DOCSLIB.ORG

C D B a Mafg Mafk Maff

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

KCTD13 PROX1 IRF3 KLRG1 TJP1 COL12A1 LMBRD2 SLC25A32 TRO RASD1 HECW2 CXCR4 IRF2BPL PTPDC1 CD55 PSAT1 A MOV10L1SPRED2 TSGA10 BAIAP2 SLC1A4 TMEM39A PPAP2B BCL3 ARL4A RAPGEF1 VEGFC F3 PHLDB2 UNC5B PRDM1 LEPREL1 NR4A2 NR4A1 RAPH1 SOCS6 SULT1B1 TNFAIP3 RGS2 GIMAP1 KIAA0513 STEAP2 HSP90B1 MAP3K8 SLC25A25 TNFRSF1B DDIT4 PLEKHA1 FRMD6 C2CD4A TSPAN11 IGIP UACA RFX2 PMAIP1 IL15RA SLC2A12 MOSPD1 GIMAP6 EDN1 ADAM19 NCOA7 CLDN5 CHSY3 BCAP29 PDP1 UBE2J1 RBPMS2 PPFIBP2 KAT2B PLAU SIPA1L3 FOXN3 C14orf93 KIF26A EGR1 ACER2 MEI1 GAB1 ASUN CNN3 SIRT1 RALGPS2 PPM1H TOX2 FAM174A DNAJB4 KLF11 GBE1 CHN2 TRIM2 IL1R1 KCNJ2 TFPI2 KLF2 PPP1R3B PPARA PPP1R15B VEGFA DSE CCRN4L NR2F2 AXL EXOC8 CCNA1 B4GALT1 GIMAP7 RQCD1 ITPRIP MYADM ICAM3 FGF2 HEY1 NFIL3 CTH PTP4A3 ATP2B1 EXT1 KLHL3 KLF9 COL13A1 MID2 GDPD1 EHD3 STRBP P4HA1 OSBP2 SEC11C NFAT5 PDGFD ENC1 DUSP5 NRCAM ANO6 KLF10 SLC40A1 C2CD4B MEIS1HOXB2_ELF1 GIMAP2 SEMA6A NR4A3 KDM6B MYO1D FAM193A CSRNP2 RASGRF2 APOO ESYT2 NINL FOXP1 HDAC9 CLK1 CDK6 SCAMP5 PHLDB3 ASGR1 HELLS DIEXF SULT1C4 MBNL1 TRAF5 GIMAP8 FOXJ2_ELF1 SIX4 TEAD4_ERG ARL4C EGR3 BACH1 ITGA6 LRRC8B MFSD2A SLC4A7 RDX PLAT PODXL USF2 NR5A2 RALGAPA2 DDX60L ANGPT2 CSF1 FOXO1_ELF1 ATP1B1 CTCF P4HA3 DNAJC3 CREG1 ACRBP BRAT1 RHOBTB1 TEAD2 ARID5A FBXL2 SLC12A4 SPRY4 CDC14A ATF3 ETV5_HOXA2 CHST11 GCNT1 ESM1 FZD8 IVNS1ABP MGP CROCC SLC39A14 DUSP7 MAP2K6 TOB2 SLC35G2 SPTY2D1 BMP4 ERG ST8SIA4 POLI MCL1 SOX13 CROT CGNL1 MXD1 ETS1 THBD BACH2 ELF1 PLAUR STC1 SGK3 ITPR2 IRAK2 PHC3 FAM198B AIM1 RNF122 ETV3 FKBP14 PIM1 MX1 TNIK MMP10 ST3GAL5 DUSP1 GULP1 INPP5A PSTPIP2 MEF2C PNP IPMK STOML1 ARHGEF18 ICOSLG MYO1E ZBTB18 PMCH MBNL3 RIOK2 TNFSF10 KITLG DHRS1 C1orf198 TMEM2 KRT80 NID2 SERP1 WNT9A PLCXD1 HBEGF PDE2A EDEM1 STEAP1 FLT1 SDPR NEDD4L SEMA6D CLASP2 ARHGAP28 SSBP3 FBXO32 ELL2 EDIL3 ACKR3 SYNM TFPI XPA DOK5 HTR1B EDEM3 ZNF217 DDI2 CPEB4 SSH1 SEL1L COLQ ZNF267 RCAN1 NAB2 PDGFA GPX8 ZBTB10 DLL4 LHX6 TSPAN13 FKBP11 PLXNA2 B4GALT5 CARS NEDD9 TMCO4 PTGER4 NANS MYRIP CTSH KRT19 SEPT5 DLG1 RCAN3 RNF144A MEGF6 VGLL4 CREM RFC3 HSD17B2 NFE2L2 MAFF CD200 PLVAP PDIA4 C12orf44 ZCCHC14 CHST1 MYBL2 SLC19A2 TRIB3 RASGRP3 YRDC RP9 SERINC5 GPR176 RNF145 AHNAK2 MCM10 KCTD12 IDH3A NR1H3 ETV5 ID3 ADAMTS9 PELO WASF3 BCL6MIDN PLLP SETD8 ATP8B1 EFNA5 ATP8B2 NT5E VIMP SLC41A2 HSPA5 M3: M1: PEX5 M2: Response to hydrogen peroxide response to endoplasmic reticulum stress Regulation of cellular extravasation Positive regulation of endothelial Cellular polysaccharide biosynthetic process PDGF receptor signaling pathway cell proliferation CCNG2 FAT1 TOX MYO15B ATP5H RBPJ CABLES2 NCAPG TCF15 POLE4 RGCC DHRS3 PFKFB3 ZNF143 PCIF1 ARSJ ZNF697 TBCC FLRT2 N4BP2 ZBTB21 DPF3 NAMPTL NUP85 ZNF76 COL4A3BP PTGS2 TRIB1 GSDMD NRG1 PRKACB TM4SF18 CNKSR3 APOLD1 ITGAV LYSMD3 IL6ST ANGPTL2 IL1A TRAF1 BHLHE40 NR2F1 NDRG1 RIPK2 SLC7A5 C10orf128 SLC38A2 SERPINH1 C8orf4 B3GNT5 CSRNP1 GADD45A TPP2 MFSD6 RAPGEF5 PALLD USP53 CREB5 CXCL2 SLC26A4 MDM2 C10orf10 B4GALT4 COL8A1 CXCL1 ZEB2 POLR3D IL8 NYNRIN IFRD1 SIK1 PDE3A SLC10A7 GABPA PER1 BIRC3 NFKB2 SMAD3 ZNF704 TIPARP TLE4 PDE4D SAT1 GARS RPS6KA1 GPR126 FOXO1_ELK1 ATP2A2 MN1 NOS3 GBP1 RUNX1 FOXO1_ETV1 PITPNC1 NUCB2 MEX3C PKIG BMX ITGB3 FOSL1 MYL4 DKK1 ZC3H12A TSHZ1 HMGN4 IRF2 TNFSF15 AQP1 CRELD2 NF1GATA3 ANK3 CCL2 TSC22D4 BBC3 LIPG SOX4 ETV5_FOXO1ERF_FOXO1 RARG HDAC5 HK2 SLC7A1 HIVEP2 JUN TMEM173 JUN-AP1 FOSL2 ABCC4 CLDN11 DIP2C NOG MOSPD2 GCH1 CD40 TSC22D2 FOSB LARP1B PIK3R1 GPRC5A SETX ATF1 EIF2B3 PALD1 FOXO1_ETV4 FARS2 HSPA13 TBX18 KCNN3 KLF4 HHIP GNG2 IL1RL1 PARP12 FAM124B ELK4 XBP1 PTX3 THSD1 SERTAD3 STC2 IDI1 ALDH1A1 RAB3B SERPINE1 TRIB2 VCAM1 SELE ITGB8 SLC9A3R2 FOXO1CHST3 GDF6 CCL20 MTHFD2 RND3 MEF2A SLC12A2 GCOM1 A2M STARD4 GATA6 CAMK2N1 IL33 RNF144B HERPUD1 DTNB SDC4 YPEL2 SOD2 IL6 FOXF1 ZC3H12C LAMP3 ADTRP DCBLD2 SLC30A7 ZFAND2A PJA2 TSPAN5 SLC33A1 IRS2 SGK1 UGDH NRG3 CXCL3 FAM185A MIS12 SLC30A1 RELB PCDH12 ALAD PPIL4 SEMA6C IL27RA CIC ICAM1 PPP1R15A M4: M5： ADAMTS1 M6: Positive regulation of cytokine secretion Nitric oxide metabolic process Muscle cell proliferation; Negative regulation of endothelial cell migration Regulation of muscle adaptation Positive regulation of leukocyte migration MAFG B C 20 D 15 VEGF 10 HPAEC HUVEC ECFC HMVEC FPKM 5 0H 1H 4H 12H MAFF 0 MAFF H0 H1 H4 H12 MAFG MAFG MAFK 10 MAFK 8 MAFK 6 Tubulin Tubulin 4 FPKM 2 0 H0 H1 H4 H12 150 MAFF 100 FPKM 50 0 H0 H1 H4 H12 Supplemental Fig. S7. MAFs are master regulators of VEGF transcriptional network. (A) Sub-networks from Fig. 7B. The VEGFA gene regulatory network contained six major sub-modules showing diverse functions. (B). Western-Blot detecting MAFs expression in four different types of ECs. HPAEC: human pulmonary artery endothelial cell; HUVEC: human umbilical vein endothelial cell; ECFC: endothelial clone forming cell, HMVEC: human dermal microvascular Endothelial cell. (C) VEGFA upregulated expression of MAF factors at H1, as revealed by RNA-seq. (D) VEGFA upregulat- ed the expression of MAF proteins at H1, as revealed by western-blot. .
Recommended publications
  • Chapter 7: Monogenic Forms of Diabetes

    Chapter 7: Monogenic Forms of Diabetes

    CHAPTER 7 MONOGENIC FORMS OF DIABETES Mark A. Sperling, MD, and Abhimanyu Garg, MD Dr. Mark A. Sperling is Emeritus Professor and Chair, University of Pittsburgh, Department of Pediatrics, Children’s Hospital of Pittsburgh of UPMC, Pittsburgh, PA. Dr. Abhimanyu Garg is Professor of Internal Medicine and Chief of the Division of Nutrition and Metabolic Diseases at University of Texas Southwestern Medical Center, Dallas, TX. SUMMARY Types 1 and 2 diabetes have multiple and complex genetic influences that interact with environmental triggers, such as viral infections or nutritional excesses, to result in their respective phenotypes: young, lean, and insulin-dependence for type 1 diabetes patients or older, overweight, and often manageable by lifestyle interventions and oral medications for type 2 diabetes patients. A small subset of patients, comprising ~2%–3% of all those diagnosed with diabetes, may have characteristics of either type 1 or type 2 diabetes but have single gene defects that interfere with insulin production, secretion, or action, resulting in clinical diabetes. These types of diabetes are known as MODY, originally defined as maturity-onset diabetes of youth, and severe early-onset forms, such as neonatal diabetes mellitus (NDM). Defects in genes involved in adipocyte development, differentiation, and death pathways cause lipodystrophy syndromes, which are also associated with insulin resistance and diabetes. Although these syndromes are considered rare, more awareness of these disorders and increased availability of genetic testing in clinical and research laboratories, as well as growing use of next generation, whole genome, or exome sequencing for clinically challenging phenotypes, are resulting in increased recognition. A correct diagnosis of MODY, NDM, or lipodystrophy syndromes has profound implications for treatment, genetic counseling, and prognosis.
  • Detection of Interacting Transcription Factors in Human Tissues Using

    Detection of Interacting Transcription Factors in Human Tissues Using

    Myšičková and Vingron BMC Genomics 2012, 13(Suppl 1):S2 http://www.biomedcentral.com/1471-2164/13/S1/S2 PROCEEDINGS Open Access Detection of interacting transcription factors in human tissues using predicted DNA binding affinity Alena Myšičková*, Martin Vingron From The Tenth Asia Pacific Bioinformatics Conference (APBC 2012) Melbourne, Australia. 17-19 January 2012 Abstract Background: Tissue-specific gene expression is generally regulated by combinatorial interactions among transcription factors (TFs) which bind to the DNA. Despite this known fact, previous discoveries of the mechanism that controls gene expression usually consider only a single TF. Results: We provide a prediction of interacting TFs in 22 human tissues based on their DNA-binding affinity in promoter regions. We analyze all possible pairs of 130 vertebrate TFs from the JASPAR database. First, all human promoter regions are scanned for single TF-DNA binding affinities with TRAP and for each TF a ranked list of all promoters ordered by the binding affinity is created. We then study the similarity of the ranked lists and detect candidates for TF-TF interaction by applying a partial independence test for multiway contingency tables. Our candidates are validated by both known protein-protein interactions (PPIs) and known gene regulation mechanisms in the selected tissue. We find that the known PPIs are significantly enriched in the groups of our predicted TF-TF interactions (2 and 7 times more common than expected by chance). In addition, the predicted interacting TFs for studied tissues (liver, muscle, hematopoietic stem cell) are supported in literature to be active regulators or to be expressed in the corresponding tissue.
  • Lncrna MAFG-AS1 Promotes the Aggressiveness of Breast Carcinoma Through Regulating Mir-339-5P/MMP15

    Lncrna MAFG-AS1 Promotes the Aggressiveness of Breast Carcinoma Through Regulating Mir-339-5P/MMP15

    European Review for Medical and Pharmacological Sciences 2019; 23: 2838-2846 LncRNA MAFG-AS1 promotes the aggressiveness of breast carcinoma through regulating miR-339-5p/MMP15 H. LI1, G.-Y. ZHANG2, C.-H. PAN3, X.-Y. ZHANG1, X.-Y. SU4 1Department of Obstetrics and Gynecology, Shandong Jiyang Public Hospital, Ji’nan, Shandong, China 2Department of Anesthesiology, Shandong Jiyang Public Hospital, Ji’nan, Shandong, China 3Department of Obstetrics and Gynecology, LanCun central hospital, Jimo, Shandong, China 4Department of Critical Care Medicine, Tai’an Central Hospital, Tai’an, Shandong, China Abstract. – OBJECTIVE: The main purposes of and is the leading cause of cancer-related deaths this study are to investigate the possible effects worldwide1. Recently, treatment strategies, such of long noncoding RNAs (lncRNAs) MAFG-AS1 on as chemotherapy, radiotherapy and molecular tar- the growth and metastasis of breast carcinoma. PATIENTS AND METHODS: geting treatment significantly improve the thera- The quantitative 2 Real Time-Polymerase Chain Reaction (qRT- peutic outcome of patients . However, the clinical PCR) assay was used to assess the MAFG-AS1 outcome of patients with breast cancer needs to level in breast cancer tissues and cells. The improve. The metastasis of cancer cells is one ma- wound healing and transwell invasion analy- jor difficulty of overcoming the poor prognosis of sis were applied to explore the invasion and mi- breast cancer patients. The epithelial-mesenchy- gration of breast cancer cell in vitro. The ex- mal transition (EMT) process of cancer cells is a pressions of epithelial-mesenchymal transition 3,4 (EMT) related markers were determined by West- crucial step during metastasis .
  • Activated Peripheral-Blood-Derived Mononuclear Cells

    Activated Peripheral-Blood-Derived Mononuclear Cells

    Transcription factor expression in lipopolysaccharide- activated peripheral-blood-derived mononuclear cells Jared C. Roach*†, Kelly D. Smith*‡, Katie L. Strobe*, Stephanie M. Nissen*, Christian D. Haudenschild§, Daixing Zhou§, Thomas J. Vasicek¶, G. A. Heldʈ, Gustavo A. Stolovitzkyʈ, Leroy E. Hood*†, and Alan Aderem* *Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103; ‡Department of Pathology, University of Washington, Seattle, WA 98195; §Illumina, 25861 Industrial Boulevard, Hayward, CA 94545; ¶Medtronic, 710 Medtronic Parkway, Minneapolis, MN 55432; and ʈIBM Computational Biology Center, P.O. Box 218, Yorktown Heights, NY 10598 Contributed by Leroy E. Hood, August 21, 2007 (sent for review January 7, 2007) Transcription factors play a key role in integrating and modulating system. In this model system, we activated peripheral-blood-derived biological information. In this study, we comprehensively measured mononuclear cells, which can be loosely termed ‘‘macrophages,’’ the changing abundances of mRNAs over a time course of activation with lipopolysaccharide (LPS). We focused on the precise mea- of human peripheral-blood-derived mononuclear cells (‘‘macro- surement of mRNA concentrations. There is currently no high- phages’’) with lipopolysaccharide. Global and dynamic analysis of throughput technology that can precisely and sensitively measure all transcription factors in response to a physiological stimulus has yet to mRNAs in a system, although such technologies are likely to be be achieved in a human system, and our efforts significantly available in the near future. To demonstrate the potential utility of advanced this goal. We used multiple global high-throughput tech- such technologies, and to motivate their development and encour- nologies for measuring mRNA levels, including massively parallel age their use, we produced data from a combination of two distinct signature sequencing and GeneChip microarrays.
  • Identifying and Mapping Cell-Type-Specific Chromatin PNAS PLUS Programming of Gene Expression

    Identifying and Mapping Cell-Type-Specific Chromatin PNAS PLUS Programming of Gene Expression

    Identifying and mapping cell-type-specific chromatin PNAS PLUS programming of gene expression Troels T. Marstranda and John D. Storeya,b,1 aLewis-Sigler Institute for Integrative Genomics, and bDepartment of Molecular Biology, Princeton University, Princeton, NJ 08544 Edited by Wing Hung Wong, Stanford University, Stanford, CA, and approved January 2, 2014 (received for review July 2, 2013) A problem of substantial interest is to systematically map variation Relating DHS to gene-expression levels across multiple cell in chromatin structure to gene-expression regulation across con- types is challenging because the DHS represents a continuous ditions, environments, or differentiated cell types. We developed variable along the genome not bound to any specific region, and and applied a quantitative framework for determining the exis- the relationship between DHS and gene expression is largely tence, strength, and type of relationship between high-resolution uncharacterized. To exploit variation across cell types and test chromatin structure in terms of DNaseI hypersensitivity and genome- for cell-type-specific relationships between DHS and gene expres- wide gene-expression levels in 20 diverse human cell types. We sion, the measurement units must be placed on a common scale, show that ∼25% of genes show cell-type-specific expression ex- the continuous DHS measure associated to each gene in a well- plained by alterations in chromatin structure. We find that distal defined manner, and all measurements considered simultaneously. regions of chromatin structure (e.g., ±200 kb) capture more genes Moreover, the chromatin and gene-expression relationship may with this relationship than local regions (e.g., ±2.5 kb), yet the local only manifest in a single cell type, making standard measures of regions show a more pronounced effect.
  • REST Mediates Androgen Receptor Actions on Gene Repression And

    REST Mediates Androgen Receptor Actions on Gene Repression And

    Published online 24 October 2013 Nucleic Acids Research, 2014, Vol. 42, No. 2 999–1015 doi:10.1093/nar/gkt921 REST mediates androgen receptor actions on gene repression and predicts early recurrence of prostate cancer Charlotte Svensson1, Jens Ceder2, Diego Iglesias-Gato1, Yin-Choy Chuan1, See Tong Pang3, Anders Bjartell2, Roxana Merino Martinez4, Laura Bott5, Leszek Helczynski6, David Ulmert2,7, Yuzhuo Wang8, Yuanjie Niu9, Colin Collins8 and Amilcar Flores-Morales1,* 1Novo Nordisk Foundation Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Downloaded from DK-2200 Copenhagen, Denmark, 2Division of Urological Cancers, Department of Clinical Sciences, Ska˚ ne University Hospital, Lund University, 20502 Malmo¨ , Sweden, 3Department of Urology, Chang Gung Memorial Hospital, Tao-Yuan 33305, Taiwan, R.O.C., 4Department of Epidemiology, Karolinska Institutet, 171 77 Stockholm, Sweden, 5Department of Cell and Molecular Biology, Karolinska Institute, 171 77 Stockholm, Sweden, 6Regional Laboratories Region Ska˚ ne, Clinical Pathology, 205 80 Malmo¨ , Sweden, 7Department of Surgery (Urology), Memorial Sloan-Kettering Cancer Center, New York, NY 100 65, USA, 8Vancouver Prostate http://nar.oxfordjournals.org/ Centre and The Department of Urologic Sciences, University of British Columbia, Vancouver, BC Canada V6H 3Z6 and 9Tianjin Institute of Urology, Tianjin Medical University, Tianjin 300 211, China Received December 19, 2012; Accepted September 20, 2013 ABSTRACT that has previously been implicated in the growth at University of British Columbia on August 12, 2014 The androgen receptor (AR) is a key regulator of NE-like castration-resistant tumors. The of prostate tumorgenesis through actions that are analysis of prostate cancer tissue microarrays not fully understood. We identified the repressor revealed that tumors with reduced expression of element (RE)-1 silencing transcription factor REST have higher probability of early recurrence, (REST) as a mediator of AR actions on gene repres- independently of their Gleason score.
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus

    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.
  • C/EBP Creates Elite Cells for Ipsc Reprogramming by Upregulating

    C/EBP Creates Elite Cells for Ipsc Reprogramming by Upregulating

    ARTICLES C/EBPα creates elite cells for iPSC reprogramming by upregulating Klf4 and increasing the levels of Lsd1 and Brd4 Bruno Di Stefano1,2,8,9,10, Samuel Collombet3,8, Janus Schou Jakobsen4,5,6,8, Michael Wierer7, Jose Luis Sardina1,2, Andreas Lackner1,2,9, Ralph Stadhouders1,2, Carolina Segura-Morales1,2, Mirko Francesconi1,2, Francesco Limone1,2, Matthias Mann7, Bo Porse4,5,6, Denis Thieffry3 and Thomas Graf1,2,10 Reprogramming somatic cells into induced pluripotent stem cells (iPSCs) is typically inefficient and has been explained by elite-cell and stochastic models. We recently reported that B cells exposed to a pulse of C/EBPα (Bα0 cells) behave as elite cells, in that they can be rapidly and efficiently reprogrammed into iPSCs by the Yamanaka factors OSKM. Here we show that C/EBPα post-transcriptionally increases the abundance of several hundred proteins, including Lsd1, Hdac1, Brd4, Med1 and Cdk9, components of chromatin-modifying complexes present at super-enhancers. Lsd1 was found to be required for B cell gene silencing and Brd4 for the activation of the pluripotency program. C/EBPα also promotes chromatin accessibility in pluripotent cells and upregulates Klf4 by binding to two haematopoietic enhancers. Bα0 cells share many properties with granulocyte/macrophage progenitors, naturally occurring elite cells that are obligate targets for leukaemic transformation, whose formation strictly requires C/EBPα. The ability to reprogram somatic into pluripotent cells has revolu- complex process, where multiple players synergistically establish new tionized stem cell research with major implications for almost all transcriptional networks and remove epigenetic barriers14. Among the fields of modern biology.
  • Picosecond-Hetero-FRET Microscopy to Probe Protein-Protein Interactions in Live Cells

    Picosecond-Hetero-FRET Microscopy to Probe Protein-Protein Interactions in Live Cells

    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector 3570 Biophysical Journal Volume 83 December 2002 3570–3577 Picosecond-Hetero-FRET Microscopy to Probe Protein-Protein Interactions in Live Cells Marc Tramier,* Isabelle Gautier,* Tristan Piolot,† Sylvie Ravalet,‡ Klaus Kemnitz,† Jacques Coppey,* Christiane Durieux,* Vincent Mignotte,‡ and Maı¨te´ Coppey-Moisan* *Institut Jacques Monod, UMR 7592, CNRS, Universite´s P6/P7, 75251 Paris Cedex 05, France; †EuroPhoton GmbH, D-12247, Berlin, Germany; and ‡ICGM, Department of Hematology, Maternite´ Port-Royal, 75014 Paris, France ABSTRACT By using a novel time- and space-correlated single-photon counting detector, we show that fluorescence resonance energy transfer (FRET) between cyan fluorescent protein (CFP) and yellow fluorescent protein (YFP) fused to herpes simplex virus thymidine kinase (TK) monomers can be used to reveal homodimerization of TK in the nucleus and cytoplasm of live cells. However, the quantification of energy transfer was limited by the intrinsic biexponential fluorescence decay of the donor CFP (lifetimes of 1.3 Ϯ 0.2 ns and 3.8 Ϯ 0.4 ns) and by the possibility of homodimer formation between two TK-CFP. In contrast, the heterodimerization of the transcriptional factor NF-E2 in the nucleus of live cells was quantified from the analysis of the fluorescence decays of GFP in terms of 1) FRET efficiency between GFP and DsRed chromophores fused to p45 and MafG, respectively, the two subunits of NF-E2 (which corresponds to an interchromophoric distance of 39 Ϯ 1 Å); and 2) fractions of GFP-p45 bound to DsRed-MafG (constant in the nucleus, varying in the range of 20% to 70% from cell to cell).
  • Evidence Revealing Deregulation of the KLF11-Mao a Pathway in Association with Chronic Stress and Depressive Disorders

    Evidence Revealing Deregulation of the KLF11-Mao a Pathway in Association with Chronic Stress and Depressive Disorders

    Neuropsychopharmacology (2015) 40, 1373–1382 & 2015 American College of Neuropsychopharmacology. All rights reserved 0893-133X/15 www.neuropsychopharmacology.org Evidence Revealing Deregulation of The KLF11-Mao A Pathway in Association with Chronic Stress and Depressive Disorders 1 1 1,2 1 3 Sharonda Harris , Shakevia Johnson , Jeremy W Duncan , Chinelo Udemgba , Jeffrey H Meyer , Paul R Albert4, Gwen Lomberk5, Raul Urrutia5, Xiao-Ming Ou1, Craig A Stockmeier1,6 and Jun Ming Wang*,1,2,7 1 2 3 Department of Psychiatry and Human Behavior, Jackson, MS, USA; Program in Neuroscience, Jackson, MS, USA; Centre for Addiction and Mental Health and Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; 4Ottawa Hospital Research Institute (Neuroscience), Ottawa, Ontario, Canada; 5Epigenetics and Chromatin Dynamics Laboratory, GI Research Unit, Mayo Clinic, Rochester, MN, 6 7 USA; Department of Psychiatry, Case Western Reserve University, Cleveland, OH, USA; Department of Pathology, University of Mississippi Medical Center, Jackson, MS, USA The biochemical pathways underlying major depressive disorder (MDD) and chronic stress are not well understood. However, it has been reported that monoamine oxidase A (MAO A, a major neurotransmitter-degrading enzyme) is significantly increased in the brains of human subjects affected with MDD and rats exposed to chronic social defeat (CSD) stress, which is used to model depression. In the current study, we compared the protein levels of a MAO A-transcriptional activator, Kruppel-like factor 11 (KLF11 , also recognized as transforming growth factor-beta-inducible early gene 2) between the brains of 18 human subjects with MDD and 18 control subjects. We found that, indeed, the expression of KLF11 is increased by 36% (po0.02) in the postmortem prefrontal cortex of human subjects with MDD compared with controls.
  • Genome-Wide DNA Methylation Analysis of KRAS Mutant Cell Lines Ben Yi Tew1,5, Joel K

    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.
  • Elucidation of the ELK1 Target Gene Network Reveals a Role in the Coordinate Regulation of Core Components of the Gene Regulation Machinery

    Elucidation of the ELK1 Target Gene Network Reveals a Role in the Coordinate Regulation of Core Components of the Gene Regulation Machinery

    Downloaded from genome.cshlp.org on October 4, 2021 - Published by Cold Spring Harbor Laboratory Press Letter Elucidation of the ELK1 target gene network reveals a role in the coordinate regulation of core components of the gene regulation machinery Joanna Boros,1,5 Ian J. Donaldson,1,5 Amanda O’Donnell,1 Zaneta A. Odrowaz,1 Leo Zeef,1 Mathieu Lupien,2,4 Clifford A. Meyer,3 X. Shirley Liu,3 Myles Brown,2 and Andrew D. Sharrocks1,6 1Faculty of Life Sciences, University of Manchester, Manchester M13 9PT, United Kingdom; 2Division of Molecular and Cellular Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute and Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA; 3Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, Massachusetts 02115, USA Transcription factors play an important role in orchestrating the activation of specific networks of genes through targeting their proximal promoter and distal enhancer regions. However, it is unclear how the specificity of downstream responses is maintained by individual members of transcription-factor families and, in most cases, what their target repertoire is. We have used ChIP-chip analysis to identify the target genes of the ETS-domain transcription factor ELK1. Two distinct modes of ELK1 target gene selection are identified; the first involves redundant promoter binding with other ETS-domain family members; the second occurs through combinatorial binding with a second transcription factor SRF, which specifies a unique group of target genes. One of the most prominent groups of genes forming the ELK1 target network includes classes involved in core gene expression control, namely, components of the basal transcriptional machinery, the spliceosome and the ribosome.