PIKfyve Inhibition Controls Inflammatory Gene Expression Networks and Activates TFEB via Oxidative Stress
Bibliographic record
Abstract
Lysosomes have a myriad of roles in cells. Defective lysosomes cause cellular/organismal dysfunction, including infection or neurological diseases. Lysosomal adaptation enhances lysosomal functions. One adaptation pathway, controlled by transcription factor EB (TFEB), stimulates lysosomal genes, boosting the stress resolution capacity. TFEB is controlled by mTOR and PIKfyve, however whether responses align is unclear. How PIKfyve inhibition activates TFEB is also mysterious. Here, two projects explored the PIKfyve-TFEB pathway. First, using transcriptomics, qRT-PCR, and Western blots in wild-type and macrophages lacking TFEB/TFE3, PIKfyve and mTOR inhibition caused differential gene expression that was mostly TFEB-dependent. PIKfyve inhibition, possibly through TFEB, may promote anti-inflammation, increasing ATF3 and decreasing FOS and IL6RA. Second, fluorescent imaging showed PIKfyve inhibition disrupts mitochondrial dynamics and promotes ROS production, driving ROS-dependent TFEB activation. PIKfyve associated diseases are often assumed to be endo-lysosomal trafficking related, but our work suggests they may depend on gene expression changes and mitochondrial disruptions.
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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".