Stems cells, big data and compendium-based analyses for identifying cell types, signalling pathways and gene regulatory networks

Data Mining of Microarray Datasets in Translational Neuroscience

Data mining involves the computational analysis of a plethora of publicly available datasets to generate new hypotheses that can be further validated by experiments for the improved understanding of the pathogenesis of neurodegenerative diseases. Although the number of sequencing datasets is on the rise, microarray analysis conducted on diverse biological samples represent a large collection of datasets with multiple web-based programs that enable efficient and convenient data analysis. In this review, we first discuss the selection of biological samples associated with neurological disorders, and the possibility of a combination of datasets, from various types of samples, to conduct an integrated analysis in order to achieve a holistic understanding of the alterations in the examined biological system. We then summarize key approaches and studies that have made use of the data mining of microarray datasets to obtain insights into translational neuroscience applications, including biomarker discovery, therapeutic development, and the elucidation of the pathogenic mechanisms of neurodegenerative diseases. We further discuss the gap to be bridged between microarray and sequencing studies to improve the utilization and combination of different types of datasets, together with experimental validation, for more comprehensive analyses. We conclude by providing future perspectives on integrating multi-omics, to advance precision phenotyping and personalized medicine for neurodegenerative diseases.

Human endometrium-derived mesenchymal stem/stromal cells application in endometrial-factor induced infertility

Endometrial-factor induced infertility remains one of the most significant pathology among all fertility disorders. Stem cell-based therapy is considered to be the next-generation approach. However, there are still issues about successfully retrieving human endometrium-derived mesenchymal stem/stromal cells (hEnMSCs). Moreover, we need to establish a better understanding of the effect of hEnMSCs on the endometrial recovery and the clinical outcome. According to these challenges we created a multi-step study. Endometrium samples were collected from females undergoing assisted reproductive technology (ART) procedure due to couple infertility. These samples were obtained using an endometrium scratching. The hEnMSCs were isolated from endometrium samples and characterized with flow cytometry analysis. Groups of endometrium injured female mice were established by the mechanical injury to uterine horns and the intraperitoneal chemotherapy. The hEnMSCs suspension was injected to some of the studied female mice at approved time intervals. Histological changes of mice uterine horns were evaluated after Masson's trichrome original staining, hematoxylin and eosin (H&E) staining. The fertility assessment of mice was performed by counting formed embryo implantation sites (ISs). The expression of fibrosis related genes (Col1a1, Col3a1, Acta2, and CD44) was evaluated by the reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Results showed that endometrium scratching is an effective procedure for mesenchymal stem/stromal cells (MSCs) collection from human endometrium. Isolated hEnMSCs met the criteria for defining MSCs. Moreover, hEnMSCs-based therapy had a demonstrably positive effect on the repair of damaged uterine horns, including a reduction of fibrosis, intensity of inflammatory cells such as lymphocytes and polymorphonuclear cells (PMNs) and the number of apoptotic bodies. The injured mice which recieved hEnMSCs had higher fertility in comparison to the untreated mice. Gene expression was reflected in histology changes and outcomes of conception. In conclusion, hEnMSCs demonstrated a positive impact on endometrium restoration and outcomes of endometrial-factor induced infertility. Further exploration is required in order to continue exploring the multifactorial associations between stem cell therapy, gene expression, endometrial changes and reproductive health, so we can identify individually effective and safe treatment strategies for endometrial-factor induced infertility, which is caused by mechanical effect or chemotherapy, in daily clinical practise.

Orienting Conflicted Graph Edges Using Genetic Algorithms to Discover Pathways in Protein-Protein Interaction Networks

Advanced computational techniques of the current era help to identify proteins from the complex biological network that interact with each other and with the cell's environment. Biological pathways are a chain of molecular actions that leads to a new molecular product creation or alters the cellular state. These pathways are helpful in the predication of many real-world issues. Rebuilding these pathways is a challenging task due to the fact that protein interactions are undirected, whereas pathways are directed. To discover these pathways in protein-protein interaction data from specified source and target, it is essential to orient protein interactions. Unfortunately, the edge orientation problem is NP-hard, which makes it challenging to develop effective algorithms. This work rebuilds biologically important pathways in a weighted network of protein interactions of yeast species. The proposed algorithm, pseudo-guided multi-objective genetic algorithm (PGMOGA) rebuilds pathways by assigning orientation to the edges of the weighted network. Extending the past research, mathematical modeling of single-objective and multi-objective functions is performed. The PGMOGA is compared with four state-of-the-art approaches, namely, random orientation plus local search (ROLS), single-objective genetic algorithm (SOGA), multi-objective genetic algorithm (MOGA), and multi random search (MRS). The comparison is based on three general and four path specific metrics. Results show that the current proposal performs better.