Understanding the Genetics and Evolution of Antimicrobial Resistance in Escherichia Coli
Bibliographic record
Abstract
Antimicrobial resistance (AMR) has emerged as a global public health challenge.The central goals of my PhD research are to better understand AMR evolution, with the longterm goal of developing novel strategies to mitigate the problem of AMR.This involves gaining comprehensive insights into the evolutionary trajectories of resistance evolution, investigation of the availability of resistance mutations and their pleiotropic consequences, and identification of genetic targets for the prevention/slowing of resistance evolution.In a systematic study, I have investigated the effects of different chromosomal quinolone resistance mutations, and the pleiotropic effects of such mutations in Escherichia coli.I identified 50 spontaneous resistance mutants on nalidixic acid, ciprofloxacin, and levofloxacin, with mutations in regions of gyrA, gyrB, and marR, which are known contributors of resistance in quinolones.Resistant isolates had increased resistance levels, widespread cross-resistance to other quinolones, and overall significant costs of resistance in the absence of antibiotic.To investigate novel strategies to combat AMR, I carried out a small RNA screen on a quinolone resistant gyrA background, to identify alternative drug targets.This screen led to the identification of 30 genes whose knockdown reduced the level of gyrase-mediated quinolone resistance in E. coli.Finally, to gain understanding of the network of genes contributing to intrinsic and phenotypic resistance, I have studied E. coli's two-component signal transduction systems (TCS).TCSs are involved in bacterial responses to many stresses, including iii antibiotics.I examined interactions between antibiotic stress and other environmental stressors in a set of eight TCS mutants.Mutants showed different types of responses to antibiotics and stressor+antibiotic combinations.In this research, I have used novel approaches to understand bacterial resistance evolution and to combat AMR.On an applied level, these studies have implications for public health strategies.This research can help lead to better selection of appropriate antibiotics, alternative drug targets involved in resistance, and has prospects for development of new therapeutic approaches for combating AMR.Alex Wong, for introducing an inexperienced PhD student to the fascinating world of bacterial evolution and genetics.I am always amazed and constantly inspired to see your critical thinking, scientific abilities, wisdom, and compassion.Thank you, for providing me this opportunity to pursue my research.Through your kind, patient and supportive supervision, today I am a better researcher with broader perspectives and overall a new evolved version of myself.It was my great honour to pursue a PhD degree under your supervision, and an incredible journey that I will proudly remember.I would like to
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".