Kv1.3 modulates neuroinflammation and neurodegeneration in Parkinson’s disease
Bibliographic record
Abstract
Characterization of the key cellular targets contributing to sustained microglial activation in neurodegenerative diseases, including Parkinson's disease (PD), and optimal modulation of these targets can provide potential treatments to halt disease progression. Here, we demonstrated that microglial Kv1.3, a voltage-gated potassium channel, was transcriptionally upregulated in response to aggregated α-synuclein (αSynAgg) stimulation in primary microglial cultures and animal models of PD, as well as in postmortem human PD brains. Patch-clamp electrophysiological studies confirmed that the observed Kv1.3 upregulation translated to increased Kv1.3 channel activity. The kinase Fyn, a risk factor for PD, modulated transcriptional upregulation and posttranslational modification of microglial Kv1.3. Multiple state-of-the-art analyses, including Duolink proximity ligation assay imaging, revealed that Fyn directly bound to Kv1.3 and posttranslationally modified its channel activity. Furthermore, we demonstrated the functional relevance of Kv1.3 in augmenting the neuroinflammatory response by using Kv1.3-KO primary microglia and the Kv1.3-specific small-molecule inhibitor PAP-1, thus highlighting the importance of Kv1.3 in neuroinflammation. Administration of PAP-1 significantly inhibited neurodegeneration and neuroinflammation in multiple animal models of PD. Collectively, our results imply that Fyn-dependent regulation of Kv1.3 channels plays an obligatory role in accentuating the neuroinflammatory response in PD and identify Kv1.3 as a potential therapeutic target for PD.
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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) | 0.000 | 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 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".