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Metagenomic analysis reveals gestational diabetes mellitus-related microbial regulators of glucose tolerance

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Abstract

Aims

Recent studies have suggested a possible association between microbiota and gestational diabetes (GDM). However, the results are inconsistent. Our objective was to investigate further the relationship between GDM and microbiota and verify the potential microbial marker.

Methods

Two complementary approaches were used for the demonstration. First, we compared the gut microbial composition of 23 GDM patients and 26 non-GDM ethnically Chinese Han pregnant women, by using whole-metagenome shotgun sequencing of their stool samples collected at the third trimester. Second, we used Q-PCR (quantitative polymerase chain reaction) to evaluate the gut microbial composition in the stool samples from another cohort of 150 Chinese pregnant women (113 Control and 37 GDM), to further confirm the potential microbial marker.

Results

The gut microbiota of GDM women show lower albeit not statistically significant (p = 0.18) alpha diversity at the species level than non-GDM women. However, the species-level beta-diversity or between-sample diversity measured by Bray–Curtis distance shows significant differences (p < 2.2e−16) between the two groups. The species Bacteroides dorei positively correlated with both OGTT (oral glucose tolerance test) 0-Hour (p = 0.0099) and OGTT 1-Hour (p = 0.0070). There is a similar trend between Bacteroides sp. 3_1_33FAA and both OGTT 0-Hour (p = 0.014) and OGTT 1-Hour (p = 0.0101) response variables. The species Alistipes putredinis negatively correlated with OGTT 1-Hour (p = 0.0172) and OGTT 2-Hour (p = 0.0147). Q-PCR validation further confirmed the association between the glucose tolerance loci of Bacteroides dorei and OGTT response.

Conclusions

Gut microbiome is related to the diabetic status of Chinese women during pregnancy. Specific species such as Bacteroides dorei associate with glucose response and could be potential monitoring and therapeutic microbial markers for GDM.

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Availability of data and material

All sequencing data underlying this work’s contributions will be made freely available.

Abbreviations

GDM:

Gestational diabetes mellitus

GTT:

Oral glucose tolerance test

RNA:

Ribonucleic acid

Q-PCR:

Quantitative polymerase chain reaction

ADA:

American Diabetes Association

DNA:

Deoxyribonucleic acid

USA:

United States of America

LefSe:

Linear discriminant analysis effect size tool

PERMANOVA:

Permutational multivariate analysis of variance

CPM:

Counts-per-million

IGV:

Integrative Genomics Viewer

BWA:

Burrows–Wheeler Aligner

ACTB:

Human beta-actin

PCoA:

Principal coordinate analysis

LDA:

Linear discriminant analysis

rRNA:

Ribosomal ribonucleic acid

EMP:

Embden–Meyerhof–Parnas pathway

PPP:

Pentose phosphate pathway

acetyl CoA:

Acetyl coenzyme A

OGTT-1-Pos:

Loci positively associated with OGTT 1-Hour response

SCFA:

Short-chain fatty acids

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Acknowledgements

The study was supported by computational resources of the Zhongshan Ophthalmic Center and the Tianhe-2 supercomputer and the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562. The XSEDE resources of the Pittsburgh Supercomputing Center’s Bridges system under the allocation TG-MCB190095 supported the computational analysis of P. W. Bible. The authors would like to thank Dr. Andrew Sand of Butler University for providing essential computational assistance.

Funding

The study was supported by Natural Science of Guangdong Province, China (2015A030313198), Youth Cultivation Project of Sun Yat-sen University, China (17ykpy24), Clinical Medical Project 5010 of Sun Yat-sen University, China (2012004), and the National Natural Science Foundation of China (81771606, 81571452).

Author information

Author notes

  1. Yanxin Wu and Paul W. Bible have contributed equally to this work.

Authors and Affiliations

  1. Department of Obstetrics and Gynecology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, People’s Republic of China

    Yanxin Wu, Wai-kit Ming, Wenjing Ding, Yuhang Long, Haitian Chen & Zilian Wang

  2. State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, 510060, People’s Republic of China

    Paul W. Bible, Xiaofeng Wen, Xifang Li, Xiuli Deng, Yuhua Deng, Shixin Guo & Lai Wei

  3. College of Arts and Sciences, Marian University, Indianapolis, IN, USA

    Paul W. Bible & Colleen L. Doçi

  4. Statistics Section, Center for Information Technology and Statistics, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, People’s Republic of China

    Sizhe Long

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  1. Yanxin Wu
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Contributions

YW collected and processed the clinical samples and analyzed and interpreted the data and wrote the manuscript. PWB wrote the manuscript and processed, analyzed, and interpreted the data. SL, WM, WD, and YL collected and processed the clinical samples. XW processed the clinical samples and performed the experiments. XL and XD processed the clinical samples. YD performed the experiments. SG carried out metagenomic sequencing experiments. CLD contributed to pathway analysis and discussion and wrote the manuscript. LW designed the study and supervised the data analysis and interpretation. HC designed the study and supervised the clinical sample collection and data interpretation. ZW designed the study and supervised the clinical sample collection and data interpretation.

Corresponding authors

Correspondence to Lai Wei, Haitian Chen or Zilian Wang.

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Conflict of interest

The authors confirm that no conflicts of interest exist.

Consent for publication

All authors have read and approved the manuscript.

Ethics approval and consent to participate

This study was approved by the research ethics committee of the First Affiliated Hospital, Sun Yat-sen University (Protocol: No. 187, 2016), and was conducted in accordance with the principles of the Declaration of Helsinki.

Informed consent

Informed consent was obtained from all patients and healthy volunteers.

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This article belongs to the topical collection "Gut Microbiome and Metabolic Disorders" managed by Massimo Federici.

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Figure S1

Neighbor joining tree of Bacteroides 16S RNA genes. The 16S RNA genes of common Bacteroides species are analyzed by sequence similarity. Values indicate neighbor-joining branch length. Smaller values indicate smaller divergence from neighboring branches. (PNG 74 kb)

Table S1

Functional Metagenomic Pathways showing differential abundance between GDM and healthy mothers. Pathways discovered using HUMANN2 software were statistically assessed for differential abundance. Mean normalized counts per million are reported for each cohort. P values are based on a Mann–Whitney–Wilcoxon test. (XLSX 9 kb)

Table S2

Associations between species abundance and clinical variables. Associations between clinical variables and species abundances across all patients were assessed using Spearman’s nonparametric correlation tests. All significant associations at p < 0.05 are reported. (XLSX 51 kb)

Table S3

Complete list of specifically enriched EC pathways from the GDM (DM) catalog. This catalog lists the p value and pathway information for all specially enriched EC functions associated with the GDM catalog. (CSV 180 kb)

Table S4

Complete list of specifically enriched EC pathways from the non-GDM (PG) catalog. This catalog lists the p value and pathway information for all specially enriched EC functions associated with the non-GDM catalog. (CSV 98 kb)

Table S5

Complete list of specifically enriched KEGG pathways from the GDM (DM) catalog. This catalog lists the p value and pathway information for all specially enriched KEGG ortholog functions associated with the GDM catalog. (CSV 87 kb)

Table S6

Complete list of specifically enriched KEGG pathways from the non-GDM (DM) catalog. This catalog lists the p value and pathway information for all specially enriched KEGG functions associated with the non-GDM catalog. (CSV 88 kb)

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Wu, Y., Bible, P.W., Long, S. et al. Metagenomic analysis reveals gestational diabetes mellitus-related microbial regulators of glucose tolerance. Acta Diabetol 57, 569–581 (2020). https://doi.org/10.1007/s00592-019-01434-2

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