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      Analysis of the microbiome in maternal, intrauterine and fetal environments based on 16S rRNA genes following different durations of membrane rupture

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      Scientific Reports
      Nature Publishing Group UK
      Microbiology, Diseases, Risk factors

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          Abstract

          The incidence of chorioamnionitis and neonatal sepsis increases with the increasing time of rupture of membranes. Changes in the amount and categories of microbiomes in maternal and fetal environments after membrane rupture have yet to be discussed. In order to determine the microbiome diversity and signature in the maternal, intrauterine, and fetal environments of different durations following membrane rupture, we collected samples of fetal membrane, amniotic fluid, cord blood and maternal peripheral blood from singleton pregnant women and divided them into five groups according to the duration of membrane rupture. DNA was isolated from the samples, and the V3V4 region of bacterial 16S rRNA genes was sequenced. We found that the alpha diversity of the fetal membrane microbiome increased significantly 12 h after membrane rupture, while the beta diversity of the amniotic fluid microbiome increased 24 h after membrane rupture. In cord blood, the mean proportion of Methylobacterium and Halomonadaceae reached the highest 12 h after membrane rupture, and the mean proportion of Prevotella reached the highest 24 h after membrane rupture. The LEfSe algorithm showed that Ruminococcus, Paludibaculum, Lachnospiraceae, and Prevotella were detected earlier in cord blood or maternal blood and then detected in fetal membranes or amniotic fluid, which may suggest a reverse infection model. In conclusion, the microbes may invade the placenta 12 h after membrane rupture and invaded the amniotic cavity 24 h after membrane rupture. In addition to the common ascending pattern of infection, the hematogenous pathway of intrauterine infection should also be considered among people with rupture of membranes.

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          QIIME allows analysis of high-throughput community sequencing data.

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            Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences

            Profiling phylogenetic marker genes, such as the 16S rRNA gene, is a key tool for studies of microbial communities but does not provide direct evidence of a community’s functional capabilities. Here we describe PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States), a computational approach to predict the functional composition of a metagenome using marker gene data and a database of reference genomes. PICRUSt uses an extended ancestral-state reconstruction algorithm to predict which gene families are present and then combines gene families to estimate the composite metagenome. Using 16S information, PICRUSt recaptures key findings from the Human Microbiome Project and accurately predicts the abundance of gene families in host-associated and environmental communities, with quantifiable uncertainty. Our results demonstrate that phylogeny and function are sufficiently linked that this ‘predictive metagenomic’ approach should provide useful insights into the thousands of uncultivated microbial communities for which only marker gene surveys are currently available.
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              STAMP: statistical analysis of taxonomic and functional profiles.

              STAMP is a graphical software package that provides statistical hypothesis tests and exploratory plots for analysing taxonomic and functional profiles. It supports tests for comparing pairs of samples or samples organized into two or more treatment groups. Effect sizes and confidence intervals are provided to allow critical assessment of the biological relevancy of test results. A user-friendly graphical interface permits easy exploration of statistical results and generation of publication-quality plots. STAMP is licensed under the GNU GPL. Python source code and binaries are available from our website at: http://kiwi.cs.dal.ca/Software/STAMP. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
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                Author and article information

                Contributors
                lixt555@126.com
                hurong@fudan.edu.cn
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                11 September 2023
                11 September 2023
                2023
                : 13
                : 15010
                Affiliations
                GRID grid.412312.7, ISNI 0000 0004 1755 1415, The Obstetrics and Gynecology Hospital of Fudan University, ; 419 Fangxie Road, Shanghai, 200011 China
                Article
                41777
                10.1038/s41598-023-41777-z
                10495440
                37696898
                24f986de-9580-4813-ae41-81c6757cbe74
                © Springer Nature Limited 2023

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 30 December 2022
                : 31 August 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 81571460
                Award Recipient :
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                © Springer Nature Limited 2023

                Uncategorized
                microbiology,diseases,risk factors
                Uncategorized
                microbiology, diseases, risk factors

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