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Record W1886753592 · doi:10.18632/oncotarget.5605

HSulf-1 deficiency dictates a metabolic reprograming of glycolysis and TCA cycle in ovarian cancer

2015· article· en· W1886753592 on OpenAlexaffabout
Susmita Mondal, Debarshi Roy, Juliana Camacho-Pereira, Ashwani Khurana, Eduardo N. Chini, Lifeng Yang, Joelle Baddour, Katherine Stilles, Seth Padmabandu, Sam Leung, Steve E. Kalloger, C. Blake Gilks, Val J. Lowe, Thomas Dierks, Edward Hammond, Keith Dredge, Deepak Nagrath, Viji Shridhar

Bibliographic record

VenueOncotarget · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsUniversity of British Columbia
FundersNational Cancer InstituteCenter for Clinical and Translational Science, Mayo ClinicNational Institutes of HealthNational Center for Advancing Translational SciencesMayo Clinic
KeywordsGlycolysisWarburg effectAnaerobic glycolysisBiologyCitric acid cycleGLUT1Lactate dehydrogenase ACell biologyGlucose transporterCancer researchBiochemistryChemistryEndocrinologyMetabolism

Abstract

fetched live from OpenAlex

// Susmita Mondal 1 , Debarshi Roy 1 , Juliana Camacho-Pereira 2,7 , Ashwani Khurana 1 , Eduardo Chini 2 , Lifeng Yang 3 , Joelle Baddour 3 , Katherine Stilles 3 , Seth Padmabandu 3 , Sam Leung 4 , Steve Kalloger 4 , Blake Gilks 4 , Val Lowe 5 , Thomas Dierks 6 , Edward Hammond 8 , Keith Dredge 8 , Deepak Nagrath 3 and Viji Shridhar 1 1 Department of Experimental Pathology, Mayo Clinic College of Medicine, Rochester, MN, USA 2 Department of Anesthesiology, Mayo Clinic College of Medicine, Rochester, MN, USA 3 Department of Chemical and Biomolecular Engineering, Rice University, Houston, TX, USA 4 Department of Pathology and Laboratory Medicine, University of British Columbia, Canada 5 Department of Nuclear Medicine, Mayo Clinic College of Medicine, Rochester, MN, USA 6 Department of Chemistry, Biochemistry I, Bielefeld University, Bielefeld, Germany 7 Institute of Medical Biochemistry Leopoldo de Meis, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil 8 Progen Pharmaceuticals Ltd, Brisbane, Queensland, Australia Correspondence to: Viji Shridhar, email: // Keywords : HSulf-1, Warburg effect, HB-EGF, ovarian cancer, c-Myc, PG545 Received : May 05, 2015 Accepted : August 27, 2015 Published : September 10, 2015 Abstract Warburg effect has emerged as a potential hallmark of many cancers. However, the molecular mechanisms that led to this metabolic state of aerobic glycolysis, particularly in ovarian cancer (OVCA) have not been completely elucidated. HSulf-1 predominantly functions by limiting the bioavailability of heparan binding growth factors and hence their downstream signaling. Here we report that HSulf-1, a known putative tumor suppressor, is a negative regulator of glycolysis. Silencing of HSulf-1 expression in OV202 cell line increased glucose uptake and lactate production by upregulating glycolytic genes such as Glut1, HKII, LDHA, as well as metabolites. Conversely, HSulf-1 overexpression in TOV21G cells resulted in the down regulation of glycolytic enzymes and reduced glycolytic phenotype, supporting the role of HSulf-1 loss in enhanced aerobic glycolysis. HSulf-1 deficiency mediated glycolytic enhancement also resulted in increased inhibitory phosphorylation of pyruvate dehydrogenase (PDH) thus blocking the entry of glucose flux into TCA cycle. Consistent with this, metabolomic and isotope tracer analysis showed reduced glucose flux into TCA cycle. Moreover, HSulf-1 loss is associated with lower oxygen consumption rate (OCR) and impaired mitochondrial function. Mechanistically, lack of HSulf-1 promotes c-Myc induction through HB-EGF-mediated p-ERK activation. Pharmacological inhibition of c-Myc reduced HB-EGF induced glycolytic enzymes implicating a major role of c-Myc in loss of HSulf-1 mediated altered glycolytic pathway in OVCA. Similarly, PG545 treatment, an agent that binds to heparan binding growth factors and sequesters growth factors away from their ligand also blocked HB-EGF signaling and reduced glucose uptake in vivo in HSulf-1 deficient cells.

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 score0.683
Threshold uncertainty score0.502

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 head0.012
GPT teacher head0.271
Teacher spread0.259 · 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

Citations29
Published2015
Admission routes2
Has abstractyes

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