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Research Article | Host-Microbe Biology

American Gut: an Open Platform for Citizen Science Microbiome Research

Daniel McDonald, Embriette Hyde, Justine W. Debelius, James T. Morton, Antonio Gonzalez, Gail Ackermann, Alexander A. Aksenov, Bahar Behsaz, Caitriona Brennan, Yingfeng Chen, Lindsay DeRight Goldasich, Pieter C. Dorrestein, Robert R. Dunn, Ashkaan K. Fahimipour, James Gaffney, Jack A. Gilbert, Grant Gogul, Jessica L. Green, Philip Hugenholtz, Greg Humphrey, Curtis Huttenhower, Matthew A. Jackson, Stefan Janssen, Dilip V. Jeste, Lingjing Jiang, Scott T. Kelley, Dan Knights, Tomasz Kosciolek, Joshua Ladau, Jeff Leach, Clarisse Marotz, Dmitry Meleshko, Alexey V. Melnik, Jessica L. Metcalf, Hosein Mohimani, Emmanuel Montassier, Jose Navas-Molina, Tanya T. Nguyen, Shyamal Peddada, Pavel Pevzner, Katherine S. Pollard, Gholamali Rahnavard, Adam Robbins-Pianka, Naseer Sangwan, Joshua Shorenstein, Larry Smarr, Se Jin Song, Timothy Spector, Austin D. Swafford, Varykina G. Thackray, Luke R. Thompson, Anupriya Tripathi, Yoshiki Vázquez-Baeza, Alison Vrbanac, Paul Wischmeyer, Elaine Wolfe, Qiyun Zhu, The American Gut Consortium, Rob Knight
Casey S. Greene, Editor
Daniel McDonald
aDepartment of Pediatrics, University of California San Diego, La Jolla, California, USA
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Embriette Hyde
aDepartment of Pediatrics, University of California San Diego, La Jolla, California, USA
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Justine W. Debelius
aDepartment of Pediatrics, University of California San Diego, La Jolla, California, USA
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James T. Morton
aDepartment of Pediatrics, University of California San Diego, La Jolla, California, USA
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Antonio Gonzalez
aDepartment of Pediatrics, University of California San Diego, La Jolla, California, USA
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Gail Ackermann
aDepartment of Pediatrics, University of California San Diego, La Jolla, California, USA
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Alexander A. Aksenov
bCollaborative Mass Spectrometry Innovation Center, University of California, San Diego, La Jolla, California, USA
cSkaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California, USA
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Bahar Behsaz
dDepartment of Computer Science and Engineering, University of California, San Diego, La Jolla, California, USA
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Caitriona Brennan
aDepartment of Pediatrics, University of California San Diego, La Jolla, California, USA
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Yingfeng Chen
eDepartment of Biology, San Diego State University, San Diego, California, USA
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Lindsay DeRight Goldasich
aDepartment of Pediatrics, University of California San Diego, La Jolla, California, USA
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Pieter C. Dorrestein
bCollaborative Mass Spectrometry Innovation Center, University of California, San Diego, La Jolla, California, USA
cSkaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California, USA
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Robert R. Dunn
fDepartment of Applied Ecology, North Carolina State University, Raleigh, North Carolina, USA
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Ashkaan K. Fahimipour
gBiology and the Built Environment Center, University of Oregon, Eugene, Oregon, USA
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James Gaffney
aDepartment of Pediatrics, University of California San Diego, La Jolla, California, USA
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Jack A. Gilbert
hDepartment of Surgery, University of Chicago, Chicago, Illinois, USA
iInstitute for Genomic and Systems Biology, University of Chicago, Chicago, Illinois, USA
jDepartment of Biosciences, Argonne National Laboratory, Chicago, Illinois, USA
kMarine Biology Laboratory, University of Chicago, Chicago, Illinois, USA
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Grant Gogul
aDepartment of Pediatrics, University of California San Diego, La Jolla, California, USA
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Jessica L. Green
gBiology and the Built Environment Center, University of Oregon, Eugene, Oregon, USA
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Philip Hugenholtz
lAustralian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, the University of Queensland, Brisbane, QLD, Australia
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Greg Humphrey
aDepartment of Pediatrics, University of California San Diego, La Jolla, California, USA
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Curtis Huttenhower
mHarvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
nThe Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
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Matthew A. Jackson
oDepartment of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
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Stefan Janssen
aDepartment of Pediatrics, University of California San Diego, La Jolla, California, USA
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Dilip V. Jeste
pDepartments of Psychiatry and Neurosciences, University of California San Diego, La Jolla, California, USA
qSam and Rose Stein Institute for Research on Aging and Center for Healthy Aging, University of California San Diego, La Jolla, California, USA
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Lingjing Jiang
aDepartment of Pediatrics, University of California San Diego, La Jolla, California, USA
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Scott T. Kelley
eDepartment of Biology, San Diego State University, San Diego, California, USA
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Dan Knights
rDepartment of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota, USA
sBiotechnology Institute, University of Minnesota, Minneapolis, Minnesota, USA
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Tomasz Kosciolek
aDepartment of Pediatrics, University of California San Diego, La Jolla, California, USA
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Joshua Ladau
tThe Gladstone Institutes, University of California, San Francisco, California, USA
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Jeff Leach
uHuman Food Project, Terlingua, Texas, USA
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Clarisse Marotz
aDepartment of Pediatrics, University of California San Diego, La Jolla, California, USA
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Dmitry Meleshko
vSt. Petersburg State University, Center for Algorithmic Biotechnology, Saint Petersburg, Russia
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Alexey V. Melnik
bCollaborative Mass Spectrometry Innovation Center, University of California, San Diego, La Jolla, California, USA
cSkaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California, USA
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Jessica L. Metcalf
wDepartment of Animal Science, Colorado State University, Fort Collins, Colorado, USA
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Hosein Mohimani
xDepartment of Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
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Emmanuel Montassier
rDepartment of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota, USA
yUniversité de Nantes, Microbiotas Hosts Antibiotics and Bacterial Resistances (MiHAR), Nantes, France
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Jose Navas-Molina
aDepartment of Pediatrics, University of California San Diego, La Jolla, California, USA
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Tanya T. Nguyen
pDepartments of Psychiatry and Neurosciences, University of California San Diego, La Jolla, California, USA
qSam and Rose Stein Institute for Research on Aging and Center for Healthy Aging, University of California San Diego, La Jolla, California, USA
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Shyamal Peddada
zDepartment of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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Pavel Pevzner
bCollaborative Mass Spectrometry Innovation Center, University of California, San Diego, La Jolla, California, USA
dDepartment of Computer Science and Engineering, University of California, San Diego, La Jolla, California, USA
aaCenter for Microbiome Innovation, University of California, San Diego, La Jolla, California, USA
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Katherine S. Pollard
tThe Gladstone Institutes, University of California, San Francisco, California, USA
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Gholamali Rahnavard
mHarvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
nThe Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
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Adam Robbins-Pianka
bbDepartment of Computer Science, University of Colorado Boulder, Boulder, Colorado, USA
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Naseer Sangwan
jDepartment of Biosciences, Argonne National Laboratory, Chicago, Illinois, USA
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Joshua Shorenstein
aDepartment of Pediatrics, University of California San Diego, La Jolla, California, USA
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Larry Smarr
dDepartment of Computer Science and Engineering, University of California, San Diego, La Jolla, California, USA
aaCenter for Microbiome Innovation, University of California, San Diego, La Jolla, California, USA
ccCalifornia Institute for Telecommunications and Information Technology (Calit2), University of California San Diego, La Jolla, California, USA
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Se Jin Song
aDepartment of Pediatrics, University of California San Diego, La Jolla, California, USA
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Timothy Spector
oDepartment of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
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Austin D. Swafford
aaCenter for Microbiome Innovation, University of California, San Diego, La Jolla, California, USA
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Varykina G. Thackray
ddDepartment of Obstetrics, Gynecology and Reproductive Sciences, University of California San Diego, La Jolla, California, USA
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Luke R. Thompson
eeOcean Chemistry and Ecosystems Division, Atlantic Oceanographic and Meteorological Laboratory, National Oceanic and Atmospheric Administration, stationed at Southwest Fisheries Science Center, La Jolla, California, USA
ffDepartment of Biological Sciences and Northern Gulf Institute, University of Southern Mississippi, Hattiesburg, Mississippi, USA
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Anupriya Tripathi
aDepartment of Pediatrics, University of California San Diego, La Jolla, California, USA
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Yoshiki Vázquez-Baeza
aDepartment of Pediatrics, University of California San Diego, La Jolla, California, USA
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Alison Vrbanac
aDepartment of Pediatrics, University of California San Diego, La Jolla, California, USA
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Paul Wischmeyer
ggDepartment of Anesthesiology and Surgery, Duke University School of Medicine, Durham, North Carolina, USA
hhDuke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, USA
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Elaine Wolfe
aDepartment of Pediatrics, University of California San Diego, La Jolla, California, USA
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Qiyun Zhu
aDepartment of Pediatrics, University of California San Diego, La Jolla, California, USA
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Rob Knight
aDepartment of Pediatrics, University of California San Diego, La Jolla, California, USA
dDepartment of Computer Science and Engineering, University of California, San Diego, La Jolla, California, USA
aaCenter for Microbiome Innovation, University of California, San Diego, La Jolla, California, USA
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Casey S. Greene
University of Pennsylvania
Roles: Editor
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DOI: 10.1128/mSystems.00031-18
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  • FIG 1 
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    FIG 1 

    Population characteristics. (A) Participants across the world have sent in samples to American Gut, although the primary geographic regions of participation are in North America and the United Kingdom; the report that a participant receives is depicted. (B) The primary sample breakdown for subsequent analyses. Red denotes the reasons that samples were removed. (C) Between the two largest populations, the United States (n = 6,634) and the United Kingdom (n = 2,071), we observe a significant difference in alpha-diversity. (D) In a meta-analysis, the largely industrialized population that makes up American Gut exhibits significant differential abundances compared to nonindustrialized populations.

  • FIG 2 
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    FIG 2 

    Blooms and effect sizes. (A) The fraction of 16S reads that recruit to bloom reads defined by Amir et al. (15) is strongly associated with the likelihood for microbial growth under aerobic culture conditions on rich medium. (B) Overlap of mass spectral features (consensus MS/MS cluster nodes; see Materials and Methods, “Molecular networking”) between AGP samples and blooms. (C) Unweighted UniFrac effect sizes. The inset shows the correlation of effect sizes when including or excluding the bloom 16S reads (Pearson r = 0.91, P = 3.76 × 10−57). (D) Weighted UniFrac effect sizes. The inset shows the correlation of the effect sizes when including or excluding bloom 16S reads (Pearson r = 0.42, P = 1.71 × 10−6); the outlier is the 16S bloom fraction of the sample.

  • FIG 3 
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    FIG 3 

    OTU and beta-diversity novelty. (A) The AGP data placed into the context of extant microbial diversity at a global scale. (B) A phylogenetic tree showing the diversity spanned by the AGP and the HMP in the context of Greengenes and the EMP. (C and D) sOTU novelty over increasing numbers of samples in the AGP (C); the AGP appears to have begun to reach saturation and is contrasted with the data from the work of Yatsunenko et al. (6) (D), which, unlike the AGP, had extremely deep sequencing per sample. (E) The minimum observed UniFrac distance between samples over increasing numbers of samples for the AGP and the HMP; the inset is from 0 to 500 samples. (F) An AGP “trading card” of an sOTU of interest (shown in full in Fig. S2).

  • FIG 4 
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    FIG 4 

    Temporal and spatial patterns. (A) Five hundred sixty-five individuals had multiple samples. Distances between samples within an individual shown at 1 month, 2 months, etc., out to over 1 year; between-subject distances are shown as BSD. Even at 1 year, the median distance between a participant’s samples is less than the median between-participant distance. (B) Within the United States, spatial processes of sOTUs appear driven by stochastic processes, as few sOTUs exhibit spatial autocorrelation (Moran’s I) on the full data set or partitions (e.g., participants older than 20 years). (C) Distance-decay relationship for Bray-Curtis dissimilarities between subject pairs that are within a 100-km (great-circle distance) neighborhood radius of one another (Mantel test r = 0.036, adjusted P = 0.03). To avoid the overplotting associated with visualization of the more than 3.4 × 105 pairwise comparisons, we visualized this relationship using two-dimensional frequency bins; darker colors indicate higher-frequency bins. Solid lines represent fits from linear models to raw data. The inset shows the largest radius (i.e., the contiguous United States). Axes are the same as in the large panel. (D) Mantel correlogram of estimated Mantel r correlations, significance of distance-decay relationships, and neighborhood size (x axis). Filled points represent neighborhood sizes for which distance-decay relationships were significant (adjusted P values < 0.05). (E) Characterizing a large bowel resection using the AGP, the EMP, a hunter-gatherer population, and ICU patients in an unweighted UniFrac principal-coordinate plot. A state change was observed in the resulting microbial community. The change in the microbial community immediately following surgery is the same as the distance between a marine sediment sample and a plant rhizosphere sample.

  • FIG 5 
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    FIG 5 

    Diversity of plants in a diet. (A) Procrustes analysis of fecal samples from n = 1,596 individuals using principal components of the VioScreen FFQ responses and principal coordinates of the unweighted UniFrac distances (M2 = 0.988) colored by diet; Procrustes tests the fit of one ordination space to another. PCA shows grouping by diets such as vegan, suggesting that self-reported diet type is consistent with differences in micronutrients and macronutrients as recorded by the FFQ; however, these dietary differences do not explain relationships between the samples in 16S space. (B) The full AGP data set, including skin and oral samples, through unweighted UniFrac and principal-coordinate analysis, highlighting a lack of apparent clustering by diet type. (C and D) Dietary conjugated linoleic acid levels as reported by the FFQ between the extremes of plant diversity consumption (C) and the levels of CLA observed by HPLC-MS (D). (E) Differential abundances of sOTUs (showing the most specific taxon name per sOTU) between those who eat fewer than 10 plants per week and those who eat over 30 per week. (F) The molecules linoleic acid (LA) and conjugated linoleic acid (CLA) (only trans-, trans-isomers are shown) were found to comprise the octadecadienoic acid found to be the key feature in this difference in number of plants consumed.

  • FIG 6 
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    FIG 6 

    (A) Compound occurrence frequency plot. Examples of compounds originating from food (piperine, black pepper alkaloid), host (stercobilin, heme catabolism product), and bacterial activity (lithocholic acid, microbially modified bile acid) or exogenous compounds such as antibiotics (rifaximin) or other drugs (lisinopril, high blood pressure medication) are shown. (B to E) Alpha- and beta-diversity assessments of antibiotic (B and C) and plant (D and E) cohorts; insets depict minimum observed beta-diversity over increasing samples.

Supplemental Material

  • Figures
  • TABLE S1 

    Summary of sample numbers and type in the other American Gut studies, sample distributions by country and territory, sample distributions by U.S. state, U.S. participant demographics, and per-sequencing-round sample accessions in EBI. Download TABLE S1, XLSX file, 0.6 MB.

    Copyright © 2018 McDonald et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • TEXT S1 

    Additional detail on the effect size comparisons, multicohort replication, projects using the American Gut Project infrastructure, and the American Gut Survey presented to participants. Download TEXT S1, DOCX file, 0.03 MB.

    Copyright © 2018 McDonald et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • TABLE S2 

    American Gut data dictionary, proportion of responses per American Gut survey question that are represented as a single question (multiselection responses were omitted as these are stored in the metadata as per response type), informal dietary questions and correlations with the food frequency questionnaire, effect size results without bloom sOTUs, and variable mapping with reference 2. Download TABLE S2, XLSX file, 0.1 MB.

    Copyright © 2018 McDonald et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • FIG S1 

    Workflow and population-scale analyses. (A) Heat map of income levels from the U.S. Census and American Gut participant locations. (B) Sample flowchart for what sample sets correspond to each analysis. (C) Using PLS-DA, we observed separation between U.S. (n = 6,634) and U.K. (n = 2,071) fecal samples. (D) We performed a principal-coordinate analysis comparing children over the age of 3 years and adults from industrialized (n = 4,643 AGP samples, n = 4,927 samples total), remote farming (n = 131), and hunter-gatherer (n = 30) lifestyles. Download FIG S1, JPG file, 1.9 MB.

    Copyright © 2018 McDonald et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • TABLE S3 

    sOTUs relevant to the balance analyses and summary of differentially abundant taxa in U.K. cohort (negative effect size indicated that the taxon is more prevalent in control [NC] subjects). Download TABLE S3, XLSX file, 0.2 MB.

    Copyright © 2018 McDonald et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • FIG S2 

    Trading cards and LS’s samples compared to ICU patients and AGP participants and diet state change analysis. (A) Unweighted UniFrac distance distributions for the sample immediately prior to surgery versus all ICU fecal samples and distances of the sample immediately following surgery versus all ICU fecal samples (Kruskal-Wallis H = 79.774, P = 4.198 × 10−19). (B) Same as panel A except comparing against all AGP fecal samples (Kruskal-Wallis H = 8117.734, P = 0.0). (C) The median distances of each sample in LS’s longitudinal data set compared to both ICU and AGP. The last presurgery sample is on day 25, and the first postsurgery sample is day 27. (D) A principal-coordinate analysis of UniFrac distances of the American Gut Project, samples from the “extreme” diet study by David et al. (25), and the Earth Microbiome Project. No obvious state change by the diet of the participants in the work of David et al. is observed. Download FIG S2, JPG file, 1.5 MB.

    Copyright © 2018 McDonald et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • FIG S3 

    Dietary levels of linoleic acid based on validated food frequency questionnaire responses; the linoleic acid detected by mass spectrometry did not differ significantly between groups consuming few and those consuming many types of plants per week. Download FIG S3, PDF file, 0.8 MB.

    Copyright © 2018 McDonald et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • FIG S4 

    Molecular novelty in the gut microbiome: molecular subnetwork of N-acyl amides (the full analysis can be found at https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=a07557dc26cc4d3f8a2076d5ae0898a2). The structural relationships in complex metabolite mixtures can be represented as networks, as described in Materials and Methods. The nodes denote unique metabolites common across multiple samples; the edges between nodes represent similarities of MS/MS spectra (the greater width of the edge denotes greater similarity); the edges are labeled with m/z differences of corresponding parent ions for the detected moieties. (A to I) Cluster/nodes of microbially derived G-protein-coupled receptor agonistic molecules detected in human fecal samples are shown. Molecules B, G, and H have been described previously (compounds 1, 2, and 4b [35] and commendamide [120]); molecules A, C, D, E, and I are previously not reported (proposed structures are shown). (J) Manual annotation of novel metabolites via comparison of experimental MS fragmentation patterns to those given in reference 39. (Top panel) Reference spectrum for compound 2 in reference 39; (bottom panel) experimental MS/MS spectrum for the parent ion m/z 611.5357. The compound is annotated as 3-(myristoyloxy)palmitoyl lysine. (K) In silico annotation using CSI:FingerID (81) for the ion with m/z 330.2640: the possible candidate structures are ranked by match percent. The top structure with 71.02% match corresponds to commendamide. (L) Manual annotation via comparison of experimental exact mass to that of the identified compound in reference 81, N-3-OH-palmitoyl ornithine. The peaks in the experimental MS/MS spectrum are examined and compared to theoretical fragments that would result from breaking bonds in the proposed structure. The structure is deemed to be consistent with the N-3-OH-palmitoyl ornithine annotation. Download FIG S4, PDF file, 1.3 MB.

    Copyright © 2018 McDonald et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • TABLE S4 

    Application of the filter for blooms to other human fecal studies which were not subjected to room-temperature shipping, taxonomy of the draft isolate genomes, the specific bloom 16S sOTUs observed, and ubiquitous colibactin-like biosynthetic gene clusters (top) and a unique surfactin-like biosynthetic gene cluster observed in the bloom isolates. Download TABLE S4, XLSX file, 0.01 MB.

    Copyright © 2018 McDonald et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • TABLE S5 

    A set of molecular features which appeared to significantly correlate with the bloom fraction and Kruskal-Wallis tests for metabolites in the antibiotic and VioScreen cohorts of samples. Download TABLE S5, XLSX file, 0.1 MB.

    Copyright © 2018 McDonald et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

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American Gut: an Open Platform for Citizen Science Microbiome Research
Daniel McDonald, Embriette Hyde, Justine W. Debelius, James T. Morton, Antonio Gonzalez, Gail Ackermann, Alexander A. Aksenov, Bahar Behsaz, Caitriona Brennan, Yingfeng Chen, Lindsay DeRight Goldasich, Pieter C. Dorrestein, Robert R. Dunn, Ashkaan K. Fahimipour, James Gaffney, Jack A. Gilbert, Grant Gogul, Jessica L. Green, Philip Hugenholtz, Greg Humphrey, Curtis Huttenhower, Matthew A. Jackson, Stefan Janssen, Dilip V. Jeste, Lingjing Jiang, Scott T. Kelley, Dan Knights, Tomasz Kosciolek, Joshua Ladau, Jeff Leach, Clarisse Marotz, Dmitry Meleshko, Alexey V. Melnik, Jessica L. Metcalf, Hosein Mohimani, Emmanuel Montassier, Jose Navas-Molina, Tanya T. Nguyen, Shyamal Peddada, Pavel Pevzner, Katherine S. Pollard, Gholamali Rahnavard, Adam Robbins-Pianka, Naseer Sangwan, Joshua Shorenstein, Larry Smarr, Se Jin Song, Timothy Spector, Austin D. Swafford, Varykina G. Thackray, Luke R. Thompson, Anupriya Tripathi, Yoshiki Vázquez-Baeza, Alison Vrbanac, Paul Wischmeyer, Elaine Wolfe, Qiyun Zhu, The American Gut Consortium, Rob Knight
mSystems May 2018, 3 (3) e00031-18; DOI: 10.1128/mSystems.00031-18

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American Gut: an Open Platform for Citizen Science Microbiome Research
Daniel McDonald, Embriette Hyde, Justine W. Debelius, James T. Morton, Antonio Gonzalez, Gail Ackermann, Alexander A. Aksenov, Bahar Behsaz, Caitriona Brennan, Yingfeng Chen, Lindsay DeRight Goldasich, Pieter C. Dorrestein, Robert R. Dunn, Ashkaan K. Fahimipour, James Gaffney, Jack A. Gilbert, Grant Gogul, Jessica L. Green, Philip Hugenholtz, Greg Humphrey, Curtis Huttenhower, Matthew A. Jackson, Stefan Janssen, Dilip V. Jeste, Lingjing Jiang, Scott T. Kelley, Dan Knights, Tomasz Kosciolek, Joshua Ladau, Jeff Leach, Clarisse Marotz, Dmitry Meleshko, Alexey V. Melnik, Jessica L. Metcalf, Hosein Mohimani, Emmanuel Montassier, Jose Navas-Molina, Tanya T. Nguyen, Shyamal Peddada, Pavel Pevzner, Katherine S. Pollard, Gholamali Rahnavard, Adam Robbins-Pianka, Naseer Sangwan, Joshua Shorenstein, Larry Smarr, Se Jin Song, Timothy Spector, Austin D. Swafford, Varykina G. Thackray, Luke R. Thompson, Anupriya Tripathi, Yoshiki Vázquez-Baeza, Alison Vrbanac, Paul Wischmeyer, Elaine Wolfe, Qiyun Zhu, The American Gut Consortium, Rob Knight
mSystems May 2018, 3 (3) e00031-18; DOI: 10.1128/mSystems.00031-18
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