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Nuclear Location Bias of HCAR1 Drives Cancer Malignancy by Multiple Routes

2022· article· en· W4225410211 on OpenAlexafffund
Mohammad Ali Mohammad Nezhady, Gaël Cagnone, Sylvain Chemtob

Bibliographic record

VenueThe FASEB Journal · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicReceptor Mechanisms and Signaling
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsNuclear localization sequenceCell biologyPhosphorylationBiologyNuclear transportInteractomeCancer cellNuclear proteinG protein-coupled receptorCancer researchProtein kinase BCancerChemistryTranscription factorSignal transductionCell nucleusGeneBiochemistryGeneticsCytoplasm

Abstract

fetched live from OpenAlex

Introduction G‐Protein Coupled Receptors (GPCR) are virtually involved in all physiological processes. HCAR1 (GPR81), as a GPCR, is endogenously activated by lactate and has been shown to promote cancer malignancy via higher level of glycolysis due to Warburg effect. Its expression level is highly elevated in many cancers and negatively correlates with patient’s prognosis. However, its mechanism of action is not well understood. On the other hand, nuclear localization of several GPCRs have been described albeit it is unusual feature for them. Additionally, it has been shown that nuclear GPCRs can perform functions distinct from their cell surface counterparts in vivo. Here we demonstrate HCAR1 has a nuclear localization and this localization pattern promotes cancer malignancy by multiple routes. Methods and Results We determined HCAR1 nuclear localization pattern by cell fractionation, immunofluorescence confocal imaging and TEM. Site‐directed mutagenesis showed ICL3 and phosphorylation of C‐terminal domains are required for nuclear localization. We also demonstrated that this localization is ligand independent and there is a pool of nuclear HCAR1 (N‐HCAR1) in the cells. We show N‐HCAR1 induces intra‐nuclear signaling through Gi and Gßγ by WB and ELISA leading to increased phosphorylation of nuclear AKT and ERK resulting in increased cancer cell survival and proliferation. Our ChIP‐sequencing data shows N‐HCAR1 binds to the genes regulating transcription and promoting expression of genes involved in cell migration and we validated this in cellulo proving N‐HCAR1 promotes migration. We identified N‐HCAR1 interactome by Bio‐ID coupled with mass spectrometry and found, it interacts with proteins involved in ribosome biogenesis, translation and DNA damage repair and our experimental data demonstrates that specifically the N‐HCAR1 promotes these three process in cellulo. Additionally, we identified the transcriptomic signature of N‐HCAR1 and showed it regulated a larger gene network than its plasma membrane counterpart. Concordantly, our in vivo tumor xenografts and tail vein injections proves that tumors without N‐HCAR1 have smaller size and tumor mass and lower metastatic rate as well. Conclusion Here we show an unusual localization of a GPCR in the nucleus and provide evidence that N‐HCAR1 specifically promotes various hallmarks of cancer malignancy. The effect of N‐HCAR1 is validate in vivo in tumor xenografts as well. Understanding these mechanisms can provide targets and cues for therapeutic developments.

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 score0.004
Threshold uncertainty score0.012

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.015
GPT teacher head0.233
Teacher spread0.218 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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