Topology association analysis in weighted protein interaction network for gene prioritization

NASA Astrophysics Data System (ADS)

Wu, Shunyao; Shao, Fengjing; Zhang, Qi; Ji, Jun; Xu, Shaojie; Sun, Rencheng; Sun, Gengxin; Du, Xiangjun; Sui, Yi

2016-11-01

Although lots of algorithms for disease gene prediction have been proposed, the weights of edges are rarely taken into account. In this paper, the strengths of topology associations between disease and essential genes are analyzed in weighted protein interaction network. Empirical analysis demonstrates that compared to other genes, disease genes are weakly connected with essential genes in protein interaction network. Based on this finding, a novel global distance measurement for gene prioritization with weighted protein interaction network is proposed in this paper. Positive and negative flow is allocated to disease and essential genes, respectively. Additionally network propagation model is extended for weighted network. Experimental results on 110 diseases verify the effectiveness and potential of the proposed measurement. Moreover, weak links play more important role than strong links for gene prioritization, which is meaningful to deeply understand protein interaction network.

A global interaction network maps a wiring diagram of cellular function

PubMed Central

Costanzo, Michael; VanderSluis, Benjamin; Koch, Elizabeth N.; Baryshnikova, Anastasia; Pons, Carles; Tan, Guihong; Wang, Wen; Usaj, Matej; Hanchard, Julia; Lee, Susan D.; Pelechano, Vicent; Styles, Erin B.; Billmann, Maximilian; van Leeuwen, Jolanda; van Dyk, Nydia; Lin, Zhen-Yuan; Kuzmin, Elena; Nelson, Justin; Piotrowski, Jeff S.; Srikumar, Tharan; Bahr, Sondra; Chen, Yiqun; Deshpande, Raamesh; Kurat, Christoph F.; Li, Sheena C.; Li, Zhijian; Usaj, Mojca Mattiazzi; Okada, Hiroki; Pascoe, Natasha; Luis, Bryan-Joseph San; Sharifpoor, Sara; Shuteriqi, Emira; Simpkins, Scott W.; Snider, Jamie; Suresh, Harsha Garadi; Tan, Yizhao; Zhu, Hongwei; Malod-Dognin, Noel; Janjic, Vuk; Przulj, Natasa; Troyanskaya, Olga G.; Stagljar, Igor; Xia, Tian; Ohya, Yoshikazu; Gingras, Anne-Claude; Raught, Brian; Boutros, Michael; Steinmetz, Lars M.; Moore, Claire L.; Rosebrock, Adam P.; Caudy, Amy A.; Myers, Chad L.; Andrews, Brenda; Boone, Charles

2017-01-01

We generated a global genetic interaction network for Saccharomyces cerevisiae, constructing over 23 million double mutants, identifying ~550,000 negative and ~350,000 positive genetic interactions. This comprehensive network maps genetic interactions for essential gene pairs, highlighting essential genes as densely connected hubs. Genetic interaction profiles enabled assembly of a hierarchical model of cell function, including modules corresponding to protein complexes and pathways, biological processes, and cellular compartments. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections among gene pairs, rather than shared functionality. The global network illustrates how coherent sets of genetic interactions connect protein complex and pathway modules to map a functional wiring diagram of the cell. PMID:27708008

The role of gene-gene interaction in the prediction of criminal behavior.

PubMed

Boutwell, Brian B; Menard, Scott; Barnes, J C; Beaver, Kevin M; Armstrong, Todd A; Boisvert, Danielle

2014-04-01

A host of research has examined the possibility that environmental risk factors might condition the influence of genes on various outcomes. Less research, however, has been aimed at exploring the possibility that genetic factors might interact to impact the emergence of human traits. Even fewer studies exist examining the interaction of genes in the prediction of behavioral outcomes. The current study expands this body of research by testing the interaction between genes involved in neural transmission. Our findings suggest that certain dopamine genes interact to increase the odds of criminogenic outcomes in a national sample of Americans. Copyright © 2014 Elsevier Inc. All rights reserved.

Identification of Human Disease Genes from Interactome Network Using Graphlet Interaction

PubMed Central

Yang, Lun; Wei, Dong-Qing; Qi, Ying-Xin; Jiang, Zong-Lai

2014-01-01

Identifying genes related to human diseases, such as cancer and cardiovascular disease, etc., is an important task in biomedical research because of its applications in disease diagnosis and treatment. Interactome networks, especially protein-protein interaction networks, had been used to disease genes identification based on the hypothesis that strong candidate genes tend to closely relate to each other in some kinds of measure on the network. We proposed a new measure to analyze the relationship between network nodes which was called graphlet interaction. The graphlet interaction contained 28 different isomers. The results showed that the numbers of the graphlet interaction isomers between disease genes in interactome networks were significantly larger than random picked genes, while graphlet signatures were not. Then, we designed a new type of score, based on the network properties, to identify disease genes using graphlet interaction. The genes with higher scores were more likely to be disease genes, and all candidate genes were ranked according to their scores. Then the approach was evaluated by leave-one-out cross-validation. The precision of the current approach achieved 90% at about 10% recall, which was apparently higher than the previous three predominant algorithms, random walk, Endeavour and neighborhood based method. Finally, the approach was applied to predict new disease genes related to 4 common diseases, most of which were identified by other independent experimental researches. In conclusion, we demonstrate that the graphlet interaction is an effective tool to analyze the network properties of disease genes, and the scores calculated by graphlet interaction is more precise in identifying disease genes. PMID:24465923

Bayesian Variable Selection for Hierarchical Gene-Environment and Gene-Gene Interactions

PubMed Central

Liu, Changlu; Ma, Jianzhong; Amos, Christopher I.

2014-01-01

We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions and gene by environment interactions in the same model. Our approach incorporates the natural hierarchical structure between the main effects and interaction effects into a mixture model, such that our methods tend to remove the irrelevant interaction effects more effectively, resulting in more robust and parsimonious models. We consider both strong and weak hierarchical models. For a strong hierarchical model, both of the main effects between interacting factors must be present for the interactions to be considered in the model development, while for a weak hierarchical model, only one of the two main effects is required to be present for the interaction to be evaluated. Our simulation results show that the proposed strong and weak hierarchical mixture models work well in controlling false positive rates and provide a powerful approach for identifying the predisposing effects and interactions in gene-environment interaction studies, in comparison with the naive model that does not impose this hierarchical constraint in most of the scenarios simulated. We illustrated our approach using data for lung cancer and cutaneous melanoma. PMID:25154630

Gene-Gene and Gene-Environment Interactions in Ulcerative Colitis

PubMed Central

Wang, Ming-Hsi; Fiocchi, Claudio; Zhu, Xiaofeng; Ripke, Stephan; Kamboh, M. Ilyas; Rebert, Nancy; Duerr, Richard H.; Achkar, Jean-Paul

2014-01-01

Genome-wide association studies (GWAS) have identified at least 133 ulcerative colitis (UC) associated loci. The role of genetic factors in clinical practice is not clearly defined. The relevance of genetic variants to disease pathogenesis is still uncertain because of not characterized gene-gene and gene-environment interactions. We examined the predictive value of combining the 133 UC risk loci with genetic interactions in an ongoing inflammatory bowel disease (IBD) GWAS. The Wellcome Trust Case-Control Consortium (WTCCC) IBD GWAS was used as a replication cohort. We applied logic regression (LR), a novel adaptive regression methodology, to search for high order interactions. Exploratory genotype correlations with UC sub-phenotypes (extent of disease, need of surgery, age of onset, extra-intestinal manifestations and primary sclerosing cholangitis (PSC)) were conducted. The combination of 133 UC loci yielded good UC risk predictability (area under the curve [AUC] of 0.86). A higher cumulative allele score predicted higher UC risk. Through LR, several lines of evidence for genetic interactions were identified and successfully replicated in the WTCCC cohort. The genetic interactions combined with the gene-smoking interaction significantly improved predictability in the model (AUC, from 0.86 to 0.89, P=3.26E-05). Explained UC variance increased from 37% to 42% after adding the interaction terms. A within case analysis found suggested genetic association with PSC. Our study demonstrates that the LR methodology allows the identification and replication of high order genetic interactions in UC GWAS datasets. UC risk can be predicted by a 133 loci and improved by adding gene-gene and gene-environment interactions. PMID:24241240

Environmental confounding in gene-environment interaction studies.

PubMed

Vanderweele, Tyler J; Ko, Yi-An; Mukherjee, Bhramar

2013-07-01

We show that, in the presence of uncontrolled environmental confounding, joint tests for the presence of a main genetic effect and gene-environment interaction will be biased if the genetic and environmental factors are correlated, even if there is no effect of either the genetic factor or the environmental factor on the disease. When environmental confounding is ignored, such tests will in fact reject the joint null of no genetic effect with a probability that tends to 1 as the sample size increases. This problem with the joint test vanishes under gene-environment independence, but it still persists if estimating the gene-environment interaction parameter itself is of interest. Uncontrolled environmental confounding will bias estimates of gene-environment interaction parameters even under gene-environment independence, but it will not do so if the unmeasured confounding variable itself does not interact with the genetic factor. Under gene-environment independence, if the interaction parameter without controlling for the environmental confounder is nonzero, then there is gene-environment interaction either between the genetic factor and the environmental factor of interest or between the genetic factor and the unmeasured environmental confounder. We evaluate several recently proposed joint tests in a simulation study and discuss the implications of these results for the conduct of gene-environment interaction studies.

Modification of the association between early adversity and obsessive-compulsive disorder by polymorphisms in the MAOA, MAOB and COMT genes.

PubMed

McGregor, N W; Hemmings, S M J; Erdman, L; Calmarza-Font, I; Stein, D J; Lochner, C

2016-12-30

The monoamine oxidases (MAOA/B) and catechol-O-methyltransferase (COMT) enzymes break down regulatory components within serotonin and dopamine pathways, and polymorphisms within these genes are candidates for OCD susceptibility. Childhood trauma has been linked OCD psychopathology, but little attention has been paid to the interactions between genes and environment in OCD aetiology. This pilot study investigated gene-by-environment interactions between childhood trauma and polymorphisms in the MAOA, MAOB and COMT genes in OCD. Ten polymorphisms (MAOA: 3 variants, MAOB: 4 variants, COMT: 3 variants) were genotyped in a cohort of OCD patients and controls. Early-life trauma was assessed using the Childhood Trauma Questionnaire (CTQ). Gene-by-gene (GxG) and gene-by-environment interactions (GxE) of the variants and childhood trauma were assessed using logistic regression models. Significant GxG interactions were found between rs362204 (COMT) and two independent polymorphisms in the MAOB gene (rs1799836 and rs6651806). Haplotype associations for OCD susceptibility were found for MAOB. Investigation of GxE interactions indicated that the sexual abuse sub-category was significantly associated with all three genes in haplotype x environment interaction analyses. Preliminary findings indicate that polymorphisms within the MAOB and COMT genes interact resulting in risk for OCD. Childhood trauma interacts with haplotypes in COMT, MAOA and MAOB, increasing risk for OCD. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Hi-C Chromatin Interaction Networks Predict Co-expression in the Mouse Cortex

PubMed Central

Hulsman, Marc; Lelieveldt, Boudewijn P. F.; de Ridder, Jeroen; Reinders, Marcel

2015-01-01

The three dimensional conformation of the genome in the cell nucleus influences important biological processes such as gene expression regulation. Recent studies have shown a strong correlation between chromatin interactions and gene co-expression. However, predicting gene co-expression from frequent long-range chromatin interactions remains challenging. We address this by characterizing the topology of the cortical chromatin interaction network using scale-aware topological measures. We demonstrate that based on these characterizations it is possible to accurately predict spatial co-expression between genes in the mouse cortex. Consistent with previous findings, we find that the chromatin interaction profile of a gene-pair is a good predictor of their spatial co-expression. However, the accuracy of the prediction can be substantially improved when chromatin interactions are described using scale-aware topological measures of the multi-resolution chromatin interaction network. We conclude that, for co-expression prediction, it is necessary to take into account different levels of chromatin interactions ranging from direct interaction between genes (i.e. small-scale) to chromatin compartment interactions (i.e. large-scale). PMID:25965262

TGMI: an efficient algorithm for identifying pathway regulators through evaluation of triple-gene mutual interaction

PubMed Central

Gunasekara, Chathura; Zhang, Kui; Deng, Wenping; Brown, Laura

2018-01-01

Abstract Despite their important roles, the regulators for most metabolic pathways and biological processes remain elusive. Presently, the methods for identifying metabolic pathway and biological process regulators are intensively sought after. We developed a novel algorithm called triple-gene mutual interaction (TGMI) for identifying these regulators using high-throughput gene expression data. It first calculated the regulatory interactions among triple gene blocks (two pathway genes and one transcription factor (TF)), using conditional mutual information, and then identifies significantly interacted triple genes using a newly identified novel mutual interaction measure (MIM), which was substantiated to reflect strengths of regulatory interactions within each triple gene block. The TGMI calculated the MIM for each triple gene block and then examined its statistical significance using bootstrap. Finally, the frequencies of all TFs present in all significantly interacted triple gene blocks were calculated and ranked. We showed that the TFs with higher frequencies were usually genuine pathway regulators upon evaluating multiple pathways in plants, animals and yeast. Comparison of TGMI with several other algorithms demonstrated its higher accuracy. Therefore, TGMI will be a valuable tool that can help biologists to identify regulators of metabolic pathways and biological processes from the exploded high-throughput gene expression data in public repositories. PMID:29579312

Gene-gene interactions and gene polymorphisms of VEGFA and EG-VEGF gene systems in recurrent pregnancy loss.

PubMed

Su, Mei-Tsz; Lin, Sheng-Hsiang; Chen, Yi-Chi; Kuo, Pao-Lin

2014-06-01

Both vascular endothelial growth factor A (VEGFA) and endocrine gland-derived vascular endothelial growth factor (EG-VEGF) systems play major roles in angiogenesis. A body of evidence suggests VEGFs regulate critical processes during pregnancy and have been associated with recurrent pregnancy loss (RPL). However, little information is available regarding the interaction of these two major major angiogenesis-related systems in early human pregnancy. This study was conducted to investigate the association of gene polymorphisms and gene-gene interaction among genes in VEGFA and EG-VEGF systems and idiopathic RPL. A total of 98 women with history of idiopathic RPL and 142 controls were included, and 5 functional SNPs selected from VEGFA, KDR, EG-VEGF (PROK1), PROKR1 and PROKR2 were genotyped. We used multifactor dimensionality reduction (MDR) analysis to choose a best model and evaluate gene-gene interactions. Ingenuity pathways analysis (IPA) was introduced to explore possible complex interactions. Two receptor gene polymorphisms [KDR (Q472H) and PROKR2 (V331M)] were significantly associated with idiopathic RPL (P<0.01). The MDR test revealed that the KDR (Q472H) polymorphism was the best loci to be associated with RPL (P=0.02). IPA revealed EG-VEGF and VEGFA systems shared several canonical signaling pathways that may contribute to gene-gene interactions, including the Akt, IL-8, EGFR, MAPK, SRC, VHL, HIF-1A and STAT3 signaling pathways. Two receptor gene polymorphisms [KDR (Q472H) and PROKR2 (V331M)] were significantly associated with idiopathic RPL. EG-VEGF and VEGFA systems shared several canonical signaling pathways that may contribute to gene-gene interactions, including the Akt, IL-8, EGFR, MAPK, SRC, VHL, HIF-1A and STAT3.

Gene-environment studies: any advantage over environmental studies?

PubMed

Bermejo, Justo Lorenzo; Hemminki, Kari

2007-07-01

Gene-environment studies have been motivated by the likely existence of prevalent low-risk genes that interact with common environmental exposures. The present study assessed the statistical advantage of the simultaneous consideration of genes and environment to investigate the effect of environmental risk factors on disease. In particular, we contemplated the possibility that several genes modulate the environmental effect. Environmental exposures, genotypes and phenotypes were simulated according to a wide range of parameter settings. Different models of gene-gene-environment interaction were considered. For each parameter combination, we estimated the probability of detecting the main environmental effect, the power to identify the gene-environment interaction and the frequency of environmentally affected individuals at which environmental and gene-environment studies show the same statistical power. The proportion of cases in the population attributable to the modeled risk factors was also calculated. Our data indicate that environmental exposures with weak effects may account for a significant proportion of the population prevalence of the disease. A general result was that, if the environmental effect was restricted to rare genotypes, the power to detect the gene-environment interaction was higher than the power to identify the main environmental effect. In other words, when few individuals contribute to the overall environmental effect, individual contributions are large and result in easily identifiable gene-environment interactions. Moreover, when multiple genes interacted with the environment, the statistical benefit of gene-environment studies was limited to those studies that included major contributors to the gene-environment interaction. The advantage of gene-environment over plain environmental studies also depends on the inheritance mode of the involved genes, on the study design and, to some extend, on the disease prevalence.

Topological and organizational properties of the products of house-keeping and tissue-specific genes in protein-protein interaction networks.

PubMed

Lin, Wen-Hsien; Liu, Wei-Chung; Hwang, Ming-Jing

2009-03-11

Human cells of various tissue types differ greatly in morphology despite having the same set of genetic information. Some genes are expressed in all cell types to perform house-keeping functions, while some are selectively expressed to perform tissue-specific functions. In this study, we wished to elucidate how proteins encoded by human house-keeping genes and tissue-specific genes are organized in human protein-protein interaction networks. We constructed protein-protein interaction networks for different tissue types using two gene expression datasets and one protein-protein interaction database. We then calculated three network indices of topological importance, the degree, closeness, and betweenness centralities, to measure the network position of proteins encoded by house-keeping and tissue-specific genes, and quantified their local connectivity structure. Compared to a random selection of proteins, house-keeping gene-encoded proteins tended to have a greater number of directly interacting neighbors and occupy network positions in several shortest paths of interaction between protein pairs, whereas tissue-specific gene-encoded proteins did not. In addition, house-keeping gene-encoded proteins tended to connect with other house-keeping gene-encoded proteins in all tissue types, whereas tissue-specific gene-encoded proteins also tended to connect with other tissue-specific gene-encoded proteins, but only in approximately half of the tissue types examined. Our analysis showed that house-keeping gene-encoded proteins tend to occupy important network positions, while those encoded by tissue-specific genes do not. The biological implications of our findings were discussed and we proposed a hypothesis regarding how cells organize their protein tools in protein-protein interaction networks. Our results led us to speculate that house-keeping gene-encoded proteins might form a core in human protein-protein interaction networks, while clusters of tissue-specific gene-encoded proteins are attached to the core at more peripheral positions of the networks.

ATHENA: A knowledge-based hybrid backpropagation-grammatical evolution neural network algorithm for discovering epistasis among quantitative trait Loci

PubMed Central

2010-01-01

Background Growing interest and burgeoning technology for discovering genetic mechanisms that influence disease processes have ushered in a flood of genetic association studies over the last decade, yet little heritability in highly studied complex traits has been explained by genetic variation. Non-additive gene-gene interactions, which are not often explored, are thought to be one source of this "missing" heritability. Methods Stochastic methods employing evolutionary algorithms have demonstrated promise in being able to detect and model gene-gene and gene-environment interactions that influence human traits. Here we demonstrate modifications to a neural network algorithm in ATHENA (the Analysis Tool for Heritable and Environmental Network Associations) resulting in clear performance improvements for discovering gene-gene interactions that influence human traits. We employed an alternative tree-based crossover, backpropagation for locally fitting neural network weights, and incorporation of domain knowledge obtainable from publicly accessible biological databases for initializing the search for gene-gene interactions. We tested these modifications in silico using simulated datasets. Results We show that the alternative tree-based crossover modification resulted in a modest increase in the sensitivity of the ATHENA algorithm for discovering gene-gene interactions. The performance increase was highly statistically significant when backpropagation was used to locally fit NN weights. We also demonstrate that using domain knowledge to initialize the search for gene-gene interactions results in a large performance increase, especially when the search space is larger than the search coverage. Conclusions We show that a hybrid optimization procedure, alternative crossover strategies, and incorporation of domain knowledge from publicly available biological databases can result in marked increases in sensitivity and performance of the ATHENA algorithm for detecting and modelling gene-gene interactions that influence a complex human trait. PMID:20875103

The Interaction Network Ontology-supported modeling and mining of complex interactions represented with multiple keywords in biomedical literature.

PubMed

Özgür, Arzucan; Hur, Junguk; He, Yongqun

2016-01-01

The Interaction Network Ontology (INO) logically represents biological interactions, pathways, and networks. INO has been demonstrated to be valuable in providing a set of structured ontological terms and associated keywords to support literature mining of gene-gene interactions from biomedical literature. However, previous work using INO focused on single keyword matching, while many interactions are represented with two or more interaction keywords used in combination. This paper reports our extension of INO to include combinatory patterns of two or more literature mining keywords co-existing in one sentence to represent specific INO interaction classes. Such keyword combinations and related INO interaction type information could be automatically obtained via SPARQL queries, formatted in Excel format, and used in an INO-supported SciMiner, an in-house literature mining program. We studied the gene interaction sentences from the commonly used benchmark Learning Logic in Language (LLL) dataset and one internally generated vaccine-related dataset to identify and analyze interaction types containing multiple keywords. Patterns obtained from the dependency parse trees of the sentences were used to identify the interaction keywords that are related to each other and collectively represent an interaction type. The INO ontology currently has 575 terms including 202 terms under the interaction branch. The relations between the INO interaction types and associated keywords are represented using the INO annotation relations: 'has literature mining keywords' and 'has keyword dependency pattern'. The keyword dependency patterns were generated via running the Stanford Parser to obtain dependency relation types. Out of the 107 interactions in the LLL dataset represented with two-keyword interaction types, 86 were identified by using the direct dependency relations. The LLL dataset contained 34 gene regulation interaction types, each of which associated with multiple keywords. A hierarchical display of these 34 interaction types and their ancestor terms in INO resulted in the identification of specific gene-gene interaction patterns from the LLL dataset. The phenomenon of having multi-keyword interaction types was also frequently observed in the vaccine dataset. By modeling and representing multiple textual keywords for interaction types, the extended INO enabled the identification of complex biological gene-gene interactions represented with multiple keywords.

Interactive effects of antioxidant genes and air pollution on respiratory function and airway disease: a HuGE review.

PubMed

Minelli, Cosetta; Wei, Igor; Sagoo, Gurdeep; Jarvis, Debbie; Shaheen, Seif; Burney, Peter

2011-03-15

Susceptibility to the respiratory effects of air pollution varies between individuals. Although some evidence suggests higher susceptibility for subjects carrying variants of antioxidant genes, findings from gene-pollution interaction studies conflict in terms of the presence and direction of interactions. The authors conducted a systematic review on antioxidant gene-pollution interactions which included 15 studies, with 12 supporting the presence of interactions. For the glutathione S-transferase M1 gene (GSTM1) (n=10 studies), only 1 study found interaction with the null genotype alone, although 5 observed interactions when GSTM1 was evaluated jointly with other genes (mainly NAD(P)H dehydrogenase [quinone] 1 (NQO1)). All studies on the glutathione S-transferase P1 (GSTP1) Ile105Val polymorphism (n=11) provided some evidence of interaction, but findings conflicted in terms of risk allele. Results were negative for glutathione S-transferase T1 (GSTT1) (n=3) and positive for heme oxygenase 1 (HMOX-1) (n=2). Meta-analysis could not be performed because there were insufficient data available for any specific gene-pollutant-outcome combination. Overall the evidence supports the presence of gene-pollution interactions, although which pollutant interacts with which gene is unclear. However, issues regarding multiple testing, selective reporting, and publication bias raise the possibility of false-positive findings. Larger studies with greater accuracy of pollution assessment and improved quality of conduct and reporting are required. © The Author 2011. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved.

Axonal guidance signaling pathway interacting with smoking in modifying the risk of pancreatic cancer: a gene- and pathway-based interaction analysis of GWAS data.

PubMed

Tang, Hongwei; Wei, Peng; Duell, Eric J; Risch, Harvey A; Olson, Sara H; Bueno-de-Mesquita, H Bas; Gallinger, Steven; Holly, Elizabeth A; Petersen, Gloria; Bracci, Paige M; McWilliams, Robert R; Jenab, Mazda; Riboli, Elio; Tjønneland, Anne; Boutron-Ruault, Marie Christine; Kaaks, Rudolph; Trichopoulos, Dimitrios; Panico, Salvatore; Sund, Malin; Peeters, Petra H M; Khaw, Kay-Tee; Amos, Christopher I; Li, Donghui

2014-05-01

Cigarette smoking is the best established modifiable risk factor for pancreatic cancer. Genetic factors that underlie smoking-related pancreatic cancer have previously not been examined at the genome-wide level. Taking advantage of the existing Genome-wide association study (GWAS) genotype and risk factor data from the Pancreatic Cancer Case Control Consortium, we conducted a discovery study in 2028 cases and 2109 controls to examine gene-smoking interactions at pathway/gene/single nucleotide polymorphism (SNP) level. Using the likelihood ratio test nested in logistic regression models and ingenuity pathway analysis (IPA), we examined 172 KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, 3 manually curated gene sets, 3 nicotine dependency gene ontology pathways, 17 912 genes and 468 114 SNPs. None of the individual pathway/gene/SNP showed significant interaction with smoking after adjusting for multiple comparisons. Six KEGG pathways showed nominal interactions (P < 0.05) with smoking, and the top two are the pancreatic secretion and salivary secretion pathways (major contributing genes: RAB8A, PLCB and CTRB1). Nine genes, i.e. ZBED2, EXO1, PSG2, SLC36A1, CLSTN1, MTHFSD, FAT2, IL10RB and ATXN2 had P interaction < 0.0005. Five intergenic region SNPs and two SNPs of the EVC and KCNIP4 genes had P interaction < 0.00003. In IPA analysis of genes with nominal interactions with smoking, axonal guidance signaling $$\\left(P=2.12\\times 1{0}^{-7}\\right)$$ and α-adrenergic signaling $$\\left(P=2.52\\times 1{0}^{-5}\\right)$$ genes were significantly overrepresented canonical pathways. Genes contributing to the axon guidance signaling pathway included the SLIT/ROBO signaling genes that were frequently altered in pancreatic cancer. These observations need to be confirmed in additional data set. Once confirmed, it will open a new avenue to unveiling the etiology of smoking-associated pancreatic cancer.

Genes2Networks: connecting lists of gene symbols using mammalian protein interactions databases.

PubMed

Berger, Seth I; Posner, Jeremy M; Ma'ayan, Avi

2007-10-04

In recent years, mammalian protein-protein interaction network databases have been developed. The interactions in these databases are either extracted manually from low-throughput experimental biomedical research literature, extracted automatically from literature using techniques such as natural language processing (NLP), generated experimentally using high-throughput methods such as yeast-2-hybrid screens, or interactions are predicted using an assortment of computational approaches. Genes or proteins identified as significantly changing in proteomic experiments, or identified as susceptibility disease genes in genomic studies, can be placed in the context of protein interaction networks in order to assign these genes and proteins to pathways and protein complexes. Genes2Networks is a software system that integrates the content of ten mammalian interaction network datasets. Filtering techniques to prune low-confidence interactions were implemented. Genes2Networks is delivered as a web-based service using AJAX. The system can be used to extract relevant subnetworks created from "seed" lists of human Entrez gene symbols. The output includes a dynamic linkable three color web-based network map, with a statistical analysis report that identifies significant intermediate nodes used to connect the seed list. Genes2Networks is powerful web-based software that can help experimental biologists to interpret lists of genes and proteins such as those commonly produced through genomic and proteomic experiments, as well as lists of genes and proteins associated with disease processes. This system can be used to find relationships between genes and proteins from seed lists, and predict additional genes or proteins that may play key roles in common pathways or protein complexes.

Discovery of gene-gene interactions across multiple independent data sets of late onset Alzheimer disease from the Alzheimer Disease Genetics Consortium.

PubMed

Hohman, Timothy J; Bush, William S; Jiang, Lan; Brown-Gentry, Kristin D; Torstenson, Eric S; Dudek, Scott M; Mukherjee, Shubhabrata; Naj, Adam; Kunkle, Brian W; Ritchie, Marylyn D; Martin, Eden R; Schellenberg, Gerard D; Mayeux, Richard; Farrer, Lindsay A; Pericak-Vance, Margaret A; Haines, Jonathan L; Thornton-Wells, Tricia A

2016-02-01

Late-onset Alzheimer disease (AD) has a complex genetic etiology, involving locus heterogeneity, polygenic inheritance, and gene-gene interactions; however, the investigation of interactions in recent genome-wide association studies has been limited. We used a biological knowledge-driven approach to evaluate gene-gene interactions for consistency across 13 data sets from the Alzheimer Disease Genetics Consortium. Fifteen single nucleotide polymorphism (SNP)-SNP pairs within 3 gene-gene combinations were identified: SIRT1 × ABCB1, PSAP × PEBP4, and GRIN2B × ADRA1A. In addition, we extend a previously identified interaction from an endophenotype analysis between RYR3 × CACNA1C. Finally, post hoc gene expression analyses of the implicated SNPs further implicate SIRT1 and ABCB1, and implicate CDH23 which was most recently identified as an AD risk locus in an epigenetic analysis of AD. The observed interactions in this article highlight ways in which genotypic variation related to disease may depend on the genetic context in which it occurs. Further, our results highlight the utility of evaluating genetic interactions to explain additional variance in AD risk and identify novel molecular mechanisms of AD pathogenesis. Copyright © 2016 Elsevier Inc. All rights reserved.

Data Imputation in Epistatic MAPs by Network-Guided Matrix Completion

PubMed Central

Žitnik, Marinka; Zupan, Blaž

2015-01-01

Abstract Epistatic miniarray profile (E-MAP) is a popular large-scale genetic interaction discovery platform. E-MAPs benefit from quantitative output, which makes it possible to detect subtle interactions with greater precision. However, due to the limits of biotechnology, E-MAP studies fail to measure genetic interactions for up to 40% of gene pairs in an assay. Missing measurements can be recovered by computational techniques for data imputation, in this way completing the interaction profiles and enabling downstream analysis algorithms that could otherwise be sensitive to missing data values. We introduce a new interaction data imputation method called network-guided matrix completion (NG-MC). The core part of NG-MC is low-rank probabilistic matrix completion that incorporates prior knowledge presented as a collection of gene networks. NG-MC assumes that interactions are transitive, such that latent gene interaction profiles inferred by NG-MC depend on the profiles of their direct neighbors in gene networks. As the NG-MC inference algorithm progresses, it propagates latent interaction profiles through each of the networks and updates gene network weights toward improved prediction. In a study with four different E-MAP data assays and considered protein–protein interaction and gene ontology similarity networks, NG-MC significantly surpassed existing alternative techniques. Inclusion of information from gene networks also allowed NG-MC to predict interactions for genes that were not included in original E-MAP assays, a task that could not be considered by current imputation approaches. PMID:25658751

Genes involved in host-parasite interactions can be revealed by their correlated expression.

PubMed

Reid, Adam James; Berriman, Matthew

2013-02-01

Molecular interactions between a parasite and its host are key to the ability of the parasite to enter the host and persist. Our understanding of the genes and proteins involved in these interactions is limited. To better understand these processes it would be advantageous to have a range of methods to predict pairs of genes involved in such interactions. Correlated gene expression profiles can be used to identify molecular interactions within a species. Here we have extended the concept to different species, showing that genes with correlated expression are more likely to encode proteins, which directly or indirectly participate in host-parasite interaction. We go on to examine our predictions of molecular interactions between the malaria parasite and both its mammalian host and insect vector. Our approach could be applied to study any interaction between species, for example, between a host and its parasites or pathogens, but also symbiotic and commensal pairings.

Allelic-based gene-gene interaction associated with quantitative traits.

PubMed

Jung, Jeesun; Sun, Bin; Kwon, Deukwoo; Koller, Daniel L; Foroud, Tatiana M

2009-05-01

Recent studies have shown that quantitative phenotypes may be influenced not only by multiple single nucleotide polymorphisms (SNPs) within a gene but also by the interaction between SNPs at unlinked genes. We propose a new statistical approach that can detect gene-gene interactions at the allelic level which contribute to the phenotypic variation in a quantitative trait. By testing for the association of allelic combinations at multiple unlinked loci with a quantitative trait, we can detect the SNP allelic interaction whether or not it can be detected as a main effect. Our proposed method assigns a score to unrelated subjects according to their allelic combination inferred from observed genotypes at two or more unlinked SNPs, and then tests for the association of the allelic score with a quantitative trait. To investigate the statistical properties of the proposed method, we performed a simulation study to estimate type I error rates and power and demonstrated that this allelic approach achieves greater power than the more commonly used genotypic approach to test for gene-gene interaction. As an example, the proposed method was applied to data obtained as part of a candidate gene study of sodium retention by the kidney. We found that this method detects an interaction between the calcium-sensing receptor gene (CaSR), the chloride channel gene (CLCNKB) and the Na, K, 2Cl cotransporter gene (CLC12A1) that contributes to variation in diastolic blood pressure.

Evolving ideas about genetics underlying insect virulence to plant resistance in rice-brown planthopper interactions.

PubMed

Kobayashi, Tetsuya

2016-01-01

Many plant-parasite interactions that include major plant resistance genes have subsequently been shown to exhibit features of gene-for-gene interactions between plant Resistance genes and parasite Avirulence genes. The brown planthopper (BPH) Nilaparvata lugens is an important pest of rice (Oryza sativa). Historically, major Resistance genes have played an important role in agriculture. As is common in gene-for-gene interactions, evolution of BPH virulence compromises the effectiveness of singly-deployed resistance genes. It is therefore surprising that laboratory studies of BPH have supported the conclusion that virulence is conferred by changes in many genes rather than a change in a single gene, as is proposed by the gene-for-gene model. Here we review the behaviour, physiology and genetics of the BPH in the context of host plant resistance. A problem for genetic understanding has been the use of various insect populations that differ in frequencies of virulent genotypes. We show that the previously proposed polygenic inheritance of BPH virulence can be explained by the heterogeneity of parental populations. Genetic mapping of Avirulence genes indicates that virulence is a monogenic trait. These evolving concepts, which have brought the gene-for-gene model back into the picture, are accelerating our understanding of rice-BPH interactions at the molecular level. Copyright © 2015 Elsevier Ltd. All rights reserved.

Gene-Gene-Environment Interactions of Serotonin Transporter, Monoamine Oxidase A and Childhood Maltreatment Predict Aggressive Behavior in Chinese Adolescents

PubMed Central

Zhang, Yun; Ming, Qing-sen; Yi, Jin-yao; Wang, Xiang; Chai, Qiao-lian; Yao, Shu-qiao

2017-01-01

Gene-environment interactions that moderate aggressive behavior have been identified independently in the serotonin transporter (5-HTT) gene and monoamine oxidase A gene (MAOA). The aim of the present study was to investigate epistasis interactions between MAOA-variable number tandem repeat (VNTR), 5-HTTlinked polymorphism (LPR) and child abuse and the effects of these on aggressive tendencies in a group of otherwise healthy adolescents. A group of 546 Chinese male adolescents completed the Child Trauma Questionnaire and Youth self-report of the Child Behavior Checklist. Buccal cells were collected for DNA analysis. The effects of childhood abuse, MAOA-VNTR, 5-HTTLPR genotypes and their interactive gene-gene-environmental effects on aggressive behavior were analyzed using a linear regression model. The effect of child maltreatment was significant, and a three-way interaction among MAOA-VNTR, 5-HTTLPR and sexual abuse (SA) relating to aggressive behaviors was identified. Chinese male adolescents with high expression of the MAOA-VNTR allele and 5-HTTLPR “SS” genotype exhibited the highest aggression tendencies with an increase in SA during childhood. The findings reported support aggression being a complex behavior involving the synergistic effects of gene-gene-environment interactions. PMID:28203149

Gene-based interaction analysis shows GABAergic genes interacting with parenting in adolescent depressive symptoms.

PubMed

Van Assche, Evelien; Moons, Tim; Cinar, Ozan; Viechtbauer, Wolfgang; Oldehinkel, Albertine J; Van Leeuwen, Karla; Verschueren, Karine; Colpin, Hilde; Lambrechts, Diether; Van den Noortgate, Wim; Goossens, Luc; Claes, Stephan; van Winkel, Ruud

2017-12-01

Most gene-environment interaction studies (G × E) have focused on single candidate genes. This approach is criticized for its expectations of large effect sizes and occurrence of spurious results. We describe an approach that accounts for the polygenic nature of most psychiatric phenotypes and reduces the risk of false-positive findings. We apply this method focusing on the role of perceived parental support, psychological control, and harsh punishment in depressive symptoms in adolescence. Analyses were conducted on 982 adolescents of Caucasian origin (M age (SD) = 13.78 (.94) years) genotyped for 4,947 SNPs in 263 genes, selected based on a literature survey. The Leuven Adolescent Perceived Parenting Scale (LAPPS) and the Parental Behavior Scale (PBS) were used to assess perceived parental psychological control, harsh punishment, and support. The Center for Epidemiologic Studies Depression Scale (CES-D) was the outcome. We used gene-based testing taking into account linkage disequilibrium to identify genes containing SNPs exhibiting an interaction with environmental factors yielding a p-value per single gene. Significant results at the corrected p-value of p < 1.90 × 10 -4 were examined in an independent replication sample of Dutch adolescents (N = 1354). Two genes showed evidence for interaction with perceived support: GABRR1 (p = 4.62 × 10 -5 ) and GABRR2 (p = 9.05 × 10 -6 ). No genes interacted significantly with psychological control or harsh punishment. Gene-based analysis was unable to confirm the interaction of GABRR1 or GABRR2 with support in the replication sample. However, for GABRR2, but not GABRR1, the correlation of the estimates between the two datasets was significant (r (46) = .32; p = .027) and a gene-based analysis of the combined datasets supported GABRR2 × support interaction (p = 1.63 × 10 -4 ). We present a gene-based method for gene-environment interactions in a polygenic context and show that genes interact differently with particular aspects of parenting. This accentuates the importance of polygenic approaches and the need to accurately assess environmental exposure in G × E. © 2017 Association for Child and Adolescent Mental Health.

Why study gene-environment interactions?

USDA-ARS?s Scientific Manuscript database

PURPOSE OF REVIEW: We examine the reasons for investigating gene-environment interactions and address recent reports evaluating interactions between genes and environmental modulators in relation to cardiovascular disease and its common risk factors. RECENT FINDINGS: Studies focusing on smoking, phy...

Genetic interaction analysis of point mutations enables interrogation of gene function at a residue-level resolution

PubMed Central

Braberg, Hannes; Moehle, Erica A.; Shales, Michael; Guthrie, Christine; Krogan, Nevan J.

2014-01-01

We have achieved a residue-level resolution of genetic interaction mapping – a technique that measures how the function of one gene is affected by the alteration of a second gene – by analyzing point mutations. Here, we describe how to interpret point mutant genetic interactions, and outline key applications for the approach, including interrogation of protein interaction interfaces and active sites, and examination of post-translational modifications. Genetic interaction analysis has proven effective for characterizing cellular processes; however, to date, systematic high-throughput genetic interaction screens have relied on gene deletions or knockdowns, which limits the resolution of gene function analysis and poses problems for multifunctional genes. Our point mutant approach addresses these issues, and further provides a tool for in vivo structure-function analysis that complements traditional biophysical methods. We also discuss the potential for genetic interaction mapping of point mutations in human cells and its application to personalized medicine. PMID:24842270

Machine Learning for Detecting Gene-Gene Interactions

PubMed Central

McKinney, Brett A.; Reif, David M.; Ritchie, Marylyn D.; Moore, Jason H.

2011-01-01

Complex interactions among genes and environmental factors are known to play a role in common human disease aetiology. There is a growing body of evidence to suggest that complex interactions are ‘the norm’ and, rather than amounting to a small perturbation to classical Mendelian genetics, interactions may be the predominant effect. Traditional statistical methods are not well suited for detecting such interactions, especially when the data are high dimensional (many attributes or independent variables) or when interactions occur between more than two polymorphisms. In this review, we discuss machine-learning models and algorithms for identifying and characterising susceptibility genes in common, complex, multifactorial human diseases. We focus on the following machine-learning methods that have been used to detect gene-gene interactions: neural networks, cellular automata, random forests, and multifactor dimensionality reduction. We conclude with some ideas about how these methods and others can be integrated into a comprehensive and flexible framework for data mining and knowledge discovery in human genetics. PMID:16722772

Predicting Protein Function by Genomic Context: Quantitative Evaluation and Qualitative Inferences

PubMed Central

Huynen, Martijn; Snel, Berend; Lathe, Warren; Bork, Peer

2000-01-01

Various new methods have been proposed to predict functional interactions between proteins based on the genomic context of their genes. The types of genomic context that they use are Type I: the fusion of genes; Type II: the conservation of gene-order or co-occurrence of genes in potential operons; and Type III: the co-occurrence of genes across genomes (phylogenetic profiles). Here we compare these types for their coverage, their correlations with various types of functional interaction, and their overlap with homology-based function assignment. We apply the methods to Mycoplasma genitalium, the standard benchmarking genome in computational and experimental genomics. Quantitatively, conservation of gene order is the technique with the highest coverage, applying to 37% of the genes. By combining gene order conservation with gene fusion (6%), the co-occurrence of genes in operons in absence of gene order conservation (8%), and the co-occurrence of genes across genomes (11%), significant context information can be obtained for 50% of the genes (the categories overlap). Qualitatively, we observe that the functional interactions between genes are stronger as the requirements for physical neighborhood on the genome are more stringent, while the fraction of potential false positives decreases. Moreover, only in cases in which gene order is conserved in a substantial fraction of the genomes, in this case six out of twenty-five, does a single type of functional interaction (physical interaction) clearly dominate (>80%). In other cases, complementary function information from homology searches, which is available for most of the genes with significant genomic context, is essential to predict the type of interaction. Using a combination of genomic context and homology searches, new functional features can be predicted for 10% of M. genitalium genes. PMID:10958638

A combination test for detection of gene-environment interaction in cohort studies.

PubMed

Coombes, Brandon; Basu, Saonli; McGue, Matt

2017-07-01

Identifying gene-environment (G-E) interactions can contribute to a better understanding of disease etiology, which may help researchers develop disease prevention strategies and interventions. One big criticism of studying G-E interaction is the lack of power due to sample size. Studies often restrict the interaction search to the top few hundred hits from a genome-wide association study or focus on potential candidate genes. In this paper, we test interactions between a candidate gene and an environmental factor to improve power by analyzing multiple variants within a gene. We extend recently developed score statistic based genetic association testing approaches to the G-E interaction testing problem. We also propose tests for interaction using gene-based summary measures that pool variants together. Although it has recently been shown that these summary measures can be biased and may lead to inflated type I error, we show that under several realistic scenarios, we can still provide valid tests of interaction. These tests use significantly less degrees of freedom and thus can have much higher power to detect interaction. Additionally, we demonstrate that the iSeq-aSum-min test, which combines a gene-based summary measure test, iSeq-aSum-G, and an interaction-based summary measure test, iSeq-aSum-I, provides a powerful alternative to test G-E interaction. We demonstrate the performance of these approaches using simulation studies and illustrate their performance to study interaction between the SNPs in several candidate genes and family climate environment on alcohol consumption using the Minnesota Center for Twin and Family Research dataset. © 2017 WILEY PERIODICALS, INC.

Gene-environment interactions in mental disorders

PubMed Central

Tsuang, Ming T; Bar, Jessica L; Stone, William S; Faraone, Stephen V

2004-01-01

Research clearly shows that both nature and nurture play important roles in the genesis of psychopathology. In this paper, we focus on 'gene-environment interaction' in mental disorders, using genetic control of sensitivity to the environment as our definition of that term. We begin with an examination of methodological issues involving gene-environment interactions, with examples concerning psychiatric and neurological conditions. Then we review the interactions in psychiatric disorders using twin, adoption and association designs. Finally, we consider gene-environment interactions in selected neurodevelopmental disorders (autism and schizophrenia). PMID:16633461

Yeast Phenomics: An Experimental Approach for Modeling Gene Interaction Networks that Buffer Disease

PubMed Central

Hartman, John L.; Stisher, Chandler; Outlaw, Darryl A.; Guo, Jingyu; Shah, Najaf A.; Tian, Dehua; Santos, Sean M.; Rodgers, John W.; White, Richard A.

2015-01-01

The genome project increased appreciation of genetic complexity underlying disease phenotypes: many genes contribute each phenotype and each gene contributes multiple phenotypes. The aspiration of predicting common disease in individuals has evolved from seeking primary loci to marginal risk assignments based on many genes. Genetic interaction, defined as contributions to a phenotype that are dependent upon particular digenic allele combinations, could improve prediction of phenotype from complex genotype, but it is difficult to study in human populations. High throughput, systematic analysis of S. cerevisiae gene knockouts or knockdowns in the context of disease-relevant phenotypic perturbations provides a tractable experimental approach to derive gene interaction networks, in order to deduce by cross-species gene homology how phenotype is buffered against disease-risk genotypes. Yeast gene interaction network analysis to date has revealed biology more complex than previously imagined. This has motivated the development of more powerful yeast cell array phenotyping methods to globally model the role of gene interaction networks in modulating phenotypes (which we call yeast phenomic analysis). The article illustrates yeast phenomic technology, which is applied here to quantify gene X media interaction at higher resolution and supports use of a human-like media for future applications of yeast phenomics for modeling human disease. PMID:25668739

Differential C3NET reveals disease networks of direct physical interactions

PubMed Central

2011-01-01

Background Genes might have different gene interactions in different cell conditions, which might be mapped into different networks. Differential analysis of gene networks allows spotting condition-specific interactions that, for instance, form disease networks if the conditions are a disease, such as cancer, and normal. This could potentially allow developing better and subtly targeted drugs to cure cancer. Differential network analysis with direct physical gene interactions needs to be explored in this endeavour. Results C3NET is a recently introduced information theory based gene network inference algorithm that infers direct physical gene interactions from expression data, which was shown to give consistently higher inference performances over various networks than its competitors. In this paper, we present, DC3net, an approach to employ C3NET in inferring disease networks. We apply DC3net on a synthetic and real prostate cancer datasets, which show promising results. With loose cutoffs, we predicted 18583 interactions from tumor and normal samples in total. Although there are no reference interactions databases for the specific conditions of our samples in the literature, we found verifications for 54 of our predicted direct physical interactions from only four of the biological interaction databases. As an example, we predicted that RAD50 with TRF2 have prostate cancer specific interaction that turned out to be having validation from the literature. It is known that RAD50 complex associates with TRF2 in the S phase of cell cycle, which suggests that this predicted interaction may promote telomere maintenance in tumor cells in order to allow tumor cells to divide indefinitely. Our enrichment analysis suggests that the identified tumor specific gene interactions may be potentially important in driving the growth in prostate cancer. Additionally, we found that the highest connected subnetwork of our predicted tumor specific network is enriched for all proliferation genes, which further suggests that the genes in this network may serve in the process of oncogenesis. Conclusions Our approach reveals disease specific interactions. It may help to make experimental follow-up studies more cost and time efficient by prioritizing disease relevant parts of the global gene network. PMID:21777411

Virus-Plus-Susceptibility Gene Interaction Determines Crohn’s Disease Gene Atg16L1 Phenotypes in Intestine

PubMed Central

Cadwell, Ken; Patel, Khushbu K.; Maloney, Nicole S.; Liu, Ta-Chiang; Ng, Aylwin C.Y.; Storer, Chad E.; Head, Richard D.; Xavier, Ramnik; Stappenbeck, Thaddeus S.; Virgin, Herbert W.

2010-01-01

SUMMARY It is unclear why disease occurs in only a small proportion of persons carrying common risk alleles of disease susceptibility genes. Here we demonstrate that an interaction between a specific virus infection and a mutation in the Crohn’s disease susceptibility gene Atg16L1 induces intestinal pathologies in mice. This virus-plus-susceptibility gene interaction generated abnormalities in granule packaging and unique patterns of gene expression in Paneth cells. Further, the response to injury induced by the toxic substance dextran sodium sulfate was fundamentally altered to include pathologies resembling aspects of Crohn’s disease. These pathologies triggered by virus-plus-susceptibility gene interaction were dependent on TNFα and IFNγ and were prevented by treatment with broad spectrum antibiotics. Thus, we provide a specific example of how a virus-plus-susceptibility gene interaction can, in combination with additional environmental factors and commensal bacteria, determine the phenotype of hosts carrying common risk alleles for inflammatory disease. PMID:20602997

Gene-environment interaction and suicidal behavior.

PubMed

Roy, Alec; Sarchiopone, Marco; Carli, Vladimir

2009-07-01

Studies have increasingly shown that gene-environment interactions are important in psychiatry. Suicidal behavior is a major public health problem. Suicide is generally considered to be a multi-determined act involving various areas of proximal and distal risk. Genetic risk factors are estimated to account for approximately 30% to 40% of the variance in suicidal behavior. In this article, the authors review relevant studies concerning the interaction between the serotonin transporter gene and environmental variables as a model of gene-environment interactions that may have an impact on suicidal behavior. The findings reviewed here suggest that there may be meaningful interactions between distal and proximal suicide risk factors that may amplify the risk of suicidal behavior. Future studies of suicidal behavior should examine both genetic and environmental variables and examine for gene-environment interactions.

Bioinformatic prediction of leader genes in human periodontitis.

PubMed

Covani, Ugo; Marconcini, Simone; Giacomelli, Luca; Sivozhelevov, Victor; Barone, Antonio; Nicolini, Claudio

2008-10-01

Genes involved in different biologic processes form complex interaction networks. However, only a few have a high number of interactions with the other genes in the network. In previous bioinformatics and experimental studies concerning the T lymphocyte cell cycle, these genes were identified and termed "leader genes." In this work, genes involved in human periodontitis were tentatively identified and ranked according to their number of interactions to obtain a preliminary, broader view of molecular mechanisms of periodontitis and plan targeted experimentation. Genes were identified with interrelated queries of several databases. The interactions among these genes were mapped and given a significance score. The weighted number of links (weighted sum of scores for every interaction in which the given gene is involved) was calculated for each gene. Genes were clustered according to this parameter. The genes in the highest cluster were termed leader genes. Sixty-one genes involved or potentially involved in periodontitis were identified. Only five were identified as leader genes, whereas 12 others were ranked in an immediately lower cluster. For 10 of 17 genes there is evidence of involvement in periodontitis; seven new genes that are potentially involved in this disease were identified. The involvement in periodontitis has been completely established for only two leader genes. We applied a validated bioinformatics algorithm to increase our knowledge of molecular mechanisms of periodontitis. Even with the limitations of this ab initio analysis, this theoretical study can suggest ad hoc experimentation targeted on significant genes and, therefore, simpler than mass-scale molecular genomics. Moreover, the identification of leader genes might suggest new potential risk factors and therapeutic targets.

Inferring gene and protein interactions using PubMed citations and consensus Bayesian networks.

PubMed

Deeter, Anthony; Dalman, Mark; Haddad, Joseph; Duan, Zhong-Hui

2017-01-01

The PubMed database offers an extensive set of publication data that can be useful, yet inherently complex to use without automated computational techniques. Data repositories such as the Genomic Data Commons (GDC) and the Gene Expression Omnibus (GEO) offer experimental data storage and retrieval as well as curated gene expression profiles. Genetic interaction databases, including Reactome and Ingenuity Pathway Analysis, offer pathway and experiment data analysis using data curated from these publications and data repositories. We have created a method to generate and analyze consensus networks, inferring potential gene interactions, using large numbers of Bayesian networks generated by data mining publications in the PubMed database. Through the concept of network resolution, these consensus networks can be tailored to represent possible genetic interactions. We designed a set of experiments to confirm that our method is stable across variation in both sample and topological input sizes. Using gene product interactions from the KEGG pathway database and data mining PubMed publication abstracts, we verify that regardless of the network resolution or the inferred consensus network, our method is capable of inferring meaningful gene interactions through consensus Bayesian network generation with multiple, randomized topological orderings. Our method can not only confirm the existence of currently accepted interactions, but has the potential to hypothesize new ones as well. We show our method confirms the existence of known gene interactions such as JAK-STAT-PI3K-AKT-mTOR, infers novel gene interactions such as RAS- Bcl-2 and RAS-AKT, and found significant pathway-pathway interactions between the JAK-STAT signaling and Cardiac Muscle Contraction KEGG pathways.

Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases

PubMed Central

Ritchie, Marylyn D; White, Bill C; Parker, Joel S; Hahn, Lance W; Moore, Jason H

2003-01-01

Background Appropriate definition of neural network architecture prior to data analysis is crucial for successful data mining. This can be challenging when the underlying model of the data is unknown. The goal of this study was to determine whether optimizing neural network architecture using genetic programming as a machine learning strategy would improve the ability of neural networks to model and detect nonlinear interactions among genes in studies of common human diseases. Results Using simulated data, we show that a genetic programming optimized neural network approach is able to model gene-gene interactions as well as a traditional back propagation neural network. Furthermore, the genetic programming optimized neural network is better than the traditional back propagation neural network approach in terms of predictive ability and power to detect gene-gene interactions when non-functional polymorphisms are present. Conclusion This study suggests that a machine learning strategy for optimizing neural network architecture may be preferable to traditional trial-and-error approaches for the identification and characterization of gene-gene interactions in common, complex human diseases. PMID:12846935

Association of methylenetetrahydrofolate reductase gene-gene interaction and haplotype with susceptibility to acute lymphoblastic leukemia in Chinese children.

PubMed

Xia, Xiaojun; Duan, Yun; Cui, Jie; Jiang, Junfeng; Lin, Li; Peng, Xiaojuan; Wang, YuHong; Guo, Bingtao; Liu, Shouhai; Lei, Xudong

2017-08-01

The aim of this study was to investigate the association of methylenetetrahydrofolate reductase (MTHFR) gene polymorphism and additional gene-gene interaction with acute lymphoblastic leukemia (ALL) risk. Logistic regression was performed to investigate the association between two single nucleotide polymorphisms (SNPs) within MTHFR gene and ALL risk and additional gene-gene interaction between rs1801133 and rs1801131. The minor allele of rs1801133 and rs1801131 is associated with decreased ALL risk, OR (95% CI) were 0.61 (0.38-0.89), and 0.68 (0.50-0.96), respectively. We also found a significantly interaction between the two SNPs, participants with rs1801133 - CT or TT and rs1801131 - AC or CC genotype have the lowest ALL risk, compared with participants with rs1801133 - CC and rs1801131 - AA genotype, OR (95% CI) was 0.32 (0.12-0.63). We did not find any haplotype between the rs1801133 and rs1801131 associated with ALL risk. rs1801133 and rs1801131 within MTHFR gene and their interaction were both associated with ALL risk in Chinese children.

A kernel regression approach to gene-gene interaction detection for case-control studies.

PubMed

Larson, Nicholas B; Schaid, Daniel J

2013-11-01

Gene-gene interactions are increasingly being addressed as a potentially important contributor to the variability of complex traits. Consequently, attentions have moved beyond single locus analysis of association to more complex genetic models. Although several single-marker approaches toward interaction analysis have been developed, such methods suffer from very high testing dimensionality and do not take advantage of existing information, notably the definition of genes as functional units. Here, we propose a comprehensive family of gene-level score tests for identifying genetic elements of disease risk, in particular pairwise gene-gene interactions. Using kernel machine methods, we devise score-based variance component tests under a generalized linear mixed model framework. We conducted simulations based upon coalescent genetic models to evaluate the performance of our approach under a variety of disease models. These simulations indicate that our methods are generally higher powered than alternative gene-level approaches and at worst competitive with exhaustive SNP-level (where SNP is single-nucleotide polymorphism) analyses. Furthermore, we observe that simulated epistatic effects resulted in significant marginal testing results for the involved genes regardless of whether or not true main effects were present. We detail the benefits of our methods and discuss potential genome-wide analysis strategies for gene-gene interaction analysis in a case-control study design. © 2013 WILEY PERIODICALS, INC.

A Nonlinear Model for Gene-Based Gene-Environment Interaction.

PubMed

Sa, Jian; Liu, Xu; He, Tao; Liu, Guifen; Cui, Yuehua

2016-06-04

A vast amount of literature has confirmed the role of gene-environment (G×E) interaction in the etiology of complex human diseases. Traditional methods are predominantly focused on the analysis of interaction between a single nucleotide polymorphism (SNP) and an environmental variable. Given that genes are the functional units, it is crucial to understand how gene effects (rather than single SNP effects) are influenced by an environmental variable to affect disease risk. Motivated by the increasing awareness of the power of gene-based association analysis over single variant based approach, in this work, we proposed a sparse principle component regression (sPCR) model to understand the gene-based G×E interaction effect on complex disease. We first extracted the sparse principal components for SNPs in a gene, then the effect of each principal component was modeled by a varying-coefficient (VC) model. The model can jointly model variants in a gene in which their effects are nonlinearly influenced by an environmental variable. In addition, the varying-coefficient sPCR (VC-sPCR) model has nice interpretation property since the sparsity on the principal component loadings can tell the relative importance of the corresponding SNPs in each component. We applied our method to a human birth weight dataset in Thai population. We analyzed 12,005 genes across 22 chromosomes and found one significant interaction effect using the Bonferroni correction method and one suggestive interaction. The model performance was further evaluated through simulation studies. Our model provides a system approach to evaluate gene-based G×E interaction.

Simultaneous learning of instantaneous and time-delayed genetic interactions using novel information theoretic scoring technique

PubMed Central

2012-01-01

Background Understanding gene interactions is a fundamental question in systems biology. Currently, modeling of gene regulations using the Bayesian Network (BN) formalism assumes that genes interact either instantaneously or with a certain amount of time delay. However in reality, biological regulations, both instantaneous and time-delayed, occur simultaneously. A framework that can detect and model both these two types of interactions simultaneously would represent gene regulatory networks more accurately. Results In this paper, we introduce a framework based on the Bayesian Network (BN) formalism that can represent both instantaneous and time-delayed interactions between genes simultaneously. A novel scoring metric having firm mathematical underpinnings is also proposed that, unlike other recent methods, can score both interactions concurrently and takes into account the reality that multiple regulators can regulate a gene jointly, rather than in an isolated pair-wise manner. Further, a gene regulatory network (GRN) inference method employing an evolutionary search that makes use of the framework and the scoring metric is also presented. Conclusion By taking into consideration the biological fact that both instantaneous and time-delayed regulations can occur among genes, our approach models gene interactions with greater accuracy. The proposed framework is efficient and can be used to infer gene networks having multiple orders of instantaneous and time-delayed regulations simultaneously. Experiments are carried out using three different synthetic networks (with three different mechanisms for generating synthetic data) as well as real life networks of Saccharomyces cerevisiae, E. coli and cyanobacteria gene expression data. The results show the effectiveness of our approach. PMID:22691450

Differential network analysis reveals the genome-wide landscape of estrogen receptor modulation in hormonal cancers

PubMed Central

Hsiao, Tzu-Hung; Chiu, Yu-Chiao; Hsu, Pei-Yin; Lu, Tzu-Pin; Lai, Liang-Chuan; Tsai, Mong-Hsun; Huang, Tim H.-M.; Chuang, Eric Y.; Chen, Yidong

2016-01-01

Several mutual information (MI)-based algorithms have been developed to identify dynamic gene-gene and function-function interactions governed by key modulators (genes, proteins, etc.). Due to intensive computation, however, these methods rely heavily on prior knowledge and are limited in genome-wide analysis. We present the modulated gene/gene set interaction (MAGIC) analysis to systematically identify genome-wide modulation of interaction networks. Based on a novel statistical test employing conjugate Fisher transformations of correlation coefficients, MAGIC features fast computation and adaption to variations of clinical cohorts. In simulated datasets MAGIC achieved greatly improved computation efficiency and overall superior performance than the MI-based method. We applied MAGIC to construct the estrogen receptor (ER) modulated gene and gene set (representing biological function) interaction networks in breast cancer. Several novel interaction hubs and functional interactions were discovered. ER+ dependent interaction between TGFβ and NFκB was further shown to be associated with patient survival. The findings were verified in independent datasets. Using MAGIC, we also assessed the essential roles of ER modulation in another hormonal cancer, ovarian cancer. Overall, MAGIC is a systematic framework for comprehensively identifying and constructing the modulated interaction networks in a whole-genome landscape. MATLAB implementation of MAGIC is available for academic uses at https://github.com/chiuyc/MAGIC. PMID:26972162

From Genes to Networks: Characterizing Gene-Regulatory Interactions in Plants.

PubMed

Kaufmann, Kerstin; Chen, Dijun

2017-01-01

Plants, like other eukaryotes, have evolved complex mechanisms to coordinate gene expression during development, environmental response, and cellular homeostasis. Transcription factors (TFs), accompanied by basic cofactors and posttranscriptional regulators, are key players in gene-regulatory networks (GRNs). The coordinated control of gene activity is achieved by the interplay of these factors and by physical interactions between TFs and DNA. Here, we will briefly outline recent technological progress made to elucidate GRNs in plants. We will focus on techniques that allow us to characterize physical interactions in GRNs in plants and to analyze their regulatory consequences. Targeted manipulation allows us to test the relevance of specific gene-regulatory interactions. The combination of genome-wide experimental approaches with mathematical modeling allows us to get deeper insights into key-regulatory interactions and combinatorial control of important processes in plants.

Recursive expectation-maximization clustering: A method for identifying buffering mechanisms composed of phenomic modules

NASA Astrophysics Data System (ADS)

Guo, Jingyu; Tian, Dehua; McKinney, Brett A.; Hartman, John L.

2010-06-01

Interactions between genetic and/or environmental factors are ubiquitous, affecting the phenotypes of organisms in complex ways. Knowledge about such interactions is becoming rate-limiting for our understanding of human disease and other biological phenomena. Phenomics refers to the integrative analysis of how all genes contribute to phenotype variation, entailing genome and organism level information. A systems biology view of gene interactions is critical for phenomics. Unfortunately the problem is intractable in humans; however, it can be addressed in simpler genetic model systems. Our research group has focused on the concept of genetic buffering of phenotypic variation, in studies employing the single-cell eukaryotic organism, S. cerevisiae. We have developed a methodology, quantitative high throughput cellular phenotyping (Q-HTCP), for high-resolution measurements of gene-gene and gene-environment interactions on a genome-wide scale. Q-HTCP is being applied to the complete set of S. cerevisiae gene deletion strains, a unique resource for systematically mapping gene interactions. Genetic buffering is the idea that comprehensive and quantitative knowledge about how genes interact with respect to phenotypes will lead to an appreciation of how genes and pathways are functionally connected at a systems level to maintain homeostasis. However, extracting biologically useful information from Q-HTCP data is challenging, due to the multidimensional and nonlinear nature of gene interactions, together with a relative lack of prior biological information. Here we describe a new approach for mining quantitative genetic interaction data called recursive expectation-maximization clustering (REMc). We developed REMc to help discover phenomic modules, defined as sets of genes with similar patterns of interaction across a series of genetic or environmental perturbations. Such modules are reflective of buffering mechanisms, i.e., genes that play a related role in the maintenance of physiological homeostasis. To develop the method, 297 gene deletion strains were selected based on gene-drug interactions with hydroxyurea, an inhibitor of ribonucleotide reductase enzyme activity, which is critical for DNA synthesis. To partition the gene functions, these 297 deletion strains were challenged with growth inhibitory drugs known to target different genes and cellular pathways. Q-HTCP-derived growth curves were used to quantify all gene interactions, and the data were used to test the performance of REMc. Fundamental advantages of REMc include objective assessment of total number of clusters and assignment to each cluster a log-likelihood value, which can be considered an indicator of statistical quality of clusters. To assess the biological quality of clusters, we developed a method called gene ontology information divergence z-score (GOid_z). GOid_z summarizes total enrichment of GO attributes within individual clusters. Using these and other criteria, we compared the performance of REMc to hierarchical and K-means clustering. The main conclusion is that REMc provides distinct efficiencies for mining Q-HTCP data. It facilitates identification of phenomic modules, which contribute to buffering mechanisms that underlie cellular homeostasis and the regulation of phenotypic expression.

iLOCi: a SNP interaction prioritization technique for detecting epistasis in genome-wide association studies

PubMed Central

2012-01-01

Background Genome-wide association studies (GWAS) do not provide a full account of the heritability of genetic diseases since gene-gene interactions, also known as epistasis are not considered in single locus GWAS. To address this problem, a considerable number of methods have been developed for identifying disease-associated gene-gene interactions. However, these methods typically fail to identify interacting markers explaining more of the disease heritability over single locus GWAS, since many of the interactions significant for disease are obscured by uninformative marker interactions e.g., linkage disequilibrium (LD). Results In this study, we present a novel SNP interaction prioritization algorithm, named iLOCi (Interacting Loci). This algorithm accounts for marker dependencies separately in case and control groups. Disease-associated interactions are then prioritized according to a novel ranking score calculated from the difference in marker dependencies for every possible pair between case and control groups. The analysis of a typical GWAS dataset can be completed in less than a day on a standard workstation with parallel processing capability. The proposed framework was validated using simulated data and applied to real GWAS datasets using the Wellcome Trust Case Control Consortium (WTCCC) data. The results from simulated data showed the ability of iLOCi to identify various types of gene-gene interactions, especially for high-order interaction. From the WTCCC data, we found that among the top ranked interacting SNP pairs, several mapped to genes previously known to be associated with disease, and interestingly, other previously unreported genes with biologically related roles. Conclusion iLOCi is a powerful tool for uncovering true disease interacting markers and thus can provide a more complete understanding of the genetic basis underlying complex disease. The program is available for download at http://www4a.biotec.or.th/GI/tools/iloci. PMID:23281813

Decoding directional genetic dependencies through orthogonal CRISPR/Cas screens | Office of Cancer Genomics

Cancer.gov

Genetic interaction studies are a powerful approach to identify functional interactions between genes. This approach can reveal networks of regulatory hubs and connect uncharacterized genes to well-studied pathways. However, this approach has previously been limited to simple gene inactivation studies. Here, we present an orthogonal CRISPR/Cas-mediated genetic interaction approach that allows the systematic activation of one gene while simultaneously knocking out a second gene in the same cell.

Gene essentiality and the topology of protein interaction networks

PubMed Central

Coulomb, Stéphane; Bauer, Michel; Bernard, Denis; Marsolier-Kergoat, Marie-Claude

2005-01-01

The mechanistic bases for gene essentiality and for cell mutational resistance have long been disputed. The recent availability of large protein interaction databases has fuelled the analysis of protein interaction networks and several authors have proposed that gene dispensability could be strongly related to some topological parameters of these networks. However, many results were based on protein interaction data whose biases were not taken into account. In this article, we show that the essentiality of a gene in yeast is poorly related to the number of interactants (or degree) of the corresponding protein and that the physiological consequences of gene deletions are unrelated to several other properties of proteins in the interaction networks, such as the average degrees of their nearest neighbours, their clustering coefficients or their relative distances. We also found that yeast protein interaction networks lack degree correlation, i.e. a propensity for their vertices to associate according to their degrees. Gene essentiality and more generally cell resistance against mutations thus seem largely unrelated to many parameters of protein network topology. PMID:16087428

Coexpression network based on natural variation in human gene expression reveals gene interactions and functions

PubMed Central

Nayak, Renuka R.; Kearns, Michael; Spielman, Richard S.; Cheung, Vivian G.

2009-01-01

Genes interact in networks to orchestrate cellular processes. Analysis of these networks provides insights into gene interactions and functions. Here, we took advantage of normal variation in human gene expression to infer gene networks, which we constructed using correlations in expression levels of more than 8.5 million gene pairs in immortalized B cells from three independent samples. The resulting networks allowed us to identify biological processes and gene functions. Among the biological pathways, we found processes such as translation and glycolysis that co-occur in the same subnetworks. We predicted the functions of poorly characterized genes, including CHCHD2 and TMEM111, and provided experimental evidence that TMEM111 is part of the endoplasmic reticulum-associated secretory pathway. We also found that IFIH1, a susceptibility gene of type 1 diabetes, interacts with YES1, which plays a role in glucose transport. Furthermore, genes that predispose to the same diseases are clustered nonrandomly in the coexpression network, suggesting that networks can provide candidate genes that influence disease susceptibility. Therefore, our analysis of gene coexpression networks offers information on the role of human genes in normal and disease processes. PMID:19797678

A deeper look at two concepts of measuring gene-gene interactions: logistic regression and interaction information revisited.

PubMed

Mielniczuk, Jan; Teisseyre, Paweł

2018-03-01

Detection of gene-gene interactions is one of the most important challenges in genome-wide case-control studies. Besides traditional logistic regression analysis, recently the entropy-based methods attracted a significant attention. Among entropy-based methods, interaction information is one of the most promising measures having many desirable properties. Although both logistic regression and interaction information have been used in several genome-wide association studies, the relationship between them has not been thoroughly investigated theoretically. The present paper attempts to fill this gap. We show that although certain connections between the two methods exist, in general they refer two different concepts of dependence and looking for interactions in those two senses leads to different approaches to interaction detection. We introduce ordering between interaction measures and specify conditions for independent and dependent genes under which interaction information is more discriminative measure than logistic regression. Moreover, we show that for so-called perfect distributions those measures are equivalent. The numerical experiments illustrate the theoretical findings indicating that interaction information and its modified version are more universal tools for detecting various types of interaction than logistic regression and linkage disequilibrium measures. © 2017 WILEY PERIODICALS, INC.

Powerful multilocus tests of genetic association in the presence of gene-gene and gene-environment interactions.

PubMed

Chatterjee, Nilanjan; Kalaylioglu, Zeynep; Moslehi, Roxana; Peters, Ulrike; Wacholder, Sholom

2006-12-01

In modern genetic epidemiology studies, the association between the disease and a genomic region, such as a candidate gene, is often investigated using multiple SNPs. We propose a multilocus test of genetic association that can account for genetic effects that might be modified by variants in other genes or by environmental factors. We consider use of the venerable and parsimonious Tukey's 1-degree-of-freedom model of interaction, which is natural when individual SNPs within a gene are associated with disease through a common biological mechanism; in contrast, many standard regression models are designed as if each SNP has unique functional significance. On the basis of Tukey's model, we propose a novel but computationally simple generalized test of association that can simultaneously capture both the main effects of the variants within a genomic region and their interactions with the variants in another region or with an environmental exposure. We compared performance of our method with that of two standard tests of association, one ignoring gene-gene/gene-environment interactions and the other based on a saturated model of interactions. We demonstrate major power advantages of our method both in analysis of data from a case-control study of the association between colorectal adenoma and DNA variants in the NAT2 genomic region, which are well known to be related to a common biological phenotype, and under different models of gene-gene interactions with use of simulated data.

5C analysis of the Epidermal Differentiation Complex locus reveals distinct chromatin interaction networks between gene-rich and gene-poor TADs in skin epithelial cells

PubMed Central

Malashchuk, Igor; Lajoie, Brian R.; Mardaryev, Andrei N.; Gdula, Michal R.; Sharov, Andrey A.; Kohwi-Shigematsu, Terumi; Fessing, Michael Y.

2017-01-01

Mammalian genomes contain several dozens of large (>0.5 Mbp) lineage-specific gene loci harbouring functionally related genes. However, spatial chromatin folding, organization of the enhancer-promoter networks and their relevance to Topologically Associating Domains (TADs) in these loci remain poorly understood. TADs are principle units of the genome folding and represents the DNA regions within which DNA interacts more frequently and less frequently across the TAD boundary. Here, we used Chromatin Conformation Capture Carbon Copy (5C) technology to characterize spatial chromatin interaction network in the 3.1 Mb Epidermal Differentiation Complex (EDC) locus harbouring 61 functionally related genes that show lineage-specific activation during terminal keratinocyte differentiation in the epidermis. 5C data validated by 3D-FISH demonstrate that the EDC locus is organized into several TADs showing distinct lineage-specific chromatin interaction networks based on their transcription activity and the gene-rich or gene-poor status. Correlation of the 5C results with genome-wide studies for enhancer-specific histone modifications (H3K4me1 and H3K27ac) revealed that the majority of spatial chromatin interactions that involves the gene-rich TADs at the EDC locus in keratinocytes include both intra- and inter-TAD interaction networks, connecting gene promoters and enhancers. Compared to thymocytes in which the EDC locus is mostly transcriptionally inactive, these interactions were found to be keratinocyte-specific. In keratinocytes, the promoter-enhancer anchoring regions in the gene-rich transcriptionally active TADs are enriched for the binding of chromatin architectural proteins CTCF, Rad21 and chromatin remodeler Brg1. In contrast to gene-rich TADs, gene-poor TADs show preferential spatial contacts with each other, do not contain active enhancers and show decreased binding of CTCF, Rad21 and Brg1 in keratinocytes. Thus, spatial interactions between gene promoters and enhancers at the multi-TAD EDC locus in skin epithelial cells are cell type-specific and involve extensive contacts within TADs as well as between different gene-rich TADs, forming the framework for lineage-specific transcription. PMID:28863138

Nonclassical Regulation of Transcription: Interchromosomal Interactions at the Malic enzyme Locus of Drosophila melanogaster

PubMed Central

Lum, Thomas E.; Merritt, Thomas J. S.

2011-01-01

Regulation of transcription can be a complex process in which many cis- and trans-interactions determine the final pattern of expression. Among these interactions are trans-interactions mediated by the pairing of homologous chromosomes. These trans-effects are wide ranging, affecting gene regulation in many species and creating complex possibilities in gene regulation. Here we describe a novel case of trans-interaction between alleles of the Malic enzyme (Men) locus in Drosophila melanogaster that results in allele-specific, non-additive gene expression. Using both empirical biochemical and predictive bioinformatic approaches, we show that the regulatory elements of one allele are capable of interacting in trans with, and modifying the expression of, the second allele. Furthermore, we show that nonlocal factors—different genetic backgrounds—are capable of significant interactions with individual Men alleles, suggesting that these trans-effects can be modified by both locally and distantly acting elements. In sum, these results emphasize the complexity of gene regulation and the need to understand both small- and large-scale interactions as more complete models of the role of trans-interactions in gene regulation are developed. PMID:21900270

Predicting human genetic interactions from cancer genome evolution.

PubMed

Lu, Xiaowen; Megchelenbrink, Wout; Notebaart, Richard A; Huynen, Martijn A

2015-01-01

Synthetic Lethal (SL) genetic interactions play a key role in various types of biological research, ranging from understanding genotype-phenotype relationships to identifying drug-targets against cancer. Despite recent advances in empirical measuring SL interactions in human cells, the human genetic interaction map is far from complete. Here, we present a novel approach to predict this map by exploiting patterns in cancer genome evolution. First, we show that empirically determined SL interactions are reflected in various gene presence, absence, and duplication patterns in hundreds of cancer genomes. The most evident pattern that we discovered is that when one member of an SL interaction gene pair is lost, the other gene tends not to be lost, i.e. the absence of co-loss. This observation is in line with expectation, because the loss of an SL interacting pair will be lethal to the cancer cell. SL interactions are also reflected in gene expression profiles, such as an under representation of cases where the genes in an SL pair are both under expressed, and an over representation of cases where one gene of an SL pair is under expressed, while the other one is over expressed. We integrated the various previously unknown cancer genome patterns and the gene expression patterns into a computational model to identify SL pairs. This simple, genome-wide model achieves a high prediction power (AUC = 0.75) for known genetic interactions. It allows us to present for the first time a comprehensive genome-wide list of SL interactions with a high estimated prediction precision, covering up to 591,000 gene pairs. This unique list can potentially be used in various application areas ranging from biotechnology to medical genetics.

Inferring gene and protein interactions using PubMed citations and consensus Bayesian networks

PubMed Central

Dalman, Mark; Haddad, Joseph; Duan, Zhong-Hui

2017-01-01

The PubMed database offers an extensive set of publication data that can be useful, yet inherently complex to use without automated computational techniques. Data repositories such as the Genomic Data Commons (GDC) and the Gene Expression Omnibus (GEO) offer experimental data storage and retrieval as well as curated gene expression profiles. Genetic interaction databases, including Reactome and Ingenuity Pathway Analysis, offer pathway and experiment data analysis using data curated from these publications and data repositories. We have created a method to generate and analyze consensus networks, inferring potential gene interactions, using large numbers of Bayesian networks generated by data mining publications in the PubMed database. Through the concept of network resolution, these consensus networks can be tailored to represent possible genetic interactions. We designed a set of experiments to confirm that our method is stable across variation in both sample and topological input sizes. Using gene product interactions from the KEGG pathway database and data mining PubMed publication abstracts, we verify that regardless of the network resolution or the inferred consensus network, our method is capable of inferring meaningful gene interactions through consensus Bayesian network generation with multiple, randomized topological orderings. Our method can not only confirm the existence of currently accepted interactions, but has the potential to hypothesize new ones as well. We show our method confirms the existence of known gene interactions such as JAK-STAT-PI3K-AKT-mTOR, infers novel gene interactions such as RAS- Bcl-2 and RAS-AKT, and found significant pathway-pathway interactions between the JAK-STAT signaling and Cardiac Muscle Contraction KEGG pathways. PMID:29049295

Gene × Smoking Interactions on Human Brain Gene Expression: Finding Common Mechanisms in Adolescents and Adults

ERIC Educational Resources Information Center

Wolock, Samuel L.; Yates, Andrew; Petrill, Stephen A.; Bohland, Jason W.; Blair, Clancy; Li, Ning; Machiraju, Raghu; Huang, Kun; Bartlett, Christopher W.

2013-01-01

Background: Numerous studies have examined gene × environment interactions (G × E) in cognitive and behavioral domains. However, these studies have been limited in that they have not been able to directly assess differential patterns of gene expression in the human brain. Here, we assessed G × E interactions using two publically available datasets…

GeneNetFinder2: Improved Inference of Dynamic Gene Regulatory Relations with Multiple Regulators.

PubMed

Han, Kyungsook; Lee, Jeonghoon

2016-01-01

A gene involved in complex regulatory interactions may have multiple regulators since gene expression in such interactions is often controlled by more than one gene. Another thing that makes gene regulatory interactions complicated is that regulatory interactions are not static, but change over time during the cell cycle. Most research so far has focused on identifying gene regulatory relations between individual genes in a particular stage of the cell cycle. In this study we developed a method for identifying dynamic gene regulations of several types from the time-series gene expression data. The method can find gene regulations with multiple regulators that work in combination or individually as well as those with single regulators. The method has been implemented as the second version of GeneNetFinder (hereafter called GeneNetFinder2) and tested on several gene expression datasets. Experimental results with gene expression data revealed the existence of genes that are not regulated by individual genes but rather by a combination of several genes. Such gene regulatory relations cannot be found by conventional methods. Our method finds such regulatory relations as well as those with multiple, independent regulators or single regulators, and represents gene regulatory relations as a dynamic network in which different gene regulatory relations are shown in different stages of the cell cycle. GeneNetFinder2 is available at http://bclab.inha.ac.kr/GeneNetFinder and will be useful for modeling dynamic gene regulations with multiple regulators.

Using protein-protein interactions for refining gene networks estimated from microarray data by Bayesian networks.

PubMed

Nariai, N; Kim, S; Imoto, S; Miyano, S

2004-01-01

We propose a statistical method to estimate gene networks from DNA microarray data and protein-protein interactions. Because physical interactions between proteins or multiprotein complexes are likely to regulate biological processes, using only mRNA expression data is not sufficient for estimating a gene network accurately. Our method adds knowledge about protein-protein interactions to the estimation method of gene networks under a Bayesian statistical framework. In the estimated gene network, a protein complex is modeled as a virtual node based on principal component analysis. We show the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae cell cycle data. The proposed method improves the accuracy of the estimated gene networks, and successfully identifies some biological facts.

Pharmacogenetics of drug-drug interaction and drug-drug-gene interaction: a systematic review on CYP2C9, CYP2C19 and CYP2D6.

PubMed

Bahar, Muh Akbar; Setiawan, Didik; Hak, Eelko; Wilffert, Bob

2017-05-01

Currently, most guidelines on drug-drug interaction (DDI) neither consider the potential effect of genetic polymorphism in the strength of the interaction nor do they account for the complex interaction caused by the combination of DDI and drug-gene interaction (DGI) where there are multiple biotransformation pathways, which is referred to as drug-drug-gene interaction (DDGI). In this systematic review, we report the impact of pharmacogenetics on DDI and DDGI in which three major drug-metabolizing enzymes - CYP2C9, CYP2C19 and CYP2D6 - are central. We observed that several DDI and DDGI are highly gene-dependent, leading to a different magnitude of interaction. Precision drug therapy should take pharmacogenetics into account when drug interactions in clinical practice are expected.

Genome-wide assessment of gene-by-smoking interactions in COPD.

PubMed

Park, Boram; Koo, So-My; An, Jaehoon; Lee, MoonGyu; Kang, Hae Yeon; Qiao, Dandi; Cho, Michael H; Sung, Joohon; Silverman, Edwin K; Yang, Hyeon-Jong; Won, Sungho

2018-06-18

Cigarette smoke exposure is a major risk factor in chronic obstructive pulmonary disease (COPD) and its interactions with genetic variants could affect lung function. However, few gene-smoking interactions have been reported. In this report, we evaluated the effects of gene-smoking interactions on lung function using Korea Associated Resource (KARE) data with the spirometric variables-forced expiratory volume in 1 s (FEV 1 ). We found that variations in FEV 1 were different among smoking status. Thus, we considered a linear mixed model for association analysis under heteroscedasticity according to smoking status. We found a previously identified locus near SOX9 on chromosome 17 to be the most significant based on a joint test of the main and interaction effects of smoking. Smoking interactions were replicated with Gene-Environment of Interaction and phenotype (GENIE), Multi-Ethnic Study of Atherosclerosis-Lung (MESA-Lung), and COPDGene studies. We found that individuals with minor alleles, rs17765644, rs17178251, rs11870732, and rs4793541, tended to have lower FEV 1 values, and lung function decreased much faster with age for smokers. There have been very few reports to replicate a common variant gene-smoking interaction, and our results revealed that statistical models for gene-smoking interaction analyses should be carefully selected.

A survey of disease connections for CD4+ T cell master genes and their directly linked genes.

PubMed

Li, Wentian; Espinal-Enríquez, Jesús; Simpfendorfer, Kim R; Hernández-Lemus, Enrique

2015-12-01

Genome-wide association studies and other genetic analyses have identified a large number of genes and variants implicating a variety of disease etiological mechanisms. It is imperative for the study of human diseases to put these genetic findings into a coherent functional context. Here we use system biology tools to examine disease connections of five master genes for CD4+ T cell subtypes (TBX21, GATA3, RORC, BCL6, and FOXP3). We compiled a list of genes functionally interacting (protein-protein interaction, or by acting in the same pathway) with the master genes, then we surveyed the disease connections, either by experimental evidence or by genetic association. Embryonic lethal genes (also known as essential genes) are over-represented in master genes and their interacting genes (55% versus 40% in other genes). Transcription factors are significantly enriched among genes interacting with the master genes (63% versus 10% in other genes). Predicted haploinsufficiency is a feature of most these genes. Disease-connected genes are enriched in this list of genes: 42% of these genes have a disease connection according to Online Mendelian Inheritance in Man (OMIM) (versus 23% in other genes), and 74% are associated with some diseases or phenotype in a Genome Wide Association Study (GWAS) (versus 43% in other genes). Seemingly, not all of the diseases connected to genes surveyed were immune related, which may indicate pleiotropic functions of the master regulator genes and associated genes. Copyright © 2015 Elsevier Ltd. All rights reserved.

Mimosa: Mixture Model of Co-expression to Detect Modulators of Regulatory Interaction

NASA Astrophysics Data System (ADS)

Hansen, Matthew; Everett, Logan; Singh, Larry; Hannenhalli, Sridhar

Functionally related genes tend to be correlated in their expression patterns across multiple conditions and/or tissue-types. Thus co-expression networks are often used to investigate functional groups of genes. In particular, when one of the genes is a transcription factor (TF), the co-expression-based interaction is interpreted, with caution, as a direct regulatory interaction. However, any particular TF, and more importantly, any particular regulatory interaction, is likely to be active only in a subset of experimental conditions. Moreover, the subset of expression samples where the regulatory interaction holds may be marked by presence or absence of a modifier gene, such as an enzyme that post-translationally modifies the TF. Such subtlety of regulatory interactions is overlooked when one computes an overall expression correlation. Here we present a novel mixture modeling approach where a TF-Gene pair is presumed to be significantly correlated (with unknown coefficient) in a (unknown) subset of expression samples. The parameters of the model are estimated using a Maximum Likelihood approach. The estimated mixture of expression samples is then mined to identify genes potentially modulating the TF-Gene interaction. We have validated our approach using synthetic data and on three biological cases in cow and in yeast. While limited in some ways, as discussed, the work represents a novel approach to mine expression data and detect potential modulators of regulatory interactions.

Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns.

PubMed

Lezon, Timothy R; Banavar, Jayanth R; Cieplak, Marek; Maritan, Amos; Fedoroff, Nina V

2006-12-12

We describe a method based on the principle of entropy maximization to identify the gene interaction network with the highest probability of giving rise to experimentally observed transcript profiles. In its simplest form, the method yields the pairwise gene interaction network, but it can also be extended to deduce higher-order interactions. Analysis of microarray data from genes in Saccharomyces cerevisiae chemostat cultures exhibiting energy metabolic oscillations identifies a gene interaction network that reflects the intracellular communication pathways that adjust cellular metabolic activity and cell division to the limiting nutrient conditions that trigger metabolic oscillations. The success of the present approach in extracting meaningful genetic connections suggests that the maximum entropy principle is a useful concept for understanding living systems, as it is for other complex, nonequilibrium systems.

Commensurate distances and similar motifs in genetic congruence and protein interaction networks in yeast

PubMed Central

Ye, Ping; Peyser, Brian D; Spencer, Forrest A; Bader, Joel S

2005-01-01

Background In a genetic interaction, the phenotype of a double mutant differs from the combined phenotypes of the underlying single mutants. When the single mutants have no growth defect, but the double mutant is lethal or exhibits slow growth, the interaction is termed synthetic lethality or synthetic fitness. These genetic interactions reveal gene redundancy and compensating pathways. Recently available large-scale data sets of genetic interactions and protein interactions in Saccharomyces cerevisiae provide a unique opportunity to elucidate the topological structure of biological pathways and how genes function in these pathways. Results We have defined congruent genes as pairs of genes with similar sets of genetic interaction partners and constructed a genetic congruence network by linking congruent genes. By comparing path lengths in three types of networks (genetic interaction, genetic congruence, and protein interaction), we discovered that high genetic congruence not only exhibits correlation with direct protein interaction linkage but also exhibits commensurate distance with the protein interaction network. However, consistent distances were not observed between genetic and protein interaction networks. We also demonstrated that congruence and protein networks are enriched with motifs that indicate network transitivity, while the genetic network has both transitive (triangle) and intransitive (square) types of motifs. These results suggest that robustness of yeast cells to gene deletions is due in part to two complementary pathways (square motif) or three complementary pathways, any two of which are required for viability (triangle motif). Conclusion Genetic congruence is superior to genetic interaction in prediction of protein interactions and function associations. Genetically interacting pairs usually belong to parallel compensatory pathways, which can generate transitive motifs (any two of three pathways needed) or intransitive motifs (either of two pathways needed). PMID:16283923

Assessment of Gene-by-Sex Interaction Effect on Bone Mineral Density

PubMed Central

Liu, Ching-Ti; Estrada, Karol; Yerges-Armstrong, Laura M.; Amin, Najaf; Evangelou, Evangelos; Li, Guo; Minster, Ryan L.; Carless, Melanie A.; Kammerer, Candace M.; Oei, Ling; Zhou, Yanhua; Alonso, Nerea; Dailiana, Zoe; Eriksson, Joel; García-Giralt, Natalia; Giroux, Sylvie; Husted, Lise Bjerre; Khusainova, Rita I.; Koromila, Theodora; Kung, Annie WaiChee; Lewis, Joshua R.; Masi, Laura; Mencej-Bedrac, Simona; Nogues, Xavier; Patel, Millan S.; Prezelj, Janez; Richards, J Brent; Sham, Pak Chung; Spector, Timothy; Vandenput, Liesbeth; Xiao, Su-Mei; Zheng, Hou-Feng; Zhu, Kun; Balcells, Susana; Brandi, Maria Luisa; Frost, Morten; Goltzman, David; González-Macías, Jesús; Karlsson, Magnus; Khusnutdinova, Elza K.; Kollia, Panagoula; Langdahl, Bente Lomholt; Ljunggren, Östen; Lorentzon, Mattias; Marc, Janja; Mellström, Dan; Ohlsson, Claes; Olmos, José M.; Ralston, Stuart H.; Riancho, José A.; Rousseau, François; Urreizti, Roser; Van Hul, Wim; Zarrabeitia, María T.; Castano-Betancourt, Martha; Demissie, Serkalem; Grundberg, Elin; Herrera, Lizbeth; Kwan, Tony; Medina-Gómez, Carolina; Pastinen, Tomi; Sigurdsson, Gunnar; Thorleifsson, Gudmar; vanMeurs, Joyce B.J.; Blangero, John; Hofman, Albert; Liu, Yongmei; Mitchell, Braxton D.; O’Connell, Jeffrey R.; Oostra, Ben A.; Rotter, Jerome I; Stefansson, Kari; Streeten, Elizabeth A.; Styrkarsdottir, Unnur; Thorsteinsdottir, Unnur; Tylavsky, Frances A.; Uitterlinden, Andre; Cauley, Jane A.; Harris, Tamara B.; Ioannidis, John P.A.; Psaty, Bruce M.; Robbins, John A; Zillikens, M. Carola; vanDuijn, Cornelia M.; Prince, Richard L.; Karasik, David; Rivadeneira, Fernando; Kiel, Douglas P.; Cupples, L. Adrienne; Hsu, Yi-Hsiang

2012-01-01

Background Sexual dimorphism in various bone phenotypes, including bone mineral density (BMD), is widely observed; however the extent to which genes explain these sex differences is unclear. To identify variants with different effects by sex, we examined gene-by-sex autosomal interactions genome-wide, and performed eQTL analysis and bioinformatics network analysis. Methods We conducted an autosomal genome-wide meta-analysis of gene-by-sex interaction on lumbar spine (LS-) and femoral neck (FN-) BMD, in 25,353 individuals from eight cohorts. In a second stage, we followed up the 12 top SNPs (P<1×10−5) in an additional set of 24,763 individuals. Gene-by-sex interaction and sex-specific effects were examined in these 12 SNPs. Results We detected one novel genome-wide significant interaction associated with LS-BMD at the Chr3p26.1-p25.1 locus, near the GRM7 gene (male effect = 0.02 & p-value = 3.0×10−5; female effect = −0.007 & p-value=3.3×10−2) and eleven suggestive loci associated with either FN- or LS-BMD in discovery cohorts. However, there was no evidence for genome-wide significant (P<5×10−8) gene-by-sex interaction in the joint analysis of discovery and replication cohorts. Conclusion Despite the large collaborative effort, no genome-wide significant evidence for gene-by-sex interaction was found influencing BMD variation in this screen of autosomal markers. If they exist, gene-by-sex interactions for BMD probably have weak effects, accounting for less than 0.08% of the variation in these traits per implicated SNP. PMID:22692763

Mapping fusiform rust resistance genes within a complex mating design of loblolly pine

Treesearch

Tania Quesada; Marcio F.R. Resende Jr.; Patricio Munoz; Jill L. Wegrzyn; David B. Neale; Matias Kirst; Gary F. Peter; Salvador A. Gezan; C.Dana Nelson; John M. Davis

2014-01-01

Fusiform rust resistance can involve gene-for-gene interactions where resistance (Fr) genes in the host interact with corresponding avirulence genes in the pathogen, Cronartium quercuum f.sp. fusiforme (Cqf). Here, we identify trees with Fr genes in a loblolly pine population derived from a complex mating design challenged with two Cqf inocula (one gall and 10 gall...

From Interaction to Co-Association —A Fisher r-To-z Transformation-Based Simple Statistic for Real World Genome-Wide Association Study

PubMed Central

Yuan, Zhongshang; Liu, Hong; Zhang, Xiaoshuai; Li, Fangyu; Zhao, Jinghua; Zhang, Furen; Xue, Fuzhong

2013-01-01

Currently, the genetic variants identified by genome wide association study (GWAS) generally only account for a small proportion of the total heritability for complex disease. One crucial reason is the underutilization of gene-gene joint effects commonly encountered in GWAS, which includes their main effects and co-association. However, gene-gene co-association is often customarily put into the framework of gene-gene interaction vaguely. From the causal graph perspective, we elucidate in detail the concept and rationality of gene-gene co-association as well as its relationship with traditional gene-gene interaction, and propose two Fisher r-to-z transformation-based simple statistics to detect it. Three series of simulations further highlight that gene-gene co-association refers to the extent to which the joint effects of two genes differs from the main effects, not only due to the traditional interaction under the nearly independent condition but the correlation between two genes. The proposed statistics are more powerful than logistic regression under various situations, cannot be affected by linkage disequilibrium and can have acceptable false positive rate as long as strictly following the reasonable GWAS data analysis roadmap. Furthermore, an application to gene pathway analysis associated with leprosy confirms in practice that our proposed gene-gene co-association concepts as well as the correspondingly proposed statistics are strongly in line with reality. PMID:23923021

Antioxidant Defense Enzyme Genes and Asthma Susceptibility: Gender-Specific Effects and Heterogeneity in Gene-Gene Interactions between Pathogenetic Variants of the Disease

PubMed Central

Polonikov, Alexey V.; Ivanov, Vladimir P.; Bogomazov, Alexey D.; Freidin, Maxim B.; Illig, Thomas; Solodilova, Maria A.

2014-01-01

Oxidative stress resulting from an increased amount of reactive oxygen species and an imbalance between oxidants and antioxidants plays an important role in the pathogenesis of asthma. The present study tested the hypothesis that genetic susceptibility to allergic and nonallergic variants of asthma is determined by complex interactions between genes encoding antioxidant defense enzymes (ADE). We carried out a comprehensive analysis of the associations between adult asthma and 46 single nucleotide polymorphisms of 34 ADE genes and 12 other candidate genes of asthma in Russian population using set association analysis and multifactor dimensionality reduction approaches. We found for the first time epistatic interactions between ADE genes underlying asthma susceptibility and the genetic heterogeneity between allergic and nonallergic variants of the disease. We identified GSR (glutathione reductase) and PON2 (paraoxonase 2) as novel candidate genes for asthma susceptibility. We observed gender-specific effects of ADE genes on the risk of asthma. The results of the study demonstrate complexity and diversity of interactions between genes involved in oxidative stress underlying susceptibility to allergic and nonallergic asthma. PMID:24895604

Reconstruction of a Functional Human Gene Network, with an Application for Prioritizing Positional Candidate Genes

PubMed Central

Franke, Lude; Bakel, Harm van; Fokkens, Like; de Jong, Edwin D.; Egmont-Petersen, Michael; Wijmenga, Cisca

2006-01-01

Most common genetic disorders have a complex inheritance and may result from variants in many genes, each contributing only weak effects to the disease. Pinpointing these disease genes within the myriad of susceptibility loci identified in linkage studies is difficult because these loci may contain hundreds of genes. However, in any disorder, most of the disease genes will be involved in only a few different molecular pathways. If we know something about the relationships between the genes, we can assess whether some genes (which may reside in different loci) functionally interact with each other, indicating a joint basis for the disease etiology. There are various repositories of information on pathway relationships. To consolidate this information, we developed a functional human gene network that integrates information on genes and the functional relationships between genes, based on data from the Kyoto Encyclopedia of Genes and Genomes, the Biomolecular Interaction Network Database, Reactome, the Human Protein Reference Database, the Gene Ontology database, predicted protein-protein interactions, human yeast two-hybrid interactions, and microarray coexpressions. We applied this network to interrelate positional candidate genes from different disease loci and then tested 96 heritable disorders for which the Online Mendelian Inheritance in Man database reported at least three disease genes. Artificial susceptibility loci, each containing 100 genes, were constructed around each disease gene, and we used the network to rank these genes on the basis of their functional interactions. By following up the top five genes per artificial locus, we were able to detect at least one known disease gene in 54% of the loci studied, representing a 2.8-fold increase over random selection. This suggests that our method can significantly reduce the cost and effort of pinpointing true disease genes in analyses of disorders for which numerous loci have been reported but for which most of the genes are unknown. PMID:16685651

Bayesian logistic regression in detection of gene-steroid interaction for cancer at PDLIM5 locus.

PubMed

Wang, Ke-Sheng; Owusu, Daniel; Pan, Yue; Xie, Changchun

2016-06-01

The PDZ and LIM domain 5 (PDLIM5) gene may play a role in cancer, bipolar disorder, major depression, alcohol dependence and schizophrenia; however, little is known about the interaction effect of steroid and PDLIM5 gene on cancer. This study examined 47 single-nucleotide polymorphisms (SNPs) within the PDLIM5 gene in the Marshfield sample with 716 cancer patients (any diagnosed cancer, excluding minor skin cancer) and 2848 noncancer controls. Multiple logistic regression model in PLINK software was used to examine the association of each SNP with cancer. Bayesian logistic regression in PROC GENMOD in SAS statistical software, ver. 9.4 was used to detect gene- steroid interactions influencing cancer. Single marker analysis using PLINK identified 12 SNPs associated with cancer (P< 0.05); especially, SNP rs6532496 revealed the strongest association with cancer (P = 6.84 × 10⁻³); while the next best signal was rs951613 (P = 7.46 × 10⁻³). Classic logistic regression in PROC GENMOD showed that both rs6532496 and rs951613 revealed strong gene-steroid interaction effects (OR=2.18, 95% CI=1.31-3.63 with P = 2.9 × 10⁻³ for rs6532496 and OR=2.07, 95% CI=1.24-3.45 with P = 5.43 × 10⁻³ for rs951613, respectively). Results from Bayesian logistic regression showed stronger interaction effects (OR=2.26, 95% CI=1.2-3.38 for rs6532496 and OR=2.14, 95% CI=1.14-3.2 for rs951613, respectively). All the 12 SNPs associated with cancer revealed significant gene-steroid interaction effects (P < 0.05); whereas 13 SNPs showed gene-steroid interaction effects without main effect on cancer. SNP rs4634230 revealed the strongest gene-steroid interaction effect (OR=2.49, 95% CI=1.5-4.13 with P = 4.0 × 10⁻⁴ based on the classic logistic regression and OR=2.59, 95% CI=1.4-3.97 from Bayesian logistic regression; respectively). This study provides evidence of common genetic variants within the PDLIM5 gene and interactions between PLDIM5 gene polymorphisms and steroid use influencing cancer.

Genome-Wide Analysis of Gene-Gene and Gene-Environment Interactions Using Closed-Form Wald Tests.

PubMed

Yu, Zhaoxia; Demetriou, Michael; Gillen, Daniel L

2015-09-01

Despite the successful discovery of hundreds of variants for complex human traits using genome-wide association studies, the degree to which genes and environmental risk factors jointly affect disease risk is largely unknown. One obstacle toward this goal is that the computational effort required for testing gene-gene and gene-environment interactions is enormous. As a result, numerous computationally efficient tests were recently proposed. However, the validity of these methods often relies on unrealistic assumptions such as additive main effects, main effects at only one variable, no linkage disequilibrium between the two single-nucleotide polymorphisms (SNPs) in a pair or gene-environment independence. Here, we derive closed-form and consistent estimates for interaction parameters and propose to use Wald tests for testing interactions. The Wald tests are asymptotically equivalent to the likelihood ratio tests (LRTs), largely considered to be the gold standard tests but generally too computationally demanding for genome-wide interaction analysis. Simulation studies show that the proposed Wald tests have very similar performances with the LRTs but are much more computationally efficient. Applying the proposed tests to a genome-wide study of multiple sclerosis, we identify interactions within the major histocompatibility complex region. In this application, we find that (1) focusing on pairs where both SNPs are marginally significant leads to more significant interactions when compared to focusing on pairs where at least one SNP is marginally significant; and (2) parsimonious parameterization of interaction effects might decrease, rather than increase, statistical power. © 2015 WILEY PERIODICALS, INC.

Interaction of childhood urbanicity and variation in dopamine genes alters adult prefrontal function as measured by functional magnetic resonance imaging (fMRI).

PubMed

Reed, Jessica L; D'Ambrosio, Enrico; Marenco, Stefano; Ursini, Gianluca; Zheutlin, Amanda B; Blasi, Giuseppe; Spencer, Barbara E; Romano, Raffaella; Hochheiser, Jesse; Reifman, Ann; Sturm, Justin; Berman, Karen F; Bertolino, Alessandro; Weinberger, Daniel R; Callicott, Joseph H

2018-01-01

Brain phenotypes showing environmental influence may help clarify unexplained associations between urban exposure and psychiatric risk. Heritable prefrontal fMRI activation during working memory (WM) is such a phenotype. We hypothesized that urban upbringing (childhood urbanicity) would alter this phenotype and interact with dopamine genes that regulate prefrontal function during WM. Further, dopamine has been hypothesized to mediate urban-associated factors like social stress. WM-related prefrontal function was tested for main effects of urbanicity, main effects of three dopamine genes-catechol-O-methyltransferase (COMT), dopamine receptor D1 (DRD1), and dopamine receptor D2 (DRD2)-and, importantly, dopamine gene-by-urbanicity interactions. For COMT, three independent human samples were recruited (total n = 487). We also studied 253 subjects genotyped for DRD1 and DRD2. 3T fMRI activation during the N-back WM task was the dependent variable, while childhood urbanicity, dopamine genotype, and urbanicity-dopamine interactions were independent variables. Main effects of dopamine genes and of urbanicity were found. Individuals raised in an urban environment showed altered prefrontal activation relative to those raised in rural or town settings. For each gene, dopamine genotype-by-urbanicity interactions were shown in prefrontal cortex-COMT replicated twice in two independent samples. An urban childhood upbringing altered prefrontal function and interacted with each gene to alter genotype-phenotype relationships. Gene-environment interactions between multiple dopamine genes and urban upbringing suggest that neural effects of developmental environmental exposure could mediate, at least partially, increased risk for psychiatric illness in urban environments via dopamine genes expressed into adulthood.

Direct protein interaction underlies gene-for-gene specificity and coevolution of the flax resistance genes and flax rust avirulence genes

PubMed Central

Dodds, Peter N.; Lawrence, Gregory J.; Catanzariti, Ann-Maree; Teh, Trazel; Wang, Ching-I. A.; Ayliffe, Michael A.; Kobe, Bostjan; Ellis, Jeffrey G.

2006-01-01

Plant resistance proteins (R proteins) recognize corresponding pathogen avirulence (Avr) proteins either indirectly through detection of changes in their host protein targets or through direct R–Avr protein interaction. Although indirect recognition imposes selection against Avr effector function, pathogen effector molecules recognized through direct interaction may overcome resistance through sequence diversification rather than loss of function. Here we show that the flax rust fungus AvrL567 genes, whose products are recognized by the L5, L6, and L7 R proteins of flax, are highly diverse, with 12 sequence variants identified from six rust strains. Seven AvrL567 variants derived from Avr alleles induce necrotic responses when expressed in flax plants containing corresponding resistance genes (R genes), whereas five variants from avr alleles do not. Differences in recognition specificity between AvrL567 variants and evidence for diversifying selection acting on these genes suggest they have been involved in a gene-specific arms race with the corresponding flax R genes. Yeast two-hybrid assays indicate that recognition is based on direct R–Avr protein interaction and recapitulate the interaction specificity observed in planta. Biochemical analysis of Escherichia coli-produced AvrL567 proteins shows that variants that escape recognition nevertheless maintain a conserved structure and stability, suggesting that the amino acid sequence differences directly affect the R–Avr protein interaction. We suggest that direct recognition associated with high genetic diversity at corresponding R and Avr gene loci represents an alternative outcome of plant–pathogen coevolution to indirect recognition associated with simple balanced polymorphisms for functional and nonfunctional R and Avr genes. PMID:16731621

Measured Gene-by-Environment Interaction in Relation to Attention-Deficit/Hyperactivity Disorder

ERIC Educational Resources Information Center

Nigg, Joel; Nikolas, Molly; Burt, S. Alexandra

2010-01-01

Objective: To summarize and evaluate the state of knowledge regarding the role of measured gene-by-environment interactions in relation to attention-deficit/hyperactivity disorder. Method: A selective review of methodologic issues was followed by a systematic search for relevant articles on measured gene-by-environment interactions; the search…

Design and analysis issues in gene and environment studies

PubMed Central

2012-01-01

Both nurture (environmental) and nature (genetic factors) play an important role in human disease etiology. Traditionally, these effects have been thought of as independent. This perspective is ill informed for non-mendelian complex disorders which result as an interaction between genetics and environment. To understand health and disease we must study how nature and nurture interact. Recent advances in human genomics and high-throughput biotechnology make it possible to study large numbers of genetic markers and gene products simultaneously to explore their interactions with environment. The purpose of this review is to discuss design and analytic issues for gene-environment interaction studies in the “-omics” era, with a focus on environmental and genetic epidemiological studies. We present an expanded environmental genomic disease paradigm. We discuss several study design issues for gene-environmental interaction studies, including confounding and selection bias, measurement of exposures and genotypes. We discuss statistical issues in studying gene-environment interactions in different study designs, such as choices of statistical models, assumptions regarding biological factors, and power and sample size considerations, especially in genome-wide gene-environment studies. Future research directions are also discussed. PMID:23253229

Design and analysis issues in gene and environment studies.

PubMed

Liu, Chen-yu; Maity, Arnab; Lin, Xihong; Wright, Robert O; Christiani, David C

2012-12-19

Both nurture (environmental) and nature (genetic factors) play an important role in human disease etiology. Traditionally, these effects have been thought of as independent. This perspective is ill informed for non-mendelian complex disorders which result as an interaction between genetics and environment. To understand health and disease we must study how nature and nurture interact. Recent advances in human genomics and high-throughput biotechnology make it possible to study large numbers of genetic markers and gene products simultaneously to explore their interactions with environment. The purpose of this review is to discuss design and analytic issues for gene-environment interaction studies in the "-omics" era, with a focus on environmental and genetic epidemiological studies. We present an expanded environmental genomic disease paradigm. We discuss several study design issues for gene-environmental interaction studies, including confounding and selection bias, measurement of exposures and genotypes. We discuss statistical issues in studying gene-environment interactions in different study designs, such as choices of statistical models, assumptions regarding biological factors, and power and sample size considerations, especially in genome-wide gene-environment studies. Future research directions are also discussed.

Interaction between the Sbcc Gene of Escherichia Coli and the Gam Gene of Phage λ

PubMed Central

Kulkarni, S. K.; Stahl, F. W.

1989-01-01

gam mutants of phage λ carrying long palindromes fail to form plaques on wild-type Escherichia coli but do grow on strains that are mutant in the sbcC gene. gam(+) λ carrying the same palindrome grow on both hosts and on a host deleted for the recB, C and D genes. These results suggest that the Gam protein of λ, known to interact also with E. coli's recBCD protein, can interact with the product of the sbcC gene. PMID:2531105

Systematic Search for Gene-Gene Interaction Effect on Prostate Cancer Risk

DTIC Science & Technology

2013-07-01

Systematic Search for Gene-Gene Interaction 5a. CONTRACT NUMBER Effect on Prostate Cancer Risk 5b. GRANT NUMBER W81XWH-09-1-0488 5c. PROGRAM...Supported by this grant ) 1. Tao S, Wang Z, Feng J, Hsu FC, Jin G, Kin ST, Zhang Z, Gronberg H, Zheng, SL, Isaacs WB, XU J, Sun J. A Genome-Wide Search for...order interactions among estrogen- metabolism genes in sporadic breast cancer. Am J Hum Genet, 69, 138-47. 48. Marchini, J., Donnelly, P. and Cardon

Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns

PubMed Central

Lezon, Timothy R.; Banavar, Jayanth R.; Cieplak, Marek; Maritan, Amos; Fedoroff, Nina V.

2006-01-01

We describe a method based on the principle of entropy maximization to identify the gene interaction network with the highest probability of giving rise to experimentally observed transcript profiles. In its simplest form, the method yields the pairwise gene interaction network, but it can also be extended to deduce higher-order interactions. Analysis of microarray data from genes in Saccharomyces cerevisiae chemostat cultures exhibiting energy metabolic oscillations identifies a gene interaction network that reflects the intracellular communication pathways that adjust cellular metabolic activity and cell division to the limiting nutrient conditions that trigger metabolic oscillations. The success of the present approach in extracting meaningful genetic connections suggests that the maximum entropy principle is a useful concept for understanding living systems, as it is for other complex, nonequilibrium systems. PMID:17138668

System-level insights into the cellular interactome of a non-model organism: inferring, modelling and analysing functional gene network of soybean (Glycine max).

PubMed

Xu, Yungang; Guo, Maozu; Zou, Quan; Liu, Xiaoyan; Wang, Chunyu; Liu, Yang

2014-01-01

Cellular interactome, in which genes and/or their products interact on several levels, forming transcriptional regulatory-, protein interaction-, metabolic-, signal transduction networks, etc., has attracted decades of research focuses. However, such a specific type of network alone can hardly explain the various interactive activities among genes. These networks characterize different interaction relationships, implying their unique intrinsic properties and defects, and covering different slices of biological information. Functional gene network (FGN), a consolidated interaction network that models fuzzy and more generalized notion of gene-gene relations, have been proposed to combine heterogeneous networks with the goal of identifying functional modules supported by multiple interaction types. There are yet no successful precedents of FGNs on sparsely studied non-model organisms, such as soybean (Glycine max), due to the absence of sufficient heterogeneous interaction data. We present an alternative solution for inferring the FGNs of soybean (SoyFGNs), in a pioneering study on the soybean interactome, which is also applicable to other organisms. SoyFGNs exhibit the typical characteristics of biological networks: scale-free, small-world architecture and modularization. Verified by co-expression and KEGG pathways, SoyFGNs are more extensive and accurate than an orthology network derived from Arabidopsis. As a case study, network-guided disease-resistance gene discovery indicates that SoyFGNs can provide system-level studies on gene functions and interactions. This work suggests that inferring and modelling the interactome of a non-model plant are feasible. It will speed up the discovery and definition of the functions and interactions of other genes that control important functions, such as nitrogen fixation and protein or lipid synthesis. The efforts of the study are the basis of our further comprehensive studies on the soybean functional interactome at the genome and microRNome levels. Additionally, a web tool for information retrieval and analysis of SoyFGNs can be accessed at SoyFN: http://nclab.hit.edu.cn/SoyFN.

System-Level Insights into the Cellular Interactome of a Non-Model Organism: Inferring, Modelling and Analysing Functional Gene Network of Soybean (Glycine max)

PubMed Central

Xu, Yungang; Guo, Maozu; Zou, Quan; Liu, Xiaoyan; Wang, Chunyu; Liu, Yang

2014-01-01

Cellular interactome, in which genes and/or their products interact on several levels, forming transcriptional regulatory-, protein interaction-, metabolic-, signal transduction networks, etc., has attracted decades of research focuses. However, such a specific type of network alone can hardly explain the various interactive activities among genes. These networks characterize different interaction relationships, implying their unique intrinsic properties and defects, and covering different slices of biological information. Functional gene network (FGN), a consolidated interaction network that models fuzzy and more generalized notion of gene-gene relations, have been proposed to combine heterogeneous networks with the goal of identifying functional modules supported by multiple interaction types. There are yet no successful precedents of FGNs on sparsely studied non-model organisms, such as soybean (Glycine max), due to the absence of sufficient heterogeneous interaction data. We present an alternative solution for inferring the FGNs of soybean (SoyFGNs), in a pioneering study on the soybean interactome, which is also applicable to other organisms. SoyFGNs exhibit the typical characteristics of biological networks: scale-free, small-world architecture and modularization. Verified by co-expression and KEGG pathways, SoyFGNs are more extensive and accurate than an orthology network derived from Arabidopsis. As a case study, network-guided disease-resistance gene discovery indicates that SoyFGNs can provide system-level studies on gene functions and interactions. This work suggests that inferring and modelling the interactome of a non-model plant are feasible. It will speed up the discovery and definition of the functions and interactions of other genes that control important functions, such as nitrogen fixation and protein or lipid synthesis. The efforts of the study are the basis of our further comprehensive studies on the soybean functional interactome at the genome and microRNome levels. Additionally, a web tool for information retrieval and analysis of SoyFGNs can be accessed at SoyFN: http://nclab.hit.edu.cn/SoyFN. PMID:25423109

Identification and functional analysis of a core gene module associated with hepatitis C virus-induced human hepatocellular carcinoma progression.

PubMed

Bai, Gaobo; Zheng, Wenling; Ma, Wenli

2018-05-01

Hepatitis C virus (HCV)-induced human hepatocellular carcinoma (HCC) progression may be due to a complex multi-step processes. The developmental mechanism of these processes is worth investigating for the prevention, diagnosis and therapy of HCC. The aim of the present study was to investigate the molecular mechanism underlying the progression of HCV-induced hepatocarcinogenesis. First, the dynamic gene module, consisting of key genes associated with progression between the normal stage and HCC, was identified using the Weighted Gene Co-expression Network Analysis tool from R language. By defining those genes in the module as seeds, the change of co-expression in differentially expressed gene sets in two consecutive stages of pathological progression was examined. Finally, interaction pairs of HCV viral proteins and their directly targeted proteins in the identified module were extracted from the literature and a comprehensive interaction dataset from yeast two-hybrid experiments. By combining the interactions between HCV and their targets, and protein-protein interactions in the Search Tool for the Retrieval of Interacting Genes database (STRING), the HCV-key genes interaction network was constructed and visualized using Cytoscape software 3.2. As a result, a module containing 44 key genes was identified to be associated with HCC progression, due to the dynamic features and functions of those genes in the module. Several important differentially co-expressed gene pairs were identified between non-HCC and HCC stages. In the key genes, cyclin dependent kinase 1 (CDK1), NDC80, cyclin A2 (CCNA2) and rac GTPase activating protein 1 (RACGAP1) were shown to be targeted by the HCV nonstructural proteins NS5A, NS3 and NS5B, respectively. The four genes perform an intermediary role between the HCV viral proteins and the dysfunctional module in the HCV key genes interaction network. These findings provided valuable information for understanding the mechanism of HCV-induced HCC progression and for seeking drug targets for the therapy and prevention of HCC.

Long-Term and Short-Term Evolutionary Impacts of Transposable Elements on Drosophila

PubMed Central

Lee, Yuh Chwen G.; Langley, Charles H.

2012-01-01

Transposable elements (TEs) are considered to be genomic parasites and their interactions with their hosts have been likened to the coevolution between host and other nongenomic, horizontally transferred pathogens. TE families, however, are vertically inherited as integral segments of the nuclear genome. This transmission strategy has been suggested to weaken the selective benefits of host alleles repressing the transposition of specific TE variants. On the other hand, the elevated rates of TE transposition and high incidences of deleterious mutations observed during the rare cases of horizontal transfers of TE families between species could create at least a transient process analogous to the influence of horizontally transmitted pathogens. Here, we formally address this analogy, using empirical and theoretical analysis to specify the mechanism of how host–TE interactions may drive the evolution of host genes. We found that host TE-interacting genes actually have more pervasive evidence of adaptive evolution than immunity genes that interact with nongenomic pathogens in Drosophila. Yet, both our theoretical modeling and empirical observations comparing Drosophila melanogaster populations before and after the horizontal transfer of P elements, which invaded D. melanogaster early last century, demonstrated that horizontally transferred TEs have only a limited influence on host TE-interacting genes. We propose that the more prevalent and constant interaction with multiple vertically transmitted TE families may instead be the main force driving the fast evolution of TE-interacting genes, which is fundamentally different from the gene-for-gene interaction of host–pathogen coevolution. PMID:22997235

Gene-Diet Interactions in Childhood Obesity

PubMed Central

Garver, William S

2011-01-01

Childhood overweight and obesity have reached epidemic proportions worldwide, and the increase in weight-associated co-morbidities including premature type 2 diabetes mellitus (T2DM) and atherosclerotic cardiovascular disease will soon become major healthcare and economic problems. A number of studies now indicate that the childhood obesity epidemic which has emerged during the past 30 years is a complex multi-factorial disease resulting from interaction of susceptibility genes with an obesogenic environment. This review will focus on gene-diet interactions suspected of having a prominent role in promoting childhood obesity. In particular, the specific genes that will be presented (FTO, MC4R, and NPC1) have recently been associated with childhood obesity through a genome-wide association study (GWAS) and were shown to interact with nutritional components to increase weight gain. Although a fourth gene (APOA2) has not yet been associated with childhood obesity, this review will also present information on what now represents the best characterized gene-diet interaction in promoting weight gain. PMID:22043166

The Rice Tungro Bacilliform Virus Gene II Product Interacts with the Coat Protein Domain of the Viral Gene III Polyprotein

PubMed Central

Herzog, Etienne; Guerra-Peraza, Orlene; Hohn, Thomas

2000-01-01

Rice tungro bacilliform virus (RTBV) is a plant pararetrovirus whose DNA genome contains four genes encoding three proteins and a large polyprotein. The function of most of the viral proteins is still unknown. To investigate the role of the gene II product (P2), we searched for interactions between this protein and other RTBV proteins. P2 was shown to interact with the coat protein (CP) domain of the viral gene III polyprotein (P3) both in the yeast two-hybrid system and in vitro. Domains involved in the P2-CP association have been identified and mapped on both proteins. To determine the importance of this interaction for viral multiplication, the infectivity of RTBV gene II mutants was investigated by agroinoculation of rice plants. The results showed that virus viability correlates with the ability of P2 to interact with the CP domain of P3. This study suggests that P2 could participate in RTBV capsid assembly. PMID:10666237

Genome-wide association studies and epistasis analyses of candidate genes related to age at menarche and age at natural menopause in a Korean population.

PubMed

Pyun, Jung-A; Kim, Sunshin; Cho, Nam H; Koh, InSong; Lee, Jong-Young; Shin, Chol; Kwack, KyuBum

2014-05-01

The aim of this study was to identify polymorphisms and gene-gene interactions that are significantly associated with age at menarche and age at menopause in a Korean population. A total of 3,452 and 1,827 women participated in studies of age at menarche and age at natural menopause, respectively. Linear regression analyses adjusted for residence area were used to perform genome-wide association studies (GWAS), candidate gene association studies, and interactions between the candidate genes for age at menarche and age at natural menopause. In GWAS, four single nucleotide polymorphisms (SNPs; rs7528241, rs1324329, rs11597068, and rs6495785) were strongly associated with age at natural menopause (lowest P = 9.66 × 10). However, GWAS of age at menarche did not reveal any strong associations. In candidate gene association studies, SNPs with P < 0.01 were selected to test their synergistic interactions. For age at natural menopause, there was a significant interaction between intronic SNPs on ADAM metallopeptidase with thrombospondin type I motif 9 (ADAMTS9) and SMAD family member 3 (SMAD3) genes (P = 9.52 × 10). For age at menarche, there were three significant interactions between three intronic SNPs on follicle-stimulating hormone receptor (FSHR) gene and one SNP located at the 3' flanking region of insulin-like growth factor 2 receptor (IGF2R) gene (lowest P = 1.95 × 10). Novel SNPs and synergistic interactions between candidate genes are significantly associated with age at menarche and age at natural menopause in a Korean population.

Gene-gene-environment interactions between drugs, transporters, receptors, and metabolizing enzymes: Statins, SLCO1B1, and CYP3A4 as an example.

PubMed

Sadee, Wolfgang

2013-09-01

Pharmacogenetic biomarker tests include mostly specific single gene-drug pairs, capable of accounting for a portion of interindividual variability in drug response and toxicity. However, multiple genes are likely to contribute, either acting independently or epistatically, with the CYP2C9-VKORC1-warfarin test panel, an example of a clinically used gene-gene-dug interaction. I discuss here further instances of gene-gene-drug interactions, including a proposed dynamic effect on statin therapy by genetic variants in both a transporter (SLCO1B1) and a metabolizing enzyme (CYP3A4) in liver cells, the main target site where statins block cholesterol synthesis. These examples set a conceptual framework for developing diagnostic panels involving multiple gene-drug combinations. Copyright © 2013 Wiley Periodicals, Inc.

AprioriGWAS, a new pattern mining strategy for detecting genetic variants associated with disease through interaction effects.

PubMed

Zhang, Qingrun; Long, Quan; Ott, Jurg

2014-06-01

Identifying gene-gene interaction is a hot topic in genome wide association studies. Two fundamental challenges are: (1) how to smartly identify combinations of variants that may be associated with the trait from astronomical number of all possible combinations; and (2) how to test epistatic interaction when all potential combinations are available. We developed AprioriGWAS, which brings two innovations. (1) Based on Apriori, a successful method in field of Frequent Itemset Mining (FIM) in which a pattern growth strategy is leveraged to effectively and accurately reduce search space, AprioriGWAS can efficiently identify genetically associated genotype patterns. (2) To test the hypotheses of epistasis, we adopt a new conditional permutation procedure to obtain reliable statistical inference of Pearson's chi-square test for the [Formula: see text] contingency table generated by associated variants. By applying AprioriGWAS to age-related macular degeneration (AMD) data, we found that: (1) angiopoietin 1 (ANGPT1) and four retinal genes interact with Complement Factor H (CFH). (2) GO term "glycosaminoglycan biosynthetic process" was enriched in AMD interacting genes. The epistatic interactions newly found by AprioriGWAS on AMD data are likely true interactions, since genes interacting with CFH are retinal genes, and GO term enrichment also verified that interaction between glycosaminoglycans (GAGs) and CFH plays an important role in disease pathology of AMD. By applying AprioriGWAS on Bipolar disorder in WTCCC data, we found variants without marginal effect show significant interactions. For example, multiple-SNP genotype patterns inside gene GABRB2 and GRIA1 (AMPA subunit 1 receptor gene). AMPARs are found in many parts of the brain and are the most commonly found receptor in the nervous system. The GABRB2 mediates the fastest inhibitory synaptic transmission in the central nervous system. GRIA1 and GABRB2 are relevant to mental disorders supported by multiple evidences.

Mutual regulatory interactions of the trunk gap genes during blastoderm patterning in the hemipteran Oncopeltus fasciatus.

PubMed

Ben-David, Jonathan; Chipman, Ariel D

2010-10-01

The early embryo of the milkweed bug, Oncopeltus fasciatus, appears as a single cell layer - the embryonic blastoderm - covering the entire egg. It is at this blastoderm stage that morphological domains are first determined, long before the appearance of overt segmentation. Central to the process of patterning the blastoderm into distinct domains are a group of transcription factors known as gap genes. In Drosophila melanogaster these genes form a network of interactions, and maintain sharp expression boundaries through strong mutual repression. Their restricted expression domains define specific areas along the entire body. We have studied the expression domains of the four trunk gap gene homologues in O. fasciatus and have determined their interactions through dsRNA gene knockdown experiments, followed by expression analyses. While the blastoderm in O. fasciatus includes only the first six segments of the embryo, the expression domains of the gap genes within these segments are broadly similar to those in Drosophila where the blastoderm includes all 15 segments. However, the interactions between the gap genes are surprisingly different from those in Drosophila, and mutual repression between the genes seems to play a much less significant role. This suggests that the well-studied interaction pattern in Drosophila is evolutionarily derived, and has evolved from a less strongly interacting network. Copyright © 2010 Elsevier Inc. All rights reserved.

Influence of SNPs in nutrient-sensitive candidate genes and gene-diet interactions on blood lipids: the DiOGenes study.

PubMed

Brahe, Lena K; Ängquist, Lars; Larsen, Lesli H; Vimaleswaran, Karani S; Hager, Jörg; Viguerie, Nathalie; Loos, Ruth J F; Handjieva-Darlenska, Teodora; Jebb, Susan A; Hlavaty, Petr; Larsen, Thomas M; Martinez, J Alfredo; Papadaki, Angeliki; Pfeiffer, Andreas F H; van Baak, Marleen A; Sørensen, Thorkild I A; Holst, Claus; Langin, Dominique; Astrup, Arne; Saris, Wim H M

2013-09-14

Blood lipid response to a given dietary intervention could be determined by the effect of diet, gene variants or gene-diet interactions. The objective of the present study was to investigate whether variants in presumed nutrient-sensitive genes involved in lipid metabolism modified lipid profile after weight loss and in response to a given diet, among overweight European adults participating in the Diet Obesity and Genes study. By multiple linear regressions, 240 SNPs in twenty-four candidate genes were investigated for SNP main and SNP-diet interaction effects on total cholesterol, LDL-cholesterol, HDL-cholesterol and TAG after an 8-week low-energy diet (only main effect) ,and a 6-month ad libitum weight maintenance diet, with different contents of dietary protein or glycaemic index. After adjusting for multiple testing, a SNP-dietary protein interaction effect on TAG was identified for lipin 1 (LPIN1) rs4315495, with a decrease in TAG of 20.26 mmol/l per A-allele/protein unit (95% CI 20.38, 20.14, P=0.000043). In conclusion, we investigated SNP-diet interactions for blood lipid profiles for 240 SNPs in twenty-four candidate genes, selected for their involvement in lipid metabolism pathways, and identified one significant interaction between LPIN1 rs4315495 and dietary protein for TAG concentration.

An integrated approach to characterize genetic interaction networks in yeast metabolism

PubMed Central

Szappanos, Balázs; Kovács, Károly; Szamecz, Béla; Honti, Frantisek; Costanzo, Michael; Baryshnikova, Anastasia; Gelius-Dietrich, Gabriel; Lercher, Martin J.; Jelasity, Márk; Myers, Chad L.; Andrews, Brenda J.; Boone, Charles; Oliver, Stephen G.; Pál, Csaba; Papp, Balázs

2011-01-01

Intense experimental and theoretical efforts have been made to globally map genetic interactions, yet we still do not understand how gene-gene interactions arise from the operation of biomolecular networks. To bridge the gap between empirical and computational studies, we: i) quantitatively measure genetic interactions between ~185,000 metabolic gene pairs in Saccharomyces cerevisiae, ii) superpose the data on a detailed systems biology model of metabolism, and iii) introduce a machine-learning method to reconcile empirical interaction data with model predictions. We systematically investigate the relative impacts of functional modularity and metabolic flux coupling on the distribution of negative and positive genetic interactions. We also provide a mechanistic explanation for the link between the degree of genetic interaction, pleiotropy, and gene dispensability. Last, we demonstrate the feasibility of automated metabolic model refinement by correcting misannotations in NAD biosynthesis and confirming them by in vivo experiments. PMID:21623372

Chemical-Gene Interactions from ToxCast Bioactivity Data ...

EPA Pesticide Factsheets

Characterizing the effects of chemicals in biological systems is often summarized by chemical-gene interactions, which have sparse coverage in the literature. The ToxCast chemical screening program has produced bioactivity data for nearly 2000 chemicals and over 450 gene targets. To evaluate the information gained from the ToxCast project, a ToxCast bioactivity network was created comprising ToxCast chemical-gene interactions based on assay data and compared to a chemical-gene association network from literature. The literature network was compiled from PubMed articles, excluding ToxCast publications, mapped to genes and chemicals. Genes were identified by curated associations available from NCBI while chemicals were identified by PubChem submissions. The frequencies of chemical-gene associations from the literature network were log-scaled and then compared to the ToxCast bioactivity network. In total, 140 times more chemical-gene associations were present in the ToxCast network in comparison to the literature-derived network highlighting the vast increase in chemical-gene interactions putatively elucidated by the ToxCast research program. There were 165 associations found in the literature network that were reproduced by ToxCast bioactivity data, and 336 associations in the literature network were not reproduced by the ToxCast bioactivity network. The literature network relies on the assumption that chemical-gene associations represent a true chemical-gene inte

Do little interactions get lost in dark random forests?

PubMed

Wright, Marvin N; Ziegler, Andreas; König, Inke R

2016-03-31

Random forests have often been claimed to uncover interaction effects. However, if and how interaction effects can be differentiated from marginal effects remains unclear. In extensive simulation studies, we investigate whether random forest variable importance measures capture or detect gene-gene interactions. With capturing interactions, we define the ability to identify a variable that acts through an interaction with another one, while detection is the ability to identify an interaction effect as such. Of the single importance measures, the Gini importance captured interaction effects in most of the simulated scenarios, however, they were masked by marginal effects in other variables. With the permutation importance, the proportion of captured interactions was lower in all cases. Pairwise importance measures performed about equal, with a slight advantage for the joint variable importance method. However, the overall fraction of detected interactions was low. In almost all scenarios the detection fraction in a model with only marginal effects was larger than in a model with an interaction effect only. Random forests are generally capable of capturing gene-gene interactions, but current variable importance measures are unable to detect them as interactions. In most of the cases, interactions are masked by marginal effects and interactions cannot be differentiated from marginal effects. Consequently, caution is warranted when claiming that random forests uncover interactions.

Macroarray expression analysis of barley susceptibility and nonhost resistance to Blumeria graminis.

PubMed

Eichmann, Ruth; Biemelt, Sophia; Schäfer, Patrick; Scholz, Uwe; Jansen, Carin; Felk, Angelika; Schäfer, Wilhelm; Langen, Gregor; Sonnewald, Uwe; Kogel, Karl-Heinz; Hückelhoven, Ralph

2006-04-01

Different formae speciales of the grass powdery mildew fungus Blumeria graminis undergo basic-compatible or basic-incompatible (nonhost) interactions with barley. Background resistance in compatible interactions and nonhost resistance require common genetic and mechanistic elements of plant defense. To build resources for differential screening for genes that potentially distinguish a compatible from an incompatible interaction on the level of differential gene expression of the plant, we constructed eight dedicated cDNA libraries, established 13.000 expressed sequence tag (EST) sequences and designed DNA macroarrays. Using macroarrays based on cDNAs derived from epidermal peels of plants pretreated with the chemical resistance activating compound acibenzolar-S-methyl, we compared the expression of barley gene transcripts in the early host interaction with B. graminis f.sp. hordei or the nonhost pathogen B. graminis f.sp. tritici, respectively. We identified 102 spots corresponding to 94 genes on the macroarray that gave significant B. graminis-responsive signals at 12 and/or 24 h after inoculation. In independent expression analyses, we confirmed the macroarray results for 11 selected genes. Although the majority of genes showed a similar expression profile in compatible versus incompatible interactions, about 30 of the 94 genes were expressed on slightly different levels in compatible versus incompatible interactions.

An interactional network of genes involved in chitin synthesis in Saccharomyces cerevisiae.

PubMed

Lesage, Guillaume; Shapiro, Jesse; Specht, Charles A; Sdicu, Anne-Marie; Ménard, Patrice; Hussein, Shamiza; Tong, Amy Hin Yan; Boone, Charles; Bussey, Howard

2005-02-16

In S. cerevisiae the beta-1,4-linked N-acetylglucosamine polymer, chitin, is synthesized by a family of 3 specialized but interacting chitin synthases encoded by CHS1, CHS2 and CHS3. Chs2p makes chitin in the primary septum, while Chs3p makes chitin in the lateral cell wall and in the bud neck, and can partially compensate for the lack of Chs2p. Chs3p requires a pathway of Bni4p, Chs4p, Chs5p, Chs6p and Chs7p for its localization and activity. Chs1p is thought to have a septum repair function after cell separation. To further explore interactions in the chitin synthase family and to find processes buffering chitin synthesis, we compiled a genetic interaction network of genes showing synthetic interactions with CHS1, CHS3 and genes involved in Chs3p localization and function and made a phenotypic analysis of their mutants. Using deletion mutants in CHS1, CHS3, CHS4, CHS5, CHS6, CHS7 and BNI4 in a synthetic genetic array analysis we assembled a network of 316 interactions among 163 genes. The interaction network with CHS3, CHS4, CHS5, CHS6, CHS7 or BNI4 forms a dense neighborhood, with many genes functioning in cell wall assembly or polarized secretion. Chitin levels were altered in 54 of the mutants in individually deleted genes, indicating a functional relationship between them and chitin synthesis. 32 of these mutants triggered the chitin stress response, with elevated chitin levels and a dependence on CHS3. A large fraction of the CHS1-interaction set was distinct from that of the CHS3 network, indicating broad roles for Chs1p in buffering both Chs2p function and more global cell wall robustness. Based on their interaction patterns and chitin levels we group interacting mutants into functional categories. Genes interacting with CHS3 are involved in the amelioration of cell wall defects and in septum or bud neck chitin synthesis, and we newly assign a number of genes to these functions. Our genetic analysis of genes not interacting with CHS3 indicate expanded roles for Chs4p, Chs5p and Chs6p in secretory protein trafficking and of Bni4p in bud neck organization.

Behavioral science and the study of gene-nutrition and gene-physical activity interactions in obesity research.

PubMed

Faith, Myles S

2008-12-01

This report summarizes emerging opportunities for behavioral science to help advance the field of gene-environment and gene-behavior interactions, based on presentations at The National Cancer Institute (NCI) Workshop, "Gene-Nutrition and Gene-Physical Activity Interactions in the Etiology of Obesity." Three opportunities are highlighted: (i) designing potent behavioral "challenges" in experiments, (ii) determining viable behavioral phenotypes for genetics studies, and (iii) identifying specific measures of the environment or environmental exposures. Additional points are underscored, including the need to incorporate novel findings from neuroimaging studies regarding motivation and drive for eating and physical activity. Advances in behavioral science theory and methods can play an important role in advancing understanding of gene-brain-behavior relationships in obesity onset.

Gene interactions in the DNA damage-response pathway identified by genome-wide RNA-interference analysis of synthetic lethality

PubMed Central

van Haaften, Gijs; Vastenhouw, Nadine L.; Nollen, Ellen A. A.; Plasterk, Ronald H. A.; Tijsterman, Marcel

2004-01-01

Here, we describe a systematic search for synthetic gene interactions in a multicellular organism, the nematode Caenorhabditis elegans. We established a high-throughput method to determine synthetic gene interactions by genome-wide RNA interference and identified genes that are required to protect the germ line against DNA double-strand breaks. Besides known DNA-repair proteins such as the C. elegans orthologs of TopBP1, RPA2, and RAD51, eight genes previously unassociated with a double-strand-break response were identified. Knockdown of these genes increased sensitivity to ionizing radiation and camptothecin and resulted in increased chromosomal nondisjunction. All genes have human orthologs that may play a role in human carcinogenesis. PMID:15326288

A distinct and replicable variant of the squamous cell carcinoma gene inositol polyphosphate-5-phosphatase modifies the susceptibility of arsenic-associated skin lesions in Bangladesh.

PubMed

Seow, Wei Jie; Pan, Wen-Chi; Kile, Molly L; Tong, Lin; Baccarelli, Andrea A; Quamruzzaman, Quazi; Rahman, Mahmuder; Mostofa, Golam; Rakibuz-Zaman, Muhammad; Kibriya, Muhammad; Ahsan, Habibul; Lin, Xihong; Christiani, David C

2015-07-01

Single-nucleotide polymorphisms (SNPs) in inflammation, one-carbon metabolism, and skin cancer genes might influence susceptibility to arsenic-induced skin lesions. A case-control study was conducted in Pabna, Bangladesh (2001-2003), and the drinking-water arsenic concentration was measured for each participant. A panel of 25 candidate SNPs was analyzed in 540 cases and 400 controls. Logistic regression was used to estimate the association between each SNP and the potential for gene-environment interactions in the skin lesion risk, with adjustments for relevant covariates. Replication testing was conducted in an independent Bangladesh population with 488 cases and 2,794 controls. In the discovery population, genetic variants in the one-carbon metabolism genes phosphatidylethanolamine N-methyltransferase (rs2278952, P for interaction  = .004; rs897453, P for interaction = .05) and dihydrofolate reductase (rs1650697, P for interaction = .02), the inflammation gene interleukin 10 (rs3024496, P for interaction =.04), and the skin cancer genes inositol polyphosphate-5-phosphatase (INPP5A; rs1133400, P for interaction = .03) and xeroderma pigmentosum complementation group C (rs2228000, P for interaction = .01) significantly modified the association between arsenic and skin lesions after adjustments for multiple comparisons. The significant gene-environment interaction between a SNP in the INPP5A gene (rs1133400) and water arsenic with respect to the skin lesion risk was successfully replicated in an independent population (P for interaction = .03). Minor allele carriers of the skin cancer gene INPP5A modified the odds of arsenic-induced skin lesions in both main and replicative populations. Genetic variation in INPP5A appears to have a role in susceptibility to arsenic toxicity. © 2015 American Cancer Society.

Interactions of HIPPI, a molecular partner of Huntingtin interacting protein HIP1, with the specific motif present at the putative promoter sequence of the caspase-1, caspase-8 and caspase-10 genes.

PubMed

Majumder, P; Choudhury, A; Banerjee, M; Lahiri, A; Bhattacharyya, N P

2007-08-01

To investigate the mechanism of increased expression of caspase-1 caused by exogenous Hippi, observed earlier in HeLa and Neuro2A cells, in this work we identified a specific motif AAAGACATG (- 101 to - 93) at the caspase-1 gene upstream sequence where HIPPI could bind. Various mutations in this specific sequence compromised the interaction, showing the specificity of the interactions. In the luciferase reporter assay, when the reporter gene was driven by caspase-1 gene upstream sequences (- 151 to - 92) with the mutation G to T at position - 98, luciferase activity was decreased significantly in green fluorescent protein-Hippi-expressing HeLa cells in comparison to that obtained with the wild-type caspase-1 gene 60 bp upstream sequence, indicating the biological significance of such binding. It was observed that the C-terminal 'pseudo' death effector domain of HIPPI interacted with the 60 bp (- 151 to - 92) upstream sequence of the caspase-1 gene containing the motif. We further observed that expression of caspase-8 and caspase-10 was increased in green fluorescent protein-Hippi-expressing HeLa cells. In addition, HIPPI interacted in vitro with putative promoter sequences of these genes, containing a similar motif. In summary, we identified a novel function of HIPPI; it binds to specific upstream sequences of the caspase-1, caspase-8 and caspase-10 genes and alters the expression of the genes. This result showed the motif-specific interaction of HIPPI with DNA, and indicates that it could act as transcription regulator.

Unifying measures of gene function and evolution.

PubMed

Wolf, Yuri I; Carmel, Liran; Koonin, Eugene V

2006-06-22

Recent genome analyses revealed intriguing correlations between variables characterizing the functioning of a gene, such as expression level (EL), connectivity of genetic and protein-protein interaction networks, and knockout effect, and variables describing gene evolution, such as sequence evolution rate (ER) and propensity for gene loss. Typically, variables within each of these classes are positively correlated, e.g. products of highly expressed genes also have a propensity to be involved in many protein-protein interactions, whereas variables between classes are negatively correlated, e.g. highly expressed genes, on average, evolve slower than weakly expressed genes. Here, we describe principal component (PC) analysis of seven genome-related variables and propose biological interpretations for the first three PCs. The first PC reflects a gene's 'importance', or the 'status' of a gene in the genomic community, with positive contributions from knockout lethality, EL, number of protein-protein interaction partners and the number of paralogues, and negative contributions from sequence ER and gene loss propensity. The next two PCs define a plane that seems to reflect the functional and evolutionary plasticity of a gene. Specifically, PC2 can be interpreted as a gene's 'adaptability' whereby genes with high adaptability readily duplicate, have many genetic interaction partners and tend to be non-essential. PC3 also might reflect the role of a gene in organismal adaptation albeit with a negative rather than a positive contribution of genetic interactions; we provisionally designate this PC 'reactivity'. The interpretation of PC2 and PC3 as measures of a gene's plasticity is compatible with the observation that genes with high values of these PCs tend to be expressed in a condition- or tissue-specific manner. Functional classes of genes substantially vary in status, adaptability and reactivity, with the highest status characteristic of the translation system and cytoskeletal proteins, highest adaptability seen in cellular processes and signalling genes, and top reactivity characteristic of metabolic enzymes.

Gene-Lifestyle Interactions in Complex Diseases: Design and Description of the GLACIER and VIKING Studies

PubMed Central

Kurbasic, Azra; Poveda, Alaitz; Chen, Yan; Ågren, Åsa; Engberg, Elisabeth; Hu, Frank B.; Johansson, Ingegerd; Barroso, Ines; Brändström, Anders; Hallmans, Göran; Renström, Frida; Franks, Paul W.

2014-01-01

Most complex diseases have well-established genetic and non-genetic risk factors. In some instances, these risk factors are likely to interact, whereby their joint effects convey a level of risk that is either significantly more or less than the sum of these risks. Characterizing these gene-environment interactions may help elucidate the biology of complex diseases, as well as to guide strategies for their targeted prevention. In most cases, the detection of gene-environment interactions will require sample sizes in excess of those needed to detect the marginal effects of the genetic and environmental risk factors. Although many consortia have been formed, comprising multiple diverse cohorts to detect gene-environment interactions, few robust examples of such interactions have been discovered. This may be because combining data across studies, usually through meta-analysis of summary data from the contributing cohorts, is often a statistically inefficient approach for the detection of gene-environment interactions. Ideally, single, very large and well-genotyped prospective cohorts, with validated measures of environmental risk factor and disease outcomes should be used to study interactions. The presence of strong founder effects within those cohorts might further strengthen the capacity to detect novel genetic effects and gene-environment interactions. Access to accurate genealogical data would also aid in studying the diploid nature of the human genome, such as genomic imprinting (parent-of-origin effects). Here we describe two studies from northern Sweden (the GLACIER and VIKING studies) that fulfill these characteristics. PMID:25396097

Gene-Lifestyle Interactions in Complex Diseases: Design and Description of the GLACIER and VIKING Studies.

PubMed

Kurbasic, Azra; Poveda, Alaitz; Chen, Yan; Agren, Asa; Engberg, Elisabeth; Hu, Frank B; Johansson, Ingegerd; Barroso, Ines; Brändström, Anders; Hallmans, Göran; Renström, Frida; Franks, Paul W

2014-12-01

Most complex diseases have well-established genetic and non-genetic risk factors. In some instances, these risk factors are likely to interact, whereby their joint effects convey a level of risk that is either significantly more or less than the sum of these risks. Characterizing these gene-environment interactions may help elucidate the biology of complex diseases, as well as to guide strategies for their targeted prevention. In most cases, the detection of gene-environment interactions will require sample sizes in excess of those needed to detect the marginal effects of the genetic and environmental risk factors. Although many consortia have been formed, comprising multiple diverse cohorts to detect gene-environment interactions, few robust examples of such interactions have been discovered. This may be because combining data across studies, usually through meta-analysis of summary data from the contributing cohorts, is often a statistically inefficient approach for the detection of gene-environment interactions. Ideally, single, very large and well-genotyped prospective cohorts, with validated measures of environmental risk factor and disease outcomes should be used to study interactions. The presence of strong founder effects within those cohorts might further strengthen the capacity to detect novel genetic effects and gene-environment interactions. Access to accurate genealogical data would also aid in studying the diploid nature of the human genome, such as genomic imprinting (parent-of-origin effects). Here we describe two studies from northern Sweden (the GLACIER and VIKING studies) that fulfill these characteristics.

Pre-Clinical Drug Prioritization via Prognosis-Guided Genetic Interaction Networks

PubMed Central

Xiong, Jianghui; Liu, Juan; Rayner, Simon; Tian, Ze; Li, Yinghui; Chen, Shanguang

2010-01-01

The high rates of failure in oncology drug clinical trials highlight the problems of using pre-clinical data to predict the clinical effects of drugs. Patient population heterogeneity and unpredictable physiology complicate pre-clinical cancer modeling efforts. We hypothesize that gene networks associated with cancer outcome in heterogeneous patient populations could serve as a reference for identifying drug effects. Here we propose a novel in vivo genetic interaction which we call ‘synergistic outcome determination’ (SOD), a concept similar to ‘Synthetic Lethality’. SOD is defined as the synergy of a gene pair with respect to cancer patients' outcome, whose correlation with outcome is due to cooperative, rather than independent, contributions of genes. The method combines microarray gene expression data with cancer prognostic information to identify synergistic gene-gene interactions that are then used to construct interaction networks based on gene modules (a group of genes which share similar function). In this way, we identified a cluster of important epigenetically regulated gene modules. By projecting drug sensitivity-associated genes on to the cancer-specific inter-module network, we defined a perturbation index for each drug based upon its characteristic perturbation pattern on the inter-module network. Finally, by calculating this index for compounds in the NCI Standard Agent Database, we significantly discriminated successful drugs from a broad set of test compounds, and further revealed the mechanisms of drug combinations. Thus, prognosis-guided synergistic gene-gene interaction networks could serve as an efficient in silico tool for pre-clinical drug prioritization and rational design of combinatorial therapies. PMID:21085674

Interactions of OsMADS1 with Floral Homeotic Genes in Rice Flower Development.

PubMed

Hu, Yun; Liang, Wanqi; Yin, Changsong; Yang, Xuelian; Ping, Baozhe; Li, Anxue; Jia, Ru; Chen, Mingjiao; Luo, Zhijing; Cai, Qiang; Zhao, Xiangxiang; Zhang, Dabing; Yuan, Zheng

2015-09-01

During reproductive development, rice plants develop unique flower organs which determine the final grain yield. OsMADS1, one of SEPALLATA-like MADS-box genes, has been unraveled to play critical roles in rice floral organ identity specification and floral meristem determinacy. However, the molecular mechanisms underlying interactions of OsMADS1 with other floral homeotic genes in regulating flower development remains largely elusive. In this work, we studied the genetic interactions of OsMADS1 with B-, C-, and D-class genes along with physical interactions among their proteins. We show that the physical and genetic interactions between OsMADS1 and OsMADS3 are essential for floral meristem activity maintenance and organ identity specification; while OsMADS1 physically and genetically interacts with OsMADS58 in regulating floral meristem determinacy and suppressing spikelet meristem reversion. We provided important genetic evidence to support the neofunctionalization of two rice C-class genes (OsMADS3 and OsMADS58) during flower development. Gene expression profiling and quantitative RT-PCR analyses further revealed that OsMADS1 affects the expression of many genes involved in floral identity and hormone signaling, and chromatin immunoprecipitation (ChIP)-PCR assay further demonstrated that OsMADS17 is a direct target gene of OsMADS1. Taken together, these results reveal that OsMADS1 has diversified regulatory functions in specifying rice floral organ and meristem identity, probably through its genetic and physical interactions with different floral homeotic regulators. Copyright © 2015 The Author. Published by Elsevier Inc. All rights reserved.

Function, dynamics and evolution of network motif modules in integrated gene regulatory networks of worm and plant.

PubMed

Defoort, Jonas; Van de Peer, Yves; Vermeirssen, Vanessa

2018-06-05

Gene regulatory networks (GRNs) consist of different molecular interactions that closely work together to establish proper gene expression in time and space. Especially in higher eukaryotes, many questions remain on how these interactions collectively coordinate gene regulation. We study high quality GRNs consisting of undirected protein-protein, genetic and homologous interactions, and directed protein-DNA, regulatory and miRNA-mRNA interactions in the worm Caenorhabditis elegans and the plant Arabidopsis thaliana. Our data-integration framework integrates interactions in composite network motifs, clusters these in biologically relevant, higher-order topological network motif modules, overlays these with gene expression profiles and discovers novel connections between modules and regulators. Similar modules exist in the integrated GRNs of worm and plant. We show how experimental or computational methodologies underlying a certain data type impact network topology. Through phylogenetic decomposition, we found that proteins of worm and plant tend to functionally interact with proteins of a similar age, while at the regulatory level TFs favor same age, but also older target genes. Despite some influence of the duplication mode difference, we also observe at the motif and module level for both species a preference for age homogeneity for undirected and age heterogeneity for directed interactions. This leads to a model where novel genes are added together to the GRNs in a specific biological functional context, regulated by one or more TFs that also target older genes in the GRNs. Overall, we detected topological, functional and evolutionary properties of GRNs that are potentially universal in all species.

Computational analysis of gene-gene interactions using multifactor dimensionality reduction.

PubMed

Moore, Jason H

2004-11-01

Understanding the relationship between DNA sequence variations and biologic traits is expected to improve the diagnosis, prevention and treatment of common human diseases. Success in characterizing genetic architecture will depend on our ability to address nonlinearities in the genotype-to-phenotype mapping relationship as a result of gene-gene interactions, or epistasis. This review addresses the challenges associated with the detection and characterization of epistasis. A novel strategy known as multifactor dimensionality reduction that was specifically designed for the identification of multilocus genetic effects is presented. Several case studies that demonstrate the detection of gene-gene interactions in common diseases such as atrial fibrillation, Type II diabetes and essential hypertension are also discussed.

Virus-Induced Gene Silencing Using Tobacco Rattle Virus as a Tool to Study the Interaction between Nicotiana attenuata and Rhizophagus irregularis.

PubMed

Groten, Karin; Pahari, Nabin T; Xu, Shuqing; Miloradovic van Doorn, Maja; Baldwin, Ian T

2015-01-01

Most land plants live in a symbiotic association with arbuscular mycorrhizal fungi (AMF) that belong to the phylum Glomeromycota. Although a number of plant genes involved in the plant-AMF interactions have been identified by analyzing mutants, the ability to rapidly manipulate gene expression to study the potential functions of new candidate genes remains unrealized. We analyzed changes in gene expression of wild tobacco roots (Nicotiana attenuata) after infection with mycorrhizal fungi (Rhizophagus irregularis) by serial analysis of gene expression (SuperSAGE) combined with next generation sequencing, and established a virus-induced gene-silencing protocol to study the function of candidate genes in the interaction. From 92,434 SuperSAGE Tag sequences, 32,808 (35%) matched with our in-house Nicotiana attenuata transcriptome database and 3,698 (4%) matched to Rhizophagus genes. In total, 11,194 Tags showed a significant change in expression (p<0.05, >2-fold change) after infection. When comparing the functions of highly up-regulated annotated Tags in this study with those of two previous large-scale gene expression studies, 18 gene functions were found to be up-regulated in all three studies mainly playing roles related to phytohormone metabolism, catabolism and defense. To validate the function of identified candidate genes, we used the technique of virus-induced gene silencing (VIGS) to silence the expression of three putative N. attenuata genes: germin-like protein, indole-3-acetic acid-amido synthetase GH3.9 and, as a proof-of-principle, calcium and calmodulin-dependent protein kinase (CCaMK). The silencing of the three plant genes in roots was successful, but only CCaMK silencing had a significant effect on the interaction with R. irregularis. Interestingly, when a highly activated inoculum was used for plant inoculation, the effect of CCaMK silencing on fungal colonization was masked, probably due to trans-complementation. This study demonstrates that large-scale gene expression studies across different species induce of a core set of genes of similar functions. However, additional factors seem to influence the overall pattern of gene expression, resulting in high variability among independent studies with different hosts. We conclude that VIGS is a powerful tool with which to investigate the function of genes involved in plant-AMF interactions but that inoculum strength can strongly influence the outcome of the interaction.

Interaction between Calpain 5, Peroxisome proliferator-activated receptor-gamma and Peroxisome proliferator-activated receptor-delta genes: a polygenic approach to obesity

PubMed Central

Sáez, María E; Grilo, Antonio; Morón, Francisco J; Manzano, Luis; Martínez-Larrad, María T; González-Pérez, Antonio; Serrano-Hernando, Javier; Ruiz, Agustín; Ramírez-Lorca, Reposo; Serrano-Ríos, Manuel

2008-01-01

Context Obesity is a multifactorial disorder, that is, a disease determined by the combined effect of genes and environment. In this context, polygenic approaches are needed. Objective To investigate the possibility of the existence of a crosstalk between the CALPAIN 10 homologue CALPAIN 5 and nuclear receptors of the peroxisome proliferator-activated receptors family. Design Cross-sectional, genetic association study and gene-gene interaction analysis. Subjects The study sample comprise 1953 individuals, 725 obese (defined as body mass index ≥ 30) and 1228 non obese subjects. Results In the monogenic analysis, only the peroxisome proliferator-activated receptor delta (PPARD) gene was associated with obesity (OR = 1.43 [1.04–1.97], p = 0.027). In addition, we have found a significant interaction between CAPN5 and PPARD genes (p = 0.038) that reduces the risk for obesity in a 55%. Conclusion Our results suggest that CAPN5 and PPARD gene products may also interact in vivo. PMID:18657264

Orphan nuclear receptor TLX recruits histone deacetylases to repress transcription and regulate neural stem cell proliferation.

PubMed

Sun, Guoqiang; Yu, Ruth T; Evans, Ronald M; Shi, Yanhong

2007-09-25

TLX is a transcription factor that is essential for neural stem cell proliferation and self-renewal. However, the molecular mechanism of TLX-mediated neural stem cell proliferation and self-renewal is largely unknown. We show here that TLX recruits histone deacetylases (HDACs) to its downstream target genes to repress their transcription, which in turn regulates neural stem cell proliferation. TLX interacts with HDAC3 and HDAC5 in neural stem cells. The HDAC5-interaction domain was mapped to TLX residues 359-385, which contains a conserved nuclear receptor-coregulator interaction motif IXXLL. Both HDAC3 and HDAC5 have been shown to be recruited to the promoters of TLX target genes along with TLX in neural stem cells. Recruitment of HDACs led to transcriptional repression of TLX target genes, the cyclin-dependent kinase inhibitor, p21(CIP1/WAF1)(p21), and the tumor suppressor gene, pten. Either inhibition of HDAC activity or knockdown of HDAC expression led to marked induction of p21 and pten gene expression and dramatically reduced neural stem cell proliferation, suggesting that the TLX-interacting HDACs play an important role in neural stem cell proliferation. Moreover, expression of a TLX peptide containing the minimal HDAC5 interaction domain disrupted the TLX-HDAC5 interaction. Disruption of this interaction led to significant induction of p21 and pten gene expression and to dramatic inhibition of neural stem cell proliferation. Taken together, these findings demonstrate a mechanism for neural stem cell proliferation through transcriptional repression of p21 and pten gene expression by TLX-HDAC interactions.

Common genetic variants in metabolism and detoxification pathways and the risk of papillary thyroid cancer

PubMed Central

Aschebrook-Kilfoy, Briseis; Neta, Gila; Brenner, Alina V; Hutchinson, Amy; Pfeiffer, Ruth M; Sturgis, Erich M; Xu, Li; Wheeler, William; Doody, Michele M; Chanock, Stephen J; Sigurdson, Alice J

2012-01-01

Relationships are unclear between polymorphisms in genes involved in metabolism and detoxification of various chemicals and papillary thyroid cancer (PTC) risk as well as their potential modification by alcohol or tobacco intake. We evaluated associations between 1647 tagging single nucleotide polymorphisms (SNPs) in 132 candidate genes/regions involved in metabolism of exogenous and endogenous compounds (Phase I/II, oxidative stress, and metal binding pathways) and PTC risk in 344 PTC cases and 452 controls. For 15 selected regions and their respective SNPs, we also assessed interaction with alcohol and tobacco use. Logistic regression models were used to evaluate the main effect of SNPs (Ptrend) and interaction with alcohol/tobacco intake. Gene- and pathway-level associations and interactions (Pgene interaction) were evaluated by combining Ptrend values using the adaptive rank-truncated product method. While we found associations between PTC risk and nine SNPs (Ptrend≤0.01) and seven genes/regions (Pregion<0.05), none remained significant after correction for the false discovery rate. We found a significant interaction between UGT2B7 and NAT1 genes and alcohol intake (Pgene interaction=0.01 and 0.02 respectively) and between the CYP26B1 gene and tobacco intake (Pgene interaction=0.02). Our results are suggestive of interaction between the genetic polymorphisms in several detoxification genes and alcohol or tobacco intake on risk of PTC. Larger studies with improved exposure assessment should address potential modification of PTC risk by alcohol and tobacco intake to confirm or refute our findings. PMID:22389382

Assessment of gene-by-sex interaction effect on bone mineral density.

PubMed

Liu, Ching-Ti; Estrada, Karol; Yerges-Armstrong, Laura M; Amin, Najaf; Evangelou, Evangelos; Li, Guo; Minster, Ryan L; Carless, Melanie A; Kammerer, Candace M; Oei, Ling; Zhou, Yanhua; Alonso, Nerea; Dailiana, Zoe; Eriksson, Joel; García-Giralt, Natalia; Giroux, Sylvie; Husted, Lise Bjerre; Khusainova, Rita I; Koromila, Theodora; Kung, Annie Waichee; Lewis, Joshua R; Masi, Laura; Mencej-Bedrac, Simona; Nogues, Xavier; Patel, Millan S; Prezelj, Janez; Richards, J Brent; Sham, Pak Chung; Spector, Timothy; Vandenput, Liesbeth; Xiao, Su-Mei; Zheng, Hou-Feng; Zhu, Kun; Balcells, Susana; Brandi, Maria Luisa; Frost, Morten; Goltzman, David; González-Macías, Jesús; Karlsson, Magnus; Khusnutdinova, Elza K; Kollia, Panagoula; Langdahl, Bente Lomholt; Ljunggren, Osten; Lorentzon, Mattias; Marc, Janja; Mellström, Dan; Ohlsson, Claes; Olmos, José M; Ralston, Stuart H; Riancho, José A; Rousseau, François; Urreizti, Roser; Van Hul, Wim; Zarrabeitia, María T; Castano-Betancourt, Martha; Demissie, Serkalem; Grundberg, Elin; Herrera, Lizbeth; Kwan, Tony; Medina-Gómez, Carolina; Pastinen, Tomi; Sigurdsson, Gunnar; Thorleifsson, Gudmar; Vanmeurs, Joyce Bj; Blangero, John; Hofman, Albert; Liu, Yongmei; Mitchell, Braxton D; O'Connell, Jeffrey R; Oostra, Ben A; Rotter, Jerome I; Stefansson, Kari; Streeten, Elizabeth A; Styrkarsdottir, Unnur; Thorsteinsdottir, Unnur; Tylavsky, Frances A; Uitterlinden, Andre; Cauley, Jane A; Harris, Tamara B; Ioannidis, John Pa; Psaty, Bruce M; Robbins, John A; Zillikens, M Carola; Vanduijn, Cornelia M; Prince, Richard L; Karasik, David; Rivadeneira, Fernando; Kiel, Douglas P; Cupples, L Adrienne; Hsu, Yi-Hsiang

2012-10-01

Sexual dimorphism in various bone phenotypes, including bone mineral density (BMD), is widely observed; however, the extent to which genes explain these sex differences is unclear. To identify variants with different effects by sex, we examined gene-by-sex autosomal interactions genome-wide, and performed expression quantitative trait loci (eQTL) analysis and bioinformatics network analysis. We conducted an autosomal genome-wide meta-analysis of gene-by-sex interaction on lumbar spine (LS) and femoral neck (FN) BMD in 25,353 individuals from 8 cohorts. In a second stage, we followed up the 12 top single-nucleotide polymorphisms (SNPs; p < 1 × 10(-5) ) in an additional set of 24,763 individuals. Gene-by-sex interaction and sex-specific effects were examined in these 12 SNPs. We detected one novel genome-wide significant interaction associated with LS-BMD at the Chr3p26.1-p25.1 locus, near the GRM7 gene (male effect = 0.02 and p = 3.0 × 10(-5) ; female effect = -0.007 and p = 3.3 × 10(-2) ), and 11 suggestive loci associated with either FN- or LS-BMD in discovery cohorts. However, there was no evidence for genome-wide significant (p < 5 × 10(-8) ) gene-by-sex interaction in the joint analysis of discovery and replication cohorts. Despite the large collaborative effort, no genome-wide significant evidence for gene-by-sex interaction was found to influence BMD variation in this screen of autosomal markers. If they exist, gene-by-sex interactions for BMD probably have weak effects, accounting for less than 0.08% of the variation in these traits per implicated SNP. © 2012 American Society for Bone and Mineral Research. Copyright © 2012 American Society for Bone and Mineral Research.

Prioritization of orphan disease-causing genes using topological feature and GO similarity between proteins in interaction networks.

PubMed

Li, Min; Li, Qi; Ganegoda, Gamage Upeksha; Wang, JianXin; Wu, FangXiang; Pan, Yi

2014-11-01

Identification of disease-causing genes among a large number of candidates is a fundamental challenge in human disease studies. However, it is still time-consuming and laborious to determine the real disease-causing genes by biological experiments. With the advances of the high-throughput techniques, a large number of protein-protein interactions have been produced. Therefore, to address this issue, several methods based on protein interaction network have been proposed. In this paper, we propose a shortest path-based algorithm, named SPranker, to prioritize disease-causing genes in protein interaction networks. Considering the fact that diseases with similar phenotypes are generally caused by functionally related genes, we further propose an improved algorithm SPGOranker by integrating the semantic similarity of GO annotations. SPGOranker not only considers the topological similarity between protein pairs in a protein interaction network but also takes their functional similarity into account. The proposed algorithms SPranker and SPGOranker were applied to 1598 known orphan disease-causing genes from 172 orphan diseases and compared with three state-of-the-art approaches, ICN, VS and RWR. The experimental results show that SPranker and SPGOranker outperform ICN, VS, and RWR for the prioritization of orphan disease-causing genes. Importantly, for the case study of severe combined immunodeficiency, SPranker and SPGOranker predict several novel causal genes.

fabp4 is central to eight obesity associated genes: a functional gene network-based polymorphic study.

PubMed

Bag, Susmita; Ramaiah, Sudha; Anbarasu, Anand

2015-01-07

Network study on genes and proteins offers functional basics of the complexity of gene and protein, and its interacting partners. The gene fatty acid-binding protein 4 (fabp4) is found to be highly expressed in adipose tissue, and is one of the most abundant proteins in mature adipocytes. Our investigations on functional modules of fabp4 provide useful information on the functional genes interacting with fabp4, their biochemical properties and their regulatory functions. The present study shows that there are eight set of candidate genes: acp1, ext2, insr, lipe, ostf1, sncg, usp15, and vim that are strongly and functionally linked up with fabp4. Gene ontological analysis of network modules of fabp4 provides an explicit idea on the functional aspect of fabp4 and its interacting nodes. The hierarchal mapping on gene ontology indicates gene specific processes and functions as well as their compartmentalization in tissues. The fabp4 along with its interacting genes are involved in lipid metabolic activity and are integrated in multi-cellular processes of tissues and organs. They also have important protein/enzyme binding activity. Our study elucidated disease-associated nsSNP prediction for fabp4 and it is interesting to note that there are four rsID׳s (rs1051231, rs3204631, rs140925685 and rs141169989) with disease allelic variation (T104P, T126P, G27D and G90V respectively). On the whole, our gene network analysis presents a clear insight about the interactions and functions associated with fabp4 gene network. Copyright © 2014 Elsevier Ltd. All rights reserved.

GBOOST: a GPU-based tool for detecting gene-gene interactions in genome-wide case control studies.

PubMed

Yung, Ling Sing; Yang, Can; Wan, Xiang; Yu, Weichuan

2011-05-01

Collecting millions of genetic variations is feasible with the advanced genotyping technology. With a huge amount of genetic variations data in hand, developing efficient algorithms to carry out the gene-gene interaction analysis in a timely manner has become one of the key problems in genome-wide association studies (GWAS). Boolean operation-based screening and testing (BOOST), a recent work in GWAS, completes gene-gene interaction analysis in 2.5 days on a desktop computer. Compared with central processing units (CPUs), graphic processing units (GPUs) are highly parallel hardware and provide massive computing resources. We are, therefore, motivated to use GPUs to further speed up the analysis of gene-gene interactions. We implement the BOOST method based on a GPU framework and name it GBOOST. GBOOST achieves a 40-fold speedup compared with BOOST. It completes the analysis of Wellcome Trust Case Control Consortium Type 2 Diabetes (WTCCC T2D) genome data within 1.34 h on a desktop computer equipped with Nvidia GeForce GTX 285 display card. GBOOST code is available at http://bioinformatics.ust.hk/BOOST.html#GBOOST.

Testing Gene-Gene Interactions in the Case-Parents Design

PubMed Central

Yu, Zhaoxia

2011-01-01

The case-parents design has been widely used to detect genetic associations as it can prevent spurious association that could occur in population-based designs. When examining the effect of an individual genetic locus on a disease, logistic regressions developed by conditioning on parental genotypes provide complete protection from spurious association caused by population stratification. However, when testing gene-gene interactions, it is unknown whether conditional logistic regressions are still robust. Here we evaluate the robustness and efficiency of several gene-gene interaction tests that are derived from conditional logistic regressions. We found that in the presence of SNP genotype correlation due to population stratification or linkage disequilibrium, tests with incorrectly specified main-genetic-effect models can lead to inflated type I error rates. We also found that a test with fully flexible main genetic effects always maintains correct test size and its robustness can be achieved with negligible sacrifice of its power. When testing gene-gene interactions is the focus, the test allowing fully flexible main effects is recommended to be used. PMID:21778736

Gene-gene interactions among genetic variants from obesity candidate genes for nonobese and obese populations in type 2 diabetes.

PubMed

Lin, Eugene; Pei, Dee; Huang, Yi-Jen; Hsieh, Chang-Hsun; Wu, Lawrence Shih-Hsin

2009-08-01

Recent studies indicate that obesity may play a key role in modulating genetic predispositions to type 2 diabetes (T2D). This study examines the main effects of both single-locus and multilocus interactions among genetic variants in Taiwanese obese and nonobese individuals to test the hypothesis that obesity-related genes may contribute to the etiology of T2D independently and/or through such complex interactions. We genotyped 11 single nucleotide polymorphisms for 10 obesity candidate genes including adrenergic beta-2-receptor surface, adrenergic beta-3-receptor surface, angiotensinogen, fat mass and obesity associated gene, guanine nucleotide binding protein beta polypeptide 3 (GNB3), interleukin 6 receptor, proprotein convertase subtilisin/kexin type 1 (PCSK1), uncoupling protein 1, uncoupling protein 2, and uncoupling protein 3. There were 389 patients diagnosed with T2D and 186 age- and sex-matched controls. Single-locus analyses showed significant main effects of the GNB3 and PCSK1 genes on the risk of T2D among the nonobese group (p = 0.002 and 0.047, respectively). Further, interactions involving GNB3 and PCSK1 were suggested among the nonobese population using the generalized multifactor dimensionality reduction method (p = 0.001). In addition, interactions among angiotensinogen, fat mass and obesity associated gene, GNB3, and uncoupling protein 3 genes were found in a significant four-locus generalized multifactor dimensionality reduction model among the obese population (p = 0.001). The results suggest that the single nucleotide polymorphisms from the obesity candidate genes may contribute to the risk of T2D independently and/or in an interactive manner according to the presence or absence of obesity.

A human functional protein interaction network and its application to cancer data analysis

PubMed Central

2010-01-01

Background One challenge facing biologists is to tease out useful information from massive data sets for further analysis. A pathway-based analysis may shed light by projecting candidate genes onto protein functional relationship networks. We are building such a pathway-based analysis system. Results We have constructed a protein functional interaction network by extending curated pathways with non-curated sources of information, including protein-protein interactions, gene coexpression, protein domain interaction, Gene Ontology (GO) annotations and text-mined protein interactions, which cover close to 50% of the human proteome. By applying this network to two glioblastoma multiforme (GBM) data sets and projecting cancer candidate genes onto the network, we found that the majority of GBM candidate genes form a cluster and are closer than expected by chance, and the majority of GBM samples have sequence-altered genes in two network modules, one mainly comprising genes whose products are localized in the cytoplasm and plasma membrane, and another comprising gene products in the nucleus. Both modules are highly enriched in known oncogenes, tumor suppressors and genes involved in signal transduction. Similar network patterns were also found in breast, colorectal and pancreatic cancers. Conclusions We have built a highly reliable functional interaction network upon expert-curated pathways and applied this network to the analysis of two genome-wide GBM and several other cancer data sets. The network patterns revealed from our results suggest common mechanisms in the cancer biology. Our system should provide a foundation for a network or pathway-based analysis platform for cancer and other diseases. PMID:20482850

Cooperation and coexpression: How coexpression networks shift in response to multiple mutualists.

PubMed

Palakurty, Sathvik X; Stinchcombe, John R; Afkhami, Michelle E

2018-04-01

A mechanistic understanding of community ecology requires tackling the nonadditive effects of multispecies interactions, a challenge that necessitates integration of ecological and molecular complexity-namely moving beyond pairwise ecological interaction studies and the "gene at a time" approach to mechanism. Here, we investigate the consequences of multispecies mutualisms for the structure and function of genomewide differential coexpression networks for the first time, using the tractable and ecologically important interaction between legume Medicago truncatula, rhizobia and mycorrhizal fungi. First, we found that genes whose expression is affected nonadditively by multiple mutualists are more highly connected in gene networks than expected by chance and had 94% greater network centrality than genes showing additive effects, suggesting that nonadditive genes may be key players in the widespread transcriptomic responses to multispecies symbioses. Second, multispecies mutualisms substantially changed coexpression network structure of 18 modules of host plant genes and 22 modules of the fungal symbionts' genes, indicating that third-party mutualists can cause significant rewiring of plant and fungal molecular networks. Third, we found that 60% of the coexpressed gene sets that explained variation in plant performance had coexpression structures that were altered by interactive effects of rhizobia and fungi. Finally, an "across-symbiosis" approach identified sets of plant and mycorrhizal genes whose coexpression structure was unique to the multiple mutualist context and suggested coupled responses across the plant-mycorrhizal interaction to rhizobial mutualists. Taken together, these results show multispecies mutualisms have substantial effects on the molecular interactions in host plants, microbes and across symbiotic boundaries. © 2018 John Wiley & Sons Ltd.

Characterization of Arabidopsis Transcriptional Responses to Different Aphid Species Reveals Genes that Contribute to Host Susceptibility and Non-host Resistance

PubMed Central

Jaouannet, Maëlle; Morris, Jenny A.; Hedley, Peter E.; Bos, Jorunn I. B.

2015-01-01

Aphids are economically important pests that display exceptional variation in host range. The determinants of diverse aphid host ranges are not well understood, but it is likely that molecular interactions are involved. With significant progress being made towards understanding host responses upon aphid attack, the mechanisms underlying non-host resistance remain to be elucidated. Here, we investigated and compared Arabidopsis thaliana host and non-host responses to aphids at the transcriptional level using three different aphid species, Myzus persicae, Myzus cerasi and Rhopalosiphum pisum. Gene expression analyses revealed a high level of overlap in the overall gene expression changes during the host and non-host interactions with regards to the sets of genes differentially expressed and the direction of expression changes. Despite this overlap in transcriptional responses across interactions, there was a stronger repression of genes involved in metabolism and oxidative responses specifically during the host interaction with M. persicae. In addition, we identified a set of genes with opposite gene expression patterns during the host versus non-host interactions. Aphid performance assays on Arabidopsis mutants that were selected based on our transcriptome analyses identified novel genes contributing to host susceptibility, host defences during interactions with M. persicae as well to non-host resistance against R. padi. Understanding how plants respond to aphid species that differ in their ability to infest plant species, and identifying the genes and signaling pathways involved, is essential for the development of novel and durable aphid control in crop plants. PMID:25993686

Detecting recurrent gene mutation in interaction network context using multi-scale graph diffusion.

PubMed

Babaei, Sepideh; Hulsman, Marc; Reinders, Marcel; de Ridder, Jeroen

2013-01-23

Delineating the molecular drivers of cancer, i.e. determining cancer genes and the pathways which they deregulate, is an important challenge in cancer research. In this study, we aim to identify pathways of frequently mutated genes by exploiting their network neighborhood encoded in the protein-protein interaction network. To this end, we introduce a multi-scale diffusion kernel and apply it to a large collection of murine retroviral insertional mutagenesis data. The diffusion strength plays the role of scale parameter, determining the size of the network neighborhood that is taken into account. As a result, in addition to detecting genes with frequent mutations in their genomic vicinity, we find genes that harbor frequent mutations in their interaction network context. We identify densely connected components of known and putatively novel cancer genes and demonstrate that they are strongly enriched for cancer related pathways across the diffusion scales. Moreover, the mutations in the clusters exhibit a significant pattern of mutual exclusion, supporting the conjecture that such genes are functionally linked. Using multi-scale diffusion kernel, various infrequently mutated genes are found to harbor significant numbers of mutations in their interaction network neighborhood. Many of them are well-known cancer genes. The results demonstrate the importance of defining recurrent mutations while taking into account the interaction network context. Importantly, the putative cancer genes and networks detected in this study are found to be significant at different diffusion scales, confirming the necessity of a multi-scale analysis.

DiffSLC: A graph centrality method to detect essential proteins of a protein-protein interaction network.

PubMed

Mistry, Divya; Wise, Roger P; Dickerson, Julie A

2017-01-01

Identification of central genes and proteins in biomolecular networks provides credible candidates for pathway analysis, functional analysis, and essentiality prediction. The DiffSLC centrality measure predicts central and essential genes and proteins using a protein-protein interaction network. Network centrality measures prioritize nodes and edges based on their importance to the network topology. These measures helped identify critical genes and proteins in biomolecular networks. The proposed centrality measure, DiffSLC, combines the number of interactions of a protein and the gene coexpression values of genes from which those proteins were translated, as a weighting factor to bias the identification of essential proteins in a protein interaction network. Potentially essential proteins with low node degree are promoted through eigenvector centrality. Thus, the gene coexpression values are used in conjunction with the eigenvector of the network's adjacency matrix and edge clustering coefficient to improve essentiality prediction. The outcome of this prediction is shown using three variations: (1) inclusion or exclusion of gene co-expression data, (2) impact of different coexpression measures, and (3) impact of different gene expression data sets. For a total of seven networks, DiffSLC is compared to other centrality measures using Saccharomyces cerevisiae protein interaction networks and gene expression data. Comparisons are also performed for the top ranked proteins against the known essential genes from the Saccharomyces Gene Deletion Project, which show that DiffSLC detects more essential proteins and has a higher area under the ROC curve than other compared methods. This makes DiffSLC a stronger alternative to other centrality methods for detecting essential genes using a protein-protein interaction network that obeys centrality-lethality principle. DiffSLC is implemented using the igraph package in R, and networkx package in Python. The python package can be obtained from git.io/diffslcpy. The R implementation and code to reproduce the analysis is available via git.io/diffslc.

A network approach to predict pathogenic genes for Fusarium graminearum.

PubMed

Liu, Xiaoping; Tang, Wei-Hua; Zhao, Xing-Ming; Chen, Luonan

2010-10-04

Fusarium graminearum is the pathogenic agent of Fusarium head blight (FHB), which is a destructive disease on wheat and barley, thereby causing huge economic loss and health problems to human by contaminating foods. Identifying pathogenic genes can shed light on pathogenesis underlying the interaction between F. graminearum and its plant host. However, it is difficult to detect pathogenic genes for this destructive pathogen by time-consuming and expensive molecular biological experiments in lab. On the other hand, computational methods provide an alternative way to solve this problem. Since pathogenesis is a complicated procedure that involves complex regulations and interactions, the molecular interaction network of F. graminearum can give clues to potential pathogenic genes. Furthermore, the gene expression data of F. graminearum before and after its invasion into plant host can also provide useful information. In this paper, a novel systems biology approach is presented to predict pathogenic genes of F. graminearum based on molecular interaction network and gene expression data. With a small number of known pathogenic genes as seed genes, a subnetwork that consists of potential pathogenic genes is identified from the protein-protein interaction network (PPIN) of F. graminearum, where the genes in the subnetwork are further required to be differentially expressed before and after the invasion of the pathogenic fungus. Therefore, the candidate genes in the subnetwork are expected to be involved in the same biological processes as seed genes, which imply that they are potential pathogenic genes. The prediction results show that most of the pathogenic genes of F. graminearum are enriched in two important signal transduction pathways, including G protein coupled receptor pathway and MAPK signaling pathway, which are known related to pathogenesis in other fungi. In addition, several pathogenic genes predicted by our method are verified in other pathogenic fungi, which demonstrate the effectiveness of the proposed method. The results presented in this paper not only can provide guidelines for future experimental verification, but also shed light on the pathogenesis of the destructive fungus F. graminearum.

Dynamic changes in the interchromosomal interaction of early histone gene loci during development of sea urchin.

PubMed

Matsushita, Masaya; Ochiai, Hiroshi; Suzuki, Ken-Ichi T; Hayashi, Sayaka; Yamamoto, Takashi; Awazu, Akinori; Sakamoto, Naoaki

2017-12-15

The nuclear positioning and chromatin dynamics of eukaryotic genes are closely related to the regulation of gene expression, but they have not been well examined during early development, which is accompanied by rapid cell cycle progression and dynamic changes in nuclear organization, such as nuclear size and chromatin constitution. In this study, we focused on the early development of the sea urchin Hemicentrotus pulcherrimus and performed three-dimensional fluorescence in situ hybridization of gene loci encoding early histones (one of the types of histone in sea urchin). There are two non-allelic early histone gene loci per sea urchin genome. We found that during the morula stage, when the early histone gene expression levels are at their maximum, interchromosomal interactions were often formed between the early histone gene loci on separate chromosomes and that the gene loci were directed to locate to more interior positions. Furthermore, these interactions were associated with the active transcription of the early histone genes. Thus, such dynamic interchromosomal interactions may contribute to the efficient synthesis of early histone mRNA during the morula stage of sea urchin development. © 2017. Published by The Company of Biologists Ltd.

Activation-dependent intrachromosomal interactions formed by the TNF gene promoter and two distal enhancers

PubMed Central

Tsytsykova, Alla V.; Rajsbaum, Ricardo; Falvo, James V.; Ligeiro, Filipa; Neely, Simon R.; Goldfeld, Anne E.

2007-01-01

Here we provide a mechanism for specific, efficient transcription of the TNF gene and, potentially, other genes residing within multigene loci. We identify and characterize highly conserved noncoding elements flanking the TNF gene, which undergo activation-dependent intrachromosomal interactions. These elements, hypersensitive site (HSS)−9 and HSS+3 (9 kb upstream and 3 kb downstream of the TNF gene, respectively), contain DNase I hypersensitive sites in naive, T helper 1, and T helper 2 primary T cells. Both HSS-9 and HSS+3 inducibly associate with acetylated histones, indicative of chromatin remodeling, bind the transcription factor nuclear factor of activated T cells (NFAT)p in vitro and in vivo, and function as enhancers of NFAT-dependent transactivation mediated by the TNF promoter. Using the chromosome conformation capture assay, we demonstrate that upon T cell activation intrachromosomal looping occurs in the TNF locus. HSS-9 and HSS+3 each associate with the TNF promoter and with each other, circularizing the TNF gene and bringing NFAT-containing nucleoprotein complexes into close proximity. TNF gene regulation thus reveals a mode of intrachromosomal interaction that combines a looped gene topology with interactions between enhancers and a gene promoter. PMID:17940009

A Combinatorial Approach to Detecting Gene-Gene and Gene-Environment Interactions in Family Studies

PubMed Central

Lou, Xiang-Yang; Chen, Guo-Bo; Yan, Lei; Ma, Jennie Z.; Mangold, Jamie E.; Zhu, Jun; Elston, Robert C.; Li, Ming D.

2008-01-01

Widespread multifactor interactions present a significant challenge in determining risk factors of complex diseases. Several combinatorial approaches, such as the multifactor dimensionality reduction (MDR) method, have emerged as a promising tool for better detecting gene-gene (G × G) and gene-environment (G × E) interactions. We recently developed a general combinatorial approach, namely the generalized multifactor dimensionality reduction (GMDR) method, which can entertain both qualitative and quantitative phenotypes and allows for both discrete and continuous covariates to detect G × G and G × E interactions in a sample of unrelated individuals. In this article, we report the development of an algorithm that can be used to study G × G and G × E interactions for family-based designs, called pedigree-based GMDR (PGMDR). Compared to the available method, our proposed method has several major improvements, including allowing for covariate adjustments and being applicable to arbitrary phenotypes, arbitrary pedigree structures, and arbitrary patterns of missing marker genotypes. Our Monte Carlo simulations provide evidence that the PGMDR method is superior in performance to identify epistatic loci compared to the MDR-pedigree disequilibrium test (PDT). Finally, we applied our proposed approach to a genetic data set on tobacco dependence and found a significant interaction between two taste receptor genes (i.e., TAS2R16 and TAS2R38) in affecting nicotine dependence. PMID:18834969

Comparative genomics of free-living Gammaproteobacteria: pathogenesis-related genes or interaction-related genes?

PubMed

Vázquez-Rosas-Landa, Mirna; Ponce-Soto, Gabriel Yaxal; Eguiarte, Luis E; Souza, V

2017-07-31

Bacteria have numerous strategies to interact with themselves and with their environment, but genes associated with these interactions are usually cataloged as pathogenic. To understand the role that these genes have not only in pathogenesis but also in bacterial interactions, we compared the genomes of eight bacteria from human-impacted environments with those of free-living bacteria from the Cuatro Ciénegas Basin (CCB), a relatively pristine oligotrophic site. Fifty-one genomes from CCB bacteria, including Pseudomonas, Vibrio, Photobacterium and Aeromonas, were analyzed. We found that the CCB strains had several virulence-related genes, 15 of which were common to all strains and were related to flagella and chemotaxis. We also identified the presence of Type III and VI secretion systems, which leads us to propose that these systems play an important role in interactions among bacterial communities beyond pathogenesis. None of the CCB strains had pathogenicity islands, despite having genes associated with antibiotics. Integrons were rare, while CRISPR elements were common. The idea that pathogenicity-related genes in many cases form part of a wider strategy used by bacteria to interact with other organisms could help us to understand the role of pathogenicity-related elements in an ecological and evolutionary framework leading toward a more inclusive One Health concept. © FEMS 2017. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

De-novo assembly and characterization of the transcriptome of Metschnikowia fructicola reveals differences in gene expression following interaction with Penicillium digitatum and grapefruit peel

PubMed Central

2013-01-01

Background The yeast Metschnikowia fructicola is an antagonist with biological control activity against postharvest diseases of several fruits. We performed a transcriptome analysis, using RNA-Seq technology, to examine the response of M. fructicola with citrus fruit and with the postharvest pathogen, Penicillium digitatum. Results More than 26 million sequencing reads were assembled into 9,674 unigenes. Approximately 50% of the unigenes could be annotated based on homology matches in the NCBI database. Based on homology, sequences were annotated with a gene description, gene ontology (GO term), and clustered into functional groups. An analysis of differential expression when the yeast was interacting with the fruit vs. the pathogen revealed more than 250 genes with specific expression responses. In the antagonist-pathogen interaction, genes related to transmembrane, multidrug transport and to amino acid metabolism were induced. In the antagonist-fruit interaction, expression of genes involved in oxidative stress, iron homeostasis, zinc homeostasis, and lipid metabolism were induced. Patterns of gene expression in the two interactions were examined at the individual transcript level by quantitative real-time PCR analysis (RT-qPCR). Conclusion This study provides new insight into the biology of the tritrophic interactions that occur in a biocontrol system such as the use of the yeast, M. fructicola for the control of green mold on citrus caused by P. digitatum. PMID:23496978

Transposon tagging of genes for cell-cell interactions in Myxococcus xanthus.

PubMed Central

Kalos, M; Zissler, J

1990-01-01

The prokaryote Myxococcus xanthus is a model for cell interactions important in multicellular behavior. We used the transposon TnphoA to specifically identify genes for cell-surface factors involved in cell interactions. From a library of 10,700 insertions of TnphoA, we isolated 36 that produced alkaline phosphatase activity. Three TnphoA insertions tagged cell motility genes, called cgl, which control the adventurous movement of cells. The products of the tagged cgl genes could function in trans upon other cells and were localized primarily in the cell envelope and extracellular space, consistent with TnphoA tagging genes for extracellular factors controlling motility. Images PMID:2172982

Inverse gene-for-gene interactions contribute additively to tan spot susceptibility in wheat.

PubMed

Liu, Zhaohui; Zurn, Jason D; Kariyawasam, Gayan; Faris, Justin D; Shi, Gongjun; Hansen, Jana; Rasmussen, Jack B; Acevedo, Maricelis

2017-06-01

Tan spot susceptibility is conferred by multiple interactions of necrotrophic effector and host sensitivity genes. Tan spot of wheat, caused by Pyrenophora tritici-repentis, is an important disease in almost all wheat-growing areas of the world. The disease system is known to involve at least three fungal-produced necrotrophic effectors (NEs) that interact with the corresponding host sensitivity (S) genes in an inverse gene-for-gene manner to induce disease. However, it is unknown if the effects of these NE-S gene interactions contribute additively to the development of tan spot. In this work, we conducted disease evaluations using different races and quantitative trait loci (QTL) analysis in a wheat recombinant inbred line (RIL) population derived from a cross between two susceptible genotypes, LMPG-6 and PI 626573. The two parental lines each harbored a single known NE sensitivity gene with LMPG-6 having the Ptr ToxC sensitivity gene Tsc1 and PI 626573 having the Ptr ToxA sensitivity gene Tsn1. Transgressive segregation was observed in the population for all races. QTL mapping revealed that both loci (Tsn1 and Tsc1) were significantly associated with susceptibility to race 1 isolates, which produce both Ptr ToxA and Ptr ToxC, and the two genes contributed additively to tan spot susceptibility. For isolates of races 2 and 3, which produce only Ptr ToxA and Ptr ToxC, only Tsn1 and Tsc1 were associated with tan spot susceptibility, respectively. This work clearly demonstrates that tan spot susceptibility in this population is due primarily to two NE-S interactions. Breeders should remove both sensitivity genes from wheat lines to obtain high levels of tan spot resistance.

Interactive Effects of in Utero Nutrition and Genetic Inheritance on Cognition: New Evidence Using Sibling Comparisons1

PubMed Central

Cook, C. Justin; Fletcher, Jason M.

2013-01-01

A large literature links early environments and later outcomes, such as cognition; however, little is known about the mechanisms. One potential mechanism is sensitivity to early environments that is moderated or amplified by the genotype. With this mechanism in mind, a complementary literature outside economics examines the interaction between genes and environments, but often problems of endogeneity and bias in estimation are uncorrected. A key issue in the literature is exploring environmental variation that is not exogenous, which is potentially problematic if there are gene-environment correlation or gene-gene interactions. Using sibling pairs with genetic data in the Wisconsin Longitudinal Study we extend a previous, and widely cited, gene-environment study that explores an interaction between the FADS2 gene, which is associated with the processing of essential fatty acids related to cognitive development, and early life nutrition in explaining later-life IQ. Our base OLS findings suggest that individuals with specific FADS2 variants gain roughly 0.15 standard deviations in IQ for each standard deviation increase in birth weight, our measure of the early nutrition environment; while, individuals with other variants of FADS2 do not have a statistically significant association with early nutrition, implying the genotype is influencing the effects of environmental exposure. When including family-level fixed effects, however, the magnitude of the gene-environment interaction is reduced by half and statistical significance dissipates, implying the interaction between FADS2 and early nutrition in explaining later life IQ may in part be due to unobserved, family-level factors. The example has wider implications for the practice of investigating gene-environment interactions when the environmental exposure is not exogenous and robustness to unobserved variation in the genome is not controlled for in the analysis. PMID:24172871

Gene-Environment Interactions in Schizophrenia: Review of Epidemiological Findings and Future Directions

PubMed Central

van Os, Jim; Rutten, Bart PF; Poulton, Richie

2008-01-01

Concern is building about high rates of schizophrenia in large cities, and among immigrants, cannabis users, and traumatized individuals, some of which likely reflects the causal influence of environmental exposures. This, in combination with very slow progress in the area of molecular genetics, has generated interest in more complicated models of schizophrenia etiology that explicitly posit gene-environment interactions (EU-GEI. European Network of Schizophrenia Networks for the Study of Gene Environment Interactions. Schizophrenia aetiology: do gene-environment interactions hold the key? [published online ahead of print April 25, 2008] Schizophr Res; S0920-9964(08) 00170–9). Although findings of epidemiological gene-environment interaction (G × E) studies are suggestive of widespread gene-environment interactions in the etiology of schizophrenia, numerous challenges remain. For example, attempts to identify gene-environment interactions cannot be equated with molecular genetic studies with a few putative environmental variables “thrown in”: G × E is a multidisciplinary exercise involving epidemiology, psychology, psychiatry, neuroscience, neuroimaging, pharmacology, biostatistics, and genetics. Epidemiological G × E studies using indirect measures of genetic risk in genetically sensitive designs have the advantage that they are able to model the net, albeit nonspecific, genetic load. In studies using direct molecular measures of genetic variation, a hypothesis-driven approach postulating synergistic effects between genes and environment impacting on a final common pathway, such as “sensitization” of mesolimbic dopamine neurotransmission, while simplistic, may provide initial focus and protection against the numerous false-positive and false-negative results that these investigations engender. Experimental ecogenetic approaches with randomized assignment may help to overcome some of the limitations of observational studies and allow for the additional elucidation of underlying mechanisms using a combination of functional enviromics and functional genomics. PMID:18791076

Blood lead levels, iron metabolism gene polymorphisms and homocysteine: a gene-environment interaction study.

PubMed

Kim, Kyoung-Nam; Lee, Mee-Ri; Lim, Youn-Hee; Hong, Yun-Chul

2017-12-01

Homocysteine has been causally associated with various adverse health outcomes. Evidence supporting the relationship between lead and homocysteine levels has been accumulating, but most prior studies have not focused on the interaction with genetic polymorphisms. From a community-based prospective cohort, we analysed 386 participants (aged 41-71 years) with information regarding blood lead and plasma homocysteine levels. Blood lead levels were measured between 2001 and 2003, and plasma homocysteine levels were measured in 2007. Interactions of lead levels with 42 genotyped single-nucleotide polymorphisms (SNPs) in five genes ( TF , HFE , CBS , BHMT and MTR ) were assessed via a 2-degree of freedom (df) joint test and a 1-df interaction test. In secondary analyses using imputation, we further assessed 58 imputed SNPs in the TF and MTHFR genes. Blood lead concentrations were positively associated with plasma homocysteine levels (p=0.0276). Six SNPs in the TF and MTR genes were screened using the 2-df joint test, and among them, three SNPs in the TF gene showed interactions with lead with respect to homocysteine levels through the 1-df interaction test (p<0.0083). Seven SNPs in the MTHFR gene were associated with homocysteine levels at an α-level of 0.05, but the associations did not persist after Bonferroni correction. These SNPs did not show interactions with lead levels. Blood lead levels were positively associated with plasma homocysteine levels measured 4-6 years later, and three SNPs in the TF gene modified the association. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Genes-environment interactions in obesity- and diabetes-associated pancreatic cancer: a GWAS data analysis.

PubMed

Tang, Hongwei; Wei, Peng; Duell, Eric J; Risch, Harvey A; Olson, Sara H; Bueno-de-Mesquita, H Bas; Gallinger, Steven; Holly, Elizabeth A; Petersen, Gloria M; Bracci, Paige M; McWilliams, Robert R; Jenab, Mazda; Riboli, Elio; Tjønneland, Anne; Boutron-Ruault, Marie Christine; Kaaks, Rudolf; Trichopoulos, Dimitrios; Panico, Salvatore; Sund, Malin; Peeters, Petra H M; Khaw, Kay-Tee; Amos, Christopher I; Li, Donghui

2014-01-01

Obesity and diabetes are potentially alterable risk factors for pancreatic cancer. Genetic factors that modify the associations of obesity and diabetes with pancreatic cancer have previously not been examined at the genome-wide level. Using genome-wide association studies (GWAS) genotype and risk factor data from the Pancreatic Cancer Case Control Consortium, we conducted a discovery study of 2,028 cases and 2,109 controls to examine gene-obesity and gene-diabetes interactions in relation to pancreatic cancer risk by using the likelihood-ratio test nested in logistic regression models and Ingenuity Pathway Analysis (IPA). After adjusting for multiple comparisons, a significant interaction of the chemokine signaling pathway with obesity (P = 3.29 × 10(-6)) and a near significant interaction of calcium signaling pathway with diabetes (P = 1.57 × 10(-4)) in modifying the risk of pancreatic cancer were observed. These findings were supported by results from IPA analysis of the top genes with nominal interactions. The major contributing genes to the two top pathways include GNGT2, RELA, TIAM1, and GNAS. None of the individual genes or single-nucleotide polymorphism (SNP) except one SNP remained significant after adjusting for multiple testing. Notably, SNP rs10818684 of the PTGS1 gene showed an interaction with diabetes (P = 7.91 × 10(-7)) at a false discovery rate of 6%. Genetic variations in inflammatory response and insulin resistance may affect the risk of obesity- and diabetes-related pancreatic cancer. These observations should be replicated in additional large datasets. A gene-environment interaction analysis may provide new insights into the genetic susceptibility and molecular mechanisms of obesity- and diabetes-related pancreatic cancer.

Development and use of the Cytoscape app GFD-Net for measuring semantic dissimilarity of gene networks

PubMed Central

Diaz-Montana, Juan J.; Diaz-Diaz, Norberto

2014-01-01

Gene networks are one of the main computational models used to study the interaction between different elements during biological processes being widely used to represent gene–gene, or protein–protein interaction complexes. We present GFD-Net, a Cytoscape app for visualizing and analyzing the functional dissimilarity of gene networks. PMID:25400907

Ant Species Differences Determined by Epistasis between Brood and Worker Genomes

PubMed Central

Linksvayer, Timothy A.

2007-01-01

Epistasis arising from physiological interactions between gene products often contributes to species differences, particularly those involved in reproductive isolation. In social organisms, phenotypes are influenced by the genotypes of multiple interacting individuals. In theory, social interactions can give rise to an additional type of epistasis between the genomes of social partners that can contribute to species differences. Using a full-factorial cross-fostering design with three species of closely related Temnothorax ants, I found that adult worker size was determined by an interaction between the genotypes of developing brood and care-giving workers, i.e. intergenomic epistasis. Such intergenomic social epistasis provides a strong signature of coevolution between social partners. These results demonstrate that just as physiologically interacting genes coevolve, diverge, and contribute to species differences, so do socially interacting genes. Coevolution and conflict between social partners, especially relatives such as parents and offspring, has long been recognized as having widespread evolutionary effects. This coevolutionary process may often result in coevolved socially-interacting gene complexes that contribute to species differences. PMID:17912371

Genetic background effects in quantitative genetics: gene-by-system interactions.

PubMed

Sardi, Maria; Gasch, Audrey P

2018-04-11

Proper cell function depends on networks of proteins that interact physically and functionally to carry out physiological processes. Thus, it seems logical that the impact of sequence variation in one protein could be significantly influenced by genetic variants at other loci in a genome. Nonetheless, the importance of such genetic interactions, known as epistasis, in explaining phenotypic variation remains a matter of debate in genetics. Recent work from our lab revealed that genes implicated from an association study of toxin tolerance in Saccharomyces cerevisiae show extensive interactions with the genetic background: most implicated genes, regardless of allele, are important for toxin tolerance in only one of two tested strains. The prevalence of background effects in our study adds to other reports of widespread genetic-background interactions in model organisms. We suggest that these effects represent many-way interactions with myriad features of the cellular system that vary across classes of individuals. Such gene-by-system interactions may influence diverse traits and require new modeling approaches to accurately represent genotype-phenotype relationships across individuals.

Systematic Search for Gene-Gene Interaction Effect on Prostate Cancer Risk

DTIC Science & Technology

2011-07-01

PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 1. REPORT DATE (DD-MM-YYYY) 2 . REPORT TYPE 3. DATES COVERED (From - To) 4. TITLE AND SUBTITLE 5a...identify SNPs in the genome that interact to have stronger effects on PCa risk in the CGEMS GWAS data, 2 ) confirm the gene-gene interaction effect on PCa...for pairs of SNPs implicated in Aim 2 among the remaining 1,893 cases and 781 controls in CAPS, and 4) fine map the genomic regions where SNPs have

Gene network biological validity based on gene-gene interaction relevance.

PubMed

Gómez-Vela, Francisco; Díaz-Díaz, Norberto

2014-01-01

In recent years, gene networks have become one of the most useful tools for modeling biological processes. Many inference gene network algorithms have been developed as techniques for extracting knowledge from gene expression data. Ensuring the reliability of the inferred gene relationships is a crucial task in any study in order to prove that the algorithms used are precise. Usually, this validation process can be carried out using prior biological knowledge. The metabolic pathways stored in KEGG are one of the most widely used knowledgeable sources for analyzing relationships between genes. This paper introduces a new methodology, GeneNetVal, to assess the biological validity of gene networks based on the relevance of the gene-gene interactions stored in KEGG metabolic pathways. Hence, a complete KEGG pathway conversion into a gene association network and a new matching distance based on gene-gene interaction relevance are proposed. The performance of GeneNetVal was established with three different experiments. Firstly, our proposal is tested in a comparative ROC analysis. Secondly, a randomness study is presented to show the behavior of GeneNetVal when the noise is increased in the input network. Finally, the ability of GeneNetVal to detect biological functionality of the network is shown.

Learning contextual gene set interaction networks of cancer with condition specificity

PubMed Central

2013-01-01

Background Identifying similarities and differences in the molecular constitutions of various types of cancer is one of the key challenges in cancer research. The appearances of a cancer depend on complex molecular interactions, including gene regulatory networks and gene-environment interactions. This complexity makes it challenging to decipher the molecular origin of the cancer. In recent years, many studies reported methods to uncover heterogeneous depictions of complex cancers, which are often categorized into different subtypes. The challenge is to identify diverse molecular contexts within a cancer, to relate them to different subtypes, and to learn underlying molecular interactions specific to molecular contexts so that we can recommend context-specific treatment to patients. Results In this study, we describe a novel method to discern molecular interactions specific to certain molecular contexts. Unlike conventional approaches to build modular networks of individual genes, our focus is to identify cancer-generic and subtype-specific interactions between contextual gene sets, of which each gene set share coherent transcriptional patterns across a subset of samples, termed contextual gene set. We then apply a novel formulation for quantitating the effect of the samples from each subtype on the calculated strength of interactions observed. Two cancer data sets were analyzed to support the validity of condition-specificity of identified interactions. When compared to an existing approach, the proposed method was much more sensitive in identifying condition-specific interactions even in heterogeneous data set. The results also revealed that network components specific to different types of cancer are related to different biological functions than cancer-generic network components. We found not only the results that are consistent with previous studies, but also new hypotheses on the biological mechanisms specific to certain cancer types that warrant further investigations. Conclusions The analysis on the contextual gene sets and characterization of networks of interaction composed of these sets discovered distinct functional differences underlying various types of cancer. The results show that our method successfully reveals many subtype-specific regions in the identified maps of biological contexts, which well represent biological functions that can be connected to specific subtypes. PMID:23418942

NATURE VERSUS NURTURE: DEATH OF A DOGMA, AND THE ROAD AHEAD

PubMed Central

Traynor, Bryan J.; Singleton, Andrew B.

2010-01-01

Interaction between the genome and the environment has been widely discussed in the literature, but has the importance ascribed to understanding these interactions been overstated? In this opinion piece, we critically discuss gene-environment interactions and attempt to answer three key questions: First, is it likely that gene-environment interactions actually exist? Second, what is the realistic value of trying to unravel these interactions, both in terms of understanding disease pathogenesis and as a means of ameliorating disease? Finally, and most importantly, do the technologies and methodologies exist to facilitate an unbiased search for gene-environment interactions? Addressing these questions highlights key areas of feasibility that must be considered in this area of research. PMID:20955927

An interactional network of genes involved in chitin synthesis in Saccharomyces cerevisiae

PubMed Central

Lesage, Guillaume; Shapiro, Jesse; Specht, Charles A; Sdicu, Anne-Marie; Ménard, Patrice; Hussein, Shamiza; Tong, Amy Hin Yan; Boone, Charles; Bussey, Howard

2005-01-01

Background In S. cerevisiae the β-1,4-linked N-acetylglucosamine polymer, chitin, is synthesized by a family of 3 specialized but interacting chitin synthases encoded by CHS1, CHS2 and CHS3. Chs2p makes chitin in the primary septum, while Chs3p makes chitin in the lateral cell wall and in the bud neck, and can partially compensate for the lack of Chs2p. Chs3p requires a pathway of Bni4p, Chs4p, Chs5p, Chs6p and Chs7p for its localization and activity. Chs1p is thought to have a septum repair function after cell separation. To further explore interactions in the chitin synthase family and to find processes buffering chitin synthesis, we compiled a genetic interaction network of genes showing synthetic interactions with CHS1, CHS3 and genes involved in Chs3p localization and function and made a phenotypic analysis of their mutants. Results Using deletion mutants in CHS1, CHS3, CHS4, CHS5, CHS6, CHS7 and BNI4 in a synthetic genetic array analysis we assembled a network of 316 interactions among 163 genes. The interaction network with CHS3, CHS4, CHS5, CHS6, CHS7 or BNI4 forms a dense neighborhood, with many genes functioning in cell wall assembly or polarized secretion. Chitin levels were altered in 54 of the mutants in individually deleted genes, indicating a functional relationship between them and chitin synthesis. 32 of these mutants triggered the chitin stress response, with elevated chitin levels and a dependence on CHS3. A large fraction of the CHS1-interaction set was distinct from that of the CHS3 network, indicating broad roles for Chs1p in buffering both Chs2p function and more global cell wall robustness. Conclusion Based on their interaction patterns and chitin levels we group interacting mutants into functional categories. Genes interacting with CHS3 are involved in the amelioration of cell wall defects and in septum or bud neck chitin synthesis, and we newly assign a number of genes to these functions. Our genetic analysis of genes not interacting with CHS3 indicate expanded roles for Chs4p, Chs5p and Chs6p in secretory protein trafficking and of Bni4p in bud neck organization. PMID:15715908

Orphan nuclear receptor TLX recruits histone deacetylases to repress transcription and regulate neural stem cell proliferation

PubMed Central

Sun, GuoQiang; Yu, Ruth T.; Evans, Ronald M.; Shi, Yanhong

2007-01-01

TLX is a transcription factor that is essential for neural stem cell proliferation and self-renewal. However, the molecular mechanism of TLX-mediated neural stem cell proliferation and self-renewal is largely unknown. We show here that TLX recruits histone deacetylases (HDACs) to its downstream target genes to repress their transcription, which in turn regulates neural stem cell proliferation. TLX interacts with HDAC3 and HDAC5 in neural stem cells. The HDAC5-interaction domain was mapped to TLX residues 359–385, which contains a conserved nuclear receptor–coregulator interaction motif IXXLL. Both HDAC3 and HDAC5 have been shown to be recruited to the promoters of TLX target genes along with TLX in neural stem cells. Recruitment of HDACs led to transcriptional repression of TLX target genes, the cyclin-dependent kinase inhibitor, p21CIP1/WAF1(p21), and the tumor suppressor gene, pten. Either inhibition of HDAC activity or knockdown of HDAC expression led to marked induction of p21 and pten gene expression and dramatically reduced neural stem cell proliferation, suggesting that the TLX-interacting HDACs play an important role in neural stem cell proliferation. Moreover, expression of a TLX peptide containing the minimal HDAC5 interaction domain disrupted the TLX–HDAC5 interaction. Disruption of this interaction led to significant induction of p21 and pten gene expression and to dramatic inhibition of neural stem cell proliferation. Taken together, these findings demonstrate a mechanism for neural stem cell proliferation through transcriptional repression of p21 and pten gene expression by TLX–HDAC interactions. PMID:17873065

Genetic interactions within inositol-related pathways are associated with longitudinal changes in ventricle size

PubMed Central

Koran, Mary Ellen I.; Hohman, Timothy J.; Meda, Shashwath A.; Thornton-Wells, Tricia A.

2013-01-01

The genetic etiology of late onset Alzheimer disease (LOAD) has proven complex, involving clinical and genetic heterogeneity and gene-gene interactions. Recent genome wide association studies (GWAS) in LOAD have led to the discovery of novel genetic risk factors; however, the investigation of gene-gene interactions has been limited. Conventional genetic studies often use binary disease status as the primary phenotype, but for complex brain-based diseases, neuroimaging data can serve as quantitative endophenotypes that correlate with disease status and closely reflect pathological changes. In the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, we tested for association of genetic interactions with longitudinal MRI measurements of the inferior lateral ventricles (ILVs), which have repeatedly shown a relationship to LOAD status and progression. We performed linear regression to evaluate the ability of pathway-derived SNP-SNP pairs to predict the slope of change in volume of the ILVs. After Bonferroni correction, we identified four significant interactions in the right ILV (RILV) corresponding to gene-gene pairs SYNJ2-PI4KA, PARD3-MYH2, PDE3A-ABHD12B and OR2L13-PRKG1 and one significant interaction in the left ILV (LILV) corresponding to SYNJ2-PI4KA. The SNP-SNP interaction corresponding to SYNJ2-PI4KA was identical in the RILV and LILV and was the most significant interaction in each (RILV: p=9.10×10−12; LILV: p=8.20×10−13). Both genes belong to the inositol phosphate signaling pathway which has been previously associated with neurodegeneration in AD and we discuss the possibility that perturbation of this pathway results in a down-regulation of the Akt cell survival pathway and, thereby, decreased neuronal survival, as reflected by increased volume of the ventricles. PMID:24077433

Direct Protein Interactions Are Responsible for Ikaros-GATA and Ikaros-Cdk9 Cooperativeness in Hematopoietic Cells

PubMed Central

Bottardi, Stefania; Mavoungou, Lionel; Bourgoin, Vincent; Mashtalir, Nazar; Affar, El Bachir

2013-01-01

Ikaros (Ik) is a critical regulator of hematopoietic gene expression. Here, we established that the Ik interactions with GATA transcription factors and cyclin-dependent kinase 9 (Cdk9), a component of the positive transcription elongation factor b (P-TEFb), are required for transcriptional activation of Ik target genes. A detailed dissection of Ik-GATA and Ik-Cdk9 protein interactions indicated that the C-terminal zinc finger domain of Ik interacts directly with the C-terminal zinc fingers of GATA1, GATA2, and GATA3, whereas the N-terminal zinc finger domain of Ik is required for interaction with the kinase and T-loop domains of Cdk9. The relevance of these interactions was demonstrated in vivo in COS-7 and primary hematopoietic cells, in which Ik facilitated Cdk9 and GATA protein recruitment to gene promoters and transcriptional activation. Moreover, the oncogenic isoform Ik6 did not efficiently interact with Cdk9 or GATA proteins in vivo and perturbed Cdk9/P-TEFb recruitment to Ik target genes, thereby affecting transcription elongation. Finally, characterization of a novel nuclear Ik isoform revealed that Ik exon 6 is dispensable for interactions with Mi2 and GATA proteins but is essential for the Cdk9 interaction. Thus, Ik is central to the Ik-GATA-Cdk9 regulatory network, which is broadly utilized for gene regulation in hematopoietic cells. PMID:23732910

Direct protein interactions are responsible for Ikaros-GATA and Ikaros-Cdk9 cooperativeness in hematopoietic cells.

PubMed

Bottardi, Stefania; Mavoungou, Lionel; Bourgoin, Vincent; Mashtalir, Nazar; Affar, El Bachir; Milot, Eric

2013-08-01

Ikaros (Ik) is a critical regulator of hematopoietic gene expression. Here, we established that the Ik interactions with GATA transcription factors and cyclin-dependent kinase 9 (Cdk9), a component of the positive transcription elongation factor b (P-TEFb), are required for transcriptional activation of Ik target genes. A detailed dissection of Ik-GATA and Ik-Cdk9 protein interactions indicated that the C-terminal zinc finger domain of Ik interacts directly with the C-terminal zinc fingers of GATA1, GATA2, and GATA3, whereas the N-terminal zinc finger domain of Ik is required for interaction with the kinase and T-loop domains of Cdk9. The relevance of these interactions was demonstrated in vivo in COS-7 and primary hematopoietic cells, in which Ik facilitated Cdk9 and GATA protein recruitment to gene promoters and transcriptional activation. Moreover, the oncogenic isoform Ik6 did not efficiently interact with Cdk9 or GATA proteins in vivo and perturbed Cdk9/P-TEFb recruitment to Ik target genes, thereby affecting transcription elongation. Finally, characterization of a novel nuclear Ik isoform revealed that Ik exon 6 is dispensable for interactions with Mi2 and GATA proteins but is essential for the Cdk9 interaction. Thus, Ik is central to the Ik-GATA-Cdk9 regulatory network, which is broadly utilized for gene regulation in hematopoietic cells.

ToxCast Data Expands Universe of Chemical-Gene Interactions (SOT)

EPA Science Inventory

Characterizing the effects of chemicals in biological systems is often summarized by chemical-gene interactions, which have sparse coverage in literature. The ToxCast chemical screening program has produced bioactivity data for nearly 2000 chemicals and over 450 gene targets. Thi...

Degrees of separation as a statistical tool for evaluating candidate genes.

PubMed

Nelson, Ronald M; Pettersson, Mats E

2014-12-01

Selection of candidate genes is an important step in the exploration of complex genetic architecture. The number of gene networks available is increasing and these can provide information to help with candidate gene selection. It is currently common to use the degree of connectedness in gene networks as validation in Genome Wide Association (GWA) and Quantitative Trait Locus (QTL) mapping studies. However, it can cause misleading results if not validated properly. Here we present a method and tool for validating the gene pairs from GWA studies given the context of the network they co-occur in. It ensures that proposed interactions and gene associations are not statistical artefacts inherent to the specific gene network architecture. The CandidateBacon package provides an easy and efficient method to calculate the average degree of separation (DoS) between pairs of genes to currently available gene networks. We show how these empirical estimates of average connectedness are used to validate candidate gene pairs. Validation of interacting genes by comparing their connectedness with the average connectedness in the gene network will provide support for said interactions by utilising the growing amount of gene network information available. Copyright © 2014 Elsevier Ltd. All rights reserved.

ReliefSeq: A Gene-Wise Adaptive-K Nearest-Neighbor Feature Selection Tool for Finding Gene-Gene Interactions and Main Effects in mRNA-Seq Gene Expression Data

PubMed Central

McKinney, Brett A.; White, Bill C.; Grill, Diane E.; Li, Peter W.; Kennedy, Richard B.; Poland, Gregory A.; Oberg, Ann L.

2013-01-01

Relief-F is a nonparametric, nearest-neighbor machine learning method that has been successfully used to identify relevant variables that may interact in complex multivariate models to explain phenotypic variation. While several tools have been developed for assessing differential expression in sequence-based transcriptomics, the detection of statistical interactions between transcripts has received less attention in the area of RNA-seq analysis. We describe a new extension and assessment of Relief-F for feature selection in RNA-seq data. The ReliefSeq implementation adapts the number of nearest neighbors (k) for each gene to optimize the Relief-F test statistics (importance scores) for finding both main effects and interactions. We compare this gene-wise adaptive-k (gwak) Relief-F method with standard RNA-seq feature selection tools, such as DESeq and edgeR, and with the popular machine learning method Random Forests. We demonstrate performance on a panel of simulated data that have a range of distributional properties reflected in real mRNA-seq data including multiple transcripts with varying sizes of main effects and interaction effects. For simulated main effects, gwak-Relief-F feature selection performs comparably to standard tools DESeq and edgeR for ranking relevant transcripts. For gene-gene interactions, gwak-Relief-F outperforms all comparison methods at ranking relevant genes in all but the highest fold change/highest signal situations where it performs similarly. The gwak-Relief-F algorithm outperforms Random Forests for detecting relevant genes in all simulation experiments. In addition, Relief-F is comparable to the other methods based on computational time. We also apply ReliefSeq to an RNA-Seq study of smallpox vaccine to identify gene expression changes between vaccinia virus-stimulated and unstimulated samples. ReliefSeq is an attractive tool for inclusion in the suite of tools used for analysis of mRNA-Seq data; it has power to detect both main effects and interaction effects. Software Availability: http://insilico.utulsa.edu/ReliefSeq.php. PMID:24339943

Construction of ontology augmented networks for protein complex prediction.

PubMed

Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian

2013-01-01

Protein complexes are of great importance in understanding the principles of cellular organization and function. The increase in available protein-protein interaction data, gene ontology and other resources make it possible to develop computational methods for protein complex prediction. Most existing methods focus mainly on the topological structure of protein-protein interaction networks, and largely ignore the gene ontology annotation information. In this article, we constructed ontology augmented networks with protein-protein interaction data and gene ontology, which effectively unified the topological structure of protein-protein interaction networks and the similarity of gene ontology annotations into unified distance measures. After constructing ontology augmented networks, a novel method (clustering based on ontology augmented networks) was proposed to predict protein complexes, which was capable of taking into account the topological structure of the protein-protein interaction network, as well as the similarity of gene ontology annotations. Our method was applied to two different yeast protein-protein interaction datasets and predicted many well-known complexes. The experimental results showed that (i) ontology augmented networks and the unified distance measure can effectively combine the structure closeness and gene ontology annotation similarity; (ii) our method is valuable in predicting protein complexes and has higher F1 and accuracy compared to other competing methods.

Effects of circadian clock genes and environmental factors on cognitive aging in old adults in a Taiwanese population.

PubMed

Lin, Eugene; Kuo, Po-Hsiu; Liu, Yu-Li; Yang, Albert C; Kao, Chung-Feng; Tsai, Shih-Jen

2017-04-11

Previous animal studies have indicated associations between circadian clock genes and cognitive impairment . In this study, we assessed whether 11 circadian clockgenes are associated with cognitive aging independently and/or through complex interactions in an old Taiwanese population. We also analyzed the interactions between environmental factors and these genes in influencing cognitive aging. A total of 634 Taiwanese subjects aged over 60 years from the Taiwan Biobank were analyzed. Mini-Mental State Examinations (MMSE) were administered to all subjects, and MMSE scores were used to evaluate cognitive function. Our data showed associations between cognitive aging and single nucleotide polymorphisms (SNPs) in 4 key circadian clock genes, CLOCK rs3749473 (p = 0.0017), NPAS2 rs17655330 (p = 0.0013), RORA rs13329238 (p = 0.0009), and RORB rs10781247 (p = 7.9 x 10-5). We also found that interactions between CLOCK rs3749473, NPAS2 rs17655330, RORA rs13329238, and RORB rs10781247 affected cognitive aging (p = 0.007). Finally, we investigated the influence of interactions between CLOCK rs3749473, RORA rs13329238, and RORB rs10781247 with environmental factors such as alcohol consumption, smoking status, physical activity, and social support on cognitive aging (p = 0.002 ~ 0.01). Our study indicates that circadian clock genes such as the CLOCK, NPAS2, RORA, and RORB genes may contribute to the risk of cognitive aging independently as well as through gene-gene and gene-environment interactions.

Key genes and pathways in measles and their interaction with environmental chemicals.

PubMed

Zhang, Rongqiang; Jiang, Hualin; Li, Fengying; Su, Ning; Ding, Yi; Mao, Xiang; Ren, Dan; Wang, Jing

2018-06-01

The aim of the present study was to explore key genes that may have a role in the pathology of measles virus infection and to clarify the interaction networks between environmental factors and differentially expressed genes (DEGs). After screening the database of the Gene Expression Omnibus of the National Center for Biotechnology Information, the dataset GSE5808 was downloaded and analyzed. A global normalization method was performed to minimize data inconsistencies and heterogeneity. DEGs during different stages of measles virus infection were explored using R software (v3.4.0). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the DEGs were performed using Cytoscape 3.4.0 software. A protein-protein interaction (PPI) network of the DEGs was obtained from the STRING database v9.05. A total of 43 DEGs were obtained from four analyzed sample groups, including 10 highly expressed genes and 33 genes with decreased expression. The most enriched pathways based on KEGG analysis were fatty acid elongation, cytokine-cytokine receptor interaction and RNA degradation. The genes mentioned in the PPI network were mainly associated with protein binding and chemokine activity. A total of 219 chemicals were identified that may, jointly or on their own, interact with the 6 DEGs between the control group and patients with measles (at hospital entry), including benzo(a)pyrene (BaP) and tetrachlorodibenzodioxin (TCDD). In conclusion, the present study revealed that chemokines and environmental chemicals, e.g. BaP and TCDD, may affect the development of measles.

Overexpression of erg1 gene in Trichoderma harzianum CECT 2413: effect on the induction of tomato defence-related genes.

PubMed

Cardoza, R E; Malmierca, M G; Gutiérrez, S

2014-09-01

To investigate the effect of the overexpression of erg1 gene of Trichoderma harzianum CECT 2413 (T34) on the Trichoderma-plant interactions and in the biocontrol ability of this fungus. Transformants of T34 strain overexpressing erg1 gene did not show effect on the ergosterol level, although a drastic decrease in the squalene level was observed in the transformants at 96 h of growth. During interaction with plants, the erg1 overexpression resulted in a reduction of the priming ability of several tomato defence-related genes belonging to the salicylate pathway, and also of the TomLoxA gene, which is related to the jasmonate pathway. Interestingly, other jasmonate-related genes, such as PINI and PINII, were slightly induced. The erg1 overexpressed transformants also showed a reduced ability to colonize tomato roots. The ergosterol biosynthetic pathway might play an important role in regulating Trichoderma-plant interactions, although this role does not seem to be restricted to the final product; instead, other intermediates such as squalene, whose role in the Trichoderma-plant interaction has not been characterized, would also play an important role. The functional analysis of genes involved in the synthesis of ergosterol could provide additional strategies to improve the ability of biocontrol of the Trichoderma strains and their interaction with plants. © 2014 The Society for Applied Microbiology.

Identification of T1D susceptibility genes within the MHC region by combining protein interaction networks and SNP genotyping data

PubMed Central

Brorsson, C.; Hansen, N. T.; Lage, K.; Bergholdt, R.; Brunak, S.; Pociot, F.

2009-01-01

Aim To develop novel methods for identifying new genes that contribute to the risk of developing type 1 diabetes within the Major Histocompatibility Complex (MHC) region on chromosome 6, independently of the known linkage disequilibrium (LD) between human leucocyte antigen (HLA)-DRB1, -DQA1, -DQB1 genes. Methods We have developed a novel method that combines single nucleotide polymorphism (SNP) genotyping data with protein–protein interaction (ppi) networks to identify disease-associated network modules enriched for proteins encoded from the MHC region. Approximately 2500 SNPs located in the 4 Mb MHC region were analysed in 1000 affected offspring trios generated by the Type 1 Diabetes Genetics Consortium (T1DGC). The most associated SNP in each gene was chosen and genes were mapped to ppi networks for identification of interaction partners. The association testing and resulting interacting protein modules were statistically evaluated using permutation. Results A total of 151 genes could be mapped to nodes within the protein interaction network and their interaction partners were identified. Five protein interaction modules reached statistical significance using this approach. The identified proteins are well known in the pathogenesis of T1D, but the modules also contain additional candidates that have been implicated in β-cell development and diabetic complications. Conclusions The extensive LD within the MHC region makes it important to develop new methods for analysing genotyping data for identification of additional risk genes for T1D. Combining genetic data with knowledge about functional pathways provides new insight into mechanisms underlying T1D. PMID:19143816

Protein-Protein Interactions (PPI) reagents: | Office of Cancer Genomics

Cancer.gov

The CTD2 Center at Emory University has a library of genes used to study protein-protein interactions in mammalian cells. These genes are cloned in different mammalian expression vectors. A list of available cancer-associated genes can be accessed below.

A functional genomics approach to dissect the mode of action of the Stagonospora nodorum effector protein SnToxA in wheat

USDA-ARS?s Scientific Manuscript database

It has now been established that the wheat pathogen Stagonospora nodorum causes disease on wheat in an inverse gene-for-gene manner through the interaction of pathogen effector proteins and corresponding dominant susceptibility host genes. One such effector, SnToxA, interacts with the Tsn1 gene to c...

The core planar cell polarity gene prickle interacts with flamingo to promote sensory axon advance in the Drosophila embryo.

PubMed

Mrkusich, Eli M; Flanagan, Dustin J; Whitington, Paul M

2011-10-01

The atypical cadherin Drosophila protein Flamingo and its vertebrate homologues play widespread roles in the regulation of both dendrite and axon growth. However, little is understood about the molecular mechanisms that underpin these functions. Whereas flamingo interacts with a well-defined group of genes in regulating planar cell polarity, previous studies have uncovered little evidence that the other core planar cell polarity genes are involved in regulation of neurite growth. We present data in this study showing that the planar cell polarity gene prickle interacts with flamingo in regulating sensory axon advance at a key choice point - the transition between the peripheral nervous system and the central nervous system. The cytoplasmic tail of the Flamingo protein is not required for this interaction. Overexpression of another core planar cell polarity gene dishevelled produces a similar phenotype to prickle mutants, suggesting that this gene may also play a role in regulation of sensory axon advance. Crown Copyright © 2011. Published by Elsevier Inc. All rights reserved.

Enhancing the gene-environment interaction framework through a quasi-experimental research design: evidence from differential responses to September 11.

PubMed

Fletcher, Jason M

2014-01-01

This article uses a gene-environment interaction framework to examine the differential responses to an objective external stressor based on genetic variation in the production of depressive symptoms. This article advances the literature by utilizing a quasi-experimental environmental exposure design, as well as a regression discontinuity design, to control for seasonal trends, which limit the potential for gene-environment correlation and allow stronger causal claims. Replications are attempted for two prominent genes (5-HTT and MAOA), and three additional genes are explored (DRD2, DRD4, and DAT1). This article provides evidence of a main effect of 9/11 on reports of feelings of sadness and fails to replicate a common finding of interaction using 5-HTT but does show support for interaction with MAOA in men. It also provides new evidence that variation in the DRD4 gene modifies an individual's response to the exposure, with individuals with no 7-repeats found to have a muted response.

Rice MADS6 Interacts with the Floral Homeotic Genes SUPERWOMAN1, MADS3, MADS58, MADS13, and DROOPING LEAF in Specifying Floral Organ Identities and Meristem Fate[C][W][OA

PubMed Central

Li, Haifeng; Liang, Wanqi; Hu, Yun; Zhu, Lu; Yin, Changsong; Xu, Jie; Dreni, Ludovico; Kater, Martin M.; Zhang, Dabing

2011-01-01

AGAMOUS-LIKE6 (AGL6) genes play essential roles in flower development, but whether and how they work with floral organ identity genes remain less understood. Here, we describe interactions of the rice (Oryza sativa) AGL6 gene MADS6 with other rice floral homeotic genes in flower development. Genetic analyses revealed that MADS6 specifies the identity of the three inner whorls and floral meristem determinacy redundantly with SUPERWOMAN1/MADS16 (B-gene) or MADS3 (C-gene). MADS6 was shown to define carpel/ovule development and floral determinacy by interacting with MADS13 (D-gene) and control the palea and floral meristem identities together with the YABBY gene DROOPING LEAF. Expression analyses revealed that the transcript levels of six B-, C-, and E-class genes were reduced in mads6-1 at the early flower developmental stage, suggesting that MADS6 is a key regulator of early flower development. Moreover, MADS6 can directly bind to a putative regulatory motif on MADS58 (C-gene), and mads6-1 mads58 displayed phenotypes similar to that of mads6-1. These results suggest that MADS6 is a key player in specifying flower development via interacting with other floral homeotic genes in rice, thus providing new insights into the mechanism by which flower development is controlled. PMID:21784949

Oxytocin receptor gene (OXTR) in relation to loneliness in adolescence: interactions with sex, parental support, and DRD2 and 5-HTTLPR genotypes.

PubMed

van Roekel, Eeske; Verhagen, Maaike; Engels, Rutger C M E; Goossens, Luc; Scholte, Ron H J

2013-10-01

Recent research has shown that loneliness, a common problem in adolescence, may have a genetic basis. The evidence, though, was limited mostly to serotonin-related and dopamine-related genes. In the present study, we focused on the oxytocin receptor gene (OXTR). Associations were examined in a longitudinal study spanning five annual waves (N=307). The relations between OXTR and loneliness were examined, as well as interactions between OXTR and sex, parental support, 5-HTTLPR genotype, and DRD2 genotype. Using Latent Growth Curve Modeling, the OXTR genotype was not directly related to loneliness. An OXTR×sex interaction was found. Girls showed a steeper decline in loneliness when they had an A allele compared with girls who were homozygous for the G allele. In addition, a gene-gene interaction or epistasis was observed. Both boys and girls who had at least one A1 allele for the DRD2 gene and also had the GG genotype for the OXTR gene showed stable levels of loneliness over time. The present study is the first to show that the GG genotype for the OXTR gene is linked to the development of loneliness in adolescence and that this association is moderated by participants' sex and their genotype for a dopamine-related gene.

Co-acting gene networks predict TRAIL responsiveness of tumour cells with high accuracy.

PubMed

O'Reilly, Paul; Ortutay, Csaba; Gernon, Grainne; O'Connell, Enda; Seoighe, Cathal; Boyce, Susan; Serrano, Luis; Szegezdi, Eva

2014-12-19

Identification of differentially expressed genes from transcriptomic studies is one of the most common mechanisms to identify tumor biomarkers. This approach however is not well suited to identify interaction between genes whose protein products potentially influence each other, which limits its power to identify molecular wiring of tumour cells dictating response to a drug. Due to the fact that signal transduction pathways are not linear and highly interlinked, the biological response they drive may be better described by the relative amount of their components and their functional relationships than by their individual, absolute expression. Gene expression microarray data for 109 tumor cell lines with known sensitivity to the death ligand cytokine tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) was used to identify genes with potential functional relationships determining responsiveness to TRAIL-induced apoptosis. The machine learning technique Random Forest in the statistical environment "R" with backward elimination was used to identify the key predictors of TRAIL sensitivity and differentially expressed genes were identified using the software GeneSpring. Gene co-regulation and statistical interaction was assessed with q-order partial correlation analysis and non-rejection rate. Biological (functional) interactions amongst the co-acting genes were studied with Ingenuity network analysis. Prediction accuracy was assessed by calculating the area under the receiver operator curve using an independent dataset. We show that the gene panel identified could predict TRAIL-sensitivity with a very high degree of sensitivity and specificity (AUC=0·84). The genes in the panel are co-regulated and at least 40% of them functionally interact in signal transduction pathways that regulate cell death and cell survival, cellular differentiation and morphogenesis. Importantly, only 12% of the TRAIL-predictor genes were differentially expressed highlighting the importance of functional interactions in predicting the biological response. The advantage of co-acting gene clusters is that this analysis does not depend on differential expression and is able to incorporate direct- and indirect gene interactions as well as tissue- and cell-specific characteristics. This approach (1) identified a descriptor of TRAIL sensitivity which performs significantly better as a predictor of TRAIL sensitivity than any previously reported gene signatures, (2) identified potential novel regulators of TRAIL-responsiveness and (3) provided a systematic view highlighting fundamental differences between the molecular wiring of sensitive and resistant cell types.

TP53 mutations, expression and interaction networks in human cancers

PubMed Central

Wang, Xiaosheng; Sun, Qingrong

2017-01-01

Although the associations of p53 dysfunction, p53 interaction networks and oncogenesis have been widely explored, a systematic analysis of TP53 mutations and its related interaction networks in various types of human cancers is lacking. Our study explored the associations of TP53 mutations, gene expression, clinical outcomes, and TP53 interaction networks across 33 cancer types using data from The Cancer Genome Atlas (TCGA). We show that TP53 is the most frequently mutated gene in a number of cancers, and its mutations appear to be early events in cancer initiation. We identified genes potentially repressed by p53, and genes whose expression correlates significantly with TP53 expression. These gene products may be especially important nodes in p53 interaction networks in human cancers. This study shows that while TP53-truncating mutations often result in decreased TP53 expression, other non-truncating TP53 mutations result in increased TP53 expression in some cancers. Survival analyses in a number of cancers show that patients with TP53 mutations are more likely to have worse prognoses than TP53-wildtype patients, and that elevated TP53 expression often leads to poor clinical outcomes. We identified a set of candidate synthetic lethal (SL) genes for TP53, and validated some of these SL interactions using data from the Cancer Cell Line Project. These predicted SL genes are promising candidates for experimental validation and the development of personalized therapeutics for patients with TP53-mutated cancers. PMID:27880943

TP53 mutations, expression and interaction networks in human cancers.

PubMed

Wang, Xiaosheng; Sun, Qingrong

2017-01-03

Although the associations of p53 dysfunction, p53 interaction networks and oncogenesis have been widely explored, a systematic analysis of TP53 mutations and its related interaction networks in various types of human cancers is lacking. Our study explored the associations of TP53 mutations, gene expression, clinical outcomes, and TP53 interaction networks across 33 cancer types using data from The Cancer Genome Atlas (TCGA). We show that TP53 is the most frequently mutated gene in a number of cancers, and its mutations appear to be early events in cancer initiation. We identified genes potentially repressed by p53, and genes whose expression correlates significantly with TP53 expression. These gene products may be especially important nodes in p53 interaction networks in human cancers. This study shows that while TP53-truncating mutations often result in decreased TP53 expression, other non-truncating TP53 mutations result in increased TP53 expression in some cancers. Survival analyses in a number of cancers show that patients with TP53 mutations are more likely to have worse prognoses than TP53-wildtype patients, and that elevated TP53 expression often leads to poor clinical outcomes. We identified a set of candidate synthetic lethal (SL) genes for TP53, and validated some of these SL interactions using data from the Cancer Cell Line Project. These predicted SL genes are promising candidates for experimental validation and the development of personalized therapeutics for patients with TP53-mutated cancers.

The Interacting Effect of the BDNF Val66Met Polymorphism and Stressful Life Events on Adolescent Depression Is Not an Artifact of Gene-Environment Correlation: Evidence from a Longitudinal Twin Study

ERIC Educational Resources Information Center

Chen, Jie; Li, Xinying; McGue, Matt

2013-01-01

Background: Confounding introduced by gene-environment correlation (rGE) may prevent one from observing a true gene-environment interaction (G × E) effect on psychopathology. The present study investigated the interacting effect of the BDNF Val66Met polymorphism and stressful life events (SLEs) on adolescent depression while controlling for the…

Antisocial Peer Affiliation and Externalizing Disorders: Evidence for Gene × Environment × Development Interaction

PubMed Central

Samek, Diana R.; Hicks, Brian M.; Keyes, Margaret A.; Iacono, William G.; McGue, Matt

2016-01-01

Gene × environment interaction contributes to externalizing disorders in adolescence, but little is known about whether such effects are long-lasting or present in adulthood. We examined gene-environment interplay in the concurrent and prospective associations between antisocial peer affiliation and externalizing disorders (antisocial behavior and substance use disorders) at ages 17, 20, 24, and 29. The sample included 1,382 same-sex twin pairs participating in the Minnesota Twin Family Study. We detected a gene × environment interaction at age 17, such that additive genetic influences on antisocial behavior and substance use disorders were greater in the context of greater antisocial peer affiliation. This gene × environment interaction was not present for antisocial behavior symptoms after age 17, but was for substance use disorder symptoms through age 29 (though effect sizes were largest at age 17). Results suggest adolescence is a critical period for the development of externalizing disorders wherein exposure to greater environmental adversity is associated with a greater expression of genetic risk. This form of gene × environment interaction may persist through young adulthood for substance use disorders, but is limited to adolescence for antisocial behavior. PMID:27580681

Antisocial peer affiliation and externalizing disorders: Evidence for Gene × Environment × Development interaction.

PubMed

Samek, Diana R; Hicks, Brian M; Keyes, Margaret A; Iacono, William G; McGue, Matt

2017-02-01

Gene × Environment interaction contributes to externalizing disorders in childhood and adolescence, but little is known about whether such effects are long lasting or present in adulthood. We examined gene-environment interplay in the concurrent and prospective associations between antisocial peer affiliation and externalizing disorders (antisocial behavior and substance use disorders) at ages 17, 20, 24, and 29. The sample included 1,382 same-sex twin pairs participating in the Minnesota Twin Family Study. We detected a Gene × Environment interaction at age 17, such that additive genetic influences on antisocial behavior and substance use disorders were greater in the context of greater antisocial peer affiliation. This Gene × Environment interaction was not present for antisocial behavior symptoms after age 17, but it was for substance use disorder symptoms through age 29 (though effect sizes were largest at age 17). The results suggest adolescence is a critical period for the development of externalizing disorders wherein exposure to greater environmental adversity is associated with a greater expression of genetic risk. This form of Gene × Environment interaction may persist through young adulthood for substance use disorders, but it appears to be limited to adolescence for antisocial behavior.

The Transcriptome of Compatible and Incompatible Interactions of Potato (Solanum tuberosum) with Phytophthora infestans Revealed by DeepSAGE Analysis

PubMed Central

Gyetvai, Gabor; Sønderkær, Mads; Göbel, Ulrike; Basekow, Rico; Ballvora, Agim; Imhoff, Maren; Kersten, Birgit; Nielsen, Kåre-Lehman; Gebhardt, Christiane

2012-01-01

Late blight, caused by the oomycete Phytophthora infestans, is the most important disease of potato (Solanum tuberosum). Understanding the molecular basis of resistance and susceptibility to late blight is therefore highly relevant for developing resistant cultivars, either by marker-assissted selection or by transgenic approaches. Specific P. infestans races having the Avr1 effector gene trigger a hypersensitive resistance response in potato plants carrying the R1 resistance gene (incompatible interaction) and cause disease in plants lacking R1 (compatible interaction). The transcriptomes of the compatible and incompatible interaction were captured by DeepSAGE analysis of 44 biological samples comprising five genotypes, differing only by the presence or absence of the R1 transgene, three infection time points and three biological replicates. 30.859 unique 21 base pair sequence tags were obtained, one third of which did not match any known potato transcript sequence. Two third of the tags were expressed at low frequency (<10 tag counts/million). 20.470 unitags matched to approximately twelve thousand potato transcribed genes. Tag frequencies were compared between compatible and incompatible interactions over the infection time course and between compatible and incompatible genotypes. Transcriptional changes were more numerous in compatible than in incompatible interactions. In contrast to incompatible interactions, transcriptional changes in the compatible interaction were observed predominantly for multigene families encoding defense response genes and genes functional in photosynthesis and CO2 fixation. Numerous transcriptional differences were also observed between near isogenic genotypes prior to infection with P. infestans. Our DeepSAGE transcriptome analysis uncovered novel candidate genes for plant host pathogen interactions, examples of which are discussed with respect to possible function. PMID:22328937

Culture as a mediator of gene-environment interaction: Cultural consonance, childhood adversity, a 2A serotonin receptor polymorphism, and depression in urban Brazil.

PubMed

Dressler, William W; Balieiro, Mauro C; Ferreira de Araújo, Luiza; Silva, Wilson A; Ernesto Dos Santos, José

2016-07-01

Research on gene-environment interaction was facilitated by breakthroughs in molecular biology in the late 20th century, especially in the study of mental health. There is a reliable interaction between candidate genes for depression and childhood adversity in relation to mental health outcomes. The aim of this paper is to explore the role of culture in this process in an urban community in Brazil. The specific cultural factor examined is cultural consonance, or the degree to which individuals are able to successfully incorporate salient cultural models into their own beliefs and behaviors. It was hypothesized that cultural consonance in family life would mediate the interaction of genotype and childhood adversity. In a study of 402 adult Brazilians from diverse socioeconomic backgrounds, conducted from 2011 to 2014, the interaction of reported childhood adversity and a polymorphism in the 2A serotonin receptor was associated with higher depressive symptoms. Further analysis showed that the gene-environment interaction was mediated by cultural consonance in family life, and that these effects were more pronounced in lower social class neighborhoods. The findings reinforce the role of the serotonergic system in the regulation of stress response and learning and memory, and how these processes in turn interact with environmental events and circumstances. Furthermore, these results suggest that gene-environment interaction models should incorporate a wider range of environmental experience and more complex pathways to better understand how genes and the environment combine to influence mental health outcomes. Copyright © 2016 Elsevier Ltd. All rights reserved.

Gene x Environment Interactions in Reading Disability and Attention-Deficit/Hyperactivity Disorder

ERIC Educational Resources Information Center

Pennington, Bruce F.; McGrath, Lauren M.; Rosenberg, Jenni; Barnard, Holly; Smith, Shelley D.; Willcutt, Erik G.; Friend, Angela; DeFries, John C.; Olson, Richard K.

2009-01-01

This article examines Gene x Environment (G x E) interactions in two comorbid developmental disorders--reading disability (RD) and attention-deficit/hyperactivity disorder (ADHD)--as a window on broader issues on G x E interactions in developmental psychology. The authors first briefly review types of G x E interactions, methods for detecting…

Apolipoprotein A2 polymorphism interacts with intakes of dairy foods to influence body weight in 2 U.S. populations

USDA-ARS?s Scientific Manuscript database

The interaction between a functional apolipoprotein A2 gene (APOA2) variant and saturated fatty acids (SFAs) for the outcome of body mass index (BMI) is among the most widely replicated gene-nutrient interactions. Whether this interaction can be extrapolated to food-based sources of SFAs, specifical...

Nature versus nurture: death of a dogma, and the road ahead.

PubMed

Traynor, Bryan J; Singleton, Andrew B

2010-10-21

Interaction between the genome and the environment has been widely discussed in the literature, but has the importance ascribed to understanding these interactions been overstated? In this opinion piece, we critically discuss gene-environment interactions and attempt to answer three key questions. First, is it likely that gene-environment interactions actually exist? Second, what is the realistic value of trying to unravel these interactions, both in terms of understanding disease pathogenesis and as a means of ameliorating disease? Finally, and most importantly, do the technologies and methodologies exist to facilitate an unbiased search for gene-environment interactions? Addressing these questions highlights key areas of feasibility that must be considered in this area of research. Copyright © 2010 Elsevier Inc. All rights reserved.

Plasticity of genetic interactions in metabolic networks of yeast.

PubMed

Harrison, Richard; Papp, Balázs; Pál, Csaba; Oliver, Stephen G; Delneri, Daniela

2007-02-13

Why are most genes dispensable? The impact of gene deletions may depend on the environment (plasticity), the presence of compensatory mechanisms (mutational robustness), or both. Here, we analyze the interaction between these two forces by exploring the condition-dependence of synthetic genetic interactions that define redundant functions and alternative pathways. We performed systems-level flux balance analysis of the yeast (Saccharomyces cerevisiae) metabolic network to identify genetic interactions and then tested the model's predictions with in vivo gene-deletion studies. We found that the majority of synthetic genetic interactions are restricted to certain environmental conditions, partly because of the lack of compensation under some (but not all) nutrient conditions. Moreover, the phylogenetic cooccurrence of synthetically interacting pairs is not significantly different from random expectation. These findings suggest that these gene pairs have at least partially independent functions, and, hence, compensation is only a byproduct of their evolutionary history. Experimental analyses that used multiple gene deletion strains not only confirmed predictions of the model but also showed that investigation of false predictions may both improve functional annotation within the model and also lead to the discovery of higher-order genetic interactions. Our work supports the view that functional redundancy may be more apparent than real, and it offers a unified framework for the evolution of environmental adaptation and mutational robustness.

Nutrigenetics and prostate cancer: 2011 and beyond.

PubMed

Yuan, Yinan; Ferguson, Lynnette R

2011-01-01

Prostate cancer runs in families and shows a clear dietary involvement. Until recently, the key risk gene(s) have proved elusive. We summarise current understandings of nutrient-gene interactions in prostate cancer risk and progression. A MEDLINE-based literature search was conducted. Hypothesis-directed candidate gene approaches provide plausible, albeit statistically weak, nutrient-gene interactions. These are based on early understandings of factors likely to impact on carcinogenesis, including both nutrient and genetic effects on androgen biosynthesis and action, xenobiotic metabolism, DNA damage and DNA repair. Non-hypothesis-directed genome-wide association studies provide much stronger evidence for other genes, not hitherto suspected for involvement. Although only a few of these have been formally tested for dietary associations in well-designed epidemiologic studies, the nature of many of the genes suggests that their activity may be regulated by nutrients. These effects may not only be relevant to prostate cancer susceptibility, but also to disease progression. It will be important to move beyond studying single nucleotide polymorphisms, into more complex chromosomal rearrangements and to epigenetic changes. For future progress, large international cohorts will not only need to provide proof of individual nutrient-gene interactions, but also to relate these to more complex nutrient-gene-gene interactions, as parts of pathways. Bioinformatics and biostatistics will be increasingly important tools in nutrigenetic studies beyond 2011. Copyright © 2011 S. Karger AG, Basel.

Arabidopsis Ensemble Reverse-Engineered Gene Regulatory Network Discloses Interconnected Transcription Factors in Oxidative Stress[W

PubMed Central

Vermeirssen, Vanessa; De Clercq, Inge; Van Parys, Thomas; Van Breusegem, Frank; Van de Peer, Yves

2014-01-01

The abiotic stress response in plants is complex and tightly controlled by gene regulation. We present an abiotic stress gene regulatory network of 200,014 interactions for 11,938 target genes by integrating four complementary reverse-engineering solutions through average rank aggregation on an Arabidopsis thaliana microarray expression compendium. This ensemble performed the most robustly in benchmarking and greatly expands upon the availability of interactions currently reported. Besides recovering 1182 known regulatory interactions, cis-regulatory motifs and coherent functionalities of target genes corresponded with the predicted transcription factors. We provide a valuable resource of 572 abiotic stress modules of coregulated genes with functional and regulatory information, from which we deduced functional relationships for 1966 uncharacterized genes and many regulators. Using gain- and loss-of-function mutants of seven transcription factors grown under control and salt stress conditions, we experimentally validated 141 out of 271 predictions (52% precision) for 102 selected genes and mapped 148 additional transcription factor-gene regulatory interactions (49% recall). We identified an intricate core oxidative stress regulatory network where NAC13, NAC053, ERF6, WRKY6, and NAC032 transcription factors interconnect and function in detoxification. Our work shows that ensemble reverse-engineering can generate robust biological hypotheses of gene regulation in a multicellular eukaryote that can be tested by medium-throughput experimental validation. PMID:25549671

Arabidopsis ensemble reverse-engineered gene regulatory network discloses interconnected transcription factors in oxidative stress.

PubMed

Vermeirssen, Vanessa; De Clercq, Inge; Van Parys, Thomas; Van Breusegem, Frank; Van de Peer, Yves

2014-12-01

The abiotic stress response in plants is complex and tightly controlled by gene regulation. We present an abiotic stress gene regulatory network of 200,014 interactions for 11,938 target genes by integrating four complementary reverse-engineering solutions through average rank aggregation on an Arabidopsis thaliana microarray expression compendium. This ensemble performed the most robustly in benchmarking and greatly expands upon the availability of interactions currently reported. Besides recovering 1182 known regulatory interactions, cis-regulatory motifs and coherent functionalities of target genes corresponded with the predicted transcription factors. We provide a valuable resource of 572 abiotic stress modules of coregulated genes with functional and regulatory information, from which we deduced functional relationships for 1966 uncharacterized genes and many regulators. Using gain- and loss-of-function mutants of seven transcription factors grown under control and salt stress conditions, we experimentally validated 141 out of 271 predictions (52% precision) for 102 selected genes and mapped 148 additional transcription factor-gene regulatory interactions (49% recall). We identified an intricate core oxidative stress regulatory network where NAC13, NAC053, ERF6, WRKY6, and NAC032 transcription factors interconnect and function in detoxification. Our work shows that ensemble reverse-engineering can generate robust biological hypotheses of gene regulation in a multicellular eukaryote that can be tested by medium-throughput experimental validation. © 2014 American Society of Plant Biologists. All rights reserved.

Next-generation analysis of cataracts: determining knowledge driven gene-gene interactions using Biofilter, and gene-environment interactions using the PhenX Toolkit.

PubMed

Pendergrass, Sarah A; Verma, Shefali S; Holzinger, Emily R; Moore, Carrie B; Wallace, John; Dudek, Scott M; Huggins, Wayne; Kitchner, Terrie; Waudby, Carol; Berg, Richard; McCarty, Catherine A; Ritchie, Marylyn D

2013-01-01

Investigating the association between biobank derived genomic data and the information of linked electronic health records (EHRs) is an emerging area of research for dissecting the architecture of complex human traits, where cases and controls for study are defined through the use of electronic phenotyping algorithms deployed in large EHR systems. For our study, 2580 cataract cases and 1367 controls were identified within the Marshfield Personalized Medicine Research Project (PMRP) Biobank and linked EHR, which is a member of the NHGRI-funded electronic Medical Records and Genomics (eMERGE) Network. Our goal was to explore potential gene-gene and gene-environment interactions within these data for 529,431 single nucleotide polymorphisms (SNPs) with minor allele frequency > 1%, in order to explore higher level associations with cataract risk beyond investigations of single SNP-phenotype associations. To build our SNP-SNP interaction models we utilized a prior-knowledge driven filtering method called Biofilter to minimize the multiple testing burden of exploring the vast array of interaction models possible from our extensive number of SNPs. Using the Biofilter, we developed 57,376 prior-knowledge directed SNP-SNP models to test for association with cataract status. We selected models that required 6 sources of external domain knowledge. We identified 5 statistically significant models with an interaction term with p-value < 0.05, as well as an overall model with p-value < 0.05 associated with cataract status. We also conducted gene-environment interaction analyses for all GWAS SNPs and a set of environmental factors from the PhenX Toolkit: smoking, UV exposure, and alcohol use; these environmental factors have been previously associated with the formation of cataracts. We found a total of 288 models that exhibit an interaction term with a p-value ≤ 1×10(-4) associated with cataract status. Our results show these approaches enable advanced searches for epistasis and gene-environment interactions beyond GWAS, and that the EHR based approach provides an additional source of data for seeking these advanced explanatory models of the etiology of complex disease/outcome such as cataracts.

Robustness of meta-analyses in finding gene × environment interactions

PubMed Central

Shi, Gang; Nehorai, Arye

2017-01-01

Meta-analyses that synthesize statistical evidence across studies have become important analytical tools for genetic studies. Inspired by the success of genome-wide association studies of the genetic main effect, researchers are searching for gene × environment interactions. Confounders are routinely included in the genome-wide gene × environment interaction analysis as covariates; however, this does not control for any confounding effects on the results if covariate × environment interactions are present. We carried out simulation studies to evaluate the robustness to the covariate × environment confounder for meta-regression and joint meta-analysis, which are two commonly used meta-analysis methods for testing the gene × environment interaction or the genetic main effect and interaction jointly. Here we show that meta-regression is robust to the covariate × environment confounder while joint meta-analysis is subject to the confounding effect with inflated type I error rates. Given vast sample sizes employed in genome-wide gene × environment interaction studies, non-significant covariate × environment interactions at the study level could substantially elevate the type I error rate at the consortium level. When covariate × environment confounders are present, type I errors can be controlled in joint meta-analysis by including the covariate × environment terms in the analysis at the study level. Alternatively, meta-regression can be applied, which is robust to potential covariate × environment confounders. PMID:28362796

Gene-Environment Interactions in Asthma: Genetic and Epigenetic Effects.

PubMed

Lee, Jong-Uk; Kim, Jeong Dong; Park, Choon-Sik

2015-07-01

Over the past three decades, a large number of genetic studies have been aimed at finding genetic variants associated with the risk of asthma, applying various genetic and genomic approaches including linkage analysis, candidate gene polymorphism studies, and genome-wide association studies (GWAS). However, contrary to general expectation, even single nucleotide polymorphisms (SNPs) discovered by GWAS failed to fully explain the heritability of asthma. Thus, application of rare allele polymorphisms in well defined phenotypes and clarification of environmental factors have been suggested to overcome the problem of 'missing' heritability. Such factors include allergens, cigarette smoke, air pollutants, and infectious agents during pre- and post-natal periods. The first and simplest interaction between a gene and the environment is a candidate interaction of both a well known gene and environmental factor in a direct physical or chemical interaction such as between CD14 and endotoxin or between HLA and allergens. Several GWAS have found environmental interactions with occupational asthma, aspirin exacerbated respiratory disease, tobacco smoke-related airway dysfunction, and farm-related atopic diseases. As one of the mechanisms behind gene-environment interaction is epigenetics, a few studies on DNA CpG methylation have been reported on subphenotypes of asthma, pitching the exciting idea that it may be possible to intervene at the junction between the genome and the environment. Epigenetic studies are starting to include data from clinical samples, which will make them another powerful tool for re-search on gene-environment interactions in asthma.

Impact of interaction between the G870A and EFEMP1 gene polymorphism on glioma risk in Chinese Han population.

PubMed

Yang, Libin; Qu, Bo; Xia, Xun; Kuang, Yongqin; Li, Jian; Fan, Kexia; Guo, Heng; Zheng, Hui; Ma, Yuan

2017-06-06

To investigate the impact of CCND1 and EFEMP1 gene polymorphism, and additional their gene-gene interactions and haplotype within EFEMP1 gene on glioma risk based on Chinese population. Logistic regression was performed to investigate association between single-nucleotide polymorphisms (SNP) and glioma risk and generalized multifactor dimensionality reduction (GMDR) was used to analyze the gene-gene interaction. Glioma risks were higher in carriers of homozygous mutant of rs603965 within CCND1 gene, rs1346787 and rs3791679 in EFEMP1 gene than those with wild-type homozygotes, OR (95%CI) were 1.67 (1.23-2.02), 1.59 (1.25-2.01) and 1.42 (1.15-1.82), respectively. GMDR analysis indicated a significant two-locus model (p=0.0010) involving rs603965 within CCND1 gene and rs1346787 within EFEMP1 gene. Overall, the cross-validation consistency of the two- locus models was 10\\ 10, and the testing accuracy is 60.17%. Participants with rs603965 - GA or AA and rs1346787- AG or GG genotype have the highest glioma risk, compared to participants with rs603965 - GG and rs1346787- AA genotype, OR (95%CI) was 3.65 (1.81-5.22). We conducted haplotype analysis for rs1346787 and rs3791679, because D' value between rs1346787 and rs3791679 was more than 0.8. The most common haplotype was rs1346787 - A and rs3791679- G haplotype, the frequency of which was 0.4905 and 0.4428 in case and control group. Polymorphism in rs603965 within CCND1 gene and rs1346787 within EFEMP1 gene and its gene- gene interaction were associated with increased glioma risk.

Differential gene expression analysis in glioblastoma cells and normal human brain cells based on GEO database.

PubMed

Wang, Anping; Zhang, Guibin

2017-11-01

The differentially expressed genes between glioblastoma (GBM) cells and normal human brain cells were investigated to performed pathway analysis and protein interaction network analysis for the differentially expressed genes. GSE12657 and GSE42656 gene chips, which contain gene expression profile of GBM were obtained from Gene Expression Omniub (GEO) database of National Center for Biotechnology Information (NCBI). The 'limma' data packet in 'R' software was used to analyze the differentially expressed genes in the two gene chips, and gene integration was performed using 'RobustRankAggreg' package. Finally, pheatmap software was used for heatmap analysis and Cytoscape, DAVID, STRING and KOBAS were used for protein-protein interaction, Gene Ontology (GO) and KEGG analyses. As results: i) 702 differentially expressed genes were identified in GSE12657, among those genes, 548 were significantly upregulated and 154 were significantly downregulated (p<0.01, fold-change >1), and 1,854 differentially expressed genes were identified in GSE42656, among the genes, 1,068 were significantly upregulated and 786 were significantly downregulated (p<0.01, fold-change >1). A total of 167 differentially expressed genes including 100 upregulated genes and 67 downregulated genes were identified after gene integration, and the genes showed significantly different expression levels in GBM compared with normal human brain cells (p<0.05). ii) Interactions between the protein products of 101 differentially expressed genes were identified using STRING and expression network was established. A key gene, called CALM3, was identified by Cytoscape software. iii) GO enrichment analysis showed that differentially expressed genes were mainly enriched in 'neurotransmitter:sodium symporter activity' and 'neurotransmitter transporter activity', which can affect the activity of neurotransmitter transportation. KEGG pathway analysis showed that the differentially expressed genes were mainly enriched in 'protein processing in endoplasmic reticulum', which can affect protein processing in endoplasmic reticulum. The results showed that: i) 167 differentially expressed genes were identified from two gene chips after integration; and ii) protein interaction network was established, and GO and KEGG pathway analyses were successfully performed to identify and annotate the key gene, which provide new insights for the studies on GBN at gene level.

A vitamin D pathway gene-gene interaction affects low-density lipoprotein cholesterol levels.

PubMed

Grave, Nathália; Tovo-Rodrigues, Luciana; da Silveira, Janaína; Rovaris, Diego Luiz; Dal Bosco, Simone Morelo; Contini, Verônica; Genro, Júlia Pasqualini

2016-12-01

Much evidence suggests an association between vitamin D deficiency and chronic diseases such as obesity and dyslipidemia. Although genetic factors play an important role in the etiology of these diseases, only a few studies have investigated the relationship between vitamin D-related genes and anthropometric and lipid profiles. The aim of this study was to investigate the association of three vitamin D-related genes with anthropometric and lipid parameters in 542 adult individuals. We analyzed the rs2228570 polymorphism in the vitamin D receptor gene (VDR), rs2134095 in the retinoid X receptor gamma gene (RXRG) and rs7041 in the vitamin D-binding protein gene (GC). Polymorphisms were genotyped by TaqMan allelic discrimination. Gene-gene interactions were evaluated by the general linear model. The functionality of the polymorphisms was investigated using the following predictors and databases: SIFT (Sorting Intolerant from Tolerant), PolyPhen-2 (Polymorphism Phenotyping v2) and Human Splicing Finder 3. We identified a significant effect of the interaction between RXRG (rs2134095) and GC (rs7041) on low-density lipoprotein cholesterol (LDL-c) levels (P=.005). Furthermore, our in silico analysis suggested a functional role for both variants in the regulation of the gene products. Our results suggest that the vitamin D-related genes RXRG and GC affect LDL-c levels. These findings are in agreement with other studies that consistently associate vitamin D and lipid profile. Together, our results corroborate the idea that analyzing gene-gene interaction would be helpful to clarify the genetic component of lipid profile. Copyright © 2016 Elsevier Inc. All rights reserved.

Computational gene network study on antibiotic resistance genes of Acinetobacter baumannii.

PubMed

Anitha, P; Anbarasu, Anand; Ramaiah, Sudha

2014-05-01

Multi Drug Resistance (MDR) in Acinetobacter baumannii is one of the major threats for emerging nosocomial infections in hospital environment. Multidrug-resistance in A. baumannii may be due to the implementation of multi-combination resistance mechanisms such as β-lactamase synthesis, Penicillin-Binding Proteins (PBPs) changes, alteration in porin proteins and in efflux pumps against various existing classes of antibiotics. Multiple antibiotic resistance genes are involved in MDR. These resistance genes are transferred through plasmids, which are responsible for the dissemination of antibiotic resistance among Acinetobacter spp. In addition, these resistance genes may also have a tendency to interact with each other or with their gene products. Therefore, it becomes necessary to understand the impact of these interactions in antibiotic resistance mechanism. Hence, our study focuses on protein and gene network analysis on various resistance genes, to elucidate the role of the interacting proteins and to study their functional contribution towards antibiotic resistance. From the search tool for the retrieval of interacting gene/protein (STRING), a total of 168 functional partners for 15 resistance genes were extracted based on the confidence scoring system. The network study was then followed up with functional clustering of associated partners using molecular complex detection (MCODE). Later, we selected eight efficient clusters based on score. Interestingly, the associated protein we identified from the network possessed greater functional similarity with known resistance genes. This network-based approach on resistance genes of A. baumannii could help in identifying new genes/proteins and provide clues on their association in antibiotic resistance. Copyright © 2014 Elsevier Ltd. All rights reserved.

Msx homeobox gene family and craniofacial development.

PubMed

Alappat, Sylvia; Zhang, Zun Yi; Chen, Yi Ping

2003-12-01

Vertebrate Msx genes are unlinked, homeobox-containing genes that bear homology to the Drosophila muscle segment homeobox gene. These genes are expressed at multiple sites of tissue-tissue interactions during vertebrate embryonic development. Inductive interactions mediated by the Msx genes are essential for normal craniofacial, limb and ectodermal organ morphogenesis, and are also essential to survival in mice, as manifested by the phenotypic abnormalities shown in knockout mice and in humans. This review summarizes studies on the expression, regulation, and functional analysis of Msx genes that bear relevance to craniofacial development in humans and mice. Key words: Msx genes, craniofacial, tooth, cleft palate, suture, development, transcription factor, signaling molecule.

STAT3 or USF2 Contributes to HIF Target Gene Specificity

PubMed Central

Pawlus, Matthew R.; Wang, Liyi; Murakami, Aya; Dai, Guanhai; Hu, Cheng-Jun

2013-01-01

The HIF1- and HIF2-mediated transcriptional responses play critical roles in solid tumor progression. Despite significant similarities, including their binding to promoters of both HIF1 and HIF2 target genes, HIF1 and HIF2 proteins activate unique subsets of target genes under hypoxia. The mechanism for HIF target gene specificity has remained unclear. Using siRNA or inhibitor, we previously reported that STAT3 or USF2 is specifically required for activation of endogenous HIF1 or HIF2 target genes. In this study, using reporter gene assays and chromatin immuno-precipitation, we find that STAT3 or USF2 exhibits specific binding to the promoters of HIF1 or HIF2 target genes respectively even when over-expressed. Functionally, HIF1α interacts with STAT3 to activate HIF1 target gene promoters in a HIF1α HLH/PAS and N-TAD dependent manner while HIF2α interacts with USF2 to activate HIF2 target gene promoters in a HIF2α N-TAD dependent manner. Physically, HIF1α HLH and PAS domains are required for its interaction with STAT3 while both N- and C-TADs of HIF2α are involved in physical interaction with USF2. Importantly, addition of functional USF2 binding sites into a HIF1 target gene promoter increases the basal activity of the promoter as well as its response to HIF2+USF2 activation while replacing HIF binding site with HBS from a HIF2 target gene does not change the specificity of the reporter gene. Importantly, RNA Pol II on HIF1 or HIF2 target genes is primarily associated with HIF1α or HIF2α in a STAT3 or USF2 dependent manner. Thus, we demonstrate here for the first time that HIF target gene specificity is achieved by HIF transcription partners that are required for HIF target gene activation, exhibit specific binding to the promoters of HIF1 or HIF2 target genes and selectively interact with HIF1α or HIF2α protein. PMID:23991099

In silico study of protein to protein interaction analysis of AMP-activated protein kinase and mitochondrial activity in three different farm animal species

NASA Astrophysics Data System (ADS)

Prastowo, S.; Widyas, N.

2018-03-01

AMP-activated protein kinase (AMPK) is cellular energy censor which works based on ATP and AMP concentration. This protein interacts with mitochondria in determine its activity to generate energy for cell metabolism purposes. For that, this paper aims to compare the protein to protein interaction of AMPK and mitochondrial activity genes in the metabolism of known animal farm (domesticated) that are cattle (Bos taurus), pig (Sus scrofa) and chicken (Gallus gallus). In silico study was done using STRING V.10 as prominent protein interaction database, followed with biological function comparison in KEGG PATHWAY database. Set of genes (12 in total) were used as input analysis that are PRKAA1, PRKAA2, PRKAB1, PRKAB2, PRKAG1, PRKAG2, PRKAG3, PPARGC1, ACC, CPT1B, NRF2 and SOD. The first 7 genes belong to gene in AMPK family, while the last 5 belong to mitochondrial activity genes. The protein interaction result shows 11, 8 and 5 metabolism pathways in Bos taurus, Sus scrofa and Gallus gallus, respectively. The top pathway in Bos taurus is AMPK signaling pathway (10 genes), Sus scrofa is Adipocytokine signaling pathway (8 genes) and Gallus gallus is FoxO signaling pathway (5 genes). Moreover, the common pathways found in those 3 species are Adipocytokine signaling pathway, Insulin signaling pathway and FoxO signaling pathway. Genes clustered in Adipocytokine and Insulin signaling pathway are PRKAA2, PPARGC1A, PRKAB1 and PRKAG2. While, in FoxO signaling pathway are PRKAA2, PRKAB1, PRKAG2. According to that, we found PRKAA2, PRKAB1 and PRKAG2 are the common genes. Based on the bioinformatics analysis, we can demonstrate that protein to protein interaction shows distinct different of metabolism in different species. However, further validation is needed to give a clear explanation.

Gene-Culture Coevolutionary Games

ERIC Educational Resources Information Center

Blute, Marion

2006-01-01

Gene-culture interactions have largely been modelled employing population genetic-type models. Moreover, in the most notable application to date, the "interactive" modes have been one way rather than bidirectional. This paper suggests using game theoretic, fully interactive models. Employing the logic utilized in population ecology for coevolution…

Protein-Protein Interaction Reagents | Office of Cancer Genomics

Cancer.gov

The CTD2 Center at Emory University has a library of genes used to study protein-protein interactions in mammalian cells. These genes are cloned in different mammalian expression vectors. A list of available cancer-associated genes can be accessed below. Emory_CTD^2_PPI_Reagents.xlsx Contact: Haian Fu

Chemical-Gene Interactions from ToxCast Bioactivity Data Expands Universe of Literature Network-Based Associations (SOT)

EPA Science Inventory

Characterizing the effects of chemicals in biological systems is often summarized by chemical-gene interactions, which have sparse coverage in the literature. The ToxCast chemical screening program has produced bioactivity data for nearly 2000 chemicals and over 450 gene targets....

Disentangling Gene-Environment Correlations and Interactions on Adolescent Depressive Symptoms

ERIC Educational Resources Information Center

Lau, Jennifer Y. F.; Eley, Thalia C.

2008-01-01

Background: Genetic risks for depression may be expressed through greater exposure towards environmental stressors (gene-environment correlation, rGE) and increased susceptibility to these stressors (gene-environment interaction, G x E). While these effects are often studied independently, evidence supports their co-occurrence on depression.…

Gene Polymorphism Association with Type 2 Diabetes and Related Gene-Gene and Gene-Environment Interactions in a Uyghur Population

PubMed Central

Xiao, Shan; Zeng, Xiaoyun; Fan, Yong; Su, Yinxia; Ma, Qi; Zhu, Jun; Yao, Hua

2016-01-01

Background We investigated the association between 8 single-nucleotide polymorphisms (SNPs) at 3 genetic loci (CDKAL1, CDKN2A/2B and FTO) with type 2 diabetes (T2D) in a Uyghur population. Material/Methods A case-control study of 879 Uyghur patients with T2D and 895 non-diabetic Uyghur controls was conducted at the Hospital of Xinjiang Medical University between 2010 and 2013. Eight SNPs in CDKAL1, CDKN2A/2B and FTO were analyzed using Sequenom MassARRAY®SNP genotyping. Factors associated with T2D were assessed by logistic regression analyses. Gene-gene and gene-environment interactions were analyzed by generalized multifactor dimensionality reduction. Results Genotype distributions of rs10811661 (CDKN2A/2B), rs7195539, rs8050136, and rs9939609 (FTO) and allele frequencies of rs8050136 and rs9939609 differed significantly between diabetes and control groups (all P<0.05). While rs10811661, rs8050136, and rs9939609 were eliminated after adjusting for covariates (P>0.05), rs7195539 distribution differed significantly in co-dominant and dominant models (P<0.05). In gene-gene interaction analysis, after adjusting for covariates the two-locus rs10811661-rs7195539 interaction model had a cross-validation consistency of 10/10 and the highest balanced accuracy of 0.5483 (P=0.014). In gene-environment interaction analysis, the 3-locus interaction model TG-HDL-family history of diabetes had a cross-validation consistency of 10/10 and the highest balanced accuracy of 0.7072 (P<0.001). The 4-locus interaction model, rs7195539-TG-HDL-family history of diabetes had a cross-validation consistency of 8/10 (P<0.001). Conclusions Polymorphisms in CDKN2A/2B and FTO, but not CDKAL1, may be associated with T2D, and alleles rs8050136 and rs9939609 are likely risk alleles for T2D in this population. There were potential interactions among CDKN2A/2B (rs10811661) – FTO (rs7195539) or FTO (rs7195539)-TG-HDL-family history of diabetes in the pathogenesis of T2D in a Uyghur population. PMID:26873362

Influence of IL1B, IL6 and IL10 gene variants and plasma fatty acid interaction on metabolic syndrome risk in a cross-sectional population-based study.

PubMed

Maintinguer Norde, Marina; Oki, Erica; Ferreira Carioca, Antonio Augusto; Teixeira Damasceno, Nágila Raquel; Fisberg, Regina Mara; Lobo Marchioni, Dirce Maria; Rogero, Marcelo Macedo

2018-04-01

Metabolic syndrome (MetS) is a cluster of interrelated risk factors for type 2 diabetes mellitus, and cardiovascular disease, with underlying inflammatory pathophysiology. Genetic variations and diet are well-known risk factor for MetS, but the interaction between these two factors is less explored. The aim of the study was to evaluate the influence of interaction between SNP of inflammatory genes (encoding interleukin (IL)-6, IL-1β and IL-10) and plasma fatty acids on the odds of MetS, in a population-based cross-sectional study. Among participants of the Health Survey - São Paulo, 301 adults (19-59 y) from whom a blood sample was collected were included. Individuals with and without MetS were compared according to their plasma inflammatory biomarkers, fatty acid profile, and genotype frequency of the IL1B (rs16944, rs1143623, rs1143627, rs1143634 and rs1143643), IL6 (rs1800795, rs1800796 and rs1800797) and IL10 (rs1554286, rs1800871, rs1800872, rs1800890 and rs3024490) genes SNP. The influence of gene-fatty acids interaction on MetS risk was investigated. IL6 gene SNP rs1800795 G allele was associated with higher odds for MetS (OR = 1.88; p = 0.017). Gene-fatty acid interaction was found between the IL1B gene SNP rs116944 and stearic acid (p inter = 0.043), and between rs1143634 and EPA (p inter = 0.017). For the IL10 gene SNP rs1800896, an interaction was found for arachidonic acid (p inter = 0.007) and estimated D5D activity (p inter = 0.019). The IL6 gene SNP rs1800795 G allele is associated with increased odds for MetS. Plasma fatty acid profile interacts with the IL1B and IL10 gene variants to modulate the odds for MetS. This and other interactions of risk factors can account for the unexplained heritability of MetS, and their elucidation can lead to new strategies for genome-customized prevention of MetS. Copyright © 2017 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

Discovering Implicit Entity Relation with the Gene-Citation-Gene Network

PubMed Central

Song, Min; Han, Nam-Gi; Kim, Yong-Hwan; Ding, Ying; Chambers, Tamy

2013-01-01

In this paper, we apply the entitymetrics model to our constructed Gene-Citation-Gene (GCG) network. Based on the premise there is a hidden, but plausible, relationship between an entity in one article and an entity in its citing article, we constructed a GCG network of gene pairs implicitly connected through citation. We compare the performance of this GCG network to a gene-gene (GG) network constructed over the same corpus but which uses gene pairs explicitly connected through traditional co-occurrence. Using 331,411 MEDLINE abstracts collected from 18,323 seed articles and their references, we identify 25 gene pairs. A comparison of these pairs with interactions found in BioGRID reveal that 96% of the gene pairs in the GCG network have known interactions. We measure network performance using degree, weighted degree, closeness, betweenness centrality and PageRank. Combining all measures, we find the GCG network has more gene pairs, but a lower matching rate than the GG network. However, combining top ranked genes in both networks produces a matching rate of 35.53%. By visualizing both the GG and GCG networks, we find that cancer is the most dominant disease associated with the genes in both networks. Overall, the study indicates that the GCG network can be useful for detecting gene interaction in an implicit manner. PMID:24358368

Interaction between nonsynonymous polymorphisms in PLA2G7 gene and smoking on the risk of coronary heart disease in a Chinese population.

PubMed

Chi, Yunpeng; Shi, Conghong; Zhang, Xiaojiang; Xi, Yang

2018-05-04

To investigate the impact of PLA2G7 polymorphism, and additional their interactions with smoking and drinking on coronary heart disease (CHD) risk based on Chinese population. GMDR model was used to screen the best gene-smoking and gene-drinking interaction combinations. Logistic regression was performed to investigate association between 4 SNPs and CHD, and the interaction effect between rs1805017 and smoking. For CHD patient-control haplotype analyses, the SHEsis online haplotype analysis software ( http://analysis.bio-x.cn/myAnalysis.php ) was employed. CHD risks were higher in carriers of homozygous mutant of rs1805017 and rs1805018 than those with wild-type homozygotes, OR (95% CI) were 1.45 (1.16-1.92) and 1.51 (1.23-1.97), respectively, but the other two SNPs, rs16874954 and rs1051931 were not significant associated with CHD risks. GMDR analysis indicated that there was a significant two-locus model (p = 0.0107) involving rs1805017 and smoking, indicating a potential gene-environment interaction between rs1805017 and smoking. But we did not found any gene-drinking and gene-gene interaction combinations in GMDR models. The haplotype R-I was observed most frequently in two groups, with 47.43 and 54.38% in the case and control group of the population, respectively. The results also indicated that the haplotype containing the rs1805017-H and rs1805018-T alleles were associated with a statistically increased CHD risk, OR (95% CI) 1.43 (1.10-1.86), p = 0.0021. Polymorphisms in rs1805017 and rs1805018, additional interaction between rs1805017 and smoking, and haplotype containing the rs1805017-H and rs1805018-T alleles were associated with increased CHD risk.

Characterization and modification of phage T7 DNA polymerase for use in DNA sequencing; Progress report, June 1, 1990--May 31, 1993

DOE Office of Scientific and Technical Information (OSTI.GOV)

Richardson, C.C.

1993-12-31

This project focuses on the DNA polymerase (gene 5 protein) of phage T7 for use in DNA sequence analysis. Gene 5 protein interacts with accessory proteins to acquire properties essential for DNA replication. One goal is to understand these interactions in order to modify the proteins for use in DNA sequencing. E. coli thioredoxin, binds to gene 5 protein and clamps it to a primer-template. They have analyzed the binding of gene 5 protein-thioredoxin to primer-templates and have defined the optimal conditions to form an extremely stable complex with a dNTP in the polymerase catalytic site. The spatial proximity ofmore » these components has been determined using fluorescence emission anisotropy. The T7 DNA binding protein, the gene 2.5 protein, interacts with gene 5 protein and gene 4 protein to increase processivity and primer synthesis, respectively. Mutant gene 2.5 proteins have been isolated that do not interact with T7 DNA polymerase and can not support T7 growth. The nucleotide binding site of the T7 helicase has been identified and mutations affecting the site provide information on how the hydrolysis of NTPs fuel its unidirectional translocation. The sequence, GTC, has been shown to be necessary and sufficient for recognition by the T7 primase. The T7 gene 5.5 protein interacts with the E. coli nucleoid protein, H-NS, and also overcomes the phage {lambda} rex restriction system.« less

Key genes and pathways in measles and their interaction with environmental chemicals

PubMed Central

Zhang, Rongqiang; Jiang, Hualin; Li, Fengying; Su, Ning; Ding, Yi; Mao, Xiang; Ren, Dan; Wang, Jing

2018-01-01

The aim of the present study was to explore key genes that may have a role in the pathology of measles virus infection and to clarify the interaction networks between environmental factors and differentially expressed genes (DEGs). After screening the database of the Gene Expression Omnibus of the National Center for Biotechnology Information, the dataset GSE5808 was downloaded and analyzed. A global normalization method was performed to minimize data inconsistencies and heterogeneity. DEGs during different stages of measles virus infection were explored using R software (v3.4.0). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the DEGs were performed using Cytoscape 3.4.0 software. A protein-protein interaction (PPI) network of the DEGs was obtained from the STRING database v9.05. A total of 43 DEGs were obtained from four analyzed sample groups, including 10 highly expressed genes and 33 genes with decreased expression. The most enriched pathways based on KEGG analysis were fatty acid elongation, cytokine-cytokine receptor interaction and RNA degradation. The genes mentioned in the PPI network were mainly associated with protein binding and chemokine activity. A total of 219 chemicals were identified that may, jointly or on their own, interact with the 6 DEGs between the control group and patients with measles (at hospital entry), including benzo(a)pyrene (BaP) and tetrachlorodibenzodioxin (TCDD). In conclusion, the present study revealed that chemokines and environmental chemicals, e.g. BaP and TCDD, may affect the development of measles. PMID:29805511

System Analysis of LWDH Related Genes Based on Text Mining in Biological Networks

PubMed Central

Miao, Yingbo; Zhang, Liangcai; Wang, Yang; Feng, Rennan; Yang, Lei; Zhang, Shihua; Jiang, Yongshuai; Liu, Guiyou

2014-01-01

Liuwei-dihuang (LWDH) is widely used in traditional Chinese medicine (TCM), but its molecular mechanism about gene interactions is unclear. LWDH genes were extracted from the existing literatures based on text mining technology. To simulate the complex molecular interactions that occur in the whole body, protein-protein interaction networks (PPINs) were constructed and the topological properties of LWDH genes were analyzed. LWDH genes have higher centrality properties and may play important roles in the complex biological network environment. It was also found that the distances within LWDH genes are smaller than expected, which means that the communication of LWDH genes during the biological process is rapid and effectual. At last, a comprehensive network of LWDH genes, including the related drugs and regulatory pathways at both the transcriptional and posttranscriptional levels, was constructed and analyzed. The biological network analysis strategy used in this study may be helpful for the understanding of molecular mechanism of TCM. PMID:25243143

HPA Axis Genes, and Their Interaction with Childhood Maltreatment, are Related to Cortisol Levels and Stress-Related Phenotypes.

PubMed

Gerritsen, Lotte; Milaneschi, Yuri; Vinkers, Christiaan H; van Hemert, Albert M; van Velzen, Laura; Schmaal, Lianne; Penninx, Brenda Wjh

2017-11-01

Stress responses are controlled by the hypothalamus pituitary adrenal (HPA)-axis and maladaptive stress responses are associated with the onset and maintenance of stress-related disorders such as major depressive disorder (MDD). Genes that play a role in the HPA-axis regulation may likely contribute to the relation between relevant neurobiological substrates and stress-related disorders. Therefore, we performed gene-wide analyses for 30 a priori literature-based genes involved in HPA-axis regulation in 2014 subjects (34% male; mean age: 42.5) to study the relations with lifetime MDD diagnosis, cortisol awakening response, and dexamethasone suppression test (DST) levels (subsample N=1472) and hippocampal and amygdala volume (3T MR images; subsample N=225). Additionally, gene by childhood maltreatment (CM) interactions were investigated. Gene-wide significant results were found for dexamethasone suppression (CYP11A1, CYP17A1, POU1F1, AKR1D1), hippocampal volume (CYP17A1, CYP11A1, HSD3B2, PROP1, AVPRA1, SRD5A1), amygdala volume (POMC, CRH, HSD3B2), and lifetime MDD diagnosis (FKBP5 and CRH), all permutation p-values<0.05. Interactions with CM were found for several genes; the strongest interactions were found for NR3C2, where the minor allele of SNP rs17581262 was related to smaller hippocampal volume, smaller amygdala volume, higher DST levels, and higher odds of MDD diagnosis only in participants with CM. As hypothesized, several HPA-axis genes are associated with stress-related endophenotypes including cortisol response and reduced brain volumes. Furthermore, we found a pleiotropic interaction between CM and the mineralocorticoid receptor gene, suggesting that this gene plays an important moderating role in stress and stress-related disorders.

[Association between MAOA-u VNTR polymorphism and its interaction with stressful life events and major depressive disorder in adolescents].

PubMed

Ma, Jing; Yu, Shun-Ying; Liang, Shan; Ding, Jun; Feng, Zhe; Yang, Fan; Gao, Wei-Jia; Lin, Jia-Ni; Huang, Chun-Xiang; Liu, Xue-Jun; Su, Lin-Yan

2013-07-01

To investigate whether the genetic polymorphism, upstream variable number of tandem repeats (uVNTR), in the monoamine oxidase A (MAOA) gene, is associated with major depressive disorder (MDD) in adolescents and to test whether there is gene-environment interaction between MAOA-uVNTR polymorphism and stressful life events (SLEs). A total of 394 Chinese Han subjects, including 187 adolescent patients with MDD and 207 normal students as a control group, were included in the study. Genotyping was performed by SNaP-shot assay. SLEs in the previous 12 months were evaluated. The groups were compared in terms of the frequency distributions of MAOA-uVNTR genotypes and alleles using statistical software. The binary logistic regression model of gene-environment interaction was established to analyze the association of the gene-environment interaction between MAOA-u VNTR genotypes and SLEs with adolescent MDD. The distribution profiles of MAOA-u VNTR genotypes and alleles were not related to the onset of MDD, severity of depression, comorbid anxiety and suicidal ideation/behavior/attempt in adolescents. The gene-environment interaction between MAOA-u VNTR genotypes and SLEs was not associated with MDD in male or female adolescents. It is not proven that MAOA-u VNTR polymorphism is associated with adolescent MDD. There is also no gene-environment interaction between MAOA-u VNTR polymorphism and SLEs that is associated with adolescent MDD.

Construction and analysis of gene-gene dynamics influence networks based on a Boolean model.

PubMed

Mazaya, Maulida; Trinh, Hung-Cuong; Kwon, Yung-Keun

2017-12-21

Identification of novel gene-gene relations is a crucial issue to understand system-level biological phenomena. To this end, many methods based on a correlation analysis of gene expressions or structural analysis of molecular interaction networks have been proposed. They have a limitation in identifying more complicated gene-gene dynamical relations, though. To overcome this limitation, we proposed a measure to quantify a gene-gene dynamical influence (GDI) using a Boolean network model and constructed a GDI network to indicate existence of a dynamical influence for every ordered pair of genes. It represents how much a state trajectory of a target gene is changed by a knockout mutation subject to a source gene in a gene-gene molecular interaction (GMI) network. Through a topological comparison between GDI and GMI networks, we observed that the former network is denser than the latter network, which implies that there exist many gene pairs of dynamically influencing but molecularly non-interacting relations. In addition, a larger number of hub genes were generated in the GDI network. On the other hand, there was a correlation between these networks such that the degree value of a node was positively correlated to each other. We further investigated the relationships of the GDI value with structural properties and found that there are negative and positive correlations with the length of a shortest path and the number of paths, respectively. In addition, a GDI network could predict a set of genes whose steady-state expression is affected in E. coli gene-knockout experiments. More interestingly, we found that the drug-targets with side-effects have a larger number of outgoing links than the other genes in the GDI network, which implies that they are more likely to influence the dynamics of other genes. Finally, we found biological evidences showing that the gene pairs which are not molecularly interacting but dynamically influential can be considered for novel gene-gene relationships. Taken together, construction and analysis of the GDI network can be a useful approach to identify novel gene-gene relationships in terms of the dynamical influence.

Evidence for gene-gene epistatic interactions among susceptibility loci for systemic lupus erythematosus.

PubMed

Hughes, Travis; Adler, Adam; Kelly, Jennifer A; Kaufman, Kenneth M; Williams, Adrienne H; Langefeld, Carl D; Brown, Elizabeth E; Alarcón, Graciela S; Kimberly, Robert P; Edberg, Jeffrey C; Ramsey-Goldman, Rosalind; Petri, Michelle; Boackle, Susan A; Stevens, Anne M; Reveille, John D; Sanchez, Elena; Martín, Javier; Niewold, Timothy B; Vilá, Luis M; Scofield, R Hal; Gilkeson, Gary S; Gaffney, Patrick M; Criswell, Lindsey A; Moser, Kathy L; Merrill, Joan T; Jacob, Chaim O; Tsao, Betty P; James, Judith A; Vyse, Timothy J; Alarcón-Riquelme, Marta E; Harley, John B; Richardson, Bruce C; Sawalha, Amr H

2012-02-01

Several confirmed genetic susceptibility loci for lupus have been described. To date, no clear evidence for genetic epistasis in lupus has been established. The aim of this study was to test for gene-gene interactions in a number of known lupus susceptibility loci. Eighteen single-nucleotide polymorphisms tagging independent and confirmed lupus susceptibility loci were genotyped in a set of 4,248 patients with lupus and 3,818 normal healthy control subjects of European descent. Epistasis was tested by a 2-step approach using both parametric and nonparametric methods. The false discovery rate (FDR) method was used to correct for multiple testing. We detected and confirmed gene-gene interactions between the HLA region and CTLA4, IRF5, and ITGAM and between PDCD1 and IL21 in patients with lupus. The most significant interaction detected by parametric analysis was between rs3131379 in the HLA region and rs231775 in CTLA4 (interaction odds ratio 1.19, Z = 3.95, P = 7.8 × 10(-5) [FDR ≤0.05], P for multifactor dimensionality reduction = 5.9 × 10(-45)). Importantly, our data suggest that in patients with lupus, the presence of the HLA lupus risk alleles in rs1270942 and rs3131379 increases the odds of also carrying the lupus risk allele in IRF5 (rs2070197) by 17% and 16%, respectively (P = 0.0028 and P = 0.0047, respectively). We provide evidence for gene-gene epistasis in systemic lupus erythematosus. These findings support a role for genetic interaction contributing to the complexity of lupus heritability. Copyright © 2012 by the American College of Rheumatology.

Specific down-regulation of spermatogenesis genes targeted by 22G RNAs in hybrid sterile males associated with an X-Chromosome introgression.

PubMed

Li, Runsheng; Ren, Xiaoliang; Bi, Yu; Ho, Vincy Wing Sze; Hsieh, Chia-Ling; Young, Amanda; Zhang, Zhihong; Lin, Tingting; Zhao, Yanmei; Miao, Long; Sarkies, Peter; Zhao, Zhongying

2016-09-01

Hybrid incompatibility (HI) prevents gene flow between species, thus lying at the heart of speciation genetics. One of the most common HIs is male sterility. Two superficially contradictory observations exist for hybrid male sterility. First, an introgression on the X Chromosome is more likely to produce male sterility than on autosome (so-called large-X theory); second, spermatogenesis genes are enriched on the autosomes but depleted on the X Chromosome (demasculinization of X Chromosome). Analysis of gene expression in Drosophila hybrids suggests a genetic interaction between the X Chromosome and autosomes that is essential for male fertility. However, the prevalence of such an interaction and its underlying mechanism remain largely unknown. Here we examine the interaction in nematode species by contrasting the expression of both coding genes and transposable elements (TEs) between hybrid sterile males and its parental nematode males. We use two lines of hybrid sterile males, each carrying an independent introgression fragment from Caenorhabditis briggsae X Chromosome in an otherwise Caenorhabditis nigoni background, which demonstrate similar defects in spermatogenesis. We observe a similar pattern of down-regulated genes that are specific for spermatogenesis between the two hybrids. Importantly, the down-regulated genes caused by the X Chromosome introgressions show a significant enrichment on the autosomes, supporting an epistatic interaction between the X Chromosome and autosomes. We investigate the underlying mechanism of the interaction by measuring small RNAs and find that a subset of 22G RNAs specifically targeting the down-regulated spermatogenesis genes is significantly up-regulated in hybrids, suggesting that perturbation of small RNA-mediated regulation may contribute to the X-autosome interaction. © 2016 Li et al.; Published by Cold Spring Harbor Laboratory Press.

Synergistic interactions of biotic and abiotic environmental stressors on gene expression.

PubMed

Altshuler, Ianina; McLeod, Anne M; Colbourne, John K; Yan, Norman D; Cristescu, Melania E

2015-03-01

Understanding the response of organisms to multiple stressors is critical for predicting if populations can adapt to rapid environmental change. Natural and anthropogenic stressors often interact, complicating general predictions. In this study, we examined the interactive and cumulative effects of two common environmental stressors, lowered calcium concentration, an anthropogenic stressor, and predator presence, a natural stressor, on the water flea Daphnia pulex. We analyzed expression changes of five genes involved in calcium homeostasis - cuticle proteins (Cutie, Icp2), calbindin (Calb), and calcium pump and channel (Serca and Ip3R) - using real-time quantitative PCR (RT-qPCR) in a full factorial experiment. We observed strong synergistic interactions between low calcium concentration and predator presence. While the Ip3R gene was not affected by the stressors, the other four genes were affected in their transcriptional levels by the combination of the stressors. Transcriptional patterns of genes that code for cuticle proteins (Cutie and Icp2) and a sarcoplasmic calcium pump (Serca) only responded to the combination of stressors, changing their relative expression levels in a synergistic response, while a calcium-binding protein (Calb) responded to low calcium stress and the combination of both stressors. The expression pattern of these genes (Cutie, Icp2, and Serca) were nonlinear, yet they were dose dependent across the calcium gradient. Multiple stressors can have complex, often unexpected effects on ecosystems. This study demonstrates that the dominant interaction for the set of tested genes appears to be synergism. We argue that gene expression patterns can be used to understand and predict the type of interaction expected when organisms are exposed simultaneously to natural and anthropogenic stressors.

CTCF-Mediated and Pax6-Associated Gene Expression in Corneal Epithelial Cell-Specific Differentiation

PubMed Central

Tsui, Shanli; Wang, Jie; Wang, Ling; Dai, Wei; Lu, Luo

2016-01-01

Background The purpose of the study is to elicit the epigenetic mechanism involving CCCTC binding factor (CTCF)-mediated chromatin remodeling that regulates PAX6 gene interaction with differentiation-associated genes to control corneal epithelial differentiation. Methods Cell cycle progression and specific keratin expressions were measured to monitor changes of differentiation-induced primary human limbal stem/progenitor (HLS/P), human corneal epithelial (HCE) and human telomerase-immortalized corneal epithelial (HTCE) cells. PAX6-interactive and differentiation-associated genes in chromatin remodeling mediated by the epigenetic factor CTCF were detected by circular chromosome conformation capture (4C) and ChIP (Chromatin immunoprecipitation)-on-chip approaches, and verified by FISH (Fluorescent in situ hybridization). Furthermore, CTCF activities were altered by CTCF-shRNA to study the effect of CTCF on mediating interaction of Pax6 and differentiation-associated genes in corneal epithelial cell fate. Results Our results demonstrated that differentiation-induced human corneal epithelial cells expressed typical corneal epithelial characteristics including morphological changes, increased keratin12 expression and G0/G1 accumulations. Expressions of CTCF and PAX6 were suppressed and elevated following the process of differentiation, respectively. During corneal epithelial cell differentiation, differentiation-induced RCN1 and ADAM17 were found interacting with PAX6 in the process of CTCF-mediated chromatin remodeling detected by 4C and verified by ChIP-on-chip and FISH. Diminished CTCF mRNA with CTCF-shRNA in HTCE cells weakened the interaction of PAX6 gene in controlling RCN1/ADAM17 and enhanced early onset of the genes in cell differentiation. Conclusion Our results explain how epigenetic factor CTCF-mediated chromatin remodeling regulates interactions between eye-specific PAX6 and those genes that are induced/associated with cell differentiation to modulate corneal epithelial cell-specific differentiation. PMID:27583466

A gene network bioinformatics analysis for pemphigoid autoimmune blistering diseases.

PubMed

Barone, Antonio; Toti, Paolo; Giuca, Maria Rita; Derchi, Giacomo; Covani, Ugo

2015-07-01

In this theoretical study, a text mining search and clustering analysis of data related to genes potentially involved in human pemphigoid autoimmune blistering diseases (PAIBD) was performed using web tools to create a gene/protein interaction network. The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database was employed to identify a final set of PAIBD-involved genes and to calculate the overall significant interactions among genes: for each gene, the weighted number of links, or WNL, was registered and a clustering procedure was performed using the WNL analysis. Genes were ranked in class (leader, B, C, D and so on, up to orphans). An ontological analysis was performed for the set of 'leader' genes. Using the above-mentioned data network, 115 genes represented the final set; leader genes numbered 7 (intercellular adhesion molecule 1 (ICAM-1), interferon gamma (IFNG), interleukin (IL)-2, IL-4, IL-6, IL-8 and tumour necrosis factor (TNF)), class B genes were 13, whereas the orphans were 24. The ontological analysis attested that the molecular action was focused on extracellular space and cell surface, whereas the activation and regulation of the immunity system was widely involved. Despite the limited knowledge of the present pathologic phenomenon, attested by the presence of 24 genes revealing no protein-protein direct or indirect interactions, the network showed significant pathways gathered in several subgroups: cellular components, molecular functions, biological processes and the pathologic phenomenon obtained from the Kyoto Encyclopaedia of Genes and Genomes (KEGG) database. The molecular basis for PAIBD was summarised and expanded, which will perhaps give researchers promising directions for the identification of new therapeutic targets.

Influence of intron length on interaction characters between post-spliced intron and its CDS in ribosomal protein genes

NASA Astrophysics Data System (ADS)

Zhao, Xiaoqing; Li, Hong; Bao, Tonglaga; Ying, Zhiqiang

2012-09-01

Many experiment evidences showed that sequence structures of introns and intron loss/gain can influence gene expression, but current mechanisms did not refer to the functions of post-spliced introns directly. We propose that postspliced introns play their functions in gene expression by interacting with their mRNA sequences and the interaction is characterized by the matched segments between introns and their CDS. In this study, we investigated the interaction characters with length series by improved Smith-Waterman local alignment software for the ribosomal protein genes in C. elegans and D. melanogaster. Our results showed that RF values of five intron groups are significantly high in the central non-conserved region and very low in 5'-end and 3'-end splicing region. It is interesting that the number of the optimal matched regions gradually increases with intron length. Distributions of the optimal matched regions are different for five intron groups. Our study revealed that there are more interaction regions between longer introns and their CDS than shorter, and it provides a positive pattern for regulating the gene expression.

A Penalized Robust Method for Identifying Gene-Environment Interactions

PubMed Central

Shi, Xingjie; Liu, Jin; Huang, Jian; Zhou, Yong; Xie, Yang; Ma, Shuangge

2015-01-01

In high-throughput studies, an important objective is to identify gene-environment interactions associated with disease outcomes and phenotypes. Many commonly adopted methods assume specific parametric or semiparametric models, which may be subject to model mis-specification. In addition, they usually use significance level as the criterion for selecting important interactions. In this study, we adopt the rank-based estimation, which is much less sensitive to model specification than some of the existing methods and includes several commonly encountered data and models as special cases. Penalization is adopted for the identification of gene-environment interactions. It achieves simultaneous estimation and identification and does not rely on significance level. For computation feasibility, a smoothed rank estimation is further proposed. Simulation shows that under certain scenarios, for example with contaminated or heavy-tailed data, the proposed method can significantly outperform the existing alternatives with more accurate identification. We analyze a lung cancer prognosis study with gene expression measurements under the AFT (accelerated failure time) model. The proposed method identifies interactions different from those using the alternatives. Some of the identified genes have important implications. PMID:24616063

Gene-gene and gene-environment interactions defining lipid-related traits.

PubMed

Ordovás, José M; Robertson, Ruairi; Cléirigh, Ellen Ní

2011-04-01

Steps towards reducing chronic disease progression are continuously being taken through the form of genomic research. Studies over the last year have highlighted more and more polymorphisms, pathways and interactions responsible for metabolic disorders such as cardiovascular disease, obesity and dyslipidemia. Many of these chronic illnesses can be partially blamed by altered lipid metabolism, combined with individual genetic components. Critical evaluation and comparison of these recent studies is essential in order to comprehend the results, conclusions and future prospects in the field of genomics as a whole. Recent literature elucidates significant gene--diet and gene--environment interactions resulting in altered lipid metabolism, inflammation and other metabolic imbalances leading to cardiovascular disease and obesity. Epigenetic and epistatic interactions are now becoming more significantly associated with such disorders, as genomic research digs deeper into the complex nature of genetic individuality and heritability. The vast array of data collected from genome-wide association studies must now be empowered and explored through more complex interaction studies, using standardized methods and larger sample sizes. In doing so the etiology of chronic disease progression will be further understood.

Inferring molecular interactions pathways from eQTL data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rashid, Imran; McDermott, Jason E.; Samudrala, Ram

Analysis of expression quantitative trait loci (eQTL) helps elucidate the connection between genotype, gene expression levels, and phenotype. However, standard statistical genetics can only attribute changes in expression levels to loci on the genome, not specific genes. Each locus can contain many genes, making it very difficult to discover which gene is controlling the expression levels of other genes. Furthermore, it is even more difficult to find a pathway of molecular interactions responsible for controlling the expression levels. Here we describe a series of techniques for finding explanatory pathways by exploring graphs of molecular interactions. We show several simple methodsmore » can find complete pathways the explain the mechanism of differential expression in eQTL data.« less

Robust and Comprehensive Analysis of 20 Osteoporosis Candidate Genes by Very High-Density Single-Nucleotide Polymorphism Screen Among 405 White Nuclear Families Identified Significant Association and Gene–Gene Interaction

PubMed Central

Xiong, Dong-Hai; Shen, Hui; Zhao, Lan-Juan; Xiao, Peng; Yang, Tie-Lin; Guo, Yan; Wang, Wei; Guo, Yan-Fang; Liu, Yong-Jun; Recker, Robert R; Deng, Hong-Wen

2007-01-01

Many “novel” osteoporosis candidate genes have been proposed in recent years. To advance our knowledge of their roles in osteoporosis, we screened 20 such genes using a set of high-density SNPs in a large family-based study. Our efforts led to the prioritization of those osteoporosis genes and the detection of gene–gene interactions. Introduction We performed large-scale family-based association analyses of 20 novel osteoporosis candidate genes using 277 single nucleotide polymorphisms (SNPs) for the quantitative trait BMD variation and the qualitative trait osteoporosis (OP) at three clinically important skeletal sites: spine, hip, and ultradistal radius (UD). Materials and Methods One thousand eight hundred seventy-three subjects from 405 white nuclear families were genotyped and analyzed with an average density of one SNP per 4 kb across the 20 genes. We conducted association analyses by SNP- and haplotype-based family-based association test (FBAT) and performed gene–gene interaction analyses using multianalytic approaches such as multifactor-dimensionality reduction (MDR) and conditional logistic regression. Results and Conclusions We detected four genes (DBP, LRP5, CYP17, and RANK) that showed highly suggestive associations (10,000-permutation derived empirical global p ≤ 0.01) with spine BMD/OP; four genes (CYP19, RANK, RANKL, and CYP17) highly suggestive for hip BMD/OP; and four genes (CYP19, BMP2, RANK, and TNFR2) highly suggestive for UD BMD/OP. The associations between BMP2 with UD BMD and those between RANK with OP at the spine, hip, and UD also met the experiment-wide stringent criterion (empirical global p ≤ 0.0007). Sex-stratified analyses further showed that some of the significant associations in the total sample were driven by either male or female subjects. In addition, we identified and validated a two-locus gene–gene interaction model involving GCR and ESR2, for which prior biological evidence exists. Our results suggested the prioritization of osteoporosis candidate genes from among the many proposed in recent years and revealed the significant gene–gene interaction effects influencing osteoporosis risk. PMID:17002564

Assessing the utility of gene co-expression stability in combination with correlation in the analysis of protein-protein interaction networks

PubMed Central

2011-01-01

Background Gene co-expression, in the form of a correlation coefficient, has been valuable in the analysis, classification and prediction of protein-protein interactions. However, it is susceptible to bias from a few samples having a large effect on the correlation coefficient. Gene co-expression stability is a means of quantifying this bias, with high stability indicating robust, unbiased co-expression correlation coefficients. We assess the utility of gene co-expression stability as an additional measure to support the co-expression correlation in the analysis of protein-protein interaction networks. Results We studied the patterns of co-expression correlation and stability in interacting proteins with respect to their interaction promiscuity, levels of intrinsic disorder, and essentiality or disease-relatedness. Co-expression stability, along with co-expression correlation, acts as a better classifier of hub proteins in interaction networks, than co-expression correlation alone, enabling the identification of a class of hubs that are functionally distinct from the widely accepted transient (date) and obligate (party) hubs. Proteins with high levels of intrinsic disorder have low co-expression correlation and high stability with their interaction partners suggesting their involvement in transient interactions, except for a small group that have high co-expression correlation and are typically subunits of stable complexes. Similar behavior was seen for disease-related and essential genes. Interacting proteins that are both disordered have higher co-expression stability than ordered protein pairs. Using co-expression correlation and stability, we found that transient interactions are more likely to occur between an ordered and a disordered protein while obligate interactions primarily occur between proteins that are either both ordered, or disordered. Conclusions We observe that co-expression stability shows distinct patterns in structurally and functionally different groups of proteins and interactions. We conclude that it is a useful and important measure to be used in concert with gene co-expression correlation for further insights into the characteristics of proteins in the context of their interaction network. PMID:22369639

Functional Interactions between Major Rice Blast Resistance Genes, Pi-ta and Pi-b, and Minor Blast Resistance QTL.

PubMed

Chen, Xinglong; Jia, Yulin; Jia, Melissa H; Pinson, Shannon; Wang, Xueyan; Wu, Bo Ming

2018-04-16

Major blast resistance (R) genes confer resistance in a gene-for-gene manner. However, little information is available on interactions between R genes. In this study, interactions between two rice blast R genes, Pi-ta and Pi-b, and other minor blast resistance quantitative trait loci (QTL) were investigated in a recombinant inbred line (RIL) population comprising of 243 RILs from a 'Cybonnet' (CYBT)×'Saber' (SB) cross. CYBT has the R gene Pi-ta and SB has Pi-b. Ten differential isolates of four Magnaporthe oryzae races (IB-1, IB-17, IB-49, and IE-1K) were used to evaluate disease reactions of the 243 RILs under greenhouse conditions. Five resistance QTL were mapped on chromosomes 2, 3, 8, 9, and 12 with a linkage map of 179 single nucleotide polymorphism (SNP) markers. Among them, qBR12 (Q1), was mapped at the Pi-ta locus and accounted for 45.41% of phenotypic variation while qBR2 (Q2) was located at the Pi-b locus and accounted for24.81%of disease reactions. An additive-by-additive epistatic interaction between Q1 (Pi-ta) and Q2 (Pi-b) was detected; they can enhance the disease resistance by an additive 0.93 using the 0 to 9 standard phenotyping method. These results suggest that Pi-ta interacts synergistically with Pi-b.

A novel approach to simulate gene-environment interactions in complex diseases.

PubMed

Amato, Roberto; Pinelli, Michele; D'Andrea, Daniel; Miele, Gennaro; Nicodemi, Mario; Raiconi, Giancarlo; Cocozza, Sergio

2010-01-05

Complex diseases are multifactorial traits caused by both genetic and environmental factors. They represent the major part of human diseases and include those with largest prevalence and mortality (cancer, heart disease, obesity, etc.). Despite a large amount of information that has been collected about both genetic and environmental risk factors, there are few examples of studies on their interactions in epidemiological literature. One reason can be the incomplete knowledge of the power of statistical methods designed to search for risk factors and their interactions in these data sets. An improvement in this direction would lead to a better understanding and description of gene-environment interactions. To this aim, a possible strategy is to challenge the different statistical methods against data sets where the underlying phenomenon is completely known and fully controllable, for example simulated ones. We present a mathematical approach that models gene-environment interactions. By this method it is possible to generate simulated populations having gene-environment interactions of any form, involving any number of genetic and environmental factors and also allowing non-linear interactions as epistasis. In particular, we implemented a simple version of this model in a Gene-Environment iNteraction Simulator (GENS), a tool designed to simulate case-control data sets where a one gene-one environment interaction influences the disease risk. The main aim has been to allow the input of population characteristics by using standard epidemiological measures and to implement constraints to make the simulator behaviour biologically meaningful. By the multi-logistic model implemented in GENS it is possible to simulate case-control samples of complex disease where gene-environment interactions influence the disease risk. The user has full control of the main characteristics of the simulated population and a Monte Carlo process allows random variability. A knowledge-based approach reduces the complexity of the mathematical model by using reasonable biological constraints and makes the simulation more understandable in biological terms. Simulated data sets can be used for the assessment of novel statistical methods or for the evaluation of the statistical power when designing a study.

LNDriver: identifying driver genes by integrating mutation and expression data based on gene-gene interaction network.

PubMed

Wei, Pi-Jing; Zhang, Di; Xia, Junfeng; Zheng, Chun-Hou

2016-12-23

Cancer is a complex disease which is characterized by the accumulation of genetic alterations during the patient's lifetime. With the development of the next-generation sequencing technology, multiple omics data, such as cancer genomic, epigenomic and transcriptomic data etc., can be measured from each individual. Correspondingly, one of the key challenges is to pinpoint functional driver mutations or pathways, which contributes to tumorigenesis, from millions of functional neutral passenger mutations. In this paper, in order to identify driver genes effectively, we applied a generalized additive model to mutation profiles to filter genes with long length and constructed a new gene-gene interaction network. Then we integrated the mutation data and expression data into the gene-gene interaction network. Lastly, greedy algorithm was used to prioritize candidate driver genes from the integrated data. We named the proposed method Length-Net-Driver (LNDriver). Experiments on three TCGA datasets, i.e., head and neck squamous cell carcinoma, kidney renal clear cell carcinoma and thyroid carcinoma, demonstrated that the proposed method was effective. Also, it can identify not only frequently mutated drivers, but also rare candidate driver genes.

Gene × Environment Interactions in Schizophrenia: Evidence from Genetic Mouse Models

PubMed Central

Marr, Julia; Bock, Gavin; Desbonnet, Lieve; Waddington, John

2016-01-01

The study of gene × environment, as well as epistatic interactions in schizophrenia, has provided important insight into the complex etiopathologic basis of schizophrenia. It has also increased our understanding of the role of susceptibility genes in the disorder and is an important consideration as we seek to translate genetic advances into novel antipsychotic treatment targets. This review summarises data arising from research involving the modelling of gene × environment interactions in schizophrenia using preclinical genetic models. Evidence for synergistic effects on the expression of schizophrenia-relevant endophenotypes will be discussed. It is proposed that valid and multifactorial preclinical models are important tools for identifying critical areas, as well as underlying mechanisms, of convergence of genetic and environmental risk factors, and their interaction in schizophrenia. PMID:27725886

Gene by environment interactions influencing reading disability and the inattentive symptom dimension of attention deficit/hyperactivity disorder.

PubMed

Rosenberg, Jenni; Pennington, Bruce F; Willcutt, Erik G; Olson, Richard K

2012-03-01

Reading disability (RD) and attention deficit/hyperactivity disorder (ADHD) are comorbid and genetically correlated, especially the inattentive dimension of ADHD (ADHD-I). However, previous research indicates that RD and ADHD enter into opposite gene by environment (G × E) interactions. This study used behavioral genetic methods to replicate these opposite G × E interactions in a sample of same-sex monozygotic and dizygotic twin pairs from the Colorado Learning Disabilities Research Center (CLDRC; DeFries et al., 1997) and to test a genetic hypothesis for why these opposite interactions occur. We replicated opposite G × E interactions for RD (bioecological) and ADHD-I (diathesis-stress) with parental education in the same sample of participants. The genetic hypothesis for this opposite pattern of interactions is that only genes specific to each disorder enter into these opposite interactions, not the shared genes underlying their comorbidity. To test this hypothesis, we used single models with an exploratory three-way interaction, in which the G × E interactions for each disorder were moderated by comorbidity. Neither three-way interaction was significant. The heritability of RD did not vary as a function of parental education and ADHD-I. Similarly, the heritability of ADHD-I did not vary as a function of parental education and RD. We documented opposite G × E interactions in RD and ADHD-I in the same overall twin sample, but the explanation for this apparent paradox remains unclear. Examining specific genes and more specific environmental factors may help resolve the paradox. © 2011 The Authors. Journal of Child Psychology and Psychiatry © 2011 Association for Child and Adolescent Mental Health.

A cis-regulatory logic simulator.

PubMed

Zeigler, Robert D; Gertz, Jason; Cohen, Barak A

2007-07-27

A major goal of computational studies of gene regulation is to accurately predict the expression of genes based on the cis-regulatory content of their promoters. The development of computational methods to decode the interactions among cis-regulatory elements has been slow, in part, because it is difficult to know, without extensive experimental validation, whether a particular method identifies the correct cis-regulatory interactions that underlie a given set of expression data. There is an urgent need for test expression data in which the interactions among cis-regulatory sites that produce the data are known. The ability to rapidly generate such data sets would facilitate the development and comparison of computational methods that predict gene expression patterns from promoter sequence. We developed a gene expression simulator which generates expression data using user-defined interactions between cis-regulatory sites. The simulator can incorporate additive, cooperative, competitive, and synergistic interactions between regulatory elements. Constraints on the spacing, distance, and orientation of regulatory elements and their interactions may also be defined and Gaussian noise can be added to the expression values. The simulator allows for a data transformation that simulates the sigmoid shape of expression levels from real promoters. We found good agreement between sets of simulated promoters and predicted regulatory modules from real expression data. We present several data sets that may be useful for testing new methodologies for predicting gene expression from promoter sequence. We developed a flexible gene expression simulator that rapidly generates large numbers of simulated promoters and their corresponding transcriptional output based on specified interactions between cis-regulatory sites. When appropriate rule sets are used, the data generated by our simulator faithfully reproduces experimentally derived data sets. We anticipate that using simulated gene expression data sets will facilitate the direct comparison of computational strategies to predict gene expression from promoter sequence. The source code is available online and as additional material. The test sets are available as additional material.

Protein-protein interaction network of gene expression in the hydrocortisone-treated keloid.

PubMed

Chen, Rui; Zhang, Zhiliang; Xue, Zhujia; Wang, Lin; Fu, Mingang; Lu, Yi; Bai, Ling; Zhang, Ping; Fan, Zhihong

2015-01-01

In order to explore the molecular mechanism of hydrocortisone in keloid tissue, the gene expression profiles of keloid samples treated with hydrocortisone were subjected to bioinformatics analysis. Firstly, the gene expression profiles (GSE7890) of five samples of keloid treated with hydrocortisone and five untreated keloid samples were downloaded from the Gene Expression Omnibus (GEO) database. Secondly, data were preprocessed using packages in R language and differentially expressed genes (DEGs) were screened using a significance analysis of microarrays (SAM) protocol. Thirdly, the DEGs were subjected to gene ontology (GO) function and KEGG pathway enrichment analysis. Finally, the interactions of DEGs in samples of keloid treated with hydrocortisone were explored in a human protein-protein interaction (PPI) network, and sub-modules of the DEGs interaction network were analyzed using Cytoscape software. Based on the analysis, 572 DEGs in the hydrocortisone-treated samples were screened; most of these were involved in the signal transduction and cell cycle. Furthermore, three critical genes in the module, including COL1A1, NID1, and PRELP, were screened in the PPI network analysis. These findings enhance understanding of the pathogenesis of the keloid and provide references for keloid therapy. © 2015 The International Society of Dermatology.

Molecular Keys to the Janthinobacterium and Duganella spp. Interaction with the Plant Pathogen Fusarium graminearum

PubMed Central

Haack, Frederike S.; Poehlein, Anja; Kröger, Cathrin; Voigt, Christian A.; Piepenbring, Meike; Bode, Helge B.; Daniel, Rolf; Schäfer, Wilhelm; Streit, Wolfgang R.

2016-01-01

Janthinobacterium and Duganella are well-known for their antifungal effects. Surprisingly, almost nothing is known on molecular aspects involved in the close bacterium-fungus interaction. To better understand this interaction, we established the genomes of 11 Janthinobacterium and Duganella isolates in combination with phylogenetic and functional analyses of all publicly available genomes. Thereby, we identified a core and pan genome of 1058 and 23,628 genes. All strains encoded secondary metabolite gene clusters and chitinases, both possibly involved in fungal growth suppression. All but one strain carried a single gene cluster involved in the biosynthesis of alpha-hydroxyketone-like autoinducer molecules, designated JAI-1. Genome-wide RNA-seq studies employing the background of two isolates and the corresponding JAI-1 deficient strains identified a set of 45 QS-regulated genes in both isolates. Most regulated genes are characterized by a conserved sequence motif within the promoter region. Among the most strongly regulated genes were secondary metabolite and type VI secretion system gene clusters. Most intriguing, co-incubation studies of J. sp. HH102 or its corresponding JAI-1 synthase deletion mutant with the plant pathogen Fusarium graminearum provided first evidence of a QS-dependent interaction with this pathogen. PMID:27833590

Adenovirus Small E1A Employs the Lysine Acetylases p300/CBP and Tumor Suppressor Rb to Repress Select Host Genes and Promote Productive Virus Infection

PubMed Central

Ferrari, Roberto; Gou, Dawei; Jawdekar, Gauri; Johnson, Sarah A.; Nava, Miguel; Su, Trent; Yousef, Ahmed F.; Zemke, Nathan R.; Pellegrini, Matteo; Kurdistani, Siavash K.; Berk, Arnold J.

2015-01-01

SUMMARY Oncogenic transformation by adenovirus small e1a depends on simultaneous interactions with the host lysine acetylases p300/CBP and the tumor suppressor RB. How these interactions influence cellular gene expression remains unclear. We find that e1a displaces RBs from E2F transcription factors and promotes p300 acetylation of RB1 K873/K874 to lock it into a repressing conformation that interacts with repressive chromatin-modifying enzymes. These repressing p300-e1a-RB1 complexes specifically interact with host genes that have unusually high p300 association within the gene body. The TGFβ-, TNF-, and interleukin-signaling pathway components are enriched among such p300-targeted genes. The p300-e1a-RB1 complex condenses chromatin in a manner dependent on HDAC activity, p300 lysine acetylase activity, the p300 bromodomain, and RB K873/K874 and e1a K239 acetylation to repress host genes that would otherwise inhibit productive virus infection. Thus, adenovirus employs e1a to repress host genes that interfere with viral replication. PMID:25525796

NaviGO: interactive tool for visualization and functional similarity and coherence analysis with gene ontology.

PubMed

Wei, Qing; Khan, Ishita K; Ding, Ziyun; Yerneni, Satwica; Kihara, Daisuke

2017-03-20

The number of genomics and proteomics experiments is growing rapidly, producing an ever-increasing amount of data that are awaiting functional interpretation. A number of function prediction algorithms were developed and improved to enable fast and automatic function annotation. With the well-defined structure and manual curation, Gene Ontology (GO) is the most frequently used vocabulary for representing gene functions. To understand relationship and similarity between GO annotations of genes, it is important to have a convenient pipeline that quantifies and visualizes the GO function analyses in a systematic fashion. NaviGO is a web-based tool for interactive visualization, retrieval, and computation of functional similarity and associations of GO terms and genes. Similarity of GO terms and gene functions is quantified with six different scores including protein-protein interaction and context based association scores we have developed in our previous works. Interactive navigation of the GO function space provides intuitive and effective real-time visualization of functional groupings of GO terms and genes as well as statistical analysis of enriched functions. We developed NaviGO, which visualizes and analyses functional similarity and associations of GO terms and genes. The NaviGO webserver is freely available at: http://kiharalab.org/web/navigo .

batman Interacts with polycomb and trithorax group genes and encodes a BTB/POZ protein that is included in a complex containing GAGA factor.

PubMed

Faucheux, M; Roignant, J-Y; Netter, S; Charollais, J; Antoniewski, C; Théodore, L

2003-02-01

Polycomb and trithorax group genes maintain the appropriate repressed or activated state of homeotic gene expression throughout Drosophila melanogaster development. We have previously identified the batman gene as a Polycomb group candidate since its function is necessary for the repression of Sex combs reduced. However, our present genetic analysis indicates functions of batman in both activation and repression of homeotic genes. The 127-amino-acid Batman protein is almost reduced to a BTB/POZ domain, an evolutionary conserved protein-protein interaction domain found in a large protein family. We show that this domain is involved in the interaction between Batman and the DNA binding GAGA factor encoded by the Trithorax-like gene. The GAGA factor and Batman codistribute on polytene chromosomes, coimmunoprecipitate from nuclear embryonic and larval extracts, and interact in the yeast two-hybrid assay. Batman, together with the GAGA factor, binds to MHS-70, a 70-bp fragment of the bithoraxoid Polycomb response element. This binding, like that of the GAGA factor, requires the presence of d(GA)n sequences. Together, our results suggest that batman belongs to a subset of the Polycomb/trithorax group of genes that includes Trithorax-like, whose products are involved in both activation and repression of homeotic genes.

Diversity and interactions of microbial functional genes under differing environmental conditions: insights from a membrane bioreactor and an oxidation ditch.

PubMed

Xia, Yu; Hu, Man; Wen, Xianghua; Wang, Xiaohui; Yang, Yunfeng; Zhou, Jizhong

2016-01-08

The effect of environmental conditions on the diversity and interactions of microbial communities has caused tremendous interest in microbial ecology. Here, we found that with identical influents but differing operational parameters (mainly mixed liquor suspended solid (MLSS) concentrations, solid retention time (SRT) and dissolved oxygen (DO) concentrations), two full-scale municipal wastewater treatment systems applying oxidation ditch (OD) and membrane bioreactor (MBR) processes harbored a majority of shared genes (87.2%) but had different overall functional gene structures as revealed by two datasets of 12-day time-series generated by a functional gene array-GeoChip 4.2. Association networks of core carbon, nitrogen and phosphorus cycling genes in each system based on random matrix theory (RMT) showed different topological properties and the MBR nodes showed an indication of higher connectivity. MLSS and DO were shown to be effective in shaping functional gene structures of the systems by statistical analyses. Higher MLSS concentrations resulting in decreased resource availability of the MBR system were thought to promote positive interactions of important functional genes. Together, these findings show the differences of functional potentials of some bioprocesses caused by differing environmental conditions and suggest that higher stress of resource limitation increased positive gene interactions in the MBR system.

Diversity and interactions of microbial functional genes under differing environmental conditions: insights from a membrane bioreactor and an oxidation ditch

NASA Astrophysics Data System (ADS)

Xia, Yu; Hu, Man; Wen, Xianghua; Wang, Xiaohui; Yang, Yunfeng; Zhou, Jizhong

2016-01-01

The effect of environmental conditions on the diversity and interactions of microbial communities has caused tremendous interest in microbial ecology. Here, we found that with identical influents but differing operational parameters (mainly mixed liquor suspended solid (MLSS) concentrations, solid retention time (SRT) and dissolved oxygen (DO) concentrations), two full-scale municipal wastewater treatment systems applying oxidation ditch (OD) and membrane bioreactor (MBR) processes harbored a majority of shared genes (87.2%) but had different overall functional gene structures as revealed by two datasets of 12-day time-series generated by a functional gene array-GeoChip 4.2. Association networks of core carbon, nitrogen and phosphorus cycling genes in each system based on random matrix theory (RMT) showed different topological properties and the MBR nodes showed an indication of higher connectivity. MLSS and DO were shown to be effective in shaping functional gene structures of the systems by statistical analyses. Higher MLSS concentrations resulting in decreased resource availability of the MBR system were thought to promote positive interactions of important functional genes. Together, these findings show the differences of functional potentials of some bioprocesses caused by differing environmental conditions and suggest that higher stress of resource limitation increased positive gene interactions in the MBR system.

Diversity and interactions of microbial functional genes under differing environmental conditions: insights from a membrane bioreactor and an oxidation ditch

PubMed Central

Xia, Yu; Hu, Man; Wen, Xianghua; Wang, Xiaohui; Yang, Yunfeng; Zhou, Jizhong

2016-01-01

The effect of environmental conditions on the diversity and interactions of microbial communities has caused tremendous interest in microbial ecology. Here, we found that with identical influents but differing operational parameters (mainly mixed liquor suspended solid (MLSS) concentrations, solid retention time (SRT) and dissolved oxygen (DO) concentrations), two full-scale municipal wastewater treatment systems applying oxidation ditch (OD) and membrane bioreactor (MBR) processes harbored a majority of shared genes (87.2%) but had different overall functional gene structures as revealed by two datasets of 12-day time-series generated by a functional gene array-GeoChip 4.2. Association networks of core carbon, nitrogen and phosphorus cycling genes in each system based on random matrix theory (RMT) showed different topological properties and the MBR nodes showed an indication of higher connectivity. MLSS and DO were shown to be effective in shaping functional gene structures of the systems by statistical analyses. Higher MLSS concentrations resulting in decreased resource availability of the MBR system were thought to promote positive interactions of important functional genes. Together, these findings show the differences of functional potentials of some bioprocesses caused by differing environmental conditions and suggest that higher stress of resource limitation increased positive gene interactions in the MBR system. PMID:26743465

batman Interacts with Polycomb and trithorax Group Genes and Encodes a BTB/POZ Protein That Is Included in a Complex Containing GAGA Factor

PubMed Central

Faucheux, M.; Roignant, J.-Y.; Netter, S.; Charollais, J.; Antoniewski, C.; Théodore, L.

2003-01-01

Polycomb and trithorax group genes maintain the appropriate repressed or activated state of homeotic gene expression throughout Drosophila melanogaster development. We have previously identified the batman gene as a Polycomb group candidate since its function is necessary for the repression of Sex combs reduced. However, our present genetic analysis indicates functions of batman in both activation and repression of homeotic genes. The 127-amino-acid Batman protein is almost reduced to a BTB/POZ domain, an evolutionary conserved protein-protein interaction domain found in a large protein family. We show that this domain is involved in the interaction between Batman and the DNA binding GAGA factor encoded by the Trithorax-like gene. The GAGA factor and Batman codistribute on polytene chromosomes, coimmunoprecipitate from nuclear embryonic and larval extracts, and interact in the yeast two-hybrid assay. Batman, together with the GAGA factor, binds to MHS-70, a 70-bp fragment of the bithoraxoid Polycomb response element. This binding, like that of the GAGA factor, requires the presence of d(GA)n sequences. Together, our results suggest that batman belongs to a subset of the Polycomb/trithorax group of genes that includes Trithorax-like, whose products are involved in both activation and repression of homeotic genes. PMID:12556479

Improving the measurement of semantic similarity by combining gene ontology and co-functional network: a random walk based approach.

PubMed

Peng, Jiajie; Zhang, Xuanshuo; Hui, Weiwei; Lu, Junya; Li, Qianqian; Liu, Shuhui; Shang, Xuequn

2018-03-19

Gene Ontology (GO) is one of the most popular bioinformatics resources. In the past decade, Gene Ontology-based gene semantic similarity has been effectively used to model gene-to-gene interactions in multiple research areas. However, most existing semantic similarity approaches rely only on GO annotations and structure, or incorporate only local interactions in the co-functional network. This may lead to inaccurate GO-based similarity resulting from the incomplete GO topology structure and gene annotations. We present NETSIM2, a new network-based method that allows researchers to measure GO-based gene functional similarities by considering the global structure of the co-functional network with a random walk with restart (RWR)-based method, and by selecting the significant term pairs to decrease the noise information. Based on the EC number (Enzyme Commission)-based groups of yeast and Arabidopsis, evaluation test shows that NETSIM2 can enhance the accuracy of Gene Ontology-based gene functional similarity. Using NETSIM2 as an example, we found that the accuracy of semantic similarities can be significantly improved after effectively incorporating the global gene-to-gene interactions in the co-functional network, especially on the species that gene annotations in GO are far from complete.

GESearch: An Interactive GUI Tool for Identifying Gene Expression Signature.

PubMed

Ye, Ning; Yin, Hengfu; Liu, Jingjing; Dai, Xiaogang; Yin, Tongming

2015-01-01

The huge amount of gene expression data generated by microarray and next-generation sequencing technologies present challenges to exploit their biological meanings. When searching for the coexpression genes, the data mining process is largely affected by selection of algorithms. Thus, it is highly desirable to provide multiple options of algorithms in the user-friendly analytical toolkit to explore the gene expression signatures. For this purpose, we developed GESearch, an interactive graphical user interface (GUI) toolkit, which is written in MATLAB and supports a variety of gene expression data files. This analytical toolkit provides four models, including the mean, the regression, the delegate, and the ensemble models, to identify the coexpression genes, and enables the users to filter data and to select gene expression patterns by browsing the display window or by importing knowledge-based genes. Subsequently, the utility of this analytical toolkit is demonstrated by analyzing two sets of real-life microarray datasets from cell-cycle experiments. Overall, we have developed an interactive GUI toolkit that allows for choosing multiple algorithms for analyzing the gene expression signatures.

Polymorphisms in genes involved in fatty acid β-oxidation interact with dietary fat intakes to modulate the plasma TG response to a fish oil supplementation.

PubMed

Bouchard-Mercier, Annie; Rudkowska, Iwona; Lemieux, Simone; Couture, Patrick; Vohl, Marie-Claude

2014-03-18

A large inter-individual variability in the plasma triglyceride (TG) response to an omega-3 polyunsaturated fatty acid (n-3 PUFA) supplementation has been observed. The objective was to examine gene-diet interaction effects on the plasma TG response after a fish oil supplementation, between single-nucleotide polymorphisms (SNPs) within genes involved in fatty acid β-oxidation and dietary fat intakes. Two hundred and eight (208) participants were recruited in the greater Quebec City area. The participants completed a six-week fish oil supplementation (5 g fish oil/day: 1.9-2.2 g EPA and 1.1 g DHA). Dietary fat intakes were measured using three-day food records. SNPs within RXRA, CPT1A, ACADVL, ACAA2, ABCD2, ACOX1 and ACAA1 genes were genotyped using TAQMAN methodology. Gene-diet interaction effects on the plasma TG response were observed for SNPs within RXRA (rs11185660, rs10881576 and rs12339187) and ACOX1 (rs17583163) genes. For rs11185660, fold changes in RXRA gene expression levels were different depending on SFA intakes for homozygotes T/T. Gene-diet interaction effects of SNPs within genes involved in fatty acid β-oxidation and dietary fat intakes may be important in understanding the inter-individual variability in plasma TG levels and in the plasma TG response to a fish oil supplementation.

Discovering mutated driver genes through a robust and sparse co-regularized matrix factorization framework with prior information from mRNA expression patterns and interaction network.

PubMed

Xi, Jianing; Wang, Minghui; Li, Ao

2018-06-05

Discovery of mutated driver genes is one of the primary objective for studying tumorigenesis. To discover some relatively low frequently mutated driver genes from somatic mutation data, many existing methods incorporate interaction network as prior information. However, the prior information of mRNA expression patterns are not exploited by these existing network-based methods, which is also proven to be highly informative of cancer progressions. To incorporate prior information from both interaction network and mRNA expressions, we propose a robust and sparse co-regularized nonnegative matrix factorization to discover driver genes from mutation data. Furthermore, our framework also conducts Frobenius norm regularization to overcome overfitting issue. Sparsity-inducing penalty is employed to obtain sparse scores in gene representations, of which the top scored genes are selected as driver candidates. Evaluation experiments by known benchmarking genes indicate that the performance of our method benefits from the two type of prior information. Our method also outperforms the existing network-based methods, and detect some driver genes that are not predicted by the competing methods. In summary, our proposed method can improve the performance of driver gene discovery by effectively incorporating prior information from interaction network and mRNA expression patterns into a robust and sparse co-regularized matrix factorization framework.

Identification of genes related to proliferative diabetic retinopathy through RWR algorithm based on protein-protein interaction network.

PubMed

Zhang, Jian; Suo, Yan; Liu, Min; Xu, Xun

2018-06-01

Proliferative diabetic retinopathy (PDR) is one of the most common complications of diabetes and can lead to blindness. Proteomic studies have provided insight into the pathogenesis of PDR and a series of PDR-related genes has been identified but are far from fully characterized because the experimental methods are expensive and time consuming. In our previous study, we successfully identified 35 candidate PDR-related genes through the shortest-path algorithm. In the current study, we developed a computational method using the random walk with restart (RWR) algorithm and the protein-protein interaction (PPI) network to identify potential PDR-related genes. After some possible genes were obtained by the RWR algorithm, a three-stage filtration strategy, which includes the permutation test, interaction test and enrichment test, was applied to exclude potential false positives caused by the structure of PPI network, the poor interaction strength, and the limited similarity on gene ontology (GO) terms and biological pathways. As a result, 36 candidate genes were discovered by the method which was different from the 35 genes reported in our previous study. A literature review showed that 21 of these 36 genes are supported by previous experiments. These findings suggest the robustness and complementary effects of both our efforts using different computational methods, thus providing an alternative method to study PDR pathogenesis. Copyright © 2017 Elsevier B.V. All rights reserved.

Functional conservation of the yeast and Arabidopsis RAD54-like genes.

PubMed

Klutstein, Michael; Shaked, Hezi; Sherman, Amir; Avivi-Ragolsky, Naomi; Shema, Efrat; Zenvirth, Drora; Levy, Avraham A; Simchen, Giora

2008-04-01

The Saccharomyces cerevisiae RAD54 gene has critical roles in DNA double-strand break repair, homologous recombination, and gene targeting. Previous results show that the yeast gene enhances gene targeting when expressed in Arabidopsis thaliana. In this work we address the trans-species compatibility of Rad54 functions. We show that overexpression of yeast RAD54 in Arabidopsis enhances DNA damage resistance severalfold. Thus, the yeast gene is active in the Arabidopsis homologous-recombination repair system. Moreover, we have identified an A. thaliana ortholog of yeast RAD54, named AtRAD54. This gene, with close sequence similarity to RAD54, complements methylmethane sulfonate (MMS) sensitivity but not UV sensitivity or gene targeting defects of rad54Delta mutant yeast cells. Overexpression of AtRAD54 in Arabidopsis leads to enhanced resistance to DNA damage. This gene's assignment as a RAD54 ortholog is further supported by the interaction of AtRad54 with AtRad51 and the interactions between alien proteins (i.e., yeast Rad54 with AtRAD51 and yeast Rad51 with AtRad54) in a yeast two-hybrid experiment. These interactions hint at the molecular nature of this interkingdom complementation, although the stronger effect of the yeast Rad54 in plants than AtRad54 in yeast might be explained by an ability of the Rad54 protein to act alone, independently of its interaction with Rad51.

The REP2 Repeats of the Genome of Neisseria meningitidis Are Associated with Genes Coordinately Regulated during Bacterial Cell Interaction

PubMed Central

Morelle, Sandrine; Carbonnelle, Etienne; Nassif, Xavier

2003-01-01

Interaction with host cells is essential in meningococcal pathogenesis especially at the blood-brain barrier. This step is likely to involve a common regulatory pathway allowing coordinate regulation of genes necessary for the interaction with endothelial cells. The analysis of the genomic sequence of Neisseria meningitidis Z2491 revealed the presence of many repeats. One of these, designated REP2, contains a −24/−12 type promoter and a ribosome binding site 5 to 13 bp before an ATG. In addition most of these REP2 sequences are located immediately upstream of an ORF. Among these REP2-associated genes are pilC1 and crgA, described as being involved in steps essential for the interaction of N. meningitidis with host cells. Furthermore, the REP2 sequences located upstream of pilC1 and crgA correspond to the previously identified promoters known to be induced during the initial localized adhesion of N. meningitidis with human cells. This characteristic led us to hypothesize that at least some of the REP2-associated genes were upregulated under the same circumstances as pilC1 and crgA. Quantitative PCR in real time demonstrated that the expression of 14 out of 16 REP2-associated genes were upregulated during the initial localized adhesion of N. meningitidis. Taken together, these data suggest that these repeats control a set of genes necessary for the efficient interaction of this pathogen with host cells. Subsequent mutational analysis was performed to address the role of these genes during meningococcus-cell interaction. PMID:12670987

Epistasis-list.org: A Curated Database of Gene-Gene and Gene-Environment Interactions in Human Epidemiology

EPA Science Inventory

The field of human genetics has experienced a paradigm shift in that common diseases are now thought to be due to the complex interactions among numerous genetic and environmental factors. This paradigm shift has prompted the development of myriad novel methods to detect such int...

Gene-Environment Interactions in Genome-Wide Association Studies: Current Approaches and New Directions

ERIC Educational Resources Information Center

Winham, Stacey J.; Biernacka, Joanna M.

2013-01-01

Background: Complex psychiatric traits have long been thought to be the result of a combination of genetic and environmental factors, and gene-environment interactions are thought to play a crucial role in behavioral phenotypes and the susceptibility and progression of psychiatric disorders. Candidate gene studies to investigate hypothesized…

Inverse gene-for-gene interactions contribute additively to tan spot susceptibility in wheat

USDA-ARS?s Scientific Manuscript database

Tan spot of wheat, caused by Pyrenophora tritici-repentis, is an important disease in almost all wheat-growing areas of the world. The disease system is known to involve at least three fungal-produced necrotrophic effectors (NEs) that interact with corresponding host sensitivity (S) genes in an inv...

Replication of Early B-cell Factor 1 (EBF1) Gene-by-psychosocial Stress Interaction Effects on Central Adiposity in a Korean Population.

PubMed

Kim, Hyun-Jin; Min, Jin-Young; Min, Kyoung-Bok

2016-09-01

Central obesity plays a major role in the development of many chronic diseases, including cardiovascular disease and cancer. Chronic stress may be involved in the pathophysiology of central obesity. Although several large-scale genome-wide association studies have reported susceptibility genes for central adiposity, the effects of interactions between genes and psychosocial stress on central adiposity have rarely been examined. A recent study focusing on Caucasians discovered the novel gene early B-cell factor 1 (EBF1) , which was associated with central obesity-related traits via interactions with stress levels. We aimed to evaluate EBF1 gene-by-stress interaction effects on central adiposity traits, including visceral adipose tissue (VAT), in Korean adults. A total of 1467 Korean adults were included in this study. We selected 22 single-nucleotide polymorphisms (SNPs) in the EBF1 gene and analyzed their interactions with stress on central adiposity using additive, dominant, and recessive genetic modeling. The four SNPs that had strong linkage disequilibrium relationships (rs10061900, rs10070743, rs4704967, and rs10056564) demonstrated significant interactions with the waist-hip ratio in the dominant model ( p int <0.007). In addition, two other SNPs (rs6556377 and rs13180086) were associated with VAT by interactions with stress levels, especially in the recessive genetic model ( p int <0.007). As stress levels increased, the mean values of central adiposity traits according to SNP genotypes exhibited gradual but significant changes ( p <0.05). These results suggest that the common genetic variants for EBF1 are associated with central adiposity through interactions with stress levels, emphasizing the importance of managing stress in the prevention of central obesity.

Genotype by watering regime interaction in cultivated tomato: lessons from linkage mapping and gene expression.

PubMed

Albert, Elise; Gricourt, Justine; Bertin, Nadia; Bonnefoi, Julien; Pateyron, Stéphanie; Tamby, Jean-Philippe; Bitton, Frédérique; Causse, Mathilde

2016-02-01

In tomato, genotype by watering interaction resulted from genotype re-ranking more than scale changes. Interactive QTLs according to watering regime were detected. Differentially expressed genes were identified in some intervals. As a result of climate change, drought will increasingly limit crop production in the future. Studying genotype by watering regime interactions is necessary to improve plant adaptation to low water availability. In cultivated tomato (Solanum lycopersicum L.), extensively grown in dry areas, well-mastered water deficits can stimulate metabolite production, increasing plant defenses and concentration of compounds involved in fruit quality, at the same time. However, few tomato Quantitative Trait Loci (QTLs) and genes involved in response to drought are identified or only in wild species. In this study, we phenotyped a population of 119 recombinant inbred lines derived from a cross between a cherry tomato and a large fruit tomato, grown in greenhouse under two watering regimes, in two locations. A large genetic variability was measured for 19 plant and fruit traits, under the two watering treatments. Highly significant genotype by watering regime interactions were detected and resulted from re-ranking more than scale changes. The population was genotyped for 679 SNP markers to develop a genetic map. In total, 56 QTLs were identified among which 11 were interactive between watering regimes. These later mainly exhibited antagonist effects according to watering treatment. Variation in gene expression in leaves of parental accessions revealed 2259 differentially expressed genes, among which candidate genes presenting sequence polymorphisms were identified under two main interactive QTLs. Our results provide knowledge about the genetic control of genotype by watering regime interactions in cultivated tomato and the possible use of deficit irrigation to improve tomato quality.

The impact of the metabotropic glutamate receptor and other gene family interaction networks on autism

PubMed Central

Hadley, Dexter; Wu, Zhi-liang; Kao, Charlly; Kini, Akshata; Mohamed-Hadley, Alisha; Thomas, Kelly; Vazquez, Lyam; Qiu, Haijun; Mentch, Frank; Pellegrino, Renata; Kim, Cecilia; Connolly, John; Pinto, Dalila; Merikangas, Alison; Klei, Lambertus; Vorstman, Jacob A.S.; Thompson, Ann; Regan, Regina; Pagnamenta, Alistair T.; Oliveira, Bárbara; Magalhaes, Tiago R.; Gilbert, John; Duketis, Eftichia; De Jonge, Maretha V.; Cuccaro, Michael; Correia, Catarina T.; Conroy, Judith; Conceição, Inês C.; Chiocchetti, Andreas G.; Casey, Jillian P.; Bolshakova, Nadia; Bacchelli, Elena; Anney, Richard; Zwaigenbaum, Lonnie; Wittemeyer, Kerstin; Wallace, Simon; Engeland, Herman van; Soorya, Latha; Rogé, Bernadette; Roberts, Wendy; Poustka, Fritz; Mouga, Susana; Minshew, Nancy; McGrew, Susan G.; Lord, Catherine; Leboyer, Marion; Le Couteur, Ann S.; Kolevzon, Alexander; Jacob, Suma; Guter, Stephen; Green, Jonathan; Green, Andrew; Gillberg, Christopher; Fernandez, Bridget A.; Duque, Frederico; Delorme, Richard; Dawson, Geraldine; Café, Cátia; Brennan, Sean; Bourgeron, Thomas; Bolton, Patrick F.; Bölte, Sven; Bernier, Raphael; Baird, Gillian; Bailey, Anthony J.; Anagnostou, Evdokia; Almeida, Joana; Wijsman, Ellen M.; Vieland, Veronica J.; Vicente, Astrid M.; Schellenberg, Gerard D.; Pericak-Vance, Margaret; Paterson, Andrew D.; Parr, Jeremy R.; Oliveira, Guiomar; Almeida, Joana; Café, Cátia; Mouga, Susana; Correia, Catarina; Nurnberger, John I.; Monaco, Anthony P.; Maestrini, Elena; Klauck, Sabine M.; Hakonarson, Hakon; Haines, Jonathan L.; Geschwind, Daniel H.; Freitag, Christine M.; Folstein, Susan E.; Ennis, Sean; Coon, Hilary; Battaglia, Agatino; Szatmari, Peter; Sutcliffe, James S.; Hallmayer, Joachim; Gill, Michael; Cook, Edwin H.; Buxbaum, Joseph D.; Devlin, Bernie; Gallagher, Louise; Betancur, Catalina; Scherer, Stephen W.; Glessner, Joseph; Hakonarson, Hakon

2014-01-01

Although multiple reports show that defective genetic networks underlie the aetiology of autism, few have translated into pharmacotherapeutic opportunities. Since drugs compete with endogenous small molecules for protein binding, many successful drugs target large gene families with multiple drug binding sites. Here we search for defective gene family interaction networks (GFINs) in 6,742 patients with the ASDs relative to 12,544 neurologically normal controls, to find potentially druggable genetic targets. We find significant enrichment of structural defects (P≤2.40E−09, 1.8-fold enrichment) in the metabotropic glutamate receptor (GRM) GFIN, previously observed to impact attention deficit hyperactivity disorder (ADHD) and schizophrenia. Also, the MXD-MYC-MAX network of genes, previously implicated in cancer, is significantly enriched (P≤3.83E−23, 2.5-fold enrichment), as is the calmodulin 1 (CALM1) gene interaction network (P≤4.16E−04, 14.4-fold enrichment), which regulates voltage-independent calcium-activated action potentials at the neuronal synapse. We find that multiple defective gene family interactions underlie autism, presenting new translational opportunities to explore for therapeutic interventions. PMID:24927284

Functional regression method for whole genome eQTL epistasis analysis with sequencing data.

PubMed

Xu, Kelin; Jin, Li; Xiong, Momiao

2017-05-18

Epistasis plays an essential rule in understanding the regulation mechanisms and is an essential component of the genetic architecture of the gene expressions. However, interaction analysis of gene expressions remains fundamentally unexplored due to great computational challenges and data availability. Due to variation in splicing, transcription start sites, polyadenylation sites, post-transcriptional RNA editing across the entire gene, and transcription rates of the cells, RNA-seq measurements generate large expression variability and collectively create the observed position level read count curves. A single number for measuring gene expression which is widely used for microarray measured gene expression analysis is highly unlikely to sufficiently account for large expression variation across the gene. Simultaneously analyzing epistatic architecture using the RNA-seq and whole genome sequencing (WGS) data poses enormous challenges. We develop a nonlinear functional regression model (FRGM) with functional responses where the position-level read counts within a gene are taken as a function of genomic position, and functional predictors where genotype profiles are viewed as a function of genomic position, for epistasis analysis with RNA-seq data. Instead of testing the interaction of all possible pair-wises SNPs, the FRGM takes a gene as a basic unit for epistasis analysis, which tests for the interaction of all possible pairs of genes and use all the information that can be accessed to collectively test interaction between all possible pairs of SNPs within two genome regions. By large-scale simulations, we demonstrate that the proposed FRGM for epistasis analysis can achieve the correct type 1 error and has higher power to detect the interactions between genes than the existing methods. The proposed methods are applied to the RNA-seq and WGS data from the 1000 Genome Project. The numbers of pairs of significantly interacting genes after Bonferroni correction identified using FRGM, RPKM and DESeq were 16,2361, 260 and 51, respectively, from the 350 European samples. The proposed FRGM for epistasis analysis of RNA-seq can capture isoform and position-level information and will have a broad application. Both simulations and real data analysis highlight the potential for the FRGM to be a good choice of the epistatic analysis with sequencing data.

Identification of Early Response Genes in Human Peripheral Leukocytes Infected with Orientia tsutsugamushi: The Emergent of a Unique Gene Expression Profile for Diagnosis of O. tsutsugamush Infection

DTIC Science & Technology

2010-01-01

dynein to move from the cell periphery to the microtubule organizing center [22]. Therefore, the initial interactions between host and intracellular...used to study host-pathogen interactions , mainly by identifying genes from pathogens that may be involved in pathogenecity and by surveying the scope...toward understanding the host-Orientia tsutsugamushi interaction at the molecular level, we used human cDNA microarray technology to examine in detail

The Association of Multiple Interacting Genes with Specific Phenotypes in Rice Using Gene Coexpression Networks1[C][W][OA

PubMed Central

Ficklin, Stephen P.; Luo, Feng; Feltus, F. Alex

2010-01-01

Discovering gene sets underlying the expression of a given phenotype is of great importance, as many phenotypes are the result of complex gene-gene interactions. Gene coexpression networks, built using a set of microarray samples as input, can help elucidate tightly coexpressed gene sets (modules) that are mixed with genes of known and unknown function. Functional enrichment analysis of modules further subdivides the coexpressed gene set into cofunctional gene clusters that may coexist in the module with other functionally related gene clusters. In this study, 45 coexpressed gene modules and 76 cofunctional gene clusters were discovered for rice (Oryza sativa) using a global, knowledge-independent paradigm and the combination of two network construction methodologies. Some clusters were enriched for previously characterized mutant phenotypes, providing evidence for specific gene sets (and their annotated molecular functions) that underlie specific phenotypes. PMID:20668062

The association of multiple interacting genes with specific phenotypes in rice using gene coexpression networks.

PubMed

Ficklin, Stephen P; Luo, Feng; Feltus, F Alex

2010-09-01

Discovering gene sets underlying the expression of a given phenotype is of great importance, as many phenotypes are the result of complex gene-gene interactions. Gene coexpression networks, built using a set of microarray samples as input, can help elucidate tightly coexpressed gene sets (modules) that are mixed with genes of known and unknown function. Functional enrichment analysis of modules further subdivides the coexpressed gene set into cofunctional gene clusters that may coexist in the module with other functionally related gene clusters. In this study, 45 coexpressed gene modules and 76 cofunctional gene clusters were discovered for rice (Oryza sativa) using a global, knowledge-independent paradigm and the combination of two network construction methodologies. Some clusters were enriched for previously characterized mutant phenotypes, providing evidence for specific gene sets (and their annotated molecular functions) that underlie specific phenotypes.

TranscriptomeBrowser 3.0: introducing a new compendium of molecular interactions and a new visualization tool for the study of gene regulatory networks.

PubMed

Lepoivre, Cyrille; Bergon, Aurélie; Lopez, Fabrice; Perumal, Narayanan B; Nguyen, Catherine; Imbert, Jean; Puthier, Denis

2012-01-31

Deciphering gene regulatory networks by in silico approaches is a crucial step in the study of the molecular perturbations that occur in diseases. The development of regulatory maps is a tedious process requiring the comprehensive integration of various evidences scattered over biological databases. Thus, the research community would greatly benefit from having a unified database storing known and predicted molecular interactions. Furthermore, given the intrinsic complexity of the data, the development of new tools offering integrated and meaningful visualizations of molecular interactions is necessary to help users drawing new hypotheses without being overwhelmed by the density of the subsequent graph. We extend the previously developed TranscriptomeBrowser database with a set of tables containing 1,594,978 human and mouse molecular interactions. The database includes: (i) predicted regulatory interactions (computed by scanning vertebrate alignments with a set of 1,213 position weight matrices), (ii) potential regulatory interactions inferred from systematic analysis of ChIP-seq experiments, (iii) regulatory interactions curated from the literature, (iv) predicted post-transcriptional regulation by micro-RNA, (v) protein kinase-substrate interactions and (vi) physical protein-protein interactions. In order to easily retrieve and efficiently analyze these interactions, we developed In-teractomeBrowser, a graph-based knowledge browser that comes as a plug-in for Transcriptome-Browser. The first objective of InteractomeBrowser is to provide a user-friendly tool to get new insight into any gene list by providing a context-specific display of putative regulatory and physical interactions. To achieve this, InteractomeBrowser relies on a "cell compartments-based layout" that makes use of a subset of the Gene Ontology to map gene products onto relevant cell compartments. This layout is particularly powerful for visual integration of heterogeneous biological information and is a productive avenue in generating new hypotheses. The second objective of InteractomeBrowser is to fill the gap between interaction databases and dynamic modeling. It is thus compatible with the network analysis software Cytoscape and with the Gene Interaction Network simulation software (GINsim). We provide examples underlying the benefits of this visualization tool for large gene set analysis related to thymocyte differentiation. The InteractomeBrowser plugin is a powerful tool to get quick access to a knowledge database that includes both predicted and validated molecular interactions. InteractomeBrowser is available through the TranscriptomeBrowser framework and can be found at: http://tagc.univ-mrs.fr/tbrowser/. Our database is updated on a regular basis.

Identification of susceptible genes for complex chronic diseases based on disease risk functional SNPs and interaction networks.

PubMed

Li, Wan; Zhu, Lina; Huang, Hao; He, Yuehan; Lv, Junjie; Li, Weimin; Chen, Lina; He, Weiming

2017-10-01

Complex chronic diseases are caused by the effects of genetic and environmental factors. Single nucleotide polymorphisms (SNPs), one common type of genetic variations, played vital roles in diseases. We hypothesized that disease risk functional SNPs in coding regions and protein interaction network modules were more likely to contribute to the identification of disease susceptible genes for complex chronic diseases. This could help to further reveal the pathogenesis of complex chronic diseases. Disease risk SNPs were first recognized from public SNP data for coronary heart disease (CHD), hypertension (HT) and type 2 diabetes (T2D). SNPs in coding regions that were classified into nonsense and missense by integrating several SNP functional annotation databases were treated as functional SNPs. Then, regions significantly associated with each disease were screened using random permutations for disease risk functional SNPs. Corresponding to these regions, 155, 169 and 173 potential disease susceptible genes were identified for CHD, HT and T2D, respectively. A disease-related gene product interaction network in environmental context was constructed for interacting gene products of both disease genes and potential disease susceptible genes for these diseases. After functional enrichment analysis for disease associated modules, 5 CHD susceptible genes, 7 HT susceptible genes and 3 T2D susceptible genes were finally identified, some of which had pleiotropic effects. Most of these genes were verified to be related to these diseases in literature. This was similar for disease genes identified from another method proposed by Lee et al. from a different aspect. This research could provide novel perspectives for diagnosis and treatment of complex chronic diseases and susceptible genes identification for other diseases. Copyright © 2017 Elsevier Inc. All rights reserved.

The Impact of Gene-Environment Dependence and Misclassification in Genetic Association Studies Incorporating Gene-Environment Interactions

PubMed Central

Lindström, Sara; Yen, Yu-Chun; Spiegelman, Donna; Kraft, Peter

2009-01-01

The possibility of gene-environment interaction can be exploited to identify genetic variants associated with disease using a joint test of genetic main effect and gene-environment interaction. We consider how exposure misclassification and dependence between the true exposure E and the tested genetic variant G affect this joint test in absolute terms and relative to three other tests: the marginal test (G), the standard test for multiplicative gene-environment interaction (GE), and the case-only test for interaction (GE-CO). All tests can have inflated Type I error rate when E and G are correlated in the underlying population. For the GE and G-GE tests this inflation is only noticeable when the gene-environment dependence is unusually strong; the inflation can be large for the GE-CO test even for modest correlation. The joint G-GE test has greater power than the GE test generally, and greater power than the G test when there is no genetic main effect and the measurement error is small to moderate. The joint G-GE test is an attractive test for assessing genetic association when there is limited knowledge about casual mechanisms a priori, even in the presence of misclassification in environmental exposure measurement and correlation between exposure and genetic variants. PMID:19521099

Intrinsic limits to gene regulation by global crosstalk

NASA Astrophysics Data System (ADS)

Friedlander, Tamar; Prizak, Roshan; Guet, Calin; Barton, Nicholas H.; Tkacik, Gasper

Gene activity is mediated by the specificity of binding interactions between special proteins, called transcription factors, and short regulatory sequences on the DNA, where different protein species preferentially bind different DNA targets. Limited interaction specificity may lead to crosstalk: a regulatory state in which a gene is either incorrectly activated due to spurious interactions or remains erroneously inactive. Since each protein can potentially interact with numerous DNA targets, crosstalk is inherently a global problem, yet has previously not been studied as such. We construct a theoretical framework to analyze the effects of global crosstalk on gene regulation, using statistical mechanics. We find that crosstalk in regulatory interactions puts fundamental limits on the reliability of gene regulation that are not easily mitigated by tuning proteins concentrations or by complex regulatory schemes proposed in the literature. Our results suggest that crosstalk imposes a previously unexplored global constraint on the functioning and evolution of regulatory networks, which is qualitatively distinct from the known constraints that act at the level of individual gene regulatory elements. The research leading to these results has received funding from the People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme (FP7/2007-2013) under REA Grant agreement Nr. 291734 (T.F.) and ERC Grant Nr. 250152 (N.B.).

Dynamic Interactions between Bombyx mori Nucleopolyhedrovirus and Its Host Cells Revealed by Transcriptome Analysis

PubMed Central

Xue, Jian; Qiao, Nan; Zhang, Wei; Cheng, Ruo-Lin; Zhang, Xiao-Qin; Bao, Yan-Yuan; Xu, Yi-Peng; Gu, Lin-Zhu

2012-01-01

Although microarray and expressed sequence tag (EST)-based approaches have been used to profile gene expression during baculovirus infection, the response of host genes to baculovirus infection and the interaction between baculovirus and its host remain largely unknown. To determine the host response to Bombyx mori nucleopolyhedrovirus infection and the dynamic interaction between the virus and its host, eight digital gene expression libraries were examined in a Bm5 cell line before infection and at 1.5, 3, 6, 12, 24, 48, and 96 h postinfection. Gene set enrichment analysis of differentially expressed genes at each time point following infection showed that gene sets including cytoskeleton, transcription, translation, energy metabolism, iron ion metabolism, and the ubiquitin-proteasome pathway were altered after viral infection. In addition, a time course depicting protein-protein interaction networks between the baculovirus and the host were constructed and revealed that viral proteins interact with a multitude of cellular machineries, such as the proteasome, cytoskeleton, and spliceosome. Several viral proteins, including IE2, CG30, PE38, and PK-1/2, were predicted to play key roles in mediating virus-host interactions. Based on these results, we tested the role of the ubiquitin-proteasome pathway and iron ion metabolism in the viral infection cycle. Treatment with a proteasome inhibitor and deferoxamine mesylate in vitro and in vivo confirmed that these pathways regulate viral infection. Taken together, these findings provide new insights into the interaction between the baculovirus and its host and identify molecular mechanisms that can be used to block viral infection and improve baculovirus expression systems. PMID:22532689

The interaction of BDNF and NTRK2 gene increases the susceptibility of paranoid schizophrenia.

PubMed

Lin, Zheng; Su, Yousong; Zhang, Chengfang; Xing, Mengjuan; Ding, Wenhua; Liao, Liwei; Guan, Yangtai; Li, Zezhi; Cui, Donghong

2013-01-01

The association between BDNF gene functional Val66Met polymorphism rs6265 and the schizophrenia is far from being consistent. In addition to the heterogeneous in schizophrenia per se leading to the inconsistent results, the interaction among multi-genes is probably playing the main role in the pathogenesis of schizophrenia, but not a single gene. Neurotrophic tyrosine kinase receptor 2 (NTRK2) is the high-affinity receptor of BDNF, and was reported to be associated with mood disorders, though no literature reported the association with schizophrenia. Thus, in the present study, total 402 patients with paranoid schizophrenia (the most common subtype of schizophrenia) and matched 406 healthy controls were recruited to investigate the role of rs6265 in BDNF, three polymorphisms in NTRK2 gene (rs1387923, rs2769605 and rs1565445) and their interaction in the susceptibility to paranoid schizophrenia in a Chinese Han population. We did not observe significant differences in allele and genotype frequencies between patients and healthy controls for all four polymorphisms separately. The haplotype analysis also showed no association between haplotype of NTRK2 genes (rs1387923, rs2769605, and rs1565445) and paranoid schizophrenia. However, we found the association between the interaction of BDNF and NTRK2 with paranoid schizophrenia by using the MDR method followed by conventional statistical analysis. The best gene-gene interaction model was a three-locus model (BDNF rs6265, NTRK2 rs1387923 and NTRK2 rs2769605), in which one low-risk and three high-risk four-locus genotype combinations were identified. Our findings implied that single polymorphism of rs6265 rs1387923, rs2769605, and rs1565445 in BDNF and NTRK2 were not associated with the development of paranoid schizophrenia in a Han population, however, the interaction of BDNF and NTRK2 genes polymorphisms (BDNF-rs6265, NTRK2-rs1387923 and NTRK2-rs2769605) may be involved in the susceptibility to paranoid schizophrenia.

The Interaction of BDNF and NTRK2 Gene Increases the Susceptibility of Paranoid Schizophrenia

PubMed Central

Zhang, Chengfang; Xing, Mengjuan; Ding, Wenhua; Liao, Liwei; Guan, Yangtai; Li, Zezhi; Cui, Donghong

2013-01-01

The association between BDNF gene functional Val66Met polymorphism rs6265 and the schizophrenia is far from being consistent. In addition to the heterogeneous in schizophrenia per se leading to the inconsistent results, the interaction among multi-genes is probably playing the main role in the pathogenesis of schizophrenia, but not a single gene. Neurotrophic tyrosine kinase receptor 2 (NTRK2) is the high-affinity receptor of BDNF, and was reported to be associated with mood disorders, though no literature reported the association with schizophrenia. Thus, in the present study, total 402 patients with paranoid schizophrenia (the most common subtype of schizophrenia) and matched 406 healthy controls were recruited to investigate the role of rs6265 in BDNF, three polymorphisms in NTRK2 gene (rs1387923, rs2769605 and rs1565445) and their interaction in the susceptibility to paranoid schizophrenia in a Chinese Han population. We did not observe significant differences in allele and genotype frequencies between patients and healthy controls for all four polymorphisms separately. The haplotype analysis also showed no association between haplotype of NTRK2 genes (rs1387923, rs2769605, and rs1565445) and paranoid schizophrenia. However, we found the association between the interaction of BDNF and NTRK2 with paranoid schizophrenia by using the MDR method followed by conventional statistical analysis. The best gene-gene interaction model was a three-locus model (BDNF rs6265, NTRK2 rs1387923 and NTRK2 rs2769605), in which one low-risk and three high-risk four-locus genotype combinations were identified. Our findings implied that single polymorphism of rs6265 rs1387923, rs2769605, and rs1565445 in BDNF and NTRK2 were not associated with the development of paranoid schizophrenia in a Han population, however, the interaction of BDNF and NTRK2 genes polymorphisms (BDNF-rs6265, NTRK2-rs1387923 and NTRK2-rs2769605) may be involved in the susceptibility to paranoid schizophrenia. PMID:24069289

Interaction between LRP5 and periostin gene polymorphisms on serum periostin levels and cortical bone microstructure.

PubMed

Pepe, J; Bonnet, N; Herrmann, F R; Biver, E; Rizzoli, R; Chevalley, T; Ferrari, S L

2018-02-01

We investigated the interaction between periostin SNPs and the SNPs of the genes assumed to modulate serum periostin levels and bone microstructure in a cohort of postmenopausal women. We identified an interaction between LRP5 SNP rs648438 and periostin SNP rs9547970 on serum periostin levels and on radial cortical porosity. The purpose of this study is to investigate the interaction between periostin gene polymorphisms (SNPs) and other genes potentially responsible for modulating serum periostin levels and bone microstructure in a cohort of postmenopausal women. In 648 postmenopausal women from the Geneva Retirees Cohort, we analyzed 6 periostin SNPs and another 149 SNPs in 14 genes, namely BMP2, CTNNB1, ESR1, ESR2, LRP5, LRP6, PTH, SPTBN1, SOST, TGFb1, TNFRSF11A, TNFSF11, TNFRSF11B and WNT16. Volumetric BMD and bone microstructure were measured by high-resolution peripheral quantitative computed tomography at the distal radius and tibia. Serum periostin levels were associated with radial cortical porosity, including after adjustment for age, BMI, and years since menopause (p = 0.036). Sixteen SNPs in the ESR1, LRP5, TNFRSF11A, SOST, SPTBN1, TNFRSF11B and TNFSF11 genes were associated with serum periostin levels (p range 0.03-0.001) whereas 26 SNPs in 9 genes were associated with cortical porosity at the radius and/or at the tibia. WNT 16 was the gene with the highest number of SNPs associated with both trabecular and cortical microstructure. The periostin SNP rs9547970 was also associated with cortical porosity (p = 0.04). In particular, SNPs in LRP5, ESR1 and near the TNFRSF11A gene were associated with both cortical porosity and serum periostin levels. Eventually, we identified an interaction between LRP5 SNP rs648438 and periostin SNP rs9547970 on serum periostin levels (interaction p = 0.01) and on radial cortical porosity (interaction p = 0.005). These results suggest that periostin expression is genetically modulated, particularly by polymorphisms in the Wnt pathway, and is thereby implicated in the genetic variation of bone microstructure.

Avirulence Genes in Cereal Powdery Mildews: The Gene-for-Gene Hypothesis 2.0.

PubMed

Bourras, Salim; McNally, Kaitlin E; Müller, Marion C; Wicker, Thomas; Keller, Beat

2016-01-01

The gene-for-gene hypothesis states that for each gene controlling resistance in the host, there is a corresponding, specific gene controlling avirulence in the pathogen. Allelic series of the cereal mildew resistance genes Pm3 and Mla provide an excellent system for genetic and molecular analysis of resistance specificity. Despite this opportunity for molecular research, avirulence genes in mildews remain underexplored. Earlier work in barley powdery mildew (B.g. hordei) has shown that the reaction to some Mla resistance alleles is controlled by multiple genes. Similarly, several genes are involved in the specific interaction of wheat mildew (B.g. tritici) with the Pm3 allelic series. We found that two mildew genes control avirulence on Pm3f: one gene is involved in recognition by the resistance protein as demonstrated by functional studies in wheat and the heterologous host Nicotiana benthamiana. A second gene is a suppressor, and resistance is only observed in mildew genotypes combining the inactive suppressor and the recognized Avr. We propose that such suppressor/avirulence gene combinations provide the basis of specificity in mildews. Depending on the particular gene combinations in a mildew race, different genes will be genetically identified as the "avirulence" gene. Additionally, the observation of two LINE retrotransposon-encoded avirulence genes in B.g. hordei further suggests that the control of avirulence in mildew is more complex than a canonical gene-for-gene interaction. To fully understand the mildew-cereal interactions, more knowledge on avirulence determinants is needed and we propose ways how this can be achieved based on recent advances in the field.

Avirulence Genes in Cereal Powdery Mildews: The Gene-for-Gene Hypothesis 2.0

PubMed Central

Bourras, Salim; McNally, Kaitlin E.; Müller, Marion C.; Wicker, Thomas; Keller, Beat

2016-01-01

The gene-for-gene hypothesis states that for each gene controlling resistance in the host, there is a corresponding, specific gene controlling avirulence in the pathogen. Allelic series of the cereal mildew resistance genes Pm3 and Mla provide an excellent system for genetic and molecular analysis of resistance specificity. Despite this opportunity for molecular research, avirulence genes in mildews remain underexplored. Earlier work in barley powdery mildew (B.g. hordei) has shown that the reaction to some Mla resistance alleles is controlled by multiple genes. Similarly, several genes are involved in the specific interaction of wheat mildew (B.g. tritici) with the Pm3 allelic series. We found that two mildew genes control avirulence on Pm3f: one gene is involved in recognition by the resistance protein as demonstrated by functional studies in wheat and the heterologous host Nicotiana benthamiana. A second gene is a suppressor, and resistance is only observed in mildew genotypes combining the inactive suppressor and the recognized Avr. We propose that such suppressor/avirulence gene combinations provide the basis of specificity in mildews. Depending on the particular gene combinations in a mildew race, different genes will be genetically identified as the “avirulence” gene. Additionally, the observation of two LINE retrotransposon-encoded avirulence genes in B.g. hordei further suggests that the control of avirulence in mildew is more complex than a canonical gene-for-gene interaction. To fully understand the mildew–cereal interactions, more knowledge on avirulence determinants is needed and we propose ways how this can be achieved based on recent advances in the field. PMID:26973683

Invariant TAD Boundaries Constrain Cell-Type-Specific Looping Interactions between Promoters and Distal Elements around the CFTR Locus

PubMed Central

Smith, Emily M.; Lajoie, Bryan R.; Jain, Gaurav; Dekker, Job

2016-01-01

Three-dimensional genome structure plays an important role in gene regulation. Globally, chromosomes are organized into active and inactive compartments while, at the gene level, looping interactions connect promoters to regulatory elements. Topologically associating domains (TADs), typically several hundred kilobases in size, form an intermediate level of organization. Major questions include how TADs are formed and how they are related to looping interactions between genes and regulatory elements. Here we performed a focused 5C analysis of a 2.8 Mb chromosome 7 region surrounding CFTR in a panel of cell types. We find that the same TAD boundaries are present in all cell types, indicating that TADs represent a universal chromosome architecture. Furthermore, we find that these TAD boundaries are present irrespective of the expression and looping of genes located between them. In contrast, looping interactions between promoters and regulatory elements are cell-type specific and occur mostly within TADs. This is exemplified by the CFTR promoter that in different cell types interacts with distinct sets of distal cell-type-specific regulatory elements that are all located within the same TAD. Finally, we find that long-range associations between loci located in different TADs are also detected, but these display much lower interaction frequencies than looping interactions within TADs. Interestingly, interactions between TADs are also highly cell-type-specific and often involve loci clustered around TAD boundaries. These data point to key roles of invariant TAD boundaries in constraining as well as mediating cell-type-specific long-range interactions and gene regulation. PMID:26748519

Genome-wide gene–gene interaction analysis for next-generation sequencing

PubMed Central

Zhao, Jinying; Zhu, Yun; Xiong, Momiao

2016-01-01

The critical barrier in interaction analysis for next-generation sequencing (NGS) data is that the traditional pairwise interaction analysis that is suitable for common variants is difficult to apply to rare variants because of their prohibitive computational time, large number of tests and low power. The great challenges for successful detection of interactions with NGS data are (1) the demands in the paradigm of changes in interaction analysis; (2) severe multiple testing; and (3) heavy computations. To meet these challenges, we shift the paradigm of interaction analysis between two SNPs to interaction analysis between two genomic regions. In other words, we take a gene as a unit of analysis and use functional data analysis techniques as dimensional reduction tools to develop a novel statistic to collectively test interaction between all possible pairs of SNPs within two genome regions. By intensive simulations, we demonstrate that the functional logistic regression for interaction analysis has the correct type 1 error rates and higher power to detect interaction than the currently used methods. The proposed method was applied to a coronary artery disease dataset from the Wellcome Trust Case Control Consortium (WTCCC) study and the Framingham Heart Study (FHS) dataset, and the early-onset myocardial infarction (EOMI) exome sequence datasets with European origin from the NHLBI's Exome Sequencing Project. We discovered that 6 of 27 pairs of significantly interacted genes in the FHS were replicated in the independent WTCCC study and 24 pairs of significantly interacted genes after applying Bonferroni correction in the EOMI study. PMID:26173972

Gene Expression Profiling of Monkeypox Virus-Infected Cells Reveals Novel Interfaces for Host-Virus Interactions

DTIC Science & Technology

2010-07-28

expression is plotted on Y -axis after normalization to mock-treated samples. Results plotted to compare calculated fold change in expression of each gene ...RESEARCH Open Access Gene expression profiling of monkeypox virus-infected cells reveals novel interfaces for host-virus interactions Abdulnaser...suppress antiviral cell defenses, exploit host cell machinery, and delay infection-induced cell death. However, a comprehensive study of all host genes

Gene-gene, gene-environment, gene-nutrient interactions and single nucleotide polymorphisms of inflammatory cytokines.

PubMed

Nadeem, Amina; Mumtaz, Sadaf; Naveed, Abdul Khaliq; Aslam, Muhammad; Siddiqui, Arif; Lodhi, Ghulam Mustafa; Ahmad, Tausif

2015-05-15

Inflammation plays a significant role in the etiology of type 2 diabetes mellitus (T2DM). The rise in the pro-inflammatory cytokines is the essential step in glucotoxicity and lipotoxicity induced mitochondrial injury, oxidative stress and beta cell apoptosis in T2DM. Among the recognized markers are interleukin (IL)-6, IL-1, IL-10, IL-18, tissue necrosis factor-alpha (TNF-α), C-reactive protein, resistin, adiponectin, tissue plasminogen activator, fibrinogen and heptoglobins. Diabetes mellitus has firm genetic and very strong environmental influence; exhibiting a polygenic mode of inheritance. Many single nucleotide polymorphisms (SNPs) in various genes including those of pro and anti-inflammatory cytokines have been reported as a risk for T2DM. Not all the SNPs have been confirmed by unifying results in different studies and wide variations have been reported in various ethnic groups. The inter-ethnic variations can be explained by the fact that gene expression may be regulated by gene-gene, gene-environment and gene-nutrient interactions. This review highlights the impact of these interactions on determining the role of single nucleotide polymorphism of IL-6, TNF-α, resistin and adiponectin in pathogenesis of T2DM.

Confirming genes influencing risk to cleft lip with/without cleft palate in a case-parent trio study.

PubMed

Beaty, T H; Taub, M A; Scott, A F; Murray, J C; Marazita, M L; Schwender, H; Parker, M M; Hetmanski, J B; Balakrishnan, P; Mansilla, M A; Mangold, E; Ludwig, K U; Noethen, M M; Rubini, M; Elcioglu, N; Ruczinski, I

2013-07-01

A collection of 1,108 case-parent trios ascertained through an isolated, nonsyndromic cleft lip with or without cleft palate (CL/P) was used to replicate the findings from a genome-wide association study (GWAS) conducted by Beaty et al. (Nat Genet 42:525-529, 2010), where four different genes/regions were identified as influencing risk to CL/P. Tagging SNPs for 33 different genes were genotyped (1,269 SNPs). All four of the genes originally identified as showing genome-wide significance (IRF6, ABCA4 and MAF, plus the 8q24 region) were confirmed in this independent sample of trios (who were primarily of European and Southeast Asian ancestry). In addition, eight genes classified as 'second tier' hits in the original study (PAX7, THADA, COL8A1/FILIP1L, DCAF4L2, GADD45G, NTN1, RBFOX3 and FOXE1) showed evidence of linkage and association in this replication sample. Meta-analysis between the original GWAS trios and these replication trios showed PAX7, COL8A1/FILIP1L and NTN1 achieved genome-wide significance. Tests for gene-environment interaction between these 33 genes and maternal smoking found evidence for interaction with two additional genes: GRID2 and ELAVL2 among European mothers (who had a higher rate of smoking than Asian mothers). Formal tests for gene-gene interaction (epistasis) failed to show evidence of statistical interaction in any simple fashion. This study confirms that many different genes influence risk to CL/P.

Gene by Environment Interaction and Resilience: Effects of Child Maltreatment and Serotonin, Corticotropin Releasing Hormone, Dopamine, and Oxytocin Genes

PubMed Central

Cicchetti, Dante; Rogosch, Fred A.

2013-01-01

In this investigation, gene-environment interaction effects in predicting resilience in adaptive functioning among maltreated and nonmaltreated low-income children (N = 595) were examined. A multi-component index of resilient functioning was derived and levels of resilient functioning were identified. Variants in four genes, 5-HTTLPR, CRHR1, DRD4 -521C/T, and OXTR, were investigated. In a series of ANCOVAs, child maltreatment demonstrated a strong negative main effect on children’s resilient functioning, whereas no main effects for any of the genotypes of the respective genes were found. However, gene-environment interactions involving genotypes of each of the respective genes and maltreatment status were obtained. For each respective gene, among children with a specific genotype, the relative advantage in resilient functioning of nonmaltreated compared to maltreated children was stronger than was the case for nonmaltreated and maltreated children with other genotypes of the respective gene. Across the four genes, a composite of the genotypes that more strongly differentiated resilient functioning between nonmaltreated and maltreated children provided further evidence of genetic variations influencing resilient functioning in nonmaltreated children, whereas genetic variation had a negligible effect on promoting resilience among maltreated children. Additional effects were observed for children based on the number of subtypes of maltreatment children experienced, as well as for abuse and neglect subgroups. Finally, maltreated and nonmaltreated children with high levels of resilience differed in their average number of differentiating genotypes. These results suggest that differential resilient outcomes are based on the interaction between genes and developmental experiences. PMID:22559122

Confirming genes influencing risk to cleft lip with/without cleft palate in a case-parent trio study

PubMed Central

Beaty, TH; Taub, MA; Scott, AF; Murray, JC; Marazita, ML; Schwender, H; Parker, MM; Hetmanski, JB; Balakrishnan, P; Mansilla, MA; Mangold, E; Ludwig, KU; Noethen, MM; Rubini, M; Elcioglu, N; Ruczinski, I

2013-01-01

A collection of 1,108 case-parent trios ascertained through an isolated, non-syndromic cleft lip with or without cleft palate (CL/P) was used to replicate the findings from a genome-wide association study (GWAS) conducted by Beaty et al. (2010) where four different genes/regions were identified as influencing risk to CL/P. Tagging SNPs for 33 different genes were genotyped (1,269 SNPs). All four of the genes originally identified as showing genome-wide significance (IRF6, ABCA4 and MAF, plus the 8q24 region) were confirmed in this independent sample of trios (who were primarily of European and Southeast Asian ancestry). In addition, eight genes classified as ‘second tier’ hits in the original study (PAX7, THADA, COL8A1/FILIP1L, DCAF4L2, GADD45G, NTN1, RBFOX3 and FOXE1) showed evidence of linkage and association in this replication sample. Meta-analysis between the original GWAS trios and these replication trios showed PAX7, COL8A1/FILIP1L and NTN1 achieved genome-wide significance. Tests for gene-environment interaction between these 33 genes and maternal smoking found evidence for interaction with two additional genes: GRID2 and ELAVL2 among European mothers (who had a higher rate of smoking than Asian mothers). Formal tests for gene-gene interaction (epistasis) failed to show evidence of statistical interaction in any simple fashion. This study confirms that many different genes influence risk to CL/P. PMID:23512105

Discovery and explanation of drug-drug interactions via text mining.

PubMed

Percha, Bethany; Garten, Yael; Altman, Russ B

2012-01-01

Drug-drug interactions (DDIs) can occur when two drugs interact with the same gene product. Most available information about gene-drug relationships is contained within the scientific literature, but is dispersed over a large number of publications, with thousands of new publications added each month. In this setting, automated text mining is an attractive solution for identifying gene-drug relationships and aggregating them to predict novel DDIs. In previous work, we have shown that gene-drug interactions can be extracted from Medline abstracts with high fidelity - we extract not only the genes and drugs, but also the type of relationship expressed in individual sentences (e.g. metabolize, inhibit, activate and many others). We normalize these relationships and map them to a standardized ontology. In this work, we hypothesize that we can combine these normalized gene-drug relationships, drawn from a very broad and diverse literature, to infer DDIs. Using a training set of established DDIs, we have trained a random forest classifier to score potential DDIs based on the features of the normalized assertions extracted from the literature that relate two drugs to a gene product. The classifier recognizes the combinations of relationships, drugs and genes that are most associated with the gold standard DDIs, correctly identifying 79.8% of assertions relating interacting drug pairs and 78.9% of assertions relating noninteracting drug pairs. Most significantly, because our text processing method captures the semantics of individual gene-drug relationships, we can construct mechanistic pharmacological explanations for the newly-proposed DDIs. We show how our classifier can be used to explain known DDIs and to uncover new DDIs that have not yet been reported.

Unconditional analyses can increase efficiency in assessing gene-environment interaction of the case-combined-control design.

PubMed

Goldstein, Alisa M; Dondon, Marie-Gabrielle; Andrieu, Nadine

2006-08-01

A design combining both related and unrelated controls, named the case-combined-control design, was recently proposed to increase the power for detecting gene-environment (GxE) interaction. Under a conditional analytic approach, the case-combined-control design appeared to be more efficient and feasible than a classical case-control study for detecting interaction involving rare events. We now propose an unconditional analytic strategy to further increase the power for detecting gene-environment (GxE) interactions. This strategy allows the estimation of GxE interaction and exposure (E) main effects under certain assumptions (e.g. no correlation in E between siblings and the same exposure frequency in both control groups). Only the genetic (G) main effect cannot be estimated because it is biased. Using simulations, we show that unconditional logistic regression analysis is often more efficient than conditional analysis for detecting GxE interaction, particularly for a rare gene and strong effects. The unconditional analysis is also at least as efficient as the conditional analysis when the gene is common and the main and joint effects of E and G are small. Under the required assumptions, the unconditional analysis retains more information than does the conditional analysis for which only discordant case-control pairs are informative leading to more precise estimates of the odds ratios.

Inherited variation in immune response genes in follicular lymphoma and diffuse large B-cell lymphoma.

PubMed

Nielsen, Kaspar Rene; Steffensen, Rudi; Haunstrup, Thure Mors; Bødker, Julie Støve; Dybkær, Karen; Baech, John; Bøgsted, Martin; Johnsen, Hans Erik

2015-01-01

Diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL) both depend on immune-mediated survival and proliferation signals from the tumor microenvironment. Inherited genetic variation influences this complex interaction. A total of 89 studies investigating immune-response genes in DLBCL and FL were critically reviewed. Relatively consistent association exists for variation in the tumor necrosis factor alpha (TNFA) and interleukin-10 loci and DLBCL risk; for DLBCL outcome association with the TNFA locus exists. Variations at chromosome 6p31-32 were associated with FL risk. Importantly, individual risk alleles have been shown to interact with each other. We suggest that the pathogenetic impact of polymorphic genes should include gene-gene interaction analysis and should be validated in preclinical model systems of normal B lymphopoiesis and B-cell malignancies. In the future, large cohort studies of interactions and genome-wide association studies are needed to extend the present findings and explore new risk alleles to be studied in preclinical models.

Learning Petri net models of non-linear gene interactions.

PubMed

Mayo, Michael

2005-10-01

Understanding how an individual's genetic make-up influences their risk of disease is a problem of paramount importance. Although machine-learning techniques are able to uncover the relationships between genotype and disease, the problem of automatically building the best biochemical model or "explanation" of the relationship has received less attention. In this paper, I describe a method based on random hill climbing that automatically builds Petri net models of non-linear (or multi-factorial) disease-causing gene-gene interactions. Petri nets are a suitable formalism for this problem, because they are used to model concurrent, dynamic processes analogous to biochemical reaction networks. I show that this method is routinely able to identify perfect Petri net models for three disease-causing gene-gene interactions recently reported in the literature.

Gene-environment interaction study for BMI reveals interactions between genetic factors and physical activity, alcohol consumption and socioeconomic status

PubMed Central

Karlsson, Torgny; Ek, Weronica E.

2017-01-01

Previous genome-wide association studies (GWAS) have identified hundreds of genetic loci to be associated with body mass index (BMI) and risk of obesity. Genetic effects can differ between individuals depending on lifestyle or environmental factors due to gene-environment interactions. In this study, we examine gene-environment interactions in 362,496 unrelated participants with Caucasian ancestry from the UK Biobank resource. A total of 94 BMI-associated SNPs, selected from a previous GWAS on BMI, were used to construct weighted genetic scores for BMI (GSBMI). Linear regression modeling was used to estimate the effect of gene-environment interactions on BMI for 131 lifestyle factors related to: dietary habits, smoking and alcohol consumption, physical activity, socioeconomic status, mental health, sleeping patterns, as well as female-specific factors such as menopause and childbirth. In total, 15 lifestyle factors were observed to interact with GSBMI, of which alcohol intake frequency, usual walking pace, and Townsend deprivation index, a measure of socioeconomic status, were all highly significant (p = 1.45*10−29, p = 3.83*10−26, p = 4.66*10−11, respectively). Interestingly, the frequency of alcohol consumption, rather than the total weekly amount resulted in a significant interaction. The FTO locus was the strongest single locus interacting with any of the lifestyle factors. However, 13 significant interactions were also observed after omitting the FTO locus from the genetic score. Our analyses indicate that many lifestyle factors modify the genetic effects on BMI with some groups of individuals having more than double the effect of the genetic score. However, the underlying causal mechanisms of gene-environmental interactions are difficult to deduce from cross-sectional data alone and controlled experiments are required to fully characterise the causal factors. PMID:28873402

Developmental expression patterns of candidate co-factors for vertebrate Six family transcription factors

PubMed Central

Neilson, Karen M.; Pignoni, Francesca; Yan, Bo; Moody, Sally A.

2010-01-01

Six family transcription factors play important roles in craniofacial development. Their transcriptional activity can be modified by co-factor proteins. Two Six genes and one co-factor gene (Eya1) are involved in the human Branchio-otic (BO) and Branchio-otic-renal (BOR) syndromes. However, mutations in Six and Eya genes only account for about half of these patients. To discover potential new causative genes, we searched the Xenopus genome for orthologues of Drosophila co-factor proteins that interact with the fly Six-related factor, SO. We identified 33 Xenopus genes with high sequence identity to 20 of the 25 fly SO-interacting proteins. We provide the developmental expression patterns of the Xenopus orthologues for 11 of the fly genes, and demonstrate that all are expressed in developing craniofacial tissues with at least partial overlap with Six1/Six2. We speculate that these genes may function as Six-interacting partners with important roles in vertebrate craniofacial development and perhaps congenital syndromes. PMID:21089078

Differentially-Expressed Pseudogenes in HIV-1 Infection.

PubMed

Gupta, Aditi; Brown, C Titus; Zheng, Yong-Hui; Adami, Christoph

2015-09-29

Not all pseudogenes are transcriptionally silent as previously thought. Pseudogene transcripts, although not translated, contribute to the non-coding RNA pool of the cell that regulates the expression of other genes. Pseudogene transcripts can also directly compete with the parent gene transcripts for mRNA stability and other cell factors, modulating their expression levels. Tissue-specific and cancer-specific differential expression of these "functional" pseudogenes has been reported. To ascertain potential pseudogene:gene interactions in HIV-1 infection, we analyzed transcriptomes from infected and uninfected T-cells and found that 21 pseudogenes are differentially expressed in HIV-1 infection. This is interesting because parent genes of one-third of these differentially-expressed pseudogenes are implicated in HIV-1 life cycle, and parent genes of half of these pseudogenes are involved in different viral infections. Our bioinformatics analysis identifies candidate pseudogene:gene interactions that may be of significance in HIV-1 infection. Experimental validation of these interactions would establish that retroviruses exploit this newly-discovered layer of host gene expression regulation for their own benefit.

Examining Gene-Environment Interactions in Comorbid Depressive and Disruptive Behavior Disorders using a Bayesian Approach

PubMed Central

Adrian, Molly; Kiff, Cara; Glazner, Chris; Kohen, Ruth; Tracy, Julia Helen; Zhou, Chuan; McCauley, Elizabeth; Stoep, Ann Vander

2015-01-01

Objective The objective of this study was to apply a Bayesian statistical analytic approach that minimizes multiple testing problems to explore the combined effects of chronic low familial support and variants in 12 candidate genes on risk for a common and debilitating childhood mental health condition. Method Bayesian mixture modeling was used to examine gene by environment interactions among genetic variants and environmental factors (family support) associated in previous studies with the occurrence of comorbid depression and disruptive behavior disorders youth, using a sample of 255 children. Results One main effects, variants in the oxytocin receptor (OXTR, rs53576) was associated with increased risk for comorbid disorders. Two significant gene x environment and one signification gene x gene interaction emerged. Variants in the nicotinic acetylcholine receptor α5 subunit (CHRNA5, rs16969968) and in the glucocorticoid receptor chaperone protein FK506 binding protein 5 (FKBP5, rs4713902) interacted with chronic low family support in association with child mental health status. One gene x gene interaction, 5-HTTLPR variant of the serotonin transporter (SERT/SLC6A4) in combination with μ opioid receptor (OPRM1, rs1799971) was associated with comorbid depression and conduct problems. Conclusions Results indicate that Bayesian modeling is a feasible strategy for conducting behavioral genetics research. This approach, combined with an optimized genetic selection strategy (Vrieze, Iacono, & McGue, 2012), revealed genetic variants involved in stress regulation ( FKBP5, SERTxOPMR), social bonding (OXTR), and nicotine responsivity (CHRNA5) in predicting comorbid status. PMID:26228411

Chapter 20: geographic variability in growth of forest trees

Treesearch

Robert Z. Callaham

1962-01-01

Tree growth, like all plant characters, is a product of the interaction of genes and environment; however, the genes, environment, and interaction are not the same for every individual of a species. Genes exert master control over the plant's growth mechanisms. They control mechanisms for responding to environment and for utilizing environment in growth. Usually...

Correcting systematic inflation in genetic association tests that consider interaction effects: application to a genome-wide association study of posttraumatic stress disorder.

PubMed

Almli, Lynn M; Duncan, Richard; Feng, Hao; Ghosh, Debashis; Binder, Elisabeth B; Bradley, Bekh; Ressler, Kerry J; Conneely, Karen N; Epstein, Michael P

2014-12-01

Genetic association studies of psychiatric outcomes often consider interactions with environmental exposures and, in particular, apply tests that jointly consider gene and gene-environment interaction effects for analysis. Using a genome-wide association study (GWAS) of posttraumatic stress disorder (PTSD), we report that heteroscedasticity (defined as variability in outcome that differs by the value of the environmental exposure) can invalidate traditional joint tests of gene and gene-environment interaction. To identify the cause of bias in traditional joint tests of gene and gene-environment interaction in a PTSD GWAS and determine whether proposed robust joint tests are insensitive to this problem. The PTSD GWAS data set consisted of 3359 individuals (978 men and 2381 women) from the Grady Trauma Project (GTP), a cohort study from Atlanta, Georgia. The GTP performed genome-wide genotyping of participants and collected environmental exposures using the Childhood Trauma Questionnaire and Trauma Experiences Inventory. We performed joint interaction testing of the Beck Depression Inventory and modified PTSD Symptom Scale in the GTP GWAS. We assessed systematic bias in our interaction analyses using quantile-quantile plots and genome-wide inflation factors. Application of the traditional joint interaction test to the GTP GWAS yielded systematic inflation across different outcomes and environmental exposures (inflation-factor estimates ranging from 1.07 to 1.21), whereas application of the robust joint test to the same data set yielded no such inflation (inflation-factor estimates ranging from 1.01 to 1.02). Simulated data further revealed that the robust joint test is valid in different heteroscedasticity models, whereas the traditional joint test is invalid. The robust joint test also has power similar to the traditional joint test when heteroscedasticity is not an issue. We believe the robust joint test should be used in candidate-gene studies and GWASs of psychiatric outcomes that consider environmental interactions. To make the procedure useful for applied investigators, we created a software tool that can be called from the popular PLINK package for analysis.

Correcting Systematic Inflation in Genetic Association Tests That Consider Interaction Effects

PubMed Central

Almli, Lynn M.; Duncan, Richard; Feng, Hao; Ghosh, Debashis; Binder, Elisabeth B.; Bradley, Bekh; Ressler, Kerry J.; Conneely, Karen N.; Epstein, Michael P.

2015-01-01

IMPORTANCE Genetic association studies of psychiatric outcomes often consider interactions with environmental exposures and, in particular, apply tests that jointly consider gene and gene-environment interaction effects for analysis. Using a genome-wide association study (GWAS) of posttraumatic stress disorder (PTSD), we report that heteroscedasticity (defined as variability in outcome that differs by the value of the environmental exposure) can invalidate traditional joint tests of gene and gene-environment interaction. OBJECTIVES To identify the cause of bias in traditional joint tests of gene and gene-environment interaction in a PTSD GWAS and determine whether proposed robust joint tests are insensitive to this problem. DESIGN, SETTING, AND PARTICIPANTS The PTSD GWAS data set consisted of 3359 individuals (978 men and 2381 women) from the Grady Trauma Project (GTP), a cohort study from Atlanta, Georgia. The GTP performed genome-wide genotyping of participants and collected environmental exposures using the Childhood Trauma Questionnaire and Trauma Experiences Inventory. MAIN OUTCOMES AND MEASURES We performed joint interaction testing of the Beck Depression Inventory and modified PTSD Symptom Scale in the GTP GWAS. We assessed systematic bias in our interaction analyses using quantile-quantile plots and genome-wide inflation factors. RESULTS Application of the traditional joint interaction test to the GTP GWAS yielded systematic inflation across different outcomes and environmental exposures (inflation-factor estimates ranging from 1.07 to 1.21), whereas application of the robust joint test to the same data set yielded no such inflation (inflation-factor estimates ranging from 1.01 to 1.02). Simulated data further revealed that the robust joint test is valid in different heteroscedasticity models, whereas the traditional joint test is invalid. The robust joint test also has power similar to the traditional joint test when heteroscedasticity is not an issue. CONCLUSIONS AND RELEVANCE We believe the robust joint test should be used in candidate-gene studies and GWASs of psychiatric outcomes that consider environmental interactions. To make the procedure useful for applied investigators, we created a software tool that can be called from the popular PLINK package for analysis. PMID:25354142

COMPUTING THERAPY FOR PRECISION MEDICINE: COLLABORATIVE FILTERING INTEGRATES AND PREDICTS MULTI-ENTITY INTERACTIONS.

PubMed

Regenbogen, Sam; Wilkins, Angela D; Lichtarge, Olivier

2016-01-01

Biomedicine produces copious information it cannot fully exploit. Specifically, there is considerable need to integrate knowledge from disparate studies to discover connections across domains. Here, we used a Collaborative Filtering approach, inspired by online recommendation algorithms, in which non-negative matrix factorization (NMF) predicts interactions among chemicals, genes, and diseases only from pairwise information about their interactions. Our approach, applied to matrices derived from the Comparative Toxicogenomics Database, successfully recovered Chemical-Disease, Chemical-Gene, and Disease-Gene networks in 10-fold cross-validation experiments. Additionally, we could predict each of these interaction matrices from the other two. Integrating all three CTD interaction matrices with NMF led to good predictions of STRING, an independent, external network of protein-protein interactions. Finally, this approach could integrate the CTD and STRING interaction data to improve Chemical-Gene cross-validation performance significantly, and, in a time-stamped study, it predicted information added to CTD after a given date, using only data prior to that date. We conclude that collaborative filtering can integrate information across multiple types of biological entities, and that as a first step towards precision medicine it can compute drug repurposing hypotheses.

COMPUTING THERAPY FOR PRECISION MEDICINE: COLLABORATIVE FILTERING INTEGRATES AND PREDICTS MULTI-ENTITY INTERACTIONS

PubMed Central

REGENBOGEN, SAM; WILKINS, ANGELA D.; LICHTARGE, OLIVIER

2015-01-01

Biomedicine produces copious information it cannot fully exploit. Specifically, there is considerable need to integrate knowledge from disparate studies to discover connections across domains. Here, we used a Collaborative Filtering approach, inspired by online recommendation algorithms, in which non-negative matrix factorization (NMF) predicts interactions among chemicals, genes, and diseases only from pairwise information about their interactions. Our approach, applied to matrices derived from the Comparative Toxicogenomics Database, successfully recovered Chemical-Disease, Chemical-Gene, and Disease-Gene networks in 10-fold cross-validation experiments. Additionally, we could predict each of these interaction matrices from the other two. Integrating all three CTD interaction matrices with NMF led to good predictions of STRING, an independent, external network of protein-protein interactions. Finally, this approach could integrate the CTD and STRING interaction data to improve Chemical-Gene cross-validation performance significantly, and, in a time-stamped study, it predicted information added to CTD after a given date, using only data prior to that date. We conclude that collaborative filtering can integrate information across multiple types of biological entities, and that as a first step towards precision medicine it can compute drug repurposing hypotheses. PMID:26776170

Multiple interactions amongst floral homeotic MADS box proteins.

PubMed Central

Davies, B; Egea-Cortines, M; de Andrade Silva, E; Saedler, H; Sommer, H

1996-01-01

Most known floral homeotic genes belong to the MADS box family and their products act in combination to specify floral organ identity by an unknown mechanism. We have used a yeast two-hybrid system to investigate the network of interactions between the Antirrhinum organ identity gene products. Selective heterodimerization is observed between MADS box factors. Exclusive interactions are detected between two factors, DEFICIENS (DEF) and GLOBOSA (GLO), previously known to heterodimerize and control development of petals and stamens. In contrast, a third factor, PLENA (PLE), which is required for reproductive organ development, can interact with the products of MADS box genes expressed at early, intermediate and late stages. We also demonstrate that heterodimerization of DEF and GLO requires the K box, a domain not found in non-plant MADS box factors, indicating that the plant MADS box factors may have different criteria for interaction. The association of PLENA and the temporally intermediate MADS box factors suggests that part of their function in mediating between the meristem and organ identity genes is accomplished through direct interaction. These data reveal an unexpectedly complex network of interactions between the factors controlling flower development and have implications for the determination of organ identity. Images PMID:8861961

Circular RNA and gene expression profiles in gastric cancer based on microarray chip technology.

PubMed

Sui, Weiguo; Shi, Zhoufang; Xue, Wen; Ou, Minglin; Zhu, Ying; Chen, Jiejing; Lin, Hua; Liu, Fuhua; Dai, Yong

2017-03-01

The aim of the present study was to screen gastric cancer (GC) tissue and adjacent tissue for differences in mRNA and circular (circRNA) expression, to analyze the differences in circRNA and mRNA expression, and to investigate the circRNA expression in gastric carcinoma and its mechanism. circRNA and mRNA differential expression profiles generated using Agilent microarray technology were analyzed in the GC tissues and adjacent tissues. qRT-PCR was used to verify the differential expression of circRNAs and mRNAs according to the interactions between circRNAs and miRNAs as well as the possible existence of miRNA and mRNA interactions. We found that: i) the circRNA expression profile revealed 1,285 significant differences in circRNA expression, with circRNA expression downregulated in 594 samples and upregulated in 691 samples via interactions with miRNAs. The qRT-PCR validation experiments showed that hsa_circRNA_400071, hsa_circRNA_000543 and hsa_circRNA_001959 expression was consistent with the microarray analysis results. ii) 29,112 genes were found in the GC tissues and adjacent tissues, including 5,460 differentially expressed genes. Among them, 2,390 differentially expressed genes were upregulated and 3,070 genes were downregulated. Gene Ontology (GO) analysis of the differentially expressed genes revealed these genes involved in biological process classification, cellular component classification and molecular function classification. Pathway analysis of the differentially expressed genes identified 83 significantly enriched genes, including 28 upregulated genes and 55 downregulated genes. iii) 69 differentially expressed circRNAs were found that might adsorb specific miRNAs to regulate the expression of their target gene mRNAs. The conclusions are: i) differentially expressed circRNAs had corresponding miRNA binding sites. These circRNAs regulated the expression of target genes through interactions with miRNAs and might become new molecular biomarkers for GC in the future. ii) Differentially expressed genes may be involved in the occurrence of GC via a variety of mechanisms. iii) CD44, CXXC5, MYH9, MALAT1 and other genes may have important implications for the occurrence and development of GC through the regulation, interaction, and mutual influence of circRNA-miRNA-mRNA via different mechanisms.

Screening for genes and subnetworks associated with pancreatic cancer based on the gene expression profile.

PubMed

Long, Jin; Liu, Zhe; Wu, Xingda; Xu, Yuanhong; Ge, Chunlin

2016-05-01

The present study aimed to screen for potential genes and subnetworks associated with pancreatic cancer (PC) using the gene expression profile. The expression profile GSE 16515 was downloaded from the Gene Expression Omnibus database, which included 36 PC tissue samples and 16 normal samples. Limma package in R language was used to screen differentially expressed genes (DEGs), which were grouped as up‑ and downregulated genes. Then, PFSNet was applied to perform subnetwork analysis for all the DEGs. Moreover, Gene Ontology (GO) and REACTOME pathway enrichment analysis of up‑ and downregulated genes was performed, followed by protein‑protein interaction (PPI) network construction using Search Tool for the Retrieval of Interacting Genes Search Tool for the Retrieval of Interacting Genes. In total, 1,989 DEGs including 1,461 up‑ and 528 downregulated genes were screened out. Subnetworks including pancreatic cancer in PC tissue samples and intercellular adhesion in normal samples were identified, respectively. A total of 8 significant REACTOME pathways for upregulated DEGs, such as hemostasis and cell cycle, mitotic were identified. Moreover, 4 significant REACTOME pathways for downregulated DEGs, including regulation of β‑cell development and transmembrane transport of small molecules were screened out. Additionally, DEGs with high connectivity degrees, such as CCNA2 (cyclin A2) and PBK (PDZ binding kinase), of the module in the protein‑protein interaction network were mainly enriched with cell‑division cycle. CCNA2 and PBK of the module and their relative pathway cell‑division cycle, and two subnetworks (pancreatic cancer and intercellular adhesion subnetworks) may be pivotal for further understanding of the molecular mechanism of PC.

Differential reconstructed gene interaction networks for deriving toxicity threshold in chemical risk assessment.

PubMed

Yang, Yi; Maxwell, Andrew; Zhang, Xiaowei; Wang, Nan; Perkins, Edward J; Zhang, Chaoyang; Gong, Ping

2013-01-01

Pathway alterations reflected as changes in gene expression regulation and gene interaction can result from cellular exposure to toxicants. Such information is often used to elucidate toxicological modes of action. From a risk assessment perspective, alterations in biological pathways are a rich resource for setting toxicant thresholds, which may be more sensitive and mechanism-informed than traditional toxicity endpoints. Here we developed a novel differential networks (DNs) approach to connect pathway perturbation with toxicity threshold setting. Our DNs approach consists of 6 steps: time-series gene expression data collection, identification of altered genes, gene interaction network reconstruction, differential edge inference, mapping of genes with differential edges to pathways, and establishment of causal relationships between chemical concentration and perturbed pathways. A one-sample Gaussian process model and a linear regression model were used to identify genes that exhibited significant profile changes across an entire time course and between treatments, respectively. Interaction networks of differentially expressed (DE) genes were reconstructed for different treatments using a state space model and then compared to infer differential edges/interactions. DE genes possessing differential edges were mapped to biological pathways in databases such as KEGG pathways. Using the DNs approach, we analyzed a time-series Escherichia coli live cell gene expression dataset consisting of 4 treatments (control, 10, 100, 1000 mg/L naphthenic acids, NAs) and 18 time points. Through comparison of reconstructed networks and construction of differential networks, 80 genes were identified as DE genes with a significant number of differential edges, and 22 KEGG pathways were altered in a concentration-dependent manner. Some of these pathways were perturbed to a degree as high as 70% even at the lowest exposure concentration, implying a high sensitivity of our DNs approach. Findings from this proof-of-concept study suggest that our approach has a great potential in providing a novel and sensitive tool for threshold setting in chemical risk assessment. In future work, we plan to analyze more time-series datasets with a full spectrum of concentrations and sufficient replications per treatment. The pathway alteration-derived thresholds will also be compared with those derived from apical endpoints such as cell growth rate.

SLFL Genes Participate in the Ubiquitination and Degradation Reaction of S-RNase in Self-compatible Peach.

PubMed

Chen, Qiuju; Meng, Dong; Gu, Zhaoyu; Li, Wei; Yuan, Hui; Duan, Xuwei; Yang, Qing; Li, Yang; Li, Tianzhong

2018-01-01

It has been proved that the gametophytic self-incompatibility (GSI), mainly exists in Rosaceae and Solanaceae, is controlled by S genes, which are two tightly linked genes located at highly polymorphic S -locus: the S-RNase for pistil specificity and the F-box gene ( SFB/SLF ) for pollen specificity, respectively. However, the roles of those genes in SI of peach are still a subject of extensive debate. In our study, we selected 37 representative varieties according to the evolution route of peach and identified their S genotypes. We cloned pollen determinant genes mutated PperSFB1m, PperSFB2m, PperSFB4m , and normal PperSFB2 , and style determinant genes PperS1-RNase, PperS2-RNase, PperS2m-RNase , and PperS4-RNase . The mutated PperSFBs encode truncated SFB proteins due to a fragment insertion. The truncated PperSFBs and normal PperSFB2 interacted with PperS-RNases demonstrated by Y2H. Normal PperSFB2 was divided into four parts: box, box-V1, V1-V2, and HVa-HVb. The box domain of PperSFB2 did not interact with PperS-RNases, both of the box-V1 and V1-V2 had interactions with PperS-RNases, while the hypervariable region of PperSFB2 HVa-HVb only interacted with PperS2-RNase showed by Y2H and BiFC assay. Bioinformatics analysis of peach genome revealed that there were other F-box genes located at S-locus, and of which three F-box genes were specifically expressed in pollen, named as PperSLFL1, PperSLFL2 , and PperSLFL3 , respectively. In phylogenetic analysis PperSLFLs clustered with Maloideae SFBB genes, and PperSFB genes were clustered into the other group with other SFB genes of Prunus . Protein interaction analysis revealed that the three PperSLFLs interacted with PperSSK1 and PperS-RNases with no allelic specificity. In vitro ubiquitination assay showed that PperSLFLs could tag ubiquitin molecules onto PperS-RNases. The above results suggest that three PperSLFLs are the appropriate candidates for the "general inhibitor," which would inactivate the S-RNases in pollen tubes, involved in the self-incompatibility of peach.

Global changes in gene expression during compatible and incompatible interactions of cowpea (Vigna unguiculata L.) with the root parasitic angiosperm Striga gesnerioides.

PubMed

Huang, Kan; Mellor, Karolina E; Paul, Shom N; Lawson, Mark J; Mackey, Aaron J; Timko, Michael P

2012-08-17

Cowpea, Vigna unguiculata L. Walp., is one of the most important food and forage legumes in the semi-arid tropics. While most domesticated forms of cowpea are susceptible to the root parasitic weed Striga gesnerioides, several cultivars have been identified that show race-specific resistance. Cowpea cultivar B301 contains the RSG3-301 gene for resistance to S. gesnerioides race SG3, but is susceptible to race SG4z. When challenged by SG3, roots of cultivar B301 develop a strong resistance response characterized by a hypersensitive reaction and cell death at the site of parasite attachment. In contrast, no visible response occurs in B301 roots parasitized by SG4z. Gene expression in the roots of the cowpea cultivar B301 during compatible (susceptible) and incompatible (resistant) interactions with S. gesnerioides races SG4z and SG3, respectively, were investigated at the early (6 days post-inoculation (dpi)) and late (13 dpi) stages of the resistance response using a Nimblegen custom design cowpea microarray. A total of 111 genes were differentially expressed in B301 roots at 6 dpi; this number increased to 2102 genes at 13 dpi. At 13 dpi, a total of 1944 genes were differentially expressed during compatible (susceptible) interactions of B301 with SG4z. Genes and pathways involved in signal transduction, programmed cell death and apoptosis, and defense response to biotic and abiotic stress were differentially expressed in the early resistance response; at the later time point, enrichment was primarily for defense-related gene expression, and genes encoding components of lignifications and secondary wall formation. In compatible interactions (B301-SG4z), multiple defense pathways were repressed, including those involved in lignin biosynthesis and secondary cell wall modifications, while cellular transport processes for nitrogen and sulfur were increased. Distinct changes in global gene expression profiles occur in host roots following successful and unsuccessful attempted parasitism by Striga. Induction of specific defense related genes and pathways defines components of a unique resistance mechanism. Some genes and pathways up-regulated in the host resistance response to SG3 are repressed in the susceptible interactions, suggesting that the parasite is targeting specific components of the host's defense. These results add to our understanding of plant-parasite interactions and the evolution of resistance to parasitic weeds.

SLFL Genes Participate in the Ubiquitination and Degradation Reaction of S-RNase in Self-compatible Peach

PubMed Central

Chen, Qiuju; Meng, Dong; Gu, Zhaoyu; Li, Wei; Yuan, Hui; Duan, Xuwei; Yang, Qing; Li, Yang; Li, Tianzhong

2018-01-01

It has been proved that the gametophytic self-incompatibility (GSI), mainly exists in Rosaceae and Solanaceae, is controlled by S genes, which are two tightly linked genes located at highly polymorphic S-locus: the S-RNase for pistil specificity and the F-box gene (SFB/SLF) for pollen specificity, respectively. However, the roles of those genes in SI of peach are still a subject of extensive debate. In our study, we selected 37 representative varieties according to the evolution route of peach and identified their S genotypes. We cloned pollen determinant genes mutated PperSFB1m, PperSFB2m, PperSFB4m, and normal PperSFB2, and style determinant genes PperS1-RNase, PperS2-RNase, PperS2m-RNase, and PperS4-RNase. The mutated PperSFBs encode truncated SFB proteins due to a fragment insertion. The truncated PperSFBs and normal PperSFB2 interacted with PperS-RNases demonstrated by Y2H. Normal PperSFB2 was divided into four parts: box, box-V1, V1-V2, and HVa-HVb. The box domain of PperSFB2 did not interact with PperS-RNases, both of the box-V1 and V1-V2 had interactions with PperS-RNases, while the hypervariable region of PperSFB2 HVa-HVb only interacted with PperS2-RNase showed by Y2H and BiFC assay. Bioinformatics analysis of peach genome revealed that there were other F-box genes located at S-locus, and of which three F-box genes were specifically expressed in pollen, named as PperSLFL1, PperSLFL2, and PperSLFL3, respectively. In phylogenetic analysis PperSLFLs clustered with Maloideae SFBB genes, and PperSFB genes were clustered into the other group with other SFB genes of Prunus. Protein interaction analysis revealed that the three PperSLFLs interacted with PperSSK1 and PperS-RNases with no allelic specificity. In vitro ubiquitination assay showed that PperSLFLs could tag ubiquitin molecules onto PperS-RNases. The above results suggest that three PperSLFLs are the appropriate candidates for the “general inhibitor,” which would inactivate the S-RNases in pollen tubes, involved in the self-incompatibility of peach. PMID:29520292

Global map of physical interactions among differentially expressed genes in multiple sclerosis relapses and remissions.

PubMed

Tuller, Tamir; Atar, Shimshi; Ruppin, Eytan; Gurevich, Michael; Achiron, Anat

2011-09-15

Multiple sclerosis (MS) is a central nervous system autoimmune inflammatory T-cell-mediated disease with a relapsing-remitting course in the majority of patients. In this study, we performed a high-resolution systems biology analysis of gene expression and physical interactions in MS relapse and remission. To this end, we integrated 164 large-scale measurements of gene expression in peripheral blood mononuclear cells of MS patients in relapse or remission and healthy subjects, with large-scale information about the physical interactions between these genes obtained from public databases. These data were analyzed with a variety of computational methods. We find that there is a clear and significant global network-level signal that is related to the changes in gene expression of MS patients in comparison to healthy subjects. However, despite the clear differences in the clinical symptoms of MS patients in relapse versus remission, the network level signal is weaker when comparing patients in these two stages of the disease. This result suggests that most of the genes have relatively similar expression levels in the two stages of the disease. In accordance with previous studies, we found that the pathways related to regulation of cell death, chemotaxis and inflammatory response are differentially expressed in the disease in comparison to healthy subjects, while pathways related to cell adhesion, cell migration and cell-cell signaling are activated in relapse in comparison to remission. However, the current study includes a detailed report of the exact set of genes involved in these pathways and the interactions between them. For example, we found that the genes TP53 and IL1 are 'network-hub' that interacts with many of the differentially expressed genes in MS patients versus healthy subjects, and the epidermal growth factor receptor is a 'network-hub' in the case of MS patients with relapse versus remission. The statistical approaches employed in this study enabled us to report new sets of genes that according to their gene expression and physical interactions are predicted to be differentially expressed in MS versus healthy subjects, and in MS patients in relapse versus remission. Some of these genes may be useful biomarkers for diagnosing MS and predicting relapses in MS patients.

Lineage-Specific Genome Architecture Links Enhancers and Non-coding Disease Variants to Target Gene Promoters.

PubMed

Javierre, Biola M; Burren, Oliver S; Wilder, Steven P; Kreuzhuber, Roman; Hill, Steven M; Sewitz, Sven; Cairns, Jonathan; Wingett, Steven W; Várnai, Csilla; Thiecke, Michiel J; Burden, Frances; Farrow, Samantha; Cutler, Antony J; Rehnström, Karola; Downes, Kate; Grassi, Luigi; Kostadima, Myrto; Freire-Pritchett, Paula; Wang, Fan; Stunnenberg, Hendrik G; Todd, John A; Zerbino, Daniel R; Stegle, Oliver; Ouwehand, Willem H; Frontini, Mattia; Wallace, Chris; Spivakov, Mikhail; Fraser, Peter

2016-11-17

Long-range interactions between regulatory elements and gene promoters play key roles in transcriptional regulation. The vast majority of interactions are uncharted, constituting a major missing link in understanding genome control. Here, we use promoter capture Hi-C to identify interacting regions of 31,253 promoters in 17 human primary hematopoietic cell types. We show that promoter interactions are highly cell type specific and enriched for links between active promoters and epigenetically marked enhancers. Promoter interactomes reflect lineage relationships of the hematopoietic tree, consistent with dynamic remodeling of nuclear architecture during differentiation. Interacting regions are enriched in genetic variants linked with altered expression of genes they contact, highlighting their functional role. We exploit this rich resource to connect non-coding disease variants to putative target promoters, prioritizing thousands of disease-candidate genes and implicating disease pathways. Our results demonstrate the power of primary cell promoter interactomes to reveal insights into genomic regulatory mechanisms underlying common diseases. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Association and Interaction Effect of AGTR1 and AGTR2 Gene Polymorphisms with Dietary Pattern on Metabolic Risk Factors of Cardiovascular Disease in Malaysian Adults

PubMed Central

Yap, Roseline Wai Kuan; Shidoji, Yoshihiro; Yap, Wai Sum; Masaki, Motofumi

2017-01-01

Gene-diet interaction using a multifactorial approach is preferred to study the multiple risk factors of cardiovascular disease (CVD). This study examined the association and gene-diet interaction effects of the angiotensin II type 1 receptor (AGTR1) gene (rs5186), and type 2 receptor (AGTR2) gene (rs1403543) polymorphisms on metabolic risk factors of CVD in Malaysian adults. CVD parameters (BMI, blood pressure, glycated hemoglobin, total cholesterol (TC), triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol (HDL-C), and TC/HDL-C ratio), and constructed dietary patterns “vegetables, fruits, and soy diet” (VFSD), and “rice, egg, and fish diet” (REFD) were obtained from previous studies. Genotyping analysis was performed by real-time PCR using Taqman probes. The subjects were 507 adults (151 Malays; 179 Chinese; and 177 Indians). Significant genetic associations were obtained on blood lipids for rs5186 in Malays and Chinese, and rs1403543 in Chinese females. The significant gene-diet interaction effects after adjusting for potential confounders were: rs5186 × VFSD on blood pressure in Malays (p = 0.016), and in Chinese on blood lipids for rs5186 × REFD (p = 0.009–0.023), and rs1403543 × VFSD in female subjects (p = 0.001–0.011). Malays and Chinese showed higher risk for blood pressure and/or lipids involving rs5186 and rs1403543 SNPs together with gene-diet interactions, but not Indians. PMID:28792482

Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence.

PubMed

Liu, Gang; Mukherjee, Bhramar; Lee, Seunggeun; Lee, Alice W; Wu, Anna H; Bandera, Elisa V; Jensen, Allan; Rossing, Mary Anne; Moysich, Kirsten B; Chang-Claude, Jenny; Doherty, Jennifer A; Gentry-Maharaj, Aleksandra; Kiemeney, Lambertus; Gayther, Simon A; Modugno, Francesmary; Massuger, Leon; Goode, Ellen L; Fridley, Brooke L; Terry, Kathryn L; Cramer, Daniel W; Ramus, Susan J; Anton-Culver, Hoda; Ziogas, Argyrios; Tyrer, Jonathan P; Schildkraut, Joellen M; Kjaer, Susanne K; Webb, Penelope M; Ness, Roberta B; Menon, Usha; Berchuck, Andrew; Pharoah, Paul D; Risch, Harvey; Pearce, Celeste Leigh

2018-02-01

There have been recent proposals advocating the use of additive gene-environment interaction instead of the widely used multiplicative scale, as a more relevant public health measure. Using gene-environment independence enhances statistical power for testing multiplicative interaction in case-control studies. However, under departure from this assumption, substantial bias in the estimates and inflated type I error in the corresponding tests can occur. In this paper, we extend the empirical Bayes (EB) approach previously developed for multiplicative interaction, which trades off between bias and efficiency in a data-adaptive way, to the additive scale. An EB estimator of the relative excess risk due to interaction is derived, and the corresponding Wald test is proposed with a general regression setting under a retrospective likelihood framework. We study the impact of gene-environment association on the resultant test with case-control data. Our simulation studies suggest that the EB approach uses the gene-environment independence assumption in a data-adaptive way and provides a gain in power compared with the standard logistic regression analysis and better control of type I error when compared with the analysis assuming gene-environment independence. We illustrate the methods with data from the Ovarian Cancer Association Consortium. © The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

β-Globin locus control region HS2 and HS3 interact structurally and functionally

PubMed Central

Jackson, David A.; McDowell, Jennifer C.; Dean, Ann

2003-01-01

The overall structure of the DNase I hypersensitive sites (HSs) that comprise the β-globin locus control region (LCR) is highly conserved among mammals, implying that the HSs have conserved functions. However, it is not well understood how the LCR HSs, either individually or collectively, activate transcription. We analyzed the interactions of HS2, HS3 and HS4 with the human ε- and β-globin genes in chromatinized episomes in fetal/embryonic K562 cells. Only HS2 activates transcription of the ε-globin gene, while all three HSs activate the β-globin gene. HS3 stimulates the β-globin gene constitutively, but HS2 and HS4 transactivation requires expression of the transcription factor EKLF, which is not present in K562 cells but is required for β-globin expression in vivo. To begin addressing how the individual HSs may interact with one another in a complex, we linked the β-globin gene to both the HS2 and HS3. HS2 and HS3 together resulted in synergistic stimulation of β-globin transcription. Unexpectedly, mutated, inactive forms of HS2 impeded the activation of the β-globin gene by HS3. Thus, there appear to be distinct interactions among the HSs and between the HSs and the globin genes. These preferential, non-exclusive interactions may underlie an important structural and functional cooperativity among the regulatory sequences of the β-globin locus in vivo. PMID:12582237

Association and Interaction Effect of AGTR1 and AGTR2 Gene Polymorphisms with Dietary Pattern on Metabolic Risk Factors of Cardiovascular Disease in Malaysian Adults.

PubMed

Yap, Roseline Wai Kuan; Shidoji, Yoshihiro; Yap, Wai Sum; Masaki, Motofumi

2017-08-09

Gene-diet interaction using a multifactorial approach is preferred to study the multiple risk factors of cardiovascular disease (CVD). This study examined the association and gene-diet interaction effects of the angiotensin II type 1 receptor ( AGTR1 ) gene (rs5186), and type 2 receptor ( AGTR2 ) gene (rs1403543) polymorphisms on metabolic risk factors of CVD in Malaysian adults. CVD parameters (BMI, blood pressure, glycated hemoglobin, total cholesterol (TC), triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol (HDL-C), and TC/HDL-C ratio), and constructed dietary patterns "vegetables, fruits, and soy diet" (VFSD), and "rice, egg, and fish diet" (REFD) were obtained from previous studies. Genotyping analysis was performed by real-time PCR using Taqman probes. The subjects were 507 adults (151 Malays; 179 Chinese; and 177 Indians). Significant genetic associations were obtained on blood lipids for rs5186 in Malays and Chinese, and rs1403543 in Chinese females. The significant gene-diet interaction effects after adjusting for potential confounders were: rs5186 × VFSD on blood pressure in Malays ( p = 0.016), and in Chinese on blood lipids for rs5186 × REFD ( p = 0.009-0.023), and rs1403543 × VFSD in female subjects ( p = 0.001-0.011). Malays and Chinese showed higher risk for blood pressure and/or lipids involving rs5186 and rs1403543 SNPs together with gene-diet interactions, but not Indians.

Mapping DNA damage-dependent genetic interactions in yeast via party mating and barcode fusion genetics.

PubMed

Díaz-Mejía, J Javier; Celaj, Albi; Mellor, Joseph C; Coté, Atina; Balint, Attila; Ho, Brandon; Bansal, Pritpal; Shaeri, Fatemeh; Gebbia, Marinella; Weile, Jochen; Verby, Marta; Karkhanina, Anna; Zhang, YiFan; Wong, Cassandra; Rich, Justin; Prendergast, D'Arcy; Gupta, Gaurav; Öztürk, Sedide; Durocher, Daniel; Brown, Grant W; Roth, Frederick P

2018-05-28

Condition-dependent genetic interactions can reveal functional relationships between genes that are not evident under standard culture conditions. State-of-the-art yeast genetic interaction mapping, which relies on robotic manipulation of arrays of double-mutant strains, does not scale readily to multi-condition studies. Here, we describe barcode fusion genetics to map genetic interactions (BFG-GI), by which double-mutant strains generated via en masse "party" mating can also be monitored en masse for growth to detect genetic interactions. By using site-specific recombination to fuse two DNA barcodes, each representing a specific gene deletion, BFG-GI enables multiplexed quantitative tracking of double mutants via next-generation sequencing. We applied BFG-GI to a matrix of DNA repair genes under nine different conditions, including methyl methanesulfonate (MMS), 4-nitroquinoline 1-oxide (4NQO), bleomycin, zeocin, and three other DNA-damaging environments. BFG-GI recapitulated known genetic interactions and yielded new condition-dependent genetic interactions. We validated and further explored a subnetwork of condition-dependent genetic interactions involving MAG1 , SLX4, and genes encoding the Shu complex, and inferred that loss of the Shu complex leads to an increase in the activation of the checkpoint protein kinase Rad53. © 2018 The Authors. Published under the terms of the CC BY 4.0 license.

The Genome-Wide Interaction Network of Nutrient Stress Genes in Escherichia coli.

PubMed

Côté, Jean-Philippe; French, Shawn; Gehrke, Sebastian S; MacNair, Craig R; Mangat, Chand S; Bharat, Amrita; Brown, Eric D

2016-11-22

Conventional efforts to describe essential genes in bacteria have typically emphasized nutrient-rich growth conditions. Of note, however, are the set of genes that become essential when bacteria are grown under nutrient stress. For example, more than 100 genes become indispensable when the model bacterium Escherichia coli is grown on nutrient-limited media, and many of these nutrient stress genes have also been shown to be important for the growth of various bacterial pathogens in vivo To better understand the genetic network that underpins nutrient stress in E. coli, we performed a genome-scale cross of strains harboring deletions in some 82 nutrient stress genes with the entire E. coli gene deletion collection (Keio) to create 315,400 double deletion mutants. An analysis of the growth of the resulting strains on rich microbiological media revealed an average of 23 synthetic sick or lethal genetic interactions for each nutrient stress gene, suggesting that the network defining nutrient stress is surprisingly complex. A vast majority of these interactions involved genes of unknown function or genes of unrelated pathways. The most profound synthetic lethal interactions were between nutrient acquisition and biosynthesis. Further, the interaction map reveals remarkable metabolic robustness in E. coli through pathway redundancies. In all, the genetic interaction network provides a powerful tool to mine and identify missing links in nutrient synthesis and to further characterize genes of unknown function in E. coli Moreover, understanding of bacterial growth under nutrient stress could aid in the development of novel antibiotic discovery platforms. With the rise of antibiotic drug resistance, there is an urgent need for new antibacterial drugs. Here, we studied a group of genes that are essential for the growth of Escherichia coli under nutrient limitation, culture conditions that arguably better represent nutrient availability during an infection than rich microbiological media. Indeed, many such nutrient stress genes are essential for infection in a variety of pathogens. Thus, the respective proteins represent a pool of potential new targets for antibacterial drugs that have been largely unexplored. We have created all possible double deletion mutants through a genetic cross of nutrient stress genes and the E. coli deletion collection. An analysis of the growth of the resulting clones on rich media revealed a robust, dense, and complex network for nutrient acquisition and biosynthesis. Importantly, our data reveal new genetic connections to guide innovative approaches for the development of new antibacterial compounds targeting bacteria under nutrient stress. Copyright © 2016 Côté et al.

Microarray analysis of differential gene expression elicited in Trametes versicolor during interspecific mycelial interactions.

PubMed

Eyre, Catherine; Muftah, Wafa; Hiscox, Jennifer; Hunt, Julie; Kille, Peter; Boddy, Lynne; Rogers, Hilary J

2010-08-01

Trametes versicolor is an important white rot fungus of both industrial and ecological interest. Saprotrophic basidiomycetes are the major decomposition agents in woodland ecosystems, and rarely form monospecific populations, therefore interspecific mycelial interactions continually occur. Interactions have different outcomes including replacement of one species by the other or deadlock. We have made subtractive cDNA libraries to enrich for genes that are expressed when T. versicolor interacts with another saprotrophic basidiomycete, Stereum gausapatum, an interaction that results in the replacement of the latter. Expressed sequence tags (ESTs) (1920) were used for microarray analysis, and their expression compared during interaction with three different fungi: S. gausapatum (replaced by T. versicolor), Bjerkandera adusta (deadlock) and Hypholoma fasciculare (replaced T. versicolor). Expression of significantly more probes changed in the interaction between T. versicolor and S. gausapatum or B. adusta compared to H. fasciculare, suggesting a relationship between interaction outcome and changes in gene expression. Copyright © 2010 The British Mycological Society. Published by Elsevier Ltd. All rights reserved.

Evolution of Genes Involved in Gamete Interaction: Evidence for Positive Selection, Duplications and Losses in Vertebrates

PubMed Central

Callebaut, Isabelle; Laurin, Michel; Pascal, Géraldine; Poupon, Anne; Goudet, Ghylène; Monget, Philippe

2012-01-01

Genes encoding proteins involved in sperm-egg interaction and fertilization exhibit a particularly fast evolution and may participate in prezygotic species isolation [1], [2]. Some of them (ZP3, ADAM1, ADAM2, ACR and CD9) have individually been shown to evolve under positive selection [3], [4], suggesting a role of positive Darwinian selection on sperm-egg interaction. However, the genes involved in this biological function have not been systematically and exhaustively studied with an evolutionary perspective, in particular across vertebrates with internal and external fertilization. Here we show that 33 genes among the 69 that have been experimentally shown to be involved in fertilization in at least one taxon in vertebrates are under positive selection. Moreover, we identified 17 pseudogenes and 39 genes that have at least one duplicate in one species. For 15 genes, we found neither positive selection, nor gene copies or pseudogenes. Genes of teleosts, especially genes involved in sperm-oolemma fusion, appear to be more frequently under positive selection than genes of birds and eutherians. In contrast, pseudogenization, gene loss and gene gain are more frequent in eutherians. Thus, each of the 19 studied vertebrate species exhibits a unique signature characterized by gene gain and loss, as well as position of amino acids under positive selection. Reflecting these clade-specific signatures, teleosts and eutherian mammals are recovered as clades in a parsimony analysis. Interestingly the same analysis places Xenopus apart from teleosts, with which it shares the primitive external fertilization, and locates it along with amniotes (which share internal fertilization), suggesting that external or internal environmental conditions of germ cell interaction may not be the unique factors that drive the evolution of fertilization genes. Our work should improve our understanding of the fertilization process and on the establishment of reproductive barriers, for example by offering new leads for experiments on genes identified as positively selected. PMID:22957080

Building a glaucoma interaction network using a text mining approach.

PubMed

Soliman, Maha; Nasraoui, Olfa; Cooper, Nigel G F

2016-01-01

The volume of biomedical literature and its underlying knowledge base is rapidly expanding, making it beyond the ability of a single human being to read through all the literature. Several automated methods have been developed to help make sense of this dilemma. The present study reports on the results of a text mining approach to extract gene interactions from the data warehouse of published experimental results which are then used to benchmark an interaction network associated with glaucoma. To the best of our knowledge, there is, as yet, no glaucoma interaction network derived solely from text mining approaches. The presence of such a network could provide a useful summative knowledge base to complement other forms of clinical information related to this disease. A glaucoma corpus was constructed from PubMed Central and a text mining approach was applied to extract genes and their relations from this corpus. The extracted relations between genes were checked using reference interaction databases and classified generally as known or new relations. The extracted genes and relations were then used to construct a glaucoma interaction network. Analysis of the resulting network indicated that it bears the characteristics of a small world interaction network. Our analysis showed the presence of seven glaucoma linked genes that defined the network modularity. A web-based system for browsing and visualizing the extracted glaucoma related interaction networks is made available at http://neurogene.spd.louisville.edu/GlaucomaINViewer/Form1.aspx. This study has reported the first version of a glaucoma interaction network using a text mining approach. The power of such an approach is in its ability to cover a wide range of glaucoma related studies published over many years. Hence, a bigger picture of the disease can be established. To the best of our knowledge, this is the first glaucoma interaction network to summarize the known literature. The major findings were a set of relations that could not be found in existing interaction databases and that were found to be new, in addition to a smaller subnetwork consisting of interconnected clusters of seven glaucoma genes. Future improvements can be applied towards obtaining a better version of this network.

Molecular interactions between tomato and the leaf mold pathogen Cladosporium fulvum.

PubMed

Rivas, Susana; Thomas, Colwyn M

2005-01-01

The interaction between tomato and the leaf mold pathogen Cladosporium fulvum is controlled in a gene-for-gene manner. This interaction has provided useful insights to the molecular basis of recognition specificity in plant disease resistance (R) proteins, disease resistance (R) gene evolution, R-protein mediated signaling, and cellular responses to pathogen attack. Tomato Cf genes encode type I membrane-associated receptor-like proteins (RLPs) comprised predominantly of extracellular leucine-rich repeats (eLRRs) and which are anchored in the plasma membrane. Cf proteins recognize fungal avirulence (Avr) peptides secreted into the leaf apoplast during infection. A direct interaction of Cf proteins with their cognate Avr proteins has not been demonstrated and the molecular mechanism of Avr protein perception is not known. Following ligand perception Cf proteins trigger a hypersensitive response (HR) and the arrest of pathogen development. Cf proteins lack an obvious signaling domain, suggesting that defense response activation is mediated through interactions with other partners. Avr protein perception results in the rapid accumulation of active oxygen species (AOS), changes in cellular ion fluxes, activation of protein kinase cascades, changes in gene expression and, possibly, targeted protein degradation. Here we review our current understanding of Cf-mediated responses in resistance to C. fulvum.

A negative genetic interaction map in isogenic cancer cell lines reveals cancer cell vulnerabilities

PubMed Central

Vizeacoumar, Franco J; Arnold, Roland; Vizeacoumar, Frederick S; Chandrashekhar, Megha; Buzina, Alla; Young, Jordan T F; Kwan, Julian H M; Sayad, Azin; Mero, Patricia; Lawo, Steffen; Tanaka, Hiromasa; Brown, Kevin R; Baryshnikova, Anastasia; Mak, Anthony B; Fedyshyn, Yaroslav; Wang, Yadong; Brito, Glauber C; Kasimer, Dahlia; Makhnevych, Taras; Ketela, Troy; Datti, Alessandro; Babu, Mohan; Emili, Andrew; Pelletier, Laurence; Wrana, Jeff; Wainberg, Zev; Kim, Philip M; Rottapel, Robert; O'Brien, Catherine A; Andrews, Brenda; Boone, Charles; Moffat, Jason

2013-01-01

Improved efforts are necessary to define the functional product of cancer mutations currently being revealed through large-scale sequencing efforts. Using genome-scale pooled shRNA screening technology, we mapped negative genetic interactions across a set of isogenic cancer cell lines and confirmed hundreds of these interactions in orthogonal co-culture competition assays to generate a high-confidence genetic interaction network of differentially essential or differential essentiality (DiE) genes. The network uncovered examples of conserved genetic interactions, densely connected functional modules derived from comparative genomics with model systems data, functions for uncharacterized genes in the human genome and targetable vulnerabilities. Finally, we demonstrate a general applicability of DiE gene signatures in determining genetic dependencies of other non-isogenic cancer cell lines. For example, the PTEN−/− DiE genes reveal a signature that can preferentially classify PTEN-dependent genotypes across a series of non-isogenic cell lines derived from the breast, pancreas and ovarian cancers. Our reference network suggests that many cancer vulnerabilities remain to be discovered through systematic derivation of a network of differentially essential genes in an isogenic cancer cell model. PMID:24104479

Interactions among variants in TXA2R, P2Y12 and GPIIIa are associated with carotid plaque vulnerability in Chinese population.

PubMed

Yi, Xingyang; Lin, Jing; Luo, Hua; Zhou, Ju; Zhou, Qiang; Wang, Yanfen; Wang, Chun

2018-04-03

The associations between variants in platelet activation-relevant genes and carotid plaque vulnerability are not fully understood. The aim of the present study was to investigate the associations of the variants in platelet activation-relevant genes and interactions among these variants with carotid plaque vulnerability. There were no significant differences in the frequencies of genotypes of the 11 variants between patients and controls. Among 396 patients, 102 patients had not carotid plaque, 106 had VP, and 188 had SP. The 11 variants were not independently associated with risk of carotid plaque vulnerability after adjusting for potential confounding variables. However, the GMDR analysis showed that there were synergistic effects of gene-gene interactions among TXA2Rr s1131882, GPIIIa rs2317676 and P2Y12 rs16863323 on carotid plaque vulnerability. The high-risk interactions among the three variants were associated with high platelet activation, and independently associated with the risk of carotid plaque vulnerability. Eleven variants in platelet activation-relevant genes were examined using mass spectrometry methods in 396 ischemic stroke patients and 291controls. Platelet-leukocyte aggregates and platelet aggregation were also measured. Carotid plaques were assessed by B-mode ultrasound. According to the results of ultrasound, the patients were stratified into three groups: non-plaque group, vulnerable plaque (VP) group and stable plaque (SP) group. Furthermore, gene-gene interactions were analyzed using generalized multifactor dimensionality reduction (GMDR) methods. The rs1131882, rs2317676, and rs16863323 three-loci interactions may confer a higher risk of carotid plaque vulnerability, and might be potential markers for plaque instability.

Schizophrenia interactome with 504 novel protein–protein interactions

PubMed Central

Ganapathiraju, Madhavi K; Thahir, Mohamed; Handen, Adam; Sarkar, Saumendra N; Sweet, Robert A; Nimgaonkar, Vishwajit L; Loscher, Christine E; Bauer, Eileen M; Chaparala, Srilakshmi

2016-01-01

Genome-wide association studies of schizophrenia (GWAS) have revealed the role of rare and common genetic variants, but the functional effects of the risk variants remain to be understood. Protein interactome-based studies can facilitate the study of molecular mechanisms by which the risk genes relate to schizophrenia (SZ) genesis, but protein–protein interactions (PPIs) are unknown for many of the liability genes. We developed a computational model to discover PPIs, which is found to be highly accurate according to computational evaluations and experimental validations of selected PPIs. We present here, 365 novel PPIs of liability genes identified by the SZ Working Group of the Psychiatric Genomics Consortium (PGC). Seventeen genes that had no previously known interactions have 57 novel interactions by our method. Among the new interactors are 19 drug targets that are targeted by 130 drugs. In addition, we computed 147 novel PPIs of 25 candidate genes investigated in the pre-GWAS era. While there is little overlap between the GWAS genes and the pre-GWAS genes, the interactomes reveal that they largely belong to the same pathways, thus reconciling the apparent disparities between the GWAS and prior gene association studies. The interactome including 504 novel PPIs overall, could motivate other systems biology studies and trials with repurposed drugs. The PPIs are made available on a webserver, called Schizo-Pi at http://severus.dbmi.pitt.edu/schizo-pi with advanced search capabilities. PMID:27336055

Polymorphisms in Genes Involved in Fatty Acid β-Oxidation Interact with Dietary Fat Intakes to Modulate the Plasma TG Response to a Fish Oil Supplementation

PubMed Central

Bouchard-Mercier, Annie; Rudkowska, Iwona; Lemieux, Simone; Couture, Patrick; Vohl, Marie-Claude

2014-01-01

A large inter-individual variability in the plasma triglyceride (TG) response to an omega-3 polyunsaturated fatty acid (n-3 PUFA) supplementation has been observed. The objective was to examine gene-diet interaction effects on the plasma TG response after a fish oil supplementation, between single-nucleotide polymorphisms (SNPs) within genes involved in fatty acid β-oxidation and dietary fat intakes. Two hundred and eight (208) participants were recruited in the greater Quebec City area. The participants completed a six-week fish oil supplementation (5 g fish oil/day: 1.9–2.2 g EPA and 1.1 g DHA). Dietary fat intakes were measured using three-day food records. SNPs within RXRA, CPT1A, ACADVL, ACAA2, ABCD2, ACOX1 and ACAA1 genes were genotyped using TAQMAN methodology. Gene-diet interaction effects on the plasma TG response were observed for SNPs within RXRA (rs11185660, rs10881576 and rs12339187) and ACOX1 (rs17583163) genes. For rs11185660, fold changes in RXRA gene expression levels were different depending on SFA intakes for homozygotes T/T. Gene-diet interaction effects of SNPs within genes involved in fatty acid β-oxidation and dietary fat intakes may be important in understanding the inter-individual variability in plasma TG levels and in the plasma TG response to a fish oil supplementation. PMID:24647074

Routine Discovery of Complex Genetic Models using Genetic Algorithms

PubMed Central

Moore, Jason H.; Hahn, Lance W.; Ritchie, Marylyn D.; Thornton, Tricia A.; White, Bill C.

2010-01-01

Simulation studies are useful in various disciplines for a number of reasons including the development and evaluation of new computational and statistical methods. This is particularly true in human genetics and genetic epidemiology where new analytical methods are needed for the detection and characterization of disease susceptibility genes whose effects are complex, nonlinear, and partially or solely dependent on the effects of other genes (i.e. epistasis or gene-gene interaction). Despite this need, the development of complex genetic models that can be used to simulate data is not always intuitive. In fact, only a few such models have been published. We have previously developed a genetic algorithm approach to discovering complex genetic models in which two single nucleotide polymorphisms (SNPs) influence disease risk solely through nonlinear interactions. In this paper, we extend this approach for the discovery of high-order epistasis models involving three to five SNPs. We demonstrate that the genetic algorithm is capable of routinely discovering interesting high-order epistasis models in which each SNP influences risk of disease only through interactions with the other SNPs in the model. This study opens the door for routine simulation of complex gene-gene interactions among SNPs for the development and evaluation of new statistical and computational approaches for identifying common, complex multifactorial disease susceptibility genes. PMID:20948983

Adenovirus small E1A employs the lysine acetylases p300/CBP and tumor suppressor Rb to repress select host genes and promote productive virus infection.

PubMed

Ferrari, Roberto; Gou, Dawei; Jawdekar, Gauri; Johnson, Sarah A; Nava, Miguel; Su, Trent; Yousef, Ahmed F; Zemke, Nathan R; Pellegrini, Matteo; Kurdistani, Siavash K; Berk, Arnold J

2014-11-12

Oncogenic transformation by adenovirus small e1a depends on simultaneous interactions with the host lysine acetylases p300/CBP and the tumor suppressor RB. How these interactions influence cellular gene expression remains unclear. We find that e1a displaces RBs from E2F transcription factors and promotes p300 acetylation of RB1 K873/K874 to lock it into a repressing conformation that interacts with repressive chromatin-modifying enzymes. These repressing p300-e1a-RB1 complexes specifically interact with host genes that have unusually high p300 association within the gene body. The TGF-β, TNF-, and interleukin-signaling pathway components are enriched among such p300-targeted genes. The p300-e1a-RB1 complex condenses chromatin in a manner dependent on HDAC activity, p300 lysine acetylase activity, the p300 bromodomain, and RB K873/K874 and e1a K239 acetylation to repress host genes that would otherwise inhibit productive virus infection. Thus, adenovirus employs e1a to repress host genes that interfere with viral replication. Copyright © 2014 Elsevier Inc. All rights reserved.

Detection of the inferred interaction network in hepatocellular carcinoma from EHCO (Encyclopedia of Hepatocellular Carcinoma genes Online)

PubMed Central

Hsu, Chun-Nan; Lai, Jin-Mei; Liu, Chia-Hung; Tseng, Huei-Hun; Lin, Chih-Yun; Lin, Kuan-Ting; Yeh, Hsu-Hua; Sung, Ting-Yi; Hsu, Wen-Lian; Su, Li-Jen; Lee, Sheng-An; Chen, Chang-Han; Lee, Gen-Cher; Lee, DT; Shiue, Yow-Ling; Yeh, Chang-Wei; Chang, Chao-Hui; Kao, Cheng-Yan; Huang, Chi-Ying F

2007-01-01

Background The significant advances in microarray and proteomics analyses have resulted in an exponential increase in potential new targets and have promised to shed light on the identification of disease markers and cellular pathways. We aim to collect and decipher the HCC-related genes at the systems level. Results Here, we build an integrative platform, the Encyclopedia of Hepatocellular Carcinoma genes Online, dubbed EHCO , to systematically collect, organize and compare the pileup of unsorted HCC-related studies by using natural language processing and softbots. Among the eight gene set collections, ranging across PubMed, SAGE, microarray, and proteomics data, there are 2,906 genes in total; however, more than 77% genes are only included once, suggesting that tremendous efforts need to be exerted to characterize the relationship between HCC and these genes. Of these HCC inventories, protein binding represents the largest proportion (~25%) from Gene Ontology analysis. In fact, many differentially expressed gene sets in EHCO could form interaction networks (e.g. HBV-associated HCC network) by using available human protein-protein interaction datasets. To further highlight the potential new targets in the inferred network from EHCO, we combine comparative genomics and interactomics approaches to analyze 120 evolutionary conserved and overexpressed genes in HCC. 47 out of 120 queries can form a highly interactive network with 18 queries serving as hubs. Conclusion This architectural map may represent the first step toward the attempt to decipher the hepatocarcinogenesis at the systems level. Targeting hubs and/or disruption of the network formation might reveal novel strategy for HCC treatment. PMID:17326819

On the role of sparseness in the evolution of modularity in gene regulatory networks

PubMed Central

2018-01-01

Modularity is a widespread property in biological systems. It implies that interactions occur mainly within groups of system elements. A modular arrangement facilitates adjustment of one module without perturbing the rest of the system. Therefore, modularity of developmental mechanisms is a major factor for evolvability, the potential to produce beneficial variation from random genetic change. Understanding how modularity evolves in gene regulatory networks, that create the distinct gene activity patterns that characterize different parts of an organism, is key to developmental and evolutionary biology. One hypothesis for the evolution of modules suggests that interactions between some sets of genes become maladaptive when selection favours additional gene activity patterns. The removal of such interactions by selection would result in the formation of modules. A second hypothesis suggests that modularity evolves in response to sparseness, the scarcity of interactions within a system. Here I simulate the evolution of gene regulatory networks and analyse diverse experimentally sustained networks to study the relationship between sparseness and modularity. My results suggest that sparseness alone is neither sufficient nor necessary to explain modularity in gene regulatory networks. However, sparseness amplifies the effects of forms of selection that, like selection for additional gene activity patterns, already produce an increase in modularity. That evolution of new gene activity patterns is frequent across evolution also supports that it is a major factor in the evolution of modularity. That sparseness is widespread across gene regulatory networks indicates that it may have facilitated the evolution of modules in a wide variety of cases. PMID:29775459

On the role of sparseness in the evolution of modularity in gene regulatory networks.

PubMed

Espinosa-Soto, Carlos

2018-05-01

Modularity is a widespread property in biological systems. It implies that interactions occur mainly within groups of system elements. A modular arrangement facilitates adjustment of one module without perturbing the rest of the system. Therefore, modularity of developmental mechanisms is a major factor for evolvability, the potential to produce beneficial variation from random genetic change. Understanding how modularity evolves in gene regulatory networks, that create the distinct gene activity patterns that characterize different parts of an organism, is key to developmental and evolutionary biology. One hypothesis for the evolution of modules suggests that interactions between some sets of genes become maladaptive when selection favours additional gene activity patterns. The removal of such interactions by selection would result in the formation of modules. A second hypothesis suggests that modularity evolves in response to sparseness, the scarcity of interactions within a system. Here I simulate the evolution of gene regulatory networks and analyse diverse experimentally sustained networks to study the relationship between sparseness and modularity. My results suggest that sparseness alone is neither sufficient nor necessary to explain modularity in gene regulatory networks. However, sparseness amplifies the effects of forms of selection that, like selection for additional gene activity patterns, already produce an increase in modularity. That evolution of new gene activity patterns is frequent across evolution also supports that it is a major factor in the evolution of modularity. That sparseness is widespread across gene regulatory networks indicates that it may have facilitated the evolution of modules in a wide variety of cases.

Using phylogenetically-informed annotation (PIA) to search for light-interacting genes in transcriptomes from non-model organisms.

PubMed

Speiser, Daniel I; Pankey, M Sabrina; Zaharoff, Alexander K; Battelle, Barbara A; Bracken-Grissom, Heather D; Breinholt, Jesse W; Bybee, Seth M; Cronin, Thomas W; Garm, Anders; Lindgren, Annie R; Patel, Nipam H; Porter, Megan L; Protas, Meredith E; Rivera, Ajna S; Serb, Jeanne M; Zigler, Kirk S; Crandall, Keith A; Oakley, Todd H

2014-11-19

Tools for high throughput sequencing and de novo assembly make the analysis of transcriptomes (i.e. the suite of genes expressed in a tissue) feasible for almost any organism. Yet a challenge for biologists is that it can be difficult to assign identities to gene sequences, especially from non-model organisms. Phylogenetic analyses are one useful method for assigning identities to these sequences, but such methods tend to be time-consuming because of the need to re-calculate trees for every gene of interest and each time a new data set is analyzed. In response, we employed existing tools for phylogenetic analysis to produce a computationally efficient, tree-based approach for annotating transcriptomes or new genomes that we term Phylogenetically-Informed Annotation (PIA), which places uncharacterized genes into pre-calculated phylogenies of gene families. We generated maximum likelihood trees for 109 genes from a Light Interaction Toolkit (LIT), a collection of genes that underlie the function or development of light-interacting structures in metazoans. To do so, we searched protein sequences predicted from 29 fully-sequenced genomes and built trees using tools for phylogenetic analysis in the Osiris package of Galaxy (an open-source workflow management system). Next, to rapidly annotate transcriptomes from organisms that lack sequenced genomes, we repurposed a maximum likelihood-based Evolutionary Placement Algorithm (implemented in RAxML) to place sequences of potential LIT genes on to our pre-calculated gene trees. Finally, we implemented PIA in Galaxy and used it to search for LIT genes in 28 newly-sequenced transcriptomes from the light-interacting tissues of a range of cephalopod mollusks, arthropods, and cubozoan cnidarians. Our new trees for LIT genes are available on the Bitbucket public repository ( http://bitbucket.org/osiris_phylogenetics/pia/ ) and we demonstrate PIA on a publicly-accessible web server ( http://galaxy-dev.cnsi.ucsb.edu/pia/ ). Our new trees for LIT genes will be a valuable resource for researchers studying the evolution of eyes or other light-interacting structures. We also introduce PIA, a high throughput method for using phylogenetic relationships to identify LIT genes in transcriptomes from non-model organisms. With simple modifications, our methods may be used to search for different sets of genes or to annotate data sets from taxa outside of Metazoa.

The case-only test for gene-environment interaction is not uniformly powerful: an empirical example

PubMed Central

Wu, Chen; Chang, Jiang; Ma, Baoshan; Miao, Xiaoping; Zhou, Yifeng; Liu, Yu; Li, Yun; Wu, Tangchun; Hu, Zhibin; Shen, Hongbing; Jia, Weihua; Zeng, Yixin; Lin, Dongxin; Kraft, Peter

2016-01-01

The case-only test has been proposed as a more powerful approach to detect gene-environment (G×E) interactions. This approach assumes that the genetic and environmental factors are independent. While it is well known that Type I error rate will increase if this assumption is violated, it is less widely appreciated that gene-environment correlation can also lead to power loss. We illustrate this phenomenon by comparing the performance of the case-only test to other approaches to detect G×E interactions in a genome-wide association study of esophageal squamous carcinoma (ESCC) in Chinese populations. Some of these approaches do not use information on the correlation between exposure and genotype (standard logistic regression), while others seek to use this information in a robust fashion to boost power without increasing Type I error (two-step, empirical Bayes and cocktail methods). G×E interactions were identified involving drinking status and two regions containing genes in the alcohol metabolism pathway, 4q23 and 12q24. Although the case-only test yielded the most significant tests of G×E interaction in the 4q23 region, the case-only test failed to identify significant interactions in the 12q24 region which were readily identified using other approaches. The low power of the case-only test in the 12q24 region is likely due to the strong inverse association between the SNPs in this region and drinking status. This example underscores the need to consider multiple approaches to detect gene-environment interactions, as different tests are more or less sensitive to different alternative hypotheses and violations of the gene-environment independence assumption. PMID:23595356

APOA2, dietary fat and body mass index: replication of a gene-diet interaction in three independent populations

USDA-ARS?s Scientific Manuscript database

Background: Nutrigenetics studies the role of genetic variation on interactions between diet and health aimed at providing more personalized dietary advice. However, replication has been very low and our aim was to study the interaction between a functional polymorphism of the APOA2 gene, food intak...

A PLSPM-Based Test Statistic for Detecting Gene-Gene Co-Association in Genome-Wide Association Study with Case-Control Design

PubMed Central

Zhang, Xiaoshuai; Yang, Xiaowei; Yuan, Zhongshang; Liu, Yanxun; Li, Fangyu; Peng, Bin; Zhu, Dianwen; Zhao, Jinghua; Xue, Fuzhong

2013-01-01

For genome-wide association data analysis, two genes in any pathway, two SNPs in the two linked gene regions respectively or in the two linked exons respectively within one gene are often correlated with each other. We therefore proposed the concept of gene-gene co-association, which refers to the effects not only due to the traditional interaction under nearly independent condition but the correlation between two genes. Furthermore, we constructed a novel statistic for detecting gene-gene co-association based on Partial Least Squares Path Modeling (PLSPM). Through simulation, the relationship between traditional interaction and co-association was highlighted under three different types of co-association. Both simulation and real data analysis demonstrated that the proposed PLSPM-based statistic has better performance than single SNP-based logistic model, PCA-based logistic model, and other gene-based methods. PMID:23620809

A PLSPM-based test statistic for detecting gene-gene co-association in genome-wide association study with case-control design.

PubMed

Zhang, Xiaoshuai; Yang, Xiaowei; Yuan, Zhongshang; Liu, Yanxun; Li, Fangyu; Peng, Bin; Zhu, Dianwen; Zhao, Jinghua; Xue, Fuzhong

2013-01-01

For genome-wide association data analysis, two genes in any pathway, two SNPs in the two linked gene regions respectively or in the two linked exons respectively within one gene are often correlated with each other. We therefore proposed the concept of gene-gene co-association, which refers to the effects not only due to the traditional interaction under nearly independent condition but the correlation between two genes. Furthermore, we constructed a novel statistic for detecting gene-gene co-association based on Partial Least Squares Path Modeling (PLSPM). Through simulation, the relationship between traditional interaction and co-association was highlighted under three different types of co-association. Both simulation and real data analysis demonstrated that the proposed PLSPM-based statistic has better performance than single SNP-based logistic model, PCA-based logistic model, and other gene-based methods.

Modeling transcriptional networks regulating secondary growth and wood formation in forest trees

Treesearch

Lijun Liu; Vladimir Filkov; Andrew Groover

2013-01-01

The complex interactions among the genes that underlie a biological process can be modeled and presented as a transcriptional network, in which genes (nodes) and their interactions (edges) are shown in a graphical form similar to a wiring diagram. A large number of genes have been identified that are expressed during the radial woody growth of tree stems (secondary...

Augmenting Microarray Data with Literature-Based Knowledge to Enhance Gene Regulatory Network Inference

PubMed Central

Kilicoglu, Halil; Shin, Dongwook; Rindflesch, Thomas C.

2014-01-01

Gene regulatory networks are a crucial aspect of systems biology in describing molecular mechanisms of the cell. Various computational models rely on random gene selection to infer such networks from microarray data. While incorporation of prior knowledge into data analysis has been deemed important, in practice, it has generally been limited to referencing genes in probe sets and using curated knowledge bases. We investigate the impact of augmenting microarray data with semantic relations automatically extracted from the literature, with the view that relations encoding gene/protein interactions eliminate the need for random selection of components in non-exhaustive approaches, producing a more accurate model of cellular behavior. A genetic algorithm is then used to optimize the strength of interactions using microarray data and an artificial neural network fitness function. The result is a directed and weighted network providing the individual contribution of each gene to its target. For testing, we used invasive ductile carcinoma of the breast to query the literature and a microarray set containing gene expression changes in these cells over several time points. Our model demonstrates significantly better fitness than the state-of-the-art model, which relies on an initial random selection of genes. Comparison to the component pathways of the KEGG Pathways in Cancer map reveals that the resulting networks contain both known and novel relationships. The p53 pathway results were manually validated in the literature. 60% of non-KEGG relationships were supported (74% for highly weighted interactions). The method was then applied to yeast data and our model again outperformed the comparison model. Our results demonstrate the advantage of combining gene interactions extracted from the literature in the form of semantic relations with microarray analysis in generating contribution-weighted gene regulatory networks. This methodology can make a significant contribution to understanding the complex interactions involved in cellular behavior and molecular physiology. PMID:24921649

Augmenting microarray data with literature-based knowledge to enhance gene regulatory network inference.

PubMed

Chen, Guocai; Cairelli, Michael J; Kilicoglu, Halil; Shin, Dongwook; Rindflesch, Thomas C

2014-06-01

Gene regulatory networks are a crucial aspect of systems biology in describing molecular mechanisms of the cell. Various computational models rely on random gene selection to infer such networks from microarray data. While incorporation of prior knowledge into data analysis has been deemed important, in practice, it has generally been limited to referencing genes in probe sets and using curated knowledge bases. We investigate the impact of augmenting microarray data with semantic relations automatically extracted from the literature, with the view that relations encoding gene/protein interactions eliminate the need for random selection of components in non-exhaustive approaches, producing a more accurate model of cellular behavior. A genetic algorithm is then used to optimize the strength of interactions using microarray data and an artificial neural network fitness function. The result is a directed and weighted network providing the individual contribution of each gene to its target. For testing, we used invasive ductile carcinoma of the breast to query the literature and a microarray set containing gene expression changes in these cells over several time points. Our model demonstrates significantly better fitness than the state-of-the-art model, which relies on an initial random selection of genes. Comparison to the component pathways of the KEGG Pathways in Cancer map reveals that the resulting networks contain both known and novel relationships. The p53 pathway results were manually validated in the literature. 60% of non-KEGG relationships were supported (74% for highly weighted interactions). The method was then applied to yeast data and our model again outperformed the comparison model. Our results demonstrate the advantage of combining gene interactions extracted from the literature in the form of semantic relations with microarray analysis in generating contribution-weighted gene regulatory networks. This methodology can make a significant contribution to understanding the complex interactions involved in cellular behavior and molecular physiology.

GSNO Reductase and β2 Adrenergic Receptor Gene-gene Interaction: Bronchodilator Responsiveness to Albuterol

PubMed Central

Choudhry, Shweta; Que, Loretta G.; Yang, Zhonghui; Liu, Limin; Eng, Celeste; Kim, Sung O.; Kumar, Gunjan; Thyne, Shannon; Chapela, Rocio; Rodriguez-Santana, Jose R.; Rodriguez-Cintron, William; Avila, Pedro C.; Stamler, Jonathan S.; Burchard, Esteban G.

2010-01-01

Background Short-acting inhaled β2-agonists such as albuterol are used for bronchodilation and are the mainstay of asthma treatment worldwide. There is significant variation in bronchodilator responsiveness to albuterol not only between individuals but also across racial/ethnic groups. The β2-adrenergic receptor (β2AR) is the target for β2-agonist drugs. The enzyme S-nitrosoglutathione reductase (GSNOR), which regulates levels of the endogenous bronchodilator S-nitrosoglutathione, has been shown to modulate the response to β2-agonists. Objective We hypothesized that there are pharmacogenetic interactions between GSNOR and β2AR gene variants which are associated with variable response to albuterol. Methods We performed family-based analyses to test for association between GSNOR gene variants and asthma and related phenotypes in 609 Puerto Rican and Mexican families with asthma. In addition, we tested these subjects for pharmacogenetic interaction between GSNOR and β2AR gene variants and responsiveness to albuterol using linear regression. Cell transfection experiments were performed to test the potential effect of the GSNOR gene variants. Results Among Puerto Ricans, several GSNOR SNPs and a haplotype in the 3′UTR were significantly associated with increased risk for asthma and lower bronchodilator responsiveness (p = 0.04 to 0.007). The GSNOR risk haplotype affects expression of GSNOR mRNA and protein, suggesting a gain of function. Furthermore, gene-gene interaction analysis provided evidence of pharmacogenetic interaction between GSNOR and β2AR gene variants and the response to albuterol in Puerto Rican (p = 0.03), Mexican (p = 0.15) and combined Puerto Rican and Mexican asthmatics (p = 0.003). Specifically, GSNOR+17059*β2AR+46 genotype combinations (TG+GG*AG and TG+GG*GG) were associated with lower bronchodilator response. Conclusion Genotyping of GSNOR and β2AR genes may be a useful in identifying Latino subjects, who might benefit from adjuvant therapy for refractory asthma. PMID:20335826

The arms race between tomato and Fusarium oxysporum.

PubMed

Takken, Frank; Rep, Martijn

2010-03-01

The interaction between tomato and Fusarium oxysporum f. sp. lycopersici has become a model system for the study of the molecular basis of disease resistance and susceptibility. Gene-for-gene interactions in this system have provided the basis for the development of tomato cultivars resistant to Fusarium wilt disease. Over the last 6 years, new insights into the molecular basis of these gene-for-gene interactions have been obtained. Highlights are the identification of three avirulence genes in F. oxysporum f. sp. lycopersici and the development of a molecular switch model for I-2, a nucleotide-binding and leucine-rich repeat-type resistance protein which mediates the recognition of the Avr2 protein. We summarize these findings here and present possible scenarios for the ongoing molecular arms race between tomato and F. oxysporum f. sp. lycopersici in both nature and agriculture.

Invariant TAD Boundaries Constrain Cell-Type-Specific Looping Interactions between Promoters and Distal Elements around the CFTR Locus.

PubMed

Smith, Emily M; Lajoie, Bryan R; Jain, Gaurav; Dekker, Job

2016-01-07

Three-dimensional genome structure plays an important role in gene regulation. Globally, chromosomes are organized into active and inactive compartments while, at the gene level, looping interactions connect promoters to regulatory elements. Topologically associating domains (TADs), typically several hundred kilobases in size, form an intermediate level of organization. Major questions include how TADs are formed and how they are related to looping interactions between genes and regulatory elements. Here we performed a focused 5C analysis of a 2.8 Mb chromosome 7 region surrounding CFTR in a panel of cell types. We find that the same TAD boundaries are present in all cell types, indicating that TADs represent a universal chromosome architecture. Furthermore, we find that these TAD boundaries are present irrespective of the expression and looping of genes located between them. In contrast, looping interactions between promoters and regulatory elements are cell-type specific and occur mostly within TADs. This is exemplified by the CFTR promoter that in different cell types interacts with distinct sets of distal cell-type-specific regulatory elements that are all located within the same TAD. Finally, we find that long-range associations between loci located in different TADs are also detected, but these display much lower interaction frequencies than looping interactions within TADs. Interestingly, interactions between TADs are also highly cell-type-specific and often involve loci clustered around TAD boundaries. These data point to key roles of invariant TAD boundaries in constraining as well as mediating cell-type-specific long-range interactions and gene regulation. Copyright © 2016 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

Interactions between MAOA and SYP polymorphisms were associated with symptoms of attention-deficit/hyperactivity disorder in Chinese Han subjects.

PubMed

Gao, Qian; Liu, Lu; Li, Hai-Mei; Tang, Yi-Lang; Wu, Zhao-Min; Chen, Yun; Wang, Yu-Feng; Qian, Qiu-Jin

2015-01-01

As candidate genes of attention--deficit/hyperactivity disorder (ADHD), monoamine oxidase A (MAOA), and synaptophysin (SYP) are both on the X chromosome, and have been suggested to be associated with the predominantly inattentive subtype (ADHD-I). The present study is to investigate the potential gene-gene interaction (G × G) between rs5905859 of MAOA and rs5906754 of SYP for ADHD in Chinese Han subjects. For family-based association study, 177 female trios were included. For case-control study, 1,462 probands and 807 normal controls were recruited. The ADHD Rating Scale-IV (ADHD-RS-IV) was used to evaluate ADHD symptoms. Pedigree-based generalized multifactor dimensionality reduction (PGMDR) for female ADHD trios indicated significant gene interaction effect of rs5905859 and rs5906754. Generalized multifactor dimensionality reduction (GMDR) indicated potential gene-gene interplay on ADHD RS-IV scores in female ADHD-I. No associations were observed in male subjects in case-control analysis. In conclusion, our findings suggested that the interaction of MAOA and SYP may be involved in the genetic mechanism of ADHD-I subtype and predict ADHD symptoms. © 2014 Wiley Periodicals, Inc.

Reduction of aflatoxin production by Aspergillus flavus and Aspergillus parasiticus in interaction with Streptomyces.

PubMed

Verheecke, C; Liboz, T; Anson, P; Diaz, R; Mathieu, F

2015-05-01

The aim of this study is to investigate aflatoxin gene expression during Streptomyces-Aspergillus interaction. Aflatoxins are carcinogenic compounds produced mainly by Aspergillus flavus and Aspergillus parasiticus. A previous study has shown that Streptomyces-A. flavus interaction can reduce aflatoxin content in vitro. Here, we first validated this same effect in the interaction with A. parasiticus. Moreover, we showed that growth reduction and aflatoxin content were correlated in A. parasiticus but not in A. flavus. Secondly, we investigated the mechanisms of action by reverse-transcriptase quantitative PCR. As microbial interaction can lead to variations in expression of household genes, the most stable [act1, βtub (and cox5 for A. parasiticus)] were chosen using geNorm software. To shed light on the mechanisms involved, we studied during the interaction the expression of five genes (aflD, aflM, aflP, aflR and aflS). Overall, the results of aflatoxin gene expression showed that Streptomyces repressed gene expression to a greater level in A. parasiticus than in A. flavus. Expression of aflR and aflS was generally repressed in both Aspergillus species. Expression of aflM was repressed and was correlated with aflatoxin B1 content. The results suggest that aflM expression could be a potential aflatoxin indicator in Streptomyces species interactions. Therefore, we demonstrate that Streptomyces can reduce aflatoxin production by both Aspergillus species and that this effect can be correlated with the repression of aflM expression. © 2015 The Authors.

Identification of crucial genes related to postmenopausal osteoporosis using gene expression profiling.

PubMed

Ma, Min; Chen, Xiaofei; Lu, Liangyu; Yuan, Feng; Zeng, Wen; Luo, Shulin; Yin, Feng; Cai, Junfeng

2016-12-01

Postmenopausal osteoporosis is a common bone disease and characterized by low bone mineral density. This study aimed to reveal key genes associated with postmenopausal osteoporosis (PMO), and provide a theoretical basis for subsequent experiments. The dataset GSE7429 was obtained from Gene Expression Omnibus. A total of 20 B cell samples (ten ones, respectively from postmenopausal women with low or high bone mineral density (BMD) were included in this dataset. Following screening of differentially expressed genes (DEGs), coexpression analysis of all genes was performed, and key genes in the coexpression network were screened using the random walk algorithm. Afterwards, functional and pathway analyses were conducted. Additionally, protein-protein interactions (PPIs) between DEGs and key genes were analyzed. A set of 308 DEGs (170 up-regulated ones and 138 down-regulated ones) between low BMD and high BMD samples were identified, and 101 key genes in the coexpression network were screened out. In the coexpression network, some genes had a higher score and degree, such as CSTA. The key genes in the coexpression network were mainly enriched in GO terms of the defense response (e.g., SERPINA1 and CST3), immune response (e.g., IL32 and CLEC7A); while, the DEGs were mainly enriched in structural constituent of cytoskeleton (e.g., CYLC2 and TUBA1B) and membrane-enclosed lumen (e.g., CCNE1 and INTS5). In the PPI network, CCNE1 interacted with REL; and TUBA1B interacted with ESR1. A series of interactions, such as CSTA/TYROBP, CCNE1/REL and TUBA1B/ESR1 might play pivotal roles in the occurrence and development of PMO.

Systems biology approach to transplant tolerance: proof of concept experiments using RNA interference (RNAi) to knock down hub genes in Jurkat and HeLa cells in vitro.

PubMed

Lwin, Wint Wah; Park, Ken; Wauson, Matthew; Gao, Qin; Finn, Patricia W; Perkins, David; Khanna, Ajai

2012-07-01

Systems biology is gaining importance in studying complex systems such as the functional interconnections of human genes [1]. To investigate the molecular interactions involved in T cell immune responses, we used databases of physical gene-gene interactions to constructed molecular interaction networks (interconnections) with R language algorithms. This helped to identify highly interconnected "hub" genes AT(1)P5C1, IL6ST, PRKCZ, MYC, FOS, JUN, and MAPK1. We hypothesized that suppression of these hub genes in the gene network would result in significant phenotypic effects on T cells and examined this in vitro. The molecular interaction networks were then analyzed and visualized with Cytoscape. Jurkat and HeLa cells were transfected with siRNA for the selected hub genes. Cell proliferation was measured using ATP luminescence and BrdU labeling, which were measured 36, 72, and 96 h after activation. Following T cell stimulation, we found a significant decrease in ATP production (P < 0.05) when the hub genes ATP5C1 and PRKCZ were knocked down using siRNA transfection, whereas no difference in ATP production was observed in siRNA transfected HeLa cells. However, HeLa cells showed a significant (P < 0.05) decrease in cell proliferation when the genes MAPK1, IL6ST, ATP5C1, JUN, and FOS were knocked down. In both Jurkat and HeLa cells, targeted gene knockdown using siRNA showed decreased cell proliferation and ATP production in both Jurkat and HeLa cells. However, Jurkat T cells and HELA cells use different hub genes to regulate activation responses. This experiment provides proof of principle of applying siRNA knockdown of T cell hub genes to evaluate their proliferative capacity and ATP production. This novel concept outlines a systems biology approach to identify hub genes for targeted therapeutics. Published by Elsevier Inc.

Mining disease genes using integrated protein-protein interaction and gene-gene co-regulation information.

PubMed

Li, Jin; Wang, Limei; Guo, Maozu; Zhang, Ruijie; Dai, Qiguo; Liu, Xiaoyan; Wang, Chunyu; Teng, Zhixia; Xuan, Ping; Zhang, Mingming

2015-01-01

In humans, despite the rapid increase in disease-associated gene discovery, a large proportion of disease-associated genes are still unknown. Many network-based approaches have been used to prioritize disease genes. Many networks, such as the protein-protein interaction (PPI), KEGG, and gene co-expression networks, have been used. Expression quantitative trait loci (eQTLs) have been successfully applied for the determination of genes associated with several diseases. In this study, we constructed an eQTL-based gene-gene co-regulation network (GGCRN) and used it to mine for disease genes. We adopted the random walk with restart (RWR) algorithm to mine for genes associated with Alzheimer disease. Compared to the Human Protein Reference Database (HPRD) PPI network alone, the integrated HPRD PPI and GGCRN networks provided faster convergence and revealed new disease-related genes. Therefore, using the RWR algorithm for integrated PPI and GGCRN is an effective method for disease-associated gene mining.

Cellular and molecular mechanisms of HIV-1 integration targeting.

PubMed

Engelman, Alan N; Singh, Parmit K

2018-07-01

Integration is central to HIV-1 replication and helps mold the reservoir of cells that persists in AIDS patients. HIV-1 interacts with specific cellular factors to target integration to interior regions of transcriptionally active genes within gene-dense regions of chromatin. The viral capsid interacts with several proteins that are additionally implicated in virus nuclear import, including cleavage and polyadenylation specificity factor 6, to suppress integration into heterochromatin. The viral integrase protein interacts with transcriptional co-activator lens epithelium-derived growth factor p75 to principally position integration within gene bodies. The integrase additionally senses target DNA distortion and nucleotide sequence to help fine-tune the specific phosphodiester bonds that are cleaved at integration sites. Research into virus-host interactions that underlie HIV-1 integration targeting has aided the development of a novel class of integrase inhibitors and may help to improve the safety of viral-based gene therapy vectors.

Insulators form gene loops by interacting with promoters in Drosophila.

PubMed

Erokhin, Maksim; Davydova, Anna; Kyrchanova, Olga; Parshikov, Alexander; Georgiev, Pavel; Chetverina, Darya

2011-09-01

Chromatin insulators are regulatory elements involved in the modulation of enhancer-promoter communication. The 1A2 and Wari insulators are located immediately downstream of the Drosophila yellow and white genes, respectively. Using an assay based on the yeast GAL4 activator, we have found that both insulators are able to interact with their target promoters in transgenic lines, forming gene loops. The existence of an insulator-promoter loop is confirmed by the fact that insulator proteins could be detected on the promoter only in the presence of an insulator in the transgene. The upstream promoter regions, which are required for long-distance stimulation by enhancers, are not essential for promoter-insulator interactions. Both insulators support basal activity of the yellow and white promoters in eyes. Thus, the ability of insulators to interact with promoters might play an important role in the regulation of basal gene transcription.

Competing endogenous RNA regulatory network in papillary thyroid carcinoma.

PubMed

Chen, Shouhua; Fan, Xiaobin; Gu, He; Zhang, Lili; Zhao, Wenhua

2018-05-11

The present study aimed to screen all types of RNAs involved in the development of papillary thyroid carcinoma (PTC). RNA‑sequencing data of PTC and normal samples were used for screening differentially expressed (DE) microRNAs (DE‑miRNAs), long non‑coding RNAs (DE‑lncRNAs) and genes (DEGs). Subsequently, lncRNA‑miRNA, miRNA‑gene (that is, miRNA‑mRNA) and gene‑gene interaction pairs were extracted and used to construct regulatory networks. Feature genes in the miRNA‑mRNA network were identified by topological analysis and recursive feature elimination analysis. A support vector machine (SVM) classifier was built using 15 feature genes, and its classification effect was validated using two microarray data sets that were downloaded from the Gene Expression Omnibus (GEO) database. In addition, Gene Ontology function and Kyoto Encyclopedia Genes and Genomes pathway enrichment analyses were conducted for genes identified in the ceRNA network. A total of 506 samples, including 447 tumor samples and 59 normal samples, were obtained from The Cancer Genome Atlas (TCGA); 16 DE‑lncRNAs, 917 DEGs and 30 DE‑miRNAs were screened. The miRNA‑mRNA regulatory network comprised 353 nodes and 577 interactions. From these data, 15 feature genes with high predictive precision (>95%) were extracted from the network and were used to form an SVM classifier with an accuracy of 96.05% (486/506) for PTC samples downloaded from TCGA, and accuracies of 96.81 and 98.46% for GEO downloaded data sets. The ceRNA regulatory network comprised 596 lines (or interactions) and 365 nodes. Genes in the ceRNA network were significantly enriched in 'neuron development', 'differentiation', 'neuroactive ligand‑receptor interaction', 'metabolism of xenobiotics by cytochrome P450', 'drug metabolism' and 'cytokine‑cytokine receptor interaction' pathways. Hox transcript antisense RNA, miRNA‑206 and kallikrein‑related peptidase 10 were nodes in the ceRNA regulatory network of the selected feature gene, and they may serve import roles in the development of PTC.

Assessing co-regulation of directly linked genes in biological networks using microarray time series analysis.

PubMed

Del Sorbo, Maria Rosaria; Balzano, Walter; Donato, Michele; Draghici, Sorin

2013-11-01

Differential expression of genes detected with the analysis of high throughput genomic experiments is a commonly used intermediate step for the identification of signaling pathways involved in the response to different biological conditions. The impact analysis was the first approach for the analysis of signaling pathways involved in a certain biological process that was able to take into account not only the magnitude of the expression change of the genes but also the topology of signaling pathways including the type of each interactions between the genes. In the impact analysis, signaling pathways are represented as weighted directed graphs with genes as nodes and the interactions between genes as edges. Edges weights are represented by a β factor, the regulatory efficiency, which is assumed to be equal to 1 in inductive interactions between genes and equal to -1 in repressive interactions. This study presents a similarity analysis between gene expression time series aimed to find correspondences with the regulatory efficiency, i.e. the β factor as found in a widely used pathway database. Here, we focused on correlations among genes directly connected in signaling pathways, assuming that the expression variations of upstream genes impact immediately downstream genes in a short time interval and without significant influences by the interactions with other genes. Time series were processed using three different similarity metrics. The first metric is based on the bit string matching; the second one is a specific application of the Dynamic Time Warping to detect similarities even in presence of stretching and delays; the third one is a quantitative comparative analysis resulting by an evaluation of frequency domain representation of time series: the similarity metric is the correlation between dominant spectral components. These three approaches are tested on real data and pathways, and a comparison is performed using Information Retrieval benchmark tools, indicating the frequency approach as the best similarity metric among the three, for its ability to detect the correlation based on the correspondence of the most significant frequency components. Copyright © 2013. Published by Elsevier Ireland Ltd.

Genome-wide gene–environment interaction analysis for asbestos exposure in lung cancer susceptibility

PubMed Central

Wei, Qingyi Wei

2012-01-01

Asbestos exposure is a known risk factor for lung cancer. Although recent genome-wide association studies (GWASs) have identified some novel loci for lung cancer risk, few addressed genome-wide gene–environment interactions. To determine gene–asbestos interactions in lung cancer risk, we conducted genome-wide gene–environment interaction analyses at levels of single nucleotide polymorphisms (SNPs), genes and pathways, using our published Texas lung cancer GWAS dataset. This dataset included 317 498 SNPs from 1154 lung cancer cases and 1137 cancer-free controls. The initial SNP-level P-values for interactions between genetic variants and self-reported asbestos exposure were estimated by unconditional logistic regression models with adjustment for age, sex, smoking status and pack-years. The P-value for the most significant SNP rs13383928 was 2.17×10–6, which did not reach the genome-wide statistical significance. Using a versatile gene-based test approach, we found that the top significant gene was C7orf54, located on 7q32.1 (P = 8.90×10–5). Interestingly, most of the other significant genes were located on 11q13. When we used an improved gene-set-enrichment analysis approach, we found that the Fas signaling pathway and the antigen processing and presentation pathway were most significant (nominal P < 0.001; false discovery rate < 0.05) among 250 pathways containing 17 572 genes. We believe that our analysis is a pilot study that first describes the gene–asbestos interaction in lung cancer risk at levels of SNPs, genes and pathways. Our findings suggest that immune function regulation-related pathways may be mechanistically involved in asbestos-associated lung cancer risk. Abbreviations:CIconfidence intervalEenvironmentFDRfalse discovery rateGgeneGSEAgene-set-enrichment analysisGWASgenome-wide association studiesi-GSEAimproved gene-set-enrichment analysis approachORodds ratioSNPsingle nucleotide polymorphism PMID:22637743

The light gene of Drosophila melanogaster encodes a homologue of VPS41, a yeast gene involved in cellular-protein trafficking.

PubMed

Warner, T S; Sinclair, D A; Fitzpatrick, K A; Singh, M; Devlin, R H; Honda, B M

1998-04-01

Mutations in a number of genes affect eye colour in Drosophila melanogaster; some of these "eye-colour" genes have been shown to be involved in various aspects of cellular transport processes. In addition, combinations of viable mutant alleles of some of these genes, such as carnation (car) combined with either light (lt) or deep-orange (dor) mutants, show lethal interactions. Recently, dor was shown to be homologous to the yeast gene PEP3 (VPS18), which is known to be involved in intracellular trafficking. We have undertaken to extend our earlier work on the lt gene, in order to examine in more detail its expression pattern and to characterize its gene product via sequencing of a cloned cDNA. The gene appears to be expressed at relatively high levels in all stages and tissues examined, and shows strong homology to VPS41, a gene involved in cellular-protein trafficking in yeast and higher eukaryotes. Further genetic experiments also point to a role for lt in transport processes: we describe lethal interactions between viable alleles of lt and dor, as well as phenotypic interactions (reductions in eye pigment) between allels of lt and another eye-colour gene, garnet (g), whose gene product has close homology to a subunit of the human adaptor complex, AP-3.

Genome-wide Hi-C analysis reveals extensive hierarchical chromatin interactions in rice.

PubMed

Dong, Qianli; Li, Ning; Li, Xiaochong; Yuan, Zan; Xie, Dejian; Wang, Xiaofei; Li, Jianing; Yu, Yanan; Wang, Jinbin; Ding, Baoxu; Zhang, Zhibin; Li, Changping; Bian, Yao; Zhang, Ai; Wu, Ying; Liu, Bao; Gong, Lei

2018-06-01

The non-random spatial packing of chromosomes in the nucleus plays a critical role in orchestrating gene expression and genome function. Here, we present a Hi-C analysis of the chromatin interaction patterns in rice (Oryza sativa L.) at hierarchical architectural levels. We confirm that rice chromosomes occupy their own territories with certain preferential inter-chromosomal associations. Moderate compartment delimitation and extensive TADs (Topologically Associated Domains) were determined to be associated with heterogeneous genomic compositions and epigenetic marks in the rice genome. We found subtle features including chromatin loops, gene loops, and off-/near-diagonal intensive interaction regions. Gene chromatin loops associated with H3K27me3 could be positively involved in gene expression. In addition to insulated enhancing effects for neighbor gene expression, the identified rice gene loops could bi-directionally (+/-) affect the expression of looped genes themselves. Finally, web-interleaved off-diagonal IHIs/KEEs (Interactive Heterochromatic Islands or KNOT ENGAGED ELEMENTs) could trap transposable elements (TEs) via the enrichment of silencing epigenetic marks. In parallel, the near-diagonal FIREs (Frequently Interacting Regions) could positively affect the expression of involved genes. Our results suggest that the chromatin packing pattern in rice is generally similar to that in Arabidopsis thaliana but with clear differences at specific structural levels. We conclude that genomic composition, epigenetic modification, and transcriptional activity could act in combination to shape global and local chromatin packing in rice. Our results confirm recent observations in rice and A. thaliana but also provide additional insights into the patterns and features of chromatin organization in higher plants. © 2018 The Authors. The Plant Journal published by John Wiley & Sons Ltd and Society for Experimental Biology.

Gene-to-gene interactions regulate endogenous pain modulation in fibromyalgia patients and healthy controls—antagonistic effects between opioid and serotonin-related genes

PubMed Central

Tour, Jeanette; Löfgren, Monika; Mannerkorpi, Kaisa; Gerdle, Björn; Larsson, Anette; Palstam, Annie; Bileviciute-Ljungar, Indre; Bjersing, Jan; Martin, Ingvar; Ernberg, Malin; Schalling, Martin; Kosek, Eva

2017-01-01

Abstract Chronic pain is associated with dysfunctional endogenous pain modulation, involving both central opioid and serotonergic (5-HT) signaling. Fibromyalgia (FM) is a chronic pain syndrome, characterized by widespread musculoskeletal pain and reduced exercise-induced hypoalgesia (EIH). In this study, we assessed the effects of 3 functional genetic polymorphisms on EIH in 130 patients with FM and 132 healthy controls. Subjects were genotyped regarding the mu-opioid receptor (OPRM1) gene (rs1799971), the serotonin transporter (5-HTT) gene (5-HTTLPR/rs25531), and the serotonin-1a receptor (5-HT1a) gene (rs6296). The patients with FM had increased pain sensitivity and reduced EIH compared with healthy controls. None of the polymorphisms had an effect on EIH on their own. We found significant gene-to-gene interactions between OPRM1 x 5-HTT and OPRM1 x 5-HT1a regarding activation of EIH, with no statistically significant difference between groups. Better EIH was found in individuals with genetically inferred strong endogenous opioid signaling (OPRM1 G) in combination with weak 5-HT tone (5-HTT low/5-HT1a G), compared with strong 5-HT tone (5-HTT high/5-HT1a CC). Based on the proposed mechanisms of these genetic variants, the findings indicate antagonistic interactions between opioid and serotonergic mechanisms during EIH. Moreover, despite different baseline pain level, similar results were detected in FM and controls, not supporting an altered interaction between opioid and 5-HT mechanisms as the basis for dysfunction of EIH in patients with FM. In summary, our results suggest that, by genetic association, the mu-opioid receptor interacts with 2 major serotonergic structures involved in 5-HT reuptake and release, to modulate EIH. PMID:28282362

Translating Mendelian and complex inheritance of Alzheimer's disease genes for predicting unique personal genome variants

PubMed Central

Regan, Kelly; Wang, Kanix; Doughty, Emily; Li, Haiquan; Li, Jianrong; Lee, Younghee; Kann, Maricel G

2012-01-01

Objective Although trait-associated genes identified as complex versus single-gene inheritance differ substantially in odds ratio, the authors nonetheless posit that their mechanistic concordance can reveal fundamental properties of the genetic architecture, allowing the automated interpretation of unique polymorphisms within a personal genome. Materials and methods An analytical method, SPADE-gen, spanning three biological scales was developed to demonstrate the mechanistic concordance between Mendelian and complex inheritance of Alzheimer's disease (AD) genes: biological functions (BP), protein interaction modeling, and protein domain implicated in the disease-associated polymorphism. Results Among Gene Ontology (GO) biological processes (BP) enriched at a false detection rate <5% in 15 AD genes of Mendelian inheritance (Online Mendelian Inheritance in Man) and independently in those of complex inheritance (25 host genes of intragenic AD single-nucleotide polymorphisms confirmed in genome-wide association studies), 16 overlapped (empirical p=0.007) and 45 were similar (empirical p<0.009; information theory). SPAN network modeling extended the canonical pathway of AD (KEGG) with 26 new protein interactions (empirical p<0.0001). Discussion The study prioritized new AD-associated biological mechanisms and focused the analysis on previously unreported interactions associated with the biological processes of polymorphisms that affect specific protein domains within characterized AD genes and their direct interactors using (1) concordant GO-BP and (2) domain interactions within STRING protein–protein interactions corresponding to the genomic location of the AD polymorphism (eg, EPHA1, APOE, and CD2AP). Conclusion These results are in line with unique-event polymorphism theory, indicating how disease-associated polymorphisms of Mendelian or complex inheritance relate genetically to those observed as ‘unique personal variants’. They also provide insight for identifying novel targets, for repositioning drugs, and for personal therapeutics. PMID:22319180

Gene-to-gene interactions regulate endogenous pain modulation in fibromyalgia patients and healthy controls-antagonistic effects between opioid and serotonin-related genes.

PubMed

Tour, Jeanette; Löfgren, Monika; Mannerkorpi, Kaisa; Gerdle, Björn; Larsson, Anette; Palstam, Annie; Bileviciute-Ljungar, Indre; Bjersing, Jan; Martin, Ingvar; Ernberg, Malin; Schalling, Martin; Kosek, Eva

2017-07-01

Chronic pain is associated with dysfunctional endogenous pain modulation, involving both central opioid and serotonergic (5-HT) signaling. Fibromyalgia (FM) is a chronic pain syndrome, characterized by widespread musculoskeletal pain and reduced exercise-induced hypoalgesia (EIH). In this study, we assessed the effects of 3 functional genetic polymorphisms on EIH in 130 patients with FM and 132 healthy controls. Subjects were genotyped regarding the mu-opioid receptor (OPRM1) gene (rs1799971), the serotonin transporter (5-HTT) gene (5-HTTLPR/rs25531), and the serotonin-1a receptor (5-HT1a) gene (rs6296). The patients with FM had increased pain sensitivity and reduced EIH compared with healthy controls. None of the polymorphisms had an effect on EIH on their own. We found significant gene-to-gene interactions between OPRM1 x 5-HTT and OPRM1 x 5-HT1a regarding activation of EIH, with no statistically significant difference between groups. Better EIH was found in individuals with genetically inferred strong endogenous opioid signaling (OPRM1 G) in combination with weak 5-HT tone (5-HTT low/5-HT1a G), compared with strong 5-HT tone (5-HTT high/5-HT1a CC). Based on the proposed mechanisms of these genetic variants, the findings indicate antagonistic interactions between opioid and serotonergic mechanisms during EIH. Moreover, despite different baseline pain level, similar results were detected in FM and controls, not supporting an altered interaction between opioid and 5-HT mechanisms as the basis for dysfunction of EIH in patients with FM. In summary, our results suggest that, by genetic association, the mu-opioid receptor interacts with 2 major serotonergic structures involved in 5-HT reuptake and release, to modulate EIH.

Integration of multi-omics data for integrative gene regulatory network inference.

PubMed

Zarayeneh, Neda; Ko, Euiseong; Oh, Jung Hun; Suh, Sang; Liu, Chunyu; Gao, Jean; Kim, Donghyun; Kang, Mingon

2017-01-01

Gene regulatory networks provide comprehensive insights and indepth understanding of complex biological processes. The molecular interactions of gene regulatory networks are inferred from a single type of genomic data, e.g., gene expression data in most research. However, gene expression is a product of sequential interactions of multiple biological processes, such as DNA sequence variations, copy number variations, histone modifications, transcription factors, and DNA methylations. The recent rapid advances of high-throughput omics technologies enable one to measure multiple types of omics data, called 'multi-omics data', that represent the various biological processes. In this paper, we propose an Integrative Gene Regulatory Network inference method (iGRN) that incorporates multi-omics data and their interactions in gene regulatory networks. In addition to gene expressions, copy number variations and DNA methylations were considered for multi-omics data in this paper. The intensive experiments were carried out with simulation data, where iGRN's capability that infers the integrative gene regulatory network is assessed. Through the experiments, iGRN shows its better performance on model representation and interpretation than other integrative methods in gene regulatory network inference. iGRN was also applied to a human brain dataset of psychiatric disorders, and the biological network of psychiatric disorders was analysed.

Schizophrenia, vitamin D, and brain development.

PubMed

Mackay-Sim, Alan; Féron, François; Eyles, Darryl; Burne, Thomas; McGrath, John

2004-01-01

Schizophrenia research is invigorated at present by the recent discovery of several plausible candidate susceptibility genes identified from genetic linkage and gene expression studies of brains from persons with schizophrenia. It is a current challenge to reconcile this gathering evidence for specific candidate susceptibility genes with the "neurodevelopmental hypothesis," which posits that schizophrenia arises from gene-environment interactions that disrupt brain development. We make the case here that schizophrenia may result not from numerous genes of small effect, but a few genes of transcriptional regulation acting during brain development. In particular we propose that low vitamin D during brain development interacts with susceptibility genes to alter the trajectory of brain development, probably by epigenetic regulation that alters gene expression throughout adult life. Vitamin D is an attractive "environmental" candidate because it appears to explain several key epidemiological features of schizophrenia. Vitamin D is an attractive "genetic" candidate because its nuclear hormone receptor regulates gene expression and nervous system development. The polygenic quality of schizophrenia, with linkage to many genes of small effect, maybe brought together via this "vitamin D hypothesis." We also discuss the possibility of a broader set of environmental and genetic factors interacting via the nuclear hormone receptors to affect the development of the brain leading to schizophrenia.

Integration of multi-omics data for integrative gene regulatory network inference

PubMed Central

Zarayeneh, Neda; Ko, Euiseong; Oh, Jung Hun; Suh, Sang; Liu, Chunyu; Gao, Jean; Kim, Donghyun

2017-01-01

Gene regulatory networks provide comprehensive insights and indepth understanding of complex biological processes. The molecular interactions of gene regulatory networks are inferred from a single type of genomic data, e.g., gene expression data in most research. However, gene expression is a product of sequential interactions of multiple biological processes, such as DNA sequence variations, copy number variations, histone modifications, transcription factors, and DNA methylations. The recent rapid advances of high-throughput omics technologies enable one to measure multiple types of omics data, called ‘multi-omics data’, that represent the various biological processes. In this paper, we propose an Integrative Gene Regulatory Network inference method (iGRN) that incorporates multi-omics data and their interactions in gene regulatory networks. In addition to gene expressions, copy number variations and DNA methylations were considered for multi-omics data in this paper. The intensive experiments were carried out with simulation data, where iGRN’s capability that infers the integrative gene regulatory network is assessed. Through the experiments, iGRN shows its better performance on model representation and interpretation than other integrative methods in gene regulatory network inference. iGRN was also applied to a human brain dataset of psychiatric disorders, and the biological network of psychiatric disorders was analysed. PMID:29354189

Gene-environment interaction: Does fluoride influence the reproductive hormones in male farmers modified by ERα gene polymorphisms?

PubMed

Ma, Qiang; Huang, Hui; Sun, Long; Zhou, Tong; Zhu, Jingyuan; Cheng, Xuemin; Duan, Lijv; Li, Zhiyuan; Cui, Liuxin; Ba, Yue

2017-12-01

The occurrence of endemic fluorosis is derived from high fluoride levels in drinking water and industrial fumes or dust. Reproductive disruption is also a major harm caused by fluoride exposure besides dental and skeletal lesions. However, few studies focus on the mechanism of fluoride exposure on male reproductive function, especially the possible interaction of fluoride exposure and gene polymorphism on male reproductive hormones. Therefore, we conducted a cross-sectional study in rural areas of Henan province in China to explore the interaction between the estrogen receptor alpha (ERα) gene and fluoride exposure on reproductive hormone levels in male farmers living in the endemic fluorosis villages. The results showed that fluoride exposure significantly increased the serum level of estradiol in the hypothalamic-pituitary-testicular (HPT) axis in male farmers. Moreover, the observations indicated that fluoride exposure and genetic markers had an interaction on serum concentration of follicle-stimulating hormone and estradiol, and the interaction among different loci of the ERα gene could impact the serum testosterone level. Findings in the present work suggest that chronic fluoride exposure in drinking water could modulate the levels of reproductive hormones in males living in endemic fluorosis areas, and the interaction between fluoride exposure and ERα polymorphisms might affect the serum levels of hormones in the HPT axis in male farmers. Copyright © 2017 Elsevier Ltd. All rights reserved.

Monoamine Oxidase A (MAOA) and Catechol-O-Methyltransferase (COMT) Gene Polymorphisms Interact with Maternal Parenting in Association with Adolescent Reactive Aggression but not Proactive Aggression: Evidence of Differential Susceptibility.

PubMed

Zhang, Wenxin; Cao, Cong; Wang, Meiping; Ji, Linqin; Cao, Yanmiao

2016-04-01

To date, whether and how gene-environment (G × E) interactions operate differently across distinct subtypes of aggression remains untested. More recently, in contrast with the diathesis-stress hypothesis, an alternative hypothesis of differential susceptibility proposes that individuals could be differentially susceptible to environments depending on their genotypes in a "for better and for worse" manner. The current study examined interactions between monoamine oxidase A (MAOA) T941G and catechol-O-methyltransferase (COMT) Val158Met polymorphisms with maternal parenting on two types of aggression: reactive and proactive. Moreover, whether these potential G × E interactions would be consistent with the diathesis-stress versus the differential susceptibility hypothesis was tested. Within the sample of 1399 Chinese Han adolescents (47.2 % girls, M age = 12.32 years, SD = 0.50), MAOA and COMT genes both interacted with positive parenting in their associations with reactive but not proactive aggression. Adolescents with T alleles/TT homozygotes of MAOA gene or Met alleles of COMT gene exhibited more reactive aggression when exposed to low positive parenting, but less reactive aggression when exposed to high positive parenting. These findings provide the first evidence for distinct G × E interaction effects on reactive versus proactive aggression and lend further support for the differential susceptibility hypothesis.

Interaction between FTO rs9939609 and the Native American-origin ABCA1 rs9282541 affects BMI in the admixed Mexican population.

PubMed

Villalobos-Comparán, Marisela; Antuna-Puente, Bárbara; Villarreal-Molina, María Teresa; Canizales-Quinteros, Samuel; Velázquez-Cruz, Rafael; León-Mimila, Paola; Villamil-Ramírez, Hugo; González-Barrios, Juan Antonio; Merino-García, José Luis; Thompson-Bonilla, María Rocío; Jarquin, Diego; Sánchez-Hernández, Osvaldo Erik; Rodríguez-Arellano, Martha Eunice; Posadas-Romero, Carlos; Vargas-Alarcón, Gilberto; Campos-Pérez, Francisco; Quiterio, Manuel; Salmerón-Castro, Jorge; Carnevale, Alessandra; Romero-Hidalgo, Sandra

2017-05-02

The aim of this study was to explore whether interactions between FTO rs9939609 and ABCA1 rs9282541 affect BMI and waist circumference (WC), and could explain previously reported population differences in FTO-obesity and FTO-BMI associations in the Mexican and European populations. A total of 3938 adults and 636 school-aged children from Central Mexico were genotyped for both polymorphisms. Subcutaneous and visceral adipose tissue biopsies from 22 class III obesity patients were analyzed for FTO and ABCA1 mRNA expression. Generalized linear models were used to test for associations and gene-gene interactions affecting BMI, WC and FTO expression. FTO and ABCA1 risk alleles were not individually associated with higher BMI or WC. However, in the absence of the ABCA1 risk allele, the FTO risk variant was significantly associated with higher BMI (P = 0.043) and marginally associated with higher WC (P = 0.067), as reported in Europeans. The gene-gene interaction affecting BMI and WC was statistically significant only in adults. FTO mRNA expression in subcutaneous abdominal adipose tissue according to ABCA1 genotype was consistent with these findings. This is the first report showing evidence of FTO and ABCA1 gene variant interactions affecting BMI, which may explain previously reported population differences. Further studies are needed to confirm this interaction.

The evolution of early cellular systems viewed through the lens of biological interactions.

PubMed

Poole, Anthony M; Lundin, Daniel; Rytkönen, Kalle T

2015-01-01

The minimal cell concept represents a pragmatic approach to the question of how few genes are required to run a cell. This is a helpful way to build a parts-list, and has been more successful than attempts to deduce a minimal gene set for life by inferring the gene repertoire of the last universal common ancestor, as few genes trace back to this hypothetical ancestral state. However, the study of minimal cellular systems is the study of biological outliers where, by practical necessity, coevolutionary interactions are minimized or ignored. In this paper, we consider the biological context from which minimal genomes have been removed. For instance, some of the most reduced genomes are from endosymbionts and are the result of coevolutionary interactions with a host; few such organisms are "free-living." As few, if any, biological systems exist in complete isolation, we expect that, as with modern life, early biological systems were part of an ecosystem, replete with organismal interactions. We favor refocusing discussions of the evolution of cellular systems on processes rather than gene counts. We therefore draw a distinction between a pragmatic minimal cell (an interesting engineering problem), a distributed genome (a system resulting from an evolutionary transition involving more than one cell) and the looser coevolutionary interactions that are ubiquitous in ecosystems. Finally, we consider the distributed genome and coevolutionary interactions between genomic entities in the context of early evolution.

A database of human genes and a gene network involved in response to tick-borne encephalitis virus infection.

PubMed

Ignatieva, Elena V; Igoshin, Alexander V; Yudin, Nikolay S

2017-12-28

Tick-borne encephalitis is caused by the neurotropic, positive-sense RNA virus, tick-borne encephalitis virus (TBEV). TBEV infection can lead to a variety of clinical manifestations ranging from slight fever to severe neurological illness. Very little is known about genetic factors predisposing to severe forms of disease caused by TBEV. The aims of the study were to compile a catalog of human genes involved in response to TBEV infection and to rank genes from the catalog based on the number of neighbors in the network of pairwise interactions involving these genes and TBEV RNA or proteins. Based on manual review and curation of scientific publications a catalog comprising 140 human genes involved in response to TBEV infection was developed. To provide access to data on all genes, the TBEVhostDB web resource ( http://icg.nsc.ru/TBEVHostDB/ ) was created. We reconstructed a network formed by pairwise interactions between TBEV virion itself, viral RNA and viral proteins and 140 genes/proteins from TBEVHostDB. Genes were ranked according to the number of interactions in the network. Two genes/proteins (CCR5 and IFNAR1) that had maximal number of interactions were revealed. It was found that the subnetworks formed by CCR5 and IFNAR1 and their neighbors were a fragments of two key pathways functioning during the course of tick-borne encephalitis: (1) the attenuation of interferon-I signaling pathway by the TBEV NS5 protein that targeted peptidase D; (2) proinflammation and tissue damage pathway triggered by chemokine receptor CCR5 interacting with CD4, CCL3, CCL4, CCL2. Among nine genes associated with severe forms of TBEV infection, three genes/proteins (CCR5, IL10, ARID1B) were found to have protein-protein interactions within the network, and two genes/proteins (IFNL3 and the IL10, that was just mentioned) were up- or down-regulated in response to TBEV infection. Based on this finding, potential mechanisms for participation of CCR5, IL10, ARID1B, and IFNL3 in the host response to TBEV infection were suggested. A database comprising 140 human genes involved in response to TBEV infection was compiled and the TBEVHostDB web resource, providing access to all genes was created. This is the first effort of integrating and unifying data on genetic factors that may predispose to severe forms of diseases caused by TBEV. The TBEVHostDB could potentially be used for assessment of risk factors for severe forms of tick-borne encephalitis and for the design of personalized pharmacological strategies for the treatment of TBEV infection.

Novel gene sets improve set-level classification of prokaryotic gene expression data.

PubMed

Holec, Matěj; Kuželka, Ondřej; Železný, Filip

2015-10-28

Set-level classification of gene expression data has received significant attention recently. In this setting, high-dimensional vectors of features corresponding to genes are converted into lower-dimensional vectors of features corresponding to biologically interpretable gene sets. The dimensionality reduction brings the promise of a decreased risk of overfitting, potentially resulting in improved accuracy of the learned classifiers. However, recent empirical research has not confirmed this expectation. Here we hypothesize that the reported unfavorable classification results in the set-level framework were due to the adoption of unsuitable gene sets defined typically on the basis of the Gene ontology and the KEGG database of metabolic networks. We explore an alternative approach to defining gene sets, based on regulatory interactions, which we expect to collect genes with more correlated expression. We hypothesize that such more correlated gene sets will enable to learn more accurate classifiers. We define two families of gene sets using information on regulatory interactions, and evaluate them on phenotype-classification tasks using public prokaryotic gene expression data sets. From each of the two gene-set families, we first select the best-performing subtype. The two selected subtypes are then evaluated on independent (testing) data sets against state-of-the-art gene sets and against the conventional gene-level approach. The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. Novel gene sets defined on the basis of regulatory interactions improve set-level classification of gene expression data. The experimental scripts and other material needed to reproduce the experiments are available at http://ida.felk.cvut.cz/novelgenesets.tar.gz.

Genetic Variants in the Bone Morphogenic Protein Gene Family Modify the Association between Residential Exposure to Traffic and Peripheral Arterial Disease

PubMed Central

Ward-Caviness, Cavin K.; Neas, Lucas M.; Blach, Colette; Haynes, Carol S.; LaRocque-Abramson, Karen; Grass, Elizabeth; Dowdy, Elaine; Devlin, Robert B.; Diaz-Sanchez, David; Cascio, Wayne E.; Lynn Miranda, Marie; Gregory, Simon G.; Shah, Svati H.; Kraus, William E.; Hauser, Elizabeth R.

2016-01-01

There is a growing literature indicating that genetic variants modify many of the associations between environmental exposures and clinical outcomes, potentially by increasing susceptibility to these exposures. However, genome-scale investigations of these interactions have been rarely performed particularly in the case of air pollution exposures. We performed race-stratified genome-wide gene-environment interaction association studies on European-American (EA, N = 1623) and African-American (AA, N = 554) cohorts to investigate the joint influence of common single nucleotide polymorphisms (SNPs) and residential exposure to traffic (“traffic exposure”)—a recognized vascular disease risk factor—on peripheral arterial disease (PAD). Traffic exposure was estimated via the distance from the primary residence to the nearest major roadway, defined as the nearest limited access highways or major arterial. The rs755249-traffic exposure interaction was associated with PAD at a genome-wide significant level (P = 2.29x10-8) in European-Americans. Rs755249 is located in the 3’ untranslated region of BMP8A, a member of the bone morphogenic protein (BMP) gene family. Further investigation revealed several variants in BMP genes associated with PAD via an interaction with traffic exposure in both the EA and AA cohorts; this included interactions with non-synonymous variants in BMP2, which is regulated by air pollution exposure. The BMP family of genes is linked to vascular growth and calcification and is a novel gene family for the study of PAD pathophysiology. Further investigation of BMP8A using the Genotype Tissue Expression Database revealed multiple variants with nominally significant (P < 0.05) interaction P-values in our EA cohort were significant BMP8A eQTLs in tissue types highlight relevant for PAD such as rs755249 (tibial nerve, eQTL P = 3.6x10-6) and rs1180341 (tibial artery, eQTL P = 5.3x10-6). Together these results reveal a novel gene, and possibly gene family, associated with PAD via an interaction with traffic air pollution exposure. These results also highlight the potential for interactions studies, particularly at the genome scale, to reveal novel biology linking environmental exposures to clinical outcomes. PMID:27082954

Prediction of the Ebola Virus Infection Related Human Genes Using Protein-Protein Interaction Network.

PubMed

Cao, HuanHuan; Zhang, YuHang; Zhao, Jia; Zhu, Liucun; Wang, Yi; Li, JiaRui; Feng, Yuan-Ming; Zhang, Ning

2017-01-01

Ebola hemorrhagic fever (EHF) is caused by Ebola virus (EBOV). It is reported that human could be infected by EBOV with a high fatality rate. However, association factors between EBOV and host still tend to be ambiguous. According to the "guilt by association" (GBA) principle, proteins interacting with each other are very likely to function similarly or the same. Based on this assumption, we tried to obtain EBOV infection-related human genes in a protein-protein interaction network using Dijkstra algorithm. We hope it could contribute to the discovery of novel effective treatments. Finally, 15 genes were selected as potential EBOV infection-related human genes. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Knowledge-driven genomic interactions: an application in ovarian cancer.

PubMed

Kim, Dokyoon; Li, Ruowang; Dudek, Scott M; Frase, Alex T; Pendergrass, Sarah A; Ritchie, Marylyn D

2014-01-01

Effective cancer clinical outcome prediction for understanding of the mechanism of various types of cancer has been pursued using molecular-based data such as gene expression profiles, an approach that has promise for providing better diagnostics and supporting further therapies. However, clinical outcome prediction based on gene expression profiles varies between independent data sets. Further, single-gene expression outcome prediction is limited for cancer evaluation since genes do not act in isolation, but rather interact with other genes in complex signaling or regulatory networks. In addition, since pathways are more likely to co-operate together, it would be desirable to incorporate expert knowledge to combine pathways in a useful and informative manner. Thus, we propose a novel approach for identifying knowledge-driven genomic interactions and applying it to discover models associated with cancer clinical phenotypes using grammatical evolution neural networks (GENN). In order to demonstrate the utility of the proposed approach, an ovarian cancer data from the Cancer Genome Atlas (TCGA) was used for predicting clinical stage as a pilot project. We identified knowledge-driven genomic interactions associated with cancer stage from single knowledge bases such as sources of pathway-pathway interaction, but also knowledge-driven genomic interactions across different sets of knowledge bases such as pathway-protein family interactions by integrating different types of information. Notably, an integration model from different sources of biological knowledge achieved 78.82% balanced accuracy and outperformed the top models with gene expression or single knowledge-based data types alone. Furthermore, the results from the models are more interpretable because they are framed in the context of specific biological pathways or other expert knowledge. The success of the pilot study we have presented herein will allow us to pursue further identification of models predictive of clinical cancer survival and recurrence. Understanding the underlying tumorigenesis and progression in ovarian cancer through the global view of interactions within/between different biological knowledge sources has the potential for providing more effective screening strategies and therapeutic targets for many types of cancer.

Meta-analysis identifies gene-by-environment interactions as demonstrated in a study of 4,965 mice.

PubMed

Kang, Eun Yong; Han, Buhm; Furlotte, Nicholas; Joo, Jong Wha J; Shih, Diana; Davis, Richard C; Lusis, Aldons J; Eskin, Eleazar

2014-01-01

Identifying environmentally-specific genetic effects is a key challenge in understanding the structure of complex traits. Model organisms play a crucial role in the identification of such gene-by-environment interactions, as a result of the unique ability to observe genetically similar individuals across multiple distinct environments. Many model organism studies examine the same traits but under varying environmental conditions. For example, knock-out or diet-controlled studies are often used to examine cholesterol in mice. These studies, when examined in aggregate, provide an opportunity to identify genomic loci exhibiting environmentally-dependent effects. However, the straightforward application of traditional methodologies to aggregate separate studies suffers from several problems. First, environmental conditions are often variable and do not fit the standard univariate model for interactions. Additionally, applying a multivariate model results in increased degrees of freedom and low statistical power. In this paper, we jointly analyze multiple studies with varying environmental conditions using a meta-analytic approach based on a random effects model to identify loci involved in gene-by-environment interactions. Our approach is motivated by the observation that methods for discovering gene-by-environment interactions are closely related to random effects models for meta-analysis. We show that interactions can be interpreted as heterogeneity and can be detected without utilizing the traditional uni- or multi-variate approaches for discovery of gene-by-environment interactions. We apply our new method to combine 17 mouse studies containing in aggregate 4,965 distinct animals. We identify 26 significant loci involved in High-density lipoprotein (HDL) cholesterol, many of which are consistent with previous findings. Several of these loci show significant evidence of involvement in gene-by-environment interactions. An additional advantage of our meta-analysis approach is that our combined study has significantly higher power and improved resolution compared to any single study thus explaining the large number of loci discovered in the combined study.

Meta-Analysis Identifies Gene-by-Environment Interactions as Demonstrated in a Study of 4,965 Mice

PubMed Central

Joo, Jong Wha J.; Shih, Diana; Davis, Richard C.; Lusis, Aldons J.; Eskin, Eleazar

2014-01-01

Identifying environmentally-specific genetic effects is a key challenge in understanding the structure of complex traits. Model organisms play a crucial role in the identification of such gene-by-environment interactions, as a result of the unique ability to observe genetically similar individuals across multiple distinct environments. Many model organism studies examine the same traits but under varying environmental conditions. For example, knock-out or diet-controlled studies are often used to examine cholesterol in mice. These studies, when examined in aggregate, provide an opportunity to identify genomic loci exhibiting environmentally-dependent effects. However, the straightforward application of traditional methodologies to aggregate separate studies suffers from several problems. First, environmental conditions are often variable and do not fit the standard univariate model for interactions. Additionally, applying a multivariate model results in increased degrees of freedom and low statistical power. In this paper, we jointly analyze multiple studies with varying environmental conditions using a meta-analytic approach based on a random effects model to identify loci involved in gene-by-environment interactions. Our approach is motivated by the observation that methods for discovering gene-by-environment interactions are closely related to random effects models for meta-analysis. We show that interactions can be interpreted as heterogeneity and can be detected without utilizing the traditional uni- or multi-variate approaches for discovery of gene-by-environment interactions. We apply our new method to combine 17 mouse studies containing in aggregate 4,965 distinct animals. We identify 26 significant loci involved in High-density lipoprotein (HDL) cholesterol, many of which are consistent with previous findings. Several of these loci show significant evidence of involvement in gene-by-environment interactions. An additional advantage of our meta-analysis approach is that our combined study has significantly higher power and improved resolution compared to any single study thus explaining the large number of loci discovered in the combined study. PMID:24415945

GIANT 2.0: genome-scale integrated analysis of gene networks in tissues.

PubMed

Wong, Aaron K; Krishnan, Arjun; Troyanskaya, Olga G

2018-05-25

GIANT2 (Genome-wide Integrated Analysis of gene Networks in Tissues) is an interactive web server that enables biomedical researchers to analyze their proteins and pathways of interest and generate hypotheses in the context of genome-scale functional maps of human tissues. The precise actions of genes are frequently dependent on their tissue context, yet direct assay of tissue-specific protein function and interactions remains infeasible in many normal human tissues and cell-types. With GIANT2, researchers can explore predicted tissue-specific functional roles of genes and reveal changes in those roles across tissues, all through interactive multi-network visualizations and analyses. Additionally, the NetWAS approach available through the server uses tissue-specific/cell-type networks predicted by GIANT2 to re-prioritize statistical associations from GWAS studies and identify disease-associated genes. GIANT2 predicts tissue-specific interactions by integrating diverse functional genomics data from now over 61 400 experiments for 283 diverse tissues and cell-types. GIANT2 does not require any registration or installation and is freely available for use at http://giant-v2.princeton.edu.

You've gotta be lucky: Coverage and the elusive gene-gene interaction.

PubMed

Reimherr, Matthew; Nicolae, Dan L

2011-01-01

Genome-wide association studies (GWAS) have led to a large number of single-SNP association findings, but there has been, so far, no investigation resulting in the discovery of a replicable gene-gene interaction. In this paper, we examine some of the possible explanations for the lack of findings, and argue that coverage of causal variation not only has a large effect on the loss in power, but that the effect is larger than in the single-SNP analyses. We show that the product of linkage disequilibrium measures, r², between causal and tested SNPs offers a good approximation to the loss in efficiency as defined by the ratio of sample sizes that lead to similar power. We also demonstrate that, in addition to the huge search space, the loss in power due to coverage when using commercially available platforms makes the search for gene-gene interactions daunting. © 2010 The Authors Annals of Human Genetics © 2010 Blackwell Publishing Ltd/University College London.

A Comparative Study on Multifactor Dimensionality Reduction Methods for Detecting Gene-Gene Interactions with the Survival Phenotype

PubMed Central

Lee, Seungyeoun; Kim, Yongkang; Kwon, Min-Seok; Park, Taesung

2015-01-01

Genome-wide association studies (GWAS) have extensively analyzed single SNP effects on a wide variety of common and complex diseases and found many genetic variants associated with diseases. However, there is still a large portion of the genetic variants left unexplained. This missing heritability problem might be due to the analytical strategy that limits analyses to only single SNPs. One of possible approaches to the missing heritability problem is to consider identifying multi-SNP effects or gene-gene interactions. The multifactor dimensionality reduction method has been widely used to detect gene-gene interactions based on the constructive induction by classifying high-dimensional genotype combinations into one-dimensional variable with two attributes of high risk and low risk for the case-control study. Many modifications of MDR have been proposed and also extended to the survival phenotype. In this study, we propose several extensions of MDR for the survival phenotype and compare the proposed extensions with earlier MDR through comprehensive simulation studies. PMID:26339630

Multi-omics approach identifies molecular mechanisms of plant-fungus mycorrhizal interaction

DOE PAGES

Larsen, Peter E.; Sreedasyam, Avinash; Trivedi, Geetika; ...

2016-01-19

In mycorrhizal symbiosis, plant roots form close, mutually beneficial interactions with soil fungi. Before this mycorrhizal interaction can be established however, plant roots must be capable of detecting potential beneficial fungal partners and initiating the gene expression patterns necessary to begin symbiosis. To predict a plant root – mycorrhizal fungi sensor systems, we analyzed in vitro experiments of Populus tremuloides (aspen tree) and Laccaria bicolor (mycorrhizal fungi) interaction and leveraged over 200 previously published transcriptomic experimental data sets, 159 experimentally validated plant transcription factor binding motifs, and more than 120-thousand experimentally validated protein-protein interactions to generate models of pre-mycorrhizal sensormore » systems in aspen root. These sensor mechanisms link extracellular signaling molecules with gene regulation through a network comprised of membrane receptors, signal cascade proteins, transcription factors, and transcription factor biding DNA motifs. Modeling predicted four pre-mycorrhizal sensor complexes in aspen that interact with fifteen transcription factors to regulate the expression of 1184 genes in response to extracellular signals synthesized by Laccaria. Predicted extracellular signaling molecules include common signaling molecules such as phenylpropanoids, salicylate, and, jasmonic acid. Lastly, this multi-omic computational modeling approach for predicting the complex sensory networks yielded specific, testable biological hypotheses for mycorrhizal interaction signaling compounds, sensor complexes, and mechanisms of gene regulation.« less

Multi-omics approach identifies molecular mechanisms of plant-fungus mycorrhizal interaction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Larsen, Peter E.; Sreedasyam, Avinash; Trivedi, Geetika

In mycorrhizal symbiosis, plant roots form close, mutually beneficial interactions with soil fungi. Before this mycorrhizal interaction can be established however, plant roots must be capable of detecting potential beneficial fungal partners and initiating the gene expression patterns necessary to begin symbiosis. To predict a plant root – mycorrhizal fungi sensor systems, we analyzed in vitro experiments of Populus tremuloides (aspen tree) and Laccaria bicolor (mycorrhizal fungi) interaction and leveraged over 200 previously published transcriptomic experimental data sets, 159 experimentally validated plant transcription factor binding motifs, and more than 120-thousand experimentally validated protein-protein interactions to generate models of pre-mycorrhizal sensormore » systems in aspen root. These sensor mechanisms link extracellular signaling molecules with gene regulation through a network comprised of membrane receptors, signal cascade proteins, transcription factors, and transcription factor biding DNA motifs. Modeling predicted four pre-mycorrhizal sensor complexes in aspen that interact with fifteen transcription factors to regulate the expression of 1184 genes in response to extracellular signals synthesized by Laccaria. Predicted extracellular signaling molecules include common signaling molecules such as phenylpropanoids, salicylate, and, jasmonic acid. Lastly, this multi-omic computational modeling approach for predicting the complex sensory networks yielded specific, testable biological hypotheses for mycorrhizal interaction signaling compounds, sensor complexes, and mechanisms of gene regulation.« less

[From stone-craved genes to Michelangelo: significance and different aspects of gene-environment interaction].

PubMed

Lazary, Judit

2017-12-01

Although genetic studies have improved a lot in recent years, without clinical relevance sometimes their significance is devalued. Reviewing the major milestones of psychogenomics it can be seen that break-through success is just a question of time. Investigations of direct effect of genetic variants on phenotypes have not yielded positive findings. However, an important step was taken by adapting the gene-environment interaction model. In this model genetic vulnerability stepped into the place of "stone craved" pathology. Further progress happened when studies of environmental factors were combined with genetic function (epigenetics). This model provided the possibility for investigation of therapeutic interventions as environmental factors and it was proven that effective treatments exert a modifying effect on gene expression. Moreover, recent developments focus on therapeutic manipulation of gene function (e.g. chemogenetics). Instead of "stone craved" genes up-to-date dynamically interacting gene function became the basis of psychogenomics in which correction of the expression is a potential therapeutic tool. Keeping in mind these trends and developments, there is no doubt that genetics will be a fundamental part of daily clinical routine in the future.

Differentially-Expressed Pseudogenes in HIV-1 Infection

PubMed Central

Gupta, Aditi; Brown, C. Titus; Zheng, Yong-Hui; Adami, Christoph

2015-01-01

Not all pseudogenes are transcriptionally silent as previously thought. Pseudogene transcripts, although not translated, contribute to the non-coding RNA pool of the cell that regulates the expression of other genes. Pseudogene transcripts can also directly compete with the parent gene transcripts for mRNA stability and other cell factors, modulating their expression levels. Tissue-specific and cancer-specific differential expression of these “functional” pseudogenes has been reported. To ascertain potential pseudogene:gene interactions in HIV-1 infection, we analyzed transcriptomes from infected and uninfected T-cells and found that 21 pseudogenes are differentially expressed in HIV-1 infection. This is interesting because parent genes of one-third of these differentially-expressed pseudogenes are implicated in HIV-1 life cycle, and parent genes of half of these pseudogenes are involved in different viral infections. Our bioinformatics analysis identifies candidate pseudogene:gene interactions that may be of significance in HIV-1 infection. Experimental validation of these interactions would establish that retroviruses exploit this newly-discovered layer of host gene expression regulation for their own benefit. PMID:26426037

Impact of Maspin Polymorphism rs2289520 G/C and Its Interaction with Gene to Gene, Alcohol Consumption Increase Susceptibility to Oral Cancer Occurrence.

PubMed

Yang, Po-Yu; Miao, Nae-Fang; Lin, Chiao-Wen; Chou, Ying-Erh; Yang, Shun-Fa; Huang, Hui-Chuan; Chang, Hsiu-Ju; Tsai, Hsiu-Ting

2016-01-01

The purpose of this study was to identify gene polymorphisms of mammary serine protease inhibitor (Maspin) specific to patients with oral cancer susceptibility and clinicopathological status. Three single-nucleotide polymorphisms (SNPs) of the Maspin gene from 741 patients with oral cancer and 601 non-cancer controls were analyzed by real-time PCR. The participants with G/G homozygotes or with G/C heterozygotes of Maspin rs2289520 polymorphism had a 2.07-fold (p = 0.01) and a 2.01-fold (p = 0.02) risk of developing oral cancer compared to those with C/C homozygotes. Moreover, gene-gene interaction increased the risk of oral cancer susceptibility among subjects expose to oral cancer related risk factors, including areca, alcohol, and tobacco consumption. G allele of Maspin rs2289520 polymorphism may be a factor that increases the susceptibility to oral cancer. The interactions of gene to oral cancer-related environmental risk factors have a synergetic effect that can further enhance oral cancer development.

An in vivo and in silico approach to study cis-antisense: a short cut to higher order response

NASA Astrophysics Data System (ADS)

Courtney, Colleen; Varanasi, Usha; Chatterjee, Anushree

2014-03-01

Antisense interactions are present in all domains of life. Typically sense, antisense RNA pairs originate from overlapping genes with convergent face to face promoters, and are speculated to be involved in gene regulation. Recent studies indicate the role of transcriptional interference (TI) in regulating expression of genes in convergent orientation. Modeling antisense, TI gene regulation mechanisms allows us to understand how organisms control gene expression. We present a modeling and experimental framework to understand convergent transcription that combines the effects of transcriptional interference and cis-antisense regulation. Our model shows that combining transcriptional interference and antisense RNA interaction adds multiple-levels of regulation which affords a highly tunable biological output, ranging from first order response to complex higher-order response. To study this system we created a library of experimental constructs with engineered TI and antisense interaction by using face-to-face inducible promoters separated by carefully tailored overlapping DNA sequences to control expression of a set of fluorescent reporter proteins. Studying this gene expression mechanism allows for an understanding of higher order behavior of gene expression networks.

Gene-Environment Interactions across Development: Exploring DRD2 Genotype and Prenatal Smoking Effects on Self-Regulation

ERIC Educational Resources Information Center

Wiebe, Sandra A.; Espy, Kimberly Andrews; Stopp, Christian; Respass, Jennifer; Stewart, Peter; Jameson, Travis R.; Gilbert, David G.; Huggenvik, Jodi I.

2009-01-01

Genetic factors dynamically interact with both pre- and postnatal environmental influences to shape development. Considerable attention has been devoted to gene-environment interactions (G x E) on important outcomes (A. Caspi & T. E. Moffitt, 2006). It is also important to consider the possibility that these G x E effects may vary across…

R Script Approach to Infer Toxoplasma Infection Mechanisms From Microarrays and Domain-Domain Protein Interactions

PubMed Central

Arenas, Ailan F; Salcedo, Gladys E; Gomez-Marin, Jorge E

2017-01-01

Pathogen-host protein-protein interaction systems examine the interactions between the protein repertoires of 2 distinct organisms. Some of these pathogen proteins interact with the host protein system and may manipulate it for their own advantages. In this work, we designed an R script by concatenating 2 functions called rowDM and rowCVmed to infer pathogen-host interaction using previously reported microarray data, including host gene enrichment analysis and the crossing of interspecific domain-domain interactions. We applied this script to the Toxoplasma-host system to describe pathogen survival mechanisms from human, mouse, and Toxoplasma Gene Expression Omnibus series. Our outcomes exhibited similar results with previously reported microarray analyses, but we found other important proteins that could contribute to toxoplasma pathogenesis. We observed that Toxoplasma ROP38 is the most differentially expressed protein among toxoplasma strains. Enrichment analysis and KEGG mapping indicated that the human retinal genes most affected by Toxoplasma infections are those related to antiapoptotic mechanisms. We suggest that proteins PIK3R1, PRKCA, PRKCG, PRKCB, HRAS, and c-JUN could be the possible substrates for differentially expressed Toxoplasma kinase ROP38. Likewise, we propose that Toxoplasma causes overexpression of apoptotic suppression human genes. PMID:29317802

Redox control of protein-DNA interactions: from molecular mechanisms to significance in signal transduction, gene expression, and DNA replication.

PubMed

Shlomai, Joseph

2010-11-01

Protein-DNA interactions play a key role in the regulation of major cellular metabolic pathways, including gene expression, genome replication, and genomic stability. They are mediated through the interactions of regulatory proteins with their specific DNA-binding sites at promoters, enhancers, and replication origins in the genome. Redox signaling regulates these protein-DNA interactions using reactive oxygen species and reactive nitrogen species that interact with cysteine residues at target proteins and their regulators. This review describes the redox-mediated regulation of several master regulators of gene expression that control the induction and suppression of hundreds of genes in the genome, regulating multiple metabolic pathways, which are involved in cell growth, development, differentiation, and survival, as well as in the function of the immune system and cellular response to intracellular and extracellular stimuli. It also discusses the role of redox signaling in protein-DNA interactions that regulate DNA replication. Specificity of redox regulation is discussed, as well as the mechanisms providing several levels of redox-mediated regulation, from direct control of DNA-binding domains through the indirect control, mediated by release of negative regulators, regulation of redox-sensitive protein kinases, intracellular trafficking, and chromatin remodeling.

Gene-gene interactions of CYP2A6 and MAOA polymorphisms on smoking behavior in Chinese male population.

PubMed

Tang, Xun; Guo, Song; Sun, Hongqiang; Song, Xuemei; Jiang, Zuonin; Sheng, Lixiang; Zhou, Dongfeng; Hu, Yonghua; Chen, Dafang

2009-05-01

Nicotine is the major psychoactive ingredient in tobacco, and is responsible for dependence through the nicotine-stimulated reward pathway mediated by the central dopaminergic system. Consequently, genetic polymorphisms in both nicotine metabolism and dopamine catabolism genes may influence smoking behavior, and interact with each other resulting in risk modulation. In this study, we investigated the association and multilocus gene-gene interactions of cytochrome P450 2A6 (CYP2A6), dopamine beta-hydroxylase (DBH), catechol O-methyl transferase (COMT), and monoamine oxidase A (MAOA) polymorphisms with smoking behavior in a community-based Chinese male population. The polymorphisms were genotyped in 203 current smokers, 66 former smokers, and 102 never smokers. Multivariate logistic regression models and the multifactor dimensionality reduction method were used to analyze the association and multilocus gene-gene interactions. Statistically significant trends were shown for increased risk of smoking initiation in participants with CYP2A6*1B/CYP2A6*1B genotypes compared with those with CYP2A6*1A/CYP2A6*1A genotypes [odds ratio (OR)=3.5, 95% confidence interval (CI)= 1.5-8.1], and participants with CYP2A6*1/CYP2A6*1 genotypes were at higher risk of smoking initiation (OR=2.4, 95% CI=1.2-4.5) and smoking persistence (OR=4.0, 95% CI=1.5-10.3) than those who have CYP2A6*4C genotypes. Moreover, the best model involved a gene-gene interaction between MAOA and CYP2A6 was characterized by the multifactor dimensionality reduction method (64.11% accuracy, P<0.001), and indicated that carriers of the combined 1460 T/O genotype for MAOA EcoRV and CYP2A6*1/CYP2A6*1 genotypes were at higher risk of smoking (OR=15.4, 95% CI=4.5-52.5). These findings suggested a substantial influence of CYP2A6 polymorphism as well as the interaction with MAOA resulting in risk modulation on smoking behavior in Chinese male population.

The FOXO1 Gene-Obesity Interaction Increases the Risk of Type 2 Diabetes Mellitus in a Chinese Han Population.

PubMed

Gong, Lilin; Li, Rong; Ren, Wei; Wang, Zengchan; Wang, Zhihong; Yang, Maosheng; Zhang, Suhua

2017-02-01

Here, we aimed to study the effect of the forkhead box O1-insulin receptor substrate 2 (FOXO1-IRS2) gene interaction and the FOXO1 and IRS2 genes-environment interaction for the risk of type 2 diabetes mellitus (T2DM) in a Chinese Han population. We genotyped 7 polymorphism sites of FOXO1 gene and IRS2 gene in 780 unrelated Chinese Han people (474 cases of T2DM, 306 cases of healthy control). The risk of T2DM in individuals with AA genotype for rs7986407 and CC genotype for rs4581585 in FOXO1 gene was 2.092 and 2.57 times higher than that with GG genotype (odds ratio [OR] = 2.092; 95% confidence interval [CI] = 1.178-3.731; P = 0.011) and TT genotype (OR = 2.571; 95% CI = 1.404-4.695; P = 0.002), respectively. The risk of T2DM in individuals with GG genotype for Gly1057Asp in IRS2 gene was 1.42 times higher than that with AA genotype (OR = 1.422; 95% CI = 1.037-1.949; P = 0.029). The other 4 single nucleotide polymorphisms (SNPs) had no significant association with T2DM (P > 0.05). Multifactor dimensionality reduction (MDR) analysis showed that the interaction between SNPs rs7986407 and rs4325426 in FOXO1 gene and waist was the best model confirmed by interaction analysis, closely associating with T2DM. There was an increased risk for T2DM in the case of non-obesity with genotype combined AA/CC, AA/AC or AG/AA for rs7986407 and rs4325426, and obesity with genotype AA for rs7986407 or AA for rs4325426 (OR = 3.976; 95% CI = 1.156-13.675; P value from sign test [P(sign)] = 0.025; P value from permutation test [P(perm)] = 0.000-0.001). Together, this study indicates an association of FOXO1 and IRS2 gene polymorphisms with T2DM in Chinese Han population, supporting FOXO1-obesity interaction as a key factor for the risk of T2DM.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhu, Yitan; Xu, Yanxun; Helseth, Donald L.

Background: Genetic interactions play a critical role in cancer development. Existing knowledge about cancer genetic interactions is incomplete, especially lacking evidences derived from large-scale cancer genomics data. The Cancer Genome Atlas (TCGA) produces multimodal measurements across genomics and features of thousands of tumors, which provide an unprecedented opportunity to investigate the interplays of genes in cancer. Methods: We introduce Zodiac, a computational tool and resource to integrate existing knowledge about cancer genetic interactions with new information contained in TCGA data. It is an evolution of existing knowledge by treating it as a prior graph, integrating it with a likelihood modelmore » derived by Bayesian graphical model based on TCGA data, and producing a posterior graph as updated and data-enhanced knowledge. In short, Zodiac realizes “Prior interaction map + TCGA data → Posterior interaction map.” Results: Zodiac provides molecular interactions for about 200 million pairs of genes. All the results are generated from a big-data analysis and organized into a comprehensive database allowing customized search. In addition, Zodiac provides data processing and analysis tools that allow users to customize the prior networks and update the genetic pathways of their interest. Zodiac is publicly available at www.compgenome.org/ZODIAC. Conclusions: Zodiac recapitulates and extends existing knowledge of molecular interactions in cancer. It can be used to explore novel gene-gene interactions, transcriptional regulation, and other types of molecular interplays in cancer.« less

Dose-sensitivity, conserved non-coding sequences, and duplicate gene retention through multiple tetraploidies in the grasses.

PubMed

Schnable, James C; Pedersen, Brent S; Subramaniam, Sabarinath; Freeling, Michael

2011-01-01

Whole genome duplications, or tetraploidies, are an important source of increased gene content. Following whole genome duplication, duplicate copies of many genes are lost from the genome. This loss of genes is biased both in the classes of genes deleted and the subgenome from which they are lost. Many or all classes are genes preferentially retained as duplicate copies are engaged in dose sensitive protein-protein interactions, such that deletion of any one duplicate upsets the status quo of subunit concentrations, and presumably lowers fitness as a result. Transcription factors are also preferentially retained following every whole genome duplications studied. This has been explained as a consequence of protein-protein interactions, just as for other highly retained classes of genes. We show that the quantity of conserved noncoding sequences (CNSs) associated with genes predicts the likelihood of their retention as duplicate pairs following whole genome duplication. As many CNSs likely represent binding sites for transcriptional regulators, we propose that the likelihood of gene retention following tetraploidy may also be influenced by dose-sensitive protein-DNA interactions between the regulatory regions of CNS-rich genes - nicknamed bigfoot genes - and the proteins that bind to them. Using grass genomes, we show that differential loss of CNSs from one member of a pair following the pre-grass tetraploidy reduces its chance of retention in the subsequent maize lineage tetraploidy.

Dose–Sensitivity, Conserved Non-Coding Sequences, and Duplicate Gene Retention Through Multiple Tetraploidies in the Grasses

PubMed Central

Schnable, James C.; Pedersen, Brent S.; Subramaniam, Sabarinath; Freeling, Michael

2011-01-01

Whole genome duplications, or tetraploidies, are an important source of increased gene content. Following whole genome duplication, duplicate copies of many genes are lost from the genome. This loss of genes is biased both in the classes of genes deleted and the subgenome from which they are lost. Many or all classes are genes preferentially retained as duplicate copies are engaged in dose sensitive protein–protein interactions, such that deletion of any one duplicate upsets the status quo of subunit concentrations, and presumably lowers fitness as a result. Transcription factors are also preferentially retained following every whole genome duplications studied. This has been explained as a consequence of protein–protein interactions, just as for other highly retained classes of genes. We show that the quantity of conserved noncoding sequences (CNSs) associated with genes predicts the likelihood of their retention as duplicate pairs following whole genome duplication. As many CNSs likely represent binding sites for transcriptional regulators, we propose that the likelihood of gene retention following tetraploidy may also be influenced by dose–sensitive protein–DNA interactions between the regulatory regions of CNS-rich genes – nicknamed bigfoot genes – and the proteins that bind to them. Using grass genomes, we show that differential loss of CNSs from one member of a pair following the pre-grass tetraploidy reduces its chance of retention in the subsequent maize lineage tetraploidy. PMID:22645525

Semantics based approach for analyzing disease-target associations.

PubMed

Kaalia, Rama; Ghosh, Indira

2016-08-01

A complex disease is caused by heterogeneous biological interactions between genes and their products along with the influence of environmental factors. There have been many attempts for understanding the cause of these diseases using experimental, statistical and computational methods. In the present work the objective is to address the challenge of representation and integration of information from heterogeneous biomedical aspects of a complex disease using semantics based approach. Semantic web technology is used to design Disease Association Ontology (DAO-db) for representation and integration of disease associated information with diabetes as the case study. The functional associations of disease genes are integrated using RDF graphs of DAO-db. Three semantic web based scoring algorithms (PageRank, HITS (Hyperlink Induced Topic Search) and HITS with semantic weights) are used to score the gene nodes on the basis of their functional interactions in the graph. Disease Association Ontology for Diabetes (DAO-db) provides a standard ontology-driven platform for describing genes, proteins, pathways involved in diabetes and for integrating functional associations from various interaction levels (gene-disease, gene-pathway, gene-function, gene-cellular component and protein-protein interactions). An automatic instance loader module is also developed in present work that helps in adding instances to DAO-db on a large scale. Our ontology provides a framework for querying and analyzing the disease associated information in the form of RDF graphs. The above developed methodology is used to predict novel potential targets involved in diabetes disease from the long list of loose (statistically associated) gene-disease associations. Copyright © 2016 Elsevier Inc. All rights reserved.

The Temporal Dynamics of Differential Gene Expression in Aspergillus fumigatus Interacting with Human Immature Dendritic Cells In Vitro

PubMed Central

Morton, Charles O.; Varga, John J.; Hornbach, Anke; Mezger, Markus; Sennefelder, Helga; Kneitz, Susanne; Kurzai, Oliver; Krappmann, Sven; Einsele, Hermann; Nierman, William C.; Rogers, Thomas R.; Loeffler, Juergen

2011-01-01

Dendritic cells (DC) are the most important antigen presenting cells and play a pivotal role in host immunity to infectious agents by acting as a bridge between the innate and adaptive immune systems. Monocyte-derived immature DCs (iDC) were infected with viable resting conidia of Aspergillus fumigatus (Af293) for 12 hours at an MOI of 5; cells were sampled every three hours. RNA was extracted from both organisms at each time point and hybridised to microarrays. iDC cell death increased at 6 h in the presence of A. fumigatus which coincided with fungal germ tube emergence; >80% of conidia were associated with iDC. Over the time course A. fumigatus differentially regulated 210 genes, FunCat analysis indicated significant up-regulation of genes involved in fermentation, drug transport, pathogenesis and response to oxidative stress. Genes related to cytotoxicity were differentially regulated but the gliotoxin biosynthesis genes were down regulated over the time course, while Aspf1 was up-regulated at 9 h and 12 h. There was an up-regulation of genes in the subtelomeric regions of the genome as the interaction progressed. The genes up-regulated by iDC in the presence of A. fumigatus indicated that they were producing a pro-inflammatory response which was consistent with previous transcriptome studies of iDC interacting with A. fumigatus germ tubes. This study shows that A. fumigatus adapts to phagocytosis by iDCs by utilising genes that allow it to survive the interaction rather than just up-regulation of specific virulence genes. PMID:21264256

GENETIC INTERACTIONS BETWEEN ADRB2 AND PTGER4 ON RESPONSES TO SALMETEROL OR MONTELUKAST IN JAPANESE PATIENTS WITH MILD PERSISTENT ASTHMA.

PubMed

Yamada, Hideyasu; Masuko, Hironori; Inui, Toshihide; Kanazawa, Jun; Yatagai, Yohei; Sakamoto, Tohru; Iijima, Hiroaki; Konno, Satoshi; Shimizu, Kaoruko; Makita, Hironi; Nishimura, Masaharu; Kokubu, Fumio; Saito, Takefumi; Endo, Takeo; Ninomiya, Hiroki; Kaneko, Norihiro; Hizawa, Nobuyuki

2016-01-01

Long-acting β 2 -agonists (LABA) and leukotriene receptor antagonists (LTRA) are two principal agents that can be added to inhaled corticosteroids (ICS) for patients with asthma that is not adequately controlled by ICS alone. In our previous study, the Gly16Arg genotype of the β 2 -adrenergic receptor (ADRB2) gene did not influence the differential bronchodilator effect of salmeterol versus montelukast as an add-on therapy to ICS within 16 weeks of follow-up (the J-Blossom study). We examined if genes encoding CYSLTR1, CYSLTR2, PTGER2 or PTGER4 could explain differential responses to salmeterol versus montelukast using the participants of the J-Blossom study. This study included 76 patients with mild-to-moderate asthma. The difference in peak expiratory flow (PEF) (ΔPEF, l/min) after 16 weeks of treatment with salmeterol (ΔPEFsal) versus montelukast (ΔPEFmon) was associated with the genotypes at each of 4 genes. In addition, multivariate analyses were used to identify a gene-gene interaction between ADRB2 gene and each of these 4 genes. Although none of 4 genes were associated with ΔPEFsal-ΔPEFmon in the univariate analyses, multivariate analysis showed that PTGER4 gene, interacting with ADRB2 Gly16Arg, was associated with ΔPEFsal-ΔPEFmon (p=0.0032). Our findings suggested that the interactions between two genetic loci at ADRB2 and PTGER4 is important in determining the differential response to salmeterol versus montelukast in patients with chronic adult asthma.

Genome-wide gene by lead exposure interaction analysis identifies UNC5D as a candidate gene for neurodevelopment.

PubMed

Wang, Zhaoxi; Claus Henn, Birgit; Wang, Chaolong; Wei, Yongyue; Su, Li; Sun, Ryan; Chen, Han; Wagner, Peter J; Lu, Quan; Lin, Xihong; Wright, Robert; Bellinger, David; Kile, Molly; Mazumdar, Maitreyi; Tellez-Rojo, Martha Maria; Schnaas, Lourdes; Christiani, David C

2017-07-28

Neurodevelopment is a complex process involving both genetic and environmental factors. Prenatal exposure to lead (Pb) has been associated with lower performance on neurodevelopmental tests. Adverse neurodevelopmental outcomes are more frequent and/or more severe when toxic exposures interact with genetic susceptibility. To explore possible loci associated with increased susceptibility to prenatal Pb exposure, we performed a genome-wide gene-environment interaction study (GWIS) in young children from Mexico (n = 390) and Bangladesh (n = 497). Prenatal Pb exposure was estimated by cord blood Pb concentration. Neurodevelopment was assessed using the Bayley Scales of Infant Development. We identified a locus on chromosome 8, containing UNC5D, and demonstrated evidence of its genome-wide significance with mental composite scores (rs9642758, p meta  = 4.35 × 10 -6 ). Within this locus, the joint effects of two independent single nucleotide polymorphisms (SNPs, rs9642758 and rs10503970) had a p-value of 4.38 × 10 -9 for mental composite scores. Correlating GWIS results with in vitro transcriptomic profiles identified one common gene, SLC1A5, which is involved in synaptic function, neuronal development, and excitotoxicity. Further analysis revealed interconnected interactions that formed a large network of 52 genes enriched with oxidative stress genes and neurodevelopmental genes. Our findings suggest that certain genetic polymorphisms within/near genes relevant to neurodevelopment might modify the toxic effects of Pb exposure via oxidative stress.

dictyExpress: a Dictyostelium discoideum gene expression database with an explorative data analysis web-based interface.

PubMed

Rot, Gregor; Parikh, Anup; Curk, Tomaz; Kuspa, Adam; Shaulsky, Gad; Zupan, Blaz

2009-08-25

Bioinformatics often leverages on recent advancements in computer science to support biologists in their scientific discovery process. Such efforts include the development of easy-to-use web interfaces to biomedical databases. Recent advancements in interactive web technologies require us to rethink the standard submit-and-wait paradigm, and craft bioinformatics web applications that share analytical and interactive power with their desktop relatives, while retaining simplicity and availability. We have developed dictyExpress, a web application that features a graphical, highly interactive explorative interface to our database that consists of more than 1000 Dictyostelium discoideum gene expression experiments. In dictyExpress, the user can select experiments and genes, perform gene clustering, view gene expression profiles across time, view gene co-expression networks, perform analyses of Gene Ontology term enrichment, and simultaneously display expression profiles for a selected gene in various experiments. Most importantly, these tasks are achieved through web applications whose components are seamlessly interlinked and immediately respond to events triggered by the user, thus providing a powerful explorative data analysis environment. dictyExpress is a precursor for a new generation of web-based bioinformatics applications with simple but powerful interactive interfaces that resemble that of the modern desktop. While dictyExpress serves mainly the Dictyostelium research community, it is relatively easy to adapt it to other datasets. We propose that the design ideas behind dictyExpress will influence the development of similar applications for other model organisms.

Somatic polyploidy is associated with the upregulation of c-MYC interacting genes and EMT-like signature

PubMed Central

Vazquez-Martin, Alejandro; Anatskaya, Olga V.; Giuliani, Alessandro; Erenpreisa, Jekaterina; Huang, Sui; Salmina, Kristine; Inashkina, Inna; Huna, Anda; Nikolsky, Nikolai N.; Vinogradov, Alexander E.

2016-01-01

The dependence of cancer on overexpressed c-MYC and its predisposition for polyploidy represents a double puzzle. We address this conundrum by cross-species transcription analysis of c-MYC interacting genes in polyploid vs. diploid tissues and cells, including human vs. mouse heart, mouse vs. human liver and purified 4n vs. 2n mouse decidua cells. Gene-by-gene transcriptome comparison and principal component analysis indicated that c-MYC interactants are significantly overrepresented among ploidy-associated genes. Protein interaction networks and gene module analysis revealed that the most upregulated genes relate to growth, stress response, proliferation, stemness and unicellularity, as well as to the pathways of cancer supported by MAPK and RAS coordinated pathways. A surprising feature was the up-regulation of epithelial-mesenchymal transition (EMT) modules embodied by the N-cadherin pathway and EMT regulators from SNAIL and TWIST families. Metabolic pathway analysis also revealed the EMT-linked features, such as global proteome remodeling, oxidative stress, DNA repair and Warburg-like energy metabolism. Genes associated with apoptosis, immunity, energy demand and tumour suppression were mostly down-regulated. Noteworthy, despite the association between polyploidy and ample features of cancer, polyploidy does not trigger it. Possibly it occurs because normal polyploidy does not go that far in embryonalisation and linked genome destabilisation. In general, the analysis of polyploid transcriptome explained the evolutionary relation of c-MYC and polyploidy to cancer. PMID:27655693

dictyExpress: a Dictyostelium discoideum gene expression database with an explorative data analysis web-based interface

PubMed Central

Rot, Gregor; Parikh, Anup; Curk, Tomaz; Kuspa, Adam; Shaulsky, Gad; Zupan, Blaz

2009-01-01

Background Bioinformatics often leverages on recent advancements in computer science to support biologists in their scientific discovery process. Such efforts include the development of easy-to-use web interfaces to biomedical databases. Recent advancements in interactive web technologies require us to rethink the standard submit-and-wait paradigm, and craft bioinformatics web applications that share analytical and interactive power with their desktop relatives, while retaining simplicity and availability. Results We have developed dictyExpress, a web application that features a graphical, highly interactive explorative interface to our database that consists of more than 1000 Dictyostelium discoideum gene expression experiments. In dictyExpress, the user can select experiments and genes, perform gene clustering, view gene expression profiles across time, view gene co-expression networks, perform analyses of Gene Ontology term enrichment, and simultaneously display expression profiles for a selected gene in various experiments. Most importantly, these tasks are achieved through web applications whose components are seamlessly interlinked and immediately respond to events triggered by the user, thus providing a powerful explorative data analysis environment. Conclusion dictyExpress is a precursor for a new generation of web-based bioinformatics applications with simple but powerful interactive interfaces that resemble that of the modern desktop. While dictyExpress serves mainly the Dictyostelium research community, it is relatively easy to adapt it to other datasets. We propose that the design ideas behind dictyExpress will influence the development of similar applications for other model organisms. PMID:19706156

Discovering disease-associated genes in weighted protein-protein interaction networks

NASA Astrophysics Data System (ADS)

Cui, Ying; Cai, Meng; Stanley, H. Eugene

2018-04-01

Although there have been many network-based attempts to discover disease-associated genes, most of them have not taken edge weight - which quantifies their relative strength - into consideration. We use connection weights in a protein-protein interaction (PPI) network to locate disease-related genes. We analyze the topological properties of both weighted and unweighted PPI networks and design an improved random forest classifier to distinguish disease genes from non-disease genes. We use a cross-validation test to confirm that weighted networks are better able to discover disease-associated genes than unweighted networks, which indicates that including link weight in the analysis of network properties provides a better model of complex genotype-phenotype associations.

Drug-conjugated polymers as gene carriers for synergistic therapeutic effect.

PubMed

Pofali, P A; Singh, B; Dandekar, P; Jain, R D; Maharjan, S; Choi, Y J; Arote, R B; Cho, C S

2016-05-01

The ability to safely and effectively transfer gene into cells is the fundamental goal of gene delivery. In spite of the best efforts of researchers around the world, gene therapy has limited success. This may be because of several limitations of delivering gene which is one of the greatest technical challenges in the modern medicine. To address these issues, many efforts have been made to bind drugs and genes together by polymers for co-delivery to achieve synergistic effect. Usually, binding interaction of drugs with polymers is either physical or chemical. In case of drug-polymer physical interaction, the efficiency of drugs generally decreases because of separation of drugs from polymers in vivo whenever it comes in contact with charged biofluid/s or cells. While chemical interaction of drug-polymer overcomes the aforementioned obstacle, several problems such as steric hindrance, solubility, and biodegradability hinder it to develop as gene carrier. Considering these benefits and pitfalls, the objective of this review is to discuss the possible extent of drug-conjugated polymers as safe and efficient gene delivery carriers for achieving synergistic effect to combat various genetic disorders. © 2015 Wiley Periodicals, Inc.

C-State: an interactive web app for simultaneous multi-gene visualization and comparative epigenetic pattern search.

PubMed

Sowpati, Divya Tej; Srivastava, Surabhi; Dhawan, Jyotsna; Mishra, Rakesh K

2017-09-13

Comparative epigenomic analysis across multiple genes presents a bottleneck for bench biologists working with NGS data. Despite the development of standardized peak analysis algorithms, the identification of novel epigenetic patterns and their visualization across gene subsets remains a challenge. We developed a fast and interactive web app, C-State (Chromatin-State), to query and plot chromatin landscapes across multiple loci and cell types. C-State has an interactive, JavaScript-based graphical user interface and runs locally in modern web browsers that are pre-installed on all computers, thus eliminating the need for cumbersome data transfer, pre-processing and prior programming knowledge. C-State is unique in its ability to extract and analyze multi-gene epigenetic information. It allows for powerful GUI-based pattern searching and visualization. We include a case study to demonstrate its potential for identifying user-defined epigenetic trends in context of gene expression profiles.

The influence of nutrigenetics on the lipid profile: interaction between genes and dietary habits.

PubMed

de Andrade, Fabiana M; Bulhões, Andréa C; Maluf, Sharbel W; Schuch, Jaqueline B; Voigt, Francine; Lucatelli, Juliana F; Barros, Alessandra C; Hutz, Mara H

2010-04-01

Nutrigenetics is a new field with few studies in Latin America. Our aim is to investigate the way in which different genes related to the lipid profile influence the response to specific dietary habits. Eight polymorphisms on seven genes were investigated in a sample (n = 567) from Porto Alegre, RS, Brazil. All the volunteers completed a food diary that was then assessed and classified into nine food groups. A number of nutrigenetic interactions were detected primarily related to the apolipoprotein E (apoE) gene. For example, frequent consumption of foods rich in polyunsaturated fat resulted in the beneficial effect of increasing HDL-C only in individuals who were not carriers of the E*4 allele of the APOE gene, whereas variations in eating habits of E*4 carriers did not affect their HDL-C (P = 0.018). Our data demonstrate for the first time nutrigenetic interactions in a Brazilian population.

Identifying New Candidate Genes and Chemicals Related to Prostate Cancer Using a Hybrid Network and Shortest Path Approach

PubMed Central

Wang, Meng; Wu, Kai; Lu, Changhong; Kong, Xiangyin

2015-01-01

Prostate cancer is a type of cancer that occurs in the male prostate, a gland in the male reproductive system. Because prostate cancer cells may spread to other parts of the body and can influence human reproduction, understanding the mechanisms underlying this disease is critical for designing effective treatments. The identification of as many genes and chemicals related to prostate cancer as possible will enhance our understanding of this disease. In this study, we proposed a computational method to identify new candidate genes and chemicals based on currently known genes and chemicals related to prostate cancer by applying a shortest path approach in a hybrid network. The hybrid network was constructed according to information concerning chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions. Many of the obtained genes and chemicals are associated with prostate cancer. PMID:26504486

Intrinsic limits to gene regulation by global crosstalk

PubMed Central

Friedlander, Tamar; Prizak, Roshan; Guet, Călin C.; Barton, Nicholas H.; Tkačik, Gašper

2016-01-01

Gene regulation relies on the specificity of transcription factor (TF)–DNA interactions. Limited specificity may lead to crosstalk: a regulatory state in which a gene is either incorrectly activated due to noncognate TF–DNA interactions or remains erroneously inactive. As each TF can have numerous interactions with noncognate cis-regulatory elements, crosstalk is inherently a global problem, yet has previously not been studied as such. We construct a theoretical framework to analyse the effects of global crosstalk on gene regulation. We find that crosstalk presents a significant challenge for organisms with low-specificity TFs, such as metazoans. Crosstalk is not easily mitigated by known regulatory schemes acting at equilibrium, including variants of cooperativity and combinatorial regulation. Our results suggest that crosstalk imposes a previously unexplored global constraint on the functioning and evolution of regulatory networks, which is qualitatively distinct from the known constraints that act at the level of individual gene regulatory elements. PMID:27489144

Genome organization and long-range regulation of gene expression by enhancers

PubMed Central

Smallwood, Andrea; Ren, Bing

2014-01-01

It is now well accepted that cell-type specific gene regulation is under the purview of enhancers. Great strides have been made recently to characterize and identify enhancers both genetically and epigenetically for multiple cell types and species, but efforts have just begun to link enhancers to their target promoters. Mapping these interactions and understanding how the 3D landscape of the genome constrains such interactions is fundamental to our understanding of mammalian gene regulation. Here, we review recent progress in mapping long-range regulatory interactions in mammalian genomes, focusing on transcriptional enhancers and chromatin organization principles. PMID:23465541

Social defeat interacts with Disc1 mutations in the mouse to affect behavior.

PubMed

Haque, F Nipa; Lipina, Tatiana V; Roder, John C; Wong, Albert H C

2012-08-01

DISC1 (Disrupted-in-schizophrenia 1) is a strong candidate susceptibility gene for psychiatric disease that was originally discovered in a family with a chromosomal translocation severing this gene. Although the family members with the translocation had an identical genetic mutation, their clinical diagnosis and presentation varied significantly. Gene-environment interactions have been proposed as a mechanism underlying the complex heritability and variable phenotype of psychiatric disorders such as major depressive disorder and schizophrenia. We hypothesized that gene-environment interactions would affect behavior in a mutant Disc1 mouse model. We examined the effect of chronic social defeat (CSD) as an environmental stressor in two lines of mice carrying different Disc1 point mutations, on behaviors relevant to psychiatric illness: locomotion in a novel open field (OF), pre-pulse inhibition (PPI) of the acoustic startle response, latent inhibition (LI), elevated plus maze (EPM), forced swim test (FST), sucrose consumption (SC), and the social interaction task for sociability and social novelty (SSN). We found that Disc1-L100P +/- and wild-type mice have similar anxiety responses to CSD, while Q31L +/- mice had a very different response. We also found evidence of significant gene-environment interactions in the OF, EPM and SSN. Copyright © 2012 Elsevier B.V. All rights reserved.

Multifactor-Dimensionality Reduction Reveals High-Order Interactions among Estrogen-Metabolism Genes in Sporadic Breast Cancer

PubMed Central

Ritchie, Marylyn D.; Hahn, Lance W.; Roodi, Nady; Bailey, L. Renee; Dupont, William D.; Parl, Fritz F.; Moore, Jason H.

2001-01-01

One of the greatest challenges facing human geneticists is the identification and characterization of susceptibility genes for common complex multifactorial human diseases. This challenge is partly due to the limitations of parametric-statistical methods for detection of gene effects that are dependent solely or partially on interactions with other genes and with environmental exposures. We introduce multifactor-dimensionality reduction (MDR) as a method for reducing the dimensionality of multilocus information, to improve the identification of polymorphism combinations associated with disease risk. The MDR method is nonparametric (i.e., no hypothesis about the value of a statistical parameter is made), is model-free (i.e., it assumes no particular inheritance model), and is directly applicable to case-control and discordant-sib-pair studies. Using simulated case-control data, we demonstrate that MDR has reasonable power to identify interactions among two or more loci in relatively small samples. When it was applied to a sporadic breast cancer case-control data set, in the absence of any statistically significant independent main effects, MDR identified a statistically significant high-order interaction among four polymorphisms from three different estrogen-metabolism genes. To our knowledge, this is the first report of a four-locus interaction associated with a common complex multifactorial disease. PMID:11404819

Exploring Plant Co-Expression and Gene-Gene Interactions with CORNET 3.0.

PubMed

Van Bel, Michiel; Coppens, Frederik

2017-01-01

Selecting and filtering a reference expression and interaction dataset when studying specific pathways and regulatory interactions can be a very time-consuming and error-prone task. In order to reduce the duplicated efforts required to amass such datasets, we have created the CORNET (CORrelation NETworks) platform which allows for easy access to a wide variety of data types: coexpression data, protein-protein interactions, regulatory interactions, and functional annotations. The CORNET platform outputs its results in either text format or through the Cytoscape framework, which is automatically launched by the CORNET website.CORNET 3.0 is the third iteration of the web platform designed for the user exploration of the coexpression space of plant genomes, with a focus on the model species Arabidopsis thaliana. Here we describe the platform: the tools, data, and best practices when using the platform. We indicate how the platform can be used to infer networks from a set of input genes, such as upregulated genes from an expression experiment. By exploring the network, new target and regulator genes can be discovered, allowing for follow-up experiments and more in-depth study. We also indicate how to avoid common pitfalls when evaluating the networks and how to avoid over interpretation of the results.All CORNET versions are available at http://bioinformatics.psb.ugent.be/cornet/ .

A robust multifactor dimensionality reduction method for detecting gene-gene interactions with application to the genetic analysis of bladder cancer susceptibility

PubMed Central

Gui, Jiang; Andrew, Angeline S.; Andrews, Peter; Nelson, Heather M.; Kelsey, Karl T.; Karagas, Margaret R.; Moore, Jason H.

2010-01-01

A central goal of human genetics is to identify and characterize susceptibility genes for common complex human diseases. An important challenge in this endeavor is the modeling of gene-gene interaction or epistasis that can result in non-additivity of genetic effects. The multifactor dimensionality reduction (MDR) method was developed as machine learning alternative to parametric logistic regression for detecting interactions in absence of significant marginal effects. The goal of MDR is to reduce the dimensionality inherent in modeling combinations of polymorphisms using a computational approach called constructive induction. Here, we propose a Robust Multifactor Dimensionality Reduction (RMDR) method that performs constructive induction using a Fisher’s Exact Test rather than a predetermined threshold. The advantage of this approach is that only those genotype combinations that are determined to be statistically significant are considered in the MDR analysis. We use two simulation studies to demonstrate that this approach will increase the success rate of MDR when there are only a few genotype combinations that are significantly associated with case-control status. We show that there is no loss of success rate when this is not the case. We then apply the RMDR method to the detection of gene-gene interactions in genotype data from a population-based study of bladder cancer in New Hampshire. PMID:21091664

Survival dimensionality reduction (SDR): development and clinical application of an innovative approach to detect epistasis in presence of right-censored data.

PubMed

Beretta, Lorenzo; Santaniello, Alessandro; van Riel, Piet L C M; Coenen, Marieke J H; Scorza, Raffaella

2010-08-06

Epistasis is recognized as a fundamental part of the genetic architecture of individuals. Several computational approaches have been developed to model gene-gene interactions in case-control studies, however, none of them is suitable for time-dependent analysis. Herein we introduce the Survival Dimensionality Reduction (SDR) algorithm, a non-parametric method specifically designed to detect epistasis in lifetime datasets. The algorithm requires neither specification about the underlying survival distribution nor about the underlying interaction model and proved satisfactorily powerful to detect a set of causative genes in synthetic epistatic lifetime datasets with a limited number of samples and high degree of right-censorship (up to 70%). The SDR method was then applied to a series of 386 Dutch patients with active rheumatoid arthritis that were treated with anti-TNF biological agents. Among a set of 39 candidate genes, none of which showed a detectable marginal effect on anti-TNF responses, the SDR algorithm did find that the rs1801274 SNP in the Fc gamma RIIa gene and the rs10954213 SNP in the IRF5 gene non-linearly interact to predict clinical remission after anti-TNF biologicals. Simulation studies and application in a real-world setting support the capability of the SDR algorithm to model epistatic interactions in candidate-genes studies in presence of right-censored data. http://sourceforge.net/projects/sdrproject/.

LHX3 interacts with inhibitor of histone acetyltransferase complex subunits LANP and TAF-1β to modulate pituitary gene regulation.

PubMed

Hunter, Chad S; Malik, Raleigh E; Witzmann, Frank A; Rhodes, Simon J

2013-01-01

LIM-homeodomain 3 (LHX3) is a transcription factor required for mammalian pituitary gland and nervous system development. Human patients and animal models with LHX3 gene mutations present with severe pediatric syndromes that feature hormone deficiencies and symptoms associated with nervous system dysfunction. The carboxyl terminus of the LHX3 protein is required for pituitary gene regulation, but the mechanism by which this domain operates is unknown. In order to better understand LHX3-dependent pituitary hormone gene transcription, we used biochemical and mass spectrometry approaches to identify and characterize proteins that interact with the LHX3 carboxyl terminus. This approach identified the LANP/pp32 and TAF-1β/SET proteins, which are components of the inhibitor of histone acetyltransferase (INHAT) multi-subunit complex that serves as a multifunctional repressor to inhibit histone acetylation and modulate chromatin structure. The protein domains of LANP and TAF-1β that interact with LHX3 were mapped using biochemical techniques. Chromatin immunoprecipitation experiments demonstrated that LANP and TAF-1β are associated with LHX3 target genes in pituitary cells, and experimental alterations of LANP and TAF-1β levels affected LHX3-mediated pituitary gene regulation. Together, these data suggest that transcriptional regulation of pituitary genes by LHX3 involves regulated interactions with the INHAT complex.

LHX3 Interacts with Inhibitor of Histone Acetyltransferase Complex Subunits LANP and TAF-1β to Modulate Pituitary Gene Regulation

PubMed Central

Witzmann, Frank A.; Rhodes, Simon J.

2013-01-01

LIM-homeodomain 3 (LHX3) is a transcription factor required for mammalian pituitary gland and nervous system development. Human patients and animal models with LHX3 gene mutations present with severe pediatric syndromes that feature hormone deficiencies and symptoms associated with nervous system dysfunction. The carboxyl terminus of the LHX3 protein is required for pituitary gene regulation, but the mechanism by which this domain operates is unknown. In order to better understand LHX3-dependent pituitary hormone gene transcription, we used biochemical and mass spectrometry approaches to identify and characterize proteins that interact with the LHX3 carboxyl terminus. This approach identified the LANP/pp32 and TAF-1β/SET proteins, which are components of the inhibitor of histone acetyltransferase (INHAT) multi-subunit complex that serves as a multifunctional repressor to inhibit histone acetylation and modulate chromatin structure. The protein domains of LANP and TAF-1β that interact with LHX3 were mapped using biochemical techniques. Chromatin immunoprecipitation experiments demonstrated that LANP and TAF-1β are associated with LHX3 target genes in pituitary cells, and experimental alterations of LANP and TAF-1β levels affected LHX3-mediated pituitary gene regulation. Together, these data suggest that transcriptional regulation of pituitary genes by LHX3 involves regulated interactions with the INHAT complex. PMID:23861948

The Skn7 Response Regulator of Saccharomyces cerevisiae Interacts with Hsf1 In Vivo and Is Required for the Induction of Heat Shock Genes by Oxidative Stress

PubMed Central

Raitt, Desmond C.; Johnson, Anthony L.; Erkine, Alexander M.; Makino, Kozo; Morgan, Brian; Gross, David S.; Johnston, Leland H.

2000-01-01

The Skn7 response regulator has previously been shown to play a role in the induction of stress-responsive genes in yeast, e.g., in the induction of the thioredoxin gene in response to hydrogen peroxide. The yeast Heat Shock Factor, Hsf1, is central to the induction of another set of stress-inducible genes, namely the heat shock genes. These two regulatory trans-activators, Hsf1 and Skn7, share certain structural homologies, particularly in their DNA-binding domains and the presence of adjacent regions of coiled-coil structure, which are known to mediate protein–protein interactions. Here, we provide evidence that Hsf1 and Skn7 interact in vitro and in vivo and we show that Skn7 can bind to the same regulatory sequences as Hsf1, namely heat shock elements. Furthermore, we demonstrate that a strain deleted for the SKN7 gene and containing a temperature-sensitive mutation in Hsf1 is hypersensitive to oxidative stress. Our data suggest that Skn7 and Hsf1 cooperate to achieve maximal induction of heat shock genes in response specifically to oxidative stress. We further show that, like Hsf1, Skn7 can interact with itself and is localized to the nucleus under normal growth conditions as well as during oxidative stress. PMID:10888672

Detecting Coevolution in Mammalian Sperm–Egg Fusion Proteins

PubMed Central

CLAW, KATRINA G.; GEORGE, RENEE D.; SWANSON, WILLIE J.

2018-01-01

SUMMARY Interactions between sperm and egg proteins can occur physically between gamete surface-binding proteins, and genetically between gamete proteins that work in complementary pathways in which they may not physically interact. Physically interacting sperm–egg proteins have been functionally identified in only a few species, and none have been verified within mammals. Candidate genes on both the sperm and egg surfaces exist, but gene deletion studies do not support functional interactions between these sperm–egg proteins; interacting sperm–egg proteins thus remain elusive. Cooperative gamete proteins undergo rapid evolution, and it is predicted that these sperm–egg proteins will also have correlated evolutionary rates due to compensatory changes on both the sperm and egg. To explore potential physical and genetic interactions in sperm–egg proteins, we sequenced four candidate genes from diverse primate species, and used regression and likelihood methods to test for signatures of coevolution between sperm–egg gene pairs. With both methods, we found that the egg protein CD9 coevolves with the sperm protein IZUMO1, suggesting a physical or genetic interaction occurs between them. With regression analysis, we found that CD9 and CRISP2 have correlated rates of evolution, and with likelihood analysis, that CD9 and CRISP1 have correlated rates. This suggests that the different tests may reflect different levels of interaction, be it physical or genetic. Coevolution tests thus provide an exploratory method for detecting potentially interacting sperm–egg protein pairs. PMID:24644026

Detecting coevolution in mammalian sperm-egg fusion proteins.

PubMed

Claw, Katrina G; George, Renee D; Swanson, Willie J

2014-06-01

Interactions between sperm and egg proteins can occur physically between gamete surface-binding proteins, and genetically between gamete proteins that work in complementary pathways in which they may not physically interact. Physically interacting sperm-egg proteins have been functionally identified in only a few species, and none have been verified within mammals. Candidate genes on both the sperm and egg surfaces exist, but gene deletion studies do not support functional interactions between these sperm-egg proteins; interacting sperm-egg proteins thus remain elusive. Cooperative gamete proteins undergo rapid evolution, and it is predicted that these sperm-egg proteins will also have correlated evolutionary rates due to compensatory changes on both the sperm and egg. To explore potential physical and genetic interactions in sperm-egg proteins, we sequenced four candidate genes from diverse primate species, and used regression and likelihood methods to test for signatures of coevolution between sperm-egg gene pairs. With both methods, we found that the egg protein CD9 coevolves with the sperm protein IZUMO1, suggesting a physical or genetic interaction occurs between them. With regression analysis, we found that CD9 and CRISP2 have correlated rates of evolution, and with likelihood analysis, that CD9 and CRISP1 have correlated rates. This suggests that the different tests may reflect different levels of interaction, be it physical or genetic. Coevolution tests thus provide an exploratory method for detecting potentially interacting sperm-egg protein pairs. © 2014 Wiley Periodicals, Inc.

Global changes in gene expression during compatible and incompatible interactions of cowpea (Vigna unguiculata L.) with the root parasitic angiosperm Striga gesnerioides

PubMed Central

2012-01-01

Background Cowpea, Vigna unguiculata L. Walp., is one of the most important food and forage legumes in the semi-arid tropics. While most domesticated forms of cowpea are susceptible to the root parasitic weed Striga gesnerioides, several cultivars have been identified that show race-specific resistance. Cowpea cultivar B301 contains the RSG3-301 gene for resistance to S. gesnerioides race SG3, but is susceptible to race SG4z. When challenged by SG3, roots of cultivar B301 develop a strong resistance response characterized by a hypersensitive reaction and cell death at the site of parasite attachment. In contrast, no visible response occurs in B301 roots parasitized by SG4z. Results Gene expression in the roots of the cowpea cultivar B301 during compatible (susceptible) and incompatible (resistant) interactions with S. gesnerioides races SG4z and SG3, respectively, were investigated at the early (6 days post-inoculation (dpi)) and late (13 dpi) stages of the resistance response using a Nimblegen custom design cowpea microarray. A total of 111 genes were differentially expressed in B301 roots at 6 dpi; this number increased to 2102 genes at 13 dpi. At 13 dpi, a total of 1944 genes were differentially expressed during compatible (susceptible) interactions of B301 with SG4z. Genes and pathways involved in signal transduction, programmed cell death and apoptosis, and defense response to biotic and abiotic stress were differentially expressed in the early resistance response; at the later time point, enrichment was primarily for defense-related gene expression, and genes encoding components of lignifications and secondary wall formation. In compatible interactions (B301 – SG4z), multiple defense pathways were repressed, including those involved in lignin biosynthesis and secondary cell wall modifications, while cellular transport processes for nitrogen and sulfur were increased. Conclusion Distinct changes in global gene expression profiles occur in host roots following successful and unsuccessful attempted parasitism by Striga. Induction of specific defense related genes and pathways defines components of a unique resistance mechanism. Some genes and pathways up-regulated in the host resistance response to SG3 are repressed in the susceptible interactions, suggesting that the parasite is targeting specific components of the host’s defense. These results add to our understanding of plant-parasite interactions and the evolution of resistance to parasitic weeds. PMID:22900582

The Rhizoctonia solani AG1-IB (isolate 7/3/14) transcriptome during interaction with the host plant lettuce (Lactuca sativa L.).

PubMed

Verwaaijen, Bart; Wibberg, Daniel; Kröber, Magdalena; Winkler, Anika; Zrenner, Rita; Bednarz, Hanna; Niehaus, Karsten; Grosch, Rita; Pühler, Alfred; Schlüter, Andreas

2017-01-01

The necrotrophic pathogen Rhizoctonia solani is one of the most economically important soil-borne pathogens of crop plants. Isolates of R. solani AG1-IB are the major pathogens responsible for bottom-rot of lettuce (Lactuca sativa L.) and are also responsible for diseases in other plant species. Currently, there is lack of information regarding the molecular responses in R. solani during the pathogenic interaction between the necrotrophic soil-borne pathogen and its host plant. The genome of R. solani AG1-IB (isolate 7/3/14) was recently established to obtain insights into its putative pathogenicity determinants. In this study, the transcriptional activity of R. solani AG1-IB was followed during the course of its pathogenic interaction with the host plant lettuce under controlled conditions. Based on visual observations, three distinct pathogen-host interaction zones on lettuce leaves were defined which covered different phases of disease progression on tissue inoculated with the AG1-IB (isolate 7/3/14). The zones were defined as: Zone 1-symptomless, Zone 2-light brown discoloration, and Zone 3-dark brown, necrotic lesions. Differences in R. solani hyphae structure in these three zones were investigated by microscopic observation. Transcriptional activity within these three interaction zones was used to represent the course of R. solani disease progression applying high-throughput RNA sequencing (RNA-Seq) analysis of samples collected from each Zone. The resulting three transcriptome data sets were analyzed for their highest expressed genes and for differentially transcribed genes between the respective interaction zones. Among the highest expressed genes was a group of not previously described genes which were transcribed exclusively during early stages of interaction, in Zones 1 and 2. Previously described importance of up-regulation in R. solani agglutinin genes during disease progression could be further confirmed; here, the corresponding genes exhibited extremely high transcription levels. Most differentially higher expressed transcripts were found within Zone 2. In Zone 3, the zone with the strongest degree of interaction, gene transcripts indicative of apoptotic activity were highly abundant. The transcriptome data presented in this work support previous models of the disease and interaction cycle of R. solani and lettuce and may influence effective techniques for control of this pathogen.

A genome-wide trans-ethnic interaction study links the PIGR-FCAMR locus to coronary atherosclerosis via interactions between genetic variants and residential exposure to traffic

PubMed Central

Neas, Lucas M.; Blach, Colette; Haynes, Carol S.; LaRocque-Abramson, Karen; Grass, Elizabeth; Dowdy, Z. Elaine; Devlin, Robert B.; Diaz-Sanchez, David; Cascio, Wayne E.; Miranda, Marie Lynn; Gregory, Simon G.; Shah, Svati H.; Kraus, William E.; Hauser, Elizabeth R.

2017-01-01

Air pollution is a worldwide contributor to cardiovascular disease mortality and morbidity. Traffic-related air pollution is a widespread environmental exposure and is associated with multiple cardiovascular outcomes such as coronary atherosclerosis, peripheral arterial disease, and myocardial infarction. Despite the recognition of the importance of both genetic and environmental exposures to the pathogenesis of cardiovascular disease, studies of how these two contributors operate jointly are rare. We performed a genome-wide interaction study (GWIS) to examine gene-traffic exposure interactions associated with coronary atherosclerosis. Using race-stratified cohorts of 538 African-Americans (AA) and 1562 European-Americans (EA) from a cardiac catheterization cohort (CATHGEN), we identify gene-by-traffic exposure interactions associated with the number of significantly diseased coronary vessels as a measure of chronic atherosclerosis. We found five suggestive (P<1x10-5) interactions in the AA GWIS, of which two (rs1856746 and rs2791713) replicated in the EA cohort (P < 0.05). Both SNPs are in the PIGR-FCAMR locus and are eQTLs in lymphocytes. The protein products of both PIGR and FCAMR are implicated in inflammatory processes. In the EA GWIS, there were three suggestive interactions; none of these replicated in the AA GWIS. All three were intergenic; the most significant interaction was in a regulatory region associated with SAMSN1, a gene previously associated with atherosclerosis and B cell activation. In conclusion, we have uncovered several novel genes associated with coronary atherosclerosis in individuals chronically exposed to increased ambient concentrations of traffic air pollution. These genes point towards inflammatory pathways that may modify the effects of air pollution on cardiovascular disease risk. PMID:28355232

Interactions of Cigarette Smoking with NAT2 Polymorphisms Impact Rheumatoid Arthritis Risk in African Americans

PubMed Central

Mikuls, Ted R.; LeVan, Tricia; Gould, Karen A.; Yu, Fang; Thiele, Geoffrey M.; Bynote, Kimberly K.; Conn, Doyt; Jonas, Beth L.; Callahan, Leigh F.; Smith, Edwin; Brasington, Richard; Moreland, Larry W.; Reynolds, Richard; Gaffo, Angelo; Bridges, S. Louis

2011-01-01

Objective To examine whether polymorphisms in genes coding for drug metabolizing enzymes (DMEs) impact rheumatoid arthritis (RA) risk due to cigarette smoking in African Americans. Methods Smoking status was evaluated in African American RA cases and non-RA controls categorized as heavy (≥ 10 pack-years) vs. other. Individuals were genotyped for a homozygous deletion polymorphism in glutathione S-transferase Mu-1 (GSTM1-null) in addition to tagging single nucleotide polymorphisms (SNPs) in N-acetyltransferase (NAT)1, NAT2, and epoxide hydrolase (EPXH1). Associations of genotypes with RA were examined using logistic regression and gene-smoking interactions were assessed. Results There were no significant associations of any DME genotype with RA. After adjustment for multiple comparisons, there were significant additive interactions between heavy smoking and NAT2 SNPs rs9987109 (Padd = 0.000003) and rs1208 (Padd = 0.00001); attributable proportions (APs) due to interaction ranged from 0.61 to 0.67. None of the multiplicative gene-smoking interactions examined remained significant after adjustment for multiple testing in overall disease risk. There was no evidence of significant gene-smoking interactions in analyses of GSTM1-null, NAT1, or EPXH1. DME gene-smoking interactions were similar when cases were limited to anti-citrullinated protein antibody (ACPA) positive individuals. Conclusion Among African Americans, RA risk imposed by heavy smoking appears to be mediated in part by genetic variation in NAT2. While further studies are needed to elucidate mechanisms underpinning these interactions, these SNPs appear to identify African American smokers at a much higher risk for RA with relative risks that are at least two-fold higher compared to non-smokers lacking these risk alleles. PMID:21989592

Interaction among variants in the SLC gene family (SLC6A14, SLC26A9, SLC11A1, and SLC9A3) and CFTR mutations with clinical markers of cystic fibrosis.

PubMed

Pereira, Stephanie V N; Ribeiro, Jose D; Bertuzzo, Carmen S; Marson, Fernando A L

2018-04-10

Cystic fibrosis (CF) is due to dysfunction of the CFTR channel and function of this channel is, in turn, affected by modifier genes that can impact the clinical phenotype. In this context, we analyzed the interaction among rs3788766*SLC6A14, rs7512462*SLC26A9, rs17235416*SLC11A1, and rs17563161*SLC9A3 variants, CFTR mutations and 40 CF severity markers by the Multifactor Dimensionality Reduction (MDR) model. A total of 164 patients with CF were included in the study. The variants in the modifier genes were identified by real-time PCR and the genotype of the CFTR gene in the diagnostic routine. Analysis of interaction between variants, CFTR mutations groupings and demographic, clinical and laboratory data were performed by the MDR. There were interaction between the rs3788766, rs7512462, rs17235416, and rs17563161 variants, and CFTR mutations with pancreatic insufficiency (PI), onset of digestive symptoms, and presence of mucoid Pseudomonas aeruginosa. Regarding PI, the interaction was observed for CFTR*rs17563161 (P-value = 0.015). Also, for onset of digestive symptoms the interaction was observed for CFTR*rs3788766*rs7512462*rs17235416*rs17563161 (P-value = 0.036). Considering the presence of mucoid P. aeruginosa, the interaction occurred for CFTR*rs3788766*rs7512462*rs17563161 (P-value = 0.035). Interaction between variants in the SLC family genes and the grouping for CFTR mutations were associated with PI, onset of digestive symptoms and mucoid P. aeruginosa, being important to determine one of the factors that may cause the diversity among the patients with CF. © 2018 Wiley Periodicals, Inc.

EINVis: a visualization tool for analyzing and exploring genetic interactions in large-scale association studies.

PubMed

Wu, Yubao; Zhu, Xiaofeng; Chen, Jian; Zhang, Xiang

2013-11-01

Epistasis (gene-gene interaction) detection in large-scale genetic association studies has recently drawn extensive research interests as many complex traits are likely caused by the joint effect of multiple genetic factors. The large number of possible interactions poses both statistical and computational challenges. A variety of approaches have been developed to address the analytical challenges in epistatic interaction detection. These methods usually output the identified genetic interactions and store them in flat file formats. It is highly desirable to develop an effective visualization tool to further investigate the detected interactions and unravel hidden interaction patterns. We have developed EINVis, a novel visualization tool that is specifically designed to analyze and explore genetic interactions. EINVis displays interactions among genetic markers as a network. It utilizes a circular layout (specially, a tree ring view) to simultaneously visualize the hierarchical interactions between single nucleotide polymorphisms (SNPs), genes, and chromosomes, and the network structure formed by these interactions. Using EINVis, the user can distinguish marginal effects from interactions, track interactions involving more than two markers, visualize interactions at different levels, and detect proxy SNPs based on linkage disequilibrium. EINVis is an effective and user-friendly free visualization tool for analyzing and exploring genetic interactions. It is publicly available with detailed documentation and online tutorial on the web at http://filer.case.edu/yxw407/einvis/. © 2013 WILEY PERIODICALS, INC.

Assessment of the reliability of protein-protein interactions and protein function prediction.

PubMed

Deng, Minghua; Sun, Fengzhu; Chen, Ting

2003-01-01

As more and more high-throughput protein-protein interaction data are collected, the task of estimating the reliability of different data sets becomes increasingly important. In this paper, we present our study of two groups of protein-protein interaction data, the physical interaction data and the protein complex data, and estimate the reliability of these data sets using three different measurements: (1) the distribution of gene expression correlation coefficients, (2) the reliability based on gene expression correlation coefficients, and (3) the accuracy of protein function predictions. We develop a maximum likelihood method to estimate the reliability of protein interaction data sets according to the distribution of correlation coefficients of gene expression profiles of putative interacting protein pairs. The results of the three measurements are consistent with each other. The MIPS protein complex data have the highest mean gene expression correlation coefficients (0.256) and the highest accuracy in predicting protein functions (70% sensitivity and specificity), while Ito's Yeast two-hybrid data have the lowest mean (0.041) and the lowest accuracy (15% sensitivity and specificity). Uetz's data are more reliable than Ito's data in all three measurements, and the TAP protein complex data are more reliable than the HMS-PCI data in all three measurements as well. The complex data sets generally perform better in function predictions than do the physical interaction data sets. Proteins in complexes are shown to be more highly correlated in gene expression. The results confirm that the components of a protein complex can be assigned to functions that the complex carries out within a cell. There are three interaction data sets different from the above two groups: the genetic interaction data, the in-silico data and the syn-express data. Their capability of predicting protein functions generally falls between that of the Y2H data and that of the MIPS protein complex data. The supplementary information is available at the following Web site: http://www-hto.usc.edu/-msms/AssessInteraction/.

Clock genes × stress × reward interactions in alcohol and substance use disorders.

PubMed

Perreau-Lenz, Stéphanie; Spanagel, Rainer

2015-06-01

Adverse life events and highly stressful environments have deleterious consequences for mental health. Those environmental factors can potentiate alcohol and drug abuse in vulnerable individuals carrying specific genetic risk factors, hence producing the final risk for alcohol- and substance-use disorders development. The nature of these genes remains to be fully determined, but studies indicate their direct or indirect relation to the stress hypothalamo-pituitary-adrenal (HPA) axis and/or reward systems. Over the past decade, clock genes have been revealed to be key-players in influencing acute and chronic alcohol/drug effects. In parallel, the influence of chronic stress and stressful life events in promoting alcohol and substance use and abuse has been demonstrated. Furthermore, the reciprocal interaction of clock genes with various HPA-axis components, as well as the evidence for an implication of clock genes in stress-induced alcohol abuse, have led to the idea that clock genes, and Period genes in particular, may represent key genetic factors to consider when examining gene × environment interaction in the etiology of addiction. The aim of the present review is to summarize findings linking clock genes, stress, and alcohol and substance abuse, and to propose potential underlying neurobiological mechanisms. Copyright © 2015 Elsevier Inc. All rights reserved.

Receptor Tyrosine Kinase MET Interactome and Neurodevelopmental Disorder Partners at the Developing Synapse

PubMed Central

Xie, Zhihui; Li, Jing; Baker, Jonathan; Eagleson, Kathie L.; Coba, Marcelo P.; Levitt, Pat

2016-01-01

Background Atypical synapse development and plasticity are implicated in many neurodevelopmental disorders (NDDs). NDD-associated, high confidence risk genes have been identified, yet little is known about functional relationships at the level of protein-protein interactions, which are the dominant molecular bases responsible for mediating circuit development. Methods Proteomics in three independent developing neocortical synaptosomal preparations identified putative interacting proteins of the ligand-activated MET receptor tyrosine kinase, an autism risk gene that mediates synapse development. The candidates were translated into interactome networks and analyzed bioinformatically. Additionally, three independent quantitative proximity ligation assays (PLA) in cultured neurons and four independent immunoprecipitation analyses of synaptosomes validated protein interactions. Results Approximately 11% (8/72) of MET-interacting proteins, including SHANK3, SYNGAP1 and GRIN2B, are associated with NDDs. Proteins in the MET interactome were translated into a novel MET interactome network based on human protein-protein interaction databases. High confidence genes from different NDD datasets that encode synaptosomal proteins were analyzed for being enriched in MET interactome proteins. This was found for autism, but not schizophrenia, bipolar disorder, major depressive disorder or attentional deficit hyperactivity disorder. There is correlated gene expression between MET and its interactive partners in developing human temporal and visual neocortices, but not with highly expressed genes that are not in the interactome. PLA and biochemical analyses demonstrate that MET-protein partner interactions are dynamically regulated by receptor activation. Conclusions The results provide a novel molecular framework for deciphering the functional relations of key regulators of synaptogenesis that contribute to both typical cortical development and to NDDs. PMID:27086544

A Novel Test for Gene-Ancestry Interactions in Genome-Wide Association Data

PubMed Central

Dunlop, Malcolm G.; Houlston, Richard S.; Tomlinson, Ian P.; Holmes, Chris C.

2012-01-01

Genome-wide association study (GWAS) data on a disease are increasingly available from multiple related populations. In this scenario, meta-analyses can improve power to detect homogeneous genetic associations, but if there exist ancestry-specific effects, via interactions on genetic background or with a causal effect that co-varies with genetic background, then these will typically be obscured. To address this issue, we have developed a robust statistical method for detecting susceptibility gene-ancestry interactions in multi-cohort GWAS based on closely-related populations. We use the leading principal components of the empirical genotype matrix to cluster individuals into “ancestry groups” and then look for evidence of heterogeneous genetic associations with disease or other trait across these clusters. Robustness is improved when there are multiple cohorts, as the signal from true gene-ancestry interactions can then be distinguished from gene-collection artefacts by comparing the observed interaction effect sizes in collection groups relative to ancestry groups. When applied to colorectal cancer, we identified a missense polymorphism in iron-absorption gene CYBRD1 that associated with disease in individuals of English, but not Scottish, ancestry. The association replicated in two additional, independently-collected data sets. Our method can be used to detect associations between genetic variants and disease that have been obscured by population genetic heterogeneity. It can be readily extended to the identification of genetic interactions on other covariates such as measured environmental exposures. We envisage our methodology being of particular interest to researchers with existing GWAS data, as ancestry groups can be easily defined and thus tested for interactions. PMID:23236349

Host Genotype and Microbiota Contribute Asymmetrically to Transcriptional Variation in the Threespine Stickleback Gut

PubMed Central

Small, Clayton M.; Milligan-Myhre, Kathryn; Bassham, Susan; Guillemin, Karen

2017-01-01

Recent studies of interactions between hosts and their resident microbes have revealed important ecological and evolutionary consequences that emerge from these complex interspecies relationships, including diseases that occur when the interactions go awry. Given the preponderance of these interactions, we hypothesized that effects of the microbiota on gene expression in the developing gut—an important aspect of host biology—would be pervasive, and that these effects would be both comparable in magnitude to and contingent on effects of the host genetic background. To evaluate the effects of the microbiota, host genotype, and their interaction on gene expression in the gut of a genetically diverse, gnotobiotic host model, the threespine stickleback (Gasterosteus aculeatus), we compared RNA-seq data among 84 larval fish. Surprisingly, we found that stickleback population and family differences explained substantially more gene expression variation than the presence of microbes. Expression levels of 72 genes, however, were affected by our microbiota treatment. These genes, including many associated with innate immunity, comprise a tractable subset of host genetic factors for precise, systems-level study of host–microbe interactions in the future. Importantly, our data also suggest subtle signatures of a statistical interaction between host genotype and the microbiota on expression patterns of genetic pathways associated with innate immunity, coagulation and complement cascades, focal adhesion, cancer, and peroxisomes. These genotype-by-environment interactions may prove to be important leads to the understanding of host genetic mechanisms commonly at the root of sometimes complex molecular relationships between hosts and their resident microbes. PMID:28391321

Genetic predispositions and parental bonding interact to shape adults' physiological responses to social distress.

PubMed

Esposito, Gianluca; Truzzi, Anna; Setoh, Peipei; Putnick, Diane L; Shinohara, Kazuyuki; Bornstein, Marc H

2017-05-15

Parental bonding and oxytocin receptor (OXTR) gene genotype each influences social abilities in adulthood. Here, we hypothesized an interaction between the two - environmental experience (parental bonding history) and genetic factors (OXTR gene genotype) - in shaping adults' social sensitivity (physiological response to distress). We assessed heart rate and peripheral temperature (tip of the nose) in 42 male adults during presentation of distress vocalizations (distress cries belonging to female human infants and adults as well as bonobo). The two physiological responses index, respectively, state of arousal and readiness to action. Participants' parental bonding in childhood was assessed through the self-report Parental Bonding Instrument. To assess participants' genetic predispositions, buccal mucosa cell samples were collected, and region rs2254298 of the oxytocin receptor gene was analyzed: previous OXTR gene findings point to associations between the G allele and better sociality (protective factor) and the A allele and poorer sociality (risk factor). We found a gene * environment interaction for susceptibility to social distress: Participants with a genetic risk factor (A carriers) with a history of high paternal overprotection showed higher heart rate increase than those without this risk factor (G/G genotype) to social distress.Also, a significant effect of the interaction between paternal care and genotype on nose temperature changes was found. This susceptibility appears to represent an indirect pathway through which genes and experiences interact to shape mature social sensitivity in males. Copyright © 2016 Elsevier B.V. All rights reserved.

Genetic predispositions and parental bonding interact to shape adults’ physiological responses to social distress

PubMed Central

Esposito, Gianluca; Truzzi, Anna; Setoh, Peipei; Putnick, Diane L.; Shinohara, Kazuyuki; Bornstein, Marc H.

2018-01-01

Parental bonding and oxytocin receptor (OXTR) gene genotype each influences social abilities in adulthood. Here, we hypothesized an interaction between the two – environmental experience (parental bonding history) and genetic factors (OXTR gene genotype) – in shaping adults’ social sensitivity (physiological response to distress). We assessed heart rate and peripheral temperature (tip of the nose) in 42 male adults during presentation of distress vocalizations (distress cries belonging to female human infants and adults as well as bonobo). The two physiological responses index, respectively, state of arousal and readiness to action. Participants’ parental bonding in childhood was assessed through the self-report Parental Bonding Instrument. To assess participants’ genetic predispositions, buccal mucosa cell samples were collected, and region rs2254298 of the oxytocin receptor gene was analyzed: previous OXTR gene findings point to associations between the G allele and better sociality (protective factor) and the A allele and poorer sociality (risk factor). We found a gene * environment interaction for susceptibility to social distress: Participants with a genetic risk factor (A carriers) with a history of high paternal overprotection showed higher heart rate increase than those without this risk factor (G/G genotype) to social distress. Also, a significant effect of the interaction between paternal care and genotype on nose temperature changes was found. This susceptibility appears to represent an indirect pathway through which genes and experiences interact to shape mature social sensitivity in males. PMID:27343933

Nutrigenomics and nutrigenetics in inflammatory bowel diseases.

PubMed

Gruber, Lisa; Lichti, Pia; Rath, Eva; Haller, Dirk

2012-10-01

Inflammatory bowel diseases (IBD) including ulcerative colitis and Crohn's disease are chronically relapsing, immune-mediated disorders of the gastrointestinal tract. A major challenge in the treatment of IBD is the heterogenous nature of these pathologies. Both, ulcerative colitis and Crohn's disease are of multifactorial etiology and feature a complex interaction of host genetic susceptibility and environmental factors such as diet and gut microbiota. Genome-wide association studies identified disease-relevant single-nucleotide polymorphisms in approximately 100 genes, but at the same time twin studies also clearly indicated a strong environmental impact in disease development. However, attempts to link dietary factors to the risk of developing IBD, based on epidemiological observations showed controversial outcomes. Yet, emerging high-throughput technologies implying complete biological systems might allow taking nutrient-gene interactions into account for a better classification of patient subsets in the future. In this context, 2 new scientific fields, "nutrigenetics" and "nutrigenomics" have been established. "Nutrigenetics," studying the effect of genetic variations on nutrient-gene interactions and "Nutrigenomics," describing the impact of nutrition on physiology and health status on the level of gene transcription, protein expression, and metabolism. It is hoped that the integration of both research areas will promote the understanding of the complex gene-environment interaction in IBD etiology and in the long-term will lead to personalized nutrition for disease prevention and treatment. This review briefly summarizes data on the impact of nutrients on intestinal inflammation, highlights nutrient-gene interactions, and addresses the potential of applying "omic" technologies in the context of IBD.

Tightly Regulated Expression of Autographa californica Multicapsid Nucleopolyhedrovirus Immediate Early Genes Emerges from Their Interactions and Possible Collective Behaviors

PubMed Central

Taka, Hitomi; Asano, Shin-ichiro; Matsuura, Yoshiharu; Bando, Hisanori

2015-01-01

To infect their hosts, DNA viruses must successfully initiate the expression of viral genes that control subsequent viral gene expression and manipulate the host environment. Viral genes that are immediately expressed upon infection play critical roles in the early infection process. In this study, we investigated the expression and regulation of five canonical regulatory immediate-early (IE) genes of Autographa californica multicapsid nucleopolyhedrovirus: ie0, ie1, ie2, me53, and pe38. A systematic transient gene-expression analysis revealed that these IE genes are generally transactivators, suggesting the existence of a highly interactive regulatory network. A genetic analysis using gene knockout viruses demonstrated that the expression of these IE genes was tolerant to the single deletions of activator IE genes in the early stage of infection. A network graph analysis on the regulatory relationships observed in the transient expression analysis suggested that the robustness of IE gene expression is due to the organization of the IE gene regulatory network and how each IE gene is activated. However, some regulatory relationships detected by the genetic analysis were contradictory to those observed in the transient expression analysis, especially for IE0-mediated regulation. Statistical modeling, combined with genetic analysis using knockout alleles for ie0 and ie1, showed that the repressor function of ie0 was due to the interaction between ie0 and ie1, not ie0 itself. Taken together, these systematic approaches provided insight into the topology and nature of the IE gene regulatory network. PMID:25816136

Deregulation of Rab and Rab Effector Genes in Bladder Cancer

PubMed Central

Ho, Joel R.; Chapeaublanc, Elodie; Kirkwood, Lisa; Nicolle, Remy; Benhamou, Simone; Lebret, Thierry; Allory, Yves; Southgate, Jennifer; Radvanyi, François; Goud, Bruno

2012-01-01

Growing evidence indicates that Rab GTPases, key regulators of intracellular transport in eukaryotic cells, play an important role in cancer. We analysed the deregulation at the transcriptional level of the genes encoding Rab proteins and Rab-interacting proteins in bladder cancer pathogenesis, distinguishing between the two main progression pathways so far identified in bladder cancer: the Ta pathway characterized by a high frequency of FGFR3 mutation and the carcinoma in situ pathway where no or infrequent FGFR3 mutations have been identified. A systematic literature search identified 61 genes encoding Rab proteins and 223 genes encoding Rab-interacting proteins. Transcriptomic data were obtained for normal urothelium samples and for two independent bladder cancer data sets corresponding to 152 and 75 tumors. Gene deregulation was analysed with the SAM (significant analysis of microarray) test or the binomial test. Overall, 30 genes were down-regulated, and 13 were up-regulated in the tumor samples. Five of these deregulated genes (LEPRE1, MICAL2, RAB23, STXBP1, SYTL1) were specifically deregulated in FGFR3-non-mutated muscle-invasive tumors. No gene encoding a Rab or Rab-interacting protein was found to be specifically deregulated in FGFR3-mutated tumors. Cluster analysis showed that the RAB27 gene cluster (comprising the genes encoding RAB27 and its interacting partners) was deregulated and that this deregulation was associated with both pathways of bladder cancer pathogenesis. Finally, we found that the expression of KIF20A and ZWINT was associated with that of proliferation markers and that the expression of MLPH, MYO5B, RAB11A, RAB11FIP1, RAB20 and SYTL2 was associated with that of urothelial cell differentiation markers. This systematic analysis of Rab and Rab effector gene deregulation in bladder cancer, taking relevant tumor subgroups into account, provides insight into the possible roles of Rab proteins and their effectors in bladder cancer pathogenesis. This approach is applicable to other group of genes and types of cancer. PMID:22724020

Expression and Sequence Variants of Inflammatory Genes; Effects on Plasma Inflammation Biomarkers Following a 6-Week Supplementation with Fish Oil.

PubMed

Cormier, Hubert; Rudkowska, Iwona; Lemieux, Simone; Couture, Patrick; Vohl, Marie-Claude

2016-03-15

(1) BACKGROUND: A growing body of literature suggest that polymorphisms (SNPs) from inflammation-related genes could possibly play a role in cytokine production and then interact with dietary n-3 fatty acids (FAs) to modulate inflammation. The aim of the present study was to test whether gene expression of selected inflammatory genes was altered following an n-3 PUFA supplementation and to test for gene-diet interactions modulating plasma inflammatory biomarker levels. (2) METHODS: 191 subjects completed a 6-week n-3 FA supplementation with 5 g/day of fish oil. Gene expression of TNF-α and IL6 was assessed in peripheral blood mononuclear cells (PBMCs) using the TaqMan technology. Genotyping of 20 SNPs from the TNF-LTA gene cluster, IL1β, IL6 and CRP genes was performed. (3) RESULTS: There was no significant reduction of plasma IL-6, TNF-α and C-reactive protein (CRP) levels after the 6-week fish oil supplementation. TNF-α and IL6 were slightly overexpressed in PBMCs after the supplementation (fold changes of 1.05 ± 0.38 and 1.18 ± 0.49, respectively (n = 191)), but relative quantification (RQ) within the -0.5 to 2.0 fold are considered as nonbiologically significant. In a MIXED model for repeated measures adjusted for the effects of age, sex and BMI, gene by supplementation interaction effects were observed for rs1143627, rs16944, rs1800797, and rs2069840 on IL6 levels, for rs2229094 on TNF-α levels and for rs1800629 on CRP levels (p < 0.05 for all). (4) CONCLUSIONS: This study shows that a 6-week n-3 FA supplementation with 5 g/day of fish oil did not alter gene expression levels of TNF-α and IL6 in PBMCs and did not have an impact on inflammatory biomarker levels. However, gene-diet interactions were observed between SNPs within inflammation-related genes modulating plasma inflammatory biomarker levels.

RUNX1 and FOXP3 interplay regulates expression of breast cancer related genes

PubMed Central

Recouvreux, María Sol; Grasso, Esteban Nicolás; Echeverria, Pablo Christian; Rocha-Viegas, Luciana; Castilla, Lucio Hernán; Schere-Levy, Carolina; Tocci, Johanna Melisa; Kordon, Edith Claudia; Rubinstein, Natalia

2016-01-01

Runx1 participation in epithelial mammary cells is still under review. Emerging data indicates that Runx1 could be relevant for breast tumor promotion. However, to date no studies have specifically evaluated the functional contribution of Runx1 to control gene expression in mammary epithelial tumor cells. It has been described that Runx1 activity is defined by protein context interaction. Interestingly, Foxp3 is a breast tumor suppressor gene. Here we show that endogenous Runx1 and Foxp3 physically interact in normal mammary cells and this interaction blocks Runx1 transcriptional activity. Furthermore we demonstrate that Runx1 is able to bind to R-spondin 3 (RSPO3) and Gap Junction protein Alpha 1 (GJA1) promoters. This binding upregulates Rspo3 oncogene expression and downregulates GJA1 tumor suppressor gene expression in a Foxp3-dependent manner. Moreover, reduced Runx1 transcriptional activity decreases tumor cell migration properties. Collectively, these data provide evidence of a new mechanism for breast tumor gene expression regulation, in which Runx1 and Foxp3 physically interact to control mammary epithelial cell gene expression fate. Our work suggests for the first time that Runx1 could be involved in breast tumor progression depending on Foxp3 availability. PMID:26735887

RUNX1 and FOXP3 interplay regulates expression of breast cancer related genes.

PubMed

Recouvreux, María Sol; Grasso, Esteban Nicolás; Echeverria, Pablo Christian; Rocha-Viegas, Luciana; Castilla, Lucio Hernán; Schere-Levy, Carolina; Tocci, Johanna Melisa; Kordon, Edith Claudia; Rubinstein, Natalia

2016-02-09

Runx1 participation in epithelial mammary cells is still under review. Emerging data indicates that Runx1 could be relevant for breast tumor promotion. However, to date no studies have specifically evaluated the functional contribution of Runx1 to control gene expression in mammary epithelial tumor cells. It has been described that Runx1 activity is defined by protein context interaction. Interestingly, Foxp3 is a breast tumor suppressor gene. Here we show that endogenous Runx1 and Foxp3 physically interact in normal mammary cells and this interaction blocks Runx1 transcriptional activity. Furthermore we demonstrate that Runx1 is able to bind to R-spondin 3 (RSPO3) and Gap Junction protein Alpha 1 (GJA1) promoters. This binding upregulates Rspo3 oncogene expression and downregulates GJA1 tumor suppressor gene expression in a Foxp3-dependent manner. Moreover, reduced Runx1 transcriptional activity decreases tumor cell migration properties. Collectively, these data provide evidence of a new mechanism for breast tumor gene expression regulation, in which Runx1 and Foxp3 physically interact to control mammary epithelial cell gene expression fate. Our work suggests for the first time that Runx1 could be involved in breast tumor progression depending on Foxp3 availability.

The influence of urban/rural residency on depressive symptoms is moderated by the serotonin receptor 2A gene.

PubMed

Jokela, Markus; Lehtimäki, Terho; Keltikangas-Järvinen, Liisa

2007-10-05

Gene-environment interactions are thought to be involved in the development of depression. Here we examined the interaction effect between urban/rural residency and the serotonin receptor 2A (HTR2A) gene on subclinical depressive symptoms. The participants were 1,224 Finnish men and women being followed in the on-going population-based study of "Cardiovascular Risk in Young Finns". Urban/rural residency was determined on the basis of a (1) subjective report and (2) the population density of the residential area. Depressive symptoms were measured in two test settings four years apart. There was a significant gene-environment interaction, such that the urban residency was associated with low depressive symptoms in individuals carrying the T/T or T/C genotype of the T102C polymorphism, but not in those carrying the C/C genotype. The T allele was associated with high depressive symptoms in remote rural areas, but with low depressive symptoms in urban or suburban areas. The gene-environment interaction was not accounted by level of education, social support, unemployment, or partnership status. The HTR2A gene may be involved in the development of depression by influencing how individuals respond to environmental conditions. (c) 2007 Wiley-Liss, Inc.

Genotypes Do Not Confer Risk For Delinquency ut Rather Alter Susceptibility to Positive and Negative Environmental Factors: Gene-Environment Interactions of BDNF Val66Met, 5-HTTLPR, and MAOA-uVNTR

PubMed Central

Comasco, Erika; Hodgins, Sheilagh; Oreland, Lars; Åslund, Cecilia

2015-01-01

Background: Previous evidence of gene-by-environment interactions associated with emotional and behavioral disorders is contradictory. Differences in findings may result from variation in valence and dose of the environmental factor, and/or failure to take account of gene-by-gene interactions. The present study investigated interactions between the brain-derived neurotrophic factor gene (BDNF Val66Met), the serotonin transporter gene-linked polymorphic region (5-HTTLPR), the monoamine oxidase A (MAOA-uVNTR) polymorphisms, family conflict, sexual abuse, the quality of the child-parent relationship, and teenage delinquency. Methods: In 2006, as part of the Survey of Adolescent Life in Västmanland, Sweden, 1 337 high-school students, aged 17–18 years, anonymously completed questionnaires and provided saliva samples for DNA analyses. Results: Teenage delinquency was associated with two-, three-, and four-way interactions of each of the genotypes and the three environmental factors. Significant four-way interactions were found for BDNF Val66Met × 5-HTTLPR×MAOA-uVNTR × family conflicts and for BDNF Val66Met × 5-HTTLPR×MAOA-uVNTR × sexual abuse. Further, the two genotype combinations that differed the most in expression levels (BDNF Val66Met Val, 5-HTTLPR LL, MAOA-uVNTR LL [girls] and L [boys] vs BDNF Val66Met Val/Met, 5-HTTLPR S/LS, MAOA-uVNTR S/SS/LS) in interaction with family conflict and sexual abuse were associated with the highest delinquency scores. The genetic variants previously shown to confer vulnerability for delinquency (BDNF Val66Met Val/Met × 5-HTTLPR S × MAOA-uVNTR S) were associated with the lowest delinquency scores in interaction with a positive child-parent relationship. Conclusions: Functional variants of the MAOA-uVNTR, 5-HTTLPR, and BDNF Val66Met, either alone or in interaction with each other, may be best conceptualized as modifying sensitivity to environmental factors that confer either risk or protection for teenage delinquency. PMID:25522433

Dual gene activation and knockout screen reveals directional dependencies in genetic networks. | Office of Cancer Genomics

Cancer.gov

Understanding the direction of information flow is essential for characterizing how genetic networks affect phenotypes. However, methods to find genetic interactions largely fail to reveal directional dependencies. We combine two orthogonal Cas9 proteins from Streptococcus pyogenes and Staphylococcus aureus to carry out a dual screen in which one gene is activated while a second gene is deleted in the same cell. We analyze the quantitative effects of activation and knockout to calculate genetic interaction and directionality scores for each gene pair.

Expression of a DNA Replication Gene Cluster in Bacteriophage T4: Genetic Linkage and the Control of Gene Product Interactions

PubMed Central

Gerald, W. L.; Karam, J. D.

1984-01-01

The results of this study bear on the relationship between genetic linkage and control of interactions between the protein products of different cistrons. In T4 bacteriophage, genes 45 and 44 encode essential components of the phage DNA replication multiprotein complex. T4 gene 45 maps directly upstream of gene 44 relative to the overall direction of reading of this region of the phage chromosome, but it is not known whether these two genes are cotranscribed. It has been shown that a nonsense lesion of T4 gene 45 exerts a cis-dominant inhibitory effect on growth of a missense mutant of gene 44 but not on growth of phage carrying the wild-type gene 44 allele. In previous work, we confirmed these observations on polarity of the gene 45 mutation but detected no polar effects by this lesion on synthesis of either mutant or wild-type gene 44 protein. In the present study, we demonstrate that mRNA for gene 44 protein is separable by gel electrophoresis from gene 45-protein-encoding mRNA. That is, the two proteins are not synthesized from one polycistronic message, and the cis-dominant inhibitory effect of the gene 45 mutation on gene 44 function is probably expressed at a posttranslational stage. We propose that close genetic linkage, whether or not it provides shared transcriptional and translational regulatory signals for certain clusters of functionally related cistrons, may determine the intracellular compartmentalization for synthesis of proteins encoded by these clusters. In prokaryotes, such linkage-dependent compartmentation may minimize the diffusion distances between gene products that are synthesized at low levels and are destined to interact. PMID:6745641

Large-scale chromatin immunoprecipitation with promoter sequence microarray analysis of the interaction of the NSs protein of Rift Valley fever virus with regulatory DNA regions of the host genome.

PubMed

Benferhat, Rima; Josse, Thibaut; Albaud, Benoit; Gentien, David; Mansuroglu, Zeyni; Marcato, Vasco; Souès, Sylvie; Le Bonniec, Bernard; Bouloy, Michèle; Bonnefoy, Eliette

2012-10-01

Rift Valley fever virus (RVFV) is a highly pathogenic Phlebovirus that infects humans and ruminants. Initially confined to Africa, RVFV has spread outside Africa and presently represents a high risk to other geographic regions. It is responsible for high fatality rates in sheep and cattle. In humans, RVFV can induce hepatitis, encephalitis, retinitis, or fatal hemorrhagic fever. The nonstructural NSs protein that is the major virulence factor is found in the nuclei of infected cells where it associates with cellular transcription factors and cofactors. In previous work, we have shown that NSs interacts with the promoter region of the beta interferon gene abnormally maintaining the promoter in a repressed state. In this work, we performed a genome-wide analysis of the interactions between NSs and the host genome using a genome-wide chromatin immunoprecipitation combined with promoter sequence microarray, the ChIP-on-chip technique. Several cellular promoter regions were identified as significantly interacting with NSs, and the establishment of NSs interactions with these regions was often found linked to deregulation of expression of the corresponding genes. Among annotated NSs-interacting genes were present not only genes regulating innate immunity and inflammation but also genes regulating cellular pathways that have not yet been identified as targeted by RVFV. Several of these pathways, such as cell adhesion, axonal guidance, development, and coagulation were closely related to RVFV-induced disorders. In particular, we show in this work that NSs targeted and modified the expression of genes coding for coagulation factors, demonstrating for the first time that this hemorrhagic virus impairs the host coagulation cascade at the transcriptional level.

Large-Scale Chromatin Immunoprecipitation with Promoter Sequence Microarray Analysis of the Interaction of the NSs Protein of Rift Valley Fever Virus with Regulatory DNA Regions of the Host Genome

PubMed Central

Benferhat, Rima; Josse, Thibaut; Albaud, Benoit; Gentien, David; Mansuroglu, Zeyni; Marcato, Vasco; Souès, Sylvie; Le Bonniec, Bernard

2012-01-01

Rift Valley fever virus (RVFV) is a highly pathogenic Phlebovirus that infects humans and ruminants. Initially confined to Africa, RVFV has spread outside Africa and presently represents a high risk to other geographic regions. It is responsible for high fatality rates in sheep and cattle. In humans, RVFV can induce hepatitis, encephalitis, retinitis, or fatal hemorrhagic fever. The nonstructural NSs protein that is the major virulence factor is found in the nuclei of infected cells where it associates with cellular transcription factors and cofactors. In previous work, we have shown that NSs interacts with the promoter region of the beta interferon gene abnormally maintaining the promoter in a repressed state. In this work, we performed a genome-wide analysis of the interactions between NSs and the host genome using a genome-wide chromatin immunoprecipitation combined with promoter sequence microarray, the ChIP-on-chip technique. Several cellular promoter regions were identified as significantly interacting with NSs, and the establishment of NSs interactions with these regions was often found linked to deregulation of expression of the corresponding genes. Among annotated NSs-interacting genes were present not only genes regulating innate immunity and inflammation but also genes regulating cellular pathways that have not yet been identified as targeted by RVFV. Several of these pathways, such as cell adhesion, axonal guidance, development, and coagulation were closely related to RVFV-induced disorders. In particular, we show in this work that NSs targeted and modified the expression of genes coding for coagulation factors, demonstrating for the first time that this hemorrhagic virus impairs the host coagulation cascade at the transcriptional level. PMID:22896612

Haplotype-based gene-gene interaction of bone morphogenetic protein 4 and interferon regulatory factor 6 in the etiology of non-syndromic cleft lip with or without cleft palate in a Chilean population.

PubMed

Blanco, Rafael; Colombo, Alicia; Pardo, Rosa; Suazo, José

2017-04-01

Non-syndromic cleft lip with or without cleft palate (NSCL/P) is the most common craniofacial birth defect in humans, the etiology of which can be dependent on the interactions of multiple genes. We previously reported haplotype associations for polymorphic variants of interferon regulatory factor 6 (IRF6), msh homeobox 1 (MSX1), bone morphogenetic protein 4 (BMP4), and transforming growth factor beta 3 (TGFB3) in Chile. Here, we analyzed the haplotype-based gene-gene interaction for markers of these genes and NSCL/P risk in the Chilean population. We genotyped 15 single nucleoptide polymorphisms (SNPs) in 152 Chilean patients and 164 controls. Linkage disequilibrium (LD) blocks were determined using the Haploview software, and phase reconstruction was performed by the Phase program. Haplotype-based interactions were evaluated using the multifactor dimensionality reduction (MDR) method. We detected two LD blocks composed of two SNPs from BMP4 (Block 1) and three SNPs from IRF6 (Block 2). Although MDR showed no statistical significance for the global interaction model involving these blocks, we found four combinations conferring a statistically significantly increased NSCL/P risk (Block 1-Block 2): T-T/T-G C-G-T/G-A-T; T-T/T-G C-G-C/C-G-C; T-T/T-G G-A-T/G-A-T; and T-T/C-G G-A-T/G-A-T. These findings may reflect the presence of a genomic region containing potential causal variants interacting in the etiology of NSCL/P and may contribute to disentangling the complex etiology of this birth defect. © 2017 Eur J Oral Sci.

Genome-wide analysis of epistasis in body mass index using multiple human populations.

PubMed

Wei, Wen-Hua; Hemani, Gib; Gyenesei, Attila; Vitart, Veronique; Navarro, Pau; Hayward, Caroline; Cabrera, Claudia P; Huffman, Jennifer E; Knott, Sara A; Hicks, Andrew A; Rudan, Igor; Pramstaller, Peter P; Wild, Sarah H; Wilson, James F; Campbell, Harry; Hastie, Nicholas D; Wright, Alan F; Haley, Chris S

2012-08-01

We surveyed gene-gene interactions (epistasis) in human body mass index (BMI) in four European populations (n<1200) via exhaustive pair-wise genome scans where interactions were computed as F ratios by testing a linear regression model fitting two single-nucleotide polymorphisms (SNPs) with interactions against the one without. Before the association tests, BMI was corrected for sex and age, normalised and adjusted for relatedness. Neither single SNPs nor SNP interactions were genome-wide significant in either cohort based on the consensus threshold (P=5.0E-08) and a Bonferroni corrected threshold (P=1.1E-12), respectively. Next we compared sub genome-wide significant SNP interactions (P<5.0E-08) across cohorts to identify common epistatic signals, where SNPs were annotated to genes to test for gene ontology (GO) enrichment. Among the epistatic genes contributing to the commonly enriched GO terms, 19 were shared across study cohorts of which 15 are previously published genome-wide association loci, including CDH13 (cadherin 13) associated with height and SORCS2 (sortilin-related VPS10 domain containing receptor 2) associated with circulating insulin-like growth factor 1 and binding protein 3. Interactions between the 19 shared epistatic genes and those involving BMI candidate loci (P<5.0E-08) were tested across cohorts and found eight replicated at the SNP level (P<0.05) in at least one cohort, which were further tested and showed limited replication in a separate European population (n>5000). We conclude that genome-wide analysis of epistasis in multiple populations is an effective approach to provide new insights into the genetic regulation of BMI but requires additional efforts to confirm the findings.

Influence of smoking status and intensity on discovery of blood pressure loci through gene-smoking interactions

PubMed Central

Fuentes, Lisa de las; Schwander, Karen; Cupples, L. Adrienne; Rao, D. C.

2015-01-01

Background Genetic variation accounts for approximately 30% of blood pressure (BP) variability but most of that variability hasn't been attributed to specific variants. Interactions between genes and BP-associated factors may explain some ‘missing heritability.’ Cigarette smoking increases BP after short-term exposure and decreases BP with longer exposure. Gene-smoking interactions have discovered novel BP loci, but the contribution of smoking status and intensity to gene discovery is unknown. Methods We analyzed gene-smoking intensity interactions for association with systolic BP (SBP) in three subgroups from the Framingham Heart Study: current smokers only (N = 1,057), current and former smokers (‘ever smokers’, N = 3,374), and all subjects (N = 6,710). We used three smoking intensity variables defined at cutoffs of 10, 15, and 20 cigarettes per day (CPD). We evaluated the 1 degree-of-freedom (df) interaction and 2df joint test using generalized estimating equations. Results Analysis of current smokers using a CPD cutoff of 10 produced two loci associated with SBP. The rs9399633 minor allele was associated with increased SBP (5 mmHg) in heavy smokers (CPD>10) but decreased SBP (7 mmHg) in light smokers (CPD≤10). The rs11717948 minor allele was associated with decreased SBP (8 mmHg) in light smokers but decreased SBP (2 mmHg) in heavy smokers. Across all nine analyses, 19 additional loci reached p < 1×10−6. Discussion Analysis of current smokers may have the highest power to detect gene-smoking interactions, despite the reduced sample size. Associations of loci near SASH1 and KLHL6/KLHL24 with SBP may be modulated by tobacco smoking. PMID:25940791

Influence of Smoking Status and Intensity on Discovery of Blood Pressure Loci Through Gene-Smoking Interactions.

PubMed

Basson, Jacob; Sung, Yun Ju; Fuentes, Lisa de Las; Schwander, Karen; Cupples, L Adrienne; Rao, D C

2015-09-01

Genetic variation accounts for approximately 30% of blood pressure (BP) variability but most of that variability has not been attributed to specific variants. Interactions between genes and BP-associated factors may explain some "missing heritability." Cigarette smoking increases BP after short-term exposure and decreases BP with longer exposure. Gene-smoking interactions have discovered novel BP loci, but the contribution of smoking status and intensity to gene discovery is unknown. We analyzed gene-smoking intensity interactions for association with systolic BP (SBP) in three subgroups from the Framingham Heart Study: current smokers only (N = 1,057), current and former smokers ("ever smokers," N = 3,374), and all subjects (N = 6,710). We used three smoking intensity variables defined at cutoffs of 10, 15, and 20 cigarettes per day (CPD). We evaluated the 1 degree-of-freedom (df) interaction and 2df joint test using generalized estimating equations. Analysis of current smokers using a CPD cutoff of 10 produced two loci associated with SBP. The rs9399633 minor allele was associated with increased SBP (5 mmHg) in heavy smokers (CPD > 10) but decreased SBP (7 mmHg) in light smokers (CPD ≤ 10). The rs11717948 minor allele was associated with decreased SBP (8 mmHg) in light smokers but decreased SBP (2 mmHg) in heavy smokers. Across all nine analyses, 19 additional loci reached P < 1 × 10(-6). Analysis of current smokers may have the highest power to detect gene-smoking interactions, despite the reduced sample size. Associations of loci near SASH1 and KLHL6/KLHL24 with SBP may be modulated by tobacco smoking. © 2015 WILEY PERIODICALS, INC.

Systematic detection of positive selection in the human-pathogen interactome and lasting effects on infectious disease susceptibility.

PubMed

Corona, Erik; Wang, Liuyang; Ko, Dennis; Patel, Chirag J

2018-01-01

Infectious disease has shaped the natural genetic diversity of humans throughout the world. A new approach to capture positive selection driven by pathogens would provide information regarding pathogen exposure in distinct human populations and the constantly evolving arms race between host and disease-causing agents. We created a human pathogen interaction database and used the integrated haplotype score (iHS) to detect recent positive selection in genes that interact with proteins from 26 different pathogens. We used the Human Genome Diversity Panel to identify specific populations harboring pathogen-interacting genes that have undergone positive selection. We found that human genes that interact with 9 pathogen species show evidence of recent positive selection. These pathogens are Yersenia pestis, human immunodeficiency virus (HIV) 1, Zaire ebolavirus, Francisella tularensis, dengue virus, human respiratory syncytial virus, measles virus, Rubella virus, and Bacillus anthracis. For HIV-1, GWAS demonstrate that some naturally selected variants in the host-pathogen protein interaction networks continue to have functional consequences for susceptibility to these pathogens. We show that selected human genes were enriched for HIV susceptibility variants (identified through GWAS), providing further support for the hypothesis that ancient humans were exposed to lentivirus pandemics. Human genes in the Italian, Miao, and Biaka Pygmy populations that interact with Y. pestis show significant signs of selection. These results reveal some of the genetic footprints created by pathogens in the human genome that may have left lasting marks on susceptibility to infectious disease.

Challenges and Opportunities in Genome-Wide Environmental Interaction (GWEI) studies

PubMed Central

Aschard, Hugues; Lutz, Sharon; Maus, Bärbel; Duell, Eric J.; Fingerlin, Tasha; Chatterjee, Nilanjan; Kraft, Peter; Van Steen, Kristel

2012-01-01

The interest in performing gene-environment interaction studies has seen a significant increase with the increase of advanced molecular genetics techniques. Practically, it became possible to investigate the role of environmental factors in disease risk and hence to investigate their role as genetic effect modifiers. The understanding that genetics is important in the uptake and metabolism of toxic substances is an example of how genetic profiles can modify important environmental risk factors to disease. Several rationales exist to set up gene-environment interaction studies and the technical challenges related to these studies – when the number of environmental or genetic risk factors is relatively small – has been described before. In the post-genomic era, it is now possible to study thousands of genes and their interaction with the environment. This brings along a whole range of new challenges and opportunities. Despite a continuing effort in developing efficient methods and optimal bioinformatics infrastructures to deal with the available wealth of data, the challenge remains how to best present and analyze Genome-Wide Environmental Interaction (GWEI) studies involving multiple genetic and environmental factors. Since GWEIs are performed at the intersection of statistical genetics, bioinformatics and epidemiology, usually similar problems need to be dealt with as for Genome-Wide Association gene-gene Interaction (GWAI) studies. However, additional complexities need to be considered which are typical for large-scale epidemiological studies, but are also related to “joining” two heterogeneous types of data in explaining complex disease trait variation or for prediction purposes. PMID:22760307

Mechanism of Mediator recruitment by tandem Gcn4 activation domains and three Gal11 activator-binding domains.

PubMed

Herbig, Eric; Warfield, Linda; Fish, Lisa; Fishburn, James; Knutson, Bruce A; Moorefield, Beth; Pacheco, Derek; Hahn, Steven

2010-05-01

Targets of the tandem Gcn4 acidic activation domains in transcription preinitiation complexes were identified by site-specific cross-linking. The individual Gcn4 activation domains cross-link to three common targets, Gal11/Med15, Taf12, and Tra1, which are subunits of four conserved coactivator complexes, Mediator, SAGA, TFIID, and NuA4. The Gcn4 N-terminal activation domain also cross-links to the Mediator subunit Sin4/Med16. The contribution of the two Gcn4 activation domains to transcription was gene specific and varied from synergistic to less than additive. Gcn4-dependent genes had a requirement for Gal11 ranging from 10-fold dependence to complete Gal11 independence, while the Gcn4-Taf12 interaction did not significantly contribute to the expression of any gene studied. Complementary methods identified three conserved Gal11 activator-binding domains that bind each Gcn4 activation domain with micromolar affinity. These Gal11 activator-binding domains contribute additively to transcription activation and Mediator recruitment at Gcn4- and Gal11-dependent genes. Although we found that the conserved Gal11 KIX domain contributes to Gal11 function, we found no evidence of specific Gcn4-KIX interaction and conclude that the Gal11 KIX domain does not function by specific interaction with Gcn4. Our combined results show gene-specific coactivator requirements, a surprising redundancy in activator-target interactions, and an activator-coactivator interaction mediated by multiple low-affinity protein-protein interactions.

Interaction between genes and macronutrient intake on the risk of developing type 2 diabetes: systematic review and findings from European Prospective Investigation into Cancer (EPIC)-InterAct.

PubMed

Li, Sherly X; Imamura, Fumiaki; Ye, Zheng; Schulze, Matthias B; Zheng, Jusheng; Ardanaz, Eva; Arriola, Larraitz; Boeing, Heiner; Dow, Courtney; Fagherazzi, Guy; Franks, Paul W; Agudo, Antonio; Grioni, Sara; Kaaks, Rudolf; Katzke, Verena A; Key, Timothy J; Khaw, Kay Tee; Mancini, Francesca R; Navarro, Carmen; Nilsson, Peter M; Onland-Moret, N Charlotte; Overvad, Kim; Palli, Domenico; Panico, Salvatore; Quirós, J Ramón; Rolandsson, Olov; Sacerdote, Carlotta; Sánchez, María-José; Slimani, Nadia; Sluijs, Ivonne; Spijkerman, Annemieke Mw; Tjonneland, Anne; Tumino, Rosario; Sharp, Stephen J; Riboli, Elio; Langenberg, Claudia; Scott, Robert A; Forouhi, Nita G; Wareham, Nicholas J

2017-07-01

Background: Gene-diet interactions have been reported to contribute to the development of type 2 diabetes (T2D). However, to our knowledge, few examples have been consistently replicated to date. Objective: We aimed to identify existing evidence for gene-macronutrient interactions and T2D and to examine the reported interactions in a large-scale study. Design: We systematically reviewed studies reporting gene-macronutrient interactions and T2D. We searched the MEDLINE, Human Genome Epidemiology Network, and WHO International Clinical Trials Registry Platform electronic databases to identify studies published up to October 2015. Eligibility criteria included assessment of macronutrient quantity (e.g., total carbohydrate) or indicators of quality (e.g., dietary fiber) by use of self-report or objective biomarkers of intake. Interactions identified in the review were subsequently examined in the EPIC (European Prospective Investigation into Cancer)-InterAct case-cohort study ( n = 21,148, with 9403 T2D cases; 8 European countries). Prentice-weighted Cox regression was used to estimate country-specific HRs, 95% CIs, and P -interaction values, which were then pooled by random-effects meta-analysis. A primary model was fitted by using the same covariates as reported in the published studies, and a second model adjusted for additional covariates and estimated the effects of isocaloric macronutrient substitution. Results: Thirteen observational studies met the eligibility criteria ( n < 1700 cases). Eight unique interactions were reported to be significant between macronutrients [carbohydrate, fat, saturated fat, dietary fiber, and glycemic load derived from self-report of dietary intake and circulating n-3 (ω-3) polyunsaturated fatty acids] and genetic variants in or near transcription factor 7-like 2 ( TCF7L2 ), gastric inhibitory polypeptide receptor ( GIPR ), caveolin 2 ( CAV2 ), and peptidase D ( PEPD ) ( P -interaction < 0.05). We found no evidence of interaction when we tried to replicate previously reported interactions. In addition, no interactions were detected in models with additional covariates. Conclusions: Eight gene-macronutrient interactions were identified for the risk of T2D from the literature. These interactions were not replicated in the EPIC-InterAct study, which mirrored the analyses undertaken in the original reports. Our findings highlight the importance of independent replication of reported interactions.

SUV39H1 interacts with HTLV-1 Tax and abrogates Tax transactivation of HTLV-1 LTR

PubMed Central

Kamoi, Koju; Yamamoto, Keiyu; Misawa, Aya; Miyake, Ariko; Ishida, Takaomi; Tanaka, Yuetsu; Mochizuki, Manabu; Watanabe, Toshiki

2006-01-01

Background Tax is the oncoprotein of HTLV-1 which deregulates signal transduction pathways, transcription of genes and cell cycle regulation of host cells. Transacting function of Tax is mainly mediated by its protein-protein interactions with host cellular factors. As to Tax-mediated regulation of gene expression of HTLV-1 and cellular genes, Tax was shown to regulate histone acetylation through its physical interaction with histone acetylases and deacetylases. However, functional interaction of Tax with histone methyltransferases (HMTase) has not been studied. Here we examined the ability of Tax to interact with a histone methyltransferase SUV39H1 that methylates histone H3 lysine 9 (H3K9) and represses transcription of genes, and studied the functional effects of the interaction on HTLV-1 gene expression. Results Tax was shown to interact with SUV39H1 in vitro, and the interaction is largely dependent on the C-terminal half of SUV39H1 containing the SET domain. Tax does not affect the methyltransferase activity of SUV39H1 but tethers SUV39H1 to a Tax containing complex in the nuclei. In reporter gene assays, co-expression of SUV39H1 represses Tax transactivation of HTLV-1 LTR promoter activity, which was dependent on the methyltransferase activity of SUV39H1. Furthermore, SUV39H1 expression is induced along with Tax in JPX9 cells. Chromatin immunoprecipitation (ChIP) analysis shows localization of SUV39H1 on the LTR after Tax induction, but not in the absence of Tax induction, in JPX9 transformants retaining HTLV-1-Luc plasmid. Immunoblotting shows higher levels of SUV39H1 expression in HTLV-1 transformed and latently infected cell lines. Conclusion Our study revealed for the first time the interaction between Tax and SUV39H1 and apparent tethering of SUV39H1 by Tax to the HTLV-1 LTR. It is speculated that Tax-mediated tethering of SUV39H1 to the LTR and induction of the repressive histone modification on the chromatin through H3 K9 methylation may be the basis for the dose-dependent repression of Tax transactivation of LTR by SUV39H1. Tax-induced SUV39H1 expression, Tax-SUV39H1 interaction and tethering to the LTR may provide a support for an idea that the above sequence of events may form a negative feedback loop that self-limits HTLV-1 viral gene expression in infected cells. PMID:16409643

Genetic Association and Gene-Gene Interaction Analyses in African American Dialysis Patients With Nondiabetic Nephropathy

PubMed Central

Bostrom, Meredith A.; Kao, W.H. Linda; Li, Man; Abboud, Hanna E.; Adler, Sharon G.; Iyengar, Sudha K.; Kimmel, Paul L.; Hanson, Robert L.; Nicholas, Susanne B.; Rasooly, Rebekah S.; Sedor, John R.; Coresh, Josef; Kohn, Orly F.; Leehey, David J.; Thornley-Brown, Denyse; Bottinger, Erwin P.; Lipkowitz, Michael S.; Meoni, Lucy A.; Klag, Michael J.; Lu, Lingyi; Hicks, Pamela J.; Langefeld, Carl D.; Parekh, Rulan S.; Bowden, Donald W.; Freedman, Barry I.

2011-01-01

Background African Americans (AAs) have increased susceptibility to non-diabetic nephropathy relative to European Americans. Study Design Follow-up of a pooled genome-wide association study (GWAS) in AA dialysis patients with nondiabetic nephropathy; novel gene-gene interaction analyses. Setting & Participants Wake Forest sample: 962 AA nondiabetic nephropathy cases; 931 non-nephropathy controls. Replication sample: 668 Family Investigation of Nephropathy and Diabetes (FIND) AA nondiabetic nephropathy cases; 804 non-nephropathy controls. Predictors Individual genotyping of top 1420 pooled GWAS-associated single nucleotide polymorphisms (SNPs) and 54 SNPs in six nephropathy susceptibility genes. Outcomes APOL1 genetic association and additional candidate susceptibility loci interacting with, or independently from, APOL1. Results The strongest GWAS associations included two non-coding APOL1 SNPs, rs2239785 (odds ratio [OR], 0.33; dominant; p = 5.9 × 10−24) and rs136148 (OR, 0.54; additive; p = 1.1 × 10−7) with replication in FIND (p = 5.0 × 10−21 and 1.9 × 10−05, respectively). Rs2239785 remained significantly associated after controlling for the APOL1 G1 and G2 coding variants. Additional top hits included a CFH SNP(OR from meta-analysis in above 3367 AA cases and controls, 0.81; additive; p = 6.8 × 10−4). The 1420 SNPs were tested for interaction with APOL1 G1 and G2 variants. Several interactive SNPs were detected, the most significant was rs16854341 in the podocin gene (NPHS2) (p = 0.0001). Limitations Non-pooled GWAS have not been performed in AA nondiabetic nephropathy. Conclusions This follow-up of a pooled GWAS provides additional and independent evidence that APOL1 variants contribute to nondiabetic nephropathy in AAs and identified additional associated and interactive non-diabetic nephropathy susceptibility genes. PMID:22119407

Epistasis Analysis for Estrogen Metabolic and Signaling Pathway Genes on Young Ischemic Stroke Patients

PubMed Central

Hsieh, Yi-Chen; Jeng, Jiann-Shing; Lin, Huey-Juan; Hu, Chaur-Jong; Yu, Chia-Chen; Lien, Li-Ming; Peng, Giia-Sheun; Chen, Chin-I; Tang, Sung-Chun; Chi, Nai-Fang; Tseng, Hung-Pin; Chern, Chang-Ming; Hsieh, Fang-I; Bai, Chyi-Huey; Chen, Yi-Rhu; Chiou, Hung-Yi; Jeng, Jiann-Shing; Tang, Sung-Chun; Yeh, Shin-Joe; Tsai, Li-Kai; Kong, Shin; Lien, Li-Ming; Chiu, Hou-Chang; Chen, Wei-Hung; Bai, Chyi-Huey; Huang, Tzu-Hsuan; Chi-Ieong, Lau; Wu, Ya-Ying; Yuan, Rey-Yue; Hu, Chaur-Jong; Sheu, Jau- Jiuan; Yu, Jia-Ming; Ho, Chun-Sum; Chen, Chin-I; Sung, Jia-Ying; Weng, Hsing-Yu; Han, Yu-Hsuan; Huang, Chun-Ping; Chung, Wen-Ting; Ke, Der-Shin; Lin, Huey-Juan; Chang, Chia-Yu; Yeh, Poh-Shiow; Lin, Kao-Chang; Cheng, Tain-Junn; Chou, Chih-Ho; Yang, Chun-Ming; Peng, Giia-Sheun; Lin, Jiann-Chyun; Hsu, Yaw-Don; Denq, Jong-Chyou; Lee, Jiunn-Tay; Hsu, Chang-Hung; Lin, Chun-Chieh; Yen, Che-Hung; Cheng, Chun-An; Sung, Yueh-Feng; Chen, Yuan-Liang; Lien, Ming-Tung; Chou, Chung-Hsing; Liu, Chia-Chen; Yang, Fu-Chi; Wu, Yi-Chung; Tso, An-Chen; Lai, Yu- Hua; Chiang, Chun-I; Tsai, Chia-Kuang; Liu, Meng-Ta; Lin, Ying-Che; Hsu, Yu-Chuan; Chen, Chih-Hung; Sung, Pi-Shan; Chern, Chang-Ming; Hu, Han-Hwa; Wong, Wen-Jang; Luk, Yun-On; Hsu, Li-Chi; Chung, Chih-Ping; Tseng, Hung-Pin; Liu, Chin-Hsiung; Lin, Chun-Liang; Lin, Hung-Chih; Hu, Chaur-Jong

2012-01-01

Background Endogenous estrogens play an important role in the overall cardiocirculatory system. However, there are no studies exploring the hormone metabolism and signaling pathway genes together on ischemic stroke, including sulfotransferase family 1E (SULT1E1), catechol-O-methyl-transferase (COMT), and estrogen receptor α (ESR1). Methods A case-control study was conducted on 305 young ischemic stroke subjects aged ≦ 50 years and 309 age-matched healthy controls. SULT1E1 -64G/A, COMT Val158Met, ESR1 c.454−397 T/C and c.454−351 A/G genes were genotyped and compared between cases and controls to identify single nucleotide polymorphisms associated with ischemic stroke susceptibility. Gene-gene interaction effects were analyzed using entropy-based multifactor dimensionality reduction (MDR), classification and regression tree (CART), and traditional multiple regression models. Results COMT Val158Met polymorphism showed a significant association with susceptibility of young ischemic stroke among females. There was a two-way interaction between SULT1E1 -64G/A and COMT Val158Met in both MDR and CART analysis. The logistic regression model also showed there was a significant interaction effect between SULT1E1 -64G/A and COMT Val158Met on ischemic stroke of the young (P for interaction = 0.0171). We further found that lower estradiol level could increase the risk of young ischemic stroke for those who carry either SULT1E1 or COMT risk genotypes, showing a significant interaction effect (P for interaction = 0.0174). Conclusions Our findings support that a significant epistasis effect exists among estrogen metabolic and signaling pathway genes and gene-environment interactions on young ischemic stroke subjects. PMID:23112845

Detecting Genetic Interactions for Quantitative Traits Using m-Spacing Entropy Measure

PubMed Central

Yee, Jaeyong; Kwon, Min-Seok; Park, Taesung; Park, Mira

2015-01-01

A number of statistical methods for detecting gene-gene interactions have been developed in genetic association studies with binary traits. However, many phenotype measures are intrinsically quantitative and categorizing continuous traits may not always be straightforward and meaningful. Association of gene-gene interactions with an observed distribution of such phenotypes needs to be investigated directly without categorization. Information gain based on entropy measure has previously been successful in identifying genetic associations with binary traits. We extend the usefulness of this information gain by proposing a nonparametric evaluation method of conditional entropy of a quantitative phenotype associated with a given genotype. Hence, the information gain can be obtained for any phenotype distribution. Because any functional form, such as Gaussian, is not assumed for the entire distribution of a trait or a given genotype, this method is expected to be robust enough to be applied to any phenotypic association data. Here, we show its use to successfully identify the main effect, as well as the genetic interactions, associated with a quantitative trait. PMID:26339620

Kinase Pathway Database: An Integrated Protein-Kinase and NLP-Based Protein-Interaction Resource

PubMed Central

Koike, Asako; Kobayashi, Yoshiyuki; Takagi, Toshihisa

2003-01-01

Protein kinases play a crucial role in the regulation of cellular functions. Various kinds of information about these molecules are important for understanding signaling pathways and organism characteristics. We have developed the Kinase Pathway Database, an integrated database involving major completely sequenced eukaryotes. It contains the classification of protein kinases and their functional conservation, ortholog tables among species, protein–protein, protein–gene, and protein–compound interaction data, domain information, and structural information. It also provides an automatic pathway graphic image interface. The protein, gene, and compound interactions are automatically extracted from abstracts for all genes and proteins by natural-language processing (NLP).The method of automatic extraction uses phrase patterns and the GENA protein, gene, and compound name dictionary, which was developed by our group. With this database, pathways are easily compared among species using data with more than 47,000 protein interactions and protein kinase ortholog tables. The database is available for querying and browsing at http://kinasedb.ontology.ims.u-tokyo.ac.jp/. PMID:12799355

Systematic analysis of the gerontome reveals links between aging and age-related diseases

PubMed Central

Fernandes, Maria; Wan, Cen; Tacutu, Robi; Barardo, Diogo; Rajput, Ashish; Wang, Jingwei; Thoppil, Harikrishnan; Thornton, Daniel; Yang, Chenhao; Freitas, Alex

2016-01-01

Abstract In model organisms, over 2,000 genes have been shown to modulate aging, the collection of which we call the ‘gerontome’. Although some individual aging-related genes have been the subject of intense scrutiny, their analysis as a whole has been limited. In particular, the genetic interaction of aging and age-related pathologies remain a subject of debate. In this work, we perform a systematic analysis of the gerontome across species, including human aging-related genes. First, by classifying aging-related genes as pro- or anti-longevity, we define distinct pathways and genes that modulate aging in different ways. Our subsequent comparison of aging-related genes with age-related disease genes reveals species-specific effects with strong overlaps between aging and age-related diseases in mice, yet surprisingly few overlaps in lower model organisms. We discover that genetic links between aging and age-related diseases are due to a small fraction of aging-related genes which also tend to have a high network connectivity. Other insights from our systematic analysis include assessing how using datasets with genes more or less studied than average may result in biases, showing that age-related disease genes have faster molecular evolution rates and predicting new aging-related drugs based on drug-gene interaction data. Overall, this is the largest systems-level analysis of the genetics of aging to date and the first to discriminate anti- and pro-longevity genes, revealing new insights on aging-related genes as a whole and their interactions with age-related diseases. PMID:28175300

Bioinformatics Analysis Reveals Genes Involved in the Pathogenesis of Ameloblastoma and Keratocystic Odontogenic Tumor.

PubMed

Santos, Eliane Macedo Sobrinho; Santos, Hércules Otacílio; Dos Santos Dias, Ivoneth; Santos, Sérgio Henrique; Batista de Paula, Alfredo Maurício; Feltenberger, John David; Sena Guimarães, André Luiz; Farias, Lucyana Conceição

2016-01-01

Pathogenesis of odontogenic tumors is not well known. It is important to identify genetic deregulations and molecular alterations. This study aimed to investigate, through bioinformatic analysis, the possible genes involved in the pathogenesis of ameloblastoma (AM) and keratocystic odontogenic tumor (KCOT). Genes involved in the pathogenesis of AM and KCOT were identified in GeneCards. Gene list was expanded, and the gene interactions network was mapped using the STRING software. "Weighted number of links" (WNL) was calculated to identify "leader genes" (highest WNL). Genes were ranked by K-means method and Kruskal-Wallis test was used (P<0.001). Total interactions score (TIS) was also calculated using all interaction data generated by the STRING database, in order to achieve global connectivity for each gene. The topological and ontological analyses were performed using Cytoscape software and BinGO plugin. Literature review data was used to corroborate the bioinformatics data. CDK1 was identified as leader gene for AM. In KCOT group, results show PCNA and TP53 . Both tumors exhibit a power law behavior. Our topological analysis suggested leader genes possibly important in the pathogenesis of AM and KCOT, by clustering coefficient calculated for both odontogenic tumors (0.028 for AM, zero for KCOT). The results obtained in the scatter diagram suggest an important relationship of these genes with the molecular processes involved in AM and KCOT. Ontological analysis for both AM and KCOT demonstrated different mechanisms. Bioinformatics analyzes were confirmed through literature review. These results may suggest the involvement of promising genes for a better understanding of the pathogenesis of AM and KCOT.

Two-Way Gene Interaction From Microarray Data Based on Correlation Methods.

PubMed

Alavi Majd, Hamid; Talebi, Atefeh; Gilany, Kambiz; Khayyer, Nasibeh

2016-06-01

Gene networks have generated a massive explosion in the development of high-throughput techniques for monitoring various aspects of gene activity. Networks offer a natural way to model interactions between genes, and extracting gene network information from high-throughput genomic data is an important and difficult task. The purpose of this study is to construct a two-way gene network based on parametric and nonparametric correlation coefficients. The first step in constructing a Gene Co-expression Network is to score all pairs of gene vectors. The second step is to select a score threshold and connect all gene pairs whose scores exceed this value. In the foundation-application study, we constructed two-way gene networks using nonparametric methods, such as Spearman's rank correlation coefficient and Blomqvist's measure, and compared them with Pearson's correlation coefficient. We surveyed six genes of venous thrombosis disease, made a matrix entry representing the score for the corresponding gene pair, and obtained two-way interactions using Pearson's correlation, Spearman's rank correlation, and Blomqvist's coefficient. Finally, these methods were compared with Cytoscape, based on BIND, and Gene Ontology, based on molecular function visual methods; R software version 3.2 and Bioconductor were used to perform these methods. Based on the Pearson and Spearman correlations, the results were the same and were confirmed by Cytoscape and GO visual methods; however, Blomqvist's coefficient was not confirmed by visual methods. Some results of the correlation coefficients are not the same with visualization. The reason may be due to the small number of data.

Relationship of the Interaction Between Two Quantitative Trait Loci with γ-Globin Expression in β-Thalassemia Intermedia Patients.

PubMed

NickAria, Shiva; Haghpanah, Sezaneh; Ramzi, Mani; Karimi, Mehran

2018-05-10

Globin switching is a significant factor on blood hemoglobin (Hb) level but its molecular mechanisms have not yet been identified, however, several quantitative trait loci (QTL) and polymorphisms involved regions on chromosomes 2p, 6q, 8q and X account for variation in the γ-globin expression level. We studied the effect of interaction between a region on intron six of the TOX gene, chromosome 8q (chr8q) and XmnI locus on the γ-globin promoter, chr11p on γ-globin expression in 150 β-thalassemia intermedia (β-TI) patients, evaluated by statistical interaction analysis. Our results showed a significant interaction between one QTL on intron six of the TOX gene (rs9693712) and XmnI locus that effect γ-globin expression. Interchromosomal interaction mediates through transcriptional machanisms to preserve true genome architectural features, chromosomes localization and DNA bending. This interaction can be a part of the unknown molecular mechanism of globin switching and regulation of gene expression.

Learning directed acyclic graphs from large-scale genomics data.

PubMed

Nikolay, Fabio; Pesavento, Marius; Kritikos, George; Typas, Nassos

2017-09-20

In this paper, we consider the problem of learning the genetic interaction map, i.e., the topology of a directed acyclic graph (DAG) of genetic interactions from noisy double-knockout (DK) data. Based on a set of well-established biological interaction models, we detect and classify the interactions between genes. We propose a novel linear integer optimization program called the Genetic-Interactions-Detector (GENIE) to identify the complex biological dependencies among genes and to compute the DAG topology that matches the DK measurements best. Furthermore, we extend the GENIE program by incorporating genetic interaction profile (GI-profile) data to further enhance the detection performance. In addition, we propose a sequential scalability technique for large sets of genes under study, in order to provide statistically significant results for real measurement data. Finally, we show via numeric simulations that the GENIE program and the GI-profile data extended GENIE (GI-GENIE) program clearly outperform the conventional techniques and present real data results for our proposed sequential scalability technique.

Differences and similarities in the serotonergic diathesis for suicide attempts and mood disorders: a 22-year longitudinal gene-environment study.

PubMed

Brezo, J; Bureau, A; Mérette, C; Jomphe, V; Barker, E D; Vitaro, F; Hébert, M; Carbonneau, R; Tremblay, R E; Turecki, G

2010-08-01

To investigate similarities and differences in the serotonergic diathesis for mood disorders and suicide attempts, we conducted a study in a cohort followed longitudinally for 22 years. A total of 1255 members of this cohort, which is representative of the French-speaking population of Quebec, were investigated. Main outcome measures included (1) mood disorders (bipolar disorder and major depression) and suicide attempts by early adulthood; (2) odds ratios and probabilities associated with 143 single nucleotide polymorphisms in 11 serotonergic genes, acting directly or as moderators in gene-environment interactions with childhood sexual or childhood physical abuse (CPA), and in gene-gene interactions; (3) regression coefficients for putative endophenotypes for mood disorders (childhood anxiousness) and suicide attempts (childhood disruptiveness). Five genes showed significant adjusted effects (HTR2A, TPH1, HTR5A, SLC6A4 and HTR1A). Of these, HTR2A variation influenced both suicide attempts and mood disorders, although through different mechanisms. In suicide attempts, HTR2A variants (rs6561333, rs7997012 and rs1885884) were involved through interactions with histories of sexual and physical abuse whereas in mood disorders through one main effect (rs9316235). In terms of phenotype-specific contributions, TPH1 variation (rs10488683) was relevant only in the diathesis for suicide attempts. Three genes contributed exclusively to mood disorders, one through a main effect (HTR5A (rs1657268)) and two through gene-environment interactions with CPA (HTR1A (rs878567) and SLC6A4 (rs3794808)). Childhood anxiousness did not mediate the effects of HTR2A and HTR5A on mood disorders, nor did childhood disruptiveness mediate the effects of TPH1 on suicide attempts. Of the serotonergic genes implicated in mood disorders and suicidal behaviors, four exhibited phenotype-specific effects, suggesting that despite their high concordance and common genetic determinants, suicide attempts and mood disorders may also have partially independent etiological pathways. To identify where these pathways diverge, we need to understand the differential, phenotype-specific gene-environment interactions such as the ones observed in the present study, using suitably powered samples.

Detection of Protein Interactions in T3S Systems Using Yeast Two-Hybrid Analysis.

PubMed

Nilles, Matthew L

2017-01-01

Two-hybrid systems, sometimes termed interaction traps, are genetic systems designed to find and analyze interactions between proteins. The most common systems are yeast based (commonly Saccharomyces cerevisae) and rely on the functional reconstitution of the GAL4 transcriptional activator. Reporter genes, such as the lacZ gene of Escherichia coli (encodes β-galactosidase), are placed under GAL4-dependent transcriptional control to provide quick and reliable detection of protein interactions. In this method the use of a yeast-based two-hybrid system is described to study protein interactions between components of type III secretion systems.

Fusing literature and full network data improves disease similarity computation.

PubMed

Li, Ping; Nie, Yaling; Yu, Jingkai

2016-08-30

Identifying relatedness among diseases could help deepen understanding for the underlying pathogenic mechanisms of diseases, and facilitate drug repositioning projects. A number of methods for computing disease similarity had been developed; however, none of them were designed to utilize information of the entire protein interaction network, using instead only those interactions involving disease causing genes. Most of previously published methods required gene-disease association data, unfortunately, many diseases still have very few or no associated genes, which impeded broad adoption of those methods. In this study, we propose a new method (MedNetSim) for computing disease similarity by integrating medical literature and protein interaction network. MedNetSim consists of a network-based method (NetSim), which employs the entire protein interaction network, and a MEDLINE-based method (MedSim), which computes disease similarity by mining the biomedical literature. Among function-based methods, NetSim achieved the best performance. Its average AUC (area under the receiver operating characteristic curve) reached 95.2 %. MedSim, whose performance was even comparable to some function-based methods, acquired the highest average AUC in all semantic-based methods. Integration of MedSim and NetSim (MedNetSim) further improved the average AUC to 96.4 %. We further studied the effectiveness of different data sources. It was found that quality of protein interaction data was more important than its volume. On the contrary, higher volume of gene-disease association data was more beneficial, even with a lower reliability. Utilizing higher volume of disease-related gene data further improved the average AUC of MedNetSim and NetSim to 97.5 % and 96.7 %, respectively. Integrating biomedical literature and protein interaction network can be an effective way to compute disease similarity. Lacking sufficient disease-related gene data, literature-based methods such as MedSim can be a great addition to function-based algorithms. It may be beneficial to steer more resources torward studying gene-disease associations and improving the quality of protein interaction data. Disease similarities can be computed using the proposed methods at http:// www.digintelli.com:8000/ .

Polyploidy Enhances F1 Pollen Sterility Loci Interactions That Increase Meiosis Abnormalities and Pollen Sterility in Autotetraploid Rice1[OPEN

PubMed Central

Wu, Jinwen; Chen, Lin; Chen, Zhixiong; Wang, Lan; Lu, Yonggen

2015-01-01

Intersubspecific autotetraploid rice (Oryza sativa ssp. indica × japonica) hybrids have greater biological and yield potentials than diploid rice. However, the low fertility of intersubspecific autotetraploid hybrids, which is largely caused by high pollen abortion rates, limits their commercial utility. To decipher the cytological and molecular mechanisms underlying allelic interactions in autotetraploid rice, we developed an autotetraploid rice hybrid that was heterozygous (SiSj) at F1 pollen sterility loci (Sa, Sb, and Sc) using near-isogenic lines. Cytological studies showed that the autotetraploid had higher percentages (>30%) of abnormal chromosome behavior and aberrant meiocytes (>50%) during meiosis than did the diploid rice hybrid control. Analysis of gene expression profiles revealed 1,888 genes that were differentially expressed between the autotetraploid and diploid hybrid lines at the meiotic stage, among which 889 and 999 were up- and down-regulated, respectively. Of the 999 down-regulated genes, 940 were associated with the combined effect of polyploidy and pollen sterility loci interactions (IPE). Gene Ontology enrichment analysis identified a prominent functional gene class consisting of seven genes related to photosystem I (Gene Ontology 0009522). Moreover, 55 meiosis-related or meiosis stage-specific genes were associated with IPE in autotetraploid rice, including Os02g0497500, which encodes a DNA repair-recombination protein, and Os02g0490000, which encodes a component of the ubiquitin-proteasome pathway. These results suggest that polyploidy enhances epistatic interactions between alleles of pollen sterility loci, thereby altering the expression profiles of important meiosis-related or meiosis stage-specific genes and resulting in high pollen sterility. PMID:26511913

Polyploidy Enhances F1 Pollen Sterility Loci Interactions That Increase Meiosis Abnormalities and Pollen Sterility in Autotetraploid Rice.

PubMed

Wu, Jinwen; Shahid, Muhammad Qasim; Chen, Lin; Chen, Zhixiong; Wang, Lan; Liu, Xiangdong; Lu, Yonggen

2015-12-01

Intersubspecific autotetraploid rice (Oryza sativa ssp. indica × japonica) hybrids have greater biological and yield potentials than diploid rice. However, the low fertility of intersubspecific autotetraploid hybrids, which is largely caused by high pollen abortion rates, limits their commercial utility. To decipher the cytological and molecular mechanisms underlying allelic interactions in autotetraploid rice, we developed an autotetraploid rice hybrid that was heterozygous (S(i)S(j)) at F1 pollen sterility loci (Sa, Sb, and Sc) using near-isogenic lines. Cytological studies showed that the autotetraploid had higher percentages (>30%) of abnormal chromosome behavior and aberrant meiocytes (>50%) during meiosis than did the diploid rice hybrid control. Analysis of gene expression profiles revealed 1,888 genes that were differentially expressed between the autotetraploid and diploid hybrid lines at the meiotic stage, among which 889 and 999 were up- and down-regulated, respectively. Of the 999 down-regulated genes, 940 were associated with the combined effect of polyploidy and pollen sterility loci interactions (IPE). Gene Ontology enrichment analysis identified a prominent functional gene class consisting of seven genes related to photosystem I (Gene Ontology 0009522). Moreover, 55 meiosis-related or meiosis stage-specific genes were associated with IPE in autotetraploid rice, including Os02g0497500, which encodes a DNA repair-recombination protein, and Os02g0490000, which encodes a component of the ubiquitin-proteasome pathway. These results suggest that polyploidy enhances epistatic interactions between alleles of pollen sterility loci, thereby altering the expression profiles of important meiosis-related or meiosis stage-specific genes and resulting in high pollen sterility. © 2015 American Society of Plant Biologists. All Rights Reserved.

Multiple OPR genes influence personality traits in substance dependent and healthy subjects in two American populations

PubMed Central

Luo, Xingguang; Zuo, Lingjun; Kranzler, Henry; Zhang, Huiping; Wang, Shuang; Gelernter, Joel

2011-01-01

Background Personality traits are among the most complex quantitative traits. Certain personality traits are associated with substance dependence (SD); genetic factors may influence both. Associations between opioid receptor (OPR) genes and SD have been reported. This study investigated the relationship between OPR genes and personality traits in a case-control sample. Methods We assessed dimensions of the five-factor model of personality in 556 subjects: 250 with SD [181 European-Americans (EAs) and 69 African-Americans (AAs)] and 306 healthy subjects (266 EAs and 40 AAs). We genotyped 20 OPRM1 markers, 8 OPRD1 markers, and 7 OPRK1 markers, and 38 unlinked ancestry-informative markers in these subjects. The relationships between OPR genes and personality traits were examined using MANCOVA, controlling for gene-gene interaction effects and potential confounders. Associations were decomposed by Roy-Bargmann Stepdown ANCOVA. Results Personality traits were associated as main or interaction effects with the haplotypes, diplotypes, alleles and genotypes at the three OPR genes (0.002

Revealing the potential pathogenesis of glioma by utilizing a glioma associated protein-protein interaction network.

PubMed

Pan, Weiran; Li, Gang; Yang, Xiaoxiao; Miao, Jinming

2015-04-01

This study aims to explore the potential mechanism of glioma through bioinformatic approaches. The gene expression profile (GSE4290) of glioma tumor and non-tumor samples was downloaded from Gene Expression Omnibus database. A total of 180 samples were available, including 23 non-tumor and 157 tumor samples. Then the raw data were preprocessed using robust multiarray analysis, and 8,890 differentially expressed genes (DEGs) were identified by using t-test (false discovery rate < 0.0005). Furthermore, 16 known glioma related genes were abstracted from Genetic Association Database. After mapping 8,890 DEGs and 16 known glioma related genes to Human Protein Reference Database, a glioma associated protein-protein interaction network (GAPN) was constructed. In addition, 51 sub-networks in GAPN were screened out through Molecular Complex Detection (score ≥ 1), and sub-network 1 was found to have the closest interaction (score = 3). What' more, for the top 10 sub-networks, Gene Ontology (GO) enrichment analysis (p value < 0.05) was performed, and DEGs involved in sub-network 1 and 2, such as BRMS1L and CCNA1, were predicted to regulate cell growth, cell cycle, and DNA replication via interacting with known glioma related genes. Finally, the overlaps of DEGs and human essential, housekeeping, tissue-specific genes were calculated (p value = 1.0, 1.0, and 0.00014, respectively) and visualized by Venn Diagram package in R. About 61% of human tissue-specific genes were DEGs as well. This research shed new light on the pathogenesis of glioma based on DEGs and GAPN, and our findings might provide potential targets for clinical glioma treatment.

A Bayesian mixture model for chromatin interaction data.

PubMed

Niu, Liang; Lin, Shili

2015-02-01

Chromatin interactions mediated by a particular protein are of interest for studying gene regulation, especially the regulation of genes that are associated with, or known to be causative of, a disease. A recent molecular technique, Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET), that uses chromatin immunoprecipitation (ChIP) and high throughput paired-end sequencing, is able to detect such chromatin interactions genomewide. However, ChIA-PET may generate noise (i.e., pairings of DNA fragments by random chance) in addition to true signal (i.e., pairings of DNA fragments by interactions). In this paper, we propose MC_DIST based on a mixture modeling framework to identify true chromatin interactions from ChIA-PET count data (counts of DNA fragment pairs). The model is cast into a Bayesian framework to take into account the dependency among the data and the available information on protein binding sites and gene promoters to reduce false positives. A simulation study showed that MC_DIST outperforms the previously proposed hypergeometric model in terms of both power and type I error rate. A real data study showed that MC_DIST may identify potential chromatin interactions between protein binding sites and gene promoters that may be missed by the hypergeometric model. An R package implementing the MC_DIST model is available at http://www.stat.osu.edu/~statgen/SOFTWARE/MDM.

Listeria arpJ gene modifies T helper type 2 subset differentiation.

PubMed

Kanoh, Makoto; Maruyama, Saho; Shen, Hua; Matsumoto, Akira; Shinomiya, Hiroto; Przybilla, Karin; Gouin, Edith; Cossart, Pascale; Goebel, Werner; Asano, Yoshihiro

2015-07-15

Although the T-cell subset differentiation pathway has been characterized extensively from the view of host gene regulation, the effects of genes of the pathogen on T-cell subset differentiation during infection have yet to be elucidated. Especially, the bacterial genes that are responsible for this shift have not yet been determined. Utilizing a single-gene-mutation Listeria panel, we investigated genes involved in the host-pathogen interaction that are required for the initiation of T-cell subset differentiation in the early phase of pathogen infection. We demonstrate that the induction of T helper types 1 and 2 (Th1 and Th2) subsets are separate phenomena and are mediated by distinct Listeria genes. We identified several candidate Listeria genes that appear to be involved in the host-Listeria interaction. Among them, arpJ is the strongest candidate gene for inhibiting Th2 subset induction. Furthermore, the analysis utilizing arpJ-deficient Listeria monocytogenes (Lm) revealed that the tumor necrosis factor (TNF) superfamily (Tnfsf) 9-TNF receptor superfamily (Tnfrsf) 9 interaction inhibits the Th2 response during Lm infection. arpJ is the candidate gene for inhibiting Th2 T-cell subset induction. The arpJ gene product influences the expression of Tnfsf/Tnfrsf on antigen-presenting cells and inhibits the Th2 T-cell subset differentiation during Listeria infection. © The Author 2015. Published by Oxford University Press on behalf of the Infectious Diseases Society of America. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Nuclear import of transcription factor BR-C is mediated by its interaction with RACK1.

PubMed

Cheng, Daojun; Qian, Wenliang; Wang, Yonghu; Meng, Meng; Wei, Ling; Li, Zhiqing; Kang, Lixia; Peng, Jian; Xia, Qingyou

2014-01-01

The transcription factor Broad Complex (BR-C) is an early ecdysone response gene in insects and contains two types of domains: two zinc finger domains for the activation of gene transcription and a Bric-a-brac/Tramtrack/Broad complex (BTB) domain for protein-protein interaction. Although the mechanism of zinc finger-mediated gene transcription is well studied, the partners interacting with the BTB domain of BR-C has not been elucidated until now. Here, we performed a yeast two-hybrid screen using the BTB domain of silkworm BR-C as bait and identified the receptor for activated C-kinase 1 (RACK1), a scaffolding/anchoring protein, as the novel partner capable of interacting with BR-C. The interaction between BR-C and RACK1 was further confirmed by far-western blotting and pull-down assays. Importantly, the disruption of this interaction, via RNAi against the endogenous RACK1 gene or deletion of the BTB domain, abolished the nuclear import of BR-C in BmN4 cells. In addition, RNAi against the endogenous PKC gene as well as phosphorylation-deficient mutation of the predicted PKC phosphorylation sites at either Ser373 or Thr406 in BR-C phenocopied RACK1 RNAi and altered the nuclear localization of BR-C. However, when BTB domain was deleted, phosphorylation mimics of either Ser373 or Thr406 had no effect on the nuclear import of BR-C. Moreover, mutating the PKC phosphorylation sites at Ser373 and Thr406 or deleting the BTB domain significantly decreased the transcriptional activation of a BR-C target gene. Given that RACK1 is necessary for recruiting PKC to close and phosphorylate target proteins, we suggest that the PKC-mediated phosphorylation and nuclear import of BR-C is determined by its interaction with RACK1. This novel finding will be helpful for further deciphering the mechanism underlying the role of BR-C proteins during insect development.

Nuclear Import of Transcription Factor BR-C Is Mediated by Its Interaction with RACK1

PubMed Central

Wang, Yonghu; Meng, Meng; Wei, Ling; Li, Zhiqing; Kang, Lixia; Peng, Jian; Xia, Qingyou

2014-01-01

The transcription factor Broad Complex (BR-C) is an early ecdysone response gene in insects and contains two types of domains: two zinc finger domains for the activation of gene transcription and a Bric-a-brac/Tramtrack/Broad complex (BTB) domain for protein-protein interaction. Although the mechanism of zinc finger-mediated gene transcription is well studied, the partners interacting with the BTB domain of BR-C has not been elucidated until now. Here, we performed a yeast two-hybrid screen using the BTB domain of silkworm BR-C as bait and identified the receptor for activated C-kinase 1 (RACK1), a scaffolding/anchoring protein, as the novel partner capable of interacting with BR-C. The interaction between BR-C and RACK1 was further confirmed by far-western blotting and pull-down assays. Importantly, the disruption of this interaction, via RNAi against the endogenous RACK1 gene or deletion of the BTB domain, abolished the nuclear import of BR-C in BmN4 cells. In addition, RNAi against the endogenous PKC gene as well as phosphorylation-deficient mutation of the predicted PKC phosphorylation sites at either Ser373 or Thr406 in BR-C phenocopied RACK1 RNAi and altered the nuclear localization of BR-C. However, when BTB domain was deleted, phosphorylation mimics of either Ser373 or Thr406 had no effect on the nuclear import of BR-C. Moreover, mutating the PKC phosphorylation sites at Ser373 and Thr406 or deleting the BTB domain significantly decreased the transcriptional activation of a BR-C target gene. Given that RACK1 is necessary for recruiting PKC to close and phosphorylate target proteins, we suggest that the PKC-mediated phosphorylation and nuclear import of BR-C is determined by its interaction with RACK1. This novel finding will be helpful for further deciphering the mechanism underlying the role of BR-C proteins during insect development. PMID:25280016

Coordinating Regulation of Gene Expression in Cardiovascular Disease: Interactions between Chromatin Modifiers and Transcription Factors

PubMed Central

Bauer, Ashley J.; Martin, Kathleen A.

2017-01-01

Cardiovascular disease is a leading cause of death with increasing economic burden. The pathogenesis of cardiovascular diseases is complex, but can arise from genetic and/or environmental risk factors. This can lead to dysregulated gene expression in numerous cell types including cardiomyocytes, endothelial cells, vascular smooth muscle cells, and inflammatory cells. While initial studies addressed transcriptional control of gene expression, epigenetics has been increasingly appreciated to also play an important role in this process through alterations in chromatin structure and gene accessibility. Chromatin-modifying proteins including enzymes that modulate DNA methylation, histone methylation, and histone acetylation can influence gene expression in numerous ways. These chromatin modifiers and their marks can promote or prevent transcription factor recruitment to regulatory regions of genes through modifications to DNA, histones, or the transcription factors themselves. This review will focus on the emerging question of how epigenetic modifiers and transcription factors interact to coordinately regulate gene expression in cardiovascular disease. While most studies have addressed the roles of either epigenetic or transcriptional control, our understanding of the integration of these processes is only just beginning. Interrogating these interactions is challenging, and improved technical approaches will be needed to fully dissect the temporal and spatial relationships between transcription factors, chromatin modifiers, and gene expression in cardiovascular disease. We summarize the current state of the field and provide perspectives on limitations and future directions. Through studies of epigenetic and transcriptional interactions, we can advance our understanding of the basic mechanisms of cardiovascular disease pathogenesis to develop novel therapeutics. PMID:28428957

Genotype-based association models of complex diseases to detect gene-gene and gene-environment interactions.

PubMed

Lobach, Iryna; Fan, Ruzong; Manga, Prashiela

A central problem in genetic epidemiology is to identify and rank genetic markers involved in a disease. Complex diseases, such as cancer, hypertension, diabetes, are thought to be caused by an interaction of a panel of genetic factors, that can be identified by markers, which modulate environmental factors. Moreover, the effect of each genetic marker may be small. Hence, the association signal may be missed unless a large sample is considered, or a priori biomedical data are used. Recent advances generated a vast variety of a priori information, including linkage maps and information about gene regulatory dependence assembled into curated pathway databases. We propose a genotype-based approach that takes into account linkage disequilibrium (LD) information between genetic markers that are in moderate LD while modeling gene-gene and gene-environment interactions. A major advantage of our method is that the observed genetic information enters a model directly thus eliminating the need to estimate haplotype-phase. Our approach results in an algorithm that is inexpensive computationally and does not suffer from bias induced by haplotype-phase ambiguity. We investigated our model in a series of simulation experiments and demonstrated that the proposed approach results in estimates that are nearly unbiased and have small variability. We applied our method to the analysis of data from a melanoma case-control study and investigated interaction between a set of pigmentation genes and environmental factors defined by age and gender. Furthermore, an application of our method is demonstrated using a study of Alcohol Dependence.

Evidence for epistasis between SLC6A4 and ITGB3 in autism etiology and in the determination of platelet serotonin levels.

PubMed

Coutinho, Ana M; Sousa, Inês; Martins, Madalena; Correia, Catarina; Morgadinho, Teresa; Bento, Celeste; Marques, Carla; Ataíde, Assunção; Miguel, Teresa S; Moore, Jason H; Oliveira, Guiomar; Vicente, Astrid M

2007-04-01

Autism is a neurodevelopmental disorder of unclear etiology. The consistent finding of platelet hyperserotonemia in a proportion of patients and its heritability within affected families suggest that genes involved in the serotonin system play a role in this disorder. The role in autism etiology of seven candidate genes in the serotonin metabolic and neurotransmission pathways and mapping to autism linkage regions (SLC6A4, HTR1A, HTR1D, HTR2A, HTR5A, TPH1 and ITGB3) was analyzed in a sample of 186 nuclear families. The impact of interactions among these genes in autism was assessed using the multifactor-dimensionality reduction (MDR) method in 186 patients and 181 controls. We further evaluated whether the effect of specific gene variants or gene interactions associated with autism etiology might be mediated by their influence on serotonin levels, using the quantitative transmission disequilibrium test (QTDT) and the restricted partition method (RPM), in a sample of 109 autistic children. We report a significant main effect of the HTR5A gene in autism (P = 0.0088), and a significant three-locus model comprising a synergistic interaction between the ITGB3 and SLC6A4 genes with an additive effect of HTR5A (P < 0.0010). In addition to the previously reported contribution of SLC6A4, we found significant associations of ITGB3 haplotypes with serotonin level distribution (P = 0.0163). The most significant models contributing to serotonin distribution were found for interactions between TPH1 rs4537731 and SLC6A4 haplotypes (P = 0.002) and between HTR1D rs6300 and SLC6A4 haplotypes (P = 0.013). In addition to the significant independent effects, evidence for interaction between SLC6A4 and ITGB3 markers was also found. The overall results implicate SLC6A4 and ITGB3 gene interactions in autism etiology and in serotonin level determination, providing evidence for a common underlying genetic mechanism and a molecular explanation for the association of platelet hyperserotonemia with autism.

Interactions between Bmp-4 and Msx-1 act to restrict gene expression to odontogenic mesenchyme.

PubMed

Tucker, A S; Al Khamis, A; Sharpe, P T

1998-08-01

Tooth development is regulated by a reciprocal series of epithelial-mesenchymal interactions. Bmp4 has been identified as a candidate signalling molecule in these interactions, initially as an epithelial signal and then later at the bud stage as a mesenchymal signal (Vainio et al. [1993] Cell 75:45-58). A target gene for Bmp4 signalling is the homeobox gene Msx-1, identified by the ability of recombinant Bmp4 protein to induce expression in mesenchyme. There is, however, no evidence that Bmp4 is the endogenous inducer of Msx-1 expression. Msx-1 and Bmp-4 show dynamic, interactive patterns of expression in oral epithelium and ectomesenchyme during the early stages of tooth development. In this study, we compare the temporal and spatial expression of these two genes to determine whether the changing expression patterns of these genes are consistent with interactions between the two molecules. We show that changes in Bmp-4 expression precede changes in Msx-1 expression. At embryonic day (E)10.5-E11.0, expression patterns are consistent with BMP4 from the epithelium, inducing or maintaining Msx-1 in underlying mesenchyme. At E11.5, Bmp-4 expression shifts from epithelium to mesenchyme and is rapidly followed by localised up-regulation of Msx-1 expression at the sites of Bmp-4 expression. Using cultured explants of developing mandibles, we confirm that exogenous BMP4 is capable of replacing the endogenous source in epithelium and inducing Msx-1 gene expression in mesenchyme. By using noggin, a BMP inhibitor, we show that endogenous Msx-1 expression can be inhibited at E10.5 and E11.5, providing the first evidence that endogenous Bmp-4 from the epithelium is responsible for regulating the early spatial expression of Msx-1. We also show that the mesenchymal shift in Bmp-4 is responsible for up-regulating Msx-1 specifically at the sites of future tooth formation. Thus, we establish that a reciprocal series of interactions act to restrict expression of both genes to future sites of tooth formation, creating a positive feedback loop that maintains expression of both genes in tooth mesenchymal cells.

Genome organization and long-range regulation of gene expression by enhancers.

PubMed

Smallwood, Andrea; Ren, Bing

2013-06-01

It is now well accepted that cell-type specific gene regulation is under the purview of enhancers. Great strides have been made recently to characterize and identify enhancers both genetically and epigenetically for multiple cell types and species, but efforts have just begun to link enhancers to their target promoters. Mapping these interactions and understanding how the 3D landscape of the genome constrains such interactions is fundamental to our understanding of mammalian gene regulation. Here, we review recent progress in mapping long-range regulatory interactions in mammalian genomes, focusing on transcriptional enhancers and chromatin organization principles. Copyright © 2013. Published by Elsevier Ltd.

A computational interactome for prioritizing genes associated with complex agronomic traits in rice (Oryza sativa).

PubMed

Liu, Shiwei; Liu, Yihui; Zhao, Jiawei; Cai, Shitao; Qian, Hongmei; Zuo, Kaijing; Zhao, Lingxia; Zhang, Lida

2017-04-01

Rice (Oryza sativa) is one of the most important staple foods for more than half of the global population. Many rice traits are quantitative, complex and controlled by multiple interacting genes. Thus, a full understanding of genetic relationships will be critical to systematically identify genes controlling agronomic traits. We developed a genome-wide rice protein-protein interaction network (RicePPINet, http://netbio.sjtu.edu.cn/riceppinet) using machine learning with structural relationship and functional information. RicePPINet contained 708 819 predicted interactions for 16 895 non-transposable element related proteins. The power of the network for discovering novel protein interactions was demonstrated through comparison with other publicly available protein-protein interaction (PPI) prediction methods, and by experimentally determined PPI data sets. Furthermore, global analysis of domain-mediated interactions revealed RicePPINet accurately reflects PPIs at the domain level. Our studies showed the efficiency of the RicePPINet-based method in prioritizing candidate genes involved in complex agronomic traits, such as disease resistance and drought tolerance, was approximately 2-11 times better than random prediction. RicePPINet provides an expanded landscape of computational interactome for the genetic dissection of agronomically important traits in rice. © 2017 The Authors The Plant Journal © 2017 John Wiley & Sons Ltd.

Gene-by-Socioeconomic Status Interaction on School Readiness

PubMed Central

Rhemtulla, Mijke; Tucker-Drob, Elliot M.

2017-01-01

In previous work with a nationally representative sample of over 1,400 monozygotic and dizygotic twins born in the United States, Tucker-Drob, Rhemtulla, Harden, Turkheimer, and Fask (2011; Psychological Science, 22, 125–133) uncovered a gene × environment interaction on scores on the Bayley Short Form test of mental ability at 2 years of age—higher socioeconomic status (SES) was associated not only with higher mental ability, but also with larger genetic contributions to individual differences in mental ability. The current study examined gene × SES interactions in mathematics skill and reading skill at 4 years of age (preschool age) in the same sample of twins, and further examined whether interactions detected at 4 years could be attributed to the persistence of the interaction previously observed at 2 years. For early mathematics skill but not early reading skill, genetic influences were more pronounced at higher levels of SES. This interaction was not accounted for by the interaction observed at 2 years. These findings indicate that SES moderates the etiological influences on certain cognitive functions at multiple stages of child development. PMID:22350185

Association of Polyaminergic Loci With Anxiety, Mood Disorders, and Attempted Suicide

PubMed Central

Fiori, Laura M.; Wanner, Brigitte; Jomphe, Valérie; Croteau, Jordie; Vitaro, Frank

2010-01-01

Background The polyamine system has been implicated in a number of psychiatric conditions, which display both alterations in polyamine levels and altered expression of genes related to polyamine metabolism. Studies have identified associations between genetic variants in spermidine/spermine N1-acetyltransferase (SAT1) and both anxiety and suicide, and several polymorphisms appear to play important roles in determining gene expression. Methodology/Principal Findings We genotyped 63 polymorphisms, spread across four polyaminergic genes (SAT1, spermine synthase (SMS), spermine oxidase (SMOX), and ornithine aminotransferase like-1 (OATL1)), in 1255 French-Canadian individuals who have been followed longitudinally for 22 years. We assessed univariate associations with anxiety, mood disorders, and attempted suicide, as assessed during early adulthood. We also investigated the involvement of gene-environment interactions in terms of childhood abuse, and assessed internalizing and externalizing symptoms as endophenotypes mediating these interactions. Overall, each gene was associated with at least one main outcome: anxiety (SAT1, SMS), mood disorders (SAT1, SMOX), and suicide attempts (SAT1, OATL1). Several SAT1 polymorphisms displayed disease-specific risk alleles, and polymorphisms in this gene were involved in gene-gene interactions with SMS to confer risk for anxiety disorders, as well as gene-environment interactions between childhood physical abuse and mood disorders. Externalizing behaviors demonstrated significant mediation with regards to the association between OATL1 and attempted suicide, however there was no evidence that externalizing or internalizing behaviors were appropriate endophenotypes to explain the associations with mood or anxiety disorders. Finally, childhood sexual abuse did not demonstrate mediating influences on any of our outcomes. Conclusions/Significance These results demonstrate that genetic variants in polyaminergic genes are associated with psychiatric conditions, each of which involves a set of separate and distinct risk alleles. As several of these polymorphisms are associated with gene expression, these findings may provide mechanisms to explain the alterations in polyamine metabolism which have been observed in psychiatric disorders. PMID:21152090

Association of polyaminergic loci with anxiety, mood disorders, and attempted suicide.

PubMed

Fiori, Laura M; Wanner, Brigitte; Jomphe, Valérie; Croteau, Jordie; Vitaro, Frank; Tremblay, Richard E; Bureau, Alexandre; Turecki, Gustavo

2010-11-30

The polyamine system has been implicated in a number of psychiatric conditions, which display both alterations in polyamine levels and altered expression of genes related to polyamine metabolism. Studies have identified associations between genetic variants in spermidine/spermine N1-acetyltransferase (SAT1) and both anxiety and suicide, and several polymorphisms appear to play important roles in determining gene expression. We genotyped 63 polymorphisms, spread across four polyaminergic genes (SAT1, spermine synthase (SMS), spermine oxidase (SMOX), and ornithine aminotransferase like-1 (OATL1)), in 1255 French-Canadian individuals who have been followed longitudinally for 22 years. We assessed univariate associations with anxiety, mood disorders, and attempted suicide, as assessed during early adulthood. We also investigated the involvement of gene-environment interactions in terms of childhood abuse, and assessed internalizing and externalizing symptoms as endophenotypes mediating these interactions. Overall, each gene was associated with at least one main outcome: anxiety (SAT1, SMS), mood disorders (SAT1, SMOX), and suicide attempts (SAT1, OATL1). Several SAT1 polymorphisms displayed disease-specific risk alleles, and polymorphisms in this gene were involved in gene-gene interactions with SMS to confer risk for anxiety disorders, as well as gene-environment interactions between childhood physical abuse and mood disorders. Externalizing behaviors demonstrated significant mediation with regards to the association between OATL1 and attempted suicide, however there was no evidence that externalizing or internalizing behaviors were appropriate endophenotypes to explain the associations with mood or anxiety disorders. Finally, childhood sexual abuse did not demonstrate mediating influences on any of our outcomes. These results demonstrate that genetic variants in polyaminergic genes are associated with psychiatric conditions, each of which involves a set of separate and distinct risk alleles. As several of these polymorphisms are associated with gene expression, these findings may provide mechanisms to explain the alterations in polyamine metabolism which have been observed in psychiatric disorders.

Association of ACE, VEGF and CCL2 gene polymorphisms with Henoch-Schönlein purpura and an evaluation of the possible interaction effects of these loci in HSP patients.

PubMed

Mohammadian, Tahereh; Bonyadi, Mortaza; Nabat, Elahe; Rafeey, Mandana

2017-07-01

Henoch-Schönlein purpura (HSP) is a multisystem, small vessel, leucocytoclastic vasculitis. It is predominantly a childhood vasculitis, rarely reported in adults. Studies have shown that several different genetic factors such as genes involved in inflammatory system and renin-angiotensin system (RAS) are important in the pathogenesis of Henoch-Schönlein purpura. The purpose of this study was to evaluate the independent effect of 3 gene polymorphisms including CCL2-2518 C/T, VEGF-634G/C and ACE(I/D) with HSP disease and their possible joint interactions in developing the disease. In this case-control study 47 HSP cases and 74 unrelated healthy controls were enrolled for evaluation. All individuals were genotyped for CCL2-2518C/T, VEGF-634G/C and ACE(I/D) gene polymorphisms. The possible association of these polymorphisms with susceptibility to develop HSP disease independently and in different joint combinations was evaluated. The frequencies of TT genotype and T allele of CCL2-2518C/T gene polymorphism and CC genotype and C allele of VEGF-634G/C gene polymorphism were significantly high in HSP children (p-values = 0.005 and = 0.007 respectively). Interestingly, studying the joint interaction of these 2 genotypes (CC genotype of VEGF G-634C and TT genotype of CCL2 C-2518T) in this cohort showed a more significant effect in the development of the disease (p < 0.000, OR = 6.009). The frequency of TT genotype of CCL2 gene when combined with II genotype of ACE gene in HSP children was significantly higher (p < 0.000, OR = 4.213). The results of this pilot study provide evidence of the possible gene-gene interaction effects of CCL2, VEGF and ACE genes in developing HSP disease.

Gene-Gene Combination Effect and Interactions among ABCA1, APOA1, SR-B1, and CETP Polymorphisms for Serum High-Density Lipoprotein-Cholesterol in the Japanese Population

PubMed Central

Nakamura, Akihiko; Niimura, Hideshi; Kuwabara, Kazuyo; Takezaki, Toshiro; Morita, Emi; Wakai, Kenji; Hamajima, Nobuyuki; Nishida, Yuichiro; Turin, Tanvir Chowdhury; Suzuki, Sadao; Ohnaka, Keizo; Uemura, Hirokazu; Ozaki, Etsuko; Hosono, Satoyo; Mikami, Haruo; Kubo, Michiaki; Tanaka, Hideo

2013-01-01

Background/Objective Gene-gene interactions in the reverse cholesterol transport system for high-density lipoprotein-cholesterol (HDL-C) are poorly understood. The present study observed gene-gene combination effect and interactions between single nucleotide polymorphisms (SNPs) in ABCA1, APOA1, SR-B1, and CETP in serum HDL-C from a cross-sectional study in the Japanese population. Methods The study population comprised 1,535 men and 1,515 women aged 35–69 years who were enrolled in the Japan Multi-Institutional Collaborative Cohort (J-MICC) Study. We selected 13 SNPs in the ABCA1, APOA1, CETP, and SR-B1 genes in the reverse cholesterol transport system. The effects of genetic and environmental factors were assessed using general linear and logistic regression models after adjusting for age, sex, and region. Principal Findings Alcohol consumption and daily activity were positively associated with HDL-C levels, whereas smoking had a negative relationship. The T allele of CETP, rs3764261, was correlated with higher HDL-C levels and had the highest coefficient (2.93 mg/dL/allele) among the 13 SNPs, which was statistically significant after applying the Bonferroni correction (p<0.001). Gene-gene combination analysis revealed that CETP rs3764261 was associated with high HDL-C levels with any combination of SNPs from ABCA1, APOA1, and SR-B1, although no gene-gene interaction was apparent. An increasing trend for serum HDL-C was also observed with an increasing number of alleles (p<0.001). Conclusions The present study identified a multiplier effect from a polymorphism in CETP with ABCA1, APOA1, and SR-B1, as well as a dose-dependence according to the number of alleles present. PMID:24376512

GeneWiz browser: An Interactive Tool for Visualizing Sequenced Chromosomes.

PubMed

Hallin, Peter F; Stærfeldt, Hans-Henrik; Rotenberg, Eva; Binnewies, Tim T; Benham, Craig J; Ussery, David W

2009-09-25

We present an interactive web application for visualizing genomic data of prokaryotic chromosomes. The tool (GeneWiz browser) allows users to carry out various analyses such as mapping alignments of homologous genes to other genomes, mapping of short sequencing reads to a reference chromosome, and calculating DNA properties such as curvature or stacking energy along the chromosome. The GeneWiz browser produces an interactive graphic that enables zooming from a global scale down to single nucleotides, without changing the size of the plot. Its ability to disproportionally zoom provides optimal readability and increased functionality compared to other browsers. The tool allows the user to select the display of various genomic features, color setting and data ranges. Custom numerical data can be added to the plot allowing, for example, visualization of gene expression and regulation data. Further, standard atlases are pre-generated for all prokaryotic genomes available in GenBank, providing a fast overview of all available genomes, including recently deposited genome sequences. The tool is available online from http://www.cbs.dtu.dk/services/gwBrowser. Supplemental material including interactive atlases is available online at http://www.cbs.dtu.dk/services/gwBrowser/suppl/.

Identifying cooperative transcriptional regulations using protein–protein interactions

PubMed Central

Nagamine, Nobuyoshi; Kawada, Yuji; Sakakibara, Yasubumi

2005-01-01

Cooperative transcriptional activations among multiple transcription factors (TFs) are important to understand the mechanisms of complex transcriptional regulations in eukaryotes. Previous studies have attempted to find cooperative TFs based on gene expression data with gene expression profiles as a measure of similarity of gene regulations. In this paper, we use protein–protein interaction data to infer synergistic binding of cooperative TFs. Our fundamental idea is based on the assumption that genes contributing to a similar biological process are regulated under the same control mechanism. First, the protein–protein interaction networks are used to calculate the similarity of biological processes among genes. Second, we integrate this similarity and the chromatin immuno-precipitation data to identify cooperative TFs. Our computational experiments in yeast show that predictions made by our method have successfully identified eight pairs of cooperative TFs that have literature evidences but could not be identified by the previous method. Further, 12 new possible pairs have been inferred and we have examined the biological relevances for them. However, since a typical problem using protein–protein interaction data is that many false-positive data are contained, we propose a method combining various biological data to increase the prediction accuracy. PMID:16126847

Interfacing cellular networks of S. cerevisiae and E. coli: Connecting dynamic and genetic information

PubMed Central

2013-01-01

Background In recent years, various types of cellular networks have penetrated biology and are nowadays used omnipresently for studying eukaryote and prokaryote organisms. Still, the relation and the biological overlap among phenomenological and inferential gene networks, e.g., between the protein interaction network and the gene regulatory network inferred from large-scale transcriptomic data, is largely unexplored. Results We provide in this study an in-depth analysis of the structural, functional and chromosomal relationship between a protein-protein network, a transcriptional regulatory network and an inferred gene regulatory network, for S. cerevisiae and E. coli. Further, we study global and local aspects of these networks and their biological information overlap by comparing, e.g., the functional co-occurrence of Gene Ontology terms by exploiting the available interaction structure among the genes. Conclusions Although the individual networks represent different levels of cellular interactions with global structural and functional dissimilarities, we observe crucial functions of their network interfaces for the assembly of protein complexes, proteolysis, transcription, translation, metabolic and regulatory interactions. Overall, our results shed light on the integrability of these networks and their interfacing biological processes. PMID:23663484

Linking Gene, Brain, and Behavior

PubMed Central

Schmidt, Louis A.; Fox, Nathan A.; Perez-Edgar, Koraly; Hamer, Dean H.

2009-01-01

Gene-environment interactions involving exogenous environmental factors are known to shape behavior and personality development. Although gene-environment interactions involving endogenous environmental factors are hypothesized to play an equally important role, this conceptual approach has not been empirically applied in the study of early-developing temperament in humans. Here we report evidence for a gene-endoenvironment (i.e., resting frontal brain electroencephalogram, EEG, asymmetry) interaction in predicting child temperament. The DRD4 gene (long allele vs. short allele) moderated the relation between resting frontal EEG asymmetry (left vs. right) at 9 months and temperament at 48 months. Children who exhibited left frontal EEG asymmetry at 9 months and who possessed the DRD4 long allele were significantly more soothable at 48 months than other children. Among children with right frontal EEG asymmetry at 9 months, those with the DRD4 long allele had significantly more difficulties focusing and sustaining attention at 48 months than those with the DRD4 short allele. Resting frontal EEG asymmetry did not influence temperament in the absence of the DRD4 long allele. We discuss how the interaction of genetic and endoenvironment factors may confer risk and protection for different behavioral styles in children. PMID:19493320

A multilevel prediction of physiological response to challenge: Interactions among child maltreatment, neighborhood crime, endothelial nitric oxide synthase gene (eNOS), and GABA(A) receptor subunit alpha-6 gene (GABRA6).

PubMed

Lynch, Michael; Manly, Jody Todd; Cicchetti, Dante

2015-11-01

Physiological response to stress has been linked to a variety of healthy and pathological conditions. The current study conducted a multilevel examination of interactions among environmental toxins (i.e., neighborhood crime and child maltreatment) and specific genetic polymorphisms of the endothelial nitric oxide synthase gene (eNOS) and GABA(A) receptor subunit alpha-6 gene (GABRA6). One hundred eighty-six children were recruited at age 4. The presence or absence of child maltreatment as well as the amount of crime that occurred in their neighborhood during the previous year were determined at that time. At age 9, the children were brought to the lab, where their physiological response to a cognitive challenge (i.e., change in the amplitude of the respiratory sinus arrhythmia) was assessed and DNA samples were collected for subsequent genotyping. The results confirmed that complex Gene × Gene, Environment × Environment, and Gene × Environment interactions were associated with different patterns of respiratory sinus arrhythmia reactivity. The implications for future research and evidence-based intervention are discussed.

Genome-Wide Analysis in Three Fusarium Pathogens Identifies Rapidly Evolving Chromosomes and Genes Associated with Pathogenicity

PubMed Central

Sperschneider, Jana; Gardiner, Donald M.; Thatcher, Louise F.; Lyons, Rebecca; Singh, Karam B.; Manners, John M.; Taylor, Jennifer M.

2015-01-01

Pathogens and hosts are in an ongoing arms race and genes involved in host–pathogen interactions are likely to undergo diversifying selection. Fusarium plant pathogens have evolved diverse infection strategies, but how they interact with their hosts in the biotrophic infection stage remains puzzling. To address this, we analyzed the genomes of three Fusarium plant pathogens for genes that are under diversifying selection. We found a two-speed genome structure both on the chromosome and gene group level. Diversifying selection acts strongly on the dispensable chromosomes in Fusarium oxysporum f. sp. lycopersici and on distinct core chromosome regions in Fusarium graminearum, all of which have associations with virulence. Members of two gene groups evolve rapidly, namely those that encode proteins with an N-terminal [SG]-P-C-[KR]-P sequence motif and proteins that are conserved predominantly in pathogens. Specifically, 29 F. graminearum genes are rapidly evolving, in planta induced and encode secreted proteins, strongly pointing toward effector function. In summary, diversifying selection in Fusarium is strongly reflected as genomic footprints and can be used to predict a small gene set likely to be involved in host–pathogen interactions for experimental verification. PMID:25994930

A Hybrid Computational Method for the Discovery of Novel Reproduction-Related Genes

PubMed Central

Chen, Lei; Chu, Chen; Kong, Xiangyin; Huang, Guohua; Huang, Tao; Cai, Yu-Dong

2015-01-01

Uncovering the molecular mechanisms underlying reproduction is of great importance to infertility treatment and to the generation of healthy offspring. In this study, we discovered novel reproduction-related genes with a hybrid computational method, integrating three different types of method, which offered new clues for further reproduction research. This method was first executed on a weighted graph, constructed based on known protein-protein interactions, to search the shortest paths connecting any two known reproduction-related genes. Genes occurring in these paths were deemed to have a special relationship with reproduction. These newly discovered genes were filtered with a randomization test. Then, the remaining genes were further selected according to their associations with known reproduction-related genes measured by protein-protein interaction score and alignment score obtained by BLAST. The in-depth analysis of the high confidence novel reproduction genes revealed hidden mechanisms of reproduction and provided guidelines for further experimental validations. PMID:25768094

Rice-arsenate interactions in hydroponics: a three-gene model for tolerance.

PubMed

Norton, Gareth J; Nigar, Meher; Williams, Paul N; Dasgupta, Tapash; Meharg, Andrew A; Price, Adam H

2008-01-01

In this study, the genetic mapping of the tolerance of root growth to 13.3 muM arsenate [As(V)] using the BalaxAzucena population is improved, and candidate genes for further study are identified. A remarkable three-gene model of tolerance is advanced, which appears to involve epistatic interaction between three major genes, two on chromosome 6 and one on chromosome 10. Any combination of two of these genes inherited from the tolerant parent leads to the plant having tolerance. Lists of potential positional candidate genes are presented. These are then refined using whole genome transcriptomics data and bioinformatics. Physiological evidence is also provided that genes related to phosphate transport are unlikely to be behind the genetic loci conferring tolerance. These results offer testable hypotheses for genes related to As(V) tolerance that might offer strategies for mitigating arsenic (As) accumulation in consumed rice.

Rice–arsenate interactions in hydroponics: a three-gene model for tolerance

PubMed Central

Norton, Gareth J.; Nigar, Meher; Dasgupta, Tapash; Meharg, Andrew A.; Price, Adam H.

2008-01-01

In this study, the genetic mapping of the tolerance of root growth to 13.3 μM arsenate [As(V)] using the Bala×Azucena population is improved, and candidate genes for further study are identified. A remarkable three-gene model of tolerance is advanced, which appears to involve epistatic interaction between three major genes, two on chromosome 6 and one on chromosome 10. Any combination of two of these genes inherited from the tolerant parent leads to the plant having tolerance. Lists of potential positional candidate genes are presented. These are then refined using whole genome transcriptomics data and bioinformatics. Physiological evidence is also provided that genes related to phosphate transport are unlikely to be behind the genetic loci conferring tolerance. These results offer testable hypotheses for genes related to As(V) tolerance that might offer strategies for mitigating arsenic (As) accumulation in consumed rice. PMID:18453529

A hybrid computational method for the discovery of novel reproduction-related genes.

PubMed

Chen, Lei; Chu, Chen; Kong, Xiangyin; Huang, Guohua; Huang, Tao; Cai, Yu-Dong

2015-01-01

Uncovering the molecular mechanisms underlying reproduction is of great importance to infertility treatment and to the generation of healthy offspring. In this study, we discovered novel reproduction-related genes with a hybrid computational method, integrating three different types of method, which offered new clues for further reproduction research. This method was first executed on a weighted graph, constructed based on known protein-protein interactions, to search the shortest paths connecting any two known reproduction-related genes. Genes occurring in these paths were deemed to have a special relationship with reproduction. These newly discovered genes were filtered with a randomization test. Then, the remaining genes were further selected according to their associations with known reproduction-related genes measured by protein-protein interaction score and alignment score obtained by BLAST. The in-depth analysis of the high confidence novel reproduction genes revealed hidden mechanisms of reproduction and provided guidelines for further experimental validations.

The Comparative Toxicogenomics Database: update 2017.

PubMed

Davis, Allan Peter; Grondin, Cynthia J; Johnson, Robin J; Sciaky, Daniela; King, Benjamin L; McMorran, Roy; Wiegers, Jolene; Wiegers, Thomas C; Mattingly, Carolyn J

2017-01-04

The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) provides information about interactions between chemicals and gene products, and their relationships to diseases. Core CTD content (chemical-gene, chemical-disease and gene-disease interactions manually curated from the literature) are integrated with each other as well as with select external datasets to generate expanded networks and predict novel associations. Today, core CTD includes more than 30.5 million toxicogenomic connections relating chemicals/drugs, genes/proteins, diseases, taxa, Gene Ontology (GO) annotations, pathways, and gene interaction modules. In this update, we report a 33% increase in our core data content since 2015, describe our new exposure module (that harmonizes exposure science information with core toxicogenomic data) and introduce a novel dataset of GO-disease inferences (that identify common molecular underpinnings for seemingly unrelated pathologies). These advancements centralize and contextualize real-world chemical exposures with molecular pathways to help scientists generate testable hypotheses in an effort to understand the etiology and mechanisms underlying environmentally influenced diseases. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Confirmatory and Competitive Evaluation of Alternative Gene-Environment Interaction Hypotheses

ERIC Educational Resources Information Center

Belsky, Jay; Pluess, Michael; Widaman, Keith F.

2013-01-01

Background: Most gene-environment interaction (GXE) research, though based on clear, vulnerability-oriented hypotheses, is carried out using exploratory rather than hypothesis-informed statistical tests, limiting power and making formal evaluation of competing GXE propositions difficult. Method: We present and illustrate a new regression technique…

Representing virus-host interactions and other multi-organism processes in the Gene Ontology.

PubMed

Foulger, R E; Osumi-Sutherland, D; McIntosh, B K; Hulo, C; Masson, P; Poux, S; Le Mercier, P; Lomax, J

2015-07-28

The Gene Ontology project is a collaborative effort to provide descriptions of gene products in a consistent and computable language, and in a species-independent manner. The Gene Ontology is designed to be applicable to all organisms but up to now has been largely under-utilized for prokaryotes and viruses, in part because of a lack of appropriate ontology terms. To address this issue, we have developed a set of Gene Ontology classes that are applicable to microbes and their hosts, improving both coverage and quality in this area of the Gene Ontology. Describing microbial and viral gene products brings with it the additional challenge of capturing both the host and the microbe. Recognising this, we have worked closely with annotation groups to test and optimize the GO classes, and we describe here a set of annotation guidelines that allow the controlled description of two interacting organisms. Building on the microbial resources already in existence such as ViralZone, UniProtKB keywords and MeGO, this project provides an integrated ontology to describe interactions between microbial species and their hosts, with mappings to the external resources above. Housing this information within the freely-accessible Gene Ontology project allows the classes and annotation structure to be utilized by a large community of biologists and users.

Long-Range Chromosome Interactions Mediated by Cohesin Shape Circadian Gene Expression

PubMed Central

Xu, Yichi; Guo, Weimin; Li, Ping; Zhang, Yan; Zhao, Meng; Fan, Zenghua; Zhao, Zhihu; Yan, Jun

2016-01-01

Mammalian circadian rhythm is established by the negative feedback loops consisting of a set of clock genes, which lead to the circadian expression of thousands of downstream genes in vivo. As genome-wide transcription is organized under the high-order chromosome structure, it is largely uncharted how circadian gene expression is influenced by chromosome architecture. We focus on the function of chromatin structure proteins cohesin as well as CTCF (CCCTC-binding factor) in circadian rhythm. Using circular chromosome conformation capture sequencing, we systematically examined the interacting loci of a Bmal1-bound super-enhancer upstream of a clock gene Nr1d1 in mouse liver. These interactions are largely stable in the circadian cycle and cohesin binding sites are enriched in the interactome. Global analysis showed that cohesin-CTCF co-binding sites tend to insulate the phases of circadian oscillating genes while cohesin-non-CTCF sites are associated with high circadian rhythmicity of transcription. A model integrating the effects of cohesin and CTCF markedly improved the mechanistic understanding of circadian gene expression. Further experiments in cohesin knockout cells demonstrated that cohesin is required at least in part for driving the circadian gene expression by facilitating the enhancer-promoter looping. This study provided a novel insight into the relationship between circadian transcriptome and the high-order chromosome structure. PMID:27135601

Biomarker Identification for Prostate Cancer and Lymph Node Metastasis from Microarray Data and Protein Interaction Network Using Gene Prioritization Method

PubMed Central

Arias, Carlos Roberto; Yeh, Hsiang-Yuan; Soo, Von-Wun

2012-01-01

Finding a genetic disease-related gene is not a trivial task. Therefore, computational methods are needed to present clues to the biomedical community to explore genes that are more likely to be related to a specific disease as biomarker. We present biomarker identification problem using gene prioritization method called gene prioritization from microarray data based on shortest paths, extended with structural and biological properties and edge flux using voting scheme (GP-MIDAS-VXEF). The method is based on finding relevant interactions on protein interaction networks, then scoring the genes using shortest paths and topological analysis, integrating the results using a voting scheme and a biological boosting. We applied two experiments, one is prostate primary and normal samples and the other is prostate primary tumor with and without lymph nodes metastasis. We used 137 truly prostate cancer genes as benchmark. In the first experiment, GP-MIDAS-VXEF outperforms all the other state-of-the-art methods in the benchmark by retrieving the truest related genes from the candidate set in the top 50 scores found. We applied the same technique to infer the significant biomarkers in prostate cancer with lymph nodes metastasis which is not established well. PMID:22654636

Prioritization of candidate disease genes by combining topological similarity and semantic similarity.

PubMed

Liu, Bin; Jin, Min; Zeng, Pan

2015-10-01

The identification of gene-phenotype relationships is very important for the treatment of human diseases. Studies have shown that genes causing the same or similar phenotypes tend to interact with each other in a protein-protein interaction (PPI) network. Thus, many identification methods based on the PPI network model have achieved good results. However, in the PPI network, some interactions between the proteins encoded by candidate gene and the proteins encoded by known disease genes are very weak. Therefore, some studies have combined the PPI network with other genomic information and reported good predictive performances. However, we believe that the results could be further improved. In this paper, we propose a new method that uses the semantic similarity between the candidate gene and known disease genes to set the initial probability vector of a random walk with a restart algorithm in a human PPI network. The effectiveness of our method was demonstrated by leave-one-out cross-validation, and the experimental results indicated that our method outperformed other methods. Additionally, our method can predict new causative genes of multifactor diseases, including Parkinson's disease, breast cancer and obesity. The top predictions were good and consistent with the findings in the literature, which further illustrates the effectiveness of our method. Copyright © 2015 Elsevier Inc. All rights reserved.

Multi-variant study of obesity risk genes in African Americans: The Jackson Heart Study.

PubMed

Liu, Shijian; Wilson, James G; Jiang, Fan; Griswold, Michael; Correa, Adolfo; Mei, Hao

2016-11-30

Genome-wide association study (GWAS) has been successful in identifying obesity risk genes by single-variant association analysis. For this study, we designed steps of analysis strategy and aimed to identify multi-variant effects on obesity risk among candidate genes. Our analyses were focused on 2137 African American participants with body mass index measured in the Jackson Heart Study and 657 common single nucleotide polymorphisms (SNPs) genotyped at 8 GWAS-identified obesity risk genes. Single-variant association test showed that no SNPs reached significance after multiple testing adjustment. The following gene-gene interaction analysis, which was focused on SNPs with unadjusted p-value<0.10, identified 6 significant multi-variant associations. Logistic regression showed that SNPs in these associations did not have significant linear interactions; examination of genetic risk score evidenced that 4 multi-variant associations had significant additive effects of risk SNPs; and haplotype association test presented that all multi-variant associations contained one or several combinations of particular alleles or haplotypes, associated with increased obesity risk. Our study evidenced that obesity risk genes generated multi-variant effects, which can be additive or non-linear interactions, and multi-variant study is an important supplement to existing GWAS for understanding genetic effects of obesity risk genes. Copyright © 2016 Elsevier B.V. All rights reserved.

Survival dimensionality reduction (SDR): development and clinical application of an innovative approach to detect epistasis in presence of right-censored data

PubMed Central

2010-01-01

Background Epistasis is recognized as a fundamental part of the genetic architecture of individuals. Several computational approaches have been developed to model gene-gene interactions in case-control studies, however, none of them is suitable for time-dependent analysis. Herein we introduce the Survival Dimensionality Reduction (SDR) algorithm, a non-parametric method specifically designed to detect epistasis in lifetime datasets. Results The algorithm requires neither specification about the underlying survival distribution nor about the underlying interaction model and proved satisfactorily powerful to detect a set of causative genes in synthetic epistatic lifetime datasets with a limited number of samples and high degree of right-censorship (up to 70%). The SDR method was then applied to a series of 386 Dutch patients with active rheumatoid arthritis that were treated with anti-TNF biological agents. Among a set of 39 candidate genes, none of which showed a detectable marginal effect on anti-TNF responses, the SDR algorithm did find that the rs1801274 SNP in the FcγRIIa gene and the rs10954213 SNP in the IRF5 gene non-linearly interact to predict clinical remission after anti-TNF biologicals. Conclusions Simulation studies and application in a real-world setting support the capability of the SDR algorithm to model epistatic interactions in candidate-genes studies in presence of right-censored data. Availability: http://sourceforge.net/projects/sdrproject/ PMID:20691091

Cationic liposome/DNA complexes: from structure to interactions with cellular membranes.

PubMed

Caracciolo, Giulio; Amenitsch, Heinz

2012-10-01

Gene-based therapeutic approaches are based upon the concept that, if a disease is caused by a mutation in a gene, then adding back the wild-type gene should restore regular function and attenuate the disease phenotype. To deliver the gene of interest, both viral and nonviral vectors are used. Viruses are efficient, but their application is impeded by detrimental side-effects. Among nonviral vectors, cationic liposomes are the most promising candidates for gene delivery. They form stable complexes with polyanionic DNA (lipoplexes). Despite several advantages over viral vectors, the transfection efficiency (TE) of lipoplexes is too low compared with those of engineered viral vectors. This is due to lack of knowledge about the interactions between complexes and cellular components. Rational design of efficient lipoplexes therefore requires deeper comprehension of the interactions between the vector and the DNA as well as the cellular pathways and mechanisms involved. The importance of the lipoplex structure in biological function is revealed in the application of synchrotron small-angle X-ray scattering in combination with functional TE measurements. According to current understanding, the structure of lipoplexes can change upon interaction with cellular membranes and such changes affect the delivery efficiency. Recently, a correlation between the mechanism of gene release from complexes, the structure, and the physical and chemical parameters of the complexes has been established. Studies aimed at correlating structure and activity of lipoplexes are reviewed herein. This is a fundamental step towards rational design of highly efficient lipid gene vectors.

Prior knowledge based mining functional modules from Yeast PPI networks with gene ontology

PubMed Central

2010-01-01

Background In the literature, there are fruitful algorithmic approaches for identification functional modules in protein-protein interactions (PPI) networks. Because of accumulation of large-scale interaction data on multiple organisms and non-recording interaction data in the existing PPI database, it is still emergent to design novel computational techniques that can be able to correctly and scalably analyze interaction data sets. Indeed there are a number of large scale biological data sets providing indirect evidence for protein-protein interaction relationships. Results The main aim of this paper is to present a prior knowledge based mining strategy to identify functional modules from PPI networks with the aid of Gene Ontology. Higher similarity value in Gene Ontology means that two gene products are more functionally related to each other, so it is better to group such gene products into one functional module. We study (i) to encode the functional pairs into the existing PPI networks; and (ii) to use these functional pairs as pairwise constraints to supervise the existing functional module identification algorithms. Topology-based modularity metric and complex annotation in MIPs will be used to evaluate the identified functional modules by these two approaches. Conclusions The experimental results on Yeast PPI networks and GO have shown that the prior knowledge based learning methods perform better than the existing algorithms. PMID:21172053

The interaction of combined effects of the BDNF and PRKCG genes and negative life events in major depressive disorder.

PubMed

Yang, Chunxia; Sun, Ning; Liu, Zhifen; Li, Xinrong; Xu, Yong; Zhang, Kerang

2016-03-30

Major depressive disorder (MDD) is a mental disorder that results from complex interplay between multiple and partially overlapping sets of susceptibility genes and environmental factors. The brain derived neurotrophic factor (BDNF) and Protein kinase C gamma type (PRKCG) are logical candidate genes in MDD. Among diverse environmental factors, negative life events have been suggested to exert a crucial impact on brain development. In the present study, we hypothesized that interactions between genetic variants in BDNF and PRKCG and negative life events may play an important role in the development of MDD. We recruited a total of 406 patients with MDD and 391 age- and gender-matched control subjects. Gene-environment interactions were analyzed using generalized multifactor dimensionality reduction (GMDR). Under a dominant model, we observed a significant three-way interaction among BDNF rs6265, PRKCG rs3745406, and negative life events. The gene-environment combination of PRKCG rs3745406 C allele, BDNF rs6265 G allele and high level of negative life events (C-G-HN) was significantly associated with MDD (OR, 5.97; 95% CI, 2.71-13.15). To our knowledge, this is the first report of evidence that the BDNF-PRKCG interaction may modify the relationship between negative life events and MDD in the Chinese population. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Systems-level analysis of risk genes reveals the modular nature of schizophrenia.

PubMed

Liu, Jiewei; Li, Ming; Luo, Xiong-Jian; Su, Bing

2018-05-19

Schizophrenia (SCZ) is a complex mental disorder with high heritability. Genetic studies (especially recent genome-wide association studies) have identified many risk genes for schizophrenia. However, the physical interactions among the proteins encoded by schizophrenia risk genes remain elusive and it is not known whether the identified risk genes converge on common molecular networks or pathways. Here we systematically investigated the network characteristics of schizophrenia risk genes using the high-confidence protein-protein interactions (PPI) from the human interactome. We found that schizophrenia risk genes encode a densely interconnected PPI network (P = 4.15 × 10 -31 ). Compared with the background genes, the schizophrenia risk genes in the interactome have significantly higher degree (P = 5.39 × 10 -11 ), closeness centrality (P = 7.56 × 10 -11 ), betweeness centrality (P = 1.29 × 10 -11 ), clustering coefficient (P = 2.22 × 10 -2 ), and shorter average shortest path length (P = 7.56 × 10 -11 ). Based on the densely interconnected PPI network, we identified 48 hub genes and 4 modules formed by highly interconnected schizophrenia genes. We showed that the proteins encoded by schizophrenia hub genes have significantly more direct physical interactions. Gene ontology (GO) analysis revealed that cell adhesion, cell cycle, immune system response, and GABR-receptor complex categories were enriched in the modules formed by highly interconnected schizophrenia risk genes. Our study reveals that schizophrenia risk genes encode a densely interconnected molecular network and demonstrates the modular nature of schizophrenia. Copyright © 2018 Elsevier B.V. All rights reserved.

Receptor Tyrosine Kinase MET Interactome and Neurodevelopmental Disorder Partners at the Developing Synapse.

PubMed

Xie, Zhihui; Li, Jing; Baker, Jonathan; Eagleson, Kathie L; Coba, Marcelo P; Levitt, Pat

2016-12-15

Atypical synapse development and plasticity are implicated in many neurodevelopmental disorders (NDDs). NDD-associated, high-confidence risk genes have been identified, yet little is known about functional relationships at the level of protein-protein interactions, which are the dominant molecular bases responsible for mediating circuit development. Proteomics in three independent developing neocortical synaptosomal preparations identified putative interacting proteins of the ligand-activated MET receptor tyrosine kinase, an autism risk gene that mediates synapse development. The candidates were translated into interactome networks and analyzed bioinformatically. Additionally, three independent quantitative proximity ligation assays in cultured neurons and four independent immunoprecipitation analyses of synaptosomes validated protein interactions. Approximately 11% (8/72) of MET-interacting proteins, including SHANK3, SYNGAP1, and GRIN2B, are associated with NDDs. Proteins in the MET interactome were translated into a novel MET interactome network based on human protein-protein interaction databases. High-confidence genes from different NDD datasets that encode synaptosomal proteins were analyzed for being enriched in MET interactome proteins. This was found for autism but not schizophrenia, bipolar disorder, major depressive disorder, or attention-deficit/hyperactivity disorder. There is correlated gene expression between MET and its interactive partners in developing human temporal and visual neocortices but not with highly expressed genes that are not in the interactome. Proximity ligation assays and biochemical analyses demonstrate that MET-protein partner interactions are dynamically regulated by receptor activation. The results provide a novel molecular framework for deciphering the functional relations of key regulators of synaptogenesis that contribute to both typical cortical development and to NDDs. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

A novel approach to exploring potential interactions among single-nucleotide polymorphisms of inflammation genes in gliomagenesis: an exploratory case-only study.

PubMed

Amirian, E Susan; Scheurer, Michael E; Liu, Yanhong; D'Amelio, Anthony M; Houlston, Richard S; Etzel, Carol J; Shete, Sanjay; Swerdlow, Anthony J; Schoemaker, Minouk J; McKinney, Patricia A; Fleming, Sarah J; Muir, Kenneth R; Lophatananon, Artitaya; Bondy, Melissa L

2011-08-01

Despite extensive research on the topic, glioma etiology remains largely unknown. Exploration of potential interactions between single-nucleotide polymorphisms (SNP) of immune genes is a promising new area of glioma research. The case-only study design is a powerful and efficient design for exploring possible multiplicative interactions between factors that are independent of one another. The purpose of our study was to use this exploratory design to identify potential pair wise SNP-SNP interactions from genes involved in several different immune-related pathways for investigation in future studies. The study population consisted of two case groups: 1,224 histologic confirmed, non-Hispanic white glioma cases from the United States and a validation population of 634 glioma cases from the United Kingdom. Polytomous logistic regression, in which one SNP was coded as the outcome and the other SNP was included as the exposure, was utilized to calculate the ORs of the likelihood of cases simultaneously having the variant alleles of two different SNPs. Potential interactions were examined only between SNPs located in different genes or chromosomes. Using this data mining strategy, we found 396 significant SNP-SNP interactions among polymorphisms of immune-related genes that were present in both the U.S. and U.K. study populations. This exploratory study was conducted for the purpose of hypothesis generation, and thus has provided several new hypotheses that can be tested using traditional case-control study designs to obtain estimates of risk. This is the first study, to our knowledge, to take this novel approach to identifying SNP-SNP interactions relevant to glioma etiology. ©2011 AACR.

Trends in gastrectomy and ADH1B and ALDH2 genotypes in Japanese alcoholic men and their gene-gastrectomy, gene-gene and gene-age interactions for risk of alcoholism.

PubMed

Yokoyama, Akira; Yokoyama, Tetsuji; Matsui, Toshifumi; Mizukami, Takeshi; Kimura, Mitsuru; Matsushita, Sachio; Higuchi, Susumu; Maruyama, Katsuya

2013-01-01

The life-time drinking profiles of Japanese alcoholics have shown that gastrectomy increases susceptibility to alcoholism. We investigated the trends in gastrectomy and alcohol dehydrogenase-1B (ADH1B) and aldehyde dehydrogenase-2 (ALDH2) genotypes and their interactions in alcoholics. This survey was conducted on 4879 Japanese alcoholic men 40 years of age or older who underwent routine gastrointestinal endoscopic screening during the period 1996-2010. ADH1B/ALDH2 genotyping was performed in 3702 patients. A history of gastrectomy was found in 508 (10.4%) patients. The reason for the gastrectomy was peptic ulcer in 317 patients and gastric cancer in 187 patients. The frequency of gastrectomy had gradually decreased from 13.3% in 1996-2000 to 10.5% in 2001-2005 and to 7.8% in 2006-2010 (P < 0.0001). ADH1B*1/*1 was less frequent in the gastrectomy group than in the non-gastrectomy group (age-adjusted prevalence: 20.4 vs. 27.6%, P = 0.006). ALDH2 genotype distribution did not differ between the two groups. The frequency of inactive ALDH2*1/*2 heterozygotes increased slightly from 13.0% in 1996-2000 to 14.0% in 2001-2005 and to 15.4% in 2006-2010 (P < 0.08). Two alcoholism-susceptibility genotypes, ADH1B*1/*1 and ALDH2*1/*1, modestly but significantly tended not to occur in the same individual (P = 0.026). The frequency of ADH1B*1/*1 decreased with ascending age groups. The high frequency of history of gastrectomy suggested that gastrectomy is still a risk factor for alcoholism, although the percentage decreased during the period. The alcoholism-susceptibility genotype ADH1B*1/*1 was less frequent in the gastrectomy group, suggesting a competitive gene-gastrectomy interaction for alcoholism. A gene-gene interaction and gene-age interactions regarding the ADH1B genotype were observed.

Gene Environment Interactions and Predictors of Colorectal Cancer in Family-Based, Multi-Ethnic Groups.

PubMed

Shiao, S Pamela K; Grayson, James; Yu, Chong Ho; Wasek, Brandi; Bottiglieri, Teodoro

2018-02-16

For the personalization of polygenic/omics-based health care, the purpose of this study was to examine the gene-environment interactions and predictors of colorectal cancer (CRC) by including five key genes in the one-carbon metabolism pathways. In this proof-of-concept study, we included a total of 54 families and 108 participants, 54 CRC cases and 54 matched family friends representing four major racial ethnic groups in southern California (White, Asian, Hispanics, and Black). We used three phases of data analytics, including exploratory, family-based analyses adjusting for the dependence within the family for sharing genetic heritage, the ensemble method, and generalized regression models for predictive modeling with a machine learning validation procedure to validate the results for enhanced prediction and reproducibility. The results revealed that despite the family members sharing genetic heritage, the CRC group had greater combined gene polymorphism rates than the family controls ( p < 0.05), on MTHFR C677T , MTR A2756G , MTRR A66G, and DHFR 19 bp except MTHFR A1298C. Four racial groups presented different polymorphism rates for four genes (all p < 0.05) except MTHFR A1298C. Following the ensemble method, the most influential factors were identified, and the best predictive models were generated by using the generalized regression models, with Akaike's information criterion and leave-one-out cross validation methods. Body mass index (BMI) and gender were consistent predictors of CRC for both models when individual genes versus total polymorphism counts were used, and alcohol use was interactive with BMI status. Body mass index status was also interactive with both gender and MTHFR C677T gene polymorphism, and the exposure to environmental pollutants was an additional predictor. These results point to the important roles of environmental and modifiable factors in relation to gene-environment interactions in the prevention of CRC.

Adjusting for background mutation frequency biases improves the identification of cancer driver genes.

PubMed

Evans, Perry; Avey, Stefan; Kong, Yong; Krauthammer, Michael

2013-09-01

A common goal of tumor sequencing projects is finding genes whose mutations are selected for during tumor development. This is accomplished by choosing genes that have more non-synonymous mutations than expected from an estimated background mutation frequency. While this background frequency is unknown, it can be estimated using both the observed synonymous mutation frequency and the non-synonymous to synonymous mutation ratio. The synonymous mutation frequency can be determined across all genes or in a gene-specific manner. This choice introduces an interesting trade-off. A gene-specific frequency adjusts for an underlying mutation bias, but is difficult to estimate given missing synonymous mutation counts. Using a genome-wide synonymous frequency is more robust, but is less suited for adjusting biases. Studying four evaluation criteria for identifying genes with high non-synonymous mutation burden (reflecting preferential selection of expressed genes, genes with mutations in conserved bases, genes with many protein interactions, and genes that show loss of heterozygosity), we find that the gene-specific synonymous frequency is superior in the gene expression and protein interaction tests. In conclusion, the use of the gene-specific synonymous mutation frequency is well suited for assessing a gene's non-synonymous mutation burden.

Homo sapiens exhibit a distinct pattern of CNV genes regulation: an important role of miRNAs and SNPs in expression plasticity.

PubMed

Dweep, Harsh; Kubikova, Nada; Gretz, Norbert; Voskarides, Konstantinos; Felekkis, Kyriacos

2015-07-16

Gene expression regulation is a complex and highly organized process involving a variety of genomic factors. It is widely accepted that differences in gene expression can contribute to the phenotypic variability between species, and that their interpretation can aid in the understanding of the physiologic variability. CNVs and miRNAs are two major players in the regulation of expression plasticity and may be responsible for the unique phenotypic characteristics observed in different lineages. We have previously demonstrated that a close interaction between these two genomic elements may have contributed to the regulation of gene expression during evolution. This work presents the molecular interactions between CNV and non CNV genes with miRNAs and other genomic elements in eight different species. A comprehensive analysis of these interactions indicates a unique nature of human CNV genes regulation as compared to other species. By using genes with short 3' UTR that abolish the "canonical" miRNA-dependent regulation, as a model, we demonstrate a distinct and tight regulation of human genes that might explain some of the unique features of human physiology. In addition, comparison of gene expression regulation between species indicated that there is a significant difference between humans and mice possibly questioning the effectiveness of the latest as experimental models of human diseases.

Functional network analysis of genes differentially expressed during xylogenesis in soc1ful woody Arabidopsis plants.

PubMed

Davin, Nicolas; Edger, Patrick P; Hefer, Charles A; Mizrachi, Eshchar; Schuetz, Mathias; Smets, Erik; Myburg, Alexander A; Douglas, Carl J; Schranz, Michael E; Lens, Frederic

2016-06-01

Many plant genes are known to be involved in the development of cambium and wood, but how the expression and functional interaction of these genes determine the unique biology of wood remains largely unknown. We used the soc1ful loss of function mutant - the woodiest genotype known in the otherwise herbaceous model plant Arabidopsis - to investigate the expression and interactions of genes involved in secondary growth (wood formation). Detailed anatomical observations of the stem in combination with mRNA sequencing were used to assess transcriptome remodeling during xylogenesis in wild-type and woody soc1ful plants. To interpret the transcriptome changes, we constructed functional gene association networks of differentially expressed genes using the STRING database. This analysis revealed functionally enriched gene association hubs that are differentially expressed in herbaceous and woody tissues. In particular, we observed the differential expression of genes related to mechanical stress and jasmonate biosynthesis/signaling during wood formation in soc1ful plants that may be an effect of greater tension within woody tissues. Our results suggest that habit shifts from herbaceous to woody life forms observed in many angiosperm lineages could have evolved convergently by genetic changes that modulate the gene expression and interaction network, and thereby redeploy the conserved wood developmental program. © 2016 The Authors. The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.

Homo sapiens exhibit a distinct pattern of CNV genes regulation: an important role of miRNAs and SNPs in expression plasticity

PubMed Central

Dweep, Harsh; Kubikova, Nada; Gretz, Norbert; Voskarides, Konstantinos; Felekkis, Kyriacos

2015-01-01

Gene expression regulation is a complex and highly organized process involving a variety of genomic factors. It is widely accepted that differences in gene expression can contribute to the phenotypic variability between species, and that their interpretation can aid in the understanding of the physiologic variability. CNVs and miRNAs are two major players in the regulation of expression plasticity and may be responsible for the unique phenotypic characteristics observed in different lineages. We have previously demonstrated that a close interaction between these two genomic elements may have contributed to the regulation of gene expression during evolution. This work presents the molecular interactions between CNV and non CNV genes with miRNAs and other genomic elements in eight different species. A comprehensive analysis of these interactions indicates a unique nature of human CNV genes regulation as compared to other species. By using genes with short 3′ UTR that abolish the “canonical” miRNA-dependent regulation, as a model, we demonstrate a distinct and tight regulation of human genes that might explain some of the unique features of human physiology. In addition, comparison of gene expression regulation between species indicated that there is a significant difference between humans and mice possibly questioning the effectiveness of the latest as experimental models of human diseases. PMID:26178010

Genetics of borderline personality disorder: systematic review and proposal of an integrative model.

PubMed

Amad, Ali; Ramoz, Nicolas; Thomas, Pierre; Jardri, Renaud; Gorwood, Philip

2014-03-01

Borderline personality disorder (BPD) is one of the most common mental disorders and is characterized by a pervasive pattern of emotional lability, impulsivity, interpersonal difficulties, identity disturbances, and disturbed cognition. Here, we performed a systematic review of the literature concerning the genetics of BPD, including familial and twin studies, association studies, and gene-environment interaction studies. Moreover, meta-analyses were performed when at least two case-control studies testing the same polymorphism were available. For each gene variant, a pooled odds ratio (OR) was calculated using fixed or random effects models. Familial and twin studies largely support the potential role of a genetic vulnerability at the root of BPD, with an estimated heritability of approximately 40%. Moreover, there is evidence for both gene-environment interactions and correlations. However, association studies for BPD are sparse, making it difficult to draw clear conclusions. According to our meta-analysis, no significant associations were found for the serotonin transporter gene, the tryptophan hydroxylase 1 gene, or the serotonin 1B receptor gene. We hypothesize that such a discrepancy (negative association studies but high heritability of the disorder) could be understandable through a paradigm shift, in which "plasticity" genes (rather than "vulnerability" genes) would be involved. Such a framework postulates a balance between positive and negative events, which interact with plasticity genes in the genesis of BPD. Copyright © 2014 Elsevier Ltd. All rights reserved.

Does parental expressed emotion moderate genetic effects in ADHD? An exploration using a genome wide association scan.

PubMed

Sonuga-Barke, Edmund J S; Lasky-Su, Jessica; Neale, Benjamin M; Oades, Robert; Chen, Wai; Franke, Barbara; Buitelaar, Jan; Banaschewski, Tobias; Ebstein, Richard; Gill, Michael; Anney, Richard; Miranda, Ana; Mulas, Fernando; Roeyers, Herbert; Rothenberger, Aribert; Sergeant, Joseph; Steinhausen, Hans Christoph; Thompson, Margaret; Asherson, Philip; Faraone, Stephen V

2008-12-05

Studies of gene x environment (G x E) interaction in ADHD have previously focused on known risk genes for ADHD and environmentally mediated biological risk. Here we use G x E analysis in the context of a genome-wide association scan to identify novel genes whose effects on ADHD symptoms and comorbid conduct disorder are moderated by high maternal expressed emotion (EE). SNPs (600,000) were genotyped in 958 ADHD proband-parent trios. After applying data cleaning procedures we examined 429,981 autosomal SNPs in 909 family trios. ADHD symptom severity and comorbid conduct disorder was measured using the Parental Account of Childhood Symptoms interview. Maternal criticism and warmth (i.e., EE) were coded by independent observers on comments made during the interview. No G x E interactions reached genome-wide significance. Nominal effects were found both with and without genetic main effects. For those with genetic main effects 36 uncorrected interaction P-values were <10(-5) implicating both novel genes as well as some previously supported candidates. These were found equally often for all of the interactions being investigated. The observed interactions in SLC1A1 and NRG3 SNPs represent reasonable candidate genes for further investigation given their previous association with several psychiatric illnesses. We find evidence for the role of EE in moderating the effects of genes on ADHD severity and comorbid conduct disorder, implicating both novel and established candidates. These findings need replicating in larger independent samples. Copyright 2008 Wiley-Liss, Inc.

Markov Logic Networks in the Analysis of Genetic Data

PubMed Central

Sakhanenko, Nikita A.

2010-01-01

Abstract Complex, non-additive genetic interactions are common and can be critical in determining phenotypes. Genome-wide association studies (GWAS) and similar statistical studies of linkage data, however, assume additive models of gene interactions in looking for genotype-phenotype associations. These statistical methods view the compound effects of multiple genes on a phenotype as a sum of influences of each gene and often miss a substantial part of the heritable effect. Such methods do not use any biological knowledge about underlying mechanisms. Modeling approaches from the artificial intelligence (AI) field that incorporate deterministic knowledge into models to perform statistical analysis can be applied to include prior knowledge in genetic analysis. We chose to use the most general such approach, Markov Logic Networks (MLNs), for combining deterministic knowledge with statistical analysis. Using simple, logistic regression-type MLNs we can replicate the results of traditional statistical methods, but we also show that we are able to go beyond finding independent markers linked to a phenotype by using joint inference without an independence assumption. The method is applied to genetic data on yeast sporulation, a complex phenotype with gene interactions. In addition to detecting all of the previously identified loci associated with sporulation, our method identifies four loci with smaller effects. Since their effect on sporulation is small, these four loci were not detected with methods that do not account for dependence between markers due to gene interactions. We show how gene interactions can be detected using more complex models, which can be used as a general framework for incorporating systems biology with genetics. PMID:20958249

An analysis of the gene interaction networks identifying the role of PARP1 in metastasis of non-small cell lung cancer.

PubMed

Chen, Kai; Li, Yajie; Xu, Hui; Zhang, Chunfeng; Li, Zhiqiang; Wang, Wei; Wang, Baofeng

2017-10-20

Though there were many researches about the effects of cancer cells on non-small cell lung cancer (NSCLC) currently, it has been rarely reported completed oncogene and its mechanism in tumors by far. Here, we used biological methods with known oncogene of NSCLC to find new oncogene and explore its functionary mechanism in NSCLC. The study firstly built NSCLC genetic interaction network based on bioinformatics methods and then combined shortest path algorithm with significance test to confirmed core genes that were closely involved with given genes; real-time qPCR was conducted to detect expression levels between patients with NSCLC and normal people; additionally, detection of PARP1's role in migration and invasion was performed by trans-well assays and wound-healing. Through gene interaction network, it was found that, core genes like PARP1, EGFR and ALK had a direct interaction. TCGA database showed that PARP1 presented strong expression in NSCLC and the expression level of metastatic NSCLC was significantly higher than that of non-metastatic NSCLC. Cell migration of NSCLC in accordance to the scratch test was suppressed by PARP1 silence but stimulated noticeably by PARP1 overexpression. According to Kaplan-meier survival curve, the higher PARP1 expression, the poorer patient survival rate and prognosis. Thus, PARP1 expression had a negative correction with patient survival rate and prognosis. New oncogene PARP1 was found from known NSCLC oncogene in terms of gene interaction network, demonstrating PARP1's impact on NSCLC cell migration.

Learning Gene Expression Through Modelling and Argumentation. A Case Study Exploring the Connections Between the Worlds of Knowledge

NASA Astrophysics Data System (ADS)

Puig, Blanca; Ageitos, Noa; Jiménez-Aleixandre, María Pilar

2017-12-01

There is emerging interest on the interactions between modelling and argumentation in specific contexts, such as genetics learning. It has been suggested that modelling might help students understand and argue on genetics. We propose modelling gene expression as a way to learn molecular genetics and diseases with a genetic component. The study is framed in Tiberghien's (2000) two worlds of knowledge, the world of "theories & models" and the world of "objects & events", adding a third component, the world of representations. We seek to examine how modelling and argumentation interact and connect the three worlds of knowledge while modelling gene expression. It is a case study of 10th graders learning about diseases with a genetic component. The research questions are as follows: (1) What argumentative and modelling operations do students enact in the process of modelling gene expression? Specifically, which operations allow connecting the three worlds of knowledge? (2) What are the interactions between modelling and argumentation in modelling gene expression? To what extent do these interactions help students connect the three worlds of knowledge and modelling gene expression? The argumentative operation of using evidence helps students to relate the three worlds of knowledge, enacted in all the connections. It seems to be a relationship among the number of interactions between modelling and argumentation, the connections between world of knowledge and students' capacity to develop a more sophisticated representation. Despite this is a case study, this approach of analysis reveals potentialities for a deeper understanding of learning genetics though scientific practices.