RESEARCH ARTICLE

Identification of hub genes and their SNP analysis in West Nilevirus infection for designing therapeutic methodologies usingRNA-Seq data

Iftikhar Aslam Tayubi1 • Ahmad Firoz2,3 • Omar M. Barukab1 • Adeel Malik4

Received: 9 March 2015 / Accepted: 19 May 2015 / Published online: 24 June 2015

� The Genetics Society of Korea and Springer-Science and Media 2015

Abstract The West Nile virus (WNV) infections are

generally asymptomatic and are considered as immediate

concerns of biodefense due to the lack of any therapeutic

remedies. In this work, we created an interaction network

of 1159 differentially expressed genes to detect potential

hub genes from WNV infected primary human macro-

phages. We go on to explore the genetic variations that can

alter the expression and function of identified hub genes

(HCLS1, SLC15A3, HCK, and LY96) using the PROVEAN

Protein Batch tool and PolyPhen-2. Community analysis of

the network revealed that these clusters were enriched in

GO terms such as inflammatory response and regulation of

proliferation. Analysis of hub genes can aid in determining

their degree of conservation and may help us in under-

standing their functional roles in biological systems. The

nsSNPs proposed in this work may be further targeted

through experimental methods for improving treatment

towards the infection of WNV.

Keywords West Nile virus � Infection � Interactionnetwork � Hub genes � Community analysis � nsSNPs

Introduction

West Nile virus (WNV) is a mosquito-borne neurotropic

viruses and belongs to a member of Japanese encephalitis

virus (JEV) serogroup in the family Flaviviridae (Heinz

et al. 2000), which comprises viruses for yellow fever and

dengue (Brinton 2002). Originally first detected in Uganda,

it has been endemically spreading in various regions across

the world, including the Middle East, some regions of

Africa, Europe and United States at different periods

(Suthar et al. 2013). However, serologically it was first

tested in Assam, India in 2006 (Chowdhury et al. 2014).

Belonging to the Flaviviridae family, it is encoded by a

*11 kb positive-sense, single-stranded RNA (ssRNA)

genome (Suthar et al. 2013). It follows a replication life

cycle, which is involved in binding to cell surface receptor,

leading to fusion with the membrane and final delivery of

infectious RNA genome into the cytoplasm (Suthar et al.

2013). The genome injected is further translated as a single

polyprotein, which undergoes subsequent cleavage by viral

and host proteases to generate structural and non-structural

proteins. The structural proteins form the virion encapsi-

dating the viral RNA, whereas, the non-structural proteins

form the replication complex, which synthesize the nega-

tive- and positive-sense viral RNA. Hence, targeting the

replication pathway and primarily the infectious genome

can aid in curbing the expression of WNV (Suthar et al.

Electronic supplementary material The online version of thisarticle (doi:10.1007/s13258-015-0297-y) contains supplementarymaterial, which is available to authorized users.

& Iftikhar Aslam Tayubi

Iftikhar.tayubi@gmail.com

1 Faculty of Computing and Information Technology, King

Abdulaziz University, Rabigh 21911,

Kingdom of Saudi Arabia

2 School of Chemistry and Biochemistry, Thapar University,

Patiala 147004, Punjab, India

3 Biomedical Informatics Center of ICMR, Post Graduate

Institute of Medical Education and Research (PGIMER),

Chandigarh 160012, India

4 Perdana University Centre for Bioinformatics, MARDI

Complex, Jalan MAEPS Perdana, 43400 Serdang, Selangor,

Malaysia

123

Genes Genom (2015) 37:679–691

DOI 10.1007/s13258-015-0297-y

2013). Additionally, targeting at gene level will also

enhance the effectiveness of therapy involved. Identifica-

tion of the prime regions of interest and further the can-

didate genes for gene therapy can be determined via RNA

sequencing (RNA-Seq) methods (Mercer et al. 2014).

The RNA-Seq method comprises of the direct

sequencing of cDNA for monitoring quantitative gene

expression (Nagalakshmi et al. 2010). It involves direct

sequencing of cDNAs using high-throughput DNA

sequencing technologies followed by mapping of reads to

the reference genome (Nagalakshmi et al. 2010). The

methodology allows identifications of introns and exons

and further helps in recognizing intronic and exonic

boundaries and boundary ends of the gene of interest

(Nagalakshmi et al. 2010). RNA-Seq is presently suggested

as cost-effective new tool, which can be applied for deci-

phering the genetic basis of diseases, and traits that could

not be detected based on previous conventional gene-dis-

covery methods (Bamshad et al. 2011). RNA-Seq has been

employed to investigate biological phenomena in diverse

areas such as viral infections (Jones et al. 2014), cancer

(Young et al. 2014) and cardiomyopathy (Christodoulou

et al. 2014). Recently, we have also used RNA-Seq anal-

ysis to explore the role of glycogenes in skeletal muscle

development in MYOGkd cells (Lee et al. 2014) and also

used precomputed expression values from mouse RNA-Seq

data to understand the role glycogenes in various biological

processes that are involved in the development of brain,

muscle, and liver tissues (Firoz et al. 2014).

RNA-Seq has also been applied to identify 1514 dif-

ferentially expressed (DE) transcripts from primary human

macrophages, in response to infection with WNV (Qian

et al. 2013). In vivo studies of WNV infection using high-

throughput digital gene expression analysis have reported

common genes that are differentially expressed in multiple

tissues upon WNV infections as well as genes that are

differentially expressed in specific tissues following WNV

infection (Pena et al. 2014). These studies have helped in

developing a framework that can aid in the discovery of

genes and their associated biological functions and in turn,

decipher their impact on causing such infectious diseases.

Therefore, in this work, we have performed a network

analysis to identify the key hubs from the list of candidates

differentially expressed transcripts that were recently

detected from RNA-Seq analysis of primary human mac-

rophages infected with WNV (Qian et al. 2013). We also

explored the Single Nucleotide Polymorphisms (SNPs)

within these potential hubs that can be suggested as the

prime regions for susceptibility towards WNV infection.

Additionally, we also explored functionally important

modules in the network of WNV infected DE genes by

using community analysis. The regions suggested in this

study can be further targeted via experimental therapeutic

technologies like gene knockout technique, gene targeting,

etc., for developing treatment towards suppressing the

pathogenic action of WNV.

Materials and methods

Datasets

In this work, 1514 differentially expressed (DE) transcripts

were used for our computational analysis and were down-

loaded from a recent study on WNV infected macrophages

(Qian et al. 2013). These DE transcripts represent the RNA-

Seq analysis originally carried out byQian et al. (2013) using

Illumina Genome Analyzer 2 and expression levels for each

transcript estimated by using maximum likelihood based

method employed in the Cufflinks program (Trapnell et al.

2010). The dataset was filtered by removing any duplicate

transcripts and represent the final set of differentially

expressed genes (DEGs) used in this study.

Network analysis

GeneMANIA [www.genemania.org/] cytoscape plugin

(Montojo et al. 2010) was utilized to determine the func-

tional interactions between DEGs based on the GO term

‘‘biological process’’ and Homo sapiens as reference spe-

cies. The predicted relationships between the genes in the

network comprises of co-expression, physical and genetic

interactions, pathways, co-localization, protein domain

similarity, and predicted interactions.

Identification of hub genes

Biological networks exhibit the scale-free property (Albert

2005) with hubs representing nodes with many connections

in the network. NetworkAnalyzer plugin of Cytoscape

(Smoot et al. 2011) was utilized to determine the hubs by

calculating the values of the node degree distribution.

Three genes with highest node degree distribution were

identified as hubs in the current network.

Community analysis

The modules with functional property were determined by

applying greedy community-structure detection algorithm

via GLay [http://brainarray.mbni.med.umich.edu/sugang/

glay] (Su et al. 2010) plugin inCytoscape. For identifying the

over-represented biological functions within each cluster,

the clusters were subjected to a functional enrichment

analysis by focusing only on communities with at least 10

nodes. The functional enrichment analysis was carried out

using the DAVID functional analysis tool.

680 Genes Genom (2015) 37:679–691

123

Computational analysis of non-synonymous SNPs

(nsSNPs) of predicted hub genes

Data mining the SNP information for hub genes identified

in humans in this study were retrieved from National

Centre for Biotechnology Information (NCBI) database

dbSNP [http://www.ncbi.nlm.nih.gov/projects/SNP]. Three

different programs were used to predict the damaged or

deleterious coding nsSNPs.

Prediction of deleterious or damaging coding

nsSNPs using the PROVEAN protein batch tool

The PROVEAN Protein Batch tool [http://provean.jcvi.org/

index.php] was used to provide PROVEAN (Protein

Variation Effect Analyzer) and SIFT (Sorting Intolerant

From Tolerant) predictions for a list of protein variants.

Both PROVEAN and SIFT are software tools that predict

whether an amino acid substitution has an impact on the

biological function of a protein if the score, lies below a

certain threshold value. In PROVEAN, the clustering of

BLAST hits is carried out, and the best 30 clusters of

closely linked sequences of the supporting sequence set

that is further employed to make the predictions. For every

supporting sequence, a delta alignment score is computed

and then averaged within and across clusters generating a

final PROVEAN score (Choi et al. 2012). A default score

threshold of -2.5 or above is considered deleterious

whereas anything less than this cut-off score has a neutral

effect. On the other hand, SIFT is a multi-step algorithm

and uses sequence homology based method to classify

amino acid substitutions (Kumar et al. 2009). The PRO-

VEAN Protein Batch tool accepts a list of protein sequence

variants as input for the predictions.

Prediction of functional modification of coding

nsSNPs by polymorphism phenotyping v2

(PolyPhen-2)

PolyPhen-2 [http://genetics.bwh.harvard.edu/pph2/] tool

was used to study the possible consequence of nsSNPs on

protein structure and function. The web server predicts the

potential effect of amino acid substitution on the stability

and function of human proteins by employing structural as

well as comparative evolutionary considerations. For each

specified amino acid residue substitution in a protein,

PolyPhen-2 mines a variety of sequence and structural

characteristics of the replacement position and supplies

them to a probabilistic classifier. For this work, we have

used the provided batch query option and selected the

default advance option for generating the predictions

(Adzhubei et al. 2010).

Results

We downloaded a list of 1514 transcripts that were deter-

mined to be differentially expressed between control

unaffected and WNV-infected samples (Qian et al. 2013).

The data were further filtered by removing any duplicate

transcripts so that each transcript represents a unique DEG.

As a result, a non-redundant set of 1159 DEGs was

Fig. 1 Flowchart showing the

overall methodology

implemented in this study

Genes Genom (2015) 37:679–691 681

123

obtained that consisted of 253 down-regulated and 906 up-

regulated genes (Supplementary Table S1). The complete

workflow of computational methodology adopted in this

study is diagrammatically represented in (Fig. 1).

Network construction and identification of hub

genes

How DEGs interact with each other and additional related

genes in the network was investigated by GeneMANIA

Cytoscape plugin (Montojo et al. 2010). A GeneMANIA

network analysis of DEGs points to enrichment of signal-

ing pathways such as cytokine-mediated and type I inter-

feron-mediated signaling pathways. The other over-

represented GO terms includes inflammatory response,

regulation of leukocyte and lymphocyte activation, and

regulation of cell activation (Table 1).

The interaction network deduced via GeneMANIA was

further analyzed in Cytoscape 2.8.2 (Smoot et al. 2011).

The initial network comprising of 952 nodes and 48,263

edges was filtered to 952 nodes and 44,785 edges by

removing duplicate edges. All the genes in the network are

represented by circles and the interactions between them

are represented as edges. The up-regulated genes are shown

in green whereas; red nodes represent down-regulated

genes. Moreover, the additional related genes predicted by

GeneMANIA are shown in cyan (Fig. 2).

The genes determined as hubs with high node degree

distribution encode, HCLS1 (Hematopoietic lineage cell-

specific protein or Hematopoietic cell-specific LYN sub-

strate 1), (Supplementary Fig. 1a) a top hub with the node

degree of 342 whereas, SLC15A3 (Solute carrier family 15

member 3) (Supplementary Fig. 1b) was the second hub

having a node degree of 323. The genes with third highest

node degree are represented by HCK (Tyrosine-protein

Table 1 Top 30 significantly

enriched gene ontology (GO)

terms detected by GeneMANIA

for differentially expressed

genes and additional related

genes

GO ID Description Q-value

GO:0019221 Cytokine-mediated signaling pathway 1.55E-39

GO:0060337 Type I interferon-mediated signaling pathway 3.75E-30

GO:0071357 Cellular response to type I interferon 3.75E-30

GO:0034340 Response to type I interferon 4.83E-30

GO:0034341 Response to interferon-gamma 4.83E-30

GO:0071346 Cellular response to interferon-gamma 6.73E-27

GO:0060333 Interferon-gamma-mediated signaling pathway 1.85E-23

GO:0051707 Response to other organism 3.10E-21

GO:0009607 Response to biotic stimulus 1.00E-20

GO:0001817 Regulation of cytokine production 1.00E-20

GO:0031347 Regulation of defense response 2.62E-20

GO:0001816 Cytokine production 7.21E-20

GO:0006954 Inflammatory response 7.52E-17

GO:0002694 Regulation of leukocyte activation 7.96E-17

GO:0050865 Regulation of cell activation 7.96E-17

GO:0051249 Regulation of lymphocyte activation 1.85E-16

GO:0046649 Lymphocyte activation 1.18E-15

GO:0045088 Regulation of innate immune response 3.63E-15

GO:0002252 Immune effector process 4.69E-15

GO:0050867 Positive regulation of cell activation 4.69E-15

GO:0002696 Positive regulation of leukocyte activation 8.86E-15

GO:0009615 Response to virus 2.20E-14

GO:0051251 Positive regulation of lymphocyte activation 2.68E-14

GO:0042110 T cell activation 3.73E-14

GO:0050778 Positive regulation of immune response 3.37E-13

GO:0031349 Positive regulation of defense response 5.70E-13

GO:0050863 Regulation of T cell activation 1.76E-12

GO:0001819 Positive regulation of cytokine production 2.05E-12

GO:0002237 Response to molecule of bacterial origin 2.25E-12

GO:0007249 I-kappaB kinase/NF-kappaB cascade 3.05E-12

682 Genes Genom (2015) 37:679–691

123

kinase) (Supplementary Fig. 1c) and LY96 (Lymphocyte

antigen 96) (Supplementary Fig. 1d) because both have a

node degree of 318.

Community analysis and functional annotation

of detected modules

Five biologically-related clusters were identified using Fast

Greedy community-structure identification algorithm

(Fig. 3). Among all the detected clusters, Cluster 1 is the

largest with 425 genes and also consists of hubs HCK and

LY96 (Fig. 3a). Cluster 2 and cluster 3 consists of 262

(Fig. 3b) and 254 (Fig. 3c) genes, respectively. Cluster 3

also consists of two hubs, SLC15A3 and HCLS1 (Fig. 3c).

Only 9 genes were observed in cluster 4 whereas cluster 5

has 2 genes (Fig. 3d). Therefore, only those communities

were selected for enrichment analyses that have at least 10

nodes. Based on this criterion, only 3 communities (Cluster

1, 2 and 3) were finally analyzed for over-representation of

GO terms.

To biologically categorize these clusters, DAVID

functional analysis tool was used to classify the genes in

each module, and observed the enrichment of GO term

‘‘Biological Process’’ in three selected modules. The top 20

statistically significant enriched GO terms for DEGs in top

3 clusters for community analysis are summarized in

(Table 2). The three most statistically significant GO terms

that were enriched in cluster 1 are response to wounding,

defense response and inflammatory response. Other

significant GO terms in this cluster include regulation of

apoptosis or programmed cell death as well as positive

regulation of response to stimulus. Cluster 2 shows higher

enrichment for GO terms related to phosphorous metabolic

processes as well as regulation of proliferation. The GO

terms of cluster 3 were mostly related to antigen processing

and presentation of peptide antigen, regulation of I-kappaB

kinase/NF-kappaB cascade and positive regulation of T

cell activation.

SNP analysis

Non-synonymous SNPs (nsSNPs) are positioned predomi-

nantly in coding regions and their consequences are

observed in phenotypic characteristics of translated protein

products (Ramensky et al. 2002). On the other hand,

influence of synonymous SNPs is not determined easily on

translated proteins probably owing to the uncertainty of

genetic code (Hunt et al. 2009). In the current study we

have focussed on deciphering the role of variants on pro-

teins and their related pathways in successive steps,

therefore we confined and selected nsSNPs for our com-

putational analysis. Figure 4 shows the distribution of

nsSNPs and total SNPs in four hub genes. From the figure

it can be observed that in spite of a large number of SNPs

identified in each hub gene, only a small percent represent

the nsSNPs. The top hub HCLS1 has 689 SNPs and 68

nsSNPs, whereas, SLC15A3 has 443 SNPs and 60 nsSNPs.

HCK has 1138 SNPs, which is highest among all four

genes, but has only 48 nsSNPs. Finally, LY96 has 796 SNPs

and only 13 nsSNPs. The estimation of ratio of synony-

mous and nonsynonymous substitution rates helps in

determining the evolutionary effect of substitutions on

protein coding genes (Yang and Nielsen 1998).

Deleterious or damaged nsSNPs by PROVEAN

protein batch tool

The PROVEAN Human protein batch tool was used for the

prediction of damaged or deleterious nsSNPs. The program

provides both PROVEAN as well as SIFT prediction

scores. The input to the PROVEAN protein batch tool

consists of detected coding nsSNPs for four hubs, that is,

68 nsSNPs for HCLS1 (UniProt ID: P14317), 60 for

SLC15A3 (UniProt ID: Q8IY34), 48 for HCK (UniProt ID:

A8K4G3), and 13 for LY96 (UniProt ID: Q9Y6Y9). The

input for each hub gene was submitted independently to

PROVEAN Human protein batch tool. Among 68 nsSNPs

for HCLS1, 29 were identified to be deleterious by PRO-

VEAN whereas 36 were identified to be damaging by SIFT

(Supplementary Table S2). For SLC15A3, 28 and 36 out of

60 nsSNPs were predicted as deleterious or damaging by

PROVEAN and SIFT, respectively (Supplementary

GPR109A

OLIG1CLEC6A

MRGPRF LRRC50F13A1CSF3C7orf68

GPR133

EREGTHBDCCL7

NR4A3OSM

SNAI1

ARNTL2

NCAPH2

AXLSOCS3ELOVL3CHST2TREML1

FPR2

DAB2

GPR132

RAB11FIP1

FN1NTNG2GPBAR1SLC11A1

IGFBP6

FSCN1RUNX2

WNT5A

IGF1SPHK1

C3orf59BMP2

LGI2

MOSC1REPS2

GNG7 SLC19A1PTGFRN

TMPRSS13SLC22A4PPARG

CYP19A1NR4A2GRB10TULP2

FAM151AGSTM1

NRGNCKB

PLEKHN1

SCIN

FSTL1

ITGA2SRC

VWF

TNFRSF4 IRAK2

MXD1

PROCR

CD80

GAS6ARHGEF11COLEC12CPXM1LAMB1

FOXC1

PTRFGJA5

MN1HS3ST3B1

CDKN1C

IL23ANUPR1

PLVAP

TNF

FXYD6PMAIP1

MSX1ITGA10

SSTR2PDGFC

RCN3CNRIP1

GFRA2

BTBD11MMP14EDN1

GPR146

RBP7PLCB2CETP

PRAM1FAM46C

LRRFIP2

PLS3SLAMF1

NKD1ACTA2

RASGRP4

RETN

ASB13ITIH4PI4K2B

PINK1

RBKS

SYNE1

ZNF618

TSPAN5

EHBP1

HIST2H2BESTAG3

CIDEB

IQSEC1

NKX3−1GCNT2

TMEM110

CLIP2NPDC1

HESX1

APOBEC3F

MAML2RRM2

PHACTR2

XBP1PIWIL4CREB3

BMPR2

GMPR

PPPDE2IL15RAKAT2B

UNC84B

ZC3H12D

IFIT2

MIA3TRIB2

TSC22D1SRGAP2ASPHD2PVRL2SEMA4ARHEBL1

CSF2RBDPEP2

ELOVL7

SPATA13

FGD2NEXN

CD38PDCD1GLS

MKLN1ATG16L2

DOK2

IGFBP4

LGALS3BPATP2A3TBC1D10CIFI27TLR3IFIT3

CORO1ASTAT4

DENND5A

MAPK12 ZFXATP2C1

MYOFTESC

SAMD4A

ENDOD1

BAALC

PLA2G16TMEM123PLA2G4CCD7

CASP7

CDKN2CGRAMD1BPHF15

CBARA1TOR1BRICTOR

TRIM25

RNF125

C1GALT1

C15orf48

ZNFX1DEK RSAD2TRIM26HSH2DFAM111ANDC80

ZNF588

POLS

KIAA0226SLC25A28SNX5RNF138USP6NL

REC8 TFGTMEM194A

NUP205C22orf28

GTPBP2 MOV10PPM1K

PNPT1SGPP2

NCOA2TJP2

VAV3

JUPACOX2

C21orf91

CBX7MDKTJP1

BCL2L11

JAK3ARID5AC1S

CDC73BCL2L13

PATL1 NCAPH

BRIP1

TMEM229BNT5C2

SFI1

RUFY3MKL2DDX43

TMEM117

FAM59A

SNX26

C14orf73

MRO

PFTK1ZNRF2GALM

CNP

IMPDH2HIST2H2AA3

STMN1

PELI3

MASTLHNRPLL

CCDC152BARD1DEF6

PHACTR4PARP10

USP9X

C3orf38

RNF6

RFKKIAA1958 PARP15

HIST1H2BC

GPR180HIST2H3A

GPBP1HIRAHIST1H2BDRPS6KC1

KIAA1109

BLZF1

PLCXD1LY6E

C2orf84ARAP2KDM6A

MSL3FAM122CTIA1

AGRN

IL27TMEM26

HIP1RABCB9MMEL1

CAMK2BCYHR1

EPHB6

PRRT2

BBC3RAB39

SBK1DYNC2H1

NOXA1

IGF2BP3

DEPDC7

AK1

NEURL3

C1orf127

GPAT2SH3PXD2B

AATK

ORAI2EGFKIAA1024

GUCY1A3RAPGEF2ENTPD5

DUSP8

CCNA1

PLEKHA4

RND1

P2RX1

SV2A

ARHGEF10LGALNT3

CAMK1MEFV

DPP4

TNK2

NTN1

KRT79DCLK2

PSCA

AFAP1EPB41L5

CPT1B

FAM179A

SNAI3CBLN3

ADCABATARHGAP23

SHANK1

SERPINF2

HSPG2SPTBN4CRYBB1

RASL11BATXN7L1AGFG2

SRGAP1

GCNT4ZDHHC19

ANXA9RAC3

KCNF1KIAA1671

ASAH2B

PTCRA

DPYSHES4EXOC3LRASGEF1C

ZBTB7CCACNA1A

FN3K

C5orf20

SLC29A2

AKAP6

GSDMA

ADCY1

RAPGEF3C20orf195

AKAP7

NIPAL4PLB1

C14orf68OSBPL6

SPTBN2

RP11−274B18.1

PNPLA7ZDHHC9TMEM151AACOT11

FCGBP

MAP2 FLYWCH2TBC1D17

EXD3GAMT

KCTD14AUTS2MYEOV

KHK

DEPDC6

ZNF589

ZNF704NANOG

CASZ1

NFIX

CHST13

SMTNL1OTOFNFIA

ATP8B2

CENPVSPTB

IQGAP3PNPLA3

OLIG2

C2orf7IQCD

SLC25A29

IQSEC3RUFY4

SFXN2SLC1A7

C1orf224

PRKCE

PIH1D2

RASAL1

EIF4BHSPA1A

C5

PIK3C2B

PLEKHM3RASL11A

MCOLN2

CACNG8FER1L6

TSKS DAB2IP

WDR86TTC39A

LINGO3

SHROOM1

C2orf71

FAM72D

C14orf182C21orf58

AKT1S1

USP41

HIST2H2AA4

CHTF18NUDCD1

HIST2H4B

HIST2H3C

HIST2H2AC

HIST2H4A

GTPBP1

CDKL1SLC41A2

RUSC1

SH2D4A

ATP10A

BDH1

CABLES1

WIPF3

C15orf52

HIST2H2AB

TMEM37

HIST2H2BF

CENPN

SLC46A1

ANUBL1

NLN

TTC3

ANKS6

GTF2I

PIGV

USP30

RASGEF1B

PTCHD1COL23A1

COL1A1

IGF2

KCNA2

ASCL2

FNDC5GPR161

FXYD7

SORCS2

C1orf183

ANKRD1ADORA1DBF4B

FZD4KLF4COL18A1

DUSP7FXYD1

MXD4PLD4

RGL3

GJB2SLC16A10MYPNICOSLG

OSBPL5DSPSLC30A4 HSPA2

MYO1A

MCOLN1

FAM125B

CHRNB2NMUR1KLK10TSPAN15

RET

ABTB2

MUSTN1ZMYND10ADRA2B

PLEKHG3KCNN1

TIAM2

SEMA3AAPLN MOSPD3

MDGA1

KCNMB4

PRLRNACADCALHM1

MAP1LC3A

FGD5IRF6GREB1

IRF4

DOCK4

MAFTNFRSF9P2RX7NEDD9IL10RAAGPAT9ID3

SLC15A3

TNFSF15LTA

DNAJB4EXT1

SEMA3GPLEC1

TNFRSF8CUX2E2F2

KLHDC7B

MAP2K6EPHB2TNFSF18

PLCL1

BCAMHDX

LSSMID2CATSPER1

RBMS2

INHBAPPP1R15A

C20orf175

NR4A1CD36

SDS

TNFAIP6FOSL2

GLT1D1

CD300E

GPR84C17orf87

OLR1

CLEC4D

FFAR2IL6

STAB1G0S2PTX3

EMR1CD163L1

PTGS2HGFPFKFB3CSRNP1OTUD1

LRRC25

CEBPB

CBWD7

TNIP3

JUNB

PTGIRADORA3S100B

SLC39A8CTSBHLX

CXCL12

DSECYP27B1

CEACAM3ENTPD1CXCL1

ADAM8

PTGER4RNF144BC10orf10

IL7R

LILRB4EBI3

FAM129A

CXCL3

HCK

STX11

CTSL1

ZFP36AMICA1CSF3R

GPR34GNG2

CLEC4E

CCL2PTPLAD2

CXCL2

GRAMD1A

S100A9

TBXAS1

TAGAP

NFKB1

KCNAB2

KIAA0040

FUT4

RCAN1

TRAF3IP2DUSP

ACSL1

BTG1

ADPRH

SOD2

RIN2

PTGER2

ADAMDEC1

CDA

NFKB2PARVG

PSTPIP2PANX1

ATF3ATF5

AMPD3

CD14

S100A4

CDC42EP2

GNSP2RY13CD5L

PDE4BNCF2

SLC2A6

MCL1GCAC6orf150NFKBIAFAR2FCER1G

RIPK2ITGB7PNRC1

MYO1G

IFI30

TYMP

APOBEC3ALGALS2

CCR7GCH1

CXCL9

HLA−DRA

SLFN12CCL5

RELBSLC38A5HLA−DMB

DAPP1GZMACASP8LMNB1

APOL4

GBP1

IL2RGARHGAP25

JAK2FAM65BHLA−B

IDO1GZMB

GIMAP4

FGL2

DOCK8

CTSC

WTAP

NFE2L3

HLA−DPB1HAPLN3

CASP4TFEC

VAMP5

CD274

TMC8PMLGRIN3AHLA−G

CASP10BTN3A2

HELBHLA−DPA1 IFI16

HLA−A

GBP4B2M

LAP3TRIM22

APOL3

BATF2

SCO2

AGTRAP

IL7GBP5

PLSCR1LYSMD2

SP140IRF9

WARSTAPBP

SAMHD1UBE2L6

TNFSF13B

SPTLC2HLA−F

BAZ1AIL15

GBP2

TANKHLA−E

CD74

FAM26F

HLA−DMA

HLA−DRB1

ELF1

BRCA2

IFITM2

SOCS1

IPCEF1

IFNGPELI1

ZNF200SECTM1ANKIB1

MYD88

RHOH

PPP1R16B

CXCL10MTHFD2

C9orf91SPATS2L

MFN1CBR1

CASP1

HCLS1

SLC46A3TICAM2

ST3GAL5

BST2LAG3

RNF19BCD180

NADK

LY96

IL2RBSIGLEC1TNFSF10SLC7A7

TDRD7

UBE2Z

OPTNUBA7 IFI35FTSJD2OAS1

RBM43RIPK1

STAT2

DDX60 EIF2AK2C19orf66

PCID2

RABGAP1LADAR

RNF213

NT5C3

TRIM5

RNF114

DDX60L

PARP12TRAFD1RFX5BTN3A1

USP18

TAP2DHX58

LCP2IFITM3

MAP3K8

LY86

HPSE

DNAJA1SERPING1

SNX10

KRR1HIVEP1LAMP3

SEMA4DSNX20

NLRP1

LGALS9HIVEP2

BTG3

IFNA13OAS3NUB1

TRIM69IFNA1

TAP1IL32OASL

BLNKIFI6

ISG15IFIH1

TCTEX1D2

CD47

STAT1

IRF7 SAMD9NMI HERC6

EPSTI1

SP140LZC3HAV1

OAS2IFI44L

SAMD9L

CXCL11

MX2DYNLT1

BTN3A3

BIRC3

TRIM21

APOBEC3G

BET1

APOL1

SLFN12L

SLFN5

PSME2FBXO6

AIM2CARD11

PHF11RTP4IFI44

GBP3

DDX58

IFIT5PSME1XAF1LBA1

XRN1PARP9DTX3L

MX1PARP14ISG20IFIT1

HERC5CD48

OTUD4RARRES3IRF1NCF1

PSMB10IFITM1TRIM38

NLRC5PSMB8

APOL6

SP110

PSMB9SP100

PBEF1

NFKBIZ

TNFRSF1BCREM

TLR2BCL2A1C1RL

ICAM1PILRA

KIAA1274

CCL13CCR1

CCL3

TNIP1

TRAF1

TYROBPSCPEP1

CCL4

CCR5SERPINB9

ITGB2

ADAM19

LILRB1

IL4I1

STOM

ADM

ZC3H12A

NBN

MS4A6A

NOD2

CD40

DRAM1

SLAMF7PDCD1LG2

IL16

LYN

MNDA

PTPRCC1QBPLEK

CFLAR

BATF3

SAMSN1CCL8

SELL

BCL3

IL1RNTNFAIP3RASSF4

C3AR1

FAS

CCL4L1

IL8S100A8

FPR3

NAMPTL

CCL4L2

IL1B

CCL3L1

CBWD2

Fig. 2 Interaction network between DEGs and related additional

genes. In this network, green nodes represent up-regulated genes

whereas red nodes represent down-regulated genes and GeneMANIA

predicted genes are shown in cyan. Hub genes are shown as purple

diamonds and represent the genes with highest node degree

Genes Genom (2015) 37:679–691 683

123

CCL3L1CCL4L1

CCL4L2IL4I1

C6orf150

S100A8ADAM8GPR84

PSTPIP2

MYO1G

TBXAS1

CEACAM3RETNCCL7

GLT1D1 CCR1

FFAR2

SOCS3CLEC4D

RASGRP4

SLC11A1

CCL3

CCL4

IL1RN

APOBEC3A

SDSTNFOSMSTX11IL8

NFKB2

BCL3

PFKFB3

GCH1

TNIP3ZC3H12AFOSL2

RAB11FIP1

IL1B

FAM46C

LRRC50

SOD2NADKCD274CLEC4E

AMPD3

TNIP1

CXCL2HS3ST3B1

EMR1IL7R

CCL8

IFI30

TAGAP

HPSE

ACSL1

GCACXCL1MXD1GBP1

CXCL3

NR4A1

OLR1

IL6NFKBIARNF19BPPP1R15A

PROCRDPP4IGF1CTSB

DEPDC6

RUNX2C10orf10

FOXC1

TLR2DRAM1BATF3

PTGIRCASP4CCL2INHBATNFAIP6

OLIG1

SPHK1

HLX

IL10RA

CTSL1

HGF

SLC22A4

NOD2SELLBCL2A1

PLEKMNDA

SERPINB9

DSE

TNFRSF1B

PTGER2FAM129A

S100A4

GNS

F13A1

DOCK4

C1S

ACOX2

HCK

NCF2CSF3R

S100A9

IFITM2CSF3

PLSCR1

SLC39A8

ADM

TNFAIP3PTGS2

ZFP36

ICAM1

KIAA0040

PANX1SNAI1

CREMCSRNP1

NBN

DUSP

MCL1NFKBIZ

PBEF1

NAMPTL

ANKRD1

NR4A3ATF3

OTUD1

FAS

HIST2H2BE

NFKB1RCAN1NFE2L3

CDKN2C

BAZ1A

XBP1

PDE4BRAPGEF2

LMNB1MFN1LRRFIP2

BTG3

MTHFD2HIVEP1IL23A

PMAIP1RND1

ELF1

OTUD4 BET1

ANKIB1

NR4A2TFG TANK

GPBP1

SLC38A5

BCL2L11DNAJA1 ZNF200WTAP

RIPK2C7orf68

PELI1PTGER4EREG

RELB

RNF144B

RICTOR

ADAMDEC1MAP3K8

JUNBSAMSN1

ACTA2

APLNPDGFC

MAML2FCGBP

GJB2

EIF4B

CABLES1

ZNRF2

MKL2

FAM59A

KIAA1958

NUP205

NUDCD1

KIAA1109

C3orf38

A

B

HNRPLL

GPR180

STMN1

TMEM117USP6NL

KAT2BMIA3NCOA2

RFK

TIA1

NFIADEPDC7

TSC22D1

SNX5

AUTS2

GUCY1A3

ID3

SLC41A2

WNT5A

TSPAN15

CNRIP1

FZD4FSTL1

EHBP1

LAMB1

MXD4

NPDC1FAM151A

COL1A1

CPXM1

FXYD1SEMA3G

GRB10

SRC

DPYS

ASAH2B

PTCHD1

FXYD6

GSTM1

NUPR1

FN1ABAT

VWF

IGFBP4AXLSRGAP2

NRGN

MYOF

PTRF

FUT4C1RL

SLC46A3

CXCL12 GPR34RCN3

GAS6

DAB2

GPR146

HSPG2CD163L1SORCS2

CDKN1CCOL18A1

SH3PXD2B

MUSTN1TMPRSS13IGFBP6

GJA5

EXT1PHACTR2GREB1

EPHB2

CD5LPPARG

RIN2CD36

ADPRHSTOM

ARNTL2

SPTLC2

NEDD9

HSPA2SNX10

PNRC1

THBDIFI16CHST2CEBPB

CTSC DENND5AFAM65B

CSF2RBG0S2

LYNPTPRC

PTX3

CFLAR

C20orf175MSX1

REPS2

C3orf59

SCPEP1MOSC1

FCER1G

CDA

CYP27B1

ITGB2LILRB4GRAMD1A

NTNG2SLC19A1

CDC42EP2

LRRC25

CLEC6AP2RX7

FPR2

AMICA1

PILRAC3AR1

P2RY13LCP2

RASSF4SLC16A10

DPEP2LILRB1NKD1

NCF1SLC7A7

FPR3C1QB

SLC2A6

MS4A6ACCL13

CD14

PTGFRN

ARHGEF10L

BAALCPLVAP

GPR109A

MRGPRF

SRGAP1

P2RX1

DOCK8

PRAM1

ATG16L2

COLEC12STAB1IGF2GNG2

SERPING1

CETP

FAR2

GPR133

ENTPD1

TYROBPFSCN1

MMP14MAF

S100B

COL23A1GPR161

GPBAR1

PTPLAD2

MCOLN1

ADORA3RBP7

CENPN

HSPA1A

BLZF1

RNF114

NDC80PCID2

RNF6

DEK

TICAM2

TMEM194A

RNF138

BARD1DNAJB4

USP9XCDC73

PI4K2B LY96

HIVEP2

KIAA1024

PIWIL4

TTC3

TRAF3IP2

C21orf91

CCNA1

IGF2BP3

PLCL1

TJP2

AGPAT9

MKLN1

DYNLT1

C1GALT1

NCAPH

CASP7

KDM6A

GLS

RRM2

DUSP7

PLS3

DSP

KLF4

ARID5A

CD38

ITGA2

SYNE1

BMPR2

BTG1EDN1

SAMD4AMN1

TOR1BBMP2ELOVL7

GALNT3

TJP1

FAM111ANT5C2

KRR1BRIP1

TCTEX1D2POLS

TMEM123HIRA

ATP2C1ZFX

BRCA2

ADC

FLYWCH2

C14orf73

FNDC5

CRYBB1AKAP6

LTA

NMUR1

PLCB2

RASAL1

WDR86

PTCRA

ATXN7L1

PARVG

PIK3C2B

ASPHD2

GCNT4

TMEM151A

PDCD1KCTD14

SMTNL1

RP11−274B18.1

TREML1

KLHDC7B

CACNG8

EXOC3L

TNFRSF4

FER1L6KCNA2

OLIG2ADCY1C20orf195

HES4

C17orf87

NOXA1

APOBEC3FCALHM1

SPTBN4

TNK2

GTPBP1

SHROOM1

C5orf20

DAB2IPRET

ARHGAP23

CASZ1CDKL1

JAK3

CLIP2

PLEC1JUP

SBK1

TRIB2 BBC3

TSKSFAM125B

HIP1R

PLD4RGL3

PLB1HDXITIH4

NANOG

NCAPH2

OSBPL6

ASCL2

KCNMB4

MAP1LC3A

AKAP7

ORAI2

IRF6

IQSEC1

NFIX

RASL11A

MYO1A

PLEKHA4

FXYD7

RAC3

ADRA2B

EGF

GFRA2BCAM

DYNC2H1CUX2

WIPF3RUFY3

RBKS

AFAP1

C15orf48

ANKS6

EPB41L5

FGD5

PLEKHN1CBLN3

HESX1

RHEBL1CYP19A1

PLEKHG3

SH2D4A

CAMK2B

SV2A

C5

DBF4B

ARHGEF11

ATP10A

GCNT2ACOT11

RNF125

RBMS2

FN3K

KCNN1

EXD3

FAM122C

CHRNB2

C1orf183

KLK10

EBI3MID2

ADAM19

TNFRSF8

PRKCESPTBN2ABTB2

AATKSTAG3

ABCB9

MDGA1

NTN1

SPTB

PNPLA3

AGFG2EPHB6

MAP2PVRL2

NACAD

SERPINF2C14orf68MCOLN2

TTC39A

SLC29A2TESC

SLC1A7ZDHHC9

PIH1D2RASL11B

TIAM2

DDX43

ANXA9

TULP2

CCDC152

NKX3−1 CD80NIPAL4KCNF1E2F2

GNG7

ZNF589ZDHHC19

ZMYND10

IQSEC3

GSDMA IL27

C1orf224

OTOFELOVL3

CHST13

SHANK1

SSTR2

CATSPER1

SLC30A4

TNFSF18

TSPAN5

LGI2MROPNPLA7

CD300E

KRT79ICOSLG

GPR132

ADORA1

RASGEF1C

RAPGEF3CPT1B

TMEM26

ANUBL1

CACNA1A

MEFV

SEMA3AITGA10MMEL1

C1orf127LINGO3C21orf58

FAM72D

RAB39

IQCD

C2orf71

PSCA

MYEOV

IQGAP3

C14orf182RASGEF1B

SFXN2

C2orf7

IMPDH2 BDH1

RPS6KC1

SPATA13

NLN

ASB13

ENTPD5

PLEKHM3

PIGV

CENPV

SLC25A29

GPAT2

PRLROSBPL5KHKSCINBCL2L13RUFY4 ATP8B2

BTBD11

LSS

TBC1D17

AK1MOSPD3

PFTK1

ZNF704

MYPN

ZNF588

DCLK2

ATF5

DUSP8

MAP2K6

IRAK2

SNX26

CAMK1

MAPK12

C15orf52

FAM179A

NEURL3

CHTF18

PRRT2

CKB

UNC84BGTF2I

ZNF618

SNAI3MASTL

KIAA1671

AKT1S1

SNX20

ZBTB7C

PINK1GAMT

CYHR1

ARAP2

PELI3

KIAA1274VAV3

Fig. 3 Communities detected

by fast greedy (GLay) clustering

algorithm are shown. In each

community a Cluster 1

b Cluster 2 c Cluster 3 d Cluster

4 e Cluster 5, up- and down-

regulated genes are shown as

green and red nodes

respectively, whereas

GeneMANIA predicted genes

are represented as cyan. Nodes

in purple diamond shape

represent hub genes

684 Genes Genom (2015) 37:679–691

123

Table S3). PROVEAN identified 21 nsSNPs as deleterious

while SIFT predicted 24 as damaging out of total 48

nsSNPs for HCK (Supplementary Table S4). Similarly, 6

and 5 nsSNPs were detected by PROVEAN and SIFT for

13 nsSNPs of LY96 gene (Supplementary Table S5).

Damaged nsSNPs by PolyPhen-2 web server

The protein sequences of each gene and their SNP substi-

tution were submitted independently to the Polyphen-2

web server. Among 68 nsSNPs for HCLS1, 47 were

identified to be damaging (Supplementary Table S2)

whereas, 37 nsSNPs were predicted to have a damaging

effect for SLC15A3 (Supplementary Table S3). Similarly,

the predicted number of damaging nsSNPs observed for

HCK was 21 out of 48 (Supplementary Table S4) and 8

nsSNPs were identified to be damaging for LY96 out of 13

nsSNPs (Supplementary Table S5). Overall, the number of

nsSNPs that are commonly predicted to be deleterious or

damaging by PROVEAN, SIFT and Polyphen-2, which

may affect protein function, is 26 for HCLS1 and

SLC15A3, 16 for HCK, and 5 for LY96 (Table 3),

respectively.

Discussion

Discovery of molecular targets and targeted therapeutics

have turned out to be a vital remedial treatment for dis-

eases, especially with the advancement of bioinformatics in

the last few decades (Dinh et al. 2007). In order to explore

the role of DEGs that were recently identified by RNA-Seq

analysis (Qian et al. 2013), and additionally related genes

involved in WNV infection, an interaction network was

constructed and node degree for each gene in the network

was calculated. HCLS1, SLC15A3, HCK and LY96 were the

genes with the highest degree and considered as hubs in the

network created from DEGs of primary human macro-

phages and additional related genes predicted by Gene-

MANIA. The most significant enriched GO terms within

the interaction network identified by GeneMANIA include

cytokine-mediated signaling and type I interferon-mediated

signaling pathways, processes related to regulation of

defense and immune response. Additional over-represented

GO terms in the network represent processes related to

immune cell activation (Table 1). This analysis is in con-

sistency with the recent study on WNV infection in which

DAVID functional annotation cluster analysis highlighted

the enrichment of processes involved in immune or defense

responses (Qian et al. 2013).

Among HCLS1, SLC15A3, HCK and LY96, only HCK

was detected as one of the hubs from a list of DEGs

whereas GeneMANIA predicted HCLS1, SLC15A3, and

LY96 genes to be a part of interaction network. These genes

may possibly play a key role in regulating the biological

process of WNV resistance and control viral infection.

HCLS1 gene codes hematopoietic cell-specific Lyn sub-

strate 1 protein (HCLS1 or HS1) that consists of an SH3

(Src homology 3) adapter domain and can trigger activa-

tion of receptor-coupled tyrosine kinases (Kitamura et al.

1989; Van Rossum et al. 2005; Huang and Burkhardt

2007). Elevated levels of HCLS1 are related with chronic

lymphoblastic leukemia (Huang et al. 2008; Scielzo et al.

2005), whereas lymphocyte precursors deficient in HCLS1

tends towards defective proliferation and differentiation of

B-lymphocytes following B cell receptor activation (Sko-

kowa et al. 2012). The members of the SLC15 protein

family have been verified to be significant drug targets at

the level of drug transport (Sasawatari et al. 2011).

SLC15A3 and SLC15A4 are the two types of histidine

transporters that have been identified in the lysosomes of

immune cells (Sakata et al. 2001). Previous studies have

established the fact that amino acids are essential in the

PHACTR4

TMEM110CBARA1

GRAMD1B

RABGAP1L

CIDEB

SIGLEC1REC8C22orf28

FTSJD2LBA1

PPM1K

CREB3

SP140L

RHOH

HELBIPCEF1MSL3

RUSC1VAMP5

PHF15RIPK1

MYD88LAMP3

UBA7

LGALS9

ISG20STAT2SP100

IFI44L

SP110

TAP2

C19orf66

SLC25A28

BATF2

APOL6

LAP3

TLR3

PLA2G16

RTP4

DDX60

IL15RA

OPTN

DTX3L

CASP1

GBP4

PSME1

EPSTI1PSME2

SLFN5

PSMB8

CXCL11GBP2

HLA−G

CXCL10PSMB10

TNFSF13B

PSMB9

STAT1

IRF9UBE2L6

TAP1

TNFSF10

BTN3A3

IFITM1

NMI

PMLOAS2

IFIH1

IFITM3

IFIT5

SLC15A3

USP18OAS3

HIST1H2BDSAMD9SPATS2L

MDK

IFI27

OASL

ISG15

IFI6PARP14

TRIM21

MX2

BST2

PHF11

PARP12

IFI44TDRD7ADAR

IRF7XAF1

MX1IFIT2

PPPDE2IFIT1

RNF213

PDCD1LG2

HLA−DPA1

FAM26F

HLA−DRB1GIMAP4

RFX5CXCL9

SOCS1

TNFSF15CASP8IL7

TRIM69SLFN12HLA−BIDO1

WARSTRAFD1

TMEM37

IL15 HLA−DMABTN3A2

TNFRSF9

CD40

SLAMF1

SLAMF7

KCNAB2TBC1D10C

TMC8

STAT4

IL16C2orf84

DAPP1

CD180SEMA4D

NLRP1

LGALS2

HLA−DMB

LYSMD2HLA−DPB1

C9orf91CD47

HLA−DRA

GZMA

GZMBIFNG

GBP5B2M

TFEC

CASP10TAPBPAPOL4

JAK2

CD74

IL2RB

ARHGAP25

ST3GAL5HAPLN3

CCR5

APOBEC3G

TRAF1

USP30

GTPBP2PARP10

AGRN

GMPR

DHX58

ZC3HAV1

ENDOD1

AGTRAP

SGPP2

FGL2

CNP

OAS1

LY6E

HERC6RSAD2

LGALS3BP

HLA−A

NEXN

PARP9

CBR1SAMD9L

TRIM5

FBXO6RBM43

XRN1

IFIT3 IFI35

DDX58HERC5

USP41C

D

ZNFX1

EIF2AK2TRIM25

TMEM229B

MOV10

PATL1NT5C3

DDX60L

PNPT1

HIST1H2BC

IFNA1

IFNA13

SLFN12L NUB1

GALM

GRIN3A GBP3

BLNKLY86

CORO1AIL2RG CCR7

CD48

DOK2

SLC46A1

IRF4KIAA0226

SFI1CARD11

PLCXD1

ZC3H12D

PARP15

HSH2D

ITGB7

UBE2Z

PLA2G4CAIM2

CCL5

LAG3TRIM38

SECTM1

IL32

SAMHD1

HCLS1

TRIM26

NLRC5

BTN3A1

BIRC3HLA−E

IRF1

HLA−F APOL3

DEF6

ATP2A3CBX7

SP140

PPP1R16B

CD7SEMA4A

FGD2

TYMP

SCO2

RARRES3

APOL1TRIM22

CBWD7

HIST2H4A

HIST2H2AA4

HIST2H2AB

HIST2H4B

HIST2H3C

HIST2H3A

HIST2H2AC

CBWD2HIST2H2BF

HIST2H2AA3

Fig. 3 continued

Genes Genom (2015) 37:679–691 685

123

Table 2 Enrichment of GO terms for DEGs in each cluster

Cluster number Term P value

1 GO:0009611, response to wounding 3.3E-28

GO:0006952, defense response 3.63E-25

GO:0006954, inflammatory response 5.64E-25

GO:0010033, response to organic substance 1.81E-15

GO:0002237, response to molecule of bacterial origin 5.35E-10

GO:0042330, taxis 5.6E-09

GO:0006935, chemotaxis 5.6E-09

GO:0042981, regulation of apoptosis 6.16E-09

GO:0043067, regulation of programmed cell death 8.52E-09

GO:0032496, response to lipopolysaccharide 8.93E-09

GO:0001817, regulation of cytokine production 8.97E-09

GO:0010941, regulation of cell death 9.62E-09

GO:0048584, positive regulation of response to stimulus 1.07E-08

GO:0048584, positive regulation of response to stimulus 1.07E-08

GO:0009617, response to bacterium 2.81E-08

GO:0001775, cell activation 2.89E-08

GO:0002684, positive regulation of immune system process 5.7E-08

GO:0002252, immune effector process 3.64E-07

GO:0002252, immune effector process 3.64E-07

GO:0002252, immune effector process 3.64E-07

2 GO:0006468, protein amino acid phosphorylation 6.19E-05

GO:0016310, phosphorylation 0.000353

GO:0046578, regulation of Ras protein signal transduction 0.000769

GO:0006793, phosphorus metabolic process 0.002414

GO:0006796, phosphate metabolic process 0.002414

GO:0042325, regulation of phosphorylation 0.002418

GO:0043242, negative regulation of protein complex disassembly 0.002544

GO:0051693, actin filament capping 0.00345

GO:0051174, regulation of phosphorus metabolic process 0.003524

GO:0019220, regulation of phosphate metabolic process 0.003524

GO:0051129, negative regulation of cellular component organization 0.003904

GO:0030835, negative regulation of actin filament depolymerization 0.004442

GO:0043244, regulation of protein complex disassembly 0.005631

GO:0050670, regulation of lymphocyte proliferation 0.006219

GO:0030834, regulation of actin filament depolymerization 0.006224

GO:0032944, regulation of mononuclear cell proliferation 0.006539

GO:0070663, regulation of leukocyte proliferation 0.006539

GO:0030837, negative regulation of actin filament polymerization 0.0069

GO:0051494, negative regulation of cytoskeleton organization 0.00736

GO:0050671, positive regulation of lymphocyte proliferation 0.00736

3 GO:0019882, antigen processing and presentation 1.35E-16

GO:0048002, antigen processing and presentation of peptide antigen 3.75E-12

GO:0002474, antigen processing and presentation of peptide antigen via MHC class I 2.22E-09

GO:0043122, regulation of I-kappaB kinase/NF-kappaB cascade 5.18E-09

GO:0002684, positive regulation of immune system process 1.27E-08

GO:0002684, positive regulation of immune system process 1.27E-08

GO:0050863, regulation of T cell activation 1.56E-08

GO:0043123, positive regulation of I-kappaB kinase/NF-kappaB cascade 1.66E-08

686 Genes Genom (2015) 37:679–691

123

regulation of immune responses (Sasawatari et al. 2011).

HCK gene is mainly expressed in cells having monocyte or

macrophage lineage (Ziegler et al. 1987) that are important

HIV-1 target cells and viral reservoirs (Martın and Bandres

1999; Orenstein 2001; Crowe et al. 2003). HCK encodes a

505-residue polypeptide, which is closely related to

pp56lck, a lymphocyte-specific protein-tyrosine kinase

(Ziegler et al. 1987). HCK also one of the members of Src

kinase family, is known to interact with NEF (HIV-1 vir-

ulence factor) and promotes viral pathogenesis of HIV/

AIDS (Trible et al. 2006). LY96 gene encodes a glyco-

protein lymphocyte antigen 96 (LY96; also denoted as

ESOP-1 or MD-2) that is involved in endotoxin recognition

and offers a foundation for antiseptic drug development

(Ohto et al. 2007). MD-2 interacts with the extracellular

domain of Toll-like receptor 4 (TLR4) and is necessary for

the activation of TLR4 by lipopolysaccharide (LPS) pre-

sent on the outer membranes of Gram-negative bacterial

cell walls (Mullen et al. 2003; Dziarski and Gupta 2000).

Recognizing the structure and function of biological

networks is indispensable for the investigation of biologi-

cal processes. Many recent studies have used network

based studies to investigate various biological problems

(Malik et al. 2014; Lee and Lee 2013). In this work, we

identified 5 functional modules or communities in the

interaction network using fast greedy algorithm imple-

mented as GLAY (Su et al. 2010) plugin for Cytoscape.

Furthermore, the functional enrichment of only 3 modules

(based on the criteria described in the methods section) was

explored by using functional annotation tool DAVID. Our

analysis shows that the modules are enriched in functions

related to defense response, inflammatory response, phos-

phorous metabolic processes and regulation of I-kappaB

kinase/NF-kappaB cascade. According to previous studies,

subjects with severe infection exhibit high expression

levels of cytokine and chemokine genes (IL-8, TNF),

antiviral signaling genes such as TMEM158 (involved in

antiviral immune signaling) (Ishikawa and Barber 2008),

Table 2 continued

Cluster number Term P value

GO:0010740, positive regulation of protein kinase cascade 2.22E-08

GO:0019884, antigen processing and presentation of exogenous antigen 2.48E-08

GO:0019884, antigen processing and presentation of exogenous antigen 2.48E-08

GO:0051249, regulation of lymphocyte activation 3.45E-08

GO:0050865, regulation of cell activation 4.18E-08

GO:0050867, positive regulation of cell activation 7.7E-08

GO:0010627, regulation of protein kinase cascade 1.47E-07

GO:0002694, regulation of leukocyte activation 1.47E-07

GO:0050870, positive regulation of T cell activation 1.48E-07

GO:0050870, positive regulation of T cell activation 1.48E-07

GO:0051251, positive regulation of lymphocyte activation 1.66E-07

GO:0046649, lymphocyte activation 2.28E-07

Fig. 4 Distribution of total

SNPs and nsSNPs in four hub

genes

Genes Genom (2015) 37:679–691 687

123

Table 3 List of nsSNPs that are commonly predicted to be damaging/deleterious/affect protein function by PROVEAN, SIFT as well as

PolyPhen-2

Gene (No. of

predicted

nsSNPS)

SNP ID AA

change

PPH2_Prob PPH2 PROVEAN

score

PROVEAN

(cutoff = -2.5)

SIFT

score

SIFT

(cutoff = 0.05)

LY96 (5) rs141631661 12 S C 0.997 Probably damaging -3.04 Deleterious 0.003 Damaging

rs199978698 29 D N 1 Probably damaging -2.54 Deleterious 0 Damaging

rs142571384 70 D Y 1 Probably damaging -7.36 Deleterious 0.002 Damaging

rs367973351 99 D H 0.999 Probably damaging -4.5 Deleterious 0.004 Damaging

rs202107994 143 E A 0.962 Probably damaging -2.85 Deleterious 0 Damaging

HCK (16) rs199815006 125 F C 1 Probably damaging -6.34 Deleterious 0 Damaging

rs372761182 168 D N 0.999 Probably damaging -3.61 Deleterious 0.002 Damaging

rs200896933 186 G R 1 Probably damaging -7.13 Deleterious 0 Damaging

rs202001086 193 R Q 0.936 Possibly damaging -3.13 Deleterious 0.001 Damaging

rs199531584 212 G R 0.996 Probably damaging -7.01 Deleterious 0 Damaging

rs189947820 239 R W 1 Probably damaging -6.86 Deleterious 0 Damaging

rs200867413 257 W R 1 Probably damaging -12.37 Deleterious 0 Damaging

rs200178250 271 T M 0.973 Probably damaging -4.89 Deleterious 0 Damaging

rs151054795 339 I T 0.981 Probably damaging -3.86 Deleterious 0.029 Damaging

rs114505022 350 A T 0.983 Probably damaging -3.52 Deleterious 0.001 Damaging

rs145632103 384 R W 1 Probably damaging -7.46 Deleterious 0 Damaging

rs377468533 392 T M 0.554 Possibly damaging -3.83 Deleterious 0.022 Damaging

rs137871205 435 R W 1 Probably damaging -6.94 Deleterious 0 Damaging

rs374360915 435 R Q 0.993 Probably damaging -3.2 Deleterious 0.001 Damaging

rs142491977 470 M V 0.773 Possibly damaging -3.13 Deleterious 0.001 Damaging

rs17093828 482 P Q 1 Probably damaging -7.15 Deleterious 0 Damaging

HCLS1 (26) rs150386736 7 G A 1 Probably damaging -4.789 Deleterious -4.789 Damaging

rs372797954 19 D E 0.999 Probably damaging -3.437 Deleterious -3.437 Damaging

rs148254045 21 W C 1 Probably damaging -11.321 Deleterious -11.321 Damaging

rs146539124 54 I T 0.986 Probably damaging -3.533 Deleterious -3.533 Damaging

rs371758156 155 R C 0.999 Probably damaging -2.749 Deleterious -2.749 Damaging

rs372191150 163 V L 0.971 Probably damaging -2.684 Deleterious -2.684 Damaging

rs34767273 166 D H 1 Probably damaging -5.588 Deleterious -5.588 Damaging

rs144275149 181 T M 1 Probably damaging -2.589 Deleterious -2.589 Damaging

rs147129061 184 H Y 1 Probably damaging -5.782 Deleterious -5.782 Damaging

rs368893881 184 H Q 1 Probably damaging -7.69 Deleterious -7.69 Damaging

rs36104070 200 I T 0.908 Possibly damaging -3.216 Deleterious -3.216 Damaging

rs143408084 203 D E 0.891 Possibly damaging -3.417 Deleterious -3.417 Damaging

rs202020296 211 G S 0.977 Probably damaging -2.55 Deleterious -2.55 Damaging

rs61749596 218 P L 0.97 Probably damaging -2.595 Deleterious -2.595 Damaging

rs144589441 225 T M 1 Probably damaging -3.399 Deleterious -3.399 Damaging

rs150713543 240 A V 0.998 Probably damaging -3.167 Deleterious -3.167 Damaging

rs145562548 291 P L 0.984 Probably damaging -2.73 Deleterious -2.73 Damaging

rs368506277 430 G W 1 Probably damaging -5.551 Deleterious -5.551 Damaging

rs376245237 441 G V 1 Probably damaging -2.85 Deleterious -2.85 Damaging

rs201252242 451 P L 1 Probably damaging -9.08 Deleterious -9.08 Damaging

rs202175021 452 D N 0.997 Probably damaging -3.99 Deleterious -3.99 Damaging

rs142545513 453 D N 1 Probably damaging -4.19 Deleterious -4.19 Damaging

rs377000996 464 G A 1 Probably damaging -5.148 Deleterious -5.148 Damaging

rs78618042 467 R Q 0.999 Probably damaging -3.049 Deleterious -3.049 Damaging

rs374286282 476 L P 1 Probably damaging -6.108 Deleterious -6.108 Damaging

rs142693789 479 A E 0.998 Probably damaging -4.51 Deleterious -4.51 Damaging

688 Genes Genom (2015) 37:679–691

123

and CD69, a C-type lectin that plays a vital role in T cell

activation, proliferation and signaling (Martın and San-

chez-Madrid 2011). Moreover, it has been observed in

subjects with history of severe infection displayed higher

expression levels of anti-inflammatory proteins such as

PI3, a protease inhibitor that acts to reduce inflammation

(Verrier et al. 2012), and TNFAIP3 that restrains NF-kappa

B activation (Song et al. 1996).

One of the prime objectives of human genetics lies in

deciphering the functional consequences of inherited vari-

ations between individuals. Even though millions of SNPs

are said to be present in the human population, the small

percentage of this set will be posing potential disease risk.

Among the genomic variants, non-synonymous SNPs are

of prime interest as they lead to amino acid change in

translated protein products, and hence, these variants are

primarily studied for their relevance in inheritable diseases

and drug sensitivity (Yue and Moult 2006). It has been

further termed as one of the promising arena for research in

the field of genomics (Karchin et al. 2005). The effect of

majority of nsSNPs is non-detrimental as they are deleted

by natural selection. Assessment of lethality of detrimental

nsSNPS is traced primarily based on phylogenetic infor-

mation (i.e. correlation with residue conservation) and

structural approaches (PolyPhen). However, studies have

also revealed many human disease genes as a resultant of

Table 3 continued

Gene (No. of

predicted

nsSNPS)

SNP ID AA

change

PPH2_Prob PPH2 PROVEAN

score

PROVEAN

(cutoff = -2.5)

SIFT

score

SIFT

(cutoff = 0.05)

SLC15A3 (26) rs377584250 51 G R 0.991 Probably damaging -4.34 Deleterious 0.002 Damaging

rs199743083 93 L P 0.994 Probably damaging -4.74 Deleterious 0.001 Damaging

rs201623464 187 V G 1 Probably damaging -6.04 Deleterious 0 Damaging

rs140918009 191 G C 0.992 Probably damaging -7.49 Deleterious 0 Damaging

rs142952480 197 R H 1 Probably damaging -3.35 Deleterious 0.007 Damaging

rs377073448 213 S L 1 Probably damaging -4.21 Deleterious 0.002 Damaging

rs140942899 256 P L 0.999 Probably damaging -5.93 Deleterious 0.002 Damaging

rs147368970 258 G D 1 Probably damaging -5.28 Deleterious 0 Damaging

rs373404805 318 P L 1 Probably damaging -6.64 Deleterious 0.003 Damaging

rs372172597 327 W R 1 Probably damaging -11.75 Deleterious 0.004 Damaging

rs34738190 337 Y C 0.998 Probably damaging -7.53 Deleterious 0 Damaging

rs188010920 369 T M 0.983 Probably damaging -3.17 Deleterious 0.021 Damaging

rs201019779 371 P L 1 Probably damaging -8.64 Deleterious 0.001 Damaging

rs140596697 392 R C 0.99 Probably Damaging -4.74 Deleterious 0.005 Damaging

rs140945002 400 R W 0.518 Possibly damaging -4.65 Deleterious 0.027 Damaging

rs199509067 405 P S 1 Probably damaging -4.34 Deleterious 0.025 Damaging

rs142111240 464 Q R 1 Probably damaging -3.54 Deleterious 0 Damaging

rs377527454 471 S G 1 Probably damaging -3.54 Deleterious 0 Damaging

rs148648672 473 I T 0.998 Probably damaging -3.85 Deleterious 0.002 Damaging

rs377256065 481 E Q 0.999 Probably damaging -2.63 Deleterious 0 Damaging

rs143864458 488 P L 1 Probably damaging -9.1 Deleterious 0 Damaging

rs367924777 489 R C 1 Probably damaging -4.81 Deleterious 0.001 Damaging

rs369243035 531 G E 1 Probably damaging -5.26 Deleterious 0 Damaging

rs374216372 537 R W 0.996 Probably damaging -3.22 Deleterious 0.023 Damaging

rs199844407 552 T M 0.999 Probably damaging -3.56 Deleterious 0.002 Damaging

rs371994103 562 R C 1 Probably damaging -3.57 Deleterious 0.001 Damaging

AA Change amino acid substitution, PPH2_Prob a classifier probability of the variation being damaging as predicted by PolyPhen-2, PPH2

PolyPhen-2, an automatic tool for the prediction of potential effect of an amino acid substitution on the structure and function of human proteins

by using physical and evolutionary comparative considerations, PROVEAN Score score that correlates with biological activity level and might be

used as an indicator for the amount of functional effect of a protein variation. If the PROVEAN score is B to a given threshold, the variation is

predicted as deleterious, whereas the variant is predicted to have a neutral effect if the score is above the threshold, PROVEAN Protein Variation

Effect Analyzer (PROVEAN) is a tool that predicts whether an amino acid substitution or indel effects the biological function of a protein, SIFT

Score scaled probability of an amino acid substitution being tolerated. Substitutions with scores\0.05 are predicted to affect protein function,

SIFT Sorting Tolerant From Intolerant (SIFT) is a tool to predict whether an amino acid substitution affects protein function

Genes Genom (2015) 37:679–691 689

123

exonic or noncoding mutations affecting regulatory regions

(Hudson 2003; Yan et al. 2002).

The findings from this work propose that the application

of computational algorithms, such as PROVEAN, SIFT,

and PolyPhen-2 analysis may offer a substitute approach to

pick target SNPs by understanding the effect of SNPs on

the functional attributes or molecular phenotype of a pro-

tein. In future studies, structural models of the mutants will

be built that may be applicable for predicting the delete-

rious nsSNPs, which in turn will be useful for additional

genotype-phenotype research as well as pharmacogenetics

studies.

Conclusion

Identification of hub genes that possess potential to cause

disease-related symptoms will lead to a comprehensive

approach to devise a therapeutic method for curing com-

plex diseases. This study determined interactions between

hub genes HCLS1, SLC15A3, HCK and LY96 from DEGs

of primary human macrophages. These genes act in signal

transduction, cell differentiation, peptide transport and

related important cellular pathways. Moreover, we short-

listed three functional modules related to defense and

inflammatory responses in WNV infected macrophages.

Hence, these clusters can be targeted in further studies to

devise a therapeutic target that stabilizes their gene

expression and aids in curbing symptoms related to WNV-

linked infection. In addition to targeting multiple targets,

gene-based approach aids in devising more personalized

approach, which further enhances its efficacy.

Acknowledgments The Deanship of Scientific Research (DSR),

King Abdulaziz University, Jeddah, funded this work under Grant No.

(830-011-D1434). The authors, therefore, acknowledge with thanks

DSR for technical and financial support.

Conflict of interest Authors declare no conflict of interest.

References

Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A,

Bork P, Kondrashov AS, Sunyaev SR (2010) A method and

server for predicting damaging missense mutations. Nat Method

7:248–249

Albert R (2005) Scale-free networks in cell biology. J Cell Sci

118:4947–4957

Bamshad MJ, Ng SB, Bigham AW, Tabor HK, Emond MJ, Nickerson

DA, Shendure J (2011) Exome sequencing as a tool for

Mendelian disease gene discovery. Nat Rev Genet 12:745–755

Brinton MA (2002) The molecular biology of West Nile virus: a new

invader of the western hemisphere. Annu Rev Microbiol

56:371–402

Choi Y, Sims GE, Murphy S, Miller JR, Chan AP (2012) Predicting

the functional effect of amino acid substitutions and indels. PLoS

One 7:e46688

Chowdhury P, Khan SA, Dutta P, Topno R, Mahanta J (2014)

Characterization of West Nile virus (WNV) isolates from

Assam, India: insights into the circulating WNV in northeastern

India. Comp Immunol Microb 37:39–47

Christodoulou DC, Wakimoto H, Onoue K, Eminaga S, Gorham JM,

DePalma SR, Herman DS, Teekakirikul P, Conner DA, McKean

DM et al (2014) 50RNA-Seq identifies Fhl1 as a genetic modifier

in cardiomyopathy. J Clin Invest 124:1364–1370

Crowe S, Zhu T, Muller WA (2003) The contribution of monocyte

infection and trafficking to viral persistence, and maintenance

of the viral reservoir in HIV infection. J Leukoc Biol

74:635–641

Dinh P, Sotiriou C, Piccart MJ (2007) The evolution of treatment

strategies: aiming at the target. Breast 16:10–16

Dziarski R, Gupta D (2000) Role of MD-2 in TLR2- and TLR4-

mediated recognition of Gram-negative and Gram-positive

bacteria and activation of chemokine genes. J Endotoxin Res

6:401–405

Firoz A, Malik A, Singh SK, Jha V, Ali A (2014) Comparative

analysis of glycogene expression in different mouse tissues using

RNA-Seq data. Int J Genomics 2014:837365

Heinz FX, Collett MS, Purcell RH, Gould EA, Howard CR, Houghton

M, Moormann RJM, Rice CM, Thiel HJ (2000) Family

Flaviviridae. In: van Regenmortel MHV, Fauquet CM, Bishop

DHL, Carstens EB, Estes MK, Lemon SM et al (eds) Virus

taxonomy: classification and nomenclature of viruses. Academic

Press, San Diego, pp 859–878

Huang Y, Burkhardt JK (2007) T-cell-receptor–dependent actin

regulatory mechanisms. J Cell Sci Rev 120:723–730

Huang Y, Comiskey EO, Dupree RS, Li S, Koleske AJ, Burkhardt JK

(2008) The c-Abl tyrosine kinase regulates actin remodeling at

the immune synapse. Blood 112:111–119

Hudson TJ (2003) Wanted: regulatory SNPs. Nat Genet 33:439–440

Hunt R, Sauna ZE, Ambudkar SV, Gottesman MM, Kimchi-Sarfaty C

(2009) Silent (synonymous) SNPs: should we care about them?

Methods Mol Biol 578:23–39

Ishikawa H, Barber GN (2008) STING is an endoplasmic reticulum

adaptor that facilitates innate immune signaling. Nature

455:674–678

Jones M, Dry IR, Frampton D, Singh M, Kanda RK, Yee MB, Kellam

P, Hollinshead M, Kinchington PR, Otoole EA et al (2014)

RNA-seq analysis of host and viral gene expression highlights

interaction between varicella zoster virus and keratinocyte

differentiation. PLoS Pathog 10:e1003896

Karchin R, Kelly L, Sali A (2005) Improving functional annotation of

non-synonymous SNPs with information theory. Pac Symp

Biocomput 10:397–408

Kitamura D, Kaneko H, Miyagoe Y, Ariyasu T, Watanabe T (1989)

Isolation and characterization of a novel human gene expressed

specifically in the cells of hematopoietic lineage. Nucl Acids Res

17:9367–9379

Kumar P, Henikoff S, Ng PC (2009) Predicting the effects of coding

non-synonymous variants on protein function using the SIFT

algorithm. Nat Protoc 4:1073–1081

Lee J, Lee J (2013) Hidden information revealed by optimal

community structure from a protein-complex bipartite network

improves protein function prediction. PLoS One 8:e60372

Lee EJ, Malik A, Pokharel S, Ahmad S, Mir BA, Cho KH, Kim J,

Kong JC, Lee DM, Chung KY et al (2014) Identification of

genes differentially expressed in myogenin knock-down bovine

muscle satellite cells during differentiation through RNA

sequencing analysis. PLoS One 9:e92447

690 Genes Genom (2015) 37:679–691

123

Malik A, Lee J, Lee J (2014) Community-based network study of

protein-carbohydrate interactions in plant lectins using glycan

array data. PLoS One 9:e95480

Martın JC, Bandres JC (1999) Cells of the monocyte-macrophage

lineage and pathogenesis of HIV-1 infection. J Acquir Immune

Defic Syndr 22:413–429

Martın P, Sanchez-Madrid F (2011) CD69: an unexpected regulator

of TH17 cell-driven inflammatory responses. Sci Signal 4:pe14

Mercer TR, Clark MB, Crawford J, Brunck ME, Gerhardt DJ, Taft RJ,

Nielsen LK, Dinger ME, Mattick JS (2014) Targeted sequencing

for gene discovery and quantification using RNA Cap-

tureSeq. Nat Protoc 5:989–1009

Montojo J, Zuberi K, Rodriguez H, Kazi F, Wright G, Donaldson SL,

Morris Q, Bader GD (2010) GeneMANIA Cytoscape plugin: fast

gene function predictions on the desktop. Bioinformatics

26:2927–2928

Mullen GE, Kennedy MN, Visintin A, Mazzoni A, Leifer CA, Davies

DR, Segal DM (2003) The role of disulfide bonds in the

assembly and function of MD-2. Proc Natl Acad Sci USA

100:3919–3924

Nagalakshmi U, Waern K, Snyder M (2010) RNA-Seq: a method for

comprehensive transcriptome analysis. Curr Protoc Mol Biol

11:1–13

Ohto U, Fukase K, Miyake K, Satow Y (2007) Crystal structures of

human MD-2 and its complex with antiendotoxic lipid IVa.

Science 316:1632–1634

Orenstein JM (2001) The macrophage in HIV infection. Immunobi-

ology 204:598–602

Pena J, Plante JA, Carillo AC, Roberts KK, Smith JK, Juelich TL,

Beasley DWC, Freiberg AN, Labute MX, Arani PN et al (2014)

Multiplexed digital mRNA profiling of the inflammatory

response in the west nile swiss webster mouse model. PLoS

Negl Trop Dis 8:e3216

Qian F, Chung L, Zheng W, Bruno V, Alexander RP, Montgomery

RR (2013) Identification of genes critical for resistance to

infection by West Nile virus using RNA-Seq analysis. Viruses

5:1664–1681

Ramensky V, Bork P, Sunyaev S (2002) Human non-synonymous

SNPs: server and survey. Nucl Acids Res 17:3894–3900

Sakata K, Yamashita T, Maeda M, Moriyama Y, Shimada S,

Tohyama M (2001) Cloning of a lymphatic peptide/histidine

transporter. Biochem J 356:53–60

Sasawatari S, Okamura T, Kasumi E, Tanaka-Furuyama K, Yanobu-

Takanashi R, Shirasawa S, Kato N, Toyama-Sorimachi N (2011)

The solute carrier family 15A4 regulates TLR9 and NOD1

functions in the innate immune system and promotes colitis in

mice. Gastroenterology 140:1513–1525

Scielzo C, Ghia P, Conti A, Bachi A, Guida G, Geuna M, Alessio M,

Cappio FC (2005) HS1 protein is differentially expressed in

chronic lymphocytic leukemia patient subsets with good or poor

prognoses. J Clin Invest 115:1644–1650

Skokowa J, Klimiankou M, Klimenkova O, Lan D, Gupta K, Hussein

K, Carrizosa E, Kusnetsova I, Li Z, Sustmann C et al (2012)

Interactions among HCLS1, HAX1 and LEF-1 proteins are

essential for G-CSF-triggered granulopoiesis. Nat Med

18:1550–1559

Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T (2011)

Cytoscape 2.8: new features for data integration and network

visualization. Bioinformatics 27:431–432

Song HY, Rothe M, Goeddel DV (1996) The tumor necrosis factor-

inducible zinc finger protein A20 interacts with TRAF1/TRAF2

and inhibits NF-kappa B activation. Proc Natl Acad Sci USA

93:6721–6725

Su G, Kuchinsky A, Morris JH, States DJ, Meng F (2010) GLay:

community structure analysis of biological networks. Bioinfor-

matics 26:3135–3137

Suthar MS, Diamond MS, Gale M Jr (2013) West Nile virus infection

and immunity. Nat Rev Microbiol 11:115–128

Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren

MJ, Salzberg SL, Wold BJ, Pachter L (2010) Transcript

assembly and quantification by RNA-Seq reveals unannotated

transcripts and isoform switching during cell differentiation. Nat

Biotechnol 28:511–515

Trible RP, Emert-Sedlak L, Smithgall TE (2006) HIV-1 Nef

selectively activates Src family kinases Hck, Lyn, and c-Src

through direct SH3 domain interaction. J Biol Chem

281:27029–27038

Van Rossum AG, Schuuring-Scholtes E, Seggelen V, Kluin PM,

Schuuring E (2005) Comparative genome analysis of cortactin

and HS1: the significance of the F-actin binding repeat domain.

BMC Genom 6:15

Verrier T, Solhonne B, Sallenave JM, Garcia-Verdugo I (2012) The

WAP protein Trappin-2/Elafin: a handyman in the regulation of

inflammatory and immune responses. Int J Biochem Cell Biol

44:1377–1380

Yan H, Yuan W, Velculescu VE, Vogelstein B, Kinzler KW (2002)

Allelic variation in human gene expression. Science 297:1143

Yang Z, Nielsen R (1998) Synonymous and nonsynonymous rate

variation in nuclear genes of mammals. J Mol Evol 46:409–418

Young A, Berry R, Holloway AF, Blackburn NB, Dickinson JL,

Sakala M, Philips JL, Moore KHB (2014) RNA-seq profiling of

a radiation resistant and radiation sensitive prostate cancer cell

line highlights opposing regulation of DNA repair and targets for

radiosensitization. BMC Cancer 14:808

Yue P, Moult J (2006) Identification and analysis of deleterious

human SNPs. J Mol Biol 356:1263–1274

Ziegler SF, Marth JD, Lewis DB, Perlmutter RM (1987) Novel

protein-tyrosine kinase gene (hck) preferentially expressed in

cells of hematopoietic origin. Mol Cell Biol 7:2276–2285

Genes Genom (2015) 37:679–691 691

123