12 research outputs found
Athletic equipment microbiota are shaped by interactions with human skin
© The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Microbiome 3 (2015): 25, doi:10.1186/s40168-015-0088-3.Americans spend the vast majority of their lives in built environments. Even traditionally outdoor pursuits, such as exercising, are often now performed indoors. Bacteria that colonize these indoor ecosystems are primarily derived from the human microbiome. The modes of human interaction with indoor surfaces and the physical conditions associated with each surface type determine the steady-state ecology of the microbial community. Bacterial assemblages associated with different surfaces in three athletic facilities, including floors, mats, benches, free weights, and elliptical handles, were sampled every other hour (8 am to 6 pm) for 2 days. Surface and equipment type had a stronger influence on bacterial community composition than the facility in which they were housed. Surfaces that were primarily in contact with human skin exhibited highly dynamic bacterial community composition and non-random co-occurrence patterns, suggesting that different host microbiomes—shaped by selective forces—were being deposited on these surfaces through time. However, bacterial assemblages found on the floors and mats changed less over time, and species co-occurrence patterns appeared random, suggesting more neutral community assembly. These longitudinal patterns highlight the dramatic turnover of microbial communities on surfaces in regular contact with human skin. By uncovering these longitudinal patterns, this study promotes a better understanding of microbe-human interactions within the built environment.MW was supported by a Weinberg College of Arts and Sciences Summer Grant from Northwestern University. This work was supported in part by the U.S. Dept. of Energy under Contract DE-AC02-06CH11357. This work was also supported by the Alfred P Sloan Foundation’s Microbiology of the Built Environment research program. SMG was supported by an EPA STAR Graduate Fellowship and the National Institutes of Health Training Grant 5 T-32 EB-009412
American Gut: an Open Platform for Citizen Science Microbiome Research
McDonald D, Hyde E, Debelius JW, et al. American Gut: an Open Platform for Citizen Science Microbiome Research. mSystems. 2018;3(3):e00031-18
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Placing changes in the microbiome in the context of the American Gut. We accumulated samples over sequencing runs to demonstrate the structural consistency in the data. We demonstrate that while the ICU dataset (https://www.ncbi.nlm.nih.gov/pubmed/27602409) falls within the American Gut samples, they do not fall close to most samples at any of the body sites. We then highlight samples from the United Kingdom, Australia, the United States and other countries to show that nationality does not overcome the variation in body site. We then highlight the utility of the American Gut in meta-analysis by reproducing results from (https://www.ncbi.nlm.nih.gov/pubmed/20668239) and (https://www.ncbi.nlm.nih.gov/pubmed/23861384), using the AGP dataset as the context for dynamic microbiome changes instead of the HMP dataset. We show rapid, complete recovery of C. diff patients following fecal material transplantation and also contextualized the change in an infant gut over time until it settles into an adult state. This demonstrates the power of the American Gut dataset, both as a cohesive study and as a context for other investigations
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The SEPP (Mirarab et al Pac Symp Biocomput 2012) fragment insertion tree used for phylogenetic analyses
American Gut Project fecal sOTU counts table
The Deblur sOTU counts table for the fecal samples used in the American Gut Project manuscript. The samples were trimmed to a common read length of 125nt, and processed by Deblur (Amir et al mSystems 2017). Blooms were removed (Amir et al mSystems 2017) and any sample with fewer than 1250 sequences was omitted. This table is not rarefied,
American Gut Project fecal sOTU relative abundance table
The Deblur sOTU relative abundance table for the fecal samples used in the American Gut Project manuscript. The samples were trimmed to a common read length of 125nt, and processed by Deblur (Amir et al mSystems 2017). Blooms were removed (Amir et al mSystems 2017) and any sample with fewer than 1250 sequences was omitted. This table is not rarefied, and is normalized to 1
American Gut collated alpha diversities
Collated alpha diversity values for the 9511 fecal samples used in the American Gut Project manuscript
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Longitudinal samples from a large bowel resection. We place longitudinal samples collected prior to and following a large bowel resection in the context of samples from the AGP, the Earth Microbiome Project (https://www.ncbi.nlm.nih.gov/pubmed/29088705), intensive care unit patients (https://www.ncbi.nlm.nih.gov/pubmed/27602409), "extreme" diet samples from (https://www.ncbi.nlm.nih.gov/pubmed/24336217), and samples from the Hadza hunter-gatherers (https://www.ncbi.nlm.nih.gov/pubmed/28839072). Unweighted UniFrac was computed on this sample set, and principal coordinates were assessed. Using EMPeror (https://www.ncbi.nlm.nih.gov/pubmed/24280061), we then animate the plot by connect successive data points gut resection time series, while rotating the data frame. We first show the how the extent of change in the microbial community, and how the samples immediately following surgery resemble fecal samples from ICU patients. In the background of the animation, a black line connects a plant rhizosphere sample to a marine sediment sample, which have the same unweighted UniFrac distance (0.78) as the longitudinal sample immediately preceding and immediately following surgery
Unweighted UniFrac distances
The unweighted UniFrac distance (Lozupone and Knight AEM 2005) matrix of the 9511 fecal samples used in the American Gut paper. UniFrac was computed using Striped UniFrac (https://github.com/biocore/unifrac). Prior to execution of UniFrac, Deblur (Amir et al mSystems 2017) was run on the samples, all bloom sOTUs were removed (Amir et al mSystems 2017), and samples were rarefied to a depth of 1250 reads (Weiss et al Microbiome 2017). For the phylogeny, fragments were inserted using SEPP (Mirarab et al Pac Symp Biocomput 2012) into the Greengenes 13_5 99% OTU tree (McDonald et al ISME 2012)