Chemo-Selective Proteomics for Discovery of Polymicrobial Interactions

Citation

Wang, Grace Zimu (2024) Chemo-Selective Proteomics for Discovery of Polymicrobial Interactions. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/1vw0-gt98. https://resolver.caltech.edu/CaltechTHESIS:03292024-213352165

Abstract

The future of microbiome research lies in our ability to engineer polymicrobial interactions toward improved host health outcomes, which requires a fundamental molecular understanding of how microbial species sense and respond to ecological competition. Chronic respiratory infection by polymicrobial communities is the leading cause of mortality and morbidity in people living with cystic fibrosis (CF). My thesis work adapts chemo-selective proteomics to dissect molecular mechanisms that drive interspecies dynamics between two notorious opportunistic pathogens dominating chronic CF infection, Pseudomonas aeruginosa and Staphylococcus aureus.

In Chapter 1, I introduce bioorthogonal noncanonical amino acid tagging (BONCAT)-based comparative proteomics, focusing on time-resolved, cell-specific, and cellular state-selective proteomic applications in the dissection of complex microbial systems. In Chapter 2, I discuss a new usage of time-resolved BONCAT to monitor immediate competition-sensing responses in interbacterial warfare. While coinfection by the Gram-negative Pseudomonas aeruginosa and the Gram-positive Staphylococcus aureus is associated with poor patient outcomes, the interspecies interactions responsible for such decline remain unknown. We discovered that P. aeruginosa senses S. aureus secreted cytotoxic peptides from a distance and preempts potential competition through activation of type six secretion system (T6SS). P. aeruginosa enhances such competition-sensing-induced antagonism through concomitant attraction toward S. aureus peptides, effectively reducing cellular distances between neighboring species and providing a competitive advantage. In Chapter 3, I discuss a new usage of cell-selective BONCAT to target protein synthesis analysis of the lowabundance organism, S. aureus, in a coculture environment predominated by P. aeruginosa. P. aeruginosa robustly outcompetes S. aureus, and conventional shotgun proteomics, which is biased toward highly abundant proteins on principle, could only identify and quantify less than 5% of total protein synthesis by S. aureus in coculture. We demonstrate that chemical enrichment affords a more than 12-fold increase in total protein abundances synthesized by S. aureus. About 50% of protein “hits” with statistically significant changes in expression were not detected in pre-enrichment lysates, highlighting BONCAT as a powerful strategy that facilitates high-resolution proteomic analysis of low-abundance organisms in polymicrobial communities.

Thesis Files