Hck SH3 domain-dependent abrogation of Nef-induced class 1 MHC down-regulation
Bibliographic record
Abstract
The ability of specific virally encoded proteins to down-regulate MHC class I molecules may enable infected cells to elude killing by CTL. In the case of HIV-1, Nef appears to be responsible for this effect. Thus, interfering with Nef-induced MHC class I down-regulation would be a strategy for increasing HIV-1-specific CTL activity, particularly towards long-lived T cell populations such as memory T cells that harbor replication-competent virus. Here, using two Nef-expressing human cell model systems, we show that a dominant-negative mutant derived from the Hck protein-tyrosine kinase, composed of the Hck N-terminal region, as well as the SH3 and SH2 domains, was able to inhibit Nef-induced MHC class I molecule down-regulation. This effect was SH3 domain dependent as it was not evident when the cells were transfected with DN-Hck-W93F, an SH3 domain mutant. The inhibitory effect of dominant-negative-Hck (DN-Hck) on Nef-induced class I down-regulation suggests that this Nef-mediated effect requires an interaction between the Nef polyproline site and an SH3-containing cellular protein that is involved in MHC class I molecule turnover. Interfering with the function of the Nef SH3 binding site in this way represents a strategy for assisting the host CTL response to clear HIV-1-infected cells.
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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 | 0.000 |
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".