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      Genomic Epidemiology of Shiga Toxin-Producing Escherichia coli Isolated from the Livestock-Food-Human Interface in South America

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          Abstract

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          Shiga toxin-producing Escherichia coli (STEC) are zoonotic pathogens that cause food-borne diseases in humans, where cattle and derived products play a key role as reservoirs and vehicles. We analyzed the genomic data of STEC strains circulating at the livestock-food-human interface in South America, extracting clinically and epidemiologically relevant information (serotypes, virulome, resistance genes, sequence types, and phylogenomics). This study included 130 STEC genomes obtained from cattle ( n = 51), beef ( n = 48), and human ( n = 31) samples. The successful expansion of O157:H7 (ST11) and non-O157 (ST16, ST21, ST223, ST443, ST677, ST679, ST2388) clones is highlighted, suggesting common activities, such as multilateral trade and travel. Circulating STEC strains analyzed exhibit high genomic diversity and harbor several genetic determinants associated with severe illness in humans, highlighting the need to establish official surveillance of this pathogen that should be focused on detecting molecular determinants of virulence and clonal relatedness, in the whole beef production chain.

          Abstract

          Shiga toxin-producing Escherichia coli (STEC) are zoonotic pathogens responsible for causing food-borne diseases in humans. While South America has the highest incidence of human STEC infections, information about the genomic characteristics of the circulating strains is scarce. The aim of this study was to analyze genomic data of STEC strains isolated in South America from cattle, beef, and humans; predicting the antibiotic resistome, serotypes, sequence types (STs), clonal complexes (CCs) and phylogenomic backgrounds. A total of 130 whole genome sequences of STEC strains were analyzed, where 39.2% were isolated from cattle, 36.9% from beef, and 23.8% from humans. The ST11 was the most predicted (20.8%) and included O-:H7 (10.8%) and O157:H7 (10%) serotypes. The successful expansion of non-O157 clones such as ST16/CC29-O111:H8 and ST21/CC29-O26:H11 is highlighted, suggesting multilateral trade and travel. Virulome analyses showed that the predominant stx subtype was stx2a (54.6%); most strains carried ehaA (96.2%), iha (91.5%) and lpfA (77.7%) genes. We present genomic data that can be used to support the surveillance of STEC strains circulating at the livestock-food-human interface in South America, in order to control the spread of critical clones “from farm to table”.

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          Fast and accurate short read alignment with Burrows–Wheeler transform

          Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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            Applied Logistic Regression

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              The EnteroBase user's guide, with case studies on Salmonella transmissions, Yersinia pestis phylogeny, and Escherichia core genomic diversity

              EnteroBase is an integrated software environment that supports the identification of global population structures within several bacterial genera that include pathogens. Here, we provide an overview of how EnteroBase works, what it can do, and its future prospects. EnteroBase has currently assembled more than 300,000 genomes from Illumina short reads from Salmonella , Escherichia , Yersinia , Clostridioides , Helicobacter , Vibrio , and Moraxella and genotyped those assemblies by core genome multilocus sequence typing (cgMLST). Hierarchical clustering of cgMLST sequence types allows mapping a new bacterial strain to predefined population structures at multiple levels of resolution within a few hours after uploading its short reads. Case Study 1 illustrates this process for local transmissions of Salmonella enterica serovar Agama between neighboring social groups of badgers and humans. EnteroBase also supports single nucleotide polymorphism (SNP) calls from both genomic assemblies and after extraction from metagenomic sequences, as illustrated by Case Study 2 which summarizes the microevolution of Yersinia pestis over the last 5000 years of pandemic plague. EnteroBase can also provide a global overview of the genomic diversity within an entire genus, as illustrated by Case Study 3, which presents a novel, global overview of the population structure of all of the species, subspecies, and clades within Escherichia .
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Animals (Basel)
                Animals (Basel)
                animals
                Animals : an Open Access Journal from MDPI
                MDPI
                2076-2615
                22 June 2021
                July 2021
                : 11
                : 7
                : 1845
                Affiliations
                [1 ]Departamento de Medicina Preventiva Animal, Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile, Santiago 8820808, Chile; fernando.sanchez@ 123456ug.uchile.cl (F.S.); beatrizescob@ 123456gmail.com (B.E.); llapierre@ 123456uchile.cl (L.L.); jacornej@ 123456uchile.cl (J.C.); ralegria@ 123456veterinaria.uchile.cl (R.A.-M.); victorneira@ 123456u.uchile.cl (V.N.)
                [2 ]Programa de Doctorado en Ciencias Silvoagropecuarias y Veterinarias, Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile, Santiago 8820808, Chile
                [3 ]Facultad de Ciencias Agropecuarias y Ambientales, Universidad Pedro de Valdivia, Santiago 8370007, Chile
                [4 ]Departamento de Fomento de la Producción Animal, Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile, Santiago 8820808, Chile; vmartine@ 123456uchile.cl
                [5 ]Department of Veterinary and Biomedical Sciences, College of Veterinary Medicine, University of Minnesota, St. Paul, MN 55108, USA; tjj@ 123456umn.edu
                [6 ]Departamento de Patología, Faculdade de Medicina Veterinária e Zootecnia, Universidade de São Paulo, São Paulo 05508-270, Brazil; dannyfuentesmv@ 123456gmail.com
                [7 ]Departamento de Microbiología, Instituto de Ciências Biomédicas, Universidade de São Paulo, São Paulo 05508-900, Brazil; eldersano@ 123456icb.usp.br (E.S.); lincopan@ 123456usp.br (N.L.)
                Author notes
                [* ]Correspondence: ngalarce@ 123456ug.uchile.cl
                Author information
                https://orcid.org/0000-0002-4288-898X
                https://orcid.org/0000-0001-5923-6219
                https://orcid.org/0000-0001-5641-9562
                https://orcid.org/0000-0002-1608-2499
                https://orcid.org/0000-0003-2845-4330
                https://orcid.org/0000-0003-0161-5800
                Article
                animals-11-01845
                10.3390/ani11071845
                8300192
                34206206
                0ce86394-03f7-4871-b3f7-6eb96fac369e
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 11 May 2021
                : 16 June 2021
                Categories
                Article

                stec,cattle,beef,molecular epidemiology,whole-genome sequencing,mlst,south america,one health

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