IL-10 Regulation by HIV-Tat in Primary Human Monocytic Cells: Involvement of Calmodulin/Calmodulin-Dependent Protein Kinase-Activated p38 MAPK and Sp-1 and CREB-1 Transcription Factors
Bibliographic record
Abstract
The anti-inflammatory cytokine, IL-10 plays an important role in HIV immunopathogenesis. The HIV accessory protein, Tat is not only critical for viral replication, but affects the host immune system by influencing cytokine production including IL-10. During HIV infection, IL-10 production by monocytic cells is up-regulated, representing a critical pathway by which HIV may induce immunodeficiency. Herein, we show that extracellular Tat-induced IL-10 expression in normal human monocytes. To understand the signaling pathways underlying HIV-Tat induced IL-10 transcription, we investigated the involvement of MAPK as well as calcium signaling and the downstream transcription factor(s). Our results suggest that Tat-induced calcium influx regulated IL-10 transcription in monocytic cells. The experiments designed to further understand the molecules involved in the calcium signaling suggested that calmodulin and calmodulin-dependent protein kinase-II (CaMK-II)-activated p38 MAPK played a role in extracellular Tat-induced IL-10 expression in primary human monocytes. Furthermore, Tat-induced IL-10 expression was regulated by p38 MAPK- and CaMK II-activated CREB-1 as well as Sp-1 transcription factors. Taken together, our results suggest that extracellular HIV-Tat induced IL-10 transcription in primary human monocytes is regulated by CREB-1 and Sp-1 transcription factors through the activation of calmodulin/CaMK-II-dependent p38 MAPK.
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 | 0.001 |
| 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".