HIV proviral burden, genetic diversity, and dynamics in viremic controllers who subsequently initiated suppressive antiretroviral therapy
Bibliographic record
Abstract
Abstract Curing HIV will require eliminating the reservoir of integrated, replication-competent proviruses that persist despite antiretroviral therapy (ART). Understanding the burden, genetic diversity and longevity of persisting proviruses in diverse individuals with HIV is critical to this goal, but these characteristics remain understudied in some groups. Among them are viremic controllers, individuals who naturally suppress HIV to low levels but for whom therapy is nevertheless recommended. We reconstructed within-host HIV evolutionary histories from longitudinal single-genome amplified viral sequences in four viremic controllers who eventually initiated ART, and used this information to characterize the age and diversity of proviruses persisting on therapy. We further leveraged these within-host proviral age distributions to estimate rates of proviral turnover prior to ART. This is an important yet understudied metric, since pre-ART proviral turnover dictates reservoir composition at ART initiation (and thereafter), which is when curative interventions, once developed, would be administered. Despite natural viremic control, all participants displayed significant within-host HIV evolution pre-therapy, where overall on-ART proviral burden and diversity broadly reflected the extent of viral replication and diversity pre-ART. Consistent with recent studies of non-controllers, the proviral pools of two participants were skewed towards sequences that integrated near ART initiation, suggesting dynamic proviral turnover during untreated infection. In contrast, proviruses recovered from the two other participants dated to time-points that were more evenly spread throughout infection, suggesting slow or negligible proviral decay following deposition. HIV cure strategies will need to overcome within-host proviral diversity, even in individuals who naturally controlled HIV replication before therapy. Importance HIV therapy is life-long because integrated, replication-competent viral copies persist within long-lived cells. To cure HIV, we need to understand when these viral reservoirs form, how large and genetically diverse they are, and how long they endure. Elite controllers, individuals who naturally suppress HIV to undetectable levels, are being intensely studied as models of HIV remission, but viremic controllers, individuals who naturally suppress HIV to low levels, remain understudied even though they too may hold valuable insights. We combined phylogenetics and mathematical modeling to reconstruct proviral seeding and decay from infection to therapy-mediated suppression in four viremic controllers. We recovered diverse proviruses persisting during therapy that broadly reflected HIV’s within-host evolutionary history, where the estimated half-lives of the persistent proviral pool during untreated infection ranged from <1 year to negligible. Cure strategies will need to contend with proviral diversity and between-host heterogeneity, even in individuals who naturally control HIV.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".