Metabolomics Analysis of Mesenchymal Stem Cells

Brown adipose tissue and alzheimer's disease

Alzheimer's disease (AD), the most common type of senile dementia, is a chronic neurodegenerative disease characterized by cognitive dysfunction and behavioral disability. The two histopathological hallmarks in this disease are the extraneuronal accumulation of amyloid-β (Aβ) and the intraneuronal deposition of neurofibrillary tangles (NFTs). Despite this, central and peripheral metabolic dysfunction, such as abnormal brain signaling, insulin resistance, inflammation, and impaired glucose utilization, have been indicated to be correlated with AD. There is solid evidence that the age-associated thermoregulatory deficit induces diverse metabolic changes associated with AD development. Brown adipose tissue (BAT) has been known as a thermoregulatory organ particularly vital during infancy. However, in recent years, BAT has been accepted as an endocrine organ, being involved in various functions that prevent AD, such as regulating energy metabolism, secreting hormones, improving insulin sensitivity, and increasing glucose utilization in adult humans. This review focuses on the mechanisms of BAT activation and the effect of aging on BAT production and signaling. Specifically, the evidence demonstrating the effect of BAT on pathological mechanisms influencing the development of AD, including insulin pathway, thermoregulation, and other hormonal pathways, are reviewed in this article.

Molecular Docking as a Therapeutic Approach for Targeting Cancer Stem Cell Metabolic Processes

Cancer stem cells (CSCs) are subpopulation of cells which have been demonstrated in a variety of cancer models and involved in cancer initiation, progression, and development. Indeed, CSCs which seem to form a small percentage of tumor cells, display resembling characteristics to natural stem cells such as self-renewal, survival, differentiation, proliferation, and quiescence. Moreover, they have some characteristics that eventually can demonstrate the heterogeneity of cancer cells and tumor progression. On the other hand, another aspect of CSCs that has been recognized as a central concern facing cancer patients is resistance to mainstays of cancer treatment such as chemotherapy and radiation. Owing to these details and the stated stemness capabilities, these immature progenitors of cancerous cells can constantly persist after different therapies and cause tumor regrowth or metastasis. Further, in both normal development and malignancy, cellular metabolism and stemness are intricately linked and CSCs dominant metabolic phenotype changes across tumor entities, patients, and tumor subclones. Hence, CSCs can be determined as one of the factors that correlate to the failure of common therapeutic approaches in cancer treatment. In this context, researchers are searching out new alternative or complementary therapies such as targeted methods to fight against cancer. Molecular docking is one of the computational modeling methods that has a new promise in cancer cell targeting through drug designing and discovering programs. In a simple definition, molecular docking methods are used to determine the metabolic interaction between two molecules and find the best orientation of a ligand to its molecular target with minimal free energy in the formation of a stable complex. As a comprehensive approach, this computational drug design method can be thought more cost-effective and time-saving compare to other conventional methods in cancer treatment. In addition, increasing productivity and quality in pharmaceutical research can be another advantage of this molecular modeling method. Therefore, in recent years, it can be concluded that molecular docking can be considered as one of the novel strategies at the forefront of the cancer battle via targeting cancer stem cell metabolic processes.

Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer

Cancer is defined as a large group of diseases that is associated with abnormal cell growth, uncontrollable cell division, and may tend to impinge on other tissues of the body by different mechanisms through metastasis. What makes cancer so important is that the cancer incidence rate is growing worldwide which can have major health, economic, and even social impacts on both patients and the governments. Thereby, the early cancer prognosis, diagnosis, and treatment can play a crucial role at the front line of combating cancer. The onset and progression of cancer can occur under the influence of complicated mechanisms and some alterations in the level of genome, proteome, transcriptome, metabolome etc. Consequently, the advent of omics science and its broad research branches (such as genomics, proteomics, transcriptomics, metabolomics, and so forth) as revolutionary biological approaches have opened new doors to the comprehensive perception of the cancer landscape. Due to the complexities of the formation and development of cancer, the study of mechanisms underlying cancer has gone beyond just one field of the omics arena. Therefore, making a connection between the resultant data from different branches of omics science and examining them in a multi-omics field can pave the way for facilitating the discovery of novel prognostic, diagnostic, and therapeutic approaches. As the volume and complexity of data from the omics studies in cancer are increasing dramatically, the use of leading-edge technologies such as machine learning can have a promising role in the assessments of cancer research resultant data. Machine learning is categorized as a subset of artificial intelligence which aims to data parsing, classification, and data pattern identification by applying statistical methods and algorithms. This acquired knowledge subsequently allows computers to learn and improve accurate predictions through experiences from data processing. In this context, the application of machine learning, as a novel computational technology offers new opportunities for achieving in-depth knowledge of cancer by analysis of resultant data from multi-omics studies. Therefore, it can be concluded that the use of artificial intelligence technologies such as machine learning can have revolutionary roles in the fight against cancer.

Impact of Magnetic Stimulation on Periodontal Ligament Stem Cells

The aim of this study was to evaluate the effect of a time-dependent magnetic field on the biological performance of periodontal ligament stem cells (PDLSCs). A Western blot analysis and Alamar Blue assay were performed to investigate the proliferative capacity of magnetically stimulated PDLSCs (PDLSCs MAG) through the study of the MAPK cascade (p-ERK1/2). The observation of ALP levels allowed the evaluation of the effect of the magnetic field on osteogenic differentiation. Metabolomics data, such as oxygen consumption rate (OCR), extracellular acidification rate (ECAR) and ATP production provided an overview of the PDLSCs MAG metabolic state. Moreover, the mitochondrial state was investigated through confocal laser scanning microscopy. Results showed a good viability for PDLSCs MAG. Magnetic stimulation can activate the ERK phosphorylation more than the FGF factor alone by promoting a better cell proliferation. Osteogenic differentiation was more effectively induced by magnetic stimulation. The metabolic panel indicated significant changes in the mitochondrial cellular respiration of PDLSCs MAG. The results suggested that periodontal ligament stem cells (PDLSCs) can respond to biophysical stimuli such as a time-dependent magnetic field, which is able to induce changes in cell proliferation and differentiation. Moreover, the magnetic stimulation also produced an effect on the cell metabolic profile. Therefore, the current study demonstrated that a time-dependent magnetic stimulation may improve the regenerative properties of PDLSCs.

The Outcome of Stem Cell-Based Therapies on the Immune Responses in Rheumatoid Arthritis

Rheumatoid arthritis as a common autoimmune inflammatory disorder with unknown etiology can affect 0.5-1% of adults in developed countries. It involves more than just the patient's joints and can be accompanied by several comorbidities and affect cardiovascular, pulmonary, and some other systems of the human body. Although cytokine-mediated pathways are mentioned to have a central role in RA pathogenesis, adaptive and innate immune systems and intracellular signaling pathways all have important roles in this process. Non-steroidal anti-inflammatory drugs, glucocorticoids, conventional disease-modifying anti-rheumatic drugs, and biological agents are some mentioned medications used for RA. They are accompanied by some adverse effects and treatment failures which elucidates the needing for novel and more powerful therapeutic approaches. Stem cell-based therapies and their beneficial effects on therapeutic processes of different diseases have been founded so far. They can be an alternative and promising therapeutic approach for RA, too; due to their effects on immune responses of the disease. This review, besides some explanations about RA characteristics, addresses the outcome of the stem cell-based therapies including mesenchymal stem cell transplantation and hematopoietic stem cell transplantation for RA and explains their effects on the disease improvement.

Mesenchymal stem cell as a novel approach to systemic sclerosis; current status and future perspectives

Systemic sclerosis is a rare chronic autoimmune disease with extensive microvascular injury, damage of endothelial cells, activation of immune responses, and progression of tissue fibrosis in the skin and various internal organs. According to epidemiological data, women's populations are more susceptible to systemic sclerosis than men. Until now, various therapeutic options are employed to manage the symptoms of the disease. Since stem cell-based treatments have developed as a novel approach to rescue from several autoimmune diseases, it seems that stem cells, especially mesenchymal stem cells as a powerful regenerative tool can also be advantageous for systemic sclerosis treatment via their remarkable properties including immunomodulatory and anti-fibrotic effects. Accordingly, we discuss the contemporary status and future perspectives of mesenchymal stem cell transplantation for systemic sclerosis.

Prospect of Stem Cell Therapy and Regenerative Medicine in Osteoporosis

The field of cell therapy and regenerative medicine can hold the promise of restoring normal tissues structure and function. Additionally, the main targets of stem cell-based therapies are chronic diseases and lifelong disabilities without definite cures such as osteoporosis. Osteoporosis as one of the important causes of morbidity in older men and post-menopausal women is characterized by reduced bone quantity or skeletal tissue atrophy that leads to an increased risk of osteoporotic fractures. The common therapeutic methods for osteoporosis only can prevent the loss of bone mass and recover the bone partially. Nevertheless, stem cell-based therapy is considered as a new approach to regenerate the bone tissue. Herein, mesenchymal stem cells as pivotal candidates for regenerative medicine purposes especially bone regeneration are the most common type of cells with anti-inflammatory, immune-privileged potential, and less ethical concerns than other types of stem cells which are investigated in osteoporosis. Based on several findings, the mesenchymal stem cells effectiveness near to a great extent depends on their secretory function. Indeed, they can be involved in the establishment of normal bone remodeling via initiation of specific molecular signaling pathways. Accordingly, the aim herein was to review the effects of stem cell-based therapies in osteoporosis.