DOCSLIB.ORG

Supplementary Figures S1-3, Tables S1-3

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Supplementary Figures S1-3, Tables S1-3 Supplementary Material Supplementary Material and Methods MEFs preparation and electroporation Dissected embryos were washed in cold PBS, head and entrails (heart, liver) were excised and used for genotyping. The remaining embryonic tissues were sliced, incubated in Trypsin/EDTA solution (15 min at 37°C; Sigma), further dissociated and cultured in Iscove's Modified Dulbecco's Medium [(IMDM); purchased from Sigma)] supplemented with 10% fetal bovine serum (FBS), penicillin/streptomycin (100 UI), 2 mM L-glutamine and 1× non-essential amino acids (all chemicals were from Gibco). Cells were electroporated in buffer (125 mM KCl, 15 mM NaCl, 3 mM glucose, 25 mM Hepes pH 7.4, 1.2 mM MgCl2) supplemented with 0.66 % (w/v) ethylene glycol (PEG) 4000 in 4 mm gap cuvettes using an ECM 830 electroporator (BTX; settings: 240V, 30 ms). Luciferase assay and NF-kB pathway activation The luciferase assay was performed according to the supplier’s protocol using Dual-Glo Luciferase Assay System (Promega). To activate the NF-ĸB pathway, cells were stimulated overnight with 20 ng/ml TNFα (stock 100 µg/ml; Peprotech), 20 ng/ml lymphotoxin (LTα; stock 10 µg/ml; Invivogen) or 100 ng/ml Pam3csk4 (stock 100 µg/ml, Invivogen). PCR primers used for ddPCR The first primer is in forward and the second primer in reverse orientation: β-ACTIN, 5’- CTAGGCGGACTATGAC-3’, 5’- GACTTGGGAGAGGACT-3’; SIRT1, 5’- CTCCAGGCAGATGCCATAAC-3’, 5’- TGCCCTTGGTTAAAATTTGG-3’; TLR2, 5’- CCTCCAATCAGGCTTCTCTG-3’, 5’- TGGAGGTTCACACACCTCTG-3’. Antibodies used for IHC and Western blotting IHC: anti-β-catenin (mouse monoclonal, EM-22, Exbio), anti-chromogranin A (rabbit polyclonal, ab15160, Abcam), anti-lysozyme (rabbit polyclonal, A0099, Dako), anti-p21 (mouse monoclonal, SXM30, BD Pharmingen), anti-p53 (rabbit polyclonal, NCL-p53-CM5p, Novocastra), anti-PCNA (rabbit polyclonal, ab18197, Abcam), anti-Tlr2 (goat polyclonal, sc-16237, Santa Cruz). The activity of brush border enzyme alkaline phosphatase was visualized using Alkaline Phosphatase Magenta™ IHC Substrate Solution (Sigma). Goblet cells were stained with Periodic acid Schiff kit (Hotchkiss-McManus) purchased from DiaPath. Western blotting: anti-α-tubulin (mouse monoclonal, TU-01, Exbio), anti-CtBP (mouse monoclonal, sc-17759, Santa Cruz), anti-p21 (mouse monoclonal, SXM30, BD Pharmingen), anti-p53 (rabbit polyclonal, NCL-p53-CM5p, Novocastra), anti-p65 (rabbit monoclonal, #8242, Cell Signalling), anti-phospho-p65 (detecting phosphorylated Ser536; rabbit monoclonal, #3033, Cell Signaling), anti-Tlr2 (rabbit monoclonal, E1J2W, Cell Signalling). Supplementary Figure and Table Legends Supplementary Figure S1. Heatmaps depicting gene expression in Hic1flox/flox MEFs treated with 4- OHT when compared to MEFs treated with vehicle (ethanol) only. Genes (299 in total) displaying significantly (q < 0.05) changed expression in at least one of the indicated time points are listed. The position of Hic1 and six genes whose expression was changed more than twice (|log FC| > 1) in at least two time points is highlighted. Supplementary Figure S2. Loss of Hic1 results in increased counts of goblet and enteroendocrine cells. A,B, goblet (A) and enteroendocrine (B) cell distribution in the indicated segments of the small intestine. Specimens obtained from four Hic1flox/flox and four Hic1flox/flox Villin-Cre+ mice were stained using Periodic acid Schiff (PAS) and an anti-chromogranin A antibody to visualize goblet and enteroendocrine cells, respectively. Stained cells (indicated by black arrowheads in the histology images on the right) were in several different fields indicated by numbers on the X axis. C, no changes in enterocytes were noted in the Hic1-deficient small intestine. Left, qRT-PCR analysis of enterocyte- specific markers hairy and enhancer of split-1 (Hes1) and sucrase-isomaltase (SI). The expression level of the respective gene in wt mice (upon normalization to Ubb) was arbitrarily set to 1. Right, staining of brush border enzyme alkaline phosphatase (AP) produced by differentiated enterocytes in the small intestine. Scale bar: 0.15 mm; error bars: SDs. Supplementary Figure S3. DSS-induced transcriptional response in the colon of Hic1flox/flox and Hic1flox/flox Villin-Cre+ mice six and nine days after DSS withdrawal. Epithelial lining of the colon obtained from four animals of each genotype was analyzed using qRT-PCR. The results were normalized to the Ubiquitin B (Ubb) housekeeping gene; the relative expression of another housekeeping gene, β-actin, is also shown. The expression level of the corresponding gene in mice without DSS treatment was arbitrarily set to 1. Error bars: SDs. Supplementary Table S1 Primers used for qRT-PCR analysis Supplementary Table S2. List of genes differentially expressed in Hic1flox/flox MEFs treated with 4- OHT for 48, 72, and 120 hours when compared to control MEFs treated (for the given time periods) with vehicle. Selection criterion: q < 0.05. Of note, none of the genes passed the selection criterion for the 24-hour time point. Supplementary Table S3. The most different ‘Gene Ontology Biological Processes’ (GO BPs) and WikiPathways categories in expression profiles of Hic1flox/flox MEFs 72 hours after addition of 4-OHT when compared to MEFs treated with vehicle only. In total, 268 gene probes passed the significance criterion (q-value < 0.05). Corresponding annotated genes (listed in Supplementary Table S2) were analyzed using the GO BP and WikiPathways 2015 Enricher datasets. The results were sorted according to the p-value; GO BPs and WikiPathways with p < 0.05 are listed. Supplementary Figure S1 24 h 48 h 72 h 120 h ILMN_2822825 Wfdc2 Hspb6 Wfdc2 Eno3 Anxa8 Ly6c1 Hspb6 AI646023 Sfrp2 Tlr2 Crabp1 Dpt Tlr2 Gas6 Tceal6 Ccl9 Prl2c3 Fhdc1 Igfbp2 Apol9b Dlk2 ILMN_1227579 Bace2 Sema4f Adssl1 Pon3 BC100451 Cox4i2 Sod3 Adamts2 ILMN_2791059 ILMN_2653725 Capn5 Klc3 Rhof Frmd8 Pdlim2 Bmp4 Hebp2 Nme5 Trappc3 Btd Elmo2 Pik3r3 Dad1 Mfap2 Acpl2 Cdc42ep2 Slc25a1 Dnajc12 Pqlc3 Taf11 Prss22 Nme7 H2−Ab1 Hoxc6 Atg9b Arpc5 Hoxc9 Zfyve21 Nanos1 5430435G22Rik Mrgprf Sema3f ILMN_2863332 Ltc4s Chst12 Nacc2 Itga3 Dyrk1b ILMN_2727536 Cbr3 Abca7 ILMN_1245221 Copz2 Tspyl3 4930583H14Rik Haghl Rnf167 Slpi Edem2 Lcmt1 C85492 Magee1 Fam110a ILMN_2590923 Gale ILMN_3144575 Ptp4a3 Rab3d Ctsw Ddit4l Gale Rassf7 Fbxo16 Sc5d Plin4 Atp6v1d Commd4 Tlcd2 Akr1b10 Tob1 Dhh ILMN_1227627 ILMN_1228783 Scx Gm347 Akr1b10 Izumo4 Enpp5 ILMN_2778799 ILMN_2607529 Insig1 Pmvk ILMN_2613923 Nudt7 Nsdhl ILMN_2706906 Dhcr24 Gjb4 Slc9a3r1 Pdlim2 Aldoc Dapk2 Tlcd2 Dapk2 Copz2 Angptl7 Ephx1 Angptl7 Cbr2 Dclk1 Uqcrh Cbr2 Ssh3 Tspan17 St3gal5 BC028528 Ly6a Angptl2 Emp3 ILMN_3162895 Gpnmb Mustn1 Pcyt2 Dbi Mvd Snn 2810432D09Rik ILMN_2731550 Fam50a Snca Hsd17b7 Arl16 Elovl6 ILMN_2654952 Nsdhl Sc4mol Mxd4 Agfg2 Hoxa5 Trappc1 Traf4 Zcchc18 Pcyt2 ILMN_2614161 Nsdhl Fdps Nsdhl Sqle Cyp51 Sqle 1500031L02Rik Myrip Cxcl12 Tbc1d17 Pcsk9 ILMN_2634083 Kbtbd4 Nde1 Zfp30 Mgst1 Shisa4 ILMN_2853342 Arf2 Asns Trim44 Zfp598 Mgp Arv1 Tgm2 Gzmd Ergic2 Pdgfb Atf4 Nppb Fam129a Chka Tmem74 Eif4enif1 Gjb2 Atf3 Rin1 Otub2 Inpp5f Myo1b Prkab1 Ift172 Gar1 Clcn3 Socs2 Krt19 Strap Lrp12 Fem1b Tgfbr1 Mcm10 Eln Rgs16 Hic1 Ndrg1 Hic1 Stmn4 Mmp9 Osmr Serpinb2 Picalm Otub2 Cdc42ep3 ILMN_2898917 Dgat2 Klf2 Fam82a2 Egr2 Ciapin1 Gfpt2 ILMN_2711948 Lrp12 ILMN_2685088 Arntl Ets2 Trit1 ILMN_1242170 ILMN_3008406 Uprt Color Key Ddx20 Stc2 Lrrc8c Crls1 Thumpd3 and Density Plot Asns 2410042D21Rik Lsm14a Lass6 Shmt2 Dhx33 Dhrs7 Ndrg1 Txnip 1500012F01Rik Zfp639 Wdr75 Slc6a9 Mpp6 0.4 ILMN_1214036 Cdh3 Agfg1 Wdr3 Fhl2 ILMN_2595732 Ch25h Slmo2 Trib3 Slc7a3 Cideb Taf5l Prpf38b 0.2 Slc1a4 Plk3 ILMN_1249579 Pparg Density Areg Ube2d2 Nat10 Zfp238 Kctd9 Trmt61a ILMN_2959330 0 Nup98 Hbegf Itfg2 H3f3b Leprotl1 Fgf21 Pole Gphn −1 0 0.5 1 Riok1 Rai14 Elavl2 Rars ILMN_2862111 Ankrd54 Cth Row Z−Score Slc7a11 Zcchc5 Supplementary Figure S2 A PAS 450 - Hic1loxP/loxP Villin-Cre+ 400 - Hic1loxP/loxP 350 loxP/loxP (per 1 slide) 300 Hic1 250 + 200 PAS 150 100 illin-Cre V Goblet cell count 50 0 loxP/loxP 1 2 3 4 5 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 --- Hic1 duodenum jejunum ileum B 50 Chromogr. A - Hic1loxP/loxP Villin-Cre+ - Hic1loxP/loxP 40 (per 1 slide) loxP/loxP 30 Hic1 + 20 Chromogr. A 10 illin-Cre V 0 loxP/loxP Enteroendocrinne cell count 1 2 3 4 5 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 --- Hic1 duodenum jejunum ileum C AP 2.0 in intestine loxP/loxP + 1.5 Hic1 illin-Cre + V 1.0 abundance AP loxP/loxP 0.5 Hic1 illin-Cre V vs. 0.0 Relative mRNA loxP/loxP loxP/loxP SI Hic1 β-actin Hes1 Hic1 Hic1 Supplementary Figure S3 Day 6 Day 9 40 40 in 35 in 35 30 30 ed intestine 25 ed intestine 25 Hic1flox/flox abundance abundance treat treat flox/flox + - 20 - 20 Hic1 Villin-Cre 15 15 10 10 Relative mRNA 5 Relative mRNA 5 control vs. DSS control vs. DSS 0 0 α α IL6 Tlr2 IL6 Tlr2 β-actin Cox2 TNF β-actin Cox2 TNF Supplementary Table S1 Primers for qRT-PCR Gene symbol Organism Sequence Actb Mouse Forward: 5'-GATCTGGCACCACACCTTCT-3' Reverse: 5'-GGGGTGTTGAAGGTCTCAAA-3' Atoh1 Mouse Forward: 5'-GCTGTGCAAGCTGAAGGG-3' Reverse: 5'-TCTTGTCGTTGTTGAAGG-3' Cox2 Mouse Forward: 5'-CGGAGAGAGTTCATCCCTGA-3' Reverse: 5'-ACCTCTCCACCAATGACCTG-3' Hic1 Mouse Forward: 5'-GAGGCTGCTGAGGTGGCTGC-3' Reverse: 5'-CTCTTGTCGCAGGACGCGCA-3' Hes1 Mouse Forward: 5'-GCTCACTTCGGACTCCATGTG-3' Reverse: 5'-GCTAGGGACTTTACGGGTAGCA-3' IL6 Mouse Forward: 5'-GATGGATGCTACCAAACTGGA-3' Reverse: 5'-GGAAATTGGGGTAGGAAGGA-3' SI Mouse Forward: 5'-TTCAAGAAATCACAACATTCAATTTACCTAG-3' Reverse: 5'-CTAAAACTTTCTTTGACATTTGAGCAA-3' Sirt1 Mouse Forward: 5'-CGGAGGGCCAGAGAGGCAGT-3' Reverse: 5'-CTCTTGCGGAGCGGCTCGTC-3'
Load more

Recommended publications
  • Differential Regulation of Gene Expression by Cholesterol Biosynthesis Inhibitors That Reduce (Pravastatin) Or Enhance (Squalest
    JPET Fast Forward. Published on May 25, 2016 as DOI: 10.1124/jpet.116.233312 This article has not been copyedited and formatted. The final version may differ from this version. Differential regulation of gene expression by cholesterol biosynthesis inhibitors that reduce (pravastatin) or enhance (squalestatin 1) nonsterol isoprenoid levels in primary cultured mouse and rat hepatocytes. Elizabeth A. Rondini, Zofia Duniec-Dmuchowski, Daniela Cukovic, Alan A. Dombkowski, and Thomas A. Kocarek Downloaded from Institute of Environmental Health Sciences, Wayne State University, Detroit, MI 48202, USA (E.A.R., Z.D-D, T.A.K.) jpet.aspetjournals.org Department of Pediatrics, Division of Clinical Pharmacology and Toxicology, Wayne State University, Detroit, MI 48202 (D.C., A.A.D) at ASPET Journals on September 27, 2021 JPET Fast Forward. Published on May 25, 2016 as DOI: 10.1124/jpet.116.233312 This article has not been copyedited and formatted. The final version may differ from this version. JPET #233312 Running title: Regulation of hepatocellular gene expression by isoprenoids Address correspondence to: Dr. Thomas A. Kocarek, Institute of Environmental Health Sciences, 6135 Woodward Avenue, IBio Building, Room 2126, Wayne State University, Detroit, MI 48202, USA. Tel: (313) 577-6580; FAX: (313) 972-8025; E-mail: [email protected] Number of text pages: 43 Downloaded from Number of tables: 2 Supplemental Number of figures: 8 jpet.aspetjournals.org Number of references: 77 Number of words in Abstract: 249 Number of words in Introduction: 745 at
    [Show full text]
  • SHARP1 (BHLHE41) Mouse Monoclonal Antibody [Clone ID: OTI3H4] Product Data
    OriGene Technologies, Inc. 9620 Medical Center Drive, Ste 200 Rockville, MD 20850, US Phone: +1-888-267-4436 [email protected] EU: [email protected] CN: [email protected] Product datasheet for TA806354 SHARP1 (BHLHE41) Mouse Monoclonal Antibody [Clone ID: OTI3H4] Product data: Product Type: Primary Antibodies Clone Name: OTI3H4 Applications: IHC, WB Recommended Dilution: WB 1:2000, IHC 1:150 Reactivity: Human Host: Mouse Isotype: IgG1 Clonality: Monoclonal Immunogen: Human recombinant protein fragment corresponding to amino acids 1-297 of human BHLHE41(NP_110389) produced in E.coli. Formulation: PBS (PH 7.3) containing 1% BSA, 50% glycerol and 0.02% sodium azide. Concentration: 1 mg/ml Purification: Purified from mouse ascites fluids or tissue culture supernatant by affinity chromatography (protein A/G) Conjugation: Unconjugated Storage: Store at -20°C as received. Stability: Stable for 12 months from date of receipt. Predicted Protein Size: 50.3 kDa Gene Name: basic helix-loop-helix family member e41 Database Link: NP_110389 Entrez Gene 79365 Human Q9C0J9 Background: This gene encodes a basic helix-loop-helix protein expressed in various tissues. The encoded protein can interact with ARNTL or compete for E-box binding sites in the promoter of PER1 and repress CLOCK/ARNTL's transactivation of PER1. This gene is believed to be involved in the control of circadian rhythm and cell differentiation. Defects in this gene are associated with the short sleep phenotype. [provided by RefSeq, Feb 2014] This product is to be used for laboratory only. Not for diagnostic or therapeutic use. View online » ©2021 OriGene Technologies, Inc., 9620 Medical Center Drive, Ste 200, Rockville, MD 20850, US 1 / 2 SHARP1 (BHLHE41) Mouse Monoclonal Antibody [Clone ID: OTI3H4] – TA806354 Synonyms: BHLHB3; DEC2; hDEC2; SHARP1 Protein Families: Transcription Factors Protein Pathways: Circadian rhythm - mammal Product images: HEK293T cells were transfected with the pCMV6- ENTRY control (Left lane) or pCMV6-ENTRY BHLHE41 ([RC206882], Right lane) cDNA for 48 hrs and lysed.
    [Show full text]
  • Identification and Characterization of Zebrafish 17Beta-HSD Type 1 and Type 3: a Comparative Analysis of Androgen/Estrogen Activity Regulators
    Institut für Experimentelle Genetik GSF-Forschungzentrum für Umwelt und Gesundheit, Neuherberg Identification and characterization of zebrafish 17beta-HSD type 1 and type 3: A comparative analysis of androgen/estrogen activity regulators Rebekka Mindnich Vollständiger Abdruck der von der Fakultät Wissenschaftszentrum Weihenstephan für Ernährung, Landnutzung und Umwelt der Technischen Universität München zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften genehmigten Dissertation. Vorsitzender: Univ.- Prof. Dr. Bertold Hock Prüfer der Dissertation: 1. Priv.-Doz. Dr. Jerzy Adamski 2. Univ.-Prof. Dr. Johannes Buchner 3. Univ.-Prof. Dr. Wolfgang Wurst Die Dissertation wurde am 30.06.2004 bei der Technischen Universität München eingereicht und durch die Fakultät Wissenschaftszentrum Weihenstephan für Ernährung, Landnutzung und Umwelt am 07.10. 2004 angenommen. Table of contents Table of contents ABSTRACT................................................................................................................................... 7 ZUSAMMENFASSUNG................................................................................................................ 9 ABBREVIATIONS....................................................................................................................... 11 1 INTRODUCTION ................................................................................................................ 13 1.1 THE AIM OF THIS STUDY ...............................................................................................
    [Show full text]
  • Analysis of Gene Expression Data for Gene Ontology
    ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION A Thesis Presented to The Graduate Faculty of The University of Akron In Partial Fulfillment of the Requirements for the Degree Master of Science Robert Daniel Macholan May 2011 ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION Robert Daniel Macholan Thesis Approved: Accepted: _______________________________ _______________________________ Advisor Department Chair Dr. Zhong-Hui Duan Dr. Chien-Chung Chan _______________________________ _______________________________ Committee Member Dean of the College Dr. Chien-Chung Chan Dr. Chand K. Midha _______________________________ _______________________________ Committee Member Dean of the Graduate School Dr. Yingcai Xiao Dr. George R. Newkome _______________________________ Date ii ABSTRACT A tremendous increase in genomic data has encouraged biologists to turn to bioinformatics in order to assist in its interpretation and processing. One of the present challenges that need to be overcome in order to understand this data more completely is the development of a reliable method to accurately predict the function of a protein from its genomic information. This study focuses on developing an effective algorithm for protein function prediction. The algorithm is based on proteins that have similar expression patterns. The similarity of the expression data is determined using a novel measure, the slope matrix. The slope matrix introduces a normalized method for the comparison of expression levels throughout a proteome. The algorithm is tested using real microarray gene expression data. Their functions are characterized using gene ontology annotations. The results of the case study indicate the protein function prediction algorithm developed is comparable to the prediction algorithms that are based on the annotations of homologous proteins.
    [Show full text]
  • Watsonjn2018.Pdf (1.780Mb)
    UNIVERSITY OF CENTRAL OKLAHOMA Edmond, Oklahoma Department of Biology Investigating Differential Gene Expression in vivo of Cardiac Birth Defects in an Avian Model of Maternal Phenylketonuria A THESIS SUBMITTED TO THE GRADUATE FACULTY In partial fulfillment of the requirements For the degree of MASTER OF SCIENCE IN BIOLOGY By Jamie N. Watson Edmond, OK June 5, 2018 J. Watson/Dr. Nikki Seagraves ii J. Watson/Dr. Nikki Seagraves Acknowledgements It is difficult to articulate the amount of gratitude I have for the support and encouragement I have received throughout my master’s thesis. Many people have added value and support to my life during this time. I am thankful for the education, experience, and friendships I have gained at the University of Central Oklahoma. First, I would like to thank Dr. Nikki Seagraves for her mentorship and friendship. I lucked out when I met her. I have enjoyed working on this project and I am very thankful for her support. I would like thank Thomas Crane for his support and patience throughout my master’s degree. I would like to thank Dr. Shannon Conley for her continued mentorship and support. I would like to thank Liz Bullen and Dr. Eric Howard for their training and help on this project. I would like to thank Kristy Meyer for her friendship and help throughout graduate school. I would like to thank my committee members Dr. Robert Brennan and Dr. Lilian Chooback for their advisement on this project. Also, I would like to thank the biology faculty and staff. I would like to thank the Seagraves lab members: Jailene Canales, Kayley Pate, Mckayla Muse, Grace Thetford, Kody Harvey, Jordan Guffey, and Kayle Patatanian for their hard work and support.
    [Show full text]
  • Identification and Characterization of TPRKB Dependency in TP53 Deficient Cancers
    Identification and Characterization of TPRKB Dependency in TP53 Deficient Cancers. by Kelly Kennaley A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Molecular and Cellular Pathology) in the University of Michigan 2019 Doctoral Committee: Associate Professor Zaneta Nikolovska-Coleska, Co-Chair Adjunct Associate Professor Scott A. Tomlins, Co-Chair Associate Professor Eric R. Fearon Associate Professor Alexey I. Nesvizhskii Kelly R. Kennaley [email protected] ORCID iD: 0000-0003-2439-9020 © Kelly R. Kennaley 2019 Acknowledgements I have immeasurable gratitude for the unwavering support and guidance I received throughout my dissertation. First and foremost, I would like to thank my thesis advisor and mentor Dr. Scott Tomlins for entrusting me with a challenging, interesting, and impactful project. He taught me how to drive a project forward through set-backs, ask the important questions, and always consider the impact of my work. I’m truly appreciative for his commitment to ensuring that I would get the most from my graduate education. I am also grateful to the many members of the Tomlins lab that made it the supportive, collaborative, and educational environment that it was. I would like to give special thanks to those I’ve worked closely with on this project, particularly Dr. Moloy Goswami for his mentorship, Lei Lucy Wang, Dr. Sumin Han, and undergraduate students Bhavneet Singh, Travis Weiss, and Myles Barlow. I am also grateful for the support of my thesis committee, Dr. Eric Fearon, Dr. Alexey Nesvizhskii, and my co-mentor Dr. Zaneta Nikolovska-Coleska, who have offered guidance and critical evaluation since project inception.
    [Show full text]
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
    Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated.
    [Show full text]
  • NSDHL, an Enzyme Involved in Cholesterol Biosynthesis, Traffics Through the Golgi and Accumulates on ER Membranes and on The
    Human Molecular Genetics, 2003, Vol. 12, No. 22 2981–2991 DOI: 10.1093/hmg/ddg321 NSDHL, an enzyme involved in cholesterol biosynthesis, traffics through the Golgi and accumulates on ER membranes and on the surface of lipid droplets Hugo Caldas1 and Gail E. Herman1,2,* Downloaded from https://academic.oup.com/hmg/article/12/22/2981/606598 by guest on 27 September 2021 1Center for Molecular and Human Genetics, Columbus Children’s Research Institute, Columbus, OH 43205, USA and 2Department of Pediatrics, The Ohio State University, Columbus, OH 43205, USA Received July 15, 2003; Revised August 29, 2003; Accepted September 11, 2003 NSDHL, for NAD(P)H steroid dehydrogenase-like, encodes a sterol dehydrogenase or decarboxylase involved in the sequential removal of two C-4 methyl groups in post-squalene cholesterol biosynthesis. Mutations in this gene are associated with human CHILD syndrome (congenital hemidysplasia with ichthyosiform nevus and limb defects), an X-linked, male lethal disorder, as well as the mouse mutations bare patches and striated. In the present study, we have investigated the subcellular localization of tagged proteins encoded by wild-type and selected mutant murine Nsdhl alleles using confocal microscopy. In addition to an ER localization commonly found for enzymes of post-squalene cholesterol biosynthesis, we have identified a novel association of NSDHL with lipid droplets, which are endoplasmic reticulum (ER)-derived cytoplasmic structures that contain a neutral lipid core. We further demonstrate that trafficking through the Golgi is necessary for ER membrane localization of the protein and propose a model for the association of NSDHL with lipid droplets.
    [Show full text]
  • 4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation
    Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4).
    [Show full text]
  • Genome-Wide DNA Methylation Analysis of KRAS Mutant Cell Lines Ben Yi Tew1,5, Joel K
    www.nature.com/scientificreports OPEN Genome-wide DNA methylation analysis of KRAS mutant cell lines Ben Yi Tew1,5, Joel K. Durand2,5, Kirsten L. Bryant2, Tikvah K. Hayes2, Sen Peng3, Nhan L. Tran4, Gerald C. Gooden1, David N. Buckley1, Channing J. Der2, Albert S. Baldwin2 ✉ & Bodour Salhia1 ✉ Oncogenic RAS mutations are associated with DNA methylation changes that alter gene expression to drive cancer. Recent studies suggest that DNA methylation changes may be stochastic in nature, while other groups propose distinct signaling pathways responsible for aberrant methylation. Better understanding of DNA methylation events associated with oncogenic KRAS expression could enhance therapeutic approaches. Here we analyzed the basal CpG methylation of 11 KRAS-mutant and dependent pancreatic cancer cell lines and observed strikingly similar methylation patterns. KRAS knockdown resulted in unique methylation changes with limited overlap between each cell line. In KRAS-mutant Pa16C pancreatic cancer cells, while KRAS knockdown resulted in over 8,000 diferentially methylated (DM) CpGs, treatment with the ERK1/2-selective inhibitor SCH772984 showed less than 40 DM CpGs, suggesting that ERK is not a broadly active driver of KRAS-associated DNA methylation. KRAS G12V overexpression in an isogenic lung model reveals >50,600 DM CpGs compared to non-transformed controls. In lung and pancreatic cells, gene ontology analyses of DM promoters show an enrichment for genes involved in diferentiation and development. Taken all together, KRAS-mediated DNA methylation are stochastic and independent of canonical downstream efector signaling. These epigenetically altered genes associated with KRAS expression could represent potential therapeutic targets in KRAS-driven cancer. Activating KRAS mutations can be found in nearly 25 percent of all cancers1.
    [Show full text]
  • Targeting Glioblastoma Stem Cells Through Disruption of the Circadian Clock
    Published OnlineFirst August 27, 2019; DOI: 10.1158/2159-8290.CD-19-0215 RESEARCH ARTICLE Targeting Glioblastoma Stem Cells through Disruption of the Circadian Clock Zhen Dong1, Guoxin Zhang1, Meng Qu2, Ryan C. Gimple1,3, Qiulian Wu1, Zhixin Qiu1, Briana C. Prager1,3, Xiuxing Wang1, Leo J.Y. Kim1,3, Andrew R. Morton3, Deobrat Dixit1, Wenchao Zhou4, Haidong Huang4, Bin Li5, Zhe Zhu1, Shideng Bao4, Stephen C. Mack6, Lukas Chavez7, Steve A. Kay2, and Jeremy N. Rich1 Downloaded from cancerdiscovery.aacrjournals.org on September 24, 2021. © 2019 American Association for Cancer Research. Published OnlineFirst August 27, 2019; DOI: 10.1158/2159-8290.CD-19-0215 ABSTRACT Glioblastomas are highly lethal cancers, containing self-renewing glioblastoma stem cells (GSC). Here, we show that GSCs, differentiated glioblastoma cells (DGC), and nonmalignant brain cultures all displayed robust circadian rhythms, yet GSCs alone displayed exquisite dependence on core clock transcription factors, BMAL1 and CLOCK, for optimal cell growth. Downregulation of BMAL1 or CLOCK in GSCs induced cell-cycle arrest and apoptosis. Chromatin immu- noprecipitation revealed that BMAL1 preferentially bound metabolic genes and was associated with active chromatin regions in GSCs compared with neural stem cells. Targeting BMAL1 or CLOCK attenu- ated mitochondrial metabolic function and reduced expression of tricarboxylic acid cycle enzymes. Small-molecule agonists of two independent BMAL1–CLOCK negative regulators, the cryptochromes and REV-ERBs, downregulated stem cell factors and reduced GSC growth. Combination of cryp- tochrome and REV-ERB agonists induced synergistic antitumor effi cacy. Collectively, these fi ndings show that GSCs co-opt circadian regulators beyond canonical circadian circuitry to promote stemness maintenance and metabolism, offering novel therapeutic paradigms.
    [Show full text]
  • BMC Genomics Biomed Central
    BMC Genomics BioMed Central Research article Open Access Histone deacetylase inhibitors: A new mode for inhibition of cholesterol metabolism Sridar V Chittur*1, Niquiche Sangster-Guity2 and Paulette J McCormick1 Address: 1Center for Functional Genomics, University at Albany, State University of New York, Cancer Research Center, One Discovery Drive, Rm 310, Rensselaer, NY 12144, USA and 2Johns Hopkins University, School of Medicine, 1550 Orleans St, CRBII Rm 456, Baltimore, MD 21231, USA Email: Sridar V Chittur* - [email protected]; Niquiche Sangster-Guity - [email protected]; Paulette J McCormick - [email protected] * Corresponding author Published: 29 October 2008 Received: 6 March 2008 Accepted: 29 October 2008 BMC Genomics 2008, 9:507 doi:10.1186/1471-2164-9-507 This article is available from: http://www.biomedcentral.com/1471-2164/9/507 © 2008 Chittur et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background: Eukaryotic gene expression is a complex process involving multiple cis and trans activating molecules to either facilitate or inhibit transcription. In recent years, many studies have focused on the role of acetylation of histone proteins in modulating transcription, whereas deacetylation of these same proteins is associated with inactivation or repression of gene expression. This study explores gene expression in HepG2 and F9 cell lines treated with Trichostatin A (TSA), a potent histone deacetylase inhibitor. Results: These experiments show that TSA treatment results in clear repression of genes involved in the cholesterol biosynthetic pathway as well as other associated pathways including fatty acid biosynthesis and glycolysis.
    [Show full text]