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      Gestational diabetes is associated with change in the gut microbiota composition in third trimester of pregnancy and postpartum

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          Abstract

          Background

          Imbalances of gut microbiota composition are linked to a range of metabolic perturbations. In the present study, we examined the gut microbiota of women with gestational diabetes mellitus (GDM) and normoglycaemic pregnant women in late pregnancy and about 8 months postpartum.

          Methods

          Gut microbiota profiles of women with GDM ( n = 50) and healthy ( n = 157) pregnant women in the third trimester and 8 months postpartum were assessed by 16S rRNA gene amplicon sequencing of the V1-V2 region. Insulin and glucose homeostasis were evaluated by a 75 g 2-h oral glucose tolerance test during and after pregnancy.

          Results

          Gut microbiota of women with GDM was aberrant at multiple levels, including phylum and genus levels, compared with normoglycaemic pregnant women. Actinobacteria at phylum level and Collinsella, Rothia and Desulfovibrio at genus level had a higher abundance in the GDM cohort. Difference in abundance of 17 species-level operational taxonomic units (OTUs) during pregnancy was associated with GDM. After adjustment for pre-pregnancy body mass index (BMI), 5 of the 17 OTUs showed differential abundance in the GDM cohort compared with the normoglycaemic pregnant women with enrichment of species annotated to Faecalibacterium and Anaerotruncus and depletion of species annotated to Clostridium (sensu stricto) and to Veillonella. OTUs assigned to Akkermansia were associated with lower insulin sensitivity while Christensenella OTUs were associated with higher fasting plasma glucose concentration. OTU richness and Shannon index decreased from late pregnancy to postpartum regardless of metabolic status. About 8 months after delivery, the microbiota of women with previous GDM was still characterised by an aberrant composition. Thirteen OTUs were differentially abundant in women with previous GDM compared with women with previous normoglycaemic pregnancy.

          Conclusion

          GDM diagnosed in the third trimester of pregnancy is associated with a disrupted gut microbiota composition compared with normoglycaemic pregnant women, and 8 months after pregnancy, differences in the gut microbiota signatures are still detectable. The gut microbiota composition of women with GDM, both during and after pregnancy, resembles the aberrant microbiota composition reported in non-pregnant individuals with type 2 diabetes and associated intermediary metabolic traits.

          Electronic supplementary material

          The online version of this article (10.1186/s40168-018-0472-x) contains supplementary material, which is available to authorized users.

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          Most cited references 47

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy.

            The Ribosomal Database Project (RDP) Classifier, a naïve Bayesian classifier, can rapidly and accurately classify bacterial 16S rRNA sequences into the new higher-order taxonomy proposed in Bergey's Taxonomic Outline of the Prokaryotes (2nd ed., release 5.0, Springer-Verlag, New York, NY, 2004). It provides taxonomic assignments from domain to genus, with confidence estimates for each assignment. The majority of classifications (98%) were of high estimated confidence (> or = 95%) and high accuracy (98%). In addition to being tested with the corpus of 5,014 type strain sequences from Bergey's outline, the RDP Classifier was tested with a corpus of 23,095 rRNA sequences as assigned by the NCBI into their alternative higher-order taxonomy. The results from leave-one-out testing on both corpora show that the overall accuracies at all levels of confidence for near-full-length and 400-base segments were 89% or above down to the genus level, and the majority of the classification errors appear to be due to anomalies in the current taxonomies. For shorter rRNA segments, such as those that might be generated by pyrosequencing, the error rate varied greatly over the length of the 16S rRNA gene, with segments around the V2 and V4 variable regions giving the lowest error rates. The RDP Classifier is suitable both for the analysis of single rRNA sequences and for the analysis of libraries of thousands of sequences. Another related tool, RDP Library Compare, was developed to facilitate microbial-community comparison based on 16S rRNA gene sequence libraries. It combines the RDP Classifier with a statistical test to flag taxa differentially represented between samples. The RDP Classifier and RDP Library Compare are available online at http://rdp.cme.msu.edu/.
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              UPARSE: highly accurate OTU sequences from microbial amplicon reads.

               Robert Edgar (2013)
              Amplified marker-gene sequences can be used to understand microbial community structure, but they suffer from a high level of sequencing and amplification artifacts. The UPARSE pipeline reports operational taxonomic unit (OTU) sequences with ≤1% incorrect bases in artificial microbial community tests, compared with >3% incorrect bases commonly reported by other methods. The improved accuracy results in far fewer OTUs, consistently closer to the expected number of species in a community.
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                Author and article information

                Affiliations
                [1 ]ISNI 0000 0001 0674 042X, GRID grid.5254.6, Novo Nordisk Foundation Center for Basic Metabolic Research, Section for Metabolic Genetics, , Faculty of Health and Medical Science, University of Copenhagen, ; Blegdamsvej 3B, 2200 Copenhagen N, Denmark
                [2 ]ISNI 0000 0004 0646 8261, GRID grid.415046.2, Department of Clinical Epidemiology, , Bispebjerg and Frederiksberg Hospital, ; Hovedvejen 5, Nordre Fasanvej 57, 2000 Frederiksberg, Copenhagen Denmark
                [3 ]Institute of Clinical Molecular Biology, Christian-Albrechts-University Kiel, University Hospital Schleswig Holstein, Campus Kiel, Rosalind-Franklin-Straße 12, 24105 Kiel, Germany
                [4 ]GRID grid.475435.4, Center for Pregnant Women with Diabetes, Department of Obstetrics, , Rigshospitalet University Hospital, ; Blegdamsvej 9, 2100 Copenhagen Ø, Denmark
                [5 ]ISNI 0000 0001 0674 042X, GRID grid.5254.6, Institute of Clinical Medicine, Faculty of Health and Medical Sciences, , University of Copenhagen, ; Blegdamdsvej 3B, 2200 Copenhagen N, Denmark
                [6 ]ISNI 0000 0004 0646 7285, GRID grid.419658.7, Steno Diabetes Center Copenhagen, ; Niels Steensens Vej 2, 2820 Gentofte, Denmark
                [7 ]ISNI 0000 0004 0646 8202, GRID grid.411905.8, Department of Obstetrics and Gynaecology, , Hvidovre University Hospital, ; Kettegaards Allé 30, 2650 Hvidovre, Denmark
                [8 ]GRID grid.475435.4, Department of Clinical Biochemistry, , Rigshospitalet University Hospital, ; Blegdamsvej 9, 2100 Copenhagen Ø, Denmark
                [9 ]ISNI 0000 0001 0728 0170, GRID grid.10825.3e, OPEN, Odense Patient Data Explorative Network, Odense University Hospital/Institute of Clinical Research, , University of Southern Denmark, ; J.B. Winsløws Vej 9 A, 3. sal, 5000 Odense, Denmark
                [10 ]GRID grid.475435.4, Department of Obstetrics and Gynaecology, , Rigshospitalet University Hospital, ; Blegdamsvej 9, 2100 Copenhagen Ø, Denmark
                [11 ]GRID grid.475435.4, Fertility Clinic 4071, Rigshospitalet University Hospital, ; Blegdamsvej 9, 2100 Copenhagen Ø, Denmark
                [12 ]ISNI 0000 0004 0646 7349, GRID grid.27530.33, Department of Obstetrics and Gynaecology, , Aalborg University Hospital, ; Reberbansgade, 9000 Aalborg, Denmark
                [13 ]ISNI 0000 0004 0646 8325, GRID grid.411900.d, Department of Obstetrics and Gynaecology, , Herlev University Hospital, ; Herlev Ringvej 75, 2730 Herlev, Denmark
                Contributors
                mie.wiinblad@sund.ku.dk
                tuehhansen@sund.ku.dk
                trine.nielsen@sund.ku.dk
                kristine.allin@regionh.dk
                m.ruehlemann@ikmb.uni-kiel.de
                pdamm@dadlnet.dk
                henrik.vestergaard@sund.ku.dk
                Ingeborg.Christina.Roerbye.Lundin@regionh.dk
                Niklas.rye.joergensen@regionh.dk
                Ole.Bjarne.Christiansen@regionh.dk
                f.heinsen@ikmb.uni-kiel.de
                a.franke@mucosa.de
                torben.hansen@sund.ku.dk
                jeannet@lauenb.org
                ORCID: http://orcid.org/0000-0002-3321-3972, oluf@sund.ku.dk
                Journal
                Microbiome
                Microbiome
                Microbiome
                BioMed Central (London )
                2049-2618
                15 May 2018
                15 May 2018
                2018
                : 6
                472
                10.1186/s40168-018-0472-x
                5952429
                29764499
                © The Author(s). 2018

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                Funding
                Funded by: Augustinus Fonden (DK)
                Award ID: 1076001001
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100003035, Aase og Ejnar Danielsens Fond;
                Award ID: 10-001605
                Categories
                Research
                Custom metadata
                © The Author(s) 2018

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