| Literature DB >> 30546309 |
Jaana van Gastel1,2, Jhana O Hendrickx1,2, Hanne Leysen1,2, Paula Santos-Otte3, Louis M Luttrell4, Bronwen Martin1, Stuart Maudsley1,2.
Abstract
G protein coupled receptors (GPCRs) were first characterized as signal transducers that elicit downstream effects through modulation of guanine (G) nucleotide-binding proteins. The pharmacotherapeutic exploitation of this signaling paradigm has created a drug-based field covering nearly 50% of the current pharmacopeia. Since the groundbreaking discoveries of the late 1990s to the present day, it is now clear however that GPCRs can also generate productive signaling cascades through the modulation of β-arrestin functionality. β-Arrestins were first thought to only regulate receptor desensitization and internalization - exemplified by the action of visual arrestin with respect to rhodopsin desensitization. Nearly 20 years ago, it was found that rather than controlling GPCR signal termination, productive β-arrestin dependent GPCR signaling paradigms were highly dependent on multi-protein complex formation and generated long-lasting cellular effects, in contrast to G protein signaling which is transient and functions through soluble second messenger systems. β-Arrestin signaling was then first shown to activate mitogen activated protein kinase signaling in a G protein-independent manner and eventually initiate protein transcription - thus controlling expression patterns of downstream proteins. While the possibility of developing β-arrestin biased or functionally selective ligands is now being investigated, no additional research has been performed on its possible contextual specificity in treating age-related disorders. The ability of β-arrestin-dependent signaling to control complex and multidimensional protein expression patterns makes this therapeutic strategy feasible, as treating complex age-related disorders will likely require therapeutics that can exert network-level efficacy profiles. It is our understanding that therapeutically targeting G protein-independent effectors such as β-arrestin will aid in the development of precision medicines with tailored efficacy profiles for disease/age-specific contextualities.Entities:
Keywords: GPCRs; age-related disorders; ligand ‘bias’; precision; tailored efficacy; β-arrestin signaling
Year: 2018 PMID: 30546309 PMCID: PMC6280185 DOI: 10.3389/fphar.2018.01369
Source DB: PubMed Journal: Front Pharmacol ISSN: 1663-9812 Impact factor: 5.810
FIGURE 1Ingenuity pathway analysis of β-arrestin1 and 2 interactome. The interactors of a protein are indicative of its function. Thus, to further investigate the function of β-arrestin1 we extracted these from seven sources: BioGrid (https://theBioGrid.org/), HPRD (Human Protein Reference Database: http://www.hprd.org/), IntAct (https://www.ebi.ac.uk/intact/), MINT (The Molecular INTeraction Database), STRING (https://string-db.org/), DIP (Database of Interacting Proteins: http://dip.mbi.ucla.edu/dip/), and CORUM (http://mips.helmholtz-muenchen.de/corum/). This resulted in a list of proteins which are proven interactors of β-arrestin1 (445) and 2 (625). (A,B) Shows the distribution of the aforementioned databanks for β-arrestin1 and 2 respectively. This dataset was further analyzed using Ingenuity Pathway Analysis (IPA). Where we extracted the following information: (C,D) the subcellular distribution – categorized in an unbiased manner using IPA protein annotation – of the interactors: Plasma Membrane, Nucleus, Cytoplasm, and Extracellular space, i, for β-arrestin1 and 2 respectively; (E,F) the functional annotation – again performed using unbiased IPA-based classification – of the interacting proteins: Cytokine, Enzyme, GPCR, growth factor, ion channel, kinase, peptidase, phosphatase, transcription regulator, translation regulator, transmembrane receptor, and transporter, β-arrestin1 and 2 respectively; and the Top 20 Canonical Pathways, β-arrestin1 and 2 respectively, related to this dataset organized in (G,H) a stacked bar chart, and (I,J) a network representation with a cut-off of 15 common genes between pathways, to increase the stringency, where the numbers depicted represent the amount of common proteins between the pathways.
FIGURE 2Analysis of β-arrestin1 and 2 interactome using STRING Interaction Network. To further analyze the networks of (A) β-arrestin1 and (B) β-arrestin2, STRING was used. To increase the stringency of the analysis the following settings were used: for Active Interaction Sources only “Experiments” and “Co-expression” were selected indicating we are only interested in data which is the result of experiments or are known to co-express, this to remove hypothetical data and suggestions. The minimum required interaction score was set to the highest level (0,9), and all unconnected nodes were removed from the network.
FIGURE 3Role of β-arrestins in aging through GeneIndexer analysis of interactome metadata. Reanalysis of the obtained interactomes for β-arrestin1 (ARRB1) and β-arrestin2 (ARRB2), using aging-related interrogation terms with GeneIndexer, in a manner similar to that described previously (Chadwick et al., 2012). The following interrogation terms were used: Ageing, Aging, Senescence, Senescent, Elderly, Elder, and Longevity (i.e., long life). These terms (inclusive of most common spellings and synonyms) were used to obtain as much information as possible as human-generated text descriptions using natural language are often variant between research groups/authors. As seen in previous bioinformatics analyses these interrogation terms all give different results. In this figure, it becomes clear that β-arrestin2 has a stronger connection to aging compared to β-arrestin1, with the exception of the interrogation terms Elderly and Elder. For the generation of this figure, the cosine similarity between the top 10 proteins and every interrogation term was averaged in order to create this table. The cosine similarity scores linking the proteins to each word are listed in Supplementary Table S3.