Protein Kinase Cε Regulation of Translocator Protein (18 kDa)<i>Tspo</i>Gene Expression Is Mediated through a MAPK Pathway Targeting STAT3 and c-Jun Transcription Factors
Bibliographic record
Abstract
Translocator protein TSPO is an 18 kDa protein implicated in numerous cell functions and is highly expressed in secretory and glandular tissues, especially in steroidogenic cells. TSPO expression is altered in pathological conditions such as certain cancers and neurological diseases. In search of the factors regulating Tspo expression, we recently showed that high levels of TSPO in steroidogenic cells may be due to high constitutive expression of protein kinase Cepsilon (PKCepsilon), while phorbol 12-myristate 13-acetate (PMA) activation of PKCepsilon drives inducible TSPO expression in nonsteroidogenic cells, likely through activator protein 1 (AP1). In this study, we aimed to identify the signal transduction pathway through which PKCepsilon regulates Tspo gene expression. The MEK1/2 specific inhibitor U0126, but not NFkappaB inhibitors, reduced basal Tspo promoter activity in TSPO-rich steroidogenic cells (MA-10 Leydig), as well as basal and PMA-induced Tspo promoter levels in TSPO-poor nonsteroidogenic cells (NIH-3T3 fibroblasts). AP1 and signal transducer and activation of transcription 3 (STAT3) have binding sites in the Tspo promoter and are downstream targets of PKCepsilon and MAPK (Raf-1-ERK1/2) pathways. PKCepsilon overexpression induced STAT3 phosphorylation in NIH-3T3 cells, while PKCepsilon knockdown reduced STAT3 and c-Jun phosphorylation in Leydig cells. MEK1/2, ERK2, c-Jun, and STAT3 knockdown reduced Tspo mRNA and protein levels in Leydig cells. Additionally, Raf-1 reduced Tspo mRNA levels in the same cells. MEK1/2, c-Jun, and STAT3 knockdown also reduced basal as well as PMA-induced Tspo mRNA levels in NIH-3T3 cells. Together, these results demonstrate that PKCepsilon regulates Tspo gene expression through a MAPK (Raf-1-MEK1/2-ERK1/2) signal transduction pathway, acting at least in part through c-Jun and STAT3 transcription factors.
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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 | 0.000 |
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".