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.     

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.     

Predictive network modeling in human induced pluripotent stem cells identifies key driver genes for insulin responsiveness

Insulin resistance (IR) precedes the development of type 2 diabetes (T2D) and increases cardiovascular disease risk. Although genome wide association studies (GWAS) have uncovered new loci associated with T2D, their contribution to explain the mechanisms leading to decreased insulin sensitivity has been very limited. Thus, new approaches are necessary to explore the genetic architecture of insulin resistance. To that end, we generated an iPSC library across the spectrum of insulin sensitivity in humans. RNA-seq based analysis of 310 induced pluripotent stem cell (iPSC) clones derived from 100 individuals allowed us to identify differentially expressed genes between insulin resistant and sensitive iPSC lines. Analysis of the co-expression architecture uncovered several insulin sensitivity-relevant gene sub-networks, and predictive network modeling identified a set of key driver genes that regulate these co-expression modules. Functional validation in human adipocytes and skeletal muscle cells (SKMCs) confirmed the relevance of the key driver candidate genes for insulin responsiveness.     

Disease-driven detection of differential inherited SNP modules from SNP network

Detection of the synergetic effects between variants, such as single-nucleotide polymorphisms (SNPs), is crucial for understanding the genetic characters of complex diseases. Here, we proposed a two-step approach to detect differentially inherited SNP modules (synergetic SNP units) from a SNP network. First, SNP-SNP interactions are identified based on prior biological knowledge, such as their adjacency on the chromosome or degree of relatedness between the functional relationships of their genes. These interactions form SNP networks. Second, disease-risk SNP modules (or sub-networks) are prioritised by their differentially inherited properties in IBD (Identity by Descent) profiles of affected and unaffected sibpairs. The search process is driven by the disease information and follows the structure of a SNP network. Simulation studies have indicated that this approach achieves high accuracy and a low false-positive rate in the identification of known disease-susceptible SNPs. Applying this method to an alcoholism dataset, we found that flexible patterns of susceptible SNP combinations do play a role in complex diseases, and some known genes were detected through these risk SNP modules. One example is GRM7, a known alcoholism gene successfully detected by a SNP module comprised of two SNPs, but neither of the two SNPs was significantly associated with the disease in single-locus analysis. These identified genes are also enriched in some pathways associated with alcoholism, including the calcium signalling pathway, axon guidance and neuroactive ligand-receptor interaction. The integration of network biology and genetic analysis provides putative functional bridges between genetic variants and candidate genes or pathways, thereby providing new insight into the aetiology of complex diseases.     

Networks of neuronal genes affected by common and rare variants in autism spectrum disorders

Autism spectrum disorders (ASD) are neurodevelopmental disorders with phenotypic and genetic heterogeneity. Recent studies have reported rare and de novo mutations in ASD, but the allelic architecture of ASD remains unclear. To assess the role of common and rare variations in ASD, we constructed a gene co-expression network based on a widespread survey of gene expression in the human brain. We identified modules associated with specific cell types and processes. By integrating known rare mutations and the results of an ASD genome-wide association study (GWAS), we identified two neuronal modules that are perturbed by both rare and common variations. These modules contain highly connected genes that are involved in synaptic and neuronal plasticity and that are expressed in areas associated with learning and memory and sensory perception. The enrichment of common risk variants was replicated in two additional samples which include both simplex and multiplex families. An analysis of the combined contribution of common variants in the neuronal modules revealed a polygenic component to the risk of ASD. The results of this study point toward contribution of minor and major perturbations in the two sub-networks of neuronal genes to ASD risk.     

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.     

Dissection of Regulatory Networks that Are Altered in Disease via Differential Co-expression

Comparing the gene-expression profiles of sick and healthy individuals can help in understanding disease. Such differential expression analysis is a well-established way to find gene sets whose expression is altered in the disease. Recent approaches to gene-expression analysis go a step further and seek differential co-expression patterns, wherein the level of co-expression of a set of genes differs markedly between disease and control samples. Such patterns can arise from a disease-related change in the regulatory mechanism governing that set of genes, and pinpoint dysfunctional regulatory networks.Here we present DICER, a new method for detecting differentially co-expressed gene sets using a novel probabilistic score for differential correlation. DICER goes beyond standard differential co-expression and detects pairs of modules showing differential co-expression. The expression profiles of genes within each module of the pair are correlated across all samples. The correlation between the two modules, however, differs markedly between the disease and normal samples.We show that DICER outperforms the state of the art in terms of significance and interpretability of the detected gene sets. Moreover, the gene sets discovered by DICER manifest regulation by disease-specific microRNA families. In a case study on Alzheimer''s disease, DICER dissected biological processes and protein complexes into functional subunits that are differentially co-expressed, thereby revealing inner structures in disease regulatory networks.     

Linking co-expression modules with phenotypes

The method for quantifying the association between co-expression module and clinical trait of interest requires application of dimensionality reduction to summaries modules as one dimensional (1D) vector. However, these methods are often linked with information loss. The amount of information lost depends upon the percentage of variance captured by the reduced 1D vector. Therefore, it is of interest to describe a method using analysis of rank (AOR) to assess the association between module and clinical trait of interest. This method works with clinical traits represented as binary class labels and can be adopted for clinical traits measured in continuous scale by dividing samples in two groups around median value. Application of the AOR method on test data for muscle gene expression profiles identifies modules significantly associated with diabetes status.     

Co-expression analysis provides important module and pathways of human dilated cardiomyopathy

Dilated cardiomyopathy (DCM) is a heart disease that injured greatly to the people wordwide. Systemic co-expression analysis for this cancer is still limited, although massive clinic experiments and gene profiling analyses had been well performed previously. Here, using the public RNA-Seq data “GSE116250” and gene annotation of Ensembl database, we built the co-expression modules for DCM by Weighted Gene Co-Expression Network Analysis, and investigated the function enrichment and protein-protein interaction (PPI) network of co-expression genes of each module by Database for Annotation, Visualization, and Integrated Discovery and Search Tool for the Retrieval of Interacting Genes/Proteins database, respectively. First, 5,000 genes in the 37 samples were screened and 11 co-expression modules were conducted. The number of genes for each module ranged from 77 to 936, with a mean of 455. Second, interaction relationships of hub-genes between pairwise modules showed great differences, suggesting relatively high-scale independence of the modules. Third, functional enrichments of the co-expression modules exhibited great differences. We found that genes in module 3 were significantly enriched in the pathways of focal adhesion and ubiquitin-mediated proteolysis. This module was inferred as the key module involved in DCM. In addition, PPI analysis revealed that the genes HSP90AA1, CTNNB1, MAPK1, GART, and PPP2CA owned the largest number of adjacency genes, unveiling that they may function importantly during the occurrence of DCM. Focal adhesion and ubiquitin-mediated proteolysis play important roles in human DCM.     

Conserved Non-Coding Regulatory Signatures in Arabidopsis Co-Expressed Gene Modules

Complex traits and other polygenic processes require coordinated gene expression. Co-expression networks model mRNA co-expression: the product of gene regulatory networks. To identify regulatory mechanisms underlying coordinated gene expression in a tissue-enriched context, ten Arabidopsis thaliana co-expression networks were constructed after manually sorting 4,566 RNA profiling datasets into aerial, flower, leaf, root, rosette, seedling, seed, shoot, whole plant, and global (all samples combined) groups. Collectively, the ten networks contained 30% of the measurable genes of Arabidopsis and were circumscribed into 5,491 modules. Modules were scrutinized for cis regulatory mechanisms putatively encoded in conserved non-coding sequences (CNSs) previously identified as remnants of a whole genome duplication event. We determined the non-random association of 1,361 unique CNSs to 1,904 co-expression network gene modules. Furthermore, the CNS elements were placed in the context of known gene regulatory networks (GRNs) by connecting 250 CNS motifs with known GRN cis elements. Our results provide support for a regulatory role of some CNS elements and suggest the functional consequences of CNS activation of co-expression in specific gene sets dispersed throughout the genome.     

FUMET: A fuzzy network module extraction technique for gene expression data

Construction of co-expression network and extraction of network modules have been an appealing area of bioinformatics research. This article presents a co-expression network construction and a biologically relevant network module extraction technique based on fuzzy set theoretic approach. The technique is able to handle both positive and negative correlations among genes. The constructed network for some benchmark gene expression datasets have been validated using topological internal and external measures. The effectiveness of network module extraction technique has been established in terms of well-known p-value, Q-value and topological statistics.     

与实验条件相关的基因功能模块聚类分析方法

针对细胞内基因功能模块化的现象,定义了“基因功能模块”和“特征功能模块”两个概念,并基于这两个概念提出一种“与实验条件相关的基因功能模块聚类算法”。该算法综合利用基因功能知识与基因表达谱信息,将基因聚类为与实验条件相关的基因功能模块。向基因表达谱中加入水平逐渐升高的数据噪音,根据基因功能模块对数据噪音的抵抗力,确定最稳定的基因功能模块,即特征功能模块。加噪音实验显示,在基因芯片技术可能发生的噪音范围内,该算法对噪音的稳健性优于层次聚类和模糊C均值聚类。将模块聚类算法应用在NCI60数据集上,发现了8个与实验条件高度相关的特征功能模块。     

Identification of hub genes associated with neutrophils infiltration in colorectal cancer

Colorectal cancer (CRC) is the leading cause of cancer-related mortality in the world. Accumulating evidence indicate that tumour infiltrating immune cells participated in cancer progression. Among them, tumour infiltrating neutrophils (TINs) are reported to play crucial role in various cancers. In this study, we used CIBERSORTx, a digital cytometry tool to evaluate the neutrophils infiltration in CRC based on gene expression data of CRC tissues from GSE39582 data set and The Cancer Genome Atlas data set (TCGA-COAD and TCGA-READ). Weighted gene co-expression network analysis (WGCNA) was conducted in GSE39582 data set to identify hub genes associated with neutrophil infiltration. The association of hub gene and neutrophils was then validated in TCGA cohorts and an independent RJ cohort. Functional analysis was performed to investigate the molecular mechanisms of the interested hub gene. We found that neutrophil infiltration is elevated in CRC tissues, and it is related to a poorer prognosis. A total of 18 gene modules are identified by WGCNA in GSE39582 data set, among which lightcyan module is significantly correlated with neutrophils infiltration. Furthermore, Superoxide Dismutase 2 (SOD2) in lightcyan module was proved to correlated with neutrophils infiltration in various cancer types. In addition, SOD2 expression is highly associated with several chemokines, including CXCL8, a neutrophils-related attractant, and functional analysis revealed that SOD2 is involved in neutrophils recruitment biological process. These results indicate that an ‘SOD2-CXCL8-neutrophil recruitment’ axis plays a potential role in colorectal cancer progression.