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

Metabolic capability and in situ activity of microorganisms in an oil reservoir.

BackgroundMicroorganisms have long been associated with oxic and anoxic degradation of hydrocarbons in oil reservoirs and oil production facilities. While we can readily determine the abundance of microorganisms in the reservoir and study their activity in the laboratory, it has been challenging to resolve what microbes are actively participating in crude oil degradation in situ and to gain insight into what metabolic pathways they deploy.ResultsHere, we describe the metabolic potential and in situ activity of microbial communities obtained from the Jiangsu Oil Reservoir (China) by an integrated metagenomics and metatranscriptomics approach. Almost complete genome sequences obtained by differential binning highlight the distinct capability of different community members to degrade hydrocarbons under oxic or anoxic condition. Transcriptomic data delineate active members of the community and give insights that Acinetobacter species completely oxidize alkanes into carbon dioxide with the involvement of oxygen, and Archaeoglobus species mainly ferment alkanes to generate acetate which could be consumed by Methanosaeta species. Furthermore, nutritional requirements based on amino acid and vitamin auxotrophies suggest a complex network of interactions and dependencies among active community members that go beyond classical syntrophic exchanges; this network defines community composition and microbial ecology in oil reservoirs undergoing secondary recovery.ConclusionOur data expand current knowledge of the metabolic potential and role in hydrocarbon metabolism of individual members of thermophilic microbial communities from an oil reservoir. The study also reveals potential metabolic exchanges based on vitamin and amino acid auxotrophies indicating the presence of complex network of interactions between microbial taxa within the community

Acetate reprograms gut microbiota during alcohol consumption.

Liver damage due to chronic alcohol use is among the most prevalent liver diseases. Alcohol consumption frequency is a strong factor of microbiota variance. Here we use isotope labeled [1-13C] ethanol, metagenomics, and metatranscriptomics in ethanol-feeding and intragastric mouse models to investigate the metabolic impacts of alcohol consumption on the gut microbiota. First, we show that although stable isotope labeled [1-13C] ethanol contributes to fatty acid pools in the liver, plasma, and cecum contents of mice, there is no evidence of ethanol metabolism by gut microbiota ex vivo under anaerobic conditions. Next, we observe through metatranscriptomics that the gut microbiota responds to ethanol-feeding by activating acetate dissimilation, not by metabolizing ethanol directly. We demonstrate that blood acetate concentrations are elevated during ethanol consumption. Finally, by increasing systemic acetate levels with glyceryl triacetate supplementation, we do not observe any impact on liver disease, but do induce similar gut microbiota alterations as chronic ethanol-feeding in mice. Our results show that ethanol is not directly metabolized by the gut microbiota, and changes in the gut microbiota linked to ethanol are a side effect of elevated acetate levels. De-trending for these acetate effects may be critical for understanding gut microbiota changes that cause alcohol-related liver disease

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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

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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)

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,

Full American Gut Project mapping file

The full American Gut Project mapping file, includes non-fecal samples

Weighted normalized UniFrac distances

The weighted normalized UniFrac distance (Lozupone et al AEM 2007) 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)