Metabolic Bi-Stability and Hysteresis in a Model Microbiome Community

Citation

Khazaei, Tahmineh (2019) Metabolic Bi-Stability and Hysteresis in a Model Microbiome Community. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/Z588-5H60. https://resolver.caltech.edu/CaltechTHESIS:05312019-135625690

Abstract

Changes in the species composition of the human microbiome are associated with a broad range of diseases, but elucidating causal mechanisms has been challenging. Some microbiome disease states persist in seemingly unfavorable conditions, e.g., the proliferation of aerobe–anaerobe communities in oxygen-exposed environments in wounds or small intestinal bacterial overgrowth. In Chapter I, using two microbes relevant to the human microbiome, we combine genome-scale mathematical modeling, bioreactor experiments, transcriptomics, and control theory to show that multi-stability and hysteresis (MSH) is a mechanism that can describe shifts to a resilient aerobe–anaerobe community. We examine the impact of changing oxygen and nutrient regimes and identify factors, including changes in metabolism and gene expression, that lead to MSH. Where MSH explains microbiome shifts, it can profoundly improve our conceptual understanding of these paradoxically persistent disease states, and thereby facilitate effective interventions.

Chapter II details a method for rapidly detecting the susceptibility and resistance of Neisseria gonorrhoeae to the antibiotic ciprofloxacin. Antimicrobial-resistant Neisseria gonorrhoeae is an urgent public-health threat, with continued worldwide incidents of infection and rising resistance to antimicrobials. Traditional culture-based methods for antibiotic susceptibility testing are unacceptably slow (1–2 days), resulting in the use of broad-spectrum antibiotics and the further development and spread of resistance. Critically needed is a rapid antibiotic susceptibility test (AST) that can guide treatment at the point-of-care. In our approach, we explore the use of RNA signatures, which are among the first cellular responses to drug exposure, as an indicator of antibiotic susceptibility. Using RNA sequencing, we identified antibiotic-responsive transcripts. Significant shifts (>4-fold change) in transcript levels occurred within 5 minutes of antibiotic exposure. We designed assays for responsive transcripts with the highest abundances and fold changes, and validated gene expression using digital PCR. Using the top two markers (porB and rpmB), we correctly determined the antibiotic susceptibility and resistance of 49 clinical isolates after 10-min exposure to ciprofloxacin. RNA signatures are therefore promising as an approach on which to build rapid AST devices for N. gonorrhoeae at the point-of-care, which is critical for disease management, surveillance, and antibiotic stewardship efforts.

Thesis Files