Direct and indirect regulation of β-glucocerebrosidase by the transcription factors <i>USF2</i> and <i>ONECUT2</i>
Bibliographic record
Abstract
Abstract Mutations in the GBA gene, which encodes the lysosomal enzyme β-glucocerebrosidase (GCase), are the most prevalent genetic susceptibility factor for Parkinson’s disease (PD). However, only approximately 20% of carriers develop the disease, suggesting the presence of genetic modifiers influencing the risk of developing PD in the presence of GBA mutations. Here we screened 1,634 human transcription factors (TFs) for their effect on GCase activity in cell lysates of the human glioblastoma line LN-229, into which we introduced the pathogenic GBA L444P variant via adenine base editing. Using a novel arrayed CRISPR activation library, we uncovered 11 TFs as regulators of GCase activity. Among these, activation of MITF and TFEC increased lysosomal GCase activity in live cells, while activation of ONECUT2 and USF2 decreased it. Conversely, ablating USF2 increased GBA mRNA and led to enhanced levels of GCase protein and activity. While MITF, TFEC, and USF2 affected GBA transcription, ONECUT2 was found to control GCase trafficking by modulating the guanine exchange factors PLEKHG4 and PLEKHG4B. Hence, our study provides a systematic approach to identifying modulators of GCase activity, expands the transcriptional landscape of GBA regulation, and deepens our understanding of the mechanisms involved in influencing GCase activity.
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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".