Recurrent Acrodysostosis-Related PKA RIα Mutant Reveals a Novel Mechanism of Aberrant PKA Deactivation
Bibliographic record
Abstract
Protein kinase A (PKA) is essential in converting extracellular signals into tightly regulated cellular responses controlling vital processes such as growth, development, and gene expression. Activation of PKA is controlled by the binding of cAMP to the regulatory subunit of PKA (R). Several mutations in the ubiquitous RIα isoform of R cause Acrodysostosis 1 (ACRO), a disease characterized by resistance to thyroid-stimulating and parathyroid hormones leading to severe congenital malformations. This work examines the recurrent R366X truncation ACRO mutant, which exhibits severe PKA hypoactivation due to loss of sensitivity to cAMP and the impairment of allosteric networks. The R366X RIα mutant has been previously studied via X-ray crystallography, but the crystal structure only captured the inhibited state and showed minimal difference from the wild type structure. Additionally, previous studies only examined the effects of ACRO mutants on the activation cycle of PKA (i.e. sensitivity to cAMP binding). Here we focus on the less understood signal termination cycle. We hypothesize that R366X acts by perturbing dynamic intermediates relevant to the PKA deactivation cycle, which are not fully recapitulated by static structures. To test our hypothesis, we combined low- and high-resolution approaches for probing protein-ligand binging, mutant stability, and identifying regions exhibiting aberrant allosteric behaviors. Based on our results, we propose a novel mechanism whereby R366X not only impairs physiological PKA activation but also accelerates PKA deactivation by increasing the rate of phosphodiesterase-catalyzed cAMP hydrolysis to 5'-AMP. Our studies shed new light on the current understanding of PKA dysregulation and ACRO's molecular etiology, outlining a multi-resolution experimental design which is transferable to other ACRO mutants.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".