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The Parkinson disease (PD) is the second most common neurodegenerative disorder affecting the central nervous system and motor functions. The biological complexity of PD is yet to reveal potential targets for intervention or to slow the disease severity. Therefore, this study aimed to compare the fidelity of blood to substantia nigra (SN) tissue gene expression from PD patients to provide a systematic approach to predict role of the key genes of PD pathobiology. Differentially expressed genes (DEGs) from multiple microarray data sets of PD blood and SN tissue from GEO database are identified. Using the theoretical network approach and variety of bioinformatic tools, we prioritized the key genes from DEGs. A total of 540 and 1024 DEGs were identified in blood and SN tissue samples, respectively. Functional pathways closely related to PD such as ERK1 and ERK2 cascades, mitogen-activated protein kinase (MAPK) signaling, Wnt, nuclear factor-κB (NF-κB), and PI3K-Akt signaling were observed by enrichment analysis. Expression patterns of 13 DEGs were similar in both blood and SN tissues. Comprehensive network topological analysis and gene regulatory networks identified additional 10 DEGs functionally connected with molecular mechanisms of PD through the mammalian target of rapamycin (mTOR), autophagy, and AMP-activated protein kinase (AMPK) signaling pathways. Potential drug molecules were identified by chemical-protein network and drug prediction analysis. These potential candidates can be further validated in vitro/in vivo to be used as biomarkers and/or novel drug targets for the PD pathology and/or to arrest or delay the neurodegeneration over the years, respectively. [ABSTRACT FROM AUTHOR]

Familial hypercholesterolemia (FH) is a monogenic lipid disorder which promotes atherosclerosis and cardiovascular diseases. Owing to the lack of sufficient published information, this study aims to identify the potential genetic biomarkers for FH by studying the global gene expression profile of blood cells. The microarray expression data of FH patients and controls was analyzed by different computational biology methods like differential expression analysis, protein network mapping, hub gene identification, functional enrichment of biological pathways, and immune cell restriction analysis. Our results showed the dysregulated expression of 115 genes connected to lipid homeostasis, immune responses, cell adhesion molecules, canonical Wnt signaling, mucin type O-glycan biosynthesis pathways in FH patients. The findings from expanded protein interaction network construction with known FH genes and subsequent Gene Ontology (GO) annotations have also supported the above findings, in addition to identifying the involvement of dysregulated thyroid hormone and ErbB signaling pathways in FH patients. The genes like CSNK1A1, JAK3, PLCG2, RALA, and ZEB2 were found to be enriched under all GO annotation categories. The subsequent phenotype ontology results have revealed JAK3I , PLCG2, and ZEB2 as key hub genes contributing to the inflammation underlying cardiovascular and immune response related phenotypes. Immune cell restriction findings show that above three genes are highly expressed by T-follicular helper CD4+ T cells, naïve B cells, and monocytes, respectively. These findings not only provide a theoretical basis to understand the role of immune dysregulations underlying the atherosclerosis among FH patients but may also pave the way to develop genomic medicine for cardiovascular diseases. [ABSTRACT FROM AUTHOR]

Myocardial infarction (MI) is the most prevalent coronary heart disease caused by the complex molecular interactions between multiple genes and environment. Here, we aim to identify potential biomarkers for the disease development and for prognosis of MI. We have used gene expression dataset (GSE66360) generated from 51 healthy controls and 49 patients experiencing acute MI and analyzed the differentially expressed genes (DEGs), protein-protein interactions (PPI), gene network-clusters to annotate the candidate pathways relevant to MI pathogenesis. Bioinformatic analysis revealed 810 DEGs. Their functional annotations have captured several MI targeting biological processes and pathways like immune response, inflammation and platelets degranulation. PPI network identify seventeen hub and bottleneck genes, whose involvement in MI was further confirmed by DisGeNET database. OpenTarget Platform reveal unique bottleneck genes as potential target for MI. Our findings identify several potential biomarkers associated with early stage MI providing a new insight into molecular mechanism underlying the disease. Unlabelled Image • Integrated bioinformatic analysis to identify potential biomarkers and/or novel drug targets for complex diseases. • Hubs, bottleneck genes can be useful biomarkers of inflammatory response in the early stage of myocardial infarction (MI). • Prioritized the MI candidate genes with DisGeNET and Open Target Platform. • Identified the key functional pathways for MI to unravel the biological basis of the disease development. • Carriers of variants in the identified MI genes may have increased risk of developing MI. [ABSTRACT FROM AUTHOR]