Evidence map›Paper›PMID 31775004›Full record

ArticleACS chemical biology2020

Quantification of ERK Kinase Activity in Biological Samples Using Differential Sensing.

Diana Zamora-Olivares, Tamer S Kaoud, Lingyu Zeng, Jacey R Pridgen, Deborah L Zhuang, Yakndara E Ekpo, Jessica R Nye, Mitchell Telles, Eric V Anslyn, Kevin N Dalby

Abstract read
In one paragraph

Article in ACS chemical biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Diana Zamora-OlivaresDepartment of Chemistry , The University of Texas at Austin , Austin , Texas 78712 , United States.ORCID 0000-0002-8898-9292
Tamer S KaoudDivision of Chemical Biology & Medicinal Chemistry, College of Pharmacy , The University of Texas at Austin , Austin , Texas 78712 , United States.ORCID 0000-0003-1298-8725
Lingyu ZengCollege of Chemistry and Molecular Science , Wuhan University , Wuhan , Hubei 430072 , China.
Jacey R PridgenDivision of Chemical Biology & Medicinal Chemistry, College of Pharmacy , The University of Texas at Austin , Austin , Texas 78712 , United States.
Deborah L ZhuangTexas Institute for Discovery Education in Science and Freshman Research Initiative , The University of Texas at Austin , Austin , Texas 78712 , United States.ORCID 0000-0003-1806-2552
Yakndara E EkpoTexas Institute for Discovery Education in Science and Freshman Research Initiative , The University of Texas at Austin , Austin , Texas 78712 , United States.
Jessica R NyeTexas Institute for Discovery Education in Science and Freshman Research Initiative , The University of Texas at Austin , Austin , Texas 78712 , United States.
Mitchell TellesTexas Institute for Discovery Education in Science and Freshman Research Initiative , The University of Texas at Austin , Austin , Texas 78712 , United States.
Eric V AnslynDepartment of Chemistry , The University of Texas at Austin , Austin , Texas 78712 , United States.ORCID 0000-0002-5137-8797
Kevin N DalbyDivision of Chemical Biology & Medicinal Chemistry, College of Pharmacy , The University of Texas at Austin , Austin , Texas 78712 , United States.ORCID 0000-0001-9272-5129

Funding

Regulation of eEF-2K an Energy and Nutrient SensorR01GM123252 · NIGMS · UNIVERSITY OF TEXAS AT AUSTIN · PI DALBY, KEVIN N, GHOSE, RANAJEET · 2017 to 2024
$2.7M
NIGMS NIH HHS R01 GM123252
6 · The paper itself

Abstract

The understanding of complex biological systems requires an ability to evaluate interacting networks of genes, proteins, and cellular reactions. Enabling technologies that support the rapid quantification of these networks will facilitate the development of biological models and help to identify treatment targets and to assess treatment plans. The biochemical process of protein phosphorylation, which underlies almost all aspects of cell signaling, is typically evaluated by immunoblotting procedures (Western blot) or more recently proteomics procedures, which provide qualitative estimates of the concentration of proteins and their modifications in cells. However, protein modifications are difficult to correlate with activity, and while immunoblotting and proteomics approaches have the potential to be quantitative, they require a complex series of steps that diminish reproducibility. Here, a complementary approach is presented that allows for the rapid quantification of a protein kinase activity in cell lysates and tumor samples. Using the activity of cellular ERK (extracellular signal-regulated kinase) as a test case, an array sensing approach that utilizes a library of differential peptide-based biosensors and chemometric tools was used to rapidly quantify nanograms of active ERK in micrograms of unfractionated cell lysates and tumor extracts. This approach has the potential both for high-throughput and for quantifying the activities of multiple protein kinases in a single biological sample. The critical advantages of this differential sensing approach over others are that it removes the need for the addition of exogenous inhibitors to suppress the activities of major off-target kinases and allows us to quantitate the amount of active kinase in tested samples rather than measuring the changes in its activity upon induction or inhibition.

Indexed as

Biosensing TechniquesCell ExtractsCell Line, TumorExtracellular Signal-Regulated MAP KinasesHumansImmunoblottingKineticsPhosphorylationProtein Processing, Post-TranslationalProteomicsRecombinant ProteinsReproducibility of ResultsSignal TransductionCell ExtractsExtracellular Signal-Regulated MAP KinasesRecombinant Proteins

Identifiers

PMID31775004
PMCPMC7085986

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.