Human Endogenous Retrovirus K(HML-2) Gag and Env specific T-cell responses are not detected in HTLV-I-infected subjects using standard peptide screening methods
Bibliographic record
Abstract
BACKGROUND: An estimated 10-20 million individuals are infected with the retrovirus human T-cell leukemia virus type 1 (HTLV-1). While the majority of these individuals remain asymptomatic, 0.3-4% develop a neurodegenerative inflammatory disease, termed HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP). HAM/TSP results in the progressive demyelination of the central nervous system and is a differential diagnosis of multiple sclerosis (MS). The etiology of HAM/TSP is unclear, but evidence points to a role for CNS-inflitrating T-cells in pathogenesis. Recently, the HTLV-1-Tax protein has been shown to induce transcription of the human endogenous retrovirus (HERV) families W, H and K. Intriguingly, numerous studies have implicated these same HERV families in MS, though this association remains controversial. RESULTS: Here, we explore the hypothesis that HTLV-1-infection results in the induction of HERV antigen expression and the elicitation of HERV-specific T-cells responses which, in turn, may be reactive against neurons and other tissues. PBMC from 15 HTLV-1-infected subjects, 5 of whom presented with HAM/TSP, were comprehensively screened for T-cell responses to overlapping peptides spanning HERV-K(HML-2) Gag and Env. In addition, we screened for responses to peptides derived from diverse HERV families, selected based on predicted binding to predicted optimal epitopes. We observed a lack of responses to each of these peptide sets. CONCLUSIONS: Thus, although the limited scope of our screening prevents us from conclusively disproving our hypothesis, the current study does not provide data supporting a role for HERV-specific T-cell responses in HTLV-1 associated immunopathology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".