Associations between the gut microbiome and metabolome in early life.

Autor:	Nguyen QP; Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Hanover, NH, USA.; Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth College, Hanover, NH, USA., Karagas MR; Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Hanover, NH, USA.; Children's Environmental Health & Disease Prevention Research Center at Dartmouth, Lebanon, NH, USA., Madan JC; Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Hanover, NH, USA.; Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth College, Hanover, NH, USA.; Children's Environmental Health & Disease Prevention Research Center at Dartmouth, Lebanon, NH, USA.; Department of Pediatrics, Children's Hospital at Dartmouth, Hanover, NH, USA., Dade E; Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Hanover, NH, USA., Palys TJ; Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Hanover, NH, USA., Morrison HG; Josephine Bay Paul Center, Marine Biological Laboratory, Woods Hole, MA, USA., Pathmasiri WW; Department of Nutrition, Nutrition Research Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. wimal_pathmasiri@unc.edu., McRitche S; Department of Nutrition, Nutrition Research Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA., Sumner SJ; Department of Nutrition, Nutrition Research Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA., Frost HR; Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth College, Hanover, NH, USA., Hoen AG; Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Hanover, NH, USA. Anne.G.Hoen@dartmouth.edu.; Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth College, Hanover, NH, USA. Anne.G.Hoen@dartmouth.edu.; Children's Environmental Health & Disease Prevention Research Center at Dartmouth, Lebanon, NH, USA. Anne.G.Hoen@dartmouth.edu.
Jazyk:	angličtina
Zdroj:	BMC microbiology [BMC Microbiol] 2021 Aug 28; Vol. 21 (1), pp. 238. Date of Electronic Publication: 2021 Aug 28.
DOI:	10.1186/s12866-021-02282-3
Abstrakt:	Background: The infant intestinal microbiome plays an important role in metabolism and immune development with impacts on lifelong health. The linkage between the taxonomic composition of the microbiome and its metabolic phenotype is undefined and complicated by redundancies in the taxon-function relationship within microbial communities. To inform a more mechanistic understanding of the relationship between the microbiome and health, we performed an integrative statistical and machine learning-based analysis of microbe taxonomic structure and metabolic function in order to characterize the taxa-function relationship in early life. Results: Stool samples collected from infants enrolled in the New Hampshire Birth Cohort Study (NHBCS) at approximately 6-weeks (n = 158) and 12-months (n = 282) of age were profiled using targeted and untargeted nuclear magnetic resonance (NMR) spectroscopy as well as DNA sequencing of the V4-V5 hypervariable region from the bacterial 16S rRNA gene. There was significant inter-omic concordance based on Procrustes analysis (6 weeks: p = 0.056; 12 months: p = 0.001), however this association was no longer significant when accounting for phylogenetic relationships using generalized UniFrac distance metric (6 weeks: p = 0.376; 12 months: p = 0.069). Sparse canonical correlation analysis showed significant correlation, as well as identifying sets of microbe/metabolites driving microbiome-metabolome relatedness. Performance of machine learning models varied across different metabolites, with support vector machines (radial basis function kernel) being the consistently top ranked model. However, predictive R 2 values demonstrated poor predictive performance across all models assessed (avg: - 5.06% -- 6 weeks; - 3.7% -- 12 months). Conversely, the Spearman correlation metric was higher (avg: 0.344-6 weeks; 0.265-12 months). This demonstrated that taxonomic relative abundance was not predictive of metabolite concentrations. Conclusions: Our results suggest a degree of overall association between taxonomic profiles and metabolite concentrations. However, lack of predictive capacity for stool metabolic signatures reflects, in part, the possible role of functional redundancy in defining the taxa-function relationship in early life as well as the bidirectional nature of the microbiome-metabolome association. Our results provide evidence in favor of a multi-omic approach for microbiome studies, especially those focused on health outcomes. (© 2021. The Author(s).)
Databáze:	MEDLINE
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