IUPHAR-DB: updated database content and new features

Chemokine receptor hetero-oligomers regulate monocyte chemotaxis

It is known that stress influences immune cell function. The underlying molecular mechanisms are unclear. We recently reported that many chemokine receptors (CRs) heteromerize with α1-adrenoceptors (α1-ARs) through which CRs are regulated. Here, we show that arginine vasopressin receptor 1A (AVPR1A) heteromerizes with all human CRs, except chemokine (C-X-C motif) receptor (CXCR)1, in recombinant systems and that such heteromers are detectable in THP-1 cells and human monocytes. We demonstrate that ligand-free AVPR1A differentially regulates the efficacy of CR partners to mediate chemotaxis and that AVPR1A ligands disrupt AVPR1A:CR heteromers, which enhances chemokine (C-C motif) receptor (CCR)1-mediated chemotaxis and inhibits CCR2-, CCR8-, and CXCR4-mediated chemotaxis. Using bioluminescence resonance energy transfer to monitor G protein activation and CRISPR/Cas9 gene-edited THP-1 cells lacking AVPR1A or α1B-AR, we show that CRs that share the propensity to heteromerize with α1B/D-ARs and AVPR1A exist and function within interdependent hetero-oligomeric complexes through which the efficacy of CRs to mediate chemotaxis is controlled. Our findings suggest that hetero-oligomers composed of CRs, α1B/D-ARs, and AVPR1A may enable stress hormones to regulate immune cell trafficking.

The IUPHAR/BPS Guide to PHARMACOLOGY in 2024

The IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb; https://www.guidetopharmacology.org) is an open-access, expert-curated, online database that provides succinct overviews and key references for pharmacological targets and their recommended experimental ligands. It includes over 3039 protein targets and 12 163 ligand molecules, including approved drugs, small molecules, peptides and antibodies. Here, we report recent developments to the resource and describe expansion in content over the six database releases made during the last two years. The database update section of this paper focuses on two areas relating to important global health challenges. The first, SARS-CoV-2 COVID-19, remains a major concern and we describe our efforts to expand the database to include a new family of coronavirus proteins. The second area is antimicrobial resistance, for which we have extended our coverage of antibacterials in partnership with AntibioticDB, a collaboration that has continued through support from GARDP. We discuss other areas of curation and also focus on our external links to resources such as PubChem that bring important synergies to the resources.

SiGra: single-cell spatial elucidation through an image-augmented graph transformer

Recent advances in high-throughput molecular imaging have pushed spatial transcriptomics technologies to subcellular resolution, which surpasses the limitations of both single-cell RNA-seq and array-based spatial profiling. The multichannel immunohistochemistry images in such data provide rich information on the cell types, functions, and morphologies of cellular compartments. In this work, we developed a method, single-cell spatial elucidation through image-augmented Graph transformer (SiGra), to leverage such imaging information for revealing spatial domains and enhancing substantially sparse and noisy transcriptomics data. SiGra applies hybrid graph transformers over a single-cell spatial graph. SiGra outperforms state-of-the-art methods on both single-cell and spot-level spatial transcriptomics data from complex tissues. The inclusion of immunohistochemistry images improves the model performance by 37% (95% CI: 27-50%). SiGra improves the characterization of intratumor heterogeneity and intercellular communication and recovers the known microscopic anatomy. Overall, SiGra effectively integrates different spatial modality data to gain deep insights into spatial cellular ecosystems.

Clinical application of serum-based proteomics technology in human tumor research

The rapid development of proteomics technology in the past decades has led to further human understanding of tumor research, and in some ways, the technology plays a very important supporting role in the early detection of tumors. Human serum has been shown to contain a variety of proteins closely related to life activities, and the dynamic change in proteins can often reflect the physiological and pathological conditions of the body. Serum has the advantage of easy extraction, so the application of proteomics technology in serum has become a hot spot and frontier area for the study of malignant tumors. However, there are still many difficulties in the standardized use of proteomic technologies, which inevitably limit the clinical application of proteomic technologies due to the heterogeneity of human proteins leading to incomplete whole proteome populations, in addition to most serum protein markers being now not highly specific in aiding the early detection of tumors. Nevertheless, further development of proteomics technologies will greatly increase our understanding of tumor biology and help discover more new tumor biomarkers with specificity that will enable medical technology.

spaCI: deciphering spatial cellular communications through adaptive graph model

Cell-cell communications are vital for biological signalling and play important roles in complex diseases. Recent advances in single-cell spatial transcriptomics (SCST) technologies allow examining the spatial cell communication landscapes and hold the promise for disentangling the complex ligand-receptor (L-R) interactions across cells. However, due to frequent dropout events and noisy signals in SCST data, it is challenging and lack of effective and tailored methods to accurately infer cellular communications. Herein, to decipher the cell-to-cell communications from SCST profiles, we propose a novel adaptive graph model with attention mechanisms named spaCI. spaCI incorporates both spatial locations and gene expression profiles of cells to identify the active L-R signalling axis across neighbouring cells. Through benchmarking with currently available methods, spaCI shows superior performance on both simulation data and real SCST datasets. Furthermore, spaCI is able to identify the upstream transcriptional factors mediating the active L-R interactions. For biological insights, we have applied spaCI to the seqFISH+ data of mouse cortex and the NanoString CosMx Spatial Molecular Imager (SMI) data of non-small cell lung cancer samples. spaCI reveals the hidden L-R interactions from the sparse seqFISH+ data, meanwhile identifies the inconspicuous L-R interactions including THBS1-ITGB1 between fibroblast and tumours in NanoString CosMx SMI data. spaCI further reveals that SMAD3 plays an important role in regulating the crosstalk between fibroblasts and tumours, which contributes to the prognosis of lung cancer patients. Collectively, spaCI addresses the challenges in interrogating SCST data for gaining insights into the underlying cellular communications, thus facilitates the discoveries of disease mechanisms, effective biomarkers and therapeutic targets.

The IUPHAR/BPS guide to PHARMACOLOGY in 2022: curating pharmacology for COVID-19, malaria and antibacterials

The IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb; www.guidetopharmacology.org) is an open-access, expert-curated database of molecular interactions between ligands and their targets. We describe expansion in content over nine database releases made during the last two years, which has focussed on three main areas of infection. The COVID-19 pandemic continues to have a major impact on health worldwide. GtoPdb has sought to support the wider research community to understand the pharmacology of emerging drug targets for SARS-CoV-2 as well as potential targets in the host to block viral entry and reduce the adverse effects of infection in patients with COVID-19. We describe how the database rapidly evolved to include a new family of Coronavirus proteins. Malaria remains a global threat to half the population of the world. Our database content continues to be enhanced through our collaboration with Medicines for Malaria Venture (MMV) on the IUPHAR/MMV Guide to MALARIA PHARMACOLOGY (www.guidetomalariapharmacology.org). Antibiotic resistance is also a growing threat to global health. In response, we have extended our coverage of antibacterials in partnership with AntibioticDB.

Integrated computational analyses reveal novel insights into the stromal microenvironment of SHH-subtype medulloblastoma

Medulloblastoma is the most common malignant brain tumour of childhood. While our understanding of this disease has progressed substantially in recent years, the role of tumour microenvironment remains unclear. Given the increasing role of microenvironment-targeted therapeutics in other cancers, this study was aimed at further exploring its role in medulloblastoma. Multiple computational techniques were used to analyze open-source bulk and single cell RNA seq data from primary samples derived from all subgroups of medulloblastoma. Gene expression is used to infer stromal subpopulations, and network-based approaches are used to identify potential therapeutic targets. Bulk data was obtained from 763 medulloblastoma samples and single cell data from an additional 7241 cells from 23 tumours. Independent bulk (285 tumours) and single cell (32,868 cells from 29 tumours) validation cohorts were used to verify results. The SHH subgroup was found to be enriched in stromal activity, including the epithelial-to-mesenchymal transition, while group 3 is comparatively stroma-suppressed. Several receptor and ligand candidates underlying this difference are identified which we find to correlate with metastatic potential of SHH medulloblastoma. Additionally, a biologically active gradient is detected within SHH medulloblastoma, from "stroma-active" to "stroma-suppressed" cells which may have relevance to targeted therapy. This study serves to further elucidate the role of the stromal microenvironment in SHH-subgroup medulloblastoma and identify novel treatment possibilities for this challenging disease.

Applications and analytical tools of cell communication based on ligand-receptor interactions at single cell level

BACKGROUND

Cellular communication is an essential feature of multicellular organisms. Binding of ligands to their homologous receptors, which activate specific cell signaling pathways, is a basic type of cellular communication and intimately linked to many degeneration processes leading to diseases.

MAIN BODY

This study reviewed the history of ligand-receptor and presents the databases which store ligand-receptor pairs. The recently applications and research tools of ligand-receptor interactions for cell communication at single cell level by using single cell RNA sequencing have been sorted out.

CONCLUSION

The summary of the advantages and disadvantages of analysis tools will greatly help researchers analyze cell communication at the single cell level. Learning cell communication based on ligand-receptor interactions by single cell RNA sequencing gives way to developing new target drugs and personalizing treatment.

Plasticity of seven-transmembrane-helix receptor heteromers in human vascular smooth muscle cells

Recently, we reported that the chemokine (C-X-C motif) receptor 4 (CXCR4) and atypical chemokine receptor 3 (ACKR3) heteromerize with α_1A/B/D-adrenoceptors (ARs) and arginine vasopressin receptor 1A (AVPR1A) in recombinant systems and in rodent and human vascular smooth muscle cells (hVSMCs). In these studies, we observed that heteromerization between two receptor partners may depend on the presence and the expression levels of other partnering receptors. To test this hypothesis and to gain initial insight into the formation of these receptor heteromers in native cells, we utilized proximity ligation assays in hVSMCs to visualize receptor-receptor proximity and systematically studied how manipulation of the expression levels of individual protomers affect heteromerization patterns among other interacting receptor partners. We confirmed subtype-specific heteromerization between endogenously expressed α_1A/B/D-ARs and detected that AVPR1A also heteromerizes with α_1A/B/D-ARs. siRNA knockdown of CXCR4 and of ACKR3 resulted in a significant re-arrangement of the heteromerization patterns among α₁-AR subtypes. Similarly, siRNA knockdown of AVPR1A significantly increased heteromerization signals for seven of the ten receptor pairs between CXCR4, ACKR3, and α_1A/B/D-ARs. Our findings suggest plasticity of seven transmembrane helix (7TM) receptor heteromerization in native cells and could be explained by a supramolecular organization of these receptors within dynamic clusters in the plasma membrane. Because we previously observed that recombinant CXCR4, ACKR3, α_1a-AR and AVPR1A form hetero-oligomeric complexes composed of 2–4 different protomers, which show signaling properties distinct from individual protomers, re-arrangements of receptor heteromerization patterns in native cells may contribute to the phenomenon of context-dependent GPCR signaling. Furthermore, these findings advise caution in the interpretation of functional consequences after 7TM receptor knockdown in experimental models. Alterations of the heteromerization patterns among other receptor partners may alter physiological and pathological responses, in particular in more complex systems, such as studies on the function of isolated organs or in in vivo experiments.

Class A G protein-coupled receptors assemble into functional higher-order hetero-oligomers

Although class A seven-transmembrane helix (7TM) receptor hetero-oligomers have been proposed, information on the assembly and function of such higher-order hetero-oligomers is not available. Utilizing bioluminescence resonance energy transfer (BRET), bimolecular luminescence/fluorescence complementation (BiLC/BiFC), and BiLC/BiFC BRET in HEK293T cells, we provide evidence that chemokine (C-X-C motif) receptor 4, atypical chemokine receptor 3, α_1a -adrenoceptor, and arginine vasopressin receptor 1A form hetero-oligomers composed of 2-4 different protomers. We show that hetero-oligomerization per se and ligand binding to individual protomers regulate agonist-induced coupling to the signaling transducers of interacting receptor partners. Our findings support the concept that receptor hetero-oligomers form supramolecular machineries with molecular signaling properties distinct from the individual protomers. These findings provide a mechanism for the phenomenon of context-dependent receptor function.

A methodology for predicting tissue-specific metabolic roles of receptors applied to subcutaneous adipose

The human biological system uses 'inter-organ' communication to achieve a state of homeostasis. This communication occurs through the response of receptors, located on target organs, to the binding of secreted ligands from source organs. Albeit years of research, the roles these receptors play in tissues is only partially understood. This work presents a new methodology based on the enrichment analysis scores of co-expression networks fed into support vector machines (SVMs) and k-NN classifiers to predict the tissue-specific metabolic roles of receptors. The approach is primarily based on the detection of coordination patterns of receptors expression. These patterns and the enrichment analysis scores of their co-expression networks were used to analyse ~ 700 receptors and predict metabolic roles of receptors in subcutaneous adipose. To facilitate supervised learning, a list of known metabolic and non-metabolic receptors was constructed using a semi-supervised approach following literature-based verification. Our approach confirms that pathway enrichment scores are good signatures for correctly classifying the metabolic receptors in adipose. We also show that the k-NN method outperforms the SVM method in classifying metabolic receptors. Finally, we predict novel metabolic roles of receptors. These predictions can enhance biological understanding and the development of new receptor-targeting metabolic drugs.

CellTalkDB: a manually curated database of ligand-receptor interactions in humans and mice

Cell-cell communications in multicellular organisms generally involve secreted ligand-receptor (LR) interactions, which is vital for various biological phenomena. Recent advancements in single-cell RNA sequencing (scRNA-seq) have effectively resolved cellular phenotypic heterogeneity and the cell-type composition of complex tissues, facilitating the systematic investigation of cell-cell communications at single-cell resolution. However, assessment of chemical-signal-dependent cell-cell communication through scRNA-seq relies heavily on prior knowledge of LR interaction pairs. We constructed CellTalkDB (http://tcm.zju.edu.cn/celltalkdb), a manually curated comprehensive database of LR interaction pairs in humans and mice comprising 3398 human LR pairs and 2033 mouse LR pairs, through text mining and manual verification of known protein-protein interactions using the STRING database, with literature-supported evidence for each pair. Compared with SingleCellSignalR, the largest LR-pair resource, CellTalkDB includes not only 2033 mouse LR pairs but also 377 additional human LR pairs. In conclusion, the data on human and mouse LR pairs contained in CellTalkDB could help to further the inference and understanding of the LR-interaction-based cell-cell communications, which might provide new insights into the mechanism underlying biological processes.

Reading the heart at single-cell resolution

The burgeoning field of single-cell transcriptomics augments our ability to scrutinize organ systems at unprecedented resolutions. Single-cell RNA sequencing (scRNA-seq) and analytical techniques have shed light on the cellular heterogeneity, developmental trajectories, intercellular communications of the cardiac system, and thus contributed much to the understanding of cardiac development, homeostasis and disorders. Although generalized protocols are well established for scRNA-seq pipelines, customized sample preparation, quality control, and data interpretation are still needed in cardiac research. In this article, we highlight major steps that impact data quality in scRNA-seq experiments, with particular focus on sample and data processing of cardiomyocytes. We also summarize popular applications of scRNA-seq, outlining general tools, caveats and examples in cardiac research.

The IUPHAR Guide to Immunopharmacology: connecting immunology and pharmacology

Given the critical role that the immune system plays in a multitude of diseases, having a clear understanding of the pharmacology of the immune system is crucial to new drug discovery and development. Here we describe the International Union of Basic and Clinical Pharmacology (IUPHAR) Guide to Immunopharmacology (GtoImmuPdb), which connects expert-curated pharmacology with key immunological concepts and aims to put pharmacological data into the hands of immunologists. In the pursuit of new therapeutics, pharmacological databases are a vital resource to researchers through providing accurate information on the fundamental science underlying drug action. This extension to the existing IUPHAR/British Pharmacological Society Guide to Pharmacology supports research into the development of drugs targeted at modulating immune, inflammatory or infectious components of disease. To provide a deeper context for how the resource can support research we show data in GtoImmuPdb relating to a case study on the targeting of vascular inflammation.

Characterization of heteromeric complexes between chemokine (C-X-C motif) receptor 4 and α1-adrenergic receptors utilizing intermolecular bioluminescence resonance energy transfer assays

Recently, we reported that chemokine (C-X-C motif) receptor 4 (CXCR4) heteromerizes with α₁-adrenergic receptors (AR) on the cell surface of vascular smooth muscle cells, through which the receptors cross-talk. Direct biophysical evidence for CXCR4:α₁-AR heteromers, however, is lacking. Here we utilized bimolecular luminescence/fluorescence complementation (BiLC/BiFC) combined with intermolecular bioluminescence resonance energy transfer (BRET) assays in HEK293T cells to evaluate CXCR4:α_1a/b/d-AR heteromerization. Atypical chemokine receptor 3 (ACKR3) and metabotropic glutamate receptor 1 (mGlu₁R) were utilized as controls. BRET between CXCR4-RLuc (Renilla reniformis) and enhanced yellow fluorescent protein (EYFP)-tagged ACKR3 or α_1a/b/d-ARs fulfilled criteria for constitutive heteromerization. BRET between CXCR4-RLuc and EYFP or mGlu₁R-EYFP were nonspecific. BRET₅₀ for CXCR4:ACKR3 and CXCR4:α_1a/b/d-AR heteromers were comparable. Stimulation of cells with phenylephrine increased BRET_max of CXCR4:α_1a/b/d-AR heteromers without affecting BRET₅₀; stimulation with CXCL12 reduced BRET_max of CXCR4:α_1a-AR heteromers, but did not affect BRET₅₀ or BRET_max/50 for CXCR4:α_1b/d-AR. A peptide analogue of transmembrane domain (TM) 2 of CXCR4 reduced BRET_max of CXCR4:α_1a/b/d-AR heteromers and increased BRET₅₀ of CXCR4:α_1a/b-AR interactions. A TM4 analogue of CXCR4 did not alter BRET. We observed CXCR4, α_1a-AR and mGlu₁R homodimerization by BiFC/BiLC, and heteromerization of homodimeric CXCR4 with proto- and homodimeric α_1a-AR by BiFC/BiLC BRET. BiFC/BiLC BRET for interactions between homodimeric CXCR4 and homodimeric mGlu₁R was nonspecific. Our findings suggest that the heteromerization affinity of CXCR4 for ACKR3 and α₁-ARs is comparable, provide evidence for conformational changes of the receptor complexes upon agonist binding and support the concept that proto- and oligomeric CXCR4 and α₁-ARs constitutively form higher-order hetero-oligomeric receptor clusters.

The IUPHAR/BPS Guide to PHARMACOLOGY in 2020: extending immunopharmacology content and introducing the IUPHAR/MMV Guide to MALARIA PHARMACOLOGY

The IUPHAR/BPS Guide to PHARMACOLOGY (www.guidetopharmacology.org) is an open-access, expert-curated database of molecular interactions between ligands and their targets. We describe significant updates made over the seven releases during the last two years. The database is notably enhanced through the continued linking of relevant pharmacology with key immunological data types as part of the IUPHAR Guide to IMMUNOPHARMACOLOGY (www.guidetoimmunopharmacology.org) and by a major new extension, the IUPHAR/MMV Guide to Malaria PHARMACOLOGY (www.guidetomalariapharmacology.org). The latter has been constructed in partnership with the Medicines for Malaria Venture, an organization dedicated to identifying, developing and delivering new antimalarial therapies that are both effective and affordable. This is in response to the global challenge of over 200 million cases of malaria and 400 000 deaths worldwide, with the majority in the WHO Africa Region. It provides new pharmacological content, including molecular targets in the malaria parasite, interaction data for ligands with antimalarial activity, and establishes curation of data from screening assays, used routinely in antimalarial drug discovery, against the whole organism. A dedicated portal has been developed to provide quick and focused access to these new data.

Comparison of Target Features for Predicting Drug-Target Interactions by Deep Neural Network Based on Large-Scale Drug-Induced Transcriptome Data

Uncovering drug-target interactions (DTIs) is pivotal to understand drug mode-of-action (MoA), avoid adverse drug reaction (ADR), and seek opportunities for drug repositioning (DR). For decades, in silico predictions for DTIs have largely depended on structural information of both targets and compounds, e.g., docking or ligand-based virtual screening. Recently, the application of deep neural network (DNN) is opening a new path to uncover novel DTIs for thousands of targets. One important question is which features for targets are most relevant to DTI prediction. As an early attempt to answer this question, we objectively compared three canonical target features extracted from: (i) the expression profiles by gene knockdown (GEPs); (ii) the protein–protein interaction network (PPI network); and (iii) the pathway membership (PM) of a target gene. For drug features, the large-scale drug-induced transcriptome dataset, or the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 dataset was used. All these features are closely related to protein function or drug MoA, of which utility is only sparsely investigated. In particular, few studies have compared the three types of target features in DNN-based DTI prediction under the same evaluation scheme. Among the three target features, the PM and the PPI network show similar performances superior to GEPs. DNN models based on both features consistently outperformed other machine learning methods such as naïve Bayes, random forest, or logistic regression.

Accessing Expert-Curated Pharmacological Data in the IUPHAR/BPS Guide to PHARMACOLOGY

The IUPHAR/BPS Guide to PHARMACOLOGY is an expert-curated, open-access database of information on drug targets and the substances that act on them. This unit describes the procedures for searching and downloading ligand-target binding data and for finding detailed annotations and the most relevant literature. The database includes concise overviews of the properties of 1,700 data-supported human drug targets and related proteins, divided into families, and 9,000 small molecule and peptide experimental ligands and approved drugs that bind to those targets. More detailed descriptions of pharmacology, function, and pathophysiology are provided for a subset of important targets. The information is reviewed regularly by expert subcommittees of the IUPHAR Committee on Receptor Nomenclature and Drug Classification. A new immunopharmacology portal has recently been added, drawing together data on immunological targets, ligands, cell types, processes and diseases. The data are available for download and can be accessed computationally via Web services. © 2018 by John Wiley & Sons, Inc.

CSgator: an integrated web platform for compound set analysis

Drug discovery typically involves investigation of a set of compounds (e.g. drug screening hits) in terms of target, disease, and bioactivity. CSgator is a comprehensive analytic tool for set-wise interpretation of compounds. It has two unique analytic features of Compound Set Enrichment Analysis (CSEA) and Compound Cluster Analysis (CCA), which allows batch analysis of compound set in terms of (i) target, (ii) bioactivity, (iii) disease, and (iv) structure. CSEA and CCA present enriched profiles of targets and bioactivities in a compound set, which leads to novel insights on underlying drug mode-of-action, and potential targets. Notably, we propose a novel concept of 'Hit Enriched Assays", i.e. bioassays of which hits are enriched among a given set of compounds. As an example, we show its utility in revealing drug mode-of-action or identifying hidden targets for anti-lymphangiogenesis screening hits. CSgator is available at http://csgator.ewha.ac.kr , and most analytic results are downloadable.

Mutagenesis of α-Conotoxins for Enhancing Activity and Selectivity for Nicotinic Acetylcholine Receptors

Nicotinic acetylcholine receptors (nAChRs) are found throughout the mammalian body and have been studied extensively because of their implication in a myriad of diseases. α-Conotoxins (α-CTxs) are peptide neurotoxins found in the venom of marine snails of genus Conus. α-CTxs are potent and selective antagonists for a variety of nAChR isoforms. Over the past 40 years, α-CTxs have proven to be valuable molecular probes capable of differentiating between closely related nAChR subtypes and have contributed greatly to understanding the physiological role of nAChRs in the mammalian nervous system. Here, we review the amino acid composition and structure of several α-CTxs that selectively target nAChR isoforms and explore strategies and outcomes for introducing mutations in native α-CTxs to direct selectivity and enhance binding affinity for specific nAChRs. This review will focus on structure-activity relationship studies involving native α-CTxs that have been rationally mutated and molecular interactions that underlie binding between ligand and nAChR isoform.

In Silico Drug-Target Profiling

Pharmacological science is trying to establish the link between chemicals, targets, and disease-related phenotypes. A plethora of chemical proteomics and structural data have been generated, thanks to the target-based approach that has dominated drug discovery at the turn of the century. There is an invaluable source of information for in silico target profiling. Prediction is based on the principle of chemical similarity (similar drugs bind similar targets) or on first principles from the biophysics of molecular interactions. In the first case, compound comparison is made through ligand-based chemical similarity search or through classifier-based machine learning approach. The 3D techniques are based on 3D structural descriptors or energy-based scoring scheme to infer a binding affinity of a compound with its putative target. More recently, a new approach based on compound set metric has been proposed in which a query compound is compared with a whole of compounds associated with a target or a family of targets. This chapter reviews the different techniques of in silico target profiling and their main applications such as inference of unwanted targets, drug repurposing, or compound prioritization after phenotypic-based screening campaigns.

Review and comparative assessment of similarity-based methods for prediction of drug–protein interactions in the druggable human proteome

Drug–protein interactions (DPIs) underlie the desired therapeutic actions and the adverse side effects of a significant majority of drugs. Computational prediction of DPIs facilitates research in drug discovery, characterization and repurposing. Similarity-based methods that do not require knowledge of protein structures are particularly suitable for druggable genome-wide predictions of DPIs. We review 35 high-impact similarity-based predictors that were published in the past decade. We group them based on three types of similarities and their combinations that they use. We discuss and compare key aspects of these methods including source databases, internal databases and their predictive models. Using our novel benchmark database, we perform comparative empirical analysis of predictive performance of seven types of representative predictors that utilize each type of similarity individually and all possible combinations of similarities. We assess predictive quality at the database-wide DPI level and we are the first to also include evaluation over individual drugs. Our comprehensive analysis shows that predictors that use more similarity types outperform methods that employ fewer similarities, and that the model combining all three types of similarities secures area under the receiver operating characteristic curve of 0.93. We offer a comprehensive analysis of sensitivity of predictive performance to intrinsic and extrinsic characteristics of the considered predictors. We find that predictive performance is sensitive to low levels of similarities between sequences of the drug targets and several extrinsic properties of the input drug structures, drug profiles and drug targets. The benchmark database and a webserver for the seven predictors are freely available at http://biomine.cs.vcu.edu/servers/CONNECTOR/.

Synthesis, Structure and Biological Activity of CIA and CIB, Two α-Conotoxins from the Predation-Evoked Venom of Conus catus

Cone snails produce a fast-acting and often paralyzing venom that is usually injected into their prey or predator through a hypodermic needle-like modified radula tooth. Many diverse compounds are found in their venom including small molecules, peptides and enzymes. However, peptidic toxins called conotoxins (10⁻40 residues and 2⁻4 disulfide bonds) largely dominate these cocktails. These disulfide rich toxins are very valuable pharmacological tools for investigating the function of ions channels, G-protein coupled receptors, transporters and enzymes. Here, we report on the synthesis, structure determination and biological activities of two α-conotoxins, CIA and CIB, found in the predatory venom of the piscivorous species Conus catus. CIA is a typical 3/5 α-conotoxin that blocks the rat muscle type nAChR with an IC₅₀ of 5.7 nM. Interestingly, CIA also inhibits the neuronal rat nAChR subtype α3β2 with an IC₅₀ of 2.06 μM. CIB is a 4/7 α-conotoxin that blocks rat neuronal nAChR subtypes, including α3β2 (IC₅₀ = 128.9 nM) and α7 (IC₅₀ = 1.51 μM). High resolution NMR structures revealed typical α-conotoxin folds for both peptides. We also investigated the in vivo effects of these toxins on fish, since both peptides were identified in the predatory venom of C. catus. Consistent with their pharmacology, CIA was highly paralytic to zebrafish (ED₅₀ = 110 μg/kg), whereas CIB did not affect the mobility of the fish. In conclusion, CIA likely participates in prey capture through muscle paralysis, while the putative ecological role of CIB remains to be elucidated.

The benefits of in silico modeling to identify possible small-molecule drugs and their off-target interactions

The research into the use of small molecules as drugs continues to be a key driver in the development of molecular databases, computer-aided drug design software and collaborative platforms. The evolution of computational approaches is driven by the essential criteria that a drug molecule has to fulfill, from the affinity to targets to minimal side effects while having adequate absorption, distribution, metabolism, and excretion (ADME) properties. A combination of ligand- and structure-based drug development approaches is already used to obtain consensus predictions of small molecule activities and their off-target interactions. Further integration of these methods into easy-to-use workflows informed by systems biology could realize the full potential of available data in the drug discovery and reduce the attrition of drug candidates.

The IUPHAR/BPS Guide to PHARMACOLOGY in 2018: updates and expansion to encompass the new guide to IMMUNOPHARMACOLOGY

The IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb, www.guidetopharmacology.org) and its precursor IUPHAR-DB, have captured expert-curated interactions between targets and ligands from selected papers in pharmacology and drug discovery since 2003. This resource continues to be developed in conjunction with the International Union of Basic and Clinical Pharmacology (IUPHAR) and the British Pharmacological Society (BPS). As previously described, our unique model of content selection and quality control is based on 96 target-class subcommittees comprising 512 scientists collaborating with in-house curators. This update describes content expansion, new features and interoperability improvements introduced in the 10 releases since August 2015. Our relationship matrix now describes ∼9000 ligands, ∼15 000 binding constants, ∼6000 papers and ∼1700 human proteins. As an important addition, we also introduce our newly funded project for the Guide to IMMUNOPHARMACOLOGY (GtoImmuPdb, www.guidetoimmunopharmacology.org). This has been 'forked' from the well-established GtoPdb data model and expanded into new types of data related to the immune system and inflammatory processes. This includes new ligands, targets, pathways, cell types and diseases for which we are recruiting new IUPHAR expert committees. Designed as an immunopharmacological gateway, it also has an emphasis on potential therapeutic interventions.

Extracting Intercellular Signaling Network of Cancer Tissues using Ligand-Receptor Expression Patterns from Whole-tumor and Single-cell Transcriptomes

Many behaviors of cancer, such as progression, metastasis and drug resistance etc., cannot be fully understood by genetic mutations or intracellular signaling alone. Instead, they are emergent properties of the cell community which forms a tumor. Studies of tumor heterogeneity reveal that many cancer behaviors critically depend on intercellular communication between cancer cells themselves and between cancer-stromal cells by secreted signaling molecules (ligands) and their cognate receptors. We analyzed public cancer transcriptome database for changes in cell-cell interactions as the characteristic of malignancy. We curated a list (>2,500 ligand-receptor pairs) and identified their joint enrichment in tumors from TCGA pan-cancer data. From single-cell RNA-Seq data for a case of melanoma and the specificity of the ligand-receptor interactions and their gene expression measured in individual cells, we constructed a map of a cell-cell communication network that indicates what signal is exchanged between which cell types in the tumor. Such networks establish a new formal phenotype of cancer which captures the cell-cell communication structure - it may open new opportunities for identifying molecular signatures of coordinated behaviors of cancer cells as a population - in turn may become a determinant of cancer progression potential and prognosis.

Protein biomarker druggability profiling

Developing automated and interactive methods for building a model by incorporating mechanistic and potentially causal annotations of ranked biomarkers of a disease or clinical condition followed by a mapping into a contextual framework in disease-linked biochemical pathways can be used for potential drug-target evaluation and for proposing new drug targets. We demonstrate the potential of this approach using ranked protein biomarkers obtained in neonatal sepsis by enrolling 127 infants (39 infants with late onset neonatal sepsis and 88 control infants) and by performing a focused proteomic profile of the sera and by applying the interactive druggability profiling algorithm (DPA) developed by us.

Analysis of the expression of human bitter taste receptors in extraoral tissues

The 25 bitter taste receptors (T2Rs) in humans perform a chemosensory function. However, very little is known about the level of expression of these receptors in different tissues. In this study, using nCounter gene expression we analyzed the expression patterns of human TAS2R transcripts in cystic fibrosis bronchial epithelial (CuFi-1), normal bronchial epithelial (NuLi-1), airway smooth muscle (ASM), pulmonary artery smooth muscle (PASM), mammary epithelial, and breast cancer cells. Our results suggest a specific pattern of TAS2R expression with TAS2R3, 4, 5, 10, 13, 19, and 50 transcripts expressed at moderate levels and TAS2R14 and TAS2R20 (or TASR49) at high levels in the various tissues analyzed. This pattern of expression is mostly independent of tissue origin and the pathological state, except in cancer cells. To elucidate the expression at the protein level, we pursued flow cytometry analysis of select T2Rs from CuFi-1 and NuLi-1 cells. The expression levels observed at the gene level by nCounter analysis correlate with the protein levels for the T2Rs analyzed. Next, to assess the functionality of the expressed T2Rs in these cells, we pursued functional assays measuring intracellular calcium mobilization after stimulation with the bitter compound quinine. Using PLC inhibitor, U-73122, we show that the calcium mobilized in these cells predominantly takes place through the Quinine-T2R-Gαβγ-PLC pathway. This report will accelerate studies aimed at analyzing the pathophysiological function of T2Rs in different extraoral tissues.

An improved approach for predicting drug-target interaction: proteochemometrics to molecular docking

Proteochemometric (PCM) methods, which use descriptors of both the interacting species, i.e. drug and the target, are being successfully employed for the prediction of drug-target interactions (DTI). However, unavailability of non-interacting dataset and determining the applicability domain (AD) of model are a main concern in PCM modeling. In the present study, traditional PCM modeling was improved by devising novel methodologies for reliable negative dataset generation and fingerprint based AD analysis. In addition, various types of descriptors and classifiers were evaluated for their performance. The Random Forest and Support Vector Machine models outperformed the other classifiers (accuracies >98% and >89% for 10-fold cross validation and external validation, respectively). The type of protein descriptors had negligible effect on the developed models, encouraging the use of sequence-based descriptors over the structure-based descriptors. To establish the practical utility of built models, targets were predicted for approved anticancer drugs of natural origin. The molecular recognition interactions between the predicted drug-target pair were quantified with the help of a reverse molecular docking approach. The majority of predicted targets are known for anticancer therapy. These results thus correlate well with anticancer potential of the selected drugs. Interestingly, out of all predicted DTIs, thirty were found to be reported in the ChEMBL database, further validating the adopted methodology. The outcome of this study suggests that the proposed approach, involving use of the improved PCM methodology and molecular docking, can be successfully employed to elucidate the intricate mode of action for drug molecules as well as repositioning them for new therapeutic applications.

Novel Agonist Bioisosteres and Common Structure-Activity Relationships for The Orphan G Protein-Coupled Receptor GPR139

GPR139 is an orphan class A G protein-coupled receptor found mainly in the central nervous system. It has its highest expression levels in the hypothalamus and striatum, regions regulating metabolism and locomotion, respectively, and has therefore been suggested as a potential target for obesity and Parkinson’s disease. The two aromatic amino acids _L-Trp and _L-Phe have been proposed as putative endogenous agonists, and three structurally related benzohydrazide, glycine benzamide, and benzotriazine surrogate agonist series have been published. Herein, we assayed 158 new analogues selected from a pharmacophore model, and identified 12 new GPR139 agonists, containing previously untested bioisosteres. Furthermore, we present the first combined structure-activity relationships, and a refined pharmacophore model to serve as a rationale for future ligand identification and optimization.

GGIP: Structure and sequence-based GPCR-GPCR interaction pair predictor

G Protein-Coupled Receptors (GPCRs) are important pharmaceutical targets. More than 30% of currently marketed pharmaceutical medicines target GPCRs. Numerous studies have reported that GPCRs function not only as monomers but also as homo- or hetero-dimers or higher-order molecular complexes. Many GPCRs exert a wide variety of molecular functions by forming specific combinations of GPCR subtypes. In addition, some GPCRs are reportedly associated with diseases. GPCR oligomerization is now recognized as an important event in various biological phenomena, and many researchers are investigating this subject. We have developed a support vector machine (SVM)-based method to predict interacting pairs for GPCR oligomerization, by integrating the structure and sequence information of GPCRs. The performance of our method was evaluated by the Receiver Operating Characteristic (ROC) curve. The corresponding area under the curve was 0.938. As far as we know, this is the only prediction method for interacting pairs among GPCRs. Our method could accelerate the analyses of these interactions, and contribute to the elucidation of the global structures of the GPCR networks in membranes. Proteins 2016; 84:1224-1233. © 2016 Wiley Periodicals, Inc.

ICEPO: the ion channel electrophysiology ontology

Ion channels are transmembrane proteins that selectively allow ions to flow across the plasma membrane and play key roles in diverse biological processes. A multitude of diseases, called channelopathies, such as epilepsies, muscle paralysis, pain syndromes, cardiac arrhythmias or hypoglycemia are due to ion channel mutations. A wide corpus of literature is available on ion channels, covering both their functions and their roles in disease. The research community needs to access this data in a user-friendly, yet systematic manner. However, extraction and integration of this increasing amount of data have been proven to be difficult because of the lack of a standardized vocabulary that describes the properties of ion channels at the molecular level. To address this, we have developed Ion Channel ElectroPhysiology Ontology (ICEPO), an ontology that allows one to annotate the electrophysiological parameters of the voltage-gated class of ion channels. This ontology is based on a three-state model of ion channel gating describing the three conformations/states that an ion channel can adopt: closed, open and inactivated. This ontology supports the capture of voltage-gated ion channel electrophysiological data from the literature in a structured manner and thus enables other applications such as querying and reasoning tools. Here, we present ICEPO (ICEPO ftp site: ftp://ftp.nextprot.org/pub/current_release/controlled_vocabularies/), as well as examples of its use.

Structure-Based Sequence Alignment of the Transmembrane Domains of All Human GPCRs: Phylogenetic, Structural and Functional Implications

The understanding of G-protein coupled receptors (GPCRs) is undergoing a revolution due to increased information about their signaling and the experimental determination of structures for more than 25 receptors. The availability of at least one receptor structure for each of the GPCR classes, well separated in sequence space, enables an integrated superfamily-wide analysis to identify signatures involving the role of conserved residues, conserved contacts, and downstream signaling in the context of receptor structures. In this study, we align the transmembrane (TM) domains of all experimental GPCR structures to maximize the conserved inter-helical contacts. The resulting superfamily-wide GpcR Sequence-Structure (GRoSS) alignment of the TM domains for all human GPCR sequences is sufficient to generate a phylogenetic tree that correctly distinguishes all different GPCR classes, suggesting that the class-level differences in the GPCR superfamily are encoded at least partly in the TM domains. The inter-helical contacts conserved across all GPCR classes describe the evolutionarily conserved GPCR structural fold. The corresponding structural alignment of the inactive and active conformations, available for a few GPCRs, identifies activation hot-spot residues in the TM domains that get rewired upon activation. Many GPCR mutations, known to alter receptor signaling and cause disease, are located at these conserved contact and activation hot-spot residue positions. The GRoSS alignment places the chemosensory receptor subfamilies for bitter taste (TAS2R) and pheromones (Vomeronasal, VN1R) in the rhodopsin family, known to contain the chemosensory olfactory receptor subfamily. The GRoSS alignment also enables the quantification of the structural variability in the TM regions of experimental structures, useful for homology modeling and structure prediction of receptors. Furthermore, this alignment identifies structurally and functionally important residues in all human GPCRs. These residues can be used to make testable hypotheses about the structural basis of receptor function and about the molecular basis of disease-associated single nucleotide polymorphisms.

G-protein coupled receptors (GPCRs) are a large superfamily of integral membrane proteins that share a characteristic 7 transmembrane helix fold. They detect various molecules outside of the cell and signal their presence to the inside of the cell. At least half of the 800 human GPCRs are potential drug targets, so understanding their structure and function is critical. Experimental structures are now available for at least one receptor from each GPCR class. The structure of the 7 helix fold is highly conserved even for receptors with very low sequence similarity. We analyze the available experimental structures and compare the common inter-helical contacts. Our analysis leads to a unified sequence-structure alignment of the GPCR superfamily that can then be used as the starting point for structure prediction of all other GPCRs. A key result of our analysis is a list of conserved contact residues and activation “hot-spots” residues that are critical for GPCR folding and function. We propose that mutations and natural variants of amino acids at these locations in the GPCRs can dramatically influence their activation state and alter intracellular signaling. This provides hypotheses for the molecular mechanisms underlying disease causing mutants for any GPCR.

Bitter taste receptors: Novel insights into the biochemistry and pharmacology

Bitter taste receptors (T2Rs) belong to the super family of G protein-coupled receptors (GPCRs). There are 25 T2Rs expressed in humans, and these interact with a large and diverse group of bitter ligands. T2Rs are expressed in many extra-oral tissues and can perform diverse physiological roles. Structure-function studies led to the identification of similarities and dissimilarities between T2Rs and Class A GPCRs including amino acid conservation and novel motifs. However, the efficacy of most of the T2R ligands is not yet elucidated and the biochemical pharmacology of T2Rs is poorly understood. Recent studies on T2Rs characterized novel ligands including blockers for these receptors that include inverse agonist and antagonists. In this review we discuss the techniques used for elucidating bitter blockers, concept of ligand bias, generic amino acid numbering, the role of cholesterol, and conserved water molecules in the biochemistry and pharmacology of T2Rs.

The Gq signalling pathway inhibits brown and beige adipose tissue

Brown adipose tissue (BAT) dissipates nutritional energy as heat via the uncoupling protein-1 (UCP1) and BAT activity correlates with leanness in human adults. Here we profile G protein-coupled receptors (GPCRs) in brown adipocytes to identify druggable regulators of BAT. Twenty-one per cent of the GPCRs link to the Gq family, and inhibition of Gq signalling enhances differentiation of human and murine brown adipocytes. In contrast, activation of Gq signalling abrogates brown adipogenesis. We further identify the endothelin/Ednra pathway as an autocrine activator of Gq signalling in brown adipocytes. Expression of a constitutively active Gq protein in mice reduces UCP1 expression in BAT, whole-body energy expenditure and the number of brown-like/beige cells in white adipose tissue (WAT). Furthermore, expression of Gq in human WAT inversely correlates with UCP1 expression. Thus, our data indicate that Gq signalling regulates brown/beige adipocytes and inhibition of Gq signalling may be a novel therapeutic approach to combat obesity.

Leukotriene E4 is a full functional agonist for human cysteinyl leukotriene type 1 receptor-dependent gene expression

Leukotriene E₄ (LTE₄) the most stable of the cysteinyl leukotrienes (cysLTs) binds poorly to classical type 1 (CysLT₁) and 2 (CysLT₂) receptors although it induces potent responses in human airways in vivo, such as bronchoconstriction, airway hyperresponsiveness and inflammatory cell influx suggesting the presence of a novel receptor that preferentially responds to LTE₄. To identify such a receptor two human mast cell lines, LAD2 and LUVA, were selected that differentially responded to LTE₄ when analysed by intracellular signalling and gene expression. Comparative transcriptome analysis and recombinant gene overexpression experiments revealed CysLT₁ as a receptor responsible for potent LTE₄-induced response in LAD2 but not in LUVA cells, an observation confirmed further by gene knockdown and selective inhibitors. Lentiviral overexpression of CysLT₁ in LUVA cells augmented intracellular calcium signalling induced by LTE₄ but did not restore full agonist responses at the gene expression level. Our data support a model where both an increased expression of Gαq-coupled CysLT₁, and sustained intracellular calcium mobilisation and extracellular signal-regulated kinase (Erk) activation, are required for LTE₄-mediated regulation of gene expression in human cells. Our study shows for the first time that CysLT₁ expression is critically important for responsiveness to LTE₄ within a human cell system.

The IUPHAR/BPS Guide to PHARMACOLOGY in 2016: towards curated quantitative interactions between 1300 protein targets and 6000 ligands

The IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb, http://www.guidetopharmacology.org) provides expert-curated molecular interactions between successful and potential drugs and their targets in the human genome. Developed by the International Union of Basic and Clinical Pharmacology (IUPHAR) and the British Pharmacological Society (BPS), this resource, and its earlier incarnation as IUPHAR-DB, is described in our 2014 publication. This update incorporates changes over the intervening seven database releases. The unique model of content capture is based on established and new target class subcommittees collaborating with in-house curators. Most information comes from journal articles, but we now also index kinase cross-screening panels. Targets are specified by UniProtKB IDs. Small molecules are defined by PubChem Compound Identifiers (CIDs); ligand capture also includes peptides and clinical antibodies. We have extended the capture of ligands and targets linked via published quantitative binding data (e.g. Ki, IC50 or Kd). The resulting pharmacological relationship network now defines a data-supported druggable genome encompassing 7% of human proteins. The database also provides an expanded substrate for the biennially published compendium, the Concise Guide to PHARMACOLOGY. This article covers content increase, entity analysis, revised curation strategies, new website features and expanded download options.

Structural basis of receptor sulfotyrosine recognition by a CC chemokine: the N-terminal region of CCR3 bound to CCL11/eotaxin-1

Trafficking of leukocytes in immune surveillance and inflammatory responses is activated by chemokines engaging their receptors. Sulfation of tyrosine residues in peptides derived from the eosinophil chemokine receptor CCR3 dramatically enhances binding to cognate chemokines. We report the structural basis of this recognition and affinity enhancement. We describe the structure of a CC chemokine (CCL11/eotaxin-1) bound to a fragment of a chemokine receptor: residues 8–23 of CCR3, including two sulfotyrosine residues. We also show that intact CCR3 is sulfated and sulfation enhances receptor activity. The CCR3 sulfotyrosine residues form hydrophobic, salt bridge and cation-p interactions with residues that are highly conserved in CC chemokines. However, the orientation of the chemokine relative to the receptor N terminus differs substantially from those observed for two CXC chemokines, suggesting that initial binding of the receptor sulfotyrosine residues guides subsequent steps in receptor activation, thereby influencing the receptor conformational changes and signaling.

Understanding peptide biology: The discovery and characterization of the novel hormone, neuronostatin

The Human Genome Project provided the opportunity to use bioinformatic approaches to discover novel, endogenous hormones. Using this approach we have identified two novel peptide hormones and review here our strategy for the identification and characterization of the hormone, neuronostatin. We describe in this mini-review our strategy for determining neuronostatin's actions in brain, heart and pancreas. More importantly, we detail our deductive reasoning strategy for the identification of a neuronostatin receptor and our progress in establishing the physiological relevance of the peptide.

A luminescent assay for real-time measurements of receptor endocytosis in living cells

Ligand-mediated endocytosis is a key autoregulatory mechanism governing the duration and intensity of signals emanating from cell surface receptors. Due to the mechanistic complexity of endocytosis and its emerging relevance in disease, simple methods capable of tracking this dynamic process in cells have become increasingly desirable. We have developed a bioluminescent reporter technology for real-time analysis of ligand-mediated receptor endocytosis using genetic fusions of NanoLuc luciferase with various G-protein-coupled receptors (GPCRs). This method is compatible with standard microplate formats, which should decrease work flows for high-throughput screens. This article also describes the application of this technology to endocytosis of epidermal growth factor receptor (EGFR), demonstrating potential applicability of the method beyond GPCRs.

Cell-Free Expression of G Protein-Coupled Receptors

The large-scale production of recombinant G protein-coupled receptors (GPCRs) is one of the major bottlenecks that hamper functional and structural studies of this important class of integral membrane proteins. Heterologous overexpression of GPCRs often results in low yields of active protein, usually due to a combination of several factors, such as low expression levels, protein insolubility, host cell toxicity, and the need to use harsh and often denaturing detergents (e.g., SDS, LDAO, OG, and DDM, among others) to extract the recombinant receptor from the host cell membrane. Many of these problematic issues are inherently linked to cell-based expression systems and can therefore be circumvented by the use of cell-free systems. In this unit, we provide a range of protocols for the production of GPCRs in a cell-free expression system. Using this system, we typically obtain GPCR expression levels of ∼1 mg per ml of reaction mixture in the continuous-exchange configuration. Although the protocols in this unit have been optimized for the cell-free expression of GPCRs, they should provide a good starting point for the production of other classes of membrane proteins, such as ion channels, aquaporins, carrier proteins, membrane-bound enzymes, and even large molecular complexes.

A draft network of ligand-receptor-mediated multicellular signalling in human

Cell-to-cell communication across multiple cell types and tissues strictly governs proper functioning of metazoans and extensively relies on interactions between secreted ligands and cell-surface receptors. Herein, we present the first large-scale map of cell-to-cell communication between 144 human primary cell types. We reveal that most cells express tens to hundreds of ligands and receptors to create a highly connected signalling network through multiple ligand-receptor paths. We also observe extensive autocrine signalling with approximately two-thirds of partners possibly interacting on the same cell type. We find that plasma membrane and secreted proteins have the highest cell-type specificity, they are evolutionarily younger than intracellular proteins, and that most receptors had evolved before their ligands. We provide an online tool to interactively query and visualize our networks and demonstrate how this tool can reveal novel cell-to-cell interactions with the prediction that mast cells signal to monoblastic lineages via the CSF1-CSF1R interacting pair.

Medicinal chemistry in the era of big data

In the era of big data medicinal chemists are exposed to an enormous amount of bioactivity data. Numerous public data sources allow for querying across medium to large data sets mostly compiled from literature. However, the data available are still quite incomplete and of mixed quality. This mini review will focus on how medicinal chemists might use such resources and how valuable the current data sources are for guiding drug discovery.

Deep sequencing reveals cell-type-specific patterns of single-cell transcriptome variation

BACKGROUND

Differentiation of metazoan cells requires execution of different gene expression programs but recent single-cell transcriptome profiling has revealed considerable variation within cells of seeming identical phenotype. This brings into question the relationship between transcriptome states and cell phenotypes. Additionally, single-cell transcriptomics presents unique analysis challenges that need to be addressed to answer this question.

RESULTS

We present high quality deep read-depth single-cell RNA sequencing for 91 cells from five mouse tissues and 18 cells from two rat tissues, along with 30 control samples of bulk RNA diluted to single-cell levels. We find that transcriptomes differ globally across tissues with regard to the number of genes expressed, the average expression patterns, and within-cell-type variation patterns. We develop methods to filter genes for reliable quantification and to calibrate biological variation. All cell types include genes with high variability in expression, in a tissue-specific manner. We also find evidence that single-cell variability of neuronal genes in mice is correlated with that in rats consistent with the hypothesis that levels of variation may be conserved.

CONCLUSIONS

Single-cell RNA-sequencing data provide a unique view of transcriptome function; however, careful analysis is required in order to use single-cell RNA-sequencing measurements for this purpose. Technical variation must be considered in single-cell RNA-sequencing studies of expression variation. For a subset of genes, biological variability within each cell type appears to be regulated in order to perform dynamic functions, rather than solely molecular noise.

Identification of novel post-transcriptional features in olfactory receptor family mRNAs

Olfactory receptor (Olfr) genes comprise the largest gene family in mice. Despite their importance in olfaction, how most Olfr mRNAs are regulated remains unexplored. Using RNA-seq analysis coupled with analysis of pre-existing databases, we found that Olfr mRNAs have several atypical features suggesting that post-transcriptional regulation impacts their expression. First, Olfr mRNAs, as a group, have dramatically higher average AU-content and lower predicted secondary structure than do control mRNAs. Second, Olfr mRNAs have a higher density of AU-rich elements (AREs) in their 3'UTR and upstream open reading frames (uORFs) in their 5 UTR than do control mRNAs. Third, Olfr mRNAs have shorter 3' UTR regions and with fewer predicted miRNA-binding sites. All of these novel properties correlated with higher Olfr expression. We also identified striking differences in the post-transcriptional features of the mRNAs from the two major classes of Olfr genes, a finding consistent with their independent evolutionary origin. Together, our results suggest that the Olfr gene family has encountered unusual selective forces in neural cells that have driven them to acquire unique post-transcriptional regulatory features. In support of this possibility, we found that while Olfr mRNAs are degraded by a deadenylation-dependent mechanism, they are largely protected from this decay in neural lineage cells.

Immunomodulatory effects mediated by serotonin

Serotonin (5-HT) induces concentration-dependent metabolic effects in diverse cell types, including neurons, entherochromaffin cells, adipocytes, pancreatic beta-cells, fibroblasts, smooth muscle cells, epithelial cells, and leukocytes. Three classes of genes regulating 5-HT function are constitutively expressed or induced in these cells: (a) membrane proteins that regulate the response to 5-HT, such as SERT, 5HTR-GPCR, and the 5HT₃-ion channels; (b) downstream signaling transduction proteins; and (c) enzymes controlling 5-HT metabolism, such as IDO and MAO, which can generate biologically active catabolites, including melatonin, kynurenines, and kynurenamines. This review covers the clinical and experimental mechanisms involved in 5-HT-induced immunomodulation. These mechanisms are cell-specific and depend on the expression of serotonergic components in immune cells. Consequently, 5-HT can modulate several immunological events, such as chemotaxis, leukocyte activation, proliferation, cytokine secretion, anergy, and apoptosis. The effects of 5-HT on immune cells may be relevant in the clinical outcome of pathologies with an inflammatory component. Major depression, fibromyalgia, Alzheimer disease, psoriasis, arthritis, allergies, and asthma are all associated with changes in the serotonergic system associated with leukocytes. Thus, pharmacological regulation of the serotonergic system may modulate immune function and provide therapeutic alternatives for these diseases.

Promiscuity and selectivity of bitter molecules and their receptors

Bitter taste is essential for survival, as it protects against consuming poisonous compounds, which are often bitter. Bitter taste perception is mediated by bitter taste receptors (TAS2Rs), a subfamily of G-protein coupled receptors (GPCRs). The number of TAS2R subtypes is species-dependent, and varies from 3 in chicken to 50 in frog. TAS2Rs present an intriguing case for studying promiscuity: some of the receptors are still orphan, or have few known agonists, while others can be activated by numerous, structurally dissimilar compounds. The ligands also vary in the repertoire of TAS2Rs that they activate: some bitter compounds are selective toward a single TAS2R, while others activate multiple TAS2Rs. Selectivity/promiscuity profile of bitter taste receptors and their compounds was explored by a chemoinformatic approach. TAS2R-promiscuous and TAS2R-selective bitter molecules were found to differ in chemical features, such as AlogP, E-state, total charge, number of rings, globularity, and heavy atom count. This allowed the prediction of bitter ligand selectivity toward TAS2Rs. Interestingly, while promiscuous TAS2Rs are activated by both TAS2R-promiscuous and TAS2R-selective compounds, almost all selective TAS2Rs in human are activated by promiscuous compounds, which are recognized by other TAS2Rs anyway. Thus, unique ligands, that may have been the evolutionary driving force for development of selective TAS2Rs, still need to be unraveled.