From Sample to Multi-Omics Conclusions in under 48 Hours

ABSTRACT Multi- omicsmethods 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 omicsdata. Here, we applied a novel integrated omicspipeline 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) metabolomicsthrough 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 metabolomicsrevealed that human skin and fermented food samples had separate chemical ecologiesand 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 samplesof 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- omicsdata 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. Omicsmethods, 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 microbiologyor other areas where complex microbial dysbioses require immediate intervention. The reason for this is the length of time required to generate and analyze omicsdata. Here, we describe the development and application of a pipeline for multi- omicsdata analysis in time frames matching those of the culture-based approaches often used for these applications. This study applied multi- omicsmethods effectively in clinically relevant time frames and sets a precedent toward their implementation in clinical medicine and industrial microbiology.