From Sample to Multi-Omics Conclusions in under 48 Hours

Robert A Quinn; Jose A Navas-Molina; Embriette R Hyde; Se Jin Song; Yoshiki Vázquez-Baeza; Greg Humphrey; James Gaffney; Jeremiah J Minich; Alexey V Melnik; Jakob Herschend; Jeff DeReus; Austin Durant; Rachel J Dutton; Mahdieh Khosroheidari; Clifford Green; Ricardo da Silva; Pieter C Dorrestein; Rob Knight

doi:10.1128/mSystems.00038-16

From Sample to Multi-Omics Conclusions in under 48 Hours

mSystems. 2016 Apr 26;1(2):e00038-16. doi: 10.1128/mSystems.00038-16. eCollection 2016 Mar-Apr.

Authors

Robert A Quinn¹, Jose A Navas-Molina², Embriette R Hyde³, Se Jin Song⁴, Yoshiki Vázquez-Baeza⁵, Greg Humphrey³, James Gaffney³, Jeremiah J Minich³, Alexey V Melnik⁶, Jakob Herschend⁶, Jeff DeReus³, Austin Durant⁷, Rachel J Dutton⁸, Mahdieh Khosroheidari⁹, Clifford Green⁹, Ricardo da Silva⁶, Pieter C Dorrestein¹⁰, Rob Knight¹¹

Affiliations

¹ Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, California, USA; Department of Pharmacology, University of California, San Diego, San Diego, California, USA.
² Department of Pediatrics, University of California, San Diego, San Diego, California, USA; Department of Computer Science and Engineering, University of California, San Diego, San Diego, California, USA.
³ Department of Pediatrics, University of California, San Diego, San Diego, California, USA.
⁴ Department of Pediatrics, University of California, San Diego, San Diego, California, USA; Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, Colorado, USA.
⁵ Department of Computer Science and Engineering, University of California, San Diego, San Diego, California, USA.
⁶ Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, California, USA.
⁷ Fermenters Club of San Diego, San Diego, California, USA.
⁸ Division of Biological Sciences, University of California, San Diego, San Diego, California, USA; Department of Pharmacology, University of California, San Diego, San Diego, California, USA.
⁹ Institute for Genomic Medicine Genomics Center, University of California, San Diego, San Diego, California, USA.
¹⁰ Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, California, USA; Department of Pediatrics, University of California, San Diego, San Diego, California, USA; Center for Microbiome Innovation, University of California, San Diego, San Diego, California, USA; Department of Pharmacology, University of California, San Diego, San Diego, California, USA.
¹¹ Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, California, USA; Department of Pediatrics, University of California, San Diego, San Diego, California, USA; Department of Computer Science and Engineering, University of California, San Diego, San Diego, California, USA; Center for Microbiome Innovation, University of California, San Diego, San Diego, California, USA.

Abstract

Multi-omics methods have greatly advanced our understanding of the biological organism and its microbial associates. However, they are not routinely used in clinical or industrial applications, due to the length of time required to generate and analyze omics data. Here, we applied a novel integrated omics pipeline for the analysis of human and environmental samples in under 48 h. Human subjects that ferment their own foods provided swab samples from skin, feces, oral cavity, fermented foods, and household surfaces to assess the impact of home food fermentation on their microbial and chemical ecology. These samples were analyzed with 16S rRNA gene sequencing, inferred gene function profiles, and liquid chromatography-tandem mass spectrometry (LC-MS/MS) metabolomics through the Qiita, PICRUSt, and GNPS pipelines, respectively. The human sample microbiomes clustered with the corresponding sample types in the American Gut Project (http://www.americangut.org), and the fermented food samples produced a separate cluster. The microbial communities of the household surfaces were primarily sourced from the fermented foods, and their consumption was associated with increased gut microbial diversity. Untargeted metabolomics revealed that human skin and fermented food samples had separate chemical ecologies and that stool was more similar to fermented foods than to other sample types. Metabolites from the fermented foods, including plant products such as procyanidin and pheophytin, were present in the skin and stool samples of the individuals consuming the foods. Some food metabolites were modified during digestion, and others were detected in stool intact. This study represents a first-of-its-kind analysis of multi-omics data that achieved time intervals matching those of classic microbiological culturing. IMPORTANCE Polymicrobial infections are difficult to diagnose due to the challenge in comprehensively cultivating the microbes present. Omics methods, such as 16S rRNA sequencing, metagenomics, and metabolomics, can provide a more complete picture of a microbial community and its metabolite production, without the biases and selectivity of microbial culture. However, these advanced methods have not been applied to clinical or industrial microbiology or other areas where complex microbial dysbioses require immediate intervention. The reason for this is the length of time required to generate and analyze omics data. Here, we describe the development and application of a pipeline for multi-omics data analysis in time frames matching those of the culture-based approaches often used for these applications. This study applied multi-omics methods effectively in clinically relevant time frames and sets a precedent toward their implementation in clinical medicine and industrial microbiology.

Keywords: 16S rRNA; fermented food; metabolome; microbiome; molecular networking; rapid response.