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. Author manuscript; available in PMC: 2018 Mar 1.
Published in final edited form as: Biochim Biophys Acta. 2017 Jan 6;1863(3):810–816. doi: 10.1016/j.bbadis.2017.01.004

The effects of PPARγ on the regulation of the TOMM40-APOE-C1 genes cluster

Shobana Subramanian 1, William K Gottschalk 1, So Young Kim 3, Allen D Roses 1,2, Ornit Chiba-Falek 1,3,*
PMCID: PMC5285471  NIHMSID: NIHMS841284  PMID: 28065845

Abstract

Chromosome 19q13.32 is a gene rich region, and has been implicated in multiple human phenotypes in adulthood including lipids traits, Alzheimer’s disease, and longevity. Peroxisome Proliferator Activated Receptor Gamma (PPARγ) is a ligand-activated nuclear transcription factor that plays a role in human complex traits that are also genetically associated with the chromosome 19q13.32 region. Here, we study the effects of PPARγ on the regional expression regulation of the genes clustered within chromosome 19q13.32, specifically TOMM40, APOE, and APOC1, applying two complementary approaches. Using the short hairpin RNA (shRNA) method in the HepG2 cell-line we knocked down PPARγ expression and measured the effect on mRNA expression. We discovered PPARγ knock down increased the levels of TOMM40-, APOE-, and APOC1-mRNAs, with the highest increase in expression observed for APOE-mRNA. To complement the PPARγ knockdown findings we also examined the effects of low doses of PPARγ agonists (nM range) on mRNA expression of these genes. Low (nM) concentrations of Pioglitazone (Pio) decreased transcription of TOMM40, APOE and APOC1 genes, with the lowest mRNA levels for each gene observed at 1.5 nM. Similar to the effect of PPARγ knockdown, the strongest response to Pioglitazone was also observed for APOE-mRNA, and Rosiglitazone (Rosi), another PPARγ agonist, produced results that were consistent with these. In conclusion, our results further established a role for PPARγ in regional transcriptional regulation of chr19q13.32, underpinning the association between PPARγ, the chr19q13.32 genes cluster, and human complex traits and disease.

Keywords: PPARγ, Chromosome 19q13.32, TOMM40, APOE, APOC1, transcription

Introduction

Chromosome 19q13.32 is a gene dense region encompassing six genes, including translocase of mitochondrial membrane 40 (TOMM40), apolipoprotein E (APOE), and apolipoprotein C1 (APOC1). Genome-wide association studies (GWAS) have implicated this gene cluster region with multiple phenotypes: lipid traits, including low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglyceride levels [1–4]; late-onset Alzheimer’s disease (LOAD), influencing both susceptibility and age of onset [5–17]; cognitive performance, including intelligence, information processing speed, and memory [18]; longevity [19]; cardiovascular risk [20, 21]; and the levels of inflammatory markers such as C-reactive protein and lipoprotein-associated phospholipase A2 [22, 23]. Furthermore, we found in Hepatitis C virus patients a significant association between the genetic heterogeneity in this region and serum lipid and apolipoprotein levels, and with fibrosis independent of serum apolipoprotein levels [24]. Collectively, these studies have demonstrated that the 19q13.32 region displays allelic heterogeneity as well as pleiotropy. However, the molecular mechanisms underlying the pleiotropic and heterogenic roles of the 19q13.32 region in human aging and disease remain elusive.

Regulation of the expression levels of the genes encoded by this region may contribute to its pleiotropic effects. It has been shown that APOE-messenger RNA (mRNA) levels are increased in LOAD brains and that cis-genetic variability contributes to differential APOE gene expression [25–27]. Furthermore, ApoE-deficient transgenic mice developed severe hypercholesterolemia, leading to atherosclerosis [28], systematic inflammation, oxidative injuries, and cardiac dysfunction in response to air pollutants [29]. Altogether, these studies suggested that APOE expression levels play an important role in the development of several conditions in adulthood. The expression regulation of other genes within the 19q13.32 region in relation to human diseases also has been investigated. For example, it has been shown that cis-modulation of APOC1 expression contributes to LOAD susceptibility [30], and that TOMM40-mRNA levels are increased in LOAD brains and regulated by an intronic poly-T variant [25–27]. These findings suggest that the genetic pleiotropic effect is mediated by molecular mechanisms of gene expression regulation including, but not limited to, transcriptional regulation.

Peroxisome Proliferator Activated Receptor Gamma (PPARγ) plays a role in obesity [31], diabetes [32], cardiovascular disease [33], and cognitive function [34]. Interestingly, as mentioned above, these traits are also genetically associated with the chromosome 19q13.32 region. PPARγ is a ligand-activated nuclear transcription factor. Studies using PPARγ agonists and antagonists showed the effect on mRNA and protein levels of different genes involved in cellular pathways of lipid metabolism [35], mitochondrial biogenesis [36], and defense against oxidation [37].

The involvement of PPARγ in the regulation of APOE expression has also been studied [38, 39], Here we complement these investigations to include the entire genomic region, studying the effects of PPARγ on the regional expression regulation of the genes clustered within chromosome 19q13.32. We used the short hairpin RNA (shRNA) method to knock down PPARγ expression and measured the effect on mRNA expression of TOMM40, APOE, and APOC1. To complement the findings with PPARγ knockdown we also examined the effects of low doses of PPARγ agonists (nM range) on mRNA expression of these genes. Our results represent new insights regarding PPARγ mediated expression control of genes transcribed from 19q13.32 in transformed cells.

Materials and Methods

Computational analysis: a prediction of potential PPARγ binding sites

The analysis of potential PPARγ binding sites was performed on the 19q13.32 sequence spanning from 30Kb upstream of the TOMM40 gene through 30Kb downstream of the APOC1 gene (overall 88,130bp, chr19: 19:45,364,477-45,452,606; GRCh37/hg19) using the Transfac Matrix Databases (v.7.0; Biobase) software and visualized on The University of California at Santa Cruz (UCSC) genome browser (Figure 1 A).

Figure 1.

Figure 1

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Analysis of PPARγ binding site enrichment in 19q13.32 chromosomal region relative to randomly selected genes.

(A) Using Transfac Matrix Databases (v.7.0; Biobase) software, and visualization on The University of California at Santa Cruz (UCSC) genome browser we analyzed PPARγ potential binding sites within the 19q13.32 chromosomal region. PPARγ binding sites are marked with black rectangles. A confirmed functional PPARγ binding site in the APOE/C1 intergenic region[59] is marked with black oval. (B) We compared the enrichment in PPARγ binding sites within the 19q13.32 chromosomal region to 100 randomly selected gene loci. The graph presents the distribution of PPARγ potential binding sites in 100 randomly selected genes. The Y-axis shows the percentage of randomly selected genes that contain a given number of PPARγ binding sites, shown on the X-axis. About 40% of randomly selected genes contain no PPARγ binding sites, and the mean number of PPARγ binding sites for these genes was 1.66 +/− 0.23, while the number of PPARγ binding sites expressed within the 19q13.32 region was 7 (marked on histogram with dotted line), which was significantly greater than the mean number of binding sites in randomly selected genes (p=0.000091).

We referred to each gene in the cluster individually and analyzed each gene +/−30Kb flanking sequences according to the following coordinates: TOMM40, chr19:45,364,477-45,436,946; APOE, chr19:45,379,039–45,442,650; APOC1, chr19:45,387,921-45,452,606 (GRCh37/hg19). As a control, we repeated the analysis of potential PPARγ binding sites for 100 randomly selected genes (the list of the randomly selected genes is provided in Supplementary Table 1), identified using a random gene set generator (http://www.molbiotools.com/randomgenesetgenerator.html). Consistently, the analysis of each of the 100 random genes includes +/−30Kb flanking sequences.

Stable transduction

We generated a HepG2 cell line with stable PPARγ knockdown using a lentiviral shRNA clone, from the MISSION® TRC1 shRNA Library (Sigma® Life Science and The RNAi Consortium (TRC)). Specifically, we used clone ID TRCN0000001673 (PPARγ 1673), with oligonucleotide sequence CAGCATTTCTACTCCACATTA. We refer to this cell line as PPARγ knockdown (PPARγ KD). To control for the effect of viral transduction and general shRNA expression, we produced a control cell line expressing shRNA targeting green fluorescent protein (GFP), using shRNA clone GFP_437 with oligonucleotide sequence ACAACAGCCACAACGTCTATA. We refer to the control cell-line as GFP. Successfully transduced cells were selected by culturing in growth media with puromycin (2 µg/mL). We confirmed the efficiency of PPARγ knockdown at the RNA level by performing quantitative reverse transcription PCR (RT-PCR) using real-time TaqMan based method (Figure 2). We conducted four independent gene expression experiments with the PPARγ KD, GFP, and untransduced HepG2 cell-lines.

Figure 2.

Figure 2

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Assessment of the reduction in PPARγ-mRNA expression in HepG2-derived PPARγ-shRNA cells.

RNA was extracted from three HepG2 derived cell-lines: PPARγ-shRNA (PPARγ KD), GFP-shRNA (GFP) and untransduced (U). The levels of PPARγ-mRNA relative to the geometric mean of GAPDH- and PPIA -mRNAs was assessed by real-time PCR and were analyzed by the 2−ΔΔCt method. The different shRNA HepG2 cell-lines are indicated on the X-axis, and the fold change of mRNA (log2 transformed) is indicated on the Y-axis. The values presented here are means levels±SEM of 4 replicates. Tukey-Kramer HSD analysis was used to determine significant differences (***, p<0.0001). PPARγ-mRNA were significantly decreased (p<0.0001) in PPARγ KD cells compared to untransduced cells (over 2-fold) and compared to GFP cells (3-fold).

Cell culture

We cultured the transduced and untransduced HepG2 cell lines in Eagle’s minimum essential medium (MEM), supplemented with 10% fetal bovine serum, 2 mM glutamax, 1 mM sodium pyruvate, 0.1 mM non-essential amino acids, and penicillin streptomycin (10,000 units/mL penicillin and 10,000 µg/mL streptomycin). Cells were maintained in a humidified incubator at 37°C and 5% CO2. For each experiment, we plated 1.5 × 105 cells onto each well of a 6-wells plate 24 hours prior drug treatment, in complete growth medium supplemented as above.

The stably transduced cell lines were maintained with 2 µg/mL puromycin and were cultured for 36 hours in puromycin-free media prior to harvesting for molecular evaluations.

Treatments with PPARg agonists

HepG2 cells were treated for 12 hours with rosiglitazone (Rosi) (Sigma, #R2408) or pioglitazone (Pio) (Sigma, #E6910) over the concentration range of 0.2, 1, 1.5, 5 and 20 nM. The final DMSO concentration was 0.05% in all wells including the zero control. In each experiment, the different Rosi or Pio concentrations were added in duplicate to six pairs of cell-wells and the experiments were performed three times, independently producing a total of six replicates for each treatment by concentration.

RNA extraction and cDNA synthesis

Total RNA was extracted from cells using TRIzol® reagent (Invitrogen, Carlsbad, CA), and purified using RNeasy Mini Kits (QIAGEN, Valencia, CA) according to the manufacturer’s protocol. The concentrations of extracted RNA samples were determined spectrophotometrically at 260 nm, and complementary DNA (cDNA) was synthesized using MultiScribe RT enzyme (Applied Biosystems, Foster City, CA) at the following conditions: 10 minutes at 25°C and 120 minutes at 37°C.

Real-time PCR

Real-time PCR was used to quantify the levels of TOMM40, APOE, APOC1, TAT and PPARγ mRNAs. Briefly, duplicates of each sample were assayed by relative quantitative realtime PCR using the QuantStudio™ 7 Flex Real-Time PCR System (Applied Biosystems) to determine the mRNA level of target genes relative to mRNAs of the housekeeping genes glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and cyclophilin A (PPIA). The expression levels of these reference genes remain consistent and were not affected by PPARγ. TaqMan expression assays were used to amplify the cDNA of the target genes and the two reference controls (Applied Biosystems, Foster City, CA, Supplementary Table 2). Each cDNA (10 ng) was amplified using TaqMan Universal PCR master mix reagent (Applied Biosystems, Foster City, CA) under the following conditions: 2 min at 50 °C, 10 min at 95 °C, 40 cycles; 15sec at 95 °C; and 1 min at 60°C. As a negative control for the specificity of the amplification, we used no-cDNA/RNA samples (no-template) on each plate. We did not detect any amplification product in control reactions. Data were analyzed with a threshold set in the linear range of amplification. The cycle number at which any particular sample crossed that threshold (Ct) was then used to determine fold difference, whereas the geometric mean of the two control genes served as a reference for normalization. Fold difference was calculated as 2−ΔΔCt, where ΔCt = [Ct(target) −Ct (reference)] and ΔΔCt = [ΔCt(sample)] - [ΔCt(calibrator)] [40]. The calibrator was a RNA sample extracted from untreated HepG2 cells, used repeatedly in each plate for normalization within and across runs. For assay validation, we generated standard curves for the target genes and each reference assay using different amounts of untreated HepG2 RNA (0.1–100 ng). In addition, the slope of the relative efficiency plot for each target gene and each internal control was determined to validate the assays. The slopes of the relative efficiency plots for TOMM40, APOE, APOC1, TAT and PPARγ and the reference genes were < 0.1, showing a standard value required for the validation of the relative quantitative method.

Statistical analysis

Relative expression levels of TOMM40-, APOE-, APOC1-, TAT- and PPARγ-mRNAs for each sample were measured in replicates and the results of all replicates were averaged. A log transformation (log 2) was performed on all mRNA levels to assure normal distribution. The statistical analysis of the shRNA cell lines represents four independent experiments. Statistical significance of differences in expression between the shRNA cell-lines were analyzed by pairwise comparisons using the Tukey-Kramer HSD test (JMP Pro12, SAS). The statistical analyses of the treatment experiments represent six replicates. The significance of the effects of the treatment conditions were analyzed by Dunnett’s method where the untreated cells were used as control in each pairwise comparison (JMP Pro12, SAS).

Results

Computational Analysis of the 19q13.32 genomic region

We searched for potential peroxisome proliferator-activated receptor-γ (PPARγ) binding sites within the 19q13.32 genomic region. For the genes within this region, TOMM40, APOE, and APOC1, we included flanking sequences of 30Kb +/− of the gene in our analysis. The analysis revealed seven potential binding sites for PPARγ (Figure 1 A).

To assess the enrichment of PPARγ binding sites of the genes within this region we performed a comparison analysis to other genomic sites. One hundred genes were selected randomly (supplementary Table 1) and a search for PPARγ potential binding sites was conducted using the same tools and parameters (i.e. Transfac Matrix Databases, gene +/− 30kb flanking sequences). The analysis showed a range of 0–13 PPARγ potential binding sites with a mode value of 0, median value of 1 and a mean value of 1.66 +/−0.23 that is significantly lower than the 7 PPARγ potential binding sites within the 19q13.32 genomic region (p=0.000091, Figure 1B). This analysis demonstrated the significant in silico enrichment of PPARγ binding sites within the 19q13.32 genomic region.

The effect of PPARγ knockdown on 19q13.32 regional gene expression

We generated a HepG2 cell-line in which PPARγ expression was knocked down. PPARγ has three known splice variants [41] and several isoforms (OMIM entry 601487). The sequence of the shRNA clone that we used to achieve the knockdown of PPARγ was expected to target all PPARγ variants and isoforms. We validated the knockdown of PPARγ and evaluated the decrease in PPARγ-mRNA levels by quantitative real-time RT-PCR (Figure 2). The levels of PPARγ-mRNA in the shRNA PPARγ knockdown cell-line (hereafter, PPARγ KD) were significantly reduced to 44% and 36% compared to the original, untransduced HepG2 cell-line and the control GFP HepG2 cells, respectively (p<0.0001; Figure 2).

We then analyzed the effect of lower PPARγ levels on the mRNA expression of TOMM40, APOE and APOC1 by real-time PCR. In general, knockdown of PPARγ resulted in increased levels of TOMM40, APOE and APOC1-mRNAs (Figure 3). The strongest effect was demonstrated for APOE-mRNA (Figure 3A). In the PPARγ KD HepG2 cell line, the levels of APOE-mRNA were significantly elevated; amounting to a 30% increase compared to the untransduced HepG2 cells (p=0.0285) and a 50% increase compared to the control GFP HepG2 cells (p=0.0039) (Figure 3A). TOMM40-mRNA was significantly increased in the PPARγ KD HepG2 cells relative to the untransduced HepG2 cells (~25%, p=0.0358, Figure 3B). The trend of higher TOMM40-mRNA was also observed when compared to the control GFP HepG2 cells; however, this increase effect was subtle (~10%) and did not reach statistical significance (p=0.4457, Figure 3B). Similar trends were observed in the analysis of APOC1-mRNA. Knockdown of PPARγ resulted in a significant increase in expression of APOC1-mRNA relative to the untransduced cells (~25%, p=0.0065, Figure 3C), and showed a smaller effect (<15%) when compared to the control GFP cells (p=0.1259, Figure 3C).

Figure 3.

Figure 3

Figure 3

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The effect of PPARγ knockdown in HepG2 cells on APOE, TOMM40 and APOC1 -mRNA levels.

RNA was extracted from three HepG2 derived cell-lines: PPARγ-shRNA (PPARγ KD), GFP-shRNA (GFP) and untransduced (U). The levels of (A) APOE-mRNA, (B) TOMM40-mRNA, and (C) APOC1-mRNA relative to the geometric mean of GAPDH and PPIA -mRNAs were assessed by real-time PCR and were analyzed by the 2−ΔΔCt method. The different shRNA HepG2 cell-lines are indicated on the X-axis, and the fold change of mRNA (log2 transformed) is indicated on the Y-axis. The values presented here are means levels±SEM of 4 replicates. Tukey-Kramer HSD analysis was used to determine significant differences (*, p<0.05; **, p<0.01). (A) APOE-mRNA showed significant increase in PPARγ KD cells compared to untransduced cells (p=0.02) and compared to GFP cells (p=0.003). (B) TOMM40-mRNA showed significant increase in PPARγ KD cells compared to untransduced cells (p=0.03), however its increased levels did not reach statistical significance compared to GFP (p=0.4). (C) APOC1-mRNA showed significant increase in PPARγ KD cells compared to untransduced cells (p=0.006) and a trend of increase compared to GFP (p=0.12).

It was shown that PPARγ acts as a transcriptional inhibitor of the tyrosine aminotransferase (TAT) gene [42]. As a control, we measured TAT-mRNA and found that PPARγ knockdown led to higher TAT-mRNA levels (Supplementary Figure 1), consistent with previous results.

The effect of low doses of PPARγ agonists on 19q13.32 regional gene expression

Pioglitazone is a PPARγ agonist. Pioglitazone (0.2–20 nM) was used to study PPARγ’s effect on TOMM40-, APOE- and APOC1-mRNA levels. Cells were treated with six different concentrations of Pioglitazone (0, 0.2, 1, 1.5, 5, and 20 nM) for 12 h, after which RNA was extracted and levels of endogenous TOMM40-, APOE- and APOC1-mRNAs were analyzed by quantitative real-time PCR. In general, exposure to Pioglitazone decreased the levels of all three transcripts. Using this pharmacological approach the strongest effect was also observed with the APOE-mRNA. Pioglitazone as low as 0.2 nM caused a slight decrease in APOE-mRNA, and the Pioglitazone-effect was maximal at 5 nM (p=0.04, Figure 4A). Further increases in Pioglitazone concentration to 20 nM produced a slight increase in APOE-mRNA compared to the treatment with 5 nM, suggesting the response had reached a plateau (Figure 4A). Similar general trends in the effects of Pioglitazone treatment were found for TOMM40-mRNA and APOC1-mRNA levels, but the overall responses were smaller than for APOE-mRNA (Figures 4B and C, respectively). The lowest TOMM40-mRNA and APOC1-mRNA levels were also detected at 5 nM; however these effects were not significant (p=0.2 and 0.09, Figures 4B and C, respectively). These results indicate that Pioglitazone-mediated activation of PPARγ was associated with a decrease in the transcription of genes within the19q13.32 chromosomal region.

Figure 4.

Figure 4

Figure 4

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APOE, TOMM40 and APOC1 -mRNA levels in HepG2 cells after Pioglitazone treatment.

Cells were treated with Pioglitazone in different concentrations (0, 0.2, 1, 1.5, 5, and 20 nM). The levels of (A) APOE-mRNA, (B) TOMM40-mRNA, and (C) APOC1-mRNA, relative to the geometric mean of GAPDH- and PPIA-mRNAs were assessed by real-time PCR and were analyzed by the 2−ΔΔCt method. The Pioglitazone concentration (nM) is indicated on the X-axis, and the fold change of mRNA (log2 transformed) is indicated on the Y-axis. The values presented here are means levels±SEM of 6 replicates. Dunnett’s analysis and student’s t-test were used to determine significant differences (PDunnett’s, pt-test <0.05) compared to the untreated cells (U), whereas (*) and (#) indicate statistical significance, respectively. (A) APOE-mRNA analysis, the strongest effect was detected at 5 nM (Dunnett’s method, P=0.0494; student’s t p=0.0125). The APOE-mRNA response to 20 nM was also significant when using student’s t-test analysis (p=0.02). (B) TOMM40-mRNA analysis, the strongest effect was detected at 5 nM but did not reach statistical significance (Dunnett’s method, P=0.2214; student’s t p=0.0646). (C) APOC1-mRNA analysis, the strongest effect was detected at 5 nM (Dunnett’s method, P=0.0934; student’s t p=0.0247). Using student’s t-test analysis the response to 1.5 nM was significant (p=0.03) and the response to 20 nM showed trend towards significance (p=0.06).

We repeated the Pioglitazone experiments with Rosiglitazone (Rosi), another thiazolidinedione PPARγ agonist, over the same low concentration range (0.2–20 nM). Rosi lowered APOE-mRNA levels over the concentrations range 0.2–1.5 nM. At the lowest levels (at 1.5 nM Rosi) there was a trend toward significance (p=0.1, Figure 5). Further increases in Rosi concentration (5–20 nM), reversed this effect and increased APOE-mRNA to levels approaching the levels in the untreated cells (Figure 5). Thus, the effect of Rosi on APOE transcription potentially occurs over a lower concentration range than Pioglitazone. In contrast to Pioglitazone, we did not detect any effect of Rosi treatment on the expression levels of TOMM40-mRNA or APOC1-mRNA (data not shown).

Figure 5.

Figure 5

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APOE -mRNA levels in HepG2 cells after Rosiglitazone treatment.

Cells were treated with Pioglitazone in different concentrations (0, 0.2, 1, 1.5, 5, and 20 nM). The levels of APOE-mRNA relative to the geometric mean of GAPDH- and PPIA-mRNAs were assessed by real-time PCR and were analyzed by the 2−ΔΔCt method. The Pioglitazone concentration (nM) is indicated on the X-axis, and the fold change of APOE-mRNA (log2 transformed) is indicated on the Y-axis. The values presented here are means levels±SEM of 6 replicates. Dunnett’s analysis and student’s t-test were used to determine significant differences (PDunnett’s, pt-test <0.05) compared to the untreated cells (U), whereas (*) and (#) indicate statistical significance, respectively. The strongest effect was detected at 1.5 nM (Dunnett’s method, P=0.1658; student’s t, p=0.0462).

Discussion

The expression of the genes in the TOMM40-APOE- APOC1 cluster is under complex regulation. Multiple control and enhancer elements embedded within this genomic region regulate tissue- and cell-specific and hormonal- and metabolite-mediated regulation [43–52]. DNA methylations within two such elements of the APOE gene control tissue-, cell- and genotype-specific APOE and TOMM40 expression [53, 54]. Cholesterol stimulates APOE expression, and this is mediated by LXR response elements within APOE enhancer elements [39, 55, 56]. PPARγ also stimulates APOE synthesis, by enhancing expression of the LXR gene [38, 39, 57, 58]. Also, a PPARγ response element (PPRE) has been identified in the APOE/APOC1 intergenic region [59], and our bioinformatics analysis revealed numerous additional potential PPARγ binding sites within the TOMM40-APOE -APOC1 genomic region (Figure 1 A), indicating a possible direct regulatory role for PPARγ-mediated transcription. To explore the regional trans effect of PPARγ on transcriptional regulation of the genes in the chromosome 19q13.32 region (TOMM40, APOE, APOC1), we took two molecular approaches in the human hepatoma cell line HepG2, knockdown by shRNA and pharmacological activation.. We discovered increased expression upon PPARγ knockdown and a decrease in transcripts levels following treatment with PPARγ agonists. Remarkably, the strongest effects we observed for both complementary approaches were on APOE-mRNA. The simplest interpretation of our results is PPARγ inhibits expression of these genes in HepG2 cells, and to the best of our knowledge is the first example of pioglitazone as an inhibitor of gene expression.

Our results conflict with an earlier report that the PPARγ agonist ciglitazone stimulated APOE expression, ApoE secretion and lipoprotein synthesis in HepG2 cells [60]. Two factors likely contribute to this discrepancy. First, ciglitazone, pioglitazone and rosiglitazone have different PPAR activation profiles [61], and it is credible that they also have different affinities for non-PPARγ receptors. Second, in the previous study cells were exposed to a higher drug concentration (5 µM) than we used (0.2–20 nM), and for a longer time (48 hours vs. 12 hours in our study), increasing the likelihood of off-target interactions. For these reasons, it is possible the ciglitazone-elicited effects on APOE and lipoprotein metabolism reported previously [60] were not due solely to PPARγ action.

By contrast, to minimize off-target effects we used a pioglitazone dose range (0.2–20 nM) that is comparable to the in vivo IC50 concentration; because human hepatocyte data is not available, we bracketed the human adipocyte IC50 (9 nM) [62]. Other investigators have demonstrated robust activation of PPARγ-responsive events in this concentration range [36, 63, 64]. Importantly, we detected significant inhibition of cell growth at the higher pioglitazone concentrations (20–50 µM; Supplementary Figure 2). Furthermore, we used a short incubation time, which we empirically determined was sufficient for transcriptional effects. Finally, the PPARγ knockdown results provided conclusive evidence that the Pio- and Rosi--elicited effects on APOE expression we observed were due to the PPARγ receptor. Inhibitor studies with the specific PPARγ antagonist GW9662 (Supplementary Figure 3) also support this conclusion.

Overall, the effects of both PPARγ activation by pioglitazone and shRNA-mediated PPARγ knockdown were subtle. PPARs constitute a family of closely related transcription factors (α, γ and δ) with over-lapping effects on lipid and glucose metabolism and inflammation [67], and all three forms are expressed in the liver which may compensate in the event of alterations in PPARγ activity. Thus, the redundancy of PPARs in HepG2 cells may have influenced the small magnitude of the effect on gene expression. Nevertheless, it has been suggested that subtle variations in the expression levels of particular genes contribute to traits and diseases in aging.

PPARγ receptors are widely distributed [66–68] and modulate cellular and physiological processes as diverse as glucose and lipid metabolism [69–71], energy balance and insulin sensitivity [72, 73], vascular and endothelial biology [74–76], inflammation [77] and neuroinflammation [78], and innate immunity [79]. They are also involved in cellular proliferation and play complicated roles in cancer [80]. Because of these diverse activities, PPARγ is an attractive pharmacological target for treating type 2 diabetes mellitus and Alzheimer’s disease, and it has been evaluated in pre-clinical studies for other neurodegenerative diseases including Parkinson’s disease and Huntington’s disease, although the post-receptor mechanisms for these beneficial effects are not completely understood.

The chr19q13.32 region includes genes that have been implicated in atherosclerosis (APOE, APOC1 cluster) and Alzheimer’s disease (TOMM40, APOE), and APOE shows the most significant and reproducible signal in genetic studies of aging. This region also possesses a number of PPARγ binding sites, and understanding how those sites regulate expression of genes in the region could facilitate the development of more efficacious therapies than are currently available. Collectively, our results established a role for PPARγ in regional transcriptional regulation of chr19q13.32, further underpinning the association between PPARγ, the chr19q13.32 genes cluster, and complex traits in adulthood and disease states.

Supplementary Material

Highlights.

  • Chromosome 19q13.32 is a gene rich region, contains the TOMM40-APOE-APOC1 genes, that exhibits a complex regulation and implicated in multiple phenotypes.

  • This region is enriched in potential PPARγ binding sites.

  • PPARγ knockdown resulted in increased levels of TOMM40, APOE and APOC1 -mRNAs, showing the strongest impact on APOE transcript levels.

  • Low doses of PPARγ agonists decreased the levels of the TOMM40, APOE and APOC1 transcripts, with the greatest effect on APOE-mRNA.

Acknowledgments

This work was funded in part by the National Institute on Aging (NIA) [R01AG040370 to O.C.].

Footnotes

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