Pleiotropic drug-resistance attenuated genomic library improves elucidation of drug mechanisms
Bibliographic record
Abstract
Identifying Saccharomyces cerevisiae genome-wide gene deletion mutants that confer hypersensitivity to a xenobiotic aids the elucidation of its mechanism of action (MoA). However, the biological activities of many xenobiotics are masked by the pleiotropic drug resistance (PDR) network which effluxes xenobiotics that are PDR substrates. The PDR network in S. cerevisiae is almost entirely under the control of two functionally homologous transcription factors Pdr1p and Pdr3p. Herein we report the construction of a PDR-attenuated haploid non-essential DMA (PA-DMA), lacking PDR1 and PDR3, which permits the MoA elucidation of xenobiotics that are PDR substrates at low concentrations. The functionality of four key cellular processes commonly activated in response to xenobiotic stress: oxidative stress response, general stress response, unfolded stress response and calcium signalling pathways were assessed in the absence of PDR1 and PDR3 genes and were found to unaltered, therefore, these key chemogenomic signatures are not lost when using the PA-DMA. Efficacy of the PA-DMA was demonstrated using cycloheximide and latrunculin A at low nanomolar concentrations to attain chemical genetic profiles that were more specific to their known main mechanisms. We also found a two-fold increase in the number of compounds that are bioactive in the pdr1Δpdr3Δ compared to the wild type strain in screening the commercially available LOPAC(1280) library. The PA-DMA should be particularly applicable to mechanism determination of xenobiotics that have limited availability, such as natural products.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".