Evolutionary Analysis of Viral Sequences in Eukaryotic Genomes
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Figure 2S 4 7 A - C 080125 CSCs 080418 CSCs - + IFN-a 48 h + IFN-a 48 h + IFN-a 72 h 6 + IFN-a 72 h 3 5 MRFI 4 2 3 2 1 1 0 0 MHC I MHC II MICA MICB ULBP-1 ULBP-2 ULBP-3 ULBP-4 MHC I MHC II MICA MICB ULBP-1 ULBP-2 ULBP-3 ULBP-4 7 B 13 080125 FBS - D 080418 FBS - + IFN-a 48 h 12 + IFN-a 48 h + IFN-a 72 h + IFN-a 72 h 6 080125 FBS 11 10 5 9 8 4 7 6 3 MRFI 5 4 2 3 2 1 1 0 0 MHC I MHC II MICA MICB ULBP-1 ULBP-2 ULBP-3 ULBP-4 MHC I MHC II MICA MICB ULBP-1 ULBP-2 ULBP-3 ULBP-4 Molecule Molecule FIGURE 4S FIGURE 5S Panel A Panel B FIGURE 6S A B C D Supplemental Results Table 1S. Modulation by IFN-α of APM in GBM CSC and FBS tumor cell lines. Molecule * Cell line IFN-α‡ HLA β2-m# HLA LMP TAP1 TAP2 class II A A HC§ 2 7 10 080125 CSCs - 1∞ (1) 3 (65) 2 (91) 1 (2) 6 (47) 2 (61) 1 (3) 1 (2) 1 (3) + 2 (81) 11 (80) 13 (99) 1 (3) 8 (88) 4 (91) 1 (2) 1 (3) 2 (68) 080125 FBS - 2 (81) 4 (63) 4 (83) 1 (3) 6 (80) 3 (67) 2 (86) 1 (3) 2 (75) + 2 (99) 14 (90) 7 (97) 5 (75) 7 (100) 6 (98) 2 (90) 1 (4) 3 (87) 080418 CSCs - 2 (51) 1 (1) 1 (3) 2 (47) 2 (83) 2 (54) 1 (4) 1 (2) 1 (3) + 2 (81) 3 (76) 5 (75) 2 (50) 2 (83) 3 (71) 1 (3) 2 (87) 1 (2) 080418 FBS - 1 (3) 3 (70) 2 (88) 1 (4) 3 (87) 2 (76) 1 (3) 1 (3) 1 (2) + 2 (78) 7 (98) 5 (99) 2 (94) 5 (100) 3 (100) 1 (4) 2 (100) 1 (2) 070104 CSCs - 1 (2) 1 (3) 1 (3) 2 (78) 1 (3) 1 (2) 1 (3) 1 (3) 1 (2) + 2 (98) 8 (100) 10 (88) 4 (89) 3 (98) 3 (94) 1 (4) 2 (86) 2 (79) * expression of APM molecules was evaluated by intracellular staining and cytofluorimetric analysis; ‡ cells were treatead or not (+/-) for 72 h with 1000 IU/ml of IFN-α; # β-2 microglobulin; § β-2 microglobulin-free HLA-A heavy chain; ∞ values are indicated as ratio between the mean of fluorescence intensity of cells stained with the selected mAb and that of the negative control; bold values indicate significant MRFI (≥ 2). -
C8cc08685k1.Pdf
Electronic Supplementary Material (ESI) for ChemComm. This journal is © The Royal Society of Chemistry 2018 Supporting Information Minimalist Linkers Suitable for Irreversible Inhibitors in Simultaneous Proteome Profiling, Live-Cell Imaging and Drug Screening Cuiping Guo,Yu Chang, Xin Wang, Chengqian Zhang, Piliang Hao*, Ke Ding and Zhengqiu Li* School of Pharmacy, Jinan University, Guangzhou, China 510632 *Corresponding author ([email protected]) 1. General Information All chemicals were purchased from commercial vendors and used without further purification, unless indicated otherwise. All reactions requiring anhydrous conditions were carried out under argon or nitrogen atmosphere using oven-dried glassware. AR-grade solvents were used for all reactions. Reaction progress was monitored by TLC on pre-coated silica plates (Merck 60 F254 nm, 0.25 µm) and spots were visualized by UV, iodine or other suitable stains. Flash column chromatography was carried out using silica gel (Qingdao Ocean). All NMR spectra (1H-NMR, 13C-NMR) were recorded on Bruker 300 MHz/400 MHz NMR spectrometers. Chemical shifts were reported in parts per million (ppm) referenced with respect to appropriate internal standards or residual solvent peaks (CDCl3 = 7.26 ppm, DMSO-d6 = 2.50 ppm). The following abbreviations were used in reporting spectra, br s (broad singlet), s (singlet), d (doublet), t (triplet), q (quartet), m (multiplet), dd (doublet of doublets). Mass spectra were obtained on Agilent LC-ESI-MS system. All analytical HPLC were carried out on Agilent system. Water with 0.1% TFA and acetonitrile with 0.1% TFA were used as eluents and the flow rate was 0.5 mL/min. -
Computational Analysis of DNA Methylation and Gene Expression Patterns in Prostate Cancer
Ieva Rauluševičiūtė Computational Analysis of DNA Methylation and Gene Expression Patterns in Prostate Cancer Master’s thesis in Molecular Medicine Trondheim, June 2018 Supervisors: dr. Morten Beck Rye and prof. Finn Drabløs Norwegian University of Science and Technology Faculty of Medicine and Health Sciences Department of Clinical and Molecular Medicine ABSTRACT DNA methylation is an important contributor for prostate cancer development and progression. It has been studied experimentally for years, but, lately, high-throughput technologies are able to produce genome-wide DNA methylation data that can be analyzed using various computational approaches. Thus, this study aims to bioinformatically investigate different DNA methylation and gene expression patterns in prostate cancer. DNA methylation data from three datasets (TCGA, Absher and Kirby) was correlated with gene expression data in order to distinguish different regulation patterns. Classically, increased DNA methylation in promoter regions is being associated with gene expression downregulation. Although, results of the present project demonstrate another robust pattern, where DNA hypermethylation in promoter regions of 1,058 common genes is accompanied by upregulated expression. After analyzing expression and methylation values in the same samples from TCGA dataset, expression overcompensation in a dataset as an explanation for upregulation was excluded. Further reasons behind the pattern were investigated using TCGA DNA methylation data with extended list of probes and includes the presence of methylated positions in CpG islands, distance to transcription start sites and alternative TSSs. As compared with the downregulated genes in the classical pattern, upregulated genes were shown to have more positions in CpG islands and closer to TSSs. Moreover, the presence of alternative TSS in prostate was demonstrated, also disclosing the limitations of methylation detection systems. -
A Molecular and Genetic Analysis of Otosclerosis
A molecular and genetic analysis of otosclerosis Joanna Lauren Ziff Submitted for the degree of PhD University College London January 2014 1 Declaration I, Joanna Ziff, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. Where work has been conducted by other members of our laboratory, this has been indicated by an appropriate reference. 2 Abstract Otosclerosis is a common form of conductive hearing loss. It is characterised by abnormal bone remodelling within the otic capsule, leading to formation of sclerotic lesions of the temporal bone. Encroachment of these lesions on to the footplate of the stapes in the middle ear leads to stapes fixation and subsequent conductive hearing loss. The hereditary nature of otosclerosis has long been recognised due to its recurrence within families, but its genetic aetiology is yet to be characterised. Although many familial linkage studies and candidate gene association studies to investigate the genetic nature of otosclerosis have been performed in recent years, progress in identifying disease causing genes has been slow. This is largely due to the highly heterogeneous nature of this condition. The research presented in this thesis examines the molecular and genetic basis of otosclerosis using two next generation sequencing technologies; RNA-sequencing and Whole Exome Sequencing. RNA–sequencing has provided human stapes transcriptomes for healthy and diseased stapes, and in combination with pathway analysis has helped identify genes and molecular processes dysregulated in otosclerotic tissue. Whole Exome Sequencing has been employed to investigate rare variants that segregate with otosclerosis in affected families, and has been followed by a variant filtering strategy, which has prioritised genes found to be dysregulated during RNA-sequencing. -
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Supplementary Figure S1. Results of flow cytometry analysis, performed to estimate CD34 positivity, after immunomagnetic separation in two different experiments. As monoclonal antibody for labeling the sample, the fluorescein isothiocyanate (FITC)- conjugated mouse anti-human CD34 MoAb (Mylteni) was used. Briefly, cell samples were incubated in the presence of the indicated MoAbs, at the proper dilution, in PBS containing 5% FCS and 1% Fc receptor (FcR) blocking reagent (Miltenyi) for 30 min at 4 C. Cells were then washed twice, resuspended with PBS and analyzed by a Coulter Epics XL (Coulter Electronics Inc., Hialeah, FL, USA) flow cytometer. only use Non-commercial 1 Supplementary Table S1. Complete list of the datasets used in this study and their sources. GEO Total samples Geo selected GEO accession of used Platform Reference series in series samples samples GSM142565 GSM142566 GSM142567 GSM142568 GSE6146 HG-U133A 14 8 - GSM142569 GSM142571 GSM142572 GSM142574 GSM51391 GSM51392 GSE2666 HG-U133A 36 4 1 GSM51393 GSM51394 only GSM321583 GSE12803 HG-U133A 20 3 GSM321584 2 GSM321585 use Promyelocytes_1 Promyelocytes_2 Promyelocytes_3 Promyelocytes_4 HG-U133A 8 8 3 GSE64282 Promyelocytes_5 Promyelocytes_6 Promyelocytes_7 Promyelocytes_8 Non-commercial 2 Supplementary Table S2. Chromosomal regions up-regulated in CD34+ samples as identified by the LAP procedure with the two-class statistics coded in the PREDA R package and an FDR threshold of 0.5. Functional enrichment analysis has been performed using DAVID (http://david.abcc.ncifcrf.gov/) -
Bioinformatics Analyses of Genomic Imprinting
Bioinformatics Analyses of Genomic Imprinting Dissertation zur Erlangung des Grades des Doktors der Naturwissenschaften der Naturwissenschaftlich-Technischen Fakultät III Chemie, Pharmazie, Bio- und Werkstoffwissenschaften der Universität des Saarlandes von Barbara Hutter Saarbrücken 2009 Tag des Kolloquiums: 08.12.2009 Dekan: Prof. Dr.-Ing. Stefan Diebels Berichterstatter: Prof. Dr. Volkhard Helms Priv.-Doz. Dr. Martina Paulsen Vorsitz: Prof. Dr. Jörn Walter Akad. Mitarbeiter: Dr. Tihamér Geyer Table of contents Summary________________________________________________________________ I Zusammenfassung ________________________________________________________ I Acknowledgements _______________________________________________________II Abbreviations ___________________________________________________________ III Chapter 1 – Introduction __________________________________________________ 1 1.1 Important terms and concepts related to genomic imprinting __________________________ 2 1.2 CpG islands as regulatory elements ______________________________________________ 3 1.3 Differentially methylated regions and imprinting clusters_____________________________ 6 1.4 Reading the imprint __________________________________________________________ 8 1.5 Chromatin marks at imprinted regions___________________________________________ 10 1.6 Roles of repetitive elements ___________________________________________________ 12 1.7 Functional implications of imprinted genes _______________________________________ 14 1.8 Evolution and parental conflict ________________________________________________ -
The Interactome of KRAB Zinc Finger Proteins Reveals the Evolutionary History of Their Functional Diversification
Resource The interactome of KRAB zinc finger proteins reveals the evolutionary history of their functional diversification Pierre-Yves Helleboid1,†, Moritz Heusel2,†, Julien Duc1, Cécile Piot1, Christian W Thorball1, Andrea Coluccio1, Julien Pontis1, Michaël Imbeault1, Priscilla Turelli1, Ruedi Aebersold2,3,* & Didier Trono1,** Abstract years ago (MYA) (Imbeault et al, 2017). Their products harbor an N-terminal KRAB (Kru¨ppel-associated box) domain related to that of Krüppel-associated box (KRAB)-containing zinc finger proteins Meisetz (a.k.a. PRDM9), a protein that originated prior to the diver- (KZFPs) are encoded in the hundreds by the genomes of higher gence of chordates and echinoderms, and a C-terminal array of zinc vertebrates, and many act with the heterochromatin-inducing fingers (ZNF) with sequence-specific DNA-binding potential (Urru- KAP1 as repressors of transposable elements (TEs) during early tia, 2003; Birtle & Ponting, 2006; Imbeault et al, 2017). KZFP genes embryogenesis. Yet, their widespread expression in adult tissues multiplied by gene and segment duplication to count today more and enrichment at other genetic loci indicate additional roles. than 350 and 700 representatives in the human and mouse Here, we characterized the protein interactome of 101 of the ~350 genomes, respectively (Urrutia, 2003; Kauzlaric et al, 2017). A human KZFPs. Consistent with their targeting of TEs, most KZFPs majority of human KZFPs including all primate-restricted family conserved up to placental mammals essentially recruit KAP1 and members target sequences derived from TEs, that is, DNA trans- associated effectors. In contrast, a subset of more ancient KZFPs posons, ERVs (endogenous retroviruses), LINEs, SINEs (long and rather interacts with factors related to functions such as genome short interspersed nuclear elements, respectively), or SVAs (SINE- architecture or RNA processing. -
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. -
Divergence of Human and Mouse Brain Transcriptome Highlights Alzheimer Disease Pathways
Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways Jeremy A. Millera,b, Steve Horvathc,d, and Daniel H. Geschwindb,c,1 aInterdepartmental Program for Neuroscience, Departments of cHuman Genetics and dBiostatistics, and bProgram in Neurogenetics and Neurobehavioral Genetics, Department of Neurology and Semel Institute, University of California, Los Angeles, CA 90095-1769 Edited by Joseph S. Takahashi, Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, TX, and approved June 1, 2010 (received for review December 10, 2009) Because mouse models play a crucial role in biomedical research works by merging data from many microarray studies, and then related to the human nervous system, understanding the similari- applying weighted gene coexpression network analysis (WGCNA) ties and differences between mouse and human brain is of (4–7). WGCNA elucidates the higher-order relationships between fundamental importance. Studies comparing transcription in hu- genes based on their coexpression relationships, delineating modules man and mouse have come to varied conclusions, in part because of of biologically related genes and permitting a robust view of their relatively small sample sizes or underpowered methodologies. transcriptome organization (3, 5, 8, 9). Within groups of highly coexpressed genes (“modules”) that comprise the core functional To better characterize gene expression differences between mouse fi and human, we took a systems-biology approach by using weighted units of the transcriptional network, WGCNA also identi es gene coexpression network analysis on more than 1,000 micro- the most highly connected, or most central genes within each module, referred to as “hubs.” We find that both gene expression arrays from brain. -
Association of Gene Ontology Categories with Decay Rate for Hepg2 Experiments These Tables Show Details for All Gene Ontology Categories
Supplementary Table 1: Association of Gene Ontology Categories with Decay Rate for HepG2 Experiments These tables show details for all Gene Ontology categories. Inferences for manual classification scheme shown at the bottom. Those categories used in Figure 1A are highlighted in bold. Standard Deviations are shown in parentheses. P-values less than 1E-20 are indicated with a "0". Rate r (hour^-1) Half-life < 2hr. Decay % GO Number Category Name Probe Sets Group Non-Group Distribution p-value In-Group Non-Group Representation p-value GO:0006350 transcription 1523 0.221 (0.009) 0.127 (0.002) FASTER 0 13.1 (0.4) 4.5 (0.1) OVER 0 GO:0006351 transcription, DNA-dependent 1498 0.220 (0.009) 0.127 (0.002) FASTER 0 13.0 (0.4) 4.5 (0.1) OVER 0 GO:0006355 regulation of transcription, DNA-dependent 1163 0.230 (0.011) 0.128 (0.002) FASTER 5.00E-21 14.2 (0.5) 4.6 (0.1) OVER 0 GO:0006366 transcription from Pol II promoter 845 0.225 (0.012) 0.130 (0.002) FASTER 1.88E-14 13.0 (0.5) 4.8 (0.1) OVER 0 GO:0006139 nucleobase, nucleoside, nucleotide and nucleic acid metabolism3004 0.173 (0.006) 0.127 (0.002) FASTER 1.28E-12 8.4 (0.2) 4.5 (0.1) OVER 0 GO:0006357 regulation of transcription from Pol II promoter 487 0.231 (0.016) 0.132 (0.002) FASTER 6.05E-10 13.5 (0.6) 4.9 (0.1) OVER 0 GO:0008283 cell proliferation 625 0.189 (0.014) 0.132 (0.002) FASTER 1.95E-05 10.1 (0.6) 5.0 (0.1) OVER 1.50E-20 GO:0006513 monoubiquitination 36 0.305 (0.049) 0.134 (0.002) FASTER 2.69E-04 25.4 (4.4) 5.1 (0.1) OVER 2.04E-06 GO:0007050 cell cycle arrest 57 0.311 (0.054) 0.133 (0.002) -
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). -
HHS Public Access Author Manuscript
HHS Public Access Author manuscript Author Manuscript Author ManuscriptNature. Author ManuscriptAuthor manuscript; Author Manuscript available in PMC 2015 June 11. Published in final edited form as: Nature. 2014 December 11; 516(7530): 242–245. doi:10.1038/nature13760. An evolutionary arms race between KRAB zinc finger genes 91/93 and SVA/L1 retrotransposons Frank MJ Jacobs1,§,*, David Greenberg1,2,¶,*, Ngan Nguyen1,3, Maximilian Haeussler1, Adam D Ewing1,¥, Sol Katzman1, Benedict Paten1, Sofie R Salama1,4, and David Haussler1,4,# 1Center for Biomolecular Science and Engineering, University of California Santa Cruz, Santa Cruz, California, United States of America 2Molecular, Cell and Developmental Biology, of California Santa Cruz, Santa Cruz, California, United States of America 3Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, California, United States of America 4Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, California, United States of America Summary Throughout evolution, primate genomes have been modified by waves of retrotransposon insertions1,2,3. For each wave, the host eventually finds a way to repress retrotransposon transcription and prevent further insertions. In mouse embryonic stem cells (mESCs), transcriptional silencing of retrotransposons requires TRIM28 (KAP1) and it’s repressive complex, which can be recruited to target sites by KRAB zinc finger proteins such as murine- specific ZFP809 which binds to integrated murine leukemia virus DNA elements and recruits