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Am J Physiol Endocrinol Metab 293: E759-E768, 2007. First published June 12, 2007; doi:10.1152/ajpendo.00191.2007
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Oxidoreductase, morphogenesis, extracellular matrix, and calcium ion-binding gene expression in streptozotocin-induced diabetic rat heart

Erik van Lunteren and Michelle Moyer

Pulmonary, Critical Care and Sleep Division, Department of Medicine, Louis Stokes Cleveland Department of Veterans Affairs Medical Center and Case Western Reserve University, Cleveland, Ohio

Submitted 27 March 2007 ; accepted in final form 8 June 2007


   ABSTRACT
TOP
 ABSTRACT
METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Diabetes has far-ranging effects on cardiac structure and function. Previous gene expression studies of the heart in animal models of type 1 diabetes concur that there is altered expression of genes involved in lipid and protein metabolism, but they diverge with regard to expression changes involving many other functional groups of genes of mechanistic importance in diabetes-induced cardiac dysfunction. To obtain additional information about these controversial areas, genome-wide expression was assessed using microarrays in left ventricle from streptozotocin-diabetic and normal rats. There were 261 genes with statistically significant altered expression of at least ±1.5-fold, of which 124 were increased and 137 reduced by diabetes. Gene ontology assignment testing identified several statistical significantly overrepresented groups among genes with altered expression, which differed for increased compared with reduced expression. Relevant gene groups with increased expression by diabetes included lipid metabolism (P < 0.001, n = 13 genes, fold change 1.5 to 14.6) and oxidoreductase activity (P < 0.001, n = 17, fold change 1.5 to 4.6). Groups with reduced expression by diabetes included morphogenesis (P < 0.00001, n = 28, fold change –1.5 to –5.1), extracellular matrix (P < 0.02, n = 9, fold change –1.5 to –3.9), cell adhesion (P < 0.05, n = 10, fold change –1.5 to –2.7), and calcium ion binding (P < 0.01, n = 13, fold change –1.5 to –3.0). Array findings were verified by quantitative PCR for 36 genes. These data combined with previous findings strengthen the evidence for diabetes-induced cardiac gene expression changes involved in cell growth and development, oxidoreductase activity, and the extracellular matrix and also point out other gene groups not previously identified as being affected, such as those involved in calcium ion homeostasis.

oxidoreductase activity; cell growth

DIABETES HAS FAR-RANGING EFFECTS on the heart, including ultrastructural alterations, impaired contractile function, and changes in pathways involved in hormonal signaling, ion homeostasis, and excitation-contraction coupling (14, 20, 37, 38, 53). Clinical consequences of diabetic effects on the heart include diabetic cardiomyopathy, increased prevalence of coronary artery disease leading to angina and myocardial infarct, and greater adverse outcomes such as heart failure and death when myocardial infarct does occur (6, 15, 30, 49, 54).

Control of cardiac cellular function occurs at many levels, ranging from modulation of gene transcription to allosteric regulation of enzyme activity. Alterations in enzyme levels and activity are linked most directly to cellular function and thereby contractile performance. However, development, aging, levels of exercise, and diseases such as diabetes may produce their effects on the function of the heart and other organs at many regulatory levels, and frequently an important event is modification of gene expression (2, 11, 13, 18, 19, 31, 34, 35, 50, 51, 58, 61). Furthermore, transcriptional events are important to elucidate, as they form the foundation of ongoing and future gene-based therapeutic approaches for cardiac disease management.

Studies examining effects of diabetes on global gene expression in noncardiac tissue have identified disturbances in a number of cellular processes and structures. Examples of perturbed systems include protein degradation and ubiquitination, carbohydrate and lipid metabolism, nitrogen metabolism, transcription and translation, morphogenesis/organogenesis, extracellular matrix including collagen, and oxidative stress (2, 11, 13, 18, 34, 35, 50, 51, 58, 61). However, there is considerable divergence among tissue types with regard to which of these systems are impacted by diabetes, as well as in the magnitude of the alterations for those systems that are affected, even for studies in which multiple tissues were examined simultaneously (31, 50). There are several advantages of examining transcriptome-wide expression changes with arrays as opposed to focusing on a small number of genes. First, it allows the identification of cardiac genes and gene groups with changed expression that previously may not have been suspected as playing a role in the pathophysiology of diabetes. These previously unsuspected genes are potentially very exciting, as they can lead to truly novel therapeutic approaches to the treatment of diabetic heart disease. Second, it places changes in expression of previously identified genes with altered expression in the context of the entire cell. That is, it allows expression changes among groups of genes with different biological functions to be compared more directly with each other with respect to things such as magnitudes and time courses of expression changes. Thus, what emerges from gene expression array studies, as opposed to studies that focus on small groups of genes, is the integrative transcriptional regulation of cellular function; i.e., one can understand how the whole system works rather than delineating information about just a single component in isolation.

Gene expression array studies of the heart in streptozotocin-diabetes concur that there is altered expression of genes involved in lipid metabolism and transport as well as in protein metabolism and biosynthesis (19, 31). However, these studies disagree in many other areas relevant to mechanisms of diabetes-induced cardiac dysfunction: Knoll et al. (31) found transcriptional changes involving organogenesis, biosynthesis, and cell growth or maintenance, whereas Gerber et al. (19) found changes involving oxidoreductase activity, the extracellular matrix, glycolysis, defense response, and induction of apoptosis. Variability in gene expression microarray findings among studies is well documented, even when identical RNA samples are analyzed with different platforms or by different laboratories using the same platform (e.g., Refs. 26, 34, 52). As a result, consistency of findings among multiple microarray studies considerably strengthens the robustness of conclusions that specific genes and/or gene groups have altered expression in the face of disease. Many of the above gene expression alterations in diabetic heart for which there is disagreement among studies (19, 31) are amenable to therapeutic interventions, so that additional information about these areas of discordance will help focus efforts to develop novel therapies for diabetes-induced cardiac disease. The purpose of the present study was, therefore, to obtain further data about cardiac gene transcriptional changes in type 1 diabetes, in particular for the following areas of controversy among previous studies: cell growth and development, oxidoreductase activity, the extracellular matrix, glycolysis, defense response, and induction of apoptosis.


   METHODS
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 ABSTRACT
 METHODS
RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Studies used streptozotocin to induce diabetes, which is a model of insulin-deficient type 1 diabetes. All procedures were approved by the institutional animal care and use committee and were in line with National Institutes of Health animal welfare guidelines. Male Wistar rats were obtained from Charles River Laboratories (Wilmington, MA) and given free access to food and water throughout the study. At the age of 8 wk, three animals were injected with 60 mg/kg intraperitoneal streptozotocin dissolved in sodium citrate buffer, and another three received buffer alone. At the age of 12 wk, the rats were deeply anesthetized (mixture of ketamine, xylazine, and acepromazine), the heart was removed surgically, and left ventricle was dissected for analysis. Compared with control rats, diabetic rats had higher fasting glucose levels (15.5 ± 0.8 vs. 3.9 ± 0.2 mM, P < 0.001), lower terminal weight (244 ± 4 vs. 352 ± 4 g, P < 0.001), lower weight gain during the first week after streptozotocin injection (4 ± 1 vs. 53 ± 8 g, P = 0.004), and a mild weight loss compared with a large weight gain during the 4 wk after streptozotocin injection (–11 ± 1 vs. 94 ± 6 g, P < 0.001).

Gene expression array studies were performed in a manner similar to that described previously (57). Total RNA was extracted using TRIzol (GIBCO-BRL, Rockville, MD), and the RNA pellets were resuspended at 1 µg RNA/µl DEPC-treated water. This was followed by a cleanup protocol with a Qiagen (Valencia, CA) RNeasy Total RNA mini kit. Total RNA was prepared for use on Affymetrix (Santa Clara, CA) microarrays, according to the directions from the manufacturer. Briefly, 8 µg of RNA was used in a reverse transcription reaction (SuperScript II; Life Technologies, Rockville, MD) to generate first-strand cDNA. After second-strand synthesis, double-strand cDNA was used in an in vitro transcription reaction to generate biotinylated cRNA. This was purified and fragmented, following which 15 µg of biotin-labeled cRNA was used in a 300-µl hybridization cocktail, which included spiked transcript controls. Two hundred milliliters of cocktail was loaded onto Affymetrix RAE 230A microarrays and hybridized for 16 h at 45°C with agitation. Standard posthybridization washes and double-stain protocols used an Affymetrix GeneChip Fluidics Station 400. Arrays were scanned using a Hewlett-Packard Gene Array scanner and analyzed with Affymetrix MAS 5.0 software. The data have been deposited in the NCBI Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) and are accessible through GEO Series accession no. GSE6880.

Statistical analysis was done with Bayesian analysis of variance for microarrays (BAM), using BAMarray software (http://www.bamarray.com) (27, 28). BAM uses a special type of inferential regularization that borrows strength across the whole data set, thereby improving statistical power while at the same time reducing the false detection rate. Genes identified by BAM as having significantly changed expression were then further selected on the basis of consistent and appropriate present and absent calls in all samples of each group per Affymetrix software (marginal calls were accepted only if that gene was called present for all other samples in that group). Subsequently signals were averaged for heart tissue from the nondiabetic and from diabetic animals, and fold changes were calculated on the basis of average values from each group. Analysis focused on genes whose expression changed at least ±1.5-fold in diabetic compared with control muscle, unless indicated otherwise. To assign biological meaning to the group of genes with changed expression, the subset of genes that met the above criteria was analyzed with the Gene Ontology (GO) classification system, using DAVID software (http://niaid.abcc.ncifcrf.gov/) (8, 24). Overrepresentation of genes with altered expression within specific GO categories was determined using the one-tailed Fisher exact probability. This was modified by the addition of a jackknifing procedure, in which a single data point is removed and the statistic is recalculated multiple times to give a distribution of probabilities that is broad if the result is highly variable and tight if the result is robust. This penalizes the significance of categories with very few (e.g., one or two) genes and favors more robust categories with larger numbers of genes. In addition, it ensures that the overrepresented gene groups remain statistically significant even in the eventthat one of the genes of the group was falsely identified as having changed expression.

Real-time PCR (rtPCR) was used to confirm changes in gene expression as described previously (57). An Applied Biosystems ABI 7900HT unit with automation attachment (Foster City, CA) was used for rtPCR. This unit is capable of collecting spectral data at multiple points during a PCR run. To execute the first step and make archive cDNA, 3 µg of total RNA was reverse transcribed in a 100-µl reaction using Applied Biosystems enzymes and reagents in accordance with the manufacturer's protocols. RNA samples were accurately quantitated using a Nanodrop Technologies ND-1000 spectrophotometer (Wilmington, DE). Equal amounts of total RNA were reverse transcribed and then used in PCR amplifications. GAPDH had very little variation in expression across the sample set and therefore was chosen as the endogenous control. Since many of the target genes of interest were signaling molecules and likely to be expressed at low levels, we opted for a low dilution factor so as to create an environment more conducive to obtaining reliable results. The cDNA reaction from above was diluted by a factor of 10. For the PCR step, 9 µl of this diluted cDNA was used for each of three replicate 15-µl reactions carried out in a 384-well plate. Rat assays were purchased from Applied Biosystems for each gene tested. Standard PCR conditions were used for the Applied Biosystems assays: 50°C for 2 min, followed by 95°C for 10 min, followed by 40 cycles of 95°C for 15 s alternating with 60°C for 1 min each. For rtPCR data analysis, values of RNA abundance were normalized for each gene with respect to the endogenous control in that sample (GAPDH), mean values for fold changes were calculated for each gene, and pairwise comparisons were made between each of the control and diabetic animals for each gene tested. PCR confirmation of gene expression array data required that 1) the direction of the mean fold changes had to be the same with quantitative PCR as with gene expression arrays, and 2) statistical testing using the unpaired t-test (two-tailed) had to indicate significance.


   RESULTS
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 ABSTRACT
 METHODS
 RESULTS
DISCUSSION
 GRANTS
 REFERENCES
 
The number of known genes with significantly altered expression in left ventricle from diabetic compared with control animals varied as a function of the fold change threshold chosen for analysis, as follows: 261 genes for at least ±1.5-fold change, 158 genes for at least ±1.7-fold change, and 91 genes for at least ±2-fold change. Irrespective of the fold change threshold, there were roughly equivalent numbers of genes with increased compared with reduced expression: 124 vs. 137, 79 vs. 77, and 45 vs. 46 genes, respectively, for the above fold change thresholds. A complete list of genes with at least ±1.5-fold changed expression is provided in APPENDIX 1 (online only).

Assignment of genes to GO groups. Assignment of genes to GO groups, and statistical testing for overrepresentation among GO groups, was performed using the 261 genes with at least ±1.5-fold changed expression. Results are depicted in Tables 1 and 2 for genes with increased and reduced expression, respectively. For biological process assignment, the major themes for genes with increased expression were physiological process and its regulation, cellular metabolism, lipid metabolism (including fatty acid oxidation), and electron transport. This contrasted with the major themes for genes with reduced expression, which included a group of GO terms revolving around morphogenesis, organ development and growth, and a smaller group related to cell adhesion. Contrasts were also noted for molecular function, with genes that had increased expression being assigned to the GO term catalytic activity as well as related but more specific terms (e.g., oxidoreductase, monooxygenase activity, ligase, inositol, and transferase activities), whereas genes with reduced expression were assigned predominantly to the GO term binding and its child terms (e.g., binding of protein, ions, metal ions, cations, calcium, polysaccharide, and actin). Finally, for cellular constituent assignment, genes with increased expression were assigned to GO terms with a strong intracellular focus, which contrasted with genes that had reduced expression for which the terms collagen, extracellular matrix, and extracellular regions were most applicable. The more focused of the above GO terms were chosen for further detailed analysis, with an emphasis on GO groups that comprised at least several genes with more than ±2-fold changed expression.


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  		Table 1. Statistically overrepresented GO terms to which genes with increased expression in left ventricle of diabetic animals were assigned

 

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  		Table 2. Statistically overrepresented GO terms to which genes with reduced expression in left ventricle of diabetic animals were assigned

 
Genes with increased expression in diabetic animals. Genes with increased expression assigned to the two most specific overrepresented GO terms are listed in Table 3. The term lipid metabolism contained a total of 13 genes, five with >2-fold and eight with 1.5- to 2-fold increased expression. Two of the genes (Cte1 and Hmgcs2) had increased expression exceeding 10-fold in magnitude and had greater augmented expression than any other gene (see APPENDIX 1). Fatty acid oxidation is a child term of lipid metabolism, and all three constituent genes (Dci, Hadha, Hadhb) were also included under lipid metabolism. The GO term oxidoreductase activity refers to catalysis of an oxidation-reduction (redox) reaction, in which one substrate acts as a hydrogen or electron donor and becomes oxidized and the other acts as a hydrogen or electron recipient and becomes reduced. Many genes within this GO group are involved in oxidative stress. For diabetic heart there were four genes with >2-fold increased expression and 13 genes with 1.5- to 2-fold increased expression related to oxidoreductase activity (Table 3). The individual genes with increased expression in diabetic heart for both of the above groups are described in further detail in the discussion section. It should be noted that four genes were assigned to both lipid metabolism and oxidoreductase activity GO groups (Hadha, Acox1, Hsd17b4, Fdft1).


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  		Table 3. Genes with increased expression that were assigned to specific statistically overrepresented GO terms

 
Genes with reduced expression in diabetic animals. The genes with diabetes-induced reduced expression that belonged to the most specific overrepresented specific GO groups are presented in Table 4. For the related terms morphogenesis, organogenesis, organ development, growth, and regulation of growth, there were 10 genes with at least 2-fold and another 18 genes with 1.5- to 2-fold reduced expression. Genes related to extracellular matrix and collagen included four with at least 2-fold downregulated expression and five with 1.5- to 2-fold reduced expression. Genes assigned to the cell adhesion GO term included six genes with at least 2-fold and another four genes with 1.5- to 2-fold reduced expression. Finally there were three genes with at least 2-fold and another 10 genes with 1.5- to 2-fold reduced expression assigned to the term calcium binding. Several genes were assigned to more than one of the above GO groups. A further description of the individual genes with reduced expression in diabetic heart is provided in the discussion section.


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  		Table 4. Genes with reduced expression that were assigned to specific statistically overrepresented GO terms

 
Comparison of changes among GO groups. Changes in gene expression among the above six GO groups were compared on the basis of the disease load index (summation of fold changes for all of the genes with altered expression) (46), as well as the average fold change for all genes with changed expression. The disease load index was higher for lipid metabolism than all GO groups other than morphogenesis (Fig. 1A), and the average fold change was higher for lipid metabolism than all other GO groups (Fig. 1B).


Figure 1
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  		Fig. 1. Comparison among 6 gene ontology (GO) groups in degree to which there was altered expression of their constituent genes. GO groups correspond to those of Tables 3 and 4. A: values for the disease load index, which is calculated as the sum of fold changes for all of the constituent genes with significantly altered expression. B: average fold change for the constituent genes with altered expression.

 
Verification with qPCR. The qPCR results are presented for 36 genes in Table 5 and confirmed gene expression microarray data. The genes tested covered a range of functions, including all six specific areas discussed above. This includes seven genes that were assigned to the GO group lipid metabolism and nine that belonged to the GO group oxidoreductase activity. For the group of genes, there was a good correlation between fold change values determined by gene expression microarrays and qPCR (Fig. 2). However, there was a tendency for PCR fold change values to be higher than those determined by expression arrays, especially for those genes with large changes in expression.


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  		Table 5. Confirmatory results by qPCR testing of select genes for which gene expression arrays indicated significantly altered expression

 

Figure 2
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  		Fig. 2. Relationship between fold changes in gene expression measured by gene expression microarray and qPCR. The relationship was highly significant and remained so even after the 3 genes with the greatest fold change were eliminated from the analysis (inset).

 

   DISCUSSION
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 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
GRANTS
 REFERENCES
 
Lipid metabolism. Among genes with more than twofold increased expression related to lipid metabolism, Cte1 and Mte1 are cytosolic and mitochondrial forms of enzymes that catalyze the reaction acyl-CoA + H2O = corresponding fatty acid + CoA, and play important roles in cellular fatty acid oxidation. They are found in abundance in heart and skeletal muscle as well as fat and liver, albeit not necessarily to equal extents. Factors that upregulate expression include fasting, a high-fat diet, and diabetes (36, 48). Hmgcs2 catalyzes a step in ketone body biosynthesis, condensing acetyl-CoA with acetoacetyl-CoA to form HMG-CoA, which is the substrate for HMG-CoA reductase. The mitochondrial (Hmgcs2) and cytosolic (Hmgcs1) forms of the enzyme are encoded by two different genes, with expression of the former increasing and the latter decreasing with starvation (1). High levels of ketone body production are hallmarks of both diabetic ketoacidosis and starvation ketosis. Sult1a1 catalyzes the transfer of a sulfate group from 3'-phosphoadenosine 5'-phosphosulfate to the hydroxyl group of an acceptor, producing the sulfated derivative; it is involved in steroid metabolism. Mob is involved in sphingolipid metabolism by catalyzing the reaction CDP-choline + N-acylsphingosine = CMP + sphingomyelin. Among genes with more modest degrees of increased expression (1.5- to 2-fold change), three play especially important roles in cellular energetics. Dci catalyzes the interconversion of 3-cis-dodecenoyl-CoA and 2-trans-dodecenoyl-CoA, a key step in fatty acid beta-oxidation (42). Hadha and Hadhb are the {alpha}- and beta-subunits of the liver mitochondrial fatty acid oxidation multienzyme complex (29). Finally, Cpt1a catalyzes the transfer of long-chain fatty acids to carnitine for translocation across the mitochondrial inner membrane, a rate-limiting step of fatty acid utilization by mitochondria under most conditions; this also provides the basis for "fuel sensing," which transduces to the cell information about the relative availability of fatty acids and glucose (16).

The above changes are consistent with the metabolic shifting of cardiac substrate utilization from carbohydrates to fatty acids with diabetes (37). Of interest is that in the present study diabetes produced very little in the way of changed expression of genes related to carbohydrate metabolism, in contrast to the substantially augmented gene expression related to lipid metabolism. The GO term carbohydrate metabolism was not overrepresented among genes with either increased or decreased expression. Furthermore, based on GO group assignment, there were only two carbohydrate metabolism-related genes with increased expression and three with reduced expression in diabetic compared with normal heart. This contrasts with the 13 lipid metabolism-related genes with increased expression and none with decreased expression.

Oxidoreductase activity. Three of the genes, Cyp2e1, Cyp4b1, and Cyp1b1, are related by their involvement in the cytochrome P-450 system. Cyp2e1 undergoes increased expression in lymphocytes from diabetic subjects (22). In the pancreas, kidney, and brain of diabetic rats, increased Cyp2e1 protein levels parallel reactive oxygen species production and lipid peroxidation; and furthermore, overexpression of Cyp2e1 augments both glutathione S-transferase A4-4 levels and mitochondrial reactive oxygen species (47). Cbr1 is one of a group of markers on chromosome 21 that show linkage to type 1 diabetes in humans (3). Decr1 has been reported previously to have increased expression in limb skeletal muscle of Zucker diabetic fatty rats (58). Fmo1 catalyzes thiobenzamide S-oxidation and has additional S- and N-oxidation activities as well, whereas Fmo3 is responsible for methimazole S-oxidation and NADPH oxidation. Fmo1 enzyme activity increases in diabetic liver but is restored to normal values with insulin treatment (4). Txnrd is a critical antioxidant enzyme for protection against oxidative stress; it functions as a peroxidase, reducing hydrogen peroxide and organic hydroperoxides including lipid hydroperoxides (43). Cat is another important component of the cellular antioxidant system by protecting the cell against hydrogen peroxide via the following reaction: 2H2O2 = O2 + 2H2O. Cat enzyme activity is increased in heart from alloxan-diabetic rodents (21). All of the above genes had increased expression with diabetes. In contrast, there were no genes assigned to the GO group oxidoreductase activity among genes with reduced expression in diabetes.

Genes with reduced expression in diabetic heart. The genes related to morphogenesis, organogenesis, organ development, growth, and regulation of growth are involved in a number of different processes, such as cell motility/contraction (Myh6, Myh10, Tpm4), growth and growth factor signaling (Igfbp5, Igf1, Wfdc1, Vegf), collagen formation (Col3a1, Col5a2), energetics during development (Ckb), progenitor cell function (Nes) (60), and cardiac angiogenesis (Tie1, Vegf) (65). Genes related to extracellular matrix and collagen included five involved directly with collagen synthesis (Col1a2, Col3a1, Col5a1, Col5a2, Col18a1) and four that comprise other components of the extracellular matrix (Fbn1, Nid, Syngr1, Lamc1). Reduced expression of many collagen-related genes as well as Fbn1 has been found consistently in limb skeletal muscle and corpus cavernosum of diabetic animals (35, 50, 51, 58). Increased Fbn1 and Fbn1 gene expression is also a feature of myocardial fibrosis (5), whereas mutations of this gene cause Marfan's syndrome, which includes among its manifestations weakening and aneurysms of the great artery walls. Genes related to cell adhesion included one of the above collagen genes (Col5a1). Cd44 is involved in adhesion during cell migration, including that of inflammatory cells. Of interest, anti-Cd44 monoclonal antibodies induce resistance to diabetes in the mouse NOD cell transfer model (59). Many of the remaining genes of the group are also involved in extracellular adhesion, including Clqr1, Cdh11, and Itga6. Finally, genes with reduced expression assigned to the term calcium binding covered a variety of cellular processes, including formation of the extracellular matrix (Fbn1, already discussed above), carbohydrate metabolism (Gpd2), cell adhesion (Cdh11), and modulation of ion channels (Calm1, Calm2), including the KCNQ family of potassium channels (64).

Other genes with changed expression in diabetic heart. Among other genes with more than twofold increased expression, two may be of particular interest despite not being assigned to overrepresented GO groups. The first is Nppa (natriuretic peptide precursor type A, with 6.9-fold increased expression), important in view of the key role that atrial natriuretic peptide plays in cardiovascular and fluid regulation. Atrial natriuretic peptide levels are increased in the plasma as well as heart atrium and ventricle of streptozotocin-diabetic rats (25), and gene expression is increased in the heart left ventricle of Zucker diabetic fatty rats (17). The other is Lepr (leptin receptor, with 2.6-fold increased expression), important due to the role of the leptin system in the regulation of body weight as well as leptin effects on cardiac contractile function (9).

There were also many genes with altered expression that are involved in various signaling pathways in the heart, such as the insulin, MAPK, protein kinase, G protein-coupled receptor, and phosphatidylinisitol systems. Genes involved in these pathways (as assigned by the GO system) are listed in Supplementary Table 1 (see online).

Comparison with gene expression array studies of diabetes in other tissues. Some of the genes with diabetes-induced changed expression in the heart also undergo altered expression in other organ systems. Based on gene expression array studies of diabetes, shared changes include the following: Cbr1, Col1a1, Col3a1, Col5a2, Decr1, Eif4ebp1, FBN1, Igfbp5, and Ptgfrn in skeletal muscle (35, 50, 58); Col5a2, Igfbp3, and Kif5b in liver (50); Hmgcs2 and Nupr1 in adipose tissue (50); Actb, Cat, Cyp1b1, Dpysl3, Fkbp4, and Inha in kidney (2, 13, 61); and Cat, Col3a1, Col5a1, Col5a2, Fbn1, Lox, and Mgst1 in corpus cavernosum (51). Together, these account for only a small proportion of the gene expression changes found in the present study, pointing to a high degree of organ specificity in the effects of diabetes.

Methodological issues. The age chosen for induction of diabetes is consistent with most previous studies of this model (10, 12, 23, 33, 39, 40, 41, 51, 55, 56). Animals were studied 4 wk after streptozotocin or buffer injection, as this amount of time postinjection is in the middle of the range used in previous studies (10, 12, 19, 23, 31, 33, 39, 40, 41, 51, 55, 56, 63). Furthermore, 4 wk is in line with the two previous diabetic heart gene expression array studies, which examined animals 2 wk post-streptozotocin (31) or 3 days, 4 wk, and 6 wk post-streptozotocin (19). The diabetic rats were not treated with insulin, similar to the majority of previous gene array studies of diabetic animals (2, 11, 13, 18, 33, 35, 50, 51, 58). Streptozotocin can produce diabetes with either intravenous or intraperitoneal administration; the latter was used in the present study on the basis of previous success with this approach (55, 56). The present study did not measure glucose values soon after streptozotocin injection. However, the markedly smaller weight gain of animals during the first week after receiving streptozotocin compared with those receiving buffer alone suggests that hyperglycemia occurred quickly, consistent with other studies, such as Knoll et al. (31), who reported glucose values >250 mg/dl at day 2 in animals receiving intraperitoneal streptozotocin. The present study did not allow examining interactions of circadian rhythm with diabetes, which has been demonstrated elegantly for clock, clock output, and select other cardiac genes by Young et al. (63). Tissue harvest for the present study started at ~8 AM, and there was no systematic difference between normal and diabetic animals in the order in which they underwent study. Nonetheless, it is possible that phase shifting of gene expression may have contributed to the present findings.

The sample size of the present study was modest but would appear to be methodologically sound, based on the following considerations. First, a number of gene expression array studies, including those examining diabetes, have used modest sample sizes with great success. Examples of diabetes studies with modest sample sizes include Lecker et al. (35), who had a sample size of two normal and two diabetic animals; Suh et al. (50), who had four normal and four diabetic animals at each age; Baelde et al. (2), who had a sample size of two normal and two diabetic animals; and Fan et al. (13), who had a sample size of two normal and three diabetic animals. Second, statistical methods designed for these types of studies, and in particular BAM, which was used in the present study, borrow strength across the data (27, 28). That is, BAM takes advantage of the fact that there are thousands of genes whose changes are tested at the same time. It thus looks at the degree of differential expression for each gene and analyzes it in the context of changes in expression (or lack thereof) in the thousands of other genes that are being analyzed simultaneously. This approach results in greater statistical power than one would obtain from statistical approaches not designed specifically for analysis of gene expression array data sets. Third, analysis of gene expression arrays typically focuses on large differences between groups, which is done to reduce the chance of identifying a false positive result. The present study set the minimum threshold at a ±1.5-fold change and found that alterations in gene expression in some instances exceeded ±10-fold in magnitude. When differences between groups are large and exceed variability within each group, then small sample sizes are sufficient to detect statistical significance irrespective of the specific statistical approach taken. Fourth, the general problem with sample sizes that are too small is lack of statistical power to identify changes; that is, one ends up with a negative study because the sample size was insufficient. The present study identified 261 genes with at least ±1.5-fold changed expression in diabetic heart. Thus, the present study does not suffer from the absence of significant changes, as would be expected to occur with too small sample sizes. Finally, to be sure the array data were valid, RT-PCR studies were performed on a large number of genes with altered expression (n = 36), and these studies confirmed the array data very well (see Table 5 and Fig. 2). The number of genes that underwent conformational PCR studies is higher than that of the vast majority of other gene expression array studies examining diabetes (2, 11, 13, 18, 33, 35, 50, 51, 58, 61).

Gene expression array studies are critically dependent on high-quality RNA. Indicators of quality include that the percentages of present calls on all arrays are greater than 40% and that 3'/M and 3'/5' ratios are less than 3.0 for beta-actin and GAPDH on all arrays. Specific values from the present study, which demonstrate high integrity of the RNA, are as follows: percent present calls on each array of 53 to 61%, beta-actin 3'/M ratio of 1.0 to 1.6, beta-actin 3'/5' ratio of 1.2 to 1.8, GADPH 3'/M ratio of 1.0 to 1.1, and GAPDH 3'/5' ratio of 1.2 to 1.8. The experience of the center performing the IVT reaction and the hybridization of the RNA to the array is another factor influencing the quality of gene expression data. All procedures from the clean-up protocol to the scanning of the microarrays were performed by the Gene Expression and Genotyping Facility of the Comprehensive Cancer Center of Case Western Reserve University (http://geacf-web.cwru.edu/geacf/index.php). This facility has been performing Affymetrix microarray studies for six years, has experience with over 5,000 Affymetrix microarray analyses, and has performed these procedures for many published papers (e.g., Refs. 7, 32, 44, 45, 46, 57, 62).

Conclusions. The present data point out the cellular components of the heart that are the most widely affected by streptozotocin-induced type 1 diabetes at a gene expression level. Downstream events such as regulation of RNA translation, posttranslational protein modifications, alterations in the rate of protein breakdown, allosteric regulation of enzyme activity, and cellular physiological alterations were not assessed in the present study. Thus the present data do not necessarily imply that comparable protein level alterations and physiological changes corresponding to the 261 genes with altered expression have occurred, similar to many other gene expression array studies of diabetes (2, 11, 13, 18, 19, 31, 34, 35, 50, 51, 58, 61) in which protein levels and physiological changes are not reported for the large numbers of genes with altered expression. The approach used in the present study of focusing on GO groups with statistical overrepresentation among genes with altered expression preferentially identifies systems with perturbations involving groups of constituent genes. These highly perturbed systems included lipid metabolism, oxidoreductase activity, morphogenesis and related terms, extracellular matrix, cell adhesion, and calcium binding, but not carbohydrate metabolism. Together, these GO groups accounted for less than one-half of all genes with at least ±1.5-fold changed expression, however, indicating that there are considerable numbers of more narrow disturbances in other cellular events as well, for example Nppa (natriuretic peptide precursor type A) and Lepr (leptin receptor). The changes in expression of genes related to lipid metabolism are consistent with data from both Knoll et al. (31) and Gerber et al. (19). On the other hand, changed gene expression related to oxidoreductase activity and the extracellular matrix is consistent with Gerber et al. (19), that related to morphogenesis and related terms is consistent with Knoll et al. (31), and that related to calcium ion binding as well as other types of binding were not reported previously. Future studies should be pursued to more comprehensively delineate diabetes-induced transcriptome-wide cardiac gene expression changes by comparing responses to long-term diabetes (many months to several years) vs. short-term (weeks) diabetes, moderate vs. severe diabetes, and treated vs. untreated diabetes, as well as by examining diabetes effects on circadian rhythmicity of expression of the entire transcriptome.


   GRANTS
TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
 GRANTS
REFERENCES
 
These studies were supported by grants from the Department of Veterans Affairs (Veterans Health Administration) as well as National Institutes of Health Grant HL-70697. The Gene Expression Array Core Facility of the Comprehensive Cancer Center of Case Western Reserve University and University Hospitals of Cleveland receives grant support from P30 CA-43703.


   ACKNOWLEDGMENTS
 
We thank Dr. Patrick Leahy from the Gene Expression Array Core Facility for valuable assistance.


   FOOTNOTES
 

Address for reprint requests and other correspondence: E. van Lunteren, Pulmonary 111J (W), Cleveland VA Medical Center, 10701 East Blvd., Cleveland, OH 44106 (e-mail: exv4{at}cwru.edu)

The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.


   REFERENCES
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 DISCUSSION
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