Genome-wide association studies of Shigella spp. and Enteroinvasive Escherichia coli isolates demonstrate an absence of genetic markers for prediction of disease severity

Abstract

Background We investigated the association of symptoms and disease severity of shigellosis patients with genetic determinants of infecting Shigella and entero-invasive Escherichia coli (EIEC), because determinants that predict disease outcome per individual patient could be used to prioritize control measures. For this purpose, genome wide association studies (GWAS) were performed using presence or absence of single genes, combinations of genes, and k-mers. All genetic variants were derived from draft genome sequences of isolates from a multicenter cross-sectional study conducted in the Netherlands during 2016 and 2017. Clinical data of patients consisting of binary/dichotomous representation of symptoms and their calculated severity scores were also available from this study. To verify the suitability of the used methods, the genetic differences between the genera Shigella and Escherichia were used as control. Results The obtained isolates were representative for a population structure as encountered in a Western European country. No association was found between single genes or combinations of genes and separate symptoms or disease severity scores. One potentially associated intergenic region was found using a k-mer approach, however, this turned out to be a false positive. Our benchmark characteristic, genus, resulted in eight associated genes and >3,000,000 k-mers, indicating adequate performance of the used algorithms. Conclusions To conclude, using several microbial GWAS methods, genetic variants in Shigella spp. and EIEC that can predict specific symptoms or a more severe course of disease were not identified, suggesting that disease severity of shigellosis is dependent on other factors than the genetic variation of the infecting bacteria. Specific genes or gene fragments of isolates from patients are unsuitable to predict outcomes and cannot be used for development, prioritization and optimization of guidelines for control measures of shigellosis or infections with EIEC.

Pathogenicity Factors of Genomic Islands in Intestinal and Extraintestinal Escherichia coli

“…Differences in the virulence level have sometimes been put forward, with Shigella considered hyper-virulent relative to EIEC, which generally induces less severe disease. Nevertheless, significant exceptions do exist and the intestinal illness is in fact indistinguishable ( Parsot, 2005 ; Michelacci et al, 2016 ; Belotserkovsky and Sansonetti, 2018 ; Hendriks et al, 2020 ). As with EHEC/STEC, the search for genetic markers that could solely explain the difference in the virulence levels might have somehow hindered the potential in the heterogeneity of the regulation of the genetic expression from one EIEC/ Shigella strain to another as well as between individual cells in an isogenic population.…”

Virulence factors of Enteric Pathogenic Escherichia coli: A Review

“…EIEC/Shigella infection commences with penetration of the pathogen into the epithelial cells in the colon, passing through the microfold cells and reaching the underlying submucosa by a transcytosis mechanism [78,79]. Disruption and damage of tight junctions caused by inflammation also give EIEC access to the underlying submucosa [80].…”

“…SigA encoded cytotoxin contributes to intestinal fluid accumulation in the EIEC or Shigella infected host [86]. There are also some other virulence factors recently been detected and identified prevalently in EIEC and Shigella isolates, including iutA, iucB, EatA, VirF, VirB, IpaJ and OspC3 implicated for aerobactin synthesis, complex siderophore iron receptor, SPATE toxin, regulation of virulence factor gene expression, control of virulence factor gene synthesis, inhibition of host cell trafficking membrane and inhibition of inflammasomes, respectively (Table) [72,79].…”

Genome-wide association studies of Shigella spp. and Enteroinvasive Escherichia coli isolates demonstrate an absence of genetic markers for prediction of disease severity

“…Output was generated as a list of associated genes per characteristic with their best pairwise comparison pvalues, sensitivity, and specificity. For each characteristic, as benchmark, a 1000 random datasets were created by shuffling the original traits randomly for a thousand times using a custom script (59).…”

“…For each symptom and severity scale, 1000 genes with the lowest 'best pairwise p-value' were used, this p-value takes population structure into account. The observed p-values of the traits were log transformed and plotted against the log transformed expected p-values of the permutation benchmark using a custom script (59). For the characteristic 'genus', Benjamini-Hochberg's method for multiple comparisons correction is used instead of pairwise p-values as the latter cannot be used to find genetic differences between the species and genera.…”