Understanding the Regulation of TFEB and Rab7 and Their Contributions to Lysosomal Adaptation
Bibliographic record
Abstract
Lysosomes can adapt their activity and biogenesis through alterations in genetic expression of lysosomal genes by transcription factor EB (TFEB), and potentially by modifying membrane trafficking through increased activation and recruitment of Rab7 to lysosomes. This research investigated how TFEB is differentially modified and what the relationship between Rab7 and mTOR inhibition is within the context of membrane trafficking. Mass spectrometry revealed 6 novel phosphorylated sites on TFEB: S108, S113, T329, T330, S331 and S465. S108 and S113 showed evidence of mTOR mediated phosphorylation, while phosphorylation on S465 is hypothesized to be mTOR-independent. All phosphorylated sites are hypothesized to result in cytoplasmic retention and TFEB inactivation. Additionally, mTOR inhibition resulted in increased lysosomal-bound Rab7 and subsequent increase in cargo trafficking and degradation. Delineating TFEB activation patterns and understanding the mechanistic pathway of Rab7 activation along with its downstream effects may help improve our understanding of key regulators of lysosomal adaptation.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".