Simvastatin Modulates the Alzheimer s Disease-Related Gene seladin-1

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1 Journal of Alzheimer s Disease 28 (2012) 1 5 IOS Press 1 Supplementary Data Simvastatin Modulates the Alzheimer s Disease-Related Gene seladin-1 Maria C. Ramos, Saleta Sierra, Carlos Ramirez, Javier Velasco and Javier S. Burgos Neuron BIO, BioPharma Division, Parque Tecnológico de Ciencias de la Salud, Edificio BIC, Armilla, Granada, Spain Accepted 8 September 2011 GENOMIC ANALYSIS RNA was extracted from of SK-N-MC cells with High Pure RNA isolation kit (Roche Diagnostics). 5 g of RNA acquired from SK-N-MC cells were amplified and labeled with Cy3 (Agilent Technologies Inc.) to produce labeled crna using Agilent low RNA input fluorescent linear amplification kits following the manufacturers protocol. After the amplification and labeling, the dye-incorporation ratio was determined using an Agilent 2100 Bioanalyser (Agilent Technologies Inc.). After washing and drying, the slides were scanned using an Agilent G2565BA scanner and the raw data were generated using Feature Extraction vs. 9.5 Image Analysis Software (Agilent Technologies Inc.). The analysis of raw data was generated with GeneSpring GX (Agilent Technologies Inc.). Data from all samples were subjected to quantile normalization [1] and the expression profiles for probes corresponding to replicated genes were averaged. Differential expression analysis for treated and control cells was performed using the rank product method [2]. A permutation-based estimation procedure with 100 random iterations was then used to Correspondence to: Javier S. Burgos, PhD, Neuron BIO, Bio- Pharma Division, Parque Tecnológico de Ciencias de la Salud, Edificio BIC, Avda. de la Innovación 1, Armilla, Granada, Spain. Tel.: ; Fax: ; jburgos@neuronbio.com. estimate the false discovery rate (FDR) value. Biological functions associated with differentially expressed genes were obtained by examining the Gene Ontology (GO) Biological Processes that were enriched in the list of genes. Correction for multiple hypothesis testing was conducted using the FDR method of Benjamini & Hochberg [3]. Gene networks and canonical pathways representing key genes were identified using Ingenuity Pathways Analysis (IPA, Ingenuity Systems Inc.) database. Briefly, the data set containing gene identifiers and corresponding fold changes was uploaded into the web-delivered application and each gene identifier was mapped to its corresponding gene object in the Ingenuity Pathways Knowledge Base (IPKB). The functional analysis identified the biological functions and/or diseases that were most significant to the data sets. Fisher s exact test was performed to calculate a p-value determining the probability that each biological function and/or disease assigned to the data set was due to chance alone. The data set was mined for significant pathways with the IPA library of canonical pathways and networks were generated by using IPA as graphical representations of the molecular relationships between genes and gene products. Differentially expressed genes were then examined, based on their GO annotation [4]. IPA was used to investigate the biological importance of the observed genome-wide changes by categorizing the obtained data into biological functions (Supplementary Table 1). The data ISSN /12/$ IOS Press and the authors. All rights reserved

2 2 M.C. Ramos et al. / Simvastatin Modulates Seladin-1 Supplementary Table 1 Biological functions as determined by Ingenuity Pathways Analysis (IPA). The table shows the top 10 functions obtained in the IPA analysis of differentially expressed genes identified in the gene expression profiles obtained for the 0.4 and 4 M simvastatin-treated cells. The p values were determined using the Benjamini-Hochberg multiple hypothesis correction method [SIMVASTATIN] 0.4 M B-H p-value [SIMVASTATIN] 4 M B-H p-value Lipid Metabolism 1.16E E-01 Lipid Metabolism 8.14E E-01 Small Molecule Biochemistry 1.16E-D7-1.22E-01 Small Molecule Biochemistry 8.14E E-01 Metabolic Disease 4.10E E-01 Metabolic Disease 6.84E E-02 Skeletal and Muscular Disorders 1.35E E-02 Molecular Transport 6.86E E-01 Endocrine System Disorders 5.71E E-02 Cellular Assembly and Organization 1.11E E-02 Hematological Disease 5.71E E-01 Drug Metabolism 1.11E E-02 Molecular Transport 2.14E E-01 Skeletal and Muscular Disorders 1.43E E-02 Developmental Disorder 2.14E E-01 Cardiovascular Disease 1.53E E-01 Genetic Disorder 2.14E E-02 Nucleic Acid Metabolsim 2.84E E-02 Cardiovascular Disease 2.14E E-01 Endocrine System Disorders 4.43E E-02 set, with the corresponding gene identifiers and fold changes, was uploaded into the web application and each gene identifier mapped to its corresponding gene object in the IPKB. Functional analysis identified the biological functions and/or diseases most significantly associated with the data sets. These included lipid metabolism, small molecule biochemistry, and metabolic disease. Network analysis was then performed to identify the relationships among the involved genes (Supplementary Fig. 1). PROTEOMIC ANALYSIS Differentially expressed proteins were examined using the GO-Tree Machine (GOTM, a GO enrichment analysis tool that compares a protein/gene list with all GO categories to identify those associated with increased protein production. The results obtained after simvastatin treatment are summarized in Supplementary Table 2. Categories showing significantly different expressions after the 4 M dataset were: lipid biosynthesis, acyl group transfer and chromatin and chromosome organization. IN VITRO qrt-pcr Total RNA isolated with the High Pure RNA isolation kit (Roche) was subjected (600 ng RNA) to reverse transcription using the RNA to cdna Kit (Applied Biosystems). cdna was mixed with the TaqMan Gene Expression Master Mix (Applied Biosystems) and the Taqman Gene Expression Assay probe ( Hs m1; Applied Biosystems). PCR (95 C for 10 min plus 40 cycles of 95 C for 15 s, 60 C for 1 min, and a final step at 25 C for 1 min) was performed in a 7500 Fast Real-Time PCR System (Applied Biosystems). The relative quantity was determined by the ddct method using SDS v1.4 software. gapdh was used as the housekeeping gene; its expression did not change at any time in PCR experiments using the Taqman Gene Expression Assay probe Hs m1 (Applied Biosystems). IN VIVO RT-qPCR All experiments were performed in accordance with the guidelines of the European Union, under the supervision of veterinary staff, and were approved by the ethics committees of Neuron BIO and the University of Granada. Male C57BL6 mice from Charles River (Barcelona, Spain), aged 18 weeks, were divided into two groups. All were housed in an air-conditioned room under a 12 L/12D photoperiod until sacrifice and had free access to water and food. Non-fasted mice were i.p-treated with a single dose of 50 mg/kg of simvastatin (12.5 mg/ml in methylcellulose 0.5% [in saline]) (dose was selected from our previous published data [5]). Control animals received an equivalent volume of the vehicle. At 32 h post-injection, mice were deeply anaesthetized with fluorane (2% with O 2 at 0.2 L/min and air at 1.5 L/min) and the whole brain extracted in RNase free conditions and conserved at 80 C until RNA extraction. The whole brain was homogenized in Mili-Q water (1 : 1 dilution) and RNA isolated from 45 mg of the homogenate using the High Pure RNA Isolation Kit (Roche). The RNA obtained (500 ng) was subjected to reverse transcription using the RNA to cdna Kit (Applied Biosystems). cdna was mixed with the TaqMan Gene Expression Master Mix (Applied Biosystems) and the Taqman Gene Expression Assay probe ( Mm m1 [mouse]; Applied Biosystems). PCR was performed

3 M.C. Ramos et al. / Simvastatin Modulates Seladin-1 3 a b c Supplementary Fig. 1. Network analysis was performed to represent relationships between genes. a) Networked genes in the 0.4 M simvastatintreated cells related to lipid metabolism, small molecule biochemistry and metabolic disease; b) genes involved in lipid metabolism, small molecule biochemistry and drug metabolism in the 4 M simvastatin-treated cells; c) genes involved in lipid metabolism, small molecule biochemistry and cardiac disease in the 4 M simvastatin-treated cells. Red icons indicate upregulated genes; the intensity reflects the increase in expression. Grey icons indicate non-regulated genes. White icons indicate non-specified genes, but involved in the network through relationships with other genes. as above in a 7500 Fast Real-Time PCR thermocycler (Applied Biosystems). The relative quantity was determined by the ddct method using SDS v1.4 software; 18S was the housekeeping gene; its expression did not change at any time in PCR experiments using the Taqman Gene Expression Assay probe Hs s1; (Applied Biosystems). IMMUNODETECTION OF SELADIN-1 Paraffin wax-embedded sections of whole brains were deparaffinated and dehydrated. Immunohistochemical labeling was performed by incubating the samples overnight with /seladin-1 rabbit monoclonal antibody (Cell Signaling Technology)

4 4 M.C. Ramos et al. / Simvastatin Modulates Seladin-1 Supplementary Table 2 Gene Ontology (GO) analysis. Differentially expressed proteins were examined with the GO-Tree Machine (GOTM, vanderbilt.edu/gotm/). The list of GO categories enriched in proteins in the proteomic profiles obtained with the 0.4 and 4 M simvastatin-treated cells is shown. The ratio of enrichment indicates the strength of the association; the p value (adjusted by multiple test adjustment) measures its significance [SIMVASTATIN] 0.4 M Ratio p LIPID BIOSYNTHESIS Cholesterol Biosynthesis E-04 Sterol Biosynthesis E-04 Lipid Biosynthesis E-04 Lipid Metabolism E-03 RIBOSOME PROTEINS Ribosome E-03 [SIMVASTATIN] 4 M LIPID BIOSYNTHESIS Cholesterol Biosynthesis E-04 Sterol Biosynthesis E-04 Lipid Biosynthesis E-03 Cellular Lipid Biosynthesis E-03 TRANSFERASE ACTIVITY/TRANSFERRING ACYL GROUPS Transferase Activity/Transferring Acyl Groups E-03 CHROMATIN AND CHROMOSOME ORGANIZATION Chromatin Assembly or Disassembly E-04 Establishment and/or Maintenance of Chromatine Architecture E-03 Chromosome Organization and Biogenesis E-03 FDPS, GGPS1 Geranyl pyrophosphate FDPS, GGPS1 simvastatin HMGCS1, Acetyl-CoA HMGCS2 HMGCR MVK ACAT2 + 3-hydroxy-3-methyl-glutaryl CoA Mevalonate Mevalonate-5-phosphate Acetoacetyl-CoA PMVK IDI1 MVD Dimethyl-allyl pyrophosphate D 3 -isopentenyl pyrophosphate Mevalonate-5-pyrophosphate Farnesyl pyrophosphate + Farnesyl pyrophosphate FDFT1 Squalene SQLE, SEC14L1 TM7SF2 LSS Dimethylcholesta-8,24-dien-3b-ol 14-Demethyl lanosterol CYP51A Lanosterol Squalene-2,3-epoxide SC4MOL Cytochrome b5(dia1) Carboxymethylcholesta- Monomethylcholesta- NSDHL Zymosterone 8,24-dien-3b-ol NSDHL 8,24-dien-3b-ol SC4MOL HSD17B7 Zymosterol Cholest-8(9)-en-3b-ol (Cholesta-8,24-dien-3b-ol) EBP EBP Cholesta-7,24-dien-3b-ol Lathosterol SC5D 7-Dehydrodesmosterol SC5D 7-Dehydrocholesterol DHCR7 Desmosterol DHCR7 Cholesterol Supplementary Diagram 1. Overview of the cholesterol biosynthesis pathway (adapted from [6]). Cholesterol is synthesized from acetyl-coa. 3-hydroxy-3-methylglutaryl CoA reductase (HMGCR) is the rate limiting enzyme since the reduction of 3-hydroxy-3-methylglutaryl CoA to mevalonate is a determining step in cholesterol formation. This enzyme is the first target for simvastatin (in orange). The last step of the cholesterol biosynthesis pathway is catalyzed by seladin-1 () (in blue). Simvastatin inhibits HMGCR at the beginning of the pathway. It also provokes the overexpression of seladin-1.

5 M.C. Ramos et al. / Simvastatin Modulates Seladin-1 5 diluted 1 : 50 in 10% blocking solution (BSA 1% in PBS-Tween 0.05%) at 4 C, and then with goat anti-rabbit biotinylated antibodies (Jackson Immuno- Research) diluted 1 : 500 in PBS at room temperature for 1 h. The samples were then processed using the Elite ABC Perox Kit (Vector Laboratories); the peroxidase reaction product was detected using 3,3 - diaminobenzidinetetrahydrochloride. Finally, samples were counterstained with Mayer s hematoxylin for 5 min and rinsed under running tap water. For negative controls, consecutive sections of each sample were incubated as describe above but without the primary or secondary antibodies. Brains of vehicle-treated mice were included to monitor the specificity of the staining. REFERENCES [1] Bolstad BM, Irizarry RA, Astrand M, Speed TP (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, [2] Breitling R, Armengaud P, Amtmann A, Herzyk P (2004) Rank products: A simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Lett 573, [3] Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: A practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol 57, [4] Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G (2000) Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25, [5] Ramirez C, Tercero I, Pineda A, Burgos JS (2011) Simvastatin is the statin that most efficiently protects against kainateinduced excitotoxicity and memory impairment. J Alzheimers Dis 24, [6] Wilcox CB, Feddes GO, Willett-Brozick JE, Hsu LC, DeLoia JA, Baysal BE (2007) Coordinate up-regulation of TMEM97 and cholesterol biosynthesis genes in normal ovarian surface epithelial cells treated with progesterone: Implications for pathogenesis of ovarian cancer. BMC Cancer 7, 223.