Nanoparticles (NPs) in biomedical applications have benefits owing to their small size. However, their intricate and sensitive nature makes an evaluation of the adverse effects of NPs on health necessary and challenging. Since there are limitations to conventional toxicological methods and omics analyses provide a more comprehensive molecular profiling of multifactorial biological systems, omics approaches are necessary to evaluate nanotoxicity. Compared to a single omics layer, integrated omics across multiple omics layers provides more sensitive and comprehensive details on NP-induced toxicity based on network integration analysis. As multi-omics data are heterogeneous and massive, computational methods such as machine learning (ML) have been applied for investigating correlation among each omics. This integration of omics and ML approaches will be helpful for analyzing nanotoxicity. To that end, mechanobiology has been applied for evaluating the biophysical changes in NPs by measuring the traction force and rigidity sensing in NP-treated cells using a sub-elastomeric pillar. Therefore, integrated omics approaches are suitable for elucidating mechanobiological effects exerted by NPs. These technologies will be valuable for expanding the safety evaluations of NPs. Here, we review the integration of omics, ML, and mechanobiology for evaluating nanotoxicity.

Cancer is a group of diseases causing abnormal cell growth, altering the genome, and invading or spreading to other parts of the body. Among therapeutic peptide drugs, anticancer peptides (ACPs) have been considered to target and kill cancer cells because cancer cells have unique characteristics such as a high negative charge and abundance of microvilli in the cell membrane when compared to a normal cell. ACPs have several advantages, such as high specificity, cost-effectiveness, low immunogenicity, minimal toxicity, and high tolerance under normal physiological conditions. However, the development and identification of ACPs are time-consuming and expensive in traditional wet-lab-based approaches. Thus, the application of artificial intelligence on the approaches can save time and reduce the cost to identify candidate ACPs. Recently, machine learning (ML), deep learning (DL), and hybrid learning (ML combined DL) have emerged into the development of ACPs without experimental analysis, owing to advances in computer power and big data from the power system. Additionally, we suggest that combination therapy with classical approaches and ACPs might be one of the impactful approaches to increase the efficiency of cancer therapy.

Parkinson’s disease (PD) and multiple system atrophy (MSA) belong to the neurodegenerative group of synucleinopathies; differential diagnosis between PD and MSA is difficult, especially at early stages, owing to their clinical and biological similarities. Thus, there is a pressing need to identify metabolic biomarkers for these diseases. The metabolic profile of the cerebrospinal fluid (CSF) is reported to be altered in PD and MSA; however, the altered metabolites remain unclear. We created a single network with altered metabolites in PD and MSA based on the literature and assessed biological functions, including metabolic disorders of the nervous system, inflammation, concentration of ATP, and neurological disorder, through bioinformatics methods. Our in-silico prediction-based metabolic networks are consistent with Parkinsonism events. Although metabolomics approaches provide a more quantitative understanding of biochemical events underlying the symptoms of PD and MSA, limitations persist in covering molecules related to neurodegenerative disease pathways. Thus, omics data, such as proteomics and microRNA, help understand the altered metabolomes mechanism. In particular, integrated omics and machine learning approaches will be helpful to elucidate the pathological mechanisms of PD and MSA. This review discusses the altered metabolites between PD and MSA in the CSF and omics approaches to discover diagnostic biomarkers.

Neurodegenerative diseases (NDDs), which are chronic and progressive diseases, are a growing health concern. Among the therapeutic methods, stem-cell-based therapy is an attractive approach to NDD treatment owing to stem cells’ characteristics such as their angiogenic ability, anti-inflammatory, paracrine, and anti-apoptotic effects, and homing ability to the damaged brain region. Human bone-marrow-derived mesenchymal stem cells (hBM-MSCs) are attractive NDD therapeutic agents owing to their widespread availability, easy attainability and in vitro manipulation and the lack of ethical issues. Ex vivo hBM-MSC expansion before transplantation is essential because of the low cell numbers in bone marrow aspirates. However, hBM-MSC quality decreases over time after detachment from culture dishes, and the ability of hBM-MSCs to differentiate after detachment from culture dishes remains poorly understood. Conventional analysis of hBM-MSCs characteristics before transplantation into the brain has several limitations. However, omics analyses provide more comprehensive molecular profiling of multifactorial biological systems. Omics and machine learning approaches can handle big data and provide more detailed characterization of hBM-MSCs. Here, we provide a brief review on the application of hBM-MSCs in the treatment of NDDs and an overview of integrated omics analysis of the quality and differentiation ability of hBM-MSCs detached from culture dishes for successful stem cell therapy.

Particulate matter (PM) in polluted air can be exposed to the human body through inhalation, ingestion, and skin contact, accumulating in various organs throughout the body. Organ accumulation of PM is a growing health concern, particularly in the cardiovascular system. PM emissions are formed in the air by solid particles, liquid droplets, and fuel – particularly diesel – combustion. PM2.5 (size < 2.5 μm particle) is a major risk factor for approximately 200,000 premature deaths annually caused by air pollution. This study assessed the deleterious effects of diesel-derived PM2.5 exposure in HL-1 mouse cardiomyocyte cell lines. The PM2.5-induced biological changes, including ultrastructure, intracellular reactive oxygen species (ROS) generation, viability, and intracellular ATP levels, were analyzed. Moreover, we analyzed changes in transcriptomics using RNA sequencing and metabolomics using gas chromatography-tandem mass spectrometry (GC-MS/MS) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) in PM2.5-treated HL-1 cells. Ultrastructural analysis using transmission electron microscopy revealed disruption of mitochondrial cristae structures in a PM2.5 dose-dependent manner. The elevation of ROS levels and reduction in cell viability and ATP levels were similarly observed in a PM2.5 dose-dependently. In addition, 6,005 genes were differentially expressed (fold change cut-off ± 4) from a total of 45,777 identified genes, and 20 amino acids (AAs) were differentially expressed (fold change cut-off ± 1.2) from a total of 28 identified AAs profiles. Using bioinformatic analysis with ingenuity pathway analysis (IPA) software, we found that the changes in the transcriptome and metabolome are highly related to changes in biological functions, including homeostasis of Ca2+, depolarization of mitochondria, the function of mitochondria, synthesis of ATP, and cardiomyopathy. Moreover, an integrated single omics network was constructed by combining the transcriptome and the metabolome. In silico prediction analysis with IPA predicted that upregulation of mitochondria depolarization, ROS generation, cardiomyopathy, suppression of Ca2+ homeostasis, mitochondrial function, and ATP synthesis occurred in PM2.5-treated HL-1 cells. In particular, the cardiac movement of HL-1 was significantly reduced after PM2.5 treatment. In conclusion, our results assessed the harmful effects of PM2.5 on mitochondrial function and analyzed the biological changes related to cardiac movement, which is potentially associated with cardiovascular diseases.