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      Dysbiosis of maternal and neonatal microbiota associated with gestational diabetes mellitus

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          Abstract

          Objective

          The initial colonisation of the human microbiota and the impact of maternal health on neonatal microbiota at birth remain largely unknown. The aim of our study is to investigate the possible dysbiosis of maternal and neonatal microbiota associated with gestational diabetes mellitus (GDM) and to estimate the potential risks of the microbial shift to neonates.

          Design

          Pregnant women and neonates suffering from GDM were enrolled and 581 maternal (oral, intestinal and vaginal) and 248 neonatal (oral, pharyngeal, meconium and amniotic fluid) samples were collected. To avoid vaginal bacteria contaminations, the included neonates were predominantly delivered by C-section, with their samples collected within seconds of delivery.

          Results

          Numerous and diverse bacterial taxa were identified from the neonatal samples, and the samples from different neonatal body sites were grouped into distinct clusters. The microbiota of pregnant women and neonates was remarkably altered in GDM, with a strong correlation between certain discriminatory bacteria and the oral glucose tolerance test. Microbes varying by the same trend across the maternal and neonatal microbiota were observed, revealing the intergenerational concordance of microbial variation associated with GDM. Furthermore, lower evenness but more depletion of KEGG orthologues and higher abundance of some viruses (eg, herpesvirus and mastadenovirus) were observed in the meconium microbiota of neonates associated with GDM.

          Conclusion

          GDM can alter the microbiota of both pregnant women and neonates at birth, which sheds light on another form of inheritance and highlights the importance of understanding the formation of early-life microbiome.

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          Most cited references14

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          Calypso: a user-friendly web-server for mining and visualizing microbiome–environment interactions

          Abstract Calypso is an easy-to-use online software suite that allows non-expert users to mine, interpret and compare taxonomic information from metagenomic or 16S rDNA datasets. Calypso has a focus on multivariate statistical approaches that can identify complex environment-microbiome associations. The software enables quantitative visualizations, statistical testing, multivariate analysis, supervised learning, factor analysis, multivariable regression, network analysis and diversity estimates. Comprehensive help pages, tutorials and videos are provided via a wiki page. Availability and Implementation: The web-interface is accessible via http://cgenome.net/calypso/. The software is programmed in Java, PERL and R and the source code is available from Zenodo (https://zenodo.org/record/50931). The software is freely available for non-commercial users. Contact: l.krause@uq.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.
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            Cesarean section and chronic immune disorders.

            Immune diseases such as asthma, allergy, inflammatory bowel disease, and type 1 diabetes have shown a parallel increase in prevalence during recent decades in westernized countries. The rate of cesarean delivery has also increased in this period and has been associated with the development of some of these diseases.
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              The interaction between vaginal microbiota, cervical length, and vaginal progesterone treatment for preterm birth risk

              Background Preterm birth is the primary cause of infant death worldwide. A short cervix in the second trimester of pregnancy is a risk factor for preterm birth. In specific patient cohorts, vaginal progesterone reduces this risk. Using 16S rRNA gene sequencing, we undertook a prospective study in women at risk of preterm birth (n = 161) to assess (1) the relationship between vaginal microbiota and cervical length in the second trimester and preterm birth risk and (2) the impact of vaginal progesterone on vaginal bacterial communities in women with a short cervix. Results Lactobacillus iners dominance at 16 weeks of gestation was significantly associated with both a short cervix <25 mm (n = 15, P < 0.05) and preterm birth <34+0 weeks (n = 18; P < 0.01; 69% PPV). In contrast, Lactobacillus crispatus dominance was highly predictive of term birth (n = 127, 98% PPV). Cervical shortening and preterm birth were not associated with vaginal dysbiosis. A longitudinal characterization of vaginal microbiota (<18, 22, 28, and 34 weeks) was then undertaken in women receiving vaginal progesterone (400 mg/OD, n = 25) versus controls (n = 42). Progesterone did not alter vaginal bacterial community structure nor reduce L. iners-associated preterm birth (<34 weeks). Conclusions L. iners dominance of the vaginal microbiota at 16 weeks of gestation is a risk factor for preterm birth, whereas L. crispatus dominance is protective against preterm birth. Vaginal progesterone does not appear to impact the pregnancy vaginal microbiota. Patients and clinicians who may be concerned about “infection risk” associated with the use of a vaginal pessary during high-risk pregnancy can be reassured. Electronic supplementary material The online version of this article (doi:10.1186/s40168-016-0223-9) contains supplementary material, which is available to authorized users.
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                Author and article information

                Journal
                Gut
                Gut
                gutjnl
                gut
                Gut
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                0017-5749
                1468-3288
                September 2018
                14 May 2018
                : 67
                : 9
                : 1614-1625
                Affiliations
                [1 ] departmentComputational Genomics Lab , Beijing Institutes of Life Science, Chinese Academy of Sciences , Beijing, China
                [2 ] departmentDepartment of Gynecology and Obstetrics , Wenzhou People’s Hospital/Wenzhou Maternal and Child Health Care Hospital , Zhejiang, China
                [3 ] departmentSavaid Medical School , University of Chinese Academy of Sciences , Beijing, China
                [4 ] departmentCenter for Excellence in Animal Evolution and Genetics , Chinese Academy of Sciences , Kunming, China
                Author notes
                [Correspondence to ] Professor Fangqing Zhao, Computational Genomics Lab, Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China; zhfq@ 123456biols.ac.cn
                Article
                gutjnl-2018-315988
                10.1136/gutjnl-2018-315988
                6109274
                29760169
                d6b094bd-9e3d-4c4b-8240-49540fa4715e
                © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

                History
                : 8 January 2018
                : 23 April 2018
                Funding
                Funded by: the Key Research Program of the Chinese Academy of Sciences;
                Funded by: National Key R&D Program;
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Categories
                Gut Microbiota
                1506
                2312
                Original article
                Custom metadata
                unlocked

                Gastroenterology & Hepatology
                gdm,microbiota,neonate,pregnancy
                Gastroenterology & Hepatology
                gdm, microbiota, neonate, pregnancy

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