Biology subjects

Hunyady, L.

Publications and source records attributed to Hunyady, L..

3 recordsLinked to original sources

bioRxiv2023

ArreSTick Motif is Responsible for GPCR-beta-Arrestin Binding Stability and Extends Phosphorylation-Dependent beta-arrestin Interactions to Non-Receptor Proteins

The binding and function of {beta}-arrestins are regulated by specific phosphorylation motifs present in G protein-coupled receptors (GPCRs). However, the exact arrangement of phosphorylated amino acids responsible for establishing a stable interaction remains unclear. To investigate this pattern, we employed a 1D sequence convolution model trained on a dataset of GPCRs that have established {beta}-arrestin binding properties. This approach allowed us to identify the amino acid pattern required for GPCRs to form stable interactions with {beta}-arrestins. This motif was named "arreSTick." Our data show that the model predicts the strength of the coupling between GPCRs and {beta}-arrestins with high accuracy, as well as the specific location of the interaction within the receptor sequence. Furthermore, we show that the arreSTick pattern is not limited to GPCRs, and is also present in numerous non-receptor proteins. Using a proximity biotinylation assay and mass spectrometry analysis, we demonstrate that the arreSTick motif controls the interaction between numerous non-receptor proteins and {beta}-arrestins. For example, the HIV-1 Tat Specific Factor 1 (HTSF1 or HTATSF1), a nuclear transcription factor, contains the arreSTick pattern, and our data show that its subcellular localization is influenced by its coupling to {beta}-arrestin2. Our findings unveil a broader regulatory role for {beta}-arrestins in phosphorylation-dependent interactions, extending beyond GPCRs to encompass non-receptor proteins as well.

biochemistry↗

bioRxiv2023

Receptor endocytosis orchestrates the spatiotemporal bias of β-arrestin signaling

The varying efficacy of biased and balanced agonists is generally explained by the stabilization of different active receptor conformations. In this study, systematic profiling of transducer activation of AT1 angiotensin receptor agonists revealed that the extent and kinetics of {beta}-arrestin binding exhibit substantial ligand-dependent differences, which however completely disappear upon the inhibition of receptor internalization. Even weak partial agonists for the {beta}- arrestin pathway acted as full or near full agonists, if receptor endocytosis was prevented, indicating that receptor conformation is not an exclusive determinant of {beta}-arrestin recruitment. The ligand-dependent variance in {beta}-arrestin translocation at endosomes was much larger than it was at the plasma membrane, showing that ligand efficacy in the {beta}-arrestin pathway is spatiotemporally determined. Experimental investigations and mathematical modeling demonstrated how multiple factors concurrently shape the effects of agonists on endosomal receptor-{beta}-arrestin binding and thus determine the extent of bias. Among others, ligand dissociation rate and G protein activity have particularly strong impact on receptor-{beta}-arrestin interaction, and their effects are integrated at endosomes. Our results highlight that endocytosis forms a key spatiotemporal platform for biased GPCR signaling and can aid the development of more efficacious functionally-selective compounds. One Sentence summaryAgonist-specific differences in {beta}-arrestin recruitment are mainly determined by the ligand dissociation rate and G protein activation at the endosomes.

cell biology↗

bioRxiv2021

Computational drug repurposing against SARS-CoV-2 reveals plasma membrane cholesterol depletion as key factor of antiviral drug activity

Comparing SARS-CoV-2 infection-induced gene expression signatures to drug treatment-induced gene expression signatures is a promising bioinformatic tool to repurpose existing drugs against SARS-CoV-2. The general hypothesis of signature based drug repurposing is that drugs with inverse similarity to a disease signature can reverse disease phenotype and thus be effective against it. However, in the case of viral infection diseases, like SARS-CoV-2, infected cells also activate adaptive, antiviral pathways, so that the relationship between effective drug and disease signature can be more ambiguous. To address this question, we analysed gene expression data from in vitro SARS-CoV-2 infected cell lines, and gene expression signatures of drugs showing anti-SARS-CoV-2 activity. Our extensive functional genomic analysis showed that both infection and treatment with in vitro effective drugs leads to activation of antiviral pathways like NFkB and JAK-STAT. Based on the similarity - and not inverse similarity - between drug and infection-induced gene expression signatures, we were able to predict the in vitro antiviral activity of drugs. We also identified SREBF1/2, key regulators of lipid metabolising enzymes, as the most activated transcription factors by several in vitro effective antiviral drugs. Using a fluorescently labeled cholesterol sensor, we showed that these drugs decrease the cholesterol levels of plasma-membrane. Supplementing drug-treated cells with cholesterol reversed the in vitro antiviral effect, suggesting the depleting plasma-membrane cholesterol plays a key role in virus inhibitory mechanism. Our results can help to more effectively repurpose approved drugs against SARS-CoV-2, and also highlights key mechanisms behind their antiviral effect. O_FIG O_LINKSMALLFIG WIDTH=171 HEIGHT=200 SRC="FIGDIR/small/459786v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@7cd823org.highwire.dtl.DTLVardef@51f699org.highwire.dtl.DTLVardef@114c555org.highwire.dtl.DTLVardef@a774ee_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗