Predictable patterns of CTL escape and reversion across host populations\n and viral subtypes in HIV-1 evolution
Bibliographic record
Abstract
The twin processes of viral evolutionary escape and reversion in response to\nhost immune pressure, in particular the cytotoxic T-lymphocyte (CTL) response,\nshape Human Immunodeficiency Virus-1 sequence evolution in infected host\npopulations. The tempo of CTL escape and reversion is known to differ between\nCTL escape variants in a given host population. Here, we ask: are rates of\nescape and reversion comparable across infected host populations? For three\ncohorts taken from three continents, we estimate escape and reversion rates at\n23 escape sites in optimally defined Gag epitopes. We find consistent escape\nrate estimates across the examined cohorts. Reversion rates are also consistent\nbetween a Canadian and South African infected host population. Certain Gag\nescape variants that incur a large replicative fitness cost are known to revert\nrapidly upon transmission. However, the relationship between escape/reversion\nrates and viral replicative capacity across a large number of epitopes has not\nbeen interrogated. We investigate this relationship by examining $in$ $vitro$\nreplicative capacities of viral sequences with minimal variation: point escape\nmutants induced in a lab strain. Remarkably, despite the complexities of\nepistatic effects exemplified by pathways to escape in famous epitopes, and the\ndiversity of both hosts and viruses, CTL escape mutants which escape rapidly\ntend to be those with the highest replicative capacity when applied as a single\npoint mutation. Similarly, mutants inducing the greatest costs to viral\nreplicative capacity tend to revert more quickly. These data suggest that\nescape rates in Gag are consistent across host populations, and that in general\nthese rates are dominated by site specific effects upon viral replicative\ncapacity.\n
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 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".