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Record W2988924036 · doi:10.1182/blood-2019-122784

The Mitochondrial Protease, Neurolysin (NLN), Regulates Respiratory Chain Complex and Supercomplex Formation and Is Necessary for AML Viability

2019· article· en· W2988924036 on OpenAlexaff
Sara Mirali, Aaron Botham, Véronique Voisin, Changjiang Xu, Jonathan St‐Germain, David Sharon, Fieke W. Hoff, Yihua Qiu, Rose Hurren, Marcela Gronda, Yulia Jitkova, G. Wei Xu, 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

VenueBlood · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHistone Deacetylase Inhibitors Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsRespiratory chainGene knockdownBiologyHaematopoiesisStem cellMyeloid leukemiaMitochondrial respiratory chainMitochondrionLeukemiaMyeloidSmall hairpin RNAMolecular biologyCancer researchCell cultureCell biologyImmunologyGenetics

Abstract

fetched live from OpenAlex

Acute myeloid leukemia (AML) cells and stem cells have unique mitochondrial characteristics with an increased reliance on oxidative phosphorylation (OXPHOS). To identify new biological vulnerabilities in the mitochondrial proteome of AML cells, we conducted an shRNA screen and identified neurolysin (NLN), a zinc metalloprotease whose mitochondrial function is not well understood and whose role in AML has not been previously reported. To begin our investigation into the role of NLN in AML, we analyzed NLN gene expression in a database of 536 AML and 73 normal bone marrow samples. NLN was overexpressed in 41% of AML samples. Overexpression of NLN in primary AML cells compared to normal hematopoietic cells was confirmed by immunoblotting. To validate the results of the screen and to determine whether NLN is required for AML growth and viability, we knocked down NLN in the leukemia cell lines OCI-AML2, MV4-11, NB4, and TEX with shRNA. NLN knockdown reduced leukemia growth and viability by 50-70%. Moreover, knockdown of NLN in AML cells reduced the clonogenic growth of leukemic cells in vitro and the engraftment of AML cells into mouse marrow after five weeks by up to 80% and 85%, respectively. The mitochondrial function of NLN is largely unknown, so we identified NLN's mitochondrial protein interactors in T-REx HEK293 cells using proximity-dependent biotin labeling (BioID) coupled with mass spectrometry (MS). This screen identified 73 mitochondrial proteins that preferentially interacted with NLN and were enriched for functions including respiratory chain complex assembly, respiratory electron transport, and mitochondrion organization. Therefore, we assessed the effects of NLN knockdown on OXPHOS. NLN knockdown reduced basal and maximal oxygen consumption, but there were no changes in the levels of individual respiratory chain complex subunits. To understand how NLN influences OXPHOS, we examined the formation of respiratory chain supercomplexes (RCS). Respiratory chain complexes I, III, and IV assemble into higher order quaternary structures called RCS, which promote efficient oxidative metabolism. NLN knockdown significantly impaired RCS formation in T-REx HEK293, OCI-AML2, and NB4 cells, which was rescued by overexpressing wild-type shRNA-resistant NLN. RCS have not been previously studied in leukemia. Therefore, we analyzed their levels in primary AML patient samples and normal hematopoietic cells. RCS assembly was increased in a subset of AML patient samples and positively correlated with NLN protein expression (R2 = 0.83, p < 0.05), suggesting that NLN mediates RCS assembly in AML. To investigate how NLN may be regulating RCS assembly, we analyzed our BioID results to identify NLN interactors that are known regulators of supercomplex formation. Among the top interactors was the known RCS regulator, LETM1. Knockdown of NLN in AML cells impaired LETM1 assembly. Of note, knockdown of LETM1 also reduced growth and oxygen consumption of AML cells. As a chemical approach to evaluate the effects of NLN inhibition on AML cells, we used the allosteric NLN inhibitor R2, (3-[(2S)-1-[(3R)-3-(2-Chlorophenyl)-2-(2-fluorophenyl)pyrazolidin-1-yl]-1-oxopropan-2-yl]-1-(adamantan-2-yl)urea), whose anti-cancer effects have not been previously reported. R2 reduced viability of AML cells, as well as two primary AML culture models, 8227 and 130578. R2 impaired RCS formation in OCI-AML2, NB4, 8227, and primary AML cells. Moreover, R2 reduced the CD34+CD38- stem cell enriched population in 8227 cells, reduced LETM1 complex assembly, and impaired OXPHOS in OCI-AML2 and 8227 cells. Finally, we assessed the effects of inhibiting NLN in mice engrafted with primary AML and normal hematopoietic cells in vivo. Treatment of mice with R2 reduced the leukemic burden in these mice without toxicity. Moreover, inhibiting NLN targeted the AML stem cells as evidenced by reduced engraftment in secondary experiments. In contrast, inhibiting NLN did not reduce the engraftment of normal hematopoietic cells. Collectively, these results demonstrate that inhibition of NLN preferentially targets AML cells and stem cells as compared to normal hematopoietic cells. In summary, we defined a novel role for NLN in RCS formation. We show that RCS are necessary for oxidative metabolism in AML and highlight NLN inhibition as a potential therapeutic strategy. Disclosures Minden: Trillium Therapetuics: Other: licensing agreement. Chan:Agios: Honoraria; AbbVie Pharmaceuticals: Research Funding; Celgene: Honoraria, Research Funding. Schimmer:Medivir Pharmaceuticals: Research Funding; Novartis Pharmaceuticals: Consultancy; Jazz Pharmaceuticals: Consultancy; Otsuka Pharmaceuticals: Consultancy.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.263
Teacher spread0.246 · 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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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