Skip to main content
PMC home page
International Journal of Medical Sciences logoLink to International Journal of Medical Sciences
. 2020 Jan 16;17(3):292–301. doi: 10.7150/ijms.37804

Altered Long Non-coding RNAs Involved in Immunological Regulation and Associated with Choroidal Neovascularization in Mice

Liwei Zhang 1,2, Huilan Zeng 1,2, Jiang-Hui Wang 3,4, Han Zhao 1,2, Boxiang Zhang 1,2, Jingling Zou 1,2, Shigeo Yoshida 5, Yedi Zhou 1,2,✉
PMCID: PMC7053346  PMID: 32132863

Abstract

Choroidal neovascularization (CNV) is a severe complication of the wet form of age-related macular degeneration (AMD). Long non-coding RNAs (lncRNAs) have been implicated in the pathogenesis of different ocular neovascular diseases. To identify the function and therapeutic potential of lncRNAs in CNV, we assessed lncRNAs and mRNA expression profile in a mouse model of laser-induced CNV by microarray analysis. The results of altered lncRNAs were validated by qRT-PCR. Bioinformatics analyses, including Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, were performed to clarify the potential biological functions and signaling pathways with which altered genes are most closely related. Moreover, to identify the interaction of lncRNAs and mRNAs, we constructed a coding-non-coding gene co-expression (CNC) network. By microarray analysis, we identified 716 altered lncRNAs and 821 altered mRNAs in CNV mice compared to controls. A CNC network profile based on 7 validated altered lncRNAs (uc009ewo.1, AK148935, uc029sdr.1, ENSMUST00000132340, AK030988, uc007mds.1, ENSMUST00000180519) as well as 282 interacted and altered mRNAs, and were connected by 713 edges. GO and KEGG analyses suggested that altered mRNAs, as well as those lncRNA-interacted mRNAs were enriched in immune system process and chemokine signaling pathway. Thus, lncRNAs are significantly altered in this mouse model of CNV and are involved in immunological regulation, suggesting that lncRNAs may play a critical role in the pathogenesis of CNV. Thus, dysregulated lncRNAs and their target genes might be promising therapeutic targets to suppress CNV in AMD.

Keywords: long non-coding RNA, choroidal neovascularization, angiogenesis, age-related macular degeneration, immunological regulation

Introduction

Age-related macular degeneration (AMD) is one of the major causes of visual impairments and blindness in developed regions 1. Choroidal neovascularization (CNV) is a severe complication of the neovascular or wet AMD 2. It is well known that vascular endothelial growth factor (VEGF) plays key roles in the pathogenesis of neovascular AMD 3, 4. Numerous studies have demonstrated that anti-VEGF therapy is effective and safe for patients with CNV due to AMD for which it has been widely used in clinics 5, 6. However, recent studies suggest that some VEGF neutralizing proteins are not effective in some patients 7, 8. There is increasing evidence that a number of factors may be responsible for non-responses including tolerance or tachyphylaxis (a term refers to the sudden decrease in response to a drug after its administration) of anti-VEGF therapies 9, 10. Therefore, there is an urgent need for the mechanistic understanding of CNV, to explore novel therapeutic targets for early intervention of CNV, which would provide potential alternatives to anti-VEGF therapies.

Long non-coding RNAs (lncRNAs) is a subtype of non-coding RNAs with the transcripts of more than 200 nucleotides 11. Although lncRNAs have no protein-coding capacity, they may regulate physiological functions as well as pathological processes in many diseases 12-15. In particular, the activation of lncRNAs might regulate the expression of protein-coding genes through sophisticated mechanisms in ocular disorders 16-19. It has been reported that lncRNAs have differential expression profiles in a mouse model of ischemia-induced retinal neovascularization20. The role of lncRNAs in AMD has also been investigated in a number of studies. A study showed that lncRNAs are differentially expressed in RPE/choroid samples in patients with early AMD compared to controls and might be involved in important regulative functions 21. Among altered lncRNAs in patients with early AMD, RP11-234O6.2 has been demonstrated as having protective effects in the aging RPE model 21. A few in vitro studies investigated the roles of several certain lncRNAs (such as ZNF503-AS1 and BANCR) in retinal pigment epithelium (RPE) cells 22, 23. Nevertheless, it still remains unclear that the expression profiles, targets, and effects of lncRNAs and their contribution to the pathogenesis of CNV as well as neovascular AMD.

Laser-induced CNV is a well-established mouse model to investigate the pathogenesis of CNV and neovascular AMD 24. Previous studies using the CNV mouse model to examine the role of VEGF and many other molecules 25-27. We recently identified altered microRNAs and tRNA-derived small RNAs in the laser-induced CNV model 28.

In the present study, microarray analyses were applied to clarify the expression profiles of lncRNAs and mRNAs in the CNV mouse model. In addition, bioinformatics analyses, including gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, were conducted to explore the related biological functions and potential signaling pathways of altered genes. Moreover, a coding-non-coding gene co-expression (CNC) network was established to investigate the correlation of differentially expressed lncRNAs and mRNAs, and to further predict the possible roles of differentially expressed lncRNAs in CNV.

Materials and Methods

Animals

C57BL/6J mice (male, aged 7-8 weeks old) (SJA Laboratory Animal Co., Ltd., Hunan, China) were used for experiments in accordance to the ARVO Statement for the Use of Animals in Ophthalmic and Vision Research. The procedures were proved by the Institutional Animal Care and Use Committee of Central South University.

Laser-induced CNV mouse model

As previously described, the CNV mouse model was induced by laser photocoagulation 27-29 with some modification. In brief, the photocoagulation was performed with 25 spots in each eye using a 532-nm diode laser (100 mW, 0.1 s duration, 50 μm). On day 7 after laser photocoagulation treatment, the eyes were enucleated. Age-matched C57BL/6J mice without the treatment were used as controls.

LncRNA and mRNA microarray analysis

Tissues of RPE-choroid-sclera complexes were collected from 4 eyes to be pooled as one sample, and a total of 6 samples (3 CNV samples and 3 control samples) were analyzed. Total RNA was isolated by using Trizol RNA extraction kit (Invitrogen, Carlsbad, CA, USA). The microarray analysis was performed by mouse lncRNA Microarray V3.0 (Arraystar, Rockville, MD, USA) as described 20. The raw data of microarray has been uploaded to the Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo/) for public access with the accession number GSE129743. Altered lncRNAs and mRNAs expression were identified by a Volcano Plot filtering [fold change (FC) ≥1.5] and P<0.05.

Quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR)

The validation of lncRNA and mRNA microarray results was further processed by qRT-PCR 30. RNA was transcribed into cDNA utilizing SuperScript Ⅲ Reverse Transcriptase (Invitrogen) following instructions of the manufacturer by Gene Amp PCR System 9700 (Applied Biosystems, Foster City, CA, USA). The qRT-PCR was performed by the ViiA 7 RT PCR System (Applied Biosystems) with the 2 × PCR Master Mix (Arraystar). LncRNAs' relative expression levels were normalized with GAPDH 31, 32. Primer used for qRT-PCR are shown in Table 1. P<0.05 was considered statistically significant differences.

Table 1.

Sequence of the primers for lncRNAs.

Forward and reverse primer Tm (℃) Product length (bp)
GAPDH (MOUSE) F:5' CACTGAGCAAGAGAGGCCCTAT3'
R:5' GCAGCGAACTTTATTGATGGTATT3'		 60 144
uc009ewo.1 F:5' GGTAAGTCCCCACTATCATTCTC 3'
R:5' AAACACCTTTGCCCTCCTC 3'		 60 184
AK148935 F:5' TTTGTTGGCTGCCTTCTTTC 3'
R:5' TGACTAACCTGTGAGTGTCCCTA 3'		 60 72
uc029sdr.1 F:5' CTCTTGATGTATCCCAGGGTG 3'
R:5' CAGGAAACACAATGCTACTCTC 3'		 60 210
ENSMUST00000132340 F:5' CGGAATGTTACTGCCCATAG 3'
R:5' TGGTCATTAGGATAAGTTCTGG 3'		 60 254
AK030988 F:5' TGGGACCTAAGGATGGAAGA 3'
R:5' AGACCAGAGCCAATGTGAGC 3'		 60 167
uc007mds.1 F:5' TTCAGCCCCGACGAGCAC 3'
R:5' GAAAGGGTTTGTTCGGCTCAC 3'		 60 187
ENSMUST00000180519 F:5' TCTGGTTCTCGGCTATGTGC 3'
R:5' AACAACAGCACAAACAGGGT 3'		 60 129
Open in a new tab

Tm: temperature. bp: base pair.

GO and KEGG pathway analyses

GO analysis (http://www.geneontology.org/) and KEGG pathway analysis (http://www.genome.jp/kegg/) were used to investigate the potential biological functions and significant pathways of the altered mRNAs or CNC-associated mRNAs.

Construction of lncRNA-mRNA co-expression network

According to validated altered lncRNAs and their related mRNAs, we established a CNC network profile to explore the relationship between lncRNAs and mRNAs. We selected Pearson correlation coefficients (PCCs)≥ 0.97 to construct the network utilizing Cytoscape version 2.8.1 software (The Cytoscape Consortium, San Diego, CA, USA) based on the PCCs of correlation analysis of lncRNA and mRNA.

Results

Expression profiles of lncRNA and mRNA in CNV mice

To assess the expressions of lncRNAs in CNV compared to control mice, microarray analysis was conducted in the collected RPE-choroid-sclera complexes. Our results showed that 716 lncRNAs were significantly altered in CNV mice compared to control mice: 442 were upregulated and 274 were downregulated (FC≥1.5, P<0.05). The top 20 up- and downregulated lncRNAs are listed in Table 2-3. Among the differentially expressed lncRNA transcripts, AK036888 tops among upregulated lncRNAs with an FC of 13.38, whereas ENSMUST00000135495 tops among downregulated lncRNAs with an FC of 7.47. The clustering analysis demonstrated the relevance of lncRNA expression patterns in CNV and control samples by showing the top 20 up- and downregulated lncRNAs (Fig. 1A). The variation of lncRNA expression between CNV and control mice is shown in a volcano plot (Fig. 1C) and a scatter plot (Fig. 1E).

Table 2.

Top 20 significantly up-regulated lncRNAs.

Sequence Name P-value FDR Fold Change Regulation Strand Relationship CNV 1 CNV 2 CNV 3 Control 1 Control 2 Control 3
AK036888 0.000171 0.150561 13.378940 up - intergenic 6.585418 6.480785 6.071276 3.077727 2.512265 2.321811
uc007pgi.1 0.014847 0.390976 9.578848 up - intergenic 5.211190 7.107776 4.430212 2.325983 2.321827 2.321811
AK078702 0.000536 0.150561 7.117427 up - intergenic 6.359641 6.132188 6.161450 3.862077 3.371296 2.925838
AK035526 0.005083 0.298875 6.657825 up + natural antisense 5.724644 7.089529 7.076339 4.231499 3.878298 3.575563
uc009ewo.1 0.000984 0.182037 5.719433 up - intron sense-overlapping 8.163315 7.829017 7.371524 5.300458 5.566779 4.949003
AK036625 0.005126 0.298875 5.353162 up - intergenic 5.726749 5.680111 5.143081 2.325983 3.345529 3.617254
ENSMUST00000148005 0.001561 0.218932 5.264145 up + exon sense-overlapping 6.509248 6.302205 5.601678 3.998450 3.479648 3.746437
ENSMUST00000153541 0.000164 0.150561 4.957192 up + exon sense-overlapping 11.456490 11.349358 10.999685 9.005207 9.098501 8.773256
AK008836 0.004393 0.293747 4.888444 up - intronic antisense 7.371524 6.772026 6.602773 4.573347 5.202004 4.102845
ENSMUST00000146254 0.000028 0.106609 4.640225 up - exon sense-overlapping 7.526886 7.218386 7.363228 5.129217 5.254380 5.082319
NR_026843 0.001265 0.203615 4.591574 up - natural antisense 6.264922 6.398417 5.991045 4.478630 3.930072 3.648715
ENSMUST00000122340 0.001105 0.191468 4.442445 up - intronic antisense 8.984519 8.526921 8.527271 6.376287 6.936308 6.272055
AK082144 0.000016 0.106609 4.424734 up - intergenic 6.554079 6.639836 6.746826 4.380675 4.513834 4.609461
AK048117 0.001609 0.221747 4.382340 up - intergenic 9.343423 9.431755 9.431267 7.627238 6.720520 7.463583
ENSMUST00000143423 0.000941 0.181475 4.307678 up + exon sense-overlapping 4.561347 5.009892 4.564078 2.963872 2.321827 2.528887
AK148935 0.000638 0.156222 4.263167 up - intergenic 8.259856 8.525405 8.333067 6.682964 6.086507 6.073080
AK047865 0.001065 0.191468 4.256438 up - intergenic 9.039629 8.963352 9.098866 7.275191 6.469285 7.088430
ENSMUST00000141975 0.000283 0.150561 4.232972 up - exon sense-overlapping 6.736901 6.384502 6.153523 4.416513 4.338800 4.274602
uc029sdr.1 0.000052 0.120829 4.185189 up + intergenic 10.768113 10.641598 10.589025 8.507404 8.798939 8.496515
NR_045384 0.010196 0.362008 4.172997 up + intergenic 5.724329 5.884554 5.893274 4.532355 3.801238 2.985313
Open in a new tab

P-values were calculated using unpaired t-test. FDR: false discovery rate. Fold change: the absolute ratio (no log scale) of average normalized intensities between CNV group and control group. CNV 1 to 3 and Control 1 to 3: each sample's normalized intensity (log2 scale). Hereinafter the same.

Table 3.

Top 20 significantly down-regulated lncRNAs.

Sequence Name P-value FDR Fold Change Regulation Strand Relationship CNV1 CNV2 CNV3 Control1 Control2 Control3
ENSMUST00000135495 0.044869 0.516189 7.465422 down - exon sense-overlapping 4.264994 6.259270 3.847529 8.927113 6.595256 7.550095
ENSMUST00000151832 0.042431 0.506906 3.682991 down + exon sense-overlapping 3.973213 5.239059 4.472576 5.519285 6.476424 7.331772
AK079094 0.000472 0.150561 2.875171 down - natural antisense 4.097720 3.991172 3.967711 5.716774 5.645347 5.265424
ENSMUST00000171338 0.003991 0.284707 2.831007 down - exon sense-overlapping 4.347292 4.591844 4.052868 6.017756 5.435850 6.042344
ENSMUST00000147716 0.002243 0.244886 2.805529 down - intergenic 7.029440 6.772935 7.329016 8.414517 8.813207 8.368486
AK030988 0.004724 0.293747 2.720887 down - intergenic 7.828629 8.353325 8.177764 9.318910 9.964228 9.408810
ENSMUST00000148202 0.005363 0.303718 2.572706 down - intergenic 3.028002 2.478576 3.041757 3.901149 4.463217 4.273829
AK005624 0.012396 0.380110 2.559854 down + bidirectional 4.166821 3.256077 3.443728 5.159213 5.085467 4.690131
AK009740 0.014086 0.383884 2.485166 down - bidirectional 2.530040 3.314212 2.545198 4.152919 4.397770 3.778787
ENSMUST00000155217 0.000964 0.182037 2.460863 down - intronic antisense 7.949919 8.236430 8.067902 9.477743 9.536148 9.137853
NR_037995 0.007062 0.335485 2.453643 down - bidirectional 7.028992 6.600705 6.301580 8.136388 8.017734 7.661932
ENSMUST00000155625 0.004510 0.293747 2.406930 down - exon sense-overlapping 2.323709 2.787010 2.712476 4.039022 4.044215 3.541541
uc007lit.1 0.039689 0.498925 2.389210 down + natural antisense 3.383898 2.440709 2.323922 3.760049 4.469326 3.688754
ENSMUST00000141135 0.027081 0.445563 2.334129 down + exon sense-overlapping 3.241041 2.759476 2.558381 3.748812 3.811546 4.667192
NR_033584 0.001977 0.240414 2.331771 down + intergenic 4.933888 5.203815 5.150325 6.451165 6.479938 6.021205
TCONS_00016118 0.007575 0.340233 2.312752 down - intronic antisense 5.561865 5.562532 4.940521 6.790447 6.549662 6.353641
AK029573 0.013265 0.380110 2.305416 down + natural antisense 4.500548 3.852448 3.562495 5.301173 5.106782 5.122617
ENSMUST00000144044 0.040861 0.503207 2.225878 down + exon sense-overlapping 4.548734 5.325275 4.294675 6.335644 5.575793 5.720369
NR_033469 0.005124 0.298875 2.225231 down + intergenic 6.507724 6.533446 6.704603 7.941327 7.926619 7.339692
AK013439 0.011666 0.378564 2.208164 down + intergenic 6.454914 6.874332 6.432539 7.343084 8.090536 7.756705
Open in a new tab

Figure 1.

Figure 1

Open in a new tab

Altered expressions of lncRNAs and mRNAs between CNV group and control group by microarray. Heat map and hierarchical clustering analysis of the top 20 up- and downregulated lncRNAs (A) and mRNAs (B) between CNV and control samples. The top column represents lncRNA and mRNA relative expression varies according to the color scale. The volcano plot presents all identified lncRNA (C) and mRNA (D) expression variation between the CNV and control samples. The horizontal green line shows the default 1.5-fold change and the vertical green line represents a P-value of 0.05. The red and green plots represent up- and downregulated RNAs with FC≥1.5 and P<0.05. The scatter plot presents all identified lncRNA (E) and mRNA (F) expression variation between the CNV and control samples. The x-axis and y-axis values are each sample's normalized values (log2 transformed). The gray line shows the default 1.5-fold changes. The red and green plots represent altered RNAs with FC≥1.5. The qRT-PCR validation of seven randomly selected lncRNAs: AK148935, ENSMUST00000132340, uc009ewo.1, uc029sdr.1, AK030988, ENSMUST00000180519, uc007mds.1 (G).

We also identified that 821 mRNAs were significantly altered in CNV mice compared to control mice: 588 were upregulated and 233 were downregulated (FC≥1.5 , P<0.05). The top 20 significantly altered mRNA are shown in Table 4-5. Among those upregulated mRNAs, Aplnr (NM_011784) tops with an FC of 11.11, while expression of Prpmp5 (NM_001024705) tops among downregulated genes with FC of 3.61. The heatmap plot revealed the clustering analysis among mRNA expression patterns by presenting the top 20 up- and downregulated mRNAs in the collected samples (Fig. 1B). A volcano plot (Fig. 1D) and a scatter plot (Fig. 1F) showed the variation of mRNA expression between CNV and control mice.

Table 4.

Top 20 significantly up-regulated mRNAs.

Sequence Name Gene Symbol P-value Fold Change Regulation Chrom CNV 1 CNV 2 CNV 3 Control 1 Control 2 Control 3
NM_011784 Aplnr 0.000012 11.110603 up chr2 7.033988 6.843315 7.167412 3.528313 3.702581 3.392226
NM_011646 Try4 0.001518 10.794919 up chr6 5.868813 6.207469 6.427937 3.563739 2.321827 2.321811
NM_008372 Il7r 0.000099 9.886701 up chr15 6.023879 5.297020 5.565189 2.325983 2.321827 2.321811
NM_177843 Gm14461 0.001185 8.779374 up chr2 7.331272 7.067940 6.677774 3.347988 4.486817 3.839826
NM_001003405 Try5 0.000012 8.310638 up chr6 5.221975 5.431142 5.596919 2.325983 2.437364 2.321811
NM_206535 Cd200r2 0.000381 7.767570 up chr16 6.312686 6.182743 5.670891 2.734021 3.294071 3.265839
NM_175406 Atp6v0d2 0.001067 7.672187 up chr4 10.914325 10.474780 10.348221 7.376840 8.241582 7.299991
NM_011089 Pira2 0.000091 7.671351 up chr7 6.094800 5.933179 5.760588 2.792479 3.300848 2.876798
NM_207244 Cd200r4 0.006075 7.548943 up chr16 6.464434 6.282340 5.594127 2.325983 3.989506 3.276587
NM_008311 Htr2b 0.000706 7.192641 up chr1 10.557041 10.345577 9.884406 7.819662 7.395292 7.032505
NM_011088 Pira11 0.000196 6.976422 up chr7 10.466558 10.217773 10.155017 7.762837 7.555972 7.113078
NM_011094 Pira7 0.000161 6.963833 up chr7 10.961688 10.969189 10.845609 8.455120 8.155355 7.766367
NM_028973 Lrrc15 0.012774 6.827810 up chr16 8.322155 8.931940 10.139378 6.901063 6.511550 5.666592
NM_178241 Cxcr1 0.003685 6.805119 up chr1 6.005635 5.869263 5.788451 3.879585 2.321827 3.162076
NM_001166672 Gm14548 0.000097 6.673624 up chr7 11.580840 11.296632 11.179657 8.847641 8.587764 8.406313
NM_019948 Clec4e 0.000563 6.596644 up chr6 5.670682 5.027602 5.075513 2.887439 2.399350 2.321811
NM_011087 Pira1 0.003576 6.187779 up chr7 5.488713 5.426346 5.204069 2.325983 3.583068 2.321811
NM_001267695 Ctss 0.000957 5.934620 up chr3 9.404564 8.806512 8.751737 6.192754 6.833984 6.228608
NM_008848 Pira6 0.000255 4.851621 up chr7 4.801415 4.635361 4.962917 2.325983 2.838959 2.399350
NM_008404 Itgb2 0.001909 4.841209 up chr10 9.670052 8.907650 8.869934 6.768681 7.213496 6.639358
Open in a new tab

Table 5.

Top 20 significantly down-regulated mRNAs.

Sequence Name Gene Symbol P-value Fold Change Regulation Chrom CNV 1 CNV 2 CNV 3 Control 1 Control 2 Control 3
NM_001024705 Prpmp5 0.014868 3.605863 down chr6 9.067530 8.986222 9.954781 11.192267 11.751723 10.615577
NM_019840 Pde4b 0.001704 2.927001 down chr4 3.256077 3.624609 3.076331 5.079052 4.632161 4.894074
NM_175296 Mael 0.013959 2.785800 down chr1 5.162247 5.565056 5.125866 7.062294 7.111513 6.113637
NM_008623 Mpz 0.008683 2.769509 down chr1 12.174806 13.137897 12.638795 13.881432 14.154913 14.324043
NM_183160 Tmem252 0.006698 2.677545 down chr19 3.883402 4.178289 4.299822 5.111757 5.964037 5.548451
NM_207280 Ccdc121 0.014313 2.450424 down chr1 2.805933 2.324675 2.323922 3.639443 3.399682 4.294497
NM_001025255 Mbp 0.000151 2.427744 down chr18 2.949733 3.182399 3.054139 4.463824 4.263468 4.297829
NM_026925 Pnlip 0.028181 2.426672 down chr19 3.116513 2.324675 2.323922 3.895614 3.380287 4.326145
NM_001113373 Shank2 0.023401 2.379186 down chr7 3.648482 3.438832 2.672217 4.131874 4.666463 4.712598
NM_001081153 Unc13c 0.034934 2.372052 down chr9 11.000760 10.198641 11.528724 12.222275 12.268342 11.975915
NM_001122594 Phlpp2 0.001196 2.356591 down chr8 3.131635 2.660591 2.931842 4.163803 4.026565 4.243805
NM_001161722 Tfeb 0.003061 2.335554 down chr17 2.690098 3.230295 2.904800 4.046248 4.066880 4.383359
NM_028807 Exoc3l4 0.026008 2.331869 down chr12 4.272006 3.720309 3.146549 5.178742 4.693262 4.931320
NM_001127685 BC048943 0.013403 2.324064 down chr12 9.447387 8.766418 8.507593 10.125555 10.236406 10.009386
NM_001204253 Clec1b 0.008321 2.321635 down chr6 4.644223 5.290836 4.979474 5.859919 6.408708 6.291329
NM_007545 Hrk 0.003712 2.290870 down chr5 7.678339 8.078760 7.425854 8.965889 8.983374 8.821377
NM_010220 Fkbp5 0.029949 2.281321 down chr17 6.708265 6.429901 7.553089 8.247108 8.176554 7.837201
NM_020599 Rlbp1 0.007127 2.235286 down chr7 8.194874 8.097004 7.941646 9.260371 9.602041 8.852490
NM_147110 Olfr570 0.003044 2.223418 down chr7 5.491363 5.307785 5.178571 6.613100 6.653939 6.169017
NM_027790 Dhrs2 0.003418 2.203722 down chr14 5.776111 6.199949 5.806094 6.968227 7.311111 6.922644
Open in a new tab

Validation of the microarray data of lncRNAs by qRT-PCR

To validate the accuracy and reliability of the microarray profiling data, seven lncRNAs (uc009ewo.1, AK148935, uc029sdr.1, ENSMUST00000132340, AK030988, uc007mds.1, ENSMUST00000180519) were randomly selected for qRT-PCR. The qRT-PCR results are consistent with the microarray analyses where expression of lncRNAs AK148935, ENSMUST00000132340, uc009ewo.1 and uc029sdr.1 were significantly upregulated and expression of lncRNAs AK030988, ENSMUST00000180519 and uc007mds.1 were significantly downregulated in CNV mice compared to control mice (Fig. 1G).

GO and KEGG pathway analyses of altered genes

GO analyses of the 821 altered mRNAs showed that upregulated genes were enriched in immune system process (ontology: biological process, GO: 0002376), extracellular region (ontology: cellular component, GO: 0005576) and protein binding (ontology: molecular function, GO: 0005515) (Fig. 2A); while downregulated genes were enriched in anion transport (ontology: biological process, GO:0006820), plasma membrane region (ontology: cellular component, GO: 0098590) and secondary active transmembrane transporter activity (ontology: molecular function, GO: 0015291) (Fig. 2B).

Figure 2.

Figure 2

Open in a new tab

The GO and KEGG analyses of altered mRNAs. The GO analyses of up- (A) and downregulated (B) mRNAs. KEGG pathway analysis of altered mRNAs indicated: the top 10 significant enriched pathways of the upregulated genes (C), and the top 5 significant enriched pathways of the downregulated genes (D).

KEGG pathway analysis of those differentially expressed mRNAs found that the upregulated genes were enriched in chemokine signaling pathway, cytokine-cytokine receptor interaction and staphylococcus aureus infection (Fig. 2C), while the downregulated genes were enriched in GABAergic synapse, Hippo signaling pathway and phototransduction (Fig. 2D).

CNC networks with GO and KEGG analyses

LncRNA and mRNA co-expression network was established based on 7 validated altered lncRNAs as well as 282 interacted mRNAs which were differentially expressed. The CNC network was composed of 289 nodes and 713 edges. There were 512 positive and 201 negative interactions within the network (Fig. 3). The lncRNA uc009ewo.1 is correlated with 138 mRNAs, ENSMUST00000132340 is correlated with 132 mRNAs, uc029sdr.1 is correlated with 152 mRNAs, ENSMUST00000180519 is correlated with 127 mRNAs, AK148935 is correlated with 76 mRNAs, while uc007mds.1 and AK030988 are correlated with 49 and 39 mRNAs respectively. According to the networks, the most relevant mRNAs are B430306N03Rik, C-type lectin receptor 4e (Clec4e) and paired immunoglobulin-like receptor A6 (Pira6), all correlated with 6 lncRNAs. To predict the functions of the lncRNAs, GO analyses and KEGG pathway analyses of those interacted mRNAs which were differentially expressed were conducted based on the results of the co-expression network. GO analysis showed that the most enriched GO terms of the targeted genes were immune system process (ontology: biological process, GO: 0002376), plasma membrane (ontology: cellular component, GO: 0005886) and protein binding (ontology: molecular function, GO: 0005515) (Fig. 4A). KEGG pathway analysis showed those targeted mRNAs were enriched in chemokine signaling pathway, osteoclast differentiation and cytokine-cytokine receptor interaction (Fig. 4B).

Figure 3.

Figure 3

Open in a new tab

CNC networks by validated lncRNAs. The co-expression network profiles of lncRNAs and mRNAs based on 7 validated lncRNAs and correlated mRNAs which were differentially expressed. This co-expression network composed of 713 edges and 289 nodes. The diamond nodes represent lncRNAs, in which yellow denotes upregulated lncRNAs and blue denotes downregulated lncRNAs. The round nodes represent mRNAs, in which green denotes upregulated mRNAs and red denotes downregulated mRNAs. Continuous and dotted lines indicate positive and negative interactions between lncRNAs and mRNAs, respectively.

Figure 4.

Figure 4

Open in a new tab

The GO and KEGG analyses of interacted and altered mRNAs in CNC networks. GO analysis of the CNC-associated altered mRNAs (A). KEGG pathway analysis of the CNC-associated altered mRNAs (B).

Discussion

Despite several studies reported important roles of particular lncRNAs such as ZNF503-AS1 and BANCR in RPE cells through in vitro studies 22, 23, none of them have investigated the role of lncRNAs in CNV. The present study profiled lncRNA and mRNA expression in the mouse model of laser-induced CNV by integrated microarray analysis. We identified 821 significantly altered mRNAs (588 upregulated; 233 downregulated) and 716 differentially expressed lncRNAs (442 upregulated; 274 downregulated) in the RPE-choroid-sclera complexes from CNV mice compared to control mice. Validation of seven randomly chosen lncRNAs by qRT-PCR further confirmed the reliability of microarray analysis. The profiles of the lncRNAs and mRNAs in CNV mice provide novel insights into our understanding of the pathogenesis of CNV.

A number of studies have reported that different cytokines, such as IL-10, IL-12, IFN-γ, IL-17, IL-18 and IL-33, may play a role in the pathogenesis of CNV 33-37. The pro- and anti-angiogenic effects of the cytokines relevant to Th1, Th2 and Th17 cells, participate in a complicated immunological network 38. It has been reported that the number of macrophages increased significantly after laser photocoagulation 24, and M1-M2 polarization of macrophages have diverse distributions and functions in laser-induced CNV as well as in wet AMD 39-41. Pro-inflammatory M1 macrophages increased in the site of CNVs 39, suggesting that inflammation might play a key role in the pathogenesis of CNV and AMD. Our GO analyses showed that the most upregulated genes participate in immune system process, whereas the downregulated genes participate in plasma membrane region. Moreover, the KEGG pathway analysis revealed that the functions of altered genes are enriched in the chemokine signaling pathway, cytokine-cytokine receptor interaction and the GABAergic synapse. Similarly, GO and KEGG pathway analyses of the lncRNA-interacted mRNAs showed that the most enriched GO terms are associated with immune system process and immune response, and the most enriched KEGG pathway is also chemokine signaling pathway. These analyses demonstrated that CNV might be mainly immune-regulated by cytokines and chemokines.

Macrophage inducible Ca2+-dependent lectin receptor (Mincle) is a member of the C-type lectin family of immune receptors, which encoded by the gene of Clec4e, and Clec4e is also a type 2 transmembrane receptor 42. As the CNC network analysis showed, Clec4e is a significantly altered gene correlated with 6 validated lncRNAs. Lv et al. reported that clec4e is induced specifically on M1 macrophages, and it plays an essential role in maintaining the phenotype of M1 macrophage in acute renal inflammation 43. Another study showed that that Clec4e enhanced proinflammatory phenotype of macrophages through activation of the unfolded protein response 44. Our previous study demonstrated that M1 macrophages mainly distribute around the site of CNVs, and inflammatory M1 macrophage-associated cytokines increased in RPE and choroid in laser-induced CNV mouse model 39. Thus Clec4e, as well as macrophage polarization are possibly involved in mRNA-lncRNA network.

Nevertheless, the relevance of lncRNAs and cytokines or macrophages in intraocular neovascularization is still unclear. Our GO analysis suggested that several altered lncRNAs are involved in the regulation of cytokines and immunological networks in the pathogenesis of CNV, however, further studies should be guaranteed to investigate the exact functions and mechanisms of the dysregulated lncRNAs. Chemokines are a group of small chemoattractant cytokines, which play great roles in inflammation and regulating angiogenesis as well as macrophage polarization 45, 46. Studies have shown that chemokine (C-X-C motif) ligand 8 (CXCL8) and monocyte chemoattractant protein-1 (MCP-1), and chemokine receptors such as CXC chemokine receptor 3 (CXCR3) have been reported to be involved in CNV and AMD pathogenesis 47, 48. A study showed that miR-539-5p attenuates experimental CNV through targeting CXC chemokine receptor 7 (CXCL7) 49, indicating that non-coding RNAs are involved in the functional effect of chemokines and their receptors that contribute to the formation of CNV. In the present study, GO and KEGG analyses showed that altered genes enriched in immune system process, immune response, chemokine signaling pathway and cytokine-cytokine receptor interaction, indicating that lncRNAs may regulate chemokines, cytokines and their receptors in the pathogenesis of CNV formation through their target genes. Thus, it is worth further investigate the mechanisms of involvement of lncRNAs in chemokines and chemokine receptors in CNV and AMD.

Zhu et al. presented identified lncRNAs involved in early AMD 21. In that study, GO and KEGG analyses showed that lncRNA-related genes enriched different functions, such as visual perception, sensory perception of light stimuli, and phototransduction pathway, but not immunological regulations. It might because that study focused on early-stage AMD patients without geographic atrophy and CNV, and our present study investigated the pathogenesis of CNV, which demonstrated different roles of lncRNAs in AMD.

We recently demonstrated that both mRNAs and lncRNAs have differential expression profiles in the oxygen-induced retinopathy (OIR) mouse model 20, which is used for investigation of retinal neovascular diseases. We recognized 539 altered mRNAs and 373 altered lncRNAs in OIR retinas (FC≥2.0, P<0.05) 20. And we tried to check the intersection of altered RNAs in both models, and identified 161 mRNAs and 53 lncRNAs which altered in OIR as well as CNV models, and the majority of the RNAs have the same trend of alteration. For instance, ENSMUST00000165968 and CD68 were up-regulated in both models, while ENSMUST00000144657 and cpa2 were down-regulated in both models. These double-dysregulated mRNAs and lncRNAs might play more essential roles in angiogenesis, and this suggested that lncRNAs could have potential roles in the pathogenesis of ocular neovascular diseases.

In conclusion, the present study reveals that lncRNAs and mRNAs are significantly altered in CNV mice through microarray analysis. Further, GO and pathway analyses indicate that altered lncRNAs are not only involved in biological processes of immune mechanisms and inflammation but also are involved in the related pathways which might contribute to CNV and AMD pathogenesis. In particular, immunological networks, including cytokines, chemokines and their receptors are mainly involved and relevant to the effect of lncRNAs in CNV. The limitation of this study is the lack of functional assessment of the identified lncRNA and mRNAs. Therefore, further studies are required to investigate the potential roles and exact mechanisms of the altered lncRNAs and mRNAs in CNV.

Acknowledgments

This work was supported by National Natural Science Foundation of China (No. 81500746 and 81800855), Natural Science Foundation of Hunan Province (No. 2018JJ3765), and Department of Science and Technology, Hunan (No.2015TP2007). The authors declare that they have no competing interests.

References

  • 1.Klein R, Peto T, Bird A, Vannewkirk MR. The epidemiology of age-related macular degeneration. American journal of ophthalmology. 2004;137:486–95. doi: 10.1016/j.ajo.2003.11.069. [DOI] [PubMed] [Google Scholar]
  • 2.Ambati J, Ambati BK, Yoo SH, Ianchulev S, Adamis AP. Age-related macular degeneration: etiology, pathogenesis, and therapeutic strategies. Survey of ophthalmology. 2003;48:257–93. doi: 10.1016/s0039-6257(03)00030-4. [DOI] [PubMed] [Google Scholar]
  • 3.van Wijngaarden P, Qureshi SH. Inhibitors of vascular endothelial growth factor (VEGF) in the management of neovascular age-related macular degeneration: a review of current practice. Clin Exp Optom. 2008;91:427–37. doi: 10.1111/j.1444-0938.2008.00305.x. [DOI] [PubMed] [Google Scholar]
  • 4.Ferrara N. Vascular endothelial growth factor and age-related macular degeneration: from basic science to therapy. Nat Med. 2010;16:1107–11. doi: 10.1038/nm1010-1107. [DOI] [PubMed] [Google Scholar]
  • 5.Kovach JL, Schwartz SG, Flynn HW Jr, Scott IU. Anti-VEGF Treatment Strategies for Wet AMD. J Ophthalmol. 2012;2012:786870. doi: 10.1155/2012/786870. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Ferrara N, Adamis AP. Ten years of anti-vascular endothelial growth factor therapy. Nat Rev Drug Discov. 2016;15:385–403. doi: 10.1038/nrd.2015.17. [DOI] [PubMed] [Google Scholar]
  • 7.Park YG, Rhu HW, Kang S, Roh YJ. New Approach of Anti-VEGF Agents for Age-Related Macular Degeneration. Journal of Ophthalmology; 2012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Eghoj MS, Sorensen TL. Tachyphylaxis during treatment of exudative age-related macular degeneration with ranibizumab. Br J Ophthalmol. 2012;96:21–3. doi: 10.1136/bjo.2011.203893. [DOI] [PubMed] [Google Scholar]
  • 9.Schaal S, Kaplan HJ, Tezel TH. Is There Tachyphylaxis to Intravitreal Anti-Vascular Endothelial Growth Factor Pharmacotherapy in Age-Related Macular Degeneration? Ophthalmology. 2008;115:2199–205. doi: 10.1016/j.ophtha.2008.07.007. [DOI] [PubMed] [Google Scholar]
  • 10.Gasperini JL, Fawzi AA, Khondkaryan A, Lam L, Chong LP, Eliott D. et al. Bevacizumab and ranibizumab tachyphylaxis in the treatment of choroidal neovascularisation. Brit J Ophthalmol. 2012;96:14–20. doi: 10.1136/bjo.2011.204685. [DOI] [PubMed] [Google Scholar]
  • 11.Mercer TR, Dinger ME, Mattick JS. Long non-coding RNAs: insights into functions. Nat Rev Genet. 2009;10:155–9. doi: 10.1038/nrg2521. [DOI] [PubMed] [Google Scholar]
  • 12.Yang L, Lin C, Jin C, Yang JC, Tanasa B, Li W. et al. lncRNA-dependent mechanisms of androgen-receptor-regulated gene activation programs. Nature. 2013;500:598–602. doi: 10.1038/nature12451. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Sun X, Yuan Y, Xiao Y, Lu Q, Yang L, Chen C, Long non-coding RNA, Bmcob, regulates osteoblastic differentiation of bone marrow mesenchymal stem cells. Biochem Biophys Res Commun; 2018. [DOI] [PubMed] [Google Scholar]
  • 14.Liu K, Yao H, Wen Y, Zhao H, Zhou N, Lei S. et al. Functional role of a long non-coding RNA LIFR-AS1/miR-29a/TNFAIP3 axis in colorectal cancer resistance to pohotodynamic therapy. Biochim Biophys Acta Mol Basis Dis. 2018;1864:2871–80. doi: 10.1016/j.bbadis.2018.05.020. [DOI] [PubMed] [Google Scholar]
  • 15.Jia J, Zhang M, Li Q, Zhou Q, Jiang Y. Long noncoding ribonucleic acid NKILA induces the endoplasmic reticulum stress/autophagy pathway and inhibits the nuclear factor-k-gene binding pathway in rats after intracerebral hemorrhage. J Cell Physiol. 2018;233:8839–49. doi: 10.1002/jcp.26798. [DOI] [PubMed] [Google Scholar]
  • 16.Yan BA, Yao J, Liu JY, Li XM, Wang XQ, Li YJ. et al. lncRNA-MIAT Regulates Microvascular Dysfunction by Functioning as a Competing Endogenous RNA. Circ Res. 2015;116:1143. doi: 10.1161/CIRCRESAHA.116.305510. -+ [DOI] [PubMed] [Google Scholar]
  • 17.Gong QY, Su GF. Roles of miRNAs and long noncoding RNAs in the progression of diabetic retinopathy. Biosci Rep; 2017. p. 37. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Xu XD, Li KR, Li XM, Yao J, Qin J, Yan B. Long non-coding RNAs: new players in ocular neovascularization. Mol Biol Rep. 2014;41:4493–505. doi: 10.1007/s11033-014-3320-5. [DOI] [PubMed] [Google Scholar]
  • 19.Huang J, Yang Y, Fang F, Liu K. MALAT1 modulates the autophagy of retinoblastoma cell through miR-124-mediated stx17 regulation. J Cell Biochem. 2018;119:3853–63. doi: 10.1002/jcb.26464. [DOI] [PubMed] [Google Scholar]
  • 20.Zhang L, Fu X, Zeng H, Wang JH, Peng Y, Zhao H. et al. Microarray Analysis of Long Non-Coding RNAs and Messenger RNAs in a Mouse Model of Oxygen-Induced Retinopathy. Int J Med Sci. 2019;16:537–47. doi: 10.7150/ijms.31274. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Zhu W, Meng YF, Xing Q, Tao JJ, Lu J, Wu Y. Identification of lncRNAs involved in biological regulation in early age-related macular degeneration. Int J Nanomed. 2017;12:7589–602. doi: 10.2147/IJN.S140275. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Chen X, Jiang C, Qin B, Liu GH, Ji JD, Sun XT, LncRNA ZNF503-AS1 promotes RPE differentiation by downregulating ZNF503 expression. Cell Death Dis; 2017. p. 8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Kutty RK, Samuel W, Duncan T, Postnikova O, Jaworski C, Nagineni CN. et al. Proinflammatory cytokine interferon-gamma increases the expression of BANCR, a long non-coding RNA, in retinal pigment epithelial cells. Cytokine. 2018;104:147–50. doi: 10.1016/j.cyto.2017.10.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Lambert V, Lecomte J, Hansen S, Blacher S, Gonzalez MLA, Struman I. et al. Laser-induced choroidal neovascularization model to study age-related macular degeneration in mice. Nat Protoc. 2013;8:2197–211. doi: 10.1038/nprot.2013.135. [DOI] [PubMed] [Google Scholar]
  • 25.Gu LP, Chen H, Tuo JS, Gao XW, Chen L. Inhibition of experimental choroidal neovascularization in mice by anti-VEGFA/VEGFR2 or non-specific siRNA. Exp Eye Res. 2010;91:433–9. doi: 10.1016/j.exer.2010.06.019. [DOI] [PubMed] [Google Scholar]
  • 26.Kobayashi Y, Yoshida S, Zhou Y, Nakama T, Ishikawa K, Kubo Y. et al. Tenascin-C secreted by transdifferentiated retinal pigment epithelial cells promotes choroidal neovascularization via integrin alpha V. Lab Invest. 2016;96:1178–88. doi: 10.1038/labinvest.2016.99. [DOI] [PubMed] [Google Scholar]
  • 27.Nakama T, Yoshida S, Ishikawa K, Kobayashi Y, Zhou Y, Nakao S. et al. Inhibition of choroidal fibrovascular membrane formation by new class of RNA interference therapeutic agent targeting periostin. Gene Ther. 2015;22:127–37. doi: 10.1038/gt.2014.112. [DOI] [PubMed] [Google Scholar]
  • 28.Zhang L, Liu S, Wang JH, Zou J, Zeng H, Zhao H. et al. Differential Expressions of microRNAs and Transfer RNA-derived Small RNAs: Potential Targets of Choroidal Neovascularization. Curr Eye Res. 2019;44:1226–35. doi: 10.1080/02713683.2019.1625407. [DOI] [PubMed] [Google Scholar]
  • 29.Zhang H, Yang Y, Takeda A, Yoshimura T, Oshima Y, Sonoda KH. et al. A Novel Platelet-Activating Factor Receptor Antagonist Inhibits Choroidal Neovascularization and Subretinal Fibrosis. PloS one. 2013;8:e68173. doi: 10.1371/journal.pone.0068173. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Cui HJ, Tao L, Li PF, Ali Y, Zhou HJ, Luo JK. et al. Altered Long Noncoding RNA and Messenger RNA Expression in Experimental Intracerebral Hemorrhage - a Preliminary Study. Cell Physiol Biochem. 2018;45:1284–301. doi: 10.1159/000487464. [DOI] [PubMed] [Google Scholar]
  • 31.Bustin SA. Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays. J Mol Endocrinol. 2000;25:169–93. doi: 10.1677/jme.0.0250169. [DOI] [PubMed] [Google Scholar]
  • 32.Yang B, Xia ZA, Zhong B, Xiong X, Sheng C, Wang Y. et al. Distinct Hippocampal Expression Profiles of Long Non-coding RNAs in an Alzheimer's Disease Model. Mol Neurobiol. 2017;54:4833–46. doi: 10.1007/s12035-016-0038-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Doyle SL, Lopez FJ, Celkova L, Brennan K, Mulfaul K, Ozaki E. et al. IL-18 Immunotherapy for Neovascular AMD: Tolerability and Efficacy in Nonhuman Primates. Invest Ophthalmol Vis Sci. 2015;56:5424–30. doi: 10.1167/iovs.15-17264. [DOI] [PubMed] [Google Scholar]
  • 34.Nakamura R, Sene A, Santeford A, Gdoura A, Kubota S, Zapata N. et al. IL10-driven STAT3 signalling in senescent macrophages promotes pathological eye angiogenesis. Nat Commun. 2015;6:7847. doi: 10.1038/ncomms8847. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Zhu Y, Tan W, Demetriades AM, Cai Y, Gao Y, Sui A. et al. Interleukin-17A neutralization alleviated ocular neovascularization by promoting M2 and mitigating M1 macrophage polarization. Immunology. 2016;147:414–28. doi: 10.1111/imm.12571. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Theodoropoulou S, Copland DA, Liu J, Wu J, Gardner PJ, Ozaki E. et al. Interleukin-33 regulates tissue remodelling and inhibits angiogenesis in the eye. J Pathol. 2017;241:45–56. doi: 10.1002/path.4816. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Kleinman ME, Yamada K, Takeda A, Chandrasekaran V, Nozaki M, Baffi JZ. et al. Sequence- and target-independent angiogenesis suppression by siRNA via TLR3. Nature. 2008;452:591–7. doi: 10.1038/nature06765. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Kwee BJ, Budina E, Najibi AJ, Mooney DJ. CD4 T-cells regulate angiogenesis and myogenesis. Biomaterials. 2018;178:109–21. doi: 10.1016/j.biomaterials.2018.06.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Zhou Y, Yoshida S, Kubo Y, Yoshimura T, Kobayashi Y, Nakama T. et al. Different distributions of M1 and M2 macrophages in a mouse model of laser-induced choroidal neovascularization. Mol Med Rep. 2017;15:3949–56. doi: 10.3892/mmr.2017.6491. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Zandi S, Nakao S, Chun KH, Fiorina P, Sun D, Arita R. et al. ROCK-isoform-specific polarization of macrophages associated with age-related macular degeneration. Cell reports. 2015;10:1173–86. doi: 10.1016/j.celrep.2015.01.050. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Zhou YD, Yoshida S, Peng YQ, Kobayashi Y, Zhang LS, Tang LS. Diverse roles of macrophages in intraocular neovascular diseases: a review. Int J Ophthalmol-Chi. 2017;10:1902–8. doi: 10.18240/ijo.2017.12.18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Lu XY, Nagata M, Yamasaki S. Mincle: 20 years of a versatile sensor of insults. Int Immunol. 2018;30:233–9. doi: 10.1093/intimm/dxy028. [DOI] [PubMed] [Google Scholar]
  • 43.Lv LL, Tang PMK, Li CJ, You YK, Li JH, Huang XR. et al. The pattern recognition receptor, Mincle, is essential for maintaining the M1 macrophage phenotype in acute renal inflammation. Kidney Int. 2017;91:587–602. doi: 10.1016/j.kint.2016.10.020. [DOI] [PubMed] [Google Scholar]
  • 44.Clement M, Basatemur G, Masters L, Baker L, Bruneval P, Iwawaki T. et al. Necrotic Cell Sensor Clec4e Promotes a Proatherogenic Macrophage Phenotype Through Activation of the Unfolded Protein Response. Circulation. 2016;134:1039–51. doi: 10.1161/CIRCULATIONAHA.116.022668. [DOI] [PubMed] [Google Scholar]
  • 45.Dimberg A. Chemokines in angiogenesis. Curr Top Microbiol Immunol. 2010;341:59–80. doi: 10.1007/82_2010_21. [DOI] [PubMed] [Google Scholar]
  • 46.Owen JL, Mohamadzadeh M. Macrophages and chemokines as mediators of angiogenesis. Front Physiol; 2013. p. 4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Fujimura S, Takahashi H, Yuda K, Ueta T, Iriyama A, Inoue T. et al. Angiostatic Effect of CXCR3 Expressed on Choroidal Neovascularization. Invest Ophthalmol Vis Sci. 2012;53:1999–2006. doi: 10.1167/iovs.11-8232. [DOI] [PubMed] [Google Scholar]
  • 48.Lechner J, Chen M, Hogg RE, Toth L, Silvestri G, Chakravarthy U, Peripheral blood mononuclear cells from neovascular age-related macular degeneration patients produce higher levels of chemokines CCL2 (MCP-1) and CXCL8 (IL-8) J Neuroinflammation; 2017. p. 14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Feng YF, Wang J, Yuan YZ, Zhang X, Shen MQ, Yuan F. miR-539-5p inhibits experimental choroidal neovascularization by targeting CXCR7. FASEB J. 2018;32:1626–39. doi: 10.1096/fj.201700640R. [DOI] [PubMed] [Google Scholar]

Articles from International Journal of Medical Sciences are provided here courtesy of Ivyspring International Publisher

RESOURCES