In Silico Identification of Interaction between Ageing and Cardiovascular Disease Genes
Received Date: Feb 23, 2019 / Accepted Date: Mar 25, 2019 / Published Date: Apr 03, 2019
Heart is the central organ that pumps pure blood to whole body through blood vessels. This circulation system involves functioning of large number of genes that interacts for proper functioning. Any malfunctioning of single gene leads to the cardiovascular diseases (CVDs) Aging is an inevitable part of life and unfortunately the risk of CVDs increases with ageing. Although numerous studies were carried on cardiovascular diseases that considered both young and aged humans, but still there are many unanswered questions as to how the genetic pathways that regulate aging influence cardiovascular disease and vice versa. Therefore, this is of great interest to identify the interaction between cardiovascular diseases and ageing genes. Genes for CVDs and ageing were collected from various databases and a network was created for their common genes. Network in analyzed to find the interaction between ageing and cardiovascular disease genes.
Keywords: Cardiovascular disease; Ageing; Interaction; Genes; Network; Databases; Nodes; Health
The most important factor of cardiovascular disease is a person's age. It is estimated that by 2030, approximately 20% of the population will be aged 65 or older. In this age group, CVD will result in 40% of all deaths and rank as the leading cause . The fields of cardiovascular disease and aging have remained largely separate. However, now this gap is beginning to close and these two fields are merging together. But still there are many unanswered questions as to how the aging genes are accessible to CVDs and are interacting with each other .
CVDs risk increases with age but now many factors like nutrition, role of social status and psychological stress is much modulating CVD risk . Costantino et al. reported that in the near future there will be more exponential increase in the prevalence of CVD due to due to daily living activities. It is a fact that additional 27 million people will have hypertension, 8 million coronary heart diseases, 4 million strokes and 3 million heart failures . So, there is an urgent need complete understanding of the mechanisms of ageing the affects the CVD risk. In present study, we tried to identify the gene interaction of ageing and CVDs.
CVDs are diseased heart conditions that include variety of diseases associated with diseased vessels, structural problems in heart and blood vessels and blood clots. Some of the common cardiovascular diseases are that we have included in our study are coronary heart disease, cerebrovascular disease, peripheral arterial disease, rheumatic heart disease, congenital heart disease, deep vein thrombosis, pulmonary embolism, cardiomyopathy, cardiac arrhythmia, hypertension, cardiac arrest, congestive heart disease, cardiac conduction disease, valvular heart diseases, aortic aneurysm, myocardial infarction, atherosclerosis and acute coronary syndrome.
In this study, we create a network of above mentioned CVDs and ageing genes to find the common interaction of both. This finding will help in further understanding of this disease interaction at molecular level. For this purpose we created two in silico networks, one for cardiovascular diseases and another for ageing. Both networks are compared for their biological pathways involved. Identified genes from common pathways are further used to create a network of ageing and cardiovascular diseases. The resultant network is then analyzed for important nodes in terms of degree and betweenness.
Collection of genes for CVDs and ageing
Genes for CVDs were collected from publicly available databases such as OMIM (https://www.omim.org/) , PubMed Central (PMC https://www.ncbi.nlm.nih.gov/pmc/) and PubMed (https://www.ncbi.nlm.nih.gov/pubmed/) . This was done through text mining using diseases specific keywords and their relevant genes. Ageing genes were retrieved from GenAge database (http://genomics.senescence.info/genes/) maintained at Human Ageing Genomic Resources (HAGR) http://genomics.senescence.info/) .
Network creation for CVDs and ageing genes
The network for genes of CVDs and ageing was created using Pathway Commons Network Visualizer (PCViz) available online at http://www.pathwaycommons.org/pcviz/ . We opted to choose only “paths-between” interactions as it consider interactions of direct paths/ edges to the queried node.
The Reactome pathway database available at http://www.reactome.org was used to explore the pathways of CVDs and ageing . Common pathways were used to manually identify common genes of CVDs and ageing.
Common gene network analysis
Common network of CVD and ageing was created using STRING available at https://string-db.org/ . It includes both physical (direct) and functional (indirect) associations. For each node of the network betweenness centrality is calculated using cytoscape . It is the measure of centrality of a node in the network to find the important nodes of the network. Betweenness centrality for node x, is calculated by summing the number of shortest paths between pairs of nodes that pass through node x divided by the total number of shortest paths between pairs of nodes. It characterizes the control of a node over the information flow of the network. A node is considered important for a network if it appears on many paths that connect pairs of nodes. The node with higher value acts as a bridge between pairs of nodes in the network .
Results and Discussion
A total of 410 genes of CVDs were collected from OMIM, PubMed and PMC. Similarly, total 305 genes of ageing and were collected from GenAge database. Table 1 shows the list of genes used for the analysis.
|VAMP8||SLC22A4||GP1BA||ACT A2||GAT A2||ZPR1||PEMT|
|ABCG8||MT HFR||SNF8||SCARB1||IT PKC||APOA4||AGT|
|T HBD||NOS3||DOT 1L||KALRN||KL||BCAP29||APOA2|
|IT GB3||FABP2||APOE||PAPPA||ANGPT L4||BT N2A1||F13B|
|IT GA2||ACE1||APOC1||PLCL2||MMP3||IRX1||T HBS4|
|KCNQ1||T NF||CX37||CACNB2||ABCC9||IT IH4||CDKN2B|
|CALM2||T GFB1||MIAT||SCN1B||SCN2B||LT A||SLC5A3|
|KCNE1||SNT A1||T NFSF4||HCN4||RYR2||EPHX2||LT A4H|
|SCN5A||KCNJ2||MYLK2||GJA5||T ECRL||T CAP||T MPO|
|ANK2||CAV3||T NNT 2||NPPA||T RDN||VCL||SLC22A5|
|CACNA1C||FLT 1||MYL3||PSEN1||T AB2||JPH2||RPS6KA3|
|LMNA||CT NNA3||T T N||DNAJC19||GAT A6||T LL1||PRDM16|
|PKP2||DES||MYL2||GAT AD1||ZIC3||PLN||T MEM87B|
|DSP||T AZ||ACT C1||PSEN2||CIT ED2||MYH7||SLC8A1|
|T GFB3||T MEM43||CHD7||SALL4||T BX20||WDPCP||T T R|
|RIT 1||SCNN1A||B3GAT 3||KMT 2D||ECE1||DT NA||LZT R1|
|BRAF||AGT R1||T KT||KDM6A||ADD1||ZFPM2||SHOC2|
|AGPAT 2||BRCA1||LMNB1||NCOR2||CT F1||EGR1||FAS|
|AGT R1||BRCA2||LRP2||NFE2L1||CT GF||EIF5A2||FGF23|
|AKT 1||BUB3||MAPK14||NFKB1||DBN1||EMD||FLT 1|
|ARNT L||CDKN1A||MIF||NUDT 1||PIK3R1||ERCC4||GH1|
|AT P5O||CEBPA||MT -CO1||PCK1||PLC2||ERCC8||GHRHR|
|AT R||CEBPB||MT 1E||PCMT 1||PMCH||PRDX1||GPX1|
|BSCL2||GSR||HESX1||SIRT 6||POLG||PROP1||HT T|
|RECQL4||GST A4||HIF1A||SLC13A1||PON1||PT EN||IGF1R|
|RELA||GST P1||HMGB1||SNCG||PPARA||PT GS2||IGF2|
|RET||GT F2H2||HMGB2||SOCS2||POU1F1||PT K2||IGFBP2|
|RICT OR||HBP1||HOXC4||SOD1||PPARG||PT PN1||IKBKB|
|SHC1||T P53||HSPA1B||KL||SPRT N||RAE1||IL7R|
|SIN3A||T P53BP1||HSPA8||T AF1||SQST M1||VEGFA||INS|
|T NF||T P63||HSPA9||T BP||SST||WRN||INSR|
|T OP1||T P73||HSPD1||T CF3||SST R3||XPA||IRS1|
|T OP2A||T PP2||HT RA2||T ERC||ST AT 3||XRCC5||IRS2|
|T OP2B||T RAP1||PDGFB||T ERF1||ST AT 5A||XRCC6||CHEK2|
|T OP3B||T RPV1||PDGFRA||T ERF2||ST AT 5B||YWHAZ||CET P|
|VCP||T XN||PDGFRB||T ERT||ST K11||ZMPST E24||CISD2|
|PIK3CB||UBB||PDPK1||T FAP2A||ST UB1||UCP1||CLOCK|
Table 1: List of genes of CVD and ageing used in the study.
From 410 genes of CVDs, related genes were eliminated as they are showing common interactions. For the leftover 339 genes of CVDs and 305 genes of ageing, network is created using PCViz and is analyzed. Figure 1 shows the network of CVDs. A total of 339 genes were input and network was created for 1171 genes which were neighbourhood genes with 5538 interactions. Similarly, for ageing a total of 305 genes were input and network was created for 1194 genes which were neighbourhood genes with 22795 interactions.
The above network is analysed by Reactome. The result shows pathway, genes involved in it, entries found in the network and information related to reaction. Based on the result, 104 genes which were involved in common pathways of CVD and ageing were taken manually. The network was created for these common genes using STRING. This network shows total 281 nodes in which 132 nodes were connected by 244 edges as shown in Figure 2.
Network created by STRING is analyzed using cytoscape. It allows analyzing the network in terms of betweenness centrality of each node. Betweenness centrality is calculated for every pair of the network and measure of how many times a node is interrupted under the assumption that information primarily flows over the shortest paths between them. Jeong et al. demonstrated the consequence of a single gene deletion in Saccharomyces cerevisiae through centrality . The same approach is used to identify the important genes in the network of CVDs and ageing.
As a result, 10 genes are found to be central to the network and are intersecting CVDs and ageing. These are NFKB1, FOXO1, HRAS, SERPINE1, VEGFA, EGFR, IRS1, TNF, PSEN1 and IL6. All these genes are involved in ageing and different CVDs like NFKB1 in Dilated cardiomyopathy, FOXO1 in carotid atherosclerosis, HRAS in congenital heart disease), SERPINE1, VEGFA and EGFR in coronary heart diseases, IRS1 and TNF in myocardial infraction), PSEN1 in Dilated cardiomyopathy and IL6 in peripheral artery disease.
Ageing is the most important risk factor affecting cardiovascular system. Here in this article, we have found out the interaction of ageing and CVDs genes. The study is very important in understanding the mechanism of ageing that increases the prevalence for CVDs. The resultant ten genes are ageing genes and are also reported to be involved in cardiovascular diseases. The ultimate goal of this research is to move towards healthy ageing. It can be achieved by understanding the ageing process to the extent where novel strategies that delay or even prevent the onset of CVDs can be implemented. Developing novel strategies will require a more integrated understanding of the ageing process and wide variety of factors that contributes to CVD risk.
Authors acknowledge the facilities of the Department of Biotechnology, Ministry of Science and Technology, Government of India, New Delhi (DBT) under the Bioinformatics Sub Centre as well as M.Sc. Biotechnology program used in the present work.
Conflict of Interest
The authors confirm that they have no conflict of interest.
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Citation: Chordia N, Patidar T, Hardia P (2019) In Silico Identification of Interaction between Ageing and Cardiovascular Disease Genes. J Health Med Informat 10: 329. DOI: 10.4172/2157-7420.1000329
Copyright: © 2019 Chordia N, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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