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From 1 Children’s Hospital, Surgical Research Laboratories, Harvard Medical School, Boston, Massachusetts; 2 Massachusetts Eye and Ear Infirmary, Retina Research Institute, Harvard Medical School, Boston, Massachusetts; and 3 Department of Vitreoretinal Surgery, Center for Ophthalmology, University of Köln, Köln, Germany.
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Abstract |
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METHODS. Long–Evans rats were made diabetic with streptozotocin. Retinal gene expression was analyzed over 3 weeks using high-density nylon filter–based cDNA arrays. Genes were sorted into clusters according to their temporal expression profiles. They were also grouped according to their potential pathophysiological significance. The in vivo gene expression profiles of selected genes were verified via RNase protection assay.
RESULTS. The rat GeneFilter contains a total of 5147 genes, of which 1691 are known genes and 3456 are expressed sequence tags (ESTs). On day 3, the expression of 27 known genes was increased by more than twofold. On days 7 and 21, the corresponding numbers were 60 and 12, respectively. A transient upregulation (>2-fold) in expression was seen in 627 of 5147 total genes. A subset of 926 genes exhibited a modest (<2-fold) decrease in expression. No genes showed a greater than twofold decrease in expression. Overall, the identity of the genes that were upregulated suggests that the response of the retina to the diabetic challenge contains an inflammatory component. Moreover, most regulatory activity occurs during the first week of diabetes.
CONCLUSIONS. The development of a rational therapy for diabetic retinopathy will be assisted by detailed knowledge regarding the molecular pathophysiology of the disease. Here, an expression profile of an underlying retinal inflammatory process in early diabetes was extracted. Beyond providing insight into the general nature of the response to a pathogenic challenge, gene expression profiling may also allow the efficient identification of potential drug targets and markers for monitoring the course of disease.
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Introduction |
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Conventional research efforts have typically focused on a particular class of molecules at a time. A broader screen of potential regulatory pathways, using methods such as subtractive hybridization and differential display PCR, although desirable, can sometimes be cumbersome and inaccurate. The advent of high-density microarray technology,6 7 8 9 with its capacity for simultaneously monitoring thousands of genes, provides a novel opportunity for a high-throughput analysis of diabetic retinal gene expression over time.
In the present study, the retinal gene expression profile of 5147 genes was studied in the retina during early diabetes and compared with the nondiabetic state. To validate the methodology, the expression profiles for selected genes were verified via RNase protection assay. Analysis of an in vivo disease model not only serves to identify disease-associated genes, but can also provide novel integrated information on the complex orchestration of gene expression throughout the genome in concert with underlying pathologic processes.
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Materials and Methods |
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RNA Extraction from Retinal Tissue
Enucleation was performed under deep anesthesia on days 3, 7,
and 21 of diabetes. Seven animals (n = 14 retinas) were
studied at each time point together with seven nondiabetic control
animals. The retinas were carefully dissected away and immediately
frozen in liquid nitrogen. The total RNA from each sample was extracted
using the guanidinium isothiocyanate extraction method with RNAzol (Tel
Test Inc., Friendswood, TX), as described previously. Briefly, the
tissue was incubated with guanidinium isothiocyanate buffer on ice,
extracted with phenol/chloroform/isoamyl-alcohol, and precipitated with
sodium acetate and glycogen carrier in isopropanol. After resuspension
of the RNA pellet, the samples were further purified by
reprecipitation. The RNA from the seven animals in each group was
pooled for the gene filter analyses.
RNA Labeling and Hybridization
For each labeling, 1 ng total RNA was reverse-transcribed in the
presence of 50 µCi of [32P]dCTP, 500 µg of
oligo-dT, and 200 units of Superscript II RT (Life Technologies, Inc.,
Rockville, MD) and DNA polymerase I (Life Technologies, Inc.). The
labeled, double-stranded cDNA was denatured and hybridized to the cDNA
Gene Filter arrays. Briefly, the Gene Filters were prehybridized at
42°C in a roller oven (Hybaid; Midwest Scientific, St. Louis, MO)
with 1.0 g/ml poly-dA (Research Genetics, Inc., Huntsville, AL) and 1.0
g/ml CotI DNA (Life Technologies, Inc.) in 5 ml of Microhyb
solution (Research Genetics, Inc.) for a least 2 hours. After an
overnight hybridization with the radiolabeled probe, the filters were
washed twice at 50°C in 2x SSC (1x SSC, 15 mM trisodium citrate,
and 150 mM NaCl), 1% SDS for 20 minutes, and once at room temperature
in 0.5x SSC, 1% SDS for 15 minutes. The filters were then exposed
overnight to a phosphor screen and scanned at 50-µm resolution with a
Phosphor Imager (MD Storm; Molecular Dynamics, Sunnyvale, CA). Scanned
images were exported as .tiff (.gel) files for image analysis in
Pathways 2.0 software (Research Genetics, Inc.). After each
hybridization, the filters were stripped by boiling in 0.5% SDS
solution and scanned for residual leftover hybridization.
Microarray cDNA Filters
Two rat microarrays were purchased from Research Genetics Inc.
The clone selection on the filter was based on the following criteria:
the clones contained the 3' untranslated gene region, had an average
size of 1 kDa, and originated from Oligo-dT primed libraries. Genes
were selected and spotted (n = 5147) on a 5 x
7-cm nylon membrane by the manufacturer. The filter had 1691 known
genes and 3456 ESTs (expressed sequence tags: partially sequenced and
studied cDNAs). To avoid system variability that may be associated with
the use of different filters, we performed sequential hybridizations on
the same filter with each of the different cDNA probes. Alteration of
hybridization results by repetitive stripping of the filter was
analyzed by comparing the first and last hybridization of each filter
using the same cDNA probe. For specific RNA probes duplicate gene
expression profiles were compared with determine reproducibility
between gene filters (n = 2).
The 5147 genes were normalized for overall background (average intensity of all genes) and compared on each filter using the Pathways 2.0 software. To exclude false-positive signals due to background noise, all spots with an intensity value less than twofold background were discarded. The numerical results (pixel intensties) were imported into an Excel spreadsheet. After raw data filtering, the total number of genes studied dropped from 5147 to 3375, whereas the number of known genes dropped from 1691 to 838. The maximal alterations in gene expression ranged from approximately one fourth to eightfold.
Data Analysis
To calculate the relative change in gene expression, the
corresponding background-corrected spot intensity values were
normalized to nondiabetic control. Genes whose change in expression
level was less than one tenth relative to the nondiabetic control were
excluded from further analysis. A value for "relative expression
index" was then obtained by taking the base 2 logarithm of the ratio
of the intensity values of day x over the corresponding
value for the nondiabetic. These relative expression indices were used
for cluster analysis using the ClustanGraphics3 software (Clustan Ltd.,
Edinburgh, United Kingdom). Direct hierarchical clustering based on the
increase in sum of square (log2; Ward method) was used.10
RNase Protection Assay
32P-labeled antisense riboprobes for
transforming growth factor (TGF) ß1 to ß3 and macrophage inhibiting
factor (MIF) 1 were prepared by T7 polymerase transcription from
respective vectors according to the manufacturer’s protocols
(PharMingen; RiboQuant, San Diego, CA). All samples were simultaneously
hybridized with an 18S riboprobe (Ambion, Austin, TX) to
normalize for variations in isolation and loading of RNA. Protected
fragments were separated on a gel of 5% acrylamide/8 M urea/1x
Tris-borate-EDTA and quantified with a PhosphorImager.
Statistical Analysis
All results are expressed as means ± SD. The data were
analyzed by ANOVA with post hoc comparisons tested using Fisher’s
protected least significant difference procedure. Differences were
considered statistically significant when P values were
<0.05.
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Results |
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General Gene Regulation
STZ injected, nondiabetic animals (nonconverters; blood
glucose < 100 mg/dl) showed <0.2% variability in retinal gene
expression compared with non–STZ-injected diabetic animals
(n = 7; data not shown). All subsequent comparisons
were to data generated from nondiabetic animals (day 0). On day 3 after
the induction of diabetes, 27 known genes showed expression levels more
than twofold. On day 7, the expression of 60 genes was increased more
than twofold, whereas on day 21 the number was 14. Of the total 3375
genes that exhibited altered expression levels in diabetes, 177 were
upregulated by more than twofold on day 3, 298 on day 7, and 82 on day
21 (see Table 1
).
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Functional Expression Patterns
The expression pattern for known genes with characterized
functions was initially examined. The analysis revealed a transient
upregulation (>2-fold) of a variety of cell cycle genes and genes
related to growth and differentiation (cytokines, growth factors, and
related genes) as well as genes operative in inflammation. Genes were
categorized into subsets of known gene function as follows:
A selection of relevant genes under each group heading is listed in Table 1 .
Confirmation of the Gene Expression Results by RNase Protection
Assay
To confirm the gene regulation observed on the filter, we
performed an RNase protection assay for selected genes: TGF-ß1 to -3
and MIF. Compared with the results generated by the GeneFilter array,
there was no significant difference in the expression pattern
(P > 0.05). Both methods showed an upregulation of RNA
levels from day 0 to day 7, with stabilization at an intermediate level
thereafter (Fig. 1)
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Discussion |
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A critical early event in the pathogenesis of diabetic retinopathy is leukocyte adhesion to the diabetic retinal vasculature.4 5 Our analysis found that genes encoding neutrophil adhesion proteins, such as the cell adhesion-like molecule, integrin alpha, vassopressin V1, VCAM-1, the glycoprotein CD44, are upregulated early in the course of diabetes in the rat retina (day 3 and day 7), whereas genes that encode proteins that reduce adhesion, such as entactin I (or nidogen-1) are downregulated (<2-fold, days 3–21; Table 1 ). The suppression of entactin-1 synthesis was recently found to disturb the aggregation of ECM molecules involved in the formation of a functional basement membrane.12 Calpactin II, a gene that codes for a molecule that interacts with integrins and regulates monocyte adhesion, is also upregulated in the retina early in diabetes (day 3). Interestingly, the gene that encodes phakoglobin, a protein that is reduced in areas where neutrophils adhere to the endothelium, was found to be downregulated in the diabetic rat retina. Platelet aggregation has been observed in human diabetes.13 Genes that regulate platelet activation and subsequent adhesion, such as cyclophilin B14 (day 7) and p7215 (day 21), were found to be upregulated later in the course of diabetes in our study (Table 1) . Therefore, genes that regulate neutrophil and platelet adhesion exhibit a temporal expression pattern in the diabetic rat retina consistent with their behavior in diabetes mellitus.
Another group of genes that was upregulated encodes for metabolic and detoxifying enzymes. Diabetic retinopathy is thought to result from chronic changes in the metabolic pathways of the retina. Hyperglycemia leads to increased intracellular glucose concentrations, accumulation of intermediary metabolites such as sorbitol, an increased lactate/pyruvate ratio, abnormal changes of the redox state (NADH/NAD+ ratio), and ultimately irreversible cell damage.16 Glucose autoxidation, protein glycation, and advanced glycation products all disturb the redox balance and have been proposed to result in oxidative stress that contributes to the development of diabetic complications.17 The activities of enzymes such as aldose reductase, lactate dehydrogenase, and aldehyde dehydrogenase has been shown to correlate with the macroangiopathy in diabetes. Moreover, the inhibition of aldose reductase may prevent the development of complications such as cataract formation and certain aspects of retinopathy in diabetic animals.18 We found that the transcription of genes involved in the above listed metabolic pathways was markedly upregulated in the retina in the STZ-rat model (Table 1) . Interestingly, we observed that the gene that encodes the ovalbumin upstream promoter gamma nuclear receptor (rCOUP), a known repressor of the aldehyde dehydrogenase gene,19 was downregulated (<2-fold; days 3–21), perhaps contributing to the increase in the transcription of the above enzyme. Genes that code for enzymes that reduce oxidative species, such as dihydropteridin reductase, were also downregulated early in the course of diabetes as well as the gene for superoxide dismutase (SOD), an important free radical scavenger. Excessive production of superoxide anion, due to reduced neutralization via the SOD enzymatic pathway, is thought to contribute to the pathophysiology of the endothelial dysfunction.20 Moreover, adenosine receptor mRNA was upregulated in the retina early in diabetes. Adenosine is a product of the metabolism of ATP and is a significant component of the retina’s compensatory hyperemic response to substrate deprivation, as occurs in diabetes.21 Adenosine receptor stimulation has a protective effect in ischemia, but its overstimulation may contribute to the cellular damage through reduction of blood flow, altered gene expression, the accumulation of superoxide radicals from adenosine catabolism, and the upregulation of nitric oxide synthase activity.22 Notably, adenosine is also believed to increase the expression of vascular endothelial growth factor.23 24 25
Another striking finding was the increased expression of genes involved in signal transduction pathways that might play an important role in the pathogenesis of endothelial and pericyte dysfunction in diabetes. The protein kinase C (PKC) and the mitogen-activated kinase (MAPK) pathways have been implicated in diabetic retinopathy, either directly or indirectly via upstream growth factors, hyperglycemia, reactive oxygen species, and products of the sorbitol pathway.26 27 PKC and MAPK have been proposed to trigger the cellular events necessary for the development of certain features of diabetic retinopathy, such as the breakdown of the blood retinal barrier and the death of endothelial cells and pericytes.28 29 We found that the main activator of PKC, diacylglycerol kinase, was upregulated as early as day 3, and PKC-binding proteins that target PKC to its potential substrates, such as PKC binding protein beta15, were upregulated later in the course of diabetes (day 21), whereas inhibitors of PKC, such as 14 to 3–3 zeta, were downregulated (<2-fold, days 7 and 21). MAPK mRNA was also markedly upregulated, as were members of the ras pathway, such as ragA and A-raf, which play an important role in the activation of MAPK by growth factors. Interestingly, the ras family members also mediate the upregulation of VEGF via the MAPK pathway. In parallel, we observed a marked upregulation of genes that encode proteins involved in the transduction of growth factor signals, such as VEGF, TGF-beta, or various interleukins. Serine threonine receptor type I (STRI) mRNA increased early (day 3) in diabetic retinopathy. Through its intrinsic kinase activity, STRI phosphorylates intracellular proteins known as Smads that form heterodimers, translocate into the nucleus and mediate transcriptional responses for cytokines such as TGF-beta, interferons, and interleukins.30 Smad 2 (<5-fold, days 3–21) and Stat 3 (<5-fold, days 3–7) were also found to be upregulated in our study, consistent with the activation of the TFG-ß pathways. Mediators of the activation pathways for platelets, hematopoietic cell tyrosine kinase (hck),31 Rap 1B32 (<5-fold, days 7–21) and phospholipase D33 or mediators in the activation of macrophages (tyrosine kinase p7234 ), or neutrophils (SAP kinase 335 ) were also upregulated in our gene filter analysis. Similarly, stress proteins were upregulated. Hsp27 and Hsp60 transcription was markedly enhanced (<5-fold, days 3–21), perhaps as a consequence of the oxidative stress that the retina endures.36 37 Tau protein kinase, which phosphorylates tau protein in response to cellular stress stimuli,38 was also upregulated (<2-fold, days 3–21). In contrast, a-synuclein, a molecular chaperone that helps proteins maintain their tertiary structure, decreased less than twofold (days 3–21). Interestingly, despite its initial upregulation, hsp70 was significantly downregulated later in the course of diabetes in our study (<2-fold, day 21). Leukocyte-derived nitric oxide has been reported to have an important role in the endothelial dysfunction and the upregulation of adhesion molecules in endothelial cells that mediate the leukocyte extravasation in nonophthalmic tissues.39 Because hsp70 is a heat shock protein that was reported to confer resistance to nitric oxide toxicity,40 this downregulation may contribute to the leukocyte-mediated endothelial damage observed in the diabetic retina (Joussen AM, Poulaki V, Adamis AP, et al., unpublished results, 2001).
Another set of genes found to be upregulated in early diabetes were genes involved both in proliferation and apoptosis. One of the central manifestations of diabetic retinopathy is the proliferation of microvascular endothelial cells under the influence of growth factors that are upregulated throughout the course of diabetes.41 In parallel, cellular stress results in endothelial and pericyte death.42 We found that antiproliferative genes such as BTG-1,43 growth arrest factors such as GADD-153 cell growth regulator,44 growth arrest mediators such as rheb45 (<2-fold, days 3–21), or the perchloric acid-soluble protein 146 were downregulated, whereas genes like N-myc47 (<2-fold, days 3–21), CGR1148 and alpha prothymosin49 (<2-fold, days 3–21), which all stimulate proliferation, are upregulated or remain constant. Genes encoding cyclins, such as cyclin D1 (<5-fold, days 3–21) and G (>5-fold, days 3–7), were also upregulated, suggesting that the retinal cells are entering the cell cycle.50 51 In addition to proliferation-related genes, we found that genes involved in apoptosis were also upregulated, such as the tumor suppressor gene p5 (<5-fold, day 7) and DRAL (<2-fold, days 3–7), a protein that induces apoptotic death through p53.52 N-myc is also known to facilitate apoptotic death induced from a variety of stimuli.53 Apoptosis promoting genes of the bcl-2 family, such as Bad,54 were upregulated (<5-fold, day 7), whereas inhibitors of apoptosis, such as the gene TEGT55 were first upregulated (>5-fold, days 3–7) and then downregulated (<2-fold, day 21). CD30, another molecule that regulates several apoptotic pathways was also found to be upregulated. CD30 signals regulate a variety of apoptotic molecules such as Fas ligand, death receptor 3, tumor necrosis factor (TNF)-related apoptosis–inducing ligand (TRAIL), TNFR-associated factor 1 (TRAF1), and cellular inhibitor of apoptosis 2 (cIAP2).56 It is possible that growth factors, cytokines, and reactive oxygen intermediates produced by inflammatory cells upregulate the pro-apoptotic genes in the endothelial cells and pericytes and collaborate with ischemia and oxidative stress to induce apoptosis.
A central finding of our gene array study was the appearance of numerous mRNAs of genes with known roles in inflammation and wound healing after the induction of diabetes. We and others have previously demonstrated that early diabetic retinopathy shows features of an inflammatory disease.4 5 57 58 59 In the present study, we found that the mRNA for macrophage inhibitory protein (MIF), a proinflammatory lymphokine involved in delayed hypersensitivity and phagocytosis, is significantly upregulated early in diabetic retinopathy.60 It has been shown that insulin and glucose regulate MIF expression in cultured adipocytes in vitro, and MIF is believed to be involved in maintaining neutrophils in the vasculature and facilitating their adhesion and local release of cytokines.61 62 Another inflammation-related gene that was upregulated was endothelin B (ETB) receptor (<2-fold, days 3–21). Its ligand, endothelin (ET), is a potent vasoconstrictor that acts as a permeability factor, perhaps in collaboration with VEGF, in diabetes. In nonophthalmic tissues, it works via a PKC-mediated mechanism and regulates extracellular matrix protein gene expression in target organs.63 ET-receptor blockade can prevent short-term diabetes in the rat.64 65 Thrombin was also upregulated (<5-fold, days 3–21). It plays an important role in platelet activation and adhesion, stimulates the expression of adhesion molecules in endothelial cells, and increases the production of cytokines that trigger the binding of leukocytes and platelets in the endothelium.66 Therefore, thrombin mechanistically couples tissue damage to inflammation. In the same context, extracellular matrix genes such as laminin A, fibronectin, and collagen were downregulated. The tissue inhibitors for matrix metalloproteinases, TIMP-1, and TIMP-2 were upregulated as well as gelatinase A (<5-fold, days 3–7), possibly contributing to the extracellular matrix turnover that characterizes diabetes.67
Last but not least, the mRNAs for various growth factors and cytokines
that are implicated in diabetic retinopathy were found to be
upregulated in our study. We have previously shown that VEGF mRNA
increases during the course of diabetes in the rat.68
We
now show that the mRNA for IGF-I, IGF-I binding proteins (<2-fold,
days 3–21), TGF-ß (<2-fold, days 7–21), and IL-1ß were
upregulated early in the diabetic retina of the rat. TGF-ß is
believed to be produced by pericytes and to act as an autocrine factor,
regulating their proliferation and their interaction with endothelial
cells.69
70
It also increases nitric oxide synthase
activity in the endothelial cells, which in turn upregulates adhesion
molecules and endothelin-1 expression.71
The activity of
the IGF axis depends on the dynamic balance between IGF and its binding
proteins that modulate IGF bioactivity. Dysregulation of this axis can
enhance vascular smooth muscle cell growth, migration, and
extracellular matrix synthesis.72
Recently, it was
proposed that the sequestration of IGF-I by the IGF binding proteins
contributes to the pathogenesis of microvascular disease in diabetes by
lowering the amounts of this trophic factor.73
Three IGF
binding protein isoforms were elevated early in diabetes. We also found
that the mRNA for IL-1ß was upregulated in the diabetic rat retina.
IL-1ß is a chemoattractive cytokine and is believed to be an effector
molecule in type I diabetes.74
It has been shown to
upregulate nitric oxide and VEGF synthesis75
and to
activate the transcription factor NF-
B in endothelial
cells.76
NF-B is an important molecule in leukocyte
activation and regulates apoptotic pathways in endothelial cells and
the response to cellular stress. It is interesting to note that the
p105 mRNA, a molecule that regulates the synthesis of the precursor for
NF-B, was also upregulated in our study (<5-fold, day 7).
Although most of the altered expression of known (named) genes after the induction of diabetes is consistent with the pathologic process, it should be noted that gene expression profiling of a tissue such as the pathologic retina does not allow one to deduce the cellular origin of the respective mRNAs, because of the cellular heterogeneity of the tissue specimen. Thus, the increase in many inflammation-related genes might simply reflect the influx of leukocytes into the diseased tissue.
Once sufficient databases of generic gene expression profiles become available, the signature expression profile of specific cell types can be extracted from whole-tissue profiles, thus enabling the monitoring of the change of cellular composition.8 Expression profiling thus represents a global approach to characterize a pathologic process at the genomic scale but should be complemented with structure preserving methods such as in situ hybridization and immunohistochemistry. However, given the completion of the human genome sequencing project and the dramatic advances in functional genomic technologies that enable genome-wide expression monitoring with instant identification of the individual genes, the paradigm for studying the molecular basis for diseases is undergoing a major shift.77 To fully embrace the advantages of a large-scale gene expression profiling, however, we have to extend our view from individual genes to global patterns (which would also include the expression of unnamed ESTs). At the simplest level of finding general patterns, a global interpretation of expression profiles can be achieved by grouping the genes based on the similarity of their expression behavior. Using hierarchical, unsupervised clustering, we found that an eight-cluster model fits the data well (Fig. 2) . Members of the same clusters exhibit similarity with respect to both their expression levels during the observation period (qualitative temporal expression profile) and the overall change in their expression levels (quantitative expression level change). The premise of such cluster analysis has been that clusters contain genes that are likely to be coregulated or serve the same biological function. However, in a more integrative view that goes beyond merely assigning functions to genes, it has been suggested that genomic regulation is organized such that genes and proteins appear in functional modules. In conjunction with pathogenesis one could for instance define an "inflammation module," as a robust, coregulated set of genes that are used as an entity in a variety of tissue damage. With the development and spread of more encompassing microarrays, the systematic search for such regulatory modules, rather than individual genes, might lead to universal principles of pathogenesis and thus improve diagnosis and therapy.
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Acknowledgements |
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Footnotes |
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Submitted for publication April 2, 2001; revised June 18, 2001; accepted July 6, 2001.
Commercial relationships policy: N.
The publication costs of this article were defrayed in part by page
charge payment. This article must therefore be marked
"advertisement" in accordance with 18 U.S.C. 
1734
solely to indicate this fact.
Corresponding author: Anthony P. Adamis, Massachusetts Eye and Ear Infirmary, Retina Research Institute, Harvard Medical School, 243 Charles Street, Boston, MA 02114. tony_adamis{at}meei.harvard.edu
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