Identification of MHC-Bound Peptides from Dendritic Cells Infected with <i>Salmonella enterica</i> Strain SL1344: Implications for a Nontyphoidal <i>Salmonella</i> Vaccine
Bibliographic record
Abstract
Worldwide Salmonella enterica infections result in substantial morbidity and mortality and are the major cause of infant bacteremia in Sub-Saharan Africa. Diseases caused by Salmonella are treatable with antibiotics, but successful antibiotic treatment has become difficult due to antimicrobial resistance and collateral effects on the microbiome. An effective vaccine together with public health efforts may be a better strategy to control these infections. Protective immunity against Salmonella depends primarily on CD4 T-cell-mediated immune responses; therefore, identifying relevant T-cell antigens is necessary for Salmonella vaccine development. We previously used a dendritic-cell-based immunoproteomics approach in our laboratory to identify T-cell antigens. The testing of these antigens as vaccine candidates against Chlamydia infection in mice yielded positive results. We applied this technology in the present study by infecting murine bone-marrow-derived dendritic cells from C57BL/6 mice with Salmonella enterica strain SL1344, followed by immunoaffinity isolation of MHC class I and II molecules and elution of bound peptides. The sequences of the peptides were identified using tandem mass spectrometry. We identified 87 MHC class-II- and 23 MHC class-I-binding Salmonella -derived peptides. Four of the 12 highest scoring class-II-binding Salmonella peptides stimulated IFN-γ production by CD4 + T cells from the spleens of mice with persistent Salmonella infection. We conclude that antigens identified by MHC immunoproteomics will be useful for Salmonella immunobiology studies and are potential Salmonella vaccine candidates. Data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the data set identifier PXD004451.
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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) | 0.000 | 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".