Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Identification of pathogenic genes and upstream regulators in age-related macular degeneration

BMC OphthalmologyBMC series – open, inclusive and trusted201717:102

https://doi.org/10.1186/s12886-017-0498-z

Received: 26 February 2017

Accepted: 16 June 2017

Published: 26 June 2017

Abstract

Background

Age-related macular degeneration (AMD) is the leading cause of irreversible blindness in older individuals. Our study aims to identify the key genes and upstream regulators in AMD.

Methods

To screen pathogenic genes of AMD, an integrated analysis was performed by using the microarray datasets in AMD derived from the Gene Expression Omnibus (GEO) database. The functional annotation and potential pathways of differentially expressed genes (DEGs) were further discovered by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. We constructed the AMD-specific transcriptional regulatory network to find the crucial transcriptional factors (TFs) which target the DEGs in AMD. Quantitative real time polymerase chain reaction (qRT-PCR) was performed to verify the DEGs and TFs obtained by integrated analysis.

Results

From two GEO datasets obtained, we identified 1280 DEGs (730 up-regulated and 550 down-regulated genes) between AMD and normal control (NC). After KEGG analysis, steroid biosynthesis is a significantly enriched pathway for DEGs. The expression of 8 genes (TNC, GRP, TRAF6, ADAMTS5, GPX3, FAP, DHCR7 and FDFT1) was detected. Except for TNC and GPX3, the other 6 genes in qRT-PCR played the same pattern with that in our integrated analysis.

Conclusions

The dysregulation of these eight genes may involve with the process of AMD. Two crucial transcription factors (c-rel and myogenin) were concluded to play a role in AMD. Especially, myogenin was associated with AMD by regulating TNC, GRP and FAP. Our finding can contribute to developing new potential biomarkers, revealing the underlying pathogenesis, and further raising new therapeutic targets for AMD.

Keywords

Age-related macular degeneration Transcription factors Differentially expressed genes Integrated analysis

Background

Age-related macular degeneration (AMD) is the leading cause of irreversible blindness in older individuals [1, 2]. Pathological deposits (drusen) between the Bruch’s membrane and retinal pigment epithelium (RPE) are closely associated with early stage AMD [3]. Late AMD can be classified into the dry form and the wet form [4]. Dry AMD is mainly caused by atrophy of RPE. Wet AMD is characterized by choroidal neovascularization (CNV), subretinal haemorrhage, photoreceptor loss, retinal detachment and visual loss [5, 6].

Age is the major risk factor associated with AMD, but beyond that, vascular endothelial growth factor (VEGF), oxidative stress, inflammation, complement system are associated with risk of AMD [7]. Despite many efforts for AMD research in recent decades, effective treatments remain absent [8, 9].

With advances in various high-throughput technologies, amounts of key genes which are associated with the process of disease were identified by microarray [1012]. Using multiple microarray datasets, integrated analysis could identified differentially expressed genes (DEGs) with more accuracy, and increased the statistical power compared with a single microarray study. Exploring the upstream transcription factors (TFs) mediating abnormal gene expression in disease status can help to understand pathophysiological changes in complex diseases.

In this study, we performed integrated analysis of multiple microarray datasets to identify DEGs between AMD and normal control (NC) groups, which may be used as potential diagnostic biomarkers for AMD. In addition, we identified several crucial TFs which target the DEGs in AMD. The aim of this study is to better characterize the molecular events and pathways of AMD and to raise new strategies of treatment for AMD.

Methods

Eligible gene expression profiles of AMD

We selected gene expression datasets of AMD on the Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.nih.gov/geo) which is the largest database of high-throughput gene expression data. Selected datasets should be whole-genome expression profile data in AMD patients and NC groups. Datasets with drug stimulation or transfection were excluded.

Identify DEGs in AMD compared to NC

Background correction was performed for the raw data. Differential expression p-values were calculated by two-tailed Student’s t-test, we combined these p-values by the inverse normal method. Finally, the DEGs between AMD and NC groups were identified with criterion of P-value < 0.05.

Functional annotation

To reveal the function and the potential pathway of DEGs, Gene Ontology (GO) classification (molecular functions, biological processes and cellular component) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were performed by using the online software GeneCodis (http://genecodis.cnb.csic.es/analysis).FDR < 0.05 was defined as the criteria of statistical significance.

AMD-specific protein-protein interaction (PPI) network construction

To further research the biological functions of DEGs, the top 30 DEGs in AMD were used to construct the PPI network by using Biological General Repository for Interaction Datasets (BioGRID) (http://thebiogrid.org/) and Cytoscape. We used nodes and edges to represent the proteins and interactions between two proteins respectively.

Construction of AMD-specific transcriptional regulatory networks

TFs could regulate gene transcription by binding to specific DNA sequences generally located in the promoter region of genes. Based on the integrated analysis, the corresponding promoters of the top ten up-regulated or down-regulated DEGs were obtained by UCSC Genome Bioinformatics (http://genome.ucsc.edu).TheTFsinvolved in regulating these DEGs were derived from the match tools in TRANSFAC which is a database of transcription factor. The transcriptional regulatory network was constructed by Cytoscape software (http://www.cytoscape.org/).

qRT-PCR confirmation

Patients with wet AMD in evolutionary stage and sex- and age-matched normal controls were recruited in our study. The exclusion criteria for AMD patients were as follows: (1) Patients with other ocular organic disease; (2) Patients with glaucoma, polypoidal choroidal vasculopathy and other ocular lesions. The NCs with ocular lesions or other serious medical illness were excluded. The detailed characteristics of AMD patients were displayed in Table 1.
Table 1

Patient characteristics

 

Case 1

Case 2

Case 3

Age (years)

78

66

77

Type

Wet

Wet

Wet

Stage

Evolutionary stage

Evolutionary stage

Evolutionary stage

UCVA

0.6

0.5

0.3

BCVA

1.2

1.4

1.2

CFT(cm)

378

362

412

Fundus performance

Choroidal neovascularization

Choroidal neovascularization

Choroidal neovascularization

FFA

Fluorescein leakage

Fluorescein leakage

Fluorescein leakage

ICGA

Choroidal neovascularization

Choroidal neovascularization

Choroidal neovascularization

OCT

Macular edema and morphological deformation

Macular edema and morphological deformation

Macular edema and morphological deformation

UCVA Uncorrected visual acuity, BCVA best corrected visual acuity, CFT central fovea thickness, FFA Fundus fluorescein angiograph, ICGA Indo cyanine green angiography, OCT Optical Coherence mography

Blood samples from 3 ADM patients (Case 1–3) and 3 NCs were collected and frozen at 80 °C within 2 h after blood withdrawal. After thawing the frozen samples at room temperature, 1 ml of the sample was used to perform RNA isolation with Trizol reagent (Invitrogen, China) according to the manufacturer’s instructions. By using SuperScript® III Reverse Transcriptase (Invitrogen, China), we generated cDNA from 1μgextracted RNA. With Power SYBR® Green PCR Master Mix (Applied Biosystems, USA), we performed quantitative PCR in an ABI 7500 real-time PCR system. Relative gene expression was analyzed using 2-ΔΔCt method. The human 18srRNA was used as endogenous control for mRNA expression in analysis.

Results

Differential expression analysis of genes in AMD

Two RNA-seq datasets (GSE90889 and GSE67898) of AMD were excluded due to that the data of these two datasets were obtained after treatment with nicotinamide and TGFβ receptor kinase inhibitor, A-83-01, respectively. Two gene expression microarray datasets (GSE50195, GSE29801) without drug stimulation or transfection were enrolled in this study (Table 2). Compared with the normal controls, 1280 DEGs in AMD were obtained with P < 0.05, among which, 730 genes were up-regulated and 550 genes were down-regulated. The top 30 most significantly up or down-regulated genes were listed in Table 3.The heat map of top 100 most significantly up or down-regulated genes (P-value < 0.05) in AMD was showed in Fig. 1.
Table 2

List of mRNA study samples from GEO

GEO accession

Author

Platform

Samples

(N:P)

Year

GSE50195

Whitmore SS

GPL17629[HuEx-1_0-st] Affymetrix Human Exon 1.0 ST Array [AltAnalyzprobeset-to-Ensembl mapping]

9:7

2013

GSE29801

Radeke MJ

GPL4133 Agilent-014850 Whole Human Genome Microarray 4x44K G4112F (Feature Number version)

151:142

2012

Table 3

Top 30 most significantly up- or down-regulated genes in AMD

GeneId

Symbol

Combined.ES

P.Value

FDR

Regulation

3371

TNC

0.583092249

4.83E-07

0.002942856

Up

341

APOC1

0.56999013

8.86E-07

0.002942856

Up

54,742

LY6K

−0.568356126

9.64E-07

0.002942856

Down

10,647

SCGB1D2

0.564849096

1.05E-06

0.002942856

Up

84,624

FNDC1

0.542691118

2.64E-06

0.005012426

Up

2922

GRP

0.543254919

2.67E-06

0.005012426

Up

11,096

ADAMTS5

0.542022774

2.72E-06

0.005012426

Up

146,802

SLC47A2

0.5296511

4.48E-06

0.006043125

Up

55,652

SLC48A1

0.529070013

4.69E-06

0.006043125

Up

5803

PTPRZ1

0.520669191

6.49E-06

0.007684835

Up

2191

FAP

0.51940808

6.88E-06

0.007684835

Up

7189

TRAF6

−0.517257899

7.61E-06

0.007741325

Down

6720

SREBF1

0.514810439

8.35E-06

0.007779644

Up

7158

TP53BP1

−0.504884978

1.20E-05

0.010594686

Down

64,111

NPVF

0.50028869

1.45E-05

0.011148798

Up

23,336

SYNM

0.501243284

1.47E-05

0.011148798

Up

116,238

TLCD1

0.498263445

1.53E-05

0.011148798

Up

2878

GPX3

0.496829715

1.64E-05

0.011475202

Up

414,328

C9orf103

0.483245526

2.70E-05

0.017394853

Up

79,047

KCTD15

−0.48163493

2.90E-05

0.018035511

Down

199,974

CYP4Z1

−0.47837158

3.37E-05

0.019718857

Down

3475

IFRD1

−0.477678809

3.41E-05

0.019718857

Down

8334

HIST1H2AC

−0.467574449

4.95E-05

0.026761718

Down

84,269

CHCHD5

0.463229503

5.82E-05

0.029154389

Up

949

SCARB1

0.464706572

5.91E-05

0.029154389

Up

7101

NR2E1

0.46403246

6.20E-05

0.029680373

Up

5393

EXOSC9

0.458296775

6.96E-05

0.030872051

Up

10,219

KLRG1

−0.454787798

7.61E-05

0.031906258

Down

23,090

ZNF423

−0.451945937

8.69E-05

0.034135755

Down

57,097

PARP11

−0.450396437

8.96E-05

0.034135755

Down

Fig. 1

Heat-map image displaying top 100 genes that were significantly up-regulated or down-regulated (P-value < 0.05) in AMD compared to normal controls

Functional annotation

As the results of GO enrichment analysis (FDR < 0.05, Additional file 1: Table S1), the DEGs were significantly enriched in regulation of transcription, DNA-dependent (FDR = 4.74E-08), cellular nitrogen compound metabolic process (FDR = 1.25E-06), cholesterol biosynthetic process (FDR = 1.30E-06), membrane (FDR = 1.53E-31), cytoplasm (FDR = 2.17E-31), protein binding (FDR = 3.70E-30) and DNA binding (FDR = 4.60E-17). After the KEGG pathway enrichment analysis (FDR < 0.05, Table 4), several pathways were significantly enriched, including valine, leucine, isoleucine degradation (FDR = 4.19E-08), steroid biosynthesis (FDR = 1.74E-07) and WNT signaling pathway (FDR = 1.02E-05). 8 DEGs (DHCR7, FDFT1, CYP27B1, TM7SF2, SQLE andSOAT2) were significantly enriched in the steroid biosynthesis which are associated with AMD (Table 5 and Fig. 2).
Table 4

The top 15 most significantly enriched pathways in AMD

Items

Items_Details

Count

P_value

FDR

Genes

Kegg:00280

Valine, leucine and isoleucine degradation

12

4.19E-08

8.51E-06

ACAT2,ALDH3A2,BCAT1,HMGCS1,BCKDHB,BCKDHA,ALDH9A1,HMGCL,ACADSB,IL4I1,PCCB,ALDH2

Kegg:00100

Steroid biosynthesis

8

1.74E-07

1.77E-05

DHCR7,FDFT1,CYP27B1,TM7SF2,SQLE,SOAT2,DHCR24,EBP

Kegg:04310

Wnt signaling pathway

18

1.02E-05

0.000516

LRP5,LEF1,NLK,CAMK2B,PRKCB,NFATC3,CSNK1E,PRKCG,FZD5,PPP2R5C,SFRP2,FZD9,APC,CTNNBIP1,ROCK1,DKK1,APC2,MMP7

Kegg:00900

Terpenoid backbone biosynthesis

6

9.28E-06

0.000628

ACAT2,HMGCS1,MVK,GGPS1,IDI1,FDPS

Kegg:05322

Systemic lupus erythematosus

13

1.99E-05

0.00081

HIST1H2BM,HIST1H2BJ,HIST1H2BD,C5,HIST1H2BL,HIST1H2BH,CTSG,CD86,HIST1H2AC,HIST3H2A,HIST1H2AE,HIST1H2BB,HIST1H2BO

Kegg:04080

Neuroactive ligand-receptor interaction

24

6.26E-05

0.002118

P2RY2,GABRB1,CHRNA3,ADRB1,GCGR,GABRE,TBXA2R,GRM7,BDKRB2,CALCRL,NMBR,NPY1R,HRH3,SSTR1,GRM5,CTSG,CHRNA10,MC3R,GRIK3,GABRP,RXFP1,TACR3,CRHR1,ADORA1

Kegg:04020

Calcium signaling pathway

18

8.83E-05

0.002242

ADRB1,CAMK2B,TBXA2R,BDKRB2,PRKCB,MYLK,PRKCG,GRM5,ERBB4,ADCY7,ITPKA,PLCZ1,SLC25A4,NOS3,TACR3,PHKG1,RYR1,PDE1A

Kegg:05200

Pathways in cancer

27

7.78E-05

0.002255

FH,LEF1,RARB,PIAS2,FGF1,PRKCB,RUNX1T1,PRKCG,NRAS,TGFB3,CDKN2B,FZD5,TGFB2,EGF,TRAF6,FZD9,APC,GSTP1,HSP90B1,HDAC1,PAX8,MAP2K1,PPARG,STAT5B,CBL,APC2,RALGDS

Kegg:00330

Arginine and proline metabolism

9

0.000145

0.003266

ALDH3A2,ADC,ALDH9A1,ARG1,NOS3,GOT2,AMD1,ALDH4A1,ALDH2

Kegg:04350

TGF-beta signaling pathway

11

0.000207

0.004199

BMP7,COMP,BMPR1B,TGFB3,ID1,CDKN2B,SMURF2,TGFB2,THBS4,ROCK1,ZFYVE9

Kegg:04012

ErbB signaling pathway

11

0.00035

0.006461

CAMK2B,PRKCB,PRKCG,NRAS,EREG,ERBB4,EGF,PAK3,MAP2K1,STAT5B,CBL

Kegg:00310

Lysine degradation

8

0.00039

0.006594

ACAT2,ALDH3A2,PIPOX,WHSC1L1,ALDH9A1,SETD8,GLT25D2,ALDH2

Kegg:04146

Peroxisome

10

0.000466

0.00727

PAOX,PIPOX,SLC27A2,MVK,AGXT,PEX7,HMGCL,NUDT12,PEX10,AMACR

Kegg:04142

Lysosome

13

0.000534

0.007738

MCOLN1,NAGPA,SCARB2,CTSF,CTSL2,ARSA,CTSG,LAMP1,ATP6AP1,SORT1,LAPTM4B,ABCB9,AP3S1

Kegg:01040

Biosynthesis of unsaturated fatty acids

5

0.000836

0.009981

PTPLB,FADS2,SCD,ACOT1,FADS1

Table 5

Differentially expressed genes enriched in steroid biosynthesis

Symbol

Gene annotation

Enzyme

Up down

DHCR7

delta 7-dehydrocholesterol reductase

EC:1.3.1.21

Up

FDFT1

Farnesyl-DiphosphateFarnesyltransferase 1.

EC:2.5.1.21

Up

CYP27B1

cytochrome P450 family 27 subfamily B member 1

EC:1.14.13.13

Up

TM7SF2

transmembrane 7 superfamily member 2

EC:1.3.1.70

Up

SQLE

squaleneepoxidase

EC:1.14.14.17

Up

SOAT2

sterol O-acyltransferase 2

EC:2.3.1.26

Down

Fig. 2

Steroid biosynthesis that enriched in differentially expressed genes of AMD

The red rectangles were represented the elements which regulated by the differentially expressed genes that enriched in steroid biosynthesis

AMD-specific protein-protein interaction (PPI) network

The PPI network of top 30 DEGs in AMD was consisted of 559 nodes and 583 edges (Fig. 3). The hub proteins were TRAF6 (degree = 225) and TP53BP1 (degree = 108).
Fig. 3

The PPI network of top 30 significantly DEGs in AMD. The blue ellipses were represented the proteins encoded by down-regulated DEGs and the red ellipses were represented the proteins encoded by up-regulated DEGs. Among which, ellipses with red border were derived from the top 30 DEGs in AMD. The green ellipses were represented other proteins

Transcriptional regulatory network

Based on TRANSFAC, 43 TFs targeting 20 DEGs (top 10 up-regulated or down-regulated genes) were identified. AMD-specific transcriptional regulatory network was constructed, which consisted of 119 TF-target interactions (Fig. 4). Among of them, c-rel, myogenin, CDP and CCAAT were top 4 TFs covering the most downstream DEGs.
Fig. 4

The AMD-specific transcriptional regulatory network. Red- and blue-color nodes represent the up- and down-regulated DEGs targeted by TFs, respectively. Green nodes denote the TFs which predicted to interact with the corresponding DEGs

qRT-PCR confirmation

To verify the expression of integrated analysis, the expression of eight selected genes including Tenascin-C (TNC), fibroblast activation protein alpha (FAP), glutathione peroxidase 3 (GPX3), gastrin-releasing peptide (GRP), ADAM metallopeptidase with thrombospondin type 1 motif 5 (ADAMTS5), TNF receptor associated factor 6 (TRAF6), DHCR7 (7-dehydrocholesterol reductase) and FDFT1 (farnesyl-diphosphate farnesyl transferase 1) were selected to tested by qRT-PCR. Since steroid biosynthesis is a significantly enriched pathway in AMD (FDR = 1.74E-07), the expression of two DEGs (DHCR7 and FDFT1) enriched in steroid biosynthesis were selected to tested by qRT-PCR. The other 6 selected DEGs for qRT-PCR confirmation were derived from the top 30 DEGs. Moreover, TNC was the most significantly DEG in AMD and TRAF6 (degree = 225) was the hub gene based on the AMD-specific PPT network. Based on previous studies, TRAF6 and GPX3 were two AMD-related DEGs [13, 14], two DEGs (FAP and GRP) were choroidal neovascularization-related genes [15, 16] and ADAMTS5 was a retinal pigment epithelium-related DEG [17] which may be involved with AMD. Hence, these six DEGs were selected to be tested by qRT-PCR as well. Except for TNC and GPX3, the expression of other 6 selected genes in qRT-PCR was consistent with our integrated analysis (Fig. 5).
Fig. 5

qRT-PCR results of differentially expressed genes in AMD

Discussion

To better reveal the pathogenesis and find more effective treatment for AMD, we performed this integrated analysis between AMD patients and NC groups. 1280 genes across the studies were consistently differentially expressed in AMD (730up-regulated and 550 down-regulated) with P<0.05. Among the top 30 up-regulated or down-regulated DEGs, we selected TNC, FAP, GPX3, GRP, ADAMTS5 and TRAF6 to verify their expression in AMD. Except for TNC and GPX3, the other 4 genes in qRT-PCR played the same pattern with that in our integrated analysis, adding evidences to the reliability of results in the integrated analysis.

As one of extracellular matrix glycol proteins produced by culture fibroblasts, TNC was reported to be up-regulated in AMD [5, 18]. According to our integrated analysis, TNC was increased as well. VEGF is a major pathogenic factor for wet AMD [18] which can promote angiogenesis, vascular permeability and induce macular eduma (ME) [19]. Animal studies have demonstrated that the up-regulation of VEGF is a cause of CNV in the RPE [20]. Since TNC can up-regulate the expression of VEGF, so we concluded that increased TNC may play a role in the process of AMD by regulating CNV. In addition, AMD is associated with degeneration of RPE [21], increased TNC may involve with AMD by repressing the adhesion and migration of RPE. However, the expression of TNC was down-regulated in the blood of AMD patients, larger scale of studies were needed.

FAP is known to be involved in the control of fibroblast growth, which was speculated to be a pioneer for the growth of corneal neovascularization [15]. The up-regulation of FAP detected in this study may be closely associated with AMD by influencing the CNV as well.

To our best knowledge, none of previous studies have reported the association between AMD and GRP. GRP was reported to be involved in the induction of angiogenesis during neuroblastoma progression [16], which showed up-regulation in AMD in our study. Hence, we suspected that increased GRP may play an important role in CNV of AMD.

ADAMTS5 (also known as ADAMTS11) is a member of ADAMTS family. The ADAMTS family has been reported to involve with embryonic development, angiogenesis and cartilage degradation [17]. Bevitt et al. reported the expression of ADAMTS genes in an ocular cell type, ARPE-19 and their regulation by TNF-α. TNF-α is known to play a role in the development of retinal neovascularisation and RPE migration in AMD [17]. Hence, ADAMTS genes may involve with AMD and other retinal pathologies by regulating the proteolytic modification of the retinal extracellular matrix (REM) [17]. According to our study, up-regulated ADAMTS5 was detected in AMD, which supported this hypothesis.

TRAF6 has been reported to involve with the process of AMD by regulating signal transduction pathway [13]. In our study, the expression of TRAF6 was down-regulated. The precise role of TRAF6 in AMD was not clear.

The expression of GPX3 has been reported to be up-regulated in the blood of late AMD patients which was presumably due to oxidative stress [14]. In our integrated analysis, the expression of GPX3 was increased. However, down-regulation of GPX3 was detected in the blood of AMD patients and further research was needed.

After function annotation analysis, we found that steroid biosynthesis was a significantly enriched pathway for DEGs. And previous studies have reported that steroids may play a role for the treatment of AMD [22, 23], while mechanism underlying was not clear [24]. As one of steroids, high level of cholesterol was suspected to increase the risk for AMD by promoting the formation of drusen [2527], since drusen is a major hallmark of AMD which contains lipid such as cholesterol [2830]. Both DHCR7 and FDFT1 are associated with cholesterol biosynthesis, which were up-regulated in AMD in our study. DHCR7 catalyzes the reduction of DHC7 to cholesterol and deficiency of DHCR7 results in decreased cholesterol and increased DHC7 levels in serum and tissue [31]. FDFT1 (also called squalene synthase) catalyzes the biosynthesis of squalene, a key cholesterol precursor [26]. So we concluded that both increased DHCR7 and FDFT1 may induce AMD by elevating the expression of cholesterol and formation of drusen.

According to the AMD-specific transcriptional regulatory networks, we identified myogenin was a common mediator of FAP, TNC and GRP, so we concluded that myogenin may be involved in AMD by regulating these target genes. As a member of Rel family, c-rel was reported to be an important mediator of various cytokine stimuli such as TNF-α [17] which suggested that c-rel may involve with the pathological process of AMD.

Conclusions

In our study, eight DEGs (TNC, FAP, GPX3, GRP, ADAMTS5, TRAF6, DHCR7 and FDFT1) were found to involve with the process of AMD by promoting the formation of drusen, regulating the process of CNV, REM and signal transduction pathway or against with oxidative stress. As a common mediator of TNC, FAP and GRP, myogenin may regulate the process of AMD and c-rel may involve with AMD by regulating TNF-α. Our finding could contribute to understanding the mechanism of AMD in molecular levels, providing clues to potential biomarkers and new therapeutic targets for the disease. The limitation of our study was that the number of samples for qRT-PCR confirmation was small. Hence, studies with larger sample size need to be performed to confirm this conclusion.

Abbreviations

ADAMTS5: 

ADAM metallopeptidase with thrombospondin type 1 motif 5

AMD: 

Age-related macular degeneration

CNV: 

choroidal neovascularization

DEGs: 

differentially expressed genes

DHCR7: 

7-dehydrocholesterol reductase

FAP: 

fibroblast activation protein alpha

FDFT: 

farnesyl-diphosphate farnesyl transferase 1.

GEO: 

Gene Expression Omnibus database

GO: 

Gene Ontology

GPX3: 

glutathione peroxidase 3

GRP: 

gastrin-releasing peptide

KEGG: 

Kyoto Encyclopedia of Genes and Genomes

ME: 

macular eduma

RPE: 

retinal pigment epithelium

TFs: 

transcription factors

TNC: 

Tenascin-C

TRAF6: 

TNF receptor associated factor 6

VEGF: 

vascular endothelial growth factor

Declarations

Acknowledgements

None.

Funding

Not applicable.

Availability of data and materials

The datasets generated during the current study are available from the corresponding author on reasonable request.

Authors’ contributions

BZ and JX contributed to the conception of the study. BZ, MYW and JX performed the data analyses.ML and YHY contributed the materials and performed the q-RT-PCR. BZ and MYW contributed significantly in writing the manuscript. All authors read and approved the final manuscript

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

All the participants’ informed consents were obtained and the research protocols were approved by the ethical committee of Affiliated hospital of Taishan medical college (No. 2016–056).

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Ophthlmology, Affiliated Hospital of Taishan Medical College
(2)
Department of Ophthlmology, Peoples Hospital of Feicheng
(3)
Department of Ophthlmology, The First Peoples Hospital of Jining
(4)
Department of Ophthlmology, The Third Peoples Hospital of Xintai

References

  1. Rosenfeld PJ, Brown DM, Heier JS, Boyer DS, Kaiser PK, Chung CY, et al. Ranibizumab for neovascular age-related macular degeneration. N Engl J Med. 2006;355(14):1419–31.View ArticlePubMedGoogle Scholar
  2. Bressler NM. Age-related macular degeneration is the leading cause of blindness. Jama J Am Med Asso. 2004;291(15):1900–1.View ArticleGoogle Scholar
  3. Hanus J, Anderson C, Wang S. RPE necroptosis in response to oxidative stress and in AMD. Ageing Res Rev. 2015;24:286–98.View ArticlePubMedPubMed CentralGoogle Scholar
  4. Hau VS, London N, Dalton M. The treatment paradigm for the implantable miniature telescope. Ophthalmol Ther. 2016:1–10.Google Scholar
  5. Afshari FT, Kwok JC, Andrews MR, Blits B, Martin KR, Faissner A, et al. Integrin activation or alpha 9 expression allows retinal pigmented epithelial cell adhesion on Bruch's membrane in wet age-related macular degeneration. Brain. 2010;133(Pt 2):448–64.View ArticlePubMedGoogle Scholar
  6. Lim LS, Mitchell P, Seddon JM, Holz FG, Wong TY. Age-related macular degeneration. Lancet. 2012;379(9827):1728–38.View ArticlePubMedGoogle Scholar
  7. Cai X, Mcginnis JF. Oxidative stress: the achilles' heel of neurodegenerative diseases of the retina. Front Biosci. 2012;17(5):1976–95.View ArticleGoogle Scholar
  8. Augustin AJ, Offermann I. Emerging drugs for age-related macular degeneration. Expert Opin on Emerg Dr. 2006;11(4):725–40.View ArticleGoogle Scholar
  9. Lin CH, Li CH, Liao PL, Tse LS, Huang WK, Cheng HW, et al. Silibinin inhibits VEGF secretion and age-related macular degeneration in a hypoxia-dependent manner through the PI-3 kinase/Akt/mTOR pathway. Br J Pharmacol. 2013;168(4):920–31.View ArticlePubMedPubMed CentralGoogle Scholar
  10. Du JY, Yang H, Tian DR, Wang QM, He L. Identification and functional analysis of differentially expressed genes related to obesity using DNA microarray. Genet Mol Res. 2014;13(13):64–72.View ArticlePubMedGoogle Scholar
  11. Huang YD, Shan W, Zeng L, Wu Y. Screening of differentially expressed genes related to bladder cancer and functional analysis with DNA microarray. Asian Pac J Cancer P. 2013;14(8):4553–7.View ArticleGoogle Scholar
  12. Chen Y, Shi JX, Pan XF, Feng J, Zhao H. DNA microarray-based screening of differentially expressed genes related to acute lung injury and functional analysis. Eur Rev Med Pharmacol Sci. 2013;17(8):1044–50.PubMedGoogle Scholar
  13. Hao Y, Weiss GM: Gene Selection from Microarray Data for Age-related Macular Degeneration by Data Mining.Google Scholar
  14. Delcourt C, Cristol JP, Léger CL, Descomps B, Papoz L. Associations of antioxidant enzymes with cataract and age-related macular degeneration. The POLA study. Pathologies Oculaires Liees a l'Age. Ophthalmology. 1999;106(2):215–22.View ArticlePubMedGoogle Scholar
  15. Shi W, Liu J, Li M, Gao H, Wang T. Expression of MMP, HPSE, and FAP in stroma promoted corneal neovascularization induced by different etiological factors. Curr Eye Res. 2010;35(11):967–77.View ArticlePubMedGoogle Scholar
  16. Kang JH, Ishola TA, Baregamian N, Mourot JM, Rychahou PG, Evers BM, et al. Bombesin induces angiogenesis and neuroblastoma growth. Cancer Lett. 2007;253(2):273–81.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Bevitt DJ, Mohamed J, Catterall JB, Li Z, Arris CE, Hiscott P, et al. Expression of ADAMTS metalloproteinases in the retinal pigment epithelium derived cell line ARPE-19: transcriptional regulation by TNFalpha. Biochim Biophys Acta. 2003;1626(1–3):83–91.View ArticlePubMedGoogle Scholar
  18. Ishikawa K, Kannan R, Hinton DR. Molecular mechanisms of subretinal fibrosis in age-related macular degeneration. Exp Eye Res. 2015;142Google Scholar
  19. Wong TY. Age-related macular degeneration: why should stroke physicians care? Stroke. 2010;41(4):575–6.View ArticlePubMedGoogle Scholar
  20. Bressler SB. Introduction: understanding the role of angiogenesis and Antiangiogenic agents in age-related macular degeneration. Ophthalmology. 2009;116(10 Suppl):S1–7.View ArticlePubMedGoogle Scholar
  21. Heller JP, Martin KR. Enhancing RPE cell-based therapy outcomes for AMD: the role of Bruch's membrane. Transl Vis Sci Technol. 2014;3(4):11–11.Google Scholar
  22. Emerson MV, Lauer DAK. Emerging therapies for the treatment of Neovascular age-related macular degeneration and diabetic macular edema. BioDrugs. 2007;21(4):245–57.View ArticlePubMedGoogle Scholar
  23. Vanderbeek BL, Bonaffini SG, Ma L: The Association between Intravitreal Steroids and Post-Injection Endophthalmitis Rates. Ophthalmology 2015, 122(11):2311–2315.e2311.Google Scholar
  24. Geltzer A, Turalba A, Vedula SS. Surgical implantation of steroids with antiangiogenic characteristics for treating neovascular age-related macular degeneration. Cochrane Db Syst Rev. 2013;1 (4):92 –92.Google Scholar
  25. Green WR. Histopathology of age-related macular degeneration. Mol Vis. 1999;5(5):384.Google Scholar
  26. Rudolf M, Curcio CA. Esterified cholesterol is highly localized to Bruch's membrane, as revealed by lipid histochemistry in wholemounts of human choroid. J Histochem Cytochem. 2009;57(8):731–9.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Mares-Perlman JA, Brady WE, Klein R, VandenLangenberg GM, Klein BE, Palta M. Dietary fat and age-related maculopathy. Arch Ophthalmol. 1995;113(6):743–8.View ArticlePubMedGoogle Scholar
  28. SHSFRACOFRCS MD. DRUSEN and their relationship to senile macular degeneration. Clin Experiment Ophthalmol. 1980;8(2):117–30.Google Scholar
  29. Green WR. Enger C: Age-related macular degeneration histopathologic studies. The 1992 Lorenz E. Zimmerman Lecture. Retina. 1993;100(10):1519–35.Google Scholar
  30. Curcio CA, Presley JB, Millican CL, Medeiros NE. Basal deposits and drusen in eyes with age-related maculopathy: evidence for solid lipid particles. Exp Eye Res. 2005;80(6):761–75.View ArticlePubMedGoogle Scholar
  31. Tierney E, Conley SK, Goodwin H, Porter FD. Analysis of short-term behavioral effects of dietary cholesterol supplementation in smith-Lemli-Opitz syndrome. Am J Med Genet A. 2010;152A(1):91–5.View ArticlePubMedPubMed CentralGoogle Scholar

Copyright

© The Author(s). 2017

Advertisement