Browse
Submit Data
Databases
API
Help

Dataset Information

11 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

An intact model for quantifying functional selectivity.

ABSTRACT: A ligand that acts on a target receptor to activate particular multiple signalling pathways with activity that is distinct from other ligands is termed ligand bias. Quantification of ligand bias is based on applying the operational model to each pathway separately and subsequent calculation of the ligand bias metric (ΔΔlogR). This approach implies independence among different pathways and causes propagation of error in the calculation. Here, we propose a semi-mechanism-based model which allows for receptor selectivity across all the pathways simultaneously (termed the 'intact operational model'). The power of the intact model for detecting ligand bias was evaluated via stochastic simulation estimation studies. It was also applied to two examples: (1) opposing effects of Gi/Gs signalling of α2-adrenergic receptors and (2) simultaneous measurement of arachidonic acid release and inositol phosphate accumulation following 5-HT_2C receptor activation. The intact operational model demonstrated greater power to detect ligand bias in the simulation. In the applications, it provided better precision of estimation and identified biased ligands that were missed by analysis of traditional methods. Issues identified in both examples might lead to different interpretations of the data. The intact operational model may elucidate greater understanding of the underlying mechanisms of functional selectivity.

SUBMITTER: Zhu X

PROVIDER: S-EPMC6384912 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

A simple method for quantifying functional selectivity and agonist bias.

Project description:Activation of seven-transmembrane (7TM) receptors by agonists does not always lead to uniform activation of all signaling pathways mediated by a given receptor. Relative to other ligands, many agonists are "biased" toward producing subsets of receptor behaviors. A hallmark of such "functional selectivity" is cell type dependence; this poses a particular problem for the profiling of agonists in whole cell test systems removed from the therapeutic one(s). Such response-specific cell-based variability makes it difficult to guide medicinal chemistry efforts aimed at identifying and optimizing therapeutically meaningful agonist bias. For this reason, we present a scale, based on the Black and Leff operational model, that contains the key elements required to describe 7TM agonism, namely, affinity (K(A) (-1)) for the receptor and efficacy (τ) in activating a particular signaling pathway. Utilizing a "transduction coefficient" term, log(τ/K(A)), this scale can statistically evaluate selective agonist effects in a manner that can theoretically inform structure-activity studies and/or drug candidate selection matrices. The bias of four chemokines for CCR5-mediated inositol phosphate production versus internalization is quantified to illustrate the practical application of this method. The independence of this method with respect to receptor density and the calculation of statistical estimates of confidence of differences are specifically discussed.

| S-EPMC3369801 | biostudies-other

Quantifying CDK inhibitor selectivity in live cells.

Project description:Concerted multidisciplinary efforts have led to the development of Cyclin-Dependent Kinase inhibitors (CDKi's) as small molecule drugs and chemical probes of intracellular CDK function. However, conflicting data has been reported on the inhibitory potency of CDKi's and a systematic characterization of affinity and selectivity against intracellular CDKs is lacking. We have developed a panel of cell-permeable energy transfer probes to quantify target occupancy for all 21 human CDKs in live cells, and present a comprehensive evaluation of intracellular isozyme potency and selectivity for a collection of 46 clinically-advanced CDKi's and tool molecules. We observed unexpected intracellular activity profiles for a number of CDKi's, offering avenues for repurposing of highly potent molecules as probes for previously unreported targets. Overall, we provide a broadly applicable method for evaluating the selectivity of CDK inhibitors in living cells, and present a refined set of tool molecules to study CDK function.

| S-EPMC7265341 | biostudies-literature

An evaluation of the operational model when applied to quantify functional selectivity.

Project description:Background and purposeFunctional selectivity describes the ability of ligands to differentially regulate multiple signalling pathways when coupled to a single receptor, and the operational model is commonly used to analyse these data. Here, we assess the mathematical properties of the operational model and evaluate the outcomes of fixing parameters on model performance.Experimental approachThe operational model was evaluated using both a mathematical identifiability analysis and simulation.Key resultsMathematical analysis revealed that the parameters R0 and KE were not independently identifiable which can be solved by considering their ratio, τ. The ratio parameter, τ, was often imprecisely estimated when only functional assay data were available and generally only the transduction coefficient R ( τKA) could be estimated precisely. The general operational model (that includes baseline and the Hill coefficient) required either the parameters Em or KA to be fixed. The normalization process largely cancelled out the mean error of the calculated Δlog (R) caused by fixing these parameters. From this analysis, it was determined that we can avoid the need for a full agonist ligand to be included in an experiment to determine Δlog (R).Conclusion and implicationsThis analysis has provided a ready-to-use understanding of current methods for quantifying functional selectivity. It showed that current methods are generally tolerant to fixing parameters. A new method was proposed that removes the need for including a high efficacy ligand in any given experiment, which allows application to large-scale screening to identify compounds with desirable features of functional selectivity.

| S-EPMC5913411 | biostudies-literature

Quantifying the Release of Biomarkers of Myocardial Necrosis from Cardiac Myocytes and Intact Myocardium.

Project description:BackgroundMyocardial infarction is diagnosed when biomarkers of cardiac necrosis exceed the 99th centile, although guidelines advocate even lower concentrations for early rule-out. We examined how many myocytes and how much myocardium these concentrations represent. We also examined if dietary troponin can confound the rule-out algorithm.MethodsIndividual rat cardiac myocytes, rat myocardium, ovine myocardium, or human myocardium were spiked into 400-μL aliquots of human serum. Blood was drawn from a volunteer after ingestion of ovine myocardium. High-sensitivity assays were used to measure cardiac troponin T (cTnT; Roche, Elecsys), cTnI (Abbott, Architect), and cardiac myosin-binding protein C (cMyC; EMD Millipore, Erenna®).ResultsThe cMyC assay could only detect the human protein. For each rat cardiac myocyte added to 400 μL of human serum, cTnT and cTnI increased by 19.0 ng/L (95% CI, 16.8-21.2) and 18.9 ng/L (95% CI, 14.7-23.1), respectively. Under identical conditions cTnT, cTnI, and cMyC increased by 3.9 ng/L (95% CI, 3.6-4.3), 4.3 ng/L (95% CI, 3.8-4.7), and 41.0 ng/L (95% CI, 38.0-44.0) per μg of human myocardium. There was no detectable change in cTnI or cTnT concentration after ingestion of sufficient ovine myocardium to increase cTnT and cTnI to approximately 1 × 108 times their lower limits of quantification.ConclusionsBased on pragmatic assumptions regarding cTn and cMyC release efficiency, circulating species, and volume of distribution, 99th centile concentrations may be exceeded by necrosis of 40 mg of myocardium. This volume is much too small to detect by noninvasive imaging.

| S-EPMC6114147 | biostudies-literature

Functional selectivity of adenosine receptor ligands.

Project description:Adenosine receptors are plasma membrane proteins that transduce an extracellular signal into the interior of the cell. Basically every mammalian cell expresses at least one of the four adenosine receptor subtypes. Recent insight in signal transduction cascades teaches us that the current classification of receptor ligands into agonists, antagonists, and inverse agonists relies very much on the experimental setup that was used. Upon activation of the receptors by the ubiquitous endogenous ligand adenosine they engage classical G protein-mediated pathways, resulting in production of second messengers and activation of kinases. Besides this well-described G protein-mediated signaling pathway, adenosine receptors activate scaffold proteins such as β-arrestins. Using innovative and sensitive experimental tools, it has been possible to detect ligands that preferentially stimulate the β-arrestin pathway over the G protein-mediated signal transduction route, or vice versa. This phenomenon is referred to as functional selectivity or biased signaling and implies that an antagonist for one pathway may be a full agonist for the other signaling route. Functional selectivity makes it necessary to redefine the functional properties of currently used adenosine receptor ligands and opens possibilities for new and more selective ligands. This review focuses on the current knowledge of functionally selective adenosine receptor ligands and on G protein-independent signaling of adenosine receptors through scaffold proteins.

| S-EPMC3146648 | biostudies-literature

Kinetics of cytokine receptor trafficking determine signaling and functional selectivity

Project description:Cytokines activate signaling via assembly of cell surface receptors, but it is unclear whether modulation of cytokine-receptor binding parameters can modify biological outcomes. We have engineered IL-6 variants with different affinities to gp130 to investigate how cytokine receptor binding dwell-times influence functional selectivity. Engineered IL-6 variants showed a range of signaling amplitudes and induced biased signaling, with changes in receptor binding dwell-times affecting more profoundly STAT1 than STAT3 phosphorylation. We show that this differential signaling arises from defective translocation of ligand-gp130 complexes to the endosomal compartment and competitive STAT1/STAT3 binding to phospho-tyrosines in gp130, and results in unique patterns of STAT3 binding to chromatin. This leads to a graded gene expression response and differences in ex vivo differentiation of Th17, Th1 and Treg cells. These results provide a molecular understanding of signaling biased by cytokine receptors, and demonstrate that manipulation of signaling thresholds is a useful strategy to decouple cytokine functional pleiotropy.

2019-12-03 | GSE130810 | GEO

Opportunities for functional selectivity in GPCR antibodies.

Project description:Monoclonal antibodies (mAbs) have been used for decades as tools to probe the biology and pharmacology of receptors in cells and tissues. They are also increasingly being developed for clinical purposes against a broad range of targets, albeit to a lesser extent for G-protein-coupled receptors (GPCRs) relative to other therapeutic targets. Recent pharmacological, structural and biophysical data have provided a great deal of new insight into the molecular details, complexity and regulation of GPCR function. Whereas GPCRs used to be viewed as having either "on" or "off" conformational states, it is now recognized that their structures may be finely tuned by ligands and other interacting proteins, leading to the selective activation of specific signaling pathways. This information coupled with new technologies for the selection of mAbs targeting GPCRs will be increasingly deployed for the development of highly selective mAbs that recognize conformational determinants leading to novel therapeutics.

| S-EPMC3729224 | biostudies-literature

Functional selectivity of adenosine A1 receptor ligands?

Project description:The concept of functional selectivity offers great potential for the development of drugs that selectively activate a specific intracellular signaling pathway. During the last few years, it has become possible to systematically analyse compound libraries on G protein-coupled receptors (GPCRs) for this 'biased' form of signaling. We screened over 800 compounds targeting the class of adenosine A(1) receptors using a β-arrestin-mediated signaling assay in U2OS cells as a G protein-independent readout for GPCR activation. A selection of compounds was further analysed in a G protein-mediated GTPγS assay. Additionally, receptor affinity of these compounds was determined in a radioligand binding assay with the agonist [(3)H]CCPA. Of all compounds tested, only LUF5589 9 might be considered as functionally selective for the G protein-dependent pathway, particularly in view of a likely overestimation of β-arrestin signaling in the U2OS cells. Altogether, our study shows that functionally selective ligands for the adenosine A(1) receptor are rare, if existing at all. A thorough analysis of biased signaling on other GPCRs also reveals that only very few compounds can be considered functionally selective. This might indicate that the concept of functional selectivity is less common than speculated.

| S-EPMC3568431 | biostudies-literature

SANTA: quantifying the functional content of molecular networks.

Project description:Linking networks of molecular interactions to cellular functions and phenotypes is a key goal in systems biology. Here, we adapt concepts of spatial statistics to assess the functional content of molecular networks. Based on the guilt-by-association principle, our approach (called SANTA) quantifies the strength of association between a gene set and a network, and functionally annotates molecular networks like other enrichment methods annotate lists of genes. As a general association measure, SANTA can (i) functionally annotate experimentally derived networks using a collection of curated gene sets and (ii) annotate experimentally derived gene sets using a collection of curated networks, as well as (iii) prioritize genes for follow-up analyses. We exemplify the efficacy of SANTA in several case studies using the S. cerevisiae genetic interaction network and genome-wide RNAi screens in cancer cell lines. Our theory, simulations, and applications show that SANTA provides a principled statistical way to quantify the association between molecular networks and cellular functions and phenotypes. SANTA is available from http://bioconductor.org/packages/release/bioc/html/SANTA.html.

| S-EPMC4161294 | biostudies-literature

Advancing Functional Utility of PAR-CLIP by Quantifying Background Binding to mRNAs and lncRNAs

Project description:We developed a method for measuring non-specific background in PAR-CLIP data demonstrating that covalently crosslinked background binding is common, reproducible and apparently universal. Furthermore, we show that quantitative determination of background is essential for identifying targets of weakly binding RNA-binding proteins and can substantially improve motif analysis. To define background binding events in PAR-CLIP data we performed the standard PAR-CLIP protocol (Hafner et al., Cell 2010.) on lysates expressing a commonly used non-RBP control, FLAG-GFP. After FLAG-tag immunopurification of UV 365nm irradiated lysates prepared from cells supplemented with 4-thiouridine (4SU), RNA was partially digested with RNase T1, radiolabeled and separated by SDS-PAGE. Reads were sequenced by Illumina HiSeq. PAR-CLIP was also performed for HuR. Included as well is a total from lysates treated like PAR-CLIP, but without immunoprecipitation (see sample description for more detail).

2013-11-07 | E-GEOD-50989 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data