Abstract
Shotgun metagenomics studies have improved our understanding of microbial population dynamics and have revealed significant contributions of microbes to gut homeostasis. They also allow in silico inference of the metagenome. While they link the microbiome with metabolic abnormalities associated with disease phenotypes, they do not capture microbial gene expression patterns that occur in response to the multitude of stimuli that constantly ambush the gut environment. Metatranscriptomics closes that gap, but its implementation is more expensive and tedious. We assessed the metabolic perturbations associated with gut inflammation using shotgun metagenomics and metatranscriptomics. Shotgun metagenomics detected changes in abundance of bacterial taxa known to be SCFA producers, which favors gut homeostasis. Bacteria in the phylum Firmicutes were found at decreased abundance, while those in phyla Bacteroidetes and Proteobacteria were found at increased abundance. Surprisingly, inferring the coding capacity of the microbiome from shotgun metagenomics data did not result in any statistically significant difference, suggesting functional redundancy in the microbiome or poor resolution of shotgun metagenomics data to profile bacterial pathways, especially when sequencing is not very deep. Obviously, the ability of metatranscriptomics libraries to detect transcripts expressed at basal (or simply low) levels is also dependent on sequencing depth. Nevertheless, metatranscriptomics informed about contrasting roles of bacteria during inflammation. Functions involved in nutrient transport, immune suppression and regulation of tissue damage were dramatically upregulated, perhaps contributed by homeostasis-promoting bacteria. Functions ostensibly increasing bacteria pathogenesis were also found upregulated, perhaps as a consequence of increased abundance of Proteobacteria. Bacterial protein synthesis appeared downregulated. In summary, shotgun metagenomics was useful to profile bacterial population composition and taxa relative abundance, but did not inform about differential gene content associated with inflammation. Metatranscriptomics was more robust for capturing bacterial metabolism in real time. Although both approaches are complementary, it is often not possible to apply them in parallel. We hope our data will help researchers to decide which approach is more appropriate for the study of different aspects of the microbiome.
Highlights
The human microbiome, which includes bacteria, archaea, eukaryotic viruses, virophages, and fungi, is tightly linked to host health
Histological analyses demonstrated that inflammation had been effectively induced in the colon of animals treated with dextran sodium sulfate (DSS) (Figure 1D); for instance, the structure of the villi was distorted and higher infiltration of inflammatory cells into the villi was observed in DSS-treated animals
A balanced microbiota is associated with gut homeostasis; detecting imbalances of the microbiome at the taxonomical and/or functional levels provides insights into disease phenotypes
Summary
The human microbiome, which includes bacteria, archaea, eukaryotic viruses, virophages, and fungi, is tightly linked to host health. Notwithstanding its importance, until recently, little was known about the microbiome, including taxonomy, assemblage into communities and metabolic contributions to host physiology (Davenport et al, 2017). Metagenomic studies aim at cataloging genes and other sequences that are contained in microorganisms’ genomes and have the potential to infer the microbiome’s functional capability (Nayfach et al, 2015); they may be inadequate to portray the spatio-temporal patterns of gene expression that occur in response to environmental stimuli like xenobiotics, dietary changes, or pathogens invasion. Studying the taxonomic composition of microbial communities provides insights into their diversity and richness, while recent metatranscriptomics and metabolomic studies have revealed that a functional redundancy is present among related bacterial taxa and that such redundancy is an important component of host’s fitness since function can be preserved despite perturbations that alter bacterial populations’ structure (Sharpton, 2018). The ultimate goal is to make causal inferences regarding the observed phenotype based on measurements of microbes’ activity and/or population dynamics
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