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Record W2524344101 · doi:10.1038/srep33684

BK channel agonist represents a potential therapeutic approach for lysosomal storage diseases

2016· article· en· W2524344101 on OpenAlexafffund
Xi Zhong, Xue Sun, Qi Cao, Gaofeng Dong, Raphael Schiffmann, Xian‐Ping Dong

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCalcium signaling and nucleotide metabolism
Canadian institutionsDalhousie University
FundersCanadian Institutes of Health ResearchDalhousie University
KeywordsAgonistComputer scienceMedicinePharmacologyBioinformaticsNeuroscienceBiologyInternal medicineReceptor

Abstract

fetched live from OpenAlex

Abstract Efficient lysosomal Ca 2+ release plays an essential role in lysosomal trafficking. We have recently shown that lysosomal big conductance Ca 2+ -activated potassium (BK) channel forms a physical and functional coupling with the lysosomal Ca 2+ release channel Transient Receptor Potential Mucolipin-1 (TRPML1). BK and TRPML1 forms a positive feedback loop to facilitate lysosomal Ca 2+ release and subsequent lysosome membrane trafficking. However, it is unclear whether the positive feedback mechanism is common for other lysosomal storage diseases (LSDs) and whether BK channel agonists rescue abnormal lysosomal storage in LSDs. In this study, we assessed the effect of BK agonist, NS1619 and NS11021 in a number of LSDs including NPC1, mild cases of mucolipidosis type IV (ML4) (TRPML1-F408∆), Niemann-Pick type A (NPA) and Fabry disease. We found that TRPML1-mediated Ca 2+ release was compromised in these LSDs. BK activation corrected the impaired Ca 2+ release in these LSDs and successfully rescued the abnormal lysosomal storage of these diseases by promoting TRPML1-mediated lysosomal exocytosis. Our study suggests that BK channel activation stimulates the TRPML1-BK positive reinforcing loop to correct abnormal lysosomal storage in LSDs. Drugs targeting BK channel represent a potential therapeutic approach for LSDs.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.178
Threshold uncertainty score0.507

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.250
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations42
Published2016
Admission routes2
Has abstractyes

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