Complex regulation of Hsf1-Skn7 activities by the catalytic subunits of PKA in Saccharomyces cerevisiae: experimental and computational evidences
Bibliographic record
Abstract
BACKGROUND: The cAMP-dependent protein kinase regulatory network (PKA-RN) regulates metabolism, memory, learning, development, and response to stress. Previous models of this network considered the catalytic subunits (CS) as a single entity, overlooking their functional individualities. Furthermore, PKA-RN dynamics are often measured through cAMP levels in nutrient-depleted cells shortly after being fed with glucose, dismissing downstream physiological processes. RESULTS: Here we show that temperature stress, along with deletion of PKA-RN genes, significantly affected HSE-dependent gene expression and the dynamics of the PKA-RN in cells growing in exponential phase. Our genetic analysis revealed complex regulatory interactions between the CS that influenced the inhibition of Hsf1/Skn7 transcription factors. Accordingly, we found new roles in growth control and stress response for Hsf1/Skn7 when PKA activity was low (cdc25Δ cells). Experimental results were used to propose an interaction scheme for the PKA-RN and to build an extension of a classic synchronous discrete modeling framework. Our computational model reproduced the experimental data and predicted complex interactions between the CS and the existence of a repressor of Hsf1/Skn7 that is activated by the CS. Additional genetic analysis identified Ssa1 and Ssa2 chaperones as such repressors. Further modeling of the new data foresaw a third repressor of Hsf1/Skn7, active only in the absence of Tpk2. By averaging the network state over all its attractors, a good quantitative agreement between computational and experimental results was obtained, as the averages reflected more accurately the population measurements. CONCLUSIONS: The assumption of PKA being one molecular entity has hindered the study of a wide range of behaviors. Additionally, the dynamics of HSE-dependent gene expression cannot be simulated accurately by considering the activity of single PKA-RN components (i.e., cAMP, individual CS, Bcy1, etc.). We show that the differential roles of the CS are essential to understand the dynamics of the PKA-RN and its targets. Our systems level approach, which combined experimental results with theoretical modeling, unveils the relevance of the interaction scheme for the CS and offers quantitative predictions for several scenarios (WT vs. mutants in PKA-RN genes and growth at optimal temperature vs. heat shock).
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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".