A genome-wide portrait of pervasive drug contaminants
Bibliographic record
Abstract
Using a validated yeast chemogenomic platform, we characterized the genome-wide effects of several pharmaceutical contaminants, including three N-nitrosamines (NDMA, NDEA and NMBA), two related compounds (DMF and 4NQO) and several of their metabolites. A collection of 4800 non-essential homozygous diploid yeast deletion strains were screened in parallel and the strain abundance was quantified by barcode sequencing. These data were used to rank deletion strains representing genes required for resistance to the compounds to delineate affected cellular pathways and to visualize the global cellular effects of these toxins in an easy-to-use searchable database. Our analysis of the N-nitrosamine screens uncovered genes (via their corresponding homozygous deletion mutants) involved in several evolutionarily conserved pathways, including: arginine biosynthesis, mitochondrial genome integrity, vacuolar protein sorting and DNA damage repair. To investigate why NDMA, NDEA and DMF caused fitness defects in strains lacking genes of the arginine pathway, we tested several N-nitrosamine metabolites (methylamine, ethylamine and formamide), and found they also affected arginine pathway mutants. Notably, each of these metabolites has the potential to produce ammonium ions during their biotransformation. We directly tested the role of ammonium ions in N-nitrosamine toxicity by treatment with ammonium sulfate and we found that ammonium sulfate also caused a growth defect in arginine pathway deletion strains. Formaldehyde, a metabolite produced from NDMA, methylamine and formamide, and which is known to cross-link free amines, perturbed deletion strains involved in chromatin remodeling and DNA repair pathways. Finally, co-administration of N-nitrosamines with ascorbic or ferulic acid did not relieve N-nitrosamine toxicity. In conclusion, we used parallel deletion mutant analysis to characterize the genes and pathways affected by exposure to N-nitrosamines and related compounds, and provide the data in an accessible, queryable database.
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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 | 0.000 | 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".