Understanding Bacterial Stress Responses to Find New Antibiotics
Bibliographic record
Abstract
The global dissemination of antibiotic resistance combined with a dearth in new antibiotics being discovered has progressively reduced the effectiveness of the clinical antibacterial arsenal. Accordingly, there is a great need to discover new antibacterial compounds that act through unique mechanisms of action. A fundamental understanding of how bacteria respond stress, including that imposed by antibiotics, can be leveraged to discover new antibacterial compounds. In this thesis, I explore different aspects of bacterial stress response, and ultimately develop a phenotypic screen that uncovered new compounds that perturb the Escherichia coli cell envelope. In Chapter 2, I explore the importance of small non-coding RNAs to E. coli survival by constructing and assessing the fitness of 1,373 double deletion mutants in nutrient-deplete media supplemented with different carbon sources. We demonstrate the importance of the sRNA chaperone, Hfq, to survival across a variety of conditions and uncover a synthetic lethal interaction between the sRNAs ArcZ and CsrC when E. coli is grown with pyruvate as a sole carbon source. In Chapter III, I describe the construction of an ordered CRISPRi collection in E. coli and showcase a methodology for conducting genetic interaction studies with essential genes. As a proof of principle, we identified a synthetic viable interaction between the essential genes involved in lipoprotein transport and Braun’s lipoprotein (Lpp). In Chapter IV, I describe a phenotypic screen for new antibacterial compounds targeting the E. coli cell envelope using a GFP-transcriptional reporter for the gene rcsA. This screen identified a new small molecule inhibitor of prolipoprotein diacyltransferase, Lgt, with narrow-spectrum activity against E. coli and Salmonella Typhimurium. Together, the work presented in this thesis advances our understanding of how bacteria respond to stress and demonstrates the importance of developing new phenotypic screens for antibiotic discovery.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".