Federica Gigliucci, F. A. Bastiaan von Meijenfeldt, Arnold Knijn, Valeria Michelacci, Gaia Scavia, Fabio Minelli, Bas E. Dutilh, Hamideh M. Ahmad, Gerwin C. Raangs, Alex W. Friedrich, John W. A. Rossen, Stefano Morabito
Abstract
The human intestinal microbiota is a homeostatic ecosystem with a remarkable impact on human health and the disruption of this equilibrium leads to an increased susceptibility to infection by numerous pathogens. In this study, we used shotgun metagenomic sequencing and two different bioinformatic approaches, based on mapping of the reads onto databases and on the reconstruction of putative draft genomes, to investigate possible changes in the composition of the intestinal microbiota in samples from patients with Shiga Toxin-producingE. coli(STEC) infection compared to healthy and healed controls, collected during an outbreak caused by a STEC O26:H11 infection. Both the bioinformatic procedures used, produced similar result with a good resolution of the taxonomic profiles of the specimens. The stool samples collected from the STEC infected patients showed a lower abundance of the members ofBifidobacterialesandClostridialesorders in comparison to controls where those microorganisms predominated. These differences seemed to correlate with the STEC infection although a flexion in the relative abundance of the Bifidobacterium genus, part of theBifidobacterialesorder, was observed also in samples from Crohn's disease patients, displaying a STEC-unrelated dysbiosis. The metagenomics also allowed to identify in the STEC positive samples, all the virulence traits present in the genomes of the STEC O26 that caused the outbreak as assessed through isolation of the epidemic strain and whole genome sequencing. The results shown represent a first evidence of the changes occurring in the intestinal microbiota of children in the course of STEC infection and indicate that metagenomics may be a promising tool for the culture-independent clinical diagnosis of the infection.
This research output has an Altmetric Attention Score of 14. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 January 2019.
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