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      Antimicrobial resistance and population genomics of multidrug-resistant Escherichia coli in pig farms in mainland China

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          Abstract

          The expanding use of antimicrobials in livestock is an important contributor to the worldwide rapid increase in antimicrobial resistance (AMR). However, large-scale studies on AMR in livestock remain scarce. Here, we report findings from surveillance of E. coli AMR in pig farms in China in 2018–2019. We isolated E. coli in 1,871 samples from pigs and their breeding environments, and found AMR in E. coli in all provinces in mainland China. We detected multidrug-resistance in 91% isolates and found resistance to last-resort drugs including colistin, carbapenems and tigecycline. We also identified a heterogeneous group of O-serogroups and sequence types among the multidrug-resistant isolates. These isolates harbored multiple resistance genes, virulence factor-encoding genes, and putative plasmids. Our data will help to understand the current AMR profiles of pigs and provide a reference for AMR control policy formulation for livestock in China.

          Abstract

          Use of antimicrobials in livestock contributes to development of antimicrobial resistance but there are few large-scale surveillance studies. Here, the authors describe E. coli surveillance in pig farms in China, reporting high levels of multidrug-resistance across all mainland provinces.

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          FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments

          Morgan N. Price, Paramvir S Dehal, Adam Arkin (2010)
          Background We recently described FastTree, a tool for inferring phylogenies for alignments with up to hundreds of thousands of sequences. Here, we describe improvements to FastTree that improve its accuracy without sacrificing scalability. Methodology/Principal Findings Where FastTree 1 used nearest-neighbor interchanges (NNIs) and the minimum-evolution criterion to improve the tree, FastTree 2 adds minimum-evolution subtree-pruning-regrafting (SPRs) and maximum-likelihood NNIs. FastTree 2 uses heuristics to restrict the search for better trees and estimates a rate of evolution for each site (the “CAT” approximation). Nevertheless, for both simulated and genuine alignments, FastTree 2 is slightly more accurate than a standard implementation of maximum-likelihood NNIs (PhyML 3 with default settings). Although FastTree 2 is not quite as accurate as methods that use maximum-likelihood SPRs, most of the splits that disagree are poorly supported, and for large alignments, FastTree 2 is 100–1,000 times faster. FastTree 2 inferred a topology and likelihood-based local support values for 237,882 distinct 16S ribosomal RNAs on a desktop computer in 22 hours and 5.8 gigabytes of memory. Conclusions/Significance FastTree 2 allows the inference of maximum-likelihood phylogenies for huge alignments. FastTree 2 is freely available at http://www.microbesonline.org/fasttree.
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            The RAST Server: Rapid Annotations using Subsystems Technology

            Ramy Aziz, Daniela Bartels, Aaron A. Best … (2008)
            Background The number of prokaryotic genome sequences becoming available is growing steadily and is growing faster than our ability to accurately annotate them. Description We describe a fully automated service for annotating bacterial and archaeal genomes. The service identifies protein-encoding, rRNA and tRNA genes, assigns functions to the genes, predicts which subsystems are represented in the genome, uses this information to reconstruct the metabolic network and makes the output easily downloadable for the user. In addition, the annotated genome can be browsed in an environment that supports comparative analysis with the annotated genomes maintained in the SEED environment. The service normally makes the annotated genome available within 12–24 hours of submission, but ultimately the quality of such a service will be judged in terms of accuracy, consistency, and completeness of the produced annotations. We summarize our attempts to address these issues and discuss plans for incrementally enhancing the service. Conclusion By providing accurate, rapid annotation freely to the community we have created an important community resource. The service has now been utilized by over 120 external users annotating over 350 distinct genomes.
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              Identification of acquired antimicrobial resistance genes

              Ea Zankari, Henrik Hasman, Salvatore Cosentino … (2012)
              Objectives Identification of antimicrobial resistance genes is important for understanding the underlying mechanisms and the epidemiology of antimicrobial resistance. As the costs of whole-genome sequencing (WGS) continue to decline, it becomes increasingly available in routine diagnostic laboratories and is anticipated to substitute traditional methods for resistance gene identification. Thus, the current challenge is to extract the relevant information from the large amount of generated data. Methods We developed a web-based method, ResFinder that uses BLAST for identification of acquired antimicrobial resistance genes in whole-genome data. As input, the method can use both pre-assembled, complete or partial genomes, and short sequence reads from four different sequencing platforms. The method was evaluated on 1862 GenBank files containing 1411 different resistance genes, as well as on 23 de- novo-sequenced isolates. Results When testing the 1862 GenBank files, the method identified the resistance genes with an ID = 100% (100% identity) to the genes in ResFinder. Agreement between in silico predictions and phenotypic testing was found when the method was further tested on 23 isolates of five different bacterial species, with available phenotypes. Furthermore, ResFinder was evaluated on WGS chromosomes and plasmids of 30 isolates. Seven of these isolates were annotated to have antimicrobial resistance, and in all cases, annotations were compatible with the ResFinder results. Conclusions A web server providing a convenient way of identifying acquired antimicrobial resistance genes in completely sequenced isolates was created. ResFinder can be accessed at www.genomicepidemiology.org. ResFinder will continuously be updated as new resistance genes are identified.
              Article 0 comments Cited 1220 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Xiangru Wang:
                ORCID: http://orcid.org/0000-0003-0191-1378
                wangxr228@mail.hzau.edu.cn
                Journal
                Journal ID (nlm-ta): Nat Commun
                Journal ID (iso-abbrev): Nat Commun
                Title: Nature Communications
                Publisher: Nature Publishing Group UK (London )
                ISSN (Electronic): 2041-1723
                Publication date (Electronic): 2 March 2022
                Publication date PMC-release: 2 March 2022
                Publication date Collection: 2022
                Volume: 13
                Electronic Location Identifier: 1116
                Affiliations
                [1 ]GRID grid.35155.37, ISNI 0000 0004 1790 4137, State Key Laboratory of Agricultural Microbiology, College of Veterinary Medicine, , Huazhong Agricultural University, ; 430070 Wuhan, China
                [2 ]GRID grid.35155.37, ISNI 0000 0004 1790 4137, Key Laboratory of Preventive Veterinary Medicine in Hubei Province, , Cooperative Innovation Centre for Sustainable Pig Production, ; 430070 Wuhan, China
                [3 ]GRID grid.35155.37, ISNI 0000 0004 1790 4137, MOA Key Laboratory of Food Safety Evaluation/National Reference Laboratory of Veterinary Drug Residue (HZAU), , Huazhong Agricultural University, ; 430070 Wuhan, China
                [4 ]Shanghai MasScience Biotechnology Institute, Shanghai, China
                Author information
                http://orcid.org/0000-0001-5249-328X
                http://orcid.org/0000-0001-9078-386X
                http://orcid.org/0000-0003-0191-1378
                Article
                Publisher ID: 28750
                DOI: 10.1038/s41467-022-28750-6
                PMC ID: 8891348
                PubMed ID: 35236849
                SO-VID: 6647eae6-ff17-44de-9f6b-2685c678a72b
                Copyright © © The Author(s) 2022
                License:

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                Date received : 6 May 2021
                Date accepted : 3 February 2022
                Funding
                Funded by: the National Natural Science Foundation of China (grant number: 31902241), the Natural Science Foundation of Hubei Province (grant number: 2020CFB525), China Postdoctoral Science Foundation (grant number: 2018M640719)
                Funded by: the China Agriculture Research System of MOF and MARA
                Funded by: the National Key R&D Program of China (grant number: 2017YFC1600100), the National Natural Science Foundation of China (grant number: 32122086), Walmart Foundation (Project number: 61626817) and Walmart Food Safety Collaboration Center
                Categories
                Subject: Article
                Custom metadata
                issue-copyright-statement © The Author(s) 2022

                ScienceOpen disciplines: Uncategorized
                Keywords: antimicrobial resistance,bacteriology,genome informatics
                Data availability:
                ScienceOpen disciplines: Uncategorized
                Keywords: antimicrobial resistance, bacteriology, genome informatics

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