Bibliographic record
Abstract
When bacteria are grown in media containing only carbon, nitrogen and essential salts, they shift in their metabolic activities to include the synthesis of essential amino acids, vitamins and other cofactors. Only 303 genes, for example, are essential for growth of E. coli on rich media and some 119 additional genes are required for growth on nutrient‐limited media. Hence compounds that target bacteria under nutrient‐limited conditions could serve as leads for novel antibacterial drugs. In fact, nutrient‐limited media probably provide a better proxy for the host environment. There have been many reports of impaired growth and attenuated virulence in pathogens due to mutations in vitamin, nucleobase and amino acid biosynthetic genes. Nevertheless, systematic searches for antibacterial chemicals have overwhelmingly emphasized rich media conditions. Thus, there is a considerable gap in antibacterial chemical space surveyed to date. In the Brown laboratory, we are trying to understand the potential of nutrient biosynthesis as a new and tractable target in drug‐resistant Gram‐negative pathogens. To this end, we have been screening libraries of structurally diverse synthetic compounds and natural products to find inhibitors of bacterial growth in minimal media. Compounds and extracts active in primary screens are subject to the addition of an array of key metabolites and pools thereof to identify suppressors of growth inhibition and provide hypotheses for physiological, genetic and biochemical experiments to elaborate mechanism of action. More recently, we are developing chemical and genomic platforms to understand the interaction of the nutrient biosynthesis apparatus with all aspects of bacterial physiology using systems approaches. The ultimate goal of these studies is to contribute fresh directions for new antibacterial therapies. Support or Funding Information I acknowledge operating funding from the Canadian Institutes of Health Research, Natural Sciences and Engineering Research Council of Canada as well as a salary support from the Canada Research Chairs Program.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.024 | 0.027 |
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".