Therapeutic Application and Structural Features of Adeno-Associated Virus Vector.

Adeno-associated virus (AAV) is characterized by non-pathogenicity, long-term infection, and broad tropism and is actively developed as a vector virus for gene therapy products. AAV is classified into more than 100 serotypes based on differences in the amino acid sequence of the capsid protein. Endocytosis involves the uptake of viral particles by AAV and accessory receptors during AAV infection. After entry into the cell, they are transported to the nucleus through the nuclear pore complex. AAVs mainly use proteoglycans as receptors to enter cells, but the types of sugar chains in proteoglycans that have binding ability are different. Therefore, it is necessary to properly evaluate the primary structure of receptor proteins, such as amino acid sequences and post-translational modifications, including glycosylation, and the higher-order structure of proteins, such as the folding of the entire capsid structure and the three-dimensional (3D) structure of functional domains, to ensure the efficacy and safety of biopharmaceuticals. To further enhance safety, it is necessary to further improve the efficiency of gene transfer into target cells, reduce the amount of vector administered, and prevent infection of non-target cells.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

Association of sick building syndrome with neuropathy target esterase (NTE) activity in Japanese.

Sick building syndrome (SBS) is a set of several clinically recognizable symptoms reported by occupants of a building without a clear cause. Neuropathy target esterase (NTE) is a membrane bound serine esterase and its reaction with organophosphates (OPs) can lead to OP-induced delayed neuropathy (OPIDN) and nerve axon degeneration. The aim of our study was to determine whether there was a difference in NTE activity in the peripheral blood mononuclear cells (PBMCs) of Japanese patients with SBS and healthy controls and whether PNPLA6 (alias NTE) gene polymorphisms were associated with SBS. We found that the enzymatic activity of NTE was significantly higher (P < 0.0005) in SBS patients compared with controls. Moreover, population with an AA genotype of a single nucleotide polymorphism (SNP), rs480208, in intron 21 of the PNPLA6 gene strongly reduced the activity of NTE. Fifty-eight SNP markers within the PNPLA6 gene were tested for association in a case-control study of 188 affected individuals and 401 age-matched controls. Only one SNP, rs480208, was statistically different in genotype distribution (P = 0.005) and allele frequency (P = 0.006) between the cases and controls (uncorrected for testing multiple SNP sites), but these were not significant by multiple corrections. The findings of the association between the enzymatic activity of NTE and SBS in Japanese show for the first time that NTE activity might be involved with SBS.

Three novel serum biomarkers, miR-1, miR-133a, and miR-206 for Limb-girdle muscular dystrophy, Facioscapulohumeral muscular dystrophy, and Becker muscular dystrophy.

OBJECTIVES: Muscular dystrophies are a clinically and genetically heterogeneous group of inherited myogenic disorders. In clinical tests for these diseases, creatine kinase (CK) is generally used as diagnostic blood-based biomarker. However, because CK levels can be altered by various other factors, such as vigorous exercise, etc., false positive is observed. Therefore, three microRNAs (miRNAs), miR-1, miR-133a, and miR-206, were previously reported as alternative biomarkers for duchenne muscular dystrophy (DMD). However, no alternative biomarkers have been established for the other muscular dystrophies. METHODS: We, therefore, evaluated whether these miR-1, miR-133a, and miR-206 can be used as powerful biomarkers using the serum from muscular dystrophy patients including DMD, myotonic dystrophy 1 (DM1), limb-girdle muscular dystrophy (LGMD), facioscapulohumeral muscular dystrophy (FSHD), becker muscular dystrophy (BMD), and distal myopathy with rimmed vacuoles (DMRV) by qualitative polymerase chain reaction (PCR) amplification assay. RESULTS: Statistical analysis indicated that all these miRNA levels in serum represented no significant differences between all muscle disorders examined in this study and controls by Bonferroni correction. However, some of these indicated significant differences without correction for testing multiple diseases (P < 0.05). The median values of miR-1 levels in the serum of patients with LGMD, FSHD, and BMD were approximately 5.5, 3.3 and 1.7 compared to that in controls, 0.68, respectively. Similarly, those of miR-133a and miR-206 levels in the serum of BMD patients were about 2.5 and 2.1 compared to those in controls, 1.03 and 1.32, respectively. CONCLUSIONS: Taken together, our data demonstrate that levels of miR-1, miR-133a, and miR-206 in serum of BMD and miR-1 in sera of LGMD and FSHD patients showed no significant differences compared with those of controls by Bonferroni correction. However, the results might need increase in sample sizes to evaluate these three miRNAs as variable biomarkers.

Current Strategies and Therapeutic Applications of Mesenchymal Stem Cell-Based Drug Delivery.

Mesenchymal stem cells (MSCs) have emerged as a promising approach for drug delivery strategies because of their unique properties. These strategies include stem cell membrane-coated nanoparticles, stem cell-derived extracellular vesicles, immunomodulatory effects, stem cell-laden scaffolds, and scaffold-free stem cell sheets. MSCs offer advantages such as low immunogenicity, homing ability, and tumor tropism, making them ideal for targeted drug delivery systems. Stem cell-derived extracellular vesicles have gained attention for their immune properties and tumor-homing abilities, presenting a potential solution for drug delivery challenges. The relationship between MSC-based drug delivery and the self-renewal and differentiation capabilities of MSCs lies in the potential of engineered MSCs to serve as effective carriers for therapeutic agents while maintaining their intrinsic properties. MSCs exhibit potent immunosuppressive functions in MSC-based drug delivery strategies. Stem cell-derived EVs have low immunogenicity and strong therapeutic potential for tissue repair and regeneration. Scaffold-free stem cell sheets represent a cutting-edge approach in regenerative medicine, offering a versatile platform for tissue engineering and regeneration across different medical specialties. MSCs have shown great potential for clinical applications in regenerative medicine because of their ability to differentiate into various cell types, secrete bioactive factors, and modulate immune responses. Researchers are exploring these innovative approaches to enhance drug delivery efficiency and effectiveness in treating various diseases.

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 [...].