A <i>Pichia</i> biosensor for high‐throughput analyses of compounds that can influence mosquito behavior
Bibliographic record
Abstract
Mosquitoes utilize their sense of smell to locate prey and feed on their blood. Repellents interfere with the biochemical cascades that detect odors. Consequently, repellants are highly effective and resource-efficient alternatives for controlling the spread of mosquito-borne illnesses. Unfortunately, the discovery of repellents is slow, laborious, and error-prone. To this end, we have taken a giant stride toward improving the speed and accuracy of repellant discovery by constructing a prototypical whole-cell biosensor for accurate detection of mosquito behavior-modifying compounds such as repellants. As a proof-of-concept, we genetically engineered Pichia pastoris to express the olfactory receptor co-receptor (Orco) of Anopheles gambiae mosquitoes. This transmembrane protein behaves like a cationic channel upon activation by stimulatory odorants. When the engineered Pichia cells are cultured in calcium-containing Hank's buffer, induction of the medium with a stimulatory odorant results in an influx of calcium ions into the cells, and the stimulatory effect is quantifiable using the calcium-sequestering fluorescent dye, fluo-4-acetoxymethyl ester. Moreover, the stimulatory effect can be titrated by adjusting either the concentration of calcium ions in the medium or the level of induction of the stimulatory odorant. Subsequent exposure of the activated Pichia cells to a repellant molecule inhibits the stimulatory effect and quenches the fluorescent signal, also in a titratable manner. Significantly, the modular architecture of the biosensor allows easy and efficient expansion of its detection range by co-expressing Orco with other olfactory receptors. The high-throughput assay is also compatible with robotic screening infrastructure, and our development represents a paradigm change for the discovery of mosquito repellants.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".