Quantifying the clonality and dynamics of the within-host HIV-1 latent reservoir
Bibliographic record
Abstract
Among people living with human immunodeficiency virus type 1 (HIV-1), the long-term persistence of a population of cells carrying transcriptionally silent integrated viral DNA (provirus) remains the primary barrier to developing an effective cure. Ongoing cell division via proliferation is generally considered to be the driving force behind the persistence of this latent HIV-1 reservoir. The contribution of this mechanism (clonal expansion) is supported by the observation that proviral sequences sampled from the reservoir are often identical. This outcome is quantified as the 'clonality' of the sample population, e.g. the fraction of provirus sequences observed more than once. However, clonality as a quantitative measure is inconsistently defined and its statistical properties are not well understood. In this Reflections article, we use mathematical and phylogenetic frameworks to formally examine the inherent problems of using clonality to characterize the dynamics and proviral composition of the reservoir. We describe how clonality is not adequate for this task due to the inherent complexity of how infected cells are 'labeled' by proviral sequences-the outcome of a sampling process from the evolutionary history of active viral replication before treatment-as well as variation in cell birth and death rates among lineages and over time. Lastly, we outline potential directions in statistical and phylogenetic research to address these issues.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".