HLA-C downregulation by HIV-1 adapts to host HLA genotype
Bibliographic record
Abstract
HIV-1 can downregulate HLA-C on infected cells, using the viral protein Vpu, and the magnitude of this downregulation varies widely between primary HIV-1 variants. The selection pressures that result in viral downregulation of HLA-C in some individuals, but preservation of surface HLA-C in others are not clear. To better understand viral immune evasion targeting HLA-C, we have characterized HLA-C downregulation by a range of primary HIV-1 viruses. 128 replication competent viral isolates from 19 individuals with effective anti-retroviral therapy, show that a substantial minority of individuals harbor latent reservoir virus which strongly downregulates HLA-C. Untreated infections display no change in HLA-C downregulation during the first 6 months of infection, but variation between viral quasispecies can be detected in chronic infection. Vpu molecules cloned from plasma of 195 treatment naïve individuals in chronic infection demonstrate that downregulation of HLA-C adapts to host HLA genotype. HLA-C alleles differ in the pressure they exert for downregulation, and individuals with higher levels of HLA-C expression favor greater viral downregulation of HLA-C. Studies of primary and mutant molecules identify 5 residues in the transmembrane region of Vpu, and 4 residues in the transmembrane domain of HLA-C, which determine interactions between Vpu and HLA. The observed adaptation of Vpu-mediated downregulation to host genotype indicates that HLA-C alleles differ in likelihood of mediating a CTL response that is subverted by viral downregulation, and that preservation of HLA-C expression is favored in the absence of these responses. Finding that latent reservoir viruses can downregulate HLA-C could have implications for HIV-1 cure therapy approaches in some individuals.
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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 | 0.000 | 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) | 0.002 | 0.044 |
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; both teacher heads agree on what is shown here.
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".