Role of the Cell Wall Microenvironment in Expression of a Heterologous SpaP-S1 Fusion Protein by <i>Streptococcus gordonii</i>
Bibliographic record
Abstract
The charge density in the cell wall microenvironment of Gram-positive bacteria is believed to influence the expression of heterologous proteins. To test this, the expression of a SpaP-S1 fusion protein, consisting of the surface protein SpaP of Streptococcus mutans and a pertussis toxin S1 fragment, was studied in the live vaccine candidate bacterium Streptococcus gordonii. Results showed that the parent strain PM14 expressed very low levels of SpaP-S1. By comparison, the dlt mutant strain, which has a mutation in the dlt operon preventing d-alanylation of the cell wall lipoteichoic acids, and another mutant strain, OB219(pPM14), which lacks the LPXTG major surface proteins SspA and SspB, expressed more SpaP-S1 than the parent. Both the dlt mutant and the OB219(pPM14) strain had a more negatively charged cell surface than PM14, suggesting that the negative charged cell wall played a role in the increase in SpaP-S1 production. Accordingly, the addition of Ca(2+), Mg(2+), and K(+), presumably increasing the positive charge of the cell wall, led to a reduction in SpaP-S1 production, while the addition of bicarbonate resulted in an increase in SpaP-S1 production. The level of SpaP-S1 production could be correlated with the level of PrsA, a peptidyl-prolyl cis/trans isomerase, in the cells. PrsA expression appears to be regulated by the cell envelope stress two-component regulatory system LiaSR. The results collectively indicate that the charge density of the cell wall microenvironment can modulate heterologous SpaP-S1 protein expression in S. gordonii and that this modulation is mediated by the level of PrsA, whose expression is regulated by the LiaSR two-component regulatory system.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".