ANALYSIS OF TFEB NUCLEO-CYTOPLASMIC SHUTTLING DYNAMICS IN RESPONSE TO NUTRIENTS AVAILABILITY
Bibliographic record
Abstract
The transcription factor EB (TFEB) is a key transcriptional regulator of lysosomal biogenesis and autophagy in response to variations in nutrient availability. TFEB subcellular localization and transcriptional activity are mainly controlled by its phosphorylation status. At the steady state, TFEB is normally phosphorylated and sequestered in the cytoplasm in an inactive form. Amino acids deprivation induces TFEB dephosphorylation and subsequent nuclear translocation, thus promoting the transcriptional activation of catabolic processes, including autophagy and lysosomal biogenesis. However, how nuclear TFEB is inactivated upon nutrient refeeding was until recently poorly understood. Our study on TFEB nucleo-cytoplasmic shuttling dynamics showed that TFEB continuously shuttles between the cytosol and the nucleus and highlighted the nuclear export as a new important checkpoint in the modulation of TFEB subcellular localization. TFEB nuclear export is mediated by the exportin CRM1, which recognizes a previously uncharacterized Nuclear Export Signal (NES) in the TFEB sequence. In addition, we found that this process requires a hierarchical multisite mTOR-dependent nuclear phosphorylation of TFEB on S142 and S138 residues, which allows its interaction with CRM1 and subsequent nuclear export in response to nutrients. Thus, our study reveals a complex scenario in which TFEB phosphorylation may occur in different subcellular compartments to finely tune its nucleo-to-cytoplasm shuttling. This model may unveil new pharmacological strategies aimed to control TFEB localization and activity by modulating its nuclear import and nuclear export in human diseases associated with autophagy or lysosomal defects, such as neurodegenerative and lysosomal storage disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".