Biology subjects

Lavin, R.

Publications and source records attributed to Lavin, R..

3 recordsLinked to original sources

bioRxiv2022

Clostridium scindens colonization of gnotobiotic mice promotes a chronic unresolving infection with Clostridioides difficile

The commensal Clostridium scindens has been regarded as a promising bacteriotherapeutic against Clostridioides difficile infection due to its ability to consume host factors that can promote C. difficile growth, and its production of the antimicrobial compound 1-acetyl-{beta}-carboline. We investigated C. scindens protective effects against C. difficile using defined colonization studies in gnotobiotic mice. Mice infected with C. difficile develop lethal infection within 48 hours. In contrast, 88% of mice pre-colonized with C. scindens survived acute infection with delayed C. difficile colonization, lower biomass, and toxin B levels at 24 hours after infection. However, two weeks post-challenge, surviving mice showed comparable levels of cecal C. difficile vegetative and spore biomass and toxin B, as seen during acute infection. After two weeks, co-colonized mice exhibited mucosal colonic hyperplasia with focal pseudomembranes, modeling a chronic and recurrent infection state. Our findings illustrate how the commensal microbiota can modulate host and pathogen interactions leading to chonic C. difficile carriage and infection.

microbiology↗

bioRxiv2021

Intrinsic instability of the dysbiotic microbiome revealed through dynamical systems inference at scale

Dynamical systems models are a powerful tool for analyzing interactions, stability, resilience, and other key properties in biomedically important microbial ecosystems, such as the gut microbiome. Challenges to modeling and inference in this setting include the large number of species present, and data sparsity/noise characteristics. Here, we introduce a Bayesian statistical method, the Microbial Dynamical Systems Inference Engine 2 (MDSINE2), which infers compact and interpretable ecosystems-scale dynamical systems models from microbiome time-series data. We model microbial dynamics as stochastic processes driven by inferred interaction modules, or groups of microbes with similar interaction structure and responses to perturbations. Additionally, we model the noise characteristics of sequencing and qPCR measurements to provide uncertainty quantification for all outputs. To evaluate MDSINE2, and provide a benchmarking resource for the community, we generated the most densely sampled microbiome time-series to date, which consists of a cohort of mice that received fecal transplants from a human donor and were then subjected to dietary and antibiotic perturbations. Benchmarking on simulated and real data demonstrate that MDSINE2 significantly outperforms state-of-the-art methods, and moreover identifies interaction modules that shed new light on ecosystems-scale interactions in the gut microbiome. We provide MDSINE2 as an open-source Python package at: https://github.com/gerberlab/MDSINE2.

systems biology↗

bioRxiv2020

The mechanisms of in vivo commensal control of Clostridioides difficile virulence

We define multiple mechanisms by which commensals protect against or worsen Clostridioides difficile infection. Leveraging new systems-level models we show how metabolically distinct species of Clostridia modulate the pathogens colonization, growth, and virulence to impact host survival. Gnotobiotic mice colonized with the amino acid fermenter Paraclostridium bifermentans survived infection while mice colonized with the butyrate- producer, Clostridium sardiniense, more rapidly succumbed. Systematic in vivo analyses revealed how each commensal altered the gut nutrient environment, modulating the pathogens metabolism, regulatory networks, and toxin production. Oral administration of P. bifermentans rescued conventional mice from lethal C. difficile infection via mechanisms identified in specifically colonized mice. Our findings lay the foundation for mechanistically informed therapies to counter C. difficile infections using systems biologic approaches to define host-commensal-pathogen interactions in vivo. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/894915v2_ufig1.gif" ALT="Figure 1"> View larger version (57K): org.highwire.dtl.DTLVardef@15a6940org.highwire.dtl.DTLVardef@b4290borg.highwire.dtl.DTLVardef@1f2af49org.highwire.dtl.DTLVardef@1d1e6b9_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

microbiology↗