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      Lack of detection of a human placenta microbiome in samples from preterm and term deliveries

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          Abstract

          Background

          Historically, the human womb has been thought to be sterile in healthy pregnancies, but this idea has been challenged by recent studies using DNA sequence-based methods, which have suggested that the womb is colonized with bacteria. For example, analysis of DNA from placenta samples yielded small proportions of microbial sequences which were proposed to represent normal bacterial colonization. However, an analysis by our group showed no distinction between background negative controls and placenta samples. Also supporting the idea that the womb is sterile is the observation that germ-free mammals can be generated by sterile delivery of neonates into a sterile isolator, after which neonates remain germ-free, which would seem to provide strong data in support of sterility of the womb.

          Results

          To probe this further and to investigate possible placental colonization associated with spontaneous preterm birth, we carried out another study comparing microbiota in placenta samples from 20 term and 20 spontaneous preterm deliveries. Both 16S rRNA marker gene sequencing and shotgun metagenomic sequencing were used to characterize placenta and control samples. We first quantified absolute amounts of bacterial 16S rRNA gene sequences using 16S rRNA gene quantitative PCR (qPCR). As in our previous study, levels were found to be low in the placenta samples and indistinguishable from negative controls. Analysis by DNA sequencing did not yield a placenta microbiome distinct from negative controls, either using marker gene sequencing as in our previous work, or with shotgun metagenomic sequencing. Several types of artifacts, including erroneous read classifications and barcode misattribution, needed to be identified and removed from the data to clarify this point.

          Conclusions

          Our findings do not support the existence of a consistent placental microbiome, in either placenta from term deliveries or spontaneous preterm births.

          Electronic supplementary material

          The online version of this article (10.1186/s40168-018-0575-4) contains supplementary material, which is available to authorized users.

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          Most cited references22

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          Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample.

          The ongoing revolution in high-throughput sequencing continues to democratize the ability of small groups of investigators to map the microbial component of the biosphere. In particular, the coevolution of new sequencing platforms and new software tools allows data acquisition and analysis on an unprecedented scale. Here we report the next stage in this coevolutionary arms race, using the Illumina GAIIx platform to sequence a diverse array of 25 environmental samples and three known "mock communities" at a depth averaging 3.1 million reads per sample. We demonstrate excellent consistency in taxonomic recovery and recapture diversity patterns that were previously reported on the basis of metaanalysis of many studies from the literature (notably, the saline/nonsaline split in environmental samples and the split between host-associated and free-living communities). We also demonstrate that 2,000 Illumina single-end reads are sufficient to recapture the same relationships among samples that we observe with the full dataset. The results thus open up the possibility of conducting large-scale studies analyzing thousands of samples simultaneously to survey microbial communities at an unprecedented spatial and temporal resolution.
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            Optimizing methods and dodging pitfalls in microbiome research

            Research on the human microbiome has yielded numerous insights into health and disease, but also has resulted in a wealth of experimental artifacts. Here, we present suggestions for optimizing experimental design and avoiding known pitfalls, organized in the typical order in which studies are carried out. We first review best practices in experimental design and introduce common confounders such as age, diet, antibiotic use, pet ownership, longitudinal instability, and microbial sharing during cohousing in animal studies. Typically, samples will need to be stored, so we provide data on best practices for several sample types. We then discuss design and analysis of positive and negative controls, which should always be run with experimental samples. We introduce a convenient set of non-biological DNA sequences that can be useful as positive controls for high-volume analysis. Careful analysis of negative and positive controls is particularly important in studies of samples with low microbial biomass, where contamination can comprise most or all of a sample. Lastly, we summarize approaches to enhancing experimental robustness by careful control of multiple comparisons and to comparing discovery and validation cohorts. We hope the experimental tactics summarized here will help researchers in this exciting field advance their studies efficiently while avoiding errors. Electronic supplementary material The online version of this article (doi:10.1186/s40168-017-0267-5) contains supplementary material, which is available to authorized users.
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              Recognizing the reagent microbiome

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                Author and article information

                Contributors
                parry@mail.med.upenn.edu
                bushman@mail.med.upenn.edu
                Journal
                Microbiome
                Microbiome
                Microbiome
                BioMed Central (London )
                2049-2618
                30 October 2018
                30 October 2018
                2018
                : 6
                : 196
                Affiliations
                [1 ]ISNI 0000 0004 1936 8972, GRID grid.25879.31, Department of Microbiology, , University of Pennsylvania School of Medicine, ; 3610 Hamilton Walk, Philadelphia, PA 19104-6076 USA
                [2 ]ISNI 0000 0001 0680 8770, GRID grid.239552.a, Division of Gastroenterology, Hepatology, and Nutrition, , The Children’s Hospital of Philadelphia, ; Philadelphia, PA 19104 USA
                [3 ]ISNI 0000 0004 1936 8972, GRID grid.25879.31, Maternal and Child Health Research Center, Department of Obstetrics and Gynecology, , University of Pennsylvania School of Medicine, ; 3400 Spruce Street, Philadelphia, PA 19104 USA
                Author information
                http://orcid.org/0000-0003-4740-4056
                Article
                575
                10.1186/s40168-018-0575-4
                6208038
                30376898
                bd7d913c-f605-4bb5-b128-ea417116505f
                © The Author(s). 2018

                Open AccessThis 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.

                History
                : 2 May 2018
                : 10 October 2018
                Funding
                Funded by: March of Dimes Foundation Prematurity Research Center at the University of Pennsylvania
                Funded by: FundRef http://dx.doi.org/10.13039/100000060, National Institute of Allergy and Infectious Diseases;
                Award ID: P30 AI045008
                Award ID: T32 AI007632
                Award ID: T32 AI007324
                Award Recipient :
                Funded by: PennCHOP Microbiome Program
                Categories
                Research
                Custom metadata
                © The Author(s) 2018

                placenta,shotgun metagenomics,16s rrna gene,microbiome,preterm birth

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