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

Abstract

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 transcriptionalmore » 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.« less

Authors:
ORCiD logo [1];  [1];  [2];  [3]
  1. Univ. of California, Berkeley, CA (United States). Dept. of Plant and Microbial Biology
  2. Univ. of Pittsburgh, PA (United States). School of Medicine. Dept. of Surgery
  3. Univ. of California, Berkeley, CA (United States). Dept. of Earth and Planetary Sciences. Dept. of Environmental Science, Policy and Management
Publication Date:
Research Org.:
Univ. of California, Berkeley, CA (United States); Univ. of Pittsburgh, PA (United States)
Sponsoring Org.:
USDOE; National Inst. of Health (NIH) (United States); Alfred P. Sloan Foundation (United States)
OSTI Identifier:
1479372
Grant/Contract Number:  
AC02-05CH11231; RAI092531A; S10 OD018174; APSF-2012-10-05
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
mSystems
Additional Journal Information:
Journal Volume: 3; Journal Issue: 1; Journal ID: ISSN 2379-5077
Publisher:
American Society for Microbiology
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; Clostridium difficile; antibiotic resistance; genome-resolved metagenomics; infant; machine learning; microbiome; resistome

Citation Formats

Rahman, Sumayah F., Olm, Matthew R., Morowitz, Michael J., and Banfield, Jillian F. Machine Learning Leveraging Genomes from Metagenomes Identifies Influential Antibiotic Resistance Genes in the Infant Gut Microbiome. United States: N. p., 2018. Web. doi:10.1128/mSystems.00123-17.
Rahman, Sumayah F., Olm, Matthew R., Morowitz, Michael J., & Banfield, Jillian F. Machine Learning Leveraging Genomes from Metagenomes Identifies Influential Antibiotic Resistance Genes in the Infant Gut Microbiome. United States. doi:10.1128/mSystems.00123-17.
Rahman, Sumayah F., Olm, Matthew R., Morowitz, Michael J., and Banfield, Jillian F. Tue . "Machine Learning Leveraging Genomes from Metagenomes Identifies Influential Antibiotic Resistance Genes in the Infant Gut Microbiome". United States. doi:10.1128/mSystems.00123-17. https://www.osti.gov/servlets/purl/1479372.
@article{osti_1479372,
title = {Machine Learning Leveraging Genomes from Metagenomes Identifies Influential Antibiotic Resistance Genes in the Infant Gut Microbiome},
author = {Rahman, Sumayah F. and Olm, Matthew R. and Morowitz, Michael J. and Banfield, Jillian F.},
abstractNote = {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.},
doi = {10.1128/mSystems.00123-17},
journal = {mSystems},
number = 1,
volume = 3,
place = {United States},
year = {Tue Jan 09 00:00:00 EST 2018},
month = {Tue Jan 09 00:00:00 EST 2018}
}

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Works referenced in this record:

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