Differential Effects of pH on the Pore-Forming Properties of <i>Bacillus thuringiensis</i> Insecticidal Crystal Toxins
Bibliographic record
Abstract
The effect of pH on the pore-forming ability of two Bacillus thuringiensis toxins, Cry1Ac and Cry1C, was examined with midgut brush border membrane vesicles isolated from the tobacco hornworm, Manduca sexta, and a light-scattering assay. In the presence of Cry1Ac, membrane permeability remained high over the entire pH range tested (6.5 to 10.5) for KCl and tetramethylammonium chloride, but was much lower at pH 6.5 than at higher pHs for potassium gluconate, sucrose, and raffinose. On the other hand, the Cry1C-induced permeability to all substrates tested was much higher at pH 6.5, 7.5, and 8.5 than at pH 9.5 and 10.5. These results indicate that the pores formed by Cry1Ac are significantly smaller at pH 6.5 than under alkaline conditions, whereas the pore-forming ability of Cry1C decreases sharply above pH 8.5. The reduced activity of Cry1C at high pH correlates well with the fact that its toxicity for M. sexta is considerably weaker than that of Cry1Aa, Cry1Ab, and Cry1Ac. However, Cry1E, despite having a toxicity comparable to that of Cry1C, formed channels as efficiently as the Cry1A toxins at pH 10.5. These results strongly suggest that although pH can influence toxin activity, additional factors also modulate toxin potency in the insect midgut.
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 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 | 0.001 |
| 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 | 0.000 |
| 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".