Sökning: WFRF:(Mustonen Ville) > (2021) > Machine Learning Pr...
Fältnamn | Indikatorer | Metadata |
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000 | 05994naa a2200457 4500 | |
001 | oai:gup.ub.gu.se/310320 | |
003 | SwePub | |
008 | 240528s2021 | |||||||||||000 ||eng| | |
024 | 7 | a https://gup.ub.gu.se/publication/3103202 URI |
024 | 7 | a https://doi.org/10.1128/mSystems.00346-212 DOI |
040 | a (SwePub)gu | |
041 | a eng | |
042 | 9 SwePub | |
072 | 7 | a ref2 swepub-contenttype |
072 | 7 | a art2 swepub-publicationtype |
100 | 1 | a Benkwitz-Bedford, Sam4 aut |
245 | 1 0 | a Machine Learning Prediction of Resistance to Subinhibitory Antimicrobial Concentrations from Escherichia coli Genomes. |
264 | 1 | c 2021 |
520 | a Escherichia coli is an important cause of bacterial infections worldwide, with multidrug-resistant strains incurring substantial costs on human lives. Besides therapeutic concentrations of antimicrobials in health care settings, the presence of subinhibitory antimicrobial residues in the environment and in clinics selects for antimicrobial resistance (AMR), but the underlying genetic repertoire is less well understood. Here, we used machine learning to predict the population doubling time and cell growth yield of 1,407 genetically diverse E. coli strains expanding under exposure to three subinhibitory concentrations of six classes of antimicrobials from single-nucleotide genetic variants, accessory gene variation, and the presence of known AMR genes. We predicted cell growth yields in the held-out test data with an average correlation (Spearman's ρ) of 0.63 (0.36 to 0.81 across concentrations) and cell doubling times with an average correlation of 0.59 (0.32 to 0.92 across concentrations), with moderate increases in sample size unlikely to improve predictions further. This finding points to the remaining missing heritability of growth under antimicrobial exposure being explained by effects that are too rare or weak to be captured unless sample size is dramatically increased, or by effects other than those conferred by the presence of individual single-nucleotide polymorphisms (SNPs) and genes. Predictions based on whole-genome information were generally superior to those based only on known AMR genes and were accurate for AMR resistance at therapeutic concentrations. We pinpointed genes and SNPs determining the predicted growth and thereby recapitulated many known AMR determinants. Finally, we estimated the effect sizes of resistance genes across the entire collection of strains, disclosing the growth effects for known resistance genes in each individual strain. Our results underscore the potential of predictive modeling of growth patterns from genomic data under subinhibitory concentrations of antimicrobials, although the remaining missing heritability poses a challenge for achieving the accuracy and precision required for clinical use. IMPORTANCE Predicting bacterial growth from genome sequences is important for a rapid characterization of strains in clinical diagnostics and to disclose candidate novel targets for anti-infective drugs. Previous studies have dissected the relationship between bacterial growth and genotype in mutant libraries for laboratory strains, yet no study so far has examined the predictive power of genome sequence in natural strains. In this study, we used a high-throughput phenotypic assay to measure the growth of a systematic collection of natural Escherichia coli strains and then employed machine learning models to predict bacterial growth from genomic data under nontherapeutic subinhibitory concentrations of antimicrobials that are common in nonclinical settings. We found a moderate to strong correlation between predicted and actual values for the different collected data sets. Moreover, we observed that the known resistance genes are still effective at sublethal concentrations, pointing to clinical implications of these concentrations. | |
650 | 7 | a MEDICIN OCH HÄLSOVETENSKAPx Medicinska och farmaceutiska grundvetenskaperx Mikrobiologi inom det medicinska området0 (SwePub)301092 hsv//swe |
650 | 7 | a MEDICAL AND HEALTH SCIENCESx Basic Medicinex Microbiology in the medical area0 (SwePub)301092 hsv//eng |
650 | 7 | a NATURVETENSKAPx Biologix Mikrobiologi0 (SwePub)106062 hsv//swe |
650 | 7 | a NATURAL SCIENCESx Biological Sciencesx Microbiology0 (SwePub)106062 hsv//eng |
650 | 7 | a NATURVETENSKAPx Data- och informationsvetenskapx Bioinformatik0 (SwePub)102032 hsv//swe |
650 | 7 | a NATURAL SCIENCESx Computer and Information Sciencesx Bioinformatics0 (SwePub)102032 hsv//eng |
653 | a antimicrobial resistance | |
653 | a deep learning | |
653 | a high-throughput assay | |
653 | a machine learning | |
653 | a whole-genome sequencing | |
700 | 1 | a Palm, Martinu Gothenburg University,Göteborgs universitet,Institutionen för kemi och molekylärbiologi,CARe - Centrum för antibiotikaresistensforskning,Department of Chemistry and Molecular Biology,Centre for antibiotic resistance research, CARe4 aut0 (Swepub:gu)xpalmz |
700 | 1 | a Demirtas, Talip Yasir4 aut |
700 | 1 | a Mustonen, Ville4 aut |
700 | 1 | a Farewell, Anne,d 1961u Gothenburg University,Göteborgs universitet,CARe - Centrum för antibiotikaresistensforskning,Institutionen för kemi och molekylärbiologi,Centre for antibiotic resistance research, CARe,Department of Chemistry and Molecular Biology4 aut0 (Swepub:gu)xfaran |
700 | 1 | a Warringer, Jonas,d 1973u Gothenburg University,Göteborgs universitet,Institutionen för kemi och molekylärbiologi,CARe - Centrum för antibiotikaresistensforskning,Department of Chemistry and Molecular Biology,Centre for antibiotic resistance research, CARe4 aut0 (Swepub:gu)xwarrj |
700 | 1 | a Parts, Leopold4 aut |
700 | 1 | a Moradigaravand, Danesh4 aut |
710 | 2 | a Göteborgs universitetb Institutionen för kemi och molekylärbiologi4 org |
773 | 0 | t mSystemsg 6:4q 6:4x 2379-5077 |
856 | 4 8 | u https://gup.ub.gu.se/publication/310320 |
856 | 4 8 | u https://doi.org/10.1128/mSystems.00346-21 |
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