Inhibition of Transient Receptor Potential Channel Mucolipin-1 (TRPML1) by Lysosomal Adenosine Involved in Severe Combined Immunodeficiency Diseases
Bibliographic record
Abstract
Impaired adenosine homeostasis has been associated with numerous human diseases. Lysosomes are referred to as the cellular recycling centers that generate adenosine by breaking down nucleic acids or ATP. Recent studies have suggested that lysosomal adenosine overload causes lysosome defects that phenocopy patients with mutations in transient receptor potential channel mucolipin-1 (TRPML1), a lysosomal Ca 2+ channel, suggesting that lysosomal adenosine overload may impair TRPML1 and then lead to subsequent lysosomal dysfunction. In this study, we demonstrate that lysosomal adenosine is elevated by deleting adenosine deaminase (ADA), an enzyme responsible for adenosine degradation. We also show that lysosomal adenosine accumulation inhibits TRPML1, which is rescued by overexpressing ENT3, the adenosine transporter situated in the lysosome membrane. Moreover, ADA deficiency results in lysosome enlargement, alkalinization, and dysfunction. These are rescued by activating TRPML1. Importantly, ADA-deficient B-lymphocytes are more vulnerable to oxidative stress, and this was rescued by TRPML1 activation. Our data suggest that lysosomal adenosine accumulation impairs lysosome function by inhibiting TRPML1 and subsequently leads to cell death in B-lymphocytes. Activating TRPML1 could be a new therapeutic strategy for those diseases.
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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".