A Comprehensive Coexpression Network Analysis in Vibrio cholerae

Abstract

<jats:title>ABSTRACT</jats:title> <jats:p>Research into the evolution and pathogenesis of <jats:named-content content-type="genus-species">Vibrio cholerae</jats:named-content> has benefited greatly from the generation of high-throughput sequencing data to drive molecular analyses. The steady accumulation of these data sets now provides a unique opportunity for <jats:italic>in silico</jats:italic> hypothesis generation via coexpression analysis. Here, we leverage all published <jats:named-content content-type="genus-species">V. cholerae</jats:named-content> RNA sequencing data, in combination with select data from other platforms, to generate a gene coexpression network that validates known gene interactions and identifies novel genetic partners across the entire <jats:named-content content-type="genus-species">V. cholerae</jats:named-content> genome. This network provides direct insights into genes influencing pathogenicity, metabolism, and transcriptional regulation, further clarifies results from previous sequencing experiments in <jats:named-content content-type="genus-species">V. cholerae</jats:named-content> (e.g., transposon insertion sequencing [Tn-seq] and chromatin immunoprecipitation sequencing [ChIP-seq]), and expands upon microarray-based findings in related Gram-negative bacteria.</jats:p> <jats:p><jats:bold>IMPORTANCE</jats:bold> Cholera is a devastating illness that kills tens of thousands of people annually. <jats:named-content content-type="genus-species">Vibrio cholerae</jats:named-content>, the causative agent of cholera, is an important model organism to investigate both bacterial pathogenesis and the impact of horizontal gene transfer on the emergence and dissemination of new virulent strains. Despite the importance of this pathogen, roughly one-third of <jats:named-content content-type="genus-species">V. cholerae</jats:named-content> genes are functionally unannotated, leaving large gaps in our understanding of this microbe. Through coexpression network analysis of existing RNA sequencing data, this work develops an approach to uncover novel gene-gene relationships and contextualize genes with no known function, which will advance our understanding of <jats:named-content content-type="genus-species">V. cholerae</jats:named-content> virulence and evolution.</jats:p>

Gene co-expression network analysis of the human gut commensal bacterium Faecalibacterium prausnitzii in R-Shiny

“…Highly correlated genes are clustered into larger modules based on similarities in their expression profiles, and members of a given module are often involved in similar functional processes [28]. To date, WGCNA has been successfully used to construct gene co-expression networks in several bacteria, including Mycobacterium tuberculosis [29], Escherichia coli [30], Lactococcus lactis [31], Vibrio cholerae [32], and Streptococcus oralis [33].…”

Independent Component Analysis Identifies the Modulons Expanding the Transcriptional Regulatory Networks of Enterohemorrhagic Escherichia coli

“…The TRNs of bacteria primarily consist of the genes whose expressions are regulated together by a specific growth condition or the presence of a specific TF(s) (Sastry et al, 2019;DuPai et al, 2020). In contrast, the modulons of bacteria consist of the genes that are identified computationally and are expressed differentially together regardless of their growth conditions and the genetic backgrounds (Saelens et al, 2018;Sastry et al, 2019;Tan et al, 2020).…”

“…The understanding of TRNs and their target genes enables the prediction of molecular mechanisms by which pathogens cause disease and survive under hostspecific conditions (Karmali, 2017). Advances in next-generation sequencing technologies facilitate analyzing the large-scale RNA-Seq and comparing the transcriptome of the pathogens grown under specific conditions or lacking a particular transcription factor(s) (TF) (Westermann et al, 2012;DuPai et al, 2020). However, the transcriptome data obtained from the genes expressed under specific experimental conditions or by a certain TF are still limited to comprehensively understand the TRNs and their target genes (Sastry et al, 2019;DuPai et al, 2020).…”

“…Advances in next-generation sequencing technologies facilitate analyzing the large-scale RNA-Seq and comparing the transcriptome of the pathogens grown under specific conditions or lacking a particular transcription factor(s) (TF) (Westermann et al, 2012;DuPai et al, 2020). However, the transcriptome data obtained from the genes expressed under specific experimental conditions or by a certain TF are still limited to comprehensively understand the TRNs and their target genes (Sastry et al, 2019;DuPai et al, 2020). Therefore, to overcome this limitation, studies have been performed to analyze bioinformatically the large-scale transcriptome data of the pathogens and to define the modulons, the independent sets of genes co-regulated under various conditions regardless of their genetic backgrounds (Saelens et al, 2018;Sastry et al, 2019;DuPai et al, 2020;Tan et al, 2020).…”

Cross-Platform Transcriptomic Data Integration, Profiling, and Mining in Vibrio cholerae

“…Multiomics big data analysis of V. cholerae has now been applied in RNA-seq, transposon insertion sequencing (Tn-seq), and chromatin immunoprecipitation sequencing (ChIP-seq). DuPai et al ( 5 ) performed weighted correlation network analysis (WGCNA) by integrating these data, then mapping the functional set of V. cholerae with an interplay network and incorporating new unknown functional genes. WGCNA can uncover similar gene expression patterns across different samples and form highly correlated co-expressed gene modules, correlating the modules with each other and with external sample traits to find candidate biomarkers ( 6 ).…”