Lipid Infusion Decreases the Expression of Nuclear Encoded Mitochondrial Genes and Increases the Expression of Extracellular Matrix Genes in Human Skeletal Muscle
2004; Elsevier BV; Volume: 280; Issue: 11 Linguagem: Inglês
10.1074/jbc.m408985200
ISSN1083-351X
AutoresDawn K. Richardson, Sangeeta Kashyap, Mandeep Bajaj, Kenneth Cusi, Steven J. Mandarino, Jean Finlayson, Ralph A. DeFronzo, Christopher P. Jenkinson, Lawrence J. Mandarino,
Tópico(s)Peroxisome Proliferator-Activated Receptors
ResumoThe association between elevated plasma free fatty acid (FFA) concentrations and insulin resistance is well known. Although the cause and effect relationship between FFAs and insulin resistance is complex, plasma FFA is negatively correlated with the expression of peroxisome proliferator activated receptor-γ cofactor-1 (PGC-1) and nuclear encoded mitochondrial genes. To test whether this association is causal, we infused a triglyceride emulsion (or saline as control) into healthy subjects to increase plasma FFA for 48 h followed by muscle biopsies, microarray analysis, quantitative real time PCR, and immunoblots. Lipid infusion increased plasma FFA concentration from 0.48 ± 0.02 to 1.73 ± 0.43 mm and decreased insulin-stimulated glucose disposal from 8.82 ± 0.69 to 6.67 ± 0.66 mg/kg·min, both with p < 0.05. PGC-1 mRNA, along with mRNAs for a number of nuclear encoded mitochondrial genes, were reduced by lipid infusion (p < 0.05). Microarray analysis also revealed that lipid infusion caused a significant overexpression of extracellular matrix genes and connective tissue growth factor. Quantitative reverse transcription PCR showed that the mRNA expression of collagens and multiple extracellular matrix genes was higher after the lipid infusion (p < 0.05). Immunoblot analysis revealed that lipid infusion also increased the expression of collagens and the connective tissue growth factor protein. These data suggest that an experimental increase in FFAs decreases the expression of PGC-1 and nuclear encoded mitochondrial genes and also increases the expression of extracellular matrix genes in a manner reminiscent of inflammation. The association between elevated plasma free fatty acid (FFA) concentrations and insulin resistance is well known. Although the cause and effect relationship between FFAs and insulin resistance is complex, plasma FFA is negatively correlated with the expression of peroxisome proliferator activated receptor-γ cofactor-1 (PGC-1) and nuclear encoded mitochondrial genes. To test whether this association is causal, we infused a triglyceride emulsion (or saline as control) into healthy subjects to increase plasma FFA for 48 h followed by muscle biopsies, microarray analysis, quantitative real time PCR, and immunoblots. Lipid infusion increased plasma FFA concentration from 0.48 ± 0.02 to 1.73 ± 0.43 mm and decreased insulin-stimulated glucose disposal from 8.82 ± 0.69 to 6.67 ± 0.66 mg/kg·min, both with p < 0.05. PGC-1 mRNA, along with mRNAs for a number of nuclear encoded mitochondrial genes, were reduced by lipid infusion (p < 0.05). Microarray analysis also revealed that lipid infusion caused a significant overexpression of extracellular matrix genes and connective tissue growth factor. Quantitative reverse transcription PCR showed that the mRNA expression of collagens and multiple extracellular matrix genes was higher after the lipid infusion (p < 0.05). Immunoblot analysis revealed that lipid infusion also increased the expression of collagens and the connective tissue growth factor protein. These data suggest that an experimental increase in FFAs decreases the expression of PGC-1 and nuclear encoded mitochondrial genes and also increases the expression of extracellular matrix genes in a manner reminiscent of inflammation. The association between elevated plasma lipid concentrations and insulin resistance is well known. Although the cause and effect relationship between lipids and insulin resistance is complex, an experimental increase in plasma free fatty acid (FFA) 1The abbreviations used are: FFA, free fatty acid; CTGF-, connective tissue growth factor; HGF, hepatocyte growth factor; MAS, Microarray Analysis Suite (software); PGC-1, peroxisome proliferator-activated receptor-γ coactivator-1; Q-RT-PCR, quantitative real time PCR; SAM, Statistical Analysis of Microarrays (software); TGF-β1, transforming growth factor-β1. concentrations induces insulin resistance in skeletal muscle in healthy humans (2Kelley D.E. Mokan M. Simoneau J.A. Mandarino L.J. J. Clin. Investig. 1993; 92: 91-98Crossref PubMed Scopus (391) Google Scholar, 3Thiebaud D. DeFronzo R.A. Jacot E. Golay A. Acheson K. Maeder E. Jequier E. Felber J.P. Metabolism. 1982; 31: 1128-1136Abstract Full Text PDF PubMed Scopus (254) Google Scholar). 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Slasky B.S. Janosky J. Am. J. Clin. Nutr. 1991; 54: 509-515Crossref PubMed Scopus (181) Google Scholar, 15Szczepaniak L.S. Babcock E.E. Schick F. Dobbins R.L. Garg A. Burns D.K. McGarry J.D. Stein D.T. Am. J. Physiol. 1999; 276: E977-E989PubMed Google Scholar). However, those studies provide no information regarding the biochemical or molecular basis of insulin resistance. Recent studies have shown that there are pronounced patterns of change in skeletal muscle gene expression from insulin-resistant subjects (16Patti M.E. Butte A.J. Crunkhorn S. Cusi K. Berria R. Kashyap S. Miyazaki Y. Kohane I. Costello M. Saccone R. Landaker E.J. Goldfine A.B. Mun E. DeFronzo R. Finlayson J. Kahn C.R. Mandarino L.J. Proc. Natl. Acad. Sci. U. S. A. 2003; 100: 8466-8471Crossref PubMed Scopus (1634) Google Scholar, 17Mootha V.K. Lindgren C.M. Eriksson K.F. Subramanian A. Sihag S. Lehar J. Puigserver P. Carlsson E. Ridderstrale M. Laurila E. Houstis N. Daly M.J. Patterson N. Mesirov J.P. Golub T.R. Tamayo P. Spiegelman B. Lander E.S. Hirschhorn J.N. Altshuler D. Groop L.C. Nat. Genet. 2003; 34: 267-273Crossref PubMed Scopus (6274) Google Scholar, 18Yang X. Pratley R.E. Tokraks S. Bogardus C. Permana P.A. Diabetologia. 2002; 45: 1584-1593Crossref PubMed Scopus (101) Google Scholar). Because insulin-resistant subjects have chronic increases in plasma FFAs, it could be argued that chronic exposure to increased FFA might lead to changes in skeletal muscle gene expression that, in turn, could produce or contribute to insulin resistance. We (16Patti M.E. Butte A.J. Crunkhorn S. Cusi K. Berria R. Kashyap S. Miyazaki Y. Kohane I. Costello M. Saccone R. Landaker E.J. Goldfine A.B. Mun E. DeFronzo R. Finlayson J. Kahn C.R. Mandarino L.J. Proc. Natl. Acad. Sci. U. S. A. 2003; 100: 8466-8471Crossref PubMed Scopus (1634) Google Scholar) and others (17Mootha V.K. Lindgren C.M. Eriksson K.F. Subramanian A. Sihag S. Lehar J. Puigserver P. Carlsson E. Ridderstrale M. Laurila E. Houstis N. Daly M.J. Patterson N. Mesirov J.P. Golub T.R. Tamayo P. Spiegelman B. Lander E.S. Hirschhorn J.N. Altshuler D. Groop L.C. Nat. Genet. 2003; 34: 267-273Crossref PubMed Scopus (6274) Google Scholar, 18Yang X. Pratley R.E. Tokraks S. Bogardus C. Permana P.A. Diabetologia. 2002; 45: 1584-1593Crossref PubMed Scopus (101) Google Scholar) have found previously that insulin-resistant subjects had decreased expression of nuclear encoded mitochondrial genes accompanied by the decreased expression of peroxisome proliferator-activated receptor-γ coactivator-1 (PGC-1), the transcriptional coactivator that drives the expression of many genes coding for proteins in mitochondria. Moreover, PGC-1 expression is inversely correlated with plasma FFA concentrations (16Patti M.E. Butte A.J. Crunkhorn S. Cusi K. Berria R. Kashyap S. Miyazaki Y. Kohane I. Costello M. Saccone R. Landaker E.J. Goldfine A.B. Mun E. DeFronzo R. Finlayson J. Kahn C.R. Mandarino L.J. Proc. Natl. Acad. Sci. U. S. A. 2003; 100: 8466-8471Crossref PubMed Scopus (1634) Google Scholar). Therefore, we set out to test the hypothesis that an experimental increase in plasma FFA concentrations would reduce the expression of nuclear encoded mitochondrial genes along with their transcriptional coactivator PGC-1. Conducting this study using a global gene expression profiling allowed us to test this hypothesis and at the same time identify novel targets of increased FFA in skeletal muscle. Subjects—Seven normoglycemic, normal glucose-tolerant Mexican American subjects without a family history of diabetes took part in this study. All subjects had normal glucose tolerance, as assessed by a 75-g oral glucose tolerance test. Subjects received a history, physical examination, and screening blood tests to ensure that they were healthy. No subject was taking any medication known to affect glucose metabolism. All subjects gave informed written consent to participate in the study, which was approved by the Institutional Review Board of the University of Texas Health Science Center at San Antonio. Study Design—Subjects were studied on two occasions separated by 3–4 weeks in random order, once with an infusion of Liposyn III (20% triglyceride emulsion largely composed of soybean oil) and once with saline as a control. Following an overnight fast, subjects reported to the General Clinical Research Center at 8 a.m., a forearm vein was catheterized, and either Liposyn III (60 ml/h) or saline was infused for 48 h. During this time, subjects were ambulatory and consumed a weight-maintaining (50% carbohydrate, 30% fat, and 20% protein) diet. After 48 h of lipid or saline infusion, an antecubital vein was catheterized, and a primed (25 μCi), continuous (0.25 μCi/min) infusion of [3-3H]glucose was begun to measure rates of glucose appearance and disappearance. A hand vein was catheterized and placed in a heated box to arterialize venous blood for the measurement of arterial glucose concentrations. One hour later, a percutaneous biopsy of the vastus lateralis muscle was performed as described previously (19Cusi K. Maezono K. Osman A. Pendergrass M. Patti M.E. Pratipanawatr T. DeFronzo R.A. Kahn C.R. Mandarino L.J. J. Clin. Investig. 2000; 105: 311-320Crossref PubMed Scopus (918) Google Scholar). Biopsy specimens (75–150 mg) were frozen immediately in liquid nitrogen and stored in liquid nitrogen until they were processed. One hour after the muscle biopsy (2 h after the start of tritiated glucose), a primed continuous (80 milliunits/(m2·min)) insulin infusion was started and continued for 240 min to quantify the effects of insulin on glucose disposal (20DeFronzo R.A. Tobin J.D. Andres R. Am. J. Physiol. 1979; 237: E214-E223PubMed Google Scholar). Throughout the insulin infusion, an infusion of 20% glucose was adjusted to maintain euglycemia (20DeFronzo R.A. Tobin J.D. Andres R. Am. J. Physiol. 1979; 237: E214-E223PubMed Google Scholar). Muscle Biopsy Processing—For mRNA analyses, muscle biopsy specimens were homogenized directly in RNAStat solution (Tel-Test Inc., Friendswood, TX), using a Polytron homogenizer (Brinkmann Instruments Westbury, NY). RNA pellets were stored in ethanol/sodium chloride solution at –80 °C. Prior to use, total RNA was purified with RNeasy and DNase I treatment (Qiagen, Chatsworth, CA). For immunoblot analysis, detergent lysates of muscle were prepared as described previously (19Cusi K. Maezono K. Osman A. Pendergrass M. Patti M.E. Pratipanawatr T. DeFronzo R.A. Kahn C.R. Mandarino L.J. J. Clin. Investig. 2000; 105: 311-320Crossref PubMed Scopus (918) Google Scholar). Microarray Analysis, Including Target Preparation, Hybridization, Staining, Scanning, and Analysis of Image—RNA was prepared for hybridization to Affymetrix (Santa Clara, CA) HG-U133A arrays according to the manufacturer's instructions. Total RNA was used as a template for the synthesis of double-stranded cDNA (Superscript double-stranded cDNA synthesis kit; Invitrogen), which was used as a template for biotin-labeled cRNA synthesis (Enzo BioArray High Yield RNA transcription labeling kit; Affymetrix). Purified (RNeasy kit; Qiagen), fragmented (35–200 nucleotides), biotinylated cRNA was hybridized to HG-U133A GeneChips overnight for 16 h at 45 °C in a rotating incubator. Following hybridization, the probe arrays were washed and stained using the GeneChip Fluidics station protocol EukGE-ES2. The protocol consisted of non-stringent and stringent washes followed by a staining procedure whereby the hybridized cRNA was fluorescently labeled using anti-biotin antibodies and a streptavidin-phycoerythrin (SAPE) solution. The intensity of bound dye was measured with an argon laser confocal scanner (GeneArray scanner; Agilent). The probe arrays were scanned twice, and the stored images were aligned and analyzed using the GeneChip software Microarray Analysis Suite (MAS) 5.0 (Affymetrix). The present call by MAS 5.0 software was 26 ± 1.5% of total genes. The 3′/5′ glyceraldehyde-3-phosphate dehydrogenase and actin expression ratios were <3 (acceptable) for all but two chips; however, all chips yielded values for the spiked controls (BIOB, BIOC, BIOD, and CREX) that were within the acceptable range. Because all positive results were subsequently confirmed using quantitative real time PCR and/or immunoblot analysis, all chips were included in the analyses. Microarray Data Expression and Analysis—A flow diagram of the steps used in analysis of the microarray data is given in supplemental Fig. 1, which is available in the on-line version of this article. The Affymetrix data acquisition programs in MAS 5.0 automatically generate a cell intensity (CEL) file from the stored images that contain a single intensity value for each probe cell on the array. The CEL files were imported into the R software package (www.r-project.org), and the probe level data were converted to expression measures using the Affy package (21Bolstad B.M. Irizarry R.A. Astrand M. Speed T.P. Bioinformatics. 2003; 19: 185-193Crossref PubMed Scopus (6463) Google Scholar) from Bioconductor. Expression values for each mRNA were obtained by the Robust Multi-array Analysis (RMA) method of Irizarry (1Irizarry R.A. Hobbs B. Collin F. Beazer-Barclay Y.D. Antonellis K.J. Scherf U. Speed T.P. Biostatistics. 2003; 4: 249-264Crossref PubMed Scopus (8516) Google Scholar), which adjusts for the background on the raw intensity scale, carries out a non-linear quantile normalization of the perfect match values, log transforms the background-adjusted perfect match values, and carries out a robust multi-chip analysis of the quantile normalized log transformed values (1Irizarry R.A. Hobbs B. Collin F. Beazer-Barclay Y.D. Antonellis K.J. Scherf U. Speed T.P. Biostatistics. 2003; 4: 249-264Crossref PubMed Scopus (8516) Google Scholar). CEL files were normalized together, and the expression values obtained were submitted to analysis with the Statistical Analysis of Microarrays (SAM) software (22Tusher V.G. Tibshirani R. Chu G. Proc. Natl. Acad. Sci. U. S. A. 2001; 98: 5116-5121Crossref PubMed Scopus (9802) Google Scholar) to identify those genes that were significantly increased or decreased. The expression values also were assembled into “gene sets” for analysis (supplemental Table II, available in the on-line version of this article), similar to that described by Mootha et al. (17Mootha V.K. Lindgren C.M. Eriksson K.F. Subramanian A. Sihag S. Lehar J. Puigserver P. Carlsson E. Ridderstrale M. Laurila E. Houstis N. Daly M.J. Patterson N. Mesirov J.P. Golub T.R. Tamayo P. Spiegelman B. Lander E.S. Hirschhorn J.N. Altshuler D. Groop L.C. Nat. Genet. 2003; 34: 267-273Crossref PubMed Scopus (6274) Google Scholar). In particular, our gene set analysis approach was based on the comparison of statistics comprised of the sum of the average differences (lipid minus saline) for each gene in a particular set divided by the variance of the average differences. The method is briefly described here. Assume a set consisting of N genes, with n subjects studied under each of two conditions. For gene j, the mean difference in expression (dj) for that gene between conditions 1 and 2 is given by Equation 1, dj=(∑i=1n(X2i−X1i))n (Eq. 1) where X1i is the expression value for subject 1, condition 1, for instance. Then, as shown in Equation 2, M=∑i=1Ndis2 (Eq. 2) the sum of the average differences in gene expression across all of the genes in the set (normalized for the variance, s2, of the average differences) is the statistic M. In the case where X1i = X2i (that is, there is no difference in gene expression between conditions 1 and 2 for all i and j) the value of M will be 0. In practice, M is calculated for each gene set based on the observed expression values for the genes in that set. An empirical distribution of expected M values for a given gene set is then derived by selecting a subset of N genes randomly 10,000 times from the entire number of genes called “present” by MAS 5.0 software. The observed M for the gene set is then compared with the distribution of expected values to determine statistical significance. The gene sets used in the analysis either were annotated previously (17Mootha V.K. Lindgren C.M. Eriksson K.F. Subramanian A. Sihag S. Lehar J. Puigserver P. Carlsson E. Ridderstrale M. Laurila E. Houstis N. Daly M.J. Patterson N. Mesirov J.P. Golub T.R. Tamayo P. Spiegelman B. Lander E.S. Hirschhorn J.N. Altshuler D. Groop L.C. Nat. Genet. 2003; 34: 267-273Crossref PubMed Scopus (6274) Google Scholar) or independently in our laboratory. A separate analysis, including gene normalization to specific samples, was conducted with GeneSpring 5.1 software (Silicon Genetics, CA) using the CHP file generated in the MAS 5.0 software. The CHP file is an output file generated from the analysis of each probe array. Filtering tools in the GeneSpring software were used to identify significantly up-regulated and down-regulated genes affected by the lipid infusion. Quantitative TaqMan Real Time PCR (Q-RT-PCR)—Muscle expression of various genes was determined using the one-step Q-RT-PCR from the total RNA used for the microarray analysis. Q-RT-PCR was performed on the ABI PRISM 7900HT sequence detection system (Applied Biosystems, Foster City, CA) using TaqMan One Step RT-PCR Master Mix reagents and the Assay On Demand gene expression primer pair and probes (Applied Biosystems). To determine the efficiencies of each primer pair and probe set, a standard curve was generated by serial dilution of an RNA sample taken from a healthy subject. Each sample was run in duplicate, and the mean value of the duplicate was used to calculate the mRNA expression of the gene of interest and an endogenous control. The quantity of the gene of interest in each sample was normalized to that of 18 S ribosomal RNA using the comparative (2–ΔΔct) method (23Livak K.J. Schmittgen T.D. Methods. 2001; 25: 402-408Crossref PubMed Scopus (127155) Google Scholar). Statistical comparisons were done using paired t tests. Immunoblot Analysis and Immunofluorescence Staining—Detergent lysates of muscle biopsies were resolved by SDS-polyacrylamide gel electrophoresis as described (19Cusi K. Maezono K. Osman A. Pendergrass M. Patti M.E. Pratipanawatr T. DeFronzo R.A. Kahn C.R. Mandarino L.J. J. Clin. Investig. 2000; 105: 311-320Crossref PubMed Scopus (918) Google Scholar). Proteins were transferred to nitrocellulose membranes, and the membranes were probed with various antibodies. Membranes were developed using Western Lightning reagents (PerkinElmer Life Sciences) and digitized and quantified using a VersaDoc 5000 imaging system (Bio-Rad). Monoclonal antibodies directed against collagens and procollagens were a generous gift of Dr. Nirmala SundarRaj at the University of Pittsburgh. Rabbit anti-connective tissue growth factor (CTGF) antibody was obtained from Torrey Pines Biolabs (Houston, TX). Five-micrometer frozen sections of muscle biopsy specimens were probed using anti-collagen I and collagen III monoclonal antibodies (gift of Dr. SundarRaj), each at a dilution of 1:500. After exposure to fluorescein isothiocyanate-conjugated goat anti-mouse IgG, images were digitized using Spot v.3.5 software (Diagnostic Instruments, Inc., Sterling Heights, MI). Statistical comparisons were done using paired t tests. Other Analyses—Plasma insulin and FFA concentrations were determined by radioimmunoassay (Diagnostic Products, Los Angeles, CA) and enzymatic kit (NEFA-C, Wako Pure Chemicals, Osaka, Japan), respectively. Plasma samples were deproteinized by the Somogyi method for the calculation of glucose-specific activity, which was used to calculate the rates of glucose metabolism (20DeFronzo R.A. Tobin J.D. Andres R. Am. J. Physiol. 1979; 237: E214-E223PubMed Google Scholar). The statistical significance of difference between means for in vivo data was determined using paired or non-paired Student's t tests where appropriate (see above for statistical analysis of microarray data). Subject Characteristics and in Vivo Data—Seven healthy subjects (three men and four women) with a mean (±S.E.) age of 43 ± 6 years and a body mass index of 24.5 ± 1.3 kg/m2 participated in the study. The fasting plasma glucose concentration was within the normal range at 92 ± 2 mg/dl. Each subject had a normal oral glucose tolerance and was studied on two occasions with 48 h of Liposyn III (60 ml/hr) or saline control infusion. Plasma FFA concentration (0.48 ± 0.02 mm) after saline increased to 1.73 ± 0.43 mm after lipid infusion (p < 0.01). Fasting plasma insulin concentrations were 4 ± 1 microunits/ml after saline and 5 ± 1 microunits/ml after lipid infusion. Basal rates of glucose appearance did not differ between the saline and lipid studies (1.92 ± 0.12 versus 2.07 ± 0.09 mg/(kg·min); p = 0.09). After 48 h of lipid or saline infusion, subjects received a 4-h euglycemic hyperinsulinemic clamp (80 milliunits/m2·min) with tritiated glucose. Steady state plasma insulin concentrations during insulin infusion were similar in the saline and lipid infusion studies (107 ± 4 versus 108 ± 5 μunits/ml). After saline, insulin increased the rate of glucose disposal to 8.82 ± 0.69 mg/(kg·min). Lipid infusion decreased the rate of insulin-stimulated glucose disposal to 6.67 ± 0.66 mg/(kg·min); p = 0.005. During the saline study, insulin completely suppressed endogenous glucose production to –0.46 ± 0.17 mg/(kg·min). After the lipid infusion, there was a tendency for reduced suppression of endogenous glucose production (0.19 ± 0.33 mg/(kg·min); p = 0.06 versus saline). Gene Set Expression Analysis—The present study was undertaken in part to test the hypothesis that an experimental increase in plasma lipids decreased the expression of nuclear encoded mitochondrial genes. Accordingly, using gene set analysis we tested whether sets of such genes were decreased in muscle after the lipid infusion. The gene expression values obtained using the Robust Multi-array Analysis method were analyzed in the gene set analysis as described under “Materials and Methods.” A number of gene sets were significantly (p < 0.05) decreased in expression after the lipid infusion (Table I). Gene set analysis revealed a significant decrease in the mitochondria_HG-U133A set of nuclear encoded mitochondrial genes. To support this observation, the c20_mitochondrial gene set, which includes a set of co-regulated genes involved in oxidative phosphorylation, also yielded significance. In addition, the uncoupling protein gene set was significantly decreased in response to the experimental increase in lipids.Table IGene sets significantly decreased in expression by lipid infusion (p < 0.05)Gene SetsMitochondria_HG-U133A_probesc20_Mitochondrial gene setUncoupling proteinsHypothetical proteinsNuclear receptorProgesteroneMAP00550_peptidoglycan_biosynthesisMAP00630_glyoxylate_and_dicarboxylate_metabolismMAP00910_nitrogen_metabolismc19_Protein biosynthesis gene setc30_Cell motility and DNA binding gene set Open table in a new tab Global gene expression profiling also allowed us to test the hypothesis that the expression of other, perhaps unpredicted genes would be changed by a lipid infusion and the consequent increase in plasma FFA. A number of gene sets significantly increased in expression following the lipid infusion including, among others, the collagen, fibronectin, and c7_extracellular matrix gene set (Table II). Also significantly increased were the GLUCO_HG-U133A set of gluconeogenesis genes and the FA_BIO_HG-U133A set, which include the fatty acid biosynthesis genes.Table IIGene sets significantly increased in expression by lipid infusion (p < 0.05)Gene SetsCollagenFibronectinc7_Extracellular matrix gene setDecarboxylaseProstaglandinProteasomeFa_Bio_HG-U133A_probesGluco_HG-U133A_probesGlycol_HG-U133A_probesMAP00010_glycolysis_gluconeogenesisMAP00040_pentose_and_glucuronate_interconversionsMAP00052_galactose_metabolismMAP00450_selenoamino_acid_metabolismMAP00472_d_arginine_and_d_ornithine_metabolismMAP00522_erythromycin_biosynthesisMAP00590_prostaglandin_and_leukotriene_metabolismMAP00600_sphingoglycolipid_metabolismMAP00710_carbon_fixationMAP00970_aminoacyl_tRNA_biosynthesisc18_Regulation of transcription gene set Open table in a new tab Single Gene Expression Analysis—Expression values obtained using the Robust Multi-array Analysis method and analyzed using SAM revealed a number of genes that were significantly increased or decreased. Likewise, the GeneSpring software also identified a number of differentially expressed genes. Lipid infusion significantly increased the expression of 198 individual genes using SAM and 90 genes with GeneSpring analysis (specific genes differentially expressed are given in supplemental Table I, a and b). Of the 198 genes increased in expression using SAM, 34 were concordant with results from the GeneSpring analysis (Table III). Many of these increasers were genes coding for extracellular matrix proteins including collagens, fibronectin, lumican, thrombospondin, and proteoglycans.Table IIIConcordant individual genes with increased expression after lipid infusionGene nameaThe word “concordant” in the title is defined as having expression levels increased using both SAM and GeneSpring analysis.Gene symbolChromosomeFold changeSAMGeneSpringMatrix metalloproteinase 2MMP216q13-q211.4>1.5LegumainLGMN14q32.11.2>1.5Disabled homolog 2, mitogen-responsive phosphoproteinDAB25p131.2>1.5Collagen, type VI, α3COL6A32q372.7>1.5LumicanLUM12q21.3-q222.5>1.5Collagen, type III, α1COL3A12q3112.3>1.5OGT-interacting protein, 106 kDaOIP1063p25.3-p24.11.1Collagen, type I, α1COL1A117q21.3-q22.19.3>1.5Collagen, type I, α2COL1A27q22.13.7>1.5Collagen, t
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