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Record W2478397997 · doi:10.1158/1538-7445.am2016-2793

Abstract 2793: ADP- dependent glucokinase enhances hypoxia- inducible factor-α target gene transactivation through modulation of ROS levels in hypoxic human cancer cells

2016· article· en· W2478397997 on OpenAlexaff
Sergio Rey, Luana Schito, Marianne Koritzinsky, Bradly G. Wouters

Bibliographic record

VenueCancer Research · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsBiologyGlycolysisGene knockdownAnaerobic glycolysisCancer cellHypoxia (environmental)GlucokinaseCell biologyHypoxia-inducible factorsCell cultureCancer researchMolecular biologyBiochemistryChemistryCancerGeneEnzymeGenetics

Abstract

fetched live from OpenAlex

Abstract ADP- dependent glucokinase (ADPGK) is a glycolytic enzyme catalyzing the reaction of glucose→glucose 6-P by using ADP instead of ATP. ADPGK is remarkably conserved across various phyla from archaea to humans. Despite an important role priming glycolysis in conditions of metabolic stress in extremophile organisms, human ADPGK does not contribute to glycolysis. Recent work shows that ADPGK is essential for ROS generation during T-cell activation. Intriguingly, ADPGK protein levels are upregulated across a panel of cancer cell lines and primary tumors. Hereby we hypothesized that the increase of cellular ROS in response to mild (∼1% O2) hypoxia is dependent on ADPGK therefore contributing to the stabilization of hypoxia- inducible factor (HIF)-1α, a central regulator of the transcriptional response of cancer cells to hypoxia. We achieved ADPGK loss-of-function (LOF) utilizing lentiviral shRNA- mediated knockdown (>75%) in HCT-116 and HT-29 (colon); or H460 (lung) cancer cell lines. Intracellular ROS levels under hypoxia (1% O2) were analyzed by CM-H2DCFDA fluorescence and flow cytometry. Protein levels of HIF-1α (and -2α), and selected HIF-α targets were assessed by immunoblot assays. HIF-1α (and -2α) transcriptional activity was measured through a RT-qPCR array developed in house targeting 84 hypoxia- responsive transcripts in HCT-116 cells exposed to 20, 10, 5, 1, .2 and <.02% O2 for 24 h. Baseline non-hypoxic oxygen consumption and glycolytic activities were measured in real-time with an automated bioanalyzer (Seahorse Biosciences) whereas 3D spheroid growth assays and HCT-116 xenografts were used to assess the effect of ADPGK LOF on tumor mass. ADPGK LOF decreased ROS and HIF-1α (-and 2α) protein levels in a cell type- and O2- dependent manner in HCT-116, HT-29 and H460 cells. Results from our RT-qPCR array show a blunted transcriptional response to hypoxia with maximal inhibition at .2% O2 (∼15 mmHg). Immunoblots confirmed a striking impairment of CA9 hypoxic induction in addition to a significant decrease of the HIF-α targets IGFBP3, ERO-1α and DDIT4. 3D spheroid assays showed a significant growth rate increase after ADPGK knockdown. Addition of the antioxidant N-Ac-Cysteine during the spheroid formation phase mimicked the effect of ADPGK LOF in control cells whereas it further enhanced cell growth in ADPGK knockdown. However, baseline O2 consumption and glycolysis were not affected. Consistently with results of 3D spheroid assays, we observed enhanced tumor growth of HCT-116 xenografts bearing ADPGK LOF. Our work uncovers a hitherto unknown function of ADPGK in cancer cells whereby it enhances ROS- dependent HIF-1α (and -2α) hypoxic stabilization and transactivation therefore limiting tumor growth. Further work will be focused on the mitochondrial origin of ADPGK- dependent hypoxic ROS generation as suggested by previous studies in primary T-lymphocytes. Citation Format: Sergio Rey, Luana Schito, Marianne Koritzinsky, Bradly G. Wouters. ADP- dependent glucokinase enhances hypoxia- inducible factor-α target gene transactivation through modulation of ROS levels in hypoxic human cancer cells. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2793.

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 score0.003
Threshold uncertainty score0.009

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)0.0030.001

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 head0.086
GPT teacher head0.380
Teacher spread0.294 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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