PhD Oral Exam - Farhan Rahman Chowdhury, Biology

When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.

Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.

Abstract

Antibiotic resistance threatens to undo many of the advancements of modern medicine. A slow antibiotic development pipeline makes it impossible to outpace bacterial evolution, making alternative strategies essential to combat resistance. In this study, I use large scale experimental evolutions powered by the soft agar gradient evolution (SAGE) platform to investigate the evolutionary trade-offs associated with antibiotic resistance, and how they can be leveraged to combat the emergence of resistance.

The study begins with the finding that a chloramphenicol (CHL) resistant Escherichia coli (E. coli) mutant exhibits a markedly reduced rate of resistance evolution against other antibiotics. I show that this slow adaptation is linked to the fitness costs associated with resistance, which bacteria often readily overcome through compensatory evolution. Further screening reveals fitness costs that are robust against mitigation via compensatory evolution, highlighting an opportunity to exploit these trade-offs to slow down the emergence of resistance. However, the translatory potential of the findings from SAGE remained unclear.

To test the utility and clinical relevance of SAGE, I first expand its applicability to a broader range of antibiotics by supplementing the evolution medium with xanthan gum. Xanthan gum is a water-binding polysaccharide that significantly reduces synaeresis of the agar-based medium, enhancing evolution in SAGE. To demonstrate its capacity to uncover resistance mechanisms I use this modified platform to characterize the evolution of resistance to the lipopeptide tridecaptin A1—an antibiotic previously thought to be impervious to resistance. I then assess the clinical applicability of the evolutionary trade-offs observed in SAGE-derived mutants by comparing outcomes from SAGE to those obtained using other widely used laboratory evolution platforms, as well as clinical bacterial datasets. These analyses reveal that SAGE more accurately reproduces clinically relevant patterns of fitness trade-offs than the alternative platforms tested. One such trade-off, collateral sensitivity (CS), has recently been proposed to be useful in mitigating resistance in sequential antibiotic therapies, where antibiotics are applied one after the other. But large-scale evolutionary studies to determine its role and effectiveness in sequential regimens were missing.

I use over 450 evolution experiments to test the role of CS in resistance mitigation in four proposed drug pairs. I find that resistance to both drugs evolves readily, and that collateral sensitivity does not hinder the evolution of multidrug resistance or promote resensitization. However, if resistance to drug B reduces susceptibility to A in an A-B drug sequence, a phenomenon I term backward CS, resistance to A can be reduced. As an example I demonstrate that β-lactam resistant E. coli cells frequently lower their resistance to β-lactams upon aminoglycoside resistance acquisition due to conflicting modifications to the proton motive force and efflux pumps. This suggests that the levels of resistance evolved can be kept in check by leveraging backwards CS to resensitize cells as antibiotic resistance evolves. However, the levels of resensitization achieved were two-fold on average, often not sufficient to reduce resistance below clinical breakpoints.

Finally, I introduce sequential antibiotic regimens composed of three drugs or “tripartite loops” to contain resistance within a closed drug cycle. Through 424 discrete adaptive laboratory evolution experiments I show that as bacteria sequentially evolve resistance to the drugs in a loop, they continually trade their past resistance for fitness gains, reverting back to sensitivity via four-to-eight-fold reductions in resistance on average. Through fitness and genomic analyses, I find that tripartite loops guide bacterial strains towards evolutionary paths that mitigate fitness costs and reverse resistance to component drugs in the loops, driving levels of resensitization not achievable through previously suggested pairwise regimens. I then apply this strategy to reproducibly resensitize or eradicate four multidrug-resistant clinical isolates over the course of 216 evolutionary experiments. Resensitization occurred even when bacteria adapted through plasmid-bound mutations instead of chromosomal changes, showing the robustness of this strategy.

In conclusion, this work demonstrates that the evolutionary trade-offs accompanying antibiotic resistance can be strategically exploited to limit or reverse resistance evolution. I highlight the importance of studying the evolutionary aspect of antibiotic resistance to inform rational treatment strategies and restore efficacy of existing antibiotics. As the pace of novel antibiotic discovery continues to lag behind resistance evolution, such evolution-based approaches may be essential for extending the lifespan of our current antimicrobial arsenal.