An immunoinformatics study reveals a new BoLA-DR-restricted CD4+ T cell epitopes on the Gag protein of bovine leukemia virus
Bibliographic record
Abstract
Bovine leukemia virus (BLV) is the causative agent of enzootic bovine leucosis (EBL), which has been reported worldwide. The expression of viral structural proteins: surface glycoprotein (gp51) and three core proteins - p15 (matrix), p24 (capsid), and p12 (nucleocapsid) induce a strong humoral and cellular immune response at first step of infection. CD4+ T-cell activation is generally induced by bovine leukocyte antigen (BoLA) region- positive antigen-presenting cells (APC) after processing of an exogenous viral antigen. Limited data are available on the BLV epitopes from the core proteins recognized by CD4+ T-cells. Thus, immunoinformatic analysis of Gag sequences obtained from 125 BLV isolates from Poland, Canada, Pakistan, Kazakhstan, Moldova and United States was performed to identify the presence of BoLA-DRB3 restricted CD4+ T-cell epitopes. The 379 15-mer overlapping peptides spanning the entire Gag sequence were run in BoLA-DRB3 allele-binding regions using a BoLA-DRB- peptide binding affinity prediction algorithm. The analysis identified 22 CD4+ T-cell peptide epitopes of variable length ranging from 17 to 22 amino acids. The predicted epitopes interacted with 73 different BoLA-DRB3 alleles found in BLV-infected cattle. Importantly, two epitopes were found to be linked with high proviral load in PBMC. A majority of dominant and subdominant epitopes showed high conservation across different viral strains, and therefore could be attractive targets for vaccine development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".