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 correspo...

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Autores principales:	Sumayah F. Rahman, Matthew R. Olm, Michael J. Morowitz, Jillian F. Banfield
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2018
Materias:	Clostridium difficile antibiotic resistance genome-resolved metagenomics infant machine learning microbiome Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/89117da0ea52466a9de801b0de9256c0
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spelling	oai:doaj.org-article:89117da0ea52466a9de801b0de9256c02021-12-02T18:39:46ZMachine Learning Leveraging Genomes from Metagenomes Identifies Influential Antibiotic Resistance Genes in the Infant Gut Microbiome10.1128/mSystems.00123-172379-5077https://doaj.org/article/89117da0ea52466a9de801b0de9256c02018-02-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSystems.00123-17https://doaj.org/toc/2379-5077ABSTRACT 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. IMPORTANCE The process of reconstructing genomes from environmental sequence data (genome-resolved metagenomics) allows unique insight into microbial systems. We apply this technique to investigate how the antibiotic resistance genes of bacteria affect their ability to flourish in the gut under various conditions. Our analysis reveals that strain-level selection in formula-fed infants drives enrichment of beta-lactamase genes in the gut resistome. Using genomes from metagenomes, we built a machine learning model to predict how organisms in the gut microbial community respond to perturbation by antibiotics. This may eventually have clinical applications.Sumayah F. RahmanMatthew R. OlmMichael J. MorowitzJillian F. BanfieldAmerican Society for MicrobiologyarticleClostridium difficileantibiotic resistancegenome-resolved metagenomicsinfantmachine learningmicrobiomeMicrobiologyQR1-502ENmSystems, Vol 3, Iss 1 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Clostridium difficile antibiotic resistance genome-resolved metagenomics infant machine learning microbiome Microbiology QR1-502
spellingShingle	Clostridium difficile antibiotic resistance genome-resolved metagenomics infant machine learning microbiome Microbiology QR1-502 Sumayah F. Rahman Matthew R. Olm Michael J. Morowitz Jillian F. Banfield Machine Learning Leveraging Genomes from Metagenomes Identifies Influential Antibiotic Resistance Genes in the Infant Gut Microbiome
description	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 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. IMPORTANCE The process of reconstructing genomes from environmental sequence data (genome-resolved metagenomics) allows unique insight into microbial systems. We apply this technique to investigate how the antibiotic resistance genes of bacteria affect their ability to flourish in the gut under various conditions. Our analysis reveals that strain-level selection in formula-fed infants drives enrichment of beta-lactamase genes in the gut resistome. Using genomes from metagenomes, we built a machine learning model to predict how organisms in the gut microbial community respond to perturbation by antibiotics. This may eventually have clinical applications.
format	article
author	Sumayah F. Rahman Matthew R. Olm Michael J. Morowitz Jillian F. Banfield
author_facet	Sumayah F. Rahman Matthew R. Olm Michael J. Morowitz Jillian F. Banfield
author_sort	Sumayah F. Rahman
title	Machine Learning Leveraging Genomes from Metagenomes Identifies Influential Antibiotic Resistance Genes in the Infant Gut Microbiome
title_short	Machine Learning Leveraging Genomes from Metagenomes Identifies Influential Antibiotic Resistance Genes in the Infant Gut Microbiome
title_full	Machine Learning Leveraging Genomes from Metagenomes Identifies Influential Antibiotic Resistance Genes in the Infant Gut Microbiome
title_fullStr	Machine Learning Leveraging Genomes from Metagenomes Identifies Influential Antibiotic Resistance Genes in the Infant Gut Microbiome
title_full_unstemmed	Machine Learning Leveraging Genomes from Metagenomes Identifies Influential Antibiotic Resistance Genes in the Infant Gut Microbiome
title_sort	machine learning leveraging genomes from metagenomes identifies influential antibiotic resistance genes in the infant gut microbiome
publisher	American Society for Microbiology
publishDate	2018
url	https://doaj.org/article/89117da0ea52466a9de801b0de9256c0
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Machine Learning Leveraging Genomes from Metagenomes Identifies Influential Antibiotic Resistance Genes in the Infant Gut Microbiome

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