Systems genetics identifies a convergent gene network for cognition and neurodevelopmental disease.

PubMed

Johnson, Michael R; Shkura, Kirill; Langley, Sarah R; Delahaye-Duriez, Andree; Srivastava, Prashant; Hill, W David; Rackham, Owen J L; Davies, Gail; Harris, Sarah E; Moreno-Moral, Aida; Rotival, Maxime; Speed, Doug; Petrovski, Slavé; Katz, Anaïs; Hayward, Caroline; Porteous, David J; Smith, Blair H; Padmanabhan, Sandosh; Hocking, Lynne J; Starr, John M; Liewald, David C; Visconti, Alessia; Falchi, Mario; Bottolo, Leonardo; Rossetti, Tiziana; Danis, Bénédicte; Mazzuferi, Manuela; Foerch, Patrik; Grote, Alexander; Helmstaedter, Christoph; Becker, Albert J; Kaminski, Rafal M; Deary, Ian J; Petretto, Enrico

2016-02-01

Genetic determinants of cognition are poorly characterized, and their relationship to genes that confer risk for neurodevelopmental disease is unclear. Here we performed a systems-level analysis of genome-wide gene expression data to infer gene-regulatory networks conserved across species and brain regions. Two of these networks, M1 and M3, showed replicable enrichment for common genetic variants underlying healthy human cognitive abilities, including memory. Using exome sequence data from 6,871 trios, we found that M3 genes were also enriched for mutations ascertained from patients with neurodevelopmental disease generally, and intellectual disability and epileptic encephalopathy in particular. M3 consists of 150 genes whose expression is tightly developmentally regulated, but which are collectively poorly annotated for known functional pathways. These results illustrate how systems-level analyses can reveal previously unappreciated relationships between neurodevelopmental disease-associated genes in the developed human brain, and provide empirical support for a convergent gene-regulatory network influencing cognition and neurodevelopmental disease.

Differentially Coexpressed Disease Gene Identification Based on Gene Coexpression Network.

PubMed

Jiang, Xue; Zhang, Han; Quan, Xiongwen

2016-01-01

Screening disease-related genes by analyzing gene expression data has become a popular theme. Traditional disease-related gene selection methods always focus on identifying differentially expressed gene between case samples and a control group. These traditional methods may not fully consider the changes of interactions between genes at different cell states and the dynamic processes of gene expression levels during the disease progression. However, in order to understand the mechanism of disease, it is important to explore the dynamic changes of interactions between genes in biological networks at different cell states. In this study, we designed a novel framework to identify disease-related genes and developed a differentially coexpressed disease-related gene identification method based on gene coexpression network (DCGN) to screen differentially coexpressed genes. We firstly constructed phase-specific gene coexpression network using time-series gene expression data and defined the conception of differential coexpression of genes in coexpression network. Then, we designed two metrics to measure the value of gene differential coexpression according to the change of local topological structures between different phase-specific networks. Finally, we conducted meta-analysis of gene differential coexpression based on the rank-product method. Experimental results demonstrated the feasibility and effectiveness of DCGN and the superior performance of DCGN over other popular disease-related gene selection methods through real-world gene expression data sets.

Identifying genome-wide immune gene variation underlying infectious disease in wildlife populations - a next generation sequencing approach in the gopher tortoise.

PubMed

Elbers, Jean P; Brown, Mary B; Taylor, Sabrina S

2018-01-19

Infectious disease is the single greatest threat to taxa such as amphibians (chytrid fungus), bats (white nose syndrome), Tasmanian devils (devil facial tumor disease), and black-footed ferrets (canine distemper virus, plague). Although understanding the genetic basis to disease susceptibility is important for the long-term persistence of these groups, most research has been limited to major-histocompatibility and Toll-like receptor genes. To better understand the genetic basis of infectious disease susceptibility in a species of conservation concern, we sequenced all known/predicted immune response genes (i.e., the immunomes) in 16 Florida gopher tortoises, Gopherus polyphemus. All tortoises produced antibodies against Mycoplasma agassizii (an etiologic agent of infectious upper respiratory tract disease; URTD) and, at the time of sampling, either had (n = 10) or lacked (n = 6) clinical signs. We found several variants associated with URTD clinical status in complement and lectin genes, which may play a role in Mycoplasma immunity. Thirty-five genes deviated from neutrality according to Tajima's D. These genes were enriched in functions relating to macromolecule and protein modifications, which are vital to immune system functioning. These results are suggestive of genetic differences that might contribute to disease severity, a finding that is consistent with other mycoplasmal diseases. This has implications for management because tortoises across their range may possess genetic variation associated with a more severe response to URTD. More generally: 1) this approach demonstrates that a broader consideration of immune genes is better able to identify important variants, and; 2) this data pipeline can be adopted to identify alleles associated with disease susceptibility or resistance in other taxa, and therefore provide information on a population's risk of succumbing to disease, inform translocations to increase genetic variation for disease resistance

Large-Scale Gene-Centric Analysis Identifies Novel Variants for Coronary Artery Disease

PubMed Central

2011-01-01

Coronary artery disease (CAD) has a significant genetic contribution that is incompletely characterized. To complement genome-wide association (GWA) studies, we conducted a large and systematic candidate gene study of CAD susceptibility, including analysis of many uncommon and functional variants. We examined 49,094 genetic variants in ∼2,100 genes of cardiovascular relevance, using a customised gene array in 15,596 CAD cases and 34,992 controls (11,202 cases and 30,733 controls of European descent; 4,394 cases and 4,259 controls of South Asian origin). We attempted to replicate putative novel associations in an additional 17,121 CAD cases and 40,473 controls. Potential mechanisms through which the novel variants could affect CAD risk were explored through association tests with vascular risk factors and gene expression. We confirmed associations of several previously known CAD susceptibility loci (eg, 9p21.3:p<10−33; LPA:p<10−19; 1p13.3:p<10−17) as well as three recently discovered loci (COL4A1/COL4A2, ZC3HC1, CYP17A1:p<5×10−7). However, we found essentially null results for most previously suggested CAD candidate genes. In our replication study of 24 promising common variants, we identified novel associations of variants in or near LIPA, IL5, TRIB1, and ABCG5/ABCG8, with per-allele odds ratios for CAD risk with each of the novel variants ranging from 1.06–1.09. Associations with variants at LIPA, TRIB1, and ABCG5/ABCG8 were supported by gene expression data or effects on lipid levels. Apart from the previously reported variants in LPA, none of the other ∼4,500 low frequency and functional variants showed a strong effect. Associations in South Asians did not differ appreciably from those in Europeans, except for 9p21.3 (per-allele odds ratio: 1.14 versus 1.27 respectively; P for heterogeneity = 0.003). This large-scale gene-centric analysis has identified several novel genes for CAD that relate to diverse biochemical and cellular functions and

Large-scale gene-centric analysis identifies novel variants for coronary artery disease.

PubMed

2011-09-01

Coronary artery disease (CAD) has a significant genetic contribution that is incompletely characterized. To complement genome-wide association (GWA) studies, we conducted a large and systematic candidate gene study of CAD susceptibility, including analysis of many uncommon and functional variants. We examined 49,094 genetic variants in ∼2,100 genes of cardiovascular relevance, using a customised gene array in 15,596 CAD cases and 34,992 controls (11,202 cases and 30,733 controls of European descent; 4,394 cases and 4,259 controls of South Asian origin). We attempted to replicate putative novel associations in an additional 17,121 CAD cases and 40,473 controls. Potential mechanisms through which the novel variants could affect CAD risk were explored through association tests with vascular risk factors and gene expression. We confirmed associations of several previously known CAD susceptibility loci (eg, 9p21.3:p<10(-33); LPA:p<10(-19); 1p13.3:p<10(-17)) as well as three recently discovered loci (COL4A1/COL4A2, ZC3HC1, CYP17A1:p<5×10(-7)). However, we found essentially null results for most previously suggested CAD candidate genes. In our replication study of 24 promising common variants, we identified novel associations of variants in or near LIPA, IL5, TRIB1, and ABCG5/ABCG8, with per-allele odds ratios for CAD risk with each of the novel variants ranging from 1.06-1.09. Associations with variants at LIPA, TRIB1, and ABCG5/ABCG8 were supported by gene expression data or effects on lipid levels. Apart from the previously reported variants in LPA, none of the other ∼4,500 low frequency and functional variants showed a strong effect. Associations in South Asians did not differ appreciably from those in Europeans, except for 9p21.3 (per-allele odds ratio: 1.14 versus 1.27 respectively; P for heterogeneity = 0.003). This large-scale gene-centric analysis has identified several novel genes for CAD that relate to diverse biochemical and cellular functions and

Differential Network Analyses of Alzheimer’s Disease Identify Early Events in Alzheimer’s Disease Pathology

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

Xia, Jing; Rocke, David M.; Perry, George

In late-onset Alzheimer’s disease (AD), multiple brain regions are not affected simultaneously. Comparing the gene expression of the affected regions to identify the differences in the biological processes perturbed can lead to greater insight into AD pathogenesis and early characteristics. We identified differentially expressed (DE) genes from single cell microarray data of four AD affected brain regions: entorhinal cortex (EC), hippocampus (HIP), posterior cingulate cortex (PCC), and middle temporal gyrus (MTG). We organized the DE genes in the four brain regions into region-specific gene coexpression networks. Differential neighborhood analyses in the coexpression networks were performed to identify genes with lowmore » topological overlap (TO) of their direct neighbors. The low TO genes were used to characterize the biological differences between two regions. Our analyses show that increased oxidative stress, along with alterations in lipid metabolism in neurons, may be some of the very early events occurring in AD pathology. Cellular defense mechanisms try to intervene but fail, finally resulting in AD pathology as the disease progresses. Furthermore, disease annotation of the low TO genes in two independent protein interaction networks has resulted in association between cancer, diabetes, renal diseases, and cardiovascular diseases.« less

Differential Network Analyses of Alzheimer’s Disease Identify Early Events in Alzheimer’s Disease Pathology

DOE PAGES

Xia, Jing; Rocke, David M.; Perry, George; ...

2014-01-01

In late-onset Alzheimer’s disease (AD), multiple brain regions are not affected simultaneously. Comparing the gene expression of the affected regions to identify the differences in the biological processes perturbed can lead to greater insight into AD pathogenesis and early characteristics. We identified differentially expressed (DE) genes from single cell microarray data of four AD affected brain regions: entorhinal cortex (EC), hippocampus (HIP), posterior cingulate cortex (PCC), and middle temporal gyrus (MTG). We organized the DE genes in the four brain regions into region-specific gene coexpression networks. Differential neighborhood analyses in the coexpression networks were performed to identify genes with lowmore » topological overlap (TO) of their direct neighbors. The low TO genes were used to characterize the biological differences between two regions. Our analyses show that increased oxidative stress, along with alterations in lipid metabolism in neurons, may be some of the very early events occurring in AD pathology. Cellular defense mechanisms try to intervene but fail, finally resulting in AD pathology as the disease progresses. Furthermore, disease annotation of the low TO genes in two independent protein interaction networks has resulted in association between cancer, diabetes, renal diseases, and cardiovascular diseases.« less

Transcriptome profiling of two maize inbreds with distinct responses to Gibberella ear rot disease to identify candidate resistance genes.

PubMed

Kebede, Aida Z; Johnston, Anne; Schneiderman, Danielle; Bosnich, Whynn; Harris, Linda J

2018-02-09

Gibberella ear rot (GER) is one of the most economically important fungal diseases of maize in the temperate zone due to moldy grain contaminated with health threatening mycotoxins. To develop resistant genotypes and control the disease, understanding the host-pathogen interaction is essential. RNA-Seq-derived transcriptome profiles of fungal- and mock-inoculated developing kernel tissues of two maize inbred lines were used to identify differentially expressed transcripts and propose candidate genes mapping within GER resistance quantitative trait loci (QTL). A total of 1255 transcripts were significantly (P ≤ 0.05) up regulated due to fungal infection in both susceptible and resistant inbreds. A greater number of transcripts were up regulated in the former (1174) than the latter (497) and increased as the infection progressed from 1 to 2 days after inoculation. Focusing on differentially expressed genes located within QTL regions for GER resistance, we identified 81 genes involved in membrane transport, hormone regulation, cell wall modification, cell detoxification, and biosynthesis of pathogenesis related proteins and phytoalexins as candidate genes contributing to resistance. Applying droplet digital PCR, we validated the expression profiles of a subset of these candidate genes from QTL regions contributed by the resistant inbred on chromosomes 1, 2 and 9. By screening global gene expression profiles for differentially expressed genes mapping within resistance QTL regions, we have identified candidate genes for gibberella ear rot resistance on several maize chromosomes which could potentially lead to a better understanding of Fusarium resistance mechanisms.

Gene therapy for sickle cell disease.

PubMed

Olowoyeye, Abiola; Okwundu, Charles I

2014-10-10

Sickle cell disease encompasses a group of genetic disorders characterized by the presence of at least one hemoglobin S (Hb S) allele, and a second abnormal allele that could allow abnormal hemoglobin polymerisation leading to a symptomatic disorder.Autosomal recessive disorders (such as sickle cell disease) are good candidates for gene therapy because a normal phenotype can be restored in diseased cells with only a single normal copy of the mutant gene. The objectives of this review are:- to determine whether gene therapy can improve survival and prevent symptoms and complications associated with sickle cell disease;- to examine the risks of gene therapy against the potential long-term gain for people with sickle cell disease. We searched the Cochrane Cystic Fibrosis and Genetic Disorders Group Haemoglobinopathies Trials Register, which comprises of references identified from comprehensive electronic database searches and searching relevant journals and abstract books of conference proceedings.Date of the most recent search of the Group's Haemoglobinopathies Trials Register: 21 July 2014. All randomised or quasi-randomised clinical trials (including any relevant phase 1, 2 or 3 trials) of gene therapy for all individuals with sickle cell disease, regardless of age or setting. No trials of gene therapy for sickle cell disease were found. No trials of gene therapy for sickle cell disease were reported. No randomised or quasi-randomised clinical trials of gene therapy for sickle cell disease were reported. Thus, no objective conclusions or recommendations in practice can be made on gene therapy for sickle cell disease. This systematic review has identified the need for well-designed, randomised controlled trials to assess the benefits and risks of gene therapy for sickle cell disease.

Positive-unlabeled learning for disease gene identification

PubMed Central

Yang, Peng; Li, Xiao-Li; Mei, Jian-Ping; Kwoh, Chee-Keong; Ng, See-Kiong

2012-01-01

Background: Identifying disease genes from human genome is an important but challenging task in biomedical research. Machine learning methods can be applied to discover new disease genes based on the known ones. Existing machine learning methods typically use the known disease genes as the positive training set P and the unknown genes as the negative training set N (non-disease gene set does not exist) to build classifiers to identify new disease genes from the unknown genes. However, such kind of classifiers is actually built from a noisy negative set N as there can be unknown disease genes in N itself. As a result, the classifiers do not perform as well as they could be. Result: Instead of treating the unknown genes as negative examples in N, we treat them as an unlabeled set U. We design a novel positive-unlabeled (PU) learning algorithm PUDI (PU learning for disease gene identification) to build a classifier using P and U. We first partition U into four sets, namely, reliable negative set RN, likely positive set LP, likely negative set LN and weak negative set WN. The weighted support vector machines are then used to build a multi-level classifier based on the four training sets and positive training set P to identify disease genes. Our experimental results demonstrate that our proposed PUDI algorithm outperformed the existing methods significantly. Conclusion: The proposed PUDI algorithm is able to identify disease genes more accurately by treating the unknown data more appropriately as unlabeled set U instead of negative set N. Given that many machine learning problems in biomedical research do involve positive and unlabeled data instead of negative data, it is possible that the machine learning methods for these problems can be further improved by adopting PU learning methods, as we have done here for disease gene identification. Availability and implementation: The executable program and data are available at http://www1.i2r

Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets

PubMed Central

Vinayagam, Arunachalam; Gibson, Travis E.; Lee, Ho-Joon; Yilmazel, Bahar; Roesel, Charles; Hu, Yanhui; Kwon, Young; Sharma, Amitabh; Liu, Yang-Yu; Perrimon, Norbert; Barabási, Albert-László

2016-01-01

The protein–protein interaction (PPI) network is crucial for cellular information processing and decision-making. With suitable inputs, PPI networks drive the cells to diverse functional outcomes such as cell proliferation or cell death. Here, we characterize the structural controllability of a large directed human PPI network comprising 6,339 proteins and 34,813 interactions. This network allows us to classify proteins as “indispensable,” “neutral,” or “dispensable,” which correlates to increasing, no effect, or decreasing the number of driver nodes in the network upon removal of that protein. We find that 21% of the proteins in the PPI network are indispensable. Interestingly, these indispensable proteins are the primary targets of disease-causing mutations, human viruses, and drugs, suggesting that altering a network’s control property is critical for the transition between healthy and disease states. Furthermore, analyzing copy number alterations data from 1,547 cancer patients reveals that 56 genes that are frequently amplified or deleted in nine different cancers are indispensable. Among the 56 genes, 46 of them have not been previously associated with cancer. This suggests that controllability analysis is very useful in identifying novel disease genes and potential drug targets. PMID:27091990

Gene therapy for sickle cell disease.

PubMed

Olowoyeye, Abiola; Okwundu, Charles I

2016-11-14

Sickle cell disease encompasses a group of genetic disorders characterized by the presence of at least one hemoglobin S (Hb S) allele, and a second abnormal allele that could allow abnormal hemoglobin polymerisation leading to a symptomatic disorder.Autosomal recessive disorders (such as sickle cell disease) are good candidates for gene therapy because a normal phenotype can be restored in diseased cells with only a single normal copy of the mutant gene. This is an update of a previously published Cochrane Review. The objectives of this review are:to determine whether gene therapy can improve survival and prevent symptoms and complications associated with sickle cell disease;to examine the risks of gene therapy against the potential long-term gain for people with sickle cell disease. We searched the Cochrane Cystic Fibrosis and Genetic Disorders Group Haemoglobinopathies Trials Register, which comprises of references identified from comprehensive electronic database searches and searching relevant journals and abstract books of conference proceedings.Date of the most recent search of the Group's Haemoglobinopathies Trials Register: 15 August 2016. All randomised or quasi-randomised clinical trials (including any relevant phase 1, 2 or 3 trials) of gene therapy for all individuals with sickle cell disease, regardless of age or setting. No trials of gene therapy for sickle cell disease were found. No trials of gene therapy for sickle cell disease were reported. No randomised or quasi-randomised clinical trials of gene therapy for sickle cell disease were reported. Thus, no objective conclusions or recommendations in practice can be made on gene therapy for sickle cell disease. This systematic review has identified the need for well-designed, randomised controlled trials to assess the benefits and risks of gene therapy for sickle cell disease.

Identifying key genes in rheumatoid arthritis by weighted gene co-expression network analysis.

PubMed

Ma, Chunhui; Lv, Qi; Teng, Songsong; Yu, Yinxian; Niu, Kerun; Yi, Chengqin

2017-08-01

This study aimed to identify rheumatoid arthritis (RA) related genes based on microarray data using the WGCNA (weighted gene co-expression network analysis) method. Two gene expression profile datasets GSE55235 (10 RA samples and 10 healthy controls) and GSE77298 (16 RA samples and seven healthy controls) were downloaded from Gene Expression Omnibus database. Characteristic genes were identified using metaDE package. WGCNA was used to find disease-related networks based on gene expression correlation coefficients, and module significance was defined as the average gene significance of all genes used to assess the correlation between the module and RA status. Genes in the disease-related gene co-expression network were subject to functional annotation and pathway enrichment analysis using Database for Annotation Visualization and Integrated Discovery. Characteristic genes were also mapped to the Connectivity Map to screen small molecules. A total of 599 characteristic genes were identified. For each dataset, characteristic genes in the green, red and turquoise modules were most closely associated with RA, with gene numbers of 54, 43 and 79, respectively. These genes were enriched in totally enriched in 17 Gene Ontology terms, mainly related to immune response (CD97, FYB, CXCL1, IKBKE, CCR1, etc.), inflammatory response (CD97, CXCL1, C3AR1, CCR1, LYZ, etc.) and homeostasis (C3AR1, CCR1, PLN, CCL19, PPT1, etc.). Two small-molecule drugs sanguinarine and papaverine were predicted to have a therapeutic effect against RA. Genes related to immune response, inflammatory response and homeostasis presumably have critical roles in RA pathogenesis. Sanguinarine and papaverine have a potential therapeutic effect against RA. © 2017 Asia Pacific League of Associations for Rheumatology and John Wiley & Sons Australia, Ltd.

Using gene chips to identify organ-specific, smooth muscle responses to experimental diabetes: potential applications to urological diseases.

PubMed

Hipp, Jason D; Davies, Kelvin P; Tar, Moses; Valcic, Mira; Knoll, Abraham; Melman, Arnold; Christ, George J

2007-02-01

To identify early diabetes-related alterations in gene expression in bladder and erectile tissue that would provide novel diagnostic and therapeutic treatment targets to prevent, delay or ameliorate the ensuing bladder and erectile dysfunction. The RG-U34A rat GeneChip (Affymetrix Inc., Sunnyvale, CA, USA) oligonucleotide microarray (containing approximately 8799 genes) was used to evaluate gene expression in corporal and male bladder tissue excised from rats 1 week after confirmation of a diabetic state, but before demonstrable changes in organ function in vivo. A conservative analytical approach was used to detect alterations in gene expression, and gene ontology (GO) classifications were used to identify biological themes/pathways involved in the aetiology of the organ dysfunction. In all, 320 and 313 genes were differentially expressed in bladder and corporal tissue, respectively. GO analysis in bladder tissue showed prominent increases in biological pathways involved in cell proliferation, metabolism, actin cytoskeleton and myosin, as well as decreases in cell motility, and regulation of muscle contraction. GO analysis in corpora showed increases in pathways related to ion channel transport and ion channel activity, while there were decreases in collagen I and actin genes. The changes in gene expression in these initial experiments are consistent with the pathophysiological characteristics of the bladder and erectile dysfunction seen later in the diabetic disease process. Thus, the observed changes in gene expression might be harbingers or biomarkers of impending organ dysfunction, and could provide useful diagnostic and therapeutic targets for a variety of progressive urological diseases/conditions (i.e. lower urinary tract symptoms related to benign prostatic hyperplasia, erectile dysfunction, etc.).

A vector space model approach to identify genetically related diseases.

PubMed

Sarkar, Indra Neil

2012-01-01

The relationship between diseases and their causative genes can be complex, especially in the case of polygenic diseases. Further exacerbating the challenges in their study is that many genes may be causally related to multiple diseases. This study explored the relationship between diseases through the adaptation of an approach pioneered in the context of information retrieval: vector space models. A vector space model approach was developed that bridges gene disease knowledge inferred across three knowledge bases: Online Mendelian Inheritance in Man, GenBank, and Medline. The approach was then used to identify potentially related diseases for two target diseases: Alzheimer disease and Prader-Willi Syndrome. In the case of both Alzheimer Disease and Prader-Willi Syndrome, a set of plausible diseases were identified that may warrant further exploration. This study furthers seminal work by Swanson, et al. that demonstrated the potential for mining literature for putative correlations. Using a vector space modeling approach, information from both biomedical literature and genomic resources (like GenBank) can be combined towards identification of putative correlations of interest. To this end, the relevance of the predicted diseases of interest in this study using the vector space modeling approach were validated based on supporting literature. The results of this study suggest that a vector space model approach may be a useful means to identify potential relationships between complex diseases, and thereby enable the coordination of gene-based findings across multiple complex diseases.

Comparing cancer vs normal gene expression profiles identifies new disease entities and common transcriptional programs in AML patients.

PubMed

Rapin, Nicolas; Bagger, Frederik Otzen; Jendholm, Johan; Mora-Jensen, Helena; Krogh, Anders; Kohlmann, Alexander; Thiede, Christian; Borregaard, Niels; Bullinger, Lars; Winther, Ole; Theilgaard-Mönch, Kim; Porse, Bo T

2014-02-06

Gene expression profiling has been used extensively to characterize cancer, identify novel subtypes, and improve patient stratification. However, it has largely failed to identify transcriptional programs that differ between cancer and corresponding normal cells and has not been efficient in identifying expression changes fundamental to disease etiology. Here we present a method that facilitates the comparison of any cancer sample to its nearest normal cellular counterpart, using acute myeloid leukemia (AML) as a model. We first generated a gene expression-based landscape of the normal hematopoietic hierarchy, using expression profiles from normal stem/progenitor cells, and next mapped the AML patient samples to this landscape. This allowed us to identify the closest normal counterpart of individual AML samples and determine gene expression changes between cancer and normal. We find the cancer vs normal method (CvN method) to be superior to conventional methods in stratifying AML patients with aberrant karyotype and in identifying common aberrant transcriptional programs with potential importance for AML etiology. Moreover, the CvN method uncovered a novel poor-outcome subtype of normal-karyotype AML, which allowed for the generation of a highly prognostic survival signature. Collectively, our CvN method holds great potential as a tool for the analysis of gene expression profiles of cancer patients.

Genome-Wide association study identifies candidate genes for Parkinson's disease in an Ashkenazi Jewish population

PubMed Central

2011-01-01

Background To date, nine Parkinson disease (PD) genome-wide association studies in North American, European and Asian populations have been published. The majority of studies have confirmed the association of the previously identified genetic risk factors, SNCA and MAPT, and two studies have identified three new PD susceptibility loci/genes (PARK16, BST1 and HLA-DRB5). In a recent meta-analysis of datasets from five of the published PD GWAS an additional 6 novel candidate genes (SYT11, ACMSD, STK39, MCCC1/LAMP3, GAK and CCDC62/HIP1R) were identified. Collectively the associations identified in these GWAS account for only a small proportion of the estimated total heritability of PD suggesting that an 'unknown' component of the genetic architecture of PD remains to be identified. Methods We applied a GWAS approach to a relatively homogeneous Ashkenazi Jewish (AJ) population from New York to search for both 'rare' and 'common' genetic variants that confer risk of PD by examining any SNPs with allele frequencies exceeding 2%. We have focused on a genetic isolate, the AJ population, as a discovery dataset since this cohort has a higher sharing of genetic background and historically experienced a significant bottleneck. We also conducted a replication study using two publicly available datasets from dbGaP. The joint analysis dataset had a combined sample size of 2,050 cases and 1,836 controls. Results We identified the top 57 SNPs showing the strongest evidence of association in the AJ dataset (p < 9.9 × 10-5). Six SNPs located within gene regions had positive signals in at least one other independent dbGaP dataset: LOC100505836 (Chr3p24), LOC153328/SLC25A48 (Chr5q31.1), UNC13B (9p13.3), SLCO3A1(15q26.1), WNT3(17q21.3) and NSF (17q21.3). We also replicated published associations for the gene regions SNCA (Chr4q21; rs3775442, p = 0.037), PARK16 (Chr1q32.1; rs823114 (NUCKS1), p = 6.12 × 10-4), BST1 (Chr4p15; rs12502586, p = 0.027), STK39 (Chr2q24.3; rs3754775, p = 0

Comparative prion disease gene expression profiling using the prion disease mimetic, cuprizone

PubMed Central

Moody, Laura R; Herbst, Allen J; Yoo, Han Sang; Vanderloo, Joshua P

2009-01-01

Identification of genes expressed in response to prion infection may elucidate biomarkers for disease, identify factors involved in agent replication, mechanisms of neuropathology and therapeutic targets. Although several groups have sought to identify gene expression changes specific to prion disease, expression profiles rife with cell population changes have consistently been identified. Cuprizone, a neurotoxicant, qualitatively mimics the cell population changes observed in prion disease, resulting in both spongiform change and astrocytosis. The use of cuprizone-treated animals as an experimental control during comparative expression profiling allows for the identification of transcripts whose expression increases during prion disease and remains unchanged during cuprizone-triggered neuropathology. In this study, expression profiles from the brains of mice preclinically and clinically infected with Rocky Mountain Laboratory (RML) mouse-adapted scrapie agent and age-matched controls were profiled using Affymetrix gene arrays. In total, 164 genes were differentially regulated during prion infection. Eighty-three of these transcripts have been previously undescribed as differentially regulated during prion disease. A 0.4% cuprizone diet was utilized as a control for comparative expression profiling. Cuprizone treatment induced spongiosis and astrocyte proliferation as indicated by glial fibrillary acidic protein (Gfap) transcriptional activation and immunohistochemistry. Gene expression profiles from brain tissue obtained from cuprizone-treated mice identified 307 differentially regulated transcript changes. After comparative analysis, 17 transcripts unaffected by cuprizone treatment but increasing in expression from preclinical to clinical prion infection were identified. Here we describe the novel use of the prion disease mimetic, cuprizone, to control for cell population changes in the brain during prion infection. PMID:19535908

Transcriptomic meta-analysis identifies gene expression characteristics in various samples of HIV-infected patients with nonprogressive disease.

PubMed

Zhang, Le-Le; Zhang, Zi-Ning; Wu, Xian; Jiang, Yong-Jun; Fu, Ya-Jing; Shang, Hong

2017-09-12

A small proportion of HIV-infected patients remain clinically and/or immunologically stable for years, including elite controllers (ECs) who have undetectable viremia (<50 copies/ml) and long-term nonprogressors (LTNPs) who maintain normal CD4 + T cell counts for prolonged periods (>10 years). However, the mechanism of nonprogression needs to be further resolved. In this study, a transcriptome meta-analysis was performed on nonprogressor and progressor microarray data to identify differential transcriptome pathways and potential biomarkers. Using the INMEX (integrative meta-analysis of expression data) program, we performed the meta-analysis to identify consistently differentially expressed genes (DEGs) in nonprogressors and further performed functional interpretation (gene ontology analysis and pathway analysis) of the DEGs identified in the meta-analysis. Five microarray datasets (81 cases and 98 controls in total), including whole blood, CD4 + and CD8 + T cells, were collected for meta-analysis. We determined that nonprogressors have reduced expression of important interferon-stimulated genes (ISGs), CD38, lymphocyte activation gene 3 (LAG-3) in whole blood, CD4 + and CD8 + T cells. Gene ontology (GO) analysis showed a significant enrichment in DEGs that function in the type I interferon signaling pathway. Upregulated pathways, including the PI3K-Akt signaling pathway in whole blood, cytokine-cytokine receptor interaction in CD4 + T cells and the MAPK signaling pathway in CD8 + T cells, were identified in nonprogressors compared with progressors. In each metabolic functional category, the number of downregulated DEGs was more than the upregulated DEGs, and almost all genes were downregulated DEGs in the oxidative phosphorylation (OXPHOS) and tricarboxylic acid (TCA) cycle in the three types of samples. Our transcriptomic meta-analysis provides a comprehensive evaluation of the gene expression profiles in major blood types of nonprogressors, providing new

Large-Scale Discovery of Disease-Disease and Disease-Gene Associations

PubMed Central

Gligorijevic, Djordje; Stojanovic, Jelena; Djuric, Nemanja; Radosavljevic, Vladan; Grbovic, Mihajlo; Kulathinal, Rob J.; Obradovic, Zoran

2016-01-01

Data-driven phenotype analyses on Electronic Health Record (EHR) data have recently drawn benefits across many areas of clinical practice, uncovering new links in the medical sciences that can potentially affect the well-being of millions of patients. In this paper, EHR data is used to discover novel relationships between diseases by studying their comorbidities (co-occurrences in patients). A novel embedding model is designed to extract knowledge from disease comorbidities by learning from a large-scale EHR database comprising more than 35 million inpatient cases spanning nearly a decade, revealing significant improvements on disease phenotyping over current computational approaches. In addition, the use of the proposed methodology is extended to discover novel disease-gene associations by including valuable domain knowledge from genome-wide association studies. To evaluate our approach, its effectiveness is compared against a held-out set where, again, it revealed very compelling results. For selected diseases, we further identify candidate gene lists for which disease-gene associations were not studied previously. Thus, our approach provides biomedical researchers with new tools to filter genes of interest, thus, reducing costly lab studies. PMID:27578529

Candidate genes for panhypopituitarism identified by gene expression profiling

PubMed Central

Mortensen, Amanda H.; MacDonald, James W.; Ghosh, Debashis

2011-01-01

Mutations in the transcription factors PROP1 and PIT1 (POU1F1) lead to pituitary hormone deficiency and hypopituitarism in mice and humans. The dysmorphology of developing Prop1 mutant pituitaries readily distinguishes them from those of Pit1 mutants and normal mice. This and other features suggest that Prop1 controls the expression of genes besides Pit1 that are important for pituitary cell migration, survival, and differentiation. To identify genes involved in these processes we used microarray analysis of gene expression to compare pituitary RNA from newborn Prop1 and Pit1 mutants and wild-type littermates. Significant differences in gene expression were noted between each mutant and their normal littermates, as well as between Prop1 and Pit1 mutants. Otx2, a gene critical for normal eye and pituitary development in humans and mice, exhibited elevated expression specifically in Prop1 mutant pituitaries. We report the spatial and temporal regulation of Otx2 in normal mice and Prop1 mutants, and the results suggest Otx2 could influence pituitary development by affecting signaling from the ventral diencephalon and regulation of gene expression in Rathke's pouch. The discovery that Otx2 expression is affected by Prop1 deficiency provides support for our hypothesis that identifying molecular differences in mutants will contribute to understanding the molecular mechanisms that control pituitary organogenesis and lead to human pituitary disease. PMID:21828248

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

Diagnostic Exome Sequencing Identifies a Novel Gene, EMILIN1, Associated with Autosomal-Dominant Hereditary Connective Tissue Disease.

PubMed

Capuano, Alessandra; Bucciotti, Francesco; Farwell, Kelly D; Tippin Davis, Brigette; Mroske, Cameron; Hulick, Peter J; Weissman, Scott M; Gao, Qingshen; Spessotto, Paola; Colombatti, Alfonso; Doliana, Roberto

2016-01-01

Heritable connective tissue diseases are a highly heterogeneous family of over 200 disorders that affect the extracellular matrix. While the genetic basis of several disorders is established, the etiology has not been discovered for a large portion of patients, likely due to rare yet undiscovered disease genes. By performing trio-exome sequencing of a 55-year-old male proband presenting with multiple symptoms indicative of a connective disorder, we identified a heterozygous missense alteration in exon 1 of the Elastin Microfibril Interfacer 1 (EMILIN1) gene, c.64G>A (p.A22T). The proband presented with ascending and descending aortic aneurysms, bilateral lower leg and foot sensorimotor peripheral neuropathy, arthropathy, and increased skin elasticity. Sanger sequencing confirmed that the EMILIN1 alteration, which maps around the signal peptide cleavage site, segregated with disease in the affected proband, mother, and son. The impaired secretion of EMILIN-1 in cells transfected with the mutant p.A22T coincided with abnormal protein accumulation within the endoplasmic reticulum. In skin biopsy of the proband, we detected less EMILIN-1 with disorganized and abnormal coarse fibrils, aggregated deposits underneath the epidermis basal lamina, and dermal cells apoptosis. These findings collectively suggest that EMILIN1 may represent a new disease gene associated with an autosomal-dominant connective tissue disorder. © 2015 The Authors. **Human Mutation published by Wiley Periodicals, Inc.

Ancestry-based stratified analysis of Immunochip data identifies novel associations with celiac disease.

PubMed

Garcia-Etxebarria, Koldo; Jauregi-Miguel, Amaia; Romero-Garmendia, Irati; Plaza-Izurieta, Leticia; Legarda, Maria; Irastorza, Iñaki; Bilbao, Jose Ramon

2016-12-01

To identify candidate genes in celiac disease (CD), we reanalyzed the whole Immunochip CD cohort using a different approach that clusters individuals based on immunoancestry prior to disease association analysis, rather than by geographical origin. We detected 636 new associated SNPs (P<7.02 × 10 -07 ) and identified 5 novel genomic regions, extended 8 others previously identified and also detected 18 isolated signals defined by one or very few significant SNPs. To test whether we could identify putative candidate genes, we performed expression analyses of several genes from the top novel region (chr2:134533564-136169524), from a previously identified locus that is now extended, and a gene marked by an isolated SNP, in duodenum biopsies of active and treated CD patients, and non-celiac controls. In the largest novel region, CCNT2 and R3HDM1 were constitutively underexpressed in disease, even after gluten removal. Moreover, several genes within this region were coexpressed in patients, but not in controls. Other novel genes like KIF21B, REL and SORD also showed altered expression in active disease. Apart from the identification of novel CD loci, these results suggest that ancestry-based stratified analysis is an efficient strategy for association studies in complex diseases.

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.

Mining biological databases for candidate disease genes

NASA Astrophysics Data System (ADS)

Braun, Terry A.; Scheetz, Todd; Webster, Gregg L.; Casavant, Thomas L.

2001-07-01

The publicly-funded effort to sequence the complete nucleotide sequence of the human genome, the Human Genome Project (HGP), has currently produced more than 93% of the 3 billion nucleotides of the human genome into a preliminary `draft' format. In addition, several valuable sources of information have been developed as direct and indirect results of the HGP. These include the sequencing of model organisms (rat, mouse, fly, and others), gene discovery projects (ESTs and full-length), and new technologies such as expression analysis and resources (micro-arrays or gene chips). These resources are invaluable for the researchers identifying the functional genes of the genome that transcribe and translate into the transcriptome and proteome, both of which potentially contain orders of magnitude more complexity than the genome itself. Preliminary analyses of this data identified approximately 30,000 - 40,000 human `genes.' However, the bulk of the effort still remains -- to identify the functional and structural elements contained within the transcriptome and proteome, and to associate function in the transcriptome and proteome to genes. A fortuitous consequence of the HGP is the existence of hundreds of databases containing biological information that may contain relevant data pertaining to the identification of disease-causing genes. The task of mining these databases for information on candidate genes is a commercial application of enormous potential. We are developing a system to acquire and mine data from specific databases to aid our efforts to identify disease genes. A high speed cluster of Linux of workstations is used to analyze sequence and perform distributed sequence alignments as part of our data mining and processing. This system has been used to mine GeneMap99 sequences within specific genomic intervals to identify potential candidate disease genes associated with Bardet-Biedle Syndrome (BBS).

Identifying Disease Associated miRNAs Based on Protein Domains.

PubMed

Qin, Gui-Min; Li, Rui-Yi; Zhao, Xing-Ming

2016-01-01

MicroRNAs (miRNAs) are a class of small endogenous non-coding genes, acting as regulators in the post-transcriptional processes. Recently, the miRNAs are found to be widely involved in different types of diseases. Therefore, the identification of disease associated miRNAs can help understand the mechanisms that underlie the disease and identify new biomarkers. However, it is not easy to identify the miRNAs related to diseases due to its extensive involvements in various biological processes. In this work, we present a new approach to identify disease associated miRNAs based on domains, the functional and structural blocks of proteins. The results on real datasets demonstrate that our method can effectively identify disease related miRNAs with high precision.

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.

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.

Analyzing the most frequent disease loci in targeted patient categories optimizes disease gene identification and test accuracy worldwide.

PubMed

Lebo, Roger V; Tonk, Vijay S

2015-01-21

Our genomewide studies support targeted testing the most frequent genetic diseases by patient category: (1) pregnant patients, (2) at-risk conceptuses, (3) affected children, and (4) abnormal adults. This approach not only identifies most reported disease causing sequences accurately, but also minimizes incorrectly identified additional disease causing loci. Diseases were grouped in descending order of occurrence from four data sets: (1) GeneTests 534 listed population prevalences, (2) 4129 high risk prenatal karyotypes, (3) 1265 affected patient microarrays, and (4) reanalysis of 25,452 asymptomatic patient results screened prenatally for 108 genetic diseases. These most frequent diseases are categorized by transmission: (A) autosomal recessive, (B) X-linked, (C) autosomal dominant, (D) microscopic chromosome rearrangements, (E) submicroscopic copy number changes, and (F) frequent ethnic diseases. Among affected and carrier patients worldwide, most reported mutant genes would be identified correctly according to one of four patient categories from at-risk couples with <64 tested genes to affected adults with 314 tested loci. Three clinically reported patient series confirmed this approach. First, only 54 targeted chromosomal sites would have detected all 938 microscopically visible unbalanced karyotypes among 4129 karyotyped POC, CVS, and amniocentesis samples. Second, 37 of 48 reported aneuploid regions were found among our 1265 clinical microarrays confirming the locations of 8 schizophrenia loci and 20 aneuploidies altering intellectual ability, while also identifying 9 of the most frequent deletion syndromes. Third, testing 15 frequent genes would have identified 124 couples with a 1 in 4 risk of a fetus with a recessive disease compared to the 127 couples identified by testing all 108 genes, while testing all mutations in 15 genes could have identified more couples. Testing the most frequent disease causing abnormalities in 1 of 8 reported disease loci [~1 of

A maize resistance gene functions against bacterial streak disease in rice.

PubMed

Zhao, Bingyu; Lin, Xinghua; Poland, Jesse; Trick, Harold; Leach, Jan; Hulbert, Scot

2005-10-25

Although cereal crops all belong to the grass family (Poacea), most of their diseases are specific to a particular species. Thus, a given cereal species is typically resistant to diseases of other grasses, and this nonhost resistance is generally stable. To determine the feasibility of transferring nonhost resistance genes (R genes) between distantly related grasses to control specific diseases, we identified a maize R gene that recognizes a rice pathogen, Xanthomonas oryzae pv. oryzicola, which causes bacterial streak disease. Bacterial streak is an important disease of rice in Asia, and no simply inherited sources of resistance have been identified in rice. Although X. o. pv. oryzicola does not cause disease on maize, we identified a maize gene, Rxo1, that conditions a resistance reaction to a diverse collection of pathogen strains. Surprisingly, Rxo1 also controls resistance to the unrelated pathogen Burkholderia andropogonis, which causes bacterial stripe of sorghum and maize. The same gene thus controls resistance reactions to both pathogens and nonpathogens of maize. Rxo1 has a nucleotide-binding site-leucine-rich repeat structure, similar to many previously identified R genes. Most importantly, Rxo1 functions after transfer as a transgene to rice, demonstrating the feasibility of nonhost R gene transfer between cereals and providing a valuable tool for controlling bacterial streak disease.

A Systems Biology Framework Identifies Molecular Underpinnings of Coronary Heart Disease

PubMed Central

Huan, Tianxiao; Zhang, Bin; Wang, Zhi; Joehanes, Roby; Zhu, Jun; Johnson, Andrew D.; Ying, Saixia; Munson, Peter J.; Raghavachari, Nalini; Wang, Richard; Liu, Poching; Courchesne, Paul; Hwang, Shih-Jen; Assimes, Themistocles L.; McPherson, Ruth; Samani, Nilesh J.; Schunkert, Heribert; Meng, Qingying; Suver, Christine; O'Donnell, Christopher J.; Derry, Jonathan; Yang, Xia; Levy, Daniel

2013-01-01

Objective Genetic approaches have identified numerous loci associated with coronary heart disease (CHD). The molecular mechanisms underlying CHD gene-disease associations, however, remain unclear. We hypothesized that genetic variants with both strong and subtle effects drive gene subnetworks that in turn affect CHD. Approach and Results We surveyed CHD-associated molecular interactions by constructing coexpression networks using whole blood gene expression profiles from 188 CHD cases and 188 age- and sex-matched controls. 24 coexpression modules were identified including one case-specific and one control-specific differential module (DM). The DMs were enriched for genes involved in B-cell activation, immune response, and ion transport. By integrating the DMs with altered gene expression associated SNPs (eSNPs) and with results of GWAS of CHD and its risk factors, the control-specific DM was implicated as CHD-causal based on its significant enrichment for both CHD and lipid eSNPs. This causal DM was further integrated with tissue-specific Bayesian networks and protein-protein interaction networks to identify regulatory key driver (KD) genes. Multi-tissue KDs (SPIB and TNFRSF13C) and tissue-specific KDs (e.g. EBF1) were identified. Conclusions Our network-driven integrative analysis not only identified CHD-related genes, but also defined network structure that sheds light on the molecular interactions of genes associated with CHD risk. PMID:23539213

Parkinson's disease candidate gene prioritization based on expression profile of midbrain dopaminergic neurons

PubMed Central

2010-01-01

Background Parkinson's disease is the second most common neurodegenerative disorder. The pathological hallmark of the disease is degeneration of midbrain dopaminergic neurons. Genetic association studies have linked 13 human chromosomal loci to Parkinson's disease. Identification of gene(s), as part of the etiology of Parkinson's disease, within the large number of genes residing in these loci can be achieved through several approaches, including screening methods, and considering appropriate criteria. Since several of the indentified Parkinson's disease genes are expressed in substantia nigra pars compact of the midbrain, expression within the neurons of this area could be a suitable criterion to limit the number of candidates and identify PD genes. Methods In this work we have used the combination of findings from six rodent transcriptome analysis studies on the gene expression profile of midbrain dopaminergic neurons and the PARK loci in OMIM (Online Mendelian Inheritance in Man) database, to identify new candidate genes for Parkinson's disease. Results Merging the two datasets, we identified 20 genes within PARK loci, 7 of which are located in an orphan Parkinson's disease locus and one, which had been identified as a disease gene. In addition to identifying a set of candidates for further genetic association studies, these results show that the criteria of expression in midbrain dopaminergic neurons may be used to narrow down the number of genes in PARK loci for such studies. PMID:20716345

A maize resistance gene functions against bacterial streak disease in rice

PubMed Central

Zhao, Bingyu; Lin, Xinghua; Poland, Jesse; Trick, Harold; Leach, Jan; Hulbert, Scot

2005-01-01

Although cereal crops all belong to the grass family (Poacea), most of their diseases are specific to a particular species. Thus, a given cereal species is typically resistant to diseases of other grasses, and this nonhost resistance is generally stable. To determine the feasibility of transferring nonhost resistance genes (R genes) between distantly related grasses to control specific diseases, we identified a maize R gene that recognizes a rice pathogen, Xanthomonas oryzae pv. oryzicola, which causes bacterial streak disease. Bacterial streak is an important disease of rice in Asia, and no simply inherited sources of resistance have been identified in rice. Although X. o. pv. oryzicola does not cause disease on maize, we identified a maize gene, Rxo1, that conditions a resistance reaction to a diverse collection of pathogen strains. Surprisingly, Rxo1 also controls resistance to the unrelated pathogen Burkholderia andropogonis, which causes bacterial stripe of sorghum and maize. The same gene thus controls resistance reactions to both pathogens and nonpathogens of maize. Rxo1 has a nucleotide-binding site-leucine-rich repeat structure, similar to many previously identified R genes. Most importantly, Rxo1 functions after transfer as a transgene to rice, demonstrating the feasibility of nonhost R gene transfer between cereals and providing a valuable tool for controlling bacterial streak disease. PMID:16230639

Identifying a gene expression signature of cluster headache in blood

PubMed Central

Eising, Else; Pelzer, Nadine; Vijfhuizen, Lisanne S.; Vries, Boukje de; Ferrari, Michel D.; ‘t Hoen, Peter A. C.; Terwindt, Gisela M.; van den Maagdenberg, Arn M. J. M.

2017-01-01

Cluster headache is a relatively rare headache disorder, typically characterized by multiple daily, short-lasting attacks of excruciating, unilateral (peri-)orbital or temporal pain associated with autonomic symptoms and restlessness. To better understand the pathophysiology of cluster headache, we used RNA sequencing to identify differentially expressed genes and pathways in whole blood of patients with episodic (n = 19) or chronic (n = 20) cluster headache in comparison with headache-free controls (n = 20). Gene expression data were analysed by gene and by module of co-expressed genes with particular attention to previously implicated disease pathways including hypocretin dysregulation. Only moderate gene expression differences were identified and no associations were found with previously reported pathogenic mechanisms. At the level of functional gene sets, associations were observed for genes involved in several brain-related mechanisms such as GABA receptor function and voltage-gated channels. In addition, genes and modules of co-expressed genes showed a role for intracellular signalling cascades, mitochondria and inflammation. Although larger study samples may be required to identify the full range of involved pathways, these results indicate a role for mitochondria, intracellular signalling and inflammation in cluster headache. PMID:28074859

Identifying key genes in glaucoma based on a benchmarked dataset and the gene regulatory network.

PubMed

Chen, Xi; Wang, Qiao-Ling; Zhang, Meng-Hui

2017-10-01

The current study aimed to identify key genes in glaucoma based on a benchmarked dataset and gene regulatory network (GRN). Local and global noise was added to the gene expression dataset to produce a benchmarked dataset. Differentially-expressed genes (DEGs) between patients with glaucoma and normal controls were identified utilizing the Linear Models for Microarray Data (Limma) package based on benchmarked dataset. A total of 5 GRN inference methods, including Zscore, GeneNet, context likelihood of relatedness (CLR) algorithm, Partial Correlation coefficient with Information Theory (PCIT) and GEne Network Inference with Ensemble of Trees (Genie3) were evaluated using receiver operating characteristic (ROC) and precision and recall (PR) curves. The interference method with the best performance was selected to construct the GRN. Subsequently, topological centrality (degree, closeness and betweenness) was conducted to identify key genes in the GRN of glaucoma. Finally, the key genes were validated by performing reverse transcription-quantitative polymerase chain reaction (RT-qPCR). A total of 176 DEGs were detected from the benchmarked dataset. The ROC and PR curves of the 5 methods were analyzed and it was determined that Genie3 had a clear advantage over the other methods; thus, Genie3 was used to construct the GRN. Following topological centrality analysis, 14 key genes for glaucoma were identified, including IL6 , EPHA2 and GSTT1 and 5 of these 14 key genes were validated by RT-qPCR. Therefore, the current study identified 14 key genes in glaucoma, which may be potential biomarkers to use in the diagnosis of glaucoma and aid in identifying the molecular mechanism of this disease.

Co-clustering phenome–genome for phenotype classification and disease gene discovery

PubMed Central

Hwang, TaeHyun; Atluri, Gowtham; Xie, MaoQiang; Dey, Sanjoy; Hong, Changjin; Kumar, Vipin; Kuang, Rui

2012-01-01

Understanding the categorization of human diseases is critical for reliably identifying disease causal genes. Recently, genome-wide studies of abnormal chromosomal locations related to diseases have mapped >2000 phenotype–gene relations, which provide valuable information for classifying diseases and identifying candidate genes as drug targets. In this article, a regularized non-negative matrix tri-factorization (R-NMTF) algorithm is introduced to co-cluster phenotypes and genes, and simultaneously detect associations between the detected phenotype clusters and gene clusters. The R-NMTF algorithm factorizes the phenotype–gene association matrix under the prior knowledge from phenotype similarity network and protein–protein interaction network, supervised by the label information from known disease classes and biological pathways. In the experiments on disease phenotype–gene associations in OMIM and KEGG disease pathways, R-NMTF significantly improved the classification of disease phenotypes and disease pathway genes compared with support vector machines and Label Propagation in cross-validation on the annotated phenotypes and genes. The newly predicted phenotypes in each disease class are highly consistent with human phenotype ontology annotations. The roles of the new member genes in the disease pathways are examined and validated in the protein–protein interaction subnetworks. Extensive literature review also confirmed many new members of the disease classes and pathways as well as the predicted associations between disease phenotype classes and pathways. PMID:22735708

Genome-wide association study for Crohn's disease in the Quebec Founder Population identifies multiple validated disease loci.

PubMed

Raelson, John V; Little, Randall D; Ruether, Andreas; Fournier, Hélène; Paquin, Bruno; Van Eerdewegh, Paul; Bradley, W E C; Croteau, Pascal; Nguyen-Huu, Quynh; Segal, Jonathan; Debrus, Sophie; Allard, René; Rosenstiel, Philip; Franke, Andre; Jacobs, Gunnar; Nikolaus, Susanna; Vidal, Jean-Michel; Szego, Peter; Laplante, Nathalie; Clark, Hilary F; Paulussen, René J; Hooper, John W; Keith, Tim P; Belouchi, Abdelmajid; Schreiber, Stefan

2007-09-11

Genome-wide association (GWA) studies offer a powerful unbiased method for the identification of multiple susceptibility genes for complex diseases. Here we report the results of a GWA study for Crohn's disease (CD) using family trios from the Quebec Founder Population (QFP). Haplotype-based association analyses identified multiple regions associated with the disease that met the criteria for genome-wide significance, with many containing a gene whose function appears relevant to CD. A proportion of these were replicated in two independent German Caucasian samples, including the established CD loci NOD2 and IBD5. The recently described IL23R locus was also identified and replicated. For this region, multiple individuals with all major haplotypes in the QFP were sequenced and extensive fine mapping performed to identify risk and protective alleles. Several additional loci, including a region on 3p21 containing several plausible candidate genes, a region near JAKMIP1 on 4p16.1, and two larger regions on chromosome 17 were replicated. Together with previously published loci, the spectrum of CD genes identified to date involves biochemical networks that affect epithelial defense mechanisms, innate and adaptive immune response, and the repair or remodeling of tissue.

Immunogenetic mechanisms leading to thyroid autoimmunity: recent advances in identifying susceptibility genes and regions.

PubMed

Brand, Oliver J; Gough, Stephen C L

2011-12-01

The autoimmune thyroid diseases (AITD) include Graves' disease (GD) and Hashimoto's thyroiditis (HT), which are characterised by a breakdown in immune tolerance to thyroid antigens. Unravelling the genetic architecture of AITD is vital to better understanding of AITD pathogenesis, required to advance therapeutic options in both disease management and prevention. The early whole-genome linkage and candidate gene association studies provided the first evidence that the HLA region and CTLA-4 represented AITD risk loci. Recent improvements in; high throughput genotyping technologies, collection of larger disease cohorts and cataloguing of genome-scale variation have facilitated genome-wide association studies and more thorough screening of candidate gene regions. This has allowed identification of many novel AITD risk genes and more detailed association mapping. The growing number of confirmed AITD susceptibility loci, implicates a number of putative disease mechanisms most of which are tightly linked with aspects of immune system function. The unprecedented advances in genetic study will allow future studies to identify further novel disease risk genes and to identify aetiological variants within specific gene regions, which will undoubtedly lead to a better understanding of AITD patho-physiology.

Immunogenetic Mechanisms Leading to Thyroid Autoimmunity: Recent Advances in Identifying Susceptibility Genes and Regions

PubMed Central

Brand, Oliver J; Gough, Stephen C.L

2011-01-01

The autoimmune thyroid diseases (AITD) include Graves’ disease (GD) and Hashimoto’s thyroiditis (HT), which are characterised by a breakdown in immune tolerance to thyroid antigens. Unravelling the genetic architecture of AITD is vital to better understanding of AITD pathogenesis, required to advance therapeutic options in both disease management and prevention. The early whole-genome linkage and candidate gene association studies provided the first evidence that the HLA region and CTLA-4 represented AITD risk loci. Recent improvements in; high throughput genotyping technologies, collection of larger disease cohorts and cataloguing of genome-scale variation have facilitated genome-wide association studies and more thorough screening of candidate gene regions. This has allowed identification of many novel AITD risk genes and more detailed association mapping. The growing number of confirmed AITD susceptibility loci, implicates a number of putative disease mechanisms most of which are tightly linked with aspects of immune system function. The unprecedented advances in genetic study will allow future studies to identify further novel disease risk genes and to identify aetiological variants within specific gene regions, which will undoubtedly lead to a better understanding of AITD patho-physiology. PMID:22654554

Integrating Gene Expression with Summary Association Statistics to Identify Genes Associated with 30 Complex Traits.

PubMed

Mancuso, Nicholas; Shi, Huwenbo; Goddard, Pagé; Kichaev, Gleb; Gusev, Alexander; Pasaniuc, Bogdan

2017-03-02

Although genome-wide association studies (GWASs) have identified thousands of risk loci for many complex traits and diseases, the causal variants and genes at these loci remain largely unknown. Here, we introduce a method for estimating the local genetic correlation between gene expression and a complex trait and utilize it to estimate the genetic correlation due to predicted expression between pairs of traits. We integrated gene expression measurements from 45 expression panels with summary GWAS data to perform 30 multi-tissue transcriptome-wide association studies (TWASs). We identified 1,196 genes whose expression is associated with these traits; of these, 168 reside more than 0.5 Mb away from any previously reported GWAS significant variant. We then used our approach to find 43 pairs of traits with significant genetic correlation at the level of predicted expression; of these, eight were not found through genetic correlation at the SNP level. Finally, we used bi-directional regression to find evidence that BMI causally influences triglyceride levels and that triglyceride levels causally influence low-density lipoprotein. Together, our results provide insight into the role of gene expression in the susceptibility of complex traits and diseases. Copyright © 2017 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

A Strategy for Identifying Quantitative Trait Genes Using Gene Expression Analysis and Causal Analysis.

PubMed

Ishikawa, Akira

2017-11-27

Large numbers of quantitative trait loci (QTL) affecting complex diseases and other quantitative traits have been reported in humans and model animals. However, the genetic architecture of these traits remains elusive due to the difficulty in identifying causal quantitative trait genes (QTGs) for common QTL with relatively small phenotypic effects. A traditional strategy based on techniques such as positional cloning does not always enable identification of a single candidate gene for a QTL of interest because it is difficult to narrow down a target genomic interval of the QTL to a very small interval harboring only one gene. A combination of gene expression analysis and statistical causal analysis can greatly reduce the number of candidate genes. This integrated approach provides causal evidence that one of the candidate genes is a putative QTG for the QTL. Using this approach, I have recently succeeded in identifying a single putative QTG for resistance to obesity in mice. Here, I outline the integration approach and discuss its usefulness using my studies as an example.

Prioritizing causal disease genes using unbiased genomic features.

PubMed

Deo, Rahul C; Musso, Gabriel; Tasan, Murat; Tang, Paul; Poon, Annie; Yuan, Christiana; Felix, Janine F; Vasan, Ramachandran S; Beroukhim, Rameen; De Marco, Teresa; Kwok, Pui-Yan; MacRae, Calum A; Roth, Frederick P

2014-12-03

Cardiovascular disease (CVD) is the leading cause of death in the developed world. Human genetic studies, including genome-wide sequencing and SNP-array approaches, promise to reveal disease genes and mechanisms representing new therapeutic targets. In practice, however, identification of the actual genes contributing to disease pathogenesis has lagged behind identification of associated loci, thus limiting the clinical benefits. To aid in localizing causal genes, we develop a machine learning approach, Objective Prioritization for Enhanced Novelty (OPEN), which quantitatively prioritizes gene-disease associations based on a diverse group of genomic features. This approach uses only unbiased predictive features and thus is not hampered by a preference towards previously well-characterized genes. We demonstrate success in identifying genetic determinants for CVD-related traits, including cholesterol levels, blood pressure, and conduction system and cardiomyopathy phenotypes. Using OPEN, we prioritize genes, including FLNC, for association with increased left ventricular diameter, which is a defining feature of a prevalent cardiovascular disorder, dilated cardiomyopathy or DCM. Using a zebrafish model, we experimentally validate FLNC and identify a novel FLNC splice-site mutation in a patient with severe DCM. Our approach stands to assist interpretation of large-scale genetic studies without compromising their fundamentally unbiased nature.

[Progress in research on pathogenic genes and gene therapy for inherited retinal diseases].

PubMed

Zhu, Ling; Cao, Cong; Sun, Jiji; Gao, Tao; Liang, Xiaoyang; Nie, Zhipeng; Ji, Yanchun; Jiang, Pingping; Guan, Minxin

2017-02-10

Inherited retinal diseases (IRDs), including retinitis pigmentosa, Usher syndrome, Cone-Rod degenerations, inherited macular dystrophy, Leber's congenital amaurosis, Leber's hereditary optic neuropathy are the most common and severe types of hereditary ocular diseases. So far more than 200 pathogenic genes have been identified. With the growing knowledge of the genetics and mechanisms of IRDs, a number of gene therapeutic strategies have been developed in the laboratory or even entered clinical trials. Here the progress of IRD research on the pathogenic genes and therapeutic strategies, particularly gene therapy, are reviewed.

Joint genetic analysis of hippocampal size in mouse and human identifies a novel gene linked to neurodegenerative disease.

PubMed

Ashbrook, David G; Williams, Robert W; Lu, Lu; Stein, Jason L; Hibar, Derrek P; Nichols, Thomas E; Medland, Sarah E; Thompson, Paul M; Hager, Reinmar

2014-10-03

Variation in hippocampal volume has been linked to significant differences in memory, behavior, and cognition among individuals. To identify genetic variants underlying such differences and associated disease phenotypes, multinational consortia such as ENIGMA have used large magnetic resonance imaging (MRI) data sets in human GWAS studies. In addition, mapping studies in mouse model systems have identified genetic variants for brain structure variation with great power. A key challenge is to understand how genetically based differences in brain structure lead to the propensity to develop specific neurological disorders. We combine the largest human GWAS of brain structure with the largest mammalian model system, the BXD recombinant inbred mouse population, to identify novel genetic targets influencing brain structure variation that are linked to increased risk for neurological disorders. We first use a novel cross-species, comparative analysis using mouse and human genetic data to identify a candidate gene, MGST3, associated with adult hippocampus size in both systems. We then establish the coregulation and function of this gene in a comprehensive systems-analysis. We find that MGST3 is associated with hippocampus size and is linked to a group of neurodegenerative disorders, such as Alzheimer's.

The Search for Autism Disease Genes

ERIC Educational Resources Information Center

Wassink, Thomas H.; Brzustowicz, Linda M.; Bartlett, Christopher W.; Szatmari, Peter

2004-01-01

Autism is a heritable disorder characterized by phenotypic and genetic complexity. This review begins by surveying current linkage, gene association, and cytogenetic studies performed with the goal of identifying autism disease susceptibility variants. Though numerous linkages and associations have been identified, they tend to diminish upon…

Defining the Role of Essential Genes in Human Disease

PubMed Central

Robertson, David L.; Hentges, Kathryn E.

2011-01-01

A greater understanding of the causes of human disease can come from identifying characteristics that are specific to disease genes. However, a full understanding of the contribution of essential genes to human disease is lacking, due to the premise that these genes tend to cause developmental abnormalities rather than adult disease. We tested the hypothesis that human orthologs of mouse essential genes are associated with a variety of human diseases, rather than only those related to miscarriage and birth defects. We segregated human disease genes according to whether the knockout phenotype of their mouse ortholog was lethal or viable, defining those with orthologs producing lethal knockouts as essential disease genes. We show that the human orthologs of mouse essential genes are associated with a wide spectrum of diseases affecting diverse physiological systems. Notably, human disease genes with essential mouse orthologs are over-represented among disease genes associated with cancer, suggesting links between adult cellular abnormalities and developmental functions. The proteins encoded by essential genes are highly connected in protein-protein interaction networks, which we find correlates with an over-representation of nuclear proteins amongst essential disease genes. Disease genes associated with essential orthologs also are more likely than those with non-essential orthologs to contribute to disease through an autosomal dominant inheritance pattern, suggesting that these diseases may actually result from semi-dominant mutant alleles. Overall, we have described attributes found in disease genes according to the essentiality status of their mouse orthologs. These findings demonstrate that disease genes do occupy highly connected positions in protein-protein interaction networks, and that due to the complexity of disease-associated alleles, essential genes cannot be ignored as candidates for causing diverse human diseases. PMID:22096564

Increased Transcript Complexity in Genes Associated with Chronic Obstructive Pulmonary Disease

PubMed Central

Lackey, Lela; McArthur, Evonne; Laederach, Alain

2015-01-01

Genome-wide association studies aim to correlate genotype with phenotype. Many common diseases including Type II diabetes, Alzheimer’s, Parkinson’s and Chronic Obstructive Pulmonary Disease (COPD) are complex genetic traits with hundreds of different loci that are associated with varied disease risk. Identifying common features in the genes associated with each disease remains a challenge. Furthermore, the role of post-transcriptional regulation, and in particular alternative splicing, is still poorly understood in most multigenic diseases. We therefore compiled comprehensive lists of genes associated with Type II diabetes, Alzheimer’s, Parkinson’s and COPD in an attempt to identify common features of their corresponding mRNA transcripts within each gene set. The SERPINA1 gene is a well-recognized genetic risk factor of COPD and it produces 11 transcript variants, which is exceptional for a human gene. This led us to hypothesize that other genes associated with COPD, and complex disorders in general, are highly transcriptionally diverse. We found that COPD-associated genes have a statistically significant enrichment in transcript complexity stemming from a disproportionately high level of alternative splicing, however, Type II Diabetes, Alzheimer’s and Parkinson’s disease genes were not significantly enriched. We also identified a subset of transcriptionally complex COPD-associated genes (~40%) that are differentially expressed between mild, moderate and severe COPD. Although the genes associated with other lung diseases are not extensively documented, we found preliminary data that idiopathic pulmonary disease genes, but not cystic fibrosis modulators, are also more transcriptionally complex. Interestingly, complex COPD transcripts are more often the product of alternative acceptor site usage. To verify the biological importance of these alternative transcripts, we used RNA-sequencing analyses to determine that COPD-associated genes are frequently

Exome Sequencing Identifies Three Novel Candidate Genes Implicated in Intellectual Disability

PubMed Central

Azam, Maleeha; Ayub, Humaira; Vissers, Lisenka E. L. M.; Gilissen, Christian; Ali, Syeda Hafiza Benish; Riaz, Moeen; Veltman, Joris A.; Pfundt, Rolph; van Bokhoven, Hans; Qamar, Raheel

2014-01-01

Intellectual disability (ID) is a major health problem mostly with an unknown etiology. Recently exome sequencing of individuals with ID identified novel genes implicated in the disease. Therefore the purpose of the present study was to identify the genetic cause of ID in one syndromic and two non-syndromic Pakistani families. Whole exome of three ID probands was sequenced. Missense variations in two plausible novel genes implicated in autosomal recessive ID were identified: lysine (K)-specific methyltransferase 2B (KMT2B), zinc finger protein 589 (ZNF589), as well as hedgehog acyltransferase (HHAT) with a de novo mutation with autosomal dominant mode of inheritance. The KMT2B recessive variant is the first report of recessive Kleefstra syndrome-like phenotype. Identification of plausible causative mutations for two recessive and a dominant type of ID, in genes not previously implicated in disease, underscores the large genetic heterogeneity of ID. These results also support the viewpoint that large number of ID genes converge on limited number of common networks i.e. ZNF589 belongs to KRAB-domain zinc-finger proteins previously implicated in ID, HHAT is predicted to affect sonic hedgehog, which is involved in several disorders with ID, KMT2B associated with syndromic ID fits the epigenetic module underlying the Kleefstra syndromic spectrum. The association of these novel genes in three different Pakistani ID families highlights the importance of screening these genes in more families with similar phenotypes from different populations to confirm the involvement of these genes in pathogenesis of ID. PMID:25405613

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

Integrative Analysis of DNA Methylation and Gene Expression Data Identifies EPAS1 as a Key Regulator of COPD

PubMed Central

Yoo, Seungyeul; Takikawa, Sachiko; Geraghty, Patrick; Argmann, Carmen; Campbell, Joshua; Lin, Luan; Huang, Tao; Tu, Zhidong; Feronjy, Robert; Spira, Avrum; Schadt, Eric E.; Powell, Charles A.; Zhu, Jun

2015-01-01

Chronic Obstructive Pulmonary Disease (COPD) is a complex disease. Genetic, epigenetic, and environmental factors are known to contribute to COPD risk and disease progression. Therefore we developed a systematic approach to identify key regulators of COPD that integrates genome-wide DNA methylation, gene expression, and phenotype data in lung tissue from COPD and control samples. Our integrative analysis identified 126 key regulators of COPD. We identified EPAS1 as the only key regulator whose downstream genes significantly overlapped with multiple genes sets associated with COPD disease severity. EPAS1 is distinct in comparison with other key regulators in terms of methylation profile and downstream target genes. Genes predicted to be regulated by EPAS1 were enriched for biological processes including signaling, cell communications, and system development. We confirmed that EPAS1 protein levels are lower in human COPD lung tissue compared to non-disease controls and that Epas1 gene expression is reduced in mice chronically exposed to cigarette smoke. As EPAS1 downstream genes were significantly enriched for hypoxia responsive genes in endothelial cells, we tested EPAS1 function in human endothelial cells. EPAS1 knockdown by siRNA in endothelial cells impacted genes that significantly overlapped with EPAS1 downstream genes in lung tissue including hypoxia responsive genes, and genes associated with emphysema severity. Our first integrative analysis of genome-wide DNA methylation and gene expression profiles illustrates that not only does DNA methylation play a ‘causal’ role in the molecular pathophysiology of COPD, but it can be leveraged to directly identify novel key mediators of this pathophysiology. PMID:25569234

Integrative analysis of DNA methylation and gene expression data identifies EPAS1 as a key regulator of COPD.

PubMed

Yoo, Seungyeul; Takikawa, Sachiko; Geraghty, Patrick; Argmann, Carmen; Campbell, Joshua; Lin, Luan; Huang, Tao; Tu, Zhidong; Foronjy, Robert F; Feronjy, Robert; Spira, Avrum; Schadt, Eric E; Powell, Charles A; Zhu, Jun

2015-01-01

Chronic Obstructive Pulmonary Disease (COPD) is a complex disease. Genetic, epigenetic, and environmental factors are known to contribute to COPD risk and disease progression. Therefore we developed a systematic approach to identify key regulators of COPD that integrates genome-wide DNA methylation, gene expression, and phenotype data in lung tissue from COPD and control samples. Our integrative analysis identified 126 key regulators of COPD. We identified EPAS1 as the only key regulator whose downstream genes significantly overlapped with multiple genes sets associated with COPD disease severity. EPAS1 is distinct in comparison with other key regulators in terms of methylation profile and downstream target genes. Genes predicted to be regulated by EPAS1 were enriched for biological processes including signaling, cell communications, and system development. We confirmed that EPAS1 protein levels are lower in human COPD lung tissue compared to non-disease controls and that Epas1 gene expression is reduced in mice chronically exposed to cigarette smoke. As EPAS1 downstream genes were significantly enriched for hypoxia responsive genes in endothelial cells, we tested EPAS1 function in human endothelial cells. EPAS1 knockdown by siRNA in endothelial cells impacted genes that significantly overlapped with EPAS1 downstream genes in lung tissue including hypoxia responsive genes, and genes associated with emphysema severity. Our first integrative analysis of genome-wide DNA methylation and gene expression profiles illustrates that not only does DNA methylation play a 'causal' role in the molecular pathophysiology of COPD, but it can be leveraged to directly identify novel key mediators of this pathophysiology.

A meta-analysis of public microarray data identifies biological regulatory networks in Parkinson's disease.

PubMed

Su, Lining; Wang, Chunjie; Zheng, Chenqing; Wei, Huiping; Song, Xiaoqing

2018-04-13

Parkinson's disease (PD) is a long-term degenerative disease that is caused by environmental and genetic factors. The networks of genes and their regulators that control the progression and development of PD require further elucidation. We examine common differentially expressed genes (DEGs) from several PD blood and substantia nigra (SN) microarray datasets by meta-analysis. Further we screen the PD-specific genes from common DEGs using GCBI. Next, we used a series of bioinformatics software to analyze the miRNAs, lncRNAs and SNPs associated with the common PD-specific genes, and then identify the mTF-miRNA-gene-gTF network. Our results identified 36 common DEGs in PD blood studies and 17 common DEGs in PD SN studies, and five of the genes were previously known to be associated with PD. Further study of the regulatory miRNAs associated with the common PD-specific genes revealed 14 PD-specific miRNAs in our study. Analysis of the mTF-miRNA-gene-gTF network about PD-specific genes revealed two feed-forward loops: one involving the SPRK2 gene, hsa-miR-19a-3p and SPI1, and the second involving the SPRK2 gene, hsa-miR-17-3p and SPI. The long non-coding RNA (lncRNA)-mediated regulatory network identified lncRNAs associated with PD-specific genes and PD-specific miRNAs. Moreover, single nucleotide polymorphism (SNP) analysis of the PD-specific genes identified two significant SNPs, and SNP analysis of the neurodegenerative disease-specific genes identified seven significant SNPs. Most of these SNPs are present in the 3'-untranslated region of genes and are controlled by several miRNAs. Our study identified a total of 53 common DEGs in PD patients compared with healthy controls in blood and brain datasets and five of these genes were previously linked with PD. Regulatory network analysis identified PD-specific miRNAs, associated long non-coding RNA and feed-forward loops, which contribute to our understanding of the mechanisms underlying PD. The SNPs identified in our

The Implicitome: A Resource for Rationalizing Gene-Disease Associations

PubMed Central

van der Horst, Eelke; Kaliyaperumal, Rajaram; Mina, Eleni; Tatum, Zuotian; Laros, Jeroen F. J.; van Mulligen, Erik M.; Schuemie, Martijn; Aten, Emmelien; Li, Tong Shu; Bruskiewich, Richard; Good, Benjamin M.; Su, Andrew I.; Kors, Jan A.; den Dunnen, Johan; van Ommen, Gert-Jan B.; Roos, Marco; ‘t Hoen, Peter A.C.; Mons, Barend; Schultes, Erik A.

2016-01-01

High-throughput experimental methods such as medical sequencing and genome-wide association studies (GWAS) identify increasingly large numbers of potential relations between genetic variants and diseases. Both biological complexity (millions of potential gene-disease associations) and the accelerating rate of data production necessitate computational approaches to prioritize and rationalize potential gene-disease relations. Here, we use concept profile technology to expose from the biomedical literature both explicitly stated gene-disease relations (the explicitome) and a much larger set of implied gene-disease associations (the implicitome). Implicit relations are largely unknown to, or are even unintended by the original authors, but they vastly extend the reach of existing biomedical knowledge for identification and interpretation of gene-disease associations. The implicitome can be used in conjunction with experimental data resources to rationalize both known and novel associations. We demonstrate the usefulness of the implicitome by rationalizing known and novel gene-disease associations, including those from GWAS. To facilitate the re-use of implicit gene-disease associations, we publish our data in compliance with FAIR Data Publishing recommendations [https://www.force11.org/group/fairgroup] using nanopublications. An online tool (http://knowledge.bio) is available to explore established and potential gene-disease associations in the context of other biomedical relations. PMID:26919047

Prediction of Human Disease Genes by Human-Mouse Conserved Coexpression Analysis

PubMed Central

Grassi, Elena; Damasco, Christian; Silengo, Lorenzo; Oti, Martin; Provero, Paolo; Di Cunto, Ferdinando

2008-01-01

Background Even in the post-genomic era, the identification of candidate genes within loci associated with human genetic diseases is a very demanding task, because the critical region may typically contain hundreds of positional candidates. Since genes implicated in similar phenotypes tend to share very similar expression profiles, high throughput gene expression data may represent a very important resource to identify the best candidates for sequencing. However, so far, gene coexpression has not been used very successfully to prioritize positional candidates. Methodology/Principal Findings We show that it is possible to reliably identify disease-relevant relationships among genes from massive microarray datasets by concentrating only on genes sharing similar expression profiles in both human and mouse. Moreover, we show systematically that the integration of human-mouse conserved coexpression with a phenotype similarity map allows the efficient identification of disease genes in large genomic regions. Finally, using this approach on 850 OMIM loci characterized by an unknown molecular basis, we propose high-probability candidates for 81 genetic diseases. Conclusion Our results demonstrate that conserved coexpression, even at the human-mouse phylogenetic distance, represents a very strong criterion to predict disease-relevant relationships among human genes. PMID:18369433

Integrative Approach to Pain Genetics Identifies Pain Sensitivity Loci across Diseases

PubMed Central

Ruau, David; Dudley, Joel T.; Chen, Rong; Phillips, Nicholas G.; Swan, Gary E.; Lazzeroni, Laura C.; Clark, J. David

2012-01-01

Identifying human genes relevant for the processing of pain requires difficult-to-conduct and expensive large-scale clinical trials. Here, we examine a novel integrative paradigm for data-driven discovery of pain gene candidates, taking advantage of the vast amount of existing disease-related clinical literature and gene expression microarray data stored in large international repositories. First, thousands of diseases were ranked according to a disease-specific pain index (DSPI), derived from Medical Subject Heading (MESH) annotations in MEDLINE. Second, gene expression profiles of 121 of these human diseases were obtained from public sources. Third, genes with expression variation significantly correlated with DSPI across diseases were selected as candidate pain genes. Finally, selected candidate pain genes were genotyped in an independent human cohort and prospectively evaluated for significant association between variants and measures of pain sensitivity. The strongest signal was with rs4512126 (5q32, ABLIM3, P = 1.3×10−10) for the sensitivity to cold pressor pain in males, but not in females. Significant associations were also observed with rs12548828, rs7826700 and rs1075791 on 8q22.2 within NCALD (P = 1.7×10−4, 1.8×10−4, and 2.2×10−4 respectively). Our results demonstrate the utility of a novel paradigm that integrates publicly available disease-specific gene expression data with clinical data curated from MEDLINE to facilitate the discovery of pain-relevant genes. This data-derived list of pain gene candidates enables additional focused and efficient biological studies validating additional candidates. PMID:22685391

Integrated Analyses of Gene Expression Profiles Digs out Common Markers for Rheumatic Diseases

PubMed Central

Wang, Lan; Wu, Long-Fei; Lu, Xin; Mo, Xing-Bo; Tang, Zai-Xiang; Lei, Shu-Feng; Deng, Fei-Yan

2015-01-01

Objective Rheumatic diseases have some common symptoms. Extensive gene expression studies, accumulated thus far, have successfully identified signature molecules for each rheumatic disease, individually. However, whether there exist shared factors across rheumatic diseases has yet to be tested. Methods We collected and utilized 6 public microarray datasets covering 4 types of representative rheumatic diseases including rheumatoid arthritis, systemic lupus erythematosus, ankylosing spondylitis, and osteoarthritis. Then we detected overlaps of differentially expressed genes across datasets and performed a meta-analysis aiming at identifying common differentially expressed genes that discriminate between pathological cases and normal controls. To further gain insights into the functions of the identified common differentially expressed genes, we conducted gene ontology enrichment analysis and protein-protein interaction analysis. Results We identified a total of eight differentially expressed genes (TNFSF10, CX3CR1, LY96, TLR5, TXN, TIA1, PRKCH, PRF1), each associated with at least 3 of the 4 studied rheumatic diseases. Meta-analysis warranted the significance of the eight genes and highlighted the general significance of four genes (CX3CR1, LY96, TLR5, and PRF1). Protein-protein interaction and gene ontology enrichment analyses indicated that the eight genes interact with each other to exert functions related to immune response and immune regulation. Conclusion The findings support that there exist common factors underlying rheumatic diseases. For rheumatoid arthritis, systemic lupus erythematosus, ankylosing spondylitis and osteoarthritis diseases, those common factors include TNFSF10, CX3CR1, LY96, TLR5, TXN, TIA1, PRKCH, and PRF1. In-depth studies on these common factors may provide keys to understanding the pathogenesis and developing intervention strategies for rheumatic diseases. PMID:26352601

DGEM--a microarray gene expression database for primary human disease tissues.

PubMed

Xia, Yuni; Campen, Andrew; Rigsby, Dan; Guo, Ying; Feng, Xingdong; Su, Eric W; Palakal, Mathew; Li, Shuyu

2007-01-01

Gene expression patterns can reflect gene regulations in human tissues under normal or pathologic conditions. Gene expression profiling data from studies of primary human disease samples are particularly valuable since these studies often span many years in order to collect patient clinical information and achieve a large sample size. Disease-to-Gene Expression Mapper (DGEM) provides a beneficial community resource to access and analyze these data; it currently includes Affymetrix oligonucleotide array datasets for more than 40 human diseases and 1400 samples. The data are normalized to the same scale and stored in a relational database. A statistical-analysis pipeline was implemented to identify genes abnormally expressed in disease tissues or genes whose expressions are associated with clinical parameters such as cancer patient survival. Data-mining results can be queried through a web-based interface at http://dgem.dhcp.iupui.edu/. The query tool enables dynamic generation of graphs and tables that are further linked to major gene and pathway resources that connect the data to relevant biology, including Entrez Gene and Kyoto Encyclopedia of Genes and Genomes (KEGG). In summary, DGEM provides scientists and physicians a valuable tool to study disease mechanisms, to discover potential disease biomarkers for diagnosis and prognosis, and to identify novel gene targets for drug discovery. The source code is freely available for non-profit use, on request to the authors.

Systems biology approach to late-onset Alzheimer's disease genome-wide association study identifies novel candidate genes validated using brain expression data and Caenorhabditis elegans experiments.

PubMed

Mukherjee, Shubhabrata; Russell, Joshua C; Carr, Daniel T; Burgess, Jeremy D; Allen, Mariet; Serie, Daniel J; Boehme, Kevin L; Kauwe, John S K; Naj, Adam C; Fardo, David W; Dickson, Dennis W; Montine, Thomas J; Ertekin-Taner, Nilufer; Kaeberlein, Matt R; Crane, Paul K

2017-10-01

We sought to determine whether a systems biology approach may identify novel late-onset Alzheimer's disease (LOAD) loci. We performed gene-wide association analyses and integrated results with human protein-protein interaction data using network analyses. We performed functional validation on novel genes using a transgenic Caenorhabditis elegans Aβ proteotoxicity model and evaluated novel genes using brain expression data from people with LOAD and other neurodegenerative conditions. We identified 13 novel candidate LOAD genes outside chromosome 19. Of those, RNA interference knockdowns of the C. elegans orthologs of UBC, NDUFS3, EGR1, and ATP5H were associated with Aβ toxicity, and NDUFS3, SLC25A11, ATP5H, and APP were differentially expressed in the temporal cortex. Network analyses identified novel LOAD candidate genes. We demonstrated a functional role for four of these in a C. elegans model and found enrichment of differentially expressed genes in the temporal cortex. Copyright © 2017 the Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

Identifying differentially expressed genes in cancer patients using a non-parameter Ising model.

PubMed

Li, Xumeng; Feltus, Frank A; Sun, Xiaoqian; Wang, James Z; Luo, Feng

2011-10-01

Identification of genes and pathways involved in diseases and physiological conditions is a major task in systems biology. In this study, we developed a novel non-parameter Ising model to integrate protein-protein interaction network and microarray data for identifying differentially expressed (DE) genes. We also proposed a simulated annealing algorithm to find the optimal configuration of the Ising model. The Ising model was applied to two breast cancer microarray data sets. The results showed that more cancer-related DE sub-networks and genes were identified by the Ising model than those by the Markov random field model. Furthermore, cross-validation experiments showed that DE genes identified by Ising model can improve classification performance compared with DE genes identified by Markov random field model. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Defining the human macula transcriptome and candidate retinal disease genes using EyeSAGE.

PubMed

Bowes Rickman, Catherine; Ebright, Jessica N; Zavodni, Zachary J; Yu, Ling; Wang, Tianyuan; Daiger, Stephen P; Wistow, Graeme; Boon, Kathy; Hauser, Michael A

2006-06-01

To develop large-scale, high-throughput annotation of the human macula transcriptome and to identify and prioritize candidate genes for inherited retinal dystrophies, based on ocular-expression profiles using serial analysis of gene expression (SAGE). Two human retina and two retinal pigment epithelium (RPE)/choroid SAGE libraries made from matched macula or midperipheral retina and adjacent RPE/choroid of morphologically normal 28- to 66-year-old donors and a human central retina longSAGE library made from 41- to 66-year-old donors were generated. Their transcription profiles were entered into a relational database, EyeSAGE, including microarray expression profiles of retina and publicly available normal human tissue SAGE libraries. EyeSAGE was used to identify retina- and RPE-specific and -associated genes, and candidate genes for retina and RPE disease loci. Differential and/or cell-type specific expression was validated by quantitative and single-cell RT-PCR. Cone photoreceptor-associated gene expression was elevated in the macula transcription profiles. Analysis of the longSAGE retina tags enhanced tag-to-gene mapping and revealed alternatively spliced genes. Analysis of candidate gene expression tables for the identified Bardet-Biedl syndrome disease gene (BBS5) in the BBS5 disease region table yielded BBS5 as the top candidate. Compelling candidates for inherited retina diseases were identified. The EyeSAGE database, combining three different gene-profiling platforms including the authors' multidonor-derived retina/RPE SAGE libraries and existing single-donor retina/RPE libraries, is a powerful resource for definition of the retina and RPE transcriptomes. It can be used to identify retina-specific genes, including alternatively spliced transcripts and to prioritize candidate genes within mapped retinal disease regions.

A whole-blood transcriptome meta-analysis identifies gene expression signatures of cigarette smoking

PubMed Central

Huan, Tianxiao; Joehanes, Roby; Schurmann, Claudia; Schramm, Katharina; Pilling, Luke C.; Peters, Marjolein J.; Mägi, Reedik; DeMeo, Dawn; O'Connor, George T.; Ferrucci, Luigi; Teumer, Alexander; Homuth, Georg; Biffar, Reiner; Völker, Uwe; Herder, Christian; Waldenberger, Melanie; Peters, Annette; Zeilinger, Sonja; Metspalu, Andres; Hofman, Albert; Uitterlinden, André G.; Hernandez, Dena G.; Singleton, Andrew B.; Bandinelli, Stefania; Munson, Peter J.; Lin, Honghuang; Benjamin, Emelia J.; Esko, Tõnu; Grabe, Hans J.; Prokisch, Holger; van Meurs, Joyce B.J.; Melzer, David; Levy, Daniel

2016-01-01

Abstract Cigarette smoking is a leading modifiable cause of death worldwide. We hypothesized that cigarette smoking induces extensive transcriptomic changes that lead to target-organ damage and smoking-related diseases. We performed a meta-analysis of transcriptome-wide gene expression using whole blood-derived RNA from 10,233 participants of European ancestry in six cohorts (including 1421 current and 3955 former smokers) to identify associations between smoking and altered gene expression levels. At a false discovery rate (FDR) <0.1, we identified 1270 differentially expressed genes in current vs. never smokers, and 39 genes in former vs. never smokers. Expression levels of 12 genes remained elevated up to 30 years after smoking cessation, suggesting that the molecular consequence of smoking may persist for decades. Gene ontology analysis revealed enrichment of smoking-related genes for activation of platelets and lymphocytes, immune response, and apoptosis. Many of the top smoking-related differentially expressed genes, including LRRN3 and GPR15, have DNA methylation loci in promoter regions that were recently reported to be hypomethylated among smokers. By linking differential gene expression with smoking-related disease phenotypes, we demonstrated that stroke and pulmonary function show enrichment for smoking-related gene expression signatures. Mediation analysis revealed the expression of several genes (e.g. ALAS2) to be putative mediators of the associations between smoking and inflammatory biomarkers (IL6 and C-reactive protein levels). Our transcriptomic study provides potential insights into the effects of cigarette smoking on gene expression in whole blood and their relations to smoking-related diseases. The results of such analyses may highlight attractive targets for treating or preventing smoking-related health effects. PMID:28158590

Generating Gene Ontology-Disease Inferences to Explore Mechanisms of Human Disease at the Comparative Toxicogenomics Database

PubMed Central

Davis, Allan Peter; Wiegers, Thomas C.; King, Benjamin L.; Wiegers, Jolene; Grondin, Cynthia J.; Sciaky, Daniela; Johnson, Robin J.; Mattingly, Carolyn J.

2016-01-01

Strategies for discovering common molecular events among disparate diseases hold promise for improving understanding of disease etiology and expanding treatment options. One technique is to leverage curated datasets found in the public domain. The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) manually curates chemical-gene, chemical-disease, and gene-disease interactions from the scientific literature. The use of official gene symbols in CTD interactions enables this information to be combined with the Gene Ontology (GO) file from NCBI Gene. By integrating these GO-gene annotations with CTD’s gene-disease dataset, we produce 753,000 inferences between 15,700 GO terms and 4,200 diseases, providing opportunities to explore presumptive molecular underpinnings of diseases and identify biological similarities. Through a variety of applications, we demonstrate the utility of this novel resource. As a proof-of-concept, we first analyze known repositioned drugs (e.g., raloxifene and sildenafil) and see that their target diseases have a greater degree of similarity when comparing GO terms vs. genes. Next, a computational analysis predicts seemingly non-intuitive diseases (e.g., stomach ulcers and atherosclerosis) as being similar to bipolar disorder, and these are validated in the literature as reported co-diseases. Additionally, we leverage other CTD content to develop testable hypotheses about thalidomide-gene networks to treat seemingly disparate diseases. Finally, we illustrate how CTD tools can rank a series of drugs as potential candidates for repositioning against B-cell chronic lymphocytic leukemia and predict cisplatin and the small molecule inhibitor JQ1 as lead compounds. The CTD dataset is freely available for users to navigate pathologies within the context of extensive biological processes, molecular functions, and cellular components conferred by GO. This inference set should aid researchers, bioinformaticists, and pharmaceutical drug

Generating Gene Ontology-Disease Inferences to Explore Mechanisms of Human Disease at the Comparative Toxicogenomics Database.

PubMed

Davis, Allan Peter; Wiegers, Thomas C; King, Benjamin L; Wiegers, Jolene; Grondin, Cynthia J; Sciaky, Daniela; Johnson, Robin J; Mattingly, Carolyn J

2016-01-01

Strategies for discovering common molecular events among disparate diseases hold promise for improving understanding of disease etiology and expanding treatment options. One technique is to leverage curated datasets found in the public domain. The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) manually curates chemical-gene, chemical-disease, and gene-disease interactions from the scientific literature. The use of official gene symbols in CTD interactions enables this information to be combined with the Gene Ontology (GO) file from NCBI Gene. By integrating these GO-gene annotations with CTD's gene-disease dataset, we produce 753,000 inferences between 15,700 GO terms and 4,200 diseases, providing opportunities to explore presumptive molecular underpinnings of diseases and identify biological similarities. Through a variety of applications, we demonstrate the utility of this novel resource. As a proof-of-concept, we first analyze known repositioned drugs (e.g., raloxifene and sildenafil) and see that their target diseases have a greater degree of similarity when comparing GO terms vs. genes. Next, a computational analysis predicts seemingly non-intuitive diseases (e.g., stomach ulcers and atherosclerosis) as being similar to bipolar disorder, and these are validated in the literature as reported co-diseases. Additionally, we leverage other CTD content to develop testable hypotheses about thalidomide-gene networks to treat seemingly disparate diseases. Finally, we illustrate how CTD tools can rank a series of drugs as potential candidates for repositioning against B-cell chronic lymphocytic leukemia and predict cisplatin and the small molecule inhibitor JQ1 as lead compounds. The CTD dataset is freely available for users to navigate pathologies within the context of extensive biological processes, molecular functions, and cellular components conferred by GO. This inference set should aid researchers, bioinformaticists, and pharmaceutical drug makers

An extended set of yeast-based functional assays accurately identifies human disease mutations

PubMed Central

Sun, Song; Yang, Fan; Tan, Guihong; Costanzo, Michael; Oughtred, Rose; Hirschman, Jodi; Theesfeld, Chandra L.; Bansal, Pritpal; Sahni, Nidhi; Yi, Song; Yu, Analyn; Tyagi, Tanya; Tie, Cathy; Hill, David E.; Vidal, Marc; Andrews, Brenda J.; Boone, Charles; Dolinski, Kara; Roth, Frederick P.

2016-01-01

We can now routinely identify coding variants within individual human genomes. A pressing challenge is to determine which variants disrupt the function of disease-associated genes. Both experimental and computational methods exist to predict pathogenicity of human genetic variation. However, a systematic performance comparison between them has been lacking. Therefore, we developed and exploited a panel of 26 yeast-based functional complementation assays to measure the impact of 179 variants (101 disease- and 78 non-disease-associated variants) from 22 human disease genes. Using the resulting reference standard, we show that experimental functional assays in a 1-billion-year diverged model organism can identify pathogenic alleles with significantly higher precision and specificity than current computational methods. PMID:26975778

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.

A hybrid network-based method for the detection of disease-related genes

NASA Astrophysics Data System (ADS)

Cui, Ying; Cai, Meng; Dai, Yang; Stanley, H. Eugene

2018-02-01

Detecting disease-related genes is crucial in disease diagnosis and drug design. The accepted view is that neighbors of a disease-causing gene in a molecular network tend to cause the same or similar diseases, and network-based methods have been recently developed to identify novel hereditary disease-genes in available biomedical networks. Despite the steady increase in the discovery of disease-associated genes, there is still a large fraction of disease genes that remains under the tip of the iceberg. In this paper we exploit the topological properties of the protein-protein interaction (PPI) network to detect disease-related genes. We compute, analyze, and compare the topological properties of disease genes with non-disease genes in PPI networks. We also design an improved random forest classifier based on these network topological features, and a cross-validation test confirms that our method performs better than previous similar studies.

Defining the Human Macula Transcriptome and Candidate Retinal Disease Genes UsingEyeSAGE

PubMed Central

Rickman, Catherine Bowes; Ebright, Jessica N.; Zavodni, Zachary J.; Yu, Ling; Wang, Tianyuan; Daiger, Stephen P.; Wistow, Graeme; Boon, Kathy; Hauser, Michael A.

2009-01-01

Purpose To develop large-scale, high-throughput annotation of the human macula transcriptome and to identify and prioritize candidate genes for inherited retinal dystrophies, based on ocular-expression profiles using serial analysis of gene expression (SAGE). Methods Two human retina and two retinal pigment epithelium (RPE)/choroid SAGE libraries made from matched macula or midperipheral retina and adjacent RPE/choroid of morphologically normal 28- to 66-year-old donors and a human central retina longSAGE library made from 41- to 66-year-old donors were generated. Their transcription profiles were entered into a relational database, EyeSAGE, including microarray expression profiles of retina and publicly available normal human tissue SAGE libraries. EyeSAGE was used to identify retina- and RPE-specific and -associated genes, and candidate genes for retina and RPE disease loci. Differential and/or cell-type specific expression was validated by quantitative and single-cell RT-PCR. Results Cone photoreceptor-associated gene expression was elevated in the macula transcription profiles. Analysis of the longSAGE retina tags enhanced tag-to-gene mapping and revealed alternatively spliced genes. Analysis of candidate gene expression tables for the identified Bardet-Biedl syndrome disease gene (BBS5) in the BBS5 disease region table yielded BBS5 as the top candidate. Compelling candidates for inherited retina diseases were identified. Conclusions The EyeSAGE database, combining three different gene-profiling platforms including the authors’ multidonor-derived retina/RPE SAGE libraries and existing single-donor retina/RPE libraries, is a powerful resource for definition of the retina and RPE transcriptomes. It can be used to identify retina-specific genes, including alternatively spliced transcripts and to prioritize candidate genes within mapped retinal disease regions. PMID:16723438

New Genes and New Insights from Old Genes: Update on Alzheimer Disease

PubMed Central

Ringman, John M.; Coppola, Giovanni

2013-01-01

Purpose of Review: This article discusses the current status of knowledge regarding the genetic basis of Alzheimer disease (AD) with a focus on clinically relevant aspects. Recent Findings: The genetic architecture of AD is complex, as it includes multiple susceptibility genes and likely nongenetic factors. Rare but highly penetrant autosomal dominant mutations explain a small minority of the cases but have allowed tremendous advances in understanding disease pathogenesis. The identification of a strong genetic risk factor, APOE, reshaped the field and introduced the notion of genetic risk for AD. More recently, large-scale genome-wide association studies are adding to the picture a number of common variants with very small effect sizes. Large-scale resequencing studies are expected to identify additional risk factors, including rare susceptibility variants and structural variation. Summary: Genetic assessment is currently of limited utility in clinical practice because of the low frequency (Mendelian mutations) or small effect size (common risk factors) of the currently known susceptibility genes. However, genetic studies are identifying with confidence a number of novel risk genes, and this will further our understanding of disease biology and possibly the identification of therapeutic targets. PMID:23558482

Renal Gene Expression Database (RGED): a relational database of gene expression profiles in kidney disease

PubMed Central

Zhang, Qingzhou; Yang, Bo; Chen, Xujiao; Xu, Jing; Mei, Changlin; Mao, Zhiguo

2014-01-01

We present a bioinformatics database named Renal Gene Expression Database (RGED), which contains comprehensive gene expression data sets from renal disease research. The web-based interface of RGED allows users to query the gene expression profiles in various kidney-related samples, including renal cell lines, human kidney tissues and murine model kidneys. Researchers can explore certain gene profiles, the relationships between genes of interests and identify biomarkers or even drug targets in kidney diseases. The aim of this work is to provide a user-friendly utility for the renal disease research community to query expression profiles of genes of their own interest without the requirement of advanced computational skills. Availability and implementation: Website is implemented in PHP, R, MySQL and Nginx and freely available from http://rged.wall-eva.net. Database URL: http://rged.wall-eva.net PMID:25252782

Renal Gene Expression Database (RGED): a relational database of gene expression profiles in kidney disease.

PubMed

Zhang, Qingzhou; Yang, Bo; Chen, Xujiao; Xu, Jing; Mei, Changlin; Mao, Zhiguo

2014-01-01

We present a bioinformatics database named Renal Gene Expression Database (RGED), which contains comprehensive gene expression data sets from renal disease research. The web-based interface of RGED allows users to query the gene expression profiles in various kidney-related samples, including renal cell lines, human kidney tissues and murine model kidneys. Researchers can explore certain gene profiles, the relationships between genes of interests and identify biomarkers or even drug targets in kidney diseases. The aim of this work is to provide a user-friendly utility for the renal disease research community to query expression profiles of genes of their own interest without the requirement of advanced computational skills. Website is implemented in PHP, R, MySQL and Nginx and freely available from http://rged.wall-eva.net. http://rged.wall-eva.net. © The Author(s) 2014. Published by Oxford University Press.

MethylMix 2.0: an R package for identifying DNA methylation genes. | Office of Cancer Genomics

Cancer.gov

DNA methylation is an important mechanism regulating gene transcription, and its role in carcinogenesis has been extensively studied. Hyper and hypomethylation of genes is a major mechanism of gene expression deregulation in a wide range of diseases. At the same time, high-throughput DNA methylation assays have been developed generating vast amounts of genome wide DNA methylation measurements. We developed MethylMix, an algorithm implemented in R to identify disease specific hyper and hypomethylated genes.

A Gene Module-Based eQTL Analysis Prioritizing Disease Genes and Pathways in Kidney Cancer.

PubMed

Yang, Mary Qu; Li, Dan; Yang, William; Zhang, Yifan; Liu, Jun; Tong, Weida

2017-01-01

Clear cell renal cell carcinoma (ccRCC) is the most common and most aggressive form of renal cell cancer (RCC). The incidence of RCC has increased steadily in recent years. The pathogenesis of renal cell cancer remains poorly understood. Many of the tumor suppressor genes, oncogenes, and dysregulated pathways in ccRCC need to be revealed for improvement of the overall clinical outlook of the disease. Here, we developed a systems biology approach to prioritize the somatic mutated genes that lead to dysregulation of pathways in ccRCC. The method integrated multi-layer information to infer causative mutations and disease genes. First, we identified differential gene modules in ccRCC by coupling transcriptome and protein-protein interactions. Each of these modules consisted of interacting genes that were involved in similar biological processes and their combined expression alterations were significantly associated with disease type. Then, subsequent gene module-based eQTL analysis revealed somatic mutated genes that had driven the expression alterations of differential gene modules. Our study yielded a list of candidate disease genes, including several known ccRCC causative genes such as BAP1 and PBRM1 , as well as novel genes such as NOD2, RRM1, CSRNP1, SLC4A2, TTLL1 and CNTN1. The differential gene modules and their driver genes revealed by our study provided a new perspective for understanding the molecular mechanisms underlying the disease. Moreover, we validated the results in independent ccRCC patient datasets. Our study provided a new method for prioritizing disease genes and pathways.

NIH Researchers Identify OCD Risk Gene

MedlinePlus

... News From NIH NIH Researchers Identify OCD Risk Gene Past Issues / Summer 2006 Table of Contents For ... and Alcoholism (NIAAA) have identified a previously unknown gene variant that doubles an individual's risk for obsessive- ...

Chromosome preference of disease genes and vectorization for the prediction of non-coding disease genes.

PubMed

Peng, Hui; Lan, Chaowang; Liu, Yuansheng; Liu, Tao; Blumenstein, Michael; Li, Jinyan

2017-10-03

Disease-related protein-coding genes have been widely studied, but disease-related non-coding genes remain largely unknown. This work introduces a new vector to represent diseases, and applies the newly vectorized data for a positive-unlabeled learning algorithm to predict and rank disease-related long non-coding RNA (lncRNA) genes. This novel vector representation for diseases consists of two sub-vectors, one is composed of 45 elements, characterizing the information entropies of the disease genes distribution over 45 chromosome substructures. This idea is supported by our observation that some substructures (e.g., the chromosome 6 p-arm) are highly preferred by disease-related protein coding genes, while some (e.g., the 21 p-arm) are not favored at all. The second sub-vector is 30-dimensional, characterizing the distribution of disease gene enriched KEGG pathways in comparison with our manually created pathway groups. The second sub-vector complements with the first one to differentiate between various diseases. Our prediction method outperforms the state-of-the-art methods on benchmark datasets for prioritizing disease related lncRNA genes. The method also works well when only the sequence information of an lncRNA gene is known, or even when a given disease has no currently recognized long non-coding genes.

Chromosome preference of disease genes and vectorization for the prediction of non-coding disease genes

PubMed Central

Peng, Hui; Lan, Chaowang; Liu, Yuansheng; Liu, Tao; Blumenstein, Michael; Li, Jinyan

2017-01-01

Disease-related protein-coding genes have been widely studied, but disease-related non-coding genes remain largely unknown. This work introduces a new vector to represent diseases, and applies the newly vectorized data for a positive-unlabeled learning algorithm to predict and rank disease-related long non-coding RNA (lncRNA) genes. This novel vector representation for diseases consists of two sub-vectors, one is composed of 45 elements, characterizing the information entropies of the disease genes distribution over 45 chromosome substructures. This idea is supported by our observation that some substructures (e.g., the chromosome 6 p-arm) are highly preferred by disease-related protein coding genes, while some (e.g., the 21 p-arm) are not favored at all. The second sub-vector is 30-dimensional, characterizing the distribution of disease gene enriched KEGG pathways in comparison with our manually created pathway groups. The second sub-vector complements with the first one to differentiate between various diseases. Our prediction method outperforms the state-of-the-art methods on benchmark datasets for prioritizing disease related lncRNA genes. The method also works well when only the sequence information of an lncRNA gene is known, or even when a given disease has no currently recognized long non-coding genes. PMID:29108274

Rational confederation of genes and diseases: NGS interpretation via GeneCards, MalaCards and VarElect.

PubMed

Rappaport, Noa; Fishilevich, Simon; Nudel, Ron; Twik, Michal; Belinky, Frida; Plaschkes, Inbar; Stein, Tsippi Iny; Cohen, Dana; Oz-Levi, Danit; Safran, Marilyn; Lancet, Doron

2017-08-18

A key challenge in the realm of human disease research is next generation sequencing (NGS) interpretation, whereby identified filtered variant-harboring genes are associated with a patient's disease phenotypes. This necessitates bioinformatics tools linked to comprehensive knowledgebases. The GeneCards suite databases, which include GeneCards (human genes), MalaCards (human diseases) and PathCards (human pathways) together with additional tools, are presented with the focus on MalaCards utility for NGS interpretation as well as for large scale bioinformatic analyses. VarElect, our NGS interpretation tool, leverages the broad information in the GeneCards suite databases. MalaCards algorithms unify disease-related terms and annotations from 69 sources. Further, MalaCards defines hierarchical relatedness-aliases, disease families, a related diseases network, categories and ontological classifications. GeneCards and MalaCards delineate and share a multi-tiered, scored gene-disease network, with stringency levels, including the definition of elite status-high quality gene-disease pairs, coming from manually curated trustworthy sources, that includes 4500 genes for 8000 diseases. This unique resource is key to NGS interpretation by VarElect. VarElect, a comprehensive search tool that helps infer both direct and indirect links between genes and user-supplied disease/phenotype terms, is robustly strengthened by the information found in MalaCards. The indirect mode benefits from GeneCards' diverse gene-to-gene relationships, including SuperPaths-integrated biological pathways from 12 information sources. We are currently adding an important information layer in the form of "disease SuperPaths", generated from the gene-disease matrix by an algorithm similar to that previously employed for biological pathway unification. This allows the discovery of novel gene-disease and disease-disease relationships. The advent of whole genome sequencing necessitates capacities to go beyond

Genome-Wide Analysis Identifies IL-18 and FUCA2 as Novel Genes Associated with Diastolic Function in African Americans with Sickle Cell Disease

PubMed Central

Sysol, Justin R.; Abbasi, Taimur; Patel, Amit R.; Lang, Roberto M.; Gupta, Akash; Garcia, Joe G. N.; Gordeuk, Victor R.; Machado, Roberto F.

2016-01-01

Background Diastolic dysfunction is common in sickle cell disease (SCD), and is associated with an increased risk of mortality. However, the molecular pathogenesis underlying this development is poorly understood. The aim of this study was to identify a gene expression profile that is associated with diastolic function in SCD, potentially elucidating molecular mechanisms behind diastolic dysfunction development. Methods Diastolic function was measured via echocardiography in 65 patients with SCD from two independent study populations. Gene expression microarray data was compared with diastolic function in both study cohorts. Candidate genes that associated in both analyses were tested for validation in a murine SCD model. Lastly, genotyping array data from the replication cohort was used to derive cis-expression quantitative trait loci (cis-eQTLs) and genetic associations within the candidate gene regions. Results Transcriptome data from both patient cohorts implicated 7 genes associated with diastolic function, and mouse SCD myocardial expression validated 3 of these genes. Genetic associations and eQTLs were detected in 2 of the 3 genes, FUCA2 and IL18. Conclusions FUCA2 and IL18 are associated with diastolic function in SCD patients, and may be involved in the pathogenesis of the disease. Genetic polymorphisms within the FUCA2 and IL18 gene regions are also associated with diastolic function in SCD, likely by affecting expression levels of the genes. PMID:27636371

Analysis of Gene Expression Profiles of Multiple Skin Diseases Identifies a Conserved Signature of Disrupted Homeostasis.

PubMed

Mills, Kevin J; Robinson, Michael K; Sherrill, Joseph D; Schnell, Daniel J; Xu, Jun

2018-05-28

Triggers of skin disease pathogenesis vary, but events associated with the elicitation of a lesion share many features in common. Our objective was to examine gene expression patterns in skin disease to develop a molecular signature of disruption of cutaneous homeostasis. Gene expression data from common inflammatory skin diseases (e.g., psoriasis, atopic dermatitis, seborrheic dermatitis and acne), and a novel statistical algorithm were used to define a unifying molecular signature referred to as the "Unhealthy Skin Signature" (USS). Using a pattern matching algorithm, analysis of public data repositories revealed that the USS is found in diverse epithelial diseases. Studies of milder disruptions of epidermal homeostasis have also shown that these conditions converge, to varying degrees, on the USS and that the degree of convergence is related directly to the severity of homeostatic disruption. The USS contains genes that had no prior published association with skin, but that play important roles in many different disease processes, supporting the importance of the USS to homeostasis. Finally, we show through pattern matching that the USS can be used to discover new potential dermatologic therapeutics. The USS provides a new means to further interrogate epithelial homeostasis and potentially develop novel therapeutics with efficacy across a spectrum of skin conditions. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Analysis of blood-based gene expression in idiopathic Parkinson disease.

PubMed

Shamir, Ron; Klein, Christine; Amar, David; Vollstedt, Eva-Juliane; Bonin, Michael; Usenovic, Marija; Wong, Yvette C; Maver, Ales; Poths, Sven; Safer, Hershel; Corvol, Jean-Christophe; Lesage, Suzanne; Lavi, Ofer; Deuschl, Günther; Kuhlenbaeumer, Gregor; Pawlack, Heike; Ulitsky, Igor; Kasten, Meike; Riess, Olaf; Brice, Alexis; Peterlin, Borut; Krainc, Dimitri

2017-10-17

To examine whether gene expression analysis of a large-scale Parkinson disease (PD) patient cohort produces a robust blood-based PD gene signature compared to previous studies that have used relatively small cohorts (≤220 samples). Whole-blood gene expression profiles were collected from a total of 523 individuals. After preprocessing, the data contained 486 gene profiles (n = 205 PD, n = 233 controls, n = 48 other neurodegenerative diseases) that were partitioned into training, validation, and independent test cohorts to identify and validate a gene signature. Batch-effect reduction and cross-validation were performed to ensure signature reliability. Finally, functional and pathway enrichment analyses were applied to the signature to identify PD-associated gene networks. A gene signature of 100 probes that mapped to 87 genes, corresponding to 64 upregulated and 23 downregulated genes differentiating between patients with idiopathic PD and controls, was identified with the training cohort and successfully replicated in both an independent validation cohort (area under the curve [AUC] = 0.79, p = 7.13E-6) and a subsequent independent test cohort (AUC = 0.74, p = 4.2E-4). Network analysis of the signature revealed gene enrichment in pathways, including metabolism, oxidation, and ubiquitination/proteasomal activity, and misregulation of mitochondria-localized genes, including downregulation of COX4I1 , ATP5A1 , and VDAC3 . We present a large-scale study of PD gene expression profiling. This work identifies a reliable blood-based PD signature and highlights the importance of large-scale patient cohorts in developing potential PD biomarkers. © 2017 American Academy of Neurology.

Genetics of Sputum Gene Expression in Chronic Obstructive Pulmonary Disease

PubMed Central

Qiu, Weiliang; Cho, Michael H.; Riley, John H.; Anderson, Wayne H.; Singh, Dave; Bakke, Per; Gulsvik, Amund; Litonjua, Augusto A.; Lomas, David A.; Crapo, James D.; Beaty, Terri H.; Celli, Bartolome R.; Rennard, Stephen; Tal-Singer, Ruth; Fox, Steven M.; Silverman, Edwin K.; Hersh, Craig P.

2011-01-01

Previous expression quantitative trait loci (eQTL) studies have performed genetic association studies for gene expression, but most of these studies examined lymphoblastoid cell lines from non-diseased individuals. We examined the genetics of gene expression in a relevant disease tissue from chronic obstructive pulmonary disease (COPD) patients to identify functional effects of known susceptibility genes and to find novel disease genes. By combining gene expression profiling on induced sputum samples from 131 COPD cases from the ECLIPSE Study with genomewide single nucleotide polymorphism (SNP) data, we found 4315 significant cis-eQTL SNP-probe set associations (3309 unique SNPs). The 3309 SNPs were tested for association with COPD in a genomewide association study (GWAS) dataset, which included 2940 COPD cases and 1380 controls. Adjusting for 3309 tests (p<1.5e-5), the two SNPs which were significantly associated with COPD were located in two separate genes in a known COPD locus on chromosome 15: CHRNA5 and IREB2. Detailed analysis of chromosome 15 demonstrated additional eQTLs for IREB2 mapping to that gene. eQTL SNPs for CHRNA5 mapped to multiple linkage disequilibrium (LD) bins. The eQTLs for IREB2 and CHRNA5 were not in LD. Seventy-four additional eQTL SNPs were associated with COPD at p<0.01. These were genotyped in two COPD populations, finding replicated associations with a SNP in PSORS1C1, in the HLA-C region on chromosome 6. Integrative analysis of GWAS and gene expression data from relevant tissue from diseased subjects has located potential functional variants in two known COPD genes and has identified a novel COPD susceptibility locus. PMID:21949713

Chapter 15: Disease Gene Prioritization

PubMed Central

Bromberg, Yana

2013-01-01

Disease-causing aberrations in the normal function of a gene define that gene as a disease gene. Proving a causal link between a gene and a disease experimentally is expensive and time-consuming. Comprehensive prioritization of candidate genes prior to experimental testing drastically reduces the associated costs. Computational gene prioritization is based on various pieces of correlative evidence that associate each gene with the given disease and suggest possible causal links. A fair amount of this evidence comes from high-throughput experimentation. Thus, well-developed methods are necessary to reliably deal with the quantity of information at hand. Existing gene prioritization techniques already significantly improve the outcomes of targeted experimental studies. Faster and more reliable techniques that account for novel data types are necessary for the development of new diagnostics, treatments, and cure for many diseases. PMID:23633938

Lentiviral vector-based insertional mutagenesis identifies genes associated with liver cancer

PubMed Central

Ranzani, Marco; Cesana, Daniela; Bartholomae, Cynthia C.; Sanvito, Francesca; Pala, Mauro; Benedicenti, Fabrizio; Gallina, Pierangela; Sergi, Lucia Sergi; Merella, Stefania; Bulfone, Alessandro; Doglioni, Claudio; von Kalle, Christof; Kim, Yoon Jun; Schmidt, Manfred; Tonon, Giovanni; Naldini, Luigi; Montini, Eugenio

2013-01-01

Transposons and γ-retroviruses have been efficiently used as insertional mutagens in different tissues to identify molecular culprits of cancer. However, these systems are characterized by recurring integrations that accumulate in tumor cells, hampering the identification of early cancer-driving events amongst bystander and progression-related events. We developed an insertional mutagenesis platform based on lentiviral vectors (LVV) by which we could efficiently induce hepatocellular carcinoma (HCC) in 3 different mouse models. By virtue of LVV’s replication-deficient nature and broad genome-wide integration pattern, LVV-based insertional mutagenesis allowed identification of 4 new liver cancer genes from a limited number of integrations. We validated the oncogenic potential of all the identified genes in vivo, with different levels of penetrance. Our newly identified cancer genes are likely to play a role in human disease, since they are upregulated and/or amplified/deleted in human HCCs and can predict clinical outcome of patients. PMID:23314173

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.

Microarray analysis identified Puccinia striiformis f. sp. tritici genes involved in infection and sporulation.

USDA-ARS?s Scientific Manuscript database

Puccinia striiformis f. sp. tritici (Pst) causes stripe rust, one of the most important diseases of wheat worldwide. To identify Pst genes involved in infection and sporulation, a custom oligonucleotide Genechip was made using sequences of 442 genes selected from Pst cDNA libraries. Microarray analy...

A Simple Screening Approach To Prioritize Genes for Functional Analysis Identifies a Role for Interferon Regulatory Factor 7 in the Control of Respiratory Syncytial Virus Disease

PubMed Central

McDonald, Jacqueline U.; Kaforou, Myrsini; Clare, Simon; Hale, Christine; Ivanova, Maria; Huntley, Derek; Dorner, Marcus; Wright, Victoria J.; Levin, Michael; Martinon-Torres, Federico; Herberg, Jethro A.

2016-01-01

ABSTRACT Greater understanding of the functions of host gene products in response to infection is required. While many of these genes enable pathogen clearance, some enhance pathogen growth or contribute to disease symptoms. Many studies have profiled transcriptomic and proteomic responses to infection, generating large data sets, but selecting targets for further study is challenging. Here we propose a novel data-mining approach combining multiple heterogeneous data sets to prioritize genes for further study by using respiratory syncytial virus (RSV) infection as a model pathogen with a significant health care impact. The assumption was that the more frequently a gene is detected across multiple studies, the more important its role is. A literature search was performed to find data sets of genes and proteins that change after RSV infection. The data sets were standardized, collated into a single database, and then panned to determine which genes occurred in multiple data sets, generating a candidate gene list. This candidate gene list was validated by using both a clinical cohort and in vitro screening. We identified several genes that were frequently expressed following RSV infection with no assigned function in RSV control, including IFI27, IFIT3, IFI44L, GBP1, OAS3, IFI44, and IRF7. Drilling down into the function of these genes, we demonstrate a role in disease for the gene for interferon regulatory factor 7, which was highly ranked on the list, but not for IRF1, which was not. Thus, we have developed and validated an approach for collating published data sets into a manageable list of candidates, identifying novel targets for future analysis. IMPORTANCE Making the most of “big data” is one of the core challenges of current biology. There is a large array of heterogeneous data sets of host gene responses to infection, but these data sets do not inform us about gene function and require specialized skill sets and training for their utilization. Here we

Novel Crohn disease locus identified by genome-wide association maps to a gene desert on 5p13.1 and modulates expression of PTGER4.

PubMed

Libioulle, Cécile; Louis, Edouard; Hansoul, Sarah; Sandor, Cynthia; Farnir, Frédéric; Franchimont, Denis; Vermeire, Séverine; Dewit, Olivier; de Vos, Martine; Dixon, Anna; Demarche, Bruno; Gut, Ivo; Heath, Simon; Foglio, Mario; Liang, Liming; Laukens, Debby; Mni, Myriam; Zelenika, Diana; Van Gossum, André; Rutgeerts, Paul; Belaiche, Jacques; Lathrop, Mark; Georges, Michel

2007-04-20

To identify novel susceptibility loci for Crohn disease (CD), we undertook a genome-wide association study with more than 300,000 SNPs characterized in 547 patients and 928 controls. We found three chromosome regions that provided evidence of disease association with p-values between 10(-6) and 10(-9). Two of these (IL23R on Chromosome 1 and CARD15 on Chromosome 16) correspond to genes previously reported to be associated with CD. In addition, a 250-kb region of Chromosome 5p13.1 was found to contain multiple markers with strongly suggestive evidence of disease association (including four markers with p < 10(-7)). We replicated the results for 5p13.1 by studying 1,266 additional CD patients, 559 additional controls, and 428 trios. Significant evidence of association (p < 4 x 10(-4)) was found in case/control comparisons with the replication data, while associated alleles were over-transmitted to affected offspring (p < 0.05), thus confirming that the 5p13.1 locus contributes to CD susceptibility. The CD-associated 250-kb region was saturated with 111 SNP markers. Haplotype analysis supports a complex locus architecture with multiple variants contributing to disease susceptibility. The novel 5p13.1 CD locus is contained within a 1.25-Mb gene desert. We present evidence that disease-associated alleles correlate with quantitative expression levels of the prostaglandin receptor EP4, PTGER4, the gene that resides closest to the associated region. Our results identify a major new susceptibility locus for CD, and suggest that genetic variants associated with disease risk at this locus could modulate cis-acting regulatory elements of PTGER4.

CNL Disease Resistance Genes in Soybean and Their Evolutionary Divergence

PubMed Central

Nepal, Madhav P; Benson, Benjamin V

2015-01-01

Disease resistance genes (R-genes) encode proteins involved in detecting pathogen attack and activating downstream defense molecules. Recent availability of soybean genome sequences makes it possible to examine the diversity of gene families including disease-resistant genes. The objectives of this study were to identify coiled-coil NBS-LRR (= CNL) R-genes in soybean, infer their evolutionary relationships, and assess structural as well as functional divergence of the R-genes. Profile hidden Markov models were used for sequence identification and model-based maximum likelihood was used for phylogenetic analysis, and variation in chromosomal positioning, gene clustering, and functional divergence were assessed. We identified 188 soybean CNL genes nested into four clades consistent to their orthologs in Arabidopsis. Gene clustering analysis revealed the presence of 41 gene clusters located on 13 different chromosomes. Analyses of the Ks-values and chromosomal positioning suggest duplication events occurring at varying timescales, and an extrapericentromeric positioning may have facilitated their rapid evolution. Each of the four CNL clades exhibited distinct patterns of gene expression. Phylogenetic analysis further supported the extrapericentromeric positioning effect on the divergence and retention of the CNL genes. The results are important for understanding the diversity and divergence of CNL genes in soybean, which would have implication in soybean crop improvement in future. PMID:25922568

CNL Disease Resistance Genes in Soybean and Their Evolutionary Divergence.

PubMed

Nepal, Madhav P; Benson, Benjamin V

2015-01-01

Disease resistance genes (R-genes) encode proteins involved in detecting pathogen attack and activating downstream defense molecules. Recent availability of soybean genome sequences makes it possible to examine the diversity of gene families including disease-resistant genes. The objectives of this study were to identify coiled-coil NBS-LRR (= CNL) R-genes in soybean, infer their evolutionary relationships, and assess structural as well as functional divergence of the R-genes. Profile hidden Markov models were used for sequence identification and model-based maximum likelihood was used for phylogenetic analysis, and variation in chromosomal positioning, gene clustering, and functional divergence were assessed. We identified 188 soybean CNL genes nested into four clades consistent to their orthologs in Arabidopsis. Gene clustering analysis revealed the presence of 41 gene clusters located on 13 different chromosomes. Analyses of the K s-values and chromosomal positioning suggest duplication events occurring at varying timescales, and an extrapericentromeric positioning may have facilitated their rapid evolution. Each of the four CNL clades exhibited distinct patterns of gene expression. Phylogenetic analysis further supported the extrapericentromeric positioning effect on the divergence and retention of the CNL genes. The results are important for understanding the diversity and divergence of CNL genes in soybean, which would have implication in soybean crop improvement in future.

Next-generation sequencing to identify candidate genes and develop diagnostic markers for a novel Phytophthora resistance gene, RpsHC18, in soybean.

PubMed

Zhong, Chao; Sun, Suli; Li, Yinping; Duan, Canxing; Zhu, Zhendong

2018-03-01

A novel Phytophthora sojae resistance gene RpsHC18 was identified and finely mapped on soybean chromosome 3. Two NBS-LRR candidate genes were identified and two diagnostic markers of RpsHC18 were developed. Phytophthora root rot caused by Phytophthora sojae is a destructive disease of soybean. The most effective disease-control strategy is to deploy resistant cultivars carrying Phytophthora-resistant Rps genes. The soybean cultivar Huachun 18 has a broad and distinct resistance spectrum to 12 P. sojae isolates. Quantitative trait loci sequencing (QTL-seq), based on the whole-genome resequencing (WGRS) of two extreme resistant and susceptible phenotype bulks from an F 2:3 population, was performed, and one 767-kb genomic region with ΔSNP-index ≥ 0.9 on chromosome 3 was identified as the RpsHC18 candidate region in Huachun 18. The candidate region was reduced to a 146-kb region by fine mapping. Nonsynonymous SNP and haplotype analyses were carried out in the 146-kb region among ten soybean genotypes using WGRS. Four specific nonsynonymous SNPs were identified in two nucleotide-binding sites-leucine-rich repeat (NBS-LRR) genes, RpsHC18-NBL1 and RpsHC18-NBL2, which were considered to be the candidate genes. Finally, one specific SNP marker in each candidate gene was successfully developed using a tetra-primer ARMS-PCR assay, and the two markers were verified to be specific for RpsHC18 and to effectively distinguish other known Rps genes. In this study, we applied an integrated genomic-based strategy combining WGRS with traditional genetic mapping to identify RpsHC18 candidate genes and develop diagnostic markers. These results suggest that next-generation sequencing is a precise, rapid and cost-effective way to identify candidate genes and develop diagnostic markers, and it can accelerate Rps gene cloning and marker-assisted selection for breeding of P. sojae-resistant soybean cultivars.

Common Marker Genes Identified from Various Sample Types for Systemic Lupus Erythematosus.

PubMed

Bing, Peng-Fei; Xia, Wei; Wang, Lan; Zhang, Yong-Hong; Lei, Shu-Feng; Deng, Fei-Yan

2016-01-01

Systemic lupus erythematosus (SLE) is a complex auto-immune disease. Gene expression studies have been conducted to identify SLE-related genes in various types of samples. It is unknown whether there are common marker genes significant for SLE but independent of sample types, which may have potentials for follow-up translational research. The aim of this study is to identify common marker genes across various sample types for SLE. Based on four public microarray gene expression datasets for SLE covering three representative types of blood-born samples (monocyte; peripheral blood mononuclear cell, PBMC; whole blood), we utilized three statistics (fold-change, FC; t-test p value; false discovery rate adjusted p value) to scrutinize genes simultaneously regulated with SLE across various sample types. For common marker genes, we conducted the Gene Ontology enrichment analysis and Protein-Protein Interaction analysis to gain insights into their functions. We identified 10 common marker genes associated with SLE (IFI6, IFI27, IFI44L, OAS1, OAS2, EIF2AK2, PLSCR1, STAT1, RNASE2, and GSTO1). Significant up-regulation of IFI6, IFI27, and IFI44L with SLE was observed in all the studied sample types, though the FC was most striking in monocyte, compared with PBMC and whole blood (8.82-251.66 vs. 3.73-74.05 vs. 1.19-1.87). Eight of the above 10 genes, except RNASE2 and GSTO1, interact with each other and with known SLE susceptibility genes, participate in immune response, RNA and protein catabolism, and cell death. Our data suggest that there exist common marker genes across various sample types for SLE. The 10 common marker genes, identified herein, deserve follow-up studies to dissert their potentials as diagnostic or therapeutic markers to predict SLE or treatment response.

Gene co-expression networks shed light into diseases of brain iron accumulation

PubMed Central

Bettencourt, Conceição; Forabosco, Paola; Wiethoff, Sarah; Heidari, Moones; Johnstone, Daniel M.; Botía, Juan A.; Collingwood, Joanna F.; Hardy, John; Milward, Elizabeth A.; Ryten, Mina; Houlden, Henry

2016-01-01

Aberrant brain iron deposition is observed in both common and rare neurodegenerative disorders, including those categorized as Neurodegeneration with Brain Iron Accumulation (NBIA), which are characterized by focal iron accumulation in the basal ganglia. Two NBIA genes are directly involved in iron metabolism, but whether other NBIA-related genes also regulate iron homeostasis in the human brain, and whether aberrant iron deposition contributes to neurodegenerative processes remains largely unknown. This study aims to expand our understanding of these iron overload diseases and identify relationships between known NBIA genes and their main interacting partners by using a systems biology approach. We used whole-transcriptome gene expression data from human brain samples originating from 101 neuropathologically normal individuals (10 brain regions) to generate weighted gene co-expression networks and cluster the 10 known NBIA genes in an unsupervised manner. We investigated NBIA-enriched networks for relevant cell types and pathways, and whether they are disrupted by iron loading in NBIA diseased tissue and in an in vivo mouse model. We identified two basal ganglia gene co-expression modules significantly enriched for NBIA genes, which resemble neuronal and oligodendrocytic signatures. These NBIA gene networks are enriched for iron-related genes, and implicate synapse and lipid metabolism related pathways. Our data also indicates that these networks are disrupted by excessive brain iron loading. We identified multiple cell types in the origin of NBIA disorders. We also found unforeseen links between NBIA networks and iron-related processes, and demonstrate convergent pathways connecting NBIAs and phenotypically overlapping diseases. Our results are of further relevance for these diseases by providing candidates for new causative genes and possible points for therapeutic intervention. PMID:26707700

Genetic Mapping and Exome Sequencing Identify Variants Associated with Five Novel Diseases

PubMed Central

Puffenberger, Erik G.; Jinks, Robert N.; Sougnez, Carrie; Cibulskis, Kristian; Willert, Rebecca A.; Achilly, Nathan P.; Cassidy, Ryan P.; Fiorentini, Christopher J.; Heiken, Kory F.; Lawrence, Johnny J.; Mahoney, Molly H.; Miller, Christopher J.; Nair, Devika T.; Politi, Kristin A.; Worcester, Kimberly N.; Setton, Roni A.; DiPiazza, Rosa; Sherman, Eric A.; Eastman, James T.; Francklyn, Christopher; Robey-Bond, Susan; Rider, Nicholas L.; Gabriel, Stacey; Morton, D. Holmes; Strauss, Kevin A.

2012-01-01

The Clinic for Special Children (CSC) has integrated biochemical and molecular methods into a rural pediatric practice serving Old Order Amish and Mennonite (Plain) children. Among the Plain people, we have used single nucleotide polymorphism (SNP) microarrays to genetically map recessive disorders to large autozygous haplotype blocks (mean = 4.4 Mb) that contain many genes (mean = 79). For some, uninformative mapping or large gene lists preclude disease-gene identification by Sanger sequencing. Seven such conditions were selected for exome sequencing at the Broad Institute; all had been previously mapped at the CSC using low density SNP microarrays coupled with autozygosity and linkage analyses. Using between 1 and 5 patient samples per disorder, we identified sequence variants in the known disease-causing genes SLC6A3 and FLVCR1, and present evidence to strongly support the pathogenicity of variants identified in TUBGCP6, BRAT1, SNIP1, CRADD, and HARS. Our results reveal the power of coupling new genotyping technologies to population-specific genetic knowledge and robust clinical data. PMID:22279524

HGPEC: a Cytoscape app for prediction of novel disease-gene and disease-disease associations and evidence collection based on a random walk on heterogeneous network.

PubMed

Le, Duc-Hau; Pham, Van-Huy

2017-06-15

Finding gene-disease and disease-disease associations play important roles in the biomedical area and many prioritization methods have been proposed for this goal. Among them, approaches based on a heterogeneous network of genes and diseases are considered state-of-the-art ones, which achieve high prediction performance and can be used for diseases with/without known molecular basis. Here, we developed a Cytoscape app, namely HGPEC, based on a random walk with restart algorithm on a heterogeneous network of genes and diseases. This app can prioritize candidate genes and diseases by employing a heterogeneous network consisting of a network of genes/proteins and a phenotypic disease similarity network. Based on the rankings, novel disease-gene and disease-disease associations can be identified. These associations can be supported with network- and rank-based visualization as well as evidences and annotations from biomedical data. A case study on prediction of novel breast cancer-associated genes and diseases shows the abilities of HGPEC. In addition, we showed prominence in the performance of HGPEC compared to other tools for prioritization of candidate disease genes. Taken together, our app is expected to effectively predict novel disease-gene and disease-disease associations and support network- and rank-based visualization as well as biomedical evidences for such the associations.

The FUN of identifying gene function in bacterial pathogens; insights from Salmonella functional genomics.

PubMed

Hammarlöf, Disa L; Canals, Rocío; Hinton, Jay C D

2013-10-01

The availability of thousands of genome sequences of bacterial pathogens poses a particular challenge because each genome contains hundreds of genes of unknown function (FUN). How can we easily discover which FUN genes encode important virulence factors? One solution is to combine two different functional genomic approaches. First, transcriptomics identifies bacterial FUN genes that show differential expression during the process of mammalian infection. Second, global mutagenesis identifies individual FUN genes that the pathogen requires to cause disease. The intersection of these datasets can reveal a small set of candidate genes most likely to encode novel virulence attributes. We demonstrate this approach with the Salmonella infection model, and propose that a similar strategy could be used for other bacterial pathogens. Copyright © 2013 Elsevier Ltd. All rights reserved.

Gene-gene and gene-environment interactions: new insights into the prevention, detection and management of coronary artery disease.

PubMed

Lanktree, Matthew B; Hegele, Robert A

2009-02-26

Despite the recent success of genome-wide association studies (GWASs) in identifying loci consistently associated with coronary artery disease (CAD), a large proportion of the genetic components of CAD and its metabolic risk factors, including plasma lipids, type 2 diabetes and body mass index, remain unattributed. Gene-gene and gene-environment interactions might produce a meaningful improvement in quantification of the genetic determinants of CAD. Testing for gene-gene and gene-environment interactions is thus a new frontier for large-scale GWASs of CAD. There are several anecdotal examples of monogenic susceptibility to CAD in which the phenotype was worsened by an adverse environment. In addition, small-scale candidate gene association studies with functional hypotheses have identified gene-environment interactions. For future evaluation of gene-gene and gene-environment interactions to achieve the same success as the single gene associations reported in recent GWASs, it will be important to pre-specify agreed standards of study design and statistical power, environmental exposure measurement, phenomic characterization and analytical strategies. Here we discuss these issues, particularly in relation to the investigation and potential clinical utility of gene-gene and gene-environment interactions in CAD.

Systematic analysis of microarray datasets to identify Parkinson's disease‑associated pathways and genes.

PubMed

Feng, Yinling; Wang, Xuefeng

2017-03-01

In order to investigate commonly disturbed genes and pathways in various brain regions of patients with Parkinson's disease (PD), microarray datasets from previous studies were collected and systematically analyzed. Different normalization methods were applied to microarray datasets from different platforms. A strategy combining gene co‑expression networks and clinical information was adopted, using weighted gene co‑expression network analysis (WGCNA) to screen for commonly disturbed genes in different brain regions of patients with PD. Functional enrichment analysis of commonly disturbed genes was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Co‑pathway relationships were identified with Pearson's correlation coefficient tests and a hypergeometric distribution‑based test. Common genes in pathway pairs were selected out and regarded as risk genes. A total of 17 microarray datasets from 7 platforms were retained for further analysis. Five gene coexpression modules were identified, containing 9,745, 736, 233, 101 and 93 genes, respectively. One module was significantly correlated with PD samples and thus the 736 genes it contained were considered to be candidate PD‑associated genes. Functional enrichment analysis demonstrated that these genes were implicated in oxidative phosphorylation and PD. A total of 44 pathway pairs and 52 risk genes were revealed, and a risk gene pathway relationship network was constructed. Eight modules were identified and were revealed to be associated with PD, cancers and metabolism. A number of disturbed pathways and risk genes were unveiled in PD, and these findings may help advance understanding of PD pathogenesis.

Gene therapy for ocular diseases.

PubMed

Liu, Melissa M; Tuo, Jingsheng; Chan, Chi-Chao

2011-05-01

The eye is an easily accessible, highly compartmentalised and immune-privileged organ that offers unique advantages as a gene therapy target. Significant advancements have been made in understanding the genetic pathogenesis of ocular diseases, and gene replacement and gene silencing have been implicated as potentially efficacious therapies. Recent improvements have been made in the safety and specificity of vector-based ocular gene transfer methods. Proof-of-concept for vector-based gene therapies has also been established in several experimental models of human ocular diseases. After nearly two decades of ocular gene therapy research, preliminary successes are now being reported in phase 1 clinical trials for the treatment of Leber congenital amaurosis. This review describes current developments and future prospects for ocular gene therapy. Novel methods are being developed to enhance the performance and regulation of recombinant adeno-associated virus- and lentivirus-mediated ocular gene transfer. Gene therapy prospects have advanced for a variety of retinal disorders, including retinitis pigmentosa, retinoschisis, Stargardt disease and age-related macular degeneration. Advances have also been made using experimental models for non-retinal diseases, such as uveitis and glaucoma. These methodological advancements are critical for the implementation of additional gene-based therapies for human ocular diseases in the near future.

Vitiligo blood transcriptomics provides new insights into disease mechanisms and identifies potential novel therapeutic targets.

PubMed

Dey-Rao, Rama; Sinha, Animesh A

2017-01-28

Significant gaps remain regarding the pathomechanisms underlying the autoimmune response in vitiligo (VL), where the loss of self-tolerance leads to the targeted killing of melanocytes. Specifically, there is incomplete information regarding alterations in the systemic environment that are relevant to the disease state. We undertook a genome-wide profiling approach to examine gene expression in the peripheral blood of VL patients and healthy controls in the context of our previously published VL-skin gene expression profile. We used several in silico bioinformatics-based analyses to provide new insights into disease mechanisms and suggest novel targets for future therapy. Unsupervised clustering methods of the VL-blood dataset demonstrate a "disease-state"-specific set of co-expressed genes. Ontology enrichment analysis of 99 differentially expressed genes (DEGs) uncovers a down-regulated immune/inflammatory response, B-Cell antigen receptor (BCR) pathways, apoptosis and catabolic processes in VL-blood. There is evidence for both type I and II interferon (IFN) playing a role in VL pathogenesis. We used interactome analysis to identify several key blood associated transcriptional factors (TFs) from within (STAT1, STAT6 and NF-kB), as well as "hidden" (CREB1, MYC, IRF4, IRF1, and TP53) from the dataset that potentially affect disease pathogenesis. The TFs overlap with our reported lesional-skin transcriptional circuitry, underscoring their potential importance to the disease. We also identify a shared VL-blood and -skin transcriptional "hot spot" that maps to chromosome 6, and includes three VL-blood dysregulated genes (PSMB8, PSMB9 and TAP1) described as potential VL-associated genetic susceptibility loci. Finally, we provide bioinformatics-based support for prioritizing dysregulated genes in VL-blood or skin as potential therapeutic targets. We examined the VL-blood transcriptome in context with our (previously published) VL-skin transcriptional profile to address

Novel Crohn Disease Locus Identified by Genome-Wide Association Maps to a Gene Desert on 5p13.1 and Modulates Expression of PTGER4

PubMed Central

Libioulle, Cécile; Louis, Edouard; Hansoul, Sarah; Sandor, Cynthia; Farnir, Frédéric; Franchimont, Denis; Vermeire, Séverine; Dewit, Olivier; de Vos, Martine; Dixon, Anna; Demarche, Bruno; Gut, Ivo; Heath, Simon; Foglio, Mario; Liang, Liming; Laukens, Debby; Mni, Myriam; Zelenika, Diana; Gossum, André Van; Rutgeerts, Paul; Belaiche, Jacques; Lathrop, Mark; Georges, Michel

2007-01-01

To identify novel susceptibility loci for Crohn disease (CD), we undertook a genome-wide association study with more than 300,000 SNPs characterized in 547 patients and 928 controls. We found three chromosome regions that provided evidence of disease association with p-values between 10−6 and 10−9. Two of these (IL23R on Chromosome 1 and CARD15 on Chromosome 16) correspond to genes previously reported to be associated with CD. In addition, a 250-kb region of Chromosome 5p13.1 was found to contain multiple markers with strongly suggestive evidence of disease association (including four markers with p < 10−7). We replicated the results for 5p13.1 by studying 1,266 additional CD patients, 559 additional controls, and 428 trios. Significant evidence of association (p < 4 × 10−4) was found in case/control comparisons with the replication data, while associated alleles were over-transmitted to affected offspring (p < 0.05), thus confirming that the 5p13.1 locus contributes to CD susceptibility. The CD-associated 250-kb region was saturated with 111 SNP markers. Haplotype analysis supports a complex locus architecture with multiple variants contributing to disease susceptibility. The novel 5p13.1 CD locus is contained within a 1.25-Mb gene desert. We present evidence that disease-associated alleles correlate with quantitative expression levels of the prostaglandin receptor EP4, PTGER4, the gene that resides closest to the associated region. Our results identify a major new susceptibility locus for CD, and suggest that genetic variants associated with disease risk at this locus could modulate cis-acting regulatory elements of PTGER4. PMID:17447842

α-cardiac actin is a novel disease gene in familial hypertrophic cardiomyopathy

PubMed Central

Mogensen, Jens; Klausen, Ib C.; Pedersen, Anders K.; Egeblad, Henrik; Bross, Peter; Kruse, Torben A.; Gregersen, Niels; Hansen, Peter S.; Baandrup, Ulrik; Børglum, Anders D.

1999-01-01

We identified the α-cardiac actin gene (ACTC) as a novel disease gene in a pedigree suffering from familial hypertrophic cardiomyopathy (FHC). Linkage analyses excluded all the previously reported FHC loci as possible disease loci in the family studied, with lod scores varying between –2.5 and –6.0. Further linkage analyses of plausible candidate genes highly expressed in the adult human heart identified ACTC as the most likely disease gene, showing a maximal lod score of 3.6. Mutation analysis of ACTC revealed an Ala295Ser mutation in exon 5 close to 2 missense mutations recently described to cause the inherited form of idiopathic dilated cardiomyopathy (IDC). ACTC is the first sarcomeric gene described in which mutations are responsible for 2 different cardiomyopathies. We hypothesize that ACTC mutations affecting sarcomere contraction lead to FHC and that mutations affecting force transmission from the sarcomere to the surrounding syncytium lead to IDC. PMID:10330430

Gene co-expression networks shed light into diseases of brain iron accumulation.

PubMed

Bettencourt, Conceição; Forabosco, Paola; Wiethoff, Sarah; Heidari, Moones; Johnstone, Daniel M; Botía, Juan A; Collingwood, Joanna F; Hardy, John; Milward, Elizabeth A; Ryten, Mina; Houlden, Henry

2016-03-01

Aberrant brain iron deposition is observed in both common and rare neurodegenerative disorders, including those categorized as Neurodegeneration with Brain Iron Accumulation (NBIA), which are characterized by focal iron accumulation in the basal ganglia. Two NBIA genes are directly involved in iron metabolism, but whether other NBIA-related genes also regulate iron homeostasis in the human brain, and whether aberrant iron deposition contributes to neurodegenerative processes remains largely unknown. This study aims to expand our understanding of these iron overload diseases and identify relationships between known NBIA genes and their main interacting partners by using a systems biology approach. We used whole-transcriptome gene expression data from human brain samples originating from 101 neuropathologically normal individuals (10 brain regions) to generate weighted gene co-expression networks and cluster the 10 known NBIA genes in an unsupervised manner. We investigated NBIA-enriched networks for relevant cell types and pathways, and whether they are disrupted by iron loading in NBIA diseased tissue and in an in vivo mouse model. We identified two basal ganglia gene co-expression modules significantly enriched for NBIA genes, which resemble neuronal and oligodendrocytic signatures. These NBIA gene networks are enriched for iron-related genes, and implicate synapse and lipid metabolism related pathways. Our data also indicates that these networks are disrupted by excessive brain iron loading. We identified multiple cell types in the origin of NBIA disorders. We also found unforeseen links between NBIA networks and iron-related processes, and demonstrate convergent pathways connecting NBIAs and phenotypically overlapping diseases. Our results are of further relevance for these diseases by providing candidates for new causative genes and possible points for therapeutic intervention. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Genomic analysis of primordial dwarfism reveals novel disease genes.

PubMed

Shaheen, Ranad; Faqeih, Eissa; Ansari, Shinu; Abdel-Salam, Ghada; Al-Hassnan, Zuhair N; Al-Shidi, Tarfa; Alomar, Rana; Sogaty, Sameera; Alkuraya, Fowzan S

2014-02-01

Primordial dwarfism (PD) is a disease in which severely impaired fetal growth persists throughout postnatal development and results in stunted adult size. The condition is highly heterogeneous clinically, but the use of certain phenotypic aspects such as head circumference and facial appearance has proven helpful in defining clinical subgroups. In this study, we present the results of clinical and genomic characterization of 16 new patients in whom a broad definition of PD was used (e.g., 3M syndrome was included). We report a novel PD syndrome with distinct facies in two unrelated patients, each with a different homozygous truncating mutation in CRIPT. Our analysis also reveals, in addition to mutations in known PD disease genes, the first instance of biallelic truncating BRCA2 mutation causing PD with normal bone marrow analysis. In addition, we have identified a novel locus for Seckel syndrome based on a consanguineous multiplex family and identified a homozygous truncating mutation in DNA2 as the likely cause. An additional novel PD disease candidate gene XRCC4 was identified by autozygome/exome analysis, and the knockout mouse phenotype is highly compatible with PD. Thus, we add a number of novel genes to the growing list of PD-linked genes, including one which we show to be linked to a novel PD syndrome with a distinct facial appearance. PD is extremely heterogeneous genetically and clinically, and genomic tools are often required to reach a molecular diagnosis.

Genomic analysis of primordial dwarfism reveals novel disease genes

PubMed Central

Shaheen, Ranad; Faqeih, Eissa; Ansari, Shinu; Abdel-Salam, Ghada; Al-Hassnan, Zuhair N.; Al-Shidi, Tarfa; Alomar, Rana; Sogaty, Sameera; Alkuraya, Fowzan S.

2014-01-01

Primordial dwarfism (PD) is a disease in which severely impaired fetal growth persists throughout postnatal development and results in stunted adult size. The condition is highly heterogeneous clinically, but the use of certain phenotypic aspects such as head circumference and facial appearance has proven helpful in defining clinical subgroups. In this study, we present the results of clinical and genomic characterization of 16 new patients in whom a broad definition of PD was used (e.g., 3M syndrome was included). We report a novel PD syndrome with distinct facies in two unrelated patients, each with a different homozygous truncating mutation in CRIPT. Our analysis also reveals, in addition to mutations in known PD disease genes, the first instance of biallelic truncating BRCA2 mutation causing PD with normal bone marrow analysis. In addition, we have identified a novel locus for Seckel syndrome based on a consanguineous multiplex family and identified a homozygous truncating mutation in DNA2 as the likely cause. An additional novel PD disease candidate gene XRCC4 was identified by autozygome/exome analysis, and the knockout mouse phenotype is highly compatible with PD. Thus, we add a number of novel genes to the growing list of PD-linked genes, including one which we show to be linked to a novel PD syndrome with a distinct facial appearance. PD is extremely heterogeneous genetically and clinically, and genomic tools are often required to reach a molecular diagnosis. PMID:24389050

Meta-Analysis of Genome-Wide Association Studies and Network Analysis-Based Integration with Gene Expression Data Identify New Suggestive Loci and Unravel a Wnt-Centric Network Associated with Dupuytren’s Disease

PubMed Central

Becker, Kerstin; Siegert, Sabine; Toliat, Mohammad Reza; Du, Juanjiangmeng; Casper, Ramona; Dolmans, Guido H.; Werker, Paul M.; Tinschert, Sigrid; Franke, Andre; Gieger, Christian; Strauch, Konstantin; Nothnagel, Michael; Nürnberg, Peter; Hennies, Hans Christian

2016-01-01

Dupuytren´s disease, a fibromatosis of the connective tissue in the palm, is a common complex disease with a strong genetic component. Up to date nine genetic loci have been found to be associated with the disease. Six of these loci contain genes that code for Wnt signalling proteins. In spite of this striking first insight into the genetic factors in Dupuytren´s disease, much of the inherited risk in Dupuytren´s disease still needs to be discovered. The already identified loci jointly explain ~1% of the heritability in this disease. To further elucidate the genetic basis of Dupuytren´s disease, we performed a genome-wide meta-analysis combining three genome-wide association study (GWAS) data sets, comprising 1,580 cases and 4,480 controls. We corroborated all nine previously identified loci, six of these with genome-wide significance (p-value < 5x10-8). In addition, we identified 14 new suggestive loci (p-value < 10−5). Intriguingly, several of these new loci contain genes associated with Wnt signalling and therefore represent excellent candidates for replication. Next, we compared whole-transcriptome data between patient- and control-derived tissue samples and found the Wnt/β-catenin pathway to be the top deregulated pathway in patient samples. We then conducted network and pathway analyses in order to identify protein networks that are enriched for genes highlighted in the GWAS meta-analysis and expression data sets. We found further evidence that the Wnt signalling pathways in conjunction with other pathways may play a critical role in Dupuytren´s disease. PMID:27467239

Omics analysis of human bone to identify genes and molecular networks regulating skeletal remodeling in health and disease.

PubMed

Reppe, Sjur; Datta, Harish K; Gautvik, Kaare M

2017-08-01

The skeleton is a metabolically active organ throughout life where specific bone cell activity and paracrine/endocrine factors regulate its morphogenesis and remodeling. In recent years, an increasing number of reports have used multi-omics technologies to characterize subsets of bone biological molecular networks. The skeleton is affected by primary and secondary disease, lifestyle and many drugs. Therefore, to obtain relevant and reliable data from well characterized patient and control cohorts are vital. Here we provide a brief overview of omics studies performed on human bone, of which our own studies performed on trans-iliacal bone biopsies from postmenopausal women with osteoporosis (OP) and healthy controls are among the first and largest. Most other studies have been performed on smaller groups of patients, undergoing hip replacement for osteoarthritis (OA) or fracture, and without healthy controls. The major findings emerging from the combined studies are: 1. Unstressed and stressed bone show profoundly different gene expression reflecting differences in bone turnover and remodeling and 2. Omics analyses comparing healthy/OP and control/OA cohorts reveal characteristic changes in transcriptomics, epigenomics (DNA methylation), proteomics and metabolomics. These studies, together with genome-wide association studies, in vitro observations and transgenic animal models have identified a number of genes and gene products that act via Wnt and other signaling systems and are highly associated to bone density and fracture. Future challenge is to understand the functional interactions between bone-related molecular networks and their significance in OP and OA pathogenesis, and also how the genomic architecture is affected in health and disease. Copyright © 2017 Elsevier Inc. All rights reserved.

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.

Discovering transnosological molecular basis of human brain diseases using biclustering analysis of integrated gene expression data.

PubMed

Cha, Kihoon; Hwang, Taeho; Oh, Kimin; Yi, Gwan-Su

2015-01-01

It has been reported that several brain diseases can be treated as transnosological manner implicating possible common molecular basis under those diseases. However, molecular level commonality among those brain diseases has been largely unexplored. Gene expression analyses of human brain have been used to find genes associated with brain diseases but most of those studies were restricted either to an individual disease or to a couple of diseases. In addition, identifying significant genes in such brain diseases mostly failed when it used typical methods depending on differentially expressed genes. In this study, we used a correlation-based biclustering approach to find coexpressed gene sets in five neurodegenerative diseases and three psychiatric disorders. By using biclustering analysis, we could efficiently and fairly identified various gene sets expressed specifically in both single and multiple brain diseases. We could find 4,307 gene sets correlatively expressed in multiple brain diseases and 3,409 gene sets exclusively specified in individual brain diseases. The function enrichment analysis of those gene sets showed many new possible functional bases as well as neurological processes that are common or specific for those eight diseases. This study introduces possible common molecular bases for several brain diseases, which open the opportunity to clarify the transnosological perspective assumed in brain diseases. It also showed the advantages of correlation-based biclustering analysis and accompanying function enrichment analysis for gene expression data in this type of investigation.

Discovering transnosological molecular basis of human brain diseases using biclustering analysis of integrated gene expression data

PubMed Central

2015-01-01

Background It has been reported that several brain diseases can be treated as transnosological manner implicating possible common molecular basis under those diseases. However, molecular level commonality among those brain diseases has been largely unexplored. Gene expression analyses of human brain have been used to find genes associated with brain diseases but most of those studies were restricted either to an individual disease or to a couple of diseases. In addition, identifying significant genes in such brain diseases mostly failed when it used typical methods depending on differentially expressed genes. Results In this study, we used a correlation-based biclustering approach to find coexpressed gene sets in five neurodegenerative diseases and three psychiatric disorders. By using biclustering analysis, we could efficiently and fairly identified various gene sets expressed specifically in both single and multiple brain diseases. We could find 4,307 gene sets correlatively expressed in multiple brain diseases and 3,409 gene sets exclusively specified in individual brain diseases. The function enrichment analysis of those gene sets showed many new possible functional bases as well as neurological processes that are common or specific for those eight diseases. Conclusions This study introduces possible common molecular bases for several brain diseases, which open the opportunity to clarify the transnosological perspective assumed in brain diseases. It also showed the advantages of correlation-based biclustering analysis and accompanying function enrichment analysis for gene expression data in this type of investigation. PMID:26043779

Utilizing Gene Tree Variation to Identify Candidate Effector Genes in Zymoseptoria tritici

PubMed Central

McDonald, Megan C.; McGinness, Lachlan; Hane, James K.; Williams, Angela H.; Milgate, Andrew; Solomon, Peter S.

2016-01-01

Zymoseptoria tritici is a host-specific, necrotrophic pathogen of wheat. Infection by Z. tritici is characterized by its extended latent period, which typically lasts 2 wks, and is followed by extensive host cell death, and rapid proliferation of fungal biomass. This work characterizes the level of genomic variation in 13 isolates, for which we have measured virulence on 11 wheat cultivars with differential resistance genes. Between the reference isolate, IPO323, and the 13 Australian isolates we identified over 800,000 single nucleotide polymorphisms, of which ∼10% had an effect on the coding regions of the genome. Furthermore, we identified over 1700 probable presence/absence polymorphisms in genes across the Australian isolates using de novo assembly. Finally, we developed a gene tree sorting method that quickly identifies groups of isolates within a single gene alignment whose sequence haplotypes correspond with virulence scores on a single wheat cultivar. Using this method, we have identified < 100 candidate effector genes whose gene sequence correlates with virulence toward a wheat cultivar carrying a major resistance gene. PMID:26837952

Identification of Genes Expressed in Premalignant Breast Disease by Microscopy-Directed Cloning

NASA Astrophysics Data System (ADS)

Jensen, Roy A.; Page, David L.; Holt, Jeffrey T.

1994-09-01

Histopathologic study of human breast biopsy samples has identified specific lesions which are associated with a high risk of development of invasive breast cancer. Presumably, these lesions (collectively termed premalignant breast disease) represent the earliest recognizable morphologic expression of fundamental molecular events that lead to the development of invasive breast cancer. To study molecular events underlying premalignant breast disease, we have developed a method for isolating RNA from histologically identified lesions from frozen human breast tissue. This method specifically obtains mRNA from breast epithelial cells and has identified three genes which are differentially expressed in premalignant breast epithelial lesions. One gene identified by this method is overexpressed in four of five noncomedo ductal carcinoma in situ lesions and appears to be the human homologue of the gene encoding the M2 subunit of ribonucleotide reductase, an enzyme involved in DNA synthesis.

Genome-Wide Architecture of Disease Resistance Genes in Lettuce

PubMed Central

Christopoulou, Marilena; Wo, Sebastian Reyes-Chin; Kozik, Alex; McHale, Leah K.; Truco, Maria-Jose; Wroblewski, Tadeusz; Michelmore, Richard W.

2015-01-01

Genome-wide motif searches identified 1134 genes in the lettuce reference genome of cv. Salinas that are potentially involved in pathogen recognition, of which 385 were predicted to encode nucleotide binding-leucine rich repeat receptor (NLR) proteins. Using a maximum-likelihood approach, we grouped the NLRs into 25 multigene families and 17 singletons. Forty-one percent of these NLR-encoding genes belong to three families, the largest being RGC16 with 62 genes in cv. Salinas. The majority of NLR-encoding genes are located in five major resistance clusters (MRCs) on chromosomes 1, 2, 3, 4, and 8 and cosegregate with multiple disease resistance phenotypes. Most MRCs contain primarily members of a single NLR gene family but a few are more complex. MRC2 spans 73 Mb and contains 61 NLRs of six different gene families that cosegregate with nine disease resistance phenotypes. MRC3, which is 25 Mb, contains 22 RGC21 genes and colocates with Dm13. A library of 33 transgenic RNA interference tester stocks was generated for functional analysis of NLR-encoding genes that cosegregated with disease resistance phenotypes in each of the MRCs. Members of four NLR-encoding families, RGC1, RGC2, RGC21, and RGC12 were shown to be required for 16 disease resistance phenotypes in lettuce. The general composition of MRCs is conserved across different genotypes; however, the specific repertoire of NLR-encoding genes varied particularly of the rapidly evolving Type I genes. These tester stocks are valuable resources for future analyses of additional resistance phenotypes. PMID:26449254

NDP gene mutations in 14 French families with Norrie disease.

PubMed

Royer, Ghislaine; Hanein, Sylvain; Raclin, Valérie; Gigarel, Nadine; Rozet, Jean-Michel; Munnich, Arnold; Steffann, Julie; Dufier, Jean-Louis; Kaplan, Josseline; Bonnefont, Jean-Paul

2003-12-01

Norrie disease is a rare X-inked recessive condition characterized by congenital blindness and occasionally deafness and mental retardation in males. This disease has been ascribed to mutations in the NDP gene on chromosome Xp11.1. Previous investigations of the NDP gene have identified largely sixty disease-causing sequence variants. Here, we report on ten different NDP gene allelic variants in fourteen of a series of 21 families fulfilling inclusion criteria. Two alterations were intragenic deletions and eight were nucleotide substitutions or splicing variants, six of them being hitherto unreported, namely c.112C>T (p.Arg38Cys), c.129C>G (p.His43Gln), c.133G>A (p.Val45Met), c.268C>T (p.Arg90Cys), c.382T>C (p.Cys128Arg), c.23479-1G>C (unknown). No NDP gene sequence variant was found in seven of the 21 families. This observation raises the issue of misdiagnosis, phenocopies, or existence of other X-linked or autosomal genes, the mutations of which would mimic the Norrie disease phenotype. Copyright 2003 Wiley-Liss, Inc.

Norrie disease gene is distinct from the monoamine oxidase genes.

PubMed

Sims, K B; Ozelius, L; Corey, T; Rinehart, W B; Liberfarb, R; Haines, J; Chen, W J; Norio, R; Sankila, E; de la Chapelle, A

1989-09-01

The genes for MAO-A and MAO-B appear to be very close to the Norrie disease gene, on the basis of loss and/or disruption of the MAO genes and activities in atypical Norrie disease patients deleted for the DXS7 locus; linkage among the MAO genes, the Norrie disease gene, and the DXS7 locus; and mapping of all these loci to the chromosomal region Xp11. The present study provides evidence that the MAO genes are not disrupted in "classic" Norrie disease patients. Genomic DNA from these "nondeletion" Norrie disease patients did not show rearrangements at the MAOA or DXS7 loci. Normal levels of MAO-A activities, as well as normal amounts and size of the MAO-A mRNA, were observed in cultured skin fibroblasts from these patients, and MAO-B activity in their platelets was normal. Catecholamine metabolites evaluated in plasma and urine were in the control range. Thus, although some atypical Norrie disease patients lack both MAO-A and MAO-B activities, MAO does not appear to be an etiologic factor in classic Norrie disease.

A novel mutation in the Norrie disease gene.

PubMed

Ott, S; Patel, R J; Appukuttan, B; Wang, X; Stout, J T

2000-04-01

Norrie disease is an X-linked recessive disorder characterized by congenital blindness and in some cases mental retardation and deafness.(1) The variability of signs among patients often complicates diagnosis. Signs such as an ocular pseudoglioma, progressive deafness, and mental disturbance are considered classic features.(2) Only one third of patients with Norrie disease have sensorineural deafness, and approximately one half of the affected individuals exhibit mental retardation, often with psychotic features.(3) Histologic analysis has suggested that retinal dysgenesis occurs early in eye development and involves cells in the inner wall of the optic cup.(4) The gene associated with Norrie disease was identified in 1992. (5,6) We report a novel mutation identified in a patient in whom Norrie disease was diagnosed.

Pathway-driven gene stability selection of two rheumatoid arthritis GWAS identifies and validates new susceptibility genes in receptor mediated signalling pathways.

PubMed

Eleftherohorinou, Hariklia; Hoggart, Clive J; Wright, Victoria J; Levin, Michael; Coin, Lachlan J M

2011-09-01

Rheumatoid arthritis (RA) is the commonest chronic, systemic, inflammatory disorder affecting ∼1% of the world population. It has a strong genetic component and a growing number of associated genes have been discovered in genome-wide association studies (GWAS), which nevertheless only account for 23% of the total genetic risk. We aimed to identify additional susceptibility loci through the analysis of GWAS in the context of biological function. We bridge the gap between pathway and gene-oriented analyses of GWAS, by introducing a pathway-driven gene stability-selection methodology that identifies potential causal genes in the top-associated disease pathways that may be driving the pathway association signals. We analysed the WTCCC and the NARAC studies of ∼5000 and ∼2000 subjects, respectively. We examined 700 pathways comprising ∼8000 genes. Ranking pathways by significance revealed that the NARAC top-ranked ∼6% laid within the top 10% of WTCCC. Gene selection on those pathways identified 58 genes in WTCCC and 61 in NARAC; 21 of those were common (P(overlap)< 10(-21)), of which 16 were novel discoveries. Among the identified genes, we validated 10 known RA associations in WTCCC and 13 in NARAC, not discovered using single-SNP approaches on the same data. Gene ontology functional enrichment analysis on the identified genes showed significant over-representation of signalling activity (P< 10(-29)) in both studies. Our findings suggest a novel model of RA genetic predisposition, which involves cell-membrane receptors and genes in second messenger signalling systems, in addition to genes that regulate immune responses, which have been the focus of interest previously.

VCP gene analyses in Japanese patients with sporadic amyotrophic lateral sclerosis identify a new mutation.

PubMed

Hirano, Makito; Nakamura, Yusaku; Saigoh, Kazumasa; Sakamoto, Hikaru; Ueno, Shuichi; Isono, Chiharu; Mitsui, Yoshiyuki; Kusunoki, Susumu

2015-03-01

Accumulating evidence has proven that mutations in the VCP gene encoding valosin-containing protein (VCP) cause inclusion body myopathy with Paget disease of the bone and frontotemporal dementia. This gene was later found to be causative for amyotrophic lateral sclerosis (ALS), a fatal neurodegenerative disease, occurring typically in elderly persons. We thus sequenced the VCP gene in 75 Japanese patients with sporadic ALS negative for mutations in other genes causative for ALS and found a novel mutation, p.Arg487His, in 1 patient. The newly identified mutant as well as known mutants rendered neuronal cells susceptible to oxidative stress. The presence of the mutation in the Japanese population extends the geographic region for involvement of the VCP gene in sporadic ALS to East Asia. Copyright © 2015 Elsevier Inc. All rights reserved.

Mapping eQTLs in the Norfolk Island Genetic Isolate Identifies Candidate Genes for CVD Risk Traits

PubMed Central

Benton, Miles C.; Lea, Rod A.; Macartney-Coxson, Donia; Carless, Melanie A.; Göring, Harald H.; Bellis, Claire; Hanna, Michelle; Eccles, David; Chambers, Geoffrey K.; Curran, Joanne E.; Harper, Jacquie L.; Blangero, John; Griffiths, Lyn R.

2013-01-01

Cardiovascular disease (CVD) affects millions of people worldwide and is influenced by numerous factors, including lifestyle and genetics. Expression quantitative trait loci (eQTLs) influence gene expression and are good candidates for CVD risk. Founder-effect pedigrees can provide additional power to map genes associated with disease risk. Therefore, we identified eQTLs in the genetic isolate of Norfolk Island (NI) and tested for associations between these and CVD risk factors. We measured genome-wide transcript levels of blood lymphocytes in 330 individuals and used pedigree-based heritability analysis to identify heritable transcripts. eQTLs were identified by genome-wide association testing of these transcripts. Testing for association between CVD risk factors (i.e., blood lipids, blood pressure, and body fat indices) and eQTLs revealed 1,712 heritable transcripts (p < 0.05) with heritability values ranging from 0.18 to 0.84. From these, we identified 200 cis-acting and 70 trans-acting eQTLs (p < 1.84 × 10−7) An eQTL-centric analysis of CVD risk traits revealed multiple associations, including 12 previously associated with CVD-related traits. Trait versus eQTL regression modeling identified four CVD risk candidates (NAAA, PAPSS1, NME1, and PRDX1), all of which have known biological roles in disease. In addition, we implicated several genes previously associated with CVD risk traits, including MTHFR and FN3KRP. We have successfully identified a panel of eQTLs in the NI pedigree and used this to implicate several genes in CVD risk. Future studies are required for further assessing the functional importance of these eQTLs and whether the findings here also relate to outbred populations. PMID:24314549

Gene Therapy for Cardiovascular Disease

PubMed Central

2003-01-01

The last decade has seen substantial advances in the development of gene therapy strategies and vector technology for the treatment of a diverse number of diseases, with a view to translating the successes observed in animal models into the clinic. Perhaps the overwhelming drive for the increase in vascular gene transfer studies is the current lack of successful long-term pharmacological treatments for complex cardiovascular diseases. The increase in cardiovascular disease to epidemic proportions has also led many to conclude that drug therapy may have reached a plateau in its efficacy and that gene therapy may represent a realistic solution to a long-term problem. Here, we discuss gene delivery approaches and target diseases. PMID:12721517

Literature mining, gene-set enrichment and pathway analysis for target identification in Behçet's disease.

PubMed

Wilson, Paul; Larminie, Christopher; Smith, Rona

2016-01-01

To use literature mining to catalogue Behçet's associated genes, and advanced computational methods to improve the understanding of the pathways and signalling mechanisms that lead to the typical clinical characteristics of Behçet's patients. To extend this technique to identify potential treatment targets for further experimental validation. Text mining methods combined with gene enrichment tools, pathway analysis and causal analysis algorithms. This approach identified 247 human genes associated with Behçet's disease and the resulting disease map, comprising 644 nodes and 19220 edges, captured important details of the relationships between these genes and their associated pathways, as described in diverse data repositories. Pathway analysis has identified how Behçet's associated genes are likely to participate in innate and adaptive immune responses. Causal analysis algorithms have identified a number of potential therapeutic strategies for further investigation. Computational methods have captured pertinent features of the prominent disease characteristics presented in Behçet's disease and have highlighted NOD2, ICOS and IL18 signalling as potential therapeutic strategies.

Human Disease Insight: An integrated knowledge-based platform for disease-gene-drug information.

PubMed

Tasleem, Munazzah; Ishrat, Romana; Islam, Asimul; Ahmad, Faizan; Hassan, Md Imtaiyaz

2016-01-01

The scope of the Human Disease Insight (HDI) database is not limited to researchers or physicians as it also provides basic information to non-professionals and creates disease awareness, thereby reducing the chances of patient suffering due to ignorance. HDI is a knowledge-based resource providing information on human diseases to both scientists and the general public. Here, our mission is to provide a comprehensive human disease database containing most of the available useful information, with extensive cross-referencing. HDI is a knowledge management system that acts as a central hub to access information about human diseases and associated drugs and genes. In addition, HDI contains well-classified bioinformatics tools with helpful descriptions. These integrated bioinformatics tools enable researchers to annotate disease-specific genes and perform protein analysis, search for biomarkers and identify potential vaccine candidates. Eventually, these tools will facilitate the analysis of disease-associated data. The HDI provides two types of search capabilities and includes provisions for downloading, uploading and searching disease/gene/drug-related information. The logistical design of the HDI allows for regular updating. The database is designed to work best with Mozilla Firefox and Google Chrome and is freely accessible at http://humandiseaseinsight.com. Copyright © 2015 King Saud Bin Abdulaziz University for Health Sciences. Published by Elsevier Ltd. All rights reserved.

Pooled Sequencing of 531 Genes in Inflammatory Bowel Disease Identifies an Associated Rare Variant in BTNL2 and Implicates Other Immune Related Genes

PubMed Central

Prescott, Natalie J.; Lehne, Benjamin; Stone, Kristina; Lee, James C.; Taylor, Kirstin; Knight, Jo; Papouli, Efterpi; Mirza, Muddassar M.; Simpson, Michael A.; Spain, Sarah L.; Lu, Grace; Fraternali, Franca; Bumpstead, Suzannah J.; Gray, Emma; Amar, Ariella; Bye, Hannah; Green, Peter; Chung-Faye, Guy; Hayee, Bu’Hussain; Pollok, Richard; Satsangi, Jack; Parkes, Miles; Barrett, Jeffrey C.; Mansfield, John C.; Sanderson, Jeremy; Lewis, Cathryn M.; Weale, Michael E.; Schlitt, Thomas; Mathew, Christopher G.

2015-01-01

The contribution of rare coding sequence variants to genetic susceptibility in complex disorders is an important but unresolved question. Most studies thus far have investigated a limited number of genes from regions which contain common disease associated variants. Here we investigate this in inflammatory bowel disease by sequencing the exons and proximal promoters of 531 genes selected from both genome-wide association studies and pathway analysis in pooled DNA panels from 474 cases of Crohn’s disease and 480 controls. 80 variants with evidence of association in the sequencing experiment or with potential functional significance were selected for follow up genotyping in 6,507 IBD cases and 3,064 population controls. The top 5 disease associated variants were genotyped in an extension panel of 3,662 IBD cases and 3,639 controls, and tested for association in a combined analysis of 10,147 IBD cases and 7,008 controls. A rare coding variant p.G454C in the BTNL2 gene within the major histocompatibility complex was significantly associated with increased risk for IBD (p = 9.65x10−10, OR = 2.3[95% CI = 1.75–3.04]), but was independent of the known common associated CD and UC variants at this locus. Rare (<1%) and low frequency (1–5%) variants in 3 additional genes showed suggestive association (p<0.005) with either an increased risk (ARIH2 c.338-6C>T) or decreased risk (IL12B p.V298F, and NICN p.H191R) of IBD. These results provide additional insights into the involvement of the inhibition of T cell activation in the development of both sub-phenotypes of inflammatory bowel disease. We suggest that although rare coding variants may make a modest overall contribution to complex disease susceptibility, they can inform our understanding of the molecular pathways that contribute to pathogenesis. PMID:25671699

Integrating genome-wide association study summaries and element-gene interaction datasets identified multiple associations between elements and complex diseases.

PubMed

He, Awen; Wang, Wenyu; Prakash, N Tejo; Tinkov, Alexey A; Skalny, Anatoly V; Wen, Yan; Hao, Jingcan; Guo, Xiong; Zhang, Feng

2018-03-01

Chemical elements are closely related to human health. Extensive genomic profile data of complex diseases offer us a good opportunity to systemically investigate the relationships between elements and complex diseases/traits. In this study, we applied gene set enrichment analysis (GSEA) approach to detect the associations between elements and complex diseases/traits though integrating element-gene interaction datasets and genome-wide association study (GWAS) data of complex diseases/traits. To illustrate the performance of GSEA, the element-gene interaction datasets of 24 elements were extracted from the comparative toxicogenomics database (CTD). GWAS summary datasets of 24 complex diseases or traits were downloaded from the dbGaP or GEFOS websites. We observed significant associations between 7 elements and 13 complex diseases or traits (all false discovery rate (FDR) < 0.05), including reported relationships such as aluminum vs. Alzheimer's disease (FDR = 0.042), calcium vs. bone mineral density (FDR = 0.031), magnesium vs. systemic lupus erythematosus (FDR = 0.012) as well as novel associations, such as nickel vs. hypertriglyceridemia (FDR = 0.002) and bipolar disorder (FDR = 0.027). Our study results are consistent with previous biological studies, supporting the good performance of GSEA. Our analyzing results based on GSEA framework provide novel clues for discovering causal relationships between elements and complex diseases. © 2017 WILEY PERIODICALS, INC.

NDRC: A Disease-Causing Genes Prioritized Method Based on Network Diffusion and Rank Concordance.

PubMed

Fang, Minghong; Hu, Xiaohua; Wang, Yan; Zhao, Junmin; Shen, Xianjun; He, Tingting

2015-07-01

Disease-causing genes prioritization is very important to understand disease mechanisms and biomedical applications, such as design of drugs. Previous studies have shown that promising candidate genes are mostly ranked according to their relatedness to known disease genes or closely related disease genes. Therefore, a dangling gene (isolated gene) with no edges in the network can not be effectively prioritized. These approaches tend to prioritize those genes that are highly connected in the PPI network while perform poorly when they are applied to loosely connected disease genes. To address these problems, we propose a new disease-causing genes prioritization method that based on network diffusion and rank concordance (NDRC). The method is evaluated by leave-one-out cross validation on 1931 diseases in which at least one gene is known to be involved, and it is able to rank the true causal gene first in 849 of all 2542 cases. The experimental results suggest that NDRC significantly outperforms other existing methods such as RWR, VAVIEN, DADA and PRINCE on identifying loosely connected disease genes and successfully put dangling genes as potential candidate disease genes. Furthermore, we apply NDRC method to study three representative diseases, Meckel syndrome 1, Protein C deficiency and Peroxisome biogenesis disorder 1A (Zellweger). Our study has also found that certain complex disease-causing genes can be divided into several modules that are closely associated with different disease phenotype.

Prioritization of Disease Susceptibility Genes Using LSM/SVD.

PubMed

Gong, Lejun; Yang, Ronggen; Yan, Qin; Sun, Xiao

2013-12-01

Understanding the role of genetics in diseases is one of the most important tasks in the postgenome era. It is generally too expensive and time consuming to perform experimental validation for all candidate genes related to disease. Computational methods play important roles for prioritizing these candidates. Herein, we propose an approach to prioritize disease genes using latent semantic mapping based on singular value decomposition. Our hypothesis is that similar functional genes are likely to cause similar diseases. Measuring the functional similarity between known disease susceptibility genes and unknown genes is to predict new disease susceptibility genes. Taking autism as an instance, the analysis results of the top ten genes prioritized demonstrate they might be autism susceptibility genes, which also indicates our approach could discover new disease susceptibility genes. The novel approach of disease gene prioritization could discover new disease susceptibility genes, and latent disease-gene relations. The prioritized results could also support the interpretive diversity and experimental views as computational evidence for disease researchers.

Genome-Wide Gene-Environment Study Identifies Glutamate Receptor Gene GRIN2A as a Parkinson's Disease Modifier Gene via Interaction with Coffee

PubMed Central

Hamza, Taye H.; Chen, Honglei; Hill-Burns, Erin M.; Rhodes, Shannon L.; Montimurro, Jennifer; Kay, Denise M.; Tenesa, Albert; Kusel, Victoria I.; Sheehan, Patricia; Eaaswarkhanth, Muthukrishnan; Yearout, Dora; Samii, Ali; Roberts, John W.; Agarwal, Pinky; Bordelon, Yvette; Park, Yikyung; Wang, Liyong; Gao, Jianjun; Vance, Jeffery M.; Kendler, Kenneth S.; Bacanu, Silviu-Alin; Scott, William K.; Ritz, Beate; Nutt, John; Factor, Stewart A.; Zabetian, Cyrus P.; Payami, Haydeh

2011-01-01

Our aim was to identify genes that influence the inverse association of coffee with the risk of developing Parkinson's disease (PD). We used genome-wide genotype data and lifetime caffeinated-coffee-consumption data on 1,458 persons with PD and 931 without PD from the NeuroGenetics Research Consortium (NGRC), and we performed a genome-wide association and interaction study (GWAIS), testing each SNP's main-effect plus its interaction with coffee, adjusting for sex, age, and two principal components. We then stratified subjects as heavy or light coffee-drinkers and performed genome-wide association study (GWAS) in each group. We replicated the most significant SNP. Finally, we imputed the NGRC dataset, increasing genomic coverage to examine the region of interest in detail. The primary analyses (GWAIS, GWAS, Replication) were performed using genotyped data. In GWAIS, the most significant signal came from rs4998386 and the neighboring SNPs in GRIN2A. GRIN2A encodes an NMDA-glutamate-receptor subunit and regulates excitatory neurotransmission in the brain. Achieving P2df = 10−6, GRIN2A surpassed all known PD susceptibility genes in significance in the GWAIS. In stratified GWAS, the GRIN2A signal was present in heavy coffee-drinkers (OR = 0.43; P = 6×10−7) but not in light coffee-drinkers. The a priori Replication hypothesis that “Among heavy coffee-drinkers, rs4998386_T carriers have lower PD risk than rs4998386_CC carriers” was confirmed: ORReplication = 0.59, PReplication = 10−3; ORPooled = 0.51, PPooled = 7×10−8. Compared to light coffee-drinkers with rs4998386_CC genotype, heavy coffee-drinkers with rs4998386_CC genotype had 18% lower risk (P = 3×10−3), whereas heavy coffee-drinkers with rs4998386_TC genotype had 59% lower risk (P = 6×10−13). Imputation revealed a block of SNPs that achieved P2df<5×10−8 in GWAIS, and OR = 0.41, P = 3×10−8 in heavy coffee-drinkers. This study is proof of concept

Identifying Cancer Driver Genes Using Replication-Incompetent Retroviral Vectors

PubMed Central

Bii, Victor M.; Trobridge, Grant D.

2016-01-01

Identifying novel genes that drive tumor metastasis and drug resistance has significant potential to improve patient outcomes. High-throughput sequencing approaches have identified cancer genes, but distinguishing driver genes from passengers remains challenging. Insertional mutagenesis screens using replication-incompetent retroviral vectors have emerged as a powerful tool to identify cancer genes. Unlike replicating retroviruses and transposons, replication-incompetent retroviral vectors lack additional mutagenesis events that can complicate the identification of driver mutations from passenger mutations. They can also be used for almost any human cancer due to the broad tropism of the vectors. Replication-incompetent retroviral vectors have the ability to dysregulate nearby cancer genes via several mechanisms including enhancer-mediated activation of gene promoters. The integrated provirus acts as a unique molecular tag for nearby candidate driver genes which can be rapidly identified using well established methods that utilize next generation sequencing and bioinformatics programs. Recently, retroviral vector screens have been used to efficiently identify candidate driver genes in prostate, breast, liver and pancreatic cancers. Validated driver genes can be potential therapeutic targets and biomarkers. In this review, we describe the emergence of retroviral insertional mutagenesis screens using replication-incompetent retroviral vectors as a novel tool to identify cancer driver genes in different cancer types. PMID:27792127

Exome sequencing in amyotrophic lateral sclerosis identifies risk genes and pathways.

PubMed

Cirulli, Elizabeth T; Lasseigne, Brittany N; Petrovski, Slavé; Sapp, Peter C; Dion, Patrick A; Leblond, Claire S; Couthouis, Julien; Lu, Yi-Fan; Wang, Quanli; Krueger, Brian J; Ren, Zhong; Keebler, Jonathan; Han, Yujun; Levy, Shawn E; Boone, Braden E; Wimbish, Jack R; Waite, Lindsay L; Jones, Angela L; Carulli, John P; Day-Williams, Aaron G; Staropoli, John F; Xin, Winnie W; Chesi, Alessandra; Raphael, Alya R; McKenna-Yasek, Diane; Cady, Janet; Vianney de Jong, J M B; Kenna, Kevin P; Smith, Bradley N; Topp, Simon; Miller, Jack; Gkazi, Athina; Al-Chalabi, Ammar; van den Berg, Leonard H; Veldink, Jan; Silani, Vincenzo; Ticozzi, Nicola; Shaw, Christopher E; Baloh, Robert H; Appel, Stanley; Simpson, Ericka; Lagier-Tourenne, Clotilde; Pulst, Stefan M; Gibson, Summer; Trojanowski, John Q; Elman, Lauren; McCluskey, Leo; Grossman, Murray; Shneider, Neil A; Chung, Wendy K; Ravits, John M; Glass, Jonathan D; Sims, Katherine B; Van Deerlin, Vivianna M; Maniatis, Tom; Hayes, Sebastian D; Ordureau, Alban; Swarup, Sharan; Landers, John; Baas, Frank; Allen, Andrew S; Bedlack, Richard S; Harper, J Wade; Gitler, Aaron D; Rouleau, Guy A; Brown, Robert; Harms, Matthew B; Cooper, Gregory M; Harris, Tim; Myers, Richard M; Goldstein, David B

2015-03-27

Amyotrophic lateral sclerosis (ALS) is a devastating neurological disease with no effective treatment. We report the results of a moderate-scale sequencing study aimed at increasing the number of genes known to contribute to predisposition for ALS. We performed whole-exome sequencing of 2869 ALS patients and 6405 controls. Several known ALS genes were found to be associated, and TBK1 (the gene encoding TANK-binding kinase 1) was identified as an ALS gene. TBK1 is known to bind to and phosphorylate a number of proteins involved in innate immunity and autophagy, including optineurin (OPTN) and p62 (SQSTM1/sequestosome), both of which have also been implicated in ALS. These observations reveal a key role of the autophagic pathway in ALS and suggest specific targets for therapeutic intervention. Copyright © 2015, American Association for the Advancement of Science.

Norrie disease gene is distinct from the monoamine oxidase genes

PubMed Central

Sims, Katherine B.; Ozelius, Laurie; Corey, Timothy; Rinehart, William B.; Liberfarb, Ruth; Haines, Jonathan; Chen, Wei Jane; Norio, Reijo; Sankila, Eeva; de la Chapelle, Albert; Murphy, Dennis L.; Gusella, James; Breakefield, Xandra O.

1989-01-01

The genes for MAO-A and MAO-B appear to be very close to the Norrie disease gene, on the basis of loss and /or disruption of the MAO genes and activities in atypical Norrie disease patients deleted for the DXS7 locus; linkage among the MAO genes, the Norrie disease gene, and the DXS7 locus; and mapping of all these loci to the chromosomal region Xp11. The present study provides evidence that the MAO genes are not disrupted in “classic” Norrie disease patients. Genomic DNA from these “nondeletion” Norrie disease patients did not show rearrangements at the MAOA or DXS7 loci. Normal levels of MAO-A activities, as well as normal amounts and size of the MAO-A mRNA, were observed in cultured skin fibroblasts from these patients, and MAO-B activity in their platelets was normal. Catecholamine metabolites evaluated in plasma and urine were in the control range. Thus, although some atypical Norrie disease patients lack both MAO-A and MAO-B activities, MAO does not appear to be an etiologic factor in classic Norrie disease. ImagesFigure 2Figure 3 PMID:2773935

Current Status and Challenges in Identifying Disease Resistance Genes in Brassica napus

PubMed Central

Neik, Ting Xiang; Barbetti, Martin J.; Batley, Jacqueline

2017-01-01

Brassica napus is an economically important crop across different continents including temperate and subtropical regions in Europe, Canada, South Asia, China and Australia. Its widespread cultivation also brings setbacks as it plays host to fungal, oomycete and chytrid pathogens that can lead to serious yield loss. For sustainable crop production, identification of resistance (R) genes in B. napus has become of critical importance. In this review, we discuss four key pathogens affecting Brassica crops: Clubroot (Plasmodiophora brassicae), Blackleg (Leptosphaeria maculans and L. biglobosa), Sclerotinia Stem Rot (Sclerotinia sclerotiorum), and Downy Mildew (Hyaloperonospora parasitica). We first review current studies covering prevalence of these pathogens on Brassica crops and highlight the R genes and QTL that have been identified from Brassica species against these pathogens. Insights into the relationships between the pathogen and its Brassica host, the unique host resistance mechanisms and how these affect resistance outcomes is also presented. We discuss challenges in identification and deployment of R genes in B. napus in relation to highly specific genetic interactions between host subpopulations and pathogen pathotypes and emphasize the need for common or shared techniques and research materials or tighter collaboration between researchers to reconcile the inconsistencies in the research outcomes. Using current genomics tools, we provide examples of how characterization and cloning of R genes in B. napus can be carried out more effectively. Lastly, we put forward strategies to breed resistant cultivars through introgressions supported by genomic approaches and suggest prospects that can be implemented in the future for a better, pathogen-resistant B. napus. PMID:29163558

Analysis of predicted loss-of-function variants in UK Biobank identifies variants protective for disease.

PubMed

Emdin, Connor A; Khera, Amit V; Chaffin, Mark; Klarin, Derek; Natarajan, Pradeep; Aragam, Krishna; Haas, Mary; Bick, Alexander; Zekavat, Seyedeh M; Nomura, Akihiro; Ardissino, Diego; Wilson, James G; Schunkert, Heribert; McPherson, Ruth; Watkins, Hugh; Elosua, Roberto; Bown, Matthew J; Samani, Nilesh J; Baber, Usman; Erdmann, Jeanette; Gupta, Namrata; Danesh, John; Chasman, Daniel; Ridker, Paul; Denny, Joshua; Bastarache, Lisa; Lichtman, Judith H; D'Onofrio, Gail; Mattera, Jennifer; Spertus, John A; Sheu, Wayne H-H; Taylor, Kent D; Psaty, Bruce M; Rich, Stephen S; Post, Wendy; Rotter, Jerome I; Chen, Yii-Der Ida; Krumholz, Harlan; Saleheen, Danish; Gabriel, Stacey; Kathiresan, Sekar

2018-04-24

Less than 3% of protein-coding genetic variants are predicted to result in loss of protein function through the introduction of a stop codon, frameshift, or the disruption of an essential splice site; however, such predicted loss-of-function (pLOF) variants provide insight into effector transcript and direction of biological effect. In >400,000 UK Biobank participants, we conduct association analyses of 3759 pLOF variants with six metabolic traits, six cardiometabolic diseases, and twelve additional diseases. We identified 18 new low-frequency or rare (allele frequency < 5%) pLOF variant-phenotype associations. pLOF variants in the gene GPR151 protect against obesity and type 2 diabetes, in the gene IL33 against asthma and allergic disease, and in the gene IFIH1 against hypothyroidism. In the gene PDE3B, pLOF variants associate with elevated height, improved body fat distribution and protection from coronary artery disease. Our findings prioritize genes for which pharmacologic mimics of pLOF variants may lower risk for disease.

Gene Therapy for Parkinson's Disease

PubMed Central

Denyer, Rachel; Douglas, Michael R.

2012-01-01

Current pharmacological and surgical treatments for Parkinson's disease offer symptomatic improvements to those suffering from this incurable degenerative neurological disorder, but none of these has convincingly shown effects on disease progression. Novel approaches based on gene therapy have several potential advantages over conventional treatment modalities. These could be used to provide more consistent dopamine supplementation, potentially providing superior symptomatic relief with fewer side effects. More radically, gene therapy could be used to correct the imbalances in basal ganglia circuitry associated with the symptoms of Parkinson's disease, or to preserve or restore dopaminergic neurons lost during the disease process itself. The latter neuroprotective approach is the most exciting, as it could theoretically be disease modifying rather than simply symptom alleviating. Gene therapy agents using these approaches are currently making the transition from the laboratory to the bedside. This paper summarises the theoretical approaches to gene therapy for Parkinson's disease and the findings of clinical trials in this rapidly changing field. PMID:22619738

Gene therapy for Parkinson's disease.

PubMed

Denyer, Rachel; Douglas, Michael R

2012-01-01

Current pharmacological and surgical treatments for Parkinson's disease offer symptomatic improvements to those suffering from this incurable degenerative neurological disorder, but none of these has convincingly shown effects on disease progression. Novel approaches based on gene therapy have several potential advantages over conventional treatment modalities. These could be used to provide more consistent dopamine supplementation, potentially providing superior symptomatic relief with fewer side effects. More radically, gene therapy could be used to correct the imbalances in basal ganglia circuitry associated with the symptoms of Parkinson's disease, or to preserve or restore dopaminergic neurons lost during the disease process itself. The latter neuroprotective approach is the most exciting, as it could theoretically be disease modifying rather than simply symptom alleviating. Gene therapy agents using these approaches are currently making the transition from the laboratory to the bedside. This paper summarises the theoretical approaches to gene therapy for Parkinson's disease and the findings of clinical trials in this rapidly changing field.

Transcriptome analysis reveals mucin 4 to be highly associated with periodontitis and identifies pleckstrin as a link to systemic diseases

PubMed Central

Lundmark, Anna; Davanian, Haleh; Båge, Tove; Johannsen, Gunnar; Koro, Catalin; Lundeberg, Joakim; Yucel-Lindberg, Tülay

2015-01-01

The multifactorial chronic inflammatory disease periodontitis, which is characterized by destruction of tooth-supporting tissues, has also been implicated as a risk factor for various systemic diseases. Although periodontitis has been studied extensively, neither disease-specific biomarkers nor therapeutic targets have been identified, nor its link with systemic diseases. Here, we analyzed the global transcriptome of periodontitis and compared its gene expression profile with those of other inflammatory conditions, including cardiovascular disease (CVD), rheumatoid arthritis (RA), and ulcerative colitis (UC). Gingival biopsies from 62 patients with periodontitis and 62 healthy subjects were subjected to RNA sequencing. The up-regulated genes in periodontitis were related to inflammation, wounding and defense response, and apoptosis, whereas down-regulated genes were related to extracellular matrix organization and structural support. The most highly up-regulated gene was mucin 4 (MUC4), and its protein product was confirmed to be over-expressed in periodontitis. When comparing the expression profile of periodontitis with other inflammatory diseases, several gene ontology categories, including inflammatory response, cell death, cell motion, and homeostatic processes, were identified as common to all diseases. Only one gene, pleckstrin (PLEK), was significantly overexpressed in periodontitis, CVD, RA, and UC, implicating this gene as an important networking link between these chronic inflammatory diseases. PMID:26686060

Comparative gene expression analysis between coronary arteries and internal mammary arteries identifies a role for the TES gene in endothelial cell functions relevant to coronary artery disease.

PubMed

Archacki, Stephen R; Angheloiu, George; Moravec, Christine S; Liu, Hui; Topol, Eric J; Wang, Qing Kenneth

2012-03-15

Coronary artery disease (CAD) is the leading cause of death worldwide. It has been established that internal mammary arteries (IMA) are resistant to the development of atherosclerosis, whereas left anterior descending (LAD) coronary arteries are athero-prone. The contrasting properties of these two arteries provide an innovative strategy to identify the genes that play important roles in the development of atherosclerosis. We carried out microarray analysis to identify genes differentially expressed between IMA and LAD. Twenty-nine genes showed significant differences in their expression levels between IMA and LAD, which included the TES gene encoding Testin. The role of TES in the cardiovascular system is unknown. Here we show that TES is involved in endothelial cell (EC) functions relevant to atherosclerosis. Western blot analysis showed higher TES expression in IMA than in LAD. Reverse transcription polymerase chain reaction and western blot analyses showed that TES was consistently and markedly down-regulated by more than 6-fold at both mRNA and protein levels in patients with CAD compared with controls without CAD (P= 0.000049). The data suggest that reduced TES expression is associated with the development of CAD. Knockdown of TES expression by small-interfering RNA promoted oxidized-LDL-mediated monocyte adhesion to ECs, EC migration and the transendothelial migration of monocytes, while the over-expression of TES in ECs blunted these processes. These results demonstrate association between reduced TES expression and CAD, establish a novel role for TES in EC functions and raise the possibility that reduced TES expression increases susceptibility to the development of CAD.

Differentiating disease subtypes by using pathway patterns constructed from gene expressions and protein networks.

PubMed

Hung, Fei-Hung; Chiu, Hung-Wen

2015-01-01

Gene expression profiles differ in different diseases. Even if diseases are at the same stage, such diseases exhibit different gene expressions, not to mention the different subtypes at a single lesion site. Distinguishing different disease subtypes at a single lesion site is difficult. In early cases, subtypes were initially distinguished by doctors. Subsequently, further differences were found through pathological experiments. For example, a brain tumor can be classified according to its origin, its cell-type origin, or the tumor site. Because of the advancements in bioinformatics and the techniques for accumulating gene expressions, researchers can use gene expression data to classify disease subtypes. Because the operation of a biopathway is closely related to the disease mechanism, the application of gene expression profiles for clustering disease subtypes is insufficient. In this study, we collected gene expression data of healthy and four myelodysplastic syndrome subtypes and applied a method that integrated protein-protein interaction and gene expression data to identify different patterns of disease subtypes. We hope it is efficient for the classification of disease subtypes in adventure.

Systems Biology-Based Investigation of Cellular Antiviral Drug Targets Identified by Gene-Trap Insertional Mutagenesis.

PubMed

Cheng, Feixiong; Murray, James L; Zhao, Junfei; Sheng, Jinsong; Zhao, Zhongming; Rubin, Donald H

2016-09-01

Viruses require host cellular factors for successful replication. A comprehensive systems-level investigation of the virus-host interactome is critical for understanding the roles of host factors with the end goal of discovering new druggable antiviral targets. Gene-trap insertional mutagenesis is a high-throughput forward genetics approach to randomly disrupt (trap) host genes and discover host genes that are essential for viral replication, but not for host cell survival. In this study, we used libraries of randomly mutagenized cells to discover cellular genes that are essential for the replication of 10 distinct cytotoxic mammalian viruses, 1 gram-negative bacterium, and 5 toxins. We herein reported 712 candidate cellular genes, characterizing distinct topological network and evolutionary signatures, and occupying central hubs in the human interactome. Cell cycle phase-specific network analysis showed that host cell cycle programs played critical roles during viral replication (e.g. MYC and TAF4 regulating G0/1 phase). Moreover, the viral perturbation of host cellular networks reflected disease etiology in that host genes (e.g. CTCF, RHOA, and CDKN1B) identified were frequently essential and significantly associated with Mendelian and orphan diseases, or somatic mutations in cancer. Computational drug repositioning framework via incorporating drug-gene signatures from the Connectivity Map into the virus-host interactome identified 110 putative druggable antiviral targets and prioritized several existing drugs (e.g. ajmaline) that may be potential for antiviral indication (e.g. anti-Ebola). In summary, this work provides a powerful methodology with a tight integration of gene-trap insertional mutagenesis testing and systems biology to identify new antiviral targets and drugs for the development of broadly acting and targeted clinical antiviral therapeutics.

Gene-Based Genome-Wide Association Analysis in European and Asian Populations Identified Novel Genes for Rheumatoid Arthritis.

PubMed

Zhu, Hong; Xia, Wei; Mo, Xing-Bo; Lin, Xiang; Qiu, Ying-Hua; Yi, Neng-Jun; Zhang, Yong-Hong; Deng, Fei-Yan; Lei, Shu-Feng

2016-01-01

Rheumatoid arthritis (RA) is a complex autoimmune disease. Using a gene-based association research strategy, the present study aims to detect unknown susceptibility to RA and to address the ethnic differences in genetic susceptibility to RA between European and Asian populations. Gene-based association analyses were performed with KGG 2.5 by using publicly available large RA datasets (14,361 RA cases and 43,923 controls of European subjects, 4,873 RA cases and 17,642 controls of Asian Subjects). For the newly identified RA-associated genes, gene set enrichment analyses and protein-protein interactions analyses were carried out with DAVID and STRING version 10.0, respectively. Differential expression verification was conducted using 4 GEO datasets. The expression levels of three selected 'highly verified' genes were measured by ELISA among our in-house RA cases and controls. A total of 221 RA-associated genes were newly identified by gene-based association study, including 71'overlapped', 76 'European-specific' and 74 'Asian-specific' genes. Among them, 105 genes had significant differential expressions between RA patients and health controls at least in one dataset, especially for 20 genes including 11 'overlapped' (ABCF1, FLOT1, HLA-F, IER3, TUBB, ZKSCAN4, BTN3A3, HSP90AB1, CUTA, BRD2, HLA-DMA), 5 'European-specific' (PHTF1, RPS18, BAK1, TNFRSF14, SUOX) and 4 'Asian-specific' (RNASET2, HFE, BTN2A2, MAPK13) genes whose differential expressions were significant at least in three datasets. The protein expressions of two selected genes FLOT1 (P value = 1.70E-02) and HLA-DMA (P value = 4.70E-02) in plasma were significantly different in our in-house samples. Our study identified 221 novel RA-associated genes and especially highlighted the importance of 20 candidate genes on RA. The results addressed ethnic genetic background differences for RA susceptibility between European and Asian populations and detected a long list of overlapped or ethnic specific RA genes. The

Norrie disease and MAO genes: nearest neighbors.

PubMed

Chen, Z Y; Denney, R M; Breakefield, X O

1995-01-01

The Norrie disease and MAO genes are tandemly arranged in the p11.4-p11.3 region of the human X chromosome in the order tel-MAOA-MAOB-NDP-cent. This relationship is conserved in the mouse in the order tel-MAOB-MAOA-NDP-cent. The MAO genes appear to have arisen by tandem duplication of an ancestral MAO gene, but their positional relationship to NDP appears to be random. Distinctive X-linked syndromes have been described for mutations in the MAOA and NDP genes, and in addition, individuals have been identified with contiguous gene syndromes due to chromosomal deletions which encompass two or three of these genes. Loss of function of the NDP gene causes a syndrome of congenital blindness and progressive hearing loss, sometimes accompanied by signs of CNS dysfunction, including variable mental retardation and psychiatric symptoms. Other mutations in the NDP gene have been found to underlie another X-linked eye disease, exudative vitreo-retinopathy. An MAOA deficiency state has been described in one family to date, with features of altered amine and amine metabolite levels, low normal intelligence, apparent difficulty in impulse control and cardiovascular difficulty in affected males. A contiguous gene syndrome in which all three genes are lacking, as well as other as yet unidentified flanking genes, results in severe mental retardation, small stature, seizures and congenital blindness, as well as altered amine and amine metabolites. Issues that remain to be resolved are the function of the NDP gene product, the frequency and phenotype of the MAOA deficiency state, and the possible occurrence and phenotype of an MAOB deficiency state.

Identifying key genes, pathways and screening therapeutic agents for manganese-induced Alzheimer disease using bioinformatics analysis.

PubMed

Ling, JunJun; Yang, Shengyou; Huang, Yi; Wei, Dongfeng; Cheng, Weidong

2018-06-01

Alzheimer disease (AD) is a progressive neurodegenerative disease, the etiology of which remains largely unknown. Accumulating evidence indicates that elevated manganese (Mn) in brain exerts toxic effects on neurons and contributes to AD development. Thus, we aimed to explore the gene and pathway variations through analysis of high through-put data in this process.To screen the differentially expressed genes (DEGs) that may play critical roles in Mn-induced AD, public microarray data regarding Mn-treated neurocytes versus controls (GSE70845), and AD versus controls (GSE48350), were downloaded and the DEGs were screened out, respectively. The intersection of the DEGs of each datasets was obtained by using Venn analysis. Then, gene ontology (GO) function analysis and KEGG pathway analysis were carried out. For screening hub genes, protein-protein interaction network was constructed. At last, DEGs were analyzed in Connectivity Map (CMAP) for identification of small molecules that overcome Mn-induced neurotoxicity or AD development.The intersection of the DEGs obtained 140 upregulated and 267 downregulated genes. The top 5 items of biological processes of GO analysis were taxis, chemotaxis, cell-cell signaling, regulation of cellular physiological process, and response to wounding. The top 5 items of KEGG pathway analysis were cytokine-cytokine receptor interaction, apoptosis, oxidative phosphorylation, Toll-like receptor signaling pathway, and insulin signaling pathway. Afterwards, several hub genes such as INSR, VEGFA, PRKACB, DLG4, and BCL2 that might play key roles in Mn-induced AD were further screened out. Interestingly, tyrphostin AG-825, an inhibitor of tyrosine phosphorylation, was predicted to be a potential agent for overcoming Mn-induced neurotoxicity or AD development.The present study provided a novel insight into the molecular mechanisms of Mn-induced neurotoxicity or AD development and screened out several small molecular candidates that might be

Perceptron ensemble of graph-based positive-unlabeled learning for disease gene identification.

PubMed

Jowkar, Gholam-Hossein; Mansoori, Eghbal G

2016-10-01

Identification of disease genes, using computational methods, is an important issue in biomedical and bioinformatics research. According to observations that diseases with the same or similar phenotype have the same biological characteristics, researchers have tried to identify genes by using machine learning tools. In recent attempts, some semi-supervised learning methods, called positive-unlabeled learning, is used for disease gene identification. In this paper, we present a Perceptron ensemble of graph-based positive-unlabeled learning (PEGPUL) on three types of biological attributes: gene ontologies, protein domains and protein-protein interaction networks. In our method, a reliable set of positive and negative genes are extracted using co-training schema. Then, the similarity graph of genes is built using metric learning by concentrating on multi-rank-walk method to perform inference from labeled genes. At last, a Perceptron ensemble is learned from three weighted classifiers: multilevel support vector machine, k-nearest neighbor and decision tree. The main contributions of this paper are: (i) incorporating the statistical properties of gene data through choosing proper metrics, (ii) statistical evaluation of biological features, and (iii) noise robustness characteristic of PEGPUL via using multilevel schema. In order to assess PEGPUL, we have applied it on 12950 disease genes with 949 positive genes from six class of diseases and 12001 unlabeled genes. Compared with some popular disease gene identification methods, the experimental results show that PEGPUL has reasonable performance. Copyright © 2016 Elsevier Ltd. All rights reserved.

Evidence for somatic gene conversion and deletion in bipolar disorder, Crohn's disease, coronary artery disease, hypertension, rheumatoid arthritis, type-1 diabetes, and type-2 diabetes.

PubMed

Ross, Kenneth Andrew

2011-02-03

During gene conversion, genetic information is transferred unidirectionally between highly homologous but non-allelic regions of DNA. While germ-line gene conversion has been implicated in the pathogenesis of some diseases, somatic gene conversion has remained technically difficult to investigate on a large scale. A novel analysis technique is proposed for detecting the signature of somatic gene conversion from SNP microarray data. The Wellcome Trust Case Control Consortium has gathered SNP microarray data for two control populations and cohorts for bipolar disorder (BD), cardiovascular disease (CAD), Crohn's disease (CD), hypertension (HT), rheumatoid arthritis (RA), type-1 diabetes (T1D) and type-2 diabetes (T2D). Using the new analysis technique, the seven disease cohorts are analyzed to identify cohort-specific SNPs at which conversion is predicted. The quality of the predictions is assessed by identifying known disease associations for genes in the homologous duplicons, and comparing the frequency of such associations with background rates. Of 28 disease/locus pairs meeting stringent conditions, 22 show various degrees of disease association, compared with only 8 of 70 in a mock study designed to measure the background association rate (P < 10-9). Additional candidate genes are identified using less stringent filtering conditions. In some cases, somatic deletions appear likely. RA has a distinctive pattern of events relative to other diseases. Similarities in patterns are apparent between BD and HT. The associations derived represent the first evidence that somatic gene conversion could be a significant causative factor in each of the seven diseases. The specific genes provide potential insights about disease mechanisms, and are strong candidates for further study.

Targeted sequencing identifies 91 neurodevelopmental disorder risk genes with autism and developmental disability biases

PubMed Central

Stessman, Holly A. F.; Xiong, Bo; Coe, Bradley P.; Wang, Tianyun; Hoekzema, Kendra; Fenckova, Michaela; Kvarnung, Malin; Gerdts, Jennifer; Trinh, Sandy; Cosemans, Nele; Vives, Laura; Lin, Janice; Turner, Tychele N.; Santen, Gijs; Ruivenkamp, Claudia; Kriek, Marjolein; van Haeringen, Arie; Aten, Emmelien; Friend, Kathryn; Liebelt, Jan; Barnett, Christopher; Haan, Eric; Shaw, Marie; Gecz, Jozef; Anderlid, Britt-Marie; Nordgren, Ann; Lindstrand, Anna; Schwartz, Charles; Kooy, R. Frank; Vandeweyer, Geert; Helsmoortel, Celine; Romano, Corrado; Alberti, Antonino; Vinci, Mirella; Avola, Emanuela; Giusto, Stefania; Courchesne, Eric; Pramparo, Tiziano; Pierce, Karen; Nalabolu, Srinivasa; Amaral, David; Scheffer, Ingrid E.; Delatycki, Martin B.; Lockhart, Paul J.; Hormozdiari, Fereydoun; Harich, Benjamin; Castells-Nobau, Anna; Xia, Kun; Peeters, Hilde; Nordenskjöld, Magnus; Schenck, Annette; Bernier, Raphael A.; Eichler, Evan E.

2017-01-01

Gene-disruptive mutations contribute to the biology of neurodevelopmental disorders (NDDs), but most pathogenic genes are not known. We sequenced 208 candidate genes from >11,730 patients and >2,867 controls. We report 91 genes with an excess of de novo mutations or private disruptive mutations in 5.7% of patients, including 38 novel NDD genes. Drosophila functional assays of a subset bolster their involvement in NDDs. We identify 25 genes that show a bias for autism versus intellectual disability and highlight a network associated with high-functioning autism (FSIQ>100). Clinical follow-up for NAA15, KMT5B, and ASH1L reveals novel syndromic and non-syndromic forms of disease. PMID:28191889

Identifying critical transitions and their leading biomolecular networks in complex diseases.

PubMed

Liu, Rui; Li, Meiyi; Liu, Zhi-Ping; Wu, Jiarui; Chen, Luonan; Aihara, Kazuyuki

2012-01-01

Identifying a critical transition and its leading biomolecular network during the initiation and progression of a complex disease is a challenging task, but holds the key to early diagnosis and further elucidation of the essential mechanisms of disease deterioration at the network level. In this study, we developed a novel computational method for identifying early-warning signals of the critical transition and its leading network during a disease progression, based on high-throughput data using a small number of samples. The leading network makes the first move from the normal state toward the disease state during a transition, and thus is causally related with disease-driving genes or networks. Specifically, we first define a state-transition-based local network entropy (SNE), and prove that SNE can serve as a general early-warning indicator of any imminent transitions, regardless of specific differences among systems. The effectiveness of this method was validated by functional analysis and experimental data.

SFM: A novel sequence-based fusion method for disease genes identification and prioritization.

PubMed

Yousef, Abdulaziz; Moghadam Charkari, Nasrollah

2015-10-21

The identification of disease genes from human genome is of great importance to improve diagnosis and treatment of disease. Several machine learning methods have been introduced to identify disease genes. However, these methods mostly differ in the prior knowledge used to construct the feature vector for each instance (gene), the ways of selecting negative data (non-disease genes) where there is no investigational approach to find them and the classification methods used to make the final decision. In this work, a novel Sequence-based fusion method (SFM) is proposed to identify disease genes. In this regard, unlike existing methods, instead of using a noisy and incomplete prior-knowledge, the amino acid sequence of the proteins which is universal data has been carried out to present the genes (proteins) into four different feature vectors. To select more likely negative data from candidate genes, the intersection set of four negative sets which are generated using distance approach is considered. Then, Decision Tree (C4.5) has been applied as a fusion method to combine the results of four independent state-of the-art predictors based on support vector machine (SVM) algorithm, and to make the final decision. The experimental results of the proposed method have been evaluated by some standard measures. The results indicate the precision, recall and F-measure of 82.6%, 85.6% and 84, respectively. These results confirm the efficiency and validity of the proposed method. Copyright © 2015 Elsevier Ltd. All rights reserved.

DRUMS: a human disease related unique gene mutation search engine.

PubMed

Li, Zuofeng; Liu, Xingnan; Wen, Jingran; Xu, Ye; Zhao, Xin; Li, Xuan; Liu, Lei; Zhang, Xiaoyan

2011-10-01

With the completion of the human genome project and the development of new methods for gene variant detection, the integration of mutation data and its phenotypic consequences has become more important than ever. Among all available resources, locus-specific databases (LSDBs) curate one or more specific genes' mutation data along with high-quality phenotypes. Although some genotype-phenotype data from LSDB have been integrated into central databases little effort has been made to integrate all these data by a search engine approach. In this work, we have developed disease related unique gene mutation search engine (DRUMS), a search engine for human disease related unique gene mutation as a convenient tool for biologists or physicians to retrieve gene variant and related phenotype information. Gene variant and phenotype information were stored in a gene-centred relational database. Moreover, the relationships between mutations and diseases were indexed by the uniform resource identifier from LSDB, or another central database. By querying DRUMS, users can access the most popular mutation databases under one interface. DRUMS could be treated as a domain specific search engine. By using web crawling, indexing, and searching technologies, it provides a competitively efficient interface for searching and retrieving mutation data and their relationships to diseases. The present system is freely accessible at http://www.scbit.org/glif/new/drums/index.html. © 2011 Wiley-Liss, Inc.

Network-Based Method for Identifying Co-Regeneration Genes in Bone, Dentin, Nerve and Vessel Tissues

PubMed Central

Pan, Hongying; Zhang, Yu-Hang; Feng, Kaiyan; Kong, XiangYin; Cai, Yu-Dong

2017-01-01

Bone and dental diseases are serious public health problems. Most current clinical treatments for these diseases can produce side effects. Regeneration is a promising therapy for bone and dental diseases, yielding natural tissue recovery with few side effects. Because soft tissues inside the bone and dentin are densely populated with nerves and vessels, the study of bone and dentin regeneration should also consider the co-regeneration of nerves and vessels. In this study, a network-based method to identify co-regeneration genes for bone, dentin, nerve and vessel was constructed based on an extensive network of protein–protein interactions. Three procedures were applied in the network-based method. The first procedure, searching, sought the shortest paths connecting regeneration genes of one tissue type with regeneration genes of other tissues, thereby extracting possible co-regeneration genes. The second procedure, testing, employed a permutation test to evaluate whether possible genes were false discoveries; these genes were excluded by the testing procedure. The last procedure, screening, employed two rules, the betweenness ratio rule and interaction score rule, to select the most essential genes. A total of seventeen genes were inferred by the method, which were deemed to contribute to co-regeneration of at least two tissues. All these seventeen genes were extensively discussed to validate the utility of the method. PMID:28974058

Network-Based Method for Identifying Co- Regeneration Genes in Bone, Dentin, Nerve and Vessel Tissues.

PubMed

Chen, Lei; Pan, Hongying; Zhang, Yu-Hang; Feng, Kaiyan; Kong, XiangYin; Huang, Tao; Cai, Yu-Dong

2017-10-02

Bone and dental diseases are serious public health problems. Most current clinical treatments for these diseases can produce side effects. Regeneration is a promising therapy for bone and dental diseases, yielding natural tissue recovery with few side effects. Because soft tissues inside the bone and dentin are densely populated with nerves and vessels, the study of bone and dentin regeneration should also consider the co-regeneration of nerves and vessels. In this study, a network-based method to identify co-regeneration genes for bone, dentin, nerve and vessel was constructed based on an extensive network of protein-protein interactions. Three procedures were applied in the network-based method. The first procedure, searching, sought the shortest paths connecting regeneration genes of one tissue type with regeneration genes of other tissues, thereby extracting possible co-regeneration genes. The second procedure, testing, employed a permutation test to evaluate whether possible genes were false discoveries; these genes were excluded by the testing procedure. The last procedure, screening, employed two rules, the betweenness ratio rule and interaction score rule, to select the most essential genes. A total of seventeen genes were inferred by the method, which were deemed to contribute to co-regeneration of at least two tissues. All these seventeen genes were extensively discussed to validate the utility of the method.

Genetic association analysis identifies variants associated with disease progression in primary sclerosing cholangitis.

PubMed

Alberts, Rudi; de Vries, Elisabeth M G; Goode, Elizabeth C; Jiang, Xiaojun; Sampaziotis, Fotis; Rombouts, Krista; Böttcher, Katrin; Folseraas, Trine; Weismüller, Tobias J; Mason, Andrew L; Wang, Weiwei; Alexander, Graeme; Alvaro, Domenico; Bergquist, Annika; Björkström, Niklas K; Beuers, Ulrich; Björnsson, Einar; Boberg, Kirsten Muri; Bowlus, Christopher L; Bragazzi, Maria C; Carbone, Marco; Chazouillères, Olivier; Cheung, Angela; Dalekos, Georgios; Eaton, John; Eksteen, Bertus; Ellinghaus, David; Färkkilä, Martti; Festen, Eleonora A M; Floreani, Annarosa; Franceschet, Irene; Gotthardt, Daniel Nils; Hirschfield, Gideon M; Hoek, Bart van; Holm, Kristian; Hohenester, Simon; Hov, Johannes Roksund; Imhann, Floris; Invernizzi, Pietro; Juran, Brian D; Lenzen, Henrike; Lieb, Wolfgang; Liu, Jimmy Z; Marschall, Hanns-Ulrich; Marzioni, Marco; Melum, Espen; Milkiewicz, Piotr; Müller, Tobias; Pares, Albert; Rupp, Christian; Rust, Christian; Sandford, Richard N; Schramm, Christoph; Schreiber, Stefan; Schrumpf, Erik; Silverberg, Mark S; Srivastava, Brijesh; Sterneck, Martina; Teufel, Andreas; Vallier, Ludovic; Verheij, Joanne; Vila, Arnau Vich; Vries, Boudewijn de; Zachou, Kalliopi; Chapman, Roger W; Manns, Michael P; Pinzani, Massimo; Rushbrook, Simon M; Lazaridis, Konstantinos N; Franke, Andre; Anderson, Carl A; Karlsen, Tom H; Ponsioen, Cyriel Y; Weersma, Rinse K

2017-08-04

Primary sclerosing cholangitis (PSC) is a genetically complex, inflammatory bile duct disease of largely unknown aetiology often leading to liver transplantation or death. Little is known about the genetic contribution to the severity and progression of PSC. The aim of this study is to identify genetic variants associated with PSC disease progression and development of complications. We collected standardised PSC subphenotypes in a large cohort of 3402 patients with PSC. After quality control, we combined 130 422 single nucleotide polymorphisms of all patients-obtained using the Illumina immunochip-with their disease subphenotypes. Using logistic regression and Cox proportional hazards models, we identified genetic variants associated with binary and time-to-event PSC subphenotypes. We identified genetic variant rs853974 to be associated with liver transplant-free survival (p=6.07×10 -9 ). Kaplan-Meier survival analysis showed a 50.9% (95% CI 41.5% to 59.5%) transplant-free survival for homozygous AA allele carriers of rs853974 compared with 72.8% (95% CI 69.6% to 75.7%) for GG carriers at 10 years after PSC diagnosis. For the candidate gene in the region, RSPO3 , we demonstrated expression in key liver-resident effector cells, such as human and murine cholangiocytes and human hepatic stellate cells. We present a large international PSC cohort, and report genetic loci associated with PSC disease progression. For liver transplant-free survival, we identified a genome-wide significant signal and demonstrated expression of the candidate gene RSPO3 in key liver-resident effector cells. This warrants further assessments of the role of this potential key PSC modifier gene. © 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.

Systematic Evaluation of Molecular Networks for Discovery of Disease Genes.

PubMed

Huang, Justin K; Carlin, Daniel E; Yu, Michael Ku; Zhang, Wei; Kreisberg, Jason F; Tamayo, Pablo; Ideker, Trey

2018-04-25

Gene networks are rapidly growing in size and number, raising the question of which networks are most appropriate for particular applications. Here, we evaluate 21 human genome-wide interaction networks for their ability to recover 446 disease gene sets identified through literature curation, gene expression profiling, or genome-wide association studies. While all networks have some ability to recover disease genes, we observe a wide range of performance with STRING, ConsensusPathDB, and GIANT networks having the best performance overall. A general tendency is that performance scales with network size, suggesting that new interaction discovery currently outweighs the detrimental effects of false positives. Correcting for size, we find that the DIP network provides the highest efficiency (value per interaction). Based on these results, we create a parsimonious composite network with both high efficiency and performance. This work provides a benchmark for selection of molecular networks in human disease research. Copyright © 2018 Elsevier Inc. All rights reserved.

Text mining and network analysis to find functional associations of genes in high altitude diseases.

PubMed

Bhasuran, Balu; Subramanian, Devika; Natarajan, Jeyakumar

2018-05-02

Travel to elevations above 2500 m is associated with the risk of developing one or more forms of acute altitude illness such as acute mountain sickness (AMS), high altitude cerebral edema (HACE) or high altitude pulmonary edema (HAPE). Our work aims to identify the functional association of genes involved in high altitude diseases. In this work we identified the gene networks responsible for high altitude diseases by using the principle of gene co-occurrence statistics from literature and network analysis. First, we mined the literature data from PubMed on high-altitude diseases, and extracted the co-occurring gene pairs. Next, based on their co-occurrence frequency, gene pairs were ranked. Finally, a gene association network was created using statistical measures to explore potential relationships. Network analysis results revealed that EPO, ACE, IL6 and TNF are the top five genes that were found to co-occur with 20 or more genes, while the association between EPAS1 and EGLN1 genes is strongly substantiated. The network constructed from this study proposes a large number of genes that work in-toto in high altitude conditions. Overall, the result provides a good reference for further study of the genetic relationships in high altitude diseases. Copyright © 2018 Elsevier Ltd. All rights reserved.

Expression screening of cancer/testis genes in prostate cancer identifies NR6A1 as a novel marker of disease progression and aggressiveness.

PubMed

Mathieu, Romain; Evrard, Bertrand; Fromont, Gaëlle; Rioux-Leclercq, Nathalie; Godet, Julie; Cathelineau, Xavier; Guillé, François; Primig, Michael; Chalmel, Frédéric

2013-07-01

Cancer/Testis (CT) genes are expressed in male gonads, repressed in most healthy somatic tissues and de-repressed in various somatic malignancies including prostate cancers (PCa). Because of their specific expression signature and their associations with tumor aggressiveness and poor outcomes, CT genes are considered to be useful biomarkers and they are also targets for the development of new anti-cancer immunotherapies. The aim of this study was to identify novel CT genes associated with hormone-sensitive prostate cancer (HSPC), and castration-resistant prostate cancer (CRPC). To identify novel CT genes we screened genes for which transcripts were detected by RNA profiling specifically in normal testis and in either HSPC or CRPC as compared to normal prostate and 44 other healthy tissues using GeneChips. The expression and clinicopathological significance of a promising candidate--NR6A1--was examined in HSPC, CRPC, and metastatic site samples using tissue microarrays. We report the identification of 98 genes detected in CRPC, HSPC and testicular samples but not in the normal controls. Among them, cellular levels of NR6A1 were found to be higher in HSPC compared to normal prostate and further increased in metastatic lesions and CRPC. Furthermore, increased NR6A1 immunoreactivity was significantly associated with a high Gleason score, advanced pT stage and cancer cell proliferation. Our results show that cellular levels of NR6A1 are correlated with disease progression in PCa. We suggest that this essential orphan nuclear receptor is a potential therapeutic target as well as a biomarker of PCa aggressiveness. Copyright © 2013 Wiley Periodicals, Inc.

Evidence for somatic gene conversion and deletion in bipolar disorder, Crohn's disease, coronary artery disease, hypertension, rheumatoid arthritis, type-1 diabetes, and type-2 diabetes

PubMed Central

2011-01-01

Background During gene conversion, genetic information is transferred unidirectionally between highly homologous but non-allelic regions of DNA. While germ-line gene conversion has been implicated in the pathogenesis of some diseases, somatic gene conversion has remained technically difficult to investigate on a large scale. Methods A novel analysis technique is proposed for detecting the signature of somatic gene conversion from SNP microarray data. The Wellcome Trust Case Control Consortium has gathered SNP microarray data for two control populations and cohorts for bipolar disorder (BD), cardiovascular disease (CAD), Crohn's disease (CD), hypertension (HT), rheumatoid arthritis (RA), type-1 diabetes (T1D) and type-2 diabetes (T2D). Using the new analysis technique, the seven disease cohorts are analyzed to identify cohort-specific SNPs at which conversion is predicted. The quality of the predictions is assessed by identifying known disease associations for genes in the homologous duplicons, and comparing the frequency of such associations with background rates. Results Of 28 disease/locus pairs meeting stringent conditions, 22 show various degrees of disease association, compared with only 8 of 70 in a mock study designed to measure the background association rate (P < 10-9). Additional candidate genes are identified using less stringent filtering conditions. In some cases, somatic deletions appear likely. RA has a distinctive pattern of events relative to other diseases. Similarities in patterns are apparent between BD and HT. Conclusions The associations derived represent the first evidence that somatic gene conversion could be a significant causative factor in each of the seven diseases. The specific genes provide potential insights about disease mechanisms, and are strong candidates for further study. Please see Commentary: http://www.biomedcentral.com/1741-7015/9/13/abstract. PMID:21291537

Constructing an integrated gene similarity network for the identification of disease genes.

PubMed

Tian, Zhen; Guo, Maozu; Wang, Chunyu; Xing, LinLin; Wang, Lei; Zhang, Yin

2017-09-20

Discovering novel genes that are involved human diseases is a challenging task in biomedical research. In recent years, several computational approaches have been proposed to prioritize candidate disease genes. Most of these methods are mainly based on protein-protein interaction (PPI) networks. However, since these PPI networks contain false positives and only cover less half of known human genes, their reliability and coverage are very low. Therefore, it is highly necessary to fuse multiple genomic data to construct a credible gene similarity network and then infer disease genes on the whole genomic scale. We proposed a novel method, named RWRB, to infer causal genes of interested diseases. First, we construct five individual gene (protein) similarity networks based on multiple genomic data of human genes. Then, an integrated gene similarity network (IGSN) is reconstructed based on similarity network fusion (SNF) method. Finally, we employee the random walk with restart algorithm on the phenotype-gene bilayer network, which combines phenotype similarity network, IGSN as well as phenotype-gene association network, to prioritize candidate disease genes. We investigate the effectiveness of RWRB through leave-one-out cross-validation methods in inferring phenotype-gene relationships. Results show that RWRB is more accurate than state-of-the-art methods on most evaluation metrics. Further analysis shows that the success of RWRB is benefited from IGSN which has a wider coverage and higher reliability comparing with current PPI networks. Moreover, we conduct a comprehensive case study for Alzheimer's disease and predict some novel disease genes that supported by literature. RWRB is an effective and reliable algorithm in prioritizing candidate disease genes on the genomic scale. Software and supplementary information are available at http://nclab.hit.edu.cn/~tianzhen/RWRB/ .

A Multiomics Approach to Identify Genes Associated with Childhood Asthma Risk and Morbidity.

PubMed

Forno, Erick; Wang, Ting; Yan, Qi; Brehm, John; Acosta-Perez, Edna; Colon-Semidey, Angel; Alvarez, Maria; Boutaoui, Nadia; Cloutier, Michelle M; Alcorn, John F; Canino, Glorisa; Chen, Wei; Celedón, Juan C

2017-10-01

Childhood asthma is a complex disease. In this study, we aim to identify genes associated with childhood asthma through a multiomics "vertical" approach that integrates multiple analytical steps using linear and logistic regression models. In a case-control study of childhood asthma in Puerto Ricans (n = 1,127), we used adjusted linear or logistic regression models to evaluate associations between several analytical steps of omics data, including genome-wide (GW) genotype data, GW methylation, GW expression profiling, cytokine levels, asthma-intermediate phenotypes, and asthma status. At each point, only the top genes/single-nucleotide polymorphisms/probes/cytokines were carried forward for subsequent analysis. In step 1, asthma modified the gene expression-protein level association for 1,645 genes; pathway analysis showed an enrichment of these genes in the cytokine signaling system (n = 269 genes). In steps 2-3, expression levels of 40 genes were associated with intermediate phenotypes (asthma onset age, forced expiratory volume in 1 second, exacerbations, eosinophil counts, and skin test reactivity); of those, methylation of seven genes was also associated with asthma. Of these seven candidate genes, IL5RA was also significant in analytical steps 4-8. We then measured plasma IL-5 receptor α levels, which were associated with asthma age of onset and moderate-severe exacerbations. In addition, in silico database analysis showed that several of our identified IL5RA single-nucleotide polymorphisms are associated with transcription factors related to asthma and atopy. This approach integrates several analytical steps and is able to identify biologically relevant asthma-related genes, such as IL5RA. It differs from other methods that rely on complex statistical models with various assumptions.

Linking genes to diseases with a SNPedia-Gene Wiki mashup

PubMed Central

2012-01-01

Background A variety of topic-focused wikis are used in the biomedical sciences to enable the mass-collaborative synthesis and distribution of diverse bodies of knowledge. To address complex problems such as defining the relationships between genes and disease, it is important to bring the knowledge from many different domains together. Here we show how advances in wiki technology and natural language processing can be used to automatically assemble ‘meta-wikis’ that present integrated views over the data collaboratively created in multiple source wikis. Results We produced a semantic meta-wiki called the Gene Wiki+ that automatically mirrors and integrates data from the Gene Wiki and SNPedia. The Gene Wiki+, available at (http://genewikiplus.org/), captures 8,047 distinct gene-disease relationships. SNPedia accounts for 4,149 of the gene-disease pairs, the Gene Wiki provides 4,377 and only 479 appear independently in both sources. All of this content is available to query and browse and is provided as linked open data. Conclusions Wikis contain increasing amounts of diverse, biological information useful for elucidating the connections between genes and disease. The Gene Wiki+ shows how wiki technology can be used in concert with natural language processing to provide integrated views over diverse underlying data sources. PMID:22541597

In-Silico Integration Approach to Identify a Key miRNA Regulating a Gene Network in Aggressive Prostate Cancer

PubMed Central

Colaprico, Antonio; Bontempi, Gianluca; Castiglioni, Isabella

2018-01-01

Like other cancer diseases, prostate cancer (PC) is caused by the accumulation of genetic alterations in the cells that drives malignant growth. These alterations are revealed by gene profiling and copy number alteration (CNA) analysis. Moreover, recent evidence suggests that also microRNAs have an important role in PC development. Despite efforts to profile PC, the alterations (gene, CNA, and miRNA) and biological processes that correlate with disease development and progression remain partially elusive. Many gene signatures proposed as diagnostic or prognostic tools in cancer poorly overlap. The identification of co-expressed genes, that are functionally related, can identify a core network of genes associated with PC with a better reproducibility. By combining different approaches, including the integration of mRNA expression profiles, CNAs, and miRNA expression levels, we identified a gene signature of four genes overlapping with other published gene signatures and able to distinguish, in silico, high Gleason-scored PC from normal human tissue, which was further enriched to 19 genes by gene co-expression analysis. From the analysis of miRNAs possibly regulating this network, we found that hsa-miR-153 was highly connected to the genes in the network. Our results identify a four-gene signature with diagnostic and prognostic value in PC and suggest an interesting gene network that could play a key regulatory role in PC development and progression. Furthermore, hsa-miR-153, controlling this network, could be a potential biomarker for theranostics in high Gleason-scored PC. PMID:29562723

Identification of Inherited Retinal Disease-Associated Genetic Variants in 11 Candidate Genes.

PubMed

Astuti, Galuh D N; van den Born, L Ingeborgh; Khan, M Imran; Hamel, Christian P; Bocquet, Béatrice; Manes, Gaël; Quinodoz, Mathieu; Ali, Manir; Toomes, Carmel; McKibbin, Martin; El-Asrag, Mohammed E; Haer-Wigman, Lonneke; Inglehearn, Chris F; Black, Graeme C M; Hoyng, Carel B; Cremers, Frans P M; Roosing, Susanne

2018-01-10

Inherited retinal diseases (IRDs) display an enormous genetic heterogeneity. Whole exome sequencing (WES) recently identified genes that were mutated in a small proportion of IRD cases. Consequently, finding a second case or family carrying pathogenic variants in the same candidate gene often is challenging. In this study, we searched for novel candidate IRD gene-associated variants in isolated IRD families, assessed their causality, and searched for novel genotype-phenotype correlations. Whole exome sequencing was performed in 11 probands affected with IRDs. Homozygosity mapping data was available for five cases. Variants with minor allele frequencies ≤ 0.5% in public databases were selected as candidate disease-causing variants. These variants were ranked based on their: (a) presence in a gene that was previously implicated in IRD; (b) minor allele frequency in the Exome Aggregation Consortium database (ExAC); (c) in silico pathogenicity assessment using the combined annotation dependent depletion (CADD) score; and (d) interaction of the corresponding protein with known IRD-associated proteins. Twelve unique variants were found in 11 different genes in 11 IRD probands. Novel autosomal recessive and dominant inheritance patterns were found for variants in Small Nuclear Ribonucleoprotein U5 Subunit 200 ( SNRNP200 ) and Zinc Finger Protein 513 ( ZNF513 ), respectively. Using our pathogenicity assessment, a variant in DEAH-Box Helicase 32 ( DHX32 ) was the top ranked novel candidate gene to be associated with IRDs, followed by eight medium and lower ranked candidate genes. The identification of candidate disease-associated sequence variants in 11 single families underscores the notion that the previously identified IRD-associated genes collectively carry > 90% of the defects implicated in IRDs. To identify multiple patients or families with variants in the same gene and thereby provide extra proof for pathogenicity, worldwide data sharing is needed.

Regulation of gene expression in the mammalian eye and its relevance to eye disease.

PubMed

Scheetz, Todd E; Kim, Kwang-Youn A; Swiderski, Ruth E; Philp, Alisdair R; Braun, Terry A; Knudtson, Kevin L; Dorrance, Anne M; DiBona, Gerald F; Huang, Jian; Casavant, Thomas L; Sheffield, Val C; Stone, Edwin M

2006-09-26

We used expression quantitative trait locus mapping in the laboratory rat (Rattus norvegicus) to gain a broad perspective of gene regulation in the mammalian eye and to identify genetic variation relevant to human eye disease. Of >31,000 gene probes represented on an Affymetrix expression microarray, 18,976 exhibited sufficient signal for reliable analysis and at least 2-fold variation in expression among 120 F(2) rats generated from an SR/JrHsd x SHRSP intercross. Genome-wide linkage analysis with 399 genetic markers revealed significant linkage with at least one marker for 1,300 probes (alpha = 0.001; estimated empirical false discovery rate = 2%). Both contiguous and noncontiguous loci were found to be important in regulating mammalian eye gene expression. We investigated one locus of each type in greater detail and identified putative transcription-altering variations in both cases. We found an inserted cREL binding sequence in the 5' flanking sequence of the Abca4 gene associated with an increased expression level of that gene, and we found a mutation of the gene encoding thyroid hormone receptor beta2 associated with a decreased expression level of the gene encoding short-wavelength sensitive opsin (Opn1sw). In addition to these positional studies, we performed a pairwise analysis of gene expression to identify genes that are regulated in a coordinated manner and used this approach to validate two previously undescribed genes involved in the human disease Bardet-Biedl syndrome. These data and analytical approaches can be used to facilitate the discovery of additional genes and regulatory elements involved in human eye disease.

Identifying potential maternal genes of Bombyx mori using digital gene expression profiling

PubMed Central

Xu, Pingzhen

2018-01-01

Maternal genes present in mature oocytes play a crucial role in the early development of silkworm. Although maternal genes have been widely studied in many other species, there has been limited research in Bombyx mori. High-throughput next generation sequencing provides a practical method for gene discovery on a genome-wide level. Herein, a transcriptome study was used to identify maternal-related genes from silkworm eggs. Unfertilized eggs from five different stages of early development were used to detect the changing situation of gene expression. The expressed genes showed different patterns over time. Seventy-six maternal genes were annotated according to homology analysis with Drosophila melanogaster. More than half of the differentially expressed maternal genes fell into four expression patterns, while the expression patterns showed a downward trend over time. The functional annotation of these material genes was mainly related to transcription factor activity, growth factor activity, nucleic acid binding, RNA binding, ATP binding, and ion binding. Additionally, twenty-two gene clusters including maternal genes were identified from 18 scaffolds. Altogether, we plotted a profile for the maternal genes of Bombyx mori using a digital gene expression profiling method. This will provide the basis for maternal-specific signature research and improve the understanding of the early development of silkworm. PMID:29462160

A Systems Approach Identifies Networks and Genes Linking Sleep and Stress: Implications for Neuropsychiatric Disorders

PubMed Central

Jiang, Peng; Scarpa, Joseph R.; Fitzpatrick, Karrie; Losic, Bojan; Gao, Vance D.; Hao, Ke; Summa, Keith C.; Yang, He S.; Zhang, Bin; Allada, Ravi; Vitaterna, Martha H.; Turek, Fred W.; Kasarskis, Andrew

2016-01-01

SUMMARY Sleep dysfunction and stress susceptibility are co-morbid complex traits, which often precede and predispose patients to a variety of neuropsychiatric diseases. Here, we demonstrate multi-level organizations of genetic landscape, candidate genes, and molecular networks associated with 328 stress and sleep traits in a chronically stressed population of 338 (C57BL/6J×A/J) F2 mice. We constructed striatal gene co-expression networks, revealing functionally and cell-type specific gene co-regulations important for stress and sleep. Using a composite ranking system, we identified network modules most relevant for 15 independent phenotypic categories, highlighting a mitochondria/synaptic module that links sleep and stress. The key network regulators of this module are overrepresented with genes implicated in neuropsychiatric diseases. Our work suggests the interplay between sleep, stress, and neuropathology emerge from genetic influences on gene expression and their collective organization through complex molecular networks, providing a framework to interrogate the mechanisms underlying sleep, stress susceptibility, and related neuropsychiatric disorders. PMID:25921536

Effect of gene polymorphisms on periodontal diseases

PubMed Central

Tarannum, Fouzia; Faizuddin, Mohamed

2012-01-01

Periodontal diseases are inflammatory diseases of supporting structures of the tooth. It results in the destruction of the supporting structures and most of the destructive processes involved are host derived. The processes leading to destruction and regeneration of the destroyed tissues are of great interest to both researchers and clinicians. The selective susceptibility of subjects for periodontitis has remained an enigma and wide varieties of risk factors have been implicated for the manifestation and progression of periodontitis. Genetic factors have been a new addition to the list of risk factors for periodontal diseases. With the availability of human genome sequence and the knowledge of the complement of the genes, it should be possible to identify the metabolic pathways involved in periodontal destruction and regeneration. Most forms of periodontitis represent a life-long account of interactions between the genome, behaviour, and environment. The current practical utility of genetic knowledge in periodontitis is limited. The information contained within the human genome can potentially lead to a better understanding of the control mechanisms modulating the production of inflammatory mediators as well as provides potential therapeutic targets for periodontal disease. Allelic variants at multiple gene loci probably influence periodontitis susceptibility. PMID:22754216

Regulatory network analysis of Epstein-Barr virus identifies functional modules and hub genes involved in infectious mononucleosis.

PubMed

Poorebrahim, Mansour; Salarian, Ali; Najafi, Saeideh; Abazari, Mohammad Foad; Aleagha, Maryam Nouri; Dadras, Mohammad Nasr; Jazayeri, Seyed Mohammad; Ataei, Atousa; Poortahmasebi, Vahdat

2017-05-01

Epstein-Barr virus (EBV) is the most common cause of infectious mononucleosis (IM) and establishes lifetime infection associated with a variety of cancers and autoimmune diseases. The aim of this study was to develop an integrative gene regulatory network (GRN) approach and overlying gene expression data to identify the representative subnetworks for IM and EBV latent infection (LI). After identifying differentially expressed genes (DEGs) in both IM and LI gene expression profiles, functional annotations were applied using gene ontology (GO) and BiNGO tools, and construction of GRNs, topological analysis and identification of modules were carried out using several plugins of Cytoscape. In parallel, a human-EBV GRN was generated using the Hu-Vir database for further analyses. Our analysis revealed that the majority of DEGs in both IM and LI were involved in cell-cycle and DNA repair processes. However, these genes showed a significant negative correlation in the IM and LI states. Furthermore, cyclin-dependent kinase 2 (CDK2) - a hub gene with the highest centrality score - appeared to be the key player in cell cycle regulation in IM disease. The most significant functional modules in the IM and LI states were involved in the regulation of the cell cycle and apoptosis, respectively. Human-EBV network analysis revealed several direct targets of EBV proteins during IM disease. Our study provides an important first report on the response to IM/LI EBV infection in humans. An important aspect of our data was the upregulation of genes associated with cell cycle progression and proliferation.

Translational informatics approach for identifying the functional molecular communicators linking coronary artery disease, infection and inflammation

PubMed Central

SHARMA, ANKIT; GHATGE, MADANKUMAR; MUNDKUR, LAKSHMI; VANGALA, RAJANI KANTH

2016-01-01

Translational informatics approaches are required for the integration of diverse and accumulating data to enable the administration of effective translational medicine specifically in complex diseases such as coronary artery disease (CAD). In the current study, a novel approach for elucidating the association between infection, inflammation and CAD was used. Genes for CAD were collected from the CAD-gene database and those for infection and inflammation were collected from the UniProt database. The cytomegalovirus (CMV)-induced genes were identified from the literature and the CAD-associated clinical phenotypes were obtained from the Unified Medical Language System. A total of 55 gene ontologies (GO) termed functional communicator ontologies were identifed in the gene sets linking clinical phenotypes in the diseasome network. The network topology analysis suggested that important functions including viral entry, cell adhesion, apoptosis, inflammatory and immune responses networked with clinical phenotypes. Microarray data was extracted from the Gene Expression Omnibus (dataset: GSE48060) for highly networked disease myocardial infarction. Further analysis of differentially expressed genes and their GO terms suggested that CMV infection may trigger a xenobiotic response, oxidative stress, inflammation and immune modulation. Notably, the current study identified γ-glutamyl transferase (GGT)-5 as a potential biomarker with an odds ratio of 1.947, which increased to 2.561 following the addition of CMV and CMV-neutralizing antibody (CMV-NA) titers. The C-statistics increased from 0.530 for conventional risk factors (CRFs) to 0.711 for GGT in combination with the above mentioned infections and CRFs. Therefore, the translational informatics approach used in the current study identified a potential molecular mechanism for CMV infection in CAD, and a potential biomarker for risk prediction. PMID:27035874

AN MHC class I immune evasion gene of Marek's disease virus

USDA-ARS?s Scientific Manuscript database

Marek's disease virus (MDV) is a widespread a-herpesvirus of chickens that causes T cell tumors. Acute, but not latent, MDV infection has previously been shown to lead to downregulation of cell-surface MHC class I (Virology 282:198–205 (2001)), but the gene(s) involved have not been identified. Here...

Microarray analysis to identify the similarities and differences of pathogenesis between aortic occlusive disease and abdominal aortic aneurysm.

PubMed

Wang, Guofu; Bi, Lechang; Wang, Gaofeng; Huang, Feilai; Lu, Mingjing; Zhu, Kai

2018-06-01

Objectives Expression profile of GSE57691 was analyzed to identify the similarities and differences between aortic occlusive disease and abdominal aortic aneurysm. Methods The expression profile of GSE57691 was downloaded from Gene Expression Omnibus database, including 20 small abdominal aortic aneurysm samples, 29 large abdominal aortic aneurysm samples, 9 aortic occlusive disease samples, and 10 control samples. Using the limma package in R, the differentially expressed genes were screened. Followed by enrichment analysis was performed for the differentially expressed genes using database for annotation, visualization, and integrated discovery online tool. Based on string online tool and Cytoscape software, protein-protein interaction network and module analyses were carried out. Moreover, integrated TF platform database and Cytoscape software were used for constructing transcriptional regulatory networks. Results As a result, 1757, 354, and 396 differentially expressed genes separately were identified in aortic occlusive disease, large abdominal aortic aneurysm, and small abdominal aortic aneurysm samples. UBB was significantly enriched in proteolysis related pathways with a high degree in three groups. SPARCL1 was another gene shared by these groups and regulated by NFIA, which had a high degree in transcriptional regulatory network. ACTB, a significant upregulated gene in abdominal aortic aneurysm samples, could be regulated by CLIC4, which was significantly enriched in cell motions. ACLY and NFIB were separately identified in aortic occlusive disease and small abdominal aortic aneurysm samples, and separately enriched in lipid metabolism and negative regulation of cell proliferation. Conclusions The downregulated UBB, NFIA, and SPARCL1 might play key roles in both aortic occlusive disease and abdominal aortic aneurysm, while the upregulated ACTB might only involve in abdominal aortic aneurysm. ACLY and NFIB were specifically involved in aortic occlusive

Mining pathway associations for disease-related pathway activity analysis based on gene expression and methylation data.

PubMed

Lee, Hyeonjeong; Shin, Miyoung

2017-01-01

The problem of discovering genetic markers as disease signatures is of great significance for the successful diagnosis, treatment, and prognosis of complex diseases. Even if many earlier studies worked on identifying disease markers from a variety of biological resources, they mostly focused on the markers of genes or gene-sets (i.e., pathways). However, these markers may not be enough to explain biological interactions between genetic variables that are related to diseases. Thus, in this study, our aim is to investigate distinctive associations among active pathways (i.e., pathway-sets) shown each in case and control samples which can be observed from gene expression and/or methylation data. The pathway-sets are obtained by identifying a set of associated pathways that are often active together over a significant number of class samples. For this purpose, gene expression or methylation profiles are first analyzed to identify significant (active) pathways via gene-set enrichment analysis. Then, regarding these active pathways, an association rule mining approach is applied to examine interesting pathway-sets in each class of samples (case or control). By doing so, the sets of associated pathways often working together in activity profiles are finally chosen as our distinctive signature of each class. The identified pathway-sets are aggregated into a pathway activity network (PAN), which facilitates the visualization of differential pathway associations between case and control samples. From our experiments with two publicly available datasets, we could find interesting PAN structures as the distinctive signatures of breast cancer and uterine leiomyoma cancer, respectively. Our pathway-set markers were shown to be superior or very comparable to other genetic markers (such as genes or gene-sets) in disease classification. Furthermore, the PAN structure, which can be constructed from the identified markers of pathway-sets, could provide deeper insights into

Systematic Evaluation of Molecular Networks for Discovery of Disease Genes. | Office of Cancer Genomics

Cancer.gov

Gene networks are rapidly growing in size and number, raising the question of which networks are most appropriate for particular applications. Here, we evaluate 21 human genome-wide interaction networks for their ability to recover 446 disease gene sets identified through literature curation, gene expression profiling, or genome-wide association studies. While all networks have some ability to recover disease genes, we observe a wide range of performance with STRING, ConsensusPathDB, and GIANT networks having the best performance overall.

Gene silencing-based disease resistance.

PubMed

Wassenegger, Michael

2002-12-01

The definition of a disease is fundamentally difficult, even if one considers only genetically based diseases. In its broadest sense, disease can be defined as any deviation from the norm that results in a physiological disadvantage. Natural selection ensures that the norm for any given species is constantly changing. In addition, some disadvantages are latent and might only manifest under certain environmental conditions. Conversely, an apparent disadvantage can carry a benefit, for example, the disease sickle-cell anemia that is an advantage in malarial areas. Because of the difficulties in giving disease a precise definition, in this review, gene silencing-based disease resistance will be restricted to the description of gene inactivation processes that contribute to maintain the physical fitness of an organism. In this sense, we are concerned with the elimination of invasive nucleic acid expressing. In numerous organisms, a variety of severe diseases are caused by the attack of invasive nucleic acids such as viruses and retroviral or transposable elements. Organisms have developed diverse mechanisms to defend themselves against such attack that include immune responses and apoptosis. Fungi, plants, invertebrates and vertebrates also enlist gene silencing systems to counteract the harmful effects of invasive nucleic acids. In particular, plants that lack interferon and immune responses have established efficient transcriptional and post-transcriptional gene silencing systems. In this review, we describe how plants defend against invasive nucleic acids and focus on the continual evolutionary battle between plants and viruses. In addition, the importance of controlling transposon activity is outlined. Finally, gene silencing-related mechanisms of genomic imprinting and X-chromosome inactivation are discussed in the context of disease resistance.

Extended exome sequencing identifies BACH2 as a novel major risk locus for Addison's disease.

PubMed

Eriksson, D; Bianchi, M; Landegren, N; Nordin, J; Dalin, F; Mathioudaki, A; Eriksson, G N; Hultin-Rosenberg, L; Dahlqvist, J; Zetterqvist, H; Karlsson, Å; Hallgren, Å; Farias, F H G; Murén, E; Ahlgren, K M; Lobell, A; Andersson, G; Tandre, K; Dahlqvist, S R; Söderkvist, P; Rönnblom, L; Hulting, A-L; Wahlberg, J; Ekwall, O; Dahlqvist, P; Meadows, J R S; Bensing, S; Lindblad-Toh, K; Kämpe, O; Pielberg, G R

2016-12-01

Autoimmune disease is one of the leading causes of morbidity and mortality worldwide. In Addison's disease, the adrenal glands are targeted by destructive autoimmunity. Despite being the most common cause of primary adrenal failure, little is known about its aetiology. To understand the genetic background of Addison's disease, we utilized the extensively characterized patients of the Swedish Addison Registry. We developed an extended exome capture array comprising a selected set of 1853 genes and their potential regulatory elements, for the purpose of sequencing 479 patients with Addison's disease and 1394 controls. We identified BACH2 (rs62408233-A, OR = 2.01 (1.71-2.37), P = 1.66 × 10 -15 , MAF 0.46/0.29 in cases/controls) as a novel gene associated with Addison's disease development. We also confirmed the previously known associations with the HLA complex. Whilst BACH2 has been previously reported to associate with organ-specific autoimmune diseases co-inherited with Addison's disease, we have identified BACH2 as a major risk locus in Addison's disease, independent of concomitant autoimmune diseases. Our results may enable future research towards preventive disease treatment. © 2016 The Authors. Journal of Internal Medicine published by John Wiley & Sons Ltd on behalf of Association for Publication of The Journal of Internal Medicine.

Gene Expression Correlated with Severe Asthma Characteristics Reveals Heterogeneous Mechanisms of Severe Disease.

PubMed

Modena, Brian D; Bleecker, Eugene R; Busse, William W; Erzurum, Serpil C; Gaston, Benjamin M; Jarjour, Nizar N; Meyers, Deborah A; Milosevic, Jadranka; Tedrow, John R; Wu, Wei; Kaminski, Naftali; Wenzel, Sally E

2017-06-01

Severe asthma (SA) is a heterogeneous disease with multiple molecular mechanisms. Gene expression studies of bronchial epithelial cells in individuals with asthma have provided biological insight and underscored possible mechanistic differences between individuals. Identify networks of genes reflective of underlying biological processes that define SA. Airway epithelial cell gene expression from 155 subjects with asthma and healthy control subjects in the Severe Asthma Research Program was analyzed by weighted gene coexpression network analysis to identify gene networks and profiles associated with SA and its specific characteristics (i.e., pulmonary function tests, quality of life scores, urgent healthcare use, and steroid use), which potentially identified underlying biological processes. A linear model analysis confirmed these findings while adjusting for potential confounders. Weighted gene coexpression network analysis constructed 64 gene network modules, including modules corresponding to T1 and T2 inflammation, neuronal function, cilia, epithelial growth, and repair mechanisms. Although no network selectively identified SA, genes in modules linked to epithelial growth and repair and neuronal function were markedly decreased in SA. Several hub genes of the epithelial growth and repair module were found located at the 17q12-21 locus, near a well-known asthma susceptibility locus. T2 genes increased with severity in those treated with corticosteroids but were also elevated in untreated, mild-to-moderate disease compared with healthy control subjects. T1 inflammation, especially when associated with increased T2 gene expression, was elevated in a subgroup of younger patients with SA. In this hypothesis-generating analysis, gene expression networks in relation to asthma severity provided potentially new insight into biological mechanisms associated with the development of SA and its phenotypes.

A 6-gene signature identifies four molecular subgroups of neuroblastoma

PubMed Central

2011-01-01

Background There are currently three postulated genomic subtypes of the childhood tumour neuroblastoma (NB); Type 1, Type 2A, and Type 2B. The most aggressive forms of NB are characterized by amplification of the oncogene MYCN (MNA) and low expression of the favourable marker NTRK1. Recently, mutations or high expression of the familial predisposition gene Anaplastic Lymphoma Kinase (ALK) was associated to unfavourable biology of sporadic NB. Also, various other genes have been linked to NB pathogenesis. Results The present study explores subgroup discrimination by gene expression profiling using three published microarray studies on NB (47 samples). Four distinct clusters were identified by Principal Components Analysis (PCA) in two separate data sets, which could be verified by an unsupervised hierarchical clustering in a third independent data set (101 NB samples) using a set of 74 discriminative genes. The expression signature of six NB-associated genes ALK, BIRC5, CCND1, MYCN, NTRK1, and PHOX2B, significantly discriminated the four clusters (p < 0.05, one-way ANOVA test). PCA clusters p1, p2, and p3 were found to correspond well to the postulated subtypes 1, 2A, and 2B, respectively. Remarkably, a fourth novel cluster was detected in all three independent data sets. This cluster comprised mainly 11q-deleted MNA-negative tumours with low expression of ALK, BIRC5, and PHOX2B, and was significantly associated with higher tumour stage, poor outcome and poor survival compared to the Type 1-corresponding favourable group (INSS stage 4 and/or dead of disease, p < 0.05, Fisher's exact test). Conclusions Based on expression profiling we have identified four molecular subgroups of neuroblastoma, which can be distinguished by a 6-gene signature. The fourth subgroup has not been described elsewhere, and efforts are currently made to further investigate this group's specific characteristics. PMID:21492432

Disease-aging network reveals significant roles of aging genes in connecting genetic diseases.

PubMed

Wang, Jiguang; Zhang, Shihua; Wang, Yong; Chen, Luonan; Zhang, Xiang-Sun

2009-09-01

One of the challenging problems in biology and medicine is exploring the underlying mechanisms of genetic diseases. Recent studies suggest that the relationship between genetic diseases and the aging process is important in understanding the molecular mechanisms of complex diseases. Although some intricate associations have been investigated for a long time, the studies are still in their early stages. In this paper, we construct a human disease-aging network to study the relationship among aging genes and genetic disease genes. Specifically, we integrate human protein-protein interactions (PPIs), disease-gene associations, aging-gene associations, and physiological system-based genetic disease classification information in a single graph-theoretic framework and find that (1) human disease genes are much closer to aging genes than expected by chance; and (2) diseases can be categorized into two types according to their relationships with aging. Type I diseases have their genes significantly close to aging genes, while type II diseases do not. Furthermore, we examine the topological characters of the disease-aging network from a systems perspective. Theoretical results reveal that the genes of type I diseases are in a central position of a PPI network while type II are not; (3) more importantly, we define an asymmetric closeness based on the PPI network to describe relationships between diseases, and find that aging genes make a significant contribution to associations among diseases, especially among type I diseases. In conclusion, the network-based study provides not only evidence for the intricate relationship between the aging process and genetic diseases, but also biological implications for prying into the nature of human diseases.

Sherlock: Detecting Gene-Disease Associations by Matching Patterns of Expression QTL and GWAS

PubMed Central

He, Xin; Fuller, Chris K.; Song, Yi; Meng, Qingying; Zhang, Bin; Yang, Xia; Li, Hao

2013-01-01

Genetic mapping of complex diseases to date depends on variations inside or close to the genes that perturb their activities. A strong body of evidence suggests that changes in gene expression play a key role in complex diseases and that numerous loci perturb gene expression in trans. The information in trans variants, however, has largely been ignored in the current analysis paradigm. Here we present a statistical framework for genetic mapping by utilizing collective information in both cis and trans variants. We reason that for a disease-associated gene, any genetic variation that perturbs its expression is also likely to influence the disease risk. Thus, the expression quantitative trait loci (eQTL) of the gene, which constitute a unique “genetic signature,” should overlap significantly with the set of loci associated with the disease. We translate this idea into a computational algorithm (named Sherlock) to search for gene-disease associations from GWASs, taking advantage of independent eQTL data. Application of this strategy to Crohn disease and type 2 diabetes predicts a number of genes with possible disease roles, including several predictions supported by solid experimental evidence. Importantly, predicted genes are often implicated by multiple trans eQTL with moderate associations. These genes are far from any GWAS association signals and thus cannot be identified from the GWAS alone. Our approach allows analysis of association data from a new perspective and is applicable to any complex phenotype. It is readily generalizable to molecular traits other than gene expression, such as metabolites, noncoding RNAs, and epigenetic modifications. PMID:23643380

The promise of disease gene discovery in South Asia

PubMed Central

Nakatsuka, Nathan; Moorjani, Priya; Rai, Niraj; Sarkar, Biswanath; Tandon, Arti; Patterson, Nick; Bhavani, Gandham SriLakshmi; Girisha, Katta Mohan; Mustak, Mohammed S; Srinivasan, Sudha; Kaushik, Amit; Vahab, Saadi Abdul; Jagadeesh, Sujatha M.; Satyamoorthy, Kapaettu; Singh, Lalji; Reich, David; Thangaraj, Kumarasamy

2017-01-01

The more than 1.5 billion people who live in South Asia are correctly viewed not as a single large population, but as many small endogamous groups. We assembled genome-wide data from over 2,800 individuals from over 260 distinct South Asian groups. We identify 81 unique groups, of which 14 have estimated census sizes of more than a million, that descend from founder events more extreme than those in Ashkenazi Jews and Finns, both of which have high rates of recessive disease due to founder events. We identify multiple examples of recessive diseases in South Asia that are the result of such founder events. This study highlights an under-appreciated opportunity for reducing disease burden among South Asians through the discovery of and testing for recessive disease genes. PMID:28714977

Analysis of neurodegenerative Mendelian genes in clinically diagnosed Alzheimer Disease

PubMed Central

Fernández, Maria Victoria; Kim, Jong Hun; Budde, John P.; Black, Kathleen; Medvedeva, Alexandra; Saef, Ben; Del-Aguila, Jorge; Ibañez, Laura; Dube, Umber; Harari, Oscar; Norton, Joanne; Chasse, Rachel; Morris, John C.; Goate, Alison

2017-01-01

Alzheimer disease (AD), Frontotemporal lobar degeneration (FTD), Amyotrophic lateral sclerosis (ALS) and Parkinson disease (PD) have a certain degree of clinical, pathological and molecular overlap. Previous studies indicate that causative mutations in AD and FTD/ALS genes can be found in clinical familial AD. We examined the presence of causative and low frequency coding variants in the AD, FTD, ALS and PD Mendelian genes, in over 450 families with clinical history of AD and over 11,710 sporadic cases and cognitive normal participants from North America. Known pathogenic mutations were found in 1.05% of the sporadic cases, in 0.69% of the cognitively normal participants and in 4.22% of the families. A trend towards enrichment, albeit non-significant, was observed for most AD, FTD and PD genes. Only PSEN1 and PINK1 showed consistent association with AD cases when we used ExAC as the control population. These results suggest that current study designs may contain heterogeneity and contamination of the control population, and that current statistical methods for the discovery of novel genes with real pathogenic variants in complex late onset diseases may be inadequate or underpowered to identify genes carrying pathogenic mutations. PMID:29091718

Analysis of neurodegenerative Mendelian genes in clinically diagnosed Alzheimer Disease.

PubMed

Fernández, Maria Victoria; Kim, Jong Hun; Budde, John P; Black, Kathleen; Medvedeva, Alexandra; Saef, Ben; Deming, Yuetiva; Del-Aguila, Jorge; Ibañez, Laura; Dube, Umber; Harari, Oscar; Norton, Joanne; Chasse, Rachel; Morris, John C; Goate, Alison; Cruchaga, Carlos

2017-11-01

Alzheimer disease (AD), Frontotemporal lobar degeneration (FTD), Amyotrophic lateral sclerosis (ALS) and Parkinson disease (PD) have a certain degree of clinical, pathological and molecular overlap. Previous studies indicate that causative mutations in AD and FTD/ALS genes can be found in clinical familial AD. We examined the presence of causative and low frequency coding variants in the AD, FTD, ALS and PD Mendelian genes, in over 450 families with clinical history of AD and over 11,710 sporadic cases and cognitive normal participants from North America. Known pathogenic mutations were found in 1.05% of the sporadic cases, in 0.69% of the cognitively normal participants and in 4.22% of the families. A trend towards enrichment, albeit non-significant, was observed for most AD, FTD and PD genes. Only PSEN1 and PINK1 showed consistent association with AD cases when we used ExAC as the control population. These results suggest that current study designs may contain heterogeneity and contamination of the control population, and that current statistical methods for the discovery of novel genes with real pathogenic variants in complex late onset diseases may be inadequate or underpowered to identify genes carrying pathogenic mutations.

The Porphyromonas gingivalis/Host Interactome Shows Enrichment in GWASdb Genes Related to Alzheimer's Disease, Diabetes and Cardiovascular Diseases

PubMed Central

Carter, Chris J.; France, James; Crean, StJohn; Singhrao, Sim K.

2017-01-01

Periodontal disease is of established etiology in which polymicrobial synergistic ecology has become dysbiotic under the influence of Porphyromonas gingivalis. Following breakdown of the host's protective oral tissue barriers, P. gingivalis migrates to developing inflammatory pathologies that associate with Alzheimer's disease (AD). Periodontal disease is a risk factor for cardiovascular disorders (CVD), type II diabetes mellitus (T2DM), AD and other chronic diseases, whilst T2DM exacerbates periodontitis. This study analyzed the relationship between the P. gingivalis/host interactome and the genes identified in genome-wide association studies (GWAS) for the aforementioned conditions using data from GWASdb (P < 1E-03) and, in some cases, from the NCBI/EBI GWAS database (P < 1E-05). Gene expression data from periodontitis or P. gingivalis microarray was compared to microarray datasets from the AD hippocampus and/or from carotid artery plaques. The results demonstrated that the host genes of the P. gingivalis interactome were significantly enriched in genes deposited in GWASdb genes related to cognitive disorders, AD and dementia, and its co-morbid conditions T2DM, obesity, and CVD. The P. gingivalis/host interactome was also enriched in GWAS genes from the more stringent NCBI-EBI database for AD, atherosclerosis and T2DM. The misregulated genes in periodontitis tissue or P. gingivalis infected macrophages also matched those in the AD hippocampus or atherosclerotic plaques. Together, these data suggest important gene/environment interactions between P. gingivalis and susceptibility genes or gene expression changes in conditions where periodontal disease is a contributory factor. PMID:29311898

The Porphyromonas gingivalis/Host Interactome Shows Enrichment in GWASdb Genes Related to Alzheimer's Disease, Diabetes and Cardiovascular Diseases.

PubMed

Carter, Chris J; France, James; Crean, StJohn; Singhrao, Sim K

2017-01-01

Periodontal disease is of established etiology in which polymicrobial synergistic ecology has become dysbiotic under the influence of Porphyromonas gingivalis . Following breakdown of the host's protective oral tissue barriers, P. gingivalis migrates to developing inflammatory pathologies that associate with Alzheimer's disease (AD). Periodontal disease is a risk factor for cardiovascular disorders (CVD), type II diabetes mellitus (T2DM), AD and other chronic diseases, whilst T2DM exacerbates periodontitis. This study analyzed the relationship between the P. gingivalis /host interactome and the genes identified in genome-wide association studies (GWAS) for the aforementioned conditions using data from GWASdb ( P < 1E-03) and, in some cases, from the NCBI/EBI GWAS database ( P < 1E-05). Gene expression data from periodontitis or P. gingivalis microarray was compared to microarray datasets from the AD hippocampus and/or from carotid artery plaques. The results demonstrated that the host genes of the P. gingivalis interactome were significantly enriched in genes deposited in GWASdb genes related to cognitive disorders, AD and dementia, and its co-morbid conditions T2DM, obesity, and CVD. The P. gingivalis /host interactome was also enriched in GWAS genes from the more stringent NCBI-EBI database for AD, atherosclerosis and T2DM. The misregulated genes in periodontitis tissue or P. gingivalis infected macrophages also matched those in the AD hippocampus or atherosclerotic plaques. Together, these data suggest important gene/environment interactions between P. gingivalis and susceptibility genes or gene expression changes in conditions where periodontal disease is a contributory factor.

Identification of susceptibility genes and genetic modifiers of human diseases

NASA Astrophysics Data System (ADS)

Abel, Kenneth; Kammerer, Stefan; Hoyal, Carolyn; Reneland, Rikard; Marnellos, George; Nelson, Matthew R.; Braun, Andreas

2005-03-01

The completion of the human genome sequence enables the discovery of genes involved in common human disorders. The successful identification of these genes is dependent on the availability of informative sample sets, validated marker panels, a high-throughput scoring technology, and a strategy for combining these resources. We have developed a universal platform technology based on mass spectrometry (MassARRAY) for analyzing nucleic acids with high precision and accuracy. To fuel this technology, we generated more than 100,000 validated assays for single nucleotide polymorphisms (SNPs) covering virtually all known and predicted human genes. We also established a large DNA sample bank comprised of more than 50,000 consented healthy and diseased individuals. This combination of reagents and technology allows the execution of large-scale genome-wide association studies. Taking advantage of MassARRAY"s capability for quantitative analysis of nucleic acids, allele frequencies are estimated in sample pools containing large numbers of individual DNAs. To compare pools as a first-pass "filtering" step is a tremendous advantage in throughput and cost over individual genotyping. We employed this approach in numerous genome-wide, hypothesis-free searches to identify genes associated with common complex diseases, such as breast cancer, osteoporosis, and osteoarthritis, and genes involved in quantitative traits like high density lipoproteins cholesterol (HDL-c) levels and central fat. Access to additional well-characterized patient samples through collaborations allows us to conduct replication studies that validate true disease genes. These discoveries will expand our understanding of genetic disease predisposition, and our ability for early diagnosis and determination of specific disease subtype or progression stage.

Regulation of gene expression in the mammalian eye and its relevance to eye disease

PubMed Central

Scheetz, Todd E.; Kim, Kwang-Youn A.; Swiderski, Ruth E.; Philp, Alisdair R.; Braun, Terry A.; Knudtson, Kevin L.; Dorrance, Anne M.; DiBona, Gerald F.; Huang, Jian; Casavant, Thomas L.; Sheffield, Val C.; Stone, Edwin M.

2006-01-01

We used expression quantitative trait locus mapping in the laboratory rat (Rattus norvegicus) to gain a broad perspective of gene regulation in the mammalian eye and to identify genetic variation relevant to human eye disease. Of >31,000 gene probes represented on an Affymetrix expression microarray, 18,976 exhibited sufficient signal for reliable analysis and at least 2-fold variation in expression among 120 F2 rats generated from an SR/JrHsd × SHRSP intercross. Genome-wide linkage analysis with 399 genetic markers revealed significant linkage with at least one marker for 1,300 probes (α = 0.001; estimated empirical false discovery rate = 2%). Both contiguous and noncontiguous loci were found to be important in regulating mammalian eye gene expression. We investigated one locus of each type in greater detail and identified putative transcription-altering variations in both cases. We found an inserted cREL binding sequence in the 5′ flanking sequence of the Abca4 gene associated with an increased expression level of that gene, and we found a mutation of the gene encoding thyroid hormone receptor β2 associated with a decreased expression level of the gene encoding short-wavelength sensitive opsin (Opn1sw). In addition to these positional studies, we performed a pairwise analysis of gene expression to identify genes that are regulated in a coordinated manner and used this approach to validate two previously undescribed genes involved in the human disease Bardet–Biedl syndrome. These data and analytical approaches can be used to facilitate the discovery of additional genes and regulatory elements involved in human eye disease. PMID:16983098

Drug repositioning for orphan genetic diseases through Conserved Anticoexpressed Gene Clusters (CAGCs)

PubMed Central

2013-01-01

Background The development of new therapies for orphan genetic diseases represents an extremely important medical and social challenge. Drug repositioning, i.e. finding new indications for approved drugs, could be one of the most cost- and time-effective strategies to cope with this problem, at least in a subset of cases. Therefore, many computational approaches based on the analysis of high throughput gene expression data have so far been proposed to reposition available drugs. However, most of these methods require gene expression profiles directly relevant to the pathologic conditions under study, such as those obtained from patient cells and/or from suitable experimental models. In this work we have developed a new approach for drug repositioning, based on identifying known drug targets showing conserved anti-correlated expression profiles with human disease genes, which is completely independent from the availability of ‘ad hoc’ gene expression data-sets. Results By analyzing available data, we provide evidence that the genes displaying conserved anti-correlation with drug targets are antagonistically modulated in their expression by treatment with the relevant drugs. We then identified clusters of genes associated to similar phenotypes and showing conserved anticorrelation with drug targets. On this basis, we generated a list of potential candidate drug-disease associations. Importantly, we show that some of the proposed associations are already supported by independent experimental evidence. Conclusions Our results support the hypothesis that the identification of gene clusters showing conserved anticorrelation with drug targets can be an effective method for drug repositioning and provide a wide list of new potential drug-disease associations for experimental validation. PMID:24088245

Co-fuse: a new class discovery analysis tool to identify and prioritize recurrent fusion genes from RNA-sequencing data.

PubMed

Paisitkriangkrai, Sakrapee; Quek, Kelly; Nievergall, Eva; Jabbour, Anissa; Zannettino, Andrew; Kok, Chung Hoow

2018-06-07

Recurrent oncogenic fusion genes play a critical role in the development of various cancers and diseases and provide, in some cases, excellent therapeutic targets. To date, analysis tools that can identify and compare recurrent fusion genes across multiple samples have not been available to researchers. To address this deficiency, we developed Co-occurrence Fusion (Co-fuse), a new and easy to use software tool that enables biologists to merge RNA-seq information, allowing them to identify recurrent fusion genes, without the need for exhaustive data processing. Notably, Co-fuse is based on pattern mining and statistical analysis which enables the identification of hidden patterns of recurrent fusion genes. In this report, we show that Co-fuse can be used to identify 2 distinct groups within a set of 49 leukemic cell lines based on their recurrent fusion genes: a multiple myeloma (MM) samples-enriched cluster and an acute myeloid leukemia (AML) samples-enriched cluster. Our experimental results further demonstrate that Co-fuse can identify known driver fusion genes (e.g., IGH-MYC, IGH-WHSC1) in MM, when compared to AML samples, indicating the potential of Co-fuse to aid the discovery of yet unknown driver fusion genes through cohort comparisons. Additionally, using a 272 primary glioma sample RNA-seq dataset, Co-fuse was able to validate recurrent fusion genes, further demonstrating the power of this analysis tool to identify recurrent fusion genes. Taken together, Co-fuse is a powerful new analysis tool that can be readily applied to large RNA-seq datasets, and may lead to the discovery of new disease subgroups and potentially new driver genes, for which, targeted therapies could be developed. The Co-fuse R source code is publicly available at https://github.com/sakrapee/co-fuse .

Coalitional game theory as a promising approach to identify candidate autism genes.

PubMed

Gupta, Anika; Sun, Min Woo; Paskov, Kelley Marie; Stockham, Nate Tyler; Jung, Jae-Yoon; Wall, Dennis Paul

2018-01-01

Despite mounting evidence for the strong role of genetics in the phenotypic manifestation of Autism Spectrum Disorder (ASD), the specific genes responsible for the variable forms of ASD remain undefined. ASD may be best explained by a combinatorial genetic model with varying epistatic interactions across many small effect mutations. Coalitional or cooperative game theory is a technique that studies the combined effects of groups of players, known as coalitions, seeking to identify players who tend to improve the performance--the relationship to a specific disease phenotype--of any coalition they join. This method has been previously shown to boost biologically informative signal in gene expression data but to-date has not been applied to the search for cooperative mutations among putative ASD genes. We describe our approach to highlight genes relevant to ASD using coalitional game theory on alteration data of 1,965 fully sequenced genomes from 756 multiplex families. Alterations were encoded into binary matrices for ASD (case) and unaffected (control) samples, indicating likely gene-disrupting, inherited mutations in altered genes. To determine individual gene contributions given an ASD phenotype, a "player" metric, referred to as the Shapley value, was calculated for each gene in the case and control cohorts. Sixty seven genes were found to have significantly elevated player scores and likely represent significant contributors to the genetic coordination underlying ASD. Using network and cross-study analysis, we found that these genes are involved in biological pathways known to be affected in the autism cases and that a subset directly interact with several genes known to have strong associations to autism. These findings suggest that coalitional game theory can be applied to large-scale genomic data to identify hidden yet influential players in complex polygenic disorders such as autism.

TOM: a web-based integrated approach for identification of candidate disease genes.

PubMed

Rossi, Simona; Masotti, Daniele; Nardini, Christine; Bonora, Elena; Romeo, Giovanni; Macii, Enrico; Benini, Luca; Volinia, Stefano

2006-07-01

The massive production of biological data by means of highly parallel devices like microarrays for gene expression has paved the way to new possible approaches in molecular genetics. Among them the possibility of inferring biological answers by querying large amounts of expression data. Based on this principle, we present here TOM, a web-based resource for the efficient extraction of candidate genes for hereditary diseases. The service requires the previous knowledge of at least another gene responsible for the disease and the linkage area, or else of two disease associated genetic intervals. The algorithm uses the information stored in public resources, including mapping, expression and functional databases. Given the queries, TOM will select and list one or more candidate genes. This approach allows the geneticist to bypass the costly and time consuming tracing of genetic markers through entire families and might improve the chance of identifying disease genes, particularly for rare diseases. We present here the tool and the results obtained on known benchmark and on hereditary predisposition to familial thyroid cancer. Our algorithm is available at http://www-micrel.deis.unibo.it/~tom/.

Common disease signatures from gene expression analysis in Huntington's disease human blood and brain.

PubMed

Mina, Eleni; van Roon-Mom, Willeke; Hettne, Kristina; van Zwet, Erik; Goeman, Jelle; Neri, Christian; A C 't Hoen, Peter; Mons, Barend; Roos, Marco

2016-08-01

Huntington's disease (HD) is a devastating brain disorder with no effective treatment or cure available. The scarcity of brain tissue makes it hard to study changes in the brain and impossible to perform longitudinal studies. However, peripheral pathology in HD suggests that it is possible to study the disease using peripheral tissue as a monitoring tool for disease progression and/or efficacy of novel therapies. In this study, we investigated if blood can be used to monitor disease severity and progression in brain. Since previous attempts using only gene expression proved unsuccessful, we compared blood and brain Huntington's disease signatures in a functional context. Microarray HD gene expression profiles from three brain regions were compared to the transcriptome of HD blood generated by next generation sequencing. The comparison was performed with a combination of weighted gene co-expression network analysis and literature based functional analysis (Concept Profile Analysis). Uniquely, our comparison of blood and brain datasets was not based on (the very limited) gene overlap but on the similarity between the gene annotations in four different semantic categories: "biological process", "cellular component", "molecular function" and "disease or syndrome". We identified signatures in HD blood reflecting a broad pathophysiological spectrum, including alterations in the immune response, sphingolipid biosynthetic processes, lipid transport, cell signaling, protein modification, spliceosome, RNA splicing, vesicle transport, cell signaling and synaptic transmission. Part of this spectrum was reminiscent of the brain pathology. The HD signatures in caudate nucleus and BA4 exhibited the highest similarity with blood, irrespective of the category of semantic annotations used. BA9 exhibited an intermediate similarity, while cerebellum had the least similarity. We present two signatures that were shared between blood and brain: immune response and spinocerebellar ataxias

Inductive matrix completion for predicting gene-disease associations.

PubMed

Natarajan, Nagarajan; Dhillon, Inderjit S

2014-06-15

Most existing methods for predicting causal disease genes rely on specific type of evidence, and are therefore limited in terms of applicability. More often than not, the type of evidence available for diseases varies-for example, we may know linked genes, keywords associated with the disease obtained by mining text, or co-occurrence of disease symptoms in patients. Similarly, the type of evidence available for genes varies-for example, specific microarray probes convey information only for certain sets of genes. In this article, we apply a novel matrix-completion method called Inductive Matrix Completion to the problem of predicting gene-disease associations; it combines multiple types of evidence (features) for diseases and genes to learn latent factors that explain the observed gene-disease associations. We construct features from different biological sources such as microarray expression data and disease-related textual data. A crucial advantage of the method is that it is inductive; it can be applied to diseases not seen at training time, unlike traditional matrix-completion approaches and network-based inference methods that are transductive. Comparison with state-of-the-art methods on diseases from the Online Mendelian Inheritance in Man (OMIM) database shows that the proposed approach is substantially better-it has close to one-in-four chance of recovering a true association in the top 100 predictions, compared to the recently proposed Catapult method (second best) that has <15% chance. We demonstrate that the inductive method is particularly effective for a query disease with no previously known gene associations, and for predicting novel genes, i.e. genes that are previously not linked to diseases. Thus the method is capable of predicting novel genes even for well-characterized diseases. We also validate the novelty of predictions by evaluating the method on recently reported OMIM associations and on associations recently reported in the literature

Republished review: Gene therapy for ocular diseases.

PubMed

Liu, Melissa M; Tuo, Jingsheng; Chan, Chi-Chao

2011-07-01

The eye is an easily accessible, highly compartmentalised and immune-privileged organ that offers unique advantages as a gene therapy target. Significant advancements have been made in understanding the genetic pathogenesis of ocular diseases, and gene replacement and gene silencing have been implicated as potentially efficacious therapies. Recent improvements have been made in the safety and specificity of vector-based ocular gene transfer methods. Proof-of-concept for vector-based gene therapies has also been established in several experimental models of human ocular diseases. After nearly two decades of ocular gene therapy research, preliminary successes are now being reported in phase 1 clinical trials for the treatment of Leber congenital amaurosis. This review describes current developments and future prospects for ocular gene therapy. Novel methods are being developed to enhance the performance and regulation of recombinant adeno-associated virus- and lentivirus-mediated ocular gene transfer. Gene therapy prospects have advanced for a variety of retinal disorders, including retinitis pigmentosa, retinoschisis, Stargardt disease and age-related macular degeneration. Advances have also been made using experimental models for non-retinal diseases, such as uveitis and glaucoma. These methodological advancements are critical for the implementation of additional gene-based therapies for human ocular diseases in the near future.

A Look to Future Directions in Gene Therapy Research for Monogenic Diseases

PubMed Central

Porteus, Matthew H; Connelly, Jon P; Pruett, Shondra M

2006-01-01

The concept of gene therapy has long appealed to biomedical researchers and clinicians because it promised to treat certain diseases at their origins. In the last several years, there have been several trials in which patients have benefited from gene therapy protocols. This progress, however, has revealed important problems, including the problem of insertional oncogenesis. In this review, which focuses on monogenic diseases, we discuss the problem of insertional oncogenesis and identify areas for future research, such as developing more quantitative assays for risk and efficacy, and ways of minimizing the genotoxic effects of gene therapy protocols, which will be important if gene therapy is to fulfill its conceptual promise. PMID:17009872

Common variants in Mendelian kidney disease genes and their association with renal function.

PubMed

Parsa, Afshin; Fuchsberger, Christian; Köttgen, Anna; O'Seaghdha, Conall M; Pattaro, Cristian; de Andrade, Mariza; Chasman, Daniel I; Teumer, Alexander; Endlich, Karlhans; Olden, Matthias; Chen, Ming-Huei; Tin, Adrienne; Kim, Young J; Taliun, Daniel; Li, Man; Feitosa, Mary; Gorski, Mathias; Yang, Qiong; Hundertmark, Claudia; Foster, Meredith C; Glazer, Nicole; Isaacs, Aaron; Rao, Madhumathi; Smith, Albert V; O'Connell, Jeffrey R; Struchalin, Maksim; Tanaka, Toshiko; Li, Guo; Hwang, Shih-Jen; Atkinson, Elizabeth J; Lohman, Kurt; Cornelis, Marilyn C; Johansson, Asa; Tönjes, Anke; Dehghan, Abbas; Couraki, Vincent; Holliday, Elizabeth G; Sorice, Rossella; Kutalik, Zoltan; Lehtimäki, Terho; Esko, Tõnu; Deshmukh, Harshal; Ulivi, Sheila; Chu, Audrey Y; Murgia, Federico; Trompet, Stella; Imboden, Medea; Kollerits, Barbara; Pistis, Giorgio; Harris, Tamara B; Launer, Lenore J; Aspelund, Thor; Eiriksdottir, Gudny; Mitchell, Braxton D; Boerwinkle, Eric; Schmidt, Helena; Hofer, Edith; Hu, Frank; Demirkan, Ayse; Oostra, Ben A; Turner, Stephen T; Ding, Jingzhong; Andrews, Jeanette S; Freedman, Barry I; Giulianini, Franco; Koenig, Wolfgang; Illig, Thomas; Döring, Angela; Wichmann, H-Erich; Zgaga, Lina; Zemunik, Tatijana; Boban, Mladen; Minelli, Cosetta; Wheeler, Heather E; Igl, Wilmar; Zaboli, Ghazal; Wild, Sarah H; Wright, Alan F; Campbell, Harry; Ellinghaus, David; Nöthlings, Ute; Jacobs, Gunnar; Biffar, Reiner; Ernst, Florian; Homuth, Georg; Kroemer, Heyo K; Nauck, Matthias; Stracke, Sylvia; Völker, Uwe; Völzke, Henry; Kovacs, Peter; Stumvoll, Michael; Mägi, Reedik; Hofman, Albert; Uitterlinden, Andre G; Rivadeneira, Fernando; Aulchenko, Yurii S; Polasek, Ozren; Hastie, Nick; Vitart, Veronique; Helmer, Catherine; Wang, Jie Jin; Stengel, Bénédicte; Ruggiero, Daniela; Bergmann, Sven; Kähönen, Mika; Viikari, Jorma; Nikopensius, Tiit; Province, Michael; Colhoun, Helen; Doney, Alex; Robino, Antonietta; Krämer, Bernhard K; Portas, Laura; Ford, Ian; Buckley, Brendan M; Adam, Martin; Thun, Gian-Andri; Paulweber, Bernhard; Haun, Margot; Sala, Cinzia; Mitchell, Paul; Ciullo, Marina; Vollenweider, Peter; Raitakari, Olli; Metspalu, Andres; Palmer, Colin; Gasparini, Paolo; Pirastu, Mario; Jukema, J Wouter; Probst-Hensch, Nicole M; Kronenberg, Florian; Toniolo, Daniela; Gudnason, Vilmundur; Shuldiner, Alan R; Coresh, Josef; Schmidt, Reinhold; Ferrucci, Luigi; van Duijn, Cornelia M; Borecki, Ingrid; Kardia, Sharon L R; Liu, Yongmei; Curhan, Gary C; Rudan, Igor; Gyllensten, Ulf; Wilson, James F; Franke, Andre; Pramstaller, Peter P; Rettig, Rainer; Prokopenko, Inga; Witteman, Jacqueline; Hayward, Caroline; Ridker, Paul M; Bochud, Murielle; Heid, Iris M; Siscovick, David S; Fox, Caroline S; Kao, W Linda; Böger, Carsten A

2013-12-01

Many common genetic variants identified by genome-wide association studies for complex traits map to genes previously linked to rare inherited Mendelian disorders. A systematic analysis of common single-nucleotide polymorphisms (SNPs) in genes responsible for Mendelian diseases with kidney phenotypes has not been performed. We thus developed a comprehensive database of genes for Mendelian kidney conditions and evaluated the association between common genetic variants within these genes and kidney function in the general population. Using the Online Mendelian Inheritance in Man database, we identified 731 unique disease entries related to specific renal search terms and confirmed a kidney phenotype in 218 of these entries, corresponding to mutations in 258 genes. We interrogated common SNPs (minor allele frequency >5%) within these genes for association with the estimated GFR in 74,354 European-ancestry participants from the CKDGen Consortium. However, the top four candidate SNPs (rs6433115 at LRP2, rs1050700 at TSC1, rs249942 at PALB2, and rs9827843 at ROBO2) did not achieve significance in a stage 2 meta-analysis performed in 56,246 additional independent individuals, indicating that these common SNPs are not associated with estimated GFR. The effect of less common or rare variants in these genes on kidney function in the general population and disease-specific cohorts requires further research.

Common Variants in Mendelian Kidney Disease Genes and Their Association with Renal Function

PubMed Central

Fuchsberger, Christian; Köttgen, Anna; O’Seaghdha, Conall M.; Pattaro, Cristian; de Andrade, Mariza; Chasman, Daniel I.; Teumer, Alexander; Endlich, Karlhans; Olden, Matthias; Chen, Ming-Huei; Tin, Adrienne; Kim, Young J.; Taliun, Daniel; Li, Man; Feitosa, Mary; Gorski, Mathias; Yang, Qiong; Hundertmark, Claudia; Foster, Meredith C.; Glazer, Nicole; Isaacs, Aaron; Rao, Madhumathi; Smith, Albert V.; O’Connell, Jeffrey R.; Struchalin, Maksim; Tanaka, Toshiko; Li, Guo; Hwang, Shih-Jen; Atkinson, Elizabeth J.; Lohman, Kurt; Cornelis, Marilyn C.; Johansson, Åsa; Tönjes, Anke; Dehghan, Abbas; Couraki, Vincent; Holliday, Elizabeth G.; Sorice, Rossella; Kutalik, Zoltan; Lehtimäki, Terho; Esko, Tõnu; Deshmukh, Harshal; Ulivi, Sheila; Chu, Audrey Y.; Murgia, Federico; Trompet, Stella; Imboden, Medea; Kollerits, Barbara; Pistis, Giorgio; Harris, Tamara B.; Launer, Lenore J.; Aspelund, Thor; Eiriksdottir, Gudny; Mitchell, Braxton D.; Boerwinkle, Eric; Schmidt, Helena; Hofer, Edith; Hu, Frank; Demirkan, Ayse; Oostra, Ben A.; Turner, Stephen T.; Ding, Jingzhong; Andrews, Jeanette S.; Freedman, Barry I.; Giulianini, Franco; Koenig, Wolfgang; Illig, Thomas; Döring, Angela; Wichmann, H.-Erich; Zgaga, Lina; Zemunik, Tatijana; Boban, Mladen; Minelli, Cosetta; Wheeler, Heather E.; Igl, Wilmar; Zaboli, Ghazal; Wild, Sarah H.; Wright, Alan F.; Campbell, Harry; Ellinghaus, David; Nöthlings, Ute; Jacobs, Gunnar; Biffar, Reiner; Ernst, Florian; Homuth, Georg; Kroemer, Heyo K.; Nauck, Matthias; Stracke, Sylvia; Völker, Uwe; Völzke, Henry; Kovacs, Peter; Stumvoll, Michael; Mägi, Reedik; Hofman, Albert; Uitterlinden, Andre G.; Rivadeneira, Fernando; Aulchenko, Yurii S.; Polasek, Ozren; Hastie, Nick; Vitart, Veronique; Helmer, Catherine; Wang, Jie Jin; Stengel, Bénédicte; Ruggiero, Daniela; Bergmann, Sven; Kähönen, Mika; Viikari, Jorma; Nikopensius, Tiit; Province, Michael; Colhoun, Helen; Doney, Alex; Robino, Antonietta; Krämer, Bernhard K.; Portas, Laura; Ford, Ian; Buckley, Brendan M.; Adam, Martin; Thun, Gian-Andri; Paulweber, Bernhard; Haun, Margot; Sala, Cinzia; Mitchell, Paul; Ciullo, Marina; Vollenweider, Peter; Raitakari, Olli; Metspalu, Andres; Palmer, Colin; Gasparini, Paolo; Pirastu, Mario; Jukema, J. Wouter; Probst-Hensch, Nicole M.; Kronenberg, Florian; Toniolo, Daniela; Gudnason, Vilmundur; Shuldiner, Alan R.; Coresh, Josef; Schmidt, Reinhold; Ferrucci, Luigi; van Duijn, Cornelia M.; Borecki, Ingrid; Kardia, Sharon L.R.; Liu, Yongmei; Curhan, Gary C.; Rudan, Igor; Gyllensten, Ulf; Wilson, James F.; Franke, Andre; Pramstaller, Peter P.; Rettig, Rainer; Prokopenko, Inga; Witteman, Jacqueline; Hayward, Caroline; Ridker, Paul M.; Bochud, Murielle; Heid, Iris M.; Siscovick, David S.; Fox, Caroline S.; Kao, W. Linda; Böger, Carsten A.

2013-01-01

Many common genetic variants identified by genome-wide association studies for complex traits map to genes previously linked to rare inherited Mendelian disorders. A systematic analysis of common single-nucleotide polymorphisms (SNPs) in genes responsible for Mendelian diseases with kidney phenotypes has not been performed. We thus developed a comprehensive database of genes for Mendelian kidney conditions and evaluated the association between common genetic variants within these genes and kidney function in the general population. Using the Online Mendelian Inheritance in Man database, we identified 731 unique disease entries related to specific renal search terms and confirmed a kidney phenotype in 218 of these entries, corresponding to mutations in 258 genes. We interrogated common SNPs (minor allele frequency >5%) within these genes for association with the estimated GFR in 74,354 European-ancestry participants from the CKDGen Consortium. However, the top four candidate SNPs (rs6433115 at LRP2, rs1050700 at TSC1, rs249942 at PALB2, and rs9827843 at ROBO2) did not achieve significance in a stage 2 meta-analysis performed in 56,246 additional independent individuals, indicating that these common SNPs are not associated with estimated GFR. The effect of less common or rare variants in these genes on kidney function in the general population and disease-specific cohorts requires further research. PMID:24029420

Gene Expression Correlated with Severe Asthma Characteristics Reveals Heterogeneous Mechanisms of Severe Disease

PubMed Central

Modena, Brian D.; Bleecker, Eugene R.; Busse, William W.; Erzurum, Serpil C.; Gaston, Benjamin M.; Jarjour, Nizar N.; Meyers, Deborah A.; Milosevic, Jadranka; Tedrow, John R.; Wu, Wei; Kaminski, Naftali

2017-01-01

Rationale: Severe asthma (SA) is a heterogeneous disease with multiple molecular mechanisms. Gene expression studies of bronchial epithelial cells in individuals with asthma have provided biological insight and underscored possible mechanistic differences between individuals. Objectives: Identify networks of genes reflective of underlying biological processes that define SA. Methods: Airway epithelial cell gene expression from 155 subjects with asthma and healthy control subjects in the Severe Asthma Research Program was analyzed by weighted gene coexpression network analysis to identify gene networks and profiles associated with SA and its specific characteristics (i.e., pulmonary function tests, quality of life scores, urgent healthcare use, and steroid use), which potentially identified underlying biological processes. A linear model analysis confirmed these findings while adjusting for potential confounders. Measurements and Main Results: Weighted gene coexpression network analysis constructed 64 gene network modules, including modules corresponding to T1 and T2 inflammation, neuronal function, cilia, epithelial growth, and repair mechanisms. Although no network selectively identified SA, genes in modules linked to epithelial growth and repair and neuronal function were markedly decreased in SA. Several hub genes of the epithelial growth and repair module were found located at the 17q12–21 locus, near a well-known asthma susceptibility locus. T2 genes increased with severity in those treated with corticosteroids but were also elevated in untreated, mild-to-moderate disease compared with healthy control subjects. T1 inflammation, especially when associated with increased T2 gene expression, was elevated in a subgroup of younger patients with SA. Conclusions: In this hypothesis-generating analysis, gene expression networks in relation to asthma severity provided potentially new insight into biological mechanisms associated with the development of SA and its

A novel method of predicting microRNA-disease associations based on microRNA, disease, gene and environment factor networks.

PubMed

Peng, Wei; Lan, Wei; Zhong, Jiancheng; Wang, Jianxin; Pan, Yi

2017-07-15

MicroRNAs have been reported to have close relationship with diseases due to their deregulation of the expression of target mRNAs. Detecting disease-related microRNAs is helpful for disease therapies. With the development of high throughput experimental techniques, a large number of microRNAs have been sequenced. However, it is still a big challenge to identify which microRNAs are related to diseases. Recently, researchers are interesting in combining multiple-biological information to identify the associations between microRNAs and diseases. In this work, we have proposed a novel method to predict the microRNA-disease associations based on four biological properties. They are microRNA, disease, gene and environment factor. Compared with previous methods, our method makes predictions not only by using the prior knowledge of associations among microRNAs, disease, environment factors and genes, but also by using the internal relationship among these biological properties. We constructed four biological networks based on the similarity of microRNAs, diseases, environment factors and genes, respectively. Then random walking was implemented on the four networks unequally. In the walking course, the associations can be inferred from the neighbors in the same networks. Meanwhile the association information can be transferred from one network to another. The results of experiment showed that our method achieved better prediction performance than other existing state-of-the-art methods. Copyright © 2017 Elsevier Inc. All rights reserved.

Transethnic genome-wide scan identifies novel Alzheimer's disease loci.

PubMed

Jun, Gyungah R; Chung, Jaeyoon; Mez, Jesse; Barber, Robert; Beecham, Gary W; Bennett, David A; Buxbaum, Joseph D; Byrd, Goldie S; Carrasquillo, Minerva M; Crane, Paul K; Cruchaga, Carlos; De Jager, Philip; Ertekin-Taner, Nilufer; Evans, Denis; Fallin, M Danielle; Foroud, Tatiana M; Friedland, Robert P; Goate, Alison M; Graff-Radford, Neill R; Hendrie, Hugh; Hall, Kathleen S; Hamilton-Nelson, Kara L; Inzelberg, Rivka; Kamboh, M Ilyas; Kauwe, John S K; Kukull, Walter A; Kunkle, Brian W; Kuwano, Ryozo; Larson, Eric B; Logue, Mark W; Manly, Jennifer J; Martin, Eden R; Montine, Thomas J; Mukherjee, Shubhabrata; Naj, Adam; Reiman, Eric M; Reitz, Christiane; Sherva, Richard; St George-Hyslop, Peter H; Thornton, Timothy; Younkin, Steven G; Vardarajan, Badri N; Wang, Li-San; Wendlund, Jens R; Winslow, Ashley R; Haines, Jonathan; Mayeux, Richard; Pericak-Vance, Margaret A; Schellenberg, Gerard; Lunetta, Kathryn L; Farrer, Lindsay A

2017-07-01

Genetic loci for Alzheimer's disease (AD) have been identified in whites of European ancestry, but the genetic architecture of AD among other populations is less understood. We conducted a transethnic genome-wide association study (GWAS) for late-onset AD in Stage 1 sample including whites of European Ancestry, African-Americans, Japanese, and Israeli-Arabs assembled by the Alzheimer's Disease Genetics Consortium. Suggestive results from Stage 1 from novel loci were followed up using summarized results in the International Genomics Alzheimer's Project GWAS dataset. Genome-wide significant (GWS) associations in single-nucleotide polymorphism (SNP)-based tests (P < 5 × 10 -8 ) were identified for SNPs in PFDN1/HBEGF, USP6NL/ECHDC3, and BZRAP1-AS1 and for the interaction of the (apolipoprotein E) APOE ε4 allele with NFIC SNP. We also obtained GWS evidence (P < 2.7 × 10 -6 ) for gene-based association in the total sample with a novel locus, TPBG (P = 1.8 × 10 -6 ). Our findings highlight the value of transethnic studies for identifying novel AD susceptibility loci. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Assessment of gene order computing methods for Alzheimer's disease

PubMed Central

2013-01-01

Background Computational genomics of Alzheimer disease (AD), the most common form of senile dementia, is a nascent field in AD research. The field includes AD gene clustering by computing gene order which generates higher quality gene clustering patterns than most other clustering methods. However, there are few available gene order computing methods such as Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Further, their performance in gene order computation using AD microarray data is not known. We thus set forth to evaluate the performances of current gene order computing methods with different distance formulas, and to identify additional features associated with gene order computation. Methods Using different distance formulas- Pearson distance and Euclidean distance, the squared Euclidean distance, and other conditions, gene orders were calculated by ACO and GA (including standard GA and improved GA) methods, respectively. The qualities of the gene orders were compared, and new features from the calculated gene orders were identified. Results Compared to the GA methods tested in this study, ACO fits the AD microarray data the best when calculating gene order. In addition, the following features were revealed: different distance formulas generated a different quality of gene order, and the commonly used Pearson distance was not the best distance formula when used with both GA and ACO methods for AD microarray data. Conclusion Compared with Pearson distance and Euclidean distance, the squared Euclidean distance generated the best quality gene order computed by GA and ACO methods. PMID:23369541

The NACP/synuclein gene: chromosomal assignment and screening for alterations in Alzheimer disease.

PubMed

Campion, D; Martin, C; Heilig, R; Charbonnier, F; Moreau, V; Flaman, J M; Petit, J L; Hannequin, D; Brice, A; Frebourg, T

1995-03-20

The major component of the vascular and plaque amyloid deposits in Alzheimer disease is the amyloid beta peptide (A beta). A second intrinsic component of amyloid, the NAC (non-A beta component of amyloid) peptide, has recently been identified, and its precursor protein was named NACP. A computer homology search allowed us to establish that the human NACP gene was homologous to the rat synuclein gene. We mapped the NACP/synuclein gene to chromosome 4 and cloned three alternatively spliced transcripts in lymphocytes derived from a normal subject. We analyzed by RT-PCR and direct sequencing the entire coding region of the NACP/synuclein gene in a group of patients with familial early onset Alzheimer disease. No mutation was found in 26 unrelated patients. Further studies are required to investigate the implication of the NACP/synuclein gene in Alzheimer disease.

Ocular findings associated with a Cys39Arg mutation in the Norrie disease gene.

PubMed

Joos, K M; Kimura, A E; Vandenburgh, K; Bartley, J A; Stone, E M

1994-12-01

To diagnose the carriers and noncarriers in a family affected with Norrie disease based on molecular analysis. Family members from three generations, including one affected patient, two obligate carriers, one carrier identified with linkage analysis, one noncarrier identified with linkage analysis, and one female family member with indeterminate carrier status, were examined clinically and electrophysiologically. Linkage analysis had previously failed to determine the carrier status of one female family member in the third generation. Blood samples were screened for mutations in the Norrie disease gene with single-strand conformation polymorphism analysis. The mutation was characterized by dideoxy-termination sequencing. Ophthalmoscopy and electroretinographic examination failed to detect the carrier state. The affected individuals and carriers in this family were found to have a transition from thymidine to cytosine in the first nucleotide of codon 39 of the Norrie disease gene, causing a cysteine-to-arginine mutation. Single-strand conformation polymorphism analysis identified a patient of indeterminate status (by linkage) to be a noncarrier of Norrie disease. Ophthalmoscopy and electroretinography could not identify carriers of this Norrie disease mutation. Single-strand conformation polymorphism analysis was more sensitive and specific than linkage analysis in identifying carriers in this family.

Transcriptome meta-analysis reveals common differential and global gene expression profiles in cystic fibrosis and other respiratory disorders and identifies CFTR regulators.

PubMed

Clarke, Luka A; Botelho, Hugo M; Sousa, Lisete; Falcao, Andre O; Amaral, Margarida D

2015-11-01

A meta-analysis of 13 independent microarray data sets was performed and gene expression profiles from cystic fibrosis (CF), similar disorders (COPD: chronic obstructive pulmonary disease, IPF: idiopathic pulmonary fibrosis, asthma), environmental conditions (smoking, epithelial injury), related cellular processes (epithelial differentiation/regeneration), and non-respiratory "control" conditions (schizophrenia, dieting), were compared. Similarity among differentially expressed (DE) gene lists was assessed using a permutation test, and a clustergram was constructed, identifying common gene markers. Global gene expression values were standardized using a novel approach, revealing that similarities between independent data sets run deeper than shared DE genes. Correlation of gene expression values identified putative gene regulators of the CF transmembrane conductance regulator (CFTR) gene, of potential therapeutic significance. Our study provides a novel perspective on CF epithelial gene expression in the context of other lung disorders and conditions, and highlights the contribution of differentiation/EMT and injury to gene signatures of respiratory disease. Copyright © 2015 Elsevier Inc. All rights reserved.

A Genome-wide CRISPR Screen in Toxoplasma Identifies Essential Apicomplexan Genes.

PubMed

Sidik, Saima M; Huet, Diego; Ganesan, Suresh M; Huynh, My-Hang; Wang, Tim; Nasamu, Armiyaw S; Thiru, Prathapan; Saeij, Jeroen P J; Carruthers, Vern B; Niles, Jacquin C; Lourido, Sebastian

2016-09-08

Apicomplexan parasites are leading causes of human and livestock diseases such as malaria and toxoplasmosis, yet most of their genes remain uncharacterized. Here, we present the first genome-wide genetic screen of an apicomplexan. We adapted CRISPR/Cas9 to assess the contribution of each gene from the parasite Toxoplasma gondii during infection of human fibroblasts. Our analysis defines ∼200 previously uncharacterized, fitness-conferring genes unique to the phylum, from which 16 were investigated, revealing essential functions during infection of human cells. Secondary screens identify as an invasion factor the claudin-like apicomplexan microneme protein (CLAMP), which resembles mammalian tight-junction proteins and localizes to secretory organelles, making it critical to the initiation of infection. CLAMP is present throughout sequenced apicomplexan genomes and is essential during the asexual stages of the malaria parasite Plasmodium falciparum. These results provide broad-based functional information on T. gondii genes and will facilitate future approaches to expand the horizon of antiparasitic interventions. Copyright © 2016 Elsevier Inc. All rights reserved.

Disease susceptibility genes shared by primary biliary cirrhosis and Crohn's disease in the Japanese population.

PubMed

Aiba, Yoshihiro; Yamazaki, Keiko; Nishida, Nao; Kawashima, Minae; Hitomi, Yuki; Nakamura, Hitomi; Komori, Atsumasa; Fuyuno, Yuta; Takahashi, Atsushi; Kawaguchi, Takaaki; Takazoe, Masakazu; Suzuki, Yasuo; Motoya, Satoshi; Matsui, Toshiyuki; Esaki, Motohiro; Matsumoto, Takayuki; Kubo, Michiaki; Tokunaga, Katsushi; Nakamura, Minoru

2015-09-01

We previously identified TNFSF15 as the most significant susceptibility gene at non-HLA loci for both primary biliary cirrhosis (PBC) and Crohn's diseases (CD) in the Japanese population. The aim of this study is to identify further disease susceptibility genes shared by PBC and CD. We selected 15 and 33 genetic variants that were significantly associated with PBC and CD, respectively, based on previously reported genome-wide association studies of the Japanese population. Next, an association study was independently performed for these genetic variants in CD (1312 CD patients and 3331 healthy controls) and PBC (1279 PBC patients and 1015 healthy controls) cohorts. Two CD susceptibility genes, ICOSLG rs2838519 and IL12B rs6556412, were also nominally associated with susceptibility to PBC (P=3.85 × 10(-2) and P=8.40 × 10(-3), respectively). Three PBC susceptibility genes, CXCR5 rs6421571, STAT4 rs7574865 and NFKB1 rs230534, were nominally associated with susceptibility to CD (P=2.82 × 10(-2), P=3.88 × 10(-2) and P=2.04 × 10(-2), respectively). The effect of ICOSLG and CXCR5 variants were concordant but the effect of STAT4, NFKB1 and IL12B variants were discordant for PBC and CD. TNFSF15 and ICOSLG-CXCR5 might constitute a shared pathogenic pathway in the development of PBC and CD in the Japanese population, whereas IL12B-STAT4-NFKB1 might constitute an opposite pathogenic pathway, reflecting the different balance between Th1 and Th17 in the two diseases.

Comparison of Expression Profiles in Ovarian Epithelium In Vivo and Ovarian Cancer Identifies Novel Candidate Genes Involved in Disease Pathogenesis

PubMed Central

Emmanuel, Catherine; Gava, Natalie; Kennedy, Catherine; Balleine, Rosemary L.; Sharma, Raghwa; Wain, Gerard; Brand, Alison; Hogg, Russell; Etemadmoghadam, Dariush; George, Joshy; Birrer, Michael J.; Clarke, Christine L.; Chenevix-Trench, Georgia; Bowtell, David D. L.; Harnett, Paul R.; deFazio, Anna

2011-01-01

Molecular events leading to epithelial ovarian cancer are poorly understood but ovulatory hormones and a high number of life-time ovulations with concomitant proliferation, apoptosis, and inflammation, increases risk. We identified genes that are regulated during the estrous cycle in murine ovarian surface epithelium and analysed these profiles to identify genes dysregulated in human ovarian cancer, using publically available datasets. We identified 338 genes that are regulated in murine ovarian surface epithelium during the estrous cycle and dysregulated in ovarian cancer. Six of seven candidates selected for immunohistochemical validation were expressed in serous ovarian cancer, inclusion cysts, ovarian surface epithelium and in fallopian tube epithelium. Most were overexpressed in ovarian cancer compared with ovarian surface epithelium and/or inclusion cysts (EpCAM, EZH2, BIRC5) although BIRC5 and EZH2 were expressed as highly in fallopian tube epithelium as in ovarian cancer. We prioritised the 338 genes for those likely to be important for ovarian cancer development by in silico analyses of copy number aberration and mutation using publically available datasets and identified genes with established roles in ovarian cancer as well as novel genes for which we have evidence for involvement in ovarian cancer. Chromosome segregation emerged as an important process in which genes from our list of 338 were over-represented including two (BUB1, NCAPD2) for which there is evidence of amplification and mutation. NUAK2, upregulated in ovarian surface epithelium in proestrus and predicted to have a driver mutation in ovarian cancer, was examined in a larger cohort of serous ovarian cancer where patients with lower NUAK2 expression had shorter overall survival. In conclusion, defining genes that are activated in normal epithelium in the course of ovulation that are also dysregulated in cancer has identified a number of pathways and novel candidate genes that may contribute

Resistance gene candidates identified by PCR with degenerate oligonucleotide primers map to clusters of resistance genes in lettuce.

PubMed

Shen, K A; Meyers, B C; Islam-Faridi, M N; Chin, D B; Stelly, D M; Michelmore, R W

1998-08-01

The recent cloning of genes for resistance against diverse pathogens from a variety of plants has revealed that many share conserved sequence motifs. This provides the possibility of isolating numerous additional resistance genes by polymerase chain reaction (PCR) with degenerate oligonucleotide primers. We amplified resistance gene candidates (RGCs) from lettuce with multiple combinations of primers with low degeneracy designed from motifs in the nucleotide binding sites (NBSs) of RPS2 of Arabidopsis thaliana and N of tobacco. Genomic DNA, cDNA, and bacterial artificial chromosome (BAC) clones were successfully used as templates. Four families of sequences were identified that had the same similarity to each other as to resistance genes from other species. The relationship of the amplified products to resistance genes was evaluated by several sequence and genetic criteria. The amplified products contained open reading frames with additional sequences characteristic of NBSs. Hybridization of RGCs to genomic DNA and to BAC clones revealed large numbers of related sequences. Genetic analysis demonstrated the existence of clustered multigene families for each of the four RGC sequences. This parallels classical genetic data on clustering of disease resistance genes. Two of the four families mapped to known clusters of resistance genes; these two families were therefore studied in greater detail. Additional evidence that these RGCs could be resistance genes was gained by the identification of leucine-rich repeat (LRR) regions in sequences adjoining the NBS similar to those in RPM1 and RPS2 of A. thaliana. Fluorescent in situ hybridization confirmed the clustered genomic distribution of these sequences. The use of PCR with degenerate oligonucleotide primers is therefore an efficient method to identify numerous RGCs in plants.

A systems approach identifies networks and genes linking sleep and stress: implications for neuropsychiatric disorders.

PubMed

Jiang, Peng; Scarpa, Joseph R; Fitzpatrick, Karrie; Losic, Bojan; Gao, Vance D; Hao, Ke; Summa, Keith C; Yang, He S; Zhang, Bin; Allada, Ravi; Vitaterna, Martha H; Turek, Fred W; Kasarskis, Andrew

2015-05-05

Sleep dysfunction and stress susceptibility are comorbid complex traits that often precede and predispose patients to a variety of neuropsychiatric diseases. Here, we demonstrate multilevel organizations of genetic landscape, candidate genes, and molecular networks associated with 328 stress and sleep traits in a chronically stressed population of 338 (C57BL/6J × A/J) F2 mice. We constructed striatal gene co-expression networks, revealing functionally and cell-type-specific gene co-regulations important for stress and sleep. Using a composite ranking system, we identified network modules most relevant for 15 independent phenotypic categories, highlighting a mitochondria/synaptic module that links sleep and stress. The key network regulators of this module are overrepresented with genes implicated in neuropsychiatric diseases. Our work suggests that the interplay among sleep, stress, and neuropathology emerges from genetic influences on gene expression and their collective organization through complex molecular networks, providing a framework for interrogating the mechanisms underlying sleep, stress susceptibility, and related neuropsychiatric disorders. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

A Heterogeneous Network Based Method for Identifying GBM-Related Genes by Integrating Multi-Dimensional Data.

PubMed

Chen Peng; Ao Li

2017-01-01

The emergence of multi-dimensional data offers opportunities for more comprehensive analysis of the molecular characteristics of human diseases and therefore improving diagnosis, treatment, and prevention. In this study, we proposed a heterogeneous network based method by integrating multi-dimensional data (HNMD) to identify GBM-related genes. The novelty of the method lies in that the multi-dimensional data of GBM from TCGA dataset that provide comprehensive information of genes, are combined with protein-protein interactions to construct a weighted heterogeneous network, which reflects both the general and disease-specific relationships between genes. In addition, a propagation algorithm with resistance is introduced to precisely score and rank GBM-related genes. The results of comprehensive performance evaluation show that the proposed method significantly outperforms the network based methods with single-dimensional data and other existing approaches. Subsequent analysis of the top ranked genes suggests they may be functionally implicated in GBM, which further corroborates the superiority of the proposed method. The source code and the results of HNMD can be downloaded from the following URL: http://bioinformatics.ustc.edu.cn/hnmd/ .

Diametrical clustering for identifying anti-correlated gene clusters.

PubMed

Dhillon, Inderjit S; Marcotte, Edward M; Roshan, Usman

2003-09-01

Clustering genes based upon their expression patterns allows us to predict gene function. Most existing clustering algorithms cluster genes together when their expression patterns show high positive correlation. However, it has been observed that genes whose expression patterns are strongly anti-correlated can also be functionally similar. Biologically, this is not unintuitive-genes responding to the same stimuli, regardless of the nature of the response, are more likely to operate in the same pathways. We present a new diametrical clustering algorithm that explicitly identifies anti-correlated clusters of genes. Our algorithm proceeds by iteratively (i). re-partitioning the genes and (ii). computing the dominant singular vector of each gene cluster; each singular vector serving as the prototype of a 'diametric' cluster. We empirically show the effectiveness of the algorithm in identifying diametrical or anti-correlated clusters. Testing the algorithm on yeast cell cycle data, fibroblast gene expression data, and DNA microarray data from yeast mutants reveals that opposed cellular pathways can be discovered with this method. We present systems whose mRNA expression patterns, and likely their functions, oppose the yeast ribosome and proteosome, along with evidence for the inverse transcriptional regulation of a number of cellular systems.

Current Status of Gene Therapy for Inherited Lung Diseases

PubMed Central

Driskell, Ryan R.; Engelhardt, John F.

2007-01-01

Gene therapy as a treatment modality for pulmonary disorders has attracted significant interest over the past decade. Since the initiation of the first clinical trials for cystic fibrosis lung disease using recombinant adenovirus in the early 1990s, the field has encountered numerous obstacles including vector inflammation, inefficient delivery, and vector production. Despite these obstacles, enthusiasm for lung gene therapy remains high. In part, this enthusiasm is fueled through the diligence of numerous researchers whose studies continue to reveal great potential of new gene transfer vectors that demonstrate increased tropism for airway epithelia. Several newly identified serotypes of adeno-associated virus have demonstrated substantial promise in animal models and will likely surface soon in clinical trials. Furthermore, an increased understanding of vector biology has also led to the development of new technologies to enhance the efficiency and selectivity of gene delivery to the lung. Although the promise of gene therapy to the lung has yet to be realized, the recent concentrated efforts in the field that focus on the basic virology of vector development will undoubtedly reap great rewards over the next decade in treating lung diseases. PMID:12524461

Identifying Future Disease Hot Spots: Infectious Disease Vulnerability Index.

PubMed

Moore, Melinda; Gelfeld, Bill; Okunogbe, Adeyemi; Paul, Christopher

2017-06-01

Recent high-profile outbreaks, such as Ebola and Zika, have illustrated the transnational nature of infectious diseases. Countries that are most vulnerable to such outbreaks might be higher priorities for technical support. RAND created the Infectious Disease Vulnerability Index to help U.S. government and international agencies identify these countries and thereby inform programming to preemptively help mitigate the spread and effects of potential transnational outbreaks. The authors employed a rigorous methodology to identify the countries most vulnerable to disease outbreaks. They conducted a comprehensive review of relevant literature to identify factors influencing infectious disease vulnerability. Using widely available data, the authors created an index for identifying potentially vulnerable countries and then ranked countries by overall vulnerability score. Policymakers should focus on the 25 most-vulnerable countries with an eye toward a potential "disease belt" in the Sahel region of Africa. The infectious disease vulnerability scores for several countries were better than what would have been predicted on the basis of economic status alone. This suggests that low-income countries can overcome economic challenges and become more resilient to public health challenges, such as infectious disease outbreaks.

FORGE Canada Consortium: outcomes of a 2-year national rare-disease gene-discovery project.

PubMed

Beaulieu, Chandree L; Majewski, Jacek; Schwartzentruber, Jeremy; Samuels, Mark E; Fernandez, Bridget A; Bernier, Francois P; Brudno, Michael; Knoppers, Bartha; Marcadier, Janet; Dyment, David; Adam, Shelin; Bulman, Dennis E; Jones, Steve J M; Avard, Denise; Nguyen, Minh Thu; Rousseau, Francois; Marshall, Christian; Wintle, Richard F; Shen, Yaoqing; Scherer, Stephen W; Friedman, Jan M; Michaud, Jacques L; Boycott, Kym M

2014-06-05

Inherited monogenic disease has an enormous impact on the well-being of children and their families. Over half of the children living with one of these conditions are without a molecular diagnosis because of the rarity of the disease, the marked clinical heterogeneity, and the reality that there are thousands of rare diseases for which causative mutations have yet to be identified. It is in this context that in 2010 a Canadian consortium was formed to rapidly identify mutations causing a wide spectrum of pediatric-onset rare diseases by using whole-exome sequencing. The FORGE (Finding of Rare Disease Genes) Canada Consortium brought together clinicians and scientists from 21 genetics centers and three science and technology innovation centers from across Canada. From nation-wide requests for proposals, 264 disorders were selected for study from the 371 submitted; disease-causing variants (including in 67 genes not previously associated with human disease; 41 of these have been genetically or functionally validated, and 26 are currently under study) were identified for 146 disorders over a 2-year period. Here, we present our experience with four strategies employed for gene discovery and discuss FORGE's impact in a number of realms, from clinical diagnostics to the broadening of the phenotypic spectrum of many diseases to the biological insight gained into both disease states and normal human development. Lastly, on the basis of this experience, we discuss the way forward for rare-disease genetic discovery both in Canada and internationally. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma

PubMed Central

Sharma, Amitabh; Menche, Jörg; Huang, C. Chris; Ort, Tatiana; Zhou, Xiaobo; Kitsak, Maksim; Sahni, Nidhi; Thibault, Derek; Voung, Linh; Guo, Feng; Ghiassian, Susan Dina; Gulbahce, Natali; Baribaud, Frédéric; Tocker, Joel; Dobrin, Radu; Barnathan, Elliot; Liu, Hao; Panettieri, Reynold A.; Tantisira, Kelan G.; Qiu, Weiliang; Raby, Benjamin A.; Silverman, Edwin K.; Vidal, Marc; Weiss, Scott T.; Barabási, Albert-László

2015-01-01

Recent advances in genetics have spurred rapid progress towards the systematic identification of genes involved in complex diseases. Still, the detailed understanding of the molecular and physiological mechanisms through which these genes affect disease phenotypes remains a major challenge. Here, we identify the asthma disease module, i.e. the local neighborhood of the interactome whose perturbation is associated with asthma, and validate it for functional and pathophysiological relevance, using both computational and experimental approaches. We find that the asthma disease module is enriched with modest GWAS P-values against the background of random variation, and with differentially expressed genes from normal and asthmatic fibroblast cells treated with an asthma-specific drug. The asthma module also contains immune response mechanisms that are shared with other immune-related disease modules. Further, using diverse omics (genomics, gene-expression, drug response) data, we identify the GAB1 signaling pathway as an important novel modulator in asthma. The wiring diagram of the uncovered asthma module suggests a relatively close link between GAB1 and glucocorticoids (GCs), which we experimentally validate, observing an increase in the level of GAB1 after GC treatment in BEAS-2B bronchial epithelial cells. The siRNA knockdown of GAB1 in the BEAS-2B cell line resulted in a decrease in the NFkB level, suggesting a novel regulatory path of the pro-inflammatory factor NFkB by GAB1 in asthma. PMID:25586491

Utilization of gene mapping and candidate gene mutation screening for diagnosing clinically equivocal conditions: a Norrie disease case study.

PubMed

Chini, Vasiliki; Stambouli, Danai; Nedelea, Florina Mihaela; Filipescu, George Alexandru; Mina, Diana; Kambouris, Marios; El-Shantil, Hatem

2014-06-01

Prenatal diagnosis was requested for an undiagnosed eye disease showing X-linked inheritance in a family. No medical records existed for the affected family members. Mapping of the X chromosome and candidate gene mutation screening identified a c.C267A[p.F89L] mutation in NPD previously described as possibly causing Norrie disease. The detection of the c.C267A[p.F89L] variant in another unrelated family confirms the pathogenic nature of the mutation for the Norrie disease phenotype. Gene mapping, haplotype analysis, and candidate gene screening have been previously utilized in research applications but were applied here in a diagnostic setting due to the scarcity of available clinical information. The clinical diagnosis and mutation identification were critical for providing proper genetic counseling and prenatal diagnosis for this family.

Methods to identify and analyze gene products involved in neuronal intracellular transport using Drosophila

PubMed Central

Neisch, Amanda L.; Avery, Adam W.; Machame, James B.; Li, Min-gang; Hays, Thomas S.

2017-01-01

Proper neuronal function critically depends on efficient intracellular transport and disruption of transport leads to neurodegeneration. Molecular pathways that support or regulate neuronal transport are not fully understood. A greater understanding of these pathways will help reveal the pathological mechanisms underlying disease. Drosophila melanogaster is the premier model system for performing large-scale genetic functional screens. Here we describe methods to carry out primary and secondary genetic screens in Drosophila aimed at identifying novel gene products and pathways that impact neuronal intracellular transport. These screens are performed using whole animal or live cell imaging of intact neural tissue to ensure integrity of neurons and their cellular environment. The primary screen is used to identify gross defects in neuronal function indicative of a disruption in microtubule-based transport. The secondary screens, conducted in both motoneurons and dendritic arborization neurons, will confirm the function of candidate gene products in intracellular transport. Together, the methodologies described here will support labs interested in identifying and characterizing gene products that alter intracellular transport in Drosophila. PMID:26794520

X-linked juvenile retinoschisis: mutations at the retinoschisis and Norrie disease gene loci?

PubMed

Hiraoka, M; Rossi, F; Trese, M T; Shastry, B S

2001-01-01

Juvenile retinoschisis (RS) and Norrie disease (ND) are X-linked recessive retinal disorders. Both disorders, in the majority of cases, are monogenic and are caused by mutations in the RS and ND genes, respectively. Here we report the identification of a family in which mutations in both the RS and ND genes are segregating with RS pathology. Although the mutations identified in this report were not functionally characterized with regard to their pathogenicity, it is likely that both of them are involved in RS pathology in the family analyzed. This suggests the complexity and digenic nature of monogenic human disorders in some cases. If this proves to be a widespread problem, it will complicate the strategies used to identify the genes involved in diseases and to develop methods for intervention.

Meta-analytic framework for sparse K-means to identify disease subtypes in multiple transcriptomic studies

PubMed Central

Huo, Zhiguang; Ding, Ying; Liu, Silvia; Oesterreich, Steffi; Tseng, George

2016-01-01

Disease phenotyping by omics data has become a popular approach that potentially can lead to better personalized treatment. Identifying disease subtypes via unsupervised machine learning is the first step towards this goal. In this paper, we extend a sparse K-means method towards a meta-analytic framework to identify novel disease subtypes when expression profiles of multiple cohorts are available. The lasso regularization and meta-analysis identify a unique set of gene features for subtype characterization. An additional pattern matching reward function guarantees consistent subtype signatures across studies. The method was evaluated by simulations and leukemia and breast cancer data sets. The identified disease subtypes from meta-analysis were characterized with improved accuracy and stability compared to single study analysis. The breast cancer model was applied to an independent METABRIC dataset and generated improved survival difference between subtypes. These results provide a basis for diagnosis and development of targeted treatments for disease subgroups. PMID:27330233

Meta-analytic framework for sparse K-means to identify disease subtypes in multiple transcriptomic studies.

PubMed

Huo, Zhiguang; Ding, Ying; Liu, Silvia; Oesterreich, Steffi; Tseng, George

Disease phenotyping by omics data has become a popular approach that potentially can lead to better personalized treatment. Identifying disease subtypes via unsupervised machine learning is the first step towards this goal. In this paper, we extend a sparse K -means method towards a meta-analytic framework to identify novel disease subtypes when expression profiles of multiple cohorts are available. The lasso regularization and meta-analysis identify a unique set of gene features for subtype characterization. An additional pattern matching reward function guarantees consistent subtype signatures across studies. The method was evaluated by simulations and leukemia and breast cancer data sets. The identified disease subtypes from meta-analysis were characterized with improved accuracy and stability compared to single study analysis. The breast cancer model was applied to an independent METABRIC dataset and generated improved survival difference between subtypes. These results provide a basis for diagnosis and development of targeted treatments for disease subgroups.

Mutations in the Norrie disease gene.

PubMed

Schuback, D E; Chen, Z Y; Craig, I W; Breakefield, X O; Sims, K B

1995-01-01

We report our experience to date in mutation identification in the Norrie disease (ND) gene. We carried out mutational analysis in 26 kindreds in an attempt to identify regions presumed critical to protein function and potentially correlated with generation of the disease phenotype. All coding exons, as well as noncoding regions of exons 1 and 2, 636 nucleotides in the noncoding region of exon 3, and 197 nucleotides of 5' flanking sequence, were analyzed for single-strand conformation polymorphisms (SSCP) by polymerase chain reaction (PCR) amplification of genomic DNA. DNA fragments that showed altered SSCP band mobilities were sequenced to locate the specific mutations. In addition to three previously described submicroscopic deletions encompassing the entire ND gene, we have now identified 6 intragenic deletions, 8 missense (seven point mutations, one 9-bp deletion), 6 nonsense (three point mutations, three single bp deletions/frameshift) and one 10-bp insertion, creating an expanded repeat in the 5' noncoding region of exon 1. Thus, mutations have been identified in a total of 24 of 26 (92%) of the kindreds we have studied to date. With the exception of two different mutations, each found in two apparently unrelated kindreds, these mutations are unique and expand the genotype database. Localization of the majority of point mutations at or near cysteine residues, potentially critical in protein tertiary structure, supports a previous protein model for norrin as member of a cystine knot growth factor family (Meitinger et al., 1993). Genotype-phenotype correlations were not evident with the limited clinical data available, except in the cases of larger submicroscopic deletions associated with a more severe neurologic syndrome.(ABSTRACT TRUNCATED AT 250 WORDS)

The application of artificial intelligence to microarray data: identification of a novel gene signature to identify bladder cancer progression.

PubMed

Catto, James W F; Abbod, Maysam F; Wild, Peter J; Linkens, Derek A; Pilarsky, Christian; Rehman, Ishtiaq; Rosario, Derek J; Denzinger, Stefan; Burger, Maximilian; Stoehr, Robert; Knuechel, Ruth; Hartmann, Arndt; Hamdy, Freddie C

2010-03-01

New methods for identifying bladder cancer (BCa) progression are required. Gene expression microarrays can reveal insights into disease biology and identify novel biomarkers. However, these experiments produce large datasets that are difficult to interpret. To develop a novel method of microarray analysis combining two forms of artificial intelligence (AI): neurofuzzy modelling (NFM) and artificial neural networks (ANN) and validate it in a BCa cohort. We used AI and statistical analyses to identify progression-related genes in a microarray dataset (n=66 tumours, n=2800 genes). The AI-selected genes were then investigated in a second cohort (n=262 tumours) using immunohistochemistry. We compared the accuracy of AI and statistical approaches to identify tumour progression. AI identified 11 progression-associated genes (odds ratio [OR]: 0.70; 95% confidence interval [CI], 0.56-0.87; p=0.0004), and these were more discriminate than genes chosen using statistical analyses (OR: 1.24; 95% CI, 0.96-1.60; p=0.09). The expression of six AI-selected genes (LIG3, FAS, KRT18, ICAM1, DSG2, and BRCA2) was determined using commercial antibodies and successfully identified tumour progression (concordance index: 0.66; log-rank test: p=0.01). AI-selected genes were more discriminate than pathologic criteria at determining progression (Cox multivariate analysis: p=0.01). Limitations include the use of statistical correlation to identify 200 genes for AI analysis and that we did not compare regression identified genes with immunohistochemistry. AI and statistical analyses use different techniques of inference to determine gene-phenotype associations and identify distinct prognostic gene signatures that are equally valid. We have identified a prognostic gene signature whose members reflect a variety of carcinogenic pathways that could identify progression in non-muscle-invasive BCa. 2009 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Population structure of the NPGS Senegalese sorghum collection and its evaluation to identify new disease resistant genes

USDA-ARS?s Scientific Manuscript database

Sorghum germplasm from West and Central Africa is cultivated in rainy and high humidity regions and is an important source of resistance genes to fungal diseases. Mold and anthracnose are two important biotic constraints to sorghum production in wet areas worldwide. Here, 158 National Plant Germplas...

ENU Mutagenesis in Mice Identifies Candidate Genes For Hypogonadism

PubMed Central

Weiss, Jeffrey; Hurley, Lisa A.; Harris, Rebecca M.; Finlayson, Courtney; Tong, Minghan; Fisher, Lisa A.; Moran, Jennifer L.; Beier, David R.; Mason, Christopher; Jameson, J. Larry

2012-01-01

Genome-wide mutagenesis was performed in mice to identify candidate genes for male infertility, for which the predominant causes remain idiopathic. Mice were mutagenized using N-ethyl-N-nitrosourea (ENU), bred, and screened for phenotypes associated with the male urogenital system. Fifteen heritable lines were isolated and chromosomal loci were assigned using low density genome-wide SNP arrays. Ten of the fifteen lines were pursued further using higher resolution SNP analysis to narrow the candidate gene regions. Exon sequencing of candidate genes identified mutations in mice with cystic kidneys (Bicc1), cryptorchidism (Rxfp2), restricted germ cell deficiency (Plk4), and severe germ cell deficiency (Prdm9). In two other lines with severe hypogonadism candidate sequencing failed to identify mutations, suggesting defects in genes with previously undocumented roles in gonadal function. These genomic intervals were sequenced in their entirety and a candidate mutation was identified in SnrpE in one of the two lines. The line harboring the SnrpE variant retains substantial spermatogenesis despite small testis size, an unusual phenotype. In addition to the reproductive defects, heritable phenotypes were observed in mice with ataxia (Myo5a), tremors (Pmp22), growth retardation (unknown gene), and hydrocephalus (unknown gene). These results demonstrate that the ENU screen is an effective tool for identifying potential causes of male infertility. PMID:22258617

Gene-chromosome locations of neuropsychiatric diseases.

PubMed

Shapshak, Paul; Somboonwit, Charurut; Sinnott, John; Commins, Deborah; Singer, Elyse; Levine, Andrew

2011-01-01

A number of genes are involved in various neuropsychiatric disorders. A comprehensive compilation of these genes is important for a better understanding of these diseases. We report an online file that lists genes by chromosome number and location. This is useful for the rapid examination of chromosome bands for genes involved in these diseases. This is not an exhaustive list and does not include single nucleotide polymorphism (SNP) results for genes that are currently being examined by genome wide association studies (GWAS) and other molecular methodologies. The database is available for free at http://www.bioinformation.net/007/paul.xls.

The NACP/synuclein gene: Chromosomal assignment and screening for alterations in Alzheimer disease

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

Campion, D.; Martin, C.; Charbonnier, F.

1995-03-20

The major component of the vascular and plaque amyloid deposits in Alzheimer disease is the amyloid {beta} peptide (A{beta}). A second intrinsic component of amyloid, the NAC (non-A{beta} component of amyloid) peptide, has recently been identified, and its precursor protein was named NACP. A computer homology search allowed us to establish that the human NACP gene was homologous to the rat synuclein gene. We mapped the NACP/synuclein gene to chromosome 4 and cloned three alternatively spliced transcripts in lymphocytes derived from a normal subject. We analyzed by RT-PCR and direct sequencing the entire coding region of the NACP/synuclein gene inmore » a group of patients with familial early onset Alzheimer disease. No mutation was found in 26 unrelated patients. Further studies are required to investigate the implication of the NACP/synuclein gene in Alzheimer disease. 21 refs., 3 tabs.« less

Alterations in cholesterol metabolism-related genes in sporadic Alzheimer's disease.

PubMed

Picard, Cynthia; Julien, Cédric; Frappier, Josée; Miron, Justin; Théroux, Louise; Dea, Doris; Breitner, John C S; Poirier, Judes

2018-06-01

Genome-wide association studies have identified several cholesterol metabolism-related genes as top risk factors for late-onset Alzheimer's disease (LOAD). We hypothesized that specific genetic variants could act as disease-modifying factors by altering the expression of those genes. Targeted association studies were conducted with available genomic, transcriptomic, proteomic, and histopathological data from 3 independent cohorts: the Alzheimer's Disease Neuroimaging Initiative (ADNI), the Quebec Founder Population (QFP), and the United Kingdom Brain Expression Consortium (UKBEC). First, a total of 273 polymorphisms located in 17 cholesterol metabolism-related loci were screened for associations with cerebrospinal fluid LOAD biomarkers beta amyloid, phosphorylated tau, and tau (from the ADNI) and with amyloid plaque and tangle densities (from the QFP). Top polymorphisms were then contrasted with gene expression levels measured in 134 autopsied healthy brains (from the UKBEC). In the end, only SREBF2 polymorphism rs2269657 showed significant dual associations with LOAD pathological biomarkers and gene expression levels. Furthermore, SREBF2 expression levels measured in LOAD frontal cortices inversely correlated with age at death; suggesting a possible influence on survival rate. Copyright © 2018 Elsevier Inc. All rights reserved.

Gene expression profiling in whole blood of patients with coronary artery disease

PubMed Central

Taurino, Chiara; Miller, William H.; McBride, Martin W.; McClure, John D.; Khanin, Raya; Moreno, María U.; Dymott, Jane A.; Delles, Christian; Dominiczak, Anna F.

2010-01-01

Owing to the dynamic nature of the transcriptome, gene expression profiling is a promising tool for discovery of disease-related genes and biological pathways. In the present study, we examined gene expression in whole blood of 12 patients with CAD (coronary artery disease) and 12 healthy control subjects. Furthermore, ten patients with CAD underwent whole-blood gene expression analysis before and after the completion of a cardiac rehabilitation programme following surgical coronary revascularization. mRNA and miRNA (microRNA) were isolated for expression profiling. Gene expression analysis identified 365 differentially expressed genes in patients with CAD compared with healthy controls (175 up- and 190 down-regulated in CAD), and 645 in CAD rehabilitation patients (196 up- and 449 down-regulated post-rehabilitation). Biological pathway analysis identified a number of canonical pathways, including oxidative phosphorylation and mitochondrial function, as being significantly and consistently modulated across the groups. Analysis of miRNA expression revealed a number of differentially expressed miRNAs, including hsa-miR-140-3p (control compared with CAD, P=0.017), hsa-miR-182 (control compared with CAD, P=0.093), hsa-miR-92a and hsa-miR-92b (post- compared with pre-exercise, P<0.01). Global analysis of predicted miRNA targets found significantly reduced expression of genes with target regions compared with those without: hsa-miR-140-3p (P=0.002), hsa-miR-182 (P=0.001), hsa-miR-92a and hsa-miR-92b (P=2.2×10−16). In conclusion, using whole blood as a ‘surrogate tissue’ in patients with CAD, we have identified differentially expressed miRNAs, differentially regulated genes and modulated pathways which warrant further investigation in the setting of cardiovascular function. This approach may represent a novel non-invasive strategy to unravel potentially modifiable pathways and possible therapeutic targets in cardiovascular disease. PMID:20528768

Global transcriptome analysis of formalin-fixed prostate cancer specimens identifies biomarkers of disease recurrence.

PubMed

Long, Qi; Xu, Jianpeng; Osunkoya, Adeboye O; Sannigrahi, Soma; Johnson, Brent A; Zhou, Wei; Gillespie, Theresa; Park, Jong Y; Nam, Robert K; Sugar, Linda; Stanimirovic, Aleksandra; Seth, Arun K; Petros, John A; Moreno, Carlos S

2014-06-15

Prostate cancer remains the second leading cause of cancer death in American men and there is an unmet need for biomarkers to identify patients with aggressive disease. In an effort to identify biomarkers of recurrence, we performed global RNA sequencing on 106 formalin-fixed, paraffin-embedded prostatectomy samples from 100 patients at three independent sites, defining a 24-gene signature panel. The 24 genes in this panel function in cell-cycle progression, angiogenesis, hypoxia, apoptosis, PI3K signaling, steroid metabolism, translation, chromatin modification, and transcription. Sixteen genes have been associated with cancer, with five specifically associated with prostate cancer (BTG2, IGFBP3, SIRT1, MXI1, and FDPS). Validation was performed on an independent publicly available dataset of 140 patients, where the new signature panel outperformed markers published previously in terms of predicting biochemical recurrence. Our work also identified differences in gene expression between Gleason pattern 4 + 3 and 3 + 4 tumors, including several genes involved in the epithelial-to-mesenchymal transition and developmental pathways. Overall, this study defines a novel biomarker panel that has the potential to improve the clinical management of prostate cancer. ©2014 American Association for Cancer Research.

Identifying disease polymorphisms from case-control genetic association data.

PubMed

Park, L

2010-12-01

In case-control association studies, it is typical to observe several associated polymorphisms in a gene region. Often the most significantly associated polymorphism is considered to be the disease polymorphism; however, it is not clear whether it is the disease polymorphism or there is more than one disease polymorphism in the gene region. Currently, there is no method that can handle these problems based on the linkage disequilibrium (LD) relationship between polymorphisms. To distinguish real disease polymorphisms from markers in LD, a method that can detect disease polymorphisms in a gene region has been developed. Relying on the LD between polymorphisms in controls, the proposed method utilizes model-based likelihood ratio tests to find disease polymorphisms. This method shows reliable Type I and Type II error rates when sample sizes are large enough, and works better with re-sequenced data. Applying this method to fine mapping using re-sequencing or dense genotyping data would provide important information regarding the genetic architecture of complex traits.

Systems Pharmacology-Based Approach of Connecting Disease Genes in Genome-Wide Association Studies with Traditional Chinese Medicine.

PubMed

Kim, Jihye; Yoo, Minjae; Shin, Jimin; Kim, Hyunmin; Kang, Jaewoo; Tan, Aik Choon

2018-01-01

Traditional Chinese medicine (TCM) originated in ancient China has been practiced over thousands of years for treating various symptoms and diseases. However, the molecular mechanisms of TCM in treating these diseases remain unknown. In this study, we employ a systems pharmacology-based approach for connecting GWAS diseases with TCM for potential drug repurposing and repositioning. We studied 102 TCM components and their target genes by analyzing microarray gene expression experiments. We constructed disease-gene networks from 2558 GWAS studies. We applied a systems pharmacology approach to prioritize disease-target genes. Using this bioinformatics approach, we analyzed 14,713 GWAS disease-TCM-target gene pairs and identified 115 disease-gene pairs with q value < 0.2. We validated several of these GWAS disease-TCM-target gene pairs with literature evidence, demonstrating that this computational approach could reveal novel indications for TCM. We also develop TCM-Disease web application to facilitate the traditional Chinese medicine drug repurposing efforts. Systems pharmacology is a promising approach for connecting GWAS diseases with TCM for potential drug repurposing and repositioning. The computational approaches described in this study could be easily expandable to other disease-gene network analysis.

Systems Pharmacology-Based Approach of Connecting Disease Genes in Genome-Wide Association Studies with Traditional Chinese Medicine

PubMed Central

Kim, Jihye; Yoo, Minjae; Shin, Jimin; Kim, Hyunmin; Kang, Jaewoo

2018-01-01

Traditional Chinese medicine (TCM) originated in ancient China has been practiced over thousands of years for treating various symptoms and diseases. However, the molecular mechanisms of TCM in treating these diseases remain unknown. In this study, we employ a systems pharmacology-based approach for connecting GWAS diseases with TCM for potential drug repurposing and repositioning. We studied 102 TCM components and their target genes by analyzing microarray gene expression experiments. We constructed disease-gene networks from 2558 GWAS studies. We applied a systems pharmacology approach to prioritize disease-target genes. Using this bioinformatics approach, we analyzed 14,713 GWAS disease-TCM-target gene pairs and identified 115 disease-gene pairs with q value < 0.2. We validated several of these GWAS disease-TCM-target gene pairs with literature evidence, demonstrating that this computational approach could reveal novel indications for TCM. We also develop TCM-Disease web application to facilitate the traditional Chinese medicine drug repurposing efforts. Systems pharmacology is a promising approach for connecting GWAS diseases with TCM for potential drug repurposing and repositioning. The computational approaches described in this study could be easily expandable to other disease-gene network analysis. PMID:29765977

Identifying Candidate Chemical-Disease Linkages ...

EPA Pesticide Factsheets

Presentation at meeting on Environmental and Epigenetic Determinants of IBD in New York, NY on identifying candidate chemical-disease linkages by using AOPs to identify molecular initiating events and using relevant high throughput assays to screen for candidate chemicals. This hazard information is combined with exposure models to inform risk assessment. Presentation at meeting on Environmental and Epigenetic Determinants of IBD in New York, NY on identifying candidate chemical-disease linkages by using AOPs to identify molecular initiating events and using relevant high throughput assays to screen for candidate chemicals. This hazard information is combined with exposure models to inform risk assessment.

Regulatory systems for hypoxia-inducible gene expression in ischemic heart disease gene therapy.

PubMed

Kim, Hyun Ah; Rhim, Taiyoun; Lee, Minhyung

2011-07-18

Ischemic heart diseases are caused by narrowed coronary arteries that decrease the blood supply to the myocardium. In the ischemic myocardium, hypoxia-responsive genes are up-regulated by hypoxia-inducible factor-1 (HIF-1). Gene therapy for ischemic heart diseases uses genes encoding angiogenic growth factors and anti-apoptotic proteins as therapeutic genes. These genes increase blood supply into the myocardium by angiogenesis and protect cardiomyocytes from cell death. However, non-specific expression of these genes in normal tissues may be harmful, since growth factors and anti-apoptotic proteins may induce tumor growth. Therefore, tight gene regulation is required to limit gene expression to ischemic tissues, to avoid unwanted side effects. For this purpose, various gene expression strategies have been developed for ischemic-specific gene expression. Transcriptional, post-transcriptional, and post-translational regulatory strategies have been developed and evaluated in ischemic heart disease animal models. The regulatory systems can limit therapeutic gene expression to ischemic tissues and increase the efficiency of gene therapy. In this review, recent progresses in ischemic-specific gene expression systems are presented, and their applications to ischemic heart diseases are discussed. Copyright © 2011 Elsevier B.V. All rights reserved.

Dawn of ocular gene therapy: implications for molecular diagnosis in retinal disease

PubMed Central

Jacques, ZANEVELD; Feng, WANG; Xia, WANG; Rui, CHEN

2013-01-01

Personalized medicine aims to utilize genomic information about patients to tailor treatment. Gene replacement therapy for rare genetic disorders is perhaps the most extreme form of personalized medicine, in that the patients’ genome wholly determines their treatment regimen. Gene therapy for retinal disorders is poised to become a clinical reality. The eye is an optimal site for gene therapy due to the relative ease of precise vector delivery, immune system isolation, and availability for monitoring of any potential damage or side effects. Due to these advantages, clinical trials for gene therapy of retinal diseases are currently underway. A necessary precursor to such gene therapies is accurate molecular diagnosis of the mutation(s) underlying disease. In this review, we discuss the application of Next Generation Sequencing (NGS) to obtain such a diagnosis and identify disease causing genes, using retinal disorders as a case study. After reviewing ocular gene therapy, we discuss the application of NGS to the identification of novel Mendelian disease genes. We then compare current, array based mutation detection methods against next NGS-based methods in three retinal diseases: Leber’s Congenital Amaurosis, Retinitis Pigmentosa, and Stargardt’s disease. We conclude that next-generation sequencing based diagnosis offers several advantages over array based methods, including a higher rate of successful diagnosis and the ability to more deeply and efficiently assay a broad spectrum of mutations. However, the relative difficulty of interpreting sequence results and the development of standardized, reliable bioinformatic tools remain outstanding concerns. In this review, recent advances NGS based molecular diagnoses are discussed, as well as their implications for the development of personalized medicine. PMID:23393028

Contig Maps and Genomic Sequencing Identify Candidate Genes in the Usher 1C Locus

PubMed Central

Higgins, Michael J.; Day, Colleen D.; Smilinich, Nancy J.; Ni, L.; Cooper, Paul R.; Nowak, Norma J.; Davies, Chris; de Jong, Pieter J.; Hejtmancik, Fielding; Evans, Glen A.; Smith, Richard J.H.; Shows, Thomas B.

1998-01-01

Usher syndrome 1C (USH1C) is a congenital condition manifesting profound hearing loss, the absence of vestibular function, and eventual retinal degeneration. The USH1C locus has been mapped genetically to a 2- to 3-cM interval in 11p14–15.1 between D11S899 and D11S861. In an effort to identify the USH1C disease gene we have isolated the region between these markers in yeast artificial chromosomes (YACs) using a combination of STS content mapping and Alu–PCR hybridization. The YAC contig is ∼3.5 Mb and has located several other loci within this interval, resulting in the order CEN-LDHA-SAA1-TPH-D11S1310-(D11S1888/KCNC1)-MYOD1-D11S902D11S921-D11S1890-TEL. Subsequent haplotyping and homozygosity analysis refined the location of the disease gene to a 400-kb interval between D11S902 and D11S1890 with all affected individuals being homozygous for the internal marker D11S921. To facilitate gene identification, the critical region has been converted into P1 artificial chromosome (PAC) clones using sequence-tagged sites (STSs) mapped to the YAC contig, Alu–PCR products generated from the YACs, and PAC end probes. A contig of >50 PAC clones has been assembled between D11S1310 and D11S1890, confirming the order of markers used in haplotyping. Three PAC clones representing nearly two-thirds of the USH1C critical region have been sequenced. PowerBLAST analysis identified six clusters of expressed sequence tags (ESTs), two known genes (BIR,SUR1) mapped previously to this region, and a previously characterized but unmapped gene NEFA (DNA binding/EF hand/acidic amino-acid-rich). GRAIL analysis identified 11 CpG islands and 73 exons of excellent quality. These data allowed the construction of a transcription map for the USH1C critical region, consisting of three known genes and six or more novel transcripts. Based on their map location, these loci represent candidate disease loci for USH1C. The NEFA gene was assessed as the USH1C locus by the sequencing of an amplified NEFA

Gene Network for Identifying the Entropy Changes of Different Modules in Pediatric Sepsis.

PubMed

Yang, Jing; Zhang, Pingli; Wang, Lumin

2016-01-01

Pediatric sepsis is a disease that threatens life of children. The incidence of pediatric sepsis is higher in developing countries due to various reasons, such as insufficient immunization and nutrition, water and air pollution, etc. Exploring the potential genes via different methods is of significance for the prevention and treatment of pediatric sepsis. This study aimed to identify potential genes associated with pediatric sepsis utilizing analysis of gene network and entropy. The mRNA expression in the blood samples collected from 20 septic children and 30 healthy controls was quantified by using Affymetrix HG-U133A microarray. Two condition-specific protein-protein interaction networks (PINs), one for the healthy control and the other one for the children with sepsis, were deduced by combining the fundamental human PINs with gene expression profiles in the two phenotypes. Subsequently, distinct modules from the two conditional networks were extracted by adopting a maximal clique-merging approach. Delta entropy (ΔS) was calculated between sepsis and control modules. Then, key genes displaying changes in gene composition were identified by matching the control and sepsis modules. Two objective modules were obtained, in which ribosomal protein RPL4 and RPL9 as well as TOP2A were probably considered as the key genes differentiating sepsis from healthy controls. According to previous reports and this work, TOP2A is the potential gene therapy target for pediatric sepsis. The relationship between pediatric sepsis and RPL4 and RPL9 needs further investigation. © 2016 The Author(s) Published by S. Karger AG, Basel.

Weighted gene co-expression network analysis of expression data of monozygotic twins identifies specific modules and hub genes related to BMI.

PubMed

Wang, Weijing; Jiang, Wenjie; Hou, Lin; Duan, Haiping; Wu, Yili; Xu, Chunsheng; Tan, Qihua; Li, Shuxia; Zhang, Dongfeng

2017-11-13

The therapeutic management of obesity is challenging, hence further elucidating the underlying mechanisms of obesity development and identifying new diagnostic biomarkers and therapeutic targets are urgent and necessary. Here, we performed differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) to identify significant genes and specific modules related to BMI based on gene expression profile data of 7 discordant monozygotic twins. In the differential gene expression analysis, it appeared that 32 differentially expressed genes (DEGs) were with a trend of up-regulation in twins with higher BMI when compared to their siblings. Categories of positive regulation of nitric-oxide synthase biosynthetic process, positive regulation of NF-kappa B import into nucleus, and peroxidase activity were significantly enriched within GO database and NF-kappa B signaling pathway within KEGG database. DEGs of NAMPT, TLR9, PTGS2, HBD, and PCSK1N might be associated with obesity. In the WGCNA, among the total 20 distinct co-expression modules identified, coral1 module (68 genes) had the strongest positive correlation with BMI (r = 0.56, P = 0.04) and disease status (r = 0.56, P = 0.04). Categories of positive regulation of phospholipase activity, high-density lipoprotein particle clearance, chylomicron remnant clearance, reverse cholesterol transport, intermediate-density lipoprotein particle, chylomicron, low-density lipoprotein particle, very-low-density lipoprotein particle, voltage-gated potassium channel complex, cholesterol transporter activity, and neuropeptide hormone activity were significantly enriched within GO database for this module. And alcoholism and cell adhesion molecules pathways were significantly enriched within KEGG database. Several hub genes, such as GAL, ASB9, NPPB, TBX2, IL17C, APOE, ABCG4, and APOC2 were also identified. The module eigengene of saddlebrown module (212 genes) was also significantly

Gene expression patterns associated with neurological disease in human HIV infection

PubMed Central

Repunte-Canonigo, Vez; Masliah, Eliezer; Lefebvre, Celine

2017-01-01

The pathogenesis and nosology of HIV-associated neurological disease (HAND) remain incompletely understood. Here, to provide new insight into the molecular events leading to neurocognitive impairments (NCI) in HIV infection, we analyzed pathway dysregulations in gene expression profiles of HIV-infected patients with or without NCI and HIV encephalitis (HIVE) and control subjects. The Gene Set Enrichment Analysis (GSEA) algorithm was used for pathway analyses in conjunction with the Molecular Signatures Database collection of canonical pathways (MSigDb). We analyzed pathway dysregulations in gene expression profiles of patients from the National NeuroAIDS Tissue Consortium (NNTC), which consists of samples from 3 different brain regions, including white matter, basal ganglia and frontal cortex of HIV-infected and control patients. While HIVE is characterized by widespread, uncontrolled inflammation and tissue damage, substantial gene expression evidence of induction of interferon (IFN), cytokines and tissue injury is apparent in all brain regions studied, even in the absence of NCI. Various degrees of white matter changes were present in all HIV-infected subjects and were the primary manifestation in patients with NCI in the absence of HIVE. In particular, NCI in patients without HIVE in the NNTC sample is associated with white matter expression of chemokines, cytokines and β-defensins, without significant activation of IFN. Altogether, the results identified distinct pathways differentially regulated over the course of neurological disease in HIV infection and provide a new perspective on the dynamics of pathogenic processes in the course of HIV neurological disease in humans. These results also demonstrate the power of the systems biology analyses and indicate that the establishment of larger human gene expression profile datasets will have the potential to provide novel mechanistic insight into the pathogenesis of neurological disease in HIV infection and

Association Analysis Suggests SOD2 as a Newly Identified Candidate Gene Associated With Leprosy Susceptibility.

PubMed

Ramos, Geovana Brotto; Salomão, Heloisa; Francio, Angela Schneider; Fava, Vinícius Medeiros; Werneck, Renata Iani; Mira, Marcelo Távora

2016-08-01

Genetic studies have identified several genes and genomic regions contributing to the control of host susceptibility to leprosy. Here, we test variants of the positional and functional candidate gene SOD2 for association with leprosy in 2 independent population samples. Family-based analysis revealed an association between leprosy and allele G of marker rs295340 (P = .042) and borderline evidence of an association between leprosy and alleles C and A of markers rs4880 (P = .077) and rs5746136 (P = .071), respectively. Findings were validated in an independent case-control sample for markers rs295340 (P = .049) and rs4880 (P = .038). These results suggest SOD2 as a newly identified gene conferring susceptibility to leprosy. © The Author 2016. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail journals.permissions@oup.com.

Novel Mutations in the ZEB1 Gene Identified in Czech and British Patients With Posterior Polymorphous Corneal Dystrophy

PubMed Central

Liskova, Petra; Tuft, Stephen J.; Gwilliam, Rhian; Ebenezer, Neil D.; Jirsova, Katerina; Prescott, Quincy; Martincova, Radka; Pretorius, Marike; Sinclair, Neil; Boase, David L.; Jeffrey, Margaret J.; Deloukas, Panos; Hardcastle, Alison J.; Filipec, Martin; Bhattacharya, Shomi S.

2009-01-01

We describe the search for mutations in six unrelated Czech and four unrelated British families with posterior polymorphous corneal dystrophy (PPCD); a relatively rare eye disorder. Coding exons and intron/exon boundaries of all three genes (VSX1, COL8A2, and ZEB1/TCF8) previously reported to be implicated in the pathogenesis of this disorder were screened by DNA sequencing. Four novel pathogenic mutations were identified in four families; two deletions, one nonsense, and one duplication within exon 7 in the ZEB1 gene located at 10p11.2. We also genotyped the Czech patients to test for a founder haplotype and lack of disease segregation with the 20p11.2 locus we previously described. Although a systematic clinical examination was not performed, our investigation does not support an association between ZEB1 changes and self reported non-ocular anomalies. In the remaining six families no disease causing mutations were identified thereby indicating that as yet unidentified gene(s) are likely to be responsible for PPCD. PMID:17437275

Genomic structure and expression of STM2, the chromosome 1 familial Alzheimer disease gene.

PubMed

Levy-Lahad, E; Poorkaj, P; Wang, K; Fu, Y H; Oshima, J; Mulligan, J; Schellenberg, G D

1996-06-01

Mutations in the gene STM2 result in autosomal dominant familial Alzheimer disease. To screen for mutations and to identify regulatory elements for this gene, the genomic DNA sequence and intron-exon structure were determined. Twelve exons including 10 coding exons were identified in a genomic region spanning 23,737 bp. The first 2 exons encode the 5'-untranslated region. Expression analysis of STM2 indicates that two transcripts of 2.4 and 2.8 kb are found in skeletal muscle, pancreas, and heart. In addition, a splice variant of the 2.4-kb transcript was identified that is the result of the use of an alternative splice acceptor site located in exon 10. The use of this site results in a transcript lacking a single glutamate. The promotor for this gene and the alternatively spliced exons leading to the 2.8-kb form of the gene remain to be identified. Expression of STM2 was high in skeletal muscle and pancreas, with comparatively low levels observed in brain. This expression pattern is intriguing since in Alzheimer disease, pathology and degeneration are observed only in the central nervous system.

Identifying key genes associated with acute myocardial infarction.

PubMed

Cheng, Ming; An, Shoukuan; Li, Junquan

2017-10-01

This study aimed to identify key genes associated with acute myocardial infarction (AMI) by reanalyzing microarray data. Three gene expression profile datasets GSE66360, GSE34198, and GSE48060 were downloaded from GEO database. After data preprocessing, genes without heterogeneity across different platforms were subjected to differential expression analysis between the AMI group and the control group using metaDE package. P < .05 was used as the cutoff for a differentially expressed gene (DEG). The expression data matrices of DEGs were imported in ReactomeFIViz to construct a gene functional interaction (FI) network. Then, DEGs in each module were subjected to pathway enrichment analysis using DAVID. MiRNAs and transcription factors predicted to regulate target DEGs were identified. Quantitative real-time polymerase chain reaction (RT-PCR) was applied to verify the expression of genes. A total of 913 upregulated genes and 1060 downregulated genes were identified in the AMI group. A FI network consists of 21 modules and DEGs in 12 modules were significantly enriched in pathways. The transcription factor-miRNA-gene network contains 2 transcription factors FOXO3 and MYBL2, and 2 miRNAs hsa-miR-21-5p and hsa-miR-30c-5p. RT-PCR validations showed that expression levels of FOXO3 and MYBL2 were significantly increased in AMI, and expression levels of hsa-miR-21-5p and hsa-miR-30c-5p were obviously decreased in AMI. A total of 41 DEGs, such as SOCS3, VAPA, and COL5A2, are speculated to have roles in the pathogenesis of AMI; 2 transcription factors FOXO3 and MYBL2, and 2 miRNAs hsa-miR-21-5p and hsa-miR-30c-5p may be involved in the regulation of the expression of these DEGs.

Systematic Analysis and Comparison of Nucleotide-Binding Site Disease Resistance Genes in a Diploid Cotton Gossypium raimondii

PubMed Central

Wei, Hengling; Li, Wei; Sun, Xiwei; Zhu, Shuijin; Zhu, Jun

2013-01-01

Plant disease resistance genes are a key component of defending plants from a range of pathogens. The majority of these resistance genes belong to the super-family that harbors a Nucleotide-binding site (NBS). A number of studies have focused on NBS-encoding genes in disease resistant breeding programs for diverse plants. However, little information has been reported with an emphasis on systematic analysis and comparison of NBS-encoding genes in cotton. To fill this gap of knowledge, in this study, we identified and investigated the NBS-encoding resistance genes in cotton using the whole genome sequence information of Gossypium raimondii. Totally, 355 NBS-encoding resistance genes were identified. Analyses of the conserved motifs and structural diversity showed that the most two distinct features for these genes are the high proportion of non-regular NBS genes and the high diversity of N-termini domains. Analyses of the physical locations and duplications of NBS-encoding genes showed that gene duplication of disease resistance genes could play an important role in cotton by leading to an increase in the functional diversity of the cotton NBS-encoding genes. Analyses of phylogenetic comparisons indicated that, in cotton, the NBS-encoding genes with TIR domain not only have their own evolution pattern different from those of genes without TIR domain, but also have their own species-specific pattern that differs from those of TIR genes in other plants. Analyses of the correlation between disease resistance QTL and NBS-encoding resistance genes showed that there could be more than half of the disease resistance QTL associated to the NBS-encoding genes in cotton, which agrees with previous studies establishing that more than half of plant resistance genes are NBS-encoding genes. PMID:23936305

A framework to identify gene expression profiles in a model of inflammation induced by lipopolysaccharide after treatment with thalidomide

PubMed Central

2012-01-01

Background Thalidomide is an anti-inflammatory and anti-angiogenic drug currently used for the treatment of several diseases, including erythema nodosum leprosum, which occurs in patients with lepromatous leprosy. In this research, we use DNA microarray analysis to identify the impact of thalidomide on gene expression responses in human cells after lipopolysaccharide (LPS) stimulation. We employed a two-stage framework. Initially, we identified 1584 altered genes in response to LPS. Modulation of this set of genes was then analyzed in the LPS stimulated cells treated with thalidomide. Results We identified 64 genes with altered expression induced by thalidomide using the rank product method. In addition, the lists of up-regulated and down-regulated genes were investigated by means of bioinformatics functional analysis, which allowed for the identification of biological processes affected by thalidomide. Confirmatory analysis was done in five of the identified genes using real time PCR. Conclusions The results showed some genes that can further our understanding of the biological mechanisms in the action of thalidomide. Of the five genes evaluated with real time PCR, three were down regulated and two were up regulated confirming the initial results of the microarray analysis. PMID:22695124

Mice, humans and haplotypes--the hunt for disease genes in SLE.

PubMed

Rigby, R J; Fernando, M M A; Vyse, T J

2006-09-01

Defining the polymorphisms that contribute to the development of complex genetic disease traits is a challenging, although increasingly tractable problem. Historically, the technical difficulties in conducting association studies across the entire human genome are such that murine models have been used to generate candidate genes for analysis in human complex diseases, such as SLE. In this article we discuss the advantages and disadvantages of this approach and specifically address some assumptions made in the transition from studying one species to another, using lupus as an example. These issues include differences in genetic structure and genetic organisation which are a reflection on the population history. Clearly there are major differences in the histories of the human population and inbred laboratory strains of mice. Both human and murine genomes do exhibit structure at the genetic level. That is to say, they comprise haplotypes which are genomic regions that carry runs of polymorphisms that are not independently inherited. Haplotypes therefore reduce the number of combinations of the polymorphisms in the DNA in that region and facilitate the identification of disease susceptibility genes in both mice and humans. There are now novel means of generating candidate genes in SLE using mutagenesis (with ENU) in mice and identifying mice that generate antinuclear autoimmunity. In addition, murine models still provide a valuable means of exploring the functional consequences of genetic variation. However, advances in technology are such that human geneticists can now screen large fractions of the human genome for disease associations using microchip technologies that provide information on upwards of 100,000 different polymorphisms. These approaches are aimed at identifying haplotypes that carry disease susceptibility mutations and rely less on the generation of candidate genes.

A genomic approach to identify hybrid incompatibility genes.

PubMed

Cooper, Jacob C; Phadnis, Nitin

2016-07-02

Uncovering the genetic and molecular basis of barriers to gene flow between populations is key to understanding how new species are born. Intrinsic postzygotic reproductive barriers such as hybrid sterility and hybrid inviability are caused by deleterious genetic interactions known as hybrid incompatibilities. The difficulty in identifying these hybrid incompatibility genes remains a rate-limiting step in our understanding of the molecular basis of speciation. We recently described how whole genome sequencing can be applied to identify hybrid incompatibility genes, even from genetically terminal hybrids. Using this approach, we discovered a new hybrid incompatibility gene, gfzf, between Drosophila melanogaster and Drosophila simulans, and found that it plays an essential role in cell cycle regulation. Here, we discuss the history of the hunt for incompatibility genes between these species, discuss the molecular roles of gfzf in cell cycle regulation, and explore how intragenomic conflict drives the evolution of fundamental cellular mechanisms that lead to the developmental arrest of hybrids.

A genomic approach to identify hybrid incompatibility genes

PubMed Central

Cooper, Jacob C.; Phadnis, Nitin

2016-01-01

ABSTRACT Uncovering the genetic and molecular basis of barriers to gene flow between populations is key to understanding how new species are born. Intrinsic postzygotic reproductive barriers such as hybrid sterility and hybrid inviability are caused by deleterious genetic interactions known as hybrid incompatibilities. The difficulty in identifying these hybrid incompatibility genes remains a rate-limiting step in our understanding of the molecular basis of speciation. We recently described how whole genome sequencing can be applied to identify hybrid incompatibility genes, even from genetically terminal hybrids. Using this approach, we discovered a new hybrid incompatibility gene, gfzf, between Drosophila melanogaster and Drosophila simulans, and found that it plays an essential role in cell cycle regulation. Here, we discuss the history of the hunt for incompatibility genes between these species, discuss the molecular roles of gfzf in cell cycle regulation, and explore how intragenomic conflict drives the evolution of fundamental cellular mechanisms that lead to the developmental arrest of hybrids. PMID:27230814

Clustering gene expression regulators: new approach to disease subtyping.

PubMed

Pyatnitskiy, Mikhail; Mazo, Ilya; Shkrob, Maria; Schwartz, Elena; Kotelnikova, Ekaterina

2014-01-01

One of the main challenges in modern medicine is to stratify different patient groups in terms of underlying disease molecular mechanisms as to develop more personalized approach to therapy. Here we propose novel method for disease subtyping based on analysis of activated expression regulators on a sample-by-sample basis. Our approach relies on Sub-Network Enrichment Analysis algorithm (SNEA) which identifies gene subnetworks with significant concordant changes in expression between two conditions. Subnetwork consists of central regulator and downstream genes connected by relations extracted from global literature-extracted regulation database. Regulators found in each patient separately are clustered together and assigned activity scores which are used for final patients grouping. We show that our approach performs well compared to other related methods and at the same time provides researchers with complementary level of understanding of pathway-level biology behind a disease by identification of significant expression regulators. We have observed the reasonable grouping of neuromuscular disorders (triggered by structural damage vs triggered by unknown mechanisms), that was not revealed using standard expression profile clustering. For another experiment we were able to suggest the clusters of regulators, responsible for colorectal carcinoma vs adenoma discrimination and identify frequently genetically changed regulators that could be of specific importance for the individual characteristics of cancer development. Proposed approach can be regarded as biologically meaningful feature selection, reducing tens of thousands of genes down to dozens of clusters of regulators. Obtained clusters of regulators make possible to generate valuable biological hypotheses about molecular mechanisms related to a clinical outcome for individual patient.

Clustering Gene Expression Regulators: New Approach to Disease Subtyping

PubMed Central

Pyatnitskiy, Mikhail; Mazo, Ilya; Shkrob, Maria; Schwartz, Elena; Kotelnikova, Ekaterina

2014-01-01

One of the main challenges in modern medicine is to stratify different patient groups in terms of underlying disease molecular mechanisms as to develop more personalized approach to therapy. Here we propose novel method for disease subtyping based on analysis of activated expression regulators on a sample-by-sample basis. Our approach relies on Sub-Network Enrichment Analysis algorithm (SNEA) which identifies gene subnetworks with significant concordant changes in expression between two conditions. Subnetwork consists of central regulator and downstream genes connected by relations extracted from global literature-extracted regulation database. Regulators found in each patient separately are clustered together and assigned activity scores which are used for final patients grouping. We show that our approach performs well compared to other related methods and at the same time provides researchers with complementary level of understanding of pathway-level biology behind a disease by identification of significant expression regulators. We have observed the reasonable grouping of neuromuscular disorders (triggered by structural damage vs triggered by unknown mechanisms), that was not revealed using standard expression profile clustering. For another experiment we were able to suggest the clusters of regulators, responsible for colorectal carcinoma vs adenoma discrimination and identify frequently genetically changed regulators that could be of specific importance for the individual characteristics of cancer development. Proposed approach can be regarded as biologically meaningful feature selection, reducing tens of thousands of genes down to dozens of clusters of regulators. Obtained clusters of regulators make possible to generate valuable biological hypotheses about molecular mechanisms related to a clinical outcome for individual patient. PMID:24416320

Positional cloning of disease genes on chromosome 16

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

Doggett, N.; Bruening, M.; Callen, D.

1996-04-01

The project seeks to elucidate the molecular basis of an important genetic disease (Batten`s disease) by molecular cloning of the affected gene by utilizing an overlapping clone map of chromosome 16. Batten disease (also known as juvenile neuronal ceroid lipofuscinosis) is a recessively inherited neurodegenerative disorder of childhood characterized by progressive loss of vision, seizures, and psychomoter disturbances. The Batten disease gene was genetically mapped to the chromosome region 16p 12.1 in close linkage with the genetic markers D16S299 and D16S298. Exon amplification of a cosmid containing D16S298 yielded a candidate gene that was disrupted by a 1 kb genomicmore » deletion in all patients containing the most common haplotype for the disease. Two separate deletions and a point mutation altering a splice site in three unrelated families have confirmed the gene as the Batten disease gene. The disease gene encodes a novel 438 amino acid membrane binding protein of unknown function.« less

Linking Genes to Cardiovascular Diseases: Gene Action and Gene–Environment Interactions

PubMed Central

2016-01-01

A unique myocardial characteristic is its ability to grow/remodel in order to adapt; this is determined partly by genes and partly by the environment and the milieu intérieur. In the “post-genomic” era, a need is emerging to elucidate the physiologic functions of myocardial genes, as well as potential adaptive and maladaptive modulations induced by environmental/epigenetic factors. Genome sequencing and analysis advances have become exponential lately, with escalation of our knowledge concerning sometimes controversial genetic underpinnings of cardiovascular diseases. Current technologies can identify candidate genes variously involved in diverse normal/abnormal morphomechanical phenotypes, and offer insights into multiple genetic factors implicated in complex cardiovascular syndromes. The expression profiles of thousands of genes are regularly ascertained under diverse conditions. Global analyses of gene expression levels are useful for cataloging genes and correlated phenotypes, and for elucidating the role of genes in maladies. Comparative expression of gene networks coupled to complex disorders can contribute insights as to how “modifier genes” influence the expressed phenotypes. Increasingly, a more comprehensive and detailed systematic understanding of genetic abnormalities underlying, for example, various genetic cardiomyopathies is emerging. Implementing genomic findings in cardiology practice may well lead directly to better diagnosing and therapeutics. There is currently evolving a strong appreciation for the value of studying gene anomalies, and doing so in a non-disjointed, cohesive manner. However, it is challenging for many—practitioners and investigators—to comprehend, interpret, and utilize the clinically increasingly accessible and affordable cardiovascular genomics studies. This survey addresses the need for fundamental understanding in this vital area. PMID:26545598

Ensemble positive unlabeled learning for disease gene identification.

PubMed

Yang, Peng; Li, Xiaoli; Chua, Hon-Nian; Kwoh, Chee-Keong; Ng, See-Kiong

2014-01-01

An increasing number of genes have been experimentally confirmed in recent years as causative genes to various human diseases. The newly available knowledge can be exploited by machine learning methods to discover additional unknown genes that are likely to be associated with diseases. In particular, positive unlabeled learning (PU learning) methods, which require only a positive training set P (confirmed disease genes) and an unlabeled set U (the unknown candidate genes) instead of a negative training set N, have been shown to be effective in uncovering new disease genes in the current scenario. Using only a single source of data for prediction can be susceptible to bias due to incompleteness and noise in the genomic data and a single machine learning predictor prone to bias caused by inherent limitations of individual methods. In this paper, we propose an effective PU learning framework that integrates multiple biological data sources and an ensemble of powerful machine learning classifiers for disease gene identification. Our proposed method integrates data from multiple biological sources for training PU learning classifiers. A novel ensemble-based PU learning method EPU is then used to integrate multiple PU learning classifiers to achieve accurate and robust disease gene predictions. Our evaluation experiments across six disease groups showed that EPU achieved significantly better results compared with various state-of-the-art prediction methods as well as ensemble learning classifiers. Through integrating multiple biological data sources for training and the outputs of an ensemble of PU learning classifiers for prediction, we are able to minimize the potential bias and errors in individual data sources and machine learning algorithms to achieve more accurate and robust disease gene predictions. In the future, our EPU method provides an effective framework to integrate the additional biological and computational resources for better disease gene predictions.

Allelic Variants of Complement Genes Associated with Dense Deposit Disease

PubMed Central

Abrera-Abeleda, Maria Asuncion; Nishimura, Carla; Frees, Kathy; Jones, Michael; Maga, Tara; Katz, Louis M.; Zhang, Yuzhou

2011-01-01

The alternative pathway of the complement cascade plays a role in the pathogenesis of dense deposit disease (DDD). Deficiency of complement factor H and mutations in CFH associate with the development of DDD, but it is unknown whether allelic variants in other complement genes also associate with this disease. We studied patients with DDD and identified previously unreported sequence alterations in several genes in addition to allelic variants and haplotypes common to patients with DDD. We found that the likelihood of developing DDD increases with the presence of two or more risk alleles in CFH and C3. To determine the functional consequence of this finding, we measured the activity of the alternative pathway in serum samples from phenotypically normal controls genotyped for variants in CFH and C3. Alternative pathway activity was higher in the presence of variants associated with DDD. Taken together, these data confirm that DDD is a complex genetic disease and may provide targets for the development of disease-specific therapies. PMID:21784901

Prenatal Exposure to Arsenic and Cadmium Impacts Infectious Disease-Related Genes within the Glucocorticoid Receptor Signal Transduction Pathway

PubMed Central

Rager, Julia E.; Yosim, Andrew; Fry, Rebecca C.

2014-01-01

There is increasing evidence that environmental agents mediate susceptibility to infectious disease. Studies support the impact of prenatal/early life exposure to the environmental metals inorganic arsenic (iAs) and cadmium (Cd) on increased risk for susceptibility to infection. The specific biological mechanisms that underlie such exposure-mediated effects remain understudied. This research aimed to identify key genes/signal transduction pathways that associate prenatal exposure to these toxic metals with changes in infectious disease susceptibility using a Comparative Genomic Enrichment Method (CGEM). Using CGEM an infectious disease gene (IDG) database was developed comprising 1085 genes with known roles in viral, bacterial, and parasitic disease pathways. Subsequently, datasets collected from human pregnancy cohorts exposed to iAs or Cd were examined in relationship to the IDGs, specifically focusing on data representing epigenetic modifications (5-methyl cytosine), genomic perturbations (mRNA expression), and proteomic shifts (protein expression). A set of 82 infection and exposure-related genes was identified and found to be enriched for their role in the glucocorticoid receptor signal transduction pathway. Given their common identification across numerous human cohorts and their known toxicological role in disease, the identified genes within the glucocorticoid signal transduction pathway may underlie altered infectious disease susceptibility associated with prenatal exposures to the toxic metals iAs and Cd in humans. PMID:25479081

Integration of targeted metabolomics and transcriptomics identifies deregulation of phosphatidylcholine metabolism in Huntington's disease peripheral blood samples.

PubMed

Mastrokolias, Anastasios; Pool, Rene; Mina, Eleni; Hettne, Kristina M; van Duijn, Erik; van der Mast, Roos C; van Ommen, GertJan; 't Hoen, Peter A C; Prehn, Cornelia; Adamski, Jerzy; van Roon-Mom, Willeke

Metabolic changes have been frequently associated with Huntington's disease (HD). At the same time peripheral blood represents a minimally invasive sampling avenue with little distress to Huntington's disease patients especially when brain or other tissue samples are difficult to collect. We investigated the levels of 163 metabolites in HD patient and control serum samples in order to identify disease related changes. Additionally, we integrated the metabolomics data with our previously published next generation sequencing-based gene expression data from the same patients in order to interconnect the metabolomics changes with transcriptional alterations. This analysis was performed using targeted metabolomics and flow injection electrospray ionization tandem mass spectrometry in 133 serum samples from 97 Huntington's disease patients (29 pre-symptomatic and 68 symptomatic) and 36 controls. By comparing HD mutation carriers with controls we identified 3 metabolites significantly changed in HD (serine and threonine and one phosphatidylcholine-PC ae C36:0) and an additional 8 phosphatidylcholines (PC aa C38:6, PC aa C36:0, PC ae C38:0, PC aa C38:0, PC ae C38:6, PC ae C42:0, PC aa C36:5 and PC ae C36:0) that exhibited a significant association with disease severity. Using workflow based exploitation of pathway databases and by integrating our metabolomics data with our gene expression data from the same patients we identified 4 deregulated phosphatidylcholine metabolism related genes ( ALDH1B1 , MBOAT1 , MTRR and PLB1 ) that showed significant association with the changes in metabolite concentrations. Our results support the notion that phosphatidylcholine metabolism is deregulated in HD blood and that these metabolite alterations are associated with specific gene expression changes.

Development of gene polymorphisms in meditators of nonalcoholic fatty liver disease

PubMed Central

Wang, Chun; Gong, Jianping; Wu, Hao

2017-01-01

Nonalcoholic fatty liver disease (NAFLD) is the most prevalent liver disease worldwide, the morbidity of which closely correlates with diversity of ethnicity, minority, family and location. Its histology spans from simple steatosis, to nonalcoholic steatohepatitis, which ultimately results in fibrosis, cirrhosis and hepatocellular carcinoma. The accelerating prevalence of NAFLD is due to an incremental incidence of metabolic syndrome that is distinguished by dyslipidemia, glucose impairment, obesity, excessive oxidative stress and adipocytokine impairment. Additionally, the pathogenesis of NAFLD is thought to be a multifactorial and complicated disease associated with lifestyle habits, nutritional factors and genetics. However, the pathogenesis and underlying mechanism in the development of NAFLD caused by genetics remains unclear. People have been increasingly emphasizing on the relationship between NAFLD and gene polymorphisms in recent years, with the aim of having a comprehensive elucidation of associated gene polymorphisms influencing the pathogenesis of the disease. In the current article, the authors attempted to critically summarize the most recently identified gene polymorphisms from the facets of glucose metabolism, fatty acid metabolism, oxidative stress and related cytokines in NAFLD that contribute to promoting the progression of the disease. PMID:28804621

Gene Therapy for Infectious Diseases

PubMed Central

Bunnell, Bruce A.; Morgan, Richard A.

1998-01-01

Gene therapy is being investigated as an alternative treatment for a wide range of infectious diseases that are not amenable to standard clinical management. Approaches to gene therapy for infectious diseases can be divided into three broad categories: (i) gene therapies based on nucleic acid moieties, including antisense DNA or RNA, RNA decoys, and catalytic RNA moieties (ribozymes); (ii) protein approaches such as transdominant negative proteins and single-chain antibodies; and (iii) immunotherapeutic approaches involving genetic vaccines or pathogen-specific lymphocytes. It is further possible that combinations of the aforementioned approaches will be used simultaneously to inhibit multiple stages of the life cycle of the infectious agent. PMID:9457428

Homophila: human disease gene cognates in Drosophila

PubMed Central

Chien, Samson; Reiter, Lawrence T.; Bier, Ethan; Gribskov, Michael

2002-01-01

Although many human genes have been associated with genetic diseases, knowing which mutations result in disease phenotypes often does not explain the etiology of a specific disease. Drosophila melanogaster provides a powerful system in which to use genetic and molecular approaches to investigate human genetic diseases. Homophila is an intergenomic resource linking the human and fly genomes in order to stimulate functional genomic investigations in Drosophila that address questions about genetic disease in humans. Homophila provides a comprehensive linkage between the disease genes compiled in Online Mendelian Inheritance in Man (OMIM) and the complete Drosophila genomic sequence. Homophila is a relational database that allows searching based on human disease descriptions, OMIM number, human or fly gene names, and sequence similarity, and can be accessed at http://homophila.sdsc.edu. PMID:11752278

Integrative multi-platform meta-analysis of gene expression profiles in pancreatic ductal adenocarcinoma patients for identifying novel diagnostic biomarkers.

PubMed

Irigoyen, Antonio; Jimenez-Luna, Cristina; Benavides, Manuel; Caba, Octavio; Gallego, Javier; Ortuño, Francisco Manuel; Guillen-Ponce, Carmen; Rojas, Ignacio; Aranda, Enrique; Torres, Carolina; Prados, Jose

2018-01-01

Applying differentially expressed genes (DEGs) to identify feasible biomarkers in diseases can be a hard task when working with heterogeneous datasets. Expression data are strongly influenced by technology, sample preparation processes, and/or labeling methods. The proliferation of different microarray platforms for measuring gene expression increases the need to develop models able to compare their results, especially when different technologies can lead to signal values that vary greatly. Integrative meta-analysis can significantly improve the reliability and robustness of DEG detection. The objective of this work was to develop an integrative approach for identifying potential cancer biomarkers by integrating gene expression data from two different platforms. Pancreatic ductal adenocarcinoma (PDAC), where there is an urgent need to find new biomarkers due its late diagnosis, is an ideal candidate for testing this technology. Expression data from two different datasets, namely Affymetrix and Illumina (18 and 36 PDAC patients, respectively), as well as from 18 healthy controls, was used for this study. A meta-analysis based on an empirical Bayesian methodology (ComBat) was then proposed to integrate these datasets. DEGs were finally identified from the integrated data by using the statistical programming language R. After our integrative meta-analysis, 5 genes were commonly identified within the individual analyses of the independent datasets. Also, 28 novel genes that were not reported by the individual analyses ('gained' genes) were also discovered. Several of these gained genes have been already related to other gastroenterological tumors. The proposed integrative meta-analysis has revealed novel DEGs that may play an important role in PDAC and could be potential biomarkers for diagnosing the disease.

Dupuytren's disease susceptibility gene, EPDR1, is involved in myofibroblast contractility.

PubMed

Staats, Kim A; Wu, Timothy; Gan, Bing S; O'Gorman, David B; Ophoff, Roel A

2016-08-01

Dupuytren's Disease is a common disorder of the connective tissue characterized by progressive and irreversible fibroblastic proliferation affecting the palmar fascia. Progressive flexion deformity appears over several months or years and although usually painless, it can result in a serious handicap causing loss of manual dexterity. There is no cure for the disease and the etiology is largely unknown. A genome-wide association study of Dupuytren's Disease identified nine susceptibility loci with the strongest genetic signal located in an intron of EPDR1, the gene encoding the Ependymin Related 1 protein. Here, we investigate the role of EPDR1 in Dupuytren's Disease. We research the role of EPDR1 by assessing gene expression in patient tissue and by gene silencing in fibroblast-populated collagen lattice (FPCL) assay, which is used as an in vitro model of Dupuytren's contractures. The three alternative transcripts produced by the EPDR1 gene are all detected in affected Dupuytren's tissue and control unaffected palmar fascia tissue. Dupuytren's tissue also contracts more in the FPCL paradigm. Dicer-substrate RNA-mediated knockdown of EPDR1 results in moderate late stage attenuation of contraction rate in FPCL, implying a role in matrix contraction. Our results suggest functional involvement of EPDR1 in the etiology of Dupuytren's Disease. Copyright © 2016. Published by Elsevier Ireland Ltd.

Mapping Gene Associations in Human Mitochondria using Clinical Disease Phenotypes

PubMed Central

Scharfe, Curt; Lu, Henry Horng-Shing; Neuenburg, Jutta K.; Allen, Edward A.; Li, Guan-Cheng; Klopstock, Thomas; Cowan, Tina M.; Enns, Gregory M.; Davis, Ronald W.

2009-01-01

Nuclear genes encode most mitochondrial proteins, and their mutations cause diverse and debilitating clinical disorders. To date, 1,200 of these mitochondrial genes have been recorded, while no standardized catalog exists of the associated clinical phenotypes. Such a catalog would be useful to develop methods to analyze human phenotypic data, to determine genotype-phenotype relations among many genes and diseases, and to support the clinical diagnosis of mitochondrial disorders. Here we establish a clinical phenotype catalog of 174 mitochondrial disease genes and study associations of diseases and genes. Phenotypic features such as clinical signs and symptoms were manually annotated from full-text medical articles and classified based on the hierarchical MeSH ontology. This classification of phenotypic features of each gene allowed for the comparison of diseases between different genes. In turn, we were then able to measure the phenotypic associations of disease genes for which we calculated a quantitative value that is based on their shared phenotypic features. The results showed that genes sharing more similar phenotypes have a stronger tendency for functional interactions, proving the usefulness of phenotype similarity values in disease gene network analysis. We then constructed a functional network of mitochondrial genes and discovered a higher connectivity for non-disease than for disease genes, and a tendency of disease genes to interact with each other. Utilizing these differences, we propose 168 candidate genes that resemble the characteristic interaction patterns of mitochondrial disease genes. Through their network associations, the candidates are further prioritized for the study of specific disorders such as optic neuropathies and Parkinson disease. Most mitochondrial disease phenotypes involve several clinical categories including neurologic, metabolic, and gastrointestinal disorders, which might indicate the effects of gene defects within the

Genetic Susceptibility to Vitiligo: GWAS Approaches for Identifying Vitiligo Susceptibility Genes and Loci

PubMed Central

Shen, Changbing; Gao, Jing; Sheng, Yujun; Dou, Jinfa; Zhou, Fusheng; Zheng, Xiaodong; Ko, Randy; Tang, Xianfa; Zhu, Caihong; Yin, Xianyong; Sun, Liangdan; Cui, Yong; Zhang, Xuejun

2016-01-01

Vitiligo is an autoimmune disease with a strong genetic component, characterized by areas of depigmented skin resulting from loss of epidermal melanocytes. Genetic factors are known to play key roles in vitiligo through discoveries in association studies and family studies. Previously, vitiligo susceptibility genes were mainly revealed through linkage analysis and candidate gene studies. Recently, our understanding of the genetic basis of vitiligo has been rapidly advancing through genome-wide association study (GWAS). More than 40 robust susceptible loci have been identified and confirmed to be associated with vitiligo by using GWAS. Most of these associated genes participate in important pathways involved in the pathogenesis of vitiligo. Many susceptible loci with unknown functions in the pathogenesis of vitiligo have also been identified, indicating that additional molecular mechanisms may contribute to the risk of developing vitiligo. In this review, we summarize the key loci that are of genome-wide significance, which have been shown to influence vitiligo risk. These genetic loci may help build the foundation for genetic diagnosis and personalize treatment for patients with vitiligo in the future. However, substantial additional studies, including gene-targeted and functional studies, are required to confirm the causality of the genetic variants and their biological relevance in the development of vitiligo. PMID:26870082

Gene Therapy for Skin Diseases

PubMed Central

Gorell, Emily; Nguyen, Ngon; Lane, Alfred; Siprashvili, Zurab

2014-01-01

The skin possesses qualities that make it desirable for gene therapy, and studies have focused on gene therapy for multiple cutaneous diseases. Gene therapy uses a vector to introduce genetic material into cells to alter gene expression, negating a pathological process. This can be accomplished with a variety of viral vectors or nonviral administrations. Although results are promising, there are several potential pitfalls that must be addressed to improve the safety profile to make gene therapy widely available clinically. PMID:24692191

Integrated rare variant-based risk gene prioritization in disease case-control sequencing studies.

PubMed

Lin, Jhih-Rong; Zhang, Quanwei; Cai, Ying; Morrow, Bernice E; Zhang, Zhengdong D

2017-12-01

Rare variants of major effect play an important role in human complex diseases and can be discovered by sequencing-based genome-wide association studies. Here, we introduce an integrated approach that combines the rare variant association test with gene network and phenotype information to identify risk genes implicated by rare variants for human complex diseases. Our data integration method follows a 'discovery-driven' strategy without relying on prior knowledge about the disease and thus maintains the unbiased character of genome-wide association studies. Simulations reveal that our method can outperform a widely-used rare variant association test method by 2 to 3 times. In a case study of a small disease cohort, we uncovered putative risk genes and the corresponding rare variants that may act as genetic modifiers of congenital heart disease in 22q11.2 deletion syndrome patients. These variants were missed by a conventional approach that relied on the rare variant association test alone.

Knowledge-based compact disease models identify new molecular players contributing to early-stage Alzheimer’s disease

PubMed Central

2013-01-01

Background High-throughput profiling of human tissues typically yield as results the gene lists comprised of a mix of relevant molecular entities with multiple false positives that obstruct the translation of such results into mechanistic hypotheses. From general probabilistic considerations, gene lists distilled for the mechanistically relevant components can be far more useful for subsequent experimental design or data interpretation. Results The input candidate gene lists were processed into different tiers of evidence consistency established by enrichment analysis across subsets of the same experiments and across different experiments and platforms. The cut-offs were established empirically through ontological and semantic enrichment; resultant shortened gene list was re-expanded by Ingenuity Pathway Assistant tool. The resulting sub-networks provided the basis for generating mechanistic hypotheses that were partially validated by literature search. This approach differs from previous consistency-based studies in that the cut-off on the Receiver Operating Characteristic of the true-false separation process is optimized by flexible selection of the consistency building procedure. The gene list distilled by this analytic technique and its network representation were termed Compact Disease Model (CDM). Here we present the CDM signature for the study of early-stage Alzheimer’s disease. The integrated analysis of this gene signature allowed us to identify the protein traffic vesicles as prominent players in the pathogenesis of Alzheimer’s. Considering the distances and complexity of protein trafficking in neurons, it is plausible that spontaneous protein misfolding along with a shortage of growth stimulation result in neurodegeneration. Several potentially overlapping scenarios of early-stage Alzheimer pathogenesis have been discussed, with an emphasis on the protective effects of AT-1 mediated antihypertensive response on cytoskeleton remodeling, along with

Comprehensive evaluation of disease- and trait-specific enrichment for eight functional elements among GWAS-identified variants.

PubMed

Markunas, Christina A; Johnson, Eric O; Hancock, Dana B

2017-07-01

Genome-wide association study (GWAS)-identified variants are enriched for functional elements. However, we have limited knowledge of how functional enrichment may differ by disease/trait and tissue type. We tested a broad set of eight functional elements for enrichment among GWAS-identified SNPs (p < 5×10 -8 ) from the NHGRI-EBI Catalog across seven disease/trait categories: cancer, cardiovascular disease, diabetes, autoimmune disease, psychiatric disease, neurological disease, and anthropometric traits. SNPs were annotated using HaploReg for the eight functional elements across any tissue: DNase sites, expression quantitative trait loci (eQTL), sequence conservation, enhancers, promoters, missense variants, sequence motifs, and protein binding sites. In addition, tissue-specific annotations were considered for brain vs. blood. Disease/trait SNPs were compared to a control set of 4809 SNPs matched to the GWAS SNPs (N = 1639) on allele frequency, gene density, distance to nearest gene, and linkage disequilibrium at ~3:1 ratio. Enrichment analyses were conducted using logistic regression, with Bonferroni correction. Overall, a significant enrichment was observed for all functional elements, except sequence motifs. Missense SNPs showed the strongest magnitude of enrichment. eQTLs were the only functional element significantly enriched across all diseases/traits. Magnitudes of enrichment were generally similar across diseases/traits, where enrichment was statistically significant. Blood vs. brain tissue effects on enrichment were dependent on disease/trait and functional element (e.g., cardiovascular disease: eQTLs P TissueDifference  = 1.28 × 10 -6 vs. enhancers P TissueDifference  = 0.94). Identifying disease/trait-relevant functional elements and tissue types could provide new insight into the underlying biology, by guiding a priori GWAS analyses (e.g., brain enhancer elements for psychiatric disease) or facilitating post hoc interpretation.

Use of deep whole-genome sequencing data to identify structure risk variants in breast cancer susceptibility genes.

PubMed

Guo, Xingyi; Shi, Jiajun; Cai, Qiuyin; Shu, Xiao-Ou; He, Jing; Wen, Wanqing; Allen, Jamie; Pharoah, Paul; Dunning, Alison; Hunter, David J; Kraft, Peter; Easton, Douglas F; Zheng, Wei; Long, Jirong

2018-03-01

Functional disruptions of susceptibility genes by large genomic structure variant (SV) deletions in germlines are known to be associated with cancer risk. However, few studies have been conducted to systematically search for SV deletions in breast cancer susceptibility genes. We analysed deep (> 30x) whole-genome sequencing (WGS) data generated in blood samples from 128 breast cancer patients of Asian and European descent with either a strong family history of breast cancer or early cancer onset disease. To identify SV deletions in known or suspected breast cancer susceptibility genes, we used multiple SV calling tools including Genome STRiP, Delly, Manta, BreakDancer and Pindel. SV deletions were detected by at least three of these bioinformatics tools in five genes. Specifically, we identified heterozygous deletions covering a fraction of the coding regions of BRCA1 (with approximately 80kb in two patients), and TP53 genes (with ∼1.6 kb in two patients), and of intronic regions (∼1 kb) of the PALB2 (one patient), PTEN (three patients) and RAD51C genes (one patient). We confirmed the presence of these deletions using real-time quantitative PCR (qPCR). Our study identified novel SV deletions in breast cancer susceptibility genes and the identification of such SV deletions may improve clinical testing.

Germline Missense Changes in the APC Gene and Their Relationship to Disease.

PubMed

Scott, Rodney J; Crooks, Renee; Rose, Lindy; Attia, John; Thakkinstian, Ammarin; Thomas, Lesley; Spigelman, Allan D; Meldrum, Cliff J

2004-05-15

Familial adenomatous polyposis (FAP) is characterized by the presence of hundreds to thousands of adenomas that carpet the entire colon and rectum. Nonsense and frameshift mutations in the adenomatous polyposis coli (APC) gene account for the majority of mutations identified to date and predispose primarily to the typical disease phenotype. Some APC mutations are associated with a milder form of the disease known as attenuated FAP. Virtually all mutations that have been described in the APC gene result in the formation of a premature stop codon and very little is known about missense mutations apart from a common Ashkenazi Jewish mutation (1307 K) and a British E1317Q missense change. The incidence of missense mutations in the APC gene has been underreported since the APC gene lends itself to analysis using an artificial transcription and translation assay known as the Protein Truncation Test (PTT) or the In Vitro Synthetic Protein assay (IVSP).In this report we have used denaturing high performance liquid chromatography to analyse the entire coding sequence of the APC gene to determine if a cohort of patients adhering to the diagnostic criteria of FAP to assess the frequency of missense mutations in the APC gene. Altogether 112 patients were studied and 22 missense mutations were identified. From the total of 22 missense changes, 13 were silent changes and the remaining 9 resulted in amino acid substitutions. One or more of these changes were identified multiple times in 62.5% of the population under study.The results reveal that missense mutations in the APC gene appear not to radically alter protein function but may be associated with more subtle processing of RNA transcripts which in turn could result in the expression of differentially spliced forms of the APC gene which may interfere with the functional activity of the APC protein.

A yeast functional screen predicts new candidate ALS disease genes

PubMed Central

Couthouis, Julien; Hart, Michael P.; Shorter, James; DeJesus-Hernandez, Mariely; Erion, Renske; Oristano, Rachel; Liu, Annie X.; Ramos, Daniel; Jethava, Niti; Hosangadi, Divya; Epstein, James; Chiang, Ashley; Diaz, Zamia; Nakaya, Tadashi; Ibrahim, Fadia; Kim, Hyung-Jun; Solski, Jennifer A.; Williams, Kelly L.; Mojsilovic-Petrovic, Jelena; Ingre, Caroline; Boylan, Kevin; Graff-Radford, Neill R.; Dickson, Dennis W.; Clay-Falcone, Dana; Elman, Lauren; McCluskey, Leo; Greene, Robert; Kalb, Robert G.; Lee, Virginia M.-Y.; Trojanowski, John Q.; Ludolph, Albert; Robberecht, Wim; Andersen, Peter M.; Nicholson, Garth A.; Blair, Ian P.; King, Oliver D.; Bonini, Nancy M.; Van Deerlin, Vivianna; Rademakers, Rosa; Mourelatos, Zissimos; Gitler, Aaron D.

2011-01-01

Amyotrophic lateral sclerosis (ALS) is a devastating and universally fatal neurodegenerative disease. Mutations in two related RNA-binding proteins, TDP-43 and FUS, that harbor prion-like domains, cause some forms of ALS. There are at least 213 human proteins harboring RNA recognition motifs, including FUS and TDP-43, raising the possibility that additional RNA-binding proteins might contribute to ALS pathogenesis. We performed a systematic survey of these proteins to find additional candidates similar to TDP-43 and FUS, followed by bioinformatics to predict prion-like domains in a subset of them. We sequenced one of these genes, TAF15, in patients with ALS and identified missense variants, which were absent in a large number of healthy controls. These disease-associated variants of TAF15 caused formation of cytoplasmic foci when expressed in primary cultures of spinal cord neurons. Very similar to TDP-43 and FUS, TAF15 aggregated in vitro and conferred neurodegeneration in Drosophila, with the ALS-linked variants having a more severe effect than wild type. Immunohistochemistry of postmortem spinal cord tissue revealed mislocalization of TAF15 in motor neurons of patients with ALS. We propose that aggregation-prone RNA-binding proteins might contribute very broadly to ALS pathogenesis and the genes identified in our yeast functional screen, coupled with prion-like domain prediction analysis, now provide a powerful resource to facilitate ALS disease gene discovery. PMID:22065782

Microarray-based identification of differentially expressed genes in extramammary Paget’s disease

PubMed Central

Lin, Jin-Ran; Liang, Jun; Zhang, Qiao-An; Huang, Qiong; Wang, Shang-Shang; Qin, Hai-Hong; Chen, Lian-Jun; Xu, Jin-Hua

2015-01-01

Extramammary Paget’s disease (EMPD) is a rare cutaneous malignancy accounting for approximately 1-2% of vulvar cancers. The rarity of this disease has caused difficulties in characterization and the molecular mechanism underlying EMPD development remains largely unclear. Here we used microarray analysis to identify differentially expressed genes in EMPD of the scrotum comparing with normal epithelium from healthy donors. Agilent single-channel microarray was used to compare the gene expression between 6 EMPD specimens and 6 normal scrotum epithelium samples. A total of 799 up-regulated genes and 723 down-regulated genes were identified in EMPD tissues. Real-time PCR was conducted to verify the differential expression of some representative genes, including ERBB4, TCF3, PAPSS2, PIK3R3, PRLR, SULT1A1, TCF7L1, and CREB3L4. Generally, the real-time PCR results were consistent with microarray data, and the expression of ERBB4, PRLR, TCF3, PIK3R3, SULT1A1, and TCF7L1 was significantly overexpressed in EMPD (P<0.05). Moreover, the overexpression of PRLR in EMPD, a receptor for the anterior pituitary hormone prolactin (PRL), was confirmed by immunohistochemistry. These data demonstrate that the differentially expressed genes from the microarray-based identification are tightly associated with EMPD occurrence. PMID:26221264

Repressor- and Activator-Type Ethylene Response Factors Functioning in Jasmonate Signaling and Disease Resistance Identified via a Genome-Wide Screen of Arabidopsis Transcription Factor Gene Expression[w

PubMed Central

McGrath, Ken C.; Dombrecht, Bruno; Manners, John M.; Schenk, Peer M.; Edgar, Cameron I.; Maclean, Donald J.; Scheible, Wolf-Rüdiger; Udvardi, Michael K.; Kazan, Kemal

2005-01-01

To identify transcription factors (TFs) involved in jasmonate (JA) signaling and plant defense, we screened 1,534 Arabidopsis (Arabidopsis thaliana) TFs by real-time quantitative reverse transcription-PCR for their altered transcript at 6 h following either methyl JA treatment or inoculation with the incompatible pathogen Alternaria brassicicola. We identified 134 TFs that showed a significant change in expression, including many APETALA2/ethylene response factor (AP2/ERF), MYB, WRKY, and NAC TF genes with unknown functions. Twenty TF genes were induced by both the pathogen and methyl JA and these included 10 members of the AP2/ERF TF family, primarily from the B1a and B3 subclusters. Functional analysis of the B1a TF AtERF4 revealed that AtERF4 acts as a novel negative regulator of JA-responsive defense gene expression and resistance to the necrotrophic fungal pathogen Fusarium oxysporum and antagonizes JA inhibition of root elongation. In contrast, functional analysis of the B3 TF AtERF2 showed that AtERF2 is a positive regulator of JA-responsive defense genes and resistance to F. oxysporum and enhances JA inhibition of root elongation. Our results suggest that plants coordinately express multiple repressor- and activator-type AP2/ERFs during pathogen challenge to modulate defense gene expression and disease resistance. PMID:16183832

Single cell gene expression profiling in Alzheimer's disease.

PubMed

Ginsberg, Stephen D; Che, Shaoli; Counts, Scott E; Mufson, Elliott J

2006-07-01

Development and implementation of microarray techniques to quantify expression levels of dozens to hundreds to thousands of transcripts simultaneously within select tissue samples from normal control subjects and neurodegenerative diseased brains has enabled scientists to create molecular fingerprints of vulnerable neuronal populations in Alzheimer's disease (AD) and related disorders. A goal is to sample gene expression from homogeneous cell types within a defined region without potential contamination by expression profiles of adjacent neuronal subpopulations and nonneuronal cells. The precise resolution afforded by single cell and population cell RNA analysis in combination with microarrays and real-time quantitative polymerase chain reaction (qPCR)-based analyses allows for relative gene expression level comparisons across cell types under different experimental conditions and disease progression. The ability to analyze single cells is an important distinction from global and regional assessments of mRNA expression and can be applied to optimally prepared tissues from animal models of neurodegeneration as well as postmortem human brain tissues. Gene expression analysis in postmortem AD brain regions including the hippocampal formation and neocortex reveals selectively vulnerable cell types share putative pathogenetic alterations in common classes of transcripts, for example, markers of glutamatergic neurotransmission, synaptic-related markers, protein phosphatases and kinases, and neurotrophins/neurotrophin receptors. Expression profiles of vulnerable regions and neurons may reveal important clues toward the understanding of the molecular pathogenesis of various neurological diseases and aid in identifying rational targets toward pharmacotherapeutic interventions for progressive, late-onset neurodegenerative disorders such as mild cognitive impairment (MCI) and AD.

DISEASES: text mining and data integration of disease-gene associations.

PubMed

Pletscher-Frankild, Sune; Pallejà, Albert; Tsafou, Kalliopi; Binder, Janos X; Jensen, Lars Juhl

2015-03-01

Text mining is a flexible technology that can be applied to numerous different tasks in biology and medicine. We present a system for extracting disease-gene associations from biomedical abstracts. The system consists of a highly efficient dictionary-based tagger for named entity recognition of human genes and diseases, which we combine with a scoring scheme that takes into account co-occurrences both within and between sentences. We show that this approach is able to extract half of all manually curated associations with a false positive rate of only 0.16%. Nonetheless, text mining should not stand alone, but be combined with other types of evidence. For this reason, we have developed the DISEASES resource, which integrates the results from text mining with manually curated disease-gene associations, cancer mutation data, and genome-wide association studies from existing databases. The DISEASES resource is accessible through a web interface at http://diseases.jensenlab.org/, where the text-mining software and all associations are also freely available for download. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Identifying key genes associated with acute myocardial infarction

PubMed Central

Cheng, Ming; An, Shoukuan; Li, Junquan

2017-01-01

Abstract Background: This study aimed to identify key genes associated with acute myocardial infarction (AMI) by reanalyzing microarray data. Methods: Three gene expression profile datasets GSE66360, GSE34198, and GSE48060 were downloaded from GEO database. After data preprocessing, genes without heterogeneity across different platforms were subjected to differential expression analysis between the AMI group and the control group using metaDE package. P < .05 was used as the cutoff for a differentially expressed gene (DEG). The expression data matrices of DEGs were imported in ReactomeFIViz to construct a gene functional interaction (FI) network. Then, DEGs in each module were subjected to pathway enrichment analysis using DAVID. MiRNAs and transcription factors predicted to regulate target DEGs were identified. Quantitative real-time polymerase chain reaction (RT-PCR) was applied to verify the expression of genes. Result: A total of 913 upregulated genes and 1060 downregulated genes were identified in the AMI group. A FI network consists of 21 modules and DEGs in 12 modules were significantly enriched in pathways. The transcription factor-miRNA-gene network contains 2 transcription factors FOXO3 and MYBL2, and 2 miRNAs hsa-miR-21-5p and hsa-miR-30c-5p. RT-PCR validations showed that expression levels of FOXO3 and MYBL2 were significantly increased in AMI, and expression levels of hsa-miR-21–5p and hsa-miR-30c-5p were obviously decreased in AMI. Conclusion: A total of 41 DEGs, such as SOCS3, VAPA, and COL5A2, are speculated to have roles in the pathogenesis of AMI; 2 transcription factors FOXO3 and MYBL2, and 2 miRNAs hsa-miR-21-5p and hsa-miR-30c-5p may be involved in the regulation of the expression of these DEGs. PMID:29049183

Prioritization of candidate disease genes by topological similarity between disease and protein diffusion profiles.

PubMed

Zhu, Jie; Qin, Yufang; Liu, Taigang; Wang, Jun; Zheng, Xiaoqi

2013-01-01

Identification of gene-phenotype relationships is a fundamental challenge in human health clinic. Based on the observation that genes causing the same or similar phenotypes tend to correlate with each other in the protein-protein interaction network, a lot of network-based approaches were proposed based on different underlying models. A recent comparative study showed that diffusion-based methods achieve the state-of-the-art predictive performance. In this paper, a new diffusion-based method was proposed to prioritize candidate disease genes. Diffusion profile of a disease was defined as the stationary distribution of candidate genes given a random walk with restart where similarities between phenotypes are incorporated. Then, candidate disease genes are prioritized by comparing their diffusion profiles with that of the disease. Finally, the effectiveness of our method was demonstrated through the leave-one-out cross-validation against control genes from artificial linkage intervals and randomly chosen genes. Comparative study showed that our method achieves improved performance compared to some classical diffusion-based methods. To further illustrate our method, we used our algorithm to predict new causing genes of 16 multifactorial diseases including Prostate cancer and Alzheimer's disease, and the top predictions were in good consistent with literature reports. Our study indicates that integration of multiple information sources, especially the phenotype similarity profile data, and introduction of global similarity measure between disease and gene diffusion profiles are helpful for prioritizing candidate disease genes. Programs and data are available upon request.

Linking disease-associated genes to regulatory networks via promoter organization

PubMed Central

Döhr, S.; Klingenhoff, A.; Maier, H.; de Angelis, M. Hrabé; Werner, T.; Schneider, R.

2005-01-01

Pathway- or disease-associated genes may participate in more than one transcriptional co-regulation network. Such gene groups can be readily obtained by literature analysis or by high-throughput techniques such as microarrays or protein-interaction mapping. We developed a strategy that defines regulatory networks by in silico promoter analysis, finding potentially co-regulated subgroups without a priori knowledge. Pairs of transcription factor binding sites conserved in orthologous genes (vertically) as well as in promoter sequences of co-regulated genes (horizontally) were used as seeds for the development of promoter models representing potential co-regulation. This approach was applied to a Maturity Onset Diabetes of the Young (MODY)-associated gene list, which yielded two models connecting functionally interacting genes within MODY-related insulin/glucose signaling pathways. Additional genes functionally connected to our initial gene list were identified by database searches with these promoter models. Thus, data-driven in silico promoter analysis allowed integrating molecular mechanisms with biological functions of the cell. PMID:15701758

Genomic structure and expression of STM2, the chromosome 1 familial Alzheimer disease gene

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

Levy-Lahad, E.; Wang, Kai; Fu, Ying Hui

1996-06-01

Mutations in the gene STM2 result in autosomal dominant familial Alzheimer disease. To screen for mutations and to identify regulatory elements for this gene, the genomic DNA sequence and intron-exon structure were determined. Twelve exons including 10 coding exons were identified in a genomic region spanning 23, 737 bp. The first 2 exons encode the 5{prime}-untranslated region. Expression analysis of STM2 indicates that two transcripts of 2.4 and 2.8 kb are found in skeletal muscle, pancreas, and heart. In addition, a splice variant of the 2.4-kb transcript was identified that is the result of the use of an alternative splicemore » acceptor site located in exon 10. The use of this site results in a transcript lacking a single glutamate. The promotor for this gene and the alternatively spliced exons leading to the 2.8-kb form of the gene remain to be identified. Expression of STM2 was high in skeletal muscle and pancreas, with comparatively low levels observed in brain. This expression pattern is intriguing since in Alzheimer disease, pathology and degeneration are observed only in the central nervous system. 19 refs., 2 figs., 3 tabs.« less

Gene Expression Differences in Peripheral Blood of Parkinson’s Disease Patients with Distinct Progression Profiles

PubMed Central

Soreq, Lilach; Lobo, Patrícia P.; Mestre, Tiago; Coelho, Miguel; Rosa, Mário M.; Gonçalves, Nilza; Wales, Pauline; Mendes, Tiago; Gerhardt, Ellen; Fahlbusch, Christiane; Bonifati, Vincenzo; Bonin, Michael; Miltenberger-Miltényi, Gabriel; Borovecki, Fran; Soreq, Hermona; Ferreira, Joaquim J.; F. Outeiro, Tiago

2016-01-01

The prognosis of neurodegenerative disorders is clinically challenging due to the inexistence of established biomarkers for predicting disease progression. Here, we performed an exploratory cross-sectional, case-control study aimed at determining whether gene expression differences in peripheral blood may be used as a signature of Parkinson’s disease (PD) progression, thereby shedding light into potential molecular mechanisms underlying disease development. We compared transcriptional profiles in the blood from 34 PD patients who developed postural instability within ten years with those of 33 patients who did not develop postural instability within this time frame. Our study identified >200 differentially expressed genes between the two groups. The expression of several of the genes identified was previously found deregulated in animal models of PD and in PD patients. Relevant genes were selected for validation by real-time PCR in a subset of patients. The genes validated were linked to nucleic acid metabolism, mitochondria, immune response and intracellular-transport. Interestingly, we also found deregulation of these genes in a dopaminergic cell model of PD, a simple paradigm that can now be used to further dissect the role of these molecular players on dopaminergic cell loss. Altogether, our study provides preliminary evidence that expression changes in specific groups of genes and pathways, detected in peripheral blood samples, may be correlated with differential PD progression. Our exploratory study suggests that peripheral gene expression profiling may prove valuable for assisting in prediction of PD prognosis, and identifies novel culprits possibly involved in dopaminergic cell death. Given the exploratory nature of our study, further investigations using independent, well-characterized cohorts will be essential in order to validate our candidates as predictors of PD prognosis and to definitively confirm the value of gene expression analysis in aiding

CFTR gene mutations in isolated chronic obstructive pulmonary disease

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

Pignatti, P.F.; Bombien, C.; Marigo, C.

1994-09-01

In order to identify a possible hereditary predisposition to the development of chronic obstructive pulmonary disease (COPD), we have looked for the presence of cystic fibrosis transmembrane regulator (CFTR) gene DNA sequence modifications in 28 unrelated patients with no signs of cystic fibrosis. The known mutations in Italian CF patients, as well as the most frequent worldwide CF mutations, were investigated. In addition, a denaturing gradient gel electrophoresis analysis of about half of the coding sequence of the gene in 56 chromosomes from the patients and in 102 chromosomes from control individuals affected by other pulmonary diseases and from normalmore » controls was performed. Nine different CFTR gene mutations and polymorphisms were found in seven patients, a highly significant increase over controls. Two of the patients were compound heterozygotes. Two frequent CF mutations were detected: deletion F508 and R117H; two rare CF mutations: R1066C and 3667ins4; and five CF sequence variants: R75Q (which was also described as a disease-causing mutation in male sterility cases due to the absence of the vasa deferentia), G576A, 2736 A{r_arrow}G, L997F, and 3271+18C{r_arrow}T. Seven (78%) of the mutations are localized in transmembrane domains. Six (86%) of the patients with defined mutations and polymorphisms had bronchiectasis. These results indicate that CFTR gene mutations and sequence alterations may be involved in the etiopathogenesis of some cases of COPD.« less

Large-scale functional RNAi screen in C. elegans identifies genes that regulate the dysfunction of mutant polyglutamine neurons

PubMed Central

2012-01-01

Background A central goal in Huntington's disease (HD) research is to identify and prioritize candidate targets for neuroprotective intervention, which requires genome-scale information on the modifiers of early-stage neuron injury in HD. Results Here, we performed a large-scale RNA interference screen in C. elegans strains that express N-terminal huntingtin (htt) in touch receptor neurons. These neurons control the response to light touch. Their function is strongly impaired by expanded polyglutamines (128Q) as shown by the nearly complete loss of touch response in adult animals, providing an in vivo model in which to manipulate the early phases of expanded-polyQ neurotoxicity. In total, 6034 genes were examined, revealing 662 gene inactivations that either reduce or aggravate defective touch response in 128Q animals. Several genes were previously implicated in HD or neurodegenerative disease, suggesting that this screen has effectively identified candidate targets for HD. Network-based analysis emphasized a subset of high-confidence modifier genes in pathways of interest in HD including metabolic, neurodevelopmental and pro-survival pathways. Finally, 49 modifiers of 128Q-neuron dysfunction that are dysregulated in the striatum of either R/2 or CHL2 HD mice, or both, were identified. Conclusions Collectively, these results highlight the relevance to HD pathogenesis, providing novel information on the potential therapeutic targets for neuroprotection in HD. PMID:22413862

Large-scale functional RNAi screen in C. elegans identifies genes that regulate the dysfunction of mutant polyglutamine neurons.

PubMed

Lejeune, François-Xavier; Mesrob, Lilia; Parmentier, Frédéric; Bicep, Cedric; Vazquez-Manrique, Rafael P; Parker, J Alex; Vert, Jean-Philippe; Tourette, Cendrine; Neri, Christian

2012-03-13

A central goal in Huntington's disease (HD) research is to identify and prioritize candidate targets for neuroprotective intervention, which requires genome-scale information on the modifiers of early-stage neuron injury in HD. Here, we performed a large-scale RNA interference screen in C. elegans strains that express N-terminal huntingtin (htt) in touch receptor neurons. These neurons control the response to light touch. Their function is strongly impaired by expanded polyglutamines (128Q) as shown by the nearly complete loss of touch response in adult animals, providing an in vivo model in which to manipulate the early phases of expanded-polyQ neurotoxicity. In total, 6034 genes were examined, revealing 662 gene inactivations that either reduce or aggravate defective touch response in 128Q animals. Several genes were previously implicated in HD or neurodegenerative disease, suggesting that this screen has effectively identified candidate targets for HD. Network-based analysis emphasized a subset of high-confidence modifier genes in pathways of interest in HD including metabolic, neurodevelopmental and pro-survival pathways. Finally, 49 modifiers of 128Q-neuron dysfunction that are dysregulated in the striatum of either R/2 or CHL2 HD mice, or both, were identified. Collectively, these results highlight the relevance to HD pathogenesis, providing novel information on the potential therapeutic targets for neuroprotection in HD. © 2012 Lejeune et al; licensee BioMed Central Ltd.

Gene expression meta-analysis identifies chromosomal regions and candidate genes involved in breast cancer metastasis.

PubMed

Thomassen, Mads; Tan, Qihua; Kruse, Torben A

2009-01-01

Breast cancer cells exhibit complex karyotypic alterations causing deregulation of numerous genes. Some of these genes are probably causal for cancer formation and local growth whereas others are causal for the various steps of metastasis. In a fraction of tumors deregulation of the same genes might be caused by epigenetic modulations, point mutations or the influence of other genes. We have investigated the relation of gene expression and chromosomal position, using eight datasets including more than 1200 breast tumors, to identify chromosomal regions and candidate genes possibly causal for breast cancer metastasis. By use of "Gene Set Enrichment Analysis" we have ranked chromosomal regions according to their relation to metastasis. Overrepresentation analysis identified regions with increased expression for chromosome 1q41-42, 8q24, 12q14, 16q22, 16q24, 17q12-21.2, 17q21-23, 17q25, 20q11, and 20q13 among metastasizing tumors and reduced gene expression at 1p31-21, 8p22-21, and 14q24. By analysis of genes with extremely imbalanced expression in these regions we identified DIRAS3 at 1p31, PSD3, LPL, EPHX2 at 8p21-22, and FOS at 14q24 as candidate metastasis suppressor genes. Potential metastasis promoting genes includes RECQL4 at 8q24, PRMT7 at 16q22, GINS2 at 16q24, and AURKA at 20q13.

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

Genes involved in muscle contractility and nutrient signaling pathways within celiac disease risk loci show differential mRNA expression.

PubMed

Montén, Caroline; Gudjonsdottir, Audur H; Browaldh, Lars; Arnell, Henrik; Nilsson, Staffan; Agardh, Daniel; Naluai, Åsa Torinsson

2015-06-30

Risk gene variants for celiac disease, identified in genome-wide linkage and association studies, might influence molecular pathways important for disease development. The aim was to examine expression levels of potential risk genes close to these variants in the small intestine and peripheral blood and also to test if the non-coding variants affect nearby gene expression levels in children with celiac disease. Intestinal biopsy and peripheral blood RNA was isolated from 167 children with celiac disease, 61 with potential celiac disease and 174 disease controls. Transcript levels for 88 target genes, selected from celiac disease risk loci, were analyzed in biopsies of a smaller sample subset by qPCR. Differentially expressed genes (3 from the pilot and 8 previously identified) were further validated in the larger sample collection (n = 402) of both tissues and correlated to nearby celiac disease risk variants. All genes were significantly down- or up-regulated in the intestinal mucosa of celiac disease children, NTS being most down-regulated (Fold change 3.6, p < 0.001). In contrast, PPP1R12B isoform C was up-regulated in the celiac disease mucosa (Fold change 1.9, p < 0.001). Allele specific expression of GLS (rs6741418, p = 0.009), INSR (rs7254060, p = 0.003) and NCALD (rs652008, p = 0.005) was also detected in the biopsies. Two genes (APPL2 and NCALD) were differentially expressed in peripheral blood but no allele specific expression was observed in this tissue. The differential expression of NTS and PPP1R12B indicate a potential role for smooth muscle contractility and cell proliferation in celiac disease, whereas other genes like GLS, NCALD and INSR suggests involvement of nutrient signaling and energy homeostasis in celiac disease pathogenesis. A disturbance in any of these pathways might contribute to development of childhood celiac disease.

Gene-Disease Network Analysis Reveals Functional Modules in Mendelian, Complex and Environmental Diseases

PubMed Central

Bauer-Mehren, Anna; Bundschus, Markus; Rautschka, Michael; Mayer, Miguel A.; Sanz, Ferran; Furlong, Laura I.

2011-01-01

Background Scientists have been trying to understand the molecular mechanisms of diseases to design preventive and therapeutic strategies for a long time. For some diseases, it has become evident that it is not enough to obtain a catalogue of the disease-related genes but to uncover how disruptions of molecular networks in the cell give rise to disease phenotypes. Moreover, with the unprecedented wealth of information available, even obtaining such catalogue is extremely difficult. Principal Findings We developed a comprehensive gene-disease association database by integrating associations from several sources that cover different biomedical aspects of diseases. In particular, we focus on the current knowledge of human genetic diseases including mendelian, complex and environmental diseases. To assess the concept of modularity of human diseases, we performed a systematic study of the emergent properties of human gene-disease networks by means of network topology and functional annotation analysis. The results indicate a highly shared genetic origin of human diseases and show that for most diseases, including mendelian, complex and environmental diseases, functional modules exist. Moreover, a core set of biological pathways is found to be associated with most human diseases. We obtained similar results when studying clusters of diseases, suggesting that related diseases might arise due to dysfunction of common biological processes in the cell. Conclusions For the first time, we include mendelian, complex and environmental diseases in an integrated gene-disease association database and show that the concept of modularity applies for all of them. We furthermore provide a functional analysis of disease-related modules providing important new biological insights, which might not be discovered when considering each of the gene-disease association repositories independently. Hence, we present a suitable framework for the study of how genetic and environmental factors