Construction and analysis of the protein-protein interaction network related to essential hypertension

Abstract

BackgroundEssential hypertension (EH) is a complex disease as a consequence of interaction between environmental factors and genetic background, but the pathogenesis of EH remains elusive. The emerging tools of network medicine offer a platform to explore a complex disease at system level. In this study, we aimed to identify the key proteins and the biological regulatory pathways involving in EH and further to explore the molecular connectivities between these pathways by the topological analysis of the Protein-protein interaction (PPI) network.ResultThe extended network including one giant network consisted of 535 nodes connected via 2572 edges and two separated small networks. 27 proteins with high BC and 28 proteins with large degree have been identified. NOS3 with highest BC and Closeness centrality located in the centre of the network. The backbone network derived from high BC proteins presents a clear and visual overview which shows all important regulatory pathways for blood pressure (BP) and the crosstalk between them. Finally, the robustness of NOS3 as central protein and accuracy of backbone were validated by 287 test networks.ConclusionOur finding suggests that blood pressure variation is orchestrated by an integrated PPI network centered on NOS3.

Systems analysis of oxidant stress in the vasculature

“…Within the network, NOS3 was identified as a node with high betweenness centrality, which is a measure of how often nodes occur on the shortest paths between other nodes and indicates a regulatory role for the node in the network. This makes biological sense as neighbors of NOS3 in the network were related to the SIRT1 pathway, antioxidants, inflammation, the renin–angiotensin system, and calcium signaling, all pathways involved in essential hypertension . One could also conclude from this network that when NOS3 is perturbed, the effect on vascular cell phenotype will extend beyond hypertension.…”

Identifying potential entry inhibitors for emerging Nipah virus by molecular docking and chemical-protein interaction network

“…Since directly interacting proteins are known to be involved in similar biological processes to regulate particular disease (Hartwell, Hopfield, Leibler, & Murray, 1999), initial interactions were extended to include those nodes from HPRD that directly interact with drug-target proteins resulting in an extended drug-target interaction network comprising of 287 nodes and 1155 edges. This methodology of extending the neighborhood of protein interactions has known to be effective for analyzing complex traits and diseases (Ran et al, 2013) (C. Chen et al, 2016. This extended drug-target network was integrated to NiV-human protein-protein interaction network (93 nodes and 126 edges) (Martinez-Gil et al, 2017) on the basis of common interaction pair across respective datasets yielding a DPI network ( Figure 6A) Table represents residue closeness values, inferred from protein structure 2VSM, and sequence conservation scores for all docking-identified ligand-binding residues.…”

Pathway mapping and development of disease‐specific biomarkers: protein‐based network biomarkers

“…Variants of aforementioned approaches have been applied to a wide range of disease phenotypes for identifying protein-based network biomarkers, such as breast cancer [5,35,36,49], colorectal cancer [30,33,100], prostate cancer [97,114], gastric cancer [115], lung cancer [37], ovarian cancer [116], acute myeloid leukaemia [39], glioma [117], ageing [23,98], Alzheimer's disease [38], inherited ataxias [118], cardiovascular diseases [6,32,34,109,119,120], chronic obstructive pulmonary disease [22], diabetes [43], asthma [121], osteoarthritis [42,122], multiple sclerosis [29], primary immunodeficiency [31], systemic inflammation [25], or acute aortic dissection [123], and demonstrated promising results. For example, Jin et al [6]developed a cardiovascular-related network based on protein information databases, and discovered network biomarkers of major adverse cardiac events (MACE) in the MS data on basis of protein knowledge.…”