E3 ubiquitin ligase CHIP facilitates cAMP and cGMP signalling cross-talk by polyubiquitinating PDE9A
Bibliographic record
Abstract
The carboxyl terminus of Hsc70-interacting protein (CHIP) is pivotal for managing misfolded and aggregated proteins via chaperone networks and degradation pathways. In a preclinical rodent model of CHIP-related ataxia, we observed that CHIP mutations lead to increased levels of phosphodiesterase 9A (PDE9A), whose role in this context remains poorly understood. Here, we investigated the molecular mechanisms underlying the role of PDE9A in CHIP-related ataxia and demonstrated that CHIP binds to PDE9A, facilitating its polyubiquitination and autophagic degradation. Conversely, dysfunctional CHIP disrupts this process, resulting in PDE9A accumulation, increased cGMP hydrolysis, and impaired PKG phosphorylation of CHIP at serine 19. This cascade further amplifies PDE9A accumulation, ultimately disrupting mitophagy and triggering neuronal apoptosis. Elevated PKA levels inhibit PDE9A degradation, further exacerbating this neuronal dysfunction. Notably, pharmacological inhibition of PDE9A via Bay 73-6691 or virus-mediated CHIP expression restored the balance of cGMP/cAMP signalling. These interventions protect against cerebellar neuropathologies, particularly Purkinje neuron mitophagy dysfunction. Thus, PDE9A upregulation considerably exacerbates ataxia associated with CHIP mutations, and targeting the interaction between PDE9A and CHIP is an innovative therapeutic strategy for CHIP-related ataxia.
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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 | 0.000 |
| 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".