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Keywords:

  • daily adaptation;
  • gene expression;
  • Grk1;
  • Pgc-1α;
  • photoreceptor cell;
  • retina

Abstract

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References

Photoreceptor cells face the challenge of adjusting their function and, possibly, their susceptibility to light damage to the marked daily changes in ambient light intensity. To achieve a better understanding of photoreceptor adaptation at the transcriptional level, this study aimed to identify genes which are under daily regulation in photoreceptor cells using microarray analysis and quantitative PCR. Included in the gene set obtained were a number of genes which up until now have not been shown to be expressed in photoreceptor cells, such as Atf3 (activating transcription factor 3) and Pde8a (phosphodiesterase 8A), and others with a known impact on phototransduction and/or photoreceptor survival, such as Grk1 (G protein-coupled receptor kinase 1) and Pgc-1α (peroxisome proliferator-activated receptor γ, coactivator 1alpha). According to their daily dynamics, the genes identified could be clustered in two groups: those with peak expression during the second part of the day which are uniformly promoted to cycle by light/dark transitions and those with peak expression during the second part of the night which are predominately driven by a clock. Since Grk1 and Pgc-1α belong in the first group, the present results support a concept in which transcriptional regulation of genes by ambient light contributes to the functional adjustment of photoreceptor cells over the 24-h period.

Abbreviations used
CT

circadian time

DD

constant darkness

LD

light/dark

LMPC

laser microdissection and pressure catapulting

ZT

Zeitgeber time

The photoreceptor cell and the mammalian retina as a whole have the ability to functionally adapt to the marked daily changes in ambient illumination which is mirrored in the daily changes of responses to light (for review, see Barlow 2001; Storch et al. 2007), which can be measured using the electroretinogram (ERG) (Cameron et al. 2008). Functional adjustment of the photoreceptor cell involves transcriptional gene regulation as is evident for the gene arylalkylamine N-acetyltransferase (Aanat) that codes for the key enzyme in melatonin formation, arylalkylamine N-acetyltransferase (EC 2.3.1.87) (for review, see Iuvone et al. 2005). Thus, transcriptional activation of the Aanat gene during darkness results in an increase of melatonin formation (Tosini and Menaker 1996; Tosini and Menaker 1998), which appears to be important for the functional adjustment of the photoreceptor cell and the retina as a whole to darkness (Baba et al. 2009; Sengupta et al. 2011). Transcriptional regulation of the Aanat gene is driven by a circadian clock (Niki et al. 1998) which is probably located within the retina (Ruan et al. 2006, 2008) or even within the photoreceptor cell itself (Tosini et al. 2007; Schneider et al. 2010; Sandu et al. 2011). The clock influences Aanat transcription directly by the action of a CLOCK:BMAL1 protein complex on E-boxes contained in the proximal promoter of the gene (Chen and Baler 2000; Tosini and Fukuhara 2003) and indirectly by gating cAMP-induced Aanat transcription via the expressional control of the enzyme adenylyl cyclase I (Fukuhara et al. 2004).

The understanding of photoreceptor adaptation at the transcriptional level is confined by an insufficient knowledge concerning the genes whose expression is regulated on a day/night basis in the photoreceptor cell. Although numerous genes have been shown to be under daily regulation in preparations of whole eye (Storch et al. 2007) or retina (Brann and Cohen 1987; Wang et al. 2001; Fukuhara et al. 2004; Humphries and Carter 2004; Kamphuis et al. 2005; Storch et al. 2007; Rath et al. 2009; Bedolla and Torre 2011; Mollema et al. 2011), only a few besides the Aanat gene have been demonstrated to display daily changes of expression in photoreceptor cells. These encompass several clock genes (Tosini et al. 2007; Schneider et al. 2010; Sandu et al. 2011) as well as the genes Drd4 (Klitten et al. 2008), Fos (Yoshida et al. 1993), Pde10a (Wolloscheck et al. 2011), Kcnv2 (Hölter et al. 2012), and Kcnb (Hölter et al. 2012).

To gain a better understanding of the functional adjustment of the photoreceptor cell to daily changes in light intensities, this study was aimed at identifying further genes which are under daily regulation in photoreceptor cells, and in particular those having an impact on phototransduction and/or resistance to light damage of photoreceptor cells. Rats were used as an experimental model as they possess (in contrast to most strains of mice) both intact photoreceptor cells and are capable of melatonin formation, while at the same time allowing effective transcriptome isolation from photoreceptor cells by microdissection (Schneider et al. 2010; Sandu et al. 2011; Wolloscheck et al. 2011).

Materials and methods

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References

Animals

Animal experimentation was carried out in accordance with the European Communities Council Directive (86/609/EEC) and the ARRIVE guidelines. Adult male Sprague-Dawley rats (received at the age of 8 weeks from Harlan Laboratories, Horst, The Netherlands; body weight: 150–180 g) were kept under standard laboratory conditions (illumination with fluorescent strip lights, 200 lux at cage level during the day and dim red light during the night; 20 ± 1°C; water and food ad libitum) under light/dark 12:12 (LD 12:12) for 3 weeks. When indicated, the rats were then kept for one cycle under dim red light and killed during the next cycle. Animals were killed at the indicated time points by decapitation following anesthesia with carbon dioxide. All dissections during the dark phase were carried out under dim red light. Retinas were rapidly removed and immediately processed as follows. For microarray analysis six animals and for quantitative polymerase chain reaction (qPCR) three animals were pooled in each sample.

Sample preparation

Fixation of the retina was carried out using the HOPE technique (Goldmann et al. 2006) and embedding with paraffin, deparaffinization with isopropanol and staining with cresyl violet was conducted as described earlier (Schneider et al. 2010).

Laser microdissection and pressure catapulting (LMPC)

To isolate photoreceptor cells from the stained sections in a contact- and contamination-free manner the LMPC technique was applied. LMPC was performed using a PALM MicroBeam system (Zeiss MicroImaging, Munich, Germany) with PALM RoboSoftware (P.A.L.M., Bernried, Germany). Under the 10× objective, photoreceptor cells were selected, cut, and catapulted into the caps of 0.5 mL microfuge tubes with an adhesive filling (PALM AdhesiveCaps; P.A.L.M.) by utilizing a pulsed UV-A nitrogen laser. Smaller areas of the sections were pooled to reach total average sample sizes of 4 million square microns per tube. To verify the purity of the preparations, photoreceptors were subjected to molecular analysis with rhodopsin as a marker for photoreceptors and tyrosine hydroxylase as a marker for inner retinal neurons. In photoreceptors collected by LMPC, the ratio of rhodopsin to tyrosine hydroxylase was increased about 60-fold compared to preparations of the whole retina. Alternatively, the whole retina was excised using a scalpel, collected in a 0.5 mL microfuge tube and immediately frozen in liquid nitrogen. Cell lysis for RNA preparation was carried out immediately after sample collection.

RNA extraction

RNA from the laser-microdissected tissue samples was isolated using the RNeasy Micro Kit (Qiagen, Hilden, Germany) following the manufacturer′s instructions. Briefly, the collected cells were lysed in a guanidine-thiocyanate-containing buffer (RLT buffer) supplied by the manufacturer. The lysates were diluted with RNase-free water and treated with proteinase K. The samples were then cleared by centrifugation, diluted with ethanol, and applied to an RNeasy MinElute Spin Column to bind RNA to the silica-gel membrane. After the first washing step, an on-column DNase treatment with RNase-free DNase I was carried out as described by the manufacturer. Isolated RNA was eluted in a final volume of 12 μL RNase-free water. The concentration of extracted RNA was determined using a NanoDrop 2000 spectrophotometer (Thermo Scientific, Wilmington, DE, USA) with measurement of the optical density at 260 and 280 nm.

Microarray

Gene expression profiling was performed using arrays of Rat230_2-type from Affymetrix. Biotinylated antisense cRNA was then prepared according to the Affymetrix standard labeling protocol. Afterward, the hybridization on the chip was performed on a GeneChip Hybridization oven 640, then dyed in the GeneChip Fluidics Station 450 and thereafter scanned with a GeneChip Scanner 3000. All equipment used was from the Affymetrix-Company (Affymetrix, High Wycombe, UK). The Affymetrix chip definition file (CDF) was used to annotate the arrays. The raw fluorescence intensity values were imported and processed using Robust Microarray Analysis (RMA) and the Expression Console 1.2 software (Affymetrix) and normalized by applying quantile normalization. The GO analysis was performed using DAVID Bioinformatics Resources (http://david.abcc.ncifcrf.gov/). The Rat230_2-array Set was used as the background for the GO analysis. The GO terms after correction for FDR at p ≤ 0.05 (Benjamini Hochberg) were selected for further analysis and interpretation.

Reverse transcription (RT) and quantitative polymerase chain reaction

cDNA was synthesized using the Verso cDNA Kit (Abgene, Hamburg, Germany), following the manufacturer's instructions. Briefly, 4.5 μL RNA solution was reverse transcribed by using anchored oligo-dT primers supplied with the kit in a final volume of 20 μL. cDNA was then diluted 1 : 3 in RNase-free water, with aliquots of 5 μL being used for PCR. Quantitative PCR was carried out in a total volume of 25 μL containing 12.5 μL ABsolute QPCR SYBR® Green Fluorescein Mix (Abgene), 0.75 μL of each primer (10 μM), 6 μL RNase-free water, and 5 μL sample. Primer sequences are listed in Table 1. PCR amplification and quantification were performed in an i-Cycler (BioRad, Munich, Germany) according to the following protocol: denaturation for 3 min at 95°C, followed by 40 cycles of 30 s at 95°C, 20 s at 60°C, and 20 s at 72°C. All amplifications were carried out in duplicate. By using agarose gel electrophoresis, the generated amplicons for all genes under examination were shown to possess the predicted sizes (Table 1). The amount of RNA was calculated from the measured threshold cycles (Ct) using a standard curve. Values were then normalized with respect to the amount of Gapdh mRNA present. The Gapdh expression does not show daily rhythmicity (p ≥ 0.05; one-way anova).

Table 1. Primer sequences
GeneAccession numberPrimer sequence 5′ to 3′Length of PCR product [bp]
Aanat NM_012818

Forward GAAGGGAGACAGCAGTTC

Reverse GTCCTGGTCTTGCCTTTG

144
Adra1b NM_016991.2

Forward TTCTGGCTGGGCTACTTC

Reverse ACTGGCACCCAAGGATAC

100
Asns NM_013079

Forward TGGGCAGAGATACCTATGGTG

Reverse GGTGGAGTGTTTCAAGGAGAC

110
Atf3 NM_012912

Forward CTGGGTCACTGGTGTTTGAG

Reverse GAGGACATCCGATGGCAAAG

103
Cdkn1b NM_031762.3

Forward AGGGCCAACAGAACAGAAG

Reverse TTTACGTCTGGCGTCGAAG

100
Cerk NM_001134861

Forward AGAGTGGCAAGTGGTATGTG

Reverse CGGATAAGGATGAGGTCAGAAG

135
Ctnnal1 NM_001106649

Forward TTCACCGATTCTGCCTACAC

Reverse GCATCCACACGGAAATGAAC

103
Dyrk2 NM_001108100

Forward GACAGGTGGACCCAACAATG

Reverse CCCTTCCCGATGACTTTGAG

111
Gapdh NM_017008

Forward ATGACTCTACCCACGGCAAG

Reverse CTGGAAGATGGTGATGGGTT

89
Grk1 NM_031096

Forward AAGACCGACCTCTGTCTG

Reverse CAGGCCACTGATGATCTG

138
Pax4 NM_031799

Forward CTCCTTCCTGTGGCTTCTTC

Reverse AGGTTGATGGCGCTTGTC

106
Pde6d NM_001108806

Forward GCAGGAGGAAGCTGTACTTG

Reverse GTCAGTGACGATGGAGGTTTG

147
Pde8a NM_198767.1

Forward ACGCCACTGCGTATTTCC

Reverse AGCTCCTTTCCCGCATTG

148
Pgc-1α NM_031347

Forward GCCGTGTGATTTACGTTGG

Reverse ATCCCGCAGATTTACGGTG

110
Pla2g1b NM_031585

Forward GGCCAAGAAGCTGGAAAG

Reverse CGGTCACAGTTGCAGATG

138
Rhobtb1 NM_001107622

Forward CCGCCTGGTGTTTACATC

Reverse ACGCTGGTAGTGGTCTTC

158

Western blot

For immunoprecipitation of Pgc-1α, three tissue samples (each obtained by pooling two retinas from two rats) were homogenized in 1 mL HEPES-sucrose buffer containing protease inhibitors. Insoluble material was pelleted. For antibody immobilization, protein A-agarose beads (30 μL bead volume; Invitrogen, Carlsbad, CA, USA) were washed four times and incubated with rabbit anti-Pgc-1α monoclonal antibody (1 : 250; Cell Signaling Technology, Danvers, MA, USA; #2178) at 4°C. Cell extracts corresponding to ~200 μg protein amounts were applied overnight to the antibody-coupled beads at 4°C. Bound proteins were recovered after extensive washes in phosphate-buffered saline. For Western blot analysis, samples were loaded on 4–12% NuPAGE Novex Bis-Tris gels (Invitrogen), separated and blotted onto polyvinylidene difluoride membrane (Westran S, Whatman Inc., Sanford, ME, USA). For immunodetection, membranes were blocked in 5% skimmed milk powder and the anti-Pgc-1α (1 : 500) or the rabbit anti-Grk1 polyclonal antibody (1 : 300; Santa Cruz Biotechnology, Santa Cruz, CA, USA; sc-84291) was applied overnight at 4°C. The horseradish-peroxidase-coupled secondary antibodies (goat anti-rabbit-horseradish-peroxidase 1 : 5000; Sigma-Aldrich, St. Louis, MO, USA; A0545) were visualized using an ECL detection system (GE Healthcare Amersham, Freiburg, Germany). To ensure that immunoreactivity was derived from equal protein amounts of homogenates staining with rabbit anti-β-actin polyclonal antibody (1 : 300; Sigma-Aldrich; A2066) was conducted. Densitometric measurement was performed using the ‘ImageJ 1.46o’ software (National Institutes of Health, Bethesda, MD, USA).

Statistical analysis

All PCR data are given as the mean with standard error of the mean (SEM) of four independent experiments. Based on the suggestion that ZT24 is equal to ZT0, cosinor analysis was used to determine the p-value for a daily dynamic, the acrophase (peak expression) and the amplitude of oscillation (half the difference between the highest and lowest values) using the statistic software “R” (version 2.12.1, freely available at www.r-project.org). All statistical analyses should be regarded as being explorative and p-values are given descriptively with no significance level being fixed.

Results

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References

Genes considered under daily regulation in photoreceptor cells

A gene under daily regulation in photoreceptor cells should show a day/night change in the transcriptome of not only photoreceptor cells but also in preparations of the whole retina (Schneider et al. 2010; Sandu et al. 2011; Wolloscheck et al. 2011; Hölter et al. 2012) from which transcriptome isolation is more effective. For these reasons, as a first step toward the identification of genes under daily regulation in photoreceptor cells, the transcriptomes of photoreceptor cells and whole retina preparations were screened by microarray analysis for genes showing analog day/night changes in both. This was done by selecting genes that fulfilled the demand of concurrently displaying a ZT6/ZT18 ratio equal or greater than twofold in microdissected photoreceptor cells and whole retina preparations. The resulting gene set comprises 40 genes (Table 2). Of these, 18 genes exhibited enhanced expression during day (Table 2A) and 22 genes showed increased expression at night (Table 2B). The gene set included genes already previously reported to be under daily regulation in photoreceptor cells, that is, Aanat (Niki et al. 1998; Schneider et al. 2010; Sandu et al. 2011), Drd4 (Klitten et al. 2008), Fos (Yoshida et al. 1993; Sandu et al. 2011), Kcnv2 (Hölter et al. 2012), Pde10a (Wolloscheck et al. 2011), and Rorβ (Sandu et al. 2011). As expected, all these genes showed elevated expression during the night. Clustering of the genes according to their molecular and biological functions (Table 3) revealed that they are distributed over different functional groups. Notably, some of the genes found to be under daily regulation by microarray analysis are important for the function, development and survival of photoreceptor cells (Table 3).

Table 2. Genes indicated by microarray analysis to display higher expression in photoreceptor cells during daytime (A) or night (B)
Probe set IDGene symbolGene nameUni gene ID ZT18/ZT6 ratio
PRCRet
  1. The genes listed exhibit a ≥ twofold change between Zeitgeber time (ZT) 6 and 18 in both photoreceptor cells (PRC) and preparations of the whole retina (Ret). The listing of the genes is performed according to the extent of their day/night change in photoreceptor cells. The genes Cars, Drd4, and Kcnv2 were analyzed by more than one probe set with similar results.

  2. a

    Genes previously reported to be under daily regulation in whole retina.

  3. b

    Genes previously reported to be under daily regulation in photoreceptor cells.

  4. c

    Genes confirmed by 24-h profiling (see Fig. 1, Table 4) to be under daily regulation in photoreceptor cells.

(A)
1369192_at Cdkn1b c cyclin-dependent kinase inhibitor 1BRn.298970.230.50
1394972_at Trove2 TROVE domain family, member 2Rn.146590.270.47
1370089_at Pgc-1α c peroxisome proliferator-activated receptor γ, coactivator 1 alphaRn.191720.280.48
1394970_at Cars cysteinyl-tRNA synthetaseRn.148650.290.48
1387925_at Asns c asparagine synthetase Rn.111720.310.29
1374794_at Kif15 kinesin family member 15Rn.452050.310.20
1374375_at RGD1560925 similar to 2610034M16Rik protein Rn.136890.340.29
1376963_at Dyrk2 c dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 2Rn.231890.340.47
1374034_at Cars cysteinyl-tRNA synthetaseRn.148650.340.37
1394676_at Oxa1l oxidase (cytochrome c) assembly 1-like Rn.105890.370.47
1370140_a_at Pax4 a c paired box 4Rn.145310.380.44
1390664_at Tmem116 transmembrane protein 116Rn.391410.400.29
1393396_at Sft2d3 SFT2 domain containing 3Rn.146920.400.47
1385698_at Cars cysteinyl-tRNA synthetaseRn.148650.420.14
1369268_at Atf3 c activating transcription factor 3Rn.96640.430.45
1369257_at Grk1 c G protein-coupled receptor kinase 1Rn.105480.450.47
1379340_at Lamc2 laminin, gamma 2 Rn.92780.470.47
1397882_at Zbtb8a zinc finger and BTB domain containing 8aRn.825640.480.44
(B)
1375987_at Cerk ceramide kinase Rn.995372.032.30
1389632_at Rhobtb1 c rho-related BTB domain containing 1Rn.37822.152.74
1390065_at Pde10a b phosphodiesterase 10ARn.448692.214.29
1390065_at Kcnv2 b potassium channel, subfamily V, member 2Rn.1686382.212.64
1371824_at Ak3l1 adenylate kinase 3-like 1Rn.10862.232.30
1394384_at Drd4 b dopamine receptor D4Rn.101592.262.30
1387255_at Aanat b c arylalkylamine N-acetyltransferaseRn.881802.203.48
1398354_at Ctnnal1 catenin (cadherin associated protein), alpha-like 1Rn.330212.402.14
1376057_at Pde8a c phosphodiesterase 8ARn.247712.414.29
1386935_at Nr4a1 a nuclear receptor subfamily 4, group A, member 1Rn.100002.502.06
1396871_at Kcnv2 b potassium channel, subfamily V, member 2Rn.1686382.552.03
1380305_at nod3l NOD3-like protein Rn.510412.602.64
1398580_at Wdr31 WD repeat domain 31Rn.389382.652.46
1372448_at Zdhhc5 zinc finger, DHHC-type containing 5Rn.42402.712.30
1371257_at Rorβ b RAR-related orphan receptor B Rn.2101572.923.73
1387520_at Drd4 b dopamine receptor D4Rn.101593.132.83
1377869_at Ccrn4l a CCR4 carbon catabolite repression 4-like (S. cerevisiae)Rn.150403.622.14
1370257_at Pla2g1b c phospholipase A2, group IB, pancreas Rn.42834.392.83
1372170_at Acy1 aminoacylase 1Rn.36794.622.30
1368574_at Adra1b c adrenoceptor alpha 1BRn.100324.652.62
1368321_at Egr1 a early growth response 1Rn.909611.92.14
1375043_at Fos b FBJ osteosarcoma oncogeneRn.10375017.52.87
Table 3. Grouping of genes according to their molecular and biological functions
  1. The genes listed refer to those identified by microarray analysis to be rhythmic in photoreceptor cells (see Table 2).

  2. a

    Genes previously reported to be under daily regulation in preparations of the whole retina.

  3. b

    Genes previously reported to be under daily regulation in photoreceptor cells.

  4. c

    Genes confirmed by 24-h profiling to be under daily regulation in photoreceptor cells (see Fig. 1; Table 4).

Specialized retinal functions
Visual perception/visual processing Grk1 c , Rorβ b
Photoreceptor cell development and differentiation Grk1 c , Pax4 a c , Rorβ b
Photoreceptor degeneration Grk1 c , Pgc-1α c
Non-specialized retinal functions
Cell cycle, cell death, and cell proliferation Asns c , Atf3 c , Cdkn1b c , Grk1 c , Nr4a1 a , Pgc-1a c , Pax4 a c
Development Pax4 a c , Rorβ b
DNA binding Atf3 c , Egr1 a , Fos b , Nr4a1 a , Pax4 a c , Pgc-1a c , Zbtb8a
Potassium ion transport Cdkn1b c , Kcnv2 b
Protein (de)phosphorylation Dyrk2 c , Grk1 c
Receptor activity Adra1b c , Drd4 b
Response to cAMP Aanat b c , Fos b , Pax4 a c
Response to drug/hormone Adra1b c , Asns c , Cdkn1b c , Drd4 b , Fos b , Grk1 c , Pax4 a c
RNA binding Pgc-1a c , Trove2
Regulation of transcription Atf3 c , Cdkn1b c , Egr1 a , Fos b , Nr4a1 a , Pax4 a c , Pde8a c , Pgc-1a c , Rorβ b , Zbtb8a
Intracellular transport/ transport vesicles Kif15, Rhobtb1 c

24-h profiling of gene expression under light/dark 12:12

To test and specify their daily regulation, the genes were subjected to daily profiling by qPCR (Fig. 1; Table 4). This effort focussed on 12 genes not yet reported to be under daily regulation in photoreceptor cells or retina.

Table 4. Cosinor analysis of the 24-h profiles shown in Fig. 1
GenePRC/LD 12:12Retina/LD 12:12Retina/DD 12:12
  p-valueAcrophaseAmplitudep-valueAcrophaseAmplitudep-valueAcrophaseAmplitude
  1. (A) Genes with peak expression during daytime (see Fig. 1a), (B) genes with peak expression at night (see Fig. 1b), (C) reference genes (see Fig. 1c). Abbreviations: constant darkness (DD), light/dark conditions (LD), photoreceptor cells (PRC).

(A)
 Asns< 0.0507.4630.4< 0.0508.2934.3> 0.050
 Atf3< 0.0506.2930.7< 0.0507.4832.3> 0.050
 Cdkn1b< 0.05010.1425.4< 0.05010.2817.7> 0.050
 Dyrk2< 0.0507.433.6< 0.05010.135.5> 0.050
 Grk1< 0.05011.135.1< 0.05012.626.1> 0.050
 Pgc-1α< 0.0508.524.7< 0.05010.529.4> 0.050
(B)
 Adra1b< 0.05019.015.3< 0.05018.438.4< 0.05026.024.5
 Cerk> 0.050< 0.05019.620.9< 0.0504.512.5
 Ctnnal1> 0.050< 0.05016.421.7> 0.050
 Pde8a< 0.05023.527.2< 0.05021.022.2< 0.0503.532.4
 Pla2g1b< 0.05017.135.1< 0.05015.820.8> 0.050
 Rhobtb1< 0.05022.423.7< 0.05022.220.2< 0.05023.50.3
(C)
 Aanat< 0.05021.328.0< 0.05021.438.4< 0.05021.526.0
 Pax4< 0.05011.128.1< 0.05011.041.3> 0.050
 Pde6d> 0.050> 0.050> 0.050
image

Figure 1. 24-h profiling of (a) genes predicted by microarray analysis to show enhanced expression during daytime, (b) genes predicted by microarray analysis to show enhanced expression at night, (c) reference genes. The mRNA levels are plotted both as a function of Zeitgeber time (ZT) and Circadian time (CT). Transcript levels are recorded in microdissected photoreceptor cells under light/dark (LD) 12:12 (left column), in preparations of whole retina under LD 12:12 (middle column) and in preparations of whole retina under constant darkness (DD) (right column) using qPCR. Statistical analysis of the 24-h profiles illustrated is provided in Table 4. The solid bars indicate the dark period. Data represent a percentage of the maximal value of transcript amount during the 24-h period. The value of ZT0 was plotted twice at both ZT0 and ZT24. Each value represents mean ± SEM (n = 4).

Download figure to PowerPoint

Consistent with the suggestion that the strategy conducted in this study – two-point day/night sampling in photoreceptor cells and whole retina preparations – is a productive and reliable approach for the detection of genes that are under daily regulation in photoreceptor cells, 10 of 12 genes tested displayed the expected 24-h rhythms in transcript amount in photoreceptor cells with a p-value of less than 0.05 (Fig. 1, left columns; Table 4). The genes (Cerk, Ctnnal1) that failed to show a statistically relevant cyclicity in photoreceptor cells (Fig. 1b, left column; Table 4B) displayed the expected day/night rhythm at least in preparations of the whole retina (Fig. 1b, middle column; Table 4B).

The recording of gene expression over multiple time points in the qPCR study provided a more complete profiling of the daily pattern of transcript amount. This allows the clustering of the genes with respect to their daily profiles. Those genes showing higher expression during the day generally display an increase in mRNA amount after light onset reaching a peak expression between ZT6 and ZT12 (Fig. 1a, left and middle columns; Table 4A). On the other hand, genes exhibiting higher expression during the night generally showed a daily pattern featuring an increase in expression occurring not earlier than the dark onset and reaching peak expression between ZT17 and ZT24 (Fig. 1b; left and middle columns). Notably, among all genes recorded, the amplitude of cycling in photoreceptor cells was most prominent for Grk1 and Pla2g1b (Table 4).

To align the results obtained with previous studies, qPCR measurement was also performed on the following reference genes: (i) Aanat, a gene under circadian regulation with peak expression during night (for review, see Iuvone et al. 2005), (ii) Pax4, a non-circadian gene with peak expression during daytime (Rath et al. 2009), and (iii) Pde6d, a gene not showing a day/night difference in microarray analysis and therefore not expected to cycle. Consistent with previous reports and the results obtained from transcriptomic profiling, daily rhythms in photoreceptor cells and preparations of the whole retina were observed for Aanat with peak expression at night and for Pax4 with peak expression during daytime (Fig. 1c, left and middle columns; Table 4C). As expected from transcriptomic profiling, Pde6d failed to show cyclicity in photoreceptor cells or preparations of the whole retina (Fig. 1c, left and middle columns; Table 4C).

24-h profiling of gene expression under constant darkness

To find out whether the daily regulation of the genes in photoreceptor cells is directly illumination-dependent or promoted by the entrainment of a self-cycling clock, rats were kept in DD for one cycle and mRNA recording was conducted during the next cycle (Fig. 1, right column; Table 4). The genes with peak expression during the day uniformly failed to cycle under DD (Fig. 1a, right column; Table 4A) and thus directly depend on ambient light/dark transitions. Four of the six genes showing nocturnal peak expression persisted to cycle under DD (Fig. 1b, right column; Table 4B), thus indicating their clock dependency.

According to previous findings (Schneider et al. 2010) and as might be expected for a gene whose expression is governed by the retinal clock system (for review, see Iuvone et al. 2005), the transcript amount of Aanat persisted to cycle under DD (Fig. 1c, right column; Table 4C). In contrast, Pax4 – which reportedly does not show a circadian regulation (Rath et al. 2009) and Pde6d whose expression is not even rhythmic under LD conditions – failed to cycle under DD (Fig. 1c, right column; Table 4C).

Comparison of mRNA levels between photoreceptor cells and retina

On the basis of the qPCR data obtained, the extent to which the daily regulation of the gene expression is specific to photoreceptor cells was investigated. For this, peak mRNA levels of each gene were compared between microdissected photoreceptor cells and preparations of the whole retina under LD 12:12 (Table 5). With the exception of Pax4, all genes confirmed to be under daily regulation in photoreceptor cells by 24-h profiling displayed a higher transcript amount in photoreceptor cells than in preparations of the whole retina. Among these genes Pde8a (44.5-fold) and Grk1 (18-fold) showed the most prominent enrichment in photoreceptor cells. Consistent with the validity of our approach, the photoreceptor-specific gene Pde6d (for review, see Ionita and Pittler 2007) was seen to show a 28-fold higher mRNA level in photoreceptor cells than in whole retina.

Table 5. Comparison of peak mRNA levels between photoreceptor cells (PRC) and preparations of the whole retina (retina) for genes with higher expression during daytime (A), genes with higher expression at night (B) and reference genes (C). The genes listed refer to those subjected to 24-h profiling (see Fig. 1; Table 4)
GenePRCRetinaPRC/retina ratio
  1. Transcript amount was determined by qPCR and represents the number of transcripts in relation to Gapdh x 105 (mean ± SEM with n = 4). Note that mRNA levels of the genes are higher in photoreceptor cells than in retina, except for Cerk, Ctnnal1, and Pax4.

  2. a

    Genes previously reported to be under daily regulation in preparations of the whole retina.

  3. b

    Genes previously reported to be under daily regulation in photoreceptor cells.

  4. c

    Genes presently confirmed by 24-h profiling to be under daily regulation in photoreceptor cells (see Fig. 1; Table 4).

(A)
 Asns c 119000 ± 25609680 ± 101012.3
 Atf3 c 15499 ± 483012829 ± 48281.2
 Cdkn1b c 771750 ± 623389509 ± 144698.6
 Dyrk2 c 105042 ± 734026409 ± 17734.0
 Grk1 c 25760 ± 114521435.95 ± 18318.0
 Pgc-1α c 5155 ± 9692485 ± 4532.1
(B)
 Adra1b c 341 ± 15119 ± 217.9
 Cerk 1540 ± 42223102 ± 97700.07
 Ctnnal1 111696 ± 40932207767 ± 776470.54
 Pde8a c 3293 ± 100974 ± 2544.5
 Pla2g1b c 13425 ± 42102120 ± 8526.3
 Rhobtb1 c 162 ± 4516 ± 610.1
(C)
 Aanat b c 566 ± 287150 ± 363.8
 Pax4 a c 175826 ± 27926309335 ± 481200.56
 Pde6d 654186 ± 18070023354 ± 497428.0

Daily changes in protein levels of Grk1 and Pgc-1α

Among the genes confirmed by 24-h profiling to cycle in photoreceptor cells, Grk1 and Pgc-1α are of particular importance for the function and pathogenesis of the photoreceptor cells (see 'Sample preparation'). Therefore, daily regulation of both genes was additionally investigated at the protein levels. For this, the strength of immunoreactivity was compared between ZT0, ZT12 and ZT18 by Western blot analysis (Fig. 2). It revealed that for both proteins the intensity of immunoreactivity increased between ZT0 and ZT18. This observation suggests that the daily rhythms in Grk1 and Pgc-1α transcript levels evoke corresponding variations in the protein levels, with the temporal lag reflecting the time necessary to translate mRNA into protein.

image

Figure 2. Western blot analysis of Grk1 (a) and Pgc-1α (b) conducted at different Zeitgeber times (ZTs) during the 24-h cycle. The upper lanes show representative Western blots with Grk1- and Pgc-1α immunostaining at ~63 kDa and ~118 kDa. The lower lanes show the β-actin signal to which the Grk1 and Pgc-1α immunostaining was normalized. The diagrams represent quantifications of immunoreactivity in relation to the corresponding β-actin signal. The solid bar indicates the dark period in LD 12:12. Data were obtained by densitometric measurement and represent percentages of the overall maximal value. Each value is the mean ± SEM (n = 4 for Grk1, n = 3 for Pgc-1α). Note that for both genes the intensity of immunoreactivity peaks around ZT18.

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Discussion

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References

As a first step toward the identification of genes whose transcriptional regulation may contribute to photoreceptor adaptation in response to the daily changes in ambient illumination, a gene set indicative for genes under daily regulation in photoreceptor cells was obtained. Since it contains numerous (Aanat: Niki et al. 1998; Drd4: Klitten et al. 2008; Fos: Yoshida et al. 1993; Kcnv2: Hölter et al. 2012; Nr1d1: Sandu et al. 2011; Pde10a: Wolloscheck et al. 2011; Rorβ: Sandu et al. 2011) (Table 2) but not all of the genes (clock genes: Schneider et al. 2010; Sandu et al. 2011; Kcnb: Hölter et al. 2012) previously reported to display cyclicity in photoreceptor cells, this gene set appears to comprise a substantial portion but not the total number of genes under daily regulation in photoreceptor cells. The lack of some relevant genes may be because of the selection criteria applied in this study which do not cover genes whose cycling in photoreceptor cells (i) is smaller than twofold, (ii) follows a 24-h profile which does not result in a difference between ZT6 and ZT18 or (iii) might be negated by a putative cycling in the inner retina in anti-phase with photoreceptor cells.

Using this approach, genes could be detected which undergo daily regulation and whose abundance in photoreceptor cells has not yet been confirmed. This provides a basis for the investigation of the precise role of these genes in the function and adaptation of photoreceptor cells and, may even reveal functional capacities of the photoreceptor cell that up until now have not been recognized or have been disregarded. Such a gene is Atf3 that encodes a member of the ATF/cyclic AMP response element-binding (ATF/CREB) family of transcription factors that – interacting with a number of other transcription factors – bind to cyclic AMP response elements (CREs) in the promoters of various genes (for review, see Hunt et al. 2012). As CRE-sequences are also abundant in the Aanat promotor (Baler et al. 1997; Haque et al. 2011) this implies that Atf3 might contribute to the rhythmicity in Aanat transcription and melatonin formation. More generally, Atf3 is considered an ‘adaptive-response’ gene whose expression is up-regulated by detrimental and stressful stimuli in many tissues including retina (Takeda et al. 2000; Prasad et al. 2010; Saul et al. 2010; Guo et al. 2011) to induce protective and regenerative events (for reviews, see Thompson et al. 2009; Hunt et al. 2012). The results presented here now suggest that Atf3 mediates cell adaptation not only in response to pathological signals but also in the context of physiological signals such as ambient lighting conditions.

More importantly, genes with a known impact on photoreceptor physiology and pathology were seen to be under daily regulation, as was the case with Grk1, a gene encoding a kinase that phosphorylates the rod visual pigment rhodopsin and in turn thus contributes to the termination of phototransduction (for review, see Arshavsky 2002) and subsequently to the maintenance of rod sensitivity (Cideciyan et al. 1998; Sakurai et al. 2011). Concurrent daily regulation of Grk1 activity through differential transcript expression (this study), protein phosphorylation (Horner et al. 2005; Osawa et al. 2011) and abundance of the Grk1 inhibitor recoverin (Wiechmann and Sinacola 1997; Zernii et al. 2011; Grigoriev et al. 2012) is consistent with a key role of Grk1 in regulating the daily adjustment of visual processing and sensitivity (for review, see Barlow 2001). Furthermore, Grk1 deficiency causes light-dependent retinal degeneration (Chen et al. 1999; Yetemian et al. 2010) and is responsible for the Oguchi form of ‘congenital stationary night blindness’ (Yamamoto et al. 1997; Khani et al. 1998; Zhang et al. 2005; Hayashi et al. 2007; Oishi et al. 2007; Azam et al. 2009). The daily regulation of Grk1 expression as reported in this study may therefore also be of pathological interest.

Daily rhythmicity in photoreceptor cells for the first time demonstrates daily changes in Pgc-1α gene expression in a tissue of the nervous system. Pgc-1α encodes a transcriptional coactivator which coordinates energy metabolism in many tissues and this appears to account for its implication in the pathogenesis of several neurodegenerative disorders including Huntington's and Parkinson's disease (for reviews, see Ross and Thompson 2006; Róna-Vörös and Weydt 2010; Turner and Schapira 2010). Pgc-1α was recently found to up-regulate the expression of several genes involved in phototransduction including Grk1 (see above) (Egger et al. 2012). Based on this observation the light-dependent 24-h cyclicity of Pgc-1α expression (this study) suggests a role for Pgc-1α in mediating the daily adjustment of phototransduction through the transcriptional regulation of relevant target genes. Pgc-1α is also reported to play a role in decreasing the light damage susceptibility of the retina and photoreceptor cells (rods), since (i) Pgc-1α-/- mice show a pronounced deterioration in retinal morphology and function upon detrimental light treatment, (ii) over-expression of Pgc-1α evoked strong anti-apoptotic effects, and (iii) Pgc-1α expression is decreased in mouse models of retinitis pigmentosa (Egger et al. 2012). The protective effect of Pgc-1α against light damage (Egger et al. 2012), together with the steady increase in Pgc-1α expression during the light phase (this study) is consistent with the possibility that the daily regulation of Pgc-1α decreases the light damage susceptibility of the retina and photoreceptor cells. Remarkably, Pgc-1α – as a coactivator of Rorα – also regulates the transcription of the clock genes Bmal1, Clock, Rev-erbα, and Rev-erbβ and thus influences clock function and physiological rhythmicity (Liu et al. 2007). The illumination-dependent regulation of Pgc-1α expression (this study) might therefore contribute to the entrainment of the photoreceptor clock/retinal clock system by light (Rohleder et al. 2006).

The daily regulation of Grk1 and Pgc-1α observed in this study was found to be directly light-dependent. This suggests that the light-dependent transcriptional regulation of genes which are able to have an effect on visual processing and/or light damage susceptibility contributes to the daily adjustment of photoreceptor function. Of interest is the finding that the daily regulation of Kcnv2, a potassium channel essential for visual signal transduction in rods and cones (Wissinger et al. 2008, 2011), has recently been observed to be directly clock-dependent (Hölter et al. 2012). A hypothetical concept is therefore proposed in which the transcriptional regulation of photoreceptor function is concurrently regulated by ambient illumination (via Grk1 and Pgc-1α) and a circadian clock (via Kcnv2).

In conclusion, the present results provide a productive and reliable basis for future research focused on how transcriptional gene regulation has an impact on the adjustment of phototransduction and light damage susceptibility of photoreceptor cells to environmental lighting conditions.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References

We thank Ute Frederiksen, Ursula Göringer-Struwe, and Kristina Schäfer for their excellent technical assistance. We also thank Ute Frederiksen and Susanne Rometsch for assistance in the preparation of the Figures, Bettina Wiechers-Schmied for secretarial help and Dr. Debra Bickes-Kelleher for linguistic assistance. The data contained in this study are included in the theses of Stefanie Kunst and Philip Hölter, Alexander Wengert and Markus Grether as a partial fulfillment of their doctorate degree at the Johannes Gutenberg University, Mainz. The study was supported by grants from the Naturwissenschaftlich-Medizinischen Forschungszentrum (NMFZ) of the University of Mainz. The authors declare no conflict of interest.

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