Silencing of enzymes involved in ceramide biosynthesis causes distinct global alterations of lipid homeostasis and gene expression
2012; Elsevier BV; Volume: 53; Issue: 8 Linguagem: Inglês
10.1194/jlr.m020941
ISSN1539-7262
AutoresWanida Ruangsiriluk, Shaun Grosskurth, Daniel Ziemek, Max Kuhn, Shelley G. Des Etages, Omar L. Francone,
Tópico(s)Ginger and Zingiberaceae research
ResumoDysregulation of ceramide synthesis has been associated with metabolic disorders such as atherosclerosis and diabetes. We examined the changes in lipid homeostasis and gene expression in Huh7 hepatocytes when the synthesis of ceramide is perturbed by knocking down serine pal mitoyltransferase subunits 1, 2, and 3 (SPTLC123) or dihydroceramide desaturase 1 (DEGS1). Although knocking down all SPTLC subunits is necessary to reduce total ceramides significantly, depleting DEGS1 is sufficient to produce a similar outcome. Lipidomic analysis of distribution and speciation of multiple lipid classes indicates an increase in phospholipids in SPTLC123-silenced cells, whereas DEGS1 depletion leads to the accumulation of sphingolipid intermediates, free fatty acids, and diacylglycerol. When cer amide synthesis is disrupted, the transcriptional profiles indicate inhibition in biosynthetic processes, downregulation of genes involved in general endomembrane traffi cking, and upregulation of endocytosis and endosomal recycling. SPTLC123 silencing strongly affects the expression of genes involved with lipid metabolism. Changes in amino acid, sugar, and nucleotide metabolism, as well as vesicle trafficking between organelles, are more prominent in DEGS1-silenced cells. These studies are the first to provide a direct and comprehensive understanding at the lipidomic and transcriptomic levels of how Huh7 hepatocytes respond to changes in the inhibition of ceramide synthesis. Dysregulation of ceramide synthesis has been associated with metabolic disorders such as atherosclerosis and diabetes. We examined the changes in lipid homeostasis and gene expression in Huh7 hepatocytes when the synthesis of ceramide is perturbed by knocking down serine pal mitoyltransferase subunits 1, 2, and 3 (SPTLC123) or dihydroceramide desaturase 1 (DEGS1). Although knocking down all SPTLC subunits is necessary to reduce total ceramides significantly, depleting DEGS1 is sufficient to produce a similar outcome. Lipidomic analysis of distribution and speciation of multiple lipid classes indicates an increase in phospholipids in SPTLC123-silenced cells, whereas DEGS1 depletion leads to the accumulation of sphingolipid intermediates, free fatty acids, and diacylglycerol. When cer amide synthesis is disrupted, the transcriptional profiles indicate inhibition in biosynthetic processes, downregulation of genes involved in general endomembrane traffi cking, and upregulation of endocytosis and endosomal recycling. SPTLC123 silencing strongly affects the expression of genes involved with lipid metabolism. Changes in amino acid, sugar, and nucleotide metabolism, as well as vesicle trafficking between organelles, are more prominent in DEGS1-silenced cells. These studies are the first to provide a direct and comprehensive understanding at the lipidomic and transcriptomic levels of how Huh7 hepatocytes respond to changes in the inhibition of ceramide synthesis. cholesteryl ester cardiolipin causal reasoning engine (algorithm) diacylglycerol dihydroceramide desaturase 1 dihydroceramide endoplasmic reticulum protein C-ets1 free cholesterol false discovery rate gene set enrichment analysis hepatic nuclear factor GTPase HRas LDL receptor lysophophatidylcholine phosphatidylcholine phosphatidylethanolamine phosphatidylinositol phosphatidylserine serine palmitoyltransferase SPTLC subunits 1, 2, 3 triacylglycerol transforming growth factor B1 Dyslipidemia leading to cardiovascular and metabolic diseases remains a major issue in many developed countries. Oversupply from diets and poor processing of lipids at a cellular level can promote greater risk toward metabolic disorders. Recent studies have become more focused on the function and regulation of different lipid classes, including sphingolipids, to understand their contribution to diseases. For instance, sphingolipids, such as ceramides, are highly elevated in the serum, liver, and muscle of type 2 diabetic patients (1Haus J.M. Kashyap S.R. Kasumov T. Zhang R. Kelly K.R. Defronzo R.A. Kirwan J.P. Plasma ceramides are elevated in obese subjects with type 2 diabetes and correlate with the severity of insulin resistance.Diabetes. 2009; 58: 337-343Crossref PubMed Scopus (446) Google Scholar) and insulin-resistant rodents (2Holland W.L. Summers S.A. Sphingolipids, insulin resistance, and metabolic disease: new insights from in vivo manipulation of sphingolipid metabolism.Endocr. Rev. 2008; 29: 381-402Crossref PubMed Scopus (427) Google Scholar). Besides being a component of lipid rafts and cell membranes, sphingolipids have been reported to function as mediators of cell growth, cell differentiation, and cell death (3Hannun Y.A. Obeid L.M. The ceramide-centric universe of lipid-mediated cell regulation: stress encounters of the lipid kind.J. Biol. Chem. 2002; 277: 25847-25850Abstract Full Text Full Text PDF PubMed Scopus (743) Google Scholar–7Hannun Y.A. Obeid L.M. Principles of bioactive lipid signalling: lessons from sphingolipids.Nat. Rev. Mol. Cell Biol. 2008; 9: 139-150Crossref PubMed Scopus (2440) Google Scholar). De novo ceramide biosynthesis starts with the first and rate-limiting step to condense serine and palmitoyl-CoA by an enzyme complex called serine palmitoyltransferase (SPTLC) producing 3-ketosphinganine (Fig. 1A). During the next step, 3-ketosphinganine is reduced by 3-ketosphinganine reductase to generate sphinganine. Depending on the tissue distribution, a certain family member of ceramide synthase (CerS) preferentially uses a certain chain-length fatty acyl-CoA to convert sphinganine to dihydroceramide (dhCer), which is then desaturated by dihydroceramide desaturase (DEGS) to yield a unique subspecies of ceramide. Currently, the major identified SPTLC subunits, SPTLC1, SPTLC2, and SPTLC3, are differentially expressed in human tissues (8Hornemann T. Richard S. Rutti M.F. Wei Y. von Eckardstein A. Cloning and initial characterization of a new subunit for mammalian serine-palmitoyltransferase.J. Biol. Chem. 2006; 281: 37275-37281Abstract Full Text Full Text PDF PubMed Scopus (139) Google Scholar). Both SPTLC2 and SPTLC3 subunits contains pyridoxal phosphate binding motif, but each appears to have a different substrate preference as shown in the overexpressed human embryonic kidney cells (9Hornemann T. Penno A. Rutti M.F. Ernst D. Kivrak-Pfiffner F. Rohrer L. von Eckardstein A. The SPTLC3 subunit of serine palmitoyltransferase generates short chain sphingoid bases.J. Biol. Chem. 2009; 284: 26322-26330Abstract Full Text Full Text PDF PubMed Scopus (121) Google Scholar). Whereas palmitate-CoA is the predominant substrate for SPTLC2, mysristoyl and lauryl-CoA are preferential substrates for SPTLC3, resulting in a different chain length of the sphingoid base. Another gene family (ssSPT) with two small subunits has recently been discovered and is believed to be associated with the major SPTLC subunits to form a large enzyme complex (10Han G. Gupta S.D. Gable K. Niranjanakumari S. Moitra P. Eichler F. Brown Jr., R.H. Harmon J.M. Dunn T.M. Identification of small subunits of mammalian serine palmitoyltransferase that confer distinct acyl-CoA substrate specificities.Proc. Natl. Acad. Sci. USA. 2009; 106: 8186-8191Crossref PubMed Scopus (175) Google Scholar). Multiple reports indicate that using myriocin to reduce SPTLC activities decreases the production of ceramide and is suggested to prevent the formation or slow the progression of atherosclerotic lesions, improve plasma lipid profiles, and prevent the onset of diabetes in rodents (11Park T.S. Rosebury W. Kindt E.K. Kowala M.C. Panek R.L. Serine palmitoyltransferase inhibitor myriocin induces the regression of atherosclerotic plaques in hyperlipidemic ApoE-deficient mice.Pharmacol. Res. 2008; 58: 45-51Crossref PubMed Scopus (86) Google Scholar–13Holland W.L. Brozinick J.T. Wang L.P. Hawkins E.D. Sargent K.M. Liu Y. Narra K. Hoehn K.L. Knotts T.A. Siesky A. et al.Inhibition of ceramide synthesis ameliorates glucocorticoid-, saturated-fat-, and obesity-induced insulin resistance.Cell Metab. 2007; 5: 167-179Abstract Full Text Full Text PDF PubMed Scopus (906) Google Scholar). Not only has inhibiting SPTLC activity by myriocin shown to be beneficial but also a small interfering RNA (siRNA) against SPTLC1 stimulates cholesterol efflux in macrophages by activating ATP cassette binding transporter 1 (ABCA1) (14Tamehiro N. Zhou S. Okuhira K. Benita Y. Brown C.E. Zhuang D.Z. Latz E. Hornemann T. von Eckardstein A. Xavier R.J. et al.SPTLC1 binds ABCA1 to negatively regulate trafficking and cholesterol efflux activity of the transporter.Biochemistry. 2008; 47: 6138-6147Crossref PubMed Scopus (41) Google Scholar). In addition, depletion of SPTLC in human macrophages shows a reduction in palmi tate-induced proinflamatory cytokine secretion of tumor necrosis factor (TNF)-α and interleukin (IL)-1β (15Håversen L. Danielsson K.N. Fogelstrand L. Wiklund O. Induction of proinflammatory cytokines by long-chain saturated fatty acids in human macrophages.Atherosclerosis. 2009; 202: 382-393Abstract Full Text Full Text PDF PubMed Scopus (187) Google Scholar). A recent genome-wide association study in European populations established a strong correlation between SPTLC3 single nucleotide polymorphisms and circulating sphingolipid levels (16Hicks A.A. Pramstaller P.P. Johansson A. Vitart V. Rudan I. Ugocsai P. Aulchenko Y. Franklin C.S. Liebisch G. Erdmann J. et al.Genetic determinants of circulating sphingolipid concentrations in European populations.PLoS Genet. 2009; 5: e1000672Crossref PubMed Scopus (149) Google Scholar). The inhibition of DEGS1 is hypothesized to benefit diabetes and cancer. DEGS1 is a protein that contains three histidine consensus motifs, a hallmark of the membrane fatty acid desaturase group. A study carried out by Holland et al. reported that heterozygous knockout mice are more sensitive to insulin and have normal glucose tolerance (13Holland W.L. Brozinick J.T. Wang L.P. Hawkins E.D. Sargent K.M. Liu Y. Narra K. Hoehn K.L. Knotts T.A. Siesky A. et al.Inhibition of ceramide synthesis ameliorates glucocorticoid-, saturated-fat-, and obesity-induced insulin resistance.Cell Metab. 2007; 5: 167-179Abstract Full Text Full Text PDF PubMed Scopus (906) Google Scholar). In addition, silencing of DEGS1 in human neuroblastoma cells decreases ceramide synthesis and inhibits cell growth and cell-cycle progression through the dephosphorylation of the retinoblastoma protein (17Kraveka J.M. Li L. Szulc Z.M. Bielawski J. Ogretmen B. Hannun Y.A. Obeid L.M. Bielawska A. Involvement of dihydroceramide desaturase in cell cycle progression in human neuroblastoma cells.J. Biol. Chem. 2007; 282: 16718-16728Abstract Full Text Full Text PDF PubMed Scopus (133) Google Scholar). Regardless of several reports describing various aspects of SPTLC and DEGS, the global changes in transcriptional regulation and lipid homeostasis responding to specific and targeted knockdown of these enzymes have not been closely examined. As liver is a major site of lipid production and homeostasis, a human hepatocarcinoma cell line, Huh7, is used as a model in this study. We utilize siRNA as a tool to knock down SPTLC or DEGS1 and to investigate the changes in lipids and gene expression as a result of ceramide reduction. Our initial hypothesis is that the changes in lipid profiles and transcriptional regulations will differ between the inhibition of SPTLC and DEGS1, although the depletion of either enzyme yields a similar reduction in the ceramide levels. Human hepatocarcinoma (Huh7) cells were obtained from Dr. Yi Luo (Pfizer Inc., Groton, CT) (18Luo Y. Shelly L. Sand T. Chang G. Jiang X.C. Identification and characterization of dual inhibitors for phospholipid transfer protein and microsomal triglyceride transfer protein.J. Pharmacol. Exp. Ther. 2010; 335: 653-658Crossref PubMed Scopus (5) Google Scholar) and maintained at 70–80% confluence in the growing media consisting of high-glucose DMEM (Life Technologies, Carlsbad, CA), 10% fetal bovine serum (BSA, Sigma-Aldrich, St. Louis, MO), 2 mM L-glutamine, 10 mM hepes, and 1% penicillin-streptomycin (Life Technologies) at 37°C, 5% CO2. Huh7 cells were seeded at 60,000 cells/well in a 12-well plate or 6 millions cells/500 cm2 plate overnight. Next day, Silencer® Select siRNA (Life Technologies) against SPTLC123 or DEGS1 gene and siLentFect™ lipid reagent (Bio-Rad, Hercules, CA) was prepared in OptiMEM according to the manufacturer protocol. Silencer® Select scramble #1 was used as a nontargeting control. The siRNA-lipid complex was added to each well to yield the final concentration of 20 nM siRNA and incubated for 24 h at 37°C, 5% CO2. Media was replaced with the fresh-growing media, and cells were incubated for another 24 h before the conditioned media (DMEM + 1% FBS + fatty acid free BSA) was added to the cells and incubated for another 24 h prior to harvest. In the QuantiGene 2.0 assay, cells were harvested by washing twice with Dulbecco's phosphate-buffered saline (D'PBS) and lysed with QuantiGene sample processing buffer (Affymetrix, Fremont, CA) according to manufacturer protocol. Corresponding gene-specific probes were purchased from Affymetrix. The QuantiGene 2.0 assay was used to detect mRNA expression of the gene of interest, and each relative light value was normalized to cyclophillin A (PPIA) expression. The expression of target genes from CRE analysis (supplementary File VI) was performed using Taqman® Gene Expression method. Total RNA of siRNA-transfected cells was isolated using RNeasy Mini Kit (Qiagene, Valencia, CA). Quantitative PCR was performed according to the manufacturing protocol, in a 7900HT Sequence Detection System (Life Technologies) using reagents and gene specific Taqman® assays (Life Technologies) as indicated in the table in supplementary File VI. Relative mRNA levels were determined using the comparative Ct method and normalized to PPIA reference gene. Cells were washed with D'PBS and lysed in 1× RIPA buffer (Sigma-Aldrich, St. Louis, MO), proteinase inhibitor cocktail and phosphatase inhibitor cocktail (Roche Diagnostics, Indianapolis, IN). The supernatant was recovered by centrifugation, and the protein concentration was quantified in the BCA assay. Protein lysates were subjected to SDS-PAGE and Western blot analysis. Antibodies purchased commercially were against human SPTLC1 (mouse monoclonal, Santa Cruz Biotechnology, Santa Cruz, CA), SPTLC2 (rabbit polyclonal, Abcam, Cambridge, MA), SPTLC3 (goat polyclonal, Santa Cruz Biotechnology), β-actin (mouse monoclonal, Sigma-Aldrich), and LDLR (goat polyclonal, R and D Systems, Minneapolis, MN). Cells were seeded and transfected with siRNA according to the transfection protocol described above and left in the growth media for 48 h. At 24 h before harvest, the growing media was replaced with the conditioned media. The transfected cells were subjected to cell proliferation or caspase 3/7 assay at the end of 24 h conditioned media treatment. Cell proliferation was performed using CyQUANT Direct Cell Proliferation assay kit (Life Technologies), and the Caspase 3/7 assay was performed using CellEvent Caspase-3/7 Green Detection Reagent (Life Technologies) following the manufacturer's protocol. Fluorescence was measured using SpectraMax Gemini EM plate reader (Molecular Devices, Sunnyvale, CA). For the SPT activity, a radiolabeled 14C-serine was added to the siRNA-transfected cells that were grown in the conditioned media for 4 h before harvest. Cells were harvested with trypsin and washed three times with cold D'PBS. Lipid was extracted using Blight-Dye method (19Bligh E.G. Dyer W.J. A rapid method of total lipid extraction and purification.Can. J. Biochem. Physiol. 1959; 37: 911-917Crossref PubMed Scopus (42689) Google Scholar) and spotted on a Baker-flex silica plate (Avantor Performance Materials, Phillipsburg, NJ) with a marker. The plate was run in a closed chamber with chloroform-methanol solvent. The signal was quantified with a scintillation detector. Total RNA isolation and hybridizations to the human genome U133 Plus 2.0 Affymetrix array chip were performed by Gene Logic. All .CEL files from the microarray experiment have been submitted to GEO (http://www.ncbi.nlm.nih.gov/geo/) and can be identified with the accession ID GSE28059 (20Barrett T. Troup D.B. Wilhite S.E. Ledoux P. Rudnev D. Evangelista C. Kim I.F. Soboleva A. Tomashevsky M. Edgar R. NCBI GEO: mining tens of millions of expression profiles–database and tools update.Nucleic Acids Res. 2007; 35: D760-D765Crossref PubMed Scopus (1087) Google Scholar). All data is minimum information about a microarray experiment (MIAME) compliant. First, .CEL files were normalized using robust multi-array analysis (RMA) (21Irizarry R.A. Hobbs B. Collin F. Beazer-Barclay Y.D. Antonellis K.J. Scherf U. Speed T.P. Exploration, normalization, and summaries of high density oligonucleotide array probe level data.Biostatistics. 2003; 4: 249-264Crossref PubMed Scopus (8454) Google Scholar). Then, pair-wise comparison between the different siRNA groups was performed using a simple linear model to calculate P values, followed by Benjamini and Hochberg false discovery rate (FDR) correction. Prior to bioin formatic analysis, Affymetrix probe sets designated with a "_x" were removed because they potentially lacked gene specificity. All remaining probes were annotated with information provided by NetAffx (Affymetrix), Gene Entrez ID (NCBI), Ingenuity Pathway Analysis (IPA; Ingenuity Sytems), and Ariadne Pathway Studios tools (MedScan Technology). For ease of comparison, bioinformatic analysis was performed so that all the treatment groups were compared with the scrambled siRNA treatment. An expression difference between treatments for a probe was considered significant if the fold-change was ≥1.5 and the FDR-adjusted P value ≤ 0.05. Hierarchical clustering was performed with Spotfire Decision Site 9.0 (http://www.spotfire.com) using a UPGMA clustering method, cosine similarity measure, and an input rank-order function. Gene set enrichment analysis (GSEA) was performed with DAVID Bioinformatics Resources 6.7, in which gene sets were considered significant if the enrichment score was greater than 2.0 (22Huang da W. Sherman B.T. Lempicki R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.Nat. Protoc. 2009; 4: 44-57Crossref PubMed Scopus (25338) Google Scholar, 23Dennis Jr., G. Sherman B.T. Hosack D.A. Yang J. Gao W. Lane H.C. Lempicki R.A. DAVID: database for annotation, visualization, and integrated discovery.Genome Biol. 2003; 4 (: P): 3Crossref PubMed Google Scholar). Pathway and network analysis was performed using IPA tools (Ingenuity Systems) and Ariadne Pathway Studios tools (MedScan Technology). Causal reasoning engine (CRE) is an algorithm to infer upstream molecular mechanisms consistent with observed expression changes (24Chindelevitch L. Ziemek D. Enayetallah A. Randhawa R. Sidders B. Brockel C. Huang E.S. Causal reasoning on biological networks: interpreting transcriptional changes.Bioinformatics. 2012; 28: 1114-1121Crossref PubMed Scopus (103) Google Scholar). Causal reasoning algorithms can be viewed as a form of gene set enrichment in which gene sets are defined by their common response to a defined molecular perturbation (e.g., inhibition of TNF activity). Two metrics to quantify the significance of an upstream hypothesis with respect to the experimental data were considered: The enrichment P value treats each set of downstream response genes as one gene set and does not take the direction of regulation into account. The correctness P value also accounts for upregulation and downregulation in the experimentally observed regulations as well as the literature-derived changes downstream of the molecular perturbation. To ensure statistical significance under a null model of randomly reassigning upregulated and downregulated transcripts to arbitrary transcript nodes, we recomputed this score 1,000 times under the null model and computed approximate P values. If the real score observed in our data was always greater than in any randomly assigned run, we noted a P value < 0.001. The user of CRE applies cutoffs for both metrics of the automatically derived hypotheses. Then the user manually interprets the hypotheses for validity and context. For the purpose of this study, hypotheses were limited to those that are one step away from the observed transcriptional data and are reasonable for the Huh7 model used in the experiment. In addition, the nominal enrichment and correctness P values were limited based on a threshold of <0.01. Ultimately, we focused on the top 20 hypotheses and note that most of them have significant enrichment P values even under the conservative Bonferroni correction for multiple testing (number of tests is ∼3,500 in both cases; resulting Bonferroni threshold is 0.05 / 3,500 = 1.4e−05). To analyze ceramides from the whole-cell lysate, Huh7 cells were lyzed with a homogenizer and immediately run on the high performance liquid chromatography (HPLC) system. Chromatography was achieved using a Phenomenex Synergi Polar-RP (2.0 × 150 mm, 4 μm, 80A) column. The injection volume of cell lysate was 30 μl with the flow rate at 0.220 ml/min. The HPLC system was coupled to an Applied Biosystems API 4000 mass spectrometer operated in positive multiple reaction monitoring (MRM) mode. The transition was monitored for ceramide C16:0, C18:0, C20:0, and C24:1. The quantity of ceramide was normalized with total protein and calculated as a percentage of the scrambled siRNA-treated sample. Subspecies of purified sphingolipids were analyzed at Lipidomics Core Facility of the Medical University of South Carolina. Briefly, to extract sphingolipids, cell pellets were sonicated twice for 30 s each in 2 ml of isopropanol-water-ethyl acetate (30:10:60, v/v). After vortex and spin down at 4,000 g for 10 min, supernatants were pooled, dried, and then further extracted following the Bligh-Dye extraction method (19Bligh E.G. Dyer W.J. A rapid method of total lipid extraction and purification.Can. J. Biochem. Physiol. 1959; 37: 911-917Crossref PubMed Scopus (42689) Google Scholar). Sphingolipids were measured by high-performance liquid chromatography-tandem mass spectrometry on a Thermo Finnigan (Waltham, MA) TSQ 7000 triple quadrupole mass spectrometer operating in a MRM positive ionization mode as previously described (25Bielawski J. Szulc Z.M. Hannun Y.A. Bielawska A. Simultaneous quantitative analysis of bioactive sphingolipids by high-performance liquid chromatography-tandem mass spectrometry.Methods. 2006; 39: 82-91Crossref PubMed Scopus (420) Google Scholar). Other lipid classes, including triacylglycerol, diacylglycerol, glycerolphospholipids, free cholesterol, cholesteryl ester, free fatty acids, cardiolipin, and lysophosphatidylcholine, were analyzed at Lipomics Technologies, West Sacramento, CA. The lipids from cell pellets were extracted in the presence of authentic internal standards by the method of Folch et al. (26Folch J. Lees M. Sloane Stanley G.H. A simple method for the isolation and purification of total lipides from animal tissues.J. Biol. Chem. 1957; 226: 497-509Abstract Full Text PDF PubMed Google Scholar) using chloroform:methanol (2:1 v/v). Individual lipid classes within each extract were separated by liquid chromatography (Agilent Technologies model 1100 series). Each lipid class was transesterified in 1% sulfuric acid in methanol in a sealed vial under a nitrogen atmosphere at 100°C for 45 min. The resulting fatty acid methyl esters were extracted from the mixture with hexane containing 0.05% butylated hydroxytoluene and were prepared for gas chromatography by sealing the hexane extracts under nitrogen. Fatty acid methyl esters were separated and quantified by capillary gas chromatography (Agilent Technologies model 6890) equipped with a 30 m DB 88MS capillary column (Agilent Technologies) and a flame ionization detector. Mass values for lipids were transformed to a ratio relative to scrambled siRNA by comparing each treatment with the average mass for the scrambled siRNA treatment. Statistic analysis was performed according to the following steps unless indicated otherwise. Treatment groups and lipid ratios were statistically compared by two-way ANOVA to calculate P values, followed by a Bonferroni FDR correction using GraphPad Prism version 5.01 for Windows (GraphPad Software, San Diego, CA). A difference between treatments for a lipid amount was considered significant if fold-change ≥ 1.5 (transformation of the calculated ratio) and the FDR-adjusted P value ≤ 0.05. Significant lipids were annotated with information provided by PubChem (NCBI), IPA tools (Ingenuity Sytems), and the Human Metabolome Database (27Wishart D.S. Knox C. Guo A.C. Eisner R. Young N. Gautam B. Hau D.D. Psychogios N. Dong E. Bouatra S. et al.HMDB: a knowledgebase for the human metabolome.Nucleic Acids Res. 2009; 37: D603-D610Crossref PubMed Scopus (1497) Google Scholar). Media from siRNA-treated cells was recovered from each well before harvest. Cells were washed with D'PBS three times before lysing with cell lysis buffer (50 mM HEPES, 1 mM EDTA, 1% NP40, 120 mM NaCl, 1% glycerol) and centrifugation. The lysate was then subjected to BCA assay for protein quantification. ApoB ELISA was performed as described previously (18Luo Y. Shelly L. Sand T. Chang G. Jiang X.C. Identification and characterization of dual inhibitors for phospholipid transfer protein and microsomal triglyceride transfer protein.J. Pharmacol. Exp. Ther. 2010; 335: 653-658Crossref PubMed Scopus (5) Google Scholar). Data was plotted using Microsoft Excel or GraphPad Prism software. Unless stated in the legend of each figure or table, a basic statistic analysis was performed using one-way ANOVA. A P value < 0.05 compared with scrambled siRNA treatment was considered as significant. We determined the mRNA levels of the three major SPTLC subunits (SPTLC1, SPTLC2, and SPTLC3) and DEGS1 in Huh7 cells. Relative to cyclophillin A, the major SPTLC subunits expressed in this cell line are SPTLC1 and SPTLC3 (Fig. 1B). In agreement with a previous study (8Hornemann T. Richard S. Rutti M.F. Wei Y. von Eckardstein A. Cloning and initial characterization of a new subunit for mammalian serine-palmitoyltransferase.J. Biol. Chem. 2006; 281: 37275-37281Abstract Full Text Full Text PDF PubMed Scopus (139) Google Scholar), the expression of SPTLC2 was noticeably low (∼10-fold lower than SPTLC1 and ∼7-fold lower than SPTLC3). The expression of DEGS1 was also detectable in Huh7 cells, although at a lower level than SPTLC1 or SPTLC3. DEGS1 and the three subunits of SPTLC were downregulated using siRNA without affecting cell morphology or viability as determined by the trypan blue staining method. There was no significant difference on mRNA knockdown 24, 48, or 72 h post transfection (data not shown). At 72 h post transfection, DEGS1, SPTLC1, and SPTLC3 mRNA levels were reduced by more than 75%. SPTLC2 mRNA was decreased by only ∼50% (Fig. 1C). Similar knockdown was obtained by using a different set of siRNA oligonucleotides (data not shown). At the protein level, the silencing effects of SPTLC siRNA were detected at 72 h post transfection (Fig. 1D). As expected, the level of SPTLC1 and SPTLC3 protein were dramatically reduced. It was more difficult to visually observe the decrease in SPTLC2 protein compared with other SPTLC subunits. Synchronizing knockdown of three SPTLC subunits yielded a reduced expression similar to knocking down an individual subunit. We were not able to specifically and conclusively detect DEGS1 protein in the whole-cell lysate from scrambled or DEGS1 siRNA-treated cells despite testing all commercially available DEGS1 antibodies. Nevertheless, we believe that the synthesis of DEGS1 was significantly decreased as shown by the changes in the levels of mRNA and different classes of sphingolipids (see Figs. 1C, E and 2A, B, D and Table 1).TABLE 1Significant changes in lipid species in SPTLC123 or DEGS1 siRNA-treated cellsLipid ClassLipid SpeciesScrambled siRNASPTLC123 siRNASPTLC123 PDEGS1 siRNADEGS1 PSPTLC123 versus DEGS1 PCeramideCer 16:01.000−1.469NS−3.408P < 0.01NSCeramideCer 24:01.000−4.485P < 0.001−2.560P < 0.001P < 0.001CeramideCer 24:11.000−3.428P < 0.001−2.152P < 0.001P < 0.001CeramideCer 26:11.000−8.185P < 0.001−3.194P < 0.01NSDihydroceramidedhCer 14:01.000−1.799NS10.072P < 0.001P < 0.001DihydroceramidedhCer 16:01.000−1.497NS12.207P < 0.001P < 0.001DihydroceramidedhCer 18:01.000−1.371NS8.332P < 0.001P < 0.001DihydroceramidedhCer 20:01.0001.268NS10.512P < 0.001P < 0.001DihydroceramidedhCer 22:01.000−1.867NS25.166P < 0.001P < 0.001DihydroceramidedhCer 22:11.000−4.376NS9.928P < 0.001P < 0.001DihydroceramidedhCer 24:01.000−3.427NS67.891P < 0.001P < 0.001DihydroceramidedhCer 24:11.000−3.847NS27.561P < 0.001P < 0.001DihydroceramidedhCer 26:01.000−1.680NS25.921P < 0.001P < 0.001DihydroceramidedhCer 26:11.000−4.689NS78.955P < 0.001P < 0.001SphinganinedhSph1.000−1.041NS7.490P < 0.001P < 0.001SphingomyelinSM 16:01.0001.461P < 0.001−2.546P < 0.001P < 0.001CardiolipinCL 16:01.0001.236P < 0.0011.195P < 0.001NSCardiolipinCL 16:1n71.0001.273P < 0.001−1.144P < 0.01P < 0.001CardiolipinCL 18:1n71.0001.210P < 0.001−1.038NSP < 0.001Cholesteryl esterCE 16:01.0001.201P < 0.051.429P < 0.001P < 0.01Free fatty acidFFA 16:01.0001.026NS1.578P < 0.001P < 0.001Free fatty acidFFA 18:1n91.000−1.156NS1.878P < 0.001P < 0.001DiacylglycerolDAG 16:01.000−1.413NS1.613P < 0.01P < 0.001TriacylglycerolTAG 16:1n71.000−1.273NS−2.645P < 0.05NSTriacylglycerolTAG 18:1n71.000−1.322NS−2.152P < 0.01NSTriacylglycerolTAG 18:1n91.000−1.657P < 0.001−2.414P < 0.001P < 0.01LysophosphatidylcholineLYPC 16:01.0001.703P < 0.011.607P < 0.05NSPhosphatidylcholinePC 16:01.0001.330P < 0.001−1.016NSP < 0.001Phosp
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