DOCSLIB.ORG

Deep Sequencing and Annotation of the Trichoplax Adhaerens Mrna

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Deep Sequencing and Annotation of the Trichoplax Adhaerens Mrna Deep Sequencing and Annotation of the Trichoplax adhaerens mRNA Transcriptome Identifies Novel Genes and a Rich Repertoire of Neural Signaling Machinery, Providing Insight into Nervous System Evolution by Yuen Yan Wong A thesis submitted in conformity with the requirements for the degree of Master of Science Department of Cell and Systems Biology University of Toronto © Copyright by Yuen Yan Wong 2018 Deep Sequencing and Annotation of the Trichoplax adhaerens mRNA Transcriptome Identifies Novel Genes and a Rich Repertoire of Neural Signaling Machinery, Providing Insight into Nervous System Evolution Yuen Yan Wong Master of Science Department of Cell and Systems Biology University of Toronto 2018 Abstract Trichoplax adhaerens is an early-diverging animal capable of motile behavior such as feeding, chemotaxis, and phototaxis, despite lacking synaptically-connected neurons and muscles. Our lab has produced a high-quality T. adhaerens transcriptome in which ~85% of the assembled genes are complete protein-coding sequences, with 2,483 newly-identified genes missed in the genome sequencing effort. One objective of this research was to identify genes involved in neural signaling. Using in silico prediction algorithms we identified an array of neuropeptide genes, GPCRs, ion channels, and synaptic scaffolding and signaling proteins. We also discovered a previously unknown group of presynaptic Rab3-Interacting Molecule (RIM) homologues in the animal phylogeny, absent in humans but present in many invertebrates. Our work sets the stage for future studies aimed at understanding the concurrent evolution of metazoan cell types, and their ability to communicate with each other through various forms of cell signaling including electrochemical signaling in the nervous system. ii Acknowledgments I would first like to thank my supervisor, Dr. Adriano Senatore, for his mentorship and guidance throughout the past two years of my Master’s studies, and his assistance in helping me prepare my thesis. Thank you Dr. Senatore for giving me the opportunity to further explore my research interests and to be able to push myself beyond my limits. Besides my advisor, I would like to thank my committee members, Dr. Mary Cheng and Dr. Robert Ness, for their guidance and support. I would like to especially thank Brian Novogradac. As a beginner in bioinformatics, I am grateful for all his great help and technical support he had been providing me throughout the two years. Next, I express my sincere gratitude to my wonderful colleagues. Thank you Sally and Alicia. Thank you so much for always being there for me, for all the sleepless nights and blood, sweat and tears we experience together while working for deadlines, and every time we celebrate our minor successes and happiness with lots of food and coffee breaks. Thank you for always being supportive and loving, and for creating all these wonderful memory with me which I will cherish for life. Thank you, Dr. Marcia Roy, for your kind support and guidance. Thank you so much for helping me with the postsynaptic proteins analysis, and in particular, thank you very much for showing me how to always love science and research! I really appreciate all of our exciting and inspiring conversations. Last, but not least, I would like to thank my family and friends for all the moral support they have given me all along. I would especially like to deliver my enormous gratitude to my parents and my siblings, for always being extremely supportive and understanding, and for always making sure I have everything I need. I am grateful to have you all by my side. iii Table of Contents ACKNOWLEDGMENTS ........................................................................................................................................... III TABLE OF CONTENTS ............................................................................................................................................ IV LIST OF FIGURES ................................................................................................................................................... VI LIST OF TABLES .................................................................................................................................................... VII LIST OF APPENDICES ........................................................................................................................................... VIII LIST OF ABBREVIATIONS/TERMINOLOGIES ........................................................................................................... IX CHAPTER 1 ............................................................................................................................................................. 1 INTRODUCTION: TRICHOPLAX ADHAERENS AS A MODEL ORGANISM FOR NEUROSCIENCE RESEARCH ........ 1 1.1 WHAT IS A NERVOUS SYSTEM? .................................................................................................................... 1 1.2 THE ANIMAL PHYLOGENY AND NERVOUS SYSTEM EVOLUTION ............................................................................. 1 1.2.1 The Animal Phylogeny ..................................................................................................................... 1 1.2.2 Controversies about the Origin of the Nervous System ..................................................................... 2 1.3 TRICHOPLAX ADHAERENS ............................................................................................................................ 3 1.3.1 What is it? ....................................................................................................................................... 3 1.3.2 Taking a Transcriptomics Approach ................................................................................................. 4 CHAPTER 2 TRANSCRIPTOME ANALYSIS OF TRICHOPLAX ADHAERENS REFLECTS A DIGESTIVE EPITHELIUM WITH CELLULAR COORDINATION AND AN ARRAY OF GENES INVOLVED IN NEURAL SIGNALING ..................................... 6 TRANSCRIPTOME ANALYSIS OF TRICHOPLAX ADHAERENS REFLECTS A DIGESTIVE EPITHELIUM WITH CELLULAR COORDINATION AND AN ARRAY OF GENES INVOLVED IN NEURAL SIGNALING ..................................... 6 2.1 METHODS............................................................................................................................................... 6 2.1.1 RNA Isolation and Illumina Sequencing ............................................................................................ 6 2.1.2 Transcriptome Production ................................................................................................................ 6 2.1.3 Transcriptome Metrics and Gene Ontology ...................................................................................... 8 2.1.4 Comparison against the Published Genome...................................................................................... 9 2.1.5 Extended and Full T. adhaerens Predicted Proteome ...................................................................... 10 2.1.6 Secretome Production.................................................................................................................... 10 2.1.7 Regulatory/Neuro-Peptide Prediction............................................................................................. 11 2.1.8 GPCRome Production and Classification Identification .................................................................... 12 2.1.9 Protein Sequence Annotations ....................................................................................................... 12 2.1.10 Cross-Phyla Domain Analysis ..................................................................................................... 13 2.2 RESULTS ............................................................................................................................................... 15 2.2.1 Transcriptome Metrics ................................................................................................................... 15 2.2.2 Gene Ontology .............................................................................................................................. 15 2.2.3 Comparison against Genome ......................................................................................................... 17 2.2.4 The Secretome ............................................................................................................................... 21 2.2.5 Regulatory/Neuro- Peptides........................................................................................................... 21 2.2.6 Neurotransmitter Biosynthesis and Degradation Pathways ............................................................ 23 2.2.7 Receptors and the GPCRome .......................................................................................................... 24 2.2.8 Channel Protein Homologues ......................................................................................................... 26 2.2.9 Pre-/Post- Synaptic Scaffolding and Signaling Protein Homologues Cross-Phyla Domain Analysis .... 27 2.2.10 Cross-Phyla Protein-Protein Interaction-Related Domain Counts Analysis ................................... 30 2.3 DISCUSSIONS ......................................................................................................................................... 30 2.3.1 A High-Quality T. adhaerens Gene Set ...........................................................................................
Load more

Recommended publications
  • Oas1b-Dependent Immune Transcriptional Profiles of West Nile
    MULTIPARENTAL POPULATIONS Oas1b-dependent Immune Transcriptional Profiles of West Nile Virus Infection in the Collaborative Cross Richard Green,*,† Courtney Wilkins,*,† Sunil Thomas,*,† Aimee Sekine,*,† Duncan M. Hendrick,*,† Kathleen Voss,*,† Renee C. Ireton,*,† Michael Mooney,‡,§ Jennifer T. Go,*,† Gabrielle Choonoo,‡,§ Sophia Jeng,** Fernando Pardo-Manuel de Villena,††,‡‡ Martin T. Ferris,†† Shannon McWeeney,‡,§,** and Michael Gale Jr.*,†,1 *Department of Immunology and †Center for Innate Immunity and Immune Disease (CIIID), University of Washington, § Seattle, Washington 98109, ‡OHSU Knight Cancer Institute, Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, and **Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, Oregon 97239, ††Department of Genetics and ‡‡Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina 27514 ABSTRACT The oligoadenylate-synthetase (Oas) gene locus provides innate immune resistance to virus KEYWORDS infection. In mouse models, variation in the Oas1b gene influences host susceptibility to flavivirus infection. Oas However, the impact of Oas variation on overall innate immune programming and global gene expression flavivirus among tissues and in different genetic backgrounds has not been defined. We examined how Oas1b acts viral infection in spleen and brain tissue to limit West Nile virus (WNV) susceptibility and disease across a range of innate immunity genetic backgrounds. The laboratory founder strains of the mouse Collaborative Cross (CC) (A/J, C57BL/6J, multiparental 129S1/SvImJ, NOD/ShiLtJ, and NZO/HlLtJ) all encode a truncated, defective Oas1b, whereas the three populations wild-derived inbred founder strains (CAST/EiJ, PWK/PhJ, and WSB/EiJ) encode a full-length OAS1B pro- Multi-parent tein.
    [Show full text]
  • Exploring the Relationship Between Gut Microbiota and Major Depressive Disorders
    E3S Web of Conferences 271, 03055 (2021) https://doi.org/10.1051/e3sconf/202127103055 ICEPE 2021 Exploring the Relationship between Gut Microbiota and Major Depressive Disorders Catherine Tian1 1Shanghai American School, Shanghai, China Abstract. Major Depressive Disorder (MDD) is a psychiatric disorder accompanied with a high rate of suicide, morbidity and mortality. With the symptom of an increasing or decreasing appetite, there is a possibility that MDD may have certain connections with gut microbiota, the colonies of microbes which reside in the human digestive system. In recent years, more and more studies started to demonstrate the links between MDD and gut microbiota from animal disease models and human metabolism studies. However, this relationship is still largely understudied, but it is very innovative since functional dissection of this relationship would furnish a new train of thought for more effective treatment of MDD. In this study, by using multiple genetic analytic tools including Allen Brain Atlas, genetic function analytical tools, and MicrobiomeAnalyst, I explored the genes that shows both expression in the brain and the digestive system to affirm that there is a connection between gut microbiota and the MDD. My approach finally identified 7 MDD genes likely to be associated with gut microbiota, implicating 3 molecular pathways: (1) Wnt Signaling, (2) citric acid cycle in the aerobic respiration, and (3) extracellular exosome signaling. These findings may shed light on new directions to understand the mechanism of MDD, potentially facilitating the development of probiotics for better psychiatric disorder treatment. 1 Introduction 1.1 Major Depressive Disorder Major Depressive Disorder (MDD) is a mood disorder that will affect the mood, behavior and other physical parts.
    [Show full text]
  • Viewed and Published Immediately Upon Acceptance Cited in Pubmed and Archived on Pubmed Central Yours — You Keep the Copyright
    BMC Genomics BioMed Central Research article Open Access Differential gene expression in ADAM10 and mutant ADAM10 transgenic mice Claudia Prinzen1, Dietrich Trümbach2, Wolfgang Wurst2, Kristina Endres1, Rolf Postina1 and Falk Fahrenholz*1 Address: 1Johannes Gutenberg-University, Institute of Biochemistry, Mainz, Johann-Joachim-Becherweg 30, 55128 Mainz, Germany and 2Helmholtz Zentrum München – German Research Center for Environmental Health, Institute for Developmental Genetics, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany Email: Claudia Prinzen - [email protected]; Dietrich Trümbach - [email protected]; Wolfgang Wurst - [email protected]; Kristina Endres - [email protected]; Rolf Postina - [email protected]; Falk Fahrenholz* - [email protected] * Corresponding author Published: 5 February 2009 Received: 19 June 2008 Accepted: 5 February 2009 BMC Genomics 2009, 10:66 doi:10.1186/1471-2164-10-66 This article is available from: http://www.biomedcentral.com/1471-2164/10/66 © 2009 Prinzen et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background: In a transgenic mouse model of Alzheimer disease (AD), cleavage of the amyloid precursor protein (APP) by the α-secretase ADAM10 prevented amyloid plaque formation, and alleviated cognitive deficits. Furthermore, ADAM10 overexpression increased the cortical synaptogenesis. These results suggest that upregulation of ADAM10 in the brain has beneficial effects on AD pathology. Results: To assess the influence of ADAM10 on the gene expression profile in the brain, we performed a microarray analysis using RNA isolated from brains of five months old mice overexpressing either the α-secretase ADAM10, or a dominant-negative mutant (dn) of this enzyme.
    [Show full text]
  • Identification of Key Genes and Pathways for Alzheimer's Disease
    Biophys Rep 2019, 5(2):98–109 https://doi.org/10.1007/s41048-019-0086-2 Biophysics Reports RESEARCH ARTICLE Identification of key genes and pathways for Alzheimer’s disease via combined analysis of genome-wide expression profiling in the hippocampus Mengsi Wu1,2, Kechi Fang1, Weixiao Wang1,2, Wei Lin1,2, Liyuan Guo1,2&, Jing Wang1,2& 1 CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China 2 Department of Psychology, University of Chinese Academy of Sciences, Beijing 10049, China Received: 8 August 2018 / Accepted: 17 January 2019 / Published online: 20 April 2019 Abstract In this study, combined analysis of expression profiling in the hippocampus of 76 patients with Alz- heimer’s disease (AD) and 40 healthy controls was performed. The effects of covariates (including age, gender, postmortem interval, and batch effect) were controlled, and differentially expressed genes (DEGs) were identified using a linear mixed-effects model. To explore the biological processes, func- tional pathway enrichment and protein–protein interaction (PPI) network analyses were performed on the DEGs. The extended genes with PPI to the DEGs were obtained. Finally, the DEGs and the extended genes were ranked using the convergent functional genomics method. Eighty DEGs with q \ 0.1, including 67 downregulated and 13 upregulated genes, were identified. In the pathway enrichment analysis, the 80 DEGs were significantly enriched in one Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, GABAergic synapses, and 22 Gene Ontology terms. These genes were mainly involved in neuron, synaptic signaling and transmission, and vesicle metabolism. These processes are all linked to the pathological features of AD, demonstrating that the GABAergic system, neurons, and synaptic function might be affected in AD.
    [Show full text]
  • Epigenetic Mechanisms Are Involved in the Oncogenic Properties of ZNF518B in Colorectal Cancer
    Epigenetic mechanisms are involved in the oncogenic properties of ZNF518B in colorectal cancer Francisco Gimeno-Valiente, Ángela L. Riffo-Campos, Luis Torres, Noelia Tarazona, Valentina Gambardella, Andrés Cervantes, Gerardo López-Rodas, Luis Franco and Josefa Castillo SUPPLEMENTARY METHODS 1. Selection of genomic sequences for ChIP analysis To select the sequences for ChIP analysis in the five putative target genes, namely, PADI3, ZDHHC2, RGS4, EFNA5 and KAT2B, the genomic region corresponding to the gene was downloaded from Ensembl. Then, zoom was applied to see in detail the promoter, enhancers and regulatory sequences. The details for HCT116 cells were then recovered and the target sequences for factor binding examined. Obviously, there are not data for ZNF518B, but special attention was paid to the target sequences of other zinc-finger containing factors. Finally, the regions that may putatively bind ZNF518B were selected and primers defining amplicons spanning such sequences were searched out. Supplementary Figure S3 gives the location of the amplicons used in each gene. 2. Obtaining the raw data and generating the BAM files for in silico analysis of the effects of EHMT2 and EZH2 silencing The data of siEZH2 (SRR6384524), siG9a (SRR6384526) and siNon-target (SRR6384521) in HCT116 cell line, were downloaded from SRA (Bioproject PRJNA422822, https://www.ncbi. nlm.nih.gov/bioproject/), using SRA-tolkit (https://ncbi.github.io/sra-tools/). All data correspond to RNAseq single end. doBasics = TRUE doAll = FALSE $ fastq-dump -I --split-files SRR6384524 Data quality was checked using the software fastqc (https://www.bioinformatics.babraham. ac.uk /projects/fastqc/). The first low quality removing nucleotides were removed using FASTX- Toolkit (http://hannonlab.cshl.edu/fastxtoolkit/).
    [Show full text]
  • Host Genetic Variation in Mucosal Immunity Pathways Influences the Upper Airway Microbiome Catherine Igartua1*, Emily R
    Igartua et al. Microbiome (2017) 5:16 DOI 10.1186/s40168-016-0227-5 RESEARCH Open Access Host genetic variation in mucosal immunity pathways influences the upper airway microbiome Catherine Igartua1*, Emily R. Davenport1,2, Yoav Gilad1,3, Dan L. Nicolae1,3,4, Jayant Pinto5† and Carole Ober1*† Abstract Background: The degree to which host genetic variation can modulate microbial communities in humans remains an open question. Here, we performed a genetic mapping study of the microbiome in two accessible upper airway sites, the nasopharynx and the nasal vestibule, during two seasons in 144 adult members of a founder population of European decent. Results: We estimated the relative abundances (RAs) of genus level bacteria from 16S rRNA gene sequences and examined associations with 148,653 genetic variants (linkage disequilibrium [LD] r2 <0.5)selectedfromamongall common variants discovered in genome sequences in this population. We identified 37 microbiome quantitative trait loci (mbQTLs) that showed evidence of association with the RAs of 22 genera (q < 0.05) and were enriched for genes in mucosal immunity pathways. The most significant association was between the RA of Dermacoccus (phylum Actinobacteria) and a variant 8 kb upstream of TINCR (rs117042385; p =1.61×10−8; q =0.002),along non-coding RNA that binds to peptidoglycan recognition protein 3 (PGLYRP3)mRNA, a gene encoding a known antimicrobial protein. A second association was between a missense variant in PGLYRP4 (rs3006458) and the RA of an unclassified genus of family Micrococcaceae (phylum Actinobacteria) (p =5.10×10−7; q=0.032). Conclusions: Our findings provide evidence of host genetic influences on upper airway microbial composition in humans and implicate mucosal immunity genes in this relationship.
    [Show full text]
  • Rph3a (NM 011286) Mouse Tagged ORF Clone – MR210001 | 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 MR210001 Rph3a (NM_011286) Mouse Tagged ORF Clone Product data: Product Type: Expression Plasmids Product Name: Rph3a (NM_011286) Mouse Tagged ORF Clone Tag: Myc-DDK Symbol: Rph3a Synonyms: 2900002P20Rik; AU022689; AW108370 Vector: pCMV6-Entry (PS100001) E. coli Selection: Kanamycin (25 ug/mL) Cell Selection: Neomycin 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 / 5 Rph3a (NM_011286) Mouse Tagged ORF Clone – MR210001 ORF Nucleotide >MR210001 ORF sequence Sequence: Red=Cloning site Blue=ORF Green=Tags(s) TTTTGTAATACGACTCACTATAGGGCGGCCGGGAATTCGTCGACTGGATCCGGTACCGAGGAGATCTGCC GCCGCGATCGCC ATGACTGACACTGTGGTGAACCGATGGATGTACCCTGGTGATGGCCCTCTGCAGTCAAATGACAAGGAAC AGCTGCAGGCAGGATGGTCCGTCCATCCTGGAGCACAGACCGACAGGCAGAGGAAGCAGGAAGAACTGAC AGACGAGGAGAAGGAGATCATCAACAGAGTGATTGCTCGGGCAGAGAAGATGGAAGCCATGGAACAGGAA CGCATTGGGCGCCTGGTGGACCGTCTGGAGACCATGAGGAAGAATGTGGCTGGAGATGGCGTGAACCGCT GCATTCTGTGTGGGGAACAGCTGGGTATGCTGGGCTCGGCCTGTGTCGTGTGTGAAGACTGTAAGAAGAA TGTCTGCACCAAGTGTGGGGTTGAGACCTCCAACAACCGTCCGCATCCGGTATGGCTCTGCAAGATCTGC CTTGAGCAGAGAGAGGTCTGGAAGCGCTCAGGAGCATGGTTCTTCAAAGGTTTCCCCAAGCAGGTCCTTC CACAGCCCATGCCTATAAAGAAGACCAAGCCCCAGCAGCCTGCTGGTGAACCGGCCACCCAGGAGCAGCC TACACCTGAGTCCAGGCATCCAGCCAGGGCTCCAGCTCGAGGTGACATGGAGGACAGGAGGCCCCCAGGG
    [Show full text]
  • Detection of H3k4me3 Identifies Neurohiv Signatures, Genomic
    viruses Article Detection of H3K4me3 Identifies NeuroHIV Signatures, Genomic Effects of Methamphetamine and Addiction Pathways in Postmortem HIV+ Brain Specimens that Are Not Amenable to Transcriptome Analysis Liana Basova 1, Alexander Lindsey 1, Anne Marie McGovern 1, Ronald J. Ellis 2 and Maria Cecilia Garibaldi Marcondes 1,* 1 San Diego Biomedical Research Institute, San Diego, CA 92121, USA; [email protected] (L.B.); [email protected] (A.L.); [email protected] (A.M.M.) 2 Departments of Neurosciences and Psychiatry, University of California San Diego, San Diego, CA 92103, USA; [email protected] * Correspondence: [email protected] Abstract: Human postmortem specimens are extremely valuable resources for investigating trans- lational hypotheses. Tissue repositories collect clinically assessed specimens from people with and without HIV, including age, viral load, treatments, substance use patterns and cognitive functions. One challenge is the limited number of specimens suitable for transcriptional studies, mainly due to poor RNA quality resulting from long postmortem intervals. We hypothesized that epigenomic Citation: Basova, L.; Lindsey, A.; signatures would be more stable than RNA for assessing global changes associated with outcomes McGovern, A.M.; Ellis, R.J.; of interest. We found that H3K27Ac or RNA Polymerase (Pol) were not consistently detected by Marcondes, M.C.G. Detection of H3K4me3 Identifies NeuroHIV Chromatin Immunoprecipitation (ChIP), while the enhancer H3K4me3 histone modification was Signatures, Genomic Effects of abundant and stable up to the 72 h postmortem. We tested our ability to use H3K4me3 in human Methamphetamine and Addiction prefrontal cortex from HIV+ individuals meeting criteria for methamphetamine use disorder or not Pathways in Postmortem HIV+ Brain (Meth +/−) which exhibited poor RNA quality and were not suitable for transcriptional profiling.
    [Show full text]
  • Patterns of Sequence Conservation in Presynaptic Neural Genes
    University of Pennsylvania ScholarlyCommons Departmental Papers (CIS) Department of Computer & Information Science November 2006 Patterns of Sequence Conservation in Presynaptic Neural Genes Dexter Hadley University of Pennsylvania Tara Murphy University of Pennsylvania Otto Valladares University of Pennsylvania Sridhar Hannenhalli University of Pennsylvania Lyle H. Ungar University of Pennsylvania, [email protected] See next page for additional authors Follow this and additional works at: https://repository.upenn.edu/cis_papers Recommended Citation Dexter Hadley, Tara Murphy, Otto Valladares, Sridhar Hannenhalli, Lyle H. Ungar, Junhyong Kim, and Maja Bucan, "Patterns of Sequence Conservation in Presynaptic Neural Genes", . November 2006. Reprinted from Genome Biology, Volume 7, Issue 11, November 2006, pages R105.1-R105.19. Publisher URL: http://genomebiology.com/2006/7/11/R105 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/cis_papers/282 For more information, please contact [email protected]. Patterns of Sequence Conservation in Presynaptic Neural Genes Abstract Background: The neuronal synapse is a fundamental functional unit in the central nervous system of animals. Because synaptic function is evolutionarily conserved, we reasoned that functional sequences of genes and related genomic elements known to play important roles in neurotransmitter release would also be conserved. Results: Evolutionary rate analysis revealed that presynaptic proteins evolve slowly, although some members of large gene families exhibit accelerated evolutionary rates relative to other family members. Comparative sequence analysis of 46 megabases spanning 150 presynaptic genes identified more than 26,000 elements that are highly conserved in eight vertebrate species, as well as a small subset of sequences (6%) that are shared among unrelated presynaptic genes.
    [Show full text]
  • Edger: Differential Analysis of Sequence Read Count Data User's
    edgeR: differential analysis of sequence read count data User’s Guide Yunshun Chen 1,2, Davis McCarthy 3,4, Matthew Ritchie 1,2, Mark Robinson 5, and Gordon Smyth 1,6 1Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia 2Department of Medical Biology, University of Melbourne, Victoria, Australia 3St Vincent’s Institute of Medical Research, Fitzroy, Victoria, Australia 4Melbourne Integrative Genomics, University of Melbourne, Victoria, Australia 5Institute of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland 6School of Mathematics and Statistics, University of Melbourne, Victoria, Australia First edition 17 September 2008 Last revised 12 May 2021 Contents 1 Introduction .............................7 1.1 Scope .................................7 1.2 Citation.................................7 1.3 How to get help ............................9 1.4 Quick start ............................... 10 2 Overview of capabilities ...................... 11 2.1 Terminology .............................. 11 2.2 Aligning reads to a genome ..................... 11 2.3 Producing a table of read counts .................. 11 2.4 Reading the counts from a file ................... 12 2.5 Pseudoalignment and quasi-mapping ............... 12 2.6 The DGEList data class ....................... 12 2.7 Filtering ................................ 13 2.8 Normalization ............................. 14 2.8.1 Normalization is only necessary for sample-specific effects .... 14 2.8.2 Sequencing depth ........................ 14 2.8.3 Effective library sizes ....................... 15 2.8.4 GC content ............................ 15 2.8.5 Gene length ........................... 16 2.8.6 Model-based normalization, not transformation .......... 16 2.8.7 Pseudo-counts .......................... 16 2 edgeR User’s Guide 2.9 Negative binomial models ...................... 17 2.9.1 Introduction ........................... 17 2.9.2 Biological coefficient of variation (BCV) .............
    [Show full text]
  • FGF2 Affects Parkinson's Disease-Associated Molecular
    fgene-11-572058 September 24, 2020 Time: 17:25 # 1 ORIGINAL RESEARCH published: 25 September 2020 doi: 10.3389/fgene.2020.572058 FGF2 Affects Parkinson’s Disease-Associated Molecular Networks Through Exosomal Rab8b/Rab31 Rohit Kumar1,2,3*†, Sainitin Donakonda4†, Stephan A. Müller1, Kai Bötzel3, Günter U. Höglinger1,2,5 and Thomas Koeglsperger1,3* 1 German Center for Neurodegenerative Diseases (DZNE), Munich, Germany, 2 Faculty of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany, 3 Department of Neurology, Ludwig Maximilian University, Munich, Germany, 4 Institute of Immunology and Experimental Oncology, Technical University of Munich, Munich, Germany, 5 Department of Neurology, Hannover Medical School, Hanover, Germany Ras-associated binding (Rab) proteins are small GTPases that regulate the trafficking of membrane components during endocytosis and exocytosis including the release of extracellular vesicles (EVs). Parkinson’s disease (PD) is one of the most prevalent Edited by: Anna Marabotti, neurodegenerative disorder in the elderly population, where pathological proteins such University of Salerno, Italy as alpha-synuclein (a-Syn) are transmitted in EVs from one neuron to another neuron Reviewed by: and ultimately across brain regions, thereby facilitating the spreading of pathology. David Meckes, We recently demonstrated fibroblast growth factor-2 (FGF2) to enhance the release Florida State University, United States Khanh N. Q. Le, of EVs and delineated the proteomic signature of FGF2-triggered EVs in cultured Taipei Medical University, Taiwan primary hippocampal neurons. Out of 235 significantly upregulated proteins, we found *Correspondence: that FGF2 specifically enriched EVs for the two Rab family members Rab8b and Rohit Kumar [email protected] Rab31.
    [Show full text]
  • Downloading and Demultiplexing Fastq Files
    bioRxiv preprint doi: https://doi.org/10.1101/2020.09.09.284505; this version posted September 9, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Transcriptome Alterations in Myotonic Dystrophy Frontal Cortex Brittney A. Otero1, Kiril Poukalov1*, Ryan P. Hildebrandt1*, Charles A. Thornton2, Kenji Jinnai3, Harutoshi Fujimura4, Takashi Kimura5, Katharine A. Hagerman6, JaCinda B. Sampson6, John W. Day6, EriC T. Wang1,7 1Dept. of Molecular Genetics & Microbiology, Center for NeuroGenetics, Genetics Institute, University of Florida 2Department of Neurology, University of Rochester Medical Center 3Department of Neurology, National Hospital Organization Hyogo-Chuo Hospital 4Department of Neurology, National Hospital Organization Toneyama Hospital 5Department of Neurology, Hyogo College of Medicine 6Department of Neurology, Stanford University 7To whom correspondence should be addressed *These authors contributed equally to this worK Abstract MyotoniC dystrophy (dystrophia myotoniCa, DM) is Caused by expanded CTG/CCTG miCrosatellite repeats, leading to multi-systemic symptoms in skeletal muscle, heart, gastrointestinal, endocrine, and central nervous systems (CNS), among others. For some patients, CNS issues can be as debilitating or more so than muscle symptoms; they include hypersomnolence, executive dysfunction, white matter atrophy, and neurofibrillary tangles. Although transCriptomes from DM type 1 (DM1) skeletal muscle have provided useful insights into pathomeChanisms and biomarkers, limited studies of transCriptomes have been performed in the CNS. To eluCidate underlying Causes of CNS dysfunCtion in patients, we have generated and analyzed RNA-seq transcriptomes from the frontal cortex of 21 DM1 patients, 4 DM type 2 (DM2) patients, and 8 unaffeCted controls.
    [Show full text]