Unique features of HLA-mediated HIV evolution in a Mexican cohort: a comparative study
Bibliographic record
Abstract
BACKGROUND: Mounting evidence indicates that HLA-mediated HIV evolution follows highly stereotypic pathways that result in HLA-associated footprints in HIV at the population level. However, it is not known whether characteristic HLA frequency distributions in different populations have resulted in additional unique footprints. METHODS: The phylogenetic dependency network model was applied to assess HLA-mediated evolution in datasets of HIV pol sequences from free plasma viruses and peripheral blood mononuclear cell (PBMC)-integrated proviruses in an immunogenetically unique cohort of Mexican individuals. Our data were compared with data from the IHAC cohort, a large multi-center cohort of individuals from Canada, Australia and the USA. RESULTS: Forty three different HLA-HIV codon associations representing 30 HLA-HIV codon pairs were observed in the Mexican cohort (q < 0.2). Strikingly, 23 (53%) of these associations differed from those observed in the well-powered IHAC cohort, strongly suggesting the existence of unique characteristics in HLA-mediated HIV evolution in the Mexican cohort. Furthermore, 17 of the 23 novel associations involved HLA alleles whose frequencies were not significantly different from those in IHAC, suggesting that their detection was not due to increased statistical power but to differences in patterns of epitope targeting. Interestingly, the consensus differed in four positions between the two cohorts and three of these positions could be explained by HLA-associated selection. Additionally, different HLA-HIV codon associations were seen when comparing HLA-mediated selection in plasma viruses and PBMC archived proviruses at the population level, with a significantly lower number of associations in the proviral dataset. CONCLUSION: Our data support universal HLA-mediated HIV evolution at the population level, resulting in detectable HLA-associated footprints in the circulating virus. However, it also strongly suggests that unique genetic backgrounds in different HIV-infected populations may influence HIV evolution in a particular direction as particular HLA-HIV codon associations are determined by specific HLA frequency distributions. Our analysis also suggests a dynamic HLA-associated evolution in HIV with fewer HLA-HIV codon associations observed in the proviral compartment, which is likely enriched in early archived HIV sequences, compared to the plasma virus compartment. These results highlight the importance of comparative HIV evolutionary studies in immunologically different populations worldwide.
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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) | 0.000 | 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".