Dynamic Gene Network Analysis of Caco-2 Cell Response to Shiga Toxin-Producing Escherichia coli-Associated Hemolytic–Uremic Syndrome

Abstract

Shiga toxin-producing Escherichia coli (STEC) O113:H21 strains are associated with human diarrhea and some strains may cause hemolytic–uremic syndrome (HUS). In Brazil, these strains are commonly found in cattle but, so far, were not isolated from HUS patients. Here, a system biology approach was used to investigate the differential transcriptomic and phenotypic responses of enterocyte-like Caco-2 cells to two STEC O113:H21 strains with similar virulence factor profiles (i.e. expressing stx2, ehxA, epeA, espA, iha, saa, sab, and subA): EH41 (Caco-2/EH41), isolated from a HUS patient in Australia, and Ec472/01 (Caco-2/Ec472), isolated from bovine feces in Brazil, during a 3 h period of bacteria–enterocyte interaction. Gene co-expression network analysis for Caco-2/EH41 revealed a quite abrupt pattern of topological variation along 3 h of enterocyte–bacteria interaction when compared with networks obtained for Caco-2/Ec472. Transcriptional module characterization revealed that EH41 induces inflammatory and apoptotic responses in Caco-2 cells just after the first hour of enterocyte–bacteria interaction, whereas the response to Ec472/01 is associated with cytoskeleton organization at the first hour, followed by the expression of immune response modulators. Scanning electron microscopy showed more intense microvilli destruction in Caco-2 cells exposed to EH41 when compared to those exposed to Ec472/01. Altogether, these results show that EH41 expresses virulence genes, inducing a distinctive host cell response, and is likely associated with severe pathogenicity.

Identification of KIF18B as a Hub Candidate Gene in the Metastasis of Clear Cell Renal Cell Carcinoma by Weighted Gene Co-expression Network Analysis

“…WGCNA have been widely used in various medical fields, such as tumor ( Xiang Z. et al, 2019 ), neurological and psychiatric disorders ( Katrinli et al, 2019 ; Tang and Liu, 2019 ), chronic disease ( Chen et al, 2019 ), and infectious diseases ( Bando et al, 2019 ). What’s more, most of the conclusions drawn from WGCNA can be further confirmed by bioinformatics analyses or biological experiments, which guarantees the high reliability of WGCNA.…”

Enteroinvasive <em>Escherichia Coli</em> is Inefficient to Escape from Autophagy during Caco-2 Cellular Infection

“…Transcriptomic and gene co-expression network analyses have been successfully used for unveiling the molecular mechanisms involved in pathogenic E. coli virulence [20,21]. Thus, to get a better understanding of the virulence differences between EIEC and S. flexneri we investigated the transcriptomic mechanisms involved in the responses of Caco-2 cells infected with EIEC or with S. flexneri.…”

Human Leukocyte Transcriptional Response to SARS-CoV-2 Infection

“…The availability of platforms for large scale gene expression analysis-mainly DNA microarrays and next generation sequencing (NGS)-has made it possible for immune response studies to migrate from a reductionist approach to one of systems biology (15), enabling a global perception of the molecular, cellular, and tissue events involved in the different types of immune response (16). Studies at this new global transcriptome scale have permitted, for instance, a better understanding of the innate and adaptative immune responses, of the defense mechanisms against different pathogens, and the evaluation of the responses to vaccination (17)(18)(19)(20)(21).…”