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A library of human gut bacterial isolates paired with longitudinal multiomics data enables mechanistic microbiome research

ABSTRACT: Fecal microbiota transplantation (FMT) is used in the treatment of microbiome-associated diseases such as Clostridium difficile infections. In order to develop synthetic therapeutics and customized disease treatments we will need to understand the bacterial communities in the stool samples used in such treatments. For this purpose, a microbiome library was generated using human stool obtained from healthy human FMT recruited by OpenBiome, a non-profit organization that provides fecal microbiome therapeutics. In addition to characterizing the bacterial populations and obtaining bacterial isolates from FMT samples, we conducted metabolite profiling with the goal of: (1) generating a library of metabolites in FMT samples, (2) Identifying metabolites associated with defined bacterial populations, and (3) identifying microbial metabolites with immunoregulatory functions. We conducted metabolite profiling on a subset consisting of 180 stool samples from 84 donors using four nontargeted liquid chromatography mass spectrometry (LC-MS) methods. Generated data were processed, isotopes removed, and adducts and fragments clustered. The identity of known metabolites was determined based on matching retention times of neat standards run in parallel with the study.

ORGANISM(S): Human Homo Sapiens

TISSUE(S): Feces

SUBMITTER: Julian Avila-Pacheco

PROVIDER: ST001192 | MetabolomicsWorkbench | Mon Jun 10 00:00:00 BST 2019

REPOSITORIES: MetabolomicsWorkbench

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Project description:Purpose: To determine whether previously observed behavioral differences in alcoholic human patients after fecal microbiota transplantation (FMT) could be transferred to mice. Methods: Fecal microbiota samples from a previously published phase 1, double-blind, randomized clinical trial of AUD-related cirrhosis patients were used to colonize germ-free mice. Fecal material was transferred to 10-15-week-old GF C57BL/6 male mice by daily gavage for 3 day. The mice were housed in sterile individually filtered cages for 15 days after which stool was collected and then they underwent the alcohol preference experiment using 2-bottle choice drinking (water and 20% ethanol v/v). Microbial DNA was isolated from stool samples by sequencing the V1 and V2 variable regions of the bacterial 16S rRNA gene were sequenced using Multitag fusion primers and sequenced on an Ion Torrent PGM next-generation sequencer. Intestinal mucosa, liver, and prefrontal cortex tissue was collected from mice at time of sacrifice. RNAseq was used to measure gene expression in pre-FMT and post-FMT samples. RNAseq data were aligned to the mouse genome (GRCm39) using STAR (version 2.7.9a) and counts were generated with HTSeq (version 0.13.5). Genes with very low counts across the study (defined as fewer than 10 counts in more than 2 samples) were eliminated before differential expression analysis. Low count genes were determined separately for each tissue type. The DESeq2 package for R was then used to measure differential expression between pre-FMT and post-FMT mice in the intestine, liver, and PFC. Benjamini and Hochberg False Discovery Rate (FDR) was used to correct for multiple testing with FDR ≤ 0.2 considered significant. Results: Mice colonized with post-FMT stool significantly reduced ethanol acceptance, intake and preference versus pre-FMT colonized mice. Microbial taxa that were higher in post-FMT humans were also associated with lower alcohol intake and preference in mice. RNAseq further showed that differential gene expression, post-FMT, occurred in the intestine rather than the liver and prefrontal cortex. Conclusions: FMT leads to significant change in gut microbiome population, which in turn alters gene expression in the intestine. FMT also significantly affects alcohol consumption. The microbiotal-intestinal interface may alter gut-liver-brain axis and reduce alcohol consumption in humans.

Project description:On going efforts are directed at understanding the mutualism between the gut microbiota and the host in breast-fed versus formula-fed infants. Due to the lack of tissue biopsies, no investigators have performed a global transcriptional (gene expression) analysis of the developing human intestine in healthy infants. As a result, the crosstalk between the microbiome and the host transcriptome in the developing mucosal-commensal environment has not been determined. In this study, we examined the host intestinal mRNA gene expression and microbial DNA profiles in full term 3 month-old infants exclusively formula fed (FF) (n=6) or breast fed (BF) (n=6) from birth to 3 months. Host mRNA microarray measurements were performed using isolated intact sloughed epithelial cells in stool samples collected at 3 months. Microbial composition from the same stool samples was assessed by metagenomic pyrosequencing. Both the host mRNA expression and bacterial microbiome phylogenetic profiles provided strong feature sets that clearly classified the two groups of babies (FF and BF). To determine the relationship between host epithelial cell gene expression and the bacterial colony profiles, the host transcriptome and functionally profiled microbiome data were analyzed in a multivariate manner. From a functional perspective, analysis of the gut microbiota's metagenome revealed that characteristics associated with virulence differed between the FF and BF babies. Using canonical correlation analysis, evidence of multivariate structure relating eleven host immunity / mucosal defense-related genes and microbiome virulence characteristics was observed. These results, for the first time, provide insight into the integrated responses of the host and microbiome to dietary substrates in the early neonatal period. Our data suggest that systems biology and computational modeling approaches that integrate “-omic” information from the host and the microbiome can identify important mechanistic pathways of intestinal development affecting the gut microbiome in the first few months of life. KEYWORDS: infant, breast-feeding, infant formula, exfoliated cells, transcriptome, metagenome, multivariate analysis, canonical correlation analysis 12 samples, 2 groups

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A library of human gut bacterial isolates paired with longitudinal multiomics data enables mechanistic microbiome research

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