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Franklin AM, Weller DL, Durso LM, Bagley M, Davis BC, Frye JG, Grim CJ, Ibekwe AM, Jahne MA, Keely SP, Kraft AL, McConn BR, Mitchell RM, Ottesen AR, Sharma M, Strain EA, Tadesse DA, Tate H, Wells JE, Williams CF, Cook KL, Kabera C, McDermott PF, Garland JL. A one health approach for monitoring antimicrobial resistance: developing a national freshwater pilot effort. FRONTIERS IN WATER 2024; 6:10.3389/frwa.2024.1359109. [PMID: 38855419 PMCID: PMC11157689 DOI: 10.3389/frwa.2024.1359109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Antimicrobial resistance (AMR) is a world-wide public health threat that is projected to lead to 10 million annual deaths globally by 2050. The AMR public health issue has led to the development of action plans to combat AMR, including improved antimicrobial stewardship, development of new antimicrobials, and advanced monitoring. The National Antimicrobial Resistance Monitoring System (NARMS) led by the United States (U.S) Food and Drug Administration along with the U.S. Centers for Disease Control and U.S. Department of Agriculture has monitored antimicrobial resistant bacteria in retail meats, humans, and food animals since the mid 1990's. NARMS is currently exploring an integrated One Health monitoring model recognizing that human, animal, plant, and environmental systems are linked to public health. Since 2020, the U.S. Environmental Protection Agency has led an interagency NARMS environmental working group (EWG) to implement a surface water AMR monitoring program (SWAM) at watershed and national scales. The NARMS EWG divided the development of the environmental monitoring effort into five areas: (i) defining objectives and questions, (ii) designing study/sampling design, (iii) selecting AMR indicators, (iv) establishing analytical methods, and (v) developing data management/analytics/metadata plans. For each of these areas, the consensus among the scientific community and literature was reviewed and carefully considered prior to the development of this environmental monitoring program. The data produced from the SWAM effort will help develop robust surface water monitoring programs with the goal of assessing public health risks associated with AMR pathogens in surface water (e.g., recreational water exposures), provide a comprehensive picture of how resistant strains are related spatially and temporally within a watershed, and help assess how anthropogenic drivers and intervention strategies impact the transmission of AMR within human, animal, and environmental systems.
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Affiliation(s)
- Alison M. Franklin
- United States (U.S.) Environmental Protection Agency, Office of Research and Development, Cincinnati, OH, United States
| | - Daniel L. Weller
- U.S. Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Lisa M. Durso
- U.S. Department of Agriculture, Agricultural Research Service (USDA, ARS), Agroecosystem Management Research, Lincoln, NE, United States
| | - Mark Bagley
- United States (U.S.) Environmental Protection Agency, Office of Research and Development, Cincinnati, OH, United States
| | - Benjamin C. Davis
- United States (U.S.) Environmental Protection Agency, Office of Research and Development, Cincinnati, OH, United States
| | - Jonathan G. Frye
- USDA ARS, U.S. National Poultry Research Center, Poultry Microbiological Safety and Processing Research Unit, Athens, GA, United States
| | - Christopher J. Grim
- Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, College Park, MD, United States
| | - Abasiofiok M. Ibekwe
- USDA, ARS, Agricultural Water Efficiency and Salinity Research Unit, Riverside, CA, United States
| | - Michael A. Jahne
- United States (U.S.) Environmental Protection Agency, Office of Research and Development, Cincinnati, OH, United States
| | - Scott P. Keely
- United States (U.S.) Environmental Protection Agency, Office of Research and Development, Cincinnati, OH, United States
| | - Autumn L. Kraft
- Oak Ridge Institute for Science and Education, USDA, ARS, Beltsville, MD, United States
| | - Betty R. McConn
- Oak Ridge Institute for Science and Education, USDA, ARS, Beltsville, MD, United States
| | - Richard M. Mitchell
- Environmental Protection Agency, Office of Water, Washington, DC, United States
| | - Andrea R. Ottesen
- Center for Veterinary Medicine, National Antimicrobial Resistance Monitoring System (NARMS), U.S. Food and Drug Administration, Laurel, MD, United States
| | - Manan Sharma
- USDA, ARS Environmental Microbial and Food Safety Laboratory, Beltsville, MD, United States
| | - Errol A. Strain
- Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, College Park, MD, United States
| | - Daniel A. Tadesse
- Center for Veterinary Medicine, National Antimicrobial Resistance Monitoring System (NARMS), U.S. Food and Drug Administration, Laurel, MD, United States
| | - Heather Tate
- Center for Veterinary Medicine, National Antimicrobial Resistance Monitoring System (NARMS), U.S. Food and Drug Administration, Laurel, MD, United States
| | - Jim E. Wells
- USDA, ARS, U.S. Meat Animal Research Center, Meat Safety and Quality, Clay Center, NE, United States
| | - Clinton F. Williams
- USDA, ARS, US Arid-Land Agricultural Research Center, Maricopa, AZ, United States
| | - Kim L. Cook
- USDA, ARS Nutrition, Food Safety and Quality National Program Staff, Beltsville, MD, United States
| | - Claudine Kabera
- Center for Veterinary Medicine, National Antimicrobial Resistance Monitoring System (NARMS), U.S. Food and Drug Administration, Laurel, MD, United States
| | - Patrick F. McDermott
- Center for Veterinary Medicine, National Antimicrobial Resistance Monitoring System (NARMS), U.S. Food and Drug Administration, Laurel, MD, United States
| | - Jay L. Garland
- United States (U.S.) Environmental Protection Agency, Office of Research and Development, Cincinnati, OH, United States
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Nguyen M, Elmore Z, Ihle C, Moen FS, Slater AD, Turner BN, Parrello B, Best AA, Davis JJ. Predicting variable gene content in Escherichia coli using conserved genes. mSystems 2023; 8:e0005823. [PMID: 37314210 PMCID: PMC10469788 DOI: 10.1128/msystems.00058-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 04/25/2023] [Indexed: 06/15/2023] Open
Abstract
Having the ability to predict the protein-encoding gene content of an incomplete genome or metagenome-assembled genome is important for a variety of bioinformatic tasks. In this study, as a proof of concept, we built machine learning classifiers for predicting variable gene content in Escherichia coli genomes using only the nucleotide k-mers from a set of 100 conserved genes as features. Protein families were used to define orthologs, and a single classifier was built for predicting the presence or absence of each protein family occurring in 10%-90% of all E. coli genomes. The resulting set of 3,259 extreme gradient boosting classifiers had a per-genome average macro F1 score of 0.944 [0.943-0.945, 95% CI]. We show that the F1 scores are stable across multi-locus sequence types and that the trend can be recapitulated by sampling a smaller number of core genes or diverse input genomes. Surprisingly, the presence or absence of poorly annotated proteins, including "hypothetical proteins" was accurately predicted (F1 = 0.902 [0.898-0.906, 95% CI]). Models for proteins with horizontal gene transfer-related functions had slightly lower F1 scores but were still accurate (F1s = 0.895, 0.872, 0.824, and 0.841 for transposon, phage, plasmid, and antimicrobial resistance-related functions, respectively). Finally, using a holdout set of 419 diverse E. coli genomes that were isolated from freshwater environmental sources, we observed an average per-genome F1 score of 0.880 [0.876-0.883, 95% CI], demonstrating the extensibility of the models. Overall, this study provides a framework for predicting variable gene content using a limited amount of input sequence data. IMPORTANCE Having the ability to predict the protein-encoding gene content of a genome is important for assessing genome quality, binning genomes from shotgun metagenomic assemblies, and assessing risk due to the presence of antimicrobial resistance and other virulence genes. In this study, we built a set of binary classifiers for predicting the presence or absence of variable genes occurring in 10%-90% of all publicly available E. coli genomes. Overall, the results show that a large portion of the E. coli variable gene content can be predicted with high accuracy, including genes with functions relating to horizontal gene transfer. This study offers a strategy for predicting gene content using limited input sequence data.
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Affiliation(s)
- Marcus Nguyen
- Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois, USA
- Consortium for Advanced Science and Engineering, University of Chicago, Chicago, Illinois, USA
| | - Zachary Elmore
- Biology Department, Hope College, Holland, Michigan, USA
| | - Clay Ihle
- Biology Department, Hope College, Holland, Michigan, USA
| | | | - Adam D. Slater
- Biology Department, Hope College, Holland, Michigan, USA
| | | | - Bruce Parrello
- Consortium for Advanced Science and Engineering, University of Chicago, Chicago, Illinois, USA
- Fellowship for Interpretation of Genomes, Burr Ridge, Illinois, USA
| | - Aaron A. Best
- Biology Department, Hope College, Holland, Michigan, USA
| | - James J. Davis
- Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois, USA
- Consortium for Advanced Science and Engineering, University of Chicago, Chicago, Illinois, USA
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3
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Chung HC, Foxx CL, Hicks JA, Stuber TP, Friedberg I, Dorman KS, Harris B. An accurate and interpretable model for antimicrobial resistance in pathogenic Escherichia coli from livestock and companion animal species. PLoS One 2023; 18:e0290473. [PMID: 37616210 PMCID: PMC10449230 DOI: 10.1371/journal.pone.0290473] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 08/09/2023] [Indexed: 08/26/2023] Open
Abstract
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.
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Affiliation(s)
- Henri C. Chung
- Department of Veterinary Microbiology and Preventive Medicine, Iowa State University, Ames, IA, United States of America
| | - Christine L. Foxx
- Research Participation Program, Oak Ridge Institute for Science and Education, Oak Ridge, TN, United States of America
| | - Jessica A. Hicks
- National Veterinary Services Laboratories, Animal and Plant Health Inspection Service, U.S. Department of Agriculture, Ames, IA, United States of America
| | - Tod P. Stuber
- National Veterinary Services Laboratories, Animal and Plant Health Inspection Service, U.S. Department of Agriculture, Ames, IA, United States of America
| | - Iddo Friedberg
- Department of Veterinary Microbiology and Preventive Medicine, Iowa State University, Ames, IA, United States of America
| | - Karin S. Dorman
- Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA, United States of America
- Department of Statistics, Iowa State University, Ames, IA, United States of America
| | - Beth Harris
- National Animal Health Laboratory Network, National Veterinary Services Laboratories, Animal and Plant Health Inspection Service, U.S. Department of Agriculture, Ames, IA, United States of America
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Perestrelo S, Amaro A, Brouwer MSM, Clemente L, Ribeiro Duarte AS, Kaesbohrer A, Karpíšková R, Lopez-Chavarrias V, Morris D, Prendergast D, Pista A, Silveira L, Skarżyńska M, Slowey R, Veldman KT, Zając M, Burgess C, Alvarez J. Building an International One Health Strain Level Database to Characterise the Epidemiology of AMR Threats: ESBL—AmpC Producing E. coli as An Example—Challenges and Perspectives. Antibiotics (Basel) 2023; 12:antibiotics12030552. [PMID: 36978419 PMCID: PMC10044432 DOI: 10.3390/antibiotics12030552] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 03/07/2023] [Accepted: 03/08/2023] [Indexed: 03/12/2023] Open
Abstract
Antimicrobial resistance (AMR) is one of the top public health threats nowadays. Among the most important AMR pathogens, Escherichia coli resistant to extended spectrum cephalosporins (ESC-EC) is a perfect example of the One Health problem due to its global distribution in animal, human, and environmental sources and its resistant phenotype, derived from the carriage of plasmid-borne extended-spectrum and AmpC β-lactamases, which limits the choice of effective antimicrobial therapies. The epidemiology of ESC-EC infection is complex as a result of the multiple possible sources involved in its transmission, and its study would require databases ideally comprising information from animal (livestock, companion, wildlife), human, and environmental sources. Here, we present the steps taken to assemble a database with phenotypic and genetic information on 10,763 ESC-EC isolates retrieved from multiple sources provided by 13 partners located in eight European countries, in the frame of the DiSCoVeR Joint Research project funded by the One Health European Joint Programme (OH-EJP), along with its strengths and limitations. This database represents a first step to help in the assessment of different geographical and temporal trends and transmission dynamics in animals and humans. The work performed highlights aspects that should be considered in future international efforts, such as the one presented here.
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Affiliation(s)
- Sara Perestrelo
- Department of Biological Safety, German Federal Institute for Risk Assessment, 10589 Berlin, Germany
| | - Ana Amaro
- Laboratory of Bacteriology and Micology, National Institute of Agrarian and Veterinary Research, National Reference for Animal Health, 2780-157 Oeiras, Portugal
| | - Michael S. M. Brouwer
- Department of Bacteriology, Host Pathogen Interaction & Diagnostics, Wageningen Bioveterinary Research, Part of Wageningen University & Research, 8221 Lelystad, The Netherlands
| | - Lurdes Clemente
- Laboratory of Bacteriology and Micology, National Institute of Agrarian and Veterinary Research, National Reference for Animal Health, 2780-157 Oeiras, Portugal
| | | | - Annemarie Kaesbohrer
- Department of Biological Safety, German Federal Institute for Risk Assessment, 10589 Berlin, Germany
- Veterinary Public Health and Epidemiology, University of Veterinary Medicine, 1210 Vienna, Austria
| | - Renata Karpíšková
- Department of Public Health, Medical Faculty, Masaryk University, 625 000 Brno, Czech Republic
| | | | - Dearbháile Morris
- Antimicrobial Resistance and Microbial Ecology Group, School of Medicine, University of Galway, H91 TK33 Galway, Ireland
| | - Deirdre Prendergast
- Backweston Laboratory Campus, Department of Agriculture, Food and the Marine, W23 X3PH Celbridge, Ireland
| | - Angela Pista
- National Reference Laboratory for Gastrointestinal Infections, Department of Infectious Diseases, National Institute of Health Doutor Ricardo Jorge, Avenida Padre Cruz, 1649-016 Lisbon, Portugal
| | - Leonor Silveira
- National Reference Laboratory for Gastrointestinal Infections, Department of Infectious Diseases, National Institute of Health Doutor Ricardo Jorge, Avenida Padre Cruz, 1649-016 Lisbon, Portugal
| | - Magdalena Skarżyńska
- Department of Microbiology, National Veterinary Research Institute, 24-100 Pulawy, Poland
| | - Rosemarie Slowey
- Backweston Laboratory Campus, Department of Agriculture, Food and the Marine, W23 X3PH Celbridge, Ireland
| | - Kees T. Veldman
- Department of Bacteriology, Host Pathogen Interaction & Diagnostics, Wageningen Bioveterinary Research, Part of Wageningen University & Research, 8221 Lelystad, The Netherlands
| | - Magdalena Zając
- Department of Microbiology, National Veterinary Research Institute, 24-100 Pulawy, Poland
| | - Catherine Burgess
- Food Safety Department, Teagasc Food Research Centre Ashtown, D15 DY05 Dublin, Ireland
| | - Julio Alvarez
- VISAVET Health Surveillance Centre, Universidad Complutense, 28040 Madrid, Spain
- Department of Animal Health, Faculty of Veterinary Medicine, Universidad Complutense, Avda. Puerta de Hierro S/N, 28040 Madrid, Spain
- Correspondence:
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5
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Datar R, Orenga S, Pogorelcnik R, Rochas O, Simner PJ, van Belkum A. Recent Advances in Rapid Antimicrobial Susceptibility Testing. Clin Chem 2021; 68:91-98. [DOI: 10.1093/clinchem/hvab207] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 09/17/2021] [Indexed: 12/30/2022]
Abstract
Abstract
Background
Antimicrobial susceptibility testing (AST) is classically performed using growth-based techniques that essentially require viable bacterial matter to become visible to the naked eye or a sophisticated densitometer.
Content
Technologies based on the measurement of bacterial density in suspension have evolved marginally in accuracy and rapidity over the 20th century, but assays expanded for new combinations of bacteria and antimicrobials have been automated, and made amenable to high-throughput turn-around. Over the past 25 years, elevated AST rapidity has been provided by nucleic acid-mediated amplification technologies, proteomic and other “omic” methodologies, and the use of next-generation sequencing. In rare cases, AST at the level of single-cell visualization was developed. This has not yet led to major changes in routine high-throughput clinical microbiological detection of antimicrobial resistance.
Summary
We here present a review of the new generation of methods and describe what is still urgently needed for their implementation in day-to-day management of the treatment of infectious diseases.
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Affiliation(s)
- Rucha Datar
- bioMérieux, Microbiology Research, La Balme Les Grottes, France
| | - Sylvain Orenga
- bioMérieux, Microbiology Research, La Balme Les Grottes, France
| | | | - Olivier Rochas
- bioMérieux, Corporate Business Development, Marcy l'Etoile, France
| | - Patricia J Simner
- Department of Pathology, Bacteriology, Division of Medical Microbiology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Alex van Belkum
- bioMérieux, Open Innovation and Partnerships, La Balme Les Grottes, France
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Aslam B, Khurshid M, Arshad MI, Muzammil S, Rasool M, Yasmeen N, Shah T, Chaudhry TH, Rasool MH, Shahid A, Xueshan X, Baloch Z. Antibiotic Resistance: One Health One World Outlook. Front Cell Infect Microbiol 2021; 11:771510. [PMID: 34900756 PMCID: PMC8656695 DOI: 10.3389/fcimb.2021.771510] [Citation(s) in RCA: 161] [Impact Index Per Article: 53.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 10/29/2021] [Indexed: 01/07/2023] Open
Abstract
Antibiotic resistance (ABR) is a growing public health concern worldwide, and it is now regarded as a critical One Health issue. One Health’s interconnected domains contribute to the emergence, evolution, and spread of antibiotic-resistant microorganisms on a local and global scale, which is a significant risk factor for global health. The persistence and spread of resistant microbial species, and the association of determinants at the human-animal-environment interface can alter microbial genomes, resulting in resistant superbugs in various niches. ABR is motivated by a well-established link between three domains: human, animal, and environmental health. As a result, addressing ABR through the One Health approach makes sense. Several countries have implemented national action plans based on the One Health approach to combat antibiotic-resistant microbes, following the Tripartite’s Commitment Food and Agriculture Organization (FAO)-World Organization for Animal Health (OIE)-World Health Organization (WHO) guidelines. The ABR has been identified as a global health concern, and efforts are being made to mitigate this global health threat. To summarize, global interdisciplinary and unified approaches based on One Health principles are required to limit the ABR dissemination cycle, raise awareness and education about antibiotic use, and promote policy, advocacy, and antimicrobial stewardship.
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Affiliation(s)
- Bilal Aslam
- Department of Microbiology, Government College University Faisalabad, Faisalabad, Pakistan
| | - Mohsin Khurshid
- Department of Microbiology, Government College University Faisalabad, Faisalabad, Pakistan
| | | | - Saima Muzammil
- Department of Microbiology, Government College University Faisalabad, Faisalabad, Pakistan
| | - Maria Rasool
- Department of Microbiology, Government College University Faisalabad, Faisalabad, Pakistan
| | - Nafeesa Yasmeen
- College of Veterinary Medicine, South China Agricultural University, Guangzhou, China
| | - Taif Shah
- Faculty of Life Science and Technology, Kunming University of Life Science and Technology, Kunming, China
| | - Tamoor Hamid Chaudhry
- Department of Microbiology, Government College University Faisalabad, Faisalabad, Pakistan.,Public Health Laboratories Division, National Institute of Health, Islamabad, Pakistan
| | | | - Aqsa Shahid
- Faculty of Rehabilitation and Allied Health Sciences, Riphah International University, Faisalabad, Pakistan
| | - Xia Xueshan
- Faculty of Life Science and Technology, Kunming University of Life Science and Technology, Kunming, China
| | - Zulqarnain Baloch
- Faculty of Life Science and Technology, Kunming University of Life Science and Technology, Kunming, China
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7
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VanOeffelen M, Nguyen M, Aytan-Aktug D, Brettin T, Dietrich EM, Kenyon RW, Machi D, Mao C, Olson R, Pusch GD, Shukla M, Stevens R, Vonstein V, Warren AS, Wattam AR, Yoo H, Davis JJ. A genomic data resource for predicting antimicrobial resistance from laboratory-derived antimicrobial susceptibility phenotypes. Brief Bioinform 2021; 22:bbab313. [PMID: 34379107 PMCID: PMC8575023 DOI: 10.1093/bib/bbab313] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 06/18/2021] [Accepted: 07/20/2021] [Indexed: 11/14/2022] Open
Abstract
Antimicrobial resistance (AMR) is a major global health threat that affects millions of people each year. Funding agencies worldwide and the global research community have expended considerable capital and effort tracking the evolution and spread of AMR by isolating and sequencing bacterial strains and performing antimicrobial susceptibility testing (AST). For the last several years, we have been capturing these efforts by curating data from the literature and data resources and building a set of assembled bacterial genome sequences that are paired with laboratory-derived AST data. This collection currently contains AST data for over 67 000 genomes encompassing approximately 40 genera and over 100 species. In this paper, we describe the characteristics of this collection, highlighting areas where sampling is comparatively deep or shallow, and showing areas where attention is needed from the research community to improve sampling and tracking efforts. In addition to using the data to track the evolution and spread of AMR, it also serves as a useful starting point for building machine learning models for predicting AMR phenotypes. We demonstrate this by describing two machine learning models that are built from the entire dataset to show where the predictive power is comparatively high or low. This AMR metadata collection is freely available and maintained on the Bacterial and Viral Bioinformatics Center (BV-BRC) FTP site ftp://ftp.bvbrc.org/RELEASE_NOTES/PATRIC_genomes_AMR.txt.
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Affiliation(s)
| | - Marcus Nguyen
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, USA
- Data Science and Learning Division, Argonne National Laboratory, Argonne, IL, USA
| | - Derya Aytan-Aktug
- National Food Institute, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Thomas Brettin
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, USA
- Computing Environment and Life Sciences, Argonne National Laboratory, Argonne, IL, USA
| | - Emily M Dietrich
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, USA
- Computing Environment and Life Sciences, Argonne National Laboratory, Argonne, IL, USA
| | - Ronald W Kenyon
- Biocomplexity Institute and Initiative, University of Virginia, Virginia, USA
| | - Dustin Machi
- Biocomplexity Institute and Initiative, University of Virginia, Virginia, USA
| | - Chunhong Mao
- Biocomplexity Institute and Initiative, University of Virginia, Virginia, USA
| | - Robert Olson
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, USA
- Data Science and Learning Division, Argonne National Laboratory, Argonne, IL, USA
| | - Gordon D Pusch
- Fellowship for Interpretation of Genomes, Burr Ridge, IL, USA
| | - Maulik Shukla
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, USA
- Data Science and Learning Division, Argonne National Laboratory, Argonne, IL, USA
| | - Rick Stevens
- Computing Environment and Life Sciences, Argonne National Laboratory, Argonne, IL, USA
- Department of Computer Science, University of Chicago, Chicago, IL, USA
| | | | - Andrew S Warren
- Biocomplexity Institute and Initiative, University of Virginia, Virginia, USA
| | - Alice R Wattam
- Data Science and Learning Division, Argonne National Laboratory, Argonne, IL, USA
- Biocomplexity Institute and Initiative, University of Virginia, Virginia, USA
| | - Hyunseung Yoo
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, USA
- Data Science and Learning Division, Argonne National Laboratory, Argonne, IL, USA
| | - James J Davis
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, USA
- Data Science and Learning Division, Argonne National Laboratory, Argonne, IL, USA
- Northwestern Argonne Institute for Science and Engineering, Evanston, IL, USA
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8
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Abstract
Antimicrobial resistance (AMR) is an important global health threat that impacts millions of people worldwide each year. Developing methods that can detect and predict AMR phenotypes can help to mitigate the spread of AMR by informing clinical decision making and appropriate mitigation strategies. Many bioinformatic methods have been developed for predicting AMR phenotypes from whole-genome sequences and AMR genes, but recent studies have indicated that predictions can be made from incomplete genome sequence data. In order to more systematically understand this, we built random forest-based machine learning classifiers for predicting susceptible and resistant phenotypes for Klebsiella pneumoniae (1,640 strains), Mycobacterium tuberculosis (2,497 strains), and Salmonella enterica (1,981 strains). We started by building models from alignments that were based on a reference chromosome for each species. We then subsampled each chromosomal alignment and built models for the resulting subalignments, finding that very small regions, representing approximately 0.1 to 0.2% of the chromosome, are predictive. In K. pneumoniae, M. tuberculosis, and S. enterica, the subalignments are able to predict multiple AMR phenotypes with at least 70% accuracy, even though most do not encode an AMR-related function. We used these models to identify regions of the chromosome with high and low predictive signals. Finally, subalignments that retain high accuracy across larger phylogenetic distances were examined in greater detail, revealing genes and intergenic regions with potential links to AMR, virulence, transport, and survival under stress conditions. IMPORTANCE Antimicrobial resistance causes thousands of deaths annually worldwide. Understanding the regions of the genome that are involved in antimicrobial resistance is important for developing mitigation strategies and preventing transmission. Machine learning models are capable of predicting antimicrobial resistance phenotypes from bacterial genome sequence data by identifying resistance genes, mutations, and other correlated features. They are also capable of implicating regions of the genome that have not been previously characterized as being involved in resistance. In this study, we generated global chromosomal alignments for Klebsiella pneumoniae, Mycobacterium tuberculosis, and Salmonella enterica and systematically searched them for small conserved regions of the genome that enable the prediction of antimicrobial resistance phenotypes. In addition to known antimicrobial resistance genes, this analysis identified genes involved in virulence and transport functions, as well as many genes with no previous implication in antimicrobial resistance.
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