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Record W4414849148 · doi:10.1038/s43856-025-01146-5

Early pandemic HIV-1 integration site preferences differ across anatomical sites

2025· article· en· W4414849148 on OpenAlexafffund
Hinissan P. Kohio, Hannah O. Ajoge, Emile A. Barua, Neel R. Vajaria, Isaac Wu, Macon D. Coleman, Sean K. Tom, Frank van der Meer, M. John Gill, Deirdre L. Church, Paul L. Beck, Christopher Power, Guido van Marle, Stephen D. Barr

Bibliographic record

VenueCommunications Medicine · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsUniversity of AlbertaUniversity of CalgaryWestern University
FundersCIHR Skin Research Training CentreCanadian Institutes of Health ResearchGovernment of Canada
KeywordsPandemicDiseasePersistence (discontinuity)PopulationData integration

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.054
GPT teacher head0.372
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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