Influence of Gag-Protease-Mediated Replication Capacity on Disease Progression in Individuals Recently Infected with HIV-1 Subtype C
Bibliographic record
Abstract
HLA class I-mediated selection of immune escape mutations in functionally important Gag epitopes may partly explain slower disease progression in HIV-1-infected individuals with protective HLA alleles. To investigate the impact of Gag function on disease progression, the replication capacities of viruses encoding Gag-protease from 60 individuals in early HIV-1 subtype C infection were assayed in an HIV-1-inducible green fluorescent protein reporter cell line and were correlated with subsequent disease progression. Replication capacities did not correlate with viral load set points (P = 0.37) but were significantly lower in individuals with below-median viral load set points (P = 0.03), and there was a trend of correlation between lower replication capacities and lower rates of CD4 decline (P = 0.09). Overall, the proportion of host HLA-specific Gag polymorphisms in or adjacent to epitopes was negatively associated with replication capacities (P = 0.04), but host HLA-B-specific polymorphisms were associated with higher viral load set points (P = 0.01). Further, polymorphisms associated with host-specific protective HLA alleles were linked with higher viral load set points (P = 0.03). These data suggest that transmission or early HLA-driven selection of Gag polymorphisms results in reduced early cytotoxic T-lymphocyte (CTL) responses and higher viral load set points. In support of the former, 46% of individuals with nonprotective alleles harbored a Gag polymorphism exclusively associated with a protective HLA allele, indicating a high rate of their transmission in sub-Saharan Africa. Overall, HIV disease progression is likely to be affected by the ability to mount effective Gag CTL responses as well as the replication capacity of the transmitted virus.
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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 | 0.002 |
| 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) | 0.001 | 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".