E2F6 Initiates Stable Epigenetic Silencing of Germline Genes During
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Table S1. List of Proteins in the BAHD1 Interactome
Table S1. List of proteins in the BAHD1 interactome BAHD1 nuclear partners found in this work yeast two-hybrid screen Name Description Function Reference (a) Chromatin adapters HP1α (CBX5) chromobox homolog 5 (HP1 alpha) Binds histone H3 methylated on lysine 9 and chromatin-associated proteins (20-23) HP1β (CBX1) chromobox homolog 1 (HP1 beta) Binds histone H3 methylated on lysine 9 and chromatin-associated proteins HP1γ (CBX3) chromobox homolog 3 (HP1 gamma) Binds histone H3 methylated on lysine 9 and chromatin-associated proteins MBD1 methyl-CpG binding domain protein 1 Binds methylated CpG dinucleotide and chromatin-associated proteins (22, 24-26) Chromatin modification enzymes CHD1 chromodomain helicase DNA binding protein 1 ATP-dependent chromatin remodeling activity (27-28) HDAC5 histone deacetylase 5 Histone deacetylase activity (23,29,30) SETDB1 (ESET;KMT1E) SET domain, bifurcated 1 Histone-lysine N-methyltransferase activity (31-34) Transcription factors GTF3C2 general transcription factor IIIC, polypeptide 2, beta 110kDa Required for RNA polymerase III-mediated transcription HEYL (Hey3) hairy/enhancer-of-split related with YRPW motif-like DNA-binding transcription factor with basic helix-loop-helix domain (35) KLF10 (TIEG1) Kruppel-like factor 10 DNA-binding transcription factor with C2H2 zinc finger domain (36) NR2F1 (COUP-TFI) nuclear receptor subfamily 2, group F, member 1 DNA-binding transcription factor with C4 type zinc finger domain (ligand-regulated) (36) PEG3 paternally expressed 3 DNA-binding transcription factor with -
Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model
Downloaded from http://www.jimmunol.org/ by guest on September 25, 2021 T + is online at: average * The Journal of Immunology , 34 of which you can access for free at: 2016; 197:1477-1488; Prepublished online 1 July from submission to initial decision 4 weeks from acceptance to publication 2016; doi: 10.4049/jimmunol.1600589 http://www.jimmunol.org/content/197/4/1477 Molecular Profile of Tumor-Specific CD8 Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A. Waugh, Sonia M. Leach, Brandon L. Moore, Tullia C. Bruno, Jonathan D. Buhrman and Jill E. Slansky J Immunol cites 95 articles Submit online. Every submission reviewed by practicing scientists ? is published twice each month by Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts http://jimmunol.org/subscription Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html http://www.jimmunol.org/content/suppl/2016/07/01/jimmunol.160058 9.DCSupplemental This article http://www.jimmunol.org/content/197/4/1477.full#ref-list-1 Information about subscribing to The JI No Triage! Fast Publication! Rapid Reviews! 30 days* Why • • • Material References Permissions Email Alerts Subscription Supplementary The Journal of Immunology The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2016 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. This information is current as of September 25, 2021. The Journal of Immunology Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A. -
The E–Id Protein Axis Modulates the Activities of the PI3K–AKT–Mtorc1
Downloaded from genesdev.cshlp.org on October 6, 2021 - Published by Cold Spring Harbor Laboratory Press The E–Id protein axis modulates the activities of the PI3K–AKT–mTORC1– Hif1a and c-myc/p19Arf pathways to suppress innate variant TFH cell development, thymocyte expansion, and lymphomagenesis Masaki Miyazaki,1,8 Kazuko Miyazaki,1,8 Shuwen Chen,1 Vivek Chandra,1 Keisuke Wagatsuma,2 Yasutoshi Agata,2 Hans-Reimer Rodewald,3 Rintaro Saito,4 Aaron N. Chang,5 Nissi Varki,6 Hiroshi Kawamoto,7 and Cornelis Murre1 1Department of Molecular Biology, University of California at San Diego, La Jolla, California 92093, USA; 2Department of Biochemistry and Molecular Biology, Shiga University of Medical School, Shiga 520-2192, Japan; 3Division of Cellular Immunology, German Cancer Research Center, D-69120 Heidelberg, Germany; 4Department of Medicine, University of California at San Diego, La Jolla, California 92093, USA; 5Center for Computational Biology, Institute for Genomic Medicine, University of California at San Diego, La Jolla, California 92093, USA; 6Department of Pathology, University of California at San Diego, La Jolla, California 92093, USA; 7Department of Immunology, Institute for Frontier Medical Sciences, Kyoto University, Kyoto 606-8507, Japan It is now well established that the E and Id protein axis regulates multiple steps in lymphocyte development. However, it remains unknown how E and Id proteins mechanistically enforce and maintain the naı¨ve T-cell fate. Here we show that Id2 and Id3 suppressed the development and expansion of innate variant follicular helper T (TFH) cells. Innate variant TFH cells required major histocompatibility complex (MHC) class I-like signaling and were associated with germinal center B cells. -
Animal Cells Anterior Epidermis Anterior Epidermis A-Neural
Anterior Epidermis Anterior Epidermis KH2012 TF common name Log2FC -Log(pvalue) Log2FC -Log(pvalue) DE in Imai Matched PWM PWM Cluster KH2012 TF common name Log2FC -Log(pvalue) Log2FC -Log(pvalue) DE in Imai Matched PWM PWM Cluster gene model Sibling Cluster Sibling Cluster Parent Cluster Parent Cluster z-score z-score gene model Sibling Cluster Sibling Cluster Parent Cluster Parent Cluster z-score z-score KH2012:KH.C11.485 Irx-B 1.0982 127.5106 0.9210 342.5323 Yes No PWM Hits No PWM Hits KH2012:KH.C1.159 E(spl)/hairy-a 1.3445 65.6302 0.6908 14.3413 Not Analyzed -15.2125 No PWM Hits KH2012:KH.L39.1 FoxH-b 0.6677 45.8148 1.1074 185.2909 No -3.3335 5.2695 KH2012:KH.C1.99 SoxB1 1.2482 73.2413 0.3331 9.3534 Not Analyzed No PWM match No PWM match KH2012:KH.C1.159 E(spl)/hairy-a 0.6233 47.2239 0.4339 77.0192 Yes -10.496 No PWM Hits KH2012:KH.C11.485 Irx-B 1.2355 72.8859 0.1608 0.0137 Not Analyzed No PWM Hits No PWM Hits KH2012:KH.C7.43 AP-2-like2 0.4991 31.7939 0.4775 68.8091 Yes 10.551 21.586 KH2012:KH.C1.1016 GCNF 0.8556 36.2030 1.3828 100.1236 Not Analyzed No PWM match No PWM match KH2012:KH.C1.99 SoxB1 0.4913 33.7808 0.3406 39.0890 No No PWM match No PWM match KH2012:KH.L108.4 CREB/ATF-a 0.6859 37.8207 0.3453 15.8154 Not Analyzed 6.405 8.6245 KH2012:KH.C7.157 Emc 0.4139 19.2080 1.1001 173.3024 Yes No PWM match No PWM match KH2012:KH.S164.12 SoxB2 0.6194 22.8414 0.6433 35.7335 Not Analyzed 8.722 17.405 KH2012:KH.C4.366 ERF2 -0.4878 -32.3767 -0.1770 -0.2316 No -10.324 9.7885 KH2012:KH.L4.17 Zinc Finger (C2H2)-18 0.6166 24.8925 0.2386 5.3130 -
1 AGING Supplementary Table 2
SUPPLEMENTARY TABLES Supplementary Table 1. Details of the eight domain chains of KIAA0101. Serial IDENTITY MAX IN COMP- INTERFACE ID POSITION RESOLUTION EXPERIMENT TYPE number START STOP SCORE IDENTITY LEX WITH CAVITY A 4D2G_D 52 - 69 52 69 100 100 2.65 Å PCNA X-RAY DIFFRACTION √ B 4D2G_E 52 - 69 52 69 100 100 2.65 Å PCNA X-RAY DIFFRACTION √ C 6EHT_D 52 - 71 52 71 100 100 3.2Å PCNA X-RAY DIFFRACTION √ D 6EHT_E 52 - 71 52 71 100 100 3.2Å PCNA X-RAY DIFFRACTION √ E 6GWS_D 41-72 41 72 100 100 3.2Å PCNA X-RAY DIFFRACTION √ F 6GWS_E 41-72 41 72 100 100 2.9Å PCNA X-RAY DIFFRACTION √ G 6GWS_F 41-72 41 72 100 100 2.9Å PCNA X-RAY DIFFRACTION √ H 6IIW_B 2-11 2 11 100 100 1.699Å UHRF1 X-RAY DIFFRACTION √ www.aging-us.com 1 AGING Supplementary Table 2. Significantly enriched gene ontology (GO) annotations (cellular components) of KIAA0101 in lung adenocarcinoma (LinkedOmics). Leading Description FDR Leading Edge Gene EdgeNum RAD51, SPC25, CCNB1, BIRC5, NCAPG, ZWINT, MAD2L1, SKA3, NUF2, BUB1B, CENPA, SKA1, AURKB, NEK2, CENPW, HJURP, NDC80, CDCA5, NCAPH, BUB1, ZWILCH, CENPK, KIF2C, AURKA, CENPN, TOP2A, CENPM, PLK1, ERCC6L, CDT1, CHEK1, SPAG5, CENPH, condensed 66 0 SPC24, NUP37, BLM, CENPE, BUB3, CDK2, FANCD2, CENPO, CENPF, BRCA1, DSN1, chromosome MKI67, NCAPG2, H2AFX, HMGB2, SUV39H1, CBX3, TUBG1, KNTC1, PPP1CC, SMC2, BANF1, NCAPD2, SKA2, NUP107, BRCA2, NUP85, ITGB3BP, SYCE2, TOPBP1, DMC1, SMC4, INCENP. RAD51, OIP5, CDK1, SPC25, CCNB1, BIRC5, NCAPG, ZWINT, MAD2L1, SKA3, NUF2, BUB1B, CENPA, SKA1, AURKB, NEK2, ESCO2, CENPW, HJURP, TTK, NDC80, CDCA5, BUB1, ZWILCH, CENPK, KIF2C, AURKA, DSCC1, CENPN, CDCA8, CENPM, PLK1, MCM6, ERCC6L, CDT1, HELLS, CHEK1, SPAG5, CENPH, PCNA, SPC24, CENPI, NUP37, FEN1, chromosomal 94 0 CENPL, BLM, KIF18A, CENPE, MCM4, BUB3, SUV39H2, MCM2, CDK2, PIF1, DNA2, region CENPO, CENPF, CHEK2, DSN1, H2AFX, MCM7, SUV39H1, MTBP, CBX3, RECQL4, KNTC1, PPP1CC, CENPP, CENPQ, PTGES3, NCAPD2, DYNLL1, SKA2, HAT1, NUP107, MCM5, MCM3, MSH2, BRCA2, NUP85, SSB, ITGB3BP, DMC1, INCENP, THOC3, XPO1, APEX1, XRCC5, KIF22, DCLRE1A, SEH1L, XRCC3, NSMCE2, RAD21. -
Hepatitis C Virus As a Unique Human Model Disease to Define
viruses Review Hepatitis C Virus as a Unique Human Model Disease to Define Differences in the Transcriptional Landscape of T Cells in Acute versus Chronic Infection David Wolski and Georg M. Lauer * Liver Center at the Gastrointestinal Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA * Correspondence: [email protected]; Tel.: +1-617-724-7515 Received: 27 June 2019; Accepted: 23 July 2019; Published: 26 July 2019 Abstract: The hepatitis C virus is unique among chronic viral infections in that an acute outcome with complete viral elimination is observed in a minority of infected patients. This unique feature allows direct comparison of successful immune responses with those that fail in the setting of the same human infection. Here we review how this scenario can be used to achieve better understanding of transcriptional regulation of T-cell differentiation. Specifically, we discuss results from a study comparing transcriptional profiles of hepatitis C virus (HCV)-specific CD8 T-cells during early HCV infection between patients that do and do not control and eliminate HCV. Identification of early gene expression differences in key T-cell differentiation molecules as well as clearly distinct transcriptional networks related to cell metabolism and nucleosomal regulation reveal novel insights into the development of exhausted and memory T-cells. With additional transcriptional studies of HCV-specific CD4 and CD8 T-cells in different stages of infection currently underway, we expect HCV infection to become a valuable model disease to study human immunity to viruses. Keywords: viral hepatitis; hepatitis C virus; T cells; transcriptional regulation; transcription factors; metabolism; nucleosome 1. -
Development and Validation of a Novel Immune-Related Prognostic Model
Wang et al. J Transl Med (2020) 18:67 https://doi.org/10.1186/s12967-020-02255-6 Journal of Translational Medicine RESEARCH Open Access Development and validation of a novel immune-related prognostic model in hepatocellular carcinoma Zheng Wang1, Jie Zhu1, Yongjuan Liu3, Changhong Liu2, Wenqi Wang2, Fengzhe Chen1* and Lixian Ma1* Abstract Background: Growing evidence has suggested that immune-related genes play crucial roles in the development and progression of hepatocellular carcinoma (HCC). Nevertheless, the utility of immune-related genes for evaluating the prognosis of HCC patients are still lacking. The study aimed to explore gene signatures and prognostic values of immune-related genes in HCC. Methods: We comprehensively integrated gene expression data acquired from 374 HCC and 50 normal tissues in The Cancer Genome Atlas (TCGA). Diferentially expressed genes (DEGs) analysis and univariate Cox regression analysis were performed to identify DEGs that related to overall survival. An immune prognostic model was constructed using the Lasso and multivariate Cox regression analyses. Furthermore, Cox regression analysis was applied to identify independent prognostic factors in HCC. The correlation analysis between immune-related signature and immune cells infltration were also investigated. Finally, the signature was validated in an external independent dataset. Results: A total of 329 diferentially expressed immune‐related genes were detected. 64 immune‐related genes were identifed to be markedly related to overall survival in HCC patients using univariate Cox regression analysis. Then we established a TF-mediated network for exploring the regulatory mechanisms of these genes. Lasso and multivariate Cox regression analyses were applied to construct the immune-based prognostic model, which consisted of nine immune‐related genes. -
Polycomb Complexes Associate with Enhancers and Promote Oncogenic Transcriptional Programs in Cancer Through Multiple Mechanisms
ARTICLE DOI: 10.1038/s41467-018-05728-x OPEN Polycomb complexes associate with enhancers and promote oncogenic transcriptional programs in cancer through multiple mechanisms Ho Lam Chan 1,2, Felipe Beckedorff 1,2, Yusheng Zhang1,2, Jenaro Garcia-Huidobro1,2,5, Hua Jiang3, Antonio Colaprico1,2, Daniel Bilbao1, Maria E. Figueroa1,2, John LaCava3,4, Ramin Shiekhattar1,2 & Lluis Morey 1,2 1234567890():,; Polycomb repressive complex 1 (PRC1) plays essential roles in cell fate decisions and development. However, its role in cancer is less well understood. Here, we show that RNF2, encoding RING1B, and canonical PRC1 (cPRC1) genes are overexpressed in breast cancer. We find that cPRC1 complexes functionally associate with ERα and its pioneer factor FOXA1 in ER+ breast cancer cells, and with BRD4 in triple-negative breast cancer cells (TNBC). While cPRC1 still exerts its repressive function, it is also recruited to oncogenic active enhancers. RING1B regulates enhancer activity and gene transcription not only by promoting the expression of oncogenes but also by regulating chromatin accessibility. Functionally, RING1B plays a divergent role in ER+ and TNBC metastasis. Finally, we show that concomitant recruitment of RING1B to active enhancers occurs across multiple cancers, highlighting an under-explored function of cPRC1 in regulating oncogenic transcriptional programs in cancer. 1 Sylvester Comprehensive Cancer Center, Biomedical Research Building, 1501 NW 10th Avenue, Miami, FL 33136, USA. 2 Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL 33136, USA. 3 Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, NY 10065, USA. 4 Institute for Systems Genetics and Department of Biochemistry and Molecular Pharmacology, New York University School of Medicine, New York, NY 10016, USA. -
1714 Gene Comprehensive Cancer Panel Enriched for Clinically Actionable Genes with Additional Biologically Relevant Genes 400-500X Average Coverage on Tumor
xO GENE PANEL 1714 gene comprehensive cancer panel enriched for clinically actionable genes with additional biologically relevant genes 400-500x average coverage on tumor Genes A-C Genes D-F Genes G-I Genes J-L AATK ATAD2B BTG1 CDH7 CREM DACH1 EPHA1 FES G6PC3 HGF IL18RAP JADE1 LMO1 ABCA1 ATF1 BTG2 CDK1 CRHR1 DACH2 EPHA2 FEV G6PD HIF1A IL1R1 JAK1 LMO2 ABCB1 ATM BTG3 CDK10 CRK DAXX EPHA3 FGF1 GAB1 HIF1AN IL1R2 JAK2 LMO7 ABCB11 ATR BTK CDK11A CRKL DBH EPHA4 FGF10 GAB2 HIST1H1E IL1RAP JAK3 LMTK2 ABCB4 ATRX BTRC CDK11B CRLF2 DCC EPHA5 FGF11 GABPA HIST1H3B IL20RA JARID2 LMTK3 ABCC1 AURKA BUB1 CDK12 CRTC1 DCUN1D1 EPHA6 FGF12 GALNT12 HIST1H4E IL20RB JAZF1 LPHN2 ABCC2 AURKB BUB1B CDK13 CRTC2 DCUN1D2 EPHA7 FGF13 GATA1 HLA-A IL21R JMJD1C LPHN3 ABCG1 AURKC BUB3 CDK14 CRTC3 DDB2 EPHA8 FGF14 GATA2 HLA-B IL22RA1 JMJD4 LPP ABCG2 AXIN1 C11orf30 CDK15 CSF1 DDIT3 EPHB1 FGF16 GATA3 HLF IL22RA2 JMJD6 LRP1B ABI1 AXIN2 CACNA1C CDK16 CSF1R DDR1 EPHB2 FGF17 GATA5 HLTF IL23R JMJD7 LRP5 ABL1 AXL CACNA1S CDK17 CSF2RA DDR2 EPHB3 FGF18 GATA6 HMGA1 IL2RA JMJD8 LRP6 ABL2 B2M CACNB2 CDK18 CSF2RB DDX3X EPHB4 FGF19 GDNF HMGA2 IL2RB JUN LRRK2 ACE BABAM1 CADM2 CDK19 CSF3R DDX5 EPHB6 FGF2 GFI1 HMGCR IL2RG JUNB LSM1 ACSL6 BACH1 CALR CDK2 CSK DDX6 EPOR FGF20 GFI1B HNF1A IL3 JUND LTK ACTA2 BACH2 CAMTA1 CDK20 CSNK1D DEK ERBB2 FGF21 GFRA4 HNF1B IL3RA JUP LYL1 ACTC1 BAG4 CAPRIN2 CDK3 CSNK1E DHFR ERBB3 FGF22 GGCX HNRNPA3 IL4R KAT2A LYN ACVR1 BAI3 CARD10 CDK4 CTCF DHH ERBB4 FGF23 GHR HOXA10 IL5RA KAT2B LZTR1 ACVR1B BAP1 CARD11 CDK5 CTCFL DIAPH1 ERCC1 FGF3 GID4 HOXA11 IL6R KAT5 ACVR2A -
Estradiol-Regulated Micrornas Control Estradiol Response in Breast Cancer Cells Poornima Bhat-Nakshatri1, Guohua Wang2, Nikail R
4850–4861 Nucleic Acids Research, 2009, Vol. 37, No. 14 Published online 14 June 2009 doi:10.1093/nar/gkp500 Estradiol-regulated microRNAs control estradiol response in breast cancer cells Poornima Bhat-Nakshatri1, Guohua Wang2, Nikail R. Collins1, Michael J. Thomson3, Tim R. Geistlinger4, Jason S. Carroll4, Myles Brown4, Scott Hammond3, Edward F. Srour2, Yunlong Liu2 and Harikrishna Nakshatri1,5,* 1Department of Surgery, 2Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, 3Department of Cell and Developmental Biology, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, 4Department of Medical Oncology, Dana-Farber Medical School, Harvard Medical School, Boston, MA and 5Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA Received May 4, 2009; Revised May 22, 2009; Accepted May 24, 2009 ABSTRACT INTRODUCTION Estradiol (E2) regulates gene expression at the Estradiol (E2) controls several biological processes by transcriptional level by functioning as a ligand for functioning as a ligand for nuclear receptors estrogen estrogen receptor alpha (ERa) and estrogen receptor receptor alpha (ERa) and beta (ERb) (1). ERs may par- beta (ERb). E2-inducible proteins c-Myc and E2Fs are ticipate in the genomic (transcriptional) and non-genomic required for optimal ERa activity and secondary actions of E2 (1,2). The genomic action involves binding of ERa to the regulatory regions of target genes either estrogen responses, respectively. We show that directly or through protein–protein interaction. DNA- E2 induces 21 microRNAs and represses seven bound ERa then recruits various co-regulatory molecules microRNAs in MCF-7 breast cancer cells; these to induce chromatin modifications that either increase or microRNAs have the potential to control 420 decrease the level of target gene transcription. -
12Q Deletions FTNW
12q deletions rarechromo.org What is a 12q deletion? A deletion from chromosome 12q is a rare genetic condition in which a part of one of the body’s 46 chromosomes is missing. When material is missing from a chromosome, it is called a deletion. What are chromosomes? Chromosomes are the structures in each of the body’s cells that carry genetic information telling the body how to develop and function. They come in pairs, one from each parent, and are numbered 1 to 22 approximately from largest to smallest. Additionally there is a pair of sex chromosomes, two named X in females, and one X and another named Y in males. Each chromosome has a short (p) arm and a long (q) arm. Looking at chromosome 12 Chromosome analysis You can’t see chromosomes with the naked eye, but if you stain and magnify them many hundreds of times under a microscope, you can see that each one has a distinctive pattern of light and dark bands. In the diagram of the long arm of chromosome 12 on page 3 you can see the bands are numbered outwards starting from the point at the top of the diagram where the short and long arms meet (the centromere). Molecular techniques If you magnify chromosome 12 about 850 times, a small piece may be visibly missing. But sometimes the missing piece is so tiny that the chromosome looks normal through a microscope. The missing section can then only be found using more sensitive molecular techniques such as FISH (fluorescence in situ hybridisation, a technique that reveals the chromosomes in fluorescent colour), MLPA (multiplex ligation-dependent probe amplification) and/or array-CGH (microarrays), a technique that shows gains and losses of tiny amounts of DNA throughout all the chromosomes. -
Supplemental Table 1. Primers and Probes for RT-Pcrs
Supplemental Table 1. Primers and probes for RT-PCRs Gene Direction Sequence Quantitative RT-PCR E2F1 Forward Primer 5’-GGA TTT CAC ACC TTT TCC TGG AT-3’ Reverse Primer 5’-CCT GGA AAC TGA CCA TCA GTA CCT-3’ Probe 5’-FAM-CGA GCT GGC CCA CTG CTC TCG-TAMRA-3' E2F2 Forward Primer 5'-TCC CAA TCC CCT CCA GAT C-3' Reverse Primer 5'-CAA GTT GTG CGA TGC CTG C-3' Probe 5' -FAM-TCC TTT TGG CCG GCA GCC G-TAMRA-3' E2F3a Forward Primer 5’-TTT AAA CCA TCT GAG AGG TAC TGA TGA-3’ Reverse Primer 5’-CGG CCC TCC GGC AA-3’ Probe 5’-FAM-CGC TTT CTC CTA GCT CCA GCC TTC G-TAMRA-3’ E2F3b Forward Primer 5’-TTT AAA CCA TCT GAG AGG TAC TGA TGA-3’ Reverse Primer 5’-CCC TTA CAG CAG CAG GCA A-3’ Probe 5’-FAM-CGC TTT CTC CTA GCT CCA GCC TTC G-TAMRA-3’ IRF-1 Forward Primer 5’-TTT GTA TCG GCC TGT GTG AAT G-3’ Reverse Primer 5’-AAG CAT GGC TGG GAC ATC A-3’ Probe 5’-FAM-CAG CTC CGG AAC AAA CAG GCA TCC TT-TAMRA-3' IRF-2 Forward Primer 5'-CGC CCC TCG GCA CTC T-3' Reverse Primer 5'-TCT TCC TAT GCA GAA AGC GAA AC-3' Probe 5'-FAM-TTC ATC GCT GGG CAC ACT ATC AGT-TAMRA-3' TBP Forward Primer 5’-CAC GAA CCA CGG CAC TGA TT-3’ Reverse Primer 5’-TTT TCT TGC TGC CAG TCT GGA C-3’ Probe 5’-FAM-TGT GCA CAG GAG CCA AGA GTG AAG A-BHQ1-3’ Primers and Probes for quantitative RT-PCRs were designed using the computer program “Primer Express” (Applied Biosystems, Foster City, CA, USA).