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      Elimination of “kitome” and “splashome” contamination results in lack of detection of a unique placental microbiome

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          Abstract

          Background

          A placental microbiome, which may be altered in gestational diabetes mellitus (GDM), has been described. However, publications raising doubts about the existence of a placental microbiome that is different than contaminants in DNA extraction kits and reagents (“kitomes”) have emerged. The aims of this study were to confirm the existence of a placental microbiome distinct from contaminants and determine if it is altered in GDM mothers.

          Results

          We first enrolled normal weight, obese and GDM mothers ( N = 17) at term elective cesarean section delivery in a pilot case control study. Bacterial DNA was extracted from placental parenchyma, maternal and cord blood, maternal vaginal-rectal swabs, and positive and negative controls with the standard Qiagen/MoBio Power Soil kit. Placentas had significantly higher copies of bacterial 16S rRNA genes than negative controls, but the placental microbiome was similar in all three groups and could not be distinguished from contaminants in blank controls. To determine the source and composition of the putative placental bacterial community identified in the pilot study, we expanded the study to 10 subjects per group ( N = 30) and increased the number and variety of negative controls ( N = 53). We modified our protocol to use an ultraclean DNA extraction kit (Qiagen QIAamp UCP with Pathogen Lysis Tube S), which reduced the “kitome” contamination, but we were still unable to distinguish a placental microbiome from contaminants in negative controls. We noted microbial DNA from the high biomass vaginal-rectal swabs and positive controls in placental and negative control samples and determined that this resulted from close proximity well-to-well cross contamination or “splashome”. We eliminated this source of contamination by repeating the sequencing run with a minimum of four wells separating high biomass from low biomass samples. This reduced the reads of bacterial 16S rRNA genes in placental samples to insignificant numbers.

          Conclusions

          We identified the problem of well-to-well contamination (“splashome”) as an additional source of error in microbiome studies of low biomass samples and found a method of eliminating it. Once “kitome” and “splashome” contaminants were eliminated, we were unable to identify a unique placental microbiome.

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          Most cited references35

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          Inherent bacterial DNA contamination of extraction and sequencing reagents may affect interpretation of microbiota in low bacterial biomass samples

          Background The advent and use of highly sensitive molecular biology techniques to explore the microbiota and microbiome in environmental and tissue samples have detected the presence of contaminating microbial DNA within reagents. These microbial DNA contaminants may distort taxonomic distributions and relative frequencies in microbial datasets, as well as contribute to erroneous interpretations and identifications. Results We herein report on the occurrence of bacterial DNA contamination within commonly used DNA extraction kits and PCR reagents and the effect of these contaminates on data interpretation. When compared to previous reports, we identified an additional 88 bacterial genera as potential contaminants of molecular biology grade reagents, bringing the total number of known contaminating microbes to 181 genera. Many of the contaminants detected are considered normal inhabitants of the human gastrointestinal tract and the environment and are often indistinguishable from those genuinely present in the sample. Conclusions Laboratories working on bacterial populations need to define contaminants present in all extraction kits and reagents used in the processing of DNA. Any unusual and/or unexpected findings need to be viewed as possible contamination as opposed to unique findings. Electronic supplementary material The online version of this article (doi:10.1186/s13099-016-0103-7) contains supplementary material, which is available to authorized users.
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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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              Investigating deep phylogenetic relationships among cyanobacteria and plastids by small subunit rRNA sequence analysis.

              Small subunit rRNA sequence data were generated for 27 strains of cyanobacteria and incorporated into a phylogenetic analysis of 1,377 aligned sequence positions from a diverse sampling of 53 cyanobacteria and 10 photosynthetic plastids. Tree inference was carried out using a maximum likelihood method with correction for site-to-site variation in evolutionary rate. Confidence in the inferred phylogenetic relationships was determined by construction of a majority-rule consensus tree based on alternative topologies not considered to be statistically significantly different from the optimal tree. The results are in agreement with earlier studies in the assignment of individual taxa to specific sequence groups. Several relationships not previously noted among sequence groups are indicated, whereas other relationships previously supported are contradicted. All plastids cluster as a strongly supported monophyletic group arising near the root of the cyanobacterial line of descent.
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                Author and article information

                Contributors
                Olomui@msu.edu
                Luisca_pe@unipamplona.edu.co
                Robert.Long@sparrow.org , Longrob4@msu.edu
                Arpita.vyas@cnsu.edu
                Olha.Krichevskiy@sparrow.org
                Luellwitz@gmail.com
                Psingh1@niu.edu
                Mulks@msu.edu
                Journal
                BMC Microbiol
                BMC Microbiol
                BMC Microbiology
                BioMed Central (London )
                1471-2180
                11 June 2020
                11 June 2020
                2020
                : 20
                : 157
                Affiliations
                [1 ]GRID grid.17088.36, ISNI 0000 0001 2150 1785, Department of Pediatrics & Human Development, Division of Neonatology, , Michigan State University, ; East Lansing, MI USA
                [2 ]GRID grid.441950.d, ISNI 0000 0001 2107 1033, Facultad de Ciencias Agrarias, , Universidad de Pamplona, ; Pamplona, Colombia
                [3 ]GRID grid.416223.0, ISNI 0000 0004 0450 5161, Department of Obstetrics & Gynecology, , Sparrow Hospital, ; Lansing, MI USA
                [4 ]GRID grid.17088.36, ISNI 0000 0001 2150 1785, Department of Obstetrics & Gynecology, , Michigan State University, ; East Lansing, MI USA
                [5 ]Department of Pediatric Endocrinology, California North State University, Elk Grove, CA USA
                [6 ]Department of Obstetrics & Gynecology, SSM Health/Dean Medical Group, Madison, WI USA
                [7 ]GRID grid.261128.e, ISNI 0000 0000 9003 8934, Department of Biological Sciences, , Northern Illinois University, ; DeKalb, IL USA
                [8 ]GRID grid.17088.36, ISNI 0000 0001 2150 1785, Department of Microbiology & Molecular Genetics, , Michigan State University, ; East Lansing, MI USA
                Author information
                http://orcid.org/0000-0002-2774-6879
                Article
                1839
                10.1186/s12866-020-01839-y
                7291729
                32527226
                40ceaa0a-8d64-4f55-876f-831e1b8a5209
                © The Author(s) 2020

                Open AccessThis 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/. 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 in a credit line to the data.

                History
                : 3 January 2020
                : 2 June 2020
                Funding
                Funded by: College of Human Medicine, Michigan State University (US)
                Award ID: RG072756-K5GSD
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2020

                Microbiology & Virology
                placenta,microbiome,kits,reagents,‘splashome’,contaminants
                Microbiology & Virology
                placenta, microbiome, kits, reagents, ‘splashome’, contaminants

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