Phylogenetic Analysis of Population-Based and Deep Sequencing Data to Identify Coevolving Sites in the nef Gene of HIV-1
Bibliographic record
Abstract
Rapidly evolving viruses such as HIV-1 display extensive sequence variation in response to host-specific selection, while simultaneously maintaining functions that are critical to replication and infectivity. This apparent conflict between diversifying and purifying selection may be resolved by an abundance of epistatic interactions such that the same functional requirements can be met by highly divergent sequences. We investigate this hypothesis by conducting an extensive characterization of sequence variation in the HIV-1 nef gene that encodes a highly variable multifunctional protein. Population-based sequences were obtained from 686 patients enrolled in the HOMER cohort in British Columbia, Canada, from which the distribution of nonsynonymous substitutions in the phylogeny was reconstructed by maximum likelihood. We used a phylogenetic comparative method on these data to identify putative epistatic interactions between residues. Two interactions (Y120/Q125 and N157/S169) were chosen to further investigate within-host evolution using HIV-1 RNA extractions from plasma samples from eight patients. Clonal sequencing confirmed strong linkage between polymorphisms at these sites in every case. We used massively parallel pyrosequencing (MPP) to reconstruct within-host evolution in these patients. Experimental error associated with MPP was quantified by performing replicates at two different stages of the protocol, which were pooled prior to analysis to reduce this source of variation. Phylogenetic reconstruction from these data revealed correlated substitutions at Y120/Q125 or N157/S169 repeated across multiple lineages in every host, indicating convergent within-host evolution shaped by epistatic interactions.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".