Structural Determinants of 5-Ht1a Receptor Interaction With Gαi Subunits
Bibliographic record
Abstract
The 5-hydroxytryptamine (5-HT) system modulates numerous physiological and behavioural processes, and dysfunction within this system underlies many behavioural disorders, such as major depression. The 5-HT1A receptor is the primary somatodendritic autoreceptor that controls the firing rate of 5-HT neurons, but is also coupled to numerous signalling pathways. An understanding of 5-HT1A receptor signalling may lead to the development of antidepressant drugs that selectively target therapeutic pathways in treating depression. The 5-HT1A receptor is coupled to inhibitory G-proteins via its intracellular loops 2 and 3. Point mutations within these loops selectively uncouple receptor signalling pathways. In this thesis, I addressed whether mutant receptors’ uncoupling from signalling pathways is associated with alteration in G-protein interaction and coupling. Using bioluminescence resonance energy transfer (BRET) to monitor receptor-G-protein interactions, we show that both wild-type and mutant receptors demonstrate a saturable interaction with Gαi protein in unstimulated conditions. Addition of 5-HT increased the BRET signal for the wild-type 5-HT1A receptor, and this increase was blocked by a 5-HT1A receptor antagonist and G-protein blocker (pertussis toxin). Mutant receptors that were deficient in Gαi signalling, but not those that still signalled to Gαi, failed to respond to receptor activation with increased receptor-Gαi interaction. Pull down studies verified the basal and agonist-induced interaction of 5-HT1A receptors with Gαi proteins. In conclusion, we have shown that the 5-HT1A receptor interacts with Gαi consistent with a pre-coupled model and that 5-HT-induced activation enhances this interaction and requires specific residues in the intracellular loops.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".