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Record W2312291368 · doi:10.1158/1538-7445.am2011-4071

Abstract 4071: Sestrins modulate the expression of the metabolic stress sensor AMPK in breast cancer cells

2011· article· en· W2312291368 on OpenAlexaff
Toran Sanli, Katja Linher, Theodoros Tsakiridis, Gurmit Singh

Bibliographic record

VenueCancer Research · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics, phytochemicals, and oxidative stress
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAMPKProtein kinase AImmunoprecipitationChemistryCell biologyGene knockdownAMP-activated protein kinaseChromatin immunoprecipitationProtein subunitCancer cellMolecular biologyCancer researchPhosphorylationBiologyGene expressionCancerBiochemistryApoptosisGene

Abstract

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Abstract Introduction: Sestrins (SESN) are stress response mediators that accumulate in cells exposed to genotoxic stresses, such as ultraviolet and ionizing radiation (IR). The three mammalian SESN (1-3) were recently shown to modulate signalling events. SESN1/2 are suggested to interact with and increasing the activity of the metabolic stress sensor AMP-activated protein kinase (AMPK). SESN1/2 have been postulated to act as scaffolding proteins that can bind targets of the AMPK metabolic pathway including AMPKα and TSC2. However, the direct mechanism in which SESN1/2 increase AMPK activity is still being elucidated. Furthermore, the ability of SESNs to modulate the expression of AMPK subunits in untreated or IR treated cells has not been examined. Methods: Immunoprecipitation of the AMPK subunits (α1-2, β1-2, γ1-3) with subunit-specific antibodies was preformed to determine the function AMPK heterotrimer complexes in MCF7 breast cancer cells. Overexpression of full length SESN2 cDNA was achieved in MCF7 cells using tetracycline inducible promoter system. SESN1/2 siRNA was introduced into cells using HiPerFect transfection reagent and the Qiagen protocol. Protein and mRNA levels were measured with western blotting and RT-PRC, respectively. Cells were treated with 0 – 8 Gy IR, and cell survival was measured using clonogenic assays. Results: Through immunoprecipitation we identified that the most prominent AMPK functional complex in MCF7 cells is the α1β1γ1 heterotrimer. SESN2 overexpression increases both mRNA and protein expression levels of AMPKα1 and AMPKβ1, and was co-immunoprecipitated with the AMPKβ1 subunit. Similarly, increasing SENS2 expression increased the phosphorylation levels of AMPKα (Thr172) and AMPKβ1 (Ser108), as well as phosphorylation of the AMPK substrate ACC. Conversely, siRNA against SESN2 decreased both AMPKα1 and AMPKβ1 mRNA and protein levels, while SESN1 siRNA blocked basal and IR-induced phosphorylation of AMPKα (Thr172). In addition, SESN2 overexpression sensitized MCF7 cells to the cytotoxic effects of IR. Conclusions: To date our work suggests that upregulation of SESNs in breast cancer cells modulates AMPK by stabilizing the AMPK (α1β1γ1) heterotrimer and by enhancing the subunit expression of AMPK. We observed that SESN2 was shown to directly interact with the AMPKβ1 subunit and also enhance its Ser108 phosphorylation, an additional marker of AMPK activity. Furthermore, silencing SESN1 expression blocks basal IR-induced AMPKα (Thr172) phosphorylation, while SESN2 overexpression acts as a radiation sensitizer in MCF7 cells. Taken together, this data indicates that SESNs not only regulate AMPK activity, but also play a role in stabilizing this enzymes basal and stress-induced expression. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4071. doi:10.1158/1538-7445.AM2011-4071

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 score0.006
Threshold uncertainty score0.019

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.054
GPT teacher head0.336
Teacher spread0.282 · 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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Citations0
Published2011
Admission routes1
Has abstractyes

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