The in vitro behavior of skin fibroblasts from patients with or without diabetic nephropathy is associated with diabetic nephropathy risk. Here we compared skin fibroblast gene expression profiles from two groups of type 1 diabetic patients: 20 with very fast (“fast-track”) versus 20 with very slow (“slow-track”) rates of development of diabetic nephropathy lesions. Gene expression profiles of skin fibroblasts grown in 25 mmol/l glucose for 36 h were assessed by Affymetrix HG-U133A GeneChips to determine the proportion of genes in a given biological pathway that were directionally consistent in their group differences. Five pathways reached statistical significance. All had significantly greater proportions of genes with higher expression levels in the fast-track group. These pathways, the first four of which are closely related and have overlapping genes, included oxidative phosphorylation (P < 0.001), electron transport system complex III (P = 0.017), citrate cycle (P = 0.037), propanoate metabolism (P = 0.044), and transcription factors (P = 0.046). These results support the concept that oxidative phosphorylation and related upstream pathways may be important in the pathogenesis of diabetic nephropathy. Whether these findings reflect inherent genetic cellular characteristics, “cell memory,” or both requires further study.

Oxidative stress represents overproduction of reactive oxygen species (ROS) relative to antioxidant defenses (1). Mitochondria, through the oxidative phosphorylation pathway, are the major cellular source of ROS (1). It has been hypothesized, based on indirect evidence, that increased oxidative stress contributes to diabetic nephropathy development (24). Mesangial cells incubated in high glucose had increased mitochondrial membrane potential and ROS production (5). Renal proximal tubular cells cultured in high glucose had increased H2O2 and lipid peroxide production and reduced antioxidant defenses, including decreased catalase activity and glutathione and endogenous antioxidants production (6). Pretreatment of these cells with rotenone (an inhibitor of the mitochondrial electron transport chain) and diphenylene iodonium (a flavoprotein enzyme inhibitor) blocked these in vitro effects of high glucose (6). Increased mitochondrial ROS production was also found in bovine aortic endothelial cells grown in high glucose, and blocking this increase prevented the downstream changes in several major biological pathways associated with the pathogenesis of diabetic nephropathy (4). Recently, these studies have been modeled into an unified hypothesis linking increased mitochondrial superoxide production to major important pathways in diabetic nephropathy pathogenesis (7). In addition, studies of superoxide dismutase-1 (SOD-1) transgenic db/db diabetic mice provided in vivo support for the role of oxidative stress in the pathogenesis of diabetic nephropathy (8). More direct evidence supporting the above hypothesis comes from studies that found increased oxidative stress and impaired antioxidant enzyme responses to high glucose in cultured skin fibroblasts from type 1 diabetic patients with diabetic nephropathy (9,10). These studies (9,10) are consistent with earlier work linking behaviors of cultured skin fibroblasts to diabetic nephropathy risk (1116).

Microarray analysis can be a powerful tool to explore gene expression profiles. However, it can also produce multiple false-positive or -negative findings. Breaking through this methodological background noise to recognize gene expression patterns related to disease has been challenging. This study compared gene expression profiles in skin fibroblasts from type 1 diabetic patients with very fast versus very slow diabetic nephropathy development by testing whether there were directionally consistent group differences in expression levels within defined biological pathways.

Participants in this study were selected from a cohort of 125 patients with >8 years of diabetes duration. This cohort, described in detail elsewhere (17), had a wide range of kidney involvement (11,12,17). Briefly, skin and kidney biopsies were obtained from each patient, and patients were ranked based on their mesangial expansion score, an estimate of the rate of development of mesangial expansion ascertained by electron microscopy morphometric analysis (11,17). The 25 patients with the highest mesangial expansion score and microalbuminuria or proteinuria were allocated to the “fast-track” and the 25 with lowest mesangial expansion score and normoalbuminuria to the “slow-track” group (11,12). Of these, 20 fast-track (all proteinuric) and 20 slow-track patients were used for the current study. Demographic characteristics were similar between the groups (Table 1). As expected, glycemia and blood pressure were higher and the glomerular filtration rate was lower in the fast-track patients. A total of 18 of 20 fast-track but none of the slow-track patients were receiving antihypertensive drugs. Glomerular structure was markedly abnormal in the fast-track group, whereas it was normal or only mildly abnormal in the slow-track group (Table 1).

Cell culture and RNA isolation.

The methods for cell culture and RNA isolation have been previously detailed (12). Briefly, skin fibroblasts were cultured in Dulbecco’s modified Eagle’s medium with 25 mmol/l glucose and 10% FCS for 36 h after synchronization for 24 h. Total RNA was isolated and its integrity evaluated, using the RNA 6000 LabChip kit and Agilent 2100 bioanalyzer (Hewlett Packard, Palo Alto, CA).

Microarray: target preparation, hybridization, and data filtering.

Biotinylated cRNA was generated from total RNA according to Affymetrix protocols (Santa Clara, CA). A hybridization mixture containing 20 μg biotinylated cRNA from each patient was hybridized to individual Affymetrix HG-U133A GeneChips. The chips were then washed, scanned, and analyzed by GeneChip Microarray Suite V.5.0. Global scaling was used on all probe sets to standardize the average signal of each array to a 500 target value, and the software provided values of expression levels of each gene relative to this average signal (18). Thus, these data are provided in relative value units. The gene expression data set was selected for further analysis as follows. First, genes that meet the criterion of at least 50% present calls in all array experiments were included in the data set. Present, absent, and marginal designations were replaced in an Excel spreadsheet with present = 1, absent = 0, and marginal = 0.2. The values for a given gene in all of the arrays were then summed, and a PAM (present, absent, and marginal) score was obtained. A score of at least 20 was required for inclusion. Second, the previously excluded data with scores <20 and >5 were analyzed, using a χ2 test to discover any asymmetric distribution in the present calls between groups for any given gene. The significance level was set at P < 0.2, Fisher’s exact test (two-tailed) was used for an entry <5, and Yates corrected test was used for an entry >5. The selected gene expression levels data were then used for directional pathway analysis.

Directional pathway analysis.

The Exploratory Visual Analysis system, which has been previously described (19,20), was modified to test directionally consistent group differences in expression levels within biological pathways defined by the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway database (available online from http://www.genome.jp/kegg/pathway.html). This system uses a 1,000-fold permutation test on each pathway to evaluate whether the occurrence of the proportion of statistically significantly (Student’s t test, P ≤ 0.05) increased/decreased genes in a biological pathway is higher than would be expected by chance (19,20). For the P-plus test in a given biological pathway, the null hypothesis is that the number of genes with increased expression levels is the same or lower than that found in a “pseudo-pathway” with an identical number of genes randomly selected from the entire database. For the P-minus test, the number of significantly decreased genes is tested. A total of 1,000 pseudo-pathway replications were performed, and the final P value was calculated by dividing the total counts by 1,000. Thus, the P value represents the probability of obtaining the observed number of genes with higher (P-plus tests) or lower (P-minus test) expression levels in a given biological pathway than by chance alone.

Real-time RT-PCR.

The methods for RT-PCR have been described previously (11,12). The sequence of the probe and the forward and reverse primers used for NDUFS1 (NADH dehydrogenase [ubiquinone] Fe-S protein 1) were: 5′-FAM-CAAAGCTGTCACAGAGGGTGCCCA-TAMRA-3′, 5′-CACAGACAATGGCCAAATGTG-3′, and 5′-CAGCATATGGATGGTTCCTCTACTG-3′, respect-ively. The sequence of the probe and the forward and reverse primers used for COX 7B (cytochrome c oxidase subunit VIIb) were: 5′-FAM-CGCACTAAATCGTCTCCAAGTTCGAAGC-TAMRA-3′, 5′-CGATGTTTCCCTTGGTCAAAA-3′, and 5′-CCTTGCCATTGTTTGCTGAA-3′, respectively. These probes were labeled with 6-FAM (6-carboxyfluorescein) at the 5′ end and TAMRA (6-carboxy-tetramethyl-rhodamine) at the 3′ end.

Skin fibroblasts from 20 patients with a very rapid development of diabetic nephropathy (fast-track group) and 20 with a very slow rate of diabetic nephropathy development (slow-track group) were cultured in Dulbecco’s modified Eagle’s medium with 25 mmol/l glucose for 36 h, and their gene expression profiles were assessed by Affymetrix HG-U133A GeneChips. At the time of skin biopsy, HbA1c (A1C) was higher in fast-track (9.4 ± 1.5%) than in slow-track (8.4 ± 1.2%, P = 0.027) patients. A total of 456 of ∼22,000 genes showed significant group differences (P < 0.05) in mRNA expression levels, with 237 higher and 219 lower in the fast- versus slow-track patients.

There was an increased proportion of genes with higher expression levels in the oxidative phosphorylation pathway (P-plus test, P < 0.001) in the fast-track compared with the slow-track group: expression levels of 10 of 127 genes on the U133A GeneChip belonging to the oxidative phosphorylation pathway were higher in the fast-track patients (Tables 2 and 3). No genes in this pathway had lower expression levels in the fast-track group. The expression levels of two of these genes, NDUFS1 and COX 7B, were confirmed by RT-PCR to be higher in the fast-track than in the slow-track patients (Table 4).

Four additional pathways, three closely related to the oxidative phosphorylation pathway, also had increased proportion of genes with higher expression levels in the fast- versus the slow-track group (Tables 2 and 3). These pathways included electron transport system complex III, citrate cycle, and propanoate metabolism. Group directional differences in transcription factors pathway were also seen (Tables 2 and 3). None of the pathways represented on the Affymetrix GeneChip had a greater proportion of genes with lower expression levels in the fast- versus the slow-track group. General cell regulation pathways, such as pyrimidine metabolism (P-plus test: P = 0.106; P-minus test: P = 0.188), lysine degradation (P-plus test: P = 0.62; P-minus test: P = 0.211), and cell cycle (P-plus test: P = 0.61; P-minus test: P = 0.136) were not different between the groups.

Several studies using single cell lines (3,4,5,6) have led to the hypothesis that oxidative phosphorylation pathway alterations may be important in diabetic nephropathy pathogenesis (2,7). The current study found directional differences in skin fibroblast gene expression levels in oxidative phosphorylation and related pathways. This is consistent with relatively greater oxidative stress in type 1 diabetic patients with diabetic nephropathy. Intracellular ROS are produced through the oxidative phosphorylation pathway by the mitochondrial electron transport chain in response to hyperglycemia (2). High glucose may result in overproduction of electron donors in the citrate cycle, which in turn generates a high mitochondrial membrane potential (2,7). It has been proposed that once a certain threshold is exceeded, high membrane potential inhibits electron transfer at mitochondrial respiratory chain complex III and increases the half-life of superoxide-generating intermediates such as ubisemiquinone (2,3,21). Thus, as a consequence of overall higher gene expression in the oxidative phosphorylation pathway, it is possible that mitochondrial membrane potential may exceed this threshold, resulting in decreased electron transfer at respiratory chain complex III and overproduction of superoxide and ROS.

Four of the five pathways with directionally consistent group differences in gene expression levels were highly clustered among >200 known biological pathways in the genome (Fig. 1). This is, to our knowledge, the first application of this new method of analysis. However, statistical methods are not yet fully developed that would allow calculation of the likelihood that these four related pathways could show directional consistent group differences by chance alone, in part because of the presence of genes that overlap in these related pathways. Nonetheless, it is intuitively very unlikely that these findings are coincidental. The propanoate metabolism pathway is connected to the citrate cycle through acetyl-CoA. Increased citrate cycle activity could further augment the oxidative phosphorylation pathway by increasing production of NADH and FADH2, causing increased production of electron donors and increased mitochondrial membrane potential. These, in turn, could inhibit electron transfer in the oxidative phosphorylation chain, resulting in increased ROS production (3,21). Importantly, the downstream consequence of these perturbations on several pathways independently linked to diabetic nephropathy risk was abrogated in experiments that blocked the oxidative stress consequences of increased mitochondrial ROS production (2,3,21). If the functional consequence of the current findings were increased ROS production, this, coupled with the already demonstrated antioxidant enzyme defects in skin fibroblasts from diabetic nephropathy patients (9,10), could result in marked oxidative stress in these cells. Because this and other skin fibroblast behaviors are related to diabetic nephropathy (913,15,16), it is tempting to hypothesize that similar processes may be occurring in the kidney.

Skin fibroblasts from the patients in this study were cultured under identical conditions for several passages before gene expression profiling was performed. However, high-glucose–induced overexpression of fibronectin and collagen IV in human cultured endothelial cells persisted after several subsequent passages in the normal glucose condition (22). The fast-track patients had higher A1C than the slow-track patients. Thus, the current study cannot definitely differentiate whether genetic or environmental (“cell memory”) effects, or both, may be operative. If the current findings represent cell memory phenomena reflecting in vivo environmental differences, there are several possible candidate influences. Glycemic control, as expected, was worse in the fast-track group. Systemic blood pressure was higher in the fast-track group. Although it is difficult to imagine how higher systemic blood pressure could influence skin fibroblast behavior, this possibility cannot be eliminated. Also, antihypertensive drugs were usually used by the fast-track but not by the slow-track patients. Drugs blocking the renin-angiotensin system have been shown to have effects in rats that persisted (23,24) far beyond any reasonable possibility that these agents had remained in these animals’ tissues (25). Although similar studies have not been performed for other antihypertensive agents, drug environmental cell memory phenomena are possible. Other environmental influences are also possible. However, our previously published data showed that there are, at least in part, heritable regulators of in vitro skin fibroblast behaviors that are associated with diabetic nephropathy risk (14). Further studies of cellular behaviors of skin fibroblasts from identical twins discordant for type 1 diabetes could help to dissect the genetic versus cell memory questions raised here. Moreover, the current findings cannot determine whether group differences in gene expression levels represent an increase in the fast-track patients, a decrease in the slow-track patients, or both. Further studies on cells from normal control subjects would be required to answer this question.

Finally, the pathway differences reported here probably do not fully explain the altered cellular phenotype that characterizes diabetic nephropathy risk (26,27). In fact, individual genes in upstream, downstream, or unrelated pathways may be equally or more important. Thus, directional pathway analysis strategies should probably not preclude other approaches that could be applied to the understanding of the cellular basis of diabetic nephropathy risk.

FIG. 1.

Pathways that are interrelated and that show directionally consistent group differences in pathway gene expression levels. OXPHOS, oxidative phosphorylation.

FIG. 1.

Pathways that are interrelated and that show directionally consistent group differences in pathway gene expression levels. OXPHOS, oxidative phosphorylation.

TABLE 1

Demographic, clinical, and renal structural characteristics of type 1 diabetic patients

Slow trackFast trackP
M/F 8/12 9/11 NS 
Age (years) 36 ± 10 38 ± 8 NS 
Diabetes duration (years) 20 ± 8 24 ± 7 NS 
A1C (%) 8.4 ± 1.2 9.4 ± 1.5 0.027 
Blood pressure (mmHg) 117 ± 9 / 69 ± 7 135 ± 11 / 77 ± 7 <0.001 
Hypertension (yes/no) 1/19 19/1 <0.001 
Antihypertensive treatments (yes/no) 0/20 18*/2 <0.001 
Albumin excretion rate (μg/min) 5.3 (0.4–15.9) 599.6 (229–4630) NA 
GFR (ml/min per 1.73 m2115 ± 13 72 ± 20 <0.001 
GBM width (nm) 415 (329–737) 768 (515–1,249) <0.001 
Vv(Mes/glom) 0.20 ± 0.02 0.56 ± 0.10 <0.001 
Mesangial expansion score −0.012 ± 0.114 1.54 ± 0.490 NA 
Slow trackFast trackP
M/F 8/12 9/11 NS 
Age (years) 36 ± 10 38 ± 8 NS 
Diabetes duration (years) 20 ± 8 24 ± 7 NS 
A1C (%) 8.4 ± 1.2 9.4 ± 1.5 0.027 
Blood pressure (mmHg) 117 ± 9 / 69 ± 7 135 ± 11 / 77 ± 7 <0.001 
Hypertension (yes/no) 1/19 19/1 <0.001 
Antihypertensive treatments (yes/no) 0/20 18*/2 <0.001 
Albumin excretion rate (μg/min) 5.3 (0.4–15.9) 599.6 (229–4630) NA 
GFR (ml/min per 1.73 m2115 ± 13 72 ± 20 <0.001 
GBM width (nm) 415 (329–737) 768 (515–1,249) <0.001 
Vv(Mes/glom) 0.20 ± 0.02 0.56 ± 0.10 <0.001 
Mesangial expansion score −0.012 ± 0.114 1.54 ± 0.490 NA 

Data are the means ± SD or median (range), unless otherwise indicated.

*

Diuretic: n = 8; α-blocker: n = 4; β-blocker: n = 2; calcium channel blocker: n =4; ACE inhibitor: n = 7; angiotensin II receptor blocker: n = 3. GBM, glomerular basement membrane (normal value = 332 ± 46); GFR, glomerular filtration rate; NA, not applicable, different by study design; Vv(Mes/glom), mesangial fractional volume (normal value = 0.20 ± 0.03).

TABLE 2

Pathways with directionally consistent group differences in gene expression levels in the fast- versus slow-track groups

PathwaysTP+P
Oxidative phosphorylation 127 10 <0.001 0.099 
Electron transport system, complex III 11 0.017 0.82 
Citrate cycle 35 0.037 0.493 
Propanoate metabolism 40 0.044 0.554 
Transcription factors 36 0.046 0.516 
PathwaysTP+P
Oxidative phosphorylation 127 10 <0.001 0.099 
Electron transport system, complex III 11 0.017 0.82 
Citrate cycle 35 0.037 0.493 
Propanoate metabolism 40 0.044 0.554 
Transcription factors 36 0.046 0.516 

T, total number of genes in the pathway; ↑, number of genes with higher expression levels in the fast-track versus slow-track groups; P+, P value for P-plus test; ↓, number of genes with lower expression levels in the fast-track versus slow-track groups; P−, P value for P-minus test.

TABLE 3

Differentially expressed genes in the five pathways with directionally consistent group differences in gene expression levels

Gene IDGene name (symbol)Gene expression levels (relative value)
PΔ
Fast trackSlow track
Oxidative phosphorylation      
    522 ATP synthase, H + transporting, mitochondrial F0 complex, subunit F6 (ATP5J) 2,806 ± 419 2,516 ± 254 0.012 
    528 ATPase, H + transporting, lysosomal 42 kDa, V1 subunit C, isoform 1 (ATP6V1C1) 503 ± 69 450 ± 90 0.045 
    1349 Cytochrome c oxidase subunit VIIb (COX7B) 2,762 ± 596 2,321 ± 324 0.007 
    4719 NADH dehydrogenase (ubiquinone) Fe-S protein 1 (NDUFS1) 812 ± 118 724 ± 91 0.012 
    4707 NADH dehydrogenase (ubiquinone) 1β subcomplex 1 (NDUFB1) 2,977 ± 739 2,569 ± 454 0.044 
    4714 NADH dehydrogenase (ubiquinone) 1β subcomplex 8 (NDUFB8) 2,233 ± 570 1,854 ± 573 0.042 
    7385 Ubiquinol-cytochrome c reductase core protein II (UQCRC2) 1,329 ± 138 1,196 ± 126 0.003 
    7381 Ubiquinol-cytochrome c reductase binding protein (UQCRB) 3,913 ± 833 3,306 ± 709 0.018 
    2110 Electron-transferring-flavoprotein dehydrogenase (ETFDH) 236 ± 31 217 ± 28 0.047 
    6391 Succinate dehydrogenase complex subunit C (SDHC) 1,090 ± 173 980 ± 140 0.033 
Electron transport system, complex III      
    7381 Ubiquinol-cytochrome c reductase binding protein (UQCRB) 3,912 ± 833 3,306 ± 709 0.018 
    7385 Ubiquinol-cytochrome c reductase core protein II (UQCRC2) 1,329 ± 138 1,196 ± 126 0.003 
Citrate cycle (TCA cycle)      
    2271 Fumarate hydratase (FH) 1,543 ± 432 1,278 ± 273 0.027 
    6391 Succinate dehydrogenase complex subunit C (SDHC) 1,090 ± 173 980 ± 140 0.033 
    8803 Succinate-CoA ligase, ADP-forming, beta subunit (SUCLA2) 912 ± 93 842 ± 105 0.031 
    1743 Dihydrolipoamide S-succinyltransferase (DLST) 1,049 ± 144 1,231 ± 194 0.002 − 
Propanoate metabolism      
    34 Acyl-CoA dehydrogenase, C-4 to C-12 straight chain (ACADM) 707 ± 167 583 ± 116 0.01 
    39 Acetyl-CoA acetyltransferase 2 (ACAT2) 616 ± 79 560 ± 67 0.022 
    8803 Succinate-CoA ligase, ADP-forming, β-subunit (SUCLA2) 912 ± 93 842 ± 105 0.031 
    31 Acetyl-CoA carboxylase-α (ACACA) 490 ± 129 581 ± 151 0.048 − 
Transcription factors      
    2958 General transcription factor IIA, 2 (GTF2A2) 2,059 ± 411 1,835 ± 251 0.045 
    2963 General transcription factor IIF, polypeptide 2 (GTF2F2) 241 ± 83 194 ± 62 0.049 
    6880 TAF9 RNA polymerase II, TATA box binding protein (TBP)-associated factor (TAF9) 1,138 ± 174 980 ± 134 0.003 
Gene IDGene name (symbol)Gene expression levels (relative value)
PΔ
Fast trackSlow track
Oxidative phosphorylation      
    522 ATP synthase, H + transporting, mitochondrial F0 complex, subunit F6 (ATP5J) 2,806 ± 419 2,516 ± 254 0.012 
    528 ATPase, H + transporting, lysosomal 42 kDa, V1 subunit C, isoform 1 (ATP6V1C1) 503 ± 69 450 ± 90 0.045 
    1349 Cytochrome c oxidase subunit VIIb (COX7B) 2,762 ± 596 2,321 ± 324 0.007 
    4719 NADH dehydrogenase (ubiquinone) Fe-S protein 1 (NDUFS1) 812 ± 118 724 ± 91 0.012 
    4707 NADH dehydrogenase (ubiquinone) 1β subcomplex 1 (NDUFB1) 2,977 ± 739 2,569 ± 454 0.044 
    4714 NADH dehydrogenase (ubiquinone) 1β subcomplex 8 (NDUFB8) 2,233 ± 570 1,854 ± 573 0.042 
    7385 Ubiquinol-cytochrome c reductase core protein II (UQCRC2) 1,329 ± 138 1,196 ± 126 0.003 
    7381 Ubiquinol-cytochrome c reductase binding protein (UQCRB) 3,913 ± 833 3,306 ± 709 0.018 
    2110 Electron-transferring-flavoprotein dehydrogenase (ETFDH) 236 ± 31 217 ± 28 0.047 
    6391 Succinate dehydrogenase complex subunit C (SDHC) 1,090 ± 173 980 ± 140 0.033 
Electron transport system, complex III      
    7381 Ubiquinol-cytochrome c reductase binding protein (UQCRB) 3,912 ± 833 3,306 ± 709 0.018 
    7385 Ubiquinol-cytochrome c reductase core protein II (UQCRC2) 1,329 ± 138 1,196 ± 126 0.003 
Citrate cycle (TCA cycle)      
    2271 Fumarate hydratase (FH) 1,543 ± 432 1,278 ± 273 0.027 
    6391 Succinate dehydrogenase complex subunit C (SDHC) 1,090 ± 173 980 ± 140 0.033 
    8803 Succinate-CoA ligase, ADP-forming, beta subunit (SUCLA2) 912 ± 93 842 ± 105 0.031 
    1743 Dihydrolipoamide S-succinyltransferase (DLST) 1,049 ± 144 1,231 ± 194 0.002 − 
Propanoate metabolism      
    34 Acyl-CoA dehydrogenase, C-4 to C-12 straight chain (ACADM) 707 ± 167 583 ± 116 0.01 
    39 Acetyl-CoA acetyltransferase 2 (ACAT2) 616 ± 79 560 ± 67 0.022 
    8803 Succinate-CoA ligase, ADP-forming, β-subunit (SUCLA2) 912 ± 93 842 ± 105 0.031 
    31 Acetyl-CoA carboxylase-α (ACACA) 490 ± 129 581 ± 151 0.048 − 
Transcription factors      
    2958 General transcription factor IIA, 2 (GTF2A2) 2,059 ± 411 1,835 ± 251 0.045 
    2963 General transcription factor IIF, polypeptide 2 (GTF2F2) 241 ± 83 194 ± 62 0.049 
    6880 TAF9 RNA polymerase II, TATA box binding protein (TBP)-associated factor (TAF9) 1,138 ± 174 980 ± 134 0.003 

Gene expression levels data are the means ± SD, expressed as values relative to the standardized average signal of 500 on each microarray chip after global scaling. Δ, direction of gene expression level difference between the fast-track versus slow-track group; +, higher expression levels in the fast-track group; −, lower expression levels in the fast-track group.

TABLE 4

Gene expression levels of NDUFS1 and COX 7B measured by RT-PCR

GenesFast trackSlow trackP
NDUFS1 1.39 ± 0.12 1.31 ± 0.17 0.026 
COX 7B 1.50 ± 0.12 1.35 ± 0.13 0.0007 
GenesFast trackSlow trackP
NDUFS1 1.39 ± 0.12 1.31 ± 0.17 0.026 
COX 7B 1.50 ± 0.12 1.35 ± 0.13 0.0007 

Data are the means ± SD. Relative values of target mRNA in samples with 0.1 μg total RNA are expressed as fold changes to the target mRNA expression in 0.1 μg reference standard total RNA. NDUFS1, NADH dehydrogenase (ubiquinone) Fe-S protein 1; COX 7B, cytochrome c oxidase subunit VIIb.

DOI: 10.2337/db05-1438

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

C.H. was supported by a mentor-based research fellowship training grant from the American Diabetes Association. M.L.C. was supported by a research fellowship training grant from Juvenile Diabetes Foundation International. This work was supported by grants from the National Institutes of Health (DK 13083, DK 54638, and DK 070210), the National Center for Research Resources (M01-RR00400), the Viking Children’s Fund, and the National Kidney Foundation of Minnesota.

We thank John Basgen and Tom Groppoli for performing the morphometric studies, Kim Pinkham for the cell culture, Michele Watrin for patient recruitment, and Sue Kupcho-Sandberg for albumin excretion rate measurements.

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