G protein-coupled receptor endocytosis generates spatiotemporal bias in ß-arrestin signaling.

The stabilization of different active conformations of G protein-coupled receptors is thought to underlie the varying efficacies of biased and balanced agonists. Here, profiling the activation of signal transducers by angiotensin II type 1 receptor (AT1R) agonists revealed that the extent and kinetics of ß-arrestin binding exhibited substantial ligand-dependent differences, which were lost when receptor internalization was inhibited. When AT1R endocytosis was prevented, even weak partial agonists of the ß-arrestin pathway acted as full or near-full agonists, suggesting that receptor conformation did not exclusively determine ß-arrestin recruitment. The ligand-dependent variance in ß-arrestin translocation was much larger at endosomes than at the plasma membrane, showing that ligand efficacy in the ß-arrestin pathway was spatiotemporally determined. Experimental investigations and mathematical modeling demonstrated how multiple factors concurrently shaped the effects of agonists on endosomal receptor-ß-arrestin binding and thus determined the extent of functional selectivity. Ligand dissociation rate and G protein activity had particularly strong, internalization-dependent effects on the receptor-ß-arrestin interaction. We also showed that endocytosis regulated the agonist efficacies of two other receptors with sustained ß-arrestin binding: the V2 vasopressin receptor and a mutant ß2-adrenergic receptor. In the absence of endocytosis, the agonist-dependent variance in ß-arrestin2 binding was markedly diminished. Our results suggest that endocytosis determines the spatiotemporal bias in GPCR signaling and can aid in the development of more efficacious, functionally selective compounds.

Cannabinoid receptor type 1 (CB1R) inhibits hypothalamic leptin signaling via ß-arrestin1 in complex with TC-PTP and STAT3.

Molecular interactions between anorexigenic leptin and orexigenic endocannabinoids, although of great metabolic significance, are not well understood. We report here that hypothalamic STAT3 signaling in mice, initiated by physiological elevations of leptin, is diminished by agonists of the cannabinoid receptor 1 (CB1R). Measurement of STAT3 activation by semi-automated confocal microscopy in cultured neurons revealed that this CB1R-mediated inhibition requires both T cell protein tyrosine phosphatase (TC-PTP) and ß-arrestin1 but is independent of changes in cAMP. Moreover, ß-arrestin1 translocates to the nucleus upon CB1R activation and binds both STAT3 and TC-PTP. Consistently, CB1R activation failed to suppress leptin signaling in ß-arrestin1 knockout mice in vivo, and in neural cells deficient in CB1R, ß-arrestin1 or TC-PTP. Altogether, CB1R activation engages ß-arrestin1 to coordinate the TC-PTP-mediated inhibition of the leptin-evoked neuronal STAT3 response. This mechanism may restrict the anorexigenic effects of leptin when hypothalamic endocannabinoid levels rise, as during fasting or in diet-induced obesity.

An Unexpected Enzyme in Vascular Smooth Muscle Cells: Angiotensin II Upregulates Cholesterol-25-Hydroxylase Gene Expression.

Angiotensin II (AngII) is a vasoactive peptide hormone, which, under pathological conditions, contributes to the development of cardiovascular diseases. Oxysterols, including 25-hydroxycholesterol (25-HC), the product of cholesterol-25-hydroxylase (CH25H), also have detrimental effects on vascular health by affecting vascular smooth muscle cells (VSMCs). We investigated AngII-induced gene expression changes in VSMCs to explore whether AngII stimulus and 25-HC production have a connection in the vasculature. RNA-sequencing revealed that Ch25h is significantly upregulated in response to AngII stimulus. The Ch25h mRNA levels were elevated robustly (~50-fold) 1 h after AngII (100 nM) stimulation compared to baseline levels. Using inhibitors, we specified that the AngII-induced Ch25h upregulation is type 1 angiotensin II receptor- and Gq/11 activity-dependent. Furthermore, p38 MAPK has a crucial role in the upregulation of Ch25h. We performed LC-MS/MS to identify 25-HC in the supernatant of AngII-stimulated VSMCs. In the supernatants, 25-HC concentration peaked 4 h after AngII stimulation. Our findings provide insight into the pathways mediating AngII-induced Ch25h upregulation. Our study elucidates a connection between AngII stimulus and 25-HC production in primary rat VSMCs. These results potentially lead to the identification and understanding of new mechanisms in the pathogenesis of vascular impairments.

Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data.

The growing availability of single-cell data, especially transcriptomics, has sparked an increased interest in the inference of cell-cell communication. Many computational tools were developed for this purpose. Each of them consists of a resource of intercellular interactions prior knowledge and a method to predict potential cell-cell communication events. Yet the impact of the choice of resource and method on the resulting predictions is largely unknown. To shed light on this, we systematically compare 16 cell-cell communication inference resources and 7 methods, plus the consensus between the methods' predictions. Among the resources, we find few unique interactions, a varying degree of overlap, and an uneven coverage of specific pathways and tissue-enriched proteins. We then examine all possible combinations of methods and resources and show that both strongly influence the predicted intercellular interactions. Finally, we assess the agreement of cell-cell communication methods with spatial colocalisation, cytokine activities, and receptor protein abundance and find that predictions are generally coherent with those data modalities. To facilitate the use of the methods and resources described in this work, we provide LIANA, a LIgand-receptor ANalysis frAmework as an open-source interface to all the resources and methods.

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.

A community challenge for a pancancer drug mechanism of action inference from perturbational profile data.

The Columbia Cancer Target Discovery and Development (CTD2) Center is developing PANACEA, a resource comprising dose-responses and RNA sequencing (RNA-seq) profiles of 25 cell lines perturbed with ∼400 clinical oncology drugs, to study a tumor-specific drug mechanism of action. Here, this resource serves as the basis for a DREAM Challenge assessing the accuracy and sensitivity of computational algorithms for de novo drug polypharmacology predictions. Dose-response and perturbational profiles for 32 kinase inhibitors are provided to 21 teams who are blind to the identity of the compounds. The teams are asked to predict high-affinity binding targets of each compound among ∼1,300 targets cataloged in DrugBank. The best performing methods leverage gene expression profile similarity analysis as well as deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessments of drug mechanisms of action.

Patient-specific Boolean models of signalling networks guide personalised treatments.

Prostate cancer is the second most occurring cancer in men worldwide. To better understand the mechanisms of tumorigenesis and possible treatment responses, we developed a mathematical model of prostate cancer which considers the major signalling pathways known to be deregulated. We personalised this Boolean model to molecular data to reflect the heterogeneity and specific response to perturbations of cancer patients. A total of 488 prostate samples were used to build patient-specific models and compared to available clinical data. Additionally, eight prostate cell line-specific models were built to validate our approach with dose-response data of several drugs. The effects of single and combined drugs were tested in these models under different growth conditions. We identified 15 actionable points of interventions in one cell line-specific model whose inactivation hinders tumorigenesis. To validate these results, we tested nine small molecule inhibitors of five of those putative targets and found a dose-dependent effect on four of them, notably those targeting HSP90 and PI3K. These results highlight the predictive power of our personalised Boolean models and illustrate how they can be used for precision oncology.

Characterization of Type 1 Angiotensin II Receptor Activation Induced Dual-Specificity MAPK Phosphatase Gene Expression Changes in Rat Vascular Smooth Muscle Cells.

Activation of the type I angiotensin receptor (AT1-R) in vascular smooth muscle cells (VSMCs) plays a crucial role in the regulation of blood pressure; however, it is also responsible for the development of pathological conditions such as vascular remodeling, hypertension and atherosclerosis. Stimulation of the VSMC by angiotensin II (AngII) promotes a broad variety of biological effects, including gene expression changes. In this paper, we have taken an integrated approach in which an analysis of AngII-induced gene expression changes has been combined with the use of small-molecule inhibitors and lentiviral-based gene silencing, to characterize the mechanism of signal transduction in response to AngII stimulation in primary rat VSMCs. We carried out Affymetrix GeneChip experiments to analyze the effects of AngII stimulation on gene expression; several genes, including DUSP5, DUSP6, and DUSP10, were identified as upregulated genes in response to stimulation. Since various dual-specificity MAPK phosphatase (DUSP) enzymes are important in the regulation of mitogen-activated protein kinase (MAPK) signaling pathways, these genes have been selected for further analysis. We investigated the kinetics of gene-expression changes and the possible signal transduction processes that lead to altered expression changes after AngII stimulation. Our data shows that the upregulated genes can be stimulated through multiple and synergistic signal transduction pathways. We have also found in our gene-silencing experiments that epidermal growth factor receptor (EGFR) transactivation is not critical in the AngII-induced expression changes of the investigated genes. Our data can help us understand the details of AngII-induced long-term effects and the pathophysiology of AT1-R. Moreover, it can help to develop potential interventions for those symptoms that are induced by the over-functioning of this receptor, such as vascular remodeling, cardiac hypertrophy or atherosclerosis.

Modeling in systems biology: Causal understanding before prediction?

Babur et al. (2021) developed the CausalPath tool to infer causal signaling interactions in high-throughput proteomics data that may foster mechanical understanding from large-scale biological datasets.

Why do pathway methods work better than they should?

Pathway analysis methods are frequently applied to cancer gene expression data to identify dysregulated pathways. These methods often infer pathway activity based on the expression of genes belonging to a given pathway, even though the proteins ultimately determine the activity of a given pathway. Furthermore, the association between gene expression levels and protein activities is not well-characterized. Here, we posit that pathway-based methods are effective not because of the correlation between expression and activity of members of a given pathway, but because pathway gene sets overlap with the genes regulated by transcription factors (TFs). Thus, pathway-based methods do not inform about the activity of the pathway of interest but rather reflect changes in TF activities.

Inferring clonal composition from multiple tumor biopsies.

Knowledge about the clonal evolution of a tumor can help to interpret the function of its genetic alterations by identifying initiating events and events that contribute to the selective advantage of proliferative, metastatic, and drug-resistant subclones. Clonal evolution can be reconstructed from estimates of the relative abundance (frequency) of subclone-specific alterations in tumor biopsies, which, in turn, inform on its composition. However, estimating these frequencies is complicated by the high genetic instability that characterizes many cancers. Models for genetic instability suggest that copy number alterations (CNAs) can influence mutation-frequency estimates and thus impede efforts to reconstruct tumor phylogenies. Our analysis suggested that accurate mutation frequency estimates require accounting for CNAs-a challenging endeavour using the genetic profile of a single tumor biopsy. Instead, we propose an optimization algorithm, Chimæra, to account for the effects of CNAs using profiles of multiple biopsies per tumor. Analyses of simulated data and tumor profiles suggested that Chimæra estimates are consistently more accurate than those of previously proposed methods and resulted in improved phylogeny reconstructions and subclone characterizations. Our analyses inferred recurrent initiating mutations in hepatocellular carcinomas, resolved the clonal composition of Wilms' tumors, and characterized the acquisition of mutations in drug-resistant prostate cancers.

Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data.

BACKGROUND: Many functional analysis tools have been developed to extract functional and mechanistic insight from bulk transcriptome data. With the advent of single-cell RNA sequencing (scRNA-seq), it is in principle possible to do such an analysis for single cells. However, scRNA-seq data has characteristics such as drop-out events and low library sizes. It is thus not clear if functional TF and pathway analysis tools established for bulk sequencing can be applied to scRNA-seq in a meaningful way. RESULTS: To address this question, we perform benchmark studies on simulated and real scRNA-seq data. We include the bulk-RNA tools PROGENy, GO enrichment, and DoRothEA that estimate pathway and transcription factor (TF) activities, respectively, and compare them against the tools SCENIC/AUCell and metaVIPER, designed for scRNA-seq. For the in silico study, we simulate single cells from TF/pathway perturbation bulk RNA-seq experiments. We complement the simulated data with real scRNA-seq data upon CRISPR-mediated knock-out. Our benchmarks on simulated and real data reveal comparable performance to the original bulk data. Additionally, we show that the TF and pathway activities preserve cell type-specific variability by analyzing a mixture sample sequenced with 13 scRNA-seq protocols. We also provide the benchmark data for further use by the community. CONCLUSIONS: Our analyses suggest that bulk-based functional analysis tools that use manually curated footprint gene sets can be applied to scRNA-seq data, partially outperforming dedicated single-cell tools. Furthermore, we find that the performance of functional analysis tools is more sensitive to the gene sets than to the statistic used.

Transfer of regulatory knowledge from human to mouse for functional genomics analysis.

Transcriptome profiling followed by differential gene expression analysis often leads to lists of genes that are hard to analyze and interpret. Functional genomics tools are powerful approaches for downstream analysis, as they summarize the large and noisy gene expression space into a smaller number of biological meaningful features. In particular, methods that estimate the activity of processes by mapping transcripts level to process members are popular. However, footprints of either a pathway or transcription factor (TF) on gene expression show superior performance over mapping-based gene sets. These footprints are largely developed for humans and their usability in the broadly-used model organism Mus musculus is uncertain. Evolutionary conservation of the gene regulatory system suggests that footprints of human pathways and TFs can functionally characterize mice data. In this paper we analyze this hypothesis. We perform a comprehensive benchmark study exploiting two state-of-the-art footprint methods, DoRothEA and an extended version of PROGENy. These methods infer TF and pathway activity, respectively. Our results show that both can recover mouse perturbations, confirming our hypothesis that footprints are conserved between mice and humans. Subsequently, we illustrate the usability of PROGENy and DoRothEA by recovering pathway/TF-disease associations from newly generated disease sets. Additionally, we provide pathway and TF activity scores for a large collection of human and mouse perturbation and disease experiments (2374). We believe that this resource, available for interactive exploration and download (https://saezlab.shinyapps.io/footprint_scores/), can have broad applications including the study of diseases and therapeutics.

Signatures of cell death and proliferation in perturbation transcriptomics data-from confounding factor to effective prediction.

Transcriptional perturbation signatures are valuable data sources for functional genomics. Linking perturbation signatures to screenings opens the possibility to model cellular phenotypes from expression data and to identify efficacious drugs. We linked perturbation transcriptomics data from the LINCS-L1000 project with cell viability information upon genetic (Achilles project) and chemical (CTRP screen) perturbations yielding more than 90 000 signature-viability pairs. An integrated analysis showed that the cell viability signature is a major factor underlying perturbation signatures. The signature is linked to transcription factors regulating cell death, proliferation and division time. We used the cell viability-signature relationship to predict viability from transcriptomics signatures, and identified and validated compounds that induce cell death in tumor cell lines. We showed that cellular toxicity can lead to unexpected similarity of signatures, confounding mechanism of action discovery. Consensus compound signatures predicted cell-specific drug sensitivity, even if the signature is not measured in the same cell line, and outperformed conventional drug-specific features. Our results can help in understanding mechanisms behind cell death and removing confounding factors of transcriptomic perturbation screens. To interactively browse our results and predict cell viability in new gene expression samples, we developed CEVIChE (CEll VIability Calculator from gene Expression; https://saezlab.shinyapps.io/ceviche/).

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen.

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.

Predicting human olfactory perception from chemical features of odor molecules.

It is still not possible to predict whether a given molecule will have a perceived odor or what olfactory percept it will produce. We therefore organized the crowd-sourced DREAM Olfaction Prediction Challenge. Using a large olfactory psychophysical data set, teams developed machine-learning algorithms to predict sensory attributes of molecules based on their chemoinformatic features. The resulting models accurately predicted odor intensity and pleasantness and also successfully predicted 8 among 19 rated semantic descriptors ("garlic," "fish," "sweet," "fruit," "burnt," "spices," "flower," and "sour"). Regularized linear models performed nearly as well as random forest-based ones, with a predictive accuracy that closely approaches a key theoretical limit. These models help to predict the perceptual qualities of virtually any molecule with high accuracy and also reverse-engineer the smell of a molecule.

Angiotensin type 1A receptor regulates ß-arrestin binding of the ß2-adrenergic receptor via heterodimerization.

Heterodimerization between angiotensin type 1A receptor (AT1R) and ß2-adrenergic receptor (ß2AR) has been shown to modulate G protein-mediated effects of these receptors. Activation of G protein-coupled receptors (GPCRs) leads to ß-arrestin binding, desensitization, internalization and G protein-independent signaling of GPCRs. Our aim was to study the effect of heterodimerization on ß-arrestin coupling. We found that ß-arrestin binding of ß2AR is affected by activation of AT1Rs. Costimulation with angiotensin II and isoproterenol markedly enhanced the interaction between ß2AR and ß-arrestins, by prolonging the lifespan of ß2AR-induced ß-arrestin2 clusters at the plasma membrane. While candesartan, a conventional AT1R antagonist, had no effect on the ß-arrestin2 binding to ß2AR, TRV120023, a ß-arrestin biased agonist, enhanced the interaction. These findings reveal a new crosstalk mechanism between AT1R and ß2AR, and suggest that enhanced ß-arrestin2 binding to ß2AR can contribute to the pharmacological effects of biased AT1R agonists.

Correction: Improved Methodical Approach for Quantitative BRET Analysis of G Protein Coupled Receptor Dimerization.

[This corrects the article DOI: 10.1371/journal.pone.0109503.].

Improved methodical approach for quantitative BRET analysis of G Protein Coupled Receptor dimerization.

G Protein Coupled Receptors (GPCR) can form dimers or higher ordered oligomers, the process of which can remarkably influence the physiological and pharmacological function of these receptors. Quantitative Bioluminescence Resonance Energy Transfer (qBRET) measurements are the gold standards to prove the direct physical interaction between the protomers of presumed GPCR dimers. For the correct interpretation of these experiments, the expression of the energy donor Renilla luciferase labeled receptor has to be maintained constant, which is hard to achieve in expression systems. To analyze the effects of non-constant donor expression on qBRET curves, we performed Monte Carlo simulations. Our results show that the decrease of donor expression can lead to saturation qBRET curves even if the interaction between donor and acceptor labeled receptors is non-specific leading to false interpretation of the dimerization state. We suggest here a new approach to the analysis of qBRET data, when the BRET ratio is plotted as a function of the acceptor labeled receptor expression at various donor receptor expression levels. With this method, we were able to distinguish between dimerization and non-specific interaction when the results of classical qBRET experiments were ambiguous. The simulation results were confirmed experimentally using rapamycin inducible heterodimerization system. We used this new method to investigate the dimerization of various GPCRs, and our data have confirmed the homodimerization of V2 vasopressin and CaSR calcium sensing receptors, whereas our data argue against the heterodimerization of these receptors with other studied GPCRs, including type I and II angiotensin, ß2 adrenergic and CB1 cannabinoid receptors.

Allosteric interactions within the AT1 angiotensin receptor homodimer: role of the conserved DRY motif.

G protein coupled receptor (GPCR) dimerization has a remarkable impact on the diversity of receptor signaling. Allosteric communication between the protomers of the dimer can alter ligand binding, receptor conformation and interactions with different effector proteins. In this study we investigated the allosteric interactions between wild type and mutant protomers of type 1 angiotensin receptor (AT1R) dimers transiently expressed in CHO cells. In our experimental setup, one protomer of the dimer was selectively stimulated and the ß-arrestin2 binding and conformation alteration of the other protomer was followed. The interaction between ß-arrestin2 and the non-stimulated protomer was monitored through a bioluminescence resonance energy transfer (BRET) based method. To measure the conformational alterations in the non-stimulated protomer directly, we also used a BRET based intramolecular receptor biosensor, which was created by inserting yellow fluorescent protein (YFP) into the 3rd intracellular loop of AT1R and fusing Renilla luciferase (RLuc) to its C terminal region. We have detected ß-arrestin2 binding, and altered conformation of the non-stimulated protomer. The cooperative ligand binding of the receptor homodimer was also observed by radioligand dissociation experiments. Mutation of the conserved DRY sequence in the activated protomer, which is also required for G protein activation, abolished all the observed allosteric effects. These data suggest that allosteric interactions in the homodimers of AT1R significantly affect the function of the non-stimulated protomer, and the conserved DRY motif has a crucial role in these interactions.