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Record W2483834911 · doi:10.1158/1538-7445.am2016-4427

Abstract 4427: Role of Smurf2 in regulation of Smad anchor necessary for receptor activation (SARA) in TGFβ-receptor signaling

2016· article· en· W2483834911 on OpenAlexaff
Sang‐Hyun Lee, John M. Di Guglielmo

Bibliographic record

VenueCancer Research · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicTGF-β signaling in diseases
Canadian institutionsWestern University
Fundersnot available
KeywordsSMADR-SMADCell biologyTransforming growth factorCancer researchUbiquitinReceptorSmad2 ProteinSignal transductionChemistryBiologyTGF alphaGrowth factorBiochemistry

Abstract

fetched live from OpenAlex

Abstract Non-small cell lung cancer (NSCLC) is a major form of lung cancer and is the leading cause of cancer mortality in the western world. Transforming growth factor β (TGFβ) deregulation leads to many human diseases: TGFβ is a tumor suppressor in normal lung epithelium; however, it switches roles and promotes lung cancer metastasis in cancer cells. TGFβ induces cell proliferation, migration and invasion in NSCLC cells. The canonical pathway of TGFβ is initiated through binding of TGFβ ligand to transmembrane Ser/Thr kinase receptors, which propagate their signaling via receptor regulated R-Smads; proteins that function as intracellular effectors in the TGFβ pathway. Once activated, R-Smads form a complex with common (Co)-Smads that translocates into the nucleus to regulate transcriptional responses. Access of R-Smads to the activated receptor complex is regulated by an adaptor protein called Smad anchor for receptor activation (SARA). SARA facilitates the activation of Smads and allow efficient Smad signalling. In addition to R-Smads and Co-Smads, inhibitory Smads (I-Smads) regulate TGFβ signalling. I-Smads block the signalling by competing against R-Smads for the association with TβR complex or by targeting receptors for ubiquitin-mediate degradation. I-Smads recruit the E3 ubiquitin ligases, Smad ubiquitination regulatory factors (Smurfs), to catalyze degradation of the receptor complex. The overall aim of the project is to characterize SARA in TGFβ receptor signalling and study the interaction of SARA with different proteins involved in the pathway, such as Smurf2 and Smad7. We first examined how SARA, Smurf2 and Smad7 influence each other by performing a multi-combination transfection study. We also examined the interaction between SARA and Smurf2 through co-immunoprecipitation. We observed that the level of SARA decrease in the presence of Smurf2 and Smad7. In the presence of Smurf2 ligase inactive mutant and Smad7, the level of SARA is somewhat recovered. However, SARA does not directly interact with Smurf2. These data together suggest that SARA and Smurf2 influence each other in a very close manner. It also suggest that it could be a transient interaction. Characterizing SARA in TGFβ receptor signalling will provide us with possible drug targets for NSCLC. Citation Format: Sanghyun Lee, John M. Di Guglielmo. Role of Smurf2 in regulation of Smad anchor necessary for receptor activation (SARA) in TGFβ-receptor signaling. [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 4427.

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)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.003

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 head0.043
GPT teacher head0.367
Teacher spread0.324 · 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
Published2016
Admission routes1
Has abstractyes

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