Molecular Vision 2017; 23:872-888
Received 13 August 2017 | Accepted 03 December 2017 | Published 05 December 2017
1Department of Psychology and Counselling, School of Psychology and Public Health, La Trobe University, Melbourne, VIC, Australia; 2Department of Biochemistry and Genetics, La Trobe Institute for Molecular Sciences, La Trobe University, Melbourne, VIC, Australia
Correspondence to: Sheila G. Crewther, School of Psychology and Public Health, La Trobe University, Plenty Road, Bundoora, VIC, Australia, 3083; Phone: +61 3 9479 2290; FAX: +61 3 9479 1956; email: S.Crewther@latrobe.edu.au
Purpose: Microarray and RNA sequencing studies in the chick model of early optically induced refractive error have implicated thousands of genes, many of which have also been linked to ocular pathologies in humans, including age-related macular degeneration (AMD), choroidal neovascularization, glaucoma, and cataract. These findings highlight the potential relevance of the chick model to understanding both refractive error development and the progression to secondary pathological complications. The present study aimed to determine whether proteomic responses to early optical defocus in the chick share similarities with these transcriptome-level changes, particularly in terms of dysregulation of pathology-related molecular processes.
Methods: Chicks were assigned to a lens condition (monocular +10 D [diopters] to induce hyperopia, −10 D to induce myopia, or no lens) on post-hatch day 5. Biometric measures were collected following a further 6 h and 48 h of rearing. The retina/RPE was then removed and prepared for liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) on an LTQ-Orbitrap Elite. Raw data were processed using MaxQuant, and differentially abundant proteins were identified using moderated t tests (fold change ≥1.5, Benjamini-Hochberg adjusted p<0.05). These differentially abundant proteins were compared with the genes and proteins implicated in previous exploratory transcriptome and proteomic studies of refractive error, as well as the genes and proteins linked to the ocular pathologies listed above for which myopia or hyperopia are risk factors. Finally, gene set enrichment analysis (GSEA) was used to assess whether gene sets from the Human Phenotype Ontology database were enriched in the lens groups relative to the no lens groups, and at the top or bottom of the protein data ranked by Spearman’s correlation with refraction at 6 and 48 h.
Results: Refractive errors of −2.63 D ± 0.31 D (mean ± standard error, SE) and 3.90 D ± 0.37 D were evident in the negative and positive lens groups, respectively, at 6 h. By 48 h, refractive compensation to both lens types was almost complete (negative lens −9.70 D ± 0.41 D, positive lens 7.70 D ± 0.44 D). More than 140 differentially abundant proteins were identified in each lens group relative to the no lens controls at both time points. No proteins were differentially abundant between the negative and positive lens groups at 6 h, and 13 were differentially abundant at 48 h. As there was substantial overlap in the proteins implicated across the six comparisons, a total of 390 differentially abundant proteins were identified. Sixty-five of these 390 proteins had previously been implicated in transcriptome studies of refractive error animal models, and 42 had previously been associated with AMD, choroidal neovascularization, glaucoma, and/or cataract in humans. The overlap of differentially abundant proteins with AMD-associated genes and proteins was statistically significant for all conditions (Benjamini-Hochberg adjusted p<0.05), with over-representation analysis implicating ontologies related to oxidative stress, cholesterol homeostasis, and melanin biosynthesis. GSEA identified significant enrichment of genes associated with abnormal electroretinogram, photophobia, and nyctalopia phenotypes in the proteins negatively correlated with ocular refraction across the lens groups at 6 h. The implicated proteins were primarily linked to photoreceptor dystrophies and mitochondrial disorders in humans.
Conclusions: Optical defocus in the chicks induces rapid changes in the abundance of many proteins in the retina/RPE that have previously been linked to inherited and age-related ocular pathologies in humans. Similar changes have been identified in a meta-analysis of chick refractive error transcriptome studies, highlighting the chick as a model for the study of optically induced stress with possible relevance to understanding the development of a range of pathological states in humans.
Myopia currently affects 23% of the world’s population, and its prevalence is rising. Approximately 4.8 billion people (50% of the population) are predicted to be myopic by 2050, nearly 1 billion of whom will be highly myopic . This poses a significant public health and socioeconomic challenge, as myopia (and high myopia in particular) increases risk for a range of sight-threatening pathologies, including cataract, glaucoma, and chorioretinal abnormalities [2-4]. Hyperopia also alters the risk of pathological complications, with several studies associating shorter axial length and/or hyperopic refraction with angle closure glaucoma and age-related macular degeneration (AMD) [3-6].
The mechanisms linking refractive errors with ocular complications are poorly understood; however, it is broadly thought that the gross structural features of myopia and hyperopia play a causative role. For example, the shorter axial length of hyperopic eyes may cause abnormalities in the size and position of anterior structures that increase risk for angle closure glaucoma [4,7,8]. Comparatively, the mechanical stress associated with distension of the vitreous chamber in myopia causes thinning and tractional changes within posterior ocular layers that potentially predispose the eye to chorioretinal abnormalities, such as retinal breaks, lacquer cracks, and posterior staphyloma [9-11]. Ultrastructural studies have demonstrated that profound myopia induction over several weeks in the chick model is sufficient to produce pathological chorioretinal abnormalities similar to those observed following decades of high myopia in humans [12-15]. During the first 4 weeks of occlusion myopia induction in the chick, cone outer segment membranes show increasingly severe signs of disruption and degeneration , and RPE cells become thinner and lose their basal infoldings [16-18]. The RPE nuclei and mitochondria also become irregular in shape [16,18], and the dispersion of the mitochondria and melanin granules within the cell becomes dysregulated . Retinal folds and detachment have been reported when occlusion is prolonged longer than 4 weeks , and by 8 weeks, lacquer crack lesions are observed in the majority of occluded chicks . In humans, these lesions predispose the development of choroidal neovascularization, which affects 5–11% of those with high myopia [2,13].
The biologic processes linked with the onset and progression of refractive error also involve genes known to be associated with the development of pathological states. The genes implicated in genome-wide association studies of refractive error in humans have pleiotropic effects in non-ocular systems where the genes are linked to a range of disorders, including diabetes, and neuropsychiatric and developmental conditions . Our recent transcriptome meta-analysis  also demonstrated significant overlap between the genes differentially expressed within the first three days of optically induced refractive error in the chick model and the genes and proteins associated with several pathologies for which myopia or hyperopia are risk factors (AMD, choroidal neovascularization, and cataract). Curiously, the overlap did not show strong specificity for the sign of ocular growth, suggesting that the non-specific features of optically induced refractive error in chicks (such as blur, loss of form vision, peripheral occlusion, or heat under the goggle) may be a sufficient source of physiological stress to induce rapid expression changes in pathology-associated genes.
Although changes in gene expression during the first 3 days of refractive error induction in the chick model have been extensively researched using discovery-driven microarray, RNA sequencing, and meta-analysis techniques, relatively little is known about the corresponding proteome changes . Thus, in the present study, we aimed to determine whether early proteomic responses to optical defocus in the chick correspond well with early transcriptome changes, particularly in terms of dysregulation of pathology-associated genes. Protein abundance changes in the retina/RPE were profiled using liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) following 6 and 48 h of optically induced myopia and hyperopia in the chick. Differentially abundant proteins were compared with the genes and proteins implicated in previous refractive error studies, as well as those associated with ocular pathologies for which myopia and hyperopia are known risk factors. To assess pathology-related genes from a more exploratory perspective, we also used gene set enrichment analysis (GSEA) to determine whether any Human Phenotype Ontology gene sets were enriched in the lens groups relative to the no lens groups, or at the top or bottom of the protein list ranked by correlation with ocular refraction.
Thirty-one male chicks (Leghorn/New Hampshire), obtained from a commercial hatchery, were raised from post hatch days 0–4 under a 12 h:12 h light-dark cycle. On post-hatch day 5, chicks were randomly assigned to a lens condition (+10 or −10 diopters [D], or no lens), and lenses (polymethyl methacrylate) attached to Velcro were fixed to the periocular feathers of the right eye. Separate no lens control animals were used because monocular treatments in chicks can affect blood flow [24,25], refraction, and axial length  in the contralateral eye. Lensing was staggered so that the two tissue collection time points were circadian matched; the chicks in group 1 were lensed 3 h into the light cycle, and the chicks in group 2 were lensed 9 h into the light cycle. All procedures adhered to the ARVO Statement for the use of Animals in Ophthalmic and Vision Research and were conducted in accordance with approved La Trobe University Animal Ethics Committee protocols.
Following 6 h (group 1) or 48 h (group 2) with the lenses attached, the chicks were anesthetized (ketamine, 45 mg/kg; xylazine, 4.5 mg/kg i.m.), and the right eye refraction and axial dimensions were determined with retinoscopy (Keeler, Vista Diagnostic Instruments, Windsor, UK) and A-Scan ultrasonography (7 MHz probe; A-Scan III, TSL: Teknar, Inc., St Louis, MO). Biometric measures were collected for five chicks per condition, with the exception of the 6 h negative lens condition where biometry could not be performed on one animal due to anesthesia effects and the 48 h no lens condition where six chicks were profiled. The chicks were then euthanized via decapitation, and their right eyes were enucleated. The section of retina/RPE between the ora serrata and 1 mm temporal to the pecten was removed under a surgical microscope (OPMI 1-FC; Zeiss, Jena, Germany), frozen in liquid nitrogen, and transferred to a −80 °C freezer. The time from initial anesthesia to tissue freezing ranged from 5 to 10 min, the order of tissue collection was counterbalanced across lens groups, and all tissue was collected in a single batch within 1 h of the designated time point (6 or 48 h).
Retina/RPE samples were resuspended in digestion buffer (8 M urea, 50 mM ammonium bicarbonate, and 10 mM DTT) and incubated for 5 h at 25 °C. Samples were then centrifuged for 15 min at 14,000 ×g, and the soluble fraction was used to determine the protein concentrations (Pierce 660 nm Protein Assay, Rockford, IL). Each sample (50 µg protein) was adjusted to 100 µl with digestion buffer, and 55 mM iodoacetamide was then added to the alkylate thiol groups at 20 °C for 35 min in the dark. The alkylated preparation was diluted to 1 M urea with 25 mM ammonium bicarbonate (pH 8.5) before sequencing-grade trypsin (Promega, Madison, WI) was added to the 5 µM final concentration. Digests were performed overnight at 37 °C. The digests were acidified with 1% (v/v) trifluoroacetic acid (TFA) and the peptides desalted on SDB-XC Empore StageTips (3M Company, St. Paul, MN), as previously described . Peptides were reconstituted in 0.1% TFA and 2% acetonitrile (ACN), and 1 µg was loaded onto C18 PepMap 100 µm ID × 2 cm trapping columns (Thermo Fisher Scientific, San Jose, CA) at 5 µl/min for 6 min, and washed for 6 min before the precolumn was switched in line with the analytical column (Vydac MS C18, 3 µm, 300 Å and 75 µm ID × 25 cm, Grace Pty. Ltd., Columbia, MD). The peptides were separated at 300 nl/min using a nonlinear ACN gradient of buffer A (0.1% formic acid and 2% ACN) and buffer B (0.1% formic acid and 80% ACN), starting at 5% buffer B to 55% over 120 min. Data were collected on an Orbitrap Elite (Thermo Fisher Scientific) in Data Dependent Acquisition mode using m/z 300–1,500 as the MS scan range. Collision induced dissociation (CID) MS/MS spectra were collected for the 20 most intense ions per MS scan. Dynamic exclusion parameters were set as follows: repeat count 1, duration 90 s, and the exclusion list size was set at 500 with early expiration disabled. Other instrument parameters for the Orbitrap were MS scan at 120,000 resolution, maximum injection time 50 ms, AGC target 1 × 106, and CID at 35% energy for a maximum injection time of 150 ms with an AGT target of 5,000. The Orbitrap Elite was operated in dual analyzer mode with the Orbitrap analyzer used for MS and the linear trap used for MS/MS.
One-way ANOVA tests using SPSS for Windows (Version 20, IBM Corp, Armonk, NY) were conducted to compare the effects of lens-wear (negative, positive, and no lens) on the refraction, axial length, and vitreous chamber depth at each time point. Tukey honest significant difference (HSD) post-hoc tests were used as required, and p values of less than 0.05 were considered statistically significant. All dependent variables were normally distributed (Shapiro-Wilks test p>0.05 and/or skewness and kurtosis within the acceptable range) and met the assumption of equal variances (Levene’s test p>0.05).
Identification and quantification of proteins were performed on raw output files from LC-ESI-MS/MS using MaxQuant (Version 220.127.116.11 [28,29]) and the built-in Andromeda search engine with the Gallus gallus UniProt FASTA database (September 2016) and common contaminants. Carbamidomethylation of cysteines was set as a fixed modification, and acetylation of protein N-termini and methionine oxidation were included as variable modifications. Parent mass tolerance was set to 5 ppm after refinement by MaxQuant, and fragment mass tolerance to 0.5 Da. Trypsin was set as the digestion enzyme with up to two missed cleavages allowed. Peptides with a minimum of seven amino-acid length were considered, and the false discovery rate (FDR) was set to 1% at the peptide and protein levels. The match-between-runs feature was used to transfer peptide identifications from one run to another based on the retention time and the mass to charge ratio. The Max LFQ algorithm  was used for label-free protein quantification.
Normalized protein group ratios obtained from MaxQuant were log2 transformed. Flagged protein groups (i.e., potential contaminants, reverse proteins, and those identified only by site) and protein groups with more than 40% missing values in any experimental condition were filtered from the results. The remaining missing values (comprising 2.51% of the data) were imputed using the hybrid QRLIC/SVD approach implemented in the imputeLCMD R package (v2.0). The data set was then normalized using the Cyclic Loess function in the Linear Models for Microarray Data R package (LIMMA; v3.30.12 ). Protein ANalysis THrough Evolutionary Relationships (PANTHER, v12)  gene ontology and protein class classifications for the 1,617 proteins remaining in the data set following filtering, imputation, and normalization are provided in Appendix 1.
Empirical Bayes moderated t tests (LIMMA R package v3.30.12) were used to identify differentially abundant proteins in the lens groups relative to no lens groups at each time point and the negative lens groups relative to positive lens groups at each time point. Protein groups displaying a fold change of greater than or equal to 1.5 (log2 FC ≥0.585) and Benjamini-Hochberg  adjusted p value of less than 0.05 were considered differentially abundant.
To enable comparisons with previous studies in other species and the use of curated human gene set files for GSEA, high confidence human orthologs for each Uniprot Accession in the data set were identified using InParanoid (v8.0) . InParanoid employs a reciprocal best hits approach that performs well in benchmarking tests of orthology inference . As orthologs could not be matched for all proteins, the data set used for the subsequent GSEA contained 1,327 identifiers. As a final step, human ortholog Uniprot IDs for the differentially abundant proteins were matched to human Ensembl gene IDs using Biomart (Homo sapiens GRCh38.p7) .
Differentially abundant proteins were compared with previous exploratory proteomic and transcriptome studies of optically induced refractive error as follows. Lists of genes and proteins implicated in transcriptome and proteomic studies published before July 2016 were obtained from Riddell and Crewther’s supplemental materials . As part of this previous study, the gene and protein lists were mapped to human ortholog Ensembl Gene IDs to enable cross-species and cross-platform comparisons. We performed a PubMed search on June 17, 2017, to update the lists with studies published after July 2016. Of the three studies published since this time [36-38], only two provided lists of implicated genes that could be used in the present analysis [37,38]. The genes implicated in these two studies were mapped to human ortholog Ensembl Gene IDs using the methods reported in our previous publication . We then compared the updated gene and protein lists with differentially abundant proteins from the present study mapped to human Ensembl gene IDs. Chord diagrams were created to display the overlapping gene and protein findings using the GOplot R package (v1.0.2) . Appendix 2 summarizes the methods used in each of the previous studies included in the comparison.
We also compared the differentially abundant proteins from the present study with genes and proteins previously associated with ocular pathologies in humans. Author-curated lists of genes associated with primary open angle glaucoma (POAG), choroidal neovascularization, cataract, sub-clinical AMD, and AMD in humans (either via genetic linkage or association or RNA or protein expression changes) were sourced from recent publications and converted to Ensembl Gene IDs using BioMart (Ensembl release 86; Homo sapiens GRCh38.p7) as described previously . The pathology-associated lists were then compared with differentially abundant proteins from the present study using the R software package GeneOverlap (v1.10.0). This package uses Fisher’s exact test to calculate the significance of overlap between each pair of lists in comparison to the genomic background. The function returns the number of intersecting findings between the two lists, the p value, and the estimated odds ratio. The null hypothesis is that the odds ratio is no larger than 1; values larger than this odds ratio indicate a positive association between lists. A contingency table was created to display the pairwise overlaps between protein and pathology lists, including the number of intersecting genes, odds ratios, and significant Benjamini-Hochberg adjusted p values. A chord diagram was created to display the overlapping gene and protein findings using the GOplot R package (v1.0.2) .
Lists of differentially abundant proteins of particular interest were further explored using the STRING database (v10.5) functional enrichment tool . This analysis identified gene ontologies and pathways that were overrepresented in the protein lists relative to the Homo sapiens genomic background.
GSEA was used to assess Human Phenotype Ontology [41,42] gene set enrichment in the lens groups relative to the no lens groups and in the protein data ranked by correlation with refraction. For the first analysis, a Homo sapiens Human Phenotype Ontology UniProt gene set file was obtained from the Bader laboratory on June 17, 2017 , and imported into the javaGSEA app  along with the data set of normalized log transformed label-free quantification (LFQ) intensity values mapped to human ortholog UniProt IDs. GSEA was then used to assess Human Phenotype Ontology gene set expression in the lens groups relative to the no lens groups at each time point. The signal-to-noise metric was used to rank proteins by the difference of the class means scaled by the standard deviation, and gene set enrichment at the top and bottom of the ranked lists was determined using 1,000 gene set permutations and an FDR q value cut-off of 0.05.
For the second analysis, correlations between refraction and normalized LFQ protein intensities across the negative lens, no lens, and positive lens groups at each time point were calculated using Spearman’s rho (Hmisc R package). The 6 h and 48 h protein lists ranked by correlation with refraction were then imported into the javaGSEA app , and GSEA was conducted using the preranked metric and 1,000 gene set permutations as described above. Leading-edge subset analysis was then used to identify the subset of proteins responsible for gene set enrichment (i.e., proteins that appeared in the ranked list at or before the point at which the running sum reached its maximum deviation from zero).
The ocular biometry measurements are illustrated in Figure 1A–C. Refractive shifts were evident in both lens groups at 6 h, and by 48 h compensation to the negative and positive 10 D lenses was almost complete (Figure 1A). One-way ANOVAs indicated that refraction was statistically significantly different between the lens groups at 6 h (F(2,11) = 68.215, p<0.001) and at 48 h (F(2,13) = 637.470, p<0.001). Post-hoc tests revealed that the refractive state of all lens groups was significantly different at both time points (p<0.01 for all comparisons). There were no statistically significant differences in the axial dimensions between the lens groups at 6 h (Figure 1B). By 48 h, however, the lens groups differed statistically significantly in axial length (F(2,13) = 8.413, p = 0.005) and vitreous chamber depth (F(2,13) = 8.539, p = 0.004). Post-hoc tests revealed that the axial length was statistically significantly shorter in the positive lens group relative to the negative lens group (p = 0.003) and that the vitreous chamber depth was statistically significantly shorter in the positive lens group relative to the no lens (p = 0.039) and negative lens (p = 0.004) groups. As expected, there was a strong negative correlation between the refraction and ocular axial dimensions (Figure 1C).
The negative and positive lens groups were first compared with age-matched no lens controls to identify differentially abundant proteins at each time point. At 6 h, 144 proteins were differentially abundant in the negative lens group, and 163 proteins were differentially abundant in the positive lens group. At 48 h, 149 proteins were differentially abundant in the negative lens group, and 173 were differentially abundant in the positive lens group. Of these differentially abundant proteins, 72 were either up- or downregulated in both lens groups at 6 h (Figure 2A), and 67 were either up- or downregulated in both lens groups at 48 h (Figure 2B). To identify abundance changes that were sensitive to the sign of defocus, we next compared the negative and positive lens groups directly at each time point. No proteins were differentially abundant between the negative and positive lens groups at 6 h, and only 13 were differentially abundant at 48 h (Figure 2C). Appendix 3 provides a detailed overview of the proteins implicated in each comparison.
Comparison of these differentially abundant proteins with the findings of previous exploratory transcriptome and proteomic studies of refractive error revealed several commonalities. Eleven of the 390 proteins identified as differentially abundant in the present study have been implicated in previous proteomic studies (Figure 3A). This relatively low number of commonalities may reflect the use of gel electrophoresis in most previous studies, as well as differences in the species, tissues, and time points profiled (see Appendix 2). In contrast, 55 of the 390 differentially abundant proteins have been implicated in previous transcriptome studies where the methodological conditions often matched those of the present study more closely (Figure 3B,C). We also compared our findings with the results of a previous transcriptome meta-analysis, which analyzed the subset of refractive error transcriptome studies conducted in the chick model. This comparison identified another ten unique commonalities with the present proteomic findings (and 12 commonalties in total; Figure 3B). Thus, 65 of the 390 differentially abundant proteins from the present study have previously been implicated at the transcriptome level (53 in individual transcriptome studies, ten in the chick transcriptome meta-analysis, and two in individual studies and the meta-analysis).
We tested each set of commonalities for over-representation of gene ontologies and pathways relative to the Homo sapiens background. No ontologies or pathways were significantly over-represented in the commonalities with previous proteomic studies. A large number of ontologies related to structural processes were over-represented in the transcriptome commonalities (Appendix 4), with the most significant over-representation occurring for the ‘axonogenesis’ ontology. Additionally, several ontologies related to melanin were over-represented in the transcriptome meta-analysis commonalities (Appendix 5). We also conducted an over-representation analysis for the remaining list of differentially abundant proteins that were not implicated in any previous transcriptome studies. Three hundred and ninety-eight ontologies and pathways were over-represented (Appendix 6). The most significant gene ontologies were related to extracellular vesicles, and the most significant pathways were ribosome and spliceosome.
Our previous meta-analysis of chick transcriptome data sets demonstrated overlap between differentially expressed genes in the chick model of early (i.e., ≤3 days) optically induced refractive errors and the genes and proteins previously linked to a range of ocular pathologies for which myopia or hyperopia are risk factors (POAG, choroidal neovascularization, cataract, sub-clinical AMD, and AMD ;). To test whether a similar association occurs at the protein level, we compared the same pathology-associated gene and protein lists with the differentially abundant proteins from the present study.
As illustrated in Figure 4A, the genes and proteins linked to AMD in humans overlapped statistically significantly with differentially abundant proteins following 6 and 48 h of negative and positive lens-wear in the chick model. The overlap with AMD-associated genes and proteins was greatest at late relative to early time points, and in the negative relative to the positive lens-group (negative 6 h odds ratio [OR] = 8.36, p<0.001; negative 48 h OR = 10.19, p<0.001; positive 6 h OR = 4.49, p = 0.002; positive 48 h OR = 4.79, p = 0.001). The differentially abundant proteins following 48 h of negative lens-wear also overlapped statistically significantly with the genes and proteins linked to choroidal neovascularization (OR = 14.20, p = 0.008). A range of ontologies were over-represented in the protein-pathology overlaps as a whole (Appendix 7). The most significant over-representation was blood microparticles (FDR<0.001). Ontologies related to physiologic stress, cholesterol homeostasis, and melanin were also implicated (Figure 4B).
In addition to comparing the overlap of differentially abundant proteins with specific ocular-pathology associated gene/protein lists, we used GSEA to assess whether any gene sets from the Human Phenotype Ontology database were enriched in response to lens-wear. This analysis tested whether the genes from any Human Phonotype Ontology gene sets were over-represented near the top or bottom of the ranked protein lists. Two methods were used to rank the proteins. In the first approach, categorical comparisons between the lens and no lens conditions at each time point were made by ranking proteins by the difference of class means scaled by the standard deviation. No significant enrichments were identified using this approach. In the second approach, proteins were ranked by Spearman’s correlation with refraction across groups at 6 h and 48 h. No significant enrichments were found in the 48 h analysis; however, at 6 h, abnormal electroretinogram, photophobia, and nyctalopia gene sets were enriched in the proteins negatively correlated with refraction, and the arrhythmia gene set was enriched in the proteins positively correlated with refraction (Table 1).
The leading-edge subsets of the abnormal electroretinogram, nyctalopia, and photophobia gene sets were highly similar and were primarily comprised of genes associated with retinal dystrophies and mitochondrial diseases (Figure 5). Consistent with these disease associations, a range of photoreceptor-related proteins were implicated, including those involved in phototransduction (OPN1LW, GUCA1A, and CNGB1), vitamin A metabolism (RPE65 and RLBP1), photoreceptor disc morphogenesis (FSCN2), protein trafficking within photoreceptors (UNC119), and photoreceptor-controlled retinal organization (RS1). The remaining leading-edge proteins were primarily related to mitochondrial function (TIMM8A, SLC25A4, and MT-CO2) and pre-mRNA splicing (PRPF4 and PRPF8). The final disease phenotype showing a significant correlation with refraction was arrhythmia. Two of the four leading-edge proteins from this phenotype gene set (GNAQ and GNA11) are associated with melanocyte development and melanopsin signaling. Mutations in these genes and NRAS (the third leading-edge protein) have been linked to melanoma in humans. The final protein, A2ML1, is a protease inhibitor involved in innate immunity.
This study is the first to use a high throughput proteomic approach to characterize early responses to optical defocus in an animal model of refractive error. The results demonstrate that proteomic responses to lens-wear are well under way by 6 h in the chick model, with more than 140 differentially abundant proteins identified in both lens groups. Moreover, the response to negative and positive lenses was highly similar at both time points (with only 13 proteins showing sign-of-defocus dependent abundance changes at 48 h). Overall, the implicated proteins showed improved concordance with the findings of previous transcriptome studies in animal models of refractive error, and substantial commonalities with the genes and proteins linked to inherited and age-related ocular pathologies in humans.
A key aim of this study was to identify commonalties between transcriptome and proteome responses to optically induced refractive error. The majority of transcriptome studies to date have profiled retina, RPE, and/or choroid tissue in the first 3 days of refractive error induction in the rapid chick model [37,45-48]. Conversely, most previous proteomic studies profiled protein abundance in the retina or sclera at later induction time points in slower animal models, such as the guinea pig [49,50], tree shrew [51,52], and mouse [53,54]. Given the diverse methodologies used, it is not surprising that our previous systematic comparison identified only 20 commonalities between the findings of all transcriptome and proteomic studies in the field . By comparison, 65 of the 390 differentially abundant proteins from the present study have previously been identified as differentially expressed at the transcriptome level [22,38,45-48,55,56]. This substantial improvement likely reflects the use of a high throughput proteomic method and the better alignment of our methodological conditions with the transcriptome data in terms of the species, time points, and tissues profiled.
Although the concordance of our proteomic results with earlier transcriptome findings is undoubtedly improved, it is still relatively low given that some of the transcriptome data were generated using similar animal models to the present study (see Appendix 2 and Appendix 8). Translation and protein degradation rates also contribute to protein abundance, and these processes have been shown to primarily affect proteins performing basic cellular and housekeeping functions following stimulation . This is consistent with the over-representation of ribosome and spliceosome proteins in our list of differentially abundant proteins that were not implicated in any previous transcriptome studies. It is also likely that a range of factors decreased the number of transcriptome-proteome commonalities in the present data comparison. Changes in the abundance of proteins with a long half-life can correlate best with mRNA abundance changes several hours earlier . Thus, it is possible that some of the protein abundance changes in the 6 h data reflect mRNA changes in the relatively unstudied  hours immediately following application of the lens. The conversion of all data to a common identifier in the present study necessarily resulted in some loss of information (e.g., when orthologs could not be identified), and systematic biases in high-throughput platforms are also known to obscure a substantial proportion of the correlation between the mRNA and protein levels . The contribution of mRNA levels to protein abundance has been shown to be high once these measurement biases are accounted for, particularly during responses to a stimulus [57,59].
The transcriptome-proteome commonalities that we identified were enriched for structural (particularly neuronal cell growth) and melanin biosynthesis ontologies, indicating that transcriptional regulation of these processes is to some extent reflected at the protein abundance level. Differential expression of genes and proteins involved in regulating cellular structure is an expected and common finding given the morphological features of refractive errors [22,37,56,60-64]. The evidence for melanin biosynthesis dysregulation in refractive error models is more limited. Mutations in the master transcription factor for melanogenesis, MIFT, are associated with microphthalmia in a range of animals [65,66]. MIFT controls the transcription of the three melanin biosynthesis genes that were differentially expressed in our previous meta-analysis of chick transcriptome studies  and downregulated at the protein level during myopia and hyperopia induction in the present study (DCT, PMEL and TYRP1 ;). More broadly, albinism is known to impair emmetropization in humans [68,69] and is associated with myopia in guinea pigs  and chicks . The melanogenesis pathway also interacts with a range of signaling processes that have previously been associated with ocular growth regulation and refractive errors, including retinoic acid, Wnt, nitric oxide, dopamine, bFGF, and TGF-beta [67,72,73].
In addition to implicating melanin biosynthesis genes, our meta-analysis of chick transcriptome studies identified differential expression of a range of complement pathway and oxidative stress genes during refractive error induction . There was no evidence for complement pathway activation at the protein level in the present study, perhaps because of the earlier time point profiled (2 days versus 2–3 days) or the tissue composition (retina/RPE versus the retina/RPE/choroid). However, a range of proteins that regulate or respond to oxidative stress were either upregulated (RHOB , LONP1 , CPEB2 , and SLC8A1 ) or downregulated (PSMB5 [78,79], CRYAB , APOA4 , SOD2 [82,83], HBA1 [84,85], PRNP , AKR1B1 , MAPT , NQO1 , RPS3 , and GPR37L1 ) following defocus. Melanin and oxidative stress pathways have been linked to AMD and choroidal neovascularization pathogenesis in humans [92-94], and a substantial proportion of the genes and proteins implicated in our meta-analysis  and the present study have been directly associated with these pathologies. The pattern of overlap with pathology-associated genes was similar at the transcriptome and proteome level; greater commonality with AMD and choroidal neovascularization genes was evident at later time points and during myopia (relative to hyperopia) induction, and many of the commonalities were not specific for the sign of defocus (i.e., the genes and proteins were up or downregulated during myopia and hyperopia induction).
A further untargeted analysis of all Human Phenotype Ontology gene sets identified enrichment of genes linked to abnormal electroretinogram, nyctalopia, and photophobia phenotypes in the proteins negatively correlated with refraction at 6 h. The implicated genes were primarily associated with photoreceptor dystrophies and mitochondrial disorders in humans. Early shifts in phototransduction and mitochondrial metabolism pathways have also been reported at the mRNA level in chicks, though not always in a sign-of-defocus-dependent manner . Inherited retinal dystrophies in humans are associated with an increased incidence of refractive errors, particularly if they affect the photoreceptors or the bipolar synapse . A recent conference abstract also reported that such genes that produce refractive error as part of a broader syndrome are enriched for polymorphisms associated with common myopia in the CREAM and 23andMe genome-wide association data , suggesting that the molecular mechanisms implicated in these disorders are relevant for understanding the basis of refractive errors in the general population.
Although changes within mitochondrial, phototransduction, melanin, and oxidative stress pathways have been linked to ocular pathologies in humans, it is unclear whether this is the case in the chick optical defocus model as little is known about the longitudinal ultrastructural and molecular effects of lens-wear [23,62]. The relevance of the identified protein abundance changes to human refractive error and associated secondary complications is also uncertain. As noted in our earlier investigation , the disruption of gene and protein expression within oxidative stress pathways could reflect the secondary effects of lens-wear such as peripheral occlusion or increased heat under the goggle. Further ultrastructural and molecular studies to address these questions are warranted, given the many advantages of chick for ophthalmic research  and the apparent potential of the model to replicate several of the molecular features of common age-related ocular disorders in humans.
In conclusion, this study compared differentially abundant proteins following 6 and 48 h of lens-wear in the chick with differentially expressed genes and proteins from previously published transcriptome and proteomic studies of refractive error, as well as the genes and proteins associated with several sight-threatening ocular pathologies in humans. The results demonstrate that cellular remodeling, melanin biosynthesis, and oxidative stress pathways are implicated at the mRNA and protein levels in animal models of refractive error. The improved concordance of the present proteomic findings with previous transcriptome studies highlights the importance of methodological choices for studies aiming to build on the existing literature and contribute to a systems-level understanding of refractive error development. We also demonstrated significant overlap between the differentially abundant proteins following lens-wear and the genes and proteins linked to AMD and choroidal neovascularization in humans. These findings highlight the chick as a model for the study of optically induced stress, with possible relevance to understanding the development of sight-threatening human ocular pathologies.
Appendix 1. PANTHER Gene Ontology (cellular component, molecular function, and biological process) and protein class classifications for the 1617 proteins in the filtered LC-ESI-MS/MS chick retina/RPE dataset.
Appendix 2. Past transcriptome and proteomic studies of refractive error animal models.
Appendix 3. Differentially abundant proteins.
Appendix 4. Functional enrichments for differentially abundant proteins also implicated in past transcriptome studies of optically-induced refractive error.
Appendix 5. Functional enrichments for differentially abundant proteins also implicated in previous transcriptome meta-analyses of optically-induced refractive error.
Appendix 6. Functional enrichments for differentially abundant proteins that were not implicated in any previous refractive error transcriptome studies.
Appendix 7. Functional enrichments for differentially abundant proteins also associated with human ocular pathologies.
Appendix 8. Comparison of single protein findings with past exploratory proteomic and transcriptome studies of refractive error.
Funding: This study was supported by an Australian Government Research Training Program Scholarship to NR. SGC (S.Crewther@latrobe.edu.au) and NR (N.Riddell@latrobe.edu.au) are co-corresponding authors for this paper.