Insights into cytokine-receptor interactions from cytokine engineering.

Cytokines exert a vast array of immunoregulatory actions critical to human biology and disease. However, the desired immunotherapeutic effects of native cytokines are often mitigated by toxicity or lack of efficacy, either of which results from cytokine receptor pleiotropy and/or undesired activation of off-target cells. As our understanding of the structural principles of cytokine-receptor interactions has advanced, mechanism-based manipulation of cytokine signaling through protein engineering has become an increasingly feasible and powerful approach. Modified cytokines, both agonists and antagonists, have been engineered with narrowed target cell specificities, and they have also yielded important mechanistic insights into cytokine biology and signaling. Here we review the theory and practice of cytokine engineering and rationalize the mechanisms of several engineered cytokines in the context of structure. We discuss specific examples of how structure-based cytokine engineering has opened new opportunities for cytokines as drugs, with a focus on the immunotherapeutic cytokines interferon, interleukin-2, and interleukin-4.

Engineered cell entry links receptor biology with single-cell genomics.

Cells communicate with each other via receptor-ligand interactions. Here, we describe lentiviral-mediated cell entry by engineered receptor-ligand interaction (ENTER) to display ligand proteins, deliver payloads, and record receptor specificity. We optimize ENTER to decode interactions between T cell receptor (TCR)-MHC peptides, antibody-antigen, and other receptor-ligand pairs. A viral presentation strategy allows ENTER to capture interactions between B cell receptor and any antigen. We engineer ENTER to deliver genetic payloads to antigen-specific T or B cells to selectively modulate cellular behavior in mixed populations. Single-cell readout of ENTER by RNA sequencing (ENTER-seq) enables multiplexed enumeration of antigen specificities, TCR clonality, cell type, and states of individual T cells. ENTER-seq of CMV-seropositive patient blood samples reveals the viral epitopes that drive effector memory T cell differentiation and inter-clonal vs. intra-clonal phenotypic diversity targeting the same epitope. ENTER technology enables systematic discovery of receptor specificity, linkage to cell fates, and antigen-specific cargo delivery.

COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas.

A dysfunctional immune response in coronavirus disease 2019 (COVID-19) patients is a recurrent theme impacting symptoms and mortality, yet a detailed understanding of pertinent immune cells is not complete. We applied single-cell RNA sequencing to 284 samples from 196 COVID-19 patients and controls and created a comprehensive immune landscape with 1.46 million cells. The large dataset enabled us to identify that different peripheral immune subtype changes are associated with distinct clinical features, including age, sex, severity, and disease stages of COVID-19. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA was found in diverse epithelial and immune cell types, accompanied by dramatic transcriptomic changes within virus-positive cells. Systemic upregulation of S100A8/A9, mainly by megakaryocytes and monocytes in the peripheral blood, may contribute to the cytokine storms frequently observed in severe patients. Our data provide a rich resource for understanding the pathogenesis of and developing effective therapeutic strategies for COVID-19.

Revealing cell-cell communication pathways with their spatially coupled gene programs.

Inference of cell-cell communication (CCC) provides valuable information in understanding the mechanisms of many important life processes. With the rise of spatial transcriptomics in recent years, many methods have emerged to predict CCCs using spatial information of cells. However, most existing methods only describe CCCs based on ligand-receptor interactions, but lack the exploration of their upstream/downstream pathways. In this paper, we proposed a new method to infer CCCs, called Intercellular Gene Association Network (IGAN). Specifically, it is for the first time that we can estimate the gene associations/network between two specific single spatially adjacent cells. By using the IGAN method, we can not only infer CCCs in an accurate manner, but also explore the upstream/downstream pathways of ligands/receptors from the network perspective, which are actually exhibited as a new panoramic cell-interaction-pathway graph, and thus provide extensive information for the regulatory mechanisms behind CCCs. In addition, IGAN can measure the CCC activity at single cell/spot resolution, and help to discover the CCC spatial heterogeneity. Interestingly, we found that CCC patterns from IGAN are highly consistent with the spatial microenvironment patterns for each cell type, which further indicated the accuracy of our method. Analyses on several public datasets validated the advantages of IGAN.

Quantitative Proteomic Analysis Reveals Functional Alterations of the Peripheral Immune System in Colorectal Cancer.

Colorectal cancer (CRC) is characterized by high morbidity, high mortality, and limited response to immunotherapies. The peripheral immune system is an important component of tumor immunity, and enhancements of peripheral immunity help to suppress tumor progression. However, the functional alterations of the peripheral immune system in CRC are unclear. Here, we used mass spectrometry-based quantitative proteomics to establish a protein expression atlas for the peripheral immune system in CRC, including plasma and five types of immune cells (CD4+ T cells, CD8+ T cells, monocytes, natural killer cells, and B cells). Synthesizing the results of the multidimensional analysis, we observed an enhanced inflammatory phenotype in CRC, including elevated expression of plasma inflammatory proteins, activation of the inflammatory pathway in monocytes, and increased inflammation-related ligand-receptor interactions. Notably, we observed tumor effects on peripheral T cells, including altered cell subpopulation ratios and suppression of cell function. Suppression of CD4+ T cell function is mainly mediated by high expression levels of protein tyrosine phosphatases. Among them, the expression of protein tyrosine phosphatase receptor type J (PTPRJ) gradually increased with CRC progression; knockdown of PTPRJ in vitro could promote T cell activation, thereby enhancing peripheral immunity. We also found that the combination of leucine-rich α-2 glycoprotein 1 (LRG1) and apolipoprotein A4 (APOA4) had the best predictive ability for colorectal cancer and has the potential to be a biomarker. Overall, this study provides a comprehensive understanding of the peripheral immune system in CRC. It also offers insights regarding the potential clinical utilities of these peripheral immune characteristics as diagnostic indicators and therapeutic targets.

Two C-terminal isoforms of Aplysia tachykinin-related peptide receptors exhibit phosphorylation-dependent and phosphorylation-independent desensitization mechanisms.

Diversity, a hallmark of G protein-coupled receptor (GPCR) signaling, partly stems from alternative splicing of a single gene generating more than one isoform for a receptor. Additionally, receptor responses to ligands can be attenuated by desensitization upon prolonged or repeated ligand exposure. Both phenomena have been demonstrated and exemplified by the deuterostome tachykinin signaling system, although the role of phosphorylation in desensitization remains a subject of debate. Here, we describe the signaling system for tachykinin-related peptides (TKRPs) in a protostome, mollusk Aplysia. We cloned the Aplysia TKRP precursor, which encodes three TKRPs (apTKRP-1, apTKRP-2a, and apTKRP-2b) containing the FXGXR-amide motif. In situ hybridization and immunohistochemistry showed predominant expression of TKRP mRNA and peptide in the cerebral ganglia. TKRPs and their posttranslational modifications were observed in extracts of central nervous system ganglia using mass spectrometry. We identified two Aplysia TKRP receptors (apTKRPRs), named apTKRPR-A and apTKRPR-B. These receptors are two isoforms generated through alternative splicing of the same gene and differ only in their intracellular C termini. Structure-activity relationship analysis of apTKRP-2b revealed that both C-terminal amidation and conserved residues of the ligand are critical for receptor activation. C-terminal truncates and mutants of apTKRPRs suggested that there is a C-terminal phosphorylation-independent desensitization for both receptors. Moreover, apTKRPR-B also exhibits phosphorylation-dependent desensitization through the phosphorylation of C-terminal Ser/Thr residues. This comprehensive characterization of the Aplysia TKRP signaling system underscores the evolutionary conservation of the TKRP and TK signaling systems, while highlighting the intricacies of receptor regulation through alternative splicing and differential desensitization mechanisms.

Identifying Spatial Co-occurrence in Healthy and InflAmed tissues (ISCHIA).

Sequencing-based spatial transcriptomics (ST) methods allow unbiased capturing of RNA molecules at barcoded spots, charting the distribution and localization of cell types and transcripts across a tissue. While the coarse resolution of these techniques is considered a disadvantage, we argue that the inherent proximity of transcriptomes captured on spots can be leveraged to reconstruct cellular networks. To this end, we developed ISCHIA (Identifying Spatial Co-occurrence in Healthy and InflAmed tissues), a computational framework to analyze the spatial co-occurrence of cell types and transcript species within spots. Co-occurrence analysis is complementary to differential gene expression, as it does not depend on the abundance of a given cell type or on the transcript expression levels, but rather on their spatial association in the tissue. We applied ISCHIA to analyze co-occurrence of cell types, ligands and receptors in a Visium dataset of human ulcerative colitis patients, and validated our findings at single-cell resolution on matched hybridization-based data. We uncover inflammation-induced cellular networks involving M cell and fibroblasts, as well as ligand-receptor interactions enriched in the inflamed human colon, and their associated gene signatures. Our results highlight the hypothesis-generating power and broad applicability of co-occurrence analysis on spatial transcriptomics data.

Predicting intercellular communication based on metabolite-related ligand-receptor interactions with MRCLinkdb.

BACKGROUND: Metabolite-associated cell communications play critical roles in maintaining human biological function. However, most existing tools and resources focus only on ligand-receptor interaction pairs where both partners are proteinaceous, neglecting other non-protein molecules. To address this gap, we introduce the MRCLinkdb database and algorithm, which aggregates and organizes data related to non-protein L-R interactions in cell-cell communication, providing a valuable resource for predicting intercellular communication based on metabolite-related ligand-receptor interactions. RESULTS: Here, we manually curated the metabolite-ligand-receptor (ML-R) interactions from the literature and known databases, ultimately collecting over 790 human and 670 mouse ML-R interactions. Additionally, we compiled information on over 1900 enzymes and 260 transporter entries associated with these metabolites. We developed Metabolite-Receptor based Cell Link Database (MRCLinkdb) to store these ML-R interactions data. Meanwhile, the platform also offers extensive information for presenting ML-R interactions, including fundamental metabolite information and the overall expression landscape of metabolite-associated gene sets (such as receptor, enzymes, and transporter proteins) based on single-cell transcriptomics sequencing (covering 35 human and 26 mouse tissues, 52 human and 44 mouse cell types) and bulk RNA-seq/microarray data (encompassing 62 human and 39 mouse tissues). Furthermore, MRCLinkdb introduces a web server dedicated to the analysis of intercellular communication based on ML-R interactions. MRCLinkdb is freely available at https://www.cellknowledge.com.cn/mrclinkdb/ . CONCLUSIONS: In addition to supplementing ligand-receptor databases, MRCLinkdb may provide new perspectives for decoding the intercellular communication and advancing related prediction tools based on ML-R interactions.

EPHB2 as a key mediator of glioma progression: Insights from microenvironmental receptor ligand-related prognostic gene signature.

Malignant gliomas, characterized by pronounced heterogeneity, a complex microenvironment, and a propensity for relapse and drug resistaniguree, pose significant challenges in oncology. This study aimed to investigate the prognostic value of Ligand and Receptor related genes (LRRGs) within the glioma microenvironment. An intersection of 71 ligand-related genes (LRGs) and 2628 receptor-related genes (RRGs) yielded a total of 69 LRRGs. Utilizing the least absolute shrinkage and selection operator (LASSO) regression analysis, a prognostic RiskScore model comprising 28 LRRGs was constructed. The model demonstrated robust prognostic value, further validated in the TCGA-GBMLGG dataset. Subsequent analyses included differential gene expression, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment (GSEA), and gene set variation (GSVA) within RiskScore groups. Additionally, evaluations of PPI, mRNA-RBP, mRNA-TF, and mRNA-drug interaction networks were conducted. Four hub genes were identified through differential expression analysis of the 28 LRRGs across various GSE datasets. A multivariate Cox prognostic model was constructed for nomogram analysis, gene mutation analysis, and related expression distribution. This study underscores the role of LRRGs in intercellular communication within the glioma microenvironment and identifies four hub genes crucial for prognostic assessment in clinical glioma patients. These findings offer a potential evaluation framework for glioma patients, enhancing our understanding of the disease and informing future therapeutic strategies.

Emerging Insights into the Structure and Function of Complement C5a Receptors.

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 ß arrestins (ßarrs). 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.

SEnSCA: Identifying possible ligand-receptor interactions and its application in cell-cell communication inference.

Multicellular organisms have dense affinity with the coordination of cellular activities, which severely depend on communication across diverse cell types. Cell-cell communication (CCC) is often mediated via ligand-receptor interactions (LRIs). Existing CCC inference methods are limited to known LRIs. To address this problem, we developed a comprehensive CCC analysis tool SEnSCA by integrating single cell RNA sequencing and proteome data. SEnSCA mainly contains potential LRI acquisition and CCC strength evaluation. For acquiring potential LRIs, it first extracts LRI features and reduces the feature dimension, subsequently constructs negative LRI samples through K-means clustering, finally acquires potential LRIs based on Stacking ensemble comprising support vector machine, 1D-convolutional neural networks and multi-head attention mechanism. During CCC strength evaluation, SEnSCA conducts LRI filtering and then infers CCC by combining the three-point estimation approach and single cell RNA sequencing data. SEnSCA computed better precision, recall, accuracy, F1 score, AUC and AUPR under most of conditions when predicting possible LRIs. To better illustrate the inferred CCC network, SEnSCA provided three visualization options: heatmap, bubble diagram and network diagram. Its application on human melanoma tissue demonstrated its reliability in CCC detection. In summary, SEnSCA offers a useful CCC inference tool and is freely available at https://github.com/plhhnu/SEnSCA.

JEBIN: analyzing gene co-expressions across multiple datasets by joint network embedding.

The inference of gene co-expression associations is one of the fundamental tasks for large-scale transcriptomic data analysis. Due to the high dimensionality and high noises in transcriptomic data, it is difficult to infer stable gene co-expression associations from single dataset. Meta-analysis of multisource data can effectively tackle this problem. We proposed Joint Embedding of multiple BIpartite Networks (JEBIN) to learn the low-dimensional consensus representation for genes by integrating multiple expression datasets. JEBIN infers gene co-expression associations in a nonlinear and global similarity manner and can integrate datasets with different distributions in linear time complexity with the gene and total sample size. The effectiveness and scalability of JEBIN were verified by simulation experiments, and its superiority over the commonly used integration methods was proved by three indexes on real biological datasets. Then, JEBIN was applied to study the gene co-expression patterns of hepatocellular carcinoma (HCC) based on multiple expression datasets of HCC and adjacent normal tissues, and further on latest HCC single-cell RNA-seq data. Results show that gene co-expressions are highly different between bulk and single-cell datasets. Finally, many differentially co-expressed ligand-receptor pairs were discovered by comparing HCC with adjacent normal data, providing candidate HCC targets for abnormal cell-cell communications.

Cell-cell communication inference and analysis in the tumour microenvironments from single-cell transcriptomics: data resources and computational strategies.

Carcinomas are complex ecosystems composed of cancer, stromal and immune cells. Communication between these cells and their microenvironments induces cancer progression and causes therapy resistance. In order to improve the treatment of cancers, it is essential to quantify crosstalk between and within various cell types in a tumour microenvironment. Focusing on the coordinated expression patterns of ligands and cognate receptors, cell-cell communication can be inferred through ligand-receptor interactions (LRIs). In this manuscript, we carry out the following work: (i) introduce pipeline for ligand-receptor-mediated intercellular communication estimation from single-cell transcriptomics and list a few available LRI-related databases and visualization tools; (ii) demonstrate seven classical intercellular communication scoring strategies, highlight four types of representative intercellular communication inference methods, including network-based approaches, machine learning-based approaches, spatial information-based approaches and other approaches; (iii) summarize the evaluation and validation avenues for intercellular communication inference and analyze the advantages and limitations for the above four types of cell-cell communication methods; (iv) comment several major challenges while provide further research directions for intercellular communication analysis in the tumour microenvironments. We anticipate that this work helps to better understand intercellular crosstalk and to further develop powerful cell-cell communication estimation tools for tumor-targeted therapy.

Characterizing dysregulations via cell-cell communications in Alzheimer's brains using single-cell transcriptomes.

BACKGROUND: Alzheimer's disease (AD) is a devastating neurodegenerative disorder affecting 44 million people worldwide, leading to cognitive decline, memory loss, and significant impairment in daily functioning. The recent single-cell sequencing technology has revolutionized genetic and genomic resolution by enabling scientists to explore the diversity of gene expression patterns at the finest resolution. Most existing studies have solely focused on molecular perturbations within each cell, but cells live in microenvironments rather than in isolated entities. Here, we leveraged the large-scale and publicly available single-nucleus RNA sequencing in the human prefrontal cortex to investigate cell-to-cell communication in healthy brains and their perturbations in AD. We uniformly processed the snRNA-seq with strict QCs and labeled canonical cell types consistent with the definitions from the BRAIN Initiative Cell Census Network. From ligand and receptor gene expression, we built a high-confidence cell-to-cell communication network to investigate signaling differences between AD and healthy brains. RESULTS: Specifically, we first performed broad communication pattern analyses to highlight that biologically related cell types in normal brains rely on largely overlapping signaling networks and that the AD brain exhibits the irregular inter-mixing of cell types and signaling pathways. Secondly, we performed a more focused cell-type-centric analysis and found that excitatory neurons in AD have significantly increased their communications to inhibitory neurons, while inhibitory neurons and other non-neuronal cells globally decreased theirs to all cells. Then, we delved deeper with a signaling-centric view, showing that canonical signaling pathways CSF, TGFß, and CX3C are significantly dysregulated in their signaling to the cell type microglia/PVM and from endothelial to neuronal cells for the WNT pathway. Finally, after extracting 23 known AD risk genes, our intracellular communication analysis revealed a strong connection of extracellular ligand genes APP, APOE, and PSEN1 to intracellular AD risk genes TREM2, ABCA1, and APP in the communication from astrocytes and microglia to neurons. CONCLUSIONS: In summary, with the novel advances in single-cell sequencing technologies, we show that cellular signaling is regulated in a cell-type-specific manner and that improper regulation of extracellular signaling genes is linked to intracellular risk genes, giving the mechanistic intra- and inter-cellular picture of AD.

Integrated proteomic, phosphoproteomic, and N-glycoproteomic analyses of small extracellular vesicles from C2C12 myoblasts identify specific PTM patterns in ligand-receptor interactions.

Small extracellular vesicles (sEVs) are important mediators of intercellular communication by transferring of functional components (proteins, RNAs, and lipids) to recipient cells. Some PTMs, including phosphorylation and N-glycosylation, have been reported to play important role in EV biology, such as biogenesis, protein sorting and uptake of sEVs. MS-based proteomic technology has been applied to identify proteins and PTM modifications in sEVs. Previous proteomic studies of sEVs from C2C12 myoblasts, an important skeletal muscle cell line, focused on identification of proteins, but no PTM information on sEVs proteins is available.In this study, we systematically analyzed the proteome, phosphoproteome, and N-glycoproteome of sEVs from C2C12 myoblasts with LC-MS/MS. In-depth analyses of the three proteomic datasets revealed that the three proteomes identified different catalogues of proteins, and PTMomic analysis could expand the identification of cargos in sEVs. At the proteomic level, a high percentage of membrane proteins, especially tetraspanins, was identified. The sEVs-derived phosphoproteome had a remarkably high level of tyrosine-phosphorylated sites. The tyrosine-phosphorylated proteins might be involved with EPH-Ephrin signaling pathway. At the level of N-glycoproteomics, several glycoforms, such as complex N-linked glycans and sialic acids on glycans, were enriched in sEVs. Retrieving of the ligand-receptor interaction in sEVs revealed that extracellular matrix (ECM) and cell adhesion molecule (CAM) represented the most abundant ligand-receptor pairs in sEVs. Mapping the PTM information on the ligands and receptors revealed that N-glycosylation mainly occurred on ECM and CAM proteins, while phosphorylation occurred on different categories of receptors and ligands. A comprehensive PTM map of ECM-receptor interaction and their components is also provided.In summary, we conducted a comprehensive proteomic and PTMomic analysis of sEVs of C2C12 myoblasts. Integrated proteomic, phosphoproteomic, and N-glycoproteomic analysis of sEVs might provide some insights about their specific uptake mechanism.

Endothelial-immune crosstalk contributes to vasculopathy in nonalcoholic fatty liver disease.

The top cause of mortality in patients with nonalcoholic fatty liver disease (NAFLD) is cardiovascular complications. However, mechanisms of NAFLD-associated vasculopathy remain understudied. Here, we show that blood outgrowth endothelial cells (BOECs) from NAFLD subjects exhibit global transcriptional upregulation of chemokines and human leukocyte antigens. In mouse models of diet-induced NAFLD, we confirm heightened endothelial expressions of CXCL12 in the aortas and the liver vasculatures, and increased retention of infiltrated leukocytes within the vessel walls. To elucidate endothelial-immune crosstalk, we performed immunoprofiling by single-cell analysis, uncovering T cell intensification in NAFLD patients. Functionally, treatment with a CXCL12-neutralizing antibody is effective at moderating the enhanced chemotactic effect of NAFLD BOECs in recruiting CD8+ T lymphocytes. Interference with the CXCL12-CXCR4 axis using a CXCR4 antagonist also averts the impact of immune cell transendothelial migration and restores endothelial barrier integrity. Clinically, we detect threefold more circulating damaged endothelial cells in NAFLD patients than in healthy controls. Our work provides insight into the modulation of interactions with effector immune cells to mitigate endothelial injury in NAFLD.

Activation and characterization of G protein-coupled receptors for CHHs in the mud crab, Scylla paramamosain.

Crustacean hyperglycemic hormone (CHH) superfamily peptides constitute a group of neurohormones, including the crustacean hyperglycemic hormone (CHH), molt-inhibiting hormone (MIH), and gonad-inhibiting hormone (GIH) or vitellogenesis-inhibiting hormone (VIH), which reportedly play an essential role in regulating various biological activities by binding to their receptors in crustaceans. Although bioinformatics analyses have identified G protein-coupled receptors (GPCRs) as potential CHH receptors, no validation through binding experiments has been carried out. This study employed a eukaryotic expression system, HEK293T cell transient transfection, and ligand-receptor interaction tests to identify the GPCRs of CHHs in the mud crab Scylla paramamosain. We found that four GPCRs (Sp-GPCR-A34-A37) were activated by their corresponding CHHs (Sp-CHH1-v1, Sp-MIH, Sp-VIH) in a dose-dependent manner. Of these, Sp-GPCR-A34 was exclusively activated by Sp-VIH; Sp-GPCR-A35 was activated by Sp-CHH1-v1 and Sp-VIH, respectively; Sp-GPCR-A36 was activated by Sp-CHH1-v1 and Sp-MIH; Sp-GPCR-A37 was exclusively activated by Sp-MIH. The half-maximal effective concentration (EC50) values for all CHHs/GPCRs pairs (both Ca2+ and cAMP signaling) were in the nanomolar range. Overall, our study provided hitherto undocumented evidence of the presence of G protein-coupled receptors of CHH in crustaceans, providing the foothold for further studies on the signaling pathways of CHHs and their corresponding GPCRs.

Single-cell RNA sequencing analysis to evaluate antimony exposure effects on cell-lineage communications within the Drosophila testicular niche.

The increasing production and prevalence of antimony (Sb)-related products raise concerns regarding its potential hazards to reproductive health. Upon environmental exposure, Sb reportedly induces testicular toxicity during spermatogenesis; moreover, it is known to affect various testicular cell populations, particularly germline stem cell populations. However, the cell-cell communication resulting from Sb exposure within the testicular niche remains poorly understood. To address this gap, herein we analyzed testicular single-cell RNA sequencing data from Sb-exposed Drosophila. Our findings revealed that the epidermal growth factor receptor (EGFR) and WNT signaling pathways were associated with the stem cell niche in Drosophila testes, which may disrupt the homeostasis of the testicular niche in Drosophila. Furthermore, we identified several ligand-receptor pairs, facilitating the elucidation of intercellular crosstalk involved in Sb-mediated reproductive toxicology. We employed scRNA-seq analysis and conducted functional verification to investigate the expression patterns of core downstream factors associated with EGFR and WNT signatures in the testes under the influence of Sb exposure. Altogether, our results shed light on the potential mechanisms of Sb exposure-mediated testicular cell-lineage communications.

Mannose Ligands for Mannose Receptor Targeting.

The mannose receptor (MR, CD 206) is an endocytic receptor primarily expressed by macrophages and dendritic cells, which plays a critical role in both endocytosis and antigen processing and presentation. MR carbohydrate recognition domains (CRDs) exhibit a high binding affinity for branched and linear oligosaccharides. Furthermore, multivalent mannose presentation on the various templates like peptides, proteins, polymers, micelles, and dendrimers was proven to be a valuable approach for the selective and efficient delivery of various therapeutically active agents to MR. This review provides a detailed account of the most relevant and recent aspects of the synthesis and application of mannosylated bioactive formulations for MR-mediated delivery in treatments of cancer and other infectious diseases. It further highlights recent findings related to the necessary structural features of the mannose-containing ligands for successful binding to the MR.

A proteomic atlas of ligand-receptor interactions at the ovine maternal-fetal interface reveals the role of histone lactylation in uterine remodeling.

Well-orchestrated maternal-fetal cross talk occurs via secreted ligands, interacting receptors, and coupled intracellular pathways between the conceptus and endometrium and is essential for successful embryo implantation. However, previous studies mostly focus on either the conceptus or the endometrium in isolation. The lack of integrated analysis impedes our understanding of early maternal-fetal cross talk. Herein, focusing on ligand-receptor complexes and coupled pathways at the maternal-fetal interface in sheep, we provide the first comprehensive proteomic map of ligand-receptor pathway cascades essential for embryo implantation. We demonstrate that these cascades are associated with cell adhesion and invasion, redox homeostasis, and the immune response. Candidate interactions and their physiological roles were further validated by functional experiments. We reveal the physical interaction of albumin and claudin 4 and their roles in facilitating embryo attachment to endometrium. We also demonstrate a novel function of enhanced conceptus glycolysis in remodeling uterine receptivity by inducing endometrial histone lactylation, a newly identified histone modification. Results from in vitro and in vivo models supported the essential role of lactate in inducing endometrial H3K18 lactylation and in regulating redox homeostasis and apoptotic balance to ensure successful implantation. By reconstructing a map of potential ligand-receptor pathway cascades at the maternal-fetal interface, our study presents new concepts for understanding molecular and cellular mechanisms that fine-tune conceptus-endometrium cross talk during implantation. This provides more direct and accurate insights for developing potential clinical intervention strategies to improve pregnancy outcomes following both natural and assisted conception.