An accurate and interpretable model for antimicrobial resistance in pathogenic Escherichia coli from livestock and companion animal species
Understanding the microbial genomic contributors to antimicrobial resistance (AMR) is essential for early detection of emerging AMR infections, a pressing global health threat in human and veterinary medicine. Here we used whole genome sequencing and antibiotic susceptibility test data from 980 dise...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2023-01-01
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| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10449230/?tool=EBI |
| _version_ | 1797736616353071104 |
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| author | Henri C. Chung Christine L. Foxx Jessica A. Hicks Tod P. Stuber Iddo Friedberg Karin S. Dorman Beth Harris |
| author_facet | Henri C. Chung Christine L. Foxx Jessica A. Hicks Tod P. Stuber Iddo Friedberg Karin S. Dorman Beth Harris |
| author_sort | Henri C. Chung |
| collection | DOAJ |
| description | Understanding the microbial genomic contributors to antimicrobial resistance (AMR) is essential for early detection of emerging AMR infections, a pressing global health threat in human and veterinary medicine. Here we used whole genome sequencing and antibiotic susceptibility test data from 980 disease causing Escherichia coli isolated from companion and farm animals to model AMR genotypes and phenotypes for 24 antibiotics. We determined the strength of genotype-to-phenotype relationships for 197 AMR genes with elastic net logistic regression. Model predictors were designed to evaluate different potential modes of AMR genotype translation into resistance phenotypes. Our results show a model that considers the presence of individual AMR genes and total number of AMR genes present from a set of genes known to confer resistance was able to accurately predict isolate resistance on average (mean F1 score = 98.0%, SD = 2.3%, mean accuracy = 98.2%, SD = 2.7%). However, fitted models sometimes varied for antibiotics in the same class and for the same antibiotic across animal hosts, suggesting heterogeneity in the genetic determinants of AMR resistance. We conclude that an interpretable AMR prediction model can be used to accurately predict resistance phenotypes across multiple host species and reveal testable hypotheses about how the mechanism of resistance may vary across antibiotics within the same class and across animal hosts for the same antibiotic. |
| first_indexed | 2024-03-12T13:16:29Z |
| format | Article |
| id | doaj.art-60e8855edec24a7784577d6d4ff6a5d9 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-03-12T13:16:29Z |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-60e8855edec24a7784577d6d4ff6a5d92023-08-27T05:31:59ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-01188An accurate and interpretable model for antimicrobial resistance in pathogenic Escherichia coli from livestock and companion animal speciesHenri C. ChungChristine L. FoxxJessica A. HicksTod P. StuberIddo FriedbergKarin S. DormanBeth HarrisUnderstanding the microbial genomic contributors to antimicrobial resistance (AMR) is essential for early detection of emerging AMR infections, a pressing global health threat in human and veterinary medicine. Here we used whole genome sequencing and antibiotic susceptibility test data from 980 disease causing Escherichia coli isolated from companion and farm animals to model AMR genotypes and phenotypes for 24 antibiotics. We determined the strength of genotype-to-phenotype relationships for 197 AMR genes with elastic net logistic regression. Model predictors were designed to evaluate different potential modes of AMR genotype translation into resistance phenotypes. Our results show a model that considers the presence of individual AMR genes and total number of AMR genes present from a set of genes known to confer resistance was able to accurately predict isolate resistance on average (mean F1 score = 98.0%, SD = 2.3%, mean accuracy = 98.2%, SD = 2.7%). However, fitted models sometimes varied for antibiotics in the same class and for the same antibiotic across animal hosts, suggesting heterogeneity in the genetic determinants of AMR resistance. We conclude that an interpretable AMR prediction model can be used to accurately predict resistance phenotypes across multiple host species and reveal testable hypotheses about how the mechanism of resistance may vary across antibiotics within the same class and across animal hosts for the same antibiotic.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10449230/?tool=EBI |
| spellingShingle | Henri C. Chung Christine L. Foxx Jessica A. Hicks Tod P. Stuber Iddo Friedberg Karin S. Dorman Beth Harris An accurate and interpretable model for antimicrobial resistance in pathogenic Escherichia coli from livestock and companion animal species PLoS ONE |
| title | An accurate and interpretable model for antimicrobial resistance in pathogenic Escherichia coli from livestock and companion animal species |
| title_full | An accurate and interpretable model for antimicrobial resistance in pathogenic Escherichia coli from livestock and companion animal species |
| title_fullStr | An accurate and interpretable model for antimicrobial resistance in pathogenic Escherichia coli from livestock and companion animal species |
| title_full_unstemmed | An accurate and interpretable model for antimicrobial resistance in pathogenic Escherichia coli from livestock and companion animal species |
| title_short | An accurate and interpretable model for antimicrobial resistance in pathogenic Escherichia coli from livestock and companion animal species |
| title_sort | accurate and interpretable model for antimicrobial resistance in pathogenic escherichia coli from livestock and companion animal species |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10449230/?tool=EBI |
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