Nesca, a novel neuronal adapter protein, links the molecular motor kinesin with the pre‐synaptic membrane protein, syntaxin‐1, in hippocampal neurons
Bibliographic record
Abstract
Vesicular transport in neurons plays a vital role in neuronal function and survival. Nesca is a novel protein that we previously identified and herein describe its pattern of expression, subcellular localization and protein-protein interactions both in vitro and in vivo. Specifically, a large proportion of Nesca is in tight association with both actin and microtubule cytoskeletal proteins. Nesca binds to F-actin, microtubules, βIII and acetylated α-tubulin, but not neurofilaments or the actin-binding protein drebrin, in in vitro-binding assays. Nesca co-immunoprecipitates with kinesin heavy chain (KIF5B) and kinesin light-chain motors as well as with the synaptic membrane precursor protein, syntaxin-1, and is a constituent of the post-synaptic density. Moreover, in vitro-binding assays indicate that Nesca directly binds KIF5B, kinesin light-chain and syntaxin-1. In contrast, Nesca does not co-immunoprecipitate with the kinesin motors KIF1B, KIF3A nor does it bind syntaxin-4 or the synaptosome-associated protein 25 kDa (SNAP-25) in vitro. Nesca expression in neurons is highly punctuate, co-stains with syntaxin-1, and is found in fractions containing markers of early endosomes and Golgi suggesting that it is involved in vesicular transport. Collectively, these data suggest that Nesca functions as an adapter involved in neuronal vesicular transport including vesicles containing soluble N-ethylmaleimide sensitive factor attachment protein receptors that are essential to exocytosis.
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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".