Open Access

Functional annotation of the human retinal pigment epithelium transcriptome

  • Judith C Booij1,
  • Simone van Soest1,
  • Sigrid MA Swagemakers2, 3,
  • Anke HW Essing1,
  • Annemieke JMH Verkerk2,
  • Peter J van der Spek2,
  • Theo GMF Gorgels1 and
  • Arthur AB Bergen1, 4Email author
BMC Genomics200910:164

DOI: 10.1186/1471-2164-10-164

Received: 10 July 2008

Accepted: 20 April 2009

Published: 20 April 2009

Abstract

Background

To determine level, variability and functional annotation of gene expression of the human retinal pigment epithelium (RPE), the key tissue involved in retinal diseases like age-related macular degeneration and retinitis pigmentosa. Macular RPE cells from six selected healthy human donor eyes (aged 63–78 years) were laser dissected and used for 22k microarray studies (Agilent technologies). Data were analyzed with Rosetta Resolver, the web tool DAVID and Ingenuity software.

Results

In total, we identified 19,746 array entries with significant expression in the RPE. Gene expression was analyzed according to expression levels, interindividual variability and functionality. A group of highly (n = 2,194) expressed RPE genes showed an overrepresentation of genes of the oxidative phosphorylation, ATP synthesis and ribosome pathways. In the group of moderately expressed genes (n = 8,776) genes of the phosphatidylinositol signaling system and aminosugars metabolism were overrepresented. As expected, the top 10 percent (n = 2,194) of genes with the highest interindividual differences in expression showed functional overrepresentation of the complement cascade, essential in inflammation in age-related macular degeneration, and other signaling pathways. Surprisingly, this same category also includes the genes involved in Bruch's membrane (BM) composition. Among the top 10 percent of genes with low interindividual differences, there was an overrepresentation of genes involved in local glycosaminoglycan turnover.

Conclusion

Our study expands current knowledge of the RPE transcriptome by assigning new genes, and adding data about expression level and interindividual variation. Functional annotation suggests that the RPE has high levels of protein synthesis, strong energy demands, and is exposed to high levels of oxidative stress and a variable degree of inflammation. Our data sheds new light on the molecular composition of BM, adjacent to the RPE, and is useful for candidate retinal disease gene identification or gene dose-dependent therapeutic studies.

Background

The retinal pigment epithelium (RPE) is a multifunctional neural-crest derived cell layer, flanked by the photoreceptor cells on the apical side and the Bruch's membrane (BM)/choroid complex on the basolateral side. Among others, the RPE supplies the photoreceptors with nutrients, regulates the ion balance in the subretinal space and recycles retinal from the photoreceptor cells, which is necessary for the continuation of the visual cycle.[1] It also phagocytoses and degrades photoreceptor outer segments and absorbs light that is projected onto the retina.[1] Finally, the RPE secretes a number of growth factors that maintain the structure and cellular differentiation of the adjacent tissues.[1]

The importance of the RPE in vision is illustrated by the major involvement of this monolayer of cells in genetically determined retinal diseases like age related macular degeneration (AMD) and retinitis pigmentosa (RP).[2] Since the great majority of genes implicated in AMD or RP are expressed in either the RPE or the photoreceptors, the identification of additional genes highly expressed in the RPE may provide valuable clues in the search for new genes involved in retinal disease. [26]

Obviously, the functional properties of RPE cells are determined by the genes they express and the proteins they encode. Although the RPE cell is one of the best studied neural cell types, [312] large scale assignment of expressed genes to the RPE has been largely dependent on RNA based studies. Assignment of proteins to the RPE has been hampered by its autofluorescence and melanin content. Large-scale RPE related expression studies were performed using cDNA arrays, serial analysis of gene expression (SAGE), expressed sequence tag (EST) analysis, and multiple RT-PCRs. The number of eyes used in these studies ranged from one to fifteen, and the number of genes under investigation from 29 to 30,000. [812] While these studies provided valuable information, they were limited in either the number of genes or the number of eyes under investigation, or they lacked specificity due to the tissue sampling method used. Moreover, most or all of these studies focused on the mean gene expression profile of all samples together, rather than documenting potential interindividual differences. [812] A robust and specific dataset on RPE expression levels from a substantial number of individuals is lacking and a great deal remains unknown with regard to the interindividual expression differences.

A number of biological processes and cellular functions of genes expressed in the RPE were described in three of the above mentioned studies.[8, 10, 12] All three identified protein metabolism and signal transduction as an important functional class of genes expressed by the RPE.[8, 10, 12] Similarly, cell structure,[8, 10] cell proliferation,[8, 10] gene transcription[10, 11] and energy metabolism were described in two out of three studies. Finally, individual studies also identified overrepresentation of membrane proteins,[10] transport or channel proteins,[10] heat shock proteins[10] and vitamin A metabolism.[11] In a recent microarray study we compared RPE gene expression in the macula with the retinal periphery and demonstrated, among other things, consistent differential expression of extracellular matrix genes corresponding with proteins in BM.[13]

The aim of the current study is to describe the gene expression levels and the interindividual variation in gene expression of native human macular RPE cells in a systematic fashion. In addition, we annotate the functions and biological pathways associated with RPE expressed (disease) genes.

To our knowledge this is the first study to present data on (interindividual differences in) human macular RPE gene expression and interindividual differences on a large scale of 22,000 genes, resulting in a further detailed description of the RPE transcriptome.

Results

RNA from six selected human macular RPE samples was hybridized to six custom made 22 k microarrays enriched for neural transcripts. We functionally annotated and analyzed the data using Rosetta Resolver, the web tool DAVID and Ingenuity software, with regard to gene expression level and variability as well as functional annotation. Furthermore, we specifically looked at the expression levels and variability of retinal disease genes.

Analysis of gene expression levels (μint)

The mean expression intensities (μint) ranged from 73 to 690,113 (arbitrary units), (see Additional file 1: Expression level and interindividual variation in all genes on the custom microarray). The distribution of μint across percentile bins of 10 percent of all genes is shown in Figure 1. We used the 90th, 50th and 10th percentile of the μint to categorize our data into groups with high (> 90th), moderate (50th–90th), low (10th–50th) and very low (< 10th) expression. We focused our analysis on the biologically most relevant gene groups with high, moderate and low gene expression levels. These categories yielded 2,194 genes with high RPE expression, 8,776 genes with moderate expression and 8,776 genes with low expression. The results of the overrepresentation analysis are presented below, and in Table 1. The overrepresentation analysis of all expressed genes,irrespective of their gene expression level (Table 1), did not yield additional functional categories apart from ECM-receptor interaction, and is not presented separately.
Table 1

Overrepresented Kegg pathways in macular RPE expressed genes with high, moderate and low expression levels and high or low levels of interindividual variability (coefficient of variation, CV).

  

expression level

  

all expression levels

high (> 90th perc)

moderate (50th–90th perc)

low (10th–50th perc)

 

all CV

ecm-receptor interaction (E)

oxidative phosphorylation (B,E)

ribosome (B,E)

ATP synthesis (B,E)

phosphatidylinositol signaling system (B,E)

aminosugars metabolism (E)

neuroactive ligand receptor interaction (B,E)

long-term depression (E)

o-glycan biosynthesis (E)

calcium signaling pathway (E)

CV

high CV

type I diabetes mellitus (B,E)

focal adhesion (B,E)

cytokine-cytokine receptor interaction (E)

complement and coagulation cascades (E)

antigen processing and presentation (E)

ecm-receptor interaction (E)

antigen processing and presentation (B,E)

complement and coagulation cascades (E)

focal adhesion (E)

cytokine-cytokine receptor interaction (E)

type I diabetes mellitus (E)

 

low CV

-

glycosaminoglycan degradation (E)

-

-

Overrepresented pathways were identified with B: a Benjamini-Hochberg corrected p value < 0.001, or E: an Ease score p value < 0.001. Perc: percentile, high CV: > 90th percentile, low CV: < 10th percentile.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-164/MediaObjects/12864_2008_Article_2048_Fig1_HTML.jpg
Figure 1

Distribution of the mean intensity (μ int ) of all genes across percentile bins of 10 percent. Each bin contains 2,194 genes. Note that the mean expression level associated with the 100th percentile bin is not displayed fully in this graph due to the height of the expression exceeding the scope of the graph.

Genes with high expression levels (μint > 90th percentile, n = 2,194)

We considered the group of highly expressed genes the most biologically relevant, and, consequently, for this group bioinformatic analysis was more extensive than for other categories. In addition to a Kegg pathway analysis, we also performed an Ingenuity analysis of the overlap between our highly expressed genes and those identified in the literature. Kegg pathway analysis revealed oxidative phosphorylation, ribosome and ATP synthesis as significantly overrepresented pathways (Benjamini-Hochberg p value < 0.001) (Table 1). There was an overlap of 1,407 genes between the highly expressed genes of the RPE transcriptome and the genes identified in retina/RPE genes identified in at least two studies in the literature.[14] Ingenuity analysis of the overlapping genes revealed oxidative phosphorylation as the most significant pathway involved. Comparison of our highly expressed genes to those expressed only in RPE studies (n = 17),[14] showed a clustering of genes in the cell-cell signaling and interaction network (Figure 2).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-164/MediaObjects/12864_2008_Article_2048_Fig2_HTML.jpg
Figure 2

Ingenuity analysis of the cross section of genes previously identified in RPE studies[14]with genes highly expressed in the RPE transcriptome. The resulting network shows a connectivity chart illustrating biological functions comprising genes, proteins and ligands related to cell-cell signaling and cell-cell interaction. This network contains 13 of the 16 genes entered into the analysis. Filled objects represent the genes entered, empty objects are genes introduced by the ingenuity software creating a connection between the entered genes.

The thirty most highly expressed RPE genes from our data set are presented in Table 2. Most notably, this list contains two glutamate transporters (SLC1A2 [genbank:AF131756] and SLC17A7 [genbank: NM_020309])[15], one of which is known to be expressed in the RPE (SLC17A7 [genbank: NM_020309])[14] and a gene (CST3 [genbank: NM_000099]), that was previously suggested to have an association with AMD,[16, 17] with known expression in the RPE.[18] The top thirty list contained three additional genes with known expression in the RPE (PTGDS [genbank: NM_000954],[17, 19]TTR [genbank: NM_000371][14, 20] and HSP90B1 [genbank: NM_003299])[21] and two genes that play a role in the protection against oxidative stress (MT1A [genbank: K01383][22], and TP53 [genbank: NM_000546][23]). Finally, we identified a number of genes with a relevant cellular function described in other tissues than the retina, like CLU [NM_001831] (complement system) and ACN9 [NM_020186] (gluconeogenesis).[24, 25]
Table 2

The thirty most highly expressed genes in macular RPE identified in six different human donors, sorted by intensity in descending order.

gene symbol

Genbank ID

mean intensity μint perc

CV perc

gene name

relevant function

KIAA0241

AA205569

99

98

KIAA0241

 

AI003379

AI003379

99

98

Transcribed locus

 

ACN9

NM_020186

99

98

ACN9 homolog

gluconeogenesis [25]

MT1A

BG191659

99

98

Metallothionein 1A

protection against reactive oxygen species[22]

SLC17A7

NM_020309

99

98

Solute carrier family 17 (sodium-dependent inorganic phosphate cotransporter), member 7

glutamate transporter, expressed in RPE[14, 15]

TP53

NM_000546

99

98

Tumor protein p53

protection against reactive oxygen species[23]

ELF2

NM_006874

99

98

E74-like factor 2 (ets domain transcription factor)

 

AI272368

AI272368

99

95

cDNA clone

 

AA807363

AA807363

99

79

cDNA clone

 

SERPINB5

NM_002639

99

98

Serpin peptidase inhibitor, clade B, member 5

 

MCM7

NM_005916

99

97

minichromosome maintenance deficient 7

 

SLC1A2

NM_004171

99

96

Solute carrier family 1 member 2

glutamate transporter[1]

HBB

NM_000518

99

99

Hemoglobin, beta

 

PTGDS

NM_000954

99

94

Prostaglandin D2 synthase 21 kDa

released from RPE during rod phagocytosis [17, 19]

T26536

T26536

99

75

cDNA clone

 

EEF1A1

NM_001402

99

80

Eukaryotic translation elongation factor 1 alpha 1

 

TTR

NM_000371

99

80

Transthyretin (prealbumin, amyloidosis type I)

maintains normal levels of retinol and retinol binding proteins in plasma [14, 20]

BE260168

BE260168

99

90

cDNA clone

 

CST3

NM_000099

99

92

Cystatin C

associated with AMD[16, 18]

ZNF503

NM_032772

99

95

Zinc finger protein 503

 

BG190000

BG190000

99

94

cDNA clone

 

BE262306

BE262306

99

80

cDNA clone

 

HSP90B1

NM_003299

99

94

Heat shock protein 90 kDa beta (Grp94), member 1

[21]

GNGT1

NM_021955

99

90

Guanine nucleotide binding protein (G protein), gamma transducing activity polypeptide 1

 

RPL3

NM_000967

99

20

Ribosomal protein L3

 

RPL41

NM_021104

99

23

Ribosomal protein L41

 

AI857840

AI857840

99

77

cDNA clone

 

PCSK7

NM_004716

99

90

Proprotein convertase subtilisin/kexin type 7

 

AL521537

AL521537

99

75

cDNA clone

 

CLU

NM_001831

99

42

Clusterin

member of complement system [24]

Among these genes we identified five genes with known expression in the RPE (SLC17A7[14], CST3[18], TTR[14], HSP90B1[21] and PTGDS[17]). The SLC17A7 gene is a glutamate transporter, like the SLC1A2 gene which is also in the top 30 highly expressed genes. The CST3 gene was previously suggested to have an association with AMD[16, 17]. The list also contains two genes with a role in the protection against oxidative stress (MT1A[22], TP53[23]). The GNGT1 gene, expressed in photoreceptors[30], suggests the inevitable presence of photoreceptor contamination. Perc: percentile.

Genes with moderate expression levels (μint 50th–90th percentile)

Upon analyzing this group of 8,776 genes, we found a statistically significant overrepresentation of the Kegg pathways phosphatidylinositol signaling and aminosugars metabolism (Benjamini-Hochberg p value < 0.001) (Table 1).

Genes with low expression levels (μint 10th–50th percentile)

Among the 8,776 genes with low expression levels there was a statistically significant overrepresentation of the neuroactive ligand-receptor interaction (Benjamini-Hochberg p value 0.001), long-term depression, O-glycan biosynthesis and calcium signaling pathways (Ease score p value < 0.001) (Table 1).

Analysis of gene expression variability (CV)

We analyzed the interindividual variability in gene expression (CV) among the 19,746 genes with expression levels in the RPE higher than the 10th percentile, (see Additional file 1: Expression level and interindividual variation in all genes on the custom microarray). Aside from the overrepresented cluster ECM-receptor interaction (Ease score p value < 0.001)(Table 1), this yielded little extra information compared to the CV assignment in subcategories of high, moderate and low expression levels (Table 1 and below), and is not presented in detail here. The thirty genes with the highest interindividual variation in expression levels in our dataset are presented in Table 3.
Table 3

The top thirty genes with the highest interindividual variation in expression levels (CV) between six healthy human donors, sorted descending by coefficient of variation (CV).

gene symbol

Genbank ID

mean intensity μint perc

CV perc

gene name

HSD17B2

NM_002153

95

99

Hydroxysteroid (17-beta) dehydrogenase 2

MYOC

NM_000261

99

99

Myocilin, trabecular meshwork inducible glucocorticoid response

OGN

NM_014057

92

99

Osteoglycin (osteoinductive factor, mimecan)

SFRP4

NM_003014

93

99

Secreted frizzled-related protein 4

AOC2

NM_009590

91

99

Amine oxidase, copper containing 2 (retina-specific)

DIO3

NM_001362

85

99

Deiodinase, iodothyronine, type III

SLC2A5

NM_003039

62

99

Solute carrier family 2 (facilitated glucose/fructose transporter), member 5

XIST

AK025198

99

99

X (inactive)-specific transcript

TFPI2

NM_006528

99

99

Tissue factor pathway inhibitor 2

CYR61

NM_001554

98

99

Cysteine-rich, angiogenic inducer, 61

FGFBP2

NM_031950

84

99

Ksp37 protein

FBP2

NM_003837

64

99

Fructose-1,6-bisphosphatase 2

EGFL6

NM_015507

74

99

EGF-like-domain, multiple 6

IL8

NM_000584

66

99

Interleukin 8

MFAP4

L38486

99

99

Microfibrillar-associated protein 4

CCL2

NM_002982

90

99

Chemokine (C-C motif) ligand 2

ZIC1

NM_003412

67

99

Zinc family member 1 (odd-paired homolog, Drosophila)

COL9A1

NM_001851

88

99

Collagen, type IX, alpha 1

CCL26

NM_006072

79

99

Chemokine (C-C motif) ligand 26

PITX2

NM_000325

89

99

Paired-like homeodomain transcription factor 2

ALDH1A1

NM_000689

78

99

Aldehyde dehydrogenase 1 family, member A1

HBG1

NM_000559

75

99

Hemoglobin, gamma A

S100A6

NM_014624

99

99

S100 calcium binding protein A6

IL6

NM_000600

77

99

Interleukin 6 (interferon, beta 2)

HBG2

NM_000184

71

99

Hemoglobin, gamma G

C13orf33

NM_032849

93

99

Chromosome 13 open reading frame 33

RBM3

NM_006743

96

99

RNA binding motif (RNP1, RRM) protein 3

CFB

NM_001710

94

99

Complement factor B

EGR1

NM_001964

99

99

Early growth response 1

PTX3

NM_002852

97

99

Pentraxin-related gene, rapidly induced by IL-1 beta

Note that although XIST, EGFL6 and RBM3 are x-chromosomal transcripts, their high interindividual variation could not be explained by the gender of the donors (data not shown). Perc: percentile.

Genes with high interindividual variability (CV > 90th percentile)

Among the 390 genes with both a high CV and high μint there was an overrepresentation of genes involved in antigen processing as well as the complement and coagulation cascades. The 824 genes with a high CV and moderate μint showed an overrepresentation of genes involved in focal adhesion and cytokine-cytokine receptor interaction, and the 762 genes with high CV and low μint showed an overrepresentation of genes involved in type I diabetes mellitus. The latter group contains mainly major histocompatibility complex genes and interleukin 1α [genbank: NM_000575].

Genes with low interindividual variability (CV < 10th percentile)

Table 4 shows the thirty genes with the most stable expression in macular RPE. Among the expressed genes (μint > 10th percentile) with stable RPE gene expression (CV < 10th percentile, n = 1,972) there were no genes overrepresented in Kegg pathways. One hundred and ninety four of these 1,972 genes had high expression levels, 1,064 had moderate expression levels and 714 had low expression levels. Using the DAVID software, a significant overrepresentation of genes in the glycosaminoglycan degradation pathway was found in the group of 194 genes with stable expression and high expression levels (Ease score p value < 0.001) (Table 1).
Table 4

The thirty genes with the least interindividual variation in macular RPE gene expression levels among six healthy human donors, sorted ascending by coefficient of variation (CV).

gene symbol

Genbank ID

mean intensity μint perc

CV perc

gene name

EXOC3

BC001511

85

< 1

Exocyst complex component 3

PDXK

AI571369

74

< 1

Pyridoxal (pyridoxine, vitamin B6) kinase

ACY1

NM_000666

63

< 1

Aminoacylase 1

SS18L1

AB014593

70

< 1

Synovial sarcoma translocation gene on chromosome 18-like 1

FOSL1

NM_005438

86

< 1

FOS-like antigen 1

FPRL2

NM_002030

56

< 1

Formyl peptide receptor-like 2

CPSF4

NM_006693

62

< 1

Cleavage and polyadenylation specific factor 4, 30 kDa

FAM110B

AK023658

40

< 1

Chromosome 8 open reading frame 72

RAB20

AW861333

20

< 1

Transcribed locus

CHD2

AW896069

15

< 1

Chromodomain helicase DNA binding protein 2

BI001591

BI001591

20

< 1

Transcribed locus

FATE1

NM_033085

37

< 1

Fetal and adult testis expressed 1

CLEC4E

NM_014358

44

< 1

C-type lectin domain family 4, member E

PARN

NM_002582

75

< 1

Poly(A)-specific ribonuclease (deadenylation nuclease)

KIAA0586

NM_014749

59

< 1

KIAA0586

TMEM156

NM_024943

41

< 1

Transmembrane protein 156

CSNK2A1

NM_001895

59

< 1

Casein kinase 2, alpha 1 polypeptide

CGI-96

NM_015703

35

< 1

CGI-96 protein

LOC442100

BM127012

26

< 1

Transcribed locus

MYST3

NM_006766

93

< 1

MYST histone acetyltransferase (monocytic leukemia) 3

ZMYND8

AF144233

33

< 1

Protein kinase C binding protein 1

C20orf11

AK025775

88

< 1

Chromosome 20 open reading frame 11

GAK

NM_005255

89

< 1

Cyclin G associated kinase

SOBP

NM_018013

67

< 1

hypothetical protein FLJ10159

ZNF665

NM_024733

16

< 1

Zinc finger protein 665

BG742052

BG742052

57

< 1

cDNA clone

OR2A7

AF327904

57

< 1

Olfactory receptor, family 2, subfamily A, member 7

PRKCE

NM_005400

18

<1

Protein kinase C, epsilon

RNF14

AB022663

88

< 1

Ring finger protein 14

SELENBP1

NM_003944

93

< 1

Selenium binding protein 1

Perc: percentile.

Gene expression analysis of known retinal disease genes

Known macular disease genes

We then investigated both the expression levels and interindividual expression differences of 14 macular disease genes in our RPE gene expression dataset (Table 5).[26] In terms of expression levels, 63 percent of the macular disease genes were found in the top 10 percent of genes with high macular RPE expression levels. In terms of variability, 50 percent of the macular disease genes were found in the top 10 percent of genes with highly variable macular RPE expression levels. In addition, none of the macular degeneration genes were found in the 10 percent of genes with stable macular RPE expression.
Table 5

Expression levels and interindividual differences of currently known macular disease genes with RPE expression[26].

gene symbol

Genbank accession

mean intensity μint (perc)

CV (perc)

high interindividual variation (CV > 10 th perc)

CFB

NM_001710

10,382

(94)

215

(99)

C3

NM_000064

9,910

(94)

145

(98)

FBLN5

NM_006329

2,823

(76)

136

(98)

PRPH2

NM_000322

32,204

(98)

86

(93)

GUCA1B

NM_002098

2,329

(72)

83

(93)

CST3

NM_000099

338

(26)

79

(92)

CFH

NM_000186

8,508

(92)

77

(91)

TIMP3

NM_000362

7,209

(91)

73

(90)

intermediate interindividual variation (CV 10 th – 90 th perc)

C2

NM_000063

1,689

(65)

71

(90)

BEST1

NM_004183

132,611

(99)

58

(84)

HTRA1

NM_002775

45,420

(99)

47

(74)

EFEMP1

NM_004105

20,292

(97)

44

(70)

C1QTNF5

NM_015645

23,226

(98)

40

(64)

TLR4

NM_003266

225

(45)

38

(60)

low interindividual variation (CV < 90 th perc)

none

-

-

 

-

 

Data are grouped by coefficient of variation (CV) in descending order into three groups, high CV (> 90th percentile, CV > 72), intermediate (CV 10th – 90th percentile) and low CV (< 10th percentile, CV < 19). Perc: percentile.

A number of genes currently known or suggested to be associated with AMD, showed high (C3 [genbank: NM_000064], CFB [genbank: NM_001710], CFH [genbank: NM_000186], HTRA1 [genbank: NM_002775], and CST3 [genbank: NM_000099]) or moderate (FBLN5 [genbank: NM_006329]) expression levels in the RPE. With the exception of HTRA1 [genbank: NM_002775], all these genes also showed high interindividual variation.

Known peripheral retinal disease genes

Finally, we analyzed the gene expression levels and interindividual differences in expression of 93 genes known to be involved in diseases of the peripheral retina[26] in our macular RPE expression dataset (Table 6).
Table 6

Expression levels and interindividual differences of currently known peripheral disease genes with RPE expression[26].

gene symbol

Genbank accession

mean intensity (μint) (percentile)

CV (percentile)

high interindividual variation (CV > 90 th percentile)

COL9A1

NM_001851

5,628

(88)

225

(100)

RBP4

NM_006744

20,291

(97)

196

(100)

COL2A1

NM_001844

527

(37)

122

(97)

GNAT1

NM_000172

100,271

(100)

80

(92)

RDH5

NM_002905

6,716

(90)

78

(92)

RLBP1

NM_000326

43,011

(99)

74

(91)

intermediate interindividual variation (CV 10 th – 90 th percentile)

PRCD

AK054729

46,964

(99)

66

(88)

GUCY2D

NM_000180

2,672

(75)

64

(87)

NPHP3

AI200954

3,266

(79)

63

(86)

IMPDH1

NM_000883

12,177

(95)

63

(86)

LRAT

NM_004744

15,935

(96)

62

(86)

LRP5

NM_002335

519

(37)

62

(86)

RD3

AV721413

11,010

(95)

61

(86)

RGR

NM_002921

39,454

(99)

57

(83)

TULP1

NM_003322

6,284

(89)

57

(83)

AHI1

AL136797

7,168

(91)

56

(82)

SEMA4A

NM_022367

2,824

(76)

54

(80)

TEAD1

AL133574

4,473

(84)

51

(78)

PANK2

NM_024960

273

(20)

48

(75)

PEX7

NM_000288

1,734

(65)

48

(75)

OAT

NM_000274

3,762

(81)

45

(71)

CDH3

NM_001793

9,589

(93)

45

(71)

BBS10

NM_024685

1,556

(63)

45

(71)

PRPF8

NM_006445

6,510

(90)

40

(64)

TIMM8A

NM_004085

913

(50)

39

(63)

FZD4

NM_012193

7,964

(92)

39

(62)

OPA3

NM_025136

20,540

(97)

39

(62)

COL11A1

NM_001854

911

(50)

38

(61)

PGK1

NM_000291

12,114

(95)

37

(59)

CYP4V2

AK022114

5,411

(87)

37

(58)

JAG1

NM_000214

1,394

(60)

35

(53)

MYO7A

NM_000260

6,691

(90)

34

(53)

BBS2

NM_031885

4,446

(84)

34

(51)

PAX2

NM_003990

2,946

(77)

33

(50)

BBS1

NM_024649

3,750

(81)

32

(46)

NYX

NM_022567

372

(28)

32

(45)

ABCC6

NM_001171

1,555

(63)

28

(35)

MERTK

NM_006343

4,763

(85)

28

(35)

ARL6

BI914103

1,023

(53)

28

(34)

MFRP

NM_031433

13,200

(96)

27

(33)

PXMP3

NM_000318

5,341

(87)

25

(25)

ALMS1

AB002326

962

(51)

23

(20)

MKKS

NM_018848

2,056

(69)

22

(16)

NDP

NM_000266

1,335

(59)

20

(14)

PHYH

NM_006214

3,293

(79)

20

(12)

WFS1

NM_006005

4,935

(86)

19

(10)

low interindividual variation (CV < 10 th percentile)

PRPF31

NM_015629

3,187

(78)

17

(06)

PEX1

NM_000466

3,008

(77)

15

(04)

PRPF3

NM_004698

7,057

(91)

14

(03)

TRIM32

NM_012210

2,277

(71)

13

(02)

CLN3

NM_000086

2,330

(72)

8

(0)

Data are grouped by coefficient of variation (CV) in descending order into three groups, high CV (> 90th percentile, CV > 72), an intermediate group (CV 10th – 90th percentile) and CV low (< 10th percentile, CV < 19).

Of this group, 32 percent were found in the 10 percent of genes with high expression levels in the macular RPE. Eleven percent of the known peripheral disease genes were found in the 10 percent of genes with high interindividual variation in expression in the macular RPE.

Discussion

This study presents the first comprehensive analysis of the macular RPE transcriptome, with a focus on interindividual differences in RPE gene expression levels. We based our analyses on microarray data from six healthy human donor eyes. In addition, we performed a Kegg pathway analysis on genes with high, moderate and low expression levels and on genes with high and low interindividual variation in expression.

Only five genes from our top 30 most highly expressed RPE genes were previously known to be expressed in the human RPE in vivo: SLC17A7 [genbank: NM_020309][14], CST3 [genbank: NM_000099])[18], PTGDS [genbank: NM_000954][17], TTR [genbank: NM_000371][14]and HSP90B1 [genbank: NM_003299])[21] illustrating the lack of knowledge on the RPE transcriptome.

Strengths and limitations of the study design

A recent statistical review suggested that a microarray study investigating a single tissue type, requires 6 biological replicate samples to draw statistically significant conclusions.[27] Consequently, we used the RPE gene expression from 6 different individuals. Previous RPE gene expression studies were based on less than six eyes, with the exception of a single cDNA microarray study limited to 4,325 genes that was based on 15 individuals. [812]

Our study design has a number of strong points and limitations, previously described in detail.[13] In summary, the strength of our study design comes from our strict selection criteria for the donor eyes (see Figure 3), the use of a laser dissection microscope for high cellular specificity and minimal tissue manipulation, large scale analysis using a 22 k microarray and a common reference design for comparison of all samples. Overall, our study was designed to minimize gene expression differences due to sampling methodology (see Figure 3) and technical causes, avoiding unnecessary mechanical handling of the freshly frozen tissue, the use of laser dissection microscopy to isolate homogeneous cell samples, stringent control of RNA quality and amplification procedures.[9, 1113] Initially, we performed dye swap experiments as technical replicates for three of our samples in order to ascertain the potential variability induced by dye bias. We observed a high correlation between the data from our analysis including and excluding the dye swap experiment (data not shown). At the same time, Dobbin (2003), Simon (2003) and others, used a similar study design as we did, and concluded that in a common reference design it is not necessary to perform dye swaps if the common reference is consistently labeled with the same dye. [28, 29] Potential gene-specific dye bias will affect all experimental samples equally, and therefore does not confound the comparisons.[27] Consequently, we decided to perform the remaining three experiments without a dye swap.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-164/MediaObjects/12864_2008_Article_2048_Fig3_HTML.jpg
Figure 3

Flow diagram of criteria used for the selection of donor eyes. Donor eyes were required to meet all selection criteria before inclusion in the study. X indicates exclusion from the study. Donors were all between 60 and 80 years old in order to exclude the presence of undetected monogenic disorders. The presence of any known eye disease or malignancy was used as an exclusion criterion since both can alter (RPE) gene expression levels. Post mortem times were required to be less than 30 hours to reduce the effects of RNA degradation. Ocular abnormalities on visual or histological inspection served as exclusion criteria, specifically any signs of early AMD, defined by us as the presence of more than 1 druse per 10 histological sections. Poor morphology of the retina was also an exclusion criterion.

One of the methodological limitations of our study was the limited number of eyes that met our selection criteria. The availability of a larger number of eyes would render more robust results with regard to interindividual variation. Nonetheless, our data give a good first impression of variability in gene expression levels in the RPE. An additional limitation is that a small amount of photoreceptor contamination was inevitably present in our RPE sample, see also table 2.[8, 13, 30] Furthermore, we cannot distinguish possible transient from permanent gene expression level differences. Our study is also limited by the fact that the measurement of gene expression of individual genes by microarray is inevitably influenced by a number of factors, like oligo design and the continuous updates of the human genome sequence. To correct for this last limitation, we focused our analysis on groups of genes with a wide range of expression levels, rather than on individual gene expression levels. Finally, our cut off criteria for high and low expression levels and interindividual differences are arbitrary. While this may indeed have consequences for individual genes, the impact on our functional analysis, which is based on large numbers of genes, will be minimal.

Despite these limitations, our data, combined with data from other retinal gene expression studies (which use a range of techniques, like SAGE and RT-PCR, that bear their own limitations),[13, 31] contributes significantly to the currently expanding knowledge of the RPE transcriptome.

Functional assessment of native gene expression in the macular RPE

The notion that the identity of a cell type is determined by the genes it expresses, prompted us to analyze the native macular RPE transcriptome. In the following section we describe the overrepresented functional groups that we identified in the RPE.

Highly expressed RPE genes and oxidative stress

Both functional annotation with DAVID and Ingenuity analysis independently indicate a statistically significant overrepresentation of genes associated with oxidative phosphorylation and ATP synthesis in our dataset. This is in line with the fact that the RPE has a high metabolic activity and energy demand. The down side of this high activity is that the RPE has to deal with large amounts of oxidative stress. The oxidative stress in the RPE is further augmented by the light projected onto the retina combined with the rich oxygen supply and lipid peroxidation in phagocytosed rod outer segments.[1, 32] Given the high level of oxidative stress, the expression of genes contributing to the defense of the RPE cell against oxidative stress is essential for cell survival. Our data confirm this notion, which is highlighted by the expression of the MT1A [genbank: K01383] gene, a metallothionein, and the TP53 [genbank: NM_000546] gene in the top 30 most highly expressed RPE genes. Metallothioneins are thought to play a role in protection against oxidative stress; addition of TP53 [genbank: NM_000546] to human cell lines leads to a 50 percent decrease in reactive oxygen species.[22, 23]

RPE and the immune system

Our data show an overrepresentation of genes with highly variable expression in a number of pathways related to the immune system. We identified the following four pathways, the complement and coagulation cascades (high expression levels), the antigen processing and presentation pathway (high expression levels) and the cytokine-cytokine receptor interaction pathway (moderate expression levels). Both the antigen processing and presentation pathway and the type 1 diabetes mellitus pathway contain MHC genes responsible for antigen presentation. Cytokine production is highly sensitive to inflammation in the RPE.[33] The cytokine-cytokine receptor pathway contains a number of chemokines, small secreted proteins involved in the chemotaxic attraction of monocytes and neutrophils. The highly variable expression of genes involved in the immune system is most likely explained by both genetic differences and a variable degree of subclinical inflammation (local or systemic) among our donors.

RPE genes and the extracellular matrix (Bruch's membrane)

The close interaction of the RPE with Bruch's membrane (BM) is exemplified by the overrepresentation of genes in two pathways. The first pathway contains genes involved in extracellular matrix (ECM) receptor interaction.[13] The ECM receptor interaction pathway, part of the focal adhesion pathway, contains collagens type I, III and IV, thrombospondin, laminin beta 1 [genbank: NM_002291], fibronectin 1 [genbank: NM_002291], reelin [genbank: NM_005045], and cd44 antigen [genbank: NM_000610]. Collagen type IV, laminin and fibronectin are all main components of basement membranes, such as BM. Surprisingly, the genes in this group showed highly variable expression, which may indicate that the molecular composition of BM is different among individuals. Alternatively, it has been described that with age, the solubility of collagens in BM decreases significantly.[34] Thus, the high variability in expression levels of collagen genes between our samples can perhaps be explained by differences in the physiological donor age.

A second pathway that connects RPE expressed genes to BM is the glycosaminoglycan (GAG) degradation pathway. There was an overrepresentation of genes with stable and high expression in this pathway. GAG synthesis has been shown in cultured RPE and GAG's are secreted into the extracellular matrix and BM.[35] Interestingly, GAG's are rapidly turned over in the RPE, and the composition of GAG in BM changes with age. [3537] Our data suggest there is a strict regulation of GAG turnover in the RPE, even in donors of different ages.

Additional RPE gene functions

In addition to the involvement of the RPE genes in oxidative stress, BM and the immune system, analysis of our data revealed the following two functional categories: protein synthesis and glutamate transport.

A high level of protein synthesis is essential for the RPE to maintain its multiple functions.[1] This is exemplified by the overrepresentation of genes with high expression in the ribosomal protein activity pathway.

Glutamate transport is an important process in the RPE. The top 30 most highly expressed RPE genes contained two glutamate transporters SLC1A2 [genbank: AF131756] and SLC17A 7 [genbank: AF131756]. The latter transporter was already known to be expressed in the human RPE in vivo.[14, 38] Glutamate is an important neurotransmitter that is released from the photoreceptors both in a light influenced fashion, and upon apoptosis. Since high concentrations of glutamate are neurotoxic, re-uptake and transport of glutamate are essential for the normal retinal homeostasis.[38]

Finally, in the overlap between previous RPE studies[14] and genes with high expression in our RPE transcriptome, we identified the cell-cell signaling and interaction network. This network contains several genes involved in signal transduction, like SLC7A2 [genbank: AL512749] and NCK2 [genbank: BC007195] further emphasizing the important role of the RPE in interaction with other cell types.[39, 40]

Comparison with literature

Comparison of our most highly expressed RPE genes to the literature revealed a distinct overlap. Schulz and coworkers recently combined different analyses of the retina/RPE/choroid transcriptome, and described 13,000 retina/RPE genes found in at least two studies.[14] Out of these 13,000, we currently assign 7,231 genes to be expressed by the RPE, 1,407 of which are highly expressed. (see Additional file 2: overlap between highly expressed RPE genes and retina/RPE genes in at least two studies) In addition, the same review[14] suggested that 246 genes were expressed only in RPE studies. We assign 137 of these 246 genes to the RPE as well; 17 out of these 137 genes have high expression levels in our RPE transcriptome analysis. (see Additional file 3: overlap between highly expressed RPE genes and genes found only in RPE studies)

Finally, of the genes previously described to be specifically expressed either in the retina or the RPE in individual studies,[14] 39 genes are also present in our RPE transcriptome analysis. Twenty two of these 39 genes had high expression levels. (see Additional file 4: overlap between highly expressed RPE genes and retina/RPE genes in single studies)

While data on interindividual variation in RPE gene expression are lacking, functional properties of RPE genes have been investigated previously.

The combined functional annotation from three studies resemble our functional annotation in the following areas: gene regulation, transcription, protein metabolism, cell proliferation, survival and signaling, energy metabolism, cytoskeleton and inflammation.[8, 10, 11] The current study adds the following more specific functional categories, oxidative phosphorylation, ATP synthesis, ribosome, phosphatidylinositol signaling and aminosugars metabolism. Among the highly expressed RPE genes we identified an overrepresentation of the complement cascade and genes involved in the composition of BM.

Gene expression analysis of known retinal disease genes

In our macular RPE sample we observed that 63 percent of genes involved in macular disorders according to the literature,[26] had high expression levels. In contrast, only 32 percent of the peripheral retinal disease genes[26] were highly expressed in our sample. These figures may be biased, since the search for candidate genes has been focused on cell-specific highly expressed genes in the first place. The figures probably reflect the fact that RPE gene expression differences exist between the retinal macula and the periphery.[13] However, our data probably also imply that the mean expression level of a gene in the RPE is informative in the search for new candidate disease genes.

With respect to the variability in gene expression, we found that the interindividual differences of currently known macular retinal disease genes were somewhat higher than the overall pattern of variation seen in the entire array. Whether or not this finding is coincidental remains to be elucidated.

Conclusion

In conclusion, we present comprehensive data on (interindividual differences of the) gene expression profile of the RPE based on 22,000 genes from six different healthy human donors. This is the first study to describe the interindividual variability in gene expression levels from a microarray analysis of the RPE transcriptome.

There was no correlation between the height of gene expression (μint) and the interindividual variability (CV) (data not shown). We noted a more than hundred fold difference in CV between genes with stable expression and genes with variable expression levels.

Our data show that the RPE most likely has high levels of protein synthesis, a high energy demand and is subject to high levels of oxidative stress as well as a variable degree of inflammation. Finally, our data show high interindividual variability in expression of ECM genes and indicate a high and constant level of glycosaminoglycan (GAG) turnover, two functions related to BM.

The fact that large interindividual differences exist in the expression of a number of known retinal disease genes has not only functional implications, but is also relevant for new candidate disease gene identification and the development of dose-dependent (gene) therapeutic strategies.

Methods

Human donor eyes

This study was performed in agreement with the declaration of Helsinki on the use of human material for research. Material used in this study was provided to us by the Corneabank Amsterdam. In order to minimize genetic heterogeneity, we selected six eyes from a total of 200 human donor eyes using strict selection criteria, (see Figure 3). In summary, donors were excluded when their age was not between 60 and 80 years, when they had an eye disease or any form of malignancy and when the time between death and enucleation of the eye was more than 30 hours. Furthermore, eyes were excluded when they showed any abnormalities upon visual or histological examination: more specifically, when more than one druse was seen in 10 histological sections, or when retinal morphology was poor. All donors were Caucasian, five were male, one was female. The donors died of cardiovascular or cerebrovascular causes or of chronic obstructive pulmonary disease. Donors did not have a known ophthalmic disorder or malignancy. Globes were enucleated between 14 and 27 hours post mortem and frozen several hours later according to a standard protocol. Donors were aged 63 to 78 years at the time of death. We chose old donors in order to minimize the likelihood of the presence of yet undiagnosed monogenic eye diseases. This does not rule out the presence of the most common retinal disease in the old eye, age related macular degeneration (AMD). Therefore the donor retinas were thoroughly screened for early signs of AMD by histological examination (the presence of more than 1 druse in 10 sections). Visual examination and histological examination, including periodic acid Schiff (PAS) staining, indicated no retinal pathology in any of the donor eyes.

RPE cell sampling

Globes were snap-frozen and stored at -80°C until use. A macular fragment of 16 mm2 with the fovea in its center was cut from each of the retinas, as described previously.[13] In summary, for each eye, 10 cryosections, 8 μm thick, spaced no more than 220 μm apart were stained with periodic-acid Schiff and microscopically examined for abnormalities, such as drusen indicative of early-AMD.

Twenty μm sections from the macular areas were used for the isolation of RPE cells. These sections were dehydrated with ethanol and air-dried before microdissection with a Laser Microdissection System (PALM, Bernried, Germany) using a pulsed laser. A total of up to 10,000 RPE cells per eye were microdissected and stored at -80° Celsius.

RNA isolation and (single) amplification

Total RNA was isolated and the mRNA component was amplified essentially as described previously.[13] Next, the amplified RNA (aRNA) samples were quantified with a nanodrop (Isogen Life Science B.V., The Netherlands) and the quality was checked on a BioAnalyzer (Agilent Technologies, Amstelveen, The Netherlands). Subsequently, aRNA samples were labeled with either a Cy3 or a Cy5 fluorescent probe.

Microarray handling

A common reference design was applied in our microarray hybridizations using the common reference sample described in the study of van Soest et al (2007).[13] In summary, the common reference sample consists of aRNA from a pool of RPE/choroid isolated from 10 donor eyes (mean age 60 years). aRNA from all six donors and the common reference sample was labeled. Subsequently, labeled aRNA from the donors was hybridized against the common reference sample to six 22 k custom arrays. Initially, a dye swap experiment was performed for three of the six donor samples in order to assess potential variability introduced by dye-bias for methodological reasons (see discussion). Dye swaps were disregarded in the final analysis. Arrays were enriched for sequences expressed in RPE, neural retina and brain (Agilent Technologies, Amstelveen, The Netherlands), (see Additional file 1: Expression level and interindividual variation in all genes on the custom microarray). Hybridization, washing and scanning were performed as described previously.[13]

Data analysis

Scanned images were processed with Feature Extraction software (v 8.5 Agilent). Data from all six hybridizations was analyzed with Rosetta Resolver software (Rosetta Inpharmatics). The signal of each of the six RPE samples was normalized using the common reference sample. This enabled a direct comparison of the six RPE samples (Figure 4). We used six biological replicates in order to draw significant conclusions.[27] For each gene we calculated the mean signal intensity (μint) and standard deviation (σ) of the six biological replicates. While a limited number of genes is present on the array more than once, for the analyses of large groups of genes we regarded the number of entries on the array equal to the number of genes. Genes were grouped according to their mean intensity (μint). We defined μint above the 90th percentile as high expression, μint between the 90th and 50th percentile as moderate expression and μint between the 50th and the 10th percentile as low expression. We considered the genes in these three groups to have potential biological significance. Genes with a μint below the 10th percentile were considered to have very low expression with a doubtful biological significance.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-164/MediaObjects/12864_2008_Article_2048_Fig4_HTML.jpg
Figure 4

Study design. A. Experimental setup. Six RPE samples from 6 different donors were hybridized to six microarrays along with the common reference sample. B. Data analysis. The common reference was used to normalize the RPE expression data from the six arrays which enabled comparison of the six individuals.

In order to describe the interindividual differences in gene expression levels between all six eyes systematically, we calculated the coefficient of variation (CV), defined as the standard deviation divided by the mean (σ/μint), for each gene. We considered genes with a CV above the 90th percentile to have "high" interindividual variation in expression and genes with a CV below the 10th percentile to have "low" interindividual variation, or stable expression. Obviously, the categories for intensity and variability of expression were chosen somewhat arbitrarily, but they were essential to facilitate systematic analysis and to minimize the number of false positive results.

A functional analysis of Kegg pathways (Kyoto Encyclopedia of Genes and Genomes) was performed on genes with high, moderate and low expression levels and on genes with high and low interindividual variation using the DAVID online software.[41] Cut off criteria used were a p-value of less than 0.001 using either a Benjamini-Hochberg correction or an Ease score, which is a modified Fisher's exact test[41, 42].

We compared our RPE transcriptome to a compilation of the mammalian retina/RPE transcriptome, which is based on multiple independent gene expression studies of combinations of the neural retina/RPE/choroid in the literature[14]. Overlap between the two datasets was analyzed using Ingenuity Pathways Analysis (Ingenuity® Systems) resulting in a connectivity network describing the underlying biology of RPE cells at the genomic and proteomic level[43].

Declarations

Acknowledgements

We thank Dr. L. Pels and co-workers of the Corneabank, Amsterdam, for providing donor eyes. This study was supported by grants from the Foundation Fighting Blindness (T-GE-0101-0172), The Netherlands Organization for Scientific Research (NWO; project 948-00-013), The Royal Netherlands Academy of Arts and Sciences (KNAW), de Algemene Nederlandse Vereniging ter Voorkoming van Blindheid (ANVVB) and het Edward en Marianne Blaauwfonds van het Amsterdams Universiteitsfonds.

Authors’ Affiliations

(1)
Department of Molecular Ophthalmogenetics, Netherlands Institute for Neuroscience (NIN), an institute of the Royal Netherlands Academy of Arts and Sciences (KNAW)
(2)
Department of Bioinformatics, Erasmus Medical Center
(3)
Department of Genetics, Erasmus Medical Center
(4)
Department of Clinical Genetics, Academic Medical Centre Amsterdam

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© Booij et al; licensee BioMed Central Ltd. 2009

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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