Gene dysregulation among virally suppressed people living with HIV links to non-AIDS defining cancer pathways
Bibliographic record
Abstract
Abstract Combination antiretroviral therapy (ART) has changed the landscape of the HIV epidemic by providing an effective means for viral suppression to people living with HIV (PLWH). Understanding living with HIV as a chronic disease requires an improved understanding of how HIV and/or ART impacts susceptibility to and development of co-occurring conditions. Genome-wide gene expression (transcriptome) differences provide a key view into biological dysregulation associated with living with HIV. Here we present the first whole blood transcriptome-wide study comparing gene expression profiles between virally suppressed PLWH and HIV negative individuals (N=555). We identify 566 genes and 5 immune cell types with differential proportions by HIV status, which were significantly enriched for immune function and cancer pathways. Leveraging quantitative trait loci (QTL) for these HIV status-associated genes, partitioned heritability, and colocalization analyses, we observed limited genetic drivers of these relationships. Our findings suggest that gene dysregulation does not return to a pre-infection state for virally suppressed PLWH, and that persistent gene dysregulation is broadly associated with immune function and cancer pathways, highlighting potential biological drivers for these causes of excess mortality and targets for pharmacological preventative treatment among PLWH.
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 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) | 0.003 | 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".