Supplementary Material Sumoylation Regulates the Chromatin

Total Page:16

File Type:pdf, Size:1020Kb

Supplementary Material Sumoylation Regulates the Chromatin Supplementary material SUMOylation Regulates the Chromatin Occupancy and Anti-Proliferative Gene Programs of Glucocorticoid Receptor Ville Paakinaho, Sanna Kaikkonen, Harri Makkonen, Vladimir Benes, and Jorma J. Palvimo A FRT wtGR GR3KR clone wt-1 wt-2 wt-3 3KR-1 3KR-2 3KR-3 - - - - - - - dex + + + + + + + kDa a-GR - 100 a-GAPDH - 35 A549 + - - HEK293-wtGR - + - - - HEK293-GR3KR + kDa a-GR - 100 a-GAPDH - 35 B wtGR GR3KR 43°C - - + + - - + + dex - + - + - + - + kDa - 170 a-GR - 130 - 100 a-GAPDH - 35 - 170 a-SUMO-1 - 130 R - 100 G - a : P I a-SUMO-2/3 - 170 - 130 - 100 Figure S1. Expression and SUMOylation of GR in stable isogenic HEK293 cells. (A) Upper panel, immunoblot analysis of three HEK293 cell clones expressing either wtGR (wt-1-3) or GR3KR (3KR-1-3). The clones and background HEK293 (FRT) cells were exposed to vehicle or dex for 17 h, and cell samples were immunoblotting with anti-GR (Santa Cruz, sc-1003) and anti- GAPDH (Santa Cruz, sc-25778) antibodies. Lower panel, comparison of the GR level in A549 cells (from ATCC) with that of the selected clones from isogenic HEK293 cells (wt-3 and 3KR-3) by immunoblotting. (B) To analyse GR SUMOylation, wtGR- and GR3KR-expressing cells were grown with of without dex for 2 h and exposed for 30 min to 43°C. Immunoprecipitation (IP) with anti- GR antibody and immunoblotting with anti-SUMO-1 (Invitrogen, 33-2400) or anti-SUMO-2/3 (MBL M114-3) antibody were performed essentially as described (Rytinki et al. 2011, Methods Mol. Biol.). A total dex- sensitivity induced for dex expression ADARB1 HLA-DRAADARB1 APPL2BBS12 ADARB1CHN2 GRHL1PLEKHA9 CHMP1BFBXO21 ZBTB16PPP2R3B HPCAL1TNFRSF10D TMEM87A GRHL1SCAND3 AKTIPJAK2 NKD1AP3S1 UPF2ACVR1B SLC25A4SNX33 ZBTB7CKLHL36 IGSF3GTPBP2 KIAA0513RPRM LIPGSTK35 SLC36A1 TMX4FIS ARNTLS1PR3 INHBBNEXN SLC9A2SATB2 MRFAP1L1OSBPL5 C9orf61OGFRL1 FOXO4OSBPL5 TXLNACLEC16A PRKAB2C5orf62 FAM105ASLC41A1 SEPX1PTTG1IP TSHZ1SFXN5 PTCHD1HIVEP1 SQLE POU3F2 ANKRD50PDE5A HOXD11HIST2H2BE TSC22D3WDR7 FAM49AURB2 MID1GCOM1 CRKMED30 ZBTB46C16orf80 MID1THAP10 SSTR2 AENPDZD2 TMEM43KBTBD11 MBOAT2PPP1R1A C17orf80MT1F RASSF4ZDHHC9 GCH1CCDC107 FAM100AMAP2K4 EIF4A3GUCY1A3 GCH1GREB1 GOT1ARCN1 PXNADAMTS8 NBNTSC22D3 TSC22D3 BIRC2COG1 PDK4KCNS3 C7orf57ZNF688 LINS1ERN1 RAD51CRHPN1 CCDC126CLPTM1L TSPAN3MN1 ZNF341ALAD GNA12SLC39A11 FKBP5STAT3 NOL3GNA12 ETNK2SPSB1 LPLNET1 NPHP4HPCAL1 XKZNF364 B PTTG1IPGJB2 TOX3 FAM104AE2F3 GCNT1JAK1 UNC84BSLC25A4 STOMITGB2 STOMEP400 KIAA0232NET1 EDG7KIAA0182 TPM2PPP1R3F ARID5BB4GALT2 PRKCQFGFRL1 TACC2TPM2 KIAA0355ANO6 RPS6KA2TUBA3D EDNRAC10orf10 TMX4ZNF784 SFRP2KAL1 RNF115CYR61 C3orf58HOXA4 8 ASB7C1QTNF1 FBXO2KRCC1 ASB7ZDHHC8P TAOK2 FLJ31568RASD1 RAD51C GPERPNMT NSDHLMSX2 - ACPL2FAM55C RNF14ACPL2 MAP2K4C6orf81 VPS37ACAMK1G TUBA3ETHSD1 COL23A1RASGRP1 ASB7BIRC3 C16orf5MRFAP1L1 PSD4SGSM1 FGF18HSD11B2 SBK1ENOX2 NET1RGS2 DGCR6LERN1 NAGKAMOT APOLD1AIFM1 ZNF259UPF2 FLJ31568ISG20 FAM65BMMP28 dex TUBA3C PGCGSTM3 ZNF18FBXO32 CLN8TUBA3D SHISA2ATP4A PHLDA1RAB20 APOL2SNN AGRP APOL2SSX2IP LIPGMTNR1A FBXO32CLN8 ZNF189ELK1 KCNA5OLFML2B C18orf54PAQR8 APODTRAF5 IGSF3B3GAT2 CHN2HMGCS1 ENPP5MERTK C18orf1BBS1 6 PHF19 OGFRL1SESN1 INPP1C10orf140 ZSWIM5TMEM91 MERTKFZD4 HIST1H1CMOCOS IGFBP5 ASB9 FUT4SLC6A3 CDH7HOXD10 HIST1H2AGHIST2H2AA3 PCDH7C4orf31 CCND1GAD1 TET1SOX12 HIST1H2ACMICB HIST1H2BDHIST1H2BJ DMDDMRT3 CDKN1ALRRTM4 DDX26BZNF503 C19orf23CXXC4 HSPA1LHIST1H2BD TMEM100HSPA6 S100PNFKBIA e HOXA9HOXA13 NTF3HOXA9 ZIC2NOS2A MID1IP1 TRIB3 g HES1CDKN1C MYC ATF3ID3 PPP1R15APRR15L RGS16 n OKL38RRS1 IRF8DDIT3 IER3SAT1 BHLHB2SGK3 EGFLAMWWP2 a STC1HOXC4 SVILSLAIN1 ZNF217SCHIP1 KLF10TGIF2 ALG3 h RFESDIRF2BP2 HMOX1C15orf39 4 PPRC1 TSC22D1HOXA10 C10orf2 c TESSTC2 FAM90A1RASSF1 OSGIN1SPRYD5 PI15MEX3B FOXA2JUN BCORSPEN GADD45AHRK PAG1IRF2BP2 d CDADC1NMI OSBPL11TF l GPERC1QTNF1 NBN CAPN7WDR60 PPP1CBING1 GTF2E1 o FAM163AELMO2 SFRS5C8orf51 FAM134BARRDC4 f CSTF3ZFP36 B3GAT1 MYOM2KLHL26 ARRDC4RP1L1 SELS TRIOBPSOX2 C3orf52OPRL1 APPBP2KHDC1 HSD17B1HOXC9 PHLDA1F3 SLC25A18ZNF57 SNAI2RGMB CRKSEPT4 SOX2HOXC6 MBSGK1 GIPC1MECP2 SGK1SGK CRKEYA1 NR2F2C17orf100 BCL11BSELS HSPA2 PCDH7PLEKHF2 DDX24 SLC39A14MB S100A13PPP3CC 2 ABCC8 PTGESABHD6 SOX18SLC44A2 TSC22D1GDF15 WDR43EGR1 HMGB3PLEK2 HOXA11ASFOXN4 RGS19TIPARP KLF15ADRB2 KLF10RFESD DUSP5KCNG1 HOXA11MME PKP2POU3F2 NOL3CNIH3 FHDC1 PROX1FOXF2 DLX1BMP6 CDKN1ARSC1A1 HOXB8GLIS3 IDI1CXXC6 HIST2H4BGFOD1 ULK1NME2 ST8SIA2SOX4 ULK1FOXQ1 RHOUSMAD6 FAM46CRBMS3 ARHGAP20ZDHHC14 NOTCH1CABLES1 PRDM8HIST2H3C DBPSLC2A14 SLC2A3NRP1 HIST2H4AMYLIP TLCD1BRD2 SOBPZNF467 HEY2IRS4 CBX2USP3 ZFXBAMBI HOXA2EFNA1 RNF14LMCD1 BTBD3NR2F1 TLE4HOXD13 MNTDDX3X C21orf69SOBP PDLIM1PITPNA 0 TLE4 GALNT10 ZNF462FZD8 PLAURIER5 ZDHHC9TSC22D3 HSPA1B wtGR GR3KR wtGR GR3KR wtGR GR3KR wtGR GR3KR wtGR GR3KR wtGR GR3KR wtGR GR3KR wtGR GR3KR CHST3 FAM43BSERTAD4 FANCBHOXC8 PLAURLYPD1 SMG1C1orf26 MEX3CHOXB5 CKS2FGF9 TSPAN9FOXA1 C3orf70CMIP TP53BP2 ITGB2MEIS1 HEY1USP9X FOXC1 ZMIZ1BLM BHLHB3ZFAND5 C3orf70PDZD8 C14orf132MEIS2 TEX10FAM62B ISG20L1CTDSPL CDX2BTG1 ELL2KIAA1804 PIM1HOXA3 Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7 Cluster 8 ACSL1 SLC7A2CKS2 RASEFFBXO5 TSPAN12ATP1A1 CNNM1C20orf177 ATP1A1 HEY1HOXA5 NDE1PIM2 KLF9PRICKLE1 TIMM8AGAS1 IGDCC3GAS1 TBX2HOXA6 TSC22D3 MAFB MERTK SGK1 IL8 JUN TSC22D1 FOXA1 LMO4 NR4A2 CDC42EP4KCTD15 BMFHCFC1 HOXA10 HELBBMF BCORINSIG1 BCORL1LAD1 TOB1HIST1H3B DLC1SOX8 FAM86ACEBPD HOXA10FAM86A KLF6PRAGMIN KLF6 BCORHIST2H3D INSIG1LMO4 PMAIP1ANKRD37 CXXC5SORBS1 HIST1H4B C14orf4PMAIP1 PRIC285CXXC5 RFESDTRIB1 KCNG1SH2B3 TMEM108SOX7 ARHGEF2PLAGL2 ZNF711KCNG1 NRP1PLD6 IL12AUNC5B NGFSOX21 KLHL15PHACTR3 SIX1HIST2H3A SOX7TRIM48 HOXC13LHX6 ZNF296GSK3B TNFRSF12AGADD45A LRRC8EPHACTR3 LDLRDDIT4 NEFLZFP64 FOXD1SPRY1 TFAP2AHCCS EFNA1AUTS2 SIX4MARVELD3 SAMD11EXT1 TUFT1IRX5 USP36PRICKLE3 FGFR3AVPI1 CNKSR3PNMA2 UBQLN2CRISPLD2 ZNF395FILIP1L NRINTNTFAP2A CALD1PER1 INHBEZNF22 ADAMTS1FOS HSPA1ACST1 R R K G 3 t R w G n i n i h h g i g i h h Figure S2. (A) Differences in dex sensitivity and total dex-induced gene expression between wtGR- and GR3KR-expressing HEK293 cells illustrated as heat maps. (Left column) Red color corresponds to higher fold-induction by dex in the GR3KR- expressing cells, blue color to that in the wtGR-expressing cells, and white color indicates that the sensitivity does not differ between cell lines. (Right column) Heat map depicting differences in total dex target gene expression level. (B) Validation of expression of one gene from each hierarchical cluster (shown in Fig. 1B) by RT-qPCR. Cells were treated with vehicle or dex for 6 h, RNA was isolated and analyzed by RT-qPCR with primers specific for TSC22D3 (cluster 1), MAFB (cluster 2), MERTK (cluster 3), SGK1 (cluster 4), IL8 (cluster 5), JUN (cluster 6), TSC22D1 (cluster 7) and FOXA1 (cluster 8). Bars represent the mean ± SD of three experiments. A B U2Os-wtGR U2Os-GR3KR SGK CACNA1I TXNIP dex-regulated MGC42367 DUSP1 MTE C20orf127 FKBP5 p<0.001 FC >1.5 and <0.7 up-regulated SGK1 SGK1 SPINK5L3 TSC22D3 SCNN1A s TUBA3D 1 TIPARP e TSC22D3 r ZFP36 n e ERRFI1 R t FOXO1 e KLF9 s g R TMEM100 K ZCCHC5 u l RHOB 6 NFKBIA G 3 c MT1G 3 t F3 LOC652968 R SLC46A3 MT1X U2Os-GR3KR w ZNF189 C2orf55 - U2Os-wtGR G MT1E 32 102 34 CEBPD - PDK4 s PHACTR3 PHACTR3 s O ZCCHC5 O 2 STK39 OR7A5 2 CHES1 FOXO3 U C10orf47 MT1E U RPS6KA2 CAV1 MT1A PRB1 TSC22D3 CA2 LRRC8A 39 120 67 CNKSR3 EDN3 IP6K3 e ADORA3 PLD5 g FAM90A7 GPR64 down-regulated n KIF13B NAV2 a CDO1 C10orf47 h RASSF4 PKP2 R c PRB2 s RGS2 2 R e d ZBTB16 r K l F3 PTGER2 n e o TSPYL2 G t e 3 GHR f t FOXO3 s g CRISPLD2 u R PNLIP l CDC42EP3 6 w SGSM1 c LAD1 6 - G SNAI2 7 18 33 DNER - SERPINA3 s PRB2 PROX1 s GALNTL4 O OGFRL1 OGFRL1 SLCO2A1 O 2 EYA2 CA2 2 CCND3 NT5DC3 U DDIT4 DKFZp451A211 U CTGF CTGF OLAH NAV2 IHPK3 ALOX5AP GPR1 TSPYL2 MUM1L1 SNAI2 PQLC3 GPIHBP1 CCL20 ALPL PROS1 TSPYL2 KCTD12 C TBC1D8 MT2A LASS6 SLC39A14 NEXN s ELF1 3 FUT4 e FSTL3 r IRF8 n e CLPTM1L t WDR60 e Ingenuity Pathway Analysis IL8 s g CREG1 SERPINE1 u l DUSP2 2 PRIC285 c CITED2 3 CAV1 KIF5C Genes dex-regulated whose expression differs; SP110 TNFSF4 ZBTB16 ZDHHC9 CXCR7 NAV2 BIRC3 U2Os-wtGR vs. U2Os-GR3KR NFIA ZFP36L2 PDZD8 NNMT PRRG1 TOP 6 Molecular and Cellular Functions NPTX2 DACT1 NFIL3 METTL7B PRB1 WNT5A C5orf62 s BNIP3 4 BCL2L1 e Pathw ay p-value r NDRG1 MAP2K1 n e SEPP1 t ITPR1 e FLRT3 s g Cellular Development 2,40E-13 - 1,27E-03 EDN2 CDO1 u l KLHL4 4 AP3S1 c NFATC1 3 Cellular Growth and Proliferation 1,86E-12 - 1,27E-03 SPRYD5 SLC2A14 MSX2 SCEL FAM90A1 ASB9 Cell Death and Survival 3,79E-12 - 1,28E-03 ING1 MCL1 GCOM1 Cellular Function and Maintenance 2,38E-09 - 1,04E-03 BIRC3 IER3 PRAGMIN PLEKHG3 ROR1 Cellular Movement 1,54E-08 - 1,14E-03 HAVCR2 GUCY1A2 JUN S100A3 EHD1 Cell Cycle 5,04E-08 - 1,20E-03 HES1 TMEM166 ZSWIM4 TIAM2 UPP1 s 5 LFNG COL16A1 e r MAP2K3 n PMEPA1 e PMEPA1 t e VEGFA s TUBA4A g CGN C15orf52 u l - GRK5 5 MARCH4 c 3 FOXS1 MYEOV SH3RF2 EPHA4 dex HBEGF RUNX1 TPST2 D CLDN1 FAM176A NFKBIA TCF4 40 25 SGK1 KRT80 *** FHL2 FHL2 ** LAMB3 CD34 20 ANKRD38 s PLAU 6 30 LCTL e e e LMCD1 r MAMLD1 n g g FRMD5 e t e n n RN7SK 15 SYNPO2L a a s DLC1 g h h SERTAD4 20 BAPX1 u l 3 c c C1orf133 c MYCN 2 d d 10 FHL2 l l ADAM19 o o TPST1 f f TGFBI 10 FRMD5 5 0 0 U2Os-wtGR U2Os-GR3KR U2Os-wtGR U2Os-GR3KR E 600 - IL8 PMEPA1 dex 10 * 2.0 *** 500 8 % 400 1.5 h e e t g g n 6 n w a a 300 h h 1.0 o c c r d 4 d l l 200 o o G f f 0.5 2 100 0 0.0 0 U2Os- U2Os- U2Os- U2Os- U2Os-wtGR U2Os-GR3KR U2Os-wtGR U2Os-GR3KR wtGR GR3KR wtGR GR3KR 48 h 72 h Figure S3. Dex target gene expression in wtGR- and GR3KR-expressing U2Os cells. RNA from U2Os cells that stably express either the wtGR (U2Os-wtGR) or the GR3KR (U2Os-GR3KR)
Recommended publications
  • 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]
  • Transcriptome Sequencing and Genome-Wide Association Analyses Reveal Lysosomal Function and Actin Cytoskeleton Remodeling in Schizophrenia and Bipolar Disorder
    Molecular Psychiatry (2015) 20, 563–572 © 2015 Macmillan Publishers Limited All rights reserved 1359-4184/15 www.nature.com/mp ORIGINAL ARTICLE Transcriptome sequencing and genome-wide association analyses reveal lysosomal function and actin cytoskeleton remodeling in schizophrenia and bipolar disorder Z Zhao1,6,JXu2,6, J Chen3,6, S Kim4, M Reimers3, S-A Bacanu3,HYu1, C Liu5, J Sun1, Q Wang1, P Jia1,FXu2, Y Zhang2, KS Kendler3, Z Peng2 and X Chen3 Schizophrenia (SCZ) and bipolar disorder (BPD) are severe mental disorders with high heritability. Clinicians have long noticed the similarities of clinic symptoms between these disorders. In recent years, accumulating evidence indicates some shared genetic liabilities. However, what is shared remains elusive. In this study, we conducted whole transcriptome analysis of post-mortem brain tissues (cingulate cortex) from SCZ, BPD and control subjects, and identified differentially expressed genes in these disorders. We found 105 and 153 genes differentially expressed in SCZ and BPD, respectively. By comparing the t-test scores, we found that many of the genes differentially expressed in SCZ and BPD are concordant in their expression level (q ⩽ 0.01, 53 genes; q ⩽ 0.05, 213 genes; q ⩽ 0.1, 885 genes). Using genome-wide association data from the Psychiatric Genomics Consortium, we found that these differentially and concordantly expressed genes were enriched in association signals for both SCZ (Po10 − 7) and BPD (P = 0.029). To our knowledge, this is the first time that a substantially large number of genes show concordant expression and association for both SCZ and BPD. Pathway analyses of these genes indicated that they are involved in the lysosome, Fc gamma receptor-mediated phagocytosis, regulation of actin cytoskeleton pathways, along with several cancer pathways.
    [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]
  • Accompanies CD8 T Cell Effector Function Global DNA Methylation
    Global DNA Methylation Remodeling Accompanies CD8 T Cell Effector Function Christopher D. Scharer, Benjamin G. Barwick, Benjamin A. Youngblood, Rafi Ahmed and Jeremy M. Boss This information is current as of October 1, 2021. J Immunol 2013; 191:3419-3429; Prepublished online 16 August 2013; doi: 10.4049/jimmunol.1301395 http://www.jimmunol.org/content/191/6/3419 Downloaded from Supplementary http://www.jimmunol.org/content/suppl/2013/08/20/jimmunol.130139 Material 5.DC1 References This article cites 81 articles, 25 of which you can access for free at: http://www.jimmunol.org/content/191/6/3419.full#ref-list-1 http://www.jimmunol.org/ Why The JI? Submit online. • Rapid Reviews! 30 days* from submission to initial decision • No Triage! Every submission reviewed by practicing scientists by guest on October 1, 2021 • Fast Publication! 4 weeks from acceptance to publication *average Subscription Information about subscribing to The Journal of Immunology is online at: http://jimmunol.org/subscription Permissions Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Email Alerts Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts The Journal of Immunology is published twice each month by The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2013 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. The Journal of Immunology Global DNA Methylation Remodeling Accompanies CD8 T Cell Effector Function Christopher D. Scharer,* Benjamin G. Barwick,* Benjamin A. Youngblood,*,† Rafi Ahmed,*,† and Jeremy M.
    [Show full text]
  • CARBOPLATIN and PACLITAXEL a Dissertation SUBMITTED
    PHARMACOGENOMICS OF CHEMOTHERAPEUTIC AGENTS: CARBOPLATIN AND PACLITAXEL A Dissertation SUBMITTED TO THE FACULTY OF UNIVERSITY OF MINNESOTA BY Taraswi Mitra Ghosh IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY Dr. Jatinder K. Lamba May 2017 © Taraswi Mitra Ghosh, 2017 ACKNOWLEDGEMENTS At this special moment when I have reached the concluding stages of my Doctoral research and am ready to embark upon another voyage in my scientific career, I would like to take the opportunity to acknowledge the contributions of the people who have shaped up my life and career. First and foremost, I would like to thank Almighty God for giving me the strength to overcome all the challenges, for giving me the motivation to learn and to acquire knowledge. I would like to thank Dr. Jatinder Lamba, my adviser. I have learned a lot from her and am extremely thankful to her. She has helped me develop my scientific temper and inquisitive thinking. I would like to express my sincere gratitude towards my committee members. Dr. Mark Kirstein, my committee chair, has been a constant support. He has helped me develop my concepts in Pharmacokinetics. His insightful questions during our discussions have encouraged me to think deep and broaden my knowledge base. Dr. Angela Birnbaum, my committee member and DGS, has always been a strong support. Working with her was a very nice and cherishable experience for me. She has provided me the confidence to undertake independent projects- an experience which I am sure will be helpful in my future as a Scientist. I would like to thank Dr.
    [Show full text]
  • Identification of Potential Key Genes and Pathway Linked with Sporadic Creutzfeldt-Jakob Disease Based on Integrated Bioinformatics Analyses
    medRxiv preprint doi: https://doi.org/10.1101/2020.12.21.20248688; this version posted December 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Identification of potential key genes and pathway linked with sporadic Creutzfeldt-Jakob disease based on integrated bioinformatics analyses Basavaraj Vastrad1, Chanabasayya Vastrad*2 , Iranna Kotturshetti 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. 3. Department of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron, Karnataka 562209, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. medRxiv preprint doi: https://doi.org/10.1101/2020.12.21.20248688; this version posted December 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Abstract Sporadic Creutzfeldt-Jakob disease (sCJD) is neurodegenerative disease also called prion disease linked with poor prognosis. The aim of the current study was to illuminate the underlying molecular mechanisms of sCJD. The mRNA microarray dataset GSE124571 was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened.
    [Show full text]
  • Single Cell Regulatory Landscape of the Mouse Kidney Highlights Cellular Differentiation Programs and Disease Targets
    ARTICLE https://doi.org/10.1038/s41467-021-22266-1 OPEN Single cell regulatory landscape of the mouse kidney highlights cellular differentiation programs and disease targets Zhen Miao 1,2,3,8, Michael S. Balzer 1,2,8, Ziyuan Ma 1,2,8, Hongbo Liu1,2, Junnan Wu 1,2, Rojesh Shrestha 1,2, Tamas Aranyi1,2, Amy Kwan4, Ayano Kondo 4, Marco Pontoglio 5, Junhyong Kim6, ✉ Mingyao Li 7, Klaus H. Kaestner2,4 & Katalin Susztak 1,2,4 1234567890():,; Determining the epigenetic program that generates unique cell types in the kidney is critical for understanding cell-type heterogeneity during tissue homeostasis and injury response. Here, we profile open chromatin and gene expression in developing and adult mouse kidneys at single cell resolution. We show critical reliance of gene expression on distal regulatory elements (enhancers). We reveal key cell type-specific transcription factors and major gene- regulatory circuits for kidney cells. Dynamic chromatin and expression changes during nephron progenitor differentiation demonstrates that podocyte commitment occurs early and is associated with sustained Foxl1 expression. Renal tubule cells follow a more complex differentiation, where Hfn4a is associated with proximal and Tfap2b with distal fate. Mapping single nucleotide variants associated with human kidney disease implicates critical cell types, developmental stages, genes, and regulatory mechanisms. The single cell multi-omics atlas reveals key chromatin remodeling events and gene expression dynamics associated with kidney development. 1 Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA. 2 Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
    [Show full text]
  • Evidence for a Role of Developmental Genes in the Origin of Obesity and Body Fat Distribution
    Evidence for a role of developmental genes in the origin of obesity and body fat distribution Stephane Gesta*, Matthias Blu¨ her†, Yuji Yamamoto*, Andrew W. Norris*, Janin Berndt†, Susan Kralisch†, Jeremie Boucher*, Choy Lewis*, and C. Ronald Kahn*‡ *Joslin Diabetes Center and Harvard Medical School, Boston, MA 02215; and †Department of Internal Medicine III, University of Leipzig, 04103 Leipzig, Germany Contributed by C. Ronald Kahn, March 9, 2006 Obesity, especially central obesity, is a hereditable trait associated In the present study, we have explored the hypothesis that with a high risk for development of diabetes and metabolic patterns of fat distribution and, perhaps, to some degree, obesity disorders. Combined gene expression analysis of adipocyte- and itself may have a developmental genetic origin. Indeed, we find preadipocyte-containing fractions from intraabdominal and sub- major differences in expression of multiple genes involved in cutaneous adipose tissue of mice revealed coordinated depot- embryonic development and pattern specification between adipo- specific differences in expression of multiple genes involved in cytes taken from intraabdominal and s.c. depots in rodents and embryonic development and pattern specification. These differ- humans. We also demonstrate similar differences in the stromo- ences were intrinsic and persisted during in vitro culture and vascular fraction (SVF)-containing preadipocytes and that these differentiation. Similar depot-specific differences in expression of differences persist in culture. Most importantly, we demonstrate developmental genes were observed in human subcutaneous ver- that some of these developmental genes exhibit changes in expres- sus visceral adipose tissue. Furthermore, in humans, several genes sion that are closely correlated with the level of obesity and the exhibited changes in expression that correlated closely with body pattern of fat distribution.
    [Show full text]
  • Supplementary Figure Legends
    1 Supplementary Figure legends 2 Supplementary Figure 1. 3 Experimental workflow. 4 5 Supplementary Figure 2. 6 IRF9 binding to promoters. 7 a) Verification of mIRF9 antibody by site-directed ChIP. IFNβ-stimulated binding of IRF9 to 8 the ISRE sequences of Mx2 was analyzed using BMDMs of WT and Irf9−/− (IRF9-/-) mice. 9 Cells were treated with 250 IU/ml of IFNβ for 1.5h. Data represent mean and SEM values of 10 three independent experiments. P-values were calculated using the paired ratio t-test (*P ≤ 11 0.05; **P ≤ 0.01, ***P ≤ 0.001). 12 b) Browser tracks showing complexes assigned as STAT-IRF9 in IFNγ treated wild type 13 BMDMs. Input, STAT2, IRF9 (scale 0-200). STAT1 (scale 0-150). 14 15 Supplementary Figure 3. 16 Experimental system for BioID. 17 a) Kinetics of STAT1, STAT2 and IRF9 synthesis in Raw 264.7 macrophages and wild type 18 BMDMs treated with 250 IU/ml as indicated. Whole-cell extracts were tested in western blot 19 for STAT1 phosphorylation at Y701 and of STAT2 at Y689 as well as total STAT1, STAT2, 20 IRF9 and GAPDH levels. The blots are representative of three independent experiments. b) 21 Irf9-/- mouse embryonic fibroblasts (MEFs) were transiently transfected with the indicated 22 expression vectors, including constitutively active IRF7-M15. One day after transfection, 23 RNA was isolated and Mx2 expression determined by qPCR. c) Myc-BirA*-IRF9 transgenic 24 Raw 264.7 were treated with increasing amounts of doxycycline (dox) (0,2µg/ml, 0,4µg/ml, 25 0,6µg/ml, 0,8µg/ml, 1mg/ml) and 50µM biotin.
    [Show full text]
  • Microrna Modulate Alveolar Epithelial Response to Cyclic Stretch
    University of Pennsylvania ScholarlyCommons Departmental Papers (BE) Department of Bioengineering 2012 MicroRNA Modulate Alveolar Epithelial Response to Cyclic Stretch Nadir Yehya University of Pennsylvania, [email protected] Adi Yerrapureddy University of Pennsylvania John Tobias University of Pennsylvania, [email protected] Susan S. Margulies University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/be_papers Part of the Biomedical Engineering and Bioengineering Commons Recommended Citation Yehya, N., Yerrapureddy, A., Tobias, J., & Margulies, S. S. (2012). MicroRNA Modulate Alveolar Epithelial Response to Cyclic Stretch. BMC Genomics, 13 (154), http://dx.doi.org/10.1186/1471-2164-13-154 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/be_papers/205 For more information, please contact [email protected]. MicroRNA Modulate Alveolar Epithelial Response to Cyclic Stretch Abstract Background MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression implicated in multiple cellular processes. Cyclic stretch of alveoli is characteristic of mechanical ventilation, and is postulated to be partly responsible for the lung injury and inflammation in entilatv or-induced lung injury. We propose that miRNAs may regulate some of the stretch response, and therefore hypothesized that miRNAs would be differentially expressed between cyclically stretched and unstretched rat alveolar epithelial cells (RAECs). Results RAECs were isolated and cultured to express type I epithelial characteristics. They were then equibiaxially stretched to 25% change in surface area at 15 cycles/minute for 1 hour or 6 hours, or served as unstretched controls, and miRNAs were extracted. Expression profiling of the miRNAs with at least 1.5-fold change over controls revealed 42 miRNAs were regulated (34 up and 8 down) with stretch.
    [Show full text]
  • Aberrant HOXC Expression Accompanies the Malignant Phenotype in Human Prostate1
    [CANCER RESEARCH 63, 5879–5888, September 15, 2003] Aberrant HOXC Expression Accompanies the Malignant Phenotype in Human Prostate1 Gary J. Miller,2 Heidi L. Miller, Adrie van Bokhoven, James R. Lambert, Priya N. Werahera, Osvaldo Schirripa,3 M. Scott Lucia, and Steven K. Nordeen4 Department of Pathology, University of Colorado Health Sciences Center, Denver, Colorado 80262 ABSTRACT breast (13, 14), and renal (15) carcinomas; melanomas (16); and squamous carcinomas of the skin (17). Because the genes implicated Dysregulation of HOX gene expression has been implicated as a factor show little consensus, the dysregulation may be a tissue-specific in malignancies for a number of years. However, no consensus has perturbation of the existing HOX expression pattern rather than a emerged regarding specific causative genes. Using a degenerate reverse transcription-PCR technique, we show up-regulation of genes from the single causative gene. Tissue-specific expression patterns have been HOXC cluster in malignant prostate cell lines and lymph node metastases. reported in kidney and colon, by Northern blot analysis (12, 15). When relative expression levels of the four HOX clusters were examined, Primary tumors in both kidney and colon showed variations in spe- lymph node metastases and cell lines derived from lymph node metastases cific HOX gene expression from the corresponding normal tissue, but exhibited very similar patterns, patterns distinct from those in benign cells overall expression patterns for individual tumors were not reported. or malignant cell lines derived from other tumor sites. Specific reverse Only primary kidney tumors were examined (15), but liver metastases transcription-PCR for HOXC4, HOXC5, HOXC6, and HOXC8 confirmed from colon tumors reportedly displayed expression of specific HOX overexpression of these genes in malignant cell lines and lymph node genes similar to that seen in either primary colon tumors or normal metastases.
    [Show full text]
  • Target Gene Gene Description Validation Diana Miranda
    Supplemental Table S1. Mmu-miR-183-5p in silico predicted targets. TARGET GENE GENE DESCRIPTION VALIDATION DIANA MIRANDA MIRBRIDGE PICTAR PITA RNA22 TARGETSCAN TOTAL_HIT AP3M1 adaptor-related protein complex 3, mu 1 subunit V V V V V V V 7 BTG1 B-cell translocation gene 1, anti-proliferative V V V V V V V 7 CLCN3 chloride channel, voltage-sensitive 3 V V V V V V V 7 CTDSPL CTD (carboxy-terminal domain, RNA polymerase II, polypeptide A) small phosphatase-like V V V V V V V 7 DUSP10 dual specificity phosphatase 10 V V V V V V V 7 MAP3K4 mitogen-activated protein kinase kinase kinase 4 V V V V V V V 7 PDCD4 programmed cell death 4 (neoplastic transformation inhibitor) V V V V V V V 7 PPP2R5C protein phosphatase 2, regulatory subunit B', gamma V V V V V V V 7 PTPN4 protein tyrosine phosphatase, non-receptor type 4 (megakaryocyte) V V V V V V V 7 EZR ezrin V V V V V V 6 FOXO1 forkhead box O1 V V V V V V 6 ANKRD13C ankyrin repeat domain 13C V V V V V V 6 ARHGAP6 Rho GTPase activating protein 6 V V V V V V 6 BACH2 BTB and CNC homology 1, basic leucine zipper transcription factor 2 V V V V V V 6 BNIP3L BCL2/adenovirus E1B 19kDa interacting protein 3-like V V V V V V 6 BRMS1L breast cancer metastasis-suppressor 1-like V V V V V V 6 CDK5R1 cyclin-dependent kinase 5, regulatory subunit 1 (p35) V V V V V V 6 CTDSP1 CTD (carboxy-terminal domain, RNA polymerase II, polypeptide A) small phosphatase 1 V V V V V V 6 DCX doublecortin V V V V V V 6 ENAH enabled homolog (Drosophila) V V V V V V 6 EPHA4 EPH receptor A4 V V V V V V 6 FOXP1 forkhead box P1 V
    [Show full text]