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In recent years, data-driven inference of cell-cell communication has helped reveal coordinated biological processes across cell types. Here, we integrate two tools, LIANA and Tensor-cell2cell, which, when combined, can deploy multiple existing methods and resources to enable the robust and flexible identification of cell-cell communication programs across multiple samples. In this work, we show how the integration of our tools facilitates the choice of method to infer cell-cell communication and subsequently perform an unsupervised deconvolution to obtain and summarize biological insights. We explain how to perform the analysis step by step in both Python and R and provide online tutorials with detailed instructions available at https://ccc-protocols.readthedocs.io/. This workflow typically takes ∼1.5 h to complete from installation to downstream visualizations on a graphics processing unit-enabled computer for a dataset of ∼63,000 cells, 10 cell types, and 12 samples.

Introduction Pituitary neuroendocrine tumors (PitNETs) are rare skull base tumors which can impart significant disability owing to their locally invasive potential. To date, the gamut of PitNET subtypes remains ill-understood at the ligand-receptor (LR) interactome level, potentially limiting therapeutic options. Here, we present findings from in silico analysis of LR complexes formed by PitNETs with clinical presentations of acromegaly, Cushing's disease, high prolactin production, and without symptoms of hormone hypersecretion. Methods Previously published PitNET gene expression data was acquired from ArrayExpress. These data represented all secretion types. LR interactions were analyzed via a crosstalk score approach. Results Cortisol (CORT) ligand was significantly involved in tumor-to-tumor signaling across all PitNET subtypes but prolactinomas, which evidenced active CORT depletion. Likewise, CCL25 ligand was implicated in 20% of the top LR complex interactions along the tumor-to-stroma signaling axis, but silent PitNETs reported unique depletion of the CCL25 ligand. Along the stroma-to-tumor signaling axis, all clinical PitNET subtypes enriched stromal vasoactive intestinal polypeptide ligand interactions with tumor secretin receptor. All clinical PitNET subtypes enriched stromal DEFB103B (human β-defensin 103B) ligand interactions with stromal chemokine receptors along the stroma-to-stroma signaling axis. In PitNETs causing Cushing's disease, immune checkpoint ligand CD274 reported high stromal expression, and prolactinomas reported low stromal expression. Moreover, prolactinomas evidenced distinctly high stromal expression of immune-exhausted T cell response marker IL10RA compared with other clinical subtypes. Conclusion Relative crosstalk score analysis revealed a great diversity of LR complex interactions across clinical PitNET subtypes and between solid tumor compartments. More data are needed to validate these findings and exact clinical importance. [ABSTRACT FROM AUTHOR]

Background: Papillary thyroid carcinoma (PTC) is the most common malignant endocrine tumour, and its incidence and prevalence are increasing considerably. Cellular heterogeneity in the tumour microenvironment is important for PTC prognosis. Spatial transcriptomics is a powerful technique for cellular heterogeneity study. Methods: In conjunction with a clinical pathologist identification method, spatial transcriptomics was employed to characterise the spatial location and RNA profiles of PTC‐associated cells within the tissue sections. The spatial RNA‐clinical signature genes for each cell type were extracted and applied to outlining the distribution regions of specific cells on the entire section. The cellular heterogeneity of each cell type was further revealed by ContourPlot analysis, monocle analysis, trajectory analysis, ligand–receptor analysis and Gene Ontology enrichment analysis. Results: The spatial distribution region of tumour cells, typical and atypical follicular cells (FCs and AFCs) and immune cells were accurately and comprehensively identified in all five PTC tissue sections. AFCs were identified as a transitional state between FCs and tumour cells, exhibiting a higher resemblance to the latter. Three tumour foci were shared among all patients out of the 13 observed. Notably, tumour foci No. 2 displayed elevated expression levels of genes associated with lower relapse‐free survival in PTC patients. We discovered key ligand–receptor interactions, including LAMB3–ITGA2, FN1–ITGA3 and FN1–SDC4, involved in the transition of PTC cells from FCs to AFCs and eventually to tumour cells. High expression of these patterns correlated with reduced relapse‐free survival. In the tumour immune microenvironment, reduced interaction between myeloid‐derived TGFB1 and TGFBR1 in tumour focus No. 2 contributed to tumourigenesis and increased heterogeneity. The spatial RNA‐clinical analysis method developed here revealed prognosis‐associated cellular heterogeneity in the PTC microenvironment. Conclusions: The occurrence of tumour foci No. 2 and three enhanced ligand–receptor interactions in the AFC area/tumour foci reduced the relapse‐free survival of PTC patients, potentially leading to improved prognostic strategies and targeted therapies for PTC patients. [ABSTRACT FROM AUTHOR]

Cell-cell communication is a fundamental process that shapes biological tissue. Historically, studies of cell-cell communication have been feasible for one or two cell types and a few genes. With the emergence of single-cell transcriptomics, we are now able to examine the genetic profiles of individual cells at unprecedented scale and depth. The availability of such data presents an exciting opportunity to construct a more comprehensive description of cell-cell communication. This review discusses the recent explosion of methods that have been developed to infer cell-cell communication from non-spatial and spatial single-cell transcriptomics, two promising technologies which have complementary strengths and limitations. We propose several avenues to propel this rapidly expanding field forward in meaningful ways.

Photopharmacology is a novel technology in drug design that aims at solving poor drug selectivity. This technology is currently in the proof-of-concept phase and relies on the photoactivation or inactivation of photochromic ligands (PCLs) to regulate biological functions and living organisms. As potential molecular tools in future agriculture 4.0, the photochromic pesticides are effective in optical control of receptors, ion channels, living behaviors, and enzymes, which displays an innovative way of pesticide discovery. In this review, we highlight the progresses of the photoisomerized insecticides and fungicides. By integrating photoswitches, such as azobenzenes and diarylethenes, into pesticide molecules, several PCLs were developed for optical regulation of important insect or fungi targets in vivo, including GABARs, RyRs, nAChRs, SURs, sodium channels, GluCls, and SDH. The results are highly significant in revealing interactions of agrochemicals with their targets, biological functions, and living behaviors, and provide powerful toolkits in understanding ligand-receptor interactions. Moreover, there is a growing demand for a diverse range of PCLs, particularly those that are sensitive to red and NIR light or sunlight, for actual field use in agriculture. We envision that this particular mode of pesticide discovery is of great promise for overcoming the challenges posed by the improper use of agrochemicals.

Mast cells play an important role in disease pathogenesis by secreting immunomodulatory molecules. Mast cells are primarily activated by the crosslinking of their high affinity IgE receptors (FceRI) by antigen bound immunoglobulin (Ig)E antibody complexes. However, mast cells can also be activated by the mas related G protein-coupled receptor X2 (MRGPRX2), in response to a range of cationic secretagogues, such as substance P (SP), which is associated with pseudo-allergic reactions. We have previously reported that the in vitro activation of mouse mast cells by basic secretagogues is mediated by the mouse orthologue of the human MRGPRX2, MRGPRB2. To further elucidate the mechanism of MRGPRX2 activation, we studied the time-dependent internalization of MRGPRX2 by human mast cells (LAD2) upon stimulation with the neuropeptide SP. In addition, we performed computational studies to identify the intermolecular forces that facilitate ligand-MRGPRX2 interaction using SP. The computational predictions were tested experimentally by activating LAD2 with SP analogs, which were missing key amino acid residues. Our data suggest that mast cell activation by SP causes internalization of MRGPRX2 within 1 min of stimulation. Hydrogen bonds (h-bonds) and salt bridges govern the biding of SP to MRGPRX2. Arg1 and Lys3 in SP are key residues that are involved in both h-bonding and salt bridge formations with Glu164 and Asp184 of MRGPRX2, respectively. In accordance, SP analogs devoid of key residues (SP1 and SP2) failed to activate MRGPRX2 degranulation. However, both SP1 and SP2 caused a comparable release of chemokine CCL2. Further, SP analogs SP1, SP2 and SP4 did not activate tumor necrosis factor (TNF) production. We further show that SP1 and SP2 limit the activity of SP on mast cells. The results provide important mechanistic insight into the events that result in mast cell activation through MRGPRX2 and highlight the important physiochemical characteristics of a peptide ligand that facilitates ligand-MRGPRX2 interactions. The results are important in understanding activation through MRGPRX2, and the intermolecular forces that govern ligand-MRGPRX2 interaction. The elucidation of important physiochemical properties within a ligand that are needed for receptor interaction will aid in designing novel therapeutics and antagonists for MRGPRX2. [ABSTRACT FROM AUTHOR]

Introduction: Currently, programmed cell death-1 (PD-1)-targeted treatment is ineffective for a sizable minority of patients, and drug resistance still cannot be overcome. Methods: To explore the mechanisms of immunotherapy and identify new therapeutic opportunities in lung adenocarcinoma (LUAD), data from patients who did and did not respond to the anti-PD-1 treatment were evaluated using single-cell RNA sequencing, and bulk RNA sequencing were collected. Results: We investigated the gene expression that respond or not respond to immunotherapy in diverse cell types and revealed transcriptional characteristics at the single-cell level. To ultimately explore the molecular response or resistance to anti-PD-1 therapy, cell-cell interactions were carried out to identify the different LRIs (ligand-receptor interactions) between untreated patients vs. no-responders, untreated patients vs. responders, and responders vs. non-responders. Next, two molecular subgroups were proposed based on 73 LRI genes, and subtype 1 had a poor survival status and was likely to be the immunosuppressive tumor subtype. Furthermore, based on the LASSO Cox regression analysis results, we found that TNFSF13, AXL, KLRK1, FAS, PROS1, and CDH1 can be distinct prognostic biomarkers, immune infiltration levels, and responses to immunotherapy in LUAD. Discussion: Altogether, the effects of immunotherapy were connected to LRIs scores, indicating that potential medications targeting these LRIs could contribute to the clinical benefit of immunotherapy. Our integrative omics analysis revealed the mechanisms underlying the anti-PD-1 therapy response and offered abundant clues for potential strategies to improve precise diagnosis and immunotherapy. [ABSTRACT FROM AUTHOR]

Mast cells play an important role in disease pathogenesis by secreting immunomodulatory molecules. Mast cells are primarily activated by the crosslinking of their high affinity IgE receptors (FcεRI) by antigen bound immunoglobulin (Ig)E antibody complexes. However, mast cells can also be activated by the mas related G protein-coupled receptor X2 (MRGPRX2), in response to a range of cationic secretagogues, such as substance P (SP), which is associated with pseudo-allergic reactions. We have previously reported that the in vitro activation of mouse mast cells by basic secretagogues is mediated by the mouse orthologue of the human MRGPRX2, MRGPRB2. To further elucidate the mechanism of MRGPRX2 activation, we studied the time-dependent internalization of MRGPRX2 by human mast cells (LAD2) upon stimulation with the neuropeptide SP. In addition, we performed computational studies to identify the intermolecular forces that facilitate ligand-MRGPRX2 interaction using SP. The computational predictions were tested experimentally by activating LAD2 with SP analogs, which were missing key amino acid residues. Our data suggest that mast cell activation by SP causes internalization of MRGPRX2 within 1 min of stimulation. Hydrogen bonds (h-bonds) and salt bridges govern the biding of SP to MRGPRX2. Arg1 and Lys3 in SP are key residues that are involved in both h-bonding and salt bridge formations with Glu164 and Asp184 of MRGPRX2, respectively. In accordance, SP analogs devoid of key residues (SP1 and SP2) failed to activate MRGPRX2 degranulation. However, both SP1 and SP2 caused a comparable release of chemokine CCL2. Further, SP analogs SP1, SP2 and SP4 did not activate tumor necrosis factor (TNF) production. We further show that SP1 and SP2 limit the activity of SP on mast cells. The results provide important mechanistic insight into the events that result in mast cell activation through MRGPRX2 and highlight the important physiochemical characteristics of a peptide ligand that facilitates ligand-MRGPRX2 interactions. The results are important in understanding activation through MRGPRX2, and the intermolecular forces that govern ligand-MRGPRX2 interaction. The elucidation of important physiochemical properties within a ligand that are needed for receptor interaction will aid in designing novel therapeutics and antagonists for MRGPRX2.

Urotensin-II is a polypeptide ligand with neurohormone-like activity. It mediates downstream signaling pathways through G-protein-coupled receptor 14 (GPR14) also known as urotensin receptor (UTR). Urotensin-II is the most potent endogenous vasoconstrictor in mammals, promoting cardiovascular remodelling, cardiac fibrosis, and cardiomyocyte hypertrophy. It is also involved in other physiological and pathological activities, including neurosecretory effects, insulin resistance, atherosclerosis, kidney disease, and carcinogenic effects. Moreover, it is a notable player in the process of inflammatory injury, which leads to the development of inflammatory diseases. Urotensin-II/UTR expression stimulates the accumulation of monocytes and macrophages, which promote the adhesion molecules expression, chemokines activation and release of inflammatory cytokines at inflammatory injury sites. Therefore, urotensin-II turns out to be an important therapeutic target for the treatment options and management of associated diseases. The main downstream signaling pathways mediated through this urotensin-II /UTR system are RhoA/ROCK, MAPKs and PI3K/AKT. Due to the importance of urotensin-II systems in biomedicine, we consolidated a network map of urotensin-II /UTR signaling. The described signaling map comprises 33 activation/inhibition events, 31 catalysis events, 15 molecular associations, 40 gene regulation events, 60 types of protein expression, and 11 protein translocation events. The urotensin-II signaling pathway map is made freely accessible through the WikiPathways Database (https://www.wikipathways.org/index.php/Pathway:WP5158). The availability of comprehensive urotensin-II signaling in the public resource will help understand the regulation and function of this pathway in normal and pathological conditions. We believe this resource will provide a platform to the scientific community in facilitating the identification of novel therapeutic drug targets for diseases associated with urotensin-II signaling. [ABSTRACT FROM AUTHOR]

Summary: Ligand‐receptor pairs play important roles in cell–cell communication for multicellular organisms in response to environmental cues. Recently, the emergence of single‐cell RNA‐sequencing (scRNA‐seq) provides unprecedented opportunities to investigate cellular communication based on ligand‐receptor expression. However, so far, no reliable ligand‐receptor interaction database is available for plant species. In this study, we developed PlantPhoneDB (https://jasonxu.shinyapps.io/PlantPhoneDB/), a pan‐plant database comprising a large number of high‐confidence ligand‐receptor pairs manually curated from seven resources. Also, we developed a PlantPhoneDB R package, which not only provided optional four scoring approaches that calculate interaction scores of ligand‐receptor pairs between cell types but also provided visualization functions to present analysis results. At the PlantPhoneDB web interface, the processed datasets and results can be searched, browsed, and downloaded. To uncover novel cell–cell communication events in plants, we applied the PlantPhoneDB R package on GSE121619 dataset to infer significant cell–cell interactions of heat‐shocked root cells in Arabidopsis thaliana. As a result, the PlantPhoneDB predicted the actively communicating AT1G28290‐AT2G14890 ligand‐receptor pair in atrichoblast–cortex cell pair in Arabidopsis thaliana. Importantly, the downstream target genes of this ligand‐receptor pair were significantly enriched in the ribosome pathway, which facilitated plants adapting to environmental changes. In conclusion, PlantPhoneDB provided researchers with integrated resources to infer cell–cell communication from scRNA‐seq datasets. [ABSTRACT FROM AUTHOR]

Biological membrane fluidity enables shape reform upon a functionally complementary encounter between a biomolecule and a synthetic nanoparticle. Nanomedicine uses ligand‐cell surface receptor affinities for nanoparticle targeting. However, due to toxicity concerns, the nature of the nanoparticle–biomembrane interaction needs exploration to realize ligand surface density and receptor–ligand interaction effects on subsequent uptake/other interaction outcomes. In this study, giant unilamellar vesicles (GUVs) were surface‐immobilized with a hCAII model receptor to study nanoparticle–complementary surface ligand interactions. We find interaction strength impacts morphological outcomes, namely, inter‐GUV adhesion, lysis, and GUV swallowing. Nanoparticle size, receptor/ligand surface densities, overall receptor/ligand concentrations and ratios affect the strength of adhesion, and thus the outcome. Interaction energy effects on GUV morphological outcomes offer fundamental insights into the design of abiological materials for interacting with biological materials. [ABSTRACT FROM AUTHOR]

Abstract Depicting a ligand-receptor complex via Interaction Fingerprints has been shown to be both a viable data visualization and an analysis tool. The spectrum of its applications ranges from simple visualization of the binding site through analysis of molecular dynamics runs, to the evaluation of the homology models and virtual screening. Here we present a novel tool derived from the Structural Interaction Fingerprints providing a detailed and unique insight into the interactions between receptor and specific regions of the ligand (grouped into pharmacophore features) in the form of a matrix, a 2D-SIFt descriptor. The provided implementation is easy to use and extends the python library, allowing the generation of interaction matrices and their manipulation (reading and writing as well as producing the average 2D-SIFt). The library for handling the interaction matrices is available via repository http://bitbucket.org/zchl/sift2d .

Coronavirus disease 2019 (COVID-19) is a pandemic whose adverse effects have been felt all over the world. As of August 2022, reports indicated that over 500 million people in the world had been infected and the number of rising deaths from the disease were slightly above 6.4 million. New variants of the causative agent, SARS-CoV-2 are emanating now and then and some are more efficacious and harder to manage. SARS-CoV-2 main protease (Mpro) has essential functions in viral gene expression and replication through proteolytic cleavage of polyproteins. Search for SARS-CoV-2 Mpro inhibitors is a vital step in the treatment and management of COVID-19. In this study, we investigated whether alkaloids with antiviral and myriad other bioactivities from the genus Lycoris can act as SARS-CoV-2 Mpro inhibitors. We conducted a computer-aided drug design study through screening optimal ligands for SARS-CoV-2 Mpro from a list of over 150 Lycoris alkaloids created from online databases such as ChEMBL, PubChem, ChemSpider, and published journal papers. The In silico study involved molecular docking of Lycoris alkaloids to SARS-CoV-2 Mpro active site, absorption, distribution, metabolism, elimination and toxicity (ADMET) screening and finally molecular dynamic (MD) simulations of the most promising ligand-SARS-CoV-2 Mpro complexes. The study identified 3,11-dimethoxy-lycoramine, narwedine, O-demethyllycoramine and epilycoramine as drug-like and lead-like Lycoris alkaloids with favorable ADMET properties and are very likely to have an inhibition activity on SARS-CoV-2 Mpro and may become potential drug candidates. DOI: http://dx.doi.org/10.5281/zenodo.7041808

Abstract 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.

Spatial transcriptomics, a groundbreaking field in cellular biology, faces the challenge of effectively deciphering complex spatial-temporal gene expression patterns. Traditional data analysis methods often fail to capture the intricate nuances of this data, limiting the depth of understanding in spatial distribution and gene interactions. In response, we present Spatial-Temporal Patterns for Downstream Analysis (STPDA), a sophisticated computational framework tailored for spatial transcriptomic data analysis. STPDA leverages high-resolution mapping to bridge the gap between genomics and histopathology, offering a comprehensive perspective on the spatial dynamics of gene expression within tissues. This approach enables a view of cellular function and organization, marking a paradigm shift in our comprehension of biological systems. By employing Autoregressive Moving Average (ARMA) and Long Short-Term Memory (LSTM) models, STPDA effectively deciphers both global and local spatio-temporal dynamics in cellular environments. This integration of spatial-temporal patterns for downstream analysis offers a transformative approach to spatial transcriptomics data analysis. STPDA excels in various single-cell analytical tasks, including the identification of ligand-receptor interactions and cell type classification. Its ability to harness spatial-temporal patterns not only matches but frequently surpasses the performance of existing state-of-the-art methods. To ensure widespread usability and impact, we have encapsulated STPDA in a scalable and accessible Python package, addressing single-cell tasks through advanced spatial-temporal pattern analysis. This development promises to enhance our understanding of cellular biology, offering novel insights and therapeutic strategies, and represents a substantial advancement in the field of spatial transcriptomics. [Display omitted] • STPDA integrates high-resolution mapping to link genomics with histopathology for spatial transcriptomic data analysis. • The framework incorporates Autoregressive Moving Average and Long Short-Term Memory models to analyze spatio-temporal patterns. • STPDA facilitates spatial-temporal patterns, supporting ligand-receptor interaction identification and cell type classification. [ABSTRACT FROM AUTHOR]

High-density lipoprotein (HDL) is a mixture of complex particles mediating reverse cholesterol transport (RCT) and several cytoprotective activities. Despite its relevance for human health, many aspects of HDL-mediated lipid trafficking and cellular signaling remain elusive at the molecular level. During HDL's journey throughout the body, its functions are mediated through interactions with cell surface receptors on different cell types. To characterize and better understand the functional interplay between HDL particles and tissue, we analyzed the surfaceome-residing receptor neighborhoods with which HDL potentially interacts. We applied a combination of chemoproteomic technologies including automated cell surface capturing (auto-CSC) and HATRIC-based ligand–receptor capturing (HATRIC-LRC) on four different cellular model systems mimicking tissues relevant for RCT. The surfaceome analysis of EA.hy926, HEPG2, foam cells, and human aortic endothelial cells (HAECs) revealed the main currently known HDL receptor scavenger receptor B1 (SCRB1), as well as 155 shared cell surface receptors representing potential HDL interaction candidates. Since vascular endothelial growth factor A (VEGF-A) was recently found as a regulatory factor of transendothelial transport of HDL, we next analyzed the VEGF-modulated surfaceome of HAEC using the auto-CSC technology. VEGF-A treatment led to the remodeling of the surfaceome of HAEC cells, including the previously reported higher surfaceome abundance of SCRB1. In total, 165 additional receptors were found on HAEC upon VEGF-A treatment representing SCRB1 co-regulated receptors potentially involved in HDL function. Using the HATRIC-LRC technology on human endothelial cells, we specifically aimed for the identification of other bona fide (co-)receptors of HDL beyond SCRB1. HATRIC-LRC enabled, next to SCRB1, the identification of the receptor tyrosine-protein kinase Mer (MERTK). Through RNA interference, we revealed its contribution to endothelial HDL binding and uptake. Furthermore, subsequent proximity ligation assays (PLAs) demonstrated the spatial vicinity of MERTK and SCRB1 on the endothelial cell surface. The data shown provide direct evidence for a complex and dynamic HDL receptome and that receptor nanoscale organization may influence binding and uptake of HDL. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) is a pandemic whose adverse effects have been felt all over the world. As of August 2022, reports indicated that over 500 million people in the world had been infected and the number of rising deaths from the disease were slightly above 6.4 million. New variants of the causative agent, SARS-CoV-2 are emanating now and then and some are more efficacious and harder to manage. SARSCoV-2 main protease (Mpro) has essential functions in viral gene expression and replication through proteolytic cleavage of polyproteins. Search for SARS-CoV-2 Mpro inhibitors is a vital step in the treatment and management of COVID-19. In this study, we investigated whether alkaloids with antiviral and myriad other bioactivities from the genus Lycoris can act as SARS-CoV-2 Mpro inhibitors. We conducted a computer-aided drug design study through screening optimal ligands for SARS-CoV-2 Mpro from a list of over 150 Lycoris alkaloids created from online databases such as ChEMBL, PubChem, ChemSpider, and published journal papers. The In silico study involved molecular docking of Lycoris alkaloids to SARS-CoV-2 Mpro active site, absorption, distribution, metabolism, elimination and toxicity (ADMET) screening and finally molecular dynamic (MD) simulations of the most promising ligand-SARS-CoV-2 Mpro complexes. The study identified 3,11-dimethoxy-lycoramine, narwedine, O-demethyllycoramine and epilycoramine as drug-like and lead-like Lycoris alkaloids with favorable ADMET properties and are very likely to have an inhibition activity on SARSCoV-2 Mpro and may become potential drug candidates. [ABSTRACT FROM AUTHOR]

Abstract Molecular knowledge of biological processes is a cornerstone in omics data analysis. Applied to single‐cell data, such analyses provide mechanistic insights into individual cells and their interactions. However, knowledge of intercellular communication is scarce, scattered across resources, and not linked to intracellular processes. To address this gap, we combined over 100 resources covering interactions and roles of proteins in inter‐ and intracellular signaling, as well as transcriptional and post‐transcriptional regulation. We added protein complex information and annotations on function, localization, and role in diseases for each protein. The resource is available for human, and via homology translation for mouse and rat. The data are accessible via OmniPath’s web service ( https://omnipathdb.org/ ), a Cytoscape plug‐in, and packages in R/Bioconductor and Python, providing access options for computational and experimental scientists. We created workflows with tutorials to facilitate the analysis of cell–cell interactions and affected downstream intracellular signaling processes. OmniPath provides a single access point to knowledge spanning intra‐ and intercellular processes for data analysis, as we demonstrate in applications studying SARS‐CoV‐2 infection and ulcerative colitis.

Pancreatic cancer is one of the most challenging cancer types in clinical treatment worldwide. This study aimed to understand the tumorigenesis mechanism and explore potential therapeutic targets for patients with pancreatic cancer. Single-cell data and expression profiles of pancreatic cancer samples and normal tissues from multiple databases were included. Comprehensive bioinformatics analyses were applied to clarify tumor microenvironment and identify key genes involved in cancer development. Immense difference of cell types was shown between tumor and normal samples. Four cell types (B cell_1, B cell_2, cancer cell_3, and CD1C+_B dendritic cell_3) were screened to be significantly associated with prognosis. Three ligand–receptor pairs, including CD74-MIF, CD74-COPA, and CD74-APP, greatly contributed to tumorigenesis. High expression of BUB1 (BUB1 Mitotic Checkpoint Serine/Threonine Kinase) was closely correlated with worse prognosis. CD1C+_B dendritic cell_3 played a key role in tumorigenesis and cancer progression possibly through CD74-MIF. BUB1 can serve as a prognostic biomarker and a therapeutic target for patients with pancreatic cancer. The study provided a novel insight into studying the molecular mechanism of pancreatic cancer development and proposed a potential strategy for exploiting new drugs.

Cancer immunotherapy targets the interplay between immune and cancer cells. In particular, interactions between cytotoxic T lymphocytes (CTLs) and cancer cells, such as PD-1 (PDCD1) binding PD-L1 (CD274), are crucial for cancer cell clearance. However, immune checkpoint inhibitors targeting these interactions are effective only in a subset of patients, requiring the identification of novel immunotherapy targets. Genome-wide clustered regularly interspaced short palindromic repeats (CRISPR) screening in either cancer or immune cells has been employed to discover regulators of immune cell function. However, CRISPR screens in a single cell type complicate the identification of essential intercellular interactions. Further, pooled screening is associated with high noise levels. Herein, we propose intercellular CRISPR screens, a computational approach for the analysis of genome-wide CRISPR screens in every interacting cell type for the discovery of intercellular interactions as immunotherapeutic targets. We used two publicly available genome-wide CRISPR screening datasets obtained while triple-negative breast cancer (TNBC) cells and CTLs were interacting. We analyzed 4825 interactions between 1391 ligands and receptors on TNBC cells and CTLs to evaluate their effects on CTL function. Intercellular CRISPR screens discovered targets of approved drugs, a few of which were not identifiable in single datasets. To evaluate the method's performance, we used data for cytokines and costimulatory molecules as they constitute the majority of immunotherapeutic targets. Combining both CRISPR datasets improved the recall of discovering these genes relative to using single CRISPR datasets over two-fold. Our results indicate that intercellular CRISPR screens can suggest novel immunotherapy targets that are not obtained through individual CRISPR screens. The pipeline can be extended to other cancer and immune cell types to discover important intercellular interactions as potential immunotherapeutic targets. [ABSTRACT FROM AUTHOR]

A critical step in malaria blood-stage infections is the invasion of red blood cells (RBCs) by merozoite forms of the Plasmodium parasite. Much progress has been made in defining the parasite ligands and host receptors that mediate this critical step. However, less well understood are the RBC biophysical determinants that influence parasite invasion. In this review we explore how Plasmodium falciparum merozoites interact with the RBC membrane during invasion to modulate RBC deformability and facilitate invasion. We further highlight RBC biomechanics-related polymorphisms that might have been selected for in human populations due to their ability to reduce parasite invasion. Such an understanding will reveal the translational potential of targeting host pathways affecting RBC biomechanical properties for the treatment of malaria. Merozoite ligands signal to the RBC membrane through multiple pathways to prime host RBCs for invasion. Merozoite attachment alters the biomechanics of the RBC membrane. Evolution has selected for mutations affecting RBC biomechanics to combat malaria. [ABSTRACT FROM AUTHOR]

Cancer immunotherapy targets the interplay between immune and cancer cells. In particular, interactions between cytotoxic T lymphocytes (CTLs) and cancer cells, such as PD-1 (PDCD1) binding PD-L1 (CD274), are crucial for cancer cell clearance. However, immune checkpoint inhibitors targeting these interactions are effective only in a subset of patients, requiring the identification of novel immunotherapy targets. Genome-wide clustered regularly interspaced short palindromic repeats (CRISPR) screening in either cancer or immune cells has been employed to discover regulators of immune cell function. However, CRISPR screens in a single cell type complicate the identification of essential intercellular interactions. Further, pooled screening is associated with high noise levels. Herein, we propose intercellular CRISPR screens, a computational approach for the analysis of genome-wide CRISPR screens in every interacting cell type for the discovery of intercellular interactions as immunotherapeutic targets. We used two publicly available genome-wide CRISPR screening datasets obtained while triple-negative breast cancer (TNBC) cells and CTLs were interacting. We analyzed 4825 interactions between 1391 ligands and receptors on TNBC cells and CTLs to evaluate their effects on CTL function. Intercellular CRISPR screens discovered targets of approved drugs, a few of which were not identifiable in single datasets. To evaluate the method’s performance, we used data for cytokines and costimulatory molecules as they constitute the majority of immunotherapeutic targets. Combining both CRISPR datasets improved the recall of discovering these genes relative to using single CRISPR datasets over two-fold. Our results indicate that intercellular CRISPR screens can suggest novel immunotherapy targets that are not obtained through individual CRISPR screens. The pipeline can be extended to other cancer and immune cell types to discover important intercellular interactions as potential immunotherapeutic targets.

The role of cell-cell communications (CCCs) is increasingly recognized as being important to differentiation, invasion, metastasis, and drug resistance in tumoral tissues. Developing CCC inference methods using traditional experimental methods are time-consuming, labor-intensive, cannot handle large amounts of data. To facilitate inference of CCCs, we proposed a computational framework, called CellMsg, which involves two primary steps: identifying ligand-receptor interactions (LRIs) and measuring the strength of LRIs-mediated CCCs. Specifically, CellMsg first identifies high-confident LRIs based on multimodal features of ligands and receptors and graph convolutional networks. Then, CellMsg measures the strength of intercellular communication by combining the identified LRIs and single-cell RNA-seq data using a three-point estimation method. Performance evaluation on four benchmark LRI datasets by five-fold cross validation demonstrated that CellMsg accurately captured the relationships between ligands and receptors, resulting in the identification of high-confident LRIs. Compared with other methods of identifying LRIs, CellMsg has better prediction performance and robustness. Furthermore, the LRIs identified by CellMsg were successfully validated through molecular docking. Finally, we examined the overlap of LRIs between CellMsg and five other classical CCC databases, as well as the intercellular crosstalk among seven cell types within a human melanoma tissue. In summary, CellMsg establishes a complete, reliable, and well-organized LRI database and an effective CCC strength evaluation method for each single-cell RNA-seq data. It provides a computational tool allowing researchers to decipher intercellular communications. CellMsg is freely available at https://github.com/pengsl-lab/CellMsg., (© The Author(s) 2025. Published by Oxford University Press.)

Background and Aims: Bowel smooth muscle experiences mechanical stress constantly during normal function, and pathologic mechanical stressors in disease states. We tested the hypothesis that pathologic mechanical stress could alter transcription to induce smooth muscle phenotypic class switching., Methods: Primary human intestinal smooth muscle cells (HISMCs), seeded on electrospun aligned poly-ε-caprolactone nano-fibrous scaffolds, were subjected to pathologic, high frequency (1 Hz) uniaxial 3% cyclic stretch (loaded) or kept unloaded in culture for 6 hours. Total RNA sequencing, qRT-PCR, and quantitative immunohistochemistry defined loading-induced changes in gene expression. NicheNet predicted how differentially expressed genes might impact HISMCs and other bowel cells., Results: Loading induced differential expression of 4537 genes in HISMCs. Loaded HISMCs had a less contractile phenotype, with increased expression of synthetic SMC genes, proinflammatory cytokines, and altered expression of axon guidance molecules, growth factors and morphogens. Many differentially expressed genes encode secreted ligands that could act cell-autonomously on smooth muscle and on other cells in the bowel wall., Discussion: HISMCs demonstrate remarkably rapid phenotypic plasticity in response to mechanical stress that may convert contractile HISMCs into proliferative, fibroblast-like cells or proinflammatory cells. These mechanical stress-induced changes in HISMC gene expression may be relevant for human bowel disease.

Angiotensin I-converting enzyme (ACE) catalyses the biosynthesis of angiotensin II, a potent blood vessel constrictor, from angiotensin I, and ACE inhibitors were recognised as medications for hypertension. Undescribed bis-γ-pyrone polypropionate compounds, callypyrones A and B were purified from the organic extract of Callyspongiidae sponge species Callyspongia diffusa by repeated chromatographic purification. Callypyrone A exhibited significantly greater attenuation potential against ACE (IC50 0.48 mM) than that displayed by callypyrone B (IC50 0.57 mM) and showed comparable activity with standard ACE inhibitor captopril (IC50 0.36 mM). Higher electronic parameters of callypyrone A (topological surface area of 108.36) combined with balanced hydrophilic-lipophilic parameter (octanol–water coefficient, log Pow 1.9), as deduced from the structure–activity relationship analyses, could further indicate the improved ligand–receptor interactions resulting in its prospective ACE inhibitory activity. In silico docking analyses of the callypyrones with ACE recorded lowest binding energy (–12.58 kcal mol−1) for callypyrone A, which further supported the antihypertensive potential of the compound. [ABSTRACT FROM AUTHOR]

Aim: The present study aimed to investigate huperzine A as an anti-Alzheimer agent based on the principle that a single compound can regulate multiple proteins and associated pathways, using system biology tools. Methodology: The simplified molecular-input line-entry system of huperzine A was retrieved from the PubChem database, and its targets were predicted using SwissTargetPrediction. These targets were matched with the proteins deposited in DisGeNET for Alzheimer disease and enriched in STRING to identify the probably regulated pathways, cellular components, biological processes, and molecular function. Furthermore, huperzine A was docked against acetylcholinesterase using AutoDock Vina, and simulations were performed with the Gromacs package to take into account the dynamics of the system and its effect on the stability and function of the ligands. Results: A total of 100 targets were predicted to be targeted by huperzine A, of which 42 were regulated at a minimum probability of 0.05. Similarly, 101 Kyoto Encyclopedia of Genes and Genomes pathways were triggered, in which neuroactive ligand–receptor interactions scored the least false discovery rate. Also, huperzine A was predicted to modulate 54 cellular components, 120 molecular functions, and 873 biological processes. Furthermore, huperzine A possessed a binding affinity of −8.7 kcal/mol with AChE and interacted within the active site of AChE via H-bonds and hydrophobic interactions. [ABSTRACT FROM AUTHOR]

Aim: The present study aimed to investigate huperzine A as an anti-Alzheimer agent based on the principle that a single compound can regulate multiple proteins and associated pathways, using system biology tools.Methodology: The simplified molecular-input line-entry system of huperzine A was retrieved from the PubChem database, and its targets were predicted using SwissTargetPrediction. These targets were matched with the proteins deposited in DisGeNET for Alzheimer disease and enriched in STRING to identify the probably regulated pathways, cellular components, biological processes, and molecular function. Furthermore, huperzine A was docked against acetylcholinesterase using AutoDock Vina, and simulations were performed with the Gromacs package to take into account the dynamics of the system and its effect on the stability and function of the ligands.Results: A total of 100 targets were predicted to be targeted by huperzine A, of which 42 were regulated at a minimum probability of 0.05. Similarly, 101 Kyoto Encyclopedia of Genes and Genomes pathways were triggered, in which neuroactive ligand–receptor interactions scored the least false discovery rate. Also, huperzine A was predicted to modulate 54 cellular components, 120 molecular functions, and 873 biological processes. Furthermore, huperzine A possessed a binding affinity of −8.7 kcal/mol with AChE and interacted within the active site of AChE via H-bonds and hydrophobic interactions.

Diet and gut microbial metabolites mediate host immune responses and are central to the maintenance of intestinal health. The metabolite-sensing G-protein coupled receptors (GPCRs) bind metabolites and trigger signals that are important for the host cell function, survival, proliferation and expansion. On the contrary, inadequate signaling of these metabolite-sensing GPCRs most likely participate to the development of diseases including inflammatory bowel diseases (IBD). In the intestine, metabolite-sensing GPCRs are highly expressed by epithelial cells and by specific subsets of immune cells. Such receptors provide an important link between immune system, gut microbiota and metabolic system. Member of these receptors, GPR35, a class A rhodopsin-like GPCR, has been shown to be activated by the metabolites tryptophan-derived kynurenic acid (KYNA), the chemokine CXCL17 and phospholipid derivate lysophosphatidic acid (LPA) species. There have been studies on GPR35 in the context of intestinal diseases since its identification as a risk gene for IBD. In this review, we discuss the pharmacology of GPR35 including its proposed endogenous and synthetic ligands as well as its antagonists. We elaborate on the risk variants of GPR35 implicated in gut-related diseases and the mechanisms by which GPR35 contribute to intestinal homeostasis.

A significant number of integrative functions in the body are mediated by the monoaminergic systems: a set of low molecular weight transmitters which are biogenic amines (serotonin and dopamine), the enzymes of their metabolism, and their receptors. This makes monoamines a major component of the nervous and endocrine systems in the body, determining the adaptogenic capabilities of the body in continually changing environmental conditions. Using our own experimental material as an example, along with published data, we discuss the regulatory role of monoamines in development, starting with the oocyte and early cleavage stage and extending to formation of the neural networks underlying behavior. The classical ligand-receptor mechanism is considered, along with the mechanism of noncanonical modification of intracellular proteins (monoaminylation) and their contribution to adaptive regulation at different stages of development. The role of monoamines as a conservative factor binding environmental signals and the physiology of the developing body is discussed. [ABSTRACT FROM AUTHOR]

Diet and gut microbial metabolites mediate host immune responses and are central to the maintenance of intestinal health. The metabolite-sensing G-protein coupled receptors (GPCRs) bind metabolites and trigger signals that are important for the host cell function, survival, proliferation and expansion. On the contrary, inadequate signaling of these metabolite-sensing GPCRs most likely participate to the development of diseases including inflammatory bowel diseases (IBD). In the intestine, metabolite-sensing GPCRs are highly expressed by epithelial cells and by specific subsets of immune cells. Such receptors provide an important link between immune system, gut microbiota and metabolic system. Member of these receptors, GPR35, a class A rhodopsin-like GPCR, has been shown to be activated by the metabolites tryptophan-derived kynurenic acid (KYNA), the chemokine CXCL17 and phospholipid derivate lysophosphatidic acid (LPA) species. There have been studies on GPR35 in the context of intestinal diseases since its identification as a risk gene for IBD. In this review, we discuss the pharmacology of GPR35 including its proposed endogenous and synthetic ligands as well as its antagonists. We elaborate on the risk variants of GPR35 implicated in gut-related diseases and the mechanisms by which GPR35 contribute to intestinal homeostasis. [ABSTRACT FROM AUTHOR]

Depicting a ligand-receptor complex via Interaction Fingerprints has been shown to be both a viable data visualization and an analysis tool. The spectrum of its applications ranges from simple visualization of the binding site through analysis of molecular dynamics runs, to the evaluation of the homology models and virtual screening. Here we present a novel tool derived from the Structural Interaction Fingerprints providing a detailed and unique insight into the interactions between receptor and specific regions of the ligand (grouped into pharmacophore features) in the form of a matrix, a 2D-SIFt descriptor. The provided implementation is easy to use and extends the python library, allowing the generation of interaction matrices and their manipulation (reading and writing as well as producing the average 2D-SIFt). The library for handling the interaction matrices is available via repository http://bitbucket.org/zchl/sift2d. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Sodium and calcium channels play fundamental roles in the physiology of excitable cells. These channels are targets for various natural toxins, synthetic drugs and insecticides. Genetic mutations in sodium and calcium channels are associated with hereditary diseases such as cardiac arrhythmias, epilepsy, myotonia, hyperalgesia and hypoalgesia. It is not surprising that the development of selective modulators of sodium and calcium channels is an important task of neuropharmacology. In recent years, crystal and cryo-electron microscopic structures of sodium and calcium channels and their complexes with toxins and drugs have been published. In these studies, structural explanations are proposed for the numerous experimental data accumulated in previous decades. In the current review, experimental structures and theoretical models of sodium and calcium channels with toxins and drugs are considered. The possible role of current-carrying cations and their binding sites in the action of some ligands is discussed. [ABSTRACT FROM AUTHOR]

Glutamatergic transmission is responsible for most of excitatory synaptic processes in the central nervous system of vertebrates. Glutamatergic synapses are involved in the vast majority of physiological and pathological processes, and their modulation has a direct impact on almost all brain functions. It is not surprising that the development and research of drugs that can affect the glutamatergic synapses has been and remains one of the priorities of neuropharmacology. Even a brief overview of this complex problem cannot fit into a single article, so the review focuses on only one of the topics, namely, the ligands which directly block the ion pores of glutamate-activated channels. [ABSTRACT FROM AUTHOR]

Molecular knowledge of biological processes is a cornerstone in omics data analysis. Applied to single‐cell data, such analyses provide mechanistic insights into individual cells and their interactions. However, knowledge of intercellular communication is scarce, scattered across resources, and not linked to intracellular processes. To address this gap, we combined over 100 resources covering interactions and roles of proteins in inter‐ and intracellular signaling, as well as transcriptional and post‐transcriptional regulation. We added protein complex information and annotations on function, localization, and role in diseases for each protein. The resource is available for human, and via homology translation for mouse and rat. The data are accessible via OmniPath's web service (https://omnipathdb.org/), a Cytoscape plug‐in, and packages in R/Bioconductor and Python, providing access options for computational and experimental scientists. We created workflows with tutorials to facilitate the analysis of cell–cell interactions and affected downstream intracellular signaling processes. OmniPath provides a single access point to knowledge spanning intra‐ and intercellular processes for data analysis, as we demonstrate in applications studying SARS‐CoV‐2 infection and ulcerative colitis. SYNOPSIS: Over 100 resources are integrated into OmniPath, a comprehensive knowledge base of intra‐ and inter‐cellular signaling. Workflows are provided and illustrated in case studies analyzing omics data in SARS‐CoV‐2 infection and ulcerative colitis. OmniPath includes 4,000,000 annotations for over 20,000 proteins.A new framework defining transmitter and receiver roles generalizes the concepts of ligand and receptor.Integrated analysis of intra‐ and intercellular signaling can be performed to study how cells affect each other in healthy and diseased conditions.Software tools and workflows in R and Python facilitate the analysis of bulk and single‐cell omics data using tools such as CellPhoneDB, NicheNet and CARNIVAL. [ABSTRACT FROM AUTHOR]

One of the important topics in structural bioinformatics refers to the analysis of protein sequences and their biological functions, as well as the assessment of protein structural similarities. These investigations play a critical role in the drug design, the homology modelling and the protein structure prediction. Therefore, it is important to evaluate the structures similarity and to identify the similar predictions. The degree of similarity of two protein 3D structures is usually measured by the root‐mean‐square distance (RMSD) between the equivalent atom pairs. The objective of this research is to present a simple procedure to calculate the RMSD between pairs of 3D structures and to align the structures in order to find the minimal value of RMSD. A web service for calculating the RMSD in Perl programming language is developed. The tool can be used in the field of bioinformatics research and computer modelling of protein structures. [ABSTRACT FROM AUTHOR]

Complement factor C5a is an integral constituent of the complement cascade critically involved in the innate immune response, and it exerts its functions via two distinct receptors, C5aR1 and C5aR2. While C5aR1 is a prototypical G-protein-coupled receptor (GPCR), C5aR2 lacks functional coupling to heterotrimeric G proteins, although both receptors efficiently recruit β arrestins (βarrs). Here, we discuss the recent studies providing direct structural details of ligand–receptor interactions, and a framework of functional bias in this system, including the differences in terms of structural motifs and transducer coupling. We also discuss the functional analogy of C5aR2 with the atypical chemokine receptors (ACKRs), and highlight the future directions to elucidate the mechanistic basis of the functional divergence of these receptors activated by a common natural agonist. The complement cascade is a critical part of our innate immune system that plays a crucial role in combating pathogenic infections. Complement C5a, a potent anaphylatoxin in the complement cascade, activates two different seven transmembrane receptors, namely C5aR1 and C5aR2. C5aR1 is a prototypical GPCR, while C5aR2, which exhibits functional analogy to ACKRs, does not exhibit any functional coupling to G proteins but engages βarrs upon activation. Crystal structures of C5aR1 in complex with antagonists have provided important insights into ligand–receptor interaction and a potential template for novel ligand discovery. Ligand-specific functional bias at C5aR1 has been discovered recently that offers a potential framework for better therapeutic intervention. [ABSTRACT FROM AUTHOR]

The root-mean-square deviation of atomic positions (RMSD) is one of the most commonly used approaches in bioinformatics. It measures the average distance between the atoms of superimposed proteins. The present study describes a program calculating RMSD between two structures. The software developed detects the surfaces of two molecular structures – a convex and a concave one and the area of their interaction by calculating RMSD between them. The program uses fragments of files from Protein Data Bank format. The Python implementation enabling RMSD computation is suggested on the ground of the Kabsh algorithm. [ABSTRACT FROM AUTHOR]

Cannabinoid receptor type 2 (CB2) is a member of the G protein-coupled receptor superfamily. CB2 plays an important role in regulating receptor desensitization and internalization. It has protective effects in chronic degenerative diseases. 3D homology models of different templates in absence of CB2 receptor crystal structures are reported in the literature, but their wide use is limited. The aims of the present study are: 1) to choose among the published crystallographic structures templates for CB2 receptor homology modelling and to evaluate them with different computational tools; 2) to investigate the interaction between the developed model of CB2 and several known cannabinoid ligands aiming to determine the structure-activity relationship by molecular docking with GOLD 5.2 software. A significant correlation between the docking results (ChemScore function) and the ligands biological activity is found. The results obtained could be further used in silico experiments of the cannabinoid receptor-ligand interactions. [ABSTRACT FROM AUTHOR]

Single-molecule pulling experiments are widely used to extract both thermodynamic and kinetic data on ligand-receptor pairs, typically by fitting different models to the probability distribution of rupture forces of the corresponding bond. Here, a theoretical model is presented that shows how a measurement of the number of binding and unbinding events as a function of the observation time can also give access to both the binding (kon) and the unbinding (koff) rates of bonds, which combined provide a well-defined bond free-energy ΔGbond. The connection between ΔGbond and the ligand-receptor binding constant measured by typical binding essays is critically discussed. The role played by the molecular construct used to tether ligands and receptors to a surface is considered, highlighting the various approximations necessary to derive general expressions that connect its structure to its contribution, termed ΔGcnf, to the bond free-energy. In this way, the validity and the assumptions underpinning widely employed formulas and experimental protocols used to extract binding constants from single-molecule experiments are assessed. Finally, the role of ΔGcnf in processes mediated by ligand-receptor binding is briefly considered, and an experiment to unambiguously measure this quantity proposed. [ABSTRACT FROM AUTHOR]