Specialized DNA Structures Act as Genomic Beacons for Integration by Evolutionarily Diverse Retroviruses
Bibliographic record
Abstract
Retroviral integration site targeting is not random and plays a critical role in expression and long-term survival of the integrated provirus. To better understand the genomic environment surrounding retroviral integration sites, we performed a meta-analysis of previously published integration site data from evolutionarily diverse retroviruses, including new experimental data from HIV-1 subtypes A, B, C and D. We show here that evolutionarily divergent retroviruses exhibit distinct integration site profiles with strong preferences for integration near non-canonical B-form DNA (non-B DNA). We also show that in vivo-derived HIV-1 integration sites are significantly more enriched in transcriptionally silent regions and transcription-silencing non-B DNA features of the genome compared to in vitro-derived HIV-1 integration sites. Integration sites from individuals infected with HIV-1 subtype A, B, C or D viruses exhibited different preferences for common genomic and non-B DNA features. In addition, we identified several integration site hotspots shared between different HIV-1 subtypes, all of which were located in the non-B DNA feature slipped DNA. Together, these data show that although evolutionarily divergent retroviruses exhibit distinct integration site profiles, they all target non-B DNA for integration. These findings provide new insight into how retroviruses integrate into genomes for long-term survival.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".