DOCSLIB.ORG

Ram Phd Thesis After Corrections 13012014 with Lola Footnote

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Ram Phd Thesis After Corrections 13012014 with Lola Footnote Mediators of pre-mRNA splicing regulate sister chromatid cohesion in mammalian cells Sriramkumar Sundaramoorthy University College London and Cancer Research UK London Research Institute PhD Supervisor: Dr. Mark Petronczki A thesis submitted for the degree of Doctor of Philosophy University College London January 2014 Declaration I Sriramkumar Sundaramoorthy 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. 2 Abstract The ‘endless forms most beautiful’ that populate our planet rely on the process of cell division to ensure equal segregation of the cellular content including the DNA to the two daughter cells. The accurate segregation of chromosomes in eukaryotes relies on connection between replicated sister chromatids, a phenomenon known as sister chromatid cohesion. Sister chromatid cohesion is mediated by a conserved ring-like protein complex known as cohesin. Defects in this process can promote aneuploidy and contribute to meiotic segregation errors with adverse consequences for developing embryos. Despite numerous advances into understanding cell division at the molecular level, we still lack a comprehensive list of the participating proteins and complexes. The aim of this thesis was to use available functional genomic and proteomic data to identify novel regulators of mitosis in human cells. Using an RNAi approach, we identified a set of factors involved in pre-mRNA splicing whose depletion prevents successful cell division. Loss of these splicing factors leads to a failure in chromosome alignment and to a protracted mitotic arrest that is dependent on the spindle assembly checkpoint. This mitotic phenotype was accompanied by a dramatic loss of sister chromatid cohesion that we could show happens as soon as DNA replication. While depletion of pre-mRNA splicing mediators had no effect on cohesin loading onto chromatin, it prevented the stable association of cohesin with chromatin. Immunoblotting revealed that the depletion of splicing factors caused a 5-fold reduction in the protein levels of Sororin, a protein required for stable association of cohesin with chromatin in post-replicative cells. Further analysis suggests erroneous splicing of Sororin pre-mRNA upon depletion of splicing factors. Importantly, the sister chromatid cohesion loss caused by depletion of splicing factors could be suppressed by a Sororin transgene that lacks introns. Our results suggest that that pre-mRNA splicing of Sororin is a rate-limiting step in the maintenance of sister chromatid cohesion in human cells. Our work reveals that a primary cellular pathology of compromised pre-mRNA splicing is a mitotic arrest accompanied by split sister chromatids. Our work linking splicing and sister chromatid cohesion has implications for the pathology of Chronic Lymphocytic Leukemia (CLL). One of the splicing factors that we implicate in sister chromatid cohesion is SF3B1, whose gene is one of the most frequently mutated genetic drivers found in CLL patients. 3 Acknowledgement At the outset, I would like to thank my supervisor Mark Petronczki for providing me not just with an opportunity to work with him but also to share his knowledge and experience about working in cell biology. I am deeply grateful to Mark for having taught me the importance of both doing science with integrity and passion while equally importantly being able to present your ideas and work in as lucid a form as possible. I would also like to thank him for instilling in me the importance for any experiment either in the lab or in life in general to have controls. I am thankful to the people in the Cell Division and Aneuploidy (CDA) lab for all their help and guidance over the years. It was a privilege to be surrounded by people smarter than myself so that I could learn as much as I have. I would like to thank Lola for being a great sounding board for all my ideas about science, theology, politics etc. and also for all her help with the many experiments. I would like to thank Sergey ‘Tovarich’ Lekomtsev for introducing me to the dark room 4 years ago and less importantly for helping me with my screen and for all the help over the past 4 years. I am grateful to Kuan-Chung for his thoughts about experiments, science, life and his ‘inspiring views’ about life and the food in London. Thanks go out to Tohru for his great guidance and for having had to endure my umpteen questions when I started out in the lab. I would also like to thank Laurent for being a brilliant ‘lab elder brother’, pointing out when I was doing wrong to make me a better researcher but also being among the first to compliment me on my rare good days. Special thanks go to Kristyna for all her help in and out of the lab over the past couple of years and for sharing all the wonderful stories about the 398 buses. I would also like to thank Murielle for her constant encouragement, help with experiments and for all the rides to Potters Bar. I am thankful to Peter Parker and Mike Howell for spending time away from their busy schedule on my thesis committee providing their valuable guidance and suggestions at every stage of my PhD at the LRI. Special thanks go to Mike and the members of the High Throughput Screening lab for their help in performing my screen 4 I am also indebted to all the people in Clare hall for making it a wonderful place to work in. Special thanks go to Marco for his help with the RT-PCR experiments, Nicola and Katharina for their help and guidance when I was starting out at Clare Hall. I am also grateful to the research administrators and assistants both at Clare hall and Lincoln’s inn fields for their excellent help in terms of my umpteen requests for visas, letters and accommodation etc. Without their help, the PhD would have easily been a much more arduous experience. I would also like to thank my friends and family for their steadfast support not just through my PhD years but also throughout my life. I am thankful to the Breast Cancer Campaign for providing me with a PhD grant for the duration of my PhD and for the special opportunity to visit the British houses of Parliament. Finally, I would like to thank Prof. Richard Dawkins for having fostered in me a sense of curiosity and wonder about the natural world and for instilling in me the perseverance to question dogmas and blind faith. 5 Table of Contents Abstract ............................................................................................................... 3 Acknowledgement .............................................................................................. 4 Table of Contents ................................................................................................ 6 Table of figures ................................................................................................... 9 List of tables ...................................................................................................... 12 Abbreviations .................................................................................................... 13 Chapter 1. Introduction .................................................................................. 15 1.1 The cell cycle ....................................................................................................... 15 1.1.1 Cell cycle phases and checkpoints ................................................................ 15 1.1.2 Regulation of the cell cycle ........................................................................... 18 1.1.3 Mitosis in mammalian cells .......................................................................... 22 1.1.4 Multiple supervisory mechanisms regulate mitosis ...................................... 25 1.2 Sister chromatid cohesion .................................................................................. 32 1.2.1 Sister chromatid cohesion is mediated by a protein complex called cohesin 33 1.2.2 Cohesin structure ........................................................................................... 34 1.2.3 Models for cohesin mediated sister chromatid cohesion .............................. 38 1.2.4 Cohesin loading onto chromatin involves the Adherin complex, the opening of the SMC1-SMC3 hinge and cohesin’s ATPase activity ....................................... 40 1.2.5 Establishment of sister chromatid cohesion happens during DNA replication .................................................................................................................. 42 1.2.6 Sororin plays a very important role in maintenance of cohesion in post- replicative cells .......................................................................................................... 44 1.2.7 Cohesin removal from mitotic chromosome happens in two distinct steps in higher eukaryotes ...................................................................................................... 46 1.2.8 Roles of cohesin beyond sister chromatid cohesion ..................................... 52 1.2.9 Cohesinopathies and cohesin in cancer ......................................................... 56 1.3 Pre-mRNA splicing ............................................................................................ 58 1.3.1 The spliceosome and pre-mRNA splicing .................................................... 59 1.3.2 Models of pre-mRNA splicing and cis-acting pre-mRNA elements in splicing .....................................................................................................................
Load more

Recommended publications
  • The Title of the Article
    Mechanism-Anchored Profiling Derived from Epigenetic Networks Predicts Outcome in Acute Lymphoblastic Leukemia Xinan Yang, PhD1, Yong Huang, MD1, James L Chen, MD1, Jianming Xie, MSc2, Xiao Sun, PhD2, Yves A Lussier, MD1,3,4§ 1Center for Biomedical Informatics and Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL 60637 USA 2State Key Laboratory of Bioelectronics, Southeast University, 210096 Nanjing, P.R.China 3The University of Chicago Cancer Research Center, and The Ludwig Center for Metastasis Research, The University of Chicago, Chicago, IL 60637 USA 4The Institute for Genomics and Systems Biology, and the Computational Institute, The University of Chicago, Chicago, IL 60637 USA §Corresponding author Email addresses: XY: [email protected] YH: [email protected] JC: [email protected] JX: [email protected] XS: [email protected] YL: [email protected] - 1 - Abstract Background Current outcome predictors based on “molecular profiling” rely on gene lists selected without consideration for their molecular mechanisms. This study was designed to demonstrate that we could learn about genes related to a specific mechanism and further use this knowledge to predict outcome in patients – a paradigm shift towards accurate “mechanism-anchored profiling”. We propose a novel algorithm, PGnet, which predicts a tripartite mechanism-anchored network associated to epigenetic regulation consisting of phenotypes, genes and mechanisms. Genes termed as GEMs in this network meet all of the following criteria: (i) they are co-expressed with genes known to be involved in the biological mechanism of interest, (ii) they are also differentially expressed between distinct phenotypes relevant to the study, and (iii) as a biomodule, genes correlate with both the mechanism and the phenotype.
    [Show full text]
  • Supp Data.Pdf
    Supplementary Methods Verhaak sub-classification The Verhaak called tumor subtype was assigned as determined in the Verhaak et al. manuscript, for those TCGA tumors that were included at the time. TCGA data for expression (Affymetrix platform, level 1) and methylation (Infinium platform level 3) were downloaded from the TCGA portal on 09/29/2011. Affymetrix data were processed using R and Biocondutor with a RMA algorithm using quantile normalization and custom CDF. Level 3 methylation data has already been processed and converted to a beta-value. The genes that make up the signature for each of the 4 Verhaak groups (i.e. Proneural, Neural, Mesenchymal, and Classical) were downloaded. For each tumor, the averaged expression of the 4 genes signatures was calculated generating 4 ‘metagene’ scores, one for each subtype, (Colman et al.2010) for every tumor. Then each subtype metagene score was z-score corrected to allow comparison among the 4 metagenes. Finally, for each tumor the subtype with the highest metagene z-score among the four was used to assign subtype. Thus by this method, a tumor is assigned to the group to which it has the strongest expression signature. The shortcoming is that if a tumor has a ‘dual personality’ – for instance has strong expression of both classical and mesenchymal signature genes, there is some arbitrariness to the tumor’s assignment. Western Blot Analysis and cell fractionation Western blot analysis was performed using standard protocols. To determine protein expression we used the following antibodies: TAZ (Sigma and BD Biosciences), YAP (Santa Cruz Biotechnology), CD44 (Cell Signaling), FN1 (BD Biosciences), TEAD1, TEAD2, TEAD3, TEAD4 (Santa Cruz Biotechnology), MST1, p-MST1, MOB1, LATS1, LATS2, 14-3-3-, 1 ACTG2, (Cell Signaling), p-LATS1/2 (Abcam), Flag (Sigma Aldrich), Actin (Calbiochem), CAV2 (BD Biosciences), CTGF (Santa Cruz), RUNX2 (Sigma Aldrich), Cylin A, Cyclin E, Cyclin B1, p-cdk1, p-cdk4 (Cell Signaling Technologies).
    [Show full text]
  • Canonical WNT Signalling Determines Lineage Specificity in Wilms Tumour
    Oncogene (2009) 28, 1063–1075 & 2009 Macmillan Publishers Limited All rights reserved 0950-9232/09 $32.00 www.nature.com/onc ORIGINAL ARTICLE Canonical WNT signalling determines lineage specificity in Wilms tumour R Fukuzawa, MR Anaka, RJ Weeks, IM Morison and AE Reeve Cancer Genetics Laboratory, Department of Biochemistry, University of Otago, Dunedin, New Zealand Wilms tumours (WTs) have two distinct types of histology to translocation of the protein to the nucleus (Clevers, with or without ectopic mesenchymal elements, suggesting 2006). Nuclear accumulation of b-catenin is therefore a that WTs arise from either the mesenchymal or epithelial surrogate marker for activation of the WNT-signalling nephrogenic lineages. Regardless of the presence or pathway in many types of cancers (Clevers, 2006). absence of CTNNB1 mutations, nuclear accumulation of Wilms tumour (WT) is an embryonal tumour that b-catenin is often observed in WTs with ectopic mesench- recapitulates kidney development. WT typically has a ymal elements. Here, we addressed the relationship triphasic histology, which consists of varying propor- between the WNT-signalling pathway and lineage in tions of blastemal, epithelial and stromal elements WTs by examining CTNNB1 and WT1 mutations, (Beckwith et al., 1990). The stromal tissues occasionally nuclear accumulation of b-catenin, tumour histology and contain ectopic mesenchymal elements such as rhabdo- gene expression profiles. In addition, we screened for myoblasts, chondrocytes and adipocytes. Remarkably, mutations in WTX, which has been proposed to be a the presence or absence of ectopic mesenchymal negative regulator of the canonical WNT-signalling elements in WT histology is associated with specific pathway. Unsupervised clustering analysis identified two precursor lesions, intralobar and perilobar nephrogenic classes of tumours: mesenchymal lineage WNT-dependent rests (ILNR and PLNR), respectively (Beckwith et al., tumours, and epithelial lineage WNT-independent tu- 1990).
    [Show full text]
  • Theses Digitisation: This Is a Digitised
    https://theses.gla.ac.uk/ Theses Digitisation: https://www.gla.ac.uk/myglasgow/research/enlighten/theses/digitisation/ This is a digitised version of the original print thesis. Copyright and moral rights for this work are retained by the author A copy can be downloaded for personal non-commercial research or study, without prior permission or charge This work cannot be reproduced or quoted extensively from without first obtaining permission in writing from the author The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the author When referring to this work, full bibliographic details including the author, title, awarding institution and date of the thesis must be given Enlighten: Theses https://theses.gla.ac.uk/ [email protected] Identification ofTopBPl Chromatin Modification Domains and Transcriptional Targets Roni H G Wright A thesis submitted to the University o f Glasgow, Faculty of Veterinary Medicine for the degree o f Doctor of Philosophy UNIVERSITY of GLASGOW i r % Division of Pathological Sciences Faculty of Veterinary Medicine September 2007 © Roni Wright :v & ProQuest Number: 10391063 All rights reserved INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if material had to be removed, a note will indicate the deletion. uest ProQuest 10391063 Published by ProQuest LLO (2017). C o pyright of the Dissertation is held by the Author.
    [Show full text]
  • Downloaded from NCBI Website [44] and Installed Extension Assay to a Local Server
    BMC Genomics BioMed Central Methodology article Open Access A highly sensitive and specific system for large-scale gene expression profiling Guohong Hu†, Qifeng Yang†, Xiangfeng Cui, Gang Yue, Marco A Azaro, Hui- Yun Wang and Honghua Li* Address: Department of Molecular Genetics, Microbiology and Immunology/The Cancer Institute of New Jersey, University of Medicine and Dentistry of New Jersey Robert Wood Johnson Medical School, Piscataway, New Jersey 08854, USA Email: Guohong Hu - [email protected]; Qifeng Yang - [email protected]; Xiangfeng Cui - [email protected]; Gang Yue - [email protected]; Marco A Azaro - [email protected]; Hui-Yun Wang - [email protected]; Honghua Li* - [email protected] * Corresponding author †Equal contributors Published: 10 January 2008 Received: 18 January 2007 Accepted: 10 January 2008 BMC Genomics 2008, 9:9 doi:10.1186/1471-2164-9-9 This article is available from: http://www.biomedcentral.com/1471-2164/9/9 © 2008 Hu et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background: Rapid progress in the field of gene expression-based molecular network integration has generated strong demand on enhancing the sensitivity and data accuracy of experimental systems. To meet the need, a high-throughput gene profiling system of high specificity and sensitivity has been developed. Results: By using specially designed primers, the new system amplifies sequences in neighboring exons separated by big introns so that mRNA sequences may be effectively discriminated from other highly related sequences including their genes, unprocessed transcripts, pseudogenes and pseudogene transcripts.
    [Show full text]
  • Primepcr™Assay Validation Report
    PrimePCR™Assay Validation Report Gene Information Gene Name zinc finger, C4H2 domain containing Gene Symbol ZC4H2 Organism Human Gene Summary This gene encodes a member of the zinc finger domain-containing protein family. This family member has a C-terminal zinc finger domain that is characterized by four cysteine residues and two histidine residues and it also includes a coiled-coil region. This protein has been detected as an autoantigen in hepatocellular carcinoma patients. This gene has been identified as a potential candidate for X-linked mental retardation. Alternative splicing results in multiple transcript variants. Gene Aliases HCA127, KIAA1166 RefSeq Accession No. NC_000023.10, NT_011669.17, NG_021200.1 UniGene ID Hs.28249 Ensembl Gene ID ENSG00000126970 Entrez Gene ID 55906 Assay Information Unique Assay ID qHsaCED0044169 Assay Type SYBR® Green Detected Coding Transcript(s) ENST00000374839, ENST00000337990, ENST00000447788, ENST00000545618 Amplicon Context Sequence CTCCTCCCTGCAATATACCATTGAGCATGTGCCAGAGTAATGGTTCTGAACAAAA GCCAACACAGATGTCAGCCTGGGGGCACTCTCAGCCTAAGGAAGCCCCTACAG CCGAGCCCTCAGCCCTAATGACTTAGGCAGTAGGTTAGGCA Amplicon Length (bp) 119 Chromosome Location X:64136772-64136920 Assay Design Exonic Purification Desalted Validation Results Efficiency (%) 99 R2 0.9996 cDNA Cq 23.51 cDNA Tm (Celsius) 85 Page 1/5 PrimePCR™Assay Validation Report gDNA Cq 24.29 Specificity (%) 100 Information to assist with data interpretation is provided at the end of this report. Page 2/5 PrimePCR™Assay Validation Report ZC4H2, Human Amplification
    [Show full text]
  • A Second Generation Human Haplotype Map of Over 3.1 Million Snps the International Hapmap Consortium1
    doi: 10.1038/nature06258 SUPPLEMENTARY INFORMATION A second generation human haplotype map of over 3.1 million SNPs The International HapMap Consortium1 Supplementary material S1 The density of common SNPs in the Phase II HapMap and the assembled human genome S2 Analysis of data quality S2.1 Analysis of amplicon structure to genotyping error S2.2 Analysis of genotype discordance from overlap with Seattle SNPs S2.3 Analysis of genotype discordance from fosmid end sequences S2.4 Analysis of monomorphism/polymorphism discrepancies S2.5 Interchromosomal LD S3. Analysis of population stratification S4. Analysis of relatedness S5. Segmental analysis of relatedness S6. Analysis of homozygosity S7. Perlegen genotyping protocols Supplementary tables Legends to supplementary figures 1 See end of manuscript for Consortium details www.nature.com/nature 1 doi: 10.1038/nature06258 SUPPLEMENTARY INFORMATION Supplementary text 1. The density of common SNPs in the Phase II HapMap and the assembled human genome. To estimate the fraction of all common variants on the autosomes that have been successfully genotyped in the consensus Phase II HapMap we note that in YRI (release 21) there are 2,334,980 SNPs with MAF≥0.05. Across the autosomes, the completed reference sequence assembled in contigs is 2.68 billion bp. Assuming that the allele frequency distribution in the YRI is well approximated by that of a simple coalescent model and using an estimate of the population mutation rate of θ = 1.2 per kb for African populations1,2 the expected number of variants with MAF≥0.05 in a sample of 120 chromosomes is 114 = θ E(S MAF≥5% ) L ∑ /1 i i=6 where L is the total length of the sequence3.
    [Show full text]
  • Copy of Supplementary Table 2
    Cluster A Cluster A SAM analysis FDR less than 5% chr Fold chr Fold NAME UNIQID location change NAME UNIQID location change WIF1 7964722 chr12 130.61 SEMA3A 8140668 chr7 0.02 DKK2 8102200 chr4 56.66 LOC138046 8147065 chr8 0.03 DKK4 8150529 chr8 54.91 ZNF804A 8046815 chr2 0.03 LGR5 7957140 chr12 41.44 CTTNBP2 8142497 chr7 0.03 ADAMTSL1 8154491 chr9 35.08 LOC645323 8113037 chr5 0.03 DKK1 7927631 chr10 28.09 KHDRBS2 8127370 chr6 0.03 GAD1 8046283 chr2 26.81 LOC727966 8127399 chr6 0.04 ZNF804B 8134018 chr7 25.46 NRN1 8123739 chr6 0.04 TNC 8163637 chr9 25.10 EGFL11 8127408 chr6 0.04 C14orf29 7974288 chr14 24.72 TMTC2 7957417 chr12 0.05 SHOX2 8091698 chr3 18.89 NA 8127393 chr6 0.05 TMEM132C 7959827 chr12 18.01 NOL4 8022856 chr18 0.06 NKD1 7995525 chr16 17.47 ENC1 8112615 chr5 0.06 CCDC68 8023401 chr18 16.55 CACNA2D1 8140579 chr7 0.07 C20orf103 8060940 chr20 16.06 C6orf32 8124280 chr6 0.07 GPR64 8171624 chrX 15.90 RELN 8141950 chr7 0.07 PDE11A 8057004 chr2 15.69 ZNF679 8133042 chr7 0.07 PGM5 8155665 chr9 15.60 GLRA2 8166127 chrX 0.08 WNT16 8135763 chr7 15.11 EOMES 8085946 chr3 0.08 ENPP3 8122071 chr6 15.06 SLC6A15 7965206 chr12 0.08 PAX3 8059301 chr2 14.78 CDH18 8111220 chr5 0.08 LOC645188 8170257 chrX 13.87 DCX 8174543 chrX 0.08 EMB 8112007 chr5 13.60 ERBB4 8058627 chr2 0.09 NA 8102210 chr4 13.34 PRL 8124185 chr6 0.09 EPHA7 8128284 chr6 13.10 LHX4 7907849 chr1 0.09 EMX2 7930857 chr10 12.49 TOX 8150962 chr8 0.09 RASL11B 8095043 chr4 12.29 MAB21L1 7970949 chr13 0.09 TNFRSF19 7968015 chr13 11.72 MPP3 8015846 chr17 0.09 GABRE 8175666 chrX 11.40
    [Show full text]
  • Supplementary Data
    SUPPLEMENTARY METHODS 1) Characterisation of OCCC cell line gene expression profiles using Prediction Analysis for Microarrays (PAM) The ovarian cancer dataset from Hendrix et al (25) was used to predict the phenotypes of the cell lines used in this study. Hendrix et al (25) analysed a series of 103 ovarian samples using the Affymetrix U133A array platform (GEO: GSE6008). This dataset comprises clear cell (n=8), endometrioid (n=37), mucinous (n=13) and serous epithelial (n=41) primary ovarian carcinomas and samples from 4 normal ovaries. To build the predictor, the Prediction Analysis of Microarrays (PAM) package in R environment was employed (http://rss.acs.unt.edu/Rdoc/library/pamr/html/00Index.html). When more than one probe described the expression of a given gene, we used the probe with the highest median absolute deviation across the samples. The dataset from Hendrix et al. (25) and the dataset of OCCC cell lines described in this manuscript were then overlaid on the basis of 11536 common unique HGNC gene symbols. Only the 99 primary ovarian cancers samples and the four normal ovary samples were used to build the predictor. Following leave one out cross-validation, a predictor based upon 126 genes was able to identify correctly the four distinct phenotypes of primary ovarian tumour samples with a misclassification rate of 18.3%. This predictor was subsequently applied to the expression data from the 12 OCCC cell lines to determine the likeliest phenotype of the OCCC cell lines compared to primary ovarian cancers. Posterior probabilities were estimated for each cell line in comparison to the following phenotypes: clear cell, endometrioid, mucinous and serous epithelial.
    [Show full text]
  • X Chromosome Cdna Microarray Screening Identifies a Functional PLP2 Promoter Polymorphism Enriched in Patients with X-Linked Mental Retardation
    Downloaded from genome.cshlp.org on September 30, 2021 - Published by Cold Spring Harbor Laboratory Press Methods X chromosome cDNA microarray screening identifies a functional PLP2 promoter polymorphism enriched in patients with X-linked mental retardation Lilei Zhang,1 Chunfa Jie,2 Cassandra Obie,1 Fatima Abidi,3 Charles E. Schwartz,3 Roger E. Stevenson,3 David Valle,1 and Tao Wang1,4 1McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore 21205, Maryland; 2Microarray Core Facility, Johns Hopkins University School of Medicine, Baltimore , Maryland; 3Greenwood Genetic Center, Greenwood 29646, South Carolina X-linked Mental Retardation (XLMR) occurs in 1 in 600 males and is highly genetically heterogeneous. We used a novel human X chromosome cDNA microarray (XCA) to survey the expression profile of X-linked genes in lymphoblasts of XLMR males. Genes with altered expression verified by Northern blot and/or quantitative PCR were considered candidates. To validate this approach, we documented the expected changes of expression in samples from a patient with a known X chromosome microdeletion and from patients with multiple copies of the X chromosome. We used our XCA to survey lymphoblast RNA samples from 43 unrelated XLMR males and found 15 genes with significant (Ն1.5-fold) reduction in expression in at least one proband. Of these, subsequent analysis confirmed altered expression in 12. We followed up one, PLP2, at Xp11.23, which exhibits approximately fourfold decreased expression in two patients. Sequencing analysis in both patients revealed a promoter variant, −113C>A, that alters the core-binding site of the transcription factor ELK1.
    [Show full text]
  • Supplementary Tables
    Supplementary Tables Supplementary Table 1. Differentially methylated genes in correlation with their expression pattern in the A4 progression model A. Hypomethylated–upregulated Genes (n= 76) ALOX5 RRAD RTN4R DSCR6 FGFR3 HTR7 WNT3A POGK PLCD3 ALPPL2 RTEL1 SEMA3B DUSP5 FOSB ITGB4 MEST PPL PSMB8 ARHGEF4 BST2 SEMA7A SLC12A7 FOXQ1 KCTD12 LETM2 PRPH PXMP2 ARNTL2 CDH3 SHC2 SLC20A2 HSPA2 KIAA0182 LIMK2 NAB1 RASIP1 ASRGL1 CLDN3 DCBLD1 SNX10 SSH1 KREMEN2 LIPE NDRG2 ATF3 CLU DCHS1 SOD3 ST3GAL4 MAL LRRC1 NR3C2 ATP8B3 CYC1 DGCR8 EBAG9 SYNGR1 TYMS MCM2 NRG2 RHOF DAGLA DISP2 FAM19A5 TNNI3 UNC5B MYB PAK6 RIPK4 DAZAP1 DOCK3 FBXO6 HSPA4L WHSC1 PNMT PCDH1 B. Hypermethylated-downregulated Genes (n= 31) ARHGAP22 TNFSF9 KLF6 LRP8 NRP1 PAPSS2 SLC43A2 TBC1D16 ASB2 DZIP1 TPM1 MDGA1 NRP2 PIK3CD SMARCA2 TLL2 C18orf1 FBN1 LHFPL2 TRIO NTNG2 PTGIS SOCS2 TNFAIP8 DIXDC1 KIFC3 LMO1 NR3C1 ODZ3 PTPRM SYNPO Supplementary Table 2. Genes enriched for different histone methylation marks in A4 progression model identified through ChIP-on-chip a. H3K4me3 (n= 978) AATF C20orf149 CUL3 FOXP1 KATNA1 NEGR1 RAN SPIN2B ABCA7 C20orf52 CWF19L1 FRK KBTBD10 NEIL1 RANBP2 SPPL2A ABCC9 C21orf13-SH3BGR CXCL3 FSIP1 KBTBD6 NELF RAPGEF3 SPRY4 ABCG2 C21orf45 CYC1 FUK KCMF1 NFKB2 RARB SPRYD3 ABHD7 C22orf32 CYorf15A FXR2 KCNH7 NGDN RASAL2 SPTLC2 ACA15 C2orf18 DAXX FZD9 KCNMB4 NKAP RASD1 SRFBP1 ACA26 C2orf29 DAZ3 G6PD KCTD18 NKTR RASEF SRI ACA3 C2orf32 DBF4 GABPB2 KDELR2 NNT RASGRF1 SRM ACA48 C2orf55 DBF4B GABRA5 KIAA0100 NOL5A RASSF1 SSH2 ACAT1 C3orf44 DBI GADD45B KIAA0226 NOLC1 RASSF3 SSH3 ACSL5
    [Show full text]
  • Epigenetic Silencing Mediated Through Activated PI3K/AKT Signaling in Breast Cancer
    SUPPLEMENTARY: Epigenetic Silencing Mediated Through Activated PI3K/AKT Signaling in Breast Cancer Tao Zuo, Ta-Ming Liu, Xun Lan, Yu-I Weng, Rulong Shen, Fei Gu, Yi-Wen Huang, Sandya Liyanarachchi, Daniel E. Deatherage, Pei-Yin Hsu, Cenny Taslim, Bhuvaneswari Ramaswamy, Charles L. Shapiro, Huey-Jen L. Lin, Alfred S. L. Cheng, Victor X. Jin, and Tim H.-M. Huang 1 Table of Content: 1. Fig. S1. Elevated AKT1 kinase activity in myrAKT1- transfected MCF10A cells. ---------------------------------------------------------------------------------------------------- page 4 2. Fig. S2. Genome-wide view of H3K27me3 in mammary epithelial cells. ---------------------------------------------------------------------------------------------------- page 5 3. Fig. S3. Biological functions of PI3K/AKT- downstream genes. ----------------- page 6 4. Fig. S4. Genomic maps of 40 genes. ------------------------------------------------- page 8 5. Fig. S5. Comparing DNA methylation levels of four CpG islands measured by meDIP-qPCR and MassARRAY.------------------------------------------------------- page 11 6. Fig. S6. Validation of DNA methylation by MBDCap-seq.----------------------- page 13 7. Table S1. Summary of H3K27me3 ChIP-seq data ------------------------------ page 14 8. Table S2. Distribution of H3K27me3 in MCF10A genome ---------------------- page 14 9. Table S3. Genes (n=488) enriched with H3K27me3 and downregulated by PI3K/AKT- signaling ---------------------------------------------------------------------- page 15 10. Table S4. Genes (n=1005) enriched
    [Show full text]