Within-host rates of insertion and deletion in the HIV-1 surface envelope glycoprotein
Bibliographic record
Abstract
Abstract Under selection by neutralizing antibodies, the HIV-1 envelope glycoprotein gp120 undergoes rapid evolution within hosts, particularly in regions encoding the five variable loops (V1-V5). Indel polymorphisms are abundant in these loops, where they can facilitate immune escape by modifying the length, composition and glycosylation profile of these structures. Here, we present a comparative analysis of within-host indel rates and characteristics within the variable regions of gp120. We analyzed a total of 3,437 HIV-1 gp120 sequences sampled longitudinally from 29 different individuals using coalescent models in BEAST. Next, we used Historian to reconstruct ancestral sequences from the resulting tree samples, and fit a Poisson generalized linear model to the distribution of indel events to estimate their rates in the five variable loops. Overall, the mean insertion and deletion rates were 1.6 × 10 − 3 and 2.5 × 10 − 3 / nt / year, respectively, with significant variation among loops. Insertions and deletions also followed similar length distributions, except for significantly longer indels in V1 and V4 and shorter indels in V5. Insertions in V1, V2, and V4 tended to create new N-linked glycosylation sites significantly more often than expected by chance, which is consistent with positive selection to alter glycosylation patterns.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".