First trimester trophoblasts forming endothelial-like tubes in vitro emulate a ‘blood vessel development’ gene expression profile

Extravillous cytotrophoblasts isolated from first trimester placenta, and immortalised cell lines derived from them, have the intrinsic ability to form endothelial-like tubes when cultured on Matrigel™ extracellular matrix. This in vitro tube formation may model placental angiogenesis and/or endovascular differentiation by trophoblasts. To interpret the relevance of this phenomenon to placental development, we used a gene expression microarray approach to identify which genes and pathways are associated with the tube-forming phenotype of HTR8/SVneo first trimester trophoblasts (HTR8-M), compared with HTR8/SVneo not forming tubes on plastic culture surface (HTR8-P). Furthermore, we used weighted gene co-expression network analysis (WGCNA) of microarray data to identify modules of co-expressed genes underlying the biological processes. There were 481 genes differentially expressed between HTR8-M and HTR8-P and these were significantly enriched for blood vessel development and related gene ontologies. WGCNA clustered the genes into 9 co-expression modules. One module was significantly associated with HTR8-M (p = 1.15E-05) and contained genes involved in actin cytoskeleton organization, cell migration and blood vessel development, consistent with tube formation on Matrigel. Another module was significantly associated with HTR8-P (p = 1.94E-05) and was enriched for genes involved in mitosis, consistent with proliferation by cells on plastic which do not differentiate. Up-regulation of angiogenesis and vascular development pathways in endovascular trophoblasts in vivo could underpin spiral artery remodelling processes, which are defective in preeclamptic pregnancies.     

GroupRank: Rank Candidate Genes in PPI Network by Differentially Expressed Gene Groups

Many cell activities are organized as a network, and genes are clustered into co-expressed groups if they have the same or closely related biological function or they are co-regulated. In this study, based on an assumption that a strong candidate disease gene is more likely close to gene groups in which all members coordinately differentially express than individual genes with differential expression, we developed a novel disease gene prioritization method GroupRank by integrating gene co-expression and differential expression information generated from microarray data as well as PPI network. A candidate gene is ranked high using GroupRank if it is differentially expressed in disease and control or is close to differentially co-expressed groups in PPI network. We tested our method on data sets of lung, kidney, leukemia and breast cancer. The results revealed GroupRank could efficiently prioritize disease genes with significantly improved AUC value in comparison to the previous method with no consideration of co-exprssed gene groups in PPI network. Moreover, the functional analyses of the major contributing gene group in gene prioritization of kidney cancer verified that our algorithm GroupRank not only ranks disease genes efficiently but also could help us identify and understand possible mechanisms in important physiological and pathological processes of disease.     

CoGA: An R Package to Identify Differentially Co-Expressed Gene Sets by Analyzing the Graph Spectra

Gene set analysis aims to identify predefined sets of functionally related genes that are differentially expressed between two conditions. Although gene set analysis has been very successful, by incorporating biological knowledge about the gene sets and enhancing statistical power over gene-by-gene analyses, it does not take into account the correlation (association) structure among the genes. In this work, we present CoGA (Co-expression Graph Analyzer), an R package for the identification of groups of differentially associated genes between two phenotypes. The analysis is based on concepts of Information Theory applied to the spectral distributions of the gene co-expression graphs, such as the spectral entropy to measure the randomness of a graph structure and the Jensen-Shannon divergence to discriminate classes of graphs. The package also includes common measures to compare gene co-expression networks in terms of their structural properties, such as centrality, degree distribution, shortest path length, and clustering coefficient. Besides the structural analyses, CoGA also includes graphical interfaces for visual inspection of the networks, ranking of genes according to their “importance” in the network, and the standard differential expression analysis. We show by both simulation experiments and analyses of real data that the statistical tests performed by CoGA indeed control the rate of false positives and is able to identify differentially co-expressed genes that other methods failed.     

Time-Course Analysis of Brain Regional Expression Network Responses to Chronic Intermittent Ethanol and Withdrawal: Implications for Mechanisms Underlying Excessive Ethanol Consumption

Long lasting abusive consumption, dependence, and withdrawal are characteristic features of alcohol use disorders (AUD). Mechanistically, persistent changes in gene expression are hypothesized to contribute to brain adaptations leading to ethanol toxicity and AUD. We employed repeated chronic intermittent ethanol (CIE) exposure by vapor chamber as a mouse model to simulate the cycles of ethanol exposure and withdrawal commonly seen with AUD. This model has been shown to induce progressive ethanol consumption in rodents. Brain CIE-responsive expression networks were identified by microarray analysis across five regions of the mesolimbic dopamine system and extended amygdala with tissue harvested from 0-hours to 7-days following CIE. Weighted Gene Correlated Network Analysis (WGCNA) was used to identify gene networks over-represented for CIE-induced temporal expression changes across brain regions. Differential gene expression analysis showed that long-lasting gene regulation occurred 7-days after the final cycle of ethanol exposure only in prefrontal cortex (PFC) and hippocampus. Across all brain regions, however, ethanol-responsive expression changes occurred mainly within the first 8-hours after removal from ethanol. Bioinformatics analysis showed that neuroinflammatory responses were seen across multiple brain regions at early time-points, whereas co-expression modules related to neuroplasticity, chromatin remodeling, and neurodevelopment were seen at later time-points and in specific brain regions (PFC or HPC). In PFC a module containing Bdnf was identified as highly CIE responsive in a biphasic manner, with peak changes at 0 hours and 5 days following CIE, suggesting a possible role in mechanisms underlying long-term molecular and behavioral response to CIE. Bioinformatics analysis of this network and several other modules identified Let-7 family microRNAs as potential regulators of gene expression changes induced by CIE. Our results suggest a complex temporal and regional pattern of widespread gene network responses involving neuroinflammatory and neuroplasticity related genes as contributing to physiological and behavioral responses to chronic ethanol.     

Approaches for extracting practical information from gene co-expression networks in plant biology

Gene co-expression, in many cases, implies the presence of a functional linkage between genes. Co-expression analysis has uncovered gene regulatory mechanisms in model organisms such as Escherichia coli and yeast. Recently, accumulation of Arabidopsis microarray data has facilitated a genome-wide inspection of gene co-expression profiles in this model plant. An approach using network analysis has provided an intuitive way to represent complex co-expression patterns between many genes. Co-expression network analysis has enabled us to extract modules, or groups of tightly co-expressed genes, associated with biological processes. Furthermore, integrated analysis of gene expression and metabolite accumulation has allowed us to hypothesize the functions of genes associated with specific metabolic processes. Co-expression network analysis is a powerful approach for data-driven hypothesis construction and gene prioritization, and provides novel insights into the system-level understanding of plant cellular processes.     

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.     

Identification of responsive gene modules by network-based gene clustering and extending: application to inflammation and angiogenesis

Background  

Cell responses to environmental stimuli are usually organized as relatively separate responsive gene modules at the molecular level. Identification of responsive gene modules rather than individual differentially expressed (DE) genes will provide important information about the underlying molecular mechanisms. Most of current methods formulate module identification as an optimization problem: find the active sub-networks in the genome-wide gene network by maximizing the objective function considering the gene differential expression and/or the gene-gene co-expression information. Here we presented a new formulation of this task: a group of closely-connected and co-expressed DE genes in the gene network are regarded as the signatures of the underlying responsive gene modules; the modules can be identified by finding the signatures and then recovering the "missing parts" by adding the intermediate genes that connect the DE genes in the gene network.     

Islands of co-expressed neighbouring genes in Arabidopsis thaliana suggest higher-order chromosome domains

Biochemical and cytogenetic experiments have led to the hypothesis that eukaryotic chromatin is organized into a series of distinct domains that are functionally independent. Two expectations of this hypothesis are: (i) adjacent genes are more frequently co-expressed than is expected by chance; and (ii) co-expressed neighbouring genes are often functionally related. Here we report that over 10% of Arabidopsis thaliana genes are within large, co-expressed chromosomal regions. Two per cent (497/22,520) of genes are highly co-expressed (r > 0.7), about five times the number expected by chance. These genes fall into 226 groups distributed across the genome, and each group typically contains two to three genes. Among the highly co-expressed groups, 40% (91/226) have genes with high amino acid sequence similarity. Nonetheless, duplicate genes alone do not explain the observed levels of co-expression. Co-expressed, non-homologous genes are transcribed in parallel, share functions, and lie close together more frequently than expected. Our results show that the A. thaliana genome contains domains of gene expression. Small domains have highly co-expressed genes that often share functional and sequence similarity and are probably co-regulated by nearby regulatory sequences. Genes within large, significantly correlated groups are typically co-regulated at a low level, suggesting the presence of large chromosomal domains.     

Evidence for an Important Role of WRKY DNA Binding Proteins in the Regulation of NPR1 Gene Expression

The Arabidopsis NPR1 gene is a positive regulator of inducible plant disease resistance. Expression of NPR1 is induced by pathogen infection or treatment with defense-inducing compounds such as salicylic acid (SA). Transgenic plants overexpressing NPR1 exhibit enhanced resistance to a broad spectrum of microbial pathogens, whereas plants underexpressing the gene are more susceptible to pathogen infection. These results suggest that regulation of NPR1 gene expression is important for the activation of plant defense responses. In the present study, we report the identification of W-box sequences in the promoter region of the NPR1 gene that are recognized specifically by SA-induced WRKY DNA binding proteins from Arabidopsis. Mutations in these W-box sequences abolished their recognition by WRKY DNA binding proteins, rendered the promoter unable to activate a downstream reporter gene, and compromised the ability of NPR1 to complement npr1 mutants for SA-induced defense gene expression and disease resistance. These results provide strong evidence that certain WRKY genes act upstream of NPR1 and positively regulate its expression during the activation of plant defense responses. Consistent with this model, we found that SA-induced expression of a number of WRKY genes was independent of NPR1.