A robust platform for chemical genomics in bacterial systems
Bibliographic record
Abstract
While genetic perturbation has been the conventional route to probing bacterial systems, small molecules are showing great promise as probes for cellular complexity. Indeed, systematic investigations of chemical-genetic interactions can provide new insights into cell networks and are often starting points for understanding the mechanism of action of novel chemical probes. We have developed a robust and sensitive platform for chemical-genomic investigations in bacteria. The approach monitors colony volume kinetically using transmissive scanning measurements, enabling acquisition of growth rates and conventional endpoint measurements. We found that chemical-genomic profiles were highly sensitive to concentration, necessitating careful selection of compound concentrations. Roughly 20,000,000 data points were collected for 15 different antibiotics. While 1052 chemical-genetic interactions were identified using the conventional endpoint biomass approach, adding interactions in growth rate resulted in 1564 interactions, a 50-200% increase depending on the drug, with many genes uncharacterized or poorly annotated. The chemical-genetic interaction maps generated from these data reveal common genes likely involved in multidrug resistance. Additionally, the maps identified deletion backgrounds exhibiting class-specific potentiation, revealing conceivable targets for combination approaches to drug discovery. This open platform is highly amenable to kinetic screening of any arrayable strain collection, be it prokaryotic or eukaryotic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".