1 Department of Biochemistry, University of Wisconsin, Madison, Wisconsin
2 Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina
3 Department of Statistics and Horticulture, University of Wisconsin, Madison, Wisconsin
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ABSTRACT |
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Type 2 diabetes is characterized by hyperglycemia resulting from a failure of the pancreatic ß-cells to compensate for insulin resistance. Obesity, which leads to insulin resistance, is a strong risk factor for the development of type 2 diabetes. However, only 20% of obese people develop diabetes; most obese people can maintain euglycemia throughout their life span, despite insulin resistance. Genetic factors play a role in determining whether an obese individual develops type 2 diabetes. It is therefore important to understand the differences underlying the two types of obesity: that which resists the onset of diabetes and that which is linked to diabetes.
We studied the link between obesity and diabetes using two mouse models of obesity. The C57BL/6J (B6)-ob/ob mouse strain, despite extreme obesity and insulin resistance, develops only mild, transient hyperglycemia under a regular chow diet (1,2). Lean BTBR mice are insulin resistant (3). When the ob mutation is introgressed onto the BTBR background through repeated backcrossing and selection of the ob allele (2), the BTBR-ob/ob mice develop severe diabetes. The fasting plasma glucose reaches 400 mg/dl at 10 weeks of age, whereas for age-matched B6-ob/ob mice, glucose remains <250 mg/dl (2).
The expression of adipogenic genes, including the transcription factor adipocyte determination factor 1/sterol regulatory element binding protein (ADD1/SREBP), is decreased in adipose tissue of ob/ob mice (4,5). These studies suggest that adipocytes in obese adipose tissue have a reduced lipogenic capacity. This has been confirmed in adipose tissue of obese human subjects (69). On the other hand, in mouse models of extreme leanness (lipodystrophy) and extreme obesity (ob/ob), hepatic expression of SREBP1 and lipogenic target genes involved in fatty acid biosynthesis are increased, leading to significantly higher rates of hepatic fatty acid synthesis in vivo (10). In human obesity, decreased lipogenic gene expression in adipose tissue is coupled with increased hepatic lipogenesis (8). These findings suggest that reduced lipogenic adipocytes, or perhaps adipocyte dysfunction, is associated with increased hepatic lipogenesis. We hypothesized that there is a shift in the "lipogenic burden" from adipose tissue to other organs, such as the liver, in animals with less lipogenic adipose tissue (11). It is unclear whether this shift is adaptive or pathological with regard to diabetes. In this study, we show that resistance to diabetes correlates with a high level of hepatic lipogenic gene expression and hepatic steatosis in B6-ob/ob mice, and that a failure to increase hepatic lipogenesis may contribute to diabetes in BTBR-ob/ob mice.
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RESEARCH DESIGN AND METHODS |
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RNA preparation.
Mice were killed by CO2 asphyxiation after a 4-h fast. Abdominal fat pads, liver, and soleus muscle were quickly transferred into liquid N2 and stored at -80°C. Total RNA was extracted from the three tissues with RNAzol reagent (Tel-Test). Intact islets were isolated from pancreas using a collagenase digestion procedure and were handpicked under a stereomicroscope. Isolated islets from each individual were preserved in TriReagent (Molecular Research Center, Cincinnati, OH) at -80°C until RNA preparation. Crude RNA samples were purified with RNeasy mini columns (Qiagen) before being subjected to microarray and RT-PCR studies.
Microarrays.
Four B6-ob/ob and four BTBR-ob/ob male mice at 14 weeks of age were used in the microarray study. RNA samples from two animals were pooled for each tissue, and each pooled RNA sample was applied to an Affymetrix MGU74A array. Because of the scarcity of islets in the BTBR-ob/ob mice, four additional mice were pooled to obtain islet RNA from these animals. A total of 16 MGU74Av2 arrays (2 strains x 4 tissues x 2 replicates = 16 arrays) were used to monitor the expression level of 12,000 genes or expressed sequence tags. The data were processed with Affymetrix Microarray Suite 4.0. Quantitative expression levels of all the transcripts were estimated using the DNA-Chip analyzer (dChip) algorithm (12). dChip models probe level data to account for artifacts such as probe-specific biases. Corrected and normalized model-based estimates of gene expression were obtained. Genes that are differentially expressed between the B6-ob/ob mice and the BTBR-ob/ob mice were determined using a statistical algorithm accounting for measurement errors and fluctuations in absolute gene expression levels (developed by Y. Lin and B.S.Y., University of Wisconsin-Madison) (13). Briefly, the raw-expression data are rank ordered by values. The ranks are converted into normal scores by stratifying into quintiles. The expression levels for each gene across experimental samples are then calculated using the normal scores. Differential expression across conditions of interest is computed by contrasting the normal scores between conditions. Robust estimates of center and spread varying across the average intensity are computed using smoothed medians and smoothed median absolute deviations, respectively. The contrasts of differential expression are standardized using the calculated center and spread. Fold changes are calculated based on the normal scores instead of raw expression data.
These standardized values should have an approximately standard normal distribution for genes that are not differentially expressed. P values are derived using a Bonferroni-style genome-wide correction. The software can be found online at http://www.stat.wisc.edu/yandell/statgen. By using normal scores and the center and spread algorithm, the method robustly adapts to changing variability across average expression levels. Upon identification of differentially expressed genes, the patterns of gene expression were tentatively assembled based on our current knowledge of gene functions.
Real-time quantitative PCR.
RNA samples from individual mouse tissues were used for RT-PCR. First-strand cDNA was synthesized from 1 µg of total RNA using Super Script II RT (Gibco BRL) primed with a mixture of oligo-dT and random hexamers. Reactions lacking the reverse transcriptase served as a control for amplification of genomic DNA. Unless otherwise specified, the reaction was carried out in a 25-µl volume in 1 x SYBR Green PCR core reagents (Applied Biosystems) containing cDNA template from 10 ng of total RNA and 6 pmol of primers. Quantitative PCR was performed on an ABI GeneAmp 5700 sequence detection system. For each gene, the cDNA samples were gridded onto a 96-well plate. For each sample, at least duplicate amplifications were performed and average measurements were taken for data analysis.
We determined the cycle at which the abundance of the accumulated PCR product crosses a specific threshold, the threshold cycle (CT) for each reaction. The difference in average CT values between ß-actin and a specific gene was calculated for each individual and is termed CT, which is comparable to the log-transformed, normalized mRNA abundance. In some instances,
CT values were further converted to relative expression levels normalized against the mean expression in B6-ob/ob mice. All values are presented as the means ± SD. Comparisons were evaluated by Students t test (two tailed).
Liver TG content.
Tissue TG content was measured with an assay based on the detection of glycerol (14). Briefly, frozen liver tissue (1030 mg) was minced in chloroform and methanol (2:1 ratio). Minced samples were incubated at -20°C overnight to release lipid before centrifugation at 1,200 rpm for 10 min at 4°C to remove tissue debris. The supernatants were washed with H2O to remove water-soluble components. Lipids in the organic phase were transferred into a new tube, dried, and redissolved with Thesit (Fluka). TG was assayed using the GPO Trinder reagent (Sigma).
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RESULTS |
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Microarrays were used to study gene expression in adipose tissue, liver, skeletal muscle, and pancreatic islets. Duplicate samples from 14-week-old male mice were used because they show the greatest phenotypic difference between B6-ob/ob and BTBR-ob/ob mice (Fig. 1). Of the 12,421 transcripts represented on the Affymetrix Mouse Genome U74A V2 arrays, 5,629 (45%) were expressed in adipose tissue (called "present" by Affymetrix software), and 4,218 (34%), 4,472 (36%), and 4,697 (38%) were expressed in liver, muscle, and islets, respectively. There was no strain difference regarding how many genes were expressed in each of the four tissues.
To identify transcripts as differentially expressed between experimental conditions, a procedure that simply uses an arbitrary fold change ratio cutoff is no longer considered effective because it ignores the fact that the variation of such ratios is not consistent across absolute expression levels (15). In this study, we used a new algorithm that uses a significance threshold that varies as a function of absolute expression level rather than an arbitrary fold change value. Figure 2 shows the dot graphs of genes identified as differentially expressed between B6-ob/ob and BTBR-ob/ob in each of the four tissues. Note that in Fig. 2, the lines defining significance thresholds address the variations in microarray data better than straight fold-change lines would. For low-abundance genes, a greater change is required to cross the significance threshold.
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The data suggest that hyperglycemia or hypertriglyceridemia in 14-week-old BTBR-ob/ob mice has no discernible effect on adipogenesis or lipogenesis in adipose tissue. On the other hand, mRNAs for a number of genes generally expressed in macrophages or inflammatory cells were increased in diabetic BTBR-ob/ob mice, including a group of 1-serine protease inhibitors, as well as some cytokines and interferon-induced genes (Table 1). The data reveals a previously unappreciated abnormality in diabetic adipose tissue.
In the liver, genes involved in the lipogenic pathway were markedly downregulated in the diabetic BTBR-ob/ob mice (Table 2). The genes include fatty acid synthase (FAS), stearoyl coenzyme A desaturase 1 (SCD1), glycerol-3-phosphate acyltransferase (GPAT), malic enzyme, the LDL receptor, sterol-C5-desaturase, and SPOT14, a known SREBP1 target gene. The data strongly suggest that lipogenesis is reduced in the liver of obese diabetic mice compared with obese nondiabetic mice.
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Low-abundance signals on microarrays are subject to high variability (15). High-abundance genes are subject to signal saturation because of the detection limit of the laser scanners. We noticed that very-low-abundance transcription factors such as SREBP1 or peroxisome proliferatoractivated receptor- (PPAR-
) did not show significant differential expression on microarrays. In addition, important lipogenic genes, such as acetyl CoA carboxylase (ACC) or malonyl CoA decarboxylase (MCD), were not represented on MGU74 arrays. For these reasons, we used quantitative RT-PCR to confirm our observations from the microarray study and also to expand our investigation on the altered gene expression profiles. Apart from its wide dynamic range of detection, RT-PCR can efficiently measure mRNA abundance of multiple individuals and thereby increase the power of statistical tests to compare two experimental groups.
Because our microarray study was carried out using 14-week-old male mice, we cannot determine whether the changes are causes or consequences of diabetes. Some changes may have been blunted under chronic hyperglycemia so that they are not detectable with microarrays. To identify gene expression changes that precede the onset of diabetes, we chose to study prediabetic 6-week-old female mice (Fig. 1B). We assume the differences seen in the prediabetic mice would likely be causative of diabetes, whereas those seen after the onset of diabetes would more likely be adaptive. Six BTBR-ob/ob mice (fasting glucose 200 mg/dl) were compared with eight B6-ob/ob mice (fasting glucose 220 mg/dl). Because the microarray studies indicated reduced lipogenic gene expression in the liver, we studied genes involved in lipogenesis. To expand our previous study in adipose tissue comparing lean and obese mice (5), we also included lean mice in this study. Below, we describe the results of these analyses.
Reduction of lipogenic gene expression in adipose tissue of obese and diabetic mice.
The expression of aP2, adipsin, ACRP30, and FIAF decreased dramatically in obese mice (Fig. 3A). The mRNA abundance of adipogenic transcriptional factors such as PPAR- and SREBP1 also dropped. Adipsin mRNA was also significantly lower in BTBR-ob/ob adipose tissue compared with B6-ob/ob.
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Hepatic steatosis in diabetes-resistant B6-ob/ob mice.
We compared gene expression in the livers of the two obese mouse strains. Decreased lipogenic gene expression was seen in BTBR-ob/ob liver at both the prediabetic and the diabetic stages (Figs. 4A and B). The reduction at 14 weeks was even greater (Fig. 4B). Unlike that in adipose tissue, the expression of SCD1 and FAS in the liver of B6-ob/ob mice did not decrease with age (Fig. 4C). The data suggest that the liver of B6-ob/ob mice maintains a high level of hepatic lipogenesis. In contrast, hepatic lipogenic gene expression remained low from the prediabetic to the diabetic stages in BTBR-ob/ob mice compared with insulin-resistant but nondiabetic B6-ob/ob mice.
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Other changes of gene expression in diabetic BTBR-ob/ob mice.
Reduced expression of fatty acid oxidation genes was also observed in multiple organs of obese diabetic mice. Acyl CoA oxidase (ACO) is an enzyme involved in the first step of the peroxisomal fatty acid oxidation pathway. In adipose tissue and soleus muscle, ACO expression decreased by 8.6- and 11.5-fold, respectively, in obese mice compared with lean mice (P < 0.01) (Fig. 5A). There was a further reduction in the soleus muscle of BTBR-ob/ob relative to B6-ob/ob (P < 0.05) (Fig. 5A, right panel). In liver, a strain difference was observed with BTBR-ob/ob mice showing an 80% reduction at 6 weeks and a 70% reduction at 14 weeks. Thus, the reduction precedes the onset of diabetes in BTBR-ob/ob mice. In each strain, the expression of ACO also decreased when animals aged (Fig. 5B). The data suggest that the obese mice have reduced fatty acid oxidation, and that there was further reduction in diabetic mice. Despite the change in ACO expression, we did not observe any change in the expression of the transcriptional factor PPAR-. It is likely that PPAR-
activity, rather than its mRNA level, mediate changes in lipid oxidation during the development of diabetes. The expression of gluconeogenic genes in BTBR-ob/ob liver was suppressed at 6 weeks but not at 14 weeks (Fig. 6). At 6 weeks, three key genes in the gluconeogenic pathway were expressed at a reduced level in BTBR-ob/ob mice, although only FBPase1 reached a significant level (Fig. 6A). An opposite trend was observed at 14 weeks; phosphoenopyruvate carboxykinase (PEPCK) was expressed at a significantly higher level in BTBR-ob/ob (Fig. 6B). This suggests an increase in hepatic gluconeogenesis at the onset of diabetes in BTBR-ob/ob mice.
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DISCUSSION |
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Microarray technology is a useful tool to detect novel aspects of disease-associated gene expression patterns. Soukas et al. (4) reported increased expression of inflammatory genes and acute-phase proteins, such as serum amyloid A3 and A4, LPS binding protein, and SV40-induced 24p3, in adipose tissue as well as in isolated adipocytes of leptin-deficient mice. The data suggest a possible link between immune system function and obesity. In the present study, multiple genes encoding 1-serine protease inhibitors, including types 1-1 through 1-6, showed coordinately elevated expression in adipose tissue of the diabetic BTBR-ob/ob mice (Table 1A). The inhibitors are antitrypsin peptides (18). Deficiency in these genes leads to early onset emphysema (19). These genes are expressed predominantly in macrophages and in the liver, and high protein concentrations are present in plasma. Additional inflammatory response genes are also found to be more highly expressed in leptin-deficient diabetic BTBR-ob/ob mice compared with leptin-deficient nondiabetic B6-ob/ob mice. The genes include cytokine subfamily B13 (AF030636), small inducible cytokine A7 (X70058) and A21a (AF035684), interferon-activated gene 202A (M31418), and interferon-inducible GTPase (AJ007971) (Table 1A).
Because a whole fat pad was used to isolate RNA for adipose tissue in this study, it is not clear whether the elevated signals of inflammatory genes are caused by an enrichment of macrophages in the fat pad of BTBR-ob/ob mice or are a result of elevated expression in the adipocytes. Taken together, the observation that acute-phase genes are turned on in leptin-deficient animals (4), and that the expression of macrophage markers is further increased in diabetes, suggests a correlation between inflammation and the disease process. We propose that these abnormalities are related to increasing insulin resistance in adipose tissue. The link between individual cytokines and insulin resistance has previously been reported. Cytokines such as tumor necrosis factor-, interleukin (IL)-1ß, IL-6, and CD36 are major regulators of adipose tissue metabolism (20,21). Adipose tissue expression of these genes is positively correlated with obesity-related insulin resistance (22). A recently identified insulin resistance marker, resistin (23), also known as Fizz3, is structurally related to a marker of inflammation, Fizz1 (24).
The reduction of lipogenic gene expression in the BTBR-ob/ob mice cannot be simply explained by insulin deficiency. At 6 weeks, BTBR-ob/ob mice had higher fasting insulin levels than B6-ob/ob mice. At 14 weeks, BTBR-ob/ob insulin levels dropped, whereas B6-ob/ob levels continued to rise. However, at both ages there was reduced expression of SREBP1 and its target genes in the livers of BTBR-ob/ob mice. The difference in gluconeogenic gene expression correlated with insulin levels. In prediabetic BTBR-ob/ob mice, where the insulin level was high, hepatic expression of FBPase1 was lower than in the nondiabetic B6-ob/ob mice. When BTBR-ob/ob mice decompensated, the expression of PEPCK was elevated to a higher level than that of the B6-ob/ob mice. The increased gluconeogenesis in liver of BTBR-ob/ob mice is therefore adaptive to the reduced insulin level.
Intriguingly, hepatic steatosis is inversely correlated with diabetes susceptibility in obese mice. B6-ob/ob mice maintained euglycemia along with increasing hepatic steatosis, whereas BTBR-ob/ob mice had no fatty liver but were severely diabetic. We propose that increased lipogenesis in B6-ob/ob mice, although undesirable when it leads to hepatic steatosis, is protective against the development of diabetes. Supporting evidence for this concept has recently emerged in the literature. For example, the PPAR- agonist GW1929, when administrated to Zucker diabetic fatty rats, increased the expression of several genes involved in lipogenesis (FAS, ATP-citrate lyase, SCD1, and acyl-CoA synthetase) in the liver (25). GW1929 produced fatty liver while ameliorating hyperglycemia. Similar observations have been reported for the antidiabetic thiazolidinediones in various mouse models of obesity and diabetes (2628). In addition, Yahagi et al. (29) recently reported that the absence of SREBP1 in B6-ob/ob mice ameliorates fatty liver but not obesity or insulin resistance, suggesting that fatty liver is not a cause of insulin resistance. Although hepatic steatosis was interpreted as a "side effect" of thiazolidinedione antidiabetic drugs (30), it may well comprise a part of their antidiabetic action.
Increased hepatic lipogenesis is also associated with reduced risk of diabetes in a lipoatrophic mouse model. The lipoatrophic A-ZIP/F-1 mouse was produced by adipose-selective expression of a dominant-negative protein (A-ZIP/F) that blocks the DNA binding of B-ZIP transcription factors of both the C/EBP and Jun families (31). Recently, Colombo et al. (32) compared the phenotype of the A-ZIP/F-1 (FVB) mutation on the FVB/N and B6 genetic backgrounds. FVB A-ZIP/F1 mice did not have fatty liver and they were severely diabetic. On the other hand, B6 A-ZIP mice showed increased levels of mRNA of lipogenic genes and a higher liver TG content, but no hyperglycemia. These observations are strikingly similar to what we have found in obese mouse models. Hepatic steatosis is therefore correlated with a lower risk of diabetes in both lipoatrophic and obese mouse models.
How might increased lipogenic capacity in the liver protect against the development of diabetes in both lipoatrophic and obese mice? A direct benefit of increased hepatic lipogenesis to the obese mice may involve alterations in fuel partitioning. Both B6-ob/ob and BTBR-ob/ob mice are leptin deficient. The animals have high food intake and low energy consumption. Thus, there is accumulation of excess nutrient, which has to be stored in lipid or might contribute to increased glucose production. Elevating the lipogenic pathway in the liver may help to deter metabolic partitioning into the gluconeogenic pathway, thus reducing the rate of glucose production in the liver. Recently, Becard et al. (33) reported the phenotype of recombinant adenovirus-mediated hepatic overexpression of SREBP-1c in streptozotocin-induced diabetic mice. The study showed that increased hepatic lipogenesis leads to a marked reduction in hyperglycemia in diabetic mice. The treatment induced lipogenic enzyme gene expression and increased liver TG content. Meanwhile, it repressed the expression of PEPCK, so that glucose homeostasis was greatly improved (33). More recently, Cao et al. (34) reported that diabetic rodents treated with a liver X receptor agonist developed increased liver TGs and normalized their plasma glucose levels.
We do not know whether the mass of lipid produced de novo is sufficient to significantly alter the flux through the gluconeogenic pathway. It is also conceivable that the products of the lipogenesis pathway might be partitioned to key regulatory sites of whole-body metabolism that could alter diabetes susceptibility. Reduced lipogenesis in adipose tissue is a common feature in these obese mice, and it may play an important physiological role. In obese mice and humans, adipose tissue, although large in size, has reduced lipogenic capacity, in part a consequence of reduced expression of SREBP-1c (49). In this study, we showed that the adipocyte lipogenic genes are dysregulated with prolonged obesity (Fig. 3C). We also show that obese mice have reduced expression of fatty acid oxidation genes (Fig. 5), suggesting a reduced capacity for lipid catabolism. The increased hepatic lipogenesis in lipoatrophy and obesity (8,10) constitutes a major shift of lipogenic burden from the adipose tissue to the liver (11). Some studies have shown a correlation between TG overaccumulation in nonadipose tissues (e.g., skeletal muscle) and insulin resistance (3538). TG accumulation in pancreatic islets might cause ß-cell lipoapoptosis and diabetes (39,40). TG accumulation in the liver may therefore prevent lipid deposition elsewhere and lipotoxic damage to the pancreatic islets.
Recently, Busch et al. (41) reported the expression profile of the ß-cell line MIN6 treated with palmitate and oleate. Similar to what we have seen in our in vivo study, they also observed that genes not normally in pancreatic islets, such as FBPase1 and FBPase2, are induced under chronic lipid exposure. In both studies, a decrease in GLUT2 expression was observed. These results raise the possibility that the BTBR-ob/ob islets might undergo lipotoxic damage. Compared with nondiabetic B6-ob/ob mice, BTBR-ob/ob females show a lower liver TG content but a significantly higher plasma TG level. The difference arises before the onset of hyperglycemia and progresses with the onset of diabetes.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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Received for publication 16 October 2002 and accepted in revised form 18 November 2002.
Additional information for this article can be found in an online appendix at http://diabetes.diabetesjournals.org.
ACO, Acyl CoA oxidase; CT, threshold cycle; dChip, DNA-Chip analyzer; IL, interleukin; PEPCK, phosphoenopyruvate carboxykinase; PPAR-, peroxisome proliferatoractivator receptor-
; SREBP, sterol regulatory element binding protein; TG, triglyceride.
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REFERENCES |
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