TFEB and TFE3 have cell-type specific expression in the brain and divergent roles in neurons
Bibliographic record
Abstract
Abstract Lysosomal dysfunction occurs in many neurodegenerative diseases, including Parkinson’s disease, and activating TFEB to enhance lysosomal biogenesis is a promising therapeutic strategy. To understand TFEB physiology in cells of the brain, we characterised TFEB expression using iPSC-derived models, and transcriptomic analysis of human and mouse brain tissue. Surprisingly, TFEB expression at the RNA and protein level was restricted to glia, whereas the related transcription factor, TFE3, was expressed ubiquitously. We identified HDAC1/2/3 as transcriptional repressors of neuronal TFEB and found the brain-penetrant HDAC inhibitor ACY-738 derepressed TFEB expression and enhanced TFE3 nuclear translocation in iPSC-dopaminergic neurons (iPSC-DaNs). We delineated the role of each transcription factor by genetic manipulation in iPSC-DaNs to reveal divergent roles in which TFEB activates mitochondrial biogenesis, whereas TFE3 enhances lysosomal biogenesis. Finally, we show TFE3 activation corrects the lysosomal dysfunction associated with GBA-N370S and SNCA-Triplication mutations in Parkinson’s patient-derived iPSC-DaNs, demonstrating therapeutic utility in neurodegeneration.
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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 | 0.001 |
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".