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Integrated metabolome analysis reveals novel connections between maternal fecal metabolome and the neonatal blood metabolome in women with gestational diabetes mellitus

Gestational Diabetes Mellitus (GDM), which is correlated with changes in the gut microbiota, is a risk factor for neonatal inborn errors of metabolism (IEMs). Maternal hyperglycemia exerts epigenetic effects on genes that encode IEM-associated enzymes, resulting in changes in the neonatal blood meta...

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Autores principales: Zhao, Chunchao, Ge, Jun, Li, Xia, Jiao, Ruifen, Li, Yuan, Quan, Huili, Li, Jianguo, Guo, Qing, Wang, Wenju
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7046769/
https://www.ncbi.nlm.nih.gov/pubmed/32107447
http://dx.doi.org/10.1038/s41598-020-60540-2
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author Zhao, Chunchao
Ge, Jun
Li, Xia
Jiao, Ruifen
Li, Yuan
Quan, Huili
Li, Jianguo
Guo, Qing
Wang, Wenju
author_facet Zhao, Chunchao
Ge, Jun
Li, Xia
Jiao, Ruifen
Li, Yuan
Quan, Huili
Li, Jianguo
Guo, Qing
Wang, Wenju
author_sort Zhao, Chunchao
collection PubMed
description Gestational Diabetes Mellitus (GDM), which is correlated with changes in the gut microbiota, is a risk factor for neonatal inborn errors of metabolism (IEMs). Maternal hyperglycemia exerts epigenetic effects on genes that encode IEM-associated enzymes, resulting in changes in the neonatal blood metabolome. However, the relationship between maternal gut microbiota and the neonatal blood metabolome remains poorly understood. This study aimed at understanding the connections between maternal gut microbiota and the neonatal blood metabolome in GDM. 1H-NMR-based untargeted metabolomics was performed on maternal fecal samples and targeted metabolomics on the matched neonatal dry blood spots from a cohort of 40 pregnant women, including 22 with GDM and 18 controls. Multi-omic association methods (including Co-Inertia Analysis and Procrustes Analysis) were applied to investigate the relationship between maternal fecal metabolome and the neonatal blood metabolome. Both maternal fecal metabolome and the matched neonatal blood metabolome could be separated along the vector of maternal hyperglycemia. A close relationship between the maternal and neonatal metabolomes was observed by multi-omic association approaches. Twelve out of thirty-two maternal fecal metabolites with altered abundances from 872 1H- NMR features (Bonferroni-adjusted P < 0.05) in women with GDM and the controls were identified, among which 8 metabolites contribute (P < 0.05 in a 999-step permutation test) to the close connection between maternal and the neonatal metabolomes in GDM. Four of these eight maternal fecal metabolites, including lysine, putrescine, guanidinoacetate, and hexadecanedioate, were negatively associated (Spearman rank correlation, coefficient value < −0.6, P < 0.05) with maternal hyperglycemia. Biotin metabolism was enriched (Bonferroni-adjusted P < 0.05 in the hypergeometric test) with the four-hyperglycemia associated fecal metabolites. The results of this study suggested that maternal fecal metabolites contribute to the connections between maternal fecal metabolome and the neonatal blood metabolome and may further affect the risk of IEMs.
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spelling pubmed-70467692020-03-05 Integrated metabolome analysis reveals novel connections between maternal fecal metabolome and the neonatal blood metabolome in women with gestational diabetes mellitus Zhao, Chunchao Ge, Jun Li, Xia Jiao, Ruifen Li, Yuan Quan, Huili Li, Jianguo Guo, Qing Wang, Wenju Sci Rep Article Gestational Diabetes Mellitus (GDM), which is correlated with changes in the gut microbiota, is a risk factor for neonatal inborn errors of metabolism (IEMs). Maternal hyperglycemia exerts epigenetic effects on genes that encode IEM-associated enzymes, resulting in changes in the neonatal blood metabolome. However, the relationship between maternal gut microbiota and the neonatal blood metabolome remains poorly understood. This study aimed at understanding the connections between maternal gut microbiota and the neonatal blood metabolome in GDM. 1H-NMR-based untargeted metabolomics was performed on maternal fecal samples and targeted metabolomics on the matched neonatal dry blood spots from a cohort of 40 pregnant women, including 22 with GDM and 18 controls. Multi-omic association methods (including Co-Inertia Analysis and Procrustes Analysis) were applied to investigate the relationship between maternal fecal metabolome and the neonatal blood metabolome. Both maternal fecal metabolome and the matched neonatal blood metabolome could be separated along the vector of maternal hyperglycemia. A close relationship between the maternal and neonatal metabolomes was observed by multi-omic association approaches. Twelve out of thirty-two maternal fecal metabolites with altered abundances from 872 1H- NMR features (Bonferroni-adjusted P < 0.05) in women with GDM and the controls were identified, among which 8 metabolites contribute (P < 0.05 in a 999-step permutation test) to the close connection between maternal and the neonatal metabolomes in GDM. Four of these eight maternal fecal metabolites, including lysine, putrescine, guanidinoacetate, and hexadecanedioate, were negatively associated (Spearman rank correlation, coefficient value < −0.6, P < 0.05) with maternal hyperglycemia. Biotin metabolism was enriched (Bonferroni-adjusted P < 0.05 in the hypergeometric test) with the four-hyperglycemia associated fecal metabolites. The results of this study suggested that maternal fecal metabolites contribute to the connections between maternal fecal metabolome and the neonatal blood metabolome and may further affect the risk of IEMs. Nature Publishing Group UK 2020-02-27 /pmc/articles/PMC7046769/ /pubmed/32107447 http://dx.doi.org/10.1038/s41598-020-60540-2 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Zhao, Chunchao
Ge, Jun
Li, Xia
Jiao, Ruifen
Li, Yuan
Quan, Huili
Li, Jianguo
Guo, Qing
Wang, Wenju
Integrated metabolome analysis reveals novel connections between maternal fecal metabolome and the neonatal blood metabolome in women with gestational diabetes mellitus
title Integrated metabolome analysis reveals novel connections between maternal fecal metabolome and the neonatal blood metabolome in women with gestational diabetes mellitus
title_full Integrated metabolome analysis reveals novel connections between maternal fecal metabolome and the neonatal blood metabolome in women with gestational diabetes mellitus
title_fullStr Integrated metabolome analysis reveals novel connections between maternal fecal metabolome and the neonatal blood metabolome in women with gestational diabetes mellitus
title_full_unstemmed Integrated metabolome analysis reveals novel connections between maternal fecal metabolome and the neonatal blood metabolome in women with gestational diabetes mellitus
title_short Integrated metabolome analysis reveals novel connections between maternal fecal metabolome and the neonatal blood metabolome in women with gestational diabetes mellitus
title_sort integrated metabolome analysis reveals novel connections between maternal fecal metabolome and the neonatal blood metabolome in women with gestational diabetes mellitus
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7046769/
https://www.ncbi.nlm.nih.gov/pubmed/32107447
http://dx.doi.org/10.1038/s41598-020-60540-2
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