Emergence of the Noncoding Cancer Genome: a Target of Genetic and Epigenetic Alterations
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Analysis of Chromatin-State Plasticity Identifies Cell-Type–Specific Regulators of H3k27me3 Patterns
Analysis of chromatin-state plasticity identifies cell-type–specific regulators of H3K27me3 patterns Luca Pinelloa,1, Jian Xub,1, Stuart H. Orkinb,c,2, and Guo-Cheng Yuana,2 aDepartment of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Heath, Boston, MA 02215; bDivision of Hematology/Oncology, Boston Children’s Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Stem Cell Institute, Harvard Medical School, Boston, MA 02115; and cHoward Hughes Medical Institute, Boston, MA 02115 Contributed by Stuart H. Orkin, December 6, 2013 (sent for review August 13, 2013) Chromatin states are highly cell-type–specific, but the underlying a significant fraction of H3K27me3 is located in distal regions. mechanisms for the establishment and maintenance of their ge- It has been proposed that distal H3K27me3 marks poised nome-wide patterns remain poorly understood. Here we present enhancers, which can be activated through replacing H3K27me3 a computational approach for investigation of chromatin-state by H3K27ac (15, 16). An important question is whether there is plasticity. We applied this approach to investigate an ENCODE a general principle controlling the plastic changes of H3K27me3 ChIP-seq dataset profiling the genome-wide distributions of the patterns across different cell types. H3K27me3 mark in 19 human cell lines. We found that the high To systematically investigate the mechanisms modulating plasticity regions (HPRs) can be divided into two functionally and chromatin-state plasticity, we developed and validated a compu- mechanistically distinct subsets, which correspond to CpG island tational approach to identify distinct lineage-restricted regulators (CGI) proximal or distal regions, respectively. -
Mouse Get4 Knockout Project (CRISPR/Cas9)
https://www.alphaknockout.com Mouse Get4 Knockout Project (CRISPR/Cas9) Objective: To create a Get4 knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Get4 gene (NCBI Reference Sequence: NM_026269 ; Ensembl: ENSMUSG00000025858 ) is located on Mouse chromosome 5. 9 exons are identified, with the ATG start codon in exon 1 and the TGA stop codon in exon 9 (Transcript: ENSMUST00000026976). Exon 2~8 will be selected as target site. Cas9 and gRNA will be co-injected into fertilized eggs for KO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Exon 2 starts from about 15.9% of the coding region. Exon 2~8 covers 75.43% of the coding region. The size of effective KO region: ~4995 bp. The KO region does not have any other known gene. Page 1 of 9 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele 5' gRNA region gRNA region 3' 1 2 3 4 5 6 7 8 9 Legends Exon of mouse Get4 Knockout region Page 2 of 9 https://www.alphaknockout.com Overview of the Dot Plot (up) Window size: 15 bp Forward Reverse Complement Sequence 12 Note: The 2000 bp section upstream of Exon 2 is aligned with itself to determine if there are tandem repeats. No significant tandem repeat is found in the dot plot matrix. So this region is suitable for PCR screening or sequencing analysis. Overview of the Dot Plot (down) Window size: 15 bp Forward Reverse Complement Sequence 12 Note: The 1505 bp section downstream of Exon 8 is aligned with itself to determine if there are tandem repeats. -
Rabbit Mab A
C 0 2 - t GFI1b (D3G2) Rabbit mAb a e r o t S Orders: 877-616-CELL (2355) [email protected] Support: 877-678-TECH (8324) 9 4 Web: [email protected] 8 www.cellsignal.com 5 # 3 Trask Lane Danvers Massachusetts 01923 USA For Research Use Only. Not For Use In Diagnostic Procedures. Applications: Reactivity: Sensitivity: MW (kDa): Source/Isotype: UniProt ID: Entrez-Gene Id: WB, F H M R Mk Endogenous 35, 42 Rabbit IgG Q5VTD9 8328 Product Usage Information Application Dilution Western Blotting 1:1000 Flow Cytometry 1:400 - 1:1600 Storage Supplied in 10 mM sodium HEPES (pH 7.5), 150 mM NaCl, 100 µg/ml BSA, 50% glycerol and less than 0.02% sodium azide. Store at –20°C. Do not aliquot the antibody. Specificity / Sensitivity GFI1b (D3G2) Rabbit mAb recognizes endogenous levels of total GFI1b protein. Species Reactivity: Human, Mouse, Rat, Monkey Source / Purification Monoclonal antibody is produced by immunizing animals with a synthetic peptide corresponding to residues surrounding Tyr119 of human GFI1b protein. Background GFI1b and its homolog GFI1 are transcriptional repressors and important regulators of erythroid and megakaryocytic development and differentiation (1,2). GFI1b negatively regulates transcription by recruiting chromatin regulatory proteins including CoREST, the histone demethylase LSD1 and HDACs 1 and 2, which associate with GFI1b via its SNAG repression domain (3). GFI1b has also been shown to control the differentiation of erythroid and megakaryocytic progenitors by regulating TGF-β signaling at the bipotent progenitor stage (4). Inactivation of GFI1b in mice leads to embryonic lethality due to failure to produce functional erythrocytes and megakaryocytes (2). -
Gfi1b (NM 008114) Mouse Tagged ORF Clone – MG204861 | Origene
OriGene Technologies, Inc. 9620 Medical Center Drive, Ste 200 Rockville, MD 20850, US Phone: +1-888-267-4436 [email protected] EU: [email protected] CN: [email protected] Product datasheet for MG204861 Gfi1b (NM_008114) Mouse Tagged ORF Clone Product data: Product Type: Expression Plasmids Product Name: Gfi1b (NM_008114) Mouse Tagged ORF Clone Tag: TurboGFP Symbol: Gfi1b Synonyms: Gfi-1B Vector: pCMV6-AC-GFP (PS100010) E. coli Selection: Ampicillin (100 ug/mL) Cell Selection: Neomycin ORF Nucleotide >MG204861 representing NM_008114 Sequence: Red=Cloning site Blue=ORF Green=Tags(s) TTTTGTAATACGACTCACTATAGGGCGGCCGGGAATTCGTCGACTGGATCCGGTACCGAGGAGATCTGCC GCCGCGATCGCC ATGCCACGGTCCTTTCTAGTGAAGAGTAAGAAGGCACACACTTACCACCAGCCCCGGGCACAGGGTGATG AGCTGGTCTGGCCTCCTGCTGTAATTCCTGTGGCAAAAGAGCATAGCCAGAGTGCCAGCCCTCTTCTCAG CACACCGCTTCCAAGCCAGACCTTGGACTGGAACACAATCAAACAGGAGCGGGAGATGTTGCTGAACCAG AGCCTTCCCAAGATGGCCTCAGCCCCAGAGGGGCCTCTCGTGACACCCCAACCCCAGGATGGGGAATCAC CACTCTCTGAGTCACCCCCTTTCTACAAGCCCAGCTTCTCCTGGGATACCTTGGCCTCCTCCTACAGCCA CAGCTACACACAGACCCCCTCCACCATGCAGTCCGCCTTCCTGGAGCGCTCCGTGAGGCTGTACGGCAGC CCCCTCGTGCCCAGCACAGAGTCTCCCTTGGACTTCCGCCTCCGCTACTCTCCAGGCATGGACACTTACC ACTGTGTCAAGTGCAACAAGGTGTTCTCCACCCCTCATGGGCTAGAAGTGCATGTCCGCCGCTCTCACAG CGGAACCCGGCCCTTTGCCTGTGATGTCTGTGGCAAAACCTTTGGCCACGCTGTGAGCTTGGAGCAGCAT ACTCACGTCCACTCACAGGAGCGAAGCTTCGAGTGCCGGATGTGTGGCAAAGCCTTCAAGCGTTCATCCA CCCTGTCCACCCACCTGCTCATCCACTCGGACACTCGGCCCTACCCCTGCCAGTTCTGTGGGAAGCGCTT CCACCAGAAGTCGGACATGAAGAAACACACCTACATCCACACAGGTGAGAAGCCCCACAAGTGCCAGGTG TGTGGGAAAGCCTTCAGCCAGAGCTCCAACCTCATCACCCACAGCCGCAAGCACACAGGCTTCAAGCCGT -
Ubiquitin-Dependent Regulation of the WNT Cargo Protein EVI/WLS Handelt Es Sich Um Meine Eigenständig Erbrachte Leistung
DISSERTATION submitted to the Combined Faculty of Natural Sciences and Mathematics of the Ruperto-Carola University of Heidelberg, Germany for the degree of Doctor of Natural Sciences presented by Lucie Magdalena Wolf, M.Sc. born in Nuremberg, Germany Date of oral examination: 2nd February 2021 Ubiquitin-dependent regulation of the WNT cargo protein EVI/WLS Referees: Prof. Dr. Michael Boutros apl. Prof. Dr. Viktor Umansky If you don’t think you might, you won’t. Terry Pratchett This work was accomplished from August 2015 to November 2020 under the supervision of Prof. Dr. Michael Boutros in the Division of Signalling and Functional Genomics at the German Cancer Research Center (DKFZ), Heidelberg, Germany. Contents Contents ......................................................................................................................... ix 1 Abstract ....................................................................................................................xiii 1 Zusammenfassung .................................................................................................... xv 2 Introduction ................................................................................................................ 1 2.1 The WNT signalling pathways and cancer ........................................................................ 1 2.1.1 Intercellular communication ........................................................................................ 1 2.1.2 WNT ligands are conserved morphogens ................................................................. -
Product Datasheet GET4 Overexpression Lysate
Product Datasheet GET4 Overexpression Lysate NBL1-08544 Unit Size: 0.1 mg Store at -80C. Avoid freeze-thaw cycles. Protocols, Publications, Related Products, Reviews, Research Tools and Images at: www.novusbio.com/NBL1-08544 Updated 3/17/2020 v.20.1 Earn rewards for product reviews and publications. Submit a publication at www.novusbio.com/publications Submit a review at www.novusbio.com/reviews/destination/NBL1-08544 Page 1 of 2 v.20.1 Updated 3/17/2020 NBL1-08544 GET4 Overexpression Lysate Product Information Unit Size 0.1 mg Concentration The exact concentration of the protein of interest cannot be determined for overexpression lysates. Please contact technical support for more information. Storage Store at -80C. Avoid freeze-thaw cycles. Buffer RIPA buffer Target Molecular Weight 36.3 kDa Product Description Description Transient overexpression lysate of chromosome 7 open reading frame 20 (C7orf20) The lysate was created in HEK293T cells, using Plasmid ID RC200220 and based on accession number NM_015949. The protein contains a C- MYC/DDK Tag. Gene ID 51608 Gene Symbol GET4 Species Human Notes HEK293T cells in 10-cm dishes were transiently transfected with a non-lipid polymer transfection reagent specially designed and manufactured for large volume DNA transfection. Transfected cells were cultured for 48hrs before collection. The cells were lysed in modified RIPA buffer (25mM Tris-HCl pH7.6, 150mM NaCl, 1% NP-40, 1mM EDTA, 1xProteinase inhibitor cocktail mix, 1mM PMSF and 1mM Na3VO4, and then centrifuged to clarify the lysate. Protein concentration was measured by BCA protein assay kit.This product is manufactured by and sold under license from OriGene Technologies and its use is limited solely for research purposes. -
Wppi: Weighting Protein-Protein Interactions
Package ‘wppi’ September 23, 2021 Type Package Title Weighting protein-protein interactions Version 1.1.2 Description Protein-protein interaction data is essential for omics data analysis and modeling. Database knowledge is general, not specific for cell type, physiological condition or any other context determining which connections are functional and contribute to the signaling. Functional annotations such as Gene Ontology and Human Phenotype Ontology might help to evaluate the relevance of interactions. This package predicts functional relevance of protein-protein interactions based on functional annotations such as Human Protein Ontology and Gene Ontology, and prioritizes genes based on network topology, functional scores and a path search algorithm. License MIT + file LICENSE URL https://github.com/AnaGalhoz37/wppi BugReports https://github.com/AnaGalhoz37/wppi/issues biocViews GraphAndNetwork, Network, Pathways, Software, GeneSignaling, GeneTarget, SystemsBiology, Transcriptomics, Annotation Encoding UTF-8 VignetteBuilder knitr Depends R(>= 4.1) Imports dplyr, igraph, logger, methods, magrittr, Matrix, OmnipathR(>= 2.99.8), progress, purrr, rlang, RCurl, stats, tibble, tidyr Suggests knitr, testthat, rmarkdown RoxygenNote 7.1.1 NeedsCompilation no git_url https://git.bioconductor.org/packages/wppi git_branch master git_last_commit 6aa91be git_last_commit_date 2021-07-15 1 2 common_neighbors Date/Publication 2021-09-23 Author Ana Galhoz [cre, aut] (<https://orcid.org/0000-0001-7402-5292>), Denes Turei [aut] (<https://orcid.org/0000-0002-7249-9379>), Michael P. Menden [aut] (<https://orcid.org/0000-0003-0267-5792>), Albert Krewinkel [ctb, cph] (pagebreak Lua filter) Maintainer Ana Galhoz <[email protected]> R topics documented: common_neighbors . .2 count_genes . .3 filter_annot_with_network . .4 functional_annot . .5 graph_from_op . .6 in_omnipath . .6 prioritization_genes . .7 process_annot . -
Bioinformatics Tools for the Analysis of Gene-Phenotype Relationships Coupled with a Next Generation Chip-Sequencing Data Processing Pipeline
Bioinformatics Tools for the Analysis of Gene-Phenotype Relationships Coupled with a Next Generation ChIP-Sequencing Data Processing Pipeline Erinija Pranckeviciene Thesis submitted to the Faculty of Graduate and Postdoctoral Studies in partial fulfillment of the requirements for the Doctorate in Philosophy degree in Cellular and Molecular Medicine Department of Cellular and Molecular Medicine Faculty of Medicine University of Ottawa c Erinija Pranckeviciene, Ottawa, Canada, 2015 Abstract The rapidly advancing high-throughput and next generation sequencing technologies facilitate deeper insights into the molecular mechanisms underlying the expression of phenotypes in living organisms. Experimental data and scientific publications following this technological advance- ment have rapidly accumulated in public databases. Meaningful analysis of currently avail- able data in genomic databases requires sophisticated computational tools and algorithms, and presents considerable challenges to molecular biologists without specialized training in bioinfor- matics. To study their phenotype of interest molecular biologists must prioritize large lists of poorly characterized genes generated in high-throughput experiments. To date, prioritization tools have primarily been designed to work with phenotypes of human diseases as defined by the genes known to be associated with those diseases. There is therefore a need for more prioritiza- tion tools for phenotypes which are not related with diseases generally or diseases with which no genes have yet been associated in particular. Chromatin immunoprecipitation followed by next generation sequencing (ChIP-Seq) is a method of choice to study the gene regulation processes responsible for the expression of cellular phenotypes. Among publicly available computational pipelines for the processing of ChIP-Seq data, there is a lack of tools for the downstream analysis of composite motifs and preferred binding distances of the DNA binding proteins. -
Genome-Wide Investigation of Cellular Functions for Trna Nucleus
Genome-wide Investigation of Cellular Functions for tRNA Nucleus- Cytoplasm Trafficking in the Yeast Saccharomyces cerevisiae DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Hui-Yi Chu Graduate Program in Molecular, Cellular and Developmental Biology The Ohio State University 2012 Dissertation Committee: Anita K. Hopper, Advisor Stephen Osmani Kurt Fredrick Jane Jackman Copyright by Hui-Yi Chu 2012 Abstract In eukaryotic cells tRNAs are transcribed in the nucleus and exported to the cytoplasm for their essential role in protein synthesis. This export event was thought to be unidirectional. Surprisingly, several lines of evidence showed that mature cytoplasmic tRNAs shuttle between nucleus and cytoplasm and their distribution is nutrient-dependent. This newly discovered tRNA retrograde process is conserved from yeast to vertebrates. Although how exactly the tRNA nuclear-cytoplasmic trafficking is regulated is still under investigation, previous studies identified several transporters involved in tRNA subcellular dynamics. At least three members of the β-importin family function in tRNA nuclear-cytoplasmic intracellular movement: (1) Los1 functions in both the tRNA primary export and re-export processes; (2) Mtr10, directly or indirectly, is responsible for the constitutive retrograde import of cytoplasmic tRNA to the nucleus; (3) Msn5 functions solely in the re-export process. In this thesis I focus on the physiological role(s) of the tRNA nuclear retrograde pathway. One possibility is that nuclear accumulation of cytoplasmic tRNA serves to modulate translation of particular transcripts. To test this hypothesis, I compared expression profiles from non-translating mRNAs and polyribosome-bound translating mRNAs collected from msn5Δ and mtr10Δ mutants and wild-type cells, in fed or acute amino acid starvation conditions. -
Downloaded from As a Tab-Delimited file, in Which Genes Are Represented in Rows and Phenotypes in Columns
cells Article Network of Interactions between ZIKA Virus Non-Structural Proteins and Human Host Proteins 1, 1,2, 2 Volha A. Golubeva y , Thales C. Nepomuceno y , Giuliana de Gregoriis , Rafael D. Mesquita 3, Xueli Li 1, Sweta Dash 1,4, Patrícia P. Garcez 5 , Guilherme Suarez-Kurtz 2 , Victoria Izumi 6, John Koomen 7, Marcelo A. Carvalho 2,8,* and Alvaro N. A. Monteiro 1,* 1 Cancer Epidemiology Program, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; [email protected] (V.A.G.); [email protected] (T.C.N.); xueli.li@moffitt.org (X.L.); sweta.dash@moffitt.org (S.D.) 2 Divisão de Pesquisa Clínica, Instituto Nacional de Câncer, Rio de Janeiro 20230-130, Brazil; [email protected] (G.d.G.); [email protected] (G.S.-K.) 3 Departamento de Bioquímica, Instituto de Química, Federal University of Rio de Janeiro, Rio de Janeiro 21941-909, Brazil; [email protected] 4 Cancer Biology PhD Program, University of South Florida, Tampa, FL 33612, USA 5 Institute of Biomedical Science, Federal University of Rio de Janeiro, Rio de Janeiro 20230-130, Brazil; [email protected] 6 Proteomics and Metabolomics Core, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; victoria.izumi@moffitt.org 7 Chemical Biology and Molecular Medicine Program, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; john.koomen@moffitt.org 8 Instituto Federal do Rio de Janeiro-IFRJ, Rio de Janeiro 20270-021, Brazil * Correspondence: [email protected] (M.A.C.); alvaro.monteiro@moffitt.org (A.N.A.M.); Tel.: +55-21-2566-7774 (M.A.C.); +813-7456321 (A.N.A.M.) These authors contributed equally to this work. -
Renoprotective Effect of Combined Inhibition of Angiotensin-Converting Enzyme and Histone Deacetylase
BASIC RESEARCH www.jasn.org Renoprotective Effect of Combined Inhibition of Angiotensin-Converting Enzyme and Histone Deacetylase † ‡ Yifei Zhong,* Edward Y. Chen, § Ruijie Liu,*¶ Peter Y. Chuang,* Sandeep K. Mallipattu,* ‡ ‡ † | ‡ Christopher M. Tan, § Neil R. Clark, § Yueyi Deng, Paul E. Klotman, Avi Ma’ayan, § and ‡ John Cijiang He* ¶ *Department of Medicine, Mount Sinai School of Medicine, New York, New York; †Department of Nephrology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China; ‡Department of Pharmacology and Systems Therapeutics and §Systems Biology Center New York, Mount Sinai School of Medicine, New York, New York; |Baylor College of Medicine, Houston, Texas; and ¶Renal Section, James J. Peters Veterans Affairs Medical Center, New York, New York ABSTRACT The Connectivity Map database contains microarray signatures of gene expression derived from approximately 6000 experiments that examined the effects of approximately 1300 single drugs on several human cancer cell lines. We used these data to prioritize pairs of drugs expected to reverse the changes in gene expression observed in the kidneys of a mouse model of HIV-associated nephropathy (Tg26 mice). We predicted that the combination of an angiotensin-converting enzyme (ACE) inhibitor and a histone deacetylase inhibitor would maximally reverse the disease-associated expression of genes in the kidneys of these mice. Testing the combination of these inhibitors in Tg26 mice revealed an additive renoprotective effect, as suggested by reduction of proteinuria, improvement of renal function, and attenuation of kidney injury. Furthermore, we observed the predicted treatment-associated changes in the expression of selected genes and pathway components. In summary, these data suggest that the combination of an ACE inhibitor and a histone deacetylase inhibitor could have therapeutic potential for various kidney diseases. -
The Changing Chromatome As a Driver of Disease: a Panoramic View from Different Methodologies
The changing chromatome as a driver of disease: A panoramic view from different methodologies Isabel Espejo1, Luciano Di Croce,1,2,3 and Sergi Aranda1 1. Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain 2. Universitat Pompeu Fabra (UPF), Barcelona, Spain 3. ICREA, Pg. Lluis Companys 23, Barcelona 08010, Spain *Corresponding authors: Luciano Di Croce ([email protected]) Sergi Aranda ([email protected]) 1 GRAPHICAL ABSTRACT Chromatin-bound proteins regulate gene expression, replicate and repair DNA, and transmit epigenetic information. Several human diseases are highly influenced by alterations in the chromatin- bound proteome. Thus, biochemical approaches for the systematic characterization of the chromatome could contribute to identifying new regulators of cellular functionality, including those that are relevant to human disorders. 2 SUMMARY Chromatin-bound proteins underlie several fundamental cellular functions, such as control of gene expression and the faithful transmission of genetic and epigenetic information. Components of the chromatin proteome (the “chromatome”) are essential in human life, and mutations in chromatin-bound proteins are frequently drivers of human diseases, such as cancer. Proteomic characterization of chromatin and de novo identification of chromatin interactors could thus reveal important and perhaps unexpected players implicated in human physiology and disease. Recently, intensive research efforts have focused on developing strategies to characterize the chromatome composition. In this review, we provide an overview of the dynamic composition of the chromatome, highlight the importance of its alterations as a driving force in human disease (and particularly in cancer), and discuss the different approaches to systematically characterize the chromatin-bound proteome in a global manner.