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CODY enables quantitatively spatiotemporal predictions on in vivo gut microbial variability induced by diet intervention

Microbial variations in the human gut are harbored in temporal and spatial heterogeneity, and quantitative prediction of spatiotemporal dynamic changes in the gut microbiota is imperative for development of tailored microbiome-directed therapeutics treatments, e.g. precision nutrition. Given the hig...

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Autores principales: Geng, Jun, Ji, Boyang, Li, Gang, López-Isunza, Felipe, Nielsen, Jens
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8020746/
https://www.ncbi.nlm.nih.gov/pubmed/33753486
http://dx.doi.org/10.1073/pnas.2019336118
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author Geng, Jun
Ji, Boyang
Li, Gang
López-Isunza, Felipe
Nielsen, Jens
author_facet Geng, Jun
Ji, Boyang
Li, Gang
López-Isunza, Felipe
Nielsen, Jens
author_sort Geng, Jun
collection PubMed
description Microbial variations in the human gut are harbored in temporal and spatial heterogeneity, and quantitative prediction of spatiotemporal dynamic changes in the gut microbiota is imperative for development of tailored microbiome-directed therapeutics treatments, e.g. precision nutrition. Given the high-degree complexity of microbial variations, subject to the dynamic interactions among host, microbial, and environmental factors, identifying how microbiota colonize in the gut represents an important challenge. Here we present COmputing the DYnamics of microbiota (CODY), a multiscale framework that integrates species-level modeling of microbial dynamics and ecosystem-level interactions into a mathematical model that characterizes spatial-specific in vivo microbial residence in the colon as impacted by host physiology. The framework quantifies spatiotemporal resolution of microbial variations on species-level abundance profiles across site-specific colon regions and in feces, independent of a priori knowledge. We demonstrated the effectiveness of CODY using cross-sectional data from two longitudinal metagenomics studies—the microbiota development during early infancy and during short-term diet intervention of obese adults. For each cohort, CODY correctly predicts the microbial variations in response to diet intervention, as validated by available metagenomics and metabolomics data. Model simulations provide insight into the biogeographical heterogeneity among lumen, mucus, and feces, which provides insight into how host physical forces and spatial structure are shaping microbial structure and functionality.
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spelling pubmed-80207462021-04-13 CODY enables quantitatively spatiotemporal predictions on in vivo gut microbial variability induced by diet intervention Geng, Jun Ji, Boyang Li, Gang López-Isunza, Felipe Nielsen, Jens Proc Natl Acad Sci U S A Biological Sciences Microbial variations in the human gut are harbored in temporal and spatial heterogeneity, and quantitative prediction of spatiotemporal dynamic changes in the gut microbiota is imperative for development of tailored microbiome-directed therapeutics treatments, e.g. precision nutrition. Given the high-degree complexity of microbial variations, subject to the dynamic interactions among host, microbial, and environmental factors, identifying how microbiota colonize in the gut represents an important challenge. Here we present COmputing the DYnamics of microbiota (CODY), a multiscale framework that integrates species-level modeling of microbial dynamics and ecosystem-level interactions into a mathematical model that characterizes spatial-specific in vivo microbial residence in the colon as impacted by host physiology. The framework quantifies spatiotemporal resolution of microbial variations on species-level abundance profiles across site-specific colon regions and in feces, independent of a priori knowledge. We demonstrated the effectiveness of CODY using cross-sectional data from two longitudinal metagenomics studies—the microbiota development during early infancy and during short-term diet intervention of obese adults. For each cohort, CODY correctly predicts the microbial variations in response to diet intervention, as validated by available metagenomics and metabolomics data. Model simulations provide insight into the biogeographical heterogeneity among lumen, mucus, and feces, which provides insight into how host physical forces and spatial structure are shaping microbial structure and functionality. National Academy of Sciences 2021-03-30 2021-03-22 /pmc/articles/PMC8020746/ /pubmed/33753486 http://dx.doi.org/10.1073/pnas.2019336118 Text en Copyright © 2021 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/ https://creativecommons.org/licenses/by-nc-nd/4.0/This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Biological Sciences
Geng, Jun
Ji, Boyang
Li, Gang
López-Isunza, Felipe
Nielsen, Jens
CODY enables quantitatively spatiotemporal predictions on in vivo gut microbial variability induced by diet intervention
title CODY enables quantitatively spatiotemporal predictions on in vivo gut microbial variability induced by diet intervention
title_full CODY enables quantitatively spatiotemporal predictions on in vivo gut microbial variability induced by diet intervention
title_fullStr CODY enables quantitatively spatiotemporal predictions on in vivo gut microbial variability induced by diet intervention
title_full_unstemmed CODY enables quantitatively spatiotemporal predictions on in vivo gut microbial variability induced by diet intervention
title_short CODY enables quantitatively spatiotemporal predictions on in vivo gut microbial variability induced by diet intervention
title_sort cody enables quantitatively spatiotemporal predictions on in vivo gut microbial variability induced by diet intervention
topic Biological Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8020746/
https://www.ncbi.nlm.nih.gov/pubmed/33753486
http://dx.doi.org/10.1073/pnas.2019336118
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