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Record W3015278998 · doi:10.1126/scitranslmed.aaz8264

The mitochondrial peptidase, neurolysin, regulates respiratory chain supercomplex formation and is necessary for AML viability

2020· article· en· W3015278998 on OpenAlexafffund
Sara Mirali, Aaron Botham, Véronique Voisin, Changjiang Xu, Jonathan St‐Germain, David Sharon, Fieke W. Hoff, Yihua Qiu, Rose Hurren, Marcela Gronda, Yulia Jitkova, Boaz Nachmias, Neil MacLean, Xiaoming Wang, Andrea Arruda, Mark D. Minden, Terzah M. Horton, Steven M. Kornblau, Steven M. Chan, Gary D. Bader, Brian Raught, Aaron D. Schimmer

Bibliographic record

VenueScience Translational Medicine · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsOccupational Cancer Research CentrePrincess Margaret Cancer CentreUniversity of Toronto
FundersCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchNational Cancer InstituteOntario Institute for Cancer ResearchNational Institutes of HealthUniversity of TorontoOntario Ministry of Health and Long-Term CareOntario Ministry of Research and InnovationPrincess Margaret Cancer Foundation
KeywordsMitochondrial respiratory chainRespiratory chainMitochondrionViability assayBiologyRespiratory systemCell biologyBiochemistryChemistryCellAnatomy

Abstract

fetched live from OpenAlex

Neurolysin (NLN) is a zinc metallopeptidase whose mitochondrial function is unclear. We found that NLN was overexpressed in almost half of patients with acute myeloid leukemia (AML), and inhibition of NLN was selectively cytotoxic to AML cells and stem cells while sparing normal hematopoietic cells. Mechanistically, NLN interacted with the mitochondrial respiratory chain. Genetic and chemical inhibition of NLN impaired oxidative metabolism and disrupted the formation of respiratory chain supercomplexes (RCS). Furthermore, NLN interacted with the known RCS regulator, LETM1, and inhibition of NLN disrupted LETM1 complex formation. RCS were increased in patients with AML and positively correlated with NLN expression. These findings demonstrate that inhibiting RCS formation selectively targets AML cells and stem cells and highlights the therapeutic potential of pharmacologically targeting NLN in AML.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.670
Threshold uncertainty score0.340

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 studies0.0000.001
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 head0.034
GPT teacher head0.285
Teacher spread0.251 · 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

Citations46
Published2020
Admission routes2
Has abstractyes

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