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Biology subjects

Young, J. B.

Publications and source records attributed to Young, J. B..

2 recordsLinked to original sources

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↗

HashSeq: A Simple, Scalable, and Conservative De Novo Variant Caller for 16S rRNA Gene Datasets

16S rRNA gene sequencing is a common and cost-effective technique for characterization of microbial communities. Recent bioinformatics methods enable high-resolution detection of sequence variants of only one nucleotide difference. In this manuscript, we utilize a very fast HashMap-based approach to detect sequence variants in six publicly available 16S rRNA gene datasets. We then use the normal distribution combined with LOESS regression to estimate background error rates as a function of sequencing depth for individual clusters of sequences. This method is computationally efficient and produces inference that yields sets of variants that are conservative and well supported by reference databases. We argue that this approach to inference is fast, simple, scalable to large datasets, and provides a high-resolution set of sequence variants which are less likely to be the result of sequencing error.

bioinformatics↗