DOCSLIB.ORG

Ii Binding Specificity of SH2 Domains Revealed by a Combinatorial

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Ii Binding Specificity of SH2 Domains Revealed by a Combinatorial Binding Specificity of SH2 Domains Revealed by a Combinatorial Peptide Library Thesis Presented in Partial Fulfillment of the Requirements for the Master’s Degree in the Graduate School of The Ohio State University By Andrew Kunys, B.S. Graduate Program in Chemistry The Ohio State University 2013 Thesis Committee: Dehua Pei, Advisor Ross Dalbey ii Copyright by Andrew Kunys 2013 iii Abstract Src homology 2 Domains (SH2 domains) are modular protein binding domains which recognize a phosphotyrosine and the residues adjacent to the phosphotyrosine. Although they are known to bind to specific phosphopeptides motifs, the exact peptide binding motifs for each of the 120 human SH2 domains are not known. These proteins are associated mainly with cellular signaling affecting processes as disparate as apoptosis and proliferation. In this work, high quality specificity data was obtained from 18 different SH2 domains by screening them individually against a One-Bead-One-Peptide library. Interestingly, no two domains have the same specificity, suggesting that subtle differences mark each domain as unique. Further, most of the domains studied recognize multiple different classes of binding peptides, which implies their role in binding may be more complicated that previously realized. The data obtained is unique, in that no other method can produce data of high enough quality to unambiguously establish the subtle specificity patterns of each domain or decipher the data obtained from a domain which can recognize multiple peptide ligand classes. This data will provide insight into the basic biology of cell signaling and should prove to be a valuable resource to researchers studying in this area. ii Acknowledgements I would like to thank, first and foremost, my mother and father for their love and support. Second, I would like to thank my colleagues and friends in Dr. Pei’s research group. I thank Yanyan Zhang and Pauline Tan for their encouragement, friendship and mentorship. I thank Nicholas Selner and Tao Liu for their encouragement and the many helpful discussions we have shared. I thank Tiffany Waller, Xianwen Chen, and Rinrada Luechapanichkul for their friendship and perspective. Finally, I thank Dr. Dehua Pei for his mentorship and support. iii Vita 2008 ……………………….........................................B.S. Chemistry, University of Michigan 2008 to present ...............................................Graduate Research Associate, Department of Chemistry, The Ohio State University 2008-2009........................................................Graduate Teaching Assistant, Department of Chemistry, The Ohio State University 2012-2013.........................................................Graduate Teaching Assistant, Center for Life Science Education, The Ohio State University Publications Kunys, A.R., Lian, W., and Pei, D. 2012. Specificity profiling of protein-binding domains using one-bead-one-compound Peptide libraries. Curr Protoc Chem Biol 4:331-355. Zhang, Y., Wavreille, A.S., Kunys, A.R., and Pei, D. 2009. The SH2 domains of inositol polyphosphate 5-phosphatases SHIP1 and SHIP2 have similar ligand specificity but different binding kinetics. Biochemistry 48:11075-11083. Fields of Study Major Field: Biological Chemistry iv Table of Contents Abstract ………………………………………………………………………………………….….……………………………………...ii Acknowledgements ……………………………………………………………………….………………….………………………iii Vita ……………………………………………………………………….……………………………………….….………………………iv List of Tables …………………………………………………………………………………………………………………………….vii List of Figures ……………………………………………………………………………………………………………………………..x Introduction SH2 domains discovery and function ………………………………………………………....…………………1 SH2 Structure and specificity determining elements ……………………………………………………3 Traditional methods of determining binding specificities .…………………………………….……….5 Determination of SH2 Specificity via One Bead One Compound libraries …..…………….……8 Experimental methods Materials ……………………………………………………….……………….............................................13 Expression of the SH2 Domains ……………………….…………………………………….……………........14 Purification and labeling of the SH2 Domains ………………………….……….………….…….………14 Covalent Labeling of SH2 Domains …………………………………………………….……..…………….....15 Library Synthesis …………………………………………………………………………………..……..................15 Screening SH2 Domains against the pY library ……………………………………..………………….....16 Partial Edman Degradation …………………………………………………………………..……………………..17 v MALDI-TOF analysis …………………………………………………….………………………………….............18 Results Library Used ….…………………………………………………………………………………………………………….19 Kinase SH2 Domains (Itk, Brk, SRMS, Frk, Btk) ………………………………….……………..……….….19 Adapter SH2 Domains (Gads, GRAP) ……………………………………………………………………………37 Shc Family SH2 Domains (Shc1, Shc2, Shc3, Shc4) ………………………………………….……….….44 Vav3 and SHE ………………………………………………………………………………………………………………59 (PLCG1-N, DAPP1, Blnk, SLA2, SH2D3A ……………………………….……………………………………..71 Discussion ……………………………………………………………………………………………………………………….……….94 Bibliography ………………………………………………………………………………………………………………….…………96 vi List of Tables Table 1. SH2 domains Examined in this study ………………………………………………………………………….23 Table 2. Peptide sequences selected from the Itk-SH2 Domain ………………………………………….…..24 Table 3. Peptide sequences selected from the Brk-SH2 Domain ………………………………….…….……25 Table 4. Peptide sequences selected from the SRMS-SH2 Domain ……………………………….…………26 Table 5. Peptide sequences selected from the Frk-SH2 Domain ………………………………….….……….27 Table6. Peptide sequences selected from the Btk-SH2 Domain ………………………………….….….……28 Table 7. Predicted Binding Partners of the Itk-SH2 Domain (Class 1) ………………….…………..….……31 Table 8. Predicted Binding Partners of the Itk-SH2 Domain (Class 2) ………………………………….……32 Table 9. Predicted Binding Partners of the Brk-SH2 Domain ……………………………………..……….……33 Table 10. Predicted Binding Partners of the SRMS-SH2 Domain ……………………………..………….……34 Table 11. Predicted Binding Partners of the Frk-SH2 Domain …………………………………………….……35 Table 12. Predicted Binding Partners of the Btk-SH2 Domain ………………………………………...….……36 Table13. Peptide sequences selected from the Gads-SH2 Domain …………………………….……………39 Table14. Peptide sequences selected from the GRAP-SH2 Domain …………………………………………40 Table 15. Predicted Binding Partners of the Gads-SH2 Domain ………………………………………..….….42 Table 16. Predicted Binding Partners of the GRAP-SH2 Domain ……………………………………..….……43 Table 17. Peptide sequences selected from the Shc1-SH2 Domain ………………………………………….47 Table 18. Peptide sequences selected from the Shc2-SH2 Domain …………………………..……………..48 Table 19. Peptide sequences selected from the Shc3-SH2 Domain ………………………………………….49 Table 20. Peptide sequences selected from the Shc4-SH2 Domain ………………………………….………50 vii Table 21. Predicted Binding Partners of the Shc1-SH2 Domain (Class 1) ………………………………….53 Table 22. Predicted Binding Partners of the Shc1-SH2 Domain (Class 2) ………………………………….54 Table 23. Predicted Binding Partners of the Shc2-SH2 Domain …………….………………………………….55 Table 24. Predicted Binding Partners of the Shc3-SH2 Domain (Class 1) ………………………………….56 Table 25. Predicted Binding Partners of the Shc3-SH2 Domain (Class 2) ………………………………….57 Table 26. Predicted Binding Partners of the Shc4-SH2 Domain ………………………….…………………….58 Table 27. Peptide sequences selected from the Vav3-SH2 Domain ………………….………………………61 Table 28. Peptide sequences selected from the SHE-SH2 Domain …………………………………..….…..62 Table 29. Predicted Binding Partners of the Vav3-SH2 Domain (Class 1) ………………………………….65 Table 30. Predicted Binding Partners of the Vav3-SH2 Domain (Class 2) ………………………………….66 Table 31. Predicted Binding Partners of the Vav3-SH2 Domain (Class 3) ………………………………….67 Table 32. Predicted Binding Partners of the Vav3-SH2 Domain (Class 4) ………………………………….68 Table 33. Predicted Binding Partners of the SHE-SH2 Domain (Class 1) ………….……………………….69 Table 34. Predicted Binding Partners of the SHE-SH2 Domain (Class 2) …………….…………………….70 Table 35. Peptide sequences selected from the PLCG1-N-SH2 Domain ………………….………………..75 Table 36. Peptide sequences selected from the DAPP1-SH2 Domain ………………….…………………..76 Table 37. Peptide sequences selected from the Blnk-SH2 Domain ………………….……………………….77 Table 38. Peptide sequences selected from the SLA2-SH2 Domain ………………….………………………78 Table 39. Peptide sequences selected from the SH2D3A-SH2 Domain ………………….…………………79 Table 40. Predicted Binding Partners of the PLCG1-N-SH2 Domain (Class 1) ……………………..…….83 Table 41. Predicted Binding Partners of the PLCG1-N-SH2 Domain (Class 2) …………..……………….84 Table 42. Predicted Binding Partners of the DAPP1-SH2 Domain (Class 1) ……………………………….85 Table 43. Predicted Binding Partners of the DAPP1-SH2 Domain (Class 2) ……………………………….86 Table 44. Predicted Binding Partners of the Blnk-SH2 Domain (Class 1) ………………………….……….87 viii Table 45. Predicted Binding Partners of the Blnk-SH2 Domain (Class 2) ………………….……………….88 Table 46. Predicted Binding Partners of the Blnk-SH2 Domain (Class 3) ………………………….……….89 Table 47. Predicted Binding Partners of the SLA2-SH2 Domain (Class 1) ………………………………….90 Table 48. Predicted Binding Partners of the SLA2-SH2 Domain (Class 2) ………………………………….91 Table 49. Predicted Binding Partners of the SH2D3A-SH2 Domain (Class 1) …………………………….92 Table 50. Predicted Binding Partners of the SH2D3A-SH2 Domain (Class 2) …………………………….93 ix List of Figures Figure 1. Src-SH2 domain bound to pYEEI ……………………………………………….………………..………………4 Figure 2. Library used for screening ……………………………………………………….……………………………….9 Figure 3. Screening Scheme ………………………………………………………………….…………………….…………..10 Figure 4. Partial
Load more

Recommended publications
  • PARSANA-DISSERTATION-2020.Pdf
    DECIPHERING TRANSCRIPTIONAL PATTERNS OF GENE REGULATION: A COMPUTATIONAL APPROACH by Princy Parsana A dissertation submitted to The Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland July, 2020 © 2020 Princy Parsana All rights reserved Abstract With rapid advancements in sequencing technology, we now have the ability to sequence the entire human genome, and to quantify expression of tens of thousands of genes from hundreds of individuals. This provides an extraordinary opportunity to learn phenotype relevant genomic patterns that can improve our understanding of molecular and cellular processes underlying a trait. The high dimensional nature of genomic data presents a range of computational and statistical challenges. This dissertation presents a compilation of projects that were driven by the motivation to efficiently capture gene regulatory patterns in the human transcriptome, while addressing statistical and computational challenges that accompany this data. We attempt to address two major difficulties in this domain: a) artifacts and noise in transcriptomic data, andb) limited statistical power. First, we present our work on investigating the effect of artifactual variation in gene expression data and its impact on trans-eQTL discovery. Here we performed an in-depth analysis of diverse pre-recorded covariates and latent confounders to understand their contribution to heterogeneity in gene expression measurements. Next, we discovered 673 trans-eQTLs across 16 human tissues using v6 data from the Genotype Tissue Expression (GTEx) project. Finally, we characterized two trait-associated trans-eQTLs; one in Skeletal Muscle and another in Thyroid. Second, we present a principal component based residualization method to correct gene expression measurements prior to reconstruction of co-expression networks.
    [Show full text]
  • MUC4/MUC16/Muc20high Signature As a Marker of Poor Prognostic for Pancreatic, Colon and Stomach Cancers
    Jonckheere and Van Seuningen J Transl Med (2018) 16:259 https://doi.org/10.1186/s12967-018-1632-2 Journal of Translational Medicine RESEARCH Open Access Integrative analysis of the cancer genome atlas and cancer cell lines encyclopedia large‑scale genomic databases: MUC4/MUC16/ MUC20 signature is associated with poor survival in human carcinomas Nicolas Jonckheere* and Isabelle Van Seuningen* Abstract Background: MUC4 is a membrane-bound mucin that promotes carcinogenetic progression and is often proposed as a promising biomarker for various carcinomas. In this manuscript, we analyzed large scale genomic datasets in order to evaluate MUC4 expression, identify genes that are correlated with MUC4 and propose new signatures as a prognostic marker of epithelial cancers. Methods: Using cBioportal or SurvExpress tools, we studied MUC4 expression in large-scale genomic public datasets of human cancer (the cancer genome atlas, TCGA) and cancer cell line encyclopedia (CCLE). Results: We identifed 187 co-expressed genes for which the expression is correlated with MUC4 expression. Gene ontology analysis showed they are notably involved in cell adhesion, cell–cell junctions, glycosylation and cell signal- ing. In addition, we showed that MUC4 expression is correlated with MUC16 and MUC20, two other membrane-bound mucins. We showed that MUC4 expression is associated with a poorer overall survival in TCGA cancers with diferent localizations including pancreatic cancer, bladder cancer, colon cancer, lung adenocarcinoma, lung squamous adeno- carcinoma, skin cancer and stomach cancer. We showed that the combination of MUC4, MUC16 and MUC20 signature is associated with statistically signifcant reduced overall survival and increased hazard ratio in pancreatic, colon and stomach cancer.
    [Show full text]
  • An Investigation Into the Genetic Architecture of Multiple System Atrophy and Familial Parkinson's Disease
    An investigation into the genetic architecture of multiple system atrophy and familial Parkinson’s disease By Monica Federoff A thesis submitted to University College London for the degree of Doctor of Philosophy Laboratory of Neurogenetics, Department of Molecular Neuroscience, Institute of Neurology, University College London (UCL) 2 I, Monica Federoff, confirm that the work presented in this thesis is my own. Information derived from other sources and collaborative work have been indicated appropriately. Signature: Date: 09/06/2016 3 Acknowledgements: When I first joined the Laboratory of Neurogenetics (LNG), NIA, NIH as a summer intern in 2008, I had minimal experience working in a laboratory and was both excited and anxious at the prospect of it. From my very first day, Dr. Andrew Singleton was incredibly welcoming and introduced me to my first mentor, Dr. Javier Simon- Sanchez. Within just ten weeks working in the lab, both Dr. Singleton and Dr. Simon- Sanchez taught me the fundamental skills in an encouraging and supportive environment. I quickly got to know others in the lab, some of whom are still here today, and I sincerely appreciate their help with my assimilation into the LNG. After returning for an additional summer and one year as an IRTA postbac, I was honored to pursue a PhD in such an intellectually stimulating and comfortable environment. I am so grateful that Dr. Singleton has been such a wonderful mentor, as he is not only a brilliant scientist, but also extremely personable and approachable. If I inquire about meeting with him, he always manages to make time in his busy schedule and provides excellent guidance and mentorship.
    [Show full text]
  • Anti-VAV3 (Aa 567-578) Polyclonal Antibody (DPABH-12442) This Product Is for Research Use Only and Is Not Intended for Diagnostic Use
    Anti-VAV3 (aa 567-578) polyclonal antibody (DPABH-12442) This product is for research use only and is not intended for diagnostic use. PRODUCT INFORMATION Antigen Description Exchange factor for GTP-binding proteins RhoA, RhoG and, to a lesser extent, Rac1. Binds physically to the nucleotide-free states of those GTPases. Plays an important role in angiogenesis. Its recruitement by phosphorylated EPHA2 is critical for EFNA1-induced RAC1 GTPase activation and vascular endothelial cell migration and assembly. Immunogen Synthetic peptide: CSGEQGTLKLPEK, corresponding to internal sequence amino acids 567-578 of Human VAV3 Isotype IgG Source/Host Goat Species Reactivity Human Purification Immunogen affinity purified Conjugate Unconjugated Applications WB, IHC-P Format Liquid Size 50 μg Buffer pH: 7.40; Constituents: 0.5% BSA, Tris buffered saline Preservative 0.02% Sodium Azide Storage Store at 4°C or at -20°C for long term storage. GENE INFORMATION Gene Name VAV3 vav 3 guanine nucleotide exchange factor [ Homo sapiens ] Official Symbol VAV3 Synonyms VAV3; vav 3 guanine nucleotide exchange factor; vav 3 oncogene; guanine nucleotide exchange factor VAV3; VAV-3; FLJ40431; 45-1 Ramsey Road, Shirley, NY 11967, USA Email: [email protected] Tel: 1-631-624-4882 Fax: 1-631-938-8221 1 © Creative Diagnostics All Rights Reserved Entrez Gene ID 10451 Protein Refseq NP_001073343 UniProt ID Q9UKW4 Chromosome Location 1p13.3 Pathway B cell receptor signaling pathway; Cell death signalling via NRAGE, NRIF and NADE; Chemokine signaling pathway; Coregulation of Androgen receptor activity; EGFR1 Signaling Pathway; Function GTPase activator activity; Rac guanyl-nucleotide exchange factor activity; SH3/SH2 adaptor activity; epidermal growth factor receptor binding; guanyl-nucleotide exchange factor activity; metal ion binding; protein binding 45-1 Ramsey Road, Shirley, NY 11967, USA Email: [email protected] Tel: 1-631-624-4882 Fax: 1-631-938-8221 2 © Creative Diagnostics All Rights Reserved.
    [Show full text]
  • Reconstruction and Analysis of Gene Networks of Human
    G C A T T A C G G C A T genes Article Reconstruction and Analysis of Gene Networks of Human Neurotransmitter Systems Reveal Genes with Contentious Manifestation for Anxiety, Depression, and Intellectual Disabilities Roman Ivanov 1,2,*, Vladimir Zamyatin 1,2, Alexandra Klimenko 1,2 , Yury Matushkin 1,2, Alexander Savostyanov 1,2,3 and Sergey Lashin 1,2 1 Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia; [email protected] (V.Z.); [email protected] (A.K.); [email protected] (Y.M.); [email protected] (A.S.); [email protected] (S.L.) 2 Novosibirsk State University, 630090 Novosibirsk, Russia 3 Institute of Physiology and Basic Medicine SB RAMS, 630117 Novosibirsk, Russia * Correspondence: [email protected] Received: 12 August 2019; Accepted: 9 September 2019; Published: 11 September 2019 Abstract: Background: The study of the biological basis of anxiety, depression, and intellectual disabilities in humans is one of the most actual problems of modern neurophysiology. Of particular interest is the study of complex interactions between molecular genetic factors, electrophysiological properties of the nervous system, and the behavioral characteristics of people. The neurobiological understanding of neuropsychiatric disorders requires not only the identification of genes that play a role in the molecular mechanisms of the occurrence and course of diseases, but also the understanding of complex interactions that occur between these genes. A systematic study of such interactions obviously contributes to the development of new methods of diagnosis, prevention, and treatment of disorders, as the orientation to allele variants of individual loci is not reliable enough, because the literature describes a number of genes, the same alleles of which can be associated with different, sometimes extremely different variants of phenotypic traits, depending on the genetic background, of their carriers, habitat, and other factors.
    [Show full text]
  • Multiple System Atrophy: Genetic Or Epigenetic?
    http://dx.doi.org/10.5607/en.2014.23.4.277 Exp Neurobiol. 2014 Dec;23(4):277-291. pISSN 1226-2560 • eISSN 2093-8144 Review Article A featured article of the special issue on Parkinsonian Syndrome Multiple System Atrophy: Genetic or Epigenetic? Edith Sturm and Nadia Stefanova* Division of Neurobiology, Department of Neurology, Innsbruck Medical University, Innsbruck A-6020, Austria Multiple system atrophy (MSA) is a rare, late-onset and fatal neurodegenerative disease including multisystem neurodegeneration and the formation of α-synuclein containing oligodendroglial cytoplasmic inclusions (GCIs), which present the hallmark of the disease. MSA is considered to be a sporadic disease; however certain genetic aspects have been studied during the last years in order to shed light on the largely unknown etiology and pathogenesis of the disease. Epidemiological studies focused on the possible impact of environmental factors on MSA disease development. This article gives an overview on the findings from genetic and epigenetic studies on MSA and discusses the role of genetic or epigenetic factors in disease pathogenesis. Key words: Multiple system atrophy, α-synuclein, neurodegeneration, genetics, epigenetics INTRODUCTION is characterized by selective wide spread neuronal cell loss, gliosis and oligodendroglial cytoplasmic inclusions (GCIs) affecting In 1969 Graham and Oppenheimer suggested the term several structures of the central nervous system [3, 6, 7]. “multiple system atrophy” (MSA) to describe and combine Neuronal loss in MSA affects the striatum, substantia nigra a set of different disorders, including olivopontocerebellar pars compacta (SNpc), cerebellum, pons, inferior olives, central atrophy (OPCA), striatonigral degeneration (SND) and Shy- autonomic nuclei and the intermediolateral column of the spinal Drager syndrome [1].
    [Show full text]
  • Chromosome Abnormalities in Two Patients with Features of Autosomal Dominant Robinow Syndrome
    ß 2007 Wiley-Liss, Inc. American Journal of Medical Genetics Part A 143A:1790–1795 (2007) Research Letter Chromosome Abnormalities in Two Patients With Features of Autosomal Dominant Robinow Syndrome Juliana F. Mazzeu,1 Ana Cristina Krepischi-Santos,1 Carla Rosenberg,1 Karoly Szuhai,2 Jeroen Knijnenburg,2 Janneke M.M. Weiss,3 Irina Kerkis,1 Zan Mustacchi,4 Guilherme Colin,5 Roˆmulo Mombach,6 Rita de Ca´ssia M. Pavanello,1 Paulo A. Otto,1 and Angela M. Vianna-Morgante1* 1Centro de Estudos do Genoma Humano, Departamento de Gene´tica e Biologia Evolutiva, Instituto de Biocieˆncias, Universidade de Sa˜o Paulo, Sa˜o Paulo, Brazil 2Department of Molecular Cell Biology, Leiden University Medical Center, Leiden, The Netherlands 3Department of Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands 4Hospital Infantil Darcy Vargas, Sa˜o Paulo, Brazil 5Departamento de Gene´tica Me´dica, Univille, Joinville, Brazil 6Centrinho Prefeito Luiz Gomes, Secretaria Municipal de Sau´de, Joinville, Brazil Received 13 April 2006; Accepted 13 December 2006 How to cite this article: Mazzeu JF, Krepischi-Santos AC, Rosenberg C, Szuhai K, Knijnenburg J, Weiss JMM, Kerkis I, Mustacchi Z, Colin G, Mombach R, Pavanello RM, Otto PA, Vianna-Morgante AM. 2007. Chromosome abnormalities in two patients with features of autosomal dominant Robinow syndrome. Am J Med Genet Part A 143A:1790–1795. To the Editor: Patient 1 Robinow syndrome [OMIM 180700] is characteriz- At age 3 4/12 years the girl was diagnosed as ed by fetal facies, mesomelic dwarfism, and hypo- affected by DRS (Fig. 1A). Detailed clinical examina- plastic genitalia.
    [Show full text]
  • Vav1 (D45G3) Rabbit Mab A
    Revision 1 C 0 2 - t Vav1 (D45G3) Rabbit mAb a e r o t S Orders: 877-616-CELL (2355) [email protected] Support: 877-678-TECH (8324) 7 5 Web: [email protected] 6 www.cellsignal.com 4 # 3 Trask Lane Danvers Massachusetts 01923 USA For Research Use Only. Not For Use In Diagnostic Procedures. Applications: Reactivity: Sensitivity: MW (kDa): Source/Isotype: UniProt ID: Entrez-Gene Id: WB, IP H Endogenous 95 Rabbit IgG P15498 7409 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, 50% glycerol and less than 0.02% sodium azide. Store at –20°C. Do not aliquot the antibody. Specificity / Sensitivity Vav1 (D45G3) Rabbit mAb recognizes endogenous levels of total Vav1 protein. Species Reactivity: Human Species predicted to react based on 100% sequence homology: Monkey Source / Purification Monoclonal antibody is produced by immunizing animals with a synthetic peptide corresponding to residues in the carboxy terminus of human Vav1 protein. Background Vav proteins belong to the Dbl family of guanine nucleotide exchange factors (GEFs) for Rho/Rac small GTPases. The three identified mammalian Vav proteins (Vav1, Vav2 and Vav3) differ in their expression. Vav1 is expressed only in hematopoietic cells and is involved in the formation of the immune synapse. Vav2 and Vav3 are more ubiquitously expressed. Vav proteins contain the Dbl homology domain, which confers GEF activity, as well as protein interaction domains that allow them to function in pathways regulating actin cytoskeleton organization (reviewed in 1).
    [Show full text]
  • Comparative Transcriptome Profiling of the Human and Mouse Dorsal Root Ganglia: an RNA-Seq-Based Resource for Pain and Sensory Neuroscience Research
    bioRxiv preprint doi: https://doi.org/10.1101/165431; this version posted October 13, 2017. 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. Title: Comparative transcriptome profiling of the human and mouse dorsal root ganglia: An RNA-seq-based resource for pain and sensory neuroscience research Short Title: Human and mouse DRG comparative transcriptomics Pradipta Ray 1, 2 #, Andrew Torck 1 , Lilyana Quigley 1, Andi Wangzhou 1, Matthew Neiman 1, Chandranshu Rao 1, Tiffany Lam 1, Ji-Young Kim 1, Tae Hoon Kim 2, Michael Q. Zhang 2, Gregory Dussor 1 and Theodore J. Price 1, # 1 The University of Texas at Dallas, School of Behavioral and Brain Sciences 2 The University of Texas at Dallas, Department of Biological Sciences # Corresponding authors Theodore J Price Pradipta Ray School of Behavioral and Brain Sciences School of Behavioral and Brain Sciences The University of Texas at Dallas The University of Texas at Dallas BSB 14.102G BSB 10.608 800 W Campbell Rd 800 W Campbell Rd Richardson TX 75080 Richardson TX 75080 972-883-4311 972-883-7262 [email protected] [email protected] Number of pages: 27 Number of figures: 9 Number of tables: 8 Supplementary Figures: 4 Supplementary Files: 6 Word count: Abstract = 219; Introduction = 457; Discussion = 1094 Conflict of interest: The authors declare no conflicts of interest Patient anonymity and informed consent: Informed consent for human tissue sources were obtained by Anabios, Inc. (San Diego, CA). Human studies: This work was approved by The University of Texas at Dallas Institutional Review Board (MR 15-237).
    [Show full text]
  • BRG1 Knockdown Inhibits Proliferation Through Multiple Cellular Pathways in Prostate Cancer Katherine A
    Giles et al. Clin Epigenet (2021) 13:37 https://doi.org/10.1186/s13148-021-01023-7 RESEARCH Open Access BRG1 knockdown inhibits proliferation through multiple cellular pathways in prostate cancer Katherine A. Giles1,2,3, Cathryn M. Gould1, Joanna Achinger‑Kawecka1,4, Scott G. Page2, Georgia R. Kafer2, Samuel Rogers2, Phuc‑Loi Luu1,4, Anthony J. Cesare2, Susan J. Clark1,4† and Phillippa C. Taberlay3*† Abstract Background: BRG1 (encoded by SMARCA4) is a catalytic component of the SWI/SNF chromatin remodelling com‑ plex, with key roles in modulating DNA accessibility. Dysregulation of BRG1 is observed, but functionally uncharacter‑ ised, in a wide range of malignancies. We have probed the functions of BRG1 on a background of prostate cancer to investigate how BRG1 controls gene expression programmes and cancer cell behaviour. Results: Our investigation of SMARCA4 revealed that BRG1 is over‑expressed in the majority of the 486 tumours from The Cancer Genome Atlas prostate cohort, as well as in a complementary panel of 21 prostate cell lines. Next, we utilised a temporal model of BRG1 depletion to investigate the molecular efects on global transcription programmes. Depleting BRG1 had no impact on alternative splicing and conferred only modest efect on global expression. How‑ ever, of the transcriptional changes that occurred, most manifested as down‑regulated expression. Deeper examina‑ tion found the common thread linking down‑regulated genes was involvement in proliferation, including several known to increase prostate cancer proliferation (KLK2, PCAT1 and VAV3). Interestingly, the promoters of genes driving proliferation were bound by BRG1 as well as the transcription factors, AR and FOXA1.
    [Show full text]
  • Comparative Transcriptome Profiling of the Human and Mouse Dorsal Root
    Research Paper Comparative transcriptome profiling of the human and mouse dorsal root ganglia: an RNA-seq–based resource for pain and sensory neuroscience research a,b a a a a a 07/20/2018 on BhDMf5ePHKav1zEoum1tQfN4a+kJLhEZgbsIHo4XMi0hCywCX1AWnYQp/IlQrHD3mH5nK33R3Qh4f27oe7zFUUf7ZAUK5aCsyqAeT54jiDxP7ZjumT3TrA== by https://journals.lww.com/pain from Downloaded Pradipta Ray , Andrew Torck , Lilyana Quigley , Andi Wangzhou , Matthew Neiman , Chandranshu Rao , Downloaded Tiffany Lama, Ji-Young Kima, Tae Hoon Kimb, Michael Q. Zhangb, Gregory Dussora, Theodore J. Pricea,* from https://journals.lww.com/pain Abstract Molecular neurobiological insight into human nervous tissues is needed to generate next-generation therapeutics for neurological disorders such as chronic pain. We obtained human dorsal root ganglia (hDRG) samples from organ donors and performed RNA- sequencing (RNA-seq) to study the hDRG transcriptional landscape, systematically comparing it with publicly available data from by BhDMf5ePHKav1zEoum1tQfN4a+kJLhEZgbsIHo4XMi0hCywCX1AWnYQp/IlQrHD3mH5nK33R3Qh4f27oe7zFUUf7ZAUK5aCsyqAeT54jiDxP7ZjumT3TrA== a variety of human and orthologous mouse tissues, including mouse DRG (mDRG). We characterized the hDRG transcriptional profile in terms of tissue-restricted gene coexpression patterns and putative transcriptional regulators, and formulated an information-theoretic framework to quantify DRG enrichment. Relevant gene families and pathways were also analyzed, including transcription factors, G-protein-coupled receptors, and ion channels. Our analyses reveal an hDRG-enriched protein-coding gene set (;140), some of which have not been described in the context of DRG or pain signaling. Most of these show conserved enrichment in mDRG and were mined for known drug–gene product interactions. Conserved enrichment of the vast majority of transcription factors suggests that the mDRG is a faithful model system for studying hDRG, because of evolutionarily conserved regulatory programs.
    [Show full text]
  • Genome-Wide Gene Expression Profiles of Ovarian Carcinoma: Identification of Molecular Targets for the Treatment of Ovarian Carcinoma
    365-384 30/3/2009 09:55 Ì Page 365 MOLECULAR MEDICINE REPORTS 2: 365-384, 2009 365 Genome-wide gene expression profiles of ovarian carcinoma: Identification of molecular targets for the treatment of ovarian carcinoma DRAGOMIRA NIKOLAEVA NIKOLOVA1,4, NIKOLAI DOGANOV2, RUMEN DIMITROV2, KRASIMIR ANGELOV3, SIEW-KEE LOW1, IVANKA DIMOVA4, DRAGA TONCHEVA4, YUSUKE NAKAMURA1 and HITOSHI ZEMBUTSU1 1Laboratory of Molecular Medicine, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo 108-8639, Japan; 2University Hospital of Obstetrics and Gynecology ‘Maichin Dom’, Sofia 1431; 3National Hospital of Oncology, Sofia 1233; 4Department of Medical Genetics, Medical University of Sofia, Sofia 1431, Bulgaria Received October 31, 2008; Accepted January 7, 2009 DOI: 10.3892/mmr_00000109 Abstract. This study aimed to clarify the molecular mecha- in women. As there are no specific indicators or symptoms of nisms involved in ovarian carcinogenesis, and to identify ovarian cancer during the early stages of the disease, the candidate molecular targets for its diagnosis and treatment. majority of patients with epithelial ovarian cancer (EOC) are The genome-wide gene expression profiles of 22 epithelial diagnosed at an advanced stage, with involvement of other ovarian carcinomas were analyzed with a microarray represent- sites such as the upper abdomen, pleural space and paraaortic ing 38,500 genes, in combination with laser microbeam lymph nodes. The cancer antigen 125 assay (CA-125) has been microdissection. A total of 273 commonly up-regulated used to screen for ovarian cancer, but is not specific; only 50% transcripts and 387 down-regulated transcripts were identified of patients with early ovarian cancer test positive using this in the ovarian carcinoma samples.
    [Show full text]