Moss, A. D. | Search bioRxiv

bioRxiv2024

Mouse Adaptation of Human Inflammatory Bowel Diseases Microbiota Enhances Colonization Efficiency and Alters Microbiome Aggressiveness Depending on Recipient Colonic Inflammatory Environment

Understanding the cause vs consequence relationship of gut inflammation and microbial dysbiosis in inflammatory bowel diseases (IBD) requires a reproducible mouse model of human-microbiota-driven experimental colitis. Our study demonstrated that human fecal microbiota transplant (FMT) transfer efficiency is an underappreciated source of experimental variability in human microbiota associated (HMA) mice. Pooled human IBD patient fecal microbiota engrafted germ-free (GF) mice with low amplicon sequence variant (ASV)-level transfer efficiency, resulting in high recipient-to-recipient variation of microbiota composition and colitis severity in HMA Il-10-/- mice. In contrast, mouse-to-mouse transfer of mouse-adapted human IBD patient microbiota transferred with high efficiency and low compositional variability resulting in highly consistent and reproducible colitis phenotypes in recipient Il-10-/- mice. Human-to-mouse FMT caused a population bottleneck with reassembly of microbiota composition that was host inflammatory environment specific. Mouse-adaptation in the inflamed Il-10-/-host reassembled a more aggressive microbiota that induced more severe colitis in serial transplant to Il-10-/- mice than the distinct microbiota reassembled in non-inflamed WT hosts. Our findings support a model of IBD pathogenesis in which host inflammation promotes aggressive resident bacteria, which further drives a feed-forward process of dysbiosis exacerbated gut inflammation. This model implies that effective management of IBD requires treating both the dysregulated host immune response and aggressive inflammation-driven microbiota. We propose that our mouse-adapted human microbiota model is an optimized, reproducible, and rigorous system to study human microbiome-driven disease phenotypes, which may be generalized to mouse models of other human microbiota-modulated diseases, including metabolic syndrome/obesity, diabetes, autoimmune diseases, and cancer.

microbiology↗

bioRxiv2022

An integrative analysis of the biological clock hypothesis in human gut microbiome

BackgroundWhile previous studies have explored the relationship between aging and the gut microbiome, it remains unclear how consistent and reproducible this association is across different cultures and groups. We performed an integrative analysis with 11 independent datasets from nine different countries to test the idea that the aging gut microbiome can be viewed as a biological clock, in which microbial changes associated with age are consistent and measurable across distinct datasets. ResultsAs expected, our Principal Coordinate Analysis found strong batch effects with study ID by far the strongest signal across datasets. Despite this large batch effect, we found a consistent signal across studies that was largely driven by sample size with only our larger cohorts showing taxa in common associated with age. Likewise, Shannon diversity and richness were not consistently associated with age across the 11 datasets, but some positive correlations with richness and host age were observed among the four largest cohorts. The taxon with the most potential as a biomarker for the aging human gut microbiome is genus Bifidobacterium, with significantly negative associations with host age in three out of the four datasets that had more than 200 samples. ConclusionThe driving force behind low reproducibility of association of age with the microbiome in previous studies appears to be inadequate sample size rather than structural differences in the microbial community based on cohort characteristics. Results from a power analysis suggest that future studies on the aging human gut microbiome consider on the order of 100-300 samples to consistently observe an age signal. With these larger sample sizes, parametric and non-parametric model yield broadly similar power.

bioinformatics↗