G Protein-Coupled Receptors Disrupted in Human Genetic Disease
Bibliographic record
Abstract
Genetic variation in G protein-coupled receptors (GPCRs) results in the disruption of GPCR function in a wide variety of human genetic diseases. In vitro strategies have been used to elucidate the molecular pathologies that underlie naturally occurring GPCR mutations. Various degrees of inactive, overactive, or constitutively active receptors have been identified. These mutations often alter ligand binding, G protein coupling, receptor desensitization, and receptor recycling. The role of inactivating and activating calcium-sensing receptor (CASR) mutations is discussed with respect to familial hypocalciuric hypercalemia (FHH) and autosomal dominant hypocalemia (ADH). Among ADH mutations, those associated with tonic-clonic seizures are discussed. Other receptors discussed include rhodopsin, thyrotropin, parathyroid hormone, melanocortin, follicle-stimulating hormone, luteinizing hormone, gonadotropin-releasing hormone (GnRHR), adrenocorticotropic hormone, vasopressin, endothelin-beta, purinergic, and the G protein associated with asthma (GPRA). Diseases caused by mutations that disrupt GPCR function are significant because they might be selectively targeted by drugs that rescue altered receptors. Examples of drug development based on targeting GPCRs mutated in disease include the calcimimetics used to compensate for some CASR mutations, obesity therapeutics targeting melanocortin receptors, interventions that alter GnRHR loss from the cell surface in idiopathic hypogonadotropic hypogonadism and novel drugs that might rescue the P2RY12 receptor in a rare bleeding disorder. The discovery of GPRA suggests that drug screens against variant GPCRs may identify novel drugs. This review of the variety of GPCRs that are disrupted in monogenic disease provides the basis for examining the significance of common pharmacogenetic variants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".