Construction and application of a co-expression network in Mycobacterium tuberculosis

Jiang, Jun; Sun, Xian; Wu, Wei; Li, Li; Wu, Hai; Zhang, Lu; Yu, Guohua; Li, Yao

doi:10.1038/srep28422

Cited by 39 publications

(37 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Major motivations for this work include the successful implementation of bacterial-focus, microarray-based co-expression networks and the lack of clear functional knowledge for a large portion of V. cholerae genes. Besides more simple guilt-by-association studies (22, 23), co-expression networks have helped to elucidate relationships in diverse microbial communities (47–50) and enable comparisons across strains and species (51–53). These works as well as the relative dearth of knowledge about the V. cholerae genome (roughly two third of genes are annotated compared to around 86% percent of all E. coli genes (54)) and the growing abundance of V. cholerae focused NGS data served as the impetus for this research.…”

Section: Discussionmentioning

confidence: 99%

“…Expression data is particularly amenable to such pooling and can be used to accurately group genes into functional modules based on their co-expression (20). In bacteria, weighted gene co-expression network analysis (WGCNA) (21) has been successfully used to underscore biologically important genes and gene-gene relationships via “guilt-by-association” approaches (22, 23). These studies have taken advantage of larger and larger heterogeneous microarray datasets to provide novel biological insights via existing data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A comprehensive co-expression network analysis inVibrio cholerae

DuPai

Wilke

Davies

2020

Preprint

View full text Add to dashboard Cite

9Research into the evolution and pathogenesis of Vibrio cholerae has benefited greatly from 10 the generation of high throughput sequencing data to drive molecular analyses. The steady 11 accumulation of these datasets now provides a unique opportunity for in silico hypothesis 12 generation via co-expression analysis. Here we leverage all published V. cholerae sequencing data, in combination with select data from other platforms, to generate a gene 14 co-expression network that validates known gene interactions and identifies novel genetic 15 partners across the entire V. cholerae genome. This network provides direct insights into 16 genes influencing pathogenicity, metabolism, and transcriptional regulation, further 17 clarifies results from previous sequencing experiments in V. cholerae (e.g. Tn-seq and ChIP-18 seq), and expands upon micro-array based findings in related gram-negative bacteria. 19 20 Importance 21Cholera is a devastating illness that kills tens of thousands of people annually. Vibrio 22 cholerae, the causative agent of cholera, is an important model organism to investigate both 23 bacterial pathogenesis and the impact of horizontal gene transfer on the emergence and 24 dissemination of new virulent strains. Despite this importance, roughly one third of V. 25 cholerae genes are functionally un-annotated, leaving large gaps in our understanding of 26 this microbe. Through co-expression network analysis of existing RNA-sequencing data, 27 this work develops an approach to uncover novel gene-gene relationships and 28 contextualize genes with no known function, which will advance our understanding of V. 29 cholerae virulence and evolution. 30 31

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A comprehensive co-expression network analysis inVibrio cholerae

DuPai

Wilke

Davies

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The interrelationship among genes in a cellular system is called Gene Co-expression Network (GCN) because genes of the same network are known to be either functionally related, controlled by the same transcriptional regulatory process or generally take part in a common biological process [18]. In a gene co-expression network, the genes signify a gene module and the edges indicate significant correlations [13]. Hence, a module is a set of genes with similar expression pattern in different samples of gene expression profiling.…”

Section: Gene Co-expressionmentioning

confidence: 99%

Multilayer Perceptron based Model of Large-Scale Gene Regulatory Network

Adigun¹,

Makolo²

2019

IJCA

View full text Add to dashboard Cite

Background: The computational reconstruction of Gene Regulatory Networks (GRNs) using different techniques have encountered the challenge of constructing large network because of many parameters to be fitted and the nature of the input data. In fact, contemporary works on GRN inference that involve the use of hybridized techniques especially Artificial Neural Network (ANN) with meta-heuristic optimization techniques have to trade off computational cost for accuracy in reconstructing large-scale GRN. This work designed an efficient feature selection algorithm with GRN model to overcome the dimension problem of input data using biological prior knowledge of co-expression and network sparseness, so as to capture and represent the actual interrelationship among genes. Methodology: The GRN model is an ensemble Multi-Layer Perceptron (MLP) network incorporating a novel feature selection algorithm termed Fuzzified Adjusted Rand Index (FARI). FARI is developed to investigate and establish the expression trends of genes in an expression profile data. A rank matrix of all genes produced by FARI shows their coexpression relationship, which is used to coordinate the selection of potential predictors as input features into the inference model. Each target gene is modeled separately by updating its parameters independently as several subproblems of the overall network. The performance of the model is subjected to synthetic, ecoli and Mtb data. Result: The result indicated an improved accuracy in the construction of large-scale GRN including a significant speedup. The result on Mtb identified CCL5 as the first expressed gene, which is the same with CCL1 identified by the experimental method. Some of the expressed genes were validated through their biological pathways showing immune responses and host susceptibility to TB. Conclusion: The included prior biological knowledge in MLP model provided the construction of an accurate large-scale GRN by reducing the potential large search space of GRN modeling. Besides, the model produced two major biological networks from the same process using the same dataset for appropriate biological validation.

show abstract

“…To explore the relationship between the genes and to construct genetic co-expression network, WGCNA was performed to convert co-expression measures into connections weight or topology overlap measure [14], which are typically used for exploring correlations at transcription levels. It is widely accepted that genes involved in the same pathway or functionality tend to exhibit similar expression pattern [15,16]. Therefore, the construction of a gene co-expression or correlation network facilitates the identification of genes with similar biological functions [17].…”

Section: Construction Of Weighted Gene Correlation Networkmentioning

confidence: 99%

The Key Candidate Genes in Tubulointerstitial Injury of Chronic Kidney Diseases (CKD) Patients as Determined by Bioinformatic Analysis

Wang¹,

Shen

Pan³

et al. 2019

Preprint

View full text Add to dashboard Cite

Background: Tubulointerstitial injury (TIL) is common in chronic kidney disease (CKD), which in turn, leads to loss of renal function. The aims of present study were to screen critical genes with tubulointerstitial lesion in CKD by weighted gene correlation network analysis (WGCNA). Methods: GSE104954 gene expression data downloaded from GEO database were used for analysis for differential expression genes(DEGs); Meanwhile, 90 expression data of tubulointerstitial samples in GSE47185 combined with clinic information were applied to WGCNA. According to the enrichment analysis and relationship with estimated glomerular filtration rate (eGFR), the eGFR-associated modules were defined, and the hub gene is selected according to the average intramodular connectivity (Kwithin); Clinical data from Nephroseq were obtained to further validate the relationship between CKD and hub genes. Results: Totally 294 DEGs were screened. Using the WGCNA, we identified 15 co-expressed modules, the blue, brown and yellow modules exhibited strongly association with eGFR, and were significantly enriched in several signaling pathways that have been reported involved in pathogenesis of CKD. Furthermore, it was found that the 4 genes (PLG, ITGB2, CTSS and CCL5) was one of the DEGs which also be identified as hub genes according to Kwithin. Finally, the Nephroseq online tool showed that the tubulointerstitial expression levels of PLG significantly positively correlated with the eGFR, while ITGB2, CTSS and CCL5 connected negatively to the eGFR. Conclusion: WGCNA is an efficient approach to system biology. By this procedure, the present study improved the understanding of the transcriptome status of renal tubulointerstitial injury in CKD.

show abstract

Construction and application of a co-expression network in Mycobacterium tuberculosis

Cited by 39 publications

References 35 publications

A comprehensive co-expression network analysis inVibrio cholerae

A comprehensive co-expression network analysis inVibrio cholerae

Multilayer Perceptron based Model of Large-Scale Gene Regulatory Network

The Key Candidate Genes in Tubulointerstitial Injury of Chronic Kidney Diseases (CKD) Patients as Determined by Bioinformatic Analysis

Contact Info

Product

Resources

About