A hierarchical approach employing metabolic and gene expression profiles to identify the pathways that confer cytotoxicity in HepG2 cells
- Zheng Li†1,
- Shireesh Srivastava†1,
- Xuerui Yang1,
- Sheenu Mittal1, 5,
- Paul Norton4,
- James Resau4,
- Brian Haab4 and
- Christina Chan1, 2, 3Email author
© Li et al; licensee BioMed Central Ltd. 2007
Received: 17 April 2007
Accepted: 11 May 2007
Published: 11 May 2007
Free fatty acids (FFA) and tumor necrosis factor alpha (TNF-α) have been implicated in the pathogenesis of many obesity-related metabolic disorders. When human hepatoblastoma cells (HepG2) were exposed to different types of FFA and TNF-α, saturated fatty acid was found to be cytotoxic and its toxicity was exacerbated by TNF-α. In order to identify the processes associated with the toxicity of saturated FFA and TNF-α, the metabolic and gene expression profiles were measured to characterize the cellular states. A computational model was developed to integrate these disparate data to reveal the underlying pathways and mechanisms involved in saturated fatty acid toxicity.
A hierarchical framework consisting of three stages was developed to identify the processes and genes that regulate the toxicity. First, discriminant analysis identified that fatty acid oxidation and intracellular triglyceride accumulation were the most relevant in differentiating the cytotoxic phenotype. Second, gene set enrichment analysis (GSEA) was applied to the cDNA microarray data to identify the transcriptionally altered pathways and processes. Finally, the genes and gene sets that regulate the metabolic responses identified in step 1 were identified by integrating the expression of the enriched gene sets and the metabolic profiles with a multi-block partial least squares (MBPLS) regression model.
The hierarchical approach suggested potential mechanisms involved in mediating the cytotoxic and cytoprotective pathways, as well as identified novel targets, such as NADH dehydrogenases, aldehyde dehydrogenases 1A1 (ALDH1A1) and endothelial membrane protein 3 (EMP3) as modulator of the toxic phenotypes. These predictions, as well as, some specific targets that were suggested by the analysis were experimentally validated.
Elevated levels of free fatty acids (FFAs) have been implicated in the pathogenesis of many obesity-related metabolic disorders [1–4], such as fatty liver disease and steatohepatitis. Dietary fatty acids produce a variety of metabolic and genetic effects on liver cells. Fatty acids compete with glucose for oxidation at the TCA cycle . Fatty acids also cause changes in the enzyme make-up of the cells by regulating the transcription of enzymes of metabolism. FFAs exert their transcriptional effects by activating transcription factors (TFs) such as sterol receptor element binding protein (SREBP), peroxisome proliferator activated receptors (PPARs) and hepatic nuclear factors (HNFs) . PPARs regulate the expression of proteins involved in fatty acid oxidation, SREBP regulates phospholipid and cholesterol synthesis and HNF affects both lipid and carbohydrate metabolism.
Tumor necrosis factor alpha (TNF-α) is another factor that has been shown to affect the function of hepatocytes in numerous ways. It has been associated with the development of hepatic insulin resistance and hepatocyte cell death [7–10]. TNF-α also activates transcription factors such as nuclear factor kappa B (NFκB) and c-Jun [11, 12]. These transcription factors alter the expression of genes involved in cellular metabolism, cell proliferation and cell death [11, 12]. Hepatocytes are known to be resistant to the cytotoxic action of TNF-α due to prompt upregulation of cytoprotective genes mediated by the activation of NF-κB in response to TNF-α . Therefore, the cytotoxic effect of TNF-α requires a secondary insult, e.g., transcriptional inhibition  or glutathione depletion .
In vivo, under conditions of obesity, hepatocytes are simultaneously exposed to elevated FFAs and TNF-α. The importance of these factors in the pathogenesis of many diseases motivated this study of the physiological, metabolic and genetic effects of the simultaneous exposures to different types of FFAs and TNF-α. Among the many responses to FFA and TNF-α, the mechanism of cell death in response to simultaneous exposure to these factors is not well characterized. Hepatocyte cell death is suggested to play an important role in the development of various hepatic disorders, e.g. in non-alcoholic steatohepatitis (NASH). Previous studies on the toxic effects of different types of FFAs on liver cells have identified that saturated FFAs are much more toxic than unsaturated FFAs [16–19]. These studies have suggested that ER-ROS (reactive oxygen species) stress , mitochondrial alterations  and lysosomal permeabilization  are the major mechanisms in the toxicity of saturated FFAs. Studies on the toxicity of saturated FFAs to other cells have suggested that increased ROS production and ceramide synthesis [20, 21] are the major mechanisms of palmitate-toxicity in those cells. Similarly, increased ceramide and ROS generation have been suggested to play important roles in the toxicity of TNF-α to hepatocytes [10, 22]. However, there has not been any study on the cytotoxic effects of simultaneous exposure to FFAs and TNF-α.
1. Metabolic changes relevant to cytotoxicity
2. Functional pathway analysis with GSEA
Gene sets used in the GSEA analysis
Fatty acid metabolism
Fatty acid biosynthesis
Programmed cell death
ERK1 Erk2 Mapk pathway
Electron Transport Chain
Enriched gene sets identified by GSEA analysis
Electron Transport Chain
Fatty acid metabolism
Fatty acids beta-oxidation
Fatty acid biosynthesis
ERK1 Erk2 Mapk pathway
3. Integrating the metabolic and the gene expression profiles to identify the pathways relevant to the cytotoxicity
To identify the genes that regulate the metabolic functions most closely associated with cytotoxicity, MBPLS models were developed to predict the metabolic processes based upon the expression data of the gene sets identified by GSEA. Ketogenesis and TG accumulation were identified to be positively and negatively related to LDH release in step 2, respectively. Therefore, two MBPLS models were developed to model BOH and TG, respectively. The MBPLS models contained 14 blocks each, corresponding to the 14 enriched gene sets identified by the GSEA. The importance of individual genes within these functional groups was identified by evaluating the regression coefficients of the genes. In particular, the genes with high positive regression coefficients to ketogenesis and TG accumulation are discussed below as the predicted roles of these genes were evaluated using inhibitors or RNA interference (RNAi).
Top ranked genes relevant to beta-hydroxbutyrate
glutathione S-transferase M5 (GSTM5)
aldehyde dehydrogenase 1 family, member A1 (ALDH1A1)
NADH dehydrogenase (ubiquinone) Fe-S protein 1, 75kDa (NADH-coenzyme Q reductase) (NDUFS1)
NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 4, 9kDa (NDUFA4)
aldehyde dehydrogenase 3 family, member B2 (ALDH3B2)
cytochrome P450, subfamily IIA (phenobarbital-inducible), polypeptide 7 (CYP2A7), transcript variant 2
v-raf murine sarcoma 3611 viral oncogene homolog 1 (ARAF1)
succinate dehydrogenase complex, subunit D, integral membrane protein (SDHD)
core promoter element binding protein (COPEB)
Nerve growth factor, beta polypeptide (NGFB)
Basal Levels of ALDH1A1 and ALDH1A3 in control cells (relative to GAPDH) measured by RT-PCR
Intracellular TG was identified to have significantly negative association to the toxicity. In agreement with this, the channeling of palmitate to TG has been shown to protect the cells from its toxic effects . Thus, genes with high positive regression coefficients to intracellular TG accumulation may be potential targets for cytoprotection. Such genes are listed in Table 5. A complete list of regression coefficients is available in the additional data file 3. Endothelial membrane protein 3 (EMP3) was the gene with greatest positive coefficient, indicating that a reduction in the expression of this gene would be cytotoxic and vice-versa. The function of EMP3 is not clearly understood, it is known to be involved in cell growth, differentiation, and apoptosis [32, 33]. Many genes involved in glutathione metabolism had positive regression coefficients. Examples of genes related to glutathione that were selected included glutamate-cysteine ligase, catalytic subunit (GCL-c), glutathione S-transferase M4 (GSTM4), microsomal glutathione S-transferase 1 (MGST1), glutathione S-transferase A2 (GSTA2) and glutathione reductase (GSR). The selection of these genes indicated a cytoprotective role of these genes, and corroborated with the role of oxidative stress in the toxicity. Finally, some genes involved in regulation of glycolysis such as hexokinase 1 (HK1) and glucokinase (hexokinase 4) regulatory protein (GCKR) had large positive regression coefficients, indicating that upregulation of glycolysis may be cytoprotective. Exposure to FFAs is associated with a reduction in cellular energetics, e.g., a decrease in glycolytic enzymes [34, 35] and a reduction in ATP synthesis due to mitochondrial uncoupling . This reduction in cellular ATP levels may play a role in the toxicity  or exacerbate it. Under these conditions, increasing glycolysis may provide an alternative route for ATP synthesis and reduce the toxicity.
4. Experimental validations
Hepatocytes are resistant to the cytotoxic effects of TNF-α due to rapid upregulation of cytoprotective genes, mediated in part by the activation of NF-κB by TNF-α. It is for this reason that most of the previous studies on the cell death caused by TNF-α employ a secondary insult, such as transcriptional inhibition or glutathione depletion [4, 15]. The observation of the dependence of TNF-α toxicity on that of palmitate suggests that the toxicity of palmitate can act as a secondary/additional insult. Because the saturated free fatty acid (palmitate) was found to have the greatest toxicity of all the treatments and the toxicity of TNF-α depended on the effect of palmitate, the subsequent validations were conducted for the palmitate conditions.
a. Validation of GSEA results
a.i. The role of mitochondria
a.ii. Lack of involvement of de novo ceramide synthesis
Top ranked genes positively related to TG
epithelial membrane protein 3 (EMP3), mRNA.
hexokinase 1 (HK1), transcript variant 5, nuclear gene encoding mitochondrial protein, mRNA.
glutamate-cysteine ligase, catalytic subunit
glucokinase (hexokinase 4) regulatory protein (GCKR), mRNA.
glutathione S-transferase M4 (GSTM4), transcript variant 3, mRNA.
acyl-Coenzyme A dehydrogenase, long chain (ACADL), mRNA.
microsomal glutathione S-transferase 1 (MGST1), transcript variant 1a, mRNA.
glutamate-cysteine ligase, catalytic subunit
glutathione S-transferase A2 (GSTA2), mRNA.
acyl-Coenzyme A dehydrogenase, C-2 to C-3 short chain (ACADS), nuclear gene encoding mitochondrial protein, mRNA.
Effect of ceramide inhibition on the toxicity of palmitate
C16 Ceramide (pmol/mg protein)
LDH released (%)
0.63 ± 0.28 *
1.27 ± 0.48 *
24.44 ± 6.39
4.71 ± 1.67
6.80 ± 2.85 *
5.15 ± 1.38
b. Validating hierarchical model predictions
b.i. Important role of NADH dehydrogenase
b.ii. The role of aldehyde dehydrogenases
b.iii. The role of endothelial membrane protein 3 (EMP3)
In this paper, a framework was developed to hierarchically integrate the metabolic and gene expression profiles to identify the genes which play important roles in determining the phenotypic responses. Application of this framework to the FFA and TNF-α induced cytotoxicity in HepG2 cells yielded novel targets to regulate the toxicity. The genes and pathways relevant to the cytotoxicity were identified and experimentally validated. For example, GSEA identified that mitochondrial alterations, but not ceramide synthesis, were associated with the toxicity. An advantage of incorporating GSEA analysis into our hierarchical approach is that incorporating knowledge-based information enhances the signal to noise ratio and the robustness of the analysis, and permits the detection of genes with modest changes . As illustrated by 1) the identification of ceramide metabolism not as an important player in the observed toxicity, which was experimentally validated, and 2) the novel targets identified by the hierarchical approach, suggest that the GSEA pathway analysis can compensate for the limited number of replicates to provide useful information. The former prediction is supported by the observation of no effect on the palmitate-induced toxicity upon inhibition of ceramide synthesis. The MBPLS prediction of the role of NADH dehydrogenase in regulating cytotoxicity was validated with complex I inhibitor studies, while those of ALDH1A1 and EMP3 were confirmed by RNAi studies.
Identification of genetic changes that control the phenotypic responses is an area of active research. This requires identification of genes that regulate the altered metabolic/physiological changes. Among the simpler models to identify such genes are the multivariate linear regression models . We also conducted multivariate linear regression analysis to relate the genetic and metabolic profiles, and found that the linear regression identified a much smaller number of genes relevant to a metabolite production or release which were assigned regression coefficients different from zero. For example, only 25 genes out of the 272 genes had weights different from zero in the linear regression model to predict BOH (see additional data file 4 for the list of genes). None of the important genes identified by MBPLS such as NADH dehydrogenases, glutathione S-transferases, ALDHs or EMP3 were selected. Linear regression model is ill-conditioned when the number of variables (genes) exceeds the number of observations (conditions), resulting in most of the regression coefficients taking on values of zero. MBPLS is a multivariate approach capable of modeling a large number of variables using a relatively small set of observations. It circumvents typical problems associated with the highly correlated and collinear nature of experimental data by projecting the data onto a few independent latent factors. These latent factors simplify the complex and diverse relationships by capturing the variable interactions contained in the original data in a new set of fewer unobserved/latent variables.
In this study, in addition to the generation of important information such as the roles of NADH dehydrogenases and the lack of involvement of de novo ceramide in the toxicity, two novel modulators of the palmitate-toxicity (ALDH1A1 and EMP3) were identified and validated. A notable point is the differences in the response of ALDH1A1 and EMP3 silencing. While silencing ALDH1A1 reduced caspase-3 activity significantly, it did not affect LDH release. On the contrary, silencing EMP3 increased LDH release without any affecting the caspase-3 activity. These differences in the cellular responses are most likely due to the cellular location of the corresponding proteins (cytosolic for ALDH1A1 and cell membrane for EMP3). Exposure to elevated levels of FFA would lead to increased omega oxidation, in which ALDHs play an important role. Omega oxidation can be a source of ROS and toxicity . Under these conditions, ALDH knock-down would reduce the omega oxidation and hence the cytotoxicity. In addition to its cytotoxic roles, ALDH1A1 may have potentially cytoprotective effects mediated by the detoxification of reactive lipid aldehydes . EMP3 is a protein of the peripheral membrane protein 22 (PMP22) family . The proteins of this family have been suggested to play important roles in cell proliferation and apoptosis . However, there has not been any study on the role of EMP3 in fatty acid toxicity. Its effect on the LDH release suggests the possibility that this protein may regulate membrane integrity.
In summary, this paper illustrated how phenotypic, metabolic and genetic profiles can be integrated hierarchically to identify phenotype relevant genes and pathways and novel targets. This approach identified the involvement of ROS generation, altered fatty acid and energy, but not ceramide metabolism in the cytotoxicity. In addition, novel targets such as ALDH1A1 and EMP3 were identified to modulate the toxicity of saturated FFA. Thus, the integration of metabolic and genetic information provides a more comprehensive picture of the perturbations as well as novel targets to regulate cellular responses.
HepG2/C3A cells and Fetal Bovine Serum were purchased from American Type Culture Collection (ATCC, Manassas, VA). Dulbecco's modified Eagle's medium with high glucose and no pyruvate (DMEM), Penicillin-Streptomycin (P/S), phosphate buffered saline (PBS, pH 7.4) and trizol reagent were purchased from Invitrogen (Carlsbad, CA). Fatty acid free bovine serum albumin (BSA) was purchased from MP Biomedicals (Chillicothe, OH). Sodium salts of all the fatty acids (palmitate, oleate and linoleate) were purchased from Sigma Aldrich chemical company (St. Louis, MO). 6-carboxy-2',7'-dichlorodihydrofluorescein diacetate, di(acetoxymethyl ester) (DCFDA dye) was obtained from Molecular Probes (Eugene, OR). Recombinant human TNF-α was from Peprotech (Rocky Hill, NJ).
One million HepG2/C3A cells were seeded into each well of a 6-well culture plate. The cells were cultured in 2 ml of medium containing DMEM supplemented with 10% fetal bovine serum (FBS) and 2% Penicillin-streptomycin (P/S). Cells were incubated at 37°C and in 10% CO2 atmosphere. After the cells reached confluence, the medium was replaced with 2 ml of the chosen medium, either HepG2, or the FFA medium containing 0.7 mM palmitate, oleate or linoleate; or the FFA-TNF-α medium. The fatty acids chosen are the most prevalent in the class of saturated (palmitate), monounsaturated (oleate) and polyunsaturated (linoleate) fatty acids in the plasma. The concentration of fatty acids chosen (0.7 mM) is commonly found in conditions of obesity. The FFAs were dissolved in 4% fatty acid-free BSA. Therefore, in addition to the HepG2 medium control, 4% fatty acid-free BSA in HepG2 medium was used as another control. TNF-α was added from a 100ug/ml stock in deionized water to make the desired final concentrations of either 20 or 100 ng/ml.
Caspase-3 assay and TUNEL staining
Cells were treated with different FFA in the presence and absence of TNF-α. Caspase-3 activity was measured using a commercially available fluorescence-based assay kit from Biomol, according to manufacturer's protocol. Data were normalized to protein measurements from parallel experiments. For TUNEL staining, cells were cultured in Labtek Chamber Slide System. TUNEL staining was performed using the Dead-End fluorimetric TUNEL system from Promega Biosciences, according to manufacturer's protocol and imaged under a fluorescence microscope (Leica).
Measurement of metabolic uptake and production
The net uptake or production of a species was calculated by the difference in the concentration of the specie in the medium, before and after the treatment. The concentrations of metabolites were measured using enzymatic assays or HPLC. The amino acids were measured by HPLC using the AccQTag method (Waters) according to the manufacturer's protocol. Briefly, the media collected after treatments were de-proteinized by adding 4X acetonitrile (by volume) and keeping on ice for 1 h, after which the samples were centrifuged and the supernatant was derivatized using the reagent in the AccQTag kit as per the instructions given. The samples were analyzed with HPLC on a Waters 2690 separations module, using an AccQTag column (Waters) and fluorescence detection (Waters detector). Intracellular triglycerides were measured by lysing the cells with 1% triton-X-100 for 24 h and measuring the triglycerides by an enzymatic assay kit from Sigma. Glucose, lactate and glycerol were measured using enzymatic analysis kits from Sigma. Free fatty acid half micro kit (Roche Diagnostics) was used to find the concentration of free fatty acids in the medium. Beta-hydroxybutyrate (BOH) released was measured using a kit from Stanbio. Acetoacetate was measured using enzymatic assay .
Fisher's discriminant analysis
FDA identifies the projection axis that maximizes the ratio of the between-group and the within-group variations. Details on the FDA algorithm can be found in . FDA was applied to identify which among the 27 measured metabolic uptake/production contributed to the separation of the different phenotypes (cytotoxic versus nontoxic). Because there were 2 classes (toxic and non-toxic), a single discriminating vector is sufficient to separate them . The relative importance of the metabolites was identified by the projection of the metabolites in the new discriminant vector space.
Cells were cultured in 10 cm tissue culture plates until confluence and then exposed to different treatments for 24 h. RNA was isolated with the Trizol reagent. The gene expression profiles were obtained with the cDNA microarrays. The microarray analyses were conducted at the Van Andel Institute, Grand Rapids, MI. The protocols are available online at . There were two biological replicates for each condition and each replicate was measured with the Cy3 and Cy5 dyes (i.e. there were two technical replicates for each biological replicate). The microarray data has been deposited at the GEO website , with a query number of GSE5441.
Measurement of reactive oxygen species
Confluent cells were treated with the control medium or 0.7 mM palmitate for different time periods. Positive control cells were treated with 2 mM H2O2 for 1 h at 37°C. Measurement of ROS was performed by flow cytometry, using 6-carboxy-2',7'-dichlorodihydrofluorescein diacetate, di(acetoxymethyl ester) (C-2938, Molecular Probes, Eugene, OR) dye. A 5 mM stock solution of the dye was prepared in DMSO, and diluted to the 5 umol/L in DMEM. After the desired treatment times, the cells were exposed to the dye for 30 min, following which the cells were washed and imaged under a fluorescence microscope. Quantitative measurements were made by reading the fluorescence of the cells in a microplate fluorimeter with excitation at 488 nm and emission at 527 nm.
Measurement of ceramide
The cells were treated with 0.7 mM palmitate in the presence or absence of 20 μM Fumonisin B1 (FB1) for 24 h, following which the lipids were extracted by the method of Bligh and Dyer . The C-16 ceramide in the samples were then quantified with LC-MS as per a previously published protocol .
Gene Set Enrichment Analysis (GSEA) of the gene data
GSEA integrates a priori knowledge of a gene's functional role with the expression data to detect concerted expression changes in a set of genes responsible for producing a phenotype. The software GSEA-P, from , was used for the GSEA analysis. Thirty seven gene sets were selected from the molecular signature database, MsigDB  functional gene group c2, as shown in Table 1. These sets included 10 metabolic pathways, 26 signal pathways and 1 cellular component. An enrichment score of a gene set S characterizes whether the set of genes randomly distributed across the list or falls mainly at the bottom or top of the list. The null hypothesis that a gene set S randomly distributes across the ranked gene list was tested with Kolmogorov-Smirnov test, with the statistical significance value estimated through 1000 random permutations of the phenotype label. The gene sets with a high significance of enrichment are considered important in separating the distinct phenotypes.
Integrating the gene expression and metabolic profiles
MBPLS is a hierarchical multivariate analysis method [49, 50], where the variables are divided into different blocks based upon a priori knowledge, for example according to different stages of a process  or different metabolic pathways in a cell . For details of the MBPLS algorithm, refer . In the MBPLS model, the different gene sets identified by GSEA formed the blocks of the MBPLS. This ensured that the blocks were separated according to the functional role of the genes. Block scores were extracted from each block to predict the metabolic uptake/productions. This facilitated the identification of an important block (gene set) to a metabolic flux, and identified the important genes within the block. Important genes sets were identified by evaluating the weights of each block and importance of individual genes were then further identified by evaluating the regression coefficients of the genes within the block. Two latent variables were selected to be extracted from each block based upon the prediction accuracy of the MBPLS model. The N-way toolbox  was applied to conduct the MBPLS modeling.
RNA interference and reverse transfection
Silencer® Validated siRNAs targeting human EMP3 and ALDH1A1 mRNA were purchased from Ambion (Austin, TX). The synthesized oligonucleotides for siRNA of EMP3 are 5'-GUCCCUGAAUCUCUGGUACtt-3' and 5'-GUACCAGAGAUUCAGGGACtc-3', and the synthesized oligonucleotides for siRNA of ALDH1A1 are 5'-GGAACAGUGUGGGUGAAUUtt-3' and 5'-AAUUCACCCACACUGUUCCtg-3'. Reverse transfection of siRNA was performed. In general, siRNA and the transfection reagent, Lipofectamine RNAiMAX (Invitrogen, Carlsbad, CA), were diluted in Opti-MEM (Invitrogen) devoid of any serum and antibiotics and added to the 6-well plates. After 20 minutes, the same numbers of HepG2 cells in antibiotic free media were plated in each well and incubated at 37°C. After 24 h of transfection, the HepG2 cells were cultured for another 24 hours in regular media with other additives, for example palmitate. Then cells were harvested, washed twice with phosphate-buffered saline and lysed.
Overexpression and forward transfection
The ALDH1A3 plasmid, pCMV6-XL4-hALDH1A3, was purchased from Origene (Rockville, MD). Transient transfection was performed according to the Lipofectamine 2000 (Invitrogen) method. In general, the HepG2 cells were seeded in 6-well plates and cultured until reaching 80% confluency. Before transfection, the cells were washed twice with phosphate buffered saline, and medium was replaced with 2 ml of Opti-MEM medium. 1 μg/well of pCMV6-XL4-hALDH1A3 was then mixed with 5ul/well of Lipofectamine 2000 in Opti-MEM, and 20 minutes later, the mixture was added to the wells. After 6–12 hours of transfection, the cells were then cultured in regular media, treated with other additives like palmitate or TNF-α, and harvested after the treatment.
Real-time quantitative RT-PCR analysis
Total RNA was extracted from cells with an RNeasy mini kit and depleted of contaminating DNA with RNase-free DNase (Qiagen, Valencia, CA). Equal amounts of total RNA (1 μg) were reverse-transcribed using an iScript cDNA synthesis kit (Bio-RAD). The first-strand cDNA was used as a template. The primers used for quantitative RT-PCR analyses of human EMP3 (5'-GTGGTCTCAGCCCTTCACA-3' and 5'-ACGTGCAGTCGTACCAGAGA-3'), human ALDH1A1 (5'-AGCCTTCACAGGATCAACAGA-3' and 5'-GTCGGCATCAGCTAACACAA-3'), human ALDH1A3 (5'-GCCCTTTATCTCGGCTCTCT-3' and 5'-CGGTGAAGGCGATCTTGT-3') and human GAPDH (5'-AACTTTGGTATCGTGGAAGGA-3' and 5'-CAGTAGAGGCAGGGATGATGT-3') were synthesized by Operon Biotechnologies, Inc. (Huntsville, AL). RT-PCR was performed in 25-μl reactions using 1/10 of the cDNA produced by reverse transcription, 0.2 μM each primer, 1 X SYBR green supermix from Bio-RAD, and an annealing temperature of 57°C for 40 cycles. Each sample was assayed in three independent RT reactions and triplicate reactions each and normalized to GAPDH expression. Negative controls included the absence of enzyme in the RT reaction and absence of template during PCR. The cycle threshold (C T ) values corresponding to the PCR cycle number at which fluorescence emission in real time reaches a threshold above the base-line emission were determined using MyIQ™ Real-Time PCR Detection System.
- ACAC :
- ALDH :
- BOH :
- EMP3 :
endothelial membrane protein 3
- FFA :
free fatty acid
- GSEA :
gene set enrichment analysis
- GST :
- HNF :
hepatic nuclear factors
- LDH :
- MBPLS :
multi-block partial least squares analysis
- NASH :
- NF-κB :
nuclear factor kappa B
- PPAR :
peroxisome proliferator activated receptors
- ROS :
Reactive oxygen species
- SREBP :
sterol receptor element binding protein
- TNF-α :
tumor necrosis factor alpha
This work is supported in part by the National Science Foundation (BES 0222747, BES 0331297, and 0425821), the National Institute of Health (1R01GM079688-01), the Environmental Protection Agency (RD83184701), the MSU Foundation and the Center for Systems Biology and the Whitaker Foundation.
- Felber JP, Golay A: Pathways from obesity to diabetes. Int J Obes Relat Metab Disord. 2002, 26 Suppl 2: S39-45. 10.1038/sj.ijo.0802126.PubMedView ArticleGoogle Scholar
- Kobayashi M: Molecular mechanism of insulin resistance. Saishin Igaku. 1998, 53 (6): 1210-1216.Google Scholar
- Tilg H: Cytokines and liver diseases. Can J Gastroenterol. 2001, 15 (10): 661-668.PubMedGoogle Scholar
- Watada H, Kawamori R: Insulin resistance and NASH. BIO Clinica. 2003, 18 (10): 874-879.Google Scholar
- Randle PJ, Garland PB, Newsholme EA, Hales CN: The glucose fatty acid cycle in obesity and maturity onset diabetes mellitus. Ann N Y Acad Sci. 1965, 131 (1): 324-333. 10.1111/j.1749-6632.1965.tb34800.x.PubMedView ArticleGoogle Scholar
- Jump DB: Fatty acid regulation of gene transcription. Crit Rev Clin Lab Sci. 2004, 41 (1): 41-78. 10.1080/10408360490278341.PubMedView ArticleGoogle Scholar
- Cheung AT, Ree D, Kolls JK, Fuselier J, Coy DH, Bryer-Ash M: An in vivo model for elucidation of the mechanism of tumor necrosis factor-alpha (TNF-alpha)-induced insulin resistance: evidence for differential regulation of insulin signaling by TNF-alpha. Endocrinology. 1998, 139 (12): 4928-4935. 10.1210/en.139.12.4928.PubMedGoogle Scholar
- Lang CH, Dobrescu C, Bagby GJ: Tumor necrosis factor impairs insulin action on peripheral glucose disposal and hepatic glucose output. Endocrinology. 1992, 130 (1): 43-52. 10.1210/en.130.1.43.PubMedGoogle Scholar
- Schwabe RF, Brenner DA: Mechanisms of Liver Injury. I. TNF-alpha-induced liver injury: role of IKK, JNK, and ROS pathways. Am J Physiol Gastrointest Liver Physiol. 2006, 290 (4): G583-9. 10.1152/ajpgi.00422.2005.PubMedView ArticleGoogle Scholar
- Ding WX, Yin XM: Dissection of the multiple mechanisms of TNF-alpha-induced apoptosis in liver injury. J Cell Mol Med. 2004, 8 (4): 445-454. 10.1111/j.1582-4934.2004.tb00469.x.PubMedView ArticleGoogle Scholar
- Heyninck K, Wullaert A, Beyaert R: Nuclear factor-kappa B plays a central role in tumour necrosis factor-mediated liver disease. Biochem Pharmacol. 2003, 66 (8): 1409-1415. 10.1016/S0006-2952(03)00491-X.PubMedView ArticleGoogle Scholar
- Brenner DA: Signal transduction during liver regeneration. J Gastroenterol Hepatol. 1998, 13 Suppl: S93-5.PubMedGoogle Scholar
- Tilg H, Diehl AM: Cytokines in alcoholic and nonalcoholic steatohepatitis. N Engl J Med. 2000, 343 (20): 1467-1476. 10.1056/NEJM200011163432007.PubMedView ArticleGoogle Scholar
- Rousset S, Bringuier A, Lardeux B, Feldmann G: Apoptosis induced by tumor necrosis factor a in human hepatoma cell lines. Falk Symposium. 113: 303-313.
- Nagai H, Matsumaru K, Feng G, Kaplowitz N: Reduced glutathione depletion causes necrosis and sensitization to tumor necrosis factor-alpha-induced apoptosis in cultured mouse hepatocytes. Hepatology. 2002, 36 (1): 55-64. 10.1053/jhep.2002.33995.PubMedView ArticleGoogle Scholar
- Ji J, Zhang L, Wang P, Mu YM, Zhu XY, Wu YY, Yu H, Zhang B, Chen SM, Sun XZ: Saturated free fatty acid, palmitic acid, induces apoptosis in fetal hepatocytes in culture. Exp Toxicol Pathol. 2005, 56 (6): 369-376. 10.1016/j.etp.2005.02.003.PubMedView ArticleGoogle Scholar
- Wei Y, Wang D, Topczewski F, Pagliassotti MJ: Saturated fatty acids induce endoplasmic reticulum stress and apoptosis independently of ceramide in liver cells. Am J Physiol Endocrinol Metab. 2006, 291 (2): E275-81. 10.1152/ajpendo.00644.2005.PubMedView ArticleGoogle Scholar
- Srivastava S, Chan C: Hydrogen peroxide and hydroxyl radicals mediate palmitate-induced cytotoxicity to hepatoma cells: relation to mitochondrial permeability transition. Free Radic Res. 2007, 41 (1): 38-49. 10.1080/10715760600943900.PubMedView ArticleGoogle Scholar
- Feldstein AE, Werneburg NW, Li Z, Bronk SF, Gores GJ: Bax inhibition protects against free fatty acid-induced lysosomal permeabilization. Am J Physiol Gastrointest Liver Physiol. 2006, 290 (6): G1339-46. 10.1152/ajpgi.00509.2005.PubMed CentralPubMedView ArticleGoogle Scholar
- Listenberger LL, Ory DS, Schaffer JE: Palmitate-induced apoptosis can occur through a ceramide-independent pathway. J Biol Chem. 2001, 276 (18): 14890-14895. 10.1074/jbc.M010286200.PubMedView ArticleGoogle Scholar
- Lu ZH, Mu YM, Wang BA, Li XL, Lu JM, Li JY, Pan CY, Yanase T, Nawata H: Saturated free fatty acids, palmitic acid and stearic acid, induce apoptosis by stimulation of ceramide generation in rat testicular Leydig cell. Biochem Biophys Res Commun. 2003, 303 (4): 1002-1007. 10.1016/S0006-291X(03)00449-2.PubMedView ArticleGoogle Scholar
- Gomez EO, Mendoza-Milla C, Ibarra-Sanchez MJ, Ventura-Gallegos JL, Zentella A: Ceramide reproduces late appearance of oxidative stress during TNF-mediated cell death in L929 cells. Biochem Biophys Res Commun. 1996, 228 (2): 505-509. 10.1006/bbrc.1996.1690.PubMedView ArticleGoogle Scholar
- Chan C, Hwang D, Stephanopoulos GN, Yarmush ML, Stephanopoulos G: Application of multivariate analysis to optimize function of cultured hepatocytes. Biotechnol Prog. 2003, 19 (2): 580-598. 10.1021/bp025660h.PubMedView ArticleGoogle Scholar
- Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005, 102 (43): 15545-15550. 10.1073/pnas.0506580102.PubMed CentralPubMedView ArticleGoogle Scholar
- Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, Puigserver P, Carlsson E, Ridderstrale M, Laurila E, Houstis N, Daly MJ, Patterson N, Mesirov JP, Golub TR, Tamayo P, Spiegelman B, Lander ES, Hirschhorn JN, Altshuler D, Groop LC: PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003, 34 (3): 267-273. 10.1038/ng1180.PubMedView ArticleGoogle Scholar
- Hwang D, Stephanopoulos G, Chan C: Inverse modeling using multi-block PLS to determine the environmental conditions that provide optimal cellular function. Bioinformatics. 2004, 20 (4): 487-499. 10.1093/bioinformatics/btg433.PubMedView ArticleGoogle Scholar
- Gill HK, Wu GY: Non-alcoholic fatty liver disease and the metabolic syndrome: effects of weight loss and a review of popular diets. Are low carbohydrate diets the answer?. World J Gastroenterol. 2006, 12 (3): 345-353.PubMed CentralPubMedGoogle Scholar
- Sanyal AJ, Campbell-Sargent C, Mirshahi F, Rizzo WB, Contos MJ, Sterling RK, Luketic VA, Shiffman ML, Clore JN: Nonalcoholic steatohepatitis: association of insulin resistance and mitochondrial abnormalities. Gastroenterology. 2001, 120 (5): 1183-1192. 10.1053/gast.2001.23256.PubMedView ArticleGoogle Scholar
- Listenberger LL, Han X, Lewis SE, Cases S, Farese RV, Ory DS, Schaffer JE: Triglyceride accumulation protects against fatty acid-induced lipotoxicity. Proc Natl Acad Sci U S A. 2003, 100 (6): 3077-3082. 10.1073/pnas.0630588100.PubMed CentralPubMedView ArticleGoogle Scholar
- Takahashi Y, Campbell EA, Hirata Y, Takayama T, Listowsky I: A basis for differentiating among the multiple human Mu-glutathione S-transferases and molecular cloning of brain GSTM5. J Biol Chem. 1993, 268 (12): 8893-8898.PubMedGoogle Scholar
- Moller IM: PLANT MITOCHONDRIA AND OXIDATIVE STRESS: Electron Transport, NADPH Turnover, and Metabolism of Reactive Oxygen Species. Annu Rev Plant Physiol Plant Mol Biol. 2001, 52: 561-591. 10.1146/annurev.arplant.52.1.561.PubMedView ArticleGoogle Scholar
- Taylor V, Suter U: Epithelial membrane protein-2 and epithelial membrane protein-3: two novel members of the peripheral myelin protein 22 gene family. Gene. 1996, 175 (1-2): 115-120. 10.1016/0378-1119(96)00134-5.PubMedView ArticleGoogle Scholar
- Jetten AM, Suter U: The peripheral myelin protein 22 and epithelial membrane protein family. Prog Nucleic Acid Res Mol Biol. 2000, 64: 97-129.PubMedView ArticleGoogle Scholar
- Dentin R, Benhamed F, Pegorier JP, Foufelle F, Viollet B, Vaulont S, Girard J, Postic C: Polyunsaturated fatty acids suppress glycolytic and lipogenic genes through the inhibition of ChREBP nuclear protein translocation. J Clin Invest. 2005, 115 (10): 2843-2854. 10.1172/JCI25256.PubMed CentralPubMedView ArticleGoogle Scholar
- Bolon C, Gauthier C, Simonnet H: Glycolysis inhibition by palmitate in renal cells cultured in a two-chamber system. Am J Physiol. 1997, 273 (5 Pt 1): C1732-8.PubMedGoogle Scholar
- Brookes PS: Mitochondrial H(+) leak and ROS generation: an odd couple. Free Radic Biol Med. 2005, 38 (1): 12-23. 10.1016/j.freeradbiomed.2004.10.016.PubMedView ArticleGoogle Scholar
- Parke DV: Mechanisms of chemical toxicity--a unifying hypothesis. Regul Toxicol Pharmacol. 1982, 2 (4): 267-286. 10.1016/0273-2300(82)90001-0.PubMedView ArticleGoogle Scholar
- Kim JS, He L, Lemasters JJ: Mitochondrial permeability transition: a common pathway to necrosis and apoptosis. Biochem Biophys Res Commun. 2003, 304 (3): 463-470. 10.1016/S0006-291X(03)00618-1.PubMedView ArticleGoogle Scholar
- Jia Z, Xu S: Clustering expressed genes on the basis of their association with a quantitative phenotype. Genet Res. 2005, 86 (3): 193-207. 10.1017/S0016672305007822.PubMedView ArticleGoogle Scholar
- Ockner RK, Kaikaus RM, Bass NM: Fatty-acid metabolism and the pathogenesis of hepatocellular carcinoma: review and hypothesis. Hepatology. 1993, 18 (3): 669-676.PubMedView ArticleGoogle Scholar
- Choudhary S, Xiao T, Vergara LA, Srivastava S, Nees D, Piatigorsky J, Ansari NH: Role of aldehyde dehydrogenase isozymes in the defense of rat lens and human lens epithelial cells against oxidative stress. Invest Ophthalmol Vis Sci. 2005, 46 (1): 259-267. 10.1167/iovs.04-0120.PubMedView ArticleGoogle Scholar
- Olsen C: An enzymatic fluorimetric micromethod for the determination of acetoacetate, -hydroxybutyrate, pyruvate and lactate. Clin Chim Acta. 1971, 33 (2): 293-300. 10.1016/0009-8981(71)90486-4.PubMedView ArticleGoogle Scholar
- Belhumeur PN, Hespanha JP, Kriegman DJ: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans Pattern Analysis and Machine Intelligence. 1997, 19 (7): 711-720. 10.1109/34.598228.View ArticleGoogle Scholar
- cDNA microarry protocol at Van Andel Institute. http://www.vai.org/Research/Services/LMT/Protocols.aspx
- GEO website . http://www.broad.harvard.edu/gsea/
- Bligh EG, Dyer WJ: A rapid method of total lipid extraction and purification. Can J Biochem Physiol. 1959, 37 (8): 911-917.PubMedView ArticleGoogle Scholar
- Yamaguchi M, Miyashita Y, Kumagai Y, Kojo S: Change in liver and plasma ceramides during D-galactosamine-induced acute hepatic injury by LC-MS/MS. Bioorg Med Chem Lett. 2004, 14 (15): 4061-4064. 10.1016/j.bmcl.2004.05.046.PubMedView ArticleGoogle Scholar
- GSEA website. http://www.ncbi.nlm.nih.gov/geo
- MacGregor JF, Jaeckle C, Kiparissides C, Koutoudi M: Process Monitoring and Diagnosis by Multi-Block PLS Methods. AIChE Journal. 1994, 40 (5): 826-838. 10.1002/aic.690400509.View ArticleGoogle Scholar
- Lopes JA, Menezes JC, Westerhuis JA, Smilde AK: Multiblock PLS analysis of an industrial pharmaceutical process. Biotechnol Bioeng. 2002, 80 (4): 419-427. 10.1002/bit.10382.PubMedView ArticleGoogle Scholar
- Andersson CA, Bro R: The N-way Toolbox for MATLAB. Chemometrics & Intelligent Laboratory Systems. 2000, 52 (1): 1-4. 10.1016/S0169-7439(00)00071-X.View ArticleGoogle Scholar
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