Proteomic Analysis of Neuroendocrine Peptidergic System Disruption Using the AtT20 Pituitary Cell Line as a Model
Bibliographic record
Abstract
Environmental pollutants may affect the activities of many cellular enzymes. The effect on the proteome of enzymatic inhibitors can be determined using two-dimensional (2D) gel electrophoresis. In neuroendocrine cells, proprotein convertases 1 and 2 (PC1 and PC2) mediate the proteolytic activation of many precursors to peptide hormones and neuropeptides. Enzymatic activities of these calcium-dependent proteinases are readily regulated by chelating agents and by heavy metals ions found in the environment. Such an inhibition could result in a potentially pathological disruption of the peptidergic system. We are interesting in finding out to what extent specific inhibition of these enzymes could affect the proteome of a neuroendocrine cell. To address this question, we used the mouse pituitary AtT20 cell line as a model. We compared the proteomic pattern of control cells to that of cells overexpressing proSAAS, a PC1-specific inhibitor. The comparison was conducted using two-dimensional (2D) gel electrophoresis, mass spectrometric identification of differing proteins and immunoblotting to confirm their identity. The 2D analysis revealed a number of alterations in the proteome of proSAAS-overexpressing cells. Mass spectrometric analysis of tryptic peptides identified two proteins found in more abundance in these cells as proSAAS and Ephrin type A receptor 2.
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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".