Cargando…
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...
Autores principales: | , , , , |
---|---|
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 |
_version_ | 1783674624405405696 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8020746 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT gengjun codyenablesquantitativelyspatiotemporalpredictionsoninvivogutmicrobialvariabilityinducedbydietintervention AT jiboyang codyenablesquantitativelyspatiotemporalpredictionsoninvivogutmicrobialvariabilityinducedbydietintervention AT ligang codyenablesquantitativelyspatiotemporalpredictionsoninvivogutmicrobialvariabilityinducedbydietintervention AT lopezisunzafelipe codyenablesquantitativelyspatiotemporalpredictionsoninvivogutmicrobialvariabilityinducedbydietintervention AT nielsenjens codyenablesquantitativelyspatiotemporalpredictionsoninvivogutmicrobialvariabilityinducedbydietintervention |