Immunological Regulation of Gut-Tropic Immune Cells by Extracellular Vesicles.

The remarkable diversity of lymphocytes, essential components of the immune system, serves as an ingenious mechanism for maximizing the efficient utilization of limited host defense resources. While cell adhesion molecules, notably in gut-tropic T cells, play a central role in this mechanism, the counterbalancing molecular details have remained elusive. Conversely, we've uncovered the molecular pathways enabling extracellular vesicles secreted by lymphocytes to reach the gut's mucosal tissues, facilitating immunological regulation. This discovery sheds light on immune fine-tuning, offering insights into immune regulation mechanisms.

Ensemble Learning, Deep Learning-Based and Molecular Descriptor-Based Quantitative Structure-Activity Relationships.

A deep learning-based quantitative structure-activity relationship analysis, namely the molecular image-based DeepSNAP-deep learning method, can successfully and automatically capture the spatial and temporal features in an image generated from a three-dimensional (3D) structure of a chemical compound. It allows building high-performance prediction models without extracting and selecting features because of its powerful feature discrimination capability. Deep learning (DL) is based on a neural network with multiple intermediate layers that makes it possible to solve highly complex problems and improve the prediction accuracy by increasing the number of hidden layers. However, DL models are too complex when it comes to understanding the derivation of predictions. Instead, molecular descriptor-based machine learning has clear features owing to the selection and analysis of features. However, molecular descriptor-based machine learning has some limitations in terms of prediction performance, calculation cost, feature selection, etc., while the DeepSNAP-deep learning method outperforms molecular descriptor-based machine learning due to the utilization of 3D structure information and the advanced computer processing power of DL.

Extracellular Vesicle-Based SARS-CoV-2 Vaccine.

Messenger ribonucleic acid (RNA) vaccines are mainly used as SARS-CoV-2 vaccines. Despite several issues concerning storage, stability, effective period, and side effects, viral vector vaccines are widely used for the prevention and treatment of various diseases. Recently, viral vector-encapsulated extracellular vesicles (EVs) have been suggested as useful tools, owing to their safety and ability to escape from neutral antibodies. Herein, we summarize the possible cellular mechanisms underlying EV-based SARS-CoV-2 vaccines.

Development, Safety, Issues, and Challenges of the SARS-CoV-2 Vaccine.

It has been reported that some mutant strains of the new coronavirus escape from neutralizing antibodies acquired by recoverees and vaccine recipients, in which the Omicron strain (B [...].

Advances in Purification, Modification, and Application of Extracellular Vesicles for Novel Clinical Treatments.

Extracellular vesicles (EV) are membrane vesicles surrounded by a lipid bilayer membrane and include microvesicles, apoptotic bodies, exosomes, and exomeres. Exosome-encapsulated microRNAs (miRNAs) released from cancer cells are involved in the proliferation and metastasis of tumor cells via angiogenesis. On the other hand, mesenchymal stem cell (MSC) therapy, which is being employed in regenerative medicine owing to the ability of MSCs to differentiate into various cells, is due to humoral factors, including messenger RNA (mRNA), miRNAs, proteins, and lipids, which are encapsulated in exosomes derived from transplanted cells. New treatments that advocate cell-free therapy using MSC-derived exosomes will significantly improve clinical practice. Therefore, using highly purified exosomes that perform their original functions is desirable. In this review, we summarized advances in the purification, modification, and application of EVs as novel strategies to treat some diseases.

Molecular Docking and Intracellular Translocation of Extracellular Vesicles for Efficient Drug Delivery.

Extracellular vesicles (EVs), including exosomes, mediate intercellular communication by delivering their contents, such as nucleic acids, proteins, and lipids, to distant target cells. EVs play a role in the progression of several diseases. In particular, programmed death-ligand 1 (PD-L1) levels in exosomes are associated with cancer progression. Furthermore, exosomes are being used for new drug-delivery systems by modifying their membrane peptides to promote their intracellular transduction via micropinocytosis. In this review, we aim to show that an efficient drug-delivery system and a useful therapeutic strategy can be established by controlling the molecular docking and intracellular translocation of exosomes. We summarise the mechanisms of molecular docking of exosomes, the biological effects of exosomes transmitted into target cells, and the current state of exosomes as drug delivery systems.

Regulation of Extracellular Vesicle-Mediated Immune Responses against Antigen-Specific Presentation.

Extracellular vesicles (EVs) produced by various immune cells, including B and T cells, macrophages, dendritic cells (DCs), natural killer (NK) cells, and mast cells, mediate intercellular communication and have attracted much attention owing to the novel delivery system of molecules in vivo. DCs are among the most active exosome-secreting cells of the immune system. EVs produced by cancer cells contain cancer antigens; therefore, the development of vaccine therapy that does not require the identification of cancer antigens using cancer-cell-derived EVs may have significant clinical implications. In this review, we summarise the molecular mechanisms underlying EV-based immune responses and their therapeutic effects on tumour vaccination.

Extracellular Vesicles as Novel Drug-Delivery Systems through Intracellular Communications.

Since it has been reported that extracellular vesicles (EVs) carry cargo using cell-to-cell comminication according to various in vivo situations, they are exprected to be applied as new drug-delivery systems (DDSs). In addition, non-coding RNAs, such as microRNAs (miRNAs), have attracted much attention as potential biomarkers in the encapsulated extracellular-vesicle (EV) form. EVs are bilayer-based lipids with heterogeneous populations of varying sizes and compositions. The EV-mediated transport of contents, which includes proteins, lipids, and nucleic acids, has attracted attention as a DDS through intracellular communication. Many reports have been made on the development of methods for introducing molecules into EVs and efficient methods for introducing them into target vesicles. In this review, we outline the possible molecular mechanisms by which miRNAs in exosomes participate in the post-transcriptional regulation of signaling pathways via cell-cell communication as novel DDSs, especially small EVs.

Therapeutic Strategy of Mesenchymal-Stem-Cell-Derived Extracellular Vesicles as Regenerative Medicine.

Extracellular vesicles (EVs) are lipid bilayer membrane particles that play critical roles in intracellular communication through EV-encapsulated informative content, including proteins, lipids, and nucleic acids. Mesenchymal stem cells (MSCs) are pluripotent stem cells with self-renewal ability derived from bone marrow, fat, umbilical cord, menstruation blood, pulp, etc., which they use to induce tissue regeneration by their direct recruitment into injured tissues, including the heart, liver, lung, kidney, etc., or secreting factors, such as vascular endothelial growth factor or insulin-like growth factor. Recently, MSC-derived EVs have been shown to have regenerative effects against various diseases, partially due to the post-transcriptional regulation of target genes by miRNAs. Furthermore, EVs have garnered attention as novel drug delivery systems, because they can specially encapsulate various target molecules. In this review, we summarize the regenerative effects and molecular mechanisms of MSC-derived EVs.

Immune Modulation Using Extracellular Vesicles Encapsulated with MicroRNAs as Novel Drug Delivery Systems.

Self-tolerance involves protection from self-reactive B and T cells via negative selection during differentiation, programmed cell death, and inhibition of regulatory T cells. The breakdown of immune tolerance triggers various autoimmune diseases, owing to a lack of distinction between self-antigens and non-self-antigens. Exosomes are non-particles that are approximately 50-130 nm in diameter. Extracellular vesicles can be used for in vivo cell-free transmission to enable intracellular delivery of proteins and nucleic acids, including microRNAs (miRNAs). miRNAs encapsulated in exosomes can regulate the molecular pathways involved in the immune response through post-transcriptional regulation. Herein, we sought to summarize and review the molecular mechanisms whereby exosomal miRNAs modulate the expression of genes involved in the immune response.

Therapeutic Application of Extracellular Vesicles-Capsulated Adeno-Associated Virus Vector via nSMase2/Smpd3, Satellite, and Immune Cells in Duchenne Muscular Dystrophy.

Duchenne muscular dystrophy (DMD) is caused by loss-of-function mutations in the dystrophin gene on chromosome Xp21. Disruption of the dystrophin-glycoprotein complex (DGC) on the cell membrane causes cytosolic Ca2+ influx, resulting in protease activation, mitochondrial dysfunction, and progressive myofiber degeneration, leading to muscle wasting and fragility. In addition to the function of dystrophin in the structural integrity of myofibers, a novel function of asymmetric cell division in muscular stem cells (satellite cells) has been reported. Therefore, it has been suggested that myofiber instability may not be the only cause of dystrophic degeneration, but rather that the phenotype might be caused by multiple factors, including stem cell and myofiber functions. Furthermore, it has been focused functional regulation of satellite cells by intracellular communication of extracellular vesicles (EVs) in DMD pathology. Recently, a novel molecular mechanism of DMD pathogenesis-circulating RNA molecules-has been revealed through the study of target pathways modulated by the Neutral sphingomyelinase2/Neutral sphingomyelinase3 (nSMase2/Smpd3) protein. In addition, adeno-associated virus (AAV) has been clinically applied for DMD therapy owing to the safety and long-term expression of transduction genes. Furthermore, the EV-capsulated AAV vector (EV-AAV) has been shown to be a useful tool for the intervention of DMD, because of the high efficacy of the transgene and avoidance of neutralizing antibodies. Thus, we review application of AAV and EV-AAV vectors for DMD as novel therapeutic strategy.

A Deep Learning-Based Quantitative Structure-Activity Relationship System Construct Prediction Model of Agonist and Antagonist with High Performance.

Molecular design and evaluation for drug development and chemical safety assessment have been advanced by quantitative structure-activity relationship (QSAR) using artificial intelligence techniques, such as deep learning (DL). Previously, we have reported the high performance of prediction models molecular initiation events (MIEs) on the adverse toxicological outcome using a DL-based QSAR method, called DeepSnap-DL. This method can extract feature values from images generated on a three-dimensional (3D)-chemical structure as a novel QSAR analytical system. However, there is room for improvement of this system's time-consumption. Therefore, in this study, we constructed an improved DeepSnap-DL system by combining the processes of generating an image from a 3D-chemical structure, DL using the image as input data, and statistical calculation of prediction-performance. Consequently, we obtained that the three prediction models of agonists or antagonists of MIEs achieved high prediction-performance by optimizing the parameters of DeepSnap, such as the angle used in the depiction of the image of a 3D-chemical structure, data-split, and hyperparameters in DL. The improved DeepSnap-DL system will be a powerful tool for computer-aided molecular design as a novel QSAR system.

Prediction Models for Agonists and Antagonists of Molecular Initiation Events for Toxicity Pathways Using an Improved Deep-Learning-Based Quantitative Structure-Activity Relationship System.

In silico approaches have been studied intensively to assess the toxicological risk of various chemical compounds as alternatives to traditional in vivo animal tests. Among these approaches, quantitative structure-activity relationship (QSAR) analysis has the advantages that it is able to construct models to predict the biological properties of chemicals based on structural information. Previously, we reported a deep learning (DL) algorithm-based QSAR approach called DeepSnap-DL for high-performance prediction modeling of the agonist and antagonist activity of key molecules in molecular initiating events in toxicological pathways using optimized hyperparameters. In the present study, to achieve high throughput in the DeepSnap-DL system-which consists of the preparation of three-dimensional molecular structures of chemical compounds, the generation of snapshot images from the three-dimensional chemical structures, DL, and statistical calculations-we propose an improved DeepSnap-DL approach. Using this improved system, we constructed 59 prediction models for the agonist and antagonist activity of key molecules in the Tox21 10K library. The results indicate that modeling of the agonist and antagonist activity with high prediction performance and high throughput can be achieved by optimizing suitable parameters in the improved DeepSnap-DL system.

A Molecular Image-Based Novel Quantitative Structure-Activity Relationship Approach, Deepsnap-Deep Learning and Machine Learning.

The quantitative structure-activity relationship (QSAR) approach has been used in numerous chemical compounds as in silico computational assessment for a long time. Further, owing to the high-performance modeling of QSAR, machine learning methods have been developed and upgraded. Particularly, the three- dimensional structure of chemical compounds has been gaining increasing attention owing to the representation of a large amount of information. However, only many of feature extraction is impossible to build models with the high-ability of the prediction. Thus, suitable extraction and effective selection of features are essential for models with excellent performance. Recently, the deep learning method has been employed to construct prediction models with very high performance using big data, especially, in the field of classification. Therefore, in this study, we developed a molecular image-based novel QSAR approach, called DeepSnap-Deep learning approach for designing high-performance models. In addition, this DeepSnap-Deep learning approach outperformed the conventional machine learnings when they are compared. Herein, we discuss the advantage and disadvantages of the machine learnings as well as the availability of the DeepSnap-Deep learning approach.

The nSMase2/Smpd3 gene modulates the severity of muscular dystrophy and the emotional stress response in mdx mice.

BACKGROUND: Duchenne muscular dystrophy (DMD) is a progressive, degenerative muscular disorder and cognitive dysfunction caused by mutations in the dystrophin gene. It is characterized by excess inflammatory responses in the muscle and repeated degeneration and regeneration cycles. Neutral sphingomyelinase 2/sphingomyelin phosphodiesterase 3 (nSMase2/Smpd3) hydrolyzes sphingomyelin in lipid rafts. This protein thus modulates inflammatory responses, cell survival or apoptosis pathways, and the secretion of extracellular vesicles in a Ca2+-dependent manner. However, its roles in dystrophic pathology have not yet been clarified. METHODS: To investigate the effects of the loss of nSMase2/Smpd3 on dystrophic muscles and its role in the abnormal behavior observed in DMD patients, we generated mdx mice lacking the nSMase2/Smpd3 gene (mdx:Smpd3 double knockout [DKO] mice). RESULTS: Young mdx:Smpd3 DKO mice exhibited reduced muscular degeneration and decreased inflammation responses, but later on they showed exacerbated muscular necrosis. In addition, the abnormal stress response displayed by mdx mice was improved in the mdx:Smpd3 DKO mice, with the recovery of brain-derived neurotrophic factor (Bdnf) expression in the hippocampus. CONCLUSIONS: nSMase2/Smpd3-modulated lipid raft integrity is a potential therapeutic target for DMD.

Molecular Image-Based Prediction Models of Nuclear Receptor Agonists and Antagonists Using the DeepSnap-Deep Learning Approach with the Tox21 10K Library.

The interaction of nuclear receptors (NRs) with chemical compounds can cause dysregulation of endocrine signaling pathways, leading to adverse health outcomes due to the disruption of natural hormones. Thus, identifying possible ligands of NRs is a crucial task for understanding the adverse outcome pathway (AOP) for human toxicity as well as the development of novel drugs. However, the experimental assessment of novel ligands remains expensive and time-consuming. Therefore, an in silico approach with a wide range of applications instead of experimental examination is highly desirable. The recently developed novel molecular image-based deep learning (DL) method, DeepSnap-DL, can produce multiple snapshots from three-dimensional (3D) chemical structures and has achieved high performance in the prediction of chemicals for toxicological evaluation. In this study, we used DeepSnap-DL to construct prediction models of 35 agonist and antagonist allosteric modulators of NRs for chemicals derived from the Tox21 10K library. We demonstrate the high performance of DeepSnap-DL in constructing prediction models. These findings may aid in interpreting the key molecular events of toxicity and support the development of new fields of machine learning to identify environmental chemicals with the potential to interact with NR signaling pathways.

LOX-1 (Lectin-Like Oxidized Low-Density Lipoprotein Receptor-1) Deletion Has Protective Effects on Stroke in the Genetic Background of Stroke-Prone Spontaneously Hypertensive Rat.

Background and Purpose- oxLDL (oxidized low-density lipoprotein) has been known for its potential to induce endothelial dysfunction and used as a major serological marker of oxidative stress. Recently, LOX-1 (lectin-like oxidized low-density lipoprotein receptor-1), a lectin-like receptor for oxLDL, has attracted attention in studies of neuronal apoptosis and stroke. We aim to investigate the impact of LOX-1-deficiency on spontaneous hypertension-related brain damage in the present study. Methods- We generated a LOX-1 deficient strain on the genetic background of stroke-prone spontaneously hypertensive rat (SHRSP), an animal model of severe hypertension and spontaneous stroke. In this new disease model with stroke-proneness, we monitored the occurrence of brain abnormalities with and without salt loading by multiple procedures including T2 weighted magnetic resonance imaging and also explored circulatory miRNAs as diagnostic biomarkers for cerebral ischemic injury by microarray analysis. Results- Both T2 weighted magnetic resonance imaging abnormalities and physiological parameter changes could be detected at significantly delayed timing in LOX-1 knockout rats compared with wild-type SHRSP, in either case of normal rat chow and salt loading (P<0.005 in all instances; n=11-20 for SHRSP and n=13-23 for LOX-1 knockout rats). There were no significant differences in the form of magnetic resonance imaging findings between the strains. A number of miRNAs expressed in the normal rat plasma, including rno-miR-150-5p and rno-miR-320-3p, showed significant changes after spontaneous brain damage in SHRSP, whereas the corresponding changes were modest or almost unnoticeable in LOX-1 knockout rats. There appeared to be the lessening of correlation of postischemic miRNA alterations between the injured brain tissue and plasma in LOX-1 knockout rats. Conclusions- Our data show that deficiency of LOX-1 has a protective effect on spontaneous brain damage in a newly generated LOX-1-deficient strain of SHRSP. Further, our analysis of miRNAs as biomarkers for ischemic brain damage supports a potential involvement of LOX-1 in blood brain barrier disruption after cerebral ischemia. Visual Overview- An online visual overview is available for this article.

Prediction Model of Aryl Hydrocarbon Receptor Activation by a Novel QSAR Approach, DeepSnap-Deep Learning.

The aryl hydrocarbon receptor (AhR) is a ligand-dependent transcription factor that senses environmental exogenous and endogenous ligands or xenobiotic chemicals. In particular, exposure of the liver to environmental metabolism-disrupting chemicals contributes to the development and propagation of steatosis and hepatotoxicity. However, the mechanisms for AhR-induced hepatotoxicity and tumor propagation in the liver remain to be revealed, due to the wide variety of AhR ligands. Recently, quantitative structure-activity relationship (QSAR) analysis using deep neural network (DNN) has shown superior performance for the prediction of chemical compounds. Therefore, this study proposes a novel QSAR analysis using deep learning (DL), called the DeepSnap-DL method, to construct prediction models of chemical activation of AhR. Compared with conventional machine learning (ML) techniques, such as the random forest, XGBoost, LightGBM, and CatBoost, the proposed method achieves high-performance prediction of AhR activation. Thus, the DeepSnap-DL method may be considered a useful tool for achieving high-throughput in silico evaluation of AhR-induced hepatotoxicity.

Prediction Model with High-Performance Constitutive Androstane Receptor (CAR) Using DeepSnap-Deep Learning Approach from the Tox21 10K Compound Library.

The constitutive androstane receptor (CAR) plays pivotal roles in drug-induced liver injury through the transcriptional regulation of drug-metabolizing enzymes and transporters. Thus, identifying regulatory factors for CAR activation is important for understanding its mechanisms. Numerous studies conducted previously on CAR activation and its toxicity focused on in vivo or in vitro analyses, which are expensive, time consuming, and require many animals. We developed a computational model that predicts agonists for the CAR using the Toxicology in the 21st Century 10k library. Additionally, we evaluate the prediction performance of novel deep learning (DL)-based quantitative structure-activity relationship analysis called the DeepSnap-DL approach, which is a procedure of generating an omnidirectional snapshot portraying three-dimensional (3D) structures of chemical compounds. The CAR prediction model, which applies a 3D structure generator tool, called CORINA-generated and -optimized chemical structures, in the DeepSnap-DL demonstrated better performance than the existing methods using molecular descriptors. These results indicate that high performance in the prediction model using the DeepSnap-DL approach may be important to prepare suitable 3D chemical structures as input data and to enable the identification of modulators of the CAR.

Optimization of a Deep-Learning Method Based on the Classification of Images Generated by Parameterized Deep Snap a Novel Molecular-Image-Input Technique for Quantitative Structure-Activity Relationship (QSAR) Analysis.

Numerous chemical compounds are distributed around the world and may affect the homeostasis of the endocrine system by disrupting the normal functions of hormone receptors. Although the risks associated with these compounds have been evaluated by acute toxicity testing in mammalian models, the chronic toxicity of many chemicals remains due to high cost of the compounds and the testing, etc. However, computational approaches may be promising alternatives and reduce these evaluations. Recently, deep learning (DL) has been shown to be promising prediction models with high accuracy for recognition of images, speech, signals, and videos since it greatly benefits from large datasets. Recently, a novel DL-based technique called DeepSnap was developed to conduct QSAR analysis using three-dimensional images of chemical structures. It can be used to predict the potential toxicity of many different chemicals to various receptors without extraction of descriptors. DeepSnap has been shown to have a very high capacity in tests using Tox21 quantitative qHTP datasets. Numerous parameters must be adjusted to use the DeepSnap method but they have not been optimized. In this study, the effects of these parameters on the performance of the DL prediction model were evaluated in terms of the loss in validation as an indicator for evaluating the performance of the DL using the toxicity information in the Tox21 qHTP database. The relations of the parameters of DeepSnap such as (1) number of molecules per SDF split into (2) zoom factor percentage, (3) atom size for van der waals percentage, (4) bond radius, (5) minimum bond distance, and (6) bond tolerance, with the validation loss following quadratic function curves, which suggests that optimal thresholds exist to attain the best performance with these prediction models. Using the parameter values set with the best performance, the prediction model of chemical compounds for CAR agonist was built using 64 images, at 105° angle, with AUC of 0.791. Thus, based on these parameters, the proposed DeepSnap-DL approach will be highly reliable and beneficial to establish models to assess the risk associated with various chemicals.