April 2012
Volume 53, Issue 4
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Glaucoma  |   April 2012
Hypoxia Regulated Gene Transcription in Human Optic Nerve Lamina Cribrosa Cells in Culture
Author Affiliations & Notes
  • Ruaidhrí P. Kirwan
    From the 1Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland, the Department of Cell Biology and Anatomy, North Texas Eye Research Institute, University of North Texas Health Sciences Center, Fort Worth, Texas, and the School of Medicine and Medical Science, University College Dublin, Dublin, Ireland.
  • Luca Felice
    From the 1Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland, the Department of Cell Biology and Anatomy, North Texas Eye Research Institute, University of North Texas Health Sciences Center, Fort Worth, Texas, and the School of Medicine and Medical Science, University College Dublin, Dublin, Ireland.
  • Abbot F. Clark
    From the 1Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland, the Department of Cell Biology and Anatomy, North Texas Eye Research Institute, University of North Texas Health Sciences Center, Fort Worth, Texas, and the School of Medicine and Medical Science, University College Dublin, Dublin, Ireland.
  • Colm J. O'Brien
    From the 1Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland, the Department of Cell Biology and Anatomy, North Texas Eye Research Institute, University of North Texas Health Sciences Center, Fort Worth, Texas, and the School of Medicine and Medical Science, University College Dublin, Dublin, Ireland.
  • Martin O. Leonard
    From the 1Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland, the Department of Cell Biology and Anatomy, North Texas Eye Research Institute, University of North Texas Health Sciences Center, Fort Worth, Texas, and the School of Medicine and Medical Science, University College Dublin, Dublin, Ireland.
  • Corresponding author: Ruaidhrí P. Kirwan, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland; ruaidhri.kirwan@ucd.ie
Investigative Ophthalmology & Visual Science April 2012, Vol.53, 2243-2255. doi:10.1167/iovs.11-6729
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      Ruaidhrí P. Kirwan, Luca Felice, Abbot F. Clark, Colm J. O'Brien, Martin O. Leonard; Hypoxia Regulated Gene Transcription in Human Optic Nerve Lamina Cribrosa Cells in Culture. Invest. Ophthalmol. Vis. Sci. 2012;53(4):2243-2255. doi: 10.1167/iovs.11-6729.

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

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Abstract

Purpose: Vascular hypoperfusion, extracellular matrix remodeling and axon loss are pathological characteristics of the glaucomatous optic nerve head. We report a novel study demonstrating transcriptional responses in optic nerve lamina cribrosa (LC) cells exposed to in vitro hypoxic stress.

Methods: Primary cultures of human glial fibrillary acid protein (GFAP) negative LC cells were generated from four donors. Cells were exposed to 24 hours of hypoxic stress (1% O2) or normoxia (21% O2). Hypoxia responsive genes were identified using Affymetrix HG-U133A microarrays (n = 3) and validated with real time PCR (n = 3). Secreted protein was measured by ELISA (n = 4) and cellular protein by Western blot (n = 4). Expression data were annotated with NIH DAVID software and putative transcription factor sites in hypoxia-responsive gene promoters were identified using Core_TF software.

Results: Hypoxia-sensitive genes included those involved in apoptosis (e.g., BNIP3), neurogenesis (e.g., STC1), extracellular matrix (e.g., MIF, DDR1/TrkE, and IGFR2), mitochondrion (e.g., CYP1B1) and angiogenesis (e.g., VEGF). Real time PCR for selected genes supported the expression changes identified by microarray. ELISA and Western blot validated corresponding changes in protein production. Promoter sequence interrogation revealed putative conserved transcription factor binding sites (e.g., HIF and CREB) in the promoters of the hypoxia responsive genes.

Conclusions: Our data show that LC cell gene expression is sensitive to reduced oxygen levels in vitro and provides bioinformatic evidence of the potential transcriptional regulators of this response.

Introduction
Primary open-angle glaucoma (POAG) is one of the leading causes of irreversible blindness worldwide 1 and is a progressive optic neuropathy that can occur in the presence or absence of chronically raised intraocular pressure (IOP). In patients in whom glaucoma develops in the absence of raised IOP (normal tension glaucoma, NTG), other pathological factors, such as reduced optic nerve head (ONH) perfusion, are implicated. 2 One of the sites of damage to the ONH in POAG and NTG is the lamina cribrosa layer, where the cellular and molecular pathology involves apoptosis of retinal ganglion cell (RGC) axons, reactive gliosis 3 and remodeling of non-axonal extracellular matrix components (e.g., increased deposition of collagen I, IV, VI, and elastin). 4,5 A reduction in ONH blood flow and perfusion pressure has also been observed in patients with raised IOP and POAG ONH damage. 6 A possible explanation for this has been shown in experimental primate studies in which compressive occlusion of arterioles perforating the anterior lamina cribrosa occurs as the IOP rises. 7 A study demonstrating increased hypoxia inducible factor-1 alpha (HIF) in the prelaminar and laminar regions of human POAG ONH tissue compared with normal controls provides further evidence that a hypoxic environment may exist in the glaucomatous lamina cribrosa. 8 Tektas et al. also show that the connective tissue sheath surrounding capillaries of the pre-laminar ONH is thicker in POAG eyes than in normal controls, a characteristic that could impair the diffusion of nutrients to the retinal ganglion cell axons. 9 Based on these studies, we hypothesize that compromise of lamina cribrosa oxygenation leads to hypoxic stress of axonal and non-axonal cellular components (e.g., lamina cribrosa glial cells), ultimately converging to a final common pathway of ONH extracellular matrix (ECM) remodeling, RGC axon loss and irreversible visual impairment. 
Glial fibrillary acid protein (GFAP) negative lamina cribrosa (LC) cells may mediate ECM remodeling and neuronal viability in the glaucomatous ONH, since not only do they share characteristics of glial derived astrocytes (e.g., expression of neurotrophins and neurotrophin receptors), they also resemble myofibroblasts (e.g., expression of alpha-smooth muscle actin, collagen I, elastin, and fibronectin). 10 In addition, we have shown previously that LC cells demonstrate a classical fibrotic response to exogenous transforming growth factor beta-1 (TGF-beta1) in vitro. 11 Lambert et al., also have shown that LC cells increase their expression of neurotrophic tyrosine kinase receptor A (Trk A), neurotrophin-3 (NT-3) and brain-derived neurotrophic factor (BDNF) in combined glucose oxygen deprivation in vitro, which underlines a possible neuromodulatory role for this cell in hypoxic environments. 12 It is clear, therefore, that the interplay between LC cells and hypoxic stress in the glaucomatous ONH has a therapeutic potential. In our study, we sought to characterize LC cell global transcriptional responses to hypoxia in vitro and subsequently identify putative transcriptional regulators of this response. 
Materials and Methods
Cell Culture
Primary GFAP-negative LC cells were generated from carefully dissected ONH tissue from four human donors and characterized as described previously. 10,13 All donors gave informed consent and the acquisition of tissue was carried out in line with the principles of the Declaration of Helsinki. Cultures were maintained in Dulbecco's modified Eagle's medium (DMEM, Sigma, Dorset, UK) supplemented with 10% fetal calf serum (FCS), penicillin (100 U/mL) and streptomycin (100 μg/mL) in 5% CO2-95% air at 37°C. Experiments were conducted on four individual LC cell lines (one cell line per donor, Fig. 1), which were seeded in triplicate or duplicate to 100 mm culture plates (5 × 106 cells per plate). All cells were used between passages 4 and 7. Confluent cells (1.5 × 107 cells) were serum starved for 24 hours and then maintained in serum-free media for a further 24 hours under normoxic conditions (21% O2, 147 torr, 37°C) or hypoxic conditions (1% O2, 20 torr, 37°C) using an atmospherically sealed cell culture hypoxia chamber (Coy Laboratories, Grass Lake, MI). A fiberoptic pH meter (pH-Optica-Micro system with a MicroTip 140 microM diameter sensor, World Precision Instruments, Stevenage, UK) was used to detect any changes in pH levels following equilibration with 5% CO2 in all cell culture conditions and in fresh media not exposed to cells. The results of these measurements are shown in Table 1. RNA was extracted and used for microarray analyses and real time PCR (n1, n2 and n3). Media supernatants and cellular lysates were prepared for protein detection by ELISA or Western blot (n1, n2, n3, and n4, Fig. 1). 
Figure 1.
 
Cell culture experimental design used in this study. A representative experiment is shown (n1). This experiment was repeated three more times to give a total of four separate experiments (n = 4). Each experiment (n1, n2, n3, n4) used duplicate or triplicate p100 plates of confluent cells derived from a separate human donor cell line, respectively (donor No. 1, donor No. 2, donor No. 3, donor No. 4). RNA was extracted and used for microarray analyses and real time PCR (n1, n2, n3). Media supernatants and cellular lysates were prepared for protein detection by ELISA or Western blot (n1, n2, n3, n4).
Figure 1.
 
Cell culture experimental design used in this study. A representative experiment is shown (n1). This experiment was repeated three more times to give a total of four separate experiments (n = 4). Each experiment (n1, n2, n3, n4) used duplicate or triplicate p100 plates of confluent cells derived from a separate human donor cell line, respectively (donor No. 1, donor No. 2, donor No. 3, donor No. 4). RNA was extracted and used for microarray analyses and real time PCR (n1, n2, n3). Media supernatants and cellular lysates were prepared for protein detection by ELISA or Western blot (n1, n2, n3, n4).
Table 1.
 
pH Values for Cell Media from LC Cells in Normoxic and Hypoxic Conditions, and in Fresh Media not Exposed to LC Cells
Table 1.
 
pH Values for Cell Media from LC Cells in Normoxic and Hypoxic Conditions, and in Fresh Media not Exposed to LC Cells
  pH Values for Cell Media from LC Cells in Normoxic and Hypoxic Conditions, and in Fresh Media not Exposed to LC Cells
RNA Preparation
Total RNA was isolated from normoxia treated and hypoxia exposed LC cells using a silica gel-based RNeasy spin column protocol (Qiagen, Valencia, CA). Total RNA was suspended in 30 μL of RNase free H2O. RNA concentrations and purity were calculated from the absorbance at 260/280 nm and 5 μg of each sample were resolved on 1.5% agarose gels to assess RNA integrity. 
Microarray Hybridization
To investigate the effect of 24 hours of hypoxic stress on global LC cell gene expression profiles, Affymetrix HG-U133A microarrays (Affymetrix, Santa Clara, CA) were used. cDNA was synthesized from total RNA using SuperScript Choice Kit (Invitrogen, Paisley, UK). Biotin-labeled cRNA was prepared from template cDNA followed by fragmentation and hybridization to Affymetrix HG-U133A arrays as per the Affymetrix protocol (Affymetrix). Arrays were then washed and fluorescently labeled before scanning with a confocal laser (Affymetrix). To ensure data reproducibility, three biologically replicated hybridizations were performed from three separate cell lines (n = 3, i.e. donor 1, donor 2 and donor 3, Fig. 1). This design yielded a total of six microarrays (three normoxia microarrays paired with three hypoxia microarrays). 
Microarray Data Analysis
Raw image (*.CEL) files were obtained through Affymetrix MAS 5.0 software. All six microarrays were normalized and processed together using Robust multichip average (RMA). This allowed statistical comparison of all microarray experimental conditions (three normoxia, three hypoxia, Fig. 2). RMA is a function within R statistical software (www.bioconductor.org) that analyzes directly from the Affymetrix microarray *.CEL image file. 14 R v2.2 was used with the installed component packages Affy v1.8.1, Tools v2.2 and Biobase v1.8. RMA performed background adjustment, noise reduction, quantile normalization and log transformation of the raw probe (gene) signal intensities. Output from this analysis was exported in a *.CSV file format and filtered using Microsoft Excel. Mean signal intensities of the probe sets from the three normoxic microarrays were compared with the mean signal intensities of the corresponding paired probe sets from the three hypoxic microarrays, and statistically significant differences in expression were calculated using the paired Student's t-test. Only probe sets with > +0.5 or < −0.5 signal log ratio (SLR) in addition to a P value of < 0.05 were considered to exhibit robust and reproducible expression changes. Since microarray analyses yield large quantities of data, we restricted our analysis to the top 50 up and down regulated genes in the set. 
Figure 2.
 
Three independent, biologically replicated microarray analyses (Panels A–C). The RMA normalized expression values for all 22,283 mRNA transcripts assayed are shown in three scatter plots (Panels A–C). Panel A (n1): Normoxia Chip 1, Hypoxia Chip 1. Panel B (n2): Normoxia Chip 2, Hypoxia Chip 2. Panel C (n3): Normoxia Chip 3, Hypoxia Chip 3. The scatter plots demonstrate the normality of the distributions of gene expression intensities for each microarray analysis following processing by the RMA software. The similarity of the scatter plot distributions illustrates the inter-microarray comparability and reproducibility of the data.
Figure 2.
 
Three independent, biologically replicated microarray analyses (Panels A–C). The RMA normalized expression values for all 22,283 mRNA transcripts assayed are shown in three scatter plots (Panels A–C). Panel A (n1): Normoxia Chip 1, Hypoxia Chip 1. Panel B (n2): Normoxia Chip 2, Hypoxia Chip 2. Panel C (n3): Normoxia Chip 3, Hypoxia Chip 3. The scatter plots demonstrate the normality of the distributions of gene expression intensities for each microarray analysis following processing by the RMA software. The similarity of the scatter plot distributions illustrates the inter-microarray comparability and reproducibility of the data.
Gene Ontology Analysis
Differentially expressed genes in hypoxia versus normoxia treated LC cells (± 0.5 SLR, P < 0.05) were annotated into biologically relevant categories using the web-based bioinformatics software package, NIH DAVID version 6.0 (National Institutes of Health, Bethesda, MD). 15  
Promoter Sequence Analysis
For each of the 100 most significantly differentially expressed genes (50 up regulated and 50 down regulated), we retrieved the putative promoter sequence 1000 DNA base pairs upstream from the first exon using the gene's Ensembl ID. These sequences were then analyzed using Core_TF software (http://grenada.lumc.nl/HumaneGenetica/CORE_TF/). This software is available freely as a web-based interface, which uses Ensembl IDs to identify common transcription factor binding sites in co-regulated genes. More precisely, the software searches for position weight matrices from the TRANSFAC R database that are over-represented in the up-loaded experimental set compared to a random set of promoters. 16 The software carries out a binomial test with the Perl module Math::Cephes (http://search.cpan.org/dist/Math- Cephes/Lib/Math/Cephes.podto) to identify transcription factor sites that are statistically over- represented in the experimental set over the random set. The first analysis searched for promoter binding site families exclusive to the up-regulated genes, and the second analysis searched for promoter binding site families exclusive to the down-regulated genes. 
Quantitative Real-Time PCR
The microarray data in this study were validated using quantitative real-time PCR. Total RNA (5 μg) samples from the same three normoxia and three hypoxia (n = 3, Fig. 1) treated LC cell lines used for the microarray analyses, and were used to synthesize first strand cDNA, using random hexamers and SuperScript II reverse transcriptase (Invitrogen, Paisley, UK). The cDNA was used for quantitative real-time PCR using Sybr Green chemistry. Sybr Green primers for: cytochrome p450, subfamily 1, polypeptide B (CYP1b1), hexokinase 2 (HK2), macrophage migration inhibitory factor 1 (MIF1), stanniocalcin 1 (STC1), vascular endothelial growth factor (VEGF), BCL2/ Adenovirus 19KDa interacting protein 3 (BNIP3), MADS box transcription enhancer factor 2, polypeptide A (MEF2A), TCDD-Inducible poly (ADP-ribose) polymerase (TIPARP), and Transferrin receptor P90, CD71 (TFRC) were designed using their RefSeq mRNA sequences with Primer3 software. 17 The nine target gene names, RefSeq identifiers and forward and reverse primer sequences are shown in Table 2. Duplicate cDNA template samples were amplified and analyzed in the Prism 7900HT sequence detection system (Applied Biosystems, Foster City, CA). Thermal cycler conditions were 10 minutes at 95°C followed by 40 cycles of 30 seconds at 95°C to denature the DNA and 30 seconds at 60°C to anneal and extend the template. Quantitative calculation of target mRNA copy number fold changes between hypoxia and normoxia samples was performed using the delta delta Ct method. The differences between cycle thresholds (Ct values) of each target gene for each sample were normalized to normoxic controls and used to calculate relative quantities in hypoxic samples. All samples also were normalized to the internal 18s RNA expression. Quantities were expressed as log base 2 (delta delta Ct). 
Table 2.
 
Selected Ontology Categories of the Differentially Expressed Genes
Table 2.
 
Selected Ontology Categories of the Differentially Expressed Genes
Enzyme-Linked Immunosorbent Assay
VEGF protein in cell medium from 1.5 × 107 cells per 100-mm plate (normoxia or hypoxia treated) was quantified using ELISA kits (R&D Systems, Minneapolis, MN). Conditioned media samples (n = 4, Fig. 1) from the experiments described in the above sections were used for this protein analysis. Concentrations of the VEGF protein standard were prepared according to the manufacturers instructions. Experimental (hypoxia) and control (normoxia) medium and standard VEGF samples were loaded in duplicate to separate 96-well plates coated with monoclonal antibodies specific for human VEGF. After washing away any unbound substances, an enzyme-linked polyclonal antibody specific for VEGF was added to the wells. Following a second wash to remove any unbound antibody-enzyme reagent, a solution of hydrogen peroxide and tetramethylbenzidine was added to the wells, after which a blue color developed in proportion to the amount of target protein bound in the initial step. The reaction was stopped using sulfuric acid and the optical density of each well was measured at 450 nm. 
Protein Isolation and Western Blot Analysis
Whole cell protein lysates were prepared from the phenol-ethanol supernatant step of TRI Reagent samples during isolation of the RNA for microarray and PCR. The protocol was carried out according to manufacturer's instructions. Briefly, 1.5 mL of acetone per 0.75 mL of TRI Reagent BD (Sigma-Aldrich, Poole, Dorset, UK) was used to precipitate protein. Samples (n = 4, Fig. 1) were incubated for 10 minutes at room temperature and centrifuged at 12,000 x g for 10 minutes at 4°C. The supernatant was discarded and the pellet washed 3 times in 2 mL of 0.3 M guanidine hydrochloride/95% ethanol wash solution for 20 minutes each. After the final wash the pellet was resuspended in 2 mL of 100% ethanol, vortexed and allow to stand for 20 minutes at room temperature. After centrifugation at 7,500 x g for 5 minutes at 4°C the supernatant was removed and the protein pellet dried under vacuum. The pellet was dissolved in lysis buffer (50 mM Tris-HCl pH 7.4 containing 150 mM NaCl, 1 mM PMSF, 1 mM EDTA, 5 mg/mL Aprotinin, 5 mg/mL Leupeptin, 1% Triton x-100, 1% sodium deoxycholate, and 1% SDS) with the aid of sonication. Protein content was quantified and normalized using the DC protein assay (Bio-Rad Laboratories, Hemel Hempstead, Hertfordshire, UK) and electrophoresed on 10% SDS-PAGE gels. Expression levels were measured using specific antibodies for BNIP3 (Santa Cruz Biotechnology, CA) and b-Actin (Sigma-Aldrich, Poole, Dorset, UK) by Western blot analysis. Following exposure, photographic films were submitted to densitometric image analysis using ImageJ software (NIH), and each 60kDa, 30kDa and 22kDa BNIP3 protein band was normalized to its corresponding 42kDa β-actin band. 
Statistical Analysis
Data are summarized as the mean ± standard error (SE) or ± standard deviation (SD). The two-tailed paired Student's t-test was used to analyze the statistical significance (*P < 0.05) of differences between mean values. 
Results
Microarray and Bioinformatic Analysis
After 24 hours of hypoxic stress, 159 genes were significantly differentially expressed (70 up-regulated and 89 down-regulated) by > +0.5 SLR or < −0.5 SLR (*P < 0.05) compared with normoxic control. The RMA normalized expression values for all 22,283 mRNA transcripts assayed are shown in three scatter plots in Figure 2 (panels A–C). Each scatter plot represents an individual microarray analysis from one of the three human donors. The scatter plots demonstrate the normality of the distributions of gene expression intensities for each biologically replicated microarray analysis. The similarity of the scatter plot distributions illustrates the inter-microarray comparability and reproducibility of our data. The microarray data are deposited and available at the Gene Expression Omnibus (GEO accession number GSE26820). Tables 3 and 4 1552 list the 50 most highly up-regulated and 50 most highly down-regulated genes, respectively. Among the differentially expressed ontological categories were: extracellular and extracellular matrix, hypoxic response, transcription factor complex, apoptosis, mitochondrion and neurogenesis (Table 2). 
Table 3.
 
Forward and Reverse Primer Sequences Used for Sybr Green Real-Time PCR Assays
Table 3.
 
Forward and Reverse Primer Sequences Used for Sybr Green Real-Time PCR Assays
  Forward and Reverse Primer Sequences Used for Sybr Green Real-Time PCR Assays
Table 4.
 
The 50 Most Highly Up-Regulated Genes in Response to Hypoxic Stress
Table 4.
 
The 50 Most Highly Up-Regulated Genes in Response to Hypoxic Stress
  The 50 Most Highly Up-Regulated Genes in Response to Hypoxic Stress
Table 5.
 
The 50 Most Highly Down-Regulated Genes in Response to Hypoxic Stress
Table 5.
 
The 50 Most Highly Down-Regulated Genes in Response to Hypoxic Stress
  The 50 Most Highly Down-Regulated Genes in Response to Hypoxic Stress
Promoter Sequence Interrogation
To unveil a panel of potential transcription factors (TFs) mediating the hypoxic stress transcriptional response in LC cells, we used Core_TF software. Core_TF analyzed the promoter sequences of the 100 most highly differentially expressed genes (top 50 up- regulated, top 50 down-regulated) in response to hypoxia and identified a number of putative regulatory transcription factor binding site families in these promoters. In the up-regulated genes the most frequently occurring putative TF binding sites included myc-associated factor X (MAX, found in 26 promoters, **P < 0.01), E2F transcription factor-1 (E2F1, found in 24 promoters, **P < 0.01), hypoxia inducible factor-1 (HIF1, found in 17 promoters, **P < 0.01, Table 6A). With specific attention to HIF, the gene promoters in whom this putative binding site was found included: BNIP3 (3 sites), MIF (1 site), and HK2 (2 sites). In the promoters of the down-regulated genes, the most frequently occurring putative binding site was the c-AMP response element-binding (CREB) family (found in 43 promoters, **P < 0.01, Table 6B). Gene promoters with CREB sites included MARS (4 sites), transferrin receptor (TFRC, 3 sites) and IGF2R (3 sites). 
Table 6.
 
Promoter Sequence Analysis Results from Core_TF Software
Table 6.
 
Promoter Sequence Analysis Results from Core_TF Software
  Promoter Sequence Analysis Results from Core_TF Software
Quantitative Real-Time PCR
A subset of the hypoxia responsive genes was chosen to validate independently the results of the microarray data. Real-time Sybr Green PCR showed that mRNA synthesis for nine target genes (CYP1B1, HK2, MIF1, STC1, VEGF, BNIP3, TIPARP, MEF2A, and TFR, Fig. 3, Panels A–I) were consistent with the direction of change reported by the three independent microarray analyses. 
Figure 3.
 
RNA and protein validation of microarray analysis. Panels A–I show nine genes whose expression was up or down regulated in response to 24 hours of hypoxic stress analyzed in independent experiments by quantitative real-time PCR. The direction of change in expression of all of these genes was consistent with the microarray analyses. (A) CYP1B1 1.3-fold increase (P > 0.05). (B) hexokinase 2 2.3-fold increase (*P < 0.05). (C) MIF 2.7-fold increase (*P < 0.05). (D) STC 1 2.8-fold increase (*P < 0.05). (E) VEGF 2.2-fold increase (*P < 0.05). (F) BNIP3 10-fold increase (*P < 0.05) (G) TIPARP 1.3-fold increase (*P < 0.05). (H) MEF2A 1.2-fold increase (P > 0.05). (I) TFRC 1.5-fold decrease (*P < 0.05). Panel J, ELISA investigated whether changes in VEGF mRNA synthesis reflected similar changes in VEGF protein production. The results of the expression changes at the protein level were significant and consistent with those at the mRNA level. VEGF protein release showed a 2.3-fold increase (normoxia = 152 pg/mL ± 19 pg/mL SE, hypoxia = 351 pg/mL ± 75 pg/mL SE, *P < 0.05, n = 4) in medium from hypoxia versus normoxia exposed LC cells.
Figure 3.
 
RNA and protein validation of microarray analysis. Panels A–I show nine genes whose expression was up or down regulated in response to 24 hours of hypoxic stress analyzed in independent experiments by quantitative real-time PCR. The direction of change in expression of all of these genes was consistent with the microarray analyses. (A) CYP1B1 1.3-fold increase (P > 0.05). (B) hexokinase 2 2.3-fold increase (*P < 0.05). (C) MIF 2.7-fold increase (*P < 0.05). (D) STC 1 2.8-fold increase (*P < 0.05). (E) VEGF 2.2-fold increase (*P < 0.05). (F) BNIP3 10-fold increase (*P < 0.05) (G) TIPARP 1.3-fold increase (*P < 0.05). (H) MEF2A 1.2-fold increase (P > 0.05). (I) TFRC 1.5-fold decrease (*P < 0.05). Panel J, ELISA investigated whether changes in VEGF mRNA synthesis reflected similar changes in VEGF protein production. The results of the expression changes at the protein level were significant and consistent with those at the mRNA level. VEGF protein release showed a 2.3-fold increase (normoxia = 152 pg/mL ± 19 pg/mL SE, hypoxia = 351 pg/mL ± 75 pg/mL SE, *P < 0.05, n = 4) in medium from hypoxia versus normoxia exposed LC cells.
ELISA
To investigate whether changes in VEGF mRNA synthesis reflected similar changes in secreted VEGF protein, we used ELISA. The results of the expression changes at the protein level were consistent with those at the mRNA level determined by microarray and real-time PCR. ELISA assays (1.5 × 107 cells/100-mm plate) showed a 2.3-fold increase (normoxia = 152 pg/mL ± 19 pg/mL, hypoxia = 351 pg/mL ± 75 pg/mL, *P < 0.05, n = 4) in hypoxia stimulated LC cell VEGF protein release compared with normoxic controls (Fig. 3, Panel J). 
Western Blot Analysis
To investigate whether changes in mRNA synthesis reflected similar changes in total cellular protein lysates, we used Western blot. BNIP3 was chosen as a representative protein whose mRNA synthesis had been the most highly up-regulated of all genes assayed by microarray and real time PCR. Western blot analysis confirmed a significant increase in cytosolic BNIP3 protein in hypoxia treated cells versus normoxic controls (*P < 0.05, n = 4, Fig. 4). 
Figure 4.
 
Western blot (Panel A) and densitometry analysis (Panel B). Panel A, BNIP3 protein observed (60kDa, 30KDa, and 22KDa bands) in hypoxia (+) treated LC cells compared with normoxic (−) controls. Panel B, relative BNIP3 protein band densitometric units normalized to b-actin. There was a significant increase in all BNIP3 protein bands in hypoxia treated LC cells versus normoxic controls (*P < 0.05, n = 4).
Figure 4.
 
Western blot (Panel A) and densitometry analysis (Panel B). Panel A, BNIP3 protein observed (60kDa, 30KDa, and 22KDa bands) in hypoxia (+) treated LC cells compared with normoxic (−) controls. Panel B, relative BNIP3 protein band densitometric units normalized to b-actin. There was a significant increase in all BNIP3 protein bands in hypoxia treated LC cells versus normoxic controls (*P < 0.05, n = 4).
Discussion
The hypothesis of this study was that in vitro hypoxia can induce LC cells to express genes involved in ECM remodeling or hypoxic stress responses. Using three biologically separate microarray analyses with validation by quantitative real time PCR, ELISA and Western blot, we reported novel data on hypoxia-regulated gene and protein expression in primary cultures of human GFAP-negative LC cells in vitro. This transcriptional analysis identified 159 genes that were robustly differentially expressed in response to hypoxic stress by greater than ±0.5 SLR. The magnitude of expression change ranged from +2.0 to −1.0 SLR. Different cell types intrinsically responded in different transcriptional ways to hypoxic stimuli so it is noteworthy, but not entirely surprising, that our observed expression changes may be smaller than other cell types. Furthermore, the quantile normalization method of RMA that we used is known to produce comparatively smaller SLR than other normalization methods, such as the average scaling method of Affymetrix MAS 5.0. 14 The return benefit on RMA's more modest SLR comes with microarray data that is more precise and less noise-laden. 18 While a greater number of microarrays would have been preferable, we have nonetheless presented a robust, biologically repeated (n = 3) microarray experiment, which is statistically significant and independently confirmed using an established validation methodology (real time PCR). We also acknowledge that we have not shown diseased tissue immunohistochemical protein changes reflective of our observed in vitro changes at the RNA level. It is possible that in the glaucomatous ONH there are waves of gene regulation and protein translation that are active during certain non-contemporaneous time points at disease initiation and progression. Thus, it may be that, depending on the temporality, the expression of genes identified in our study may not necessarily be mirrored in all tissue protein analyses. However, our data do provide a tool for directing these studies in the future. 
Using NIH David software, we classified the differentially expressed genes into pathologically relevant categories, which included ECM, apoptosis, hypoxic response and neurogenesis. Our data provide a useful addition to other studies that have examined the vascular theory of glaucoma either by investigation of the direct effect of hypoxia on RGC axon viability 19 or on the production of neurotoxic molecules (e.g., iNOS) in optic nerve head astrocytes (ONHAs). 20 Although alterations in oxygen tension in pig and cat optic nerve heads and reduced lumenal diameter of primate lamina cribrosa blood vessels occur in response to experimentally raised intraocular pressure, 7,21,22 there is little direct evidence regarding the tissue oxygen level present in these pathological contexts in humans. This led us to an obvious technical dilemma of choosing a pathologically meaningful in vitro hypoxic environment with which to expose the LC cells. In lieu of definitive human in vivo normal or glaucomatous lamina cribrosa PO2 values, we selected experimental parameters of 1% O2 (a PO2 equivalent to 20 torr/20 mmHg) drawn from studies that also have examined the effects of in vitro hypoxic stress on cellular gene expression profiles. 23,24  
Media pH reduction can occur under hypoxic conditions, especially if there is excessive cellular glycolytic activity. This could have induced LC cell gene expression changes in hypoxia-treated cells that were not present in normoxic control cells. The authors addressed this potential variation by measuring pH in (i) equilibrated media not exposed to LC cells, (ii) equilibrated media exposed to LC cells under normoxic conditions, and (iii) equilibrated media exposed to LC cells under hypoxic conditions. While the mean pH was higher in fresh media not exposed to cells (pH 7.4) than in media that had been exposed to cells, the pH between normoxic (pH 7.25) and hypoxic (pH 7.24) cell media was similar. 
The most highly up-regulated gene determined by microarray and real time PCR with protein validation by Western blot was BCL2/adenovirus E1B 19 kDa protein-interacting protein 3 (BNIP3). BNIP3 is a member of a family of interacting proteins that regulate cell death. 25 BNIP3 functions by binding to and suppressing the anti-apoptotic activity of other proteins (e.g., Bcl-X) and is one of the most abundantly induced genes by HIF. 25,26 In models of cardiac ischemia, BNIP3 knock-out animals show reduced ECM remodeling in the myocardial infarct zone compared to wild-type animals. 27 BNIP3, therefore, is thought to play a role in amplifying ECM changes in tissues exposed to hypoxic stress. Two other up-regulated genes in our data set, MIF1 and discoidin domain receptor family member 1 (DDR1/Trk E), also have biological functions related to the ECM, to TGF-beta and to neurotrophin signaling pathways. MIF1 is a pro-inflammatory cytokine that, when inhibited, can reduce TGF-beta mediated ECM remodeling in animal models of lung fibrosis. 28 DDR1/Trk E is a tyrosine kinase receptor activated by collagen and modulates cell interaction with the ECM. 29 DDR1/Trk E also is the receptor for nerve growth factor (NGF), which is a member of the neurotrophin family of hormones responsible for maintenance and differentiation of neural crest cells. 30 Lambert et al. previously found that Trk receptor protein expression (TrkA) was increased significantly in LC cells exposed to combined oxygen glucose deprivation, 12 a result that demonstrates a consistency with our present study. DDR1/Trk E also has been suggested as a potential amplifier of fibrosis by accelerating collagen synthesis and activating matrix metalloproteinases. 31,32 In tandem with ECM remodeling, there is loss of RGC axons in the human glaucomatous optic nerve head. Neuroprotection against hypoxic stress in this context is a potentially important direction for new glaucoma treatments. 33 We found hypoxic up-regulation of STC1, DPYSL4/CRMP3, and DDR1/TrkE, all of which have known neuromodulatory effects, 34 which points to a potential role for the LC cell in controlling RGC axon viability. 
One of the outcomes of this study was the novel demonstration of a classical hypoxia driven adaptive response in the LC cell. We report a significant increase in VEGF protein release from hypoxia stimulated LC cells versus normoxic controls, in addition to significant transcriptional increases in VEGF mRNA by microarray and real time PCR analyses. Other than opposing hypoxia and inducing pro-fibrotic effects, VEGF has direct effects on neurons, promoting neurogenesis and neuron survival. 35 In addition to VEGF, our results showed up-regulated mRNA for EGL nine homolog 1 (C. Elegans, EGLN1), which was particularly intriguing because EGLN1 is a key oxygen sensor responsible for maintaining low levels of HIF under normoxic conditions. 36 Up-regulation of EGLN1 in our experiments points to a potential auto-regulatory mechanism for controlling HIF activity in hypoxic LC cells. 
Core_TF software analysis of the promoters of the genes in our data revealed the presence of cross species conserved transcription factor binding sites (e.g., HIF and CREB, Table 6), indicating their putative functional role in regulating hypoxia-responsive transcription in LC cells. The detection of HIF particularly was encouraging and serves to bioinformatically validate the hypoxic conditions used in our experiments. Up-regulated genes from the microarray data with HIF promoter sites as identified by Core_TF included BNIP3, MIF, and HK2 (these genes also were validated by real time PCR). Several CREB binding sites were identified in the promoters of down-regulated genes from the microarray data, including MARS and TFRC (TFRC also was validated by real time PCR). Hypoxic degradation and inactivation of CREB, with consequent silencing of CREB driven transcription, occurs in other cell systems. 37 We reason that a similar mechanism might exist for the hypoxia-driven down-regulation of genes, such as TFRC in our experiments. While it is acknowledged that additional molecular studies are required to confirm the in silico HIF and CREB observations presented here, our data are still novel and informative, especially as no previously published data exists on LC cell transcription factor activation in hypoxia. 
In conclusion, our study represents the first microarray expression and bioinformatic transcription factor analysis of LC cells exposed to hypoxic stress in vitro. The data increase our understanding of the mechanisms of vascular insult in the ONH and provide support for targeting hypoxic stress-induced transcription as a potential future POAG or NTG treatment. 
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Footnotes
 Supported by a Science Foundation Ireland Grant (MOL) and a Mater College for Post Graduate Education and Research Grant (RPK and CJO'B).
Footnotes
 Disclosure: R.P. Kirwan, None; L. Felica, None; A.F. Clark, None; C.J. O'Brien, None; M.O. Leonard, None
Footnotes
 Presented by RPK as an ARVO oral free paper (www.iovs.org/E-Abstract 2373).
Figure 1.
 
Cell culture experimental design used in this study. A representative experiment is shown (n1). This experiment was repeated three more times to give a total of four separate experiments (n = 4). Each experiment (n1, n2, n3, n4) used duplicate or triplicate p100 plates of confluent cells derived from a separate human donor cell line, respectively (donor No. 1, donor No. 2, donor No. 3, donor No. 4). RNA was extracted and used for microarray analyses and real time PCR (n1, n2, n3). Media supernatants and cellular lysates were prepared for protein detection by ELISA or Western blot (n1, n2, n3, n4).
Figure 1.
 
Cell culture experimental design used in this study. A representative experiment is shown (n1). This experiment was repeated three more times to give a total of four separate experiments (n = 4). Each experiment (n1, n2, n3, n4) used duplicate or triplicate p100 plates of confluent cells derived from a separate human donor cell line, respectively (donor No. 1, donor No. 2, donor No. 3, donor No. 4). RNA was extracted and used for microarray analyses and real time PCR (n1, n2, n3). Media supernatants and cellular lysates were prepared for protein detection by ELISA or Western blot (n1, n2, n3, n4).
Figure 2.
 
Three independent, biologically replicated microarray analyses (Panels A–C). The RMA normalized expression values for all 22,283 mRNA transcripts assayed are shown in three scatter plots (Panels A–C). Panel A (n1): Normoxia Chip 1, Hypoxia Chip 1. Panel B (n2): Normoxia Chip 2, Hypoxia Chip 2. Panel C (n3): Normoxia Chip 3, Hypoxia Chip 3. The scatter plots demonstrate the normality of the distributions of gene expression intensities for each microarray analysis following processing by the RMA software. The similarity of the scatter plot distributions illustrates the inter-microarray comparability and reproducibility of the data.
Figure 2.
 
Three independent, biologically replicated microarray analyses (Panels A–C). The RMA normalized expression values for all 22,283 mRNA transcripts assayed are shown in three scatter plots (Panels A–C). Panel A (n1): Normoxia Chip 1, Hypoxia Chip 1. Panel B (n2): Normoxia Chip 2, Hypoxia Chip 2. Panel C (n3): Normoxia Chip 3, Hypoxia Chip 3. The scatter plots demonstrate the normality of the distributions of gene expression intensities for each microarray analysis following processing by the RMA software. The similarity of the scatter plot distributions illustrates the inter-microarray comparability and reproducibility of the data.
Figure 3.
 
RNA and protein validation of microarray analysis. Panels A–I show nine genes whose expression was up or down regulated in response to 24 hours of hypoxic stress analyzed in independent experiments by quantitative real-time PCR. The direction of change in expression of all of these genes was consistent with the microarray analyses. (A) CYP1B1 1.3-fold increase (P > 0.05). (B) hexokinase 2 2.3-fold increase (*P < 0.05). (C) MIF 2.7-fold increase (*P < 0.05). (D) STC 1 2.8-fold increase (*P < 0.05). (E) VEGF 2.2-fold increase (*P < 0.05). (F) BNIP3 10-fold increase (*P < 0.05) (G) TIPARP 1.3-fold increase (*P < 0.05). (H) MEF2A 1.2-fold increase (P > 0.05). (I) TFRC 1.5-fold decrease (*P < 0.05). Panel J, ELISA investigated whether changes in VEGF mRNA synthesis reflected similar changes in VEGF protein production. The results of the expression changes at the protein level were significant and consistent with those at the mRNA level. VEGF protein release showed a 2.3-fold increase (normoxia = 152 pg/mL ± 19 pg/mL SE, hypoxia = 351 pg/mL ± 75 pg/mL SE, *P < 0.05, n = 4) in medium from hypoxia versus normoxia exposed LC cells.
Figure 3.
 
RNA and protein validation of microarray analysis. Panels A–I show nine genes whose expression was up or down regulated in response to 24 hours of hypoxic stress analyzed in independent experiments by quantitative real-time PCR. The direction of change in expression of all of these genes was consistent with the microarray analyses. (A) CYP1B1 1.3-fold increase (P > 0.05). (B) hexokinase 2 2.3-fold increase (*P < 0.05). (C) MIF 2.7-fold increase (*P < 0.05). (D) STC 1 2.8-fold increase (*P < 0.05). (E) VEGF 2.2-fold increase (*P < 0.05). (F) BNIP3 10-fold increase (*P < 0.05) (G) TIPARP 1.3-fold increase (*P < 0.05). (H) MEF2A 1.2-fold increase (P > 0.05). (I) TFRC 1.5-fold decrease (*P < 0.05). Panel J, ELISA investigated whether changes in VEGF mRNA synthesis reflected similar changes in VEGF protein production. The results of the expression changes at the protein level were significant and consistent with those at the mRNA level. VEGF protein release showed a 2.3-fold increase (normoxia = 152 pg/mL ± 19 pg/mL SE, hypoxia = 351 pg/mL ± 75 pg/mL SE, *P < 0.05, n = 4) in medium from hypoxia versus normoxia exposed LC cells.
Figure 4.
 
Western blot (Panel A) and densitometry analysis (Panel B). Panel A, BNIP3 protein observed (60kDa, 30KDa, and 22KDa bands) in hypoxia (+) treated LC cells compared with normoxic (−) controls. Panel B, relative BNIP3 protein band densitometric units normalized to b-actin. There was a significant increase in all BNIP3 protein bands in hypoxia treated LC cells versus normoxic controls (*P < 0.05, n = 4).
Figure 4.
 
Western blot (Panel A) and densitometry analysis (Panel B). Panel A, BNIP3 protein observed (60kDa, 30KDa, and 22KDa bands) in hypoxia (+) treated LC cells compared with normoxic (−) controls. Panel B, relative BNIP3 protein band densitometric units normalized to b-actin. There was a significant increase in all BNIP3 protein bands in hypoxia treated LC cells versus normoxic controls (*P < 0.05, n = 4).
Table 1.
 
pH Values for Cell Media from LC Cells in Normoxic and Hypoxic Conditions, and in Fresh Media not Exposed to LC Cells
Table 1.
 
pH Values for Cell Media from LC Cells in Normoxic and Hypoxic Conditions, and in Fresh Media not Exposed to LC Cells
  pH Values for Cell Media from LC Cells in Normoxic and Hypoxic Conditions, and in Fresh Media not Exposed to LC Cells
Table 2.
 
Selected Ontology Categories of the Differentially Expressed Genes
Table 2.
 
Selected Ontology Categories of the Differentially Expressed Genes
Table 3.
 
Forward and Reverse Primer Sequences Used for Sybr Green Real-Time PCR Assays
Table 3.
 
Forward and Reverse Primer Sequences Used for Sybr Green Real-Time PCR Assays
  Forward and Reverse Primer Sequences Used for Sybr Green Real-Time PCR Assays
Table 4.
 
The 50 Most Highly Up-Regulated Genes in Response to Hypoxic Stress
Table 4.
 
The 50 Most Highly Up-Regulated Genes in Response to Hypoxic Stress
  The 50 Most Highly Up-Regulated Genes in Response to Hypoxic Stress
Table 5.
 
The 50 Most Highly Down-Regulated Genes in Response to Hypoxic Stress
Table 5.
 
The 50 Most Highly Down-Regulated Genes in Response to Hypoxic Stress
  The 50 Most Highly Down-Regulated Genes in Response to Hypoxic Stress
Table 6.
 
Promoter Sequence Analysis Results from Core_TF Software
Table 6.
 
Promoter Sequence Analysis Results from Core_TF Software
  Promoter Sequence Analysis Results from Core_TF Software
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