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COVID-19 has created a global pandemic with high morbidity and mortality in 2020. Novel coronavirus (nCoV), also known as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2), is responsible for this deadly disease. International Committee on Taxonomy of Viruses (ICTV) has declared that nCoV is highly genetically similar to SARS-CoV epidemic in 2003 (89% similarity). Limited number of clinically validated Human-nCoV protein interaction data is available in the literature. With this hypothesis, the present work focuses on developing a computational model for nCoV-Human protein interaction network, using the experimentally validated SARS-CoV-Human protein interactions. Initially, level-1 and level-2 human spreader proteins are identified in SARS-CoV-Human interaction network, using Susceptible-Infected-Susceptible (SIS) model. These proteins are considered as potential human targets for nCoV bait proteins. A gene-ontology based fuzzy affinity function has been used to construct the nCoV-Human protein interaction network at 99.98% specificity threshold. This also identifies the level-1 human spreaders for COVID-19 in human protein-interaction network. Level-2 human spreaders are subsequently identified using the SIS model. The derived host-pathogen interaction network is finally validated using 7 potential FDA listed drugs for COVID-19 with significant overlap between the known drug target proteins and the identified spreader proteins., Comment: Total number of pages is 12. Total number of figures is 7. Total number of tables is 1

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., Highlights • Amyotrophic Lateral Sclerosis protein-protein interaction network (ALS-PPIN) followed a hierarchical scale-free nature. • We identified 17 bottleneck-hubs in the ALS-PPIN. • Among bottleneck-hubs we found CDC5L, SNW1, TP53, SOD1, and VCP were the high degree nodes (hubs) in the ALS-PPIN. • CDC5L is the effective communicator with all five modules in the ALS-PPIN and followed by SNW1 and TP53. • Modules are highly associated with various disease classes like neoplasms, nervous systems and others.

Highlights • LC degeneration associated with cognitive decline in PD patients. • LC degeneration correlated with extensive network disruption. • LC degeneration may contribute to cognitive decline through disorganized brain function., Degeneration of the locus coeruleus (LC) is recognized as a critical hallmark of Parkinson's disease (PD). Recent studies have reported that noradrenaline produced from the LC has critical effects on brain functional organization. However, it is unknown if LC degeneration in PD contributes to cognitive/motor manifestations through modulating brain functional organization. This study enrolled 94 PD patients and 68 healthy controls, and LC integrity was measured using the contrast-to-noise ratio of the LC (CNRLC) calculated from T1-weighted magnetic resonance imaging. We used graph-theory-based network analysis to characterize brain functional organization. The relationships among LC degeneration, network disruption, and cognitive/motor manifestations in PD were assessed. Whether network disruption was a mediator between LC degeneration and cognitive/motor impairments was assessed further. In addition, an independent PD subgroup (n = 35) having functional magnetic resonance scanning before and after levodopa administration was enrolled to evaluate whether LC degeneration-related network deficiencies were independent of dopamine deficiency. We demonstrated that PD patients have significant LC degeneration compared to healthy controls. CNRLC was positively correlated with Montreal Cognitive Assessment score and the nodal efficiency (NE) of several cognitive-related regions. Lower NE of the superior temporal gyrus was a mediator between LC degeneration and cognitive impairment in PD. However, levodopa treatment could not normalize the reduced NE of the superior temporal gyrus (mediator). In conclusion, we provided evidence for the relationship between LC degeneration and extensive network disruption in PD, and highlight the role of network disorganization in LC degeneration-related cognitive impairment.