Biology subjects

DeFelice, B. C.

Publications and source records attributed to DeFelice, B. C..

4 recordsLinked to original sources

bioRxiv2023

Multiomics characterization of cell type repertoires for urine liquid biopsies

Urine is assayed alongside blood in medicine, yet current clinical diagnostic tests utilize only a small fraction of its total biomolecular repertoire, potentially foregoing high-resolution insights into human health and disease. In this work, we characterized the joint landscapes of transcriptomic and metabolomic signals in human urine. We also compared the urine transcriptome to plasma cell-free RNA, identifying a distinct cell type repertoire and enrichment for metabolic signal. Untargeted metabolomic measurements identified a complementary set of pathways to the transcriptomic analysis. Our findings suggest that urine is a promising biofluid yielding prognostic and detailed insights for hard-to-biopsy tissues with low representation in the blood, offering promise for a new generation of liquid biopsies.

genomics↗

bioRxiv2023

Retroviral infection and commensal bacteria dependently alter the metabolomic profile in a sterile organ

Both viruses and bacteria produce pathogen associated molecular patterns that may affect microbial pathogenesis and anti-microbial responses. Additionally, bacteria produce metabolites while viruses could change metabolic profiles of the infected cells. Here, we used an unbiased metabolomics approach to profile metabolites in spleens and blood of Murine Leukemia Virus-infected mice monocolonized with Lactobacillus murinus to show that viral infection significantly changes the metabolite profile of monocolonized mice. We hypothesize that these changes could contribute to viral pathogenesis or to the host response against the virus and thus, open a new avenue for future investigations.

microbiology↗

bioRxiv2022

Resource competition predicts assembly of in vitro gut bacterial communities

Members of microbial communities interact via a plethora of mechanisms, including resource competition, cross-feeding, and pH modulation. However, the relative contributions of these mechanisms to community dynamics remain uncharacterized. Here, we develop a framework to distinguish the effects of resource competition from other interaction mechanisms by integrating data from growth measurements in spent media, synthetic community assembly, and metabolomics with consumer-resource models. When applied to human gut commensals, our framework revealed that resource competition alone could explain most pairwise interactions. The resource-competition landscape inferred from metabolomic profiles of individual species predicted assembly compositions, demonstrating that resource competition is a dominant driver of in vitro community assembly. Moreover, the identification and incorporation of interactions other than resource competition, including pH-mediated effects and cross-feeding, improved model predictions. Our work provides an experimental and modeling framework to characterize and quantify interspecies interactions in vitro that should advance mechanistically principled engineering of microbial communities.

ecology↗

bioRxiv2021

A metabolomics pipeline enables mechanistic interrogation of the gut microbiome

Gut microbes modulate host phenotypes and are associated with numerous health effects in humans, ranging from cancer immunotherapy response to metabolic disease and obesity. However, difficulty in accurate and high-throughput functional analysis of human gut microbes has hindered defining mechanistic connections between individual microbial strains and host phenotypes. One key way the gut microbiome influences host physiology is through the production of small molecules1-3, yet progress in elucidating this chemical interplay has been hindered by limited tools calibrated to detect products of anaerobic biochemistry in the gut. Here we construct a microbiome-focused, integrated mass-spectrometry pipeline to accelerate the identification of microbiota-dependent metabolites (MDMs) in diverse sample types. We report the metabolic profiles of 178 gut microbe strains using our library of 833 metabolites. Leveraging this metabolomics resource we establish deviations in the relationships between phylogeny and metabolism, use machine learning to discover novel metabolism in Bacteroides, and employ comparative genomics-based discovery of candidate biochemical pathways. MDMs can be detected in diverse biofluids in gnotobiotic and conventional mice and traced back to corresponding metabolomic profiles of cultured bacteria. Collectively, our microbiome-focused metabolomics pipeline and interactive metabolomics profile explorer are a powerful tool for characterizing microbe and microbe-host interactions.

microbiology↗