Molecular Basis for the Interaction Between Integrator Subunits Ints9 and Ints11 and Its Functional Importance
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A Genetic Screen Identifies the Triple T Complex Required for DNA Damage Signaling and ATM and ATR Stability
Downloaded from genesdev.cshlp.org on September 27, 2021 - Published by Cold Spring Harbor Laboratory Press A genetic screen identifies the Triple T complex required for DNA damage signaling and ATM and ATR stability Kristen E. Hurov, Cecilia Cotta-Ramusino, and Stephen J. Elledge1 Howard Hughes Medical Institute and Department of Genetics, Harvard Medical School, Division of Genetics, Brigham and Women’s Hospital, Boston, Massachusetts 02115, USA In response to DNA damage, cells activate a complex signal transduction network called the DNA damage response (DDR). To enhance our current understanding of the DDR network, we performed a genome-wide RNAi screen to identify genes required for resistance to ionizing radiation (IR). Along with a number of known DDR genes, we discovered a large set of novel genes whose depletion leads to cellular sensitivity to IR. Here we describe TTI1 (Tel two-interacting protein 1) and TTI2 as highly conserved regulators of the DDR in mammals. TTI1 and TTI2 protect cells from spontaneous DNA damage, and are required for the establishment of the intra-S and G2/M checkpoints. TTI1 and TTI2 exist in multiple complexes, including a 2-MDa complex with TEL2 (telomere maintenance 2), called the Triple T complex, and phosphoinositide-3-kinase-related protein kinases (PIKKs) such as ataxia telangiectasia-mutated (ATM). The components of the TTT complex are mutually dependent on each other, and act as critical regulators of PIKK abundance and checkpoint signaling. [Keywords: TTI1; TEL2; TTI2; PIKK; TTT complex; IR sensitivity] Supplemental material is available at http://www.genesdev.org. Received April 5, 2010; revised version accepted July 22, 2010. -
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. -
Variation in Protein Coding Genes Identifies Information
bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Animal complexity and information flow 1 1 2 3 4 5 Variation in protein coding genes identifies information flow as a contributor to 6 animal complexity 7 8 Jack Dean, Daniela Lopes Cardoso and Colin Sharpe* 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Institute of Biological and Biomedical Sciences 25 School of Biological Science 26 University of Portsmouth, 27 Portsmouth, UK 28 PO16 7YH 29 30 * Author for correspondence 31 [email protected] 32 33 Orcid numbers: 34 DLC: 0000-0003-2683-1745 35 CS: 0000-0002-5022-0840 36 37 38 39 40 41 42 43 44 45 46 47 48 49 Abstract bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Animal complexity and information flow 2 1 Across the metazoans there is a trend towards greater organismal complexity. How 2 complexity is generated, however, is uncertain. Since C.elegans and humans have 3 approximately the same number of genes, the explanation will depend on how genes are 4 used, rather than their absolute number. -
Molecular Basis of the Function of Transcriptional Enhancers
cells Review Molecular Basis of the Function of Transcriptional Enhancers 1,2, 1, 1,3, Airat N. Ibragimov y, Oleg V. Bylino y and Yulii V. Shidlovskii * 1 Laboratory of Gene Expression Regulation in Development, Institute of Gene Biology, Russian Academy of Sciences, 34/5 Vavilov St., 119334 Moscow, Russia; [email protected] (A.N.I.); [email protected] (O.V.B.) 2 Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Institute of Gene Biology, Russian Academy of Sciences, 34/5 Vavilov St., 119334 Moscow, Russia 3 I.M. Sechenov First Moscow State Medical University, 8, bldg. 2 Trubetskaya St., 119048 Moscow, Russia * Correspondence: [email protected]; Tel.: +7-4991354096 These authors contributed equally to this study. y Received: 30 May 2020; Accepted: 3 July 2020; Published: 5 July 2020 Abstract: Transcriptional enhancers are major genomic elements that control gene activity in eukaryotes. Recent studies provided deeper insight into the temporal and spatial organization of transcription in the nucleus, the role of non-coding RNAs in the process, and the epigenetic control of gene expression. Thus, multiple molecular details of enhancer functioning were revealed. Here, we describe the recent data and models of molecular organization of enhancer-driven transcription. Keywords: enhancer; promoter; chromatin; transcriptional bursting; transcription factories; enhancer RNA; epigenetic marks 1. Introduction Gene transcription is precisely organized in time and space. The process requires the participation of hundreds of molecules, which form an extensive interaction network. Substantial progress was achieved recently in our understanding of the molecular processes that take place in the cell nucleus (e.g., see [1–9]). -
PDF Output of CLIC (Clustering by Inferred Co-Expression)
PDF Output of CLIC (clustering by inferred co-expression) Dataset: Num of genes in input gene set: 14 Total number of genes: 16493 CLIC PDF output has three sections: 1) Overview of Co-Expression Modules (CEMs) Heatmap shows pairwise correlations between all genes in the input query gene set. Red lines shows the partition of input genes into CEMs, ordered by CEM strength. Each row shows one gene, and the brightness of squares indicates its correlations with other genes. Gene symbols are shown at left side and on the top of the heatmap. 2) Details of each CEM and its expansion CEM+ Top panel shows the posterior selection probability (dataset weights) for top GEO series datasets. Bottom panel shows the CEM genes (blue rows) as well as expanded CEM+ genes (green rows). Each column is one GEO series dataset, sorted by their posterior probability of being selected. The brightness of squares indicates the gene's correlations with CEM genes in the corresponding dataset. CEM+ includes genes that co-express with CEM genes in high-weight datasets, measured by LLR score. 3) Details of each GEO series dataset and its expression profile: Top panel shows the detailed information (e.g. title, summary) for the GEO series dataset. Bottom panel shows the background distribution and the expression profile for CEM genes in this dataset. Overview of Co-Expression Modules (CEMs) with Dataset Weighting Scale of average Pearson correlations Num of Genes in Query Geneset: 14. Num of CEMs: 1. 0.0 0.2 0.4 0.6 0.8 1.0 Cpsf3l Polr2b Ints3 Ints7 Ints1 Ints4 Ints9 Ints2 -
Enhancer Rnas Are an Important Regulatory Layer of the Epigenome
REVIEW ARTICLE https://doi.org/10.1038/s41594-020-0446-0 Enhancer RNAs are an important regulatory layer of the epigenome Vittorio Sartorelli 1 and Shannon M. Lauberth 2 ✉ Noncoding RNAs (ncRNAs) direct a remarkable number of diverse functions in development and disease through their regula- tion of transcription, RNA processing and translation. Leading the charge in the RNA revolution is a class of ncRNAs that are synthesized at active enhancers, called enhancer RNAs (eRNAs). Here, we review recent insights into the biogenesis of eRNAs and the mechanisms underlying their multifaceted functions and consider how these findings could inform future investigations into enhancer transcription and eRNA function. he explosion of high-throughput sequencing data has Many different models for how enhancers function in gene con- revealed the complexity and diversity of the transcriptome. trol have been proposed since their initial discovery nearly four TThese data have also unexpectedly revealed that only 1–2% decades ago19–21. Specifically, there is considerable evidence demon- of the transcriptome provides instructions for the synthesis of strating that looping of distal enhancers to their target promoters is functional proteins, while the remaining 98–99% gives rise to a required for enhancer function (reviewed in ref. 22). For example, plethora of ncRNAs, including transfer RNAs (tRNAs), ribosomal a key study revealed that experimental induction of chromatin RNAs (rRNAs), intronic RNAs, small nuclear (sn)RNAs, small looping between the mouse β-globin (Hbb) promoter and its asso- nucleolar (sno)RNAs, microRNAs (miRNAs) and long noncoding ciated enhancer region results in transcriptional activation of the RNAs (lncRNAs). A recent addition to the expanding list of regu- Hbb gene23. -
Regulates Target Gene Transcription Via Single-Stranded DNA Response Elements
TLS/FUS (translocated in liposarcoma/fused in sarcoma) regulates target gene transcription via single-stranded DNA response elements Adelene Y. Tan1, Todd R. Riley, Tristan Coady, Harmen J. Bussemaker, and James L. Manley2 Department of Biological Sciences, Columbia University, New York, NY 10027 Contributed by James L. Manley, February 29, 2012 (sent for review December 9, 2011) TLS/FUS (TLS) is a multifunctional protein implicated in a wide range TLS has also been linked to splicing. It contains an RNP-type of cellular processes, including transcription and mRNA processing, RNA-binding domain and associates directly with SR protein as well as in both cancer and neurological disease. However, little is splicing factors (11). TET proteins have been detected in spliceo- currently known about TLS target genes and how they are recog- somes (12), and TLS was found associated with RNAP II and nized. Here, we used ChIP and promoter microarrays to identify snRNPs in a transcription and splicing complex in vitro (13). It is genes potentially regulated by TLS. Among these genes, we detected unclear whether and how TLS recruits splicing factors to sites of a number that correlate with previously known functions of TLS, active transcription, but one possibility is through its interaction and confirmed TLS occupancy at several of them by ChIP. We also with TBP and the TFIID complex. detected changes in mRNA levels of these target genes in cells where Here we provide insight into TLS regulation of RNAP II-tran- scribed genes. We used ChIP followed by promoter microarray TLS levels were altered, indicative of both activation and repression. -
PHF22 (INTS12) (NM 020395) Human Over-Expression Lysate Product Data
OriGene Technologies, Inc. 9620 Medical Center Drive, Ste 200 Rockville, MD 20850, US Phone: +1-888-267-4436 [email protected] EU: [email protected] CN: [email protected] Product datasheet for LC412498 PHF22 (INTS12) (NM_020395) Human Over-expression Lysate Product data: Product Type: Over-expression Lysates Description: INTS12 HEK293T cell transient overexpression lysate (as WB positive control) Species: Human Expression Host: HEK293T Expression cDNA Clone TrueORF Clone RC204838 or AA Sequence: Tag: C-Myc/DDK Detection Antibodies: Clone OTI4C5, Anti-DDK (FLAG) monoclonal antibody (TA50011-100) Accession Number: NM_020395, NP_065128 Synonyms: INT12; PHF22; SBBI22 Predicted MW: 48.8 kDa Components: 1 vial of 20 ug lyophilized gene specific transient over-expression cell lysate Storage: The lysate can be shipped at ambient temperature. Upon receiving, store the sample at - 20°C. Lysate samples can be reconstituted with SDS Sample Buffer. Avoid repeated freeze- thaw cycles after reconstitution. Lysate samples are stable for 12 months from date of receipt when stored at -20°C. Preparation: HEK293T cells in 10-cm dishes were transiently transfected withM egaTran Transfection Reagent (TT200002) and 5ug TrueORF cDNA plasmid. Transfected cells were cultured for 48hrs before collection. The cells were lysed in modified RIPA buffer (25mM Tris-HCl pH7.6, 150mM NaCl, 1% NP-40, 1mM EDTA, 1xProteinase inhibitor cocktail mix (Sigma), 1mM PMSF and 1mM Na3VO4), and then centrifuged to clarify the lysate. Protein concentration was measured by BCA kit (Thermo Scientific Inc.). To facilitate transportation and protein, the products are supplied as lyophilized proteins. RefSeq: NP_065128 Locus ID: 57117 Cytogenetics: 4q24 Protein Families: Druggable Genome, Transcription Factors This product is to be used for laboratory only. -
Bioinformatics Analysis for the Identification of Differentially Expressed Genes and Related Signaling Pathways in H
Bioinformatics analysis for the identification of differentially expressed genes and related signaling pathways in H. pylori-CagA transfected gastric cancer cells Dingyu Chen*, Chao Li, Yan Zhao, Jianjiang Zhou, Qinrong Wang and Yuan Xie* Key Laboratory of Endemic and Ethnic Diseases , Ministry of Education, Guizhou Medical University, Guiyang, China * These authors contributed equally to this work. ABSTRACT Aim. Helicobacter pylori cytotoxin-associated protein A (CagA) is an important vir- ulence factor known to induce gastric cancer development. However, the cause and the underlying molecular events of CagA induction remain unclear. Here, we applied integrated bioinformatics to identify the key genes involved in the process of CagA- induced gastric epithelial cell inflammation and can ceration to comprehend the potential molecular mechanisms involved. Materials and Methods. AGS cells were transected with pcDNA3.1 and pcDNA3.1::CagA for 24 h. The transfected cells were subjected to transcriptome sequencing to obtain the expressed genes. Differentially expressed genes (DEG) with adjusted P value < 0.05, | logFC |> 2 were screened, and the R package was applied for gene ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The differential gene protein–protein interaction (PPI) network was constructed using the STRING Cytoscape application, which conducted visual analysis to create the key function networks and identify the key genes. Next, the Submitted 20 August 2020 Kaplan–Meier plotter survival analysis tool was employed to analyze the survival of the Accepted 11 March 2021 key genes derived from the PPI network. Further analysis of the key gene expressions Published 15 April 2021 in gastric cancer and normal tissues were performed based on The Cancer Genome Corresponding author Atlas (TCGA) database and RT-qPCR verification. -
Enhancer Rnas: Transcriptional Regulators and Workmates of Namirnas in Myogenesis
Odame et al. Cell Mol Biol Lett (2021) 26:4 https://doi.org/10.1186/s11658-021-00248-x Cellular & Molecular Biology Letters REVIEW Open Access Enhancer RNAs: transcriptional regulators and workmates of NamiRNAs in myogenesis Emmanuel Odame , Yuan Chen, Shuailong Zheng, Dinghui Dai, Bismark Kyei, Siyuan Zhan, Jiaxue Cao, Jiazhong Guo, Tao Zhong, Linjie Wang, Li Li* and Hongping Zhang* *Correspondence: [email protected]; zhp@sicau. Abstract edu.cn miRNAs are well known to be gene repressors. A newly identifed class of miRNAs Farm Animal Genetic Resources Exploration termed nuclear activating miRNAs (NamiRNAs), transcribed from miRNA loci that and Innovation Key exhibit enhancer features, promote gene expression via binding to the promoter and Laboratory of Sichuan enhancer marker regions of the target genes. Meanwhile, activated enhancers pro- Province, College of Animal Science and Technology, duce endogenous non-coding RNAs (named enhancer RNAs, eRNAs) to activate gene Sichuan Agricultural expression. During chromatin looping, transcribed eRNAs interact with NamiRNAs University, Chengdu 611130, through enhancer-promoter interaction to perform similar functions. Here, we review China the functional diferences and similarities between eRNAs and NamiRNAs in myogen- esis and disease. We also propose models demonstrating their mutual mechanism and function. We conclude that eRNAs are active molecules, transcriptional regulators, and partners of NamiRNAs, rather than mere RNAs produced during enhancer activation. Keywords: Enhancer RNA, NamiRNAs, MicroRNA, Myogenesis, Transcriptional regulator Introduction Te identifcation of lin-4 miRNA in Caenorhabditis elegans in 1993 [1] triggered research to discover and understand small microRNAs’ (miRNAs) mechanisms. Recently, some miRNAs are reported to activate target genes during transcription via base pairing to the 3ʹ or 5ʹ untranslated regions (3ʹ or 5ʹ UTRs), the promoter [2], and the enhancer regions [3]. -
(Q36.1;Q24) with a Concurrent Submicroscopic Del(4)(Q23q24) in an Adult with Polycythemia Vera
cancers Case Report A Novel Acquired t(2;4)(q36.1;q24) with a Concurrent Submicroscopic del(4)(q23q24) in An Adult with Polycythemia Vera Eigil Kjeldsen Cancer Cytogenetic Section, HemoDiagnostic Laboratory, Department of Hematology, Aarhus University Hospital, Tage-Hansens Gade 2, DK-8000 Aarhus C, Denmark; [email protected]; Tel.: +45-7846-7398; Fax: +45-7846-7399 Received: 6 June 2018; Accepted: 21 June 2018; Published: 25 June 2018 Abstract: Background: Polycythemia vera (PV) is a clonal myeloid stem cell disease characterized by a growth-factor independent erythroid proliferation with an inherent tendency to transform into overt acute myeloid malignancy. Approximately 95% of the PV patients harbor the JAK2V617F mutation while less than 35% of the patients harbor cytogenetic abnormalities at the time of diagnosis. Methods and Results: Here we present a JAK2V617F positive PV patient where G-banding revealed an apparently balanced t(2;4)(q35;q21), which was confirmed by 24-color karyotyping. Oligonucleotide array-based Comparative Genomic Hybridization (aCGH) analysis revealed an interstitial 5.4 Mb large deletion at 4q23q24. Locus-specific fluorescent in situ hybridization (FISH) analyses confirmed the mono-allelic 4q deletion and that it was located on der(4)t(2;4). Additional locus-specific bacterial artificial chromosome (BAC) probes and mBanding refined the breakpoint on chromosome 2. With these methods the karyotype was revised to 46,XX,t(2;4)(q36.1;q24)[18]/46,XX[7]. Conclusions: This is the first report on a PV patient associated with an acquired novel t(2;4)(q36.1;q24) and a concurrent submicroscopic deletion del(4)(q23q24). -
INTS14 Form a Functional Module of Integrator That Binds Nucleic Acids and the Cleavage Module
ARTICLE https://doi.org/10.1038/s41467-020-17232-2 OPEN INTS10–INTS13–INTS14 form a functional module of Integrator that binds nucleic acids and the cleavage module Kevin Sabath1, Melanie L. Stäubli1, Sabrina Marti1, Alexander Leitner 2, Murielle Moes 1 & ✉ Stefanie Jonas 1 ′ 1234567890():,; The Integrator complex processes 3 -ends of spliceosomal small nuclear RNAs (snRNAs). Furthermore, it regulates transcription of protein coding genes by terminating transcription after unstable pausing. The molecular basis for Integrator’s functions remains obscure. Here, we show that INTS10, Asunder/INTS13 and INTS14 form a separable, functional Integrator module. The structure of INTS13-INTS14 reveals a strongly entwined complex with a unique chain interlink. Unexpected structural homology to the Ku70-Ku80 DNA repair complex suggests nucleic acid affinity. Indeed, the module displays affinity for DNA and RNA but prefers RNA hairpins. While the module plays an accessory role in snRNA maturation, it has a stronger influence on transcription termination after pausing. Asunder/INTS13 directly binds Integrator’s cleavage module via a conserved C-terminal motif that is involved in snRNA processing and required for spermatogenesis. Collectively, our data establish INTS10-INTS13- INTS14 as a nucleic acid-binding module and suggest that it brings cleavage module and target transcripts into proximity. 1 Institute of Molecular Biology and Biophysics, ETH Zurich, Otto-Stern-Weg 5, CH-8093 Zurich, Switzerland. 2 Institute of Molecular Systems Biology, ETH ✉ Zurich, Zurich, Switzerland. email: [email protected] NATURE COMMUNICATIONS | (2020) 11:3422 | https://doi.org/10.1038/s41467-020-17232-2 | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-020-17232-2 NA polymerase II (RNAPII) requires essential processing development in Drosophila and Xenopus and shown to be factors to terminate transcription and to release its primary required for correct mitosis in human cells36,38,39.