Using Viper, a Package for Virtual Inference of Protein-Activity by Enriched Regulon Analysis
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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. -
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. -
Supplemental Materials ZNF281 Enhances Cardiac Reprogramming
Supplemental Materials ZNF281 enhances cardiac reprogramming by modulating cardiac and inflammatory gene expression Huanyu Zhou, Maria Gabriela Morales, Hisayuki Hashimoto, Matthew E. Dickson, Kunhua Song, Wenduo Ye, Min S. Kim, Hanspeter Niederstrasser, Zhaoning Wang, Beibei Chen, Bruce A. Posner, Rhonda Bassel-Duby and Eric N. Olson Supplemental Table 1; related to Figure 1. Supplemental Table 2; related to Figure 1. Supplemental Table 3; related to the “quantitative mRNA measurement” in Materials and Methods section. Supplemental Table 4; related to the “ChIP-seq, gene ontology and pathway analysis” and “RNA-seq” and gene ontology analysis” in Materials and Methods section. Supplemental Figure S1; related to Figure 1. Supplemental Figure S2; related to Figure 2. Supplemental Figure S3; related to Figure 3. Supplemental Figure S4; related to Figure 4. Supplemental Figure S5; related to Figure 6. Supplemental Table S1. Genes included in human retroviral ORF cDNA library. Gene Gene Gene Gene Gene Gene Gene Gene Symbol Symbol Symbol Symbol Symbol Symbol Symbol Symbol AATF BMP8A CEBPE CTNNB1 ESR2 GDF3 HOXA5 IL17D ADIPOQ BRPF1 CEBPG CUX1 ESRRA GDF6 HOXA6 IL17F ADNP BRPF3 CERS1 CX3CL1 ETS1 GIN1 HOXA7 IL18 AEBP1 BUD31 CERS2 CXCL10 ETS2 GLIS3 HOXB1 IL19 AFF4 C17ORF77 CERS4 CXCL11 ETV3 GMEB1 HOXB13 IL1A AHR C1QTNF4 CFL2 CXCL12 ETV7 GPBP1 HOXB5 IL1B AIMP1 C21ORF66 CHIA CXCL13 FAM3B GPER HOXB6 IL1F3 ALS2CR8 CBFA2T2 CIR1 CXCL14 FAM3D GPI HOXB7 IL1F5 ALX1 CBFA2T3 CITED1 CXCL16 FASLG GREM1 HOXB9 IL1F6 ARGFX CBFB CITED2 CXCL3 FBLN1 GREM2 HOXC4 IL1F7 -
A Flexible Microfluidic System for Single-Cell Transcriptome Profiling
www.nature.com/scientificreports OPEN A fexible microfuidic system for single‑cell transcriptome profling elucidates phased transcriptional regulators of cell cycle Karen Davey1,7, Daniel Wong2,7, Filip Konopacki2, Eugene Kwa1, Tony Ly3, Heike Fiegler2 & Christopher R. Sibley 1,4,5,6* Single cell transcriptome profling has emerged as a breakthrough technology for the high‑resolution understanding of complex cellular systems. Here we report a fexible, cost‑efective and user‑ friendly droplet‑based microfuidics system, called the Nadia Instrument, that can allow 3′ mRNA capture of ~ 50,000 single cells or individual nuclei in a single run. The precise pressure‑based system demonstrates highly reproducible droplet size, low doublet rates and high mRNA capture efciencies that compare favorably in the feld. Moreover, when combined with the Nadia Innovate, the system can be transformed into an adaptable setup that enables use of diferent bufers and barcoded bead confgurations to facilitate diverse applications. Finally, by 3′ mRNA profling asynchronous human and mouse cells at diferent phases of the cell cycle, we demonstrate the system’s ability to readily distinguish distinct cell populations and infer underlying transcriptional regulatory networks. Notably this provided supportive evidence for multiple transcription factors that had little or no known link to the cell cycle (e.g. DRAP1, ZKSCAN1 and CEBPZ). In summary, the Nadia platform represents a promising and fexible technology for future transcriptomic studies, and other related applications, at cell resolution. Single cell transcriptome profling has recently emerged as a breakthrough technology for understanding how cellular heterogeneity contributes to complex biological systems. Indeed, cultured cells, microorganisms, biopsies, blood and other tissues can be rapidly profled for quantifcation of gene expression at cell resolution. -
Kruppel-Like Factor 9 Inhibits Glioblastoma Stemness
KRUPPEL-LIKE FACTOR 9 INHIBITS GLIOBLASTOMA STEMNESS THROUGH GLOBAL TRANSCRIPTION REPRESSION AND INHIBITION OF INTEGRIN ALPHA 6 AND CD151 By Jessica Tilghman A dissertation submitted to Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland October, 2015 Abstract Glioblastoma (GBM) stem cells (GSCs) represent tumor-propagating cells with stem-like characteristics (stemness) that contribute disproportionately to GBM drug resistance and tumor recurrence. Understanding the mechanisms supporting GSC stemness is important for developing novel strategies that target tumor propagation to inhibit cancer progression and improve patient survival. Krüppel-like factor 9 (KLF9) has emerged as a regulator of cell differentiation, neural development, and oncogenesis; however, the molecular basis for KLF9’s diverse contextual functions has been unclear. We establish for the first time a genome-wide map of KLF9-regulated targets in human glioblastoma stem-like cells, and show that KLF9 functions as a transcriptional repressor and thereby regulates multiple signaling pathways involved in oncogenesis and regulation of cancer stem-like phenotype. A detailed analysis of two novel KLF9 targets suggests that KLF9 inhibits glioma cell stemness by repressing expression of integrin α6 and CD151. The expression of one candidate KLF9 target gene ITGA6 coding for integrin α6 was verified to be downregulated by KLF9 in GSCs. ITGA6 transcription repression by KLF9 altered GBM neurosphere cell behavior as evidenced by reduced cell adhesion to and migration through membrane coated with the integrin α6 ligand laminin. Forced expression of integrin α6 partially rescued GBM neurosphere cells from the differentiating and adhesion/migration-inhibiting effects of KLF9. -
WO 2019/079361 Al 25 April 2019 (25.04.2019) W 1P O PCT
(12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization I International Bureau (10) International Publication Number (43) International Publication Date WO 2019/079361 Al 25 April 2019 (25.04.2019) W 1P O PCT (51) International Patent Classification: CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, C12Q 1/68 (2018.01) A61P 31/18 (2006.01) DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, C12Q 1/70 (2006.01) HR, HU, ID, IL, IN, IR, IS, JO, JP, KE, KG, KH, KN, KP, KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, (21) International Application Number: MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, PCT/US2018/056167 OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, (22) International Filing Date: SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, 16 October 2018 (16. 10.2018) TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. (25) Filing Language: English (84) Designated States (unless otherwise indicated, for every kind of regional protection available): ARIPO (BW, GH, (26) Publication Language: English GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ, (30) Priority Data: UG, ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, RU, TJ, 62/573,025 16 October 2017 (16. 10.2017) US TM), European (AL, AT, BE, BG, CH, CY, CZ, DE, DK, EE, ES, FI, FR, GB, GR, HR, HU, ΓΕ , IS, IT, LT, LU, LV, (71) Applicant: MASSACHUSETTS INSTITUTE OF MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, TECHNOLOGY [US/US]; 77 Massachusetts Avenue, TR), OAPI (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW, Cambridge, Massachusetts 02139 (US). -
Key Genes Regulating Skeletal Muscle Development and Growth in Farm Animals
animals Review Key Genes Regulating Skeletal Muscle Development and Growth in Farm Animals Mohammadreza Mohammadabadi 1 , Farhad Bordbar 1,* , Just Jensen 2 , Min Du 3 and Wei Guo 4 1 Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman 77951, Iran; [email protected] 2 Center for Quantitative Genetics and Genomics, Aarhus University, 8210 Aarhus, Denmark; [email protected] 3 Washington Center for Muscle Biology, Department of Animal Sciences, Washington State University, Pullman, WA 99163, USA; [email protected] 4 Muscle Biology and Animal Biologics, Animal and Dairy Science, University of Wisconsin-Madison, Madison, WI 53558, USA; [email protected] * Correspondence: [email protected] Simple Summary: Skeletal muscle mass is an important economic trait, and muscle development and growth is a crucial factor to supply enough meat for human consumption. Thus, understanding (candidate) genes regulating skeletal muscle development is crucial for understanding molecular genetic regulation of muscle growth and can be benefit the meat industry toward the goal of in- creasing meat yields. During the past years, significant progress has been made for understanding these mechanisms, and thus, we decided to write a comprehensive review covering regulators and (candidate) genes crucial for muscle development and growth in farm animals. Detection of these genes and factors increases our understanding of muscle growth and development and is a great help for breeders to satisfy demands for meat production on a global scale. Citation: Mohammadabadi, M.; Abstract: Farm-animal species play crucial roles in satisfying demands for meat on a global scale, Bordbar, F.; Jensen, J.; Du, M.; Guo, W. -
Tgfβ-Regulated Gene Expression by Smads and Sp1/KLF-Like Transcription Factors in Cancer VOLKER ELLENRIEDER
ANTICANCER RESEARCH 28 : 1531-1540 (2008) Review TGFβ-regulated Gene Expression by Smads and Sp1/KLF-like Transcription Factors in Cancer VOLKER ELLENRIEDER Signal Transduction Laboratory, Internal Medicine, Department of Gastroenterology and Endocrinology, University of Marburg, Marburg, Germany Abstract. Transforming growth factor beta (TGF β) controls complex induces the canonical Smad signaling molecules which vital cellular functions through its ability to regulate gene then translocate into the nucleus to regulate transcription (2). The expression. TGFβ binding to its transmembrane receptor cellular response to TGF β can be extremely variable depending kinases initiates distinct intracellular signalling cascades on the cell type and the activation status of a cell at a given time. including the Smad signalling and transcription factors and also For instance, TGF β induces growth arrest and apoptosis in Smad-independent pathways. In normal epithelial cells, TGF β healthy epithelial cells, whereas it can also promote tumor stimulation induces a cytostatic program which includes the progression through stimulation of cell proliferation and the transcriptional repression of the c-Myc oncogene and the later induction of an epithelial-to-mesenchymal transition of tumor induction of the cell cycle inhibitors p15 INK4b and p21 Cip1 . cells (1, 3). In the last decade it has become clear that both the During carcinogenesis, however, many tumor cells lose their tumor suppressing and the tumor promoting functions of TGF β ability to respond to TGF β with growth inhibition, and instead, are primarily regulated on the level of gene expression through activate genes involved in cell proliferation, invasion and Smad-dependent and -independent mechanisms (1, 2, 4). -
GATA2 Regulates Mast Cell Identity and Responsiveness to Antigenic Stimulation by Promoting Chromatin Remodeling at Super- Enhancers
ARTICLE https://doi.org/10.1038/s41467-020-20766-0 OPEN GATA2 regulates mast cell identity and responsiveness to antigenic stimulation by promoting chromatin remodeling at super- enhancers Yapeng Li1, Junfeng Gao 1, Mohammad Kamran1, Laura Harmacek2, Thomas Danhorn 2, Sonia M. Leach1,2, ✉ Brian P. O’Connor2, James R. Hagman 1,3 & Hua Huang 1,3 1234567890():,; Mast cells are critical effectors of allergic inflammation and protection against parasitic infections. We previously demonstrated that transcription factors GATA2 and MITF are the mast cell lineage-determining factors. However, it is unclear whether these lineage- determining factors regulate chromatin accessibility at mast cell enhancer regions. In this study, we demonstrate that GATA2 promotes chromatin accessibility at the super-enhancers of mast cell identity genes and primes both typical and super-enhancers at genes that respond to antigenic stimulation. We find that the number and densities of GATA2- but not MITF-bound sites at the super-enhancers are several folds higher than that at the typical enhancers. Our studies reveal that GATA2 promotes robust gene transcription to maintain mast cell identity and respond to antigenic stimulation by binding to super-enhancer regions with dense GATA2 binding sites available at key mast cell genes. 1 Department of Immunology and Genomic Medicine, National Jewish Health, Denver, CO 80206, USA. 2 Center for Genes, Environment and Health, National Jewish Health, Denver, CO 80206, USA. 3 Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, ✉ CO 80045, USA. email: [email protected] NATURE COMMUNICATIONS | (2021) 12:494 | https://doi.org/10.1038/s41467-020-20766-0 | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-020-20766-0 ast cells (MCs) are critical effectors in immunity that at key MC genes. -
Toolbox Model of Evolution of Prokaryotic Metabolic Networks and Their Regulation
Toolbox model of evolution of prokaryotic metabolic networks and their regulation Sergei Maslova,1, Sandeep Krishnab, Tin Yau Panga,c, and Kim Sneppenb aDepartment of Condensed Matter Physics and Materials Science, Brookhaven National Laboratory, Upton, NY 11973; bNiels Bohr Institute, Blegdamsvej 17, DK-2100 Copenhagen, Denmark; and cDepartment of Physics and Astronomy, Stony Brook University, Stony Brook, NY 11794-3800 Edited by David J. Lipman, National Institutes of Health, Bethesda, MD, and approved April 16, 2009 (received for review March 23, 2009) It has been reported that the number of transcription factors A simple evolutionary model explains both these empirical encoded in prokaryotic genomes scales approximately quadrati- observations in a unified framework based on modular functional cally with their total number of genes. We propose a conceptual design of prokaryotic metabolic networks and their regulation. explanation of this finding and illustrate it using a simple model in which metabolic and regulatory networks of prokaryotes are Toolbox View of Metabolic Networks shaped by horizontal gene transfer of coregulated metabolic Metabolic networks are composed of many semiautonomous func- pathways. Adapting to a new environmental condition monitored tional modules corresponding to traditional metabolic pathways (8) by a new transcription factor (e.g., learning to use another nutri- or their subunits (9). Constituent genes of such evolutionary ent) involves both acquiring new enzymes and reusing some of the modules tend to cooccur (be either all present or all absent) in enzymes already encoded in the genome. As the repertoire of genomes (9, 10). These pathways overlap with each other to form branched, interconnected metabolic networks. -
An Improved Bind-N-Seq Strategy to Determine Protein-DNA Interactions Validated Using the Bacterial Transcriptional Regulator Yipr Shi-Qi An1,2* , Miguel A
An et al. BMC Microbiology (2020) 20:1 https://doi.org/10.1186/s12866-019-1672-7 METHODOLOGY ARTICLE Open Access An improved bind-n-seq strategy to determine protein-DNA interactions validated using the bacterial transcriptional regulator YipR Shi-qi An1,2* , Miguel A. Valvano2, Yan-hua Yu3, Jeremy S. Webb1 and Guillermo Lopez Campos2 Abstract Background: Interactions between transcription factors and DNA lie at the centre of many biological processes including DNA recombination, replication, repair and transcription. Most bacteria encode diverse proteins that act as transcription factors to regulate various traits. Several technologies for identifying protein–DNA interactions at the genomic level have been developed. Bind-n-seq is a high-throughput in vitro method first deployed to analyse DNA interactions associated with eukaryotic zinc-finger proteins. The method has three steps (i) binding protein to a randomised oligonucleotide DNA target library, (ii) deep sequencing of bound oligonucleotides, and (iii) a computational algorithm to define motifs among the sequences. The classical Bind-n-seq strategy suffers from several limitations including a lengthy wet laboratory protocol and a computational algorithm that is difficult to use. We introduce here an improved, rapid,andsimplifiedBind-n-seqprotocolcoupledwithauser-friendly downstream data analysis and handling algorithm, which has been optimized for bacterial target proteins. We validate this new protocol by showing the successful characterisation of the DNA-binding specificities of YipR (YajQ interacting protein regulator), a well- known transcriptional regulator of virulence genes in the bacterial phytopathogen Xanthomonas campestris pv. campestris (Xcc). Results: The improved Bind-n-seq approach identified several DNA binding motif sequences for YipR, in particular the CCCTCTC motif, which were located in the promoter regions of 1320 Xcc genes.