Gene expression differences in differentially methylated sites associated with HIV status and cocaine use
Bibliographic record
Abstract
Abstract Background Epigenome studies of human HIV-1 (HIV) in whole blood have uncovered a growing list of differentially methylated genes associated with either HIV acquisition, disease progression, or both. Cocaine use is associated with increased disease severity, and methylation changes in some of the HIV-associated genes mediate this effect. Many of these genes are critical players in innate immune response, including both regulators and targets of interferon-alpha and NF-kB activation. However, no study to date has evaluated the gene expression dynamics for these genes in the context of HIV. Methods Targeted gene expression analyses were performed on 588 people who used illicit drugs within a harmonized cohort comprised of the Vancouver People Who Inject Drugs Study (VPWIDS) using RNAseq in whole blood, including 227 people living with HIV (PLWH). Eighteen genes were selected from six recent epigenome-wide association studies to test for differential expression by HIV status. Both gene-level and transcript-level expression changes were estimated using negative binomial regression models. Results Nine of the 18 target genes exhibited significant upregulation in PLWH after multiple hypothesis testing correction: EPSTI1, IFI44L, IFIT3, MX1, NLRC5, PARP9, PLSCR1, RIN2 , and RSAD2 . Transcript-level analysis detected additional upregulation of isoforms for genes CD44, RASSF3 , and TAP1 . Stratified analysis by cocaine use revealed MX1 and RSAD2 to be exclusively upregulated among PLWH who recently used cocaine. Pathway analysis identified significant dysregulation in the interferon alpha/beta signaling pathway. Conclusions We confirm the dysregulation of genes previously reported to have differential methylation among PLWH. Results from this study support the model of epigenetic changes altering gene expression for key immune genes such as NLRC5 and MX1 , and demonstrate systemic dysregulation of genes involved in innate immune function.
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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 | 0.001 | 0.001 |
| 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".