July 2002
Volume 43, Issue 7
Cornea  |   July 2002
Microarray Analysis of Corneal Fibroblast Gene Expression after Interleukin-1 Treatment
Author Affiliations
  • Vinit B. Mahajan
    From the Departments of Microbiology and Molecular Genetics and
  • Cui Wei
    Department of Ophthalmology, University of Southern California Keck School of Medicine, Los Angeles, California.
  • Peter J. McDonnell, III
    Ophthalmology, University of California Irvine, Irvine, California; and the
    Department of Ophthalmology, University of Southern California Keck School of Medicine, Los Angeles, California.
Investigative Ophthalmology & Visual Science July 2002, Vol.43, 2143-2151. doi:
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    • Get Citation

      Vinit B. Mahajan, Cui Wei, Peter J. McDonnell; Microarray Analysis of Corneal Fibroblast Gene Expression after Interleukin-1 Treatment. Invest. Ophthalmol. Vis. Sci. 2002;43(7):2143-2151.

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

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purpose. To identify changes in gene expression in human corneal fibroblasts after exposure to interleukin-1α.

methods. RNA was isolated from cultured human corneal fibroblasts after treatment with interleukin-1α and subjected to DNA microarray analysis. Changes in gene expression were determined by comparison with untreated cells in three independent experiments after a Bayesian statistical analysis of variance.

results. Changes in gene expression were reproducibly observed in 165 genes representing previously identified and novel chemokines, matrix molecules, membrane receptors, angiogenic mediators, and transcription factors that correlated with pathophysiological responses to inflammation. Dramatic increases in gene expression were observed with exodus-1 (CCL20), MMP-12, and RhoA.

conclusions. DNA microarray analysis of the corneal fibroblast response to interleukin-1α provides important insight into modeling changes in gene expression and suggests novel therapeutic targets for the control of corneal inflammation.

The cornea is a transparent, five-layered tissue that serves as a critical barrier to ocular infection and injury. The stromal layer constitutes 90% of the corneal thickness and is a frequent site of inflammation after infection, trauma, or surgery. Although the inflammatory response may initially help control an infection or seal a wound, all too often the process leads to edema, collagen disorganization, neovascularization, ulceration, and scarring. Current anti-inflammatory treatment is frequently inadequate to prevent destruction of tissue and loss of corneal transparency. 1 2 In severe cases, the only option is corneal transplantation, which is itself subject to failure after excessive inflammation. Thus, uncontrolled inflammation within the cornea may significantly decrease visual acuity or end in blindness. 3 Understanding the endogenous inflammatory response is critical for developing improved treatment options for the more than 40,000 patients annually who undergo corneal transplantation because of cornea-related blindness. 4  
Interleukin-(IL)-1α is an important mediator of early inflammation in the cornea. 5 6 7 8 9 The cytokine IL-1α is produced early by epithelial cells and resident corneal fibroblasts and later by invading leukocytes. 7 10 Expression of IL-1α is upregulated after corneal grafting, 11 infection, 12 epithelial trauma, 6 and alkali burns. 13 As one of the earliest cytokines released into the corneal stroma, IL-1α amplifies the inflammatory response through membrane receptors expressed by fibroblasts. 7 14 During dynamic phenotypic changes, these cells alter their shape, become motile, elaborate additional inflammatory factors, and regulate stromal matrix components. 8 15 16 This response is in part due to altered gene expression mediated by IL-1α, yet only a limited number of these genes are known. 
With the advent of gene therapy and the increased number of small-molecule pharmaceuticals, it is increasingly important to discover which genes might serve as targets in controlling excessive inflammation. DNA microarrays offer a powerful method to identify potential targets by simultaneously screening expression changes in thousands of genes. 17 In a culture model of corneal inflammation, we applied this technique to monitor early changes in gene expression in corneal fibroblasts exposed to IL-1α. Using novel statistical software, a model for genes involved in corneal inflammation was developed with the hope that this approach may eventually identify specific therapeutic avenues for the reduction of corneal opacification. 
Materials and Methods
Cells Culture and RNA Isolation
Cell culture reagents were purchased form Gibco-BRL (Grand Island, NY) and IL-1α from Calbiochem (La Jolla, CA). A fresh human cornea was obtained from the Lions Doheney Eye Bank (Los Angeles, CA). A central section measuring 6 mm in diameter was excised, and epithelial and endothelial layers were scraped away. The remaining stromal layer was minced, and explants were cultured in Eagle’s minimum essential medium with 20% fetal bovine serum, 100 IU penicillin G, and 100 μg/mL streptomycin in 35-mm dishes (Corning, Inc., Corning, NY). After 14 days, corneal fibroblasts became confluent and were subcultured. Fifth-passage corneal fibroblasts were then grown on 100-mm dishes in DMEM supplemented with 10% fetal bovine serum until 80% to 90% confluent. Cells were then rinsed and cultured in cell culture medium (Opti-MEM; Gibco) for an additional 72 hours, as previously described. 18 Corneal fibroblasts were treated for 24 hours with either vehicle (EtOH) or IL-1α (10 ng/mL) in three independent experiments. Afterward, cells were trypsinized, and approximately 20 μg total RNA was isolated with an RNA extraction system (RNeasy; Qiagen, Valencia, CA), according to the manufacturer’s protocol. The RNA concentration was determined by spectroscopy, and the quality was confirmed by gel electrophoresis. 
cRNA Preparation and Array Hybridization
Total RNA was converted into double-stranded cDNA using a custom kit (SuperScript II Double-Stranded cDNA Synthesis Kit; Life Technologies, Gaithersburg, MD). After ethanol extraction, in vitro transcription reactions were performed (BioArray HighYield RNA Transcript Labeling Kit; Enzo Biochemicals, Inc., Farmingdale, NY) according to the manufacturer’s protocol. Purified, labeled cRNA was quantified by spectrophotometric analysis, qualitatively analyzed for size distribution by gel electrophoresis, and fragmented to 30 to 60 base fragments with Tris-acetate (pH 8.1; 40 mM), KOAc (100 mM), and MgOAc (30 mM) in a 20-μL volume heated for 35 minutes at 94°C. Protocols and instrumentation setups, including total RNA samples, hybridization to U95A human microarrys, washing, staining, and scanning were followed as recommended in the manufacturer’s technical manual (GeneChip Microarrays; Affymetrix, Santa Clara, CA). 
Data Analysis
The resultant arrays were scanned twice in the system’s confocal scanner (HP GeneArray Scanner; Hewlett Packard, Palo Alto, CA). Data analysis was first performed with the software accompanying the microarrays (GeneChip Expression Analysis Software, ver. 3.3; Affymetrix) to obtain average difference intensities. The Cyber-T software package developed at the University of California, Irvine, California, (provided in the public domain by the National Center for Genome Resources, Santa Fe, NM, at http://genomics.biochem.uci.edu/genex/cybert/) was then used to determine average intensity values of genes across three independent experiments and to compare values for control- and IL-1α–treated cells. This list was further restricted based on whether genes were consistently present or consistently absent in three independent experiments. By using a Bayesian statistical analysis, significant changes were ranked by the assignment of probabilities. 19 Genes displaying intensities lower than 30 were empirically considered absent. Multiples of changes in gene expression were calculated by comparing average intensities in control with IL-1α treatment. For genes that were entirely absent in either control or IL-1α treatment but present in the other condition, relative multiples of change were assigned by comparing intensities with a normalized value determined by the software (Affymetrix). 
The U95A human gene microarray is composed of oligonucleotides representing approximately 12,000 full-length, partially sequenced, and expressed sequence tag genes. Of the sequences represented, approximately 7700 were detected in cRNA produced from cultured human corneal fibroblasts, and 3532 of these correlated with complete cDNAs. Pair-wise comparison of gene expression levels in three independent control experiments displayed an overall correlation coefficient of R 2 = 0.95, suggesting excellent reproducibility (data not shown). 
Corneal fibroblasts were treated with IL-1α for 24 hours, as in previous studies, 18 and cRNA was generated for microarray analysis. A common approach to identifying significant changes in gene expression is based on selection by multiples of change. After IL-1α treatment, 1115 genes displayed a greater than threefold expression change (data not shown) on average. Yet, many of these were changes with high interexperimental variance and probably represented experimental artifacts. An alternative approach is based on applying the t-test within a Bayesian statistical framework and using probabilities rather than multiples of change to rank significant expression changes. This method was preferred, because it more conservatively identifies genes with expression changes and reduces false-positive findings in microarray studies, with few replicates. 19 20  
After statistical analysis, significant changes in messenger RNA levels were detected in only 14 genes with P < 0.01 (Table 1 , asterisks). Expanding the selection of genes to those with a P < 0.05 generated an additional 118 genes (Table 1) . Some of the genes were especially noteworthy for their inflammatory roles and are described in the results (Table 1) . In addition, 22 genes absent in untreated cells were detected after IL-1α treatment (Table 2) , and 25 genes became undetectable (Table 3) . Several unknown or incomplete sequences were also identified. This included 55 sequences with a P < 0.05: 6 detected only after IL-1α treatment and 16 that became undetectable after IL-1α treatment (data not shown). Altogether, these results suggest that the number of highly reproducible changes in gene expression were limited to approximately 2% of the sequences sampled. This is in spite of the extremely sensitive detection system and application of a highly potent cytokine. 
Identification of Control Genes
Studies demonstrate that gene detection by microarrays correlates with standard methods such as Northern analysis and RT-PCR. 21 In untreated corneal fibroblasts, for example, the microarray experiment detected 521 corneal genes previously catalogued by various methods. 22 At the same time, genes specifically not expressed by passaged corneal fibroblasts, such as the type 2 IL-1 receptor, collagenase, and stromelysin were appropriately absent. 23 24 To further confirm whether previously described changes in gene expression in IL-1α–treated corneal fibroblasts were evident in these microarray studies, we examined genes for IL-1α, regulated on activation normal T-cell expressed and secreted (RANTES), and granulocyte-macrophage–colony-stimulating factor (GM-CSF)-1. 18 25 These genes served as internal controls, and, consistent with previous studies, each of these genes was upregulated. In treated cells IL-1α gene expression increased sixfold, RANTES eightfold, and GM-CSF-1 sevenfold (Tables 1 2) . These findings lend strong support to the statistical methods used for gene selection and the validity of the microarray analysis within the biological context. Some IL-1α–responsive genes such as hepatocyte growth factor, 26 monocyte chemotactic protein-1, 18 and GRO-α, 27 were not represented on the microarray and could not be tested. 
IL-1α–responsive genes have also been studied in cells of different tissue origin. The microarray analysis identified similar expression changes not previously described in the corneal fibroblasts. Some of these were the modulation of interferon-β2, endothelin-1, platelet-derived growth factor (PDGF)-α receptor, thrombospondin, manganese superoxide dismutase, Gro-β, Gro-γ, plasminogen activator-inhibitor (PAI) 2, c-myc, and IL-6. 10 No expression changes were observed in inducible nitric oxide synthase (iNOS), cyclooxygenase (COX)-2, tissue inhibitor of matrix metalloproteinase (TIMP)-1, metalloproteinase (MMP)-1, IL-8, or keratinocyte growth factor. This may reflect either a tissue-specific expression or response, or in the case corneal fibroblast genes, differences in the experimental conditions. 24 28  
Secreted Inflammatory Genes
As one of the earliest expressed cytokines, IL-1α propagates the expression of additional cytokines that amplify the inflammatory process. Genes for both IL-1α and IL-1β were upregulated (Table 1 2) . It is important to note that the increased IL-1β RNA detected may reflect increased stabilization of its mRNA rather than increased transcription and that some IL-1–responsive genes, such as keratinocyte growth factor, may depend specifically on the IL-1β isoform. 26 29 Interferon-β2a, which is identical with IL-6, was upregulated and may have a protective role during corneal infection. 30 Interferon-α, which displays antiviral, antiparasitic, and antiproliferative activities, 31 was downregulated. This could create a permissive environment for fibroblast mitogenesis and viral infection. 
Chemokines are cytokines that recruit leukocytes to sites of inflammation. 32 In response to IL-1α, for example, corneal fibroblasts upregulate and secrete MCP-1 (CCL2), RANTES (CCL5), and GROα (CXCL1). 6 18 The microarray experiments identified two other growth-regulated oncogenes (GRO) genes: GRO-β (XXCL2) and GRO-γ (CXCL3). The chemokine exodus-1/MIP-3α/LARC (CCL20), 33 34 displayed a dramatic 29-fold increase in expression. Monocyte chemotactic protein-2 (CCL8) and ENA-78 (CXCL5) were upregulated as well. 35 Together, these chemokines bind to various G-protein–coupled chemokine receptors expressed by neutrophils, eosinophils, monocytes, and T cells and could attract a full range of leukocytes from the limbal region into the corneal stroma. 36  
Cellular Inflammation Genes
IL-1α regulation of genes expressed at the membrane fundamentally alters the fibroblast responsiveness to extracellular inflammatory factors. Membrane receptors for IL-7 and IL-15 were upregulated. Expression of gp130, a membrane receptor/signal transducer for IL-6, 37 38 increased fivefold, and together with an increase in IL-6, suggests the coordinated expression of a signaling pathway. Receptor responses to the cytokine glia-derived neurotrophic factor (GDNF) are modified by RETL-2, which increased threefold. 39 In contrast, the cytokine receptor for IL-11 was downregulated, suggesting a possible decreased responsiveness to the antiapoptotic signal generated by IL-11. 40 In addition, the decreased level of major histocompatibility complex (MHC) class II HLA-DR β-1 suggests that antigen presentation mediated by the fibroblasts may be diminished. 41  
Modulation of gene transcription is an essential feature of cytokine signaling, and expression changes in several transcriptional factors were observed (Tables 1 2 3) . Of particular note was the increased expression of signal transducer and activator of transcription (STAT)-4 and R-κB. STAT proteins have dual functions: first binding to cytokine receptors and then migrating into the nucleus and directly activating transcription. STAT4 is essential for IL-12 signals that lead to production of interferon-γ. 42 43 The recently identified R-κB is a homologue of nuclear factor (NF)-κB, the prototypical inflammatory transcription factor. Although R-κB is known to regulate interleukin-2 receptor α-chain gene expression, 44 its conceivably broad transcriptional and inflammatory effects remain to be explored. In contrast to transcription factors, an alternative mechanism for gene regulation is the degradation of newly formed mRNA. The observed increase in the nuclear protein HEM45 may have this effect, as suggested by its putative role in degrading viral mRNA. 45  
An important mechanism of IL-1α–mediated inflammation is the activation of eicosanoid signaling by modulating arachidonic acid metabolism. 10 No effects were observed on phospholipase A2 or COX-2 expression. In contrast to the cyclooxygenase pathway, the lipoxygenase pathway appeared to be activated early with the observed increase in leukotriene C4 synthase. 46 Among other effects, its secreted product leukotriene-C4 (LTC4) increases microvascular permeability. 47  
Corneal Transparency and Extracellular Matrix Genes
One of the remarkable features of the cornea is its essential transparency to light—a transparency that is diminished after injury. This transparency depends on the extracellular collagen composition and organization, and corneal fibroblasts are a primary source of extracellular matrix proteins in the stroma. TGFβ, for example, regulates collagen synthesis and deposition and is an immunosuppressant in the anterior chamber. 48 49 Activation of TGFβ is dependent on thrombospondin-1, which increased fourfold after IL-1α treatment. This may affect both the matrix structure and regulation of inflammation. 50 An observed decrease in the TGFβ type III receptor suggests IL-1α may modulate specific TGF signaling pathways. 51 52 bone morphogenic protein (BMP)-7, which regulates specific collagen deposition, displayed a decreased expression. 53 In addition to these signaling molecules, IL-1α induced a threefold increase in α-1 type VII collagen (COL7A1), which has a specific role in anchoring the basal cells of the corneal epithelium to the stroma. 54 Also observed was a threefold decrease in α-5 collagen type IV (COL4A5), which helps form corneal basement membranes. 55  
Collagen matrix organization is regulated by accessory proteins. Proteoglycans maintain corneal transparency by regulating collagen fibril spacing and corneal hydration. A sevenfold increase in chondroitin 6-sulfotransferase–like protein was detected. Expression of this enzyme may be critical for maturation of the keratan sulfate proteoglycans, which are the major proteoglycans in the cornea. 56 57 At the same time, a fivefold decrease in the chondroitin sulfate proteoglycan versican was also detected. Other accessory proteins identified were matrilin-3, which forms collagen-dependent and -independent fibrils, 58 and thrombospondin-1. 59  
Secreted proteases are known to play a major role in remodeling the stromal extracellular matrix during corneal wound healing. 60 61 62 In response to IL-1α, fibroblasts displayed a 10-fold increase in human metalloelastase (HME/MMP-12), which was originally identified in macrophages. 63 MMP-12 has multiple extracellular matrix substrates and disrupts basement membranes. 64 65 No expression changes in membrane type matrix metalloproteinase-1, -2, or -4 were detected. Although MMP-1 gene regulation was not detected under these experimental conditions, the observed increase in manganese superoxide dismutase, and leukotriene C4 synthase may eventually signal an increase in MMP-1. 66 67 Secreted proteases are regulated in turn by a various inhibitors. RECK is a membrane protein that inhibits MMP-9 activity. 68 Decreased RECK suggests a concomitant increase in MMP-9 proteolytic activity, which is associated with corneal tissue destruction. 61 62 In contrast, IL-1α induced the increased expression of two protease inhibitors: squamous cell carcinoma antigen (SCCA) 2 (leupin) and PAI-2, which may help control the degradation of the stromal matrix. 69 70 There were no observed expression changes in tissue inhibitor of metalloproteinase-2, -3, or -4. 
The avascular and relatively acellular quality of the stroma is essential to maintaining corneal transparency. Stromal neovascularization during inflammation and subsequent wound healing interferes with corneal light transmission, and IL-1 is implicated in this process. 71 72 Several angiogenic factors were identified after IL-1α treatment including endothelin (EDN)-1, 73 74 heparin-binding epidermal growth factor (HB-EGF), 75 GM-CSF-1, 76 and connective tissue growth factor (CTGF). 77 At the same time, changes in the potent angiogenic factor VEGF, which is not detected in stromal fibroblasts, was appropriately absent. 78 79 Similar to stromal neovascularization, proliferation of stromal fibroblasts interferes with corneal clarity. Some of the angiogenic factors, such as HB-EGF and CTGF, are also fibroblast mitogens. 80 81 82 Enhanced expression of the PDGF receptor may prime fibroblasts for PDGF mitogenic effects. 83 Increased expression of the follistatin-related protein (FLRG), a BMP-2 inhibitor, may protect specifically against BMP-2’s proliferative effects. 84 85  
Cell Cytoskeletal and Motility Genes
Corneal fibroblasts normally lie flat between collagen lamella and extend filopodia to adjacent fibroblasts. After injury, however, filopodial extensions retract, actin networks form, and the cells become motile. 15 16 IL-1α is known to have a chemorepulsive effect on corneal fibroblasts in vitro and at the site of IL-1α injection in vivo. 83 86 The microarray identified several genes that may contribute to these processes. 
One of the most dramatic changes was the 46-fold increase in RhoA gene expression. The small guanosine triphosphatase (GTPase) RhoA is a signaling molecule that provides a link between extracellular signals and dynamic changes in the actin cytoskeleton. 87 It is associated with membrane retraction by the formation of actin stress fibers and focal adhesions and was recently identified in IL-1 receptor signal transduction. 88 Several intracellular proteins that directly regulate the cytoskeleton were recognized. These included actin-binding proteins, such as the LIM and the LIM domain binding protein (LDB)-1; the short form of β-II spectrin, α-catenin-like protein; and T-platstin. 89 Expression changes in microtubule-related proteins included alternatively spliced microtubule-associated protein (MAP)2 and the tubulin-binding protein RP-1. 90  
The transformation of quiescent keratocytes to mobile fibroblasts involves modulation of cellular interactions with extracellular matrix. Enzymes that degrade the matrix as previously described lead to loss of cell anchorage, whereas other matrix proteins, such as thrombospondin and PDGF signals, may promote motility. 83 91 BMP-2 and -4 are also chemoattractive signals, but no significant changes in gene expression were observed other than the increased BMP-2 inhibitor FLRG. Another component is the differential expression of cell adhesion molecules. A threefold decrease in HNK-1 sulfotransferase, which alters the cell adhesion activity of HNK-1, was detected along with a decreased expression of protocadherin 68. In contrast, increased expression of Nr-CAM/hBRAVO and syndecan-1 was observed. 92 93 Finally, transmembrane integrin proteins connect matrix molecules such as collagen and laminin to intracellular actin and transmit cell motility signals. 94 95 After IL-1α treatment, integrin-α expression increased, whereas integrin β-5 and laminin α-4 decreased. Together, these changes in gene expression may influence fibroblast cytoskeletal reorganization and motility during IL-1α mediated inflammation. 
The pathophysiological mechanisms of corneal inflammation and how best to treat patients are poorly understood. Activation of the endogenous inflammatory response by IL-1 has both beneficial and detrimental effects. 5 After stromal injury, some degree of inflammation is essential for wound healing and control of infection. At the same time, however, inhibition of IL-1 may be favorable under certain conditions. 96 97 We hypothesized that identifying IL-1–responsive genes may help develop models for more specific therapeutic interventions. Studies with cultured cells are a well-established method for the initial identification of cytokine-responsive genes. Which corneal fibroblast phenotype represents the best therapeutic target remains to be determined, 8 98 and although the complex leukocyte effects are not represented, cytokine studies have nevertheless provided important insights. 6 The application of DNA microarray technologies, we expected, would identify additional cytokine responsive genes and help develop models for further investigation. 17 99  
A model for the response to IL-1α in corneal fibroblasts is presented in Figure 1 . Genes thought to contribute to inflammation, maintenance of corneal transparency, and fibroblast motility and cytoskeletal rearrangement are emphasized and organized by their predicted protein localization. Such a model represents an exciting starting point for further investigation, yet it is critical to be aware of the limitations imposed by the model and inherent in microarray studies. The observed gene expression may reflect either increased transcription or increased mRNA stability, and whether the mRNA is translated into protein is not certain. Distinguishing tissue culture artifact from genuine expression changes requires in vivo studies. For example, Il-1α–responsive genes may be regulated by serum starvation or by an intrinsic IL-1α autocrine loop activated after trypsin-passage. 28 The expense associated with commercial DNA microarray analysis precludes the comprehensive study of time and dose responses, but the generation of custom gene chips containing only genes in the model will now allow such studies to be conducted at far less cost. Appropriate statistical tools have been lacking to interpret the seemingly thousands of changes in gene expression revealed by microarrays in the context of low replication, errors from multiple measurements, and determining significance by multiples of change. These pitfalls were avoided in this study with the use of the Cyber-T statistical software. Finally, it is important to recall that DNA microarray results do not reveal other important mechanisms that regulate proteins directly rather than through gene expression. 
These caveats notwithstanding, DNA microanalysis was an effective and efficient screening method. Several previously known IL-1–responsive genes were identified, supporting the statistical and experimental methods. Some novel genes representing chemokines, chemotactic factors, cell adhesion molecules, interleukin receptors, matrix metalloproteinase, protease inhibitors, proteoglycans, angiogenic factors, cytoskeletal regulators, and transcription factors were identified. Each of these molecules represent potential targets, but what genes warrant further investigation? Regulation of MMPs is promising, 60 and this study correctly identified MMP-9 as an important candidate. Inhibition of MMP-9 has already shown some success as an adjunctive therapy in clinical studies, 100 and results suggest that targeting MMP-12, identified in this study, may also be important. Even with multiple redundant chemokine expression, targeting single chemokines or their receptors can dramatically reduce inflammation. 36 We identified chemokine targets not previously described in the cornea. Perhaps more important are the G-protein receptors (CXCR2 and CCR1, -2, -3, -5, and -6) implicated by this array of chemokines. It is interesting to speculate that therapies may be narrowed toward receptors expressed during leukocyte reactions specific to allergic, bacterial, or viral inflammation. Regulation of RhoA may control fibroblast motility and eventually wound contraction and corneal scarring, and specific inhibitors are available to test such a hypothesis. Finally, corneal gene therapy makes possible the targeting of specific transcription factors, such as STAT4 and R-κB, which have conceivably broad roles in inflammation. 101 102 Taken together, the observations based on this DNA microarray experiment provide fertile ground for developing and testing novel hypotheses to reduce cornea-related blindness. 
Table 1.
Genes Expression Changes after IL-1α Treatment
Table 1.
Genes Expression Changes after IL-1α Treatment
Accession Number Gene Description Change (×) Accession Number Gene Description Change (×)
Extracellular Inflammation  U50062 RIP protein kinase 2.4
 M29893 Low-molecular-mass GTP-binding protein (ral) −2.1
  U64197 Exodus-1∗ 29.2  L33881 Human protein kinase C iota isoform −2.8
  Y16645 Monocyte chemotactic protein-2 14.8  AF039945 Synaptojanin 2B −3.0
  M21121 T-cell–specific protein (RANTES)∗ 8.3  U37352 Phosphatase 2A B′ alpha1 regulatory subunit −3.4
  M13207 GM-CSF-1 7.2  Y13493 Protein kinase Dyrk2 −3.7
  M15330 Interleukin-1-beta (IL-1β) 6.5  L20321 Serine/threonine kinase stk2∗ −3.9
  M36821 GRO-gamma∗ 4.1  M59371 Human protein tyrosine kinase∗ −5.4
  X04430 IFN-beta 2a (IL-6) 3.9  Metabolic
  M36820 GRO-beta 3.2  M20681 Glucose transporter-like protein-III (GLUT3) −3.1
  X78686 ENA-78 3.1  M81118 Alcohol dehydrogenase chi polypeptide (ADH5) −2.3
 Matrix  U46689 Microsomal aldehyde dehydrogenase (ALD10) −2.3
  L23808 Human metalloproteinase (HME) MMP-12∗ 10.4  AF025887 Glutathione S-transferase A4-4 (GSTA4) −2.9
  J05008 Homo sapiens endothelin-1 (EDN1)∗ 8.8  U37100 Aldose reductase–like peptide −2.8
  Y00630 Plasminogen activator-inhibitor 2, (PAI-2) 7.7  X13589 Aromatase (estrogen synthetase) 3.3
  X14787 Thrombospondin 4.1  X85019 UDP-GalNAc-polypeptide N-acetylgalactosaminyltransferase (T2) 3.1
  L02870 Alpha-1 type VII collagen (COL7A1) 3.5
  U76702 Follistatin-related protein (FLRG) 3.2  AF020543 Palmitoyl-protein thioesterase-2 (PPT2) 2.6
  M60278 Heparin-binding EGF-like growth factor 2.6  D86181 Galactocerebrosidase −2.5
  M14113 Coagulation factor VIII-C −2.7  S69189 Peroxisomal acyl-coenzyme A oxidase −2.5
  M58526 Alpha-5 collagen type IV (COL4A5) −3.2  M76665 11-beta-Hydroxysteroid dehydrogenase (HSD11) 2.5
  X78947 Connective tissue growth factor −3.2  AF029893 i-beta-1,3-N-Acetylglucosaminyltransferase −2.4
  X15998 Chondroilin sulphate proteoglycan versican, V1 splice-variant −3.4  U43944 Cytosolic NADP(+)-dependent malic enzyme −2.8
 L29254 (clone P1–5) l-Iditol-2 dehydrogenase −2.7
  K02054 HUMGRP5E Human gastrin-releasing peptide −3.8  L46590 Very long chain acyl-CoA dehydrogenase 2.9
  AB011538 MEGF5∗ −4.1 Miscellaneous
  S78569 Laminin alpha 4 chain∗ −5.4  AL022165 Chondroitin 6-sulfotransferase-like protein 7.1
Membrane Protein  D29992 Placental protein 5 (PP5) 6.9
  S46950 Adenosine A2 receptor 9.7  M92357 B94 3.8
  U55258 hBRAVO/Nr-CAM precursor 9.1  AJ225089 2–5′ Oligoadenylate synthelase 59 kDa isoform 3.6
  M57230 Membrane glycoprotein gp130 5.4  X54162 64 Kd autoantigen expressed in thyroid and extra-ocular muscle 3.3
  U08023 Cellular proto-oncogene (c-mer) 5.1
  Z48199 Syndecan-1 gene (exons 2-5) 4.7  AF048730 Cyclin T1 3.0
  S77812 Vascular endothelial growth factor receptor/VEGF receptor/cell surface tyrosine kinase 4.7  J02611 Apolipoprotein D 2.8
 X07834 Manganese superoxide dismutase 2.6
  M23263 Androgen receptor (HUMARB) 4.3  U11313 Sterol carrier protein-X/sterol carrier protein-2 (SCP-X/SCP-2) −2.2
  AF035121 KDR/flk-1 (VEGF receptor) 4.0
  AL022314 Novel trypsin family protein with class A LDL receptor domains 3.6  M15796 Human cyclin protein gene −2.2
 AJ001625 Pex3 −2.4
  L10126 Serine/threonine kinase receptor-2-3 (SKR2-3) 3.5  AL022326 Synaptogyrin 1A (SYNGR1A) −2.4
  U77643 K12 protein precursor 3.4  U68723 Checkpoint suppressor 1 −2.4
  U67784 Orphan G-protein–coupled receptor (RDC1) 3.4  S74728 Antiquitin=26g turgor protein homologue −2.5
  U31628 Interleukin-15 receptor alpha chain precursor (IL15RA) 3.2  U70063 Human acid ceramidase −2.5
 D64109 Tob family (Tob2) −2.5
  X76079 Platelet-derived growth factor alpha receptor 3.1  D83004 Ubiquitin-conjugating enzyme E2 −2.6
  AF057169 Bestrophin (VMD2), alternatively spliced product 3.1  AF070523 JWA −2.6
  U97145 RET ligand 2 (RETL2) 3.0  AB023421 Heat shock protein apg-1 −2.7
  M29696 Interleukin-7 receptor (IL-7) 2.9  U92436 MMAC1 (tumor supressor) −2.8
  U50136 Leukotriene C4 synthase (LTC4S) 2.3  AF006010 Progestin-induced protein (DD5) −2.9
  U07695 Human tyrosine kinase (HTK) −2.5  M13452 Lamin A∗ −3.2
  AF009425 C18orf1, alternative splicing variant alpha-2 −2.7  X82554 SPHAR gene for cyclin-related protein −3.5
  L06328 Voltage-dependent anion channel isoform 2 (VDAC) −2.8  M32886 Sorcin CP-22 −3.8
 AF005081 Skin-specific protein (xp320) −4.2
  L07594 Transforming growth factor-beta type III receptor −2.8 Nuclear
  AF070594 HNK-1 sulfotransferase −2.8   U88964 HEM45 8.4
  U81375 hENT1 −2.9   D21205 Estrogen-responsive finger protein 6.4
  S59184 Related to receptor tyrosine kinase −3.0   M14660 ISG-54K gene (interferon-stimulated gene) 4.4
  U32324 Human interleukin-11 receptor alpha chain −3.5   L02932 Peroxisome proliferator–activated receptor 3.9
  D50406 RECK −3.9   L78440 STAT4 3.8
  AL035081 Similar to Xenopus laevis mRNA for KDEL receptor −4.5   X63759 hTNP2 3.7
 AB015332 HRIHFB2018 3.4
Cytosolic Structural   U51096 Cdx2 3.1
  V00568 c-Myc oncogene 3.3
  X68742 Integrin, alpha subunit∗ 7.0   M97935 Transcription factor ISGF-3 3.9
  X90761 hHa2 (keratin) 3.4   U19969 Two-handed zinc finger protein ZEB mRNA −2.3
  L43821 Enhancer of filamentation (HEF1) 2.9   AF008442 RNA polymerase I subunit hRPA39 mRNA −2.5
  M22299 T-plastin −2.2   AF020043 Chromosome-associated polypeptide (HCAP) −2.6
  AF035812 Dynein light intermediate chain 2 (LIC2) −2.3   AJ223321 RP58 gene −2.7
  X53002 Integrin beta-5 subunit −2.5   V01512 Cellular oncogene c-fos −2.7
  S67247 Smooth muscle myosin heavy chain isoform SMemb −2.6   AL031670 Similar to Zinc finger, C3HC4 type (RING finger) −2.8
 AB006909 A-type microphthalmia associated transcription factor −2.9
  X94232 RP1 −2.6
  U97067 Alpha-catenin-like protein −2.6   AF054284 Spliceosomal protein SAP 155 −3.0
  U49957 LIM protein (LPP)∗ −4.3   U10686 MAGE-11 antigen (MAGE11) −3.2
 Signaling   D13666 Osteoblast-specific factor 2 (OSF-2os)∗ −3.6
  U19261 Epstein-Barr virus–induced protein (TNF signaling) 6.2   M97815 Cellular Retinoic acid-binding protein II (CRABP-II)∗ −5.0
  X68277 CL 100 protein tyrosine phosphatase 4.4 Mitochondrial
  U77735 Pim-2 protooncogene homolog (pim-2h) 3.7   X69433 Mitochondrial isocitrate dehydrogenase (NADP) −2.9
  X79780 YPT3 3.2   Y14494 Mitochondrial carrier protein ARALAR1 −3.2
  AF041434 Potentially prenylated protein tyrosine phosphatase hPRL-3 2.5   U22028 Human cytochrome P450 (CYP2A13) −3.6
Table 2.
Genes Expressed after IL-1α Treatment
Table 2.
Genes Expressed after IL-1α Treatment
Accession Number Gene Description Change (×)
  M28983 Interleukin 1 alpha 6.0
  U19557 Squamous cell carcinoma antigen 2 (SCCA2) 9.2
 Membrane Protein
  M81830 Somatostatin receptor isoform 2 (SSTR2) 9.9
  U10485 Lymphoid-restricted membrane protein (Jaw1) 9.7
  L02750 Potassium channel 4.6
  U89330 Microtubule-associated protein 2 (MAP2), alternatively spliced 12.7
  AJ005694 Short form of beta II spectrin 10.2
 Signaling Proteins
  M12174 Ras-related rho (RhoA) 45.9
  M93426 Protein tyrosine phosphatase zeta-polypeptide (PTPRZ) 8.0
  AB001872 Leucine zipper–bearing kinase 4.0
  AB001466 Efs1 2.9
 Metabolic Enzymes
  U10473 p4betaGT/3 beta-1,4-galactosyltransferase 8.2
  L31801 Monocarboxylate transporter 1 (SLC16A1) 5.1
  U22961 Similarity to l-glycerol-3-phosphate-NAD oxidoreductase 3.9
  AF040639 Aflatoxin B1-aldehyde reductase 3.9
  U16811 Bak 9.7
  U52969 PEP19 (PCP4) 2.4
  U22376 Human (c-myb) gene 14.2
  AF059194 MAFK 12.5
  M21535 Erg protein (ets-related gene, HUMERG11) 5.7
  L49219 RB1 5.5
  X80878 R-kappaB 5.3
Table 3.
Genes Undetectable after IL-1α Treatment
Table 3.
Genes Undetectable after IL-1α Treatment
Accession Number Gene Description Fold-change
  M28585 Leukocyte interferon alpha, clone pIFN105 −5.7
  AJ224741 Matrilin-3 −3.1
  X51801 OP-1 (BMP-7) −3.1
Membrane Protein
  M29540 Carcinoembryonic antigen (CEA) −6.3
  M32578 MHC class II HLA-DR beta-1 mRNA (DR2.3) −5.2
  M69296 Estrogen receptor-related protein −5.0
  AF029343 Protocadherin 68 (PCH68) −4.6
  U49516 Human serotonin 5-HT2c receptor −3.6
  U66582 GammaC-crystallin (CRYGC) −13.2
  AF052389 LIM domain binding protein (LDB1) −12.5
  D50370 Nucleosome assembly protein −6.4
  X95191 Delta-sarcoglycan −6.1
  S76756 4R-MAP2 −4.7
  J03756 Growth hormone-variant-1 (GH1) and variant-2 (GH2) −23.0
  L11706 Hormone-sensitive lipase (LIPE) gene −21.1
  U07620 MAP kinase −6.2
  M60724 p70 Ribosomal S6 kinase alpha-I −3.7
  AB015228 RALDH2-T −12.5
  D83017 Nel-related protein −6.3
  AJ005821 X-like 1 −5.1
  AF007833 Kruppel-related zinc finger protein hcKrox −19.8
  U66619 SWI/SNF complex 60 KDa subunit (BAF60c) −15.7
  U66561 Kruppel-related zinc finger protein (ZNF184) −13.2
  M93119 Zinc-finger DNA-binding motifs (IA-1) −8.3
  S82986 HOXC6 −6.9
Figure 1.
Model for IL-1α–mediated inflammatory response by corneal fibroblasts. IL-1α responsive genes with putative activities during inflammation, maintenance of corneal transparency, and modulation of the cytoskeleton and cellular motility are presented. Genes are arranged according to their predicted protein localization in various cellular compartments. Genes with increased expression (bold type) and decreased expression (normal type).
Figure 1.
Model for IL-1α–mediated inflammatory response by corneal fibroblasts. IL-1α responsive genes with putative activities during inflammation, maintenance of corneal transparency, and modulation of the cytoskeleton and cellular motility are presented. Genes are arranged according to their predicted protein localization in various cellular compartments. Genes with increased expression (bold type) and decreased expression (normal type).
The authors thank G. Wesley Hatfield and She-pin Hung for assistance with the statistical software and J. Denis Heck and Kim Nguyen for assistance with the microarrays. 
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