Regulation of LRRK2 promoter activity and gene expression by Sp1
Bibliographic record
Abstract
BACKGROUND: The dopaminergic neurodegeneration in the nigrostriatal pathway is a prominent neuropathological feature of Parkinson's disease (PD). Mutations in various genes have been linked to familial PD, and leucine-rich repeat kinase 2 (LRRK2) gene is one of them. LRRK2 is a large complex protein, belonging to the ROCO family of proteins. Recent studies suggest that the level of LRRK2 protein is one of the contributing factors to PD pathogenesis. However, it remains elusive how LRRK2 is regulated at the transcriptional and translational level. RESULTS: In this study, we cloned a 1738 bp 5'-flanking region of the human LRRK2 gene. The transcriptional start site (TSS) was located to 135 bp upstream of translational start site and the fragment -118 to +133 bp had the minimum promoter activity required for transcription. There were two functional Sp1- responsive elements on the human LRRK2 gene promoter revealed by electrophoretic mobility shift assay (EMSA). Sp1 overexpression promoted LRRK2 transcription and translation in the cellular model. On the contrary, application of mithramycin A inhibited LRRK2 transcriptional and translational activities. CONCLUSION: This is the first study indicating that Sp1 signaling plays an important role in the regulation of human LRRK2 gene expression. It suggests that controlling LRRK2 level by manipulating Sp1 signaling may be beneficial to attenuate PD-related neuropathology.
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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) | 0.002 | 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".