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Genomics-Based Reconstruction and Predictive Profiling of Amino Acid Biosynthesis in the Human Gut Microbiome
The human gut microbiota (HGM) have an impact on host health and disease. Amino acids are building blocks of proteins and peptides, also serving as precursors of many essential metabolites including nucleotides, cofactors, etc. Many HGM community members are unable to synthesize some amino acids (au...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9026213/ https://www.ncbi.nlm.nih.gov/pubmed/35456791 http://dx.doi.org/10.3390/microorganisms10040740 |
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author | Ashniev, German A. Petrov, Sergey N. Iablokov, Stanislav N. Rodionov, Dmitry A. |
author_facet | Ashniev, German A. Petrov, Sergey N. Iablokov, Stanislav N. Rodionov, Dmitry A. |
author_sort | Ashniev, German A. |
collection | PubMed |
description | The human gut microbiota (HGM) have an impact on host health and disease. Amino acids are building blocks of proteins and peptides, also serving as precursors of many essential metabolites including nucleotides, cofactors, etc. Many HGM community members are unable to synthesize some amino acids (auxotrophs), while other members possess complete biosynthetic pathways for these nutrients (prototrophs). Metabolite exchange between auxotrophs and prototrophs affects microbial community structure. Previous studies of amino acid biosynthetic phenotypes were limited to model species or narrow taxonomic groups of bacteria. We analyzed over 2800 genomes representing 823 cultured HGM species with the aim to reconstruct biosynthetic pathways for proteinogenic amino acids. The genome context analysis of incomplete pathway variants allowed us to identify new potential enzyme variants in amino acid biosynthetic pathways. We further classified the studied organisms with respect to their pathway variants and inferred their prototrophic vs. auxotrophic phenotypes. A cross-species comparison was applied to assess the extent of conservation of the assigned phenotypes at distinct taxonomic levels. The obtained reference collection of binary metabolic phenotypes was used for predictive metabolic profiling of HGM samples from several large metagenomic datasets. The established approach for metabolic phenotype profiling will be useful for prediction of overall metabolic properties, interactions, and responses of HGM microbiomes as a function of dietary variations, dysbiosis and other perturbations. |
format | Online Article Text |
id | pubmed-9026213 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-90262132022-04-23 Genomics-Based Reconstruction and Predictive Profiling of Amino Acid Biosynthesis in the Human Gut Microbiome Ashniev, German A. Petrov, Sergey N. Iablokov, Stanislav N. Rodionov, Dmitry A. Microorganisms Article The human gut microbiota (HGM) have an impact on host health and disease. Amino acids are building blocks of proteins and peptides, also serving as precursors of many essential metabolites including nucleotides, cofactors, etc. Many HGM community members are unable to synthesize some amino acids (auxotrophs), while other members possess complete biosynthetic pathways for these nutrients (prototrophs). Metabolite exchange between auxotrophs and prototrophs affects microbial community structure. Previous studies of amino acid biosynthetic phenotypes were limited to model species or narrow taxonomic groups of bacteria. We analyzed over 2800 genomes representing 823 cultured HGM species with the aim to reconstruct biosynthetic pathways for proteinogenic amino acids. The genome context analysis of incomplete pathway variants allowed us to identify new potential enzyme variants in amino acid biosynthetic pathways. We further classified the studied organisms with respect to their pathway variants and inferred their prototrophic vs. auxotrophic phenotypes. A cross-species comparison was applied to assess the extent of conservation of the assigned phenotypes at distinct taxonomic levels. The obtained reference collection of binary metabolic phenotypes was used for predictive metabolic profiling of HGM samples from several large metagenomic datasets. The established approach for metabolic phenotype profiling will be useful for prediction of overall metabolic properties, interactions, and responses of HGM microbiomes as a function of dietary variations, dysbiosis and other perturbations. MDPI 2022-03-30 /pmc/articles/PMC9026213/ /pubmed/35456791 http://dx.doi.org/10.3390/microorganisms10040740 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ashniev, German A. Petrov, Sergey N. Iablokov, Stanislav N. Rodionov, Dmitry A. Genomics-Based Reconstruction and Predictive Profiling of Amino Acid Biosynthesis in the Human Gut Microbiome |
title | Genomics-Based Reconstruction and Predictive Profiling of Amino Acid Biosynthesis in the Human Gut Microbiome |
title_full | Genomics-Based Reconstruction and Predictive Profiling of Amino Acid Biosynthesis in the Human Gut Microbiome |
title_fullStr | Genomics-Based Reconstruction and Predictive Profiling of Amino Acid Biosynthesis in the Human Gut Microbiome |
title_full_unstemmed | Genomics-Based Reconstruction and Predictive Profiling of Amino Acid Biosynthesis in the Human Gut Microbiome |
title_short | Genomics-Based Reconstruction and Predictive Profiling of Amino Acid Biosynthesis in the Human Gut Microbiome |
title_sort | genomics-based reconstruction and predictive profiling of amino acid biosynthesis in the human gut microbiome |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9026213/ https://www.ncbi.nlm.nih.gov/pubmed/35456791 http://dx.doi.org/10.3390/microorganisms10040740 |
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