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Record W2891192951 · doi:10.3791/58416

Monitoring Hippo Signaling Pathway Activity Using a Luciferase-based Large Tumor Suppressor (LATS) Biosensor

2018· article· en· W2891192951 on OpenAlexafffund
Taha Azad, Kazem Nouri, Helena J. Janse van Rensburg, Yawei Hao, Xiaolong Yang

Bibliographic record

VenueJournal of Visualized Experiments · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHippo pathway signaling and YAP/TAZ
Canadian institutionsQueen's University
FundersCanadian Institutes of Health Research
KeywordsHippo signaling pathwayRegulatorContext (archaeology)KinaseSuppressorBiologyCell biologySignal transductionLuciferaseComputational biologyCancer researchCancerBiochemistryGeneticsTransfectionCell cultureGene

Abstract

fetched live from OpenAlex

The Hippo signaling pathway is a conserved regulator of organ size and has important roles in the development and cancer biology. Due to technical challenges, it remains difficult to assess the activity of this signaling pathway and interpret it within a biological context. The existing literature on large tumor suppressor (LATS) relies on methods that are qualitative and cannot easily be scaled-up for screening. Recently, we have developed a bioluminescence-based biosensor to monitor the kinase activity of LATS-a core component of the Hippo kinase cascade. Here, we describe procedures for how this LATS biosensor (LATS-BS) can be used to characterize Hippo pathway regulators. First, we provide a detailed protocol for investigating the effect of an overexpressed protein candidate (e.g., VEGFR2) on LATS activity using the LATS-BS. Then, we show how the LATS-BS can be used for a small-scale kinase inhibitor screen. This protocol can feasibly be scaled-up to perform larger screens, which undoubtedly will identify novel regulators of the Hippo pathway.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.043
GPT teacher head0.400
Teacher spread0.358 · 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.

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

Citations9
Published2018
Admission routes2
Has abstractyes

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