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Supplementary Data Chemical genomics identifies the unfolded protein response as a target for selective cancer cell killing during glucose deprivation Sakae Saito1, Aki Furuno1, Junko Sakurai1, Asami Sakamoto1, Hae-Ryong Park2, Kazuo Shin-ya3, Takashi Tsuruo1, Akihiro Tomida1 1 Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, Tokyo, Japan 2 Department of Food Science and Biotechnology, Kyungnam University, Masan, Korea. 3 Biological Information Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan - 1 - Supplemental Methods Reporter assay. The reporter assay was performed as described previously (1). HT1080 cells were cultured overnight in a 12-well plate (3 u 105 cells/well), and transfection was performed. The cells were incubated for 8 hours with a transfection mixture containing 500 ng of firefly luciferase–containing reporter plasmids (pGRP78pro160-Luc(1) or FLAG-tagged XBP1-Luc) and 1 ng of renilla luciferase–containing plasmid phRL-CMV (Promega, WI, USA) as an internal control. The medium was then replaced with fresh growth medium, and the cells were incubated for another 4 hours. Subsequently, the cells were reseeded in a 96-well plate (5 u 103 cells/well), cultured overnight, and treated for 18 hours with various concentrations of versipelostatin (VST), biguanides or pyrvinium pamoate with or without 10 mM 2-deoxy-D-glucose (2DG) or 5 Pg/mL of Tunicamycin (TM). Relative activity of firefly luciferase to renilla luciferase was determined using the Dual-Glo Luciferase Assay System (Promega). Immunoblot analysis. Cells were lysed in 1u sodium dodecyl sulfate (SDS) sample buffer (62.5 mM Tris-HCl, pH 6.8, 2% SDS, 5% 2-mercaptoethanol, and 10% glycerol), and protein concentrations of the lysates were measured with a Bio-Rad protein assay kit (Bio-Rad, CA, USA). Equal amounts of proteins were resolved on a 4%–20% SDS-polyacrylamide gradient gel and transferred by electroblotting onto a nitrocellulose membrane. Immunoblots were probed with the following antibodies: mouse monoclonal anti-KDEL (for detection of GRP78 and GRP94; StressGen, BC, Canada), rabbit polyclonal anti-eIF2D, anti-phospho-eIF2D (Ser51), rabbit monoclonal anti-AMPKD, anti-phospho-AMPKD (Thr172) (Cell Signaling Technology, Inc., MA, - 2 - USA), rabbit polyclonal anti-CREB-2 (ATF4) (Santa Cruz Biotechnology, Inc., CA, USA), anti-FLAG M2 (to detect FLAG-tagged ATF6 proteins; Sigma) and anti-E-actin (internal control; Sigma). Rabbit polyclonal anti-EIF2AK3 (PERK) antibody was raised against synthetic peptide corresponding to the cytoplasmic domain CKDESTDWPLSSPSP of the human PERK protein. The specific signals were detected with horseradish peroxidase–conjugated second antibody (Amersham Pharmacia Biotech, Tokyo, Japan) and a chemiluminescence detection system (PerkinElmer, Inc., MA, USA). Microarray expression profiles. Total RNA from cultured cells was isolated using the RNeasy RNA purification kit (Qiagen, CA, USA). The quality of total RNA was analyzed using the RNA 6000 Nano LabChip kit on a 2100 Bioanalyzer (Agilent Technologies, CA, USA). cRNA targets for hybridization to GeneChip were prepared by reverse transcription from 5 Pg of total RNA. Targets were then labeled with biotin before fragmentation according to standard Affymetrix protocols. Hybridization to GeneChip Human Genome U133 Plus 2.0 arrays (Affymetrix) was carried out using Fluidics Station 450 and GeneChip Scanner 3000 (Affymetrix). Quantitative real-time PCR. Total RNA from HeLa cell lines was isolated by using the RNeasy RNA purification kit (Qiagen) with DNase I treatment to digest genomic DNA contamination. As a reference, QPCR Reference Human Total RNA (Stratagene, CA, USA) was used. First-strand cDNAs were synthesized by reverse transcription from 5 Pg of total RNA using the SuperScrip II First-Strand Synthesis System (Invitrogen) primed with oligo-(dT)12-18 and random hexamers according to the manufacturer’s instructions. The quantitative real-time PCR was then performed with FAM-labeled D-LUX primer sets (Invitrogen) in 16 genes selected from the UPR-CS - 3 - signature genes. The specific primers were designed with the D-LUX Designer software (Invitogen), and E-actin was used as a control in all experiments. All of the primer sequences are described to Supplemental Table S4. The PCR reaction mixtures were set up with Platinum Quantitative PCR SuperMix-UDG (Invitrogen) according to the manufacturer’s instructions. Each 20 Pl reaction contains 100 nM of each gene-specific primer and the template cDNA (2 nM). The two-step PCR cycling condition consisted of incubation at 50 qC for 15 min, then at 95 qC for 2 min, followed by 45 cycles each at 95 qC for 15 sec and 62 qC for 30 sec. Reactions were conducted in the 96-well spectrofluorometric thermal cycler ABI PRISM 7700 (Applied Biosystems, CA, USA). Fluorescence was monitored during every PCR cycle at the annealing step. - 4 - Supplemental Table S1. Summary of the experimental conditions for 42 microarray samples. Gene expression profiles were determined with Affymetrix Human Genome U133 plus 2.0 microarrays in 4 cancer cell lines. The cells were treated with VST, biguanides or pyruvinium pamoate in adequate dosage under normal or ER stress conditions for 15 or 18 hours. The exposure time periods were sufficient to activate the UPR in those cells but not to allow apparent overt cell death, even in the presence of UPR modulators. A total of 42 samples were classified into 4 groups: cells under normal growth conditions (control, black), under ER stress conditions (stress, red), under ER stress conditions with a UPR modulator (stress+ modulator, blue) and under normal conditions with a UPR modulator (modulator, green). GF, glucose-free; 2-DG, 2-deoxyglucose; TM, tunicamycin; VST, versipelostatin; PP, pyrvinium pamoate. HeLa HT-29 HT1080 MKN74 Control 18h(1), 15h(2) 18h(1) (2) 18h(1) (2) (3) (6) 18h VST 1PM,18h 10PM,18h 3PM,18h 1PM,18h Buformin ņņ 300PM,18h ņ Metformin ņņ 10mM,18h ņ Phenformin ņņ 100PM,18h ņ PP ņņ 0.1PM, 18h ņ GF 18h(1), 15h(2) 18h(1) (2) 18h 18h GF/VST 1PM,18h 10PM,18h 3PM,18h 1PM,18h GF/Buformin 50PM,15h ņņņ GF/Metformin 1.5mM,15h ņņņ GF/Phenformin 15PM,15h ņņņ 2DG 5mM,18h ņ 10mM,18h(1) (4) ņ 2DG/VST 5mM/1PM, 18h ņ 10mM/3PM,18h ņ 2DG/Buformin ņņ 10mM/300PM, 18h ņ 2DG/Metformin ņņ 10mM/10mM, 18h ņ 2DG/Phenformin ņņ 10mM/100PM, 18h ņ 2DG/PP ņņ 10mM/0.1PM, 18h ņ TM 1Pg/mL,18h ņ 5Pg/mL,18h ņ TM/VST ņņ 5Pg/mL /3PM,18h ņ - 5 - Supplemental Table S2. List of CEL files. To identify the Glucose Deprivation signature genes and the VST/biguanides signature genes, RMA and SAM analysis was performed with .respectively ,(غ) and 10 samples (ع) data sets from 16 samples Sample Name .CEL file Paired/Baseline SAM غع HeLa_Cont UPR_HeLa_Cont.CEL ņ ع HeLa_Cont-2 UPR_HeLa_Cont-2.CEL ņ ع HeLa_GF UPR_HeLa_GF.CEL HeLa_Cont غ HeLa_VST UPR_HeLa_VST.CEL HeLa_Cont HeLa_GF+VST UPR_HeLa_GF+VST.CEL HeLa_Cont ع HeLa_2DG UPR_HeLa_2DG.CEL HeLa_Cont-2 HeLa_TM UPR_HeLa_TM.CEL HeLa_Cont-2 HeLa_GF-2 UPR_HeLa_GF-2.CEL HeLa_Cont-2 HeLa_2DG+VST UPR_HeLa_2DG+VST.CEL HeLa_Cont-2 HeLa_GF+Bu UPR_HeLa_GF+Bu.CEL HeLa_Cont-2 HeLa_GF+Met UPR_HeLa_GF+Met.CEL HeLa_Cont-2 HeLa_GF+Phen UPR_HeLa_GF+Phen.CEL HeLa_Cont-2 غع HT-29_Cont UPR_HT-29_Cont.CEL ņ ع HT-29_Cont-2 UPR_HT-29_Cont-2.CEL ņ ع HT-29_GF UPR_HT-29_GF.CEL HT-29_Cont غ HT-29_VST UPR_HT-29_VST.CEL HT-29_Cont HT-29_GF+VST UPR_HT-29_GF+VST.CEL HT-29_Cont غع HT-29_GF-2 UPR_HT-29_GF-2.CEL HT-29_Cont-2 ع HT1080_Cont UPR_HT1080_Cont.CEL ņ ع HT1080_Cont-2 UPR_HT1080_Cont-2.CEL ņ غ HT1080_Cont-3 UPR_HT1080_Cont-3.CEL ņ ع HT1080_Cont-6 UPR_HT1080_Cont-6.CEL ņ ع HT1080_GF UPR_HT1080_GF.CEL HT1080_Cont غ HT1080_VST UPR_HT1080_VST.CEL HT1080_Cont HT1080_GF+VST UPR_HT1080_GF+VST.CEL HT1080_Cont ع HT1080_2DG UPR_HT1080_2DG.CEL HT1080_Cont-2 HT1080_PP UPR_HT1080_PP.CEL HT1080_Cont-2 HT1080_2DG+PP UPR_HT1080_2DG+PP.CEL HT1080_Cont-2 HT1080_Bu UPR_HT1080_Bu.CEL HT1080_Cont-3 HT1080_Met UPR_HT1080_Met.CEL HT1080_Cont-3 غ HT1080_Phen UPR_HT1080_Phen.CEL HT1080_Cont-3 HT1080_2DG+Bu UPR_HT1080_2DG+Bu.CEL HT1080_Cont-3 HT1080_2DG+Met UPR_HT1080_2DG+Met.CEL HT1080_Cont-3 HT1080_2DG+Phen UPR_HT1080_2DG+Phen.CEL HT1080_Cont-3 ع HT1080_2DG-4 UPR_HT1080_2DG-4.CEL HT1080_Cont-6 HT1080_TM UPR_HT1080_TM.CEL HT1080_Cont-6 HT1080_2DG+VST UPR_HT1080_2DG+VST.CEL HT1080_Cont-6 HT1080_TM+VST UPR_HT1080_TM+VST.CEL HT1080_Cont-6 غع MKN74_Cont UPR_MKN74_Cont.CEL ņ غ MKN74_VST UPR_MKN74_VST.CEL MKN74_Cont ع MKN74_GF UPR_MKN74_GF.CEL MKN74_Cont MKN74_GF+VST UPR_MKN74_GF+VST.CEL MKN74_Cont - 6 - Supplemental Table S3. List of the Glucose Deprivation signature genes. The annotation data was obtained from NetAffyx (Affymetrix). Data shown are sorted by Score (d) from SAM analysis. A. Up-regulated 148 probe sets (97 genes) Score Fold q-value Probe Set ID Gene Symbol Gene Title (d) Change (%) 1558080_s_at LOC144871 hypothetical protein LOC144871 15.667 3.65 0 230265_at SEL1L Sel-1 suppressor of lin-12-like (C. elegans) 14.395 4.00 0 208499_s_at DNAJC3 DnaJ (Hsp40) homolog, subfamily C, member 3 13.925 3.73 0 202061_s_at SEL1L sel-1 suppressor of lin-12-like (C. elegans) 13.159 4.30 0 235341_at DNAJC3 DnaJ (Hsp40) homolog, subfamily C, member 3 13.033 3.33 0 203252_at CDK2AP2 CDK2-associated protein 2 12.927 2.26 0 heat shock 70kDa protein 5 (glucose-regulated 211936_at HSPA5 12.834 2.71 0 protein, 78kDa) 225284_at LOC144871 hypothetical protein LOC144871 12.546 2.56 0 200825_s_at HYOU1 hypoxia up-regulated 1 12.123 3.85 0 homocysteine-inducible, endoplasmic reticulum 217168_s_at HERPUD1 12.055
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    G C A T T A C G G C A T genes Article Identification of Key Genes and Pathways in Pancreatic Cancer Gene Expression Profile by Integrative Analysis Wenzong Lu * , Ning Li and Fuyuan Liao Department of Biomedical Engineering, College of Electronic and Information Engineering, Xi’an Technological University, Xi’an 710021, China * Correspondence: [email protected]; Tel.: +86-29-86173358 Received: 6 July 2019; Accepted: 7 August 2019; Published: 13 August 2019 Abstract: Background: Pancreatic cancer is one of the malignant tumors that threaten human health. Methods: The gene expression profiles of GSE15471, GSE19650, GSE32676 and GSE71989 were downloaded from the gene expression omnibus database including pancreatic cancer and normal samples. The differentially expressed genes between the two types of samples were identified with the Limma package using R language. The gene ontology functional and pathway enrichment analyses of differentially-expressed genes were performed by the DAVID software followed by the construction of a protein–protein interaction network. Hub gene identification was performed by the plug-in cytoHubba in cytoscape software, and the reliability and survival analysis of hub genes was carried out in The Cancer Genome Atlas gene expression data. Results: The 138 differentially expressed genes were significantly enriched in biological processes including cell migration, cell adhesion and several pathways, mainly associated with extracellular matrix-receptor interaction and focal adhesion pathway in pancreatic cancer. The top hub genes, namely thrombospondin 1, DNA topoisomerase II alpha, syndecan 1, maternal embryonic leucine zipper kinase and proto-oncogene receptor tyrosine kinase Met were identified from the protein–protein interaction network.
  • Integration of Text Mining with Systems Biology Provides New Insight Into the Pathogenesis of Diabetic Neuropathy

    Integration of Text Mining with Systems Biology Provides New Insight Into the Pathogenesis of Diabetic Neuropathy

    INTEGRATION OF TEXT MINING WITH SYSTEMS BIOLOGY PROVIDES NEW INSIGHT INTO THE PATHOGENESIS OF DIABETIC NEUROPATHY by Junguk Hur A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Bioinformatics) in The University of Michigan 2010 Doctoral Committee: Professor Eva L. Feldman, Co-Chair Professor Hosagrahar V. Jagadish, Co-Chair Professor Matthias Kretzler Research Assistant Professor Maureen Sartor Professor David J. States, University of Texas Junguk Hur © 2010 All Rights Reserved DEDICATION To my family ii ACKNOWLEDGMENTS Over the past few years, I have been tremendously fortunate to have the company and mentorship of the most wonderful and smartest scientists I know. My advisor, Prof. Eva Feldman, guided me through my graduate studies with constant support, encouragement, enthusiasm and infinite patience. I would like to thank her for being a mom in my academic life and raising me to become a better scientist. I would also like to thank my co-advisors Prof. H. V. Jagadish and Prof. David States. They provided sound advice and inspiration that have been instrumental in my Ph.D. study. I am also very grateful to Prof. Matthias Kretzler and Prof. Maureen Sartor for being an active part of my committee as well as for their continuous encouragement and guidance of my work. I would also like to thank Prof. Daniel Burns, Prof. Margit Burmeister, Prof. Gil Omenn and Prof. Brain Athey from the Bioinformatics Program, who have been very generous with their support and advice about academic life. I am also very grateful to Sherry, Julia and Judy, who helped me through the various administrative processes with cheerful and encouraging dispositions.
  • Stem Cell-Based Reporter Assays for the Identification of Carcinogenic Properties of (Non)-Genotoxic Chemicals

    Stem Cell-Based Reporter Assays for the Identification of Carcinogenic Properties of (Non)-Genotoxic Chemicals

    UNIVERSIDADE DE LISBOA Faculdade de Ciências Departamento de Biologia Animal Stem cell-based reporter assays for the identification of carcinogenic properties of (non)-genotoxic chemicals Cátia Sofia Alão Gonçalves DISSERTAÇÃO MESTRADO EM BIOLOGIA HUMANA E AMBIENTE 2013 UNIVERSIDADE DE LISBOA Faculdade de Ciências Departamento de Biologia Animal Stem cell-based reporter assays for the identification of carcinogenic properties of (non)-genotoxic chemicals Cátia Sofia Alão Gonçalves Dissertação orientada por: Dr. Harry Vrieling (Toxicogenetic/LUMC) Professora Dra. Deodália Dias (DBA/FCUL) DISSERTAÇÃO MESTRADO EM BIOLOGIA HUMANA E AMBIENTE 2013 Acknowledgements First of all I would like to express my deep and sincere gratitude to Harry Vrieling, for having welcomed me in your workgroup and give me the opportunity to develop my dissertation. I also have to thank Giel Hendriks for the useful discussions and uplifting spirits that allowed me to develop my dissertation and allowed me to develop, not only the professional level, but also the personal one. A special thanks to the entire lab, in particular to Brunno for helping me with the BAC recombennering and the great talks, to Fabienne for the tips on how to culture the mES cell lines and for her constant assistance in the Lab and to Binni for helping me understand a little better the Dutch culture. I wish to thank all of my colleagues at the Department of Toxicogenetics, at the Leiden University Medical Centre, for all the support. I thank Professor Deodália Dias for all the support provided under this thesis and also in my training. I want to thank to all the friends that I made during my time in Leiden, without them, probably this dissertation would not be completed.