High density array screening to identify the genetic requirements for transition metal tolerance in Saccharomyces cerevisiae
Why this work is in the frame
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Bibliographic record
Abstract
Biological systems have developed with a strong dependence on transition metals for accomplishing a number of biochemical reactions. Iron, copper, manganese and zinc are essential for virtually all forms of life with their unique chemistries contributing to a variety of physiological processes including oxygen transport, generation of cellular energy and protein structure and function. Properties of these metals (and to a lesser extent nickel and cobalt) that make them so essential to life also make them extremely cytotoxic in many cases through the formation of damaging oxygen radicals via Fenton chemistry. While life has evolved to exploit the chemistries of transition metals to drive physiological reactions, systems have concomitantly evolved to protect against the damaging effects of these same metals. Saccharomyces cerevisiae is a valuable tool for studying metal homeostasis with many of the genes identified thus far having homologs in higher eukaryotes including humans. Using high density arrays, we have screened a haploid S. cerevisiae deletion set containing 4786 non-essential gene deletions for strains sensitive to each of Fe, Cu, Mn, Ni, Zn and Co and then integrated the six screens using cluster analysis to identify pathways that are unique to individual metals and others with function shared between metals. Genes with no previous implication in metal homeostasis were found to contribute to sensitivity to each metal. Significant overlap was observed between the strains that were sensitive to Mn, Ni, Zn and Co with many of these strains lacking genes for the high affinity Fe transport pathway and genes involved in vacuolar transport and acidification. The results from six genome-wide metal tolerance screens show that there is some commonality between the cellular defenses against the toxicity of Mn, Ni, Zn and Co with Fe and Cu requiring different systems. Additionally, potential new factors been identified that function in tolerance to each of the six metals.
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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 | 0.000 | 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 it