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Record W4402714479 · doi:10.1038/s41418-024-01381-8

The IDH1-R132H mutation aggravates cisplatin-induced acute kidney injury by promoting ferroptosis through disrupting NDUFA1 and FSP1 interaction

2024· article· en· W4402714479 on OpenAlexaff
Kunmei Lai, Zhimin Chen, Siyi Lin, Keng Ye, Ying Yuan, Guoping Li, Yankun Song, Huabin Ma, Tak W. Mak, Yanfang Xu

Bibliographic record

VenueCell Death and Differentiation · 2024
Typearticle
Languageen
FieldMedicine
TopicFerroptosis and cancer prognosis
Canadian institutionsPrincess Margaret Cancer Centre
FundersFujian Medical UniversityXiamen UniversityFujian Provincial Health CommissionNational Natural Science Foundation of China
KeywordsCisplatinIDH1Lipid peroxidationCancer researchMutationNephrotoxicityAcute kidney injuryProgrammed cell deathKidneyOxidative stressChemistryMedicineBiologyGeneChemotherapyApoptosisGeneticsBiochemistryInternal medicine

Abstract

fetched live from OpenAlex

The IDH1-R132H mutation is implicated in the development of various tumors. Whether cisplatin, a common chemotherapeutic agent, induces more significant renal toxicity in individuals with the IDH1-R132H mutation remains unclear. In this study, we observed that the IDH1-R132H mutation exacerbates mitochondrial lipid peroxidation and dysfunction in renal tubules, rendering the kidneys more susceptible to cisplatin-induced ferroptosis. The IDH1-R132H mutation increases methylation of the Ndufa1 promoter, thereby suppressing NDUFA1 transcription and translation. This suppression disrupts NDUFA1's interaction with FSP1, reducing its resistance to cisplatin-induced tubular epithelial cell death. As a consequence, ROS accumulates, lipid peroxidation occurs, and ferroptosis is triggered, thereby promoting acute kidney injury. In summary, this study elucidates a novel mechanism underlying cisplatin-induced nephrotoxicity and provides valuable insights for the development of personalized treatment strategies for tumor patients carrying the IDH1-R132H mutation.

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)0.000
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 score0.183
Threshold uncertainty score0.591

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.016
GPT teacher head0.293
Teacher spread0.277 · 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

Citations28
Published2024
Admission routes1
Has abstractyes

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