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

More Info

  • Email [email protected]
  • 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
  • Undergraduate Opportunities
  • Graduate Opportunities
  • UVA Engineering

Connect With Us

Influential Parameters for the Analysis of Intracellular Parasite Metabolomics

  • Home
  • Blog Details
April 20 2018
  • Published Works

mSphere. 2018 Apr 18;3(2):e00097-18. doi: 10.1128/mSphere.00097-18. Print 2018 Apr 25.

ABSTRACT

Metabolomics is increasingly popular for the study of pathogens. For the malaria parasite Plasmodium falciparum, both targeted and untargeted metabolomics have improved our understanding of pathogenesis, host-parasite interactions, and antimalarial drug treatment and resistance. However, purification and analysis procedures for performing metabolomics on intracellular pathogens have not been explored. Here, we purified in vitro-grown ring-stage intraerythrocytic P. falciparum parasites for untargeted metabolomics studies; the small size of this developmental stage amplifies the challenges associated with metabolomics studies as the ratio between host and parasite biomass is maximized. Following metabolite identification and data preprocessing, we explored multiple confounding factors that influence data interpretation, including host contamination and normalization approaches (including double-stranded DNA, total protein, and parasite numbers). We conclude that normalization parameters have large effects on differential abundance analysis and recommend the thoughtful selection of these parameters. However, normalization does not remove the contribution from the parasite’s extracellular environment (culture media and host erythrocyte). In fact, we found that extraparasite material is as influential on the metabolome as treatment with a potent antimalarial drug with known metabolic effects (artemisinin). Because of this influence, we could not detect significant changes associated with drug treatment. Instead, we identified metabolites predictive of host and medium contamination that could be used to assess sample purification. Our analysis provides the first quantitative exploration of the effects of these factors on metabolomics data analysis; these findings provide a basis for development of improved experimental and analytical methods for future metabolomics studies of intracellular organisms.IMPORTANCE Molecular characterization of pathogens such as the malaria parasite can lead to improved biological understanding and novel treatment strategies. However, the distinctive biology of the Plasmodium parasite, including its repetitive genome and the requirement for growth within a host cell, hinders progress toward these goals. Untargeted metabolomics is a promising approach to learn about pathogen biology. By measuring many small molecules in the parasite at once, we gain a better understanding of important pathways that contribute to the parasite’s response to perturbations such as drug treatment. Although increasingly popular, approaches for intracellular parasite metabolomics and subsequent analysis are not well explored. The findings presented in this report emphasize the critical need for improvements in these areas to limit misinterpretation due to host metabolites and to standardize biological interpretation. Such improvements will aid both basic biological investigations and clinical efforts to understand important pathogens.

PMID:29669882 | PMC:PMC5907652 | DOI:10.1128/mSphere.00097-18

Previous Post Next Post

Recent Posts

  • Fecal sphingolipids predict parenteral nutrition associated cholestasis in the neonatal intensive care unit
  • Comparative analyses of parasites with a comprehensive database of genome-scale metabolic models
  • Comparative analyses of parasites with a comprehensive database of geno-scale metabolic models
  • Quantifying cumulative phenotypic and genomic evidence for procedural generation of metabolic network reconstructions
  • Computational approaches to understanding Clostridioides difficile metabolism and virulence
Logo

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

Related Links

  • PubMed
  • Undergraduate Opportunities
  • Graduate Opportunities
  • UVA Engineering

Contact Info

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

  • Email: [email protected]
  • Phone (434) 924-8195

© Copyright 2021 Papin Lab. Designed by Sabres Media LLC

  • Home
  • Publications
  • Meet Our Team
  • Contact