Identification of Primary Antimicrobial Resistance Drivers in Agricultural Nontyphoidal Salmonella enterica Serovars by Using Machine Learning
Identifieur interne : 000282 ( Pmc/Checkpoint ); précédent : 000281; suivant : 000283Identification of Primary Antimicrobial Resistance Drivers in Agricultural Nontyphoidal Salmonella enterica Serovars by Using Machine Learning
Auteurs : Finlay Maguire [Canada] ; Muhammad Attiq Rehman [Canada] ; Catherine Carrillo [Canada] ; Moussa S. Diarra [Canada] ; Robert G. Beiko [Canada]Source :
- mSystems [ 2379-5077 ] ; 2019.
Abstract
Antimicrobial resistance (AMR) represents an existential threat to the function of modern medicine. Genomics and machine learning methods are being increasingly used to analyze and predict AMR. This type of surveillance is very important to try to reduce the impact of AMR. Machine learning models are typically trained using genomic data, but the aspects of the genomes that they use to make predictions are rarely analyzed. In this work, we showed how, by using different types of machine learning models and performing this analysis, it is possible to identify the key genes underlying AMR in nontyphoidal
Url:
DOI: 10.1128/mSystems.00211-19
PubMed: 31387929
PubMed Central: 6687941
Affiliations:
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Identification of Primary Antimicrobial Resistance Drivers in Agricultural Nontyphoidal <named-content content-type="genus-species">Salmonella enterica</named-content> Serovars by Using Machine Learning</title><author><name sortKey="Maguire, Finlay" sort="Maguire, Finlay" uniqKey="Maguire F" first="Finlay" last="Maguire">Finlay Maguire</name><affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada</addr-line></nlm:aff><country xml:lang="fr">Canada</country><wicri:regionArea>Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia</wicri:regionArea><wicri:noRegion>Nova Scotia</wicri:noRegion></affiliation></author><author><name sortKey="Rehman, Muhammad Attiq" sort="Rehman, Muhammad Attiq" uniqKey="Rehman M" first="Muhammad Attiq" last="Rehman">Muhammad Attiq Rehman</name><affiliation wicri:level="1"><nlm:aff id="aff2"><addr-line>Guelph Research and Development Center, Agriculture and Agri-Food Canada (AAFC), Guelph, Ontario, Canada</addr-line></nlm:aff><country xml:lang="fr">Canada</country><wicri:regionArea>Guelph Research and Development Center, Agriculture and Agri-Food Canada (AAFC), Guelph, Ontario</wicri:regionArea><wicri:noRegion>Ontario</wicri:noRegion></affiliation></author><author><name sortKey="Carrillo, Catherine" sort="Carrillo, Catherine" uniqKey="Carrillo C" first="Catherine" last="Carrillo">Catherine Carrillo</name><affiliation wicri:level="1"><nlm:aff id="aff3"><addr-line>Canadian Food Inspection Agency (CFIA), Ottawa, Ontario, Canada</addr-line></nlm:aff><country xml:lang="fr">Canada</country><wicri:regionArea>Canadian Food Inspection Agency (CFIA), Ottawa, Ontario</wicri:regionArea><wicri:noRegion>Ontario</wicri:noRegion></affiliation></author><author><name sortKey="Diarra, Moussa S" sort="Diarra, Moussa S" uniqKey="Diarra M" first="Moussa S." last="Diarra">Moussa S. Diarra</name><affiliation wicri:level="1"><nlm:aff id="aff2"><addr-line>Guelph Research and Development Center, Agriculture and Agri-Food Canada (AAFC), Guelph, Ontario, Canada</addr-line></nlm:aff><country xml:lang="fr">Canada</country><wicri:regionArea>Guelph Research and Development Center, Agriculture and Agri-Food Canada (AAFC), Guelph, Ontario</wicri:regionArea><wicri:noRegion>Ontario</wicri:noRegion></affiliation></author><author><name sortKey="Beiko, Robert G" sort="Beiko, Robert G" uniqKey="Beiko R" first="Robert G." last="Beiko">Robert G. Beiko</name><affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada</addr-line></nlm:aff><country xml:lang="fr">Canada</country><wicri:regionArea>Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia</wicri:regionArea><wicri:noRegion>Nova Scotia</wicri:noRegion></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">31387929</idno><idno type="pmc">6687941</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6687941</idno><idno type="RBID">PMC:6687941</idno><idno type="doi">10.1128/mSystems.00211-19</idno><date when="2019">2019</date><idno type="wicri:Area/Pmc/Corpus">001256</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">001256</idno><idno type="wicri:Area/Pmc/Curation">001256</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">001256</idno><idno type="wicri:Area/Pmc/Checkpoint">000282</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">000282</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Identification of Primary Antimicrobial Resistance Drivers in Agricultural Nontyphoidal <named-content content-type="genus-species">Salmonella enterica</named-content> Serovars by Using Machine Learning</title><author><name sortKey="Maguire, Finlay" sort="Maguire, Finlay" uniqKey="Maguire F" first="Finlay" last="Maguire">Finlay Maguire</name><affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada</addr-line></nlm:aff><country xml:lang="fr">Canada</country><wicri:regionArea>Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia</wicri:regionArea><wicri:noRegion>Nova Scotia</wicri:noRegion></affiliation></author><author><name sortKey="Rehman, Muhammad Attiq" sort="Rehman, Muhammad Attiq" uniqKey="Rehman M" first="Muhammad Attiq" last="Rehman">Muhammad Attiq Rehman</name><affiliation wicri:level="1"><nlm:aff id="aff2"><addr-line>Guelph Research and Development Center, Agriculture and Agri-Food Canada (AAFC), Guelph, Ontario, Canada</addr-line></nlm:aff><country xml:lang="fr">Canada</country><wicri:regionArea>Guelph Research and Development Center, Agriculture and Agri-Food Canada (AAFC), Guelph, Ontario</wicri:regionArea><wicri:noRegion>Ontario</wicri:noRegion></affiliation></author><author><name sortKey="Carrillo, Catherine" sort="Carrillo, Catherine" uniqKey="Carrillo C" first="Catherine" last="Carrillo">Catherine Carrillo</name><affiliation wicri:level="1"><nlm:aff id="aff3"><addr-line>Canadian Food Inspection Agency (CFIA), Ottawa, Ontario, Canada</addr-line></nlm:aff><country xml:lang="fr">Canada</country><wicri:regionArea>Canadian Food Inspection Agency (CFIA), Ottawa, Ontario</wicri:regionArea><wicri:noRegion>Ontario</wicri:noRegion></affiliation></author><author><name sortKey="Diarra, Moussa S" sort="Diarra, Moussa S" uniqKey="Diarra M" first="Moussa S." last="Diarra">Moussa S. Diarra</name><affiliation wicri:level="1"><nlm:aff id="aff2"><addr-line>Guelph Research and Development Center, Agriculture and Agri-Food Canada (AAFC), Guelph, Ontario, Canada</addr-line></nlm:aff><country xml:lang="fr">Canada</country><wicri:regionArea>Guelph Research and Development Center, Agriculture and Agri-Food Canada (AAFC), Guelph, Ontario</wicri:regionArea><wicri:noRegion>Ontario</wicri:noRegion></affiliation></author><author><name sortKey="Beiko, Robert G" sort="Beiko, Robert G" uniqKey="Beiko R" first="Robert G." last="Beiko">Robert G. Beiko</name><affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada</addr-line></nlm:aff><country xml:lang="fr">Canada</country><wicri:regionArea>Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia</wicri:regionArea><wicri:noRegion>Nova Scotia</wicri:noRegion></affiliation></author></analytic><series><title level="j">mSystems</title><idno type="eISSN">2379-5077</idno><imprint><date when="2019">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Antimicrobial resistance (AMR) represents an existential threat to the function of modern medicine. Genomics and machine learning methods are being increasingly used to analyze and predict AMR. This type of surveillance is very important to try to reduce the impact of AMR. Machine learning models are typically trained using genomic data, but the aspects of the genomes that they use to make predictions are rarely analyzed. In this work, we showed how, by using different types of machine learning models and performing this analysis, it is possible to identify the key genes underlying AMR in nontyphoidal <italic>Salmonella</italic> (NTS). NTS is among the leading cause of foodborne illness globally; however, AMR in NTS has not been heavily studied within the food chain itself. Therefore, in this work we performed a broad-scale analysis of the AMR in NTS isolates from commercial chicken farms and identified some priority AMR genes for surveillance.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Gardy, Jl" uniqKey="Gardy J">JL Gardy</name></author><author><name sortKey="Loman, Nj" uniqKey="Loman N">NJ Loman</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Robinson, T" uniqKey="Robinson T">T Robinson</name></author><author><name sortKey="Bu, D" uniqKey="Bu D">D Bu</name></author><author><name sortKey="Carrique Mas, J" uniqKey="Carrique Mas J">J Carrique-Mas</name></author><author><name sortKey="Fevre, E" uniqKey="Fevre E">E Fèvre</name></author><author><name sortKey="Gilbert, M" uniqKey="Gilbert M">M Gilbert</name></author><author><name sortKey="Grace, D" uniqKey="Grace D">D Grace</name></author><author><name sortKey="Hay, S" uniqKey="Hay S">S 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sortKey="Olsen, Gj" uniqKey="Olsen G">GJ Olsen</name></author><author><name sortKey="Davis, Jj" uniqKey="Davis J">JJ Davis</name></author><author><name sortKey="Disz, T" uniqKey="Disz T">T Disz</name></author><author><name sortKey="Edwards, Ra" uniqKey="Edwards R">RA Edwards</name></author><author><name sortKey="Gerdes, S" uniqKey="Gerdes S">S Gerdes</name></author><author><name sortKey="Parrello, B" uniqKey="Parrello B">B Parrello</name></author><author><name sortKey="Shukla, M" uniqKey="Shukla M">M Shukla</name></author><author><name sortKey="Vonstein, V" uniqKey="Vonstein V">V Vonstein</name></author><author><name sortKey="Wattam, A" uniqKey="Wattam A">A Wattam</name></author><author><name sortKey="Xia, F" uniqKey="Xia F">F Xia</name></author><author><name sortKey="Stevens, R" uniqKey="Stevens R">R Stevens</name></author></analytic></biblStruct></listBibl></div1></back></TEI><pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">mSystems</journal-id><journal-id journal-id-type="iso-abbrev">mSystems</journal-id><journal-id journal-id-type="hwp">msys</journal-id><journal-id journal-id-type="pmc">msys</journal-id><journal-id journal-id-type="publisher-id">mSystems</journal-id><journal-title-group><journal-title>mSystems</journal-title></journal-title-group><issn pub-type="epub">2379-5077</issn><publisher><publisher-name>American Society for Microbiology</publisher-name><publisher-loc>1752 N St., N.W., Washington, DC</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">31387929</article-id><article-id pub-id-type="pmc">6687941</article-id><article-id pub-id-type="publisher-id">mSystems00211-19</article-id><article-id pub-id-type="doi">10.1128/mSystems.00211-19</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="overline"><subject>Clinical Science and Epidemiology</subject></subj-group></article-categories><title-group><article-title>Identification of Primary Antimicrobial Resistance Drivers in Agricultural Nontyphoidal <named-content content-type="genus-species">Salmonella enterica</named-content> Serovars by Using Machine Learning</article-title><alt-title alt-title-type="running-head">Nontyphoidal <italic>Salmonella</italic> AMR Analysis and Prediction</alt-title><alt-title alt-title-type="short-authors">Maguire et al.</alt-title></title-group><contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="false">https://orcid.org/0000-0002-1203-9514</contrib-id><name><surname>Maguire</surname><given-names>Finlay</given-names></name><xref ref-type="aff" rid="aff1"><sup>a</sup></xref></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="false">https://orcid.org/0000-0002-2062-1969</contrib-id><name><surname>Rehman</surname><given-names>Muhammad Attiq</given-names></name><xref ref-type="aff" rid="aff2"><sup>b</sup></xref></contrib><contrib contrib-type="author"><name><surname>Carrillo</surname><given-names>Catherine</given-names></name><xref ref-type="aff" rid="aff3"><sup>c</sup></xref></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid" authenticated="false">https://orcid.org/0000-0003-2966-345X</contrib-id><name><surname>Diarra</surname><given-names>Moussa S.</given-names></name><xref ref-type="aff" rid="aff2"><sup>b</sup></xref></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="false">https://orcid.org/0000-0002-5065-4980</contrib-id><name><surname>Beiko</surname><given-names>Robert G.</given-names></name><xref ref-type="aff" rid="aff1"><sup>a</sup></xref></contrib><aff id="aff1"><label>a</label><addr-line>Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada</addr-line></aff><aff id="aff2"><label>b</label><addr-line>Guelph Research and Development Center, Agriculture and Agri-Food Canada (AAFC), Guelph, Ontario, Canada</addr-line></aff><aff id="aff3"><label>c</label><addr-line>Canadian Food Inspection Agency (CFIA), Ottawa, Ontario, Canada</addr-line></aff></contrib-group><contrib-group><contrib contrib-type="editor"><name><surname>Gilbert</surname><given-names>Jack A.</given-names></name><role>Editor</role><aff>University of California San Diego</aff></contrib></contrib-group><author-notes><corresp id="cor1">Address correspondence to Moussa S. Diarra, <email>moussa.diarra@canada.ca</email>.</corresp><fn fn-type="other"><p><bold>Citation</bold> Maguire F, Rehman MA, Carrillo C, Diarra MS, Beiko RG. 2019. Identification of primary antimicrobial resistance drivers in agricultural nontyphoidal <italic>Salmonella enterica</italic> serovars by using machine learning. mSystems 4:e00211-19. <ext-link ext-link-type="uri" xlink:href="https://doi.org/10.1128/mSystems.00211-19">https://doi.org/10.1128/mSystems.00211-19</ext-link>.</p></fn></author-notes><pub-date pub-type="epub"><day>6</day><month>8</month><year>2019</year></pub-date><pub-date pub-type="collection"><season>Jul-Aug</season><year>2019</year></pub-date><volume>4</volume><issue>4</issue><elocation-id>e00211-19</elocation-id><history><date date-type="received"><day>22</day><month>3</month><year>2019</year></date><date date-type="accepted"><day>11</day><month>7</month><year>2019</year></date></history><permissions><copyright-statement>© Crown copyright 2019.</copyright-statement><copyright-year>2019</copyright-year><copyright-holder>Crown</copyright-holder><license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/"><license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International license</ext-link>.</license-p></license></permissions><self-uri content-type="pdf" xlink:href="mSystems.00211-19.pdf"></self-uri><abstract abstract-type="precis"><p>Antimicrobial resistance (AMR) represents an existential threat to the function of modern medicine. Genomics and machine learning methods are being increasingly used to analyze and predict AMR. This type of surveillance is very important to try to reduce the impact of AMR. Machine learning models are typically trained using genomic data, but the aspects of the genomes that they use to make predictions are rarely analyzed. In this work, we showed how, by using different types of machine learning models and performing this analysis, it is possible to identify the key genes underlying AMR in nontyphoidal <italic>Salmonella</italic> (NTS). NTS is among the leading cause of foodborne illness globally; however, AMR in NTS has not been heavily studied within the food chain itself. Therefore, in this work we performed a broad-scale analysis of the AMR in NTS isolates from commercial chicken farms and identified some priority AMR genes for surveillance.</p></abstract><abstract><title>ABSTRACT</title><p>Nontyphoidal <italic>Salmonella</italic> (NTS) is a leading global cause of bacterial foodborne morbidity and mortality. Our ability to treat severe NTS infections has been impaired by increasing antimicrobial resistance (AMR). To understand and mitigate the global health crisis AMR represents, we need to link the observed resistance phenotypes with their underlying genomic mechanisms. Broiler chickens represent a key reservoir and vector for NTS infections, but isolates from this setting have been characterized in only very low numbers relative to clinical isolates. In this study, we sequenced and assembled 97 genomes encompassing 7 serotypes isolated from broiler chicken in farms in British Columbia between 2005 and 2008. Through application of machine learning (ML) models to predict the observed AMR phenotype from this genomic data, we were able to generate highly (0.92 to 0.99) precise logistic regression models using known AMR gene annotations as features for 7 antibiotics (amoxicillin-clavulanic acid, ampicillin, cefoxitin, ceftiofur, ceftriaxone, streptomycin, and tetracycline). Similarly, we also trained “reference-free” k-mer-based set-covering machine phenotypic prediction models (0.91 to 1.0 precision) for these antibiotics. By combining the inferred k-mers and logistic regression weights, we identified the primary drivers of AMR for the 7 studied antibiotics in these isolates. With our research representing one of the largest studies of a diverse set of NTS isolates from broiler chicken, we can thus confirm that the <italic>AmpC</italic>-like <italic>CMY-2</italic> β-lactamase is a primary driver of β-lactam resistance and that the phosphotransferases <italic>APH(6)-Id</italic> and <italic>APH(3″-Ib)</italic> are the principal drivers of streptomycin resistance in this important ecosystem.</p><p><bold>IMPORTANCE</bold> Antimicrobial resistance (AMR) represents an existential threat to the function of modern medicine. Genomics and machine learning methods are being increasingly used to analyze and predict AMR. This type of surveillance is very important to try to reduce the impact of AMR. Machine learning models are typically trained using genomic data, but the aspects of the genomes that they use to make predictions are rarely analyzed. In this work, we showed how, by using different types of machine learning models and performing this analysis, it is possible to identify the key genes underlying AMR in nontyphoidal <italic>Salmonella</italic> (NTS). NTS is among the leading cause of foodborne illness globally; however, AMR in NTS has not been heavily studied within the food chain itself. Therefore, in this work we performed a broad-scale analysis of the AMR in NTS isolates from commercial chicken farms and identified some priority AMR genes for surveillance.</p></abstract><kwd-group><title>KEYWORDS</title><kwd>AMR prediction</kwd><kwd><italic>Salmonella</italic></kwd><kwd>antimicrobial resistance</kwd><kwd>food chain</kwd><kwd>genomics</kwd><kwd>machine learning</kwd></kwd-group><funding-group><award-group id="award1"><funding-source><institution-wrap><institution>Donald Hill Family Fellow</institution></institution-wrap></funding-source><principal-award-recipient><name><surname>Maguire</surname><given-names>Finlay</given-names></name></principal-award-recipient></award-group><award-group id="award2"><funding-source><institution-wrap><institution>Gouvernement du Canada | Canadian Institutes of Health Research (CIHR)</institution><institution-id>https://doi.org/10.13039/501100000024</institution-id></institution-wrap></funding-source><principal-award-recipient><name><surname>Beiko</surname><given-names>Robert G.</given-names></name></principal-award-recipient><principal-award-recipient><name><surname>Maguire</surname><given-names>Finlay</given-names></name></principal-award-recipient></award-group><award-group id="award3"><funding-source><institution-wrap><institution>Genome Canada (Génome Canada)</institution><institution-id>https://doi.org/10.13039/100008762</institution-id></institution-wrap></funding-source><principal-award-recipient><name><surname>Maguire</surname><given-names>Finlay</given-names></name></principal-award-recipient></award-group><award-group id="award4"><funding-source><institution-wrap><institution>Genome Canada (Génome Canada)</institution><institution-id>https://doi.org/10.13039/100008762</institution-id></institution-wrap></funding-source><principal-award-recipient><name><surname>Beiko</surname><given-names>Robert G.</given-names></name></principal-award-recipient></award-group></funding-group><counts><count count="9" count-type="supplementary-material"></count><fig-count count="5"></fig-count><table-count count="0"></table-count><equation-count count="0"></equation-count><ref-count count="96"></ref-count><page-count count="17"></page-count><word-count count="11714"></word-count></counts><custom-meta-group><custom-meta><meta-name>cover-date</meta-name><meta-value>July/August 2019</meta-value></custom-meta></custom-meta-group></article-meta></front></pmc><affiliations><list><country><li>Canada</li></country></list><tree><country name="Canada"><noRegion><name sortKey="Maguire, Finlay" sort="Maguire, Finlay" uniqKey="Maguire F" first="Finlay" last="Maguire">Finlay Maguire</name></noRegion><name sortKey="Beiko, Robert G" sort="Beiko, Robert G" uniqKey="Beiko R" first="Robert G." last="Beiko">Robert G. Beiko</name><name sortKey="Carrillo, Catherine" sort="Carrillo, Catherine" uniqKey="Carrillo C" first="Catherine" last="Carrillo">Catherine Carrillo</name><name sortKey="Diarra, Moussa S" sort="Diarra, Moussa S" uniqKey="Diarra M" first="Moussa S." last="Diarra">Moussa S. Diarra</name><name sortKey="Rehman, Muhammad Attiq" sort="Rehman, Muhammad Attiq" uniqKey="Rehman M" first="Muhammad Attiq" last="Rehman">Muhammad Attiq Rehman</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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