Developing discriminate model and comparative analysis of differentially expressed genes and pathways for bloodstream samples of diabetes mellitus type 2

Integrative system biology and mathematical modeling of genetic networks identifies shared biomarkers for obesity and diabetes

Obesity and type 2 and diabetes mellitus (T2D) are two dual epidemics whose shared genetic pathological mechanisms are still far from being fully understood. Therefore, this study is aimed at discovering key genes, molecular mechanisms, and new drug targets for obesity and T2D by analyzing the genome wide gene expression data with different computational biology approaches. In this study, the RNA-sequencing data of isolated primary human adipocytes from individuals who are lean, obese, and T2D was analyzed by an integrated framework consisting of gene expression, protein interaction network (PIN), tissue specificity, and druggability approaches. Our findings show a total of 1932 unique differentially expressed genes (DEGs) across the diabetes versus obese group comparison (p≤0.05). The PIN analysis of these 1932 DEGs identified 190 high centrality network (HCN) genes, which were annotated against 3367 GO terms and functional pathways, like response to insulin signaling, phosphorylation, lipid metabolism, glucose metabolism, etc. (p≤0.05). By applying additional PIN and topological parameters to 190 HCN genes, we further mapped 25 high confidence genes, functionally connected with diabetes and obesity traits. Interestingly, ERBB2, FN1, FYN, HSPA1A, HBA1, and ITGB1 genes were found to be tractable by small chemicals, antibodies, and/or enzyme molecules. In conclusion, our study highlights the potential of computational biology methods in correlating expression data to topological parameters, functional relationships, and druggability characteristics of the candidate genes involved in complex metabolic disorders with a common etiological basis.

Decoding Common Features of Neurodegenerative Disorders: From Differentially Expressed Genes to Pathways

BACKGROUND

Neurodegeneration is a progressive/irreversible loss of neurons, building blocks of our nervous system. Their degeneration gradually collapses the entire structural and functional system manifesting in myriads of clinical disorders categorized as Neurodegenerative Disorders (NDs) such as Alzheimer's Disease, (AD), Parkinson's Disease (PD), Frontotemporal Dementia (FTD) and Amyotrophic Lateral Sclerosis (ALS). NDs are characterized by a puzzling interplay of molecular and cellular defects affecting subset of neuronal populations in specific affected brain areas.

OBJECTIVE

In present study, comparative in silico analysis was performed by utilizing gene expression datasets of AD, PD, FTD and ALS to identify potential common features to gain insights into complex molecular pathophysiology of the selected NDs.

METHODS

Gene expression data of four disorders were subjected to the identification of Differential Gene Expression (DEG) and their mapping on biological processes, KEGG pathways and molecular functions. Detailed comparative analysis was performed to highlight the common grounds of these dis-orders at various stages.

RESULTS

Astoundingly, 106 DEGs were found to be common across all disorders. Alongwith in total 100 GO terms and 7 KEGG pathways were found to be significantly enriched across all disorders. EGFR, CDC42 and CREBBP have been identified as the significantly interacting nodes in gene-gene in-teraction and in Protein-Protein Interaction (PPI) network as well. Furthermore, interaction of common DEGs targets with miRNA's has been scrutinized.

CONCLUSION

The complex molecular underpinnings of these disorders are currently elusive. Despite heterogeneous clinical and pathological expressions, common features have been recognized in many NDs which provide evidence of their convergence.

Clinical relevance of epigenetics in the onset and management of type 2 diabetes mellitus

Epigenetics is involved in the altered expression of gene networks that underlie insulin resistance and insufficiency. Major genes controlling β-cell differentiation and function, such as PAX4, PDX1, and GLP1 receptor, are epigenetically controlled. Epigenetics can cause insulin resistance through immunomediated pro-inflammatory actions related to several factors, such as NF-kB, osteopontin, and Toll-like receptors. Hereafter, we provide a critical and comprehensive summary on this topic with a particular emphasis on translational and clinical aspects. We discuss the effect of epigenetics on β-cell regeneration for cell replacement therapy, the emerging bioinformatics approaches for analyzing the epigenetic contribution to type 2 diabetes mellitus (T2DM), the epigenetic core of the transgenerational inheritance hypothesis in T2DM, and the epigenetic clinical trials on T2DM. Therefore, prevention or reversion of the epigenetic changes occurring during T2DM development may reduce the individual and societal burden of the disease.

Advances in translational bioinformatics facilitate revealing the landscape of complex disease mechanisms

Advances of high-throughput technologies have rapidly produced more and more data from DNAs and RNAs to proteins, especially large volumes of genome-scale data. However, connection of the genomic information to cellular functions and biological behaviours relies on the development of effective approaches at higher systems level. In particular, advances in RNA-Seq technology has helped the studies of transcriptome, RNA expressed from the genome, while systems biology on the other hand provides more comprehensive pictures, from which genes and proteins actively interact to lead to cellular behaviours and physiological phenotypes. As biological interactions mediate many biological processes that are essential for cellular function or disease development, it is important to systematically identify genomic information including genetic mutations from GWAS (genome-wide association study), differentially expressed genes, bidirectional promoters, intrinsic disordered proteins (IDP) and protein interactions to gain deep insights into the underlying mechanisms of gene regulations and networks. Furthermore, bidirectional promoters can co-regulate many biological pathways, where the roles of bidirectional promoters can be studied systematically for identifying co-regulating genes at interactive network level. Combining information from different but related studies can ultimately help revealing the landscape of molecular mechanisms underlying complex diseases such as cancer.