Suppression of Gq and PLC gene expression has a small effect on quantum bumps in vivo in Periplaneta americana
Bibliographic record
Abstract
Visual signal transmission by Drosophila melanogaster photoreceptors is mediated by a Gq protein that activates a phospholipase C (PLC). Mutations and deficiencies in expression of either of these proteins cause severe defects in phototransduction. Here we investigated whether these proteins are also involved in the cockroach, Periplaneta americana, phototransduction by silencing Gq α-subunit (Gqα) and phosphoinositide-specific phospholipase C (PLC) by RNA interference and observing responses to single photons (quantum bumps, QB). We found (1) non-specific decreases in membrane resistance, membrane capacitance and absolute sensitivity in the photoreceptors of both Gqα and PLC knockdowns, and (2) small changes in QB statistics. Despite significant decreases in expressions of Gq and PLC mRNA, the changes in QB properties were surprisingly modest, with mean latencies increasing by ~ 10%, and without significant decrease in their amplitudes. To better understand our results, we used a mathematical model of the phototransduction cascade. By modifying the Gq and PLC abundances, and diffusion rates for Gq, we found that QB latencies and amplitudes deteriorated noticeably only after large decreases in the protein levels, especially when Gq diffusion was slow. Also, reduction in Gq but not PLC lowered quantum efficiency. These results suggest that expression of the proteins may be redundant.
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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) | 0.001 | 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 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".