alexa
Reach Us +44 3308 085114
In Silico Identification of Interaction between Ageing and Cardiovascular Disease Genes
ISSN: 2157-7420

Journal of Health & Medical Informatics
Open Access

Like us on:

Our Group organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations
700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)
All submissions of the EM system will be redirected to Online Manuscript Submission System. Authors are requested to submit articles directly to Online Manuscript Submission System of respective journal.
  • Research Article   
  • J Health Med Informat 2019, Vol 10(2): 329
  • DOI: 10.4172/2157-7420.1000329

In Silico Identification of Interaction between Ageing and Cardiovascular Disease Genes

Nikita Chordia*, Teena Patidar and Priyesh Hardia
Bioinformatics Sub-Center, School of Biotechnology, Takshashila Campus, Devi Ahilya University, Khandwa Road, Indore-452001, India
*Corresponding Author: Nikita Chordia, Bioinformatics Sub-Center, School of Biotechnology, Takshashila Campus, Devi Ahilya University, Khandwa Road, Indore-452001, India, Tel: 0091-731-2470373; 0091-9685281822, Email: [email protected]

Received Date: Feb 23, 2019 / Accepted Date: Mar 25, 2019 / Published Date: Apr 03, 2019

Abstract

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

Introduction

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 [1]. 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 [2].

CVDs risk increases with age but now many factors like nutrition, role of social status and psychological stress is much modulating CVD risk [3]. 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 [4]. 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.

Methodology

Collection of genes for CVDs and ageing

Genes for CVDs were collected from publicly available databases such as OMIM (https://www.omim.org/) [5], PubMed Central (PMC https://www.ncbi.nlm.nih.gov/pmc/) and PubMed (https://www.ncbi.nlm.nih.gov/pubmed/) [6]. 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/) [7].

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/ [8]. We opted to choose only “paths-between” interactions as it consider interactions of direct paths/ edges to the queried node.

Pathway analysis

The Reactome pathway database available at http://www.reactome.org was used to explore the pathways of CVDs and ageing [9]. 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/ [10]. It includes both physical (direct) and functional (indirect) associations. For each node of the network betweenness centrality is calculated using cytoscape [11]. 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 [12].

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.

CVD Genes
MEF2A ARH PPARG2 MRPS6 ANKS1A IFNW1 COL4A1
CYP27A1 ABCA1 ADIPOQ PON2 T CF21 IFNA21 COL4A2
ST6GALNAC5 LCAT FABP4 HNF1A ADT RP ABO HHIPL1
LRP6 APOC2 PLA2G7 MRAS KCNK5 LIPA ADAMT S7
LDLR SELE KLF14 LPA PLG KIAA1462 FURIN
PCSK9 GLUL INSIG1 KDR ZC3HC1 CYP17A1 FES
APOB IL10 ABCG1 IL6R HDAC9 CNNM2 SMG6
GIP ABCG5 APOC3 ZEB2 LPL NT 5C2 SRR
VAMP5 EDNRA CX3CL1 IL18 T RIB1 PDGFD RASD1
VAMP8 SLC22A4 GP1BA ACT A2 GAT A2 ZPR1 PEMT
GGCX CET P ATP5G1 HMOX1 CX3CR1 APOA5 UBE2Z
ABCG8 MT HFR SNF8 SCARB1 IT PKC APOA4 AGT
F7 CCL2 SF3A2 CD36 UGT 1A1 APOA1 SELP
OLR1 CCR2 AP3D1 CRP SERPINE1 BRAP LRP8
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
CD40LG EDN1 IRS1 GCLC GSBS LIPI CDKN2A
KCNQ1 T NF CX37 CACNB2 ABCC9 IT IH4 CDKN2B
CALM2 T GFB1 MIAT SCN1B SCN2B LT A SLC5A3
KCNH2 PSMA6 GCLM SCN3B NUP155 GHR SH2B3
KCNE1 SNT A1 T NFSF4 HCN4 RYR2 EPHX2 LT A4H
KCNE2 KCNE3 F13A1 KCND3 CASQ2 T NNC1 ALOX5AP
SCN5A KCNJ2 MYLK2 GJA5 T ECRL T CAP T MPO
ANK2 CAV3 T NNT 2 NPPA T RDN VCL SLC22A5
KCNJ5 SCN4B T PM1 RBM20 DPP6 FLNC CRYAB
ALG10 AKAP9 MYBPC3 FKT N KCNJ8 MYOZ2 PIGT
CALM1 CNBP PRKAG2 EYA4 GPD1L LDB3 LAMA4
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
JUP DMD CSRP3 RAF1 GJA1 NEXN LAMP2
DSG2 SGCD NR2F2 SDHA NKX2-5 MYPN NFKB1
T GFB3 T MEM43 CHD7 SALL4 T BX20 WDPCP T T R
DSC2 GNB3 JAG1 T BX1 BMPR2 CBL GDF1
RIT 1 SCNN1A B3GAT 3 KMT 2D ECE1 DT NA LZT R1
BRAF AGT R1 T KT KDM6A ADD1 ZFPM2 SHOC2
SOS2 CYP11B2 T BX5 EVC2 CYP3A5 PIGL NRAS
WNK1 NR3C2 HRAS EVC NOS2 MKKS T FAP2B
WNK4 CYP11B1 MAP2K1 F2 PT GIS ARHGAP31 PRDM6
HSD11B1 SCNN1B MAP2K2 F5 AT P1B1 CRELD1 NKX2-6
SLC9B1 RGS5 SEMA3E HABP2 KNG1 WRB PT PN11
KCNMB1 ACVRL1 ESR1 F9 FGG ADAMTS1 KRAS
MEX3C KCNK3 ATHS PROS1 HIVEP1 CYBA SOS1
G6PC3 DGUOK SOAT1 PROC STXBP5 VEGFA TNNI3K
SARS2 DYRK1B LIPG SERPINC1 VWF FOXO1 PTRF
CPS1 SORT1 CD5L PROZ F11 FGB DMPK
SMAD9 CACNA1H ADAMTS4 SERPINA10 CYP4V2 SIRT1 GATA5
PDE3A PEE1 IL6 IL1B TFPI FLNA ALK2
AKAP10 CAV1 P2RY12 ALDH2 MIF NOTCH1 RUNX2
EMD LIPC ICAM1 EGFR MMP1 SMAD6 LRP1
TRPM4 HP MBL2 IL1R1 SLC39A2 DCHS1 ADRB1
CLCNKA FBN1 FCN2 CTLA4 MAGP2 KLK1 ADRA2C
ADA LOX TLR2 MASP2 BGN TIMP2 GRK5
ADRB2 TGFBR1 TGFB2 MYH11 MLCK MMP9 IL17RA
HSPB27 TGFBR2 COL3A1 PRKG1 MMP2 EFEMP2 IL17A
FRMD4B SMAD3 FOXE3 MFAP5 MAT2A TIMP3  
Ageing Genes
A2M BDNF LEP MYC CREB1 EFEMP1 ESR1
ABL1 BLM LEPR NBN CREBBP EGF FEN1
ADCY5 BMI1 LMNA NCOR1 CSNK1E EGFR FGF21
AGPAT 2 BRCA1 LMNB1 NCOR2 CT F1 EGR1 FAS
AGT R1 BRCA2 LRP2 NFE2L1 CT GF EIF5A2 FGF23
AIFM1 BUB1B MAP3K5 NFE2L2 CT NNB1 ELN FGFR1
AKT 1 BUB3 MAPK14 NFKB1 DBN1 EMD FLT 1
APEX1 C1QA MAPK3 NFKB2 DDIT 3 EP300 FOS
APOC3 CACNA1A MAPK8 NFKBIA DGAT 1 EPOR FOXM1
APOE CAT MAPK9 NGF DLL3 EPS8 FOXO1
APP CCNA2 MAPT NGFR E2F1 ERBB2 FOXO3
APT X CDC42 MAX NOG EEF1A1 ERCC1 FOXO4
AR CDK1 MDM2 NR3C1 EEF1E1 ERCC2 GCLC
ARHGAP1 CDK7 MED1 NRG1 EEF2 ERCC3 GCLM
ARNT L CDKN1A MIF NUDT 1 PIK3R1 ERCC4 GH1
AT F2 CDKN2A MLH1 PAPPA PIN1 ERCC5 GHR
AT M CDKN2B MSRA PARP1 PLAU ERCC6 GHRH
AT P5O CEBPA MT -CO1 PCK1 PLC2 ERCC8 GHRHR
AT R CEBPB MT 1E PCMT 1 PMCH PRDX1 GPX1
BAK1 GRN MT OR PCNA PML PRKCA GPX4
BAX GSK3A MXD1 SIRT 1 POLB PRKCD  
BCL2 GSK3B MXI1 SIRT 3 POLD1 PRKDC GRB2
BSCL2 GSR HESX1 SIRT 6 POLG PROP1 HT T
RB1 GSS HIC1 SIRT 7 POLA1 PSEN1 IGF1
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
RGN H2AFX HOXB7   PPARGC1A PT K2B IGFBP3
RICT OR HBP1 HOXC4 SOD1 PPARG PT PN1 IKBKB
RPA1 HDAC1 HRAS JAK2 PPM1D PT PN11 IL2
S100B HDAC2 HSF1 JUN PPP1CA PYCR1 IL2RG
SDHC HDAC3 HSP90AA1 JUND SOD2 RED51 IL6
SERPINE1 HELLS HSPA1A KCNA3 SP1 RAD52 IL7
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
CNR1 UBE2I PIK3CA T FDP1 SUMO1 UCP2 CLU
COQ7 UCHL1 PEX5 T GFB1 SUN1 UCP3  

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.

medical-informatics-connected-neighborhood

Figure 1: Network of CVD genes complete network including connected and free nodes (grey nodes represent the connected neighborhood genes and nodes with black outline represent the query genes).

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.

medical-informatics-co-expression-gene

Figure 2: Network created by STRING. Different colour shows different interactions like gene neighbourhood, co-expression, gene fusion, gene cooccurrence and text mining.

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 [13]. 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.

Conclusion

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.

Acknowledgement

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.

References

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.

Select your language of interest to view the total content in your interested language

Post Your Comment Citation
Share This Article
Article Usage
  • Total views: 249
  • [From(publication date): 0-0 - Nov 18, 2019]
  • Breakdown by view type
  • HTML page views: 216
  • PDF downloads: 33
Top