Early pandemic HIV-1 integration site preferences differ across anatomical sites
Bibliographic record
Abstract
HIV-1 persists in the body even when treatment suppresses viral replication. This persistence is due in part to the virus integrating into the DNA of infected cells. While it is known that HIV-1 can integrate into many different tissues, it remains unclear whether integration patterns differ across anatomical sites. This study investigated how the location and characteristics of HIV-1 integration sites vary across distinct tissues in people living with HIV-1 subtype B during the early years of the pandemic, before modern treatment was widely available. Integration site data were obtained from matched samples from the esophagus, blood, stomach, duodenum, and colon, and from unmatched brain tissue. We evaluated how frequently the virus integrated near different genomic features, including gene regions, repetitive elements, and predicted DNA structures, and compared integration patterns across tissues and individuals. We show that integration site patterns differ by tissue. In brain tissue, HIV integrates less frequently into genes and more frequently into specific repetitive elements and accessible regions of DNA. We also find that integration near unusual DNA shapes varies by tissue, and that certain integration hotspots are shared while others are unique. Genes involved in HIV-1-related diseases are frequently targeted across tissues. This study reveals that HIV-1 integration patterns are shaped by the tissue environment. These findings suggest that the long-term persistence of HIV-1 depends in part on tissue-specific integration site features, with potential implications for disease risk and treatment strategies. Kohio et al. map HIV-1 integration sites across multiple tissues from early infections. The results identify brain-specific patterns and tissue-dependent preferences that may influence HIV persistence and disease risk. HIV can stay in the body for life by hiding inside the DNA of infected cells. This makes it hard to completely remove the virus, even with strong treatment. In this study, researchers explored whether HIV hides in different ways depending on where in the body the infected cells are found. They examined tissues from several parts of the body from people living with HIV before modern treatments were available. The study found that HIV inserts its genetic material in different spots depending on the tissue. For example, in the brain, the virus avoids genes and hides in less active parts of the DNA. These findings may help scientists understand why HIV acts differently in different areas of the body, which could improve future treatments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".