Early pandemic HIV-1 integration site preferences differ across anatomical reservoirs
Bibliographic record
Abstract
Abstract Despite effective viral suppression with modern antiretroviral therapy (ART), HIV-1 persists in latent reservoirs across multiple tissues. Integration into the host genome is essential for viral persistence, yet the characteristics of these reservoir sites across anatomical locations remain poorly understood. To address this, we analyzed integration sites from matched esophagus, PBL/PBMC, stomach, duodenum, colon, and unmatched brain tissue samples of individuals infected with HIV-1 subtype B. The virus used in this study was from 1993, an early stage of the HIV pandemic, providing insights into integration patterns before extensive ART use. Our analysis examined genomic feature enrichment, proximity to non-B DNA structures, integration hotspots, and site overlap across tissues and individuals. We identified a distinct integration pattern in brain tissue, characterized by reduced gene targeting and increased enrichment in Short Interspersed Nuclear Elements (SINEs) and DNase I hypersensitivity sites (DHS). Tissue-specific preferences for integration near non-B DNA structures were evident, alongside shared and unique hotspots across tissues and individuals. Notably, genes associated with HIV-related diseases were frequent integration targets. These findings underscore the complex interplay between viral integration, host genetics, and tissue-specific factors, highlighting the potential role of integration sites in disease development.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".