American Gut: an Open Platform for Citizen Science Microbiome Research

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

We show that a citizen science, self-selected cohort shipping samples through the mail at room temperature recaptures many known microbiome results from clinically collected cohorts and reveals new ones. Of particular interest is integrating n = 1 study data with the population data, showing that the extent of microbiome change after events such as surgery can exceed differences between distinct environmental biomes, and the effect of diverse plants in the diet, which we confirm with untargeted metabolomics on hundreds of samples.

ABSTRACT

Although much work has linked the human microbiome to specific phenotypes and lifestyle variables, data from different projects have been challenging to integrate and the extent of microbial and molecular diversity in human stool remains unknown. Using standardized protocols from the Earth Microbiome Project and sample contributions from over 10,000 citizen-scientists, together with an open research network, we compare human microbiome specimens primarily from the United States, United Kingdom, and Australia to one another and to environmental samples. Our results show an unexpected range of beta-diversity in human stool microbiomes compared to environmental samples; demonstrate the utility of procedures for removing the effects of overgrowth during room-temperature shipping for revealing phenotype correlations; uncover new molecules and kinds of molecular communities in the human stool metabolome; and examine emergent associations among the microbiome, metabolome, and the diversity of plants that are consumed (rather than relying on reductive categorical variables such as veganism, which have little or no explanatory power). We also demonstrate the utility of the living data resource and cross-cohort comparison to confirm existing associations between the microbiome and psychiatric illness and to reveal the extent of microbiome change within one individual during surgery, providing a paradigm for open microbiome research and education.

IMPORTANCE We show that a citizen science, self-selected cohort shipping samples through the mail at room temperature recaptures many known microbiome results from clinically collected cohorts and reveals new ones. Of particular interest is integrating n = 1 study data with the population data, showing that the extent of microbiome change after events such as surgery can exceed differences between distinct environmental biomes, and the effect of diverse plants in the diet, which we confirm with untargeted metabolomics on hundreds of samples.

Related collections

Most cited references 42

Record: found
Abstract: found
Article: found

Is Open Access

EMPeror: a tool for visualizing high-throughput microbial community data

Yoshiki Vázquez-Baeza, Meg Pirrung, Antonio González González … (2013)

Background As microbial ecologists take advantage of high-throughput sequencing technologies to describe microbial communities across ever-increasing numbers of samples, new analysis tools are required to relate the distribution of microbes among larger numbers of communities, and to use increasingly rich and standards-compliant metadata to understand the biological factors driving these relationships. In particular, the Earth Microbiome Project drives these needs by profiling the genomic content of tens of thousands of samples across multiple environment types. Findings Features of EMPeror include: ability to visualize gradients and categorical data, visualize different principal coordinates axes, present the data in the form of parallel coordinates, show taxa as well as environmental samples, dynamically adjust the size and transparency of the spheres representing the communities on a per-category basis, dynamically scale the axes according to the fraction of variance each explains, show, hide or recolor points according to arbitrary metadata including that compliant with the MIxS family of standards developed by the Genomic Standards Consortium, display jackknifed-resampled data to assess statistical confidence in clustering, perform coordinate comparisons (useful for procrustes analysis plots), and greatly reduce loading times and overall memory footprint compared with existing approaches. Additionally, ease of sharing, given EMPeror’s small output file size, enables agile collaboration by allowing users to embed these visualizations via emails or web pages without the need for extra plugins. Conclusions Here we present EMPeror, an open source and web browser enabled tool with a versatile command line interface that allows researchers to perform rapid exploratory investigations of 3D visualizations of microbial community data, such as the widely used principal coordinates plots. EMPeror includes a rich set of controllers to modify features as a function of the metadata. By being specifically tailored to the requirements of microbial ecologists, EMPeror thus increases the speed with which insight can be gained from large microbiome datasets.

0 comments Cited 562 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Mass spectral molecular networking of living microbial colonies.

J. Watrous, P. Roach, T Alexandrov … (2012)

Integrating the governing chemistry with the genomics and phenotypes of microbial colonies has been a "holy grail" in microbiology. This work describes a highly sensitive, broadly applicable, and cost-effective approach that allows metabolic profiling of live microbial colonies directly from a Petri dish without any sample preparation. Nanospray desorption electrospray ionization mass spectrometry (MS), combined with alignment of MS data and molecular networking, enabled monitoring of metabolite production from live microbial colonies from diverse bacterial genera, including Bacillus subtilis, Streptomyces coelicolor, Mycobacterium smegmatis, and Pseudomonas aeruginosa. This work demonstrates that, by using these tools to visualize small molecular changes within bacterial interactions, insights can be gained into bacterial developmental processes as a result of the improved organization of MS/MS data. To validate this experimental platform, metabolic profiling was performed on Pseudomonas sp. SH-C52, which protects sugar beet plants from infections by specific soil-borne fungi [R. Mendes et al. (2011) Science 332:1097-1100]. The antifungal effect of strain SH-C52 was attributed to thanamycin, a predicted lipopeptide encoded by a nonribosomal peptide synthetase gene cluster. Our technology, in combination with our recently developed peptidogenomics strategy, enabled the detection and partial characterization of thanamycin and showed that it is a monochlorinated lipopeptide that belongs to the syringomycin family of antifungal agents. In conclusion, the platform presented here provides a significant advancement in our ability to understand the spatiotemporal dynamics of metabolite production in live microbial colonies and communities.

0 comments Cited 298 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Searching molecular structure databases with tandem mass spectra using CSI:FingerID.

Kai Dührkop, Huibin Shen, Marvin Meusel … (2015)

Metabolites provide a direct functional signature of cellular state. Untargeted metabolomics experiments usually rely on tandem MS to identify the thousands of compounds in a biological sample. Today, the vast majority of metabolites remain unknown. We present a method for searching molecular structure databases using tandem MS data of small molecules. Our method computes a fragmentation tree that best explains the fragmentation spectrum of an unknown molecule. We use the fragmentation tree to predict the molecular structure fingerprint of the unknown compound using machine learning. This fingerprint is then used to search a molecular structure database such as PubChem. Our method is shown to improve on the competing methods for computational metabolite identification by a considerable margin.

0 comments Cited 277 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Casey S. Greene: Role: Editor

Journal

Journal ID (nlm-ta): mSystems

Journal ID (iso-abbrev): mSystems

Journal ID (hwp): msys

Journal ID (pmc): msys

Journal ID (publisher-id): mSystems

Title: mSystems

Publisher: American Society for Microbiology (1752 N St., N.W., Washington, DC )

ISSN (Electronic): 2379-5077

Publication date (Electronic): 15 May 2018

Publication date Collection: May-Jun 2018

Volume: 3

Issue: 3

Electronic Location Identifier: e00031-18

Affiliations

[a ]Department of Pediatrics, University of California San Diego, La Jolla, California, USA

[b ]Collaborative Mass Spectrometry Innovation Center, University of California, San Diego, La Jolla, California, USA

[c ]Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California, USA

[d ]Department of Computer Science and Engineering, University of California, San Diego, La Jolla, California, USA

[e ]Department of Biology, San Diego State University, San Diego, California, USA

[f ]Department of Applied Ecology, North Carolina State University, Raleigh, North Carolina, USA

[g ]Biology and the Built Environment Center, University of Oregon, Eugene, Oregon, USA

[h ]Department of Surgery, University of Chicago, Chicago, Illinois, USA

[i ]Institute for Genomic and Systems Biology, University of Chicago, Chicago, Illinois, USA

[j ]Department of Biosciences, Argonne National Laboratory, Chicago, Illinois, USA

[k ]Marine Biology Laboratory, University of Chicago, Chicago, Illinois, USA

[l ]Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, the University of Queensland, Brisbane, QLD, Australia

[m ]Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA

[n ]The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA

[o ]Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom

[p ]Departments of Psychiatry and Neurosciences, University of California San Diego, La Jolla, California, USA

[q ]Sam and Rose Stein Institute for Research on Aging and Center for Healthy Aging, University of California San Diego, La Jolla, California, USA

[r ]Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota, USA

[s ]Biotechnology Institute, University of Minnesota, Minneapolis, Minnesota, USA

[t ]The Gladstone Institutes, University of California, San Francisco, California, USA

[u ]Human Food Project, Terlingua, Texas, USA

[v ]St. Petersburg State University, Center for Algorithmic Biotechnology, Saint Petersburg, Russia

[w ]Department of Animal Science, Colorado State University, Fort Collins, Colorado, USA

[x ]Department of Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

[y ]Université de Nantes, Microbiotas Hosts Antibiotics and Bacterial Resistances (MiHAR), Nantes, France

[z ]Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA

[aa ]Center for Microbiome Innovation, University of California, San Diego, La Jolla, California, USA

[bb ]Department of Computer Science, University of Colorado Boulder, Boulder, Colorado, USA

[cc ]California Institute for Telecommunications and Information Technology (Calit2), University of California San Diego, La Jolla, California, USA

[dd ]Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Diego, La Jolla, California, USA

[ee ]Ocean Chemistry and Ecosystems Division, Atlantic Oceanographic and Meteorological Laboratory, National Oceanic and Atmospheric Administration, stationed at Southwest Fisheries Science Center, La Jolla, California, USA

[ff ]Department of Biological Sciences and Northern Gulf Institute, University of Southern Mississippi, Hattiesburg, Mississippi, USA

[gg ]Department of Anesthesiology and Surgery, Duke University School of Medicine, Durham, North Carolina, USA

[hh ]Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, USA

University of Pennsylvania

Author notes

Address correspondence to Rob Knight, robknight@ 123456ucsd.edu .

D. McDonald and E. Hyde contributed equally to this work.

Citation McDonald D, Hyde E, Debelius JW, Morton JT, Gonzalez A, Ackermann G, Aksenov AA, Behsaz B, Brennan C, Chen Y, DeRight Goldasich L, Dorrestein PC, Dunn RR, Fahimipour AK, Gaffney J, Gilbert JA, Gogul G, Green JL, Hugenholtz P, Humphrey G, Huttenhower C, Jackson MA, Janssen S, Jeste DV, Jiang L, Kelley ST, Knights D, Kosciolek T, Ladau J, Leach J, Marotz C, Meleshko D, Melnik AV, Metcalf JL, Mohimani H, Montassier E, Navas-Molina J, Nguyen TT, Peddada S, Pevzner P, Pollard KS, Rahnavard G, Robbins-Pianka A, Sangwan N, Shorenstein J, Smarr L, Song SJ, Spector T, Swafford AD, Thackray VG, Thompson LR, Tripathi A, Vázquez-Baeza Y, Vrbanac A, Wischmeyer P, Wolfe E, Zhu Q, The American Gut Consortium, Knight R. 2018. American Gut: an open platform for citizen science microbiome research. mSystems 3:e00031-18. https://doi.org/10.1128/mSystems.00031-18.