Psychoactive pharmaceuticals induce fish gene expression profiles associated with human idiopathic autism

Idiopathic autism, caused by genetic susceptibility interacting with unknown environmental triggers, has increased dramatically in the past 25 years. Identifying environmental triggers has been difficult due to poorly understood pathophysiology and subjective definitions of autism. The use of antidepressants by pregnant women has been associated with autism. These and other unmetabolized psychoactive pharmaceuticals (UPPs) have also been found in drinking water from surface sources, providing another possible exposure route and raising questions about human health consequences. Here, we examined gene expression patterns of fathead minnows treated with a mixture of three psychoactive pharmaceuticals (fluoxetine, venlafaxine & carbamazepine) in dosages intended to be similar to the highest observed conservative estimates of environmental concentrations. We conducted microarray experiments examining brain tissue of fish exposed to individual pharmaceuticals and a mixture of all three. We used gene-class analysis to test for enrichment of gene sets involved with ten human neurological disorders. Only sets associated with idiopathic autism were unambiguously enriched. We found that UPPs induce autism-like gene expression patterns in fish. Our findings suggest a new potential trigger for idiopathic autism in genetically susceptible individuals involving an overlooked source of environmental contamination.     

PAGE: Parametric Analysis of Gene Set Enrichment

Background  

Gene set enrichment analysis (GSEA) is a microarray data analysis method that uses predefined gene sets and ranks of genes to identify significant biological changes in microarray data sets. GSEA is especially useful when gene expression changes in a given microarray data set is minimal or moderate.     

Temporal gene induction patterns in sheepshead minnows exposed to 17beta-estradiol

Gene arrays provide a powerful method to examine changes in gene expression in fish due to chemical exposures in the environment. In this study, we expanded an existing gene array for sheepshead minnows (Cyprinodon variegatus) (SHM) and used it to examine temporal changes in gene expression for male SHM exposed to 100 ng 17beta-estradiol (E(2))/L for five time points between 0 and 48 hr. We found that in addition to the induction of genes involved in oocyte development (vitellogenin [VTG], zona radiata [ZRP]), other genes involved in metabolism and the inflammatory response are also affected. We identified five patterns of temporal induction in genes whose expression was modified due to E(2) exposure. We validated the gene array data for the expression of VTG 1, VTG 2, ZRP 2 and ZRP 3 and found that with low levels of exogenous E(2) (100 ng E(2)/L) exposure, ZRP expression precedes VTG expression. However, at higher concentrations of E(2) (500 ng E(2)/L), the difference in temporal expression appears to be lost. Exposure to high levels of environmental contaminants may affect the normal ordered expression of genes required for reproduction. Gene expression profiling using arrays promises to be a valuable tool in the field of environmental toxicology. As more genes are identified for species used in toxicological testing, researchers will be better able to predict adverse effects to chemical exposures and to understand the relationships between changes in gene expression and changes in phenotype.     

Inferring biological functions and associated transcriptional regulators using gene set expression coherence analysis

Background  

Gene clustering has been widely used to group genes with similar expression pattern in microarray data analysis. Subsequent enrichment analysis using predefined gene sets can provide clues on which functional themes or regulatory sequence motifs are associated with individual gene clusters. In spite of the potential utility, gene clustering and enrichment analysis have been used in separate platforms, thus, the development of integrative algorithm linking both methods is highly challenging.     

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

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.     

Coexpression network analysis linked H2AFJ to chemoradiation resistance in colorectal cancer

Neoadjuvant chemoradiotherapy (CRT) resistance is a complex phenomenon and it remains a major problem for patients with a priori resistant tumor. Therefore, there is a strong need to investigate molecular biomarkers which may guide for treatment decision-making. In our study, weighted gene coexpression network analysis was applied to identify CRT-resistance hub modules in 12 colorectal cancer (CRC) cell lines with different CRT sensitivities from GSE20298 data set. The green module and purple module had the highest correlations with CRT resistance. Gene ontology enrichment analysis indicated that the function of these two modules focused on interferon-mediated signaling pathway, immune response, chromatin modulation, Rho GTPases activities, and regulation of apoptotic process. Then, 15 hub genes in both the coexpression and protein-protein interaction networks were selected. Among these hub genes, higher H2A histone family member J (H2AFJ) expression was independently validated in patient cohorts from two testing data sets of GSE46862 and GSE68204 to be related to CRT resistance. The receiver operating characteristic curve showed that H2AFJ could efficiently distinguish CRT-resistance cases from CRT-sensitive cases in another two testing data sets. Furthermore, meta-analysis of 12 Gene Expression Omnibus–sourced data sets showed that H2AFJ messenger RNA levels were significantly higher in CRC tissues than in normal colon tissues. High H2AFJ expression was correlated with a significant worse event- and relapse-free survival by analyzing the data from the R2: Genomics Analysis and Visualization Platform. Gene set enrichment analysis determined that the mechanism of H2AFJ-mediated CRT resistance might involve the ERK5 (MAPK7), human immunodeficiency virus Nef (HIV Nef), and inflammatory pathways. This study is the first, to the best of our knowledge, to implicate and verify H2AFJ as an effective new marker for CRT response prediction.     

榕小蜂基因共表达研究

榕小蜂的雌雄个体之间存在很大表型差异,以至于很难将雌雄个体彼此联系在一起.以对叶榕传粉榕小蜂作为材料,利用"加权基因共表达网络分析"软件(WGCNA),对榕小蜂的基因组和转录组进行分析,结果发现,5个基因共表达模块,分别用蓝色、蓝绿色、棕色、绿色和黄色标识,其中2个模块偏爱在雌蜂中表达,3个模块偏爱在蛹中表达.基因本体(GO)分析发现在蓝绿色和黄色表达模块中发现3个功能富集的基因集合.在蓝绿色基因表达模块中发现2个基因集合,分别与细胞周期和核苷酸结合活性有关;在黄色基因表达模块中发现1个基因结合,与细胞分化有关,尤其是与神经发育有关.     

Identifying Gene Interaction Enrichment for Gene Expression Data

Gene set analysis allows the inclusion of knowledge from established gene sets, such as gene pathways, and potentially improves the power of detecting differentially expressed genes. However, conventional methods of gene set analysis focus on gene marginal effects in a gene set, and ignore gene interactions which may contribute to complex human diseases. In this study, we propose a method of gene interaction enrichment analysis, which incorporates knowledge of predefined gene sets (e.g. gene pathways) to identify enriched gene interaction effects on a phenotype of interest. In our proposed method, we also discuss the reduction of irrelevant genes and the extraction of a core set of gene interactions for an identified gene set, which contribute to the statistical variation of a phenotype of interest. The utility of our method is demonstrated through analyses on two publicly available microarray datasets. The results show that our method can identify gene sets that show strong gene interaction enrichments. The enriched gene interactions identified by our method may provide clues to new gene regulation mechanisms related to the studied phenotypes. In summary, our method offers a powerful tool for researchers to exhaustively examine the large numbers of gene interactions associated with complex human diseases, and can be a useful complement to classical gene set analyses which only considers single genes in a gene set.     

ADGO: analysis of differentially expressed gene sets using composite GO annotation

MOTIVATION: Genes are typically expressed in modular manners in biological processes. Recent studies reflect such features in analyzing gene expression patterns by directly scoring gene sets. Gene annotations have been used to define the gene sets, which have served to reveal specific biological themes from expression data. However, current annotations have limited analytical power, because they are classified by single categories providing only unary information for the gene sets. RESULTS: Here we propose a method for discovering composite biological themes from expression data. We intersected two annotated gene sets from different categories of Gene Ontology (GO). We then scored the expression changes of all the single and intersected sets. In this way, we were able to uncover, for example, a gene set with the molecular function F and the cellular component C that showed significant expression change, while the changes in individual gene sets were not significant. We provided an exemplary analysis for HIV-1 immune response. In addition, we tested the method on 20 public datasets where we found many 'filtered' composite terms the number of which reached approximately 34% (a strong criterion, 5% significance) of the number of significant unary terms on average. By using composite annotation, we can derive new and improved information about disease and biological processes from expression data. AVAILABILITY: We provide a web application (ADGO: http://array.kobic.re.kr/ADGO) for the analysis of differentially expressed gene sets with composite GO annotations. The user can analyze Affymetrix and dual channel array (spotted cDNA and spotted oligo microarray) data for four species: human, mouse, rat and yeast. CONTACT: chu@kribb.re.kr SUPPLEMENTARY INFORMATION: http://array.kobic.re.kr/ADGO.     

Computational methods for analysis of cellular functions and pathways collectively targeted by differentially expressed microRNA.

This report presents computational methods of analysis of cellular processes, functions, and pathways affected by differentially expressed microRNA, a statistical basis of the gene enrichment analysis method, a modification of enrichment analysis method accounting for combinatorial targeting of Gene Ontology categories by multiple miRNAs and examples of the global functional profiling of predicted targets of differentially expressed miRNAs in cancer. We have also summarized an application of Ingenuity Pathway Analysis tools for in depth analysis of microRNA target sets that may be useful for the biological interpretation of microRNA profiling data. To illustrate the utility of these methods, we report the main results of our recent computational analysis of five published datasets of aberrantly expressed microRNAs in five human cancers (pancreatic cancer, breast cancer, colon cancer, lung cancer, and lymphoma). Using a combinatorial target prediction algorithm and statistical enrichment analysis, we have determined Gene Ontology categories as well as biological functions, disease categories, toxicological categories, and signaling pathways that are: targeted by multiple microRNAs; statistically significantly enriched with target genes; and known to be affected in specific cancers. Our recent computational analysis of predicted targets of co-expressed miRNAs in five human cancers suggests that co-expressed miRNAs provide systemic compensatory response to the abnormal phenotypic changes in cancer cells by targeting a broad range of functional categories and signaling pathways reportedly affected in a particular cancer.     

Transcriptome Profiling of Human Ulcerative Colitis Mucosa Reveals Altered Expression of Pathways Enriched in Genetic Susceptibility Loci

Human colonic mucosa altered by inflammation due to ulcerative colitis (UC) displays a drastically altered pattern of gene expression compared with healthy tissue. We aimed to understand the underlying molecular pathways influencing these differences by analyzing three publically-available, independently-generated microarray datasets of gene expression from endoscopic biopsies of the colon. Gene set enrichment analysis (GSEA) revealed that all three datasets share 87 gene sets upregulated in UC lesions and 8 gene sets downregulated (false discovery rate <0.05). The upregulated pathways were dominated by gene sets involved in immune function and signaling, as well as the control of mitosis. We applied pathway analysis to genotype data derived from genome-wide association studies (GWAS) of UC, consisting of 5,584 cases and 11,587 controls assembled from eight European-ancestry cohorts. The upregulated pathways derived from the gene expression data showed a highly significant overlap with pathways derived from the genotype data (33 of 56 gene sets, hypergeometric P = 1.49×10^–19). This study supports the hypothesis that heritable variation in gene expression as measured by GWAS signals can influence key pathways in the development of disease, and that comparison of genetic susceptibility loci with gene expression signatures can differentiate key drivers of inflammation from secondary effects on gene expression of the inflammatory process.     

Administration of a maple syrup extract to mitigate their hepatic inflammation induced by a high-fat diet: a transcriptome analysis

Effects of the administration of maple syrup extract (MSX) on hepatic gene expression were investigated in mice fed a high-fat diet. Gene annotation enrichment analysis based on gene ontology revealed some changes in the expression of genes related to lipid metabolism and the immune response in MSX-fed mice. Detailed analysis of these data indicated that MSX ingestion mitigates hepatic inflammation.     

Analysis of sample set enrichment scores: assaying the enrichment of sets of genes for individual samples in genome-wide expression profiles

MOTIVATION: Gene expression profiling experiments in cell lines and animal models characterized by specific genetic or molecular perturbations have yielded sets of genes annotated by the perturbation. These gene sets can serve as a reference base for interrogating other expression datasets. For example, a new dataset in which a specific pathway gene set appears to be enriched, in terms of multiple genes in that set evidencing expression changes, can then be annotated by that reference pathway. We introduce in this paper a formal statistical method to measure the enrichment of each sample in an expression dataset. This allows us to assay the natural variation of pathway activity in observed gene expression data sets from clinical cancer and other studies. RESULTS: Validation of the method and illustrations of biological insights gleaned are demonstrated on cell line data, mouse models, and cancer-related datasets. Using oncogenic pathway signatures, we show that gene sets built from a model system are indeed enriched in the model system. We employ ASSESS for the use of molecular classification by pathways. This provides an accurate classifier that can be interpreted at the level of pathways instead of individual genes. Finally, ASSESS can be used for cross-platform expression models where data on the same type of cancer are integrated over different platforms into a space of enrichment scores. AVAILABILITY: Versions are available in Octave and Java (with a graphical user interface). Software can be downloaded at http://people.genome.duke.edu/assess.     

Comparative study of gene set enrichment methods

Background  

The analysis of high-throughput gene expression data with respect to sets of genes rather than individual genes has many advantages. A variety of methods have been developed for assessing the enrichment of sets of genes with respect to differential expression. In this paper we provide a comparative study of four of these methods: Fisher's exact test, Gene Set Enrichment Analysis (GSEA), Random-Sets (RS), and Gene List Analysis with Prediction Accuracy (GLAPA). The first three methods use associative statistics, while the fourth uses predictive statistics. We first compare all four methods on simulated data sets to verify that Fisher's exact test is markedly worse than the other three approaches. We then validate the other three methods on seven real data sets with known genetic perturbations and then compare the methods on two cancer data sets where our a priori knowledge is limited.