TLR2 and TLR4 signaling shapes specific antibody responses to <i>Salmonella typhi</i> antigens
Bibliographic record
Abstract
TLR directly induce innate immune responses by sensing a variety of microbial components and are critical for the fine-tuning of subsequent adaptive immune responses. However, their impact and mechanism of action on antibody responses against bacterial antigens are not yet fully understood. Salmonella enterica serovar Typhi (S. typhi) porins have been characterized as inducers of long-lasting specific antibody responses in mice. In this report, we show that immunization of TLR4-deficient (TLR4(-/-)), myeloid differentiating gene 88-deficient and Toll/IL-R domain-containing adaptor-inducing IFN-beta-deficient mice with S. typhi porins led to significantly reduced B-cell responses. TLR2(-/-) mice, as well, showed reduced IgG titers with a more pronounced impairment in the production of IgG3 anti-porins antibodies. Adoptive transfer of TLR2(-/-)- or TLR4(-/-)-B cells into B-cell-deficient mice revealed a direct effect of TLR4 on B cells for the primary IgM response, whereas stimulation of B cells via TLR2 was important for IgG production. Furthermore, S. typhi porins were found to efficiently elicit maturation of CD11c(+) conventional DC. Taken together, S. typhi porins represent not only a suitable B-cell antigen for vaccination, but exhibit potent TLR-dependent stimulatory functions on B cells and DC, which help to further enhance and shape the antibody response.
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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) | 0.002 | 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".