HDAC6 Inhibition Promotes Transcription Factor EB Activation and Is Protective in Experimental Kidney Disease
Bibliographic record
Abstract
To contend with the deleterious effects of accumulating misfolded protein aggregates or damaged organelles cells rely on a system of quality control processes, among them the autophagy-lysosome pathway. This pathway is itself controlled by a master regulator transcription factor termed transcription factor EB (TFEB). When TFEB localizes to the cell nucleus it promotes the expression of a number of genes involved in protein clearance. Here, we set out to determine (1) whether TFEB expression is altered in chronic kidney disease (CKD); (2) whether inhibition of the cytosolic deacetylase histone deacetylase 6 (HDAC6) affects TFEB acetylation and nuclear localization; and (3) whether HDAC6 inhibition, in turn, alters the natural history of experimental CKD. TFEB mRNA and protein levels were observed to be diminished in the kidneys of humans with diabetic kidney disease, accompanied by accumulation of the protein aggregate adaptor protein p62 in tubule epithelial cells. In cultured NRK-52E cells, HDAC6 inhibition with the small molecule inhibitor Tubastatin A acetylated TFEB, increasing TFEB localization to the nucleus and attenuating cell death. In a rat model of CKD, Tubastatin A prevented the accumulation of misfolded protein aggregates in tubule epithelial cells, attenuated proteinuria progression, limited tubule cell death and diminished tubulointerstitial collagenous matrix deposition. These findings point to the common occurrence of dysregulated quality control processes in CKD and they suggest that TFEB downregulation may contribute to tubule injury in CKD. They also identify a regulatory relationship between HDAC6 and TFEB. HDAC6 inhibitors and TFEB activators both warrant further investigation as treatments for CKD.
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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) | 0.001 | 0.001 |
| 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) | 0.004 | 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".