August 2002
Volume 43, Issue 8
Free
Biochemistry and Molecular Biology  |   August 2002
Microarray Analysis of Gene Expression in the Aging Human Retina
Author Affiliations
  • Shigeo Yoshida
    From the Departments of Ophthalmology and Visual Sciences and Human Genetics, W. K. Kellogg Eye Center, University of Michigan, Ann Arbor, Michigan.
  • Beverly M. Yashar
    From the Departments of Ophthalmology and Visual Sciences and Human Genetics, W. K. Kellogg Eye Center, University of Michigan, Ann Arbor, Michigan.
  • Suja Hiriyanna
    From the Departments of Ophthalmology and Visual Sciences and Human Genetics, W. K. Kellogg Eye Center, University of Michigan, Ann Arbor, Michigan.
  • Anand Swaroop
    From the Departments of Ophthalmology and Visual Sciences and Human Genetics, W. K. Kellogg Eye Center, University of Michigan, Ann Arbor, Michigan.
Investigative Ophthalmology & Visual Science August 2002, Vol.43, 2554-2560. doi:https://doi.org/
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      Shigeo Yoshida, Beverly M. Yashar, Suja Hiriyanna, Anand Swaroop; Microarray Analysis of Gene Expression in the Aging Human Retina. Invest. Ophthalmol. Vis. Sci. 2002;43(8):2554-2560. doi: https://doi.org/.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

purpose. To develop gene expression profiles of young and elderly human retinas and identify candidate genes for aging-associated retinal diseases.

methods. Gene microarray slides containing 2400 human genes (primarily neuronal) were hybridized to biotin or dinitrophenyl (DNP)-labeled target cDNAs that were synthesized using total RNAs from young (13–14 years) and elderly (62–74 years) human retinas. Hybridization signals were visualized with cyanine (Cy)-5 or Cy-3 fluorescent reporter molecules, and the fluorescence intensities of the images were analyzed by computer. Northern blot analysis and real-time quantitative reverse transcription PCR (qRT-PCR) were performed to validate the microarray results.

results. Of the 2400 genes represented on the microarray slides, more than 50% hybridized to the retinal cDNA targets. Expression of a majority of these genes was not altered during aging; nonetheless, changes in the expression of 24 genes were detected between young and elderly retinas. These genes could be clustered into four categories: energy metabolism, stress response, cell growth, and neuronal transmission/signaling. Northern blot analysis and qRT-PCR results confirmed the changes in expression of 8 of 10 genes examined.

conclusions. Using commercially available slide microarrays, the authors show that aging of the human retina is associated with changes in patterns of gene expression. This analysis suggests that pathways involved in stress response and energy metabolism play key roles in retinal aging. These studies demonstrate the utility of gene microarrays in identifying global patterns of retinal gene expression and lay the foundation for future studies defining the genetic basis of aging-associated retinal diseases, such as age-related macular degeneration.

Aging is a universal event in all eukaryotes. The strongest evidence of genetic control of aging comes from investigations in model systems (Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, and Mus musculus), suggesting that changes in a single gene can have a substantial impact on life span. 1 2 Although hormone signaling, gene silencing, cellular stress, and/or oxidative stress have been implicated, it is as yet unclear how these are linked to the regulation of aging. Nevertheless, these studies provide a framework for considering diseases of aging in higher organisms as both time-dependent and genetically regulated events. 
Aging in humans is associated with progressive and perhaps irreversible impairment of physiological functions, including vision. Age-related macular degeneration (AMD) is a major cause of untreatable vision loss in the elderly. The origin of this disease is dependent on complex interactions between genetic and environmental risk factors—the two strongest risk factors being age and family history. 3 Vision loss in AMD is attributed to photoreceptor dysfunction, which is caused by abnormalities of the retinal pigment epithelium (RPE), Bruch’s membrane, and/or choriocapillaris. 4 The physiological changes in the aging retina are remarkably similar, albeit less severe, to the pathologic changes in AMD tissue. In both aging and AMD, multiple factors, including DNA damage and oxidative stress, are believed to contribute to the pathology. In light of these studies, we hypothesized that a broader understanding of the molecular events that modulate aging of the retina would provide insights into the pathogenesis of AMD. 
Although a change in the activity of a single gene can be directly correlated to a cellular phenotype (which forms the theoretical basis for linkage analysis of Mendelian traits), the molecular definition of complex phenotypes (e.g., aging and AMD) depends on the interaction of multiple genes and cellular pathways. Microarray technology offers an opportunity to delineate the complex gene expression profiles of specialized cell types. 5 6 7 8 9 10 A unique strength of this methodology is that gene discovery is hypothesis independent. We have used commercially produced gene microarrays to examine changes in retinal gene expression, with the goal of identifying genes and cellular events that contribute to retinal aging. In this report, we demonstrate the utility of this technology, report that aging of the human retina is accompanied by discrete changes in gene expression patterns, and present evidence for aging-associated pathways in the retina. 
Materials and Methods
Human Retinal Tissue Specimens and RNA Preparation
The procurement and use of human tissues was in compliance with the tenets of the Declaration of Helsinki. Donor eyes were obtained from the National Disease Research Interchange (NDRI; Philadelphia, PA) as either whole globes or posterior poles with the cornea removed. The eyes were enucleated within 2 to 6 hours of death and shipped on ice in sterile containers. The globes were hemisected by a circumferential incision around the pars plana. The retina was dissected, snap frozen, and stored at −80°C until use. The tissue was rejected if the donor had a history of ocular or systemic disease or if the tissue showed phenotypic abnormalities on visual examination. The time from death until dissection was 24 to 40 hours. Total RNA was purified using the guanidium isothiocyanate method (TRIzol reagent; GibcoBRL, Gaithersburg, MD). The amount and the quality of each RNA preparation were checked by electrophoresis, and only those showing intact 18S and 28S ribosomal RNA bands were used for further analyses. 
Microarray Experimentation
Microarray slides (Micromax) were purchased from NEN Life Science Products, Inc. (Boston, MA) and contained 2400 human genes (called probes). Of these, 80% are expressed in the brain. (A complete description of the genes on the microarray slides is available at http://www.umich.edu/∼retina/yoshida-iovs.html) 
Target Labeling.
Four micrograms of total RNA samples and 350 pg of plant control RNA were converted into either biotin or dinitrophenyl (DNP) cDNA targets using reverse transcriptase (Micromax cDNA Microarray System; NEN Life Science Products; see http://www.nen.com/pdf/MMMANUALPDF.pdf/ for details). To evaluate target labeling, aliquots of labeled cDNAs and a control cDNA of known concentration were serially diluted, individually spotted onto a membrane (GeneScreen; NEN Life Science Products) and fixed by UV cross-linking at 1200 μJ. The membrane filters were developed by chemiluminescence, as described (the enhanced chemiluminescence protocol, NEN Life Science Products). The concentration of test cDNA was determined by identifying the dilution spot with an intensity that most closely matched the least dense intensity spot of the control cDNA, and the cDNA concentrations were calculated by multiplying the target sample dilution factor by the concentration of the control cDNA producing the least dense equivalent spot intensity (data not shown). Labeled cDNAs were used as targets in the hybridization reactions if a 1:800 dilution spot of the target cDNA was visible and the difference between the paired biotin cDNA and the DNP cDNA mass yields was less than fourfold. 
Hybridization.
A mixture of equal specific activity of biotin- and DNP-labeled cDNA targets was simultaneously hybridized to the microarray slide at 65°C for 16 hours. After hybridization, slides were washed at room temperature in 0.5× SSC-0.01% SDS for 5 minutes and 0.06× SSC-0.01% SDS for 5 minutes, followed by a 2-minute wash in 0.06× SSC. Biotin and DNP hybridization signals were then detected with Cy-5 and -3 fluorescent reporter molecules, respectively (Tyramide Signal Amplification [TSA protocol]; NEN Life Science Products). 
Image Acquisition and Analysis.
Fluorescence intensities were determined by NEN from images taken with The ScanArray 3000 (GSI Lumonics, Inc., Watertown, MA), which was equipped with laser excitation sources and interference filters appropriate for the Cy-3 and -5 fluors. Data were then filtered to remove values from poorly hybridized spots, so that the intensity levels of more than 60% of the pixels in a single spot were greater than the local background. 11 The young-to-elderly ratio measurements for each remaining spot on each array were calculated by computer (ScanAlyze software; http://genome-www.stanford.edu/software). A median ratio for each hybridized slide was then calculated based on the ratio of fluors in the young and elderly target populations (see the Results section). 8 12 These values were converted to 1, and this conversion factor was used to normalize the results of the hybridization. 
Northern Blot Analysis
Total RNA (5 μg) was separated on a 1% formaldehyde-agarose gel and transferred to nylon membrane (Hybond N+; Amersham Pharmacia Biotech, Piscataway, NJ). The cDNA clones for the human KIAA 0120, TGF-β receptor–interacting protein 1 (TRIP1) and IFN-responsive transcription factor subunit (ISGF3G) were obtained from Research Genetics (Huntsville, AL). Human cDNAs for creatine kinase B (CKB), transferrin (TF) and glyceralaldehyde-3-phosphate dehydrogenase (GAPDH) had been isolated in our laboratory from a subtracted retinal library. 13 Purified cDNA inserts were labeled with [α-32P] dCTP using a DNA-labeling system (Multiprime; Amersham Pharmacia Biotech) and hybridized to Northern blot analysis. The blots were normalized against ribosomal RNA, and image intensities were quantified by densitometry of computer (Image Beta 3b software; Scion Corp., Frederick, MD). 
Real-Time qRT-PCR
qRT-PCR was performed with gene-specific primers using sequences derived from GenBank (Table 1) . Purified retinal RNA was prepared from three human samples from young individuals (ages 16, 16, and 18 years) and three from elderly persons (ages 70, 74, and 78 years). These retinal samples were different from those used in the microarray analyses. First-strand cDNA was synthesized with reverse transcriptase (Invitrogen, Carlsbad, CA) and oligo-dT priming. Real-time PCR was performed in triplicate for each individual, with a commercial system (iCycler; Bio-Rad, Hercules, CA) and fluorescence detection (SYBR Green; PE-Applied Biosystems, Foster City, CA), as described herein. The 50-μL reactions were performed in 96-well plates with optical sealing tape (Bio-Rad) and contained the following components: 50 mM Tris (pH 8.3), 3 mM MgCl2, 0.5 mg/mL BSA, 200 μM each dNTP, 0.33× green fluorescence, 0.5 U Taq polymerase (AmpliTaq; PE-Applied Biosystems), two primers at an optimal concentration, and the cDNA template or water. To confirm the specificity of the PCR reactions, products were separated by electrophoresis in 1.0% agarose gels and visualized by ethidium bromide staining. Changes in gene expression were quantified by calculating the average value of the triplicate reactions from each of the three young individuals and comparing that to the average derived from the triplicate analysis of each of the three elderly individuals. This generated a total of nine pair-wise comparisons, from which the mean ± SEM were calculated. Each reaction was normalized against human β-actin (ACTB). 
Results
cDNA microarrays (Micromax; NEN Life Sciences) containing 2400 human genes (called probes) were hybridized to cDNA targets, which were synthesized from human retinal RNAs of individuals in age groups that represented two distinct stages, young (ages 13–14 years) and elderly (ages 62–72 years). Two young and three elderly RNA samples were used to ensure that any expression changes in the targets were attributable to the age of the tissue and not to the genetic or environmental backgrounds of the contributing individual. In addition, the biotin and DNP tags were exchanged between young and elderly targets in replicate experiments to control for the preferential incorporation of one fluor over the other. Hybridization experiments included the following pairs of labeled cDNA targets: 14 years-DNP and 62 years-biotin; 72 years-DNP and 14 years-biotin; 13 years-DNP and 66 years-biotin; and 66-year-DNP and 13 years-biotin. In this system, cDNA targets are tagged with two different fluorescent labels (red and green). Genes that are expressed at approximately equal levels in the two targets appear as yellow spots, and preferential expression of genes in one tissue type is detected as red or green fluorescence (images not shown). Quantitative differences in the levels of gene expression between retinas from young and elderly individuals are represented by a scatter plot, and the differential level of expression at each point in the scatter plot is determined by comparing the expression level at the x coordinate (young retina) and the expression level of the gene at the y coordinate (elderly retina; data not shown). Extracted data are then filtered using ScanAlyze to remove intensities of poorly hybridizing spots. 
After filtration, 52% of the 2400 genes were found to hybridize strongly with the retinal cDNA targets, suggesting that approximately 1250 genes on the microarray slide are expressed in the human retina. Data were further normalized based on the expectation that the expression levels of the majority of retinal genes are not altered during aging (see the Materials and Methods section). (The raw data for all experiments can be accessed at the web site www.umich.edu/∼retina/yoshida-iovs.html.) We then defined a level of significance for variations in the expression of individual genes; if a two-fold change in expression (i.e., probes whose Cy3-to-Cy5 ratio is greater than +2 or <0.5) was observed in three of four experiments, a gene was considered to be differentially expressed. Based on this criterion, the level of expression of most of the genes on the chip was constant between the young and elderly retinas. However, a small subset of the genes demonstrated significant and reproducible alterations in mRNA levels. 
Our comparative hybridization studies demonstrated that retinal aging is associated with alterations in mRNA levels, which reflect a change in gene expression and/or mRNA stability. The data revealed that 17 (1.4%) genes were expressed at higher levels in retinas from young individuals (referred as young-dominant), whereas 7 (0.6%) genes were designated as elderly-dominant (Table 2) . To establish the validity of the microarray data, we analyzed the patterns of expression of six genes by Northern blot analysis, using the same sources of young and elderly retinal RNAs (Fig. 1) . A comparison of the hybridization intensities indicated that mRNA levels of KIAA0120, TRIP1, and ISGF3G were elevated in retinas from young individuals by 3.5-, 5.0-, and 3.8-fold, respectively (Figs. 1A 1B 1C) , whereas CKB expression in the elderly retinas was nearly twice that in the young retina (Fig. 1D) . Two control genes, TF and GAPDH, showed nearly equivalent levels of gene expression in the young and elderly retinas (Figs. 1E 1F) . In each case, the levels of gene expression on the Northern analyses were consistent with those observed in the microarray experiments. 
To further validate cDNA microarray data, RNA was purified from new sources of young (three samples) and elderly human (three samples) retinas and used to examine the expression of nine differentially expressed genes (of these, TRIP1, ISGF3G, and CKB had been analyzed by Northern blot analysis; see Fig. 1B 1C 1D , respectively), with qRT-PCR analysis. The microarray and qRT-PCR results were consistent for seven of the nine genes evaluated (Table 2) . The expression of two genes, ISGF3G and COL7A1, was not consistent with the microarray results, demonstrating the impact of intrasample variability in human studies and the importance of replication and validation by independent methodology. The microarray and Northern blot results indicated that ISGF3G was preferentially expressed in young retina, but the qRT-PCR analysis suggested preferential expression in the elderly retina. An equivalent deviation was noted for COL7A1, which was identified as an elderly-dominant gene by microarray analysis but showed higher expression in young retina by qRT-PCR analysis. 
To identify potential biological relationships among similarly upregulated genes, we used the recently developed PubGene database (http://www.pubgene.org) 14 to identify citation-based gene network associations (Figs. 2A 2B) . This database includes 10 million MEDLINE records (provided in the public domain from the National Center for Biotechnology Information, Bethesda, MD). We submitted gene symbols of all extracted genes (except ISGF3G and COL7A1) to PubGene and obtained cocitation neighborhoods. The network of eight of the young-dominant genes defined a cocitation neighborhood with somatostatin (SST) at the center, whereas another network of four elderly-dominant genes centered at interleukin-1α (IL1A). 
Discussion
This is the first report to describe gene expression changes in aging retina by using slide microarrays. Our analysis demonstrates that aging of the human retina is associated with changes in the patterns of gene expression. In the present study, the gene expression profile of 13 to 14 years (young) human retina was compared with the profile of 62 to 72 years (elderly) retinas. We show that several genes involved in cell growth and protein processing are preferentially expressed in retinas of young individuals, whereas genes involved in stress response are expressed at higher levels in elderly retinas. Although some of the differences in gene expression may represent the retinal maturation process (because of the age of the young tissue), our findings are consistent with previous reports of transcriptional profiling of aging in mouse skeletal muscle and brain, which suggested a decreased use of biosynthetic pathways and increased reliance on genes involved in stress response. 7 15 It is not yet possible to determine the importance of any individual aging gene to the process in all organisms; however, the commonality of the results at the level of the cellular pathways support the hypothesis that common molecular mechanisms may regulate aging in different tissues and cell types. 
Although microarray technology has the potential to define global patterns of gene expression, the completeness of this catalog is dependent on the arrays that are analyzed. Because we used a commercially produced array containing less than 10% of the genes in the human genome, the analysis was expected to identify only a restricted number of differentially expressed genes. Despite the limitation, this analysis has identified several interesting genes, including a candidate retinal aging gene, CKB. Creatine kinases, including the cytosolic and mitochondrial isoforms, are thought to play a central role in cellular energy metabolism by transporting energy from the site of production in the mitochondrion to that of utilization. Previous studies demonstrated that CKB is expressed in the retina, 13 with the highest concentrations in inner segments of the photoreceptors and in the plexiform layers. 16 Studies of mouse brain aging have also found evidence for age-related increase in the expression of CKB. 7 Higher expression of CKB has been linked to cellular energy stress and may reflect the extent of brain damage. 17 These associations are particularly intriguing because expression of several specific stress response genes is also enhanced in the aging retina. Finally, the ability of our unselected array to identify candidate aging gene(s) underscores the power of this technology to generate a hypothesis-independent expression profile of the aging retina. 
The analysis of data from microarray studies presents a major challenge, 18 and the interpretation of the biological characteristics of genes in each cluster has remained primarily a manual and subjective task. In an attempt to perform a more objective analysis, we used the recently launched PubGene database. 14 Although the database is biased toward well-studied genes that are extensively reported in the literature relative to newly discovered genes, it offers a method for rapidly establishing potential associations between genes and functional pathways. Our analysis positions interleukin-1α (IL1A), a potent mediator of inflammation and immunity, in the center of the literature network of elderly-dominant genes (Fig. 2B) . This is consistent with the accumulating evidence that aging is associated with inflammatory response. 7 19 The observation that Somatostatin positions at the center of the young-dominant network (Fig. 2A) suggests that this neuropeptide may play a more significant role in regulating retinal function than previously envisaged. 
Although cDNA microarray technology can provide considerable new insights into gene expression, many aspects require additional development. These include better methods for target labeling, image acquisition, and processing; reduction of intra- and interslide variations, and clustering of gene expression data. 10 18 20 We used the TSA system in this analysis, because this was the only available labeling method for small amounts of human RNA when the studies were initiated. Because this is an amplification-based method, the TSA system may introduce some level of bias during label incorporation, reverse transcription, and signal amplification. Our recent studies have demonstrated that another microarray detection system (3DNA Submicro Expression Array Detection System; Genisphere, Hatfield, PA) provides more consistent hybridization data in slide arrays. 21 Another possible approach is to complement the slide microarrays with oligonucleotide-based microarrays (e.g., GeneChips from Affymetrix, Santa Clara, CA), in which a two-probe pair strategy is used to minimize cross-hybridization and background signal. 22  
In addition to the variables associated with the cDNA microarrays, a high sample-to-sample variation is inherent in human tissue samples. 23 An optimized method of donor eye preservation 24 may reduce such variations. In identical (isogenic) biological systems, the expression of a single gene can fluctuate and exhibit gene-specific patterns of noise. 25 To control for both biological and methodological noise, we used multiple sources of young and elderly retinal tissue and exchanged the label (Cy-3 and Cy-5) between the tissue sources. In addition, we increased the significance of our analysis by performing multiple hybridizations. 26 This allowed us to generate results with a certain level of significance and to focus only on genes that show larger changes in RNA levels. However, the ability to collect more profiles in parallel may directly influence the extraction of useful data, especially in investigations that use human tissue. 27 The variations in gene expression patterns that were observed by using complementary methods with different sets of retinal samples emphasize the importance of analyzing a sufficiently large pool of samples to minimize sample-to-sample variations. Additional investigations with custom gene microarrays of expressed sequence tags (ESTs) generated from retina-RPE libraries 13 28 29 30 31 and with an increased number of retinas at various ages are necessary to obtain a comprehensive profile of aging-associated changes in gene expression. Our studies have laid the foundation for future global profiling of retinal and RPE gene expression during development, aging, and disease. 
 
Table 1.
 
Gene-Specific Primers
Table 1.
 
Gene-Specific Primers
Gene Forward Primer Reverse Primer
ISGF3G 5′-CCTCACCTTCATCTACACGGG-3′ 5′-ATTCAGTGTTACCTGGAACTTCGG-3′
TRIP1 5′-GCCAAGGACCCTATCGTCAATG-3′ 5′-CACAAAGCACTGGTAGCCCATC-3′
PIG7 5′-GCAGGACGTGGACCATTACT-3′ 5′-CCCCCAAAAGAAGACATGAA-3′
CTNNA1 5′-AAGTAGAAGCAGCCGTGGAA-3′ 5′-CGTCCTGCTTCTGACATCAA-3′
USP9X 5′-TCAGGATGTGGGTCGTTACA-3′ 5′-TGTCTGCCAAGCCTTTTCTT-3′
GABRB3 5′-CTTGACAATCGAGTGGCTGA-3′ 5′-CAATCCTTTCCACTCCGGTA-3′
COL7A1 5′-GATGACCCACGGACAGAGTT-3′ 5′-ACTTCCCGTCTGTGATCAGG-3′
CKB 5′-TGCTCATCGAGATGGAACAG-3′ 5′-TACCAAGGGTGACGGAAGTC-3′
AMY2A 5′-GGTTCAGGTCTCTCCACCAA-3′ 5′-CCATCATTGAAATCCCATCC-3′
ACTB 5′-CTCCTGAGCGCAAGTACTCC-3′ 5′-GTCACCTTCACCGTTCCAGT-3′
Table 2.
 
Changes in Gene Expression in Young Versus Elderly Retina
Table 2.
 
Changes in Gene Expression in Young Versus Elderly Retina
Gene Name Gene Symbol Accession No. Change (-fold)
Microarray qRT-PCR
Young
 Cell growth regulation
  Interferon-inducible fragment (cDNA 6-16) IFI616 X02492 −70.7 ∼−3.5
  IFN-responsive transcription factor subunit* ISGF3G M87503 −5.0 ∼ −2.3 1.4 ± 0.6
  TGF-β receptor interacting protein 1* TRIP1 U36764 −6.1 ∼ −2.2 −2.3 ± 1.4
  LPS-induced TNF-α factor PIG7 AF010312 −13.1 ∼ −2.0 −6.3 ± 0.9
  α1(E)-catenin CTNNA1 L23805 −3.4 ∼ −3.0 −1.6 ± 0.5
 DNA/RNA synthesis
  Tetratricopeptide repeat domain 3 TTC3 D84296 −7.5 ∼ −4.4
  Heterogeneous nuclear ribonucleoprotein F HNRPF L28010 −6.3 ∼−3.2
  Arginine methyltransferase HRMT1LI X99209 −4.1 ∼ −2.8
 Protein processing/trafficing
  26S proteasome subunit p97 PSMD2 D78151 −3.6 ∼ −2.0
  Ubiquitin hydrolase USP9X X98296 −9.8 ∼ −2.0 −3.6 ± 0.7
  Membrane cofactor protein MCP Y00651 −2.7 ∼−2.3
 Energy metabolism
  Phosphoglycerate mutase PGAM1 J04173 −14.3 ∼ −3.6
  Muscle specific enolase ENO3 X51957 −7.9 ∼ −2.4
 Membrane protein
  α 2δ calcium channel isoform I CACNA2D2 AF042793 −8.9 ∼ −2.2
  GABA receptor β-3 subunit GABRB3 M82919 −4.6 ∼ −2.6 −3.5 ± 0.8
  p64 CLCP protein CLIC1 X87689 −3.2 ∼ −2.3
 Others
  KIAA0120* KIAA0120 D21261 −4.6 ∼−2.0
Elderly
 Stress response
  Cold inducible RNA-binding protein CIRBP D78134 2.4 ∼ 3.8
  α-1 Type VII collagen COL7A1 L02870 2.6 ∼ 3.4 −1.2 ± 1.1
  Myristoylated alanine-rich protein kinase C MACS D10522 2.1 ∼ 3.3
 Energy metabolism
  Creatine kinase B* CKB L47647 2.2 ∼ 6.8 6.3 ± 1.2
 Others
  Endogenous retrovirus type C oncovirus N/A M74509 2.0 ∼ 5.1
  B-cell CLL/lymphoma 9 BCL9 Y13620 2.3 ∼ 3.0
  Pancreatic amylase AMY2A M28443 2.6 ∼ 5.3 1.6 ± 0.6
Figure 1.
 
Northern blot analysis of microarray-extracted retinal genes. Northern blot analysis of total RNA from retinas of young (lane 1) and elderly (lane 2) individuals were hybridized with 32P-labeled cDNA probes. The following cDNAs were used: (A) KIAA0120, (B) TGF-β receptor interacting protein 1, (C) IFN-responsive transcription factor subunit, (D) creatine kinase B, (E) transferrin, (F) GADPH. The ethidium bromide staining of 28S and 18S ribosomal RNA (shown below the autoradiograph) demonstrates the integrity and equal loading of RNA samples.
Figure 1.
 
Northern blot analysis of microarray-extracted retinal genes. Northern blot analysis of total RNA from retinas of young (lane 1) and elderly (lane 2) individuals were hybridized with 32P-labeled cDNA probes. The following cDNAs were used: (A) KIAA0120, (B) TGF-β receptor interacting protein 1, (C) IFN-responsive transcription factor subunit, (D) creatine kinase B, (E) transferrin, (F) GADPH. The ethidium bromide staining of 28S and 18S ribosomal RNA (shown below the autoradiograph) demonstrates the integrity and equal loading of RNA samples.
Figure 2.
 
The cocitation neighborhood of genes differentially expressed in young (A) and elderly (B) retinas. The subset of gene symbols in each cluster submitted to PubGene (i.e., those identified in this report) are in italic within the gray boxes. The genes at the center of each network are shown in bold in the white rectangle. The numbers next to the lines connecting two gene symbols represent MEDLINE citations in which both genes are cited. Connecting lines with no numbers indicate only one publication where the two genes are cited. SST, somatostatin, and IL1A, interleukin 1-α.
Figure 2.
 
The cocitation neighborhood of genes differentially expressed in young (A) and elderly (B) retinas. The subset of gene symbols in each cluster submitted to PubGene (i.e., those identified in this report) are in italic within the gray boxes. The genes at the center of each network are shown in bold in the white rectangle. The numbers next to the lines connecting two gene symbols represent MEDLINE citations in which both genes are cited. Connecting lines with no numbers indicate only one publication where the two genes are cited. SST, somatostatin, and IL1A, interleukin 1-α.
The authors thank Jeffrey Trent for allowing one of them (AS) to visit the microarray facility at National Human Genome Research Institute and for introducing the basics of this emerging technology; Paul R. Lichter and Marvin Sears for their trust and support during the early stages of this work; Robert Thompson for suggestions; colleagues of the microarray facility, Mohammad Othman, Sepideh Zareparsi, Rafal Farjo and Jindan Yu, for constructive discussions; and the staff of National Disease Research Interchange (Philadelphia, Pennsylvania) and Midwest Eye Bank (Ann Arbor, Michigan) for assistance in acquiring human tissues. 
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Figure 1.
 
Northern blot analysis of microarray-extracted retinal genes. Northern blot analysis of total RNA from retinas of young (lane 1) and elderly (lane 2) individuals were hybridized with 32P-labeled cDNA probes. The following cDNAs were used: (A) KIAA0120, (B) TGF-β receptor interacting protein 1, (C) IFN-responsive transcription factor subunit, (D) creatine kinase B, (E) transferrin, (F) GADPH. The ethidium bromide staining of 28S and 18S ribosomal RNA (shown below the autoradiograph) demonstrates the integrity and equal loading of RNA samples.
Figure 1.
 
Northern blot analysis of microarray-extracted retinal genes. Northern blot analysis of total RNA from retinas of young (lane 1) and elderly (lane 2) individuals were hybridized with 32P-labeled cDNA probes. The following cDNAs were used: (A) KIAA0120, (B) TGF-β receptor interacting protein 1, (C) IFN-responsive transcription factor subunit, (D) creatine kinase B, (E) transferrin, (F) GADPH. The ethidium bromide staining of 28S and 18S ribosomal RNA (shown below the autoradiograph) demonstrates the integrity and equal loading of RNA samples.
Figure 2.
 
The cocitation neighborhood of genes differentially expressed in young (A) and elderly (B) retinas. The subset of gene symbols in each cluster submitted to PubGene (i.e., those identified in this report) are in italic within the gray boxes. The genes at the center of each network are shown in bold in the white rectangle. The numbers next to the lines connecting two gene symbols represent MEDLINE citations in which both genes are cited. Connecting lines with no numbers indicate only one publication where the two genes are cited. SST, somatostatin, and IL1A, interleukin 1-α.
Figure 2.
 
The cocitation neighborhood of genes differentially expressed in young (A) and elderly (B) retinas. The subset of gene symbols in each cluster submitted to PubGene (i.e., those identified in this report) are in italic within the gray boxes. The genes at the center of each network are shown in bold in the white rectangle. The numbers next to the lines connecting two gene symbols represent MEDLINE citations in which both genes are cited. Connecting lines with no numbers indicate only one publication where the two genes are cited. SST, somatostatin, and IL1A, interleukin 1-α.
Table 1.
 
Gene-Specific Primers
Table 1.
 
Gene-Specific Primers
Gene Forward Primer Reverse Primer
ISGF3G 5′-CCTCACCTTCATCTACACGGG-3′ 5′-ATTCAGTGTTACCTGGAACTTCGG-3′
TRIP1 5′-GCCAAGGACCCTATCGTCAATG-3′ 5′-CACAAAGCACTGGTAGCCCATC-3′
PIG7 5′-GCAGGACGTGGACCATTACT-3′ 5′-CCCCCAAAAGAAGACATGAA-3′
CTNNA1 5′-AAGTAGAAGCAGCCGTGGAA-3′ 5′-CGTCCTGCTTCTGACATCAA-3′
USP9X 5′-TCAGGATGTGGGTCGTTACA-3′ 5′-TGTCTGCCAAGCCTTTTCTT-3′
GABRB3 5′-CTTGACAATCGAGTGGCTGA-3′ 5′-CAATCCTTTCCACTCCGGTA-3′
COL7A1 5′-GATGACCCACGGACAGAGTT-3′ 5′-ACTTCCCGTCTGTGATCAGG-3′
CKB 5′-TGCTCATCGAGATGGAACAG-3′ 5′-TACCAAGGGTGACGGAAGTC-3′
AMY2A 5′-GGTTCAGGTCTCTCCACCAA-3′ 5′-CCATCATTGAAATCCCATCC-3′
ACTB 5′-CTCCTGAGCGCAAGTACTCC-3′ 5′-GTCACCTTCACCGTTCCAGT-3′
Table 2.
 
Changes in Gene Expression in Young Versus Elderly Retina
Table 2.
 
Changes in Gene Expression in Young Versus Elderly Retina
Gene Name Gene Symbol Accession No. Change (-fold)
Microarray qRT-PCR
Young
 Cell growth regulation
  Interferon-inducible fragment (cDNA 6-16) IFI616 X02492 −70.7 ∼−3.5
  IFN-responsive transcription factor subunit* ISGF3G M87503 −5.0 ∼ −2.3 1.4 ± 0.6
  TGF-β receptor interacting protein 1* TRIP1 U36764 −6.1 ∼ −2.2 −2.3 ± 1.4
  LPS-induced TNF-α factor PIG7 AF010312 −13.1 ∼ −2.0 −6.3 ± 0.9
  α1(E)-catenin CTNNA1 L23805 −3.4 ∼ −3.0 −1.6 ± 0.5
 DNA/RNA synthesis
  Tetratricopeptide repeat domain 3 TTC3 D84296 −7.5 ∼ −4.4
  Heterogeneous nuclear ribonucleoprotein F HNRPF L28010 −6.3 ∼−3.2
  Arginine methyltransferase HRMT1LI X99209 −4.1 ∼ −2.8
 Protein processing/trafficing
  26S proteasome subunit p97 PSMD2 D78151 −3.6 ∼ −2.0
  Ubiquitin hydrolase USP9X X98296 −9.8 ∼ −2.0 −3.6 ± 0.7
  Membrane cofactor protein MCP Y00651 −2.7 ∼−2.3
 Energy metabolism
  Phosphoglycerate mutase PGAM1 J04173 −14.3 ∼ −3.6
  Muscle specific enolase ENO3 X51957 −7.9 ∼ −2.4
 Membrane protein
  α 2δ calcium channel isoform I CACNA2D2 AF042793 −8.9 ∼ −2.2
  GABA receptor β-3 subunit GABRB3 M82919 −4.6 ∼ −2.6 −3.5 ± 0.8
  p64 CLCP protein CLIC1 X87689 −3.2 ∼ −2.3
 Others
  KIAA0120* KIAA0120 D21261 −4.6 ∼−2.0
Elderly
 Stress response
  Cold inducible RNA-binding protein CIRBP D78134 2.4 ∼ 3.8
  α-1 Type VII collagen COL7A1 L02870 2.6 ∼ 3.4 −1.2 ± 1.1
  Myristoylated alanine-rich protein kinase C MACS D10522 2.1 ∼ 3.3
 Energy metabolism
  Creatine kinase B* CKB L47647 2.2 ∼ 6.8 6.3 ± 1.2
 Others
  Endogenous retrovirus type C oncovirus N/A M74509 2.0 ∼ 5.1
  B-cell CLL/lymphoma 9 BCL9 Y13620 2.3 ∼ 3.0
  Pancreatic amylase AMY2A M28443 2.6 ∼ 5.3 1.6 ± 0.6
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