ATR-16 Syndrome: Mechanisms Linking Monosomy to Phenotype
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Regulation and Dysregulation of Chromosome Structure in Cancer
Regulation and Dysregulation of Chromosome Structure in Cancer The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Hnisz, Denes et al. “Regulation and Dysregulation of Chromosome Structure in Cancer.” Annual Review of Cancer Biology 2, 1 (March 2018): 21–40 © 2018 Annual Reviews As Published https://doi.org/10.1146/annurev-cancerbio-030617-050134 Version Author's final manuscript Citable link http://hdl.handle.net/1721.1/117286 Terms of Use Creative Commons Attribution-Noncommercial-Share Alike Detailed Terms http://creativecommons.org/licenses/by-nc-sa/4.0/ Regulation and dysregulation of chromosome structure in cancer Denes Hnisz1*, Jurian Schuijers1, Charles H. Li1,2, Richard A. Young1,2* 1 Whitehead Institute for Biomedical Research, 455 Main Street, Cambridge, MA 02142, USA 2 Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA * Corresponding authors Corresponding Authors: Denes Hnisz Whitehead Institute for Biomedical Research 455 Main Street Cambridge, MA 02142 Tel: (617) 258-7181 Fax: (617) 258-0376 [email protected] Richard A. Young Whitehead Institute for Biomedical Research 455 Main Street Cambridge, MA 02142 Tel: (617) 258-5218 Fax: (617) 258-0376 [email protected] 1 Summary Cancer arises from genetic alterations that produce dysregulated gene expression programs. Normal gene regulation occurs in the context of chromosome loop structures called insulated neighborhoods, and recent studies have shown that these structures are altered and can contribute to oncogene dysregulation in various cancer cells. We review here the types of genetic and epigenetic alterations that influence neighborhood structures and contribute to gene dysregulation in cancer, present models for insulated neighborhoods associated with the most prominent human oncogenes, and discuss how such models may lead to further advances in cancer diagnosis and therapy. -
AXIN1 Protein Full-Length Recombinant Human Protein Expressed in Sf9 Cells
Catalog # Aliquot Size A71-30G-20 20 µg A71-30G-50 50 µg AXIN1 Protein Full-length recombinant human protein expressed in Sf9 cells Catalog # A71-30G Lot # E3330-6 Product Description Purity Full-length recombinant human AXIN1 was expressed by baculovirus in Sf9 insect cells using an N-terminal GST tag. This gene accession number is BC044648. The purity of AXIN1 protein was Gene Aliases determined to be >75% by densitometry. AXIN; MGC52315 Approx. MW 135 kDa. Formulation Recombinant protein stored in 50mM Tris-HCl, pH 7.5, 50mM NaCl, 10mM glutathione, 0.1mM EDTA, 0.25mM DTT, 0.1mM PMSF, 25% glycerol. Storage and Stability Store product at –70oC. For optimal storage, aliquot target into smaller quantities after centrifugation and store at recommended temperature. For most favorable performance, avoid repeated handling and multiple freeze/thaw cycles. Scientific Background AXIN 1 encodes a cytoplasmic protein which contains a regulation of G-protein signaling (RGS) domain and a dishevelled and axin (DIX) domain that interacts with adenomatosis polyposis coli, catenin beta-1, glycogen synthase kinase 3 beta, protein phosphate 2, and itself. AXIN1 has both positive and negative regulatory roles in Wnt-beta-catenin signaling. AXIN1 is a core component of a 'destruction complex' that promotes AXIN1 Protein phosphorylation and polyubiquitination of cytoplasmic beta- Full-length recombinant human protein expressed in Sf9 cells catenin, resulting in beta-catenin proteasomal degradation in the absence of Wnt signaling. Nuclear accumulation of AXIN1 can Catalog # A71-30G positively influence beta-catenin-mediated transcription during Lot # E3330-6 Wnt signalling and can induce apoptosis (1). -
In Silico Prediction of High-Resolution Hi-C Interaction Matrices
ARTICLE https://doi.org/10.1038/s41467-019-13423-8 OPEN In silico prediction of high-resolution Hi-C interaction matrices Shilu Zhang1, Deborah Chasman 1, Sara Knaack1 & Sushmita Roy1,2* The three-dimensional (3D) organization of the genome plays an important role in gene regulation bringing distal sequence elements in 3D proximity to genes hundreds of kilobases away. Hi-C is a powerful genome-wide technique to study 3D genome organization. Owing to 1234567890():,; experimental costs, high resolution Hi-C datasets are limited to a few cell lines. Computa- tional prediction of Hi-C counts can offer a scalable and inexpensive approach to examine 3D genome organization across multiple cellular contexts. Here we present HiC-Reg, an approach to predict contact counts from one-dimensional regulatory signals. HiC-Reg pre- dictions identify topologically associating domains and significant interactions that are enri- ched for CCCTC-binding factor (CTCF) bidirectional motifs and interactions identified from complementary sources. CTCF and chromatin marks, especially repressive and elongation marks, are most important for HiC-Reg’s predictive performance. Taken together, HiC-Reg provides a powerful framework to generate high-resolution profiles of contact counts that can be used to study individual locus level interactions and higher-order organizational units of the genome. 1 Wisconsin Institute for Discovery, 330 North Orchard Street, Madison, WI 53715, USA. 2 Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53715, USA. *email: [email protected] NATURE COMMUNICATIONS | (2019) 10:5449 | https://doi.org/10.1038/s41467-019-13423-8 | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-13423-8 he three-dimensional (3D) organization of the genome has Results Temerged as an important component of the gene regulation HiC-Reg for predicting contact count using Random Forests. -
The Wnt Pathway Scaffold Protein Axin Promotes Signaling Specificity by Suppressing Competing Kinase Reactions
bioRxiv preprint doi: https://doi.org/10.1101/768242; this version posted September 13, 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. The Wnt pathway scaffold protein Axin promotes signaling specificity by suppressing competing kinase reactions Maire Gavagan1,2, Erin Fagnan1,2, Elizabeth B. Speltz1, and Jesse G. Zalatan1,* 1Department of Chemistry, University of Washington, Seattle, WA 98195, USA 2These authors contributed equally to this work *Correspondence: [email protected] 1 bioRxiv preprint doi: https://doi.org/10.1101/768242; this version posted September 13, 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. Abstract GSK3β is a multifunctional kinase that phosphorylates β-catenin in the Wnt signaling network and also acts on other protein targets in response to distinct cellular signals. To test the long-standing hypothesis that the scaffold protein Axin specifically accelerates β-catenin phosphorylation, we measured GSK3β reaction rates with multiple substrates in a minimal, biochemically-reconstituted system. We observed an unexpectedly small, ~2-fold Axin-mediated rate increase for the β-catenin reaction. The much larger effects reported previously may have arisen because Axin can rescue GSK3β from an inactive state that occurs only under highly specific conditions. -
New Approaches to Functional Process Discovery in HPV 16-Associated Cervical Cancer Cells by Gene Ontology
Cancer Research and Treatment 2003;35(4):304-313 New Approaches to Functional Process Discovery in HPV 16-Associated Cervical Cancer Cells by Gene Ontology Yong-Wan Kim, Ph.D.1, Min-Je Suh, M.S.1, Jin-Sik Bae, M.S.1, Su Mi Bae, M.S.1, Joo Hee Yoon, M.D.2, Soo Young Hur, M.D.2, Jae Hoon Kim, M.D.2, Duck Young Ro, M.D.2, Joon Mo Lee, M.D.2, Sung Eun Namkoong, M.D.2, Chong Kook Kim, Ph.D.3 and Woong Shick Ahn, M.D.2 1Catholic Research Institutes of Medical Science, 2Department of Obstetrics and Gynecology, College of Medicine, The Catholic University of Korea, Seoul; 3College of Pharmacy, Seoul National University, Seoul, Korea Purpose: This study utilized both mRNA differential significant genes of unknown function affected by the display and the Gene Ontology (GO) analysis to char- HPV-16-derived pathway. The GO analysis suggested that acterize the multiple interactions of a number of genes the cervical cancer cells underwent repression of the with gene expression profiles involved in the HPV-16- cancer-specific cell adhesive properties. Also, genes induced cervical carcinogenesis. belonging to DNA metabolism, such as DNA repair and Materials and Methods: mRNA differential displays, replication, were strongly down-regulated, whereas sig- with HPV-16 positive cervical cancer cell line (SiHa), and nificant increases were shown in the protein degradation normal human keratinocyte cell line (HaCaT) as a con- and synthesis. trol, were used. Each human gene has several biological Conclusion: The GO analysis can overcome the com- functions in the Gene Ontology; therefore, several func- plexity of the gene expression profile of the HPV-16- tions of each gene were chosen to establish a powerful associated pathway, identify several cancer-specific cel- cervical carcinogenesis pathway. -
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. -
Renal Cell Neoplasms Contain Shared Tumor Type–Specific Copy Number Variations
The American Journal of Pathology, Vol. 180, No. 6, June 2012 Copyright © 2012 American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.ajpath.2012.01.044 Tumorigenesis and Neoplastic Progression Renal Cell Neoplasms Contain Shared Tumor Type–Specific Copy Number Variations John M. Krill-Burger,* Maureen A. Lyons,*† The annual incidence of renal cell carcinoma (RCC) has Lori A. Kelly,*† Christin M. Sciulli,*† increased steadily in the United States for the past three Patricia Petrosko,*† Uma R. Chandran,†‡ decades, with approximately 58,000 new cases diag- 1,2 Michael D. Kubal,§ Sheldon I. Bastacky,*† nosed in 2010, representing 3% of all malignancies. Anil V. Parwani,*†‡ Rajiv Dhir,*†‡ and Treatment of RCC is complicated by the fact that it is not a single disease but composes multiple tumor types with William A. LaFramboise*†‡ different morphological characteristics, clinical courses, From the Departments of Pathology* and Biomedical and outcomes (ie, clear-cell carcinoma, 82% of RCC ‡ Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania; cases; type 1 or 2 papillary tumors, 11% of RCC cases; † the University of Pittsburgh Cancer Institute, Pittsburgh, chromophobe tumors, 5% of RCC cases; and collecting § Pennsylvania; and Life Technologies, Carlsbad, California duct carcinoma, approximately 1% of RCC cases).2,3 Benign renal neoplasms are subdivided into papillary adenoma, renal oncocytoma, and metanephric ade- Copy number variant (CNV) analysis was performed on noma.2,3 Treatment of RCC often involves surgical resec- renal cell carcinoma (RCC) specimens (chromophobe, tion of a large renal tissue component or removal of the clear cell, oncocytoma, papillary type 1, and papillary entire affected kidney because of the relatively large size of type 2) using high-resolution arrays (1.85 million renal tumors on discovery and the availability of a life-sus- probes). -
Download Download
Supplementary Figure S1. Results of flow cytometry analysis, performed to estimate CD34 positivity, after immunomagnetic separation in two different experiments. As monoclonal antibody for labeling the sample, the fluorescein isothiocyanate (FITC)- conjugated mouse anti-human CD34 MoAb (Mylteni) was used. Briefly, cell samples were incubated in the presence of the indicated MoAbs, at the proper dilution, in PBS containing 5% FCS and 1% Fc receptor (FcR) blocking reagent (Miltenyi) for 30 min at 4 C. Cells were then washed twice, resuspended with PBS and analyzed by a Coulter Epics XL (Coulter Electronics Inc., Hialeah, FL, USA) flow cytometer. only use Non-commercial 1 Supplementary Table S1. Complete list of the datasets used in this study and their sources. GEO Total samples Geo selected GEO accession of used Platform Reference series in series samples samples GSM142565 GSM142566 GSM142567 GSM142568 GSE6146 HG-U133A 14 8 - GSM142569 GSM142571 GSM142572 GSM142574 GSM51391 GSM51392 GSE2666 HG-U133A 36 4 1 GSM51393 GSM51394 only GSM321583 GSE12803 HG-U133A 20 3 GSM321584 2 GSM321585 use Promyelocytes_1 Promyelocytes_2 Promyelocytes_3 Promyelocytes_4 HG-U133A 8 8 3 GSE64282 Promyelocytes_5 Promyelocytes_6 Promyelocytes_7 Promyelocytes_8 Non-commercial 2 Supplementary Table S2. Chromosomal regions up-regulated in CD34+ samples as identified by the LAP procedure with the two-class statistics coded in the PREDA R package and an FDR threshold of 0.5. Functional enrichment analysis has been performed using DAVID (http://david.abcc.ncifcrf.gov/) -
Multi-Modal Meta-Analysis of 1494 Hepatocellular Carcinoma Samples Reveals
Author Manuscript Published OnlineFirst on September 21, 2018; DOI: 10.1158/1078-0432.CCR-18-0088 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Multi-modal meta-analysis of 1494 hepatocellular carcinoma samples reveals significant impact of consensus driver genes on phenotypes Kumardeep Chaudhary1, Olivier B Poirion1, Liangqun Lu1,2, Sijia Huang1,2, Travers Ching1,2, Lana X Garmire1,2,3* 1Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI 96813, USA 2Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, HI 96822, USA 3Current affiliation: Department of Computational Medicine and Bioinformatics, Building 520, 1600 Huron Parkway, Ann Arbor, MI 48109 Short Title: Impact of consensus driver genes in hepatocellular carcinoma * To whom correspondence should be addressed. Lana X. Garmire, Department of Computational Medicine and Bioinformatics Medical School, University of Michigan Building 520, 1600 Huron Parkway Ann Arbor-48109, MI, USA, Phone: +1-(734) 615-5510 Current email address: [email protected] Grant Support: This research was supported by grants K01ES025434 awarded by NIEHS through funds provided by the trans-NIH Big Data to Knowledge (BD2K) initiative (http://datascience.nih.gov/bd2k), P20 COBRE GM103457 awarded by NIH/NIGMS, NICHD R01 HD084633 and NLM R01LM012373 and Hawaii Community Foundation Medical Research Grant 14ADVC-64566 to Lana X Garmire. 1 Downloaded from clincancerres.aacrjournals.org on October 1, 2021. © 2018 American Association for Cancer Research. Author Manuscript Published OnlineFirst on September 21, 2018; DOI: 10.1158/1078-0432.CCR-18-0088 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. -
DNA Replication Stress Response Involving PLK1, CDC6, POLQ
DNA replication stress response involving PLK1, CDC6, POLQ, RAD51 and CLASPIN upregulation prognoses the outcome of early/mid-stage non-small cell lung cancer patients C. Allera-Moreau, I. Rouquette, B. Lepage, N. Oumouhou, M. Walschaerts, E. Leconte, V. Schilling, K. Gordien, L. Brouchet, Mb Delisle, et al. To cite this version: C. Allera-Moreau, I. Rouquette, B. Lepage, N. Oumouhou, M. Walschaerts, et al.. DNA replica- tion stress response involving PLK1, CDC6, POLQ, RAD51 and CLASPIN upregulation prognoses the outcome of early/mid-stage non-small cell lung cancer patients. Oncogenesis, Nature Publishing Group: Open Access Journals - Option C, 2012, 1, pp.e30. 10.1038/oncsis.2012.29. hal-00817701 HAL Id: hal-00817701 https://hal.archives-ouvertes.fr/hal-00817701 Submitted on 9 Jun 2021 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution - NonCommercial - NoDerivatives| 4.0 International License Citation: Oncogenesis (2012) 1, e30; doi:10.1038/oncsis.2012.29 & 2012 Macmillan Publishers Limited All rights reserved 2157-9024/12 www.nature.com/oncsis ORIGINAL ARTICLE DNA replication stress response involving PLK1, CDC6, POLQ, RAD51 and CLASPIN upregulation prognoses the outcome of early/mid-stage non-small cell lung cancer patients C Allera-Moreau1,2,7, I Rouquette2,7, B Lepage3, N Oumouhou3, M Walschaerts4, E Leconte5, V Schilling1, K Gordien2, L Brouchet2, MB Delisle1,2, J Mazieres1,2, JS Hoffmann1, P Pasero6 and C Cazaux1 Lung cancer is the leading cause of cancer deaths worldwide. -
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. -
Depicting Gene Co-Expression Networks Underlying Eqtls Nathalie Vialaneix, Laurence Liaubet, Magali San Cristobal
Depicting gene co-expression networks underlying eQTLs Nathalie Vialaneix, Laurence Liaubet, Magali San Cristobal To cite this version: Nathalie Vialaneix, Laurence Liaubet, Magali San Cristobal. Depicting gene co-expression networks underlying eQTLs. Haja N. Kadarmideen. Systems Biology in Animal Production and Health vol 2, 2, Springer International Publishing, pp.1-31, 2016, 978-3-319-43330-1. 10.1007/978-3-319-43332-5_1. hal-01390589 HAL Id: hal-01390589 https://hal.archives-ouvertes.fr/hal-01390589 Submitted on 2 Nov 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Depicting gene co-expression networks underlying eQTLs Nathalie Villa-Vialaneix, Laurence Liaubet and Magali SanCristobal Abstract Deciphering the biological mechanisms underlying a list of genes whose expression is under partial genetic control (i.e., having at least one eQTL) may not be as easy as for a list of differential genes. Indeed, no specific phenotype (e.g., health or production phenotype) is linked to the list of transcripts under study. There is a need to find a coherent biological interpretation of a list of genes under (partial) genetic control. We propose a pipeline using appropriate statistical tools to build a co-expression network from the list of genes, then to finely depict the network structure.