DOCSLIB.ORG

Dissecting Aberrant Proteolytic Ubiquitin Networks in Cancer

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Dissecting Aberrant Proteolytic Ubiquitin Networks in Cancer TECHNISCHE UNIVERSITÄT MÜNCHEN Fakultät für Medizin III. Medizinische Klinik und Poliklinik Klinikum rechts der Isar Dissecting aberrant proteolytic ubiquitin networks in cancer Carmen Gloria Richter (M.Sc.) Vollständiger Abdruck der von der Fakultät für Medizin der Technischen Universität München zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften (Dr. rer. nat.) genehmigten Dissertation. Vorsitzender: Prof. Dr. Dr. Andreas Pichlmair Prüfer der Dissertation: 1. Prof. Dr. Florian Bassermann 2. Prof. Dr. Bernhard Küster 3. Prof. Dr. Sebastian Theurich Die Dissertation wurde am 01.08.2019 bei der Technischen Universität München eingereicht und durch die Fakultät für Medizin am 07.04.2020 angenommen. Content 1 Summary ................................................................................................................... 1 2 Introduction .............................................................................................................. 3 2.1 Multiple myeloma ............................................................................................................3 2.1.1 Pathophysiology .........................................................................................................3 2.1.2 Clinical manifestation and diagnosis ...........................................................................5 2.1.3 Treatment ...................................................................................................................6 2.1.4 The role of MYC in multiple myeloma .........................................................................7 2.2 The ubiquitin code ...........................................................................................................7 2.2.1 Ubiquitin .....................................................................................................................8 2.2.2 Ubiquitylation cascade ...............................................................................................9 2.2.3 SCF family of ubiquitin E3 ligases .............................................................................10 2.2.4 The human F-box protein FBXL13 ............................................................................12 2.2.5 Deubiquitinases ........................................................................................................12 2.2.6 The OTU family DUB OTUD6B ..................................................................................14 2.2.7 The proteasome ........................................................................................................15 2.3 LIN28 ..............................................................................................................................16 2.3.1 Physiological role of LIN28 .......................................................................................16 2.3.2 The role of LIN28B in cancer.....................................................................................18 2.4 The cell cycle .................................................................................................................20 2.4.1 Overview of the cell cycle .........................................................................................20 2.4.2 G1/S transition ..........................................................................................................22 2.5 Centrosomes .................................................................................................................22 2.5.1 Centrosome functions and structure .........................................................................22 2.5.2 The centrosome cycle ...............................................................................................23 2.6 Aim of the study ............................................................................................................25 3 Material ................................................................................................................... 27 3.1 Devices and instruments ..............................................................................................27 3.2 Consumables .................................................................................................................28 3.3 Chemicals and reagents ...............................................................................................29 3.4 Commercial kits ............................................................................................................32 3.5 Enzymes .........................................................................................................................32 3.6 Oligonucleotides ...........................................................................................................33 3.6.1 Cloning oligonucleotides ...........................................................................................33 I 3.6.2 Oligonucleotides for amplicon deep-sequencing ...................................................... 34 3.6.3 qPCR oligonucleotides ............................................................................................. 34 3.6.4 Sequencing oligonucleotides .................................................................................... 35 3.7 Sequences of shRNAs and siRNAs .............................................................................. 35 3.7.1 Sequences of shRNAs .............................................................................................. 35 3.7.2 Sequences of siRNAs ............................................................................................... 36 3.8 Plasmids ........................................................................................................................ 36 3.9 Bacteria .......................................................................................................................... 38 3.10 Standards for DNA and proteins electrophoresis ....................................................... 38 3.11 Antibodies ...................................................................................................................... 38 3.11.1 Primary antibodies .................................................................................................... 38 3.11.2 Custom made primary antibodies ............................................................................. 39 3.11.3 Conjugated primary antibodies ................................................................................. 39 3.11.4 Conjugated secondary antibodies ............................................................................ 39 3.12 Cell lines ........................................................................................................................ 40 3.13 Cell culture media and supplements ........................................................................... 40 3.14 Patient samples ............................................................................................................. 41 3.15 Solutions and buffers .................................................................................................... 41 3.16 Software and databases ............................................................................................... 44 4 Methods................................................................................................................... 45 4.1 Molecular cloning .......................................................................................................... 45 4.1.1 Polymerase chain reaction (PCR) .............................................................................. 45 4.1.2 Agarose gel electrophoresis and gel purification ...................................................... 45 4.1.3 DNA restriction digest and ligation............................................................................ 45 4.1.4 DNA mutagenesis ..................................................................................................... 46 4.1.5 Bacterial transformation ............................................................................................ 46 4.1.6 DNA extraction from bacteria ................................................................................... 47 4.1.7 Annealing of shRNA oligonucleotides ....................................................................... 47 4.2 Generation of CRISPR/Cas9 sgRNA libraries ............................................................. 47 4.2.1 Designing CRISPR/Cas9 libraries ............................................................................. 47 4.2.2 Annealing and dilution of libraries ............................................................................. 48 4.2.3 Vector digest and ligation of libraries ........................................................................ 48 4.2.4 Bacterial transformation and harvesting of libraries .................................................. 48 4.2.5 CRISPR/Cas9 screen ................................................................................................ 49 4.2.6 Sample preparation for Illumina next-generation sequencing ................................... 49 II 4.2.7 Illumina MiSeq sequencing .......................................................................................50 4.3 Gene expression analysis .............................................................................................51 4.3.1 RNA extraction from eukaryotic cells ........................................................................51
Load more

Recommended publications
  • Dissecting the Genetic Relationship Between Cardiovascular Risk Factors and Alzheimer's Disease
    UC San Diego UC San Diego Previously Published Works Title Dissecting the genetic relationship between cardiovascular risk factors and Alzheimer's disease. Permalink https://escholarship.org/uc/item/7137q6g1 Journal Acta neuropathologica, 137(2) ISSN 0001-6322 Authors Broce, Iris J Tan, Chin Hong Fan, Chun Chieh et al. Publication Date 2019-02-01 DOI 10.1007/s00401-018-1928-6 Peer reviewed eScholarship.org Powered by the California Digital Library University of California Acta Neuropathologica https://doi.org/10.1007/s00401-018-1928-6 ORIGINAL PAPER Dissecting the genetic relationship between cardiovascular risk factors and Alzheimer’s disease Iris J. Broce1 · Chin Hong Tan1,2 · Chun Chieh Fan3 · Iris Jansen4 · Jeanne E. Savage4 · Aree Witoelar5 · Natalie Wen6 · Christopher P. Hess1 · William P. Dillon1 · Christine M. Glastonbury1 · Maria Glymour7 · Jennifer S. Yokoyama8 · Fanny M. Elahi8 · Gil D. Rabinovici8 · Bruce L. Miller8 · Elizabeth C. Mormino9 · Reisa A. Sperling10,11 · David A. Bennett12 · Linda K. McEvoy13 · James B. Brewer13,14,15 · Howard H. Feldman14 · Bradley T. Hyman10 · Margaret Pericak‑Vance16 · Jonathan L. Haines17,18 · Lindsay A. Farrer19,20,21,22,23 · Richard Mayeux24,25,26 · Gerard D. Schellenberg27 · Kristine Yafe7,8,28 · Leo P. Sugrue1 · Anders M. Dale3,13,14 · Danielle Posthuma4 · Ole A. Andreassen5 · Celeste M. Karch6 · Rahul S. Desikan1 Received: 22 September 2018 / Revised: 28 October 2018 / Accepted: 28 October 2018 © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Cardiovascular (CV)- and lifestyle-associated risk factors (RFs) are increasingly recognized as important for Alzheimer’s disease (AD) pathogenesis. Beyond the ε4 allele of apolipoprotein E (APOE), comparatively little is known about whether CV-associated genes also increase risk for AD.
    [Show full text]
  • Mouse Germ Line Mutations Due to Retrotransposon Insertions Liane Gagnier1, Victoria P
    Gagnier et al. Mobile DNA (2019) 10:15 https://doi.org/10.1186/s13100-019-0157-4 REVIEW Open Access Mouse germ line mutations due to retrotransposon insertions Liane Gagnier1, Victoria P. Belancio2 and Dixie L. Mager1* Abstract Transposable element (TE) insertions are responsible for a significant fraction of spontaneous germ line mutations reported in inbred mouse strains. This major contribution of TEs to the mutational landscape in mouse contrasts with the situation in human, where their relative contribution as germ line insertional mutagens is much lower. In this focussed review, we provide comprehensive lists of TE-induced mouse mutations, discuss the different TE types involved in these insertional mutations and elaborate on particularly interesting cases. We also discuss differences and similarities between the mutational role of TEs in mice and humans. Keywords: Endogenous retroviruses, Long terminal repeats, Long interspersed elements, Short interspersed elements, Germ line mutation, Inbred mice, Insertional mutagenesis, Transcriptional interference Background promoter and polyadenylation motifs and often a splice The mouse and human genomes harbor similar types of donor site [10, 11]. Sequences of full-length ERVs can TEs that have been discussed in many reviews, to which encode gag, pol and sometimes env, although groups of we refer the reader for more in depth and general infor- LTR retrotransposons with little or no retroviral hom- mation [1–9]. In general, both human and mouse con- ology also exist [6–9]. While not the subject of this re- tain ancient families of DNA transposons, none view, ERV LTRs can often act as cellular enhancers or currently active, which comprise 1–3% of these genomes promoters, creating chimeric transcripts with genes, and as well as many families or groups of retrotransposons, have been implicated in other regulatory functions [11– which have caused all the TE insertional mutations in 13].
    [Show full text]
  • Tumor-Associated Antigens Identified Early in Mouse Mammary Tumor Development Can Be Effective Vaccine Targets
    Vaccine 37 (2019) 3552–3561 Contents lists available at ScienceDirect Vaccine journal homepage: www.elsevier.com/locate/vaccine Tumor-associated antigens identified early in mouse mammary tumor development can be effective vaccine targets ⇑ Sasha E. Stanton a, , Ekram Gad a, Lauren R. Corulli a, Hailing Lu a,1, Mary L. Disis a a Cancer Vaccine Institute, University of Washington, Seattle WA, 98109, USA article info abstract Article history: Breast cancer vaccines composed of antigens identified by serological analysis of cDNA expression Received 25 June 2018 libraries (SEREX) induce antigen specific immune responses in patients but have had disappointing clin- Received in revised form 5 April 2019 ical benefits. While many attempts to modify the adjuvants and vaccine method have been tried, one Accepted 9 May 2019 issue not addressed was whether the SEREX tumor-associated antigens identified from late stages of dis- Available online 21 May 2019 ease were ideal targets. We questioned in the transgenic TgMMTV-neu mouse model whether the antigen repertoire is distinct between early and late stage breast cancer and whether the antigens identified via Keywords: SEREX from transgenic mice with early or late stage tumors would elicit differential anti-tumor effects to Breast cancer prevention address this question. Vaccine antigens Th1 Three early stage antigens, Pdhx, Stk39, and Otud6B, were identified from a SEREX screen of mice prior DNA vaccines to development of palpable lesions. Formulated into a vaccine, each early antigen inhibited tumor growth Mouse mammary tumor models (p < 0.0001). The antigens identified from mice with late stage tumors (Swap70, Gsn, and Arhgef2) were unable to inhibit tumor growth when used as vaccines (for example Gsn p = 0.26).
    [Show full text]
  • A Clinicopathological and Molecular Genetic Analysis of Low-Grade Glioma in Adults
    A CLINICOPATHOLOGICAL AND MOLECULAR GENETIC ANALYSIS OF LOW-GRADE GLIOMA IN ADULTS Presented by ANUSHREE SINGH MSc A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy Brain Tumour Research Centre Research Institute in Healthcare Sciences Faculty of Science and Engineering University of Wolverhampton November 2014 i DECLARATION This work or any part thereof has not previously been presented in any form to the University or to any other body whether for the purposes of assessment, publication or for any other purpose (unless otherwise indicated). Save for any express acknowledgments, references and/or bibliographies cited in the work, I confirm that the intellectual content of the work is the result of my own efforts and of no other person. The right of Anushree Singh to be identified as author of this work is asserted in accordance with ss.77 and 78 of the Copyright, Designs and Patents Act 1988. At this date copyright is owned by the author. Signature: Anushree Date: 30th November 2014 ii ABSTRACT The aim of the study was to identify molecular markers that can determine progression of low grade glioma. This was done using various approaches such as IDH1 and IDH2 mutation analysis, MGMT methylation analysis, copy number analysis using array comparative genomic hybridisation and identification of differentially expressed miRNAs using miRNA microarray analysis. IDH1 mutation was present at a frequency of 71% in low grade glioma and was identified as an independent marker for improved OS in a multivariate analysis, which confirms the previous findings in low grade glioma studies.
    [Show full text]
  • Supplementary Materials
    Supplementary materials Supplementary Table S1: MGNC compound library Ingredien Molecule Caco- Mol ID MW AlogP OB (%) BBB DL FASA- HL t Name Name 2 shengdi MOL012254 campesterol 400.8 7.63 37.58 1.34 0.98 0.7 0.21 20.2 shengdi MOL000519 coniferin 314.4 3.16 31.11 0.42 -0.2 0.3 0.27 74.6 beta- shengdi MOL000359 414.8 8.08 36.91 1.32 0.99 0.8 0.23 20.2 sitosterol pachymic shengdi MOL000289 528.9 6.54 33.63 0.1 -0.6 0.8 0 9.27 acid Poricoic acid shengdi MOL000291 484.7 5.64 30.52 -0.08 -0.9 0.8 0 8.67 B Chrysanthem shengdi MOL004492 585 8.24 38.72 0.51 -1 0.6 0.3 17.5 axanthin 20- shengdi MOL011455 Hexadecano 418.6 1.91 32.7 -0.24 -0.4 0.7 0.29 104 ylingenol huanglian MOL001454 berberine 336.4 3.45 36.86 1.24 0.57 0.8 0.19 6.57 huanglian MOL013352 Obacunone 454.6 2.68 43.29 0.01 -0.4 0.8 0.31 -13 huanglian MOL002894 berberrubine 322.4 3.2 35.74 1.07 0.17 0.7 0.24 6.46 huanglian MOL002897 epiberberine 336.4 3.45 43.09 1.17 0.4 0.8 0.19 6.1 huanglian MOL002903 (R)-Canadine 339.4 3.4 55.37 1.04 0.57 0.8 0.2 6.41 huanglian MOL002904 Berlambine 351.4 2.49 36.68 0.97 0.17 0.8 0.28 7.33 Corchorosid huanglian MOL002907 404.6 1.34 105 -0.91 -1.3 0.8 0.29 6.68 e A_qt Magnogrand huanglian MOL000622 266.4 1.18 63.71 0.02 -0.2 0.2 0.3 3.17 iolide huanglian MOL000762 Palmidin A 510.5 4.52 35.36 -0.38 -1.5 0.7 0.39 33.2 huanglian MOL000785 palmatine 352.4 3.65 64.6 1.33 0.37 0.7 0.13 2.25 huanglian MOL000098 quercetin 302.3 1.5 46.43 0.05 -0.8 0.3 0.38 14.4 huanglian MOL001458 coptisine 320.3 3.25 30.67 1.21 0.32 0.9 0.26 9.33 huanglian MOL002668 Worenine
    [Show full text]
  • Clinical Validation of the Tempus Xt Next-Generation Targeted Oncology Sequencing Assay
    www.oncotarget.com Oncotarget, 2019, Vol. 10, (No. 24), pp: 2384-2396 Research Paper Clinical validation of the tempus xT next-generation targeted oncology sequencing assay Nike Beaubier1, Robert Tell1, Denise Lau1, Jerod R. Parsons1, Stephen Bush1, Jason Perera1, Shelly Sorrells1, Timothy Baker1, Alan Chang1, Jackson Michuda1, Catherine Iguartua1, Shelley MacNeil1, Kaanan Shah1, Philip Ellis1, Kimberly Yeatts1, Brett Mahon1, Timothy Taxter1, Martin Bontrager1, Aly Khan1, Robert Huether1, Eric Lefkofsky1 and Kevin P. White1 1Tempus Labs Inc., Chicago, IL 60654, USA Correspondence to: Nike Beaubier, email: [email protected] Kevin P. White, email: [email protected] Keywords: tumor profiling, next-generation sequencing assay validation Received: August 03, 2018 Accepted: February 03, 2019 Published: March 22, 2019 Copyright: Beaubier et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License 3.0 (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ABSTRACT We developed and clinically validated a hybrid capture next generation sequencing assay to detect somatic alterations and microsatellite instability in solid tumors and hematologic malignancies. This targeted oncology assay utilizes tumor- normal matched samples for highly accurate somatic alteration calling and whole transcriptome RNA sequencing for unbiased identification of gene fusion events. The assay was validated with a combination of clinical specimens and cell lines, and recorded a sensitivity of 99.1% for single nucleotide variants, 98.1% for indels, 99.9% for gene rearrangements, 98.4% for copy number variations, and 99.9% for microsatellite instability detection. This assay presents a wide array of data for clinical management and clinical trial enrollment while conserving limited tissue.
    [Show full text]
  • Greg's Awesome Thesis
    Analysis of alignment error and sitewise constraint in mammalian comparative genomics Gregory Jordan European Bioinformatics Institute University of Cambridge A dissertation submitted for the degree of Doctor of Philosophy November 30, 2011 To my parents, who kept us thinking and playing This dissertation is the result of my own work and includes nothing which is the out- come of work done in collaboration except where specifically indicated in the text and acknowledgements. This dissertation is not substantially the same as any I have submitted for a degree, diploma or other qualification at any other university, and no part has already been, or is currently being submitted for any degree, diploma or other qualification. This dissertation does not exceed the specified length limit of 60,000 words as defined by the Biology Degree Committee. November 30, 2011 Gregory Jordan ii Analysis of alignment error and sitewise constraint in mammalian comparative genomics Summary Gregory Jordan November 30, 2011 Darwin College Insight into the evolution of protein-coding genes can be gained from the use of phylogenetic codon models. Recently sequenced mammalian genomes and powerful analysis methods developed over the past decade provide the potential to globally measure the impact of natural selection on pro- tein sequences at a fine scale. The detection of positive selection in particular is of great interest, with relevance to the study of host-parasite conflicts, immune system evolution and adaptive dif- ferences between species. This thesis examines the performance of methods for detecting positive selection first with a series of simulation experiments, and then with two empirical studies in mammals and primates.
    [Show full text]
  • Deubiquitylases in Developmental Ubiquitin Signaling and Congenital Diseases
    Cell Death & Differentiation (2021) 28:538–556 https://doi.org/10.1038/s41418-020-00697-5 REVIEW ARTICLE Deubiquitylases in developmental ubiquitin signaling and congenital diseases 1 1,2 1 Mohammed A. Basar ● David B. Beck ● Achim Werner Received: 16 October 2020 / Revised: 20 November 2020 / Accepted: 24 November 2020 / Published online: 17 December 2020 This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2020 Abstract Metazoan development from a one-cell zygote to a fully formed organism requires complex cellular differentiation and communication pathways. To coordinate these processes, embryos frequently encode signaling information with the small protein modifier ubiquitin, which is typically attached to lysine residues within substrates. During ubiquitin signaling, a three-step enzymatic cascade modifies specific substrates with topologically unique ubiquitin modifications, which mediate changes in the substrate’s stability, activity, localization, or interacting proteins. Ubiquitin signaling is critically regulated by deubiquitylases (DUBs), a class of ~100 human enzymes that oppose the conjugation of ubiquitin. DUBs control many essential cellular functions and various aspects of human physiology and development. Recent genetic studies have fi 1234567890();,: 1234567890();,: identi ed mutations in several DUBs that cause developmental disorders. Here we review principles controlling DUB activity and substrate recruitment that allow these enzymes to regulate ubiquitin signaling during development. We summarize key mechanisms of how DUBs control embryonic and postnatal differentiation processes, highlight developmental disorders that are caused by mutations in particular DUB members, and describe our current understanding of how these mutations disrupt development. Finally, we discuss how emerging tools from human disease genetics will enable the identification and study of novel congenital disease-causing DUBs.
    [Show full text]
  • University of Groningen Development of Bioinformatic Tools And
    University of Groningen Development of bioinformatic tools and application of novel statistical methods in genome- wide analysis van der Most, Peter Johannes IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below. Document Version Publisher's PDF, also known as Version of record Publication date: 2017 Link to publication in University of Groningen/UMCG research database Citation for published version (APA): van der Most, P. J. (2017). Development of bioinformatic tools and application of novel statistical methods in genome-wide analysis. University of Groningen. Copyright Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons). The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne- amendment. Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons the number of authors shown on this cover page is limited to 10 maximum. Download date: 07-10-2021 Chapter 8 Genome-wide survival meta-analysis of age at first cannabis use Camelia C.
    [Show full text]
  • Whole Exome Sequencing in Families at High Risk for Hodgkin Lymphoma: Identification of a Predisposing Mutation in the KDR Gene
    Hodgkin Lymphoma SUPPLEMENTARY APPENDIX Whole exome sequencing in families at high risk for Hodgkin lymphoma: identification of a predisposing mutation in the KDR gene Melissa Rotunno, 1 Mary L. McMaster, 1 Joseph Boland, 2 Sara Bass, 2 Xijun Zhang, 2 Laurie Burdett, 2 Belynda Hicks, 2 Sarangan Ravichandran, 3 Brian T. Luke, 3 Meredith Yeager, 2 Laura Fontaine, 4 Paula L. Hyland, 1 Alisa M. Goldstein, 1 NCI DCEG Cancer Sequencing Working Group, NCI DCEG Cancer Genomics Research Laboratory, Stephen J. Chanock, 5 Neil E. Caporaso, 1 Margaret A. Tucker, 6 and Lynn R. Goldin 1 1Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 2Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 3Ad - vanced Biomedical Computing Center, Leidos Biomedical Research Inc.; Frederick National Laboratory for Cancer Research, Frederick, MD; 4Westat, Inc., Rockville MD; 5Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; and 6Human Genetics Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA ©2016 Ferrata Storti Foundation. This is an open-access paper. doi:10.3324/haematol.2015.135475 Received: August 19, 2015. Accepted: January 7, 2016. Pre-published: June 13, 2016. Correspondence: [email protected] Supplemental Author Information: NCI DCEG Cancer Sequencing Working Group: Mark H. Greene, Allan Hildesheim, Nan Hu, Maria Theresa Landi, Jennifer Loud, Phuong Mai, Lisa Mirabello, Lindsay Morton, Dilys Parry, Anand Pathak, Douglas R. Stewart, Philip R. Taylor, Geoffrey S. Tobias, Xiaohong R. Yang, Guoqin Yu NCI DCEG Cancer Genomics Research Laboratory: Salma Chowdhury, Michael Cullen, Casey Dagnall, Herbert Higson, Amy A.
    [Show full text]
  • Human Lectins, Their Carbohydrate Affinities and Where to Find Them
    biomolecules Review Human Lectins, Their Carbohydrate Affinities and Where to Review HumanFind Them Lectins, Their Carbohydrate Affinities and Where to FindCláudia ThemD. Raposo 1,*, André B. Canelas 2 and M. Teresa Barros 1 1, 2 1 Cláudia D. Raposo * , Andr1 é LAQVB. Canelas‐Requimte,and Department M. Teresa of Chemistry, Barros NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829‐516 Caparica, Portugal; [email protected] 12 GlanbiaLAQV-Requimte,‐AgriChemWhey, Department Lisheen of Chemistry, Mine, Killoran, NOVA Moyne, School E41 of ScienceR622 Co. and Tipperary, Technology, Ireland; canelas‐ [email protected] NOVA de Lisboa, 2829-516 Caparica, Portugal; [email protected] 2* Correspondence:Glanbia-AgriChemWhey, [email protected]; Lisheen Mine, Tel.: Killoran, +351‐212948550 Moyne, E41 R622 Tipperary, Ireland; [email protected] * Correspondence: [email protected]; Tel.: +351-212948550 Abstract: Lectins are a class of proteins responsible for several biological roles such as cell‐cell in‐ Abstract:teractions,Lectins signaling are pathways, a class of and proteins several responsible innate immune for several responses biological against roles pathogens. such as Since cell-cell lec‐ interactions,tins are able signalingto bind to pathways, carbohydrates, and several they can innate be a immuneviable target responses for targeted against drug pathogens. delivery Since sys‐ lectinstems. In are fact, able several to bind lectins to carbohydrates, were approved they by canFood be and a viable Drug targetAdministration for targeted for drugthat purpose. delivery systems.Information In fact, about several specific lectins carbohydrate were approved recognition by Food by andlectin Drug receptors Administration was gathered for that herein, purpose. plus Informationthe specific organs about specific where those carbohydrate lectins can recognition be found by within lectin the receptors human was body.
    [Show full text]
  • FBXO24 Rabbit Polyclonal Antibody – TA330492 | Origene
    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 TA330492 FBXO24 Rabbit Polyclonal Antibody Product data: Product Type: Primary Antibodies Applications: WB Recommended Dilution: WB Reactivity: Human Host: Rabbit Isotype: IgG Clonality: Polyclonal Immunogen: The immunogen for anti-FBXO24 antibody: synthetic peptide directed towards the middle region of human FBXO24. Synthetic peptide located within the following region: LCATRECLYILSSHDIEQHAPYRHLPASRVVGTPEPSLGARAPQDPGGMA Formulation: Liquid. Purified antibody supplied in 1x PBS buffer with 0.09% (w/v) sodium azide and 2% sucrose. Note that this product is shipped as lyophilized powder to China customers. Conjugation: Unconjugated Storage: Store at -20°C as received. Stability: Stable for 12 months from date of receipt. Predicted Protein Size: 65 kDa Gene Name: F-box protein 24 Database Link: NP_277041 Entrez Gene 26261 Human O75426 This product is to be used for laboratory only. Not for diagnostic or therapeutic use. View online » ©2021 OriGene Technologies, Inc., 9620 Medical Center Drive, Ste 200, Rockville, MD 20850, US 1 / 2 FBXO24 Rabbit Polyclonal Antibody – TA330492 Background: FBXO24 is a member of the F-box protein family which is characterized by an approximately 40 amino acid motif, the F-box. The F-box proteins constitute one of the four subunits of the ubiquitin protein ligase complex called SCFs (SKP1-cullin-F-box), which function in phosphorylation-dependent ubiquitination. The F-box proteins are divided into 3 classes: Fbws containing WD-40 domains, Fbls containing leucine-rich repeats, and Fbxs containing either different protein-protein interaction modules or no recognizable motifs.
    [Show full text]