DOCSLIB.ORG

Evolution of Vertebrate Opioid Receptors

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

Evolution of vertebrate opioid receptors Susanne Dreborg, Go¨ rel Sundstro¨ m, Tomas A. Larsson, and Dan Larhammar* Department of Neuroscience, Uppsala University, Box 593, SE-75124 Uppsala, Sweden Edited by Tomas Ho¨kfelt, Karolinska Institutet, Stockholm, Sweden, and approved August 15, 2008 (received for review June 9, 2008) The opioid peptides and receptors have prominent roles in pain Many vertebrate gene families have been found to have transmission and reward mechanisms in mammals. The evolution expanded in the early stages of vertebrate evolution, before the of the opioid receptors has so far been little studied, with only a radiation of jawed vertebrates. However, the high degree of few reports on species other than tetrapods. We have investigated sequence divergence over such large evolutionary distances species representing a broader range of vertebrates and found that often obscures orthology–paralogy relationships. Investigation the four opioid receptor types (delta, kappa, mu, and NOP) are of conserved synteny may facilitate identification of orthologs present in most of the species. The gene relationships were and gives important clues to the mechanisms by which the genes deduced by using both phylogenetic analyses and chromosomal were duplicated. We used this approach to investigate the location relative to 20 neighboring gene families in databases of evolution of a few other gene families, namely the neuropeptide assembled genomes. The combined results show that the verte- Y (NPY) family of peptides (27) and the large family of NPY brate opioid receptor gene family arose by quadruplication of a receptors (28). These families were found to have expanded as large chromosomal block containing at least 14 other gene fami- a result of extensive chromosome duplications, most likely lies. The quadruplication seems to coincide with, and, therefore, resulting from two tetraploidizations, i.e., genome duplications, probably resulted from, the two proposed genome duplications in that occurred early in vertebrate evolution (29). These genome early vertebrate evolution. We conclude that the quartet of opioid duplications, often referred to as 1R and 2R, occurred after the receptors was already present at the origin of jawed vertebrates divergence of tunicates and lancelets (30) from vertebrates but Ϸ450 million years ago. A few additional opioid receptor gene before the divergence of cartilaginous fishes and bony verte- duplications have occurred in bony fishes. Interestingly, the brates (31). For the cyclostomes (lampreys and hagfishes) the ancestral receptor gene duplications coincide with the origin of picture is not completely clear but based on analyses of a limited the four opioid peptide precursor genes. Thus, the complete number of gene families they seem to have undergone the first tetraploidization (1R) but not the second (2R) (32–36). vertebrate opioid system was already established in the first As the opioid receptor genes are located on four different jawed vertebrates. chromosomes in human (1, 6, 8, and 20) we decided to investigate whether they arose by duplication of a single ancestral opioid chromosome ͉ G protein-coupled receptor ͉ gene duplication receptor gene in the two tetraploidizations. Other investigators have also suggested that studies of chromosomal location may everal opioid peptides, including endorphin and enkephalins, shed light on opioid receptor evolution (37). We describe here Sare important regulators of nociceptive neurotransmission an investigation, using a combination of sequence-based phy- and reward mechanisms in mammals. Specific binding sites in the logenies and gene locations for the opioid receptors and their brain for opioid compounds were first reported in 1973 (1–3), neighboring families that shows that they expanded by gene and it was soon evident that more than one type of binding site duplications in conjunction with the proposed tetraploidizations existed (4). Subsequently three distinct opioid receptors were in early vertebrate evolution. identified and designated delta, kappa, and mu. These receptors were cloned and found to be encoded by separate genes belong- Results ing to the superfamily of rhodopsin-like G protein-coupled To investigate whether the opioid receptor genes arose by receptors (GPCRs) (5–8). The genes for the opioid receptors duplications of a single ancestral gene in the two basal vertebrate (OPR) have been named OPRD1 (delta), OPRK1 (kappa), and tetraploidizations we have analyzed the opioid receptor gene OPRM1 (mu) by the HUGO Gene Nomenclature Committee family and 20 of the neighboring gene families phylogenetically. (HGNC). Specifically, we wanted to find out whether these gene families Homology searches resulted in the discovery of a fourth were duplicated in the same time period, i.e., after the diver- receptor in both rodents and humans initially named ORL1 for gence of invertebrate chordates and vertebrates but before the opioid receptor-like (9) or LC132 (10). This receptor shows divergence of bony fishes and tetrapods because this is the time 48–49% identity to the other three human receptors, which span in which the two tetraploidizations took place. The gene display 55–58% identity among one another. The receptor has families were analyzed phylogenetically by making both neigh- been named NOP by the International Union of Basic and bor-joining (NJ) trees and quartet-puzzling maximum likelihood Clinical Pharmacology and its gene has been named OPRL1 by (QP) trees in which species that diverged before 2R were used HGNC. An endogenous peptide ligand with some similarity to as outgroups to provide relative dating of the gene duplications. the other opioid peptides was discovered and named nociceptin The opioid receptors were analyzed in human (Homo sapiens), (11) or orphanin FQ (12). mouse (Mus musculus), dog (Canis familiaris), cow (Bos taurus), The evolution of the endogenous opioid peptide ligands has been studied extensively and the major peptide ligands are Author contributions: S.D., G.S., T.A.L., and D.L. designed research; S.D. and G.S. performed generated from four prepropeptides that are encoded by sepa- research; S.D., G.S., T.A.L., and D.L. analyzed data; and S.D., G.S., T.A.L., and D.L. wrote the rate genes in tetrapods. The genes arose by duplications in the paper. common ancestor of tetrapods and bony fishes (13). Opioid The authors declare no conflict of interest. receptor sequences have been reported for a few nonmammalian This article is a PNAS Direct Submission. tetrapods (14–18) and a few teleost fishes (19–24), and a partial *To whom correspondence should be addressed. E-mail: [email protected]. sequence has been reported for a hagfish (19). Functional studies This article contains supporting information online at www.pnas.org/cgi/content/full/ EVOLUTION in amphibians and bony fishes have shown that the opioid system 0805590105/DCSupplemental. is involved in nociception also in these species (25, 26). © 2008 by The National Academy of Sciences of the USA www.pnas.org͞cgi͞doi͞10.1073͞pnas.0805590105 PNAS ͉ October 7, 2008 ͉ vol. 105 ͉ no. 40 ͉ 15487–15492 Downloaded by guest on September 29, 2021 Dme.3L Bfl.sc203 Hsa.17.SSTR2 gray short-tailed opossum (Monodelphis domestica), chicken 63 Hsa.22.SSTR3 (Gallus gallus), western clawed frog (Xenopus tropicalis), rough- Hsa.16.SSTR5 Hsa.20.SSTR4 skinned newt (Taricha granulosa), zebrafish (Danio rerio), 95 Hsa.14.SSTR1 71 Hsa.20.NPBWR2 medaka (Oryzias latipes), stickleback (Gasterosteus aculeatus), NPBWR2 71 Bta.Un.b and spotted green pufferfish (Tetraodon nigroviridis). We found Mdo.1.a* at least four opioid receptor genes in the genome databases for Ola.7 Gac.XII.a most of these species. The zebrafish and medaka have duplicates 65 Dre.23..b of the OPRK1 and/or the OPRD1 genes, whereas the OPRL1 52 Xtr.sc1400.a* Hsa.8.NPBWR1 63 gene is missing in medaka and the OPRM1 gene is missing in Bta.14.b NPBWR1 80 Cfa.29.a spotted green pufferfish [Fig. 1 and supporting information (SI) 66 96 Mmu.1.b Fig. S1]. However, this does not necessarily mean that these Mdo.3.b genes have been lost because their absence may simply be due to 57 Cmi.AAVX01065032.1 Xtr.sc83.a* 84 incomplete sequencing of the genomes or poor genome assembly 66 Gga.2.a in the databases. The human opioid receptor sequences were Pma.co2020 97 Pma.co7520 Hsa.20.OPRL1 used for blastp searches of the Florida lancelet (Branchiostoma 90 Bta.13 floridae) database and the elephant shark (Callorhinchus milii) 94 Cfa.24 91 database, but this produced no reasonable hits. Mmu.2 OPRL1 The phylogenetic tree (Fig. 1) shows that the neuropeptide 76 Mdo.1.b 65 Tgr.L B/W (NPBW) receptors are closely related to the opioid recep- 95 Gga.20 57 Xtr.sc1400.b tors, i.e., closer than to any other GPCRs. This is supported by Dre.23.a 94 Tni.Un.b* the chromosomal locations because NPBWR1 is located next to 99 100 Gac.XII.b OPRK1 on human chromosome 8 (287 kb downstream) and Hsa.8.OPRK1 50 Cfa.29.b NPBWR2 is situated on human chromosome 20 next to OPRL1 91 Bta.14.a 78 (only 26 kb downstream) (Fig. 2 and 3). Such close linkage can Mmu.1.a 98 be seen in most of the species that have NPBW receptors. An Mdo.3.a 97 Gga.2.b OPRK1 earlier split of the somatostatin receptors from the opioid/ Tgr.K NPBW receptors is suggested by the fact that the vertebrate 96 Xtr.sc83.b* 85 Ola.20* somatostatin receptors cluster with a Florida lancelet sequence 91 93 Tni.6* and a fruit fly (Drosophila melanogaster) allostatin C receptor Gac.XXI* 52 Ola.17 sequence in the phylogenetic tree (Fig. 1). Both the opioid and 64 92 95 Gac.III the NPBW receptor families seem to have expanded in the time Tni.Un.a* 85 Dre.2 period coinciding with the tetraploidizations in early vertebrate Hsa.6.OPRM1 62 Cfa.1 evolution.
Recommended publications
  • Analysis of Trans Esnps Infers Regulatory Network Architecture

    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.
  • Antisense RNA Polymerase II Divergent Transcripts Are P-Tefb Dependent and Substrates for the RNA Exosome

    Antisense RNA Polymerase II Divergent Transcripts Are P-Tefb Dependent and Substrates for the RNA Exosome

    Antisense RNA polymerase II divergent transcripts are P-TEFb dependent and substrates for the RNA exosome Ryan A. Flynna,1,2, Albert E. Almadaa,b,1, Jesse R. Zamudioa, and Phillip A. Sharpa,b,3 aDavid H. Koch Institute for Integrative Cancer Research, Cambridge, MA 02139; and bDepartment of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139 Contributed by Phillip A. Sharp, May 12, 2011 (sent for review March 3, 2011) Divergent transcription occurs at the majority of RNA polymerase II PII carboxyl-terminal domain (CTD) at serine 2, DSIF, and (RNAPII) promoters in mouse embryonic stem cells (mESCs), and NELF results in the dissociation of NELF from the elongation this activity correlates with CpG islands. Here we report the char- complex and continuation of transcription (13). More recently acterization of upstream antisense transcription in regions encod- it was recognized, in mESCs, that c-Myc stimulates transcription ing transcription start site associated RNAs (TSSa-RNAs) at four at over a third of all cellular promoters by recruitment of P-TEFb divergent CpG island promoters: Isg20l1, Tcea1, Txn1, and Sf3b1. (12). Intriguingly in these same cells, NELF and DSIF have bi- We find that upstream antisense RNAs (uaRNAs) have distinct modal binding profiles at divergent TSSs. This suggests divergent capped 5′ termini and heterogeneous nonpolyadenylated 3′ ends. RNAPII complexes might be poised for signals controlling elon- uaRNAs are short-lived with average half-lives of 18 minutes and gation and opens up the possibility that in the antisense direction are present at 1–4 copies per cell, approximately one RNA per DNA P-TEF-b recruitment may be regulating release for productive template.
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus

    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.
  • See Also Figure 1

    See Also Figure 1

    Figure S1. Box-and-whisker plots depicting the range of expression values per developmental stage, with DESeq normalization (A) or quantile normalization (B). See also Figure 1. Figure S2. Lv-Setmar expression has low variation over developmental time. A. A plot of Lv-setmar versus Lv-ubiquitin expression over time demonstrates that Lv-setmar exhibits less temporal variation than Lv-ubiquitin. B. A representative gel showing Lv-setmar qPCR products amplified from cDNAs representing each sequenced stage in this study, demonstrating comparable product levels and an absence of spurious amplification products. See also Figure 1E. Figure S3. LvEDGE database. Screen shots showing the home page (A), the search window (B), an example search with a temporal expression plot (C), and the numerical data reflected in the plot (D) for the LvEDGE public database, which hosts the data described herein. stage 1 2 3 4 5 6 7 8 9 10 11 Category Subcategory 2-cell 60-cell EB HB TVP MB EG MG LG EP LP meiotic Cell Division Cytokinesis Mitosis checkpoint cell division recombination cell cycle stem cell left-right cell left-right Development maintenance asymmetry morphogenesis asymmetry regulation of multicellular organismal process cell soma cell soma Gene Expression chromatin SWI/SNF Control Chromatin modification chromatin binding complex methylated histone Binding negative sequence- sequence- sequence- regulation of sequence- specific DNA specific DNA specific DNA transcription specific DNA sequence-specific DNA binding binding binding binding factor activity
  • 'Next- Generation' Sequencing Data Analysis

    'Next- Generation' Sequencing Data Analysis

    Novel Algorithm Development for ‘Next- Generation’ Sequencing Data Analysis Agne Antanaviciute Submitted in accordance with the requirements for the degree of Doctor of Philosophy University of Leeds School of Medicine Leeds Institute of Biomedical and Clinical Sciences 12/2017 ii The candidate confirms that the work submitted is her own, except where work which has formed part of jointly-authored publications has been included. The contribution of the candidate and the other authors to this work has been explicitly given within the thesis where reference has been made to the work of others. This copy has been supplied on the understanding that it is copyright material and that no quotation from the thesis may be published without proper acknowledgement ©2017 The University of Leeds and Agne Antanaviciute The right of Agne Antanaviciute to be identified as Author of this work has been asserted by her in accordance with the Copyright, Designs and Patents Act 1988. Acknowledgements I would like to thank all the people who have contributed to this work. First and foremost, my supervisors Dr Ian Carr, Professor David Bonthron and Dr Christopher Watson, who have provided guidance, support and motivation. I could not have asked for a better supervisory team. I would also like to thank my collaborators Dr Belinda Baquero and Professor Adrian Whitehouse for opening new, interesting research avenues. A special thanks to Dr Belinda Baquero for all the hard wet lab work without which at least half of this thesis would not exist. Thanks to everyone at the NGS Facility – Carolina Lascelles, Catherine Daley, Sally Harrison, Ummey Hany and Laura Crinnion – for the generation of NGS data used in this work and creating a supportive and stimulating work environment.
  • A Genome-Wide Library of MADM Mice for Single-Cell Genetic Mosaic Analysis

    A Genome-Wide Library of MADM Mice for Single-Cell Genetic Mosaic Analysis

    bioRxiv preprint doi: https://doi.org/10.1101/2020.06.05.136192; this version posted June 6, 2020. 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. Contreras et al., A Genome-wide Library of MADM Mice for Single-Cell Genetic Mosaic Analysis Ximena Contreras1, Amarbayasgalan Davaatseren1, Nicole Amberg1, Andi H. Hansen1, Johanna Sonntag1, Lill Andersen2, Tina Bernthaler2, Anna Heger1, Randy Johnson3, Lindsay A. Schwarz4,5, Liqun Luo4, Thomas Rülicke2 & Simon Hippenmeyer1,6,# 1 Institute of Science and Technology Austria, Am Campus 1, 3400 Klosterneuburg, Austria 2 Institute of Laboratory Animal Science, University of Veterinary Medicine Vienna, Vienna, Austria 3 Department of Biochemistry and Molecular Biology, University of Texas, Houston, TX 77030, USA 4 HHMI and Department of Biology, Stanford University, Stanford, CA 94305, USA 5 Present address: St. Jude Children’s Research Hospital, Memphis, TN 38105, USA 6 Lead contact #Correspondence and requests for materials should be addressed to S.H. ([email protected]) 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.06.05.136192; this version posted June 6, 2020. 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. Contreras et al., SUMMARY Mosaic Analysis with Double Markers (MADM) offers a unique approach to visualize and concomitantly manipulate genetically-defined cells in mice with single-cell resolution.
  • Identification of Transcriptomic Differences Between Lower

    Identification of Transcriptomic Differences Between Lower

    International Journal of Molecular Sciences Article Identification of Transcriptomic Differences between Lower Extremities Arterial Disease, Abdominal Aortic Aneurysm and Chronic Venous Disease in Peripheral Blood Mononuclear Cells Specimens Daniel P. Zalewski 1,*,† , Karol P. Ruszel 2,†, Andrzej St˛epniewski 3, Dariusz Gałkowski 4, Jacek Bogucki 5 , Przemysław Kołodziej 6 , Jolanta Szyma ´nska 7 , Bartosz J. Płachno 8 , Tomasz Zubilewicz 9 , Marcin Feldo 9,‡ , Janusz Kocki 2,‡ and Anna Bogucka-Kocka 1,‡ 1 Chair and Department of Biology and Genetics, Medical University of Lublin, 4a Chod´zkiSt., 20-093 Lublin, Poland; [email protected] 2 Chair of Medical Genetics, Department of Clinical Genetics, Medical University of Lublin, 11 Radziwiłłowska St., 20-080 Lublin, Poland; [email protected] (K.P.R.); [email protected] (J.K.) 3 Ecotech Complex Analytical and Programme Centre for Advanced Environmentally Friendly Technologies, University of Marie Curie-Skłodowska, 39 Gł˛ebokaSt., 20-612 Lublin, Poland; [email protected] 4 Department of Pathology and Laboratory Medicine, Rutgers-Robert Wood Johnson Medical School, One Robert Wood Johnson Place, New Brunswick, NJ 08903-0019, USA; [email protected] 5 Chair and Department of Organic Chemistry, Medical University of Lublin, 4a Chod´zkiSt., Citation: Zalewski, D.P.; Ruszel, K.P.; 20-093 Lublin, Poland; [email protected] St˛epniewski,A.; Gałkowski, D.; 6 Laboratory of Diagnostic Parasitology, Chair and Department of Biology and Genetics, Medical University of Bogucki, J.; Kołodziej, P.; Szyma´nska, Lublin, 4a Chod´zkiSt., 20-093 Lublin, Poland; [email protected] J.; Płachno, B.J.; Zubilewicz, T.; Feldo, 7 Department of Integrated Paediatric Dentistry, Chair of Integrated Dentistry, Medical University of Lublin, M.; et al.
  • Research Article Identification of Microrna-451A As a Novel Circulating Biomarker for Colorectal Cancer Diagnosis

    Research Article Identification of Microrna-451A As a Novel Circulating Biomarker for Colorectal Cancer Diagnosis

    Hindawi BioMed Research International Volume 2020, Article ID 5236236, 18 pages https://doi.org/10.1155/2020/5236236 Research Article Identification of microRNA-451a as a Novel Circulating Biomarker for Colorectal Cancer Diagnosis Zhen Zhang ,1 Dai Zhang,2 Yaping Cui,1 Yongsheng Qiu,3 Changhong Miao,4 and Xihua Lu 1 1Department of Anesthesiology, Affiliated Cancer Hospital of ZhengZhou University, Henan Cancer Hospital, ZhengZhou, China 2Department of Laboratory Medicine, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China 3Department of Anesthesiology, Children’s Hospital Affiliated to Zhengzhou University, Zhengzhou, China 4Department of Anesthesiology, Affiliated Cancer Hospital of Fudan University, Shanghai, China Correspondence should be addressed to Zhen Zhang; [email protected] and Xihua Lu; [email protected] Received 3 July 2020; Accepted 10 August 2020; Published 27 August 2020 Academic Editor: David A. McClellan Copyright © 2020 Zhen Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background. Colorectal cancer (CRC) is one of the leading causes of cancer death worldwide. Successful treatment of CRC relies on accurate early diagnosis, which is currently a challenge due to its complexity and personalized pathologies. Thus, novel molecular biomarkers are needed for early CRC detection. Methods. Gene and microRNA microarray profiling of CRC tissues and miRNA- seq data were analyzed. Candidate microRNA biomarkers were predicted using both CRC-specific network and miRNA-BD tool. Validation analyses were carried out to interrogate the identified candidate CRC biomarkers.
  • Supplementary Table S4. FGA Co-Expressed Gene List in LUAD

    Supplementary Table S4. FGA Co-Expressed Gene List in LUAD

    Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase
  • Identifying Compartment-Specific Non-HLA Targets After Renal Transplantation by Integrating Transcriptome and ‘‘Antibodyome’’ Measures

    Identifying Compartment-Specific Non-HLA Targets After Renal Transplantation by Integrating Transcriptome and ‘‘Antibodyome’’ Measures

    Identifying compartment-specific non-HLA targets after renal transplantation by integrating transcriptome and ‘‘antibodyome’’ measures Li Lia, Persis Wadiab, Rong Chenc, Neeraja Kambhamd, Maarten Naesensa, Tara K. Sigdela, David B. Miklosb, Minnie M. Sarwala,1, and Atul J. Buttea,c,1 aDepartment of Pediatrics, Blood and Marrow Transplantation Division, Departments of bMedicine and dPathology, and cCenter for Biomedical Informatics Research, Stanford University, 300 Pasteur Drive, Stanford, CA 94304 Communicated by Mark M. Davis, Stanford University School of Medicine, Stanford, CA, January 27, 2009 (received for review May 22, 2008) We have conducted an integrative genomics analysis of serological antigens, may be associated with chronic renal allograft histological responses to non-HLA targets after renal transplantation, with the injury (11). Antibodies against Agrin, the most abundant heparin aim of identifying the tissue specificity and types of immunogenic sulfate proteoglycan present in the glomerular basal membrane, non-HLA antigenic targets after transplantation. Posttransplant have been implicated in transplant glomerulopathy (12), and ago- antibody responses were measured by paired comparative analysis nistic antibodies against the Angiotensin II type 1 receptor of pretransplant and posttransplant serum samples from 18 pedi- (AT1R-AA) were described in renal allograft recipients with severe atric renal transplant recipients, measured against 5,056 unique vascular types of rejection and malignant hypertension (13). protein targets on the ProtoArray platform. The specificity of It is expected that there are many more unidentified non-HLA antibody responses were measured against gene expression levels non-ABO immune antigens that might evoke specific antibody specific to the kidney, and 2 other randomly selected organs (heart responses after renal transplantation (8).
  • Systematic Screening Reveals a Role for BRCA1 in the Response to Transcription-Associated DNA Damage

    Systematic Screening Reveals a Role for BRCA1 in the Response to Transcription-Associated DNA Damage

    Downloaded from genesdev.cshlp.org on October 6, 2021 - Published by Cold Spring Harbor Laboratory Press RESOURCE/METHODOLOGY Systematic screening reveals a role for BRCA1 in the response to transcription-associated DNA damage Sarah J. Hill,1,2 Thomas Rolland,1,2,3 Guillaume Adelmant,1,4,5 Xianfang Xia,1,2,3 Matthew S. Owen,1,2,3 Amelie Dricot,1,2,3 Travis I. Zack,1,6 Nidhi Sahni,1,2,3 Yves Jacob,1,2,3,7,8,9 Tong Hao,1,2,3 Kristine M. McKinney,1,2 Allison P. Clark,1,2 Deepak Reyon,10,11,12 Shengdar Q. Tsai,10,11,12 J. Keith Joung,10,11,12 Rameen Beroukhim,1,6,13 Jarrod A. Marto,1,4,5 Marc Vidal,1,2,3 Suzanne Gaudet,1,2,3 David E. Hill,1,2,3,14 and David M. Livingston1,2,14 1Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA; 2Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA; 3Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA; 4Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts 02115, USA; 5Blais Proteomics Center, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA; 6The Broad Institute, Cambridge, Massachusetts 02142, USA; 7Departement de Virologie, Unite de Gen etique Moleculaire des Virus a ARN, Institut Pasteur, F-75015 Paris, France; 8UMR3569, Centre National de la Recherche Scientifique, F-75015 Paris, France; 9UnitedeG en etique Moleculaire des Virus a ARN, Universite Paris Diderot, F-75015 Paris, France; 10Molecular Pathology Unit, Center for Computational and Integrative Biology, 11Center for Cancer Research, Massachusetts General Hospital, Charlestown, Massachusetts 02129, USA; 12Department of Pathology, Harvard Medical School, Boston, Massachusetts 02115, USA; 13Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA BRCA1 is a breast and ovarian tumor suppressor.
  • 1 Substitution Mapping in Dahl Rats Identifies Two Distinct Blood

    1 Substitution Mapping in Dahl Rats Identifies Two Distinct Blood

    Genetics: Published Articles Ahead of Print, published on October 8, 2006 as 10.1534/genetics.106.061747 1 Substitution Mapping in Dahl Rats Identifies Two Distinct Blood Pressure Quantitative Trait Loci within 1.12 Mb and 1.25 Mb Intervals on Chromosome 3. Soon Jin Lee, Jun Liu, Allison M. Westcott, Joshua A. Vieth, Sarah J. DeRaedt, Siming Yang, Bina Joe, and George T. Cicila Department of Physiology, Pharmacology, Metabolism, and Cardiovascular Sciences University of Toledo College of Medicine, 3035 Arlington Avenue Toledo, Ohio 43614 2 Running Head: Congenic Substrains and Blood Pressure Correspondence to: George T. Cicila, Ph.D. University of Toledo College of Medicine Department of Physiology, Pharmacology, Metabolism, and Cardiovascular Sciences 3035 Arlington Avenue Toledo, Ohio 43614 Phone: (419) 383-4171 Fax: (419) 383-6168 e-mail: [email protected] Key Words: genetic hypertension, inbred rat strains, Dahl salt-sensitive rat, Dahl salt- resistant rat, salt-sensitivity 3 Abstract Substitution mapping was used to refine the localization of blood pressure (BP) quantitative trait loci (QTLs) within the congenic region of S.R-Edn3 rats located at the q-terminus of rat chromosome 3 (RNO3). An F2(SxS.R-Edn3) population (n=173) was screened to identify rats having crossovers within the congenic region of RNO3 and six congenic substrains were developed that carry shorter segments of R-rat derived RNO3. Five of the six congenic substrains had significantly lower BP compared to the parental S rat. The lack of BP lowering effect demonstrated by the S.R(ET3x5) substrain and the BP lowering effect retained by S.R(ET3x2) substrain, together define the RNO3 BP QTL-containing region as approximately 4.64 Mb.