Lysosomes finely control macrophage inflammatory function via regulating the release of lysosomal Fe2+ through TRPML1 channel
Bibliographic record
Abstract
Lysosomes are best known for their roles in inflammatory responses by engaging in autophagy to remove inflammasomes. Here, we describe an unrecognized role for the lysosome, showing that it finely controls macrophage inflammatory function by manipulating the lysosomal Fe2+—prolyl hydroxylase domain enzymes (PHDs)—NF-κB—interleukin 1 beta (IL1B) transcription pathway that directly links lysosomes with inflammatory responses. TRPML1, a lysosomal cationic channel, is activated secondarily to ROS elevation upon inflammatory stimuli, which in turn suppresses IL1B transcription, thus limiting the excessive production of IL-1β in macrophages. Mechanistically, the suppression of IL1B transcription caused by TRPML1 activation results from its modulation on the release of lysosomal Fe2+, which subsequently activates PHDs. The activated PHDs then represses transcriptional activity of NF-κB, ultimately resulting in suppressed IL1B transcription. More importantly, in vivo stimulation of TRPML1 ameliorates multiple clinical signs of Dextran sulfate sodium-induced colitis in mice, suggesting TRPML1 has potential in treating inflammatory bowel disease. Lysosomes are best known for their roles in inflammatory responses via autophagy. Here, Xing et al., find that lysosomes regulate macrophage inflammatory responses by finely controlling interleukin-1β production, altering lysosomal Fe2+ release through the TRPML1 channel.
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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 | 0.000 |
| 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".