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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Obesity (Silver Spring). Author manuscript; available in PMC 2009 January 6.
Published in final edited form as:
PMCID: PMC2614385
NIHMSID: NIHMS52141

Brief Genetic Analysis

Effects of Ritonavir on Adipocyte Gene Expression: Evidence for a Stress-Related Response

Abstract

To understand the molecular mechanisms underlying the development of dyslipidemia and lipodystrophy that occurs after administration of aspartic acid protease inhibitors, we examined transcriptional profiles using cDNA microarrays in 3T3-L1 adipocytes exposed for 2 - 21 days to 10 μM ritonavir. The expression levels of ~12,000 transcripts were assessed using the MgU74Av2 mouse microarray chip. Ritonavir altered gene expression of inflammatory cytokines, stress response genes localized to endoplasmic reticulum, oxidative stress genes, apoptosis related genes, and expression of genes involved in cell adhesion and extracellular matrix remodeling. Microarray analysis also identified a novel gene down-regulated by ritonavir, Cidea, whose expression levels may affect free fatty acid metabolism. These changes suggest a unique, stress-related pattern in adipocytes induced by chronic exposure to the protease inhibitor, ritonavir.

Introduction

The mechanisms underlying the pathogenesis of human immunodeficiency virus associated lipodystrophy syndrome are not well understood, but involve redistribution of adipose tissue as well as metabolic abnormalities in adipocyte function (1). Highly active antiretroviral therapy that includes protease inhibitors has been associated with peripheral lipoatrophy (2) and visceral adiposity (3), and protease inhibitor mediated lipoatrophy has been proposed to be due to alterations in adipocyte differentiation and lipolysis (4).

Two previous short-term investigations have employed microarray approaches in 3T3-L1 adipocytes to identify potential candidate genes whose expression might explain the development of protease inhibitor-mediated lipodystrophy syndrome (5, 6). However, longer term effects of protease inhibitors on gene expression have not been examined. We hypothesized that protease inhibitor-induced lipodystrophy syndrome involves coordinated cumulative changes in adipocyte gene expression that would indicate stress-related cellular activation. We therefore examined changes in gene expression in 3T3-L1 adipocytes treated chronically with the protease inhibitor, ritonavir.

Results and Discussion

Gene expression was compared by microarray between control and 10 μM ritonavir-treated 3T3-L1 cells at 2, 6, 10, 14, and 21 days after initiation of differentiation. Tables 1A, 1B, and 1C show fold changes in gene expression throughout the 21 days of ritonavir exposure. There was no effect of ritonavir treatment on genes required for the adipocyte phenotype that are expressed early in differentiation, confirming previously reported findings (7). While there was a small but significant decrease in C/EBPα expression at 6 and 10 days (p<0.01, p<0.05), respectively, by 14 and 21 days the expression was equivalent to vehicle (Figure 1A). PPARγ gene expression was unchanged at all time points (Figure 1B) which agrees with previous reports (8). None of the other adipocyte-specific differentiation genes examined were significantly altered by ritonavir treatment (Table 1A). There were no significant differences in expression of genes relevant for insulin signaling (Table 1A).

Figure 1
Real Time PCR expression ratios showing changes in: (A) CCAAT/enhancer binding protein (C/EBPα), (B) peroxisome proliferator activated receptor gamma (PPARγ), (C) phosphoenolpyruvate carboxykinase 1 (Pck1), ( D) cytochrome c oxidase, subunit ...
Table 1
Effects of 10μM ritonavir on gene expression over time in 3T3-L1 adipocytes

Cellular stress in adipocytes is often accompanied by the onset of inflammation, endoplasmic reticulum and oxidative stress, which is then followed by physical damage to adipocytes and surrounding endothelial cells (9, 10). We observed an expression pattern of inflammation induced by ritonavir different from that found in obesity (11). Typically, in obesity, inflammation is characterized by increased secretion of TNFα and leptin as well as decreased secretion of adiponectin by adipocytes leading to infiltration of macrophages (11). While we were not able to examine changes in TNFα by real time PCR due to low expression in these 3T3-L1 adipocytes, leptin gene expression was unchanged (Table 1B). Adiponectin gene expression was significantly reduced with short term (2d) and chronic (14 and 21d) exposure to ritonavir (p<0.02, Figure 1E), a finding consistent with increased oxidative stress (12). Our results agree with Lagathu, et al who demonstrated that acute treatment of fully differentiated human adipocytes with 10 μM ritonavir resulted in increased ROS production and decreased adiponectin protein expression (13). Further, ritonavir treatment significantly increased expression of interleukin 9 (p<0.0007) but not interleukin 6 (p=0.22), chemokine ligand 9 (p<0.0002), and tumor necrosis factor receptor family, member 1b (p75; p<0.02) with the largest increases at 14 and 21 days (Table 1B, Figure 2A). Our results differ from those of Vernochet, et al., who reported significant inductions from 5 days’ exposure to 10 μM ritonavir in TNFα and IL-6 expression in cultured human adipocytes (14) and from Grigem, et al. who reported that short-term ritonavir exposure increased TNFα receptor expression in a dose dependent manner (8), probably because of the different cells and time course examined.

Figure 2
Real Time PCR expression ratios showing changes in (A) tumor necrosis factor receptor superfamily, member 1b (Tnfrsf1b), (B) activating transcription factor 3 (Atf3), (C) MAP kinase-interacting serine/threonine kinase 2 (Mknk2), (D) Bcl2-associated X ...

Endoplasmic reticulum (ER) stress has been implicated as a response to metabolic stress (15). One metabolic stress reported in protease inhibitor treated adipocytes is excess basal free fatty acid release (16-18). We observed a trend towards an increase (p=0.073) in activating transcription factor 3 (Atf 3), a significant increase in Atf4 (p<0.0008) expression, and a significant decrease (p<0.0003) in phosphoenolpyruvate carboxykinase (Pck1) expression (Tables 1A and 1B). Reverse transcriptase real time PCR (RT-PCR) for Atf3 and Pck1 showed the largest increase (p<0.01) in Atf3 expression at 14 days (Figure 2B) and the largest decrease (p<0.01) in Pck1 expression at 10 days (Figure 1C). Parker, et al. also showed a significant increase in Atf3 gene expression by RT-PCR in 3T3-L1 adipocytes treated with ritonavir for 16-24 hours, and a significant decrease in Pck1 gene expression with 24 hour exposure to 30 μM lopinavir or 10 μM nelfinavir (6). Atf3 activation has been suggested to be a cellular response to stress through which cells attempt to prevent JNK-induced apoptosis, because it induces the anti-apoptotic factor, heat shock protein 27 (19). Pck1 is believed to be a key enzyme for glyceroneogenesis. Decreased Pck1 expression has also been observed in metabolic syndrome and correlated with excess free fatty acid release (20). Further, flavin containing monooxygenase (FMO1) expression was significantly decreased (p<0.0001, Table 1B). This decrease is another ER insult, in that yeast FMO1 activity is important in maintaining the ER oxidative state necessary for folding proteins containing disulfide bonds (21). Interestingly, in rat primary hepatocytes, ritonavir treatment induced apoptosis through activating the unfolded protein response in ER (22) where FMO1 is localized. Other genes induced in expression during ER stress include CHOP and an active splice variant of XBP-1 (23, 24). There were no differences in CHOP-10 expression or total XBP-1 expression by microarray analysis during ritonavir treatment (Table 1B). We examined the splicing pattern of XBP-1 at 14 and 21 days, which were the time points where other stress markers were significantly changed (Figures 1D & E and 2A, C, & D). There were no significant differences (14 day: p<0.82, 21 day: p<0.69) in the ratio of spliced XBP-1 transcript to unspliced XBP-1 transcript in ritonavir- vs. vehicle-treated cells at 14 days (0.163±0.014 vs. 0.173±0.015) and 21 days (0.124±0.041 vs. 0.149±0.047), respectively. Therefore, in 3T3-L1 adipocytes, ritonavir treatment did not induce expression of genes known to initiate the unfolded protein response in ER (24, 25).

Microarray analysis suggested that ritonavir treated adipocytes may have oxidative stress, because cytochrome C oxidase, subunit VIa, polypeptide 2 (Cox6a2) expression was significantly increased (p<0.0005; Table 1B). These data are consistent with the observed increase in MAP kinase-interacting serine/threonine kinase 2 (Mknk2; p<0.013) and Bcl2-associated X protein (Bax) expression (p<0.0315; Table 1B). Cox6a2, Mknk2, and Bax expression were confirmed by RT-PCR, with Mknk2 showing increases at 2, 6, and 21 days, preceding increased Cox6a2 expression at 14 and 21 days, and Bax induction at 21 days (Figure 1D and Figure 2C & D). These changes, collectively, suggest a cytokine induced stress response (26, 27) which could lead to apoptosis or necrosis (28). Indeed, endothelial cells treated with 15 μM ritonavir produced 32% more superoxide anion and had increased permeability when compared to controls (29). In addition, ritonavir treatment of human endothelial cells induced cytotoxicity and subsequent necrosis through mitochondrial damage (30). Further, treatment of human adipocytes with 20 μM ritonavir induced apoptosis (31).

Changes in the extracellular matrix may also contribute to the susceptibility of adipocytes to invasion/apoptosis (32, 33). Microarray analysis revealed decreased expression of many genes involved in cell adhesion such as: latent transforming growth factor beta binding protein 1 (Ltbp1, p<0.028) which was confirmed by RT-PCR (p<0.01 at 21 days, Figure 2E), transforming growth factor, beta induced (p<0.019), microfibrillar associated protein 5 (p<0.0034), dermatopontin (p<0.0001), integrin alpha 6 (p<0.0002), and tissue inhibitor metalloproteinase 3 (Timp 3, p<0.0003; Table 1C). Ltbp1 is important for association of a cytokine, TGF-β with the extracellular matrix, and lower expression of Ltbp1 might result in increased TGF-β signaling (34). Timp3 is also involved in remodeling of extracellular matrix, and decreased expression by ritonavir (Table 1C) would result in increased matrix metalloproteinase (MMP) activity. Indeed, ritonavir treatment of 3T3-L1 cells reversed the decrease in MMP-9 activity normally seen during adipogenesis (35).

Lastly, gene profiling demonstrated significantly lower (p<0.0093, Table 1B, Figure 2F) cell death-inducing DNA fragmentation factor, alpha subunit-like effector A (Cidea) in ritonavir treated 3T3-L1 cells. This finding has not been previously reported. Cidea expression in 293T cells induced apoptosis by a caspase independent mechanism (36), but perhaps more importantly, low Cidea expression has been shown to be related to increased basal lipolysis in both human and mouse adipocytes (37, 38). Although free fatty acid release was not measured in these studies, we and others have previously shown increased basal free fatty acid release in 3T3-L1 adipocytes chronically treated with 10 μM ritonavir (16) or acutely treated with nelfinavir in doses between 5-40 μM (17).

Gene profiling in 3T3-L1 cells chronically exposed to ritonavir has revealed a unique pattern of adipocyte response that includes distinct effects on inflammatory mediators, ER and oxidative stress, and potential remodeling of extracellular matrix proteins which may predispose adipocytes to apoptosis. Future studies confirming these findings should be carried out using other protease inhibitors such as atazanavir that may have a more favorable metabolic profile.

Experimental Methods

Cell Culture and Protease Inhibitor Treatment

Murine 3T3-L1 cells (ATCC; Manassas, VA) were grown on Corning/Costar dishes (Corning, NY) in a 5% CO2 atmosphere at 37°C and maintained in Dulbecco’s Modified Eagle’s medium (DMEM, 4500 mg glucose/liter) supplemented with 10% fetal bovine serum, 2 mM glutamine, 8 μg/ml biotin, 110 μg/ml pyruvate, 100 units/ml penicillin, and 100 μg/ml streptomycin. Confluent cells were differentiated by addition of 10-6 M dexamethasone, 0.5 mM isobutylmethylxanthine, and 5 μg/ml insulin to DMEM medium with 10% fetal bovine serum for 3 days, with medium changed once every 24 hours. Cells receiving ritonavir were given differentiation medium containing a final concentration of 10 μM ritonavir dissolved in 0.1% ethanol as previously described (16) in both differentiation cocktail and DMEM maintenance medium as specified below. Purified ritonavir was graciously provided by Abbot Laboratories under a materials transfer agreement. Control cells were given 0.1% ethanol (vehicle) containing medium without ritonavir. After 3 days, the differentiation medium was withdrawn, and cells were maintained in DMEM with 10% FBS plus either vehicle or 10 μM ritonavir for a total of 21 days, with addition of fresh medium every 24 hours.

Microarray Analysis

Cells were harvested at 2, 6, 10, 14, and 21 days for total RNA using Trizol reagent (Invitrogen; Carlsbad, CA). Equivalent amounts of total RNA from six 10 cm dishes (treated identically) were pooled, and poly A RNA was then purified with Ambion poly A purest kit (Ambion; Austin, TX) from the pooled RNA sample. One poly A RNA sample treated with either vehicle or ritonavir from each time point was hybridized to Affymetrix (Santa Clara, CA) MgU74Av2 gene expression arrays. The time course experiment, poly A RNA purification, and hybridization to Affymetrix MgU74Av2 gene expression arrays was repeated a second time.

Real Time PCR

Total RNA samples (as described above) were DNase treated (Invitrogen), purified by phenol/chloroform/isoamyl alcohol (Invitrogen) extraction, and precipitated with 70% ethanol. Concentration and integrity of DNase treated RNA was determined with an Agilent (Santa Clara, CA) 2100 bioanalyzer. Approximately 250 ng of RNA was reversed transcribed using the Superscript III Platinum Two-Step qRT-PCR kit (Invitrogen) to make cDNA template used for real time PCR reactions. Applied Biosystems (Foster City, CA) TaqMan fluorescent assays were used to determine relative mRNA expression for each gene studied. To examine the effect of ritonavir on relative mRNA expression, expression ratios were calculated taking into account the efficiency of the PCR reaction for both reference and target genes. The reference gene for all analyses was 18s ribosomal RNA.

Semi-quantitative PCR for XBP-1 Transcripts

cDNA templates were diluted (1:5) in PCR hot start mix (SuperArray Biosciences, Frederick, MD). For amplification of unspliced (U-XBP1) and spliced (S- XBP1) transcripts, a single pair of primers was used: sense; GAA CCA GGA GTT AAG AAC ACG and antisense; AGG CAA CAG TGT CAG AGT CC (Invitrogen, Carlsbad, CA) as previously described (39). PCR conditions: 95°C for 15min followed by 40 cycles of 95°C for 30sec, 60°C for 30sec, and 72°C for 30sec (40). PCR products were electrophoresed on 4-12% gradient tris borate EDTA (TBE) polyacrylamide gels (Invitrogen, Carlsbad, CA). Presence of S-XBP1 was confirmed by both amplification of spliced variant with sequence specific primers (40), and by comparison to positive control samples generously provided by Brian D. Dynlacht (New York University School of Medicine, NYU Cancer Institute). TBE gels were stained with 0.5 μg/ml ethidium bromide followed by quantitation of U-XBP1 and S-XBP1 bands using ImageQuant software (GE Healthcare, Piscataway, NJ).

Statistical Analysis

Intensities from the microarray hybridization were calculated using Affymetrix ArraySuite (Version 4) from the individual experiments and quality control of the two experiments was assessed using a model based analysis (41). Change call and expression data for each gene from the two replicates were compared for consistency between experiments. A correlation coefficient was calculated from log transformed expression indices (for each time point within treatment) and ranged between 0.88-0.91. Since both experiments were consistent with respect to the change call comparisons and direction of trends, intensities for those genes called present were averaged across both experiments. A linear regression model was developed to examine the effect of ritonavir on gene expression over time with the following equation:

equation M1

The overall mean is m, and the other terms denote deviation from the mean due to treatment effect (Trti), time effect (TimeJ), treatment by time interaction ((Trt × Time)ij), and error (eijk). Transcripts were tested for significance at the p<0.05 level for treatment, time, or treatment by time interaction. Fold changes in gene expression due to ritonavir treatment are reported for each time point with the p value for Trti in Tables 1A, 1B, and 1C. Additional data (adjusted intensities from microarray hybridization with 95% confidence limits) are included separately as an appendix.

For confirmation of gene expression with real time PCR, mean within time ratios from both microarray experiments were calculated for each time point comparing relative expression of ritonavir treated cells to vehicle (42). The mean within time ratios averaged across the 2 experiments were compared to a hypothesized mean=1, and were considered significantly different from 1 at p<0.05. All means are reported with standard errors. The ratio of S-XBP1 to U-XBP1 was calculated, and subjected to arcsine-square root transformation before analysis using unpaired t-tests. Non-transformed means with SEM’s are reported in the text.

Supplementary Material

Appendix

Acknowledgements

This research was supported by the Intramural Research Program of the NIH, grant Z01-HD-000641 from the National Institute of Child Health and Human Development, National Institutes of Health (to JAY).

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