Identification of MHC bound peptides from murine bone marrow derived dendritic cells infected with <i>Salmonella</i> (VAC9P.1108)
Bibliographic record
Abstract
Abstract Salmonella infections cause a spectrum of diseases ranging from self-limiting diarrhea to life-threatening systemic disease. Diseases caused by Salmonella are treatable with antibiotics but successful antibiotic treatment has become increasingly difficult due to antimicrobial resistance. An effective vaccine is therefore the best strategy to reduce these diseases. A clear understanding of host immunity to Salmonella is essential to aid in the development of a vaccine. Protective immunity against Salmonella depends on a wide range of innate and adaptive immune mechanisms and T cell-mediated immune responses are important in the host control of intracellular Salmonella infection. Our laboratory used an immunoproteomics approach to identify Chlamydia T cell antigens that exhibited significant protection against Chlamydia infection in mice when used as vaccines. These results demonstrate that T cell antigens identified by immunoproteomics can be successfully exploited as T cell vaccines against an intracellular pathogen. Recently we generated dendritic cells from bone marrow of C57BL/6 mice and infected with Salmonella entericaSL1344 followed by elution of MHC class I and class II-bound peptides. The sequences of the purified peptides were then identified using tandem mass spectrometry. We identified 87 MHC class II and 23 MHC class I Salmonella derived peptides. These antigens are of use in Salmonella immunobiology research and as potential Salmonella vaccine candidates.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".