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Record W4323319185 · doi:10.3390/curroncol30030231

Inhibition of Sphingosine Kinase 2 Results in PARK2-Mediated Mitophagy and Induces Apoptosis in Multiple Myeloma

2023· article· en· W4323319185 on OpenAlexvenueno aff
Jian Wu, Shengjun Fan, Daniel Feinberg, Xiaobei Wang, Shaima Jabbar, Yubin Kang

Bibliographic record

VenueCurrent Oncology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSphingolipid Metabolism and Signaling
Canadian institutionsnot available
FundersNational Cancer InstituteNational Heart, Lung, and Blood InstituteDuke Cancer InstituteNational Institutes of Health
KeywordsMitophagySphingosine kinaseSphingosineCell biologyBiologyDownregulation and upregulationMitochondrionApoptosisKinaseSphingosine-1-phosphateGeneticsAutophagyGeneReceptor

Abstract

fetched live from OpenAlex

Mitophagy plays an important role in maintaining mitochondrial homeostasis by clearing damaged mitochondria. Sphingosine kinase 2 (SK2), a type of sphingosine kinase, is an important metabolic enzyme involved in generating sphingosine-1-phosphate. Its expression level is elevated in many cancers and is associated with poor clinical outcomes. However, the relationship between SK2 and mitochondrial dysfunction remains unclear. We found that the genetic downregulation of SK2 or treatment with ABC294640, a specific inhibitor of SK2, induced mitophagy and apoptosis in multiple myeloma cell lines. We showed that mitophagy correlates with apoptosis induction and likely occurs through the SET/PP2AC/PARK2 pathway, where inhibiting PP2AC activity may rescue this process. Furthermore, we found that PP2AC and PARK2 form a complex, suggesting that they might regulate mitophagy through protein-protein interactions. Our study demonstrates the important role of SK2 in regulating mitophagy and provides new insights into the mechanism of mitophagy in multiple myeloma.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.055
GPT teacher head0.329
Teacher spread0.274 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2023
Admission routes1
Has abstractyes

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