Selective Antagonism of Receptor Signaling Using Antisense RNA to Deplete G-Protein Subunits
Bibliographic record
Abstract
The molecular identification and characterization of the components of receptor-signaling pathways has revealed a striking redundancy and diversity of signaling elements. For example, G protein-coupled receptors bind to a diversity of ligands, ranging from classical low-molecular-weight monoammes like serotonin (5HT) or dopamine, to large glycoproteins such as gonadotropins ( 1 ). Within a given receptor family, multiple subtypes of receptors have been identified: for example, the serotonin-receptor family comprises over 15 distinct receptors ( 2 ). An analogous multiplicity of subtypes extsts within the families of G proteins ( 3 , 4 ) and effecters, such as phospholipases, adenylyl cyclases, protein kinases, and ion channels ( 5 – 9 ). Indeed, low-stringency cDNA-screening techmques have led to the identification of homologs of unknown function, such as orphan receptors ( 10 ). Biochemical characterization of purified proteins in vitro, or by overexpression of then cDNAs in transfected cell lines has been instrumental in defining the properties of these signal-transduction elements. However, these approaches may distort the interactions that occur in situ because of abnormally high expression of the various signaling components and nonphysiological optimization of assay conditions. Pharmacological approaches have been very useful in defining the physiological roles of cloned receptors, but are limited by the availability of specific receptor agonists and antagonists. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".