Gene Expression Profiling of Host Response in Models of Acute HIV Infection
Bibliographic record
Abstract
HIV infection is characterized by a host response composed of adaptive and innate immunity that partially limits viral replication; however, it ultimately fails in eradicating the virus. To model host gene expression during acute HIV infection, we infected cynomolgus macaques with the SIV/HIV-1 chimeric virus, SHIV89.6P, and profiled gene expression in peripheral blood over a 5-wk period using a high density cDNA microarray. We demonstrate that viral challenge induced a widespread suppression of genes regulating innate immunity, including the LPS receptors, CD14 and TLR4. An overexpression of 16 IFN-stimulated genes was also observed in response to infection; however, it did not correlate with control over viral titers. A statistical analysis of the dataset identified 10 genes regulating apoptosis with differential expression during the first 2 wk of infection (p < 0.004). Quantitative real-time PCR verified transcriptional increases in IFN-alpha-inducible genes and decreases in genes regulating innate immunity. Therefore, the persistence of high viral loads despite an extensive IFN response suggests that HIV can resist in vivo IFN treatment despite published reports of in vitro efficacy. The transcriptional suppression of genes regulating innate immunity may allow HIV to evade acute host responses and establish a chronic infection and may reduce innate host defense against opportunistic infections.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".