Cannabinoid receptor type 1 (CB1R) inhibits hypothalamic leptin signaling via β-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 β-arrestin1 but is independent of changes in cAMP. Moreover, β-arrestin1 translocates to the nucleus upon CB1R activation and binds both STAT3 and TC-PTP. Consistently, CB1R activation failed to suppress leptin signaling in β-arrestin1 knockout mice in vivo, and in neural cells deficient in CB1R, β-arrestin1 or TC-PTP. Altogether, CB1R activation engages β-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.

Combi-seq for multiplexed transcriptome-based profiling of drug combinations using deterministic barcoding in single-cell droplets

Anti-cancer therapies often exhibit only short-term effects. Tumors typically develop drug resistance causing relapses that might be tackled with drug combinations. Identification of the right combination is challenging and would benefit from high-content, high-throughput combinatorial screens directly on patient biopsies. However, such screens require a large amount of material, normally not available from patients. To address these challenges, we present a scalable microfluidic workflow, called Combi-Seq, to screen hundreds of drug combinations in picoliter-size droplets using transcriptome changes as a readout for drug effects. We devise a deterministic combinatorial DNA barcoding approach to encode treatment conditions, enabling the gene expression-based readout of drug effects in a highly multiplexed fashion. We apply Combi-Seq to screen the effect of 420 drug combinations on the transcriptome of K562 cells using only ~250 single cell droplets per condition, to successfully predict synergistic and antagonistic drug pairs, as well as their pathway activities.

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.

Multiple methods to infer cell-cell communication (CCC) from single cell data are currently available. Here, the authors systematically compare 16 CCC inference resources and 7 methods, and develop the LIANA framework as an interface to use and compare all these approaches.

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.

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.

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.

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Drug-perturbed RNA sequencing data can be used to identify drug targets

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Technology-based drug-target definitions often subsume literature definitions

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Literature and screening datasets provide complementary information on drug mechanisms

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.

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.

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 β-arrestin binding of the β2-adrenergic receptor via heterodimerization

Heterodimerization between angiotensin type 1A receptor (AT₁R) and β₂-adrenergic receptor (β₂AR) has been shown to modulate G protein-mediated effects of these receptors. Activation of G protein-coupled receptors (GPCRs) leads to β-arrestin binding, desensitization, internalization and G protein-independent signaling of GPCRs. Our aim was to study the effect of heterodimerization on β-arrestin coupling. We found that β-arrestin binding of β₂AR is affected by activation of AT₁Rs. Costimulation with angiotensin II and isoproterenol markedly enhanced the interaction between β₂AR and β-arrestins, by prolonging the lifespan of β₂AR-induced β-arrestin2 clusters at the plasma membrane. While candesartan, a conventional AT₁R antagonist, had no effect on the β-arrestin2 binding to β₂AR, TRV120023, a β-arrestin biased agonist, enhanced the interaction. These findings reveal a new crosstalk mechanism between AT₁R and β₂AR, and suggest that enhanced β-arrestin2 binding to β₂AR can contribute to the pharmacological effects of biased AT₁R agonists.

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, β2 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 β-arrestin2 binding and conformation alteration of the other protomer was followed. The interaction between β-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 β-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.

Mechanisms of angiotensin II-mediated regulation of aldosterone synthase expression in H295R human adrenocortical and rat adrenal glomerulosa cells

In adrenal zona glomerulosa cells angiotensin II (Ang II) is a key regulator of steroidogenesis. Our purpose was to compare the mechanisms of Ang II-induced changes in the expression level of early transcription factors NR4A1 (NGFIB) and NR4A2 (Nurr1) genes, and the CYP11B2 gene encoding aldosterone synthase in H295R human adrenocortical tumor cells and in primary rat adrenal glomerulosa cells. Real-time PCR studies have demonstrated that Ang II increased the expression levels of NR4A1 and NR4A2 in H295R cells within 1 h after stimulation, which persisted up to 6 h; whereas in rat adrenal glomerulosa cells the kinetics of the expression of these genes were more rapid and transient. Ang II also induced prolonged nuclear translocation of Nurr1 and NGFIB proteins in both cell types. Studies using MEK inhibitor (PD98059, 20 microM), protein kinase C inhibitor (BIM1, 3 microM) and calmodulin kinase (CAMK) inhibitor (KN93, 10 microM) revealed that in rat adrenal glomerulosa cells CAMK-mediated mechanisms play a predominant role in the regulation of CYP11B2. In accordance with earlier findings, in H295R cells MEK inhibition increased the expression of NR4A1, NR4A2 and CYP11B2 genes, however, it decreased the Ang II-induced gene expression levels, suggesting that ERK activation has a role in control of expression of these genes. No such mechanism was detected in rat glomerulosa cells. Sar1-Ile4-Ile8-AngII, which can cause G protein-independent ERK activation, also stimulated the expression of CYP11B2 in H295R cells. These data suggest that the previously reported CAMK-mediated stimulation of early transcription factors NGFIB and Nurr1 has a predominant role in Ang II-induced CYP11B2 activation in rat adrenal glomerulosa cells, whereas in H295R cells ERK activation and G protein-independent mechanisms also contribute to this process.