Amyotrophic Lateral Sclerosis (ALS) is a fatal disease, progressive nature characterizes by loss of both upper and lower motor neuron functions. One of the major challenge is to understand the mechanism of ALS multifactorial nature. We aimed to explore some key genes related to ALS through bioinformatics methods for its therapeutic intervention. Here, we applied a systems biology approach involving experimentally validated 148 ALS-associated proteins and construct ALS protein-protein interaction network (ALS-PPIN). The network was further statistically analysed and identified bottleneck-hubs. The network is also subjected to identify modules which could have similar functions. The interaction between the modules and bottleneck-hubs provides the functional regulatory role of the ALS mechanism. The ALS-PPIN demonstrated a hierarchical scale-free nature. We identified 17 bottleneck-hubs, in which CDC5L, SNW1, TP53, SOD1, and VCP were the high degree nodes (hubs) in ALS-PPIN. CDC5L was found to control highly cluster modules and play a vital role in the stability of the overall network followed by SNW1, TP53, SOD1, and VCP. HSPA5 and HSPA8 acting as a common connector for CDC5L and TP53 bottleneck-hubs. The functional and disease association analysis showed ALS has a strong correlation with mRNA processing, protein deubiquitination, and neoplasms, nervous system, immune system disease classes. In the future, biochemical investigation of the observed bottleneck-hubs and their interacting partners could provide a further understanding of their role in the pathophysiology of ALS.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease caused by the loss of upper and lower motor neurons. The sporadic ALS (sALS) is a multigenic disorder and the complex mechanisms underlying its onset are still not fully delineated. Despite the recent scientific advancements, certain aspects of ALS pathogenic targets need to be yet clarified. The aim of the presented study is to identify potential genetic biomarkers and drug targets for sALS, by analysing gene expression profiles, presented in the publicly available GSE68605 dataset, of motor neurons cells obtained from sALS patients. We used different computational approaches including differential expression analysis, protein network mapping, candidate protein biomarker (CPB) identification, elucidation of the role of functional modules, and molecular docking analysis. The resultant top ten up- and downregulated genes were further used to construct protein-protein interaction network (PPIN). The PPIN analysis resulted in identifying four CPBs (namely RIOK2, AKT1, CTNNB1, and TNF) that commonly overlapped with one another in network parameters (degree, bottleneck and maximum neighbourhood component). The RIOK2 protein emerged as a potential mediator of top five functional modules that are associated with RNA binding, lipoprotein particle receptor binding in pre-ribosome, and interferon, cytokine-mediated signaling pathway. Furthermore, molecular docking analysis revealed that cyclosporine exhibited the highest binding affinity (-8.6 kJ/mol) with RIOK2, and surpassed the FDA-approved ALS drugs, such as riluzole and edaravone. This suggested that cyclosporine may serve as a promising candidate for targeting RIOK2 downregulation observed in sALS patients. In order to validate our computational results, it is suggested that in vitro and in vivo studies may be conducted in future to provide a more detailed understanding of ALS diagnosis, prognosis, and therapeutic intervention.

Colorectal cancer is one of the leading causes of cancer-associated mortality across the worldwide. One of the major challenges in colorectal cancer is the understanding of the regulatory mechanisms of biological molecules. In this study, we aimed to identify novel key molecules in colorectal cancer by using a computational systems biology approach. We constructed the colorectal protein–protein interaction network which followed hierarchical scale-free nature. We identified TP53, CTNBB1, AKT1, EGFR, HRAS, JUN, RHOA, and EGF as bottleneck-hubs. The HRAS showed the largest interacting strength with functional subnetworks, having strong correlation with protein phosphorylation, kinase activity, signal transduction, and apoptotic processes. Furthermore, we constructed the bottleneck-hubs’ regulatory networks with their transcriptional (transcription factor) and post-transcriptional (miRNAs) regulators, which exhibited the important key regulators. We observed miR-429, miR-622, and miR-133b and transcription factors (EZH2, HDAC1, HDAC4, AR, NFKB1, and KLF4) regulates four bottleneck-hubs (TP53, JUN, AKT1 and EGFR) at the motif level. In future, biochemical investigation of the observed key regulators could provide further understanding about their role in the pathophysiology of colorectal cancer.