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      Quantitative sequencing clarifies the role of disruptor taxa, oral microbiota, and strict anaerobes in the human small-intestine microbiome

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          Abstract

          Background

          Upper gastrointestinal (GI) disorders and abdominal pain afflict between 12 and 30% of the worldwide population and research suggests these conditions are linked to the gut microbiome. Although large-intestine microbiota have been linked to several GI diseases, the microbiota of the human small intestine and its relation to human disease has been understudied. The small intestine is the major site for immune surveillance in the gut, and compared with the large intestine, it has greater than 100 times the surface area and a thinner and more permeable mucus layer.

          Results

          Using quantitative sequencing, we evaluated total and taxon-specific absolute microbial loads from 250 duodenal-aspirate samples and 21 paired duodenum-saliva samples from participants in the REIMAGINE study. Log-transformed total microbial loads spanned 5 logs and were normally distributed. Paired saliva-duodenum samples suggested potential transmission of oral microbes to the duodenum, including organisms from the HACEK group. Several taxa, including Klebsiella, Escherichia, Enterococcus, and Clostridium, seemed to displace strict anaerobes common in the duodenum, so we refer to these taxa as disruptors. Disruptor taxa were enriched in samples with high total microbial loads and in individuals with small intestinal bacterial overgrowth (SIBO). Absolute loads of disruptors were associated with more severe GI symptoms, highlighting the value of absolute taxon quantification when studying small-intestine health and function.

          Conclusion

          This study provides the largest dataset of the absolute abundance of microbiota from the human duodenum to date. The results reveal a clear relationship between the oral microbiota and the duodenal microbiota and suggest an association between the absolute abundance of disruptor taxa, SIBO, and the prevalence of severe GI symptoms.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s40168-021-01162-2.

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          Most cited references61

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          DADA2: High resolution sample inference from Illumina amplicon data

          We present DADA2, a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. In several mock communities DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.
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            The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

            SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.
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              Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin

              Background Taxonomic classification of marker-gene sequences is an important step in microbiome analysis. Results We present q2-feature-classifier (https://github.com/qiime2/q2-feature-classifier), a QIIME 2 plugin containing several novel machine-learning and alignment-based methods for taxonomy classification. We evaluated and optimized several commonly used classification methods implemented in QIIME 1 (RDP, BLAST, UCLUST, and SortMeRNA) and several new methods implemented in QIIME 2 (a scikit-learn naive Bayes machine-learning classifier, and alignment-based taxonomy consensus methods based on VSEARCH, and BLAST+) for classification of bacterial 16S rRNA and fungal ITS marker-gene amplicon sequence data. The naive-Bayes, BLAST+-based, and VSEARCH-based classifiers implemented in QIIME 2 meet or exceed the species-level accuracy of other commonly used methods designed for classification of marker gene sequences that were evaluated in this work. These evaluations, based on 19 mock communities and error-free sequence simulations, including classification of simulated “novel” marker-gene sequences, are available in our extensible benchmarking framework, tax-credit (https://github.com/caporaso-lab/tax-credit-data). Conclusions Our results illustrate the importance of parameter tuning for optimizing classifier performance, and we make recommendations regarding parameter choices for these classifiers under a range of standard operating conditions. q2-feature-classifier and tax-credit are both free, open-source, BSD-licensed packages available on GitHub.
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                Author and article information

                Contributors
                jbarlow@caltech.edu
                Gabriela.Leite@cshs.org
                aromano@caltech.edu
                Rashin.Sedighi@cshs.org
                Christine.Chang@cshs.org
                shreyacelly@yahoo.com
                Ali.Rezaie@cshs.org
                Ruchi.Mathur@cshs.org
                Mark.Pimentel@cshs.org
                rustem.admin@caltech.edu
                Journal
                Microbiome
                Microbiome
                Microbiome
                BioMed Central (London )
                2049-2618
                2 November 2021
                2 November 2021
                2021
                : 9
                : 214
                Affiliations
                [1 ]GRID grid.20861.3d, ISNI 0000000107068890, Division of Biology and Biological Engineering, , California Institute of Technology, ; 1200 E. California Blvd, Pasadena, CA 91125 USA
                [2 ]GRID grid.50956.3f, ISNI 0000 0001 2152 9905, Medically Associated Science and Technology (MAST) Program, , Cedars-Sinai Medical Center, ; Los Angeles, CA 90048 USA
                [3 ]GRID grid.20861.3d, ISNI 0000000107068890, Division of Chemistry and Chemical Engineering, , California Institute of Technology, ; 1200 E. California Blvd, Pasadena, CA 91125 USA
                [4 ]GRID grid.50956.3f, ISNI 0000 0001 2152 9905, Division of Digestive and Liver Diseases, , Cedars-Sinai Medical Center, ; Los Angeles, CA 90048 USA
                [5 ]GRID grid.50956.3f, ISNI 0000 0001 2152 9905, Division of Endocrinology, Diabetes, and Metabolism, , Cedars-Sinai Medical Center, ; Los Angeles, CA 90048 USA
                Author information
                http://orcid.org/0000-0002-3680-4399
                Article
                1162
                10.1186/s40168-021-01162-2
                8561862
                34724979
                e8593eb2-c5c1-4bd6-86db-c4b84767d6c8
                © The Author(s) 2021

                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
                : 15 June 2021
                : 14 September 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100009576, kenneth rainin foundation;
                Award ID: 2018-1207
                Award Recipient :
                Funded by: jacobs institute for molecular engineering for medicine (caltech)
                Award ID: n/a
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, national institutes of health;
                Award ID: T32GM112592
                Categories
                Research
                Custom metadata
                © The Author(s) 2021

                duodenum,saliva,hacek,human small intestinal microbiome,ibs,sibo,enterobacteriaceae,lactobacillus,constipation,bloating

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