Literature DB >> 28987343

Comparison of gene co-expression networks in Pseudomonas aeruginosa and Staphylococcus aureus reveals conservation in some aspects of virulence.

Nazanin Hosseinkhan1, Zaynab Mousavian2, Ali Masoudi-Nejad3.   

Abstract

Pseudomonas aeruginosa and Staphylococcus aureus are two evolutionary distant bacterial species that are frequently isolated from persistent infections such as chronic infectious wounds and severe lung infections in cystic fibrosis patients. To the best of our knowledge no comprehensive genome scale co-expression study has been already conducted on these two species and in most cases only the expression of very few genes has been the subject of investigation. In this study, in order to investigate the level of expressional conservation between these two species, using heterogeneous gene expression datasets the weighted gene co-expression network analysis (WGCNA) approach was applied to study both single and cross species genome scale co-expression patterns of these two species. Single species co-expression network analysis revealed that in P. aeruginosa, genes involved in quorum sensing (QS), iron uptake, nitrate respiration and type III secretion systems and in S. aureus, genes associated with the regulation of carbon metabolism, fatty acid-phospholipids metabolism and proteolysis represent considerable co-expression across a variety of experimental conditions. Moreover, the comparison of gene co-expression networks between P. aeruginosa and S. aureus was led to the identification of four co-expressed gene modules in both species totally consisting of 318 genes. Several genes related to two component signal transduction systems, small colony variants (SCVs) morphotype and protein complexes were found in the detected modules. We believe that targeting the key players among the identified co-expressed orthologous genes will be a potential intervention strategy to control refractory co-infections caused by these two bacterial species.
Copyright © 2017 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Cross species; Gene co-expression network; P. aeruginosa; S. aureus

Mesh:

Year:  2017        PMID: 28987343     DOI: 10.1016/j.gene.2017.10.005

Source DB:  PubMed          Journal:  Gene        ISSN: 0378-1119            Impact factor:   3.688


  2 in total

1.  Machine learning from Pseudomonas aeruginosa transcriptomes identifies independently modulated sets of genes associated with known transcriptional regulators.

Authors:  Akanksha Rajput; Hannah Tsunemoto; Anand V Sastry; Richard Szubin; Kevin Rychel; Joseph Sugie; Joe Pogliano; Bernhard O Palsson
Journal:  Nucleic Acids Res       Date:  2022-04-22       Impact factor: 19.160

2.  Identification of Modules With Similar Gene Regulation and Metabolic Functions Based on Co-expression Data.

Authors:  Edgardo Galán-Vásquez; Ernesto Perez-Rueda
Journal:  Front Mol Biosci       Date:  2019-12-13
  2 in total

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