Machine Learning Leveraging Genomes from Metagenomes Identifies Influential Antibiotic Resistance Genes in the Infant Gut Microbiome

Rahman, Sumayah F.; Olm, Matthew R.; Morowitz, Michael J.; Banfield, Jillian F.

doi:10.1128/mSystems.00123-17

Title: Machine Learning Leveraging Genomes from Metagenomes Identifies Influential Antibiotic Resistance Genes in the Infant Gut Microbiome

Journal Article · Tue Jan 09 00:00:00 EST 2018 · mSystems

DOI:https://doi.org/10.1128/mSystems.00123-17· OSTI ID:1479372

^[1]; Olm, Matthew R. ^[1]; Morowitz, Michael J. ^[2]; Banfield, Jillian F. ^[3]

Univ. of California, Berkeley, CA (United States). Dept. of Plant and Microbial Biology
Univ. of Pittsburgh, PA (United States). School of Medicine. Dept. of Surgery
Univ. of California, Berkeley, CA (United States). Dept. of Earth and Planetary Sciences. Dept. of Environmental Science, Policy and Management

Antibiotic resistance in pathogens is extensively studied, and yet little is known about how antibiotic resistance genes of typical gut bacteria influence microbiome dynamics. Here, we leveraged genomes from metagenomes to investigate how genes of the premature infant gut resistome correspond to the ability of bacteria to survive under certain environmental and clinical conditions. We found that formula feeding impacts the resistome. Random forest models corroborated by statistical tests revealed that the gut resistome of formula-fed infants is enriched in class D beta-lactamase genes. Interestingly, Clostridium difficile strains harboring this gene are at higher abundance in formula-fed infants than C. difficile strains lacking this gene. Organisms with genes for major facilitator superfamily drug efflux pumps have higher replication rates under all conditions, even in the absence of antibiotic therapy. Using a machine learning approach, we identified genes that are predictive of an organism’s direction of change in relative abundance after administration of vancomycin and cephalosporin antibiotics. The most accurate results were obtained by reducing annotated genomic data to five principal components classified by boosted decision trees. Among the genes involved in predicting whether an organism increased in relative abundance after treatment are those that encode subclass B2 beta-lactamases and transcriptional regulators of vancomycin resistance. This demonstrates that machine learning applied to genome-resolved metagenomics data can identify key genes for survival after antibiotics treatment and predict how organisms in the gut microbiome will respond to antibiotic administration.

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Research Organization:: Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE; National Inst. of Health (NIH) (United States); Alfred P. Sloan Foundation (United States)

Grant/Contract Number:: AC02-05CH11231; RAI092531A; S10 OD018174; APSF-2012-10-05

OSTI ID:: 1479372

Journal Information:: mSystems, Vol. 3, Issue 1; ISSN 2379-5077

Publisher:: American Society for MicrobiologyCopyright Statement

Country of Publication:: United States

Language:: English

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Title: Machine Learning Leveraging Genomes from Metagenomes Identifies Influential Antibiotic Resistance Genes in the Infant Gut Microbiome

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