RNF219 Regulates CCR4-NOT Function in Mrna Translation and Deadenylation
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Analysis of Trans Esnps Infers Regulatory Network Architecture
Analysis of trans eSNPs infers regulatory network architecture Anat Kreimer Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2014 © 2014 Anat Kreimer All rights reserved ABSTRACT Analysis of trans eSNPs infers regulatory network architecture Anat Kreimer eSNPs are genetic variants associated with transcript expression levels. The characteristics of such variants highlight their importance and present a unique opportunity for studying gene regulation. eSNPs affect most genes and their cell type specificity can shed light on different processes that are activated in each cell. They can identify functional variants by connecting SNPs that are implicated in disease to a molecular mechanism. Examining eSNPs that are associated with distal genes can provide insights regarding the inference of regulatory networks but also presents challenges due to the high statistical burden of multiple testing. Such association studies allow: simultaneous investigation of many gene expression phenotypes without assuming any prior knowledge and identification of unknown regulators of gene expression while uncovering directionality. This thesis will focus on such distal eSNPs to map regulatory interactions between different loci and expose the architecture of the regulatory network defined by such interactions. We develop novel computational approaches and apply them to genetics-genomics data in human. We go beyond pairwise interactions to define network motifs, including regulatory modules and bi-fan structures, showing them to be prevalent in real data and exposing distinct attributes of such arrangements. We project eSNP associations onto a protein-protein interaction network to expose topological properties of eSNPs and their targets and highlight different modes of distal regulation. -
Mayr, Annu Rev Genet 2017
GE51CH09-Mayr ARI 12 October 2017 10:21 Annual Review of Genetics Regulation by 3-Untranslated Regions Christine Mayr Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; email: [email protected] Annu. Rev. Genet. 2017. 51:171–94 Keywords First published as a Review in Advance on August noncoding RNA, regulatory RNA, alternative 3-UTRs, alternative 30, 2017 polyadenylation, cotranslational protein complex formation, cellular The Annual Review of Genetics is online at organization, mRNA localization, RNA-binding proteins, cooperativity, genet.annualreviews.org accessibility of regulatory elements https://doi.org/10.1146/annurev-genet-120116- 024704 Abstract Copyright c 2017 by Annual Reviews. 3 -untranslated regions (3 -UTRs) are the noncoding parts of mRNAs. Com- All rights reserved pared to yeast, in humans, median 3-UTR length has expanded approx- imately tenfold alongside an increased generation of alternative 3-UTR isoforms. In contrast, the number of coding genes, as well as coding region length, has remained similar. This suggests an important role for 3-UTRs in the biology of higher organisms. 3-UTRs are best known to regulate ANNUAL REVIEWS Further diverse fates of mRNAs, including degradation, translation, and localiza- Click here to view this article's tion, but they can also function like long noncoding or small RNAs, as has Annu. Rev. Genet. 2017.51:171-194. Downloaded from www.annualreviews.org online features: • Download figures as PPT slides been shown for whole 3 -UTRs as well as for cleaved fragments. Further- • Navigate linked references • Download citations more, 3 -UTRs determine the fate of proteins through the regulation of Access provided by Memorial Sloan-Kettering Cancer Center on 11/30/17. -
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
Evidence for Differential Alternative Splicing in Blood of Young Boys With
Stamova et al. Molecular Autism 2013, 4:30 http://www.molecularautism.com/content/4/1/30 RESEARCH Open Access Evidence for differential alternative splicing in blood of young boys with autism spectrum disorders Boryana S Stamova1,2,5*, Yingfang Tian1,2,4, Christine W Nordahl1,3, Mark D Shen1,3, Sally Rogers1,3, David G Amaral1,3 and Frank R Sharp1,2 Abstract Background: Since RNA expression differences have been reported in autism spectrum disorder (ASD) for blood and brain, and differential alternative splicing (DAS) has been reported in ASD brains, we determined if there was DAS in blood mRNA of ASD subjects compared to typically developing (TD) controls, as well as in ASD subgroups related to cerebral volume. Methods: RNA from blood was processed on whole genome exon arrays for 2-4–year-old ASD and TD boys. An ANCOVA with age and batch as covariates was used to predict DAS for ALL ASD (n=30), ASD with normal total cerebral volumes (NTCV), and ASD with large total cerebral volumes (LTCV) compared to TD controls (n=20). Results: A total of 53 genes were predicted to have DAS for ALL ASD versus TD, 169 genes for ASD_NTCV versus TD, 1 gene for ASD_LTCV versus TD, and 27 genes for ASD_LTCV versus ASD_NTCV. These differences were significant at P <0.05 after false discovery rate corrections for multiple comparisons (FDR <5% false positives). A number of the genes predicted to have DAS in ASD are known to regulate DAS (SFPQ, SRPK1, SRSF11, SRSF2IP, FUS, LSM14A). In addition, a number of genes with predicted DAS are involved in pathways implicated in previous ASD studies, such as ROS monocyte/macrophage, Natural Killer Cell, mTOR, and NGF signaling. -
Towards a Molecular Understanding of Microrna-Mediated Gene Silencing
REVIEWS NON-CODING RNA Towards a molecular understanding of microRNA-mediated gene silencing Stefanie Jonas and Elisa Izaurralde Abstract | MicroRNAs (miRNAs) are a conserved class of small non-coding RNAs that assemble with Argonaute proteins into miRNA-induced silencing complexes (miRISCs) to direct post-transcriptional silencing of complementary mRNA targets. Silencing is accomplished through a combination of translational repression and mRNA destabilization, with the latter contributing to most of the steady-state repression in animal cell cultures. Degradation of the mRNA target is initiated by deadenylation, which is followed by decapping and 5ʹ‑to‑3ʹ exonucleolytic decay. Recent work has enhanced our understanding of the mechanisms of silencing, making it possible to describe in molecular terms a continuum of direct interactions from miRNA target recognition to mRNA deadenylation, decapping and 5ʹ‑to‑3ʹ degradation. Furthermore, an intricate interplay between translational repression and mRNA degradation is emerging. Deadenylation MicroRNAs (miRNAs) are conserved post-transcriptional recruit additional protein partners to mediate silenc- 5,6 Shortening of mRNA poly(A) regulators of gene expression that are integral to ing . Silencing occurs through a combination of tails. In eukaryotes, this almost all known biological processes, including translational repression, deadenylation, decapping and process is catalysed by the cell growth, proliferation and differentiation, as well 5ʹ‑to‑3ʹ mRNA degradation5,6 (FIG. 1). The GW182 pro- consecutive but partially as organismal metabolism and development1. The teins play a central part in this process and are among redundant action of two 5,6 cytoplasmic deadenylase number of miRNAs encoded within the genomes of the most extensively studied AGO partners . -
1 Canonical BAF Complex in Regulatory T Cells 2 3 Chin
bioRxiv preprint doi: https://doi.org/10.1101/2020.02.26.964981; this version posted February 27, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1 A genome-wide CRISPR screen reveals a role for the BRD9-containing non- 2 canonical BAF complex in regulatory T cells 3 4 Chin-San Loo1,3,#, Jovylyn Gatchalian2,#, Yuqiong Liang1, Mathias Leblanc1, Mingjun 5 Xie1, Josephine Ho2, Bhargav Venkatraghavan1, Diana C. Hargreaves2*, and Ye 6 Zheng1* 7 8 1. NOMIS Center for Immunobiology and Microbial Pathogenesis, Salk Institute for 9 Biological Studies 10 2. Molecular and Cellular Biology Laboratory, Salk Institute for Biological Studies 11 3. Division of Biological Sciences, University of California, San Diego 12 # Co-first authors 13 * Co-corresponding authors 14 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.02.26.964981; this version posted February 27, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 15 Summary 16 Regulatory T cells (Tregs) play a pivotal role in suppressing auto-reactive T cells 17 and maintaining immune homeostasis. Treg development and function are 18 dependent on the transcription factor Foxp3. Here we performed a genome-wide 19 CRISPR/Cas9 knockout screen to identify the regulators of Foxp3 in mouse 20 primary Tregs. The results showed that Foxp3 regulators are highly enriched in 21 genes encoding SWI/SNF and SAGA complex subunits. Among the three 22 SWI/SNF-related complexes, the non-canonical or ncBAF (also called GBAF or 23 BRD9-containing BAF) complex promoted the expression of Foxp3, whereas the 24 PBAF complex repressed its expression. -
Plenary and Platform Abstracts
American Society of Human Genetics 68th Annual Meeting PLENARY AND PLATFORM ABSTRACTS Abstract #'s Tuesday, October 16, 5:30-6:50 pm: 4. Featured Plenary Abstract Session I Hall C #1-#4 Wednesday, October 17, 9:00-10:00 am, Concurrent Platform Session A: 6. Variant Insights from Large Population Datasets Ballroom 20A #5-#8 7. GWAS in Combined Cancer Phenotypes Ballroom 20BC #9-#12 8. Genome-wide Epigenomics and Non-coding Variants Ballroom 20D #13-#16 9. Clonal Mosaicism in Cancer, Alzheimer's Disease, and Healthy Room 6A #17-#20 Tissue 10. Genetics of Behavioral Traits and Diseases Room 6B #21-#24 11. New Frontiers in Computational Genomics Room 6C #25-#28 12. Bone and Muscle: Identifying Causal Genes Room 6D #29-#32 13. Precision Medicine Initiatives: Outcomes and Lessons Learned Room 6E #33-#36 14. Environmental Exposures in Human Traits Room 6F #37-#40 Wednesday, October 17, 4:15-5:45 pm, Concurrent Platform Session B: 24. Variant Interpretation Practices and Resources Ballroom 20A #41-#46 25. Integrated Variant Analysis in Cancer Genomics Ballroom 20BC #47-#52 26. Gene Discovery and Functional Models of Neurological Disorders Ballroom 20D #53-#58 27. Whole Exome and Whole Genome Associations Room 6A #59-#64 28. Sequencing-based Diagnostics for Newborns and Infants Room 6B #65-#70 29. Omics Studies in Alzheimer's Disease Room 6C #71-#76 30. Cardiac, Valvular, and Vascular Disorders Room 6D #77-#82 31. Natural Selection and Human Phenotypes Room 6E #83-#88 32. Genetics of Cardiometabolic Traits Room 6F #89-#94 Wednesday, October 17, 6:00-7:00 pm, Concurrent Platform Session C: 33. -
The Complex SNP and CNV Genetic Architecture of the Increased Risk of Congenital Heart Defects in Down Syndrome
Downloaded from genome.cshlp.org on September 24, 2021 - Published by Cold Spring Harbor Laboratory Press Research The complex SNP and CNV genetic architecture of the increased risk of congenital heart defects in Down syndrome M. Reza Sailani,1,2 Periklis Makrythanasis,1 Armand Valsesia,3,4,5 Federico A. Santoni,1 Samuel Deutsch,1 Konstantin Popadin,1 Christelle Borel,1 Eugenia Migliavacca,1 Andrew J. Sharp,1,20 Genevieve Duriaux Sail,1 Emilie Falconnet,1 Kelly Rabionet,6,7,8 Clara Serra-Juhe´,7,9 Stefano Vicari,10 Daniela Laux,11 Yann Grattau,12 Guy Dembour,13 Andre Megarbane,12,14 Renaud Touraine,15 Samantha Stora,12 Sofia Kitsiou,16 Helena Fryssira,16 Chariklia Chatzisevastou-Loukidou,16 Emmanouel Kanavakis,16 Giuseppe Merla,17 Damien Bonnet,11 Luis A. Pe´rez-Jurado,7,9 Xavier Estivill,6,7,8 Jean M. Delabar,18 and Stylianos E. Antonarakis1,2,19,21 1–19[Author affiliations appear at the end of the paper.] Congenital heart defect (CHD) occurs in 40% of Down syndrome (DS) cases. While carrying three copies of chromosome 21 increases the risk for CHD, trisomy 21 itself is not sufficient to cause CHD. Thus, additional genetic variation and/or environmental factors could contribute to the CHD risk. Here we report genomic variations that in concert with trisomy 21, determine the risk for CHD in DS. This case-control GWAS includes 187 DS with CHD (AVSD = 69, ASD = 53, VSD = 65) as cases, and 151 DS without CHD as controls. Chromosome 21–specific association studies revealed rs2832616 and rs1943950 as CHD risk alleles (adjusted genotypic P-values <0.05). -
CNOT2 Antibody A
C 0 2 - t CNOT2 Antibody a e r o t S Orders: 877-616-CELL (2355) [email protected] Support: 877-678-TECH (8324) 5 5 Web: [email protected] 9 www.cellsignal.com 6 # 3 Trask Lane Danvers Massachusetts 01923 USA For Research Use Only. Not For Use In Diagnostic Procedures. Applications: Reactivity: Sensitivity: MW (kDa): Source: UniProt ID: Entrez-Gene Id: WB, IP H M R Mk Endogenous 62 Rabbit Q9NZN8 4848 Product Usage Information Application Dilution Western Blotting 1:1000 Immunoprecipitation 1:50 Storage Supplied in 10 mM sodium HEPES (pH 7.5), 150 mM NaCl, 100 µg/ml BSA and 50% glycerol. Store at –20°C. Do not aliquot the antibody. Specificity / Sensitivity CNOT2 Antibody recognizes endogenous levels of total CNOT2 protein. Species Reactivity: Human, Mouse, Rat, Monkey Source / Purification Polyclonal antibodies are produced by immunizing animals with a synthetic peptide corresponding to residues near the carboxy terminus of human CNOT2 protein. Antibodies are purified by protein A and peptide affinity chromatography. Background The evolutionarily conserved CCR4-NOT (CNOT) complex regulates mRNA metabolism in eukaryotic cells (1). This regulation occurs at different levels of mRNA synthesis and degradation, including transcription initiation, elongation, deadenylation, and degradation (1). Multiple components, including CNOT1, CNOT2, CNOT3, CNOT4, CNOT6, CNOT6L, CNOT7, CNOT8, CNOT9, and CNOT10 have been identified in this complex (2). In addition, subunit composition of this complex has been shown to vary among different tissues (3). 1. Denis, C.L. and Chen, J. (2003) Prog Nucleic Acid Res Mol Biol 73, 221-50. 2. Lau, N.C. et al. -
A Study on Acute Myeloid Leukemias with Trisomy 8, 11, Or 13, Monosomy 7, Or Deletion 5Q
Leukemia (2005) 19, 1224–1228 & 2005 Nature Publishing Group All rights reserved 0887-6924/05 $30.00 www.nature.com/leu Genomic gains and losses influence expression levels of genes located within the affected regions: a study on acute myeloid leukemias with trisomy 8, 11, or 13, monosomy 7, or deletion 5q C Schoch1, A Kohlmann1, M Dugas1, W Kern1, W Hiddemann1, S Schnittger1 and T Haferlach1 1Laboratory for Leukemia Diagnostics, Department of Internal Medicine III, University Hospital Grosshadern, Ludwig-Maximilians-University, Munich, Germany We performed microarray analyses in AML with trisomies 8 aim of this study to investigate whether gains and losses on the (n ¼ 12), 11 (n ¼ 7), 13 (n ¼ 7), monosomy 7 (n ¼ 9), and deletion genomic level translate into altered genes expression also in 5q (n ¼ 7) as sole changes to investigate whether genomic gains and losses translate into altered expression levels of other areas of the genome in AML. genes located in the affected chromosomal regions. Controls were 104 AML with normal karyotype. In subgroups with trisomy, the median expression of genes located on gained Materials and methods chromosomes was higher, while in AML with monosomy 7 and deletion 5q the median expression of genes located in deleted Samples regions was lower. The 50 most differentially expressed genes, as compared to all other subtypes, were equally distributed Bone marrow samples of AML patients at diagnosis were over the genome in AML subgroups with trisomies. In contrast, 30 and 86% of the most differentially expressed genes analyzed: 12 cases with trisomy 8 (AML-TRI8), seven with characteristic for AML with 5q deletion and monosomy 7 are trisomy 11 (AML-TRI11), seven with trisomy 13 (AML-TRI13), located on chromosomes 5 or 7. -
CNOT4 Antibody (Pab)
21.10.2014CNOT4 antibody (pAb) Rabbit Anti-Human/Mouse/Rat CCR4-NOT Transcription Complex Subunit 4 (NOT4, NOT4H) Instruction Manual Catalog Number PK-AB718-4813 Synonyms CNOT4 Antibody: CCR4-NOT transcription complex subunit 4, NOT4, NOT4H Description CNOT4 is a component of the CCR4-NOT transcription complex, a complex that is implicated in the repression of RNA polymerase II transcription. In the CCR4-NOT complex, CNOT4 acts as an E3 ubiquitin-protein ligase and interacts with a subset of E2 ubiquitin-conjugating enzymes through a unique C4C4 RING domain. This E3 ligase activity was shown to be dependent on the selective and specific interaction with the ubiquitin conjugating enzyme UbcH5B. In yeast, mutations in CNOT4 that prevented its interaction with the UbcH5B homolog UBC4 caused increased sensitivity to hydroxyurea, heat shock, and hygromycin B, suggesting that CNOT4 and UbcH5B are involved in stress response in vivo. Multiple isoforms of CNOT4 are known to exist. Quantity 100 µg Source / Host Rabbit Immunogen CNOT4 antibody was raised against a 19 amino acid peptide near the amino terminus of the human CNOT4. Purification Method Affinity chromatography purified via peptide column. Clone / IgG Subtype Polyclonal antibody Species Reactivity Human, Mouse, Rat Specificity Formulation Antibody is supplied in PBS containing 0.02% sodium azide. Reconstitution During shipment, small volumes of antibody will occasionally become entrapped in the seal of the product vial. For products with volumes of 200 μl or less, we recommend gently tapping the vial on a hard surface or briefly centrifuging the vial in a tabletop centrifuge to dislodge any liquid in the container’s cap. -
Whole Exome Sequencing in Families at High Risk for Hodgkin Lymphoma: Identification of a Predisposing Mutation in the KDR Gene
Hodgkin Lymphoma SUPPLEMENTARY APPENDIX Whole exome sequencing in families at high risk for Hodgkin lymphoma: identification of a predisposing mutation in the KDR gene Melissa Rotunno, 1 Mary L. McMaster, 1 Joseph Boland, 2 Sara Bass, 2 Xijun Zhang, 2 Laurie Burdett, 2 Belynda Hicks, 2 Sarangan Ravichandran, 3 Brian T. Luke, 3 Meredith Yeager, 2 Laura Fontaine, 4 Paula L. Hyland, 1 Alisa M. Goldstein, 1 NCI DCEG Cancer Sequencing Working Group, NCI DCEG Cancer Genomics Research Laboratory, Stephen J. Chanock, 5 Neil E. Caporaso, 1 Margaret A. Tucker, 6 and Lynn R. Goldin 1 1Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 2Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 3Ad - vanced Biomedical Computing Center, Leidos Biomedical Research Inc.; Frederick National Laboratory for Cancer Research, Frederick, MD; 4Westat, Inc., Rockville MD; 5Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; and 6Human Genetics Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA ©2016 Ferrata Storti Foundation. This is an open-access paper. doi:10.3324/haematol.2015.135475 Received: August 19, 2015. Accepted: January 7, 2016. Pre-published: June 13, 2016. Correspondence: [email protected] Supplemental Author Information: NCI DCEG Cancer Sequencing Working Group: Mark H. Greene, Allan Hildesheim, Nan Hu, Maria Theresa Landi, Jennifer Loud, Phuong Mai, Lisa Mirabello, Lindsay Morton, Dilys Parry, Anand Pathak, Douglas R. Stewart, Philip R. Taylor, Geoffrey S. Tobias, Xiaohong R. Yang, Guoqin Yu NCI DCEG Cancer Genomics Research Laboratory: Salma Chowdhury, Michael Cullen, Casey Dagnall, Herbert Higson, Amy A.