Erratum to: Host lifestyle affects human microbiota on daily timescales

David, Lawrence A.; Materna, Arne; Friedman, Jonathan; Baptista, Maria I. C.; Blackburn, Matthew; Perrotta, Alessandra; Erdman, Susan E.; Alm, Eric J.

doi:10.1186/s13059-016-0988-y

Cited by 13 publications

(19 citation statements)

References 1 publication

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“…The human gut harbors between 500 and 1000 distinct microorganisms whose metabolic activities have numerous implications for human health [1]. While few longitudinal studies exists, the composition of the gut microbiome is generally considered to be relatively stable over time, with approximately 70 % of an individual's gut microbiota retained throughout their lifetime [2]; however, the proportion of different bacteria taxa can rapidly change in response to changes in diet [2,3] or an individual's state of health [4,5]. More importantly, the human microbiome is often a better predictor of host phenotype, explaining 20 % of the variation among individuals, than is the host genome, which explains only 1.9 % [6].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Gut Microbiota Following Changes in Training Volume Among Swimmers

et al. 2020

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Exercise can influence gut microbial community structure and diversity; however, the temporal dynamics of this association have rarely been explored. Here we characterized fecal microbiota in response to short term changes in training volume. Fecal samples, body composition, and training logs were collected from Division I NCAA collegiate swimmers during peak training through their in-season taper in 2016 (n=9) and 2017 (n=7), capturing a systematic reduction in training volume near the conclusion of their athletic season. Fecal microbiota were characterized using 16S rRNA V4 amplicon sequencing and multivariate statistical analysis, Spearman rank correlations, and random forest models. Peak training volume, measured as swimming distance, decreased significantly during the study period from 32.6±4.8 km/wk to 11.3±8.1 km/wk (ANOVA, p<0.05); however, body composition showed no significant changes. Coinciding with the decrease in training volume, the microbial community structure showed a significant decrease in overall microbial diversity, a decrease in microbial community structural similarity, and a decrease in the proportion of the bacterial genera Faecalibacterium and Coprococcus. Together these data demonstrate a significant association between short-term changes in training volume and microbial composition and structure in the gut; future research will establish whether these changes are associated with energy balance or nutrient intake.

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Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Gut Microbiota Following Changes in Training Volume Among Swimmers

et al. 2020

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“…Here we explore the use of on publicly available datasets studying Crohn’s disease microbiota (Gevers et al [21]), oral cancer microbiota (Schmidt et al [22]), and the variation of microbiota as a function of time (David et al [23]). With the larger Gevers et al Crohn’s dataset, we validate the ability of to capture microbial profile “signatures” (configurations of taxa which are groups with specific ratios/relative abundances of co-occurring taxa).…”

Section: Resultsmentioning

confidence: 99%

Themetagenomics: Exploring Thematic Structure and Predicted Functionality of 16s rRNA Amplicon Data

Woloszynek

Zhao

Simpson

et al. 2019

Preprint

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Analysis of microbiome data involves identifying co-occurring groups of taxa associated with sample features of interest (e.g., disease state). Elucidating such relations is often difficult as microbiome data are compositional, sparse, and have high dimensionality. Also, the configuration of co-occurring taxa may represent overlapping subcommunities that contribute to sample characteristics such as host status. Preserving the configuration of co-occurring microbes rather than detecting specific indicator species is more likely to facilitate biologically meaningful interpretations. Additionally, analyses that use taxonomic relative abundances to predict the abundances of different gene functions aggregate predicted functional profiles across taxa. This precludes straightforward identification of predicted functional components associated with subsets of co-occurring taxa. We provide an approach to explore co-occurring taxa using “topics” generated via a topic model and link these topics to specific sample features (e.g., disease state). Rather than inferring predicted functional content based on overall taxonomic relative abundances, we instead focus on inference of functional content within topics, which we parse by estimating interactions between topics and pathways through a multilevel, fully Bayesian regression model. We apply our methods to three publicly available 16S amplicon sequencing datasets: an inflammatory bowel disease dataset from Gevers et al., an oral cancer dataset from Schmidt et al., and a time-series dataset from David et al. Using our topic model approach to uncover latent structure in 16S rRNA amplicon surveys, investigators can (1) capture groups of co-occurring taxa termed topics; (2) uncover within-topic functional potential; (3) link taxa co-occurrence, gene function, and environmental/host features; and (4) explore the way in which sets of co-occurring taxa behave and evolve over time. These methods have been implemented in a freely available R package: https://github.com/EESI/themetagenomics.

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“…For example, Allen et al instructed subjects to bring fresh samples to the lab within 30 min of defecation [20]; Cronin et al asked participants to provide fresh samples, and those were transported at room temperature to the lab or held at 4 °C for up to 12 hours prior to analysis [21]; Munukka et al stated that samples were frozen immediately at − 20 °C and were kept at − 80 °C until processing [22]. Moreover, although the human gut microbiome composition tends to be fairly stable, temporal dynamics can be easily produced [65,66], suggesting that repeated measurements are required to fully investigate the gut microbiome composition. No included study described possible mitigations to such changes.…”

Section: Sample Collection and Analysis Factorsmentioning

confidence: 99%

Attributes of Physical Activity and Gut Microbiome in Adults: A Systematic Review

et al. 2020

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Growing evidence shows the contribution of physical activity interventions to the gut microbiome. However, specific physical activity characteristics that can modify the gut microbiome are unknown. This review’s aim was to explore the contribution of physical activity intervention characteristics on human gut microbiome composition, in terms of diversity, specific bacterial groups, and associated gut microbiome metabolites. A literature search in PubMed; Cochrane Library; CINAHL-EBSCO; SCOPUS; Web of Science; ClinicalTrials.gov; PROSPERO; and ProQuest. Five studies met the inclusion criteria of a physical activity intervention duration of at least five weeks, with any description of the type or dose used. All included studies reported an endurance training; two studies used endurance and an additional muscle-strengthening training regimen. All studies reported using a dietary intervention control. Reported gut microbiome α-diversity changes were non-significant, β-diversity changes were mixed (three studies reported an increase, two reported non-significant changes). All studies reported significant changes in the abundances of specific bacterial/archaea groups and bacteria-related metabolites following interventions. In conclusion, physical activity (regardless of specific characteristics) has significant contribution to gut microbiome composition and associated metabolites. There are no current recommendations for physical activity to promote gut microbiome composition. Future studies should focus on the contribution of current recommended physical activity dose to gut microbiome composition.

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Erratum to: Host lifestyle affects human microbiota on daily timescales

Cited by 13 publications

References 1 publication

Comparative Analysis of Gut Microbiota Following Changes in Training Volume Among Swimmers

Comparative Analysis of Gut Microbiota Following Changes in Training Volume Among Swimmers

Themetagenomics: Exploring Thematic Structure and Predicted Functionality of 16s rRNA Amplicon Data

Attributes of Physical Activity and Gut Microbiome in Adults: A Systematic Review

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