Biochemical and genetic approaches to understanding the functions of the protein tyrosine phosphatase-sigma
Bibliographic record
Abstract
PTP-sigma, a developmentally regulated receptor protein tyrosine phosphatase of the LAR family, is highly expressed in the nervous system and is involved in axon guidance. In this study, two potential substrates of PTP-sigma have been detected by substrate trapping and two-dimensional electrophoresis. In addition, an expression pattern of PTP-sigma in adult non-neural mouse tissues has been established and opens new research avenues for the function of PTP-sigma. The high levels of PTP-sigma in fat led us to look into a possible role for this enzyme in insulin signaling. Insulin stimulated PTP-sigma knockout adipose tissue showed an hyperphosphorylation of the insulin receptor (IR). GH-deficiency is predominantly responsible for glucose homeostasis defects of PTP-sigma knockout mice and could mask the effect of the absence of PTP-sigma in insulin signaling in vivo. To overcome this, adipocytes were isolated. Upon insulin stimulation of PTP-sigma knockout adipocytes, an increase in IR and a decrease in Akt phosphorylation were observed, suggesting that the IR could be a substrate of PTP-sigma and that PTP-sigma could be involved in the regulation of the activation of Akt. The elucidation of the mechanisms of action of PTP-sigma in insulin signaling may lead to the development of new treatments in diabetes and obesity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".