© kamla-raj 2018 int j hum genet, 18(2): 137-151 (2018...

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Screening Hub Genes in Microbial Keratitis Based on Gibbs Sampling Analysis Qiao-Ling Wang 1 , Meng-Hui Zhang 2 and Xi Chen 3* 1 Department of Ophthalmology, The Second People’s Hospital of Jinan, Jinan 250012, Shandong, P.R. China 2 Department of General Surgery, The Fourth People’s Hospital of Jinan, Jinan 250031, Shandong, P.R. China 3 Department of Ophthalmology, The Ninth People’s Hospital of Chongqing, Chongqing 400700, P.R. China KEYWORDS Gibbs Sampling. Hub Gene. Key Differential Pathway. Markov Chain. Microbial Keratitis. Probability ABSTRACT This study aimed to identify potential key pathways and hub genes. The enrichment pathways were chosen through merging the gene expression data from microarray data with KEGG pathways. Gibbs sampling was performed to screen out significant pathways and the pathway gene set was determined. Finally, hub genes were identified using Gibbs sampling and statistics. A total of 278 pathways were chosen according to gene overlap greater than 5. Markov chain (MC) was established based on the enrichment of the gene expression profile in each pathway using Gibbs sampling and then 26 significant pathways were determined judging by adj_á pi greater than 0.8. Additionally, 1167 pathway gene sets were found out. Finally, 26 pathways were chosen as key pathways and 5 hub genes were identified on basis of their importance, which will contribute to elucidating potential molecular mechanism of microbial keratitis. We identified potential key pathways and hub genes in microbial keratitis. Address for correspondence: Xi Chen Department of Ophthalmology, The Ninth People’s Hospital of Chongqing, Chongqing 400700, P.R. China E-mail: [email protected] INTRODUCTION Microbial keratitis is an infective ophthalmic disease, caused by spectrums of pathogenic microorganisms including bacteria, viruses, fun- gi, Acanthamoeba and other prokaryote patho- gens. In spite of recent advances in antimicrobi- al measures, due to ensuing excessive inflamma- tory response and ocular tissue damage, large numbers of patients’ conditions fail to achieve effective controls or continue to deteriorate, and eventually patients have to undergo corneal transplantation or even enucleation. According- ly, it is imperative to have a bearing on relevant physiopathologic mechanisms, which inevitably will be a breakthrough for diagnosing, treating or monitoring microbial keratitis. Microbial keratitis initiation and progression relate to intricate biological process that involves various genes alterations and diverse molecular mechanisms underlying the pathophysiology. With the aid of bioinformatics analyses, a pow- erful approach emerging of proteome analysis in tear protein profile has indicated that glutare- doxin-related protein, lipocalin, prolactin induc- ible protein, serum albumin precursor, apolipo- protein, lacritin precursor (Ananthi et al. 2008; Ananthi et al. 2013; Kandhavelu et al. 2017). Sev- eral molecular pathways were reported to involve in the development of microbial keratitis, name- ly, p38 mitogen-activated protein kinases (MAPK) pathway (Hua et al. 2017), Toll-like re- ceptor-3 (TLR3)/ TIR domain-containing adap- tor inducing IFN-β (TRIF) pathway (Park et al. 2015), spleen-tyrosine kinase (Syk) signaling (Liu et al. 2015). With the increasingly biologi- cal importance of highly connective hub genes that play a pivotal role in maintaining interac- tion network, hub genes or key pathways iden- tified were particularly crucial for uncovering the pathogenesis of microbial keratitis. In the wake of rapidly diverse bioinformatics method- ologies, the understanding of underlying phys- iopathologic molecular mechanisms of disorders has been greatly improved. Here, in this work, coupled with microarray data, Kyoto Encyclo- pedia of Genes and Genomes (KEGG) enrich- ment, Gibbs sampling, the researchers identi- fied key pathways and hub genes associated with microbial keratitis. © Kamla-Raj 2018 Int J Hum Genet, 18(2): 137-151 (2018) DOI: 10.31901/24566330.2018/18.2.697

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Screening Hub Genes in Microbial Keratitis Based on GibbsSampling Analysis

Qiao-Ling Wang1, Meng-Hui Zhang2 and Xi Chen3*

1Department of Ophthalmology, The Second People’s Hospital of Jinan, Jinan 250012,Shandong, P.R. China

2Department of General Surgery, The Fourth People’s Hospital of Jinan, Jinan 250031,Shandong, P.R. China

3Department of Ophthalmology, The Ninth People’s Hospital of Chongqing,Chongqing 400700, P.R. China

KEYWORDS Gibbs Sampling. Hub Gene. Key Differential Pathway. Markov Chain. Microbial Keratitis. Probability

ABSTRACT This study aimed to identify potential key pathways and hub genes. The enrichment pathways werechosen through merging the gene expression data from microarray data with KEGG pathways. Gibbs sampling wasperformed to screen out significant pathways and the pathway gene set was determined. Finally, hub genes wereidentified using Gibbs sampling and statistics. A total of 278 pathways were chosen according to gene overlap greaterthan 5. Markov chain (MC) was established based on the enrichment of the gene expression profile in each pathwayusing Gibbs sampling and then 26 significant pathways were determined judging by adj_ápi greater than 0.8. Additionally,1167 pathway gene sets were found out. Finally, 26 pathways were chosen as key pathways and 5 hub genes wereidentified on basis of their importance, which will contribute to elucidating potential molecular mechanism ofmicrobial keratitis. We identified potential key pathways and hub genes in microbial keratitis.

Address for correspondence:Xi ChenDepartment of Ophthalmology,The Ninth People’s Hospital of Chongqing,Chongqing 400700, P.R. ChinaE-mail: [email protected]

INTRODUCTION

Microbial keratitis is an infective ophthalmicdisease, caused by spectrums of pathogenicmicroorganisms including bacteria, viruses, fun-gi, Acanthamoeba and other prokaryote patho-gens. In spite of recent advances in antimicrobi-al measures, due to ensuing excessive inflamma-tory response and ocular tissue damage, largenumbers of patients’ conditions fail to achieveeffective controls or continue to deteriorate, andeventually patients have to undergo cornealtransplantation or even enucleation. According-ly, it is imperative to have a bearing on relevantphysiopathologic mechanisms, which inevitablywill be a breakthrough for diagnosing, treatingor monitoring microbial keratitis.

Microbial keratitis initiation and progressionrelate to intricate biological process that involvesvarious genes alterations and diverse molecularmechanisms underlying the pathophysiology.With the aid of bioinformatics analyses, a pow-

erful approach emerging of proteome analysisin tear protein profile has indicated that glutare-doxin-related protein, lipocalin, prolactin induc-ible protein, serum albumin precursor, apolipo-protein, lacritin precursor (Ananthi et al. 2008;Ananthi et al. 2013; Kandhavelu et al. 2017). Sev-eral molecular pathways were reported to involvein the development of microbial keratitis, name-ly, p38 mitogen-activated protein kinases(MAPK) pathway (Hua et al. 2017), Toll-like re-ceptor-3 (TLR3)/ TIR domain-containing adap-tor inducing IFN-β (TRIF) pathway (Park et al.2015), spleen-tyrosine kinase (Syk) signaling(Liu et al. 2015). With the increasingly biologi-cal importance of highly connective hub genesthat play a pivotal role in maintaining interac-tion network, hub genes or key pathways iden-tified were particularly crucial for uncoveringthe pathogenesis of microbial keratitis. In thewake of rapidly diverse bioinformatics method-ologies, the understanding of underlying phys-iopathologic molecular mechanisms of disordershas been greatly improved. Here, in this work,coupled with microarray data, Kyoto Encyclo-pedia of Genes and Genomes (KEGG) enrich-ment, Gibbs sampling, the researchers identi-fied key pathways and hub genes associatedwith microbial keratitis.

© Kamla-Raj 2018 Int J Hum Genet, 18(2): 137-151 (2018)DOI: 10.31901/24566330.2018/18.2.697

138 QIAO-LING WANG, MENG-HUI ZHANG AND XI CHEN

Objective

In the present study, the researchersachieved 278 enrichment pathways based ongenes intersection greater than 5 from microar-ray data performed on samples with 15 keratitit-ides and 12 normal. Moreover, Gibbs samplingwas implemented to identify 26 key differentialpathways. Then, the researchers applied Gibbssampling to acquire 5 hub genes through differ-ential pathway gene sets. Overall, hub genes anal-ysis improved comprehending of microbial kerati-tis through providing relevant gene changes thatoccur during microbial keratitis progression anddiscovered the key biomarkers with potential util-ity for clinical diagnosis, therapy, and surveillanceof microbial keratitis progression.

METHODOLOGY

Microarray Data Acquisition andPreprocessing

The gene expression profile, with the acces-sion number of E-GEOD-58291 (Chidambaram etal. 2017), was downloaded from ArrayExpress(http://www.ebi.ac.uk/arrayexpress/). In the mi-croarray data of E-GEOD-58291, there were 15keratitis samples and 12 normal samples. Theprobe annotation data were downloaded andthen each probe was mapped to matched genesymbols. Amongst which the probe was discard-ed if it can’t match any one gene, or the averageexpression value was computed if there weremultiple probes match one gene. After data pre-processing, the researchers eventually acquiredthe gene expression matrix containing 14,832genes.

EGG Pathway Enrichment

Pathway enrichment analysis of gene expres-sion matrix was conducted based on the KEGGpathway database. The researchers merged thegene expression data with KEGG regulatory path-ways and chose 278 pathways judging by geneoverlap greater than 5.

Gibbs Sampling

In order to obtain the pivotal pathways andkey genes, Gibbs sampling was introduced.Gibbs sampling is a Markov chain (MC) Monte

Carlo (MCMC) algorithm extensively applied instatistical inference that generates samples froma sequence of different conditional probabilitydistributions. To executing Gibbs sampling, theabove 278 pathways were converted into MC.

According to the enrichment of the geneexpression profile in each pathway, the mean val-ue of gene expression in each pathway was com-puted in normal and keratitis states. The normalstate served as the initial state and the keratitisstate served as prior state, then the third statewas inferred by the prior state, in turn the state nwas gained according to this model extrapolated.

An empty Gibbs sampling set comprising ak-dimensional (k = 278) random vector was de-fined. Following, n samples MC data set con-taining the initial value and prior value were de-posited into the empty Gibbs sampling set. Then,a k-dimensional vector was initialized, k-1 ele-ments of this vector were fixed, the remainingelement was extracted, like this cycled k timeswhich was equal to updating the whole vectorsand also generate a novel sample. The third statewas obtained. Ultimately, through n cycles ofGibbs sampling, a MC was established.

Differential Pathways Analysis

On basis on the posterior value of pathwaysgenerated by the MC and utilizing the probabil-ity formula αpi, the probability of each pathwaywas acquired. Then, by statistical analysis (stu-dent’s t-test) of the pathways expression in kerati-tis and normal states, the P values were calcu-lated and according to it, the pathways wereranked (ranki). Combining αpi and P value, cor-rection coefficient (Rvalue) was calculated andthen adj-αpi (multiplying αpi by Rvalue) was calcu-lated. Key differential pathways were selectedjudging by adj_αpi greater than 0.8. Related com-putational formulas were as follows.

Hub Genes Identification

The genes in the differential pathways wereanalyzed to find the pathway gene set and theirfrequencies in differential pathways were count-

α = ∑1000−20001000 − 2000 + 1 R = 1 − adj_α = × pi

HUB GENES IN MICROBIAL KERATITIS 139

ed. Subsequently, pathway gene set were con-verted into MC and then Gibbs sampling wasperformed. Finally, differential pathway genesof which alfa-adj were greater than 0.8 were cho-sen as hub genes.

RESULTS

KEGG Pathway Enrichment Analysis

By data preprocessing, a total of 14832 geneswere obtained and these genes were enrichedinto the KEGG pathway with 287 pathways con-taining 6,894 genes. The enrichment analysissuggested that 278 pathways were determinedbased on the gene overlap greater than 5.

Differential Pathways Identification

To further determine key pathways, the re-searchers used the Gibbs sampling via comput-ing adj_pi to gain the probabilities distributionof enriched pathways shown in Figure 1, in theimage, a total of 26 key pathways were identifiedaccording to the adj-pi > 0.8, including NF-kap-pa B signaling, TNF signaling, Cytokine-cytok-ine receptor interaction, Phagosome, Hemato-

poietic cell lineage, Toll-like receptor signalinget al. As shown in Table 1, the expression valuesof these pathways in normal and keratitis statessuggested that these pathways were upregulat-ed significantly in keratitis groups compared tonormal group (P < 0.05), correspondingly, theheat map of pathways expression levels werepresented in Figure 2. Compared with normalgroup, these 26 significant pathways in keratitisgroup were higher expressed.

Pathway Gene Set Selection

In an effort to find out the hub genes are ofimportance in gene expression network, 1167genes in significant pathways were needed tobe analyzed. Gene set in 26 significant pathwayscomprised 82 genes in Rheumatoid arthritis sig-naling pathway, 195 genes in Cytokine-cytokinereceptor interaction signaling pathway, 150genes in Tuberculosis signaling, 158 genes inChemokine signaling pathway, 115 genes in Os-teoclast differentiation signaling pathway, 49genes in Staphylococcus aureus infection path-way et al. Several genes may appear in multi-pathways and a total of 1167 genes were con-tained in 26 key differential pathways and the

A Probabilities of differential pathways B Density of probabilities of differential pathways

Differential pathways Differential pathways

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Fig. 1. The probabilities distribution of 278 pathways. The X axis denoted the pathways, and the Y axisrepresented the posterior value of the pathways. A. The adjusted posterior value distribution of 278pathways. B. The density of 278 differential pathways posterior value distribution. Pathway was con-sidered as the key differential pathway judging by adj_pi > 0.8.Source: Author

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A P

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MA

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MA

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3CA

;PIK

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3C

G;

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CZ

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T1;

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T3;

FOX

O3;

CH

UK

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BK

B;I

KB

KG

;N F

KB

IA;N

FKB

IB;N

FKB

1;R

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; G

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; VA

V3;

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1; R

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GN

B2;

GN

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GN

G5;

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G7;

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G8

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; G

NG

12;G

NG

T1

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1;E

L

MO

1;D

OC

K2;

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CA

R

1;C

RK

; C

RK

L;P

TK

2B;

PLC

B1;

PLC

B2;

PLC

B4;

R A

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IAM

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CF1

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CD

Ost

eocl

ast

diff

eren

tiatio

n11

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SF1;

CSF

1R;G

RB

2; M

APK

1; M

APK

3; P

IK3C

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; PI

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K3R

1;PI

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2;PI

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3 ;A

KT

1;A

KT

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NG

;IFN

GR

1; I

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R2;

STA

T1;

IL

1A;I

L1B

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NF;

TN

FRSF

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2 ;T

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TG

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FRSF

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CK

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FKB

IA;R

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A;N

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TC

1;

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12;

MA

PK13

;MA

PK14

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PK8;

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PK9;

FO

S;FO

SB;F

OSL

2;FO

SL1;

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;JU

ND

;JU

N B

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CY

BB

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BA

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CF1

; N

CF4

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K;T

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AR

;L

ILR

B2;

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RB

1;L

IL

RB

5;L

ILR

B4;

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RB

3;L

ILR

A3;

LIL

RA

2; L

ILR

A4;

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RA

6;L

ILR

A5;

FC

GR

1A;F

CG

R2A

;FC

GR

2B;F

CG

R3B

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EM

2;SI

RPA

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IRPB

1;T

YR

OB

P;SY

K;L

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;PLC

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3CA

;PPP

3CB

;PPP

3CC

;PPP

3R1

;CR

EB1;

SPI1

;MIT

F;C

TSK

; AC

P5;

ITG

B3;

PPA

RG

;IFN

AR

1; I

FNA

R2;

JAK

1;TY

K2;

STA

T2;IR

F9;S

OC

S1;

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S3;G

AB

2;FH

L2;

CY

LD;S

QST

M1

Stap

hylo

cocc

us a

ureu

s in

fect

ion

49FG

G;C

3;C

FB;C

FD;

CFH

;MA

SP1;

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A;C

1QB

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C;C

1R;C

1S;C

2;C

4A;C

5;C

3A R

1;C

5AR

1;FC

GR

1A;F

CG

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; FC

GR

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; FP

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2;FP

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;SE

LPL

G;S

EL

P;IC

AM

1;IT

GA

L;IT

GA

M;I

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2;D

SG1;

HLA

-DM

A;H

LA-D

MB

;HLA

-DO

A;H

LA-D

OB

;HLA

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PA1;

HL

A-D

PB1;

HL

A-D

QA

1;H

LA

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A2;

HL

A-D

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1;H

LA

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A;H

LA

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B1;

HL

A-

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B3;

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A-D

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4;H

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FR;I

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Pha

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132

VAM

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CT

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RO

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A-C

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A-F

; H

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A-E

; H

LA

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A;H

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B;H

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A;H

LA

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B;H

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1;H

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1; H

LA

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QA

1; H

LA

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A2;

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QB

1;H

LA

-DR

A;H

LA

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A-D

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3;H

LA

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B4;

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A-

DR

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B5B

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; E

EA

1;PI

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; H

GS;

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AB

7B;D

YN

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1;D

YN

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1;D

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2;D

YN

C1L

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UB

A1B

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BA

4A;T

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A;T

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AL

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BB

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BB

3;T

UB

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BB

2B

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PO;C

TSL;

CTSS

;SEC

61A

1;SE

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2;SE

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;SEC

61G

;TA

P1;T

A

142 QIAO-LING WANG, MENG-HUI ZHANG AND XI CHENTa

ble

1: C

ontd

...

Pat

hway

Num

ber

Mem

ber

P2;C

ALR

;CA

NX

;FC

AR

;FC

GR

1A;F

CG

R2A

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GR

2B;F

CG

R3B

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AM

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B2

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3;C

OL

EC

11;C

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12

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THB

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OM

P;TH

BS2

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BS3

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BS4

;TL

R2;

TLR

6;TL

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CD

14;P

LA2R

1;M

RC1;C

D209

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C7A;

MSR

1;MAR

CO;O

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se35

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HLA

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HL

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LA

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HL

A-D

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4;H

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CD

80;C

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dia

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us37

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CD

36;C

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2; I

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3; I

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4;IT

GA

5;IT

GA

6;H

LA-D

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Infl

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(IB

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PB1;

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A1;

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A2;

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DR

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HL

A-D

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test

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mun

e ne

twor

k fo

r Ig

A p

rodu

ctio

n38

CD

80;C

D86

; H

LA

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LA

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B;H

LA

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HL

A-D

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DPB

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A-D

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LA

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B1;

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RA

; H

LA

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LA

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TAB

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B2;

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FKB

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FKB

1;R

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A;I

L1A

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1B;T

NF;

NO

S2;I

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FB2;

TG

FB3;

C3;

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AM

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CG

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GA

4;IT

G

B1;

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S2;N

CF1

;N

CF2

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F4;

CY

BA

; M

APK

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APK

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JUN

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PK11

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APK

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APK

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MA

PK14

; IF

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;IFN

GR

1; I

FNG

R2;

JAK

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K2;

STAT

1;H

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MA

;HLA

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LA-

DO

A;H

LA

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B;

HL

A-D

PA1;

HL

A-D

PB1;

HL

A-D

QA

1;H

LA

-DQ

A2;

HL

A-

DQ

B1;

HL

A-D

RA

;HL

A-D

RB

1;

HL

A-D

RB

3;H

LA

-DR

B4;

HL

A-

DR

B5;

MA

RC

KSL

1;PT

PN6;

PRK

CB

Nat

ural

kill

er c

ell

med

iate

d cy

toto

xici

ty98

HLA

-A;H

LA-B

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LA-F

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KIR

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; K

LRC

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AM

1;IC

A M

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3; V

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1;VA

V2;

RA

C1;

RA

C2;

RA

C3;

PAK

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AP2

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P 2K

2; M

APK

1; M

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F;C

SF2;

IFN

G;K

IR2D

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YR

OB

P;LC

K;F

CG

R3B

;FC

ER1G

;CD

247;

ZAP

HUB GENES IN MICROBIAL KERATITIS 143

70;S

YK

;LC

P2;L

AT;

PLC

G1;

PL

CG

2;PI

K3C

A;

PIK

3CD

;PIK

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IK3R

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5; P

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N;S

HC

1;SH

C2;

SHC

3;

SH C

4;G

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S1;

SOS2

; H

RA

S;K

RA

S;N

RA

S; A

RA

F;B

RA

F;R

AF1

; M

ICB

; M

IC A

; U

LBP1

; U

LBP2

; R

AET

1G;

RA

ET1L

;R

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T1E

; H

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; PP

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CC

; PP

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1;N

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K C

A;

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CB

;SH

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;IFN

GR

1;IF

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1; I

FNA

R2;

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F10;

TN

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10A

;TN

FRSF

10B

; TN

FRSF

10C

; TN

FRSF

10D

; FA

S; G

ZMB

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ASP

3;B

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NA

SEL;

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JC3;

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APK

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; TL

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SOC

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REL

A;T

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S2

Tabl

e 1:

Con

td...

Pat

hway

Num

ber

Mem

ber

144 QIAO-LING WANG, MENG-HUI ZHANG AND XI CHEN

Allo

graf

t re

ject

ion

31H

LA-A

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Aut

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DR

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2

Tabl

e 1:

Con

td...

Pat

hway

Num

ber

Mem

ber

HUB GENES IN MICROBIAL KERATITIS 145

concrete results are presented in SupplementTable 1.

Hub Genes Identification

Finally, to acquire the potential hub genesinvolved in keratitis, Gibbs sampling was reuti-lized to calculate probabilities of pathway geneset. As presented in Figure 3, there were 5 hubgenes identified based on adj_αpi > 0.8, includ-ing CXCL5, MARCO, FCER1G, RAC2, HCK.Their expression values in normal and keratitisstates suggested that they were enhanced mark-edly in keratitis group when compared with nor-mal group (Fig. 4, P < 0.05), accordingly, the heatmap of hub genes expression levels in twogroups were exhibited in Figure 5. These hubgenes were shown to be higher expressed inkeratitis group on comparing normal group.

DISCUSSION

In the current study, the gene expressionmatrix with 14832 genes was gained through datapreprocessing and then 278 pathways were ob-tained via KEGG enrichment analysis. Moreover,26 significant pathways were chose by Gibbssampling. Furthermore, 5 hub genes werescreened by analyzing pathway gene set usingGibbs sampling. These results demonstrated thatidentified key pathways and hub genes wouldprovide valuable information on the progress ofmicrobial keratitis and offer new insights for in-vestigating molecule mechanisms potentiallyinvolved in the microbial keratitis.

Microbial keratitis is recognized as the lead-ing eye-threatening disease of keratitis, charac-terised by the presence of white/yellowish infil-trates in the corneal stroma, with/without anoverlaying corneal epithelial defect, and relatedwith strong signs of inflammation (Upadhyay etal. 2015), which caused considerable inconve-nience to patients. In an effort to uncover theunderlying molecular mechanisms that drive themicrobial keratitis development, more and moreresearchers have spared no efforts to carry outexperiments. It was suggested that activity ofToll-like receptors (TLRs) signaling resulting inthe activation of nuclear factor-kB (NF-kB) andproduction of proinflammatory cytokines likeTNF-α, enable to trigger the earliest immune re-sponses that lead to inflammation in ocular im-munology (Pandey et al. 2013). Beyond that,A

sthm

a23

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Con

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Pat

hway

Num

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146 QIAO-LING WANG, MENG-HUI ZHANG AND XI CHEN

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HUB GENES IN MICROBIAL KERATITIS 147

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148 QIAO-LING WANG, MENG-HUI ZHANG AND XI CHEN

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HUB GENES IN MICROBIAL KERATITIS 149

activation of TLR2 and TLR4 through NF-kap-paB could contribute to pathogenesis of kerato-mycosis (Jie et al. 2009). In addition, Toll-likereceptor 4 signalling pathway was activated in arat model of Acanthamoeba keratitis (Ren andWu 2011). TNF-α and IL-6 was reported to facil-itate corneal lymphangiogenesis during acuteHSV-1 infection (Bryant-Hudson et al. 2014). Con-

sistent with this. This study demonstrated that aseries of inflammation signaling pathways, suchas NF-kappa B signaling, TNF signaling path-way, Toll-like receptor signaling pathway wereidentified using bioinformatics measurement.

Previous research demonstrated that kerati-tis was associated with longstanding rheuma-toid arthritis, besides, morbidity and mortality

Fig. 5. Heat map of the top 5 hub genes. Red: high expression level; Greeen: lower expression level.Source: Author

150 QIAO-LING WANG, MENG-HUI ZHANG AND XI CHEN

of which were increasingly improved (Domnga-ng Noche et al. 2016; Lee et al. 2016; Petrushkinet al. 2016). HLA-DR expression was reported tobe considered as a biomarker of inflammationfor multicenter clinical trials of ocular surfacedisease (Epstein et al. 2013). Accumulating evi-dence showed that peripheral ulcerative kerati-tis was associated with tuberculosis in a child(Al-Mendalawi 2016), mycobacterium tubercu-losis may lead to formation of interstitial kerati-tis (Gupta et al. 2015). Cytokines and chemok-ines served as small proteins played an impor-tant proinflammatory or anti-inflammatory rolein modulating the herpes simplex keratitis (Azheret al. 2017). It is reported that chemokine CXCL10inhibition of hem- and lymph-angiogenesisemerged in inflamed corneas (Gao et al. 2017). Ithas also reported that IL-8, IL-6 and IL-1â ex-pressions were increased in tears samples ofpatients with microbial keratitis compared withnegative normal (Santacruz et al. 2015). The im-portance of long-lasting Ag presentation in in-ducing peripheral T cell tolerance was also de-termined in the model of herpes stromal keratitisautoimmune disease (Raimondi et al. 2006). Thus,in line with above evidence, results of this studydetermined that Rheumatoid arthritis pathway,Tuberculosis pathway, chemokine signalingpathway, Staphylococcus aureus infection sig-naling pathway were selected.

Based on Gibbs sampling, several hub geneslike RAC2, MARCO, CXCL5, HCK, FCER1Gwere identified and these key genes may con-tribute to predict, monitor and even treat micro-bial keratitis. Lin et al demonstrated that CXCL5/lipopolysaccharide (LPS)-induced chemokinemediated neutrophil recruitment into the corneaduring LPS keratitis (Lin et al. 2007). A previousinvestigation determined that CXCL5 as a pro-inflammatory chemokine was downregulated inmice corneal with alkali burns after treated withtofacitinib (Sakimoto and Ishimori 2016). Sweetet al demonstrated that the presence of FcR com-mon γ-chain (Fcer1g) or/and MyD88 were re-sponsible for proinflammatory responses to ex-ogenous antigens while the absence of themreduced extrafollicular plasmablast response(Sweet and Nickerson 2017). It is suggested thatLck/Hck/Fgr triple knockout enhanced antiviralsensing and resistance substantially while ec-topic expression of them dampened cells antivi-ral defense (Liu et al. 2017). Nonetheless, thisstudy is only a bioinformatics screening for biom-

arkers in microbial keratitis preliminarily and fur-ther in-depth investigations in molecular patho-genesis and validations with several experimentsare still demanded.

CONCLUSION

Collectively, this study found out several keypathways associated with microbial keratitisbased on bioinformatics method, accordingly,monitoring these signaling pathways might aidprediction or treatment of microbial keratitis oc-currence and development. Importantly, hubgenes in microbial keratitis, namely, RAC2, MAR-CO, CXCL5, HCK, FCER1G were identified onthe basis of pathway gene set using Gibbs sam-pling, which were likely to be prognostic anddiagnostic implications of microbial keratitis.

RECOMMENDATIONS

Findings from the present study will providethe molecular basis for the understanding ofmicrobial keratitis pathogenesis, implying a novelstrategy for relieving the burden of microbialkeratitis.

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Paper received for publication on March 2018Paper accepted for publication on May 2018