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      Gut microbiome pattern reflects healthy aging and predicts survival in humans

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          Abstract

          The gut microbiome has important effects on human health, yet its importance in human aging remains unclear. Here we demonstrate that, starting in mid-to-late adulthood, gut microbiomes become increasingly unique to individuals with age. We leverage three independent cohorts comprising over 9000 individuals and find that compositional uniqueness is strongly associated with microbially produced amino acid derivatives circulating in the bloodstream. In older age (over ~80 years), healthy individuals show continued microbial drift toward a unique compositional state, whereas this drift is absent in less healthy individuals. The identified microbiome pattern of healthy aging is characterized by a depletion of core genera found across most humans, primarily Bacteroides. Retaining a high Bacteroides dominance into older age, or having a low gut microbiome uniqueness measure, predicts decreased survival in a four-year follow-up. Our analysis identifies increasing compositional uniqueness of the gut microbiome as a component of healthy aging, which is characterized by distinct microbial metabolic outputs in the blood.

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          Most cited references58

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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

            phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data

            Background The analysis of microbial communities through DNA sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data. Results Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research. Conclusions The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.
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              FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments

              Background We recently described FastTree, a tool for inferring phylogenies for alignments with up to hundreds of thousands of sequences. Here, we describe improvements to FastTree that improve its accuracy without sacrificing scalability. Methodology/Principal Findings Where FastTree 1 used nearest-neighbor interchanges (NNIs) and the minimum-evolution criterion to improve the tree, FastTree 2 adds minimum-evolution subtree-pruning-regrafting (SPRs) and maximum-likelihood NNIs. FastTree 2 uses heuristics to restrict the search for better trees and estimates a rate of evolution for each site (the “CAT” approximation). Nevertheless, for both simulated and genuine alignments, FastTree 2 is slightly more accurate than a standard implementation of maximum-likelihood NNIs (PhyML 3 with default settings). Although FastTree 2 is not quite as accurate as methods that use maximum-likelihood SPRs, most of the splits that disagree are poorly supported, and for large alignments, FastTree 2 is 100–1,000 times faster. FastTree 2 inferred a topology and likelihood-based local support values for 237,882 distinct 16S ribosomal RNAs on a desktop computer in 22 hours and 5.8 gigabytes of memory. Conclusions/Significance FastTree 2 allows the inference of maximum-likelihood phylogenies for huge alignments. FastTree 2 is freely available at http://www.microbesonline.org/fasttree.
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                Author and article information

                Journal
                101736592
                48119
                Nat Metab
                Nat Metab
                Nature metabolism
                2522-5812
                12 May 2021
                18 February 2021
                February 2021
                18 August 2021
                : 3
                : 2
                : 274-286
                Affiliations
                [1 ]Institute for Systems Biology, Seattle, WA 98109
                [2 ]Oregon Health and Science University, Portland, OR 97239
                [3 ]Herbert Wertheim School of Public Health and Human Longevity Science at UCSD and Department of Medicine, UCSD School of Medicine, La Jolla, CA 92093
                [4 ]Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA 15261
                [5 ]Center for Musculoskeletal Health, Department of Internal Medicine, University of California Davis Medical Center, Sacramento, CA 95817
                [6 ]Current address: Lifestyle Medicine Institute, Redlands, CA 92374
                [7 ]eScience Institute, University of Washington, Seattle, WA 98195
                [8 ]Department of Bioengineering, University of Washington, Seattle, WA 98195
                [9 ]Current address: Onegevity Health, New York, NY 10019
                Author notes

                Author contributions

                T.W., S.M.G., L.H., E.S.O., and N.P conceptualized the study. T.W., J.W., J.L., J.A.C., S.M.G., and E.S.O participated in study design. T.W., C.D., N.R., S.P., J.W., J.L., J.C.E., A.Z., and J.T.Y. performed data analysis and figure generation. G.G. and M.R. aided in dissimilarity analysis. G.G., M.R., N.E.L., J.Z., J.A.C and D.M.K. assisted in results interpretation. A.T.M. and J.C.L. managed the logistics of data collection and integration. T.W., S.M.G., E.S.O and N.P. were the primary writers of the paper, with contributions from all authors. All authors read and approved the final manuscript.

                [* ]Correspondence and requests for materials should be addressed to sgibbons@ 123456isbscience.org , orwoll@ 123456ohsu.edu , and nprice@ 123456isbscience.org .
                Article
                NIHMS1664974
                10.1038/s42255-021-00348-0
                8169080
                33619379
                d017351d-6b51-460d-b7f9-a6c04b9ca022

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