A Multi-Omics Protocol for Swine Feces to Elucidate Longitudinal Dynamics in Microbiome Structure and Function

Abstract

Swine are regarded as promising biomedical models, but the dynamics of their gastrointestinal microbiome have been much less investigated than that of humans or mice. The aim of this study was to establish an integrated multi-omics protocol to investigate the fecal microbiome of healthy swine. To this end, a preparation and analysis protocol including integrated sample preparation for meta-omics analyses of deep-frozen feces was developed. Subsequent data integration linked microbiome composition with function, and metabolic activity with protein inventories, i.e., 16S rRNA data and expressed proteins, and identified proteins with corresponding metabolites. 16S rRNA gene amplicon and metaproteomics analyses revealed a fecal microbiome dominated by Prevotellaceae, Lactobacillaceae, Lachnospiraceae, Ruminococcaceae and Clostridiaceae. Similar microbiome compositions in feces and colon, but not ileum samples, were observed, showing that feces can serve as minimal-invasive proxy for porcine colon microbiomes. Longitudinal dynamics in composition, e.g., temporal decreased abundance of Lactobacillaceae and Streptococcaceae during the experiment, were not reflected in microbiome function. Instead, metaproteomics and metabolomics showed a rather stable functional state, as evident from short-chain fatty acids (SCFA) profiles and associated metaproteome functions, pointing towards functional redundancy among microbiome constituents. In conclusion, our pipeline generates congruent data from different omics approaches on the taxonomy and functionality of the intestinal microbiome of swine.

Influenza A H1N1 Induced Disturbance of the Respiratory and Fecal Microbiome of German Landrace Pigs – a Multi-Omics Characterization

“…By employing an integrated multi-omics approach consisting of 16S rRNA gene sequencing, metaproteomics, and metabolomics on fecal samples from healthy and infected swine, structure and function of the gastrointestinal microbiome were elucidated prior to and during sublethal influenza A H1N1pdm09 infection. Subsequently, the structural and functional composition of the gut microbiome was compared to a reanalyzed data set of the healthy porcine microbiome from the same experiment ( 35 ). 16S rRNA gene sequencing identified 912 (±88) ASVs from feces of the IAV-infected animals, which is in good accordance to the ASV numbers detected for the gastrointestinal microbiome of healthy swine ( 35 ).…”

“…Subsequently, the structural and functional composition of the gut microbiome was compared to a reanalyzed data set of the healthy porcine microbiome from the same experiment ( 35 ). 16S rRNA gene sequencing identified 912 (±88) ASVs from feces of the IAV-infected animals, which is in good accordance to the ASV numbers detected for the gastrointestinal microbiome of healthy swine ( 35 ). According to Shannon index, infected animals had a significantly increased (Welsh t test, P < 0.05) microbiome diversity ( P = 0.02) (Table S2A and B).…”

“…Moreover, most murine studies were based on the characterization of the microbiome using 16S rRNA gene sequencing data, leaving the functional characterization of the microbiome largely unexplored. In the collaborative and interdisciplinary project “KoInfekt,” we are currently investigating how monocausal IAV infections and bactoviral coinfections affect host fitness, health status, immune response, and microbiome, employing swine as a model ( 31 – 35 ). For instance, Schwaiger and colleagues analyzed the systemic as well as the local immune response of the same pigs as those used in this study ( 31 ).…”

Metaproteomics—An Advantageous Option in Studies of Host-Microbiota Interaction

“…This method could be also be a part of a multi-omics approach in studies aiming to elucidate longitudinal dynamics in the microbiome structure [ 30 ] or in studies of modulating gut microbiota composition in a preclinical neonatal model [ 31 ].…”

“…It is also important to note that host and microbiota interactions involve delicate interplay between factors such as age, genetics, immunity and dietary habits, which could act as potential confounding variables. Establishing an integrated multi-omics protocol will also be beneficial for the development of metaproteomics as either a stand-alone approach or as part of a complex strategy [ 30 ].…”