DOCSLIB.ORG

“Rlk” and Receptor-Like Proteins L

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

COMPUTATIONAL IDENTIFICATION, PHYLOGENETIC AND SYNTENY ANALYSIS OF RECEPTOR-LIKE KINASES “RLK” AND RECEPTOR-LIKE PROTEINS “RLP” IN LEGUMES A Dissertation Submitted to the Graduate Faculty of the North Dakota State University of Agriculture and Applied Science By Daniel Restrepo Montoya In Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY Major Program: Genomics and Bioinformatics October 2018 Fargo, North Dakota North Dakota State University GraDuate School Title COMPUTATIONAL IDENTIFICATION, PHYLOGENETIC AND SYNTENY ANALYSIS OF RECEPTOR-LIKE KINASES “RLK” AND RECEPTOR-LIKE PROTEINS “RLP” IN LEGUMES By Daniel Restrepo Montoya The Supervisory Committee certifies that this disquisition complies with North Dakota State University’s regulations and meets the accepted standards for the degree of DOCTOR OF PHILOSOPHY SUPERVISORY COMMITTEE: Dr. Juan M. Osorno Co-Chair Dr. Phillip E. McClean Co-Chair Dr. Julia Bowsher Dr. Robert Brueggemann ApproveD: 11/16/2018 Dr. Phillip E. McClean Date Department Chair ABSTRACT Legumes are considered the second most important family of crop plants after the grass family based on economic relevance. In recent years, the field of legume genomics has expanded due to advancements in high-throughput sequencing and genotyping technologies. To date, no published comparative genomic analysis explores receptor-like kinases “RLK” and receptor-like proteins “RLP” among legume genomes. Evaluating these RLK and RLP should provide a source of new information because extensive genetic and phenotypic studies have already discovered the diverse roles of RLK and RLP in cell development, disease resistance, and stress responses among other functions. This study demonstrates that a computational logical approach for classifying the RLK/RLP in legumes/non-legumes is statistically well supported and can be used in other plant species. The analysis of RLK/RLP of 7 legumes and 3 non-legume species evaluated suggests that about 2% are RLK and less than 1% of the proteins are RLP. The results suggest a dynamic evolution of RLK and RLP in the legume family. In fact, between 66% to 85% of RLK and 83% to 88% of RLP belong to orthologous clusters among the species evaluated. The remaining RLK and RLP proteins are classified as singletons. The ratio of the pairwise synteny blocks of RLK/RLP among legumes shows a 1:1 relationship. The exception is G. max, which shows an approximately 2:1 ratio due to its recent whole genome duplication (G. max vs. the other six legumes). The other legumes show evidence of a similar proportion of plasma membrane proteins among the legume pairwise synteny blocks. iii ACKNOWLEDGMENTS I would like to thank Dr. Juan M. Osorno and Dr. Phil McClean for their patience and for teaching me about agriculture, breeding, genetics, genomics, bioinformatics, and much more. Dr. Robert Brueggeman for its invaluable tips, insights, and recommendations about plant pathology. Dr Julia Bowsher for its recommendations and support. The Fulbright scholarship. The Francisco Jose de Caldas – Colciencias scholarship, the North Dakota State University, the ILLiad interlibrary loan service system, and the NDSU dry breeding program. The Google Cloud platform service and the free extended trials. Dr. Mark Miller and Dr. Wayne Pfeiffer and the Cipres-Xsede platform. Dr. Luis F. Niño and Ing. Jonathan Narvaez and the Scientific Cluster at Universidad Nacional de ColomBia. Dr. Henrik Nielsen at DTU. The NCBI staff - Dr. Peter S. Cooper, Dr. Wayne Matten. The OrthoMCL support team. The MCScanX community. Rebecca Kutty and Michael Stein for their editing suggestions, and finally the *NIX, python, and bash communities. iv DEDICATION Simon, Maria, the everlasting gardener, and the regretful fisherman. v TABLE OF CONTENTS ABSTRACT ................................................................................................................................... iii ACKNOWLEDGMENTS ............................................................................................................. iv DEDICATION ................................................................................................................................ v LIST OF TABLES ......................................................................................................................... ix LIST OF FIGURES ........................................................................................................................ x LIST OF APPENDIX TABLES .................................................................................................... xi LIST OF APPENDIX FIGURES .................................................................................................. xii CHAPTER 1. INTRODUCTION ................................................................................................... 1 CHAPTER 2. COMPUTATIONAL IDENTIFICATION OF RLK AND RLP IN LEGUMES .... 5 Abstract ........................................................................................................................... 5 Background ................................................................................................................ 5 Results ........................................................................................................................ 5 Conclusions ................................................................................................................ 6 Background of RLK and RLP ........................................................................................ 7 Methods ........................................................................................................................ 13 Datasets .................................................................................................................... 13 Computational identification of RLK and RLP ....................................................... 14 Independent evaluation of predictive performance .................................................. 17 Results ........................................................................................................................... 18 Performance prediction of RLK and RLP ................................................................ 18 Summary of predicted RLK and RLP ...................................................................... 19 Summary of the presence and prevalence of functional domains ............................ 29 Discussion ..................................................................................................................... 34 Conclusions ................................................................................................................... 39 CHAPTER 3. PHYLOGENETIC ANALYSIS OF RLK AND RLP IN LEGUMES .................. 42 vi Abstract ......................................................................................................................... 42 Background .............................................................................................................. 42 Results ...................................................................................................................... 42 Conclusions .............................................................................................................. 43 Background of RLK and RLP in legumes .................................................................... 43 Methods ........................................................................................................................ 48 Datasets .................................................................................................................... 48 Orthology analysis ................................................................................................... 49 Hierarchical clustering analysis of RLK and RLP ................................................... 49 Results ........................................................................................................................... 50 Orthology and hierarchical clustering analyses of RLK .......................................... 50 Orthology of RLK-nonRD ....................................................................................... 52 Hierarchical clustering analysis of RLK .................................................................. 53 Orthology and hierarchical clustering domain analysis of RLP .............................. 55 Hierarchical clustering analysis of RLP ................................................................... 56 Discussion ..................................................................................................................... 58 Conclusions ................................................................................................................... 60 CHAPTER 4. SYNTENY ANALYSIS OF RLK AND RLP IN LEGUMES .............................. 62 Abstract ......................................................................................................................... 62 Background .............................................................................................................. 62 Results ...................................................................................................................... 62 Conclusions .............................................................................................................. 63 Background of synteny in legumes ..............................................................................
Recommended publications
  • Investigation of the Underlying Hub Genes and Molexular Pathogensis in Gastric Cancer by Integrated Bioinformatic Analyses

    Investigation of the Underlying Hub Genes and Molexular Pathogensis in Gastric Cancer by Integrated Bioinformatic Analyses

    bioRxiv preprint doi: https://doi.org/10.1101/2020.12.20.423656; this version posted December 22, 2020. 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. Investigation of the underlying hub genes and molexular pathogensis in gastric cancer by integrated bioinformatic analyses Basavaraj Vastrad1, Chanabasayya Vastrad*2 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2020.12.20.423656; this version posted December 22, 2020. 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. Abstract The high mortality rate of gastric cancer (GC) is in part due to the absence of initial disclosure of its biomarkers. The recognition of important genes associated in GC is therefore recommended to advance clinical prognosis, diagnosis and and treatment outcomes. The current investigation used the microarray dataset GSE113255 RNA seq data from the Gene Expression Omnibus database to diagnose differentially expressed genes (DEGs). Pathway and gene ontology enrichment analyses were performed, and a proteinprotein interaction network, modules, target genes - miRNA regulatory network and target genes - TF regulatory network were constructed and analyzed. Finally, validation of hub genes was performed. The 1008 DEGs identified consisted of 505 up regulated genes and 503 down regulated genes.
  • Neurological Disorders, Genetic Correlations, and the Role of Exome Sequencing

    Neurological Disorders, Genetic Correlations, and the Role of Exome Sequencing

    Journal of Translational Science Review Article ISSN: 2059-268X Neurological disorders, genetic correlations, and the role of exome sequencing Tony L Brown1* and Theresa M Meloche2 1Columbia University, USA 2Advanced Research and Human Development Institute, USA Abstract Genomic information access and utilization by researchers and clinicians have barely begun the journey for fulfillment of their full potential in the research and clinical arenas. Exciting is the potential depth and breadth of research, clinical applications, and more personalized medicine, that remain on the horizon. Exome sequencing has clarified the responsibilities of over 130 genes, greatly expanding the medical genetics database and enabling the development of orphan disease- based pharmaceuticals. The focus of our research efforts was to review several literature sources related to rare genomic disease research and exome sequencing, as well as to review the new research and diagnostic strategies that were utilized. Using a systems approach, under discussion are neurology, neuropathy, and the central nervous and musculoskeletal systems. Also discussed will be the topics of inborn errors of metabolism, and the genetic targets related to developmental delay. Recommendations for future research will also be discussed. Exome sequencing neuronal ceroid lipofuscinoses the most common group of inherited neurological degenerative disorders [6]. Whether examining the A review of new strategies for rare genomic disease research mitochondrial defect implicated in prenatal ventriculomegaly
  • Gene Promoter and Exon DNA Methylation Changes in Colon

    Gene Promoter and Exon DNA Methylation Changes in Colon

    Molnár et al. BMC Cancer (2018) 18:695 https://doi.org/10.1186/s12885-018-4609-x RESEARCH ARTICLE Open Access Gene promoter and exon DNA methylation changes in colon cancer development – mRNA expression and tumor mutation alterations Béla Molnár1,2*†, Orsolya Galamb1†, Bálint Péterfia2, Barnabás Wichmann1, István Csabai3, András Bodor3,4, Alexandra Kalmár1, Krisztina Andrea Szigeti2, Barbara Kinga Barták2, Zsófia Brigitta Nagy2, Gábor Valcz1, Árpád V. Patai2, Péter Igaz1,2 and Zsolt Tulassay1,2 Abstract Background: DNA mutations occur randomly and sporadically in growth-related genes, mostly on cytosines. Demethylation of cytosines may lead to genetic instability through spontaneous deamination. Aims were whole genome methylation and targeted mutation analysis of colorectal cancer (CRC)-related genes and mRNA expression analysis of TP53 pathway genes. Methods: Long interspersed nuclear element-1 (LINE-1) BS-PCR followed by pyrosequencing was performed for the estimation of global DNA metlyation levels along the colorectal normal-adenoma-carcinoma sequence. Methyl capture sequencing was done on 6 normal adjacent (NAT), 15 adenomatous (AD) and 9 CRC tissues. Overall quantitative methylation analysis, selection of top hyper/hypomethylated genes, methylation analysis on mutation regions and TP53 pathway gene promoters were performed. Mutations of 12 CRC-related genes (APC, BRAF, CTNNB1, EGFR, FBXW7, KRAS, NRAS, MSH6, PIK3CA, SMAD2, SMAD4, TP53) were evaluated. mRNA expression of TP53 pathway genes was also analyzed. Results: According to the LINE-1 methylation results, overall hypomethylation was observed along the normal- adenoma-carcinoma sequence. Within top50 differential methylated regions (DMRs), in AD-N comparison TP73, NGFR, PDGFRA genes were hypermethylated, FMN1, SLC16A7 genes were hypomethylated.
  • Nicotinic Receptors in Sleep-Related Hypermotor Epilepsy: Pathophysiology and Pharmacology

    Nicotinic Receptors in Sleep-Related Hypermotor Epilepsy: Pathophysiology and Pharmacology

    brain sciences Review Nicotinic Receptors in Sleep-Related Hypermotor Epilepsy: Pathophysiology and Pharmacology Andrea Becchetti 1,* , Laura Clara Grandi 1 , Giulia Colombo 1 , Simone Meneghini 1 and Alida Amadeo 2 1 Department of Biotechnology and Biosciences, University of Milano-Bicocca, 20126 Milano, Italy; [email protected] (L.C.G.); [email protected] (G.C.); [email protected] (S.M.) 2 Department of Biosciences, University of Milano, 20133 Milano, Italy; [email protected] * Correspondence: [email protected] Received: 13 October 2020; Accepted: 21 November 2020; Published: 25 November 2020 Abstract: Sleep-related hypermotor epilepsy (SHE) is characterized by hyperkinetic focal seizures, mainly arising in the neocortex during non-rapid eye movements (NREM) sleep. The familial form is autosomal dominant SHE (ADSHE), which can be caused by mutations in genes encoding subunits of the neuronal nicotinic acetylcholine receptor (nAChR), Na+-gated K+ channels, as well as non-channel signaling proteins, such as components of the gap activity toward rags 1 (GATOR1) macromolecular complex. The causative genes may have different roles in developing and mature brains. Under this respect, nicotinic receptors are paradigmatic, as different pathophysiological roles are exerted by distinct nAChR subunits in adult and developing brains. The widest evidence concerns α4 and β2 subunits. These participate in heteromeric nAChRs that are major modulators of excitability in mature neocortical circuits as well as regulate postnatal synaptogenesis. However, growing evidence implicates mutant α2 subunits in ADSHE, which poses interpretive difficulties as very little is known about the function of α2-containing (α2*) nAChRs in the human brain.
  • Functional Annotation and Curation of Hypothetical Proteins Present in a Newly Emerged Serotype 1C of Shigella Flexneri

    Functional Annotation and Curation of Hypothetical Proteins Present in a Newly Emerged Serotype 1C of Shigella Flexneri

    G C A T T A C G G C A T genes Article Functional Annotation and Curation of Hypothetical Proteins Present in A Newly Emerged Serotype 1c of Shigella flexneri: Emphasis on Selecting Targets for Virulence and Vaccine Design Studies Tanuka Sen and Naresh K. Verma * Division of Biomedical Science and Biochemistry, Research School of Biology, The Australian National University, Canberra, ACT 2601, Australia; [email protected] * Correspondence: [email protected] Received: 12 February 2020; Accepted: 19 March 2020; Published: 23 March 2020 Abstract: Shigella flexneri is the principal cause of bacillary dysentery, contributing significantly to the global burden of diarrheal disease. The appearance and increase in the multi-drug resistance among Shigella strains, necessitates further genetic studies and development of improved/new drugs against the pathogen. The presence of an abundance of hypothetical proteins in the genome and how little is known about them, make them interesting genetic targets. The present study aims to carry out characterization of the hypothetical proteins present in the genome of a newly emerged serotype of S. flexneri (strain Y394), toward their novel regulatory functions using various bioinformatics databases/tools. Analysis of the genome sequence rendered 4170 proteins, out of which 721 proteins were annotated as hypothetical proteins (HPs) with no known function. The amino acid sequences of these HPs were evaluated using a combination of latest bioinformatics tools based on homology search against functionally identified proteins. Functional domains were considered as the basis to infer the biological functions of HPs in this case and the annotation helped in assigning various classes to the proteins such as signal transducers, lipoproteins, enzymes, membrane proteins, transporters, virulence, and binding proteins.
  • Structural Basis for Mammalian Nucleotide Sugar Transport Shivani Ahuja, Matthew R Whorton*

    Structural Basis for Mammalian Nucleotide Sugar Transport Shivani Ahuja, Matthew R Whorton*

    RESEARCH ARTICLE Structural basis for mammalian nucleotide sugar transport Shivani Ahuja, Matthew R Whorton* Vollum Institute, Oregon Health & Science University, Portland, United States Abstract Nucleotide-sugar transporters (NSTs) are critical components of the cellular glycosylation machinery. They transport nucleotide-sugar conjugates into the Golgi lumen, where they are used for the glycosylation of proteins and lipids, and they then subsequently transport the nucleotide monophosphate byproduct back to the cytoplasm. Dysregulation of human NSTs causes several debilitating diseases, and NSTs are virulence factors for many pathogens. Here we present the first crystal structures of a mammalian NST, the mouse CMP-sialic acid transporter (mCST), in complex with its physiological substrates CMP and CMP-sialic acid. Detailed visualization of extensive protein-substrate interactions explains the mechanisms governing substrate selectivity. Further structural analysis of mCST’s unique lumen-facing partially-occluded conformation, coupled with the characterization of substrate-induced quenching of mCST’s intrinsic tryptophan fluorescence, reveals the concerted conformational transitions that occur during substrate transport. These results provide a framework for understanding the effects of disease-causing mutations and the mechanisms of this diverse family of transporters. DOI: https://doi.org/10.7554/eLife.45221.001 Introduction Nucleotide-sugar transporters (NSTs) are products of the solute carrier 35 (SLC35) gene family in humans and are
  • Robles JTO Supplemental Digital Content 1

    Robles JTO Supplemental Digital Content 1

    Supplementary Materials An Integrated Prognostic Classifier for Stage I Lung Adenocarcinoma based on mRNA, microRNA and DNA Methylation Biomarkers Ana I. Robles1, Eri Arai2, Ewy A. Mathé1, Hirokazu Okayama1, Aaron Schetter1, Derek Brown1, David Petersen3, Elise D. Bowman1, Rintaro Noro1, Judith A. Welsh1, Daniel C. Edelman3, Holly S. Stevenson3, Yonghong Wang3, Naoto Tsuchiya4, Takashi Kohno4, Vidar Skaug5, Steen Mollerup5, Aage Haugen5, Paul S. Meltzer3, Jun Yokota6, Yae Kanai2 and Curtis C. Harris1 Affiliations: 1Laboratory of Human Carcinogenesis, NCI-CCR, National Institutes of Health, Bethesda, MD 20892, USA. 2Division of Molecular Pathology, National Cancer Center Research Institute, Tokyo 104-0045, Japan. 3Genetics Branch, NCI-CCR, National Institutes of Health, Bethesda, MD 20892, USA. 4Division of Genome Biology, National Cancer Center Research Institute, Tokyo 104-0045, Japan. 5Department of Chemical and Biological Working Environment, National Institute of Occupational Health, NO-0033 Oslo, Norway. 6Genomics and Epigenomics of Cancer Prediction Program, Institute of Predictive and Personalized Medicine of Cancer (IMPPC), 08916 Badalona (Barcelona), Spain. List of Supplementary Materials Supplementary Materials and Methods Fig. S1. Hierarchical clustering of based on CpG sites differentially-methylated in Stage I ADC compared to non-tumor adjacent tissues. Fig. S2. Confirmatory pyrosequencing analysis of DNA methylation at the HOXA9 locus in Stage I ADC from a subset of the NCI microarray cohort. 1 Fig. S3. Methylation Beta-values for HOXA9 probe cg26521404 in Stage I ADC samples from Japan. Fig. S4. Kaplan-Meier analysis of HOXA9 promoter methylation in a published cohort of Stage I lung ADC (J Clin Oncol 2013;31(32):4140-7). Fig. S5. Kaplan-Meier analysis of a combined prognostic biomarker in Stage I lung ADC.
  • Analysis of Membrane Transporter Systems Expressed During Symbiotic Nitrogen Fixation in the Model Legume Medicago Truncatula

    Analysis of Membrane Transporter Systems Expressed During Symbiotic Nitrogen Fixation in the Model Legume Medicago Truncatula

    Graduate Theses, Dissertations, and Problem Reports 2018 ANALYSIS OF MEMBRANE TRANSPORTER SYSTEMS EXPRESSED DURING SYMBIOTIC NITROGEN FIXATION IN THE MODEL LEGUME MEDICAGO TRUNCATULA Christina Laureen Wyman Follow this and additional works at: https://researchrepository.wvu.edu/etd Recommended Citation Wyman, Christina Laureen, "ANALYSIS OF MEMBRANE TRANSPORTER SYSTEMS EXPRESSED DURING SYMBIOTIC NITROGEN FIXATION IN THE MODEL LEGUME MEDICAGO TRUNCATULA" (2018). Graduate Theses, Dissertations, and Problem Reports. 7280. https://researchrepository.wvu.edu/etd/7280 This Dissertation is protected by copyright and/or related rights. It has been brought to you by the The Research Repository @ WVU with permission from the rights-holder(s). You are free to use this Dissertation in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you must obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/ or on the work itself. This Dissertation has been accepted for inclusion in WVU Graduate Theses, Dissertations, and Problem Reports collection by an authorized administrator of The Research Repository @ WVU. For more information, please contact [email protected]. ANALYSIS OF MEMBRANE TRANSPORTER SYSTEMS EXPRESSED DURING SYMBIOTIC NITROGEN FIXATION IN THE MODEL LEGUME MEDICAGO TRUNCATULA Christina Laureen Wyman Dissertation submitted to Davis College of Agriculture, Natural Resources and Design at West Virginia University in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Genetics and Developmental Biology Vagner Benedito, Ph.D., Chair Jonathan Cumming, Ph.D. Daniel Panaccione, Ph.D. Nicole Waterland, Ph.D.
  • Content Based Search in Gene Expression Databases and a Meta-Analysis of Host Responses to Infection

    Content Based Search in Gene Expression Databases and a Meta-Analysis of Host Responses to Infection

    Content Based Search in Gene Expression Databases and a Meta-analysis of Host Responses to Infection A Thesis Submitted to the Faculty of Drexel University by Francis X. Bell in partial fulfillment of the requirements for the degree of Doctor of Philosophy November 2015 c Copyright 2015 Francis X. Bell. All Rights Reserved. ii Acknowledgments I would like to acknowledge and thank my advisor, Dr. Ahmet Sacan. Without his advice, support, and patience I would not have been able to accomplish all that I have. I would also like to thank my committee members and the Biomed Faculty that have guided me. I would like to give a special thanks for the members of the bioinformatics lab, in particular the members of the Sacan lab: Rehman Qureshi, Daisy Heng Yang, April Chunyu Zhao, and Yiqian Zhou. Thank you for creating a pleasant and friendly environment in the lab. I give the members of my family my sincerest gratitude for all that they have done for me. I cannot begin to repay my parents for their sacrifices. I am eternally grateful for everything they have done. The support of my sisters and their encouragement gave me the strength to persevere to the end. iii Table of Contents LIST OF TABLES.......................................................................... vii LIST OF FIGURES ........................................................................ xiv ABSTRACT ................................................................................ xvii 1. A BRIEF INTRODUCTION TO GENE EXPRESSION............................. 1 1.1 Central Dogma of Molecular Biology........................................... 1 1.1.1 Basic Transfers .......................................................... 1 1.1.2 Uncommon Transfers ................................................... 3 1.2 Gene Expression ................................................................. 4 1.2.1 Estimating Gene Expression ............................................ 4 1.2.2 DNA Microarrays ......................................................
  • Heliorhodopsin Evolution Is Driven by Photosensory Promiscuity in Monoderms 2 Paul-Adrian Bulzu1, Vinicius Silva Kavagutti1,2, Maria-Cecilia Chiriac1, Charlotte 3 D

    Heliorhodopsin Evolution Is Driven by Photosensory Promiscuity in Monoderms 2 Paul-Adrian Bulzu1, Vinicius Silva Kavagutti1,2, Maria-Cecilia Chiriac1, Charlotte 3 D

    bioRxiv preprint doi: https://doi.org/10.1101/2021.02.01.429150; this version posted February 2, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 Heliorhodopsin evolution is driven by photosensory promiscuity in monoderms 2 Paul-Adrian Bulzu1, Vinicius Silva Kavagutti1,2, Maria-Cecilia Chiriac1, Charlotte 3 D. Vavourakis3, Keiichi Inoue4, Hideki Kandori5,6, Adrian-Stefan Andrei7, Rohit 4 Ghai1* 5 1Department of Aquatic Microbial Ecology, Institute of Hydrobiology, Biology Centre of 6 the Academy of Sciences of the Czech Republic, České Budějovice, Czech Republic. 7 2Department of Ecosystem Biology, Faculty of Science, University of South Bohemia, 8 Branišovská 1760, České Budějovice, Czech Republic. 9 3EUTOPS, Research Institute for Biomedical Aging Research, University of Innsbruck, 10 Austria. 11 4The Institute for Solid State Physics, The University of Tokyo, Kashiwa, Japan. 12 5Department of Life Science and Applied Chemistry, Nagoya Institute of Technology, 13 Showa, Nagoya 466-8555, Japan. 14 6OptoBioTechnology Research Center, Nagoya Institute of Technology, Showa, Nagoya 15 466-8555, Japan 16 7Limnological Station, Department of Plant and Microbial Biology, University of Zurich, 17 Kilchberg, Switzerland. 18 *Corresponding author: Rohit Ghai 19 Department of Aquatic Microbial Ecology, Institute of Hydrobiology, Biology Centre of 20 the Academy 21 of Sciences of the Czech Republic, Na Sádkách 7, 370 05, České Budějovice, Czech 22 Republic. 23 Phone: +420 387 775 881 24 Fax: +420 385 310 248 25 E-mail: [email protected] 26 bioRxiv preprint doi: https://doi.org/10.1101/2021.02.01.429150; this version posted February 2, 2021.
  • Functional Specialization in Nucleotide Sugar Transporters Occurred Through Differentiation of the Gene Cluster Eama

    Functional Specialization in Nucleotide Sugar Transporters Occurred Through Differentiation of the Gene Cluster Eama

    Västermark et al. BMC Evolutionary Biology 2011, 11:123 http://www.biomedcentral.com/1471-2148/11/123 RESEARCHARTICLE Open Access Functional specialization in nucleotide sugar transporters occurred through differentiation of the gene cluster EamA (DUF6) before the radiation of Viridiplantae Åke Västermark1*, Markus Sällman Almén1, Martin W Simmen2, Robert Fredriksson1 and Helgi B Schiöth1 Abstract Background: The drug/metabolite transporter superfamily comprises a diversity of protein domain families with multiple functions including transport of nucleotide sugars. Drug/metabolite transporter domains are contained in both solute carrier families 30, 35 and 39 proteins as well as in acyl-malonyl condensing enzyme proteins. In this paper, we present an evolutionary analysis of nucleotide sugar transporters in relation to the entire superfamily of drug/metabolite transporters that considers crucial intra-protein duplication events that have shaped the transporters. We use a method that combines the strengths of hidden Markov models and maximum likelihood to find relationships between drug/metabolite transporter families, and branches within families. Results: We present evidence that the triose-phosphate transporters, domain unknown function 914, uracil- diphosphate glucose-N-acetylglucosamine, and nucleotide sugar transporter families have evolved from a domain duplication event before the radiation of Viridiplantae in the EamA family (previously called domain unknown function 6). We identify previously unknown branches in the solute carrier 30, 35 and 39 protein families that emerged simultaneously as key physiological developments after the radiation of Viridiplantae, including the “35C/E” branch of EamA, which formed in the lineage of T. adhaerens (Animalia). We identify a second cluster of DMTs, called the domain unknown function 1632 cluster, which has non-cytosolic N- and C-termini, and thus appears to have been formed from a different domain duplication event.
  • The Complexity, Challenges and Benefits of Comparing Two Transporter Classification Systems in TCDB and Pfam Zachary Chiang*, Ake Vastermark*, Marco Punta, Penelope C

    The Complexity, Challenges and Benefits of Comparing Two Transporter Classification Systems in TCDB and Pfam Zachary Chiang*, Ake Vastermark*, Marco Punta, Penelope C

    Briefings in Bioinformatics, 16(5), 2015, 865–872 doi: 10.1093/bib/bbu053 Advance Access Publication Date: 21 January 2015 Paper The complexity, challenges and benefits of comparing two transporter classification systems in TCDB and Pfam Zachary Chiang*, Ake Vastermark*, Marco Punta, Penelope C. Coggill, Jaina Mistry, Robert D. Finn and Milton H. Saier, Jr Corresponding author. Milton H. Saier, Jr., Department of Molecular Biology, UC San Diego, La Jolla, CA 92093-0116. Tel.: þ1-858-534-4084; Fax: þ1-858-534- 7108. E-mail: [email protected] *These authors contributed equally to this work. Abstract Transport systems comprise roughly 10% of all proteins in a cell, playing critical roles in many processes. Improving and expanding their classification is an important goal that can affect studies ranging from comparative genomics to potential drug target searches. It is not surprising that different classification systems for transport proteins have arisen, be it within a specialized database, focused on this functional class of proteins, or as part of a broader classification system for all proteins. Two such databases are the Transporter Classification Database (TCDB) and the Protein family (Pfam) database. As part of a long-term endeavor to improve consistency between the two classification systems, we have compared transporter annota- tions in the two databases to understand the rationale for differences and to improve both systems. Differences sometimes reflect the fact that one database has a particular transporter family while the other does not. Differing family definitions and hierarchical organizations were reconciled, resulting in recognition of 69 Pfam ‘Domains of Unknown Function’, which proved to be transport protein families to be renamed using TCDB annotations.