Codon usage and amino acid usage influence genes expression level.

PubMed

Paul, Prosenjit; Malakar, Arup Kumar; Chakraborty, Supriyo

2018-02-01

Highly expressed genes in any species differ in the usage frequency of synonymous codons. The relative recurrence of an event of the favored codon pair (amino acid pairs) varies between gene and genomes due to varying gene expression and different base composition. Here we propose a new measure for predicting the gene expression level, i.e., codon plus amino bias index (CABI). Our approach is based on the relative bias of the favored codon pair inclination among the genes, illustrated by analyzing the CABI score of the Medicago truncatula genes. CABI showed strong correlation with all other widely used measures (CAI, RCBS, SCUO) for gene expression analysis. Surprisingly, CABI outperforms all other measures by showing better correlation with the wet-lab data. This emphasizes the importance of the neighboring codons of the favored codon in a synonymous group while estimating the expression level of a gene.

A method to identify differential expression profiles of time-course gene data with Fourier transformation.

PubMed

Kim, Jaehee; Ogden, Robert Todd; Kim, Haseong

2013-10-18

Time course gene expression experiments are an increasingly popular method for exploring biological processes. Temporal gene expression profiles provide an important characterization of gene function, as biological systems are both developmental and dynamic. With such data it is possible to study gene expression changes over time and thereby to detect differential genes. Much of the early work on analyzing time series expression data relied on methods developed originally for static data and thus there is a need for improved methodology. Since time series expression is a temporal process, its unique features such as autocorrelation between successive points should be incorporated into the analysis. This work aims to identify genes that show different gene expression profiles across time. We propose a statistical procedure to discover gene groups with similar profiles using a nonparametric representation that accounts for the autocorrelation in the data. In particular, we first represent each profile in terms of a Fourier basis, and then we screen out genes that are not differentially expressed based on the Fourier coefficients. Finally, we cluster the remaining gene profiles using a model-based approach in the Fourier domain. We evaluate the screening results in terms of sensitivity, specificity, FDR and FNR, compare with the Gaussian process regression screening in a simulation study and illustrate the results by application to yeast cell-cycle microarray expression data with alpha-factor synchronization.The key elements of the proposed methodology: (i) representation of gene profiles in the Fourier domain; (ii) automatic screening of genes based on the Fourier coefficients and taking into account autocorrelation in the data, while controlling the false discovery rate (FDR); (iii) model-based clustering of the remaining gene profiles. Using this method, we identified a set of cell-cycle-regulated time-course yeast genes. The proposed method is general and can be potentially used to identify genes which have the same patterns or biological processes, and help facing the present and forthcoming challenges of data analysis in functional genomics.

Machine Learning–Based Differential Network Analysis: A Study of Stress-Responsive Transcriptomes in Arabidopsis[W

PubMed Central

Ma, Chuang; Xin, Mingming; Feldmann, Kenneth A.; Wang, Xiangfeng

2014-01-01

Machine learning (ML) is an intelligent data mining technique that builds a prediction model based on the learning of prior knowledge to recognize patterns in large-scale data sets. We present an ML-based methodology for transcriptome analysis via comparison of gene coexpression networks, implemented as an R package called machine learning–based differential network analysis (mlDNA) and apply this method to reanalyze a set of abiotic stress expression data in Arabidopsis thaliana. The mlDNA first used a ML-based filtering process to remove nonexpressed, constitutively expressed, or non-stress-responsive “noninformative” genes prior to network construction, through learning the patterns of 32 expression characteristics of known stress-related genes. The retained “informative” genes were subsequently analyzed by ML-based network comparison to predict candidate stress-related genes showing expression and network differences between control and stress networks, based on 33 network topological characteristics. Comparative evaluation of the network-centric and gene-centric analytic methods showed that mlDNA substantially outperformed traditional statistical testing–based differential expression analysis at identifying stress-related genes, with markedly improved prediction accuracy. To experimentally validate the mlDNA predictions, we selected 89 candidates out of the 1784 predicted salt stress–related genes with available SALK T-DNA mutagenesis lines for phenotypic screening and identified two previously unreported genes, mutants of which showed salt-sensitive phenotypes. PMID:24520154

A deep auto-encoder model for gene expression prediction.

PubMed

Xie, Rui; Wen, Jia; Quitadamo, Andrew; Cheng, Jianlin; Shi, Xinghua

2017-11-17

Gene expression is a key intermediate level that genotypes lead to a particular trait. Gene expression is affected by various factors including genotypes of genetic variants. With an aim of delineating the genetic impact on gene expression, we build a deep auto-encoder model to assess how good genetic variants will contribute to gene expression changes. This new deep learning model is a regression-based predictive model based on the MultiLayer Perceptron and Stacked Denoising Auto-encoder (MLP-SAE). The model is trained using a stacked denoising auto-encoder for feature selection and a multilayer perceptron framework for backpropagation. We further improve the model by introducing dropout to prevent overfitting and improve performance. To demonstrate the usage of this model, we apply MLP-SAE to a real genomic datasets with genotypes and gene expression profiles measured in yeast. Our results show that the MLP-SAE model with dropout outperforms other models including Lasso, Random Forests and the MLP-SAE model without dropout. Using the MLP-SAE model with dropout, we show that gene expression quantifications predicted by the model solely based on genotypes, align well with true gene expression patterns. We provide a deep auto-encoder model for predicting gene expression from SNP genotypes. This study demonstrates that deep learning is appropriate for tackling another genomic problem, i.e., building predictive models to understand genotypes' contribution to gene expression. With the emerging availability of richer genomic data, we anticipate that deep learning models play a bigger role in modeling and interpreting genomics.

Tissue Non-Specific Genes and Pathways Associated with Diabetes: An Expression Meta-Analysis.

PubMed

Mei, Hao; Li, Lianna; Liu, Shijian; Jiang, Fan; Griswold, Michael; Mosley, Thomas

2017-01-21

We performed expression studies to identify tissue non-specific genes and pathways of diabetes by meta-analysis. We searched curated datasets of the Gene Expression Omnibus (GEO) database and identified 13 and five expression studies of diabetes and insulin responses at various tissues, respectively. We tested differential gene expression by empirical Bayes-based linear method and investigated gene set expression association by knowledge-based enrichment analysis. Meta-analysis by different methods was applied to identify tissue non-specific genes and gene sets. We also proposed pathway mapping analysis to infer functions of the identified gene sets, and correlation and independent analysis to evaluate expression association profile of genes and gene sets between studies and tissues. Our analysis showed that PGRMC1 and HADH genes were significant over diabetes studies, while IRS1 and MPST genes were significant over insulin response studies, and joint analysis showed that HADH and MPST genes were significant over all combined data sets. The pathway analysis identified six significant gene sets over all studies. The KEGG pathway mapping indicated that the significant gene sets are related to diabetes pathogenesis. The results also presented that 12.8% and 59.0% pairwise studies had significantly correlated expression association for genes and gene sets, respectively; moreover, 12.8% pairwise studies had independent expression association for genes, but no studies were observed significantly different for expression association of gene sets. Our analysis indicated that there are both tissue specific and non-specific genes and pathways associated with diabetes pathogenesis. Compared to the gene expression, pathway association tends to be tissue non-specific, and a common pathway influencing diabetes development is activated through different genes at different tissues.

ARNetMiT R Package: association rules based gene co-expression networks of miRNA targets.

PubMed

Özgür Cingiz, M; Biricik, G; Diri, B

2017-03-31

miRNAs are key regulators that bind to target genes to suppress their gene expression level. The relations between miRNA-target genes enable users to derive co-expressed genes that may be involved in similar biological processes and functions in cells. We hypothesize that target genes of miRNAs are co-expressed, when they are regulated by multiple miRNAs. With the usage of these co-expressed genes, we can theoretically construct co-expression networks (GCNs) related to 152 diseases. In this study, we introduce ARNetMiT that utilize a hash based association rule algorithm in a novel way to infer the GCNs on miRNA-target genes data. We also present R package of ARNetMiT, which infers and visualizes GCNs of diseases that are selected by users. Our approach assumes miRNAs as transactions and target genes as their items. Support and confidence values are used to prune association rules on miRNA-target genes data to construct support based GCNs (sGCNs) along with support and confidence based GCNs (scGCNs). We use overlap analysis and the topological features for the performance analysis of GCNs. We also infer GCNs with popular GNI algorithms for comparison with the GCNs of ARNetMiT. Overlap analysis results show that ARNetMiT outperforms the compared GNI algorithms. We see that using high confidence values in scGCNs increase the ratio of the overlapped gene-gene interactions between the compared methods. According to the evaluation of the topological features of ARNetMiT based GCNs, the degrees of nodes have power-law distribution. The hub genes discovered by ARNetMiT based GCNs are consistent with the literature.

Partial Least Squares Based Gene Expression Analysis in EBV- Positive and EBV-Negative Posttransplant Lymphoproliferative Disorders.

PubMed

Wu, Sa; Zhang, Xin; Li, Zhi-Ming; Shi, Yan-Xia; Huang, Jia-Jia; Xia, Yi; Yang, Hang; Jiang, Wen-Qi

2013-01-01

Post-transplant lymphoproliferative disorder (PTLD) is a common complication of therapeutic immunosuppression after organ transplantation. Gene expression profile facilitates the identification of biological difference between Epstein-Barr virus (EBV) positive and negative PTLDs. Previous studies mainly implemented variance/regression analysis without considering unaccounted array specific factors. The aim of this study is to investigate the gene expression difference between EBV positive and negative PTLDs through partial least squares (PLS) based analysis. With a microarray data set from the Gene Expression Omnibus database, we performed PLS based analysis. We acquired 1188 differentially expressed genes. Pathway and Gene Ontology enrichment analysis identified significantly over-representation of dysregulated genes in immune response and cancer related biological processes. Network analysis identified three hub genes with degrees higher than 15, including CREBBP, ATXN1, and PML. Proteins encoded by CREBBP and PML have been reported to be interact with EBV before. Our findings shed light on expression distinction of EBV positive and negative PTLDs with the hope to offer theoretical support for future therapeutic study.

A transversal approach to predict gene product networks from ontology-based similarity

PubMed Central

Chabalier, Julie; Mosser, Jean; Burgun, Anita

2007-01-01

Background Interpretation of transcriptomic data is usually made through a "standard" approach which consists in clustering the genes according to their expression patterns and exploiting Gene Ontology (GO) annotations within each expression cluster. This approach makes it difficult to underline functional relationships between gene products that belong to different expression clusters. To address this issue, we propose a transversal analysis that aims to predict functional networks based on a combination of GO processes and data expression. Results The transversal approach presented in this paper consists in computing the semantic similarity between gene products in a Vector Space Model. Through a weighting scheme over the annotations, we take into account the representativity of the terms that annotate a gene product. Comparing annotation vectors results in a matrix of gene product similarities. Combined with expression data, the matrix is displayed as a set of functional gene networks. The transversal approach was applied to 186 genes related to the enterocyte differentiation stages. This approach resulted in 18 functional networks proved to be biologically relevant. These results were compared with those obtained through a standard approach and with an approach based on information content similarity. Conclusion Complementary to the standard approach, the transversal approach offers new insight into the cellular mechanisms and reveals new research hypotheses by combining gene product networks based on semantic similarity, and data expression. PMID:17605807

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.

Discovery and validation of a glioblastoma co-expressed gene module

PubMed Central

Dunwoodie, Leland J.; Poehlman, William L.; Ficklin, Stephen P.; Feltus, Frank Alexander

2018-01-01

Tumors exhibit complex patterns of aberrant gene expression. Using a knowledge-independent, noise-reducing gene co-expression network construction software called KINC, we created multiple RNAseq-based gene co-expression networks relevant to brain and glioblastoma biology. In this report, we describe the discovery and validation of a glioblastoma-specific gene module that contains 22 co-expressed genes. The genes are upregulated in glioblastoma relative to normal brain and lower grade glioma samples; they are also hypo-methylated in glioblastoma relative to lower grade glioma tumors. Among the proneural, neural, mesenchymal, and classical glioblastoma subtypes, these genes are most-highly expressed in the mesenchymal subtype. Furthermore, high expression of these genes is associated with decreased survival across each glioblastoma subtype. These genes are of interest to glioblastoma biology and our gene interaction discovery and validation workflow can be used to discover and validate co-expressed gene modules derived from any co-expression network. PMID:29541392

Discovery and validation of a glioblastoma co-expressed gene module.

PubMed

Dunwoodie, Leland J; Poehlman, William L; Ficklin, Stephen P; Feltus, Frank Alexander

2018-02-16

Tumors exhibit complex patterns of aberrant gene expression. Using a knowledge-independent, noise-reducing gene co-expression network construction software called KINC, we created multiple RNAseq-based gene co-expression networks relevant to brain and glioblastoma biology. In this report, we describe the discovery and validation of a glioblastoma-specific gene module that contains 22 co-expressed genes. The genes are upregulated in glioblastoma relative to normal brain and lower grade glioma samples; they are also hypo-methylated in glioblastoma relative to lower grade glioma tumors. Among the proneural, neural, mesenchymal, and classical glioblastoma subtypes, these genes are most-highly expressed in the mesenchymal subtype. Furthermore, high expression of these genes is associated with decreased survival across each glioblastoma subtype. These genes are of interest to glioblastoma biology and our gene interaction discovery and validation workflow can be used to discover and validate co-expressed gene modules derived from any co-expression network.

Characterization of basal gene expression trends over a diurnal cycle in Xiphophorus maculatus skin, brain and liver.

PubMed

Lu, Yuan; Reyes, Jose; Walter, Sean; Gonzalez, Trevor; Medrano, Geraldo; Boswell, Mikki; Boswell, William; Savage, Markita; Walter, Ronald

2018-06-01

Evolutionarily conserved diurnal circadian mechanisms maintain oscillating patterns of gene expression based on the day-night cycle. Xiphophorus fish have been used to evaluate transcriptional responses after exposure to various light sources and it was determined that each source incites distinct genetic responses in skin tissue. However, basal expression levels of genes that show oscillating expression patterns in day-night cycle, may affect the outcomes of such experiments, since basal gene expression levels at each point in the circadian path may influence the profile of identified light responsive genes. Lack of knowledge regarding diurnal fluctuations in basal gene expression patterns may confound the understanding of genetic responses to external stimuli (e.g., light) since the dynamic nature of gene expression implies animals subjected to stimuli at different times may be at very different stages within the continuum of genetic homeostasis. We assessed basal gene expression changes over a 24-hour period in 200 select Xiphophorus gene targets known to transcriptionally respond to various types of light exposure. We identified 22 genes in skin, 36 genes in brain and 28 genes in liver that exhibit basal oscillation of expression patterns. These genes, including known circadian regulators, produced the expected expression patterns over a 24-hour cycle when compared to circadian regulatory genes identified in other species, especially human and other vertebrate animal models. Our results suggest the regulatory network governing diurnal oscillating gene expression is similar between Xiphophorus and other vertebrates for the three Xiphophorus organs tested. In addition, we were able to categorize light responsive gene sets in Xiphophorus that do, and do not, exhibit circadian based oscillating expression patterns. Copyright © 2017 Elsevier Inc. All rights reserved.

Genome-Based Genetic Tool Development for Bacillus methanolicus: Theta- and Rolling Circle-Replicating Plasmids for Inducible Gene Expression and Application to Methanol-Based Cadaverine Production.

PubMed

Irla, Marta; Heggeset, Tonje M B; Nærdal, Ingemar; Paul, Lidia; Haugen, Tone; Le, Simone B; Brautaset, Trygve; Wendisch, Volker F

2016-01-01

Bacillus methanolicus is a thermophilic methylotroph able to overproduce amino acids from methanol, a substrate not used for human or animal nutrition. Based on our previous RNA-seq analysis a mannitol inducible promoter and a putative mannitol activator gene mtlR were identified. The mannitol inducible promoter was applied for controlled gene expression using fluorescent reporter proteins and a flow cytometry analysis, and improved by changing the -35 promoter region and by co-expression of the mtlR regulator gene. For independent complementary gene expression control, the heterologous xylose-inducible system from B. megaterium was employed and a two-plasmid gene expression system was developed. Four different replicons for expression vectors were compared with respect to their copy number and stability. As an application example, methanol-based production of cadaverine was shown to be improved from 11.3 to 17.5 g/L when a heterologous lysine decarboxylase gene cadA was expressed from a theta-replicating rather than a rolling-circle replicating vector. The current work on inducible promoter systems and compatible theta- or rolling circle-replicating vectors is an important extension of the poorly developed B. methanolicus genetic toolbox, valuable for genetic engineering and further exploration of this bacterium.

Genome-Based Genetic Tool Development for Bacillus methanolicus: Theta- and Rolling Circle-Replicating Plasmids for Inducible Gene Expression and Application to Methanol-Based Cadaverine Production

PubMed Central

Irla, Marta; Heggeset, Tonje M. B.; Nærdal, Ingemar; Paul, Lidia; Haugen, Tone; Le, Simone B.; Brautaset, Trygve; Wendisch, Volker F.

2016-01-01

Bacillus methanolicus is a thermophilic methylotroph able to overproduce amino acids from methanol, a substrate not used for human or animal nutrition. Based on our previous RNA-seq analysis a mannitol inducible promoter and a putative mannitol activator gene mtlR were identified. The mannitol inducible promoter was applied for controlled gene expression using fluorescent reporter proteins and a flow cytometry analysis, and improved by changing the -35 promoter region and by co-expression of the mtlR regulator gene. For independent complementary gene expression control, the heterologous xylose-inducible system from B. megaterium was employed and a two-plasmid gene expression system was developed. Four different replicons for expression vectors were compared with respect to their copy number and stability. As an application example, methanol-based production of cadaverine was shown to be improved from 11.3 to 17.5 g/L when a heterologous lysine decarboxylase gene cadA was expressed from a theta-replicating rather than a rolling-circle replicating vector. The current work on inducible promoter systems and compatible theta- or rolling circle-replicating vectors is an important extension of the poorly developed B. methanolicus genetic toolbox, valuable for genetic engineering and further exploration of this bacterium. PMID:27713731

Applicability of a gene expression based prediction method to SD and Wistar rats: an example of CARCINOscreen®.

PubMed

Matsumoto, Hiroshi; Saito, Fumiyo; Takeyoshi, Masahiro

2015-12-01

Recently, the development of several gene expression-based prediction methods has been attempted in the fields of toxicology. CARCINOscreen® is a gene expression-based screening method to predict carcinogenicity of chemicals which target the liver with high accuracy. In this study, we investigated the applicability of the gene expression-based screening method to SD and Wistar rats by using CARCINOscreen®, originally developed with F344 rats, with two carcinogens, 2,4-diaminotoluen and thioacetamide, and two non-carcinogens, 2,6-diaminotoluen and sodium benzoate. After the 28-day repeated dose test was conducted with each chemical in SD and Wistar rats, microarray analysis was performed using total RNA extracted from each liver. Obtained gene expression data were applied to CARCINOscreen®. Predictive scores obtained by the CARCINOscreen® for known carcinogens were > 2 in all strains of rats, while non-carcinogens gave prediction scores below 0.5. These results suggested that the gene expression based screening method, CARCINOscreen®, can be applied to SD and Wistar rats, widely used strains in toxicological studies, by setting of an appropriate boundary line of prediction score to classify the chemicals into carcinogens and non-carcinogens.

pySAPC, a python package for sparse affinity propagation clustering: Application to odontogenesis whole genome time series gene-expression data.

PubMed

Cao, Huojun; Amendt, Brad A

2016-11-01

Developmental dental anomalies are common forms of congenital defects. The molecular mechanisms of dental anomalies are poorly understood. Systematic approaches such as clustering genes based on similar expression patterns could identify novel genes involved in dental anomalies and provide a framework for understanding molecular regulatory mechanisms of these genes during tooth development (odontogenesis). A python package (pySAPC) of sparse affinity propagation clustering algorithm for large datasets was developed. Whole genome pair-wise similarity was calculated based on expression pattern similarity based on 45 microarrays of several stages during odontogenesis. pySAPC identified 743 gene clusters based on expression pattern similarity during mouse tooth development. Three clusters are significantly enriched for genes associated with dental anomalies (with FDR <0.1). The three clusters of genes have distinct expression patterns during odontogenesis. Clustering genes based on similar expression profiles recovered several known regulatory relationships for genes involved in odontogenesis, as well as many novel genes that may be involved with the same genetic pathways as genes that have already been shown to contribute to dental defects. By using sparse similarity matrix, pySAPC use much less memory and CPU time compared with the original affinity propagation program that uses a full similarity matrix. This python package will be useful for many applications where dataset(s) are too large to use full similarity matrix. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang. Copyright © 2016. Published by Elsevier B.V.

Case-based retrieval framework for gene expression data.

PubMed

Anaissi, Ali; Goyal, Madhu; Catchpoole, Daniel R; Braytee, Ali; Kennedy, Paul J

2015-01-01

The process of retrieving similar cases in a case-based reasoning system is considered a big challenge for gene expression data sets. The huge number of gene expression values generated by microarray technology leads to complex data sets and similarity measures for high-dimensional data are problematic. Hence, gene expression similarity measurements require numerous machine-learning and data-mining techniques, such as feature selection and dimensionality reduction, to be incorporated into the retrieval process. This article proposes a case-based retrieval framework that uses a k-nearest-neighbor classifier with a weighted-feature-based similarity to retrieve previously treated patients based on their gene expression profiles. The herein-proposed methodology is validated on several data sets: a childhood leukemia data set collected from The Children's Hospital at Westmead, as well as the Colon cancer, the National Cancer Institute (NCI), and the Prostate cancer data sets. Results obtained by the proposed framework in retrieving patients of the data sets who are similar to new patients are as follows: 96% accuracy on the childhood leukemia data set, 95% on the NCI data set, 93% on the Colon cancer data set, and 98% on the Prostate cancer data set. The designed case-based retrieval framework is an appropriate choice for retrieving previous patients who are similar to a new patient, on the basis of their gene expression data, for better diagnosis and treatment of childhood leukemia. Moreover, this framework can be applied to other gene expression data sets using some or all of its steps.

A method to identify differential expression profiles of time-course gene data with Fourier transformation

PubMed Central

2013-01-01

Background Time course gene expression experiments are an increasingly popular method for exploring biological processes. Temporal gene expression profiles provide an important characterization of gene function, as biological systems are both developmental and dynamic. With such data it is possible to study gene expression changes over time and thereby to detect differential genes. Much of the early work on analyzing time series expression data relied on methods developed originally for static data and thus there is a need for improved methodology. Since time series expression is a temporal process, its unique features such as autocorrelation between successive points should be incorporated into the analysis. Results This work aims to identify genes that show different gene expression profiles across time. We propose a statistical procedure to discover gene groups with similar profiles using a nonparametric representation that accounts for the autocorrelation in the data. In particular, we first represent each profile in terms of a Fourier basis, and then we screen out genes that are not differentially expressed based on the Fourier coefficients. Finally, we cluster the remaining gene profiles using a model-based approach in the Fourier domain. We evaluate the screening results in terms of sensitivity, specificity, FDR and FNR, compare with the Gaussian process regression screening in a simulation study and illustrate the results by application to yeast cell-cycle microarray expression data with alpha-factor synchronization. The key elements of the proposed methodology: (i) representation of gene profiles in the Fourier domain; (ii) automatic screening of genes based on the Fourier coefficients and taking into account autocorrelation in the data, while controlling the false discovery rate (FDR); (iii) model-based clustering of the remaining gene profiles. Conclusions Using this method, we identified a set of cell-cycle-regulated time-course yeast genes. The proposed method is general and can be potentially used to identify genes which have the same patterns or biological processes, and help facing the present and forthcoming challenges of data analysis in functional genomics. PMID:24134721

RefEx, a reference gene expression dataset as a web tool for the functional analysis of genes.

PubMed

Ono, Hiromasa; Ogasawara, Osamu; Okubo, Kosaku; Bono, Hidemasa

2017-08-29

Gene expression data are exponentially accumulating; thus, the functional annotation of such sequence data from metadata is urgently required. However, life scientists have difficulty utilizing the available data due to its sheer magnitude and complicated access. We have developed a web tool for browsing reference gene expression pattern of mammalian tissues and cell lines measured using different methods, which should facilitate the reuse of the precious data archived in several public databases. The web tool is called Reference Expression dataset (RefEx), and RefEx allows users to search by the gene name, various types of IDs, chromosomal regions in genetic maps, gene family based on InterPro, gene expression patterns, or biological categories based on Gene Ontology. RefEx also provides information about genes with tissue-specific expression, and the relative gene expression values are shown as choropleth maps on 3D human body images from BodyParts3D. Combined with the newly incorporated Functional Annotation of Mammals (FANTOM) dataset, RefEx provides insight regarding the functional interpretation of unfamiliar genes. RefEx is publicly available at http://refex.dbcls.jp/.

RefEx, a reference gene expression dataset as a web tool for the functional analysis of genes

PubMed Central

Ono, Hiromasa; Ogasawara, Osamu; Okubo, Kosaku; Bono, Hidemasa

2017-01-01

Gene expression data are exponentially accumulating; thus, the functional annotation of such sequence data from metadata is urgently required. However, life scientists have difficulty utilizing the available data due to its sheer magnitude and complicated access. We have developed a web tool for browsing reference gene expression pattern of mammalian tissues and cell lines measured using different methods, which should facilitate the reuse of the precious data archived in several public databases. The web tool is called Reference Expression dataset (RefEx), and RefEx allows users to search by the gene name, various types of IDs, chromosomal regions in genetic maps, gene family based on InterPro, gene expression patterns, or biological categories based on Gene Ontology. RefEx also provides information about genes with tissue-specific expression, and the relative gene expression values are shown as choropleth maps on 3D human body images from BodyParts3D. Combined with the newly incorporated Functional Annotation of Mammals (FANTOM) dataset, RefEx provides insight regarding the functional interpretation of unfamiliar genes. RefEx is publicly available at http://refex.dbcls.jp/. PMID:28850115

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

Design-Based Learning for Biology: Genetic Engineering Experience Improves Understanding of Gene Expression

ERIC Educational Resources Information Center

Ellefson, Michelle R.; Brinker, Rebecca A.; Vernacchio, Vincent J.; Schunn, Christian D.

2008-01-01

Gene expression is a difficult topic for students to learn and comprehend, at least partially because it involves various biochemical structures and processes occurring at the microscopic level. Designer Bacteria, a design-based learning (DBL) unit for high-school students, applies principles of DBL to the teaching of gene expression. Throughout…

Integrated pathway-based transcription regulation network mining and visualization based on gene expression profiles.

PubMed

Kibinge, Nelson; Ono, Naoaki; Horie, Masafumi; Sato, Tetsuo; Sugiura, Tadao; Altaf-Ul-Amin, Md; Saito, Akira; Kanaya, Shigehiko

2016-06-01

Conventionally, workflows examining transcription regulation networks from gene expression data involve distinct analytical steps. There is a need for pipelines that unify data mining and inference deduction into a singular framework to enhance interpretation and hypotheses generation. We propose a workflow that merges network construction with gene expression data mining focusing on regulation processes in the context of transcription factor driven gene regulation. The pipeline implements pathway-based modularization of expression profiles into functional units to improve biological interpretation. The integrated workflow was implemented as a web application software (TransReguloNet) with functions that enable pathway visualization and comparison of transcription factor activity between sample conditions defined in the experimental design. The pipeline merges differential expression, network construction, pathway-based abstraction, clustering and visualization. The framework was applied in analysis of actual expression datasets related to lung, breast and prostrate cancer. Copyright © 2016 Elsevier Inc. All rights reserved.

Array-based comparative genomic hybridization-guided identification of reference genes for normalization of real-time quantitative polymerase chain reaction assay data for lymphomas, histiocytic sarcomas, and osteosarcomas of dogs.

PubMed

Tsai, Pei-Chien; Breen, Matthew

2012-09-01

To identify suitable reference genes for normalization of real-time quantitative PCR (RT-qPCR) assay data for common tumors of dogs. Malignant lymph node (n = 8), appendicular osteosarcoma (9), and histiocytic sarcoma (12) samples and control samples of various nonneoplastic canine tissues. Array-based comparative genomic hybridization (aCGH) data were used to guide selection of 9 candidate reference genes. Expression stability of candidate reference genes and 4 commonly used reference genes was determined for tumor samples with RT-qPCR assays and 3 software programs. LOC611555 was the candidate reference gene with the highest expression stability among the 3 tumor types. Of the commonly used reference genes, expression stability of HPRT was high in histiocytic sarcoma samples, and expression stability of Ubi and RPL32 was high in osteosarcoma samples. Some of the candidate reference genes had higher expression stability than did the commonly used reference genes. Data for constitutively expressed genes with high expression stability are required for normalization of RT-qPCR assay results. Without such data, accurate quantification of gene expression in tumor tissue samples is difficult. Results of the present study indicated LOC611555 may be a useful RT-qPCR assay reference gene for multiple tissue types. Some commonly used reference genes may be suitable for normalization of gene expression data for tumors of dogs, such as lymphomas, osteosarcomas, or histiocytic sarcomas.

Identifying osteosarcoma metastasis associated genes by weighted gene co-expression network analysis (WGCNA).

PubMed

Tian, Honglai; Guan, Donghui; Li, Jianmin

2018-06-01

Osteosarcoma (OS), the most common malignant bone tumor, accounts for the heavy healthy threat in the period of children and adolescents. OS occurrence usually correlates with early metastasis and high death rate. This study aimed to better understand the mechanism of OS metastasis.Based on Gene Expression Omnibus (GEO) database, we downloaded 4 expression profile data sets associated with OS metastasis, and selected differential expressed genes. Weighted gene co-expression network analysis (WGCNA) approach allowed us to investigate the most OS metastasis-correlated module. Gene Ontology functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were used to give annotation of selected OS metastasis-associated genes.We select 897 differential expressed genes from OS metastasis and OS non-metastasis groups. Based on these selected genes, WGCNA further explored 142 genes included in the most OS metastasis-correlated module. Gene Ontology functional and KEGG pathway enrichment analyses showed that significantly OS metastasis-associated genes were involved in pathway correlated with insulin-like growth factor binding.Our research figured out several potential molecules participating in metastasis process and factors acting as biomarker. With this study, we could better explore the mechanism of OS metastasis and further discover more therapy targets.

Co-expression network with protein-protein interaction and transcription regulation in malaria parasite Plasmodium falciparum.

PubMed

Yu, Fu-Dong; Yang, Shao-You; Li, Yuan-Yuan; Hu, Wei

2013-04-10

Malaria continues to be one of the most severe global infectious diseases, as a major threat to human health and economic development. Network-based biological analysis is a promising approach to uncover key genes and biological processes from a network viewpoint, which could not be recognized from individual gene-based signatures. We integrated gene co-expression profile with protein-protein interaction and transcriptional regulation information to construct a comprehensive gene co-expression network of Plasmodium falciparum. Based on this network, we identified 10 core modules by using ICE (Iterative Clique Enumeration) algorithm, which were essential for malaria parasite development in intraerythrocytic developmental cycle (IDC) stages. In each module, all genes were highly correlated probably due to co-regulation or formation of a protein complex. Some of these genes were recognized to be differentially coexpressed among three close-by IDC stages. The gene of prpf8 (PFD0265w) encoding pre-mRNA processing splicing factor 8 product was identified as DCGs (differentially co-expressed genes) among IDC stages, although this gene function was seldom reported in previous researches. Integrating the species-specific gene prediction and differential co-expression gene detection, we found some modules could perform species-specific functions according to some of genes in these modules were species-specific genes, like the module 10. Furthermore, in order to reveal the underlying mechanisms of the erythrocyte invasion by P. falciparum, Steiner Tree algorithm was employed to identify the invasion subnetwork from our gene co-expression network. The subnetwork-based analysis indicated that some important Plasmodium parasite specific genes could corporate with each other and be co-regulated during the parasite invasion process, which including a head-to-head gene pair of PfRH2a (PF13_0198) and PfRH2b (MAL13P1.176). This study based on gene co-expression network could shed new insights on the mechanisms of pathogenesis, even virulence and P. falciparum development. Crown Copyright © 2012. Published by Elsevier B.V. All rights reserved.

ConGEMs: Condensed Gene Co-Expression Module Discovery Through Rule-Based Clustering and Its Application to Carcinogenesis.

PubMed

Mallik, Saurav; Zhao, Zhongming

2017-12-28

For transcriptomic analysis, there are numerous microarray-based genomic data, especially those generated for cancer research. The typical analysis measures the difference between a cancer sample-group and a matched control group for each transcript or gene. Association rule mining is used to discover interesting item sets through rule-based methodology. Thus, it has advantages to find causal effect relationships between the transcripts. In this work, we introduce two new rule-based similarity measures-weighted rank-based Jaccard and Cosine measures-and then propose a novel computational framework to detect condensed gene co-expression modules ( C o n G E M s) through the association rule-based learning system and the weighted similarity scores. In practice, the list of evolved condensed markers that consists of both singular and complex markers in nature depends on the corresponding condensed gene sets in either antecedent or consequent of the rules of the resultant modules. In our evaluation, these markers could be supported by literature evidence, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway and Gene Ontology annotations. Specifically, we preliminarily identified differentially expressed genes using an empirical Bayes test. A recently developed algorithm-RANWAR-was then utilized to determine the association rules from these genes. Based on that, we computed the integrated similarity scores of these rule-based similarity measures between each rule-pair, and the resultant scores were used for clustering to identify the co-expressed rule-modules. We applied our method to a gene expression dataset for lung squamous cell carcinoma and a genome methylation dataset for uterine cervical carcinogenesis. Our proposed module discovery method produced better results than the traditional gene-module discovery measures. In summary, our proposed rule-based method is useful for exploring biomarker modules from transcriptomic data.

Non-parent of Origin Expression of Numerous Effector Genes Indicates a Role of Gene Regulation in Host Adaption of the Hybrid Triticale Powdery Mildew Pathogen.

PubMed

Praz, Coraline R; Menardo, Fabrizio; Robinson, Mark D; Müller, Marion C; Wicker, Thomas; Bourras, Salim; Keller, Beat

2018-01-01

Powdery mildew is an important disease of cereals. It is caused by one species, Blumeria graminis , which is divided into formae speciales each of which is highly specialized to one host. Recently, a new form capable of growing on triticale ( B.g. triticale ) has emerged through hybridization between wheat and rye mildews ( B.g. tritici and B.g. secalis , respectively). In this work, we used RNA sequencing to study the molecular basis of host adaptation in B.g. triticale . We analyzed gene expression in three B.g. tritici isolates, two B.g. secalis isolates and two B.g. triticale isolates and identified a core set of putative effector genes that are highly expressed in all formae speciales . We also found that the genes differentially expressed between isolates of the same form as well as between different formae speciales were enriched in putative effectors. Their coding genes belong to several families including some which contain known members of mildew avirulence ( Avr ) and suppressor ( Svr ) genes. Based on these findings we propose that effectors play an important role in host adaptation that is mechanistically based on Avr-Resistance gene-Svr interactions. We also found that gene expression in the B.g. triticale hybrid is mostly conserved with the parent-of-origin, but some genes inherited from B.g. tritici showed a B.g. secalis -like expression. Finally, we identified 11 unambiguous cases of putative effector genes with hybrid-specific, non-parent of origin gene expression, and we propose that they are possible determinants of host specialization in triticale mildew. These data suggest that altered expression of multiple effector genes, in particular Avr and Svr related factors, might play a role in mildew host adaptation based on hybridization.

Framework for reanalysis of publicly available Affymetrix® GeneChip® data sets based on functional regions of interest.

PubMed

Saka, Ernur; Harrison, Benjamin J; West, Kirk; Petruska, Jeffrey C; Rouchka, Eric C

2017-12-06

Since the introduction of microarrays in 1995, researchers world-wide have used both commercial and custom-designed microarrays for understanding differential expression of transcribed genes. Public databases such as ArrayExpress and the Gene Expression Omnibus (GEO) have made millions of samples readily available. One main drawback to microarray data analysis involves the selection of probes to represent a specific transcript of interest, particularly in light of the fact that transcript-specific knowledge (notably alternative splicing) is dynamic in nature. We therefore developed a framework for reannotating and reassigning probe groups for Affymetrix® GeneChip® technology based on functional regions of interest. This framework addresses three issues of Affymetrix® GeneChip® data analyses: removing nonspecific probes, updating probe target mapping based on the latest genome knowledge and grouping probes into gene, transcript and region-based (UTR, individual exon, CDS) probe sets. Updated gene and transcript probe sets provide more specific analysis results based on current genomic and transcriptomic knowledge. The framework selects unique probes, aligns them to gene annotations and generates a custom Chip Description File (CDF). The analysis reveals only 87% of the Affymetrix® GeneChip® HG-U133 Plus 2 probes uniquely align to the current hg38 human assembly without mismatches. We also tested new mappings on the publicly available data series using rat and human data from GSE48611 and GSE72551 obtained from GEO, and illustrate that functional grouping allows for the subtle detection of regions of interest likely to have phenotypical consequences. Through reanalysis of the publicly available data series GSE48611 and GSE72551, we profiled the contribution of UTR and CDS regions to the gene expression levels globally. The comparison between region and gene based results indicated that the detected expressed genes by gene-based and region-based CDFs show high consistency and regions based results allows us to detection of changes in transcript formation.

A novel approach for human whole transcriptome analysis based on absolute gene expression of microarray data.

PubMed

Bikel, Shirley; Jacobo-Albavera, Leonor; Sánchez-Muñoz, Fausto; Cornejo-Granados, Fernanda; Canizales-Quinteros, Samuel; Soberón, Xavier; Sotelo-Mundo, Rogerio R; Del Río-Navarro, Blanca E; Mendoza-Vargas, Alfredo; Sánchez, Filiberto; Ochoa-Leyva, Adrian

2017-01-01

In spite of the emergence of RNA sequencing (RNA-seq), microarrays remain in widespread use for gene expression analysis in the clinic. There are over 767,000 RNA microarrays from human samples in public repositories, which are an invaluable resource for biomedical research and personalized medicine. The absolute gene expression analysis allows the transcriptome profiling of all expressed genes under a specific biological condition without the need of a reference sample. However, the background fluorescence represents a challenge to determine the absolute gene expression in microarrays. Given that the Y chromosome is absent in female subjects, we used it as a new approach for absolute gene expression analysis in which the fluorescence of the Y chromosome genes of female subjects was used as the background fluorescence for all the probes in the microarray. This fluorescence was used to establish an absolute gene expression threshold, allowing the differentiation between expressed and non-expressed genes in microarrays. We extracted the RNA from 16 children leukocyte samples (nine males and seven females, ages 6-10 years). An Affymetrix Gene Chip Human Gene 1.0 ST Array was carried out for each sample and the fluorescence of 124 genes of the Y chromosome was used to calculate the absolute gene expression threshold. After that, several expressed and non-expressed genes according to our absolute gene expression threshold were compared against the expression obtained using real-time quantitative polymerase chain reaction (RT-qPCR). From the 124 genes of the Y chromosome, three genes (DDX3Y, TXLNG2P and EIF1AY) that displayed significant differences between sexes were used to calculate the absolute gene expression threshold. Using this threshold, we selected 13 expressed and non-expressed genes and confirmed their expression level by RT-qPCR. Then, we selected the top 5% most expressed genes and found that several KEGG pathways were significantly enriched. Interestingly, these pathways were related to the typical functions of leukocytes cells, such as antigen processing and presentation and natural killer cell mediated cytotoxicity. We also applied this method to obtain the absolute gene expression threshold in already published microarray data of liver cells, where the top 5% expressed genes showed an enrichment of typical KEGG pathways for liver cells. Our results suggest that the three selected genes of the Y chromosome can be used to calculate an absolute gene expression threshold, allowing a transcriptome profiling of microarray data without the need of an additional reference experiment. Our approach based on the establishment of a threshold for absolute gene expression analysis will allow a new way to analyze thousands of microarrays from public databases. This allows the study of different human diseases without the need of having additional samples for relative expression experiments.

Identification and VIGS-based characterization of Bx1 ortholog in rye (Secale cereale L.)

PubMed Central

Groszyk, Jolanta; Kowalczyk, Mariusz; Yanushevska, Yuliya; Stochmal, Anna; Rakoczy-Trojanowska, Monika

2017-01-01

The first step of the benzoxazinoid (BX) synthesis pathway is catalyzed by an enzyme with indole-3-glycerol phosphate lyase activity encoded by 3 genes, Bx1, TSA and Igl. A gene highly homologous to maize and wheat Bx1 has been identified in rye. The goal of the study was to analyze the gene and to experimentally verify its role in the rye BX biosynthesis pathway as a rye ortholog of the Bx1 gene. Expression of the gene showed peak values 3 days after imbibition (dai) and at 21 dai it was undetectable. Changes of the BX content in leaves were highly correlated with the expression pattern until 21 dai. In plants older than 21 dai despite the undetectable expression of the analyzed gene there was still low accumulation of BXs. Function of the gene was verified by correlating its native expression and virus-induced silencing with BX accumulation. Barley stripe mosaic virus (BSMV)-based vectors were used to induce transcriptional (TGS) and posttranscriptional (PTGS) silencing of the analyzed gene. Both strategies (PTGS and TGS) significantly reduced the transcript level of the analyzed gene, and this was highly correlated with lowered BX content. Inoculation with virus-based vectors specifically induced expression of the analyzed gene, indicating up-regulation by biotic stressors. This is the first report of using the BSMV-based system for functional analysis of rye gene. The findings prove that the analyzed gene is a rye ortholog of the Bx1 gene. Its expression is developmentally regulated and is strongly induced by biotic stress. Stable accumulation of BXs in plants older than 21 dai associated with undetectable expression of ScBx1 indicates that the function of the ScBx1 in the BX biosynthesis is redundant with another gene. We anticipate that the unknown gene is a putative ortholog of the Igl, which still remains to be identified in rye. PMID:28234909

Identification and VIGS-based characterization of Bx1 ortholog in rye (Secale cereale L.).

PubMed

Groszyk, Jolanta; Kowalczyk, Mariusz; Yanushevska, Yuliya; Stochmal, Anna; Rakoczy-Trojanowska, Monika; Orczyk, Waclaw

2017-01-01

The first step of the benzoxazinoid (BX) synthesis pathway is catalyzed by an enzyme with indole-3-glycerol phosphate lyase activity encoded by 3 genes, Bx1, TSA and Igl. A gene highly homologous to maize and wheat Bx1 has been identified in rye. The goal of the study was to analyze the gene and to experimentally verify its role in the rye BX biosynthesis pathway as a rye ortholog of the Bx1 gene. Expression of the gene showed peak values 3 days after imbibition (dai) and at 21 dai it was undetectable. Changes of the BX content in leaves were highly correlated with the expression pattern until 21 dai. In plants older than 21 dai despite the undetectable expression of the analyzed gene there was still low accumulation of BXs. Function of the gene was verified by correlating its native expression and virus-induced silencing with BX accumulation. Barley stripe mosaic virus (BSMV)-based vectors were used to induce transcriptional (TGS) and posttranscriptional (PTGS) silencing of the analyzed gene. Both strategies (PTGS and TGS) significantly reduced the transcript level of the analyzed gene, and this was highly correlated with lowered BX content. Inoculation with virus-based vectors specifically induced expression of the analyzed gene, indicating up-regulation by biotic stressors. This is the first report of using the BSMV-based system for functional analysis of rye gene. The findings prove that the analyzed gene is a rye ortholog of the Bx1 gene. Its expression is developmentally regulated and is strongly induced by biotic stress. Stable accumulation of BXs in plants older than 21 dai associated with undetectable expression of ScBx1 indicates that the function of the ScBx1 in the BX biosynthesis is redundant with another gene. We anticipate that the unknown gene is a putative ortholog of the Igl, which still remains to be identified in rye.

PROSPECT improves cis-acting regulatory element prediction by integrating expression profile data with consensus pattern searches

PubMed Central

Fujibuchi, Wataru; Anderson, John S. J.; Landsman, David

2001-01-01

Consensus pattern and matrix-based searches designed to predict cis-acting transcriptional regulatory sequences have historically been subject to large numbers of false positives. We sought to decrease false positives by incorporating expression profile data into a consensus pattern-based search method. We have systematically analyzed the expression phenotypes of over 6000 yeast genes, across 121 expression profile experiments, and correlated them with the distribution of 14 known regulatory elements over sequences upstream of the genes. Our method is based on a metric we term probabilistic element assessment (PEA), which is a ranking of potential sites based on sequence similarity in the upstream regions of genes with similar expression phenotypes. For eight of the 14 known elements that we examined, our method had a much higher selectivity than a naïve consensus pattern search. Based on our analysis, we have developed a web-based tool called PROSPECT, which allows consensus pattern-based searching of gene clusters obtained from microarray data. PMID:11574681

Transient, Inducible, Placenta-Specific Gene Expression in Mice

PubMed Central

Fan, Xiujun; Petitt, Matthew; Gamboa, Matthew; Huang, Mei; Dhal, Sabita; Druzin, Maurice L.; Wu, Joseph C.

2012-01-01

Molecular understanding of placental functions and pregnancy disorders is limited by the absence of methods for placenta-specific gene manipulation. Although persistent placenta-specific gene expression has been achieved by lentivirus-based gene delivery methods, developmentally and physiologically important placental genes have highly stage-specific functions, requiring controllable, transient expression systems for functional analysis. Here, we describe an inducible, placenta-specific gene expression system that enables high-level, transient transgene expression and monitoring of gene expression by live bioluminescence imaging in mouse placenta at different stages of pregnancy. We used the third generation tetracycline-responsive tranactivator protein Tet-On 3G, with 10- to 100-fold increased sensitivity to doxycycline (Dox) compared with previous versions, enabling unusually sensitive on-off control of gene expression in vivo. Transgenic mice expressing Tet-On 3G were created using a new integrase-based, site-specific approach, yielding high-level transgene expression driven by a ubiquitous promoter. Blastocysts from these mice were transduced with the Tet-On 3G-response element promoter-driving firefly luciferase using lentivirus-mediated placenta-specific gene delivery and transferred into wild-type pseudopregnant recipients for placenta-specific, Dox-inducible gene expression. Systemic Dox administration at various time points during pregnancy led to transient, placenta-specific firefly luciferase expression as early as d 5 of pregnancy in a Dox dose-dependent manner. This system enables, for the first time, reliable pregnancy stage-specific induction of gene expression in the placenta and live monitoring of gene expression during pregnancy. It will be widely applicable to studies of both placental development and pregnancy, and the site-specific Tet-On G3 mouse will be valuable for studies in a broad range of tissues. PMID:23011919

Identifying spatially similar gene expression patterns in early stage fruit fly embryo images: binary feature versus invariant moment digital representations

PubMed Central

Gurunathan, Rajalakshmi; Van Emden, Bernard; Panchanathan, Sethuraman; Kumar, Sudhir

2004-01-01

Background Modern developmental biology relies heavily on the analysis of embryonic gene expression patterns. Investigators manually inspect hundreds or thousands of expression patterns to identify those that are spatially similar and to ultimately infer potential gene interactions. However, the rapid accumulation of gene expression pattern data over the last two decades, facilitated by high-throughput techniques, has produced a need for the development of efficient approaches for direct comparison of images, rather than their textual descriptions, to identify spatially similar expression patterns. Results The effectiveness of the Binary Feature Vector (BFV) and Invariant Moment Vector (IMV) based digital representations of the gene expression patterns in finding biologically meaningful patterns was compared for a small (226 images) and a large (1819 images) dataset. For each dataset, an ordered list of images, with respect to a query image, was generated to identify overlapping and similar gene expression patterns, in a manner comparable to what a developmental biologist might do. The results showed that the BFV representation consistently outperforms the IMV representation in finding biologically meaningful matches when spatial overlap of the gene expression pattern and the genes involved are considered. Furthermore, we explored the value of conducting image-content based searches in a dataset where individual expression components (or domains) of multi-domain expression patterns were also included separately. We found that this technique improves performance of both IMV and BFV based searches. Conclusions We conclude that the BFV representation consistently produces a more extensive and better list of biologically useful patterns than the IMV representation. The high quality of results obtained scales well as the search database becomes larger, which encourages efforts to build automated image query and retrieval systems for spatial gene expression patterns. PMID:15603586

Network-based differential gene expression analysis suggests cell cycle related genes regulated by E2F1 underlie the molecular difference between smoker and non-smoker lung adenocarcinoma

PubMed Central

2013-01-01

Background Differential gene expression (DGE) analysis is commonly used to reveal the deregulated molecular mechanisms of complex diseases. However, traditional DGE analysis (e.g., the t test or the rank sum test) tests each gene independently without considering interactions between them. Top-ranked differentially regulated genes prioritized by the analysis may not directly relate to the coherent molecular changes underlying complex diseases. Joint analyses of co-expression and DGE have been applied to reveal the deregulated molecular modules underlying complex diseases. Most of these methods consist of separate steps: first to identify gene-gene relationships under the studied phenotype then to integrate them with gene expression changes for prioritizing signature genes, or vice versa. It is warrant a method that can simultaneously consider gene-gene co-expression strength and corresponding expression level changes so that both types of information can be leveraged optimally. Results In this paper, we develop a gene module based method for differential gene expression analysis, named network-based differential gene expression (nDGE) analysis, a one-step integrative process for prioritizing deregulated genes and grouping them into gene modules. We demonstrate that nDGE outperforms existing methods in prioritizing deregulated genes and discovering deregulated gene modules using simulated data sets. When tested on a series of smoker and non-smoker lung adenocarcinoma data sets, we show that top differentially regulated genes identified by the rank sum test in different sets are not consistent while top ranked genes defined by nDGE in different data sets significantly overlap. nDGE results suggest that a differentially regulated gene module, which is enriched for cell cycle related genes and E2F1 targeted genes, plays a role in the molecular differences between smoker and non-smoker lung adenocarcinoma. Conclusions In this paper, we develop nDGE to prioritize deregulated genes and group them into gene modules by simultaneously considering gene expression level changes and gene-gene co-regulations. When applied to both simulated and empirical data, nDGE outperforms the traditional DGE method. More specifically, when applied to smoker and non-smoker lung cancer sets, nDGE results illustrate the molecular differences between smoker and non-smoker lung cancer. PMID:24341432

EXP-PAC: providing comparative analysis and storage of next generation gene expression data.

PubMed

Church, Philip C; Goscinski, Andrzej; Lefèvre, Christophe

2012-07-01

Microarrays and more recently RNA sequencing has led to an increase in available gene expression data. How to manage and store this data is becoming a key issue. In response we have developed EXP-PAC, a web based software package for storage, management and analysis of gene expression and sequence data. Unique to this package is SQL based querying of gene expression data sets, distributed normalization of raw gene expression data and analysis of gene expression data across experiments and species. This package has been populated with lactation data in the international milk genomic consortium web portal (http://milkgenomics.org/). Source code is also available which can be hosted on a Windows, Linux or Mac APACHE server connected to a private or public network (http://mamsap.it.deakin.edu.au/~pcc/Release/EXP_PAC.html). Copyright © 2012 Elsevier Inc. All rights reserved.

iPcc: a novel feature extraction method for accurate disease class discovery and prediction

PubMed Central

Ren, Xianwen; Wang, Yong; Zhang, Xiang-Sun; Jin, Qi

2013-01-01

Gene expression profiling has gradually become a routine procedure for disease diagnosis and classification. In the past decade, many computational methods have been proposed, resulting in great improvements on various levels, including feature selection and algorithms for classification and clustering. In this study, we present iPcc, a novel method from the feature extraction perspective to further propel gene expression profiling technologies from bench to bedside. We define ‘correlation feature space’ for samples based on the gene expression profiles by iterative employment of Pearson’s correlation coefficient. Numerical experiments on both simulated and real gene expression data sets demonstrate that iPcc can greatly highlight the latent patterns underlying noisy gene expression data and thus greatly improve the robustness and accuracy of the algorithms currently available for disease diagnosis and classification based on gene expression profiles. PMID:23761440

Transcriptomic analysis in the developing zebrafish embryo after compound exposure: Individual gene expression and pathway regulation

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

Hermsen, Sanne A.B., E-mail: Sanne.Hermsen@rivm.nl; Department of Toxicogenomics, Maastricht University, P.O. Box 616, 6200 MD, Maastricht; Institute for Risk Assessment Sciences

2013-10-01

The zebrafish embryotoxicity test is a promising alternative assay for developmental toxicity. Classically, morphological assessment of the embryos is applied to evaluate the effects of compound exposure. However, by applying differential gene expression analysis the sensitivity and predictability of the test may be increased. For defining gene expression signatures of developmental toxicity, we explored the possibility of using gene expression signatures of compound exposures based on commonly expressed individual genes as well as based on regulated gene pathways. Four developmental toxic compounds were tested in concentration-response design, caffeine, carbamazepine, retinoic acid and valproic acid, and two non-embryotoxic compounds, D-mannitol andmore » saccharin, were included. With transcriptomic analyses we were able to identify commonly expressed genes, which were mostly development related, after exposure to the embryotoxicants. We also identified gene pathways regulated by the embryotoxicants, suggestive of their modes of action. Furthermore, whereas pathways may be regulated by all compounds, individual gene expression within these pathways can differ for each compound. Overall, the present study suggests that the use of individual gene expression signatures as well as pathway regulation may be useful starting points for defining gene biomarkers for predicting embryotoxicity. - Highlights: • The zebrafish embryotoxicity test in combination with transcriptomics was used. • We explored two approaches of defining gene biomarkers for developmental toxicity. • Four compounds in concentration-response design were tested. • We identified commonly expressed individual genes as well as regulated gene pathways. • Both approaches seem suitable starting points for defining gene biomarkers.« less

Microarray Meta-Analysis Identifies Acute Lung Injury Biomarkers in Donor Lungs That Predict Development of Primary Graft Failure in Recipients

PubMed Central

Haitsma, Jack J.; Furmli, Suleiman; Masoom, Hussain; Liu, Mingyao; Imai, Yumiko; Slutsky, Arthur S.; Beyene, Joseph; Greenwood, Celia M. T.; dos Santos, Claudia

2012-01-01

Objectives To perform a meta-analysis of gene expression microarray data from animal studies of lung injury, and to identify an injury-specific gene expression signature capable of predicting the development of lung injury in humans. Methods We performed a microarray meta-analysis using 77 microarray chips across six platforms, two species and different animal lung injury models exposed to lung injury with or/and without mechanical ventilation. Individual gene chips were classified and grouped based on the strategy used to induce lung injury. Effect size (change in gene expression) was calculated between non-injurious and injurious conditions comparing two main strategies to pool chips: (1) one-hit and (2) two-hit lung injury models. A random effects model was used to integrate individual effect sizes calculated from each experiment. Classification models were built using the gene expression signatures generated by the meta-analysis to predict the development of lung injury in human lung transplant recipients. Results Two injury-specific lists of differentially expressed genes generated from our meta-analysis of lung injury models were validated using external data sets and prospective data from animal models of ventilator-induced lung injury (VILI). Pathway analysis of gene sets revealed that both new and previously implicated VILI-related pathways are enriched with differentially regulated genes. Classification model based on gene expression signatures identified in animal models of lung injury predicted development of primary graft failure (PGF) in lung transplant recipients with larger than 80% accuracy based upon injury profiles from transplant donors. We also found that better classifier performance can be achieved by using meta-analysis to identify differentially-expressed genes than using single study-based differential analysis. Conclusion Taken together, our data suggests that microarray analysis of gene expression data allows for the detection of “injury" gene predictors that can classify lung injury samples and identify patients at risk for clinically relevant lung injury complications. PMID:23071521

Selection of low-variance expressed Malus x domestica (apple) genes for use as quantitative PCR reference genes (housekeepers)

USDA-ARS?s Scientific Manuscript database

To accurately measure gene expression using PCR-based approaches, there is the need for reference genes that have low variance in expression (housekeeping genes) to normalise the data for RNA quantity and quality. For non-model species such as Malus x domestica (apples), previously, the selection of...

Covariance Structure Models for Gene Expression Microarray Data

ERIC Educational Resources Information Center

Xie, Jun; Bentler, Peter M.

2003-01-01

Covariance structure models are applied to gene expression data using a factor model, a path model, and their combination. The factor model is based on a few factors that capture most of the expression information. A common factor of a group of genes may represent a common protein factor for the transcript of the co-expressed genes, and hence, it…

[Study on action mechanism and material base of compound Danshen dripping pills in treatment of carotid atherosclerosis based on techniques of gene expression profile and molecular fingerprint].

PubMed

Zhou, Wei; Song, Xiang-gang; Chen, Chao; Wang, Shu-mei; Liang, Sheng-wang

2015-08-01

Action mechanism and material base of compound Danshen dripping pills in treatment of carotid atherosclerosis were discussed based on gene expression profile and molecular fingerprint in this paper. First, gene expression profiles of atherosclerotic carotid artery tissues and histologically normal tissues in human body were collected, and were screened using significance analysis of microarray (SAM) to screen out differential gene expressions; then differential genes were analyzed by Gene Ontology (GO) analysis and KEGG pathway analysis; to avoid some genes with non-outstanding differential expression but biologically importance, Gene Set Enrichment Analysis (GSEA) were performed, and 7 chemical ingredients with higher negative enrichment score were obtained by Cmap method, implying that they could reversely regulate the gene expression profiles of pathological tissues; and last, based on the hypotheses that similar structures have similar activities, 336 ingredients of compound Danshen dripping pills were compared with 7 drug molecules in 2D molecular fingerprints method. The results showed that 147 differential genes including 60 up-regulated genes and 87 down regulated genes were screened out by SAM. And in GO analysis, Biological Process ( BP) is mainly concerned with biological adhesion, response to wounding and inflammatory response; Cellular Component (CC) is mainly concerned with extracellular region, extracellular space and plasma membrane; while Molecular Function (MF) is mainly concerned with antigen binding, metalloendopeptidase activity and peptide binding. KEGG pathway analysis is mainly concerned with JAK-STAT, RIG-I like receptor and PPAR signaling pathway. There were 10 compounds, such as hexadecane, with Tanimoto coefficients greater than 0.85, which implied that they may be the active ingredients (AIs) of compound Danshen dripping pills in treatment of carotid atherosclerosis (CAs). The present method can be applied to the research on material base and molecular action mechanism of TCM.

A Modified ABCDE Model of Flowering in Orchids Based on Gene Expression Profiling Studies of the Moth Orchid Phalaenopsis aphrodite

PubMed Central

Lee, Ann-Ying; Chen, Chun-Yi; Chang, Yao-Chien Alex; Chao, Ya-Ting; Shih, Ming-Che

2013-01-01

Previously we developed genomic resources for orchids, including transcriptomic analyses using next-generation sequencing techniques and construction of a web-based orchid genomic database. Here, we report a modified molecular model of flower development in the Orchidaceae based on functional analysis of gene expression profiles in Phalaenopsis aphrodite (a moth orchid) that revealed novel roles for the transcription factors involved in floral organ pattern formation. Phalaenopsis orchid floral organ-specific genes were identified by microarray analysis. Several critical transcription factors including AP3, PI, AP1 and AGL6, displayed distinct spatial distribution patterns. Phylogenetic analysis of orchid MADS box genes was conducted to infer the evolutionary relationship among floral organ-specific genes. The results suggest that gene duplication MADS box genes in orchid may have resulted in their gaining novel functions during evolution. Based on these analyses, a modified model of orchid flowering was proposed. Comparison of the expression profiles of flowers of a peloric mutant and wild-type Phalaenopsis orchid further identified genes associated with lip morphology and peloric effects. Large scale investigation of gene expression profiles revealed that homeotic genes from the ABCDE model of flower development classes A and B in the Phalaenopsis orchid have novel functions due to evolutionary diversification, and display differential expression patterns. PMID:24265826

Partial least squares based gene expression analysis in estrogen receptor positive and negative breast tumors.

PubMed

Ma, W; Zhang, T-F; Lu, P; Lu, S H

2014-01-01

Breast cancer is categorized into two broad groups: estrogen receptor positive (ER+) and ER negative (ER-) groups. Previous study proposed that under trastuzumab-based neoadjuvant chemotherapy, tumor initiating cell (TIC) featured ER- tumors response better than ER+ tumors. Exploration of the molecular difference of these two groups may help developing new therapeutic strategies, especially for ER- patients. With gene expression profile from the Gene Expression Omnibus (GEO) database, we performed partial least squares (PLS) based analysis, which is more sensitive than common variance/regression analysis. We acquired 512 differentially expressed genes. Four pathways were found to be enriched with differentially expressed genes, involving immune system, metabolism and genetic information processing process. Network analysis identified five hub genes with degrees higher than 10, including APP, ESR1, SMAD3, HDAC2, and PRKAA1. Our findings provide new understanding for the molecular difference between TIC featured ER- and ER+ breast tumors with the hope offer supports for therapeutic studies.

AUCTSP: an improved biomarker gene pair class predictor.

PubMed

Kagaris, Dimitri; Khamesipour, Alireza; Yiannoutsos, Constantin T

2018-06-26

The Top Scoring Pair (TSP) classifier, based on the concept of relative ranking reversals in the expressions of pairs of genes, has been proposed as a simple, accurate, and easily interpretable decision rule for classification and class prediction of gene expression profiles. The idea that differences in gene expression ranking are associated with presence or absence of disease is compelling and has strong biological plausibility. Nevertheless, the TSP formulation ignores significant available information which can improve classification accuracy and is vulnerable to selecting genes which do not have differential expression in the two conditions ("pivot" genes). We introduce the AUCTSP classifier as an alternative rank-based estimator of the magnitude of the ranking reversals involved in the original TSP. The proposed estimator is based on the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) and as such, takes into account the separation of the entire distribution of gene expression levels in gene pairs under the conditions considered, as opposed to comparing gene rankings within individual subjects as in the original TSP formulation. Through extensive simulations and case studies involving classification in ovarian, leukemia, colon, breast and prostate cancers and diffuse large b-cell lymphoma, we show the superiority of the proposed approach in terms of improving classification accuracy, avoiding overfitting and being less prone to selecting non-informative (pivot) genes. The proposed AUCTSP is a simple yet reliable and robust rank-based classifier for gene expression classification. While the AUCTSP works by the same principle as TSP, its ability to determine the top scoring gene pair based on the relative rankings of two marker genes across all subjects as opposed to each individual subject results in significant performance gains in classification accuracy. In addition, the proposed method tends to avoid selection of non-informative (pivot) genes as members of the top-scoring pair.

Co-regulation of the atrial natriuretic factor and cardiac myosin light chain-2 genes during alpha-adrenergic stimulation of neonatal rat ventricular cells. Identification of cis sequences within an embryonic and a constitutive contractile protein gene which mediate inducible expression.

PubMed

Knowlton, K U; Baracchini, E; Ross, R S; Harris, A N; Henderson, S A; Evans, S M; Glembotski, C C; Chien, K R

1991-04-25

To study the mechanisms which mediate the transcriptional activation of cardiac genes during alpha adrenergic stimulation, the present study examined the regulated expression of three cardiac genes, a ventricular embryonic gene (atrial natriuretic factor, ANF), a constitutively expressed contractile protein gene (cardiac MLC-2), and a cardiac sodium channel gene. alpha 1-Adrenergic stimulation activates the expression and release of ANF from neonatal ventricular cells. As assessed by RNase protection analyses, treatment with alpha-adrenergic agonists increases the steady-state levels of ANF mRNA by greater than 15-fold. However, a rat cardiac sodium channel gene mRNA is not induced, indicating that alpha-adrenergic stimulation does not lead to an increase in the expression of all cardiac genes. Studies employing a series of rat ANF luciferase and rat MLC-2 luciferase fusion genes identify 315- and 92-base pair cis regulatory sequences within an embryonic gene (ANF) and a constitutively expressed contractile protein gene (MLC-2), respectively, which mediate alpha-adrenergic-inducible gene expression. Transfection of various ANF luciferase reporters into neonatal rat ventricular cells demonstrated that upstream sequences which mediate tissue-specific expression (-3003 to -638) can be segregated from those responsible for inducibility. The lack of inducibility of a cardiac Na+ channel gene, and the segregation of ANF gene sequences which mediate cardiac specific from those which mediate inducible expression, provides further insight into the relationship between muscle-specific and inducible expression during cardiac myocyte hypertrophy. Based on these results, a testable model is proposed for the induction of embryonic cardiac genes and constitutively expressed contractile protein genes and the noninducibility of a subset of cardiac genes during alpha-adrenergic stimulation of neonatal rat ventricular cells.

Tissue and cell-type co-expression networks of transcription factors and wood component genes in Populus trichocarpa.

PubMed

Shi, Rui; Wang, Jack P; Lin, Ying-Chung; Li, Quanzi; Sun, Ying-Hsuan; Chen, Hao; Sederoff, Ronald R; Chiang, Vincent L

2017-05-01

Co-expression networks based on transcriptomes of Populus trichocarpa major tissues and specific cell types suggest redundant control of cell wall component biosynthetic genes by transcription factors in wood formation. We analyzed the transcriptomes of five tissues (xylem, phloem, shoot, leaf, and root) and two wood forming cell types (fiber and vessel) of Populus trichocarpa to assemble gene co-expression subnetworks associated with wood formation. We identified 165 transcription factors (TFs) that showed xylem-, fiber-, and vessel-specific expression. Of these 165 TFs, 101 co-expressed (correlation coefficient, r > 0.7) with the 45 secondary cell wall cellulose, hemicellulose, and lignin biosynthetic genes. Each cell wall component gene co-expressed on average with 34 TFs, suggesting redundant control of the cell wall component gene expression. Co-expression analysis showed that the 101 TFs and the 45 cell wall component genes each has two distinct groups (groups 1 and 2), based on their co-expression patterns. The group 1 TFs (44 members) are predominantly xylem and fiber specific, and are all highly positively co-expressed with the group 1 cell wall component genes (30 members), suggesting their roles as major wood formation regulators. Group 1 TFs include a lateral organ boundary domain gene (LBD) that has the highest number of positively correlated cell wall component genes (36) and TFs (47). The group 2 TFs have 57 members, including 14 vessel-specific TFs, and are generally less correlated with the cell wall component genes. An exception is a vessel-specific basic helix-loop-helix (bHLH) gene that negatively correlates with 20 cell wall component genes, and may function as a key transcriptional suppressor. The co-expression networks revealed here suggest a well-structured transcriptional homeostasis for cell wall component biosynthesis during wood formation.

Identifying Epigenetic Biomarkers using Maximal Relevance and Minimal Redundancy Based Feature Selection for Multi-Omics Data.

PubMed

Mallik, Saurav; Bhadra, Tapas; Maulik, Ujjwal

2017-01-01

Epigenetic Biomarker discovery is an important task in bioinformatics. In this article, we develop a new framework of identifying statistically significant epigenetic biomarkers using maximal-relevance and minimal-redundancy criterion based feature (gene) selection for multi-omics dataset. Firstly, we determine the genes that have both expression as well as methylation values, and follow normal distribution. Similarly, we identify the genes which consist of both expression and methylation values, but do not follow normal distribution. For each case, we utilize a gene-selection method that provides maximal-relevant, but variable-weighted minimum-redundant genes as top ranked genes. For statistical validation, we apply t-test on both the expression and methylation data consisting of only the normally distributed top ranked genes to determine how many of them are both differentially expressed andmethylated. Similarly, we utilize Limma package for performing non-parametric Empirical Bayes test on both expression and methylation data comprising only the non-normally distributed top ranked genes to identify how many of them are both differentially expressed and methylated. We finally report the top-ranking significant gene-markerswith biological validation. Moreover, our framework improves positive predictive rate and reduces false positive rate in marker identification. In addition, we provide a comparative analysis of our gene-selection method as well as othermethods based on classificationperformances obtained using several well-known classifiers.

Structure-related clustering of gene expression fingerprints of thp-1 cells exposed to smaller polycyclic aromatic hydrocarbons.

PubMed

Wan, B; Yarbrough, J W; Schultz, T W

2008-01-01

This study was undertaken to test the hypothesis that structurally similar PAHs induce similar gene expression profiles. THP-1 cells were exposed to a series of 12 selected PAHs at 50 microM for 24 hours and gene expressions profiles were analyzed using both unsupervised and supervised methods. Clustering analysis of gene expression profiles revealed that the 12 tested chemicals were grouped into five clusters. Within each cluster, the gene expression profiles are more similar to each other than to the ones outside the cluster. One-methylanthracene and 1-methylfluorene were found to have the most similar profiles; dibenzothiophene and dibenzofuran were found to share common profiles with fluorine. As expression pattern comparisons were expanded, similarity in genomic fingerprint dropped off dramatically. Prediction analysis of microarrays (PAM) based on the clustering pattern generated 49 predictor genes that can be used for sample discrimination. Moreover, a significant analysis of Microarrays (SAM) identified 598 genes being modulated by tested chemicals with a variety of biological processes, such as cell cycle, metabolism, and protein binding and KEGG pathways being significantly (p < 0.05) affected. It is feasible to distinguish structurally different PAHs based on their genomic fingerprints, which are mechanism based.

Finding gene regulatory network candidates using the gene expression knowledge base.

PubMed

Venkatesan, Aravind; Tripathi, Sushil; Sanz de Galdeano, Alejandro; Blondé, Ward; Lægreid, Astrid; Mironov, Vladimir; Kuiper, Martin

2014-12-10

Network-based approaches for the analysis of large-scale genomics data have become well established. Biological networks provide a knowledge scaffold against which the patterns and dynamics of 'omics' data can be interpreted. The background information required for the construction of such networks is often dispersed across a multitude of knowledge bases in a variety of formats. The seamless integration of this information is one of the main challenges in bioinformatics. The Semantic Web offers powerful technologies for the assembly of integrated knowledge bases that are computationally comprehensible, thereby providing a potentially powerful resource for constructing biological networks and network-based analysis. We have developed the Gene eXpression Knowledge Base (GeXKB), a semantic web technology based resource that contains integrated knowledge about gene expression regulation. To affirm the utility of GeXKB we demonstrate how this resource can be exploited for the identification of candidate regulatory network proteins. We present four use cases that were designed from a biological perspective in order to find candidate members relevant for the gastrin hormone signaling network model. We show how a combination of specific query definitions and additional selection criteria derived from gene expression data and prior knowledge concerning candidate proteins can be used to retrieve a set of proteins that constitute valid candidates for regulatory network extensions. Semantic web technologies provide the means for processing and integrating various heterogeneous information sources. The GeXKB offers biologists such an integrated knowledge resource, allowing them to address complex biological questions pertaining to gene expression. This work illustrates how GeXKB can be used in combination with gene expression results and literature information to identify new potential candidates that may be considered for extending a gene regulatory network.

Regional and temporal differences in gene expression of LH(BETA)T(AG) retinoblastoma tumors.

PubMed

Houston, Samuel K; Pina, Yolanda; Clarke, Jennifer; Koru-Sengul, Tulay; Scott, William K; Nathanson, Lubov; Schefler, Amy C; Murray, Timothy G

2011-07-23

The purpose of this study was to evaluate by microarray the hypothesis that LH(BETA)T(AG) retinoblastoma tumors exhibit regional and temporal variations in gene expression. LH(BETA)T(AG) mice aged 12, 16, and 20 weeks were euthanatized (n = 9). Specimens were taken from five tumor areas (apex, anterior lateral, center, base, and posterior lateral). Samples were hybridized to gene microarrays. The data were preprocessed and analyzed, and genes with a P < 0.01, according to the ANOVA models, and a log(2)-fold change >2.5 were considered to be differentially expressed. Differentially expressed genes were analyzed for overlap with known networks by using pathway analysis tools. There were significant temporal (P < 10(-8)) and regional differences in gene expression for LH(BETA)T(AG) retinoblastoma tumors. At P < 0.01 and log(2)-fold change >2.5, there were significant changes in gene expression of 190 genes apically, 84 genes anterolaterally, 126 genes posteriorly, 56 genes centrally, and 134 genes at the base. Differentially expressed genes overlapped with known networks, with significant involvement in regulation of cellular proliferation and growth, response to oxygen levels and hypoxia, regulation of cellular processes, cellular signaling cascades, and angiogenesis. There are significant temporal and regional variations in the LH(BETA)T(AG) retinoblastoma model. Differentially expressed genes overlap with key pathways that may play pivotal roles in murine retinoblastoma development. These findings suggest the mechanisms involved in tumor growth and progression in murine retinoblastoma tumors and identify pathways for analysis at a functional level, to determine significance in human retinoblastoma. Microarray analysis of LH(BETA)T(AG) retinal tumors showed significant regional and temporal variations in gene expression, including dysregulation of genes involved in hypoxic responses and angiogenesis.

A SoxC gene related to larval shell development and co-expression analysis of different shell formation genes in early larvae of oyster.

PubMed

Liu, Gang; Huan, Pin; Liu, Baozhong

2017-06-01

Among the potential larval shell formation genes in mollusks, most are expressed in cells surrounding the shell field during the early phase of shell formation. The only exception (cgi-tyr1) is expressed in the whole larval mantle and thus represents a novel type of expression pattern. This study reports another gene with such an expression pattern. The gene encoded a SoxC homolog of the Pacific oyster Crassostrea gigas and was named cgi-soxc. Whole-mount in situ hybridization revealed that the gene was highly expressed in the whole larval mantle of early larvae. Based on its spatiotemporal expression, cgi-soxc is hypothesized to be involved in periostracum biogenesis, biomineralization, and regulation of cell proliferation. Furthermore, we investigated the interrelationship between cgi-soxc expression and two additional potential shell formation genes, cgi-tyr1 and cgi-gata2/3. The results confirmed co-expression of the three genes in the larval mantle of early D-veliger. Nevertheless, cgi-gata2/3 was only expressed in the mantle edge, and the other two genes were expressed in all mantle cells. Based on the spatial expression patterns of the three genes, two cell groups were identified from the larval mantle (tyr1 + /soxc + /gata2/3 + cells and tyr1 + /soxc + /gata2/3 - cells) and are important to study the differentiation and function of this tissue. The results of this study enrich our knowledge on the structure and function of larval mantle and provide important information to understand the molecular mechanisms of larval shell formation.

Analysis of genetic association using hierarchical clustering and cluster validation indices.

PubMed

Pagnuco, Inti A; Pastore, Juan I; Abras, Guillermo; Brun, Marcel; Ballarin, Virginia L

2017-10-01

It is usually assumed that co-expressed genes suggest co-regulation in the underlying regulatory network. Determining sets of co-expressed genes is an important task, based on some criteria of similarity. This task is usually performed by clustering algorithms, where the genes are clustered into meaningful groups based on their expression values in a set of experiment. In this work, we propose a method to find sets of co-expressed genes, based on cluster validation indices as a measure of similarity for individual gene groups, and a combination of variants of hierarchical clustering to generate the candidate groups. We evaluated its ability to retrieve significant sets on simulated correlated and real genomics data, where the performance is measured based on its detection ability of co-regulated sets against a full search. Additionally, we analyzed the quality of the best ranked groups using an online bioinformatics tool that provides network information for the selected genes. Copyright © 2017 Elsevier Inc. All rights reserved.

Identification of Id4 as a regulator of BRCA1 expression by using a ribozyme-library-based inverse genomics approach

PubMed Central

Beger, Carmela; Pierce, Leigh N.; Krüger, Martin; Marcusson, Eric G.; Robbins, Joan M.; Welcsh, Piri; Welch, Peter J.; Welte, Karl; King, Mary-Claire; Barber, Jack R.; Wong-Staal, Flossie

2001-01-01

Expression of the breast and ovarian cancer susceptibility gene BRCA1 is down-regulated in sporadic breast and ovarian cancer cases. Therefore, the identification of genes involved in the regulation of BRCA1 expression might lead to new insights into the pathogenesis and treatment of these tumors. In the present study, an “inverse genomics” approach based on a randomized ribozyme gene library was applied to identify cellular genes regulating BRCA1 expression. A ribozyme gene library with randomized target recognition sequences was introduced into human ovarian cancer-derived cells stably expressing a selectable marker [enhanced green fluorescence protein (EGFP)] under the control of the BRCA1 promoter. Cells in which BRCA1 expression was upregulated by particular ribozymes were selected through their concomitant increase in EGFP expression. The cellular target gene of one ribozyme was identified to be the dominant negative transcriptional regulator Id4. Modulation of Id4 expression resulted in inversely regulated expression of BRCA1. In addition, increase in Id4 expression was associated with the ability of cells to exhibit anchorage-independent growth, demonstrating the biological relevance of this gene. Our data suggest that Id4 is a crucial gene regulating BRCA1 expression and might therefore be important for the BRCA1 regulatory pathway involved in the pathogenesis of sporadic breast and ovarian cancer. PMID:11136250

Reduction of wobble-position GC bases in Corynebacteria genes and enhancement of PCR and heterologous expression.

PubMed

Sanli, G; Blaber, S I; Blaber, M

2001-01-01

Corynebacteria codon usage exhibits an overall GC content of 67%, and a wobble-position GC content of 88%. Escherichia coli, on the other hand has an overall GC content of 51%, and a wobble-position GC content of 55%. The high GC content of Corynebacteria genes results in an unfavorable codon preference for heterologous expression, and can present difficulties for polymerase-based manipulations due to secondary-structure effects. Since these characteristics are due primarily to base composition at the wobble-position, synthetic genes can, in principle, be designed to eliminate these problems and retain the wild-type amino acid sequence. Such genes would obviate the need for special additives or bases during in vitro polymerase-based manipulation and mutant host strains containing uncommon tRNA's for heterologous expression. We have evaluated synthetic genes with reduced wobble-position G/C content using two variants of the enzyme 2,5-diketo-D-gluconic acid reductase (2,5-DKGR A and B) from Corynebacterium. The wild-type genes are refractory to polymerase-based manipulations and exhibit poor heterologous expression in enteric bacteria. The results indicate that a subset of codons for five amino acids (alanine, arginine, glutamate, glycine and valine) contribute the greatest contribution to reduction in G/C content at the wobble-position. Furthermore, changes in codons for two amino acids (leucine and proline) enhance bias for expression in enteric bacteria without affecting the overall G/C content. The synthetic genes are readily amplified using polymerase-based methodologies, and exhibit high levels of heterologous expression in E. coli.

A P-Norm Robust Feature Extraction Method for Identifying Differentially Expressed Genes

PubMed Central

Liu, Jian; Liu, Jin-Xing; Gao, Ying-Lian; Kong, Xiang-Zhen; Wang, Xue-Song; Wang, Dong

2015-01-01

In current molecular biology, it becomes more and more important to identify differentially expressed genes closely correlated with a key biological process from gene expression data. In this paper, based on the Schatten p-norm and Lp-norm, a novel p-norm robust feature extraction method is proposed to identify the differentially expressed genes. In our method, the Schatten p-norm is used as the regularization function to obtain a low-rank matrix and the Lp-norm is taken as the error function to improve the robustness to outliers in the gene expression data. The results on simulation data show that our method can obtain higher identification accuracies than the competitive methods. Numerous experiments on real gene expression data sets demonstrate that our method can identify more differentially expressed genes than the others. Moreover, we confirmed that the identified genes are closely correlated with the corresponding gene expression data. PMID:26201006

A P-Norm Robust Feature Extraction Method for Identifying Differentially Expressed Genes.

PubMed

Liu, Jian; Liu, Jin-Xing; Gao, Ying-Lian; Kong, Xiang-Zhen; Wang, Xue-Song; Wang, Dong

2015-01-01

In current molecular biology, it becomes more and more important to identify differentially expressed genes closely correlated with a key biological process from gene expression data. In this paper, based on the Schatten p-norm and Lp-norm, a novel p-norm robust feature extraction method is proposed to identify the differentially expressed genes. In our method, the Schatten p-norm is used as the regularization function to obtain a low-rank matrix and the Lp-norm is taken as the error function to improve the robustness to outliers in the gene expression data. The results on simulation data show that our method can obtain higher identification accuracies than the competitive methods. Numerous experiments on real gene expression data sets demonstrate that our method can identify more differentially expressed genes than the others. Moreover, we confirmed that the identified genes are closely correlated with the corresponding gene expression data.

Integrating Colon Cancer Microarray Data: Associating Locus-Specific Methylation Groups to Gene Expression-Based Classifications.

PubMed

Barat, Ana; Ruskin, Heather J; Byrne, Annette T; Prehn, Jochen H M

2015-11-23

Recently, considerable attention has been paid to gene expression-based classifications of colorectal cancers (CRC) and their association with patient prognosis. In addition to changes in gene expression, abnormal DNA-methylation is known to play an important role in cancer onset and development, and colon cancer is no exception to this rule. Large-scale technologies, such as methylation microarray assays and specific sequencing of methylated DNA, have been used to determine whole genome profiles of CpG island methylation in tissue samples. In this article, publicly available microarray-based gene expression and methylation data sets are used to characterize expression subtypes with respect to locus-specific methylation. A major objective was to determine whether integration of these data types improves previously characterized subtypes, or provides evidence for additional subtypes. We used unsupervised clustering techniques to determine methylation-based subgroups, which are subsequently annotated with three published expression-based classifications, comprising from three to six subtypes. Our results showed that, while methylation profiles provide a further basis for segregation of certain (Inflammatory and Goblet-like) finer-grained expression-based subtypes, they also suggest that other finer-grained subtypes are not distinctive and can be considered as a single subtype.

Integrating Colon Cancer Microarray Data: Associating Locus-Specific Methylation Groups to Gene Expression-Based Classifications

PubMed Central

Barat, Ana; Ruskin, Heather J.; Byrne, Annette T.; Prehn, Jochen H. M.

2015-01-01

Recently, considerable attention has been paid to gene expression-based classifications of colorectal cancers (CRC) and their association with patient prognosis. In addition to changes in gene expression, abnormal DNA-methylation is known to play an important role in cancer onset and development, and colon cancer is no exception to this rule. Large-scale technologies, such as methylation microarray assays and specific sequencing of methylated DNA, have been used to determine whole genome profiles of CpG island methylation in tissue samples. In this article, publicly available microarray-based gene expression and methylation data sets are used to characterize expression subtypes with respect to locus-specific methylation. A major objective was to determine whether integration of these data types improves previously characterized subtypes, or provides evidence for additional subtypes. We used unsupervised clustering techniques to determine methylation-based subgroups, which are subsequently annotated with three published expression-based classifications, comprising from three to six subtypes. Our results showed that, while methylation profiles provide a further basis for segregation of certain (Inflammatory and Goblet-like) finer-grained expression-based subtypes, they also suggest that other finer-grained subtypes are not distinctive and can be considered as a single subtype. PMID:27600244

A novel approach for human whole transcriptome analysis based on absolute gene expression of microarray data

PubMed Central

Bikel, Shirley; Jacobo-Albavera, Leonor; Sánchez-Muñoz, Fausto; Cornejo-Granados, Fernanda; Canizales-Quinteros, Samuel; Soberón, Xavier; Sotelo-Mundo, Rogerio R.; del Río-Navarro, Blanca E.; Mendoza-Vargas, Alfredo; Sánchez, Filiberto

2017-01-01

Background In spite of the emergence of RNA sequencing (RNA-seq), microarrays remain in widespread use for gene expression analysis in the clinic. There are over 767,000 RNA microarrays from human samples in public repositories, which are an invaluable resource for biomedical research and personalized medicine. The absolute gene expression analysis allows the transcriptome profiling of all expressed genes under a specific biological condition without the need of a reference sample. However, the background fluorescence represents a challenge to determine the absolute gene expression in microarrays. Given that the Y chromosome is absent in female subjects, we used it as a new approach for absolute gene expression analysis in which the fluorescence of the Y chromosome genes of female subjects was used as the background fluorescence for all the probes in the microarray. This fluorescence was used to establish an absolute gene expression threshold, allowing the differentiation between expressed and non-expressed genes in microarrays. Methods We extracted the RNA from 16 children leukocyte samples (nine males and seven females, ages 6–10 years). An Affymetrix Gene Chip Human Gene 1.0 ST Array was carried out for each sample and the fluorescence of 124 genes of the Y chromosome was used to calculate the absolute gene expression threshold. After that, several expressed and non-expressed genes according to our absolute gene expression threshold were compared against the expression obtained using real-time quantitative polymerase chain reaction (RT-qPCR). Results From the 124 genes of the Y chromosome, three genes (DDX3Y, TXLNG2P and EIF1AY) that displayed significant differences between sexes were used to calculate the absolute gene expression threshold. Using this threshold, we selected 13 expressed and non-expressed genes and confirmed their expression level by RT-qPCR. Then, we selected the top 5% most expressed genes and found that several KEGG pathways were significantly enriched. Interestingly, these pathways were related to the typical functions of leukocytes cells, such as antigen processing and presentation and natural killer cell mediated cytotoxicity. We also applied this method to obtain the absolute gene expression threshold in already published microarray data of liver cells, where the top 5% expressed genes showed an enrichment of typical KEGG pathways for liver cells. Our results suggest that the three selected genes of the Y chromosome can be used to calculate an absolute gene expression threshold, allowing a transcriptome profiling of microarray data without the need of an additional reference experiment. Discussion Our approach based on the establishment of a threshold for absolute gene expression analysis will allow a new way to analyze thousands of microarrays from public databases. This allows the study of different human diseases without the need of having additional samples for relative expression experiments. PMID:29230367

Differentially co-expressed interacting protein pairs discriminate samples under distinct stages of HIV type 1 infection.

PubMed

Yoon, Dukyong; Kim, Hyosil; Suh-Kim, Haeyoung; Park, Rae Woong; Lee, KiYoung

2011-01-01

Microarray analyses based on differentially expressed genes (DEGs) have been widely used to distinguish samples across different cellular conditions. However, studies based on DEGs have not been able to clearly determine significant differences between samples of pathophysiologically similar HIV-1 stages, e.g., between acute and chronic progressive (or AIDS) or between uninfected and clinically latent stages. We here suggest a novel approach to allow such discrimination based on stage-specific genetic features of HIV-1 infection. Our approach is based on co-expression changes of genes known to interact. The method can identify a genetic signature for a single sample as contrasted with existing protein-protein-based analyses with correlational designs. Our approach distinguishes each sample using differentially co-expressed interacting protein pairs (DEPs) based on co-expression scores of individual interacting pairs within a sample. The co-expression score has positive value if two genes in a sample are simultaneously up-regulated or down-regulated. And the score has higher absolute value if expression-changing ratios are similar between the two genes. We compared characteristics of DEPs with that of DEGs by evaluating their usefulness in separation of HIV-1 stage. And we identified DEP-based network-modules and their gene-ontology enrichment to find out the HIV-1 stage-specific gene signature. Based on the DEP approach, we observed clear separation among samples from distinct HIV-1 stages using clustering and principal component analyses. Moreover, the discrimination power of DEPs on the samples (70-100% accuracy) was much higher than that of DEGs (35-45%) using several well-known classifiers. DEP-based network analysis also revealed the HIV-1 stage-specific network modules; the main biological processes were related to "translation," "RNA splicing," "mRNA, RNA, and nucleic acid transport," and "DNA metabolism." Through the HIV-1 stage-related modules, changing stage-specific patterns of protein interactions could be observed. DEP-based method discriminated the HIV-1 infection stages clearly, and revealed a HIV-1 stage-specific gene signature. The proposed DEP-based method might complement existing DEG-based approaches in various microarray expression analyses.

A biomarker-based screen of a gene expression compendium reveals regulation of Nrf2 by CAR and STAT5b

EPA Science Inventory

Computational approaches were developed to identify factors that regulate Nrf2 in a large gene expression compendium of microarray profiles including >2000 comparisons which queried the effects of chemicals, genes, diets, and infectious agents on gene expression in the mouse l...

At-TAX: a whole genome tiling array resource for developmental expression analysis and transcript identification in Arabidopsis thaliana

PubMed Central

Laubinger, Sascha; Zeller, Georg; Henz, Stefan R; Sachsenberg, Timo; Widmer, Christian K; Naouar, Naïra; Vuylsteke, Marnik; Schölkopf, Bernhard; Rätsch, Gunnar; Weigel, Detlef

2008-01-01

Gene expression maps for model organisms, including Arabidopsis thaliana, have typically been created using gene-centric expression arrays. Here, we describe a comprehensive expression atlas, Arabidopsis thaliana Tiling Array Express (At-TAX), which is based on whole-genome tiling arrays. We demonstrate that tiling arrays are accurate tools for gene expression analysis and identified more than 1,000 unannotated transcribed regions. Visualizations of gene expression estimates, transcribed regions, and tiling probe measurements are accessible online at the At-TAX homepage. PMID:18613972

Identification of Human HK Genes and Gene Expression Regulation Study in Cancer from Transcriptomics Data Analysis

PubMed Central

Zhang, Zhang; Liu, Jingxing; Wu, Jiayan; Yu, Jun

2013-01-01

The regulation of gene expression is essential for eukaryotes, as it drives the processes of cellular differentiation and morphogenesis, leading to the creation of different cell types in multicellular organisms. RNA-Sequencing (RNA-Seq) provides researchers with a powerful toolbox for characterization and quantification of transcriptome. Many different human tissue/cell transcriptome datasets coming from RNA-Seq technology are available on public data resource. The fundamental issue here is how to develop an effective analysis method to estimate expression pattern similarities between different tumor tissues and their corresponding normal tissues. We define the gene expression pattern from three directions: 1) expression breadth, which reflects gene expression on/off status, and mainly concerns ubiquitously expressed genes; 2) low/high or constant/variable expression genes, based on gene expression level and variation; and 3) the regulation of gene expression at the gene structure level. The cluster analysis indicates that gene expression pattern is higher related to physiological condition rather than tissue spatial distance. Two sets of human housekeeping (HK) genes are defined according to cell/tissue types, respectively. To characterize the gene expression pattern in gene expression level and variation, we firstly apply improved K-means algorithm and a gene expression variance model. We find that cancer-associated HK genes (a HK gene is specific in cancer group, while not in normal group) are expressed higher and more variable in cancer condition than in normal condition. Cancer-associated HK genes prefer to AT-rich genes, and they are enriched in cell cycle regulation related functions and constitute some cancer signatures. The expression of large genes is also avoided in cancer group. These studies will help us understand which cell type-specific patterns of gene expression differ among different cell types, and particularly for cancer. PMID:23382867

Gene selection for tumor classification using neighborhood rough sets and entropy measures.

PubMed

Chen, Yumin; Zhang, Zunjun; Zheng, Jianzhong; Ma, Ying; Xue, Yu

2017-03-01

With the development of bioinformatics, tumor classification from gene expression data becomes an important useful technology for cancer diagnosis. Since a gene expression data often contains thousands of genes and a small number of samples, gene selection from gene expression data becomes a key step for tumor classification. Attribute reduction of rough sets has been successfully applied to gene selection field, as it has the characters of data driving and requiring no additional information. However, traditional rough set method deals with discrete data only. As for the gene expression data containing real-value or noisy data, they are usually employed by a discrete preprocessing, which may result in poor classification accuracy. In this paper, we propose a novel gene selection method based on the neighborhood rough set model, which has the ability of dealing with real-value data whilst maintaining the original gene classification information. Moreover, this paper addresses an entropy measure under the frame of neighborhood rough sets for tackling the uncertainty and noisy of gene expression data. The utilization of this measure can bring about a discovery of compact gene subsets. Finally, a gene selection algorithm is designed based on neighborhood granules and the entropy measure. Some experiments on two gene expression data show that the proposed gene selection is an effective method for improving the accuracy of tumor classification. Copyright © 2017 Elsevier Inc. All rights reserved.

Inferring causal genomic alterations in breast cancer using gene expression data

PubMed Central

2011-01-01

Background One of the primary objectives in cancer research is to identify causal genomic alterations, such as somatic copy number variation (CNV) and somatic mutations, during tumor development. Many valuable studies lack genomic data to detect CNV; therefore, methods that are able to infer CNVs from gene expression data would help maximize the value of these studies. Results We developed a framework for identifying recurrent regions of CNV and distinguishing the cancer driver genes from the passenger genes in the regions. By inferring CNV regions across many datasets we were able to identify 109 recurrent amplified/deleted CNV regions. Many of these regions are enriched for genes involved in many important processes associated with tumorigenesis and cancer progression. Genes in these recurrent CNV regions were then examined in the context of gene regulatory networks to prioritize putative cancer driver genes. The cancer driver genes uncovered by the framework include not only well-known oncogenes but also a number of novel cancer susceptibility genes validated via siRNA experiments. Conclusions To our knowledge, this is the first effort to systematically identify and validate drivers for expression based CNV regions in breast cancer. The framework where the wavelet analysis of copy number alteration based on expression coupled with the gene regulatory network analysis, provides a blueprint for leveraging genomic data to identify key regulatory components and gene targets. This integrative approach can be applied to many other large-scale gene expression studies and other novel types of cancer data such as next-generation sequencing based expression (RNA-Seq) as well as CNV data. PMID:21806811

A statistical method for measuring activation of gene regulatory networks.

PubMed

Esteves, Gustavo H; Reis, Luiz F L

2018-06-13

Gene expression data analysis is of great importance for modern molecular biology, given our ability to measure the expression profiles of thousands of genes and enabling studies rooted in systems biology. In this work, we propose a simple statistical model for the activation measuring of gene regulatory networks, instead of the traditional gene co-expression networks. We present the mathematical construction of a statistical procedure for testing hypothesis regarding gene regulatory network activation. The real probability distribution for the test statistic is evaluated by a permutation based study. To illustrate the functionality of the proposed methodology, we also present a simple example based on a small hypothetical network and the activation measuring of two KEGG networks, both based on gene expression data collected from gastric and esophageal samples. The two KEGG networks were also analyzed for a public database, available through NCBI-GEO, presented as Supplementary Material. This method was implemented in an R package that is available at the BioConductor project website under the name maigesPack.

Systems biology definition of the core proteome of metabolism and expression is consistent with high-throughput data.

PubMed

Yang, Laurence; Tan, Justin; O'Brien, Edward J; Monk, Jonathan M; Kim, Donghyuk; Li, Howard J; Charusanti, Pep; Ebrahim, Ali; Lloyd, Colton J; Yurkovich, James T; Du, Bin; Dräger, Andreas; Thomas, Alex; Sun, Yuekai; Saunders, Michael A; Palsson, Bernhard O

2015-08-25

Finding the minimal set of gene functions needed to sustain life is of both fundamental and practical importance. Minimal gene lists have been proposed by using comparative genomics-based core proteome definitions. A definition of a core proteome that is supported by empirical data, is understood at the systems-level, and provides a basis for computing essential cell functions is lacking. Here, we use a systems biology-based genome-scale model of metabolism and expression to define a functional core proteome consisting of 356 gene products, accounting for 44% of the Escherichia coli proteome by mass based on proteomics data. This systems biology core proteome includes 212 genes not found in previous comparative genomics-based core proteome definitions, accounts for 65% of known essential genes in E. coli, and has 78% gene function overlap with minimal genomes (Buchnera aphidicola and Mycoplasma genitalium). Based on transcriptomics data across environmental and genetic backgrounds, the systems biology core proteome is significantly enriched in nondifferentially expressed genes and depleted in differentially expressed genes. Compared with the noncore, core gene expression levels are also similar across genetic backgrounds (two times higher Spearman rank correlation) and exhibit significantly more complex transcriptional and posttranscriptional regulatory features (40% more transcription start sites per gene, 22% longer 5'UTR). Thus, genome-scale systems biology approaches rigorously identify a functional core proteome needed to support growth. This framework, validated by using high-throughput datasets, facilitates a mechanistic understanding of systems-level core proteome function through in silico models; it de facto defines a paleome.

Transcriptome dynamics along axolotl regenerative development are consistent with an extensive reduction in gene expression heterogeneity in dedifferentiated cells

PubMed Central

2017-01-01

Although in recent years the study of gene expression variation in the absence of genetic or environmental cues or gene expression heterogeneity has intensified considerably, many basic and applied biological fields still remain unaware of how useful the study of gene expression heterogeneity patterns might be for the characterization of biological systems and/or processes. Largely based on the modulator effect chromatin compaction has for gene expression heterogeneity and the extensive changes in chromatin compaction known to occur for specialized cells that are naturally or artificially induced to revert to less specialized states or dedifferentiate, I recently hypothesized that processes that concur with cell dedifferentiation would show an extensive reduction in gene expression heterogeneity. The confirmation of the existence of such trend could be of wide interest because of the biomedical and biotechnological relevance of cell dedifferentiation-based processes, i.e., regenerative development, cancer, human induced pluripotent stem cells, or plant somatic embryogenesis. Here, I report the first empirical evidence consistent with the existence of an extensive reduction in gene expression heterogeneity for processes that concur with cell dedifferentiation by analyzing transcriptome dynamics along forearm regenerative development in Ambystoma mexicanum or axolotl. Also, I briefly discuss on the utility of the study of gene expression heterogeneity dynamics might have for the characterization of cell dedifferentiation-based processes, and the engineering of tools that afforded better monitoring and modulating such processes. Finally, I reflect on how a transitional reduction in gene expression heterogeneity for dedifferentiated cells can promote a long-term increase in phenotypic heterogeneity following cell dedifferentiation with potential adverse effects for biomedical and biotechnological applications. PMID:29134148

A Pathway Based Classification Method for Analyzing Gene Expression for Alzheimer's Disease Diagnosis.

PubMed

Voyle, Nicola; Keohane, Aoife; Newhouse, Stephen; Lunnon, Katie; Johnston, Caroline; Soininen, Hilkka; Kloszewska, Iwona; Mecocci, Patrizia; Tsolaki, Magda; Vellas, Bruno; Lovestone, Simon; Hodges, Angela; Kiddle, Steven; Dobson, Richard Jb

2016-01-01

Recent studies indicate that gene expression levels in blood may be able to differentiate subjects with Alzheimer's disease (AD) from normal elderly controls and mild cognitively impaired (MCI) subjects. However, there is limited replicability at the single marker level. A pathway-based interpretation of gene expression may prove more robust. This study aimed to investigate whether a case/control classification model built on pathway level data was more robust than a gene level model and may consequently perform better in test data. The study used two batches of gene expression data from the AddNeuroMed (ANM) and Dementia Case Registry (DCR) cohorts. Our study used Illumina Human HT-12 Expression BeadChips to collect gene expression from blood samples. Random forest modeling with recursive feature elimination was used to predict case/control status. Age and APOE ɛ4 status were used as covariates for all analysis. Gene and pathway level models performed similarly to each other and to a model based on demographic information only. Any potential increase in concordance from the novel pathway level approach used here has not lead to a greater predictive ability in these datasets. However, we have only tested one method for creating pathway level scores. Further, we have been able to benchmark pathways against genes in datasets that had been extensively harmonized. Further work should focus on the use of alternative methods for creating pathway level scores, in particular those that incorporate pathway topology, and the use of an endophenotype based approach.

Accelerated Evolution of Developmentally Biased Genes in the Tetraphenic Ant Cardiocondyla obscurior.

PubMed

Schrader, Lukas; Helanterä, Heikki; Oettler, Jan

2017-03-01

Plastic gene expression underlies phenotypic plasticity and plastically expressed genes evolve under different selection regimes compared with ubiquitously expressed genes. Social insects are well-suited models to elucidate the evolutionary dynamics of plastic genes for their genetically and environmentally induced discrete polymorphisms. Here, we study the evolution of plastically expressed genes in the ant Cardiocondyla obscurior-a species that produces two discrete male morphs in addition to the typical female polymorphism of workers and queens. Based on individual-level gene expression data from 28 early third instar larvae, we test whether the same evolutionary dynamics that pertain to plastically expressed genes in adults also pertain to genes with plastic expression during development. In order to quantify plasticity of gene expression over multiple contrasts, we develop a novel geometric measure. For genes expressed during development, we show that plasticity of expression is positively correlated with evolutionary rates. We furthermore find a strong correlation between expression plasticity and expression variation within morphs, suggesting a close link between active and passive plasticity of gene expression. Our results support the notion of relaxed selection and neutral processes as important drivers in the evolution of adaptive plasticity. © The Author 2016. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Adult mouse brain gene expression patterns bear an embryologic imprint

PubMed Central

Zapala, Matthew A.; Hovatta, Iiris; Ellison, Julie A.; Wodicka, Lisa; Del Rio, Jo A.; Tennant, Richard; Tynan, Wendy; Broide, Ron S.; Helton, Rob; Stoveken, Barbara S.; Winrow, Christopher; Lockhart, Daniel J.; Reilly, John F.; Young, Warren G.; Bloom, Floyd E.; Lockhart, David J.; Barlow, Carrolee

2005-01-01

The current model to explain the organization of the mammalian nervous system is based on studies of anatomy, embryology, and evolution. To further investigate the molecular organization of the adult mammalian brain, we have built a gene expression-based brain map. We measured gene expression patterns for 24 neural tissues covering the mouse central nervous system and found, surprisingly, that the adult brain bears a transcriptional “imprint” consistent with both embryological origins and classic evolutionary relationships. Embryonic cellular position along the anterior–posterior axis of the neural tube was shown to be closely associated with, and possibly a determinant of, the gene expression patterns in adult structures. We also observed a significant number of embryonic patterning and homeobox genes with region-specific expression in the adult nervous system. The relationships between global expression patterns for different anatomical regions and the nature of the observed region-specific genes suggest that the adult brain retains a degree of overall gene expression established during embryogenesis that is important for regional specificity and the functional relationships between regions in the adult. The complete collection of extensively annotated gene expression data along with data mining and visualization tools have been made available on a publicly accessible web site (www.barlow-lockhart-brainmapnimhgrant.org). PMID:16002470

Multiplex cDNA quantification method that facilitates the standardization of gene expression data

PubMed Central

Gotoh, Osamu; Murakami, Yasufumi; Suyama, Akira

2011-01-01

Microarray-based gene expression measurement is one of the major methods for transcriptome analysis. However, current microarray data are substantially affected by microarray platforms and RNA references because of the microarray method can provide merely the relative amounts of gene expression levels. Therefore, valid comparisons of the microarray data require standardized platforms, internal and/or external controls and complicated normalizations. These requirements impose limitations on the extensive comparison of gene expression data. Here, we report an effective approach to removing the unfavorable limitations by measuring the absolute amounts of gene expression levels on common DNA microarrays. We have developed a multiplex cDNA quantification method called GEP-DEAN (Gene expression profiling by DCN-encoding-based analysis). The method was validated by using chemically synthesized DNA strands of known quantities and cDNA samples prepared from mouse liver, demonstrating that the absolute amounts of cDNA strands were successfully measured with a sensitivity of 18 zmol in a highly multiplexed manner in 7 h. PMID:21415008

Non-parent of Origin Expression of Numerous Effector Genes Indicates a Role of Gene Regulation in Host Adaption of the Hybrid Triticale Powdery Mildew Pathogen

PubMed Central

Praz, Coraline R.; Menardo, Fabrizio; Robinson, Mark D.; Müller, Marion C.; Wicker, Thomas; Bourras, Salim; Keller, Beat

2018-01-01

Powdery mildew is an important disease of cereals. It is caused by one species, Blumeria graminis, which is divided into formae speciales each of which is highly specialized to one host. Recently, a new form capable of growing on triticale (B.g. triticale) has emerged through hybridization between wheat and rye mildews (B.g. tritici and B.g. secalis, respectively). In this work, we used RNA sequencing to study the molecular basis of host adaptation in B.g. triticale. We analyzed gene expression in three B.g. tritici isolates, two B.g. secalis isolates and two B.g. triticale isolates and identified a core set of putative effector genes that are highly expressed in all formae speciales. We also found that the genes differentially expressed between isolates of the same form as well as between different formae speciales were enriched in putative effectors. Their coding genes belong to several families including some which contain known members of mildew avirulence (Avr) and suppressor (Svr) genes. Based on these findings we propose that effectors play an important role in host adaptation that is mechanistically based on Avr-Resistance gene-Svr interactions. We also found that gene expression in the B.g. triticale hybrid is mostly conserved with the parent-of-origin, but some genes inherited from B.g. tritici showed a B.g. secalis-like expression. Finally, we identified 11 unambiguous cases of putative effector genes with hybrid-specific, non-parent of origin gene expression, and we propose that they are possible determinants of host specialization in triticale mildew. These data suggest that altered expression of multiple effector genes, in particular Avr and Svr related factors, might play a role in mildew host adaptation based on hybridization. PMID:29441081

Refinement of light-responsive transcript lists using rice oligonucleotide arrays: evaluation of gene-redundancy.

PubMed

Jung, Ki-Hong; Dardick, Christopher; Bartley, Laura E; Cao, Peijian; Phetsom, Jirapa; Canlas, Patrick; Seo, Young-Su; Shultz, Michael; Ouyang, Shu; Yuan, Qiaoping; Frank, Bryan C; Ly, Eugene; Zheng, Li; Jia, Yi; Hsia, An-Ping; An, Kyungsook; Chou, Hui-Hsien; Rocke, David; Lee, Geun Cheol; Schnable, Patrick S; An, Gynheung; Buell, C Robin; Ronald, Pamela C

2008-10-06

Studies of gene function are often hampered by gene-redundancy, especially in organisms with large genomes such as rice (Oryza sativa). We present an approach for using transcriptomics data to focus functional studies and address redundancy. To this end, we have constructed and validated an inexpensive and publicly available rice oligonucleotide near-whole genome array, called the rice NSF45K array. We generated expression profiles for light- vs. dark-grown rice leaf tissue and validated the biological significance of the data by analyzing sources of variation and confirming expression trends with reverse transcription polymerase chain reaction. We examined trends in the data by evaluating enrichment of gene ontology terms at multiple false discovery rate thresholds. To compare data generated with the NSF45K array with published results, we developed publicly available, web-based tools (www.ricearray.org). The Oligo and EST Anatomy Viewer enables visualization of EST-based expression profiling data for all genes on the array. The Rice Multi-platform Microarray Search Tool facilitates comparison of gene expression profiles across multiple rice microarray platforms. Finally, we incorporated gene expression and biochemical pathway data to reduce the number of candidate gene products putatively participating in the eight steps of the photorespiration pathway from 52 to 10, based on expression levels of putatively functionally redundant genes. We confirmed the efficacy of this method to cope with redundancy by correctly predicting participation in photorespiration of a gene with five paralogs. Applying these methods will accelerate rice functional genomics.

Candidate gene prioritization by network analysis of differential expression using machine learning approaches

PubMed Central

2010-01-01

Background Discovering novel disease genes is still challenging for diseases for which no prior knowledge - such as known disease genes or disease-related pathways - is available. Performing genetic studies frequently results in large lists of candidate genes of which only few can be followed up for further investigation. We have recently developed a computational method for constitutional genetic disorders that identifies the most promising candidate genes by replacing prior knowledge by experimental data of differential gene expression between affected and healthy individuals. To improve the performance of our prioritization strategy, we have extended our previous work by applying different machine learning approaches that identify promising candidate genes by determining whether a gene is surrounded by highly differentially expressed genes in a functional association or protein-protein interaction network. Results We have proposed three strategies scoring disease candidate genes relying on network-based machine learning approaches, such as kernel ridge regression, heat kernel, and Arnoldi kernel approximation. For comparison purposes, a local measure based on the expression of the direct neighbors is also computed. We have benchmarked these strategies on 40 publicly available knockout experiments in mice, and performance was assessed against results obtained using a standard procedure in genetics that ranks candidate genes based solely on their differential expression levels (Simple Expression Ranking). Our results showed that our four strategies could outperform this standard procedure and that the best results were obtained using the Heat Kernel Diffusion Ranking leading to an average ranking position of 8 out of 100 genes, an AUC value of 92.3% and an error reduction of 52.8% relative to the standard procedure approach which ranked the knockout gene on average at position 17 with an AUC value of 83.7%. Conclusion In this study we could identify promising candidate genes using network based machine learning approaches even if no knowledge is available about the disease or phenotype. PMID:20840752

dictyExpress: a Dictyostelium discoideum gene expression database with an explorative data analysis web-based interface.

PubMed

Rot, Gregor; Parikh, Anup; Curk, Tomaz; Kuspa, Adam; Shaulsky, Gad; Zupan, Blaz

2009-08-25

Bioinformatics often leverages on recent advancements in computer science to support biologists in their scientific discovery process. Such efforts include the development of easy-to-use web interfaces to biomedical databases. Recent advancements in interactive web technologies require us to rethink the standard submit-and-wait paradigm, and craft bioinformatics web applications that share analytical and interactive power with their desktop relatives, while retaining simplicity and availability. We have developed dictyExpress, a web application that features a graphical, highly interactive explorative interface to our database that consists of more than 1000 Dictyostelium discoideum gene expression experiments. In dictyExpress, the user can select experiments and genes, perform gene clustering, view gene expression profiles across time, view gene co-expression networks, perform analyses of Gene Ontology term enrichment, and simultaneously display expression profiles for a selected gene in various experiments. Most importantly, these tasks are achieved through web applications whose components are seamlessly interlinked and immediately respond to events triggered by the user, thus providing a powerful explorative data analysis environment. dictyExpress is a precursor for a new generation of web-based bioinformatics applications with simple but powerful interactive interfaces that resemble that of the modern desktop. While dictyExpress serves mainly the Dictyostelium research community, it is relatively easy to adapt it to other datasets. We propose that the design ideas behind dictyExpress will influence the development of similar applications for other model organisms.

dictyExpress: a Dictyostelium discoideum gene expression database with an explorative data analysis web-based interface

PubMed Central

Rot, Gregor; Parikh, Anup; Curk, Tomaz; Kuspa, Adam; Shaulsky, Gad; Zupan, Blaz

2009-01-01

Background Bioinformatics often leverages on recent advancements in computer science to support biologists in their scientific discovery process. Such efforts include the development of easy-to-use web interfaces to biomedical databases. Recent advancements in interactive web technologies require us to rethink the standard submit-and-wait paradigm, and craft bioinformatics web applications that share analytical and interactive power with their desktop relatives, while retaining simplicity and availability. Results We have developed dictyExpress, a web application that features a graphical, highly interactive explorative interface to our database that consists of more than 1000 Dictyostelium discoideum gene expression experiments. In dictyExpress, the user can select experiments and genes, perform gene clustering, view gene expression profiles across time, view gene co-expression networks, perform analyses of Gene Ontology term enrichment, and simultaneously display expression profiles for a selected gene in various experiments. Most importantly, these tasks are achieved through web applications whose components are seamlessly interlinked and immediately respond to events triggered by the user, thus providing a powerful explorative data analysis environment. Conclusion dictyExpress is a precursor for a new generation of web-based bioinformatics applications with simple but powerful interactive interfaces that resemble that of the modern desktop. While dictyExpress serves mainly the Dictyostelium research community, it is relatively easy to adapt it to other datasets. We propose that the design ideas behind dictyExpress will influence the development of similar applications for other model organisms. PMID:19706156

Transcriptional profiling in facioscapulohumeral muscular dystrophy to identify candidate biomarkers

PubMed Central

Rahimov, Fedik; King, Oliver D.; Leung, Doris G.; Bibat, Genila M.; Emerson, Charles P.; Kunkel, Louis M.; Wagner, Kathryn R.

2012-01-01

Facioscapulohumeral muscular dystrophy (FSHD) is a progressive neuromuscular disorder caused by contractions of repetitive elements within the macrosatellite D4Z4 on chromosome 4q35. The pathophysiology of FSHD is unknown and, as a result, there is currently no effective treatment available for this disease. To better understand the pathophysiology of FSHD and develop mRNA-based biomarkers of affected muscles, we compared global analysis of gene expression in two distinct muscles obtained from a large number of FSHD subjects and their unaffected first-degree relatives. Gene expression in two muscle types was analyzed using GeneChip Gene 1.0 ST arrays: biceps, which typically shows an early and severe disease involvement; and deltoid, which is relatively uninvolved. For both muscle types, the expression differences were mild: using relaxed cutoffs for differential expression (fold change ≥1.2; nominal P value <0.01), we identified 191 and 110 genes differentially expressed between affected and control samples of biceps and deltoid muscle tissues, respectively, with 29 genes in common. Controlling for a false-discovery rate of <0.25 reduced the number of differentially expressed genes in biceps to 188 and in deltoid to 7. Expression levels of 15 genes altered in this study were used as a “molecular signature” in a validation study of an additional 26 subjects and predicted them as FSHD or control with 90% accuracy based on biceps and 80% accuracy based on deltoids. PMID:22988124

Gene and enhancer trap tagging of vascular-expressed genes in poplar trees

Treesearch

Andrew Groover; Joseph R. Fontana; Gayle Dupper; Caiping Ma; Robert Martienssen; Steven Strauss; Richard Meilan

2004-01-01

We report a gene discovery system for poplar trees based on gene and enhancer traps. Gene and enhancer trap vectors carrying the β-glucuronidase (GUS) reporter gene were inserted into the poplar genome via Agrobacterium tumefaciens transformation, where they reveal the expression pattern of genes at or near the insertion sites. Because GUS...

PINTA: a web server for network-based gene prioritization from expression data

PubMed Central

Nitsch, Daniela; Tranchevent, Léon-Charles; Gonçalves, Joana P.; Vogt, Josef Korbinian; Madeira, Sara C.; Moreau, Yves

2011-01-01

PINTA (available at http://www.esat.kuleuven.be/pinta/; this web site is free and open to all users and there is no login requirement) is a web resource for the prioritization of candidate genes based on the differential expression of their neighborhood in a genome-wide protein–protein interaction network. Our strategy is meant for biological and medical researchers aiming at identifying novel disease genes using disease specific expression data. PINTA supports both candidate gene prioritization (starting from a user defined set of candidate genes) as well as genome-wide gene prioritization and is available for five species (human, mouse, rat, worm and yeast). As input data, PINTA only requires disease specific expression data, whereas various platforms (e.g. Affymetrix) are supported. As a result, PINTA computes a gene ranking and presents the results as a table that can easily be browsed and downloaded by the user. PMID:21602267

Fractal Clustering and Knowledge-driven Validation Assessment for Gene Expression Profiling.

PubMed

Wang, Lu-Yong; Balasubramanian, Ammaiappan; Chakraborty, Amit; Comaniciu, Dorin

2005-01-01

DNA microarray experiments generate a substantial amount of information about the global gene expression. Gene expression profiles can be represented as points in multi-dimensional space. It is essential to identify relevant groups of genes in biomedical research. Clustering is helpful in pattern recognition in gene expression profiles. A number of clustering techniques have been introduced. However, these traditional methods mainly utilize shape-based assumption or some distance metric to cluster the points in multi-dimension linear Euclidean space. Their results shows poor consistence with the functional annotation of genes in previous validation study. From a novel different perspective, we propose fractal clustering method to cluster genes using intrinsic (fractal) dimension from modern geometry. This method clusters points in such a way that points in the same clusters are more self-affine among themselves than to the points in other clusters. We assess this method using annotation-based validation assessment for gene clusters. It shows that this method is superior in identifying functional related gene groups than other traditional methods.

Exploring of the molecular mechanism of rhinitis via bioinformatics methods

PubMed Central

Song, Yufen; Yan, Zhaohui

2018-01-01

The aim of this study was to analyze gene expression profiles for exploring the function and regulatory network of differentially expressed genes (DEGs) in pathogenesis of rhinitis by a bioinformatics method. The gene expression profile of GSE43523 was downloaded from the Gene Expression Omnibus database. The dataset contained 7 seasonal allergic rhinitis samples and 5 non-allergic normal samples. DEGs between rhinitis samples and normal samples were identified via the limma package of R. The webGestal database was used to identify enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of the DEGs. The differentially co-expressed pairs of the DEGs were identified via the DCGL package in R, and the differential co-expression network was constructed based on these pairs. A protein-protein interaction (PPI) network of the DEGs was constructed based on the Search Tool for the Retrieval of Interacting Genes database. A total of 263 DEGs were identified in rhinitis samples compared with normal samples, including 125 downregulated ones and 138 upregulated ones. The DEGs were enriched in 7 KEGG pathways. 308 differential co-expression gene pairs were obtained. A differential co-expression network was constructed, containing 212 nodes. In total, 148 PPI pairs of the DEGs were identified, and a PPI network was constructed based on these pairs. Bioinformatics methods could help us identify significant genes and pathways related to the pathogenesis of rhinitis. Steroid biosynthesis pathway and metabolic pathways might play important roles in the development of allergic rhinitis (AR). Genes such as CDC42 effector protein 5, solute carrier family 39 member A11 and PR/SET domain 10 might be also associated with the pathogenesis of AR, which provided references for the molecular mechanisms of AR. PMID:29257233

A gene expression biomarker identifies in vitro and in vivo ERα modulators in a human gene expression compendium

EPA Science Inventory

We propose the use of gene expression profiling to complement the chemical characterization currently based on HTS assay data and present a case study relevant to the Endocrine Disruptor Screening Program. We have developed computational methods to identify estrogen receptor &alp...

Gene expression patterns combined with network analysis identify hub genes associated with bladder cancer.

PubMed

Bi, Dongbin; Ning, Hao; Liu, Shuai; Que, Xinxiang; Ding, Kejia

2015-06-01

To explore molecular mechanisms of bladder cancer (BC), network strategy was used to find biomarkers for early detection and diagnosis. The differentially expressed genes (DEGs) between bladder carcinoma patients and normal subjects were screened using empirical Bayes method of the linear models for microarray data package. Co-expression networks were constructed by differentially co-expressed genes and links. Regulatory impact factors (RIF) metric was used to identify critical transcription factors (TFs). The protein-protein interaction (PPI) networks were constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and clusters were obtained through molecular complex detection (MCODE) algorithm. Centralities analyses for complex networks were performed based on degree, stress and betweenness. Enrichment analyses were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Co-expression networks and TFs (based on expression data of global DEGs and DEGs in different stages and grades) were identified. Hub genes of complex networks, such as UBE2C, ACTA2, FABP4, CKS2, FN1 and TOP2A, were also obtained according to analysis of degree. In gene enrichment analyses of global DEGs, cell adhesion, proteinaceous extracellular matrix and extracellular matrix structural constituent were top three GO terms. ECM-receptor interaction, focal adhesion, and cell cycle were significant pathways. Our results provide some potential underlying biomarkers of BC. However, further validation is required and deep studies are needed to elucidate the pathogenesis of BC. Copyright © 2015 Elsevier Ltd. All rights reserved.

Random forests-based differential analysis of gene sets for gene expression data.

PubMed

Hsueh, Huey-Miin; Zhou, Da-Wei; Tsai, Chen-An

2013-04-10

In DNA microarray studies, gene-set analysis (GSA) has become the focus of gene expression data analysis. GSA utilizes the gene expression profiles of functionally related gene sets in Gene Ontology (GO) categories or priori-defined biological classes to assess the significance of gene sets associated with clinical outcomes or phenotypes. Many statistical approaches have been proposed to determine whether such functionally related gene sets express differentially (enrichment and/or deletion) in variations of phenotypes. However, little attention has been given to the discriminatory power of gene sets and classification of patients. In this study, we propose a method of gene set analysis, in which gene sets are used to develop classifications of patients based on the Random Forest (RF) algorithm. The corresponding empirical p-value of an observed out-of-bag (OOB) error rate of the classifier is introduced to identify differentially expressed gene sets using an adequate resampling method. In addition, we discuss the impacts and correlations of genes within each gene set based on the measures of variable importance in the RF algorithm. Significant classifications are reported and visualized together with the underlying gene sets and their contribution to the phenotypes of interest. Numerical studies using both synthesized data and a series of publicly available gene expression data sets are conducted to evaluate the performance of the proposed methods. Compared with other hypothesis testing approaches, our proposed methods are reliable and successful in identifying enriched gene sets and in discovering the contributions of genes within a gene set. The classification results of identified gene sets can provide an valuable alternative to gene set testing to reveal the unknown, biologically relevant classes of samples or patients. In summary, our proposed method allows one to simultaneously assess the discriminatory ability of gene sets and the importance of genes for interpretation of data in complex biological systems. The classifications of biologically defined gene sets can reveal the underlying interactions of gene sets associated with the phenotypes, and provide an insightful complement to conventional gene set analyses. Copyright © 2012 Elsevier B.V. All rights reserved.

A Stationary Wavelet Entropy-Based Clustering Approach Accurately Predicts Gene Expression

PubMed Central

Nguyen, Nha; Vo, An; Choi, Inchan

2015-01-01

Abstract Studying epigenetic landscapes is important to understand the condition for gene regulation. Clustering is a useful approach to study epigenetic landscapes by grouping genes based on their epigenetic conditions. However, classical clustering approaches that often use a representative value of the signals in a fixed-sized window do not fully use the information written in the epigenetic landscapes. Clustering approaches to maximize the information of the epigenetic signals are necessary for better understanding gene regulatory environments. For effective clustering of multidimensional epigenetic signals, we developed a method called Dewer, which uses the entropy of stationary wavelet of epigenetic signals inside enriched regions for gene clustering. Interestingly, the gene expression levels were highly correlated with the entropy levels of epigenetic signals. Dewer separates genes better than a window-based approach in the assessment using gene expression and achieved a correlation coefficient above 0.9 without using any training procedure. Our results show that the changes of the epigenetic signals are useful to study gene regulation. PMID:25383910

Classification of early-stage non-small cell lung cancer by weighing gene expression profiles with connectivity information.

PubMed

Zhang, Ao; Tian, Suyan

2018-05-01

Pathway-based feature selection algorithms, which utilize biological information contained in pathways to guide which features/genes should be selected, have evolved quickly and become widespread in the field of bioinformatics. Based on how the pathway information is incorporated, we classify pathway-based feature selection algorithms into three major categories-penalty, stepwise forward, and weighting. Compared to the first two categories, the weighting methods have been underutilized even though they are usually the simplest ones. In this article, we constructed three different genes' connectivity information-based weights for each gene and then conducted feature selection upon the resulting weighted gene expression profiles. Using both simulations and a real-world application, we have demonstrated that when the data-driven connectivity information constructed from the data of specific disease under study is considered, the resulting weighted gene expression profiles slightly outperform the original expression profiles. In summary, a big challenge faced by the weighting method is how to estimate pathway knowledge-based weights more accurately and precisely. Only until the issue is conquered successfully will wide utilization of the weighting methods be impossible. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Identification and resolution of artifacts in the interpretation of imprinted gene expression.

PubMed

Proudhon, Charlotte; Bourc'his, Déborah

2010-12-01

Genomic imprinting refers to genes that are epigenetically programmed in the germline to express exclusively or preferentially one allele in a parent-of-origin manner. Expression-based genome-wide screening for the identification of imprinted genes has failed to uncover a significant number of new imprinted genes, probably because of the high tissue- and developmental-stage specificity of imprinted gene expression. A very large number of technical and biological artifacts can also lead to the erroneous evidence of imprinted gene expression. In this article, we focus on three common sources of potential confounding effects: (i) random monoallelic expression in monoclonal cell populations, (ii) genetically determined monoallelic expression and (iii) contamination or infiltration of embryonic tissues with maternal material. This last situation specifically applies to genes that occur as maternally expressed in the placenta. Beside the use of reciprocal crosses that are instrumental to confirm the parental specificity of expression, we provide additional methods for the detection and elimination of these situations that can be misinterpreted as cases of imprinted expression.

Investigating a multigene prognostic assay based on significant pathways for Luminal A breast cancer through gene expression profile analysis.

PubMed

Gao, Haiyan; Yang, Mei; Zhang, Xiaolan

2018-04-01

The present study aimed to investigate potential recurrence-risk biomarkers based on significant pathways for Luminal A breast cancer through gene expression profile analysis. Initially, the gene expression profiles of Luminal A breast cancer patients were downloaded from The Cancer Genome Atlas database. The differentially expressed genes (DEGs) were identified using a Limma package and the hierarchical clustering analysis was conducted for the DEGs. In addition, the functional pathways were screened using Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses and rank ratio calculation. The multigene prognostic assay was exploited based on the statistically significant pathways and its prognostic function was tested using train set and verified using the gene expression data and survival data of Luminal A breast cancer patients downloaded from the Gene Expression Omnibus. A total of 300 DEGs were identified between good and poor outcome groups, including 176 upregulated genes and 124 downregulated genes. The DEGs may be used to effectively distinguish Luminal A samples with different prognoses verified by hierarchical clustering analysis. There were 9 pathways screened as significant pathways and a total of 18 DEGs involved in these 9 pathways were identified as prognostic biomarkers. According to the survival analysis and receiver operating characteristic curve, the obtained 18-gene prognostic assay exhibited good prognostic function with high sensitivity and specificity to both the train and test samples. In conclusion the 18-gene prognostic assay including the key genes, transcription factor 7-like 2, anterior parietal cortex and lymphocyte enhancer factor-1 may provide a new method for predicting outcomes and may be conducive to the promotion of precision medicine for Luminal A breast cancer.

Differential expression profiles and pathways of genes in sugarcane leaf at elongation stage in response to drought stress

PubMed Central

Li, Changning; Nong, Qian; Solanki, Manoj Kumar; Liang, Qiang; Xie, Jinlan; Liu, Xiaoyan; Li, Yijie; Wang, Weizan; Yang, Litao; Li, Yangrui

2016-01-01

Water stress causes considerable yield losses in sugarcane. To investigate differentially expressed genes under water stress, a pot experiment was performed with the sugarcane variety GT21 at three water-deficit levels (mild, moderate, and severe) during the elongation stage and gene expression was analyzed using microarray technology. Physiological parameters of sugarcane showed significant alterations in response to drought stress. Based on the expression profile of 15,593 sugarcane genes, 1,501 (9.6%) genes were differentially expressed under different water-level treatments; 821 genes were upregulated and 680 genes were downregulated. A gene similarity analysis showed that approximately 62.6% of the differentially expressed genes shared homology with functional proteins. In a Gene Ontology (GO) analysis, 901 differentially expressed genes were assigned to 36 GO categories. Moreover, 325 differentially expressed genes were classified into 101 pathway categories involved in various processes, such as the biosynthesis of secondary metabolites, ribosomes, carbon metabolism, etc. In addition, some unannotated genes were detected; these may provide a basis for studies of water-deficit tolerance. The reliability of the observed expression patterns was confirmed by RT-PCR. The results of this study may help identify useful genes for improving drought tolerance in sugarcane. PMID:27170459

Analysis of genetic association in Listeria and Diabetes using Hierarchical Clustering and Silhouette Index

NASA Astrophysics Data System (ADS)

Pagnuco, Inti A.; Pastore, Juan I.; Abras, Guillermo; Brun, Marcel; Ballarin, Virginia L.

2016-04-01

It is usually assumed that co-expressed genes suggest co-regulation in the underlying regulatory network. Determining sets of co-expressed genes is an important task, where significative groups of genes are defined based on some criteria. This task is usually performed by clustering algorithms, where the whole family of genes, or a subset of them, are clustered into meaningful groups based on their expression values in a set of experiment. In this work we used a methodology based on the Silhouette index as a measure of cluster quality for individual gene groups, and a combination of several variants of hierarchical clustering to generate the candidate groups, to obtain sets of co-expressed genes for two real data examples. We analyzed the quality of the best ranked groups, obtained by the algorithm, using an online bioinformatics tool that provides network information for the selected genes. Moreover, to verify the performance of the algorithm, considering the fact that it doesn’t find all possible subsets, we compared its results against a full search, to determine the amount of good co-regulated sets not detected.

Differential Expression of Zinc Transporters in Prostate Epithelia of Racial Groups

DTIC Science & Technology

2012-09-01

of significant genes or proteins in the prostate cancers taken from AAs versus those from European Americans (EAs)?” Because there is a well...may be differentially expressed in AAs and EAs. A study of the genes and proteins which influence the expression of any gene confirmed to be...type of TMA, based on long term clinical follow up was used to address the question of whether hZIP gene and protein expression is associated with

Microarray and comparative genomics-based identification of genes and gene regulatory regions of the mouse immune system

PubMed Central

Hutton, John J; Jegga, Anil G; Kong, Sue; Gupta, Ashima; Ebert, Catherine; Williams, Sarah; Katz, Jonathan D; Aronow, Bruce J

2004-01-01

Background In this study we have built and mined a gene expression database composed of 65 diverse mouse tissues for genes preferentially expressed in immune tissues and cell types. Using expression pattern criteria, we identified 360 genes with preferential expression in thymus, spleen, peripheral blood mononuclear cells, lymph nodes (unstimulated or stimulated), or in vitro activated T-cells. Results Gene clusters, formed based on similarity of expression-pattern across either all tissues or the immune tissues only, had highly significant associations both with immunological processes such as chemokine-mediated response, antigen processing, receptor-related signal transduction, and transcriptional regulation, and also with more general processes such as replication and cell cycle control. Within-cluster gene correlations implicated known associations of known genes, as well as immune process-related roles for poorly described genes. To characterize regulatory mechanisms and cis-elements of genes with similar patterns of expression, we used a new version of a comparative genomics-based cis-element analysis tool to identify clusters of cis-elements with compositional similarity among multiple genes. Several clusters contained genes that shared 5–6 cis-elements that included ETS and zinc-finger binding sites. cis-Elements AP2 EGRF ETSF MAZF SP1F ZF5F and AREB ETSF MZF1 PAX5 STAT were shared in a thymus-expressed set; AP4R E2FF EBOX ETSF MAZF SP1F ZF5F and CREB E2FF MAZF PCAT SP1F STAT cis-clusters occurred in activated T-cells; CEBP CREB NFKB SORY and GATA NKXH OCT1 RBIT occurred in stimulated lymph nodes. Conclusion This study demonstrates a series of analytic approaches that have allowed the implication of genes and regulatory elements that participate in the differentiation, maintenance, and function of the immune system. Polymorphism or mutation of these could adversely impact immune system functions. PMID:15504237

The tunable pReX expression vector enables optimizing the T7-based production of membrane and secretory proteins in E. coli.

PubMed

Kuipers, Grietje; Karyolaimos, Alexandros; Zhang, Zhe; Ismail, Nurzian; Trinco, Gianluca; Vikström, David; Slotboom, Dirk Jan; de Gier, Jan-Willem

2017-12-16

To optimize the production of membrane and secretory proteins in Escherichia coli, it is critical to harmonize the expression rates of the genes encoding these proteins with the capacity of their biogenesis machineries. Therefore, we engineered the Lemo21(DE3) strain, which is derived from the T7 RNA polymerase-based BL21(DE3) protein production strain. In Lemo21(DE3), the T7 RNA polymerase activity can be modulated by the controlled co-production of its natural inhibitor T7 lysozyme. This setup enables to precisely tune target gene expression rates in Lemo21(DE3). The t7lys gene is expressed from the pLemo plasmid using the titratable rhamnose promoter. A disadvantage of the Lemo21(DE3) setup is that the system is based on two plasmids, a T7 expression vector and pLemo. The aim of this study was to simplify the Lemo21(DE3) setup by incorporating the key elements of pLemo in a standard T7-based expression vector. By incorporating the gene encoding the T7 lysozyme under control of the rhamnose promoter in a standard T7-based expression vector, pReX was created (ReX stands for Regulated gene eXpression). For two model membrane proteins and a model secretory protein we show that the optimized production yields obtained with the pReX expression vector in BL21(DE3) are similar to the ones obtained with Lemo21(DE3) using a standard T7 expression vector. For another secretory protein, a c-type cytochrome, we show that pReX, in contrast to Lemo21(DE3), enables the use of a helper plasmid that is required for the maturation and hence the production of this heme c protein. Here, we created pReX, a T7-based expression vector that contains the gene encoding the T7 lysozyme under control of the rhamnose promoter. pReX enables regulated T7-based target gene expression using only one plasmid. We show that with pReX the production of membrane and secretory proteins can be readily optimized. Importantly, pReX facilitates the use of helper plasmids. Furthermore, the use of pReX is not restricted to BL21(DE3), but it can in principle be used in any T7 RNAP-based strain. Thus, pReX is a versatile alternative to Lemo21(DE3).

Clustering cancer gene expression data by projective clustering ensemble

PubMed Central

Yu, Xianxue; Yu, Guoxian

2017-01-01

Gene expression data analysis has paramount implications for gene treatments, cancer diagnosis and other domains. Clustering is an important and promising tool to analyze gene expression data. Gene expression data is often characterized by a large amount of genes but with limited samples, thus various projective clustering techniques and ensemble techniques have been suggested to combat with these challenges. However, it is rather challenging to synergy these two kinds of techniques together to avoid the curse of dimensionality problem and to boost the performance of gene expression data clustering. In this paper, we employ a projective clustering ensemble (PCE) to integrate the advantages of projective clustering and ensemble clustering, and to avoid the dilemma of combining multiple projective clusterings. Our experimental results on publicly available cancer gene expression data show PCE can improve the quality of clustering gene expression data by at least 4.5% (on average) than other related techniques, including dimensionality reduction based single clustering and ensemble approaches. The empirical study demonstrates that, to further boost the performance of clustering cancer gene expression data, it is necessary and promising to synergy projective clustering with ensemble clustering. PCE can serve as an effective alternative technique for clustering gene expression data. PMID:28234920

Microarray-based gene expression profiling in patients with cryopyrin-associated periodic syndromes defines a disease-related signature and IL-1-responsive transcripts.

PubMed

Balow, James E; Ryan, John G; Chae, Jae Jin; Booty, Matthew G; Bulua, Ariel; Stone, Deborah; Sun, Hong-Wei; Greene, James; Barham, Beverly; Goldbach-Mansky, Raphaela; Kastner, Daniel L; Aksentijevich, Ivona

2013-06-01

To analyse gene expression patterns and to define a specific gene expression signature in patients with the severe end of the spectrum of cryopyrin-associated periodic syndromes (CAPS). The molecular consequences of interleukin 1 inhibition were examined by comparing gene expression patterns in 16 CAPS patients before and after treatment with anakinra. We collected peripheral blood mononuclear cells from 22 CAPS patients with active disease and from 14 healthy children. Transcripts that passed stringent filtering criteria (p values≤false discovery rate 1%) were considered as differentially expressed genes (DEG). A set of DEG was validated by quantitative reverse transcription PCR and functional studies with primary cells from CAPS patients and healthy controls. We used 17 CAPS and 66 non-CAPS patient samples to create a set of gene expression models that differentiates CAPS patients from controls and from patients with other autoinflammatory conditions. Many DEG include transcripts related to the regulation of innate and adaptive immune responses, oxidative stress, cell death, cell adhesion and motility. A set of gene expression-based models comprising the CAPS-specific gene expression signature correctly classified all 17 samples from an independent dataset. This classifier also correctly identified 15 of 16 post-anakinra CAPS samples despite the fact that these CAPS patients were in clinical remission. We identified a gene expression signature that clearly distinguished CAPS patients from controls. A number of DEG were in common with other systemic inflammatory diseases such as systemic onset juvenile idiopathic arthritis. The CAPS-specific gene expression classifiers also suggest incomplete suppression of inflammation at low doses of anakinra.

Microarray-based gene expression profiling in patients with cryopyrin-associated periodic syndromes defines a disease-related signature and IL-1-responsive transcripts

PubMed Central

Balow, James E; Ryan, John G; Chae, Jae Jin; Booty, Matthew G; Bulua, Ariel; Stone, Deborah; Sun, Hong-Wei; Greene, James; Barham, Beverly; Goldbach-Mansky, Raphaela; Kastner, Daniel L; Aksentijevich, Ivona

2014-01-01

Objective To analyse gene expression patterns and to define a specific gene expression signature in patients with the severe end of the spectrum of cryopyrin-associated periodic syndromes (CAPS). The molecular consequences of interleukin 1 inhibition were examined by comparing gene expression patterns in 16 CAPS patients before and after treatment with anakinra. Methods We collected peripheral blood mononuclear cells from 22 CAPS patients with active disease and from 14 healthy children. Transcripts that passed stringent filtering criteria (p values ≤ false discovery rate 1%) were considered as differentially expressed genes (DEG). A set of DEG was validated by quantitative reverse transcription PCR and functional studies with primary cells from CAPS patients and healthy controls. We used 17 CAPS and 66 non-CAPS patient samples to create a set of gene expression models that differentiates CAPS patients from controls and from patients with other autoinflammatory conditions. Results Many DEG include transcripts related to the regulation of innate and adaptive immune responses, oxidative stress, cell death, cell adhesion and motility. A set of gene expression-based models comprising the CAPS-specific gene expression signature correctly classified all 17 samples from an independent dataset. This classifier also correctly identified 15 of 16 postanakinra CAPS samples despite the fact that these CAPS patients were in clinical remission. Conclusions We identified a gene expression signature that clearly distinguished CAPS patients from controls. A number of DEG were in common with other systemic inflammatory diseases such as systemic onset juvenile idiopathic arthritis. The CAPS-specific gene expression classifiers also suggest incomplete suppression of inflammation at low doses of anakinra. PMID:23223423

Discovery of new candidate genes for rheumatoid arthritis through integration of genetic association data with expression pathway analysis.

PubMed

Shchetynsky, Klementy; Diaz-Gallo, Lina-Marcella; Folkersen, Lasse; Hensvold, Aase Haj; Catrina, Anca Irinel; Berg, Louise; Klareskog, Lars; Padyukov, Leonid

2017-02-02

Here we integrate verified signals from previous genetic association studies with gene expression and pathway analysis for discovery of new candidate genes and signaling networks, relevant for rheumatoid arthritis (RA). RNA-sequencing-(RNA-seq)-based expression analysis of 377 genes from previously verified RA-associated loci was performed in blood cells from 5 newly diagnosed, non-treated patients with RA, 7 patients with treated RA and 12 healthy controls. Differentially expressed genes sharing a similar expression pattern in treated and untreated RA sub-groups were selected for pathway analysis. A set of "connector" genes derived from pathway analysis was tested for differential expression in the initial discovery cohort and validated in blood cells from 73 patients with RA and in 35 healthy controls. There were 11 qualifying genes selected for pathway analysis and these were grouped into two evidence-based functional networks, containing 29 and 27 additional connector molecules. The expression of genes, corresponding to connector molecules was then tested in the initial RNA-seq data. Differences in the expression of ERBB2, TP53 and THOP1 were similar in both treated and non-treated patients with RA and an additional nine genes were differentially expressed in at least one group of patients compared to healthy controls. The ERBB2, TP53. THOP1 expression profile was successfully replicated in RNA-seq data from peripheral blood mononuclear cells from healthy controls and non-treated patients with RA, in an independent collection of samples. Integration of RNA-seq data with findings from association studies, and consequent pathway analysis implicate new candidate genes, ERBB2, TP53 and THOP1 in the pathogenesis of RA.

Partitioning of functional gene expression data using principal points.

PubMed

Kim, Jaehee; Kim, Haseong

2017-10-12

DNA microarrays offer motivation and hope for the simultaneous study of variations in multiple genes. Gene expression is a temporal process that allows variations in expression levels with a characterized gene function over a period of time. Temporal gene expression curves can be treated as functional data since they are considered as independent realizations of a stochastic process. This process requires appropriate models to identify patterns of gene functions. The partitioning of the functional data can find homogeneous subgroups of entities for the massive genes within the inherent biological networks. Therefor it can be a useful technique for the analysis of time-course gene expression data. We propose a new self-consistent partitioning method of functional coefficients for individual expression profiles based on the orthonormal basis system. A principal points based functional partitioning method is proposed for time-course gene expression data. The method explores the relationship between genes using Legendre coefficients as principal points to extract the features of gene functions. Our proposed method provides high connectivity in connectedness after clustering for simulated data and finds a significant subsets of genes with the increased connectivity. Our approach has comparative advantages that fewer coefficients are used from the functional data and self-consistency of principal points for partitioning. As real data applications, we are able to find partitioned genes through the gene expressions found in budding yeast data and Escherichia coli data. The proposed method benefitted from the use of principal points, dimension reduction, and choice of orthogonal basis system as well as provides appropriately connected genes in the resulting subsets. We illustrate our method by applying with each set of cell-cycle-regulated time-course yeast genes and E. coli genes. The proposed method is able to identify highly connected genes and to explore the complex dynamics of biological systems in functional genomics.

Validating internal controls for quantitative plant gene expression studies.

PubMed

Brunner, Amy M; Yakovlev, Igor A; Strauss, Steven H

2004-08-18

Real-time reverse transcription PCR (RT-PCR) has greatly improved the ease and sensitivity of quantitative gene expression studies. However, accurate measurement of gene expression with this method relies on the choice of a valid reference for data normalization. Studies rarely verify that gene expression levels for reference genes are adequately consistent among the samples used, nor compare alternative genes to assess which are most reliable for the experimental conditions analyzed. Using real-time RT-PCR to study the expression of 10 poplar (genus Populus) housekeeping genes, we demonstrate a simple method for determining the degree of stability of gene expression over a set of experimental conditions. Based on a traditional method for analyzing the stability of varieties in plant breeding, it defines measures of gene expression stability from analysis of variance (ANOVA) and linear regression. We found that the potential internal control genes differed widely in their expression stability over the different tissues, developmental stages and environmental conditions studied. Our results support that quantitative comparisons of candidate reference genes are an important part of real-time RT-PCR studies that seek to precisely evaluate variation in gene expression. The method we demonstrated facilitates statistical and graphical evaluation of gene expression stability. Selection of the best reference gene for a given set of experimental conditions should enable detection of biologically significant changes in gene expression that are too small to be revealed by less precise methods, or when highly variable reference genes are unknowingly used in real-time RT-PCR experiments.

Diurnal Transcriptome and Gene Network Represented through Sparse Modeling in Brachypodium distachyon.

PubMed

Koda, Satoru; Onda, Yoshihiko; Matsui, Hidetoshi; Takahagi, Kotaro; Yamaguchi-Uehara, Yukiko; Shimizu, Minami; Inoue, Komaki; Yoshida, Takuhiro; Sakurai, Tetsuya; Honda, Hiroshi; Eguchi, Shinto; Nishii, Ryuei; Mochida, Keiichi

2017-01-01

We report the comprehensive identification of periodic genes and their network inference, based on a gene co-expression analysis and an Auto-Regressive eXogenous (ARX) model with a group smoothly clipped absolute deviation (SCAD) method using a time-series transcriptome dataset in a model grass, Brachypodium distachyon . To reveal the diurnal changes in the transcriptome in B. distachyon , we performed RNA-seq analysis of its leaves sampled through a diurnal cycle of over 48 h at 4 h intervals using three biological replications, and identified 3,621 periodic genes through our wavelet analysis. The expression data are feasible to infer network sparsity based on ARX models. We found that genes involved in biological processes such as transcriptional regulation, protein degradation, and post-transcriptional modification and photosynthesis are significantly enriched in the periodic genes, suggesting that these processes might be regulated by circadian rhythm in B. distachyon . On the basis of the time-series expression patterns of the periodic genes, we constructed a chronological gene co-expression network and identified putative transcription factors encoding genes that might be involved in the time-specific regulatory transcriptional network. Moreover, we inferred a transcriptional network composed of the periodic genes in B. distachyon , aiming to identify genes associated with other genes through variable selection by grouping time points for each gene. Based on the ARX model with the group SCAD regularization using our time-series expression datasets of the periodic genes, we constructed gene networks and found that the networks represent typical scale-free structure. Our findings demonstrate that the diurnal changes in the transcriptome in B. distachyon leaves have a sparse network structure, demonstrating the spatiotemporal gene regulatory network over the cyclic phase transitions in B. distachyon diurnal growth.

Identification and handling of artifactual gene expression profiles emerging in microarray hybridization experiments

PubMed Central

Brodsky, Leonid; Leontovich, Andrei; Shtutman, Michael; Feinstein, Elena

2004-01-01

Mathematical methods of analysis of microarray hybridizations deal with gene expression profiles as elementary units. However, some of these profiles do not reflect a biologically relevant transcriptional response, but rather stem from technical artifacts. Here, we describe two technically independent but rationally interconnected methods for identification of such artifactual profiles. Our diagnostics are based on detection of deviations from uniformity, which is assumed as the main underlying principle of microarray design. Method 1 is based on detection of non-uniformity of microarray distribution of printed genes that are clustered based on the similarity of their expression profiles. Method 2 is based on evaluation of the presence of gene-specific microarray spots within the slides’ areas characterized by an abnormal concentration of low/high differential expression values, which we define as ‘patterns of differentials’. Applying two novel algorithms, for nested clustering (method 1) and for pattern detection (method 2), we can make a dual estimation of the profile’s quality for almost every printed gene. Genes with artifactual profiles detected by method 1 may then be removed from further analysis. Suspicious differential expression values detected by method 2 may be either removed or weighted according to the probabilities of patterns that cover them, thus diminishing their input in any further data analysis. PMID:14999086

Global transgenerational gene expression dynamics in two newly synthesized allohexaploid wheat (Triticum aestivum) lines

PubMed Central

2012-01-01

Background Alteration in gene expression resulting from allopolyploidization is a prominent feature in plants, but its spectrum and extent are not fully known. Common wheat (Triticum aestivum) was formed via allohexaploidization about 10,000 years ago, and became the most important crop plant. To gain further insights into the genome-wide transcriptional dynamics associated with the onset of common wheat formation, we conducted microarray-based genome-wide gene expression analysis on two newly synthesized allohexaploid wheat lines with chromosomal stability and a genome constitution analogous to that of the present-day common wheat. Results Multi-color GISH (genomic in situ hybridization) was used to identify individual plants from two nascent allohexaploid wheat lines between Triticum turgidum (2n = 4x = 28; genome BBAA) and Aegilops tauschii (2n = 2x = 14; genome DD), which had a stable chromosomal constitution analogous to that of common wheat (2n = 6x = 42; genome BBAADD). Genome-wide analysis of gene expression was performed for these allohexaploid lines along with their parental plants from T. turgidum and Ae. tauschii, using the Affymetrix Gene Chip Wheat Genome-Array. Comparison with the parental plants coupled with inclusion of empirical mid-parent values (MPVs) revealed that whereas the great majority of genes showed the expected parental additivity, two major patterns of alteration in gene expression in the allohexaploid lines were identified: parental dominance expression and non-additive expression. Genes involved in each of the two altered expression patterns could be classified into three distinct groups, stochastic, heritable and persistent, based on their transgenerational heritability and inter-line conservation. Strikingly, whereas both altered patterns of gene expression showed a propensity of inheritance, identity of the involved genes was highly stochastic, consistent with the involvement of diverse Gene Ontology (GO) terms. Nonetheless, those genes showing non-additive expression exhibited a significant enrichment for vesicle-function. Conclusions Our results show that two patterns of global alteration in gene expression are conditioned by allohexaploidization in wheat, that is, parental dominance expression and non-additive expression. Both altered patterns of gene expression but not the identity of the genes involved are likely to play functional roles in stabilization and establishment of the newly formed allohexaploid plants, and hence, relevant to speciation and evolution of T. aestivum. PMID:22277161

Analyses of the NAC transcription factor gene family in Gossypium raimondii Ulbr.: chromosomal location, structure, phylogeny, and expression patterns.

PubMed

Shang, Haihong; Li, Wei; Zou, Changsong; Yuan, Youlu

2013-07-01

NAC domain proteins are plant-specific transcription factors known to play diverse roles in various plant developmental processes. In the present study, we performed the first comprehensive study of the NAC gene family in Gossypium raimondii Ulbr., incorporating phylogenetic, chromosomal location, gene structure, conserved motif, and expression profiling analyses. We identified 145 NAC transcription factor (NAC-TF) genes that were phylogenetically clustered into 18 distinct subfamilies. Of these, 127 NAC-TF genes were distributed across the 13 chromosomes, 80 (55%) were preferentially retained duplicates located in both duplicated regions and six were located in triplicated chromosomal regions. The majority of NAC-TF genes showed temporal-, spatial-, and tissue-specific expression patterns based on transcriptomic and qRT-PCR analyses. However, the expression patterns of several duplicate genes were partially redundant, suggesting the occurrence of sub-functionalization during their evolution. Based on their genomic organization, we concluded that genomic duplications contributed significantly to the expansion of the NAC-TF gene family in G. raimondii. Comprehensive analysis of their expression profiles could provide novel insights into the functional divergence among members of the NAC gene family in G. raimondii. © 2013 Institute of Botany, Chinese Academy of Sciences.

A Digital Gene Expression-Based Bovine Gene Atlas Evaluating 92 Adult, Juvenile and Fetal Cattle Tissues

USDA-ARS?s Scientific Manuscript database

A comprehensive transcriptome survey, or “Gene Atlas,” provides information essential for a complete understanding of the genomic biology of an organism. Using a digital gene expression approach, we developed a Gene Atlas of RNA abundance in 92 adult, juvenile and fetal cattle tissues. The samples...

Integrated genome-wide Alu methylation and transcriptome profiling analyses reveal novel epigenetic regulatory networks associated with autism spectrum disorder.

PubMed

Saeliw, Thanit; Tangsuwansri, Chayanin; Thongkorn, Surangrat; Chonchaiya, Weerasak; Suphapeetiporn, Kanya; Mutirangura, Apiwat; Tencomnao, Tewin; Hu, Valerie W; Sarachana, Tewarit

2018-01-01

Alu elements are a group of repetitive elements that can influence gene expression through CpG residues and transcription factor binding. Altered gene expression and methylation profiles have been reported in various tissues and cell lines from individuals with autism spectrum disorder (ASD). However, the role of Alu elements in ASD remains unclear. We thus investigated whether Alu elements are associated with altered gene expression profiles in ASD. We obtained five blood-based gene expression profiles from the Gene Expression Omnibus database and human Alu-inserted gene lists from the TranspoGene database. Differentially expressed genes (DEGs) in ASD were identified from each study and overlapped with the human Alu-inserted genes. The biological functions and networks of Alu-inserted DEGs were then predicted by Ingenuity Pathway Analysis (IPA). A combined bisulfite restriction analysis of lymphoblastoid cell lines (LCLs) derived from 36 ASD and 20 sex- and age-matched unaffected individuals was performed to assess the global DNA methylation levels within Alu elements, and the Alu expression levels were determined by quantitative RT-PCR. In ASD blood or blood-derived cells, 320 Alu-inserted genes were reproducibly differentially expressed. Biological function and pathway analysis showed that these genes were significantly associated with neurodevelopmental disorders and neurological functions involved in ASD etiology. Interestingly, estrogen receptor and androgen signaling pathways implicated in the sex bias of ASD, as well as IL-6 signaling and neuroinflammation signaling pathways, were also highlighted. Alu methylation was not significantly different between the ASD and sex- and age-matched control groups. However, significantly altered Alu methylation patterns were observed in ASD cases sub-grouped based on Autism Diagnostic Interview-Revised scores compared with matched controls. Quantitative RT-PCR analysis of Alu expression also showed significant differences between ASD subgroups. Interestingly, Alu expression was correlated with methylation status in one phenotypic ASD subgroup. Alu methylation and expression were altered in LCLs from ASD subgroups. Our findings highlight the association of Alu elements with gene dysregulation in ASD blood samples and warrant further investigation. Moreover, the classification of ASD individuals into subgroups based on phenotypes may be beneficial and could provide insights into the still unknown etiology and the underlying mechanisms of ASD.

Transcriptome-wide analysis of WRKY transcription factors in wheat and their leaf rust responsive expression profiling.

PubMed

Satapathy, Lopamudra; Singh, Dharmendra; Ranjan, Prashant; Kumar, Dhananjay; Kumar, Manish; Prabhu, Kumble Vinod; Mukhopadhyay, Kunal

2014-12-01

WRKY, a plant-specific transcription factor family, has important roles in pathogen defense, abiotic cues and phytohormone signaling, yet little is known about their roles and molecular mechanism of function in response to rust diseases in wheat. We identified 100 TaWRKY sequences using wheat Expressed Sequence Tag database of which 22 WRKY sequences were novel. Identified proteins were characterized based on their zinc finger motifs and phylogenetic analysis clustered them into six clades consisting of class IIc and class III WRKY proteins. Functional annotation revealed major functions in metabolic and cellular processes in control plants; whereas response to stimuli, signaling and defense in pathogen inoculated plants, their major molecular function being binding to DNA. Tag-based expression analysis of the identified genes revealed differential expression between mock and Puccinia triticina inoculated wheat near isogenic lines. Gene expression was also performed with six rust-related microarray experiments at Gene Expression Omnibus database. TaWRKY10, 15, 17 and 56 were common in both tag-based and microarray-based differential expression analysis and could be representing rust specific WRKY genes. The obtained results will bestow insight into the functional characterization of WRKY transcription factors responsive to leaf rust pathogenesis that can be used as candidate genes in molecular breeding programs to improve biotic stress tolerance in wheat.

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.

Network-based co-expression analysis for exploring the potential diagnostic biomarkers of metastatic melanoma.

PubMed

Wang, Li-Xin; Li, Yang; Chen, Guan-Zhi

2018-01-01

Metastatic melanoma is an aggressive skin cancer and is one of the global malignancies with high mortality and morbidity. It is essential to identify and verify diagnostic biomarkers of early metastatic melanoma. Previous studies have systematically assessed protein biomarkers and mRNA-based expression characteristics. However, molecular markers for the early diagnosis of metastatic melanoma have not been identified. To explore potential regulatory targets, we have analyzed the gene microarray expression profiles of malignant melanoma samples by co-expression analysis based on the network approach. The differentially expressed genes (DEGs) were screened by the EdgeR package of R software. A weighted gene co-expression network analysis (WGCNA) was used for the identification of DEGs in the special gene modules and hub genes. Subsequently, a protein-protein interaction network was constructed to extract hub genes associated with gene modules. Finally, twenty-four important hub genes (RASGRP2, IKZF1, CXCR5, LTB, BLK, LINGO3, CCR6, P2RY10, RHOH, JUP, KRT14, PLA2G3, SPRR1A, KRT78, SFN, CLDN4, IL1RN, PKP3, CBLC, KRT16, TMEM79, KLK8, LYPD3 and LYPD5) were treated as valuable factors involved in the immune response and tumor cell development in tumorigenesis. In addition, a transcriptional regulatory network was constructed for these specific modules or hub genes, and a few core transcriptional regulators were found to be mostly associated with our hub genes, including GATA1, STAT1, SP1, and PSG1. In summary, our findings enhance our understanding of the biological process of malignant melanoma metastasis, enabling us to identify specific genes to use for diagnostic and prognostic markers and possibly for targeted therapy.

A Filter Feature Selection Method Based on MFA Score and Redundancy Excluding and It's Application to Tumor Gene Expression Data Analysis.

PubMed

Li, Jiangeng; Su, Lei; Pang, Zenan

2015-12-01

Feature selection techniques have been widely applied to tumor gene expression data analysis in recent years. A filter feature selection method named marginal Fisher analysis score (MFA score) which is based on graph embedding has been proposed, and it has been widely used mainly because it is superior to Fisher score. Considering the heavy redundancy in gene expression data, we proposed a new filter feature selection technique in this paper. It is named MFA score+ and is based on MFA score and redundancy excluding. We applied it to an artificial dataset and eight tumor gene expression datasets to select important features and then used support vector machine as the classifier to classify the samples. Compared with MFA score, t test and Fisher score, it achieved higher classification accuracy.

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.

Prostate cancer-associated gene expression alterations determined from needle biopsies.

PubMed

Qian, David Z; Huang, Chung-Ying; O'Brien, Catherine A; Coleman, Ilsa M; Garzotto, Mark; True, Lawrence D; Higano, Celestia S; Vessella, Robert; Lange, Paul H; Nelson, Peter S; Beer, Tomasz M

2009-05-01

To accurately identify gene expression alterations that differentiate neoplastic from normal prostate epithelium using an approach that avoids contamination by unwanted cellular components and is not compromised by acute gene expression changes associated with tumor devascularization and resulting ischemia. Approximately 3,000 neoplastic and benign prostate epithelial cells were isolated using laser capture microdissection from snap-frozen prostate biopsy specimens provided by 31 patients who subsequently participated in a clinical trial of preoperative chemotherapy. cDNA synthesized from amplified total RNA was hybridized to custom-made microarrays composed of 6,200 clones derived from the Prostate Expression Database. Expression differences for selected genes were verified using quantitative reverse transcription-PCR. Comparative analyses identified 954 transcript alterations associated with cancer (q < 0.01%), including 149 differentially expressed genes with no known functional roles. Gene expression changes associated with ischemia and surgical removal of the prostate gland were absent. Genes up-regulated in prostate cancer were statistically enriched in categories related to cellular metabolism, energy use, signal transduction, and molecular transport. Genes down-regulated in prostate cancers were enriched in categories related to immune response, cellular responses to pathogens, and apoptosis. A heterogeneous pattern of androgen receptor expression changes was noted. In exploratory analyses, androgen receptor down-regulation was associated with a lower probability of cancer relapse after neoadjuvant chemotherapy followed by radical prostatectomy. Assessments of tumor phenotypes based on gene expression for treatment stratification and drug targeting of oncogenic alterations may best be ascertained using biopsy-based analyses where the effects of ischemia do not complicate interpretation.

Prostate Cancer-Associated Gene Expression Alterations Determined from Needle Biopsies

PubMed Central

Qian, David Z.; Huang, Chung-Ying; O'Brien, Catherine A.; Coleman, Ilsa M.; Garzotto, Mark; True, Lawrence D.; Higano, Celestia S.; Vessella, Robert; Lange, Paul H.; Nelson, Peter S.; Beer, Tomasz M.

2010-01-01

Purpose To accurately identify gene expression alterations that differentiate neoplastic from normal prostate epithelium using an approach that avoids contamination by unwanted cellular components and is not compromised by acute gene expression changes associated with tumor devascularization and resulting ischemia. Experimental Design Approximately 3,000 neoplastic and benign prostate epithelial cells were isolated using laser capture microdissection from snap-frozen prostate biopsy specimens provided by 31 patients who subsequently participated in a clinical trial of preoperative chemotherapy. cDNA synthesized from amplified total RNA was hybridized to custom-made microarrays comprised of 6200 clones derived from the Prostate Expression Database. Expression differences for selected genes were verified using quantitative RT-PCR. Results Comparative analyses identified 954 transcript alterations associated with cancer (q value <0.01%) including 149 differentially expressed genes with no known functional roles. Gene expression changes associated with ischemia and surgical removal of the prostate gland were absent. Genes up-regulated in prostate cancer were statistically enriched in categories related to cellular metabolism, energy utilization, signal transduction, and molecular transport. Genes down-regulated in prostate cancers were enriched in categories related to immune response, cellular responses to pathogens, and apoptosis. A heterogeneous pattern of AR expression changes was noted. In exploratory analyses, AR down regulation was associated with a lower probability of cancer relapse after neoadjuvant chemotherapy followed by radical prostatectomy. Conclusions Assessments of tumor phenotypes based on gene expression for treatment stratification and drug targeting of oncogenic alterations may best be ascertained using biopsy-based analyses where the effects of ischemia do not complicate interpretation. PMID:19366833

Porcine transcriptome analysis based on 97 non-normalized cDNA libraries and assembly of 1,021,891 expressed sequence tags

PubMed Central

Gorodkin, Jan; Cirera, Susanna; Hedegaard, Jakob; Gilchrist, Michael J; Panitz, Frank; Jørgensen, Claus; Scheibye-Knudsen, Karsten; Arvin, Troels; Lumholdt, Steen; Sawera, Milena; Green, Trine; Nielsen, Bente J; Havgaard, Jakob H; Rosenkilde, Carina; Wang, Jun; Li, Heng; Li, Ruiqiang; Liu, Bin; Hu, Songnian; Dong, Wei; Li, Wei; Yu, Jun; Wang, Jian; Stærfeldt, Hans-Henrik; Wernersson, Rasmus; Madsen, Lone B; Thomsen, Bo; Hornshøj, Henrik; Bujie, Zhan; Wang, Xuegang; Wang, Xuefei; Bolund, Lars; Brunak, Søren; Yang, Huanming; Bendixen, Christian; Fredholm, Merete

2007-01-01

Background Knowledge of the structure of gene expression is essential for mammalian transcriptomics research. We analyzed a collection of more than one million porcine expressed sequence tags (ESTs), of which two-thirds were generated in the Sino-Danish Pig Genome Project and one-third are from public databases. The Sino-Danish ESTs were generated from one normalized and 97 non-normalized cDNA libraries representing 35 different tissues and three developmental stages. Results Using the Distiller package, the ESTs were assembled to roughly 48,000 contigs and 73,000 singletons, of which approximately 25% have a high confidence match to UniProt. Approximately 6,000 new porcine gene clusters were identified. Expression analysis based on the non-normalized libraries resulted in the following findings. The distribution of cluster sizes is scaling invariant. Brain and testes are among the tissues with the greatest number of different expressed genes, whereas tissues with more specialized function, such as developing liver, have fewer expressed genes. There are at least 65 high confidence housekeeping gene candidates and 876 cDNA library-specific gene candidates. We identified differential expression of genes between different tissues, in particular brain/spinal cord, and found patterns of correlation between genes that share expression in pairs of libraries. Finally, there was remarkable agreement in expression between specialized tissues according to Gene Ontology categories. Conclusion This EST collection, the largest to date in pig, represents an essential resource for annotation, comparative genomics, assembly of the pig genome sequence, and further porcine transcription studies. PMID:17407547

Cloning of Trametes versicolar genes induced by nitrogen starvation

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

Trudel, P.; Courchesne, D.; Roy, C.

1988-06-01

We have screened a genomic library of Trametes versicolar for genes whose expression is associated with nitrogen starvation, which has been shown to induce ligninolytic activity. Using two different approaches based on differential expression, we isolated 29 clones. These were shown by restriction mapping and cross-hybridization to code for 11 distinct differentially expressed genes. Northern analysis of the kinetics of expression of these genes revealed that at least four of them have kinetics of induction that parallel kinetics of induction of ligninolytic activity.

Food-grade host/vector expression system for Lactobacillus casei based on complementation of plasmid-associated phospho-beta-galactosidase gene lacG.

PubMed

Takala, T M; Saris, P E J; Tynkkynen, S S H

2003-01-01

A new food-grade host/vector system for Lactobacillus casei based on lactose selection was constructed. The wild-type non-starter host Lb. casei strain E utilizes lactose via a plasmid-encoded phosphotransferase system. For food-grade cloning, a stable lactose-deficient mutant was constructed by deleting a 141-bp fragment from the phospho-beta-galactosidase gene lacG via gene replacement. The deletion resulted in an inactive phospho-beta-galactosidase enzyme with an internal in-frame deletion of 47 amino acids. A complementation plasmid was constructed containing a replicon from Lactococcus lactis, the lacG gene from Lb. casei, and the constitutive promoter of pepR for lacG expression from Lb. rhamnosus. The expression of the lacG gene from the resulting food-grade plasmid pLEB600 restored the ability of the lactose-negative mutant strain to grow on lactose to the wild-type level. The vector pLEB600 was used for expression of the proline iminopeptidase gene pepI from Lb. helveticus in Lb. casei. The results show that the food-grade expression system reported in this paper can be used for expression of foreign genes in Lb. casei.

Life-cycle and growth-phase-dependent regulation of the ubiquitin genes of Trypanosoma cruzi.

PubMed

Manning-Cela, Rebeca; Jaishankar, Sobha; Swindle, John

2006-07-01

Trypanosoma cruzi, the causative agent of Chagas disease, exhibits a complex life cycle that is accompanied by the stage-specific gene expression. At the molecular level, very little is known about gene regulation in trypanosomes. Complex gene organizations coupled with polycistronic transcription units make the analysis of regulated gene expression difficult in trypanosomes. The ubiquitin genes of T. cruzi are a good example of this complexity. They are organized as a single cluster containing five ubiquitin fusion (FUS) and five polyubiquitin (PUB) genes that are polycistronically transcribed but expressed differently in response to developmental and environmental changes. Gene replacements were used to study FUS and PUB gene expression at different stages of growth and at different points in the life cycle of T. cruzi. Based on the levels of reporter gene expression, it was determined that FUS1 expression was downregulated as the parasites approached stationary phase, whereas PUB12.5 polyubiquitin gene expression increased. Conversely, FUS1 expression increases when epimastigotes and amastigotes differentiate into trypomastigotes, whereas the expression of PUB12.5 decreases when epimastigotes differentiate into amastigotes and trypomastigotes. Although the level of CAT activity in logarithmic growing epimastigotes is six- to seven-fold higher when the gene was expressed from the FUS1 locus than when expressed from the PUB12.5 locus, the rate of transcription from the two loci was the same implying that post-transcriptional mechanisms play a dominant role in the regulation of gene expression.

In silico identification and comparative analysis of differentially expressed genes in human and mouse tissues

PubMed Central

Pao, Sheng-Ying; Lin, Win-Li; Hwang, Ming-Jing

2006-01-01

Background Screening for differentially expressed genes on the genomic scale and comparative analysis of the expression profiles of orthologous genes between species to study gene function and regulation are becoming increasingly feasible. Expressed sequence tags (ESTs) are an excellent source of data for such studies using bioinformatic approaches because of the rich libraries and tremendous amount of data now available in the public domain. However, any large-scale EST-based bioinformatics analysis must deal with the heterogeneous, and often ambiguous, tissue and organ terms used to describe EST libraries. Results To deal with the issue of tissue source, in this work, we carefully screened and organized more than 8 million human and mouse ESTs into 157 human and 108 mouse tissue/organ categories, to which we applied an established statistic test using different thresholds of the p value to identify genes differentially expressed in different tissues. Further analysis of the tissue distribution and level of expression of human and mouse orthologous genes showed that tissue-specific orthologs tended to have more similar expression patterns than those lacking significant tissue specificity. On the other hand, a number of orthologs were found to have significant disparity in their expression profiles, hinting at novel functions, divergent regulation, or new ortholog relationships. Conclusion Comprehensive statistics on the tissue-specific expression of human and mouse genes were obtained in this very large-scale, EST-based analysis. These statistical results have been organized into a database, freely accessible at our website , for easy searching of human and mouse tissue-specific genes and for investigating gene expression profiles in the context of comparative genomics. Comparative analysis showed that, although highly tissue-specific genes tend to exhibit similar expression profiles in human and mouse, there are significant exceptions, indicating that orthologous genes, while sharing basic genomic properties, could result in distinct phenotypes. PMID:16626500

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

PubMed

Wang, Anping; Zhang, Guibin

2017-11-01

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

Pluripotency, Differentiation, and Reprogramming: A Gene Expression Dynamics Model with Epigenetic Feedback Regulation

PubMed Central

Miyamoto, Tadashi; Furusawa, Chikara; Kaneko, Kunihiko

2015-01-01

Embryonic stem cells exhibit pluripotency: they can differentiate into all types of somatic cells. Pluripotent genes such as Oct4 and Nanog are activated in the pluripotent state, and their expression decreases during cell differentiation. Inversely, expression of differentiation genes such as Gata6 and Gata4 is promoted during differentiation. The gene regulatory network controlling the expression of these genes has been described, and slower-scale epigenetic modifications have been uncovered. Although the differentiation of pluripotent stem cells is normally irreversible, reprogramming of cells can be experimentally manipulated to regain pluripotency via overexpression of certain genes. Despite these experimental advances, the dynamics and mechanisms of differentiation and reprogramming are not yet fully understood. Based on recent experimental findings, we constructed a simple gene regulatory network including pluripotent and differentiation genes, and we demonstrated the existence of pluripotent and differentiated states from the resultant dynamical-systems model. Two differentiation mechanisms, interaction-induced switching from an expression oscillatory state and noise-assisted transition between bistable stationary states, were tested in the model. The former was found to be relevant to the differentiation process. We also introduced variables representing epigenetic modifications, which controlled the threshold for gene expression. By assuming positive feedback between expression levels and the epigenetic variables, we observed differentiation in expression dynamics. Additionally, with numerical reprogramming experiments for differentiated cells, we showed that pluripotency was recovered in cells by imposing overexpression of two pluripotent genes and external factors to control expression of differentiation genes. Interestingly, these factors were consistent with the four Yamanaka factors, Oct4, Sox2, Klf4, and Myc, which were necessary for the establishment of induced pluripotent stem cells. These results, based on a gene regulatory network and expression dynamics, contribute to our wider understanding of pluripotency, differentiation, and reprogramming of cells, and they provide a fresh viewpoint on robustness and control during development. PMID:26308610

Short Vegetative Phase-Like MADS-Box Genes Inhibit Floral Meristem Identity in Barley1[W][OA

PubMed Central

Trevaskis, Ben; Tadege, Million; Hemming, Megan N.; Peacock, W. James; Dennis, Elizabeth S.; Sheldon, Candice

2007-01-01

Analysis of the functions of Short Vegetative Phase (SVP)-like MADS-box genes in barley (Hordeum vulgare) indicated a role in determining meristem identity. Three SVP-like genes are expressed in vegetative tissues of barley: Barley MADS1 (BM1), BM10, and Vegetative to Reproductive Transition gene 2. These genes are induced by cold but are repressed during floral development. Ectopic expression of BM1 inhibited spike development and caused floral reversion in barley, with florets at the base of the spike replaced by tillers. Head emergence was delayed in plants that ectopically express BM1, primarily by delayed development after the floral transition, but expression levels of the barley VRN1 gene (HvVRN1) were not affected. Ectopic expression of BM10 inhibited spike development and caused partial floral reversion, where florets at the base of the spike were replaced by inflorescence-like structures, but did not affect heading date. Floral reversion occurred more frequently when BM1 and BM10 ectopic expression lines were grown in short-day conditions. BM1 and BM10 also inhibited floral development and caused floral reversion when expressed in Arabidopsis (Arabidopsis thaliana). We conclude that SVP-like genes function to suppress floral meristem identity in winter cereals. PMID:17114273

Comparative ecological transcriptomics and the contribution of gene expression to the evolutionary potential of a threatened fish.

PubMed

Brauer, Chris J; Unmack, Peter J; Beheregaray, Luciano B

2017-12-01

Understanding whether small populations with low genetic diversity can respond to rapid environmental change via phenotypic plasticity is an outstanding research question in biology. RNA sequencing (RNA-seq) has recently provided the opportunity to examine variation in gene expression, a surrogate for phenotypic variation, in nonmodel species. We used a comparative RNA-seq approach to assess expression variation within and among adaptively divergent populations of a threatened freshwater fish, Nannoperca australis, found across a steep hydroclimatic gradient in the Murray-Darling Basin, Australia. These populations evolved under contrasting selective environments (e.g., dry/hot lowland; wet/cold upland) and represent opposite ends of the species' spectrum of genetic diversity and population size. We tested the hypothesis that environmental variation among isolated populations has driven the evolution of divergent expression at ecologically important genes using differential expression (DE) analysis and an anova-based comparative phylogenetic expression variance and evolution model framework based on 27,425 de novo assembled transcripts. Additionally, we tested whether gene expression variance within populations was correlated with levels of standing genetic diversity. We identified 290 DE candidate transcripts, 33 transcripts with evidence for high expression plasticity, and 50 candidates for divergent selection on gene expression after accounting for phylogenetic structure. Variance in gene expression appeared unrelated to levels of genetic diversity. Functional annotation of the candidate transcripts revealed that variation in water quality is an important factor influencing expression variation for N. australis. Our findings suggest that gene expression variation can contribute to the evolutionary potential of small populations. © 2017 John Wiley & Sons Ltd.

Adaptation of video game UVW mapping to 3D visualization of gene expression patterns

NASA Astrophysics Data System (ADS)

Vize, Peter D.; Gerth, Victor E.

2007-01-01

Analysis of gene expression patterns within an organism plays a critical role in associating genes with biological processes in both health and disease. During embryonic development the analysis and comparison of different gene expression patterns allows biologists to identify candidate genes that may regulate the formation of normal tissues and organs and to search for genes associated with congenital diseases. No two individual embryos, or organs, are exactly the same shape or size so comparing spatial gene expression in one embryo to that in another is difficult. We will present our efforts in comparing gene expression data collected using both volumetric and projection approaches. Volumetric data is highly accurate but difficult to process and compare. Projection methods use UV mapping to align texture maps to standardized spatial frameworks. This approach is less accurate but is very rapid and requires very little processing. We have built a database of over 180 3D models depicting gene expression patterns mapped onto the surface of spline based embryo models. Gene expression data in different models can easily be compared to determine common regions of activity. Visualization software, both Java and OpenGL optimized for viewing 3D gene expression data will also be demonstrated.

Recrudescence Mechanisms and Gene Expression Profile of the Reproductive Tracts from Chickens during the Molting Period

PubMed Central

Ahn, Suzie E.; Lim, Chul-Hong; Lee, Jin-Young; Bae, Seung-Min; Kim, Jinyoung; Bazer, Fuller W.; Song, Gwonhwa

2013-01-01

The reproductive system of chickens undergoes dynamic morphological and functional tissue remodeling during the molting period. The present study identified global gene expression profiles following oviductal tissue regression and regeneration in laying hens in which molting was induced by feeding high levels of zinc in the diet. During the molting and recrudescence processes, progressive morphological and physiological changes included regression and re-growth of reproductive organs and fluctuations in concentrations of testosterone, progesterone, estradiol and corticosterone in blood. The cDNA microarray analysis of oviductal tissues revealed the biological significance of gene expression-based modulation in oviductal tissue during its remodeling. Based on the gene expression profiles, expression patterns of selected genes such as, TF, ANGPTL3, p20K, PTN, AvBD11 and SERPINB3 exhibited similar patterns in expression with gradual decreases during regression of the oviduct and sequential increases during resurrection of the functional oviduct. Also, miR-1689* inhibited expression of Sp1, while miR-17-3p, miR-22* and miR-1764 inhibited expression of STAT1. Similarly, chicken miR-1562 and miR-138 reduced the expression of ANGPTL3 and p20K, respectively. These results suggest that these differentially regulated genes are closely correlated with the molecular mechanism(s) for development and tissue remodeling of the avian female reproductive tract, and that miRNA-mediated regulation of key genes likely contributes to remodeling of the avian reproductive tract by controlling expression of those genes post-transcriptionally. The discovered global gene profiles provide new molecular candidates responsible for regulating morphological and functional recrudescence of the avian reproductive tract, and provide novel insights into understanding the remodeling process at the genomic and epigenomic levels. PMID:24098561

Gene Selection and Cancer Classification: A Rough Sets Based Approach

NASA Astrophysics Data System (ADS)

Sun, Lijun; Miao, Duoqian; Zhang, Hongyun

Indentification of informative gene subsets responsible for discerning between available samples of gene expression data is an important task in bioinformatics. Reducts, from rough sets theory, corresponding to a minimal set of essential genes for discerning samples, is an efficient tool for gene selection. Due to the compuational complexty of the existing reduct algoritms, feature ranking is usually used to narrow down gene space as the first step and top ranked genes are selected . In this paper,we define a novel certierion based on the expression level difference btween classes and contribution to classification of the gene for scoring genes and present a algorithm for generating all possible reduct from informative genes.The algorithm takes the whole attribute sets into account and find short reduct with a significant reduction in computational complexity. An exploration of this approach on benchmark gene expression data sets demonstrates that this approach is successful for selecting high discriminative genes and the classification accuracy is impressive.

Validating internal controls for quantitative plant gene expression studies

PubMed Central

Brunner, Amy M; Yakovlev, Igor A; Strauss, Steven H

2004-01-01

Background Real-time reverse transcription PCR (RT-PCR) has greatly improved the ease and sensitivity of quantitative gene expression studies. However, accurate measurement of gene expression with this method relies on the choice of a valid reference for data normalization. Studies rarely verify that gene expression levels for reference genes are adequately consistent among the samples used, nor compare alternative genes to assess which are most reliable for the experimental conditions analyzed. Results Using real-time RT-PCR to study the expression of 10 poplar (genus Populus) housekeeping genes, we demonstrate a simple method for determining the degree of stability of gene expression over a set of experimental conditions. Based on a traditional method for analyzing the stability of varieties in plant breeding, it defines measures of gene expression stability from analysis of variance (ANOVA) and linear regression. We found that the potential internal control genes differed widely in their expression stability over the different tissues, developmental stages and environmental conditions studied. Conclusion Our results support that quantitative comparisons of candidate reference genes are an important part of real-time RT-PCR studies that seek to precisely evaluate variation in gene expression. The method we demonstrated facilitates statistical and graphical evaluation of gene expression stability. Selection of the best reference gene for a given set of experimental conditions should enable detection of biologically significant changes in gene expression that are too small to be revealed by less precise methods, or when highly variable reference genes are unknowingly used in real-time RT-PCR experiments. PMID:15317655

Validation of the Lung Subtyping Panel in Multiple Fresh-Frozen and Formalin-Fixed, Paraffin-Embedded Lung Tumor Gene Expression Data Sets.

PubMed

Faruki, Hawazin; Mayhew, Gregory M; Fan, Cheng; Wilkerson, Matthew D; Parker, Scott; Kam-Morgan, Lauren; Eisenberg, Marcia; Horten, Bruce; Hayes, D Neil; Perou, Charles M; Lai-Goldman, Myla

2016-06-01

Context .- A histologic classification of lung cancer subtypes is essential in guiding therapeutic management. Objective .- To complement morphology-based classification of lung tumors, a previously developed lung subtyping panel (LSP) of 57 genes was tested using multiple public fresh-frozen gene-expression data sets and a prospectively collected set of formalin-fixed, paraffin-embedded lung tumor samples. Design .- The LSP gene-expression signature was evaluated in multiple lung cancer gene-expression data sets totaling 2177 patients collected from 4 platforms: Illumina RNAseq (San Diego, California), Agilent (Santa Clara, California) and Affymetrix (Santa Clara) microarrays, and quantitative reverse transcription-polymerase chain reaction. Gene centroids were calculated for each of 3 genomic-defined subtypes: adenocarcinoma, squamous cell carcinoma, and neuroendocrine, the latter of which encompassed both small cell carcinoma and carcinoid. Classification by LSP into 3 subtypes was evaluated in both fresh-frozen and formalin-fixed, paraffin-embedded tumor samples, and agreement with the original morphology-based diagnosis was determined. Results .- The LSP-based classifications demonstrated overall agreement with the original clinical diagnosis ranging from 78% (251 of 322) to 91% (492 of 538 and 869 of 951) in the fresh-frozen public data sets and 84% (65 of 77) in the formalin-fixed, paraffin-embedded data set. The LSP performance was independent of tissue-preservation method and gene-expression platform. Secondary, blinded pathology review of formalin-fixed, paraffin-embedded samples demonstrated concordance of 82% (63 of 77) with the original morphology diagnosis. Conclusions .- The LSP gene-expression signature is a reproducible and objective method for classifying lung tumors and demonstrates good concordance with morphology-based classification across multiple data sets. The LSP panel can supplement morphologic assessment of lung cancers, particularly when classification by standard methods is challenging.

Differential Responses of Soleus and Plantaris Muscle Fibers to Overloading

NASA Astrophysics Data System (ADS)

Kawano, Fuminori; Shibaguchi, Tsubasa; Ohira, Takashi; Nakai, Naoya; Ohira, Yoshinobu

2013-02-01

Responses of slow and fast fibers in soleus and plantaris muscles of adult rats to overloading by the tendon transection of synergists were studied. Overloading-related hypertrophy was noted in the slow fibers of plantaris and soleus, although the magnitude was greater in plantaris. Five genes with minor expression in slow soleus muscle were identified by microarray analysis. Base-line expressions of these genes in slow fibers of plantaris were also low. Further, repressive effects of overloading on these genes were seen in some fast fibers of plantaris, not in whole plantaris and soleus. The data suggested that the repression of particular genes might be related to the pronounced morphological response of fibers expressing type II, including I+II, myosin heavy chain (MyHC), although these genes with lower base-line expression in slow fibers did not respond to overloading.

Clustering change patterns using Fourier transformation with time-course gene expression data.

PubMed

Kim, Jaehee

2011-01-01

To understand the behavior of genes, it is important to explore how the patterns of gene expression change over a period of time because biologically related gene groups can share the same change patterns. In this study, the problem of finding similar change patterns is induced to clustering with the derivative Fourier coefficients. This work is aimed at discovering gene groups with similar change patterns which share similar biological properties. We developed a statistical model using derivative Fourier coefficients to identify similar change patterns of gene expression. We used a model-based method to cluster the Fourier series estimation of derivatives. We applied our model to cluster change patterns of yeast cell cycle microarray expression data with alpha-factor synchronization. It showed that, as the method clusters with the probability-neighboring data, the model-based clustering with our proposed model yielded biologically interpretable results. We expect that our proposed Fourier analysis with suitably chosen smoothing parameters could serve as a useful tool in classifying genes and interpreting possible biological change patterns.

Automated Protocol for Large-Scale Modeling of Gene Expression Data.

PubMed

Hall, Michelle Lynn; Calkins, David; Sherman, Woody

2016-11-28

With the continued rise of phenotypic- and genotypic-based screening projects, computational methods to analyze, process, and ultimately make predictions in this field take on growing importance. Here we show how automated machine learning workflows can produce models that are predictive of differential gene expression as a function of a compound structure using data from A673 cells as a proof of principle. In particular, we present predictive models with an average accuracy of greater than 70% across a highly diverse ∼1000 gene expression profile. In contrast to the usual in silico design paradigm, where one interrogates a particular target-based response, this work opens the opportunity for virtual screening and lead optimization for desired multitarget gene expression profiles.

Cell-Type–Specific Transcriptional Profiles of the Dimorphic Pathogen Penicillium marneffei Reflect Distinct Reproductive, Morphological, and Environmental Demands

PubMed Central

Pasricha, Shivani; Payne, Michael; Canovas, David; Pase, Luke; Ngaosuwankul, Nathamon; Beard, Sally; Oshlack, Alicia; Smyth, Gordon K.; Chaiyaroj, Sansanee C.; Boyce, Kylie J.; Andrianopoulos, Alex

2013-01-01

Penicillium marneffei is an opportunistic human pathogen endemic to Southeast Asia. At 25° P. marneffei grows in a filamentous hyphal form and can undergo asexual development (conidiation) to produce spores (conidia), the infectious agent. At 37° P. marneffei grows in the pathogenic yeast cell form that replicates by fission. Switching between these growth forms, known as dimorphic switching, is dependent on temperature. To understand the process of dimorphic switching and the physiological capacity of the different cell types, two microarray-based profiling experiments covering approximately 42% of the genome were performed. The first experiment compared cells from the hyphal, yeast, and conidiation phases to identify “phase or cell-state–specific” gene expression. The second experiment examined gene expression during the dimorphic switch from one morphological state to another. The data identified a variety of differentially expressed genes that have been organized into metabolic clusters based on predicted function and expression patterns. In particular, C-14 sterol reductase–encoding gene ergM of the ergosterol biosynthesis pathway showed high-level expression throughout yeast morphogenesis compared to hyphal. Deletion of ergM resulted in severe growth defects with increased sensitivity to azole-type antifungal agents but not amphotericin B. The data defined gene classes based on spatio-temporal expression such as those expressed early in the dimorphic switch but not in the terminal cell types and those expressed late. Such classifications have been helpful in linking a given gene of interest to its expression pattern throughout the P. marneffei dimorphic life cycle and its likely role in pathogenicity. PMID:24062530

Logic Learning Machine and standard supervised methods for Hodgkin's lymphoma prognosis using gene expression data and clinical variables.

PubMed

Parodi, Stefano; Manneschi, Chiara; Verda, Damiano; Ferrari, Enrico; Muselli, Marco

2018-03-01

This study evaluates the performance of a set of machine learning techniques in predicting the prognosis of Hodgkin's lymphoma using clinical factors and gene expression data. Analysed samples from 130 Hodgkin's lymphoma patients included a small set of clinical variables and more than 54,000 gene features. Machine learning classifiers included three black-box algorithms ( k-nearest neighbour, Artificial Neural Network, and Support Vector Machine) and two methods based on intelligible rules (Decision Tree and the innovative Logic Learning Machine method). Support Vector Machine clearly outperformed any of the other methods. Among the two rule-based algorithms, Logic Learning Machine performed better and identified a set of simple intelligible rules based on a combination of clinical variables and gene expressions. Decision Tree identified a non-coding gene ( XIST) involved in the early phases of X chromosome inactivation that was overexpressed in females and in non-relapsed patients. XIST expression might be responsible for the better prognosis of female Hodgkin's lymphoma patients.

Mining Gene Regulatory Networks by Neural Modeling of Expression Time-Series.

PubMed

Rubiolo, Mariano; Milone, Diego H; Stegmayer, Georgina

2015-01-01

Discovering gene regulatory networks from data is one of the most studied topics in recent years. Neural networks can be successfully used to infer an underlying gene network by modeling expression profiles as times series. This work proposes a novel method based on a pool of neural networks for obtaining a gene regulatory network from a gene expression dataset. They are used for modeling each possible interaction between pairs of genes in the dataset, and a set of mining rules is applied to accurately detect the subjacent relations among genes. The results obtained on artificial and real datasets confirm the method effectiveness for discovering regulatory networks from a proper modeling of the temporal dynamics of gene expression profiles.

Analyzing gene expression from relative codon usage bias in Yeast genome: a statistical significance and biological relevance.

PubMed

Das, Shibsankar; Roymondal, Uttam; Sahoo, Satyabrata

2009-08-15

Based on the hypothesis that highly expressed genes are often characterized by strong compositional bias in terms of codon usage, there are a number of measures currently in use that quantify codon usage bias in genes, and hence provide numerical indices to predict the expression levels of genes. With the recent advent of expression measure from the score of the relative codon usage bias (RCBS), we have explicitly tested the performance of this numerical measure to predict the gene expression level and illustrate this with an analysis of Yeast genomes. In contradiction with previous other studies, we observe a weak correlations between GC content and RCBS, but a selective pressure on the codon preferences in highly expressed genes. The assertion that the expression of a given gene depends on the score of relative codon usage bias (RCBS) is supported by the data. We further observe a strong correlation between RCBS and protein length indicating natural selection in favour of shorter genes to be expressed at higher level. We also attempt a statistical analysis to assess the strength of relative codon bias in genes as a guide to their likely expression level, suggesting a decrease of the informational entropy in the highly expressed genes.

Identification and resolution of artifacts in the interpretation of imprinted gene expression

PubMed Central

Proudhon, Charlotte

2010-01-01

Genomic imprinting refers to genes that are epigenetically programmed in the germline to express exclusively or preferentially one allele in a parent-of-origin manner. Expression-based genome-wide screening for the identification of imprinted genes has failed to uncover a significant number of new imprinted genes, probably because of the high tissue- and developmental-stage specificity of imprinted gene expression. A very large number of technical and biological artifacts can also lead to the erroneous evidence of imprinted gene expression. In this article, we focus on three common sources of potential confounding effects: (i) random monoallelic expression in monoclonal cell populations, (ii) genetically determined monoallelic expression and (iii) contamination or infiltration of embryonic tissues with maternal material. This last situation specifically applies to genes that occur as maternally expressed in the placenta. Beside the use of reciprocal crosses that are instrumental to confirm the parental specificity of expression, we provide additional methods for the detection and elimination of these situations that can be misinterpreted as cases of imprinted expression. PMID:20829207

Evaluation and selection of reliable reference genes for gene expression under abiotic stress in cotton (Gossypium hirsutum L.).

PubMed

Wang, Min; Wang, Qinglian; Zhang, Baohong

2013-11-01

Reference genes are critical for normalization of the gene expression level of target genes. The widely used housekeeping genes may change their expression levels at different tissue under different treatment or stress conditions. Therefore, systematical evaluation on the housekeeping genes is required for gene expression analysis. Up to date, no work was performed to evaluate the housekeeping genes in cotton under stress treatment. In this study, we chose 10 housekeeping genes to systematically assess their expression levels at two different tissues (leaves and roots) under two different abiotic stresses (salt and drought) with three different concentrations. Our results show that there is no best reference gene for all tissues at all stress conditions. The reliable reference gene should be selected based on a specific condition. For example, under salt stress, UBQ7, GAPDH and EF1A8 are better reference genes in leaves; TUA10, UBQ7, CYP1, GAPDH and EF1A8 were better in roots. Under drought stress, UBQ7, EF1A8, TUA10, and GAPDH showed less variety of expression level in leaves and roots. Thus, it is better to identify reliable reference genes first before performing any gene expression analysis. However, using a combination of housekeeping genes as reference gene may provide a new strategy for normalization of gene expression. In this study, we found that combination of four housekeeping genes worked well as reference genes under all the stress conditions. © 2013.

Microarray-based gene expression profiling to elucidate effectiveness of fermented Codonopsis lanceolata in mice.

PubMed

Choi, Woon Yong; Kim, Ji Seon; Park, Sung Jin; Ma, Choong Je; Lee, Hyeon Yong

2014-04-08

In this study, the effect of Codonopsis lanceolata fermented by lactic acid on controlling gene expression levels related to obesity was observed in an oligonucleotide chip microarray. Among 8170 genes, 393 genes were up regulated and 760 genes were down regulated in feeding the fermented C. lanceolata (FCL). Another 374 genes were up regulated and 527 genes down regulated without feeding the sample. The genes were not affected by the FCL sample. It was interesting that among those genes, Chytochrome P450, Dmbt1, LOC76487, and thyroid hormones, etc., were mostly up or down regulated. These genes are more related to lipid synthesis. We could conclude that the FCL possibly controlled the gene expression levels related to lipid synthesis, which resulted in reducing obesity. However, more detailed protein expression experiments should be carried out.

A systems-based approach to analyse the host response in murine lung macrophages challenged with respiratory syncytial virus

PubMed Central

2013-01-01

Background Respiratory syncytial virus (RSV) is an important cause of lower respiratory tract infection in young children. The degree of disease severity is determined by the host response to infection. Lung macrophages play an important early role in the host response to infection and we have used a systems-based approach to examine the host response in RSV-infected lung-derived macrophage cells. Results Lung macrophage cells could be efficiently infected (>95%) with RSV in vitro, and the expression of several virus structural proteins could be detected. Although we failed to detect significant levels of virus particle production, virus antigen could be detected up until 96 hours post-infection (hpi). Microarray analysis indicated that 20,086 annotated genes were expressed in the macrophage cells, and RSV infection induced an 8.9% and 11.3% change in the global gene transcriptome at 4 hpi and 24 hpi respectively. Genes showing up-regulated expression were more numerous and exhibited higher changes in expression compared to genes showing down-regulated expression. Based on gene ontology, genes with cytokine, antiviral, cell death, and signal transduction functions showed the highest increases in expression, while signalling transduction, RNA binding and protein kinase genes showed the greatest reduction in expression levels. Analysis of the global gene expression profile using pathway enrichment analysis confirmed that up-regulated expression of pathways related to pathogen recognition, interferon signalling and antigen presentation occurred in the lung macrophage cells challenged with RSV. Conclusion Our data provided a comprehensive analysis of RSV-induced gene expression changes in lung macrophages. Although virus gene expression was detected, our data was consistent with an abortive infection and this correlated with the activation of several antivirus signalling pathways such as interferon type I signalling and cell death signalling. RSV infection induced a relatively large increase in pro-inflammatory cytokine expression, however the maintenance of this pro-inflammatory response was not dependent on the production of infectious virus particles. The sustained pro-inflammatory response even in the absence of a productive infection suggests that drugs that control the pro-inflammatory response may be useful in the treatment of patients with severe RSV infection. PMID:23506210

Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks

PubMed Central

Marbach, Daniel; Roy, Sushmita; Ay, Ferhat; Meyer, Patrick E.; Candeias, Rogerio; Kahveci, Tamer; Bristow, Christopher A.; Kellis, Manolis

2012-01-01

Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the network inference problem in a machine-learning framework and use both supervised and unsupervised methods to predict regulatory edges by integrating transcription factor (TF) binding, evolutionarily conserved sequence motifs, gene expression, and chromatin modification data sets as input features. Applying these methods to Drosophila melanogaster, we predict ∼300,000 regulatory edges in a network of ∼600 TFs and 12,000 target genes. We validate our predictions using known regulatory interactions, gene functional annotations, tissue-specific expression, protein–protein interactions, and three-dimensional maps of chromosome conformation. We use the inferred network to identify putative functions for hundreds of previously uncharacterized genes, including many in nervous system development, which are independently confirmed based on their tissue-specific expression patterns. Last, we use the regulatory network to predict target gene expression levels as a function of TF expression, and find significantly higher predictive power for integrative networks than for motif or ChIP-based networks. Our work reveals the complementarity between physical evidence of regulatory interactions (TF binding, motif conservation) and functional evidence (coordinated expression or chromatin patterns) and demonstrates the power of data integration for network inference and studies of gene regulation at the systems level. PMID:22456606

Gene-based association study of genes linked to hippocampal sclerosis of aging neuropathology: GRN, TMEM106B, ABCC9, and KCNMB2

PubMed Central

Katsumata, Yuriko; Nelson, Peter T.; Ellingson, Sally R.; Fardo, David W.

2017-01-01

Hippocampal sclerosis of aging (HS-Aging) is a common neurodegenerative condition associated with dementia. To learn more about genetic risk of HS-Aging pathology, we tested gene-based associations of the GRN, TMEM106B, ABCC9, and KCNMB2 genes, which were reported to be associated with HS-Aging pathology in previous studies. Genetic data were obtained from the Alzheimer’s Disease Genetics Consortium (ADGC), linked to autopsy-derived neuropathological outcomes from the National Alzheimer’s Coordinating Center (NACC). Of the 3,251 subjects included in the study, 271 (8.3%) were identified as an HS-Aging case. The significant gene-based association between the ABCC9 gene and HS-Aging appeared to be driven by a region in which a significant haplotype-based association was found. We tested this haplotype as an expression Quantitative Trait Locus (eQTL) using two different public-access brain gene expression databases. The HS-Aging pathology protective ABCC9 haplotype was associated with decreased ABCC9 expression, indicating a possible toxic gain of function. PMID:28131462

GENE-Counter: A Computational Pipeline for the Analysis of RNA-Seq Data for Gene Expression Differences

PubMed Central

Di, Yanming; Schafer, Daniel W.; Wilhelm, Larry J.; Fox, Samuel E.; Sullivan, Christopher M.; Curzon, Aron D.; Carrington, James C.; Mockler, Todd C.; Chang, Jeff H.

2011-01-01

GENE-counter is a complete Perl-based computational pipeline for analyzing RNA-Sequencing (RNA-Seq) data for differential gene expression. In addition to its use in studying transcriptomes of eukaryotic model organisms, GENE-counter is applicable for prokaryotes and non-model organisms without an available genome reference sequence. For alignments, GENE-counter is configured for CASHX, Bowtie, and BWA, but an end user can use any Sequence Alignment/Map (SAM)-compliant program of preference. To analyze data for differential gene expression, GENE-counter can be run with any one of three statistics packages that are based on variations of the negative binomial distribution. The default method is a new and simple statistical test we developed based on an over-parameterized version of the negative binomial distribution. GENE-counter also includes three different methods for assessing differentially expressed features for enriched gene ontology (GO) terms. Results are transparent and data are systematically stored in a MySQL relational database to facilitate additional analyses as well as quality assessment. We used next generation sequencing to generate a small-scale RNA-Seq dataset derived from the heavily studied defense response of Arabidopsis thaliana and used GENE-counter to process the data. Collectively, the support from analysis of microarrays as well as the observed and substantial overlap in results from each of the three statistics packages demonstrates that GENE-counter is well suited for handling the unique characteristics of small sample sizes and high variability in gene counts. PMID:21998647

Predicting Gene Expression Level from Relative Codon Usage Bias: An Application to Escherichia coli Genome

PubMed Central

Roymondal, Uttam; Das, Shibsankar; Sahoo, Satyabrata

2009-01-01

We present an expression measure of a gene, devised to predict the level of gene expression from relative codon bias (RCB). There are a number of measures currently in use that quantify codon usage in genes. Based on the hypothesis that gene expressivity and codon composition is strongly correlated, RCB has been defined to provide an intuitively meaningful measure of an extent of the codon preference in a gene. We outline a simple approach to assess the strength of RCB (RCBS) in genes as a guide to their likely expression levels and illustrate this with an analysis of Escherichia coli (E. coli) genome. Our efforts to quantitatively predict gene expression levels in E. coli met with a high level of success. Surprisingly, we observe a strong correlation between RCBS and protein length indicating natural selection in favour of the shorter genes to be expressed at higher level. The agreement of our result with high protein abundances, microarray data and radioactive data demonstrates that the genomic expression profile available in our method can be applied in a meaningful way to the study of cell physiology and also for more detailed studies of particular genes of interest. PMID:19131380

Prediction of operon-like gene clusters in the Arabidopsis thaliana genome based on co-expression analysis of neighboring genes.

PubMed

Wada, Masayoshi; Takahashi, Hiroki; Altaf-Ul-Amin, Md; Nakamura, Kensuke; Hirai, Masami Y; Ohta, Daisaku; Kanaya, Shigehiko

2012-07-15

Operon-like arrangements of genes occur in eukaryotes ranging from yeasts and filamentous fungi to nematodes, plants, and mammals. In plants, several examples of operon-like gene clusters involved in metabolic pathways have recently been characterized, e.g. the cyclic hydroxamic acid pathways in maize, the avenacin biosynthesis gene clusters in oat, the thalianol pathway in Arabidopsis thaliana, and the diterpenoid momilactone cluster in rice. Such operon-like gene clusters are defined by their co-regulation or neighboring positions within immediate vicinity of chromosomal regions. A comprehensive analysis of the expression of neighboring genes therefore accounts a crucial step to reveal the complete set of operon-like gene clusters within a genome. Genome-wide prediction of operon-like gene clusters should contribute to functional annotation efforts and provide novel insight into evolutionary aspects acquiring certain biological functions as well. We predicted co-expressed gene clusters by comparing the Pearson correlation coefficient of neighboring genes and randomly selected gene pairs, based on a statistical method that takes false discovery rate (FDR) into consideration for 1469 microarray gene expression datasets of A. thaliana. We estimated that A. thaliana contains 100 operon-like gene clusters in total. We predicted 34 statistically significant gene clusters consisting of 3 to 22 genes each, based on a stringent FDR threshold of 0.1. Functional relationships among genes in individual clusters were estimated by sequence similarity and functional annotation of genes. Duplicated gene pairs (determined based on BLAST with a cutoff of E<10(-5)) are included in 27 clusters. Five clusters are associated with metabolism, containing P450 genes restricted to the Brassica family and predicted to be involved in secondary metabolism. Operon-like clusters tend to include genes encoding bio-machinery associated with ribosomes, the ubiquitin/proteasome system, secondary metabolic pathways, lipid and fatty-acid metabolism, and the lipid transfer system. Copyright © 2012 Elsevier B.V. All rights reserved.

Homeobox genes and melatonin synthesis: regulatory roles of the cone-rod homeobox transcription factor in the rodent pineal gland.

PubMed

Rohde, Kristian; Møller, Morten; Rath, Martin Fredensborg

2014-01-01

Nocturnal synthesis of melatonin in the pineal gland is controlled by a circadian rhythm in arylalkylamine N-acetyltransferase (AANAT) enzyme activity. In the rodent, Aanat gene expression displays a marked circadian rhythm; release of norepinephrine in the gland at night causes a cAMP-based induction of Aanat transcription. However, additional transcriptional control mechanisms exist. Homeobox genes, which are generally known to encode transcription factors controlling developmental processes, are also expressed in the mature rodent pineal gland. Among these, the cone-rod homeobox (CRX) transcription factor is believed to control pineal-specific Aanat expression. Based on recent advances in our understanding of Crx in the rodent pineal gland, we here suggest that homeobox genes play a role in adult pineal physiology both by ensuring pineal-specific Aanat expression and by facilitating cAMP response element-based circadian melatonin production.

Homeobox Genes and Melatonin Synthesis: Regulatory Roles of the Cone-Rod Homeobox Transcription Factor in the Rodent Pineal Gland

PubMed Central

Rath, Martin Fredensborg

2014-01-01

Nocturnal synthesis of melatonin in the pineal gland is controlled by a circadian rhythm in arylalkylamine N-acetyltransferase (AANAT) enzyme activity. In the rodent, Aanat gene expression displays a marked circadian rhythm; release of norepinephrine in the gland at night causes a cAMP-based induction of Aanat transcription. However, additional transcriptional control mechanisms exist. Homeobox genes, which are generally known to encode transcription factors controlling developmental processes, are also expressed in the mature rodent pineal gland. Among these, the cone-rod homeobox (CRX) transcription factor is believed to control pineal-specific Aanat expression. Based on recent advances in our understanding of Crx in the rodent pineal gland, we here suggest that homeobox genes play a role in adult pineal physiology both by ensuring pineal-specific Aanat expression and by facilitating cAMP response element-based circadian melatonin production. PMID:24877149

Transcriptome analysis of the Tan sheep testes: Differential expression of antioxidant enzyme-related genes and proteins in response to dietary vitamin E supplementation.

PubMed

Xu, Chenchen; Zuo, Zhaoyun; Liu, Kun; Jia, Huina; Zhang, Yuwei; Luo, Hailing

2016-03-15

Gene-chip technology was employed to study the effect of dietary vitamin E on gene expression in sheep testes based on our previous research. Thirty-five male Tan sheep (20-30 days after weaning) with similar body weight were randomly allocated into five groups and supplemented 0, 20, 100, 200 and 2,000 IU sheep(-1)day(-1) vitamin E (treatments denoted as E0, E20, E100, E200, and E2000, respectively) for 120 days. At the end of the study the sheep were slaughtered and the testis samples were immediately collected and stored in liquid nitrogen. Differences in gene expression between different treated groups were identified. Based on GO enrichment analysis and the KEGG database to evaluate the gene expression data we found that vitamin E might affect genes in the testes by modulating the oxidation level, by affecting the expression of various receptors and transcription factors in biological pathways, and by regulating the expression of metabolism-associated genes. The effect of vitamin E supplementation on the expression of oxidative enzyme-related genes was detected by quantitative real-time PCR (qRT-PCR) and Western blot. The results show that dietary vitamin E, at various doses, can significantly increase (P<0.05) the mRNA and protein expression of Glutathione peroxidase 3 and Glutathione S-transferase alpha 1. In addition, the results of qRT-PCR of the antioxidant enzyme genes were consistent with those obtained using the gene chip microarray analysis. In summary, the dietary vitamin E treatment altered the expression of a number of genes in sheep testes. The increase in the mRNA and protein levels of antioxidant enzyme genes, coupled with the elevation in the activity of the antioxidant enzymes were primarily responsible for the improved reproductive performance promoted by dietary vitamin E. Copyright © 2015 Elsevier B.V. All rights reserved.

Intra- and interspecies gene expression models for predicting drug response in canine osteosarcoma.

PubMed

Fowles, Jared S; Brown, Kristen C; Hess, Ann M; Duval, Dawn L; Gustafson, Daniel L

2016-02-19

Genomics-based predictors of drug response have the potential to improve outcomes associated with cancer therapy. Osteosarcoma (OS), the most common primary bone cancer in dogs, is commonly treated with adjuvant doxorubicin or carboplatin following amputation of the affected limb. We evaluated the use of gene-expression based models built in an intra- or interspecies manner to predict chemosensitivity and treatment outcome in canine OS. Models were built and evaluated using microarray gene expression and drug sensitivity data from human and canine cancer cell lines, and canine OS tumor datasets. The "COXEN" method was utilized to filter gene signatures between human and dog datasets based on strong co-expression patterns. Models were built using linear discriminant analysis via the misclassification penalized posterior algorithm. The best doxorubicin model involved genes identified in human lines that were co-expressed and trained on canine OS tumor data, which accurately predicted clinical outcome in 73 % of dogs (p = 0.0262, binomial). The best carboplatin model utilized canine lines for gene identification and model training, with canine OS tumor data for co-expression. Dogs whose treatment matched our predictions had significantly better clinical outcomes than those that didn't (p = 0.0006, Log Rank), and this predictor significantly associated with longer disease free intervals in a Cox multivariate analysis (hazard ratio = 0.3102, p = 0.0124). Our data show that intra- and interspecies gene expression models can successfully predict response in canine OS, which may improve outcome in dogs and serve as pre-clinical validation for similar methods in human cancer research.

A framework for analyzing the relationship between gene expression and morphological, topological, and dynamical patterns in neuronal networks.

PubMed

de Arruda, Henrique Ferraz; Comin, Cesar Henrique; Miazaki, Mauro; Viana, Matheus Palhares; Costa, Luciano da Fontoura

2015-04-30

A key point in developmental biology is to understand how gene expression influences the morphological and dynamical patterns that are observed in living beings. In this work we propose a methodology capable of addressing this problem that is based on estimating the mutual information and Pearson correlation between the intensity of gene expression and measurements of several morphological properties of the cells. A similar approach is applied in order to identify effects of gene expression over the system dynamics. Neuronal networks were artificially grown over a lattice by considering a reference model used to generate artificial neurons. The input parameters of the artificial neurons were determined according to two distinct patterns of gene expression and the dynamical response was assessed by considering the integrate-and-fire model. As far as single gene dependence is concerned, we found that the interaction between the gene expression and the network topology, as well as between the former and the dynamics response, is strongly affected by the gene expression pattern. In addition, we observed a high correlation between the gene expression and some topological measurements of the neuronal network for particular patterns of gene expression. To our best understanding, there are no similar analyses to compare with. A proper understanding of gene expression influence requires jointly studying the morphology, topology, and dynamics of neurons. The proposed framework represents a first step towards predicting gene expression patterns from morphology and connectivity. Copyright © 2015. Published by Elsevier B.V.

Gene Expression (mRNA) Markers for Differentiating between Malignant and Benign Follicular Thyroid Tumours

PubMed Central

Wojtas, Bartosz; Pfeifer, Aleksandra; Oczko-Wojciechowska, Malgorzata; Krajewska, Jolanta; Czarniecka, Agnieszka; Kukulska, Aleksandra; Eszlinger, Markus; Musholt, Thomas; Stokowy, Tomasz; Swierniak, Michal; Stobiecka, Ewa; Chmielik, Ewa; Rusinek, Dagmara; Tyszkiewicz, Tomasz; Halczok, Monika; Hauptmann, Steffen; Lange, Dariusz; Jarzab, Michal; Paschke, Ralf; Jarzab, Barbara

2017-01-01

Distinguishing between follicular thyroid cancer (FTC) and follicular thyroid adenoma (FTA) constitutes a long-standing diagnostic problem resulting in equivocal histopathological diagnoses. There is therefore a need for additional molecular markers. To identify molecular differences between FTC and FTA, we analyzed the gene expression microarray data of 52 follicular neoplasms. We also performed a meta-analysis involving 14 studies employing high throughput methods (365 follicular neoplasms analyzed). Based on these two analyses, we selected 18 genes differentially expressed between FTA and FTC. We validated them by quantitative real-time polymerase chain reaction (qRT-PCR) in an independent set of 71 follicular neoplasms from formaldehyde-fixed paraffin embedded (FFPE) tissue material. We confirmed differential expression for 7 genes (CPQ, PLVAP, TFF3, ACVRL1, ZFYVE21, FAM189A2, and CLEC3B). Finally, we created a classifier that distinguished between FTC and FTA with an accuracy of 78%, sensitivity of 76%, and specificity of 80%, based on the expression of 4 genes (CPQ, PLVAP, TFF3, ACVRL1). In our study, we have demonstrated that meta-analysis is a valuable method for selecting possible molecular markers. Based on our results, we conclude that there might exist a plausible limit of gene classifier accuracy of approximately 80%, when follicular tumors are discriminated based on formalin-fixed postoperative material. PMID:28574441

Gene Expression (mRNA) Markers for Differentiating between Malignant and Benign Follicular Thyroid Tumours.

PubMed

Wojtas, Bartosz; Pfeifer, Aleksandra; Oczko-Wojciechowska, Malgorzata; Krajewska, Jolanta; Czarniecka, Agnieszka; Kukulska, Aleksandra; Eszlinger, Markus; Musholt, Thomas; Stokowy, Tomasz; Swierniak, Michal; Stobiecka, Ewa; Chmielik, Ewa; Rusinek, Dagmara; Tyszkiewicz, Tomasz; Halczok, Monika; Hauptmann, Steffen; Lange, Dariusz; Jarzab, Michal; Paschke, Ralf; Jarzab, Barbara

2017-06-02

Distinguishing between follicular thyroid cancer (FTC) and follicular thyroid adenoma (FTA) constitutes a long-standing diagnostic problem resulting in equivocal histopathological diagnoses. There is therefore a need for additional molecular markers. To identify molecular differences between FTC and FTA, we analyzed the gene expression microarray data of 52 follicular neoplasms. We also performed a meta-analysis involving 14 studies employing high throughput methods (365 follicular neoplasms analyzed). Based on these two analyses, we selected 18 genes differentially expressed between FTA and FTC. We validated them by quantitative real-time polymerase chain reaction (qRT-PCR) in an independent set of 71 follicular neoplasms from formaldehyde-fixed paraffin embedded (FFPE) tissue material. We confirmed differential expression for 7 genes ( CPQ , PLVAP , TFF3 , ACVRL1 , ZFYVE21 , FAM189A2 , and CLEC3B ). Finally, we created a classifier that distinguished between FTC and FTA with an accuracy of 78%, sensitivity of 76%, and specificity of 80%, based on the expression of 4 genes ( CPQ , PLVAP , TFF3 , ACVRL1 ). In our study, we have demonstrated that meta-analysis is a valuable method for selecting possible molecular markers. Based on our results, we conclude that there might exist a plausible limit of gene classifier accuracy of approximately 80%, when follicular tumors are discriminated based on formalin-fixed postoperative material.

Analysis of differential gene expression by bead-based fiber-optic array in nonfunctioning pituitary adenomas.

PubMed

Jiang, Z; Gui, S; Zhang, Y

2011-05-01

Nonfunctioning pituitary adenomas (NFPAs) are relatively common, accounting for 30% of all pituitary adenomas; however, their pathogenesis remains enigmatic. To explore the possible pathogenesis of NFPAs, we used fiber-optic BeadArray to examine gene expression in 5 NFPAs compared with 3 normal pituitaries. 4 differentially expressed genes were chosen randomly for validation by reverse transcriptase-real time quantitative polymerase chain reaction (RT-qPCR). We then analyzed the differentially expressed gene profile with Kyoto Encyclopedia of Genes and Genomes (KEGG). The array analysis indentified significant increases in the expression of 1,402 genes and 383 expressed sequence tags (ESTs), and decreases in 1,697 genes and 113 ESTs in the NFPAs. Bioinformatic and pathway analysis showed that the genes HIGD1B, FAM5C, PMAIP1 and the pathway cell-cycle regulation may play an important role in tumorigenesis and progression of NFPAs. Our data suggest fiber-optic BeadArray combined with pathway analysis of differential gene expression profile appears to be a valid approach for investigating the pathogenesis of tumors. © Georg Thieme Verlag KG Stuttgart · New York.

Exploring candidate biomarkers for lung and prostate cancers using gene expression and flux variability analysis.

PubMed

Asgari, Yazdan; Khosravi, Pegah; Zabihinpour, Zahra; Habibi, Mahnaz

2018-02-19

Genome-scale metabolic models have provided valuable resources for exploring changes in metabolism under normal and cancer conditions. However, metabolism itself is strongly linked to gene expression, so integration of gene expression data into metabolic models might improve the detection of genes involved in the control of tumor progression. Herein, we considered gene expression data as extra constraints to enhance the predictive powers of metabolic models. We reconstructed genome-scale metabolic models for lung and prostate, under normal and cancer conditions to detect the major genes associated with critical subsystems during tumor development. Furthermore, we utilized gene expression data in combination with an information theory-based approach to reconstruct co-expression networks of the human lung and prostate in both cohorts. Our results revealed 19 genes as candidate biomarkers for lung and prostate cancer cells. This study also revealed that the development of a complementary approach (integration of gene expression and metabolic profiles) could lead to proposing novel biomarkers and suggesting renovated cancer treatment strategies which have not been possible to detect using either of the methods alone.

Differential gene expressions in testes of L2 strain Taiwan country chicken in response to acute heat stress.

PubMed

Wang, Shih-Han; Cheng, Chuen-Yu; Tang, Pin-Chi; Chen, Chih-Feng; Chen, Hsin-Hsin; Lee, Yen-Pai; Huang, San-Yuan

2013-01-15

Acute heat stress affects genes involved in spermatogenesis in mammals. However, there is apparently no elaborate research on the effects of acute heat stress on gene expression in avian testes. The purpose of this study was to investigate global gene expression in testes of the L2 strain of Taiwan country chicken after acute heat stress. Twelve roosters, 45 weeks old, were allocated into four groups, including control roosters kept at 25 °C, roosters subjected to 38 °C acute heat stress for 4 hours without recovery, with 2-hour recovery, and with 6-hour recovery, respectively. Testis samples were collected for RNA isolation and microarray analysis. Based on gene expression profiles, 169 genes were upregulated and 140 genes were downregulated after heat stress using a cutoff value of twofold or greater change. Based on gene ontology analysis, differentially expressed genes were mainly related to response to stress, transport, signal transduction, and metabolism. A functional network analysis displayed that heat shock protein genes and related chaperones were the major upregulated groups in chicken testes after acute heat stress. A quantitative real-time polymerase chain reaction analysis of mRNA expressions of HSP70, HSP90AA1, BAG3, SERPINB2, HSP25, DNAJA4, CYP3A80, CIRBP, and TAGLN confirmed the results of the microarray analysis. Because the HSP genes (HSP25, HSP70, and HSP90AA1) and the antiapoptotic BAG3 gene were dramatically altered in heat-stressed chicken testes, we concluded that these genes were important factors in the avian testes under acute heat stress. Whether these genes could be candidate genes for thermotolerance in roosters requires further investigation. Copyright © 2013 Elsevier Inc. All rights reserved.

Double-filter identification of vascular-expressed genes using Arabidopsis plants with vascular hypertrophy and hypotrophy.

PubMed

Ckurshumova, Wenzislava; Scarpella, Enrico; Goldstein, Rochelle S; Berleth, Thomas

2011-08-01

Genes expressed in vascular tissues have been identified by several strategies, usually with a focus on mature vascular cells. In this study, we explored the possibility of using two opposite types of altered tissue compositions in combination with a double-filter selection to identify genes with a high probability of vascular expression in early organ primordia. Specifically, we generated full-transcriptome microarray profiles of plants with (a) genetically strongly reduced and (b) pharmacologically vastly increased vascular tissues and identified a reproducible cohort of 158 transcripts that fulfilled the dual requirement of being underrepresented in (a) and overrepresented in (b). In order to assess the predictive value of our identification scheme for vascular gene expression, we determined the expression patterns of genes in two unbiased subsamples. First, we assessed the expression patterns of all twenty annotated transcription factor genes from the cohort of 158 genes and found that seventeen of the twenty genes were preferentially expressed in leaf vascular cells. Remarkably, fifteen of these seventeen vascular genes were clearly expressed already very early in leaf vein development. Twelve genes with published leaf expression patterns served as a second subsample to monitor the representation of vascular genes in our cohort. Of those twelve genes, eleven were preferentially expressed in leaf vascular tissues. Based on these results we propose that our compendium of 158 genes represents a sample that is highly enriched for genes expressed in vascular tissues and that our approach is particularly suited to detect genes expressed in vascular cell lineages at early stages of their inception. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Transcriptional profiling of root-knot nematode induced feeding sites in cowpea (Vigna unguiculata L. Walp.) using a soybean genome array.

PubMed

Das, Sayan; Ehlers, Jeffrey D; Close, Timothy J; Roberts, Philip A

2010-08-19

The locus Rk confers resistance against several species of root-knot nematodes (Meloidogyne spp., RKN) in cowpea (Vigna unguiculata). Based on histological and reactive oxygen species (ROS) profiles, Rk confers a delayed but strong resistance mechanism without a hypersensitive reaction-mediated cell death process, which allows nematode development but blocks reproduction. Responses to M. incognita infection in roots of resistant genotype CB46 and a susceptible near-isogenic line (null-Rk) were investigated using a soybean Affymetrix GeneChip expression array at 3 and 9 days post-inoculation (dpi). At 9 dpi 552 genes were differentially expressed in incompatible interactions (infected resistant tissue compared with non-infected resistant tissue) and 1,060 genes were differentially expressed in compatible interactions (infected susceptible tissue compared with non-infected susceptible tissue). At 3 dpi the differentially expressed genes were 746 for the incompatible and 623 for the compatible interactions. When expression between infected resistant and susceptible genotypes was compared, 638 and 197 genes were differentially expressed at 9 and 3 dpi, respectively. In comparing the differentially expressed genes in response to nematode infection, a greater number and proportion of genes were down-regulated in the resistant than in the susceptible genotype, whereas more genes were up-regulated in the susceptible than in the resistant genotype. Gene ontology based functional categorization revealed that the typical defense response was partially suppressed in resistant roots, even at 9 dpi, allowing nematode juvenile development. Differences in ROS concentrations, induction of toxins and other defense related genes seem to play a role in this unique resistance mechanism.

GSNFS: Gene subnetwork biomarker identification of lung cancer expression data.

PubMed

Doungpan, Narumol; Engchuan, Worrawat; Chan, Jonathan H; Meechai, Asawin

2016-12-05

Gene expression has been used to identify disease gene biomarkers, but there are ongoing challenges. Single gene or gene-set biomarkers are inadequate to provide sufficient understanding of complex disease mechanisms and the relationship among those genes. Network-based methods have thus been considered for inferring the interaction within a group of genes to further study the disease mechanism. Recently, the Gene-Network-based Feature Set (GNFS), which is capable of handling case-control and multiclass expression for gene biomarker identification, has been proposed, partly taking into account of network topology. However, its performance relies on a greedy search for building subnetworks and thus requires further improvement. In this work, we establish a new approach named Gene Sub-Network-based Feature Selection (GSNFS) by implementing the GNFS framework with two proposed searching and scoring algorithms, namely gene-set-based (GS) search and parent-node-based (PN) search, to identify subnetworks. An additional dataset is used to validate the results. The two proposed searching algorithms of the GSNFS method for subnetwork expansion are concerned with the degree of connectivity and the scoring scheme for building subnetworks and their topology. For each iteration of expansion, the neighbour genes of a current subnetwork, whose expression data improved the overall subnetwork score, is recruited. While the GS search calculated the subnetwork score using an activity score of a current subnetwork and the gene expression values of its neighbours, the PN search uses the expression value of the corresponding parent of each neighbour gene. Four lung cancer expression datasets were used for subnetwork identification. In addition, using pathway data and protein-protein interaction as network data in order to consider the interaction among significant genes were discussed. Classification was performed to compare the performance of the identified gene subnetworks with three subnetwork identification algorithms. The two searching algorithms resulted in better classification and gene/gene-set agreement compared to the original greedy search of the GNFS method. The identified lung cancer subnetwork using the proposed searching algorithm resulted in an improvement of the cross-dataset validation and an increase in the consistency of findings between two independent datasets. The homogeneity measurement of the datasets was conducted to assess dataset compatibility in cross-dataset validation. The lung cancer dataset with higher homogeneity showed a better result when using the GS search while the dataset with low homogeneity showed a better result when using the PN search. The 10-fold cross-dataset validation on the independent lung cancer datasets showed higher classification performance of the proposed algorithms when compared with the greedy search in the original GNFS method. The proposed searching algorithms provide a higher number of genes in the subnetwork expansion step than the greedy algorithm. As a result, the performance of the subnetworks identified from the GSNFS method was improved in terms of classification performance and gene/gene-set level agreement depending on the homogeneity of the datasets used in the analysis. Some common genes obtained from the four datasets using different searching algorithms are genes known to play a role in lung cancer. The improvement of classification performance and the gene/gene-set level agreement, and the biological relevance indicated the effectiveness of the GSNFS method for gene subnetwork identification using expression data.

A plasmid-based Escherichia coli gene expression system with cell-to-cell variation below the extrinsic noise limit

PubMed Central

2017-01-01

Experiments in synthetic biology and microbiology can benefit from protein expression systems with low cell-to-cell variability (noise) and expression levels precisely tunable across a useful dynamic range. Despite advances in understanding the molecular biology of microbial gene regulation, many experiments employ protein-expression systems exhibiting high noise and nearly all-or-none responses to induction. I present an expression system that incorporates elements known to reduce gene expression noise: negative autoregulation and bicistronic transcription. I show by stochastic simulation that while negative autoregulation can produce a more gradual response to induction, bicistronic expression of a repressor and gene of interest can be necessary to reduce noise below the extrinsic limit. I synthesized a plasmid-based system incorporating these principles and studied its properties in Escherichia coli cells, using flow cytometry and fluorescence microscopy to characterize induction dose-response, induction/repression kinetics and gene expression noise. By varying ribosome binding site strengths, expression levels from 55–10,740 molecules/cell were achieved with noise below the extrinsic limit. Individual strains are inducible across a dynamic range greater than 20-fold. Experimental comparison of different regulatory networks confirmed that bicistronic autoregulation reduces noise, and revealed unexpectedly high noise for a conventional expression system with a constitutively expressed transcriptional repressor. I suggest a hybrid, low-noise expression system to increase the dynamic range. PMID:29084263

Query-based biclustering of gene expression data using Probabilistic Relational Models.

PubMed

Zhao, Hui; Cloots, Lore; Van den Bulcke, Tim; Wu, Yan; De Smet, Riet; Storms, Valerie; Meysman, Pieter; Engelen, Kristof; Marchal, Kathleen

2011-02-15

With the availability of large scale expression compendia it is now possible to view own findings in the light of what is already available and retrieve genes with an expression profile similar to a set of genes of interest (i.e., a query or seed set) for a subset of conditions. To that end, a query-based strategy is needed that maximally exploits the coexpression behaviour of the seed genes to guide the biclustering, but that at the same time is robust against the presence of noisy genes in the seed set as seed genes are often assumed, but not guaranteed to be coexpressed in the queried compendium. Therefore, we developed ProBic, a query-based biclustering strategy based on Probabilistic Relational Models (PRMs) that exploits the use of prior distributions to extract the information contained within the seed set. We applied ProBic on a large scale Escherichia coli compendium to extend partially described regulons with potentially novel members. We compared ProBic's performance with previously published query-based biclustering algorithms, namely ISA and QDB, from the perspective of bicluster expression quality, robustness of the outcome against noisy seed sets and biological relevance.This comparison learns that ProBic is able to retrieve biologically relevant, high quality biclusters that retain their seed genes and that it is particularly strong in handling noisy seeds. ProBic is a query-based biclustering algorithm developed in a flexible framework, designed to detect biologically relevant, high quality biclusters that retain relevant seed genes even in the presence of noise or when dealing with low quality seed sets.

Identification of promising host-induced silencing targets among genes preferentially transcribed in haustoria of Puccinia

USDA-ARS?s Scientific Manuscript database

Expression of dsRNA fragments of rust pathogen genes in wheat seedlings through the barley stripe mosaic virus (BSMV) based host-induced gene silencing (HIGS) system can reduce the expression of the corresponding genes in the rust fungus. The highest levels of suppression have generally been observe...

An RNA-Seq based gene expression atlas of the common bean.

PubMed

O'Rourke, Jamie A; Iniguez, Luis P; Fu, Fengli; Bucciarelli, Bruna; Miller, Susan S; Jackson, Scott A; McClean, Philip E; Li, Jun; Dai, Xinbin; Zhao, Patrick X; Hernandez, Georgina; Vance, Carroll P

2014-10-06

Common bean (Phaseolus vulgaris) is grown throughout the world and comprises roughly 50% of the grain legumes consumed worldwide. Despite this, genetic resources for common beans have been lacking. Next generation sequencing, has facilitated our investigation of the gene expression profiles associated with biologically important traits in common bean. An increased understanding of gene expression in common bean will improve our understanding of gene expression patterns in other legume species. Combining recently developed genomic resources for Phaseolus vulgaris, including predicted gene calls, with RNA-Seq technology, we measured the gene expression patterns from 24 samples collected from seven tissues at developmentally important stages and from three nitrogen treatments. Gene expression patterns throughout the plant were analyzed to better understand changes due to nodulation, seed development, and nitrogen utilization. We have identified 11,010 genes differentially expressed with a fold change ≥ 2 and a P-value < 0.05 between different tissues at the same time point, 15,752 genes differentially expressed within a tissue due to changes in development, and 2,315 genes expressed only in a single tissue. These analyses identified 2,970 genes with expression patterns that appear to be directly dependent on the source of available nitrogen. Finally, we have assembled this data in a publicly available database, The Phaseolus vulgaris Gene Expression Atlas (Pv GEA), http://plantgrn.noble.org/PvGEA/ . Using the website, researchers can query gene expression profiles of their gene of interest, search for genes expressed in different tissues, or download the dataset in a tabular form. These data provide the basis for a gene expression atlas, which will facilitate functional genomic studies in common bean. Analysis of this dataset has identified genes important in regulating seed composition and has increased our understanding of nodulation and impact of the nitrogen source on assimilation and distribution throughout the plant.

Stable and Efficient Gene Transfer into the Retina Using an HIV-Based Lentiviral Vector

NASA Astrophysics Data System (ADS)

Miyoshi, Hiroyuki; Takahashi, Masayo; Gage, Fred H.; Verma, Inder M.

1997-09-01

The development of methods for efficient gene transfer to terminally differentiated retinal cells is important to study the function of the retina as well as for gene therapy of retinal diseases. We have developed a lentiviral vector system based on the HIV that can transduce terminally differentiated neurons of the brain in vivo. In this study, we have evaluated the ability of HIV vectors to transfer genes into retinal cells. An HIV vector containing a gene encoding the green fluorescent protein (GFP) was injected into the subretinal space of rat eyes. The GFP gene under the control of the cytomegalovirus promoter was efficiently expressed in both photoreceptor cells and retinal pigment epithelium. However, the use of the rhodopsin promoter resulted in expression predominantly in photoreceptor cells. Most successfully transduced eyes showed that photoreceptor cells in >80% of the area of whole retina expressed the GFP. The GFP expression persisted for at least 12 weeks with no apparent decrease. The efficient gene transfer into photoreceptor cells by HIV vectors will be useful for gene therapy of retinal diseases such as retinitis pigmentosa.

Mind-controlled transgene expression by a wireless-powered optogenetic designer cell implant.

PubMed

Folcher, Marc; Oesterle, Sabine; Zwicky, Katharina; Thekkottil, Thushara; Heymoz, Julie; Hohmann, Muriel; Christen, Matthias; Daoud El-Baba, Marie; Buchmann, Peter; Fussenegger, Martin

2014-11-11

Synthetic devices for traceless remote control of gene expression may provide new treatment opportunities in future gene- and cell-based therapies. Here we report the design of a synthetic mind-controlled gene switch that enables human brain activities and mental states to wirelessly programme the transgene expression in human cells. An electroencephalography (EEG)-based brain-computer interface (BCI) processing mental state-specific brain waves programs an inductively linked wireless-powered optogenetic implant containing designer cells engineered for near-infrared (NIR) light-adjustable expression of the human glycoprotein SEAP (secreted alkaline phosphatase). The synthetic optogenetic signalling pathway interfacing the BCI with target gene expression consists of an engineered NIR light-activated bacterial diguanylate cyclase (DGCL) producing the orthogonal second messenger cyclic diguanosine monophosphate (c-di-GMP), which triggers the stimulator of interferon genes (STING)-dependent induction of synthetic interferon-β promoters. Humans generating different mental states (biofeedback control, concentration, meditation) can differentially control SEAP production of the designer cells in culture and of subcutaneous wireless-powered optogenetic implants in mice.

Transcriptome assembly and digital gene expression atlas of the rainbow trout

USDA-ARS?s Scientific Manuscript database

Background: Transcriptome analysis is a preferred method for gene discovery, marker development and gene expression profiling in non-model organisms. Previously, we sequenced a transcriptome reference using Sanger-based and 454-pyrosequencing, however, a transcriptome assembly is still incomplete an...

Differential expression profiles of miRNAs induced by vaccination followed by Marek’s disease virus challenge at cytolytic stage in chickens resistant or susceptible to Marek’s disease

USDA-ARS?s Scientific Manuscript database

Mounting evidence shows microRNAs (miRNAs) directly regulate gene expression post-transcriptionally through base-pairing with regions in the 3’-untranslated sequences of target gene mRNAs, which results in dysregulation of gene expression/translation and subsequently modulates cellular processes. We...

Variable directionality of gene expression changes across generations does not constitute negative evidence of epigenetic inheritance.

PubMed

Sharma, Abhay

2015-01-01

Transgenerational epigenetic inheritance in mammals has been controversial due to inherent difficulties in its experimental demonstration. A recent report has, however, opened a new front in the ongoing debate by claiming that endocrine disrupting chemicals, contrary to previous findings, do not cause effects across generations. This claim is based on the observation that gene expression changes induced by these chemicals in the exposed and unexposed generations are mainly in the opposite direction. This analysis shows that the pattern of gene expression reported in the two generations is not expected by chance and is suggestive of transmission across generations. A meta-analysis of diverse data sets related to endocrine disruptor-induced transgenerational gene expression alterations, including the data provided in the said report, further suggests that effects of endocrine disrupting chemicals persist in unexposed generations. Based on the prior evidence of phenotypic variability and gene expression alterations in opposite direction between generations, it is argued here that calling evidence of mismatched directionality in gene expression in experiments testing potential of environmental agents in inducing epigenetic inheritance of phenotypic traits as negative is untenable. This is expected to settle the newly raised doubts over epigenetic inheritance in mammals.

AhR-mediated gene expression in the developing mouse telencephalon.

PubMed

Gohlke, Julia M; Stockton, Pat S; Sieber, Stella; Foley, Julie; Portier, Christopher J

2009-11-01

We hypothesize that TCDD-induced developmental neurotoxicity is modulated through an AhR-dependent interaction with key regulatory neuronal differentiation pathways during telencephalon development. To test this hypothesis we examined global gene expression in both dorsal and ventral telencephalon tissues in E13.5 AhR-/- and wildtype mice exposed to TCDD or vehicle. Consistent with previous biochemical, pathological and behavioral studies, our results suggest TCDD initiated changes in gene expression in the developing telencephalon are primarily AhR-dependent, as no statistically significant gene expression changes are evident after TCDD exposure in AhR-/- mice. Based on a gene regulatory network for neuronal specification in the developing telencephalon, the present analysis suggests differentiation of GABAergic neurons in the ventral telencephalon is compromised in TCDD exposed and AhR-/- mice. In addition, our analysis suggests Sox11 may be directly regulated by AhR based on gene expression and comparative genomics analyses. In conclusion, this analysis supports the hypothesis that AhR has a specific role in the normal development of the telencephalon and provides a mechanistic framework for neurodevelopmental toxicity of chemicals that perturb AhR signaling.

Analysis of temporal gene expression profiles: clustering by simulated annealing and determining the optimal number of clusters.

PubMed

Lukashin, A V; Fuchs, R

2001-05-01

Cluster analysis of genome-wide expression data from DNA microarray hybridization studies has proved to be a useful tool for identifying biologically relevant groupings of genes and samples. In the present paper, we focus on several important issues related to clustering algorithms that have not yet been fully studied. We describe a simple and robust algorithm for the clustering of temporal gene expression profiles that is based on the simulated annealing procedure. In general, this algorithm guarantees to eventually find the globally optimal distribution of genes over clusters. We introduce an iterative scheme that serves to evaluate quantitatively the optimal number of clusters for each specific data set. The scheme is based on standard approaches used in regular statistical tests. The basic idea is to organize the search of the optimal number of clusters simultaneously with the optimization of the distribution of genes over clusters. The efficiency of the proposed algorithm has been evaluated by means of a reverse engineering experiment, that is, a situation in which the correct distribution of genes over clusters is known a priori. The employment of this statistically rigorous test has shown that our algorithm places greater than 90% genes into correct clusters. Finally, the algorithm has been tested on real gene expression data (expression changes during yeast cell cycle) for which the fundamental patterns of gene expression and the assignment of genes to clusters are well understood from numerous previous studies.

Comprehensive analysis of pathway or functionally related gene expression in the National Cancer Institute's anticancer screen.

PubMed

Huang, Ruili; Wallqvist, Anders; Covell, David G

2006-03-01

We have analyzed the level of gene coregulation, using gene expression patterns measured across the National Cancer Institute's 60 tumor cell panels (NCI(60)), in the context of predefined pathways or functional categories annotated by KEGG (Kyoto Encyclopedia of Genes and Genomes), BioCarta, and GO (Gene Ontology). Statistical methods were used to evaluate the level of gene expression coherence (coordinated expression) by comparing intra- and interpathway gene-gene correlations. Our results show that gene expression in pathways, or groups of functionally related genes, has a significantly higher level of coherence than that of a randomly selected set of genes. Transcriptional-level gene regulation appears to be on a "need to be" basis, such that pathways comprising genes encoding closely interacting proteins and pathways responsible for vital cellular processes or processes that are related to growth or proliferation, specifically in cancer cells, such as those engaged in genetic information processing, cell cycle, energy metabolism, and nucleotide metabolism, tend to be more modular (lower degree of gene sharing) and to have genes significantly more coherently expressed than most signaling and regular metabolic pathways. Hierarchical clustering of pathways based on their differential gene expression in the NCI(60) further revealed interesting interpathway communications or interactions indicative of a higher level of pathway regulation. The knowledge of the nature of gene expression regulation and biological pathways can be applied to understanding the mechanism by which small drug molecules interfere with biological systems.

Expression-based clustering of CAZyme-encoding genes of Aspergillus niger.

PubMed

Gruben, Birgit S; Mäkelä, Miia R; Kowalczyk, Joanna E; Zhou, Miaomiao; Benoit-Gelber, Isabelle; De Vries, Ronald P

2017-11-23

The Aspergillus niger genome contains a large repertoire of genes encoding carbohydrate active enzymes (CAZymes) that are targeted to plant polysaccharide degradation enabling A. niger to grow on a wide range of plant biomass substrates. Which genes need to be activated in certain environmental conditions depends on the composition of the available substrate. Previous studies have demonstrated the involvement of a number of transcriptional regulators in plant biomass degradation and have identified sets of target genes for each regulator. In this study, a broad transcriptional analysis was performed of the A. niger genes encoding (putative) plant polysaccharide degrading enzymes. Microarray data focusing on the initial response of A. niger to the presence of plant biomass related carbon sources were analyzed of a wild-type strain N402 that was grown on a large range of carbon sources and of the regulatory mutant strains ΔxlnR, ΔaraR, ΔamyR, ΔrhaR and ΔgalX that were grown on their specific inducing compounds. The cluster analysis of the expression data revealed several groups of co-regulated genes, which goes beyond the traditionally described co-regulated gene sets. Additional putative target genes of the selected regulators were identified, based on their expression profile. Notably, in several cases the expression profile puts questions on the function assignment of uncharacterized genes that was based on homology searches, highlighting the need for more extensive biochemical studies into the substrate specificity of enzymes encoded by these non-characterized genes. The data also revealed sets of genes that were upregulated in the regulatory mutants, suggesting interaction between the regulatory systems and a therefore even more complex overall regulatory network than has been reported so far. Expression profiling on a large number of substrates provides better insight in the complex regulatory systems that drive the conversion of plant biomass by fungi. In addition, the data provides additional evidence in favor of and against the similarity-based functions assigned to uncharacterized genes.

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

A transcriptome-based examination of blood group expression

PubMed Central

Noh, S.-J.; Lee, Y.T.; Byrnes, C.; Miller, J.L.

2011-01-01

Over the last two decades, red cell biologists witnessed a vast expansion of genetic-based information pertaining to blood group antigens and their carrier molecules. Genetic progress has led to a better comprehension of the associated antigens. To assist with studies concerning the integrated regulation and function of blood groups, transcript levels for each of the 36 associated genes were studied. Profiles using mRNA from directly sampled reticulocytes and cultured primary erythroblasts are summarized in this report. Transcriptome profiles suggest a highly regulated pattern of blood group gene expression during erythroid differentiation and ontogeny. Approximately one-third of the blood group carrier genes are transcribed in an erythroid-specific fashion. Low-level and indistinct expression was noted for most of the carbohydrate-associated genes. Methods are now being developed to further explore and manipulate expression of the blood group genes at all stages of human erythropoiesis. PMID:20685146

Bayesian median regression for temporal gene expression data

NASA Astrophysics Data System (ADS)

Yu, Keming; Vinciotti, Veronica; Liu, Xiaohui; 't Hoen, Peter A. C.

2007-09-01

Most of the existing methods for the identification of biologically interesting genes in a temporal expression profiling dataset do not fully exploit the temporal ordering in the dataset and are based on normality assumptions for the gene expression. In this paper, we introduce a Bayesian median regression model to detect genes whose temporal profile is significantly different across a number of biological conditions. The regression model is defined by a polynomial function where both time and condition effects as well as interactions between the two are included. MCMC-based inference returns the posterior distribution of the polynomial coefficients. From this a simple Bayes factor test is proposed to test for significance. The estimation of the median rather than the mean, and within a Bayesian framework, increases the robustness of the method compared to a Hotelling T2-test previously suggested. This is shown on simulated data and on muscular dystrophy gene expression data.

A Glycine Riboswitch in Streptococcus pyogenes Controls Expression of a Sodium:Alanine Symporter Family Protein Gene.

PubMed

Khani, Afsaneh; Popp, Nicole; Kreikemeyer, Bernd; Patenge, Nadja

2018-01-01

Regulatory RNAs play important roles in the control of bacterial gene expression. In this study, we investigated gene expression regulation by a putative glycine riboswitch located in the 5'-untranslated region of a sodium:alanine symporter family (SAF) protein gene in the group A Streptococcus pyogenes serotype M49 strain 591. Glycine-dependent gene expression mediated by riboswitch activity was studied using a luciferase reporter gene system. Maximal reporter gene expression was observed in the absence of glycine and in the presence of low glycine concentrations. Differences in glycine-dependent gene expression were not based on differential promoter activity. Expression of the SAF protein gene and the downstream putative cation efflux protein gene was investigated in wild-type bacteria by RT-qPCR transcript analyses. During growth in the presence of glycine (≥1 mM), expression of the genes were downregulated. Northern blot analyses revealed premature transcription termination in the presence of high glycine concentrations. Growth in the presence of 0.1 mM glycine led to the production of a full-length transcript. Furthermore, stability of the SAF protein gene transcript was drastically reduced in the presence of glycine. We conclude that the putative glycine riboswitch in S. pyogenes serotype M49 strain 591 represses expression of the SAF protein gene and the downstream putative cation efflux protein gene in the presence of high glycine concentrations. Sequence and secondary structure comparisons indicated that the streptococcal riboswitch belongs to the class of tandem aptamer glycine riboswitches.

MARQ: an online tool to mine GEO for experiments with similar or opposite gene expression signatures.

PubMed

Vazquez, Miguel; Nogales-Cadenas, Ruben; Arroyo, Javier; Botías, Pedro; García, Raul; Carazo, Jose M; Tirado, Francisco; Pascual-Montano, Alberto; Carmona-Saez, Pedro

2010-07-01

The enormous amount of data available in public gene expression repositories such as Gene Expression Omnibus (GEO) offers an inestimable resource to explore gene expression programs across several organisms and conditions. This information can be used to discover experiments that induce similar or opposite gene expression patterns to a given query, which in turn may lead to the discovery of new relationships among diseases, drugs or pathways, as well as the generation of new hypotheses. In this work, we present MARQ, a web-based application that allows researchers to compare a query set of genes, e.g. a set of over- and under-expressed genes, against a signature database built from GEO datasets for different organisms and platforms. MARQ offers an easy-to-use and integrated environment to mine GEO, in order to identify conditions that induce similar or opposite gene expression patterns to a given experimental condition. MARQ also includes additional functionalities for the exploration of the results, including a meta-analysis pipeline to find genes that are differentially expressed across different experiments. The application is freely available at http://marq.dacya.ucm.es.

Massive Collection of Full-Length Complementary DNA Clones and Microarray Analyses:. Keys to Rice Transcriptome Analysis

NASA Astrophysics Data System (ADS)

Kikuchi, Shoshi

2009-02-01

Completion of the high-precision genome sequence analysis of rice led to the collection of about 35,000 full-length cDNA clones and the determination of their complete sequences. Mapping of these full-length cDNA sequences has given us information on (1) the number of genes expressed in the rice genome; (2) the start and end positions and exon-intron structures of rice genes; (3) alternative transcripts; (4) possible encoded proteins; (5) non-protein-coding (np) RNAs; (6) the density of gene localization on the chromosome; (7) setting the parameters of gene prediction programs; and (8) the construction of a microarray system that monitors global gene expression. Manual curation for rice gene annotation by using mapping information on full-length cDNA and EST assemblies has revealed about 32,000 expressed genes in the rice genome. Analysis of major gene families, such as those encoding membrane transport proteins (pumps, ion channels, and secondary transporters), along with the evolution from bacteria to higher animals and plants, reveals how gene numbers have increased through adaptation to circumstances. Family-based gene annotation also gives us a new way of comparing organisms. Massive amounts of data on gene expression under many kinds of physiological conditions are being accumulated in rice oligoarrays (22K and 44K) based on full-length cDNA sequences. Cluster analyses of genes that have the same promoter cis-elements, that have similar expression profiles, or that encode enzymes in the same metabolic pathways or signal transduction cascades give us clues to understanding the networks of gene expression in rice. As a tool for that purpose, we recently developed "RiCES", a tool for searching for cis-elements in the promoter regions of clustered genes.

A detailed transcript-level probe annotation reveals alternative splicing based microarray platform differences

PubMed Central

Lee, Joseph C; Stiles, David; Lu, Jun; Cam, Margaret C

2007-01-01

Background Microarrays are a popular tool used in experiments to measure gene expression levels. Improving the reproducibility of microarray results produced by different chips from various manufacturers is important to create comparable and combinable experimental results. Alternative splicing has been cited as a possible cause of differences in expression measurements across platforms, though no study to this point has been conducted to show its influence in cross-platform differences. Results Using probe sequence data, a new microarray probe/transcript annotation was created based on the AceView Aug05 release that allowed for the categorization of genes based on their expression measurements' susceptibility to alternative splicing differences across microarray platforms. Examining gene expression data from multiple platforms in light of the new categorization, genes unsusceptible to alternative splicing differences showed higher signal agreement than those genes most susceptible to alternative splicing differences. The analysis gave rise to a different probe-level visualization method that can highlight probe differences according to transcript specificity. Conclusion The results highlight the need for detailed probe annotation at the transcriptome level. The presence of alternative splicing within a given sample can affect gene expression measurements and is a contributing factor to overall technical differences across platforms. PMID:17708771

Intra and Interspecific Variations of Gene Expression Levels in Yeast Are Largely Neutral: (Nei Lecture, SMBE 2016, Gold Coast).

PubMed

Yang, Jian-Rong; Maclean, Calum J; Park, Chungoo; Zhao, Huabin; Zhang, Jianzhi

2017-09-01

It is commonly, although not universally, accepted that most intra and interspecific genome sequence variations are more or less neutral, whereas a large fraction of organism-level phenotypic variations are adaptive. Gene expression levels are molecular phenotypes that bridge the gap between genotypes and corresponding organism-level phenotypes. Yet, it is unknown whether natural variations in gene expression levels are mostly neutral or adaptive. Here we address this fundamental question by genome-wide profiling and comparison of gene expression levels in nine yeast strains belonging to three closely related Saccharomyces species and originating from five different ecological environments. We find that the transcriptome-based clustering of the nine strains approximates the genome sequence-based phylogeny irrespective of their ecological environments. Remarkably, only ∼0.5% of genes exhibit similar expression levels among strains from a common ecological environment, no greater than that among strains with comparable phylogenetic relationships but different environments. These and other observations strongly suggest that most intra and interspecific variations in yeast gene expression levels result from the accumulation of random mutations rather than environmental adaptations. This finding has profound implications for understanding the driving force of gene expression evolution, genetic basis of phenotypic adaptation, and general role of stochasticity in evolution. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Gene expression profiling via LongSAGE in a non-model plant species: a case study in seeds of Brassica napus

PubMed Central

Obermeier, Christian; Hosseini, Bashir; Friedt, Wolfgang; Snowdon, Rod

2009-01-01

Background Serial analysis of gene expression (LongSAGE) was applied for gene expression profiling in seeds of oilseed rape (Brassica napus ssp. napus). The usefulness of this technique for detailed expression profiling in a non-model organism was demonstrated for the highly complex, neither fully sequenced nor annotated genome of B. napus by applying a tag-to-gene matching strategy based on Brassica ESTs and the annotated proteome of the closely related model crucifer A. thaliana. Results Transcripts from 3,094 genes were detected at two time-points of seed development, 23 days and 35 days after pollination (DAP). Differential expression showed a shift from gene expression involved in diverse developmental processes including cell proliferation and seed coat formation at 23 DAP to more focussed metabolic processes including storage protein accumulation and lipid deposition at 35 DAP. The most abundant transcripts at 23 DAP were coding for diverse protease inhibitor proteins and proteases, including cysteine proteases involved in seed coat formation and a number of lipid transfer proteins involved in embryo pattern formation. At 35 DAP, transcripts encoding napin, cruciferin and oleosin storage proteins were most abundant. Over both time-points, 18.6% of the detected genes were matched by Brassica ESTs identified by LongSAGE tags in antisense orientation. This suggests a strong involvement of antisense transcript expression in regulatory processes during B. napus seed development. Conclusion This study underlines the potential of transcript tagging approaches for gene expression profiling in Brassica crop species via EST matching to annotated A. thaliana genes. Limits of tag detection for low-abundance transcripts can today be overcome by ultra-high throughput sequencing approaches, so that tag-based gene expression profiling may soon become the method of choice for global expression profiling in non-model species. PMID:19575793

Reconstructing regulatory networks from the dynamic plasticity of gene expression by mutual information

PubMed Central

Wang, Jianxin; Chen, Bo; Wang, Yaqun; Wang, Ningtao; Garbey, Marc; Tran-Son-Tay, Roger; Berceli, Scott A.; Wu, Rongling

2013-01-01

The capacity of an organism to respond to its environment is facilitated by the environmentally induced alteration of gene and protein expression, i.e. expression plasticity. The reconstruction of gene regulatory networks based on expression plasticity can gain not only new insights into the causality of transcriptional and cellular processes but also the complex regulatory mechanisms that underlie biological function and adaptation. We describe an approach for network inference by integrating expression plasticity into Shannon’s mutual information. Beyond Pearson correlation, mutual information can capture non-linear dependencies and topology sparseness. The approach measures the network of dependencies of genes expressed in different environments, allowing the environment-induced plasticity of gene dependencies to be tested in unprecedented details. The approach is also able to characterize the extent to which the same genes trigger different amounts of expression in response to environmental changes. We demonstrated the usefulness of this approach through analysing gene expression data from a rabbit vein graft study that includes two distinct blood flow environments. The proposed approach provides a powerful tool for the modelling and analysis of dynamic regulatory networks using gene expression data from distinct environments. PMID:23470995

Using RNA-seq data to select reference genes for normalizing gene expression in apple roots.

PubMed

Zhou, Zhe; Cong, Peihua; Tian, Yi; Zhu, Yanmin

2017-01-01

Gene expression in apple roots in response to various stress conditions is a less-explored research subject. Reliable reference genes for normalizing quantitative gene expression data have not been carefully investigated. In this study, the suitability of a set of 15 apple genes were evaluated for their potential use as reliable reference genes. These genes were selected based on their low variance of gene expression in apple root tissues from a recent RNA-seq data set, and a few previously reported apple reference genes for other tissue types. Four methods, Delta Ct, geNorm, NormFinder and BestKeeper, were used to evaluate their stability in apple root tissues of various genotypes and under different experimental conditions. A small panel of stably expressed genes, MDP0000095375, MDP0000147424, MDP0000233640, MDP0000326399 and MDP0000173025 were recommended for normalizing quantitative gene expression data in apple roots under various abiotic or biotic stresses. When the most stable and least stable reference genes were used for data normalization, significant differences were observed on the expression patterns of two target genes, MdLecRLK5 (MDP0000228426, a gene encoding a lectin receptor like kinase) and MdMAPK3 (MDP0000187103, a gene encoding a mitogen-activated protein kinase). Our data also indicated that for those carefully validated reference genes, a single reference gene is sufficient for reliable normalization of the quantitative gene expression. Depending on the experimental conditions, the most suitable reference genes can be specific to the sample of interest for more reliable RT-qPCR data normalization.

Using RNA-seq data to select reference genes for normalizing gene expression in apple roots

PubMed Central

Zhou, Zhe; Cong, Peihua; Tian, Yi

2017-01-01

Gene expression in apple roots in response to various stress conditions is a less-explored research subject. Reliable reference genes for normalizing quantitative gene expression data have not been carefully investigated. In this study, the suitability of a set of 15 apple genes were evaluated for their potential use as reliable reference genes. These genes were selected based on their low variance of gene expression in apple root tissues from a recent RNA-seq data set, and a few previously reported apple reference genes for other tissue types. Four methods, Delta Ct, geNorm, NormFinder and BestKeeper, were used to evaluate their stability in apple root tissues of various genotypes and under different experimental conditions. A small panel of stably expressed genes, MDP0000095375, MDP0000147424, MDP0000233640, MDP0000326399 and MDP0000173025 were recommended for normalizing quantitative gene expression data in apple roots under various abiotic or biotic stresses. When the most stable and least stable reference genes were used for data normalization, significant differences were observed on the expression patterns of two target genes, MdLecRLK5 (MDP0000228426, a gene encoding a lectin receptor like kinase) and MdMAPK3 (MDP0000187103, a gene encoding a mitogen-activated protein kinase). Our data also indicated that for those carefully validated reference genes, a single reference gene is sufficient for reliable normalization of the quantitative gene expression. Depending on the experimental conditions, the most suitable reference genes can be specific to the sample of interest for more reliable RT-qPCR data normalization. PMID:28934340

A cis-regulatory logic simulator.

PubMed

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

2007-07-27

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

Mining microarray data at NCBI's Gene Expression Omnibus (GEO)*.

PubMed

Barrett, Tanya; Edgar, Ron

2006-01-01

The Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) has emerged as the leading fully public repository for gene expression data. This chapter describes how to use Web-based interfaces, applications, and graphics to effectively explore, visualize, and interpret the hundreds of microarray studies and millions of gene expression patterns stored in GEO. Data can be examined from both experiment-centric and gene-centric perspectives using user-friendly tools that do not require specialized expertise in microarray analysis or time-consuming download of massive data sets. The GEO database is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.

Non-invasive imaging using reporter genes altering cellular water permeability

NASA Astrophysics Data System (ADS)

Mukherjee, Arnab; Wu, Di; Davis, Hunter C.; Shapiro, Mikhail G.

2016-12-01

Non-invasive imaging of gene expression in live, optically opaque animals is important for multiple applications, including monitoring of genetic circuits and tracking of cell-based therapeutics. Magnetic resonance imaging (MRI) could enable such monitoring with high spatiotemporal resolution. However, existing MRI reporter genes based on metalloproteins or chemical exchange probes are limited by their reliance on metals or relatively low sensitivity. Here we introduce a new class of MRI reporters based on the human water channel aquaporin 1. We show that aquaporin overexpression produces contrast in diffusion-weighted MRI by increasing tissue water diffusivity without affecting viability. Low aquaporin levels or mixed populations comprising as few as 10% aquaporin-expressing cells are sufficient to produce MRI contrast. We characterize this new contrast mechanism through experiments and simulations, and demonstrate its utility in vivo by imaging gene expression in tumours. Our results establish an alternative class of sensitive, metal-free reporter genes for non-invasive imaging.

Benzaldehyde Schiff bases regulation to the metabolism, hemolysis, and virulence genes expression in vitro and their structure-microbicidal activity relationship.

PubMed

Xia, Lei; Xia, Yu-Fen; Huang, Li-Rong; Xiao, Xiao; Lou, Hua-Yong; Liu, Tang-Jingjun; Pan, Wei-Dong; Luo, Heng

2015-06-05

There is an urgent need to develop new antibacterial agents because of multidrug resistance by bacteria and fungi. Schiff bases (aldehyde or ketone-like compounds) exhibit intense antibacterial characteristics, and are therefore, promising candidates as antibacterial agents. To investigate the mechanism of action of newly designed benzaldehyde Schiff bases, a series of high-yielding benzaldehyde Schiff bases were synthesized, and their structures were determined by NMR and MS spectra data. The structure-microbicidal activity relationship of derivatives was investigated, and the antibacterial mechanisms were investigated by gene assays for the expression of functional genes in vitro using Escherichia coli, Staphylococcus aureus, and Bacillus subtilis. The active compounds were selective for certain active groups. The polar substitution of the R2 group of the amino acids in the Schiff bases, affected the antibacterial activity against E. coli and S. aureus; specific active group at the R3 or R4 groups of the acylhydrazone Schiff bases could improve their inhibitory activity against these three tested organisms. The antibacterial mechanism of the active benzaldehyde Schiff bases appeared to regulate the expression of metabolism-associated genes in E. coli, hemolysis-associated genes in B. subtilis, and key virulence genes in S. aureus. Some benzaldehyde Schiff bases were bactericidal to all the three strains and appeared to regulate gene expression associated with metabolism, hemolysis, and virulence, in vitro. The newly designed benzaldehyde Schiff bases possessed unique antibacterial activity and might be potentially useful for prophylactic or therapeutic intervention of bacterial infections. Copyright © 2015. Published by Elsevier Masson SAS.

Schistosoma mansoni: resistant specific infection-induced gene expression in Biomphalaria glabrata identified by fluorescent-based differential display.

PubMed

Lockyer, Anne E; Noble, Leslie R; Rollinson, David; Jones, Catherine S

2004-01-01

The freshwater tropical snail Biomphalaria glabrata is an intermediate host for Schistosoma mansoni, the causative agent of human intestinal schistosomiasis, and strains differ in their susceptibility to parasite infection. Changes in gene expression in response to parasite infection have been simultaneously examined in a susceptible strain (NHM1742) and a resistant strain (NHM1981) using a newly developed fluorescent-based differential display method. Such RNA profiling techniques allow the examination of changes in gene expression in response to parasite infection, without requiring previous sequence knowledge, or selecting candidate genes that may be involved in the complex neuroendocrine or defence systems of the snail. Thus, novel genes may be identified. Ten transcripts were initially identified, present only in the profiles derived from snails of the resistant strain when exposed to infection. The differential expression of five of these genes, including HSP70 and several novel transcripts with one containing at least two globin-like domains, has been confirmed by semi-quantitative RT-PCR.

Expression analysis in response to drought stress in soybean: Shedding light on the regulation of metabolic pathway genes.

PubMed

Guimarães-Dias, Fábia; Neves-Borges, Anna Cristina; Viana, Antonio Americo Barbosa; Mesquita, Rosilene Oliveira; Romano, Eduardo; de Fátima Grossi-de-Sá, Maria; Nepomuceno, Alexandre Lima; Loureiro, Marcelo Ehlers; Alves-Ferreira, Márcio

2012-06-01

Metabolomics analysis of wild type Arabidopsis thaliana plants, under control and drought stress conditions revealed several metabolic pathways that are induced under water deficit. The metabolic response to drought stress is also associated with ABA dependent and independent pathways, allowing a better understanding of the molecular mechanisms in this model plant. Through combining an in silico approach and gene expression analysis by quantitative real-time PCR, the present work aims at identifying genes of soybean metabolic pathways potentially associated with water deficit. Digital expression patterns of Arabidopsis genes, which were selected based on the basis of literature reports, were evaluated under drought stress condition by Genevestigator. Genes that showed strong induction under drought stress were selected and used as bait to identify orthologs in the soybean genome. This allowed us to select 354 genes of putative soybean orthologs of 79 Arabidopsis genes belonging to 38 distinct metabolic pathways. The expression pattern of the selected genes was verified in the subtractive libraries available in the GENOSOJA project. Subsequently, 13 genes from different metabolic pathways were selected for validation by qPCR experiments. The expression of six genes was validated in plants undergoing drought stress in both pot-based and hydroponic cultivation systems. The results suggest that the metabolic response to drought stress is conserved in Arabidopsis and soybean plants.

Rapid construction of a Bacterial Artificial Chromosomal (BAC) expression vector using designer DNA fragments.

PubMed

Chen, Chao; Zhao, Xinqing; Jin, Yingyu; Zhao, Zongbao Kent; Suh, Joo-Won

2014-11-01

Bacterial artificial chromosomal (BAC) vectors are increasingly being used in cloning large DNA fragments containing complex biosynthetic pathways to facilitate heterologous production of microbial metabolites for drug development. To express inserted genes using Streptomyces species as the production hosts, an integration expression cassette is required to be inserted into the BAC vector, which includes genetic elements encoding a phage-specific attachment site, an integrase, an origin of transfer, a selection marker and a promoter. Due to the large sizes of DNA inserted into the BAC vectors, it is normally inefficient and time-consuming to assemble these fragments by routine PCR amplifications and restriction-ligations. Here we present a rapid method to insert fragments to construct BAC-based expression vectors. A DNA fragment of about 130 bp was designed, which contains upstream and downstream homologous sequences of both BAC vector and pIB139 plasmid carrying the whole integration expression cassette. In-Fusion cloning was performed using the designer DNA fragment to modify pIB139, followed by λ-RED-mediated recombination to obtain the BAC-based expression vector. We demonstrated the effectiveness of this method by rapid construction of a BAC-based expression vector with an insert of about 120 kb that contains the entire gene cluster for biosynthesis of immunosuppressant FK506. The empty BAC-based expression vector constructed in this study can be conveniently used for construction of BAC libraries using either microbial pure culture or environmental DNA, and the selected BAC clones can be directly used for heterologous expression. Alternatively, if a BAC library has already been constructed using a commercial BAC vector, the selected BAC vectors can be manipulated using the method described here to get the BAC-based expression vectors with desired gene clusters for heterologous expression. The rapid construction of a BAC-based expression vector facilitates heterologous expression of large gene clusters for drug discovery. Copyright © 2014 Elsevier Inc. All rights reserved.

Statistical indicators of collective behavior and functional clusters in gene networks of yeast

NASA Astrophysics Data System (ADS)

Živković, J.; Tadić, B.; Wick, N.; Thurner, S.

2006-03-01

We analyze gene expression time-series data of yeast (S. cerevisiae) measured along two full cell-cycles. We quantify these data by using q-exponentials, gene expression ranking and a temporal mean-variance analysis. We construct gene interaction networks based on correlation coefficients and study the formation of the corresponding giant components and minimum spanning trees. By coloring genes according to their cell function we find functional clusters in the correlation networks and functional branches in the associated trees. Our results suggest that a percolation point of functional clusters can be identified on these gene expression correlation networks.

A Dual-Color Reporter Assay of Cohesin-Mediated Gene Regulation in Budding Yeast Meiosis.

PubMed

Fan, Jinbo; Jin, Hui; Yu, Hong-Guo

2017-01-01

In this chapter, we describe a quantitative fluorescence-based assay of gene expression using the ratio of the reporter green fluorescence protein (GFP) to the internal red fluorescence protein (RFP) control. With this dual-color heterologous reporter assay, we have revealed cohesin-regulated genes and discovered a cis-acting DNA element, the Ty1-LTR, which interacts with cohesin and regulates gene expression during yeast meiosis. The method described here provides an effective cytological approach for quantitative analysis of global gene expression in budding yeast meiosis.

RNA-Seq Analysis of Developing Pecan (Carya illinoinensis) Embryos Reveals Parallel Expression Patterns among Allergen and Lipid Metabolism Genes.

PubMed

Mattison, Christopher P; Rai, Ruhi; Settlage, Robert E; Hinchliffe, Doug J; Madison, Crista; Bland, John M; Brashear, Suzanne; Graham, Charles J; Tarver, Matthew R; Florane, Christopher; Bechtel, Peter J

2017-02-22

The pecan nut is a nutrient-rich part of a healthy diet full of beneficial fatty acids and antioxidants, but can also cause allergic reactions in people suffering from food allergy to the nuts. The transcriptome of a developing pecan nut was characterized to identify the gene expression occurring during the process of nut development and to highlight those genes involved in fatty acid metabolism and those that commonly act as food allergens. Pecan samples were collected at several time points during the embryo development process including the water, gel, dough, and mature nut stages. Library preparation and sequencing were performed using Illumina-based mRNA HiSeq with RNA from four time points during the growing season during August and September 2012. Sequence analysis with Trinotate software following the Trinity protocol identified 133,000 unigenes with 52,267 named transcripts and 45,882 annotated genes. A total of 27,312 genes were defined by GO annotation. Gene expression clustering analysis identified 12 different gene expression profiles, each containing a number of genes. Three pecan seed storage proteins that commonly act as allergens, Car i 1, Car i 2, and Car i 4, were significantly up-regulated during the time course. Up-regulated fatty acid metabolism genes that were identified included acyl-[ACP] desaturase and omega-6 desaturase genes involved in oleic and linoleic acid metabolism. Notably, a few of the up-regulated acyl-[ACP] desaturase and omega-6 desaturase genes that were identified have expression patterns similar to the allergen genes based upon gene expression clustering and qPCR analysis. These findings suggest the possibility of coordinated accumulation of lipids and allergens during pecan nut embryogenesis.

Solexa-Sequencing Based Transcriptome Study of Plaice Skin Phenotype in Rex Rabbits (Oryctolagus cuniculus)

PubMed Central

Pan, Lei; Liu, Yan; Wei, Qiang; Xiao, Chenwen; Ji, Quanan; Bao, Guolian; Wu, Xinsheng

2015-01-01

Background Fur is an important genetically-determined characteristic of domestic rabbits; rabbit furs are of great economic value. We used the Solexa sequencing technology to assess gene expression in skin tissues from full-sib Rex rabbits of different phenotypes in order to explore the molecular mechanisms associated with fur determination. Methodology/Principal Findings Transcriptome analysis included de novo assembly, gene function identification, and gene function classification and enrichment. We obtained 74,032,912 and 71,126,891 short reads of 100 nt, which were assembled into 377,618 unique sequences by Trinity strategy (N50=680 nt). Based on BLAST results with known proteins, 50,228 sequences were identified at a cut-off E-value ≥ 10-5. Using Blast to Gene Ontology (GO), Clusters of Orthologous Groups (KOG) and Kyoto Encyclopedia of Genes and Genomes (KEGG), we obtained several genes with important protein functions. A total of 308 differentially expressed genes were obtained by transcriptome analysis of plaice and un-plaice phenotype animals; 209 additional differentially expressed genes were not found in any database. These genes included 49 that were only expressed in plaice skin rabbits. The novel genes may play important roles during skin growth and development. In addition, 99 known differentially expressed genes were assigned to PI3K-Akt signaling, focal adhesion, and ECM-receptor interactin, among others. Growth factors play a role in skin growth and development by regulating these signaling pathways. We confirmed the altered expression levels of seven target genes by qRT-PCR. And chosen a key gene for SNP to found the differentially between plaice and un-plaice phenotypes rabbit. Conclusions/Significance The rabbit transcriptome profiling data provide new insights in understanding the molecular mechanisms underlying rabbit skin growth and development. PMID:25955442

A novel gene expression-based prognostic scoring system to predict survival in gastric cancer

DOE PAGES

Wang, Pin; Wang, Yunshan; Hang, Bo; ...

2016-07-11

Analysis of gene expression patterns in gastric cancer (GC) can help to identify a comprehensive panel of gene biomarkers for predicting clinical outcomes and to discover potential new therapeutic targets. Here, a multi-step bioinformatics analytic approach was developed to establish a novel prognostic scoring system for GC. We first identified 276 genes that were robustly differentially expressed between normal and GC tissues, of which, 249 were found to be significantly associated with overall survival (OS) by univariate Cox regression analysis. The biological functions of 249 genes are related to cell cycle, RNA/ncRNA process, acetylation and extracellular matrix organization. A networkmore » was generated for view of the gene expression architecture of 249 genes in 265 GCs. Finally, we applied a canonical discriminant analysis approach to identify a 53-gene signature and a prognostic scoring system was established based on a canonical discriminant function of 53 genes. The prognostic scores strongly predicted patients with GC to have either a poor or good OS. Our study raises the prospect that the practicality of GC patient prognosis can be assessed by this prognostic scoring system.« less

Principal Angle Enrichment Analysis (PAEA): Dimensionally Reduced Multivariate Gene Set Enrichment Analysis Tool

PubMed Central

Clark, Neil R.; Szymkiewicz, Maciej; Wang, Zichen; Monteiro, Caroline D.; Jones, Matthew R.; Ma’ayan, Avi

2016-01-01

Gene set analysis of differential expression, which identifies collectively differentially expressed gene sets, has become an important tool for biology. The power of this approach lies in its reduction of the dimensionality of the statistical problem and its incorporation of biological interpretation by construction. Many approaches to gene set analysis have been proposed, but benchmarking their performance in the setting of real biological data is difficult due to the lack of a gold standard. In a previously published work we proposed a geometrical approach to differential expression which performed highly in benchmarking tests and compared well to the most popular methods of differential gene expression. As reported, this approach has a natural extension to gene set analysis which we call Principal Angle Enrichment Analysis (PAEA). PAEA employs dimensionality reduction and a multivariate approach for gene set enrichment analysis. However, the performance of this method has not been assessed nor its implementation as a web-based tool. Here we describe new benchmarking protocols for gene set analysis methods and find that PAEA performs highly. The PAEA method is implemented as a user-friendly web-based tool, which contains 70 gene set libraries and is freely available to the community. PMID:26848405

Principal Angle Enrichment Analysis (PAEA): Dimensionally Reduced Multivariate Gene Set Enrichment Analysis Tool.

PubMed

Clark, Neil R; Szymkiewicz, Maciej; Wang, Zichen; Monteiro, Caroline D; Jones, Matthew R; Ma'ayan, Avi

2015-11-01

Gene set analysis of differential expression, which identifies collectively differentially expressed gene sets, has become an important tool for biology. The power of this approach lies in its reduction of the dimensionality of the statistical problem and its incorporation of biological interpretation by construction. Many approaches to gene set analysis have been proposed, but benchmarking their performance in the setting of real biological data is difficult due to the lack of a gold standard. In a previously published work we proposed a geometrical approach to differential expression which performed highly in benchmarking tests and compared well to the most popular methods of differential gene expression. As reported, this approach has a natural extension to gene set analysis which we call Principal Angle Enrichment Analysis (PAEA). PAEA employs dimensionality reduction and a multivariate approach for gene set enrichment analysis. However, the performance of this method has not been assessed nor its implementation as a web-based tool. Here we describe new benchmarking protocols for gene set analysis methods and find that PAEA performs highly. The PAEA method is implemented as a user-friendly web-based tool, which contains 70 gene set libraries and is freely available to the community.

A novel gene expression-based prognostic scoring system to predict survival in gastric cancer

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

Wang, Pin; Wang, Yunshan; Hang, Bo

Analysis of gene expression patterns in gastric cancer (GC) can help to identify a comprehensive panel of gene biomarkers for predicting clinical outcomes and to discover potential new therapeutic targets. Here, a multi-step bioinformatics analytic approach was developed to establish a novel prognostic scoring system for GC. We first identified 276 genes that were robustly differentially expressed between normal and GC tissues, of which, 249 were found to be significantly associated with overall survival (OS) by univariate Cox regression analysis. The biological functions of 249 genes are related to cell cycle, RNA/ncRNA process, acetylation and extracellular matrix organization. A networkmore » was generated for view of the gene expression architecture of 249 genes in 265 GCs. Finally, we applied a canonical discriminant analysis approach to identify a 53-gene signature and a prognostic scoring system was established based on a canonical discriminant function of 53 genes. The prognostic scores strongly predicted patients with GC to have either a poor or good OS. Our study raises the prospect that the practicality of GC patient prognosis can be assessed by this prognostic scoring system.« less

Genome-Wide Identification and Transcriptome-Based Expression Profiling of the Sox Gene Family in the Nile Tilapia (Oreochromis niloticus)

PubMed Central

Wei, Ling; Yang, Chao; Tao, Wenjing; Wang, Deshou

2016-01-01

The Sox transcription factor family is characterized with the presence of a Sry-related high-mobility group (HMG) box and plays important roles in various biological processes in animals, including sex determination and differentiation, and the development of multiple organs. In this study, 27 Sox genes were identified in the genome of the Nile tilapia (Oreochromis niloticus), and were classified into seven groups. The members of each group of the tilapia Sox genes exhibited a relatively conserved exon-intron structure. Comparative analysis showed that the Sox gene family has undergone an expansion in tilapia and other teleost fishes following their whole genome duplication, and group K only exists in teleosts. Transcriptome-based analysis demonstrated that most of the tilapia Sox genes presented stage-specific and/or sex-dimorphic expressions during gonadal development, and six of the group B Sox genes were specifically expressed in the adult brain. Our results provide a better understanding of gene structure and spatio-temporal expression of the Sox gene family in tilapia, and will be useful for further deciphering the roles of the Sox genes during sex determination and gonadal development in teleosts. PMID:26907269

Genome-Wide Identification and Transcriptome-Based Expression Profiling of the Sox Gene Family in the Nile Tilapia (Oreochromis niloticus).

PubMed

Wei, Ling; Yang, Chao; Tao, Wenjing; Wang, Deshou

2016-02-23

The Sox transcription factor family is characterized with the presence of a Sry-related high-mobility group (HMG) box and plays important roles in various biological processes in animals, including sex determination and differentiation, and the development of multiple organs. In this study, 27 Sox genes were identified in the genome of the Nile tilapia (Oreochromis niloticus), and were classified into seven groups. The members of each group of the tilapia Sox genes exhibited a relatively conserved exon-intron structure. Comparative analysis showed that the Sox gene family has undergone an expansion in tilapia and other teleost fishes following their whole genome duplication, and group K only exists in teleosts. Transcriptome-based analysis demonstrated that most of the tilapia Sox genes presented stage-specific and/or sex-dimorphic expressions during gonadal development, and six of the group B Sox genes were specifically expressed in the adult brain. Our results provide a better understanding of gene structure and spatio-temporal expression of the Sox gene family in tilapia, and will be useful for further deciphering the roles of the Sox genes during sex determination and gonadal development in teleosts.

Selection and validation of reference genes for gene expression analysis in apomictic and sexual Cenchrus ciliaris

PubMed Central

2013-01-01

Background Apomixis is a naturally occurring asexual mode of seed reproduction resulting in offspring genetically identical to the maternal plant. Identifying differential gene expression patterns between apomictic and sexual plants is valuable to help deconstruct the trait. Quantitative RT-PCR (qRT-PCR) is a popular method for analyzing gene expression. Normalizing gene expression data using proper reference genes which show stable expression under investigated conditions is critical in qRT-PCR analysis. We used qRT-PCR to validate expression and stability of six potential reference genes (EF1alpha, EIF4A, UBCE, GAPDH, ACT2 and TUBA) in vegetative and reproductive tissues of B-2S and B-12-9 accessions of C. ciliaris. Findings Among tissue types evaluated, EF1alpha showed the highest level of expression while TUBA showed the lowest. When all tissue types were evaluated and compared between genotypes, EIF4A was the most stable reference gene. Gene expression stability for specific ovary stages of B-2S and B-12-9 was also determined. Except for TUBA, all other tested reference genes could be used for any stage-specific ovary tissue normalization, irrespective of the mode of reproduction. Conclusion Our gene expression stability assay using six reference genes, in sexual and apomictic accessions of C. ciliaris, suggests that EIF4A is the most stable gene across all tissue types analyzed. All other tested reference genes, with the exception of TUBA, could be used for gene expression comparison studies between sexual and apomictic ovaries over multiple developmental stages. This reference gene validation data in C. ciliaris will serve as an important base for future apomixis-related transcriptome data validation. PMID:24083672

Problem-Based Test: The Effect of Fibroblast Growth Factor on Gene Expression

ERIC Educational Resources Information Center

Szeberenyi, Jozsef

2011-01-01

This paper shows the results of an experiment in which the effects of fibroblast growth factor (FGF), actinomycin D (Act D; an inhibitor of transcription), and cycloheximide (CHX; an inhibitor of translation) were studied on the expression of two genes: a gene called "Fnk" and the gene coding for glyceraldehyde-3-phosphate dehydrogenase (GAPDH).…

Adaptation and evolution of deep-sea scale worms (Annelida: Polynoidae): insights from transcriptome comparison with a shallow-water species

NASA Astrophysics Data System (ADS)

Zhang, Yanjie; Sun, Jin; Chen, Chong; Watanabe, Hiromi K.; Feng, Dong; Zhang, Yu; Chiu, Jill M. Y.; Qian, Pei-Yuan; Qiu, Jian-Wen

2017-04-01

Polynoid scale worms (Polynoidae, Annelida) invaded deep-sea chemosynthesis-based ecosystems approximately 60 million years ago, but little is known about their genetic adaptation to the extreme deep-sea environment. In this study, we reported the first two transcriptomes of deep-sea polynoids (Branchipolynoe pettiboneae, Lepidonotopodium sp.) and compared them with the transcriptome of a shallow-water polynoid (Harmothoe imbricata). We determined codon and amino acid usage, positive selected genes, highly expressed genes and putative duplicated genes. Transcriptome assembly produced 98,806 to 225,709 contigs in the three species. There were more positively charged amino acids (i.e., histidine and arginine) and less negatively charged amino acids (i.e., aspartic acid and glutamic acid) in the deep-sea species. There were 120 genes showing clear evidence of positive selection. Among the 10% most highly expressed genes, there were more hemoglobin genes with high expression levels in both deep-sea species. The duplicated genes related to DNA recombination and metabolism, and gene expression were only enriched in deep-sea species. Deep-sea scale worms adopted two strategies of adaptation to hypoxia in the chemosynthesis-based habitats (i.e., rapid evolution of tetra-domain hemoglobin in Branchipolynoe or high expression of single-domain hemoglobin in Lepidonotopodium sp.).

Adaptation and evolution of deep-sea scale worms (Annelida: Polynoidae): insights from transcriptome comparison with a shallow-water species

PubMed Central

Zhang, Yanjie; Sun, Jin; Chen, Chong; Watanabe, Hiromi K.; Feng, Dong; Zhang, Yu; Chiu, Jill M.Y.; Qian, Pei-Yuan; Qiu, Jian-Wen

2017-01-01

Polynoid scale worms (Polynoidae, Annelida) invaded deep-sea chemosynthesis-based ecosystems approximately 60 million years ago, but little is known about their genetic adaptation to the extreme deep-sea environment. In this study, we reported the first two transcriptomes of deep-sea polynoids (Branchipolynoe pettiboneae, Lepidonotopodium sp.) and compared them with the transcriptome of a shallow-water polynoid (Harmothoe imbricata). We determined codon and amino acid usage, positive selected genes, highly expressed genes and putative duplicated genes. Transcriptome assembly produced 98,806 to 225,709 contigs in the three species. There were more positively charged amino acids (i.e., histidine and arginine) and less negatively charged amino acids (i.e., aspartic acid and glutamic acid) in the deep-sea species. There were 120 genes showing clear evidence of positive selection. Among the 10% most highly expressed genes, there were more hemoglobin genes with high expression levels in both deep-sea species. The duplicated genes related to DNA recombination and metabolism, and gene expression were only enriched in deep-sea species. Deep-sea scale worms adopted two strategies of adaptation to hypoxia in the chemosynthesis-based habitats (i.e., rapid evolution of tetra-domain hemoglobin in Branchipolynoe or high expression of single-domain hemoglobin in Lepidonotopodium sp.). PMID:28397791

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.

Selection and Validation of Reference Genes for Quantitative Real-Time PCR in Buckwheat (Fagopyrum esculentum) Based on Transcriptome Sequence Data

PubMed Central

Demidenko, Natalia V.; Logacheva, Maria D.; Penin, Aleksey A.

2011-01-01

Quantitative reverse transcription PCR (qRT-PCR) is one of the most precise and widely used methods of gene expression analysis. A necessary prerequisite of exact and reliable data is the accurate choice of reference genes. We studied the expression stability of potential reference genes in common buckwheat (Fagopyrum esculentum) in order to find the optimal reference for gene expression analysis in this economically important crop. Recently sequenced buckwheat floral transcriptome was used as source of sequence information. Expression stability of eight candidate reference genes was assessed in different plant structures (leaves and inflorescences at two stages of development and fruits). These genes are the orthologs of Arabidopsis genes identified as stable in a genome-wide survey gene of expression stability and a traditionally used housekeeping gene GAPDH. Three software applications – geNorm, NormFinder and BestKeeper - were used to estimate expression stability and provided congruent results. The orthologs of AT4G33380 (expressed protein of unknown function, Expressed1), AT2G28390 (SAND family protein, SAND) and AT5G46630 (clathrin adapter complex subunit family protein, CACS) are revealed as the most stable. We recommend using the combination of Expressed1, SAND and CACS for the normalization of gene expression data in studies on buckwheat using qRT-PCR. These genes are listed among five the most stably expressed in Arabidopsis that emphasizes utility of the studies on model plants as a framework for other species. PMID:21589908

Medium-throughput processing of whole mount in situ hybridisation experiments into gene expression domains.

PubMed

Crombach, Anton; Cicin-Sain, Damjan; Wotton, Karl R; Jaeger, Johannes

2012-01-01

Understanding the function and evolution of developmental regulatory networks requires the characterisation and quantification of spatio-temporal gene expression patterns across a range of systems and species. However, most high-throughput methods to measure the dynamics of gene expression do not preserve the detailed spatial information needed in this context. For this reason, quantification methods based on image bioinformatics have become increasingly important over the past few years. Most available approaches in this field either focus on the detailed and accurate quantification of a small set of gene expression patterns, or attempt high-throughput analysis of spatial expression through binary pattern extraction and large-scale analysis of the resulting datasets. Here we present a robust, "medium-throughput" pipeline to process in situ hybridisation patterns from embryos of different species of flies. It bridges the gap between high-resolution, and high-throughput image processing methods, enabling us to quantify graded expression patterns along the antero-posterior axis of the embryo in an efficient and straightforward manner. Our method is based on a robust enzymatic (colorimetric) in situ hybridisation protocol and rapid data acquisition through wide-field microscopy. Data processing consists of image segmentation, profile extraction, and determination of expression domain boundary positions using a spline approximation. It results in sets of measured boundaries sorted by gene and developmental time point, which are analysed in terms of expression variability or spatio-temporal dynamics. Our method yields integrated time series of spatial gene expression, which can be used to reverse-engineer developmental gene regulatory networks across species. It is easily adaptable to other processes and species, enabling the in silico reconstitution of gene regulatory networks in a wide range of developmental contexts.

Using rabies virus vaccine strain SRV9 as viral vector to express exogenous gene.

PubMed

Wang, Hualei; Jin, Hongli; Feng, Na; Zheng, Xuexing; Li, Ling; Qi, Yinglin; Liang, Meng; Zhao, Yongkun; Wang, Tiecheng; Gao, Yuwei; Tu, Changchun; Jin, Ningyi; Yang, Songtao; Xia, Xianzhu

2015-04-01

Rabies virus (RABV) can cause a fatal neurological disease in human and animals, and vaccines were generally applied for the immunoprophylaxis of rabies. Here, a recombinant viral vector carrying the exogenous gene expression component between phosphoprotein (P) and matrix protein (M) genes of RABV was constructed based on the vaccine strain SRV9 used in China. To develop a reverse genetic system, the full-length cDNA plasmids of SRV9 were constructed using the eukaryotic expression vector pCI or pcDNA3.1(+). However, recovery efficiency based on the pcDNA3.1 vector was significantly higher than that of the pCI vector. The exogenous gene expression component PE-PS-BsiWI-PmeI or PS-BsiWI-PmeI-PE was introduced in different locations between the P and M genes of SRV9. When the enhanced green fluorescent protein (eGFP) was used as a reporter gene, both locations could rescue recombinant RABV (rRABV) expressing eGFP with high efficiency. Characterization of rRABV expressing eGFP in vitro revealed that its growth was similar to that of the parental virus. Animal experiments showed that rRABV expressing eGFP could replicate and express eGFP in the brains of suckling mice. Furthermore, rRABV of SRV9 was nonpathogenic for 3-week-old mice and could be cleared from the central nervous system at 5 days post-inoculation. Our results showed that the recombinant SRV9 virus could be used as a useful viral vector for exogenous gene expression.

Inhibition of histone deacetylation and DNA methylation improves gene expression mediated by the adeno-associated virus/phage in cancer cells.

PubMed

Kia, Azadeh; Yata, Teerapong; Hajji, Nabil; Hajitou, Amin

2013-10-22

Bacteriophage (phage), viruses that infect bacteria only, have become promising vectors for targeted systemic delivery of genes to cancer, although, with poor efficiency. We previously designed an improved phage vector by incorporating cis genetic elements of adeno-associated virus (AAV). This novel AAV/phage hybrid (AAVP) specifically targeted systemic delivery of therapeutic genes into tumors. To advance the AAVP vector, we recently introduced the stress-inducible Grp78 tumor specific promoter and found that this dual tumor-targeted AAVP provides persistent gene expression, over time, in cancer cells compared to silenced gene expression from the CMV promoter in the parental AAVP. Herein, we investigated the effect of histone deacetylation and DNA methylation on AAVP-mediated gene expression in cancer cells and explored the effect of cell confluence state on AAVP gene expression efficacy. Using a combination of AAVP expressing the GFP reporter gene, flow cytometry, inhibitors of histone deacetylation, and DNA methylation, we have demonstrated that histone deacetylation and DNA methylation are associated with silencing of gene expression from the CMV promoter in the parental AAVP. Importantly, inhibitors of histone deacetylases boost gene expression in cancer cells from the Grp78 promoter in the dual tumor-targeted AAVP. However, cell confluence had no effect on AAVP-guided gene expression. Our findings prove that combination of histone deacetylase inhibitor drugs with the Grp78 promoter is an effective approach to improve AAVP-mediated gene expression in cancer cells and should be considered for AAVP-based clinical cancer gene therapy.

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 correlated with BMI (r = 0.56, P = 0.04), and hub genes of KCNN1 and AQP10 were differentially expressed. We identified significant genes and specific modules potentially related to BMI based on the gene expression profile data of monozygotic twins. The findings may help further elucidate the underlying mechanisms of obesity development and provide novel insights to research potential gene biomarkers and signaling pathways for obesity treatment. Further analysis and validation of the findings reported here are important and necessary when more sample size is acquired.

ATNT: an enhanced system for expression of polycistronic secondary metabolite gene clusters in Aspergillus niger.

PubMed

Geib, Elena; Brock, Matthias

2017-01-01

Fungi are treasure chests for yet unexplored natural products. However, exploitation of their real potential remains difficult as a significant proportion of biosynthetic gene clusters appears silent under standard laboratory conditions. Therefore, elucidation of novel products requires gene activation or heterologous expression. For heterologous gene expression, we previously developed an expression platform in Aspergillus niger that is based on the transcriptional regulator TerR and its target promoter P terA . In this study, we extended this system by regulating expression of terR  by the doxycycline inducible Tet-on system. Reporter genes cloned under the control of the target promoter P terA remained silent in the absence of doxycycline, but were strongly expressed when doxycycline was added. Reporter quantification revealed that the coupled system results in about five times higher expression rates compared to gene expression under direct control of the Tet-on system. As production of secondary metabolites generally requires the expression of several biosynthetic genes, the suitability of the self-cleaving viral peptide sequence P2A was tested in this optimised expression system. P2A allowed polycistronic expression of genes required for Asp-melanin formation in combination with the gene coding for the red fluorescent protein tdTomato. Gene expression and Asp-melanin formation was prevented in the absence of doxycycline and strongly induced by addition of doxycycline. Fluorescence studies confirmed the correct subcellular localisation of the respective enzymes. This tightly regulated but strongly inducible expression system enables high level production of secondary metabolites most likely even those with toxic potential. Furthermore, this system is compatible with polycistronic gene expression and, thus, suitable for the discovery of novel natural products.

Regulatory RNA at the root of animals: dynamic expression of developmental lincRNAs in the calcisponge Sycon ciliatum.

PubMed

Bråte, Jon; Adamski, Marcin; Neumann, Ralf S; Shalchian-Tabrizi, Kamran; Adamska, Maja

2015-12-22

Long non-coding RNAs (lncRNAs) play important regulatory roles during animal development, and it has been hypothesized that an RNA-based gene regulation was important for the evolution of developmental complexity in animals. However, most studies of lncRNA gene regulation have been performed using model animal species, and very little is known about this type of gene regulation in non-bilaterians. We have therefore analysed RNA-Seq data derived from a comprehensive set of embryogenesis stages in the calcareous sponge Sycon ciliatum and identified hundreds of developmentally expressed intergenic lncRNAs (lincRNAs) in this species. In situ hybridization of selected lincRNAs revealed dynamic spatial and temporal expression during embryonic development. More than 600 lincRNAs constitute integral parts of differentially expressed gene modules, which also contain known developmental regulatory genes, e.g. transcription factors and signalling molecules. This study provides insights into the non-coding gene repertoire of one of the earliest evolved animal lineages, and suggests that RNA-based gene regulation was probably present in the last common ancestor of animals. © 2015 The Authors.

Precise integration of inducible transcriptional elements (PrIITE) enables absolute control of gene expression.

PubMed

Pinto, Rita; Hansen, Lars; Hintze, John; Almeida, Raquel; Larsen, Sylvester; Coskun, Mehmet; Davidsen, Johanne; Mitchelmore, Cathy; David, Leonor; Troelsen, Jesper Thorvald; Bennett, Eric Paul

2017-07-27

Tetracycline-based inducible systems provide powerful methods for functional studies where gene expression can be controlled. However, the lack of tight control of the inducible system, leading to leakiness and adverse effects caused by undesirable tetracycline dosage requirements, has proven to be a limitation. Here, we report that the combined use of genome editing tools and last generation Tet-On systems can resolve these issues. Our principle is based on precise integration of inducible transcriptional elements (coined PrIITE) targeted to: (i) exons of an endogenous gene of interest (GOI) and (ii) a safe harbor locus. Using PrIITE cells harboring a GFP reporter or CDX2 transcription factor, we demonstrate discrete inducibility of gene expression with complete abrogation of leakiness. CDX2 PrIITE cells generated by this approach uncovered novel CDX2 downstream effector genes. Our results provide a strategy for characterization of dose-dependent effector functions of essential genes that require absence of endogenous gene expression. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

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

PubMed

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

2015-01-01

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

Ion channel gene expression predicts survival in glioma patients

PubMed Central

Wang, Rong; Gurguis, Christopher I.; Gu, Wanjun; Ko, Eun A; Lim, Inja; Bang, Hyoweon; Zhou, Tong; Ko, Jae-Hong

2015-01-01

Ion channels are important regulators in cell proliferation, migration, and apoptosis. The malfunction and/or aberrant expression of ion channels may disrupt these important biological processes and influence cancer progression. In this study, we investigate the expression pattern of ion channel genes in glioma. We designate 18 ion channel genes that are differentially expressed in high-grade glioma as a prognostic molecular signature. This ion channel gene expression based signature predicts glioma outcome in three independent validation cohorts. Interestingly, 16 of these 18 genes were down-regulated in high-grade glioma. This signature is independent of traditional clinical, molecular, and histological factors. Resampling tests indicate that the prognostic power of the signature outperforms random gene sets selected from human genome in all the validation cohorts. More importantly, this signature performs better than the random gene signatures selected from glioma-associated genes in two out of three validation datasets. This study implicates ion channels in brain cancer, thus expanding on knowledge of their roles in other cancers. Individualized profiling of ion channel gene expression serves as a superior and independent prognostic tool for glioma patients. PMID:26235283

ROKU: a novel method for identification of tissue-specific genes.

PubMed

Kadota, Koji; Ye, Jiazhen; Nakai, Yuji; Terada, Tohru; Shimizu, Kentaro

2006-06-12

One of the important goals of microarray research is the identification of genes whose expression is considerably higher or lower in some tissues than in others. We would like to have ways of identifying such tissue-specific genes. We describe a method, ROKU, which selects tissue-specific patterns from gene expression data for many tissues and thousands of genes. ROKU ranks genes according to their overall tissue specificity using Shannon entropy and detects tissues specific to each gene if any exist using an outlier detection method. We evaluated the capacity for the detection of various specific expression patterns using synthetic and real data. We observed that ROKU was superior to a conventional entropy-based method in its ability to rank genes according to overall tissue specificity and to detect genes whose expression pattern are specific only to objective tissues. ROKU is useful for the detection of various tissue-specific expression patterns. The framework is also directly applicable to the selection of diagnostic markers for molecular classification of multiple classes.

Molecular Structure-Based Large-Scale Prediction of Chemical-Induced Gene Expression Changes.

PubMed

Liu, Ruifeng; AbdulHameed, Mohamed Diwan M; Wallqvist, Anders

2017-09-25

The quantitative structure-activity relationship (QSAR) approach has been used to model a wide range of chemical-induced biological responses. However, it had not been utilized to model chemical-induced genomewide gene expression changes until very recently, owing to the complexity of training and evaluating a very large number of models. To address this issue, we examined the performance of a variable nearest neighbor (v-NN) method that uses information on near neighbors conforming to the principle that similar structures have similar activities. Using a data set of gene expression signatures of 13 150 compounds derived from cell-based measurements in the NIH Library of Integrated Network-based Cellular Signatures program, we were able to make predictions for 62% of the compounds in a 10-fold cross validation test, with a correlation coefficient of 0.61 between the predicted and experimentally derived signatures-a reproducibility rivaling that of high-throughput gene expression measurements. To evaluate the utility of the predicted gene expression signatures, we compared the predicted and experimentally derived signatures in their ability to identify drugs known to cause specific liver, kidney, and heart injuries. Overall, the predicted and experimentally derived signatures had similar receiver operating characteristics, whose areas under the curve ranged from 0.71 to 0.77 and 0.70 to 0.73, respectively, across the three organ injury models. However, detailed analyses of enrichment curves indicate that signatures predicted from multiple near neighbors outperformed those derived from experiments, suggesting that averaging information from near neighbors may help improve the signal from gene expression measurements. Our results demonstrate that the v-NN method can serve as a practical approach for modeling large-scale, genomewide, chemical-induced, gene expression changes.

Dominant genetics using a yeast genomic library under the control of a strong inducible promoter.

PubMed

Ramer, S W; Elledge, S J; Davis, R W

1992-12-01

In Saccharomyces cerevisiae, numerous genes have been identified by selection from high-copy-number libraries based on "multicopy suppression" or other phenotypic consequences of overexpression. Although fruitful, this approach suffers from two major drawbacks. First, high copy number alone may not permit high-level expression of tightly regulated genes. Conversely, other genes expressed in proportion to dosage cannot be identified if their products are toxic at elevated levels. This work reports construction of a genomic DNA expression library for S. cerevisiae that circumvents both limitations by fusing randomly sheared genomic DNA to the strong, inducible yeast GAL1 promoter, which can be regulated by carbon source. The library obtained contains 5 x 10(7) independent recombinants, representing a breakpoint at every base in the yeast genome. This library was used to examine aberrant gene expression in S. cerevisiae. A screen for dominant activators of yeast mating response identified eight genes that activate the pathway in the absence of exogenous mating pheromone, including one previously unidentified gene. One activator was a truncated STE11 gene lacking approximately 1000 base pairs of amino-terminal coding sequence. In two different clones, the same GAL1 promoter-proximal ATG is in-frame with the coding sequence of STE11, suggesting that internal initiation of translation there results in production of a biologically active, truncated STE11 protein. Thus this library allows isolation based on dominant phenotypes of genes that might have been difficult or impossible to isolate from high-copy-number libraries.

Development of a tightly regulated and highly responsive copper-inducible gene expression system and its application to control of flowering time.

PubMed

Saijo, Takanori; Nagasawa, Akitsu

2014-01-01

A newly developed copper-inducible gene expression system overcame the mixed results reported earlier, worked well both in cultured cells and a whole plant, and enabled to control flowering timing. Copper is one of the essential microelements and is readily taken up by plants. However, to date, it has rarely been used to control the expression of genes of interest, probably due to the inefficiency of the gene expression systems. In this study, we successfully developed a copper-inducible gene expression system that is based on the regulation of the yeast metallothionein gene. This system can be applied in the field and regulated at approximately one-hundredth of the rate used for registered copper-based fungicides. In the presence of copper, a translational fusion of the ACE1 transcription factor with the VP16 activation domain (VP16AD) of herpes simplex virus strongly activated transcription of the GFP gene in transgenic Arabidopsis. Interestingly, insertion of the To71 sequence, a 5'-untranslated region of the 130k/180k gene of tomato mosaic virus, upstream of the GFP gene reduced the basal expression of GFP in the absence of copper to almost negligible levels, even in soil-grown plants that were supplemented with ordinary liquid nutrients. Exposure of plants to 100 μM copper resulted in an over 1,000-fold induction ratio at the transcriptional level of GFP. This induction was copper-specific and dose-dependent with rapid and reversible responses. Using this expression system, we also succeeded in regulating floral transition by copper treatment. These results indicate that our newly developed copper-inducible system can accelerate gene functional analysis in model plants and can be used to generate novel agronomic traits in crop species.

Functional genomics annotation of a statistical epistasis network associated with bladder cancer susceptibility.

PubMed

Hu, Ting; Pan, Qinxin; Andrew, Angeline S; Langer, Jillian M; Cole, Michael D; Tomlinson, Craig R; Karagas, Margaret R; Moore, Jason H

2014-04-11

Several different genetic and environmental factors have been identified as independent risk factors for bladder cancer in population-based studies. Recent studies have turned to understanding the role of gene-gene and gene-environment interactions in determining risk. We previously developed the bioinformatics framework of statistical epistasis networks (SEN) to characterize the global structure of interacting genetic factors associated with a particular disease or clinical outcome. By applying SEN to a population-based study of bladder cancer among Caucasians in New Hampshire, we were able to identify a set of connected genetic factors with strong and significant interaction effects on bladder cancer susceptibility. To support our statistical findings using networks, in the present study, we performed pathway enrichment analyses on the set of genes identified using SEN, and found that they are associated with the carcinogen benzo[a]pyrene, a component of tobacco smoke. We further carried out an mRNA expression microarray experiment to validate statistical genetic interactions, and to determine if the set of genes identified in the SEN were differentially expressed in a normal bladder cell line and a bladder cancer cell line in the presence or absence of benzo[a]pyrene. Significant nonrandom sets of genes from the SEN were found to be differentially expressed in response to benzo[a]pyrene in both the normal bladder cells and the bladder cancer cells. In addition, the patterns of gene expression were significantly different between these two cell types. The enrichment analyses and the gene expression microarray results support the idea that SEN analysis of bladder in population-based studies is able to identify biologically meaningful statistical patterns. These results bring us a step closer to a systems genetic approach to understanding cancer susceptibility that integrates population and laboratory-based studies.

Polycistronic gene expression in Aspergillus niger.

PubMed

Schuetze, Tabea; Meyer, Vera

2017-09-25

Genome mining approaches predict dozens of biosynthetic gene clusters in each of the filamentous fungal genomes sequenced so far. However, the majority of these gene clusters still remain cryptic because they are not expressed in their natural host. Simultaneous expression of all genes belonging to a biosynthetic pathway in a heterologous host is one approach to activate biosynthetic gene clusters and to screen the metabolites produced for bioactivities. Polycistronic expression of all pathway genes under control of a single and tunable promoter would be the method of choice, as this does not only simplify cloning procedures, but also offers control on timing and strength of expression. However, polycistronic gene expression is a feature not commonly found in eukaryotic host systems, such as Aspergillus niger. In this study, we tested the suitability of the viral P2A peptide for co-expression of three genes in A. niger. Two genes descend from Fusarium oxysporum and are essential to produce the secondary metabolite enniatin (esyn1, ekivR). The third gene (luc) encodes the reporter luciferase which was included to study position effects. Expression of the polycistronic gene cassette was put under control of the Tet-On system to ensure tunable gene expression in A. niger. In total, three polycistronic expression cassettes which differed in the position of luc were constructed and targeted to the pyrG locus in A. niger. This allowed direct comparison of the luciferase activity based on the position of the luciferase gene. Doxycycline-mediated induction of the Tet-On expression cassettes resulted in the production of one long polycistronic mRNA as proven by Northern analyses, and ensured comparable production of enniatin in all three strains. Notably, gene position within the polycistronic expression cassette matters, as, luciferase activity was lowest at position one and had a comparable activity at positions two and three. The P2A peptide can be used to express at least three genes polycistronically in A. niger. This approach can now be applied to heterologously express entire secondary metabolite gene clusters polycistronically or to co-express any genes of interest in equimolar amounts.

Determining Cutoff Point of Ensemble Trees Based on Sample Size in Predicting Clinical Dose with DNA Microarray Data.

PubMed

Yılmaz Isıkhan, Selen; Karabulut, Erdem; Alpar, Celal Reha

2016-01-01

Background/Aim . Evaluating the success of dose prediction based on genetic or clinical data has substantially advanced recently. The aim of this study is to predict various clinical dose values from DNA gene expression datasets using data mining techniques. Materials and Methods . Eleven real gene expression datasets containing dose values were included. First, important genes for dose prediction were selected using iterative sure independence screening. Then, the performances of regression trees (RTs), support vector regression (SVR), RT bagging, SVR bagging, and RT boosting were examined. Results . The results demonstrated that a regression-based feature selection method substantially reduced the number of irrelevant genes from raw datasets. Overall, the best prediction performance in nine of 11 datasets was achieved using SVR; the second most accurate performance was provided using a gradient-boosting machine (GBM). Conclusion . Analysis of various dose values based on microarray gene expression data identified common genes found in our study and the referenced studies. According to our findings, SVR and GBM can be good predictors of dose-gene datasets. Another result of the study was to identify the sample size of n = 25 as a cutoff point for RT bagging to outperform a single RT.

Selecting and validating reference genes for quantitative real-time PCR in Plutella xylostella (L.).

PubMed

You, Yanchun; Xie, Miao; Vasseur, Liette; You, Minsheng

2018-05-01

Gene expression analysis provides important clues regarding gene functions, and quantitative real-time PCR (qRT-PCR) is a widely used method in gene expression studies. Reference genes are essential for normalizing and accurately assessing gene expression. In the present study, 16 candidate reference genes (ACTB, CyPA, EF1-α, GAPDH, HSP90, NDPk, RPL13a, RPL18, RPL19, RPL32, RPL4, RPL8, RPS13, RPS4, α-TUB, and β-TUB) from Plutella xylostella were selected to evaluate gene expression stability across different experimental conditions using five statistical algorithms (geNorm, NormFinder, Delta Ct, BestKeeper, and RefFinder). The results suggest that different reference genes or combinations of reference genes are suitable for normalization in gene expression studies of P. xylostella according to the different developmental stages, strains, tissues, and insecticide treatments. Based on the given experimental sets, the most stable reference genes were RPS4 across different developmental stages, RPL8 across different strains and tissues, and EF1-α across different insecticide treatments. A comprehensive and systematic assessment of potential reference genes for gene expression normalization is essential for post-genomic functional research in P. xylostella, a notorious pest with worldwide distribution and a high capacity to adapt and develop resistance to insecticides.

A proteomic chronology of gene expression through the cell cycle in human myeloid leukemia cells.

PubMed

Ly, Tony; Ahmad, Yasmeen; Shlien, Adam; Soroka, Dominique; Mills, Allie; Emanuele, Michael J; Stratton, Michael R; Lamond, Angus I

2014-01-01

Technological advances have enabled the analysis of cellular protein and RNA levels with unprecedented depth and sensitivity, allowing for an unbiased re-evaluation of gene regulation during fundamental biological processes. Here, we have chronicled the dynamics of protein and mRNA expression levels across a minimally perturbed cell cycle in human myeloid leukemia cells using centrifugal elutriation combined with mass spectrometry-based proteomics and RNA-Seq, avoiding artificial synchronization procedures. We identify myeloid-specific gene expression and variations in protein abundance, isoform expression and phosphorylation at different cell cycle stages. We dissect the relationship between protein and mRNA levels for both bulk gene expression and for over ∼6000 genes individually across the cell cycle, revealing complex, gene-specific patterns. This data set, one of the deepest surveys to date of gene expression in human cells, is presented in an online, searchable database, the Encyclopedia of Proteome Dynamics (http://www.peptracker.com/epd/). DOI: http://dx.doi.org/10.7554/eLife.01630.001.

A proteomic chronology of gene expression through the cell cycle in human myeloid leukemia cells

PubMed Central

Ly, Tony; Ahmad, Yasmeen; Shlien, Adam; Soroka, Dominique; Mills, Allie; Emanuele, Michael J; Stratton, Michael R; Lamond, Angus I

2014-01-01

Technological advances have enabled the analysis of cellular protein and RNA levels with unprecedented depth and sensitivity, allowing for an unbiased re-evaluation of gene regulation during fundamental biological processes. Here, we have chronicled the dynamics of protein and mRNA expression levels across a minimally perturbed cell cycle in human myeloid leukemia cells using centrifugal elutriation combined with mass spectrometry-based proteomics and RNA-Seq, avoiding artificial synchronization procedures. We identify myeloid-specific gene expression and variations in protein abundance, isoform expression and phosphorylation at different cell cycle stages. We dissect the relationship between protein and mRNA levels for both bulk gene expression and for over ∼6000 genes individually across the cell cycle, revealing complex, gene-specific patterns. This data set, one of the deepest surveys to date of gene expression in human cells, is presented in an online, searchable database, the Encyclopedia of Proteome Dynamics (http://www.peptracker.com/epd/). DOI: http://dx.doi.org/10.7554/eLife.01630.001 PMID:24596151

Creating and validating cis-regulatory maps of tissue-specific gene expression regulation

PubMed Central

O'Connor, Timothy R.; Bailey, Timothy L.

2014-01-01

Predicting which genomic regions control the transcription of a given gene is a challenge. We present a novel computational approach for creating and validating maps that associate genomic regions (cis-regulatory modules–CRMs) with genes. The method infers regulatory relationships that explain gene expression observed in a test tissue using widely available genomic data for ‘other’ tissues. To predict the regulatory targets of a CRM, we use cross-tissue correlation between histone modifications present at the CRM and expression at genes within 1 Mbp of it. To validate cis-regulatory maps, we show that they yield more accurate models of gene expression than carefully constructed control maps. These gene expression models predict observed gene expression from transcription factor binding in the CRMs linked to that gene. We show that our maps are able to identify long-range regulatory interactions and improve substantially over maps linking genes and CRMs based on either the control maps or a ‘nearest neighbor’ heuristic. Our results also show that it is essential to include CRMs predicted in multiple tissues during map-building, that H3K27ac is the most informative histone modification, and that CAGE is the most informative measure of gene expression for creating cis-regulatory maps. PMID:25200088

Gene expression pattern recognition algorithm inferences to classify samples exposed to chemical agents

NASA Astrophysics Data System (ADS)

Bushel, Pierre R.; Bennett, Lee; Hamadeh, Hisham; Green, James; Ableson, Alan; Misener, Steve; Paules, Richard; Afshari, Cynthia

2002-06-01

We present an analysis of pattern recognition procedures used to predict the classes of samples exposed to pharmacologic agents by comparing gene expression patterns from samples treated with two classes of compounds. Rat liver mRNA samples following exposure for 24 hours with phenobarbital or peroxisome proliferators were analyzed using a 1700 rat cDNA microarray platform. Sets of genes that were consistently differentially expressed in the rat liver samples following treatment were stored in the MicroArray Project System (MAPS) database. MAPS identified 238 genes in common that possessed a low probability (P < 0.01) of being randomly detected as differentially expressed at the 95% confidence level. Hierarchical cluster analysis on the 238 genes clustered specific gene expression profiles that separated samples based on exposure to a particular class of compound.

Partition resampling and extrapolation averaging: approximation methods for quantifying gene expression in large numbers of short oligonucleotide arrays.

PubMed

Goldstein, Darlene R

2006-10-01

Studies of gene expression using high-density short oligonucleotide arrays have become a standard in a variety of biological contexts. Of the expression measures that have been proposed to quantify expression in these arrays, multi-chip-based measures have been shown to perform well. As gene expression studies increase in size, however, utilizing multi-chip expression measures is more challenging in terms of computing memory requirements and time. A strategic alternative to exact multi-chip quantification on a full large chip set is to approximate expression values based on subsets of chips. This paper introduces an extrapolation method, Extrapolation Averaging (EA), and a resampling method, Partition Resampling (PR), to approximate expression in large studies. An examination of properties indicates that subset-based methods can perform well compared with exact expression quantification. The focus is on short oligonucleotide chips, but the same ideas apply equally well to any array type for which expression is quantified using an entire set of arrays, rather than for only a single array at a time. Software implementing Partition Resampling and Extrapolation Averaging is under development as an R package for the BioConductor project.

Detrimental effect of expression of Bt endotoxin Cry1Ac on in vitro regeneration, in vivo growth and development of tobacco and cotton transgenics.

PubMed

Rawat, Preeti; Singh, Amarjeet Kumar; Ray, Krishna; Chaudhary, Bhupendra; Kumar, Sanjeev; Gautam, Taru; Kanoria, Shaveta; Kaur, Gurpreet; Kumar, Paritosh; Pental, Deepak; Burma, Pradeep Kumar

2011-06-01

High levels of expression of the cry1Ac gene from Bacillus thuringiensis cannot be routinely achieved in transgenic plants despite modifications made in the gene to improve its expression. This has been attributed to the instability of the transcript in a few reports. In the present study, based on the genetic transformation of cotton and tobacco, we show that the expression of the Cry1Ac endotoxin has detrimental effects on both the in vitro and in vivo growth and development of transgenic plants. A number of experiments on developing transgenics in cotton with different versions of cry1Ac gene showed that the majority of the plants did not express any Cry1Ac protein. Based on Southern blot analysis, it was also observed that a substantial number of lines did not contain the cry1Ac gene cassette although they contained the marker gene nptII. More significantly, all the lines that showed appreciable levels of expression were found to be phenotypically abnormal. Experiments on transformation of tobacco with different constructs expressing the cry1Ac gene showed that in vitro regeneration was inhibited by the encoded protein. Further, out of a total of 145 independent events generated with the different cry1Ac gene constructs in tobacco, only 21 showed expression of the Cry1Ac protein, confirming observations made in cotton that regenerants that express high levels of the Cry1Ac protein are selected against during regeneration of transformed events. This problem was circumvented by targeting the Cry1Ac protein to the chloroplast, which also significantly improved the expression of the protein.

Cardiac transcriptional response to acute and chronic angiotensin II treatments.

PubMed

Larkin, Jennie E; Frank, Bryan C; Gaspard, Renee M; Duka, Irena; Gavras, Haralambos; Quackenbush, John

2004-07-08

Exposure of experimental animals to increased angiotensin II (ANG II) induces hypertension associated with cardiac hypertrophy, inflammation, and myocardial necrosis and fibrosis. Some of the most effective antihypertensive treatments are those that antagonize ANG II. We investigated cardiac gene expression in response to acute (24 h) and chronic (14 day) infusion of ANG II in mice; 24-h treatment induces hypertension, and 14-day treatment induces hypertension and extensive cardiac hypertrophy and necrosis. For genes differentially expressed in response to ANG II treatment, we tested for significant regulation of pathways, based on Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Microarray Pathway Profiler (GenMAPP) databases, as well as functional classes based on Gene Ontology (GO) terms. Both acute and chronic ANG II treatments resulted in decreased expression of mitochondrial metabolic genes, notably those for the electron transport chain and Krebs-TCA cycle; chronic ANG II treatment also resulted in decreased expression of genes involved in fatty acid metabolism. In contrast, genes involved in protein translation and ribosomal activity increased expression following both acute and chronic ANG II treatments. Some classes of genes showed differential response between acute and chronic ANG II treatments. Acute treatment increased expression of genes involved in oxidative stress and amino acid metabolism, whereas chronic treatments increased cytoskeletal and extracellular matrix genes, second messenger cascades responsive to ANG II, and amyloidosis genes. Although a functional linkage between Alzheimer disease, hypertension, and high cholesterol has been previously documented in studies of brain tissue, this is the first demonstration of induction of Alzheimer disease pathways by hypertension in heart tissue. This study provides the most comprehensive available survey of gene expression changes in response to acute and chronic ANG II treatment, verifying results from disparate studies, and suggests mechanisms that provide novel insight into the etiology of hypertensive heart disease and possible therapeutic interventions that may help to mitigate its effects.

Selection and evaluation of reference genes for expression studies with quantitative PCR in the model fungus Neurospora crassa under different environmental conditions in continuous culture.

PubMed

Cusick, Kathleen D; Fitzgerald, Lisa A; Pirlo, Russell K; Cockrell, Allison L; Petersen, Emily R; Biffinger, Justin C

2014-01-01

Neurospora crassa has served as a model organism for studying circadian pathways and more recently has gained attention in the biofuel industry due to its enhanced capacity for cellulase production. However, in order to optimize N. crassa for biotechnological applications, metabolic pathways during growth under different environmental conditions must be addressed. Reverse-transcription quantitative PCR (RT-qPCR) is a technique that provides a high-throughput platform from which to measure the expression of a large set of genes over time. The selection of a suitable reference gene is critical for gene expression studies using relative quantification, as this strategy is based on normalization of target gene expression to a reference gene whose expression is stable under the experimental conditions. This study evaluated twelve candidate reference genes for use with N. crassa when grown in continuous culture bioreactors under different light and temperature conditions. Based on combined stability values from NormFinder and Best Keeper software packages, the following are the most appropriate reference genes under conditions of: (1) light/dark cycling: btl, asl, and vma1; (2) all-dark growth: btl, tbp, vma1, and vma2; (3) temperature flux: btl, vma1, act, and asl; (4) all conditions combined: vma1, vma2, tbp, and btl. Since N. crassa exists as different cell types (uni- or multi-nucleated), expression changes in a subset of the candidate genes was further assessed using absolute quantification. A strong negative correlation was found to exist between ratio and threshold cycle (CT) values, demonstrating that CT changes serve as a reliable reflection of transcript, and not gene copy number, fluctuations. The results of this study identified genes that are appropriate for use as reference genes in RT-qPCR studies with N. crassa and demonstrated that even with the presence of different cell types, relative quantification is an acceptable method for measuring gene expression changes during growth in bioreactors.

Identification of novel risk genes associated with type 1 diabetes mellitus using a genome-wide gene-based association analysis.

PubMed

Qiu, Ying-Hua; Deng, Fei-Yan; Li, Min-Jing; Lei, Shu-Feng

2014-11-01

Type 1 diabetes mellitus is a serious disorder characterized by destruction of pancreatic β-cells, culminating in absolute insulin deficiency. Genetic factors contribute to the susceptibility of type 1 diabetes mellitus. The aim of the present study was to identify more susceptibility genes of type 1 diabetes mellitus. We carried out an initial gene-based genome-wide association study in a total of 4,075 type 1 diabetes mellitus cases and 2,604 controls by using the Gene-based Association Test using Extended Simes procedure. Furthermore, we carried out replication studies, differential expression analysis and functional annotation clustering analysis to support the significance of the identified susceptibility genes. We identified 452 genes associated with type 1 diabetes mellitus, even after adapting the genome-wide threshold for significance (P < 9.05E-04). Among these genes, 171 were newly identified for type 1 diabetes mellitus, which were ignored in single-nucleotide polymorphism-based association analysis and were not previously reported. We found that 53 genes have supportive evidence from replication studies and/or differential expression studies. In particular, seven genes including four non-human leukocyte antigen (HLA) genes (RASIP1, STRN4, BCAR1 and MYL2) are replicated in at least one independent population and also differentially expressed in peripheral blood mononuclear cells or monocytes. Furthermore, the associated genes tend to enrich in immune-related pathways or Gene Ontology project terms. The present results suggest the high power of gene-based association analysis in detecting disease-susceptibility genes. Our findings provide more insights into the genetic basis of type 1 diabetes mellitus.

Comparison of gene co-networks reveals the molecular mechanisms of the rice (Oryza sativa L.) response to Rhizoctonia solani AG1 IA infection.

PubMed

Zhang, Jinfeng; Zhao, Wenjuan; Fu, Rong; Fu, Chenglin; Wang, Lingxia; Liu, Huainian; Li, Shuangcheng; Deng, Qiming; Wang, Shiquan; Zhu, Jun; Liang, Yueyang; Li, Ping; Zheng, Aiping

2018-05-05

Rhizoctonia solani causes rice sheath blight, an important disease affecting the growth of rice (Oryza sativa L.). Attempts to control the disease have met with little success. Based on transcriptional profiling, we previously identified more than 11,947 common differentially expressed genes (TPM > 10) between the rice genotypes TeQing and Lemont. In the current study, we extended these findings by focusing on an analysis of gene co-expression in response to R. solani AG1 IA and identified gene modules within the networks through weighted gene co-expression network analysis (WGCNA). We compared the different genes assigned to each module and the biological interpretations of gene co-expression networks at early and later modules in the two rice genotypes to reveal differential responses to AG1 IA. Our results show that different changes occurred in the two rice genotypes and that the modules in the two groups contain a number of candidate genes possibly involved in pathogenesis, such as the VQ protein. Furthermore, these gene co-expression networks provide comprehensive transcriptional information regarding gene expression in rice in response to AG1 IA. The co-expression networks derived from our data offer ideas for follow-up experimentation that will help advance our understanding of the translational regulation of rice gene expression changes in response to AG1 IA.

Bone-related gene profiles in developing calvaria.

PubMed

Cho, Je-Yoel; Lee, Won-Bong; Kim, Hyun-Jung; Mi Woo, Kyung; Baek, Jeong-Hwa; Choi, Je-Yong; Hur, Cheol-Gu; Ryoo, Hyun-Mo

2006-05-10

Generating a comprehensive understanding of osteogenesis-related gene profiles is very important in the development of new treatments for osteopenic conditions. Developing calvaria undergoes a typical intramembranous bone-forming process. To identify genes associated with osteoblast differentiation, we isolated total RNAs from parietal bones, that represent active osteoblasts, and sutural mesenchyme, that represents osteoprogenitor cells, and comprehensively analyzed their gene expression profiles using an oligo-based Affymetrix microarray chip containing 22,690 probes. About 2100 genes with "Present" calls had more than 2-fold higher expression in bone compared to sutures while 73 of these genes had more than 8-fold expression. Some of these genes are already known to be bone-related biomarkers: VitD receptor, bone sialoprotein, osteocalcin, osteopontin, MMP13, etc. Eight genes were selected and subjected to confirmation by quantitative real-time RT-PCR analyses. All the genes tested showed higher expression in bones, ranging from 5- to 140-fold. Several of these genes are ESTs while others are already known but their functions in osteogenesis were not previously known. Most genes of the BMP and FGF families probed in the Genechip analysis were more highly expressed in bone tissues compared to suture. All differentially-expressed Runx and Dlx family genes also showed higher expression in bone. These results imply that our data is valid and can be used as a good standard for the mining of osteogenesis-related genes.

Reliable reference genes for normalization of gene expression data in tea plants (Camellia sinensis) exposed to metal stresses.

PubMed

Wang, Ming-Le; Li, Qing-Hui; Xin, Hua-Hong; Chen, Xuan; Zhu, Xu-Jun; Li, Xing-Hui

2017-01-01

Tea plants [Camellia sinensis (L.) O. Kuntze] are an important leaf-type crop that are widely used for the production of non-alcoholic beverages in the world. Exposure to excessive amounts of heavy metals adversely affects the quality and yield of tea leaves. To analyze the molecular responses of tea plants to heavy metals, a reliable quantification of gene expression is important and of major importance herein is the normalization of the measured expression levels for the target genes. Ideally, stably expressed reference genes should be evaluated in all experimental systems. In this study, 12 candidate reference genes (i.e., 18S rRNA, Actin, CYP, EF-1α, eIF-4α, GAPDH, MON1, PP2AA3, TBP, TIP41, TUA, and UBC) were cloned from tea plants, and the stability of their expression was examined systematically in 60 samples exposed to diverse heavy metals (i.e., manganese, aluminum, copper, iron, and zinc). Three Excel-based algorithms (geNorm, NormFinder, and BestKeeper) were used to evaluate the expression stability of these genes. PP2AA3 and 18S rRNA were the most stably expressed genes, even though their expression profiles exhibited some variability. Moreover, commonly used reference genes (i.e., GAPDH and TBP) were the least appropriate reference genes for most samples. To further validate the suitability of the analyzed reference genes, the expression level of a phytochelatin synthase gene (i.e., CsPCS1) was determined using the putative reference genes for data normalizations. Our results may be beneficial for future studies involving the quantification of relative gene expression levels in tea plants.

Reliable reference genes for normalization of gene expression data in tea plants (Camellia sinensis) exposed to metal stresses

PubMed Central

Wang, Ming-Le; Li, Qing-Hui; Xin, Hua-Hong; Chen, Xuan; Zhu, Xu-Jun

2017-01-01

Tea plants [Camellia sinensis (L.) O. Kuntze] are an important leaf-type crop that are widely used for the production of non-alcoholic beverages in the world. Exposure to excessive amounts of heavy metals adversely affects the quality and yield of tea leaves. To analyze the molecular responses of tea plants to heavy metals, a reliable quantification of gene expression is important and of major importance herein is the normalization of the measured expression levels for the target genes. Ideally, stably expressed reference genes should be evaluated in all experimental systems. In this study, 12 candidate reference genes (i.e., 18S rRNA, Actin, CYP, EF-1α, eIF-4α, GAPDH, MON1, PP2AA3, TBP, TIP41, TUA, and UBC) were cloned from tea plants, and the stability of their expression was examined systematically in 60 samples exposed to diverse heavy metals (i.e., manganese, aluminum, copper, iron, and zinc). Three Excel-based algorithms (geNorm, NormFinder, and BestKeeper) were used to evaluate the expression stability of these genes. PP2AA3 and 18S rRNA were the most stably expressed genes, even though their expression profiles exhibited some variability. Moreover, commonly used reference genes (i.e., GAPDH and TBP) were the least appropriate reference genes for most samples. To further validate the suitability of the analyzed reference genes, the expression level of a phytochelatin synthase gene (i.e., CsPCS1) was determined using the putative reference genes for data normalizations. Our results may be beneficial for future studies involving the quantification of relative gene expression levels in tea plants. PMID:28453515

How powerful are summary-based methods for identifying expression-trait associations under different genetic architectures?

PubMed

Veturi, Yogasudha; Ritchie, Marylyn D

2018-01-01

Transcriptome-wide association studies (TWAS) have recently been employed as an approach that can draw upon the advantages of genome-wide association studies (GWAS) and gene expression studies to identify genes associated with complex traits. Unlike standard GWAS, summary level data suffices for TWAS and offers improved statistical power. Two popular TWAS methods include either (a) imputing the cis genetic component of gene expression from smaller sized studies (using multi-SNP prediction or MP) into much larger effective sample sizes afforded by GWAS - TWAS-MP or (b) using summary-based Mendelian randomization - TWAS-SMR. Although these methods have been effective at detecting functional variants, it remains unclear how extensive variability in the genetic architecture of complex traits and diseases impacts TWAS results. Our goal was to investigate the different scenarios under which these methods yielded enough power to detect significant expression-trait associations. In this study, we conducted extensive simulations based on 6000 randomly chosen, unrelated Caucasian males from Geisinger's MyCode population to compare the power to detect cis expression-trait associations (within 500 kb of a gene) using the above-described approaches. To test TWAS across varying genetic backgrounds we simulated gene expression and phenotype using different quantitative trait loci per gene and cis-expression /trait heritability under genetic models that differentiate the effect of causality from that of pleiotropy. For each gene, on a training set ranging from 100 to 1000 individuals, we either (a) estimated regression coefficients with gene expression as the response using five different methods: LASSO, elastic net, Bayesian LASSO, Bayesian spike-slab, and Bayesian ridge regression or (b) performed eQTL analysis. We then sampled with replacement 50,000, 150,000, and 300,000 individuals respectively from the testing set of the remaining 5000 individuals and conducted GWAS on each set. Subsequently, we integrated the GWAS summary statistics derived from the testing set with the weights (or eQTLs) derived from the training set to identify expression-trait associations using (a) TWAS-MP (b) TWAS-SMR (c) eQTL-based GWAS, or (d) standalone GWAS. Finally, we examined the power to detect functionally relevant genes using the different approaches under the considered simulation scenarios. In general, we observed great similarities among TWAS-MP methods although the Bayesian methods resulted in improved power in comparison to LASSO and elastic net as the trait architecture grew more complex while training sample sizes and expression heritability remained small. Finally, we observed high power under causality but very low to moderate power under pleiotropy.

Microarray expression profiling identifies genes with altered expression in HDL-deficient mice

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

Callow, Matthew J.; Dudoit, Sandrine; Gong, Elaine L.

2000-05-05

Based on the assumption that severe alterations in the expression of genes known to be involved in HDL metabolism may affect the expression of other genes we screened an array of over 5000 mouse expressed sequence tags (ESTs) for altered gene expression in the livers of two lines of mice with dramatic decreases in HDL plasma concentrations. Labeled cDNA from livers of apolipoprotein AI (apo AI) knockout mice, Scavenger Receptor BI (SR-BI) transgenic mice and control mice were co-hybridized to microarrays. Two-sample t-statistics were used to identify genes with altered expression levels in the knockout or transgenic mice compared withmore » the control mice. In the SR-BI group we found 9 array elements representing at least 5 genes to be significantly altered on the basis of an adjusted p value of less than 0.05. In the apo AI knockout group 8 array elements representing 4 genes were altered compared with the control group (p < 0.05). Several of the genes identified in the SR-BI transgenic suggest altered sterol metabolism and oxidative processes. These studies illustrate the use of multiple-testing methods for the identification of genes with altered expression in replicated microarray experiments of apo AI knockout and SR-BI transgenic mice.« less

NCBI GEO: mining millions of expression profiles--database and tools.

PubMed

Barrett, Tanya; Suzek, Tugba O; Troup, Dennis B; Wilhite, Stephen E; Ngau, Wing-Chi; Ledoux, Pierre; Rudnev, Dmitry; Lash, Alex E; Fujibuchi, Wataru; Edgar, Ron

2005-01-01

The Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) is the largest fully public repository for high-throughput molecular abundance data, primarily gene expression data. The database has a flexible and open design that allows the submission, storage and retrieval of many data types. These data include microarray-based experiments measuring the abundance of mRNA, genomic DNA and protein molecules, as well as non-array-based technologies such as serial analysis of gene expression (SAGE) and mass spectrometry proteomic technology. GEO currently holds over 30,000 submissions representing approximately half a billion individual molecular abundance measurements, for over 100 organisms. Here, we describe recent database developments that facilitate effective mining and visualization of these data. Features are provided to examine data from both experiment- and gene-centric perspectives using user-friendly Web-based interfaces accessible to those without computational or microarray-related analytical expertise. The GEO database is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.

Mining Microarray Data at NCBI’s Gene Expression Omnibus (GEO)*

PubMed Central

Barrett, Tanya; Edgar, Ron

2006-01-01

Summary The Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) has emerged as the leading fully public repository for gene expression data. This chapter describes how to use Web-based interfaces, applications, and graphics to effectively explore, visualize, and interpret the hundreds of microarray studies and millions of gene expression patterns stored in GEO. Data can be examined from both experiment-centric and gene-centric perspectives using user-friendly tools that do not require specialized expertise in microarray analysis or time-consuming download of massive data sets. The GEO database is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo. PMID:16888359

Transcriptional profiling of immune-related genes in Pacific white shrimp (Litopenaeus vannamei) during ontogenesis.

PubMed

Quispe, Ruth L; Justino, Emily B; Vieira, Felipe N; Jaramillo, Michael L; Rosa, Rafael D; Perazzolo, Luciane M

2016-11-01

We have performed here a gene expression analysis to determine the developmental stage at the main genes involved in crustacean immune response begin to be expressed and their changes in mRNA abundance during shrimp development. By using a quantitative PCR-based approach, we have measured the mRNA abundance of 24 immune-related genes from different functional categories in twelve developmental stages ranging from fertilized eggs to larval and postlarval stages and also in juveniles. We showed for the first time that the main genes from the RNAi-based post-transcriptional pathway involved in shrimp antiviral immunity are transcribed in all developmental stages, but exhibit a diverse pattern of gene expression during shrimp ontogenesis. On the other hand, hemocyte-expressed genes mainly involved in antimicrobial defenses appeared to be transcribed in larval stages, indicating that hematopoiesis initiates early in development. Moreover, transcript levels of some genes were early detected in fertilized eggs at 0-4 h post-spawning, suggesting a maternal contribution of immune-related transcripts to shrimp progeny. Altogether, our results provide important clues regarding the ontogenesis of hemocytes as well the establishment of antiviral and antimicrobial defenses in shrimp. Copyright © 2016 Elsevier Ltd. All rights reserved.

Unstable Expression of Commonly Used Reference Genes in Rat Pancreatic Islets Early after Isolation Affects Results of Gene Expression Studies.

PubMed

Kosinová, Lucie; Cahová, Monika; Fábryová, Eva; Týcová, Irena; Koblas, Tomáš; Leontovyč, Ivan; Saudek, František; Kříž, Jan

2016-01-01

The use of RT-qPCR provides a powerful tool for gene expression studies; however, the proper interpretation of the obtained data is crucially dependent on accurate normalization based on stable reference genes. Recently, strong evidence has been shown indicating that the expression of many commonly used reference genes may vary significantly due to diverse experimental conditions. The isolation of pancreatic islets is a complicated procedure which creates severe mechanical and metabolic stress leading possibly to cellular damage and alteration of gene expression. Despite of this, freshly isolated islets frequently serve as a control in various gene expression and intervention studies. The aim of our study was to determine expression of 16 candidate reference genes and one gene of interest (F3) in isolated rat pancreatic islets during short-term cultivation in order to find a suitable endogenous control for gene expression studies. We compared the expression stability of the most commonly used reference genes and evaluated the reliability of relative and absolute quantification using RT-qPCR during 0-120 hrs after isolation. In freshly isolated islets, the expression of all tested genes was markedly depressed and it increased several times throughout the first 48 hrs of cultivation. We observed significant variability among samples at 0 and 24 hrs but substantial stabilization from 48 hrs onwards. During the first 48 hrs, relative quantification failed to reflect the real changes in respective mRNA concentrations while in the interval 48-120 hrs, the relative expression generally paralleled the results determined by absolute quantification. Thus, our data call into question the suitability of relative quantification for gene expression analysis in pancreatic islets during the first 48 hrs of cultivation, as the results may be significantly affected by unstable expression of reference genes. However, this method could provide reliable information from 48 hrs onwards.

Mindfulness-Based Stress Reduction training reduces loneliness and pro-inflammatory gene expression in older adults: a small randomized controlled trial.

PubMed

Creswell, J David; Irwin, Michael R; Burklund, Lisa J; Lieberman, Matthew D; Arevalo, Jesusa M G; Ma, Jeffrey; Breen, Elizabeth Crabb; Cole, Steven W

2012-10-01

Lonely older adults have increased expression of pro-inflammatory genes as well as increased risk for morbidity and mortality. Previous behavioral treatments have attempted to reduce loneliness and its concomitant health risks, but have had limited success. The present study tested whether the 8-week Mindfulness-Based Stress Reduction (MBSR) program (compared to a Wait-List control group) reduces loneliness and downregulates loneliness-related pro-inflammatory gene expression in older adults (N = 40). Consistent with study predictions, mixed effect linear models indicated that the MBSR program reduced loneliness, compared to small increases in loneliness in the control group (treatment condition × time interaction: F(1,35) = 7.86, p = .008). Moreover, at baseline, there was an association between reported loneliness and upregulated pro-inflammatory NF-κB-related gene expression in circulating leukocytes, and MBSR downregulated this NF-κB-associated gene expression profile at post-treatment. Finally, there was a trend for MBSR to reduce C Reactive Protein (treatment condition × time interaction: (F(1,33) = 3.39, p = .075). This work provides an initial indication that MBSR may be a novel treatment approach for reducing loneliness and related pro-inflammatory gene expression in older adults. Copyright © 2012 Elsevier Inc. All rights reserved.

Mindfulness-Based Stress Reduction Training Reduces Loneliness and Pro-Inflammatory Gene Expression in Older Adults: A Small Randomized Controlled Trial

PubMed Central

Creswell, J. David; Irwin, Michael R.; Burklund, Lisa J.; Lieberman, Matthew D.; Arevalo, Jesusa M. G.; Ma, Jeffrey; Breen, Elizabeth Crabb; Cole, Steven W.

2013-01-01

Lonely older adults have increased expression of pro-inflammatory genes as well as increased risk for morbidity and mortality. Previous behavioral treatments have attempted to reduce loneliness and its concomitant health risks, but have had limited success. The present study tested whether the 8-week Mindfulness-Based Stress Reduction (MBSR) program (compared to a Wait-List control group) reduces loneliness and downregulates loneliness-related pro-inflammatory gene expression in older adults (N=40). Consistent with study predictions, mixed effect linear models indicated that the MBSR program reduced loneliness, compared to small increases in loneliness in the control group (treatment condition × time interaction: F(1,35)=7.86, p=.008). Moreover, at baseline, there was an association between reported loneliness and upregulated pro-inflammatory NF-κB-related gene expression in circulating leukocytes, and MBSR downregulated this NF-κB-associated gene expression profile at post-treatment. Finally, there was a trend for MBSR to reduce C Reactive Protein (treatment condition × time interaction: (F(1,33)=3.39, p=.075). This work provides an initial indication that MBSR may be a novel treatment approach for reducing loneliness and related pro-inflammatory gene expression in older adults. PMID:22820409

Study of five novel non-synonymous polymorphisms in human brain-expressed genes in a Colombian sample.

PubMed

Ojeda, Diego A; Forero, Diego A

2014-10-01

Non-synonymous single nucleotide polymorphisms (nsSNPs) in brain-expressed genes represent interesting candidates for genetic research in neuropsychiatric disorders. To study novel nsSNPs in brain-expressed genes in a sample of Colombian subjects. We applied an approach based on in silico mining of available genomic data to identify and select novel nsSNPs in brain-expressed genes. We developed novel genotyping assays, based in allele-specific PCR methods, for these nsSNPs and genotyped them in 171 Colombian subjects. Five common nsSNPs (rs6855837; p.Leu395Ile, rs2305160; p.Thr394Ala, rs10503929; p.Met289Thr, rs2270641; p.Thr4Pro and rs3822659; p.Ser735Ala) were studied, located in the CLOCK, NPAS2, NRG1, SLC18A1 and WWC1 genes. We reported allele and genotype frequencies in a sample of South American healthy subjects. There is previous experimental evidence, arising from genome-wide expression and association studies, for the involvement of these genes in several neuropsychiatric disorders and endophenotypes, such as schizophrenia, mood disorders or memory performance. Frequencies for these nsSNPSs in the Colombian samples varied in comparison to different HapMap populations. Future study of these nsSNPs in brain-expressed genes, a synaptogenomics approach, will be important for a better understanding of neuropsychiatric diseases and endophenotypes in different populations.

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

PubMed

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

2017-03-01

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

Multi-membership gene regulation in pathway based microarray analysis

PubMed Central

2011-01-01

Background Gene expression analysis has been intensively researched for more than a decade. Recently, there has been elevated interest in the integration of microarray data analysis with other types of biological knowledge in a holistic analytical approach. We propose a methodology that can be facilitated for pathway based microarray data analysis, based on the observation that a substantial proportion of genes present in biochemical pathway databases are members of a number of distinct pathways. Our methodology aims towards establishing the state of individual pathways, by identifying those truly affected by the experimental conditions based on the behaviour of such genes. For that purpose it considers all the pathways in which a gene participates and the general census of gene expression per pathway. Results We utilise hill climbing, simulated annealing and a genetic algorithm to analyse the consistency of the produced results, through the application of fuzzy adjusted rand indexes and hamming distance. All algorithms produce highly consistent genes to pathways allocations, revealing the contribution of genes to pathway functionality, in agreement with current pathway state visualisation techniques, with the simulated annealing search proving slightly superior in terms of efficiency. Conclusions We show that the expression values of genes, which are members of a number of biochemical pathways or modules, are the net effect of the contribution of each gene to these biochemical processes. We show that by manipulating the pathway and module contribution of such genes to follow underlying trends we can interpret microarray results centred on the behaviour of these genes. PMID:21939531

Multi-membership gene regulation in pathway based microarray analysis.

PubMed

Pavlidis, Stelios P; Payne, Annette M; Swift, Stephen M

2011-09-22

Gene expression analysis has been intensively researched for more than a decade. Recently, there has been elevated interest in the integration of microarray data analysis with other types of biological knowledge in a holistic analytical approach. We propose a methodology that can be facilitated for pathway based microarray data analysis, based on the observation that a substantial proportion of genes present in biochemical pathway databases are members of a number of distinct pathways. Our methodology aims towards establishing the state of individual pathways, by identifying those truly affected by the experimental conditions based on the behaviour of such genes. For that purpose it considers all the pathways in which a gene participates and the general census of gene expression per pathway. We utilise hill climbing, simulated annealing and a genetic algorithm to analyse the consistency of the produced results, through the application of fuzzy adjusted rand indexes and hamming distance. All algorithms produce highly consistent genes to pathways allocations, revealing the contribution of genes to pathway functionality, in agreement with current pathway state visualisation techniques, with the simulated annealing search proving slightly superior in terms of efficiency. We show that the expression values of genes, which are members of a number of biochemical pathways or modules, are the net effect of the contribution of each gene to these biochemical processes. We show that by manipulating the pathway and module contribution of such genes to follow underlying trends we can interpret microarray results centred on the behaviour of these genes.

The Rice B-Box Zinc Finger Gene Family: Genomic Identification, Characterization, Expression Profiling and Diurnal Analysis

PubMed Central

Huang, Jianyan; Zhao, Xiaobo; Weng, Xiaoyu; Wang, Lei; Xie, Weibo

2012-01-01

Background The B-box (BBX) -containing proteins are a class of zinc finger proteins that contain one or two B-box domains and play important roles in plant growth and development. The Arabidopsis BBX gene family has recently been re-identified and renamed. However, there has not been a genome-wide survey of the rice BBX (OsBBX) gene family until now. Methodology/Principal Findings In this study, we identified 30 rice BBX genes through a comprehensive bioinformatics analysis. Each gene was assigned a uniform nomenclature. We described the chromosome localizations, gene structures, protein domains, phylogenetic relationship, whole life-cycle expression profile and diurnal expression patterns of the OsBBX family members. Based on the phylogeny and domain constitution, the OsBBX gene family was classified into five subfamilies. The gene duplication analysis revealed that only chromosomal segmental duplication contributed to the expansion of the OsBBX gene family. The expression profile of the OsBBX genes was analyzed by Affymetrix GeneChip microarrays throughout the entire life-cycle of rice cultivar Zhenshan 97 (ZS97). In addition, microarray analysis was performed to obtain the expression patterns of these genes under light/dark conditions and after three phytohormone treatments. This analysis revealed that the expression patterns of the OsBBX genes could be classified into eight groups. Eight genes were regulated under the light/dark treatments, and eleven genes showed differential expression under at least one phytohormone treatment. Moreover, we verified the diurnal expression of the OsBBX genes using the data obtained from the Diurnal Project and qPCR analysis, and the results indicated that many of these genes had a diurnal expression pattern. Conclusions/Significance The combination of the genome-wide identification and the expression and diurnal analysis of the OsBBX gene family should facilitate additional functional studies of the OsBBX genes. PMID:23118960

Inferring gene dependency network specific to phenotypic alteration based on gene expression data and clinical information of breast cancer.

PubMed

Zhou, Xionghui; Liu, Juan

2014-01-01

Although many methods have been proposed to reconstruct gene regulatory network, most of them, when applied in the sample-based data, can not reveal the gene regulatory relations underlying the phenotypic change (e.g. normal versus cancer). In this paper, we adopt phenotype as a variable when constructing the gene regulatory network, while former researches either neglected it or only used it to select the differentially expressed genes as the inputs to construct the gene regulatory network. To be specific, we integrate phenotype information with gene expression data to identify the gene dependency pairs by using the method of conditional mutual information. A gene dependency pair (A,B) means that the influence of gene A on the phenotype depends on gene B. All identified gene dependency pairs constitute a directed network underlying the phenotype, namely gene dependency network. By this way, we have constructed gene dependency network of breast cancer from gene expression data along with two different phenotype states (metastasis and non-metastasis). Moreover, we have found the network scale free, indicating that its hub genes with high out-degrees may play critical roles in the network. After functional investigation, these hub genes are found to be biologically significant and specially related to breast cancer, which suggests that our gene dependency network is meaningful. The validity has also been justified by literature investigation. From the network, we have selected 43 discriminative hubs as signature to build the classification model for distinguishing the distant metastasis risks of breast cancer patients, and the result outperforms those classification models with published signatures. In conclusion, we have proposed a promising way to construct the gene regulatory network by using sample-based data, which has been shown to be effective and accurate in uncovering the hidden mechanism of the biological process and identifying the gene signature for phenotypic change.

Inducible, tunable and multiplex human gene regulation using CRISPR-Cpf1-based transcription factors | Office of Cancer Genomics

Cancer.gov

Targeted and inducible regulation of mammalian gene expression is a broadly important research capability that may also enable development of novel therapeutics for treating human diseases. Here we demonstrate that a catalytically inactive RNA-guided CRISPR-Cpf1 nuclease fused to transcriptional activation domains can up-regulate endogenous human gene expression. We engineered drug-inducible Cpf1-based activators and show how this system can be used to tune the regulation of endogenous gene transcription in human cells.

Identification of differentially expressed genes through RNA sequencing in goats (Capra hircus) at different postnatal stages

PubMed Central

Li, Qian; Lin, Sen

2017-01-01

Intramuscular fat (IMF) content and fatty acid composition of longissimus dorsi muscle (LM) change with growth, which partially determines the flavor and nutritional value of goat (Capra hircus) meat. However, unlike cattle, little information is available on the transcriptome-wide changes during different postnatal stages in small ruminants, especially goats. In this study, the sequencing reads of goat LM tissues collected from kid, youth, and adult period were mapped to the goat genome. Results showed that out of total 24 689 Unigenes, 20 435 Unigenes were annotated. Based on expected number of fragments per kilobase of transcript sequence per million base pairs sequenced (FPKM), 111 annotated differentially expressed genes (DEGs) were identified among different postnatal stages, which were subsequently assigned to 16 possible expression patterns by series-cluster analysis. Functional classification by Gene Ontology (GO) analysis was used for selecting the genes showing highest expression related to lipid metabolism. Finally, we identified the node genes for lipid metabolism regulation using co-expression analysis. In conclusion, these data may uncover candidate genes having functional roles in regulation of goat muscle development and lipid metabolism during the various growth stages in goats. PMID:28800357

Identification of differentially expressed genes through RNA sequencing in goats (Capra hircus) at different postnatal stages.

PubMed

Lin, Yaqiu; Zhu, Jiangjiang; Wang, Yong; Li, Qian; Lin, Sen

2017-01-01

Intramuscular fat (IMF) content and fatty acid composition of longissimus dorsi muscle (LM) change with growth, which partially determines the flavor and nutritional value of goat (Capra hircus) meat. However, unlike cattle, little information is available on the transcriptome-wide changes during different postnatal stages in small ruminants, especially goats. In this study, the sequencing reads of goat LM tissues collected from kid, youth, and adult period were mapped to the goat genome. Results showed that out of total 24 689 Unigenes, 20 435 Unigenes were annotated. Based on expected number of fragments per kilobase of transcript sequence per million base pairs sequenced (FPKM), 111 annotated differentially expressed genes (DEGs) were identified among different postnatal stages, which were subsequently assigned to 16 possible expression patterns by series-cluster analysis. Functional classification by Gene Ontology (GO) analysis was used for selecting the genes showing highest expression related to lipid metabolism. Finally, we identified the node genes for lipid metabolism regulation using co-expression analysis. In conclusion, these data may uncover candidate genes having functional roles in regulation of goat muscle development and lipid metabolism during the various growth stages in goats.

Molecular Characterization of Heterologous HIV-1gp120 Gene Expression Disruption in Mycobacterium bovis BCG Host Strain: A Critical Issue for Engineering Mycobacterial Based-Vaccine Vectors

PubMed Central

Joseph, Joan; Fernández-Lloris, Raquel; Pezzat, Elías; Saubi, Narcís; Cardona, Pere-Joan; Mothe, Beatriz; Gatell, Josep Maria

2010-01-01

Mycobacterium bovis Bacillus Calmette-Guérin (BCG) as a live vector of recombinant bacterial vaccine is a promising system to be used. In this study, we evaluate the disrupted expression of heterologous HIV-1gp120 gene in BCG Pasteur host strain using replicative vectors pMV261 and pJH222. pJH222 carries a lysine complementing gene in BCG lysine auxotrophs. The HIV-1 gp120 gene expression was regulated by BCG hsp60 promoter (in plasmid pMV261) and Mycobacteria spp. α-antigen promoter (in plasmid pJH222). Among 14 rBCG:HIV-1gp120 (pMV261) colonies screened, 12 showed a partial deletion and two showed a complete deletion. However, deletion was not observed in all 10 rBCG:HIV-1gp120 (pJH222) colonies screened. In this study, we demonstrated that E. coli/Mycobacterial expression vectors bearing a weak promoter and lysine complementing gene in a recombinant lysine auxotroph of BCG could prevent genetic rearrangements and disruption of HIV 1gp120 gene expression, a key issue for engineering Mycobacterial based vaccine vectors. PMID:20617151

Simultaneous Overexpression of Functional Human HO-1, E5NT and ENTPD1 Protects Murine Fibroblasts against TNF-α-Induced Injury In Vitro

PubMed Central

Cinti, Alessandro; De Giorgi, Marco; Chisci, Elisa; Arena, Claudia; Galimberti, Gloria; Farina, Laura; Bugarin, Cristina; Rivolta, Ilaria; Gaipa, Giuseppe; Smolenski, Ryszard Tom; Cerrito, Maria Grazia; Lavitrano, Marialuisa; Giovannoni, Roberto

2015-01-01

Several biomedical applications, such as xenotransplantation, require multiple genes simultaneously expressed in eukaryotic cells. Advances in genetic engineering technologies have led to the development of efficient polycistronic vectors based on the use of the 2A self-processing oligopeptide. The aim of this work was to evaluate the protective effects of the simultaneous expression of a novel combination of anti-inflammatory human genes, ENTPD1, E5NT and HO-1, in eukaryotic cells. We produced an F2A system-based multicistronic construct to express three human proteins in NIH3T3 cells exposed to an inflammatory stimulus represented by tumor necrosis factor alpha (TNF-α), a pro-inflammatory cytokine which plays an important role during inflammation, cell proliferation, differentiation and apoptosis and in the inflammatory response during ischemia/reperfusion injury in several organ transplantation settings. The protective effects against TNF-α-induced cytotoxicity and cell death, mediated by HO-1, ENTPD1 and E5NT genes were better observed in cells expressing the combination of genes as compared to cells expressing each single gene and the effect was further improved by administrating enzymatic substrates of the human genes to the cells. Moreover, a gene expression analyses demonstrated that the expression of the three genes has a role in modulating key regulators of TNF-α signalling pathway, namely Nemo and Tnfaip3, that promoted pro-survival phenotype in TNF-α injured cells. These results could provide new insights in the research of protective mechanisms in transplantation settings. PMID:26513260

Allen Brain Atlas-Driven Visualizations: a web-based gene expression energy visualization tool.

PubMed

Zaldivar, Andrew; Krichmar, Jeffrey L

2014-01-01

The Allen Brain Atlas-Driven Visualizations (ABADV) is a publicly accessible web-based tool created to retrieve and visualize expression energy data from the Allen Brain Atlas (ABA) across multiple genes and brain structures. Though the ABA offers their own search engine and software for researchers to view their growing collection of online public data sets, including extensive gene expression and neuroanatomical data from human and mouse brain, many of their tools limit the amount of genes and brain structures researchers can view at once. To complement their work, ABADV generates multiple pie charts, bar charts and heat maps of expression energy values for any given set of genes and brain structures. Such a suite of free and easy-to-understand visualizations allows for easy comparison of gene expression across multiple brain areas. In addition, each visualization links back to the ABA so researchers may view a summary of the experimental detail. ABADV is currently supported on modern web browsers and is compatible with expression energy data from the Allen Mouse Brain Atlas in situ hybridization data. By creating this web application, researchers can immediately obtain and survey numerous amounts of expression energy data from the ABA, which they can then use to supplement their work or perform meta-analysis. In the future, we hope to enable ABADV across multiple data resources.

Microarray‑based bioinformatics analysis of the prospective target gene network of key miRNAs influenced by long non‑coding RNA PVT1 in HCC.

PubMed

Zhang, Yu; Mo, Wei-Jia; Wang, Xiao; Zhang, Tong-Tong; Qin, Yuan; Wang, Han-Lin; Chen, Gang; Wei, Dan-Ming; Dang, Yi-Wu

2018-05-02

The long non‑coding RNA (lncRNA) PVT1 plays vital roles in the tumorigenesis and development of various types of cancer. However, the potential expression profiling, functions and pathways of PVT1 in HCC remain unknown. PVT1 was knocked down in SMMC‑7721 cells, and a miRNA microarray analysis was performed to detect the differentially expressed miRNAs. Twelve target prediction algorithms were used to predict the underlying targets of these differentially expressed miRNAs. Bioinformatics analysis was performed to explore the underlying functions, pathways and networks of the targeted genes. Furthermore, the relationship between PVT1 and the clinical parameters in HCC was confirmed based on the original data in the TCGA database. Among the differentially expressed miRNAs, the top two upregulated and downregulated miRNAs were selected for further analysis based on the false discovery rate (FDR), fold‑change (FC) and P‑values. Based on the TCGA database, PVT1 was obviously highly expressed in HCC, and a statistically higher PVT1 expression was found for sex (male), ethnicity (Asian) and pathological grade (G3+G4) compared to the control groups (P<0.05). Furthermore, Gene Ontology (GO) analysis revealed that the target genes were involved in complex cellular pathways, such as the macromolecule biosynthetic process, compound metabolic process, and transcription. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that the MAPK and Wnt signaling pathways may be correlated with the regulation of the four candidate miRNAs. The results therefore provide significant information on the differentially expressed miRNAs associated with PVT1 in HCC, and we hypothesized that PVT1 may play vital roles in HCC by regulating different miRNAs or target gene expression (particularly MAPK8) via the MAPK or Wnt signaling pathways. Thus, further investigation of the molecular mechanism of PVT1 in HCC is needed.

[Effect of gene optimization on the expression and purification of HDV small antigen produced by genetic engineering].

PubMed

Ding, Jun-Ying; Meng, Qing-Ling; Guo, Min-Zhuo; Yi, Yao; Su, Qiu-Dong; Lu, Xue-Xin; Qiu, Feng; Bi, Sheng-Li

2012-10-01

To study the effect of gene optimization on the expression and purification of HDV small antigen produced by genetic engineering. Based on the colon preference of E. coli, the HDV small antigen original gene from GenBank was optimized. Both the original gene and the optimized gene expressed in prokaryotic cells, SDS-PAGE was made to analyze the protein expression yield and to decide which protein expression style was more proportion than the other. Furthermore, two antigens were purified by chromatography in order to compare the purity by SDS-PAGE and Image Lab software. SDS-PAGE indicated that the molecular weight of target proteins from two groups were the same as we expected. Gene optimization resulted in the higher yield and it could make the product more soluble. After chromatography, the purity of target protein from optimized gene was up to 96.3%, obviously purer than that from original gene. Gene optimization could increase the protein expression yield and solubility of genetic engineering HDV small antigen. In addition, the product from the optimized gene group was easier to be purified for diagnosis usage.

STARNET 2: a web-based tool for accelerating discovery of gene regulatory networks using microarray co-expression data

PubMed Central

Jupiter, Daniel; Chen, Hailin; VanBuren, Vincent

2009-01-01

Background Although expression microarrays have become a standard tool used by biologists, analysis of data produced by microarray experiments may still present challenges. Comparison of data from different platforms, organisms, and labs may involve complicated data processing, and inferring relationships between genes remains difficult. Results STARNET 2 is a new web-based tool that allows post hoc visual analysis of correlations that are derived from expression microarray data. STARNET 2 facilitates user discovery of putative gene regulatory networks in a variety of species (human, rat, mouse, chicken, zebrafish, Drosophila, C. elegans, S. cerevisiae, Arabidopsis and rice) by graphing networks of genes that are closely co-expressed across a large heterogeneous set of preselected microarray experiments. For each of the represented organisms, raw microarray data were retrieved from NCBI's Gene Expression Omnibus for a selected Affymetrix platform. All pairwise Pearson correlation coefficients were computed for expression profiles measured on each platform, respectively. These precompiled results were stored in a MySQL database, and supplemented by additional data retrieved from NCBI. A web-based tool allows user-specified queries of the database, centered at a gene of interest. The result of a query includes graphs of correlation networks, graphs of known interactions involving genes and gene products that are present in the correlation networks, and initial statistical analyses. Two analyses may be performed in parallel to compare networks, which is facilitated by the new HEATSEEKER module. Conclusion STARNET 2 is a useful tool for developing new hypotheses about regulatory relationships between genes and gene products, and has coverage for 10 species. Interpretation of the correlation networks is supported with a database of previously documented interactions, a test for enrichment of Gene Ontology terms, and heat maps of correlation distances that may be used to compare two networks. The list of genes in a STARNET network may be useful in developing a list of candidate genes to use for the inference of causal networks. The tool is freely available at , and does not require user registration. PMID:19828039

Identification of Primary Transcriptional Regulation of Cell Cycle-Regulated Genes upon DNA Damage

PubMed Central

Zhou, Tong; Chou, Jeff; Mullen, Thomas E.; Elkon, Rani; Zhou, Yingchun; Simpson, Dennis A.; Bushel, Pierre R.; Paules, Richard S.; Lobenhofer, Edward K.; Hurban, Patrick; Kaufmann, William K.

2007-01-01

The changes in global gene expression in response to DNA damage may derive from either direct induction or repression by transcriptional regulation or indirectly by synchronization of cells to specific cell cycle phases, such as G1 or G2. We developed a model that successfully estimated the expression levels of >400 cell cycle-regulated genes in normal human fibroblasts based on the proportions of cells in each phase of the cell cycle. By isolating effects on the gene expression associated with the cell cycle phase redistribution after genotoxin treatment, the direct transcriptional target genes were distinguished from genes for which expression changed secondary to cell synchronization. Application of this model to ionizing radiation (IR)-treated normal human fibroblasts identified 150 of 406 cycle-regulated genes as putative direct transcriptional targets of IR-induced DNA damage. Changes in expression of these genes after IR treatment derived from both direct transcriptional regulation and cell cycle synchronization. PMID:17404513

The genetic architecture of gene expression levels in wild baboons.

PubMed

Tung, Jenny; Zhou, Xiang; Alberts, Susan C; Stephens, Matthew; Gilad, Yoav

2015-02-25

Primate evolution has been argued to result, in part, from changes in how genes are regulated. However, we still know little about gene regulation in natural primate populations. We conducted an RNA sequencing (RNA-seq)-based study of baboons from an intensively studied wild population. We performed complementary expression quantitative trait locus (eQTL) mapping and allele-specific expression analyses, discovering substantial evidence for, and surprising power to detect, genetic effects on gene expression levels in the baboons. eQTL were most likely to be identified for lineage-specific, rapidly evolving genes; interestingly, genes with eQTL significantly overlapped between baboons and a comparable human eQTL data set. Our results suggest that genes vary in their tolerance of genetic perturbation, and that this property may be conserved across species. Further, they establish the feasibility of eQTL mapping using RNA-seq data alone, and represent an important step towards understanding the genetic architecture of gene expression in primates.

The genetic architecture of gene expression levels in wild baboons

PubMed Central

Tung, Jenny; Zhou, Xiang; Alberts, Susan C; Stephens, Matthew; Gilad, Yoav

2015-01-01

Primate evolution has been argued to result, in part, from changes in how genes are regulated. However, we still know little about gene regulation in natural primate populations. We conducted an RNA sequencing (RNA-seq)-based study of baboons from an intensively studied wild population. We performed complementary expression quantitative trait locus (eQTL) mapping and allele-specific expression analyses, discovering substantial evidence for, and surprising power to detect, genetic effects on gene expression levels in the baboons. eQTL were most likely to be identified for lineage-specific, rapidly evolving genes; interestingly, genes with eQTL significantly overlapped between baboons and a comparable human eQTL data set. Our results suggest that genes vary in their tolerance of genetic perturbation, and that this property may be conserved across species. Further, they establish the feasibility of eQTL mapping using RNA-seq data alone, and represent an important step towards understanding the genetic architecture of gene expression in primates. DOI: http://dx.doi.org/10.7554/eLife.04729.001 PMID:25714927

Genetic divergence in the transcriptional engram of chronic alcohol abuse: A laser-capture RNA-seq study of the mouse mesocorticolimbic system.

PubMed

Mulligan, Megan K; Mozhui, Khyobeni; Pandey, Ashutosh K; Smith, Maren L; Gong, Suzhen; Ingels, Jesse; Miles, Michael F; Lopez, Marcelo F; Lu, Lu; Williams, Robert W

2017-02-01

Genetic factors that influence the transition from initial drinking to dependence remain enigmatic. Recent studies have leveraged chronic intermittent ethanol (CIE) paradigms to measure changes in brain gene expression in a single strain at 0, 8, 72 h, and even 7 days following CIE. We extend these findings using LCM RNA-seq to profile expression in 11 brain regions in two inbred strains - C57BL/6J (B6) and DBA/2J (D2) - 72 h following multiple cycles of ethanol self-administration and CIE. Linear models identified differential expression based on treatment, region, strain, or interactions with treatment. Nearly 40% of genes showed a robust effect (FDR < 0.01) of region, and hippocampus CA1, cortex, bed nucleus stria terminalis, and nucleus accumbens core had the highest number of differentially expressed genes after treatment. Another 8% of differentially expressed genes demonstrated a robust effect of strain. As expected, based on similar studies in B6, treatment had a much smaller impact on expression; only 72 genes (p < 0.01) are modulated by treatment (independent of region or strain). Strikingly, many more genes (415) show a strain-specific and largely opposite response to treatment and are enriched in processes related to RNA metabolism, transcription factor activity, and mitochondrial function. Over 3 times as many changes in gene expression were detected in D2 compared to B6, and weighted gene co-expression network analysis (WGCNA) module comparison identified more modules enriched for treatment effects in D2. Substantial strain differences exist in the temporal pattern of transcriptional neuroadaptation to CIE, and these may drive individual differences in risk of addiction following excessive alcohol consumption. Copyright © 2016 Elsevier Inc. All rights reserved.

Abiotic conditions leading to FUM gene expression and fumonisin accumulation by Fusarium proliferatum strains grown on a wheat-based substrate.

PubMed

Cendoya, Eugenia; Pinson-Gadais, Laetitia; Farnochi, María C; Ramirez, María L; Chéreau, Sylvain; Marcheguay, Giselè; Ducos, Christine; Barreau, Christian; Richard-Forget, Florence

2017-07-17

Fusarium proliferatum produces fumonisins B not only on maize but also on diverse crops including wheat. Using a wheat-based medium, the effects of abiotic factors, temperature and water activity (a W ), on growth, fumonisin biosynthesis, and expression of FUM genes were compared for three F. proliferatum strains isolated from durum wheat in Argentina. Although all isolates showed similar profiles of growth, the fumonisin production profiles were slightly different. Regarding FUM gene transcriptional control, both FUM8 and FUM19 expression showed similar behavior in all tested conditions. For both genes, expression at 25°C correlated with fumonisin production, regardless of the a w conditions. However, at 15°C, these two genes were as highly expressed as at 25°C although the amounts of toxin were very weak, suggesting that the kinetics of fumonisin production was slowed at 15°C. This study provides useful baseline data on conditions representing a low or a high risk for contamination of wheat kernels with fumonisins. Copyright © 2017 Elsevier B.V. All rights reserved.

Standardized Cannabis sativa extract attenuates tau and stathmin gene expression in the melanoma cell line.

PubMed

Vaseghi, Golnaz; Taki, Mohamad Javad; Javanmard, Shaghayegh Haghjooy

2017-10-01

Metastasis is the main cause of death in patients with melanoma. Cannabis-based medicines are effective adjunctive drugs in cancer patients. Tau and Stathmin proteins are the key proteins in cancer metastasis. Here we have investigated the effect of a standardized Cannabis sativa extract on cell migration and Tau and Stathmin gene expression in the melanoma cell line. In the treatment group, melanoma (B1617) was treated 48 hr with various concentrations of standardized C. sativa extract. Cells with no treatment were considered as the control group, then study was followed by Quantitative RT-Real Time PCR assay. Relative gene expression was calculated by the ΔΔct method. Migration assay was used to evaluate cancer metastasis. Tau and stathmin gene expression was significantly decreased compared to the control group. Cell migration was also significantly reduced compared to controls. C. sativa decreased tau and stathmin gene expression and cancer metastasis. The results may have some clinical relevance for the use of cannabis-based medicines in patients with metastatic melanoma.

Field Evaluation of a Fluorogenic Probe-Based PCR Assay for Identification of a Visceral Leishmaniasis Gene Target

DTIC Science & Technology

2004-06-01

encodes protein required for amastigote development, which can ultimately be expressed in humans as VL (3, 4, 5). The leishmaniasises are also expressed ...Leishmania surveillance at Tallil Air Base, south central Iraq, expressed concern of a potential leishmaniasis outbreak situation. In response, we...site. That L. donovani promastigote-to-amastigote development, and VL pathogenesis, requires an A2 gene family encoded factor defines this protein

On-the-fly selection of cell-specific enhancers, genes, miRNAs and proteins across the human body using SlideBase

PubMed Central

Ienasescu, Hans; Li, Kang; Andersson, Robin; Vitezic, Morana; Rennie, Sarah; Chen, Yun; Vitting-Seerup, Kristoffer; Lagoni, Emil; Boyd, Mette; Bornholdt, Jette; de Hoon, Michiel J. L.; Kawaji, Hideya; Lassmann, Timo; Hayashizaki, Yoshihide; Forrest, Alistair R. R.; Carninci, Piero; Sandelin, Albin

2016-01-01

Genomics consortia have produced large datasets profiling the expression of genes, micro-RNAs, enhancers and more across human tissues or cells. There is a need for intuitive tools to select subsets of such data that is the most relevant for specific studies. To this end, we present SlideBase, a web tool which offers a new way of selecting genes, promoters, enhancers and microRNAs that are preferentially expressed/used in a specified set of cells/tissues, based on the use of interactive sliders. With the help of sliders, SlideBase enables users to define custom expression thresholds for individual cell types/tissues, producing sets of genes, enhancers etc. which satisfy these constraints. Changes in slider settings result in simultaneous changes in the selected sets, updated in real time. SlideBase is linked to major databases from genomics consortia, including FANTOM, GTEx, The Human Protein Atlas and BioGPS. Database URL: http://slidebase.binf.ku.dk PMID:28025337

The promises and pitfalls of RNA-interference-based therapeutics

PubMed Central

Castanotto, Daniela; Rossi, John J.

2009-01-01

The discovery that gene expression can be controlled by the Watson–Crick base-pairing of small RNAs with messenger RNAs containing complementary sequence — a process known as RNA interference — has markedly advanced our understanding of eukaryotic gene regulation and function. The ability of short RNA sequences to modulate gene expression has provided a powerful tool with which to study gene function and is set to revolutionize the treatment of disease. Remarkably, despite being just one decade from its discovery, the phenomenon is already being used therapeutically in human clinical trials, and biotechnology companies that focus on RNA-interference-based therapeutics are already publicly traded. PMID:19158789

Digital analysis of the expression levels of multiple colorectal cancer-related genes by multiplexed digital-PCR coupled with hydrogel bead-array.

PubMed

Qi, Zongtai; Ma, Yinjiao; Deng, Lili; Wu, Haiping; Zhou, Guohua; Kajiyama, Tomoharu; Kambara, Hideki

2011-06-07

To digitally analyze expression levels of multiple genes in one reaction, we proposed a method termed as 'MDHB' (Multiplexed Digital-PCR coupled with Hydrogel Bead-array). The template for bead-based emulsion PCR (emPCR) was prepared by reverse transcription using sequence-tagged primers. The beads recovered from emPCR were immobilized with hydrogel to form a single-bead layer on a chip, and then decoded by gene-specific probe hybridization and Cy3-dUTP based primer extension reaction. The specificity of probe hybridization was improved by using electrophoresis to remove mismatched probes on the bead's surface. The number of positive beads reflects the abundance of expressed genes; the expression levels of target genes were normalized to a housekeeping gene and expressed as the number ratio of green beads to red beads. The discrimination limit of MDHB is 0.1% (i.e., one target molecule from 1000 background molecules), and the sensitivity of the method is below 100 cells when using the β-actin gene as the detection target. We have successfully employed MDHB to detect the relative expression levels of four colorectal cancer (CRC)-related genes (c-myc, COX-2, MMP7, and DPEP1) in 8 tissue samples and 9 stool samples from CRC patients, giving the detection rates of 100% and 77%, respectively. The results suggest that MDHB could be a potential tool for early non-invasive diagnosis of CRC.

Transcript Profile Analyses of Maize Silks Reveal Effective Activation of Genes Involved in Microtubule-Based Movement, Ubiquitin-Dependent Protein Degradation, and Transport in the Pollination Process

PubMed Central

Chen, Hao; Sun, Wei; Zhang, Xian Sheng

2013-01-01

Pollination is the first crucial step of sexual reproduction in flowering plants, and it requires communication and coordination between the pollen and the stigma. Maize (Zea mays) is a model monocot with extraordinarily long silks, and a fully sequenced genome, but little is known about the mechanism of its pollen–stigma interactions. In this study, the dynamic gene expression of silks at four different stages before and after pollination was analyzed. The expression profiles of immature silks (IMS), mature silks (MS), and silks at 20 minutes and 3 hours after pollination (20MAP and 3HAP, respectively) were compared. In total, we identified 6,337 differentially expressed genes in silks (SDEG) at the four stages. Among them, the expression of 172 genes were induced upon pollination, most of which participated in RNA binding, processing and transcription, signal transduction, and lipid metabolism processes. Genes in the SDEG dataset could be divided into 12 time-course clusters according to their expression patterns. Gene Ontology (GO) enrichment analysis revealed that many genes involved in microtubule-based movement, ubiquitin-mediated protein degradation, and transport were predominantly expressed at specific stages, indicating that they might play important roles in the pollination process of maize. These results add to current knowledge about the pollination process of grasses and provide a foundation for future studies on key genes involved in the pollen–silk interaction in maize. PMID:23301084

Transcriptome Analysis of ABA/JA-Dual Responsive Genes in Rice Shoot and Root.

PubMed

Kim, Jin-Ae; Bhatnagar, Nikita; Kwon, Soon Jae; Min, Myung Ki; Moon, Seok-Jun; Yoon, In Sun; Kwon, Taek-Ryoun; Kim, Sun Tae; Kim, Beom-Gi

2018-01-01

The phytohormone abscisic acid (ABA) enables plants to adapt to adverse environmental conditions through the modulation of metabolic pathways and of growth and developmental programs. We used comparative microarray analysis to identify genes exhibiting ABA-dependent expression and other hormone-dependent expression among them in Oryza sativa shoot and root. We identified 854 genes as significantly up- or down-regulated in root or shoot under ABA treatment condition. Most of these genes had similar expression profiles in root and shoot under ABA treatment condition, whereas 86 genes displayed opposite expression responses in root and shoot. To examine the crosstalk between ABA and other hormones, we compared the expression profiles of the ABA-dependently regulated genes under several different hormone treatment conditions. Interestingly, around half of the ABA-dependently expressed genes were also regulated by jasmonic acid based on microarray data analysis. We searched the promoter regions of these genes for cis-elements that could be responsible for their responsiveness to both hormones, and found that ABRE and MYC2 elements, among others, were common to the promoters of genes that were regulated by both ABA and JA. These results show that ABA and JA might have common gene expression regulation system and might explain why the JA could function for both abiotic and biotic stress tolerance.

An analysis of gene expression in PTSD implicates genes involved in the glucocorticoid receptor pathway and neural responses to stress

PubMed Central

Logue, Mark W.; Smith, Alicia K.; Baldwin, Clinton; Wolf, Erika J.; Guffanti, Guia; Ratanatharathorn, Andrew; Stone, Annjanette; Schichman, Steven A.; Humphries, Donald; Binder, Elisabeth B.; Arloth, Janine; Menke, Andreas; Uddin, Monica; Wildman, Derek; Galea, Sandro; Aiello, Allison E.; Koenen, Karestan C.; Miller, Mark W.

2015-01-01

We examined the association between posttraumatic stress disorder (PTSD) and gene expression using whole blood samples from a cohort of trauma-exposed white non-Hispanic male veterans (115 cases and 28 controls). 10,264 probes of genes and gene transcripts were analyzed. We found 41 that were differentially expressed in PTSD cases versus controls (multiple-testing corrected p<0.05). The most significant was DSCAM, a neurological gene expressed widely in the developing brain and in the amygdala and hippocampus of the adult brain. We then examined the 41 differentially expressed genes in a meta-analysis using two replication cohorts and found significant associations with PTSD for 7 of the 41 (p<0.05), one of which (ATP6AP1L) survived multiple-testing correction. There was also broad evidence of overlap across the discovery and replication samples for the entire set of genes implicated in the discovery data based on the direction of effect and an enrichment of p<0.05 significant probes beyond what would be expected under the null. Finally, we found that the set of differentially expressed genes from the discovery sample was enriched for genes responsive to glucocorticoid signaling with most showing reduced expression in PTSD cases compared to controls. PMID:25867994

Construction of diagnosis system and gene regulatory networks based on microarray analysis.

PubMed

Hong, Chun-Fu; Chen, Ying-Chen; Chen, Wei-Chun; Tu, Keng-Chang; Tsai, Meng-Hsiun; Chan, Yung-Kuan; Yu, Shyr Shen

2018-05-01

A microarray analysis generally contains expression data of thousands of genes, but most of them are irrelevant to the disease of interest, making analyzing the genes concerning specific diseases complicated. Therefore, filtering out a few essential genes as well as their regulatory networks is critical, and a disease can be easily diagnosed just depending on the expression profiles of a few critical genes. In this study, a target gene screening (TGS) system, which is a microarray-based information system that integrates F-statistics, pattern recognition matching, a two-layer K-means classifier, a Parameter Detection Genetic Algorithm (PDGA), a genetic-based gene selector (GBG selector) and the association rule, was developed to screen out a small subset of genes that can discriminate malignant stages of cancers. During the first stage, F-statistic, pattern recognition matching, and a two-layer K-means classifier were applied in the system to filter out the 20 critical genes most relevant to ovarian cancer from 9600 genes, and the PDGA was used to decide the fittest values of the parameters for these critical genes. Among the 20 critical genes, 15 are associated with cancer progression. In the second stage, we further employed a GBG selector and the association rule to screen out seven target gene sets, each with only four to six genes, and each of which can precisely identify the malignancy stage of ovarian cancer based on their expression profiles. We further deduced the gene regulatory networks of the 20 critical genes by applying the Pearson correlation coefficient to evaluate the correlationship between the expression of each gene at the same stages and at different stages. Correlationships between gene pairs were calculated, and then, three regulatory networks were deduced. Their correlationships were further confirmed by the Ingenuity pathway analysis. The prognostic significances of the genes identified via regulatory networks were examined using online tools, and most represented biomarker candidates. In summary, our proposed system provides a new strategy to identify critical genes or biomarkers, as well as their regulatory networks, from microarray data. Copyright © 2018. Published by Elsevier Inc.

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 study not only greatly increases our understanding of genetic susceptibility to RA, but also provides important insights into the ethno-genetic homogeneity and heterogeneity of RA in both ethnicities.

LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures.

PubMed

Duan, Qiaonan; Flynn, Corey; Niepel, Mario; Hafner, Marc; Muhlich, Jeremy L; Fernandez, Nicolas F; Rouillard, Andrew D; Tan, Christopher M; Chen, Edward Y; Golub, Todd R; Sorger, Peter K; Subramanian, Aravind; Ma'ayan, Avi

2014-07-01

For the Library of Integrated Network-based Cellular Signatures (LINCS) project many gene expression signatures using the L1000 technology have been produced. The L1000 technology is a cost-effective method to profile gene expression in large scale. LINCS Canvas Browser (LCB) is an interactive HTML5 web-based software application that facilitates querying, browsing and interrogating many of the currently available LINCS L1000 data. LCB implements two compacted layered canvases, one to visualize clustered L1000 expression data, and the other to display enrichment analysis results using 30 different gene set libraries. Clicking on an experimental condition highlights gene-sets enriched for the differentially expressed genes from the selected experiment. A search interface allows users to input gene lists and query them against over 100 000 conditions to find the top matching experiments. The tool integrates many resources for an unprecedented potential for new discoveries in systems biology and systems pharmacology. The LCB application is available at http://www.maayanlab.net/LINCS/LCB. Customized versions will be made part of the http://lincscloud.org and http://lincs.hms.harvard.edu websites. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Characterization of an In Vivo Z-DNA Detection Probe Based on a Cell Nucleus Accumulating Intrabody.

PubMed

Gulis, Galina; Silva, Izabel Cristina Rodrigues; Sousa, Herdson Renney; Sousa, Isabel Garcia; Bezerra, Maryani Andressa Gomes; Quilici, Luana Salgado; Maranhao, Andrea Queiroz; Brigido, Marcelo Macedo

2016-09-01

Left-handed Z-DNA is a physiologically unstable DNA conformation, and its existence in vivo can be attributed to localized torsional distress. Despite evidence for the existence of Z-DNA in vivo, its precise role in the control of gene expression is not fully understood. Here, an in vivo probe based on an anti-Z-DNA intrabody is proposed for native Z-DNA detection. The probe was used for chromatin immunoprecipitation of potential Z-DNA-forming sequences in the human genome. One of the isolated putative Z-DNA-forming sequences was cloned upstream of a reporter gene expression cassette under control of the CMV promoter. The reporter gene encoded an antibody fragment fused to GFP. Transient co-transfection of this vector along with the Z-probe coding vector improved reporter gene expression. This improvement was demonstrated by measuring reporter gene mRNA and protein levels and the amount of fluorescence in co-transfected CHO-K1 cells. These results suggest that the presence of the anti-Z-DNA intrabody can interfere with a Z-DNA-containing reporter gene expression. Therefore, this in vivo probe for the detection of Z-DNA could be used for global correlation of Z-DNA-forming sequences and gene expression regulation.

Genome-Wide Identification, Evolution and Expression Analysis of mTERF Gene Family in Maize

PubMed Central

Zhao, Yanxin; Cai, Manjun; Zhang, Xiaobo; Li, Yurong; Zhang, Jianhua; Zhao, Hailiang; Kong, Fei; Zheng, Yonglian; Qiu, Fazhan

2014-01-01

Plant mitochondrial transcription termination factor (mTERF) genes comprise a large family with important roles in regulating organelle gene expression. In this study, a comprehensive database search yielded 31 potential mTERF genes in maize (Zea mays L.) and most of them were targeted to mitochondria or chloroplasts. Maize mTERF were divided into nine main groups based on phylogenetic analysis, and group IX represented the mitochondria and species-specific clade that diverged from other groups. Tandem and segmental duplication both contributed to the expansion of the mTERF gene family in the maize genome. Comprehensive expression analysis of these genes, using microarray data and RNA-seq data, revealed that these genes exhibit a variety of expression patterns. Environmental stimulus experiments revealed differential up or down-regulation expression of maize mTERF genes in seedlings exposed to light/dark, salts and plant hormones, respectively, suggesting various important roles of maize mTERF genes in light acclimation and stress-related responses. These results will be useful for elucidating the roles of mTERF genes in the growth, development and stress response of maize. PMID:24718683

System Biology Approach: Gene Network Analysis for Muscular Dystrophy.

PubMed

Censi, Federica; Calcagnini, Giovanni; Mattei, Eugenio; Giuliani, Alessandro

2018-01-01

Phenotypic changes at different organization levels from cell to entire organism are associated to changes in the pattern of gene expression. These changes involve the entire genome expression pattern and heavily rely upon correlation patterns among genes. The classical approach used to analyze gene expression data builds upon the application of supervised statistical techniques to detect genes differentially expressed among two or more phenotypes (e.g., normal vs. disease). The use of an a posteriori, unsupervised approach based on principal component analysis (PCA) and the subsequent construction of gene correlation networks can shed a light on unexpected behaviour of gene regulation system while maintaining a more naturalistic view on the studied system.In this chapter we applied an unsupervised method to discriminate DMD patient and controls. The genes having the highest absolute scores in the discrimination between the groups were then analyzed in terms of gene expression networks, on the basis of their mutual correlation in the two groups. The correlation network structures suggest two different modes of gene regulation in the two groups, reminiscent of important aspects of DMD pathogenesis.

Fight or flight? - Flight increases immune gene expression but does not help to fight an infection.

PubMed

Woestmann, L; Kvist, J; Saastamoinen, M

2017-03-01

Flight represents a key trait in most insects, being energetically extremely demanding, yet often necessary for foraging and reproduction. Additionally, dispersal via flight is especially important for species living in fragmented landscapes. Even though, based on life-history theory, a negative relationship may be expected between flight and immunity, a number of previous studies have indicated flight to induce an increased immune response. In this study, we assessed whether induced immunity (i.e. immune gene expression) in response to 15-min forced flight treatment impacts individual survival of bacterial infection in the Glanville fritillary butterfly (Melitaea cinxia). We were able to confirm previous findings of flight-induced immune gene expression, but still observed substantially stronger effects on both gene expression levels and life span due to bacterial infection compared to flight treatment. Even though gene expression levels of some immunity-related genes were elevated due to flight, these individuals did not show increased survival of bacterial infection, indicating that flight-induced immune activation does not completely protect them from the negative effects of bacterial infection. Finally, an interaction between flight and immune treatment indicated a potential trade-off: flight treatment increased immune gene expression in naïve individuals only, whereas in infected individuals no increase in immune gene expression was induced by flight. Our results suggest that the up-regulation of immune genes upon flight is based on a general stress response rather than reflecting an adaptive response to cope with potential infections during flight or in new habitats. © 2016 The Authors. Journal of Evolutionary Biology Published by John Wiley & Sons ltd on behalf of European Society for Evolutionary Biology.

LEGO: a novel method for gene set over-representation analysis by incorporating network-based gene weights

PubMed Central

Dong, Xinran; Hao, Yun; Wang, Xiao; Tian, Weidong

2016-01-01

Pathway or gene set over-representation analysis (ORA) has become a routine task in functional genomics studies. However, currently widely used ORA tools employ statistical methods such as Fisher’s exact test that reduce a pathway into a list of genes, ignoring the constitutive functional non-equivalent roles of genes and the complex gene-gene interactions. Here, we develop a novel method named LEGO (functional Link Enrichment of Gene Ontology or gene sets) that takes into consideration these two types of information by incorporating network-based gene weights in ORA analysis. In three benchmarks, LEGO achieves better performance than Fisher and three other network-based methods. To further evaluate LEGO’s usefulness, we compare LEGO with five gene expression-based and three pathway topology-based methods using a benchmark of 34 disease gene expression datasets compiled by a recent publication, and show that LEGO is among the top-ranked methods in terms of both sensitivity and prioritization for detecting target KEGG pathways. In addition, we develop a cluster-and-filter approach to reduce the redundancy among the enriched gene sets, making the results more interpretable to biologists. Finally, we apply LEGO to two lists of autism genes, and identify relevant gene sets to autism that could not be found by Fisher. PMID:26750448

LEGO: a novel method for gene set over-representation analysis by incorporating network-based gene weights.

PubMed

Dong, Xinran; Hao, Yun; Wang, Xiao; Tian, Weidong

2016-01-11

Pathway or gene set over-representation analysis (ORA) has become a routine task in functional genomics studies. However, currently widely used ORA tools employ statistical methods such as Fisher's exact test that reduce a pathway into a list of genes, ignoring the constitutive functional non-equivalent roles of genes and the complex gene-gene interactions. Here, we develop a novel method named LEGO (functional Link Enrichment of Gene Ontology or gene sets) that takes into consideration these two types of information by incorporating network-based gene weights in ORA analysis. In three benchmarks, LEGO achieves better performance than Fisher and three other network-based methods. To further evaluate LEGO's usefulness, we compare LEGO with five gene expression-based and three pathway topology-based methods using a benchmark of 34 disease gene expression datasets compiled by a recent publication, and show that LEGO is among the top-ranked methods in terms of both sensitivity and prioritization for detecting target KEGG pathways. In addition, we develop a cluster-and-filter approach to reduce the redundancy among the enriched gene sets, making the results more interpretable to biologists. Finally, we apply LEGO to two lists of autism genes, and identify relevant gene sets to autism that could not be found by Fisher.

CGO: utilizing and integrating gene expression microarray data in clinical research and data management.

PubMed

Bumm, Klaus; Zheng, Mingzhong; Bailey, Clyde; Zhan, Fenghuang; Chiriva-Internati, M; Eddlemon, Paul; Terry, Julian; Barlogie, Bart; Shaughnessy, John D

2002-02-01

Clinical GeneOrganizer (CGO) is a novel windows-based archiving, organization and data mining software for the integration of gene expression profiling in clinical medicine. The program implements various user-friendly tools and extracts data for further statistical analysis. This software was written for Affymetrix GeneChip *.txt files, but can also be used for any other microarray-derived data. The MS-SQL server version acts as a data mart and links microarray data with clinical parameters of any other existing database and therefore represents a valuable tool for combining gene expression analysis and clinical disease characteristics.

OpenFlyData: an exemplar data web integrating gene expression data on the fruit fly Drosophila melanogaster.

PubMed

Miles, Alistair; Zhao, Jun; Klyne, Graham; White-Cooper, Helen; Shotton, David

2010-10-01

Integrating heterogeneous data across distributed sources is a major requirement for in silico bioinformatics supporting translational research. For example, genome-scale data on patterns of gene expression in the fruit fly Drosophila melanogaster are widely used in functional genomic studies in many organisms to inform candidate gene selection and validate experimental results. However, current data integration solutions tend to be heavy weight, and require significant initial and ongoing investment of effort. Development of a common Web-based data integration infrastructure (a.k.a. data web), using Semantic Web standards, promises to alleviate these difficulties, but little is known about the feasibility, costs, risks or practical means of migrating to such an infrastructure. We describe the development of OpenFlyData, a proof-of-concept system integrating gene expression data on D. melanogaster, combining Semantic Web standards with light-weight approaches to Web programming based on Web 2.0 design patterns. To support researchers designing and validating functional genomic studies, OpenFlyData includes user-facing search applications providing intuitive access to and comparison of gene expression data from FlyAtlas, the BDGP in situ database, and FlyTED, using data from FlyBase to expand and disambiguate gene names. OpenFlyData's services are also openly accessible, and are available for reuse by other bioinformaticians and application developers. Semi-automated methods and tools were developed to support labour- and knowledge-intensive tasks involved in deploying SPARQL services. These include methods for generating ontologies and relational-to-RDF mappings for relational databases, which we illustrate using the FlyBase Chado database schema; and methods for mapping gene identifiers between databases. The advantages of using Semantic Web standards for biomedical data integration are discussed, as are open issues. In particular, although the performance of open source SPARQL implementations is sufficient to query gene expression data directly from user-facing applications such as Web-based data fusions (a.k.a. mashups), we found open SPARQL endpoints to be vulnerable to denial-of-service-type problems, which must be mitigated to ensure reliability of services based on this standard. These results are relevant to data integration activities in translational bioinformatics. The gene expression search applications and SPARQL endpoints developed for OpenFlyData are deployed at http://openflydata.org. FlyUI, a library of JavaScript widgets providing re-usable user-interface components for Drosophila gene expression data, is available at http://flyui.googlecode.com. Software and ontologies to support transformation of data from FlyBase, FlyAtlas, BDGP and FlyTED to RDF are available at http://openflydata.googlecode.com. SPARQLite, an implementation of the SPARQL protocol, is available at http://sparqlite.googlecode.com. All software is provided under the GPL version 3 open source license.

Gene expression in cerebral ischemia: a new approach for neuroprotection.

PubMed

Millán, Mónica; Arenillas, Juan

2006-01-01

Cerebral ischemia is one of the strongest stimuli for gene induction in the brain. Hundreds of genes have been found to be induced by brain ischemia. Many genes are involved in neurodestructive functions such as excitotoxicity, inflammatory response and neuronal apoptosis. However, cerebral ischemia is also a powerful reformatting and reprogramming stimulus for the brain through neuroprotective gene expression. Several genes may participate in both cellular responses. Thus, isolation of candidate genes for neuroprotection strategies and interpretation of expression changes have been proven difficult. Nevertheless, many studies are being carried out to improve the knowledge of the gene activation and protein expression following ischemic stroke, as well as in the development of new therapies that modify biochemical, molecular and genetic changes underlying cerebral ischemia. Owing to the complexity of the process involving numerous critical genes expressed differentially in time, space and concentration, ongoing therapeutic efforts should be based on multiple interventions at different levels. By modification of the acute gene expression induced by ischemia or the apoptotic gene program, gene therapy is a promising treatment but is still in a very experimental phase. Some hurdles will have to be overcome before these therapies can be introduced into human clinical stroke trials. Copyright 2006 S. Karger AG, Basel.

A stable RNA virus-based vector for citrus trees

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

Folimonov, Alexey S.; Folimonova, Svetlana Y.; Bar-Joseph, Moshe

Virus-based vectors are important tools in plant molecular biology and plant genomics. A number of vectors based on viruses that infect herbaceous plants are in use for expression or silencing of genes in plants as well as screening unknown sequences for function. Yet there is a need for useful virus-based vectors for woody plants, which demand much greater stability because of the longer time required for systemic infection and analysis. We examined several strategies to develop a Citrus tristeza virus (CTV)-based vector for transient expression of foreign genes in citrus trees using a green fluorescent protein (GFP) as a reporter.more » These strategies included substitution of the p13 open reading frame (ORF) by the ORF of GFP, construction of a self-processing fusion of GFP in-frame with the major coat protein (CP), or expression of the GFP ORF as an extra gene from a subgenomic (sg) mRNA controlled either by a duplicated CTV CP sgRNA controller element (CE) or an introduced heterologous CE of Beet yellows virus. Engineered vector constructs were examined for replication, encapsidation, GFP expression during multiple passages in protoplasts, and for their ability to infect, move, express GFP, and be maintained in citrus plants. The most successful vectors based on the 'add-a-gene' strategy have been unusually stable, continuing to produce GFP fluorescence after more than 4 years in citrus trees.« less

Population Level Purifying Selection and Gene Expression Shape Subgenome Evolution in Maize.

PubMed

Pophaly, Saurabh D; Tellier, Aurélien

2015-12-01

The maize ancestor experienced a recent whole-genome duplication (WGD) followed by gene erosion which generated two subgenomes, the dominant subgenome (maize1) experiencing fewer deletions than maize2. We take advantage of available extensive polymorphism and gene expression data in maize to study purifying selection and gene expression divergence between WGD retained paralog pairs. We first report a strong correlation in nucleotide diversity between duplicate pairs, except for upstream regions. We then show that maize1 genes are under stronger purifying selection than maize2. WGD retained genes have higher gene dosage and biased Gene Ontologies consistent with previous studies. The relative gene expression of paralogs across tissues demonstrates that 98% of duplicate pairs have either subfunctionalized in a tissuewise manner or have diverged consistently in their expression thereby preventing functional complementation. Tissuewise subfunctionalization seems to be a hallmark of transcription factors, whereas consistent repression occurs for macromolecular complexes. We show that dominant gene expression is a strong determinant of the strength of purifying selection, explaining the inferred stronger negative selection on maize1 genes. We propose a novel expression-based classification of duplicates which is more robust to explain observed polymorphism patterns than the subgenome location. Finally, upstream regions of repressed genes exhibit an enrichment in transposable elements which indicates a possible mechanism for expression divergence. © The Author 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Differential gene expression in the siphonophore Nanomia bijuga (Cnidaria) assessed with multiple next-generation sequencing workflows.

PubMed

Siebert, Stefan; Robinson, Mark D; Tintori, Sophia C; Goetz, Freya; Helm, Rebecca R; Smith, Stephen A; Shaner, Nathan; Haddock, Steven H D; Dunn, Casey W

2011-01-01

We investigated differential gene expression between functionally specialized feeding polyps and swimming medusae in the siphonophore Nanomia bijuga (Cnidaria) with a hybrid long-read/short-read sequencing strategy. We assembled a set of partial gene reference sequences from long-read data (Roche 454), and generated short-read sequences from replicated tissue samples that were mapped to the references to quantify expression. We collected and compared expression data with three short-read expression workflows that differ in sample preparation, sequencing technology, and mapping tools. These workflows were Illumina mRNA-Seq, which generates sequence reads from random locations along each transcript, and two tag-based approaches, SOLiD SAGE and Helicos DGE, which generate reads from particular tag sites. Differences in expression results across workflows were mostly due to the differential impact of missing data in the partial reference sequences. When all 454-derived gene reference sequences were considered, Illumina mRNA-Seq detected more than twice as many differentially expressed (DE) reference sequences as the tag-based workflows. This discrepancy was largely due to missing tag sites in the partial reference that led to false negatives in the tag-based workflows. When only the subset of reference sequences that unambiguously have tag sites was considered, we found broad congruence across workflows, and they all identified a similar set of DE sequences. Our results are promising in several regards for gene expression studies in non-model organisms. First, we demonstrate that a hybrid long-read/short-read sequencing strategy is an effective way to collect gene expression data when an annotated genome sequence is not available. Second, our replicated sampling indicates that expression profiles are highly consistent across field-collected animals in this case. Third, the impacts of partial reference sequences on the ability to detect DE can be mitigated through workflow choice and deeper reference sequencing.

Differential Gene Expression in the Siphonophore Nanomia bijuga (Cnidaria) Assessed with Multiple Next-Generation Sequencing Workflows

PubMed Central

Siebert, Stefan; Robinson, Mark D.; Tintori, Sophia C.; Goetz, Freya; Helm, Rebecca R.; Smith, Stephen A.; Shaner, Nathan; Haddock, Steven H. D.; Dunn, Casey W.

2011-01-01

We investigated differential gene expression between functionally specialized feeding polyps and swimming medusae in the siphonophore Nanomia bijuga (Cnidaria) with a hybrid long-read/short-read sequencing strategy. We assembled a set of partial gene reference sequences from long-read data (Roche 454), and generated short-read sequences from replicated tissue samples that were mapped to the references to quantify expression. We collected and compared expression data with three short-read expression workflows that differ in sample preparation, sequencing technology, and mapping tools. These workflows were Illumina mRNA-Seq, which generates sequence reads from random locations along each transcript, and two tag-based approaches, SOLiD SAGE and Helicos DGE, which generate reads from particular tag sites. Differences in expression results across workflows were mostly due to the differential impact of missing data in the partial reference sequences. When all 454-derived gene reference sequences were considered, Illumina mRNA-Seq detected more than twice as many differentially expressed (DE) reference sequences as the tag-based workflows. This discrepancy was largely due to missing tag sites in the partial reference that led to false negatives in the tag-based workflows. When only the subset of reference sequences that unambiguously have tag sites was considered, we found broad congruence across workflows, and they all identified a similar set of DE sequences. Our results are promising in several regards for gene expression studies in non-model organisms. First, we demonstrate that a hybrid long-read/short-read sequencing strategy is an effective way to collect gene expression data when an annotated genome sequence is not available. Second, our replicated sampling indicates that expression profiles are highly consistent across field-collected animals in this case. Third, the impacts of partial reference sequences on the ability to detect DE can be mitigated through workflow choice and deeper reference sequencing. PMID:21829563

A regulation probability model-based meta-analysis of multiple transcriptomics data sets for cancer biomarker identification.

PubMed

Xie, Xin-Ping; Xie, Yu-Feng; Wang, Hong-Qiang

2017-08-23

Large-scale accumulation of omics data poses a pressing challenge of integrative analysis of multiple data sets in bioinformatics. An open question of such integrative analysis is how to pinpoint consistent but subtle gene activity patterns across studies. Study heterogeneity needs to be addressed carefully for this goal. This paper proposes a regulation probability model-based meta-analysis, jGRP, for identifying differentially expressed genes (DEGs). The method integrates multiple transcriptomics data sets in a gene regulatory space instead of in a gene expression space, which makes it easy to capture and manage data heterogeneity across studies from different laboratories or platforms. Specifically, we transform gene expression profiles into a united gene regulation profile across studies by mathematically defining two gene regulation events between two conditions and estimating their occurring probabilities in a sample. Finally, a novel differential expression statistic is established based on the gene regulation profiles, realizing accurate and flexible identification of DEGs in gene regulation space. We evaluated the proposed method on simulation data and real-world cancer datasets and showed the effectiveness and efficiency of jGRP in identifying DEGs identification in the context of meta-analysis. Data heterogeneity largely influences the performance of meta-analysis of DEGs identification. Existing different meta-analysis methods were revealed to exhibit very different degrees of sensitivity to study heterogeneity. The proposed method, jGRP, can be a standalone tool due to its united framework and controllable way to deal with study heterogeneity.

Expression Analysis of Stress-Related Genes in Kernels of Different Maize (Zea mays L.) Inbred Lines with Different Resistance to Aflatoxin Contamination

PubMed Central

Jiang, Tingbo; Zhou, Boru; Luo, Meng; Abbas, Hamed K.; Kemerait, Robert; Lee, Robert Dewey; Scully, Brian T.; Guo, Baozhu

2011-01-01

This research examined the expression patterns of 94 stress-related genes in seven maize inbred lines with differential expressions of resistance to aflatoxin contamination. The objective was to develop a set of genes/probes associated with resistance to A. flavus and/or aflatoxin contamination. Ninety four genes were selected from previous gene expression studies with abiotic stress to test the differential expression in maize lines, A638, B73, Lo964, Lo1016, Mo17, Mp313E, and Tex6, using real-time RT-PCR. Based on the relative-expression levels, the seven maize inbred lines clustered into two different groups. One group included B73, Lo1016 and Mo17, which had higher levels of aflatoxin contamination and lower levels of overall gene expression. The second group which included Tex6, Mp313E, Lo964 and A638 had lower levels of aflatoxin contamination and higher overall levels of gene expressions. A total of six “cross-talking” genes were identified between the two groups, which are highly expressed in the resistant Group 2 but down-regulated in susceptible Group 1. When further subjected to drought stress, Tex6 expressed more genes up-regulated and B73 has fewer genes up-regulated. The transcript patterns and interactions measured in these experiments indicate that the resistant mechanism is an interconnected process involving many gene products and transcriptional regulators, as well as various host interactions with environmental factors, particularly, drought and high temperature. PMID:22069724

Gene expression analysis predicts insect venom anaphylaxis in indolent systemic mastocytosis.

PubMed

Niedoszytko, M; Bruinenberg, M; van Doormaal, J J; de Monchy, J G R; Nedoszytko, B; Koppelman, G H; Nawijn, M C; Wijmenga, C; Jassem, E; Elberink, J N G Oude

2011-05-01

Anaphylaxis to insect venom (Hymenoptera) is most severe in patients with mastocytosis and may even lead to death. However, not all patients with mastocytosis suffer from anaphylaxis. The aim of the study was to analyze differences in gene expression between patients with indolent systemic mastocytosis (ISM) and a history of insect venom anaphylaxis (IVA) compared to those patients without a history of anaphylaxis, and to determine the predictive use of gene expression profiling. Whole-genome gene expression analysis was performed in peripheral blood cells. Twenty-two adults with ISM were included: 12 with a history of IVA and 10 without a history of anaphylaxis of any kind. Significant differences in single gene expression corrected for multiple testing were found for 104 transcripts (P < 0.05). Gene ontology analysis revealed that the differentially expressed genes were involved in pathways responsible for the development of cancer and focal and cell adhesion suggesting that the expression of genes related to the differentiation state of cells is higher in patients with a history of anaphylaxis. Based on the gene expression profiles, a naïve Bayes prediction model was built identifying patients with IVA. In ISM, gene expression profiles are different between patients with a history of IVA and those without. These findings might reflect a more pronounced mast cells dysfunction in patients without a history of anaphylaxis. Gene expression profiling might be a useful tool to predict the risk of anaphylaxis on insect venom in patients with ISM. Prospective studies are needed to substantiate any conclusions. © 2010 John Wiley & Sons A/S.

Gene expression analysis of pancreatic cell lines reveals genes overexpressed in pancreatic cancer.

PubMed

Alldinger, Ingo; Dittert, Dag; Peiper, Matthias; Fusco, Alberto; Chiappetta, Gennaro; Staub, Eike; Lohr, Matthias; Jesnowski, Ralf; Baretton, Gustavo; Ockert, Detlef; Saeger, Hans-Detlev; Grützmann, Robert; Pilarsky, Christian

2005-01-01

Pancreatic cancer is one of the leading causes of cancer-related death. Using DNA gene expression analysis based on a custom made Affymetrix cancer array, we investigated the expression pattern of both primary and established pancreatic carcinoma cell lines. We analyzed the gene expression of 5 established pancreatic cancer cell lines (AsPC-1, BxPC-3, Capan-1, Capan-2 and HPAF II) and 5 primary isolates, 1 of them derived from benign pancreatic duct cells. Out of 1,540 genes which were expressed in at least 3 experiments, we found 122 genes upregulated and 18 downregulated in tumor cell lines compared to benign cells with a fold change >3. Several of the upregulated genes (like Prefoldin 5, ADAM9 and E-cadherin) have been associated with pancreatic cancer before. The other differentially regulated genes, however, play a so far unknown role in the course of human pancreatic carcinoma. By means of immunohistochemistry we could show that thymosin beta-10 (TMSB10), upregulated in tumor cell lines, is expressed in human pancreatic carcinoma, but not in non-neoplastic pancreatic tissue, suggesting a role for TMSB10 in the carcinogenesis of pancreatic carcinoma. Using gene expression profiling of pancreatic cell lines we were able to identify genes differentially expressed in pancreatic adenocarcinoma, which might contribute to pancreatic cancer development. Copyright 2005 S. Karger AG, Basel.

Relationship between gene expression and GC-content in mammals: statistical significance and biological relevance.

PubMed

Sémon, Marie; Mouchiroud, Dominique; Duret, Laurent

2005-02-01

Mammalian chromosomes are characterized by large-scale variations of DNA base composition (the so-called isochores). In contradiction with previous studies, Lercher et al. (Hum. Mol. Genet., 12, 2411, 2003) recently reported a strong correlation between gene expression breadth and GC-content, suggesting that there might be a selective pressure favoring the concentration of housekeeping genes in GC-rich isochores. We reassessed this issue by examining in human and mouse the correlation between gene expression and GC-content, using different measures of gene expression (EST, SAGE and microarray) and different measures of GC-content. We show that correlations between GC-content and expression are very weak, and may vary according to the method used to measure expression. Such weak correlations have a very low predictive value. The strong correlations reported by Lercher et al. (2003) are because of the fact that they measured variables over neighboring genes windows. We show here that using gene windows artificially enhances the correlation. The assertion that the expression of a given gene depends on the GC-content of the region where it is located is therefore not supported by the data.

Systemic bioinformatics analysis of skeletal muscle gene expression profiles of sepsis

PubMed Central

Yang, Fang; Wang, Yumei

2018-01-01

Sepsis is a type of systemic inflammatory response syndrome with high morbidity and mortality. Skeletal muscle dysfunction is one of the major complications of sepsis that may also influence the outcome of sepsis. The aim of the present study was to explore and identify potential mechanisms and therapeutic targets of sepsis. Systemic bioinformatics analysis of skeletal muscle gene expression profiles from the Gene Expression Omnibus was performed. Differentially expressed genes (DEGs) in samples from patients with sepsis and control samples were screened out using the limma package. Differential co-expression and coregulation (DCE and DCR, respectively) analysis was performed based on the Differential Co-expression Analysis package to identify differences in gene co-expression and coregulation patterns between the control and sepsis groups. Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways of DEGs were identified using the Database for Annotation, Visualization and Integrated Discovery, and inflammatory, cancer and skeletal muscle development-associated biological processes and pathways were identified. DCE and DCR analysis revealed several potential therapeutic targets for sepsis, including genes and transcription factors. The results of the present study may provide a basis for the development of novel therapeutic targets and treatment methods for sepsis. PMID:29805480

Base composition and expression level of human genes.

PubMed

Arhondakis, Stilianos; Auletta, Fabio; Torelli, Giuseppe; D'Onofrio, Giuseppe

2004-01-21

It is well known that the gene distribution is non-uniform in the human genome, reaching the highest concentration in the GC-rich isochores. Also the amino acid frequencies, and the hydrophobicity, of the corresponding encoded proteins are affected by the high GC level of the genes localized in the GC-rich isochores. It was hypothesized that the gene expression level as well is higher in GC-rich compared to GC-poor isochores [Mol. Biol. Evol. 10 (1993) 186]. Several features of human genes and proteins, namely expression level, coding and non-coding lengths, and hydrophobicity were investigated in the present paper. The results support the hypothesis reported above, since all the parameters so far studied converge to the same conclusion, that the average expression level of the GC-rich genes is significantly higher than that of the GC-poor genes.

Expression of the Laccase Gene from a White Rot Fungus in Pichia pastoris Can Enhance the Resistance of This Yeast to H2O2-Mediated Oxidative Stress by Stimulating the Glutathione-Based Antioxidative System

PubMed Central

Fan, Fangfang; Zhuo, Rui; Ma, Fuying; Gong, Yangmin; Wan, Xia; Jiang, Mulan

2012-01-01

Laccase is a copper-containing polyphenol oxidase that has great potential in industrial and biotechnological applications. Previous research has suggested that fungal laccase may be involved in the defense against oxidative stress, but there is little direct evidence supporting this hypothesis, and the mechanism by which laccase protects cells from oxidative stress also remains unclear. Here, we report that the expression of the laccase gene from white rot fungus in Pichia pastoris can significantly enhance the resistance of yeast to H2O2-mediated oxidative stress. The expression of laccase in yeast was found to confer a strong ability to scavenge intracellular H2O2 and to protect cells from lipid oxidative damage. The mechanism by which laccase gene expression increases resistance to oxidative stress was then investigated further. We found that laccase gene expression in Pichia pastoris could increase the level of glutathione-based antioxidative activity, including the intracellular glutathione levels and the enzymatic activity of glutathione peroxidase, glutathione reductase, and γ-glutamylcysteine synthetase. The transcription of the laccase gene in Pichia pastoris was found to be enhanced by the oxidative stress caused by exogenous H2O2. The stimulation of laccase gene expression in response to exogenous H2O2 stress further contributed to the transcriptional induction of the genes involved in the glutathione-dependent antioxidative system, including PpYAP1, PpGPX1, PpPMP20, PpGLR1, and PpGSH1. Taken together, these results suggest that the expression of the laccase gene in Pichia pastoris can enhance the resistance of yeast to H2O2-mediated oxidative stress by stimulating the glutathione-based antioxidative system to protect the cell from oxidative damage. PMID:22706050

Analysis of host response to bacterial infection using error model based gene expression microarray experiments

PubMed Central

Stekel, Dov J.; Sarti, Donatella; Trevino, Victor; Zhang, Lihong; Salmon, Mike; Buckley, Chris D.; Stevens, Mark; Pallen, Mark J.; Penn, Charles; Falciani, Francesco

2005-01-01

A key step in the analysis of microarray data is the selection of genes that are differentially expressed. Ideally, such experiments should be properly replicated in order to infer both technical and biological variability, and the data should be subjected to rigorous hypothesis tests to identify the differentially expressed genes. However, in microarray experiments involving the analysis of very large numbers of biological samples, replication is not always practical. Therefore, there is a need for a method to select differentially expressed genes in a rational way from insufficiently replicated data. In this paper, we describe a simple method that uses bootstrapping to generate an error model from a replicated pilot study that can be used to identify differentially expressed genes in subsequent large-scale studies on the same platform, but in which there may be no replicated arrays. The method builds a stratified error model that includes array-to-array variability, feature-to-feature variability and the dependence of error on signal intensity. We apply this model to the characterization of the host response in a model of bacterial infection of human intestinal epithelial cells. We demonstrate the effectiveness of error model based microarray experiments and propose this as a general strategy for a microarray-based screening of large collections of biological samples. PMID:15800204

Systematic identification of human housekeeping genes possibly useful as references in gene expression studies.

PubMed

Caracausi, Maria; Piovesan, Allison; Antonaros, Francesca; Strippoli, Pierluigi; Vitale, Lorenza; Pelleri, Maria Chiara

2017-09-01

The ideal reference, or control, gene for the study of gene expression in a given organism should be expressed at a medium‑high level for easy detection, should be expressed at a constant/stable level throughout different cell types and within the same cell type undergoing different treatments, and should maintain these features through as many different tissues of the organism. From a biological point of view, these theoretical requirements of an ideal reference gene appear to be best suited to housekeeping (HK) genes. Recent advancements in the quality and completeness of human expression microarray data and in their statistical analysis may provide new clues toward the quantitative standardization of human gene expression studies in biology and medicine, both cross‑ and within‑tissue. The systematic approach used by the present study is based on the Transcriptome Mapper tool and exploits the automated reassignment of probes to corresponding genes, intra‑ and inter‑sample normalization, elaboration and representation of gene expression values in linear form within an indexed and searchable database with a graphical interface recording quantitative levels of expression, expression variability and cross‑tissue width of expression for more than 31,000 transcripts. The present study conducted a meta‑analysis of a pool of 646 expression profile data sets from 54 different human tissues and identified actin γ 1 as the HK gene that best fits the combination of all the traditional criteria to be used as a reference gene for general use; two ribosomal protein genes, RPS18 and RPS27, and one aquaporin gene, POM121 transmembrane nucleporin C, were also identified. The present study provided a list of tissue‑ and organ‑specific genes that may be most suited for the following individual tissues/organs: Adipose tissue, bone marrow, brain, heart, kidney, liver, lung, ovary, skeletal muscle and testis; and also provides in these cases a representative, quantitative portrait of the relative, typical gene‑expression profile in the form of searchable database tables.

Automated Discovery of Functional Generality of Human Gene Expression Programs

PubMed Central

Gerber, Georg K; Dowell, Robin D; Jaakkola, Tommi S; Gifford, David K

2007-01-01

An important research problem in computational biology is the identification of expression programs, sets of co-expressed genes orchestrating normal or pathological processes, and the characterization of the functional breadth of these programs. The use of human expression data compendia for discovery of such programs presents several challenges including cellular inhomogeneity within samples, genetic and environmental variation across samples, uncertainty in the numbers of programs and sample populations, and temporal behavior. We developed GeneProgram, a new unsupervised computational framework based on Hierarchical Dirichlet Processes that addresses each of the above challenges. GeneProgram uses expression data to simultaneously organize tissues into groups and genes into overlapping programs with consistent temporal behavior, to produce maps of expression programs, which are sorted by generality scores that exploit the automatically learned groupings. Using synthetic and real gene expression data, we showed that GeneProgram outperformed several popular expression analysis methods. We applied GeneProgram to a compendium of 62 short time-series gene expression datasets exploring the responses of human cells to infectious agents and immune-modulating molecules. GeneProgram produced a map of 104 expression programs, a substantial number of which were significantly enriched for genes involved in key signaling pathways and/or bound by NF-κB transcription factors in genome-wide experiments. Further, GeneProgram discovered expression programs that appear to implicate surprising signaling pathways or receptor types in the response to infection, including Wnt signaling and neurotransmitter receptors. We believe the discovered map of expression programs involved in the response to infection will be useful for guiding future biological experiments; genes from programs with low generality scores might serve as new drug targets that exhibit minimal “cross-talk,” and genes from high generality programs may maintain common physiological responses that go awry in disease states. Further, our method is multipurpose, and can be applied readily to novel compendia of biological data. PMID:17696603

Identification of genes expressed in the hermaphrodite germ line of C. elegans using SAGE

PubMed Central

Wang, Xin; Zhao, Yongjun; Wong, Kim; Ehlers, Peter; Kohara, Yuji; Jones, Steven J; Marra, Marco A; Holt, Robert A; Moerman, Donald G; Hansen, Dave

2009-01-01

Background Germ cells must progress through elaborate developmental stages from an undifferentiated germ cell to a fully differentiated gamete. Some of these stages include exiting mitosis and entering meiosis, progressing through the various stages of meiotic prophase, adopting either a male (sperm) or female (oocyte) fate, and completing meiosis. Additionally, many of the factors needed to drive embryogenesis are synthesized in the germ line. To increase our understanding of the genes that might be necessary for the formation and function of the germ line, we have constructed a SAGE library from hand dissected C. elegans hermaphrodite gonads. Results We found that 4699 genes, roughly 21% of all known C. elegans genes, are expressed in the adult hermaphrodite germ line. Ribosomal genes are highly expressed in the germ line; roughly four fold above their expression levels in the soma. We further found that 1063 of the germline-expressed genes have enriched expression in the germ line as compared to the soma. A comparison of these 1063 germline-enriched genes with a similar list of genes prepared using microarrays revealed an overlap of 460 genes, mutually reinforcing the two lists. Additionally, we identified 603 germline-enriched genes, supported by in situ expression data, which were not previously identified. We also found >4 fold enrichment for RNA binding proteins in the germ line as compared to the soma. Conclusion Using multiple technological platforms provides a more complete picture of global gene expression patterns. Genes involved in RNA metabolism are expressed at a significantly higher level in the germ line than the soma, suggesting a stronger reliance on RNA metabolism for control of the expression of genes in the germ line. Additionally, the number and expression level of germ line expressed genes on the X chromosome is lower than expected based on a random distribution. PMID:19426519

PathMAPA: a tool for displaying gene expression and performing statistical tests on metabolic pathways at multiple levels for Arabidopsis.

PubMed

Pan, Deyun; Sun, Ning; Cheung, Kei-Hoi; Guan, Zhong; Ma, Ligeng; Holford, Matthew; Deng, Xingwang; Zhao, Hongyu

2003-11-07

To date, many genomic and pathway-related tools and databases have been developed to analyze microarray data. In published web-based applications to date, however, complex pathways have been displayed with static image files that may not be up-to-date or are time-consuming to rebuild. In addition, gene expression analyses focus on individual probes and genes with little or no consideration of pathways. These approaches reveal little information about pathways that are key to a full understanding of the building blocks of biological systems. Therefore, there is a need to provide useful tools that can generate pathways without manually building images and allow gene expression data to be integrated and analyzed at pathway levels for such experimental organisms as Arabidopsis. We have developed PathMAPA, a web-based application written in Java that can be easily accessed over the Internet. An Oracle database is used to store, query, and manipulate the large amounts of data that are involved. PathMAPA allows its users to (i) upload and populate microarray data into a database; (ii) integrate gene expression with enzymes of the pathways; (iii) generate pathway diagrams without building image files manually; (iv) visualize gene expressions for each pathway at enzyme, locus, and probe levels; and (v) perform statistical tests at pathway, enzyme and gene levels. PathMAPA can be used to examine Arabidopsis thaliana gene expression patterns associated with metabolic pathways. PathMAPA provides two unique features for the gene expression analysis of Arabidopsis thaliana: (i) automatic generation of pathways associated with gene expression and (ii) statistical tests at pathway level. The first feature allows for the periodical updating of genomic data for pathways, while the second feature can provide insight into how treatments affect relevant pathways for the selected experiment(s).

PathMAPA: a tool for displaying gene expression and performing statistical tests on metabolic pathways at multiple levels for Arabidopsis

PubMed Central

Pan, Deyun; Sun, Ning; Cheung, Kei-Hoi; Guan, Zhong; Ma, Ligeng; Holford, Matthew; Deng, Xingwang; Zhao, Hongyu

2003-01-01

Background To date, many genomic and pathway-related tools and databases have been developed to analyze microarray data. In published web-based applications to date, however, complex pathways have been displayed with static image files that may not be up-to-date or are time-consuming to rebuild. In addition, gene expression analyses focus on individual probes and genes with little or no consideration of pathways. These approaches reveal little information about pathways that are key to a full understanding of the building blocks of biological systems. Therefore, there is a need to provide useful tools that can generate pathways without manually building images and allow gene expression data to be integrated and analyzed at pathway levels for such experimental organisms as Arabidopsis. Results We have developed PathMAPA, a web-based application written in Java that can be easily accessed over the Internet. An Oracle database is used to store, query, and manipulate the large amounts of data that are involved. PathMAPA allows its users to (i) upload and populate microarray data into a database; (ii) integrate gene expression with enzymes of the pathways; (iii) generate pathway diagrams without building image files manually; (iv) visualize gene expressions for each pathway at enzyme, locus, and probe levels; and (v) perform statistical tests at pathway, enzyme and gene levels. PathMAPA can be used to examine Arabidopsis thaliana gene expression patterns associated with metabolic pathways. Conclusion PathMAPA provides two unique features for the gene expression analysis of Arabidopsis thaliana: (i) automatic generation of pathways associated with gene expression and (ii) statistical tests at pathway level. The first feature allows for the periodical updating of genomic data for pathways, while the second feature can provide insight into how treatments affect relevant pathways for the selected experiment(s). PMID:14604444

A Cancer Gene Selection Algorithm Based on the K-S Test and CFS.

PubMed

Su, Qiang; Wang, Yina; Jiang, Xiaobing; Chen, Fuxue; Lu, Wen-Cong

2017-01-01

To address the challenging problem of selecting distinguished genes from cancer gene expression datasets, this paper presents a gene subset selection algorithm based on the Kolmogorov-Smirnov (K-S) test and correlation-based feature selection (CFS) principles. The algorithm selects distinguished genes first using the K-S test, and then, it uses CFS to select genes from those selected by the K-S test. We adopted support vector machines (SVM) as the classification tool and used the criteria of accuracy to evaluate the performance of the classifiers on the selected gene subsets. This approach compared the proposed gene subset selection algorithm with the K-S test, CFS, minimum-redundancy maximum-relevancy (mRMR), and ReliefF algorithms. The average experimental results of the aforementioned gene selection algorithms for 5 gene expression datasets demonstrate that, based on accuracy, the performance of the new K-S and CFS-based algorithm is better than those of the K-S test, CFS, mRMR, and ReliefF algorithms. The experimental results show that the K-S test-CFS gene selection algorithm is a very effective and promising approach compared to the K-S test, CFS, mRMR, and ReliefF algorithms.

A View of the Therapy for Bell's Palsy Based on Molecular Biological Analyses of Facial Muscles.

PubMed

Moriyama, Hiroshi; Mitsukawa, Nobuyuki; Itoh, Masahiro; Otsuka, Naruhito

2017-12-01

Details regarding the molecular biological features of Bell's palsy have not been widely reported in textbooks. We genetically analyzed facial muscles and clarified these points. We performed genetic analysis of facial muscle specimens from Japanese patients with severe (House-Brackmann facial nerve grading system V) and moderate (House-Brackmann facial nerve grading system III) dysfunction due to Bell's palsy. Microarray analysis of gene expression was performed using specimens from the healthy and affected sides, and gene expression was compared. Changes in gene expression were defined as an affected side/healthy side ratio of >1.5 or <0.5. We observed that the gene expression in Bell's palsy changes with the degree of facial nerve palsy. Especially, muscle, neuron, and energy category genes tended to fluctuate with the degree of facial nerve palsy. It is expected that this study will aid in the development of new treatments and diagnostic/prognostic markers based on the severity of facial nerve palsy.

Sig2GRN: a software tool linking signaling pathway with gene regulatory network for dynamic simulation.

PubMed

Zhang, Fan; Liu, Runsheng; Zheng, Jie

2016-12-23

Linking computational models of signaling pathways to predicted cellular responses such as gene expression regulation is a major challenge in computational systems biology. In this work, we present Sig2GRN, a Cytoscape plugin that is able to simulate time-course gene expression data given the user-defined external stimuli to the signaling pathways. A generalized logical model is used in modeling the upstream signaling pathways. Then a Boolean model and a thermodynamics-based model are employed to predict the downstream changes in gene expression based on the simulated dynamics of transcription factors in signaling pathways. Our empirical case studies show that the simulation of Sig2GRN can predict changes in gene expression patterns induced by DNA damage signals and drug treatments. As a software tool for modeling cellular dynamics, Sig2GRN can facilitate studies in systems biology by hypotheses generation and wet-lab experimental design. http://histone.scse.ntu.edu.sg/Sig2GRN/.

Transcriptional profiling of murine osteoblast differentiation based on RNA-seq expression analyses.

PubMed

Khayal, Layal Abo; Grünhagen, Johannes; Provazník, Ivo; Mundlos, Stefan; Kornak, Uwe; Robinson, Peter N; Ott, Claus-Eric

2018-04-11

Osteoblastic differentiation is a multistep process characterized by osteogenic induction of mesenchymal stem cells, which then differentiate into proliferative pre-osteoblasts that produce copious amounts of extracellular matrix, followed by stiffening of the extracellular matrix, and matrix mineralization by hydroxylapatite deposition. Although these processes have been well characterized biologically, a detailed transcriptional analysis of murine primary calvaria osteoblast differentiation based on RNA sequencing (RNA-seq) analyses has not previously been reported. Here, we used RNA-seq to obtain expression values of 29,148 genes at four time points as murine primary calvaria osteoblasts differentiate in vitro until onset of mineralization was clearly detectable by microscopic inspection. Expression of marker genes confirmed osteogenic differentiation. We explored differential expression of 1386 protein-coding genes using unsupervised clustering and GO analyses. 100 differentially expressed lncRNAs were investigated by co-expression with protein-coding genes that are localized within the same topologically associated domain. Additionally, we monitored expression of 237 genes that are silent or active at distinct time points and compared differential exon usage. Our data represent an in-depth profiling of murine primary calvaria osteoblast differentiation by RNA-seq and contribute to our understanding of genetic regulation of this key process in osteoblast biology. Copyright © 2018 Elsevier Inc. All rights reserved.

Synergistic Effect of Auto-Activation and Small RNA Regulation on Gene Expression

NASA Astrophysics Data System (ADS)

Xiong, Li-Ping; Ma, Yu-Qiang; Tang, Lei-Han

2010-09-01

Auto-activation and small ribonucleic acid (RNA)-mediated regulation are two important mechanisms in controlling gene expression. We study the synergistic effect of these two regulations on gene expression. It is found that under this combinatorial regulation, gene expression exhibits bistable behaviors at the transition regime, while each of these two regulations, if working solely, only leads to monostability. Within the stochastic framework, the base pairing strength between sRNA and mRNA plays an important role in controlling the transition time between on and off states. The noise strength of protein number in the off state approaches 1 and is smaller than that in the on state. The noise strength also depends on which parameters, the feedback strength or the synthesis rate of small RNA, are tuned in switching the gene expression on and off. Our findings may provide a new insight into gene-regulation mechanism and can be applied in synthetic biology.

A high resolution atlas of gene expression in the domestic sheep (Ovis aries)

PubMed Central

Farquhar, Iseabail L.; Young, Rachel; Lefevre, Lucas; Pridans, Clare; Tsang, Hiu G.; Afrasiabi, Cyrus; Watson, Mick; Whitelaw, C. Bruce; Freeman, Tom C.; Archibald, Alan L.; Hume, David A.

2017-01-01

Sheep are a key source of meat, milk and fibre for the global livestock sector, and an important biomedical model. Global analysis of gene expression across multiple tissues has aided genome annotation and supported functional annotation of mammalian genes. We present a large-scale RNA-Seq dataset representing all the major organ systems from adult sheep and from several juvenile, neonatal and prenatal developmental time points. The Ovis aries reference genome (Oar v3.1) includes 27,504 genes (20,921 protein coding), of which 25,350 (19,921 protein coding) had detectable expression in at least one tissue in the sheep gene expression atlas dataset. Network-based cluster analysis of this dataset grouped genes according to their expression pattern. The principle of ‘guilt by association’ was used to infer the function of uncharacterised genes from their co-expression with genes of known function. We describe the overall transcriptional signatures present in the sheep gene expression atlas and assign those signatures, where possible, to specific cell populations or pathways. The findings are related to innate immunity by focusing on clusters with an immune signature, and to the advantages of cross-breeding by examining the patterns of genes exhibiting the greatest expression differences between purebred and crossbred animals. This high-resolution gene expression atlas for sheep is, to our knowledge, the largest transcriptomic dataset from any livestock species to date. It provides a resource to improve the annotation of the current reference genome for sheep, presenting a model transcriptome for ruminants and insight into gene, cell and tissue function at multiple developmental stages. PMID:28915238

A high resolution atlas of gene expression in the domestic sheep (Ovis aries).

PubMed

Clark, Emily L; Bush, Stephen J; McCulloch, Mary E B; Farquhar, Iseabail L; Young, Rachel; Lefevre, Lucas; Pridans, Clare; Tsang, Hiu G; Wu, Chunlei; Afrasiabi, Cyrus; Watson, Mick; Whitelaw, C Bruce; Freeman, Tom C; Summers, Kim M; Archibald, Alan L; Hume, David A

2017-09-01

Sheep are a key source of meat, milk and fibre for the global livestock sector, and an important biomedical model. Global analysis of gene expression across multiple tissues has aided genome annotation and supported functional annotation of mammalian genes. We present a large-scale RNA-Seq dataset representing all the major organ systems from adult sheep and from several juvenile, neonatal and prenatal developmental time points. The Ovis aries reference genome (Oar v3.1) includes 27,504 genes (20,921 protein coding), of which 25,350 (19,921 protein coding) had detectable expression in at least one tissue in the sheep gene expression atlas dataset. Network-based cluster analysis of this dataset grouped genes according to their expression pattern. The principle of 'guilt by association' was used to infer the function of uncharacterised genes from their co-expression with genes of known function. We describe the overall transcriptional signatures present in the sheep gene expression atlas and assign those signatures, where possible, to specific cell populations or pathways. The findings are related to innate immunity by focusing on clusters with an immune signature, and to the advantages of cross-breeding by examining the patterns of genes exhibiting the greatest expression differences between purebred and crossbred animals. This high-resolution gene expression atlas for sheep is, to our knowledge, the largest transcriptomic dataset from any livestock species to date. It provides a resource to improve the annotation of the current reference genome for sheep, presenting a model transcriptome for ruminants and insight into gene, cell and tissue function at multiple developmental stages.

Multiple biomarkers in molecular oncology. II. Molecular diagnostics applications in breast cancer management.

PubMed

Malinowski, Douglas P

2007-05-01

In recent years, the application of genomic and proteomic technologies to the problem of breast cancer prognosis and the prediction of therapy response have begun to yield encouraging results. Independent studies employing transcriptional profiling of primary breast cancer specimens using DNA microarrays have identified gene expression profiles that correlate with clinical outcome in primary breast biopsy specimens. Recent advances in microarray technology have demonstrated reproducibility, making clinical applications more achievable. In this regard, one such DNA microarray device based upon a 70-gene expression signature was recently cleared by the US FDA for application to breast cancer prognosis. These DNA microarrays often employ at least 70 gene targets for transcriptional profiling and prognostic assessment in breast cancer. The use of PCR-based methods utilizing a small subset of genes has recently demonstrated the ability to predict the clinical outcome in early-stage breast cancer. Furthermore, protein-based immunohistochemistry methods have progressed from using gene clusters and gene expression profiling to smaller subsets of expressed proteins to predict prognosis in early-stage breast cancer. Beyond prognostic applications, DNA microarray-based transcriptional profiling has demonstrated the ability to predict response to chemotherapy in early-stage breast cancer patients. In this review, recent advances in the use of multiple markers for prognosis of disease recurrence in early-stage breast cancer and the prediction of therapy response will be discussed.

Gene silencing in primary and metastatic tumors by small interfering RNA delivery in mice: quantitative analysis using melanoma cells expressing firefly and sea pansy luciferases.

PubMed

Takahashi, Yuki; Nishikawa, Makiya; Kobayashi, Naoki; Takakura, Yoshinobu

2005-07-20

Silencing of oncogenes or other genes contributing to tumor malignancy or progression by RNA interference (RNAi) offers a promising approach to treating tumor patients. To achieve RNAi-based tumor therapy, a small interfering RNA (siRNA) or siRNA-expressing vector needs to be delivered to tumor cells, but little information about its in vivo delivery has been reported. In this study, we examined whether the expression of the target gene in tumor cells can be suppressed by the delivery of RNAi effectors to primary and metastatic tumor cells. To quantitatively evaluate the RNAi effects in tumor cells, mouse melanoma B16-BL6 cells were stably transfected with both firefly (a model target gene) and sea pansy (an internal standard gene) luciferase genes to obtain B16-BL6/dual Luc cells. The target gene expression in subcutaneous primary tumors of B16-BL6/dual Luc cells was significantly suppressed by direct injection of the RNAi effectors followed by electroporation. The expression in metastatic hepatic tumors was also significantly reduced by an intravenous injection of either RNAi effector by the hydrodynamics-based procedure. These results indicate that the both RNAi effectors have a potential to silence target gene in tumor cells in vivo when successfully delivered to tumor cells.

Evaluation and Validation of Housekeeping Genes as Reference for Gene Expression Studies in Pigeonpea (Cajanus cajan) Under Drought Stress Conditions

PubMed Central

Sinha, Pallavi; Singh, Vikas K.; Suryanarayana, V.; Krishnamurthy, L.; Saxena, Rachit K.; Varshney, Rajeev K.

2015-01-01

Gene expression analysis using quantitative real-time PCR (qRT-PCR) is a very sensitive technique and its sensitivity depends on the stable performance of reference gene(s) used in the study. A number of housekeeping genes have been used in various expression studies in many crops however, their expression were found to be inconsistent under different stress conditions. As a result, species specific housekeeping genes have been recommended for different expression studies in several crop species. However, such specific housekeeping genes have not been reported in the case of pigeonpea (Cajanus cajan) despite the fact that genome sequence has become available for the crop. To identify the stable housekeeping genes in pigeonpea for expression analysis under drought stress conditions, the relative expression variations of 10 commonly used housekeeping genes (EF1α, UBQ10, GAPDH, 18SrRNA, 25SrRNA, TUB6, ACT1, IF4α, UBC and HSP90) were studied on root, stem and leaves tissues of Asha (ICPL 87119). Three statistical algorithms geNorm, NormFinder and BestKeeper were used to define the stability of candidate genes. geNorm analysis identified IF4α and TUB6 as the most stable housekeeping genes however, NormFinder analysis determined IF4α and HSP90 as the most stable housekeeping genes under drought stress conditions. Subsequently validation of the identified candidate genes was undertaken in qRT-PCR based gene expression analysis of uspA gene which plays an important role for drought stress conditions in pigeonpea. The relative quantification of the uspA gene varied according to the internal controls (stable and least stable genes), thus highlighting the importance of the choice of as well as validation of internal controls in such experiments. The identified stable and validated housekeeping genes will facilitate gene expression studies in pigeonpea especially under drought stress conditions. PMID:25849964

Evaluation and validation of housekeeping genes as reference for gene expression studies in pigeonpea (Cajanus cajan) under drought stress conditions.

PubMed

Sinha, Pallavi; Singh, Vikas K; Suryanarayana, V; Krishnamurthy, L; Saxena, Rachit K; Varshney, Rajeev K

2015-01-01

Gene expression analysis using quantitative real-time PCR (qRT-PCR) is a very sensitive technique and its sensitivity depends on the stable performance of reference gene(s) used in the study. A number of housekeeping genes have been used in various expression studies in many crops however, their expression were found to be inconsistent under different stress conditions. As a result, species specific housekeeping genes have been recommended for different expression studies in several crop species. However, such specific housekeeping genes have not been reported in the case of pigeonpea (Cajanus cajan) despite the fact that genome sequence has become available for the crop. To identify the stable housekeeping genes in pigeonpea for expression analysis under drought stress conditions, the relative expression variations of 10 commonly used housekeeping genes (EF1α, UBQ10, GAPDH, 18SrRNA, 25SrRNA, TUB6, ACT1, IF4α, UBC and HSP90) were studied on root, stem and leaves tissues of Asha (ICPL 87119). Three statistical algorithms geNorm, NormFinder and BestKeeper were used to define the stability of candidate genes. geNorm analysis identified IF4α and TUB6 as the most stable housekeeping genes however, NormFinder analysis determined IF4α and HSP90 as the most stable housekeeping genes under drought stress conditions. Subsequently validation of the identified candidate genes was undertaken in qRT-PCR based gene expression analysis of uspA gene which plays an important role for drought stress conditions in pigeonpea. The relative quantification of the uspA gene varied according to the internal controls (stable and least stable genes), thus highlighting the importance of the choice of as well as validation of internal controls in such experiments. The identified stable and validated housekeeping genes will facilitate gene expression studies in pigeonpea especially under drought stress conditions.

Simple Shared Motifs (SSM) in conserved region of promoters: a new approach to identify co-regulation patterns.

PubMed

Gruel, Jérémy; LeBorgne, Michel; LeMeur, Nolwenn; Théret, Nathalie

2011-09-12

Regulation of gene expression plays a pivotal role in cellular functions. However, understanding the dynamics of transcription remains a challenging task. A host of computational approaches have been developed to identify regulatory motifs, mainly based on the recognition of DNA sequences for transcription factor binding sites. Recent integration of additional data from genomic analyses or phylogenetic footprinting has significantly improved these methods. Here, we propose a different approach based on the compilation of Simple Shared Motifs (SSM), groups of sequences defined by their length and similarity and present in conserved sequences of gene promoters. We developed an original algorithm to search and count SSM in pairs of genes. An exceptional number of SSM is considered as a common regulatory pattern. The SSM approach is applied to a sample set of genes and validated using functional gene-set enrichment analyses. We demonstrate that the SSM approach selects genes that are over-represented in specific biological categories (Ontology and Pathways) and are enriched in co-expressed genes. Finally we show that genes co-expressed in the same tissue or involved in the same biological pathway have increased SSM values. Using unbiased clustering of genes, Simple Shared Motifs analysis constitutes an original contribution to provide a clearer definition of expression networks.

Simple Shared Motifs (SSM) in conserved region of promoters: a new approach to identify co-regulation patterns

PubMed Central

2011-01-01

Background Regulation of gene expression plays a pivotal role in cellular functions. However, understanding the dynamics of transcription remains a challenging task. A host of computational approaches have been developed to identify regulatory motifs, mainly based on the recognition of DNA sequences for transcription factor binding sites. Recent integration of additional data from genomic analyses or phylogenetic footprinting has significantly improved these methods. Results Here, we propose a different approach based on the compilation of Simple Shared Motifs (SSM), groups of sequences defined by their length and similarity and present in conserved sequences of gene promoters. We developed an original algorithm to search and count SSM in pairs of genes. An exceptional number of SSM is considered as a common regulatory pattern. The SSM approach is applied to a sample set of genes and validated using functional gene-set enrichment analyses. We demonstrate that the SSM approach selects genes that are over-represented in specific biological categories (Ontology and Pathways) and are enriched in co-expressed genes. Finally we show that genes co-expressed in the same tissue or involved in the same biological pathway have increased SSM values. Conclusions Using unbiased clustering of genes, Simple Shared Motifs analysis constitutes an original contribution to provide a clearer definition of expression networks. PMID:21910886

Single-cell RNA sequencing reveals gene expression signatures of breast cancer-associated endothelial cells.

PubMed

Sun, Zhengda; Wang, Chih-Yang; Lawson, Devon A; Kwek, Serena; Velozo, Hugo Gonzalez; Owyong, Mark; Lai, Ming-Derg; Fong, Lawrence; Wilson, Mark; Su, Hua; Werb, Zena; Cooke, Daniel L

2018-02-16

Tumor endothelial cells (TEC) play an indispensible role in tumor growth and metastasis although much of the detailed mechanism still remains elusive. In this study we characterized and compared the global gene expression profiles of TECs and control ECs isolated from human breast cancerous tissues and reduction mammoplasty tissues respectively by single cell RNA sequencing (scRNA-seq). Based on the qualified scRNA-seq libraries that we made, we found that 1302 genes were differentially expressed between these two EC phenotypes. Both principal component analysis (PCA) and heat map-based hierarchical clustering separated the cancerous versus control ECs as two distinctive clusters, and MetaCore disease biomarker analysis indicated that these differentially expressed genes are highly correlated with breast neoplasm diseases. Gene Set Enrichment Analysis software (GSEA) enriched these genes to extracellular matrix (ECM) signal pathways and highlighted 127 ECM-associated genes. External validation verified some of these ECM-associated genes are not only generally overexpressed in various cancer tissues but also specifically overexpressed in colorectal cancer ECs and lymphoma ECs. In conclusion, our data demonstrated that ECM-associated genes play pivotal roles in breast cancer EC biology and some of them could serve as potential TEC biomarkers for various cancers.

Gene coexpression measures in large heterogeneous samples using count statistics.

PubMed

Wang, Y X Rachel; Waterman, Michael S; Huang, Haiyan

2014-11-18

With the advent of high-throughput technologies making large-scale gene expression data readily available, developing appropriate computational tools to process these data and distill insights into systems biology has been an important part of the "big data" challenge. Gene coexpression is one of the earliest techniques developed that is still widely in use for functional annotation, pathway analysis, and, most importantly, the reconstruction of gene regulatory networks, based on gene expression data. However, most coexpression measures do not specifically account for local features in expression profiles. For example, it is very likely that the patterns of gene association may change or only exist in a subset of the samples, especially when the samples are pooled from a range of experiments. We propose two new gene coexpression statistics based on counting local patterns of gene expression ranks to take into account the potentially diverse nature of gene interactions. In particular, one of our statistics is designed for time-course data with local dependence structures, such as time series coupled over a subregion of the time domain. We provide asymptotic analysis of their distributions and power, and evaluate their performance against a wide range of existing coexpression measures on simulated and real data. Our new statistics are fast to compute, robust against outliers, and show comparable and often better general performance.

Gene expression analysis of copper tolerance and wood decay in the brown rot fungus Fibroporia radiculosa

USDA-ARS?s Scientific Manuscript database

Many brown rot fungi are capable of rapidly degrading wood and are copper-tolerant. To better understand the genes that control these processes, we examined gene expression of Fibroporia radiculosa growing on wood treated with a copper-based preservative that combined copper carbonate with dimethyld...

Integrating Data Clustering and Visualization for the Analysis of 3D Gene Expression Data

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

Data Analysis and Visualization; nternational Research Training Group ``Visualization of Large and Unstructured Data Sets,'' University of Kaiserslautern, Germany; Computational Research Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA 94720, USA

2008-05-12

The recent development of methods for extracting precise measurements of spatial gene expression patterns from three-dimensional (3D) image data opens the way for new analyses of the complex gene regulatory networks controlling animal development. We present an integrated visualization and analysis framework that supports user-guided data clustering to aid exploration of these new complex datasets. The interplay of data visualization and clustering-based data classification leads to improved visualization and enables a more detailed analysis than previously possible. We discuss (i) integration of data clustering and visualization into one framework; (ii) application of data clustering to 3D gene expression data; (iii)more » evaluation of the number of clusters k in the context of 3D gene expression clustering; and (iv) improvement of overall analysis quality via dedicated post-processing of clustering results based on visualization. We discuss the use of this framework to objectively define spatial pattern boundaries and temporal profiles of genes and to analyze how mRNA patterns are controlled by their regulatory transcription factors.« less

Effects of gravity changes on gene expression of BDNF and serotonin receptors in the mouse brain.

PubMed

Ishikawa, Chihiro; Li, Haiyan; Ogura, Rin; Yoshimura, Yuko; Kudo, Takashi; Shirakawa, Masaki; Shiba, Dai; Takahashi, Satoru; Morita, Hironobu; Shiga, Takashi

2017-01-01

Spaceflight entails various stressful environmental factors including microgravity. The effects of gravity changes have been studied extensively on skeletal, muscular, cardiovascular, immune and vestibular systems, but those on the nervous system are not well studied. The alteration of gravity in ground-based animal experiments is one of the approaches taken to address this issue. Here we investigated the effects of centrifugation-induced gravity changes on gene expression of brain-derived neurotrophic factor (BDNF) and serotonin receptors (5-HTRs) in the mouse brain. Exposure to 2g hypergravity for 14 days showed differential modulation of gene expression depending on regions of the brain. BDNF expression was decreased in the ventral hippocampus and hypothalamus, whereas increased in the cerebellum. 5-HT1BR expression was decreased in the cerebellum, whereas increased in the ventral hippocampus and caudate putamen. In contrast, hypergravity did not affect gene expression of 5-HT1AR, 5-HT2AR, 5-HT2CR, 5-HT4R and 5-HT7R. In addition to hypergravity, decelerating gravity change from 2g hypergravity to 1g normal gravity affected gene expression of BDNF, 5-HT1AR, 5-HT1BR, and 5-HT2AR in various regions of the brain. We also examined involvement of the vestibular organ in the effects of hypergravity. Surgical lesions of the inner ear's vestibular organ removed the effects induced by hypergravity on gene expression, which suggests that the effects of hypergravity are mediated through the vestibular organ. In summary, we showed that gravity changes induced differential modulation of gene expression of BDNF and 5-HTRs (5-HT1AR, 5-HT1BR and 5-HT2AR) in some brain regions. The modulation of gene expression may constitute molecular bases that underlie behavioral alteration induced by gravity changes.

Effects of gravity changes on gene expression of BDNF and serotonin receptors in the mouse brain

PubMed Central

Yoshimura, Yuko; Kudo, Takashi; Shirakawa, Masaki; Shiba, Dai; Takahashi, Satoru; Morita, Hironobu

2017-01-01

Spaceflight entails various stressful environmental factors including microgravity. The effects of gravity changes have been studied extensively on skeletal, muscular, cardiovascular, immune and vestibular systems, but those on the nervous system are not well studied. The alteration of gravity in ground-based animal experiments is one of the approaches taken to address this issue. Here we investigated the effects of centrifugation-induced gravity changes on gene expression of brain-derived neurotrophic factor (BDNF) and serotonin receptors (5-HTRs) in the mouse brain. Exposure to 2g hypergravity for 14 days showed differential modulation of gene expression depending on regions of the brain. BDNF expression was decreased in the ventral hippocampus and hypothalamus, whereas increased in the cerebellum. 5-HT1BR expression was decreased in the cerebellum, whereas increased in the ventral hippocampus and caudate putamen. In contrast, hypergravity did not affect gene expression of 5-HT1AR, 5-HT2AR, 5-HT2CR, 5-HT4R and 5-HT7R. In addition to hypergravity, decelerating gravity change from 2g hypergravity to 1g normal gravity affected gene expression of BDNF, 5-HT1AR, 5-HT1BR, and 5-HT2AR in various regions of the brain. We also examined involvement of the vestibular organ in the effects of hypergravity. Surgical lesions of the inner ear’s vestibular organ removed the effects induced by hypergravity on gene expression, which suggests that the effects of hypergravity are mediated through the vestibular organ. In summary, we showed that gravity changes induced differential modulation of gene expression of BDNF and 5-HTRs (5-HT1AR, 5-HT1BR and 5-HT2AR) in some brain regions. The modulation of gene expression may constitute molecular bases that underlie behavioral alteration induced by gravity changes. PMID:28591153

Differential gene expression patterns between smokers and non‐smokers: cause or consequence?

PubMed Central

Jansen, Rick; Brooks, Andy; Willemsen, Gonneke; van Grootheest, Gerard; de Geus, Eco; Smit, Jan H.; Penninx, Brenda W.; Boomsma, Dorret I.

2015-01-01

Abstract The molecular mechanisms causing smoking‐induced health decline are largely unknown. To elucidate the molecular pathways involved in cause and consequences of smoking behavior, we conducted a genome‐wide gene expression study in peripheral blood samples targeting 18 238 genes. Data of 743 smokers, 1686 never smokers and 890 ex‐smokers were available from two population‐based cohorts from the Netherlands. In addition, data of 56 monozygotic twin pairs discordant for ever smoking were used. One hundred thirty‐two genes were differentially expressed between current smokers and never smokers (P < 1.2 × 10−6, Bonferroni correction). The most significant genes were G protein‐coupled receptor 15 (P < 1 × 10−150) and leucine‐rich repeat neuronal 3 (P < 1 × 10−44). The smoking‐related genes were enriched for immune system, blood coagulation, natural killer cell and cancer pathways. By taking the data of ex‐smokers into account, expression of these 132 genes was classified into reversible (94 genes), slowly reversible (31 genes), irreversible (6 genes) or inconclusive (1 gene). Expression of 6 of the 132 genes (three reversible and three slowly reversible) was confirmed to be reactive to smoking as they were differentially expressed in monozygotic pairs discordant for smoking. Cis‐expression quantitative trait loci for GPR56 and RARRES3 (downregulated in smokers) were associated with increased number of cigarettes smoked per day in a large genome‐wide association meta‐analysis, suggesting a causative effect of GPR56 and RARRES3 expression on smoking behavior. In conclusion, differential gene expression patterns in smokers are extensive and cluster in several underlying disease pathways. Gene expression differences seem mainly direct consequences of smoking, and largely reversible after smoking cessation. However, we also identified DNA variants that may influence smoking behavior via the mediating gene expression. PMID:26594007

Identification and comprehensive evaluation of reference genes for RT-qPCR analysis of host gene-expression in Brassica juncea-aphid interaction using microarray data.

PubMed

Ram, Chet; Koramutla, Murali Krishna; Bhattacharya, Ramcharan

2017-07-01

Brassica juncea is a chief oil yielding crop in many parts of the world including India. With advancement of molecular techniques, RT-qPCR based study of gene-expression has become an integral part of experimentations in crop breeding. In RT-qPCR, use of appropriate reference gene(s) is pivotal. The virtue of the reference genes, being constant in expression throughout the experimental treatments, needs to be validated case by case. Appropriate reference gene(s) for normalization of gene-expression data in B. juncea during the biotic stress of aphid infestation is not known. In the present investigation, 11 reference genes identified from microarray database of Arabidopsis-aphid interaction at a cut off FDR ≤0.1, along with two known reference genes of B. juncea, were analyzed for their expression stability upon aphid infestation. These included 6 frequently used and 5 newly identified reference genes. Ranking orders of the reference genes in terms of expression stability were calculated using advanced statistical approaches such as geNorm, NormFinder, delta Ct and BestKeeper. The analysis suggested CAC, TUA and DUF179 as the most suitable reference genes. Further, normalization of the gene-expression data of STP4 and PR1 by the most and the least stable reference gene, respectively has demonstrated importance and applicability of the recommended reference genes in aphid infested samples of B. juncea. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

[Stability analysis of reference gene based on real-time PCR in Artemisia annua under cadmium treatment].

PubMed

Zhou, Liang-Yun; Mo, Ge; Wang, Sheng; Tang, Jin-Fu; Yue, Hong; Huang, Lu-Qi; Shao, Ai-Juan; Guo, Lan-Ping

2014-03-01

In this study, Actin, 18S rRNA, PAL, GAPDH and CPR of Artemisia annua were selected as candidate reference genes, and their gene-specific primers for real-time PCR were designed, then geNorm, NormFinder, BestKeeper, Delta CT and RefFinder were used to evaluate their expression stability in the leaves of A. annua under treatment of different concentrations of Cd, with the purpose of finding a reliable reference gene to ensure the reliability of gene-expression analysis. The results showed that there were some significant differences among the candidate reference genes under different treatments and the order of expression stability of candidate reference gene was Actin > 18S rRNA > PAL > GAPDH > CPR. These results suggested that Actin, 18S rRNA and PAL could be used as ideal reference genes of gene expression analysis in A. annua and multiple internal control genes were adopted for results calibration. In addition, differences in expression stability of candidate reference genes in the leaves of A. annua under the same concentrations of Cd were observed, which suggested that the screening of candidate reference genes was needed even under the same treatment. To our best knowledge, this study for the first time provided the ideal reference genes under Cd treatment in the leaves of A. annua and offered reference for the gene expression analysis of A. annua under other conditions.

Gene Expression-Based Survival Prediction in Lung Adenocarcinoma: A Multi-Site, Blinded Validation Study

PubMed Central

Shedden, Kerby; Taylor, Jeremy M.G.; Enkemann, Steve A.; Tsao, Ming S.; Yeatman, Timothy J.; Gerald, William L.; Eschrich, Steve; Jurisica, Igor; Venkatraman, Seshan E.; Meyerson, Matthew; Kuick, Rork; Dobbin, Kevin K.; Lively, Tracy; Jacobson, James W.; Beer, David G.; Giordano, Thomas J.; Misek, David E.; Chang, Andrew C.; Zhu, Chang Qi; Strumpf, Dan; Hanash, Samir; Shepherd, Francis A.; Ding, Kuyue; Seymour, Lesley; Naoki, Katsuhiko; Pennell, Nathan; Weir, Barbara; Verhaak, Roel; Ladd-Acosta, Christine; Golub, Todd; Gruidl, Mike; Szoke, Janos; Zakowski, Maureen; Rusch, Valerie; Kris, Mark; Viale, Agnes; Motoi, Noriko; Travis, William; Sharma, Anupama

2009-01-01

Although prognostic gene expression signatures for survival in early stage lung cancer have been proposed, for clinical application it is critical to establish their performance across different subject populations and in different laboratories. Here we report a large, training-testing, multi-site blinded validation study to characterize the performance of several prognostic models based on gene expression for 442 lung adenocarcinomas. The hypotheses proposed examined whether microarray measurements of gene expression either alone or combined with basic clinical covariates (stage, age, sex) can be used to predict overall survival in lung cancer subjects. Several models examined produced risk scores that substantially correlated with actual subject outcome. Most methods performed better with clinical data, supporting the combined use of clinical and molecular information when building prognostic models for early stage lung cancer. This study also provides the largest available set of microarray data with extensive pathological and clinical annotation for lung adenocarcinomas. PMID:18641660

Regulated Expression of Adenoviral Vectors-Based Gene Therapies

PubMed Central

Curtin, James F.; Candolfi, Marianela; Puntel, Mariana; Xiong, Weidong; Muhammad, A. K. M.; Kroeger, Kurt; Mondkar, Sonali; Liu, Chunyan; Bondale, Niyati; Lowenstein, Pedro R.; Castro, Maria G.

2008-01-01

Summary Regulatable promoter systems allow gene expression to be tightly controlled in vivo. This is highly desirable for the development of safe, efficacious adenoviral vectors that can be used to treat human diseases in the clinic. Ideally, regulatable cassettes should have minimal gene expression in the “OFF” state, and expression should quickly reach therapeutic levels in the “ON” state. In addition, the components of regulatable cassettes should be non-toxic at physiological concentrations and should not be immunogenic, especially when treating chronic illness that requires long-lasting gene expression. In this chapter, we will describe in detail protocols to develop and validate first generation (Ad) and high-capacity adenoviral (HC-Ad) vectors that express therapeutic genes under the control of the TetON regulatable system. Our laboratory has successfully used these protocols to regulate the expression of marker genes, immune stimulatory genes, and toxins for cancer gene therapeutics, i.e., glioma that is a deadly form of brain cancer. We have shown that this third generation TetON regulatable system, incorporating a doxycycline (DOX)-sensitive rtTA2S-M2 inducer and tTSKid silencer, is non-toxic, relatively non-immunogenic, and can tightly regulate reporter transgene expression downstream of a TRE promoter from adenoviral vectors in vitro and also in vivo. PMID:18470649

Cloning and Expression of Yak Active Chymosin in Pichia pastoris

PubMed Central

Luo, Fan; Jiang, Wei Hua; Yang, Yuan Xiao; Li, Jiang; Jiang, Ming Feng

2016-01-01

Rennet, a complex of enzymes found in the stomachs of ruminants, is an important component for cheese production. In our study, we described that yak chymosin gene recombinant Pichia pastoris strain could serve as a novel source for rennet production. Yaks total RNA was extracted from the abomasum of an unweaned yak. The yak preprochymosin, prochymosin, and chymosin genes from total RNA were isolated using gene specific primers based on cattle chymosin gene sequence respectively and analyzed their expression pattern byreal time-polymerase chain reaction. The result showed that the chymosin gene expression level of the sucking yaks was 11.45 times higher than one of adult yaks and yak chymosin belongs to Bovidae family in phylogenetic analysis. To express each, the preprochymosin, prochymosin, and chymosin genes were ligated into the expression vector pPICZαA, respectively, and were expressed in Pichia pastoris X33. The results showed that all the recombinant clones of P. pastoris containing the preprochymosin, prochymosin or chymosin genes could produce the active form of recombinant chymosin into the culture supernatant. Heterologous expressed prochymosin (14.55 Soxhlet unit/mL) had the highest enzyme activity of the three expressed chymosin enzymes. Therefore, we suggest that the yak chymosin gene recombinant Pichia pastoris strain could provide an alternative source of rennet production. PMID:27004812

Cloning and Expression of Yak Active Chymosin in Pichia pastoris.

PubMed

Luo, Fan; Jiang, Wei Hua; Yang, Yuan Xiao; Li, Jiang; Jiang, Ming Feng

2016-09-01

Rennet, a complex of enzymes found in the stomachs of ruminants, is an important component for cheese production. In our study, we described that yak chymosin gene recombinant Pichia pastoris strain could serve as a novel source for rennet production. Yaks total RNA was extracted from the abomasum of an unweaned yak. The yak preprochymosin, prochymosin, and chymosin genes from total RNA were isolated using gene specific primers based on cattle chymosin gene sequence respectively and analyzed their expression pattern byreal time-polymerase chain reaction. The result showed that the chymosin gene expression level of the sucking yaks was 11.45 times higher than one of adult yaks and yak chymosin belongs to Bovidae family in phylogenetic analysis. To express each, the preprochymosin, prochymosin, and chymosin genes were ligated into the expression vector pPICZαA, respectively, and were expressed in Pichia pastoris X33. The results showed that all the recombinant clones of P. pastoris containing the preprochymosin, prochymosin or chymosin genes could produce the active form of recombinant chymosin into the culture supernatant. Heterologous expressed prochymosin (14.55 Soxhlet unit/mL) had the highest enzyme activity of the three expressed chymosin enzymes. Therefore, we suggest that the yak chymosin gene recombinant Pichia pastoris strain could provide an alternative source of rennet production.

Arabidopsis Gene Family Profiler (aGFP)--user-oriented transcriptomic database with easy-to-use graphic interface.

PubMed

Dupl'áková, Nikoleta; Renák, David; Hovanec, Patrik; Honysová, Barbora; Twell, David; Honys, David

2007-07-23

Microarray technologies now belong to the standard functional genomics toolbox and have undergone massive development leading to increased genome coverage, accuracy and reliability. The number of experiments exploiting microarray technology has markedly increased in recent years. In parallel with the rapid accumulation of transcriptomic data, on-line analysis tools are being introduced to simplify their use. Global statistical data analysis methods contribute to the development of overall concepts about gene expression patterns and to query and compose working hypotheses. More recently, these applications are being supplemented with more specialized products offering visualization and specific data mining tools. We present a curated gene family-oriented gene expression database, Arabidopsis Gene Family Profiler (aGFP; http://agfp.ueb.cas.cz), which gives the user access to a large collection of normalised Affymetrix ATH1 microarray datasets. The database currently contains NASC Array and AtGenExpress transcriptomic datasets for various tissues at different developmental stages of wild type plants gathered from nearly 350 gene chips. The Arabidopsis GFP database has been designed as an easy-to-use tool for users needing an easily accessible resource for expression data of single genes, pre-defined gene families or custom gene sets, with the further possibility of keyword search. Arabidopsis Gene Family Profiler presents a user-friendly web interface using both graphic and text output. Data are stored at the MySQL server and individual queries are created in PHP script. The most distinguishable features of Arabidopsis Gene Family Profiler database are: 1) the presentation of normalized datasets (Affymetrix MAS algorithm and calculation of model-based gene-expression values based on the Perfect Match-only model); 2) the choice between two different normalization algorithms (Affymetrix MAS4 or MAS5 algorithms); 3) an intuitive interface; 4) an interactive "virtual plant" visualizing the spatial and developmental expression profiles of both gene families and individual genes. Arabidopsis GFP gives users the possibility to analyze current Arabidopsis developmental transcriptomic data starting with simple global queries that can be expanded and further refined to visualize comparative and highly selective gene expression profiles.

Mapping cis- and trans-regulatory effects across multiple tissues in twins

PubMed Central

Grundberg, Elin; Small, Kerrin S.; Hedman, Åsa K.; Nica, Alexandra C.; Buil, Alfonso; Keildson, Sarah; Bell, Jordana T.; Yang, Tsun-Po; Meduri, Eshwar; Barrett, Amy; Nisbett, James; Sekowska, Magdalena; Wilk, Alicja; Shin, So-Youn; Glass, Daniel; Travers, Mary; Min, Josine L.; Ring, Sue; Ho, Karen; Thorleifsson, Gudmar; Kong, Augustine; Thorsteindottir, Unnur; Ainali, Chrysanthi; Dimas, Antigone S.; Hassanali, Neelam; Ingle, Catherine; Knowles, David; Krestyaninova, Maria; Lowe, Christopher E.; Di Meglio, Paola; Montgomery, Stephen B.; Parts, Leopold; Potter, Simon; Surdulescu, Gabriela; Tsaprouni, Loukia; Tsoka, Sophia; Bataille, Veronique; Durbin, Richard; Nestle, Frank O.; O’Rahilly, Stephen; Soranzo, Nicole; Lindgren, Cecilia M.; Zondervan, Krina T.; Ahmadi, Kourosh R.; Schadt, Eric E.; Stefansson, Kari; Smith, George Davey; McCarthy, Mark I.; Deloukas, Panos; Dermitzakis, Emmanouil T.; Spector, Tim D.

2013-01-01

Sequence-based variation in gene expression is a key driver of disease risk. Common variants regulating expression in cis have been mapped in many eQTL studies typically in single tissues from unrelated individuals. Here, we present a comprehensive analysis of gene expression across multiple tissues conducted in a large set of mono- and dizygotic twins that allows systematic dissection of genetic (cis and trans) and non-genetic effects on gene expression. Using identity-by-descent estimates, we show that at least 40% of the total heritable cis-effect on expression cannot be accounted for by common cis-variants, a finding which exposes the contribution of low frequency and rare regulatory variants with respect to both transcriptional regulation and complex trait susceptibility. We show that a substantial proportion of gene expression heritability is trans to the structural gene and identify several replicating trans-variants which act predominantly in a tissue-restricted manner and may regulate the transcription of many genes. PMID:22941192

Cellular immunity to viral antigens limits E1-deleted adenoviruses for gene therapy.

PubMed Central

Yang, Y; Nunes, F A; Berencsi, K; Furth, E E; Gönczöl, E; Wilson, J M

1994-01-01

An important limitation that has emerged in the use of adenoviruses for gene therapy has been loss of recombinant gene expression that occurs concurrent with the development of pathology in the organ expressing the transgene. We have used liver-directed approaches to gene therapy in mice to study mechanisms that underlie the problems with transient expression and pathology that have characterized in vivo applications of first-generation recombinant adenoviruses (i.e., those deleted of E1a and E1b). Our data are consistent with the following hypothesis. Cells harboring the recombinant viral genome express the transgene as desired; however, low-level expression of viral genes also occurs. A virus-specific cellular immune response is stimulated that leads to destruction of the genetically modified hepatocytes, massive hepatitis, and repopulation of the liver with nontransgene-containing hepatocytes. These findings suggest approaches for improving recombinant adenoviruses that are based on further crippling the virus to limit expression of nondeleted viral genes. Images PMID:8183921

Regulation of Silk Genes by Hox and Homeodomain Proteins in the Terminal Differentiated Silk Gland of the Silkworm Bombyx mori

PubMed Central

Takiya, Shigeharu; Tsubota, Takuya; Kimoto, Mai

2016-01-01

The silk gland of the silkworm Bombyx mori is a long tubular organ that is divided into several subparts along its anteroposterior (AP) axis. As a trait of terminal differentiation of the silk gland, several silk protein genes are expressed with unique regional specificities. Most of the Hox and some of the homeobox genes are also expressed in the differentiated silk gland with regional specificities. The expression patterns of Hox genes in the silk gland roughly correspond to those in embryogenesis showing “colinearity”. The central Hox class protein Antennapedia (Antp) directly regulates the expression of several middle silk gland–specific silk genes, whereas the Lin-1/Isl-1/Mec3 (LIM)-homeodomain transcriptional factor Arrowhead (Awh) regulates the expression of posterior silk gland–specific genes for silk fiber proteins. We summarize our results and discuss the usefulness of the silk gland of Bombyx mori for analyzing the function of Hox genes. Further analyses of the regulatory mechanisms underlying the region-specific expression of silk genes will provide novel insights into the molecular bases for target-gene selection and regulation by Hox and homeodomain proteins. PMID:29615585

Gene-based meta-analysis of genome-wide association study data identifies independent single-nucleotide polymorphisms in ANXA6 as being associated with systemic lupus erythematosus in Asian populations.

PubMed

Zhang, Jing; Zhang, Lu; Zhang, Yan; Yang, Jing; Guo, Mengbiao; Sun, Liangdan; Pan, Hai-Feng; Hirankarn, Nattiya; Ying, Dingge; Zeng, Shuai; Lee, Tsz Leung; Lau, Chak Sing; Chan, Tak Mao; Leung, Alexander Moon Ho; Mok, Chi Chiu; Wong, Sik Nin; Lee, Ka Wing; Ho, Marco Hok Kung; Lee, Pamela Pui Wah; Chung, Brian Hon-Yin; Chong, Chun Yin; Wong, Raymond Woon Sing; Mok, Mo Yin; Wong, Wilfred Hing Sang; Tong, Kwok Lung; Tse, Niko Kei Chiu; Li, Xiang-Pei; Avihingsanon, Yingyos; Rianthavorn, Pornpimol; Deekajorndej, Thavatchai; Suphapeetiporn, Kanya; Shotelersuk, Vorasuk; Ying, Shirley King Yee; Fung, Samuel Ka Shun; Lai, Wai Ming; Garcia-Barceló, Maria-Mercè; Cherny, Stacey S; Sham, Pak Chung; Cui, Yong; Yang, Sen; Ye, Dong Qing; Zhang, Xue-Jun; Lau, Yu Lung; Yang, Wanling

2015-11-01

Previous genome-wide association studies (GWAS), which were mainly based on single-variant analysis, have identified many systemic lupus erythematosus (SLE) susceptibility loci. However, the genetic architecture of this complex disease is far from being understood. The aim of this study was to investigate whether using a gene-based analysis may help to identify novel loci, by considering global evidence of association from a gene or a genomic region rather than focusing on evidence for individual variants. Based on the results of a meta-analysis of 2 GWAS of SLE conducted in 2 Asian cohorts, we performed an in-depth gene-based analysis followed by replication in a total of 4,626 patients and 7,466 control subjects of Asian ancestry. Differential allelic expression was measured by pyrosequencing. More than one-half of the reported SLE susceptibility loci showed evidence of independent effects, and this finding is important for understanding the mechanisms of association and explaining disease heritability. ANXA6 was detected as a novel SLE susceptibility gene, with several single-nucleotide polymorphisms (SNPs) contributing independently to the association with disease. The risk allele of rs11960458 correlated significantly with increased expression of ANXA6 in peripheral blood mononuclear cells from heterozygous healthy control subjects. Several other associated SNPs may also regulate ANXA6 expression, according to data obtained from public databases. Higher expression of ANXA6 in patients with SLE was also reported previously. Our study demonstrated the merit of using gene-based analysis to identify novel susceptibility loci, especially those with independent effects, and also demonstrated the widespread presence of loci with independent effects in SLE susceptibility genes. © 2015, American College of Rheumatology.

Global gene expression analysis of apple fruit development from the floral bud to ripe fruit

PubMed Central

Janssen, Bart J; Thodey, Kate; Schaffer, Robert J; Alba, Rob; Balakrishnan, Lena; Bishop, Rebecca; Bowen, Judith H; Crowhurst, Ross N; Gleave, Andrew P; Ledger, Susan; McArtney, Steve; Pichler, Franz B; Snowden, Kimberley C; Ward, Shayna

2008-01-01

Background Apple fruit develop over a period of 150 days from anthesis to fully ripe. An array representing approximately 13000 genes (15726 oligonucleotides of 45–55 bases) designed from apple ESTs has been used to study gene expression over eight time points during fruit development. This analysis of gene expression lays the groundwork for a molecular understanding of fruit growth and development in apple. Results Using ANOVA analysis of the microarray data, 1955 genes showed significant changes in expression over this time course. Expression of genes is coordinated with four major patterns of expression observed: high in floral buds; high during cell division; high when starch levels and cell expansion rates peak; and high during ripening. Functional analysis associated cell cycle genes with early fruit development and three core cell cycle genes are significantly up-regulated in the early stages of fruit development. Starch metabolic genes were associated with changes in starch levels during fruit development. Comparison with microarrays of ethylene-treated apple fruit identified a group of ethylene induced genes also induced in normal fruit ripening. Comparison with fruit development microarrays in tomato has been used to identify 16 genes for which expression patterns are similar in apple and tomato and these genes may play fundamental roles in fruit development. The early phase of cell division and tissue specification that occurs in the first 35 days after pollination has been associated with up-regulation of a cluster of genes that includes core cell cycle genes. Conclusion Gene expression in apple fruit is coordinated with specific developmental stages. The array results are reproducible and comparisons with experiments in other species has been used to identify genes that may play a fundamental role in fruit development. PMID:18279528

Global gene expression analysis of apple fruit development from the floral bud to ripe fruit.

PubMed

Janssen, Bart J; Thodey, Kate; Schaffer, Robert J; Alba, Rob; Balakrishnan, Lena; Bishop, Rebecca; Bowen, Judith H; Crowhurst, Ross N; Gleave, Andrew P; Ledger, Susan; McArtney, Steve; Pichler, Franz B; Snowden, Kimberley C; Ward, Shayna

2008-02-17

Apple fruit develop over a period of 150 days from anthesis to fully ripe. An array representing approximately 13000 genes (15726 oligonucleotides of 45-55 bases) designed from apple ESTs has been used to study gene expression over eight time points during fruit development. This analysis of gene expression lays the groundwork for a molecular understanding of fruit growth and development in apple. Using ANOVA analysis of the microarray data, 1955 genes showed significant changes in expression over this time course. Expression of genes is coordinated with four major patterns of expression observed: high in floral buds; high during cell division; high when starch levels and cell expansion rates peak; and high during ripening. Functional analysis associated cell cycle genes with early fruit development and three core cell cycle genes are significantly up-regulated in the early stages of fruit development. Starch metabolic genes were associated with changes in starch levels during fruit development. Comparison with microarrays of ethylene-treated apple fruit identified a group of ethylene induced genes also induced in normal fruit ripening. Comparison with fruit development microarrays in tomato has been used to identify 16 genes for which expression patterns are similar in apple and tomato and these genes may play fundamental roles in fruit development. The early phase of cell division and tissue specification that occurs in the first 35 days after pollination has been associated with up-regulation of a cluster of genes that includes core cell cycle genes. Gene expression in apple fruit is coordinated with specific developmental stages. The array results are reproducible and comparisons with experiments in other species has been used to identify genes that may play a fundamental role in fruit development.

A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae

PubMed Central

Nookaew, Intawat; Papini, Marta; Pornputtapong, Natapol; Scalcinati, Gionata; Fagerberg, Linn; Uhlén, Matthias; Nielsen, Jens

2012-01-01

RNA-seq, has recently become an attractive method of choice in the studies of transcriptomes, promising several advantages compared with microarrays. In this study, we sought to assess the contribution of the different analytical steps involved in the analysis of RNA-seq data generated with the Illumina platform, and to perform a cross-platform comparison based on the results obtained through Affymetrix microarray. As a case study for our work we, used the Saccharomyces cerevisiae strain CEN.PK 113-7D, grown under two different conditions (batch and chemostat). Here, we asses the influence of genetic variation on the estimation of gene expression level using three different aligners for read-mapping (Gsnap, Stampy and TopHat) on S288c genome, the capabilities of five different statistical methods to detect differential gene expression (baySeq, Cuffdiff, DESeq, edgeR and NOISeq) and we explored the consistency between RNA-seq analysis using reference genome and de novo assembly approach. High reproducibility among biological replicates (correlation ≥0.99) and high consistency between the two platforms for analysis of gene expression levels (correlation ≥0.91) are reported. The results from differential gene expression identification derived from the different statistical methods, as well as their integrated analysis results based on gene ontology annotation are in good agreement. Overall, our study provides a useful and comprehensive comparison between the two platforms (RNA-seq and microrrays) for gene expression analysis and addresses the contribution of the different steps involved in the analysis of RNA-seq data. PMID:22965124

Laser-capture micro dissection combined with next-generation sequencing analysis of cell type-specific deafness gene expression in the mouse cochlea.

PubMed

Nishio, Shin-Ya; Takumi, Yutaka; Usami, Shin-Ichi

2017-05-01

Cochlear implantation (CI), which directly stimulates the cochlear nerves, is the most effective and widely used medical intervention for patients with severe to profound sensorineural hearing loss. The etiology of the hearing loss is speculated to have a major influence of CI outcomes, particularly in cases resulting from mutations in genes preferentially expressed in the spiral ganglion region. To elucidate precise gene expression levels in each part of the cochlea, we performed laser-capture micro dissection in combination with next-generation sequencing analysis and determined the expression levels of all known deafness-associated genes in the organ of Corti, spiral ganglion, lateral wall, and spiral limbs. The results were generally consistent with previous reports based on immunocytochemistry or in situ hybridization. As a notable result, the genes associated with many kinds of syndromic hearing loss (such as Clpp, Hars2, Hsd17b4, Lars2 for Perrault syndrome, Polr1c and Polr1d for Treacher Collins syndrome, Ndp for Norrie Disease, Kal for Kallmann syndrome, Edn3 and Snai2 for Waardenburg Syndrome, Col4a3 for Alport syndrome, Sema3e for CHARGE syndrome, Col9a1 for Sticker syndrome, Cdh23, Cib2, Clrn1, Pcdh15, Ush1c, Ush2a, Whrn for Usher syndrome and Wfs1 for Wolfram syndrome) showed higher levels of expression in the spiral ganglion than in other parts of the cochlea. This dataset will provide a base for more detailed analysis in order to clarify gene functions in the cochlea as well as predict CI outcomes based on gene expression data. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

RNA expression of genes involved in cytarabine metabolism and transport predicts cytarabine response in acute myeloid leukemia.

PubMed

Abraham, Ajay; Varatharajan, Savitha; Karathedath, Sreeja; Philip, Chepsy; Lakshmi, Kavitha M; Jayavelu, Ashok Kumar; Mohanan, Ezhilpavai; Janet, Nancy Beryl; Srivastava, Vivi M; Shaji, Ramachandran V; Zhang, Wei; Abraham, Aby; Viswabandya, Auro; George, Biju; Chandy, Mammen; Srivastava, Alok; Mathews, Vikram; Balasubramanian, Poonkuzhali

2015-07-01

Variation in terms of outcome and toxic side effects of treatment exists among acute myeloid leukemia (AML) patients on chemotherapy with cytarabine (Ara-C) and daunorubicin (Dnr). Candidate Ara-C metabolizing gene expression in primary AML cells is proposed to account for this variation. Ex vivo Ara-C sensitivity was determined in primary AML samples using MTT assay. mRNA expression of candidate Ara-C metabolizing genes were evaluated by RQPCR analysis. Global gene expression profiling was carried out for identifying differentially expressed genes between exvivo Ara-C sensitive and resistant samples. Wide interindividual variations in ex vivo Ara-C cytotoxicity were observed among samples from patients with AML and were stratified into sensitive, intermediately sensitive and resistant, based on IC50 values obtained by MTT assay. RNA expression of deoxycytidine kinase (DCK), human equilibrative nucleoside transporter-1 (ENT1) and ribonucleotide reductase M1 (RRM1) were significantly higher and cytidine deaminase (CDA) was significantly lower in ex vivo Ara-C sensitive samples. Higher DCK and RRM1 expression in AML patient's blast correlated with better DFS. Ara-C resistance index (RI), a mathematically derived quotient was proposed based on candidate gene expression pattern. Ara-C ex vivo sensitive samples were found to have significantly lower RI compared with resistant as well as samples from patients presenting with relapse. Patients with low RI supposedly highly sensitive to Ara-C were found to have higher incidence of induction death (p = 0.002; RR: 4.35 [95% CI: 1.69-11.22]). Global gene expression profiling undertaken to find out additional contributors of Ara-C resistance identified many apoptosis as well as metabolic pathway genes to be differentially expressed between Ara-C resistant and sensitive samples. This study highlights the importance of evaluating expression of candidate Ara-C metabolizing genes in predicting ex vivo drug response as well as treatment outcome. RI could be a predictor of ex vivo Ara-C response irrespective of cytogenetic and molecular risk groups and a potential biomarker for AML treatment outcome and toxicity. Original submitted 22 December 2014; Revision submitted 9 April 2015.

UNCLES: method for the identification of genes differentially consistently co-expressed in a specific subset of datasets.

PubMed

Abu-Jamous, Basel; Fa, Rui; Roberts, David J; Nandi, Asoke K

2015-06-04

Collective analysis of the increasingly emerging gene expression datasets are required. The recently proposed binarisation of consensus partition matrices (Bi-CoPaM) method can combine clustering results from multiple datasets to identify the subsets of genes which are consistently co-expressed in all of the provided datasets in a tuneable manner. However, results validation and parameter setting are issues that complicate the design of such methods. Moreover, although it is a common practice to test methods by application to synthetic datasets, the mathematical models used to synthesise such datasets are usually based on approximations which may not always be sufficiently representative of real datasets. Here, we propose an unsupervised method for the unification of clustering results from multiple datasets using external specifications (UNCLES). This method has the ability to identify the subsets of genes consistently co-expressed in a subset of datasets while being poorly co-expressed in another subset of datasets, and to identify the subsets of genes consistently co-expressed in all given datasets. We also propose the M-N scatter plots validation technique and adopt it to set the parameters of UNCLES, such as the number of clusters, automatically. Additionally, we propose an approach for the synthesis of gene expression datasets using real data profiles in a way which combines the ground-truth-knowledge of synthetic data and the realistic expression values of real data, and therefore overcomes the problem of faithfulness of synthetic expression data modelling. By application to those datasets, we validate UNCLES while comparing it with other conventional clustering methods, and of particular relevance, biclustering methods. We further validate UNCLES by application to a set of 14 real genome-wide yeast datasets as it produces focused clusters that conform well to known biological facts. Furthermore, in-silico-based hypotheses regarding the function of a few previously unknown genes in those focused clusters are drawn. The UNCLES method, the M-N scatter plots technique, and the expression data synthesis approach will have wide application for the comprehensive analysis of genomic and other sources of multiple complex biological datasets. Moreover, the derived in-silico-based biological hypotheses represent subjects for future functional studies.

RNase One Gene Isolation, Expression, and Affinity Purification Models Research Experimental Progression and Culminates with Guided Inquiry-Based Experiments

ERIC Educational Resources Information Center

Bailey, Cheryl P.

2009-01-01

This new biochemistry laboratory course moves through a progression of experiments that generates a platform for guided inquiry-based experiments. RNase One gene is isolated from prokaryotic genomic DNA, expressed as a tagged protein, affinity purified, and tested for activity and substrate specificity. Student pairs present detailed explanations…

Genetic and Transcriptomic Bases of Intestinal Epithelial Barrier Dysfunction in Inflammatory Bowel Disease.

PubMed

Vancamelbeke, Maaike; Vanuytsel, Tim; Farré, Ricard; Verstockt, Sare; Ferrante, Marc; Van Assche, Gert; Rutgeerts, Paul; Schuit, Frans; Vermeire, Séverine; Arijs, Ingrid; Cleynen, Isabelle

2017-10-01

Intestinal barrier defects are common in patients with inflammatory bowel disease (IBD). To identify which components could underlie these changes, we performed an in-depth analysis of epithelial barrier genes in IBD. A set of 128 intestinal barrier genes was selected. Polygenic risk scores were generated based on selected barrier gene variants that were associated with Crohn's disease (CD) or ulcerative colitis (UC) in our study. Gene expression was analyzed using microarray and quantitative reverse transcription polymerase chain reaction. Influence of barrier gene variants on expression was studied by cis-expression quantitative trait loci mapping and comparing patients with low- and high-risk scores. Barrier risk scores were significantly higher in patients with IBD than controls. At single-gene level, the associated barrier single-nucleotide polymorphisms were most significantly enriched in PTGER4 for CD and HNF4A for UC. As a group, the regulating proteins were most enriched for CD and UC. Expression analysis showed that many epithelial barrier genes were significantly dysregulated in active CD and UC, with overrepresentation of mucus layer genes. In uninflamed CD ileum and IBD colon, most barrier gene levels restored to normal, except for MUC1 and MUC4 that remained persistently increased compared with controls. Expression levels did not depend on cis-regulatory variants nor combined genetic risk. We found genetic and transcriptomic dysregulations of key epithelial barrier genes and components in IBD. Of these, we believe that mucus genes, in particular MUC1 and MUC4, play an essential role in the pathogenesis of IBD and could represent interesting targets for treatment.

A gene expression resource generated by genome-wide lacZ profiling in the mouse

PubMed Central

Tuck, Elizabeth; Estabel, Jeanne; Oellrich, Anika; Maguire, Anna Karin; Adissu, Hibret A.; Souter, Luke; Siragher, Emma; Lillistone, Charlotte; Green, Angela L.; Wardle-Jones, Hannah; Carragher, Damian M.; Karp, Natasha A.; Smedley, Damian; Adams, Niels C.; Bussell, James N.; Adams, David J.; Ramírez-Solis, Ramiro; Steel, Karen P.; Galli, Antonella; White, Jacqueline K.

2015-01-01

ABSTRACT Knowledge of the expression profile of a gene is a critical piece of information required to build an understanding of the normal and essential functions of that gene and any role it may play in the development or progression of disease. High-throughput, large-scale efforts are on-going internationally to characterise reporter-tagged knockout mouse lines. As part of that effort, we report an open access adult mouse expression resource, in which the expression profile of 424 genes has been assessed in up to 47 different organs, tissues and sub-structures using a lacZ reporter gene. Many specific and informative expression patterns were noted. Expression was most commonly observed in the testis and brain and was most restricted in white adipose tissue and mammary gland. Over half of the assessed genes presented with an absent or localised expression pattern (categorised as 0-10 positive structures). A link between complexity of expression profile and viability of homozygous null animals was observed; inactivation of genes expressed in ≥21 structures was more likely to result in reduced viability by postnatal day 14 compared with more restricted expression profiles. For validation purposes, this mouse expression resource was compared with Bgee, a federated composite of RNA-based expression data sets. Strong agreement was observed, indicating a high degree of specificity in our data. Furthermore, there were 1207 observations of expression of a particular gene in an anatomical structure where Bgee had no data, indicating a large amount of novelty in our data set. Examples of expression data corroborating and extending genotype-phenotype associations and supporting disease gene candidacy are presented to demonstrate the potential of this powerful resource. PMID:26398943

Vaccine induced differential expressions of miRNAs at cytolytic stage in chickens resistant or susceptible to Marek’s disease

USDA-ARS?s Scientific Manuscript database

Gene expression regulation is critical for all cellular processes since dysregulation of it often results in elevated disease risk and compromised cellular immunity. MicroRNAs (miRNAs) directly regulate gene expression post-transcriptionally through base-pairing with regions in the 3’-untranslated s...

Validation of the β-amy1 transcription profiling assay and selection of reference genes suited for a RT-qPCR assay in developing barley caryopsis.

PubMed

Ovesná, Jaroslava; Kučera, Ladislav; Vaculová, Kateřina; Štrymplová, Kamila; Svobodová, Ilona; Milella, Luigi

2012-01-01

Reverse transcription coupled with real-time quantitative PCR (RT-qPCR) is a frequently used method for gene expression profiling. Reference genes (RGs) are commonly employed to normalize gene expression data. A limited information exist on the gene expression and profiling in developing barley caryopsis. Expression stability was assessed by measuring the cycle threshold (Ct) range and applying both the GeNorm (pair-wise comparison of geometric means) and Normfinder (model-based approach) principles for the calculation. Here, we have identified a set of four RGs suitable for studying gene expression in the developing barley caryopsis. These encode the proteins GAPDH, HSP90, HSP70 and ubiquitin. We found a correlation between the frequency of occurrence of a transcript in silico and its suitability as an RG. This set of RGs was tested by comparing the normalized level of β-amylase (β-amy1) transcript with directly measured quantities of the BMY1 gene product in the developing barley caryopsis. This panel of genes could be used for other gene expression studies, as well as to optimize β-amy1 analysis for study of the impact of β-amy1 expression upon barley end-use quality.

Comprehensive analysis of area-specific and time-dependent changes in gene expression in the motor cortex of macaque monkeys during recovery from spinal cord injury.

PubMed

Higo, Noriyuki; Sato, Akira; Yamamoto, Tatsuya; Oishi, Takao; Nishimura, Yukio; Murata, Yumi; Onoe, Hirotaka; Isa, Tadashi; Kojima, Toshio

2018-05-01

The present study aimed to assess the molecular bases of cortical compensatory mechanisms following spinal cord injury in primates. To accomplish this, comprehensive changes in gene expression were investigated in the bilateral primary motor cortex (M1), dorsal premotor cortex (PMd), and ventral premotor cortex (PMv) after a unilateral lesion of the lateral corticospinal tract (l-CST). At 2 weeks after the lesion, a large number of genes exhibited altered expression levels in the contralesional M1, which is directly linked to the lesioned l-CST. Gene ontology and network analyses indicated that these changes in gene expression are involved in the atrophy and plasticity changes observed in neurons. Orchestrated gene expression changes were present when behavioral recovery was attained 3 months after the lesion, particularly among the bilateral premotor areas, and a large number of these genes are involved in plasticity. Moreover, several genes abundantly expressed in M1 of intact monkeys were upregulated in both the PMd and PMv after the l-CST lesion. These area-specific and time-dependent changes in gene expression may underlie the molecular mechanisms of functional recovery following a lesion of the l-CST. © 2018 Wiley Periodicals, Inc.

An integrative systems genetics approach reveals potential causal genes and pathways related to obesity.

PubMed

Kogelman, Lisette J A; Zhernakova, Daria V; Westra, Harm-Jan; Cirera, Susanna; Fredholm, Merete; Franke, Lude; Kadarmideen, Haja N

2015-10-20

Obesity is a multi-factorial health problem in which genetic factors play an important role. Limited results have been obtained in single-gene studies using either genomic or transcriptomic data. RNA sequencing technology has shown its potential in gaining accurate knowledge about the transcriptome, and may reveal novel genes affecting complex diseases. Integration of genomic and transcriptomic variation (expression quantitative trait loci [eQTL] mapping) has identified causal variants that affect complex diseases. We integrated transcriptomic data from adipose tissue and genomic data from a porcine model to investigate the mechanisms involved in obesity using a systems genetics approach. Using a selective gene expression profiling approach, we selected 36 animals based on a previously created genomic Obesity Index for RNA sequencing of subcutaneous adipose tissue. Differential expression analysis was performed using the Obesity Index as a continuous variable in a linear model. eQTL mapping was then performed to integrate 60 K porcine SNP chip data with the RNA sequencing data. Results were restricted based on genome-wide significant single nucleotide polymorphisms, detected differentially expressed genes, and previously detected co-expressed gene modules. Further data integration was performed by detecting co-expression patterns among eQTLs and integration with protein data. Differential expression analysis of RNA sequencing data revealed 458 differentially expressed genes. The eQTL mapping resulted in 987 cis-eQTLs and 73 trans-eQTLs (false discovery rate < 0.05), of which the cis-eQTLs were associated with metabolic pathways. We reduced the eQTL search space by focusing on differentially expressed and co-expressed genes and disease-associated single nucleotide polymorphisms to detect obesity-related genes and pathways. Building a co-expression network using eQTLs resulted in the detection of a module strongly associated with lipid pathways. Furthermore, we detected several obesity candidate genes, for example, ENPP1, CTSL, and ABHD12B. To our knowledge, this is the first study to perform an integrated genomics and transcriptomics (eQTL) study using, and modeling, genomic and subcutaneous adipose tissue RNA sequencing data on obesity in a porcine model. We detected several pathways and potential causal genes for obesity. Further validation and investigation may reveal their exact function and association with obesity.

ROKU: a novel method for identification of tissue-specific genes

PubMed Central

Kadota, Koji; Ye, Jiazhen; Nakai, Yuji; Terada, Tohru; Shimizu, Kentaro

2006-01-01

Background One of the important goals of microarray research is the identification of genes whose expression is considerably higher or lower in some tissues than in others. We would like to have ways of identifying such tissue-specific genes. Results We describe a method, ROKU, which selects tissue-specific patterns from gene expression data for many tissues and thousands of genes. ROKU ranks genes according to their overall tissue specificity using Shannon entropy and detects tissues specific to each gene if any exist using an outlier detection method. We evaluated the capacity for the detection of various specific expression patterns using synthetic and real data. We observed that ROKU was superior to a conventional entropy-based method in its ability to rank genes according to overall tissue specificity and to detect genes whose expression pattern are specific only to objective tissues. Conclusion ROKU is useful for the detection of various tissue-specific expression patterns. The framework is also directly applicable to the selection of diagnostic markers for molecular classification of multiple classes. PMID:16764735

Tightly regulated, high-level expression from controlled copy number vectors based on the replicon of temperate phage N15.

PubMed

Mardanov, Andrey V; Strakhova, Taisia S; Smagin, Vladimir A; Ravin, Nikolai V

2007-06-15

A new Escherichia coli host/vector system has been developed to allow a dual regulation of both the plasmid copy number and gene expression. The new pN15E vectors are low copy number plasmids based on the replicon of temperate phage N15, comprising the repA replicase gene and cB repressor gene, controlling the plasmid copy number. Regulation of pN15E copy number is achieved through arabinose-inducible expression of phage N15 antirepressor protein, AntA, whose gene was integrated into the chromosome of the host strain under control of the PBAD promoter. The host strain also carried phage N15 partition operon, sop, allowing stable inheritance of pN15E vectors in the absence of selection pressure. In the first vector, pN15E4, the same PBAD promoter controls expression of a cloned gene. The second vector, pN15E6, carries the phage T5 promoter with a double lac operator repression module thus allowing independent regulation of promoter activity and copy number. Using the lacZ gene to monitor expression in these vectors, we show that the ratio of induction/repression can be about 7600-fold for pN15E4 and more than 15,000-fold for pN15E6. The low copy number of these vectors ensures very low basal level of expression allowing cloning genes encoding toxic products that was demonstrated by the stable maintenance of a gene encoding a restriction endonuclease in pN15E4. The tight control of transcription and the potential to regulate gene activities quantitatively over wide ranges will open up new approaches in the study of gene function in vivo and controlled expression of heterologous genes.

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/.

Gene expression-based molecular diagnostic system for malignant gliomas is superior to histological diagnosis.

PubMed

Shirahata, Mitsuaki; Iwao-Koizumi, Kyoko; Saito, Sakae; Ueno, Noriko; Oda, Masashi; Hashimoto, Nobuo; Takahashi, Jun A; Kato, Kikuya

2007-12-15

Current morphology-based glioma classification methods do not adequately reflect the complex biology of gliomas, thus limiting their prognostic ability. In this study, we focused on anaplastic oligodendroglioma and glioblastoma, which typically follow distinct clinical courses. Our goal was to construct a clinically useful molecular diagnostic system based on gene expression profiling. The expression of 3,456 genes in 32 patients, 12 and 20 of whom had prognostically distinct anaplastic oligodendroglioma and glioblastoma, respectively, was measured by PCR array. Next to unsupervised methods, we did supervised analysis using a weighted voting algorithm to construct a diagnostic system discriminating anaplastic oligodendroglioma from glioblastoma. The diagnostic accuracy of this system was evaluated by leave-one-out cross-validation. The clinical utility was tested on a microarray-based data set of 50 malignant gliomas from a previous study. Unsupervised analysis showed divergent global gene expression patterns between the two tumor classes. A supervised binary classification model showed 100% (95% confidence interval, 89.4-100%) diagnostic accuracy by leave-one-out cross-validation using 168 diagnostic genes. Applied to a gene expression data set from a previous study, our model correlated better with outcome than histologic diagnosis, and also displayed 96.6% (28 of 29) consistency with the molecular classification scheme used for these histologically controversial gliomas in the original article. Furthermore, we observed that histologically diagnosed glioblastoma samples that shared anaplastic oligodendroglioma molecular characteristics tended to be associated with longer survival. Our molecular diagnostic system showed reproducible clinical utility and prognostic ability superior to traditional histopathologic diagnosis for malignant glioma.

Generation of TALE-Based Designer Epigenome Modifiers.

PubMed

Nitsch, Sandra; Mussolino, Claudio

2018-01-01

Manipulation of gene expression can be facilitated by editing the genome or the epigenome. Precise genome editing is traditionally achieved by using designer nucleases which are generally exploited to eliminate a specific gene product. Upon the introduction of a site-specific DNA double-strand break (DSB) by the nuclease, endogenous DSB repair mechanisms are in turn harnessed to induce DNA sequence changes that can result in target gene inactivation. Minimal off-target effects can be obtained by endowing designer nucleases with the highly specific DNA-binding domain (DBD) derived from transcription activator-like effectors (TALEs). In contrast, epigenome editing allows gene expression control without inducing changes in the DNA sequence by specifically altering epigenetic marks, as histone tails modifications or DNA methylation patterns within promoter or enhancer regions. Importantly, this approach allows both up- and downregulation of the target gene expression, and the effect is generally reversible. TALE-based designer epigenome modifiers combine the high specificity of TALE-derived DBDs with the power of epigenetic modifier domains to induce fast and long-lasting changes in the epigenetic landscape of a target gene and control its expression. Here we provide a detailed description for the generation of TALE-based designer epigenome modifiers and of a suitable reporter cell line to easily monitor their activity.

Revealing cell cycle control by combining model-based detection of periodic expression with novel cis-regulatory descriptors

PubMed Central

Andersson, Claes R; Hvidsten, Torgeir R; Isaksson, Anders; Gustafsson, Mats G; Komorowski, Jan

2007-01-01

Background We address the issue of explaining the presence or absence of phase-specific transcription in budding yeast cultures under different conditions. To this end we use a model-based detector of gene expression periodicity to divide genes into classes depending on their behavior in experiments using different synchronization methods. While computational inference of gene regulatory circuits typically relies on expression similarity (clustering) in order to find classes of potentially co-regulated genes, this method instead takes advantage of known time profile signatures related to the studied process. Results We explain the regulatory mechanisms of the inferred periodic classes with cis-regulatory descriptors that combine upstream sequence motifs with experimentally determined binding of transcription factors. By systematic statistical analysis we show that periodic classes are best explained by combinations of descriptors rather than single descriptors, and that different combinations correspond to periodic expression in different classes. We also find evidence for additive regulation in that the combinations of cis-regulatory descriptors associated with genes periodically expressed in fewer conditions are frequently subsets of combinations associated with genes periodically expression in more conditions. Finally, we demonstrate that our approach retrieves combinations that are more specific towards known cell-cycle related regulators than the frequently used clustering approach. Conclusion The results illustrate how a model-based approach to expression analysis may be particularly well suited to detect biologically relevant mechanisms. Our new approach makes it possible to provide more refined hypotheses about regulatory mechanisms of the cell cycle and it can easily be adjusted to reveal regulation of other, non-periodic, cellular processes. PMID:17939860

Clinical value of miR-198-5p in lung squamous cell carcinoma assessed using microarray and RT-qPCR.

PubMed

Liang, Yue-Ya; Huang, Jia-Cheng; Tang, Rui-Xue; Chen, Wen-Jie; Chen, Peng; Cen, Wei-Luan; Shi, Ke; Gao, Li; Gao, Xiang; Liu, An-Gui; Peng, Xiao-Tong; Chen, Gang; Huang, Su-Ning; Fang, Ye-Ying; Gu, Yong-Yao

2018-02-02

To examine the clinical value of miR-198-5p in lung squamous cell carcinoma (LUSC). Gene Expression Omnibus (GEO) microarray datasets were used to explore the miR-198-5p expression and its diagnostic value in LUSC. Real-time reverse transcription quantitative polymerase chain reaction was used to evaluate the expression of miR-198-5p in 23 formalin-fixed, paraffin-embedded (FFPE) LUSC tissues and corresponding non-cancerous tissues. The correlation between miR-198-5p expression and clinic pathological features was assessed. Meanwhile, putative target messenger RNAs of miR-198-5p were identified based on the analysis of differentially expressed genes in the Cancer Genome Atlas (TCGA) and 12 miRNA prediction tools. Subsequently, the putative target genes were sent to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses. MiR-198-5p was low expressed in LUSC tissues. The combined standard mean difference (SMD) values of miR-198-5p expression based on GEO datasets were - 0.30 (95% confidence interval (CI) - 0.54, - 0.06) and - 0.39 (95% CI - 0.83, 0.05) using fixed effect model and random effect model, respectively. The sensitivity and specificity were not sufficiently high, as the area under the curve (AUC) was 0.7749 (Q* = 0.7143) based on summarized receiver operating characteristic (SROC) curves constructed using GEO datasets. Based on the in-house RT-qPCR, miR-198-5p expression was 4.3826 ± 1.7660 in LUSC tissues and 4.4522 ± 1.8263 in adjacent normal tissues (P = 0.885). The expression of miR-198-5p was significantly higher in patients with early TNM stages (I-II) than that in cases with advanced TNM stages (III-IV) (5.4400 ± 1.5277 vs 3.5690 ± 1.5228, P = 0.008). Continuous variable-based meta-analysis of GEO and PCR data displayed the SMD values of - 0.26 (95% CI - 0.48, - 0.04) and - 0.34 (95% CI - 0.71, 0.04) based on fixed and random effect models, respectively. As for the diagnostic value of miR-198-5p, the AUC based on the SROC curve using GEO and PCR data was 0.7351 (Q* = 0.6812). In total, 542 genes were identified as the targets of miR-198-5p. The most enriched Gene Ontology terms were epidermis development among biological processes, cell junction among cellular components, and protein dimerization activity among molecule functions. The pathway of non-small cell lung cancer was the most significant pathway identified using Kyoto Encyclopedia of Genes and Genomes analysis. The expression of miR-198-5p is related to the TNM stage. Thus, miR-198-5p might play an important role via its target genes in LUSC.

A long non-coding RNA expression profile can predict early recurrence in hepatocellular carcinoma after curative resection.

PubMed

Lv, Yufeng; Wei, Wenhao; Huang, Zhong; Chen, Zhichao; Fang, Yuan; Pan, Lili; Han, Xueqiong; Xu, Zihai

2018-06-20

The aim of this study was to develop a novel long non-coding RNA (lncRNA) expression signature to accurately predict early recurrence for patients with hepatocellular carcinoma (HCC) after curative resection. Using expression profiles downloaded from The Cancer Genome Atlas database, we identified multiple lncRNAs with differential expression between early recurrence (ER) group and non-early recurrence (non-ER) group of HCC. Least absolute shrinkage and selection operator (LASSO) for logistic regression models were used to develop a lncRNA-based classifier for predicting ER in the training set. An independent test set was used to validated the predictive value of this classifier. Futhermore, a co-expression network based on these lncRNAs and its highly related genes was constructed and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of genes in the network were performed. We identified 10 differentially expressed lncRNAs, including 3 that were upregulated and 7 that were downregulated in ER group. The lncRNA-based classifier was constructed based on 7 lncRNAs (AL035661.1, PART1, AC011632.1, AC109588.1, AL365361.1, LINC00861 and LINC02084), and its accuracy was 0.83 in training set, 0.87 in test set and 0.84 in total set. And ROC curve analysis showed the AUROC was 0.741 in training set, 0.824 in the test set and 0.765 in total set. A functional enrichment analysis suggested that the genes of which is highly related to 4 lncRNAs were involved in immune system. This 7-lncRNA expression profile can effectively predict the early recurrence after surgical resection for HCC. This article is protected by copyright. All rights reserved.

Gene expression changes in the retina following subretinal injection of human neural progenitor cells into a rodent model for retinal degeneration.

PubMed

Jones, Melissa K; Lu, Bin; Saghizadeh, Mehrnoosh; Wang, Shaomei

2016-01-01

Retinal degenerative diseases (RDDs) affect millions of people and are the leading cause of vision loss. Although treatment options for RDDs are limited, stem and progenitor cell-based therapies have great potential to halt or slow the progression of vision loss. Our previous studies have shown that a single subretinal injection of human forebrain derived neural progenitor cells (hNPCs) into the Royal College of Surgeons (RCS) retinal degenerate rat offers long-term preservation of photoreceptors and visual function. Furthermore, neural progenitor cells are currently in clinical trials for treating age-related macular degeneration; however, the molecular mechanisms of stem cell-based therapies are largely unknown. This is the first study to analyze gene expression changes in the retina of RCS rats following subretinal injection of hNPCs using high-throughput sequencing. RNA-seq data of retinas from RCS rats injected with hNPCs (RCS(hNPCs)) were compared to sham surgery in RCS (RCS(sham)) and wild-type Long Evans (LE(sham)) rats. Differential gene expression patterns were determined with in silico analysis and confirmed with qRT-PCR. Function, biologic, cellular component, and pathway analyses were performed on differentially expressed genes and investigated with immunofluorescent staining experiments. Analysis of the gene expression data sets identified 1,215 genes that were differentially expressed between RCS(sham) and LE(sham) samples. Additionally, 283 genes were differentially expressed between the RCS(hNPCs) and RCS(sham) samples. Comparison of these two gene sets identified 68 genes with inverse expression (termed rescue genes), including Pdc, Rp1, and Cdc42ep5. Functional, biologic, and cellular component analyses indicate that the immune response is enhanced in RCS(sham). Pathway analysis of the differential expression gene sets identified three affected pathways in RCS(hNPCs), which all play roles in phagocytosis signaling. Immunofluorescent staining detected the increased presence of macrophages and microglia in RCS(sham) retinas, which decreased in RCS(hNPCs) retinas similar to the patterns detected in LE(sham). The results from this study provide evidence of the gene expression changes that occur following treatment with hNPCs in the degenerating retina. This information can be used in future studies to potentially enhance or predict responses to hNPC and other stem cell therapies for retinal degenerative diseases.

Concordant integrative gene set enrichment analysis of multiple large-scale two-sample expression data sets.

PubMed

Lai, Yinglei; Zhang, Fanni; Nayak, Tapan K; Modarres, Reza; Lee, Norman H; McCaffrey, Timothy A

2014-01-01

Gene set enrichment analysis (GSEA) is an important approach to the analysis of coordinate expression changes at a pathway level. Although many statistical and computational methods have been proposed for GSEA, the issue of a concordant integrative GSEA of multiple expression data sets has not been well addressed. Among different related data sets collected for the same or similar study purposes, it is important to identify pathways or gene sets with concordant enrichment. We categorize the underlying true states of differential expression into three representative categories: no change, positive change and negative change. Due to data noise, what we observe from experiments may not indicate the underlying truth. Although these categories are not observed in practice, they can be considered in a mixture model framework. Then, we define the mathematical concept of concordant gene set enrichment and calculate its related probability based on a three-component multivariate normal mixture model. The related false discovery rate can be calculated and used to rank different gene sets. We used three published lung cancer microarray gene expression data sets to illustrate our proposed method. One analysis based on the first two data sets was conducted to compare our result with a previous published result based on a GSEA conducted separately for each individual data set. This comparison illustrates the advantage of our proposed concordant integrative gene set enrichment analysis. Then, with a relatively new and larger pathway collection, we used our method to conduct an integrative analysis of the first two data sets and also all three data sets. Both results showed that many gene sets could be identified with low false discovery rates. A consistency between both results was also observed. A further exploration based on the KEGG cancer pathway collection showed that a majority of these pathways could be identified by our proposed method. This study illustrates that we can improve detection power and discovery consistency through a concordant integrative analysis of multiple large-scale two-sample gene expression data sets.

The significance of alternative transcripts for Caenorhabditis elegans transcription factor genes, based on expression pattern analysis

PubMed Central

2013-01-01

Background Sequence-specific DNA-binding proteins, with their paramount importance in the regulation of expression of the genetic material, are encoded by approximately 5% of the genes in an animal’s genome. But it is unclear to what extent alternative transcripts from these genes may further increase the complexity of the transcription factor complement. Results Of the 938 potential C. elegans transcription factor genes, 197 were annotated in WormBase as encoding at least two distinct isoforms. Evaluation of prior evidence identified, with different levels of confidence, 50 genes with alternative transcript starts, 23 with alternative transcript ends, 35 with alternative splicing and 34 with alternative transcripts generated by a combination of mechanisms, leaving 55 that were discounted. Expression patterns were determined for transcripts for a sample of 29 transcription factor genes, concentrating on those with alternative transcript starts for which the evidence was strongest. Seamless fosmid recombineering was used to generate reporter gene fusions with minimal modification to assay expression of specific transcripts while maintaining the broad genomic DNA context and alternative transcript production. Alternative transcription factor gene transcripts were typically expressed with identical or substantially overlapping distributions rather than in distinct domains. Conclusions Increasingly sensitive sequencing technologies will reveal rare transcripts but many of these are clearly non-productive. The majority of the transcription factor gene alternative transcripts that are productive may represent tolerable noise rather than encoding functionally distinct isoforms. PMID:23586691

Combining Evidence of Preferential Gene-Tissue Relationships from Multiple Sources

PubMed Central

Guo, Jing; Hammar, Mårten; Öberg, Lisa; Padmanabhuni, Shanmukha S.; Bjäreland, Marcus; Dalevi, Daniel

2013-01-01

An important challenge in drug discovery and disease prognosis is to predict genes that are preferentially expressed in one or a few tissues, i.e. showing a considerably higher expression in one tissue(s) compared to the others. Although several data sources and methods have been published explicitly for this purpose, they often disagree and it is not evident how to retrieve these genes and how to distinguish true biological findings from those that are due to choice-of-method and/or experimental settings. In this work we have developed a computational approach that combines results from multiple methods and datasets with the aim to eliminate method/study-specific biases and to improve the predictability of preferentially expressed human genes. A rule-based score is used to merge and assign support to the results. Five sets of genes with known tissue specificity were used for parameter pruning and cross-validation. In total we identify 3434 tissue-specific genes. We compare the genes of highest scores with the public databases: PaGenBase (microarray), TiGER (EST) and HPA (protein expression data). The results have 85% overlap to PaGenBase, 71% to TiGER and only 28% to HPA. 99% of our predictions have support from at least one of these databases. Our approach also performs better than any of the databases on identifying drug targets and biomarkers with known tissue-specificity. PMID:23950964

Comparison of RNA-seq and microarray-based models for clinical endpoint prediction.

PubMed

Zhang, Wenqian; Yu, Ying; Hertwig, Falk; Thierry-Mieg, Jean; Zhang, Wenwei; Thierry-Mieg, Danielle; Wang, Jian; Furlanello, Cesare; Devanarayan, Viswanath; Cheng, Jie; Deng, Youping; Hero, Barbara; Hong, Huixiao; Jia, Meiwen; Li, Li; Lin, Simon M; Nikolsky, Yuri; Oberthuer, André; Qing, Tao; Su, Zhenqiang; Volland, Ruth; Wang, Charles; Wang, May D; Ai, Junmei; Albanese, Davide; Asgharzadeh, Shahab; Avigad, Smadar; Bao, Wenjun; Bessarabova, Marina; Brilliant, Murray H; Brors, Benedikt; Chierici, Marco; Chu, Tzu-Ming; Zhang, Jibin; Grundy, Richard G; He, Min Max; Hebbring, Scott; Kaufman, Howard L; Lababidi, Samir; Lancashire, Lee J; Li, Yan; Lu, Xin X; Luo, Heng; Ma, Xiwen; Ning, Baitang; Noguera, Rosa; Peifer, Martin; Phan, John H; Roels, Frederik; Rosswog, Carolina; Shao, Susan; Shen, Jie; Theissen, Jessica; Tonini, Gian Paolo; Vandesompele, Jo; Wu, Po-Yen; Xiao, Wenzhong; Xu, Joshua; Xu, Weihong; Xuan, Jiekun; Yang, Yong; Ye, Zhan; Dong, Zirui; Zhang, Ke K; Yin, Ye; Zhao, Chen; Zheng, Yuanting; Wolfinger, Russell D; Shi, Tieliu; Malkas, Linda H; Berthold, Frank; Wang, Jun; Tong, Weida; Shi, Leming; Peng, Zhiyu; Fischer, Matthias

2015-06-25

Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model. We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models. We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.

Bioinformatics approaches for cross-species liver cancer analysis based on microarray gene expression profiling

PubMed Central

Fang, H; Tong, W; Perkins, R; Shi, L; Hong, H; Cao, X; Xie, Q; Yim, SH; Ward, JM; Pitot, HC; Dragan, YP

2005-01-01

Background The completion of the sequencing of human, mouse and rat genomes and knowledge of cross-species gene homologies enables studies of differential gene expression in animal models. These types of studies have the potential to greatly enhance our understanding of diseases such as liver cancer in humans. Genes co-expressed across multiple species are most likely to have conserved functions. We have used various bioinformatics approaches to examine microarray expression profiles from liver neoplasms that arise in albumin-SV40 transgenic rats to elucidate genes, chromosome aberrations and pathways that might be associated with human liver cancer. Results In this study, we first identified 2223 differentially expressed genes by comparing gene expression profiles for two control, two adenoma and two carcinoma samples using an F-test. These genes were subsequently mapped to the rat chromosomes using a novel visualization tool, the Chromosome Plot. Using the same plot, we further mapped the significant genes to orthologous chromosomal locations in human and mouse. Many genes expressed in rat 1q that are amplified in rat liver cancer map to the human chromosomes 10, 11 and 19 and to the mouse chromosomes 7, 17 and 19, which have been implicated in studies of human and mouse liver cancer. Using Comparative Genomics Microarray Analysis (CGMA), we identified regions of potential aberrations in human. Lastly, a pathway analysis was conducted to predict altered human pathways based on statistical analysis and extrapolation from the rat data. All of the identified pathways have been known to be important in the etiology of human liver cancer, including cell cycle control, cell growth and differentiation, apoptosis, transcriptional regulation, and protein metabolism. Conclusion The study demonstrates that the hepatic gene expression profiles from the albumin-SV40 transgenic rat model revealed genes, pathways and chromosome alterations consistent with experimental and clinical research in human liver cancer. The bioinformatics tools presented in this paper are essential for cross species extrapolation and mapping of microarray data, its analysis and interpretation. PMID:16026603

Weighted gene co-expression network analysis reveals potential genes involved in early metamorphosis process in sea cucumber Apostichopus japonicus.

PubMed

Li, Yongxin; Kikuchi, Mani; Li, Xueyan; Gao, Qionghua; Xiong, Zijun; Ren, Yandong; Zhao, Ruoping; Mao, Bingyu; Kondo, Mariko; Irie, Naoki; Wang, Wen

2018-01-01

Sea cucumbers, one main class of Echinoderms, have a very fast and drastic metamorphosis process during their development. However, the molecular basis under this process remains largely unknown. Here we systematically examined the gene expression profiles of Japanese common sea cucumber (Apostichopus japonicus) for the first time by RNA sequencing across 16 developmental time points from fertilized egg to juvenile stage. Based on the weighted gene co-expression network analysis (WGCNA), we identified 21 modules. Among them, MEdarkmagenta was highly expressed and correlated with the early metamorphosis process from late auricularia to doliolaria larva. Furthermore, gene enrichment and differentially expressed gene analysis identified several genes in the module that may play key roles in the metamorphosis process. Our results not only provide a molecular basis for experimentally studying the development and morphological complexity of sea cucumber, but also lay a foundation for improving its emergence rate. Copyright © 2017 Elsevier Inc. All rights reserved.

[Cloning and characterization of genes differentially expressed in human dental pulp cells and gingival fibroblasts].

PubMed

Wang, Zhong-dong; Wu, Ji-nan; Zhou, Lin; Ling, Jun-qi; Guo, Xi-min; Xiao, Ming-zhen; Zhu, Feng; Pu, Qin; Chai, Yu-bo; Zhao, Zhong-liang

2007-02-01

To study the biological properties of human dental pulp cells (HDPC) by cloning and analysis of genes differentially expressed in HDPC in comparison with human gingival fibroblasts (HGF). HDPC and HGF were cultured and identified by immunocytochemistry. HPDC and HGF subtractive cDNA library was established by PCR-based modified subtractive hybridization, genes differentially expressed by HPDC were cloned, sequenced and compared to find homogeneous sequence in GenBank by BLAST. Cloning and sequencing analysis indicate 12 genes differentially expressed were obtained, in which two were unknown genes. Among the 10 known genes, 4 were related to signal transduction, 2 were related to trans-membrane transportation (both cell membrane and nuclear membrane), and 2 were related to RNA splicing mechanisms. The biological properties of HPDC are determined by the differential expression of some genes and the growth and differentiation of HPDC are associated to the dynamic protein synthesis and secretion activities of the cell.

Targeting pancreatic expressed PAX genes for the treatment of diabetes mellitus and pancreatic neuroendocrine tumors.

PubMed

Martin-Montalvo, Alejandro; Lorenzo, Petra I; López-Noriega, Livia; Gauthier, Benoit R

2017-01-01

Four members of the PAX family, PAX2, PAX4, PAX6 and PAX8 are known to be expressed in the pancreas. Accumulated evidences indicate that several pancreatic expressed PAX genes play a significant role in pancreatic development/functionality and alterations in these genes are involved in the pathogenesis of pancreatic diseases. Areas covered: In this review, we summarize the ongoing research related to pancreatic PAX genes in diabetes mellitus and pancreatic neuroendocrine tumors. We dissect the current knowledge at different levels; from mechanistic studies in cell lines performed to understand the molecular processes controlled by pancreatic PAX genes, to in vivo studies using rodent models that over-express or lack specific PAX genes. Finally, we describe human studies associating variants on pancreatic-expressed PAX genes with pancreatic diseases. Expert opinion: Based on the current literature, we propose that future interventions to treat pancreatic neuroendocrine tumors and diabetes mellitus could be developed via the modulation of PAX4 and/or PAX6 regulated pathways.

From Saccharomyces cerevisiae to human: The important gene co-expression modules.

PubMed

Liu, Wei; Li, Li; Ye, Hua; Chen, Haiwei; Shen, Weibiao; Zhong, Yuexian; Tian, Tian; He, Huaqin

2017-08-01

Network-based systems biology has become an important method for analyzing high-throughput gene expression data and gene function mining. Yeast has long been a popular model organism for biomedical research. In the current study, a weighted gene co-expression network analysis algorithm was applied to construct a gene co-expression network in Saccharomyces cerevisiae . Seventeen stable gene co-expression modules were detected from 2,814 S. cerevisiae microarray data. Further characterization of these modules with the Database for Annotation, Visualization and Integrated Discovery tool indicated that these modules were associated with certain biological processes, such as heat response, cell cycle, translational regulation, mitochondrion oxidative phosphorylation, amino acid metabolism and autophagy. Hub genes were also screened by intra-modular connectivity. Finally, the module conservation was evaluated in a human disease microarray dataset. Functional modules were identified in budding yeast, some of which are associated with patient survival. The current study provided a paradigm for single cell microorganisms and potentially other organisms.

Exploring codon context bias for synthetic gene design of a thermostable invertase in Escherichia coli.

PubMed

Pek, Han Bin; Klement, Maximilian; Ang, Kok Siong; Chung, Bevan Kai-Sheng; Ow, Dave Siak-Wei; Lee, Dong-Yup

2015-01-01

Various isoforms of invertases from prokaryotes, fungi, and higher plants has been expressed in Escherichia coli, and codon optimisation is a widely-adopted strategy for improvement of heterologous enzyme expression. Successful synthetic gene design for recombinant protein expression can be done by matching its translational elongation rate against heterologous host organisms via codon optimization. Amongst the various design parameters considered for the gene synthesis, codon context bias has been relatively overlooked compared to individual codon usage which is commonly adopted in most of codon optimization tools. In addition, matching the rates of transcription and translation based on secondary structure may lead to enhanced protein folding. In this study, we evaluated codon context fitness as design criterion for improving the expression of thermostable invertase from Thermotoga maritima in Escherichia coli and explored the relevance of secondary structure regions for folding and expression. We designed three coding sequences by using (1) a commercial vendor optimized gene algorithm, (2) codon context for the whole gene, and (3) codon context based on the secondary structure regions. Then, the codon optimized sequences were transformed and expressed in E. coli. From the resultant enzyme activities and protein yield data, codon context fitness proved to have the highest activity as compared to the wild-type control and other criteria while secondary structure-based strategy is comparable to the control. Codon context bias was shown to be a relevant parameter for enhancing enzyme production in Escherichia coli by codon optimization. Thus, we can effectively design synthetic genes within heterologous host organisms using this criterion. Copyright © 2015 Elsevier Inc. All rights reserved.

Developmental transcriptional profiling reveals key insights into Triticeae reproductive development.

PubMed

Tran, Frances; Penniket, Carolyn; Patel, Rohan V; Provart, Nicholas J; Laroche, André; Rowland, Owen; Robert, Laurian S

2013-06-01

Despite their importance, there remains a paucity of large-scale gene expression-based studies of reproductive development in species belonging to the Triticeae. As a first step to address this deficiency, a gene expression atlas of triticale reproductive development was generated using the 55K Affymetrix GeneChip(®) wheat genome array. The global transcriptional profiles of the anther/pollen, ovary and stigma were analyzed at concurrent developmental stages, and co-expressed as well as preferentially expressed genes were identified. Data analysis revealed both novel and conserved regulatory factors underlying Triticeae floral development and function. This comprehensive resource rests upon detailed gene annotations, and the expression profiles are readily accessible via a web browser. © 2013 Her Majesty the Queen in Right of Canada as represented by the Minister of Agriculture and Agri-Food Canada.

Genomic organization and expression analysis of a farnesyl diphosphate synthase gene (FPPS2) in apples (Malus domestica Borkh.).

PubMed

Yuan, Kejun; Wang, Changjun; Xin, Li; Zhang, Anning; Ai, Chengxiang

2013-07-25

A farnesyl diphosphate synthase gene (FPPS2), which contains 11 introns and 12 exons, was isolated from the apple cultivar "White Winter Pearmain". When it was compared to our previously reported FPPS1, its each intron size was different, its each exon size was the same as that of FPPS1 gene, 30 nucleotide differences were found in its coding sequence. Based on these nucleotide differences, specific primers were designed to perform expression analysis; the results showed that it expressed in both fruit and leaf, its expression level was obviously lower than that of FPPS1 gene in fruit which was stored at 4°C for 5 weeks. This is the first report concerning two FPPS genes and their expression comparison in apples. Copyright © 2013 Elsevier B.V. All rights reserved.

Creating genetic resistance to HIV.

PubMed

Burnett, John C; Zaia, John A; Rossi, John J

2012-10-01

HIV/AIDS remains a chronic and incurable disease, in spite of the notable successes of combination antiretroviral therapy. Gene therapy offers the prospect of creating genetic resistance to HIV that supplants the need for antiviral drugs. In sight of this goal, a variety of anti-HIV genes have reached clinical testing, including gene-editing enzymes, protein-based inhibitors, and RNA-based therapeutics. Combinations of therapeutic genes against viral and host targets are designed to improve the overall antiviral potency and reduce the likelihood of viral resistance. In cell-based therapies, therapeutic genes are expressed in gene modified T lymphocytes or in hematopoietic stem cells that generate an HIV-resistant immune system. Such strategies must promote the selective proliferation of the transplanted cells and the prolonged expression of therapeutic genes. This review focuses on the current advances and limitations in genetic therapies against HIV, including the status of several recent and ongoing clinical studies. Copyright © 2012 Elsevier Ltd. All rights reserved.

LPL is the strongest prognostic factor in a comparative analysis of RNA-based markers in early chronic lymphocytic leukemia.

PubMed

Kaderi, Mohd Arifin; Kanduri, Meena; Buhl, Anne Mette; Sevov, Marie; Cahill, Nicola; Gunnarsson, Rebeqa; Jansson, Mattias; Smedby, Karin Ekström; Hjalgrim, Henrik; Jurlander, Jesper; Juliusson, Gunnar; Mansouri, Larry; Rosenquist, Richard

2011-08-01

The expression levels of LPL, ZAP70, TCL1A, CLLU1 and MCL1 have recently been proposed as prognostic factors in chronic lymphocytic leukemia. However, few studies have systematically compared these different RNA-based markers. Using real-time quantitative PCR, we measured the mRNA expression levels of these genes in unsorted samples from 252 newly diagnosed chronic lymphocytic leukemia patients and correlated our data with established prognostic markers (for example Binet stage, CD38, IGHV gene mutational status and genomic aberrations) and clinical outcome. High expression levels of all RNA-based markers, except MCL1, predicted shorter overall survival and time to treatment, with LPL being the most significant. In multivariate analysis including the RNA-based markers, LPL expression was the only independent prognostic marker for overall survival and time to treatment. When studying LPL expression and the established markers, LPL expression retained its independent prognostic strength for overall survival. All of the RNA-based markers, albeit with varying ability, added prognostic information to established markers, with LPL expression giving the most significant results. Notably, high LPL expression predicted a worse outcome in good-prognosis subgroups, such as patients with mutated IGHV genes, Binet stage A, CD38 negativity or favorable cytogenetics. In particular, the combination of LPL expression and CD38 could further stratify Binet stage A patients. LPL expression is the strongest RNA-based prognostic marker in chronic lymphocytic leukemia that could potentially be applied to predict outcome in the clinical setting, particularly in the large group of patients with favorable prognosis.

Microarray-based cancer prediction using soft computing approach.

PubMed

Wang, Xiaosheng; Gotoh, Osamu

2009-05-26

One of the difficulties in using gene expression profiles to predict cancer is how to effectively select a few informative genes to construct accurate prediction models from thousands or ten thousands of genes. We screen highly discriminative genes and gene pairs to create simple prediction models involved in single genes or gene pairs on the basis of soft computing approach and rough set theory. Accurate cancerous prediction is obtained when we apply the simple prediction models for four cancerous gene expression datasets: CNS tumor, colon tumor, lung cancer and DLBCL. Some genes closely correlated with the pathogenesis of specific or general cancers are identified. In contrast with other models, our models are simple, effective and robust. Meanwhile, our models are interpretable for they are based on decision rules. Our results demonstrate that very simple models may perform well on cancerous molecular prediction and important gene markers of cancer can be detected if the gene selection approach is chosen reasonably.

Digital gene expression for non-model organisms

PubMed Central

Hong, Lewis Z.; Li, Jun; Schmidt-Küntzel, Anne; Warren, Wesley C.; Barsh, Gregory S.

2011-01-01

Next-generation sequencing technologies offer new approaches for global measurements of gene expression but are mostly limited to organisms for which a high-quality assembled reference genome sequence is available. We present a method for gene expression profiling called EDGE, or EcoP15I-tagged Digital Gene Expression, based on ultra-high-throughput sequencing of 27-bp cDNA fragments that uniquely tag the corresponding gene, thereby allowing direct quantification of transcript abundance. We show that EDGE is capable of assaying for expression in >99% of genes in the genome and achieves saturation after 6–8 million reads. EDGE exhibits very little technical noise, reveals a large (106) dynamic range of gene expression, and is particularly suited for quantification of transcript abundance in non-model organisms where a high-quality annotated genome is not available. In a direct comparison with RNA-seq, both methods provide similar assessments of relative transcript abundance, but EDGE does better at detecting gene expression differences for poorly expressed genes and does not exhibit transcript length bias. Applying EDGE to laboratory mice, we show that a loss-of-function mutation in the melanocortin 1 receptor (Mc1r), recognized as a Mendelian determinant of yellow hair color in many different mammals, also causes reduced expression of genes involved in the interferon response. To illustrate the application of EDGE to a non-model organism, we examine skin biopsy samples from a cheetah (Acinonyx jubatus) and identify genes likely to control differences in the color of spotted versus non-spotted regions. PMID:21844123

Biased gene expression in early honeybee larval development

PubMed Central

2013-01-01

Background Female larvae of the honeybee (Apis mellifera) develop into either queens or workers depending on nutrition. This nutritional stimulus triggers different developmental trajectories, resulting in adults that differ from each other in physiology, behaviour and life span. Results To understand how these trajectories are established we have generated a comprehensive atlas of gene expression throughout larval development. We found substantial differences in gene expression between worker and queen-destined larvae at 6 hours after hatching. Some of these early changes in gene expression are maintained throughout larval development, indicating that caste-specific developmental trajectories are established much earlier than previously thought. Within our gene expression data we identified processes that potentially underlie caste differentiation. Queen-destined larvae have higher expression of genes involved in transcription, translation and protein folding early in development with a later switch to genes involved in energy generation. Using RNA interference, we were able to demonstrate that one of these genes, hexamerin 70b, has a role in caste differentiation. Both queen and worker developmental trajectories are associated with the expression of genes that have alternative splice variants, although only a single variant of a gene tends to be differentially expressed in a given caste. Conclusions Our data, based on the biases in gene expression early in development together with published data, supports the idea that caste development in the honeybee consists of two phases; an initial biased phase of development, where larvae can still switch to the other caste by differential feeding, followed by commitment to a particular developmental trajectory. PMID:24350621

Microarray profiling of gene expression in human adipocytes in response to anthocyanins.

PubMed

Tsuda, Takanori; Ueno, Yuki; Yoshikawa, Toshikazu; Kojo, Hitoshi; Osawa, Toshihiko

2006-04-14

Adipocyte dysfunction is strongly associated with the development of obesity and insulin resistance. It is accepted that the regulation of adipocytokine secretion or the adipocyte specific gene expression is one of the most important targets for the prevention of obesity and amelioration of insulin sensitivity. Recently, we demonstrated that anthocyanins, which are pigments widespread in the plant kingdom, have the potency of anti-obesity in mice and the enhancement adipocytokine secretion and its gene expression in adipocytes. In this study, we have shown the gene expression profile in human adipocytes treated with anthocyanins (cyanidin 3-glucoside; C3G or cyanidin; Cy). The human adipocytes were treated with 100 microM C3G, Cy or vehicle for 24 h. The total RNA from the adipocytes was isolated and carried out GeneChip microarray analysis. Based on the gene expression profile, we demonstrated the significant changes of adipocytokine expression (up-regulation of adiponectin and down-regulation of plasminogen activator inhibitor-1 and interleukin-6). Some of lipid metabolism related genes (uncoupling protein2, acylCoA oxidase1 and perilipin) also significantly induced in both common the C3G or Cy treatment groups. These studies have provided an overview of the gene expression profiles in human adipocytes treated with anthocyanins and demonstrated that anthocyanins can regulate adipocytokine gene expression to ameliorate adipocyte function related with obesity and diabetes that merit further investigation.

Optimal consistency in microRNA expression analysis using reference-gene-based normalization.

PubMed

Wang, Xi; Gardiner, Erin J; Cairns, Murray J

2015-05-01

Normalization of high-throughput molecular expression profiles secures differential expression analysis between samples of different phenotypes or biological conditions, and facilitates comparison between experimental batches. While the same general principles apply to microRNA (miRNA) normalization, there is mounting evidence that global shifts in their expression patterns occur in specific circumstances, which pose a challenge for normalizing miRNA expression data. As an alternative to global normalization, which has the propensity to flatten large trends, normalization against constitutively expressed reference genes presents an advantage through their relative independence. Here we investigated the performance of reference-gene-based (RGB) normalization for differential miRNA expression analysis of microarray expression data, and compared the results with other normalization methods, including: quantile, variance stabilization, robust spline, simple scaling, rank invariant, and Loess regression. The comparative analyses were executed using miRNA expression in tissue samples derived from subjects with schizophrenia and non-psychiatric controls. We proposed a consistency criterion for evaluating methods by examining the overlapping of differentially expressed miRNAs detected using different partitions of the whole data. Based on this criterion, we found that RGB normalization generally outperformed global normalization methods. Thus we recommend the application of RGB normalization for miRNA expression data sets, and believe that this will yield a more consistent and useful readout of differentially expressed miRNAs, particularly in biological conditions characterized by large shifts in miRNA expression.

Stem cell-based gene therapy activated using magnetic hyperthermia to enhance the treatment of cancer

PubMed Central

Yin, Perry T.; Shah, Shreyas; Pasquale, Nicholas J.; Garbuzenko, Olga B.; Minko, Tamara; Lee, Ki-Bum

2015-01-01

Stem cell-based gene therapies, wherein stem cells are genetically engineered to express therapeutic molecules, have shown tremendous potential for cancer applications owing to their innate ability to home to tumors. However, traditional stem cell-based gene therapies are hampered by our current inability to control when the therapeutic genes are actually turned on, thereby resulting in detrimental side effects. Here, we report the novel application of magnetic core-shell nanoparticles for the dual purpose of delivering and activating a heat-inducible gene vector that encodes TNF-related apoptosis-inducing ligand (TRAIL) in adipose-derived mesenchymal stem cells (AD-MSCs). By combining the tumor tropism of the AD-MSCs with the spatiotemporal MCNP-based delivery and activation of TRAIL expression, this platform provides an attractive means with which to enhance our control over the activation of stem cell-based gene therapies. In particular, we found that these engineered AD-MSCs retained their innate ability to proliferate, differentiate, and, most importantly, home to tumors, making them ideal cellular carriers. Moreover, exposure of the engineered AD-MSCS to mild magnetic hyperthermia resulted in the selective expression of TRAIL from the engineered AD-MSCs and, as a result, induced significant ovarian cancer cell death in vitro and in vivo. PMID:26720500

Stem cell-based gene therapy activated using magnetic hyperthermia to enhance the treatment of cancer.

PubMed

Yin, Perry T; Shah, Shreyas; Pasquale, Nicholas J; Garbuzenko, Olga B; Minko, Tamara; Lee, Ki-Bum

2016-03-01

Stem cell-based gene therapies, wherein stem cells are genetically engineered to express therapeutic molecules, have shown tremendous potential for cancer applications owing to their innate ability to home to tumors. However, traditional stem cell-based gene therapies are hampered by our current inability to control when the therapeutic genes are actually turned on, thereby resulting in detrimental side effects. Here, we report the novel application of magnetic core-shell nanoparticles for the dual purpose of delivering and activating a heat-inducible gene vector that encodes TNF-related apoptosis-inducing ligand (TRAIL) in adipose-derived mesenchymal stem cells (AD-MSCs). By combining the tumor tropism of the AD-MSCs with the spatiotemporal MCNP-based delivery and activation of TRAIL expression, this platform provides an attractive means with which to enhance our control over the activation of stem cell-based gene therapies. In particular, we found that these engineered AD-MSCs retained their innate ability to proliferate, differentiate, and, most importantly, home to tumors, making them ideal cellular carriers. Moreover, exposure of the engineered AD-MSCS to mild magnetic hyperthermia resulted in the selective expression of TRAIL from the engineered AD-MSCs and, as a result, induced significant ovarian cancer cell death in vitro and in vivo. Copyright © 2015 Elsevier Ltd. All rights reserved.

Role of miR-1 expression in clear cell renal cell carcinoma (ccRCC): A bioinformatics study based on GEO, ArrayExpress microarrays and TCGA database.

PubMed

Yan, Hai-Biao; Huang, Jia-Cheng; Chen, You-Rong; Yao, Jian-Ni; Cen, Wei-Ning; Li, Jia-Yi; Jiang, Yi-Fan; Chen, Gang; Li, Sheng-Hua

2018-02-01

To investigate the clinical value and potential molecular mechanisms of miR-1 in clear cell renal cell carcinoma (ccRCC). We searched the Gene Expression Omnibus (GEO), ArrayExpress, several online publication databases and the Cancer Genome Atlas (TCGA). Continuous variable meta-analysis and diagnostic meta-analysis were conducted, both in Stata 14, to show the expression of miR-1 in ccRCC. Furthermore, we acquired the potential targets of miR-1 from datasets that transfected miR-1 into ccRCC cells, online prediction databases, differentially expressed genes from TCGA and literature. Subsequently bioinformatics analysis based on aforementioned selected target genes was conducted. The combined effect was -0.92 with the 95% confidence interval (CI) of -1.08 to -0.77 based on fixed effect model (I 2  = 81.3%, P < 0.001). No publication bias was found in our investigation. Sensitivity analysis showed that GSE47582 and 2 TCGA studies might cause heterogeneity. After eliminating them, the combined effect was -0.47 (95%CI: -0.78, -0.16) with I 2  = 18.3%. As for the diagnostic meta-analysis, the combined sensitivity and specificity were 0.90 (95%CI: 0.61, 0.98) and 0.63 (95%CI: 0.39, 0.82). The area under the curve (AUC) in the summarized receiver operating characteristic (SROC) curve was 0.83 (95%CI: 0.80, 0.86). No publication bias was found (P = 0.15). We finally got 67 genes which were defined the promising target genes of miR-1 in ccRCC. The most three significant KEGG pathways based on the aforementioned genes were Complement and coagulation cascades, ECM-receptor interaction and Focal adhesion. The downregulation of miR-1 might play an important role in ccRCC by targeting its target genes. Copyright © 2017 Elsevier GmbH. All rights reserved.

Annotation of gene function in citrus using gene expression information and co-expression networks

PubMed Central

2014-01-01

Background The genus Citrus encompasses major cultivated plants such as sweet orange, mandarin, lemon and grapefruit, among the world’s most economically important fruit crops. With increasing volumes of transcriptomics data available for these species, Gene Co-expression Network (GCN) analysis is a viable option for predicting gene function at a genome-wide scale. GCN analysis is based on a “guilt-by-association” principle whereby genes encoding proteins involved in similar and/or related biological processes may exhibit similar expression patterns across diverse sets of experimental conditions. While bioinformatics resources such as GCN analysis are widely available for efficient gene function prediction in model plant species including Arabidopsis, soybean and rice, in citrus these tools are not yet developed. Results We have constructed a comprehensive GCN for citrus inferred from 297 publicly available Affymetrix Genechip Citrus Genome microarray datasets, providing gene co-expression relationships at a genome-wide scale (33,000 transcripts). The comprehensive citrus GCN consists of a global GCN (condition-independent) and four condition-dependent GCNs that survey the sweet orange species only, all citrus fruit tissues, all citrus leaf tissues, or stress-exposed plants. All of these GCNs are clustered using genome-wide, gene-centric (guide) and graph clustering algorithms for flexibility of gene function prediction. For each putative cluster, gene ontology (GO) enrichment and gene expression specificity analyses were performed to enhance gene function, expression and regulation pattern prediction. The guide-gene approach was used to infer novel roles of genes involved in disease susceptibility and vitamin C metabolism, and graph-clustering approaches were used to investigate isoprenoid/phenylpropanoid metabolism in citrus peel, and citric acid catabolism via the GABA shunt in citrus fruit. Conclusions Integration of citrus gene co-expression networks, functional enrichment analysis and gene expression information provide opportunities to infer gene function in citrus. We present a publicly accessible tool, Network Inference for Citrus Co-Expression (NICCE, http://citrus.adelaide.edu.au/nicce/home.aspx), for the gene co-expression analysis in citrus. PMID:25023870

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

PubMed Central

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

2015-01-01

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

Gene expression profiling reveals two separate mechanisms regulating apoptosis in rectal carcinomas in vivo

PubMed Central

de Bruin, Elza C.; van de Pas, Simone; van de Velde, Cornelis J. H.; van Krieken, J. Han J. M.; Peltenburg, Lucy T. C.; Marijnen, Corrie A. M.

2007-01-01

The level of apoptosis in rectal carcinomas of patients treated by surgery only predicts local failure; patients with intrinsically high-apoptotic tumors develop less local recurrences than patients with low levels of apoptosis. To identify genes involved in this intrinsic apoptotic process in vivo, 47 rectal tumors with known apoptotic phenotype (24 low- and 23 high-apoptotic) were analyzed by oligonucleotide microarray technology. We identified several genes differentially expressed between low- and high-apoptotic tumors. Unsupervised clustering of the tumors based on expression levels of these genes separated the low-apoptotic from the high-apoptotic tumors, indicating a gene expression-dependent regulation. In addition, this clustering revealed two subgroups of high-apoptotic tumors. One high-apoptotic subgroup showed subtle differences in mRNA and protein expression of the known apoptotic regulators BAX, cIAP2 and ARC compared to the low-apoptotic tumors. The other subgroup of high-apoptotic tumors showed high expression of immune-related genes; predominantly HLA class II and chemokines, but also HLA class I and interferon-inducible genes were highly expressed. Immunohistochemistry revealed HLA-DR expression in epithelial tumor cells in 70% of these high-apoptotic tumors. The expression data suggest that high levels of apoptosis in rectal carcinoma patients can be the result of either slightly altered expression of known pro- and anti-apoptotic genes or high expression of immune-related genes. Electronic supplementary material The online version of this article (doi: 10.1007/s10495-007-0088-2) contains supplementary material, which is available to authorized users. PMID:17610066

Expression of Hormonal Carcinogenesis Genes and Related Regulatory microRNAs in Uterus and Ovaries of DDT-Treated Female Rats.

PubMed

Kalinina, T S; Kononchuk, V V; Gulyaeva, L F

2017-10-01

The insecticide dichlorodiphenyltrichloroethane (DDT) is a nonmutagenic xenobiotic compound able to exert estrogen-like effects resulting in activation of estrogen receptor-α (ERα) followed by changed expression of its downstream target genes. In addition, studies performed over recent years suggest that DDT may also influence expression of microRNAs. However, an impact of DDT on expression of ER, microRNAs, and related target genes has not been fully elucidated. Here, using real-time PCR, we assessed changes in expression of key genes involved in hormonal carcinogenesis as well as potentially related regulatory oncogenic/tumor suppressor microRNAs and their target genes in the uterus and ovaries of female Wistar rats during single and chronic multiple-dose DDT exposure. We found that applying DDT results in altered expression of microRNAs-221, -222, -205, -126a, and -429, their target genes (Pten, Dicer1), as well as genes involved in hormonal carcinogenesis (Esr1, Pgr, Ccnd1, Cyp19a1). Notably, Cyp19a1 expression seems to be also regulated by microRNAs-221, -222, and -205. The data suggest that epigenetic effects induced by DDT as a potential carcinogen may be based on at least two mechanisms: (i) activation of ERα followed by altered expression of the target genes encoding receptor Pgr and Ccnd1 as well as impaired expression of Cyp19a1, affecting, thereby, cell hormone balance; and (ii) changed expression of microRNAs resulting in impaired expression of related target genes including reduced level of Cyp19a1 mRNA.

Digital Gene Expression Analysis Based on De Novo Transcriptome Assembly Reveals New Genes Associated with Floral Organ Differentiation of the Orchid Plant Cymbidium ensifolium

PubMed Central

Yang, Fengxi; Zhu, Genfa

2015-01-01

Cymbidium ensifolium belongs to the genus Cymbidium of the orchid family. Owing to its spectacular flower morphology, C. ensifolium has considerable ecological and cultural value. However, limited genetic data is available for this non-model plant, and the molecular mechanism underlying floral organ identity is still poorly understood. In this study, we characterize the floral transcriptome of C. ensifolium and present, for the first time, extensive sequence and transcript abundance data of individual floral organs. After sequencing, over 10 Gb clean sequence data were generated and assembled into 111,892 unigenes with an average length of 932.03 base pairs, including 1,227 clusters and 110,665 singletons. Assembled sequences were annotated with gene descriptions, gene ontology, clusters of orthologous group terms, the Kyoto Encyclopedia of Genes and Genomes, and the plant transcription factor database. From these annotations, 131 flowering-associated unigenes, 61 CONSTANS-LIKE (COL) unigenes and 90 floral homeotic genes were identified. In addition, four digital gene expression libraries were constructed for the sepal, petal, labellum and gynostemium, and 1,058 genes corresponding to individual floral organ development were identified. Among them, eight MADS-box genes were further investigated by full-length cDNA sequence analysis and expression validation, which revealed two APETALA1/AGL9-like MADS-box genes preferentially expressed in the sepal and petal, two AGAMOUS-like genes particularly restricted to the gynostemium, and four DEF-like genes distinctively expressed in different floral organs. The spatial expression of these genes varied distinctly in different floral mutant corresponding to different floral morphogenesis, which validated the specialized roles of them in floral patterning and further supported the effectiveness of our in silico analysis. This dataset generated in our study provides new insights into the molecular mechanisms underlying floral patterning of Cymbidium and supports a valuable resource for molecular breeding of the orchid plant. PMID:26580566

TRAM (Transcriptome Mapper): database-driven creation and analysis of transcriptome maps from multiple sources

PubMed Central

2011-01-01

Background Several tools have been developed to perform global gene expression profile data analysis, to search for specific chromosomal regions whose features meet defined criteria as well as to study neighbouring gene expression. However, most of these tools are tailored for a specific use in a particular context (e.g. they are species-specific, or limited to a particular data format) and they typically accept only gene lists as input. Results TRAM (Transcriptome Mapper) is a new general tool that allows the simple generation and analysis of quantitative transcriptome maps, starting from any source listing gene expression values for a given gene set (e.g. expression microarrays), implemented as a relational database. It includes a parser able to assign univocal and updated gene symbols to gene identifiers from different data sources. Moreover, TRAM is able to perform intra-sample and inter-sample data normalization, including an original variant of quantile normalization (scaled quantile), useful to normalize data from platforms with highly different numbers of investigated genes. When in 'Map' mode, the software generates a quantitative representation of the transcriptome of a sample (or of a pool of samples) and identifies if segments of defined lengths are over/under-expressed compared to the desired threshold. When in 'Cluster' mode, the software searches for a set of over/under-expressed consecutive genes. Statistical significance for all results is calculated with respect to genes localized on the same chromosome or to all genome genes. Transcriptome maps, showing differential expression between two sample groups, relative to two different biological conditions, may be easily generated. We present the results of a biological model test, based on a meta-analysis comparison between a sample pool of human CD34+ hematopoietic progenitor cells and a sample pool of megakaryocytic cells. Biologically relevant chromosomal segments and gene clusters with differential expression during the differentiation toward megakaryocyte were identified. Conclusions TRAM is designed to create, and statistically analyze, quantitative transcriptome maps, based on gene expression data from multiple sources. The release includes FileMaker Pro database management runtime application and it is freely available at http://apollo11.isto.unibo.it/software/, along with preconfigured implementations for mapping of human, mouse and zebrafish transcriptomes. PMID:21333005

Identification of Cell Cycle-Regulated Genes by Convolutional Neural Network.

PubMed

Liu, Chenglin; Cui, Peng; Huang, Tao

2017-01-01

The cell cycle-regulated genes express periodically with the cell cycle stages, and the identification and study of these genes can provide a deep understanding of the cell cycle process. Large false positives and low overlaps are big problems in cell cycle-regulated gene detection. Here, a computational framework called DLGene was proposed for cell cycle-regulated gene detection. It is based on the convolutional neural network, a deep learning algorithm representing raw form of data pattern without assumption of their distribution. First, the expression data was transformed to categorical state data to denote the changing state of gene expression, and four different expression patterns were revealed for the reported cell cycle-regulated genes. Then, DLGene was applied to discriminate the non-cell cycle gene and the four subtypes of cell cycle genes. Its performances were compared with six traditional machine learning methods. At last, the biological functions of representative cell cycle genes for each subtype are analyzed. Our method showed better and more balanced performance of sensitivity and specificity comparing to other machine learning algorithms. The cell cycle genes had very different expression pattern with non-cell cycle genes and among the cell-cycle genes, there were four subtypes. Our method not only detects the cell cycle genes, but also describes its expression pattern, such as when its highest expression level is reached and how it changes with time. For each type, we analyzed the biological functions of the representative genes and such results provided novel insight to the cell cycle mechanisms. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Isolation of ripening-related genes from ethylene/1-MCP treated papaya through RNA-seq.

PubMed

Shen, Yan Hong; Lu, Bing Guo; Feng, Li; Yang, Fei Ying; Geng, Jiao Jiao; Ming, Ray; Chen, Xiao Jing

2017-08-31

Since papaya is a typical climacteric fruit, exogenous ethylene (ETH) applications can induce premature and quicker ripening, while 1-methylcyclopropene (1-MCP) slows down the ripening processes. Differential gene expression in ETH or 1-MCP-treated papaya fruits accounts for the ripening processes. To isolate the key ripening-related genes and better understand fruit ripening mechanisms, transcriptomes of ETH or 1-MCP-treated, and non-treated (Control Group, CG) papaya fruits were sequenced using Illumina Hiseq2500. A total of 18,648 (1-MCP), 19,093 (CG), and 15,321 (ETH) genes were detected, with the genes detected in the ETH-treatment being the least. This suggests that ETH may inhibit the expression of some genes. Based on the differential gene expression (DGE) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, 53 fruit ripening-related genes were selected: 20 cell wall-related genes, 18 chlorophyll and carotenoid metabolism-related genes, four proteinases and their inhibitors, six plant hormone signal transduction pathway genes, four transcription factors, and one senescence-associated gene. Reverse transcription quantitative PCR (RT-qPCR) analyses confirmed the results of RNA-seq and verified that the expression pattern of six genes is consistent with the fruit senescence process. Based on the expression profiling of genes in carbohydrate metabolic process, chlorophyll metabolism pathway, and carotenoid metabolism pathway, the mechanism of pulp softening and coloration of papaya was deduced and discussed. We illustrate that papaya fruit softening is a complex process with significant cell wall hydrolases, such as pectinases, cellulases, and hemicellulases involved in the process. Exogenous ethylene accelerates the coloration of papaya changing from green to yellow. This is likely due to the inhibition of chlorophyll biosynthesis and the α-branch of carotenoid metabolism. Chy-b may play an important role in the yellow color of papaya fruit. Comparing the differential gene expression in ETH/1-MCP-treated papaya using RNA-seq is a sound approach to isolate ripening-related genes. The results of this study can improve our understanding of papaya fruit ripening molecular mechanism and reveal candidate fruit ripening-related genes for further research.

Conditional transgenic mouse models: from the basics to genome-wide sets of knockouts and current studies of tissue regeneration.

PubMed

Bockamp, Ernesto; Sprengel, Rolf; Eshkind, Leonid; Lehmann, Thomas; Braun, Jan M; Emmrich, Frank; Hengstler, Jan G

2008-03-01

Many mouse models are currently available, providing avenues to elucidate gene function and to recapitulate specific pathological conditions. To a large extent, successful translation of clinical evidence or analytical data into appropriate mouse models is possible through progress in transgenic or gene-targeting technology. Beginning with a review of standard mouse transgenics and conventional gene targeting, this article will move on to discussing the basics of conditional gene expression: the tetracycline (tet)-off and tet-on systems based on the transactivators tet-controlled transactivator (Tta) and reverse tet-on transactivator (rtTA) that allow downregulation or induction of gene expression; Cre or Flp recombinase-mediated modifications, including excision, inversion, insertion and interchromosomal translocation; combination of the tet and Cre systems, permitting inducible knockout, reporter gene activation or activation of point mutations; the avian retroviral system based on delivery of rtTA specifically into cells expressing the avian retroviral receptor, which enables cell type-specific, inducible gene expression; the tamoxifen system, one of the most frequently applied steroid receptor-based systems, allows rapid activation of a fusion protein between the gene of interest and a mutant domain of the estrogen receptor, whereby activation does not depend on transcription; and techniques for cell type-specific ablation. The diphtheria toxin receptor system offers the advantage that it can be combined with the 'zoo' of Cre recombinase driver mice. Having described the basics we move on to the cutting edge: generation of genome-wide sets of conditional knockout mice. To this end, large ongoing projects apply two strategies: gene trapping based on random integration of trapping vectors into introns leading to truncation of the transcript, and gene targeting, representing the directed approach using homologous recombination. It can be expected that in the near future genome-wide sets of such mice will be available. Finally, the possibilities of conditional expression systems for investigating gene function in tissue regeneration will be illustrated by examples for neurodegenerative disease, liver regeneration and wound healing of the skin.

Reverse engineering the gap gene network of Drosophila melanogaster.

PubMed

Perkins, Theodore J; Jaeger, Johannes; Reinitz, John; Glass, Leon

2006-05-01

A fundamental problem in functional genomics is to determine the structure and dynamics of genetic networks based on expression data. We describe a new strategy for solving this problem and apply it to recently published data on early Drosophila melanogaster development. Our method is orders of magnitude faster than current fitting methods and allows us to fit different types of rules for expressing regulatory relationships. Specifically, we use our approach to fit models using a smooth nonlinear formalism for modeling gene regulation (gene circuits) as well as models using logical rules based on activation and repression thresholds for transcription factors. Our technique also allows us to infer regulatory relationships de novo or to test network structures suggested by the literature. We fit a series of models to test several outstanding questions about gap gene regulation, including regulation of and by hunchback and the role of autoactivation. Based on our modeling results and validation against the experimental literature, we propose a revised network structure for the gap gene system. Interestingly, some relationships in standard textbook models of gap gene regulation appear to be unnecessary for or even inconsistent with the details of gap gene expression during wild-type development.

Systematic comparison of co-expression of multiple recombinant thermophilic enzymes in Escherichia coli BL21(DE3).

PubMed

Chen, Hui; Huang, Rui; Zhang, Y-H Percival

2017-06-01

The precise control of multiple heterologous enzyme expression levels in one Escherichia coli strain is important for cascade biocatalysis, metabolic engineering, synthetic biology, natural product synthesis, and studies of complexed proteins. We systematically investigated the co-expression of up to four thermophilic enzymes (i.e., α-glucan phosphorylase (αGP), phosphoglucomutase (PGM), glucose 6-phosphate dehydrogenase (G6PDH), and 6-phosphogluconate dehydrogenase (6PGDH)) in E. coli BL21(DE3) by adding T7 promoter or T7 terminator of each gene for multiple genes in tandem, changing gene alignment, and comparing one or two plasmid systems. It was found that the addition of T7 terminator after each gene was useful to decrease the influence of the upstream gene. The co-expression of the four enzymes in E. coli BL21(DE3) was demonstrated to generate two NADPH molecules from one glucose unit of maltodextrin, where NADPH was oxidized to convert xylose to xylitol. The best four-gene co-expression system was based on two plasmids (pET and pACYC) which harbored two genes. As a result, apparent enzymatic activities of the four enzymes were regulated to be at similar levels and the overall four-enzyme activity was the highest based on the formation of xylitol. This study provides useful information for the precise control of multi-enzyme-coordinated expression in E. coli BL21(DE3).

Combined SOM-portrayal of gene expression and DNA methylation landscapes disentangles modes of epigenetic regulation in glioblastoma.

PubMed

Hopp, Lydia; Löffler-Wirth, Henry; Galle, Jörg; Binder, Hans

2018-06-11

We present here a novel method that enables unraveling the interplay between gene expression and DNA methylation in complex diseases such as cancer. The method is based on self-organizing maps and allows for analysis of data landscapes from 'governed by methylation' to 'governed by expression'. We identified regulatory modules of coexpressed and comethylated genes in high-grade gliomas: two modes are governed by genes hypermethylated and underexpressed in IDH-mutated cases, while two other modes reflect immune and stromal signatures in the classical and mesenchymal subtypes. A fifth mode with proneural characteristics comprises genes of repressed and poised chromatin states active in healthy brain. Two additional modes enrich genes either in active or repressed chromatin states. The method disentangles the interplay between gene expression and methylation. It has the potential to integrate also mutation and copy number data and to apply to large sample cohorts.

GTA: a game theoretic approach to identifying cancer subnetwork markers.

PubMed

Farahmand, S; Goliaei, S; Ansari-Pour, N; Razaghi-Moghadam, Z

2016-03-01

The identification of genetic markers (e.g. genes, pathways and subnetworks) for cancer has been one of the most challenging research areas in recent years. A subset of these studies attempt to analyze genome-wide expression profiles to identify markers with high reliability and reusability across independent whole-transcriptome microarray datasets. Therefore, the functional relationships of genes are integrated with their expression data. However, for a more accurate representation of the functional relationships among genes, utilization of the protein-protein interaction network (PPIN) seems to be necessary. Herein, a novel game theoretic approach (GTA) is proposed for the identification of cancer subnetwork markers by integrating genome-wide expression profiles and PPIN. The GTA method was applied to three distinct whole-transcriptome breast cancer datasets to identify the subnetwork markers associated with metastasis. To evaluate the performance of our approach, the identified subnetwork markers were compared with gene-based, pathway-based and network-based markers. We show that GTA is not only capable of identifying robust metastatic markers, it also provides a higher classification performance. In addition, based on these GTA-based subnetworks, we identified a new bonafide candidate gene for breast cancer susceptibility.

Gene expression-based detection of radiation exposure in mice after treatment with granulocyte colony-stimulating factor and lipopolysaccharide.

PubMed

Tucker, James D; Grever, William E; Joiner, Michael C; Konski, Andre A; Thomas, Robert A; Smolinski, Joseph M; Divine, George W; Auner, Gregory W

2012-02-01

In a large-scale nuclear incident, many thousands of people may be exposed to a wide range of radiation doses. Rapid biological dosimetry will be required on an individualized basis to estimate the exposures and to make treatment decisions. To ameliorate the adverse effects of exposure, victims may be treated with one or more cytokine growth factors, including granulocyte colony-stimulating factor (G-CSF), which has therapeutic efficacy for treating radiation-induced bone marrow ablation by stimulating granulopoiesis. The existence of infections and the administration of G-CSF each may confound the ability to achieve reliable dosimetry by gene expression analysis. In this study, C57BL/6 mice were used to determine the extent to which G-CSF and lipopolysaccharide (LPS, which simulates infection by gram-negative bacteria) alter the expression of genes that are either radiation-responsive or non-responsive, i.e., show potential for use as endogenous controls. Mice were acutely exposed to (60)Co γ rays at either 0 Gy or 6 Gy. Two hours later the animals were injected with either 0.1 mg/kg of G-CSF or 0.3 mg/kg of LPS. Expression levels of 96 different gene targets were evaluated in peripheral blood after an additional 4 or 24 h using real-time quantitative PCR. The results indicate that the expression levels of some genes are altered by LPS, but altered expression after G-CSF treatment was generally not observed. The expression levels of many genes therefore retain utility for biological dosimetry or as endogenous controls. These data suggest that PCR-based quantitative gene expression analyses may have utility in radiation biodosimetry in humans even in the presence of an infection or after treatment with G-CSF.

Molecular cloning, sequence characterization and recombinant expression of Nanog gene in goat fibroblast cells using lentiviral based expression system.

PubMed

Singhal, Dinesh K; Singhal, Raxita; Malik, Hruda N; Kumar, Surender; Kumar, Sudarshan; Mohanty, Ashok K; Kaushik, Jai K; Malakar, Dhruba

2014-01-01

Nanog is a homeodomain containing protein which plays important roles in regulation of signaling pathways for maintenance and induction of pluripotency in stem cells. Because of its unique expression in stem cells it is also regarded as pluripotency marker. In this study goat Nanog (gNanog) gene has been amplified, cloned and characterized at sequence level with successful over-expression in CHO-K1 cell line using a lentiviral based system. gNanog ORF is 903 bp long which codes for Nanog protein of size 300 amino acids (aas). Complete nucleotide sequence shows some evolutionary mutation in goat in comparision to other species. Protein sequence of goat is highly similar to other species. Overall, gNanog nucleotide sequence and predicted protein sequence showed high similarity and minimum divergence with cattle (96 % identity/4 % divergence) and buffalo (94/5 %) while low similarity and high divergence with pig (84/15 %), human (81/23 %) and mouse (69/40 %) indicating evolutionary closeness of gNanog to cattle and buffalo. gNanog lentiviral expression construct was prepared for over-expression of Nanog gene in adult goat fibroblast cells. Lentiviral expression construct of Nanog enabled continuous protein expression for induction and maintenance of pluripotency. Western blotting revealed the expression of Nanog gene at protein level which supported that the lentiviral expression system is highly promising for Nanog protein expression in differentiated goat cell.

Biomarkers of Exposure to Toxic Substances. Volume 2: Genomics: Unique Patterns of Differential Gene Expression and Pathway Perturbation Resulting from Exposure to Nephrotoxins with Regional Specific Toxicity

DTIC Science & Technology

2009-05-01

of chemicals agents . Changes in gene expression are among the most sensitive indicators of chemical exposure. Toxicogenomics, which is based on DNA...assessing gene expression changes and subsequently the mechanism of renal injury following exposure to nephrotoxins selected for their regional...Serine Treatment on Selected Serum Chemistry Parameters ........................ 8 Table 4: Effect of PUR Treatment on Selected Serum Chemistry

Automatic Control of Gene Expression in Mammalian Cells.

PubMed

Fracassi, Chiara; Postiglione, Lorena; Fiore, Gianfranco; di Bernardo, Diego

2016-04-15

Automatic control of gene expression in living cells is paramount importance to characterize both endogenous gene regulatory networks and synthetic circuits. In addition, such a technology can be used to maintain the expression of synthetic circuit components in an optimal range in order to ensure reliable performance. Here we present a microfluidics-based method to automatically control gene expression from the tetracycline-inducible promoter in mammalian cells in real time. Our approach is based on the negative-feedback control engineering paradigm. We validated our method in a monoclonal population of cells constitutively expressing a fluorescent reporter protein (d2EYFP) downstream of a minimal CMV promoter with seven tet-responsive operator motifs (CMV-TET). These cells also constitutively express the tetracycline transactivator protein (tTA). In cells grown in standard growth medium, tTA is able to bind the CMV-TET promoter, causing d2EYFP to be maximally expressed. Upon addition of tetracycline to the culture medium, tTA detaches from the CMV-TET promoter, thus preventing d2EYFP expression. We tested two different model-independent control algorithms (relay and proportional-integral (PI)) to force a monoclonal population of cells to express an intermediate level of d2EYFP equal to 50% of its maximum expression level for up to 3500 min. The control input is either tetracycline-rich or standard growth medium. We demonstrated that both the relay and PI controllers can regulate gene expression at the desired level, despite oscillations (dampened in the case of the PI controller) around the chosen set point.

Transcriptome profiling analysis reveals biomarkers in colon cancer samples of various differentiation

PubMed Central

Yu, Tonghu; Zhang, Huaping; Qi, Hong

2018-01-01

The aim of the present study was to investigate more colon cancer-related genes in different stages. Gene expression profile E-GEOD-62932 was extracted for differentially expressed gene (DEG) screening. Series test of cluster analysis was used to obtain significant trending models. Based on the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases, functional and pathway enrichment analysis were processed and a pathway relation network was constructed. Gene co-expression network and gene signal network were constructed for common DEGs. The DEGs with the same trend were clustered and in total, 16 clusters with statistical significance were obtained. The screened DEGs were enriched into small molecule metabolic process and metabolic pathways. The pathway relation network was constructed with 57 nodes. A total of 328 common DEGs were obtained. Gene signal network was constructed with 71 nodes. Gene co-expression network was constructed with 161 nodes and 211 edges. ABCD3, CPT2, AGL and JAM2 are potential biomarkers for the diagnosis of colon cancer. PMID:29928385

Comparison between effects of free curcumin and curcumin loaded NIPAAm-MAA nanoparticles on telomerase and PinX1 gene expression in lung cancer cells.

PubMed

Badrzadeh, Fariba; Akbarzadeh, Abolfazl; Zarghami, Nosratollah; Yamchi, Mohammad Rahmati; Zeighamian, Vahide; Tabatabae, Fateme Sadate; Taheri, Morteza; Kafil, Hossein Samadi

2014-01-01

Herbal compounds such as curcumin which decrease telomerase and gene expression have been considered as beneficial tools for lung cancer treatment. In this article, we compared the effects of pure curcumin and curcumin-loaded NIPAAm-MAA nanoparticles on telomerase and PinX1 gene expression in a lung cancer cell line. A tetrazolium-based assay was used for determination of cytotoxic effects of curcumin on the Calu-6 lung cancer cell line and telomerase and pinX1 gene expression was measured with real-time PCR. MTT assay showed that Curcumin-loaded NIPAAm-MAA inhibited the growth of the Calu-6 lung cancer cell line in a time and dose-dependent manner. Our q-PCR results showed that the expression of telomerase gene was effectively reduced as the concentration of curcumin-loaded NIPAAm-MAA increased while expression of the PinX1 gene became elevated. The results showed that curcumin- loaded- NIPAAm-MAA exerted cytotoxic effects on the Calu-6 cell line through down-regulation of telomerase and stimulation of pinX1 gene expression. NIPPAm-MAA could be good carrier for such kinds of hydrophobic agent.

In silico selection of expression reference genes with demonstrated stability in barley among a diverse set of tissues and cultivars

USDA-ARS?s Scientific Manuscript database

Premise of the study: Reference genes are selected based on the assumption of temporal and spatial expression stability and on their widespread use in model species. They are often used in new target species without validation, presumed as stable. For barley, reference gene validation is lacking, bu...

Selection of Reference Genes for Expression Studies of Xenobiotic Adaptation in Tetranychus urticae.

PubMed

Morales, Mariany Ashanty; Mendoza, Bianca Marie; Lavine, Laura Corley; Lavine, Mark Daniel; Walsh, Douglas Bruce; Zhu, Fang

2016-01-01

Quantitative real-time PCR (qRT-PCR) is an extensively used, high-throughput method to analyze transcriptional expression of genes of interest. An appropriate normalization strategy with reliable reference genes is required for calculating gene expression across diverse experimental conditions. In this study, we aim to identify the most stable reference genes for expression studies of xenobiotic adaptation in Tetranychus urticae, an extremely polyphagous herbivore causing significant yield reduction of agriculture. We chose eight commonly used housekeeping genes as candidates. The qRT-PCR expression data for these genes were evaluated from seven populations: a susceptible and three acaricide resistant populations feeding on lima beans, and three other susceptible populations which had been shifted host from lima beans to three other plant species. The stability of the candidate reference genes was then assessed using four different algorithms (comparative ΔCt method, geNorm, NormFinder, and BestKeeper). Additionally, we used an online web-based tool (RefFinder) to assign an overall final rank for each candidate gene. Our study found that CycA and Rp49 are best for investigating gene expression in acaricide susceptible and resistant populations. GAPDH, Rp49, and Rpl18 are best for host plant shift studies. And GAPDH and Rp49 were the most stable reference genes when investigating gene expression under changes in both experimental conditions. These results will facilitate research in revealing molecular mechanisms underlying the xenobiotic adaptation of this notorious agricultural pest.

Selection of Reference Genes for Expression Studies of Xenobiotic Adaptation in Tetranychus urticae

PubMed Central

Morales, Mariany Ashanty; Mendoza, Bianca Marie; Lavine, Laura Corley; Lavine, Mark Daniel; Walsh, Douglas Bruce; Zhu, Fang

2016-01-01

Quantitative real-time PCR (qRT-PCR) is an extensively used, high-throughput method to analyze transcriptional expression of genes of interest. An appropriate normalization strategy with reliable reference genes is required for calculating gene expression across diverse experimental conditions. In this study, we aim to identify the most stable reference genes for expression studies of xenobiotic adaptation in Tetranychus urticae, an extremely polyphagous herbivore causing significant yield reduction of agriculture. We chose eight commonly used housekeeping genes as candidates. The qRT-PCR expression data for these genes were evaluated from seven populations: a susceptible and three acaricide resistant populations feeding on lima beans, and three other susceptible populations which had been shifted host from lima beans to three other plant species. The stability of the candidate reference genes was then assessed using four different algorithms (comparative ΔCt method, geNorm, NormFinder, and BestKeeper). Additionally, we used an online web-based tool (RefFinder) to assign an overall final rank for each candidate gene. Our study found that CycA and Rp49 are best for investigating gene expression in acaricide susceptible and resistant populations. GAPDH, Rp49, and Rpl18 are best for host plant shift studies. And GAPDH and Rp49 were the most stable reference genes when investigating gene expression under changes in both experimental conditions. These results will facilitate research in revealing molecular mechanisms underlying the xenobiotic adaptation of this notorious agricultural pest. PMID:27570487

oPOSSUM: identification of over-represented transcription factor binding sites in co-expressed genes

PubMed Central

Ho Sui, Shannan J.; Mortimer, James R.; Arenillas, David J.; Brumm, Jochen; Walsh, Christopher J.; Kennedy, Brian P.; Wasserman, Wyeth W.

2005-01-01

Targeted transcript profiling studies can identify sets of co-expressed genes; however, identification of the underlying functional mechanism(s) is a significant challenge. Established methods for the analysis of gene annotations, particularly those based on the Gene Ontology, can identify functional linkages between genes. Similar methods for the identification of over-represented transcription factor binding sites (TFBSs) have been successful in yeast, but extension to human genomics has largely proved ineffective. Creation of a system for the efficient identification of common regulatory mechanisms in a subset of co-expressed human genes promises to break a roadblock in functional genomics research. We have developed an integrated system that searches for evidence of co-regulation by one or more transcription factors (TFs). oPOSSUM combines a pre-computed database of conserved TFBSs in human and mouse promoters with statistical methods for identification of sites over-represented in a set of co-expressed genes. The algorithm successfully identified mediating TFs in control sets of tissue-specific genes and in sets of co-expressed genes from three transcript profiling studies. Simulation studies indicate that oPOSSUM produces few false positives using empirically defined thresholds and can tolerate up to 50% noise in a set of co-expressed genes. PMID:15933209

Comparative Genomics of Non-TNL Disease Resistance Genes from Six Plant Species.

PubMed

Nepal, Madhav P; Andersen, Ethan J; Neupane, Surendra; Benson, Benjamin V

2017-09-30

Disease resistance genes (R genes), as part of the plant defense system, have coevolved with corresponding pathogen molecules. The main objectives of this project were to identify non-Toll interleukin receptor, nucleotide-binding site, leucine-rich repeat (nTNL) genes and elucidate their evolutionary divergence across six plant genomes. Using reference sequences from Arabidopsis , we investigated nTNL orthologs in the genomes of common bean, Medicago , soybean, poplar, and rice. We used Hidden Markov Models for sequence identification, performed model-based phylogenetic analyses, visualized chromosomal positioning, inferred gene clustering, and assessed gene expression profiles. We analyzed 908 nTNL R genes in the genomes of the six plant species, and classified them into 12 subgroups based on the presence of coiled-coil (CC), nucleotide binding site (NBS), leucine rich repeat (LRR), resistance to Powdery mildew 8 (RPW8), and BED type zinc finger domains. Traditionally classified CC-NBS-LRR (CNL) genes were nested into four clades (CNL A-D) often with abundant, well-supported homogeneous subclades of Type-II R genes. CNL-D members were absent in rice, indicating a unique R gene retention pattern in the rice genome. Genomes from Arabidopsis , common bean, poplar and soybean had one chromosome without any CNL R genes. Medicago and Arabidopsis had the highest and lowest number of gene clusters, respectively. Gene expression analyses suggested unique patterns of expression for each of the CNL clades. Differential gene expression patterns of the nTNL genes were often found to correlate with number of introns and GC content, suggesting structural and functional divergence.

Comparative Genomics of Non-TNL Disease Resistance Genes from Six Plant Species

PubMed Central

Andersen, Ethan J.; Neupane, Surendra; Benson, Benjamin V.

2017-01-01

Disease resistance genes (R genes), as part of the plant defense system, have coevolved with corresponding pathogen molecules. The main objectives of this project were to identify non-Toll interleukin receptor, nucleotide-binding site, leucine-rich repeat (nTNL) genes and elucidate their evolutionary divergence across six plant genomes. Using reference sequences from Arabidopsis, we investigated nTNL orthologs in the genomes of common bean, Medicago, soybean, poplar, and rice. We used Hidden Markov Models for sequence identification, performed model-based phylogenetic analyses, visualized chromosomal positioning, inferred gene clustering, and assessed gene expression profiles. We analyzed 908 nTNL R genes in the genomes of the six plant species, and classified them into 12 subgroups based on the presence of coiled-coil (CC), nucleotide binding site (NBS), leucine rich repeat (LRR), resistance to Powdery mildew 8 (RPW8), and BED type zinc finger domains. Traditionally classified CC-NBS-LRR (CNL) genes were nested into four clades (CNL A-D) often with abundant, well-supported homogeneous subclades of Type-II R genes. CNL-D members were absent in rice, indicating a unique R gene retention pattern in the rice genome. Genomes from Arabidopsis, common bean, poplar and soybean had one chromosome without any CNL R genes. Medicago and Arabidopsis had the highest and lowest number of gene clusters, respectively. Gene expression analyses suggested unique patterns of expression for each of the CNL clades. Differential gene expression patterns of the nTNL genes were often found to correlate with number of introns and GC content, suggesting structural and functional divergence. PMID:28973974

Parallel human genome analysis: microarray-based expression monitoring of 1000 genes.

PubMed Central

Schena, M; Shalon, D; Heller, R; Chai, A; Brown, P O; Davis, R W

1996-01-01

Microarrays containing 1046 human cDNAs of unknown sequence were printed on glass with high-speed robotics. These 1.0-cm2 DNA "chips" were used to quantitatively monitor differential expression of the cognate human genes using a highly sensitive two-color hybridization assay. Array elements that displayed differential expression patterns under given experimental conditions were characterized by sequencing. The identification of known and novel heat shock and phorbol ester-regulated genes in human T cells demonstrates the sensitivity of the assay. Parallel gene analysis with microarrays provides a rapid and efficient method for large-scale human gene discovery. Images Fig. 1 Fig. 2 Fig. 3 PMID:8855227

Genome-wide association analysis links multiple psychiatric liability genes to oscillatory brain activity.

PubMed

Smit, Dirk J A; Wright, Margaret J; Meyers, Jacquelyn L; Martin, Nicholas G; Ho, Yvonne Y W; Malone, Stephen M; Zhang, Jian; Burwell, Scott J; Chorlian, David B; de Geus, Eco J C; Denys, Damiaan; Hansell, Narelle K; Hottenga, Jouke-Jan; McGue, Matt; van Beijsterveldt, Catharina E M; Jahanshad, Neda; Thompson, Paul M; Whelan, Christopher D; Medland, Sarah E; Porjesz, Bernice; Lacono, William G; Boomsma, Dorret I

2018-06-26

Oscillatory activity is crucial for information processing in the brain, and has a long history as a biomarker for psychopathology. Variation in oscillatory activity is highly heritable, but current understanding of specific genetic influences remains limited. We performed the largest genome-wide association study to date of oscillatory power during eyes-closed resting electroencephalogram (EEG) across a range of frequencies (delta 1-3.75 Hz, theta 4-7.75 Hz, alpha 8-12.75 Hz, and beta 13-30 Hz) in 8,425 subjects. Additionally, we performed KGG positional gene-based analysis and brain-expression analyses. GABRA2-a known genetic marker for alcohol use disorder and epilepsy-significantly affected beta power, consistent with the known relation between GABA A interneuron activity and beta oscillations. Tissue-specific SNP-based imputation of gene-expression levels based on the GTEx database revealed that hippocampal GABRA2 expression may mediate this effect. Twenty-four genes at 3p21.1 were significant for alpha power (FDR q < .05). SNPs in this region were linked to expression of GLYCTK in hippocampal tissue, and GNL3 and ITIH4 in the frontal cortex-genes that were previously implicated in schizophrenia and bipolar disorder. In sum, we identified several novel genetic variants associated with oscillatory brain activity; furthermore, we replicated and advanced understanding of previously known genes associated with psychopathology (i.e., schizophrenia and alcohol use disorders). Importantly, these psychopathological liability genes affect brain functioning, linking the genes' expression to specific cortical/subcortical brain regions. © 2018 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Expression analysis of some genes regulated by retinoic acid in controls and triadimefon-exposed embryos: is the amphibian Xenopus laevis a suitable model for gene-based comparative teratology?

PubMed

Di Renzo, Francesca; Rossi, Federica; Bacchetta, Renato; Prati, Mariangela; Giavini, Erminio; Menegola, Elena

2011-06-01

The use of nonmammal models in teratological studies is a matter of debate and seems to be justified if the embryotoxic mechanism involves conserved processes. Published data on mammals and Xenopus laevis suggest that azoles are teratogenic by altering the endogenous concentration of retinoic acid (RA). The expression of some genes (Shh, Ptch-1, Gsc, and Msx2) controlled by retinoic acid is downregulated in rat embryos exposed at the phylotypic stage to the triazole triadimefon (FON). In order to propose X. laevis as a model for gene-based comparative teratology, this work evaluates the expression of Shh, Ptch-1, Gsc, and Msx2 in FON-exposed X. laevis embryos. Embryos, exposed to a high concentration level (500 µM) of FON from stage 13 till 17, were examined at stages 17, 27, and 47. Stage 17 and 27 embryos were processed to perform quantitative RT-PCR. The developmental rate was never affected by FON at any considered stage. FON-exposed stage 47 larvae showed the typical craniofacial malformations. A significant downregulation of Gsc was observed in FON-exposed stage 17 embryos. Shh, Ptch-1, Msx2 showed a high fluctuation of expression both in control and in FON-exposed samples both at stages 17 and 27. The downregulation of Gsc mimics the effects of FON on rat embryos, showing for this gene a common effect of FON in the two vertebrate classes. The high fluctuation observed in the gene expression of the other genes, however, suggests that X. laevis at this stage has limited utility for gene-based comparative teratology. © 2011 Wiley-Liss, Inc.

Changes in gene expression associated with response to neoadjuvant chemotherapy in breast cancer.

PubMed

Hannemann, Juliane; Oosterkamp, Hendrika M; Bosch, Cathy A J; Velds, Arno; Wessels, Lodewyk F A; Loo, Claudette; Rutgers, Emiel J; Rodenhuis, Sjoerd; van de Vijver, Marc J

2005-05-20

At present, clinically useful markers predicting response of primary breast carcinomas to either doxorubicin-cyclophosphamide (AC) or doxorubicin-docetaxel (AD) are lacking. We investigated whether gene expression profiles of the primary tumor could be used to predict treatment response to either of those chemotherapy regimens. Within a single-institution, randomized, phase II trial, patients with locally advanced breast cancer received six courses of either AC (n = 24) or AD (n = 24) neoadjuvant chemotherapy. Gene expression profiles were generated from core-needle biopsies obtained before treatment and correlated with the response of the primary tumor to the chemotherapy administered. Additionally, pretreatment gene expression profiles were compared with those in tumors remaining after chemotherapy. Ten (20%) of 48 patients showed a (near) pathologic complete remission of the primary tumor after treatment. No gene expression pattern correlating with response could be identified for all patients or for the AC or AD groups separately. The comparison of the pretreatment biopsy and the tumor excised after chemotherapy revealed differences in gene expression in tumors that showed a partial remission but not in tumors that did not respond to chemotherapy. No gene expression profile predicting the response of primary breast carcinomas to AC- or AD-based neoadjuvant chemotherapy could be detected in this interim analysis. More subtle differences in gene expression are likely to be present but can only be reliably identified by studying a larger group of patients. Response of a breast tumor to neoadjuvant chemotherapy results in alterations in gene expression.

Robust transcriptional tumor signatures applicable to both formalin-fixed paraffin-embedded and fresh-frozen samples

PubMed Central

Cheng, Jun; He, Jun; Liu, Huaping; Cai, Hao; Hong, Guini; Zhang, Jiahui; Li, Na; Ao, Lu; Guo, Zheng

2017-01-01

Formalin-fixed paraffin-embedded (FFPE) samples represent a valuable resource for clinical researches. However, FFPE samples are usually considered an unreliable source for gene expression analysis due to the partial RNA degradation. In this study, through comparing gene expression profiles between FFPE samples and paired fresh-frozen (FF) samples for three cancer types, we firstly showed that expression measurements of thousands of genes had at least two-fold change in FFPE samples compared with paired FF samples. Therefore, for a transcriptional signature based on risk scores summarized from the expression levels of the signature genes, the risk score thresholds trained from FFPE (or FF) samples could not be applied to FF (or FFPE) samples. On the other hand, we found that more than 90% of the relative expression orderings (REOs) of gene pairs in the FF samples were maintained in their paired FFPE samples and largely unaffected by the storage time. The result suggested that the REOs of gene pairs were highly robust against partial RNA degradation in FFPE samples. Finally, as a case study, we developed a REOs-based signature to distinguish liver cirrhosis from hepatocellular carcinoma (HCC) using FFPE samples. The signature was validated in four datasets of FFPE samples and eight datasets of FF samples. In conclusion, the valuable FFPE samples can be fully exploited to identify REOs-based diagnostic and prognostic signatures which could be robustly applicable to both FF samples and FFPE samples with degraded RNA. PMID:28036264

On construction of stochastic genetic networks based on gene expression sequences.

PubMed

Ching, Wai-Ki; Ng, Michael M; Fung, Eric S; Akutsu, Tatsuya

2005-08-01

Reconstruction of genetic regulatory networks from time series data of gene expression patterns is an important research topic in bioinformatics. Probabilistic Boolean Networks (PBNs) have been proposed as an effective model for gene regulatory networks. PBNs are able to cope with uncertainty, corporate rule-based dependencies between genes and discover the sensitivity of genes in their interactions with other genes. However, PBNs are unlikely to use directly in practice because of huge amount of computational cost for obtaining predictors and their corresponding probabilities. In this paper, we propose a multivariate Markov model for approximating PBNs and describing the dynamics of a genetic network for gene expression sequences. The main contribution of the new model is to preserve the strength of PBNs and reduce the complexity of the networks. The number of parameters of our proposed model is O(n2) where n is the number of genes involved. We also develop efficient estimation methods for solving the model parameters. Numerical examples on synthetic data sets and practical yeast data sequences are given to demonstrate the effectiveness of the proposed model.

Identifying novel glioma associated pathways based on systems biology level meta-analysis.

PubMed

Hu, Yangfan; Li, Jinquan; Yan, Wenying; Chen, Jiajia; Li, Yin; Hu, Guang; Shen, Bairong

2013-01-01

With recent advances in microarray technology, including genomics, proteomics, and metabolomics, it brings a great challenge for integrating this "-omics" data to analysis complex disease. Glioma is an extremely aggressive and lethal form of brain tumor, and thus the study of the molecule mechanism underlying glioma remains very important. To date, most studies focus on detecting the differentially expressed genes in glioma. However, the meta-analysis for pathway analysis based on multiple microarray datasets has not been systematically pursued. In this study, we therefore developed a systems biology based approach by integrating three types of omics data to identify common pathways in glioma. Firstly, the meta-analysis has been performed to study the overlapping of signatures at different levels based on the microarray gene expression data of glioma. Among these gene expression datasets, 12 pathways were found in GeneGO database that shared by four stages. Then, microRNA expression profiles and ChIP-seq data were integrated for the further pathway enrichment analysis. As a result, we suggest 5 of these pathways could be served as putative pathways in glioma. Among them, the pathway of TGF-beta-dependent induction of EMT via SMAD is of particular importance. Our results demonstrate that the meta-analysis based on systems biology level provide a more useful approach to study the molecule mechanism of complex disease. The integration of different types of omics data, including gene expression microarrays, microRNA and ChIP-seq data, suggest some common pathways correlated with glioma. These findings will offer useful potential candidates for targeted therapeutic intervention of glioma.

Identifying gnostic predictors of the vaccine response.

PubMed

Haining, W Nicholas; Pulendran, Bali

2012-06-01

Molecular predictors of the response to vaccination could transform vaccine development. They would allow larger numbers of vaccine candidates to be rapidly screened, shortening the development time for new vaccines. Gene-expression based predictors of vaccine response have shown early promise. However, a limitation of gene-expression based predictors is that they often fail to reveal the mechanistic basis of their ability to classify response. Linking predictive signatures to the function of their component genes would advance basic understanding of vaccine immunity and also improve the robustness of vaccine prediction. New analytic tools now allow more biological meaning to be extracted from predictive signatures. Functional genomic approaches to perturb gene expression in mammalian cells permit the function of predictive genes to be surveyed in highly parallel experiments. The challenge for vaccinologists is therefore to use these tools to embed mechanistic insights into predictors of vaccine response. Copyright © 2012 Elsevier Ltd. All rights reserved.

Identifying gnostic predictors of the vaccine response

PubMed Central

Haining, W. Nicholas; Pulendran, Bali

2012-01-01

Molecular predictors of the response to vaccination could transform vaccine development. They would allow larger numbers of vaccine candidates to be rapidly screened, shortening the development time for new vaccines. Gene-expression based predictors of vaccine response have shown early promise. However, a limitation of gene-expression based predictors is that they often fail to reveal the mechanistic basis for their ability to classify response. Linking predictive signatures to the function of their component genes would advance basic understanding of vaccine immunity and also improve the robustness of outcome classification. New analytic tools now allow more biological meaning to be extracted from predictive signatures. Functional genomic approaches to perturb gene expression in mammalian cells permit the function of predictive genes to be surveyed in highly parallel experiments. The challenge for vaccinologists is therefore to use these tools to embed mechanistic insights into predictors of vaccine response. PMID:22633886

Caste development and reproduction: a genome-wide analysis of hallmarks of insect eusociality

PubMed Central

Cristino, A S; Nunes, F M F; Lobo, C H; Bitondi, M M G; Simões, Z L P; Da Fontoura Costa, L; Lattorff, H M G; Moritz, R F A; Evans, J D; Hartfelder, K

2006-01-01

The honey bee queen and worker castes are a model system for developmental plasticity. We used established expressed sequence tag information for a Gene Ontology based annotation of genes that are differentially expressed during caste development. Metabolic regulation emerged as a major theme, with a caste-specific difference in the expression of oxidoreductases vs. hydrolases. Motif searches in upstream regions revealed group-specific motifs, providing an entry point to cis-regulatory network studies on caste genes. For genes putatively involved in reproduction, meiosis-associated factors came out as highly conserved, whereas some determinants of embryonic axes either do not have clear orthologs (bag of marbles, gurken, torso), or appear to be lacking (trunk) in the bee genome. Our results are the outcome of a first genome-based initiative to provide an annotated framework for trends in gene regulation during female caste differentiation (representing developmental plasticity) and reproduction. PMID:17069641

Modeling Bi-modality Improves Characterization of Cell Cycle on Gene Expression in Single Cells

PubMed Central

Danaher, Patrick; Finak, Greg; Krouse, Michael; Wang, Alice; Webster, Philippa; Beechem, Joseph; Gottardo, Raphael

2014-01-01

Advances in high-throughput, single cell gene expression are allowing interrogation of cell heterogeneity. However, there is concern that the cell cycle phase of a cell might bias characterizations of gene expression at the single-cell level. We assess the effect of cell cycle phase on gene expression in single cells by measuring 333 genes in 930 cells across three phases and three cell lines. We determine each cell's phase non-invasively without chemical arrest and use it as a covariate in tests of differential expression. We observe bi-modal gene expression, a previously-described phenomenon, wherein the expression of otherwise abundant genes is either strongly positive, or undetectable within individual cells. This bi-modality is likely both biologically and technically driven. Irrespective of its source, we show that it should be modeled to draw accurate inferences from single cell expression experiments. To this end, we propose a semi-continuous modeling framework based on the generalized linear model, and use it to characterize genes with consistent cell cycle effects across three cell lines. Our new computational framework improves the detection of previously characterized cell-cycle genes compared to approaches that do not account for the bi-modality of single-cell data. We use our semi-continuous modelling framework to estimate single cell gene co-expression networks. These networks suggest that in addition to having phase-dependent shifts in expression (when averaged over many cells), some, but not all, canonical cell cycle genes tend to be co-expressed in groups in single cells. We estimate the amount of single cell expression variability attributable to the cell cycle. We find that the cell cycle explains only 5%–17% of expression variability, suggesting that the cell cycle will not tend to be a large nuisance factor in analysis of the single cell transcriptome. PMID:25032992

RNA interference of tubulin genes has lethal effects in Mythimna separate.

PubMed

Wang, Jin-da; Wang, Ya-Ru; Wang, Yong-Zhi; Wang, Wei-Zhong; Wang, Rong; Gao, San-Ji

2018-05-23

RNAi (RNA interference) is a technology for silencing expression of target genes via sequence-specific double-stranded RNA (dsRNA). Recently, dietary introduction of bacterially expressed dsRNA has shown great potential in the field of pest management. Identification of potential candidate genes for RNAi is the first step in this application. The oriental armyworm, Mythimna separata Walker (Lepidoptera: Noctuidae) is a polyphagous, migratory pest, and outbreaks have led to severe crop damage in China. In the present study, two tubulin genes were chosen as target genes because of their crucial role in insect development. Both Msα-tubulin and Msβ-tubulin genes are expressed across all life stages and are highly expressed in the head and epidermis. Feeding of bacterially expressed dsRNA of Msα-tubulin and Msβ-tubulin to third-instar larvae knocked down target mRNAs. A lethal phenotype was observed with knockdown of Msα-tubulin and Msβ-tubulin concurrent with reduction in body weight. Bacterially expressed dsRNA can be used to control M. separata, and tubulin genes could be effective candidate genes for an RNAi-based control strategy of this pest. Copyright © 2017. Published by Elsevier B.V.

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

Gelb, Bruce D; Tartaglia, Marco; Pennacchio, Len

Diagnostic and therapeutic applications for Noonan Syndrome are described. The diagnostic and therapeutic applications are based on certain mutations in a RAS-specific guanine nucleotide exchange factor gene SOS1 or its expression product. The diagnostic and therapeutic applications are also based on certain mutations in a serine/threonine protein kinase gene RAF1 or its expression product thereof. Also described are nucleotide sequences, amino acid sequences, probes, and primers related to RAF1 or SOS1, and variants thereof, as well as host cells expressing such variants.

Prediction of regulatory gene pairs using dynamic time warping and gene ontology.

PubMed

Yang, Andy C; Hsu, Hui-Huang; Lu, Ming-Da; Tseng, Vincent S; Shih, Timothy K

2014-01-01

Selecting informative genes is the most important task for data analysis on microarray gene expression data. In this work, we aim at identifying regulatory gene pairs from microarray gene expression data. However, microarray data often contain multiple missing expression values. Missing value imputation is thus needed before further processing for regulatory gene pairs becomes possible. We develop a novel approach to first impute missing values in microarray time series data by combining k-Nearest Neighbour (KNN), Dynamic Time Warping (DTW) and Gene Ontology (GO). After missing values are imputed, we then perform gene regulation prediction based on our proposed DTW-GO distance measurement of gene pairs. Experimental results show that our approach is more accurate when compared with existing missing value imputation methods on real microarray data sets. Furthermore, our approach can also discover more regulatory gene pairs that are known in the literature than other methods.

Gene expression information improves reliability of receptor status in breast cancer patients

PubMed Central

Kenn, Michael; Schlangen, Karin; Castillo-Tong, Dan Cacsire; Singer, Christian F.; Cibena, Michael; Koelbl, Heinz; Schreiner, Wolfgang

2017-01-01

Immunohistochemical (IHC) determination of receptor status in breast cancer patients is frequently inaccurate. Since it directs the choice of systemic therapy, it is essential to increase its reliability. We increase the validity of IHC receptor expression by additionally considering gene expression (GE) measurements. Crisp therapeutic decisions are based on IHC estimates, even if they are borderline reliable. We further improve decision quality by a responsibility function, defining a critical domain for gene expression. Refined normalization is devised to file any newly diagnosed patient into existing data bases. Our approach renders receptor estimates more reliable by identifying patients with questionable receptor status. The approach is also more efficient since the rate of conclusive samples is increased. We have curated and evaluated gene expression data, together with clinical information, from 2880 breast cancer patients. Combining IHC with gene expression information yields a method more reliable and also more efficient as compared to common practice up to now. Several types of possibly suboptimal treatment allocations, based on IHC receptor status alone, are enumerated. A ‘therapy allocation check’ identifies patients possibly miss-classified. Estrogen: false negative 8%, false positive 6%. Progesterone: false negative 14%, false positive 11%. HER2: false negative 2%, false positive 50%. Possible implications are discussed. We propose an ‘expression look-up-plot’, allowing for a significant potential to improve the quality of precision medicine. Methods are developed and exemplified here for breast cancer patients, but they may readily be transferred to diagnostic data relevant for therapeutic decisions in other fields of oncology. PMID:29100391

In Silico Prediction and Validation of Gfap as an miR-3099 Target in Mouse Brain.

PubMed

Abidin, Shahidee Zainal; Leong, Jia-Wen; Mahmoudi, Marzieh; Nordin, Norshariza; Abdullah, Syahril; Cheah, Pike-See; Ling, King-Hwa

2017-08-01

MicroRNAs are small non-coding RNAs that play crucial roles in the regulation of gene expression and protein synthesis during brain development. MiR-3099 is highly expressed throughout embryogenesis, especially in the developing central nervous system. Moreover, miR-3099 is also expressed at a higher level in differentiating neurons in vitro, suggesting that it is a potential regulator during neuronal cell development. This study aimed to predict the target genes of miR-3099 via in-silico analysis using four independent prediction algorithms (miRDB, miRanda, TargetScan, and DIANA-micro-T-CDS) with emphasis on target genes related to brain development and function. Based on the analysis, a total of 3,174 miR-3099 target genes were predicted. Those predicted by at least three algorithms (324 genes) were subjected to DAVID bioinformatics analysis to understand their overall functional themes and representation. The analysis revealed that nearly 70% of the target genes were expressed in the nervous system and a significant proportion were associated with transcriptional regulation and protein ubiquitination mechanisms. Comparison of in situ hybridization (ISH) expression patterns of miR-3099 in both published and in-house-generated ISH sections with the ISH sections of target genes from the Allen Brain Atlas identified 7 target genes (Dnmt3a, Gabpa, Gfap, Itga4, Lxn, Smad7, and Tbx18) having expression patterns complementary to miR-3099 in the developing and adult mouse brain samples. Of these, we validated Gfap as a direct downstream target of miR-3099 using the luciferase reporter gene system. In conclusion, we report the successful prediction and validation of Gfap as an miR-3099 target gene using a combination of bioinformatics resources with enrichment of annotations based on functional ontologies and a spatio-temporal expression dataset.

Changing expression of vertebrate immunity genes in an anthropogenic environment: a controlled experiment.

PubMed

Hablützel, Pascal I; Brown, Martha; Friberg, Ida M; Jackson, Joseph A

2016-09-01

The effect of anthropogenic environments on the function of the vertebrate immune system is a problem of general importance. For example, it relates to the increasing rates of immunologically-based disease in modern human populations and to the desirability of identifying optimal immune function in domesticated animals. Despite this importance, our present understanding is compromised by a deficit of experimental studies that make adequately matched comparisons between wild and captive vertebrates. We transferred post-larval fishes (three-spined sticklebacks), collected in the wild, to an anthropogenic (captive) environment. We then monitored, over 11 months, how the systemic expression of immunity genes changed in comparison to cohort-matched wild individuals in the originator population (total n = 299). We found that a range of innate (lyz, defbl2, il1r-like, tbk1) and adaptive (cd8a, igmh) immunity genes were up-regulated in captivity, accompanied by an increase in expression of the antioxidant enzyme, gpx4a. For some genes previously known to show seasonality in the wild, this appeared to be reduced in captive fishes. Captive fishes tended to express immunity genes, including igzh, foxp3b, lyz, defbl2, and il1r-like, more variably. Furthermore, although gene co-expression patterns (analyzed through gene-by-gene correlations and mutual information theory based networks) shared common structure in wild and captive fishes, there was also significant divergence. For one gene in particular, defbl2, high expression was associated with adverse health outcomes in captive fishes. Taken together, these results demonstrate widespread regulatory changes in the immune system in captive populations, and that the expression of immunity genes is more constrained in the wild. An increase in constitutive systemic immune activity, such as we observed here, may alter the risk of immunopathology and contribute to variance in health in vertebrate populations exposed to anthropogenic environments.

A Green-Light-Responsive System for the Control of Transgene Expression in Mammalian and Plant Cells.

PubMed

Chatelle, Claire; Ochoa-Fernandez, Rocio; Engesser, Raphael; Schneider, Nils; Beyer, Hannes M; Jones, Alex R; Timmer, Jens; Zurbriggen, Matias D; Weber, Wilfried

2018-05-18

The ever-increasing complexity of synthetic gene networks and applications of synthetic biology requires precise and orthogonal gene expression systems. Of particular interest are systems responsive to light as they enable the control of gene expression dynamics with unprecedented resolution in space and time. While broadly used in mammalian backgrounds, however, optogenetic approaches in plant cells are still limited due to interference of the activating light with endogenous photoreceptors. Here, we describe the development of the first synthetic light-responsive system for the targeted control of gene expression in mammalian and plant cells that responds to the green range of the light spectrum in which plant photoreceptors have minimal activity. We first engineered a system based on the light-sensitive bacterial transcription factor CarH and its cognate DNA operator sequence CarO from Thermus thermophilus to control gene expression in mammalian cells. The system was functional in various mammalian cell lines, showing high induction (up to 350-fold) along with low leakiness, as well as high reversibility. We quantitatively described the systems characteristics by the development and experimental validation of a mathematical model. Finally, we transferred the system into A. thaliana protoplasts and demonstrated gene repression in response to green light. We expect that this system will provide new opportunities in applications based on synthetic gene networks and will open up perspectives for optogenetic studies in mammalian and plant cells.

2A self-cleaving peptide-based multi-gene expression system in the silkworm Bombyx mori

PubMed Central

Wang, Yuancheng; Wang, Feng; Wang, Riyuan; Zhao, Ping; Xia, Qingyou

2015-01-01

Fundamental and applied studies of silkworms have entered the functional genomics era. Here, we report a multi-gene expression system (MGES) based on 2A self-cleaving peptide (2A), which regulates the simultaneous expression and cleavage of multiple gene targets in the silk gland of transgenic silkworms. First, a glycine-serine-glycine spacer (GSG) was found to significantly improve the cleavage efficiency of 2A. Then, the cleavage efficiency of six types of 2As with GSG was analyzed. The shortest porcine teschovirus-1 2A (P2A-GSG) exhibited the highest cleavage efficiency in all insect cell lines that we tested. Next, P2A-GSG successfully cleaved the artificial human serum albumin (66 kDa) linked with human acidic fibroblast growth factor (20.2 kDa) fusion genes and vitellogenin receptor fragment (196 kD) of silkworm linked with EGFP fusion genes, importantly, vitellogenin receptor protein was secreted to the outside of cells. Furthermore, P2A-GSG successfully mediated the simultaneous expression and cleavage of a DsRed and EGFP fusion gene in silk glands and caused secretion into the cocoon of transgenic silkworms using our sericin1 expression system. We predicted that the MGES would be an efficient tool for gene function research and innovative research on various functional silk materials in medicine, cosmetics, and other biomedical areas. PMID:26537835

Analyzing gene expression profiles in dilated cardiomyopathy via bioinformatics methods.

PubMed

Wang, Liming; Zhu, L; Luan, R; Wang, L; Fu, J; Wang, X; Sui, L

2016-10-10

Dilated cardiomyopathy (DCM) is characterized by ventricular dilatation, and it is a common cause of heart failure and cardiac transplantation. This study aimed to explore potential DCM-related genes and their underlying regulatory mechanism using methods of bioinformatics. The gene expression profiles of GSE3586 were downloaded from Gene Expression Omnibus database, including 15 normal samples and 13 DCM samples. The differentially expressed genes (DEGs) were identified between normal and DCM samples using Limma package in R language. Pathway enrichment analysis of DEGs was then performed. Meanwhile, the potential transcription factors (TFs) and microRNAs (miRNAs) of these DEGs were predicted based on their binding sequences. In addition, DEGs were mapped to the cMap database to find the potential small molecule drugs. A total of 4777 genes were identified as DEGs by comparing gene expression profiles between DCM and control samples. DEGs were significantly enriched in 26 pathways, such as lymphocyte TarBase pathway and androgen receptor signaling pathway. Furthermore, potential TFs (SP1, LEF1, and NFAT) were identified, as well as potential miRNAs (miR-9, miR-200 family, and miR-30 family). Additionally, small molecules like isoflupredone and trihexyphenidyl were found to be potential therapeutic drugs for DCM. The identified DEGs (PRSS12 and FOXG1), potential TFs, as well as potential miRNAs, might be involved in DCM.

Analyzing gene expression profiles in dilated cardiomyopathy via bioinformatics methods

PubMed Central

Wang, Liming; Zhu, L.; Luan, R.; Wang, L.; Fu, J.; Wang, X.; Sui, L.

2016-01-01

Dilated cardiomyopathy (DCM) is characterized by ventricular dilatation, and it is a common cause of heart failure and cardiac transplantation. This study aimed to explore potential DCM-related genes and their underlying regulatory mechanism using methods of bioinformatics. The gene expression profiles of GSE3586 were downloaded from Gene Expression Omnibus database, including 15 normal samples and 13 DCM samples. The differentially expressed genes (DEGs) were identified between normal and DCM samples using Limma package in R language. Pathway enrichment analysis of DEGs was then performed. Meanwhile, the potential transcription factors (TFs) and microRNAs (miRNAs) of these DEGs were predicted based on their binding sequences. In addition, DEGs were mapped to the cMap database to find the potential small molecule drugs. A total of 4777 genes were identified as DEGs by comparing gene expression profiles between DCM and control samples. DEGs were significantly enriched in 26 pathways, such as lymphocyte TarBase pathway and androgen receptor signaling pathway. Furthermore, potential TFs (SP1, LEF1, and NFAT) were identified, as well as potential miRNAs (miR-9, miR-200 family, and miR-30 family). Additionally, small molecules like isoflupredone and trihexyphenidyl were found to be potential therapeutic drugs for DCM. The identified DEGs (PRSS12 and FOXG1), potential TFs, as well as potential miRNAs, might be involved in DCM. PMID:27737314

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

PubMed Central

2013-01-01

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

Generation of a Tet-On Expression System to Study Transactivation Ability of Tax-2.

PubMed

Bignami, Fabio; Sozzi, Riccardo Alessio; Pilotti, Elisabetta

2017-01-01

HTLV Tax proteins (Tax-1 and Tax-2) are known to be able to transactivate several host cellular genes involved in complex molecular pathways. Here, we describe a stable and regulated high-level expression model based on Tet-On system, to study the capacity of Tax-2 to transactivate host genes. In particular, the Jurkat Tet-On cell line suitable for evaluating the ability of Tax-2 to stimulate transactivation of a specific host gene, CCL3L1 (C-C motif chemokine ligand 3 like 1 gene), was selected. Then, a plasmid expressing tax-2 gene under control of a tetracycline-response element was constructed. To avoid the production of a fusion protein between the report gene and the inserted gene, a bidirectional plasmid was designed. Maximum expression and fast response time were achieved by using nucleofection technology as transfection method. After developing an optimized protocol for efficiently transferring tax-2 gene in Jurkat Tet-On cellular model and exposing transfected cells to Dox (doxycycline, a tetracycline derivate), a kinetics of tax-2 expression through TaqMan Real-time PCR assay was determined.

ABC gene expression profiles have clinical importance and possibly form a new hallmark of cancer.

PubMed

Dvorak, Pavel; Pesta, Martin; Soucek, Pavel

2017-05-01

Adenosine triphosphate-binding cassette proteins constitute a large family of active transporters through extracellular and intracellular membranes. Increased drug efflux based on adenosine triphosphate-binding cassette protein activity is related to the development of cancer cell chemoresistance. Several articles have focused on adenosine triphosphate-binding cassette gene expression profiles (signatures), based on the expression of all 49 human adenosine triphosphate-binding cassette genes, in individual tumor types and reported connections to established clinicopathological features. The aim of this study was to test our theory about the existence of adenosine triphosphate-binding cassette gene expression profiles common to multiple types of tumors, which may modify tumor progression and provide clinically relevant information. Such general adenosine triphosphate-binding cassette profiles could constitute a new attribute of carcinogenesis. Our combined cohort consisted of tissues from 151 cancer patients-breast, colorectal, and pancreatic carcinomas. Standard protocols for RNA isolation and quantitative real-time polymerase chain reaction were followed. Gene expression data from individual tumor types as well as a merged tumor dataset were analyzed by bioinformatics tools. Several general adenosine triphosphate-binding cassette profiles, with differences in gene functions, were established and shown to have significant relations to clinicopathological features such as tumor size, histological grade, or clinical stage. Genes ABCC7, A3, A8, A12, and C8 prevailed among the most upregulated or downregulated ones. In conclusion, the results supported our theory about general adenosine triphosphate-binding cassette gene expression profiles and their importance for cancer on clinical as well as research levels. The presence of ABCC7 (official symbol CFTR) among the genes with key roles in the profiles supports the emerging evidence about its crucial role in various cancers. Graphical abstract.

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.

Differential co-expression analysis reveals a novel prognostic gene module in ovarian cancer.

PubMed

Gov, Esra; Arga, Kazim Yalcin

2017-07-10

Ovarian cancer is one of the most significant disease among gynecological disorders that women suffered from over the centuries. However, disease-specific and effective biomarkers were still not available, since studies have focused on individual genes associated with ovarian cancer, ignoring the interactions and associations among the gene products. Here, ovarian cancer differential co-expression networks were reconstructed via meta-analysis of gene expression data and co-expressed gene modules were identified in epithelial cells from ovarian tumor and healthy ovarian surface epithelial samples to propose ovarian cancer associated genes and their interactions. We propose a novel, highly interconnected, differentially co-expressed, and co-regulated gene module in ovarian cancer consisting of 84 prognostic genes. Furthermore, the specificity of the module to ovarian cancer was shown through analyses of datasets in nine other cancers. These observations underscore the importance of transcriptome based systems biomarkers research in deciphering the elusive pathophysiology of ovarian cancer, and here, we present reciprocal interplay between candidate ovarian cancer genes and their transcriptional regulatory dynamics. The corresponding gene module might provide new insights on ovarian cancer prognosis and treatment strategies that continue to place a significant burden on global health.

Pathway-Based Concentration Response Profiles from Toxicogenomics Data

EPA Science Inventory

Microarray analysis of gene expression of in vitro systems could be a powerful tool for assessing chemical hazard. Differentially expressed genes specific to cells, chemicals, and concentrations can be organized into molecular pathways that inform mode of action. An important par...

A Java tool for dynamic web-based 3D visualization of anatomy and overlapping gene or protein expression patterns.

PubMed

Gerth, Victor E; Vize, Peter D

2005-04-01

The Gene Expression Viewer is a web-launched three-dimensional visualization tool, tailored to compare surface reconstructions of multi-channel image volumes generated by confocal microscopy or micro-CT.

Molecular Signatures Discriminating the Male and the Female Sexual Pathways in the Pearl Oyster Pinctada margaritifera

PubMed Central

Teaniniuraitemoana, Vaihiti; Huvet, Arnaud; Levy, Peva; Gaertner-Mazouni, Nabila; Gueguen, Yannick; Le Moullac, Gilles

2015-01-01

The genomics of economically important marine bivalves is studied to provide better understanding of the molecular mechanisms underlying their different reproductive strategies. The recently available gonad transcriptome of the black-lip pearl oyster Pinctada margaritifera is a novel and powerful resource to study these mechanisms in marine mollusks displaying hermaphroditic features. In this study, RNAseq quantification data of the P. margaritifera gonad transcriptome were analyzed to identify candidate genes in histologically-characterized gonad samples to provide molecular signatures of the female and male sexual pathway in this pearl oyster. Based on the RNAseq data set, stringent expression analysis identified 1,937 contigs that were differentially expressed between the gonad histological categories. From the hierarchical clustering analysis, a new reproduction model is proposed, based on a dual histo-molecular analytical approach. Nine candidate genes were identified as markers of the sexual pathway: 7 for the female pathway and 2 for the male one. Their mRNA levels were assayed by real-time PCR on a new set of gonadic samples. A clustering method revealed four principal expression patterns based on the relative gene expression ratio. A multivariate regression tree realized on these new samples and validated on the previously analyzed RNAseq samples showed that the sexual pathway of P. margaritifera can be predicted by a 3-gene-pair expression ratio model of 4 different genes: pmarg-43476, pmarg-foxl2, pmarg-54338 and pmarg-fem1-like. This 3-gene-pair expression ratio model strongly suggests only the implication of pmarg-foxl2 and pmarg-fem1-like in the sex inversion of P. margaritifera. This work provides the first histo-molecular model of P. margaritifera reproduction and a gene expression signature of its sexual pathway discriminating the male and female pathways. These represent useful tools for understanding and studying sex inversion, sex differentiation and sex determinism in this species and other related species for aquaculture purposes such as genetic selection programs. PMID:25815473

Gene networks and the evolution of plant morphology.

PubMed

Das Gupta, Mainak; Tsiantis, Miltos

2018-06-06

Elaboration of morphology depends on the precise orchestration of gene expression by key regulatory genes. The hierarchy and relationship among the participating genes is commonly known as gene regulatory network (GRN). Therefore, the evolution of morphology ultimately occurs by the rewiring of gene network structures or by the co-option of gene networks to novel domains. The availability of high-resolution expression data combined with powerful statistical tools have opened up new avenues to formulate and test hypotheses on how diverse gene networks influence trait development and diversity. Here we summarize recent studies based on both big-data and genetics approaches to understand the evolution of plant form and physiology. We also discuss recent genome-wide investigations on how studying open-chromatin regions may help study the evolution of gene expression patterns. Copyright © 2018. Published by Elsevier Ltd.

DEXTER: Disease-Expression Relation Extraction from Text.

PubMed

Gupta, Samir; Dingerdissen, Hayley; Ross, Karen E; Hu, Yu; Wu, Cathy H; Mazumder, Raja; Vijay-Shanker, K

2018-01-01

Gene expression levels affect biological processes and play a key role in many diseases. Characterizing expression profiles is useful for clinical research, and diagnostics and prognostics of diseases. There are currently several high-quality databases that capture gene expression information, obtained mostly from large-scale studies, such as microarray and next-generation sequencing technologies, in the context of disease. The scientific literature is another rich source of information on gene expression-disease relationships that not only have been captured from large-scale studies but have also been observed in thousands of small-scale studies. Expression information obtained from literature through manual curation can extend expression databases. While many of the existing databases include information from literature, they are limited by the time-consuming nature of manual curation and have difficulty keeping up with the explosion of publications in the biomedical field. In this work, we describe an automated text-mining tool, Disease-Expression Relation Extraction from Text (DEXTER) to extract information from literature on gene and microRNA expression in the context of disease. One of the motivations in developing DEXTER was to extend the BioXpress database, a cancer-focused gene expression database that includes data derived from large-scale experiments and manual curation of publications. The literature-based portion of BioXpress lags behind significantly compared to expression information obtained from large-scale studies and can benefit from our text-mined results. We have conducted two different evaluations to measure the accuracy of our text-mining tool and achieved average F-scores of 88.51 and 81.81% for the two evaluations, respectively. Also, to demonstrate the ability to extract rich expression information in different disease-related scenarios, we used DEXTER to extract information on differential expression information for 2024 genes in lung cancer, 115 glycosyltransferases in 62 cancers and 826 microRNA in 171 cancers. All extractions using DEXTER are integrated in the literature-based portion of BioXpress.Database URL: http://biotm.cis.udel.edu/DEXTER.

Using Poisson mixed-effects model to quantify transcript-level gene expression in RNA-Seq.

PubMed

Hu, Ming; Zhu, Yu; Taylor, Jeremy M G; Liu, Jun S; Qin, Zhaohui S

2012-01-01

RNA sequencing (RNA-Seq) is a powerful new technology for mapping and quantifying transcriptomes using ultra high-throughput next-generation sequencing technologies. Using deep sequencing, gene expression levels of all transcripts including novel ones can be quantified digitally. Although extremely promising, the massive amounts of data generated by RNA-Seq, substantial biases and uncertainty in short read alignment pose challenges for data analysis. In particular, large base-specific variation and between-base dependence make simple approaches, such as those that use averaging to normalize RNA-Seq data and quantify gene expressions, ineffective. In this study, we propose a Poisson mixed-effects (POME) model to characterize base-level read coverage within each transcript. The underlying expression level is included as a key parameter in this model. Since the proposed model is capable of incorporating base-specific variation as well as between-base dependence that affect read coverage profile throughout the transcript, it can lead to improved quantification of the true underlying expression level. POME can be freely downloaded at http://www.stat.purdue.edu/~yuzhu/pome.html. yuzhu@purdue.edu; zhaohui.qin@emory.edu Supplementary data are available at Bioinformatics online.

An all-in-one, Tet-On 3G inducible PiggyBac system for human pluripotent stem cells and derivatives.

PubMed

Randolph, Lauren N; Bao, Xiaoping; Zhou, Chikai; Lian, Xiaojun

2017-05-08

Human pluripotent stem cells (hPSCs) offer tremendous promise in tissue engineering and cell-based therapies due to their unique combination of two properties: pluripotency and unlimited proliferative capacity. However, directed differentiation of hPSCs to clinically relevant cell lineages is needed to achieve the goal of hPSC-based therapies. This requires a deep understanding of how cell signaling pathways converge on the nucleus to control differentiation and the ability to dissect gene function in a temporal manner. Here, we report the use of the PiggyBac transposon and a Tet-On 3G drug-inducible gene expression system to achieve versatile inducible gene expression in hPSC lines. Our new system, XLone, offers improvement over previous Tet-On systems with significantly reduced background expression and increased sensitivity to doxycycline. Transgene expression in hPSCs is tightly regulated in response to doxycycline treatment. In addition, the PiggyBac elements in our XLone construct provide a rapid and efficient strategy for generating stable transgenic hPSCs. Our inducible gene expression PiggyBac transposon system should facilitate the study of gene function and directed differentiation in human stem cells.

Ion Channel Gene Expression in Lung Adenocarcinoma: Potential Role in Prognosis and Diagnosis

PubMed Central

Ko, Jae-Hong; Gu, Wanjun; Lim, Inja; Bang, Hyoweon; Ko, Eun A.; Zhou, Tong

2014-01-01

Ion channels are known to regulate cancer processes at all stages. The roles of ion channels in cancer pathology are extremely diverse. We systematically analyzed the expression patterns of ion channel genes in lung adenocarcinoma. First, we compared the expression of ion channel genes between normal and tumor tissues in patients with lung adenocarcinoma. Thirty-seven ion channel genes were identified as being differentially expressed between the two groups. Next, we investigated the prognostic power of ion channel genes in lung adenocarcinoma. We assigned a risk score to each lung adenocarcinoma patient based on the expression of the differentially expressed ion channel genes. We demonstrated that the risk score effectively predicted overall survival and recurrence-free survival in lung adenocarcinoma. We also found that the risk scores for ever-smokers were higher than those for never-smokers. Multivariate analysis indicated that the risk score was a significant prognostic factor for survival, which is independent of patient age, gender, stage, smoking history, Myc level, and EGFR/KRAS/ALK gene mutation status. Finally, we investigated the difference in ion channel gene expression between the two major subtypes of non-small cell lung cancer: adenocarcinoma and squamous-cell carcinoma. Thirty ion channel genes were identified as being differentially expressed between the two groups. We suggest that ion channel gene expression can be used to improve the subtype classification in non-small cell lung cancer at the molecular level. The findings in this study have been validated in several independent lung cancer cohorts. PMID:24466154

Mapping gene expression patterns during myeloid differentiation using the EML hematopoietic progenitor cell line.

PubMed

Du, Yang; Campbell, Janee L; Nalbant, Demet; Youn, Hyewon; Bass, Ann C Hughes; Cobos, Everardo; Tsai, Schickwann; Keller, Jonathan R; Williams, Simon C

2002-07-01

The detailed examination of the molecular events that control the early stages of myeloid differentiation has been hampered by the relative scarcity of hematopoietic stem cells and the lack of suitable cell line models. In this study, we examined the expression of several myeloid and nonmyeloid genes in the murine EML hematopoietic stem cell line. Expression patterns for 19 different genes were examined by Northern blotting and RT-PCR in RNA samples from EML, a variety of other immortalized cell lines, and purified murine hematopoietic stem cells. Representational difference analysis (RDA) was performed to identify differentially expressed genes in EML. Expression patterns of genes encoding transcription factors (four members of the C/EBP family, GATA-1, GATA-2, PU.1, CBFbeta, SCL, and c-myb) in EML were examined and were consistent with the proposed functions of these proteins in hematopoietic differentiation. Expression levels of three markers of terminal myeloid differentiation (neutrophil elastase, proteinase 3, and Mac-1) were highest in EML cells at the later stages of differentiation. In a search for genes that were differentially expressed in EML cells during myeloid differentiation, six cDNAs were isolated. These included three known genes (lysozyme, histidine decarboxylase, and tryptophan hydroxylase) and three novel genes. Expression patterns of known genes in differentiating EML cells accurately reflected their expected expression patterns based on previous studies. The identification of three novel genes, two of which encode proteins that may act as regulators of hematopoietic differentiation, suggests that EML is a useful model system for the molecular analysis of hematopoietic differentiation.

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.

DNA polymerase β variant Ile260Met generates global gene expression changes related to cellular transformation

PubMed Central

Sweasy, Joann B.

2012-01-01

Maintenance of genomic stability is essential for cellular survival. The base excision repair (BER) pathway is critical for resolution of abasic sites and damaged bases, estimated to occur 20,000 times in cells daily. DNA polymerase β (Pol β) participates in BER by filling DNA gaps that result from excision of damaged bases. Approximately 30% of human tumours express Pol β variants, many of which have altered fidelity and activity in vitro and when expressed, induce cellular transformation. The prostate tumour variant Ile260Met transforms cells and is a sequence-context-dependent mutator. To test the hypothesis that mutations induced in vivo by Ile260Met lead to cellular transformation, we characterized the genome-wide expression profile of a clone expressing Ile260Met as compared with its non-induced counterpart. Using a 1.5-fold minimum cut-off with a false discovery rate (FDR) of <0.05, 912 genes exhibit altered expression. Microarray results were confirmed by quantitative real-time polymerase chain reaction (qRT-PCR) and revealed unique expression profiles in other clones. Gene Ontology (GO) clusters were analyzed using Ingenuity Pathways Analysis to identify altered gene networks and associated nodes. We determined three nodes of interest that exhibited dysfunctional regulation of downstream gene products without themselves having altered expression. One node, peroxisome proliferator-activated protein γ (PPARG), was sequenced and found to contain a coding region mutation in PPARG2 only in transformed cells. Further analysis suggests that this mutation leads to dominant negative activity of PPARG2. PPARG is a transcription factor implicated to have tumour suppressor function. This suggests that the PPARG2 mutant may have played a role in driving cellular transformation. We conclude that PPARG induces cellular transformation by a mutational mechanism. PMID:22914675

Robustness of equations that define molecular subtypes of glioblastoma tumors based on five transcripts measured by RT-PCR.

PubMed

Castells, Xavier; Acebes, Juan José; Majós, Carles; Boluda, Susana; Julià-Sapé, Margarida; Candiota, Ana Paula; Ariño, Joaquín; Barceló, Anna; Arús, Carles

2015-01-01

Glioblastoma (Gb) is one of the most deadly tumors. Its molecular subtypes are yet to be fully characterized while the attendant efforts for personalized medicine need to be intensified in relation to glioblastoma diagnosis, treatment, and prognosis. Several molecular signatures based on gene expression microarrays were reported, but the use of microarrays for routine clinical practice is challenged by attendant economic costs. Several authors have proposed discriminant equations based on RT-PCR. Still, the discriminant threshold is often incompletely described, which makes proper validation difficult. In a previous work, we have reported two Gb subtypes based on the expression levels of four genes: CHI3L1, LDHA, LGALS1, and IGFBP3. One Gb subtype presented with low expression of the four genes mentioned, and of MGMT in a large portion of the patients (with anticipated high methylation of its promoter), and mutated IDH1. Here, we evaluate the robustness of the equations fitted with these genes using RT-PCR values in a set of 64 cases and importantly, define an unequivocal discriminant threshold with a view to prognostic implications. We developed two approaches to generate the discriminant equations: 1) using the expression level of the four genes mentioned above, and 2) using those genes displaying the highest correlation with survival among the aforementioned four ones, plus MGMT, as an attempt to further reduce the number of genes. The ease of equations' applicability, reduction in cost for raw data, and robustness in terms of resampling-based classification accuracy warrant further evaluation of these equations to discern Gb tumor biopsy heterogeneity at molecular level, diagnose potential malignancy, and prognosis of individual patients with glioblastomas.

Statistical approach for selection of biologically informative genes.

PubMed

Das, Samarendra; Rai, Anil; Mishra, D C; Rai, Shesh N

2018-05-20

Selection of informative genes from high dimensional gene expression data has emerged as an important research area in genomics. Many gene selection techniques have been proposed so far are either based on relevancy or redundancy measure. Further, the performance of these techniques has been adjudged through post selection classification accuracy computed through a classifier using the selected genes. This performance metric may be statistically sound but may not be biologically relevant. A statistical approach, i.e. Boot-MRMR, was proposed based on a composite measure of maximum relevance and minimum redundancy, which is both statistically sound and biologically relevant for informative gene selection. For comparative evaluation of the proposed approach, we developed two biological sufficient criteria, i.e. Gene Set Enrichment with QTL (GSEQ) and biological similarity score based on Gene Ontology (GO). Further, a systematic and rigorous evaluation of the proposed technique with 12 existing gene selection techniques was carried out using five gene expression datasets. This evaluation was based on a broad spectrum of statistically sound (e.g. subject classification) and biological relevant (based on QTL and GO) criteria under a multiple criteria decision-making framework. The performance analysis showed that the proposed technique selects informative genes which are more biologically relevant. The proposed technique is also found to be quite competitive with the existing techniques with respect to subject classification and computational time. Our results also showed that under the multiple criteria decision-making setup, the proposed technique is best for informative gene selection over the available alternatives. Based on the proposed approach, an R Package, i.e. BootMRMR has been developed and available at https://cran.r-project.org/web/packages/BootMRMR. This study will provide a practical guide to select statistical techniques for selecting informative genes from high dimensional expression data for breeding and system biology studies. Published by Elsevier B.V.

In Situ Detection of MicroRNA Expression with RNAscope Probes.

PubMed

Yin, Viravuth P

2018-01-01

Elucidating the spatial resolution of gene transcripts provides important insight into potential gene function. MicroRNAs are short, singled-stranded noncoding RNAs that control gene expression through base-pair complementarity with target mRNAs in the 3' untranslated region (UTR) and inhibiting protein expression. However, given their small size of ~22- to 24-nt and low expression levels, standard in situ hybridization detection methods are not amendable for microRNA spatial resolution. Here, I describe a technique that employs RNAscope probe design and propriety amplification technology that provides simultaneous single molecule detection of individual microRNA and its target gene. This method allows for rapid and sensitive detection of noncoding RNA transcripts in frozen tissue sections.

Recombinant adeno-associated virus targets passenger gene expression to cones in primate retina

NASA Astrophysics Data System (ADS)

Mancuso, Katherine; Hendrickson, Anita E.; Connor, Thomas B., Jr.; Mauck, Matthew C.; Kinsella, James J.; Hauswirth, William W.; Neitz, Jay; Neitz, Maureen

2007-05-01

Recombinant adeno-associated virus (rAAV) is a promising vector for gene therapy of photoreceptor-based diseases. Previous studies have demonstrated that rAAV serotypes 2 and 5 can transduce both rod and cone photoreceptors in rodents and dogs, and it can target rods, but not cones in primates. Here we report that using a human cone-specific enhancer and promoter to regulate expression of a green fluorescent protein (GFP) reporter gene in an rAAV-5 vector successfully targeted expression of the reporter gene to primate cones, and the time course of GFP expression was able to be monitored in a living animal using the RetCam II digital imaging system.

Energy sources and levels influenced on performance parameters, thyroid hormones, and HSP70 gene expression of broiler chickens under heat stress.

PubMed

Raghebian, Majid; Sadeghi, Ali Asghar; Aminafshar, Mehdi

2016-12-01

The present study was conducted to evaluate the effects of energy sources and levels on body and organs weights, thyroid hormones, and heat shock protein (HSP70) gene expression in broilers under heat stress. In a completely randomized design, 600 1-day-old Cobb chickens were assigned to five dietary treatments and four replicates. The chickens were fed diet based on corn as main energy source and energy level based on Cobb standard considered as control (C), corn-based diet with 3 % lesser energy than the control (T1), corn-based diet with 6 % lesser energy than the control (T2), corn and soybean oil-based diet according to Cobb standard (T3), and corn and soybean oil-based diet with 3 % upper energy than the control (T4). Temperature was increased to 34 °C for 8 h daily from days 12 to 41 of age to induce heat stress. The chickens in T1 and T2 had lower thyroid hormones and corticosterone levels than those in C, T3, and T4. The highest liver weight was for C and the lowest one was for T4. The highest gene expression was found in chickens fed T4 diet, and the lowest gene expression was for those in T2 group. The highest feed intake and worse feed conversion ratio was related to chickens in T2. The chickens in T3 and T4 had higher feed intake and weight gain than those in C. The results showed that the higher energy level supplied from soybean oil could enhance gene expression of HSP70 and decline the level of corticosterone and thyroid hormones and consequently improved performance.

Discovering functional modules by topic modeling RNA-Seq based toxicogenomic data.

PubMed

Yu, Ke; Gong, Binsheng; Lee, Mikyung; Liu, Zhichao; Xu, Joshua; Perkins, Roger; Tong, Weida

2014-09-15

Toxicogenomics (TGx) endeavors to elucidate the underlying molecular mechanisms through exploring gene expression profiles in response to toxic substances. Recently, RNA-Seq is increasingly regarded as a more powerful alternative to microarrays in TGx studies. However, realizing RNA-Seq's full potential requires novel approaches to extracting information from the complex TGx data. Considering read counts as the number of times a word occurs in a document, gene expression profiles from RNA-Seq are analogous to a word by document matrix used in text mining. Topic modeling aiming at to discover the latent structures in text corpora would be helpful to explore RNA-Seq based TGx data. In this study, topic modeling was applied on a typical RNA-Seq based TGx data set to discover hidden functional modules. The RNA-Seq based gene expression profiles were transformed into "documents", on which latent Dirichlet allocation (LDA) was used to build a topic model. We found samples treated by the compounds with the same modes of actions (MoAs) could be clustered based on topic similarities. The topic most relevant to each cluster was identified as a "marker" topic, which was interpreted by gene enrichment analysis with MoAs then confirmed by compound and pathways associations mined from literature. To further validate the "marker" topics, we tested topic transferability from RNA-Seq to microarrays. The RNA-Seq based gene expression profile of a topic specifically associated with peroxisome proliferator-activated receptors (PPAR) signaling pathway was used to query samples with similar expression profiles in two different microarray data sets, yielding accuracy of about 85%. This proof-of-concept study demonstrates the applicability of topic modeling to discover functional modules in RNA-Seq data and suggests a valuable computational tool for leveraging information within TGx data in RNA-Seq era.

Potential roles for transposable elements in creating imprinted expression.

PubMed

Anderson, Sarah N; Springer, Nathan M

2018-04-01

Changes in gene expression can have profound effects on phenotype. Nature has provided many complex patterns of gene regulation such as imprinting. Imprinted genes exhibit differences in the expression of the maternal and paternal alleles, even though they reside in the same nucleus with access to the same trans-acting factors. Significant attention has been focused on the potential reasons that imprinted expression could be beneficial and stabilized by selection. However, less attention has focused on understanding how imprinted expression might arise or decay. We discuss the evidence for frequent turnover of imprinted expression based on evolutionary analyses in plants and the potential role for transposable elements (TEs) in creating imprinted expression patterns. Copyright © 2018 Elsevier Ltd. All rights reserved.

Finding gene clusters for a replicated time course study

PubMed Central

2014-01-01

Background Finding genes that share similar expression patterns across samples is an important question that is frequently asked in high-throughput microarray studies. Traditional clustering algorithms such as K-means clustering and hierarchical clustering base gene clustering directly on the observed measurements and do not take into account the specific experimental design under which the microarray data were collected. A new model-based clustering method, the clustering of regression models method, takes into account the specific design of the microarray study and bases the clustering on how genes are related to sample covariates. It can find useful gene clusters for studies from complicated study designs such as replicated time course studies. Findings In this paper, we applied the clustering of regression models method to data from a time course study of yeast on two genotypes, wild type and YOX1 mutant, each with two technical replicates, and compared the clustering results with K-means clustering. We identified gene clusters that have similar expression patterns in wild type yeast, two of which were missed by K-means clustering. We further identified gene clusters whose expression patterns were changed in YOX1 mutant yeast compared to wild type yeast. Conclusions The clustering of regression models method can be a valuable tool for identifying genes that are coordinately transcribed by a common mechanism. PMID:24460656

DNetDB: The human disease network database based on dysfunctional regulation mechanism.

PubMed

Yang, Jing; Wu, Su-Juan; Yang, Shao-You; Peng, Jia-Wei; Wang, Shi-Nuo; Wang, Fu-Yan; Song, Yu-Xing; Qi, Ting; Li, Yi-Xue; Li, Yuan-Yuan

2016-05-21

Disease similarity study provides new insights into disease taxonomy, pathogenesis, which plays a guiding role in diagnosis and treatment. The early studies were limited to estimate disease similarities based on clinical manifestations, disease-related genes, medical vocabulary concepts or registry data, which were inevitably biased to well-studied diseases and offered small chance of discovering novel findings in disease relationships. In other words, genome-scale expression data give us another angle to address this problem since simultaneous measurement of the expression of thousands of genes allows for the exploration of gene transcriptional regulation, which is believed to be crucial to biological functions. Although differential expression analysis based methods have the potential to explore new disease relationships, it is difficult to unravel the upstream dysregulation mechanisms of diseases. We therefore estimated disease similarities based on gene expression data by using differential coexpression analysis, a recently emerging method, which has been proved to be more potential to capture dysfunctional regulation mechanisms than differential expression analysis. A total of 1,326 disease relationships among 108 diseases were identified, and the relevant information constituted the human disease network database (DNetDB). Benefiting from the use of differential coexpression analysis, the potential common dysfunctional regulation mechanisms shared by disease pairs (i.e. disease relationships) were extracted and presented. Statistical indicators, common disease-related genes and drugs shared by disease pairs were also included in DNetDB. In total, 1,326 disease relationships among 108 diseases, 5,598 pathways, 7,357 disease-related genes and 342 disease drugs are recorded in DNetDB, among which 3,762 genes and 148 drugs are shared by at least two diseases. DNetDB is the first database focusing on disease similarity from the viewpoint of gene regulation mechanism. It provides an easy-to-use web interface to search and browse the disease relationships and thus helps to systematically investigate etiology and pathogenesis, perform drug repositioning, and design novel therapeutic interventions.Database URL: http://app.scbit.org/DNetDB/ #.

MiRNA-TF-gene network analysis through ranking of biomolecules for multi-informative uterine leiomyoma dataset.

PubMed

Mallik, Saurav; Maulik, Ujjwal

2015-10-01

Gene ranking is an important problem in bioinformatics. Here, we propose a new framework for ranking biomolecules (viz., miRNAs, transcription-factors/TFs and genes) in a multi-informative uterine leiomyoma dataset having both gene expression and methylation data using (statistical) eigenvector centrality based approach. At first, genes that are both differentially expressed and methylated, are identified using Limma statistical test. A network, comprising these genes, corresponding TFs from TRANSFAC and ITFP databases, and targeter miRNAs from miRWalk database, is then built. The biomolecules are then ranked based on eigenvector centrality. Our proposed method provides better average accuracy in hub gene and non-hub gene classifications than other methods. Furthermore, pre-ranked Gene set enrichment analysis is applied on the pathway database as well as GO-term databases of Molecular Signatures Database with providing a pre-ranked gene-list based on different centrality values for comparing among the ranking methods. Finally, top novel potential gene-markers for the uterine leiomyoma are provided. Copyright © 2015 Elsevier Inc. All rights reserved.

Ontology based molecular signatures for immune cell types via gene expression analysis

PubMed Central

2013-01-01

Background New technologies are focusing on characterizing cell types to better understand their heterogeneity. With large volumes of cellular data being generated, innovative methods are needed to structure the resulting data analyses. Here, we describe an ‘Ontologically BAsed Molecular Signature’ (OBAMS) method that identifies novel cellular biomarkers and infers biological functions as characteristics of particular cell types. This method finds molecular signatures for immune cell types based on mapping biological samples to the Cell Ontology (CL) and navigating the space of all possible pairwise comparisons between cell types to find genes whose expression is core to a particular cell type’s identity. Results We illustrate this ontological approach by evaluating expression data available from the Immunological Genome project (IGP) to identify unique biomarkers of mature B cell subtypes. We find that using OBAMS, candidate biomarkers can be identified at every strata of cellular identity from broad classifications to very granular. Furthermore, we show that Gene Ontology can be used to cluster cell types by shared biological processes in order to find candidate genes responsible for somatic hypermutation in germinal center B cells. Moreover, through in silico experiments based on this approach, we have identified genes sets that represent genes overexpressed in germinal center B cells and identify genes uniquely expressed in these B cells compared to other B cell types. Conclusions This work demonstrates the utility of incorporating structured ontological knowledge into biological data analysis – providing a new method for defining novel biomarkers and providing an opportunity for new biological insights. PMID:24004649

Dynamic network reconstruction from gene expression data applied to immune response during bacterial infection.

PubMed

Guthke, Reinhard; Möller, Ulrich; Hoffmann, Martin; Thies, Frank; Töpfer, Susanne

2005-04-15

The immune response to bacterial infection represents a complex network of dynamic gene and protein interactions. We present an optimized reverse engineering strategy aimed at a reconstruction of this kind of interaction networks. The proposed approach is based on both microarray data and available biological knowledge. The main kinetics of the immune response were identified by fuzzy clustering of gene expression profiles (time series). The number of clusters was optimized using various evaluation criteria. For each cluster a representative gene with a high fuzzy-membership was chosen in accordance with available physiological knowledge. Then hypothetical network structures were identified by seeking systems of ordinary differential equations, whose simulated kinetics could fit the gene expression profiles of the cluster-representative genes. For the construction of hypothetical network structures singular value decomposition (SVD) based methods and a newly introduced heuristic Network Generation Method here were compared. It turned out that the proposed novel method could find sparser networks and gave better fits to the experimental data. Reinhard.Guthke@hki-jena.de.

Gene selection for the reconstruction of stem cell differentiation trees: a linear programming approach.

PubMed

Ghadie, Mohamed A; Japkowicz, Nathalie; Perkins, Theodore J

2015-08-15

Stem cell differentiation is largely guided by master transcriptional regulators, but it also depends on the expression of other types of genes, such as cell cycle genes, signaling genes, metabolic genes, trafficking genes, etc. Traditional approaches to understanding gene expression patterns across multiple conditions, such as principal components analysis or K-means clustering, can group cell types based on gene expression, but they do so without knowledge of the differentiation hierarchy. Hierarchical clustering can organize cell types into a tree, but in general this tree is different from the differentiation hierarchy itself. Given the differentiation hierarchy and gene expression data at each node, we construct a weighted Euclidean distance metric such that the minimum spanning tree with respect to that metric is precisely the given differentiation hierarchy. We provide a set of linear constraints that are provably sufficient for the desired construction and a linear programming approach to identify sparse sets of weights, effectively identifying genes that are most relevant for discriminating different parts of the tree. We apply our method to microarray gene expression data describing 38 cell types in the hematopoiesis hierarchy, constructing a weighted Euclidean metric that uses just 175 genes. However, we find that there are many alternative sets of weights that satisfy the linear constraints. Thus, in the style of random-forest training, we also construct metrics based on random subsets of the genes and compare them to the metric of 175 genes. We then report on the selected genes and their biological functions. Our approach offers a new way to identify genes that may have important roles in stem cell differentiation. tperkins@ohri.ca Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Isolation and Identification of Genes Activating Uas2-Dependent Adh2 Expression in Saccharomyces Cerevisiae

PubMed Central

Donoviel, M. S.; Young, E. T.

1996-01-01

Two cis-acting elements have been identified that act synergistically to regulate expression of the glucose-repressed alcohol dehydrogenase 2 (ADH2) gene. UAS1 is bound by the trans-activator Adr1p. UAS2 is thought to be the binding site for an unidentified regulatory protein. A genetic selection based on a UAS2-dependent ADH2 reporter was devised to isolate genes capable of activating UAS2-dependent transcription. One set of UAS2-dependent genes contained SPT6/CRE2/SSN20. Multicopy SPT6 caused improper expression of chromosomal ADH2. A second set of UAS2-dependent clones contained a previously uncharacterized open reading frame designated MEU1 (Multicopy Enhancer of UAS2). A frame shift mutation in MEU1 abolished its ability to activate UAS2-dependent gene expression. Multicopy MEU1 expression suppressed the constitutive ADH2 expression caused by cre2-1. Disruption of MEU1 reduced endogenous ADH2 expression about twofold but had no effect on cell viability or growth. No homologues of MEU1 were identified by low-stringency Southern hybridization of yeast genomic DNA, and no significant homologues were found in the sequence data bases. A MEU1/β-gal fusion protein was not localized to a particular region of the cell. MEU1 is linked to PPR1 on chromosome XII. PMID:8807288

Down-regulation of BAX gene during carcinogenesis and acquisition of resistance to 5-FU in colorectal cancer.

PubMed

Manoochehri, Mehdi; Karbasi, Ashraf; Bandehpour, Mojgan; Kazemi, Bahram

2014-04-01

Carcinogenesis and resistance to chemotherapy could be as results of expression variations in apoptosis regulating genes. Changes in the expression of apoptosis interfering genes may contribute to colorectal carcinogenesis and resistance to 5-Flourouracil (5-FU) during treatment schedule period. The present study aimed to evaluate the expression of pro-apoptotic and anti-apoptotic genes in colorectal cancer tumor tissues, normal adjacent tissues, and tumor colorectal cancer cell line during acquiring resistance to 5-FU in HT-29 based on Bolus treatment protocol. The normal and tumor tissues were obtained from hospital after surgery and total RNA was extracted for expression analysis. The HT-29 colorectal cancer cell line was cultured and exposed with 5-FU in three stages based on Bolus protocol. The MTT assay and Real Time PCR were carried out to determine the sensitivity to the drug and expression of desired genes, respectively. The obtained data showed that Proapoptotic genes, BAX and BID, were down-regulated in resistant derivate cells compared to wild type HT-29 cells. On the other hand Antiapoptotic genes, CIAP1 and XIAP, showed upregulation in resistant cells compared to wild type ones. Furthermore, BAX and FAS genes showed down-regulation in tumor samples in comparison to normal adjacent tissues. In conclusion, the results of our study suggest that BAX down-regulation could contribute as an important factor during both colorectal carcinogenesis and cell resistance to 5-FU.

Altered retinal microRNA expression profiles in early diabetic retinopathy: an in silico analysis.

PubMed

Xiong, Fen; Du, Xinhua; Hu, Jianyan; Li, Tingting; Du, Shanshan; Wu, Qiang

2014-07-01

MicroRNAs (miRNAs) - as negative regulators of target genes - are associated with various human diseases, but their precise role(s) in diabetic retinopathy (DR) remains to be elucidated. The aim of this study was to elucidate the involvement of miRNAs in early DR using in silico analysis to explore their gene expression patterns. We used the streptozotocin (STZ)-induced diabetic rat to investigate the roles of miRNAs in early DR. Retinal miRNA expression profiles from diabetic versus healthy control rats were examined by miRNA array analysis. Based on several bioinformatic systems, specifically, gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, we identified signatures of the potential pathological processes, gene functions, and signaling pathways that are influenced by dysregulated miRNAs. We used quantitative real-time polymerase chain reaction (qRT-PCR) to validate six (i.e. those with significant changes in expression levels) of the 17 miRNAs that were detected in the miRNA array. We also describe the significant role of the miRNA-gene network, which is based on the interactions between miRNAs and target genes. GO analysis of the 17 miRNAs detected in the miRNA array analysis revealed the most prevalent miRNAs to be those related to biological processes, olfactory bulb development and axonogenesis. These miRNAs also exert significant influence on additional pathways, including the mitogen-activated protein and calcium signaling pathways. Six of the seventeen miRNAs were chosen for qRT-PCR validation. With the exception of a slight difference in miRNA-350, our results are in close agreement with the differential expressions detected by array analysis. This study, which describes miRNA expression during the early developmental phases of DR, revealed extensive miRNA interactions. Based on both their target genes and signaling pathways, we suggest that miRNAs perform critical regulatory functions during the early stages of DR evolution.

Identification and Validation of Reference Genes and Their Impact on Normalized Gene Expression Studies across Cultivated and Wild Cicer Species

PubMed Central

Reddy, Palakolanu Sudhakar; Sri Cindhuri, Katamreddy; Sivaji Ganesh, Adusumalli; Sharma, Kiran Kumar

2016-01-01

Quantitative Real-Time PCR (qPCR) is a preferred and reliable method for accurate quantification of gene expression to understand precise gene functions. A total of 25 candidate reference genes including traditional and new generation reference genes were selected and evaluated in a diverse set of chickpea samples. The samples used in this study included nine chickpea genotypes (Cicer spp.) comprising of cultivated and wild species, six abiotic stress treatments (drought, salinity, high vapor pressure deficit, abscisic acid, cold and heat shock), and five diverse tissues (leaf, root, flower, seedlings and seed). The geNorm, NormFinder and RefFinder algorithms used to identify stably expressed genes in four sample sets revealed stable expression of UCP and G6PD genes across genotypes, while TIP41 and CAC were highly stable under abiotic stress conditions. While PP2A and ABCT genes were ranked as best for different tissues, ABCT, UCP and CAC were most stable across all samples. This study demonstrated the usefulness of new generation reference genes for more accurate qPCR based gene expression quantification in cultivated as well as wild chickpea species. Validation of the best reference genes was carried out by studying their impact on normalization of aquaporin genes PIP1;4 and TIP3;1, in three contrasting chickpea genotypes under high vapor pressure deficit (VPD) treatment. The chickpea TIP3;1 gene got significantly up regulated under high VPD conditions with higher relative expression in the drought susceptible genotype, confirming the suitability of the selected reference genes for expression analysis. This is the first comprehensive study on the stability of the new generation reference genes for qPCR studies in chickpea across species, different tissues and abiotic stresses. PMID:26863232

Identification and Validation of Reference Genes and Their Impact on Normalized Gene Expression Studies across Cultivated and Wild Cicer Species.

PubMed

Reddy, Dumbala Srinivas; Bhatnagar-Mathur, Pooja; Reddy, Palakolanu Sudhakar; Sri Cindhuri, Katamreddy; Sivaji Ganesh, Adusumalli; Sharma, Kiran Kumar

2016-01-01

Quantitative Real-Time PCR (qPCR) is a preferred and reliable method for accurate quantification of gene expression to understand precise gene functions. A total of 25 candidate reference genes including traditional and new generation reference genes were selected and evaluated in a diverse set of chickpea samples. The samples used in this study included nine chickpea genotypes (Cicer spp.) comprising of cultivated and wild species, six abiotic stress treatments (drought, salinity, high vapor pressure deficit, abscisic acid, cold and heat shock), and five diverse tissues (leaf, root, flower, seedlings and seed). The geNorm, NormFinder and RefFinder algorithms used to identify stably expressed genes in four sample sets revealed stable expression of UCP and G6PD genes across genotypes, while TIP41 and CAC were highly stable under abiotic stress conditions. While PP2A and ABCT genes were ranked as best for different tissues, ABCT, UCP and CAC were most stable across all samples. This study demonstrated the usefulness of new generation reference genes for more accurate qPCR based gene expression quantification in cultivated as well as wild chickpea species. Validation of the best reference genes was carried out by studying their impact on normalization of aquaporin genes PIP1;4 and TIP3;1, in three contrasting chickpea genotypes under high vapor pressure deficit (VPD) treatment. The chickpea TIP3;1 gene got significantly up regulated under high VPD conditions with higher relative expression in the drought susceptible genotype, confirming the suitability of the selected reference genes for expression analysis. This is the first comprehensive study on the stability of the new generation reference genes for qPCR studies in chickpea across species, different tissues and abiotic stresses.

Selection of reference genes for quantitative real-time PCR normalization in Panax ginseng at different stages of growth and in different organs.

PubMed

Liu, Jing; Wang, Qun; Sun, Minying; Zhu, Linlin; Yang, Michael; Zhao, Yu

2014-01-01

Quantitative real-time reverse transcription PCR (qRT-PCR) has become a widely used method for gene expression analysis; however, its data interpretation largely depends on the stability of reference genes. The transcriptomics of Panax ginseng, one of the most popular and traditional ingredients used in Chinese medicines, is increasingly being studied. Furthermore, it is vital to establish a series of reliable reference genes when qRT-PCR is used to assess the gene expression profile of ginseng. In this study, we screened out candidate reference genes for ginseng using gene expression data generated by a high-throughput sequencing platform. Based on the statistical tests, 20 reference genes (10 traditional housekeeping genes and 10 novel genes) were selected. These genes were tested for the normalization of expression levels in five growth stages and three distinct plant organs of ginseng by qPCR. These genes were subsequently ranked and compared according to the stability of their expressions using geNorm, NormFinder, and BestKeeper computational programs. Although the best reference genes were found to vary across different samples, CYP and EF-1α were the most stable genes amongst all samples. GAPDH/30S RPS20, CYP/60S RPL13 and CYP/QCR were the optimum pair of reference genes in the roots, stems, and leaves. CYP/60S RPL13, CYP/eIF-5A, aTUB/V-ATP, eIF-5A/SAR1, and aTUB/pol IIa were the most stably expressed combinations in each of the five developmental stages. Our study serves as a foundation for developing an accurate method of qRT-PCR and will benefit future studies on gene expression profiles of Panax Ginseng.

Challenges in projecting clustering results across gene expression-profiling datasets.

PubMed

Lusa, Lara; McShane, Lisa M; Reid, James F; De Cecco, Loris; Ambrogi, Federico; Biganzoli, Elia; Gariboldi, Manuela; Pierotti, Marco A

2007-11-21

Gene expression microarray studies for several types of cancer have been reported to identify previously unknown subtypes of tumors. For breast cancer, a molecular classification consisting of five subtypes based on gene expression microarray data has been proposed. These subtypes have been reported to exist across several breast cancer microarray studies, and they have demonstrated some association with clinical outcome. A classification rule based on the method of centroids has been proposed for identifying the subtypes in new collections of breast cancer samples; the method is based on the similarity of the new profiles to the mean expression profile of the previously identified subtypes. Previously identified centroids of five breast cancer subtypes were used to assign 99 breast cancer samples, including a subset of 65 estrogen receptor-positive (ER+) samples, to five breast cancer subtypes based on microarray data for the samples. The effect of mean centering the genes (i.e., transforming the expression of each gene so that its mean expression is equal to 0) on subtype assignment by method of centroids was assessed. Further studies of the effect of mean centering and of class prevalence in the test set on the accuracy of method of centroids classifications of ER status were carried out using training and test sets for which ER status had been independently determined by ligand-binding assay and for which the proportion of ER+ and ER- samples were systematically varied. When all 99 samples were considered, mean centering before application of the method of centroids appeared to be helpful for correctly assigning samples to subtypes, as evidenced by the expression of genes that had previously been used as markers to identify the subtypes. However, when only the 65 ER+ samples were considered for classification, many samples appeared to be misclassified, as evidenced by an unexpected distribution of ER+ samples among the resultant subtypes. When genes were mean centered before classification of samples for ER status, the accuracy of the ER subgroup assignments was highly dependent on the proportion of ER+ samples in the test set; this effect of subtype prevalence was not seen when gene expression data were not mean centered. Simple corrections such as mean centering of genes aimed at microarray platform or batch effect correction can have undesirable consequences because patient population effects can easily be confused with these assay-related effects. Careful thought should be given to the comparability of the patient populations before attempting to force data comparability for purposes of assigning subtypes to independent subjects.

svdPPCS: an effective singular value decomposition-based method for conserved and divergent co-expression gene module identification.

PubMed

Zhang, Wensheng; Edwards, Andrea; Fan, Wei; Zhu, Dongxiao; Zhang, Kun

2010-06-22

Comparative analysis of gene expression profiling of multiple biological categories, such as different species of organisms or different kinds of tissue, promises to enhance the fundamental understanding of the universality as well as the specialization of mechanisms and related biological themes. Grouping genes with a similar expression pattern or exhibiting co-expression together is a starting point in understanding and analyzing gene expression data. In recent literature, gene module level analysis is advocated in order to understand biological network design and system behaviors in disease and life processes; however, practical difficulties often lie in the implementation of existing methods. Using the singular value decomposition (SVD) technique, we developed a new computational tool, named svdPPCS (SVD-based Pattern Pairing and Chart Splitting), to identify conserved and divergent co-expression modules of two sets of microarray experiments. In the proposed methods, gene modules are identified by splitting the two-way chart coordinated with a pair of left singular vectors factorized from the gene expression matrices of the two biological categories. Importantly, the cutoffs are determined by a data-driven algorithm using the well-defined statistic, SVD-p. The implementation was illustrated on two time series microarray data sets generated from the samples of accessory gland (ACG) and malpighian tubule (MT) tissues of the line W118 of M. drosophila. Two conserved modules and six divergent modules, each of which has a unique characteristic profile across tissue kinds and aging processes, were identified. The number of genes contained in these models ranged from five to a few hundred. Three to over a hundred GO terms were over-represented in individual modules with FDR < 0.1. One divergent module suggested the tissue-specific relationship between the expressions of mitochondrion-related genes and the aging process. This finding, together with others, may be of biological significance. The validity of the proposed SVD-based method was further verified by a simulation study, as well as the comparisons with regression analysis and cubic spline regression analysis plus PAM based clustering. svdPPCS is a novel computational tool for the comparative analysis of transcriptional profiling. It especially fits the comparison of time series data of related organisms or different tissues of the same organism under equivalent or similar experimental conditions. The general scheme can be directly extended to the comparisons of multiple data sets. It also can be applied to the integration of data sets from different platforms and of different sources.

Characterization of distinct classes of differential gene expression in osteoblast cultures from non-syndromic craniosynostosis bone.

PubMed

Rojas-Peña, Monica L; Olivares-Navarrete, Rene; Hyzy, Sharon; Arafat, Dalia; Schwartz, Zvi; Boyan, Barbara D; Williams, Joseph; Gibson, Greg

2014-01-01

Craniosynostosis, the premature fusion of one or more skull sutures, occurs in approximately 1 in 2500 infants, with the majority of cases non-syndromic and of unknown etiology. Two common reasons proposed for premature suture fusion are abnormal compression forces on the skull and rare genetic abnormalities. Our goal was to evaluate whether different sub-classes of disease can be identified based on total gene expression profiles. RNA-Seq data were obtained from 31 human osteoblast cultures derived from bone biopsy samples collected between 2009 and 2011, representing 23 craniosynostosis fusions and 8 normal cranial bones or long bones. No differentiation between regions of the skull was detected, but variance component analysis of gene expression patterns nevertheless supports transcriptome-based classification of craniosynostosis. Cluster analysis showed 4 distinct groups of samples; 1 predominantly normal and 3 craniosynostosis subtypes. Similar constellations of sub-types were also observed upon re-analysis of a similar dataset of 199 calvarial osteoblast cultures. Annotation of gene function of differentially expressed transcripts strongly implicates physiological differences with respect to cell cycle and cell death, stromal cell differentiation, extracellular matrix (ECM) components, and ribosomal activity. Based on these results, we propose non-syndromic craniosynostosis cases can be classified by differences in their gene expression patterns and that these may provide targets for future clinical intervention.

Characterization of Distinct Classes of Differential Gene Expression in Osteoblast Cultures from Non-Syndromic Craniosynostosis Bone

PubMed Central

Rojas-Peña, Monica L.; Olivares-Navarrete, Rene; Hyzy, Sharon; Arafat, Dalia; Schwartz, Zvi; Boyan, Barbara D.; Williams, Joseph; Gibson, Greg

2014-01-01

Craniosynostosis, the premature fusion of one or more skull sutures, occurs in approximately 1 in 2500 infants, with the majority of cases non-syndromic and of unknown etiology. Two common reasons proposed for premature suture fusion are abnormal compression forces on the skull and rare genetic abnormalities. Our goal was to evaluate whether different sub-classes of disease can be identified based on total gene expression profiles. RNA-Seq data were obtained from 31 human osteoblast cultures derived from bone biopsy samples collected between 2009 and 2011, representing 23 craniosynostosis fusions and 8 normal cranial bones or long bones. No differentiation between regions of the skull was detected, but variance component analysis of gene expression patterns nevertheless supports transcriptome-based classification of craniosynostosis. Cluster analysis showed 4 distinct groups of samples; 1 predominantly normal and 3 craniosynostosis subtypes. Similar constellations of sub-types were also observed upon re-analysis of a similar dataset of 199 calvarial osteoblast cultures. Annotation of gene function of differentially expressed transcripts strongly implicates physiological differences with respect to cell cycle and cell death, stromal cell differentiation, extracellular matrix (ECM) components, and ribosomal activity. Based on these results, we propose non-syndromic craniosynostosis cases can be classified by differences in their gene expression patterns and that these may provide targets for future clinical intervention. PMID:25184005

Fine mapping and identification of candidate genes for the sy-2 locus in a temperature-sensitive chili pepper (Capsicum chinense).

PubMed

Liu, Li; Venkatesh, Jelli; Jo, Yeong Deuk; Koeda, Sota; Hosokawa, Munetaka; Kang, Jin-Ho; Goritschnig, Sandra; Kang, Byoung-Cheorl

2016-08-01

The sy - 2 temperature-sensitive gene from Capsicum chinense was fine mapped to a 138.8-kb region at the distal portion of pepper chromosome 1. Based on expression analyses, two putative F-box genes were identified as sy - 2 candidate genes. Seychelles-2 ('sy-2') is a temperature-sensitive natural mutant of Capsicum chinense, which exhibits an abnormal leaf phenotype when grown at temperatures below 24 °C. We previously showed that the sy-2 phenotype is controlled by a single recessive gene, sy-2, located on pepper chromosome 1. In this study, a high-resolution genetic and physical map for the sy-2 locus was constructed using two individual F2 mapping populations derived from a cross between C. chinense mutant 'sy-2' and wild-type 'No. 3341'. The sy-2 gene was fine mapped to a 138.8-kb region between markers SNP 5-5 and SNP 3-8 at the distal portion of chromosome 1, based on comparative genomic analysis and genomic information from pepper. The sy-2 target region was predicted to contain 27 genes. Expression analysis of these predicted genes showed a differential expression pattern for ORF10 and ORF20 between mutant and wild-type plants; with both having significantly lower expression in 'sy-2' than in wild-type plants. In addition, the coding sequences of both ORF10 and ORF20 contained single nucleotide polymorphisms (SNPs) causing amino acid changes, which may have important functional consequences. ORF10 and ORF20 are predicted to encode F-box proteins, which are components of the SCF complex. Based on the differential expression pattern and the presence of nonsynonymous SNPs, we suggest that these two putative F-box genes are most likely responsible for the temperature-sensitive phenotypes in pepper. Further investigation of these genes may enable a better understanding of the molecular mechanisms of low temperature sensitivity in plants.

Selective elimination of long INterspersed element-1 expressing tumour cells by targeted expression of the HSV-TK suicide gene

PubMed Central

Chendeb, Mariam; Schneider, Robert; Davidson, Irwin; Fadloun, Anas

2017-01-01

In gene therapy, effective and selective suicide gene expression is crucial. We exploited the endogenous Long INterspersed Element-1 (L1) machinery often reactivated in human cancers to integrate the Herpes Simplex Virus Thymidine Kinase (HSV-TK) suicide gene selectively into the genome of cancer cells. We developed a plasmid-based system directing HSV-TK expression only when reverse transcribed and integrated in the host genome via the endogenous L1 ORF1/2 proteins and an Alu element. Delivery of these new constructs into cells followed by Ganciclovir (GCV) treatment selectively induced mortality of L1 ORF1/2 protein expressing cancer cells, but had no effect on primary cells that do not express L1 ORF1/2. This novel strategy for selective targeting of tumour cells provides high tolerability as the HSV-TK gene cannot be expressed without reverse transcription and integration, and high selectivity as these processes take place only in cancer cells expressing high levels of functional L1 ORF1/2. PMID:28415677

Identification of stable reference genes in differentiating human pluripotent stem cells.

PubMed

Holmgren, Gustav; Ghosheh, Nidal; Zeng, Xianmin; Bogestål, Yalda; Sartipy, Peter; Synnergren, Jane

2015-06-01

Reference genes, often referred to as housekeeping genes (HKGs), are frequently used to normalize gene expression data based on the assumption that they are expressed at a constant level in the cells. However, several studies have shown that there may be a large variability in the gene expression levels of HKGs in various cell types. In a previous study, employing human embryonic stem cells (hESCs) subjected to spontaneous differentiation, we observed that the expression of commonly used HKG varied to a degree that rendered them inappropriate to use as reference genes under those experimental settings. Here we present a substantially extended study of the HKG signature in human pluripotent stem cells (hPSC), including nine global gene expression datasets from both hESC and human induced pluripotent stem cells, obtained during directed differentiation toward endoderm-, mesoderm-, and ectoderm derivatives. Sets of stably expressed genes were compiled, and a handful of genes (e.g., EID2, ZNF324B, CAPN10, and RABEP2) were identified as generally applicable reference genes in hPSCs across all cell lines and experimental conditions. The stability in gene expression profiles was confirmed by reverse transcription quantitative PCR analysis. Taken together, the current results suggest that differentiating hPSCs have a distinct HKG signature, which in some aspects is different from somatic cell types, and underscore the necessity to validate the stability of reference genes under the actual experimental setup used. In addition, the novel putative HKGs identified in this study can preferentially be used for normalization of gene expression data obtained from differentiating hPSCs. Copyright © 2015 the American Physiological Society.

Optimal Scaling of Digital Transcriptomes

PubMed Central

Glusman, Gustavo; Caballero, Juan; Robinson, Max; Kutlu, Burak; Hood, Leroy

2013-01-01

Deep sequencing of transcriptomes has become an indispensable tool for biology, enabling expression levels for thousands of genes to be compared across multiple samples. Since transcript counts scale with sequencing depth, counts from different samples must be normalized to a common scale prior to comparison. We analyzed fifteen existing and novel algorithms for normalizing transcript counts, and evaluated the effectiveness of the resulting normalizations. For this purpose we defined two novel and mutually independent metrics: (1) the number of “uniform” genes (genes whose normalized expression levels have a sufficiently low coefficient of variation), and (2) low Spearman correlation between normalized expression profiles of gene pairs. We also define four novel algorithms, one of which explicitly maximizes the number of uniform genes, and compared the performance of all fifteen algorithms. The two most commonly used methods (scaling to a fixed total value, or equalizing the expression of certain ‘housekeeping’ genes) yielded particularly poor results, surpassed even by normalization based on randomly selected gene sets. Conversely, seven of the algorithms approached what appears to be optimal normalization. Three of these algorithms rely on the identification of “ubiquitous” genes: genes expressed in all the samples studied, but never at very high or very low levels. We demonstrate that these include a “core” of genes expressed in many tissues in a mutually consistent pattern, which is suitable for use as an internal normalization guide. The new methods yield robustly normalized expression values, which is a prerequisite for the identification of differentially expressed and tissue-specific genes as potential biomarkers. PMID:24223126

Multiple abiotic stimuli are integrated in the regulation of rice gene expression under field conditions.

PubMed

Plessis, Anne; Hafemeister, Christoph; Wilkins, Olivia; Gonzaga, Zennia Jean; Meyer, Rachel Sarah; Pires, Inês; Müller, Christian; Septiningsih, Endang M; Bonneau, Richard; Purugganan, Michael

2015-11-26

Plants rely on transcriptional dynamics to respond to multiple climatic fluctuations and contexts in nature. We analyzed the genome-wide gene expression patterns of rice (Oryza sativa) growing in rainfed and irrigated fields during two distinct tropical seasons and determined simple linear models that relate transcriptomic variation to climatic fluctuations. These models combine multiple environmental parameters to account for patterns of expression in the field of co-expressed gene clusters. We examined the similarities of our environmental models between tropical and temperate field conditions, using previously published data. We found that field type and macroclimate had broad impacts on transcriptional responses to environmental fluctuations, especially for genes involved in photosynthesis and development. Nevertheless, variation in solar radiation and temperature at the timescale of hours had reproducible effects across environmental contexts. These results provide a basis for broad-based predictive modeling of plant gene expression in the field.

Hybrid coexpression link similarity graph clustering for mining biological modules from multiple gene expression datasets.

PubMed

Salem, Saeed; Ozcaglar, Cagri

2014-01-01

Advances in genomic technologies have enabled the accumulation of vast amount of genomic data, including gene expression data for multiple species under various biological and environmental conditions. Integration of these gene expression datasets is a promising strategy to alleviate the challenges of protein functional annotation and biological module discovery based on a single gene expression data, which suffers from spurious coexpression. We propose a joint mining algorithm that constructs a weighted hybrid similarity graph whose nodes are the coexpression links. The weight of an edge between two coexpression links in this hybrid graph is a linear combination of the topological similarities and co-appearance similarities of the corresponding two coexpression links. Clustering the weighted hybrid similarity graph yields recurrent coexpression link clusters (modules). Experimental results on Human gene expression datasets show that the reported modules are functionally homogeneous as evident by their enrichment with biological process GO terms and KEGG pathways.

Artificial genetic selection for an efficient translation initiation site for expression of human RACK1 gene in Escherichia coli

PubMed Central

Zhelyabovskaya, Olga B.; Berlin, Yuri A.; Birikh, Klara R.

2004-01-01

In bacterial expression systems, translation initiation is usually the rate limiting and the least predictable stage of protein synthesis. Efficiency of a translation initiation site can vary dramatically depending on the sequence context. This is why many standard expression vectors provide very poor expression levels of some genes. This notion persuaded us to develop an artificial genetic selection protocol, which allows one to find for a given target gene an individual efficient ribosome binding site from a random pool. In order to create Darwinian pressure necessary for the genetic selection, we designed a system based on translational coupling, in which microorganism survival in the presence of antibiotic depends on expression of the target gene, while putting no special requirements on this gene. Using this system we obtained superproducing constructs for the human protein RACK1 (receptor for activated C kinase). PMID:15034151

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

PubMed

Xi, Jianing; Wang, Minghui; Li, Ao

2018-06-05

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

Molecular profiling of tumor progression in head and neck cancer.

PubMed

Belbin, Thomas J; Singh, Bhuvanesh; Smith, Richard V; Socci, Nicholas D; Wreesmann, Volkert B; Sanchez-Carbayo, Marta; Masterson, Jessica; Patel, Snehal; Cordon-Cardo, Carlos; Prystowsky, Michael B; Childs, Geoffrey

2005-01-01

To assess gene expression changes associated with tumor progression in patients with squamous cell carcinoma of the oral cavity. A microarray containing 17 840 complementary DNA clones was used to measure gene expression changes associated with tumor progression in 9 patients with squamous cell carcinoma of the oral cavity. Samples were taken for analysis from the primary tumor, nodal metastasis, and "normal" mucosa from the patients' oral cavity. Tertiary care facility. Patients Nine patients with stage III or stage IV untreated oral cavity squamous cell carcinoma. Our analysis to categorize genes based on their expression patterns has identified 140 genes that consistently increased in expression during progression from normal tissue to invasive tumor and subsequently to metastatic node (in at least 4 of the 9 cases studied). A similar list of 94 genes has been identified that decreased in expression during tumor progression and metastasis. We validated this gene discovery approach by selecting moesin (a member of the ezrin/radixin/moesin [ERM] family of cytoskeletal proteins) and one of the genes that consistently increased in expression during tumor progression for subsequent immunohistochemical analysis using a head and neck squamous cell carcinoma tissue array. A distinct pattern of gene expression, with progressive up- or down-regulation of expression, is found during the progression from histologically normal tissue to primary carcinoma and to nodal metastasis.

Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes

PubMed Central

Roy, Janine; Aust, Daniela; Knösel, Thomas; Rümmele, Petra; Jahnke, Beatrix; Hentrich, Vera; Rückert, Felix; Niedergethmann, Marco; Weichert, Wilko; Bahra, Marcus; Schlitt, Hans J.; Settmacher, Utz; Friess, Helmut; Büchler, Markus; Saeger, Hans-Detlev; Schroeder, Michael; Pilarsky, Christian; Grützmann, Robert

2012-01-01

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice. PMID:22615549

Gene expression analysis of E. coli strains provides insights into the role of gene regulation in diversification

PubMed Central

Vital, Marius; Chai, Benli; Østman, Bjørn; Cole, James; Konstantinidis, Konstantinos T; Tiedje, James M

2015-01-01

Escherichia coli spans a genetic continuum from enteric strains to several phylogenetically distinct, atypical lineages that are rare in humans, but more common in extra-intestinal environments. To investigate the link between gene regulation, phylogeny and diversification in this species, we analyzed global gene expression profiles of four strains representing distinct evolutionary lineages, including a well-studied laboratory strain, a typical commensal (enteric) strain and two environmental strains. RNA-Seq was employed to compare the whole transcriptomes of strains grown under batch, chemostat and starvation conditions. Highly differentially expressed genes showed a significantly lower nucleotide sequence identity compared with other genes, indicating that gene regulation and coding sequence conservation are directly connected. Overall, distances between the strains based on gene expression profiles were largely dependent on the culture condition and did not reflect phylogenetic relatedness. Expression differences of commonly shared genes (all four strains) and E. coli core genes were consistently smaller between strains characterized by more similar primary habitats. For instance, environmental strains exhibited increased expression of stress defense genes under carbon-limited growth and entered a more pronounced survival-like phenotype during starvation compared with other strains, which stayed more alert for substrate scavenging and catabolism during no-growth conditions. Since those environmental strains show similar genetic distance to each other and to the other two strains, these findings cannot be simply attributed to genetic relatedness but suggest physiological adaptations. Our study provides new insights into ecologically relevant gene-expression and underscores the role of (differential) gene regulation for the diversification of the model bacterial species. PMID:25343512

LEAP: constructing gene co-expression networks for single-cell RNA-sequencing data using pseudotime ordering.

PubMed

Specht, Alicia T; Li, Jun

2017-03-01

To construct gene co-expression networks based on single-cell RNA-Sequencing data, we present an algorithm called LEAP, which utilizes the estimated pseudotime of the cells to find gene co-expression that involves time delay. R package LEAP available on CRAN. jun.li@nd.edu. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Shrinkage estimation of effect sizes as an alternative to hypothesis testing followed by estimation in high-dimensional biology: applications to differential gene expression.

PubMed

Montazeri, Zahra; Yanofsky, Corey M; Bickel, David R

2010-01-01

Research on analyzing microarray data has focused on the problem of identifying differentially expressed genes to the neglect of the problem of how to integrate evidence that a gene is differentially expressed with information on the extent of its differential expression. Consequently, researchers currently prioritize genes for further study either on the basis of volcano plots or, more commonly, according to simple estimates of the fold change after filtering the genes with an arbitrary statistical significance threshold. While the subjective and informal nature of the former practice precludes quantification of its reliability, the latter practice is equivalent to using a hard-threshold estimator of the expression ratio that is not known to perform well in terms of mean-squared error, the sum of estimator variance and squared estimator bias. On the basis of two distinct simulation studies and data from different microarray studies, we systematically compared the performance of several estimators representing both current practice and shrinkage. We find that the threshold-based estimators usually perform worse than the maximum-likelihood estimator (MLE) and they often perform far worse as quantified by estimated mean-squared risk. By contrast, the shrinkage estimators tend to perform as well as or better than the MLE and never much worse than the MLE, as expected from what is known about shrinkage. However, a Bayesian measure of performance based on the prior information that few genes are differentially expressed indicates that hard-threshold estimators perform about as well as the local false discovery rate (FDR), the best of the shrinkage estimators studied. Based on the ability of the latter to leverage information across genes, we conclude that the use of the local-FDR estimator of the fold change instead of informal or threshold-based combinations of statistical tests and non-shrinkage estimators can be expected to substantially improve the reliability of gene prioritization at very little risk of doing so less reliably. Since the proposed replacement of post-selection estimates with shrunken estimates applies as well to other types of high-dimensional data, it could also improve the analysis of SNP data from genome-wide association studies.

A self-initiating eukaryotic transient gene expression system based on contransfection of bacteriophage T7 RNA polymerase and DNA vectors containing a T7 autogene.

PubMed Central

Chen, X; Li, Y; Xiong, K; Wagner, T E

1994-01-01

A novel cytoplasmic gene expression system has been developed. This system differs from other expression systems in that it relies on the co-delivery of plasmid DNA and T7 RNA polymerase (RNAP) during transfection. The plasmid contains a T7 RNAP gene driven by the T7 promoter (T7 autogene) and a functional/reporter gene driven by another T7 promoter (T7T7/T7-gene construct). Once this DNA-enzyme complex is introduced into eukaryotic cells, the transcription of the T7 RNAP and the functional/reporter genes is initiated by the co-delivered T7 RNAP. The T7 RNAP, which is responsible for the initiation and maintenance of expression of both T7 and functional/reporter genes, is replenished by translation of newly synthesized T7 mRNA. This T7 system was designed in such a manner that the expression of the functional/reporter genes can occur in the cytoplasm and does not require any nuclear involvement. When transfected by either a pT7T7/T7Luc or a pT7T7/T7hGH plasmids with the cointroduced T7 RNAP, mouse L cells were found to express high levels of luciferase immediately after transfection, apparently due to the cytoplasmic gene expression; the expression of human growth hormone (hGH) could be sustained for at least 6 days. Both T7 and hGH mRNA were expressed by the cells transfected with pT7T7/T7hGH. These results suggest that this cytoplasmic expression system may be used for certain targets of somatic gene therapy. Images PMID:8029020

Identification of conserved drought stress responsive gene-network across tissues and developmental stages in rice.

PubMed

Smita, Shuchi; Katiyar, Amit; Pandey, Dev Mani; Chinnusamy, Viswanathan; Archak, Sunil; Bansal, Kailash Chander

2013-01-01

Identification of genes that are coexpressed across various tissues and environmental stresses is biologically interesting, since they may play coordinated role in similar biological processes. Genes with correlated expression patterns can be best identified by using coexpression network analysis of transcriptome data. In the present study, we analyzed the temporal-spatial coordination of gene expression in root, leaf and panicle of rice under drought stress and constructed network using WGCNA and Cytoscape. Total of 2199 differentially expressed genes (DEGs) were identified in at least three or more tissues, wherein 88 genes have coordinated expression profile among all the six tissues under drought stress. These 88 highly coordinated genes were further subjected to module identification in the coexpression network. Based on chief topological properties we identified 18 hub genes such as ABC transporter, ATP-binding protein, dehydrin, protein phosphatase 2C, LTPL153 - Protease inhibitor, phosphatidylethanolaminebinding protein, lactose permease-related, NADP-dependent malic enzyme, etc. Motif enrichment analysis showed the presence of ABRE cis-elements in the promoters of > 62% of the coordinately expressed genes. Our results suggest that drought stress mediated upregulated gene expression was coordinated through an ABA-dependent signaling pathway across tissues, at least for the subset of genes identified in this study, while down regulation appears to be regulated by tissue specific pathways in rice.

T-cell lymphomas associated gene expression signature: Bioinformatics analysis based on gene expression Omnibus.

PubMed

Zhou, Lei-Lei; Xu, Xiao-Yue; Ni, Jie; Zhao, Xia; Zhou, Jian-Wei; Feng, Ji-Feng

2018-06-01

Due to the low incidence and the heterogeneity of subtypes, the biological process of T-cell lymphomas is largely unknown. Although many genes have been detected in T-cell lymphomas, the role of these genes in biological process of T-cell lymphomas was not further analyzed. Two qualified datasets were downloaded from Gene Expression Omnibus database. The biological functions of differentially expressed genes were evaluated by gene ontology enrichment and KEGG pathway analysis. The network for intersection genes was constructed by the cytoscape v3.0 software. Kaplan-Meier survival curves and log-rank test were employed to assess the association between differentially expressed genes and clinical characters. The intersection mRNAs were proved to be associated with fundamental processes of T-cell lymphoma cells. These intersection mRNAs were involved in the activation of some cancer-related pathways, including PI3K/AKT, Ras, JAK-STAT, and NF-kappa B signaling pathway. PDGFRA, CXCL12, and CCL19 were the most significant central genes in the signal-net analysis. The results of survival analysis are not entirely credible. Our findings uncovered aberrantly expressed genes and a complex RNA signal network in T-cell lymphomas and indicated cancer-related pathways involved in disease initiation and progression, providing a new insight for biotargeted therapy in T-cell lymphomas. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.