HIV Downregulates Interferon-Stimulated Genes in Primary Macrophages
Bibliographic record
Abstract
HIV is able to outpace the innate immune response, including that mediated by interferon (IFN), to establish a productive infection. Primary macrophages, however, may be protected from HIV infection by treatment with type I IFN before virus exposure. The ability of HIV to modulate the type I IFN-mediated innate immune response when it encounters a cell that has already been exposed to IFN remains poorly defined. The optimal pretreatment time (12 h) and the most potent HIV-inhibitors (e.g., IFN-α2 and -ω) were identified to investigate the ability of HIV to modulate an established type I IFN response. Gene expression at the level of the entire transcriptome was then compared between primary macrophages treated with type I IFNs, as opposed to treated with IFNs and then infected with HIV. Although HIV was not able to establish a robust infection, the virus was able to downregulate a number of IFN-stimulated genes (ISGs) with a fold change greater than 1.5 (i.e., AXL, IFI27, IFI44, IFI44L, ISG15, OAS1, OAS3, and XAF1). The downregulation of OAS1 by the presence of HIV was confirmed by real-time quantitative polymerase chain reaction. In conclusion, even though HIV replication is significantly inhibited by IFN pretreatment, the virus is able to downregulate the transcription of known antiviral ISGs (e.g., IFI44, ISG15, and OAS1).
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".