Biology subjects

Plichta, D. R.

Publications and source records attributed to Plichta, D. R..

3 recordsLinked to original sources

bioRxiv2025

Discovery of the metalloenzyme IsmB revises a pathway for coprostanol formation by the human gut microbiome

High levels of circulating cholesterol are associated with human cardiovascular diseases and an altered gut microbiome. Still, major gaps exist in our understanding of the interactions of cholesterol with gut microbes. The reductive transformation of cholesterol to the poorly absorbed sterol coprostanol by human gut bacteria has long been known, but the genetic and biochemical basis for this activity is only partially elucidated. Here, we discover and characterize a gut bacterial enzyme that catalyzes the reduction of cholestenone to coprostanone, the second step in the intestinal sterol metabolism (ism) pathway for coprostanol production. We identify a gene encoding a previously unknown 5{beta}-reductase, IsmB, a new member of the Fe-S cluster flavoenzyme superfamily, in the coprostanol producing organism Eubacterium coprostanoligenes. Biochemical characterization of IsmB confirms it is an anaerobic Fe-S cluster flavoenzyme and reveals specificity for reduction of an unanticipated intermediate, 5-cholesten-3-one, to coprostanone, revising the ism pathway. We also identify and characterize homologs of IsmB encoded in uncultured human gut bacteria that also encode the previously identified ism pathway enzyme IsmA, further supporting the role of IsmB in coprostanol formation. Finally, analysis of human stool metagenomics and metabolomics datasets further confirms the relevance of IsmB in the human gut microbiome, and analyses of human serum metabolomics from Framingham Heart Study participants reveal negative correlation between serum cholesterol levels and the presence of IsmA/IsmB encoders in the gut. Together, these results show the utility of combining biochemistry and stool metagenomic analysis for gut microbial enzyme discovery, and suggests IsmA/IsmB-encoding gut bacteria carry potential benefits for cholesterol homeostasis and cardiovascular health.

biochemistry↗

bioRxiv2023

Protein Language Models Uncover Carbohydrate-Active Enzyme Function in Metagenomic

In metagenomics, the pool of uncharacterized microbial enzymes presents a challenge for functional annotation. Among these, carbohydrate-active enzymes (CAZymes) stand out due to their pivotal roles in various biological processes related to host health and nutrition. Here, we present CAZyLingua, the first tool that harnesses protein language model embeddings to build a deep learning framework that facilitates the annotation of CAZymes in metagenomic datasets. Our benchmarking results showed on average a higher F1 score (reflecting an average of precision and recall) on the annotated genomes of Bacteroides thetaiotaomicron, Eggerthella lenta and Ruminococcus gnavus compared to the traditional sequence homology-based method in dbCAN2. We applied our tool to a paired mother/infant longitudinal dataset and revealed unannotated CAZymes linked to microbial development during infancy. When applied to metagenomic datasets derived from patients affected by fibrosis-prone diseases such as Crohns disease and IgG4-related disease, CAZyLingua uncovered CAZymes associated with disease and healthy states. In each of these metagenomic catalogs, CAZyLingua discovered new annotations that were previously overlooked by traditional sequence homology tools. Overall, the deep learning model CAZyLingua can be applied in combination with existing tools to unravel intricate CAZyme evolutionary profiles and patterns, contributing to a more comprehensive understanding of microbial metabolic dynamics.

bioinformatics↗

bioRxiv2021

Genome binning of viral entities from bulk metagenomics data

Despite the accelerating number of uncultivated virus sequences discovered in metagenomics and their apparent importance for health and disease, the human gut virome and its interactions with bacteria in the gastrointestinal are not well understood. In addition, a paucity of whole-virome datasets from subjects with gastrointestinal diseases is preventing a deeper understanding of the viromes role in disease and in gastrointestinal ecology as a whole. By combining a deep-learning based metagenomics binning algorithm with paired metagenome and metavirome datasets we developed the Phages from Metagenomics Binning (PHAMB) approach for binning thousands of viral genomes directly from bulk metagenomics data. Simultaneously our methodology enables clustering of viral genomes into accurate taxonomic viral populations. We applied this methodology on the Human Microbiome Project 2 (HMP2) cohort and recovered 6,077 HQ genomes from 1,024 viral populations and explored viral-host interactions. We show that binning can be advantageously applied to existing and future metagenomes to illuminate viral ecological dynamics with other microbiome constituents.

genomics↗