Functional Characterization of CNOT3 Variants Identified in Familial Adenomatous Polyposis Adenomas
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Functional Annotation of Exon Skipping Event in Human Pora Kim1,*,†, Mengyuan Yang1,†,Keyiya2, Weiling Zhao1 and Xiaobo Zhou1,3,4,*
D896–D907 Nucleic Acids Research, 2020, Vol. 48, Database issue Published online 23 October 2019 doi: 10.1093/nar/gkz917 ExonSkipDB: functional annotation of exon skipping event in human Pora Kim1,*,†, Mengyuan Yang1,†,KeYiya2, Weiling Zhao1 and Xiaobo Zhou1,3,4,* 1School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA, 2College of Electronics and Information Engineering, Tongji University, Shanghai, China, 3McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA and 4School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA Received August 13, 2019; Revised September 21, 2019; Editorial Decision October 03, 2019; Accepted October 03, 2019 ABSTRACT been used as therapeutic targets (3–8). For example, MET has lost the binding site of E3 ubiquitin ligase CBL through Exon skipping (ES) is reported to be the most com- exon 14 skipping event (9), resulting in an enhanced expres- mon alternative splicing event due to loss of func- sion level of MET. MET amplification drives the prolifera- tional domains/sites or shifting of the open read- tion of tumor cells. Multiple tyrosine kinase inhibitors, such ing frame (ORF), leading to a variety of human dis- as crizotinib, cabozantinib and capmatinib, have been used eases and considered therapeutic targets. To date, to treat patients with MET exon 14 skipping (10). Another systematic and intensive annotations of ES events example is the dystrophin gene (DMD) in Duchenne mus- based on the skipped exon units in cancer and cular dystrophy (DMD), a progressive neuromuscular dis- normal tissues are not available. -
Sarkar Et Al. SUPPLEMENTAL INFORMATION
Sarkar et al. SUPPLEMENTAL INFORMATION Figure S1 A 10 20 30 40 50 MADKRKLQGE IDRCLKKVSE GVEQFEDIWQ KLHNAANANQ KEKYEADLKK 60 70 80 90 100 EIKKLQRLRD QIKTWVASNE IKDKRQLIEN RKLIETQMER FKVVERETKT Predicted 110 120 130 140 150 coiled-coil KAYSKEGLGL AQKVDPAQKE KEEVGQWLTN TIDTLNMQVD QFESEVESLS domain 160 170 180 190 200 (1-241) VQTRKKKGDK DKQDRIEGLK RHIEKHRYHV RMLETILRML DNDSILVDAI 210 220 230 240 250 RKIKDDVEYY VDSSQDPDFE ENEFLYDDLD LEDIPQALVA TSPPSHSHME 260 270 280 290 300 DEIFNQSSST PTSTTSSSPI PPSPANCTTE NSEDDKKRGR ST* DSEVSQSP * 310 320 330 340 350 AKNGSKPVHS NQHPQSPAVP PTYPSGPPPT TSALSSTPGN NGASTPAAPT 360 370 380 390 400 SALGPKASPA PSHNSGTPAP YAQAVAPPNA SGPSNAQPRP PSAQPSGGSG 410 420 430 440 450 Intrinsically GGSGGSSSNS NSGTGGGAGK QNGATSYSSV VADSPAEVTL SSSGGSSASS disordered region 460 470 480 490 500 (242-605) QALGPTSGPH NPAPSTSKES STAAPSGAGN VASGSGNNSG GPSLLVPLPV 510 520 530 540 550 NPPSSPTPSF SEAKAAGTLL NGPPQFSTTP EIKAPEPLSS LKSMAERAAI 560 570 580 590 600 SSGIEDPVPT LHLTDRDIIL SSTSAPPTSS QPPLQLSEVN IPLSLGVCPL 610 620 630 640 650 GPVSLTKEQL YQQAMEEAAW HHMPHPSDSE RIRQYLPRNP CPTPPYHHQM 660 670 680 690 700 NAR/CS PPPHSDTVEF YQRLSTETLF FIFYYLEGTK AQYLAAKALK KQSWRFHTKY 710 720 730 740 750 NOT box (654-751) MMWFQRHEEP KTITDEFEQG TYIYFDYEKW GQRKKEGFTF EYRYLEDRDLQ B 2 Supplementary Figure 1: A. Sequence of the mouse CNOT3 protein (UNIPROT Q8K0V4). Vertical blue lines delineate the domains of the protein that are shown in Figure 1B. The green-shaded box indicates the the NOT box region, which is required for the interaction between CNOT3 and Aurora B (Figure 1C). Aurora B consensus phosphorylation sites are underlined. The red box and text indicate the putative nuclear localization sequence. * indicates residues 292 and 294, which were mutated in this study. B. Phosphorylation sites detected on the CNOT3 protein by in vivo proteomic discovery mass pectrometry. -
Supplemental Information
Supplemental information Dissection of the genomic structure of the miR-183/96/182 gene. Previously, we showed that the miR-183/96/182 cluster is an intergenic miRNA cluster, located in a ~60-kb interval between the genes encoding nuclear respiratory factor-1 (Nrf1) and ubiquitin-conjugating enzyme E2H (Ube2h) on mouse chr6qA3.3 (1). To start to uncover the genomic structure of the miR- 183/96/182 gene, we first studied genomic features around miR-183/96/182 in the UCSC genome browser (http://genome.UCSC.edu/), and identified two CpG islands 3.4-6.5 kb 5’ of pre-miR-183, the most 5’ miRNA of the cluster (Fig. 1A; Fig. S1 and Seq. S1). A cDNA clone, AK044220, located at 3.2-4.6 kb 5’ to pre-miR-183, encompasses the second CpG island (Fig. 1A; Fig. S1). We hypothesized that this cDNA clone was derived from 5’ exon(s) of the primary transcript of the miR-183/96/182 gene, as CpG islands are often associated with promoters (2). Supporting this hypothesis, multiple expressed sequences detected by gene-trap clones, including clone D016D06 (3, 4), were co-localized with the cDNA clone AK044220 (Fig. 1A; Fig. S1). Clone D016D06, deposited by the German GeneTrap Consortium (GGTC) (http://tikus.gsf.de) (3, 4), was derived from insertion of a retroviral construct, rFlpROSAβgeo in 129S2 ES cells (Fig. 1A and C). The rFlpROSAβgeo construct carries a promoterless reporter gene, the β−geo cassette - an in-frame fusion of the β-galactosidase and neomycin resistance (Neor) gene (5), with a splicing acceptor (SA) immediately upstream, and a polyA signal downstream of the β−geo cassette (Fig. -
Sarkar Et Al. SUPPLEMENTAL INFORMATION
Sarkar et al. SUPPLEMENTAL INFORMATION Figure S1 A 10 20 30 40 50 MADKRKLQGE IDRCLKKVSE GVEQFEDIWQ KLHNAANANQ KEKYEADLKK 60 70 80 90 100 EIKKLQRLRD QIKTWVASNE IKDKRQLIEN RKLIETQMER FKVVERETKT Predicted 110 120 130 140 150 coiled-coil KAYSKEGLGL AQKVDPAQKE KEEVGQWLTN TIDTLNMQVD QFESEVESLS domain 160 170 180 190 200 (1-241) VQTRKKKGDK DKQDRIEGLK RHIEKHRYHV RMLETILRML DNDSILVDAI 210 220 230 240 250 RKIKDDVEYY VDSSQDPDFE ENEFLYDDLD LEDIPQALVA TSPPSHSHME 260 270 280 290 300 DEIFNQSSST PTSTTSSSPI PPSPANCTTE NSEDDKKRGR ST* DSEVSQSP * 310 320 330 340 350 AKNGSKPVHS NQHPQSPAVP PTYPSGPPPT TSALSSTPGN NGASTPAAPT 360 370 380 390 400 SALGPKASPA PSHNSGTPAP YAQAVAPPNA SGPSNAQPRP PSAQPSGGSG 410 420 430 440 450 Intrinsically GGSGGSSSNS NSGTGGGAGK QNGATSYSSV VADSPAEVTL SSSGGSSASS disordered region 460 470 480 490 500 (242-605) QALGPTSGPH NPAPSTSKES STAAPSGAGN VASGSGNNSG GPSLLVPLPV 510 520 530 540 550 NPPSSPTPSF SEAKAAGTLL NGPPQFSTTP EIKAPEPLSS LKSMAERAAI 560 570 580 590 600 SSGIEDPVPT LHLTDRDIIL SSTSAPPTSS QPPLQLSEVN IPLSLGVCPL 610 620 630 640 650 GPVSLTKEQL YQQAMEEAAW HHMPHPSDSE RIRQYLPRNP CPTPPYHHQM 660 670 680 690 700 NAR/CS PPPHSDTVEF YQRLSTETLF FIFYYLEGTK AQYLAAKALK KQSWRFHTKY 710 720 730 740 750 NOT box (654-751) MMWFQRHEEP KTITDEFEQG TYIYFDYEKW GQRKKEGFTF EYRYLEDRDLQ B 2 Supplementary Figure 1: A. Sequence of the mouse CNOT3 protein (UNIPROT Q8K0V4). Vertical blue lines delineate the domains of the protein that are shown in Figure 1B. The green-shaded box indicates the the NOT box region, which is required for the interaction between CNOT3 and Aurora B (Figure 1C). Aurora B consensus phosphorylation sites are underlined. The red box and red text indicate the putative nuclear localization sequence. * indicates residues 292 and 294, which were mutated in this study. B. Phosphorylation sites detected on the CNOT3 protein by in vivo proteomic discovery mass pectrometry. -
Susceptibility Loci of CNOT6 in the General Mrna Degradation Pathway and Lung Cancer Risk-A Re-Analysis of Eight Gwass
Providence St. Joseph Health Providence St. Joseph Health Digital Commons Articles, Abstracts, and Reports 4-1-2017 Susceptibility loci of CNOT6 in the general mRNA degradation pathway and lung cancer risk-A re-analysis of eight GWASs. Fei Zhou Yanru Wang Hongliang Liu Neal Ready Younghun Han See next page for additional authors Follow this and additional works at: https://digitalcommons.psjhealth.org/publications Part of the Oncology Commons Authors Fei Zhou, Yanru Wang, Hongliang Liu, Neal Ready, Younghun Han, Rayjean J Hung, Yonathan Brhane, John McLaughlin, Paul Brennan, Heike Bickeböller, Albert Rosenberger, Richard S Houlston, Neil Caporaso, Maria Teresa Landi, Irene Brüske, Angela Risch, Yuanqing Ye, Xifeng Wu, David C Christiani, Gary Goodman, Chu Chen, Christopher I Amos, and Qingyi Wei HHS Public Access Author manuscript Author ManuscriptAuthor Manuscript Author Mol Carcinog Manuscript Author . Author manuscript; Manuscript Author available in PMC 2017 April 01. Published in final edited form as: Mol Carcinog. 2017 April ; 56(4): 1227–1238. doi:10.1002/mc.22585. Susceptibility loci of CNOT6 in the general mRNA degradation pathway and lung cancer risk - a re-analysis of eight GWASs Fei Zhou1,2,3,4,5,*, Yanru Wang1,2,*, Hongliang Liu1,2, Neal Ready1,2, Younghun Han6, Rayjean J. Hung7, Yonathan Brhane7, John McLaughlin8, Paul Brennan9, Heike Bickeböller10, Albert Rosenberger10, Richard S. Houlston11, Neil Caporaso12, Maria Teresa Landi12, Irene Brüske13, Angela Risch14, Yuanqing Ye15, Xifeng Wu15, David C. Christiani16, Gary Goodman17,18, -
The CCR4-NOT Complex Contributes to Repression of Major
www.nature.com/scientificreports OPEN The CCR4-NOT complex contributes to repression of Major Histocompatibility Complex class II Received: 20 January 2017 Accepted: 3 May 2017 transcription Published: xx xx xxxx Alfonso Rodríguez-Gil 1, Olesja Ritter1, Vera V. Saul1, Jochen Wilhelm2, Chen-Yuan Yang3, Rudolf Grosschedl3, Yumiko Imai4, Keiji Kuba4, Michael Kracht5 & M. Lienhard Schmitz1 The multi-subunit CCR4 (carbon catabolite repressor 4)-NOT (Negative on TATA) complex serves as a central coordinator of all different steps of eukaryotic gene expression. Here we performed a systematic and comparative analysis of cells where the CCR4-NOT subunits CNOT1, CNOT2 or CNOT3 were individually downregulated using doxycycline-inducible shRNAs. Microarray experiments showed that downregulation of either CNOT subunit resulted in elevated expression of major histocompatibility complex class II (MHC II) genes which are found in a gene cluster on chromosome 6. Increased expression of MHC II genes after knock-down or knock-out of either CNOT subunit was seen in a variety of cell systems and also in naïve macrophages from CNOT3 conditional knock-out mice. CNOT2-mediated repression of MHC II genes occurred also in the absence of the master regulator class II transactivator (CIITA) and did not cause detectable changes of the chromatin structure at the chromosomal MHC II locus. CNOT2 downregulation resulted in an increased de novo transcription of mRNAs whereas tethering of CNOT2 to a regulatory region governing MHC II expression resulted in diminished transcription. These results expand the known repertoire of CCR4-NOT members for immune regulation and identify CNOT proteins as a novel group of corepressors restricting class II expression. -
NIH Public Access Author Manuscript Cell
NIH Public Access Author Manuscript Cell. Author manuscript; available in PMC 2013 September 14. NIH-PA Author ManuscriptPublished NIH-PA Author Manuscript in final edited NIH-PA Author Manuscript form as: Cell. 2012 September 14; 150(6): 1274–1286. doi:10.1016/j.cell.2012.04.040. Circuitry and dynamics of human transcription factor regulatory networks Shane Neph1,*, Andrew B. Stergachis1,*, Alex Reynolds1, Richard Sandstrom1, Elhanan Borenstein1,2,3,¥, and John A. Stamatoyannopoulos1,4,¥ 1Department of Genome Sciences, University of Washington, Seattle, WA 98195 2Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195 3Santa Fe Institute, Santa Fe, NM 87501 4Department of Medicine, University of Washington, Seattle, WA 98195 SUMMARY The combinatorial cross-regulation of hundreds of sequence-specific transcription factors defines a regulatory network that underlies cellular identity and function. Here we use genome-wide maps of in vivo DNaseI footprints to assemble an extensive core human regulatory network comprising connections among 475 sequence-specific transcription factors, and to analyze the dynamics of these connections across 41 diverse cell and tissue types. We find that human transcription factor networks are highly cell-selective and are driven by cohorts of factors that include regulators with previously unrecognized roles in control of cellular identity. Moreover, we identify many widely expressed factors that impact transcriptional regulatory networks in a cell-selective manner. Strikingly, in spite of their inherent diversity, all cell type regulatory networks independently converge on a common architecture that closely resembles the topology of living neuronal networks. Together, our results provide the first description of the circuitry, dynamics, and organizing principles of the human transcription factor regulatory network. -
A Single Gene Network Accurately Predicts Phenotypic Effects of Gene Perturbation in Caenorhabditis Elegans
ARTICLES A single gene network accurately predicts phenotypic effects of gene perturbation in Caenorhabditis elegans Insuk Lee1,4, Ben Lehner2–4, Catriona Crombie2, Wendy Wong2, Andrew G Fraser2 & Edward M Marcotte1 The fundamental aim of genetics is to understand how an organism’s phenotype is determined by its genotype, and implicit in this is predicting how changes in DNA sequence alter phenotypes. A single network covering all the genes of an organism might guide such predictions down to the level of individual cells and tissues. To validate this approach, we computationally generated a network covering most C. elegans genes and tested its predictive capacity. Connectivity within this network predicts essentiality, identifying this relationship as an evolutionarily conserved biological principle. Critically, the network makes tissue-specific predictions—we accurately identify genes for most systematically assayed loss-of-function phenotypes, which span diverse http://www.nature.com/naturegenetics cellular and developmental processes. Using the network, we identify 16 genes whose inactivation suppresses defects in the retinoblastoma tumor suppressor pathway, and we successfully predict that the dystrophin complex modulates EGF signaling. We conclude that an analogous network for human genes might be similarly predictive and thus facilitate identification of disease genes and rational therapeutic targets. The central goal of genetics is to understand how heritable informa- not explicitly reflect multiple cell types, tissues or stages of -
Transcriptional Regulator CNOT3 Defines an Aggressive Colorectal Cancer Subtype
Published OnlineFirst November 29, 2016; DOI: 10.1158/0008-5472.CAN-16-1346 Cancer Tumor and Stem Cell Biology Research Transcriptional Regulator CNOT3 Defines an Aggressive Colorectal Cancer Subtype Paloma Cejas1,2,3, Alessia Cavazza1,2, C.N. Yandava1, Victor Moreno3, David Horst4, Juan Moreno-Rubio3, Emilio Burgos5, Marta Mendiola3,5, Len Taing1, Ajay Goel6, Jaime Feliu3, and Ramesh A. Shivdasani1,2 Abstract Cancer cells exhibit dramatic alterations of chromatin organi- primarily to genes whose expression was affected by CNOT3 loss, zation at cis-regulatory elements, but the molecular basis, extent, and also at sites modulated in certain types of colorectal cancers. and impact of these alterations are still being unraveled. Here, we These target genes were implicated in ESC and cancer self-renewal identify extensive genome-wide modification of sites bearing the and fell into two distinct groups: those dependent on CNOT3 and active histone mark H3K4me2 in primary human colorectal MYC for optimal transcription and those repressed by CNOT3 cancers, as compared with corresponding benign precursor ade- binding and promoter hypermethylation. Silencing CNOT3 in nomas. Modification of certain colorectal cancer sites highlighted colorectal cancer cells resulted in replication arrest. In clinical þ the activity of the transcription factor CNOT3, which is known to specimens, early-stage tumors that included >5% CNOT3 cells control self-renewal of embryonic stem cells (ESC). In primary exhibited a correlation to worse clinical outcomes compared with colorectal cancer cells, we observed a scattered pattern of CNOT3 tumors with little to no CNOT3 expression. Together, our findings expression, as might be expected for a tumor-initiating cell mark- implicate CNOT3 in the coordination of colonic epithelial cell er. -
Hepatic Posttranscriptional Network Comprised of CCR4–NOT Deadenylase and FGF21 Maintains Systemic Metabolic Homeostasis
Hepatic posttranscriptional network comprised of CCR4–NOT deadenylase and FGF21 maintains systemic metabolic homeostasis Masahiro Moritaa,b,c,1,2, Nadeem Siddiquid,e,1, Sakie Katsumuraa,b, Christopher Rouyad,e, Ola Larssonf, Takeshi Nagashimag, Bahareh Hekmatnejadh,i, Akinori Takahashij, Hiroshi Kiyonarik, Mengwei Zanga,b, René St-Arnaudh,i, Yuichi Oikel, Vincent Giguèred,e,m, Ivan Topisirovicd,m,n, Mariko Okada-Hatakeyamag,o, Tadashi Yamamotoj,2, and Nahum Sonenbergd,e,2 aDepartment of Molecular Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229; bBarshop Institute for Longevity and Aging Studies, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229; cInstitute of Resource Development and Analysis, Kumamoto University, 860-0811 Kumamoto, Japan; dDepartment of Biochemistry, McGill University, Montreal, QC H3A 1A3, Canada; eGoodman Cancer Research Centre, McGill University, Montreal, QC H3A 1A3, Canada; fDepartment of Oncology-Pathology, Scilifelab, Karolinska Institutet, SE-171 76 Stockholm, Sweden; gLaboratory for Integrated Cellular Systems, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045 Kanagawa, Japan; hResearch Centre, Shriners Hospital for Children–Canada, Montreal, QC H4A 0A9, Canada; iDepartment of Human Genetics, McGill University, Montreal, QC H3A 2T5, Canada; jCell Signal Unit, Okinawa Institute of Science and Technology Graduate University, Onna-son, 904-0495 Okinawa, Japan; kLaboratories for Animal Resource Development and Genetic Engineering, RIKEN Center for Biosystems Dynamics Research, Kobe, 650-0047 Hyogo, Japan; lDepartment of Molecular Genetics, Graduate School of Medical Sciences, Kumamoto University, 860-8556 Kumamoto, Japan; mGerald Bronfman Department of Oncology, McGill University, Montreal, QC H2W 1S6, Canada; nLady Davis Institute for Medical Research, Sir Mortimer B. -
High-Resolution Genome-Wide DNA Methylation Maps of Mouse Primary
Chatterjee et al. Epigenetics & Chromatin 2014, 7:35 http://www.epigeneticsandchromatin.com/content/7/1/35 RESEARCH Open Access High-resolution genome-wide DNA methylation maps of mouse primary female dermal fibroblasts and keratinocytes Raghunath Chatterjee1,2†, Ximiao He1†, Di Huang3, Peter FitzGerald4, Andrew Smith5 and Charles Vinson1* Abstract Background: Genome-wide DNA methylation at a single nucleotide resolution in different primary cells of the mammalian genome helps to determine the characteristics and functions of tissue-specific hypomethylated regions (TS-HMRs). We determined genome-wide cytosine methylation maps at 91X and 36X coverage of newborn female mouse primary dermal fibroblasts and keratinocytes and compared with mRNA-seq gene expression data. Results: These high coverage methylation maps were used to identify HMRs in both cell types. A total of 2.91% of the genome are in keratinocyte HMRs, and 2.15% of the genome are in fibroblast HMRs with 1.75% being common. Half of the TS-HMRs are extensions of common HMRs, and the remaining are unique TS-HMRs. Four levels of CG methylation are observed: 1) total unmethylation for CG dinucleotides in HMRs in CGIs that are active in all tissues; 2) 10% to 40% methylation for TS-HMRs; 3) 60% methylation for TS-HMRs in cells types where they are not in HMRs; and 4) 70% methylation for the nonfunctioning part of the genome. SINE elements are depleted inside the TS-HMRs, while highly enriched in the surrounding regions. Hypomethylation at the last exon shows gene repression, while demethylation toward the gene body positively correlates with gene expression. -
39UTR Shortening Identifies High-Risk Cancers with Targeted Dysregulation
OPEN 39UTR shortening identifies high-risk SUBJECT AREAS: cancers with targeted dysregulation of GENE REGULATORY NETWORKS the ceRNA network REGULATORY NETWORKS Li Li1*, Duolin Wang2*, Mengzhu Xue1*, Xianqiang Mi1, Yanchun Liang2 & Peng Wang1,3 Received 1 2 15 April 2014 Key Laboratory of Systems Biology, Shanghai Advanced Research Institute, Chinese Academy of Sciences, College of Computer Science and Technology, Jilin University, 3School of Life Science and Technology, ShanghaiTech University. Accepted 3 June 2014 Competing endogenous RNA (ceRNA) interactions form a multilayered network that regulates gene Published expression in various biological pathways. Recent studies have demonstrated novel roles of ceRNA 23 June 2014 interactions in tumorigenesis, but the dynamics of the ceRNA network in cancer remain unexplored. Here, we examine ceRNA network dynamics in prostate cancer from the perspective of alternative cleavage and polyadenylation (APA) and reveal the principles of such changes. Analysis of exon array data revealed that both shortened and lengthened 39UTRs are abundant. Consensus clustering with APA data stratified Correspondence and cancers into groups with differing risks of biochemical relapse and revealed that a ceRNA subnetwork requests for materials enriched with cancer genes was specifically dysregulated in high-risk cancers. The novel connection between should be addressed to 39UTR shortening and ceRNA network dysregulation was supported by the unusually high number of P.W. (wangpeng@ microRNA response elements (MREs) shared by the dysregulated ceRNA interactions and the significantly sari.ac.cn) altered 39UTRs. The dysregulation followed a fundamental principle in that ceRNA interactions connecting genes that show opposite trends in expression change are preferentially dysregulated. This targeted dysregulation is responsible for the majority of the observed expression changes in genes with significant * These authors ceRNA dysregulation and represents a novel mechanism underlying aberrant oncogenic expression.