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.
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.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.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.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.
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.
Duchenne muscular dystrophy (DMD) is a progressive neuromuscular disorder. Here, we show that the CD63 antigen, which is located on the surface of extracellular vesicles (EVs), is associated with increased levels of muscle-abundant miRNAs, namely myomiRs miR-1, miR-133a, and miR-206, in the sera of DMD patients and mdx mice. Furthermore, the release of EVs from the murine myoblast C2C12 cell line was found to be modulated by intracellular ceramide levels in a Ca2+-dependent manner. Next, to investigate the effects of EVs on cell survival, C2C12 myoblasts and myotubes were cultured with EVs from the sera of mdx mice or C2C12 cells overexpressing myomiRs in presence of cellular stresses. Both the exposure of C2C12 myoblasts and myotubes to EVs from the serum of mdx mice, and the overexpression of miR-133a in C2C12 cells in presence of cellular stress resulted in a significant decrease in cell death. Finally, to assess whether miRNAs regulate skeletal muscle regeneration in vivo, we intraperitoneally injected GW4869 (an inhibitor of exosome secretion) into mdx mice for 5 and 10 days. Levels of miRNAs and creatine kinase in the serum of GW4869-treated mdx mice were significantly downregulated compared with those of controls. The tibialis anterior muscles of the GW4869-treated mdx mice showed a robust decrease in Evans blue dye uptake. Collectively, these results indicate that EVs and myomiRs might protect the skeletal muscle of mdx mice from degeneration.
Endometriosis is a chronic disease caused by the presence of endometrial tissue in ectopic locations outside the uterus. Chronic exposure to the environmental pollutant dioxin has been correlated with an increased incidence in the development of endometriosis in non-human primates. We have therefore examined whether there is an association between the polymorphisms of ten dioxin detoxification genes and endometriosis in Japanese women.This was a pilot study in which 100 patients with endometriosis and 143 controls were enrolled. The prevalence of five microsatellite and 28 single nucleotide polymorphism markers within ten dioxin detoxification genes (AhR, AHRR, ARNT, CYP1A1, CYP2E1, EPHX1, GSTM1, GSTP1, GSTT1, NAT2) was examined.Taking into account that this analysis was a preliminary study due to its small sample size and genetic power, the results did not show any statistically significant difference between the cases and controls for any of the allele and genotype frequency distributions examined. In addition, no significant associations between the allele/genotype of all polymorphisms and the stage (I-II or III-IV) of endometriosis were observed.Based on the findings of this pilot study, we conclude the polymorphisms of the ten dioxin detoxification genes analyzed did not contribute to the etiology of endometriosis among our patients.
Computational intelligence (CI) uses applied computational methods for problem-solving inspired by the behavior of humans and animals. Biological systems are used to construct software to solve complex problems, and one type of such system is an artificial immune system (AIS), which imitates the immune system of a living body. AISs have been used to solve problems that require identification and learning, such as computer virus identification and removal, image identification, and function optimization problems. In the body’s immune system, a wide variety of cells work together to distinguish between the self and non-self and to eliminate the non-self. AISs enable learning and discrimination by imitating part or all of the mechanisms of a living body’s immune system. Certainly, some deep neural networks have exceptional performance that far surpasses that of humans in certain tasks, but to build such a network, a huge amount of data is first required. These networks are used in a wide range of applications, such as extracting knowledge from a large amount of data, learning from past actions, and creating the optimal solution (the optimization problem). A new technique for pre-training natural language processing (NLP) software ver.9.1by using transformers called Bidirectional Encoder Representations (BERT) builds on recent research in pre-training contextual representations, including Semi-Supervised Sequence Learning, Generative Pre-Training, ELMo (Embeddings from Language Models), which is a method for obtaining distributed representations that consider context, and ULMFit (Universal Language Model Fine-Tuning). BERT is a method that can address the issue of the need for large amounts of data, which is inherent in large-scale models, by using pre-learning with unlabeled data. An optimization problem involves “finding a solution that maximizes or minimizes an objective function under given constraints”. In recent years, machine learning approaches that consider pattern recognition as an optimization problem have become popular. This pattern recognition is an operation that associates patterns observed as spatial and temporal changes in signals with classes to which they belong. It involves identifying and retrieving predetermined features and rules from data; however, the features and rules here are not logical information, but are found in images, sounds, etc. Therefore, pattern recognition is generally conducted by supervised learning. Based on a new theory that deals with the process by which the immune system learns from past infection experiences, the clonal selection of immune cells can be viewed as a learning rule of reinforcement learning.
Protein three-dimensional structural analysis using artificial intelligence is attracting attention in various fields, such as the estimation of vaccine structure and stability. In particular, when using the spike protein in vaccines, the major issues in the construction of SARS-CoV-2 vaccines are their weak abilities to attack the virus and elicit immunity for a short period. Structural information about new viruses is essential for understanding their properties and creating effective vaccines. However, determining the structure of a protein through experiments is a lengthy and laborious process. Therefore, a new computational approach accelerated the elucidation process and made predictions more accurate. Using advanced machine learning technology called deep neural networks, it has become possible to predict protein structures directly from protein and gene sequences. We summarize the advances in antiviral therapy with the SARS-CoV-2 vaccine and extracellular vesicles via computational analysis.
We and others have reported that human NF-κB inhibitor-like-1 (NFKBIL1) was a putative susceptible gene for autoimmune diseases such as rheumatoid arthritis (RA). However, its precise role in the pathogenesis of RA is still largely unknown. In this study, we generated transgenic mice expressing human NFKBIL1 (NFKBIL1-Tg) and examined whether NFKBIL1 plays some role(s) in the development of autoimmune arthritis. In both a collagen-induced arthritis model and a collagen antibody-induced arthritis model, NFKBIL1-Tg mice showed resistance to arthritis compared to control mice, indicating that the gene product of NFKBIL1 was involved in the control of thusly induced arthritis. Total spleen cells of NFKBIL1-Tg mouse showed decreased proliferation to mitogenic stimuli, consistent with its resistance to arthritis. Unexpectedly, purified T cells of NFKBIL1-Tg mouse showed increased proliferation and cytokine production. This apparent discrepancy was accounted for by the impaired functions of antigen-presenting cells of NFKBIL1-Tg mouse; both T/B cell-depleted spleen cells and bone marrow-derived dendritic cells of the Tg mouse induced less prominent proliferation and IL-2 production of T cells. Furthermore, dendritic cells (DCs) derived from NFKBIL1-Tg mouse showed lower expression of co-stimulatory molecules and decreased production of inflammatory cytokines when they were activated by lipopolysaccharide. Taken together, these results indicated that NFKBIL1 affected the pathogenesis of RA at least in part through the regulation of DC functions.
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