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1From the Guerrieri Center for Genetic Engineering and Molecular Ophthalmology at the Wilmer Eye Institute; the 3Department of Ophthalmology, Wilmer Eye Institute; 4Departments of Biostatistics, 8Oncology, 9Pathology, 10Molecular Biology and Genetics, 7Neuroscience and the 11McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland; 5Santen Pharmaceutical, Takayama, Japan; and 6Hôpital Saint-Antoine, Paris, France.
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Abstract |
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METHODS. A microarray was constructed containing cDNA fragments corresponding to genes known or postulated to be involved in normal retinal function, development, and disease. Gene expression in rd1 retina was compared with age-matched control retinas at three time points: the peak of rod degeneration (postnatal day [P]14), early in cone degeneration (P35), and during cone degeneration (P50). Selected microarray results were confirmed with real-time PCR. The cellular distribution of one of the differentially expressed genes, dickkopf 3 (Dkk3), was assessed by in situ hybridization.
RESULTS. At each stage of degeneration, there was only limited overlap of the genes that showed increased expression, suggesting the involvement of temporally distinct molecular pathways. Genes active in transport mechanisms and in signaling pathways were differentially expressed during rod degeneration, whereas genes with functions in protein modification and cellular metabolism were differentially expressed during cone degeneration. Increased expression of genes involved in cell proliferation pathways and oxidative stress was observed at each time point.
CONCLUSIONS. These microarray results provide clues to understanding the molecular pathways underlying photoreceptor degeneration and indicate directions for future studies. In addition, comparisons of normal and degenerated retina identified numerous genes and ESTs that are potentially enriched in rod photoreceptors.
Although loss of rods has substantial functional consequences such as night blindness and some constriction of visual fields, rod death by itself does not cause severe vision loss. Rather, it is the delayed wave of nonautonomous cone photoreceptor degeneration that results in blindness. The mechanism by which death of rods leads to death of cones is unknown. Several lines of evidence suggest that cone viability may depend on diffusible or contact-mediated factors,4 5 6 but it is not yet established whether their absence results in cone death. It has been proposed that these survival factors may be rod derived.7 Identification of gene expression changes that accompany cone degeneration is an important step toward distinguishing between these possibilities and generating new hypotheses.
Naturally occurring and experimentally generated rodent models have been valuable for exploring the histopathological, functional, and molecular changes that occur during retinal degeneration.8 9 One well-studied model is the retinal degeneration (rd1) mutant mouse, which has a deficiency in the rod photoreceptor-specific cGMP phosphodiesterase ß-subunit (ßPDE), a mutation that also causes retinal degeneration in humans.10 The phenotype of the rd1 mutant allows one to address several questions applicable to many of the retinal dystrophies. For example, although early changes in cGMP and calcium dynamics11 12 can be directly attributed to the ßPDE mutation, the molecular events that lead to rod death and delayed cone degeneration are unclear. Recent data have described morphologic and molecular changes in inner retinal cells during and after photoreceptor degeneration in rd1 and other models,13 14 15 yet the role of these cells in promoting or protecting photoreceptors from apoptosis is unknown. In addition, a variety of neurotrophic factors have been reported to reduce the rate of photoreceptor loss in rd1 and other retinal degenerations (Refs. 16 , 17 and references therein). Although Müller cells and microglia have been implicated in this process,18 19 20 the molecular mechanisms involved remain to be established.
Because rod and cone photoreceptor death and the reactive changes that occur in neighboring retinal cells are likely to involve the coordinated expression of multiple genes, one approach to identifying pathways involved, as well as individual disease-causing genes, is to monitor alterations in gene expression associated with degeneration.21 22 Among the methods being used to profile retinal gene expression, data mining of cDNA libraries23 24 25 and serial analysis of gene expression (SAGE)26 are well suited for in-depth quantitation of gene expression under a single experimental state. In comparison, microarray technologies have the advantage of being able to measure gene expression changes across multiple experimental conditions or different disease states.27 28 29 In a study of the rd1 mouse utilizing a 588-gene cDNA macroarray, Jones et al.30 found increased expression of one gene, nucleoside diphosphate kinase nm23-M2, which is involved in transcriptional regulation and suppression of metastasis.30 With a 205-gene apoptosis-related macroarray, upregulation of frizzled-related protein-2 (SFRP2) was detected in donor retinas from patients with retinitis pigmentosa,31 and subsequent experiments have provided additional data implicating involvement of the Wnt signaling pathway.32 Further demonstrating the power of these approaches, both SAGE33 and microarray analysis34 have helped in the identification of inosine monophosphate dehydrogenase type 1 (IMPDH1) as the mutated gene in a form of autosomal dominant retinitis pigmentosa (RP10).
In this study, we developed a custom mouse retinal cDNA microarray that includes genes that have predicted expression in retinal neurons and glia. We used the array to analyze gene expression in degenerating rd1 retina and identified genes and molecular pathways that are potentially involved in rod photoreceptor death. Furthermore, by comparing a time series of degeneration, we identified gene expression changes that may mediate disease progression—in particular, the non–cell-autonomous death of cone photoreceptors. An additional finding was the identification of novel retina-enriched genes and expressed sequence tags (ESTs), several of which map to known human disease loci and can be considered as potential candidate genes for retinal diseases.
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Methods |
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The clones were rearrayed into 96-well plates, PCR amplified, purified, and eluted in TE. PCR-amplified clones were suspended in a final concentration of 50% dimethylsulfoxide (DMSO) and arrayed onto silylated slides (SuperAmine; Telechem, Sunnyvale, CA) using an arrayer (Microgrid II; Biorobotics, Cambridge, UK) with 100-µm-tip quill pins (Biorobotics). The spots were printed in duplicate on the array, separated by half the length of the slide. Spot quality was verified by fluorescence staining (Sybr-Green II; Molecular Probes, Eugene, OR) on several slides from each print.
RNA Isolation and Probe Labeling
All procedures involving mice were performed in accordance with the ARVO Statement for the Use of Animals in Ophthalmic and Vision Research and were approved by the Animal Care and Use Committee at The Johns Hopkins University. Tissue samples were obtained from homozygous rd1/rd1 mice on a C57BL/6 background (for postnatal day [P]14 and P50),39 or homozygous rd1/rd1 mice on a C3H background (for P35) and age-matched control C57BL/6 mice and were processed immediately or flash frozen and stored at –80°C. Retinas were dissected under a microscope to exclude pigmented epithelium and other extraretinal tissue. Total RNA was isolated using phenol-chloroform extraction (TRIzol; Invitrogen, Carlsbad, CA). Retinas from four animals were pooled to account for the small amount of retinal tissue and low RNA yield per retina and also to minimize the effect of biological variability. A reference sample was made containing 30% retina RNA and 70% brain RNA, derived from a mix of strains at various ages, from juvenile to adult. RNA integrity was assessed by gel electrophoresis with A260/A280 absorbance ratios and by analyzing an aliquot (Bioanalyzer; Agilent, Palo Alto, CA).
Microarray Hybridization
Details of arraying and hybridization methodologies are included in the Supplemental Methods (available at www.iovs.org/cgi/content/full/45/9/2929/DC1) and were reported previously.35 Probe preparation and hybridization were based on published protocols.40 41 A minimum of four replicates were performed of each experiment, and dye swaps were used to minimize the potential for differential dye effects. The RNA used in replicate experiments for the mutant mice was from different preparations. A reference sample experimental design (wild-type versus reference and mutant versus reference) was used for time points P14 and P50, and a direct comparison design (wild-type versus mutant) was used at time point P35. This difference in comparison design was not predicted to affect the final results because each time point was analyzed separately to determine significant differential expression, and all subsequent clustering analyses were performed on the calculated mutant–wild-type ratios. Furthermore, we calculated the distribution of the expression ratios at each time point by dividing the ratios into groups that increased by 0.1 log2 units. The distributions for the three time points were very similar, indicating that the microarray results from the two designs are highly comparable and that the mixed design did not introduce experimental bias (see the Results section and Fig. 2B ).
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Data Analysis
Spot-finding, image analysis, and quantification were performed on the scanned microarray data TIFF files (Imagene software; Biodiscovery, Inc., Marina del Rey, CA). A linear combination method was applied to calculate the adjusted intensity measurements for arrays with multiple scans, as described in Chowers et al.41 Local background was subtracted from the spot signals, and the data were log2 transformed. Low-intensity spots were flagged and then treated by flooring to a threshold defined by the average intensity of all low-intensity spots within the same array. Flagged spots with intensity lower than the threshold were upgraded to the threshold, whereas spots with intensity higher than the threshold remained at the original values. This floor method maximized the proportion of informative spots included for later analysis, and at the same time minimized the effect of poor-quality data arising from low-intensity signals. If a spot had both cy3 and cy5 channels floored, it was not included in the normalization process, and the final log ratio of this spot was assigned as zero. For all other spots, we first did loess normalization based on multivariate analysis (MVA) plots.42 Plate effects and pin effects were checked for each array based on box plots. For arrays with a plate/pin effect, a median normalization was applied within each plate/pin, and the median log ratio was subtracted from the loess-normalized log ratio. Statistical analysis of the normalized and transformed data was performed with the Significance Analysis of Microarrays (SAM) program created by Tusher et al.43
An alternative clustering analysis was also developed because our experiments included a limited number of time points and experimental conditions. T statistics were calculated between all successive time points as follows: Suppose there are n time points in the experiment. First, t statistics are calculated for the n time points for each gene i as
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m and w are the average expression ratios for mutant and wild-type, Vm and Vw are the within-group variances for mutant and wild-type, and n1 and n2 are the number of measurements for mutant and wild-type. Next, considering two successive time points, we defined expression changes of "up," "down," or "no change," by choosing a set of threshold values for the t statistics. With combinations of different expression changes at each time step, all possible expression patterns can be constructed. For example, for an experiment with three time points, we would have eight different patterns ("up–up," "up–down," "no change–up," and so on). Permutation analysis was performed to evaluate the statistical significance for each pattern. For each expression pattern, we counted the numbers of genes that have satisfied the criteria for the pattern before and after the permutation, and the ratio between these two numbers was used as a conservative estimate of the false-discovery rate.
The statistical significance of the distribution of functional classes of the differentially expressed genes was determined as follows: If the differentially expressed genes tend to come from a particular functional group, the frequency of differentially expressed genes for this group would be significantly higher than its corresponding frequency in the total gene set on the array. Binomial and Pearson 
2 tests44 were performed to check whether the distribution of the percentage of differentially expressed genes that fall into various functional categories is different from the distribution of the percentage of all genes that fall into each functional group. When each functional group is considered separately, the number of genes that fall into a single group follows a binomial distribution. Based on the null distribution of genes in the functional classes (Fig. 1) , we calculated the probability for the number of observed differentially expressed genes that fall into a functional group.
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In Situ Hybridization
Probe preparation and hybridization of mouse dickkopf 3 was performed as previously described, with minor modification.46 The detailed protocol can be found in the Supplementary Methods.
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Results |
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Various optimization and validation studies for probe labeling and hybridization were performed with the printed arrays. For example, in our experience, PCR products resuspended in 50% DMSO produced better spot morphologies and hybridization intensities than those in SSC-containing buffers (3x SSC or 25%DMSO/3x SSC; data not shown). We also found that aminosilane-coated slides produced more consistent results than those that were polylysine-coated, and various aminoallyl UTP-to-TTP ratios were tested to determine which gave the most favorable dye incorporation and signal-to-noise ratios. All subsequent analyses were performed with the optimized protocol described in the Methods section, which is similar to the protocol we recently reported for human retinal arrays.41
To assess microarray reliability, identical samples were compared using a self–self hybridization. Scatterplot analysis revealed a high degree of correlation, with an R2 of 0.9619 (Fig. 2A) . Only seven genes of the 10,752 spots on the slide (0.065%; each gene printed twice) showed a greater than twofold ratio of normalized cy5/cy3 values. As expected, low-intensity signals were more variable: All the genes that had spurious differential expression had intensity levels close to background. This test also demonstrated that there was minimal artifactual variation from dye effect.
Identification of Genes Differentially Expressed during Retinal Degeneration
A time-series experiment was performed to compare the gene expression profiles of normal and degenerating homozygous rd1 retinas. The time points analyzed (P14, corresponding to the peak of rod degeneration; P35, post-rod and early cone degeneration; and P50, during cone degeneration) were chosen in an effort to maximize the likelihood of identifying genes that promote progression of degeneration, particularly genes that contribute to cone photoreceptor death.
To determine whether experimental bias was introduced by the mixed experimental design used in this study (see the Methods section for the experimental design used) we calculated the distribution of the expression ratios of the three time points (P14, P35, and P50). The overall distributions of expression ratios were very similar at each time point (Fig. 2B) , indicating that the results from the pair-wise and reference designs are comparable and that there was no detectable introduced bias. The two later time points, P35 and P50, completely overlap, suggesting that the observed larger number of expression changes at P35 (described later) reflects true biological events and are not an artifact due to the study design.
The SAM algorithm43 was used to determine the likelihood that observed expression level changes reflect true expression level differences ("real" change) rather than artifacts due to chance (see the Methods section for the normalization and statistical protocols used). Instead of using a predefined n-fold ratio cutoff, a false-discovery rate (FDR) was calculated by considering the variability in experimental measurements and comparing the expression ratios from the microarrays with ratios calculated by randomly permuting the control and experimental groups. Gene expression changes were considered significantly different between wild-type and mutant if they were identified in the SAM analysis using a low FDR. We first established which genes were differentially expressed at each of the time points by using FDR levels in the same low range (high stringency: 1.2% for P14, 0.2% for P35, and 0.5% for P50). At P14, 70 genes were differentially expressed: 69 decreased and 1 increased. At P35, 177 genes were decreased and 33 were increased, and at P50, 144 genes were decreased and 62 were increased (Table 1 and Supplemental Table 1 at www.iovs.org/cgi/content/full/45/9/2929/DC1). Increasing the FDR for the P14 analysis to 16% resulted in a larger number of differentially expressed genes (118 decreased and 62 increased). We chose to use this FDR level to allow for comparable subsequent analyses and clustering of the three time points, realizing that a fraction of the identified genes would not represent truly differentially expressed genes.
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As expected, genes known to be differentially expressed in rod photoreceptors had decreased expression at each of the three time points. In contrast, there was little overlap in the identity of the genes with increased expression. Forty-three genes (of 62) that were upregulated at P50 were not upregulated at P14 (Fig. 4B , Table 1 and Supplemental Table 1 ). Therefore, the array results may help to distinguish rod degeneration from cone degeneration. For example, different intercellular signaling and protein-modification genes were upregulated during cone degeneration at P50 than during rod degeneration at P14. Several calcium-activated proteins were upregulated only at P50 (e.g., calpain, calmodulin). In contrast, genes involved in cellular transport were more highly represented during rod degeneration than during cone degeneration, particularly those involved in modulating ion transport and neuronal transmission, such as Ly6 and NMDA1 subunit zeta 1. These data suggest the involvement of different molecular events in rod and cone death.
We also compared the annotation of the differentially expressed genes to the frequencies of the functional classes on the entire array (Fig. 4A) . We performed binomial and Pearson 2 tests to determine whether the distribution of the percentage of differentially expressed genes that fall into various functional categories is different from their distribution in the set of all genes on the array (see Fig. 1 and the Methods section). Several functional groups had P < 0.001 (Fig. 4) , indicating that they had significantly higher or lower representation than expected (we chose a conservative probability cutoff because multiple groups were being tested).
The large data set of gene expression changes was clustered into smaller groups to identify genes that were behaving similarly, to infer biological information from the gene expression data. Standard clustering methods, such as hierarchical clustering, self-organizing maps or k-means clustering, are based on the distances between pair-wise gene expression profiles (using correlation coefficients). These methods are not optimal if only a few time points or experimental conditions are being compared, as was the case in this study. Therefore, a clustering method was developed that allowed us to determine genes that clustered based on a statistically significant threshold (see the Methods section).
The clustering analysis demonstrated that the temporal expression patterns varied among the genes, Some genes showed continuous increases, others decreased, and others had time-point–specific expression changes. Because we were interested in the progression of cone degeneration, the genes were clustered based on the change in expression from P14 to P35 and from P35 to P50. Eight different clusters were identified based on the direction of gene expression changes between P35 and P14 and P50 and P35 (Fig. 5 ; gene lists of each cluster are available in Supplemental Table 3 at www.iovs.org/cgi/content/full/45/9/2929/DC1). To assess the predictability of this clustering method, we determined which cluster contained known retinal genes. As expected, many of the genes that showed decreased expression at each time point, those in clusters 2 (down–down) and 6 (down–no change), are known retina-enriched genes. The largest number of genes was in cluster 4 (down–up), which contained genes with mutant/wild-type expression ratios that dipped at P35, and in cluster 3, which had the opposite pattern (up–down), showing a mutant/wild-type ratio that peaked at P35. For example, the expression ratios for follistatin-like 2, insulin-like growth factor–binding protein 7, and several different crystallins peaked during early stages of cone degeneration (up–down), whereas the expression ratios for numerous genes involved in protein turnover, such as proteases, ubiquitin conjugating proteins, and chaperones, were higher during rod and cone degeneration and dipped at P35 (down–up). The coordinated expression changes may indicate that clustered genes participate in similar biological processes.
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We used in situ hybridization to examine the cellular expression pattern of one of the more interesting differentially expressed genes, dickkopf 3 (Dkk3). Dkk3 is a member of the dickkopf family of Wnt-signaling regulatory genes50 and has been implicated in the control of cell proliferation.51 52 53 Dkk3 has also been found in a recent study to inhibit Wnt signaling in PC12 cells,54 but does not appear to in other contexts.55 56 57 Expression of the Dkk3 gene has not been reported in degenerating retina, although the expression patterns of several dickkopf family members and Wnt-related genes have been described in the developing and adult normal eye.58 59 Therefore, we were interested in determining whether the cellular distribution of Dkk3 changes during retinal degeneration.
Dkk3 had a more than threefold higher expression in rd1 animals at P35, compared with wild-type mice, and a twofold higher expression at P50. There was 1.6 fold higher expression at P14. Using in situ hybridization, we found that Dkk3 was expressed in inner nuclear layer and ganglion cell layer cells in normal and degenerating tissue (Fig. 6) . The overall cellular distribution pattern did not change during degeneration.
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The clones on the array (known as well as novel genes) may facilitate the identification of genes that cause hereditary retinal diseases. To generate a list of candidate genes, we determined the chromosomal position of the human counterpart of the genes that had reduced expression in our analyses. As shown in Table 2 , 42 of the mouse clones in the down–down and down–no change clusters had human homologues that mapped within chromosomal intervals implicated in retinal diseases (as defined by the RetNet database; known genes with causative mutations, and their corresponding chromosomal regions, were omitted). Despite the large size of some of these regions, the strategy of combining expression data with the chromosome position of the genes and with known retinal disease loci provides a powerful suggestion of a gene’s involvement in disease. Curiously, most of the mapped genes are involved in neuronal signaling. Several novel genes were also localized to disease regions and will be interesting to explore further.
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Discussion |
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We found particularly provocative the observation that several genes with products known to provide protection against oxidative damage, including antioxidant protein 2,63 glutathione peroxidase 2,64 and paraoxonase,65 were upregulated during the period of cone degeneration. This raises the question, could oxidative damage contribute to cone death after rods disappear? Rods and cones receive their oxygen supply from choroidal vessels that have very high flow and contain fenestrations that allow pooling of plasma, separated from the photoreceptors only by Bruch’s membrane and the retinal pigmented epithelium. A high oxygen diffusion gradient allows adequate oxygenation through the entire outer nuclear layer. The elevated oxygen supply is balanced by extreme oxygen utilization by photoreceptors, which are packed with mitochondria to support the activity of the Na/K pump required to maintain the dark current. As rods die, there is decreased oxygen demand, potentially resulting in excessive oxygen supply to the remaining cones. High levels of tissue oxygen increase oxidative stress, which can overwhelm protective mechanisms, as demonstrated by the observation that prolonged hyperoxia causes photoreceptor cell death.66 67 Thus, it seems reasonable to consider a possible contribution of oxidative damage to cone cell death.
Another interesting observation was that a group of genes usually associated with tissue growth and differentiation showed increased expression in rd1 retina during cone degeneration. Included among this group were the Wnt pathway gene dickkopf 3, membrane glycoprotein M6, insulin-like growth factor binding protein 5, and Bin-1 (also known as myc box dependent interacting protein 1). The products of these genes may act as part of signaling pathways that activate apoptosis, attempt to block apoptosis, or attempt to upregulate protective cell functions. For example, although dickkopf 3 activity in the retina has not been explored, it has been reported to be downregulated in lung carcinoma,51 52 suggesting that it may participate in the control of cellular proliferation. Increased expression of the Wnt-related genes SFRP and frizzled-4 have been demonstrated in human retinal degenerations,31 32 68 and Wnt is known to regulate apoptosis in vitro.69 70 Membrane glycoprotein M6 is associated with calcium-mediated neuronal differentiation of PC12 cells,71 and insulin-like growth factor–binding proteins are known to mediate neuronal differentiation.72
Genes that had increased expression, particularly genes involved in growth and differentiation, could be part of the glial hypertrophy and proliferation that accompany photoreceptor degeneration. The role of retinal glia cells in cone photoreceptor degeneration in rd1 mice is unclear, although during rod degeneration there are reports of increased glial fibrillary acidic protein (GFAP)73 and immediate-early genes in Müller cells.74 In our analysis, we found that vimentin, an intermediate filament protein associated with Müller cell hypertrophy75 and apoptosis76 was increased at time points P35 and P50. Pleiotrophin, a gene that is found in glia in the brain and is involved in communication with neurons77 was increased at P50. Several immune-related genes had increased expression, such as the lymphocyte antigen 6 complex, consistent with activation of microglia,78 which have also been implicated in retinal degeneration.20
Müller cells are believed to have a protective effect on rod photoreceptors in several damage paradigms by providing neurotrophins, a well-described class of growth factors.18 19 79 It is plausible that glial reactive changes attempt to protect cones in rd1, possibly through actions of the growth and differentiation genes that are differentially expressed. Detailed cell localization studies are necessary to demonstrate whether glia cells, and which type, express genes that we found upregulated on the array.
Several crystallin genes also showed expression changes during rod and cone death. For example, crystallin beta A4 was increased 4-fold in mutant P35 retinas and 2.8-fold at P14. A recent proteomics study of rd1 retinas demonstrated that several 
- and ß-crystallin family members were induced in rod degeneration.80 Similarly, Jones et al.81 reported increased expression of -crystallin at P18 in rd1 retinas, which was distributed predominantly in the inner retina and possibly in ganglion cells and Müller end processes. In contrast, at P50, when the majority of cones are gone, there was more than a twofold decreased expression of crystallin beta A4 and alpha 1-crystallin.
Crystallins are the major structural proteins in the lens and are present in other tissues, including retina.82 83 84 The extralenticular function of -crystallins includes chaperone and antiapoptosis activities, and they play important roles in differentiation, development, and neurodegeneration.85 86 87 ß-Crystallins have also been reported to have chaperone activity during cellular stress,86 88 89 but their role in degenerated retina is not known. Crystallins are phosphorylated through cAMP-dependent and -independent pathways,90 91 suggesting that they interact with signaling-related proteins. -Crystallin inhibited oxidative stress-induced apoptosis in RPE cells in culture,92 and in a lens epithelial cell line, oxidative stress-induced apoptosis was associated with decreased -crystallin.93 Introduction of -crystallin into the lens cells prevented H2O2-induced apoptosis through inhibition of caspase-3 activation.93 Based on these studies, it is possible that crystallins are involved in protecting cones from death caused by oxidative damage. This possibility clearly warrants further exploration.
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Footnotes |
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Supported by grants from the National Eye Institute, Macula Vision Foundation, and Santen Pharmaceutical Co., Ltd., and by generous gifts from Mr. and Mrs. Marshall and Stevie Wishnack and from Mr. and Mrs. Robert and Clarice Smith. ASH is supported by a Canadian Institutes of Health Research Senior Research Fellowship. PAC is the George S. and Dolores Dore Eccles Professor of Ophthalmology and Neuroscience; DJZ is the Guerrieri Professor of Genetic Engineering and Molecular Ophthalmology and is a recipient of the Senior Investigator Award from Research to Prevent Blindness, Inc. Funding for the study was provided by Santen Pharmaceutical Co., Ltd. Under a licensing agreement between Santen Pharmaceutical and The Johns Hopkins University, ASH, DJZ, IC, MK, and RHF are entitled to a share of royalties received by the university on sales of products described in the article. The terms of this arrangement are managed by The Johns Hopkins University in accordance with its conflict of interest policies.
Submitted for publication October 27, 2003; revised March 26 and April 19, 2004; accepted April 21, 2004.
Disclosure: A.S. Hackam, (P); R. Strom, None; D. Liu, None; J. Qian, None; C. Wang, None; D. Otteson, None; T. Gunatilaka, None; R.H. Farkas, None; I. Chowers, (P); M. Kageyama, Santen Pharmaceutical (E, P); T. Leveillard, None; J.-A. Sahel, None; P.A. Campochiaro, None; G. Parmigiani, None; D.J. Zack, Santen Pharmaceutical (F, P)
The publication costs of this article were defrayed in part by page charge payment. This article must therefore be marked "advertisement" in accordance with 18 U.S.C. 
1734 solely to indicate this fact.
Corresponding author: Donald J. Zack, 809 Maumenee Building, The Johns Hopkins University School of Medicine, 600 N. Wolfe Street, Baltimore, MD 21287; dzack{at}bs.jhmi.edu.
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