Jason Papin
University of Virginia, VA·Biomedical Engineering
Antibiotic-resistant bacteria are a serious threat. Pseudomonas aeruginosa and Staphylococcus aureus alone cause more than 30,000 and 300,000 drug-resistant infections per year in the United States.
- Fields
Biomedical Engineering
Microbiology & Immunology
Computer Science
- Topics
antimicrobial resistance
antibiotic resistance
Pseudomonas aeruginosa
- Grants
- 1 known grant, through May 2027
What they work on
- Build computational metabolic network models of antibiotic-resistant and antibiotic-sensitive Pseudomonas aeruginosa and Staphylococcus aureus in body-like environments, to predict genes and pathways that support the evolution of resistance.
- Test the models' predictions experimentally, combining phenotyping (measuring how strains grow and use nutrients) with modeling to find metabolic processes that can be adjusted to select against resistant bacteria.
- Compare a Gram-negative and a Gram-positive pathogen to determine whether metabolic dependencies behind resistance are specific to one species or a general adaptation.
- Develop methods that fold gene-activity data into metabolic models and use ensemble modeling for uncertainty, and apply machine learning to find metabolic states linked to resistance in patient isolates.
More about the research
These bacteria have quickly evolved resistance to every new antibiotic introduced against them. This project asks a less familiar question: does a bacterium's metabolism, the network of chemical reactions it uses to take in nutrients and grow, help it become resistant? And could that metabolism be targeted so resistant populations grow poorly? The lab's own data and others' show that resistant strains of both species differ in their metabolism as a whole.
Why it matters
Doctors are running out of antibiotics that work against drug-resistant Pseudomonas aeruginosa and Staphylococcus aureus infections, which number in the hundreds of thousands each year in the United States. By finding metabolic genes and pathways that resistant bacteria depend on, this work could point to new drug targets that select against resistant populations. Its model-driven approach could also be applied to other resistant pathogens.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Berthony Deslouches
University of Pittsburgh, PA·Microbiology & Immunology
Determine how the cationic (positively charged) and hydrophobic motifs of engineered PAX peptides each contribute to recognizing and killing bacteria, by designing peptide series that vary one valine and one arginine at a time.
Sara Abboud
University of California-Berkeley, CA·Microbiology & Immunology·Early-career
Test whether the nerve sensor channels TRPV1 and TRPA1 change mucin-related gene activity in corneal and conjunctival cells, using RNA sequencing confirmed with qRT-PCR.
Robert Clubb
University of California-Los Angeles, CA·Biochemistry
Determine how the C. diphtheriae surface receptor HbpA captures human hemoglobin, and test whether it works with other heme-binding receptors to distort hemoglobin and trigger heme release.
The school
University of Virginia: 43 labs on PI Pages, $814M a year on research, 413 research doctorates a year.
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