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      The gut microbiota is associated with immune cell dynamics in humans

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          SUMMARY

          The gut microbiota influences development 13 and homeostasis 47 of the mammalian immune system, and is associated with human inflammatory- 8 and immune diseases 9, 10 as well as patients’ responses to immunotherapy 1114 . Still, our understanding of how gut bacteria modulate the immune system remains limited, particularly in humans where a lack of deliberate manipulations makes inference challenging. Here we study hundreds of hospitalized—and closely monitored—cancer patients receiving hematopoietic cell transplantation as they recover from chemotherapy and stem cell engraftment. This aggressive treatment causes large shifts in both circulatory immune cell and microbiota populations, allowing the relationships between the two to be studied simultaneously. Analysis of observed daily changes in circulating neutrophil, lymphocyte and monocyte counts and >10,000 longitudinal microbiota samples from patients revealed consistent associations between gut bacteria and immune cell dynamics. High-resolution clinical metadata and Bayesian inference allowed us to compare the effects of bacterial genera relative to those of immunomodulatory medications, revealing a considerable influence of the gut microbiota—in concert and over time—on systemic immune cell dynamics. Our analysis establishes and quantifies the link between the gut microbiota and the human immune system, with implications for microbiota-driven modulation of immunity.

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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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            Is Open Access

            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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              Regression Shrinkage and Selection Via the Lasso

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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                7 October 2020
                25 November 2020
                December 2020
                25 May 2021
                : 588
                : 7837
                : 303-307
                Affiliations
                [1) ]Institute for Computational Medicine, NYU Langone Health, New York, NY, USA
                [2) ]Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
                [3) ]Adult Bone Marrow Transplantation Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
                [4) ]Weill Cornell Medical College, New York, NY, USA
                [5) ]Infectious Disease Service, Department of Medicine, and Immunology Program, Sloan Kettering Institute, New York, NY, USA.
                [6) ]Harvard University, T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA 02115
                [7) ]Sahlgrenska Cancer Center, Department of Microbiology and Immunology, Institute of Biomedicine, University of Gothenburg, SE-405 30, Gothenburg, Sweden
                [8) ]Department of Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
                [9) ]Division of Hematologic Malignancies and Cellular Therapy, Duke University School of Medicine
                Author notes
                Correspondence to Jonas Schluter ( Jonas.schluter@ 123456nyulangone.org ) and Joao Xavier ( xavierj@ 123456mskcc.org ).

                Author Contributions

                J.S. and J.B.X. wrote the manuscript. J.S. and J.B.X. designed the analyses with expert help from R.N.. J.U.P. and Y.T. contributed to the clinical data preparation, B.P.T. provided the 16S data processing pipelines, K.A.M, M.S., A.S., S.M., M.F., M.S.P, T.M.H., M.A.P., M.R.M.v.d.B. provided clinical context and helped with variable selection, N.J.C., M.L., L.B., A.B, A.D.S. provided clinical and other data from Duke, A.D. provided the shotgun processing pipelines. E.F., L.A.A. and R.J.W. processed patients’ stool samples, including for 16S sequencing, shotgun metagenomics and qPCR quantification of total 16S rRNA gene. All authors contributed to the writing and interpretation of the results.

                Article
                NIHMS1634150
                10.1038/s41586-020-2971-8
                7725892
                33239790
                e9bd99df-1779-447c-aaa2-082f24699e19

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