Logo Logo
  • Home
  • Publications
  • Meet Our Team
  • Contact

More Info

  • Email papinlab@virgina.edu
  • Phone Office: (434) 924-8195 Computational lab: (434) 982-6267 Wet lab: (434) 924-8640
  • Location 415 Lane Road, Room 2041 Charlottesville, VA 22903

Related Links

  • PubMed
  • UVA Engineering

Connect With Us

Comparative analyses of parasites with a comprehensive database of genome-scale metabolic models

  • Home
  • Blog Details
February 23 2022
  • Published Works

PLoS Comput Biol. 2022 Feb 23;18(2):e1009870. doi: 10.1371/journal.pcbi.1009870. eCollection 2022 Feb.

ABSTRACT

Protozoan parasites cause diverse diseases with large global impacts. Research on the pathogenesis and biology of these organisms is limited by economic and experimental constraints. Accordingly, studies of one parasite are frequently extrapolated to infer knowledge about another parasite, across and within genera. Model in vitro or in vivo systems are frequently used to enhance experimental manipulability, but these systems generally use species related to, yet distinct from, the clinically relevant causal pathogen. Characterization of functional differences among parasite species is confined to post hoc or single target studies, limiting the utility of this extrapolation approach. To address this challenge and to accelerate parasitology research broadly, we present a functional comparative analysis of 192 genomes, representing every high-quality, publicly-available protozoan parasite genome including Plasmodium, Toxoplasma, Cryptosporidium, Entamoeba, Trypanosoma, Leishmania, Giardia, and other species. We generated an automated metabolic network reconstruction pipeline optimized for eukaryotic organisms. These metabolic network reconstructions serve as biochemical knowledgebases for each parasite, enabling qualitative and quantitative comparisons of metabolic behavior across parasites. We identified putative differences in gene essentiality and pathway utilization to facilitate the comparison of experimental findings and discovered that phylogeny is not the sole predictor of metabolic similarity. This knowledgebase represents the largest collection of genome-scale metabolic models for both pathogens and eukaryotes; with this resource, we can predict species-specific functions, contextualize experimental results, and optimize selection of experimental systems for fastidious species.

PMID:35196325 | PMC:PMC8901074 | DOI:10.1371/journal.pcbi.1009870

Previous Post Next Post

Recent Posts

  • Identifying metabolic shifts in Crohn’s disease using’ omics-driven contextualized computational metabolic network models
  • Ten simple rules for launching an academic research career
  • Metabolic Network Models of the Gardnerella Pangenome Identify Key Interactions with the Vaginal Environment
  • Enterococci enhance Clostridioides difficile pathogenesis
  • Genome-scale metabolic modeling reveals increased reliance on valine catabolism in clinical isolates of Klebsiella pneumoniae
Logo

Computational Systems Biology Laboratory; The research group of Dr. Jason Papin in the Department of Biomedical Engineering at the University of Virginia.

Related Links

  • PubMed
  • UVA Engineering

Contact Info

The research group of Dr. Jason Papin in the Department of Biomedical Engineering at the University of Virginia

  • Email: papinlab@virginia.edu
  • Phone (434) 924-8195

  • Home
  • Publications
  • Meet Our Team
  • Contact