Regional and subcellular distribution of the receptor‐targeting protein PIST in the rat central nervous system
Bibliographic record
Abstract
Protein interacting specifically with Tc10, PIST, is a Golgi-associated sorting protein involved in regulating cell-surface targeting of plasma membrane receptors. The present study provides the first comprehensive description of PIST distribution in the mammalian central nervous system and of its subcellular localization by immunocytochemistry. PIST is distributed widely throughout the neuraxis, predominantly associated with neuronal cell bodies and dendrites. In hippocampal neurons, in vitro and in situ, PIST displayed a patchy subcellular distribution in an area surrounding the nucleus and extending into one of the major dendrites. By colocalization with the trans-Golgi marker TGN38, we were able to show that PIST is associated largely but not exclusively with the trans-Golgi network in central neurons. High or moderate to high levels of PIST-like immunoreactivity were found in cortical areas, in particular in layer V of the neocortex. The motor cortex was most strongly labeled. Also, the piriform and insular cortices displayed strong PIST labeling. In the hippocampus, CA2 but not CA1 or CA3 pyramidal cells displayed strong PIST-labeling, extending into their apical dendrites. In the thalamus, ventrolateral and laterodorsal nuclei were most strongly stained, whereas in the hypothalamus the supraoptic nucleus stood out with strong immunoreactivity. Strikingly, in the brainstem all cranial nerve motor nuclei were PIST-positive at varying levels, which is in keeping with the prominent expression of PIST in forebrain motor areas. This selective distribution of PIST suggests that the protein serves distinctive roles in specific neuronal populations, establishing functionally distinct zones, for instance, in the hippocampus.
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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 | 0.001 | 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 | 0.001 |
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".