Disparate Regulation of LPS-Induced MAPK Signaling and IL-12p40 Expression Between Different Myeloid Cell Types with and without HIV Infection
Bibliographic record
Abstract
Studies from our laboratory and those of others have implicated lipopolysaccharide (LPS)-induced MAPK signaling as an important pathway in the regulation of cytokine expression. In this article, the regulation of IL-12 expression in two different human myeloid cell populations was evaluated. In primary monocytes, the inhibition of p38 enhanced IL-12 production, whereas it downregulated IL-12 production in THP-1 cells. The role of MAPK signaling in transcription factor binding to the IL-12p40 promoter was subsequently determined. In primary monocytes, ERK and p38 inhibition increased binding of AP-1 and Sp1, respectively, to the IL-12p40 promoter, while JNK inhibition increased NF-kappaB, AP-1, and Sp1 binding. In THP-1 cells, p38, ERK, and JNK inhibition increased NF-kappaB and Sp1 binding to the IL-12p40 promoter, while inhibiting AP-1 binding. In monocytes, mutations in the NF-kappaB, AP-1, Sp1, or Ets-2 binding sites resulted in complete inhibition of LPS-stimulated IL-12p40 promoter activity using a luciferase-based assay. In contrast, promoter activity was abrogated in THP-1 cells only when the Sp1 or Ets-2 binding sites were mutated. Transcription factor binding to the IL-12p40 promoter following in-vitro HIV infection demonstrated several differences between monocytes and THP-1 cells. Infection with HIV produced an increase in NF-kappaB, AP-1, and Sp1 binding in primary monocytes. In contrast, binding of Ets-2 was dramatically impaired following HIV infection of monocytes, but was unaffected in THP-1 cells. These data clearly show that although LPS induces IL-12p40 expression in primary monocytes and THP-1 cells, the signaling pathways involved and the effect of HIV infection differ and can have disparate effects in these two cell types.
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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 | 0.000 |
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".