bso

Biological Systems: Open Access

ISSN - 2329-6577

44-7723-59-8358

Research Article - (2015) Volume 4, Issue 2

Gene-Gene Interaction Mapping Of Human Cytomegalic Virus through System Biology Approach

Vijaylaxmi Saxena, Supriya Dixit* and Alfisha Ashraf
Bioinformatics Infrastructure Facility, Centre of DBT (Govt. India), Dayanand Girl’s P.G. College, India
*Corresponding Author: Supriya Dixit, Bioinformatics Infrastructure Facility, Centre of DBT (Govt. India), Dayanand Girl’s P.G. College, Kanpur (U.P), India, Tel: 09415125252 Email:

Abstract

Systems biology is concerned with the study of biological systems, by investigating the components of cellular networks and their interactions. The objective of present study is to build gene-gene interaction network of human cytomegalovirus genes with human genes and other influenza causing genes which helps to identify pathways, recognize gene function and find potential drug targets for cytomegalovirus visualized through cytoscape and its plugin. So, genetic interaction is logical interaction between two genes and more than that affects any organism phenotypically. Human cytomegalovirus has many strategies to survive the attack of the host. Human cytomegalovirus infection of host cells induces cellular activation and disturbance of the cell cycle. Further functional analysis was done to know functionally active genes to cause infection and also these genes will be used as targets to prevent infection spread through virus and then ontology analysis was performed to those functionally active genes describes gene products in terms of their associated biological processes, cellular components and molecular functions by using clueGO Plugin.

Keywords: System biology; Functional analysis; Ontology analysis

Introduction

Systems biology underpinning inter and intra-cellular dynamical networks, by means of signal and system-oriented approaches, applying experimental high throughput and whole genome techniques, integrating computational methods with experimental efforts. Emergence, robustness and modularity are the three basic concepts that are crucial to understanding complex biological system [1]. The main aspect of system biology is translating the biological information into models [2]. Drug discovery and the design of multiple drug therapies and therapeutic gene circuits and complex engineering product are the applications of systems biology to medical practice [3]. Human cytomegalovirus (HCMV) is an enveloped DNA virus that, like other members of the herpes virus family, establishes lifelong latency following primary infection Cytomegalovirus. Cytoscape is an open source bioinformatics software platform for visualizing molecular interaction networks and integrating with gene profiles and other state data. Cytoscape is most commonly used for biological research applications. The tool is best used in conjunction with large databases of gene expression data, proteinprotein interaction, protein-DNA interaction, and genetic interactions that are increasingly available for humans and model organisms [4]. Several useful plugins are available for Cytoscape to extend its capabilities for example network analyzer plug-in. The study of gene - gene interaction play an important role in the search for the cause of human disease and the main aim of establishment of this study is To Build and Analyse Gene-Gene Interaction Network of HCMV genes. The network of hcmv genes are used for further studies such as Functional and Ontology Analysis of HCMV genes.

Materials and Methods

Materials

Data source

Gene card is a database of human genes that provide information about their functions, genomic views, proteins and protein domains, transcripts, orthology, paralogs, their expression localization, interaction and involvement in pathway, disease and disorders on all known human genes. Total 129 genes (Table 1) have been extracted from gene card to build a network.

Gene name GCID from gene cards PMID
RAB6A ­ GC11MO73386 7798313
ENSG00000183214 GC06Pn31357 19793804
ENSG00000235233 GC06P131410 Not found
RSAD2 GC02P007005 Not found
ENSG00000206206 GC06MK33264 20444888
ENSG00000206279 GC06Mn3215 20444888
ENSG00000227046 GC06Mj33207 20444888
ENSG00000231617 GC06Mm33456 20444888
MIR20A GC13P092001 23768492
DAXX GC06MO33286 17942542
ENSG00000206235 GC06MI32943 9175839
ENSG00000206297 GC06Mn32741 9175839
ENSG00000206299 GC06Mn32718 9175839
ENSG00000223481 GC06Mk32767 9175839
ENSG00000224212 GC06Mk32790 9175839
ENSG00000225967 GC06Mi32773 9175839
ENSG00000226173 GC06MI32966 9175839
ENSG00000227816 GC06Mi32796 9175839
ENSG00000228582 GC06Mj32703 9175839
ENSG00000230705 GC06Mm32846 9175839
ENSG00000232326 GC06Mo32879 9175839
ENSG00000232367 GC06Mj32735 9175839
ENSG00000237599 GC06Mm23815 9175839
MIR17 GC13P092002 23768492
TMEM147 GC19P036038 17188320
GGH GC08M063928 1328481
LINC01194 GC05P012578 17400331
MRGPRXI GC11M018955 16352349
MPZ GC01M161274 17765268
HNRNPH3 GC10P070090 21320693
MICA GC06P031373 16951502
RAB11FIP4 GC17P029718 19761540
CBR1 GC21P037442 21320693
FKBP10 GC17P039968 21320693
IL32 GC16P003153 23402302
TMEM43 GC03P014142 21320693
TRIM23 GC05M064885 19176615
CX3CL1 GC16P057406 19605482
DDX39A GC19M014521 20610707
LBR GC01M2255899 15018860
CAMKK1 GC17M003763 Not found
CD69 GC12M010498 19152985
CHAF1A GC19P004402 21445097
LILRB1 GC19P055085 23348966
MICB GC06P031465 23625227
PDIA4 GC07M148700 21320693
TAPT1 GC04M016162 10640539
WDR26 GC01M224573 21320693
ANXA2 GC15M060639 12456502
CFLAR GC02P201980 17056549
ACLY GC17M040023 21320693
ACTL6A GC03P179280 21320693
AGTR2 GC0XP115216 18534055
ANAPC10 GC04M145916 22792066
ANAPC7 GC12M110810 22792066
ANAPC5 GC12M121746 22792066
CEBPA GC19M033790 19631360
EEF2K GC16P022217 Not found
EIF2AK3 GC02M088857 23592989
IFI16 GC01P0158969 22291595
KAT5 GC11P065479 21320693
NUDT21 GC16M056463 21320693
RAB11A GC15P066018 19761540
RAB1A GC02M065297 21320693
SPI1 GC11M049902 18308397
TAP1 GC06M032812 9175839
TAP2 GC06M032789 9175839
THBS2 GC06M169615 11563036
TLR3 GC04P186990 19914718
DDX39B GC06M031522 20610707
ANPEP GC15M090328 Not found
CAMKK2 GC12M121675 Not found
CD59 GC11M03721 7594597
CDC23 GC05M137552 22792066
DDB1 GC11M061066 21320693
EGR1 GC05P137801 10623574
EP400 GC12P132434 21320693
HFE GC06P026087 12456502
HLA-G GC06P029794 9687527
MSR1 GC08M016009 19914718
PSMB6 GC17P004699 21320693
PSMB4 GC01P151372 21320693
PSME3 GC17P040985 21320693
RUVBL2 GC19P049497 21320693
RUVBL1 GC03M127783 21320693
TLR9 GC03M052255 19914718
UBR5 GC08M103265 21320693
CAMK2B GC07M044225 Not found
CAMK2A GC05M149579 Not found
CDC27 GC17M045195 22792066
E2F1 GC20M032263 14695446
EIF4A1 GC17P007476 23747307
HSPA5 GC09M127997 21221131
KPNA1 GC03M122140 12610148
PML GC15P074287 Not found
PSMA3 GC14P058711 21320693
PSMC6 GC14P053173 21320693
PSMD3 GC17P038137 21320693
RAN GC12P131356 21320693
SUMO1 GC02M203070 21816224
THBS1 GC15P039873 11563036
TRRAP GC07P098475 21320693
WT1 GC11M032365 10623574
AGTR1 GC03P148415 18534055
CAMK2G GC10M075572 Not found
CDK2 GC12P056360 14695446
FLNB GC03P057969 12559625
GPT GC08P145728 15797363
HLA-C GC06M031236 9687527
HLA-DQA1 GC06P032595 12443029
PSMD2 CG03P184016 21320693
PSMC4 GC19P040477 21320693
TLR2 GC04P154612 18053251
IL10 GC01M206940 15018860
IL4 GC05P132009 15018860
STAT3 GC17M040465 21320693
CAMK2D GC04M114372 NOT FOUND
HLA-A GC06P030186 12443029
HLA-DQB1 GC06M032629 12443029
ICAM1 GC19P010381 9154389
PIK3CG GC07P106505 19427341
IL6 GC07P022765 23555719
MAPK1 GC22M022108 16650413
CASP3 GC04M185548 14695446
HLA-DRB1 GC06M032546 12443029
NFKB1 GC04P103422 19427341
CD55 GC01P207494 7594597
MAPK14 GC06P035995 17229385
TNF GC06P031543 22486303
TP53 GC17M007565 17400331

Table 1: The table shows genes of HCMV with their respective GCID.

Tool

Cytoscape is an open source bioinformatics software platform and it provides basic functionality to layout and queries the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations [4,5]. Cytoscape has been used to construct HCMV network for Analysis of HCMV gene.

ClueGO is a cytoscape plug-in that enhances biological interpretation of large lists of genes. ClueGO integrates Gene Ontology (GO) terms as well as KEGG/BioCarta pathways and creates a functionally organized GO/pathway term network. CluePedia provides a comprehensive view on a pathway or process by investigating experimental [5].

Method

biological-systems-Workflow

Figure 1: Flowchart of Workflow

Results and Discussion

The genes involved in infection spread through cytomegalo virus find through GeneCards database and various publish literature. Hence, gene-gene interaction network was built. The interaction network is imported through Cytoscape’s web services option. However the study was aimed to find out the genes involved in cytomegalic infection in humans hence, genes from other species was removed but genes of influenza virus is in the network because patient’s infected with influenza have elevated level of genes which are found to be causative agents of cytomegalovirus. Network containing self-loops, duplicate edges were removed and gene-gene interaction network contains 13923 genes and 35714 interactions (Figure 2).

biological-systems-gene-gene-interation

Figure 2: The above diagram shows gene-gene interation network of cytomegalo virus, influenza virus and human gene.

The main objective behind genetic interaction network is to understand the relationship between the genotypes and phenotypes of individuals which could be important key for identifying genetic variants responsible for disease. Generally, unexpected phenotypic changes will occurred when two or more genetic variants will interact with each other. Hence, genetic interaction network will help to map affected gene and their related biological process or pathways to develop successful therapeutic strategies further.

Analysis using CluePedia plugin

Although it was large gene interaction network and the research aimed to do functional analysis of gene dataset and this analysis was done through CluePedia plugin. Functional analysis helps to determine which gene is functional modules form list of genes of interest. CluePedia plugin helps to identify functionally participating group of genes in cytomegalic infection with their interaction type like expression, binding etc (Figure 3).

biological-systems-CluePedia-plugin

Figure 3: The above diagram shows enrichment analysis of gene through CluePedia plugin involved in infections spread through HCMV. Dark green line shows activation expression of gene and yellow line shows binding expression of genes.

Analysis through ClueGO Plugin

47 genes found functionally enriched among 129 genes and with the help of ClueGO plugin further analysis was done. Go term analysis was performed through ClueGo plugin and for categorizing 47 genes into GO term parameters taken as defaulted.

Go term analysis was performed through ClueGO plugin and for categorizing 47 genes into GO term, some parameter was set. Kappa (ะบ) Statistics is used to examine interrater and intrarater reliability of data in relation to clinical diagnosis or classification and assessment finding. These data require to access specific reliability that’s why kappa statistics used. The range of possible values of kappa is from –1 to 1, though it usually falls between 0 and 1. Unity represents perfect agreement, indicating that the raters agree in their classification of every case. Zero indicates agreement no better than that expected by chance. A negative kappa would indicate agreement worse than that expected by chance [6].

The main aim of ontology analysis of functionally enriched genes is to know which gene upregulates and down regulates in certain biological process. ClueGO analysis performs automatically the calculation of the terms and groups significance. P-Value correction method is selected in ClueGO selection panel, then on the network and on the charts the corrected P-Value will be represented.

The terms and groups significance can be found in the ClueGo browser.

The chart showed in Figure 4; mark the level of the significance for terms and groups using:

biological-systems-biological-processes

Figure 4: Graphical representation of biological processes found for 47 genes.

1. **: if the term/group is over significant, P-Value <0.001.

2. *: if the term/group is significant, 0.001

3. . (Dot): 0.05

The below table shows biological process and name of genes involved in those biological process (Table 2).

Function Groups Group Genes
Cellular Senescence** Group 1 CDK2|CDK4|CDK6|E2F1|EP400|IL6|MAPK14|NFKB1|STAT3|TP53
Cellular responses to stress** Group 6 CDK2|CDK4|CDK6|E2F1|EP400|IL6|MAPK14|NFKB1|STAT3|TP53
Complement cascade** Group 3 CD55|CD59
ER-Phagosome pathway Group 5 HLA-G|TAP1
Extrinsic Pathway for Apoptosis** Group 0 CFLAR|TNF
Immunoregulatory interactions between a Lymphoid and a non-Lymphoid cell* Group 2 CAMK2B|HLA-G|ICAM1|LILRB1
Integrin cell surface interactions None 0 ICAM1|THBS1
Interferon alpha/beta signaling None 1 ADAR|EGR1|HLA-G
Toll-Like Receptors Cascades** Group 4 IFI16|MAPK14|NFKB1|TLR2|TLR3|TLR9
Transcriptional Regulation of White Adipocyte Differentiation None 2 CDK4|NFKB1|TNF

Table 2: The above table shows significant biological process founded for 47 genes.

According to previous studies HCMV infection could disrupt mucosal surfaces, predisposing the patient to superinfection, or it could cause alterations in humoral and cell-mediated immunity [7]. In ontology analysis the over-significant pathways were found are Cellular Senescence is a process in which aging is occurred in single cell at individual level and there is an arrest of cell cycle to encounter oncogenic stress and infected cell will eliminated. Cellular stress response is a reaction in which structure and function of macromolecules is changed due to fluctuations in extracellular conditions of cells. In Extrinsic Pathway for Apoptosis the extrinsic ligand which can cause harm is leads to death by binding to TNF receptor and

Toll-Like Receptors Cascades belong to a family of transmembrane proteins that can recognize and discriminate a diverse array of microbial antigens and also it plays important role in innate immunity system.

Somewhere these all over-significant process leads towards destruction of cell which can be oncogenic further and but in HCMV infection normal functions of all these pathways is disrupt primarily and afterwards infection can lead to chronic condition. Effective vaccines or drugs are not available for cytomegalo virus so by triggering these pathways and genes and their products which were affects the pathways successful therapeutic strategies will be developed.

Conclusion

The genetic interaction study of human cytomegalovirus was done using Cytoscape tool and its various plugins. This study focuses on building and analyzing the gene-gene interaction network for cytomegalovirus. Gene-gene interaction network was retrieve from Cytoscape web services and network contains 13923 nodes and 35714 edges of human gene and genes causing influenza infection. Functional analysis of HCMV genes found to spread disease was performed with the help of CluePedia plugin, a total of 47 genes were predicted in functional analysis. Ontology analysis of those 47 genes was performed through ClueGO plugin to predict significant biological processes.

Acknowledgements

I like to put my sincere acknowledgements to DBT for providing us such platform and financial assistance. And my sincere thanks to BIF Center at D.G.P.G. College, Kanpur and all staff members there.

References

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Citation: Saxena V, Dixit S, Ashraf A (2015) Gene-Gene Interaction Mapping Of Human Cytomegalic Virus through System Biology Approach. Biol syst Open Access 4:141.

Copyright: © 2015 Saxena 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.