Microarray study reveals that HIV-1 induces rapid type-I interferon-dependent p53 mRNA up-regulation in human primary CD4+T cells
Bibliographic record
Abstract
BACKGROUND: Infection with HIV-1 has been shown to alter expression of a large array of host cell genes. However, previous studies aimed at investigating the putative HIV-1-induced modulation of host gene expression have been mostly performed in established human cell lines. To better approximate natural conditions, we monitored gene expression changes in a cell population highly enriched in human primary CD4+ T lymphocytes exposed to HIV-1 using commercial oligonucleotide microarrays from Affymetrix. RESULTS: We report here that HIV-1 influences expression of genes related to many important biological processes such as DNA repair, cellular cycle, RNA metabolism and apoptosis. Notably, expression of the p53 tumor suppressor and genes involved in p53 homeostasis such as GADD34 were up-regulated by HIV-1 at the mRNA level. This observation is distinct from the previously reported p53 phosphorylation and stabilization at the protein level, which precedes HIV-1-induced apoptosis. We present evidence that the HIV-1-mediated increase in p53 gene expression is associated with virus-mediated induction of type-I interferon (i.e. IFN-alpha and IFN-beta). CONCLUSION: These observations have important implications for our understanding of HIV-1 pathogenesis, particularly in respect to the virus-induced depletion of CD4+ T cells.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".