Cell Growth Inhibition and Functioning of Human Somatostatin Receptor Type 2 Are Modulated by Receptor Heterodimerization
Bibliographic record
Abstract
Somatostatin (SST) analogs have been successfully used in the medical treatment of acromegaly, caused by GH hypersecreting pituitary adenomas. Patients on SST analogs rarely develop tachyphylaxis despite years of continuous administration. It has been recently proposed that a functional association between SST receptor (SSTR) subtypes 2 and 5 exists to account for this behavior; however, a physical interaction has yet to be identified. Using both coimmunoprecipitation and photobleaching fluorescence resonance energy transfer microscopy techniques, we determined that SSTR2 and SSTR5 heterodimerize. Surprisingly, selective activation of SSTR2 and not SSTR5, or their costimulation, modulates the association. The SSTR2-selective agonist L-779,976 is more efficacious at inhibiting adenylate cyclase, activating ERK1/2, and inducing the cyclin-dependent kinase inhibitor p27(Kip1) in cells expressing both SSTR2 and SSTR5 compared with SSTR2 alone. Furthermore, cell growth inhibition by L-779,976 treatment was markedly extended in coexpressing cells. Trafficking of SSTR2 is also affected upon heterodimerization, an attribute corresponding to modifications in beta-arrestin association kinetics. Activation of SSTR2 results in the recruitment and stable association of beta-arrestin, followed by receptor internalization and intracellular receptor pooling. In contrast, heterodimerization increases the recycling rate of internalized SSTR2 by destabilizing its interaction with beta-arrestin. Given that SST analogs show preferential binding to SSTR2, these data provide a mechanism for their effectiveness in controlling pituitary tumors and the absence of tolerance seen in patients undergoing long-term administration.
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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 | 0.001 |
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".