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      Impact of Different Exercise Modalities on the Human Gut Microbiome

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          Abstract

          In this study we examined changes to the human gut microbiome resulting from an eight-week intervention of either cardiorespiratory exercise (CRE) or resistance training exercise (RTE). Twenty-eight subjects (21 F; aged 18–26) were recruited for our CRE study and 28 subjects (17 F; aged 18–33) were recruited for our RTE study. Fecal samples for gut microbiome profiling were collected twice weekly during the pre-intervention phase (three weeks), intervention phase (eight weeks), and post-intervention phase (three weeks). Pre/post VO 2max, three repetition maximum (3RM), and body composition measurements were conducted. Heart rate ranges for CRE were determined by subjects’ initial VO 2max test. RTE weight ranges were established by subjects’ initial 3RM testing for squat, bench press, and bent-over row. Gut microbiota were profiled using 16S rRNA gene sequencing. Microbiome sequence data were analyzed with QIIME 2. CRE resulted in initial changes to the gut microbiome which were not sustained through or after the intervention period, while RTE resulted in no detectable changes to the gut microbiota. For both CRE and RTE, we observe some evidence that the baseline microbiome composition may be predictive of exercise gains. This work suggests that the human gut microbiome can change in response to a new exercise program, but the type of exercise likely impacts whether a change occurs. The changes observed in our CRE intervention resemble a disturbance to the microbiome, where an initial shift is observed followed by a return to the baseline state. More work is needed to understand how sustained changes to the microbiome occur, resulting in differences that have been reported in cross sectional studies of athletes and non-athletes.

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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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            Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

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              Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms

              DNA sequencing continues to decrease in cost with the Illumina HiSeq2000 generating up to 600 Gb of paired-end 100 base reads in a ten-day run. Here we present a protocol for community amplicon sequencing on the HiSeq2000 and MiSeq Illumina platforms, and apply that protocol to sequence 24 microbial communities from host-associated and free-living environments. A critical question as more sequencing platforms become available is whether biological conclusions derived on one platform are consistent with what would be derived on a different platform. We show that the protocol developed for these instruments successfully recaptures known biological results, and additionally that biological conclusions are consistent across sequencing platforms (the HiSeq2000 versus the MiSeq) and across the sequenced regions of amplicons.
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                Author and article information

                Journal
                Sports (Basel)
                Sports (Basel)
                sports
                Sports
                MDPI
                2075-4663
                21 January 2021
                February 2021
                : 9
                : 2
                : 14
                Affiliations
                [1 ]Department of Health Sciences, Northern Arizona University, Flagstaff, AZ 86011, USA; Anthony.Santos@ 123456nau.edu (A.C.S.); Jay.Sutliffe@ 123456nau.edu (J.S.)
                [2 ]Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ 86011, USA; ams379@ 123456nau.edu (A.S.); Talima.Pearson@ 123456nau.edu (T.P.); Emily.Cope@ 123456nau.edu (E.C.); Greg.Caporaso@ 123456nau.edu (J.G.C.)
                [3 ]Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ 86011, USA; skyman@ 123456tgen.org
                [4 ]School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ 86011, USA; Kyle.Winfree@ 123456nau.edu
                [5 ]Department of Mathematics and Statistics, Northern Arizona University, Flagstaff, AZ 86011, USA; Derek.Sonderegger@ 123456nau.edu
                Author notes
                Author information
                https://orcid.org/0000-0003-3696-041X
                https://orcid.org/0000-0002-8865-1670
                Article
                sports-09-00014
                10.3390/sports9020014
                7909775
                33494210
                eaafe53c-62eb-4d2c-b167-6e70d24b92e9
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 18 December 2020
                : 16 January 2021
                Categories
                Article

                gut,microbiome,exercise,resistance training,cardiorespiratory fitness

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