Chemical-genetic interrogation of small molecule mechanism of action in S. cerevisiae
Bibliographic record
Abstract
The budding yeast S. cerevisiae is widely used as a model organism to study biological processes that are conserved among eukaryotes. Di fferent genomic approaches have been applied successfully to interrogate the mode of action of small molecules and their combinations. In this thesis, these technologies were applied to di fferent sets of chemical compounds in the context of two collaborative projects. In addition to insight into the mode of action of these molecules, novel approaches for analysis of chemical-genetic pro files to integrate GO annotation, genetic interactions and protein complex data have been developed. The fi rst project was motivated by a pressing need to design novel therapeutic strategies to combat infections caused by opportunistic fungal pathogens. Systematic screens of 1180 FDA approved drugs identifi ed 148 small molecules that exhibit synergy in combination with uconcazole, a widely used anti-fungal drug (Wright lab, McMaster University, Canada). Genome-wide chemical-genetic profiles for 6 of these drugs revealed two di fferent modes of action of synergy. Five of the compounds a ffected membrane integrity; these chemical-genetic interactions were supported by microscopy analysis and sorbitol rescue assays. The sixth compound targets a distinct membrane-associated pathway, sphingolipid biosynthesis. These results not only give insight into the mechanism of the synergistic interactions, they also provide starting points for the prediction of synergistic anti-fungal combinations with potential clinical applications. The second project characterised compounds that aff ected melanocytes in a chemical screen in zebra fish (Patton lab, Edinburgh). Chemical-genetic screens in S.cerevisiae enabled us to show that melanocyte pigmentation reducing compounds do so by interfering with copper metabolism. Further, we found that defects in intracellular AP1 and AP3 trafficking pathways cause sensitivity to low copper conditions. Surprisingly, we observed that the widely-used MAP-kinase inhibitor U0126 a ffects copper metabolism. A nitrofuran compound was found to speci fically promote melanocyte cell death in zebrafi sh. This enabled us to study off -target eff ects of these compounds that are used to treat trypanosome infections. Nifurtimox is a nitrofuran prodrug that is activated by pathogen-specifi c nitroreductases. Using yeast and zebra fish we were able to show that nitrofurans are also bioactivated by host-specifi c aldehyde dehydrogenases suggesting that a combination therapy with an aldehyde dehydrogenase inhibitor might reduce side e ffects associated with nifurtimox.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".