Systematic expression analysis of ligand-receptor pairs reveals important cell-to-cell interactions inside glioma.
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ABSTRACT:
SUBMITTER: Yuan D
PROVIDER: S-EPMC6532229 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Interactions between different cell types are critical for a plethora of biological processes, such as the immune response. We recently developed a novel technology, called LIPSTIC (labeling of immune partnership by SorTagging intercellular contacts), that allows for identifying cells undergoing specific interactions thanks to an enzymatic labeling reaction. Our work demonstrated the use of this technology to monitor interactions between immune cells, both in vitro and in vivo, by the genetic engineering of CD40 and CD40L, an essential costimulatory axis between antigen-presenting cells and T cells. Here we describe protocols to design novel LIPSTIC-engineered ligand and receptor pairs, clone constructs into retroviral expression vector, perform their initial validation, and use them to measure interactions ex vivo. This information will be useful to investigators interested in exploiting the LIPSTIC technology to track their favorite immune interaction. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Design of LIPSTIC-engineered ligand and receptor pairs Basic Protocol 2: Cloning of LIPSTIC-engineered ligand and receptor pairs Basic Protocol 3: Validation of LIPSTIC-engineered ligand and receptor pairs in 293T cells Basic Protocol 4: Measuring interaction with LIPSTIC in immune cells ex vivo.
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Project description:Modeling signal transduction in cancer cells has implications for targeting new therapies and inferring the mechanisms that improve or threaten a patient's treatment response. For transcriptome-wide studies, it has been proposed that simple correlation between a ligand and receptor pair implies a relationship to the disease process. Statistically, a differential correlation (DC) analysis across groups stratified by prognosis can link the pair to clinical outcomes. While the prognostic effect and the apparent change in correlation are both biological consequences of activation of the signaling mechanism, a correlation-driven analysis does not clearly capture this assumption and makes inefficient use of continuous survival phenotypes. To augment the correlation hypothesis, we propose that a regression framework assuming a patient-specific, latent level of signaling activation exists and generates both prognosis and correlation. Data from these systems can be inferred via interaction terms in survival regression models allowing signal transduction models beyond one pair at a time and adjusting for other factors. We illustrate the use of this model on ovarian cancer data from the Cancer Genome Atlas (TCGA) and discuss how the finding may be used to develop markers to guide targeted molecular therapies.
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Project description:BackgroundRegulation of cellular events is, often, initiated via extracellular signaling. Extracellular signaling occurs when a circulating ligand interacts with one or more membrane-bound receptors. Identification of receptor-ligand pairs is thus an important and specific form of PPI prediction.ResultsGiven a set of disparate data sources (expression data, domain content, and phylogenetic profile) we seek to predict new receptor-ligand pairs. We create a combined kernel classifier and assess its performance with respect to the Database of Ligand-Receptor Partners (DLRP) 'golden standard' as well as the method proposed by Gertz et al. Among our findings, we discover that our predictions for the tgfβ family accurately reconstruct over 76% of the supported edges (0.76 recall and 0.67 precision) of the receptor-ligand bipartite graph defined by the DLRP "golden standard". In addition, for the tgfβ family, the combined kernel classifier is able to relatively improve upon the Gertz et al. work by a factor of approximately 1.5 when considering that our method has an F-measure of 0.71 while that of Gertz et al. has a value of 0.48.ConclusionsThe prediction of receptor-ligand pairings is a difficult and complex task. We have demonstrated that using kernel learning on multiple data sources provides a stronger alternative to the existing method in solving this task.
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Project description:MotivationIntracellular communication is crucial to many biological processes, such as differentiation, development, homeostasis and inflammation. Single-cell transcriptomics provides an unprecedented opportunity for studying cell-cell communications mediated by ligand-receptor interactions. Although computational methods have been developed to infer cell type-specific ligand-receptor interactions from one single-cell transcriptomics profile, there is lack of approaches considering ligand and receptor simultaneously to identifying dysregulated interactions across conditions from multiple single-cell profiles.ResultsWe developed scLR, a statistical method for examining dysregulated ligand-receptor interactions between two conditions. scLR models the distribution of the product of ligands and receptors expressions and accounts for inter-sample variances and small sample sizes. scLR achieved high sensitivity and specificity in simulation studies. scLR revealed important cytokine signaling between macrophages and proliferating T cells during severe acute COVID-19 infection, and activated TGF-β signaling from alveolar type II cells in the pathogenesis of pulmonary fibrosis.Availability and implementationscLR is freely available at https://github.com/cyhsuTN/scLR.Supplementary informationSupplementary data are available at Bioinformatics online.
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Project description:Bacterial surface ligands mediate interactions with the host cell during association that determines the specific outcome for the host-microbe association. The association begins with receptors on the host cell binding ligands on the microbial cell to form a partnership that initiates responses in both cells. Methods to determine the specific cognate partnerships are lacking. Determining these molecular interactions between the host and microbial surfaces are difficult, yet crucial in defining biologically important events that are triggered during association of the microbiome, and critical in defining the initiating signal from the host membrane that results in pathology or commensal association. In this study, we designed an approach to discover cognate host-microbe receptor/ligand pairs using a covalent cross-linking strategy with whole cells. Protein/protein cross-linking occurred when the interacting molecules were within 9-12 Å, allowing for identification of specific pairs of proteins from the host and microbe that define the molecular interaction during association. To validate the method three different bacteria with three previously known protein/protein partnerships were examined. The exact interactions were confirmed and led to discovery of additional partnerships that were not recognized as cognate partners, but were previously reported to be involved in bacterial interactions. Additionally, three unknown receptor/ligand partners were discovered and validated with in vitro infection assays by blocking the putative host receptor and deleting the bacterial ligand. Subsequently, Salmonella enterica sv. Typhimurium was cross-linked to differentiated colonic epithelial cells (caco-2) to discover four previously unknown host receptors bound to three previously undefined host ligands for Salmonella. This approach resulted in a priori discovery of previously unknown and biologically important molecules for host/microbe association that were casually reported to mediate bacterial invasion. The whole cell cross-linking approach promises to enable discovery of possible targets to modulate interaction of the microbiome with the host that are important in infection and commensalism, both of with initiate a host response.
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Project description:B cell precursor acute lymphoblastic leukemia (BCP-ALL) is a blood cancer that originates from the abnormal proliferation of B-lymphoid progenitors. Cell population components and cell-cell interaction in the bone marrow microenvironment are significant factors for progression, relapse, and therapy resistance of BCP-ALL. In this study, we identified specifically expressed genes in B cells and myeloid cells by analyzing single-cell RNA sequencing data for seven BCP-ALL samples and four healthy samples obtained from a public database. Integrating 1356 bulk RNA sequencing samples from a public database and our previous study, we found a total of 57 significant ligand-receptor pairs (24 upregulated and 33 downregulated) in the autocrine crosstalk network of B cells. Via assessment of the communication between B cells and myeloid cells, another 29 ligand-receptor pairs were discovered, some of which notably affected survival outcomes. A score-based model was constructed with least absolute shrinkage and selection operator (LASSO) using these ligand-receptor pairs. Patients with higher scores had poorer prognoses. This model can be applied to create predictions for both pediatric and adult BCP-ALL patients.
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Project description:The tumour microenvironment (TME) of clear cell renal cell carcinoma (ccRCC) comprises multiple cell types, which promote tumour progression and modulate drug resistance and immune cell infiltrations via ligand-receptor (LR) interactions. However, the interactions, expression patterns, and clinical relevance of LR in the TME in ccRCC are insufficiently characterised. This study characterises the complex composition of the TME in ccRCC by analysing the single-cell sequencing (scRNA-seq) data of patients with ccRCC from the Gene expression omnibus database. On analysing the scRNA-seq data combined with the cancer genome atlas kidney renal clear cell carcinoma (TCGA-KIRC) dataset, 46 LR-pairs were identified that were significantly correlated and had prognostic values. Furthermore, a new molecular subtyping model was proposed based on these 46 LR-pairs. Molecular subtyping was performed in two ccRCC cohorts, revealing significant differences in prognosis between the subtypes of the two ccRCC cohorts. Different molecular subtypes exhibited different clinicopathological features, mutational, pathway, and immune signatures. Finally, the LR.score model that was constructed using ten essential LR-pairs that were identified based on LASSO Cox regression analysis revealed that the model could accurately predict the prognosis of patients with ccRCC. In addition, the differential expression of ten LR-pairs in tumour and normal cell lines was identified. Further functional experiments showed that CX3CL1 can exert anti-tumorigenic role in ccRCC cell line. Altogether, the effects of immunotherapy were connected to LR.scores, indicating that potential medications targeting these LR-pairs could contribute to the clinical benefit of immunotherapy. Therefore, this study identifies LR-pairs that could be effective biomarkers and predictors for molecular subtyping and immunotherapy effects in ccRCC. Targeting LR-pairs provides a new direction for immunotherapy regimens and prognostic evaluations in ccRCC.
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Project description:Cell-to-cell interactions (CCIs) through ligand-receptor (LR) pairs in the tumor microenvironment underlie the poor prognosis of pancreatic ductal adenocarcinoma (PDAC). However, there is scant knowledge of the association of CCIs with PDAC prognosis, which is critical to the identification of potential therapeutic candidates. Here, we sought to identify the LR pairs associated with PDAC patient prognosis by integrating survival analysis and single-cell CCI prediction. Via survival analysis using gene expression from cancer cohorts, we found 199 prognostic LR pairs. CCI prediction based on single-cell RNA-seq data revealed the enriched LR pairs associated with poor prognosis. Notably, the CCIs involved epithelial tumor cells, cancer-associated fibroblasts, and tumor-associated macrophages through integrin-related and ANXA1-FPR pairs. Finally, we determined that CCIs involving 33 poor-prognostic LR pairs were associated with tumor grade. Although the clinical implication of the set of LR pairs must be determined, our results may provide potential therapeutic targets in PDAC.
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Project description:Cell surface receptors have been extensively studied because they initiate and regulate signal transduction cascades leading to a variety of functional cellular outcomes. An important class of immune receptors (e.g., T-cell antigen receptors) whose ligands are anchored to the surfaces of other cells remain poorly understood. The mechanism by which ligand binding initiates receptor phosphorylation, a process termed "receptor triggering", remains controversial. Recently, direct measurements of the (two-dimensional) receptor-ligand complex lifetimes at cell-cell interface were found to be smaller than (three-dimensional) lifetimes in solution but the underlying mechanism is unknown. At the cell-cell interface, the receptor-ligand complex spans a short intermembrane distance (15 nm) compared to long surface molecules (LSMs) whose ectodomains span >40 nm and these LSMs include phosphatases (e.g., CD45) that dephosphorylate the receptor. It has been proposed that size-based segregation of LSMs from a receptor-ligand complex is a mechanism of receptor triggering but it is unclear whether the mechanochemistry supports such small-scale segregation. Here we present a nanometer-scale mathematical model that couples membrane elasticity with the compressional stiffness and lateral mobility of LSMs. We find robust supradiffusive segregation of LSMs from a single receptor-ligand complex. The model predicts that LSM redistribution will result in a time-dependent tension on the complex leading to a decreased two-dimensional lifetime. Interestingly, the model predicts a nonlinear relationship between the three- and two-dimensional lifetimes, which can enhance the ability of receptors to discriminate between similar ligands.
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Project description:The homodimeric, activating natural killer cell receptor NKG2D interacts with multiple monomeric ligands polyspecifically, yet without central conformational flexibility. Crystal structures of multiple NKG2D-ligand interactions have identified the NKG2D tyrosine pair Tyr 152 and Tyr 199 as forming multiple specific but diverse interactions with MICA and related proteins. Here we systematically altered each tyrosine to tryptophan, phenylalanine, isoleucine, leucine, valine, serine, and alanine to measure the effect of mutation on affinity and thermodynamics for binding a range of similar ligands: MICA, the higher-affinity ligand MICB, and MICdesign, a high-affinity version of MICA that shares all NKG2D contact residues with MICA. Affinity and residue size were related: tryptophan could often substitute for tyrosine without loss of affinity; loss of the tyrosine hydroxyl through mutation to phenylalanine was tolerated more at position 152 than 199; and the smallest residues coincide with lowest affinities in general. NKG2D mutant van't Hoff binding thermodynamics generally show that substitution of other residues for tyrosine causes a moderate positive or flat van't Hoff slope consistent with moderate loss of binding enthalpy. One set of NKG2D mutations caused MICA to adopt a positive van't Hoff slope corresponding to absorption of heat, and another set caused MICB to adopt a negative slope of greater heat release than wild-type. MICdesign shared one example of the first set with MICA and one of the second set with MICB. When the NKG2D mutation affinities were arranged according to change in nonpolar surface area and compared to results from specific antibody-antigen and protein-peptide interactions, it was found that hydrophobic surface loss in NKG2D reduced binding affinity less than reported in the other contexts. The hydrophobic effect at the center of the NKG2D binding appears more similar to that at the periphery of an antibody-antigen binding site than at its center. Therefore the polyspecific NKG2D binding site is more tolerant of structural alteration in general than either an antibody-antigen or protein-peptide binding site, and this tolerance may adapt NKG2D to a broad range of protein surfaces with micromolar affinity.
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