Predicting Antimicrobial Resistance Using Partial Genome Alignments

Journal Article · Tue Jun 15 04:00:00 EDT 2021 · mSystems

DOI:https://doi.org/10.1128/msystems.00185-21· OSTI ID:1854526

Aytan-Aktug, D. ^[1]; Nguyen, M. ^[2]; Clausen, P. C. ^[3]; Stevens, R. L. ^[2]; Aarestrup, F. M. ^[1]; Lund, O. ^[1]; Davis, J. J. ^[4]

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1. Technical Univ. of Denmark, Lyngby (Denmark)
2. Univ. of Chicago, IL (United States). Consortium for Advanced Science and Engineering; Argonne National Lab. (ANL), Argonne, IL (United States)
3. National Food Institute, Technical University of Denmark, Kongens Lyngby, Denmark
4. Univ. of Chicago, IL (United States). Consortium for Advanced Science and Engineering; Argonne National Lab. (ANL), Argonne, IL (United States); Northwest Argonne Institute for Science and Engineering, Evanston, IL (United States)

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.

• Research Organization: Argonne National Laboratory (ANL), Argonne, IL (United States)
• Sponsoring Organization: Defense Advanced Research Projects Agency; National Institutes of Health (NIH). National Institute of Allergy and Infectious Diseases (NIAID); Novo Nordisk Foundation
• Grant/Contract Number: AC02-06CH11357
• OSTI ID: 1854526
• Journal Information: mSystems, Journal Name: mSystems Journal Issue: 3 Vol. 6; ISSN 2379-5077
• Publisher: American Society for Microbiology
• Country of Publication: United States
• Language: English

Related Subjects

59 BASIC BIOLOGICAL SCIENCES
AMR
Antimicrobial resistance
ML
machine learning
random forest