Pro and Anti Tumorigenic Effect of Proteinases in Intestinal Tumorigenesis

Total Page:16

File Type:pdf, Size:1020Kb

Pro and Anti Tumorigenic Effect of Proteinases in Intestinal Tumorigenesis PRO AND ANTI TUMORIGENIC EFFECT OF PROTEINASES IN INTESTINAL TUMORIGENESIS By Mark Jason Sinnamon Dissertation Submitted to the Faculty of the Graduate School of Vanderbilt University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY in Cancer Biology May, 2008 Nashville, Tennessee Approved: Professor Lynn M. Matrisian Professor Barbara Fingleton Professor Ambra Pozzi Professor Simon W. Hayward Professor Kevin G. Osteen CANCER BIOLOGY PRO AND ANTI TUMORIGENIC EFFECT OF PROTEINASES IN INTESTINAL TUMORIGENESIS MARK JASON SINNAMON Dissertation under the direction of Professor Lynn M. Matrisian Matrix metalloproteinases (MMPs) are classically associated with late stage metastases, though previous work by our lab and others have expanded the role of MMPs to all stages of tumor development. Using a genetic model of intestinal tumorigenesis, the Min (multiple intestinal neoplasia) mouse, we previously demonstrated a role for MMPs, in particular MMP-7, in the development of intestinal adenomas. To further explore the role of proteinases in the development of intestinal neoplasia, I created a novel microarray to examine more than 500 proteases for differential expression in intestinal tumors. Relative microarray analysis found that MMPs-10, -13, and -14 have the highest fold change in expression in tumor compared to normal samples, while absolute microarray analysis indicated that MMPs-9, -12, -15 and -19 are present in tumor samples but not normal intestine. As a result of this screen, I generated Min mice genetically deficient for various proteinases that were differentially detected in Min adenomas (MMP-2, -9, -12, and -19). Genetic ablation of MMP-2, -12, or -19 did not affect tumor number or size, however, MMP-9 deficient Min mice developed 25% fewer tumors than littermate controls. Further, in the context of intestinal tumors, the major cellular source of MMP-9 is neutrophils thus suggesting a pro-tumorigenic role of inflammation in early tumorigenesis. Additionally, our screen detected several mast cell produced proteinases in tumor tissue that were absent from normal intestine. To examine the effect of mast cell proteinases in tumorigenesis, I generated mast cell-deficient Min mice. Surprisingly, these mice developed 50% more tumors than mast cell Min mice, thus indicating that mast cells function in an anti- tumorigenic role early during intestinal tumorigenesis. Thus, these studies demonstrate that proteinases and infiltrating inflammatory cells function in both pro- and anti-tumorigenic roles during early intestinal tumor development. Approved_____________________________________________Date:______________ ORIGINAL PUBLICATIONS Acuff, H.B., Sinnamon, M., Fingleton, B., Boone, B., Levy, S.E., Chen, X., Pozzi, A., Carbone, D.P., Schwartz, D.R., Moin, K., Sloane, B.F., Matrisian, L.M. (2006) Analysis of host- and tumor-derived proteinases using a custom dual species microarray reveals a protective role for stromal matrix metalloproteinase-12 in non-small cell lung cancer. Cancer Res. 2006 Aug 15;66(16):7968-75. Schwartz, D.R., Moin, K., Yao, B., Matrisian, L.M., Coussens, L.M., Bugge, T.H., Fingleton, B., Acuff, H.B., Sinnamon, M., Nassar, H., Platts, A.E., Krawetz, S.A., Linebaugh, B.E., Sloane, B.F. (2007) Hu/Mu ProtIn oligonucleotide microarray: dual- species array for profiling protease and protease inhibitor gene expression in tumors and their microenvironment. Mol Cancer Res. May;5(5):443-54. Sinnamon, M.J., Carter, K.J., Sims, L.P., Lafleur, B., Fingleton, B., Matrisian, L.M. (2007) A protective role for mast cells in intestinal tumorigenesis. Carcinogenesis. 2008 Feb 6; (in press) Sinnamon, M.J., Carter, K.J., Fingleton, B., Matrisian, L.M. Effect of stromal MMP ablation on tumorigenesis in the Min model system. (in preparation) iii ACKNOWLEDGEMENTS Firstly, I would firstly like to thank my mentor, Dr. Lynn Matrisian, for her support, guidance, and confidence throughout the course of this thesis project. I would like to thank the current and former members of my dissertation committee for their guidance and assistance: Dr. Kevin Osteen, Dr. Barbara Fingleton, Dr. Simon Hayward, Dr. Ambra Pozzi, and Dr. Robert Whitehead. I would especially like to acknowledge those researchers who have contributed reagents and technical expertise critical for assays needed to complete this work, namely Dr. Hayward, Dr. Pozzi, and Dr. Jamie Lee. Additionally, I would like to acknowledge past and current members of the Matrisian Laboratory including Dr. Howard Crawford, Dr. Tracy Vargo-Gogola, Dr. Rebecca Wagenaar-Miller, Dr. Mark Gustavson, Dr. Permila Harrell, Dr. Heath Acuff, Dr. Oliver McIntyre, Dr. Conor Lynch, Dr. Michael Van Saun, Dr. Michelle Martin, Dr. Lee Gorden, Kathy Carter, Sophie Thiolloy, and Randy Scherer. I would like to especially acknowledge Dr. Barbara Fingleton and Dr. Lisa McCawley for their continued support whether it be constructing a talk, proofreading a paper, or listening to me talk myself out of that day’s “greatest idea ever”. Lastly, I truly appreciate the support and encouragement from my family and friends throughout this process. iv TABLE OF CONTENTS Page ACKNOWLEDGEMENTS ............................................................................................... iv LIST OF FIGURES ......................................................................................................... viii LIST OF TABLES ...............................................................................................................x LIST OF ABBREVIATIONS ............................................................................................ xi Chapter I. INTRODUCTION ..................................................................................................1 Colorectal Cancer .........................................................................................1 General Characteristics, Staging, Pathology, Genetics ..........................1 The Min Mouse as a Model Organism of Intestinal Tumorigenesis .....8 MMPs and Cancer ......................................................................................11 The Matrix Metalloproteinase Family .................................................11 Regulation of MMP Activity ...............................................................17 MMPs in Tumorigenesis and Cancer Progression ...............................20 II. MATERIALS AND METHODS ...........................................................................26 Mice Breeding and Genotyping .................................................................26 Preparation of Genomic DNA .............................................................26 Min Mice ..............................................................................................26 Generation of MMP-2-/- Min Mice ......................................................27 Generation of MMP-2-/-; MMP-7-/- Min Mice .....................................28 v Generation of MMP-9-/- Min Mice .......................................................28 Generation of MMP-12-/- Min Mice ....................................................30 Generation of MMP-19-/- Min Mice ....................................................30 Generation of c-kitW-sh/W-sh Min Mice ..................................................31 Tissue Collection, Processing, and Tumor Measurements ........................32 Tissue Collection and Tumor Measurements ......................................32 RNA Isolation, Microarray Analysis, and Real-Time PCR .......................32 RNA Isolation ......................................................................................32 cDNA Microarray Construction ..........................................................33 Affymetrix Hu/Mu ProtIn Array Construction ....................................48 Real Time PCR Analysis .....................................................................49 Histochemical and Immunohistochemical Analyses .................................49 Chloroacetate Reaction (CAE) to Demonstrate Mast Cells .................49 Immunohistochemistry ........................................................................50 Visualization of Proliferation and Apoptosis .......................................50 Demonstration of Leukocyte Populations ............................................51 MMP-9 and von Willebrand Factor IHC .............................................52 Immunohistochemical Quantification ..................................................53 Immunofluorescent Co-localization of MMP-9 ...................................53 III. RESULTS .............................................................................................................55 MMPs in Mouse Models of Colon Cancer ................................................55 Microarray Profiling of Intestinal Adenomas ......................................55 Analysis of Heterogeneity of MMP Expression in Tumors .................61 The Role of MMP-2 in Intestinal Tumorigenesis ......................................65 The Role of MMP-12 in Intestinal Tumorigenesis ....................................68 The Role of MMP-19 in Intestinal Tumorigenesis ....................................71 vi The Role of MMP-9 in Intestinal Tumorigenesis ......................................73 IV. AN ANTI-TUMORIGENIC ROLE FOR MAST CELLS ...................................78 Introduction ................................................................................................78 Results ........................................................................................................79
Recommended publications
  • The ELIXIR Core Data Resources: ​Fundamental Infrastructure for The
    Supplementary Data: The ELIXIR Core Data Resources: fundamental infrastructure ​ for the life sciences The “Supporting Material” referred to within this Supplementary Data can be found in the Supporting.Material.CDR.infrastructure file, DOI: 10.5281/zenodo.2625247 (https://zenodo.org/record/2625247). ​ ​ Figure 1. Scale of the Core Data Resources Table S1. Data from which Figure 1 is derived: Year 2013 2014 2015 2016 2017 Data entries 765881651 997794559 1726529931 1853429002 2715599247 Monthly user/IP addresses 1700660 2109586 2413724 2502617 2867265 FTEs 270 292.65 295.65 289.7 311.2 Figure 1 includes data from the following Core Data Resources: ArrayExpress, BRENDA, CATH, ChEBI, ChEMBL, EGA, ENA, Ensembl, Ensembl Genomes, EuropePMC, HPA, IntAct /MINT , InterPro, PDBe, PRIDE, SILVA, STRING, UniProt ● Note that Ensembl’s compute infrastructure physically relocated in 2016, so “Users/IP address” data are not available for that year. In this case, the 2015 numbers were rolled forward to 2016. ● Note that STRING makes only minor releases in 2014 and 2016, in that the interactions are re-computed, but the number of “Data entries” remains unchanged. The major releases that change the number of “Data entries” happened in 2013 and 2015. So, for “Data entries” , the number for 2013 was rolled forward to 2014, and the number for 2015 was rolled forward to 2016. The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences ​ 1 Figure 2: Usage of Core Data Resources in research The following steps were taken: 1. API calls were run on open access full text articles in Europe PMC to identify articles that ​ ​ mention Core Data Resource by name or include specific data record accession numbers.
    [Show full text]
  • Deep Profiling of Protease Substrate Specificity Enabled by Dual Random and Scanned Human Proteome Substrate Phage Libraries
    Deep profiling of protease substrate specificity enabled by dual random and scanned human proteome substrate phage libraries Jie Zhoua, Shantao Lib, Kevin K. Leunga, Brian O’Donovanc, James Y. Zoub,d, Joseph L. DeRisic,d, and James A. Wellsa,d,e,1 aDepartment of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158; bDepartment of Biomedical Data Science, Stanford University, Stanford, CA 94305; cDepartment of Biochemistry and Biophysics, University of California, San Francisco, CA 94158; dChan Zuckerberg Biohub, San Francisco, CA 94158; and eDepartment of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158 Edited by Benjamin F. Cravatt, Scripps Research Institute, La Jolla, CA, and approved August 19, 2020 (received for review May 11, 2020) Proteolysis is a major posttranslational regulator of biology inside lysate and miss low abundance proteins and those simply not and outside of cells. Broad identification of optimal cleavage sites expressed in cell lines tested that typically express only half their and natural substrates of proteases is critical for drug discovery genomes (13). and to understand protease biology. Here, we present a method To potentially screen a larger and more diverse sequence that employs two genetically encoded substrate phage display space, investigators have developed genetically encoded substrate libraries coupled with next generation sequencing (SPD-NGS) that phage (14, 15) or yeast display libraries (16, 17). Degenerate DNA allows up to 10,000-fold deeper sequence coverage of the typical six- sequences (up to 107) encoding random peptides were fused to a to eight-residue protease cleavage sites compared to state-of-the-art phage or yeast coat protein gene for a catch-and-release strategy synthetic peptide libraries or proteomics.
    [Show full text]
  • Annual Scientific Report 2013 on the Cover Structure 3Fof in the Protein Data Bank, Determined by Laponogov, I
    EMBL-European Bioinformatics Institute Annual Scientific Report 2013 On the cover Structure 3fof in the Protein Data Bank, determined by Laponogov, I. et al. (2009) Structural insight into the quinolone-DNA cleavage complex of type IIA topoisomerases. Nature Structural & Molecular Biology 16, 667-669. © 2014 European Molecular Biology Laboratory This publication was produced by the External Relations team at the European Bioinformatics Institute (EMBL-EBI) A digital version of the brochure can be found at www.ebi.ac.uk/about/brochures For more information about EMBL-EBI please contact: [email protected] Contents Introduction & overview 3 Services 8 Genes, genomes and variation 8 Molecular atlas 12 Proteins and protein families 14 Molecular and cellular structures 18 Chemical biology 20 Molecular systems 22 Cross-domain tools and resources 24 Research 26 Support 32 ELIXIR 36 Facts and figures 38 Funding & resource allocation 38 Growth of core resources 40 Collaborations 42 Our staff in 2013 44 Scientific advisory committees 46 Major database collaborations 50 Publications 52 Organisation of EMBL-EBI leadership 61 2013 EMBL-EBI Annual Scientific Report 1 Foreword Welcome to EMBL-EBI’s 2013 Annual Scientific Report. Here we look back on our major achievements during the year, reflecting on the delivery of our world-class services, research, training, industry collaboration and European coordination of life-science data. The past year has been one full of exciting changes, both scientifically and organisationally. We unveiled a new website that helps users explore our resources more seamlessly, saw the publication of ground-breaking work in data storage and synthetic biology, joined the global alliance for global health, built important new relationships with our partners in industry and celebrated the launch of ELIXIR.
    [Show full text]
  • Global Cellular Response to Chemotherapy- Induced
    RESEARCH ARTICLE elife.elifesciences.org Global cellular response to chemotherapy- induced apoptosis Arun P Wiita1,2, Etay Ziv3,4, Paul J Wiita5, Anatoly Urisman1,6, Olivier Julien1, Alma L Burlingame1, Jonathan S Weissman3,7, James A Wells1,3* 1Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, United States; 2Department of Laboratory Medicine, University of California, San Francisco, San Francisco, United States; 3Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, United States; 4Department of Radiology, University of California, San Francisco, San Francisco, United States; 5Department of Physics, The College of New Jersey, Ewing, United States; 6Department of Pathology, University of California, San Francisco, San Francisco, United States; 7Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, United States Abstract How cancer cells globally struggle with a chemotherapeutic insult before succumbing to apoptosis is largely unknown. Here we use an integrated systems-level examination of transcription, translation, and proteolysis to understand these events central to cancer treatment. As a model we study myeloma cells exposed to the proteasome inhibitor bortezomib, a first-line therapy. Despite robust transcriptional changes, unbiased quantitative proteomics detects production of only a few critical anti-apoptotic proteins against a background of general translation inhibition. Simultaneous ribosome profiling further reveals potential translational regulation of stress response genes. Once the apoptotic machinery is engaged, degradation by caspases is largely independent of upstream bortezomib effects. Moreover, previously uncharacterized non-caspase proteolytic events also participate in cellular deconstruction. Our systems-level data also support co-targeting the anti-apoptotic regulator HSF1 to promote cell death by bortezomib.
    [Show full text]
  • Strategic Plan 2011-2016
    Strategic Plan 2011-2016 Wellcome Trust Sanger Institute Strategic Plan 2011-2016 Mission The Wellcome Trust Sanger Institute uses genome sequences to advance understanding of the biology of humans and pathogens in order to improve human health. -i- Wellcome Trust Sanger Institute Strategic Plan 2011-2016 - ii - Wellcome Trust Sanger Institute Strategic Plan 2011-2016 CONTENTS Foreword ....................................................................................................................................1 Overview .....................................................................................................................................2 1. History and philosophy ............................................................................................................ 5 2. Organisation of the science ..................................................................................................... 5 3. Developments in the scientific portfolio ................................................................................... 7 4. Summary of the Scientific Programmes 2011 – 2016 .............................................................. 8 4.1 Cancer Genetics and Genomics ................................................................................ 8 4.2 Human Genetics ...................................................................................................... 10 4.3 Pathogen Variation .................................................................................................. 13 4.4 Malaria
    [Show full text]
  • Comparative Genomics of the Major Parasitic Worms
    Comparative genomics of the major parasitic worms International Helminth Genomes Consortium Supplementary Information Introduction ............................................................................................................................... 4 Contributions from Consortium members ..................................................................................... 5 Methods .................................................................................................................................... 6 1 Sample collection and preparation ................................................................................................................. 6 2.1 Data production, Wellcome Trust Sanger Institute (WTSI) ........................................................................ 12 DNA template preparation and sequencing................................................................................................. 12 Genome assembly ........................................................................................................................................ 13 Assembly QC ................................................................................................................................................. 14 Gene prediction ............................................................................................................................................ 15 Contamination screening ............................................................................................................................
    [Show full text]
  • Biological Databases for Human Research
    Genomics Proteomics Bioinformatics 13 (2015) 55–63 HOSTED BY Genomics Proteomics Bioinformatics www.elsevier.com/locate/gpb www.sciencedirect.com RESOURCE REVIEW Biological Databases for Human Research Dong Zou #,a, Lina Ma #,b, Jun Yu *,c, Zhang Zhang *,d CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China Received 1 January 2015; revised 16 January 2015; accepted 16 January 2015 Available online 21 February 2015 Handled by Ge Gao KEYWORDS Abstract The completion of the Human Genome Project lays a foundation for systematically Human; studying the human genome from evolutionary history to precision medicine against diseases. Database; With the explosive growth of biological data, there is an increasing number of biological databases Big data; that have been developed in aid of human-related research. Here we present a collection of human- Database category; related biological databases and provide a mini-review by classifying them into different categories Curation according to their data types. As human-related databases continue to grow not only in count but also in volume, challenges are ahead in big data storage, processing, exchange and curation. Introduction from various database resources in an automated manner. Therefore, developing databases to deal with gigantic volumes of biological data is a fundamentally essential task in bioinfor- As biological data accumulate at larger scales and increase at matics. To be short, biological databases integrate enormous exponential paces, thanks principally to higher-throughput amounts of omics data, serving as crucially important and lower-cost DNA sequencing technologies, the number of resources and becoming increasingly indispensable for scien- biological databases that have been developed to manage such tists from wet-lab biologists to in silico bioinformaticians.
    [Show full text]
  • MEROPS: the Peptidase Database Neil D
    Published online 5 November 2009 Nucleic Acids Research, 2010, Vol. 38, Database issue D227–D233 doi:10.1093/nar/gkp971 MEROPS: the peptidase database Neil D. Rawlings*, Alan J. Barrett and Alex Bateman The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SA, UK Received September 11, 2009; Revised October 12, 2009; Accepted October 14, 2009 ABSTRACT which are grouped into clans. A family contains proteins that can be shown to be related by sequence comparison Peptidases, their substrates and inhibitors are of alone, whereas a clan contains proteins where the great relevance to biology, medicine and biotech- sequences are so distantly related that similarity can nology. The MEROPS database (http://merops only be seen by comparing structures. Sequence analysis .sanger.ac.uk) aims to fulfil the need for an is restricted to that portion of the protein directly respon- integrated source of information about these. sible for peptidase or inhibitor activity which is termed the The database has a hierarchical classification ‘peptidase unit’ or the ‘inhibitor unit’, respectively. in which homologous sets of peptidases and A peptidase or inhibitor unit will normally correspond protein inhibitors are grouped into protein species, to a structural domain, and some proteins will contain which are grouped into families, which are in turn more than one peptidase or inhibitor domain. Examples grouped into clans. The classification framework is are potato virus Y polyprotein which contains three peptidase units, each in a different family, and turkey used for attaching information at each level. ovomucoid, which contains three inhibitor units all in An important focus of the database has become the same family.
    [Show full text]
  • What Helminth Genomes Have Taught Us About Parasite Evolution
    SPECIAL ISSUE ARTICLE S85 What helminth genomes have taught us about parasite evolution MAGDALENA ZAROWIECKI* and MATT BERRIMAN Parasite Genomics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK (Received 4 June 2014; revised 11 August 2014; accepted 14 August 2014; first published online 8 December 2014) SUMMARY The genomes of more than 20 helminths have now been sequenced. Here we perform a meta-analysis of all sequenced genomes of nematodes and Platyhelminthes, and attempt to address the question of what are the defining characteristics of helminth genomes. We find that parasitic worms lack systems for surface antigenic variation, instead maintaining infections using their surfaces as the first line of defence against the host immune system, with several expanded gene families of genes associated with the surface and tegument. Parasite excretory/secretory products evolve rapidly, and proteases even more so, with each parasite exhibiting unique modifications of its protease repertoire. Endoparasitic flatworms show striking losses of metabolic capabilities, not matched by nematodes. All helminths do however exhibit an overall reduction in auxiliary metabolism (biogenesis of co-factors and vitamins). Overall, the prevailing pattern is that there are few commonalities between the genomes of independently evolved parasitic worms, with each parasite having undergone specific adaptations for their particular niche. Key words: parasite genomics, phylogeny, comparative transcriptomics, evolution of parasitism, Cestoda, Trematoda, Nematoda. INTRODUCTION Humans are parasitized by two major groups of parasitic worms; the Nematoda (roundworms) Parasitic worms (helminths) cause some of the and Platyhelminthes (flatworms). Within flatworms most devastating threats to human health and liveli- endoparasitism is believed to have arisen only once hoods.
    [Show full text]
  • Substrate-Driven Mapping of the Degradome by Comparison of Sequence Logos
    Substrate-Driven Mapping of the Degradome by Comparison of Sequence Logos Julian E. Fuchs, Susanne von Grafenstein, Roland G. Huber, Christian Kramer, Klaus R. Liedl* Institute of General, Inorganic and Theoretical Chemistry, and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innsbruck, Austria Abstract Sequence logos are frequently used to illustrate substrate preferences and specificity of proteases. Here, we employed the compiled substrates of the MEROPS database to introduce a novel metric for comparison of protease substrate preferences. The constructed similarity matrix of 62 proteases can be used to intuitively visualize similarities in protease substrate readout via principal component analysis and construction of protease specificity trees. Since our new metric is solely based on substrate data, we can engraft the protease tree including proteolytic enzymes of different evolutionary origin. Thereby, our analyses confirm pronounced overlaps in substrate recognition not only between proteases closely related on sequence basis but also between proteolytic enzymes of different evolutionary origin and catalytic type. To illustrate the applicability of our approach we analyze the distribution of targets of small molecules from the ChEMBL database in our substrate-based protease specificity trees. We observe a striking clustering of annotated targets in tree branches even though these grouped targets do not necessarily share similarity on protein sequence level. This highlights the value and applicability of knowledge acquired from peptide substrates in drug design of small molecules, e.g., for the prediction of off-target effects or drug repurposing. Consequently, our similarity metric allows to map the degradome and its associated drug target network via comparison of known substrate peptides.
    [Show full text]
  • HMMER Web Server Documentation Release 1.0
    HMMER web server Documentation Release 1.0 Rob Finn & Simon Potter EMBL-EBI Oct 16, 2019 Contents 1 Target databases 3 1.1 Sequence databases...........................................3 1.2 Profile HMM databases.........................................4 1.3 Search provenance............................................4 2 Searches 5 2.1 Search query...............................................5 2.2 Query examples.............................................6 2.3 Default search parameters........................................6 2.3.1 phmmer.............................................6 2.3.2 hmmscan............................................6 2.3.3 hmmsearch...........................................6 2.3.4 jackhmmer...........................................6 2.4 Databases.................................................7 2.4.1 Sequence databases.......................................7 2.4.2 HMM databases.........................................7 2.5 Thresholds................................................7 2.5.1 Significance thresholds.....................................7 2.5.2 Reporting thresholds......................................8 2.5.3 Gathering thresholds......................................9 2.5.4 Gene3D and Superfamily thresholds..............................9 3 Advanced search options 11 3.1 Taxonomy Restrictions.......................................... 11 3.1.1 Search.............................................. 11 3.1.2 Pre-defined Taxonomic Tree.................................. 11 3.2 Customisation of results........................................
    [Show full text]
  • VIEW Open Access Are Protozoan Metacaspases Potential Parasite Killers? Benoît Meslin1, Habib Zalila2, Nicolas Fasel2, Stephane Picot1, Anne-Lise Bienvenu1*
    Meslin et al. Parasites & Vectors 2011, 4:26 http://www.parasitesandvectors.com/content/4/1/26 REVIEW Open Access Are protozoan metacaspases potential parasite killers? Benoît Meslin1, Habib Zalila2, Nicolas Fasel2, Stephane Picot1, Anne-Lise Bienvenu1* Abstract Mechanisms concerning life or death decisions in protozoan parasites are still imperfectly understood. Comparison with higher eukaryotes has led to the hypothesis that caspase-like enzymes could be involved in death pathways. This hypothesis was reinforced by the description of caspase-related sequences in the genome of several parasites, including Plasmodium, Trypanosoma and Leishmania. Although several teams are working to decipher the exact role of metacaspases in protozoan parasites, partial, conflicting or negative results have been obtained with respect to the relationship between protozoan metacaspases and cell death. The aim of this paper is to review current knowledge of protozoan parasite metacaspases within a drug targeting perspective. Metacaspases: a twenty-first century history However, it demonstrated the requirement to explore the In the late nineties, Aravind from Bethesda was the first to role of metacaspases, as an aid to determining whether describe orthologues of caspases [1]. This paved the way renaming these enzymes in agreement with their definitive for Uren and colleagues to describe paracaspases from ani- specificity is needed. mals and slime mould, and metacaspases from plants, Although only recently described and their function fungi and protozoa, in the beginning of 21st century [2]. poorly explored, metacaspases could be considered as Caspases are limited to metazoans, while metacaspases are potential targets for new specific treatments against the missing from them: leading to the hypothesis that meta- main protozoan parasites affecting humans.
    [Show full text]