Effect of Insect Larval Midgut Proteases on the Activity of <i>Bacillus thuringiensis</i> Cry Toxins
Bibliographic record
Abstract
To test the possibility that proteolytic cleavage by midgut juice enzymes could enhance or inhibit the activity of Bacillus thuringiensis insecticidal toxins, once activated, the effects of different toxins on the membrane potential of the epithelial cells of isolated Manduca sexta midguts in the presence and absence of midgut juice were measured. While midgut juice had little effect on the activity of Cry1Aa, Cry1Ac, Cry1Ca, Cry1Ea, and R233A, a mutant of Cry1Aa from which one of the four salt bridges linking domains I and II of the toxin was eliminated, it greatly increased the activity of Cry1Ab. In addition, when tested in the presence of a cocktail of protease inhibitors or when boiled, midgut juice retained almost completely its capacity to enhance Cry1Ab activity, suggesting that proteases were not responsible for the stimulation. On the other hand, in the absence of midgut juice, the cocktail of protease inhibitors also enhanced the activity of Cry1Ab, suggesting that proteolytic cleavage by membrane proteases could render the toxin less effective. The lower toxicity of R233A, despite a similar in vitro pore-forming ability, compared with Cry1Aa, cannot be accounted for by an increased susceptibility to midgut proteases. Although these assays were performed under conditions approaching those found in the larval midgut, the depolarizing activities of the toxins correlated only partially with their toxicities.
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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 | 0.000 |
| 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".