Cerebrospinal Fluid (CSF) Proteomics: Methods and Protocols [1st ed. 2019] 978-1-4939-9705-3, 978-1-4939-9706-0

This volume focuses on protein analysis, including a wide range of the use of mass spectrometry and other protein method

402 71 13MB

English Pages XV, 557 [554] Year 2019

Report DMCA / Copyright

DOWNLOAD FILE

Polecaj historie

Cerebrospinal Fluid (CSF) Proteomics: Methods and Protocols [1st ed. 2019]
 978-1-4939-9705-3, 978-1-4939-9706-0

Table of contents :
Front Matter ....Pages i-xv
Front Matter ....Pages 1-1
The Human Brain Proteome Project: Biological and Technological Challenges (Joaquín Fernández-Irigoyen, Fernando Corrales, Enrique Santamaría)....Pages 3-23
Front Matter ....Pages 25-25
Guidelines for CSF Processing and Biobanking: Impact on the Identification and Development of Optimal CSF Protein Biomarkers (Yanaika S. Hok-A-Hin, Eline A. J. Willemse, Charlotte E. Teunissen, Marta Del Campo)....Pages 27-50
Functional Analyses of Embryonic Cerebrospinal Fluid Proteins (Teresa Caprile, Francisco Lamus, María Isabel Alonso, Hernán Montecinos, Angel Gato)....Pages 51-60
CSF Sample Preparation for Data-Independent Acquisition (Katalin Barkovits, Lars Tönges, Katrin Marcus)....Pages 61-67
Sample Fractionation Techniques for CSF Peptide Spectral Library Generation (Sandra Pacharra, Katrin Marcus, Caroline May)....Pages 69-77
Front Matter ....Pages 79-79
Application of 2D-DIGE and iTRAQ Workflows to Analyze CSF in Gliomas (Aishwarya A. Rao, Kanika Mehta, Nikita Gahoi, Sanjeeva Srivastava)....Pages 81-110
Peptidomic Workflow Applied to Cerebrospinal Fluid Analysis (Rustam H. Ziganshin, Sergey I. Kovalchuk, Igor V. Azarkin)....Pages 111-118
Quantitative Evaluation of Different Protein Fractions of Cerebrospinal Fluid Using 18O Labeling (Ramona Birke, Eberhard Krause, Michael Schümann, Ingolf E. Blasig, Reiner F. Haseloff)....Pages 119-128
A Versatile Workflow for Cerebrospinal Fluid Proteomic Analysis with Mass Spectrometry: A Matter of Choice between Deep Coverage and Sample Throughput (Charlotte Macron, Antonio Núñez Galindo, Ornella Cominetti, Loïc Dayon)....Pages 129-154
Determination of Cerebrospinal Fluid Proteome Variations by Isobaric Labeling Coupled with Strong Cation-Exchange Chromatography and Tandem Mass Spectrometry (Mercedes Lachén-Montes, Andrea González-Morales, Joaquín Fernández-Irigoyen, Enrique Santamaría)....Pages 155-168
SWATH Mass Spectrometry Applied to Cerebrospinal Fluid Differential Proteomics: Establishment of a Sample-Specific Method (Sandra I. Anjo, Cátia Santa, Bruno Manadas)....Pages 169-189
Front Matter ....Pages 191-191
Top-Down Proteomics Applied to Human Cerebrospinal Fluid (Marina Gay, Ester Sánchez-Jiménez, Laura Villarreal, Mar Vilanova, Romain Huguet, Gianluca Arauz-Garofalo et al.)....Pages 193-219
Application of an Aptamer-Based Proteomics Assay (SOMAscan™) in Rat Cerebrospinal Fluid (Alba Simats, Laura Ramiro, Joan Montaner, Teresa García-Berrocoso)....Pages 221-231
Monitoring the Cerebrospinal Fluid Cytokine Profile Using Membrane-Based Antibody Arrays (Andrea González-Morales, Mercedes Lachén-Montes, Joaquín Fernández-Irigoyen, Enrique Santamaría)....Pages 233-246
Selective and Sensitive Mass Spectrometric Identification of Immune Complex Antigens in Cerebrospinal Fluid (Nozomi Aibara, Kaname Ohyama)....Pages 247-253
CSF N-Glycoproteomics Using MALDI MS Techniques in Neurodegenerative Diseases (Angela Messina, Angelo Palmigiano, Rosaria Ornella Bua, Donata Agata Romeo, Rita Barone, Luisa Sturiale et al.)....Pages 255-272
Deployment of Label-Free Quantitative Olfactory Proteomics to Detect Cerebrospinal Fluid Biomarker Candidates in Synucleinopathies (Mercedes Lachén-Montes, Andrea González-Morales, Joaquín Fernández-Irigoyen, Enrique Santamaría)....Pages 273-289
An Improved Assay for Quantitation of Cerebrospinal Fluid Cystatin C Using Liquid Chromatography Tandem Mass Spectrometry (Abdullah Md Sheikh, Atsushi Nagai)....Pages 291-302
Array-Based Profiling of Proteins and Autoantibody Repertoires in CSF (Elisa Pin, Ronald Sjöberg, Eni Andersson, Cecilia Hellström, Jennie Olofsson, August Jernbom Falk et al.)....Pages 303-318
Front Matter ....Pages 319-319
Untargeted Metabolomics Relative Quantification by SWATH Mass Spectrometry Applied to Cerebrospinal Fluid (Vera M. Mendes, Margarida Coelho, Bruno Manadas)....Pages 321-336
Co-extraction for Metabolomics and Proteomics from a Single CSF Sample (Philipp Hörmann, Katalin Barkovits, Katrin Marcus, Karsten Hiller)....Pages 337-342
Characterization of Alzheimer’s Disease Micro-RNA Profile in Exosome-Enriched CSF Samples (Javier Riancho, Ana Santurtun, Pascual Sánchez-Juan)....Pages 343-352
Mass Spectrometry Applied to Human Cerebrospinal Fluid Lipidome (Laura Millán, Joaquín Fernández-Irigoyen, Enrique Santamaría, Rebeca Mayo)....Pages 353-361
Front Matter ....Pages 363-363
Trait Loci Mapping and CSF Proteome (Daimei Sasayama, Kotaro Hattori, Hiroshi Kunugi)....Pages 365-376
Essential Features and Use Cases of the Cerebrospinal Fluid Proteome Resource (CSF-PR) (Astrid Guldbrandsen, Yehia Mokhtar Farag, Ragnhild Reehorst Lereim, Frode S. Berven, Harald Barsnes)....Pages 377-391
Bioinformatics to Tackle the Biological Meaning of Human Cerebrospinal Fluid Proteome (Fábio Trindade, Rita Nogueira-Ferreira, Paulo Bastos, Francisco Amado, Rita Ferreira, Rui Vitorino)....Pages 393-553
Back Matter ....Pages 555-557

Citation preview

Methods in Molecular Biology 2044

Enrique Santamaría Joaquín Fernández-Irigoyen Editors

Cerebrospinal Fluid (CSF) Proteomics Methods and Protocols

METHODS

IN

MOLECULAR BIOLOGY

Series Editor John M. Walker School of Life and Medical Sciences University of Hertfordshire Hatfield, Hertfordshire, UK

For further volumes: http://www.springer.com/series/7651

For over 35 years, biological scientists have come to rely on the research protocols and methodologies in the critically acclaimed Methods in Molecular Biology series. The series was the first to introduce the step-by-step protocols approach that has become the standard in all biomedical protocol publishing. Each protocol is provided in readily-reproducible step-bystep fashion, opening with an introductory overview, a list of the materials and reagents needed to complete the experiment, and followed by a detailed procedure that is supported with a helpful notes section offering tips and tricks of the trade as well as troubleshooting advice. These hallmark features were introduced by series editor Dr. John Walker and constitute the key ingredient in each and every volume of the Methods in Molecular Biology series. Tested and trusted, comprehensive and reliable, all protocols from the series are indexed in PubMed.

Cerebrospinal Fluid (CSF) Proteomics Methods and Protocols

Edited by

Enrique Santamaría and Joaquín Fernández-Irigoyen Proteomics Unit, Clinical Neuroproteomics Laboratory, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Proteored-ISCIII, Pamplona, Spain

Editors Enrique Santamarı´a Proteomics Unit, Clinical Neuroproteomics Laboratory, Navarrabiomed, Complejo Hospitalario de Navarra (CHN) Universidad Pu´blica de Navarra (UPNA), IdiSNA, Proteored-ISCIII Pamplona, Spain

Joaquı´n Ferna´ndez-Irigoyen Proteomics Unit, Clinical Neuroproteomics Laboratory, Navarrabiomed, Complejo Hospitalario de Navarra (CHN) Universidad Pu´blica de Navarra (UPNA), IdiSNA, Proteored-ISCIII Pamplona, Spain

ISSN 1064-3745 ISSN 1940-6029 (electronic) Methods in Molecular Biology ISBN 978-1-4939-9705-3 ISBN 978-1-4939-9706-0 (eBook) https://doi.org/10.1007/978-1-4939-9706-0 © Springer Science+Business Media, LLC, part of Springer Nature 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Cover Caption: Cover design by Marı´a Arraiza from Communication and Design Unit (Navarrabiomed-Spain) This Humana imprint is published by the registered company Springer Science+Business Media, LLC, part of Springer Nature. The registered company address is: 233 Spring Street, New York, NY 10013, U.S.A.

Preface Due to the complexity of the molecular organization of the brain, the characterization of protein profiles within the specific brain structures and cerebrospinal fluid (CSF) forms an essential part of unearthing the molecular perturbation associated with neuropsychiatric disorders and neurodegenerative diseases. However, despite CSF being considered the best compartment for the analysis of protein and peptide biomarkers to study brain disorders and to support CNS drug development, it presents intrinsic characteristics that hamper the in-depth routine analysis in a straightforward manner. Although we are in the early stages of a learning curve, the application of quantitative proteomics is emerging as a powerful strategy to profile CSF proteomes in normal and pathological states, increasing our understanding of human brain biology and pushing the detection and validation of consistent CSF biomarkers in the neurology field. The themes discussed within CSF Proteomics: Methods and Protocols will be mainly focused on protein analysis, encompassing a wide spectrum of the utility of mass spectrometry and different protein methods within neurobiological disciplines. Specifically, this book will bring a collection of detailed protocols written by experienced professionals around the world to cover a variety of experimental workflows used in CSF proteomics. Chapters will include international guidelines for CSF processing and biobanking, CSF sample preparation methods, shotgun, and targeted proteomic workflows (based on 18O and isobaric labeling, label-free quantitative proteomics, SWATH, top-down proteomics, protein arrays, and multiple reaction monitoring) in order to achieve finer molecular resolution in proteomic studies of CSF. Moreover, additional chapters will focus on experimental strategies based on other –omics applied to CSF metabolome, lipidome, and microRNAome. Finally, several chapters will describe different bioinformatic pipelines useful to analyze and integrate the molecular information derived from high-throughput transcriptomic and proteomic experiments performed in CSF. All these approaches useful to quantify CSF proteomes, detect potential biomarker candidates, identify posttranslational modifications, and characterize molecular interactomes are beginning to shed new light on the translation, integration, and interpretation of the metabolic regulation that occurs in brain disorders at CSF level. We consider that this book will be a useful resource for graduate students or junior postdoctoral fellows interested in starting a journey in CSF proteotyping, as well as established researchers seeking valuable insight into the growing utility of proteomics in neuroscience. Enrique Santamarı´a Joaquı´n Ferna´ndez-Irigoyen

Pamplona, Spain

v

Contents Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

PART I

INTRODUCTION

1 The Human Brain Proteome Project: Biological and Technological Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Joaquı´n Ferna´ndez-Irigoyen, Fernando Corrales, and Enrique Santamarı´a

PART II

3

CEREBROSPINAL FLUID SAMPLE PREPARATION METHODS FOR PROTEOMIC WORKFLOWS

2 Guidelines for CSF Processing and Biobanking: Impact on the Identification and Development of Optimal CSF Protein Biomarkers. . . . . . . . . . Yanaika S. Hok-A-Hin, Eline A. J. Willemse, Charlotte E. Teunissen, and Marta Del Campo 3 Functional Analyses of Embryonic Cerebrospinal Fluid Proteins . . . . . . . . . . . . . . Teresa Caprile, Francisco Lamus, Marı´a Isabel Alonso, Herna´n Montecinos, and Angel Gato 4 CSF Sample Preparation for Data-Independent Acquisition . . . . . . . . . . . . . . . . . . Katalin Barkovits, Lars To¨nges, and Katrin Marcus 5 Sample Fractionation Techniques for CSF Peptide Spectral Library Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sandra Pacharra, Katrin Marcus, and Caroline May

PART III

v xi

27

51

61

69

SHOTGUN CEREBROSPINAL FLUID PROTEOMICS

6 Application of 2D-DIGE and iTRAQ Workflows to Analyze CSF in Gliomas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 Aishwarya A. Rao, Kanika Mehta, Nikita Gahoi, and Sanjeeva Srivastava 7 Peptidomic Workflow Applied to Cerebrospinal Fluid Analysis . . . . . . . . . . . . . . . 111 Rustam H. Ziganshin, Sergey I. Kovalchuk, and Igor V. Azarkin 8 Quantitative Evaluation of Different Protein Fractions of Cerebrospinal Fluid Using 18O Labeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 ¨ mann, Ramona Birke, Eberhard Krause, Michael Schu Ingolf E. Blasig, and Reiner F. Haseloff

vii

viii

Contents

9 A Versatile Workflow for Cerebrospinal Fluid Proteomic Analysis with Mass Spectrometry: A Matter of Choice between Deep Coverage and Sample Throughput . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 ˜ ez Galindo, Ornella Cominetti, Charlotte Macron, Antonio Nu´n and Loı¨c Dayon 10 Determination of Cerebrospinal Fluid Proteome Variations by Isobaric Labeling Coupled with Strong Cation-Exchange Chromatography and Tandem Mass Spectrometry . . . . . . . . . . . . . . . . . . . . . . . . . . 155 Mercedes Lache´n-Montes, Andrea Gonza´lez-Morales, Joaquı´n Ferna´ndez-Irigoyen, and Enrique Santamarı´a 11 SWATH Mass Spectrometry Applied to Cerebrospinal Fluid Differential Proteomics: Establishment of a Sample-Specific Method . . . . . . . . . . 169 Sandra I. Anjo, Ca´tia Santa, and Bruno Manadas

PART IV 12

13

14

15

16

17

18

CEREBROSPINAL FLUID SUBPROTEOMICS

Top-Down Proteomics Applied to Human Cerebrospinal Fluid . . . . . . . . . . . . . . Marina Gay, Ester Sa´nchez-Jime´nez, Laura Villarreal, Mar Vilanova, Romain Huguet, Gianluca Arauz-Garofalo, Mireia Dı´az-Lobo, Daniel Lopez-Ferrer, and Marta Vilaseca Application of an Aptamer-Based Proteomics Assay (SOMAscan™) in Rat Cerebrospinal Fluid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alba Simats, Laura Ramiro, Joan Montaner, and Teresa Garcı´a-Berrocoso Monitoring the Cerebrospinal Fluid Cytokine Profile Using Membrane-Based Antibody Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Andrea Gonza´lez-Morales, Mercedes Lache´n-Montes, Joaquı´n Ferna´ndez-Irigoyen, and Enrique Santamarı´a Selective and Sensitive Mass Spectrometric Identification of Immune Complex Antigens in Cerebrospinal Fluid . . . . . . . . . . . . . . . . . . . . . . . Nozomi Aibara and Kaname Ohyama CSF N-Glycoproteomics Using MALDI MS Techniques in Neurodegenerative Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Angela Messina, Angelo Palmigiano, Rosaria Ornella Bua, Donata Agata Romeo, Rita Barone, Luisa Sturiale, Mario Zappia, and Domenico Garozzo Deployment of Label-Free Quantitative Olfactory Proteomics to Detect Cerebrospinal Fluid Biomarker Candidates in Synucleinopathies. . . . . Mercedes Lache´n-Montes, Andrea Gonza´lez-Morales, Joaquı´n Ferna´ndez-Irigoyen, and Enrique Santamarı´a An Improved Assay for Quantitation of Cerebrospinal Fluid Cystatin C Using Liquid Chromatography Tandem Mass Spectrometry . . . . . . . Abdullah Md Sheikh and Atsushi Nagai

193

221

233

247

255

273

291

Contents

19

ix

Array-Based Profiling of Proteins and Autoantibody Repertoires in CSF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303 Elisa Pin, Ronald Sjo¨berg, Eni Andersson, Cecilia Hellstro¨m, Jennie Olofsson, August Jernbom Falk, Sofia Bergstro¨m, Julia Remnesta˚l, David Just, Peter Nilsson, and Anna Ma˚nberg

PART V

OTHER–OMICS APPLIED TO CEREBROSPINAL FLUID

20

Untargeted Metabolomics Relative Quantification by SWATH Mass Spectrometry Applied to Cerebrospinal Fluid . . . . . . . . . . . . . . . . . . . . . . . . . Vera M. Mendes, Margarida Coelho, and Bruno Manadas 21 Co-extraction for Metabolomics and Proteomics from a Single CSF Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Philipp Ho¨rmann, Katalin Barkovits, Katrin Marcus, and Karsten Hiller 22 Characterization of Alzheimer’s Disease Micro-RNA Profile in Exosome-Enriched CSF Samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Javier Riancho, Ana Santurtun, and Pascual Sa´nchez-Juan 23 Mass Spectrometry Applied to Human Cerebrospinal Fluid Lipidome. . . . . . . . . Laura Milla´n, Joaquı´n Ferna´ndez-Irigoyen, Enrique Santamarı´a, and Rebeca Mayo

PART VI 24 25

26

321

337

343 353

BIOINFORMATICS APPLIED TO CEREBROSPINAL FLUID

Trait Loci Mapping and CSF Proteome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365 Daimei Sasayama, Kotaro Hattori, and Hiroshi Kunugi Essential Features and Use Cases of the Cerebrospinal Fluid Proteome Resource (CSF-PR). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377 Astrid Guldbrandsen, Yehia Mokhtar Farag, Ragnhild Reehorst Lereim, Frode S. Berven, and Harald Barsnes Bioinformatics to Tackle the Biological Meaning of Human Cerebrospinal Fluid Proteome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393 Fa´bio Trindade, Rita Nogueira-Ferreira, Paulo Bastos, Francisco Amado, Rita Ferreira, and Rui Vitorino

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

555

Contributors NOZOMI AIBARA  Unit of Medical Pharmacy, Department of Pharmacy Practice, Graduate School of Biomedical Sciences, Nagasaki University, Nagasaki, Japan MARI´A ISABEL ALONSO  Departamento de Anatomı´a y Radiologı´a, Facultad de Medicina, Universidad de Valladolid, Valladolid, Spain FRANCISCO AMADO  QOPNA & LAQV-REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro, Portugal ENI ANDERSSON  Division of Affinity Proteomics, Department of Protein Science, SciLifeLab, KTH—Royal Institute of Technology, Stockholm, Sweden SANDRA I. ANJO  CNC—Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal GIANLUCA ARAUZ-GAROFALO  Mass Spectrometry and Proteomics Core Facility, Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain IGOR V. AZARKIN  Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russian Federation KATALIN BARKOVITS  Medizinisches Proteom-Center, Faculty of Medicine, Ruhr-Universit€ at Bochum, Bochum, Germany RITA BARONE  CNR, Istituto per i Polimeri, Compositi e i Biomateriali Catania, Catania, Italy; Child Neurology and Psychiatry Unit, Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy; Pediatric Neurology Unit, Department of Pediatrics, University of Catania, Catania, Italy HARALD BARSNES  Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway; Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway PAULO BASTOS  iBiMED, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal SOFIA BERGSTRO¨M  Division of Affinity Proteomics, Department of Protein Science, SciLifeLab, KTH—Royal Institute of Technology, Stockholm, Sweden FRODE S. BERVEN  Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway RAMONA BIRKE  Leibniz-Forschungsinstitut fu¨r Molekulare Pharmakologie, Berlin, Germany INGOLF E. BLASIG  Leibniz-Forschungsinstitut fu¨r Molekulare Pharmakologie, Berlin, Germany ROSARIA ORNELLA BUA  CNR, Istituto per i Polimeri, Compositi e i Biomateriali Catania, Catania, Italy TERESA CAPRILE  Departamento de Biologı´a Celular, Facultad de Ciencias Biologicas, Universidad de Concepcion, Bio Bio Region, Chile MARGARIDA COELHO  CNC—Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; Chemistry Department, Faculty of Sciences and Technology, University of Coimbra, Coimbra, Portugal ORNELLA COMINETTI  Proteomics, Nestle´ Institute of Health Sciences, Nestle´ Research, Lausanne, Switzerland

xi

xii

Contributors

FERNANDO CORRALES  Functional Proteomics Laboratory, Proteored-ISCIII, CIBERehd, Madrid, Spain LOI¨C DAYON  Proteomics, Nestle´ Institute of Health Sciences, Nestle´ Research, Lausanne, Switzerland MARTA DEL CAMPO  Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, Amsterdam, The Netherlands MIREIA DI´AZ-LOBO  Mass Spectrometry and Proteomics Core Facility, Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain AUGUST JERNBOM FALK  Division of Affinity Proteomics, Department of Protein Science, SciLifeLab, KTH—Royal Institute of Technology, Stockholm, Sweden YEHIA MOKHTAR FARAG  Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway; Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway JOAQUI´N FERNA´NDEZ-IRIGOYEN  Proteomics Unit, Clinical Neuroproteomics Laboratory, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pu´blica de Navarra (UPNA), IdiSNA, Proteored-ISCIII, Pamplona, Spain RITA FERREIRA  QOPNA & LAQV-REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro, Portugal NIKITA GAHOI  Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India; Centre for Research in Nanotechnology and Sciences, Indian Institute of Technology Bombay, Mumbai, India TERESA GARCI´A-BERROCOSO  Neurovascular Research Laboratory, Vall d’Hebron Institute of Research (VHIR), Universitat Auto`noma de Barcelona, Barcelona, Spain DOMENICO GAROZZO  CNR, Istituto per i Polimeri, Compositi e i Biomateriali Catania, Catania, Italy ANGEL GATO  Departamento de Anatomı´a y Radiologı´a, Facultad de Medicina, Universidad de Valladolid, Valladolid, Spain MARINA GAY  Mass Spectrometry and Proteomics Core Facility, Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain ANDREA GONZA´LEZ-MORALES  Proteomics Unit, Clinical Neuroproteomics Laboratory, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pu´blica de Navarra (UPNA), IdiSNA, Proteored-ISCIII, Pamplona, Spain ASTRID GULDBRANDSEN  Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway; Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway REINER F. HASELOFF  Leibniz-Forschungsinstitut fu¨r Molekulare Pharmakologie, Berlin, Germany KOTARO HATTORI  Department of Mental Disorder Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan; Medical Genome Center, National Center of Neurology and Psychiatry, Tokyo, Japan CECILIA HELLSTRO¨M  Division of Affinity Proteomics, Department of Protein Science, SciLifeLab, KTH—Royal Institute of Technology, Stockholm, Sweden KARSTEN HILLER  Department of Bioinformatics and Biochemistry, Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universit€ a t Braunschweig, Braunschweig, Germany; Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany

Contributors

xiii

YANAIKA S. HOK-A-HIN  Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, Amsterdam, The Netherlands PHILIPP HO¨RMANN  Department of Bioinformatics and Biochemistry, Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universit€ a t Braunschweig, Braunschweig, Germany ROMAIN HUGUET  Thermo Fisher Scientific, San Jose, CA, USA DAVID JUST  Division of Affinity Proteomics, Department of Protein Science, SciLifeLab, KTH—Royal Institute of Technology, Stockholm, Sweden SERGEY I. KOVALCHUK  Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russian Federation EBERHARD KRAUSE  Leibniz-Forschungsinstitut fu¨r Molekulare Pharmakologie, Berlin, Germany HIROSHI KUNUGI  Department of Mental Disorder Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan MERCEDES LACHE´N-MONTES  Proteomics Unit, Clinical Neuroproteomics Laboratory, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pu´blica de Navarra (UPNA), IdiSNA, Proteored-ISCIII, Pamplona, Spain FRANCISCO LAMUS  Departamento de Anatomı´a y Radiologı´a, Facultad de Medicina, Universidad de Valladolid, Valladolid, Spain RAGNHILD REEHORST LEREIM  Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway; Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway DANIEL LO´PEZ-FERRER  Thermo Fisher Scientific, San Jose, CA, USA CHARLOTTE MACRON  Proteomics, Nestle´ Institute of Health Sciences, Nestle´ Research, Lausanne, Switzerland BRUNO MANADAS  CNC—Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal ANNA MA˚NBERG  Division of Affinity Proteomics, Department of Protein Science, SciLifeLab, KTH—Royal Institute of Technology, Stockholm, Sweden KATRIN MARCUS  Medizinisches Proteom-Center, Medical Faculty, Ruhr-Universit€ at Bochum, Bochum, Germany CAROLINE MAY  Medizinisches Proteom-Center, Medical Faculty, Ruhr-Universit€ at Bochum, Bochum, Germany REBECA MAYO  Metabolomics Department, OWL, Derio, Bizkaia, Spain KANIKA MEHTA  Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India VERA M. MENDES  CNC—Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal ANGELA MESSINA  CNR, Istituto per i Polimeri, Compositi e i Biomateriali Catania, Catania, Italy LAURA MILLA´N  Metabolomics Department, OWL, Derio, Bizkaia, Spain JOAN MONTANER  Neurovascular Research Laboratory, Vall d’Hebron Institute of Research (VHIR), Universitat Auto`noma de Barcelona, Barcelona, Spain HERNA´N MONTECINOS  Departamento de Biologı´a Celular, Facultad de Ciencias Biologicas, Universidad de Concepcion, Bio Bio Region, Chile ATSUSHI NAGAI  Department of Laboratory Medicine, Faculty of Medicine, Shimane University, Izumo, Japan

xiv

Contributors

PETER NILSSON  Division of Affinity Proteomics, Department of Protein Science, SciLifeLab, KTH—Royal Institute of Technology, Stockholm, Sweden RITA NOGUEIRA-FERREIRA  Unidade de Investigac¸a˜o Cardiovascular (UnIC), Departamento de Cirurgia e Fisiologia, Faculdade de Medicina, Universidade do Porto, Porto, Portugal ANTONIO NU´N˜EZ GALINDO  Proteomics, Nestle´ Institute of Health Sciences, Nestle´ Research, Lausanne, Switzerland KANAME OHYAMA  Unit of Medical Pharmacy, Department of Pharmacy Practice, Graduate School of Biomedical Sciences, Nagasaki University, Nagasaki, Japan JENNIE OLOFSSON  Division of Affinity Proteomics, Department of Protein Science, SciLifeLab, KTH—Royal Institute of Technology, Stockholm, Sweden SANDRA PACHARRA  Medizinisches Proteom-Center, Medical Faculty, Ruhr-Universit€ at Bochum, Bochum, Germany ANGELO PALMIGIANO  CNR, Istituto per i Polimeri, Compositi e i Biomateriali Catania, Catania, Italy ELISA PIN  Division of Affinity Proteomics, Department of Protein Science, SciLifeLab, KTH—Royal Institute of Technology, Stockholm, Sweden LAURA RAMIRO  Neurovascular Research Laboratory, Vall d’Hebron Institute of Research (VHIR), Universitat Auto`noma de Barcelona, Barcelona, Spain AISHWARYA A. RAO  Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India JULIA REMNESTA˚L  Division of Affinity Proteomics, Department of Protein Science, SciLifeLab, KTH—Royal Institute of Technology, Stockholm, Sweden JAVIER RIANCHO  Service of Neurology, Hospital Sierrallana, IDIVAL-CIBERNED, Torrelavega, Spain DONATA AGATA ROMEO  CNR, Istituto per i Polimeri, Compositi e i Biomateriali Catania, Catania, Italy ESTER SA´NCHEZ-JIME´NEZ  Mass Spectrometry and Proteomics Core Facility, Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain PASCUAL SA´NCHEZ-JUAN  Service of Neurology, University Hospital Marques de ValdecillaIDIVAL.CIBERNED, Santander, Spain CA´TIA SANTA  CNC—Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; Institute for Interdisciplinary Research, University of Coimbra, Coimbra, Portugal ENRIQUE SANTAMARI´A  Proteomics Unit, Clinical Neuroproteomics Laboratory, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pu´blica de Navarra (UPNA), IdiSNA, Proteored-ISCIII, Pamplona, Spain ANA SANTURTUN  Department of Physiology and Pharmacology, IDIVAL, University of Cantabria, Santander, Spain DAIMEI SASAYAMA  Department of Mental Disorder Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan; Department of Psychiatry, Shinshu University School of Medicine, Nagano, Japan MICHAEL SCHU¨MANN  Leibniz-Forschungsinstitut fu¨r Molekulare Pharmakologie, Berlin, Germany ABDULLAH MD SHEIKH  Department of Laboratory Medicine, Faculty of Medicine, Shimane University, Izumo, Japan

Contributors

xv

ALBA SIMATS  Neurovascular Research Laboratory, Vall d’Hebron Institute of Research (VHIR), Universitat Auto`noma de Barcelona, Barcelona, Spain RONALD SJO¨BERG  Division of Affinity Proteomics, Department of Protein Science, SciLifeLab, KTH—Royal Institute of Technology, Stockholm, Sweden SANJEEVA SRIVASTAVA  Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India LUISA STURIALE  CNR, Istituto per i Polimeri, Compositi e i Biomateriali Catania, Catania, Italy CHARLOTTE E. TEUNISSEN  Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, Amsterdam, The Netherlands LARS TO¨NGES  Department of Neurology, Ruhr-Universit€ a t Bochum at St Josef-Hospital, Bochum, Germany FA´BIO TRINDADE  iBiMED, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal; Unidade de Investigac¸a˜o Cardiovascular (UnIC), Departamento de Cirurgia e Fisiologia, Faculdade de Medicina, Universidade do Porto, Porto, Portugal MAR VILANOVA  Mass Spectrometry and Proteomics Core Facility, Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain MARTA VILASECA  Mass Spectrometry and Proteomics Core Facility, Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain LAURA VILLARREAL  Mass Spectrometry and Proteomics Core Facility, Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain RUI VITORINO  iBiMED, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal; Unidade de Investigac¸a˜o Cardiovascular (UnIC), Departamento de Cirurgia e Fisiologia, Faculdade de Medicina, Universidade do Porto, Porto, Portugal ELINE A. J. WILLEMSE  Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, Amsterdam, The Netherlands MARIO ZAPPIA  Section of Neurosciences, Department GF Ingrassia, University of Catania, Catania, Italy RUSTAM H. ZIGANSHIN  Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russian Federation

Part I Introduction

Chapter 1 The Human Brain Proteome Project: Biological and Technological Challenges Joaquı´n Ferna´ndez-Irigoyen, Fernando Corrales, and Enrique Santamarı´a Abstract Brain proteomics has become a method of choice that allows zooming-in where neuropathophysiological alterations are taking place, detecting protein mediators that might eventually be measured in cerebrospinal fluid (CSF) as potential neuropathologically derived biomarkers. Following this hypothesis, mass spectrometry-based neuroproteomics has emerged as a powerful approach to profile neural proteomes derived from brain structures and CSF in order to map the extensive protein catalog of the human brain. This chapter provides a historical perspective on the Human Brain Proteome Project (HBPP), some recommendation to the experimental design in neuroproteomic projects, and a brief description of relevant technological and computational innovations that are emerging in the neurobiology field thanks to the proteomics community. Importantly, this chapter highlights recent discoveries from the biology- and disease-oriented branch of the HBPP (B/D-HBPP) focused on spatiotemporal proteomic characterizations of mouse models of neurodegenerative diseases, elucidation of proteostatic networks in different types of dementia, the characterization of unresolved clinical phenotypes, and the discovery of novel biomarker candidates in CSF. Key words Proteomics, Human brain, Mass spectrometry

1

Introduction The complexity of the human brain is reflected in its ~90 billion neurons and at least an equal number of glial cells, which can be further subdivided into hundreds of different types on the basis of molecular properties, morphology, and connectivity patterns [1]. Trillions of synaptic connections between these cell types contribute to the definition of around 900 neuroanatomical subdivisions in the adult human brain [2]. The growing application of omicsbased science has enabled the simultaneous examination of thousands of genes, transcripts, and proteins in the brain, using high-throughput approaches and innovative analytical tools for data integration [1, 3]. In general, the deployment of transcriptomics has revealed specific regional transcriptional signatures that

Enrique Santamarı´a and Joaquı´n Ferna´ndez-Irigoyen (eds.), Cerebrospinal Fluid (CSF) Proteomics: Methods and Protocols, Methods in Molecular Biology, vol. 2044, https://doi.org/10.1007/978-1-4939-9706-0_1, © Springer Science+Business Media, LLC, part of Springer Nature 2019

3

4

Joaquı´n Ferna´ndez-Irigoyen et al.

are regulated in a spatiotemporal manner across mammalian brains, increasing the complexity of the structural and molecular organization of this organ [4–9]. Taking into account that proteins are the dynamic molecules regulating most biological functions, the systematic analysis of the brain proteome is needed to functionally interpret the information provided by transcriptome-wide approaches [1]. Furthermore, given the segmented functions of the brain, the characterization of protein profiles within specific areas is essential to decipher the molecular basis for structure specialization as well as the proteostasis impairment associated with neurodegenerative and psychiatric diseases.

2

From Basic to Clinical Neuroproteomics Proteomics is commonly used to encompass multidisciplinary approaches combining different technologies that aim to study the entire spectrum of protein modulation (structure, interaction, expression, abundance, and modification). The rise in the proteomics field was evidenced with the introduction of different highresolution approaches as advanced separation techniques, highresolution mass spectrometry, and computational workflows, catalyzing the scope of biological studies to proteome-wide measurements [10–13]. The term “neuroproteomics” was coined for the first time by Kim et al. in 2004, in a reference to the implementation of proteomics in the molecular analysis of the central nervous system [14]. In general, this term comprehends the application of proteomic methods to assess the qualitative and quantitative aspects of central and peripheral nervous system protein contents, encompassing global dynamics events underlying brain-related disorders ranging from neurotrauma-related injuries (traumatic brain and spinal cord injuries, stroke, etc.), neuropsychiatric syndromes (schizophrenia, depression, etc.), and neurodegenerative diseases (tauopathies, synucleinopathies, etc.). During the last decade, the use of mass spectrometry in neuroscience has been progressively increased, in parallel with the use of proteomics in brain biopsy tissues (Fig. 1). Some reviews have highlighted the technological implementation of proteomics in neuroscience [3, 15–17] and the application of proteomic approaches (Fig. 2) to understand the molecular background contributing to the progression of neurological disorders [18, 19]. The term “clinical neuroproteomics” refers to the subfield of proteomics aimed at developing a better understanding of human central/peripheral nervous system disease and discovering characteristic neurological protein markers [20]. Prior to starting any clinical neuroproteomic study, it is imperative to draft an exhaustively considered experimental design. This is often the most important point of the whole workflow as it defines whether the

The Human Brain Proteome

5

Fig. 1 Number of peer-reviewed publications related to the use of mass spectrometry and proteomics in brain biology. The chart summarizes the number of scientific peer-reviewed publications obtained by searching the terms “brain and mass spectrometry,” “brain and proteomics,” “cerebrospinal fluid and mass spectrometry,” and “cerebrospinal fluid and proteomics” in the PubMed database during the period 2008–2017

approaches followed will meet the study objectives. Briefly, a good experimental design describes the following items: 1. Study background. 2. Definition of study objective. 3. Selection and background of subjects (i.e., including controls and subjects with an overlap in clinical presentation and brain neuropathology) and sample preparation (postmortem brain, organelles, synapse, protein complex, CSF, serum/plasma). 4. Experimental methodology (targeted proteomics, shotgun proteomics in combination with other omics, etc.). 5. Statistical data evaluation and validation approach. 6. Potential follow-up studies (Fig. 3). Brain proteomics is an ideal approach that allows zooming-in where pathophysiological changes are taking place, detecting protein mediators that might eventually be measured in biofluids as potential neuropathologically derived biomarkers. However, despite advances in sensitivity in mass spectrometry and in the development of proteomic workflows applied in neuroscience, protein yield from neural tissues is often a harsh limiting factor when considering the quantities needed for proteomics. The nervous system is composed of tangled mixtures of different cell types,

6

Joaquı´n Ferna´ndez-Irigoyen et al.

Neuroproteomic toolbox

Imaging

Relative quantitation

Absolute quantitation PSAQ

MALDI-IMS Label-based

Label-free

2D-DIGE

LF-MS peak intensity

ICPL

LF-MS spectral counting

ICAT

SRM/MRM/PRM

TMT

SWATH

ITRAQ

TDMS

AQUA QCAT

SILAC SILAM Dimethyl labeling

Fig. 2 Proteomic workflows used for relative and absolute protein/peptide quantitation in neuroproteomic projects (MALDI-IMS MALDI imaging mass spectrometry, 2D-DIGE two-dimensional differential gel electrophoresis, ICPL isotope-coded protein labeling, ICAT isotope-coded affinity tag, TMT tandem mass tag, iTRAQ isobaric tags for relative and absolute quantitation, SILAC stable isotope labeling by amino acids in cell culture, SILAM stable isotope labeling in mammals, LF-MS label-free mass spectrometry, SRM selected reaction monitoring, MRM multiple reaction monitoring, PRM parallel reaction monitoring, SWATH sequential window acquisition of all theoretical fragment-ion spectra, TDMS top-down mass spectrometry, PSAQ protein standard absolute quantification, AQUA absolute quantification, QCAT quantitation by concatenated tryptic peptides)

presenting major sampling and analysis challenges [21, 22]. Moreover, neurons are highly polarized cells and protein products are not uniformly distributed. Specific compartments such as soma, axon, dendrites, and synapsis contain a unique composition of proteins [23] being involved in specific biological networks and pathways [24]. Unless enrichment strategies for sample preparation (at cellular or subcellular level) are used, results may often be difficult to interpret and bias toward the identification of abundant proteins. An additional complication in working with human brain is the existence of several factors (pH, temperature of storage) that may interfere with tissue and molecular preservation of this organ. These factors are related with long postmortem delay between death and sample processing for storage, generating protein artifacts, and posttranslational modifications (PTMs) due to protein degradation events [25–27]. Despite the bottlenecks mentioned before, the brain proteomics community is continuously contributing to the repertoire of mammalian brain proteomes not only at qualitative but also at quantitative level [19, 28], providing basic information for understanding the pathophysiology and

The Human Brain Proteome

7

Delineation of the natural history of the neurological syndrome

Clinical question definition

Experimental design

Choice of biological sample

Standardization of sample handling, processing and storage

Analytical platform and methodology

Biostatistics

Confirmation of biomarkers identity and validity

Larger cohorts, multicenter trials, prospective screening

Established assay sensitivity & specificity

Development of point-of-care tests

Fig. 3 Overview of the key phases of the pathway to biomarker discovery

progression of neurological disorders as well as protein mediators that may be used as potential therapeutic agents or even explored in biofluids as candidate biomarkers for diagnosis and evolution.

3

The Human Brain Proteome Project The Human Brain Proteome Project (HBPP) [29] is an open international project under the patronage of the Human Proteome Organization (HUPO) that aims: 1. To analyze the brain proteome of human as well as mouse models in healthy, neurodiseased, and aged status. 2. To perform quantitative proteomics as well as complementary gene expression profiling on disease-related brain areas and body fluids in order to push new diagnostic approaches. 3. To exchange knowledge and data with other international initiatives in the neuroproteomic field. 4. To make neuroproteomic research and its results available in the scientific community and society.

8

Joaquı´n Ferna´ndez-Irigoyen et al.

Since its creation in 2002 (chaired, structured, and organized by professors Helmut E. Meyer and Joaquim Close), significant progress has been made in the field of neuroproteomics. In the first phase of the HBPP, research efforts were made to characterize brain proteomes of mice [30, 31], completing mouse databases and proving the applicability of different proteomic approaches for the investigation of brain proteomes. Eighteen different groups across Europe, Asia, and the USA were involved in a pilot phase [32, 33] with the aim to perform differential quantitative proteome analysis of mouse brain of three different ages and to identify as many proteins as possible using biopsy and autopsy human brain specimens. Thirty-seven different analytical approaches were accomplished [34], and a centralized data reprocessing strategy was elaborated [35] in this multicentric effort. Finally, combining datasets derived from bidimensional electrophoresis and 2D-LC-MS/ MS, a non-redundant set of 1804 human brain proteins were identified and functionally annotated, creating the first human brain proteome database [36]. Afterward, and coinciding with the surge in shotgun proteomics, proteome-wide analysis of 18 anatomical regions (pituitary gland, cortex superior temporal gyrus, cortex middle temporal gyrus, thalamus, anterior temporal lobe, corpus callosum, hippocampus, visual cortex, cerebellum, olfactory bulb, locus ceruleus, substantia nigra, amygdala, nucleus basalis of Meynert, caudate nucleus, putamen, medial globus pallidus, and lateral globus pallidus) have been performed by independent groups using different technological platforms identifying more than 7000 protein species in healthy human brain [28, 37]. Moreover, subcellular proteomics has allowed the characterization of the protein content of specific neuronal compartments (such as neuromelanin granules, mitochondria, nucleus, membranes, axogliasomes, postsynaptic density, and myelin), establishing a subcellular reference proteome to a depth of 3000 proteins [28]. In parallel, the construction of a human antibody library [38] has allowed the creation of specific tissue profiling maps for 100 proteins across 25 areas of rat brain, beginning to generate a brain atlas based on immunohistochemistry [39]. Until 2015, approximately 10,500 proteins (corresponding to 7800 proteincoding genes) were detected in healthy human brain using proteomic methods. A meta-analysis based on all these proteome datasets was made available to the scientific community, generating an updated human brain proteome database, protein interaction data for human brain proteins, a compilation of synaptic protein-coding genes, anatomical correlation of proteomic and transcriptomic profiles stored in Allen Brain Atlas, pathways analysis of the identified human brain proteome, and human brain proteome classification according to chromosomal origin [28]. More recently, the human brain proteome has been complemented by additional characterizations of the protein components derived from the pituitary,

The Human Brain Proteome

9

corpus callosum, and anterior temporal lobe [40, 41]. With respect to murine brain, Sharma et al. have performed state-of-the-art proteome-wide analysis of the cerebellum, hippocampus, thalamus, striatum, brainstem, olfactory bulb, motor cortex, prefrontal cortex, corpus callosum, and the optic nerve derived from C57BL/6 mice. For that, a combination of different proteomic strategies was used, unambiguously identifying more than 11,000 proteins in all brain structures analyzed, being the most extensive characterization of a mammalian brain proteome [42]. However, proteomic approaches have to deal with the increment in protein isoforms due to multiple posttranslational modifications (PTMs). Considering that a protein may undergo between 2 and 20 PTMs in average [43], shotgun proteomics has been able to reveal protein expression for approximately 50–75% of the human genome [44, 45], and approximately 260,000 PTM sites have been identified in the human proteome [46], it is not easy to predict how many protein species are expressed during the development of the human brain. Thanks to the implementation of effective enrichment strategies, novel high-performance instrumentation, and bioinformatics algorithms with rigorous scoring, modification-specific proteomics allows the detection and quantitation of thousands of PTM sites in a single experiment [13, 46]. Using mass spectrometry-based proteomics, PTMs like S-nitrosylation, citrullination, O-GlcNAcylation, deamination, N-glycosylation, phosphorylation, ubiquitination, acetylation, arginylation, Omannosylation, deamidation, sumoylation, and methylation have been mapped in different areas of Drosophila, rat, mouse, or human brain [47–62]. Overall, the widespread application of PTMomics has led to a new dimension in our knowledge of the complexity of biological processes mediated by PTMs (and their potential crosstalk) in mammalian brain. The definition of the site-specific functions and the elucidation of the pathways that catalyze and control its own homeostasis in healthy, diseased, and aged brain cells remain challenges for the future. 3.1 Biology/DiseaseDriven Human Proteome Project: Focus on the Brain

The Human Proteome Project (HPP) is an initiative of the Human Proteome Organization (HUPO) with the mission of characterizing the human proteome, providing resources for the accurate quantification of human proteins, and making this knowledge available to the broad scientific community. The idea of grouping all activities running under the umbrella of HUPO since 2001 was firstly discussed during the HUPO World Congress in Amsterdam [63, 64], and the HPP was officially presented in 2010 in Sydney [65, 66]. The main aim was the configuration of a detailed map of all human proteins in their biological context to define functional models and the regulatory events in response to physiological and pathological conditions as the basis to develop novel diagnostic, prognostic, and therapeutic strategies. To this end, three

10

Joaquı´n Ferna´ndez-Irigoyen et al.

technological pillars were defined, namely, mass spectrometry (MS), affinity reagents, and knowledgebase (KB) [65]. The MS pillar aims to set a collection of assays to accurately quantify at least one polypeptide from each human coding gene. The coordinates to quantify more than 166,000 peptides by SRM, covering 99.7% of the annotated human proteins, some splicing variants, non-synonymous mutations, and posttranslational modifications [67], are already available at the human SRMAtlas (http:// www.srmatlas.org). Besides, special attention is paid to standardization to provide traceability and reproducibility to MS experiments facilitating data sharing across labs [68]. In parallel, the Proteomics Identification Database (PRIDE) was developed [69] as a public repository to make accessible all MS data generated (http://www.ebi.ac.uk/pride). The goal of the affinity reagents pillar is to obtain antibodies to detect and quantify at least one polypeptide product for each human gene in all cell types, tissues, and organs and define their subcellular distribution and the age-dependent expression under physiological and pathological conditions. Currently, the Human Protein Atlas (HPA, http:// www.proteinatlas.org) contains information of more than 18,000 proteins based on 26,000 antibodies [70, 71]. The KB role is to create a knowledge platform of human proteins that integrates all structural and functional information available, as well as the information generated by the HPP initiatives under stringent quality standards. High-density MS datasets are reanalyzed and deposited in PeptideAtlas that contains curated information for 1,400,000 peptides belonging to 15,898 proteins (http://www.peptideatlas. org). neXtProt integrates protein information from different sources, including PeptideAtlas, HPA, and UniProtKB-Swiss-Prot among others [72, 73], to offer a comprehensive knowledge platform with 20,230 proteins, 42,241 proteoforms (splicing), more than 200,000 protein-protein binary interactions, about 200,000 posttranslational modifications, and more than 5 million SAP variants (http://nextprot.org). A Pathology Pillar was launched in 2017 to provide support on human sample handling and assistance to generate links between proteomics profiles and histological information. In addition to the aims mainly associated with the Pillars, two complementary sub-projects were started: the ChromosomeCentric HPP (C-HPP) and the Biology- and Disease-Driven HPP (B/D-HPP). The C-HPP is a consortium of 25 international research teams that initially adopt a specific chromosome to characterize its associated proteome [74]. The main aim was to detect the proteins for which experimental information supporting their existence is still available: the missing proteins. A recent initiative of the C-HPP aims to characterize proteins with unknown function (uPE1) [75]; work is in progress in collaboration with B/D-HPP teams to enhance the annotation of uPE1 proteins in specific

The Human Brain Proteome

11

biological contexts. The B/D-HPP is an open initiative aiming to gather life scientists specialized in particular areas to generate new concepts in biology using proteomics resources. The overall idea is to organize the human proteome in functional modules as the basis of human cell, tissue, and organ complexity, integrating technology and biological knowledge [76]. The B/D-HPP currently integrates 18 initiatives: Brain, Liver, Plasma, Kidney and Urine, Skeletal Muscle, EyeOme, PediOme, Extreme Conditions, Diabetes, Cancer, Infectious Diseases, Immunopeptidomics, Cardiovascular, Mitochondria, Model Organisms, Protein Aggregation, Food and Nutrition, and Glycoproteomics (https://www.hupo.org/B/DHPP). In addition to dissemination activities in collaboration with scientific and clinical societies, the B/D-HPP groups aim to define lists of highly cited proteins in their field (termed popular proteins) (https://www.hupo.org/B/D-HPP) that might prove useful to identify driver mechanisms of disease and to develop precision medicine strategies [77]. To this end, specific Web tools have been developed to perform systematic searches in bibliographic databases to extract the top ranking proteins and metabolites according to their co-citation index with a particular physiological or pathological process [78–81]. The biology- and disease-oriented branch of the HBPP (B/DHBPP) has been established with the main goal of supporting the broad application of state-of-the-art proteomic measurements by life scientists studying the molecular mechanisms of biological processes involved in brain functionality [76]. To date, B/DHBPP researchers [82] have used a broad spectrum of methods (Fig. 4) and have fostered strong cooperation between scientists from different fields such as neurobiology, neuroanatomy, and neuropathology to understand the biogenesis of the most common forms of dementia like Alzheimer’s disease, Parkinson’s disease, frontotemporal dementia (FTD), and other neurodegenerative and psychiatric diseases. During the last years, important conclusions are emerging in the neurobiology field thanks to the proteomics community: 1. Shotgun proteome-wide approaches applied to mouse models of neurodegenerative diseases have revealed that spatiotemporal analyses are highly useful to identify protein mediators involved in the aggregate-mediated toxicity in Huntington’s and Alzheimer’s diseases [83–85]. 2. Proteome data obtained in mouse models of tauopathies and synucleinopathies point out that the induced proteostatic imbalance diminishes the capacity of neural cells to prevent aberrant conformational changes, altering not only the expression levels but also the solubility of hundreds of cellular proteins [86].

Enzymatic digestion

Brain

Affinity enrichment (PTMs)

Proteomics (LC-MS/MS)

Subcellular fractionation FACS Enrichment of specific cellular subpopulations

Enzymatic digestion

CSF

Tissue sectioning

MALDI-IMS

Abundant Protein depletion

Enzymatic digestion

Affinity enrichment (PTMs)

Proteomics (LC-MS/MS, arrays…)

LCM

Molecular data integration

Clinical and neuropathological associated information

Whole tissue

Genomics Transcriptomics Metabolomics lipidomics

Joaquı´n Ferna´ndez-Irigoyen et al.

12

Fig. 4 Schematic graph representing some technical workflows suitable for neuroproteomic studies (MALDIIMS MALDI imaging mass spectrometry, LCM laser capture microdissection, FACS fluorescence-activated cell sorting)

3. Top-down proteomic discipline is allowing the generation of the Mouse Brain Proteoform Atlas, identifying brain intact proteoforms that contribute to specific phenotypes among murine strains [87]. 4. Until now, most of the neuroproteomics workflows focused on the characterization of human AD and PD neurodegeneration, although informative, have ignored the neuropathological progression of the disease across AD/PD related-brain structures. Deciphering the progressive proteome-wide alterations that occur in a stage-dependent manner in early-affected brain

The Human Brain Proteome

13

structures has unveiled the biochemical pathways initially affected during the progressive neurodegeneration, identifying potential novel therapeutic targets and biomarkers in AD and PD [88–91]. 5. Differences observed in the protein composition of amyloid plaques between rapidly progressive Alzheimer’s disease (rpAD) and typical sporadic Alzheimer’s disease (sAD) have suggested that rpAD may be considered a novel subtype of AD [92]. 6. Based on the assumption that AD and PD share common underlying mechanisms due to the overlap in clinical presentation and brain neuropathology, cross-disease proteomic analyses have demonstrated pathology-specific molecular pathways and protein signatures that are common in AD and PD [90, 93]. 7. Thanks to systems-level proteomic approaches, clinical phenotypes and genetic mechanisms underlying the amyotrophic lateral sclerosis and frontotemporal dementia (ALS-FTD) disease spectrum have been elegantly resolved [94]. 8. The application of 3D matrix-assisted laser desorption/ionization (MALDI) imaging is elucidating lesion-specific molecular mechanisms occurring post-traumatic brain injury/spinal cord injury (TBI/SCI), identifying novel therapeutic targets [95, 96]. 9. Novel associations between brain-enriched proteins and neuropathological substrates (Abeta 1–42, tau, phospho-tau) have been identified in older adults with cognitive impairment by differential cerebrospinal fluid (CSF) proteomics [97]. 3.2 Innovative Technological and Computational Developments

Conventional mass spectrometry (MS)-based proteomics approaches have been usually ineffective for mapping protein expression in well-annotated archival formalin-fixed, paraffinembedded (FFPE) brain tissues [98], as well as in tissue sections with complex spatial resolution, mainly due to the limited overall sensitivity of traditional proteomic workflows. However, recent technological advancements have yielded reliable recovery for brain proteome quantitation in these specific contexts. Djuric et al. identified novel markers of neurodevelopment by profiling the fetal brain neurodevelopment-related protein dynamics through the differential analysis between neural progenitor cells and neuron-rich cellular layers of the FFPE cerebrum with respect to differentiated neural progenitor cells derived from induced pluripotent stem cells (iPSCs) [99]. On the other hand, applying spatially resolved proteomics based on laser capture microdissection (LCM) with nanoliter-scale sample preparation system (nanoPOTS), Zhu et al. demonstrate that this workflow facilitated by

14

Joaquı´n Ferna´ndez-Irigoyen et al.

DMSO as capture solvent can detect and comparatively quantify 1000 proteins with a spatial resolution of 100 μm, corresponding to just 10–18 rat brain cortical cells [100]. Due to existing methods for metabolic protein labeling in vivo usually access all cell types, Alvarez-Castelao B et al. developed an approach to isolate, analyze, and quantitate cell-type-specific proteomes based on the establishment of a transgenic mouse line with Cre-recombinase-induced expression of a mutant methionyl-tRNA synthetase, enabling the cell-type-specific labeling of nascent proteins that may be isolated by affinity purification. Using this method in combination with imaging and mass spectrometry, they labeled and specifically analyzed the proteome of hippocampal excitatory neurons and Purkinje neurons [101]. In an effort to combine proteomic data with clinical technologies, Roy M et al. integrated the composition of postsynaptic proteomes in human neocortical regions with genetic and behavioral data, functional magnetic resonance (fMRI), and positron emission tomography (PET) imaging records. Interestingly, the integration of large-scale GWAS with regional proteome data identified the same cortical region for smoking behavior as detected by fMRI data, linking postsynaptic proteomes, genetics, and brain imaging [102]. Improvements in data acquisition and computational workflows associated with mass spectrometry data are also revolutionizing the classical way to obtain and interpret brain proteomes. On the one hand, highly specialized laboratories are able to detect more than 10,000 proteins derived from murine brain in only 100 min by single-shot proteomics using BoxCar acquisition, extending the sensitivity into the low-attomolar range [103]. On the other hand, thanks to advances in DNA sequencing technologies that have made complete sequencing of human genomes/transcriptomes routine, proteogenomics is experiencing heightened significance. In this approach, customized protein sequence databases generated using genomic and transcriptomic information are used to facilitate the identification of novel peptides (not present in reference protein sequence databases) from mass spectrometry-based proteomic data [104]. Using different proteogenomic-like approaches, more than 950 novel single amino acid variants (SAAVs) have been detected in human brain, where around 50% of the reference and SAAV peptide pairs were equally abundant, supporting the notion that both alleles are equally expressed [105]. Using a different deep integration of RNA-Seq and LC-MS/MS data, 125 protein variants have been identified in glioma stem cells, being a novel resource for potential targets against glioblastoma [106]. In summary, the widespread implantation and establishment of these proteomic innovations will surely promote advancing knowledge about the proteomic portrait of the human brain, leading to novel developments in neurologic differential diagnosis and therapies.

The Human Brain Proteome

15

In our opinion, the full potential of neuroproteomics will be achieved as a result of: (1) Implementation of novel sample processing and targeted methods (2) Characterization of the PTM profilings (3) Integration with other “omics” disciplines, (4) Development of system biology tools for meta-analysis, making possible the knowledge and data exchange with specific repositories on mRNA levels (Allen Brain Atlas), gene expression profiles (Gene Expression Nervous System Atlas), and neuroanatomy (BrainMaps), with the final goal to decipher the global brain protein diversity at structural, cellular, and subcellular level 3.3 Cerebrospinal Fluid (CSF)

The selection of a fluid in close proximity to a diseased organ may increase the probability of finding a biomarker panel originating from a pathological tissue. In that respect, CSF, which is in closest contact with the brain, represents a useful reservoir of potential clinically relevant biomarkers for neurological diseases [107, 108]. Several reports have demonstrated the presence of at least 4500 proteins in human CSF [28, 109]. In addition, studies focused on PTM characterizations have pointed out the presence of phosphorylated, glycosylated, oxidized, and carbonylated proteins in human CSF. However, despite the fact that cerebrospinal fluid (CSF) is considered the best compartment for the analysis of protein and peptide biomarkers to study neurological disorders and to support CNS drug development [110], the number of publications remains certainly constant during the last decade (Fig. 1). Human CSF has a 100-fold lower protein concentration than plasma/serum (0.6 and 80 mg/ml, respectively) with a considerable dynamic range, ranging from albumin in micromolar amounts to cytokines in picomolar quantities, which hampers the in-depth analysis of the CSF proteome in a straightforward manner [110]. Moreover, the blood contamination and blood cell hemolysis during CSF collection may dramatically disturb the proteome analysis. During the last years, many CSF proteomics biomarker studies have been published, but not all present an adequate statistical power calculation or sufficient number of technical replicates to compensate the variation of the proteomic workflow, hampering independent validation phases of the proposed biomarker candidates. All these reasons support the notion that CSF proteomics field has not yet reached a mature stage (Fig. 1). Although we are in the early stages of a learning curve, the development of highperformance instrumentation, the combination of shotgun and targeted proteomic approaches, and the implementation of novel bioinformatics workflows will push the detection and validation of consistent CSF biomarkers in the neurology field in the near future.

16

Joaquı´n Ferna´ndez-Irigoyen et al.

Acknowledgments The Clinical Neuroproteomics Laboratory of Navarrabiomed participates in the HUPO Brain Proteome Project and is lined up with the Spanish Initiative on the Human Proteome Project (SpHPP). The Proteomics Unit of Navarrabiomed is a member of Proteored, PRB3-ISCIII, and is supported by grant PT17/0019, of the PE I + D + i 2013–2016, funded by ISCIII and ERDF. Part of the work described here was funded by grants from the Spanish Ministry of Economy and Competitiveness (MINECO) (Ref. SAF201459340-R), Department of Economic Development from the Government of Navarra (Ref. PC023-PC024, PC025, PC081-82, and PI059), and Obra Social la Caixa to E.S. References 1. Kitchen RR, Rozowsky JS, Gerstein MB, Nairn AC (2014) Decoding neuroproteomics: integrating the genome, translatome and functional anatomy. Nat Neurosci 17 (11):1491–1499. https://doi.org/10.1038/ nn.3829 2. Hawrylycz MJ, Lein ES, Guillozet-Bongaarts AL, Shen EH, Ng L, Miller JA, van de Lagemaat LN, Smith KA, Ebbert A, Riley ZL, Abajian C, Beckmann CF, Bernard A, Bertagnolli D, Boe AF, Cartagena PM, Chakravarty MM, Chapin M, Chong J, Dalley RA, Daly BD, Dang C, Datta S, Dee N, Dolbeare TA, Faber V, Feng D, Fowler DR, Goldy J, Gregor BW, Haradon Z, Haynor DR, Hohmann JG, Horvath S, Howard RE, Jeromin A, Jochim JM, Kinnunen M, Lau C, Lazarz ET, Lee C, Lemon TA, Li L, Li Y, Morris JA, Overly CC, Parker PD, Parry SE, Reding M, Royall JJ, Schulkin J, Sequeira PA, Slaughterbeck CR, Smith SC, Sodt AJ, Sunkin SM, Swanson BE, Vawter MP, Williams D, Wohnoutka P, Zielke HR, Geschwind DH, Hof PR, Smith SM, Koch C, Grant SG, Jones AR (2012) An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489(7416):391–399. https://doi.org/10.1038/nature11405 3. Bayes A, Grant SG (2009) Neuroproteomics: understanding the molecular organization and complexity of the brain. Nat Rev Neurosci 10(9):635–646. https://doi.org/10. 1038/nrn2701 4. Kang HJ, Kawasawa YI, Cheng F, Zhu Y, Xu X, Li M, Sousa AM, Pletikos M, Meyer KA, Sedmak G, Guennel T, Shin Y, Johnson MB, Krsnik Z, Mayer S, Fertuzinhos S, Umlauf S, Lisgo SN, Vortmeyer A, Weinberger DR, Mane S, Hyde TM, Huttner A,

Reimers M, Kleinman JE, Sestan N (2011) Spatio-temporal transcriptome of the human brain. Nature 478(7370):483–489. https:// doi.org/10.1038/nature10523 5. Miller JA, Ding SL, Sunkin SM, Smith KA, Ng L, Szafer A, Ebbert A, Riley ZL, Royall JJ, Aiona K, Arnold JM, Bennet C, Bertagnolli D, Brouner K, Butler S, Caldejon S, Carey A, Cuhaciyan C, Dalley RA, Dee N, Dolbeare TA, Facer BA, Feng D, Fliss TP, Gee G, Goldy J, Gourley L, Gregor BW, Gu G, Howard RE, Jochim JM, Kuan CL, Lau C, Lee CK, Lee F, Lemon TA, Lesnar P, McMurray B, Mastan N, Mosqueda N, Naluai-Cecchini T, Ngo NK, Nyhus J, Oldre A, Olson E, Parente J, Parker PD, Parry SE, Stevens A, Pletikos M, Reding M, Roll K, Sandman D, Sarreal M, Shapouri S, Shapovalova NV, Shen EH, Sjoquist N, Slaughterbeck CR, Smith M, Sodt AJ, Williams D, Zollei L, Fischl B, Gerstein MB, Geschwind DH, Glass IA, Hawrylycz MJ, Hevner RF, Huang H, Jones AR, Knowles JA, Levitt P, Phillips JW, Sestan N, Wohnoutka P, Dang C, Bernard A, Hohmann JG, Lein ES (2014) Transcriptional landscape of the prenatal human brain. Nature 508 (7495):199–206. https://doi.org/10.1038/ nature13185 6. Lein ES, Hawrylycz MJ, Ao N, Ayres M, Bensinger A, Bernard A, Boe AF, Boguski MS, Brockway KS, Byrnes EJ, Chen L, Chen TM, Chin MC, Chong J, Crook BE, Czaplinska A, Dang CN, Datta S, Dee NR, Desaki AL, Desta T, Diep E, Dolbeare TA, Donelan MJ, Dong HW, Dougherty JG, Duncan BJ, Ebbert AJ, Eichele G, Estin LK, Faber C, Facer BA, Fields R, Fischer SR, Fliss TP, Frensley C, Gates SN, Glattfelder KJ,

The Human Brain Proteome Halverson KR, Hart MR, Hohmann JG, Howell MP, Jeung DP, Johnson RA, Karr PT, Kawal R, Kidney JM, Knapik RH, Kuan CL, Lake JH, Laramee AR, Larsen KD, Lau C, Lemon TA, Liang AJ, Liu Y, Luong LT, Michaels J, Morgan JJ, Morgan RJ, Mortrud MT, Mosqueda NF, Ng LL, Ng R, Orta GJ, Overly CC, Pak TH, Parry SE, Pathak SD, Pearson OC, Puchalski RB, Riley ZL, Rockett HR, Rowland SA, Royall JJ, Ruiz MJ, Sarno NR, Schaffnit K, Shapovalova NV, Sivisay T, Slaughterbeck CR, Smith SC, Smith KA, Smith BI, Sodt AJ, Stewart NN, Stumpf KR, Sunkin SM, Sutram M, Tam A, Teemer CD, Thaller C, Thompson CL, Varnam LR, Visel A, Whitlock RM, Wohnoutka PE, Wolkey CK, Wong VY, Wood M, Yaylaoglu MB, Young RC, Youngstrom BL, Yuan XF, Zhang B, Zwingman TA, Jones AR (2007) Genome-wide atlas of gene expression in the adult mouse brain. Nature 445 (7124):168–176. https://doi.org/10.1038/ nature05453 7. Morris JA, Royall JJ, Bertagnolli D, Boe AF, Burnell JJ, Byrnes EJ, Copeland C, Desta T, Fischer SR, Goldy J, Glattfelder KJ, Kidney JM, Lemon T, Orta GJ, Parry SE, Pathak SD, Pearson OC, Reding M, Shapouri S, Smith KA, Soden C, Solan BM, Weller J, Takahashi JS, Overly CC, Lein ES, Hawrylycz MJ, Hohmann JG, Jones AR (2010) Divergent and nonuniform gene expression patterns in mouse brain. Proc Natl Acad Sci U S A 107 (44):19049–19054. https://doi.org/10. 1073/pnas.1003732107 8. Bakken TE, Miller JA, Ding SL, Sunkin SM, Smith KA, Ng L, Szafer A, Dalley RA, Royall JJ, Lemon T, Shapouri S, Aiona K, Arnold J, Bennett JL, Bertagnolli D, Bickley K, Boe A, Brouner K, Butler S, Byrnes E, Caldejon S, Carey A, Cate S, Chapin M, Chen J, Dee N, Desta T, Dolbeare TA, Dotson N, Ebbert A, Fulfs E, Gee G, Gilbert TL, Goldy J, Gourley L, Gregor B, Gu G, Hall J, Haradon Z, Haynor DR, Hejazinia N, Hoerder-Suabedissen A, Howard R, Jochim J, Kinnunen M, Kriedberg A, Kuan CL, Lau C, Lee CK, Lee F, Luong L, Mastan N, May R, Melchor J, Mosqueda N, Mott E, Ngo K, Nyhus J, Oldre A, Olson E, Parente J, Parker PD, Parry S, Pendergraft J, Potekhina L, Reding M, Riley ZL, Roberts T, Rogers B, Roll K, Rosen D, Sandman D, Sarreal M, Shapovalova N, Shi S, Sjoquist N, Sodt AJ, Townsend R, Velasquez L, Wagley U, Wakeman WB, White C, Bennett C, Wu J, Young R, Youngstrom BL, Wohnoutka P, Gibbs RA, Rogers J, Hohmann JG, Hawrylycz MJ, Hevner RF, Molnar Z,

17

Phillips JW, Dang C, Jones AR, Amaral DG, Bernard A, Lein ES (2016) A comprehensive transcriptional map of primate brain development. Nature 535(7612):367–375. https:// doi.org/10.1038/nature18637 9. Xu C, Li Q, Efimova O, He L, Tatsumoto S, Stepanova V, Oishi T, Udono T, Yamaguchi K, Shigenobu S, Kakita A, Nawa H, Khaitovich P, Go Y (2018) Human-specific features of spatial gene expression and regulation in eight brain regions. Genome Res 28(8):1097–1110. https://doi.org/10.1101/gr.231357.117 10. Patterson SD, Aebersold RH (2003) Proteomics: the first decade and beyond. Nat Genet 33(Suppl):311–323. https://doi.org/10. 1038/ng1106ng1106 11. Aebersold R, Mann M (2003) Mass spectrometry-based proteomics. Nature 422 (6928):198–207. https://doi.org/10.1038/ nature01511nature01511 12. Ahrens CH, Brunner E, Qeli E, Basler K, Aebersold R (2010) Generating and navigating proteome maps using mass spectrometry. Nat Rev Mol Cell Biol 11(11):789–801. https://doi.org/10.1038/nrm2973 13. Aebersold R, Mann M (2016) Massspectrometric exploration of proteome structure and function. Nature 537 (7620):347–355. https://doi.org/10.1038/ nature19949 14. Kim SI, Voshol H, van Oostrum J, Hastings TG, Cascio M, Glucksman MJ (2004) Neuroproteomics: expression profiling of the brain’s proteomes in health and disease. Neurochem Res 29(6):1317–1331 15. Liao L, McClatchy DB, Yates JR (2009) Shotgun proteomics in neuroscience. Neuron 63 (1):12–26. https://doi.org/10.1016/j.neu ron.2009.06.011 16. Hosp F, Mann M (2017) A primer on concepts and applications of proteomics in neuroscience. Neuron 96(3):558–571. https://doi. org/10.1016/j.neuron.2017.09.025 17. Scifo E, Calza G, Fuhrmann M, Soliymani R, Baumann M, Lalowski M (2017) Recent advances in applying mass spectrometry and systems biology to determine brain dynamics. Expert Rev Proteomics 14(6):545–559. https://doi.org/10.1080/14789450.2017. 1335200 18. Sun F, Cavalli V (2010) Neuroproteomics approaches to decipher neuronal regeneration and degeneration. Mol Cell Proteomics 9 (5):963–975. https://doi.org/10.1074/ mcp.R900003-MCP200

18

Joaquı´n Ferna´ndez-Irigoyen et al.

19. Craft GE, Chen A, Nairn AC (2013) Recent advances in quantitative neuroproteomics. Methods 61(3):186–218. https://doi.org/ 10.1016/j.ymeth.2013.04.008 20. Shoemaker LD, Achrol AS, Sethu P, Steinberg GK, Chang SD (2012) Clinical neuroproteomics and biomarkers: from basic research to clinical decision making. Neurosurgery 70 (3):518–525. https://doi.org/10.1227/ NEU.0b013e3182333a26 21. Twiss JL, Fainzilber M (2016) Neuroproteomics: how many angels can be identified in an extract from the head of a pin? Mol Cell Proteomics 15(2):341–343. https://doi.org/10. 1074/mcp.E116.057828 22. Vercauteren FG, Bergeron JJ, Vandesande F, Arckens L, Quirion R (2004) Proteomic approaches in brain research and neuropharmacology. Eur J Pharmacol 500 (1–3):385–398. https://doi.org/10.1016/j. ejphar.2004.07.039 23. Tribl F, Meyer HE, Marcus K (2008) Analysis of organelles within the nervous system: impact on brain and organelle functions. Expert Rev Proteomics 5(2):333–351. https://doi.org/10.1586/14789450.5.2. 333 24. Grant SG, Blackstock WP (2001) Proteomics in neuroscience: from protein to network. J Neurosci 21(21):8315–8318 25. Fountoulakis M, Hardmeier R, Hoger H, Lubec G (2001) Postmortem changes in the level of brain proteins. Exp Neurol 167 (1):86–94. https://doi.org/10.1006/exnr. 2000.7529 26. Franzen B, Yang Y, Sunnemark D, Wickman M, Ottervald J, Oppermann M, Sandberg K (2003) Dihydropyrimidinase related protein-2 as a biomarker for temperature and time dependent post mortem changes in the mouse brain proteome. Proteomics 3(10):1920–1929. https://doi.org/ 10.1002/pmic.200300535 27. ElHajj Z, Cachot A, Muller T, Riederer IM, Riederer BM (2016) Effects of postmortem delays on protein composition and oxidation. Brain Res Bull 121:98–104. https://doi.org/ 10.1016/j.brainresbull.2016.01.005 28. Fernandez-Irigoyen J, Labarga A, Zabaleta A, de Morentin XM, Perez-Valderrama E, Zelaya MV, Santamaria E (2015) Toward defining the anatomo-proteomic puzzle of the human brain: an integrative analysis. Proteomics Clin Appl 9(9–10):796–807. https://doi.org/10. 1002/prca.201400127 29. Hamacher M, Meyer HE (2005) HUPO brain proteome project: aims and needs in

proteomics. Expert Rev Proteomics 2 (1):1–3. https://doi.org/10.1586/ 14789450.2.1.1 30. Gauss C, Kalkum M, Lowe M, Lehrach H, Klose J (1999) Analysis of the mouse proteome. (I) Brain proteins: separation by two-dimensional electrophoresis and identification by mass spectrometry and genetic variation. Electrophoresis 20(3):575–600. https://doi.org/10.1002/(SICI)1522-2683 (19990301)20:33.0. CO;2-3 31. Klose J, Nock C, Herrmann M, Stuhler K, Marcus K, Bluggel M, Krause E, Schalkwyk LC, Rastan S, Brown SD, Bussow K, Himmelbauer H, Lehrach H (2002) Genetic analysis of the mouse brain proteome. Nat Genet 30(4):385–393. https://doi.org/10. 1038/ng861 32. Hamacher M, Apweiler R, Arnold G, Becker A, Bluggel M, Carrette O, Colvis C, Dunn MJ, Frohlich T, Fountoulakis M, van Hall A, Herberg F, Ji J, Kretzschmar H, Lewczuk P, Lubec G, Marcus K, Martens L, Palacios Bustamante N, Park YM, Pennington SR, Robben J, Stuhler K, Reidegeld KA, Riederer P, Rossier J, Sanchez JC, Schrader M, Stephan C, Tagle D, Thiele H, Wang J, Wiltfang J, Yoo JS, Zhang C, Klose J, Meyer HE (2006) HUPO brain proteome project: summary of the pilot phase and introduction of a comprehensive data reprocessing strategy. Proteomics 6(18):4890–4898. https://doi.org/10.1002/pmic.200600295 33. Hamacher M, Marcus K, Stephan C, Klose J, Park YM, Meyer HE (2008) HUPO brain proteome project: toward a code of conduct. Mol Cell Proteomics 7(2):457 34. Reidegeld KA, Muller M, Stephan C, Bluggel M, Hamacher M, Martens L, Korting G, Chamrad DC, Parkinson D, Apweiler R, Meyer HE, Marcus K (2006) The power of cooperative investigation: summary and comparison of the HUPO brain proteome project pilot study results. Proteomics 6(18):4997–5014. https://doi.org/10. 1002/pmic.200600305 35. Stephan C, Reidegeld KA, Hamacher M, van Hall A, Marcus K, Taylor C, Jones P, Muller M, Apweiler R, Martens L, Korting G, Chamrad DC, Thiele H, Bluggel M, Parkinson D, Binz PA, Lyall A, Meyer HE (2006) Automated reprocessing pipeline for searching heterogeneous mass spectrometric data of the HUPO brain proteome project pilot phase. Proteomics 6 (18):5015–5029. https://doi.org/10.1002/ pmic.200600294

The Human Brain Proteome 36. Mueller M, Martens L, Reidegeld KA, Hamacher M, Stephan C, Bluggel M, Korting G, Chamrad D, Scheer C, Marcus K, Meyer HE, Apweiler R (2006) Functional annotation of proteins identified in human brain during the HUPO brain proteome project pilot study. Proteomics 6 (18):5059–5075. https://doi.org/10.1002/ pmic.200600194 37. Fernandez-Irigoyen J, Zelaya MV, PerezValderrama E, Santamaria E (2015) New insights into the human brain proteome: protein expression profiling of deep brain stimulation target areas. J Proteome 127 (Pt B):395–405. https://doi.org/10.1016/ j.jprot.2015.03.032 38. Persson A, Hober S, Uhlen M (2006) A human protein atlas based on antibody proteomics. Curr Opin Mol Ther 8(3):185–190 39. Mulder J, Bjorling E, Jonasson K, Wernerus H, Hober S, Hokfelt T, Uhlen M (2009) Tissue profiling of the mammalian central nervous system using human antibody-based proteomics. Mol Cell Proteomics 8(7):1612–1622. https://doi.org/10. 1074/mcp.M800539-MCP200 40. Saia-Cereda VM, Santana AG, Schmitt A, Falkai P, Martins-de-Souza D (2017) The nuclear proteome of White and Gray matter from schizophrenia postmortem brains. Mol Neuropsychiatry 3(1):37–52. https://doi. org/10.1159/000477299 41. Liu X, Guo Z, Sun H, Li W, Sun W (2017) Comprehensive map and functional annotation of human pituitary and thyroid proteome. J Proteome Res 16(8):2680–2691. https://doi.org/10.1021/acs.jproteome. 6b00914 42. Sharma K, Schmitt S, Bergner CG, Tyanova S, Kannaiyan N, Manrique-Hoyos N, Kongi K, Cantuti L, Hanisch UK, Philips MA, Rossner MJ, Mann M, Simons M (2015) Cell typeand brain region-resolved mouse brain proteome. Nat Neurosci 18(12):1819–1831. https://doi.org/10.1038/nn.4160 43. Fountoulakis M (2004) Application of proteomics technologies in the investigation of the brain. Mass Spectrom Rev 23(4):231–258. https://doi.org/10.1002/mas.10075 44. Ezkurdia I, Juan D, Rodriguez JM, Frankish A, Diekhans M, Harrow J, Vazquez J, Valencia A, Tress ML (2014) Multiple evidence strands suggest that there may be as few as 19,000 human protein-coding genes. Hum Mol Genet 23(22):5866–5878. https://doi.org/10.1093/hmg/ddu309 45. Wilhelm M, Schlegl J, Hahne H, Gholami AM, Lieberenz M, Savitski MM, Ziegler E,

19

Butzmann L, Gessulat S, Marx H, Mathieson T, Lemeer S, Schnatbaum K, Reimer U, Wenschuh H, Mollenhauer M, Slotta-Huspenina J, Boese JH, Bantscheff M, Gerstmair A, Faerber F, Kuster B (2014) Mass-spectrometry-based draft of the human proteome. Nature 509(7502):582–587. https://doi.org/10.1038/nature13319 46. Doll S, Burlingame AL (2015) Mass spectrometry-based detection and assignment of protein posttranslational modifications. ACS Chem Biol 10(1):63–71. https://doi. org/10.1021/cb500904b 47. Amal H, Barak B, Bhat V, Gong G, Joughin BA, Wishnok JS, Feng G, Tannenbaum SR (2018) Shank3 mutation in a mouse model of autism leads to changes in the S-nitrosoproteome and affects key proteins involved in vesicle release and synaptic function. Mol Psychiatry. https://doi.org/10.1038/s41380018-0113-6 48. Lee CY, Wang D, Wilhelm M, Zolg DP, Schmidt T, Schnatbaum K, Reimer U, Ponten F, Uhlen M, Hahne H, Kuster B (2018) Mining the human tissue proteome for protein citrullination. Mol Cell Proteomics 17(7):1378–1391. https://doi.org/10. 1074/mcp.RA118.000696 49. Wang S, Yang F, Petyuk VA, Shukla AK, Monroe ME, Gritsenko MA, Rodland KD, Smith RD, Qian WJ, Gong CX, Liu T (2017) Quantitative proteomics identifies altered O-GlcNAcylation of structural, synaptic and memory-associated proteins in Alzheimer’s disease. J Pathol 243(1):78–88. https://doi. org/10.1002/path.4929 50. Attilio PJ, Flora M, Kamnaksh A, Bradshaw DJ, Agoston D, Mueller GP (2017) The effects of blast exposure on protein deimination in the brain. Oxidative Med Cell Longev 2017:8398072. https://doi.org/10.1155/ 2017/8398072 51. Fang P, Wang XJ, Xue Y, Liu MQ, Zeng WF, Zhang Y, Zhang L, Gao X, Yan GQ, Yao J, Shen HL, Yang PY (2016) In-depth mapping of the mouse brain N-glycoproteome reveals widespread N-glycosylation of diverse brain proteins. Oncotarget 7(25):38796–38809. https://doi.org/10.18632/oncotarget.9737 52. Kohansal-Nodehi M, Chua JJ, Urlaub H, Jahn R, Czernik D (2016) Analysis of protein phosphorylation in nerve terminal reveals extensive changes in active zone proteins upon exocytosis. elife 5. https://doi.org/10. 7554/eLife.14530 53. Seneviratne U, Nott A, Bhat VB, Ravindra KC, Wishnok JS, Tsai LH, Tannenbaum SR (2016) S-nitrosation of proteins relevant to

20

Joaquı´n Ferna´ndez-Irigoyen et al.

Alzheimer’s disease during early stages of neurodegeneration. Proc Natl Acad Sci U S A 113 (15):4152–4157. https://doi.org/10.1073/ pnas.1521318113 54. Ramirez J, Martinez A, Lectez B, Lee SY, Franco M, Barrio R, Dittmar G, Mayor U (2015) Proteomic analysis of the ubiquitin landscape in the Drosophila embryonic nervous system and the adult photoreceptor cells. PLoS One 10(10):e0139083. https://doi. org/10.1371/journal.pone.0139083 55. Bouchut A, Chawla AR, Jeffers V, Hudmon A, Sullivan WJ Jr (2015) Proteome-wide lysine acetylation in cortical astrocytes and alterations that occur during infection with brain parasite Toxoplasma gondii. PLoS One 10(3):e0117966. https://doi. org/10.1371/journal.pone.0117966 56. Trinidad JC, Barkan DT, Gulledge BF, Thalhammer A, Sali A, Schoepfer R, Burlingame AL (2012) Global identification and characterization of both O-GlcNAcylation and phosphorylation at the murine synapse. Mol Cell Proteomics 11(8):215–229. https://doi.org/10.1074/mcp.O112. 018366 57. Decca MB, Bosc C, Luche S, Brugiere S, Job D, Rabilloud T, Garin J, Hallak ME (2006) Protein arginylation in rat brain cytosol: a proteomic analysis. Neurochem Res 31 (3):401–409. https://doi.org/10.1007/ s11064-005-9037-z 58. Khidekel N, Ficarro SB, Peters EC, HsiehWilson LC (2004) Exploring the O-GlcNAc proteome: direct identification of O-GlcNAcmodified proteins from the brain. Proc Natl Acad Sci U S A 101(36):13132–13137. https://doi.org/10.1073/pnas. 0403471101 59. Bartels MF, Winterhalter PR, Yu J, Liu Y, Lommel M, Mohrlen F, Hu H, Feizi T, Westerlind U, Ruppert T, Strahl S (2016) Protein O-Mannosylation in the murine brain: occurrence of mono-O-Mannosyl Glycans and identification of new substrates. PLoS One 11(11):e0166119. https://doi. org/10.1371/journal.pone.0166119 60. Serra A, Gallart-Palau X, Wei J, Sze SK (2016) Characterization of glutamine deamidation by long-length electrostatic repulsionhydrophilic interaction chromatographytandem mass spectrometry (LERLIC-MS/ MS) in shotgun proteomics. Anal Chem 88 (21):10573–10582. https://doi.org/10. 1021/acs.analchem.6b02688 61. Matsuzaki S, Lee L, Knock E, Srikumar T, Sakurai M, Hazrati LN, Katayama T, Staniszewski A, Raught B, Arancio O, Fraser

PE (2015) SUMO1 affects synaptic function, spine density and memory. Sci Rep 5:10730. https://doi.org/10.1038/srep10730 62. Zareba-Koziol M, Szwajda A, Dadlez M, Wyslouch-Cieszynska A, Lalowski M (2014) Global analysis of S-nitrosylation sites in the wild type (APP) transgenic mouse brain-clues for synaptic pathology. Mol Cell Proteomics 13(9):2288–2305. https://doi.org/10. 1074/mcp.M113.036079 63. Gavin AC, Aebersold R, Heck AJ (2008) Meeting report on the 7th world congress of the human proteome organization (HUPO) in Amsterdam: proteome biology. Mol Cell Proteomics 7(11):2288–2291. https://doi. org/10.1074/mcp.H800011-MCP200 64. Baker MS (2009) Building the ‘practical’ human proteome project - the next big thing in basic and clinical proteomics. Curr Opin Mol Ther 11(6):600–602 65. Legrain P, Aebersold R, Archakov A, Bairoch A, Bala K, Beretta L, Bergeron J, Borchers C, Corthals GL, Costello CE, Deutsch EW, Domon B, Hancock W, He F, Hochstrasser D, Marko-Varga G, Salekdeh GH, Sechi S, Snyder M, Srivastava S, Uhlen M, Hu CH, Yamamoto T, Paik YK, Omenn GS (2011) The human proteome project: current state and future direction. Mol Cell Proteomics. https://doi.org/10. 1074/mcp.O111.009993 66. Hancock W, Omenn G, Legrain P, Paik YK (2011) Proteomics, human proteome project, and chromosomes. J Proteome Res 10 (1):210. https://doi.org/10.1021/ pr101099h 67. Kusebauch U, Campbell DS, Deutsch EW, Chu CS, Spicer DA, Brusniak MY, Slagel J, Sun Z, Stevens J, Grimes B, Shteynberg D, Hoopmann MR, Blattmann P, Ratushny AV, Rinner O, Picotti P, Carapito C, Huang CY, Kapousouz M, Lam H, Tran T, Demir E, Aitchison JD, Sander C, Hood L, Aebersold R, Moritz RL (2016) Human SRMAtlas: a resource of targeted assays to quantify the complete human proteome. Cell 166(3):766–778. https://doi.org/10.1016/ j.cell.2016.06.041 68. Deutsch EW, Orchard S, Binz PA, Bittremieux W, Eisenacher M, Hermjakob H, Kawano S, Lam H, Mayer G, Menschaert G, Perez-Riverol Y, Salek RM, Tabb DL, Tenzer S, Vizcaino JA, Walzer M, Jones AR (2017) Proteomics standards initiative: fifteen years of progress and future work. J Proteome Res 16(12):4288–4298. https:// doi.org/10.1021/acs.jproteome.7b00370

The Human Brain Proteome 69. Jones P, Cote RG, Martens L, Quinn AF, Taylor CF, Derache W, Hermjakob H, Apweiler R (2006) PRIDE: a public repository of protein and peptide identifications for the proteomics community. Nucleic Acids Res 34(Database issue):D659–D663. https:// doi.org/10.1093/nar/gkj138 70. Uhlen M, Fagerberg L, Hallstrom BM, Lindskog C, Oksvold P, Mardinoglu A, Sivertsson A, Kampf C, Sjostedt E, Asplund A, Olsson I, Edlund K, Lundberg E, Navani S, Szigyarto CA, Odeberg J, Djureinovic D, Takanen JO, Hober S, Alm T, Edqvist PH, Berling H, Tegel H, Mulder J, Rockberg J, Nilsson P, Schwenk JM, Hamsten M, von Feilitzen K, Forsberg M, Persson L, Johansson F, Zwahlen M, von Heijne G, Nielsen J, Ponten F (2015) Proteomics. Tissue-based map of the human proteome. Science 347 (6220):1260419. https://doi.org/10.1126/ science.1260419 71. Thul PJ, Lindskog C (2017) The human protein atlas: a spatial map of the human proteome. Protein Sci 27(1):233–244. https://doi. org/10.1002/pro.3307 72. Lane L, Argoud-Puy G, Britan A, Cusin I, Duek PD, Evalet O, Gateau A, Gaudet P, Gleizes A, Masselot A, Zwahlen C, Bairoch A (2012) neXtProt: a knowledge platform for human proteins. Nucleic Acids Res 40 (Database issue):D76–D83. https://doi. org/10.1093/nar/gkr1179 73. Gaudet P, Michel PA, Zahn-Zabal M, Britan A, Cusin I, Domagalski M, Duek PD, Gateau A, Gleizes A, Hinard V, Rech de Laval V, Lin J, Nikitin F, Schaeffer M, Teixeira D, Lane L, Bairoch A (2017) The neXtProt knowledgebase on human proteins: 2017 update. Nucleic Acids Res 45(D1): D177–D182. https://doi.org/10.1093/ nar/gkw1062 74. Paik YK, Omenn GS, Uhlen M, Hanash S, Marko-Varga G, Aebersold R, Bairoch A, Yamamoto T, Legrain P, Lee HJ, Na K, Jeong SK, He F, Binz PA, Nishimura T, Keown P, Baker MS, Yoo JS, Garin J, Archakov A, Bergeron J, Salekdeh GH, Hancock WS (2012) Standard guidelines for the chromosome-centric human proteome project. J Proteome Res 11(4):2005–2013. https://doi.org/10.1021/pr200824a 75. Paik YK, Omenn GS, Hancock WS, Lane L, Overall CM (2017) Advances in the chromosome-centric human proteome project: looking to the future. Expert Rev Proteomics 14(12):1059–1071. https://doi.org/ 10.1080/14789450.2017.1394189

21

76. Aebersold R, Bader GD, Edwards AM, van Eyk JE, Kussmann M, Qin J, Omenn GS (2013) The biology/disease-driven human proteome project (B/D-HPP): enabling protein research for the life sciences community. J Proteome Res 12(1):23–27. https://doi.org/ 10.1021/pr301151m 77. Van Eyk JE, Snyder MP (2018) Precision medicine: role of proteomics in changing clinical management and care. J Proteome Res. https://doi.org/10.1021/acs.jproteome. 8b00504 78. Lau E, Venkatraman V, Thomas CT, Wu JC, Van Eyk JE, Lam MPY (2018) Identifying high-priority proteins across the human diseasome using semantic similarity. J Proteome Res. https://doi.org/10.1021/acs. jproteome.8b00393 79. Lam MP, Venkatraman V, Xing Y, Lau E, Cao Q, Ng DC, Su AI, Ge J, Van Eyk JE, Ping P (2016) Data-driven approach to determine popular proteins for targeted proteomics translation of six organ systems. J Proteome Res 15(11):4126–4134. https:// doi.org/10.1021/acs.jproteome.6b00095 80. Yu KH, Lee TM, Chen YJ, Re C, Kou SC, Chiang JH, Snyder M, Kohane IS (2018) A cloud-based metabolite and chemical prioritization system for the biology/disease-driven human proteome project. J Proteome Res. https://doi.org/10.1021/acs.jproteome. 8b00378 81. Yu KH, Lee TM, Wang CS, Chen YJ, Re C, Kou SC, Chiang JH, Kohane IS, Snyder M (2018) Systematic protein prioritization for targeted proteomics studies through literature mining. J Proteome Res 17 (4):1383–1396. https://doi.org/10.1021/ acs.jproteome.7b00772 82. Van Eyk JE, Corrales FJ, Aebersold R, Cerciello F, Deutsch EW, Roncada P, Sanchez JC, Yamamoto T, Yang P, Zhang H, Omenn GS (2016) Highlights of the biology and disease-driven human proteome project, 2015-2016. J Proteome Res 15 (11):3979–3987. https://doi.org/10.1021/ acs.jproteome.6b00444 83. Hosp F, Gutierrez-Angel S, Schaefer MH, Cox J, Meissner F, Hipp MS, Hartl FU, Klein R, Dudanova I, Mann M (2017) Spatiotemporal proteomic profiling of huntington’s disease inclusions reveals widespread loss of protein function. Cell Rep 21 (8):2291–2303. https://doi.org/10.1016/j. celrep.2017.10.097 84. Savas JN, Wang YZ, DeNardo LA, MartinezBartolome S, McClatchy DB, Hark TJ, Shanks NF, Cozzolino KA, Lavallee-

22

Joaquı´n Ferna´ndez-Irigoyen et al.

Adam M, Smukowski SN, Park SK, Kelly JW, Koo EH, Nakagawa T, Masliah E, Ghosh A, Yates JR 3rd (2017) Amyloid accumulation drives proteome-wide alterations in mouse models of Alzheimer’s disease-like pathology. Cell Rep 21(9):2614–2627. https://doi.org/ 10.1016/j.celrep.2017.11.009 85. Lachen-Montes M, Gonzalez-Morales A, de Morentin XM, Perez-Valderrama E, Ausin K, Zelaya MV, Serna A, Aso E, Ferrer I, Fernandez-Irigoyen J, Santamaria E (2016) An early dysregulation of FAK and MEK/ERK signaling pathways precedes the beta-amyloid deposition in the olfactory bulb of APP/PS1 mouse model of Alzheimer’s disease. J Proteome 148:149–158. https://doi. org/10.1016/j.jprot.2016.07.032 86. Pace MC, Xu G, Fromholt S, Howard J, Crosby K, Giasson BI, Lewis J, Borchelt DR (2018) Changes in proteome solubility indicate widespread proteostatic disruption in mouse models of neurodegenerative disease. Acta Neuropathol. https://doi.org/10. 1007/s00401-018-1895-y 87. Davis RG, Park HM, Kim K, Greer JB, Fellers RT, LeDuc RD, Romanova EV, Rubakhin SS, Zombeck JA, Wu C, Yau PM, Gao P, van Nispen AJ, Patrie SM, Thomas PM, Sweedler JV, Rhodes JS, Kelleher NL (2018) Top-down proteomics enables comparative analysis of brain proteoforms between mouse strains. Anal Chem 90(6):3802–3810. https://doi.org/10.1021/acs.analchem. 7b04108 88. Zelaya MV, Perez-Valderrama E, de Morentin XM, Tunon T, Ferrer I, Luquin MR, Fernandez-Irigoyen J, Santamaria E (2015) Olfactory bulb proteome dynamics during the progression of sporadic Alzheimer’s disease: identification of common and distinct olfactory targets across Alzheimer-related co-pathologies. Oncotarget 6 (37):39437–39456. https://doi.org/10. 18632/oncotarget.6254 89. Hondius DC, van Nierop P, Li KW, Hoozemans JJ, van der Schors RC, van Haastert ES, van der Vies SM, Rozemuller AJ, Smit AB (2016) Profiling the human hippocampal proteome at all pathologic stages of Alzheimer’s disease. Alzheimers Dement 12(6):654–668. https://doi.org/10.1016/j.jalz.2015.11. 002 90. Lachen-Montes M, Gonzalez-Morales A, Iloro I, Elortza F, Ferrer I, Gveric D, Fernandez-Irigoyen J, Santamaria E (2018) Unveiling the olfactory proteostatic disarrangement in Parkinson’s disease by proteome-wide profiling. Neurobiol Aging

73:123–134. https://doi.org/10.1016/j. neurobiolaging.2018.09.018 91. Lachen-Montes M, Gonzalez-Morales A, Zelaya MV, Perez-Valderrama E, Ausin K, Ferrer I, Fernandez-Irigoyen J, Santamaria E (2017) Olfactory bulb neuroproteomics reveals a chronological perturbation of survival routes and a disruption of prohibitin complex during Alzheimer’s disease progression. Sci Rep 7(1):9115. https://doi.org/10. 1038/s41598-017-09481-x 92. Drummond E, Nayak S, Faustin A, Pires G, Hickman RA, Askenazi M, Cohen M, Haldiman T, Kim C, Han X, Shao Y, Safar JG, Ueberheide B, Wisniewski T (2017) Proteomic differences in amyloid plaques in rapidly progressive and sporadic Alzheimer’s disease. Acta Neuropathol 133(6):933–954. https://doi. org/10.1007/s00401-017-1691-0 93. Ping L, Duong DM, Yin L, Gearing M, Lah JJ, Levey AI, Seyfried NT (2018) Global quantitative analysis of the human brain proteome in Alzheimer’s and Parkinson’s disease. Sci Data 5:180036. https://doi.org/10. 1038/sdata.2018.36 94. Umoh ME, Dammer EB, Dai J, Duong DM, Lah JJ, Levey AI, Gearing M, Glass JD, Seyfried NT (2018) A proteomic network approach across the ALS-FTD disease spectrum resolves clinical phenotypes and genetic vulnerability in human brain. EMBO Mol Med 10(1):48–62. https://doi.org/10. 15252/emmm.201708202 95. Devaux S, Cizkova D, Quanico J, Franck J, Nataf S, Pays L, Hauberg-Lotte L, Maass P, Kobarg JH, Kobeissy F, Meriaux C, Wisztorski M, Slovinska L, Blasko J, Cigankova V, Fournier I, Salzet M (2016) Proteomic analysis of the Spatio-temporal based molecular kinetics of acute spinal cord injury identifies a time- and segment-specific window for effective tissue repair. Mol Cell Proteomics 15(8):2641–2670. https://doi. org/10.1074/mcp.M115.057794 96. Mallah K, Quanico J, Trede D, Kobeissy F, Zibara K, Salzet M, Fournier I (2018) Lipid changes associated with traumatic brain injury revealed by 3D MALDI-MSI. Anal Chem 90 (17):10568–10576. https://doi.org/10. 1021/acs.analchem.8b02682 97. Dayon L, Nunez Galindo A, Wojcik J, Cominetti O, Corthesy J, Oikonomidi A, Henry H, Kussmann M, Migliavacca E, Severin I, Bowman GL, Popp J (2018) Alzheimer disease pathology and the cerebrospinal fluid proteome. Alzheimers Res Ther 10 (1):66. https://doi.org/10.1186/s13195018-0397-4

The Human Brain Proteome 98. Tanca A, Pagnozzi D, Addis MF (2012) Setting proteins free: progresses and achievements in proteomics of formalin-fixed, paraffin-embedded tissues. Proteomics Clin Appl 6 (1–2):7–21. https://doi.org/10.1002/prca. 201100044 99. Djuric U, Rodrigues DC, Batruch I, Ellis J, Shannon P, Diamandis P (2017) Spatiotemporal proteomic profiling of human cerebral development. Mol Cell Proteomics 16 (9):1548–1562. https://doi.org/10.1074/ mcp.M116.066274 100. Zhu Y, Dou M, Piehowski PD, Liang Y, Wang F, Chu RK, Chrisler WB, Smith JN, Schwarz KC, Shen Y, Shukla AK, Moore RJ, Smith RD, Qian WJ, Kelly RT (2018) Spatially resolved proteome mapping of laser capture microdissected tissue with automated sample transfer to Nanodroplets. Mol Cell Proteomics 17(9):1864–1874. https://doi. org/10.1074/mcp.TIR118.000686 101. Alvarez-Castelao B, Schanzenbacher CT, Hanus C, Glock C, Tom Dieck S, Dorrbaum AR, Bartnik I, Nassim-Assir B, Ciirdaeva E, Mueller A, Dieterich DC, Tirrell DA, Langer JD, Schuman EM (2017) Cell-type-specific metabolic labeling of nascent proteomes in vivo. Nat Biotechnol 35(12):1196–1201. https://doi.org/10.1038/nbt.4016 102. Roy M, Sorokina O, Skene N, Simonnet C, Mazzo F, Zwart R, Sher E, Smith C, Armstrong JD, Grant SGN (2018) Proteomic analysis of postsynaptic proteins in regions of the human neocortex. Nat Neurosci 21 (1):130–138. https://doi.org/10.1038/ s41593-017-0025-9 103. Meier F, Geyer PE, Virreira Winter S, Cox J, Mann M (2018) BoxCar acquisition method enables single-shot proteomics at a depth of 10,000 proteins in 100 minutes. Nat Methods 15(6):440–448. https://doi.org/10. 1038/s41592-018-0003-5 104. Nesvizhskii AI (2014) Proteogenomics: concepts, applications and computational

23

strategies. Nat Methods 11(11):1114–1125. https://doi.org/10.1038/nmeth.3144 105. Wingo TS, Duong DM, Zhou M, Dammer EB, Wu H, Cutler DJ, Lah JJ, Levey AI, Seyfried NT (2017) Integrating nextgeneration genomic sequencing and mass spectrometry to estimate allele-specific protein abundance in human brain. J Proteome Res 16(9):3336–3347. https://doi.org/10. 1021/acs.jproteome.7b00324 106. Mostovenko E, Vegvari A, Rezeli M, Lichti CF, Fenyo D, Wang Q, Lang FF, Sulman EP, Sahlin KB, Marko-Varga G, Nilsson CL (2018) Large scale identification of variant proteins in Glioma stem cells. ACS Chem Neurosci 9(1):73–79. https://doi.org/10. 1021/acschemneuro.7b00362 107. Meeter LH, Kaat LD, Rohrer JD, van Swieten JC (2017) Imaging and fluid biomarkers in frontotemporal dementia. Nat Rev Neurol 13 (7):406–419. https://doi.org/10.1038/ nrneurol.2017.75 108. Lleo A, Cavedo E, Parnetti L, Vanderstichele H, Herukka SK, Andreasen N, Ghidoni R, Lewczuk P, Jeromin A, Winblad B, Tsolaki M, Mroczko B, Visser PJ, Santana I, Svenningsson P, Blennow K, Aarsland D, Molinuevo JL, Zetterberg H, Mollenhauer B (2015) Cerebrospinal fluid biomarkers in trials for Alzheimer and Parkinson diseases. Nat Rev Neurol 11(1):41–55. https://doi. org/10.1038/nrneurol.2014.232 109. Macron C, Lane L, Nunez Galindo A, Dayon L (2018) Deep dive on the proteome of human cerebrospinal fluid: a valuable data resource for biomarker discovery and missing protein identification. J Proteome Res. https://doi.org/10.1021/acs.jproteome. 8b00300 110. van Gool AJ, Hendrickson RC (2012) The proteomic toolbox for studying cerebrospinal fluid. Expert Rev Proteomics 9(2):165–179. https://doi.org/10.1586/epr.12.6

Part II Cerebrospinal Fluid Sample Preparation Methods for Proteomic Workflows

Chapter 2 Guidelines for CSF Processing and Biobanking: Impact on the Identification and Development of Optimal CSF Protein Biomarkers Yanaika S. Hok-A-Hin, Eline A. J. Willemse, Charlotte E. Teunissen, and Marta Del Campo Abstract The field of neurological diseases strongly needs biomarkers for early diagnosis and optimal stratification of patients in clinical trials or to monitor disease progression. Cerebrospinal fluid (CSF) is one of the main sources for the identification of novel protein biomarkers for neurological diseases. Despite the enormous efforts employed to identify novel CSF biomarkers, the high variability observed across different studies has hampered their validation and implementation in clinical practice. Such variability is partly caused by the effect of different pre-analytical confounding factors on protein stability, highlighting the importance to develop and comply with standardized operating procedures. In this chapter, we describe the international consensus pre-analytical guidelines for CSF processing and biobanking that have been established during the last decade, with a special focus on the influence of pre-analytical confounders on the global CSF proteome. In addition, we provide novel results on the influence of different delayed storage and freeze/ thaw conditions on the CSF proteome using two novel large multiplex protein arrays (SOMAscan and Olink). Compliance to consensus guidelines will likely facilitate the successful development and implementation of CSF protein biomarkers in both research and clinical settings, ultimately facilitating the successful development of disease-modifying therapies. Key words CSF, Proteomics, Biomarkers, Neurology, Guidelines, Biobanking, Pre-analytical confounding factors, Stability, Reproducibility

1

Introduction Biomarkers for neurological diseases are strongly needed for early diagnosis, to monitor disease progression or for optimal selection of cases for clinical trials. Access to such biomarkers will likely facilitate the successful development of future novel therapies

Electronic supplementary material: The online version of this chapter (https://doi.org/10.1007/978-1-49399706-0_2) contains supplementary material, which is available to authorized users. Enrique Santamarı´a and Joaquı´n Ferna´ndez-Irigoyen (eds.), Cerebrospinal Fluid (CSF) Proteomics: Methods and Protocols, Methods in Molecular Biology, vol. 2044, https://doi.org/10.1007/978-1-4939-9706-0_2, © Springer Science+Business Media, LLC, part of Springer Nature 2019

27

28

Yanaika S. Hok-A-Hin et al.

[1, 2]. Antemortem cerebrospinal fluid (CSF) is one of the main sources used for discovery of such biomarkers since it is in direct contact with the central nervous system, and thus it can reflect the pathophysiological processes that the brain undergoes [2]. For example, the core biomarkers used in the field of Alzheimer’s disease (AD) are found in CSF and represent the classical hallmarks of AD pathology: a decrease of amyloid β 42 (Aβ42) levels reflects senile plaque pathology, while an increase of hyperphosphorylated Tau (P-tau) and total Tau (T-tau) reflects neurofibrillary tangle (NFT) formation and axonal degeneration [3–5]. Despite the enormous efforts and investments into the development of novel CSF biomarkers for neurological diseases, the variability found across different studies due to laboratoryassociated errors has partly hampered their implementation in clinical practice. Importantly, it has been shown that 50–75% of total laboratory errors occur during the pre-analytical phase (e.g., errors in tube labeling, delayed storage, blood contamination) [6]. Good examples of this problem are the classical AD CSF biomarkers, which have shown to have high variability across different studies [2, 7–9] reaching in some cases inter-assay and interlaboratory coefficient variations of 20–35% [10–12]. These CSF biomarkers, discovered more than 20 years ago, have been recently included in the AD diagnostic guidelines to be used within research settings or to support that dementia is likely caused by an underlying AD pathology, but they are still not suitable for clinical routine care [5]. Thus, the strong impact that pre-analytical confounding factors can cause on CSF biomarker analysis has urged the harmonization of laboratory procedures across different centers leading to the development of standard operating procedures (SOP) and international guidelines for CSF sample processing and biobanking [13–24]. The recommendations and guidelines to control for pre-analytical confounding factors in CSF analyses are mainly based on the results obtained from the classical AD CSF biomarkers (especially Aβ) but also α-synuclein, a protein biomarker that could contribute to the diagnosis of Parkinson’s disease (PD) or dementia with Lewy bodies (DLB) [25]. However, whether pre-analytical confounding factors affect most of the CSF proteome remains unknown. A good understanding of the stability of the CSF proteome will help to better design the proteomics studies for the discovery and validation of novel CSF biomarkers that are continuously ongoing and maximize the use of limited amount of precious, well-characterized antemortem CSF samples available for most neurological disorders. Here, we describe the guidelines on CSF sample processing and biobanking to minimize the influence of pre-analytical confounding factor in the analysis of CSF protein biomarkers, with a special focus on studies performed in the global CSF proteome. In

Guidelines for CSF Processing and Biobanking

29

addition, we present new evidence on the stability of the CSF proteome after different delayed storage and freeze/thaw conditions using two novel large multiplex protein arrays covering more than 1000 proteins (SOMAscan [26] and Olink [27]).

2

Pre-analytical Factors Influencing the Analysis of CSF Proteins In the following paragraphs, we discuss the international guidelines for CSF sample processing and biobanking, describing the effects of CSF collection procedures (e.g., blood contamination) and laboratory processing factors (e.g., storage temperature) (Table 1) not only on specific proteins but also on global CSF protein analyses. A general overview of the proteins affected by the different preanalytical confounding factors is included in Table 2.

2.1 Time of the Day of CSF Withdrawal

Circadian rhythms can influence the analysis of specific biochemical compounds, and thus, the time of the day of CSF withdrawal may ultimately affect the concentration of CSF protein biomarkers [28]. Multiple studies investigated the effect of diurnal variation on CSF biomarkers for AD and PD, but only minor variations in protein levels were observed [29–35]. Since most lumbar punctures (LP) are performed during clinical appointments, standardizing the time of withdrawal is difficult. Nonetheless, it is recommended to record the time of CSF withdrawal to identify the potential influence of the diurnal variation on novel CSF protein biomarker candidates.

2.2 CSF Withdrawal (Location of LP, Type of Needle, Collection Tube)

CSF is obtained by LP from the vertebral body L3–L5 using atraumatic or small-gauge needles to minimize post-LP headaches [22, 36, 37]. There are no experimental evidences indicating whether the type of needle used in LP affects CSF protein measurements. CSF protein measurements can however be influenced by the surface and physiochemical properties of the collection tubes used during CSF withdrawal. For example, hydrophobic peptides were differently affected by the type of collection tubes and the optimal measurements were achieved using low-adsorption polypropylene (PP) plastic tubes [38]. Multiple studies demonstrated that Aβ42 levels decrease up to 48% with the use of most types of CSF collection tubes [39–41]. Such effects have however not been observed in α-synuclein [42]. The international consortium of the European Union Joint Programme Neurodegenerative Disease Research “BIOMARKAPD” was initiated to standardize and harmonize the use of biomarkers in AD and PD [43]. This consortium agreed to use Sarstedt PP tubes for CSF collection (cat no: 62,610,018, 10 mL collection tube, round base; cat no. 62,554,502, 15 mL tube with conical base to use if a pellet is necessary) for biomarker research since they have the lowest

30

Yanaika S. Hok-A-Hin et al.

Table 1 Collection protocol for CSF Item no. Procedure

Recommended for CSF

A. Collection procedure 1.

Time of day of withdrawal and storage

Record date and time of collection

2.

Preferred volume

At least 12 mL. First 1–2 mL for routine CSF assessment. Last 10 mL for biobanking Record volume taken and fraction used for biobanking, if applicable

3.

Location

Intervertebral space L3–L5

4.

If blood contamination occurs

Do not process further Criteria for blood contamination: Up to 500 red blood cells/μL Record number of blood cells in diagnostic samples

5.

Type of needle

Atraumatic

6.

Type of collection tube

Polypropylene tubes, screw cap, volume > 10 mL

7.

Other body fluids that should be measured simultaneously

Serum, plasma (EDTA is preferred over citrate)

8.

Other materials that should be measured simultaneously

DNA

B. Processing for storage 9.

Storage temperature until freezing Room temperature before, during, and after centrifugation

10.

Centrifugation conditions

2000  g (1800–2200), 10 min at room temperature

11.

Time delay between withdrawal, processing, and freezing

Before centrifugation: 30–60 min max 2 h After centrifugation, samples should be aliquoted and frozen immediately, with a maximal delay of 2 h

12.

Type of tube for aliquoting

Small polypropylene tubes with screw cap Record manufacturers

13.

Aliquots

A minimum of two aliquots is recommended The advised research sample volume of 10 mL should be enough for >10 aliquots

14.

Volume of aliquots

Minimum of 0.1 mL. Depending on total volume of tube: 0.2, 0.5, and 1 mL. Preferably tubes are filled up to 75% of the volume

15.

Coding

Unique codes. Freezing-proof labels. Ideally barcodes to facilitate searching, to aid in blinding the analysis and to protect patient privacy

16.

Freezing temperature

Freeze CSF at 80  C

Guidelines for CSF Processing and Biobanking

31

Table 2 Overview of proteins affected by different pre-analytical factors Affected protein

Sensitive

Effect

Method

Study

Apolipoprotein C1

Volume withdrawal

Increased gradient (in tenth mL CSF)

MALDI-TOF MS

[46]

Aβ42

CSF collection tubes

Decreased

Lactadherin

ELISA

[39–41]



Olink

Figure 1



Delayed storage 1 week at 4 C Decreased

Dipeptidyl peptidase 2

Delayed storage 1 week at 4 C Decreased

Olink

Figure 1

Transthyretin

Delayed storage 4 h at RT

Decreased

SELDI-TOF MS

[63]

Transthyretin S-cysteine Delayed storage 4 h at RT

Decreased

SELDI-TOF MS

[63]

α-Mannosidase

Delayed storage 48 h at RT

Decreased

Fluorimetric assays [61]

α-Fucosidase

Delayed storage 48 h at RT

Decreased

Fluorimetric assays [61]

β-Mannosidase

Delayed storage 48 h at RT

Decreased

Fluorimetric assays [61]

β-Galactosidase

Delayed storage 48 h at RT

Decreased

Fluorimetric assays [61]

β-Glucocerebrosidase

Delayed storage 48 h at RT

Decreased

Fluorimetric assays [61]

β-Hexosaminidase

Delayed storage 48 h at RT

Decreased

Fluorimetric assays [61]

Cathepsin D

Delayed storage 48 h at RT

Decreased

Fluorimetric assays [61]

Cathepsin E

Delayed storage 48 h at RT

Decreased

Fluorimetric assays [61]

SLIT and NTRK2-LIKE Delayed storage 1 week at RT Decreased PROTEIN 2

Olink

Figure 1

Interleuking-17D

Delayed storage 1 week at RT Decreased

Olink

Figure 1

Opticin

Delayed storage 1 week at RT Decreased

Olink

Figure 1

Tissue alpha-Lfucosidase

Delayed storage 1 week at RT Decreased

Olink

Figure 1

Bone morphogenetic protein 4

Delayed storage 1 week at RT Decreased

Olink

Figure 1

Thimet oligopeptidase

Delayed storage 1 week at RT Decreased

Olink

Figure 1

Cathepsin L1

Delayed storage 1 week at RT Increased

Olink

Figure 1

Flavin reductase

Delayed storage 1 week at RT Increased

Olink

Figure 1

Hydroxyacylglutathione Delayed storage 1 week at RT Increased hydrolase, mitochondrial

Olink

Figure 1

Follistatin

Delayed storage 1 week at RT Decreased

Olink

Figure 1

Alpha-L-iduronidase

Delayed storage 24 h  1 week Decreased at RT

SOMAscan

Figure 1 (continued)

32

Yanaika S. Hok-A-Hin et al.

Table 2 (continued) Affected protein

Sensitive

Cathepsin A

Effect

Method

Study

Delayed storage 1 week at RT Decreased

SOMAscan

Figure 1

Protein kinase B alpha/ beta/gamma (RAC family)

Delayed storage 1 week at RT Decreased

SOMAscan

Figure 1

Erythrocyte membrane protein 4.1

Delayed storage 1 week at RT Decreased

SOMAscan

Figure 1

Carboxypeptidase E

Delayed storage 1 week at RT Decreased

SOMAscan

Figure 1

C-Src kinase CSK

Delayed storage 1 week at RT Decreased

SOMAscan

Figure 1

Chordin-like 1

Delayed storage 1 week at RT Decreased

SOMAscan

Figure 1

CD30 ligand, TNFSF8, CD153

Delayed storage 1 week at RT Decreased

SOMAscan

Figure 1

Ephrin type-A receptor 5 Delayed storage 1 week at RT Decreased

SOMAscan

Figure 1

Nucleotide diphosphate Delayed storage 1 week at RT Decreased kinase B, Nm23-H2

SOMAscan

Figure 1

Leptin receptor

Delayed storage 1 week at RT Decreased

SOMAscan

Figure 1

Glyceraldehyde-3phosphate dehydrogenase

Delayed processing 24 h at RT Decreased

SOMAscan

Figure 1

Acid phosphatase 1, soluble adipocyte acid phosphatase LMW-PTP

Delayed processing 24 h at RT Decreased

SOMAscan

Figure 1

Endoplasmic reticulum resident protein 29 ERp29

Delayed processing 24 h at RT Decreased

SOMAscan

Figure 1

NCC27 chloride intracellular channel protein 1

Delayed processing 24 h at RT Decreased

SOMAscan

Figure 1

Transthyretin

Freeze/thaw (1 cycle)

Decreased

SELDI-TOF MS

[63]

α-Mannosidase

Freeze/thaw (1 cycle)

Decreased

Fluorimetric assays [61]

α-Fucosidase

Freeze/thaw (2 cycles)

Increased

Fluorimetric assays [61]

β-Mannosidase

Freeze/thaw (2 cycles)

Increased

Fluorimetric assays [61]

β-Hexosaminidase

Freeze/thaw (2 cycles)

Increased

Fluorimetric assays [61]

Transglutaminase 3

Freeze/thaw (4 cycles)

Decreased

SOMAscan

β-Galactosidase

Freeze/thaw (5 cycles)

Decreased

Fluorimetric assays [61]

β-Glucocerebrosidase

Freeze/thaw (5 cycles)

Decreased

Fluorimetric assays [61]

Cathepsin D

Freeze/thaw (5 cycles)

Decreased

Fluorimetric assays [61]

Figure 1

(continued)

Guidelines for CSF Processing and Biobanking

33

Table 2 (continued) Affected protein

Sensitive

Effect

Method

Study

Protein kinase B alpha/ beta/gamma (RAC family)

Freeze/thaw (8 cycles)

Decreased

Olink

Figure 1

C-type lectin domain family 11 member A

Freeze/thaw (8 cycles)

Decreased

Olink

Figure 1

Lipopolysaccharidebinding protein

Freeze/thaw (8 cycles)

Decreased

SOMAscan

Figure 1

Aβ42

Aliquoting volume (low volumes)

Decreased

MSD

[66]

Aβ42

Aliquoting volume (low volumes)

Decreased

ELISA

[67, 70]

α-Mannosidase

Storage at 20  C

Complement C3

Decreased

Fluorimetric assays [61]



Increased

MALDI-TOF MS

[54]



MALDI-TOF MS Undergoes N-terminal cleavage

[72]

Storage at 20 C

Cystatin C

Storage at 20 C

β-Galactosidase

Long-term storage at 80  C Increased (5 weeks)

Fluorimetric assays [61]

β-Hexosaminidase

Long-term storage at 80  C Increased (15 weeks)

Fluorimetric assays [61]

α-Mannosidase

Long-term storage at 80  C Decreased (40 weeks)

Fluorimetric assays [61]

α-Fucosidase

Long-term storage at 80  C Increased (40 weeks)

Fluorimetric assays [61]

β-Glucocerebrosidase

Long-term storage at 80  C Decreased (40 weeks)

Fluorimetric assays [61]

Cathepsin D

Long-term storage at 80  C Decreased (40 weeks)

Fluorimetric assays [61]

Cathepsin E

Long-term storage at 80  C Decreased (40 weeks)

Fluorimetric assays [61]

CSF cerebrospinal fluid, h hours, MSD Meso Scale Discovery, RT room temperature

binding capacity [40, 44]. Different studies have shown that the use of Tween®-20 can prevent protein wall adsorption, thereby keeping the Aβ42 concentrations stable [41, 45] and suggesting an additional option to control for the type of tube used. However, it is important to note that the addition of chemical agents could interfere with other CSF proteins and is thus not preferred for research purposes.

34

Yanaika S. Hok-A-Hin et al.

Taken together, LP should be performed between L3 and L5 vertebral bodies using an atraumatic or small-gauge needles and CSF should be collected in 10 mL Sarstedt PP tubes. Any changes in these guidelines should be documented. 2.3 CSF Volume of Withdrawal

Passive diffusion of the CSF creates a rostrocaudal gradient that can ultimately affect CSF protein concentration [46–48]. The CSF volume that is collected during an LP can influence the protein concentration since small CSF volumes may represent the composition of the lumbar dural sack, but larger volumes likely reflect the rostral spinal or ventricular CSF [49]. Comparing protein levels in samples coming from a 2 mL LP to those coming from a 15 mL LP could then lead to inaccurate results. Matrix-assisted laser desorption/ionization time-of-flight analyzer mass spectrometry (MALDI-TOF MS) of serially collected CSF samples (e.g., first mL compared to tenth mL) showed that out of 41 proteins, only apolipoprotein C1 was significantly increased in the tenth mL CSF sample [46] (Table 2). Even though only 1 out of 41 proteins was affected by the CSF ventricularlumbar gradient, the influence of CSF withdrawal volume on other CSF proteins cannot be excluded. Thus, it is recommended to collect always a standard volume of 12 mL of CSF. Noteworthy, the volume of CSF withdrawal does not correlate to post-lumbar risk or complaints [37].

2.4 Blood Contamination

Traumatic taps occur in 14–20% of standard lumbar punctures causing blood contamination in the CSF [50]. The protein concentration in blood is 200- to 400-fold higher compared to CSF [51], and thus, small amounts of blood can already affect the CSF proteome providing incorrect CSF protein biomarker results. For instance, CSF biomarker candidates such as vascular endothelial growth factor (VEGF) and neuron-specific enolase (NSE) are present in high concentrations in blood [52, 53]; and NSE levels were found to increase linearly with increased hemolysis of blood in CSF [53]. A number of studies investigated the effect of blood contamination on the CSF proteome. MALDI-TOF analyses have shown that presence of blood proteins (e.g., hemoglobin) decreases the intensity of the CSF proteomic pattern [54, 55]. Furthermore, two studies using liquid chromatography coupled mass spectrometry (LC-MS/MS) showed that blood contamination increased the levels of more than 200 proteins in CSF [51, 56], including catalase, peroxiredoxin, and carbonic anhydrase. These data highlight the importance of controlling for blood contamination for accurate measurements of CSF proteins. Interestingly, the effect of blood contamination was strongly reduced when CSF samples were centrifuged after collection [52, 54, 55].

Guidelines for CSF Processing and Biobanking

35

To minimize the effect of potential blood contamination, it is recommended to eliminate the first 2 mL of CSF during collection [23] and thereafter centrifuge the samples according to standardized protocol (2000  g for 10 min at room temperature (RT)). Nonetheless, CSF samples containing an erythrocyte count above 500/μL should be excluded for biomarker studies, although lower percentages are always preferred [13]. Hemoglobin might also be a useful quality indicator for CSF hemolysis [54, 56] though specific thresholds still need to be established. 2.5 Collection of Serum, Plasma, and DNA/RNA Linked to the CSF Sample

It is advised to collect and biobank matched serum and/or plasma samples, since novel proteomics technologies may allow in the near future to reliably measure biomarkers for neurological disorders in blood. Strong correlations between blood-CSF measurements provide additional support that potential blood-based biomarkers likely reflect specific changes within the central nervous system. Noteworthy, blood-based biomarkers are time- and cost-efficient and less invasive and allow longitudinal screening to monitor disease progression. Compared to CSF, blood contains more cells, and therefore, it is expected that blood biomarkers are more sensitive to the pre-analytical confounders. Guidelines for the standardization of blood biomarkers in AD have been established [57], but it is expected that these will be updated in the future based on novel experimental data. Furthermore, DNA/RNA collection could be of interest since genetic information allows not only comparison of specific genotypes, but it will also allow to analyze associations between proteome, transcriptome, and genome, providing new insights into the pathological processes of a specific disease [58].

2.6 Storage Temperature Prior to Processing

The effect of storing CSF at different temperatures (room temperature or 4  C) before further processing (e.g., spinning and aliquoting) on the stability of CSF proteins has not been extensively studied yet. Previous studies analyzing CSF neurofilament light and progranulin showed that these proteins were not affected by different temperatures prior to processing [59, 60]. Therefore, CSF samples can be kept at either room temperature or 4  C after withdrawal until further processing.

2.7 Time Delay Between CSF Withdrawal, Processing, and Freezing

Shortly after withdrawal, CSF should be further processed (i.e., centrifuged), aliquoted, and frozen. However, time periods between CSF withdrawal and processing (delayed processing) or between processing and freezing (delayed storage) can vary across laboratories and might influence protein concentrations. The effect of delayed processing (i.e., the time between CSF collection and centrifugation) has not been extensively studied yet. It was recently shown that CSF progranulin levels remained stable if samples were kept for up to 24 h after withdrawal [60]. Noteworthy, we have now analyzed the effect of 24-h delayed processing at

36

Yanaika S. Hok-A-Hin et al.

Table 3 Percentage of proteins that remained stable under different pre-analytical conditions using large multiplex protein arrays (SOMAscan and Olink) Percentage of stable proteins Temp

Time

SOMAscan

Olink



Delayed storage

4 C RT RT

168 h 24 h 168 h

88.1% 84.3% 68.3%

92.9% 89.7% 85.8%

Delayed processing

4 C RT

24 h 24 h

75.6% 73.5%

X X

f/t cycles

4 8

81.6% 77.5%a

91.9% 86.3%

Results for the Olink cardiovascular III panel (89 proteins) based on n ¼ 2 instead of n ¼ 3. Specifics regarding the statistical analysis and criteria established to determine protein stability are included in Subheading 3. Temp temperature, f/t freeze/thaw, RT room temperature, h hour. X means data not available

a

different temperatures (RT and 4  C) in the 1129 proteins measured with an aptamer-based proteomic array (SOMAscan [26]; detailed explanation of the method and statistical analysis can be found in Subheading 3). We observed that 25% of the proteins measured were affected by a delay processing of 24 h independently of the temperature (Tables 2 and 3, Fig. 1), suggesting that a delayed storage can partially influence the stability of CSF proteome. More experimental evidence exists regarding the effect of delayed storage (i.e., the time between centrifugation and freezing) on CSF protein stability. It has been observed that the activity of different lysosomal enzymes (e.g., α-mannosidase or α-fucosidase among others) decreases after a delayed storage of 48 h at RT [61]. A MALDI-TOF study showed changes in CSF peptide pattern already after 30 min of delayed storage [65], while others revealed that the CSF proteome remained stable after a delayed storage of 2 or 6 h [54, 55, 62]. However, surface-enhanced laser desorption ionization time-of-flight (SELDI-TOF) study focusing on transthyretin and transthyretin S-cysteine has shown that a delayed storage of 4 h affected the peak intensity for these proteins [63]. We have also analyzed the effect of delayed storage (24 h and 1 week) at different temperatures (RT and 4  C) on a total of 1603 CSF proteins using the aptamer-based array (1129 proteins) as well as an antibody-based protein array (Olink [27]; 831 proteins, detailed explanation of the method can be found in Subheading 3). We observed that more than 85% of the proteins measured with both platforms remained stable when storage of the samples was delayed for 1 week at 4  C (Table 3). The percentage of CSF stable

Guidelines for CSF Processing and Biobanking

37

Fig. 1 Stability of all proteins measured with SOMAscan and Olink under different pre-analytical storage and f/t conditions. The y-as presents the protein levels as relative units and the x-as presents the number of proteins measured. The difference between the condition and the reference sample is expressed as 95% CI and plotted in a vertical line per protein. Red dots indicate the mid-CI value. Mid-CI values are ordered from far from 0 (left) to close to 0 (right). Dotted horizontal lines indicate the threshold 95% CI value for criterion 2. ∗Results for the Olink cardiovascular III panel (89 proteins) were based on n ¼ 2 instead of n ¼ 3. f/t freeze/thaw, RT room temperature

proteins was slightly lower when samples were kept for 1 week at RT, a condition that is usually avoided in most experimental and clinical procedures. An overview of the effect of the different delayed storage conditions for all proteins measured using both protein platforms is presented in Fig. 1.

38

Yanaika S. Hok-A-Hin et al.

Fig. 1 (continued)

Guidelines for CSF Processing and Biobanking

39

Taken together, these data suggest that most of the CSF proteins are not affected by extreme delay processing or storage. However, it is strongly recommended to adhere to the international guidelines recommending to not exceed 4 h between CSF withdrawal, processing, and storage. 2.8 Freeze/Thaw Cycles

Freezing and thawing can influence protein stability, ultimately affecting the measurements of such proteins [64]. The activity of multiple lysosome enzymes in CSF such as α-fucosidase or β-mannosidase was increased after two freeze/thaw cycles [61]. Proteomics studies have shown that the levels of transthyretin and its isoforms were approximately 15% decreased after one freeze/thaw cycle (Table 2) [63, 65]. However, no changes in the overall CSF proteome stability have been observed after up to four freeze/thaw cycles [54, 62]. Interestingly, we have analyzed the effect of one, four, and eight freeze/thaw cycles on a total of 1603 CSF proteins analyzed with two multiplex protein arrays (SOMAscan and Olink). More than 85% of CSF proteins analyzed with SOMAscan platform remained stable after eight freeze/thaw cycles (Table 3, Fig. 1). The proteins measured with Olink platform showed more susceptibility to freeze/thaw cycles since 82% and 76% remained stable after four and eight freeze/thaw cycles, respectively (Tables 2 and 3, Fig. 1). These data suggest that most of the CSF proteins are not affected after a considerable large number of freeze/thaw cycles. Nonetheless, it is of great importance to always accurately document the number of freeze/thaw cycles that CSF samples have undergone and always ponder the potential effect that freeze/ thaw may play in specific CSF proteins. If possible, the effect of freeze/thaw cycles on the specific biomarker of interest should be ultimately tested experimentally as highlighted by assay validation guidelines [21]. It is recommended to minimize the number of freeze/thaw cycles to two, especially for individual immunoassay analysis during the validation and implementation phases.

2.9

Aliquoting of CSF after processing is necessary in order to reduce the number of freeze/thaw cycles. When aliquots are prepared, both the type of tube and the volume of aliquots should be taken into consideration since it may impact the concentration of CSF proteins. Similar to CSF collection tubes (see Subheading 2.2), PP tubes should be used for aliquoting. The volume of aliquoting is not trivial, as the ratio between the tube surface area and the volume of CSF (surface/volume) may affect the degree of protein absorbance to the tube wall, ultimately affecting protein measurements [66–68]. For example, in 1.5 mL PP tubes, aliquots of 0.5 mL showed a reduction of 13.6% Aβ42 levels compared to aliquots containing 1.5 mL of CSF [67]. Furthermore, the volume

CSF Aliquoting

40

Yanaika S. Hok-A-Hin et al.

of aliquots may influence the evaporation process differently, which could impact the final protein measurements. However, no evaporation was observed after storing different volumes at 20  C or 80  C [69]. Whether CSF aliquot volume affects the concentration of other novel CSF biomarker candidates remains to be investigated. To minimize the potential effects of surface/volume ratio and evaporation on the final CSF protein measurements, it is recommended to fill aliquots tube up to 75% [70]. Noteworthy, all CSF samples (including aliquots) should be provided with a unique code linked to a protected online database providing patient information (e.g., clinical or informed consent information). Barcode labels are preferred because this enables automation, doubleblinded research and protects patient privacy. Labels should be suitable for long-term storage at 80  C. 2.10 Freezing Temperature

The temperature at which CSF is frozen (e.g., 20  C, 80  C, and liquid nitrogen (N2)) could also affect the stability of CSF proteins [62]. The changes on pH during storage can induce oxidative modifications of proteins [71], which can change the availability of the epitopes detected with immunological assays and thus affect protein measurements. The activity of lysosome enzymes in CSF (e.g., α-mannosidase) was decreased in samples stored at 20  C compared to those stored at 80  C [61]. MALDI-TOF MS analysis comparing CSF storage at 20  C, 80  C, and N2 showed no significant variation in the CSF peptide profile [62]. However, differences in the stability of specific CSF proteins were found when comparing CSF storage at 20  C and 80  C. For instance, cystatin C undergoes proteolysis during storage at 20  C but not at 80  C storage [54, 55, 72] and complement C3 levels were increased in CSF samples stored at 20  C compared to those stored at 80  C (Table 2) [54]. Thus, to ensure CSF protein stability and minimize pre-analytical variance, it is recommended to store CSF samples at 80  C.

2.11 Long-Term Storage of CSF

Understanding CSF protein stability after long-term storage is very relevant since the use of historical patient cohorts is important not only to increase sample size but also to perform longitudinal studies and identify prognostic and monitoring biomarkers. Despite the influence of long-term storage on the CSF proteome has not been thoroughly studied yet, it has been observed that the classical AD CSF biomarkers remained stable for up to 12 years when stored at 80  C [73]. Noteworthy, several lysosomal enzymes (e.g., α-mannosidase, β-glucocerebrosidase, cathepsins) were shown to be significantly decreased upon 40-week storage at 80  C [61]. Nonetheless, more research is needed to understand the processes that occur in the CSF proteome upon long-term storage.

Guidelines for CSF Processing and Biobanking

3

41

Methods Samples

CSF was collected as leftovers from diagnostics at the Alzheimer Center of the VU University Medical Center (VUmc, Amsterdam, The Netherlands) [74]. The CSF had been centrifuged at 2000  g for 10 min before storage at 20  C. Aliquots of pooled CSF were typically 500 μL and were stored in 1.5 mL polypropylene tubes with screw cap (Sarstedt, Nu¨mbrecht, Germany). All samples were blinded and sent to either Olink (Uppsala, Sweden) or SomaLogic, Inc. (Boulder, CO), on dry ice. This study was in line with the institutional research code and donors gave informed consent.

3.2 Exposure to Delayed Storage and f/t Stability

Six pools were prepared from anonymized CSF (processed as described above) for analysis of the effects of delayed storage and f/t stability, prepared according to the standard operating procedure for stability testing [21]. Aliquots of the pools were stored 1 week at 4  C or 1 h, 1 day, or 1 week at RT, for delayed storage stability (n ¼ 3), or underwent 1, 4, or 8 freeze/thaw cycles for freeze/thaw stability (n ¼ 3). Thawing was for 2 h at RT and freezing was at 80  C minimally overnight. Samples were stored at 80  C until further analysis. Reference samples were stored at 80  C directly at time point zero.

3.3 Exposure to Delayed Processing

Samples for analysis of the effect of delayed processing (n ¼ 3) on proteins present in the SOMAscan were prepared from fresh CSF and were kept at either 4  C or RT for 1, 2, or 24 h, before centrifugation at 2000  g for 10 min and final storage at 80  C. Reference samples were stored at 80  C directly at time point zero after centrifugation at 2000  g for 10 min. This condition was tested only for SOMAscan.

3.4

The SOMAscan panel (SomaLogic, Inc., Boulder, Colorado) is a high-throughput targeted protein discovery platform that contains aptamers, in the used version detecting 1129 proteins [26]. Aptamers are synthetic DNA strands that were selected to bind proteins in an antibody-like manner; however, the aptamers have a better stability and can be better reproduced than antibodies. The targets of the SOMAscan aptamers include major gene families including receptors, kinases, growth factors, and hormones and span a diverse collection of secreted, intracellular, and extracellular proteins or domains [75]. Samples were measured in three different dilutions to enable the appropriate measurement range for all aptamers within one sample. The protein-bound aptamers are hybridized to custom DNA microarrays before fluorescent readout. Proteins are measured in the femtomolar range and are expressed as relative fluorescence units (RFU). SOMAscan results were normalized in three sequential steps: (1) hybridization

3.1

SOMAscan Assay

42

Yanaika S. Hok-A-Hin et al.

control normalization to reduce variation between microarray plates; (2) median signal normalization to reduce systematic bias within a single plate run, using three different dilutions of the same sample; (3) calibration for each aptamer to a common pooled calibrator sample. The limit of detection (LOD) values were not available. 3.5

Olink Assay

3.6 Statistical Analysis SOMAscan and Olink

The immunoassay panels from Olink (Uppsala, Sweden) allow high-throughput targeted protein biomarker discovery. Each Olink panel is focused on a specific area of disease or key biology process, targeting 92 established and/or exploratory biomarkers. In the current study, we screened 11 panels: the cardiometabolic (v.3601), cardiovascular II (v.5002), cardiovascular III (v.6101), cell regulation (v.3701), development (v.3501), immune response (v.3201), inflammation (v.3004), metabolism (v.3401), neurology (v.8001), oncology II (v.7001), and organ damage (v.3301). A full list of the targeted proteins can be found at Olink’s website (www. olink.com/products). Olink’s technology is based on the use of antibodies that have a single DNA oligo sequence attached, which can only hybridize and amplify with the DNA oligo sequence of its complementary antibody when bound to the same protein, resulting in a highly sensitive quantification as PCR-like readout. All samples were run in one plate. Quality control testing included comparing the internal control of each sample to assure sample quality. After quality control checks performed by Olink, results from 949 proteins were reported in relative units. Protein levels measured with Olink were expressed as relative units on a log2 scale. LOD was defined by Olink as three times the standard deviation over background. Olink’s quality control testing included the comparison of an internal control in each sample to assure sample quality. For each of the tested condition (delayed processing, delayed storage, or freeze/thaw stability), we compared the results of the most extreme time point or cycle to the reference sample in a linear model correcting for pool. The contrast between the tested condition and the reference sample was expressed as 95% confidence interval (CI). The 95% CIs were judged on “95% CI criterion 1,” whether the 95% CIs included 0 (0 included in 95% CI means no significant change), and “95% CI size criterion 2,” whether the 95% CIs were within the thresholds (indicating a small 95% CI and thus a reliable estimation of the effect) (Fig. 2). Those 95% CI thresholds were calculated for SOMAscan and Olink separately, based on the variation of the 95% CI ranges in the subset of proteins that were overlapping in both panels (a total of 357) for the most extreme conditions, i.e., delayed storage of 168 h at RT and 4  C and 8 f/t cycles (excluding 8 f/t for Olink

Guidelines for CSF Processing and Biobanking

43

Fig. 2 Example of the statistical criteria for stability. The green arrow points out a protein that satisfies criterion 1, since 0 is included in the 95% CI, and it satisfies criterion 2, since the 95% CI is smaller than the red dotted threshold lines (set at 1 and 1). This protein would be regarded as stable. The blue arrow points out a protein that does not satisfy criteria 1, but it does satisfy criteria 2. This protein would not be regarded as stable. The red arrow points out a protein that satisfies criteria 1, but it does not satisfy criteria 2. This protein would not be regarded as stable

Table 4 95% CI threshold calculation for stability of proteins between the condition in the left column and the reference sample. The outlier range was defined as the range between Q1–1.5 IQR and Q3 + 1.5 IQR 95% CI range SOMAscan Overlapping proteins (357)

Median IQR

Outlier range

95% CI range Olink Outlier range/2 Median IQR

Outlier range

Outlier range/2

Delayed storage 168 h 4  C 0.226

0.224 0.897

0.448

0.761

0.650 2.600

1.300

Delayed storage 168 h RT

0.277

0.365 1.459

0.729

0.758

0.799 3.195

1.598

8 f/t cycles

0.290

0.250 1.000

0.501

a

a

a

Average

1.119

 0.56

a

2.898

 1.45

Excluded because results for the Olink cardiovascular III panel (89 proteins) were based on n ¼ 2 instead of n ¼ 3. CI confidence interval, f/t freeze/thaw, RT room temperature, h hour, IQR interquartile range

a

since n ¼ 2 instead of 3 for proteins of the cardiovascular III panel) (Table 4). This design was chosen to detect variation in pre-analytical storage conditions that was not attributed to the common variation of protein levels per technology. We calculated the average outlier range, i.e., the range between Q1–1.5 ∗interquartile range (IQR) and Q3 + 1.5 ∗ IQR over the conditions, and divided this range by 2 to get the value for the positive and

44

Yanaika S. Hok-A-Hin et al.

negative threshold. For SOMAscan the threshold was between 0.56 and 0.56; for Olink the threshold was calculated between 1.45 and 1.45. If both criteria were met, the protein was regarded as stable.

4

Conclusion The large variation in CSF protein biomarker measurements has partly hampered the validation of novel CSF biomarker candidates and their ultimate implementation in clinical practice. This variation might be greatly reduced by minimizing the effect of preanalytical confounding factors. Therefore, international consensus guidelines for CSF processing and biobanking have been established, which can be regularly updated with new experimental data on novel CSF biomarker candidates. In this chapter we have summarized the guidelines for CSF processing and biobanking with a special focus on the effect over the global CSF proteome. Additionally, we report new evidences about the effect of delayed storage conditions and freeze/thaw cycles in a large set of CSF proteins (a total of 1603). Despite the fact that different proteomics studies have identified specific proteins that are certainly sensitive to pre-analytical confounding factors (e.g., complement C3, cystatin C, transthyretin), the data suggest that most of the CSF proteome remains stable even under extreme (and, thus, unusual) conditions. This data is important, since CSF samples under different pre-analytical conditions (e.g., delayed storage, freeze/thaw cycles) might be combined for the discovery and validation of CSF biomarker studies, allowing to increase the sample size and thus the power of the proteomics analysis. Nonetheless, it is essential to track the different pre-analytical conditions of each sample and to always consider the potential effect of the confounding factors on proteins of interest within discovery/validation of CSF biomarker studies, since specific proteins that have not been identified as clinically relevant yet might appear sensitive to nonstandard pre-analytical conditions. An overview of the CSF proteins that are sensitive to specific preanalytical confounding factors identified so far is presented in Table 2. Studies showed that hydrophobic peptides, like Aβ42, adhered to laboratory plastics such as tubes. It may be of interest to identify other patterns (e.g., biochemical, structural) that define whether a specific CSF protein is more prone to be affected by a certain pre-analytical conditions, for which additional research is needed. Noteworthy, experimental evidence regarding the influence of the pre-analytical confounding factors on the CSF proteome is limited. Therefore, more experiments and new updates to the current guidelines are expected in the future, especially with new

Guidelines for CSF Processing and Biobanking

45

emerging technologies that will facilitate the identification and validation of novel biomarkers [76]. Furthermore, the influence of the pre-analytical confounding factors on novel CSF biomarkers should always be tested prior to implementation in clinical practice [59, 60, 77]. Identifying and controlling the influence of pre-analytical confounding factors on CSF protein analysis will ultimately facilitate the development of successful CSF protein biomarkers for different neurological disorders. References 1. Rifai N, Gillette MA, Carr SA (2006) Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat Biotechnol 24(8):971–983. https://doi.org/10. 1038/nbt1235 2. Mattsson N (2011) CSF biomarkers in neurodegenerative diseases. Clin Chem Lab Med 49 (3):345–352. https://doi.org/10.1515/ CCLM.2011.082 3. Blennow K, Hampel H, Weiner M, Zetterberg H (2010) Cerebrospinal fluid and plasma biomarkers in Alzheimer disease. Nat Rev Neurol 6(3):131–144. https://doi.org/10.1038/ nrneurol.2010.4 4. Schoonenboom NS, Mulder C, Vanderstichele H, Van Elk EJ, Kok A, Van Kamp GJ, Scheltens P, Blankenstein MA (2005) Effects of processing and storage conditions on amyloid beta (1-42) and tau concentrations in cerebrospinal fluid: implications for use in clinical practice. Clin Chem 51 (1):189–195. https://doi.org/10.1373/ clinchem.2004.039735 5. Jack CR Jr, Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, Holtzman DM, Jagust W, Jessen F, Karlawish J, Liu E, Molinuevo JL, Montine T, Phelps C, Rankin KP, Rowe CC, Scheltens P, Siemers E, Snyder HM, Sperling R, Contributors (2018) NIA-AA research framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement 14(4):535–562. https://doi.org/10. 1016/j.jalz.2018.02.018 6. Plebani M, Sciacovelli L, Aita A, Chiozza ML (2014) Harmonization of pre-analytical quality indicators. Biochem Med (Zagreb) 24 (1):105–113. https://doi.org/10.11613/ BM.2014.012 7. Sunderland T, Linker G, Mirza N, Putnam KT, Friedman DL, Kimmel LH, Bergeson J, Manetti GJ, Zimmermann M, Tang B, Bartko JJ, Cohen RM (2003) Decreased Β-Amyloid1-42 and increased tau levels in cerebrospinal fluid of patients with Alzheimer disease. JAMA 289

(16):2094–2103. https://doi.org/10.1001/ jama.289.16.2094 8. Mollenhauer B, El-Agnaf OMA, Marcus K, Trenkwalder C, Schlossmacher MG (2010) Quantification of α-synuclein in cerebrospinal fluid as a biomarker candidate: review of the literature and considerations for future studies. Biomark Med 4(5):683–699. https://doi.org/ 10.2217/bmm.10.90. https://www. futuremedicine.com/doi/abs/10.2217/ bmm.10.90 9. Mattsson N, Andreasson U, Persson S, Carrillo MC, Collins S, Chalbot S, Cutler N, DufourRainfray D, Fagan AM, Heegaard NH, Robin Hsiung GY, Hyman B, Iqbal K, Kaeser SA, Lachno DR, Lleo A, Lewczuk P, Molinuevo JL, Parchi P, Regeniter A, Rissman RA, Rosenmann H, Sancesario G, Schroder J, Shaw LM, Teunissen CE, Trojanowski JQ, Vanderstichele H, Vandijck M, Verbeek MM, Zetterberg H, Blennow K, Alzheimer’s Association QCPWG (2013) CSF biomarker variability in the Alzheimer’s association quality control program. Alzheimers Dement 9 (3):251–261. https://doi.org/10.1016/j.jalz. 2013.01.010 10. Lewczuk P, Beck G, Ganslandt O, Esselmann H, Deisenhammer F, Regeniter A, Petereit HF, Tumani H, Gerritzen A, Oschmann P, Schroder J, Schonknecht P, Zimmermann K, Hampel H, Burger K, Otto M, Haustein S, Herzog K, Dannenberg R, Wurster U, Bibl M, Maler JM, Reubach U, Kornhuber J, Wiltfang J (2006) International quality control survey of neurochemical dementia diagnostics. Neurosci Lett 409(1):1–4. https://doi.org/10.1016/j. neulet.2006.07.009 11. Verwey NA, van der Flier WM, Blennow K, Clark C, Sokolow S, De Deyn PP, Galasko D, Hampel H, Hartmann T, Kapaki E, Lannfelt L, Mehta PD, Parnetti L, Petzold A, Pirttila T, Saleh L, Skinningsrud A, Swieten JC, Verbeek MM, Wiltfang J, Younkin S, Scheltens P, Blankenstein MA (2009) A worldwide multicentre

46

Yanaika S. Hok-A-Hin et al.

comparison of assays for cerebrospinal fluid biomarkers in Alzheimer’s disease. Ann Clin Biochem 46(Pt 3):235–240. https://doi.org/ 10.1258/acb.2009.008232 12. Mattsson N, Zetterberg H, Blennow K (2010) Lessons from multicenter studies on CSF biomarkers for Alzheimer’s disease. Int J Alzheimers Dis 2010. https://doi.org/10.4061/ 2010/610613 13. Teunissen CE, Petzold A, Bennett JL, Berven FS, Brundin L, Comabella M, Franciotta D, Frederiksen JL, Fleming JO, Furlan R, Hintzen RQ, Hughes SG, Johnson MH, Krasulova E, Kuhle J, Magnone MC, Rajda C, Rejdak K, Schmidt HK, van Pesch V, Waubant E, Wolf C, Giovannoni G, Hemmer B, Tumani H, Deisenhammer F (2009) A consensus protocol for the standardization of CSF collection and biobank. Neurology 73:1914–1922 14. Deisenhammera F, Egga R, Giovannonib G, Hemmerc B, Petzoldd A, Sellebjerge F, Teunissen C, Tumanig H (2009) EFNS guidelines on disease-specific CSF investigations. Eur J Neurol 16:760–770. https://doi.org/ 10.1111/j.1468-1331.2009.02595.x 15. del Campo M, Mollenhauer B, Bertolotto A, Engelborghs S, Hampel H, Simonsen AH, Kapaki E, Kruse N, Le Bastard N, Lehmann S, Molinuevo JL, Parnetti L, PerretLiaudet A, Saez-Valero J, Saka E, Urbani A, Vanmechelen E, Verbeek M, Visser PJ, Teunissen C (2012) Recommendations to standardize preanalytical confounding factors in Alzheimer’s and Parkinson’s disease cerebrospinal fluid biomarkers: an update. Biomark Med 6(4):419–430. https://doi.org/10. 2217/bmm.12.46 16. Otto M, Bowser R, Turner M, Berry J, Brettschneider J, Connor J, Costa J, Cudkowicz M, Glass J, Jahn O, Lehnert S, Malaspina A, Parnetti L, Petzold A, Shaw P, Sherman A, Steinacker P, Sussmuth S, Teunissen C, Tumani H, Wuolikainen A, Ludolph A, Volcano G (2012) Roadmap and standard operating procedures for biobanking and discovery of neurochemical markers in ALS. Amyotroph Lateral Scler 13(1):1–10. https://doi.org/10.3109/17482968.2011. 627589 17. Vanderstichele H, Bibl M, Engelborghs S, Le Bastard N, Lewczuk P, Molinuevo JL, Parnetti L, Perret-Liaudet A, Shaw LM, Teunissen C, Wouters D, Blennow K (2012) Standardization of preanalytical aspects of cerebrospinal fluid biomarker testing for Alzheimer’s disease diagnosis: a consensus paper from the Alzheimer’s biomarkers

standardization initiative. Alzheimers Dement 8(1):65–73. https://doi.org/10.1016/j.jalz. 2011.07.004 18. Teunissen CE, Tumani H, Engelborghs S, Mollenhauer B (2014) Biobanking of CSF: international standardization to optimize biomarker development. Clin Biochem 47 (4-5):288–292. https://doi.org/10.1016/j. clinbiochem.2013.12.024 19. Willemse EAJ, Teunissen CE (2015) Importance of pre-analytical stability for CSF biomarker testing. Cerebrospinal Fluid Clin Neurol:59–77. https://doi.org/10.1007/ 978-3-319-01225-4_5 20. Willemse EAJ, Teunissen CE (2015) Biobanking of cerebrospinal fluid for biomarker analysis in neurological diseases. Adv Exp Med Biol 864:79–93. https://doi.org/10.1007/978-3319-20579-3_7 21. Andreasson U, Perret-Liaudet A, van Waalwijk van Doorn LJ, Blennow K, Chiasserini D, Engelborghs S, Fladby T, Genc S, Kruse N, Kuiperij HB, Kulic L, Lewczuk P, Mollenhauer B, Mroczko B, Parnetti L, Vanmechelen E, Verbeek MM, Winblad B, Zetterberg H, Koel-Simmelink M, Teunissen CE (2015) A practical guide to immunoassay method validation. Front Neurol 6:179. https://doi.org/10.3389/fneur.2015.00179 22. Engelborghs S, Niemantsverdriet E, Struyfs H, Blennow K, Brouns R, Comabella M, Dujmovic I, van der Flier W, Frolich L, Galimberti D, Gnanapavan S, Hemmer B, Hoff E, Hort J, Iacobaeus E, Ingelsson M, Jan de Jong F, Jonsson M, Khalil M, Kuhle J, Lleo A, de Mendonca A, Molinuevo JL, Nagels G, Paquet C, Parnetti L, Roks G, RosaNeto P, Scheltens P, Skarsgard C, Stomrud E, Tumani H, Visser PJ, Wallin A, Winblad B, Zetterberg H, Duits F, Teunissen CE (2017) Consensus guidelines for lumbar puncture in patients with neurological diseases. Alzheimers Dement (Amst) 8:111–126. https://doi.org/ 10.1016/j.dadm.2017.04.007 23. Hansson O, Mikulskis A, Fagan AM, Teunissen C, Zetterberg H, Vanderstichele H, Molinuevo JL, Shaw LM, Vandijck M, Verbeek MM, Savage M, Mattsson N, Lewczuk P, Batrla R, Rutz S, Dean RA, Blennow K (2018) The impact of preanalytical variables on measuring cerebrospinal fluid biomarkers for Alzheimer’s disease diagnosis: a review. Alzheimers Dement 14(10):1313–1333. https:// doi.org/10.1016/j.jalz.2018.05.008 24. Deisenhammer F, Bartos A, Egg R, Gilhus NE, Giovannoni G, Rauer S, Sellebjerg F, Force ET (2006) Guidelines on routine cerebrospinal fluid analysis. Report from an EFNS task force.

Guidelines for CSF Processing and Biobanking Eur J Neurol 13(9):913–922. https://doi.org/ 10.1111/j.1468-1331.2006.01493.x 25. Simonsen AH, Kuiperij B, Mukhtar O, El-Agnaf A, Engelborghs S, Herukka S, Parnetti L, Rektorova I, Vanmechelen E, Kapaki E, Verbeek M, Mollenhauer B (2015) The utility of α-synuclein as biofluid marker in neurodegenerative diseases: a systematic review of the literature. Biomark Med 10(1) 26. Gold L, Ayers D, Bertino J, Bock C, Bock A, Brody EN, Carter J, Dalby AB, Eaton BE, Fitzwater T, Flather D, Forbes A, Foreman T, Fowler C, Gawande B, Goss M, Gunn M, Gupta S, Halladay D, Heil J, Heilig J, Hicke B, Husar G, Janjic N, Jarvis T, Jennings S, Katilius E, Keeney TR, Kim N, Koch TH, Kraemer S, Kroiss L, Le N, Levine D, Lindsey W, Lollo B, Mayfield W, Mehan M, Mehler R, Nelson SK, Nelson M, Nieuwlandt D, Nikrad M, Ochsner U, Ostroff RM, Otis M, Parker T, Pietrasiewicz S, Resnicow DI, Rohloff J, Sanders G, Sattin S, Schneider D, Singer B, Stanton M, Sterkel A, Stewart A, Stratford S, Vaught JD, Vrkljan M, Walker JJ, Watrobka M, Waugh S, Weiss A, Wilcox SK, Wolfson A, Wolk SK, Zhang C, Zichi D (2010) Aptamer-based multiplexed proteomic technology for biomarker discovery. PLoS One 5(12):e15004. https://doi.org/10. 1371/journal.pone.0015004 27. Carlyle BC, Trombetta BA, Arnold SE (2018) Proteomic approaches for the discovery of biofluid biomarkers of neurodegenerative dementias. Proteomes 6(3). https://doi.org/10. 3390/proteomes6030032 28. Murillo-Rodriguez E, Desarnaud F, ProsperoGarcia O (2006) Diurnal variation of arachidonoylethanolamine, palmitoylethanolamide and oleoylethanolamide in the brain of the rat. Life Scienes 79(1):30–37. https://doi.org/10. 1016/j.lfs.2005.12.028 29. Bjerke M, Portelius E, Minthon L, Wallin A, Anckarsater H, Anckarsater R, Andreasen N, Zetterberg H, Andreasson U, Blennow K (2010) Confounding factors influencing amyloid Beta concentration in cerebrospinal fluid. Int J Alzheimers Dis 2010. https://doi.org/ 10.4061/2010/986310 30. Spies PE, Slats D, Rikkert MG, Tseng J, Claassen JA, Verbeek MM (2011) CSF alpha-synuclein concentrations do not fluctuate over hours and are not correlated to amyloid beta in humans. Neurosci Lett 504(3):336–338. https://doi. org/10.1016/j.neulet.2011.09.063 31. Moghekar A, Goh J, Li M, Albert M, O’Brien RJ (2012) Cerebrospinal fluid Abeta and tau level fluctuation in an older clinical cohort.

47

Arch Neurol 69(2):246–250. https://doi. org/10.1001/archneurol.2011.732 32. Slats D, Claassen JA, Spies PE, Borm G, Besse KT, van Aalst W, Tseng J, Sjogren MJ, Olde Rikkert MG, Verbeek MM (2012) Hourly variability of cerebrospinal fluid biomarkers in Alzheimer’s disease subjects and healthy older volunteers. Neurobiol Aging 33(4):831 e831–831 e839. https://doi.org/10.1016/j. neurobiolaging.2011.07.008 33. Cicognola C, Chiasserini D, Parnetti L (2015) Preanalytical confounding factors in the analysis of cerebrospinal fluid biomarkers for Alzheimer’s disease: the issue of diurnal variation. Front Neurol 6:143. https://doi.org/10. 3389/fneur.2015.00143 34. Cicognola C, Chiasserini D, Eusebi P, Andreasson U, Vanderstichele H, Zetterberg H, Parnetti L, Blennow K (2016) No diurnal variation of classical and candidate biomarkers of Alzheimer’s disease in CSF. Mol Neurodegener 11(1):65. https://doi.org/10. 1186/s13024-016-0130-3 35. Lucey BP, Fagan AM, Holtzman DM, Morris JC, Bateman RJ (2017) Diurnal oscillation of CSF Abeta and other AD biomarkers. Mol Neurodegener 12(1):36. https://doi.org/10. 1186/s13024-017-0161-4 36. Engedal TS, Ording H, Vilholm OJ (2015) Changing the needle for lumbar punctures: results from a prospective study. Clin Neurol Neurosurg 130:74–79. https://doi.org/10. 1016/j.clineuro.2014.12.020 37. Duits FH, Martinez-Lage P, Paquet C, Engelborghs S, Lleo A, Hausner L, Molinuevo JL, Stomrud E, Farotti L, Ramakers I, Tsolaki M, Skarsgard C, Astrand R, Wallin A, Vyhnalek M, Holmber-Clausen M, Forlenza OV, Ghezzi L, Ingelsson M, Hoff EI, Roks G, de Mendonca A, Papma JM, Izagirre A, Taga M, Struyfs H, Alcolea DA, Frolich L, Balasa M, Minthon L, Twisk JWR, Persson S, Zetterberg H, van der Flier WM, Teunissen CE, Scheltens P, Blennow K (2016) Performance and complications of lumbar puncture in memory clinics: results of the multicenter lumbar puncture feasibility study. Alzheimers Dement 12(2):154–163. https://doi. org/10.1016/j.jalz.2015.08.003 38. Kraut A, Marcellin M, Adrait A, Kuhn L, Louwagie M, Kieffer-Jaquinod S, Lebert D, Masselon CD, Dupuis A, Bruley C, Jaquinod M, Garin J, Gallagher-Gambarelli M (2009) Peptide storage: are you getting the best return on your investment? Defining optimal storage conditions for proteomic samples. J Proteome Res 8:3778–3378

48

Yanaika S. Hok-A-Hin et al.

39. Perret-Liaudet A, Pelpel M, Tholance Y, Dumont B, Vanderstichele H, Zorzi W, ElMoualij B, Schraen S, Moreaud O, Gabelle A, Thouvenot E, Thomas-Anterion C, Touchon J, Krolak-Salmon P, Kovacs GG, Coudreuse A, Quadrio I, Lehmann S (2012) Cerebrospinal fluid collection tubes: a critical issue for Alzheimer disease diagnosis. Clin Chem 58(4):787–795 40. Lehmann S, Schraen S, Quadrio I, Paquet C, Bombois S, Delaby C, Dorey A, Dumurgier J, Hirtz C, Krolak-Salmon P, Laplanche JL, Moreaud O, Peoc’h K, Rouaud O, Sablonniere B, Thouvenot E, Touchon J, Vercruysse O, Hugon J, Gabelle A, Pasquier F, Perret-Liaudet A (2014) Impact of harmonization of collection tubes on Alzheimer’s disease diagnosis. Alzheimers Dement 10 (5 Suppl):S390–S394 e392. https://doi.org/ 10.1016/j.jalz.2013.06.008 41. Toombs A, Paterson RW, Schott JM, Zetterberg H (2014) Amyloid-beta 42 adsorption following serial tube transfer. Alzheimers Res Ther 6:5 42. Mollenhauer B, Batrla R, El-Agnaf O, Galasko DR, Lashuel HA, Merchant KM, Shaw LM, Selkoe DJ, Umek R, Vanderstichele H, Zetterberg H, Zhang J, Caspell-Garcia C, Coffey C, Hutten SJ, Frasier M, Taylor P, Investigating Synuclein Consortium of the Michael JFFfPsR (2017) A user’s guide for alpha-synuclein biomarker studies in biological fluids: perianalytical considerations. Mov Disord 32(8):1117–1130. https://doi.org/10. 1002/mds.27090 43. Reijs BL, Teunissen CE, Goncharenko N, Betsou F, Blennow K, Baldeiras I, Brosseron F, Cavedo E, Fladby T, Froelich L, Gabryelewicz T, Gurvit H, Kapaki E, Koson P, Kulic L, Lehmann S, Lewczuk P, Lleo A, Maetzler W, de Mendonca A, Miller AM, Molinuevo JL, Mollenhauer B, Parnetti L, Rot U, Schneider A, Simonsen AH, Tagliavini F, Tsolaki M, Verbeek MM, Verhey FR, Zboch M, Winblad B, Scheltens P, Zetterberg H, Visser PJ (2015) The central biobank and virtual biobank of BIOMARKAPD: a resource for studies on neurodegenerative diseases. Front Neurol 6:216. https:// doi.org/10.3389/fneur.2015.00216 44. Willemse E, van Uffelen K, Brix B, Engelborghs S, Vanderstichele H, Teunissen C (2017) How to handle adsorption of cerebrospinal fluid amyloid beta (1-42) in laboratory practice? Identifying problematic handlings and resolving the issue by use of the Abeta42/Abeta40 ratio. Alzheimers Dement 13(8):885–892. https://doi.org/10.1016/j. jalz.2017.01.010

45. Pica-Mendez AM, Tanen M, Dallob A, Tanaka W, Laterza OF (2010) Nonspecific binding of Abeta42 to polypropylene tubes and the effect of Tween-20. Clin Chim Acta 411(21-22):1833. https://doi.org/10.1016/ j.cca.2010.07.019 46. Simonsen AH, Bech S, Laursen I, Salvesen L, Winge K, Waldemar G, Werdelin L, Nielsen JE, McGuire JN, Hjermind LE (2010) Proteomic investigations of the ventriculo-lumbar gradient in human CSF. J Neurosci Methods 191 (2):244–248. https://doi.org/10.1016/j. jneumeth.2010.06.017 47. Tarnaris A, Toma AK, Chapman MD, Petzold A, Keir G, Kitchen ND, Watkins LD (2011) Rostrocaudal dynamics of CSF biomarkers. Neurochem Res 36(3):528–532. https:// doi.org/10.1007/s11064-010-0374-1 48. Brandner S, Thaler C, Lewczuk P, Lelental N, Buchfelder M, Kleindienst A (2013) Neuroprotein dynamics in the cerebrospinal fluid: intraindividual concomitant ventricular and lumbar measurements. Eur Neurol 70 (3-4):189–194. https://doi.org/10.1159/ 000352032 49. Reiber H (2003) Proteins in cerebrospinal fluid and blood, barriers, CSF flow rate and sourcerelated dynamics. Restor Neurol Neurosci 21:79–96 50. Axel Petzold A, Sharpe LT, Keir G (2006) Spectrophotometry for cerebrospinal fluid pigment analysis. Neurocrit Care 4(6):153–162. https://doi.org/10.1385/Neurocrit 51. You J-S, Gelfanova V, Knierman MD, Witzmann FA, Wang M, Hale JE (2005) The impact of blood contamination on the proteome of cerebrospinal fluid. Proteomics 5 (1):290–296. https://doi.org/10.1002/ pmic.200400889 52. Yang J, Dombrowski SM, Deshpande A, Krajcir N, El-Khoury S, Krishnan C, Luciano MG (2011) Stability analysis of vascular endothelial growth factor in cerebrospinal fluid. Neurochem Res 36(11):1947–1954. https:// doi.org/10.1007/s11064-011-0517-z 53. Ramont L, Thoannes H, Volondat A, Chastang F, Millet MC, Maquart FX (2005) Effects of hemolysis and storage condition on neuron-specific enolase (NSE) in cerebrospinal fluid and serum: implications in clinical practice. Clin Chem Lab Med 43(11):1215–1217. https://doi.org/10.1515/CCLM.2005.210 54. Jimenez CR, Koel-Simmelink M, Pham TV, van der Voort L, Teunissen CE (2007) Endogeneous peptide profiling of cerebrospinal fluid by MALDI-TOF mass spectrometry: optimization of magnetic bead-based peptide capture and analysis of preanalytical variables.

Guidelines for CSF Processing and Biobanking Proteomics Clin Appl 1(11):1385–1392. https://doi.org/10.1002/prca.200700330 55. Berven FS, Kroksveen AC, Berle M, Rajalahti T, Flikka K, Arneberg R, Myhr KM, Vedeler C, Kvalheim OM, Ulvik RJ (2007) Pre-analytical influence on the low molecular weight cerebrospinal fluid proteome. Proteomics Clin Appl 1(7):699–711. https://doi. org/10.1002/prca.200700126 56. Aasebo E, Opsahl JA, Bjorlykke Y, Myhr KM, Kroksveen AC, Berven FS (2014) Effects of blood contamination and the rostro-caudal gradient on the human cerebrospinal fluid proteome. PLoS One 9(3):e90429. https://doi. org/10.1371/journal.pone.0090429 57. O’Bryant SE, Gupta V, Henriksen K, Edwards M, Jeromin A, Lista S, Bazenet C, Soares H, Lovestone S, Hampel H, Montine T, Blennow K, Foroud T, Carrillo M, Graff-Radford N, Laske C, Breteler M, Shaw L, Trojanowski JQ, Schupf N, Rissman RA, Fagan AM, Oberoi P, Umek R, Weiner MW, Grammas P, Posner H, Martins R, Star B, groups Bw (2015) Guidelines for the standardization of preanalytic variables for blood-based biomarker studies in Alzheimer’s disease research. Alzheimers Dement 11(5):549–560. https://doi.org/10. 1016/j.jalz.2014.08.099 58. Fehlbaum-Beurdeley P, Jarrige-Le Prado AC, Pallares D, Carriere J, Guihal C, Soucaille C, Rouet F, Drouin D, Sol O, Jordan H, Wu D, Lei L, Einstein R, Schweighoffer F, Bracco L (2010) Toward an Alzheimer’s disease diagnosis via high-resolution blood gene expression. Alzheimers Dement 6(1):25–38. https://doi. org/10.1016/j.jalz.2009.07.001 59. Koel-Simmelink MJ, Vennegoor A, Killestein J, Blankenstein MA, Norgren N, Korth C, Teunissen CE (2014) The impact of pre-analytical variables on the stability of neurofilament proteins in CSF, determined by a novel validated SinglePlex Luminex assay and ELISA. J Immunol Methods 402(1-2):43–49. https://doi. org/10.1016/j.jim.2013.11.008 60. Willemse EAJ, Durieux-Lu S, van der Flier WM, Pijnenburg YAL, de Jonge R, Teunissen CE (2016) Stability of progranulin under preanalytical conditions in serum and cerebrospinal fluid. J Alzheimers Dis 53(1):107–116 61. Persichetti E, Chiasserini D, Parnetti L, Eusebi P, Paciotti S, De Carlo C, Codini M, Tambasco N, Rossi A, El-Agnaf OM, Calabresi P, Beccari T (2014) Factors influencing the measurement of lysosomal enzymes activity in human cerebrospinal fluid. PLoS One 9(7):e101453. https://doi.org/10. 1371/journal.pone.0101453

49

62. Bruegel M, Planert M, Baumann S, Focke A, Bergh FT, Leichtle A, Ceglarek U, Thiery J, Fiedler GM (2009) Standardized peptidome profiling of human cerebrospinal fluid by magnetic bead separation and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. J Proteome 72(4):608–615. https:// doi.org/10.1016/j.jprot.2008.11.018 63. Simonsen AH, Bahl JM, Danborg PB, Lindstrom V, Larsen SO, Grubb A, Heegaard NH, Waldemar G (2013) Pre-analytical factors influencing the stability of cerebrospinal fluid proteins. J Neurosci Methods 215 (2):234–240. https://doi.org/10.1016/j. jneumeth.2013.03.011 64. Klener J, Hofbauerova K, Bartos A, Ricny J, Ripova D, Kopecky V (2014) Instability of cerebrospinal fluid after delayed storage and repeated freezing: a holistic study by drop coating deposition Raman spectroscopy. Clin Chem Lab Med 52(5):657–664. https://doi. org/10.1515/cclm-2013-0800 65. Rosenling T, Slim CL, Christin C, Coulier L, Shi S, Stoop MP, Bosman J, Suits F, Horvatovich PL, Stockhofe-Zurwieden N, Vreeken R, Hankemeier T, van Gool AJ, Luider TM, Bischoff R (2009) The effect of preanalytical factors on stability of the proteome and selected metabolites in CSF. J Proteome Res 8 (12):5511–5522 66. Toombs J, Paterson RW, Lunn MP, Nicholas JM, Fox NC, Chapman MD, Schott JM, Zetterberg H (2013) Identification of an important potential confound in CSF AD studies: aliquot volume. Clin Chem Lab Med 51 (12):2311–2317. https://doi.org/10.1515/ cclm-2013-0293 67. Vanderstichele HM, Janelidze S, Demeyer L, Coart E, Stoops E, Herbst V, Mauroo K, Brix B, Hansson O (2016) Optimized standard operating procedures for the analysis of cerebrospinal fluid Abeta42 and the ratios of Abeta isoforms using low protein binding tubes. J Alzheimers Dis 53(3):1121–1132. https:// doi.org/10.3233/JAD-160286 68. Delaby C, Munoz L, Torres S, Nadal A, Le Bastard N, Lehmann S, Lleo A, Alcolea D (2019) Impact of CSF storage volume on the analysis of Alzheimer’s disease biomarkers on an automated platform. Clin Chim Acta 490:98–101. https://doi.org/10.1016/j.cca. 2018.12.021 69. Willemse EA, Koel-Simmelink MJ, DurieuxLu S, van der Flier WM, Teunissen CE (2015) Standard biobanking conditions prevent evaporation of body fluid samples. Clin Chim Acta 442:141–145. https://doi.org/10.1016/j. cca.2015.01.036

50

Yanaika S. Hok-A-Hin et al.

70. Leitao MJ, Baldeiras I, Herukka SK, Pikkarainen M, Leinonen V, Simonsen AH, Perret-Liaudet A, Fourier A, Quadrio I, Veiga PM, de Oliveira CR (2015) Chasing the effects of pre-analytical confounders - a multicenter study on CSF-AD biomarkers. Front Neurol 6:153. https://doi.org/10.3389/fneur.2015. 00153 71. Poulsen K, Bahl JM, Tanassi JT, Simonsen AH, Heegaard NH (2012) Characterization and stability of transthyretin isoforms in cerebrospinal fluid examined by immunoprecipitation and high-resolution mass spectrometry of intact protein. Methods 56(2):284–292. https://doi.org/10.1016/j.ymeth.2011.12. 009 72. Carrette O, Burkhard PR, Hughes S, Hochstrasser DF, Sanchez JC (2005) Truncated cystatin C in cerebrospiral fluid: technical [corrected] artefact or biological process? Proteomics 5(12):3060–3065. https://doi.org/10. 1002/pmic.200402039 73. Willemse EAJ, van Uffelen KWJ, van der Flier WM, Teunissen CE (2017) Effect of long-term storage in biobanks on cerebrospinal fluid biomarker Abeta1-42, T-tau, and P-tau values. Alzheimers Dement (Amst) 8:45–50. https:// doi.org/10.1016/j.dadm.2017.03.005

74. van der Flier WM, Pijnenburg YA, Prins N, Lemstra AW, Bouwman FH, Teunissen CE, van Berckel BN, Stam CJ, Barkhof F, Visser PJ, van Egmond E, Scheltens P (2014) Optimizing patient care and research: the Amsterdam dementia cohort. J Alzheimers Dis 41 (1):313–327. https://doi.org/10.3233/JAD132306 75. SomaLogic Inc., SOMAscan technical note 76. Molinuevo JL, Ayton S, Batrla R, Bednar MM, Bittner T, Cummings J, Fagan AM, Hampel H, Mielke MM, Mikulskis A, O’Bryant S, Scheltens P, Sevigny J, Shaw LM, Soares HD, Tong G, Trojanowski JQ, Zetterberg H, Blennow K (2018) Current state of Alzheimer’s fluid biomarkers. Acta Neuropathol 136 (6):821–853. https://doi.org/10.1007/ s00401-018-1932-x 77. Kvartsberg H, Portelius E, Andreasson U, Brinkmalm G, Hellwig K, Lelental N, Kornhuber J, Hansson O, Minthon L, Spitzer P, Maler JM, Zetterberg H, Blennow K, Lewczuk P (2015) Characterization of the postsynaptic protein neurogranin in paired cerebrospinal fluid and plasma samples from Alzheimer’s disease patients and healthy controls. Alzheimers Res Ther 7(1):40. https:// doi.org/10.1186/s13195-015-0124-3

Chapter 3 Functional Analyses of Embryonic Cerebrospinal Fluid Proteins Teresa Caprile, Francisco Lamus, Marı´a Isabel Alonso, Herna´n Montecinos, and Angel Gato Abstract The embryonic cerebrospinal fluid (eCSF) influences neuroepithelial cell behavior, affecting proliferation, differentiation, and survival. One major question to resolve in the field is to precisely describe the eCSF molecules responsible and to understand how these molecules interact in order to exert their functions. Here we describe an in vitro protocol to analyze the influence of eCSF components on neuroepithelium development. Key words Embryonic cerebrospinal fluid, Development, Protein interaction, Chick embryo, Neurogenesis, Mesencephalic explant

1

Introduction After anterior neuropore closure, the cranial region of the neural tube enlarges and generates the encephalic vesicles. During early developmental stages, these vesicles are delineated by the neuroepithelium, a pseudostratified epithelium that will eventually generate all the neurons and glial cells of the anterior adult central nervous system. The brain vesicles are filled with embryonic cerebrospinal fluid (eCSF), which plays important roles in encephalic development, regulating the survival, proliferation, and differentiation of the neuroepithelial progenitors [1–3]. At the time of maximum proliferation and differentiation of neuroepithelial progenitor cells, the vertebrate eCSF displays a dynamic expression pattern of several proteins including essential growth and survival factors [3, 4] such as fibroblast growth factors (FGFs) [5], bone morphogenetic proteins (BMP) [6], lipoproteins [7], and subcommissural organ spondin (SCO-sp) [8] among others. The importance of such diffusible factors has been demonstrated in vitro on embryonic mesencephalic and cortical explants which only develop

Enrique Santamarı´a and Joaquı´n Ferna´ndez-Irigoyen (eds.), Cerebrospinal Fluid (CSF) Proteomics: Methods and Protocols, Methods in Molecular Biology, vol. 2044, https://doi.org/10.1007/978-1-4939-9706-0_3, © Springer Science+Business Media, LLC, part of Springer Nature 2019

51

52

Teresa Caprile et al.

normally in the presence of eCSF [9]. Similar results were observed with adult brain neural stem cells, which remain competent when responding to the neurogenic influence of eCSF [9]. By contrast, the adult CSF loses the neurogenic inductive properties, illustrating the crucial importance to study the molecular composition and functions of this fluid at embryonic stages. Here we describe a detailed protocol to study the neurogenic and proliferative effects of eCSF factors on mesencephalic embryonic explants and a guide to adapt this protocol to similar studies, such as the analysis of functional eCSF’s protein interaction.

2 2.1

Materials Equipment

1. Egg incubator calibrated at 37–39  C. 2. Biological Safety Cabinet class II. 3. Forced-air incubator at 37  C and 5% CO2. 4. Autoclaved dissection tools: thin forceps (Dumont #5), ophthalmology surgery micro-scissors, and thin tungsten needle. 5. Stereo zoom microscope. 6. Borosilicate glass capillaries (1.5 mm O.D.  0.86 mm I.D.). 7. Vertical micropipette puller. 8. Micro-aspirator device (PLI-100 pico-injector, Harvard Apparatus) under micromanipulator control (Leica Micromanipulator). 9. Nitrocellulose membrane (filter type 0.0.8 μm AABP from Millipore). 10. Steel grille. 11. Teflon rings of 2 mm high and 15 mm diameter. 12. Four wells in vitro culture plates 15 mm diameter (NUNC). 13. PAP pen. 14. Chemical hood. 15. Laboratory Freeze Dryer.

2.2 Reagents and Materials

1. Fertilized chick eggs from stage 20 to 25 according to Hamburger and Hamilton (H.H.). 2. Ringer’s solution: NaCl 7.2 g, CaCl2 (anhydrous) 0.17 g or CaCl2·2H2O 0.23 g, KCl 0.37 g; make up to 1 liter with deionized water, autoclave, and store at 4  C. 3. DMEM–F12 medium. 4. Carnoy’s fluid: Prepare this fixative in a hood just before use. In 50 ml falcon tube, add 18 ml of ethanol, 3 ml of acetic acid glacial, and 9 ml of chloroform.

Functional Analyses of Embryonic Cerebrospinal Fluid Proteins

53

5. Tris-phosphate buffer: Add 1.2 g·Na2HPO4 anhydrous (8.4 mM), 0.48 g·KH2PO4 anhydrous (3.5 mM), 7.0 g·NaCl (120 mM), and 1.25 g TRIS (10 mM). Adjust pH to 7.8 with the appropriate volume of concentrated HCl. Bring final volume to 1 l with deionized water. 6. Tris-phosphate-BSA 1%: Add 1 g of albumin serum bovine to 100 ml of Tris-phosphate buffer, stir until clear, and if necessary, heat the solution to 40  C. 7. Protein of the eCSF to analyze. 8. Primary antibody against the protein to analyze, anti-BrdU (G3G4, Developmental Studies Hybridoma Bank, University of Iowa, Iowa City, IA) and anti-βIII tubulin (clone Tuj1, Promega, Madison, WI, USA). 9. Secondary antibody: Alexa Fluor 546 Goat anti-Mouse (Invitrogen, Carlsbad, CA, USA). 10. Nuclear stain: TO-PRO-3 (Invitrogen, Carlsbad, CA, USA).

3 3.1

Methods eCSF Aspiration

Here we described our eCSF extraction technique, which is based on a careful CSF microaspiration from the mesencephalic cavity. It is necessary to use several chick embryos from the same developmental stage until enough eCSF is obtained for subsequent analysis or experimental explants culture. We employed stages ranging from E20 H.H. to 25 H.H. which coincides with the period of brain development before choroid plexus functional development. 1. Put the fertilized eggs in the incubator for 3–4.5 days (H.H.20–25) depending of the desired stage. 2. In order to visualize the embryo, open an upper window in the eggshell in horizontal position. Check that the embryo is at the desired stage. 3. Cut the embryo and their vascular area using thin forceps (Dumont #5) and micro scissors and place it in a Petri dish with Ringer’s solution. 4. Eliminate the extra embryonic membranes, including the amnion, by traction with thin forceps. 5. Wash carefully the embryos in fresh Ringer’s solution and then aspirate the solution leaving the embryo on the bottom of the Petri dish. 6. Place a glass micropipette (see Note 1), connected to a microaspirator device and handled under micromanipulator control, close to the mesencephalic vesicle which is clearly identified by transillumination.

54

Teresa Caprile et al.

Fig. 1 Images set showing the procedure to obtain and culture neuroepithelial mesencephalic explants from HH 21 stage chick embryos. (a) Lateral view of the embryo showing the forceps holding and the tungsten needle (arrowhead) cutting the ectoderm at the mesencephalic-rhombencephalic isthmus (discontinuous line) and the elimination of the ectoderm by traction (white arrow). Asterisk shows the mesencephalic cavity. (b) Micro scissors cutting the mesencephalic neuroepithelium through the discontinuous line. (c) Neuroepithelial tissue handled by aspiration with a glass micropipette. (d) Opening the explant sealed borders to expose the apical surface. (e) Neuroepithelial fragment ready to be placed onto the filter membrane. (f) Neuroepithelial fragment showing the stitch-up against the filter membrane (arrowheads). (g) Neuroepithelial explant over the filter membrane supported by the Teflon ring and the steel grille, in contact with the culture media. (h) Diagram showing the disposition of culture system components

7. Introduce the tip of the micropipette until it reaches the middle of the mesencephalic cavity (asterisk in Fig. 1a) (see Note 2). 8. Make a negative pressure of 0.4 PSI in order to slowly aspirate the eCSF of the brain cavities (see Notes 3 and 4). 9. Collect the obtained eCSF in an Eppendorf tube and maintain it at 4  C to avoid protein degradation.

Functional Analyses of Embryonic Cerebrospinal Fluid Proteins

55

10. Once the required volume of eCSF is obtained, proceed to lyophilize it with a Laboratory Freeze Dryer (Telstar) device and store at 40  C until needed. 11. For use, restored the initial eCSF volume with distilled and deionized water. 3.2 Organotypic Cultures of Chick Embryos Mesencephalic Roof

The handling of the embryos and the culture procedures must be done under sterile conditions using Biological Safety Cabinet class II, autoclaved dissection tools, sterile plastic culture material, sterile solutions, and a culture room previously irradiated with ultraviolet light. 1. Prepare 15 mm wells culture dishes as follows: On the bottom of each culture well, place a 2 mm high Teflon ring. Over the ring place a thin steel grille which provides mechanical support to the explant allowing direct contact with the culture medium (Fig. 1g, h). 2. Boil in distilled water for 20 min several small squares (0.5  0.5 cm) of nitrocellulose membrane (filter type 0.8 μm AABP) to clean stain components. 3. Prepare and keep at 4  C the different culture mediums before dissecting the explants (see Subheading 3.3). 4. Proceed with the eggs as described in the eCSF aspiration section. After 3–4.5 days in the incubator, open an upper window in the egg, cut the embryo and their vascular area, and place it in a Petri dish with Ringer’s solution. Eliminate the extra embryonic membranes including the amnion by traction with thin forceps and wash carefully the embryos in fresh Ringer’s solution and then aspirate the solution leaving the embryo at Petri dish. 5. Take off the ectoderm that covers the mesencephalic vesicle. In order to avoid external influences, it is important to be careful with this step. Using a thin tungsten needle, make a transversal incision in the ectodermal surface at the level of the mesencephalon–rhombencephalon furrow, and then take off the ectoderm by anterior traction (Fig. 1a). Eliminate the mesenchymal tissue present in the basal side of this furrow with the tungsten needle. As a result of this step, the mesencephalic neuroepithelium is exposed. 6. Using thin forceps, hold the head of the embryo against the bottom of the Petri dish, avoiding movement of the head during neuroepithelium cutting (Fig. 1b). 7. Using ophthalmology surgery micro-scissors, cut the dorsal and lateral part of the mesencephalic neuroepithelium symmetrically, with the median line of the roof crossing the explants in anterior-posterior way (Fig. 1b) (see Notes 5 and 6).

56

Teresa Caprile et al.

8. Aspirate the isolated mesencephalic explants with an inverted glass pipette, and place it in a Petri dish containing a small square (0.5  0.5 cm) of Millipore filter previously boiled (Fig. 1c, d). 9. A filter paper must be placed with the dark side over the top, and the oval neuroepithelial explant must be carefully opened at the sealed borders using the tip of micro forceps in an opening movement. This procedure is performed under binocular microscope and transillumination control (Fig. 1e) (see Note 7). 10. The final procedure must be made with epi-illumination control and involve a neuroepithelial “stitch-up” in the filter paper support just giving peripheral micropunctions with a tungsten needle making the tissue penetrate and hold in the filter paper (Fig. 1f) (see Note 8). 11. Carefully place the nitrocellulose filter with the mesencephalic explant on top of the steel grille in direct contact (but not immersed) with the culture medium. The explants are now ready to initiate the in vitro culture with different conditions. 3.3 Culture Conditions

1. Maintain the mesencephalic explants at 37  C with 5% CO2 for 24 h (maximum 48 h) in the different culture mediums. One hour before to finish the culture mediums. Add 0.01 mM 5-bromo-2´-deoxyuridine (BrdU, Sigma) during the last hour of culture. 2. The required media to analyze the function of eCSF factor are: (a) Negative control: Maintain the explant in DMEM–F12 for 24 h. (b) Positive control: Maintain the explant in 20% eCSF in DMEM–F12. (c) Gain-of-function assay: Maintain the explant in presence of the protein of interest diluted in DMEM–F12 for 24 h. The concentration of the protein must be near the concentration described in native eCSF. (d) Antibody control: Maintain the explants in presence of the protein of interest diluted in DMEM–F12 for 24 h and in presence of its antibody. This point is important in order to check the functional immunoblockage of the protein since not all the antibodies efficiently result in functional inhibition. (e) Loss-of-function assay: Maintain the explant in 20% eCSF in DMEM–F12 in presence of the antibody against the protein of interest.

Functional Analyses of Embryonic Cerebrospinal Fluid Proteins

57

3. Analyze by immunohistochemistry the differentiation and proliferation of the explants maintained in each of these five conditions. 3.4 Immunohistochemistry

1. After 24 h in the respective culture medium, fix the explants in Carnoy for 20 min. 2. Wash three times for 5 min with ethanol 100% and two times in xylene. 3. Infiltrate and embed in Paraplast. 4. Orient the explant to obtain frontal sections of the mesencephalic explant. 5. Cut 5–8 μm thick sections, choose the middle sections of the explant, and float them in a water bath to stretch the sections. 6. Mount sections in a polylysine-coated glass slide and leave them at least 12 h previous to the next step to ensure the adhesion. 7. Deparaffinize the sections by two successive xylene baths, 5 min each. 8. Hydrate the sections by passing through decreasing concentration of alcohol baths: two changes of absolute ethanol, 5 min each, 95% alcohol for 2 min, and 70% alcohol for 2 min. 9. Wash briefly in distilled water. 10. Place the slides in Tris-buffer phosphate for 10 min, and block in TRIS-BSA 1% for 1 h. 11. Using the PAP pen, make a circle in the slide around the explant section. Inside the circle put a drop (30 μl approx.) of the mouse monoclonal primary antibodies raised against antiBrdU (G3G4, Developmental Studies Hybridoma Bank, University of Iowa, Iowa City, IA) 1:2 in Tris-BSA 1% or the βIII tubulin 1:200 (clone Tuj1, Promega, Madison, WI, USA). Incubate overnight. 12. Rinse slides three times in Tris-buffer, 10 min each. 13. Incubate with To-PRO 3 (1:1000) and the second antibody Alexa Fluor 546 Goat anti-Mouse (Invitrogen, Carlsbad, CA) 1:100 in Tris-BSA 1% for 2 h at room temperature. From this step forward, slides should be protected from light. 14. Rinse slides three times in Tris-buffer, 10 min each. 15. Mount a coverslip on glass slides with Fluoromount. 16. Using a confocal microscope, observe and photograph the whole explant in order to analyze and compare the immunostaining with the different culture conditions.

58

Teresa Caprile et al.

3.5 Proliferation and Differentiation Analysis

1. Using the ImageJ program (free download available at https:// imagej.nih.gov/ij), measure the total explant area. 2. Transform the image obtained with the anti-BrDu antibody to binary image and count total number of particles (number of nuclei). Show on a graph the proliferation as number of positive nucleus versus total area. 3. Transform the image obtained with anti-Tubulin βIII to binary image and measure the area positive for the signal. Express the differentiation factor as positive area versus total area ratio. 4. Each condition is performed in triplicate, error bars represent s. e.m., and statistical analyses are performed using ANOVA. Differences are considered significant for p < 0.05.

3.6 Alternative Experimental Approach

As we stated before, the main objective of this technique was to maintain dissected neuroepithelium tissue (from earliest stages of development) in an in vitro culture system, and despite the limited culture duration (not more than 48 h), it is a critical period to study the neuroepithelial cells response to different experimental conditions because their intense proliferative and neurogenic activity. This technique, together with the eCSF micro-aspiration from embryonic brain cavity, was initially developed to study the direct influence of eCSF on neuroepithelial cell behavior by addition or not of eCSF to the culture medium or locally by microimplantation of latex microbeads soaked in eCSF [1, 10]. In the detailed protocol, the role of some specific components can be studied by addition to the defined culture media or by immunoblocking of the biological activity in eCSF [5, 8]. The functional interaction of two eCSF proteins can be also studied by this method, by the addition of both proteins simultaneously, or by studying if the immunoblockage of one protein also generates a functional blockage of the other protein (suggesting that both proteins form a complex) [11]. Alternatively, other experimental strategies can be developed based on this technique. For instance, the mesencephalic explants can be dissected together with the mesencephalic–rhombencephalic isthmus (a well know cell signals provider center during development) allowing the study of their direct influence on the behavior of mesencephalic cells or on gene expression [12]. Also, small fragments of other organizing centers can be explanted, such as the mesencephalic–prosencephalic isthmus, and used to generate “ectopic” explants in the mesencephalic cultured tissue and evaluate their spatial influence. Finally, biological sensors can be included in the experimental setup before the introduction of mesencephalic explants, such as F9.8 cells which allow the detection of retinoic acid-dependent activity [13, 14].

Functional Analyses of Embryonic Cerebrospinal Fluid Proteins

4

59

Notes 1. To make glass micropipettes, we use borosilicate glass capillaries (1.5 mm O.D.  0.86 mm I.D. from Harvard Apparatus). The tube end is initially pre-stretched with a Narishige puller PB7 to obtain a short micropipette cone. Afterward final stretching was applied. Finally, the micropipettes were beveled in a Narishige EG400, under gas tube blowing conditions to avoid glass obstruction, until reaching 80–100 μm inner diameter in the tip which was controlled by a Narishige MF9 microscope. Micropipettes were carefully stored in a box avoiding contact with the tip. There is also the possibility to buy the micropipettes prepulled (WPI instruments). 2. A proper bevel in the pipette tip is relevant to avoid cellular contamination during neuroepithelium puncture. 3. Put special attention to avoid the contact of the micropipette tip with the collapsing mesencephalic wall. Usually the amount of eCSF obtained from each embryo ranges from 2 to 10 μl depending on the embryonic developmental stage. 4. Alternatively, the eCSF aspiration can be done with a flexible silicone rubber nosepiece connected with a mouthpiece (Sigma A5177). 5. It is important to exclude the mesencephalic-rhombencephalic isthmic tissue in the explants to avoid interference with the isthmic signals. 6. Scissor cutting promote adherence between the neuroepithelial borders giving an oval morphology which must be preserved until tissue placement over the nitrocellulose filter in order to identify the apical surface of the neuroepithelium (Fig. 1d). 7. It is relevant to maintain visual control of the apical surface of the neuroepithelial explants which must be placed with the apical surface in contact with the filter paper support. 8. To avoid explant folds, gently make a soft traction with a tungsten needle before “stitch-up”. This step is necessary to ensure the apical contact of the explant with the culture medium through the porous nitrocellulose membrane.

Acknowledgments This work was supported by 216.031.112.1-0 VRID Enlace and 1191860 FONDECYT grants to T.C. and by Ministerio de Educacio´n y Ciencia (BFU207/6516) Junta de Castilla y Leo´n (Consejerı´a de Educacio´n, GR195) to A.G. The authors thank Claudia Montecinos for the elaboration of the Fig. 1 scheme.

60

Teresa Caprile et al.

References 1. Gato A, Moro J, Alonso M, Bueno D, De la Maza A, Martin C (2005) Embryonic cerebrospinal fluid regulates neuroepithelial survival, proliferation, and neurogenesis in chick embryos. AnatRecA Discov MolCell 284:475–484 2. Gato A, Desmond M (2009) Why the embryo still matters: CSF and the neuroepithelium as interdependent regulators of embryonic brain growth, morphogenesis and histiogenesis. DevBiol 327:263–272 3. Zappaterra MW, Lehtinen MK (2012) The cerebrospinal fluid: regulator of neurogenesis, behavior, and beyond. Cell MolLife Sci 69:2863–2878 4. Parada C, Gato A, Aparicio M, Bueno D (2006) Proteome analysis of chick embryonic cerebrospinal fluid. Proteomics 6:312–320 5. Martin C, Bueno D, Alonso M, Moro J, Martin P, Carnicero E, Gato A (2006) FGF2 plays a key role in embryonic cerebrospinal fluid trophic properties over chick embryo neuroepithelial stem cells. Dev Biol 297:402–416 6. Dattatreyamurty B, Roux E, Horbinski C, Kaplan P, Robak L, Beck H, Lein P, Higgins D, Chandrasekaran V (2001) Cerebrospinal fluid contains biologically active BMP-7. ExpNeurol 172(2):273–281 7. Parada C, Escola-Gil J, Bueno D (2008) Low-density lipoproteins from embryonic cerebrospinal fluid are required for neural differentiation. JNeurosciRes 86:2674–2684 8. Vera A, Stanic K, Montecinos H, Torrejon M, Marcellini S, Caprile T (2013) SCO-spondin from embryonic cerebrospinal fluid is required for neurogenesis during early brain development. Front Neurosci 80:1–14

9. Alonso MI, Lamus F, Carnicero E, Moro JA, de la Mano A, Ferna´ndez JMF, Desmond ME, Gato A (2017) Embryonic cerebrospinal fluid increases neurogenic activity in the brain ventricular-subventricular zone of adult mice. Front Neuroanat 11:124. https://doi.org/10. 3389/fnana.2017.00124 10. Martin C, Alonso MI, Santiago C, Moro JA, De la Mano A, Carretero R, Gato A (2009) Early embryonic brain development in rats requires the trophic influence of cerebrospinal fluid. Int J Dev Neurosci 27(7):733–740. https://doi. org/10.1016/j.ijdevneu.2009.06.002 11. Vera A, Recabal A, Saldivia N, Stanic K, Torrejon M, Montecinos H, Caprile T (2015) Interaction between SCO-spondin and low density lipoproteins from embryonic cerebrospinal fluid modulates their roles in early neurogenesis. Front Neuroanat 9:72 12. Parada C, Martı´n C, Alonso MI, Moro JA, Bueno D, Gato A (2005) Embryonic cerebrospinal fluid collaborates with the isthmic organizer to regulate mesencephalic gene expression. J Neurosci Res 82(3):333–345 13. Alonso MI, Martı´n C, Carnicero E, Bueno D, Gato A (2011) Cerebrospinal fluid control of neurogenesis induced by retinoic acid during early brain development. Dev Dyn 240 (7):1650–1659. https://doi.org/10.1002/ dvdy.22657 14. Alonso MI, Carnicero E, Carretero R, de la Mano A, Moro JA, Lamus F, Martı´n C, Gato A (2014) Retinoic acid, under cerebrospinal fluid control, induces neurogenesis during early brain development. J Dev Biol 2 (2):72–83. https://doi.org/10.3390/ jdb2020072

Chapter 4 CSF Sample Preparation for Data-Independent Acquisition Katalin Barkovits, Lars To¨nges, and Katrin Marcus Abstract To study changes in neurological diseases and to identify disease-related mechanisms or biomarkers for diagnosis, cerebrospinal fluid (CSF) is frequently used for proteomic-based discovery. In the last years, development and application of mass spectrometry (MS) techniques have made essential contributions to proteomic studies including protein identification as well as quantification. Until recently, biomarker discovery studies were performed through bottom-up proteomics utilizing data-dependent acquisition. However, drawbacks like stochastic selection of precursor ions cause the exclusion of low-abundant ions from fragmentation as well as from data analysis leading to technical variances among different samples and result in inconsistent data sets. In contrast, data-independent acquisition (DIA) enables almost complete and reproducible quantitative analysis gaining more and more interest as a method for reliable MS-based protein quantification. Besides the utilization of a proper analysis platform, a prerequisite for biomarker studies is the selection of suitable samples and sample processing strategies. Especially for CSF, blood contamination has tremendous impact on the quantitative analysis. In addition, complex processing methods such as protein or peptide fractionation prior to MS analysis can lead to variabilities that affect the reliability of the quantitative results. Here we present methods to evaluate in a first step the CSF quality in regard to blood contamination for the subsequent MS-based sample preparation and finally a DIA method for the analysis of CSF. Key words Blood contamination, In-solution digest, Data-independent acquisition, Data-dependent acquisition, Mass spectrometry

1

Introduction Cerebrospinal fluid (CSF) serves as a valuable specimen for biomarker discovery in the field of neurological disorders. Since it is in direct contact with the extracellular space of the brain as well as the spinal cord, disease-specific changes could be reflected in this body fluid [1, 2]. Many proteomic-based studies applied liquid chromatography-mass spectrometry (LC-MS) for CSF protein biomarker discovery to perform quantitative differential profiling in order to reveal differences in the proteome between healthy individuals and patients suffering from neurodegenerative diseases like Alzheimer’s and Parkinson’s disease [3]. However, the large dynamic range of CSF protein concentrations makes the

Enrique Santamarı´a and Joaquı´n Ferna´ndez-Irigoyen (eds.), Cerebrospinal Fluid (CSF) Proteomics: Methods and Protocols, Methods in Molecular Biology, vol. 2044, https://doi.org/10.1007/978-1-4939-9706-0_4, © Springer Science+Business Media, LLC, part of Springer Nature 2019

61

62

Katalin Barkovits et al.

identification and quantification of low-abundant potential protein biomarkers challenging [4]. Therefore, in several studies pre-analytical sample processing steps were utilized like depletion of high-abundant proteins, in gel or strong cation exchange fractionation to enhance proteome coverage [5–7]. These methods involve the possibility of introducing sample preparation variabilities. In depletion, for example, there is a potential risk that low-abundant proteins might be reduced by co-depletion from the sample, making their detection more difficult [8]. On the other hand, large amounts of starting material are required, but usually only small amounts of CSF are available. Furthermore, each additional step during sample processing leads to a reduction of reproducibility; hence approaches with less complex sample preparation are required for LC-MS-based quantitative CSF analysis [9]. Besides the sample preparation-based variances, also other aspects like sampling strategy, sample collection, and storage can lead to variabilities affecting reliable detection of biomarkers [10]. For example, during CSF collection by lumbar punctures, unwanted vascular bleeding can occur, causing peripheral blood to contaminate CSF [11]. Usually determination of red blood cells is used to exclude blood-contaminated samples, which is only accurate if the cells are not lysed in the CSF until cell counting [12]. Hence, careful selection of the samples to be analyzed is a prerequisite for a successful quantitative LC-MS-based workflow. Moreover, the choice of an adequate LC-MS method for reproducible and accurate protein quantification must be made. Various quantitative label-free approaches were performed with CSF utilizing data-dependent acquisition (DDA), a precursor ion abundancebased quantification method. The stochastic selection of high intensive ions within this scan mode holds the risk to exclude low intensive ions from fragmentation as well as from data analysis [13]. This can lead to technical variances among different samples and results in inconsistent data sets. In contrast to this, the scan mode data-independent acquisition (DIA) overcomes several limitations of DDA. In particular, all detectable precursor ions are used for fragmentation and subsequent quantification ensuring the generation of comprehensive and reproducible data sets as already demonstrated by several DIA-based studies [14, 15]. Recently we could show for “native” CSF (no depletion or fractionation was performed) that DIA increased the number of identified protein groups from 600 to over 1500 compared to a conventional DDA method [16]. Moreover, utilizing a substantia nigra spectral library for CSF DIA, over 60 brain-originated proteins could be identified compared to only 11 with DDA (for more information about CSF peptide spectral library generation, see Chapter 5). In summary, DIA provides a great platform for accurate and reproducible MS-based label-free quantification of “native” CSF with the opportunity to use comprehensive spectral libraries to gain an in-depth CSF proteome analysis.

CSF Sample Preparation for DIA

2

63

Materials

2.1 Combur10-Test® Strip Analysis

1. 50 μl CSF.

2.2 In-Solution Trypsin Digest

1. 20 μl CSF.

2. Combur10-Test® strip.

2. Sample Buffer: 0.1% RapiGest SF Surfactant (Waters) in 50 mM ammonium bicarbonate. 3. 100 mM 1,4-dithiothreitol in water. 4. 250 mM iodoacetamide solution (see Note 1). 5. 0.5 μg/μl trypsin in 50 mM acetic acid. 6. 10% trifluoroacetic acid in water (see Note 2). 7. Incubator shaker with lid heating or heated chamber. 8. Microcentrifuge with adjustable rotor speed up to 16,000  g. 9. 0.5 ml reaction tubes.

2.3 DataIndependent Acquisition

3

1. Q Exactive HF mass spectrometer (Thermo Fisher).

Methods (Fig. 1)

3.1 Combur10-Test® Strip Analysis

1. Remove a test strip from the test strip tube and apply 25 μl CSF each to the ERY and the Hb test field (see Note 3). 2. After 60 s compare the reaction colors of the two test fields with the color comparison scale on the test strip tube. 3. Exclude CSF samples for subsequent LC-MS analysis with a classification of 4+ for ERY and Hb (see Note 4).

3.2 In-Solution Trypsin Digest

1. Add 20 μl sample buffer to 20 μl CSF and mix gently. 2. Add 2 μl 1,4-dithiothreitol solution to a final concentration of 5 mM. Incubate at 56  C for 20 min at 350 rpm in an incubator shaker. 3. Let the sample cool to room temperature. 4. Add 2.5 μl iodoacetamide solution to a final concentration of 15 mM. Incubate at room temperature for 30 min in the dark. 5. Add 0.8 μl trypsin solution (see Note 5). Incubate at 37  C overnight (approximately 16–18 h). 6. Stop digestion by adding 2.3 μl 10% trifluoroacetic acid to a final concentration of 0.5%.

64

Katalin Barkovits et al.

Fig. 1 Label-free proteomic workflow for DIA-based CSF analytic. “Native” CSF is used in a first step to evaluate sample quality by utilizing the Combur10-Test® strip. In addition to the standard red blood cell count, this analysis enables to reveal possible blood-contaminated CSF due to lysed red blood cells. Afterward suitable CSF is processed for the subsequent mass spectrometry-based analysis using data-independent acquisition

7. Incubate the sample at 37  C for 45 min (see Note 6). 8. Centrifuge the sample at 16,000  g for 15 min and transfer the supernatant into a new Eppendorf tube (see Note 7). 9. Determine peptide concentration, e.g., by amino acid analysis as described in Plum et al. [17], and prepare 500 ng for nanoHPLC-ESI-MS/MS analysis (see Note 8). 3.3 DataIndependent Acquisition

1. The mass spectrometer (MS) setting as tabulated below for a CSF DIA method is intended for a 90-min HPLC gradient with the solvent system: (A) 0.1% formic acid; (B) 84% acetonitrile in 0.1% formic acid and a peptide separation from 5% to 40% B with a flow rate of 400 nl per min (see Note 9). Mass spectrometer parameters for CSF DIA method: Polarity

Positive

Full MS Microscans Resolution Automatic gain control (AGC) target Maximum IT Scan range

1 35,000 3e6 80 ms 400–1200 m/z (continued)

CSF Sample Preparation for DIA

4

Polarity

Positive

DIA Microscans Resolution AGC target Maximum IT Loop count Isolation window Isolation offset Normalized collision energy

1 35,000 1e6 Auto 20 40 m/z 0 m/z 27

65

Notes 1. Prepare iodoacetamide solution freshly before use. 2. Use only high-purity trifluoroacetic acid. 3. The Combur10-Test® strip is originally designed for the analysis of urine. For the evaluation of CSF blood contamination, only the two test fields ERY and Hb are utilized. Therefore, the recommended volume of 25 μl is directly pipetted on the test fields. 4. The result of the Combur10-Test® strip should be combined with the standard values of the red blood cell (RBC) count and not evaluated as a single result. The recommended cutoff value of 4+ was evaluated in an in-house CSF blood spike-in study, which showed that it was possible to determine blood contaminations, especially if erythrocytes were lysed, which would remain undetected by a single RBC count. 5. Use an enzyme-to-sample ratio of approximately 1:20 based on the protein concentration of the sample. 6. In the manufacturer’s manual, it is described that a slight cloudiness should be observed; however for CSF samples, no cloudiness will be observed. 7. After centrifugation, no visible precipitate of the hydrolytic RapiGest SF by-products will be observed. In order to avoid transfer of the by-products, transfer only 40 μl of the supernatant into a new reaction tube. 8. Be aware that DIA data analysis with, for example, the software Spectronaut (Biognosys) requires specific standard peptides called iRT peptides, which are spiked into each individual sample prior to LC-MS analysis. 9. Parameters are specific for analysis on a Q Exactive MS system. If another MS system is used for the analysis, it may be necessary to adjust these parameters. Moreover, parameters have to be adapted when using other HPLC conditions.

66

Katalin Barkovits et al.

Acknowledgment This work was supported by the Medical Faculty at RUB (FoRUM), the European Union (NISCI, GA no. 681094), and PURE, a project of North Rhine-Westphalia, a federal German state. References 1. Shi M, Caudle WM, Zhang J (2009) Biomarker discovery in neurodegenerative diseases: a proteomic approach. Neurobiol Dis 35(2):157–164. https://doi.org/10.1016/j. nbd.2008.09.004 2. Kroksveen AC, Opsahl JA, Aye TT, Ulvik RJ, Berven FS (2011) Proteomics of human cerebrospinal fluid: discovery and verification of biomarker candidates in neurodegenerative diseases using quantitative proteomics. J Proteome 74(4):371–388. https://doi.org/10. 1016/j.jprot.2010.11.010 3. Cilento EM, Jin L, Stewart T, Shi M, Sheng L, Zhang J (2018) Mass spectrometry: a platform for biomarker discovery and validation for Alzheimer’s and Parkinson’s diseases. J Neurochem. https://doi.org/10.1111/jnc.14635 4. Roche S, Gabelle A, Lehmann S (2008) Clinical proteomics of the cerebrospinal fluid: towards the discovery of new biomarkers. Proteomics Clin Appl 2(3):428–436. https://doi. org/10.1002/prca.200780040 5. Begcevic I, Brinc D, Drabovich AP, Batruch I, Diamandis EP (2016) Identification of brainenriched proteins in the cerebrospinal fluid proteome by LC-MS/MS profiling and mining of the human protein atlas. Clin Proteomics 13:11. https://doi.org/10.1186/s12014016-9111-3 6. Zhang Y, Guo Z, Zou L, Yang Y, Zhang L, Ji N, Shao C, Sun W, Wang Y (2015) A comprehensive map and functional annotation of the normal human cerebrospinal fluid proteome. J Proteome 119:90–99. https://doi. org/10.1016/j.jprot.2015.01.017 7. Guldbrandsen A, Vethe H, Farag Y, Oveland E, Garberg H, Berle M, Myhr KM, Opsahl JA, Barsnes H, Berven FS (2014) In-depth characterization of the cerebrospinal fluid (CSF) proteome displayed through the CSF proteome resource (CSF-PR). Mol Cell Proteomics 13 (11):3152–3163. https://doi.org/10.1074/ mcp.M114.038554 8. Granger J, Siddiqui J, Copeland S, Remick D (2005) Albumin depletion of human plasma also removes low abundance proteins including

the cytokines. Proteomics 5(18):4713–4718. https://doi.org/10.1002/pmic.200401331 9. Piehowski PD, Petyuk VA, Orton DJ, Xie F, Moore RJ, Ramirez-Restrepo M, Engel A, Lieberman AP, Albin RL, Camp DG, Smith RD, Myers AJ (2013) Sources of technical variability in quantitative LC-MS proteomics: human brain tissue sample analysis. J Proteome Res 12 (5):2128–2137. https://doi.org/10.1021/ pr301146m 10. Teunissen CE, Tumani H, Engelborghs S, Mollenhauer B (2014) Biobanking of CSF: international standardization to optimize biomarker development. Clin Biochem 47 (4–5):288–292. https://doi.org/10.1016/j. clinbiochem.2013.12.024 11. Petzold A, Sharpe LT, Keir G (2006) Spectrophotometry for cerebrospinal fluid pigment analysis. Neurocrit Care 4(2):153–162. https://doi.org/10.1385/NCC:4:2:153 12. del Campo M, Mollenhauer B, Bertolotto A, Engelborghs S, Hampel H, Simonsen AH, Kapaki E, Kruse N, Le Bastard N, Lehmann S, Molinuevo JL, Parnetti L, PerretLiaudet A, Sa´ez-Valero J, Saka E, Urbani A, Vanmechelen E, Verbeek M, Visser PJ, Teunissen C (2012) Recommendations to standardize preanalytical confounding factors in Alzheimer’s and Parkinson’s disease cerebrospinal fluid biomarkers: an update. Biomark Med 6(4):419–430. https://doi.org/10. 2217/bmm.12.46 13. Law KP, Lim YP (2013) Recent advances in mass spectrometry: data independent analysis and hyper reaction monitoring. Expert Rev Proteomics 10(6):551–566. https://doi.org/ 10.1586/14789450.2013.858022 14. Hu A, Noble WS, Wolf-Yadlin A (2016) Technical advances in proteomics: new developments in data-independent acquisition. F1000Res 5. https://doi.org/10.12688/ f1000research.7042.1 15. Lin L, Zheng J, Yu Q, Chen W, Xing J, Chen C, Tian R (2018) High throughput and accurate serum proteome profiling by integrated sample preparation technology and

CSF Sample Preparation for DIA single-run data independent mass spectrometry analysis. J Proteome 174:9–16. https://doi. org/10.1016/j.jprot.2017.12.014 16. Barkovits K, Linden A, Galozzi S, Schilde L, Pacharra S, Mollenhauer B, Stoepel N, Steinbach S, May C, Uszkoreit J, Eisenacher M, Marcus K (2018) Characterization of cerebrospinal fluid via data-independent acquisition mass spectrometry. J Proteome Res 17(10):3418–3430. https://doi.org/10. 1021/acs.jproteome.8b00308

67

17. Plum S, Helling S, Theiss C, Leite REP, May C, Jacob-Filho W, Eisenacher M, Kuhlmann K, Meyer HE, Riederer P, Grinberg LT, Gerlach M, Marcus K (2013) Combined enrichment of neuromelanin granules and synaptosomes from human substantia nigra pars compacta tissue for proteomic analysis. J Proteome 94:202–206. https://doi.org/10. 1016/j.jprot.2013.07.015

Chapter 5 Sample Fractionation Techniques for CSF Peptide Spectral Library Generation Sandra Pacharra, Katrin Marcus, and Caroline May Abstract Data-independent acquisition (DIA) is becoming more prominent as a method for comprehensive proteomic analysis of clinical samples due to its ability to acquire essentially all fragment ion spectra in a single LC-ESI-MS/MS experiment. Since the direct correlation between a precursor and its fragment ions is lost when acquiring all ions in a defined m/z range, one data analysis strategy is using so-called peptide spectral libraries. These are usually generated by measuring similar biological samples in data-dependent (DDA) mode. The peptide spectral library content is a major limitation for the successful identification from DIA data. This is because a fragment ion spectrum from the sample can only be matched, and thus identified, when it is present in the peptide spectral library. In order to enhance peptide spectral library size, the sample for generating the peptide spectral library can be subjected to extended separation strategies prior to DDA. These strategies are of special relevance for biological samples containing a few very high-abundant proteins, such as CSF, as they enlarge the identification of low-abundant proteins. In instances of CSF separation, suitable methods include the 1D SDS-PAGE of proteins and high-pH reversed-phase peptide fractionation. Both methods are based on different protein/peptide characteristics, are complementary with one another, and are inexpensive and easy to establish. Ideally, DDA spectra from samples generated with both methods combine to achieve a comprehensive spectral library. Key words Peptide fractionation, SDS-PAGE, In-gel digest, In-solution digest, High-pH reversed phase, Spectral library, DIA

1

Introduction In the past, label-free proteomic studies were typically performed in data-dependent acquisition mode (DDA), where a fixed number of precursor ions were selected for fragmentation (TopN method). These ions were normally individual to the sample and the most abundant ones within the sample. Therefore, the fragmentation following the identification of the lower-abundant ions using DDA was often not satisfactory. This is because low-abundant peptides, belonging to low-abundant proteins, are often identified as unrepresented.

Enrique Santamarı´a and Joaquı´n Ferna´ndez-Irigoyen (eds.), Cerebrospinal Fluid (CSF) Proteomics: Methods and Protocols, Methods in Molecular Biology, vol. 2044, https://doi.org/10.1007/978-1-4939-9706-0_5, © Springer Science+Business Media, LLC, part of Springer Nature 2019

69

70

Sandra Pacharra et al.

To circumvent this problem, a data-independent acquisition (DIA)-based approach can be applied (for a detailed description, see [1]). As opposed to DDA, in DIA mass spectrometric analysis, all ions in a predefined m/z range (or, time window) are fragmented. This, in principle, leads to a complete map of the fragment ion spectra. Due to this, DIA is gaining progressively more interest as a method for reliable, as well as comprehensive, label-free quantification of proteins. Indeed, it has already been applied successfully in several biomarker discovery and clinical studies [2–6]. However, it should be noted that the precursor to fragment ion assignment is lost during DIA, because the generated fragment ion spectra may be a result of several precursor ions. Therefore, precursor identification from DIA data requires more complex data analysis strategies, which can be achieved either in a direct interpretation mode or by using so-called peptide spectral libraries [7]. These spectral libraries are usually generated by measuring similar biological samples in the DDA mode and storing the elution profile information, as well as the precursor-fragment characteristics [8]. It is common to combine repeated DDA measurements of a representative sample (or sample pool) into a peptide spectral library in order to maximize peptide coverage [9]. This approach still suffers from an incomplete inclusion of particularly low-abundant peptides, due to the typical TopN precursor selection in DDA experiments. However, peptide spectral library depth can be improved by applying sample pre-fractionation prior to the DDA measurement. This approach is especially recommended when samples cover a broad range of protein abundance, such as CSF where only a few proteins are highly abundant, with the rest being of low abundance. In order to generate a comprehensive CSF peptide spectral library, it is recommended that the CSF pool be fractionated before DDA measurements are taken. This can be done on peptide as well as protein level. For protein levels, one possibility is separation using one-dimensional sodium dodecyl sulfate polyacrylamide gel electrophoresis (1D SDS-PAGE), followed by in-gel trypsin digestion. In terms of peptide level, high-pH reversed-phase peptide fractionation can be used after in-solution trypsin digest. Ideally, the DDA measurements of all these fractions are combined into one comprehensive CSF peptide spectral library. This chapter describes an optimized strategy for CSF peptide spectral library generation (Fig. 1).

2

Materials

2.1 Sample Preparation

1. 120 μl CSF. 2. Centrifuge.

CSF Peptide Spectral Library Generation

71

Fig. 1 Graphical workflow overview. For peptide spectral library generation, two separation strategies were applied for CSF. On the one hand, CSF was first fractionated by 1D-SDS PAGE followed by in-gel digestion and mass spectrometric measurements of the peptide of single bands in DDA mode. On the other hand, CSF was digested in-solution, fractionated by high-pH reversed-phase peptide fractionation, followed by mass spectrometric measurement of each fraction in DDA mode. Combining spectra from both strategies enhances peptide spectral library content (∗modified image taken from http://planetorbitrap.com/, Thermo Fisher Scientific Inc., USA)

3.

20  C freezer.

4. Icebox. 5. 0.5 ml reaction tubes. 6. 100% acetone. 2.2 Protein Separation Using 1D SDS-PAGE

1. LDS sample buffer pH 8.5: 26.5 mM Tris–HCl, 35.25 mM Tris base, 2% LDS, 10% glycerol, 0.055 mM Coomassie blue G250, and 0.045 mM phenol red. 2. 2 mM DTT solution. 3. Vortexer. 4. Thermomixer. 5. Icebox. 6. Empty gel cassettes, size 8 cm  8 cm (mini size), 1.5 mm thickness. 7. Gel cassette handcast system. 8. Horizontal electrophoresis apparatus inclusive electrodes. 9. Gel comb.

72

Sandra Pacharra et al.

10. Power supply. 11. 4  C refrigerator. 12. Separation gel buffer for 12% gels: 357 mM Bis-Tris, 40% (v/v) acrylamide/bis-acrylamide (30%/0.4%), 0.1% (w/v) APS (40%). 13. Running buffer: 50 mM MOPS, 50 mM Tris, 3.5 mM SDS, 2.7 mM EDTA. 2.3

Gel Staining

1. Plastic box. 2. Staining solution SimplyBlue™ SafeStain, Thermo Fisher Scientific.

2.4 In-Gel Trypsin Digest

1. Glass vials. 2. Timer. 3. Thermomixer. 4. Scalpel. 5. Red caps. 6. Vacuum concentrator. 7. Buffer A: 10 mM ammonium bicarbonate buffer. 8. Buffer B: 5 mM ammonium bicarbonate buffer in 50% (v/v) acetonitrile. 9. 50 mM iodoacetamide solution. 10. Trypsin solution: 0.033 μg/μl trypsin in 50 mM ammonium bicarbonate.

2.5 Peptide Extraction

1. Ultrasonic bath. 2. Glass vials for mass spectrometric measurements. 3. Hamilton syringe. 4. Extraction solution: 0.1% trifluoroacetic acid, 20% acetonitrile.

2.6 In-Solution Trypsin Digest

1. DIGE solution: 30 mM TRIS hydrochloride pH 8.5, 7 M urea, 2 M thiourea. 2. Sample buffer: 50 mM ammonium bicarbonate. 3. Incubator shaker. 4. 1,4-Dithiothreitol solution: 200 mM 1,4-dithiothreitol in water. 5. 550 mM iodoacetamide solution (see Note 1). 6. 1 μg/μl trypsin in 50 mM acetic acid. 7. Incubator shaker with lid heating or heated chamber. 8. 10% trifluoroacetic acid in water. 9. Microcentrifuge with adjustable rotor speed up to 16,000  g.

CSF Peptide Spectral Library Generation

73

Table 1 Preparation of the high-pH step-elution solutions. 8 triethylamine solutions with increasing acetonitrile content, 1 ml each Number

2.7 High-pH Reversed-Phase Peptide Fractionation

Acetonitrile (%)

Acetonitrile (μl)

0.1% Triethylamine (μl)

1

5.0

50

950

2

7.5

75

925

3

10.0

100

900

4

12.5

125

875

5

15.0

150

850

6

17.5

175

825

7

20.0

200

800

8

50.0

500

500

1. Vacuum concentrator. 2. Equilibration solution: 0.1% trifluoroacetic acid in water. 3. Microcentrifuge with adjustable rotor speed up to 16,000  g. 4. Pierce High-pH Reversed-Phase Peptide Fractionation Kit: Reversed-phase fractionation spin columns and 0.1% triethylamine in water. 5. High-pH step-elution solutions: 8 triethylamine solutions with increasing acetonitrile content according to Table 1 (1 ml each).

3

Methods

3.1 Sample Preparation

1. To remove the remaining lipids, the 120 μl CSF is displaced with 1:4 (v/v) acetone and incubated at 20  C overnight in a freezer. 2. To remove the acetone, the sample is centrifuged for 4 min at 5000  g and 4  C and the supernatant is discarded. 3. The sediment is air-dried in the opened reaction tube for 5 min on ice.

3.2 Protein Separation Using 1D SDS-PAGE

1. The sediment is solubilized in a 20 μl LDS sample buffer. 2. For reduction of the disulfide bridges and denaturation, 1:10 (v/v) 2 mM DTT is added, followed by incubation for 10 min at 350 rpm and 90  C in a thermomixer. 3. For removal of the insoluble components, and the condensation of the buffer in the lid of the reaction tube, the sample is again centrifuged for 4 min at 5000  g at RT.

74

Sandra Pacharra et al.

4. The resulting supernatant is the sample used for 1D SDS-PAGE. 5. Add the APS solution to the separation gel buffer and shake it gently to avoid air bubbles. 6. Cast the gel cassette top-down in the gel cassette handcast station until 0.5 cm below the top. 7. Air bubbles must be prevented in every step to avoid disturbance of the focusing. 8. Place the comb between the gel cassettes on the top without removing the gel cassette of the handcast station. 9. For polymerization, incubate the gel cassette in the handcast station for 45 min. 10. All steps are performed at RT. 11. Gels can be stored for up to 1 week at 4  C in a refrigerator packed in a wet tissue to avoid dehydration of the gel surface. 12. For 1D SDS-PAGE, fill up the electrophoresis apparatus with the running buffer. 13. Pipette the CSF sample into the sample pocket. Air bubbles must be prevented to avoid disturbance of the focusing. 14. Start focusing by applying a stepwise voltage gradient: 50 V for 15 min and 150 V for 45 min at room temperature. 15. After focusing, the gel can be stained with SimplyBlue™ SafeStain according to the manufacturer’s protocol. 3.3 In-Gel Trypsin Digest

1. Cut each lane into 12 single bands per lane. 2. Transfer each single band into a single glass vial. 3. For destaining and adjusting of the pH for trypsin digest, incubate the gel pieces in 20 μl ammonium bicarbonate buffer for 10 min. 4. Remove the ammonium bicarbonate buffer. 5. Add 50% (v/v) 50 mM ammonium bicarbonate with 50% (v/v) acetonitrile to the bands and incubate again for 10 min. 6. Repeat the incubation cycle three times in total. 7. For reduction and modification of the disulfide bridges, after the second incubation with 50 mM ammonium bicarbonate, discard the ammonium bicarbonate buffer. 8. Each glass vial is filled with 50 μl of 10 mM dithiothreitol, followed by incubation for 1 h at 350 rpm and 56  C in a thermomixer.

CSF Peptide Spectral Library Generation

75

9. Discard the dithiothreitol solution, and fill up each glass vial with 50 μl of 50 mM iodoacetamide, followed by incubation for 45 min at room temperature in the dark. 10. Discard the iodoacetamide solution, and continue the destaining protocol with 50% (v/v) 50 mM ammonium bicarbonate with 50% (v/v) 100% acetonitrile. 3.4 Peptide Extraction

1. The gel pieces are dried in a vacuum concentrator and resuspended in 6 μl of trypsin solution. Digestion is performed overnight. 2. Peptides are eluted by incubating the gel pieces twice for 15 min in 30 μl extraction solution in an ice-cold ultrasonic bath. 3. All of the peptide extracts, resulting from the bands of the complete SDS gel electrophoresis, are used for nano-HPLCESI-MS/MS (see Note 2).

3.5 In-Solution Trypsin Digest

1. The sediment from Subheading 3.1 (approximately 50 μg protein) is solubilized in 20 μl DIGE solution. 2. Dilute your sample (see Note 3) with 26.4 μl sample buffer to a final volume of 46.4 μl. 3. Add 1.25 μl 1,4-dithiothreitol solution to a final concentration of 5 mM. Incubate at 56  C for 20 min at 350 rpm in an incubator shaker. 4. Add 1.38 μl iodoacetamide solution to a final concentration of 15 mM. Incubate at ambient temperature for 30 min in the dark. 5. Dilute with 50 μl sample buffer before adding 1 μl trypsin solution (see Note 4). Incubate at 37  C for 16–18 h. 6. Add 2.5 μl 10% trifluoroacetic acid and centrifuge at 16,000  g for 10 min.

3.6 High-pH Reversed-Phase Peptide Fractionation

1. Dry the peptide solution in a vacuum concentrator and dissolve in 300 μl equilibration solution. Spin the sample at 16,000  g for 5 min. 2. Follow the instructions of the Pierce High-pH Reversed-Phase Peptide Fractionation Kit user manual for column conditioning and fractionation of digest sample. 3. Dry all resulting fractions in a vacuum concentrator and resuspend in 60 μl equilibration solution. 4. Determine peptide concentration (see Note 5), e.g., by amino acid analysis following acidic hydrolysis, and prepare 200–800 ng of each fraction for nano-HPLC-ESI-MS/MS analysis (see Note 2).

76

4

Sandra Pacharra et al.

Notes 1. Prepare iodoacetamide solution freshly before use. 2. For DIA spectral library generation, it is advisable to add reference peptides (e.g., the iRT peptides) to each sample before measurement. When using iRT peptides, dilute solubilized iRT peptides 1:10 in 0.1% TFA and add 1 μl of this solution to each sample. 3. 10–100 μg of protein sample is suitable for high-pH reversedphase peptide fractionation. 4. Use an enzyme-to-sample ratio of approximately 1:50. 5. Between different samples, the peptide concentration per fraction might vary depending on protein abundance differences.

Acknowledgment This work was supported by the Deutsche Parkinson Gesellschaft, Medical Faculty at RUB (FoRUM); the European Union (NISCI, GA no. 681094); the German Federal Ministry of Education and Research (WTZ with Brazil, FKZ 01DN14023); the HUPO Brain Proteome Project (HBPP), PURE, a project of North RhineWestphalia, a federal German state; and de.NBI, a project of the German Federal Ministry of Education and Research [FKZ 031 A 534A]. References 1. Ludwig C, Gillet L, Rosenberger G, Amon S, Collins BC, Aebersold R (2018) Dataindependent acquisition-based SWATH-MS for quantitative proteomics: a tutorial. Mol Syst Biol 14(8):e8126. https://doi.org/10.15252/msb. 20178126 2. Anjo SI, Santa C, Manadas B (2017) SWATHMS as a tool for biomarker discovery: from basic research to clinical applications. Proteomics 17 (3–4). https://doi.org/10.1002/pmic. 201600278 3. Muntel J, Xuan Y, Berger ST, Reiter L, Bachur R, Kentsis A, Steen H (2015) Advancing urinary protein biomarker discovery by dataindependent acquisition on a QuadrupoleOrbitrap mass spectrometer. J Proteome Res 14(11):4752–4762. https://doi.org/10.1021/ acs.jproteome.5b00826 4. Liu Y, Buil A, Collins BC, Gillet LC, Blum LC, Cheng LY, Vitek O, Mouritsen J, Lachance G, Spector TD, Dermitzakis ET, Aebersold R (2015) Quantitative variability of 342 plasma

proteins in a human twin population. Mol Syst Biol 11(1):786. https://doi.org/10.15252/ msb.20145728 5. Ahrman E, Hallgren O, Malmstrom L, Hedstrom U, Malmstrom A, Bjermer L, Zhou XH, Westergren-Thorsson G, Malmstrom J (2018) Quantitative proteomic characterization of the lung extracellular matrix in chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis. J Proteome. https://doi. org/10.1016/j.jprot.2018.02.027 6. Song Y, Zhong L, Zhou J, Lu M, Xing T, Ma L, Shen J (2017) Data-independent acquisitionbased quantitative proteomic analysis reveals potential biomarkers of kidney cancer. Proteomics Clin Appl 11(11–12). https://doi.org/10. 1002/prca.201700066 7. Bilbao A, Varesio E, Luban J, Strambio-DeCastillia C, Hopfgartner G, Muller M, Lisacek F (2015) Processing strategies and software solutions for data-independent acquisition in mass spectrometry. Proteomics 15

CSF Peptide Spectral Library Generation (5–6):964–980. https://doi.org/10.1002/ pmic.201400323 8. Gillet LC, Navarro P, Tate S, Rost H, Selevsek N, Reiter L, Bonner R, Aebersold R (2012) Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol Cell Proteomics 11(6). https://doi.org/10.1074/mcp.O111.016717

77

9. Bruderer R, Bernhardt OM, Gandhi T, Miladinovic SM, Cheng LY, Messner S, Ehrenberger T, Zanotelli V, Butscheid Y, Escher C, Vitek O, Rinner O, Reiter L (2015) Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated threedimensional liver microtissues. Mol Cell Proteomics 14(5):1400–1410. https://doi.org/10. 1074/mcp.M114.044305

Part III Shotgun Cerebrospinal Fluid Proteomics

Chapter 6 Application of 2D-DIGE and iTRAQ Workflows to Analyze CSF in Gliomas Aishwarya A. Rao, Kanika Mehta, Nikita Gahoi, and Sanjeeva Srivastava Abstract Proteomics is an indispensable tool for disease biomarker discovery. It is widely used for the analysis of biological fluids such as cerebrospinal fluid (CSF), blood, and saliva, which further aids in our understanding of disease incidence and progression. CSF is often the biospecimen of choice in case of intracranial tumors, as rapid changes in the tumor microenvironment can be easily assessed due to its close proximity to the brain. On the contrary studies comprising of serum or plasma samples do not truly reflect the underlying molecular alterations due to the presence of protective blood-brain barrier. We have described in here the detailed workflows for two advanced proteomics techniques, namely, 2D-DIGE (two-dimensional difference in-gel electrophoresis) and iTRAQ (isobaric tag for relative and absolute quantitation), for CSF analysis. Both of these techniques are very sensitive and widely used for quantitative proteomics analysis. Key words Sample preparation, DIGE, Cyanine dyes, Internal standard, Isobaric labeling, iTRAQ, Reaction quenching, Sample fractionation, Mass spectrometric analysis

1

Introduction Gliomas are one of the most aggressive and lethal central nervous system (CNS) tumors of glial cell origin [1], causing extensive neurological destruction [2]. They are known to exhibit highly heterogeneous morphology [3], which is the main cause of poor prognosis, leading to substantial morbidity [4]. Gliomas represent 26% of all primary brain and CNS tumors and 81% of malignant tumors [5]. Additionally, they account for ~46.5% of tumors in children and adolescents aged 0–19 years, highlighting the need for a robust, accurate, and early diagnosis. The current screening modalities for gliomas, viz., radiological investigations and biopsy, are cumbersome, lack molecular signatures, and offer limited patient

Aishwarya A. Rao and Kanika Mehta are the joint first authors. Enrique Santamarı´a and Joaquı´n Ferna´ndez-Irigoyen (eds.), Cerebrospinal Fluid (CSF) Proteomics: Methods and Protocols, Methods in Molecular Biology, vol. 2044, https://doi.org/10.1007/978-1-4939-9706-0_6, © Springer Science+Business Media, LLC, part of Springer Nature 2019

81

82

Aishwarya A. Rao et al.

compliance and hence should be accompanied with more discerning surrogate approaches. In this light, studies involving biological fluids, such as serum [6, 7], cerebrospinal fluid (CSF) [8], and urine [9], have gained huge impetus and are being currently explored for biomarker discovery. Since CSF bathes and protects the CNS, it is a potent source of circulating molecules from the tumor microenvironment, making it one of the most appropriate biospecimens to understand the dysregulated cancer pathobiology [10]. Proteomics-based CSF investigation of glioma is deemed as a promising strategy for early prognosis and/or diagnosis, as it helps in the identification of key effector molecules and underlying perturbed molecular mechanisms [11, 12]. Apart from the CNS malignancies, CSF is also extensively analyzed in several neurodegenerative diseases like Parkinson’s disease and Alzheimer’s disease, to understand disease progression and identify potential therapeutic targets [13–16]. However, CSF studies are often circumvented due to the inherent limitations posed by the sample itself, for instance, its high salt content and an overall low protein yield [17]. This can be addressed by a thorough and efficient sample preparation methodology which becomes extremely crucial as CSF is a precious biospecimen, collected through an invasive lumbar puncture procedure [18]. We have described here, in detail, our laboratory standardized workflows for two high-throughput quantitative proteomics techniques, i.e., 2D-DIGE and iTRAQ for CSF analysis. 2D-DIGE is an advanced version of the traditionally used 2-DE (two-dimensional gel electrophoresis). Its advantages include higher sensitivity, accuracy, and quantitative reproducibility as compared to the traditional 2-DE [19]. Moreover, it allows comparison of samples from two different conditions in the same gel, thereby obliterating gel-to-gel variations. This method, first described by Unlu et al. [20], allows comparison across large datasets with lesser number of gels, saving a lot of time and energy and facilitating better analysis. In 2D-DIGE, fluorescent dyes called CyDyes are used for labeling the proteins in the samples. The CyDyes that are used, viz., Cy2, Cy3, and Cy5, have similar mass, charge, pH insensitivity, and photostability. The only difference between them is that each of them emits a distinct, highly sensitive signal which could be scanned at different wavelengths [21, 22]. During labeling, the α-amino group of the lysine residue binds to the -NHS ester reactive group of the CyDye, resulting in the replacement of the positive charge on lysine with the positive charge of the CyDye. Hence, the isoelectric point (pI) of the protein remains unaltered, while the mass of the protein increases by 500 Da. The samples are always labeled with the minimum amount of dye to make sure that each protein molecule in the sample is labeled with only one dye molecule [23]. Usually, the labeling efficacy in each sample is not more than 3% of the total proteins present in the sample.

Proteomics Workflow for Glioma CSF Analysis

83

iTRAQ reagents, on the other hand, are commonly used for the state-of-the-art mass spectrometry-based quantitative proteomics analysis. The iTRAQ reagent consists of three components: reporter group, balancer group, and peptide reactive group. Four different reporter groups, i.e., 114, 115, 116, and 117, are available in iTRAQ 4-plex kit, which can be used to label four different individual samples simultaneously. All the four reagents have the same peptide reactive group that covalently binds to the N-terminal and lysine side chain of proteins. The balancer group in each tag has a different mass (28–31 Da) depending upon the mass of reporter ion (114–117 Da), making the overall mass same for all the four tags (145 Da) [24]. Once the individual samples (containing digested peptides) are labeled with the iTRAQ reagent, the reaction is quenched and all the four reactions are pooled into one tube. During MS/MS fragmentation, the intensities of reporter ion peaks (114–117) are measured and their ratio helps in estimating the relative peptide amount present in the different samples. Thus, iTRAQ is a powerful tool for studying global changes in protein expression levels across different biological samples and is suitable for several biological fluids like serum, CSF, plasma, etc.

2

Materials Prepare all the reagents using distilled water and store them at room temperature unless mentioned otherwise. For iTRAQ, all the reagents should be of MS grade quality.

2.1

2D-DIGE

2.1.1 Sample Preparation Protein Extraction

Desalting the Sample Using a Desalting Kit

1. Acetone. 2. Rehydration buffer (RHB): 6 M urea, 2 M thiourea, 2% CHAPS, and 0.002% of 1% bromophenol blue. Dissolve 360.36 mg urea, 152.24 mg thiourea, and 20 mg CHAPS in 900 μL of MilliQ water. Add bromophenol blue and make up the volume to 1 mL with MilliQ water. 1. 2-D Clean-Up Kit (GE Healthcare). 2. 2-D Quant Kit (GE, Healthcare) or Pierce™ BCA Protein Assay Kit (Thermo Scientific) or Quick Start™ Bradford 1 Dye Reagent (Bio-Rad).

84

Aishwarya A. Rao et al.

2.1.2 Dye Preparation and Sample Labeling

1. 99.8% anhydrous Aldrich).

dimethylformamide

Preparation of CyDyes

2. CyDye™ DIGE (GE Healthcare).

Labeling of CSF Samples with CyDyes

1. 100 mM NaOH: Dissolve 4 mg of sodium hydroxide in 900 μL of MilliQ water. Make up the volume to 1 mL with MilliQ water.

Fluor

Minimal

(DMF)

Dye

(Sigma-

Labeling

Kit

2. 10 mM lysine: Dissolve 1.83 mg of L-lysine in 900 μL of MilliQ water. Make up the volume to 1 mL with MilliQ water. 2.1.3 Gel Run Rehydration of IPG Strips

1. Reswelling tray: Immobiline™ DryStrip reswelling Tray (GE Healthcare). 2. 1% (v/v) IPG buffer in RHB: Add 5 μL of IPG buffer (GE Healthcare) to 495 μL of RHB. 3. 1% DTT in 1% IPG + RHB: Add 5 mg of dithiothreitol to the tube containing 1% IPG buffer in RHB and mix well till the powder dissolves completely. 4. IPG strips: Immobiline™ DryStrip gels (GE Healthcare). 5. Cover fluid oil: DryStrip cover fluid (GE Healthcare).

First Dimensional Isoelectric Focusing

1. IPGphor unit: Ettan IPGphor3 isoelectric focusing unit (GE Healthcare). 2. Manifold ceramic tray: GE IPGphor™ manifold ceramic tray (GE Healthcare).

Second Dimensional SDS-PAGE

1. Gel casting unit: GE Ettan™ DALT gel caster cassette (GE Healthcare). 2. 12.5% SDS-PAGE resolving gel: (a) 1.5 M Tris–HCl: Dissolve 18.21 g Trizma base in 90 mL MilliQ water and dissolve it completely. Adjust the pH to 8.8 using 1 N HCl and make up the volume to 100 mL. (b) 10% SDS: Dissolve 100 mg of sodium dodecyl sulfate to 900 μL of MilliQ water. Make up the volume to 1 mL with MilliQ water. (c) 10% APS: Dissolve 100 mg of ammonium persulfate in 900 μL of MilliQ water. Make up the volume to 1 mL with MilliQ water. (d) For each gel add 62.5 mL of 30% Acrylamide/Bis Solution 37.5:1 (Bio-Rad), 37.5 mL of 1.5 M Tris–HCl, 1 mL of 10% SDS and 48 mL of MilliQ water to a 500 mL conical flask. Mix thoroughly and de-gas. Add 1 mL of 10% APS and 50 μL of PlusOne TEMED

Proteomics Workflow for Glioma CSF Analysis

85

(GE Healthcare). Mix thoroughly and pour the resulting solution into the funnel-shaped space of the Gel casting unit. 3. 0.1% SDS solution: Dissolve 100 mg of sodium dodecyl sulfate (Sigma-Aldrich) in 90 mL of MilliQ water in a 250 mL conical flask. Make up the volume to 100 mL with MilliQ water. 4. Equilibration buffer: 6 M urea, 75 mM Tris–HCl pH 8.8, 29.3% (v/v) glycerol, 2% (w/v) SDS, and 0.002% (w/v) of 1% bromophenol blue: (a) 75 mM Tris–HCl: Dissolve 454.275 mg Trizma base in 25 mL MilliQ water in a glass bottle and dissolve it completely. Adjust the pH to 8.8 using 1 N HCl. (b) Equilibration buffer: In the above mentioned 75 mM Tris–HCl solution, dissolve 18.018 g urea, 14.65 mL PlusOne Glycerol 87% (GE Healthcare), and 1 g SDS. Add bromophenol blue, mix thoroughly, and make up the volume to 50 mL. 5. 10 mg/mL DTT in equilibration buffer: Dissolve 100 mg DTT in 10 mL of equilibration buffer. 6. 25 mg/mL iodoacetamide (IAA) in equilibration buffer: Dissolve 250 mg iodoacetamide in 10 mL of equilibration buffer. 7. SDS electrophoresis running buffer: (a) 10 buffer: Dissolve 30 g of Trizma base, 144 g of Emparta® ACS Glycine (Merck), and 10 g of SDS in 1 L of MilliQ water. The pH of the buffer should be 8.3. Usually, no pH adjustment is required. Store the buffer at room temperature. (b) 2 buffer: Take 200 mL of 10 buffer in a 2 L beaker and make up the volume to 1 L with MilliQ water. (c) 1 buffer: Take 100 mL of 10 buffer in a 2 L beaker and make up the volume to 1 L with MilliQ water. 8. Agarose sealing solution (100 mL): Dissolve 750 mg of agarose and 200 μL of 1% bromophenol blue in 90 mL of 1 SDS electrophoresis buffer. Make up the volume to 100 mL with 1 SDS electrophoresis buffer. 2.1.4 Gel Scanning Using Typhoon FLA9500 Gel Scanner

1. Typhoon FLA9500 gel scanner.

2.1.5 Analysis of Images

1. DeCyder software.

86

Aishwarya A. Rao et al.

2.1.6 In-Gel Digestion

1. Ammonium bicarbonate (ABC).

Reagent Preparation

2. Acetonitrile LC-MS grade. 3. Trypsin: Pierce™ Trypsin Protease, MS Grade. 4. Formic acid LC-MS grade.

Gel Cutting

1. Methanol.

Washing and Dehydration

1. Vortexer: Spinix Vortex Shaker (Tarsons). 2. Small centrifuge: SPINWIN-MC00 (Tarsons).

Reduction and Alkylation

1. Tris(2-carboxyethyl)phosphine hydrochloride (TCEP). 2. 0.5 M IAA (prepare fresh): Weigh 92.48 mg of IAA and dissolve in 1 mL of MilliQ water. Store at 4  C.

Dehydration and Digestion

1. Speedvac.

Extraction

1. Sonicator.

Desalting of the Sample

1. C18 material: Empore™ Octadecyl C18 47 mm extraction discs (3M Empore). 2. 0.1% formic acid: Add 5 μL of formic acid LC-MS grade to 4995 μL of Pierce™ Water LC-MS Grade (Thermo Fisher Scientific).

Sample Submission for Mass Spectrometric Analysis

1. μDrop Plate (Thermo Fisher Scientific).

2.2

1. Solution A (6 M urea buffer, pH 8.0): Weigh 302.85 mg of Trizma base (50 mM), 18 g of urea (6 M), 219.15 mg of NaCl (75 mM) and 4.76 mg of MgCl2 (1 mM) and make up the volume to 50 mL using MilliQ water.

iTRAQ

2.2.1 Sample Preparation Using Vacuum Concentrator

2.2.2 QC Check Using SDS-PAGE

2. Multiskan GO (Thermo Fisher Scientific).

2. 2-D Quant Kit (GE, Healthcare)/Pierce™ BCA Protein Assay Kit (Thermo Scientific)/Quick Start™ Bradford 1 Dye Reagent (Bio-Rad). Store at 4  C. 1. Tris buffer solution (pH 8.8): Add 18.17 g Trizma base (1.5 M) to 75 mL water. Adjust pH using dilute HCl and make up the volume to 100 mL. 2. Tris buffer solution (pH 6.8): Add 6.06 g Trizma base (0.5 M) to 75 mL water. Adjust pH using dilute HCl and make up the volume to 100 mL. 3. 10% ammonium persulfate solution: Dissolve 10 mg of ammonium persulfate in 100 μL of water. Cover the tube with aluminum foil.

Proteomics Workflow for Glioma CSF Analysis

87

4. 10% SDS solution: Dissolve 10 mg of sodium dodecyl sulfate in 100 μL of water. 5. TEMED (N,N,N,N0 -tetramethylethylenediamine) Healthcare). Store at 4  C.

(GE

6. Resolving gel (for three small gels): 6 mL ReadySol (Bio-Rad), 4.9 mL water, 3.8 mL 1.5 M Tris–HCl (pH 8.8), 150 μL 10% SDS solution, 150 μL 10% APS solution, 6 μL TEMED. 7. Stacking gel (for three small gels): 1 mL ReadySol (Bio-Rad), 4.1 mL water, 750 μL 0.5 M Tris–HCl (pH 6.8), 60 μL 10% SDS solution, 60 μL 10% APS solution, 6 μL TEMED. 8. 5 SDS loading buffer (5 mL): 1.6 mL 10% SDS, 1 mL of 0.5 M Tris buffer solution (pH 6.8), 0.8 mL glycerol, 0.4 mL β-mercaptoethanol, 0.4 mL 1% bromophenol blue (GE Healthcare) and add 3.9 mL of deionized water. 9. 10 SDS electrophoresis running buffer: 30 g of Trizma base (250 mM), 144 g of glycine (1.92 M), 10 g SDS (1% w/v). Volume raised to 1 L using distilled water. 10. Staining solution: Add 400 mL methanol (40%) and 70 mL acetic acid (7%) to 530 mL water (53%). Add 1 tablet of PhastGel Coomassie Blue to this 1 L solution. 11. Destaining solution: Add 400 mL methanol (40%) and 70 mL acetic acid (7%) to 530 mL water (53%). 2.2.3 Proteolytic Digestion Using Trypsin

1. TEAB (tetraethylammonium bromide) (Sigma): Take 1 M TEAB and dilute it to 0.5 M by taking an equal volume of MilliQ water. 2. 0.5 M TCEP (Tris(2-carboxyethyl)phosphine) (Sigma): Store at 4  C. 3. 0.5 M IAA (Sigma): Weigh 92.48 mg of IAA and dissolve in 1 mL of MilliQ water. Always prepare IAA fresh and store it at 4  C. 4. Solution B (pH 8.0): 25 mM Tris (151.425 mg) and 1 mM CaCl2 (5.549 mg). Make up the volume to 50 mL using MilliQ water. 5. Pierce™ Trypsin Protease, MS Grade: Reconstitute one vial of trypsin (100 μg) in 400 μL of 0.5 M TEAB (concentration ¼ 0.25 μg/μL). Give a short vortex and store at 20  C.

2.2.4 Sample Cleanup

1. C18 material: Empore™ Octadecyl C18 47 mm Extraction Discs (3M Empore). 2. 0.1% formic acid LC-MS grade: *Add 5 μL formic acid in 5 mL in MilliQ water. 3. 50% acetonitrile LC-MS grade in 0.1% formic acid: *Add 1 mL acetonitrile and 2 μL formic acid to 1 mL water.

88

Aishwarya A. Rao et al.

4. 40% acetonitrile in 0.1% formic acid: ∗Add 0.8 mL acetonitrile and 2 μL formic acid to 1.2 mL water. 5. 60% acetonitrile in 0.1% formic acid: ∗Add 1.2 mL acetonitrile and 2 μL formic acid to 0.8 mL water. 6. 80% acetonitrile in 0.1% formic acid: ∗Add 1.6 mL acetonitrile and 2 μL formic acid to 0.4 mL water. ∗ The final volume of the reagents may vary depending on the sample number. 2.2.5 iTRAQ Labeling

1. AB Sciex iTRAQ Reagent 4-plex kit. Store at 20  C. 2. Millipore Absolute ethanol.

2.2.6 Sample Fractionation

1. C-18 material: Empore™ Octadecyl C-18 47 mm Extraction Discs (3M Empore). 2. Sigma LC-MS grade methanol. 3. 0.1% Pierce™ LC-MS grade trifluoroacetic acid: ∗Add 2 μL trifluoroacetic acid in 2 mL in MilliQ water. 4. 80% acetonitrile in 0.1% trifluoroacetic acid: ∗Add 1.6 mL acetonitrile and 2 μL trifluoroacetic acid to 0.4 mL water. 5. 10% acetonitrile in 0.1% trifluoroacetic acid: ∗Add 0.8 mL acetonitrile and 2 μL trifluoroacetic acid to 1.2 mL water. 6. 15% acetonitrile in 0.1% trifluoroacetic acid: ∗Add 1.2 mL acetonitrile and 2 μL trifluoroacetic acid to 0.8 mL water. 7. 20% acetonitrile in 0.1% trifluoroacetic acid: ∗Add 1.6 mL acetonitrile and 2 μL trifluoroacetic acid to 0.4 mL water. ∗ The final volumes of the reagents may vary depending on the sample number.

2.2.7 Mass Spectrometric Analysis

3

1. 0.1% formic acid: Add 5 μL formic acid in 5 mL of MilliQ water.

Methods

3.1 2D-DIGE Workflow

A detailed schematic representation of the 2D-DIGE workflow is shown in Fig. 1.

3.1.1 Sample Preparation

1. Take the CSF samples from the 80  C freezer and place them immediately on ice. Allow the samples to thaw completely on ice.

Protein Extraction

2. Take 500 μL of CSF sample in a 1.5 mL microcentrifuge tube (see Note 1). 3. Sonicate the samples on ice at 20% amplitude using a pulse of 5 s ON and 5 s OFF for a total of 8 cycles (see Note 2).

Proteomics Workflow for Glioma CSF Analysis

89

Fig. 1 Schematic representation of the methodology adapted to identify differentially expressed proteins from CSF using 2D-DIGE approach

4. Divide each sample equally into two 1.5 mL tubes. 5. Add 1.2 mL of ice-cold acetone to the microcentrifuge tubes and vortex briefly. 6. Incubate the tubes at 20  C for 4 h with intermittent vortexing to precipitate the proteins (see Note 3). 7. Set the temperature of a centrifuge to 4  C and centrifuge the tubes at 13,000  g for 30 min. 8. For each microcentrifuge tube, collect the supernatant in a fresh 1.5 mL tube. Then split the volume equally into three 1.5 mL tubes. 9. Wash the pellet twice with an excess of ice-cold acetone to make sure that the lipid content is reduced further. 10. To the three tubes containing the supernatant, add another 1 mL of ice-cold acetone and vortex briefly. 11. Incubate the tubes overnight at 20  C to precipitate any remaining protein in the supernatant. 12. Next day, centrifuge the tubes again at 13,000  g for 30 min at 4  C. Discard the supernatant completely.

90

Aishwarya A. Rao et al.

13. Now there would be a total of eight tubes per CSF sample (two tubes containing the pellet obtained after 4 h of incubation with acetone and six tubes in which the supernatants were split). 14. Dissolve the pellet in one tube in 100 μL of rehydration buffer (RHB). Transfer the resulting solution to the second tube containing the pellet and dissolve the pellet completely. Repeat this procedure for all the remaining six tubes. 15. Now add another 100 μL of RHB to the first tube and make sure that any protein sticking to the walls of the tube is dissolved completely. Transfer this 100 μL of RHB to another tube and repeat the above step for all the six tubes. Finally, add this 100 μL solution to the last tube. The final tube contains CSF protein in 200 μL of RHB. Desalting the Sample Using a Desalting Kit (See Note 4)

1. For each CSF sample, split the protein mixture into two 1.5 mL tubes. 2. To both the tubes, add 300 μL of precipitant provided in the kit. Vortex briefly and incubate on ice for 15 min. 3. To each tube, add 300 μL of coprecipitant provided in the kit. Vortex briefly to ensure proper mixing of the solution. 4. Centrifuge the tubes at 4  C for 5 min at 15,000  g. Discard the supernatant. 5. Repeat the above step to collect all the remaining supernatant and discard it completely. 6. Carefully overlay the pellet with 40 μL of coprecipitant. Incubate on ice for 5 min. 7. Centrifuge the tubes at 4  C for 5 min at 15,000  g. Discard the supernatant completely. 8. Disperse the pellet in 40 μL of distilled water by tapping or vortexing. 9. To each tube add 1 mL of chilled wash buffer and 5 μL of wash additive provided in the kit. Incubate for 1 h with intermittent vortexing. 10. Centrifuge the tubes at 4  C for 10 min at 18,000  g. Discard the supernatant completely. 11. Air-dry the pellet for a maximum of 5 min. 12. Add 50 μL of RHB to each tube and dissolve the pellet completely. 13. Pool the resulting solution in both the tubes to get a final volume of 100 μL of protein extract for each CSF sample. 14. Quantify the protein content of the resulting sample using 2-D Quant Kit from GE Healthcare as per the manufacturer’s

Proteomics Workflow for Glioma CSF Analysis

91

protocol. Alternatively, one can use BCA assay kit from Pierce or Bradford assay kit from Bio-Rad and follow the manufacturer’s protocol. 15. Store the protein extracts at 20  C. 3.1.2 Dye Preparation and Sample Labeling

1. Before reconstitution, keep the CyDye Kit at room temperature for 10 min.

Preparation of CyDyes (See Note 5)

2. Briefly centrifuge the CyDye tubes to bring all the dye powder to the bottom of the tubes. 3. Add 15 μL of DMF to every 5 nmol/L of dye to get a final concentration of 333 pmol/μL of dye (see Note 6). 4. Place the caps on the CyDye tubes tightly. Vortex the tubes vigorously for 30 s. 5. Centrifuge the CyDye tubes at 12,000  g for 30 s to ensure that no dye is sticking to the walls of the tubes. 6. This reconstituted CyDye can be stored at 80  C and should be used within a month.

Labeling of CSF Samples with CyDyes

CyDye labeling of CSF samples has been schematically represented in Fig. 2.

Fig. 2 Schematic representation of sample labeling. CyDye labeling is performed in 2D-DIGE technique for the identification of differentially abundant protein in normal and diseased conditions. The rightmost panel represents the 3D view of a differentially expressed protein in different samples, identified in Biological Variation Analysis (BVA) using DeCyder 2D software

92

Aishwarya A. Rao et al.

1. Take two CSF samples per 2D-DIGE gel. Treat one as “test” and the other as “control.” 2. Prepare two microcentrifuge tubes for each CSF sample containing 60 μg of protein. 3. Prepare one tube for “internal standard” by mixing 30 μg of protein from both samples used in this experiment. 4. Adjust the pH of each sample to pH 8.5 with the help of 100 mM·NaOH. 5. To each sample tube add 1.2 μL of reconstituted CyDye to get a final concentration of 400 pmol CyDye/60 μg protein as mentioned below: (a) Label the first set of CSF samples as follows: Test with Cy3 and control with Cy5. (b) Now label the second set of CSF samples as follows: Test with Cy5 and control with Cy3 (see Note 7). (c) Label the internal standard with Cy2. 6. Briefly vortex the tubes to mix the sample and dye and centrifuge the tubes to bring the solution to the bottom of the tube. 7. Incubate the tubes containing sample and dye mixture for 1 h on ice in the dark. 8. Add 1 μL of 10 mM/L-lysine to each tube to stop the reaction. 9. Briefly vortex and centrifuge the tubes. 10. Incubate the tubes in the dark for another 30 min on ice. 11. Add all the three sets of labeled samples to one microcentrifuge tube and mix well. Bring the final volume to 350 μL by adding RHB. 3.1.3 Gel Run Rehydration of IPG Strips

1. Clean the reswelling tray using nonionic detergents and rinse thoroughly with distilled water. Using lint-free tissues, dry the tray completely. 2. Make sure the reswelling tray is leveled. 3. Add 1% (v/v) IPG buffer and 1% DTT to the rehydration buffer. Mix thoroughly. 4. Dilute the mixture of labeled samples with rehydration buffer containing IPG and DTT according to the length of the strip being used for the experiment (see Note 8). 5. Add the labeled samples and rehydration buffer mix to individual channels of the reswelling tray. 6. Pick up the IPG strip from the positive end edge with the help of forceps and remove the plastic cover from the strip. 7. Place the strip gently in the channel, taking care that no air bubbles are formed. The gel side of the strip should be facing

Proteomics Workflow for Glioma CSF Analysis

93

down and the positive end of the strip should lie against the sloped edge of the channel. 8. Press the strip down very gently to remove any air bubble and incubate for 1 h at room temperature. 9. Add 3 mL of cover fluid oil over each strip to prevent evaporation of the sample and crystallization of the urea in the rehydration buffer. Also, add 1 mL of cover fluid oil in all the remaining channels. 10. Gently slide the lid onto the tray. 11. Incubate the strip at room temperature for a minimum of 10–12 h. However, for 18 cm and 24 cm strips, the incubation time could exceed to 15–18 h (see Note 9). First Dimensional Isoelectric Focusing

1. Check whether the IPGphor unit is properly leveled and turn on the system. 2. Clean the manifold ceramic tray thoroughly with nonionic detergent and distilled water. Dry it completely with the help of lint-free tissue and place it onto the unit. 3. Pour fresh cover fluid oil into the channels where the strips are to be placed. Also, add fresh cover fluid oil to the immediately adjacent channels on either side. In the remaining lanes, pour the cover fluid oil used previously. 4. With the help of forceps, carefully remove the IPG strip from the reswelling try and drain the excess cover fluid oil by tapping the edge of the strip on a tissue paper. 5. Gently slide the strip into one channel of the manifold, making sure that the gel side is now facing upward. 6. Place electrode wicks/thick tissue paper wicks on a fresh piece of aluminum foil and soak them in distilled water. Position each wick at the end of the strip such that it is in contact with the gel (see Note 10). 7. Carefully position the electrodes over these wicks and lock the electrodes in place. 8. Close the lid of the IPGphor unit and start the IEF run with the protocol set according to the sample type and the strip length. For CSF samples, we carry out isoelectric focusing using Ettan IPGphor3 isoelectric focusing unit (GE Healthcare) for a total of 77,800 Vh1 (see Note 11).

Second Dimension: SDS-PAGE

1. Keep the gel casting unit ready. Using distilled water, make sure there are no leakages. 2. Prepare the cocktail for a 12.5% SDS-PAGE resolving gel and pour it into the casting unit. Spray 0.1% SDS solution over the

94

Aishwarya A. Rao et al.

top of the gel surface to prevent its contact with air. Leave the gel for polymerization. 3. Prepare the equilibration buffer. 4. With the help of forceps, pick up the IPG strip from the manifold channel and remove the excess oil by tapping its edge onto a lint-free tissue. 5. Place the IPG strip in a plastic tray containing the equilibration buffer and 10 mg/mL DTT. Place the plastic tray on a rocker and allow the solution to flow over the gel at low speed for 15 min. 6. Prepare the second equilibration solution by adding 25 mg/ mL iodoacetamide to the equilibration buffer. 7. After the reducing step is completed, place the strip in another tray containing the second equilibration solution and rock the tray at low speed for 15 min. 8. On completion of the alkylation step, wash the IPG strip with 1 SDS electrophoresis running buffer. 9. Remove the excess polymerized gel.

SDS

solution

placed

over

the

10. Carefully slide the IPG strip on top of the resolving gel, taking care that no air bubbles get trapped at the interface. The IPG strip can be gently pressed down onto the gel to remove any air bubble. 11. Seal the IPG strip and the resolving gel with melted agarose. 12. Pour 1SDS electrophoresis running buffer into the buffer tank up to the level demarcated on the running unit. 13. Place the gel casting unit in a buffer tank and fill the inner buffer tank with 2 running buffer. 14. Plug the electrodes onto the running unit and switch on the power supply (see Note 12). 15. Once the electrophoresis is completed, switch off the power supply and remove the glass plates. 3.1.4 Gel Scanning

1. After the completion of the second dimension PAGE, clean the glass plates thoroughly using lint-free tissues, taking care that no contaminants like dust, hair, and fibers remain on the glass plates. 2. Place the glass plates in the tray of the ETTAN DALT stage taking care that they are properly oriented as indicated on the instrument. Clamp them to keep them in place. 3. Turn on the Typhoon scanner.

Proteomics Workflow for Glioma CSF Analysis

95

4. After the orange-colored light stops blinking, the front panel opens and the DIGE stage gets inserted in a proper orientation, indicated by a clicking sound. 5. The front panel then closes automatically and the instrument is now ready to scan the gels. 6. Scan the gels using the Typhoon FLA9500 software:

3.1.5 Analysis of images

l

On the desktop of the attached computer, click on the Typhoon FLA9500 icon. A window with multiple options will open.

l

Click on the 2D-DIGE icon. Another window with multiple options for scanning DIGE gels opens.

l

Fill in the fields specified for Gel name, Scan date, and Destination folder.

l

Select the ETTAN DALT stage as platform for scanning. The scanning area will now be displayed on the window.

l

Notice that two gels can be scanned at a time. If only one gel is to be scanned, then right-click on the area demarcated for the second gel and click delete.

l

Choose the appropriate wavelengths for the three different dyes, viz., Cy2, Cy3, and Cy5.

l

Check the box in front of Cy2 to set it as standard.

l

Set the PTM for all the three dyes as 500.

l

Set the resolution for all the three dyes as 100 μM.

l

Click Preview to check for the presence or absence of spots.

l

Now click Scan. A window opens which shows the progress of the scan.

l

The scanner gives a beep sound after the gels have been scanned at all the three wavelengths.

l

If the gels need to be re-scanned, click on “Return.” This will reopen the scanning window in which the above procedure could be repeated.

l

Once the scanning is complete, close the window. Save the images in both the “.mel” and “.tiff” formats in the Destination folder specified before.

DeCyder software is used for the analysis of gel images as per the following steps: Step 1: Loading of image 1. Click on the DeCyder software on the desktop. 2. Select the “Image Loader” option. 3. Upload images obtained from one gel for Cy2, Cy3, and Cy5 in both the “.gel” and “.tiff” formats.

96

Aishwarya A. Rao et al.

4. Repeat the above step for all other gel images that need to be analyzed. Step 2: Editing the images 1. Select all the imported gel images. 2. Select the “Edit Gel Images” tab. The Cy2 images of all gels will be displayed. These Cy2 images were earlier selected as Standard for each gel. 3. Crop and rotate the Cy2 images as required. The Cy3 and Cy5 gel images are cropped using the same dimensions. 4. Save the changes and close the window. Step 3: Importing the images for analysis 1. Click on the “New Project” icon at the right corner of the screen. 2. Create a new folder and name it appropriately. 3. Import the cropped images into this folder. Step 4: Analysis of the images The DeCyder software has three options for image analysis of the gels. Each of these options is explained below: Differential In-Gel Analysis (DIA) (See Note 13)

1. Click on DIA to create a new workspace. 2. Import the Cy2, Cy3, and Cy5 images of one gel into this workspace. 3. Add the estimated number of spots and process the images. 4. DIA shows the 3D views of all the spots and also displays the Max Slope and the Max Volume for each spot. 5. Protein spots usually have a Max Slope between 1 and 1.5. 6. Click on “Exclude Filters” to exclude the areas showing peaks due to the presence of dust particles.

Biological Variation Analysis (BVA) (See Note 14)

1. Click on BVA to create a new workspace. 2. Create two groups—Control and Test. 3. Sort the gel images into these two groups as required. 4. Use the following modes: (a) Spot map mode: l The Cy2 images, i.e., the Standards, of all the gels are shown in this mode. (b) Match mode: l Add new matches, create new spots, merge spots, and split the spots that are demarcated by the software using the options provided in this mode.

Proteomics Workflow for Glioma CSF Analysis

97

l

Check each spot manually and add or change spots as required.

l

Create a match set to incorporate all the changes.

l

This helps in marking those spots which are present but not detected by the software in accurate matching of the gels.

(c) Protein mode: l This mode has options for displaying the statistical values for each and every spot on all the gels, along with the corresponding spot number on the Master gel. It also shows the number of gels in which a particular spot has appeared. l

Select the protein parameters as desired from the given list: student’s T-test, one-way ANOVA, two-way ANOVA, and fold change. Values for each spot will be displayed.

l

Refine the data using these values to set thresholds for selecting or discarding the spots.

(d) Appearance mode: l This mode gives the comparison of the peak heights, widths, and areas for each spot. (e) 3D view: l Using this mode check the authenticity of each spot, i.e., whether it is truly a protein spot or a dust particle. l

Compare the 3D view of the spots with one another.

l

Also, compare spots chosen by the software with the spots chosen manually.

l

Check whether proper matching of the spots has been done by the software.

(f) Graph View: l Check the relative abundance of individual spots, which correspond to proteins, in each gel. l

Batch Processor (See Note 15)

Calculate the average values of all the Control samples and all the Experimental samples. These values would indicate whether a protein has been upregulated or downregulated in Experimental vs. Control samples.

1. Select all the gels that need to be analyzed simultaneously. 2. Create the DIA and BVA batch list. 3. Select one of the Cy2 gel images as the Master gel. Other gel images will be matched against this gel image. 4. Click on Run.

98

Aishwarya A. Rao et al.

3.1.6 In-Gel Digestion

Reagent Preparation

Once the gel images have been analyzed and compared, identify the spots that show differential expression in the two or more conditions under study. Mix the samples once again without adding the dyes. Rehydrate the same length of strip used for 2D-DIGE with this sample mix and carry out the IEF and SDS-PAGE runs as mentioned before. Stain the gel overnight using Coomassie Blue stain. Destain the gel the next day and scan it. Store the gel in MilliQ water, and mark the spots showing differential expression on a printed version of the scanned gel image. 1. 100 mM ammonium bicarbonate stock solution (ABC): Add 79.06 mg·NH4HCO3 to 10 mL MilliQ water in a 15 mL falcon tube. 2. 50 mM ABC: Take 5 mL of 100 mM ABC stock solution in 15 mL falcon tube and demarcate it as 50 mM ABC. Add 5 mL of MilliQ water to the tube. 3. 25 mM ABC: Take 5 mL of 50 mM ABC solution in 15 mL falcon tube and demarcate it as 25 mM ABC. Add 5 mL of MilliQ water to the tube. 4. Solution A: Take 5 mL of 25 mM ABC solution in 15 mL falcon tube and demarcate it as Solution A. Add 10 mL of ACN to the tube and mix thoroughly. 5. 10 mM dithiothreitol (DTT): Add 1.5 mg DTT to 1 mL of 100 mM ABC stock solution in a 1.5 mL Eppendorf tube and mix until it dissolves. 6. 50 mM iodoacetamide (IAA): Add 10 mg IAA to 1 mL of 100 mM ABC Stock Solution in a 1.5 mL Eppendorf tube wrapped in silver foil and mix until it dissolves. 7. 0.1 μg/μL trypsin: Dissolve 20 μg of Pierce™ Trypsin Protease, MS Grade, from Thermo Fisher Scientific in 200 μL of ice-cold 50 mM ABC. 8. Extraction solutions: These solutions are to be freshly prepared the next day: (a) 0.1% formic acid (FA) stock solution: Add 1 μL FA to 999 μL MilliQ water in a 1.5 mL Eppendorf tube and mix well. (b) 50% acetonitrile (ACN) in 0.1% FA: Mix 50 μL 0.1% FA and 50 μL of ACN in a 1.5 mL Eppendorf tube and mix well. (c) 60% Acetonitrile (ACN) in 0.1% FA: Mix 40 μL 0.1% FA and 60 μL of ACN in a 1.5 mL Eppendorf tube and mix well. (d) 80% Acetonitrile (ACN) in 0.1% FA: Mix 20 μL 0.1% FA and 80 μL of ACN in a 1.5 mL Eppendorf tube and mix well.

Proteomics Workflow for Glioma CSF Analysis

99

9. 0.1% Formic acid (FA) in acetonitrile (ACN): Add 1 μL FA to 999 μL acetonitrile in a 1.5 mL Eppendorf tube and mix well. 10. 40% acetonitrile (ACN) in 0.1% FA: Mix 60 μL 0.1% FA and 40 μL of ACN in a 1.5 mL Eppendorf tube and mix well. Gel Cutting

1. Rinse the gels thoroughly with 25 mM ABC and place them in a laminar flow. 2. Wash one 500 μL Axygen tube for each gel piece with 100 μL methanol followed by 100 μL MilliQ water. Repeat this washing step one more time. 3. Using a sterile razor, cut the protein band from the gel and place it on a glass slide. 4. Further cut the protein band into small pieces using the razor and transfer the gel pieces to the Axygen tube.

Washing and Dehydration

1. Add 100 μL of 25 mM ABC to the tube containing the gel pieces and vortex the tube for 10 min. 2. Give the tube a brief spin to bring down all the gel pieces and discard the supernatant completely. 3. Add 100 μL of Solution A to the tube containing the gel pieces and vortex the tube for 10 min. 4. Give the tube a brief spin to bring down all the gel pieces and discard the supernatant completely (see Note 16).

Reduction and Alkylation

1. Add 50 μL of 10 mM DTT to the gel pieces and vortex briefly. 2. Place the tubes at 56  C for 1 h. 3. Vortex briefly and give the tubes a short spin to bring down all the gel pieces. 4. Discard the supernatant completely. 5. Add 100 μL of 25 mM ABC to the gel pieces in order to rinse them. 6. Vortex the tubes for 5 min and give the tubes a short spin to bring down all the gel pieces. 7. Discard the supernatant completely. 8. Add 50 μL of 50 mM IAA to the gel pieces and vortex briefly. 9. Place the tubes at in the dark for 30 min. 10. Vortex briefly and give the tubes a short spin to bring down all the gel pieces. 11. Discard the supernatant completely. 12. Add 100 μL of 25 mM ABC to the gel pieces in order to rinse them. 13. Vortex the tubes for 5 min and give the tubes a short spin to bring down all the gel pieces.

100

Aishwarya A. Rao et al.

14. Discard the supernatant completely. 15. Add 100 μL of Solution A to the gel pieces. 16. Vortex the tubes for 5 min and give the tubes a short spin to bring down all the gel pieces. 17. Discard the supernatant completely. 18. Add 100 μL of 25 mM ABC to the gel pieces in order to rinse them. 19. Vortex the tubes for 5 min and give the tubes a short spin to bring down all the gel pieces. 20. Discard the supernatant completely. 21. Add 100 μL of Solution A to the gel pieces. 22. Vortex the tubes for 5 min and give the tubes a short spin to bring down all the gel pieces. 23. Discard the supernatant completely. Dehydration and Digestion

1. Speedvac the Axygen tubes for 15 min on medium mode to dehydrate the gel pieces. Alternatively, place the tubes at 60  C for 5 min in a dry bath and then cool the tubes by placing it at 4  C. 2. Place the tubes on ice. 3. Add 3 μL of 0.1 μg/μL trypsin and 7 μL of 50 mM ABC to the gel pieces in the tubes. 4. Incubate the tubes on ice for 30 min. 5. Add 30 μL of 50 mM ABC to cover the gel pieces in the tubes. 6. Incubate at 37  C overnight (16 h) on a shaking dry bath.

Extraction

1. Give the tubes a short spin. 2. Collect the supernatant in a freshly cleaned Axygen tube. 3. To the tubes containing gel pieces, add100 μL of 50% ACN in 0.1% FA and vortex for 10 min. 4. Briefly spin the tubes and transfer all the supernatant to the Axygen tubes mentioned in step 2. 5. To the tubes containing gel pieces, add100 μL of 60% ACN in 0.1% FA. 6. Sonicate the solution for 1 min at 25% amplitude with 2 s pulse on and 1 s off using a small probe. 7. Vortex the tube for 10 min. 8. Briefly spin the tubes and transfer all the supernatant to the Axygen tubes mentioned in step 2. 9. To the tubes containing gel pieces, add100 μL of 80% ACN in 0.1% FA.

Proteomics Workflow for Glioma CSF Analysis

101

10. Sonicate the solution for 1 min at 25% amplitude with 2 s pulses on and 1 s off using a small probe. 11. Vortex the tubes for 10 min. 12. Briefly spin the tubes and transfer all the supernatant to the Axygen tubes mentioned in step 2. 13. Speedvac the tubes mentioned in step 2 on medium mode till the solution dries completely. Desalting of the Sample

1. Take 200 μL autopipette tips and pack two plugs of C18 material into each tip, as described by Rappsilber et al. [25]. This tip would be capable of binding ~20 μg of peptides in the sample. Hence, prepare additional tips for each sample if required. 2. Add 20 μL of 0.1% FA to the dried sample. 3. Vortex the tubes for 10 min and give the tubes a short spin to bring down all the samples. 4. Add 40 μL of 50% ACN in 0.1% FA to activate the column. 5. Fix the tip to the end of a 5 mL syringe (without needle), and gradually depress the plunger till all the liquid elutes from the C18 column. 6. Repeat steps 4 and 5 two more times. 7. Further activate the column by adding 40 μL of 0.1% FA in ACN and passing the liquid through the column using the syringe as described in step 5. Repeat this step two more times. 8. Wash the column thrice with 40 μL of 0.1% FA. 9. Discard all the eluent collected so far. 10. Add 20 μL of the reconstituted sample to the column and pass the liquid through the column with the help of the syringe. Collect the eluent in a fresh tube. 11. Add the eluent again onto the column and repeat the above step, collecting the resulting eluent in the same tube. Repeat this step four more times. This will ensure binding of maximum number of peptides to the C18 column. 12. Wash the column thrice with 40 μL of 0.1% FA, adding the eluents to the same tube. 13. Elute the peptides into fresh tubes using 40 μL of 40% ACN in 0.1% FA, followed by 40 μL of 60% ACN in 0.1% FA and 40 μL of 80% ACN in 0.1% FA. 14. Dry the resulting desalted eluent solution using a Speedvac in the medium mode. 15. Store the dried tubes at 80  C until the next use.

102

Aishwarya A. Rao et al.

Sample Submission for Mass Spectrometric Analysis

1. Reconstitute the dried sample in 15 μL of 0.1% FA. 2. Take 1 μL of the sample in a tube and dilute it by adding 4 μL of 0.1% FA. 3. Load 2 μL of the resulting solution in duplicates onto a Thermo μDrop Plate and quantify the concentration of peptides in the sample by using Multiskan GO. Use 0.1% FA as the reference blank and calculate the concentration of the peptides in the solution. 4. Aliquot 10 μg of peptide mix into a separate tube and submit the sample for protein identification by the peptide mass fingerprinting method using a MALDI-TOF/TOF instrument. Alternately, dilute the sample with 0.1% FA such that ~1 μg of the sample will be present in 4 μL of solution injected into a hybrid or tribrid mass spectrometer for protein identification.

3.2

iTRAQ Workflow

3.2.1 Sample Preparation

A detailed schematic representation of the iTRAQ workflow is shown in Fig. 3. 1. Take 1 mL of CSF sample and completely dry it using a centrifugal vacuum concentrator (see Note 17). 2. Reconstitute the dried pellet in 40 μL of 6 M urea buffer (Solution A) and vortex it for 10 min (see Note 18). 3. Perform protein quantification to estimate the total amount present in the sample using the 2-D Quant Kit from GE Healthcare as per the manufacturer’s protocol. Alternatively, one can use BCA assay kit from Pierce or Bradford assay kit from Bio-Rad and follow the manufacturer’s protocol (see Note 19).

3.2.2 QC Check Using SDS-PAGE

1. Clamp the glass plates into the holding cassette and lock the cassette. Check for any possible leakage by adding water (observe for 5–10 min). Discard the water by inverting the cassette. 2. Prepare a 12% resolving gel solution, and, using a pipette, add it between the glass plates till three/fourth of the area and let it polymerize. Add water or 0.1% SDS solution over the gel to avoid any contact with air, which facilitates gel polymerization. 3. Prepare a 5% stacking gel solution and pour it over the polymerized resolving gel. Immediately place the comb in between the glass plates and allow the stacking gel to polymerize. 4. Once the gel is polymerized, place the glass plates in the running apparatus and fill it with 1 running buffer till the mark on the apparatus. If using one gel, use a dummy glass plate along with your gel, in the unit. 5. Carefully remove the comb to prevent breakage of the wells.

Fig. 3 Schematic representation of the experimental workflow used for iTRAQ-based quantitative proteomics. The schematic illustrates the multiplexing of samples in a single experiment. The spectra obtained will provide the peptide sequence, while the intensity of the reporter ion (zoomed-in) provides information about the protein abundance in different samples

104

Aishwarya A. Rao et al.

6. Take volume corresponding to 5 μg protein from each sample to check the overall sample profile. Add 3 μL of 5 sample buffer and make up the volume to 15 μL using distilled water (see Note 20). 7. Add the samples into the wells and start the current at a constant voltage of 100 V. The sample run may take 2–3 h to complete. 8. Once the dye front has left the bottom of the gel, stop the current. Carefully remove the glass plates and place the resolving gel into the staining solution. 9. Destain the gel after approximately 3 h and scan it. 3.2.3 In-Solution Digestion of CSF Samples

1. Take sample volume equivalent to100 μg of protein amount and add 0.5 M TEAB buffer to have the same total volume for all the samples. Alternatively, one can use the dissolution buffer present in the AB Sciex iTRAQ® Reagent 4-plex kit to make up the volume. 2. Check the pH using a pH paper strip; use a drop of the sample on the paper and check the color as per the indicator. In case the pH is less than 8.0, add an additional amount of 0.5 M TEAB or dissolution buffer (see Note 21). 3. Reduce the protein sample using 0.5 M TCEP at a final concentration of 20 mM in the reaction mix. Incubate the reaction mix at 37  C for 1 h (see Note 22). 4. Following reduction, alkylate the reduced proteins with 0.5 M IAA at a final concentration of 37.5 mM. Incubate the reaction mix in dark for 30 min at RT (see Note 23). 5. Dilute the contents four times with Solution B (25 mM Tris pH 8.0, 1 mM CaCl2). 6. Digest using 13.4 μL (~3.3 μg) trypsin enzyme (MS grade from either Promega or Pierce) in a 1:30 ratio (trypsin/protein) and incubate at 37  C for 16 h (Thermo Shaking Incubator, speed 850 rpm) (see Note 24). 7. Following digestion, concentrate the digested products in a centrifugal vacuum concentrator at medium or low speed. 8. Store the dried samples at 80  C till the next use.

3.2.4 Sample Cleanup Using C18 Columns

1. Prepare C18 stage tips using Empore C18 extraction discs [25]. Pack two plugs of C18 material into each stage tip (200 μL pipette tips) for a total binding capacity of ~20 μg (see Note 25). 2. Reconstitute the dried peptides in 100 μL of freshly prepared 0.1% formic acid and vortex the tubes for 10 min to ensure thorough mixing. Give the tubes a brief short spin so that everything comes down at the bottom of the tubes.

Proteomics Workflow for Glioma CSF Analysis

105

3. Activate five tips initially with 20 μL of 50% acetonitrile in 0.1% formic acid and then with 20 μL of 100% acetonitrile in 0.1% formic acid. 4. Subsequently, wash the tips three times with 20 μL of 0.1% formic acid. 5. Pass 20 μL of reconstituted peptides from each tip (six times) for peptides to bind efficiently to the C18 column. 6. Wash the tips by passing 20 μL of 0.1% formic acid (three times). 7. Elute the peptides serially with 20 μL of 40% acetonitrile in 0.1% formic acid, 60% acetonitrile in 0.1% formic acid and 80% acetonitrile in 0.1% formic acid. 8. Dry the peptides using centrifugal vacuum concentrator at medium or low speed. 9. Store the dried tubes at 80  C until the next use. 3.2.5 iTRAQ 4-plex Labeling

1. Reconstitute the dried peptides in 25 μL of 0.5 M TEAB or dissolution buffer. Take 1 μL of the sample, dilute it to 5 μL by adding 4 μL of 0.5 M TEAB or dissolution buffer, and quantify by loading 2 μL in duplicates using Thermo Multiskan GO. 2. Perform peptide quantification to determine the peptide amount for labeling. 3. Take equal amount of digested peptides from all the samples for iTRAQ labeling (35–50 μg). As per your peptide quantification values, make up the volume using dissolution buffer so that all the sample tubes have the same volume. It will depend on the peptide amount to be used for labeling. The chemical structure of the iTRAQ reagents is shown in Fig. 4. The positions of the reporter group, the amine-specific peptide-reactive group, and the isobaric tag are shown in detail. 4. Allow each required vial of the iTRAQ reagent to reach room temperature. Give a short spin to bring the solutions to the bottom of the tubes. The volume of each label will be 20–25 μL (see Note 26). 5. Add 70 μL of ethanol (present in the kit) to each vial followed by brief vortexing and spinning. 6. Transfer the entire contents of each iTRAQ reagent (114/115/116/117) into two sample tubes. For example, in case one has two sets/biological replicates, transfer half volume of iTRAQ 114 reagent to the respective set 1 and set 2 tubes (~40 μL each).

106

Aishwarya A. Rao et al.

Fig. 4 Representative chemical structure of iTRAQ tag showing. (a) Reporter group mass that retains charge, (b) balance group to maintain the total mass of the isobaric tag to 145, and (c) NHS-ester-activated group that binds to the N-terminal of the protein

7. To ensure high labeling efficiency, add ~100 μL of ethanol to maintain a 70:30 ratio of organic/aqueous content (see Note 27). CAUTION: The amount of ethanol required may vary accordingly to your peptide sample volume. 8. Vortex each tube to mix, and then spin. 9. Incubate the tubes at room temperature for 2 h. 10. Quench the reaction using 200 μL of MilliQ water. CAUTION: Again, the amount of water required may vary according to the organic and aqueous volume. 11. Incubate the tubes at room temperature for 30 min. 12. After quenching, spin each iTRAQ reagent-labeled sample tube to bring the solution to the bottom of the tube. 13. Combine the contents of each iTRAQ reagent-labeled sample (114, 115, 116 and 117) into one tube (per sample set). 14. Vortex to mix, and then spin. 15. Dry the sample in a centrifugal vacuum concentrator (see Note 28). 3.2.6 Sample Fractionation Post iTRAQ Labeling

1. Prepare two C-18 tips per iTRAQ 4-plex labeling set. 2. Reconstitute the dried pooled sample in 100 μL of 0.1% trifluoroacetic acid and vortex for 10 min. Give the tubes a brief

Proteomics Workflow for Glioma CSF Analysis

107

short spin so that everything comes down at the bottom of the tubes. 3. Condition the tips twice with 100 μL of methanol and discard the liquid from the collection vial. 4. Wash the tips twice with 100 μL of 80% acetonitrile in 0.1% trifluoroacetic acid. 5. Equilibrate the tips trifluoroacetic acid.

twice

with

100

μL

of

0.1%

6. Pass 50 μL sample from each tip 6–8 times. 7. Wash the tips twice with 100 μL of 0.1% trifluoroacetic acid. 8. Elute using different concentration of acetonitrile: 10%, 15%, 20%, 25%, 30%, 35%, 50%, 70%, and 90% (take 100 μL in each case). 9. Pool the last three eluents, i.e., 50%, 70%, and 90%, in one tube to make a total of seven fractions. 3.2.7 Sample Submission for Mass Spectrometric Analysis

1. Reconstitute the dried samples in 15 μL of 0.1% formic acid. 2. Take 1 μL of the sample, dilute it to 5 μL by adding 4 μL of 0.1% formic acid, and quantify using Multiskan GO by loading 2 μL in duplicates on Thermo μDrop plate. 3. Dilute the samples with 0.1% formic acid to inject ~1 μg from each fraction in the mass spectrometer.

4

Notes 1. Discard all tips used for pipetting the CSF samples directly in a separate beaker containing disinfectant solution. Once all such tips are collected, wrap them in a foil and transfer them to a biohazard bag immediately. Wash the beaker thoroughly with soap and water. 2. The samples should be preferably kept on ice during sonication. 3. CSF samples typically have very high lipid content which could potentially interfere with the labeling of the samples with the dye. Treating the samples with ice-cold acetone helps to decrease the lipid content of the sample and ensures efficient dye-labeling. 4. The desalting step is done to remove the high salt content in CSF samples which could interfere with the MS run. 5. CyDyes need to be prepared only when a fresh DIGE kit is being used. Before preparing the dyes, make 1 mL aliquots of 99.8% anhydrous dimethylformamide (DMF) in ambercolored microcentrifuge tubes or clear microcentrifuge tubes

108

Aishwarya A. Rao et al.

wrapped in silver foil to protect them from light and avoid contamination with water. For every reconstitution, a fresh aliquot should be used. 6. Cy2 shows a deep yellow, Cy3 a deep red, and Cy5 a deep blue color. 7. Dye-swapping is performed to eliminate any artifacts resulting due to dye effects. 8. The volume of rehydration buffer containing IPG and DTT, mixed with the sample to rehydrate the IPG strips, varies as per the strip length. For a 7 cm strip, 125 μL of the rehydration buffer mix is used; for 11 cm strip, 200 μL of the rehydration buffer mix; for 18 cm strip, 350 μL of the rehydration buffer mix; and for 24 cm strip, 450 μL of the rehydration buffer mix is added. In our lab, we typically use IPG strips of 18 cm length and a pH range of 4–7. 9. In our lab, the 18 cm IPG strips are passively rehydrated for 16 h with the desalted and labeled sample solution. 10. Proper placement of the wicks is critical since they will help in conductivity and absorbing salts and other interfering material that move toward the edges of the strip during electrophoresis. 11. If your sample has salts or any other interfering compounds like detergents, IEF will stop during the run. Make sure that your sample is cleaned of these contaminants. 12. In our lab, electrophoresis for CSF samples is carried out initially at 100 V for 1 h and then at 350 V till the dye front runs out of the gel. 13. The Differential In-Gel Analysis (DIA) option of the DeCyder software is used for the analysis of the Cy2, Cy3, and Cy5 images obtained from one gel or gels which were run as technical replicates. 14. The Biological Variation Analysis (BVA) option of the DeCyder software is used for the analysis of the Cy2, Cy3, and Cy5 images obtained from gels used for running biological replicates. 15. The Batch Processor option of the DeCyder software is used for comparing a large number of gels simultaneously. 16. The Washing and Dehydration steps should be performed at least three times. If the stain is still seen in the gel pieces, repeat all the four steps for an additional 1–2 more times. Make sure that the last wash is always with 100 μL of Solution A. 17. All vacuum drying steps should be carried at low or medium speed. Do not perform these steps at high speed. 18. One may still see a small pellet even after adding 6 M urea. This is fine as here we have directly dried the CSF without any

Proteomics Workflow for Glioma CSF Analysis

109

cleaning step and not all sample contents will get dissolved in urea. Use the supernatant for all the subsequent steps, i.e., quantification and iTRAQ labeling, leaving the small pellet aside. 19. This is an important step, and all the samples should be quantified in duplicates to obtain an accurate quantification value. 20. Since the sample has urea, do not heat the samples before running them on an SDS-PAGE gel. 21. This is an important step as trypsin works best at pH 8–8.5. 22. If TCEP is not available, then one could use DTT as well. 23. Since IAA is light sensitive, incubation should be done in dark. 24. If one does not have a shaking incubator, then the samples can be kept in a normal dry bath or static incubator maintained at 37  C. 25. This step is done to remove salts or any other contamination which could interfere with the mass spectrometric run. 26. Vortexing is always done to uniformly mix the contents inside a vial, especially after any thawing process. Vortexing is often followed by a short spin (which can be performed using a bench-top Tarsons centrifuge which has a low speed) to bring the contents sticking at the wall to the bottom. 27. Also, please use absolute ethanol for adding the surplus amount of ethanol as the iTRAQ kits have limited volume of ethanol. 28. Since the pooled volume cannot be vacuum dried alone in one tube, one may dry them separately and pool before fractionation.

Acknowledgment We would like to thank the High-Resolution Mass SpectrometryBased Proteomics Research and Training Facility at IIT Bombay supported by the Department of Biotechnology (BT/PR13114/ INF/22/206/2015) for processing the samples submitted for protein identification and quantification. References 1. Rao JS (2003) Molecular mechanisms of glioma invasiveness: the role of proteases. Nat Rev Cancer 3(7):489–501 2. Maher EA, Furnari FB, Bachoo RM, Rowitch DH, Louis DN, Cavenee WK, DePinho RA (2001) Malignant glioma: genetics and biology

of a grave matter. Genes Dev 15 (11):1311–1333 3. Gladson CL, Prayson RA, Liu WM (2010) The pathobiology of glioma tumors. Annu Rev Pathol 5:33–50 4. Schittenhelm J (2017) Recent advances in subtyping tumors of the central nervous system

110

Aishwarya A. Rao et al.

using molecular data. Expert Rev Mol Diagn 17(1):83–94 5. Ostrom QT, Gittleman H, Truitt G, Boscia A, Kruchko C, Barnholtz-Sloan JS (2018) CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2011-2015. Neuro Oncol 20(suppl_4):iv1–iv86 6. Gollapalli K, Ray S, Srivastava R, Renu D, Singh P, Dhali S, BajpaiDikshit J, Srikanth R, Moiyadi A, Srivastava S (2012) Investigation of serum proteome alterations in human glioblastoma multiforme. Proteomics 14:2378–2390 7. Somasundaram K, Nijaguna MB, Kumar DM (2009) Serum proteomics of glioma: methods and applications. Expert Rev Mol Diagn 7:695–707 8. Gahoi N, Malhotra D, Moiyadi A, Varma SG, Gandhi MN, Srivastava S (2018) Multipronged proteomic analysis to study the glioma pathobiology using cerebrospinal fluid samples. Proteomics Clin Appl 3:e1700056 9. Brown KJ, Seol H, Pillai DK, Sankoorikal BJ, Formolo CA, Mac J, Edwards NJ, Rose MC, Hathout Y (2013) The human secretome atlas initiative: implications in health and disease conditions. Biochim Biophys Acta 1834 (11):2454–2461 10. Verheul C, Kleijn A, Lamfers MLM (2017) Cerebrospinal fluid biomarkers of malignancies located in the central nervous system. Handb Clin Neurol 146:139–169 11. Shen F, Zhang Y, Yao Y, Hua W, Zhang HS, Wu JS, Zhong P, Zhou LF (2014) Proteomic analysis of cerebrospinal fluid: toward the identification of biomarkers for gliomas. Neurosurg Rev 37(3):367–380 12. Tan Z, Liu R, Zheng L, Hao S, Fu C, Li Z, Deng X, Jang T, Merchant M, Whitin JC, Guo M, Cohen HJ, Recht L, Ling XB (2015) Cerebrospinal fluid protein dynamic driver network: at the crossroads of brain tumorigenesis. Methods 83:36–43 13. Guo J, Sun Z, Xiao S, Liu D, Jin G, Wang E, Zhou J, Zhou J (2009) Proteomic analysis of the cerebrospinal fluid of Parkinson’s disease patients. Cell Res 12:1401–1403 14. Khoonsari PE, H€aggmark A, Lo¨nnberg M, Mikus M, Kilander L, Lannfelt L, Bergquist J, Ingelsson M, Nilsson P, Kultima K, Shevchenko G (2016) Analysis of the cerebrospinal fluid proteome in Alzheimer’s disease. PLoS One 11(3):e0150672 ¨ hrfelt A, Brinkmalm G, 15. Sjo¨din S, Hansson O, O Zetterberg H, Brinkmalm A, Blennow K (2017) Mass spectrometric analysis of

cerebrospinal fluid ubiquitin in Alzheimer’s disease and Parkinsonian disorders. Proteomics Clin Appl 11(11–12) ˜ ez Galindo A, Wojcik J, 16. Dayon L, Nu´n Cominetti O, Corthe´sy J, Oikonomidi A, Henry H, Kussmann M, Migliavacca E, Severin I, Bowman GL, Popp J (2018) Alzheimer disease pathology and the cerebrospinal fluid proteome. Alzheimers Res Ther 10(1):66 17. Yuan X, Russell T, Wood G, Desiderio DM (2002) Analysis of the human lumbar cerebrospinal fluid proteome. Electrophoresis 7-8:1185–1196 18. Shalaby T, Achini F, Grotzer MA (2016) Targeting cerebrospinal fluid for discovery of brain cancer biomarkers. J Cancer Metastasis Treat 2:176–187 19. Marouga R, David S, Hawkins E (2005) The development of the DIGE system: 2D fluorescence difference gel analysis technology. Anal Bioanal Chem 382:669 20. Unlu¨ M, Morgan ME, Minden JS (1997) Difference gel electrophoresis: a single gel method for detecting changes in protein extracts. Electrophoresis 18(11):2071–2077 21. Gharbi S, Gaffney P, Yang A, Zvelebil MJ, Cramer R, Waterfield MD, Timms JF (2002) Evaluation of two-dimensional differential gel electrophoresis for proteomic expression analysis of a model breast cancer cell system. Mol Cell Proteomics 1(2):91–98 22. Ohlendieck K (ed) (2018) Difference gel electrophoresis methods and protocols. Methods in molecular biology, vol 1664. Humana Press, New York, NY, p 3 23. Alban A, David SO, Bjorkesten L, Andersson C, Sloge E, Lewis S, Currie I (2003) A novel experimental design for comparative two-dimensional gel analysis: two-dimensional difference gel electrophoresis incorporating a pooled internal standard. Proteomics 3(1):36–44 24. Ross PL, Huang YN, Marchese JN, Williamson B, Parker K, Hattan S, Khainovski N, Pillai S, Dey S, Daniels S, Purkayastha S, Juhasz P, Martin S, BartletJones M, He F, Jacobson A, Pappin DJ (2004) Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics 3(12):1154–1169 25. Rappsilber J, Mann M, Ishihama Y (2007) Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips. Nat Protoc 2 (8):1896–1906

Chapter 7 Peptidomic Workflow Applied to Cerebrospinal Fluid Analysis Rustam H. Ziganshin, Sergey I. Kovalchuk, and Igor V. Azarkin Abstract Proteo-peptidomic profiling of biofluids is used to identify disease biomarkers and to study molecular mechanisms of pathology development. Previously, we studied changes in cerebrospinal fluid (CSF) and blood plasma associated with Guillain-Barre syndrome (GBS)—a rare and severe disorder of the peripheral nervous system with an unknown etiology. Here, we describe the workflow for the analysis of endogenous peptides from CSF. The procedure covers sample preparation, liquid chromatography-mass spectrometry (LC-MS) analysis, and bioinformatics analysis and allows identification of more than 1100 peptides from 181 protein groups in ~3 h from a single CSF sample derived from non-neurological, non-oncological patients. Key words Endogenous peptides, Peptide isolation, Peptidome, Cerebrospinal fluid, LC-MS, SDBRPS StageTips

1

Introduction Cerebrospinal fluid (CSF) major functions are neuronal system protection and osmotic pressure maintenance. It also plays an important role in the metabolism of nervous tissue, absorbing catabolic products and supplying nerve cells with the substances necessary both for their functioning and regulation [1]. CSF proteo-peptidomic profile depends on the pathophysiological state of the organism, reflecting molecular mechanisms of various neurodegenerative diseases and providing a potential source of their diagnostic signature molecules. Proteome analysis of CSF can be carried out by any of the standard proteomic workflow, for example, by prefractionation of proteins by 1D SDS-PAGE, followed by in-gel trypsin digestion and LC-MS analysis of the extracted peptide fragments [2]. At the same time, a comprehensive peptidome analysis of CSF is a much less trivial task. Among the biological fluids, CSF composition is

Enrique Santamarı´a and Joaquı´n Ferna´ndez-Irigoyen (eds.), Cerebrospinal Fluid (CSF) Proteomics: Methods and Protocols, Methods in Molecular Biology, vol. 2044, https://doi.org/10.1007/978-1-4939-9706-0_7, © Springer Science+Business Media, LLC, part of Springer Nature 2019

111

112

Rustam H. Ziganshin et al.

close to blood plasma/serum. And endogenous peptide analysis in CSF has the same problems as blood peptidome analysis: extremely low endogenous peptide concentration and exceptionally high diversity of peptides against a background of high protein concentration with several major high-abundant proteins further complicated by almost quantitative association of peptides with highabundant proteins [3, 4]. There are two groups of approaches for desorption of peptides from high-abundant plasma proteins: sample treatment by high concentrations of organic solvents (mainly acetonitrile) [5–7] and dissociation of peptide-protein complexes with chaotropic substances in the presence of reducing reagents (such as urea with DTT) [8, 9]. Subsequent separation of peptides and proteins can be also carried out in several ways. When peptides are desorbed from proteins by high organic solvent concentration, a significant amount of protein material is removed as a result of precipitation [8, 10–12]. Also, peptides can be separated by ultrafiltration [13–16] or by various types of liquid chromatography [9, 17–20]. Previously we compared most of the published methods within one experimental setup [21]. Unique peptide identification numbers ranged from 176 to 2156 from method to method. The best results were obtained by peptides extraction from plasma with 2% sodium deoxycholate (SDC) upon heating, while the others (such as acetonitrile extraction, extraction with 6 M guanidine hydrochloride upon heating, and differential solubilization) were not as good. CSF peptide extraction is different from plasma and is not so well studied. We tested different peptide extraction approaches on CSF, and here we describe our best optimized method based on heating the CSF sample in the presence of SDC and reducing/alkylation agents with subsequent peptide isolation using SDB-RPS StageTips.

2

Materials

2.1 Solutions and Reagents

1. Milli-Q water (H2O). 2. 1 M Tris–HCl pH 9.56 (stock solution): weigh 0.15 g Tris–HCl and 1.09 g Tris base, transfer to the 15 ml Falcon tube, and add 9.16 ml water. Mix and store at 4  C (see Note 1). 3. 0.2 M Tris(2-carboxyethyl)phosphine hydrochloride (TCEP) (stock solution): weigh 58 mg of TCEP in a 1.5 ml microcentrifuge tube and add 0.96 ml·H2O. Mix and store at 20  C (see Note 2). 4. 0.4 M 2-chloroacetamide (2-CAA) (stock solution): weigh 37.4 mg CAA in a 1.5 ml microcentrifuge tube and add 0.98 ml H2O. Mix and store at 20  C (see Note 3).

Peptidomic Workflow Applied to Cerebrospinal Fluid Analysis

113

5. Sodium deoxycholate (SDC). 6. Acetonitrile, HPLC grade. 7. Trifluoroacetic acid (TFA) Solution 1: 10% TFA in H2O (see Note 4). 8. TFA Solution 2: 1% TFA in H2O (see Note 4). 9. TFA Solution 3: 0.2% TFA in H2O (see Note 4). 10. Ethyl acetate, HPLC grade. 11. Elution Solution: 50% acetonitrile, 5% ammonia in H2O (see Note 5). 12. Loading Solution: 2% acetonitrile, 0.1% TFA, 97.9% H2O. 2.2 SDB-RPS Stage Tips

SDB-RPS StageTips are prepared as described earlier [18] with modifications. Two pieces of Empore SDB-RPS membrane (3 M) are stamped out using a blunt-ended Hamilton needle (part# 91014: Metal (N) Hub, Point Style 3, gauge 14) and forced into the 200-μl pipette tip end by a piece of 1/1600 OD PEEK tubing (1535, Upchurch Scientific). The membrane stack is further pressed down and compressed with a piece of a 1/1600 OD PEEK tubing.

2.3

O-Tube

It is used as a holder for the StageTip and for liquid collection during centrifugal StageTip filtration. Either a 2 ml microcentrifuge tube (72.691, Sarstedt) with an opening punctured in the tube’s lid or an Eppendorf tube with a pipette tip lid-adapter can be used. SDB-RPS StageTip and O-tube comprise the Spin-unit (see Note 6).

2.4

Equipment

1. Bench-top centrifuge (5415D, Eppendorf). 2. Mixing and heating block suitable for handling 1.5 ml microcentrifuge tubes (ThermoMixer C, Eppendorf). 3. Vortexer (IKA Vortex 3, IKA). 4. Vacuum Concentrator (SpeedVac, Thermo). 5. Ultrasonic water bath (S100 Elmasonic, Elma).

3

Methods The protocol consists of the following steps: 1. Heating-assisted peptide desorption from proteins by SDC in reducing/alkylation conditions. 2. Peptide isolation using SDB-RPS StageTips. 3. LC-MS analysis. 4. Bioinformatics analysis.

114

Rustam H. Ziganshin et al.

3.1 Peptide Desorption from Proteins

1. CSF samples were obtained from the patients under sterile conditions using standard lumbar puncture techniques. After needle administration and mandrin removal, the cerebrospinal liquid (1 ml) obtained from the middle portion was collected in a dry clean tube without preservatives, immediately frozen, and stored at 80  C. 2. Weigh 12 mg SDC in a 1.5 ml microcentrifuge tube. 3. Thaw CSF sample on ice and centrifuge at 15,000  g for 10 min at 4  C. 4. Into the tube with SDC, transfer 500 μl of the freshly centrifuged CSF sample, 30 μl of 1 M Tris pH 9.56, 30 μl of 0.2 M TCEP, 30 μl of 0.4 M CAA, and mix by vortexing until SDC is dissolved. The total volume is around 600 μl. 5. Heat the solution at 95  C for 10 min using mixing and heating block (at 500 RPM). 6. Cool solution to ambient temperature and add 60 μl of TFA solution 1 to precipitate deoxycholic acid. 7. Add 600 μl of ethyl acetate to the tube and vortex it vigorously for 30 s. 8. Centrifuge the sample for 1 min at 15,000  g, and collect and discard the upper ethyl acetate layer by a 200 μl pipette. The ethyl acetate layer must be carefully removed as fully as possible. 9. Repeat steps 7 to 8 two more times.

3.2 Peptide Isolation Using SDB-RPS StageTips

1. Prepare two SDB-RPS StageTips for each CSF sample. 2. Insert SDB-RPS StageTips into O-tubes and fill them with the prepared CSF solution: add 300 μl of CSF sample per StageTip (600 μl total). 3. Centrifuge Spin-unit at 300  g until the CSF sample goes through the StageTips (6–7 min). Discard the flow-through. 4. Load 100 μl of TFA Solution 2 and 100 μl of ethyl acetate onto StageTips and centrifuge at 300  g until all the solution goes through (3–5 min). Discard the flow-through (see Note 7). 5. Load 100 μl of TFA Solution 2 onto StageTips and centrifuge at 300  g until all the solution goes through (3–5 min). Discard the flow-through. 6. Load 100 μl of TFA Solution 3 onto StageTips and centrifuge at 300  g until all the solution goes through (3–5 min). Discard the flow-through. 7. Transfer the StageTips into new O-tubes. Load 50 μl of Elution Solution onto StageTips and centrifuge at 300  g until all the solution goes through.

Peptidomic Workflow Applied to Cerebrospinal Fluid Analysis

115

8. Mix the eluates from O-tubes in a single 0.5 ml microcentrifuge tube, freeze it in liquid nitrogen, dry using vacuum concentrator, and store at 80  C until the LC-MS analyses (see Note 8). 3.3

LC-MS Analysis

Prepare the peptide sample for the analysis: add 12 μl of Loading Solution and dissolve by 1 min sonication in an ultrasonic water bath. Centrifuge for 10 min at 15,000  g and transfer the solution into the autosampler vial. LC-MS analysis is performed with an Ultimate 3000 Nano LC System (Thermo Fisher Scientific) coupled to a Q Exactive HF benchtop Orbitrap mass spectrometer (Thermo Fisher Scientific) via a nanoelectrospray source (Thermo Fisher Scientific). The mobile phases are as follows: (a) 0.1% (v/v) formic acid in H2O and (b) 0.1% (v/v) formic acid, 80% (v/v) acetonitrile, 19.9% (v/v) H2O. Samples are loaded onto a trapping column (100 μm ID, 20 mm length, packed in-house with Aeris Peptide XB-C18 2.6 μm resin (Phenomenex)) in mobile phase A at flow rate 5 μl/min for 5 min and separated with a 137 min linear gradient of mobile phase B (see Table 1) at a flow rate of 350 nl/min on a 50-cm 75-μm ID column packed in-house with Aeris Peptide XB-C18 2.6 μm resin (Phenomenex) [22]. The precolumn and column are heated to 40  C. Peptides are analyzed on the Q Exactive HF benchtop Orbitrap mass spectrometer (Thermo Fisher Scientific), with one full scan (300–1400 m/z, R ¼ 60,000 at 200 m/z), AGC target 3e6 ions, followed by up to 15 datadependent MS/MS scans with higher-energy collisional dissociation (HCD) (AGC target 1e5, max fill time 60 ms, isolation window 1.2 m/z, normalized collision energy (NCE) 28%, underfill ratio 2%, resolution 15,000, fixed first mass 100 m/z). Other settings (charge exclusion, unassigned, 1, >6; peptide match, preferred; exclude isotopes, on; dynamic exclusion, 30 s) were enabled. An example of the expected total ion current chromatogram for CSF peptide isolate is shown in Fig. 1. Table 1 Chromatographic gradient used for separation of CSF peptides Time (min)

Mobile phase B (%)

0

4

4

6

96

28

116

45

123

100

128

100

129

4

137

4

116

Rustam H. Ziganshin et al.

Fig. 1 Total ion current chromatogram obtained for CSF peptide isolate

3.4 Bioinformatics Analysis

4

MS raw files were analyzed by PEAKS Studio 8.0 (Bioinformatics Solutions Inc.) [23], and peak lists were searched against human UniProt FASTA (canonical and isoform) database version of July 2016 (176,494 entries) with cysteine carbamidomethylation as a fixed modification and methionine oxidation and asparagine and glutamine deamidation as variable modifications. False discovery rate was set to 0.01 for Peptide-Spectrum matches and was determined by searching a reverse database. No enzyme specificity was set in the database search. Peptide identification was performed with an allowed initial precursor mass deviation up to 10 p.p.m. and an allowed fragment mass deviation 0.05 Da. Expected number of identifications is >1000 unique peptides from >150 protein groups from a single CSF sample from a single LC-MS run.

Notes 1. The Tris–HCl stock solution can be stored at 4  C for 1 month. 2. The TCEP stock solution can be prepared in large quantity, aliquoted, and stored at 20  C indefinitely.

Peptidomic Workflow Applied to Cerebrospinal Fluid Analysis

117

3. The 2-CAA stock solution prepared in large batches could be frozen in aliquots and stored at 20  C for 1 month. Aliquots should be thawed only once. 4. The TFA solutions can be stored at room temperature in a tightly closed container for 1 month. 5. Prepare daily. 6. The opening could be punctured in the tube lid using a small electric drill with a drill diameter of 4 mm. 7. Deoxycholic acid must be removed completely from the peptide sample in order to prevent its precipitation in a trapping column during LC-MS analyses. 8. Freezing eluates by liquid nitrogen speeds up the drying process and helps resolubilization. If accidentally melted during the drying process, and dried from liquid state, do not dry the sample completely: keep 1–2 μl of the liquid to reduce peptide absorption to plastic and to help resolubilization. References 1. Illes S (2017) More than a drainage fluid: the role of CSF in signaling in the brain and other effects on brain tissue. Handb Clin Neurol 146:33–46. https://doi.org/10.1016/B9780-12-804279-3.00003-4 2. Ziganshin RH, Ivanova OM, Lomakin YA, Belogurov AA Jr, Kovalchuk SI, Azarkin IV, Arapidi GP, Anikanov NA, Shender VO, Piradov MA, Suponeva NA, Vorobyeva AA, Gabibov AG, Ivanov VT, Govorun VM (2016) The pathogenesis of the demyelinating form of Guillain-Barre syndrome (GBS): proteopeptidomic and immunological profiling of physiological fluids. Mol Cell Proteomics 15 (7):2366–2378. https://doi.org/10.1074/ mcp.M115.056036 3. Lowenthal MS, Mehta AI, Frogale K, Bandle RW, Araujo RP, Hood BL, Veenstra TD, Conrads TP, Goldsmith P, Fishman D, Petricoin EF 3rd, Liotta LA (2005) Analysis of albuminassociated peptides and proteins from ovarian cancer patients. Clin Chem 51 (10):1933–1945. https://doi.org/10.1373/ clinchem.2005.052944 4. Mehta AI, Ross S, Lowenthal MS, Fusaro V, Fishman DA, Petricoin EF 3rd, Liotta LA (2003) Biomarker amplification by serum carrier protein binding. Dis Markers 19(1):1–10 5. Chertov O, Biragyn A, Kwak LW, Simpson JT, Boronina T, Hoang VM, Prieto DA, Conrads TP, Veenstra TD, Fisher RJ (2004) Organic solvent extraction of proteins and peptides from serum as an effective sample preparation

for detection and identification of biomarkers by mass spectrometry. Proteomics 4 (4):1195–1203. https://doi.org/10.1002/ pmic.200300677 6. Tucholska M, Florentinus A, Williams D, Marshall JG (2010) The endogenous peptides of normal human serum extracted from the acetonitrile-insoluble precipitate using modified aqueous buffer with analysis by LC-ESIPaul ion trap and Qq-TOF. J Proteome 73 (6):1254–1269. https://doi.org/10.1016/j. jprot.2010.02.022 7. Williams D, Ackloo S, Zhu P, Bowden P, Evans KR, Addison CL, Lock C, Marshall JG (2010) Precipitation and selective extraction of human serum endogenous peptides with analysis by quadrupole time-of-flight mass spectrometry reveals posttranslational modifications and low-abundance peptides. Anal Bioanal Chem 396(3):1223–1247. https://doi.org/10. 1007/s00216-009-3345-0 8. Orvisky E, Drake SK, Martin BM, AbdelHamid M, Ressom HW, Varghese RS, An Y, Saha D, Hortin GL, Loffredo CA, Goldman R (2006) Enrichment of low molecular weight fraction of serum for MS analysis of peptides associated with hepatocellular carcinoma. Proteomics 6(9):2895–2902. https://doi.org/10. 1002/pmic.200500443 9. Tirumalai RS, Chan KC, Prieto DA, Issaq HJ, Conrads TP, Veenstra TD (2003) Characterization of the low molecular weight human serum proteome. Mol Cell Proteomics 2

118

Rustam H. Ziganshin et al.

(10):1096–1103. https://doi.org/10.1074/ mcp.M300031-MCP200 10. Wu J, An Y, Pu H, Shan Y, Ren X, An M, Wang Q, Wei S, Ji J (2010) Enrichment of serum low-molecular-weight proteins using C18 absorbent under urea/dithiothreitol denatured environment. Anal Biochem 398 (1):34–44. https://doi.org/10.1016/j.ab. 2009.10.047 11. Kawashima Y, Fukutomi T, Tomonaga T, Takahashi H, Nomura F, Maeda T, Kodera Y (2010) High-yield peptide-extraction method for the discovery of subnanomolar biomarkers from small serum samples. J Proteome Res 9 (4):1694–1705. https://doi.org/10.1021/ pr9008018 12. Greening DW, Simpson RJ (2010) A centrifugal ultrafiltration strategy for isolating the low-molecular weight ( df4[,1]=snp_id > write.table(df4, "genotype.txt", append=F,quote=F, row.names=F, col.names=F, sep="\t") # outputs the generated file

The file ‘genotype.txt’ is generated for use in subsequent analyses. 3.4.1 CSF Protein Concentration Data

The protein concentration data need to be formatted as a text file with a header line indicating the name of each sample plus one line per protein. The first column is the protein ID and the subsequent columns are the normalized concentration in each sample. Supplementary File 3 (CSF_protein.txt) is an example file derived from our previous study [1].

3.4.2 Covariates Data

The covariates data file consists of a header line indicating the name of each sample plus one line per covariate. In the example file ‘covariate.txt’ (Supplementary File 4) derived from our previous study [1], the second row shows the sex and the third row shows the age of the participants.

3.4.3 Gene Location

This file consists of a header line plus one line per protein. The first column is the gene ID indicating the gene that expresses the protein. The second to fourth columns are the chromosome number, starting position, and ending position of the gene, respectively. Supplementary File 5 (gene_location.txt) is an example file derived from our previous study [1].

3.4.4 SNP Location

This file consists of a header line plus one line per SNP. The first column is the SNP ID. The second and third columns are the chromosome number and the position of the SNP. A sample file is provided as Supplementary File 6 (SNP_position.txt). Save the above five files in the same folder. After setting your working directory to that folder with ‘setwd’ function, Matrix eQTL can be run in R by the following commands.

372

Daimei Sasayama et al.

> library(MatrixEQTL); > useModel=modelLINEAR; # modelANOVA or modelLINEAR or modelLINEAR_CROSS (see Note 7) > SNP_file_name=paste("genotype.txt",sep=""); > snps_location_file_name=paste("SNP_position.txt", sep=""); > expression_file_name=paste("CSF_protein.txt",sep=""); > gene_location_file_name=paste("gene_location.txt", sep=""); > covariates_file_name=paste("covariate.txt",sep="") > output_file_name_cis= "cis_pqtl.txt"; > output_file_name_tra= "trans_pqtl.txt"; > pvOutputThreshold_cis=1e-3; > pvOutputThreshold_tra=1e-5; # (see Note 8) > errorCovariance=numeric(); # Set to numeric() for identity > cisDist=5e6; # Distance for local gene-SNP pairs (see Note 9) > snps=SlicedData$new(); # (see Note 10) > snps$fileDelimiter="\t"; # the TAB character > snps$fileOmitCharacters="NA"; # denote missing values > snps$fileSkipRows=1; # one row of column labels > snps$fileSkipColumns=1; # one column of row labels; > snps$fileSliceSize=2000; # read file in pieces of 2,000 rows > snps$LoadFile( SNP_file_name ); > gene=SlicedData$new(); > gene$fileDelimiter="\t"; # the TAB character > gene$fileOmitCharacters="NA"; # denote missing values; > gene$fileSkipRows=1; # one row of column labels > gene$fileSkipColumns=1; # one column of row labels > gene$fileSliceSize=2000; # read file in slices of 2,000 rows > gene$LoadFile(expression_file_name); > cvrt=SlicedData$new(); > cvrt$fileDelimiter="\t"; # the TAB character > cvrt$fileOmitCharacters="NA"; # denote missing values; > cvrt$fileSkipRows=1; # one row of column labels > cvrt$fileSkipColumns=1; # one column of row labels > if(length(covariates_file_name)>0) {cvrt$LoadFile(covariates_file_name);} > snpspos = read.table(snps_location_file_name, header = TRUE, stringsAsFactors = FALSE); > genepos = read.table(gene_location_file_name, header = TRUE, stringsAsFactors = FALSE);

CSF Protein Trait Loci Mapping

373

> me=Matrix_eQTL_main(snps=snps, gene=gene, cvrt=cvrt, output_file_name=output_file_name_tra, pvOutputThreshold=pvOutputThreshold_tra, useModel=useModel, errorCovariance=errorCovariance, cis=output_file_name_cis,

verbose=TRUE,

output_file_name.

pvOutputThreshold.cis=pvOutputThreshold_cis,

snpspos=snpspos, genepos=genepos, cisDist=cisDist, pvalue.hist="qqplot", min.pv. by.genesnp=FALSE,noFDRsaveMemory=FALSE); > cat(’Analysis done in: ’, me$time.in.sec, ’ seconds’, ’\n’); > cat(’Detected local eQTLs:’, ’\n’); > cat(’Detected distant eQTLs:’, ’\n’); > plot(me) # This plots the Q-Q plot of local and distant p-values

A Q-Q plot (Fig. 2) and two files, ‘cis_pqtl.txt’ and’ trans_pqtl. txt’ (Supplementary Files 7 and 8), will be generated. ‘cis_pqtl.txt’ and’ trans_pqtl.txt’ show the results of the pQTL analysis and include the beta value, t-stat, p-value, and false discovery rate for each significant association. In this chapter, we showed a step-by-step example of a pQTL analysis using sample data. The methods shown here which were adapted from various eQTL studies may be potentially applicable to future metabolomic quantitative trait loci (mQTL) mapping studies as well. As mentioned, it is preferable to standardize the procedure of lumbar puncture to minimize the pre-analytical error. However, strict standardization may not be necessary for achieving optimal results if adequate sample size is obtained. Nevertheless, factors that may potentially influence the CSF protein levels should be recorded so that the influence of such factors may be investigated in the future. The main analytical challenges that remain include normalization of protein concentration data and multiple testing. Although various normalization techniques and multiple testing correction strategies have been proposed, each method has advantages and disadvantages. We recommend that multiple statistical approaches be used and compared to validate the findings of the study.

4

Notes 1. Human CSF production is known to be affected by circadian rhythms [17]. However, little is known concerning the influence of circadian rhythms on CSF protein levels. Although the concentrations of some CSF proteins have been reported as having circadian fluctuation, they were only slightly higher than the variability of the assay [18, 19]. A study investigating the effects of various factors, such as fasting, smoking, exercise, caffeine intake, and alcohol use, is difficult to perform. Thus,

374

Daimei Sasayama et al.

experimental evidence in humans regarding the influence of these factors on CSF proteins are scarce. 2. The use of atraumatic (pencil-point) needles could reduce the risk of post-lumbar puncture headache [20]. A larger needle gauge was significantly associated with increased post-lumbar puncture headache when traditional cutting needles were used. However, a similar association was not observed when atraumatic needles were used [21]. 3. It is preferable that the position during CSF collection should not be changed within the same study. The concentration gradient within the lumbar sac may differ in sitting and decubitus positions [22]. 4. Local anesthetics are often performed with 23 G needles. However, it has been reported that thinner needles are less painful [23]. Thus, a 27 G or thinner needle is recommended for lidocaine infiltration. 5. According to a study by Peskind et al. [24], the amount of CSF collected was unrelated with the risk of post-lumbar puncture headache. Position during lumbar puncture or minutes of recumbent rest following lumbar puncture also did not have significant influence on post-lumbar puncture headache. 6. Kauwe et al. [25] examined the associations between 5.8 M SNPs and 59 proteins in a total of 574 CSF samples and identified 335 SNPs associated with 5 CSF proteins (nominal p < 1.47  1010; Bonferroni-corrected p < 0.05 for 342 million tests). Sasayama et al. [1] examined the associations between 514,227 SNPs and 1126 proteins in 133 CSF samples and identified 446 SNPs associated with 65 CSF proteins (nominal p < 7.65  109; false discovery rate-corrected p < 0.01; |Spearman’s rho| > 0.462). The genotype data and the CSF protein concentration data used in the study by Sasayama et al. can be downloaded from GEO (GSE83708, GSE83709, and GSE83710). 7. The ‘useModel’ parameter can be set to ‘modelLINEAR’, ‘modelANOVA’, or ‘modelLINEAR_CROSS’. ‘modelLINEAR’ assumes an additive effect of the genotype, analysis of variance (ANOVA) allows to have both additive and dominant effects, and the last model tests for the significance of the interaction between the genotype and the last covariate in the covariates data file. 8. The p-value threshold will determine the threshold for saving SNP–protein associations in the output file. For a large dataset, it is recommended that the threshold be set to a low value, especially for the trans analysis, to avoid an excessively large output file.

CSF Protein Trait Loci Mapping

375

9. The ‘cisDist’ parameter determines the maximum distance for the gene–SNP pair to be identified as cis. Many studies define cis-eQTL as an SNP associated with expression levels of a gene within a 1 Mb (megabase) distance. However, Kirsten et al. [26] showed that the density of eQTLs was enriched even at a distance of 5 Mb away from the transcribed region of the gene. Therefore, we defined cis-eQTL as an SNP within 5 Mb in our example analysis. 10. The file delimiter can be set to tabulation “\t”, comma “,”, space ““, etc. The string representation for missing values, the number of rows with column labels, and the number of columns with row labels can alsobe set. References 1. Sasayama D, Hattori K, Ogawa S, Yokota Y, Matsumura R, Teraishi T, Hori H, Ota M, Yoshida S, Kunugi H (2017) Genome-wide quantitative trait loci mapping of the human cerebrospinal fluid proteome. Hum Mol Genet 26(1):44–51. https://doi.org/10.1093/ hmg/ddw366 2. Sasayama D, Hori H, Nakamura S, Miyata R, Teraishi T, Hattori K, Ota M, Yamamoto N, Higuchi T, Amano N, Kunugi H (2013) Identification of single nucleotide polymorphisms regulating peripheral blood mRNA expression with genome-wide significance: an eQTL study in the Japanese population. PLoS One 8(1): e54967. https://doi.org/10.1371/journal. pone.0054967 3. Petretto E, Mangion J, Dickens NJ, Cook SA, Kumaran MK, Lu H, Fischer J, Maatz H, Kren V, Pravenec M, Hubner N, Aitman TJ (2006) Heritability and tissue specificity of expression quantitative trait loci. PLoS Genet 2(10):e172. https://doi.org/10.1371/jour nal.pgen.0020172 4. Stranger BE, Forrest MS, Clark AG, Minichiello MJ, Deutsch S, Lyle R, Hunt S, Kahl B, Antonarakis SE, Tavare S, Deloukas P, Dermitzakis ET (2005) Genome-wide associations of gene expression variation in humans. PLoS Genet 1(6):e78 5. Kim Y, Xia K, Tao R, Giusti-Rodriguez P, Vladimirov V, van den Oord E, Sullivan PF (2014) A meta-analysis of gene expression quantitative trait loci in brain. Transl Psychiatry 4:e459. https://doi.org/10.1038/tp.2014.96 6. Lourdusamy A, Newhouse S, Lunnon K, Proitsi P, Powell J, Hodges A, Nelson SK, Stewart A, Williams S, Kloszewska I, Mecocci P, Soininen H, Tsolaki M, Vellas B, Lovestone S, AddNeuroMed C, Dobson R, Alzheimer’s Disease Neuroimaging I (2012)

Identification of cis-regulatory variation influencing protein abundance levels in human plasma. Hum Mol Genet 21(16):3719–3726. https://doi.org/10.1093/hmg/dds186 7. Wu L, Candille SI, Choi Y, Xie D, Jiang L, LiPook-Than J, Tang H, Snyder M (2013) Variation and genetic control of protein abundance in humans. Nature 499(7456):79–82. https:// doi.org/10.1038/nature12223 8. Tarnaris A, Toma AK, Chapman MD, Petzold A, Keir G, Kitchen ND, Watkins LD (2011) Rostrocaudal dynamics of CSF biomarkers. Neurochem Res 36(3):528–532. https:// doi.org/10.1007/s11064-010-0374-1 9. Teunissen CE, Petzold A, Bennett JL, Berven FS, Brundin L, Comabella M, Franciotta D, Frederiksen JL, Fleming JO, Furlan R, Hintzen RQ, Hughes SG, Johnson MH, Krasulova E, Kuhle J, Magnone MC, Rajda C, Rejdak K, Schmidt HK, van Pesch V, Waubant E, Wolf C, Giovannoni G, Hemmer B, Tumani H, Deisenhammer F (2009) A consensus protocol for the standardization of cerebrospinal fluid collection and biobanking. Neurology 73(22):1914–1922. https://doi. org/10.1212/WNL.0b013e3181c47cc2 10. Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ (2015) Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4:7. https://doi. org/10.1186/s13742-015-0047-8 11. Tanisawa K, Arai Y, Hirose N, Shimokata H, Yamada Y, Kawai H, Kojima M, Obuchi S, Hirano H, Yoshida H, Suzuki H, Fujiwara Y, Ihara K, Sugaya M, Arai T, Mori S, Sawabe M, Sato N, Muramatsu M, Higuchi M, Liu YW, Kong QP, Tanaka M (2017) Exome-wide association study identifies CLEC3B missense variant p.S106G as being associated with extreme longevity in east asian populations. J Gerontol

376

Daimei Sasayama et al.

A Biol Sci Med Sci 72(3):309–318. https:// doi.org/10.1093/gerona/glw074 12. Wang D, Sun Y, Stang P, Berlin JA, Wilcox MA, Li Q (2009) Comparison of methods for correcting population stratification in a genome-wide association study of rheumatoid arthritis: principal-component analysis versus multidimensional scaling. BMC Proc 3(Suppl 7):S109 13. Valikangas T, Suomi T, Elo LL (2018) A systematic evaluation of normalization methods in quantitative label-free proteomics. Brief Bioinform 19(1):1–11. https://doi.org/10.1093/ bib/bbw095 14. Sun W (2012) A statistical framework for eQTL mapping using RNA-seq data. Biometrics 68(1):1–11. https://doi.org/10. 1111/j.1541-0420.2011.01654.x 15. Shabalin AA (2012) Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics (Oxford, England) 28 (10):1353–1358. https://doi.org/10.1093/ bioinformatics/bts163 16. R Core Team (2015) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna 17. Nilsson C, Stahlberg F, Thomsen C, Henriksen O, Herning M, Owman C (1992) Circadian variation in human cerebrospinal fluid production measured by magnetic resonance imaging. Am J Phys 262(1 Pt 2): R20–R24. https://doi.org/10.1152/ajpregu. 1992.262.1.R20 18. Bateman RJ, Wen G, Morris JC, Holtzman DM (2007) Fluctuations of CSF amyloid-beta levels: implications for a diagnostic and therapeutic biomarker. Neurology 68(9):666–669. https://doi.org/10.1212/01.wnl. 0000256043.50901.e3 19. Moghekar A, Goh J, Li M, Albert M, O’Brien RJ (2012) Cerebrospinal fluid Abeta and tau level fluctuation in an older clinical cohort. Arch Neurol 69(2):246–250. https://doi. org/10.1001/archneurol.2011.732 20. Carson D, Serpell M (1996) Choosing the best needle for diagnostic lumbar puncture. Neurology 47(1):33–37

21. Zorrilla-Vaca A, Mathur V, Wu CL, Grant MC (2018) The impact of spinal needle selection on postdural puncture headache: a metaanalysis and metaregression of randomized studies. Reg Anesth Pain Med 43 (5):502–508. https://doi.org/10.1097/AAP. 0000000000000775 22. Siever L, Kraemer H, Sack R, Angwin P, Berger P, Zarcone V, Barchas J, Brodie HK (1975) Gradients of biogenic amine metabolites in cerebrospinal fluid. Dis Nerv Syst 36 (1):13–16 23. Wago KJ, Skarsvag TI, Lundbom JS, Tangen LF, Ballo S, Hjelseng T, Finsen V (2016) The importance of needle gauge for pain during injection of lidocaine. J Plast Surg Hand Surg 50(2):115–118. https://doi.org/10.3109/ 2000656X.2015.1111223 24. Peskind ER, Riekse R, Quinn JF, Kaye J, Clark CM, Farlow MR, Decarli C, Chabal C, Vavrek D, Raskind MA, Galasko D (2005) Safety and acceptability of the research lumbar puncture. Alzheimer Dis Assoc Disord 19 (4):220–225 25. Kauwe JS, Bailey MH, Ridge PG, Perry R, Wadsworth ME, Hoyt KL, Staley LA, Karch CM, Harari O, Cruchaga C, Ainscough BJ, Bales K, Pickering EH, Bertelsen S, Alzheimer’s Disease Neuroimaging I, Fagan AM, Holtzman DM, Morris JC, Goate AM (2014) Genome-wide association study of CSF levels of 59 Alzheimer’s disease candidate proteins: significant associations with proteins involved in amyloid processing and inflammation. PLoS Genet 10(10):e1004758. https://doi.org/10. 1371/journal.pgen.1004758 26. Kirsten H, Al-Hasani H, Holdt L, Gross A, Beutner F, Krohn K, Horn K, Ahnert P, Burkhardt R, Reiche K, Hackermuller J, Loffler M, Teupser D, Thiery J, Scholz M (2015) Dissecting the genetics of the human transcriptome identifies novel trait-related trans-eQTLs and corroborates the regulatory relevance of non-protein coding locidagger. Hum Mol Genet 24(16):4746–4763. https:// doi.org/10.1093/hmg/ddv194

Chapter 25 Essential Features and Use Cases of the Cerebrospinal Fluid Proteome Resource (CSF-PR) Astrid Guldbrandsen, Yehia Mokhtar Farag, Ragnhild Reehorst Lereim, Frode S. Berven, and Harald Barsnes Abstract Every year, a large number of published studies present biomarkers for various neurological disorders. Many of these studies are based on mass spectrometry proteomics data and describe comparison of the abundance of proteins in cerebrospinal fluid between two or more disease groups. As the number of such studies is growing, it is no longer straightforward to obtain an overview of which specific proteins are increased or decreased between the numerous relevant diseases and their many subcategories, or to see the larger picture or trends between related diseases. To alleviate this situation, we therefore mined the literature for mass spectrometry–based proteomics studies including quantitative protein data from cerebrospinal fluid of patients with multiple sclerosis, Alzheimer’s disease, and Parkinson’s disease and organized the extracted data in the Cerebrospinal Fluid Proteome Resource (CSF-PR). CSF-PR is freely available online at http://probe.uib.no/csf-pr, is highly interactive, and allows for easy navigation, visualization, and export of the published scientific data. This chapter will guide the user through some of the most important features of the tool and show examples of the suggested use cases. Key words Mass spectrometry, Cerebrospinal fluid, Neurodegenerative/neurological disorders/diseases, Biomarkers, Data mining, Data visualization, Multiple sclerosis, Parkinson’s disease, Alzheimer’s disease

1

Introduction Cerebrospinal fluid (CSF) surrounds the brain and spinal cord and is believed to be a promising source for biomarkers for diseases affecting the central nervous system, as the detected proteins may reflect ongoing neurological processes [1–4]. Mass spectrometry–based proteomics is a method of choice for such studies and can measure thousands of proteins and peptides from a single sample. During the last 10–15 years, numerous papers describing mass spectrometry results from CSF proteome mapping and biomarker studies have been published [5–9]. These types of studies often result in large data matrices, but usually only a small portion of the

Enrique Santamarı´a and Joaquı´n Ferna´ndez-Irigoyen (eds.), Cerebrospinal Fluid (CSF) Proteomics: Methods and Protocols, Methods in Molecular Biology, vol. 2044, https://doi.org/10.1007/978-1-4939-9706-0_25, © Springer Science+Business Media, LLC, part of Springer Nature 2019

377

378

Astrid Guldbrandsen et al.

results are highlighted and discussed in the paper. Furthermore, while some authors provide all the underlying raw data (both identification and quantification), this is unfortunately not the default. We therefore saw the need to extract all protein- and peptiderelated data across the relevant mass spectrometry studies and organize it in a common format for easy navigation, and make this information freely available to the scientific community. In 2014, we created the first version of the CSF Proteome Resource (CSF-PR), mainly as a repository of the mass spectrometry data associated with our comprehensive CSF proteome mapping study [10]. The initial goal was to organize our large-scale CSF data in a structured manner, but also to share protein and peptide lists (with additional details) with other researchers. Next, we switched the focus to quantitative CSF data, resulting in an expanded and redesigned version of CSF-PR, to also include quantitative results, both from our own group and from other research groups. To achieve this, we mined the available literature for studies presenting protein abundance comparisons in CSF from patients with multiple sclerosis (MS), Alzheimer’s disease (AD), and Parkinson’s disease (PD), focusing on studies using mass spectrometry, and organized the extracted data in a revised version of CSF-PR, referred to as CSF-PR 2.0 [9]. When choosing the data to include, we applied specific quality control filters for the literature searches related to technology, methodological setup, and sample size. Since the initial release of CSF-PR 2.0, an additional 12 datasets from eight recent publications have been added [11–18], resulting in CSF-PR now containing a total of 97 datasets from 25 publications. New relevant studies fulfilling the criteria are continuously being added. Several useful features for browsing, sorting, filtering, and investigating the included data are available. This makes CSF-PR ideal for, for example, finding which proteins that are identified and quantified in CSF, investigating relevant literature for potential biomarker proteins, comparing protein abundances between diseases, or selecting promising proteotypic peptides to use in targeted analyses [19–22].

2

Methods A description of CSF-PR features and a step-by-step guide through the resource is presented here, starting from the welcome page. The guide is divided into several sections for easy navigation and mainly focuses on the quantitative data.

Essential Features and Use Cases of CSF-PR

379

Fig. 1 Overview of the CSF-PR welcome page, where the different sections of the webpage can be accessed. A status of the data in the resource is shown in the upper left corner, along with release notes 2.1 The CSF-PR Welcome Page

When entering the CSF-PR web page, an overview of the resource status and sections is displayed (Fig. 1). The user can choose to navigate to either (1) the manually curated quantitative data extracted from the publications [9], (2) the identification data from the abovementioned CSF mapping paper [10], (3) make a search across all resource data, or (4) compare their own protein data to the data in the resource.

2.2 Quantification Data

The quantification data tab takes you to an overview of the disease categories for which there is quantitative data in CSF-PR (Fig. 2), and the user can choose an individual disease category or inspect all diseases at the same time. After selection, an overview of the selected data is presented as a heat map (Fig. 3), displaying the different disease subcategories for which data are available, plus the number of available datasets for each comparison.

2.2.1 Filtering, Customizing, and Investigating the Quantitative Data

Several options for filtering, customizing, and investigating the CSF-PR data are available. The user can either select specific comparisons from the heat map, or click Select all disease group comparisons in the bottom right corner to selects all datasets. Selection of cells will result in yellow edges around the cell (see Notes 1 and 2). By navigating to the top left corner in the heat map, the Dataset filters window can be opened (Fig. 4). This view provides an overview of the distribution of various data properties, related to publication year, study type, technology, analytical approach etc., and can be used to filter for specific data properties. Upon each selected

380

Astrid Guldbrandsen et al.

Fig. 2 Overview of the available disease categories in CSF-PR. The user can click to view a selected disease category or to load data for all disease categories. Additionally, the number of datasets in each disease category is indicated as a fraction of the total number of datasets in the repository (n ¼ 97)

filter, the pie charts are updated to display the current distribution, providing the user full control over the data being displayed. Given that different papers often use highly specific disease subgroups and because the categorization of the various disease subgroups is a subject of debate, we decided to use the same subgroup names in the heat map as is provided in the paper. However, we added the possibility to rename and combine existing groups, for example, to reduce the number of disease subgroup comparisons in the heat map and plots (Fig. 5a, see Note 3). There is also a possibility to reorder the disease subgroups or exclude certain groups, to better organize and optimize the views and plots in the resource (Fig. 5b). These features are available through the Recombine disease groups and Reorder and select disease groups icons in the top left corner of the heat map (see Fig. 3). Whenever a selection is made from the heat map, new tabs/ levels become visible to the right. The second tab includes a bubble plot providing an overview of the direction of regulation between the disease subgroups for the currently selected proteins (Fig. 6),

Essential Features and Use Cases of CSF-PR

381

Fig. 3 Heat map displaying some of the available disease categories and disease subgroup comparisons. The number inside each cell shows the number of datasets available for this specific comparison; the darker the color, the more datasets are available

with the bubble size representing the number of proteins changed in the specific direction between the disease subgroups. One, several, or all bubbles can be selected. By default, all are selected. The next tab provides a table with all the comparison- and protein-specific data. Plots showing the direction of regulation between all the disease group comparisons are shown for each protein. The table can be sorted on protein accession number or name, or on a specific value (direction of regulation) in a specific comparison (Fig. 7) (see Notes 4 and 5). To view more details for a specific protein, the protein can be selected from the table, upon which a new Protein Details tab will

382

Astrid Guldbrandsen et al.

Fig. 4 The Dataset Explorer allows the user to explore properties of the currently selected data, and filter for specific properties. Numbers in the charts indicate the number of datasets the given filter will apply to. Multiple selections are supported and the charts adapts as filters are applied

Fig. 5 Pop-up dialogs where the user can (a) perform recombination or renaming of disease groups, or (b) select and sort the disease groups

Essential Features and Use Cases of CSF-PR

383

Fig. 6 Bubble plot giving an overview of the number of proteins found increased, decreased, or equal between the various disease subgroups. Selection can be made by clicking the bubbles

Fig. 7 The protein table lists all the proteins under the current selection and shows a plot illustrating how the proteins change across the various disease group comparisons

become available below the Protein Table in the side panel. This tab presents all the results related to the selected protein from the relevant publications under the current filtration. The graph at the top summarizes the protein comparison results from each dataset in a single triangle as either Increased, Decreased, or Equal (Fig. 8) (see Notes 6–9). Below the graph is a table containing the available peptide details from the datasets illustrated in the graph. This information can be useful in the selection of proteotypic peptides as is described in more detail below.

384

Astrid Guldbrandsen et al.

Fig. 8 (a) Overview of the datasets giving evidence for increased (red triangle), decreased (green triangle), or equal (blue square) abundance of the selected protein across the various comparisons (x-axis, not labelled in this view). Each data point represents a specific protein fold change (disease groups A vs. B) in a specific dataset. The specific comparison can be found by hovering over the symbol. (b) Protein sequence coverage overview showing the peptides used for quantification in the specific selected dataset. Red indicates increased abundance and green indicated decreased abundance. Dark red and dark green indicate statistically significant changes in disease group A vs. B 2.3 Searching for Proteins or Peptides

2.3.1 Searching for a Specific Protein/ Peptide of Interest: Basic

The search tab in the home page allows the user to search for specific proteins (or peptides) across all or selected datasets in CSF-PR. This is useful to figure out if certain proteins are identified and/or quantified in CSF (see Note 10). It can also be used to inspect protein regulation across disease groups and categories. Multiple search keys are supported. 1. Navigate to the Search tab in the CSF-PR welcome page. 2. Type either protein name(s), accession number(s), or peptide sequence(s) and click the appropriate search term as well as the disease (if restricting the search to a specific disease). 3. Load the results, which will then be visible in the same framework as described above. 4. Observe the protein abundance status across various disease group comparisons in the plot. Expand to view a labelled xaxis. This plot shows the average results for all datasets quantifying this protein between the two specific disease groups. 5. Sort or filter the plot, drop comparisons from the view, flip disease groups A and B, or export to Excel using the icons in the bottom right corner. Click on any data point to view specific dataset details, as is described above. Click the accession number to view related UniProt (https://www.uniprot.org) information about the specific protein.

Essential Features and Use Cases of CSF-PR

385

6. To view more detailed and dataset specific protein results, navigate to the Protein Details tab. Here, it becomes clear how many datasets that support the summarized results shown in the Protein Table. 2.3.2 Evaluating/ Selecting Biomarker Candidates

As part of a biomarker discovery project, it is necessary to evaluate the list of candidate proteins, in terms of their potential as markers of one or several categories (diagnostic, prognostic, etc.). For this purpose, CSF-PR is a very useful tool, as it can easily give the user an overview of what other researchers have already found regarding your potential markers, in relation to MS, AD, and PD. This can tell you something about each marker’s potential sensitivity and selectivity, and could also provide some indications as to which processes each marker can monitor. 1. Navigate to the search section and insert the list of biomarker candidates to evaluate. Observe which ones are found in the database. The proteins not found in CSF-PR might be difficult to identify or quantify using standard proteomics methods. 2. Load the search results and inspect the candidate protein abundance across the various disease group comparisons. 3. By sorting the table in various ways, and perhaps merging and reordering the patient groups, interesting trends across disease categories and subgroups can be revealed. Some questions that might be relevant in this setting: (a) Which studies have found the protein and what methods did they use? (b) Is the protein only differentially expressed in your disease of interest? (c) Does the protein seem to be affected by treatment? (d) If the protein is similarly affected across different diseases, which processes are common for these diseases? (e) What is the planned control group for your followup/verification experiments? Is it suitable? 4. By navigating to the heat map tab and clicking the dataset filters icon, you can observe the various features of the data, related to approaches (discovery/targeted) and specific quantification methods used, for example, selected/parallel reaction monitoring (SRM/PRM), tandem mass tag (TMT), or label-free. If you, for example, are interested in using SRM or PRM for your follow-up experiments, it may be a good idea to apply the filter for SRM/PRM or targeted and mass spectrometry, as you will then only see proteins that have previously been quantified by SRM/PRM. 5. Based on the information obtained from CSF-PR, one can hopefully arrive at a better selection of candidate markers to proceed with in further experiments.

386

Astrid Guldbrandsen et al.

2.3.3 Investigating Peptide Information for Targeted Assays (SRM/PRM)

CSF-PR is a great tool to use if you are in the process of verifying potential biomarker candidates. Biomarker discovery experiments often result in a number of potential candidates, as outlined above. The selection of which peptides to use as surrogates for your proteins of interest is not always straightforward. Some guideline papers have been published on the selection of such peptides [23, 24], but it can also be a good idea to observe which peptides have worked well for other researchers. To investigate peptide details for a protein of interest, follow these steps: 1. Navigate to the Search panel and search for your protein of interest. You will see your results appearing if there are any hits for this protein in the resource. Hits can be either in the identification data [10] or in the quantification data [9]. You can choose either to load only data for a specific disease category (MS, AD or PD) or to load all data. 2. To get all available information on the protein, load all data. Your protein will appear alone in the protein table (see Note 11). Click in the table to open the next level tab, which is the Protein Details. 3. In the Protein Details tab, all relevant peptide data for your protein of interest can be found in the table below the overview plot (see Fig. 8). 4. To view detailed dataset and protein specific information, click any of the triangles or the comparison in the table. 5. To see the publication from which the specific dataset has been extracted, click the publication link, taking you to the publication in PubMed. 6. The Protein Coverage section in the peptide overview gives information about which specific peptides have been used in each dataset to arrive at the final quantitative conclusion for the protein (see Fig. 8). The coverage across the full protein sequence is shown here, and when available, also the quantitative results for each peptide, where abundance (increase, decrease, equal) and significance status is illustrated by color coding (see Note 12). 7. Hover over the peptide to see the sequence plus the start and end indexes. 8. Click a peptide to view detailed peptide-specific data (e.g., sequence, fold change, p-value, comments) in a pop-up window. You will find that not all peptides are identified for most proteins, or even has the same abundance or significance status, and there may be many reasons for this such as post-translational modifications. This information is crucial in the selection of appropriate

Essential Features and Use Cases of CSF-PR

387

peptides to represent a protein, and can save much time in peptide suitability testing. 2.4 Compare to Your Own Data

2.4.1 Comparison of Your Own Data to the Data in CSF-PR

The compare section of CSF-PR makes it possible to insert protein results from your own experiments as the search key, and reveal how your protein data compares to what is previously known in the field. 1. Navigate to the Compare tab in the CSF-PR welcome page. 2. Select or enter the disease groups that best describe your specific comparison. 3. Insert the list of protein accession numbers into the appropriate fields, based on their observed increased, decreased on equal abundance between the two disease groups used in your study. 4. Click compare. The Results Overview will appear giving an overview of which proteins are found in the search, in which disease categories the proteins are found, plus the total number of hits. Make selections from the pie chart if desired, or load all data. 5. After loading the data, the protein data will be available in the same framework (heat map, plots, and tables) as described above, but the inserted user data is now integrated with the resource data (Fig. 9). 6. The data can now be inspected, sorted, and filtered as described above, with the user data always visible and directly comparable with the resource data.

3

Notes 1. By default, only the CSF datasets are visible in the heat map, and pooled samples are included. Some serum datasets have also been included in the resource, if they represented verification of initial CSF studies. If the user wants to also display the serum datasets, this must be specifically selected by clicking the red drop symbol in the upper left corner of the heat map. In this area is also the possibility to remove datasets from studies that have employed pooling of samples. 2. In the lower right corner, other tabs are also available, such as the possibility to allow for multiple selections from the heat map, to clear filters and to export heat map data. It is also possible to view all the datasets and publications from which the datasets are collected. This overview will appear in a pop-up window, and clicking each dataset shows metadata for the dataset, related to, for example, experimental details, technology, protein and peptide numbers, and patient features. A link to the original papers is also provided in this window.

388

Astrid Guldbrandsen et al.

Fig. 9 (a) The bubble plot with integrated user data (far left). (b) The protein table (expanded view to show the comparisons) with the user data results (increased) integrated and shown as a light red horizontal line. (c) The protein details plot shown with the user data results (increased) integrated and shown as a light red horizontal line. (d) The protein details plot shown with the user data results (decreased) integrated and shown as a light green horizontal line

Essential Features and Use Cases of CSF-PR

389

3. The recombination of disease groups feature can be useful for advanced users, but should be used with caution. We recommend looking into the original publications for patient details. 4. Detailed sorting and filtering options for the protein table is available by clicking the Sort or filter comparison icon in the bottom right corner. Here, specific comparisons can also be removed from the table. The possibility to switch/flip disease groups is also available through the Switch disease groups icon, as is the opportunity to Clear all applied filters. 5. Data can be exported to Excel at this level and all other levels in CSF-PR. This is available through the Excel icon in the lower right corner. 6. By default, all individual datasets are represented by individual data points, but the user can choose to see the summarized trend for all datasets combined by clicking the Show/hide individual datasets icon on the right. By selecting this view, only one averaged data point will be shown for each comparison. 7. Hover over each data point to see the details related to the disease groups compared, the number of patients, and the pvalue (if available), or click on a data point to get a pop-up with even more details related to the protein and the dataset. 8. The size of the triangle represents the number of patients in the specific study (the larger the triangle, the more patients included in the study). Furthermore, the color and placement indicate the increased, decreased, or equal abundance of the protein in the specific A vs. B comparison. 9. The graph can be ordered by trend by clicking the Order dataset by trend icon in the bottom right corner. 10. Please keep in mind that CSF-PR does not represent a complete overview of all proteins and peptides found in CSF, as not all types of publications are included. Some were excluded based on our specific criteria related to, for example, methods, patient number, disease groups, or study type [9]. 11. To be able to see the disease group comparison, either hover over the specific data point in the plot or click the icon in the upper right corner of the plot window to get a labelled x-axis. 12. To see only the significantly changed peptides, click the Show/ hide not significant and stable peptides icon at the bottom right.

Acknowledgements A.G. and H.B. are supported by the Research Council of Norway. H.B., Y.F., and R.R.L. are supported by Bergen Research Foundation. F.B. is supported by the Western Norway Regional Health Authority.

390

Astrid Guldbrandsen et al.

References 1. Reiber H (2001) Dynamics of brain-derived proteins in cerebrospinal fluid. Clin Chim Acta 310(2):173–186 2. Shevchenko G, Konzer A, Musunuri S, Bergquist J (2015) Neuroproteomics tools in clinical practice. Biochim Biophys Acta 1854 (7):705–717. https://doi.org/10.1016/j. bbapap.2015.01.016 3. Johanson CE, Duncan JA III, Klinge PM, Brinker T, Stopa EG, Silverberg GD (2008) Multiplicity of cerebrospinal fluid functions: new challenges in health and disease. Cerebrospinal Fluid Res 5:10. https://doi.org/10. 1186/1743-8454-5-10 4. Carlyle BC, Trombetta BA, Arnold SE (2018) Proteomic approaches for the discovery of biofluid biomarkers of neurodegenerative dementias. Proteomes 6(3):PMC6161166. https:// doi.org/10.3390/proteomes6030032 5. Kroksveen AC, Opsahl JA, Guldbrandsen A, Myhr KM, Oveland E, Torkildsen O, Berven FS (2015) Cerebrospinal fluid proteomics in multiple sclerosis. Biochim Biophys Acta 1854 (7):746–756. https://doi.org/10.1016/j. bbapap.2014.12.013 6. Kroksveen AC, Opsahl JA, Aye TT, Ulvik RJ, Berven FS (2011) Proteomics of human cerebrospinal fluid: discovery and verification of biomarker candidates in neurodegenerative diseases using quantitative proteomics. J Proteome 74(4):371–388. https://doi.org/10. 1016/j.jprot.2010.11.010 7. Portelius E, Brinkmalm G, Pannee J, Zetterberg H, Blennow K, Dahlen R, Brinkmalm A, Gobom J (2017) Proteomic studies of cerebrospinal fluid biomarkers of Alzheimer’s disease: an update. Expert Rev Proteomics 14(11):1007–1020. https://doi. org/10.1080/14789450.2017.1384697 8. Roche S, Gabelle A, Lehmann S (2008) Clinical proteomics of the cerebrospinal fluid: towards the discovery of new biomarkers. Proteomics Clin Appl 2(3):428–436. https://doi. org/10.1002/prca.200780040 9. Guldbrandsen A, Farag Y, Kroksveen AC, Oveland E, Lereim RR, Opsahl JA, Myhr KM, Berven FS, Barsnes H (2017) CSF-PR 2.0: an interactive literature guide to quantitative cerebrospinal fluid mass spectrometry data from neurodegenerative disorders. Mol Cell Proteomics 16(2):300–309. https://doi.org/ 10.1074/mcp.O116.064477 10. Guldbrandsen A, Vethe H, Farag Y, Oveland E, Garberg H, Berle M, Myhr KM, Opsahl JA, Barsnes H, Berven FS (2014) In-depth

characterization of the cerebrospinal fluid (CSF) proteome displayed through the CSF proteome resource (CSF-PR). Mol Cell Proteomics 13(11):3152–3163. https://doi.org/ 10.1074/mcp.M114.038554 11. Begcevic I, Brinc D, Brown M, MartinezMorillo E, Goldhardt O, Grimmer T, Magdolen V, Batruch I, Diamandis EP (2018) Brain-related proteins as potential CSF biomarkers of Alzheimer’s disease: a targeted mass spectrometry approach. J Proteome 182:12–20. https://doi.org/10.1016/j.jprot. 2018.04.027 12. Begcevic I, Brinc D, Dukic L, Simundic AM, Zavoreo I, Basic Kes V, Martinez-Morillo E, Batruch I, Drabovich AP, Diamandis EP (2018) Targeted mass spectrometry-based assays for relative quantification of 30 brainrelated proteins and their clinical applications. J Proteome Res 17(7):2282–2292. https:// doi.org/10.1021/acs.jproteome.7b00768 13. Pavelek Z, Vysata O, Tambor V, Pimkova K, Vu DL, Kuca K, Stourac P, Valis M (2016) Proteomic analysis of cerebrospinal fluid for relapsing-remitting multiple sclerosis and clinically isolated syndrome. Biomed Rep 5 (1):35–40. https://doi.org/10.3892/br. 2016.668 14. Brinkmalm G, Sjodin S, Simonsen AH, Hasselbalch SG, Zetterberg H, Brinkmalm A, Blennow K (2018) A parallel reaction monitoring mass spectrometric method for analysis of potential CSF biomarkers for Alzheimer’s disease. Proteomics Clin Appl 12(1). https://doi. org/10.1002/prca.201700131 15. Kroksveen AC, Guldbrandsen A, Vaudel M, Lereim RR, Barsnes H, Myhr KM, Torkildsen O, Berven FS (2017) In-depth cerebrospinal fluid quantitative proteome and deglycoproteome analysis: presenting a comprehensive picture of pathways and processes affected by multiple sclerosis. J Proteome Res 16(1):179–194. https://doi.org/10.1021/ acs.jproteome.6b00659 16. Paterson RW, Heywood WE, Heslegrave AJ, Magdalinou NK, Andreasson U, Sirka E, Bliss E, Slattery CF, Toombs J, Svensson J, Johansson P, Fox NC, Zetterberg H, Mills K, Schott JM (2016) A targeted proteomic multiplex CSF assay identifies increased malate dehydrogenase and other neurodegenerative biomarkers in individuals with Alzheimer’s disease pathology. Transl Psychiatry 6(11):e952. https://doi.org/10.1038/tp.2016.194 17. Stoop MP, Runia TF, Stingl C, van der Vuurst de Vries RM, Luider TM, Hintzen RQ (2017)

Essential Features and Use Cases of CSF-PR Decreased neuro-axonal proteins in CSF at first attack of suspected multiple sclerosis. Proteomics Clin Appl 11(11–12). https://doi.org/ 10.1002/prca.201700005 18. Timirci-Kahraman O, Karaaslan Z, Tuzun E, Kurtuncu M, Baykal AT, Gunduz T, Tuzuner MB, Akgun E, Gurel B, Eraksoy M, Kucukali CI (2018) Identification of candidate biomarkers in converting and non-converting clinically isolated syndrome by proteomics analysis of cerebrospinal fluid. Acta Neurol Belg. https://doi.org/10.1007/s13760-018-09544 19. Macron C, Lane L, Nunez Galindo A, Dayon L (2018) Deep dive on the proteome of human cerebrospinal fluid: a valuable data resource for biomarker discovery and missing protein identification. J Proteome Res. https://doi.org/ 10.1021/acs.jproteome.8b00300 20. Barkovits K, Linden A, Galozzi S, Schilde L, Pacharra S, Mollenhauer B, Stoepel N, Steinbach S, May C, Uszkoreit J, Eisenacher M, Marcus K (2018) Characterization of cerebrospinal fluid via data-independent acquisition mass spectrometry. J Proteome Res 17(10):3418–3430. https://doi.org/10. 1021/acs.jproteome.8b00308

391

21. Sathe G, Na CH, Renuse S, Madugundu A, Albert M, Moghekar A, Pandey A (2018) Phosphotyrosine profiling of human cerebrospinal fluid. Clin Proteomics 15:29. https:// doi.org/10.1186/s12014-018-9205-1 22. Martin NA, Nawrocki A, Molnar V, Elkjaer ML, Thygesen EK, Palkovits M, Acs P, Sejbaek T, Nielsen HH, Hegedus Z, Sellebjerg F, Molnar T, Barbosa EGV, Alcaraz N, Gallyas F Jr, Svenningsen AF, Baumbach J, Lassmann H, Larsen MR, Illes Z (2018) Orthologous proteins of experimental de- and remyelination are differentially regulated in the CSF proteome of multiple sclerosis subtypes. PLoS One 13(8):e0202530. https:// doi.org/10.1371/journal.pone.0202530 23. Lange V, Picotti P, Domon B, Aebersold R (2008) Selected reaction monitoring for quantitative proteomics: a tutorial. Mol Syst Biol 4:222. https://doi.org/10.1038/msb.2008. 61 24. Bollinger JG, Stergachis AB, Johnson RS, Egertson JD, MacCoss MJ (2016) Selecting optimal peptides for targeted proteomic experiments in human plasma using in vitro synthesized proteins as analytical standards. Methods Mol Biol 1410:207–221. https:// doi.org/10.1007/978-1-4939-3524-6_12

Chapter 26 Bioinformatics to Tackle the Biological Meaning of Human Cerebrospinal Fluid Proteome Fa´bio Trindade, Rita Nogueira-Ferreira, Paulo Bastos, Francisco Amado, Rita Ferreira, and Rui Vitorino Abstract Cerebrospinal fluid (CSF) is a source of valuable information concerning brain disorders. The technical advances of high-throughput omics platforms to analyze body fluids can generate a huge amount of data, whose translation to biological meaning is a challenge. Several bioinformatic tools have emerged to help handling this data into systems biology comprehensively. Herein, we describe a step-by-step tutorial for CSF proteome data analysis in the set of neurodegenerative diseases using (1) ClueGO+CluePedia tool to perform cluster-based analysis envisioning the characterization of the biological processes dysregulated in neurodegenerative diseases including Alzheimer’s and Parkinson’s diseases; (2) Cytoscape to map diseasespecific proteins; (3) SecretomeP to inquire the secretion pathway of CSF proteins; and (4) STRING to identify biological processes modulated by secreted CSF proteins based on protein-protein interaction analysis. This step-by-step guide might help researchers to better characterize disease pathogenesis and to identify putative disease biomarkers. Key words CSF proteome, Protein-protein interaction, ClueGO, SecretomeP, Neurodegenerative diseases, STRING, Candidate markers

1

Introduction Cerebrospinal fluid (CSF) is an important source of potential biomarkers as a result of its proximity with the brain parenchyma. CSF is also generated from brain interstitial fluid, thus providing a more direct reflection of biochemical changes within the central nervous system (CNS) [1, 2]. There has been a growing interest in the application of CSF proteomics to search for CNS disease-specific biomarkers. Most of the proteomic approaches used nowadays rely on mass spectrometry (MS), combining gel-free shotgun MS approaches with depletion of high-abundance proteins for the detection and quantification of low-abundance proteins [2–4]. The latest advances in high-throughput MS have driven

Enrique Santamarı´a and Joaquı´n Ferna´ndez-Irigoyen (eds.), Cerebrospinal Fluid (CSF) Proteomics: Methods and Protocols, Methods in Molecular Biology, vol. 2044, https://doi.org/10.1007/978-1-4939-9706-0_26, © Springer Science+Business Media, LLC, part of Springer Nature 2019

393

394

Fa´bio Trindade et al.

the identification of thousands of proteins in CSF [5]. But, as a result, the new challenge of digging biological meaning out of big data has emerged in CSF proteome research. As such, bioinformatics is now viewed as a cornerstone of life and health sciences because it accelerates the integration, translation, and interpretation of big data into meaningful discoveries. Several bioinformatic tools (commercialized and freely available) have been released to help researchers dealing with large lists of proteins, envisioning the identification of the molecular processes modulated by a given pathophysiological condition and the identification of associated biomarkers. Commercialized tools are widely popular as they tend to offer more user-friendly environments, while open-source programs are more flexible in terms of the possibilities to modify existing algorithms [6, 7]. Since different programs might produce dissimilar and (in some cases) contradictory results, the selection of a specific software/algorithm is crucial and should take into account the following variables: (1) MS methodology applied (depletion or enrichment procedures, labeling for quantitative purposes, normalization, protein concentration) [7], (2) data format support (e.g., Excel or Notepad), and (3) interface provided (source code vs user-friendly interface) [8]. Herein, we exemplify following a step-by-step guide the applicability of the freely available bioinformatic tools ClueGO+CluePedia from Cytoscape [8], SecretomeP [9], and STRING [10, 11] to the integration of a CSF protein dataset. Cytoscape was selected since it is an open-source software for complex network analysis and visualization. There are several plug-ins available, being ClueGO and CluePedia, generally used to perform cluster-based analysis aiming at the identification of the biological processes specific to each condition and to, simultaneously, detect proteins behind those same processes [8, 12]. In the present chapter, ClueGO and CluePedia are applied to the comparison of CSF proteomes across neurodegenerative diseases, considering differently expressed CSF proteins (based on data listed in [2]). To get a deeper view into how the CSF proteome is modulated, the SecretomeP was applied for the identification of proteins’ origin, if they are predicted to be secreted or not. SecretomeP uses amino acid sequences and artificial neural networks to predict the existence and location of signal peptide cleavage sites. A protein is predicted to be classically secreted if it presents a signal peptide probability above a specified threshold (D-cutoff score  0.45) [13]. Moreover, SecretomeP uses a neural network that combines six protein characteristics (number of atoms, number of positively charged residues, presence of transmembrane helices, presence of low-complexity regions, presence of pro-peptides, and subcellular localization) to predict if a protein is non-classically secreted, based on a neural network score (NN-score) > 0.5 [9]. Altogether, the information given by this bioinformatic tool allows predicting if a secreted CSF protein is

Bioinformatics Applied to CSF Proteome

395

present within trafficking vesicles or if it is released upon cell injury. This analysis helps identifying the origin of disease-specific altered proteins and its association with a given pathophysiological condition. The analysis of proteins secreted by the conventional and nonconventional pathways using STRING makes possible the identification of biological processes modulated by these same proteins. STRING analysis relies on annotated functional interactions between proteins [11]. The applicability of these bioinformatic tools is exemplified below.

2

Materials The choice of a bioinformatic tool depends on the biological or clinical question to be investigated. For example, we compare CSF proteomes between distinct neurodegenerative diseases, Alzheimer’s disease (AD), Parkinson’s disease (PD), dementia with Lewy bodies (DLB), frontotemporal dementia (FTD), and Huntington disease (HD), in order to decipher specifically dysregulated biological processes or disease-specific markers.

2.1

Datasets

2.2 Freely Available Bioinformatic Web Tools and Databases

Table 1 (created based on [2]). UniProtKB (http://www.uniprot.org/). ClueGO (version 2.5.2) and CluePedia (version 1.5.2.) apps for Cytoscape (version 3.7.0, http://www.cytoscape.org/). SecretomeP version SecretomeP/).

2.0

(http://www.cbs.dtu.dk/services/

STRING version 10.5 (https://string-db.org/).

3

Methods

3.1 Step-by-Step Analysis of CSF Proteomes Across Neurodegenerative Diseases Using Cytoscape and ClueGO +CluePedia Plug-ins

1. For the identification of the biological processes more representative for each disease, run ClueGO plug-in (Apps ! ClueGO v2.5.2 + CluePedia v1.5.2). 2. On the ClueGO panel, define the number of variables: analysis type, cluster list(s), organism, identifiers type, ontology, statistical test, PV correction, advanced statistical options, network specificity, and advanced settings. (a) At the “Load Marker List(s)” panel select Homo Sapiens [9606]. (b) Add five windows at the “Load Marker List(s)” panel (see Note 1).

Gene

BASP1

BDNF

CGREF1

CHGA

CHGB

IGFBP5

NRXN1

NPTX1

NPTXR

PLXDC2

PEBP1

EFCAB14

RCN2

CHGB

SCG3

IGF2

Accession

P80723

P23560

Q99674

P10645

P05060

P24593

Q9ULB1

Q15818

O95502

Q6UX71

P30086

O75071

Q14257

P05060

Q8WXD2

P01344

Insulin-like growth factor II

Secretogranin-3

Secretogranin-1

Reticulocalbin-2

EF-hand calcium-binding domaincontaining protein 14

Phosphatidylethanolamine-binding protein 1

Plexin domain-containing protein 2

Neuronal pentraxin receptor

Neuronal pentraxin-1

Neurexin-1

Insulin-like growth factor-binding protein 5

Secretogranin-1

Chromogranin-A

Cell growth regulator with EF hand domain protein 1

Brain-derived neurotrophic factor

Brain acid soluble protein 1

Protein

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Disease

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Disease type

+





+













+











Change

Table 1 List of CSF proteins modulated by the neurodegenerative diseases Alzheimer’s disease (AD), Parkinson’s disease (PD), dementia with Lewy bodies (DLB), frontotemporal dementia (FTD), and Huntington disease (HD)

396 Fa´bio Trindade et al.

CAMK2B

TBC1D10A

CDH13

PRKCSH

GOLGB1

LTBP2

NBL1

SMARCAD1

ORM1

APOA2

APOD

APOE

APOH

COL1A2

CUTA

GOLM1

XPO5

LYNX1

NFASC

Q13554

Q9BXI6

P55290

P14314

Q14789

Q14767

P41271

Q9H4L7

P02763

P02652

P05090

P02649

P02749

P08123

O60888

Q8NBJ4

Q9HAV4

Q9BZG9

O94856

Neurofascin

Ly-6/neurotoxin-like protein 1

Exportin-5

Golgi membrane protein 1

Protein CutA

Collagen alpha-2(I) chain

Beta-2-glycoprotein 1

Apolipoprotein E

Apolipoprotein D

Apolipoprotein A-II

Alpha-1-acid glycoprotein 1

SWI/SNF-related matrix-associated actin-dependent regulator of chromatin subfamily A containing DEAD/H box 1

Neuroblastoma suppressor of tumorigenicity 1

Latent-transforming growth factor beta-binding protein 2

Golgin subfamily B member 1

Glucosidase 2 subunit beta

Cadherin-13

TBC1 domain family member 10A

Calcium/calmodulin-dependent protein kinase type II subunit beta

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Alzheimer’s disease

Alzheimer’s disease

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

(continued)





+

+



+

+



+

+

+

+

+

+







+



Bioinformatics Applied to CSF Proteome 397

Gene

PTPRN

SORT1

CLEC3B

AGT

ENO2

HERC4

KLK6

LCAT

PCSK1N

SOD1

TCERG1

ATP6AP1

COCH

MGP

SPON1

AMBP

C2

CYTL1

Accession

Q16849

Q99523

P05452

P01019

P09104

A0A024QZN8

Q92876

P04180

Q9UHG2

P00441

O14776

Q15904

O43405

P08493

Q9HCB6

P02760

P06681

Q9NRR1

Table 1 (continued)

Cytokine-like protein 1 (Protein C17)

Complement C2

Protein AMBP

Spondin-1

Matrix Gla protein

Cochlin

V-type proton ATPase subunit S1

Transcription elongation regulator 1

Superoxide dismutase

ProSAAS

Phosphatidylcholine-sterol acyltransferase

Kallikrein-6

Hect domain and RLD 4

Gamma-enolase

Angiotensinogen

Tetranectin

Sortilin

Receptor-type tyrosine-protein phosphatase-like N

Protein

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Disease

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Disease type

+



+

+

+

+



+







+

+







+



Change

398 Fa´bio Trindade et al.

FGB

HLA-B

HLA-E

HP

ITIH1

UACA

SORCS3

APOA2

APOE

TTR

RBP4

B2M

B2M

CST3

VGF

A1BG

KNG1

CLU

APOE

P02675

P03989

P13747

P00738

P19827

Q9BZF9

Q9UPU3

P02652

P02649

P02766

P02753

P61769

P61769

P01034

O15240

P04217

P01042

P10909

P02649

Apolipoprotein E

Clusterin

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

α-1β glycoprotein

Kininogen precursor

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Neurosecretory protein VGF

Cystatin C

Beta-2-microglobulin

Beta-2-microglobulin

Retinol-binding protein 4

Transthyretin

Apolipoprotein E

Apolipoprotein

VPS10 domain-containing receptor SorCS3

Uveal autoantigen with coiled-coil domains and ankyrin repeats

Inter-alpha-trypsin inhibitor heavy chain H1

Haptoglobin

HLA class I histocompatibility antigen alpha chain E

HLA class I histocompatibility antigen B-27 alpha chain

Fibrinogen beta chain

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

(continued)











+

+

+

+

+









+

+

+

+

+

Bioinformatics Applied to CSF Proteome 399

Gene

PTGDS

APOA1

CCPG1

SERPINA1

TTR

ALB

AHSG

MAPT

MAPT

MAPT

MAPT

MAPT

MAPT

MAPT

MAPT

MAPT

MAPT

MAPT

MAPT

MAPT

Accession

P41222

P02647

Q9ULG6

P01009

P02766

P02768

P02765

P10636

P10636

P10636

P10636

P10636

P10636

P10636

P10636

P10636

P10636

P10636

P10636

P10636

Table 1 (continued)

Microtubule-associated protein tau

Microtubule-associated protein tau

Microtubule-associated protein tau

Microtubule-associated protein tau

Microtubule-associated protein tau

Microtubule-associated protein tau

Microtubule-associated protein tau

Microtubule-associated protein tau

Microtubule-associated protein tau

Microtubule-associated protein tau

Microtubule-associated protein tau

Microtubule-associated protein tau

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

α-2-HS glycoprotein

Microtubule-associated protein tau

Alzheimer’s disease

Serum albumin

Alzheimer’s disease

Alzheimer’s disease

α-1 antitrypsin

Transthyretin

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Disease

Cell cycle progression 8 protein

Apolipoprotein A-I

protaglandin D2 synthase

Protein

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Disease type

+

+

+

+

+

+

+

+

+

+

+

+

+







+







Change

400 Fa´bio Trindade et al.

MAPT

MAPT

MAPT

MAPT

MAPT

MAPT

SERPINA1

CLU

APOE

SERPINC1

SERPINA3

AGT

NUCB1

CST3

APP

APOC1

C3

PSAP

B2M

RNASE1

A1BG

C3

P10636

P10636

P10636

P10636

P10636

P10636

P01009

P10909

P02649

P01008

P01011

P01019

Q02818

P01034

P05067

P02654

P01024

P07602

P61769

P07998

P04217

P01024

Alzheimer’s disease

Alzheimer’s disease

α-1β glycoprotein

Complement C3

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Ribonuclease pancreatic

Beta-2-microglobulin

Prosaposin

Complement C3

Apolipoprotein C-I

Amyloid beta A4 protein

Cystatin C

Nucleobindin-1

Angiotensinogen

Alpha-1-antichymotrypsin

Antithrombin-III

Apolipoprotein E

Alzheimer’s disease

Alzheimer’s disease

α-1 antitrypsin

Clusterin

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Microtubule-associated protein tau

Microtubule-associated protein tau

Microtubule-associated protein tau

Microtubule-associated protein tau

Microtubule-associated protein tau

Microtubule-associated protein tau

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

(continued)





+



+

+



+











+

+



+

+

+

+

+

+

Bioinformatics Applied to CSF Proteome 401

Gene

CHI3L1

PTGDS

CST3

TXN

B2M

VGF

NPTXR

SNAP25

MAPT

MAPT

MAPT

AFM

COL18A1

A1BG

A2M

APLP2

APP

APOC1

Accession

P36222

P41222

P01034

P10599

P61769

O15240

O95502

P60880

P10636

P10636

P10636

P43652

P39060

P04217

P01023

Q06481

P05067

P02654

Table 1 (continued)

Alzheimer’s disease Alzheimer’s disease Alzheimer’s disease Alzheimer’s disease

α1B-Glycoprotein precursor

α2-Macroglobulin precursor

Amyloid β(A4) precursorlike protein 2 isoform 4

Amyloid β A4 protein isoform g

Alzheimer’s disease

Alzheimer’s disease

α1-Type XVIII collagen isoform 3 precursor

Apolipoprotein C-I precursor

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Disease

Afamin precursor

Microtubule-associated protein tau

Microtubule-associated protein tau

Microtubule-associated protein tau

Synaptosomal-associated protein 25

Neuronal pentraxin receptor-1

Identified as neuronal secretory protein VGF

Beta-2-microglobulin

Thioredoxin

Cystatin C

Prostaglandin-H2 D-isomerase

Chitinase-3-like protein 1

Protein

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Disease type



+





+

+

+

+

+

+

+





+

+

+

+

+

Change

402 Fa´bio Trindade et al.

BTD

NSG1

CLSTN3

CTSL

CD99

CP

CTBS

C2

C4A

C4B

C5

C6

C8B

CFB

ENO2

ALDOA

GRIA4

P43251

P42857

Q9BQT9

P07711

P14209

P00450

Q01459

P06681

P0C0L4

P0C0L5

P01031

P13671

P07358

P00751

P09104

P04075

P48058

Glutamate receptor, ionotrophic, AMPA 4 isoform 3 precursor

Fructose-bisphosphate aldolase A

Enolase 2

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Complement component 8, β polypeptide preproprotein

Complement factor B preproprotein

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Complement component 6 precursor

Complement component 5 preproprotein

Complement component 4B preproprotein

Complement component 4A preproprotein

Complement component 2 isoform 2 preproprotein

Di-N-acetyl-chitobiase

Ceruloplasmin precursor

CD99 antigen isoform b precursor

Cathepsin L1 preproprotein

Calsyntenin 3

Brain neuron cytoplasmic protein 1

Biotinidase precursor

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

(continued)

+

+

+

+

+

+

+





+

+

+

+





+

+

Bioinformatics Applied to CSF Proteome 403

Gene

SHISA7

IGFBP6

ITIH1

L1CAM

LAMB2

LTBP2

LRG1

MCAM

NEO1

NEGR1

OGN

PGLYRP2

PAM

PLG

PVRL1

Accession

A6NL88

P24592

P19827

P32004

P55268

Q14767

P02750

P43121

Q92859

Q7Z3B1

P20774

Q96PD5

P19021

P00747

Q15223

Table 1 (continued)

Poliovirus receptor-related 1 isoform 1

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Peptidylglycine α-amidating monooxygenase isoform a preproprotein

Plasminogen

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Peptidoglycan recognition protein 2 precursor

Osteoglycin preproprotein

Neuronal growth regulator 1

Neogenin homologue 1

Alzheimer’s disease

Alzheimer’s disease

Leucine-rich α2-glycoprotein 1

Melanoma cell adhesion molecule

Alzheimer’s disease

Alzheimer’s disease

Laminin, β2 precursor

Latent transforming growth factor β-binding protein 2

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Disease

L1 cell adhesion molecule isoform 3 precursor

Inter-α (globulin) inhibitor H1

Insulinlike growth factor binding protein 6

Hypothetical protein LOC729956 (Homo sapiens)

Protein

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Disease type

+

+



+

+

+



+

+





+

+

+



Change

404 Fa´bio Trindade et al.

PDIA3

PTPRN

PDIA3

PTPRN

PKM

SMOC1

SPP1

SPP1

SPP1

SEZ6L

SERPINA3

VASN

SPON1

SOD3

TF

P30101

Q16849

P30101

Q16849

P14618

Q9H4F8

P10451

P10451

P10451

Q9BYH1

P01011

Q6EMK4

Q9HCB6

P08294

P02787

Transferrin

Superoxide dismutase 3, extracellular precursor

Spondin 1, extracellular matrix protein

Slitlike 2

Serpin peptidase inhibitor, clade A, member 3 precursor

Seizure-related 6 homologue (mouse)-like precursor

Secreted phosphoprotein 1 isoform c

Secreted phosphoprotein 1 isoform b

Secreted phosphoprotein 1 isoform a

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Ig γ3-chain C region

Secreted modular calcium-binding protein 1 isoform 1

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Pyruvate kinase, muscle isoform M1

Protein-L-isoaspartate (d-aspartate) O-methyltransferase

Protein tyrosine phosphatase, receptor type, N precursor

Protein disulfide-isomerase A3 precursor

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

(continued)

+

+

+

+

+

+



+

+



+

+



+

+

Bioinformatics Applied to CSF Proteome 405

Gene

VGF

ANXA1

ANXA1

CHGB

CST3

SCG2

CALM1

S100A1

VGF

PSAP

PTGDS

PTGDS

NPTXR

B2M

APOA1

CHGA

CUTA

Accession

O15240

P04083

Q5TZZ9

P05060

P01034

P13521

P62158

P23297

O15240

P07602

A0A024R8G3

P41222

O95502

P61769

P02647

P10645

O60888

Table 1 (continued)

Protein CutA

Chromogranin-A

Apolipoprotein A-I

Beta-2-microglobulin [Cleaved into: Beta-2-microglobulin form pI 5.3]

Neuronal pentraxin receptor

Prostaglandin-H2 D-isomerase

Prostaglandin D2 synthase 21 kDa

Prosaposin

Neurosecretory protein VGF [cleaved into: neuroendocrine regulatory peptide-1]

Protein S100-A1

Calmodulin

Secretogranin-2

Cystatin-C

Secretogranin-1

Annexin

Annexin A1

VGF nerve growth factor-inducible precursor

Protein

Alzheimer’s disease

Alzheimer’s disease

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Alzheimer’s disease

Alzheimer’s disease

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Alzheimer’s disease

Alzheimer’s disease

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Disease type

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Disease



+

+





+





















+

Change

406 Fa´bio Trindade et al.

GSN

CP

ITIH4

NRCAM

NRCAM

CLSTN1

B4GAT1

SERPINF1

CNDP1

MAN1A1

CADM3

APOC2

CA4

TTR

CDH2

SCG3

SERPINA3

KNG1

VTN

CPE

AGT

P06396

P00450

Q14624

Q92823

Q4KMQ7

O94985

O43505

P36955

Q96KN2

P33908

Q8N126

P02655

P22748

P02766

P19022

Q8WXD2

P01011

P01042

P04004

P16870

P01019

Angiotensinogen

Carboxypeptidase E

Vitronectin

Kininogen-1

Alpha-1-antichymotrypsin

Secretogranin-3

Cadherin-2

Transthyretin

Carbonic anhydrase 4

Apolipoprotein C-II

Cell adhesion molecule 3

Mannosyl-oligosaccharide 1,2-alpha-mannosidase IA

Beta-Ala-His dipeptidase

Pigment epithelium-derived factor

Beta-1,4-glucuronyltransferase 1

Calsyntenin-1

NRCAM protein

Neuronal cell adhesion molecule

Inter-alpha-trypsin inhibitor heavy chain H4

Ceruloplasmin

Gelsolin

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

(continued)















+

























Bioinformatics Applied to CSF Proteome 407

Gene

GOT1

A2M

APOH

CHI3L1

APOE

CLU

C4B

PSAP

A2M

FBLN1

C4B

HSP90AA1

GSN

SERPINA3

APOH

AGT

SERPINF1

HP

TTR

Accession

P17174

P01023

P02749

P36222

P02649

P10909

P0C0L5

P07602

P01023

P23142

P0C0L5

P07900

P06396

P01011

P02749

P01019

P36955

P00738

P02766

Table 1 (continued)

Alzheimer’s disease

β2-Glycoprotein

TTR dimer

Haptoglobin

PEDF

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

α1-Antichymotrypsin

Angiotensinogen

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Gelsolin isoform

Heat shock protein HSP 90-alpha

C4B1

Alzheimer’s disease

Alzheimer’s disease

α2-macroglobulin

Fibulin 1

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Alzheimer’s disease

Disease

Prosaposin

Complement C4-B

Clusterin

Apolipoprotein E

Chitinase-3-like protein 1

Beta-2-glycoprotein 1

Alpha-2-macroglobulin

Aspartate aminotransferase, cytoplasmic

Protein

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Disease type

+

+

+

+

+







+

+





+





+

+

+



Change

408 Fa´bio Trindade et al.

CTD

BASP1

CARTPT

LTBP4

NXPH4

NPTX1

PVALB

PLXDC2

PENK

SIRPA

SST

BSG

PTPRN2

ITGA7

P07339

P80723

Q16568

Q8N2S1

O95158

Q15818

P20472

Q6UX71

P01210

P78324

P61278

P35613

Q92932

Q13683

Integrin alpha-7

Receptor-type tyrosine-protein phosphatase N2

Basigin

Somatostatin

Tyrosine-protein phosphatase non-receptor type substrate 1

Proenkephalin-A

Plexin domain-containing protein 2

Parvalbumin alpha

Neuronal pentraxin-1

Neurexophilin-4

Latent-transforming growth factor beta-binding protein 4

Cocaine- and amphetamineregulated transcript protein

Brain acid soluble protein 1

Cathepsin D

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Alzheimer’s disease

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

(continued)

+











+

+

+



+

+

+



Bioinformatics Applied to CSF Proteome 409

Gene

FAIM2

LTBP2

SMARCAD1

APOC2

APOC3

CUTA

GOLM1

HPX

SLC39A10

XPO5

LTBP2

Accession

Q9BWQ8

Q14767

Q9H4L7

P02655

P02656

O60888

Q8NBJ4

P02790

Q9ULF5

Q9HAV4

Q14767

Table 1 (continued)

Latent-transforming growth factor beta-binding protein 2

Exportin-5

Zinc transporter ZIP10

Hemopexin

Golgi membrane protein 1

Protein CutA

Apolipoprotein C-III

Apolipoprotein C-II

SWI/SNF-related matrix-associated actin-dependent regulator of chromatin subfamily A containing DEAD/H box 1

Latent-transforming growth factor beta-binding protein 2

Protein lifeguard 2

Protein

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Disease type

Dementia with Lewy bodies

Dementia with Lewy bodies

Disease

+



+

+



+

+

+







Change

410 Fa´bio Trindade et al.

NPTXR

NUCB1

FAM3C

SORT1

CLEC3B

TTR

PDE12

AGT

APOC1

F5

ENO2

LYZ

B3GNT2

A0A024R1P8

Q02818

Q92520

Q99523

P05452

P02766

Q6L8Q7

P01019

P02654

P12259

P09104

P61626

Q9NY97

Dementia with Lewy bodies Dementia with Lewy bodies

N-Acetyllactosaminide beta-1-3-Nacetylglucosaminyltransferase 2

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Lysozyme C

Gamma-enolase

Coagulation factor V

Apolipoprotein C-I

Angiotensinogen

20 ,50 -phosphodiesterase 12

Transthyretin

Tetranectin

Sortilin

Protein FAM3C

Nucleobindin-1

Neuronal pentraxin receptor

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

(continued)

+

+

+





+



+

+



+

+



Bioinformatics Applied to CSF Proteome 411

Gene

ROCK1

SORL1

SCG5

ENPP2

SULF2

SOD1

TCERG1

NCAM1

CDH13

CD99L2

PIGR

ITIH4

Accession

Q13464

Q92673

P05408

Q13822

Q8IWU5

P00441

O14776

P13591

P55290

Q8TCZ2

P01833

Q14624

Table 1 (continued)

Inter-alpha-trypsin inhibitor heavy chain H4

Polymeric immunoglobulin receptor

CD99 antigen-like protein 2

Cadherin-13

Neural cell adhesion molecule 1

Transcription elongation regulator 1

Superoxide dismutase

Extracellular sulfatase Sulf-2

Ectonucleotide pyrophosphatase/ phosphodiesterase family member 2

Neuroendocrine protein 7B2

Sortilin-related receptor

Rho-associated protein kinase 1

Protein

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia with Lewy bodies Dementia with Lewy bodies

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Disease type

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Disease

+

+









+







+



Change

412 Fa´bio Trindade et al.

C4A

C3

CHGA

PTGDS

GPX3

F2

CNTN1

TTR

ENO1

APOA4

ALDOC

IGFBP2

APLP1

PKM

P0C0L4

P01024

P10645

P41222

P22352

P00734

Q12860

P02766

P06733

P06727

P09972

P18065

P51693

P14618

Pyruvate kinase isozymes M1/M2

Amyloid-like protein 1

Insulin-like growth factor-binding protein 2

Fructose-bisphosphate aldolase C

Apolipoprotein A-IV

Alpha-enolase

Transthyretin

Contactin-1

Prothrombin

Glutathione peroxidase 3

Prostaglandin-H2 D-isomerase

Chromogranin-A

Complement C3

Complement C4-A

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

(continued)

+

+

+

+

+

+

+

+

+

+

+

+

+

+

Bioinformatics Applied to CSF Proteome 413

Gene

GM2A

SERPINF1

GCNT2

ALB

AGT

CST3

CST3

SNCA

ALB

PCSK1N

APOE

RBP4

Accession

P17900

P36955

Q06430

P02768

P01019

P01034

P01034

P37840

P02768

Q9UHG2

P02649

P02753

Table 1 (continued)

Retinol-binding protein 4

Apolipoprotein E

ProSAAS

Serum albumin

Alpha-synuclein

Cystatin C

Cystatin C

Angiotensinogen

Frontotemporal dementia

Frontotemporal dementia

Frontotemporal dementia

Frontotemporal dementia

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

Dementia with Lewy bodies

N-Acetyllactosaminide beta-1,3-Nacetylglucosaminyl transferase

Serum albumin

Dementia with Lewy bodies

Dementia with Lewy bodies

Disease

Pigment epithelium-derived factor precursor

Ganglioside GM2 activator

Protein

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Disease type







+

+

+







+

+

+

Change

414 Fa´bio Trindade et al.

SERPINF1

HP

F2

APOA4

HP

APLP1

CP

CSF1R

EPHA4

LRP1

SPP1

TIMP1

APOB

GPR37

SERPINC1

SERPINC1

APOA1

C3

CDH8

KLK6

P36955

P00738

P00734

P06727

P00738

P51693

P00450

P07333

P54764

Q07954

P10451

P01033

P04114

O15354

P01008

P01008

P02647

P01024

P55286

Q92876

Kallikrein-6

Cadherin-8

Complement C3

Apolipoprotein A-I

Antithrombin-III

Antithrombin-III

Prosaposin receptor GPR37

Apolipoprotein B-100

Metalloproteinase inhibitor 1

Osteopontin

Prolow-density lipoprotein receptor-related protein 1

Ephrin type-A receptor 4

Macrophage colony-stimulating factor 1 receptor

Ceruloplasmin

Amyloid-like protein

Haptoglobin

Apolipoprotein A-IV

Prothrombin

Haptoglobin

Pigment epithelium-derived factor

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Huntington’s disease

Huntington’s disease

Huntington’s disease

Frontotemporal dementia

Frontotemporal dementia

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

(continued)









+

+

+

+





+

+







+

+

+





Bioinformatics Applied to CSF Proteome 415

Gene

PRDX2

UBA52

APLP1

CGREF1

CHGB

NPPC

IGFBP5

NRXN1

PRNP

SIRPA

RTN4

PLK2

BSG

LYST

APP

GOLGB1

Accession

P32119

P62987

P51693

Q99674

P05060

P23582

P24593

Q9ULB1

Q53YK7

P78324

Q9NQC3

Q9NYY3

P35613

Q99698

P05067

Q14789

Table 1 (continued)

Golgin subfamily B member 1

Amyloid beta A4 protein

Lysosomal-trafficking regulator

Basigin

Serine/threonine-protein kinase PLK2

Reticulon-4

Tyrosine-protein phosphatase non-receptor type substrate 1

Major prion protein

Neurexin-1

Insulin-like growth factor-binding protein 5

C-type natriuretic peptide

Secretogranin-1

Cell growth regulator with EF hand domain protein 1

Amyloid-like protein 1

Ubiquitin-60S ribosomal protein L40

Peroxiredoxin-2

Protein

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Disease

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Disease type

+

+



+





+

+

+



+

+

+



+

+

Change

416 Fa´bio Trindade et al.

HBEGF

ORM1

APOA2

APOC1

APOC3

APOH

APOM

HOOK3

AJAP1

SLC39A10

KRT8

RBP4

SELM

CP

CST3

HERC4

KLK6

F2

PKM

SELENBP1

GC

COCH

Q99075

P02763

P02652

P02654

P02656

P02749

O95445

Q86VS8

Q9UKB5

Q9ULF5

P05787

P02753

Q8WWX9

P00450

P01034

A0A024QZN8

Q92876

P00734

P14618

Q13228

P02774

O43405

Cochlin

Vitamin D-binding protein

Selenium-binding protein 1

Pyruvate kinase PKM

Prothrombin

Kallikrein-6

Hect domain and RLD 4

Cystatin-C

Ceruloplasmin

Selenoprotein M

Retinol-binding protein 4

Keratin, type II cytoskeletal 8

Zinc transporter ZIP10

Adherens junction-associated protein 1

Protein Hook homolog 3

Apolipoprotein M

Beta-2-glycoprotein 1

Apolipoprotein C-III

Apolipoprotein C-I

Apolipoprotein A-II

Alpha-1-acid glycoprotein 1

Proheparin-binding EGF-like growth factor

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

(continued)





+

+





































Bioinformatics Applied to CSF Proteome 417

Gene

ECM1

ITGA7

CD99L2

PIGR

APP

APOA1

SOD3

CLEC3B

PARK7

SNCA

PPP1R12A

DHRS7B

APOA1

APOA1

CEP120

CLEC3B

Accession

Q16610

Q13683

Q8TCZ2

P01833

P05067

P02647

P08294

P05452

Q99497

P37840

O14974

Q6IAN0

P02647

P02647

Q8N960

P05452

Table 1 (continued)

Tetranectin

Centrosomal protein of 120 kDa

Apolipoprotein A-I

Apolipoprotein A-I

Dehydrogenase/reductase SDR family member 7B

Protein phosphatase 1 regulatory subunit 12A

Alpha-synuclein

Protein deglycase DJ-1

Tetranectin

Extracellular superoxide dismutase

Apolipoprotein A1

Amyloid beta A4 protein

Polymeric immunoglobulin receptor

CD99 antigen-like protein 2

Splice isoform 3 of integrin alpha-7

Extracellular matrix protein 1

Protein

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Parkinson’s disease

Disease

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Dementia and primarily neurodegenerative

Disease type





+













+

+

+



+





Change

418 Fa´bio Trindade et al.

Bioinformatics Applied to CSF Proteome

419

(c) In the “ClueGO Settings,” “Ontologies/Pathways” panel, select GO Biological processes (see Note 2), and in the “GO Term/Pathway Selection” panel, select five genes per cluster and 10% genes (see Note 3). (d) Select the statistical test (Bonferroni, Bonferroni stepdown, or Benjamini-Hochberg) at the “Advanced Statistical Options” panel (see Note 4). (e) Select network specificity (Global, Medium, or Detailed network) (see Note 5). 3. Import protein data. (a) Go to Table 1, and copy the accession number of the proteins modulated by AD. (b) Paste the information in the first window at “Load Marker List(s)” panel. In our example, the color associated to each disease was yellow, AD; dark blue, DLB; pink, FTD; light blue, HT; brown, PD. (c) Repeat the procedure for the other four diseases in study. 4. Start the analysis. 5. At the “Visual Style” panel, set the view style as “clusters” instead of “groups” in order to allow the network to be differentiated by cluster color as previously selected (each color corresponds to one of the diseases in study). 6. At the right side of the “Table Panel” (CluePedia), select “Update” to depict the proteins (represented as gene name in the network). The network can be rearranged in the working space by simply mouse-selecting specific nodes and dragging them to empty or less crowded spaces or by clicking in the bar below in one of the predefined dispositions. 7. Go to “File” ! “Export” ! “Network to Image. . . .” Select the desired file format, name it (e.g., Fig. 1), and find the proper location. Files should be preferably saved as .png. Maximum resolution is achieved at maximum zoom and with “inches” selected (see Note 6). (a) This analysis highlights the biological processes modulated by each disease under study. In our example, only AD (yellow nodes) and PD (brown nodes) seem to modulate specific biological processes: “platelet degranulation” and “amyloid fibril formation” in the case of AD and “cellular response to amyloid-beta” in the case of PD. Gray nodes represent the biological processes that are common to all the studied conditions. 8. Open Cytoscape v3.7.0.

420

Fa´bio Trindade et al.

Fig. 1 GO enrichment analysis of the CSF proteome performed with ClueGO+CluePedia plug-ins from Cytoscape, highlighting the biological processes modulated by the neurodegenerative diseases. Yellow nodes refer to the biological processes modulated by Alzheimer’s disease (AD), brown nodes to the ones regulated by Parkinson’s disease (PD), and gray nodes represent the ones that are common to all the neurodegenerative diseases considered (Table 1)

9. Select “File” ! “Import” ! “Network from File” ! Select “Table 1.” A new window pops up. (a) In the Gene column, select the green dot (Source Node). (b) In the Disease column, select “◉” (Target Node). (c) In the Change column, select “▶” (Interaction type; see Note 7). 10. Select “Tools” ! “Network Analyzer” ! “Network Analysis” ! “Analyze Network. . . .” (a) Select “Treat network as undirected” ! OK (see Note 8). (b) In the “Result Panel,” select “Visualize Parameters.” (c) Choose “Betweeness Centrality” at “Map Node Size to:” (d) Choose “Betweeness Centrality” at “Map Node Color to:” ! “Low values to dark colors.” (e) Choose “Edge Betweeness” at “Map Edge Size to:” (f) Choose “Edge Betweeness” at “Map Edge Color to:” ! “Low values to dark colors” (see Note 9). 11. Go to “File” ! “Export” ! “Network to Image. . . .” Select the desired file format, name it (e.g., Fig. 2), and find the proper location. Files should be preferentially saved as .png.

Bioinformatics Applied to CSF Proteome

421

Fig. 2 Network depicting betweenness centrality of the differentially expressed CSF proteins across the neurodegenerative diseases studied. Proteins represented as smaller nodes (blue) represent those with higher biomarker potential as they were found dysregulated in only one of the five conditions at scope. Gene name has correspondence to protein name in Table 1

Maximum resolution is achieved at maximum zoom and with “inches” selected (see Note 6). (a) This analysis allows the identification of common and unique proteins per disease. In the resulting network, a snapshot of the relevance of each protein per condition is attained. Larger nodes and thicker edges represent proteins associated to more conditions and higher number of evidences, respectively. From this analysis, a first filter to identify potential disease biomarkers can be applied. Ideally, proteins uniquely associated to one condition should be regarded as preferable candidates. Still, one should mind that many other considerations are needed in the pipeline toward the definition of potential biomarkers (e.g., protein secretion should be minded—see Subheading 3.2).

422

Fa´bio Trindade et al.

3.2 Step-by-Step Analysis of CSF Proteome Using SecretomeP for the Identification of Proteins’ Origin

1. To use this bioinformatic tool, data from Table 1 must be converted into FASTA format. (a) Go to UniProt ! select “Retrieve/ID mapping,” and paste protein accession number in the window “1. Provide your identifiers.” Keep the default “UniProt KB” option. (b) Select “Download” ! “Download all,” “Format: FASTA (canonical),” “Uncompressed,” and “Go” (see Note 10). 2. Open SecretomeP. 3. At the panel “Submit a file in FASTA format directly from your local disk,” choose the downloaded file, and “Open.” 4. Select “Mammalian” and “Submit” (see Note 11). 5. Copy the data retrieved from the analysis to an excel file (e.g., Table 2). 6. The warnings meaning: (a) “Signal peptide predicted by SignalP” means that the protein is predicted to be secreted by the classical pathway. (b) “–” And an NN score above 0.6 means that the protein is predicted to be secreted by the non-classical pathway. (c) “–” and an NN score below 0.6 means that the protein is not predicted to be secreted; however, it might result from cell injury. Data retrieved from our analysis highlighted that 70% of the up- or downregulated proteins in the CSF across neurodegenerative diseases are secreted (mostly by the canonical pathway, while 4.5% were predicted as secreted within vesicles). From these, the classically secreted calsyntenin-1, which is involved in APP metabolism, was found downregulated, whereas the non-classically secreted SNAP25 was found upregulated in AD (Table 2). Calsyntenin-1 and APP undergo axonal/axoplasmic transportation, and perturbation in this trafficking leads to the increased production of Aβ [14]. SNAP25 regulates neurotransmitter release, representing an important role in synaptic specific neuronal system [15]. The non-secreted proteins, such as the transcription elongation regulator 1 and the rho-associated protein kinase 1, might result from cell injury. However, SecretomeP does not take into account tissue origin (see Note 12).

3.2.1 Step-by-Step Analysis of the Biological Processes Modulated by Putative Secreted Proteins Using STRING

To identify the most relevant biological processes involving proteins secreted by the canonical and noncanonical pathways, STRING tool (see Note 13) was used for the analysis of data retrieved from SecretomeP. 1. Open STRING v10.5 (https://string-db.org/). 2. Select “Multiple proteins” in the right side of the panel.

Bioinformatics Applied to CSF Proteome

423

Table 2 Data retrieved from SecretomeP analysis highlighting the proteins predicted to be secreted by the classical pathway and by the non-classical pathway and the ones not expected to be secreted # Name

NN-score

Odds

Weighted

Warning

O14776

0.371

0.727

0.001



O43505

0.914

6.114

0.012

Signal peptide predicted by SignalP

O60888

0.978

7.351

0.015

Signal peptide predicted by SignalP

O94985

0.436

3.823

0.008

Signal peptide predicted by SignalP

O95158

0.714

0.893

0.002

Signal peptide predicted by SignalP

P00441

0.648

1.749

0.003



P00734

0.618

1.468

0.003

Signal peptide predicted by SignalP

P00738

0.693

1.97

0.004

Signal peptide predicted by SignalP

P01011

0.839

4.032

0.008

Signal peptide predicted by SignalP

P01019

0.751

2.652

0.005

Signal peptide predicted by SignalP

P01023

0.596

1.403

0.003

Signal peptide predicted by SignalP

P01024

0.618

1.51

0.003

Signal peptide predicted by SignalP

P01042

0.489

0.983

0.002

Signal peptide predicted by SignalP

P01210

0.861

4.715

0.009

Signal peptide predicted by SignalP

P01833

0.17

0.345

0.001

Signal peptide predicted by SignalP

P02649

0.88

5.064

0.01

Signal peptide predicted by SignalP

P02654

0.945

6.716

0.013

Signal peptide predicted by SignalP

P02655

0.964

7.346

0.015

Signal peptide predicted by SignalP

P02656

0.855

4.832

0.01

Signal peptide predicted by SignalP

P02749

0.591

1.347

0.003

Signal peptide predicted by SignalP

P02766

0.911

6.077

0.012

Signal peptide predicted by SignalP

P02790

0.669

1.757

0.004

Signal peptide predicted by SignalP

P04004

0.891

0.342

0.001

Signal peptide predicted by SignalP

P05408

0.686

2.491

0.005

Signal peptide predicted by SignalP

P05452

0.554

1.226

0.002

Signal peptide predicted by SignalP

P06396

0.553

1.215

0.002

Signal peptide predicted by SignalP

P06727

0.459

0.914

0.002

Signal peptide predicted by SignalP

P06733

0.536

1.177

0.002



P07339

0.758

2.734

0.005

Signal peptide predicted by SignalP

P07602

0.785

3.009

0.006

Signal peptide predicted by SignalP (continued)

424

Fa´bio Trindade et al.

Table 2 (continued) # Name

NN-score

Odds

Weighted

Warning

P07900

0.173

0.35

0.001



P09104

0.599

1.408

0.003



P09972

0.323

0.598

0.001



P0C0L4

0.399

0.671

0.001

Signal peptide predicted by SignalP

P0C0L5

0.397

0.749

0.001

Signal peptide predicted by SignalP

P10645

0.392

0.753

0.002

Signal peptide predicted by SignalP

P10909

0.826

3.793

0.008

Signal peptide predicted by SignalP

P12259

0.319

0.589

0.001

Signal peptide predicted by SignalP

P13591

0.359

0.723

0.001

Signal peptide predicted by SignalP

P16870

0.464

0.933

0.002

Signal peptide predicted by SignalP

P17174

0.439

0.853

0.002



P18065

0.886

5.441

0.011

Signal peptide predicted by SignalP

P19022

0.203

0.408

0.001

Signal peptide predicted by SignalP

P20472

0.286

0.537

0.001



P22352

0.899

5.695

0.011

Signal peptide predicted by SignalP

P22748

0.396

0.74

0.001

Signal peptide predicted by SignalP

P23142

0.589

1.373

0.003

Signal peptide predicted by SignalP

P33908

0.437

2.455

0.005

Signal peptide predicted by SignalP

P35613

0.502

1.255

0.003

Signal peptide predicted by SignalP

P36222

0.645

1.603

0.003

Signal peptide predicted by SignalP

P36955

0.825

0.877

0.002

Signal peptide predicted by SignalP

P41222

0.772

0.526

0.001

Signal peptide predicted by SignalP

P51693

0.243

0.465

0.001

Signal peptide predicted by SignalP

P55290

0.601

1.438

0.003

Signal peptide predicted by SignalP

P61278

0.814

0.64

0.001

Signal peptide predicted by SignalP

P61626

0.915

6.267

0.013

Signal peptide predicted by SignalP

P78324

0.197

0.386

0.001

Signal peptide predicted by SignalP

P80723

0.47

0.966

0.002



Q02818

0.305

0.566

0.001

Signal peptide predicted by SignalP

Q12860

0.621

1.53

0.003

Signal peptide predicted by SignalP

Q13464

0.196

0.387

0.001

– (continued)

Bioinformatics Applied to CSF Proteome

425

Table 2 (continued) # Name

NN-score

Odds

Weighted

Warning

Q13683

0.536

1.159

0.002

Signal peptide predicted by SignalP

Q13822

0.488

1.004

0.002

Signal peptide predicted by SignalP

Q14624

0.406

0.778

0.002

Signal peptide predicted by SignalP

Q14767

0.401

0.777

0.002

Signal peptide predicted by SignalP

Q15818

0.6

1.409

0.003

Signal peptide predicted by SignalP

Q16568

0.923

6.239

0.012

Signal peptide predicted by SignalP

Q6L8Q7

0.514

1.051

0.002



Q6UX71

0.169

5.492

0.011

Signal peptide predicted by SignalP

Q8IWU5

0.607

1.543

0.003

Signal peptide predicted by SignalP

Q8N126

0.389

0.843

0.002

Signal peptide predicted by SignalP

Q8N2S1

0.28

2.793

0.006

Signal peptide predicted by SignalP

Q8NBJ4

0.547

1.23

0.002

Signal peptide predicted by SignalP

Q8TCZ2

0.269

0.514

0.001

Signal peptide predicted by SignalP

Q8WXD2

0.357

0.764

0.002

Signal peptide predicted by SignalP

Q92520

0.821

3.92

0.008

Signal peptide predicted by SignalP

Q92673

0.534

1.135

0.002

Signal peptide predicted by SignalP

Q92823

0.332

3.649

0.007

Signal peptide predicted by SignalP

Q92932

0.608

1.404

0.003

Signal peptide predicted by SignalP

Q96KN2

0.81

3.459

0.007

Signal peptide predicted by SignalP

Q99523

0.442

0.912

0.002

Signal peptide predicted by SignalP

Q9BWQ8

0.613

2.321

0.005



Q9H4L7

0.336

0.62

0.001



Q9HAV4

0.455

0.892

0.002



Q9NY97

0.535

1.141

0.002

Signal peptide predicted by SignalP

Q9ULF5

0.388

0.754

0.002

Signal peptide predicted by SignalP

A0A024R1

0.758

2.911

0.006

Signal peptide predicted by SignalP

Q4KMQ7

0.498

1.02

0.002

Signal peptide predicted by SignalP

A6NL88

0.414

0.818

0.002

Signal peptide predicted by SignalP

O15240

0.315

0.611

0.001

Signal peptide predicted by SignalP

O60888

0.978

7.351

0.015

Signal peptide predicted by SignalP

O95502

0.88

5.479

0.011

Signal peptide predicted by SignalP (continued)

426

Fa´bio Trindade et al.

Table 2 (continued) # Name

NN-score

Odds

Weighted

Warning

P00450

0.676

1.869

0.004

Signal peptide predicted by SignalP

P00747

0.403

0.749

0.001

Signal peptide predicted by SignalP

P00751

0.457

0.888

0.002

Signal peptide predicted by SignalP

P01008

0.645

1.653

0.003

Signal peptide predicted by SignalP

P01011

0.839

4.032

0.008

Signal peptide predicted by SignalP

P01019

0.751

2.652

0.005

Signal peptide predicted by SignalP

P01023

0.596

1.404

0.003

Signal peptide predicted by SignalP

P01024

0.618

1.51

0.003

Signal peptide predicted by SignalP

P01031

0.626

1.508

0.003

Signal peptide predicted by SignalP

P01034

0.937

6.803

0.014

Signal peptide predicted by SignalP

P02647

0.848

4.266

0.009

Signal peptide predicted by SignalP

P02649

0.88

5.064

0.01

Signal peptide predicted by SignalP

P02654

0.945

6.716

0.013

Signal peptide predicted by SignalP

P02750

0.72

2.273

0.005

Signal peptide predicted by SignalP

P02787

0.478

0.972

0.002

Signal peptide predicted by SignalP

P04075

0.356

0.658

0.001



P04083

0.511

1.052

0.002



P04217

0.829

3.758

0.008

Signal peptide predicted by SignalP

P05060

0.452

0.96

0.002

Signal peptide predicted by SignalP

P05067

0.441

0.871

0.002

Signal peptide predicted by SignalP

P06396

0.553

1.215

0.002

Signal peptide predicted by SignalP

P06681

0.66

1.726

0.003

Signal peptide predicted by SignalP

P07358

0.565

3.429

0.007

Signal peptide predicted by SignalP

P07602

0.785

3.009

0.006

Signal peptide predicted by SignalP

P07711

0.514

1.052

0.002

Signal peptide predicted by SignalP

P07998

0.772

2.982

0.006

Signal peptide predicted by SignalP

P08294

0.773

3.27

0.007

Signal peptide predicted by SignalP

P09104

0.599

1.408

0.003



P0C0L4

0.399

0.754

0.002

Signal peptide predicted by SignalP

P0C0L5

0.397

0.749

0.001

Signal peptide predicted by SignalP

P0DP23

0.676

2.172

0.004

– (continued)

Bioinformatics Applied to CSF Proteome

427

Table 2 (continued) # Name

NN-score

Odds

Weighted

Warning

P0DP24

0.676

2.172

0.004



P0DP25

0.676

2.172

0.004



P10451

0.656

2.743

0.005

Signal peptide predicted by SignalP

P10599

0.37

0.7

0.001



P10636

0.317

0.587

0.001



P10645

0.392

0.753

0.002

Signal peptide predicted by SignalP

P10909

0.826

3.793

0.008

Signal peptide predicted by SignalP

P13521

0.388

0.727

0.001

Signal peptide predicted by SignalP

P13671

0.464

0.92

0.002

Signal peptide predicted by SignalP

P14209

0.733

1.385

0.003

Signal peptide predicted by SignalP

P14618

0.42

0.798

0.002



P19021

0.362

0.683

0.001

Signal peptide predicted by SignalP

P19827

0.515

1.087

0.002

Signal peptide predicted by SignalP

P20774

0.805

3.512

0.007

Signal peptide predicted by SignalP

P23297

0.761

2.751

0.006



P24592

0.675

1.911

0.004

Signal peptide predicted by SignalP

P30101

0.554

1.192

0.002

Signal peptide predicted by SignalP

P32004

0.554

2.793

0.006

Signal peptide predicted by SignalP

P36222

0.645

1.603

0.003

Signal peptide predicted by SignalP

P39060

0.166

0.331

0.001

Signal peptide predicted by SignalP

P41222

0.772

2.793

0.006

Signal peptide predicted by SignalP

P42857

0.378

0.602

0.001



P43121

0.256

0.491

0.001

Signal peptide predicted by SignalP

P43251

0.72

2.244

0.004

Signal peptide predicted by SignalP

P43652

0.449

0.865

0.002

Signal peptide predicted by SignalP

P48058

0.629

1.552

0.003

Signal peptide predicted by SignalP

P55268

0.261

0.492

0.001

Signal peptide predicted by SignalP

P60880

0.791

3.16

0.006



P61769

0.907

5.869

0.012

Signal peptide predicted by SignalP

Q01459

0.819

0.643

0.001

Signal peptide predicted by SignalP

Q02818

0.305

0.566

0.001

Signal peptide predicted by SignalP (continued)

428

Fa´bio Trindade et al.

Table 2 (continued) # Name

NN-score

Odds

Weighted

Warning

Q06481

0.507

1.125

0.002

Signal peptide predicted by SignalP

Q14624

0.406

0.778

0.002

Signal peptide predicted by SignalP

Q14767

0.401

0.777

0.002

Signal peptide predicted by SignalP

Q15223

0.246

0.468

0.001

Signal peptide predicted by SignalP

Q16849

0.294

0.629

0.001

Signal peptide predicted by SignalP

Q6EMK4

0.216

0.421

0.001

Signal peptide predicted by SignalP

Q7Z3B1

0.775

2.867

0.006

Signal peptide predicted by SignalP

Q92859

0.246

0.491

0.001

Signal peptide predicted by SignalP

Q96PD5

0.655

1.75

0.004

Signal peptide predicted by SignalP

Q9BQT9

0.404

0.782

0.002

Signal peptide predicted by SignalP

Q9BYH1

0.647

1.68

0.003

Signal peptide predicted by SignalP

Q9H4F8

0.349

3.645

0.007

Signal peptide predicted by SignalP

Q9HCB6

0.327

0.755

0.002

Signal peptide predicted by SignalP

A0A024R8

0.772

1.295

0.003

Signal peptide predicted by SignalP

Q5TZZ9

0.511

1.052

0.002



O14776

0.371

0.727

0.001



O15240

0.315

0.611

0.001

Signal peptide predicted by SignalP

O43405

0.655

1.753

0.004

Signal peptide predicted by SignalP

O60888

0.978

7.351

0.015

Signal peptide predicted by SignalP

O75071

0.771

3.311

0.007



O94856

0.354

0.674

0.001

Signal peptide predicted by SignalP

O95502

0.88

5.479

0.011

Signal peptide predicted by SignalP

P00441

0.648

1.749

0.003



P00738

0.693

1.97

0.004

Signal peptide predicted by SignalP

P01009

0.852

4.343

0.009

Signal peptide predicted by SignalP

P01019

0.751

2.115

0.004

Signal peptide predicted by SignalP

P01034

0.937

6.803

0.014

Signal peptide predicted by SignalP

P01042

0.489

0.983

0.002

Signal peptide predicted by SignalP

P01344

0.864

4.719

0.009

Signal peptide predicted by SignalP

P02647

0.848

4.266

0.009

Signal peptide predicted by SignalP

P02649

0.88

5.064

0.01

Signal peptide predicted by SignalP (continued)

Bioinformatics Applied to CSF Proteome

429

Table 2 (continued) # Name

NN-score

Odds

Weighted

Warning

P02652

0.849

3.743

0.007

Signal peptide predicted by SignalP

P02675

0.534

1.192

0.002

Signal peptide predicted by SignalP

P02749

0.591

1.347

0.003

Signal peptide predicted by SignalP

P02753

0.858

4.584

0.009

Signal peptide predicted by SignalP

P02760

0.769

2.837

0.006

Signal peptide predicted by SignalP

P02763

0.691

2.034

0.004

Signal peptide predicted by SignalP

P02765

0.553

1.248

0.002

Signal peptide predicted by SignalP

P02766

0.911

6.077

0.012

Signal peptide predicted by SignalP

P02768

0.467

0.91

0.002

Signal peptide predicted by SignalP

P03989

0.278

0.543

0.001

Signal peptide predicted by SignalP

P04180

0.85

4.337

0.009

Signal peptide predicted by SignalP

P04217

0.829

3.758

0.008

Signal peptide predicted by SignalP

P05060

0.452

0.96

0.002

Signal peptide predicted by SignalP

P05090

0.881

5.275

0.011

Signal peptide predicted by SignalP

P05452

0.554

1.226

0.002

Signal peptide predicted by SignalP

P06681

0.66

1.726

0.003

Signal peptide predicted by SignalP

P08123

0.337

0.653

0.001

Signal peptide predicted by SignalP

P08493

0.731

0.574

0.001

Signal peptide predicted by SignalP

P09104

0.599

1.408

0.003



P0DP57

0.978

7.351

0.015

Signal peptide predicted by SignalP

P0DP58

0.615

1.942

0.004

Signal peptide predicted by SignalP

P10636

0.317

0.587

0.001



P10645

0.392

0.753

0.002

Signal peptide predicted by SignalP

P10909

0.826

3.793

0.008

Signal peptide predicted by SignalP

P13747

0.096

5.869

0.012

Signal peptide predicted by SignalP

P14314

0.308

2.816

0.006

Signal peptide predicted by SignalP

P19827

0.515

1.087

0.002

Signal peptide predicted by SignalP

P23560

0.824

3.625

0.007

Signal peptide predicted by SignalP

P24593

0.92

6.429

0.013

Signal peptide predicted by SignalP

P30086

0.672

2.652

0.005



P41222

0.772

2.793

0.006

Signal peptide predicted by SignalP (continued)

430

Fa´bio Trindade et al.

Table 2 (continued) # Name

NN-score

Odds

Weighted

Warning

P41271

0.586

1.378

0.003

Signal peptide predicted by SignalP

P55290

0.601

1.438

0.003

Signal peptide predicted by SignalP

P61769

0.907

0.211

0

Signal peptide predicted by SignalP

P80723

0.47

0.966

0.002



Q13554

0.424

0.801

0.002



Q14257

0.909

5.997

0.012

Signal peptide predicted by SignalP

Q14767

0.401

0.777

0.002

Signal peptide predicted by SignalP

Q14789

0.238

0.454

0.001



Q15818

0.6

1.409

0.003

Signal peptide predicted by SignalP

Q15904

0.623

1.734

0.003

Signal peptide predicted by SignalP

Q16849

0.294

0.629

0.001

Signal peptide predicted by SignalP

Q6UX71

0.169

0.342

0.001

Signal peptide predicted by SignalP

Q8NBJ4

0.547

1.23

0.002

Signal peptide predicted by SignalP

Q8WXD2

0.357

0.671

0.001

Signal peptide predicted by SignalP

Q92876

0.771

2.878

0.006

Signal peptide predicted by SignalP

Q99523

0.442

0.912

0.002

Signal peptide predicted by SignalP

Q99674

0.829

4.497

0.009

Signal peptide predicted by SignalP

Q9BXI6

0.201

0.395

0.001



Q9BZF9

0.184

0.367

0.001



Q9H4L7

0.336

0.62

0.001



Q9HAV4

0.455

0.892

0.002



Q9HCB6

0.327

0.602

0.001

Signal peptide predicted by SignalP

Q9NRR1

0.928

6.309

0.013

Signal peptide predicted by SignalP

Q9UHG2

0.707

2.093

0.004

Signal peptide predicted by SignalP

Q9ULB1

0.429

0.818

0.002

Signal peptide predicted by SignalP

Q9ULG6

0.848

4.702

0.009



Q9UPU3

0.307

0.577

0.001

Signal peptide predicted by SignalP

A0A024QZ

0.588

1.319

0.003



Bioinformatics Applied to CSF Proteome

431

3. Go to Table 2, and copy the UniProt accession number of the proteins secreted by the canonical pathway (identified with “signal peptide predicted by SignalP”). 4. In the STRING webpage, paste the data to the “List of Names” space. 5. Select Homo sapiens in the “Organism” settings (see Note 14). 6. Select “Search.” 7. A list of proteins and corresponding annotations can be visualized in a new STRING webpage. 8. Check if all proteins are correctly annotated. 9. Then, select “continue.” 10. A network of protein-protein interactions can be visualized. To identify the most relevant biological processes, select “∑ Analysis.” In this tab, the biological processes (as defined by GO) are listed according to their FDR. As can be seen, the top five biological processes modulated in CSF by secreted proteins are “regulation of biological quality,” “negative regulation of hydrolase activity,” “response to stress,” “platelet degranulation,” and “response to stimulus” (Table 3). 11. Repeat the same procedure for the proteins secreted in vesicles (identified with “-” and with an NN score higher than 0.6 in Table 2, which correspond to seven distinct proteins). No significant biological processes were retrieved from this analysis. This step-by-step guide exemplifies the usefulness of some user-friendly bioinformatic tools in the integrated analysis of proteome data. These analyses might be of great interest to (1) better understand the biological processes modulated by neurological diseases through the analysis of CSF with tools such as ClueGO +CluePedia and STRING, (2) quickly identify disease-specific proteins with Cytoscape, and (3) unveil the CSF proteins’ origin considering if they are expected to be secreted or not using tools as SecretomeP. The integration of data retrieved from these analyses will help to define putative disease markers of neurological conditions for further validation.

4

Notes 1. The number of boxes created should correspond to the number of conditions in study. 2. The ClueGO network was created using the gene ontology update from 4 September 2018.

GO.0065008 Regulation of biological quality

Pathway ID

Pathway description 62 6.43E14 ENSP00000205948, ENSP00000216361, ENSP00000216492, ENSP00000218230, ENSP00000220478, ENSP00000227667, ENSP00000233813, ENSP00000236850, ENSP00000237014, ENSP00000245907, ENSP00000249330, ENSP00000252486, ENSP00000254722, ENSP00000260197, ENSP00000261908, ENSP00000263574, ENSP00000264005, ENSP00000264025, ENSP00000264613, ENSP00000265023, ENSP00000265983, ENSP00000266546, ENSP00000269141, ENSP00000289749, ENSP00000295718, ENSP00000295897, ENSP00000297268, ENSP00000300175, ENSP00000303550, ENSP00000306099, ENSP00000307549, ENSP00000308541, ENSP00000308938, ENSP00000309148, ENSP00000311905, ENSP00000315130, ENSP00000323929, ENSP00000331544, ENSP00000333769, ENSP00000348068, ENSP00000348307, ENSP00000348888, ENSP00000350425, ENSP00000355627, ENSP00000356671, ENSP00000356771, ENSP00000356969, ENSP00000358777, ENSP00000359074, ENSP00000360519, ENSP00000366513, ENSP00000368314, ENSP00000376793, ENSP00000378394, ENSP00000378517, ENSP00000385142, ENSP00000385834, ENSP00000386104, ENSP00000391826, ENSP00000406381, ENSP00000414303, ENSP00000452120

Observed False gene discovery count rate Matching proteins in your network (IDs)

Table 3 Biological processes modulated by putative secreted proteins according to STRING analysis

A2M, AGT, ALB, APLP2, APOA1, APOA2, APOA4, APOC3, APOE, APOH, ATP6AP1, BDNF, BSG, C3, CDH2, CHGA, CLSTN1, CLSTN3, CLU, COCH, COL1A2, CP, CPE, CYTL1, ENSG00000224916, F2, F5, FBLN1, FGB, HPX, IGF2, IGFBP5, ITGA7, KLK6, KNG1, L1CAM, LCAT, LTBP4, NBL1, NEO1, NPTX1, NRCAM, NRXN1, PCSK1N, PIGR, PLG, PSAP, PTPRN, PVRL1, RBP4, SCG3, SCG5, SERPINA1, SERPINA3, SERPINC1, SERPINF1, SIRPA, SORL1, SPP1, TF, TTR, VGF

Matching proteins in your network (labels)

432 Fa´bio Trindade et al.

25 2.64E13 ENSP00000223642, ENSP00000226218, ENSP00000227667, ENSP00000236850, ENSP00000245907, ENSP00000252491, ENSP00000254722, ENSP00000256637, ENSP00000260197, ENSP00000263574, ENSP00000265023, ENSP00000265132, ENSP00000266041, ENSP00000273283, ENSP00000284981, ENSP00000323929, ENSP00000348068, ENSP00000355627, ENSP00000356671, ENSP00000356969, ENSP00000366124, ENSP00000374069, ENSP00000376793, ENSP00000393887, ENSP00000396688 63 3.17E12 ENSP00000205948, ENSP00000216361, ENSP00000216492, ENSP00000218230, ENSP00000220478, ENSP00000223642, ENSP00000226218, ENSP00000249330, ENSP00000252455, ENSP00000252486, ENSP00000255409, ENSP00000259396, ENSP00000261267, ENSP00000263413, ENSP00000264036, ENSP00000265023, ENSP00000266041, ENSP00000287641, ENSP00000295718, ENSP00000295897, ENSP00000296777, ENSP00000297268, ENSP00000300289, ENSP00000304133, ENSP00000306099, ENSP00000306864, ENSP00000307156, ENSP00000308541, ENSP00000308938, ENSP00000309148, ENSP00000315130, ENSP00000318472, ENSP00000323929, ENSP00000324025, ENSP00000324248, ENSP00000331544, ENSP00000333769, ENSP00000345179, ENSP00000345344, ENSP00000345968, ENSP00000347665, ENSP00000348068, ENSP00000348170, ENSP00000348307,

GO.0051346 Negative regulation of hydrolase activity

GO.0006950 Response to stress

(continued)

A2M, AGT, AHSG, ALB, APOA2, APOA4, APOD, APOE, APOH, B2M, BDNF, BSG, C4A, C5, C6, C8B, CARTPT, CFB, CGREF1, CHGA, CHI3L1, CLU, COCH, COL18A1, COL1A2, CST3, CTSL1, F2, F5, FBLN1, FGB, GPX3, GSN, HP, IGF2, ITIH4, KLK6, KNG1, L1CAM, LAMB2, LYZ, MCAM, NCAM1, ORM1, PCSK1N, PDIA3, PENK, PGLYRP2, PLG, PRKCSH, PSAP, PTPRN, SCG2, SCG3, SERPINA1, SERPINA3, SERPINC1, SIRPA, SPP1, SST, VASN, VGF, VTN

A2M, AGT, AHSG, AMBP, APLP2, APOA1, APOA2, APOC1, APOC3, APP, C3, C4A, C5, CST3, ITIH1, ITIH4, KNG1, PTPRN2, SERPINA1, SERPINA3, SERPINC1, SERPINF1, SORL1, SORT1, VTN

Bioinformatics Applied to CSF Proteome 433

91 3.24E12 ENSP00000205948, ENSP00000216361, ENSP00000216492, ENSP00000218230, ENSP00000220478, ENSP00000221891, ENSP00000223642, ENSP00000226218, ENSP00000227667, ENSP00000233809, ENSP00000233813, ENSP00000236850, ENSP00000237014, ENSP00000245907, ENSP00000252455, ENSP00000252486, ENSP00000254722, ENSP00000256637, ENSP00000259396, ENSP00000260197, ENSP00000261267, ENSP00000261908,

GO.0050896 Response to stimulus

ENSP00000350425, ENSP00000355627, ENSP00000356671, ENSP00000356771, ENSP00000356969, ENSP00000359074, ENSP00000360281, ENSP00000362924, ENSP00000366124, ENSP00000373477, ENSP00000376793, ENSP00000378394, ENSP00000378517, ENSP00000391826, ENSP00000393887, ENSP00000396688, ENSP00000414303, ENSP00000416561, ENSP00000452780

Observed False gene discovery count rate Matching proteins in your network (IDs)

14 3.24E12 ENSP00000220478, ENSP00000236850, ENSP00000265023, ENSP00000284981, ENSP00000295897, ENSP00000306099, ENSP00000308938, ENSP00000315130, ENSP00000323929, ENSP00000348068, ENSP00000356771, ENSP00000378394, ENSP00000385834, ENSP00000391826

Pathway description

GO.0002576 Platelet degranulation

Pathway ID

Table 3 (continued)

A2M, AGT, AHSG, ALB, APLP1, APLP2, APOA1, APOA2, APOA4, APOC3, APOD, APOE, APOH, ATP6AP1, B3GNT1, B3GNT2, C3, C4A, C5, C6, C8B, CA4, CARTPT, CDH13, CFB, CGREF1, CHGA, CLEC3B, CLU, CNTN1, COCH, COL18A1, CPE, CST3, CTSL1, CYTL1, ENSG00000224916, F2, F5, FBLN1, FGB, GPX3, GRIA4, HP, IGF2, IGFBP2, IGFBP5, IGFBP6,

A2M, ALB, APOA1, APP, CLU, F5, FGB, IGF2, KNG1, PLG, PSAP, SCG3, SERPINA1, TF

Matching proteins in your network (labels)

434 Fa´bio Trindade et al.

ENSP00000261978, ENSP00000263273, ENSP00000263413, ENSP00000263574, ENSP00000264005, ENSP00000264036, ENSP00000265023, ENSP00000266041, ENSP00000282499, ENSP00000295897, ENSP00000296130, ENSP00000296777, ENSP00000300175, ENSP00000300289, ENSP00000300900, ENSP00000301464, ENSP00000303550, ENSP00000304133, ENSP00000305595, ENSP00000306099, ENSP00000306864, ENSP00000307156, ENSP00000307549, ENSP00000308541, ENSP00000308938, ENSP00000309096, ENSP00000309148, ENSP00000315130, ENSP00000318472, ENSP00000323929, ENSP00000324025, ENSP00000325660, ENSP00000331544, ENSP00000333593, ENSP00000344786, ENSP00000345179, ENSP00000345344, ENSP00000345968, ENSP00000347665, ENSP00000348068, ENSP00000348170, ENSP00000348307, ENSP00000348888, ENSP00000350425, ENSP00000353007, ENSP00000355110, ENSP00000355627, ENSP00000356771, ENSP00000356969, ENSP00000358715, ENSP00000358777, ENSP00000359074, ENSP00000360281, ENSP00000360519, ENSP00000360687, ENSP00000366124, ENSP00000373477, ENSP00000374069, ENSP00000376793, ENSP00000378517, ENSP00000385142, ENSP00000386104, ENSP00000391826, ENSP00000393887, ENSP00000396688, ENSP00000406381, ENSP00000408632, ENSP00000416561, ENSP00000452120 (continued)

ITGA7, ITIH4, KLK6, KNG1, L1CAM, LAMB2, LCAT, LTBP2, LYZ, MCAM, NCAM1, NEO1, NFASC, NPTX1, NRXN1, NUCB1, NXPH4, ORM1, PCSK1N, PDIA3, PGLYRP2, PIGR, PLG, PRKCSH, PTGDS, PTPRN2, RBP4, SCG2, SCG3, SCG5, SERPINA1, SERPINA3, SERPINF1, SIRPA, SMOC1, SORCS3, SORL1, SORT1, SPP1, SULF2, TTR, VASN, VTN

Bioinformatics Applied to CSF Proteome 435

25 3.94E12 ENSP00000216492, ENSP00000220478, ENSP00000236850, ENSP00000249330, ENSP00000255409, ENSP00000261978, ENSP00000265023, ENSP00000284981, ENSP00000295718, ENSP00000295897, ENSP00000296777, ENSP00000304133, ENSP00000306099, ENSP00000308938, ENSP00000311905, ENSP00000315130, ENSP00000323929, ENSP00000348068, ENSP00000355627, ENSP00000356771, ENSP00000378394, ENSP00000385142, ENSP00000385834, ENSP00000391826, ENSP00000414303 19 7.55E12 ENSP00000223642, ENSP00000226218, ENSP00000245907, ENSP00000254722, ENSP00000260197, ENSP00000263574, ENSP00000265023, ENSP00000265132, ENSP00000266041, ENSP00000273283, ENSP00000284981, ENSP00000323929, ENSP00000348068, ENSP00000355627, ENSP00000356671, ENSP00000366124,

GO.0032940 Secretion by cell

GO.0010951 Negative regulation of endopeptidase activity

Observed False gene discovery count rate Matching proteins in your network (IDs) 13 3.24E12 ENSP00000205948, ENSP00000223642, ENSP00000245907, ENSP00000263413, ENSP00000265023, ENSP00000306099, ENSP00000308541, ENSP00000315130, ENSP00000323929, ENSP00000331544, ENSP00000360281, ENSP00000396688, ENSP00000416561

Pathway description

GO.0072376 Protein activation cascade

Pathway ID

Table 3 (continued)

A2M, AGT, AHSG, AMBP, APLP2, APP, C3, C4A, C5, CST3, ITIH1, ITIH4, KNG1, SERPINA1, SERPINA3, SERPINC1, SERPINF1, SORL1, VTN

A2M, AGT, ALB, APOA1, APP, BDNF, CARTPT, CHGA, CHI3L1, CLU, F5, FGB, IGF2, KNG1, LTBP2, LTBP4, NRXN1, PLG, PSAP, PTPRN, SCG2, SCG3, SERPINA1, TF, VGF

A2M, APOH, C3, C4A, C5, C6, C8B, CFB, CLU, F2, FBLN1, FGB, KNG1

Matching proteins in your network (labels)

436 Fa´bio Trindade et al.

22 8.13E12 ENSP00000226218, ENSP00000236671, ENSP00000237014, ENSP00000263574, ENSP00000284981, ENSP00000297268, ENSP00000306099, ENSP00000308938, ENSP00000311905, ENSP00000318472, ENSP00000323929, ENSP00000331544, ENSP00000333769, ENSP00000345344, ENSP00000347665, ENSP00000353007, ENSP00000355627, ENSP00000362924, ENSP00000366124, ENSP00000378517, ENSP00000385142, ENSP00000452120 69 1.23E11 ENSP00000205948, ENSP00000216492, ENSP00000220478, ENSP00000221891, ENSP00000223642, ENSP00000226218, ENSP00000226355, ENSP00000227667, ENSP00000236850, ENSP00000249330, ENSP00000252486, ENSP00000252491, ENSP00000255409, ENSP00000256637, ENSP00000259396, ENSP00000259486, ENSP00000260197, ENSP00000261978, ENSP00000264005, ENSP00000264025, ENSP00000264613, ENSP00000265023, ENSP00000265132, ENSP00000265983, ENSP00000269141, ENSP00000282499, ENSP00000284981, ENSP00000289749, ENSP00000295718, ENSP00000295897, ENSP00000296777, ENSP00000297268, ENSP00000300175, ENSP00000300289, ENSP00000300900, ENSP00000304133, ENSP00000306099, ENSP00000308541, ENSP00000308938, ENSP00000311905, ENSP00000315130, ENSP00000323929, ENSP00000333769, ENSP00000344786,

GO.0030198 Extracellular matrix organization

GO.0051179 Localization

ENSP00000376793, ENSP00000393887, ENSP00000396688

(continued)

A2M, AFM, AGT, AHSG, ALB, AMBP, APLP1, APOA1, APOA2, APOA4, APOC1, APOC3, APOD, APOE, APOH, APP, BDNF, BSG, C4A, C5, CA4, CADM3, CARTPT, CDH13, CDH2, CHGA, CHI3L1, CLU, COL1A2, CP, CPE, ENPP2, ENSG00000224916, F2, F5, FGB, GRIA4, GSN, HP, HPX, IGF2, ITGA7, KNG1, L1CAM, LCAT, LTBP2, LTBP4, NBL1, NFASC, NRXN1, ORM1, PDIA3, PIGR, PLG, PSAP, PTGDS, PTPRN, PVRL1, RBP4, SCG2, SCG3, SCG5, SERPINA1, SIRPA, SORL1, SORT1, SPP1, VGF, VTN

A2M, AGT, APLP2, APP, BSG, COL18A1, COL1A2, CST3, CTSD, CTSL1, FBLN1, FGB, GSN, ITGA7, LTBP4, NCAM1, NRXN1, PLG, SPP1, SULF2, TTR, VTN

Bioinformatics Applied to CSF Proteome 437

Pathway description

GO.0009605 Response to external stimulus

Pathway ID

Table 3 (continued)

44 3.68E11 ENSP00000216361, ENSP00000216492, ENSP00000218230, ENSP00000223642, ENSP00000227667, ENSP00000233809, ENSP00000237014, ENSP00000249330, ENSP00000255409, ENSP00000259486, ENSP00000261267, ENSP00000261908, ENSP00000264025, ENSP00000284981, ENSP00000287641, ENSP00000295897, ENSP00000296777, ENSP00000304133, ENSP00000305595, ENSP00000306099, ENSP00000307156, ENSP00000308541, ENSP00000309096, ENSP00000315130, ENSP00000318472, ENSP00000324248, ENSP00000325660, ENSP00000344786, ENSP00000345968, ENSP00000348170,

ENSP00000345179, ENSP00000348068, ENSP00000348170, ENSP00000348307, ENSP00000348888, ENSP00000350425, ENSP00000355627, ENSP00000356771, ENSP00000356969, ENSP00000357106, ENSP00000359074, ENSP00000360519, ENSP00000360687, ENSP00000362924, ENSP00000378394, ENSP00000378517, ENSP00000385142, ENSP00000386104, ENSP00000391826, ENSP00000393887, ENSP00000396688, ENSP00000406381, ENSP00000408632, ENSP00000414303, ENSP00000452120

Observed False gene discovery count rate Matching proteins in your network (IDs)

AGT, ALB, APOA2, APOA4, APOC3, APP, B2M, B3GNT1, B3GNT2, BDNF, C4A, C5, CARTPT, CHGA, CHI3L1, CLU, CNTN1, COCH, CST3, ENPP2, ENSG00000224916, F2, FGB, GSN, HP, IGFBP2, L1CAM, LAMB2, LYZ, NCAM1, NEO1, NFASC, NRXN1, PCSK1N, PENK, PGLYRP2, PVRL1, RBP4, SCG2, SERPINC1, SPP1, SST, TTR, VGF

Matching proteins in your network (labels)

438 Fa´bio Trindade et al.

GO.0048583 Regulation of response to stimulus

GO.0034369 Plasma lipoprotein particle remodeling

59 4.18E11 ENSP00000205948, ENSP00000216361, ENSP00000223642, ENSP00000226218, ENSP00000227667, ENSP00000233809, ENSP00000233813, ENSP00000236850, ENSP00000245907, ENSP00000248933, ENSP00000252486, ENSP00000254722, ENSP00000255409, ENSP00000260197, ENSP00000261908, ENSP00000263574, ENSP00000265023, ENSP00000265132, ENSP00000265983, ENSP00000269141, ENSP00000284981, ENSP00000289749, ENSP00000296777, ENSP00000300289, ENSP00000301464, ENSP00000302621, ENSP00000304133, ENSP00000306099, ENSP00000306864, ENSP00000308541, ENSP00000308938, ENSP00000309148, ENSP00000311905, ENSP00000315130, ENSP00000323929, ENSP00000331544, ENSP00000345179, ENSP00000345344, ENSP00000348170, ENSP00000348888, ENSP00000353007, ENSP00000355627, ENSP00000356671, ENSP00000356969, ENSP00000358777, ENSP00000359074,

9 4.18E11 ENSP00000227667, ENSP00000236850, ENSP00000252486, ENSP00000252491, ENSP00000264005, ENSP00000350425, ENSP00000355627, ENSP00000356969, ENSP00000406381

ENSP00000350425, ENSP00000355627, ENSP00000356671, ENSP00000356969, ENSP00000359074, ENSP00000360519, ENSP00000362924, ENSP00000366124, ENSP00000378517, ENSP00000385142, ENSP00000396688, ENSP00000406381, ENSP00000414303, ENSP00000452780

(continued)

A2M, AGT, AHSG, AMBP, APLP2, APOA1, APOA2, APOC3, APOD, APOE, APOH, APP, ATP6AP1, BDNF, C3, C4A, C5, C8B, CARTPT, CDH13, CDH2, CFB, CHI3L1, CLU, COCH, CTSL1, F2, FBLN1, FGB, GSN, HP, HPX, IGF2, IGFBP2, IGFBP5, IGFBP6, KLK6, KNG1, L1CAM, LRG1, LTBP4, NBL1, NEO1, NRXN1, PDIA3, PIGR, PLG, PSAP, PTGDS, SCG2, SERPINC1, SERPINF1, SEZ6L, SORL1, SPP1, SULF2, TF, VASN, VTN

AGT, APOA1, APOA2, APOA4, APOC1, APOC3, APOE, ENSG00000224916, LCAT

Bioinformatics Applied to CSF Proteome 439

Pathway description

GO.0051239 Regulation of multicellular organismal process

Pathway ID

Table 3 (continued)

49 4.68E11 ENSP00000205948, ENSP00000223642, ENSP00000226218, ENSP00000227667, ENSP00000228938, ENSP00000233813, ENSP00000236850, ENSP00000245907, ENSP00000252486, ENSP00000252491, ENSP00000255409, ENSP00000259396, ENSP00000260197, ENSP00000263413, ENSP00000264005, ENSP00000264025, ENSP00000265023, ENSP00000266546, ENSP00000269141, ENSP00000289749, ENSP00000296777, ENSP00000302621, ENSP00000306099, ENSP00000306864, ENSP00000308541, ENSP00000308938, ENSP00000309148, ENSP00000315130, ENSP00000325660, ENSP00000331544, ENSP00000345179, ENSP00000345968, ENSP00000350425, ENSP00000353007, ENSP00000355110, ENSP00000355627, ENSP00000356671, ENSP00000356969, ENSP00000358777, ENSP00000360519, ENSP00000366124, ENSP00000366513,

ENSP00000360281, ENSP00000360687, ENSP00000362924, ENSP00000378394, ENSP00000378517, ENSP00000385142, ENSP00000385834, ENSP00000391826, ENSP00000393887, ENSP00000396688, ENSP00000408632, ENSP00000414303, ENSP00000416561

Observed False gene discovery count rate Matching proteins in your network (IDs)

AGT, AHSG, APOA1, APOA2, APOA4, APOC1, APOC3, APOD, APOE, APOH, ATP6AP1, BDNF, C3, C5, C6, CARTPT, CDH2, CHI3L1, CLSTN1, CLSTN3, CLU, CNTN1, CST3, ENSG00000224916, F2, FBLN1, FGB, IGFBP5, KLK6, KNG1, LCAT, LRG1, MGP, NBL1, NRCAM, NRXN1, ORM1, PGLYRP2, PLG, PVRL1, RBP4, SERPINC1, SMOC1, SORL1, SPP1, SULF2, TF, VASN, VTN

Matching proteins in your network (labels)

440 Fa´bio Trindade et al.

8 9.37E11 ENSP00000227667, ENSP00000236850, ENSP00000252486, ENSP00000264005, ENSP00000315130, ENSP00000350425, ENSP00000356969, ENSP00000406381

27 7.90E11 ENSP00000205948, ENSP00000220478, ENSP00000236850, ENSP00000245907, ENSP00000264036, ENSP00000265023, ENSP00000284981, ENSP00000295897, ENSP00000297268, ENSP00000306099, ENSP00000308541, ENSP00000308938, ENSP00000309148, ENSP00000315130, ENSP00000323929, ENSP00000331544, ENSP00000333769, ENSP00000345179, ENSP00000348068, ENSP00000348307, ENSP00000356671, ENSP00000356771, ENSP00000359074, ENSP00000362924, ENSP00000378394, ENSP00000385834, ENSP00000391826

GO.0042060 Wound healing

GO.0043691 Reverse cholesterol transport

21 7.71E11 ENSP00000205948, ENSP00000223642, ENSP00000226218, ENSP00000236850, ENSP00000252486, ENSP00000254722, ENSP00000265023, ENSP00000306099, ENSP00000308541, ENSP00000308938, ENSP00000323929, ENSP00000345179, ENSP00000345968, ENSP00000355627, ENSP00000356671, ENSP00000360281, ENSP00000362924, ENSP00000378517, ENSP00000393887, ENSP00000396688, ENSP00000416561

GO.1903034 Regulation of response to wounding

ENSP00000368314, ENSP00000378517, ENSP00000385142, ENSP00000385834, ENSP00000393887, ENSP00000406381, ENSP00000414303

(continued)

APOA1, APOA2, APOA4, APOC3, APOE, CLU, ENSG00000224916, LCAT

A2M, ALB, APOA1, APOD, APOH, APP, BSG, C3, CLU, COL1A2, F2, F5, FBLN1, FGB, GSN, IGF2, KLK6, KNG1, L1CAM, MCAM, PLG, PSAP, SCG3, SERPINA1, SERPINC1, SIRPA, TF

A2M, AGT, AHSG, APOA1, APOD, APOE, APOH, C4A, C5, C8B, CFB, F2, FGB, GSN, KNG1, PGLYRP2, PLG, SERPINC1, SERPINF1, SPP1, VTN

Bioinformatics Applied to CSF Proteome 441

20 1.94E10 ENSP00000223642, ENSP00000226218, ENSP00000245907, ENSP00000254722, ENSP00000260197, ENSP00000263574, ENSP00000265023, ENSP00000265132, ENSP00000266041, ENSP00000273283, ENSP00000284981, ENSP00000308541, ENSP00000323929, ENSP00000348068, ENSP00000355627, ENSP00000356671, ENSP00000366124, ENSP00000376793, ENSP00000393887, ENSP00000396688 58 3.33E10 ENSP00000205948, ENSP00000216492, ENSP00000220478, ENSP00000221891, ENSP00000226218, ENSP00000226355, ENSP00000227667, ENSP00000236850,

GO.0045861 Negative regulation of proteolysis

GO.0006810 Transport

Observed False gene discovery count rate Matching proteins in your network (IDs) 28 1.04E10 ENSP00000205948, ENSP00000220478, ENSP00000245907, ENSP00000264036, ENSP00000265023, ENSP00000284981, ENSP00000295897, ENSP00000297268, ENSP00000306099, ENSP00000307156, ENSP00000308541, ENSP00000308938, ENSP00000309148, ENSP00000315130, ENSP00000323929, ENSP00000331544, ENSP00000333769, ENSP00000345179, ENSP00000348068, ENSP00000348307, ENSP00000356671, ENSP00000356771, ENSP00000359074, ENSP00000362924, ENSP00000366124, ENSP00000378394, ENSP00000385834, ENSP00000391826

Pathway description

GO.0009611 Response to wounding

Pathway ID

Table 3 (continued)

A2M, AFM, AGT, AHSG, ALB, AMBP, APLP1, APOA1, APOA2, APOA4, APOC1, APOC3, APOD, APOE, APOH, APP, BDNF, BSG, C4A, CA4,

A2M, AGT, AHSG, AMBP, APLP2, APP, C3, C4A, C5, CST3, F2, ITIH1, ITIH4, KNG1, SERPINA1, SERPINA3, SERPINC1, SERPINF1, SORL1, VTN

A2M, ALB, APOD, APOH, APP, BSG, C3, CLU, COL1A2, CST3, F2, F5, FBLN1, FGB, GSN, IGF2, KLK6, KNG1, L1CAM, LAMB2, MCAM, PLG, PSAP, SCG3, SERPINA1, SERPINC1, SIRPA, TF

Matching proteins in your network (labels)

442 Fa´bio Trindade et al.

GO.0043086 Negative regulation of catalytic activity

A2M, AGT, AMBP, APLP2, APOA1, APOA2, APOC1, APOC3, APOE, APP, C3, C4A, C5, CST3, ENSG00000224916, HP, ITIH1, ITIH4, KNG1, PTPRN2, SCG5, SERPINA1, SERPINA3, SERPINC1, SERPINF1, SORL1, SORT1, VTN

28 7.01E10 ENSP00000223642, ENSP00000226218, ENSP00000227667, ENSP00000236850, ENSP00000245907, ENSP00000252486, ENSP00000252491, ENSP00000254722, ENSP00000256637, ENSP00000260197, ENSP00000263574, ENSP00000265023, ENSP00000265132, ENSP00000266041, ENSP00000273283, ENSP00000284981, ENSP00000300175, ENSP00000323929,

(continued)

CARTPT, CHGA, CHI3L1, CLU, COL1A2, CP, ENPP2, ENSG00000224916, F5, FGB, GRIA4, GSN, HP, HPX, IGF2, KNG1, LCAT, LTBP2, LTBP4, NFASC, NRXN1, ORM1, PDIA3, PIGR, PLG, PSAP, PTGDS, PTPRN, PVRL1, RBP4, SCG2, SCG3, SCG5, SERPINA1, SORL1, SORT1, VGF, VTN

ENSP00000249330, ENSP00000252486, ENSP00000252491, ENSP00000255409, ENSP00000256637, ENSP00000259396, ENSP00000259486, ENSP00000260197, ENSP00000261978, ENSP00000264005, ENSP00000264025, ENSP00000264613, ENSP00000265023, ENSP00000265132, ENSP00000265983, ENSP00000282499, ENSP00000284981, ENSP00000295718, ENSP00000295897, ENSP00000296777, ENSP00000297268, ENSP00000300175, ENSP00000300289, ENSP00000300900, ENSP00000304133, ENSP00000306099, ENSP00000308938, ENSP00000311905, ENSP00000315130, ENSP00000323929, ENSP00000333769, ENSP00000344786, ENSP00000345179, ENSP00000348068, ENSP00000348170, ENSP00000348888, ENSP00000350425, ENSP00000355627, ENSP00000356771, ENSP00000356969, ENSP00000360519, ENSP00000360687, ENSP00000362924, ENSP00000378394, ENSP00000385142, ENSP00000391826, ENSP00000393887, ENSP00000396688, ENSP00000406381, ENSP00000414303

Bioinformatics Applied to CSF Proteome 443

23 8.96E10 ENSP00000205948, ENSP00000220478, ENSP00000236850, ENSP00000245907, ENSP00000265023, ENSP00000284981, ENSP00000295897, ENSP00000297268, ENSP00000306099, ENSP00000308541, ENSP00000308938, ENSP00000315130, ENSP00000323929, ENSP00000331544, ENSP00000333769, ENSP00000348068, ENSP00000348307, ENSP00000356671, ENSP00000356771, ENSP00000359074, ENSP00000378394, ENSP00000385834, ENSP00000391826

GO.0007596 Blood coagulation

ENSP00000348068, ENSP00000348170, ENSP00000355627, ENSP00000356671, ENSP00000356969, ENSP00000366124, ENSP00000374069, ENSP00000376793, ENSP00000396688, ENSP00000406381

Observed False gene discovery count rate Matching proteins in your network (IDs)

20 7.12E10 ENSP00000223642, ENSP00000226218, ENSP00000245907, ENSP00000254722, ENSP00000260197, ENSP00000263574, ENSP00000265023, ENSP00000265132, ENSP00000266041, ENSP00000273283, ENSP00000284981, ENSP00000323929, ENSP00000348068, ENSP00000355627, ENSP00000356671, ENSP00000362924, ENSP00000366124, ENSP00000376793, ENSP00000393887, ENSP00000396688

Pathway description

GO.0052548 Regulation of endopeptidase activity

Pathway ID

Table 3 (continued)

A2M, ALB, APOA1, APOH, APP, BSG, C3, CLU, COL1A2, F2, F5, FBLN1, FGB, IGF2, KNG1, L1CAM, PLG, PSAP, SCG3, SERPINA1, SERPINC1, SIRPA, TF

A2M, AGT, AHSG, AMBP, APLP2, APP, C3, C4A, C5, CST3, GSN, ITIH1, ITIH4, KNG1, SERPINA1, SERPINA3, SERPINC1, SERPINF1, SORL1, VTN

Matching proteins in your network (labels)

444 Fa´bio Trindade et al.

20 1.34E09 ENSP00000223642, ENSP00000226218, ENSP00000245907, ENSP00000254722, ENSP00000260197, ENSP00000263574, ENSP00000265023, ENSP00000265132, ENSP00000266041, ENSP00000273283, ENSP00000323929, ENSP00000331544, ENSP00000348068, ENSP00000355627, ENSP00000356671, ENSP00000362924, ENSP00000366124, ENSP00000376793, ENSP00000393887, ENSP00000396688 6 1.37E09 ENSP00000236850, ENSP00000252486, ENSP00000252491, ENSP00000350425, ENSP00000355627, ENSP00000356969 7 2.05E09 ENSP00000227667, ENSP00000236850, ENSP00000252486, ENSP00000252491, ENSP00000350425, ENSP00000356969, ENSP00000406381 35 2.22E09 ENSP00000216361, ENSP00000216492, ENSP00000223642, ENSP00000226218, ENSP00000245907, ENSP00000249330, ENSP00000252455, ENSP00000255409, ENSP00000259396, ENSP00000261267, ENSP00000263413, ENSP00000265023, ENSP00000266041, ENSP00000284981, ENSP00000304133, ENSP00000306099, ENSP00000308541, ENSP00000315130, ENSP00000318472, ENSP00000324248, ENSP00000345344, ENSP00000345968,

GO.0010873 Positive regulation of cholesterol esterification

GO.0033700 Phospholipid efflux

GO.0006952 Defense response

7 1.24E09 ENSP00000227667, ENSP00000236850, ENSP00000252486, ENSP00000252491, ENSP00000264005, ENSP00000350425, ENSP00000356969

GO.0052547 Regulation of peptidase activity

GO.0034375 High-density lipoprotein particle remodeling

(continued)

AHSG, APOA2, APOA4, APP, B2M, BDNF, C3, C4A, C5, C6, C8B, CFB, CHGA, CHI3L1, CLU, COCH, CST3, CTSL1, F2, FGB, HP, ITIH4, KNG1, LYZ, NCAM1, ORM1, PENK, PGLYRP2, PRKCSH, SCG2, SERPINA1, SERPINA3, SPP1, VGF, VTN

APOA1, APOA2, APOA4, APOC1, APOC3, APOE, ENSG00000224916

AGT, APOA1, APOA2, APOA4, APOC1, APOE

A2M, AGT, AHSG, AMBP, APLP2, C3, C4A, C5, CST3, FBLN1, GSN, ITIH1, ITIH4, KNG1, SERPINA1, SERPINA3, SERPINC1, SERPINF1, SORL1, VTN

APOA1, APOA2, APOA4, APOC1, APOC3, APOE, LCAT

Bioinformatics Applied to CSF Proteome 445

16 4.08E09 ENSP00000205948, ENSP00000223642, ENSP00000226218, ENSP00000236850, ENSP00000252486, ENSP00000254722, ENSP00000265023, ENSP00000289749, ENSP00000296777, ENSP00000306099, ENSP00000308541, ENSP00000308938, ENSP00000345179, ENSP00000356671, ENSP00000378517, ENSP00000385142

GO.0032102 Negative regulation of response to external stimulus

APOA1, APOD, APOE, APOH, C5, CARTPT, F2, FGB, KNG1, NBL1, NRXN1, PLG, SERPINC1, SERPINF1, SPP1, VTN

APOA1, APOA4, APOC1, APOC3, APOH, ENSG00000224916, SORT1

7 3.36E09 ENSP00000205948, ENSP00000227667, ENSP00000236850, ENSP00000252491, ENSP00000256637, ENSP00000350425, ENSP00000406381

GO.0051004 Regulation of lipoprotein lipase activity

Matching proteins in your network (labels)

A2M, ALB, APOA1, APP, CLU, COL1A2, F2, F5, FGB, IGF2, KNG1, PLG, PSAP, SCG3, SERPINA1, TF

ENSP00000348068, ENSP00000348170, ENSP00000350425, ENSP00000356969, ENSP00000360281, ENSP00000366124, ENSP00000376793, ENSP00000378517, ENSP00000393887, ENSP00000396688, ENSP00000414303, ENSP00000416561, ENSP00000452780

Observed False gene discovery count rate Matching proteins in your network (IDs)

16 3.29E09 ENSP00000220478, ENSP00000236850, ENSP00000265023, ENSP00000284981, ENSP00000295897, ENSP00000297268, ENSP00000306099, ENSP00000308541, ENSP00000308938, ENSP00000315130, ENSP00000323929, ENSP00000348068, ENSP00000356771, ENSP00000378394, ENSP00000385834, ENSP00000391826

Pathway description

GO.0030168 Platelet activation

Pathway ID

Table 3 (continued)

446 Fa´bio Trindade et al.

43 4.74E09 ENSP00000216361, ENSP00000223642, ENSP00000226218, ENSP00000227667, ENSP00000233809, ENSP00000233813, ENSP00000236850, ENSP00000245907, ENSP00000252486, ENSP00000252491, ENSP00000254722, ENSP00000259486, ENSP00000260197, ENSP00000264005, ENSP00000264025, ENSP00000266546, ENSP00000269141, ENSP00000301464, ENSP00000306099, ENSP00000306864, ENSP00000308541, ENSP00000309148, ENSP00000311905, ENSP00000315130, ENSP00000324025, ENSP00000325660, ENSP00000331544, ENSP00000345179, ENSP00000350364, ENSP00000355627, ENSP00000356969, ENSP00000366124, ENSP00000366513, ENSP00000368314, ENSP00000378517, ENSP00000385142, ENSP00000391826, ENSP00000393887, ENSP00000396688, ENSP00000406381, ENSP00000408632, ENSP00000414303, ENSP00000452120 52 5.12E09 ENSP00000205948, ENSP00000216492, ENSP00000220478, ENSP00000226355, ENSP00000227667, ENSP00000236850, ENSP00000237014, ENSP00000249330, ENSP00000252486, ENSP00000252491, ENSP00000255409, ENSP00000256637, ENSP00000260197, ENSP00000261978, ENSP00000264005, ENSP00000264025, ENSP00000264613, ENSP00000265023, ENSP00000265983, ENSP00000269141, ENSP00000282499, ENSP00000284981, ENSP00000295718, ENSP00000295897, ENSP00000296777, ENSP00000300289,

GO.0051128 Regulation of cellular component organization

GO.1902578 Single-organism localization

(continued)

A2M, AFM, AGT, ALB, APOA1, APOA2, APOA4, APOC1, APOC3, APOD, APOE, APOH, APP, BDNF, BSG, C4A, CA4, CARTPT, CDH2, CHGA, CHI3L1, CLU, CP, CPE, ENSG00000224916, F5, FGB, GRIA4, GSN, HPX, IGF2, KNG1, LCAT, LTBP2, LTBP4, NFASC, NPTX1, NRXN1, PDIA3, PIGR, PLG, PSAP, PTPRN, PVRL1, RBP4, SCG2, SCG3, SERPINA1, SORL1, SORT1, TTR, VGF

AGT, AHSG, APOA1, APOA2, APOC1, APOC3, APOD, APOE, BDNF, C3, C4A, C5, CDH13, CDH2, CGREF1, CLSTN1, CLSTN3, CLU, CNTN1, COCH, CST3, ENPP2, ENSG00000224916, F2, FBLN1, FGB, IGF2, IGFBP2, IGFBP5, IGFBP6, ITGA7, KLK6, LCAT, LTBP4, NEGR1, NRCAM, NRXN1, PVRL1, SERPINF1, SORL1, SPP1, VASN, VTN

Bioinformatics Applied to CSF Proteome 447

Pathway description

GO.0044765 Single-organism transport

Pathway ID

Table 3 (continued)

50 6.76E09 ENSP00000205948, ENSP00000216492, ENSP00000220478, ENSP00000226355, ENSP00000227667, ENSP00000236850, ENSP00000237014, ENSP00000249330, ENSP00000252486, ENSP00000252491, ENSP00000255409, ENSP00000256637, ENSP00000260197, ENSP00000261978, ENSP00000264005, ENSP00000264025, ENSP00000264613, ENSP00000265023, ENSP00000265983, ENSP00000282499, ENSP00000284981, ENSP00000295718, ENSP00000295897, ENSP00000296777, ENSP00000300289, ENSP00000300900, ENSP00000304133, ENSP00000306099, ENSP00000307549, ENSP00000308938,

ENSP00000300900, ENSP00000304133, ENSP00000306099, ENSP00000307549, ENSP00000308938, ENSP00000311905, ENSP00000315130, ENSP00000323929, ENSP00000333769, ENSP00000344786, ENSP00000345179, ENSP00000348068, ENSP00000348888, ENSP00000350425, ENSP00000355627, ENSP00000356771, ENSP00000356969, ENSP00000360519, ENSP00000362924, ENSP00000378394, ENSP00000385142, ENSP00000386104, ENSP00000391826, ENSP00000396688, ENSP00000406381, ENSP00000414303

Observed False gene discovery count rate Matching proteins in your network (IDs)

A2M, AFM, AGT, ALB, APOA1, APOA2, APOA4, APOC1, APOC3, APOD, APOE, APOH, APP, BDNF, BSG, C4A, CA4, CARTPT, CHGA, CHI3L1, CLU, CP, ENSG00000224916, F5, FGB, GRIA4, GSN, HPX, IGF2, KNG1, LCAT, LTBP2, LTBP4, NFASC, NPTX1, NRXN1, PDIA3, PIGR, PLG, PSAP, PTPRN, PVRL1, RBP4, SCG2, SCG3, SERPINA1, SORL1, SORT1, TTR, VGF

Matching proteins in your network (labels)

448 Fa´bio Trindade et al.

28 1.38E08 ENSP00000205948, ENSP00000226218, ENSP00000227667, ENSP00000233813, ENSP00000236850, ENSP00000252486, ENSP00000252491, ENSP00000259396, ENSP00000260197, ENSP00000265023, ENSP00000284981, ENSP00000296777, ENSP00000306099, ENSP00000306864, ENSP00000308541, ENSP00000308938, ENSP00000331544, ENSP00000345179, ENSP00000345968, ENSP00000355627, ENSP00000356969, ENSP00000360519, ENSP00000360687, ENSP00000366124, ENSP00000378517, ENSP00000393887, ENSP00000406381, ENSP00000414303

9 1.07E08 ENSP00000227667, ENSP00000236850, ENSP00000252486, ENSP00000252491, ENSP00000264005, ENSP00000315130, ENSP00000350425, ENSP00000356969, ENSP00000406381

GO.0030301 Cholesterol transport

GO.0051241 Negative regulation of multicellular organismal process

9 8.85E09 ENSP00000227667, ENSP00000236850, ENSP00000252486, ENSP00000252491, ENSP00000264005, ENSP00000350425, ENSP00000355627, ENSP00000356969, ENSP00000406381

GO.0097006 Regulation of plasma lipoprotein particle levels

ENSP00000311905, ENSP00000315130, ENSP00000323929, ENSP00000333769, ENSP00000344786, ENSP00000345179, ENSP00000348068, ENSP00000348888, ENSP00000350425, ENSP00000355627, ENSP00000356771, ENSP00000356969, ENSP00000360519, ENSP00000362924, ENSP00000378394, ENSP00000385142, ENSP00000391826, ENSP00000396688, ENSP00000406381, ENSP00000414303

(continued)

AGT, AHSG, APOA1, APOA2, APOC1, APOC3, APOD, APOE, APOH, APP, BDNF, CARTPT, CST3, ENSG00000224916, F2, FBLN1, FGB, IGFBP5, KNG1, ORM1, PGLYRP2, PLG, PTGDS, RBP4, SORL1, SPP1, VASN, VTN

APOA1, APOA2, APOA4, APOC1, APOC3, APOE, CLU, ENSG00000224916, LCAT

AGT, APOA1, APOA2, APOA4, APOC1, APOC3, APOE, ENSG00000224916, LCAT

Bioinformatics Applied to CSF Proteome 449

Pathway description

GO.0032501 Multicellular organismal process

GO.0034370 Triglyceride-rich lipoprotein particle remodeling

Pathway ID

Table 3 (continued)

73 1.98E08 ENSP00000205948, ENSP00000216361, ENSP00000216492, ENSP00000218230, ENSP00000220478, ENSP00000221891, ENSP00000223642, ENSP00000227667, ENSP00000228938, ENSP00000233809, ENSP00000233813, ENSP00000236671, ENSP00000236850, ENSP00000245907, ENSP00000248933, ENSP00000249330, ENSP00000252486, ENSP00000252491, ENSP00000255409, ENSP00000256637, ENSP00000260197, ENSP00000261908, ENSP00000263413, ENSP00000263574, ENSP00000264005, ENSP00000264025, ENSP00000264036, ENSP00000265023, ENSP00000265132, ENSP00000266546, ENSP00000287641, ENSP00000289749, ENSP00000295897, ENSP00000296777, ENSP00000297268, ENSP00000300900, ENSP00000303550, ENSP00000304133, ENSP00000305595, ENSP00000306099, ENSP00000306477, ENSP00000307156, ENSP00000308541, ENSP00000308938, ENSP00000309096, ENSP00000311905, ENSP00000315130, ENSP00000318472,

6 1.60E08 ENSP00000236850, ENSP00000252486, ENSP00000264005, ENSP00000350425, ENSP00000356969, ENSP00000406381

Observed False gene discovery count rate Matching proteins in your network (IDs)

A2M, AGT, ALB, AMBP, APLP1, APLP2, APOA1, APOA2, APOA4, APOC1, APOC3, APOD, APOE, APOH, B3GNT1, B3GNT2, BTD, C3, C5, C6, CA4, CARTPT, CDH13, CHGA, CHI3L1, CLSTN3, CLU, COCH, COL1A2, CPE, CST3, CTSD, CTSL1, CYTL1, ENSG00000224916, F2, FAM3C, FGB, IGFBP2, IGFBP5, KNG1, L1CAM, LAMB2, LCAT, LTBP4, MCAM, MGP, NBL1, NCAM1, NEO1, NFASC, NRXN1, PCSK1N, PENK, PIGR, PLG, PVRL1, RBP4, SCG2, SCG3, SERPINA1, SERPINA3, SERPINC1, SEZ6L, SIRPA, SMOC1, SORCS3, SORL1, SORT1, SPP1, SST, SULF2, VGF

APOA1, APOA2, APOA4, APOE, ENSG00000224916, LCAT

Matching proteins in your network (labels)

450 Fa´bio Trindade et al.

15 2.50E08 ENSP00000205948, ENSP00000227667, ENSP00000236850, ENSP00000252486, ENSP00000252491, ENSP00000260197, ENSP00000264005, ENSP00000295897, ENSP00000315130, ENSP00000345179, ENSP00000350425, ENSP00000356969, ENSP00000360519, ENSP00000378394, ENSP00000406381 71 2.54E08 ENSP00000205948, ENSP00000216361, ENSP00000216492, ENSP00000218230, ENSP00000220478, ENSP00000221891, ENSP00000223642, ENSP00000227667, ENSP00000228938, ENSP00000233813, ENSP00000236671, ENSP00000236850, ENSP00000245907, ENSP00000248933, ENSP00000249330, ENSP00000252486, ENSP00000252491, ENSP00000255409, ENSP00000256637, ENSP00000260197, ENSP00000261908, ENSP00000263413, ENSP00000263574, ENSP00000264005, ENSP00000264025, ENSP00000264036,

GO.0006869 Lipid transport

GO.0044707 Single-multicellular organism process

ENSP00000323929, ENSP00000324248, ENSP00000344786, ENSP00000345179, ENSP00000345344, ENSP00000348068, ENSP00000348307, ENSP00000348888, ENSP00000350425, ENSP00000353007, ENSP00000353025, ENSP00000355110, ENSP00000355627, ENSP00000356671, ENSP00000356969, ENSP00000358715, ENSP00000359074, ENSP00000360519, ENSP00000366124, ENSP00000376793, ENSP00000378517, ENSP00000385142, ENSP00000386104, ENSP00000406381, ENSP00000408632

(continued)

A2M, AGT, ALB, APLP1, APLP2, APOA1, APOA2, APOA4, APOC1, APOC3, APOD, APOE, APOH, B3GNT1, B3GNT2, BTD, C3, C5, C6, CA4, CARTPT, CDH13, CHGA, CHI3L1, CLSTN3, CLU, COCH, COL1A2, CPE, CST3, CTSD, CTSL1, CYTL1, ENSG00000224916, F2, FAM3C, FGB, IGFBP5, KNG1, L1CAM, LAMB2, LCAT, LTBP4, MCAM, MGP, NBL1, NCAM1, NEO1, NFASC, NRXN1, PCSK1N, PENK,

ALB, APOA1, APOA2, APOA4, APOC1, APOC3, APOD, APOE, APOH, CLU, ENSG00000224916, LCAT, PSAP, RBP4, SORL1 Bioinformatics Applied to CSF Proteome 451

Pathway description

GO.0048585 Negative regulation of response to stimulus

Pathway ID

Table 3 (continued)

PIGR, PLG, PVRL1, RBP4, SCG2, SCG3, SERPINA1, SERPINA3, SERPINC1, SEZ6L, SIRPA, SMOC1, SORCS3, SORL1, SORT1, SPP1, SST, SULF2, VGF

A2M, AGT, AHSG, AMBP, APOA1, APOA2, APOD, APOE, APOH, C5, CARTPT, CDH2, CLU, F2, FBLN1, FGB, HP, IGFBP5, KNG1, NBL1, NRXN1, PGLYRP2, PLG, PSAP,

32 2.67E08 ENSP00000205948, ENSP00000223642, ENSP00000226218, ENSP00000233813, ENSP00000236850, ENSP00000252486, ENSP00000254722, ENSP00000260197, ENSP00000265023, ENSP00000265132,

Matching proteins in your network (labels)

ENSP00000265023, ENSP00000266546, ENSP00000287641, ENSP00000289749, ENSP00000295897, ENSP00000296777, ENSP00000297268, ENSP00000300900, ENSP00000303550, ENSP00000304133, ENSP00000305595, ENSP00000306099, ENSP00000306477, ENSP00000307156, ENSP00000308541, ENSP00000308938, ENSP00000309096, ENSP00000311905, ENSP00000315130, ENSP00000318472, ENSP00000323929, ENSP00000324248, ENSP00000344786, ENSP00000345179, ENSP00000345344, ENSP00000348068, ENSP00000348307, ENSP00000348888, ENSP00000350425, ENSP00000353007, ENSP00000353025, ENSP00000355110, ENSP00000355627, ENSP00000356671, ENSP00000356969, ENSP00000358715, ENSP00000359074, ENSP00000360519, ENSP00000366124, ENSP00000376793, ENSP00000378517, ENSP00000385142, ENSP00000386104, ENSP00000406381, ENSP00000408632

Observed False gene discovery count rate Matching proteins in your network (IDs)

452 Fa´bio Trindade et al.

A2M, AGT, AMBP, APLP2, APOA1, APOA2, APOC1, APOC3, APOE, APP, B2M, C3, C4A, C5, CST3, ENSG00000224916, HP, ITIH1, ITIH4, KNG1, PTPRN2, SCG5, SERPINA1, SERPINA3, SERPINC1, SERPINF1, SORL1, SORT1, VTN

A2M, AGT, AHSG, APOA1, APOD, APOE, APOH, B2M, C4A, C5, C8B, CARTPT, CDH13, CFB, F2, FGB, KNG1, NBL1, NRXN1, PGLYRP2, PLG, SCG2, SERPINC1, SERPINF1, SPP1, VTN

29 3.25E08 ENSP00000223642, ENSP00000226218, ENSP00000227667, ENSP00000236850, ENSP00000245907, ENSP00000252486, ENSP00000252491, ENSP00000254722, ENSP00000256637, ENSP00000260197, ENSP00000263574, ENSP00000265023, ENSP00000265132, ENSP00000266041, ENSP00000273283, ENSP00000284981, ENSP00000300175, ENSP00000323929, ENSP00000348068, ENSP00000348170, ENSP00000355627, ENSP00000356671, ENSP00000356969, ENSP00000366124, ENSP00000374069, ENSP00000376793, ENSP00000396688, ENSP00000406381, ENSP00000452780 26 3.56E08 ENSP00000205948, ENSP00000223642, ENSP00000226218, ENSP00000236850, ENSP00000252486, ENSP00000254722, ENSP00000265023, ENSP00000289749, ENSP00000296777, ENSP00000304133, ENSP00000306099, ENSP00000308541, ENSP00000308938, ENSP00000323929, ENSP00000345179, ENSP00000345968, ENSP00000355627, ENSP00000356671,

GO.0044092 Negative regulation of molecular function

GO.0032101 Regulation of response to external stimulus

(continued)

SCG2, SERPINC1, SERPINF1, SORL1, SPP1, SULF2, VASN, VTN

ENSP00000269141, ENSP00000289749, ENSP00000296777, ENSP00000304133, ENSP00000306099, ENSP00000306864, ENSP00000308541, ENSP00000308938, ENSP00000315130, ENSP00000323929, ENSP00000331544, ENSP00000345179, ENSP00000345968, ENSP00000348170, ENSP00000353007, ENSP00000355627, ENSP00000356671, ENSP00000356969, ENSP00000378394, ENSP00000378517, ENSP00000385142, ENSP00000393887

Bioinformatics Applied to CSF Proteome 453

24 6.01E08 ENSP00000226218, ENSP00000252486, ENSP00000254722, ENSP00000260197, ENSP00000263574, ENSP00000265023, ENSP00000265132, ENSP00000266041, ENSP00000273283, ENSP00000296130, ENSP00000308541, ENSP00000311905, ENSP00000315130, ENSP00000323929, ENSP00000331544, ENSP00000348068, ENSP00000355627, ENSP00000356671, ENSP00000360281, ENSP00000362924,

GO.0030162 Regulation of proteolysis

ENSP00000360281, ENSP00000378517, ENSP00000385142, ENSP00000393887, ENSP00000396688, ENSP00000408632, ENSP00000416561, ENSP00000452780

Observed False gene discovery count rate Matching proteins in your network (IDs)

27 5.51E08 ENSP00000221891, ENSP00000226218, ENSP00000261908, ENSP00000264025, ENSP00000264036, ENSP00000265132, ENSP00000266546, ENSP00000284981, ENSP00000306099, ENSP00000307156, ENSP00000318472, ENSP00000324025, ENSP00000325660, ENSP00000344786, ENSP00000347665, ENSP00000348307, ENSP00000350364, ENSP00000350425, ENSP00000355627, ENSP00000357106, ENSP00000359403, ENSP00000370588, ENSP00000378517, ENSP00000385142, ENSP00000408632, ENSP00000452120, ENSP00000452780

Pathway description

GO.0007155 Cell adhesion

Pathway ID

Table 3 (continued)

A2M, AGT, AHSG, AMBP, APLP2, APOE, C8B, CFB, CLEC3B, CLU, CST3, F2, FBLN1, GSN, ITIH1, ITIH4, KNG1, LTBP4, SERPINA1, SERPINA3, SERPINC1, SERPINF1, SORL1, VTN

AGT, AMBP, APLP1, APOA4, APP, B2M, CADM3, CD99, CD99L2, CDH13, CGREF1, CLSTN3, CNTN1, COL18A1, FGB, ITGA7, LAMB2, MCAM, NCAM1, NEGR1, NEO1, NFASC, NRXN1, PVRL1, SIRPA, SPP1, VTN

Matching proteins in your network (labels)

454 Fa´bio Trindade et al.

43 9.55E08 ENSP00000223642, ENSP00000226218, ENSP00000227667, ENSP00000233809, ENSP00000233813, ENSP00000236850, ENSP00000248933, ENSP00000252486, ENSP00000255409, ENSP00000260197,

GO.0009966 Regulation of signal transduction

(continued)

A2M, AGT, AHSG, AMBP, APLP2, APOA1, APOC3, APOD, APOE, APP, ATP6AP1, BDNF, C5, CARTPT, CDH13, CDH2, CHI3L1, CLU, F2, FBLN1, FGB, GSN, HPX,

A2M, AHSG, ALB, AMBP, APLP1, APOA1, APOE, APP, C4A, CHGA, CLU, COL1A2, ENPP2, F5, FGB, GSN, HP, HPX, IGF2, KNG1, PIGR, PLG, PSAP, SCG3, SERPINA1, SORL1, SORT1, TF, VTN

A2M, C4A, C5, C8B, CFB, SERPINC1, VTN

7 8.62E08 ENSP00000223642, ENSP00000226218, ENSP00000323929, ENSP00000356671, ENSP00000360281, ENSP00000396688, ENSP00000416561 29 8.67E08 ENSP00000216492, ENSP00000220478, ENSP00000221891, ENSP00000226218, ENSP00000236850, ENSP00000252486, ENSP00000256637, ENSP00000259486, ENSP00000260197, ENSP00000265023, ENSP00000265132, ENSP00000265983, ENSP00000284981, ENSP00000295897, ENSP00000297268, ENSP00000306099, ENSP00000308938, ENSP00000315130, ENSP00000323929, ENSP00000348068, ENSP00000348170, ENSP00000348888, ENSP00000356771, ENSP00000362924, ENSP00000378394, ENSP00000385834, ENSP00000391826, ENSP00000393887, ENSP00000396688

ALB, APOA1, APOA2, APOA4, APOC1, APOC3, APOD, APOE, APOH, CLU, ENSG00000224916, LCAT, PSAP, RBP4, SORL1

15 8.34E08 ENSP00000205948, ENSP00000227667, ENSP00000236850, ENSP00000252486, ENSP00000252491, ENSP00000260197, ENSP00000264005, ENSP00000295897, ENSP00000315130, ENSP00000345179, ENSP00000350425, ENSP00000356969, ENSP00000360519, ENSP00000378394, ENSP00000406381

GO.0016192 Vesicle-mediated transport

GO.2000257 Regulation of protein activation cascade

GO.0010876 Lipid localization

ENSP00000366124, ENSP00000376793, ENSP00000393887, ENSP00000416561

Bioinformatics Applied to CSF Proteome 455

Pathway description

GO.0023051 Regulation of signaling

Pathway ID

Table 3 (continued)

46 1.01E07 ENSP00000223642, ENSP00000226218, ENSP00000227667, ENSP00000233809, ENSP00000233813, ENSP00000236850, ENSP00000248933, ENSP00000252486, ENSP00000255409, ENSP00000260197, ENSP00000261908, ENSP00000263574, ENSP00000265132, ENSP00000265983, ENSP00000266546, ENSP00000269141, ENSP00000284981, ENSP00000289749, ENSP00000300175, ENSP00000300289, ENSP00000301464, ENSP00000302621,

ENSP00000261908, ENSP00000263574, ENSP00000265132, ENSP00000265983, ENSP00000269141, ENSP00000284981, ENSP00000289749, ENSP00000296777, ENSP00000300289, ENSP00000301464, ENSP00000302621, ENSP00000304133, ENSP00000306099, ENSP00000306864, ENSP00000308541, ENSP00000309148, ENSP00000311905, ENSP00000315130, ENSP00000323929, ENSP00000331544, ENSP00000345179, ENSP00000353007, ENSP00000355627, ENSP00000358777, ENSP00000359074, ENSP00000362924, ENSP00000378394, ENSP00000385142, ENSP00000385834, ENSP00000391826, ENSP00000393887, ENSP00000408632, ENSP00000414303

Observed False gene discovery count rate Matching proteins in your network (IDs)

A2M, AGT, AHSG, AMBP, APLP2, APOA1, APOC3, APOD, APOE, APP, ATP6AP1, BDNF, C5, CDH13, CDH2, CHI3L1, CLSTN1, CLSTN3, CLU, F2, FBLN1, FGB, GSN, HPX, IGF2, IGFBP2, IGFBP5, IGFBP6, KLK6, L1CAM, LRG1, LTBP4, NBL1, NEO1, NRXN1, PDIA3, PSAP, RBP4, SCG2, SCG5, SEZ6L, SORL1, SULF2, TF, VASN, VTN

IGF2, IGFBP2, IGFBP5, IGFBP6, KLK6, L1CAM, LRG1, LTBP4, NBL1, NEO1, NRXN1, PDIA3, PSAP, SCG2, SEZ6L, SORL1, SULF2, TF, VASN, VTN

Matching proteins in your network (labels)

456 Fa´bio Trindade et al.

41 2.56E07 ENSP00000205948, ENSP00000216492, ENSP00000223642, ENSP00000226218, ENSP00000227667, ENSP00000233813, ENSP00000236850, ENSP00000245907, ENSP00000252486, ENSP00000252491, ENSP00000254722, ENSP00000259486, ENSP00000260197, ENSP00000261908, ENSP00000263273, ENSP00000264036, ENSP00000265023, ENSP00000266546, ENSP00000269141, ENSP00000287641, ENSP00000289749, ENSP00000300175, ENSP00000300289, ENSP00000306099,

7 1.46E07 ENSP00000227667, ENSP00000236850, ENSP00000252486, ENSP00000252491, ENSP00000350425, ENSP00000356969, ENSP00000406381

GO.0033344 Cholesterol efflux

GO.0032879 Regulation of localization

12 1.36E07 ENSP00000205948, ENSP00000226218, ENSP00000236850, ENSP00000252486, ENSP00000254722, ENSP00000265023, ENSP00000306099, ENSP00000308541, ENSP00000308938, ENSP00000345179, ENSP00000356671, ENSP00000378517

GO.1903035 Negative regulation of response to wounding

ENSP00000304133, ENSP00000306099, ENSP00000306864, ENSP00000308541, ENSP00000309148, ENSP00000311905, ENSP00000315130, ENSP00000323929, ENSP00000331544, ENSP00000345179, ENSP00000353007, ENSP00000355627, ENSP00000358777, ENSP00000359074, ENSP00000360519, ENSP00000362924, ENSP00000366513, ENSP00000378394, ENSP00000385142, ENSP00000385834, ENSP00000391826, ENSP00000393887, ENSP00000408632, ENSP00000414303

(continued)

AGT, AHSG, APOA1, APOA2, APOA4, APOC1, APOC3, APOD, APOE, APOH, ATP6AP1, C3, C4A, C5, CDH13, CDH2, CHGA, CLSTN3, CNTN1, COL18A1, ENPP2, ENSG00000224916, F2, FBLN1, FGB, GSN, IGFBP5, KNG1, MCAM, NBL1, NEO1, NRXN1, NUCB1, PDIA3, RBP4, SCG5, SERPINF1, SORL1, SST, TF, VTN

APOA1, APOA2, APOA4, APOC1, APOC3, APOE, ENSG00000224916

APOA1, APOD, APOE, APOH, F2, FGB, KNG1, PLG, SERPINC1, SERPINF1, SPP1, VTN

Bioinformatics Applied to CSF Proteome 457

Pathway description

6 2.96E07 ENSP00000227667, ENSP00000236850, ENSP00000252486, ENSP00000252491, ENSP00000350425, ENSP00000356969 39 3.07E07 ENSP00000216492, ENSP00000220478, ENSP00000236850, ENSP00000249330, ENSP00000252486, ENSP00000255409, ENSP00000256637, ENSP00000260197, ENSP00000261978, ENSP00000265023, ENSP00000269141, ENSP00000284981, ENSP00000295718, ENSP00000295897,

GO.0051641 Cellular localization

15 2.92E07 ENSP00000216492, ENSP00000220478, ENSP00000236850, ENSP00000265023, ENSP00000284981, ENSP00000295897, ENSP00000306099, ENSP00000308938, ENSP00000315130, ENSP00000323929, ENSP00000348068, ENSP00000356771, ENSP00000378394, ENSP00000385834, ENSP00000391826

ENSP00000308541, ENSP00000325660, ENSP00000331544, ENSP00000345179, ENSP00000347665, ENSP00000350425, ENSP00000355627, ENSP00000356969, ENSP00000358777, ENSP00000360519, ENSP00000362924, ENSP00000385142, ENSP00000385834, ENSP00000393887, ENSP00000396688, ENSP00000406381, ENSP00000408632

Observed False gene discovery count rate Matching proteins in your network (IDs)

GO.0034377 Plasma lipoprotein particle assembly

GO.0006887 Exocytosis

Pathway ID

Table 3 (continued)

A2M, AGT, ALB, APOA1, APOE, APP, ATP6AP1, BDNF, BSG, CARTPT, CDH13, CDH2, CHGA, CHI3L1, CLU, CPE, F5, FGB, GSN, IGF2, KNG1, LTBP2, LTBP4, NFASC, NPTX1, NRXN1, PDIA3, PIGR, PLG, PSAP, PTPRN, SCG2, SCG3,

APOA1, APOA2, APOA4, APOC1, APOC3, APOE

A2M, ALB, APOA1, APP, CHGA, CLU, F5, FGB, IGF2, KNG1, PLG, PSAP, SCG3, SERPINA1, TF

Matching proteins in your network (labels)

458 Fa´bio Trindade et al.

A2M, AGT, AHSG, APOA1, APOD, APOE, C4A, C5, C8B, CFB, PGLYRP2, SERPINC1, SERPINF1, VTN

A2M, AGT, AHSG, AMBP, APLP2, APOA1, APOC3, APOD, APOE, APP, ATP6AP1, BDNF, C5, CDH13, CDH2, CHI3L1, CLSTN1, CLSTN3, CLU, F2, FBLN1, FGB, GSN, HPX, IGF2, IGFBP2, IGFBP5, IGFBP6, KLK6, L1CAM, LRG1, LTBP4, NBL1, NEO1, NRXN1, PDIA3, PSAP, RBP4, SCG2, SCG5, SEZ6L, SORL1, SULF2, TF, VASN, VTN

14 4.73E07 ENSP00000223642, ENSP00000226218, ENSP00000236850, ENSP00000252486, ENSP00000254722, ENSP00000323929, ENSP00000345179, ENSP00000345968, ENSP00000355627, ENSP00000356671, ENSP00000360281, ENSP00000393887, ENSP00000396688, ENSP00000416561 46 4.80E07 ENSP00000223642, ENSP00000226218, ENSP00000227667, ENSP00000233809, ENSP00000233813, ENSP00000236850, ENSP00000248933, ENSP00000252486, ENSP00000255409, ENSP00000260197, ENSP00000261908, ENSP00000263574, ENSP00000265132, ENSP00000265983, ENSP00000266546, ENSP00000269141, ENSP00000284981, ENSP00000289749, ENSP00000300175, ENSP00000300289, ENSP00000301464, ENSP00000302621, ENSP00000304133, ENSP00000306099, ENSP00000306864, ENSP00000308541, ENSP00000309148, ENSP00000311905, ENSP00000315130, ENSP00000323929,

GO.0050727 Regulation of inflammatory response

GO.0010646 Regulation of cell communication

(continued)

SCG5, SERPINA1, SORL1, SORT1, TF, VGF

ENSP00000296777, ENSP00000300175, ENSP00000300289, ENSP00000304133, ENSP00000306099, ENSP00000307549, ENSP00000308938, ENSP00000311905, ENSP00000315130, ENSP00000323929, ENSP00000333769, ENSP00000344786, ENSP00000348068, ENSP00000348888, ENSP00000355627, ENSP00000356771, ENSP00000358777, ENSP00000362924, ENSP00000378394, ENSP00000385142, ENSP00000385834, ENSP00000386104, ENSP00000391826, ENSP00000408632, ENSP00000414303

Bioinformatics Applied to CSF Proteome 459

26 8.00E07 ENSP00000223642, ENSP00000226218, ENSP00000233813, ENSP00000245907, ENSP00000252486, ENSP00000254722, ENSP00000260197, ENSP00000263574, ENSP00000265023, ENSP00000265132, ENSP00000266041, ENSP00000273283, ENSP00000284981, ENSP00000308541, ENSP00000315130, ENSP00000323929, ENSP00000331544, ENSP00000345179, ENSP00000348068, ENSP00000350425, ENSP00000355627, ENSP00000356671, ENSP00000366124, ENSP00000376793, ENSP00000393887, ENSP00000396688

GO.0051248 Negative regulation of protein metabolic process

ENSP00000331544, ENSP00000345179, ENSP00000353007, ENSP00000355627, ENSP00000358777, ENSP00000359074, ENSP00000360519, ENSP00000362924, ENSP00000366513, ENSP00000378394, ENSP00000385142, ENSP00000385834, ENSP00000391826, ENSP00000393887, ENSP00000408632, ENSP00000414303

Observed False gene discovery count rate Matching proteins in your network (IDs)

11 5.07E07 ENSP00000227667, ENSP00000236850, ENSP00000252486, ENSP00000252491, ENSP00000264005, ENSP00000295897, ENSP00000315130, ENSP00000350425, ENSP00000356969, ENSP00000360519, ENSP00000406381

Pathway description

GO.0015850 Organic hydroxy compound transport

Pathway ID

Table 3 (continued)

A2M, AGT, AHSG, AMBP, APLP2, APOA4, APOD, APOE, APP, C3, C4A, C5, CLU, CST3, F2, FBLN1, IGFBP5, ITIH1, ITIH4, KNG1, SERPINA1, SERPINA3, SERPINC1, SERPINF1, SORL1, VTN

ALB, APOA1, APOA2, APOA4, APOC1, APOC3, APOE, CLU, ENSG00000224916, LCAT, RBP4

Matching proteins in your network (labels)

460 Fa´bio Trindade et al.

5 8.23E07 ENSP00000236850, ENSP00000252486, ENSP00000264005, ENSP00000350425, ENSP00000406381 36 9.04E07 ENSP00000205948, ENSP00000216361, ENSP00000223642, ENSP00000226218, ENSP00000233813, ENSP00000236850, ENSP00000245907, ENSP00000255409, ENSP00000261908, ENSP00000265983, ENSP00000269141, ENSP00000296777, ENSP00000300289, ENSP00000302621, ENSP00000304133, ENSP00000306099, ENSP00000308541, ENSP00000308938, ENSP00000309148, ENSP00000315130, ENSP00000345344, ENSP00000345968, ENSP00000353007, ENSP00000355627, ENSP00000358777, ENSP00000359074, ENSP00000360281, ENSP00000362924, ENSP00000378394, ENSP00000385834, ENSP00000391826, ENSP00000396688, ENSP00000408632, ENSP00000414303, ENSP00000416561, ENSP00000452780 17 9.46E07 ENSP00000223642, ENSP00000245907, ENSP00000255409, ENSP00000259396, ENSP00000261267, ENSP00000265023, ENSP00000266041, ENSP00000304133, ENSP00000308541, ENSP00000348068, ENSP00000348170, ENSP00000356969, ENSP00000376793, ENSP00000378517, ENSP00000393887, ENSP00000396688, ENSP00000414303 64 1.09E06 ENSP00000218230, ENSP00000221891, ENSP00000223642, ENSP00000227667, ENSP00000233809, ENSP00000236850,

GO.0034372 Very-low-density lipoprotein particle remodeling

GO.0048584 Positive regulation of response to stimulus

GO.0006954 Inflammatory response

GO.0007154 Cell communication

(continued)

A2M, AGT, ALB, APLP1, APLP2, APOA1, APOA2, APOA4, APOC3, APOE, APP, ATP6AP1, B2M, BSG,

AHSG, APOA2, BDNF, C3, C4A, C5, CHI3L1, F2, HP, ITIH4, KNG1, LYZ, ORM1, SCG2, SERPINA1, SERPINA3, SPP1

AGT, APOA1, APOH, ATP6AP1, B2M, BDNF, C3, C4A, C5, C8B, CARTPT, CDH13, CDH2, CFB, CHI3L1, CLU, COCH, CTSL1, F2, FGB, GSN, HPX, IGF2, IGFBP5, KLK6, L1CAM, LRG1, NEO1, PDIA3, PGLYRP2, PLG, PSAP, SCG2, SULF2, TF, VTN

APOA1, APOA4, APOE, ENSG00000224916, LCAT

Bioinformatics Applied to CSF Proteome 461

Pathway ID

Table 3 (continued)

Pathway description ENSP00000237014, ENSP00000245907, ENSP00000249330, ENSP00000252455, ENSP00000252486, ENSP00000255409, ENSP00000256637, ENSP00000259486, ENSP00000260197, ENSP00000261978, ENSP00000263574, ENSP00000264025, ENSP00000266546, ENSP00000282499, ENSP00000284981, ENSP00000287641, ENSP00000295897, ENSP00000297268, ENSP00000300175, ENSP00000300289, ENSP00000301464, ENSP00000303550, ENSP00000304133, ENSP00000306099, ENSP00000307549, ENSP00000308541, ENSP00000315130, ENSP00000318472, ENSP00000323929, ENSP00000324248, ENSP00000325660, ENSP00000333593, ENSP00000333769, ENSP00000344786, ENSP00000345344, ENSP00000345968, ENSP00000348888, ENSP00000350425, ENSP00000353007, ENSP00000355110, ENSP00000355627, ENSP00000356969, ENSP00000358715, ENSP00000358777, ENSP00000359074, ENSP00000360519, ENSP00000362924, ENSP00000368314, ENSP00000374069, ENSP00000378394, ENSP00000385142, ENSP00000385834, ENSP00000386104, ENSP00000391826, ENSP00000406381, ENSP00000408632, ENSP00000452120, ENSP00000452780

Observed False gene discovery count rate Matching proteins in your network (IDs)

C3, C5, CDH13, CHI3L1, CLSTN3, CLU, CNTN1, COL1A2, CPE, CTSL1, CYTL1, ENPP2, ENSG00000224916, F2, FGB, GRIA4, GSN, IGF2, IGFBP2, IGFBP6, ITGA7, L1CAM, LTBP2, NCAM1, NFASC, NPTX1, NRCAM, NRXN1, NXPH4, PCSK1N, PDIA3, PENK, PGLYRP2, PIGR, PRKCSH, PSAP, PTPRN2, PVRL1, RBP4, SCG2, SCG5, SMOC1, SORCS3, SORL1, SORT1, SST, SULF2, TF, TTR, VGF

Matching proteins in your network (labels)

462 Fa´bio Trindade et al.

GO.0044700 Single organism signaling

GO.0034381 Plasma lipoprotein particle clearance 63 1.19E06 ENSP00000218230, ENSP00000221891, ENSP00000223642, ENSP00000227667, ENSP00000233809, ENSP00000236850, ENSP00000237014, ENSP00000245907, ENSP00000249330, ENSP00000252455, ENSP00000252486, ENSP00000255409, ENSP00000256637, ENSP00000259486, ENSP00000260197, ENSP00000261978, ENSP00000263574, ENSP00000264025, ENSP00000266546, ENSP00000282499, ENSP00000284981, ENSP00000287641, ENSP00000297268, ENSP00000300175, ENSP00000300289, ENSP00000301464, ENSP00000303550, ENSP00000304133, ENSP00000306099, ENSP00000307549, ENSP00000308541, ENSP00000315130, ENSP00000318472, ENSP00000323929, ENSP00000324248, ENSP00000325660, ENSP00000333593, ENSP00000333769, ENSP00000344786, ENSP00000345344, ENSP00000345968, ENSP00000348888, ENSP00000350425, ENSP00000353007, ENSP00000355110, ENSP00000355627, ENSP00000356969, ENSP00000358715, ENSP00000358777, ENSP00000359074, ENSP00000360519, ENSP00000362924, ENSP00000368314, ENSP00000374069, ENSP00000378394, ENSP00000385142, ENSP00000385834, ENSP00000386104, ENSP00000391826, ENSP00000406381, ENSP00000408632, ENSP00000452120, ENSP00000452780

6 1.09E06 ENSP00000227667, ENSP00000236850, ENSP00000252486, ENSP00000252491, ENSP00000356969, ENSP00000406381

(continued)

A2M, AGT, APLP1, APLP2, APOA1, APOA2, APOA4, APOC3, APOE, APP, ATP6AP1, B2M, BSG, C3, C5, CDH13, CHI3L1, CLSTN3, CLU, CNTN1, COL1A2, CPE, CTSL1, CYTL1, ENPP2, ENSG00000224916, F2, FGB, GRIA4, GSN, IGF2, IGFBP2, IGFBP6, ITGA7, L1CAM, LTBP2, NCAM1, NFASC, NPTX1, NRCAM, NRXN1, NXPH4, PCSK1N, PDIA3, PENK, PGLYRP2, PIGR, PRKCSH, PSAP, PTPRN2, PVRL1, RBP4, SCG2, SCG5, SMOC1, SORCS3, SORL1, SORT1, SST, SULF2, TF, TTR, VGF

APOA1, APOA2, APOC1, APOC3, APOE, ENSG00000224916

Bioinformatics Applied to CSF Proteome 463

Pathway description

GO.0042221 Response to chemical

Pathway ID

Table 3 (continued)

53 1.21E06 ENSP00000216492, ENSP00000221891, ENSP00000223642, ENSP00000233809, ENSP00000233813, ENSP00000245907, ENSP00000252486, ENSP00000254722, ENSP00000256637, ENSP00000259486, ENSP00000261908, ENSP00000261978, ENSP00000263273, ENSP00000264005, ENSP00000264025, ENSP00000266041, ENSP00000284981, ENSP00000296130, ENSP00000300900, ENSP00000304133, ENSP00000305595, ENSP00000306099, ENSP00000306864, ENSP00000307156, ENSP00000307549, ENSP00000309096, ENSP00000311905, ENSP00000315130, ENSP00000318472, ENSP00000325660, ENSP00000333769, ENSP00000344786, ENSP00000345179, ENSP00000345344, ENSP00000347665, ENSP00000348170, ENSP00000348888, ENSP00000356671, ENSP00000356969, ENSP00000358777, ENSP00000359074, ENSP00000360519, ENSP00000360687, ENSP00000362924, ENSP00000366124, ENSP00000373477, ENSP00000378394, ENSP00000378517, ENSP00000385142, ENSP00000391826, ENSP00000396688, ENSP00000408632, ENSP00000414303

Observed False gene discovery count rate Matching proteins in your network (IDs)

APLP1, APOA2, APOD, APOE, APP, ATP6AP1, B3GNT1, B3GNT2, BDNF, BSG, C3, C4A, C5, CA4, CDH13, CHGA, CLEC3B, CLU, CNTN1, COL18A1, CST3, CTSL1, ENPP2, FGB, GPX3, GSN, HP, IGF2, IGFBP2, IGFBP5, ITIH4, L1CAM, LAMB2, LCAT, LTBP2, LTBP4, NCAM1, NEO1, NFASC, NPTX1, NRXN1, NUCB1, PIGR, PSAP, PTGDS, PVRL1, RBP4, SCG2, SERPINC1, SERPINF1, SORT1, SPP1, VASN

Matching proteins in your network (labels)

464 Fa´bio Trindade et al.

(continued)

A2M, AGT, AHSG, ALB, AMBP, APLP1, APLP2, APOA1, APOA2, APOA4, APOC1, APOC3, APOD, APOE, APOH, C3, C4A, C5, CDH13, CHGA, CLU, COL18A1, CST3, ENSG00000224916, F2, FBLN1, FGB, GSN, HP, IGFBP5, IGFBP6, ITIH1, ITIH4, KNG1, NBL1, NEO1, NRXN1, OGN, ORM1, PDIA3, PGLYRP2, PLG, PSAP, PTGDS, PTPRN2, RBP4, SCG2, SCG5, SERPINA1, SERPINA3, SORL1, SORT1, SPP1, SST, SULF2, VASN, VTN

57 1.54E06 ENSP00000205948, ENSP00000216492, ENSP00000221891, ENSP00000223642, ENSP00000226218, ENSP00000227667, ENSP00000233813, ENSP00000236850, ENSP00000245907, ENSP00000252486, ENSP00000252491, ENSP00000256637, ENSP00000259396, ENSP00000260197, ENSP00000261908, ENSP00000262551, ENSP00000263574, ENSP00000265023, ENSP00000265132, ENSP00000266041, ENSP00000273283, ENSP00000287641, ENSP00000289749, ENSP00000295897, ENSP00000300175, ENSP00000300289, ENSP00000301464, ENSP00000304133, ENSP00000306099, ENSP00000306864, ENSP00000308541, ENSP00000308938, ENSP00000315130, ENSP00000323929, ENSP00000331544, ENSP00000345179, ENSP00000345968, ENSP00000347665, ENSP00000348068, ENSP00000348170,

GO.0048519 Negative regulation of biological process

APOA1, APOA2, APOA4, APOC1, APOC3, APOE, ENSG00000224916

7 1.47E06 ENSP00000227667, ENSP00000236850, ENSP00000252486, ENSP00000252491, ENSP00000350425, ENSP00000356969, ENSP00000406381

GO.0032374 Regulation of cholesterol transport

A2M, AGT, ALB, APOA1, APP, B2M, CHGA, CLU, COL1A2, CST3, F2, F5, FGB, IGF2, KNG1, PLG, PSAP, SCG3, SERPINA1, TF

20 1.40E06 ENSP00000216492, ENSP00000220478, ENSP00000236850, ENSP00000265023, ENSP00000284981, ENSP00000295897, ENSP00000297268, ENSP00000306099, ENSP00000308541, ENSP00000308938, ENSP00000315130, ENSP00000323929, ENSP00000348068, ENSP00000355627, ENSP00000356771, ENSP00000366124, ENSP00000378394, ENSP00000385834, ENSP00000391826, ENSP00000452780

GO.0001775 Cell activation

Bioinformatics Applied to CSF Proteome 465

GO.0007165 Signal transduction

60 1.84E06 ENSP00000218230, ENSP00000221891, ENSP00000223642, ENSP00000227667, ENSP00000233809, ENSP00000236850,

6 1.79E06 ENSP00000223642, ENSP00000226218, ENSP00000323929, ENSP00000360281, ENSP00000396688, ENSP00000416561

GO.0030449 Regulation of complement activation

ENSP00000350425, ENSP00000353007, ENSP00000355627, ENSP00000356969, ENSP00000360519, ENSP00000360687, ENSP00000362924, ENSP00000366124, ENSP00000374069, ENSP00000376793, ENSP00000378394, ENSP00000378517, ENSP00000385142, ENSP00000393887, ENSP00000396688, ENSP00000406381, ENSP00000408632

Observed False gene discovery count rate Matching proteins in your network (IDs)

23 1.70E06 ENSP00000227667, ENSP00000233813, ENSP00000236850, ENSP00000249330, ENSP00000252486, ENSP00000254722, ENSP00000261908, ENSP00000263574, ENSP00000264005, ENSP00000264613, ENSP00000265023, ENSP00000265983, ENSP00000296777, ENSP00000307549, ENSP00000308541, ENSP00000350425, ENSP00000355627, ENSP00000356969, ENSP00000358777, ENSP00000360519, ENSP00000385834, ENSP00000406381, ENSP00000452780

Pathway description

GO.0048878 Chemical homeostasis

Pathway ID

Table 3 (continued)

A2M, AGT, APLP1, APLP2, APOA1, APOA2, APOA4, APOC3, APOE, APP, ATP6AP1, B2M, BSG, C3, C5,

A2M, C4A, C5, C8B, CFB, VTN

AGT, APLP2, APOA1, APOA2, APOA4, APOC3, APOE, ATP6AP1, B2M, CARTPT, CP, ENSG00000224916, F2, HPX, IGFBP5, KNG1, LCAT, NEO1, NPTX1, RBP4, SERPINF1, TF, VGF

Matching proteins in your network (labels)

466 Fa´bio Trindade et al.

A2M, C4A, C5, C8B, CFB, HPX, VTN

7 2.04E06 ENSP00000223642, ENSP00000226218, ENSP00000265983, ENSP00000323929, ENSP00000360281, ENSP00000396688, ENSP00000416561 28 2.10E06 ENSP00000205948, ENSP00000223642, ENSP00000226218, ENSP00000227667, ENSP00000236850, ENSP00000245907, ENSP00000252491, ENSP00000254722,

GO.0002920 Regulation of humoral immune response

GO.0051336 Regulation of hydrolase activity

(continued)

A2M, AHSG, AMBP, APLP2, APOA1, APOA2, APOA4, APOC1, APOC3, APOH, C3, C4A, C5, CST3, ENSG00000224916, FBLN1, GSN,

CARTPT, CDH13, CHI3L1, CLU, CNTN1, COL1A2, CPE, CTSL1, CYTL1, ENPP2, ENSG00000224916, F2, FGB, GRIA4, GSN, IGF2, IGFBP2, IGFBP6, ITGA7, L1CAM, LTBP2, NCAM1, NRXN1, NXPH4, PCSK1N, PDIA3, PENK, PGLYRP2, PIGR, PRKCSH, PSAP, PTPRN, PTPRN2, PVRL1, RBP4, SCG2, SCG5, SMOC1, SORCS3, SORL1, SORT1, SST, SULF2, TF, TTR

ENSP00000237014, ENSP00000245907, ENSP00000252455, ENSP00000252486, ENSP00000255409, ENSP00000256637, ENSP00000259486, ENSP00000260197, ENSP00000261978, ENSP00000263574, ENSP00000264025, ENSP00000282499, ENSP00000284981, ENSP00000287641, ENSP00000295718, ENSP00000296777, ENSP00000297268, ENSP00000300175, ENSP00000300289, ENSP00000301464, ENSP00000303550, ENSP00000304133, ENSP00000306099, ENSP00000308541, ENSP00000315130, ENSP00000318472, ENSP00000323929, ENSP00000324248, ENSP00000325660, ENSP00000333593, ENSP00000333769, ENSP00000345344, ENSP00000345968, ENSP00000348888, ENSP00000350425, ENSP00000353007, ENSP00000355110, ENSP00000355627, ENSP00000356969, ENSP00000358715, ENSP00000358777, ENSP00000359074, ENSP00000360519, ENSP00000362924, ENSP00000374069, ENSP00000378394, ENSP00000385142, ENSP00000385834, ENSP00000386104, ENSP00000391826, ENSP00000406381, ENSP00000408632, ENSP00000452120, ENSP00000452780

Bioinformatics Applied to CSF Proteome 467

AGT, AHSG, APOA1, APOE, APOH, ATP6AP1, BDNF, C3, C5, C6, CARTPT, CHI3L1, CLSTN1,

37 2.23E06 ENSP00000205948, ENSP00000216361, ENSP00000223642, ENSP00000228938, ENSP00000233813, ENSP00000236850,

GO.0050793 Regulation of developmental process

ITIH1, ITIH4, KNG1, PTPRN2, SERPINA1, SERPINA3, SERPINC1, SERPINF1, SORL1, SORT1, VTN

Matching proteins in your network (labels)

AGT, APOA2, APOE, APOH, ATP6AP1, BDNF, C3, C5, C6, CARTPT, CHI3L1, CLSTN1, CLSTN3, CLU, CNTN1, ENPP2, ENSG00000224916, F2, FGB, KNG1, LRG1, NBL1, NEGR1, NRCAM, NRXN1, PLG, SPP1, SULF2, TF

ENSP00000256637, ENSP00000260197, ENSP00000263574, ENSP00000265023, ENSP00000265132, ENSP00000266041, ENSP00000273283, ENSP00000323929, ENSP00000331544, ENSP00000348068, ENSP00000350425, ENSP00000356671, ENSP00000356969, ENSP00000362924, ENSP00000366124, ENSP00000374069, ENSP00000376793, ENSP00000393887, ENSP00000396688, ENSP00000406381

Observed False gene discovery count rate Matching proteins in your network (IDs)

29 2.14E06 ENSP00000205948, ENSP00000223642, ENSP00000245907, ENSP00000252486, ENSP00000255409, ENSP00000259486, ENSP00000263413, ENSP00000265023, ENSP00000266546, ENSP00000289749, ENSP00000296777, ENSP00000302621, ENSP00000306099, ENSP00000308541, ENSP00000308938, ENSP00000315130, ENSP00000325660, ENSP00000350364, ENSP00000353007, ENSP00000355627, ENSP00000356969, ENSP00000358777, ENSP00000366513, ENSP00000368314, ENSP00000378517, ENSP00000385142, ENSP00000385834, ENSP00000406381, ENSP00000414303

Pathway description

GO.0051240 Positive regulation of multicellular organismal process

Pathway ID

Table 3 (continued)

468 Fa´bio Trindade et al.

GO.0044710 Single-organism metabolic process

GO.0006953 Acute-phase response

56 2.50E06 ENSP00000205948, ENSP00000223642, ENSP00000227667, ENSP00000233813, ENSP00000236671, ENSP00000236850, ENSP00000245907, ENSP00000252486, ENSP00000259486, ENSP00000260197, ENSP00000262551, ENSP00000263413, ENSP00000263574, ENSP00000264005, ENSP00000264613, ENSP00000265023, ENSP00000265132, ENSP00000265983, ENSP00000284981, ENSP00000295897, ENSP00000297268, ENSP00000300289, ENSP00000303550, ENSP00000304133, ENSP00000305595, ENSP00000306099, ENSP00000306100, ENSP00000306477, ENSP00000308541, ENSP00000309096,

7 2.34E06 ENSP00000259396, ENSP00000266041, ENSP00000308541, ENSP00000348068, ENSP00000348170, ENSP00000376793, ENSP00000393887

ENSP00000245907, ENSP00000252486, ENSP00000255409, ENSP00000260197, ENSP00000261908, ENSP00000263413, ENSP00000264025, ENSP00000266546, ENSP00000289749, ENSP00000296777, ENSP00000302621, ENSP00000306099, ENSP00000306864, ENSP00000308541, ENSP00000311905, ENSP00000325660, ENSP00000331544, ENSP00000345968, ENSP00000355110, ENSP00000355627, ENSP00000358777, ENSP00000360519, ENSP00000362924, ENSP00000366124, ENSP00000366513, ENSP00000368314, ENSP00000378517, ENSP00000385142, ENSP00000393887, ENSP00000414303, ENSP00000452120

(continued)

A2M, ALB, AMBP, APLP2, APOA1, APOA2, APOA4, APOC3, APOD, APOE, APOH, APP, B3GNT1, B3GNT2, BDNF, BSG, BTD, C3, C4A, C5, C6, C8B, CFB, CLU, COL18A1, COL1A2, CP, CTBS, CTSD, CTSL1, CYTL1, ENPP2, ENSG00000224916, F2, FBLN1, FGB, GPX3, GSN, HPX, IGF2, IGFBP5, KLK6, KNG1, LCAT, MAN1A1, OGN, PAM, PDIA3, PSAP, PTGDS, RBP4, SCG2, SOD3, SORL1, SULF2, TF

AHSG, F2, HP, ITIH4, ORM1, SERPINA1, SERPINA3

CLSTN3, CNTN1, COCH, CST3, F2, FBLN1, FGB, GSN, IGFBP5, ITGA7, LRG1, LTBP4, MGP, NBL1, NEO1, NRCAM, NRXN1, PGLYRP2, PVRL1, RBP4, SMOC1, SORL1, SPP1, VASN

Bioinformatics Applied to CSF Proteome 469

33 3.97E06 ENSP00000216492, ENSP00000220478, ENSP00000236850, ENSP00000249330,

7 3.15E06 ENSP00000223642, ENSP00000245907, ENSP00000263413, ENSP00000315130, ENSP00000360281, ENSP00000396688, ENSP00000416561

GO.0006956 Complement activation

GO.0051649 Establishment of localization in cell

5 3.11E06 ENSP00000227667, ENSP00000252486, ENSP00000252491, ENSP00000356969, ENSP00000406381

GO.0032375 Negative regulation of cholesterol transport

ENSP00000309148, ENSP00000315130, ENSP00000323929, ENSP00000331544, ENSP00000333769, ENSP00000345179, ENSP00000345344, ENSP00000347665, ENSP00000350425, ENSP00000353007, ENSP00000356969, ENSP00000357453, ENSP00000359664, ENSP00000360281, ENSP00000360519, ENSP00000360687, ENSP00000362924, ENSP00000371554, ENSP00000373477, ENSP00000378394, ENSP00000385834, ENSP00000391826, ENSP00000396688, ENSP00000406381, ENSP00000414303, ENSP00000416561

Observed False gene discovery count rate Matching proteins in your network (IDs)

6 2.85E06 ENSP00000205948, ENSP00000265023, ENSP00000306099, ENSP00000308541, ENSP00000323929, ENSP00000331544

Pathway description

GO.0072378 Blood coagulation, fibrin clot formation

Pathway ID

Table 3 (continued)

A2M, AGT, ALB, APOA1, APOE, APP, BDNF, BSG, CARTPT, CHGA,

C3, C4A, C5, C6, C8B, CFB, CLU

APOA2, APOC1, APOC3, APOE, ENSG00000224916

A2M, APOH, F2, FBLN1, FGB, KNG1

Matching proteins in your network (labels)

470 Fa´bio Trindade et al.

APOE, APOH, F2, FGB, KNG1, PLG, VTN

7 4.18E06 ENSP00000205948, ENSP00000226218, ENSP00000252486, ENSP00000265023, ENSP00000306099, ENSP00000308541, ENSP00000308938 69 4.18E06 ENSP00000216492, ENSP00000218230, ENSP00000221891, ENSP00000223642, ENSP00000227667, ENSP00000233809, ENSP00000236850, ENSP00000237014, ENSP00000245907, ENSP00000252455, ENSP00000252486, ENSP00000254722, ENSP00000256637, ENSP00000259486, ENSP00000260197, ENSP00000261978, ENSP00000263574, ENSP00000264025, ENSP00000282499, ENSP00000284981, ENSP00000287641, ENSP00000295718,

GO.0030195 Negative regulation of blood coagulation

GO.0051716 Cellular response to stimulus

(continued)

A2M, AGT, ALB, APLP1, APLP2, APOA1, APOA2, APOA4, APOC3, APOD, APOE, APP, ATP6AP1, B2M, BSG, C3, C5, CARTPT, CDH13, CHGA, CLEC3B, CLU, CNTN1, CPE, CST3, CTSL1, CYTL1, ENPP2, ENSG00000224916, F2, FGB, GRIA4, GSN, IGF2, IGFBP2, IGFBP6, ITGA7, L1CAM, LAMB2, LTBP2, NCAM1, NPTX1, NRXN1, NXPH4, PCSK1N, PDIA3, PENK,

AHSG, APOA2, F2, HP, ITIH4, ORM1, SERPINA1, SERPINA3

8 4.18E06 ENSP00000259396, ENSP00000266041, ENSP00000308541, ENSP00000348068, ENSP00000348170, ENSP00000356969, ENSP00000376793, ENSP00000393887

CHI3L1, CLU, F5, FGB, IGF2, KNG1, LTBP2, LTBP4, NFASC, NPTX1, NRXN1, PDIA3, PLG, PSAP, PTPRN, SCG2, SCG3, SCG5, SERPINA1, SORL1, SORT1, TF, VGF

GO.0002526 Acute inflammatory response

ENSP00000252486, ENSP00000255409, ENSP00000256637, ENSP00000260197, ENSP00000261978, ENSP00000265023, ENSP00000284981, ENSP00000295718, ENSP00000295897, ENSP00000296777, ENSP00000300175, ENSP00000300289, ENSP00000304133, ENSP00000306099, ENSP00000307549, ENSP00000308938, ENSP00000311905, ENSP00000315130, ENSP00000323929, ENSP00000333769, ENSP00000344786, ENSP00000348068, ENSP00000355627, ENSP00000356771, ENSP00000378394, ENSP00000385142, ENSP00000385834, ENSP00000391826, ENSP00000414303

Bioinformatics Applied to CSF Proteome 471

Pathway description

GO.0010901 Regulation of verylow-density lipoprotein particle remodeling

Pathway ID

Table 3 (continued)

4 4.94E06 ENSP00000227667, ENSP00000236850, ENSP00000356969, ENSP00000406381

ENSP00000295897, ENSP00000296130, ENSP00000296777, ENSP00000300175, ENSP00000300289, ENSP00000301464, ENSP00000303550, ENSP00000304133, ENSP00000306099, ENSP00000306864, ENSP00000307156, ENSP00000307549, ENSP00000308541, ENSP00000315130, ENSP00000318472, ENSP00000323929, ENSP00000324248, ENSP00000325660, ENSP00000333593, ENSP00000333769, ENSP00000345179, ENSP00000345344, ENSP00000345968, ENSP00000348888, ENSP00000350425, ENSP00000353007, ENSP00000355110, ENSP00000355627, ENSP00000356969, ENSP00000358715, ENSP00000358777, ENSP00000359074, ENSP00000360519, ENSP00000362924, ENSP00000366124, ENSP00000371554, ENSP00000374069, ENSP00000378394, ENSP00000378517, ENSP00000385142, ENSP00000385834, ENSP00000386104, ENSP00000391826, ENSP00000406381, ENSP00000408632, ENSP00000452120, ENSP00000452780

Observed False gene discovery count rate Matching proteins in your network (IDs)

APOA1, APOA2, APOC3, ENSG00000224916

PGLYRP2, PIGR, PRKCSH, PSAP, PTPRN, PTPRN2, PVRL1, RBP4, SCG2, SCG5, SERPINF1, SMOC1, SOD3, SORCS3, SORL1, SORT1, SPP1, SST, SULF2, TF, TTR, VASN

Matching proteins in your network (labels)

472 Fa´bio Trindade et al.

(continued)

A2M, C4A, C5, C8B, CFB, CLEC3B, GSN, VTN

8 5.43E06 ENSP00000223642, ENSP00000226218, ENSP00000296130, ENSP00000323929, ENSP00000360281, ENSP00000362924, ENSP00000396688, ENSP00000416561

GO.0070613 Regulation of protein processing

A2M, AGT, AHSG, ALB, AMBP, APLP1, APLP2, APOA1, APOA2, APOA4, APOC1, APOC3, APOD, APOE, APOH, C3, C4A, C5, CDH13, CHGA, CLU, COL18A1, CST3, ENSG00000224916, F2, FBLN1, FGB, GSN, HP, IGFBP5, IGFBP6, ITIH1, ITIH4, KNG1, NBL1, NEO1, NRXN1, OGN, PGLYRP2, PLG, PSAP, PTGDS, RBP4, SCG2, SERPINA1, SERPINA3, SERPINC1, SORL1, SPP1, SST, SULF2, VASN, VTN

53 5.21E06 ENSP00000205948, ENSP00000216492, ENSP00000221891, ENSP00000223642, ENSP00000226218, ENSP00000227667, ENSP00000233813, ENSP00000236850, ENSP00000245907, ENSP00000252486, ENSP00000252491, ENSP00000260197, ENSP00000261908, ENSP00000262551, ENSP00000263574, ENSP00000265023, ENSP00000265132, ENSP00000266041, ENSP00000273283, ENSP00000287641, ENSP00000289749, ENSP00000295897, ENSP00000301464, ENSP00000304133, ENSP00000306099, ENSP00000306864, ENSP00000308541, ENSP00000308938, ENSP00000315130, ENSP00000323929, ENSP00000331544, ENSP00000345179, ENSP00000345968, ENSP00000347665, ENSP00000348068, ENSP00000348170, ENSP00000350425, ENSP00000353007, ENSP00000355627, ENSP00000356671, ENSP00000356969, ENSP00000360519, ENSP00000360687, ENSP00000362924, ENSP00000366124, ENSP00000376793, ENSP00000378394, ENSP00000378517, ENSP00000385142, ENSP00000393887, ENSP00000396688, ENSP00000406381, ENSP00000408632

GO.0048523 Negative regulation of cellular process

APOA1, APOA2, APOE, ENSG00000224916

4 4.94E06 ENSP00000236850, ENSP00000252486, ENSP00000356969, ENSP00000406381

GO.0034384 High-density lipoprotein particle clearance

Bioinformatics Applied to CSF Proteome 473

29 5.43E06 ENSP00000205948, ENSP00000216361, ENSP00000223642, ENSP00000226218, ENSP00000236850, ENSP00000252486, ENSP00000254722, ENSP00000265023, ENSP00000265132, ENSP00000265983, ENSP00000306099, ENSP00000308541, ENSP00000308938, ENSP00000315130, ENSP00000323929, ENSP00000345179, ENSP00000345344, ENSP00000348170, ENSP00000355627, ENSP00000356671, ENSP00000360281, ENSP00000362924,

GO.0080134 Regulation of response to stress

Observed False gene discovery count rate Matching proteins in your network (IDs) 34 5.43E06 ENSP00000205948, ENSP00000227667, ENSP00000236850, ENSP00000237014, ENSP00000249330, ENSP00000252486, ENSP00000252491, ENSP00000255409, ENSP00000256637, ENSP00000260197, ENSP00000261978, ENSP00000264005, ENSP00000265983, ENSP00000295718, ENSP00000295897, ENSP00000296777, ENSP00000300175, ENSP00000300289, ENSP00000300900, ENSP00000304133, ENSP00000311905, ENSP00000315130, ENSP00000333769, ENSP00000344786, ENSP00000345179, ENSP00000350425, ENSP00000355627, ENSP00000356969, ENSP00000358777, ENSP00000360519, ENSP00000378394, ENSP00000385834, ENSP00000406381, ENSP00000414303

Pathway description

GO.0071702 Organic substance transport

Pathway ID

Table 3 (continued)

A2M, AGT, AHSG, AMBP, APOA1, APOD, APOE, APOH, B2M, C4A, C5, C8B, CFB, CLU, COCH, CTSL1, F2, FGB, GSN, HP, HPX, KNG1, PLG, PSAP, SERPINC1, SERPINF1, SPP1, TF, VTN

AGT, ALB, APOA1, APOA2, APOA4, APOC1, APOC3, APOD, APOE, APOH, ATP6AP1, BDNF, BSG, CA4, CARTPT, CHI3L1, CLU, ENSG00000224916, HPX, LCAT, LTBP2, LTBP4, NFASC, PDIA3, PSAP, PTPRN, RBP4, SCG2, SCG5, SORL1, SORT1, TF, TTR, VGF

Matching proteins in your network (labels)

474 Fa´bio Trindade et al.

6 7.66E06 ENSP00000223642, ENSP00000245907, ENSP00000263413, ENSP00000315130, ENSP00000360281, ENSP00000396688

C3, C4A, C5, C6, C8B, CLU

(continued)

AHSG, APOA1, APOE, APOH, APP, ATP6AP1, BDNF, C3, C5, C6, CARTPT, CDH2, CHI3L1, CLSTN1, CLSTN3, CNTN1, ENPP2, F2, KLK6, LRG1, MGP, NBL1, NRCAM, NRXN1, PGLYRP2, PVRL1, RBP4, SORL1, SPP1, VASN

30 7.62E06 ENSP00000205948, ENSP00000223642, ENSP00000228938, ENSP00000236850, ENSP00000245907, ENSP00000252486, ENSP00000255409, ENSP00000259486, ENSP00000260197, ENSP00000263413, ENSP00000264025, ENSP00000266546, ENSP00000269141, ENSP00000284981, ENSP00000289749, ENSP00000296777, ENSP00000302621, ENSP00000306864, ENSP00000308541, ENSP00000309148, ENSP00000325660, ENSP00000345968, ENSP00000358777, ENSP00000360519, ENSP00000366513, ENSP00000368314, ENSP00000378517, ENSP00000385142, ENSP00000393887, ENSP00000414303

GO.2000026 Regulation of multicellular organismal development

GO.0006958 Complement activation, classical pathway

AGT, APOA1, APOA2, APOA4, APOC1, APOC3, APOD, APOE, C3, ENSG00000224916, GOLM1, PSAP, SERPINA3

13 6.56E06 ENSP00000227667, ENSP00000236850, ENSP00000245907, ENSP00000252486, ENSP00000252491, ENSP00000345179, ENSP00000350425, ENSP00000355627, ENSP00000356969, ENSP00000373363, ENSP00000376793, ENSP00000378394, ENSP00000406381

GO.0019216 Regulation of lipid metabolic process

APOA1, APOA2, APOC1, APOC3, SORT1

5 6.24E06 ENSP00000227667, ENSP00000236850, ENSP00000252491, ENSP00000256637, ENSP00000356969

GO.0060192 Negative regulation of lipase activity

ENSP00000378394, ENSP00000378517, ENSP00000385834, ENSP00000393887, ENSP00000396688, ENSP00000416561, ENSP00000452780

Bioinformatics Applied to CSF Proteome 475

A2M, AGT, AHSG, AMBP, APLP2, APOA1, APOA2, APOA4, APOD, APOE, ATP6AP1, BDNF, C8B, CARTPT, CDH2, CFB, CHI3L1, CLEC3B, CLU, CST3, ENPP2, F2, FGB, GSN, HPX, IGF2, IGFBP5, ITIH1, ITIH4, KNG1, LTBP4, PSAP, SERPINA1, SERPINA3, SERPINC1, SERPINF1, SORL1, TF, VTN

39 8.85E06 ENSP00000226218, ENSP00000233813, ENSP00000236850, ENSP00000252486, ENSP00000254722, ENSP00000255409, ENSP00000259486, ENSP00000260197, ENSP00000263574, ENSP00000265023, ENSP00000265132, ENSP00000265983, ENSP00000266041, ENSP00000269141, ENSP00000273283, ENSP00000296130, ENSP00000296777, ENSP00000306099, ENSP00000308541, ENSP00000311905, ENSP00000315130, ENSP00000323929, ENSP00000345179, ENSP00000348068, ENSP00000350425, ENSP00000355627,

GO.0051246 Regulation of protein metabolic process

Matching proteins in your network (labels) AGT, APOA1, ATP6AP1, BDNF, C5, CARTPT, CDH13, CDH2, CHI3L1, CLSTN1, CLSTN3, CLU, F2, FGB, GSN, HPX, IGF2, IGFBP5, KLK6, L1CAM, LRG1, NEO1, NRXN1, PDIA3, PSAP, RBP4, SULF2, TF, VTN

Observed False gene discovery count rate Matching proteins in your network (IDs) 29 7.66E06 ENSP00000223642, ENSP00000226218, ENSP00000233813, ENSP00000236850, ENSP00000255409, ENSP00000261908, ENSP00000265983, ENSP00000266546, ENSP00000269141, ENSP00000296777, ENSP00000300289, ENSP00000302621, ENSP00000306099, ENSP00000308541, ENSP00000309148, ENSP00000315130, ENSP00000353007, ENSP00000355627, ENSP00000358777, ENSP00000359074, ENSP00000360519, ENSP00000362924, ENSP00000366513, ENSP00000378394, ENSP00000385142, ENSP00000385834, ENSP00000391826, ENSP00000408632, ENSP00000414303

Pathway description

GO.0023056 Positive regulation of signaling

Pathway ID

Table 3 (continued)

476 Fa´bio Trindade et al.

4 9.91E06 ENSP00000236850, ENSP00000252486, ENSP00000350425, ENSP00000356969 4 9.91E06 ENSP00000227667, ENSP00000252486, ENSP00000252491, ENSP00000406381

GO.0034382 Chylomicron remnant clearance

35 9.91E06 ENSP00000205948, ENSP00000227667, ENSP00000236850, ENSP00000249330, ENSP00000252486, ENSP00000252491, ENSP00000255409, ENSP00000260197, ENSP00000261978, ENSP00000264005, ENSP00000269141, ENSP00000289749, ENSP00000295718, ENSP00000295897, ENSP00000300175, ENSP00000300289, ENSP00000304133, ENSP00000315130, ENSP00000333769, ENSP00000344786, ENSP00000345179, ENSP00000348888, ENSP00000350425, ENSP00000355627, ENSP00000356969, ENSP00000357106, ENSP00000358777, ENSP00000360519, ENSP00000362924, ENSP00000368314, ENSP00000378394, ENSP00000385142, ENSP00000385834, ENSP00000386104, ENSP00000406381

9 9.43E06 ENSP00000236850, ENSP00000252486, ENSP00000252491, ENSP00000260197, ENSP00000263574, ENSP00000264005, ENSP00000284981, ENSP00000350425, ENSP00000356969

GO.0034380 High-density lipoprotein particle assembly

GO.0033036 Macromolecule localization

GO.0008203 Cholesterol metabolic process

ENSP00000356671, ENSP00000356969, ENSP00000358777, ENSP00000360281, ENSP00000362924, ENSP00000366124, ENSP00000376793, ENSP00000378394, ENSP00000385834, ENSP00000391826, ENSP00000393887, ENSP00000414303, ENSP00000416561

APOC1, APOC3, APOE, ENSG00000224916 (continued)

APOA1, APOA2, APOA4, APOE

AGT, ALB, APOA1, APOA2, APOA4, APOC1, APOC3, APOD, APOE, APOH, ATP6AP1, BSG, CADM3, CDH2, CHI3L1, CLU, CPE, ENSG00000224916, GSN, LCAT, LTBP2, NBL1, NFASC, NRCAM, NRXN1, PDIA3, PIGR, PSAP, PTPRN, RBP4, SCG2, SCG5, SORL1, TF, VGF

APLP2, APOA1, APOA2, APOA4, APOC1, APOE, APP, LCAT, SORL1

Bioinformatics Applied to CSF Proteome 477

27 1.40E05 ENSP00000227667, ENSP00000233813, ENSP00000236850, ENSP00000249330, ENSP00000252486, ENSP00000254722, ENSP00000261908, ENSP00000263574, ENSP00000264005, ENSP00000264613, ENSP00000265023, ENSP00000265983, ENSP00000295897, ENSP00000296777, ENSP00000300289, ENSP00000303550, ENSP00000307549, ENSP00000308541,

8 1.36E05 ENSP00000205948, ENSP00000226218, ENSP00000252486, ENSP00000265023, ENSP00000306099, ENSP00000308541, ENSP00000308938, ENSP00000356671

GO.0030193 Regulation of blood coagulation

GO.0042592 Homeostatic process

23 1.13E05 ENSP00000223642, ENSP00000226218, ENSP00000233813, ENSP00000245907, ENSP00000252486, ENSP00000254722, ENSP00000260197, ENSP00000263574, ENSP00000265023, ENSP00000265132, ENSP00000266041, ENSP00000273283, ENSP00000284981, ENSP00000308541, ENSP00000323929, ENSP00000331544, ENSP00000348068, ENSP00000355627, ENSP00000356671, ENSP00000366124, ENSP00000376793, ENSP00000393887, ENSP00000396688

GO.0032269 Negative regulation of cellular protein metabolic process

Observed False gene discovery count rate Matching proteins in your network (IDs) 6 1.09E05 ENSP00000227667, ENSP00000236850, ENSP00000252491, ENSP00000350425, ENSP00000355627, ENSP00000406381

Pathway description

GO.0042304 Regulation of fatty acid biosynthetic process

Pathway ID

Table 3 (continued)

AGT, ALB, APLP2, APOA1, APOA2, APOA4, APOC3, APOE, ATP6AP1, CARTPT, CP, CYTL1, ENSG00000224916, F2, HPX, IGFBP5, KNG1, LCAT, NEO1, NPTX1, PDIA3, PIGR, RBP4, SERPINA3, SERPINF1, TF, VGF

APOE, APOH, F2, FGB, KNG1, PLG, SERPINC1, VTN

A2M, AGT, AHSG, AMBP, APLP2, APOE, APP, C3, C4A, C5, CST3, F2, FBLN1, IGFBP5, ITIH1, ITIH4, KNG1, SERPINA1, SERPINA3, SERPINC1, SERPINF1, SORL1, VTN

AGT, APOA1, APOA4, APOC1, APOC3, ENSG00000224916

Matching proteins in your network (labels)

478 Fa´bio Trindade et al.

GO.0007275 Multicellular organismal development

GO.0002455 Humoral immune response mediated by circulating immunoglobulin 53 1.77E05 ENSP00000205948, ENSP00000221891, ENSP00000223642, ENSP00000228938, ENSP00000233813, ENSP00000245907, ENSP00000248933, ENSP00000249330, ENSP00000252486, ENSP00000255409, ENSP00000256637, ENSP00000260197, ENSP00000261908, ENSP00000263413, ENSP00000263574, ENSP00000264025, ENSP00000264036, ENSP00000266546, ENSP00000289749, ENSP00000297268, ENSP00000300900, ENSP00000303550, ENSP00000304133, ENSP00000305595, ENSP00000306477, ENSP00000307156, ENSP00000308541, ENSP00000309096, ENSP00000311905, ENSP00000315130, ENSP00000318472, ENSP00000331544, ENSP00000333769, ENSP00000344786, ENSP00000345179, ENSP00000350425, ENSP00000353007, ENSP00000353025, ENSP00000355110, ENSP00000355627, ENSP00000356969, ENSP00000359074, ENSP00000360519, ENSP00000362924, ENSP00000366124, ENSP00000378394, ENSP00000378517, ENSP00000385142, ENSP00000385834, ENSP00000386104,

6 1.54E05 ENSP00000223642, ENSP00000245907, ENSP00000263413, ENSP00000315130, ENSP00000360281, ENSP00000396688

ENSP00000348888, ENSP00000350425, ENSP00000355627, ENSP00000356969, ENSP00000358777, ENSP00000360519, ENSP00000376793, ENSP00000385834, ENSP00000406381

(continued)

AGT, AHSG, APLP1, APLP2, APOA2, APOA4, APOD, APOE, APOH, B2M, B3GNT1, B3GNT2, BSG, BTD, C3, C5, C6, CA4, CDH13, CHI3L1, CLSTN3, CLU, COL1A2, CPE, CST3, CYTL1, F2, FAM3C, FBLN1, GSN, IGFBP5, L1CAM, LAMB2, LTBP4, MCAM, MGP, NBL1, NCAM1, NEO1, NFASC, NRXN1, PSAP, PVRL1, RBP4, SCG2, SEZ6L, SMOC1, SORL1, SORT1, SPP1, SULF2, TF, VGF

C3, C4A, C5, C6, C8B, CLU

Bioinformatics Applied to CSF Proteome 479

36 2.04E05 ENSP00000205948, ENSP00000226218, ENSP00000227667, ENSP00000236850, ENSP00000245907, ENSP00000252486, ENSP00000252491, ENSP00000254722, ENSP00000255409, ENSP00000256637, ENSP00000260197, ENSP00000263574, ENSP00000265023, ENSP00000265132, ENSP00000266041, ENSP00000273283, ENSP00000296777, ENSP00000300175, ENSP00000315130, ENSP00000323929, ENSP00000331544, ENSP00000348068, ENSP00000348170, ENSP00000350425, ENSP00000355627, ENSP00000356671, ENSP00000356969, ENSP00000362924, ENSP00000366124, ENSP00000374069, ENSP00000376793, ENSP00000378394, ENSP00000385834, ENSP00000391826, ENSP00000396688, ENSP00000406381

GO.0050790 Regulation of catalytic activity

ENSP00000393887, ENSP00000408632, ENSP00000452780

Observed False gene discovery count rate Matching proteins in your network (IDs)

14 1.78E05 ENSP00000233809, ENSP00000233813, ENSP00000252486, ENSP00000266546, ENSP00000301464, ENSP00000308541, ENSP00000311905, ENSP00000324025, ENSP00000355627, ENSP00000366513, ENSP00000368314, ENSP00000378517, ENSP00000408632, ENSP00000414303

Pathway description

GO.0001558 Regulation of cell growth

Pathway ID

Table 3 (continued)

A2M, AGT, AMBP, APLP2, APOA1, APOA2, APOA4, APOC1, APOC3, APOE, APOH, C3, C4A, CARTPT, CHI3L1, CLU, CST3, ENSG00000224916, FBLN1, GSN, HP, IGF2, ITIH1, ITIH4, KNG1, PSAP, PTPRN2, SCG5, SERPINA1, SERPINA3, SERPINC1, SERPINF1, SORL1, SORT1, TF, VTN

AGT, APOE, BDNF, CDH13, CGREF1, CLSTN1, CLSTN3, F2, IGFBP2, IGFBP5, IGFBP6, LTBP4, NRCAM, SPP1

Matching proteins in your network (labels)

480 Fa´bio Trindade et al.

9 2.08E05 ENSP00000205948, ENSP00000226218, ENSP00000252486, ENSP00000265023, ENSP00000306099, ENSP00000308541, ENSP00000308938, ENSP00000356671, ENSP00000362924 7 2.21E05 ENSP00000227667, ENSP00000236850, ENSP00000252486, ENSP00000264005, ENSP00000350425, ENSP00000356969, ENSP00000406381 19 2.24E05 ENSP00000205948, ENSP00000223642, ENSP00000226218, ENSP00000233813, ENSP00000252486, ENSP00000254722, ENSP00000259486, ENSP00000260197, ENSP00000264036, ENSP00000287641, ENSP00000289749, ENSP00000304133, ENSP00000331544, ENSP00000345179, ENSP00000347665, ENSP00000355627, ENSP00000362924, ENSP00000385834, ENSP00000408632 18 2.55E05 ENSP00000216492, ENSP00000223642, ENSP00000236850, ENSP00000259486, ENSP00000261908, ENSP00000264025, ENSP00000284981, ENSP00000304133, ENSP00000305595, ENSP00000307156, ENSP00000309096, ENSP00000318472, ENSP00000325660, ENSP00000344786, ENSP00000359074, ENSP00000378517, ENSP00000385142, ENSP00000414303 48 2.64E05 ENSP00000205948, ENSP00000221891, ENSP00000226218, ENSP00000228938, ENSP00000233813, ENSP00000245907, ENSP00000248933, ENSP00000249330, ENSP00000252486, ENSP00000255409, ENSP00000260197, ENSP00000261908,

GO.0061041 Regulation of wound healing

GO.0042632 Cholesterol homeostasis

GO.0040012 Regulation of locomotion

GO.0006935 Chemotaxis

GO.0048731 System development

(continued)

AGT, AHSG, APLP1, APLP2, APOA2, APOA4, APOD, APOE, APOH, B2M, B3GNT1, B3GNT2, BSG, BTD, C3, CA4, CDH13, CHI3L1, CLSTN3, CLU, COL1A2, CPE, CST3, CYTL1, F2, GSN, IGFBP5,

APOA1, APP, B3GNT1, B3GNT2, BDNF, C5, CHGA, CNTN1, ENPP2, L1CAM, LAMB2, NCAM1, NEO1, NFASC, NRXN1, PVRL1, SCG2, SPP1

AGT, APOD, APOE, APOH, C5, CDH13, COL18A1, ENPP2, FBLN1, GSN, IGFBP5, MCAM, NBL1, SCG2, SERPINF1, SORL1, SST, TF, VTN

APOA1, APOA2, APOA4, APOC3, APOE, ENSG00000224916, LCAT

APOE, APOH, F2, FGB, GSN, KNG1, PLG, SERPINC1, VTN

Bioinformatics Applied to CSF Proteome 481

8 2.72E05 ENSP00000227667, ENSP00000236850, ENSP00000237014, ENSP00000252486, ENSP00000350425, ENSP00000356969, ENSP00000360519, ENSP00000406381

GO.0007603 Phototransduction, visible light

ENSP00000263574, ENSP00000264025, ENSP00000264036, ENSP00000266546, ENSP00000289749, ENSP00000297268, ENSP00000300900, ENSP00000303550, ENSP00000304133, ENSP00000305595, ENSP00000306477, ENSP00000307156, ENSP00000308541, ENSP00000309096, ENSP00000315130, ENSP00000318472, ENSP00000333769, ENSP00000344786, ENSP00000345179, ENSP00000350425, ENSP00000353007, ENSP00000355110, ENSP00000355627, ENSP00000356969, ENSP00000359074, ENSP00000360519, ENSP00000362924, ENSP00000366124, ENSP00000378394, ENSP00000378517, ENSP00000385142, ENSP00000385834, ENSP00000386104, ENSP00000393887, ENSP00000408632, ENSP00000452780

Observed False gene discovery count rate Matching proteins in your network (IDs)

8 2.72E05 ENSP00000218230, ENSP00000256637, ENSP00000296777, ENSP00000300175, ENSP00000324248, ENSP00000333593, ENSP00000358715, ENSP00000386104

Pathway description

GO.0007218 Neuropeptide signaling pathway

Pathway ID

Table 3 (continued)

APOA1, APOA2, APOA4, APOC3, APOE, ENSG00000224916, RBP4, TTR

CARTPT, CPE, NXPH4, PCSK1N, PENK, SCG5, SORCS3, SORT1

L1CAM, LAMB2, MCAM, MGP, NBL1, NCAM1, NEO1, NFASC, NRXN1, PSAP, PVRL1, RBP4, SCG2, SEZ6L, SMOC1, SORL1, SPP1, SULF2, TF, VGF, VTN

Matching proteins in your network (labels)

482 Fa´bio Trindade et al.

APOA1, APOE, APOH, BDNF, C3, C5, C6, CDH2, CHI3L1, COCH, CST3, F2, FBLN1, FGB, GSN, ITGA7, LRG1, NRCAM, SERPINF1, SPP1, VASN

21 2.84E05 ENSP00000205948, ENSP00000216361, ENSP00000223642, ENSP00000236850, ENSP00000245907, ENSP00000252486, ENSP00000254722, ENSP00000255409, ENSP00000263413, ENSP00000269141, ENSP00000302621, ENSP00000306099, ENSP00000306864, ENSP00000308541, ENSP00000331544, ENSP00000362924, ENSP00000366124, ENSP00000368314, ENSP00000378517, ENSP00000414303, ENSP00000452120 6 3.17E05 ENSP00000227667, ENSP00000236850, ENSP00000252486, ENSP00000259486, ENSP00000350425, ENSP00000356969 18 3.17E05 ENSP00000205948, ENSP00000223642, ENSP00000226218, ENSP00000233813, ENSP00000252486, ENSP00000254722, ENSP00000259486, ENSP00000260197, ENSP00000264036, ENSP00000287641, ENSP00000289749, ENSP00000331544, ENSP00000345179, ENSP00000347665, ENSP00000355627, ENSP00000362924, ENSP00000385834, ENSP00000408632

GO.0022603 Regulation of anatomical structure morphogenesis

GO.0046503 Glycerolipid catabolic process

GO.2000145 Regulation of cell motility

(continued)

AGT, APOD, APOE, APOH, C5, CDH13, COL18A1, ENPP2, FBLN1, GSN, IGFBP5, MCAM, NBL1, SERPINF1, SORL1, SST, TF, VTN

APOA1, APOA2, APOA4, APOC3, APOE, ENPP2

AHSG, ALB, AMBP, APLP1, APOA1, APOE, APP, C4A, COL1A2, ENPP2, GSN, HP, HPX, SORL1, SORT1, VTN

16 2.84E05 ENSP00000221891, ENSP00000226218, ENSP00000236850, ENSP00000252486, ENSP00000256637, ENSP00000259486, ENSP00000260197, ENSP00000265132, ENSP00000265983, ENSP00000284981, ENSP00000295897, ENSP00000297268, ENSP00000348170, ENSP00000362924, ENSP00000393887, ENSP00000396688

GO.0006897 Endocytosis

Bioinformatics Applied to CSF Proteome 483

Pathway description

GO.0065009 Regulation of molecular function

GO.0010903 Negative regulation of very-lowdensity lipoprotein particle remodeling

Pathway ID

Table 3 (continued)

40 3.28E05 ENSP00000205948, ENSP00000226218, ENSP00000227667, ENSP00000236850, ENSP00000245907, ENSP00000252486, ENSP00000252491, ENSP00000254722, ENSP00000255409, ENSP00000256637, ENSP00000260197, ENSP00000263574, ENSP00000265023, ENSP00000265132, ENSP00000266041, ENSP00000273283, ENSP00000296777, ENSP00000300175, ENSP00000303550, ENSP00000315130, ENSP00000323929, ENSP00000331544, ENSP00000348068, ENSP00000348170, ENSP00000350425, ENSP00000355627, ENSP00000356671, ENSP00000356969, ENSP00000362924, ENSP00000366124, ENSP00000374069, ENSP00000376793, ENSP00000378394, ENSP00000385142, ENSP00000385834, ENSP00000391826, ENSP00000396688, ENSP00000406381, ENSP00000414303, ENSP00000452780

3 3.28E05 ENSP00000227667, ENSP00000236850, ENSP00000356969

Observed False gene discovery count rate Matching proteins in your network (IDs)

A2M, AGT, AMBP, APLP2, APOA1, APOA2, APOA4, APOC1, APOC3, APOE, APOH, B2M, BDNF, C3, C4A, CARTPT, CHI3L1, CLU, CST3, CYTL1, ENSG00000224916, FBLN1, GSN, HP, IGF2, ITIH1, ITIH4, KNG1, NRXN1, PSAP, PTPRN2, SCG5, SERPINA1, SERPINA3, SERPINC1, SERPINF1, SORL1, SORT1, TF, VTN

APOA1, APOA2, APOC3

Matching proteins in your network (labels)

484 Fa´bio Trindade et al.

5 3.83E05 ENSP00000223642, ENSP00000261267, ENSP00000263413, ENSP00000295897, ENSP00000360281

ALB, C5, C6, C8B, LYZ

(continued)

AGT, APOD, APOE, APOH, BDNF, CDH13, CGREF1, COL18A1, FBLN1, IGFBP5, IGFBP6, OGN, PLG, PTGDS, RBP4, SCG2, SERPINF1, SST

18 3.79E05 ENSP00000205948, ENSP00000233813, ENSP00000252486, ENSP00000254722, ENSP00000262551, ENSP00000287641, ENSP00000301464, ENSP00000304133, ENSP00000308938, ENSP00000324025, ENSP00000331544, ENSP00000345179, ENSP00000347665, ENSP00000355627, ENSP00000360519, ENSP00000360687, ENSP00000408632, ENSP00000414303

GO.0008285 Negative regulation of cell proliferation

GO.0019835 Cytolysis

AGT, APOA1, ATP6AP1, BDNF, C5, CARTPT, CDH13, CDH2, CHI3L1, CLSTN1, CLSTN3, CLU, F2, FGB, GSN, HPX, IGF2, IGFBP5, KLK6, L1CAM, LRG1, NEO1, NRXN1, PDIA3, PSAP, RBP4, SULF2, TF, VTN

29 3.59E05 ENSP00000223642, ENSP00000226218, ENSP00000233813, ENSP00000236850, ENSP00000255409, ENSP00000261908, ENSP00000265983, ENSP00000266546, ENSP00000269141, ENSP00000296777, ENSP00000300289, ENSP00000302621, ENSP00000306099, ENSP00000308541, ENSP00000309148, ENSP00000315130, ENSP00000353007, ENSP00000355627, ENSP00000358777, ENSP00000359074, ENSP00000360519, ENSP00000362924, ENSP00000366513, ENSP00000378394, ENSP00000385142, ENSP00000385834, ENSP00000391826, ENSP00000408632, ENSP00000414303

GO.0010647 Positive regulation of cell communication

APOE, CLU, SORL1

3 3.28E05 ENSP00000252486, ENSP00000260197, ENSP00000315130

GO.1902996 Regulation of neurofibrillary tangle assembly

APOE, CLU, SORL1

3 3.28E05 ENSP00000252486, ENSP00000260197, ENSP00000315130

GO.1902947 Regulation of tau-protein kinase activity

Bioinformatics Applied to CSF Proteome 485

Pathway description

11 5.72E05 ENSP00000218230, ENSP00000237014, ENSP00000249330, ENSP00000295718, ENSP00000296777, ENSP00000300175, ENSP00000309148, ENSP00000311905, ENSP00000355627, ENSP00000360519, ENSP00000386104 17 6.14E05 ENSP00000205948, ENSP00000223642, ENSP00000226218, ENSP00000233813, ENSP00000252486, ENSP00000254722, ENSP00000259486, ENSP00000260197, ENSP00000264036, ENSP00000287641, ENSP00000289749, ENSP00000331544, ENSP00000345179, ENSP00000347665, ENSP00000355627, ENSP00000362924, ENSP00000408632 7 6.20E05 ENSP00000227667, ENSP00000236850, ENSP00000252486, ENSP00000350425, ENSP00000356969, ENSP00000360519, ENSP00000406381 4 6.97E05 ENSP00000205948, ENSP00000236850, ENSP00000350425, ENSP00000406381

GO.0030334 Regulation of cell migration

GO.0001523 Retinoid metabolic process

GO.0051006 Positive regulation of lipoprotein lipase activity

9 4.09E05 ENSP00000248933, ENSP00000252486, ENSP00000266546, ENSP00000269141, ENSP00000284981, ENSP00000307156, ENSP00000344786, ENSP00000368314, ENSP00000385142

Observed False gene discovery count rate Matching proteins in your network (IDs)

GO.0010817 Regulation of hormone levels

GO.0050808 Synapse organization

Pathway ID

Table 3 (continued)

APOA1, APOA4, APOH, ENSG00000224916

APOA1, APOA2, APOA4, APOC3, APOE, ENSG00000224916, RBP4

AGT, APOD, APOE, APOH, C5, CDH13, COL18A1, ENPP2, FBLN1, GSN, IGFBP5, MCAM, NBL1, SERPINF1, SORL1, SST, VTN

AGT, CARTPT, CPE, KLK6, LTBP4, PCSK1N, PTPRN, RBP4, SCG5, TTR, VGF

APOE, APP, CDH2, CLSTN3, LAMB2, NFASC, NRCAM, NRXN1, SEZ6L

Matching proteins in your network (labels)

486 Fa´bio Trindade et al.

17

0.0001 ENSP00000233809, ENSP00000233813, ENSP00000245907, ENSP00000252486, ENSP00000266546, ENSP00000284981, ENSP00000301464, ENSP00000308541, ENSP00000311905, ENSP00000324025,

4 9.98E05 ENSP00000227667, ENSP00000236850, ENSP00000350425, ENSP00000406381

GO.0010896 Regulation of triglyceride catabolic process

GO.0040008 Regulation of growth

A2M, AGT, AHSG, AMBP, APLP2, APOA1, APOE, ATP6AP1, BDNF, C4A, C6, CARTPT, CDH2, CHI3L1, CLEC3B, CLU, CST3, ENPP2, F2, FGB, GSN, HPX, IGF2, IGFBP5, ITIH1, ITIH4, KNG1, PSAP, SERPINA1, SERPINA3, SERPINC1, SERPINF1, SORL1, TF, VTN

35 9.62E05 ENSP00000226218, ENSP00000233813, ENSP00000236850, ENSP00000252486, ENSP00000254722, ENSP00000255409, ENSP00000259486, ENSP00000260197, ENSP00000263413, ENSP00000263574, ENSP00000265023, ENSP00000265132, ENSP00000265983, ENSP00000266041, ENSP00000269141, ENSP00000273283, ENSP00000296130, ENSP00000296777, ENSP00000306099, ENSP00000308541, ENSP00000315130, ENSP00000323929, ENSP00000348068, ENSP00000355627, ENSP00000356671, ENSP00000358777, ENSP00000362924, ENSP00000366124, ENSP00000376793, ENSP00000378394, ENSP00000385834, ENSP00000391826, ENSP00000393887, ENSP00000396688, ENSP00000414303

GO.0032268 Regulation of cellular protein metabolic process

(continued)

AGT, APOE, APP, BDNF, C3, CDH13, CGREF1, CLSTN1, CLSTN3, F2, IGFBP2, IGFBP5, IGFBP6, LTBP4, NRCAM, RBP4, SPP1

APOA1, APOA4, APOC3, ENSG00000224916

ALB, AMBP, APOA1, APOE, COL1A2, ENPP2, HP, HPX, SORL1, VTN

10 9.00E05 ENSP00000226218, ENSP00000236850, ENSP00000252486, ENSP00000259486, ENSP00000260197, ENSP00000265132, ENSP00000265983, ENSP00000295897, ENSP00000297268, ENSP00000348170

GO.0006898 Receptor-mediated endocytosis

APOA1, APOA2, APOA4, APOC1, APOC3, ENSG00000224916

6 8.99E05 ENSP00000227667, ENSP00000236850, ENSP00000252491, ENSP00000350425, ENSP00000356969, ENSP00000406381

GO.0050994 Regulation of lipid catabolic process

Bioinformatics Applied to CSF Proteome 487

Pathway description

GO.0048518 Positive regulation of biological process

Pathway ID

Table 3 (continued)

58

0.0001 ENSP00000205948, ENSP00000216361, ENSP00000216492, ENSP00000223642, ENSP00000226218, ENSP00000233809, ENSP00000233813, ENSP00000236850, ENSP00000245907, ENSP00000252486, ENSP00000252491, ENSP00000255409, ENSP00000259486, ENSP00000260197, ENSP00000264036, ENSP00000265023, ENSP00000265983, ENSP00000266546, ENSP00000289749, ENSP00000296130, ENSP00000296777, ENSP00000300289, ENSP00000302621, ENSP00000303550, ENSP00000304133, ENSP00000306099, ENSP00000308541, ENSP00000308938, ENSP00000309148, ENSP00000315130, ENSP00000325660, ENSP00000345344, ENSP00000345968, ENSP00000347665, ENSP00000348170, ENSP00000350364, ENSP00000350425, ENSP00000353007, ENSP00000355627, ENSP00000356969, ENSP00000358777, ENSP00000359074, ENSP00000360281, ENSP00000360519, ENSP00000366124, ENSP00000366513, ENSP00000368314, ENSP00000378517,

ENSP00000355627, ENSP00000360519, ENSP00000366513, ENSP00000368314, ENSP00000378517, ENSP00000408632, ENSP00000414303

Observed False gene discovery count rate Matching proteins in your network (IDs)

AGT, AHSG, APOA1, APOA2, APOA4, APOC1, APOE, APOH, ATP6AP1, B2M, BDNF, C3, C4A, C5, C8B, CARTPT, CDH13, CFB, CHGA, CHI3L1, CLEC3B, CLSTN1, CLSTN3, CLU, CNTN1, COCH, COL18A1, CST3, CTSL1, CYTL1, ENPP2, ENSG00000224916, F2, FGB, HP, HPX, IGF2, IGFBP2, IGFBP5, KLK6, KNG1, L1CAM, LRG1, MCAM, NBL1, NEGR1, NRCAM, NRXN1, PDIA3, PGLYRP2, PLG, RBP4, SCG2, SORL1, SPP1, SULF2, TF, VTN

Matching proteins in your network (labels)

488 Fa´bio Trindade et al.

GO.0009725 Response to hormone

20

8

0.000108 ENSP00000233809, ENSP00000233813, ENSP00000236850, ENSP00000245907, ENSP00000249330, ENSP00000254722, ENSP00000256637, ENSP00000264005, ENSP00000295718, ENSP00000300900,

0.000106 ENSP00000205948, ENSP00000227667, ENSP00000252491, ENSP00000256637, ENSP00000350425, ENSP00000355627, ENSP00000356969, ENSP00000406381

0.000103 ENSP00000233809, ENSP00000245907, ENSP00000254722, ENSP00000264005, ENSP00000306099, ENSP00000307549, ENSP00000333769, ENSP00000347665, ENSP00000348170, ENSP00000350425, ENSP00000362924, ENSP00000366124, ENSP00000414303, ENSP00000452780

14

GO.0010035 Response to inorganic substance

GO.0060191 Regulation of lipase activity

0.000103 ENSP00000218230, ENSP00000227667, ENSP00000233809, ENSP00000236850, ENSP00000237014, ENSP00000249330, ENSP00000252486, ENSP00000254722, ENSP00000255409, ENSP00000284981, ENSP00000287641, ENSP00000306864, ENSP00000308541, ENSP00000347665, ENSP00000350425, ENSP00000355627, ENSP00000356969, ENSP00000360519, ENSP00000366124, ENSP00000371554, ENSP00000385142, ENSP00000406381, ENSP00000414303

23

GO.0009628 Response to abiotic stimulus

ENSP00000385142, ENSP00000385834, ENSP00000391826, ENSP00000393887, ENSP00000396688, ENSP00000406381, ENSP00000408632, ENSP00000414303, ENSP00000416561, ENSP00000452780

(continued)

APOA1, APOA2, ATP6AP1, BDNF, BSG, C3, CA4, CST3, CTSL1, FGB, IGF2, IGFBP2, IGFBP5, LCAT, PTGDS, PTPRN, SERPINF1, SORT1, SPP1, VGF

AGT, APOA2, APOA4, APOC1, APOC3, APOH, ENSG00000224916, SORT1

APOA4, B2M, BDNF, BSG, C3, COL18A1, CST3, FGB, GSN, HP, IGFBP2, LCAT, NPTX1, SERPINF1

AGT, APOA1, APOA2, APOA4, APOC3, APOE, APP, BDNF, CHI3L1, COL18A1, CST3, ENSG00000224916, F2, IGFBP2, NRXN1, PCSK1N, RBP4, SERPINF1, SOD3, SST, TTR, VASN, VGF

Bioinformatics Applied to CSF Proteome 489

0.000114 ENSP00000227667, ENSP00000236850, ENSP00000252486

3

8

GO.0032489 Regulation of Cdc42 protein signal transduction

GO.0022617 Extracellular matrix disassembly

0.000119 ENSP00000236671, ENSP00000297268, ENSP00000308938, ENSP00000323929, ENSP00000333769, ENSP00000345344, ENSP00000347665, ENSP00000378517

0.000114 ENSP00000223642, ENSP00000226218, ENSP00000233813, ENSP00000236850, ENSP00000255409, ENSP00000261908, ENSP00000265983, ENSP00000269141, ENSP00000296777, ENSP00000300289, ENSP00000302621, ENSP00000306099, ENSP00000308541, ENSP00000309148, ENSP00000315130, ENSP00000353007, ENSP00000355627, ENSP00000358777, ENSP00000359074, ENSP00000362924, ENSP00000378394, ENSP00000385834, ENSP00000391826, ENSP00000408632, ENSP00000414303

ENSP00000306099, ENSP00000333769, ENSP00000345344, ENSP00000356969, ENSP00000358777, ENSP00000360687, ENSP00000366124, ENSP00000378517, ENSP00000391826, ENSP00000414303

Observed False gene discovery count rate Matching proteins in your network (IDs)

25

Pathway description

GO.0009967 Positive regulation of signal transduction

Pathway ID

Table 3 (continued)

A2M, BSG, COL18A1, COL1A2, CTSD, CTSL1, PLG, SPP1

APOA1, APOC3, APOE

AGT, APOA1, ATP6AP1, BDNF, C5, CARTPT, CDH13, CDH2, CHI3L1, CLU, F2, FGB, GSN, HPX, IGF2, IGFBP5, KLK6, L1CAM, LRG1, NEO1, PDIA3, PSAP, SULF2, TF, VTN

Matching proteins in your network (labels)

490 Fa´bio Trindade et al.

0.000133 ENSP00000223642, ENSP00000245907, ENSP00000360281, ENSP00000416561

0.000137 ENSP00000221891, ENSP00000223642, ENSP00000226218, ENSP00000236850, ENSP00000252486, ENSP00000252491, ENSP00000255409, ENSP00000259486, ENSP00000260197, ENSP00000263574, ENSP00000265132, ENSP00000265983, ENSP00000269141, ENSP00000284981, ENSP00000296777, ENSP00000306099, ENSP00000308541, ENSP00000315130, ENSP00000331544, ENSP00000355627, ENSP00000358777, ENSP00000378394, ENSP00000385834, ENSP00000391826, ENSP00000393887, ENSP00000406381, ENSP00000414303

4

27

GO.0019220 Regulation of phosphate metabolic process

GO.0006957 Complement activation, alternative pathway

0.000128 ENSP00000221891, ENSP00000233809, ENSP00000233813, ENSP00000236850, ENSP00000245907, ENSP00000249330, ENSP00000254722, ENSP00000264005, ENSP00000287641, ENSP00000295718, ENSP00000300900, ENSP00000333769, ENSP00000350425, ENSP00000356969, ENSP00000360687, ENSP00000362924, ENSP00000366124, ENSP00000378517, ENSP00000414303

19

GO.0014070 Response to organic cyclic compound

0.000126 ENSP00000227667, ENSP00000236850, ENSP00000245907, ENSP00000252486, ENSP00000252491, ENSP00000350425, ENSP00000355627, ENSP00000406381

8

GO.0046890 Regulation of lipid biosynthetic process

(continued)

AGT, AHSG, AMBP, APLP1, APLP2, APOA1, APOC1, APOE, APP, ATP6AP1, BDNF, C5, CARTPT, CDH2, CHI3L1, CLU, ENPP2, ENSG00000224916, F2, FBLN1, FGB, HPX, IGF2, PSAP, SORL1, TF, VTN

C3, C5, C8B, CFB

APLP1, APOA1, APOA2, APOA4, BDNF, BSG, C3, CA4, CST3, GSN, IGFBP2, IGFBP5, LCAT, PTGDS, PTPRN, SERPINF1, SPP1, SST, VGF

AGT, APOA1, APOA4, APOC1, APOC3, APOE, C3, ENSG00000224916

Bioinformatics Applied to CSF Proteome 491

0.00014 ENSP00000216492, ENSP00000223642, ENSP00000236850, ENSP00000259486, ENSP00000261908, ENSP00000264025, ENSP00000269141, ENSP00000284981, ENSP00000297268, ENSP00000304133, ENSP00000305595, ENSP00000307156, ENSP00000308541, ENSP00000309096, ENSP00000318472, ENSP00000325660, ENSP00000333769, ENSP00000344786, ENSP00000348307, ENSP00000359074, ENSP00000378517, ENSP00000385142, ENSP00000408632, ENSP00000414303, ENSP00000452120 0.000145 ENSP00000221891, ENSP00000233809, ENSP00000233813, ENSP00000245907, ENSP00000252486, ENSP00000254722, ENSP00000256637, ENSP00000287641, ENSP00000297268, ENSP00000307549, ENSP00000333769, ENSP00000345179, ENSP00000347665, ENSP00000348170, ENSP00000350425, ENSP00000356969, ENSP00000358777, ENSP00000360519, ENSP00000362924, ENSP00000366124, ENSP00000371554, ENSP00000373477, ENSP00000378517, ENSP00000391826, ENSP00000414303

25

GO.1901700 Response to oxygencontaining compound

Observed False gene discovery count rate Matching proteins in your network (IDs) 25

Pathway description

GO.0006928 Movement of cell or subcellular component

Pathway ID

Table 3 (continued)

APLP1, APOA2, APOA4, APOD, APOE, ATP6AP1, BDNF, BSG, C3, COL18A1, COL1A2, CST3, GPX3, GSN, HP, IGF2, IGFBP2, IGFBP5, NPTX1, RBP4, SERPINF1, SOD3, SORT1, SPP1, SST

APOA1, APP, B3GNT1, B3GNT2, BDNF, BSG, C5, CDH13, CDH2, CHGA, CNTN1, COL1A2, ENPP2, F2, ITGA7, L1CAM, LAMB2, NCAM1, NEO1, NFASC, NRXN1, PVRL1, SCG2, SIRPA, SPP1

Matching proteins in your network (labels)

492 Fa´bio Trindade et al.

6

24

GO.0042325 Regulation of phosphorylation

29

GO.0051049 Regulation of transport

GO.0034103 Regulation of tissue remodeling

15

GO.0061564 Axon development

0.000221 ENSP00000223642, ENSP00000226218, ENSP00000236850, ENSP00000252486, ENSP00000255409, ENSP00000259486, ENSP00000260197, ENSP00000263574, ENSP00000265132, ENSP00000265983, ENSP00000269141, ENSP00000284981, ENSP00000296777, ENSP00000306099,

0.000221 ENSP00000296777, ENSP00000355627, ENSP00000358777, ENSP00000366124, ENSP00000378517, ENSP00000385834

0.000187 ENSP00000216492, ENSP00000223642, ENSP00000226218, ENSP00000227667, ENSP00000236850, ENSP00000245907, ENSP00000252486, ENSP00000252491, ENSP00000260197, ENSP00000261908, ENSP00000263273, ENSP00000265023, ENSP00000300175, ENSP00000300289, ENSP00000306099, ENSP00000308541, ENSP00000325660, ENSP00000331544, ENSP00000345179, ENSP00000350425, ENSP00000355627, ENSP00000356969, ENSP00000358777, ENSP00000360519, ENSP00000385142, ENSP00000393887, ENSP00000396688, ENSP00000406381, ENSP00000408632

0.000146 ENSP00000236850, ENSP00000261908, ENSP00000264025, ENSP00000284981, ENSP00000305595, ENSP00000307156, ENSP00000307549, ENSP00000309096, ENSP00000318472, ENSP00000325660, ENSP00000344786, ENSP00000345179, ENSP00000359074, ENSP00000385142, ENSP00000414303

(continued)

AGT, AHSG, AMBP, APLP2, APOA1, APOE, APP, ATP6AP1, BDNF, C5, CARTPT, CDH2, CHI3L1, CLU, ENPP2, F2, FBLN1, FGB, HPX, IGF2, PSAP, SORL1, TF, VTN

AGT, ATP6AP1, CARTPT, CST3, SPP1, TF

AGT, AHSG, APOA1, APOA2, APOA4, APOC1, APOC3, APOD, APOE, ATP6AP1, C3, C4A, C5, CDH13, CHGA, CNTN1, ENSG00000224916, F2, FBLN1, FGB, KNG1, NEO1, NRXN1, NUCB1, PDIA3, RBP4, SCG5, SORL1, VTN

APOA1, APOD, APP, B3GNT1, B3GNT2, BDNF, CNTN1, L1CAM, LAMB2, NCAM1, NEO1, NFASC, NPTX1, NRXN1, PVRL1

Bioinformatics Applied to CSF Proteome 493

Pathway description

GO.0050794 Regulation of cellular process

Pathway ID

Table 3 (continued)

85

0.000224 ENSP00000205948, ENSP00000216361, ENSP00000216492, ENSP00000221891, ENSP00000223642, ENSP00000226218, ENSP00000227667, ENSP00000233809, ENSP00000233813, ENSP00000236850, ENSP00000237014, ENSP00000245907, ENSP00000248933, ENSP00000252455, ENSP00000252486, ENSP00000252491, ENSP00000255409, ENSP00000256637, ENSP00000259486, ENSP00000260197, ENSP00000261978, ENSP00000262551, ENSP00000263273, ENSP00000263413, ENSP00000264005, ENSP00000264036, ENSP00000265023, ENSP00000265132, ENSP00000265983, ENSP00000266041, ENSP00000266546, ENSP00000273283, ENSP00000282499, ENSP00000289749, ENSP00000295718, ENSP00000295897, ENSP00000296130, ENSP00000297268, ENSP00000300175, ENSP00000301464, ENSP00000302621, ENSP00000304133, ENSP00000306099, ENSP00000306864, ENSP00000308541, ENSP00000308938,

ENSP00000308541, ENSP00000315130, ENSP00000331544, ENSP00000355627, ENSP00000358777, ENSP00000378394, ENSP00000385834, ENSP00000391826, ENSP00000393887, ENSP00000414303

Observed False gene discovery count rate Matching proteins in your network (IDs)

A2M, AGT, AHSG, ALB, AMBP, APLP1, APOA1, APOA2, APOA4, APOC1, APOC3, APOD, APOE, APOH, ATP6AP1, B2M, BDNF, BSG, C3, C4A, C5, C6, CDH13, CHGA, CHI3L1, CLEC3B, CLSTN1, CLSTN3, CLU, COCH, COL1A2, CPE, CST3, CTSL1, ENPP2, ENSG00000224916, F2, FGB, GRIA4, HP, HPX, IGF2, IGFBP2, IGFBP5, IGFBP6, ITGA7, ITIH1, ITIH4, KNG1, L1CAM, LCAT, LRG1, LTBP2, MCAM, NBL1, NCAM1, NRCAM, NRXN1, NUCB1, NXPH4, OGN, PENK, PIGR, PLG, PRKCSH, PSAP, PTGDS, PTPRN, PTPRN2, RBP4, SCG2, SCG5, SERPINA1, SERPINA3, SERPINC1, SEZ6L, SORCS3, SORL1, SORT1, SPP1, SULF2, TF, TTR, VASN, VTN

Matching proteins in your network (labels)

494 Fa´bio Trindade et al.

GO.0001932 Regulation of protein phosphorylation

23

0.000227 ENSP00000223642, ENSP00000226218, ENSP00000236850, ENSP00000252486, ENSP00000255409, ENSP00000259486, ENSP00000260197, ENSP00000263574, ENSP00000265132, ENSP00000265983, ENSP00000269141, ENSP00000284981, ENSP00000296777, ENSP00000306099, ENSP00000308541, ENSP00000315130, ENSP00000331544, ENSP00000355627, ENSP00000358777, ENSP00000378394, ENSP00000385834, ENSP00000391826, ENSP00000414303

ENSP00000315130, ENSP00000318472, ENSP00000323929, ENSP00000324248, ENSP00000333593, ENSP00000333769, ENSP00000345179, ENSP00000345344, ENSP00000348068, ENSP00000348170, ENSP00000348888, ENSP00000350425, ENSP00000353007, ENSP00000355627, ENSP00000356671, ENSP00000356969, ENSP00000358715, ENSP00000358777, ENSP00000359074, ENSP00000360519, ENSP00000360687, ENSP00000366124, ENSP00000366513, ENSP00000368314, ENSP00000374069, ENSP00000376793, ENSP00000378394, ENSP00000378517, ENSP00000385142, ENSP00000385834, ENSP00000386104, ENSP00000391826, ENSP00000393887, ENSP00000396688, ENSP00000406381, ENSP00000408632, ENSP00000414303, ENSP00000452120, ENSP00000452780

(continued)

AGT, AMBP, APLP2, APOA1, APOE, APP, ATP6AP1, BDNF, C5, CARTPT, CDH2, CHI3L1, CLU, ENPP2, F2, FBLN1, FGB, HPX, IGF2, PSAP, SORL1, TF, VTN

Bioinformatics Applied to CSF Proteome 495

0.00024 ENSP00000205948, ENSP00000221891, ENSP00000223642, ENSP00000228938, ENSP00000233809, ENSP00000233813, ENSP00000245907, ENSP00000248933, ENSP00000249330, ENSP00000252486, ENSP00000255409, ENSP00000256637, ENSP00000260197, ENSP00000263413, ENSP00000263574, ENSP00000264025, ENSP00000264036, ENSP00000266546, ENSP00000289749, ENSP00000297268, ENSP00000300900, ENSP00000302621, ENSP00000303550, ENSP00000304133, ENSP00000305595, ENSP00000306477, ENSP00000307156, ENSP00000308541, ENSP00000309096, ENSP00000311905, ENSP00000315130, ENSP00000318472, ENSP00000323929, ENSP00000331544, ENSP00000333769, ENSP00000344786, ENSP00000345179, ENSP00000350425, ENSP00000353007, ENSP00000353025,

54

GO.0044767 Single-organism developmental process

0.000233 ENSP00000261908, ENSP00000263574, ENSP00000264613, ENSP00000265983, ENSP00000284981, ENSP00000358777, ENSP00000385834, ENSP00000452780

8

GO.0055076 Transition metal ion homeostasis

0.000233 ENSP00000236850, ENSP00000350425, ENSP00000355627, ENSP00000406381

Observed False gene discovery count rate Matching proteins in your network (IDs) 4

Pathway description

GO.0045723 Positive regulation of fatty acid biosynthetic process

Pathway ID

Table 3 (continued)

A2M, AGT, AHSG, APLP1, APLP2, APOA2, APOA4, APOD, APOE, APOH, B2M, B3GNT1, B3GNT2, BSG, BTD, C3, C5, C6, CA4, CDH13, CHI3L1, CLSTN3, CLU, COL1A2, CPE, CST3, CYTL1, F2, FAM3C, FBLN1, IGFBP2, IGFBP5, L1CAM, LAMB2, LRG1, LTBP4, MCAM, MGP, NBL1, NCAM1, NFASC, NRXN1, PSAP, PVRL1, RBP4, SCG2, SEZ6L, SMOC1, SORL1, SORT1, SPP1, SULF2, TF, VGF

APLP2, APP, ATP6AP1, B2M, CP, HPX, NEO1, TF

AGT, APOA1, APOA4, ENSG00000224916

Matching proteins in your network (labels)

496 Fa´bio Trindade et al.

5

3

5

GO.0051005 Negative regulation of lipoprotein lipase activity

GO.0090207 Regulation of triglyceride metabolic process

23

3

GO.0046464 Acylglycerol catabolic process

GO.0040011 Locomotion

GO.0010916 Negative regulation of very-lowdensity lipoprotein particle clearance

0.000248 ENSP00000227667, ENSP00000236850, ENSP00000245907, ENSP00000350425, ENSP00000406381

0.000248 ENSP00000227667, ENSP00000252491, ENSP00000256637

0.000248 ENSP00000227667, ENSP00000236850, ENSP00000252486, ENSP00000350425, ENSP00000356969

0.000248 ENSP00000216492, ENSP00000223642, ENSP00000259486, ENSP00000261908, ENSP00000269141, ENSP00000284981, ENSP00000297268, ENSP00000304133, ENSP00000305595, ENSP00000307156, ENSP00000308541, ENSP00000309096, ENSP00000318472, ENSP00000325660, ENSP00000333769, ENSP00000344786, ENSP00000348307, ENSP00000359074, ENSP00000378517, ENSP00000385142, ENSP00000408632, ENSP00000414303, ENSP00000452120

0.000248 ENSP00000227667, ENSP00000252491, ENSP00000406381

ENSP00000355110, ENSP00000355627, ENSP00000356969, ENSP00000359074, ENSP00000360519, ENSP00000366124, ENSP00000378394, ENSP00000378517, ENSP00000385142, ENSP00000385834, ENSP00000386104, ENSP00000393887, ENSP00000408632, ENSP00000452780

(continued)

APOA1, APOA4, APOC3, C3, ENSG00000224916

APOC1, APOC3, SORT1

APOA1, APOA2, APOA4, APOC3, APOE

APP, B3GNT1, B3GNT2, BDNF, BSG, C5, CDH13, CDH2, CHGA, CNTN1, COL1A2, ENPP2, F2, ITGA7, L1CAM, LAMB2, NCAM1, NEO1, NFASC, NRXN1, SCG2, SIRPA, SPP1

APOC1, APOC3, ENSG00000224916

Bioinformatics Applied to CSF Proteome 497

0.000276 ENSP00000223642, ENSP00000227667, ENSP00000233813, ENSP00000236850, ENSP00000245907, ENSP00000248933, ENSP00000252486, ENSP00000255409, ENSP00000260197, ENSP00000265132, ENSP00000265983, ENSP00000269141, ENSP00000296777, ENSP00000306099, ENSP00000308541, ENSP00000315130, ENSP00000323929, ENSP00000331544, ENSP00000355627, ENSP00000358777, ENSP00000359074, ENSP00000378394, ENSP00000385834, ENSP00000391826, ENSP00000408632 0.000322 ENSP00000264025, ENSP00000269141, ENSP00000357106, ENSP00000359074, ENSP00000385142

25

5

GO.0007157 Heterophilic cell–cell adhesion via plasma membrane cell adhesion molecules

0.000276 ENSP00000252486, ENSP00000266546, ENSP00000269141, ENSP00000284981, ENSP00000368314, ENSP00000385142

GO.1902531 Regulation of intracellular signal transduction

6

GO.0007416 Synapse assembly

0.000264 ENSP00000223642, ENSP00000245907, ENSP00000263413, ENSP00000306099, ENSP00000315130, ENSP00000360281, ENSP00000396688, ENSP00000416561

Observed False gene discovery count rate Matching proteins in your network (IDs) 8

Pathway description

GO.0006959 Humoral immune response

Pathway ID

Table 3 (continued)

CADM3, CDH2, L1CAM, NRXN1, PVRL1

A2M, AGT, AMBP, APOA1, APOC3, APOE, ATP6AP1, C3, C5, CARTPT, CDH13, CDH2, CHI3L1, CLU, F2, FBLN1, FGB, HPX, IGF2, IGFBP5, L1CAM, PSAP, SEZ6L, SORL1, TF

APOE, APP, CDH2, CLSTN3, NRCAM, NRXN1

C3, C4A, C5, C6, C8B, CFB, CLU, FGB

Matching proteins in your network (labels)

498 Fa´bio Trindade et al.

0.000344 ENSP00000221891, ENSP00000233809, ENSP00000233813, ENSP00000236850, ENSP00000245907, ENSP00000254722, ENSP00000256637, ENSP00000261978, ENSP00000264005, ENSP00000295718, ENSP00000296130, ENSP00000300900, ENSP00000306099, ENSP00000311905, ENSP00000333769, ENSP00000345344, ENSP00000356969, ENSP00000358777, ENSP00000360687, ENSP00000362924, ENSP00000366124, ENSP00000378517, ENSP00000391826, ENSP00000408632, ENSP00000414303

25

11

GO.0009719 Response to endogenous stimulus

GO.0010038 Response to metal ion

0.000358 ENSP00000233809, ENSP00000245907, ENSP00000254722, ENSP00000264005, ENSP00000306099, ENSP00000307549, ENSP00000333769, ENSP00000362924, ENSP00000371554, ENSP00000414303, ENSP00000452780

0.000341 ENSP00000236850, ENSP00000237014, ENSP00000252486, ENSP00000252491, ENSP00000259486, ENSP00000260197, ENSP00000263574, ENSP00000264005, ENSP00000284981, ENSP00000350425, ENSP00000356969, ENSP00000360519

12

GO.0006066 Alcohol metabolic process

0.000338 ENSP00000236850, ENSP00000252486, ENSP00000252491, ENSP00000260197, ENSP00000263574, ENSP00000264005, ENSP00000284981, ENSP00000295897, ENSP00000350425, ENSP00000356969

10

GO.0008202 Steroid metabolic process

(continued)

B2M, BDNF, BSG, C3, FGB, GSN, IGFBP2, LCAT, NPTX1, SERPINF1, SOD3

APLP1, APOA1, APOA2, ATP6AP1, BDNF, BSG, C3, CA4, CDH13, CLEC3B, CST3, CTSL1, FGB, GSN, IGF2, IGFBP2, IGFBP5, LCAT, LTBP2, LTBP4, PTGDS, PTPRN, SERPINF1, SORT1, SPP1

APLP2, APOA1, APOA2, APOA4, APOC1, APOE, APP, ENPP2, LCAT, RBP4, SORL1, TTR

ALB, APLP2, APOA1, APOA2, APOA4, APOC1, APOE, APP, LCAT, SORL1

Bioinformatics Applied to CSF Proteome 499

51

GO.0048522 Positive regulation of cellular process

0.000391 ENSP00000223642, ENSP00000226218, ENSP00000233809, ENSP00000233813, ENSP00000236850, ENSP00000245907, ENSP00000252486, ENSP00000252491, ENSP00000255409, ENSP00000259486, ENSP00000260197, ENSP00000263413, ENSP00000264036, ENSP00000265023, ENSP00000265983, ENSP00000266546, ENSP00000289749, ENSP00000296130, ENSP00000296777, ENSP00000300289, ENSP00000302621, ENSP00000303550, ENSP00000304133, ENSP00000306099, ENSP00000308541, ENSP00000309148, ENSP00000315130, ENSP00000325660, ENSP00000347665, ENSP00000348170, ENSP00000350364, ENSP00000350425, ENSP00000353007, ENSP00000355627, ENSP00000356969, ENSP00000358777, ENSP00000359074, ENSP00000360519, ENSP00000366124, ENSP00000366513,

0.000385 ENSP00000227667, ENSP00000236850, ENSP00000252486, ENSP00000252491, ENSP00000260197, ENSP00000306864, ENSP00000315130, ENSP00000331544, ENSP00000345179, ENSP00000356969, ENSP00000362924, ENSP00000366124, ENSP00000378517, ENSP00000385142, ENSP00000406381

Observed False gene discovery count rate Matching proteins in your network (IDs) 15

Pathway description

GO.0051129 Negative regulation of cellular component organization

Pathway ID

Table 3 (continued)

AGT, AHSG, APOA1, APOA2, APOA4, APOC1, APOE, ATP6AP1, BDNF, C3, C4A, C5, C6, CARTPT, CDH13, CHI3L1, CLEC3B, CLSTN1, CLSTN3, CLU, CNTN1, COL18A1, CST3, CYTL1, ENPP2, ENSG00000224916, F2, FGB, HP, HPX, IGF2, IGFBP2, IGFBP5, KLK6, KNG1, L1CAM, LRG1, MCAM, NBL1, NEGR1, NRCAM, NRXN1, PDIA3, PSAP, RBP4, SCG2, SORL1, SPP1, SULF2, TF, VTN

APOA1, APOA2, APOC1, APOC3, APOD, APOE, CLU, CST3, ENSG00000224916, FBLN1, GSN, NRXN1, SORL1, SPP1, VASN

Matching proteins in your network (labels)

500 Fa´bio Trindade et al.

0.000448 ENSP00000252486, ENSP00000260197, ENSP00000315130

3

4 4

7

8

GO.1902430 Negative regulation of beta-amyloid formation

GO.0007597 Blood coagulation, intrinsic pathway

GO.0010954 Positive regulation of protein processing

GO.0006641 Triglyceride metabolic process

GO.0031099 Regeneration

0.000469 ENSP00000205948, ENSP00000252486, ENSP00000307156, ENSP00000309148, ENSP00000345179, ENSP00000350425, ENSP00000356969, ENSP00000362924

0.000461 ENSP00000205948, ENSP00000227667, ENSP00000236850, ENSP00000252486, ENSP00000252491, ENSP00000350425, ENSP00000356969

0.00045 ENSP00000245907, ENSP00000263413, ENSP00000296130, ENSP00000362924

0.00045 ENSP00000205948, ENSP00000265023, ENSP00000308541, ENSP00000323929

0.000402 ENSP00000218230, ENSP00000233809, ENSP00000236850, ENSP00000249330, ENSP00000252486, ENSP00000287641, ENSP00000295897, ENSP00000296777, ENSP00000356671, ENSP00000362924, ENSP00000366124, ENSP00000378517, ENSP00000414303

13

GO.0031667 Response to nutrient levels

ENSP00000368314, ENSP00000378394, ENSP00000378517, ENSP00000385142, ENSP00000385834, ENSP00000391826, ENSP00000393887, ENSP00000396688, ENSP00000406381, ENSP00000408632, ENSP00000414303

(continued)

APOA2, APOA4, APOD, APOE, APOH, GSN, KLK6, LAMB2

APOA1, APOA2, APOA4, APOC1, APOC3, APOE, APOH

C3, C6, CLEC3B, GSN

A2M, APOH, F2, KNG1

APOE, CLU, SORL1

ALB, APOA1, APOE, BDNF, CARTPT, CST3, GSN, IGFBP2, PCSK1N, SERPINC1, SPP1, SST, VGF

Bioinformatics Applied to CSF Proteome 501

17

35

GO.0031347 Regulation of defense response

GO.0009892 Negative regulation of metabolic process

0.000573 ENSP00000221891, ENSP00000223642, ENSP00000226218, ENSP00000227667, ENSP00000233813, ENSP00000236850, ENSP00000245907, ENSP00000252486, ENSP00000252491, ENSP00000256637, ENSP00000260197, ENSP00000263574, ENSP00000265023, ENSP00000265132,

0.000556 ENSP00000216361, ENSP00000223642, ENSP00000226218, ENSP00000236850, ENSP00000252486, ENSP00000254722, ENSP00000265983, ENSP00000323929, ENSP00000345179, ENSP00000345344, ENSP00000355627, ENSP00000356671, ENSP00000360281, ENSP00000393887, ENSP00000396688, ENSP00000416561, ENSP00000452780

0.00055 ENSP00000216361, ENSP00000223642, ENSP00000226218, ENSP00000233809, ENSP00000236850, ENSP00000245907, ENSP00000259396, ENSP00000265132, ENSP00000265983, ENSP00000289749, ENSP00000296777, ENSP00000315130, ENSP00000323929, ENSP00000345179, ENSP00000345344, ENSP00000348888, ENSP00000356969, ENSP00000358777, ENSP00000360281, ENSP00000360519, ENSP00000362924, ENSP00000391826, ENSP00000396688, ENSP00000416561

Observed False gene discovery count rate Matching proteins in your network (IDs) 24

Pathway description

GO.0002682 Regulation of immune system process

Pathway ID

Table 3 (continued)

A2M, AGT, AHSG, AMBP, APLP1, APLP2, APOA1, APOA2, APOA4, APOC1, APOC3, APOD, APOE, APP, C3, C4A, C5, CLU, CST3, ENSG00000224916, F2, FBLN1, HP, IGFBP5, ITIH1, ITIH4, KNG1, PTPRN2, SCG5, SERPINA1,

A2M, AGT, AHSG, APOA1, APOD, APOE, B2M, C4A, C5, C8B, CFB, COCH, CTSL1, HPX, SERPINC1, SERPINF1, VTN

A2M, AMBP, APOA1, APOA2, APOD, ATP6AP1, C3, C4A, C5, C8B, CARTPT, CFB, CLU, COCH, CTSL1, GSN, HPX, IGF2, IGFBP2, NBL1, ORM1, PIGR, RBP4, VTN

Matching proteins in your network (labels)

502 Fa´bio Trindade et al.

0.00058 ENSP00000223642, ENSP00000233813, ENSP00000236850, ENSP00000245907, ENSP00000255409, ENSP00000265983, ENSP00000269141, ENSP00000296777, ENSP00000306099, ENSP00000308541, ENSP00000315130, ENSP00000355627, ENSP00000358777, ENSP00000359074, ENSP00000378394, ENSP00000385834, ENSP00000391826, ENSP00000408632

18

16

GO.1902533 Positive regulation of intracellular signal transduction

GO.0031175 Neuron projection development

0.000609 ENSP00000252486, ENSP00000261908, ENSP00000264025, ENSP00000284981, ENSP00000289749, ENSP00000305595, ENSP00000307156, ENSP00000307549, ENSP00000309096, ENSP00000318472, ENSP00000325660, ENSP00000344786, ENSP00000345179, ENSP00000350425, ENSP00000359074, ENSP00000385142

0.000576 ENSP00000205948, ENSP00000223642, ENSP00000245907, ENSP00000254722, ENSP00000255409, ENSP00000259486, ENSP00000263413, ENSP00000302621, ENSP00000355627

9

GO.0045765 Regulation of angiogenesis

ENSP00000266041, ENSP00000273283, ENSP00000284981, ENSP00000300175, ENSP00000308541, ENSP00000315130, ENSP00000323929, ENSP00000331544, ENSP00000345179, ENSP00000348068, ENSP00000348170, ENSP00000350425, ENSP00000355627, ENSP00000356671, ENSP00000356969, ENSP00000366124, ENSP00000374069, ENSP00000376793, ENSP00000393887, ENSP00000396688, ENSP00000406381

(continued)

APOA4, APOD, APOE, APP, B3GNT1, B3GNT2, CNTN1, L1CAM, LAMB2, NBL1, NCAM1, NEO1, NFASC, NPTX1, NRXN1, PVRL1

AGT, APOA1, ATP6AP1, C3, C5, CARTPT, CDH13, CDH2, CHI3L1, CLU, F2, FGB, HPX, IGF2, IGFBP5, L1CAM, PSAP, TF

AGT, APOH, C3, C5, C6, CHI3L1, ENPP2, LRG1, SERPINF1

SERPINA3, SERPINC1, SORL1, SORT1, VTN

Bioinformatics Applied to CSF Proteome 503

0.000643 ENSP00000252486, ENSP00000264025, ENSP00000266546, ENSP00000269141, ENSP00000366513, ENSP00000385142, ENSP00000414303

7

30

3

GO.0050807 Regulation of synapse organization

GO.0002376 Immune system process

GO.0030300 Regulation of intestinal cholesterol absorption

0.000713 ENSP00000236850, ENSP00000350425, ENSP00000356969

0.000713 ENSP00000216492, ENSP00000223642, ENSP00000226218, ENSP00000236671, ENSP00000245907, ENSP00000252455, ENSP00000259486, ENSP00000263413, ENSP00000264025, ENSP00000284981, ENSP00000297268, ENSP00000300289, ENSP00000304133, ENSP00000306099, ENSP00000308541, ENSP00000315130, ENSP00000318472, ENSP00000333769, ENSP00000345344, ENSP00000345968, ENSP00000348170, ENSP00000348307, ENSP00000348888, ENSP00000350425, ENSP00000359074, ENSP00000360281, ENSP00000378517, ENSP00000385834, ENSP00000396688, ENSP00000416561

0.000609 ENSP00000236850, ENSP00000252486, ENSP00000252491, ENSP00000350425, ENSP00000355627, ENSP00000356969, ENSP00000406381

Observed False gene discovery count rate Matching proteins in your network (IDs) 7

Pathway description

GO.0045834 Positive regulation of lipid metabolic process

Pathway ID

Table 3 (continued)

APOA1, APOA2, APOA4

APOA4, APP, BSG, C3, C4A, C5, C6, C8B, CFB, CHGA, CLU, COL1A2, CTSD, CTSL1, ENPP2, F2, FGB, HP, L1CAM, NCAM1, PDIA3, PGLYRP2, PIGR, PRKCSH, PVRL1, SCG2, SIRPA, SPP1, TF, VTN

APOE, BDNF, CDH2, CLSTN1, CLSTN3, NRXN1, PVRL1

AGT, APOA1, APOA2, APOA4, APOC1, APOE, ENSG00000224916

Matching proteins in your network (labels)

504 Fa´bio Trindade et al.

0.000816 ENSP00000223642, ENSP00000226218, ENSP00000252486, ENSP00000255409, ENSP00000259486, ENSP00000260197, ENSP00000263413, ENSP00000265983, ENSP00000269141, ENSP00000284981, ENSP00000296130, ENSP00000296777, ENSP00000306099, ENSP00000308541, ENSP00000315130, ENSP00000331544, ENSP00000355627, ENSP00000356969, ENSP00000358777, ENSP00000362924, ENSP00000378394, ENSP00000385834, ENSP00000391826, ENSP00000414303

24

47

GO.0051247 Positive regulation of protein metabolic process

GO.0019538 Protein metabolic process

0.000827 ENSP00000205948, ENSP00000218230, ENSP00000223642, ENSP00000227667, ENSP00000233809, ENSP00000233813, ENSP00000236671, ENSP00000245907, ENSP00000252491, ENSP00000260197, ENSP00000263413, ENSP00000265023, ENSP00000265132, ENSP00000265983, ENSP00000284981, ENSP00000295718, ENSP00000295897, ENSP00000300175, ENSP00000300289, ENSP00000301464,

0.000789 ENSP00000223642, ENSP00000236850, ENSP00000245907, ENSP00000252486, ENSP00000255409, ENSP00000259486, ENSP00000261908, ENSP00000263413, ENSP00000266546, ENSP00000269141, ENSP00000289749, ENSP00000302621, ENSP00000306099, ENSP00000325660, ENSP00000350364, ENSP00000355627, ENSP00000358777, ENSP00000366513, ENSP00000368314, ENSP00000385142, ENSP00000414303

21

GO.0051094 Positive regulation of developmental process

(continued)

A2M, ALB, AMBP, APOA2, APOA4, APOC1, APOC3, APOH, APP, B2M, B3GNT1, B3GNT2, C3, C4A, C5, C6, C8B, CFB, CLU, CNDP1, CTSD, CTSL1, CYTL1, ENSG00000224916, F2, FBLN1, FGB, GSN, HPX, IGF2, IGFBP2, IGFBP5, IGFBP6, KLK6, KNG1, LTBP4, MAN1A1, PCSK1N, PDIA3,

AGT, APOA2, APOE, APP, ATP6AP1, BDNF, C5, C6, CARTPT, CDH2, CHI3L1, CLEC3B, CLU, ENPP2, F2, FBLN1, FGB, GSN, HPX, IGF2, PSAP, SORL1, TF, VTN

AGT, APOA1, APOE, ATP6AP1, BDNF, C3, C5, C6, CDH2, CHI3L1, CLSTN1, CLSTN3, CNTN1, ENPP2, FGB, LRG1, NBL1, NEGR1, NEO1, NRCAM, NRXN1

Bioinformatics Applied to CSF Proteome 505

36

GO.0048513 Organ development

0.000872 ENSP00000205948, ENSP00000221891, ENSP00000228938, ENSP00000233813, ENSP00000236850, ENSP00000248933, ENSP00000249330, ENSP00000254722, ENSP00000255409, ENSP00000263574, ENSP00000264025, ENSP00000284981,

0.000831 ENSP00000223642, ENSP00000245907, ENSP00000252486, ENSP00000255409, ENSP00000260197, ENSP00000265132, ENSP00000269141, ENSP00000296777, ENSP00000306099, ENSP00000331544, ENSP00000355627, ENSP00000358777, ENSP00000378394, ENSP00000385834, ENSP00000391826

ENSP00000303550, ENSP00000304133, ENSP00000305595, ENSP00000306099, ENSP00000308541, ENSP00000308938, ENSP00000309096, ENSP00000309148, ENSP00000311905, ENSP00000315130, ENSP00000323929, ENSP00000331544, ENSP00000345344, ENSP00000350425, ENSP00000351682, ENSP00000353007, ENSP00000356969, ENSP00000357453, ENSP00000360281, ENSP00000362924, ENSP00000374069, ENSP00000385834, ENSP00000391826, ENSP00000396688, ENSP00000406381, ENSP00000416561, ENSP00000452780

Observed False gene discovery count rate Matching proteins in your network (IDs)

15

Pathway description

GO.0043408 Regulation of MAPK cascade

Pathway ID

Table 3 (continued)

AGT, APLP1, APLP2, APOA1, APOA2, APOD, APOH, APP, B2M, BDNF, BSG, CA4, CHI3L1, CLEC3B, CNTN1, COL18A1, COL1A2, CPE, CST3, CYTL1, IGF2, IGFBP5, ITGA7, LAMB2, MGP, NRXN1,

AGT, AMBP, APOE, ATP6AP1, C3, C5, CARTPT, CDH2, CHI3L1, FBLN1, FGB, IGF2, PSAP, SORL1, TF

PLG, PTPRN, PTPRN2, SCG2, SCG5, SORL1, SULF2, TF

Matching proteins in your network (labels)

506 Fa´bio Trindade et al.

GO.0048856 Anatomical structure development

48

0.000939 ENSP00000205948, ENSP00000221891, ENSP00000223642, ENSP00000228938, ENSP00000233813, ENSP00000245907, ENSP00000248933, ENSP00000249330, ENSP00000252486, ENSP00000255409, ENSP00000256637, ENSP00000260197, ENSP00000263413, ENSP00000263574, ENSP00000264025, ENSP00000264036, ENSP00000266546, ENSP00000289749, ENSP00000297268, ENSP00000300900, ENSP00000303550, ENSP00000304133, ENSP00000305595, ENSP00000306477, ENSP00000307156, ENSP00000308541, ENSP00000309096, ENSP00000315130, ENSP00000318472, ENSP00000333769, ENSP00000344786, ENSP00000345179, ENSP00000350425, ENSP00000353007, ENSP00000355110, ENSP00000355627, ENSP00000356969, ENSP00000359074, ENSP00000360519, ENSP00000366124, ENSP00000378394, ENSP00000378517, ENSP00000385142, ENSP00000385834, ENSP00000386104, ENSP00000393887, ENSP00000408632, ENSP00000452780

ENSP00000296130, ENSP00000297268, ENSP00000300900, ENSP00000303550, ENSP00000307156, ENSP00000325660, ENSP00000333769, ENSP00000345179, ENSP00000347665, ENSP00000353007, ENSP00000355110, ENSP00000355627, ENSP00000356969, ENSP00000360519, ENSP00000366124, ENSP00000378394, ENSP00000378517, ENSP00000385142, ENSP00000385834, ENSP00000386104, ENSP00000391826, ENSP00000414303, ENSP00000452120, ENSP00000452780

(continued)

AGT, AHSG, APLP1, APLP2, APOA2, APOA4, APOD, APOE, APOH, B2M, B3GNT1, B3GNT2, BSG, BTD, C3, C5, C6, CA4, CDH13, CHI3L1, CLSTN3, CLU, COL1A2, CPE, CST3, CYTL1, F2, IGFBP5, L1CAM, LAMB2, MCAM, MGP, NBL1, NCAM1, NFASC, NRXN1, PSAP, PVRL1, RBP4, SCG2, SEZ6L, SMOC1, SORL1, SORT1, SPP1, SULF2, TF, VGF

PSAP, PVRL1, RBP4, SERPINF1, SEZ6L, SMOC1, SPP1, SULF2, TF, VGF

Bioinformatics Applied to CSF Proteome 507

Pathway description

0.00106 ENSP00000236671, ENSP00000297268, ENSP00000309148, ENSP00000345344, ENSP00000347665, ENSP00000350425 0.00106 ENSP00000227667, ENSP00000252486, ENSP00000252491, ENSP00000345179, ENSP00000356969, ENSP00000406381

6

6

13

GO.0044243 Multicellular organismal catabolic process

GO.0045833 Negative regulation of lipid metabolic process

GO.0051051 Negative regulation of transport

0.00106 ENSP00000216492, ENSP00000227667, ENSP00000236850, ENSP00000252486, ENSP00000252491, ENSP00000261908,

0.00106 ENSP00000236850, ENSP00000350425, ENSP00000406381

3

GO.0010898 Positive regulation of triglyceride catabolic process

0.000969 ENSP00000252486, ENSP00000264025, ENSP00000266546, ENSP00000269141, ENSP00000284981, ENSP00000355627, ENSP00000366513, ENSP00000385142, ENSP00000414303

0.000962 ENSP00000261908, ENSP00000264025, ENSP00000284981, ENSP00000305595, ENSP00000307156, ENSP00000309096, ENSP00000318472, ENSP00000325660, ENSP00000344786, ENSP00000359074, ENSP00000385142, ENSP00000414303

0.000944 ENSP00000236850, ENSP00000350425, ENSP00000356969, ENSP00000406381

9

12

4

Observed False gene discovery count rate Matching proteins in your network (IDs)

GO.0050803 Regulation of synapse structure or activity

GO.0007411 Axon guidance

GO.0050996 Positive regulation of lipid catabolic process

Pathway ID

Table 3 (continued)

AGT, APOA1, APOA2, APOC1, APOC3, APOD, APOE, CARTPT,

APOA2, APOC1, APOC3, APOD, APOE, ENSG00000224916

APOA4, COL18A1, COL1A2, CTSD, CTSL1, KLK6

APOA1, APOA4, ENSG00000224916

AGT, APOE, APP, BDNF, CDH2, CLSTN1, CLSTN3, NRXN1, PVRL1

APP, B3GNT1, B3GNT2, BDNF, CNTN1, L1CAM, LAMB2, NCAM1, NEO1, NFASC, NRXN1, PVRL1

APOA1, APOA2, APOA4, ENSG00000224916

Matching proteins in your network (labels)

508 Fa´bio Trindade et al.

0.00119 ENSP00000233809, ENSP00000236850, ENSP00000245907, ENSP00000254722, ENSP00000264005, ENSP00000287641, ENSP00000295718, ENSP00000300900, ENSP00000356969, ENSP00000360687, ENSP00000366124, ENSP00000378517 0.00127 ENSP00000223642, ENSP00000226218, ENSP00000236850, ENSP00000245907, ENSP00000252486, ENSP00000254722, ENSP00000259486, ENSP00000266546, ENSP00000269141, ENSP00000306099, ENSP00000315130, ENSP00000325660, ENSP00000350364, ENSP00000355627, ENSP00000366513, ENSP00000385142, ENSP00000391826, ENSP00000393887, ENSP00000396688, ENSP00000406381, ENSP00000414303

12

21

GO.0048545 Response to steroid hormone

GO.0051130 Positive regulation of cellular component organization

0.00118 ENSP00000216361, ENSP00000236850, ENSP00000252486, ENSP00000259486, ENSP00000269141, ENSP00000306099, ENSP00000306864, ENSP00000308541, ENSP00000331544, ENSP00000368314, ENSP00000378517, ENSP00000414303, ENSP00000452120

13

GO.0022604 Regulation of cell morphogenesis

0.00118 ENSP00000226218, ENSP00000236850, ENSP00000306099, ENSP00000308938, ENSP00000331544, ENSP00000345179, ENSP00000378517, ENSP00000408632

8

GO.0010810 Regulation of cell–substrate adhesion

ENSP00000296777, ENSP00000300289, ENSP00000331544, ENSP00000345179, ENSP00000355627, ENSP00000356969, ENSP00000406381

(continued)

AGT, AHSG, APOA1, APOE, BDNF, C3, C4A, C5, CDH2, CLSTN1, CLSTN3, CLU, CNTN1, ENPP2, ENSG00000224916, FGB, IGF2, NEGR1, NRXN1, SERPINF1, VTN

APOA1, APOA2, C3, CA4, CST3, IGFBP2, LCAT, PTGDS, PTPRN, SERPINF1, SPP1, SST

APOA1, APOE, BDNF, CDH2, COCH, ENPP2, F2, FBLN1, FGB, ITGA7, NRCAM, SPP1, VASN

APOA1, APOD, CDH13, FBLN1, FGB, PLG, SPP1, VTN

CHGA, ENSG00000224916, FBLN1, NEO1, PDIA3

Bioinformatics Applied to CSF Proteome 509

13

4

GO.0031102 Neuron projection regeneration

GO.0007409 Axonogenesis

8

GO.0006022 Aminoglycan metabolic process

20

GO.0006629 Lipid metabolic process

0.0015 ENSP00000261908, ENSP00000264025, ENSP00000284981, ENSP00000305595, ENSP00000307156, ENSP00000307549, ENSP00000309096, ENSP00000318472, ENSP00000325660, ENSP00000344786, ENSP00000359074, ENSP00000385142, ENSP00000414303

0.00149 ENSP00000252486, ENSP00000307156, ENSP00000345179, ENSP00000350425

0.00145 ENSP00000262551, ENSP00000266041, ENSP00000273283, ENSP00000303550, ENSP00000305595, ENSP00000309096, ENSP00000345968, ENSP00000359664

0.00139 ENSP00000205948, ENSP00000227667, ENSP00000236850, ENSP00000245907, ENSP00000252486, ENSP00000259486, ENSP00000260197, ENSP00000263574, ENSP00000264005, ENSP00000284981, ENSP00000295897, ENSP00000315130, ENSP00000345179, ENSP00000350425, ENSP00000355627, ENSP00000356969, ENSP00000360519, ENSP00000360687, ENSP00000378394, ENSP00000406381

0.0013 ENSP00000216492, ENSP00000236850, ENSP00000252486, ENSP00000266546, ENSP00000269141, ENSP00000315130, ENSP00000355627, ENSP00000362924, ENSP00000366513, ENSP00000385142, ENSP00000414303

Observed False gene discovery count rate Matching proteins in your network (IDs) 11

Pathway description

GO.0044089 Positive regulation of cellular component biogenesis

Pathway ID

Table 3 (continued)

APP, B3GNT1, B3GNT2, BDNF, CNTN1, L1CAM, LAMB2, NCAM1, NEO1, NFASC, NPTX1, NRXN1, PVRL1

APOA4, APOD, APOE, LAMB2

B3GNT1, B3GNT2, CTBS, CYTL1, ITIH1, ITIH4, OGN, PGLYRP2

AGT, ALB, APLP2, APOA1, APOA2, APOA4, APOC3, APOD, APOE, APOH, APP, C3, CLU, ENPP2, ENSG00000224916, LCAT, PSAP, PTGDS, RBP4, SORL1

AGT, APOA1, APOE, BDNF, CDH2, CHGA, CLSTN1, CLSTN3, CLU, GSN, NRXN1

Matching proteins in your network (labels)

510 Fa´bio Trindade et al.

0.0015 ENSP00000236850, ENSP00000237014, ENSP00000252486, ENSP00000252491, ENSP00000259486, ENSP00000260197, ENSP00000263574, ENSP00000264005, ENSP00000284981, ENSP00000295897, ENSP00000350425, ENSP00000356969, ENSP00000360519 0.00175 ENSP00000252486, ENSP00000295718, ENSP00000345179, ENSP00000348170, ENSP00000350425, ENSP00000366124, ENSP00000371554, ENSP00000373477 0.0018 ENSP00000236850, ENSP00000252486, ENSP00000350425, ENSP00000355627, ENSP00000406381 0.00188 ENSP00000264025, ENSP00000266546, ENSP00000269141, ENSP00000357106, ENSP00000359074, ENSP00000366513, ENSP00000385142, ENSP00000408632

13

8

5

8

6

9

GO.1901615 Organic hydroxy compound metabolic process

GO.0000302 Response to reactive oxygen species

GO.0046889 Positive regulation of lipid biosynthetic process

GO.0098742 Cell–cell adhesion via plasmamembrane adhesion molecules

GO.0007631 Feeding behavior

GO.0040013 Negative regulation of locomotion

0.00197 ENSP00000205948, ENSP00000223642, ENSP00000233813, ENSP00000252486, ENSP00000254722, ENSP00000289749, ENSP00000331544, ENSP00000345179, ENSP00000362924

0.00197 ENSP00000263574, ENSP00000284981, ENSP00000296777, ENSP00000350364, ENSP00000355627, ENSP00000414303

0.0015 ENSP00000205948, ENSP00000308541, ENSP00000308938

3

GO.0051918 Negative regulation of fibrinolysis

(continued)

APOD, APOE, APOH, C5, FBLN1, GSN, IGFBP5, NBL1, SERPINF1

AGT, APLP2, APP, BDNF, CARTPT, NEGR1

CADM3, CDH13, CDH2, CLSTN1, CLSTN3, L1CAM, NRXN1, PVRL1

AGT, APOA1, APOA4, APOE, ENSG00000224916

APOA4, APOD, APOE, CST3, GPX3, HP, PTPRN, SOD3

ALB, APLP2, APOA1, APOA2, APOA4, APOC1, APOE, APP, ENPP2, LCAT, RBP4, SORL1, TTR

APOH, F2, PLG

Bioinformatics Applied to CSF Proteome 511

Pathway description

0.00223 ENSP00000218230, ENSP00000221891, ENSP00000223642, ENSP00000227667, ENSP00000236850, ENSP00000245907, ENSP00000252486, ENSP00000256637, ENSP00000259486, ENSP00000263574, ENSP00000287641, ENSP00000296777, ENSP00000300175, ENSP00000324248, ENSP00000333593, ENSP00000355627, ENSP00000358715, ENSP00000378394, ENSP00000386104, ENSP00000414303

20

33

GO.0007186 G-protein coupled receptor signaling pathway

GO.0010033 Response to organic substance

0.00226 ENSP00000221891, ENSP00000233809, ENSP00000233813, ENSP00000236850,

0.0022 ENSP00000223642, ENSP00000226218, ENSP00000252486, ENSP00000255409, ENSP00000259486, ENSP00000263413, ENSP00000265983, ENSP00000269141, ENSP00000284981, ENSP00000296130, ENSP00000296777, ENSP00000306099, ENSP00000308541, ENSP00000315130, ENSP00000331544, ENSP00000355627, ENSP00000358777, ENSP00000362924, ENSP00000378394, ENSP00000385834, ENSP00000391826, ENSP00000414303

0.00218 ENSP00000236850, ENSP00000252486, ENSP00000260197, ENSP00000289749, ENSP00000295897, ENSP00000300289, ENSP00000362924

22

7

Observed False gene discovery count rate Matching proteins in your network (IDs)

GO.0032270 Positive regulation of cellular protein metabolic process

GO.0051235 Maintenance of location

Pathway ID

Table 3 (continued)

APLP1, APOA1, APOA2, ATP6AP1, BDNF, BSG, C3, C4A, CA4, CDH13,

AGT, APLP1, APLP2, APOA1, APOC3, APOE, BDNF, C3, C5, CARTPT, CPE, ENPP2, NXPH4, PCSK1N, PENK, PSAP, SCG5, SORCS3, SORT1, SST

AGT, APOE, APP, ATP6AP1, BDNF, C5, C6, CARTPT, CDH2, CHI3L1, CLEC3B, CLU, ENPP2, F2, FBLN1, FGB, GSN, HPX, IGF2, PSAP, TF, VTN

ALB, APOA1, APOE, GSN, NBL1, PDIA3, SORL1

Matching proteins in your network (labels)

512 Fa´bio Trindade et al.

0.00241 ENSP00000245907, ENSP00000263413

0.00241 ENSP00000345179, ENSP00000350425

2

2

6

2

2

GO.0001970 Positive regulation of activation of membrane attack complex

GO.0034443 Negative regulation of lipoprotein oxidation

GO.0055072 Iron ion homeostasis

GO.0060621 Negative regulation of cholesterol import

GO.1902998 Positive regulation of neurofibrillary tangle assembly

0.00241 ENSP00000252486, ENSP00000315130

0.00241 ENSP00000227667, ENSP00000356969

0.00241 ENSP00000261908, ENSP00000264613, ENSP00000265983, ENSP00000358777, ENSP00000385834, ENSP00000452780

0.00241 ENSP00000306100, ENSP00000345968

2

GO.0001519 Peptide amidation

ENSP00000245907, ENSP00000254722, ENSP00000256637, ENSP00000261978, ENSP00000264005, ENSP00000266041, ENSP00000295718, ENSP00000296130, ENSP00000300900, ENSP00000306099, ENSP00000307549, ENSP00000311905, ENSP00000315130, ENSP00000318472, ENSP00000333769, ENSP00000345344, ENSP00000356969, ENSP00000358777, ENSP00000360519, ENSP00000360687, ENSP00000362924, ENSP00000366124, ENSP00000373477, ENSP00000378394, ENSP00000378517, ENSP00000391826, ENSP00000396688, ENSP00000408632, ENSP00000414303

APOE, CLU

APOA2, APOC3

(continued)

ATP6AP1, B2M, CP, HPX, NEO1, TF

APOA4, APOD

C3, C6

PAM, PGLYRP2

CLEC3B, CLU, CST3, CTSL1, FGB, GPX3, GSN, IGF2, IGFBP2, IGFBP5, ITIH4, LCAT, LTBP2, LTBP4, NCAM1, NPTX1, PSAP, PTGDS, PTPRN, RBP4, SERPINF1, SORT1, SPP1

Bioinformatics Applied to CSF Proteome 513

Pathway description

0.00267 ENSP00000252486, ENSP00000355627, ENSP00000356969

0.00267 ENSP00000205948, ENSP00000233813, ENSP00000252486, ENSP00000260197, ENSP00000269141, ENSP00000284981, ENSP00000296777, ENSP00000306864, ENSP00000308541, ENSP00000331544, ENSP00000345968, ENSP00000355627, ENSP00000360519, ENSP00000366124, ENSP00000378517, ENSP00000393887, ENSP00000414303

3

17

GO.0034374 Low-density lipoprotein particle remodeling

GO.0051093 Negative regulation of developmental process

0.00254 ENSP00000263574, ENSP00000264613, ENSP00000265983, ENSP00000284981, ENSP00000358777, ENSP00000385834

6

GO.0046916 Cellular transition metal ion homeostasis

0.00248 ENSP00000252486, ENSP00000260197, ENSP00000264025, ENSP00000266546, ENSP00000269141, ENSP00000284981, ENSP00000289749, ENSP00000308541, ENSP00000309148, ENSP00000325660, ENSP00000366513, ENSP00000368314, ENSP00000378517, ENSP00000385142, ENSP00000414303

0.00244 ENSP00000226218, ENSP00000227667, ENSP00000245907, ENSP00000252491, ENSP00000393887, ENSP00000396688, ENSP00000406381, ENSP00000408632

15

8

Observed False gene discovery count rate Matching proteins in your network (IDs)

GO.0051960 Regulation of nervous system development

GO.0030100 Regulation of endocytosis

Pathway ID

Table 3 (continued)

AGT, AHSG, APOE, APOH, APP, BDNF, CARTPT, CDH2, CST3, F2, FBLN1, IGFBP5, PGLYRP2, RBP4, SORL1, SPP1, VASN

AGT, APOA2, APOE

APLP2, APP, ATP6AP1, CP, HPX, TF

APOE, APP, BDNF, CDH2, CLSTN1, CLSTN3, CNTN1, F2, KLK6, NBL1, NRCAM, NRXN1, PVRL1, SORL1, SPP1

AHSG, APOC1, APOC3, C3, C4A, CDH13, ENSG00000224916, VTN

Matching proteins in your network (labels)

514 Fa´bio Trindade et al.

0.00304 ENSP00000252486, ENSP00000264036, ENSP00000269141, ENSP00000297268, ENSP00000304133, ENSP00000345179, ENSP00000347665, ENSP00000355627, ENSP00000368314, ENSP00000385142, ENSP00000408632, ENSP00000452120 0.0031 ENSP00000261908, ENSP00000264025, ENSP00000284981, ENSP00000305595, ENSP00000307156, ENSP00000307549, ENSP00000309096, ENSP00000318472, ENSP00000325660, ENSP00000344786, ENSP00000359074, ENSP00000385142, ENSP00000414303 0.00316 ENSP00000296777, ENSP00000358777, ENSP00000378517, ENSP00000385834 0.00321 ENSP00000264025, ENSP00000266546, ENSP00000269141, ENSP00000357106, ENSP00000359074, ENSP00000366513, ENSP00000408632 0.00321 ENSP00000226218, ENSP00000227667, ENSP00000245907, ENSP00000252491, ENSP00000260197, ENSP00000306099, ENSP00000358777, ENSP00000393887, ENSP00000396688, ENSP00000406381, ENSP00000408632

12

13

4 7

11

GO.0001568 Blood vessel development

GO.0048667 Cell morphogenesis involved in neuron differentiation

GO.0045124 Regulation of bone resorption

GO.0007156 Homophilic cell adhesion via plasma membrane adhesion molecules

GO.0060627 Regulation of vesicle-mediated transport

0.0028 ENSP00000227667, ENSP00000236850, ENSP00000350425, ENSP00000406381

4

GO.0070328 Triglyceride homeostasis

0.0028 ENSP00000227667, ENSP00000236850, ENSP00000252486, ENSP00000350425

4

GO.0019433 Triglyceride catabolic process

(continued)

AHSG, APOC1, APOC3, ATP6AP1, C3, C4A, CDH13, ENSG00000224916, FGB, SORL1, VTN

CADM3, CDH13, CDH2, CLSTN1, CLSTN3, L1CAM, PVRL1

ATP6AP1, CARTPT, SPP1, TF

APP, B3GNT1, B3GNT2, BDNF, CNTN1, L1CAM, LAMB2, NCAM1, NEO1, NFASC, NPTX1, NRXN1, PVRL1

AGT, APOD, APOE, CDH13, CDH2, COL18A1, COL1A2, ITGA7, MCAM, NRCAM, NRXN1, SCG2

APOA1, APOA4, APOC3, ENSG00000224916

APOA1, APOA4, APOC3, APOE

Bioinformatics Applied to CSF Proteome 515

0.00335 ENSP00000223642, ENSP00000226218, ENSP00000252486, ENSP00000255409, ENSP00000259486, ENSP00000265983, ENSP00000269141, ENSP00000296777, ENSP00000306099, ENSP00000308541, ENSP00000315130, ENSP00000355627, ENSP00000358777, ENSP00000378394, ENSP00000385834, ENSP00000391826, ENSP00000406381, ENSP00000414303

18

29

GO.0045937 Positive regulation of phosphate metabolic process

GO.0009653 Anatomical structure morphogenesis

0.00371 ENSP00000221891, ENSP00000226218, ENSP00000228938, ENSP00000233813, ENSP00000252486, ENSP00000264025, ENSP00000264036, ENSP00000269141, ENSP00000284981, ENSP00000289749, ENSP00000297268, ENSP00000304133, ENSP00000305595, ENSP00000307156, ENSP00000307549, ENSP00000309096, ENSP00000315130, ENSP00000318472, ENSP00000325660, ENSP00000333769, ENSP00000345179, ENSP00000355627,

0.00333 ENSP00000216492, ENSP00000223642, ENSP00000245907, ENSP00000263413, ENSP00000315130, ENSP00000360281, ENSP00000396688

7

GO.0002443 Leukocyte mediated immunity

0.00328 ENSP00000226218, ENSP00000236850, ENSP00000306099, ENSP00000331544, ENSP00000378517, ENSP00000408632

Observed False gene discovery count rate Matching proteins in your network (IDs) 6

Pathway description

GO.0010811 Positive regulation of cell–substrate adhesion

Pathway ID

Table 3 (continued)

AGT, APLP1, APOD, APOE, APP, B3GNT1, B3GNT2, BSG, CDH13, CDH2, CLU, CNTN1, COL1A2, CPE, GSN, IGF2, IGFBP5, L1CAM, LAMB2, MCAM, MGP, NBL1, NCAM1, NPTX1, NRXN1, PVRL1, RBP4, SCG2, VTN

AGT, APOE, ATP6AP1, BDNF, C5, CARTPT, CDH2, CHI3L1, CLU, ENPP2, ENSG00000224916, F2, FGB, HPX, IGF2, PSAP, TF, VTN

C3, C4A, C5, C6, C8B, CHGA, CLU

APOA1, CDH13, FBLN1, FGB, SPP1, VTN

Matching proteins in your network (labels)

516 Fa´bio Trindade et al.

0.00387 ENSP00000223642, ENSP00000226218, ENSP00000236850, ENSP00000265983, ENSP00000323929, ENSP00000345968, ENSP00000356969, ENSP00000360281, ENSP00000360519, ENSP00000396688, ENSP00000416561

11

7

16

GO.0002697 Regulation of immune effector process

GO.0042445 Hormone metabolic process

GO.0001934 Positive regulation of protein phosphorylation

0.00421 ENSP00000223642, ENSP00000226218, ENSP00000255409, ENSP00000259486, ENSP00000265983, ENSP00000269141, ENSP00000296777, ENSP00000306099, ENSP00000308541, ENSP00000315130, ENSP00000355627, ENSP00000358777, ENSP00000378394, ENSP00000385834, ENSP00000391826, ENSP00000414303

0.00404 ENSP00000218230, ENSP00000237014, ENSP00000300175, ENSP00000309148, ENSP00000355627, ENSP00000360519, ENSP00000386104

0.0038 ENSP00000261908, ENSP00000264025, ENSP00000284981, ENSP00000289749, ENSP00000305595, ENSP00000307156, ENSP00000307549, ENSP00000309096, ENSP00000318472, ENSP00000325660, ENSP00000344786, ENSP00000359074, ENSP00000385142

13

GO.0048812 Neuron projection morphogenesis

0.00372 ENSP00000205948, ENSP00000223642, ENSP00000233813, ENSP00000252486, ENSP00000254722, ENSP00000289749, ENSP00000331544, ENSP00000345179

8

GO.2000146 Negative regulation of cell motility

ENSP00000359074, ENSP00000360519, ENSP00000362924, ENSP00000385142, ENSP00000386104, ENSP00000391826, ENSP00000408632

(continued)

AGT, ATP6AP1, BDNF, C5, CARTPT, CDH2, CHI3L1, CLU, ENPP2, F2, FGB, HPX, IGF2, PSAP, TF, VTN

AGT, CPE, KLK6, PCSK1N, RBP4, SCG5, TTR

A2M, APOA1, APOA2, C4A, C5, C8B, CFB, HPX, PGLYRP2, RBP4, VTN

APP, B3GNT1, B3GNT2, CNTN1, L1CAM, LAMB2, NBL1, NCAM1, NEO1, NFASC, NPTX1, NRXN1, PVRL1

APOD, APOE, APOH, C5, FBLN1, IGFBP5, NBL1, SERPINF1

Bioinformatics Applied to CSF Proteome 517

GO.0030030 Cell projection organization

GO.0035023 Regulation of Rho protein signal transduction

GO.0003008 System process

18

4

25

5

GO.0046470 Phosphatidylcholine metabolic process

0.00436 ENSP00000252486, ENSP00000261908, ENSP00000264025, ENSP00000284981, ENSP00000289749, ENSP00000305595, ENSP00000307156, ENSP00000307549,

0.00431 ENSP00000227667, ENSP00000236850, ENSP00000252486, ENSP00000269141

0.00431 ENSP00000216361, ENSP00000216492, ENSP00000252486, ENSP00000254722, ENSP00000263574, ENSP00000264036, ENSP00000265023, ENSP00000296777, ENSP00000297268, ENSP00000305595, ENSP00000307156, ENSP00000324248, ENSP00000344786, ENSP00000347665, ENSP00000348888, ENSP00000353007, ENSP00000355627, ENSP00000356771, ENSP00000358715, ENSP00000360519, ENSP00000362924, ENSP00000368314, ENSP00000376793, ENSP00000385142, ENSP00000414303

0.00423 ENSP00000236850, ENSP00000259486, ENSP00000264005, ENSP00000350425, ENSP00000356969

0.00423 ENSP00000358777, ENSP00000378517, ENSP00000385834

Observed False gene discovery count rate Matching proteins in your network (IDs) 3

Pathway description

GO.0045780 Positive regulation of bone resorption

Pathway ID

Table 3 (continued)

APOA4, APOD, APOE, APP, B3GNT1, B3GNT2, CDH13, CNTN1, GSN, L1CAM, LAMB2, NBL1, NCAM1,

APOA1, APOC3, APOE, CDH2

AGT, APLP2, APOE, B3GNT2, BDNF, CARTPT, CHGA, COCH, COL18A1, COL1A2, F5, GSN, KNG1, LAMB2, MCAM, NFASC, NRCAM, NRXN1, PENK, PIGR, RBP4, SERPINA3, SERPINF1, SORCS3, SULF2

APOA1, APOA2, APOA4, ENPP2, LCAT

ATP6AP1, SPP1, TF

Matching proteins in your network (labels)

518 Fa´bio Trindade et al.

12

10

7

30

GO.0001944 Vasculature development

GO.0009416 Response to light stimulus

GO.0002250 Adaptive immune response

GO.0031324 Negative regulation of cellular metabolic process

0.00479 ENSP00000221891, ENSP00000223642, ENSP00000226218, ENSP00000227667, ENSP00000233813, ENSP00000245907, ENSP00000252486, ENSP00000252491, ENSP00000254722, ENSP00000260197, ENSP00000263574, ENSP00000265023, ENSP00000265132, ENSP00000266041, ENSP00000273283, ENSP00000284981, ENSP00000308541, ENSP00000315130, ENSP00000323929, ENSP00000331544, ENSP00000345179, ENSP00000348068, ENSP00000348170, ENSP00000350425, ENSP00000355627, ENSP00000356671, ENSP00000366124, ENSP00000376793, ENSP00000393887, ENSP00000396688

0.00479 ENSP00000223642, ENSP00000245907, ENSP00000263413, ENSP00000315130, ENSP00000345344, ENSP00000360281, ENSP00000396688

0.00455 ENSP00000227667, ENSP00000236850, ENSP00000237014, ENSP00000252486, ENSP00000284981, ENSP00000350425, ENSP00000356969, ENSP00000360519, ENSP00000406381, ENSP00000414303

0.00443 ENSP00000252486, ENSP00000264036, ENSP00000269141, ENSP00000297268, ENSP00000304133, ENSP00000345179, ENSP00000347665, ENSP00000355627, ENSP00000368314, ENSP00000385142, ENSP00000408632, ENSP00000452120

ENSP00000309096, ENSP00000318472, ENSP00000325660, ENSP00000344786, ENSP00000345179, ENSP00000350425, ENSP00000359074, ENSP00000362924, ENSP00000385142, ENSP00000408632

(continued)

A2M, AGT, AHSG, AMBP, APLP1, APLP2, APOA4, APOC1, APOC3, APOD, APOE, APP, C3, C4A, C5, CLU, CST3, F2, FBLN1, HP, IGFBP5, ITIH1, ITIH4, KNG1, SERPINA1, SERPINA3, SERPINC1, SERPINF1, SORL1, VTN

C3, C4A, C5, C6, C8B, CLU, CTSL1

APOA1, APOA2, APOA4, APOC3, APOE, APP, BDNF, ENSG00000224916, RBP4, TTR

AGT, APOD, APOE, CDH13, CDH2, COL18A1, COL1A2, ITGA7, MCAM, NRCAM, NRXN1, SCG2

NEO1, NFASC, NPTX1, NRXN1, PVRL1

Bioinformatics Applied to CSF Proteome 519

4 13

10

GO.0016486 Peptide hormone processing

GO.0098609 Cell–cell adhesion

GO.0015711 Organic anion transport

0.00541 ENSP00000227667, ENSP00000236850, ENSP00000252486, ENSP00000252491, ENSP00000295897, ENSP00000300900, ENSP00000350425, ENSP00000356969, ENSP00000406381, ENSP00000414303

0.00522 ENSP00000264025, ENSP00000266546, ENSP00000306099, ENSP00000344786, ENSP00000350364, ENSP00000350425, ENSP00000357106, ENSP00000359074, ENSP00000366513, ENSP00000385142, ENSP00000408632, ENSP00000452120, ENSP00000452780

0.00522 ENSP00000218230, ENSP00000300175, ENSP00000355627, ENSP00000386104

0.00518 ENSP00000245907, ENSP00000263413, ENSP00000265983

3

GO.0044087 Regulation of cellular component biogenesis

GO.0002922 Positive regulation of humoral immune response

Observed False gene discovery count rate Matching proteins in your network (IDs) 0.00499 ENSP00000216492, ENSP00000236850, ENSP00000252486, ENSP00000260197, ENSP00000264005, ENSP00000264025, ENSP00000266546, ENSP00000269141, ENSP00000315130, ENSP00000345179, ENSP00000355627, ENSP00000366513, ENSP00000385142, ENSP00000414303

Pathway description 14

Pathway ID

Table 3 (continued)

ALB, APOA1, APOA2, APOA4, APOC1, APOC3, APOE, BDNF, CA4, ENSG00000224916

APOA4, B2M, CADM3, CDH13, CLSTN1, CLSTN3, FGB, ITGA7, L1CAM, NEGR1, NFASC, NRXN1, PVRL1

AGT, CPE, PCSK1N, SCG5

C3, C6, HPX

AGT, APOA1, APOD, APOE, BDNF, CDH2, CHGA, CLSTN1, CLSTN3, CLU, LCAT, NRXN1, PVRL1, SORL1

Matching proteins in your network (labels)

520 Fa´bio Trindade et al.

0.00593 ENSP00000223642, ENSP00000245907, ENSP00000255409, ENSP00000269141, ENSP00000296777, ENSP00000306099, ENSP00000355627, ENSP00000358777, ENSP00000378394, ENSP00000385834, ENSP00000391826 0.00598 ENSP00000261908, ENSP00000264025, ENSP00000284981, ENSP00000305595, ENSP00000307156, ENSP00000307549, ENSP00000309096, ENSP00000318472, ENSP00000325660, ENSP00000344786, ENSP00000347665, ENSP00000359074, ENSP00000385142, ENSP00000414303 0.00598 ENSP00000236671, ENSP00000297268, ENSP00000309148, ENSP00000345344, ENSP00000347665 0.00598 ENSP00000252486, ENSP00000252491

0.00598 ENSP00000221891, ENSP00000414303

0.00598 ENSP00000252486, ENSP00000269141

11

14

5

2

2

2

GO.0043410 Positive regulation of MAPK cascade

GO.0000904 Cell morphogenesis involved in differentiation

GO.0030574 Collagen catabolic process

GO.0034447 Very-low-density lipoprotein particle clearance

GO.0071874 Cellular response to norepinephrine stimulus

GO.1901626 Regulation of postsynaptic membrane organization

0.00577 ENSP00000226218, ENSP00000233809, ENSP00000236850, ENSP00000265023, ENSP00000306099, ENSP00000308938, ENSP00000331544, ENSP00000345179, ENSP00000359074, ENSP00000362924, ENSP00000378517, ENSP00000391826, ENSP00000408632

13

GO.0030155 Regulation of cell adhesion

APOE, CDH2

APLP1, BDNF

APOC1, APOE

(continued)

COL18A1, COL1A2, CTSD, CTSL1, KLK6

APP, B3GNT1, B3GNT2, BDNF, CNTN1, COL18A1, L1CAM, LAMB2, NCAM1, NEO1, NFASC, NPTX1, NRXN1, PVRL1

AGT, ATP6AP1, C3, C5, CARTPT, CDH2, CHI3L1, FGB, IGF2, PSAP, TF

APOA1, APOD, CDH13, FBLN1, FGB, GSN, IGF2, IGFBP2, KNG1, L1CAM, PLG, SPP1, VTN

Bioinformatics Applied to CSF Proteome 521

17

15

GO.0060284 Regulation of cell development

6

GO.0042157 Lipoprotein metabolic process

GO.0045087 Innate immune response

7

GO.0030203 Glycosaminoglycan metabolic process

0.00696 ENSP00000236850, ENSP00000252486, ENSP00000260197, ENSP00000269141, ENSP00000284981, ENSP00000289749, ENSP00000306099, ENSP00000306864, ENSP00000308541, ENSP00000309148, ENSP00000325660, ENSP00000331544, ENSP00000358777, ENSP00000368314, ENSP00000378517

0.00664 ENSP00000216492, ENSP00000223642, ENSP00000226218, ENSP00000245907, ENSP00000252455, ENSP00000263413, ENSP00000284981, ENSP00000306099, ENSP00000315130, ENSP00000318472, ENSP00000345344, ENSP00000345968, ENSP00000350425, ENSP00000360281, ENSP00000396688, ENSP00000416561, ENSP00000452780

0.00658 ENSP00000227667, ENSP00000252491, ENSP00000295897, ENSP00000350425, ENSP00000356969, ENSP00000406381

0.00611 ENSP00000262551, ENSP00000266041, ENSP00000273283, ENSP00000303550, ENSP00000305595, ENSP00000309096, ENSP00000345968

0.00605 ENSP00000296777, ENSP00000355627, ENSP00000366124

Observed False gene discovery count rate Matching proteins in your network (IDs) 3

Pathway description

GO.0034104 Negative regulation of tissue remodeling

Pathway ID

Table 3 (continued)

APOA1, APOE, APP, ATP6AP1, CDH2, CNTN1, F2, FBLN1, FGB, KLK6, NBL1, NRCAM, SORL1, SPP1, VASN

APOA4, APP, B2M, C3, C4A, C5, C6, C8B, CFB, CHGA, CLU, CTSL1, FGB, NCAM1, PGLYRP2, PRKCSH, VTN

ALB, APOA2, APOA4, APOC1, APOC3, ENSG00000224916

B3GNT1, B3GNT2, CYTL1, ITIH1, ITIH4, OGN, PGLYRP2

AGT, CARTPT, CST3

Matching proteins in your network (labels)

522 Fa´bio Trindade et al.

0.00765 ENSP00000226218, ENSP00000248933, ENSP00000263574, ENSP00000284981, ENSP00000306477, ENSP00000307156, ENSP00000307549, ENSP00000309148, ENSP00000315130, ENSP00000325660, ENSP00000345179, ENSP00000355627, ENSP00000362924, ENSP00000366124, ENSP00000368314, ENSP00000385142 0.00774 ENSP00000236850, ENSP00000252486, ENSP00000269141, ENSP00000306099, ENSP00000306864, ENSP00000331544, ENSP00000368314, ENSP00000378517, ENSP00000414303

16

9

4 27

GO.0007417 Central nervous system development

GO.0010769 Regulation of cell morphogenesis involved in differentiation

GO.0007566 Embryo implantation

GO.0007399 Nervous system development

0.00825 ENSP00000221891, ENSP00000226218, ENSP00000248933, ENSP00000252486, ENSP00000260197, ENSP00000261908, ENSP00000263574, ENSP00000264025, ENSP00000266546, ENSP00000289749, ENSP00000305595, ENSP00000306477, ENSP00000307156, ENSP00000308541, ENSP00000309096, ENSP00000315130, ENSP00000318472, ENSP00000344786, ENSP00000345179, ENSP00000350425, ENSP00000353007, ENSP00000355627, ENSP00000359074, ENSP00000362924,

0.00806 ENSP00000331544, ENSP00000333769, ENSP00000366124, ENSP00000378517

0.00738 ENSP00000252486, ENSP00000269141, ENSP00000348888, ENSP00000385142

4

GO.0043113 Receptor clustering

0.00732 ENSP00000233809, ENSP00000236850, ENSP00000287641, ENSP00000356671, ENSP00000362924, ENSP00000378517, ENSP00000414303

7

GO.0007584 Response to nutrient

(continued)

AGT, APLP1, APLP2, APOA4, APOD, APOE, B3GNT1, B3GNT2, BTD, CLSTN3, CLU, CST3, F2, GSN, L1CAM, LAMB2, NBL1, NCAM1, NEO1, NFASC, NRXN1, PVRL1, SEZ6L, SORL1, SPP1, SULF2, VTN

BSG, CST3, FBLN1, SPP1

APOA1, APOE, BDNF, CDH2, FBLN1, FGB, NRCAM, SPP1, VASN

AGT, APLP2, APOD, APP, BTD, CLU, CNTN1, CST3, GSN, KLK6, LAMB2, NPTX1, NRCAM, NRXN1, SEZ6L, VTN

APOE, CDH2, NRXN1, PIGR

APOA1, BDNF, GSN, IGFBP2, SERPINC1, SPP1, SST

Bioinformatics Applied to CSF Proteome 523

0.00876 ENSP00000236850, ENSP00000307156, ENSP00000345179, ENSP00000366124 0.00876 ENSP00000226218, ENSP00000261908, ENSP00000289749, ENSP00000302621, ENSP00000306864, ENSP00000311905, ENSP00000353007, ENSP00000355627 0.00887 ENSP00000252486, ENSP00000254722, ENSP00000259486, ENSP00000269141, ENSP00000309148, ENSP00000325660, ENSP00000350364, ENSP00000368314, ENSP00000378517, ENSP00000385142, ENSP00000414303

4 8

11

81

GO.0048678 Response to axon injury

GO.0090287 Regulation of cellular response to growth factor stimulus

GO.0031344 Regulation of cell projection organization

GO.0050789 Regulation of biological process

0.00887 ENSP00000205948, ENSP00000216492, ENSP00000221891, ENSP00000223642, ENSP00000226218, ENSP00000227667, ENSP00000228938, ENSP00000233809, ENSP00000233813, ENSP00000236850, ENSP00000237014, ENSP00000245907,

0.00866 ENSP00000236850, ENSP00000307156, ENSP00000345179

3

GO.0031103 Axon regeneration

0.00839 ENSP00000264025, ENSP00000266546, ENSP00000366513, ENSP00000385142, ENSP00000414303

ENSP00000366124, ENSP00000378517, ENSP00000385142

Observed False gene discovery count rate Matching proteins in your network (IDs)

5

Pathway description

GO.0051963 Regulation of synapse assembly

Pathway ID

Table 3 (continued)

A2M, AGT, AHSG, ALB, AMBP, APLP1, APOA1, APOA2, APOA4, APOC1, APOC3, APOD, APOE, APOH, ATP6AP1, BDNF, BSG, C3, C4A, C5, C8B, CDH13, CFB, CHGA, CHI3L1, CLEC3B,

APOE, BDNF, CDH2, CNTN1, ENPP2, KLK6, NEGR1, NRCAM, NRXN1, SERPINF1, SPP1

AGT, LRG1, LTBP4, NBL1, NEO1, SULF2, VASN, VTN

APOA1, APOD, CST3, LAMB2

APOA1, APOD, LAMB2

BDNF, CLSTN1, CLSTN3, NRXN1, PVRL1

Matching proteins in your network (labels)

524 Fa´bio Trindade et al.

ENSP00000248933, ENSP00000252455, ENSP00000252486, ENSP00000252491, ENSP00000255409, ENSP00000256637, ENSP00000259396, ENSP00000260197, ENSP00000261978, ENSP00000262551, ENSP00000263273, ENSP00000264005, ENSP00000264036, ENSP00000265023, ENSP00000265132, ENSP00000265983, ENSP00000266041, ENSP00000266546, ENSP00000273283, ENSP00000282499, ENSP00000289749, ENSP00000295718, ENSP00000295897, ENSP00000296130, ENSP00000297268, ENSP00000300175, ENSP00000301464, ENSP00000302621, ENSP00000304133, ENSP00000306099, ENSP00000306864, ENSP00000308541, ENSP00000308938, ENSP00000315130, ENSP00000318472, ENSP00000323929, ENSP00000324248, ENSP00000333593, ENSP00000333769, ENSP00000345179, ENSP00000345344, ENSP00000348068, ENSP00000348170, ENSP00000348888, ENSP00000350425, ENSP00000353007, ENSP00000355627, ENSP00000356969, ENSP00000358777, ENSP00000359074, ENSP00000360281, ENSP00000360519, ENSP00000366124, ENSP00000366513, ENSP00000368314, ENSP00000373363, ENSP00000376793, ENSP00000378517, ENSP00000385142, ENSP00000385834, ENSP00000386104, ENSP00000391826, ENSP00000393887, ENSP00000396688, ENSP00000406381, ENSP00000408632, ENSP00000414303, ENSP00000416561, ENSP00000452120 (continued)

CLSTN1, CLSTN3, CLU, COL1A2, CPE, CST3, CTSL1, ENSG00000224916, F2, FGB, GOLM1, GRIA4, HP, HPX, IGF2, IGFBP2, IGFBP5, IGFBP6, ITGA7, ITIH1, ITIH4, KNG1, L1CAM, LCAT, LRG1, LTBP2, MCAM, MGP, NBL1, NCAM1, NRCAM, NRXN1, NUCB1, NXPH4, OGN, ORM1, PENK, PIGR, PLG, PRKCSH, PTPRN, RBP4, SCG2, SCG5, SERPINA1, SERPINA3, SEZ6L, SORL1, SORT1, SPP1, SULF2, TF, TTR, VASN, VTN

Bioinformatics Applied to CSF Proteome 525

7

8

83

GO.0007565 Female pregnancy

GO.0050900 Leukocyte migration

GO.0065007 Biological regulation

0.00943 ENSP00000205948, ENSP00000216492, ENSP00000220478, ENSP00000221891, ENSP00000223642, ENSP00000226218, ENSP00000227667, ENSP00000228938, ENSP00000233809, ENSP00000233813, ENSP00000236850, ENSP00000245907, ENSP00000248933, ENSP00000249330, ENSP00000252455, ENSP00000252486, ENSP00000252491, ENSP00000255409, ENSP00000256637, ENSP00000259396, ENSP00000260197, ENSP00000261978, ENSP00000262551, ENSP00000263273, ENSP00000264005, ENSP00000264036,

0.00943 ENSP00000216492, ENSP00000297268, ENSP00000304133, ENSP00000308541, ENSP00000333769, ENSP00000348307, ENSP00000359074, ENSP00000378517

0.00913 ENSP00000233809, ENSP00000265132, ENSP00000331544, ENSP00000333769, ENSP00000355627, ENSP00000366124, ENSP00000378517

0.0089 ENSP00000252486, ENSP00000254722, ENSP00000263574, ENSP00000264613, ENSP00000265023, ENSP00000265983, ENSP00000296777, ENSP00000307549, ENSP00000308541, ENSP00000355627, ENSP00000358777, ENSP00000385834

Observed False gene discovery count rate Matching proteins in your network (IDs) 12

Pathway description

GO.0055082 Cellular chemical homeostasis

Pathway ID

Table 3 (continued)

A2M, AGT, AHSG, ALB, AMBP, APLP1, APOA1, APOA2, APOA4, APOC1, APOC3, APOD, APOE, APOH, ATP6AP1, B2M, BDNF, BSG, C3, C4A, C5, C8B, CDH13, CFB, CHGA, CHI3L1, CLEC3B, CLSTN1, CLSTN3, CLU, COL1A2, CP, CPE, CST3, CTSL1, ENSG00000224916, F2, F5, FGB, GOLM1, GRIA4, HP, IGF2, IGFBP2, IGFBP5, IGFBP6, ITGA7, ITIH1, ITIH4, KNG1, LCAT, LRG1, LTBP2, MCAM, MGP, NBL1, NCAM1,

BSG, CHGA, COL1A2, F2, L1CAM, SCG2, SIRPA, SPP1

AGT, AMBP, BSG, CST3, FBLN1, IGFBP2, SPP1

AGT, APLP2, APOE, ATP6AP1, CARTPT, CP, F2, HPX, KNG1, NPTX1, SERPINF1, TF

Matching proteins in your network (labels)

526 Fa´bio Trindade et al.

GO.0042127 Regulation of cell proliferation

22

0.00967 ENSP00000205948, ENSP00000233809, ENSP00000233813, ENSP00000252486, ENSP00000254722, ENSP00000262551, ENSP00000287641, ENSP00000301464, ENSP00000302621, ENSP00000304133, ENSP00000308541, ENSP00000308938,

ENSP00000264613, ENSP00000265023, ENSP00000265132, ENSP00000266041, ENSP00000266546, ENSP00000273283, ENSP00000282499, ENSP00000289749, ENSP00000295897, ENSP00000296130, ENSP00000297268, ENSP00000300175, ENSP00000301464, ENSP00000302621, ENSP00000304133, ENSP00000306099, ENSP00000306864, ENSP00000307549, ENSP00000308541, ENSP00000308938, ENSP00000315130, ENSP00000318472, ENSP00000323929, ENSP00000324248, ENSP00000333593, ENSP00000333769, ENSP00000345179, ENSP00000345344, ENSP00000348068, ENSP00000348170, ENSP00000348307, ENSP00000348888, ENSP00000350425, ENSP00000353007, ENSP00000355627, ENSP00000356771, ENSP00000356969, ENSP00000358777, ENSP00000360281, ENSP00000360519, ENSP00000366124, ENSP00000366513, ENSP00000368314, ENSP00000373363, ENSP00000376793, ENSP00000378517, ENSP00000385142, ENSP00000386104, ENSP00000391826, ENSP00000393887, ENSP00000396688, ENSP00000406381, ENSP00000408632, ENSP00000414303, ENSP00000416561, ENSP00000452120, ENSP00000452780

(continued)

AGT, APOD, APOE, APOH, BDNF, CDH13, CGREF1, CST3, F2, FBLN1, IGF2, IGFBP2, IGFBP5, IGFBP6, LRG1, OGN, PLG, PTGDS, RBP4, SCG2, SERPINF1, SST

NPTX1, NRCAM, NRXN1, NUCB1, NXPH4, OGN, ORM1, PENK, PIGR, PLG, PRKCSH, RBP4, SCG2, SCG3, SCG5, SERPINA1, SERPINA3, SEZ6L, SIRPA, SORL1, SORT1, SPP1, SULF2, VASN, VGF, VTN

Bioinformatics Applied to CSF Proteome 527

3

6

4 9

GO.0048261 Negative regulation of receptormediated endocytosis

GO.0009306 Protein secretion

GO.0090303 Positive regulation of wound healing

GO.0046486 Glycerolipid metabolic process

0.0103 ENSP00000205948, ENSP00000227667, ENSP00000236850, ENSP00000252486, ENSP00000252491, ENSP00000259486, ENSP00000264005, ENSP00000350425, ENSP00000356969

0.0102 ENSP00000205948, ENSP00000226218, ENSP00000308541, ENSP00000308938

0.0102 ENSP00000249330, ENSP00000255409, ENSP00000261978, ENSP00000295718, ENSP00000304133, ENSP00000355627

0.01 ENSP00000227667, ENSP00000252491, ENSP00000406381

0.00997 ENSP00000227667, ENSP00000236671, ENSP00000236850, ENSP00000252486, ENSP00000259486, ENSP00000262551, ENSP00000265132, ENSP00000297268, ENSP00000309148, ENSP00000345344, ENSP00000347665, ENSP00000350425, ENSP00000356969, ENSP00000359664, ENSP00000373477, ENSP00000406381

ENSP00000324025, ENSP00000331544, ENSP00000345179, ENSP00000355627, ENSP00000360519, ENSP00000360687, ENSP00000366124, ENSP00000391826, ENSP00000408632, ENSP00000414303

Observed False gene discovery count rate Matching proteins in your network (IDs)

16

Pathway description

GO.0044712 Single-organism catabolic process

Pathway ID

Table 3 (continued)

APOA1, APOA2, APOA4, APOC1, APOC3, APOE, APOH, ENPP2, LCAT

APOH, F2, PLG, VTN

AGT, CHI3L1, LTBP2, PTPRN, SCG2, VGF

APOC1, APOC3, ENSG00000224916

AMBP, APOA1, APOA2, APOA4, APOC3, APOE, COL18A1, COL1A2, CTBS, CTSD, CTSL1, ENPP2, ENSG00000224916, GPX3, KLK6, OGN

Matching proteins in your network (labels)

528 Fa´bio Trindade et al.

0.0113 ENSP00000226218, ENSP00000269141, ENSP00000307156, ENSP00000315130, ENSP00000355627, ENSP00000362924 0.0113 ENSP00000236850, ENSP00000252486, ENSP00000254722, ENSP00000323929, ENSP00000345179, ENSP00000356671

2

5

6

6

GO.0097118 Neuroligin clustering involved in postsynaptic membrane assembly

GO.0009914 Hormone transport

GO.0010001 Glial cell differentiation

GO.0031348 Negative regulation of defense response

0.0112 ENSP00000237014, ENSP00000249330, ENSP00000295718, ENSP00000296777, ENSP00000311905

0.0108 ENSP00000269141, ENSP00000385142

0.0108 ENSP00000252486, ENSP00000261908, ENSP00000263574, ENSP00000264613, ENSP00000265023, ENSP00000265983, ENSP00000308541, ENSP00000355627, ENSP00000358777, ENSP00000385834, ENSP00000452780

11

GO.0055065 Metal ion homeostasis

0.0108 ENSP00000252486, ENSP00000264036, ENSP00000269141, ENSP00000304133, ENSP00000345179, ENSP00000347665, ENSP00000368314, ENSP00000385142, ENSP00000408632, ENSP00000452120

0.0108 ENSP00000350425, ENSP00000406381

10

2

GO.0034371 Chylomicron remodeling

0.0108 ENSP00000350425, ENSP00000373477

GO.0048514 Blood vessel morphogenesis

2

GO.0006982 Response to lipid hydroperoxide

(continued)

A2M, APOA1, APOD, APOE, SERPINC1, SERPINF1

AGT, CDH2, CLU, GSN, LAMB2, VTN

CARTPT, LTBP4, PTPRN, TTR, VGF

CDH2, NRXN1

AGT, APLP2, APOE, ATP6AP1, B2M, CP, F2, HPX, KNG1, NEO1, TF

APOD, APOE, CDH13, CDH2, COL18A1, ITGA7, MCAM, NRCAM, NRXN1, SCG2

APOA4, ENSG00000224916

APOA4, GPX3

Bioinformatics Applied to CSF Proteome 529

Pathway description

3

15

15

GO.0050776 Regulation of immune response

GO.0044255 Cellular lipid metabolic process

15

0.0122 ENSP00000205948, ENSP00000227667, ENSP00000236850, ENSP00000245907, ENSP00000252486, ENSP00000252491, ENSP00000259486, ENSP00000264005, ENSP00000350425, ENSP00000355627, ENSP00000356969, ENSP00000360519, ENSP00000360687, ENSP00000378394, ENSP00000406381

0.0116 ENSP00000216361, ENSP00000223642, ENSP00000226218, ENSP00000236850, ENSP00000245907, ENSP00000265132, ENSP00000265983, ENSP00000315130, ENSP00000323929, ENSP00000345344, ENSP00000348888, ENSP00000356969, ENSP00000360281, ENSP00000396688, ENSP00000416561

0.0114 ENSP00000227667, ENSP00000252491, ENSP00000356969

0.0114 ENSP00000252486, ENSP00000261908, ENSP00000264025, ENSP00000284981, ENSP00000289749, ENSP00000305595, ENSP00000307156, ENSP00000307549, ENSP00000309096, ENSP00000318472, ENSP00000325660, ENSP00000345179, ENSP00000350425, ENSP00000359074, ENSP00000385142

Observed False gene discovery count rate Matching proteins in your network (IDs)

GO.0050995 Negative regulation of lipid catabolic process

GO.0048666 Neuron development

Pathway ID

Table 3 (continued)

AGT, APOA1, APOA2, APOA4, APOC1, APOC3, APOE, APOH, C3, ENPP2, ENSG00000224916, LCAT, PSAP, PTGDS, RBP4

A2M, AMBP, APOA1, APOA2, C3, C4A, C5, C8B, CFB, CLU, COCH, CTSL1, HPX, PIGR, VTN

APOA2, APOC1, APOC3

APOA4, APOD, APOE, APP, B3GNT1, B3GNT2, CNTN1, L1CAM, LAMB2, NBL1, NCAM1, NEO1, NPTX1, NRXN1, PVRL1

Matching proteins in your network (labels)

530 Fa´bio Trindade et al.

29

GO.0070887 Cellular response to chemical stimulus

0.0132 ENSP00000216492, ENSP00000221891, ENSP00000223642, ENSP00000233809, ENSP00000233813, ENSP00000254722, ENSP00000256637, ENSP00000261978, ENSP00000287641, ENSP00000295718, ENSP00000296130, ENSP00000304133, ENSP00000306099, ENSP00000306864, ENSP00000307549, ENSP00000311905, ENSP00000318472, ENSP00000345344, ENSP00000350425, ENSP00000358777, ENSP00000362924, ENSP00000366124, ENSP00000371554, ENSP00000378394, ENSP00000378517, ENSP00000391826, ENSP00000408632, ENSP00000414303, ENSP00000452780

0.0127 ENSP00000226218, ENSP00000252486, ENSP00000260197, ENSP00000261908, ENSP00000264025, ENSP00000289749, ENSP00000305595, ENSP00000307156, ENSP00000307549, ENSP00000308541, ENSP00000309096, ENSP00000309148, ENSP00000315130, ENSP00000318472, ENSP00000345179, ENSP00000350425, ENSP00000355627, ENSP00000359074, ENSP00000362924, ENSP00000378517, ENSP00000385142

0.0126 ENSP00000205948, ENSP00000226218, ENSP00000245907, ENSP00000308541, ENSP00000308938, ENSP00000355627

6

21

0.0122 ENSP00000236850, ENSP00000252486, ENSP00000261908, ENSP00000263574, ENSP00000264613, ENSP00000265023, ENSP00000265983, ENSP00000308541, ENSP00000355627, ENSP00000358777, ENSP00000385834, ENSP00000452780

12

GO.0022008 Neurogenesis

GO.1903036 Positive regulation of response to wounding

GO.0050801 Ion homeostasis

(continued)

APLP1, APOA4, ATP6AP1, B2M, BDNF, C5, CDH13, CHGA, CLEC3B, CST3, CTSL1, FGB, GSN, IGF2, IGFBP2, IGFBP5, LTBP2, LTBP4, NCAM1, NPTX1, PSAP, PTPRN, SCG2, SERPINF1, SOD3, SORT1, SPP1, SST, VASN

AGT, APOA4, APOD, APOE, B3GNT1, B3GNT2, CLU, F2, GSN, KLK6, L1CAM, LAMB2, NBL1, NCAM1, NEO1, NPTX1, NRXN1, PVRL1, SORL1, SPP1, VTN

AGT, APOH, C3, F2, PLG, VTN

AGT, APLP2, APOA1, APOE, ATP6AP1, B2M, CP, F2, HPX, KNG1, NEO1, TF

Bioinformatics Applied to CSF Proteome 531

0.0137 ENSP00000216361, ENSP00000223642, ENSP00000233809, ENSP00000245907, ENSP00000265983, ENSP00000315130, ENSP00000345344, ENSP00000345968, ENSP00000358777, ENSP00000360281, ENSP00000360519, ENSP00000391826, ENSP00000396688, ENSP00000416561, ENSP00000452780

15

8

5

20

GO.0002684 Positive regulation of immune system process

GO.0010721 Negative regulation of cell development

GO.0051651 Maintenance of location in cell

GO.0006955 Immune response

0.0143 ENSP00000216492, ENSP00000223642, ENSP00000226218, ENSP00000245907, ENSP00000252455, ENSP00000259486,

0.0142 ENSP00000252486, ENSP00000260197, ENSP00000295897, ENSP00000300289, ENSP00000362924

0.0137 ENSP00000252486, ENSP00000260197, ENSP00000284981, ENSP00000306864, ENSP00000308541, ENSP00000331544, ENSP00000378517, ENSP00000414303

0.0136 ENSP00000216361, ENSP00000216492, ENSP00000249330, ENSP00000261267, ENSP00000306099, ENSP00000345968, ENSP00000348170

7

GO.0042742 Defense response to bacterium

0.0136 ENSP00000205948, ENSP00000223642, ENSP00000233813, ENSP00000252486, ENSP00000254722, ENSP00000289749, ENSP00000345179

Observed False gene discovery count rate Matching proteins in your network (IDs) 7

Pathway description

GO.0030336 Negative regulation of cell migration

Pathway ID

Table 3 (continued)

APOA4, APP, B2M, C3, C4A, C5, C6, C8B, CFB, CHGA, CLU, CTSL1,

ALB, APOE, GSN, PDIA3, SORL1

APOE, APP, BDNF, F2, FBLN1, SORL1, SPP1, VASN

ATP6AP1, B2M, C3, C4A, C5, C8B, CFB, CLU, COCH, CTSL1, HPX, IGF2, IGFBP2, PGLYRP2, RBP4

CHGA, COCH, FGB, HP, LYZ, PGLYRP2, VGF

APOD, APOE, APOH, C5, IGFBP5, NBL1, SERPINF1

Matching proteins in your network (labels)

532 Fa´bio Trindade et al.

13

3

GO.0042730 Fibrinolysis

GO.0019725 Cellular homeostasis

0.0148 ENSP00000205948, ENSP00000252486, ENSP00000302621, ENSP00000304133, ENSP00000408632

5

GO.0001936 Regulation of endothelial cell proliferation

0.015 ENSP00000252486, ENSP00000254722, ENSP00000263574, ENSP00000264613, ENSP00000265023, ENSP00000265983, ENSP00000296777, ENSP00000300289, ENSP00000307549, ENSP00000308541, ENSP00000355627, ENSP00000358777, ENSP00000385834

0.0148 ENSP00000306099, ENSP00000308541, ENSP00000308938

0.0144 ENSP00000304133, ENSP00000306099, ENSP00000347665, ENSP00000362924

0.0144 ENSP00000223642, ENSP00000236850, ENSP00000260197, ENSP00000261908, ENSP00000263273, ENSP00000266546, ENSP00000269141, ENSP00000296777, ENSP00000300289, ENSP00000306099, ENSP00000331544, ENSP00000345179, ENSP00000356969, ENSP00000360519, ENSP00000362924, ENSP00000385142

4

16

GO.1904035 Regulation of epithelial cell apoptotic process

GO.0032880 Regulation of protein localization

ENSP00000263413, ENSP00000264025, ENSP00000284981, ENSP00000306099, ENSP00000315130, ENSP00000318472, ENSP00000345344, ENSP00000345968, ENSP00000348888, ENSP00000350425, ENSP00000360281, ENSP00000396688, ENSP00000416561, ENSP00000452780

(continued)

AGT, APLP2, APOE, ATP6AP1, CARTPT, CP, F2, HPX, KNG1, NPTX1, PDIA3, SERPINF1, TF

F2, FGB, PLG

APOE, APOH, CDH13, LRG1, SCG2

COL18A1, FGB, GSN, SCG2

APOA1, APOA2, APOD, C5, CARTPT, CDH2, CLSTN3, FBLN1, FGB, GSN, NEO1, NRXN1, NUCB1, PDIA3, RBP4, SORL1

ENPP2, FGB, NCAM1, PGLYRP2, PIGR, PRKCSH, PVRL1, VTN

Bioinformatics Applied to CSF Proteome 533

0.0161 ENSP00000252486, ENSP00000254722, ENSP00000269141, ENSP00000309148, ENSP00000325660, ENSP00000350364, ENSP00000368314, ENSP00000378517, ENSP00000414303 0.0164 ENSP00000236850, ENSP00000356969

0.0164 ENSP00000236850, ENSP00000345179

0.0164 ENSP00000305595, ENSP00000309096

0.0164 ENSP00000233809, ENSP00000233813, ENSP00000301464

0.0164 ENSP00000245907, ENSP00000308541, ENSP00000309148

2

2

2

3

3

GO.0002740 Negative regulation of cytokine secretion involved in immune response

GO.0014012 Peripheral nervous system axon regeneration

GO.0030311 Poly-Nacetyllactosamine biosynthetic process

GO.0043567 Regulation of insulin-like growth factor receptor signaling pathway

GO.0045745 Positive regulation of G-protein coupled receptor protein signaling pathway

Observed False gene discovery count rate Matching proteins in your network (IDs) 9

Pathway description

GO.0010975 Regulation of neuron projection development

Pathway ID

Table 3 (continued)

C3, F2, KLK6

IGFBP2, IGFBP5, IGFBP6

B3GNT1, B3GNT2

APOA1, APOD

APOA1, APOA2

APOE, BDNF, CDH2, CNTN1, KLK6, NEGR1, NRCAM, SERPINF1, SPP1

Matching proteins in your network (labels)

534 Fa´bio Trindade et al.

0.0164 ENSP00000245907, ENSP00000396688

2

8

GO.2000427 Positive regulation of apoptotic cell clearance

GO.0051604 Protein maturation

0.0169 ENSP00000205948, ENSP00000218230, ENSP00000260197, ENSP00000300175, ENSP00000306099, ENSP00000309148, ENSP00000355627, ENSP00000386104

0.0164 ENSP00000252486, ENSP00000315130

2

GO.1902004 Positive regulation of beta-amyloid formation

0.0164 ENSP00000216492, ENSP00000223642, ENSP00000236850, ENSP00000261908, ENSP00000265023, ENSP00000300175, ENSP00000306099, ENSP00000331544, ENSP00000355627, ENSP00000356969, ENSP00000358777, ENSP00000360519, ENSP00000385142 0.0164 ENSP00000252486, ENSP00000385142

13

GO.0051046 Regulation of secretion

0.0164 ENSP00000261908, ENSP00000264025, ENSP00000284981, ENSP00000289749, ENSP00000305595, ENSP00000307156, ENSP00000307549, ENSP00000309096, ENSP00000318472, ENSP00000325660, ENSP00000344786, ENSP00000359074, ENSP00000362924, ENSP00000385142

2

14

GO.0048858 Cell projection morphogenesis

0.0164 ENSP00000226218, ENSP00000233809, ENSP00000236850, ENSP00000306099, ENSP00000331544, ENSP00000359074, ENSP00000378517, ENSP00000391826, ENSP00000408632

GO.0097114 NMDA glutamate receptor clustering

9

GO.0045785 Positive regulation of cell adhesion

(continued)

AGT, APOH, CPE, FGB, KLK6, PCSK1N, SCG5, SORL1

C3, C4A

APOE, CLU

APOE, NRXN1

AGT, APOA1, APOA2, ATP6AP1, C5, CHGA, FBLN1, FGB, KNG1, NEO1, NRXN1, RBP4, SCG5

APP, B3GNT1, B3GNT2, CNTN1, GSN, L1CAM, LAMB2, NBL1, NCAM1, NEO1, NFASC, NPTX1, NRXN1, PVRL1

APOA1, CDH13, FBLN1, FGB, IGF2, IGFBP2, L1CAM, SPP1, VTN

Bioinformatics Applied to CSF Proteome 535

Pathway description

14

16

GO.0000902 Cell morphogenesis

0.0178 ENSP00000261908, ENSP00000264025, ENSP00000284981, ENSP00000289749, ENSP00000305595, ENSP00000307156,

0.0174 ENSP00000216492, ENSP00000223642, ENSP00000236850, ENSP00000269141, ENSP00000297268, ENSP00000304133, ENSP00000308541, ENSP00000333769, ENSP00000348307, ENSP00000359074, ENSP00000368314, ENSP00000378517, ENSP00000408632, ENSP00000452120

0.0173 ENSP00000236850, ENSP00000252486, ENSP00000254722, ENSP00000345179, ENSP00000356671

5

GO.0050728 Negative regulation of inflammatory response

GO.0016477 Cell migration

0.0173 ENSP00000264036, ENSP00000265023, ENSP00000353007, ENSP00000355627, ENSP00000362924

0.0172 ENSP00000226218, ENSP00000228938, ENSP00000233813, ENSP00000254722, ENSP00000255409, ENSP00000264025, ENSP00000296130, ENSP00000303550, ENSP00000307156, ENSP00000309148, ENSP00000333769, ENSP00000345179, ENSP00000347665, ENSP00000353007, ENSP00000355627, ENSP00000360519, ENSP00000362924, ENSP00000366124, ENSP00000378394, ENSP00000378517, ENSP00000414303, ENSP00000452120

5

22

Observed False gene discovery count rate Matching proteins in your network (IDs)

GO.0003014 Renal system process

GO.0009888 Tissue development

Pathway ID

Table 3 (continued)

APP, B3GNT1, B3GNT2, CLU, CNTN1, COL18A1, GSN, L1CAM,

APOA1, BSG, C5, CDH13, CDH2, CHGA, COL1A2, F2, ITGA7, L1CAM, NRCAM, SCG2, SIRPA, SPP1

APOA1, APOD, APOE, SERPINC1, SERPINF1

AGT, GSN, KNG1, MCAM, SULF2

AGT, APOD, BDNF, BSG, CHI3L1, CLEC3B, COL18A1, CST3, CYTL1, GSN, IGFBP5, ITGA7, KLK6, LAMB2, MGP, PSAP, PVRL1, RBP4, SERPINF1, SPP1, SULF2, VTN

Matching proteins in your network (labels)

536 Fa´bio Trindade et al.

6

3 9

10

5

4 8

GO.0051384 Response to glucocorticoid

GO.0002021 Response to dietary excess

GO.0006979 Response to oxidative stress

GO.0042493 Response to drug

GO.0060193 Positive regulation of lipase activity

GO.0048588 Developmental cell growth

GO.0048638 Regulation of developmental growth

0.0206 ENSP00000245907, ENSP00000252486, ENSP00000284981, ENSP00000355627, ENSP00000360519, ENSP00000368314, ENSP00000378517, ENSP00000414303

0.0206 ENSP00000284981, ENSP00000307156, ENSP00000355627, ENSP00000414303

0.0196 ENSP00000205948, ENSP00000236850, ENSP00000350425, ENSP00000355627, ENSP00000406381

0.0194 ENSP00000233809, ENSP00000236850, ENSP00000287641, ENSP00000300900, ENSP00000345179, ENSP00000347665, ENSP00000356969, ENSP00000366124, ENSP00000414303, ENSP00000452780

0.0194 ENSP00000252486, ENSP00000284981, ENSP00000295718, ENSP00000345179, ENSP00000348170, ENSP00000350425, ENSP00000366124, ENSP00000371554, ENSP00000373477

0.0183 ENSP00000218230, ENSP00000249330, ENSP00000252486

0.0178 ENSP00000233809, ENSP00000245907, ENSP00000254722, ENSP00000264005, ENSP00000356969, ENSP00000360687

ENSP00000307549, ENSP00000309096, ENSP00000315130, ENSP00000318472, ENSP00000325660, ENSP00000344786, ENSP00000347665, ENSP00000359074, ENSP00000362924, ENSP00000385142

(continued)

AGT, APOE, APP, BDNF, C3, NRCAM, RBP4, SPP1

AGT, APP, BDNF, LAMB2

AGT, APOA1, APOA4, APOH, ENSG00000224916

APOA1, APOA2, APOD, B2M, BDNF, CA4, COL18A1, CST3, IGFBP2, SST

APOA4, APOD, APOE, APP, CST3, GPX3, HP, PTPRN, SOD3

APOE, PCSK1N, VGF

APOA2, C3, IGFBP2, LCAT, PTGDS, SERPINF1

LAMB2, NBL1, NCAM1, NEO1, NFASC, NPTX1, NRXN1, PVRL1

Bioinformatics Applied to CSF Proteome 537

0.0213 ENSP00000226218, ENSP00000259486, ENSP00000263574, ENSP00000265983, ENSP00000284981, ENSP00000355627, ENSP00000391826

7

27

GO.0050730 Regulation of peptidyl-tyrosine phosphorylation

GO.0044281 Small molecule metabolic process

0.022 ENSP00000227667, ENSP00000236850, ENSP00000237014, ENSP00000245907, ENSP00000252486, ENSP00000252491, ENSP00000259486, ENSP00000260197, ENSP00000262551, ENSP00000263574, ENSP00000264005, ENSP00000284981, ENSP00000295897, ENSP00000303550, ENSP00000305595, ENSP00000306477,

0.0213 ENSP00000249330, ENSP00000252486, ENSP00000255409, ENSP00000260197, ENSP00000261978, ENSP00000269141, ENSP00000289749, ENSP00000295718, ENSP00000300175, ENSP00000300289, ENSP00000304133, ENSP00000315130, ENSP00000333769, ENSP00000344786, ENSP00000348888, ENSP00000355627, ENSP00000357106, ENSP00000358777, ENSP00000362924, ENSP00000368314, ENSP00000385142, ENSP00000385834, ENSP00000386104

23

GO.0008104 Protein localization

0.0209 ENSP00000265023, ENSP00000308541, ENSP00000355627, ENSP00000358777, ENSP00000378517, ENSP00000385834

Observed False gene discovery count rate Matching proteins in your network (IDs) 6

Pathway description

GO.0032846 Positive regulation of homeostatic process

Pathway ID

Table 3 (continued)

AGT, ALB, APLP2, APOA1, APOA2, APOA4, APOC1, APOC3, APOE, APP, B3GNT1, B3GNT2, BDNF, BSG, BTD, C3, CYTL1, ENPP2, ENSG00000224916, LCAT, OGN, PSAP, PTGDS, RBP4, SORL1, SULF2, TTR

AGT, APLP2, APP, ENPP2, HPX, IGF2, VTN

AGT, APOE, ATP6AP1, BSG, CADM3, CDH2, CHI3L1, CLU, CPE, GSN, LTBP2, NBL1, NFASC, NRCAM, NRXN1, PDIA3, PIGR, PTPRN, SCG2, SCG5, SORL1, TF, VGF

AGT, ATP6AP1, F2, KNG1, SPP1, TF

Matching proteins in your network (labels)

538 Fa´bio Trindade et al.

0.0227 ENSP00000252486, ENSP00000269141, ENSP00000385142

3

3

2 15

GO.0001941 Postsynaptic membrane organization

GO.0006656 Phosphatidylcholine biosynthetic process

GO.1900221 Regulation of betaamyloid clearance

GO.0051050 Positive regulation of transport

0.0231 ENSP00000223642, ENSP00000226218, ENSP00000245907, ENSP00000252486, ENSP00000260197, ENSP00000265023, ENSP00000296777, ENSP00000306099, ENSP00000308541, ENSP00000325660, ENSP00000355627, ENSP00000358777, ENSP00000360519, ENSP00000393887, ENSP00000396688

0.0227 ENSP00000252486, ENSP00000315130

0.0227 ENSP00000236850, ENSP00000264005, ENSP00000356969

0.0225 ENSP00000233813, ENSP00000236850, ENSP00000252486, ENSP00000260197, ENSP00000265132, ENSP00000269141, ENSP00000289749, ENSP00000296777, ENSP00000304133, ENSP00000306099, ENSP00000306864, ENSP00000315130, ENSP00000331544, ENSP00000345179, ENSP00000353007, ENSP00000355627, ENSP00000393887, ENSP00000414303

18

GO.0023057 Negative regulation of signaling

ENSP00000309096, ENSP00000333769, ENSP00000350425, ENSP00000353007, ENSP00000355627, ENSP00000356969, ENSP00000360519, ENSP00000360687, ENSP00000378394, ENSP00000406381, ENSP00000414303

(continued)

AGT, AHSG, APOE, ATP6AP1, C3, C4A, C5, CARTPT, CNTN1, F2, FGB, KNG1, RBP4, SORL1, VTN

APOE, CLU

APOA1, APOA2, LCAT

APOE, CDH2, NRXN1

AGT, AHSG, AMBP, APOA1, APOD, APOE, BDNF, CARTPT, CDH2, CLU, FBLN1, FGB, IGFBP5, NBL1, SCG2, SORL1, SULF2, VASN

Bioinformatics Applied to CSF Proteome 539

0.0235 ENSP00000216492, ENSP00000223642, ENSP00000236850, ENSP00000261908, ENSP00000300175, ENSP00000306099, ENSP00000331544, ENSP00000355627, ENSP00000356969, ENSP00000358777, ENSP00000360519, ENSP00000385142 0.0241 ENSP00000233813, ENSP00000236850, ENSP00000252486, ENSP00000260197, ENSP00000265132, ENSP00000269141, ENSP00000289749, ENSP00000296777, ENSP00000304133, ENSP00000306099, ENSP00000306864, ENSP00000315130, ENSP00000331544, ENSP00000345179, ENSP00000353007, ENSP00000355627, ENSP00000393887, ENSP00000414303

12

18

GO.1903530 Regulation of secretion by cell

GO.0010648 Negative regulation of cell communication

0.0235 ENSP00000248933, ENSP00000254722, ENSP00000263574, ENSP00000296777, ENSP00000324248, ENSP00000350364, ENSP00000355627, ENSP00000358715, ENSP00000366124, ENSP00000385142, ENSP00000414303

11

GO.0007610 Behavior

0.0234 ENSP00000264025, ENSP00000269141, ENSP00000306099, ENSP00000344786, ENSP00000350364, ENSP00000350425, ENSP00000359074, ENSP00000385142, ENSP00000452120, ENSP00000452780

Observed False gene discovery count rate Matching proteins in your network (IDs) 10

Pathway description

GO.0016337 Single organismal cell–cell adhesion

Pathway ID

Table 3 (continued)

AGT, AHSG, AMBP, APOA1, APOD, APOE, BDNF, CARTPT, CDH2, CLU, FBLN1, FGB, IGFBP5, NBL1, SCG2, SORL1, SULF2, VASN

AGT, APOA1, APOA2, ATP6AP1, C5, CHGA, FBLN1, FGB, NEO1, NRXN1, RBP4, SCG5

AGT, APLP2, BDNF, CARTPT, CST3, NEGR1, NRXN1, PENK, SERPINF1, SEZ6L, SORCS3

APOA4, B2M, CDH2, FGB, ITGA7, L1CAM, NEGR1, NFASC, NRXN1, PVRL1

Matching proteins in your network (labels)

540 Fa´bio Trindade et al.

0.0249 ENSP00000223642, ENSP00000245907, ENSP00000353007

0.0249 ENSP00000269141, ENSP00000385142, ENSP00000408632

3

3

9

22

GO.0010575 Positive regulation of vascular endothelial growth factor production

GO.0016339 Calcium-dependent cell–cell adhesion via plasma membrane cell adhesion molecules

GO.0002683 Negative regulation of immune system process

GO.1901564 Organonitrogen compound metabolic process

0.025 ENSP00000218230, ENSP00000236850, ENSP00000259486, ENSP00000262551, ENSP00000264005, ENSP00000265132, ENSP00000265983, ENSP00000266041,

0.025 ENSP00000223642, ENSP00000236850, ENSP00000265132, ENSP00000289749, ENSP00000296777, ENSP00000323929, ENSP00000345179, ENSP00000345968, ENSP00000356969

0.0248 ENSP00000233809, ENSP00000236850, ENSP00000245907, ENSP00000254722, ENSP00000264005, ENSP00000287641, ENSP00000295718, ENSP00000300900, ENSP00000356969, ENSP00000360519, ENSP00000360687, ENSP00000366124, ENSP00000378517, ENSP00000414303

14

GO.0033993 Response to lipid

0.0245 ENSP00000223642, ENSP00000236850, ENSP00000245907, ENSP00000259396, ENSP00000315130, ENSP00000331544, ENSP00000345179, ENSP00000345968, ENSP00000353007, ENSP00000355627, ENSP00000356969

11

GO.0001817 Regulation of cytokine production

(continued)

AGT, AMBP, APOA1, APOA2, APOA4, B3GNT1, B3GNT2, BDNF, BTD, CPE, CTBS, CYTL1, ENPP2, HPX,

A2M, AMBP, APOA1, APOA2, APOD, C5, CARTPT, NBL1, PGLYRP2

CDH13, CDH2, NRXN1

C3, C5, SULF2

APOA1, APOA2, BDNF, C3, CA4, CST3, IGFBP2, LCAT, PTGDS, PTPRN, RBP4, SERPINF1, SPP1, SST

AGT, APOA1, APOA2, APOD, C3, C5, CLU, FBLN1, ORM1, PGLYRP2, SULF2

Bioinformatics Applied to CSF Proteome 541

22

18

GO.0048468 Cell development

GO.0060341 Regulation of cellular localization

0.0267 ENSP00000216492, ENSP00000223642, ENSP00000236850, ENSP00000260197, ENSP00000261908, ENSP00000263273, ENSP00000269141, ENSP00000300175, ENSP00000306099, ENSP00000308541, ENSP00000331544, ENSP00000345179,

0.0259 ENSP00000252486, ENSP00000261908, ENSP00000264025, ENSP00000269141, ENSP00000284981, ENSP00000289749, ENSP00000305595, ENSP00000307156, ENSP00000307549, ENSP00000309096, ENSP00000315130, ENSP00000318472, ENSP00000325660, ENSP00000345179, ENSP00000347665, ENSP00000350425, ENSP00000353007, ENSP00000355627, ENSP00000359074, ENSP00000362924, ENSP00000366124, ENSP00000385142

0.0254 ENSP00000266546, ENSP00000366513, ENSP00000385142, ENSP00000414303

ENSP00000273283, ENSP00000300175, ENSP00000303550, ENSP00000305595, ENSP00000306100, ENSP00000306477, ENSP00000309096, ENSP00000350425, ENSP00000355627, ENSP00000356969, ENSP00000359664, ENSP00000378394, ENSP00000386104, ENSP00000414303

Observed False gene discovery count rate Matching proteins in your network (IDs)

4

Pathway description

GO.0051965 Positive regulation of synapse assembly

Pathway ID

Table 3 (continued)

AGT, APOA1, APOA2, APOD, ATP6AP1, C5, CDH2, CHGA, F2, FBLN1, FGB, GSN, NEO1, NRXN1, NUCB1, RBP4, SCG5, SORL1

AGT, APOA4, APOD, APOE, APP, B3GNT1, B3GNT2, CDH2, CLU, CNTN1, COL18A1, CST3, GSN, L1CAM, LAMB2, NBL1, NCAM1, NEO1, NPTX1, NRXN1, PVRL1, SULF2

BDNF, CLSTN1, CLSTN3, NRXN1

ITIH1, ITIH4, LCAT, OGN, PAM, PCSK1N, PSAP, SCG5

Matching proteins in your network (labels)

542 Fa´bio Trindade et al.

14

14

3 3

2

GO.0072359 Circulatory system development

GO.0051180 Vitamin transport

GO.0018146 Keratan sulfate biosynthetic process

GO.0018158 Protein oxidation

9

GO.0040017 Positive regulation of locomotion

GO.0072358 Cardiovascular system development

4

GO.0030072 Peptide hormone secretion

0.0298 ENSP00000236850, ENSP00000356969

0.0298 ENSP00000262551, ENSP00000305595, ENSP00000309096

0.0272 ENSP00000226355, ENSP00000236850, ENSP00000360519

0.0268 ENSP00000252486, ENSP00000264036, ENSP00000269141, ENSP00000297268, ENSP00000304133, ENSP00000345179, ENSP00000347665, ENSP00000355627, ENSP00000360519, ENSP00000368314, ENSP00000385142, ENSP00000386104, ENSP00000408632, ENSP00000452120

0.0268 ENSP00000252486, ENSP00000264036, ENSP00000269141, ENSP00000297268, ENSP00000304133, ENSP00000345179, ENSP00000347665, ENSP00000355627, ENSP00000360519, ENSP00000368314, ENSP00000385142, ENSP00000386104, ENSP00000408632, ENSP00000452120

0.0268 ENSP00000226218, ENSP00000259486, ENSP00000264036, ENSP00000304133, ENSP00000331544, ENSP00000347665, ENSP00000355627, ENSP00000385834, ENSP00000408632

0.0268 ENSP00000249330, ENSP00000295718, ENSP00000296777, ENSP00000311905

ENSP00000355627, ENSP00000356969, ENSP00000358777, ENSP00000360519, ENSP00000362924, ENSP00000385142

APOA1, APOA2

B3GNT1, B3GNT2, OGN

AFM, APOA1, RBP4

(continued)

AGT, APOD, APOE, CDH13, CDH2, COL18A1, COL1A2, CPE, ITGA7, MCAM, NRCAM, NRXN1, RBP4, SCG2

AGT, APOD, APOE, CDH13, CDH2, COL18A1, COL1A2, CPE, ITGA7, MCAM, NRCAM, NRXN1, RBP4, SCG2

AGT, CDH13, COL18A1, ENPP2, FBLN1, MCAM, SCG2, TF, VTN

CARTPT, LTBP4, PTPRN, VGF

Bioinformatics Applied to CSF Proteome 543

0.0298 ENSP00000344786, ENSP00000368314

0.0298 ENSP00000248933, ENSP00000385142 0.0298 ENSP00000252491, ENSP00000406381

0.0298 ENSP00000252486, ENSP00000269141

0.0315 ENSP00000236850, ENSP00000252486, ENSP00000264005, ENSP00000350425, ENSP00000356969 0.0315 ENSP00000236850, ENSP00000265132, ENSP00000323929, ENSP00000345968, ENSP00000356969

2

2 2

2

8

5

5

GO.0045162 Clustering of voltage-gated sodium channels

GO.0060074 Synapse maturation

GO.0060696 Regulation of phospholipid catabolic process

GO.1901631 Positive regulation of presynaptic membrane organization

GO.0001101 Response to acid chemical

GO.0046165 Alcohol biosynthetic process

GO.0050777 Negative regulation of immune response

0.0315 ENSP00000233809, ENSP00000254722, ENSP00000287641, ENSP00000297268, ENSP00000347665, ENSP00000360519, ENSP00000362924, ENSP00000414303

0.0298 ENSP00000344786, ENSP00000368314, ENSP00000452120

Observed False gene discovery count rate Matching proteins in your network (IDs) 3

Pathway description

GO.0034113 Heterotypic cell–cell adhesion

Pathway ID

Table 3 (continued)

A2M, AMBP, APOA1, APOA2, PGLYRP2

APOA1, APOA2, APOA4, APOE, LCAT

BDNF, COL18A1, COL1A2, GSN, IGFBP2, RBP4, SERPINF1, SST

APOE, CDH2

APOC1, ENSG00000224916

NRXN1, SEZ6L

NFASC, NRCAM

ITGA7, NFASC, NRCAM

Matching proteins in your network (labels)

544 Fa´bio Trindade et al.

0.034 ENSP00000226218, ENSP00000269141, ENSP00000307156, ENSP00000315130, ENSP00000355627, ENSP00000362924 0.034 ENSP00000216492, ENSP00000236850, ENSP00000261908, ENSP00000296777, ENSP00000331544, ENSP00000356969 0.0345 ENSP00000216492, ENSP00000223642, ENSP00000236850, ENSP00000269141, ENSP00000297268, ENSP00000304133, ENSP00000308541, ENSP00000333769, ENSP00000348307, ENSP00000359074, ENSP00000368314, ENSP00000378517, ENSP00000408632, ENSP00000452120 0.0352 ENSP00000223642, ENSP00000289749, ENSP00000345179

6

5

3

6

6

14

3

GO.0042593 Glucose homeostasis

GO.0045766 Positive regulation of angiogenesis

GO.0048662 Negative regulation of smooth muscle cell proliferation

GO.0042063 Gliogenesis

GO.1903531 Negative regulation of secretion by cell

GO.0051674 Localization of cell

GO.0002686 Negative regulation of leukocyte migration

0.0324 ENSP00000233813, ENSP00000262551, ENSP00000345179

0.0324 ENSP00000223642, ENSP00000245907, ENSP00000255409, ENSP00000263413, ENSP00000302621

0.0324 ENSP00000233813, ENSP00000249330, ENSP00000254722, ENSP00000296777, ENSP00000307549, ENSP00000360519

0.0324 ENSP00000252486, ENSP00000265023, ENSP00000355627

3

GO.0042311 Vasodilation

0.0323 ENSP00000223642, ENSP00000236850, ENSP00000261908, ENSP00000296777, ENSP00000306099, ENSP00000331544, ENSP00000356969, ENSP00000360519, ENSP00000385142

9

GO.0050708 Regulation of protein secretion

APOD, C5, NBL1

(continued)

APOA1, BSG, C5, CDH13, CDH2, CHGA, COL1A2, F2, ITGA7, L1CAM, NRCAM, SCG2, SIRPA, SPP1

APOA1, APOA2, CARTPT, CHGA, FBLN1, NEO1

AGT, CDH2, CLU, GSN, LAMB2, VTN

APOD, IGFBP5, OGN

C3, C5, C6, CHI3L1, LRG1

CARTPT, IGFBP5, NPTX1, RBP4, SERPINF1, VGF

AGT, APOE, KNG1

APOA1, APOA2, C5, CARTPT, FBLN1, FGB, NEO1, NRXN1, RBP4

Bioinformatics Applied to CSF Proteome 545

0.0375 ENSP00000269141, ENSP00000385142

2

3

GO.0097104 Postsynaptic membrane assembly

GO.0001937 Negative regulation of endothelial cell proliferation

0.038 ENSP00000205948, ENSP00000252486, ENSP00000304133

0.0375 ENSP00000264613, ENSP00000265983, ENSP00000358777, ENSP00000385834

4

GO.0006879 Cellular iron ion homeostasis

0.0375 ENSP00000264036, ENSP00000353007

2

0.0374 ENSP00000216361, ENSP00000223642, ENSP00000245907, ENSP00000265983, ENSP00000315130, ENSP00000345344, ENSP00000345968, ENSP00000360281, ENSP00000396688, ENSP00000416561, ENSP00000452780

11

GO.0050778 Positive regulation of immune response

GO.0003094 Glomerular filtration

0.0368 ENSP00000223642, ENSP00000236850, ENSP00000260197, ENSP00000261908, ENSP00000263273, ENSP00000296777, ENSP00000300289, ENSP00000306099, ENSP00000331544, ENSP00000345179, ENSP00000356969, ENSP00000360519, ENSP00000385142

13

GO.0051223 Regulation of protein transport

0.0358 ENSP00000295897, ENSP00000303550, ENSP00000348888, ENSP00000360519, ENSP00000376793, ENSP00000385834

Observed False gene discovery count rate Matching proteins in your network (IDs) 6

Pathway description

GO.0001894 Tissue homeostasis

Pathway ID

Table 3 (continued)

APOE, APOH, SCG2

CDH2, NRXN1

ATP6AP1, CP, HPX, TF

MCAM, SULF2

B2M, C3, C4A, C5, C8B, CFB, CLU, COCH, CTSL1, HPX, PGLYRP2

APOA1, APOA2, APOD, C5, CARTPT, FBLN1, FGB, NEO1, NRXN1, NUCB1, PDIA3, RBP4, SORL1

ALB, CYTL1, PIGR, RBP4, SERPINA3, TF

Matching proteins in your network (labels)

546 Fa´bio Trindade et al.

0.0386 ENSP00000252486, ENSP00000263574, ENSP00000264613, ENSP00000265023, ENSP00000265983, ENSP00000308541, ENSP00000355627, ENSP00000358777, ENSP00000385834 0.0394 ENSP00000226218, ENSP00000227667, ENSP00000252491, ENSP00000406381

0.041 ENSP00000205948, ENSP00000218230, ENSP00000300175, ENSP00000306099, ENSP00000309148, ENSP00000355627, ENSP00000386104 0.0411 ENSP00000252486, ENSP00000254722, ENSP00000269141, ENSP00000284981, ENSP00000289749, ENSP00000309148,

9

4

7

10

GO.0006875 Cellular metal ion homeostasis

GO.0048259 Regulation of receptormediated endocytosis

GO.0016485 Protein processing

GO.0045664 Regulation of neuron differentiation

0.038 ENSP00000228938, ENSP00000233813, ENSP00000252486, ENSP00000256637, ENSP00000260197, ENSP00000264025, ENSP00000289749, ENSP00000302621, ENSP00000303550, ENSP00000305595, ENSP00000307156, ENSP00000307549, ENSP00000308541, ENSP00000309096, ENSP00000309148, ENSP00000315130, ENSP00000318472, ENSP00000323929, ENSP00000345179, ENSP00000347665, ENSP00000350425, ENSP00000353007, ENSP00000355110, ENSP00000355627, ENSP00000359074, ENSP00000362924, ENSP00000366124, ENSP00000378394, ENSP00000378517, ENSP00000385142, ENSP00000385834, ENSP00000391826, ENSP00000452120, ENSP00000452780

34

GO.0030154 Cell differentiation

(continued)

APOE, APP, CDH2, CNTN1, KLK6, NBL1, NEGR1, NRCAM, SERPINF1, SPP1

AGT, APOH, CPE, FGB, KLK6, PCSK1N, SCG5

APOC1, APOC3, ENSG00000224916, VTN

AGT, APLP2, APOE, ATP6AP1, CP, F2, HPX, KNG1, TF

A2M, AGT, APOA4, APOD, APOE, B2M, B3GNT1, B3GNT2, CLU, COL18A1, CST3, CYTL1, F2, GSN, IGF2, IGFBP5, ITGA7, KLK6, L1CAM, LAMB2, LRG1, MGP, NBL1, NCAM1, NPTX1, NRXN1, PSAP, PVRL1, SMOC1, SORL1, SORT1, SPP1, SULF2, TF

Bioinformatics Applied to CSF Proteome 547

9

6

GO.0051146 Striated muscle cell differentiation

0.0426 ENSP00000233813, ENSP00000256637, ENSP00000261908, ENSP00000269141, ENSP00000355627, ENSP00000391826

0.0425 ENSP00000248933, ENSP00000254722, ENSP00000284981, ENSP00000296777, ENSP00000324248, ENSP00000358715, ENSP00000366124, ENSP00000385142, ENSP00000414303

0.0425 ENSP00000205948, ENSP00000223642, ENSP00000226218, ENSP00000236850, ENSP00000252486, ENSP00000252491, ENSP00000255409, ENSP00000260197, ENSP00000284981, ENSP00000296777, ENSP00000303550, ENSP00000315130, ENSP00000331544, ENSP00000350425, ENSP00000355627, ENSP00000356969, ENSP00000362924, ENSP00000378394, ENSP00000385834, ENSP00000391826, ENSP00000406381, ENSP00000414303

22

GO.0044093 Positive regulation of molecular function

GO.0044708 Single-organism behavior

0.0413 ENSP00000284981, ENSP00000368314, ENSP00000414303

ENSP00000325660, ENSP00000350364, ENSP00000368314, ENSP00000378517

Observed False gene discovery count rate Matching proteins in your network (IDs)

3

Pathway description

GO.0008038 Neuron recognition

Pathway ID

Table 3 (continued)

AGT, CDH2, IGF2, IGFBP5, NEO1, SORT1

APP, BDNF, CARTPT, CST3, NRXN1, PENK, SERPINF1, SEZ6L, SORCS3

AGT, APOA1, APOA2, APOA4, APOC1, APOE, APOH, APP, BDNF, C5, CARTPT, CHI3L1, CLU, CYTL1, ENSG00000224916, FBLN1, GSN, IGF2, PSAP, SORL1, TF, VTN

APP, BDNF, NRCAM

Matching proteins in your network (labels)

548 Fa´bio Trindade et al.

0.0433 ENSP00000262551, ENSP00000266041, ENSP00000273283, ENSP00000305595, ENSP00000309096 0.0442 ENSP00000205948, ENSP00000252486, ENSP00000254722 0.0442 ENSP00000284981, ENSP00000344786, ENSP00000368314 0.0444 ENSP00000233813, ENSP00000252486, ENSP00000260197, ENSP00000269141, ENSP00000284981, ENSP00000296777, ENSP00000306864, ENSP00000308541, ENSP00000331544, ENSP00000345968, ENSP00000378517, ENSP00000414303

7

5

3

3 12

GO.0016042 Lipid catabolic process

GO.1903510 Mucopolysaccharide metabolic process

GO.0010596 Negative regulation of endothelial cell migration

GO.0042551 Neuron maturation

GO.0045596 Negative regulation of cell differentiation

0.0433 ENSP00000227667, ENSP00000236850, ENSP00000252486, ENSP00000259486, ENSP00000350425, ENSP00000356969, ENSP00000406381

0.043 ENSP00000264025, ENSP00000269141, ENSP00000357106, ENSP00000408632

4

GO.0034332 Adherens junction organization

0.0429 ENSP00000233813, ENSP00000236850, ENSP00000252486, ENSP00000260197, ENSP00000261908, ENSP00000284981, ENSP00000289749, ENSP00000296777, ENSP00000306099, ENSP00000306864, ENSP00000308541, ENSP00000311905, ENSP00000325660, ENSP00000331544, ENSP00000345968, ENSP00000355110, ENSP00000355627, ENSP00000358777, ENSP00000368314, ENSP00000378517

20

GO.0045595 Regulation of cell differentiation

(continued)

APOE, APP, BDNF, CARTPT, CDH2, F2, FBLN1, IGFBP5, PGLYRP2, SORL1, SPP1, VASN

APP, NFASC, NRCAM

APOE, APOH, SERPINF1

B3GNT1, B3GNT2, ITIH1, ITIH4, OGN

APOA1, APOA2, APOA4, APOC3, APOE, ENPP2, ENSG00000224916

CADM3, CDH13, CDH2, PVRL1

AGT, APOA1, APOE, APP, ATP6AP1, CARTPT, CNTN1, F2, FBLN1, FGB, IGFBP5, LTBP4, NBL1, NEO1, NRCAM, PGLYRP2, SMOC1, SORL1, SPP1, VASN

Bioinformatics Applied to CSF Proteome 549

Pathway description

16

2

7

8

4

4

GO.0034379 Very-low-density lipoprotein particle assembly

GO.0001503 Ossification

GO.0048589 Developmental growth

GO.0042058 Regulation of epidermal growth factor receptor signaling pathway

GO.0061387 Regulation of extent of cell growth

4

0.0485 ENSP00000252486, ENSP00000368314, ENSP00000378517, ENSP00000414303

0.0485 ENSP00000263574, ENSP00000284981, ENSP00000355627, ENSP00000408632

0.048 ENSP00000233813, ENSP00000307156, ENSP00000309148, ENSP00000345179, ENSP00000355627, ENSP00000362924, ENSP00000378394, ENSP00000414303

0.0474 ENSP00000228938, ENSP00000233813, ENSP00000256637, ENSP00000296130, ENSP00000378517, ENSP00000391826, ENSP00000393887

0.0456 ENSP00000227667, ENSP00000252491

0.0453 ENSP00000233813, ENSP00000236850, ENSP00000252486, ENSP00000260197, ENSP00000265132, ENSP00000269141, ENSP00000289749, ENSP00000304133, ENSP00000306099, ENSP00000306864, ENSP00000315130, ENSP00000331544, ENSP00000345179, ENSP00000353007, ENSP00000355627, ENSP00000393887

0.0445 ENSP00000307156, ENSP00000315130, ENSP00000355627, ENSP00000362924

Observed False gene discovery count rate Matching proteins in your network (IDs)

GO.0009968 Negative regulation of signal transduction

GO.0021782 Glial cell development

Pathway ID

Table 3 (continued)

APOE, BDNF, NRCAM, SPP1

AGT, APLP2, APP, CDH13

AGT, APOD, BDNF, GSN, IGFBP5, KLK6, LAMB2, PSAP

AHSG, CLEC3B, IGF2, IGFBP5, MGP, SORT1, SPP1

APOC1, APOC3

AGT, AHSG, AMBP, APOA1, APOD, APOE, CDH2, CLU, FBLN1, FGB, IGFBP5, NBL1, SCG2, SORL1, SULF2, VASN

AGT, CLU, GSN, LAMB2

Matching proteins in your network (labels)

550 Fa´bio Trindade et al.

Bioinformatics Applied to CSF Proteome

551

3. Adjusting the number of genes per cluster as well as the percentage of genes is important to manage the network’s biological output. This is especially important when dealing with big datasets, as to represent only biological processes covered by a higher number or percentage of genes. Otherwise, a crowded and meaningless network is obtained. This should be done on a trial-and-error basis. 4. The standard test is the two-side hypergeometric that filters significant over- and underrepresented GO terms. 5. “Global network” provides a general information regarding the biological processes, whereas the “Detailed network” shows more specific and informative ontologies underlying aspects of the studied gene product. Network specificity may also be tuned by defining specific GO ranges, from 0 (very general) to 20 (very detailed). 6. Maximum resolution is achieved at 600 dpi. 7. The selection of gene “▶” in the Change column and “◉” in the Disease column defines the type of interaction with focus on the disease, the target node. With these definitions the gene name can be visualized, and the multiple associations to the conditions in analysis can be depicted. 8. To study associations between proteins and diseases, no directionality is required, i.e., the statements “condition A is associated to protein B” and “protein B is associated to condition A” mean the same. Still, when working with protein-protein interactions, e.g., when studying signaling pathways, directionality is sometimes required. In other words, in a reaction cascade A ! B ! C (where A activates B and this activates C) is not biologically equal to C ! B ! A. In such cases, make sure to “Treat the network as directed.” 9. Nodes with high degree are known as hub proteins, which are essential in the network structure and functionality point. The power law of node degree distribution and betweenness centrality distribution must be performed and implies proteins with high centrality values computed by Network Analyzer. 10. Only 100 proteins with no more than 4000 amino acids might be analyzed at once. So, more than one FASTA file will be necessary for the analysis of all proteins listed in Table 1. 11. You might want to write your email in the page created by SecretomeP to receive the data retrieved from SecretomeP analysis. 12. The Human Protein Atlas maps all the human proteins in cells, tissues, and organs using integration of various omics technologies, including antibody-based imaging, mass spectrometrybased proteomics, transcriptomics, and systems biology. The

552

Fa´bio Trindade et al.

Tissue Atlas shows the distribution of the proteins across all major tissues and organs in the human body. By crossing the data retrieved from SecretomeP with information from Human Protein Atlas, more insights about proteins’ origin might be obtained. For instance, for any given protein, one can investigate whether it is brain-specific, enriched in the brain, or if it is ubiquitous. 13. The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database is commonly used to predict proteinprotein interaction information, allowing the rapid identification of the most prevalent biological processes in a set of proteome data. Similar to ClueGO, STRING offers the possibility to perform gene ontology enrichment analysis. 14. Version 10.5 of STRING covers more than 2000 completely sequenced organisms.

Acknowledgments Acknowledgments are due to the University of Aveiro, University of Porto, and FCT/MCT for the financial support for the QOPNA Research Unit (FCT UID/QUI/00062/2019), iBiMED (UID/BIM/04501/2013 and POCI-01-0145-FEDER-007628), and Unidade de Investigac¸˜ao Cardiovascular (UID/IC/00051/ 2013) through national founds and, where applicable, co-financed by the FEDER, within the PT2020 Partnership Agreement, and to the Portuguese NMR Network. F.T. and R.V. are supported by individual fellowship grants (SFRH/BD/111633/ 2015 and IF/00286/2015, respectively). References 1. Jeromin A, Bowser R (2017) Biomarkers in neurodegenerative diseases. Adv Neurobiol 15:491–528. https://doi.org/10.1007/9783-319-57193-5_20 2. Bastos P, Ferreira R, Manadas B, Moreira PI, Vitorino R (2017) Insights into the human brain proteome: disclosing the biological meaning of protein networks in cerebrospinal fluid. Crit Rev Clin Lab Sci 54(3):185–204. https:// doi.org/10.1080/10408363.2017.1299682 ˜ ez Galindo A, Dayon L 3. Macron C, Lane L, Nu´n (2018) Deep dive on the proteome of human cerebrospinal fluid: a valuable data resource for biomarker discovery and missing protein identification. J Proteome Res 17(12):4113–4126. https://doi.org/10.1021/acs.jproteome. 8b00300

4. Kroksveen AC, Guldbrandsen A, Vaudel M, Lereim RR, Barsnes H, Myhr K-M, Torkildsen Ø, Berven FS (2017) In-depth cerebrospinal fluid quantitative proteome and deglycoproteome analysis: presenting a comprehensive picture of pathways and processes affected by multiple sclerosis. J Proteome Res 16 (1):179–194. https://doi.org/10.1021/acs. jproteome.6b00659 5. Guldbrandsen A, Farag Y, Kroksveen AC, Oveland E, Lereim RR, Opsahl JA, Myhr K-M, Berven FS, Barsnes H (2017) CSF-PR 2.0: an interactive literature guide to quantitative cerebrospinal fluid mass spectrometry data from neurodegenerative disorders. Mol Cell Proteomics 16(2):300–309. https://doi.org/ 10.1074/mcp.O116.064477

Bioinformatics Applied to CSF Proteome 6. Havugimana PC, Hu P, Emili A (2017) Protein complexes, big data, machine learning and integrative proteomics: lessons learned over a decade of systematic analysis of protein interaction networks. Expert Rev Proteomics 14 (10):845–855. https://doi.org/10.1080/ 14789450.2017.1374179 7. Calderon-Gonzalez KG, Hernandez-Monge J, Herrera-Aguirre ME, Luna-Arias JP (2016) Bioinformatics tools for proteomics data interpretation. Adv Exp Med Biol 919:281–341. https://doi.org/10.1007/978-3-319-414485_16 8. Bindea G, Galon J, Mlecnik B (2013) CluePedia cytoscape plugin: pathway insights using integrated experimental and in silico data. Bioinformatics (Oxford, England) 29 (5):661–663. https://doi.org/10.1093/bioin formatics/btt019 9. Bendtsen JD, Jensen LJ, Blom N, von Heijne G, Brunak S (2004) Feature-based prediction of non-classical and leaderless protein secretion. Protein Eng Des Sel 17(4):349–356. https://doi.org/10.1093/protein/gzh037 10. von Mering C, Huynen M, Jaeggi D, Schmidt S, Bork P, Snel B (2003) STRING: a database of predicted functional associations between proteins. Nucleic Acids Res 31 (1):258–261 11. Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P, Doerks T, Stark M, Muller J, Bork P, Jensen LJ, von Mering C (2011) The STRING database in

553

2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res 39(Database):D561–D568. https:// doi.org/10.1093/nar/gkq973 12. Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A, Fridman W-H, Page`s F, Trajanoski Z, Galon J (2009) ClueGO: a cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics (Oxford, England) 25(8):1091–1093. https://doi.org/10.1093/bioinformatics/ btp101 13. Petersen TN, Brunak S, von Heijne G, Nielsen H (2011) SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Methods 8:785. https://doi.org/10.1038/ nmeth.1701 14. Vagnoni A, Perkinton MS, Gray EH, Francis PT, Noble W, Miller CCJ (2012) Calsyntenin1 mediates axonal transport of the amyloid precursor protein and regulates Aβ production. Hum Mol Genet 21(13):2845–2854. https:// doi.org/10.1093/hmg/dds109 15. Brinkmalm A, Brinkmalm G, Honer WG, Fro¨lich L, Hausner L, Minthon L, Hansson O, Wallin A, Zetterberg H, ¨ hrfelt A (2014) SNAP-25 is a Blennow K, O promising novel cerebrospinal fluid biomarker for synapse degeneration in Alzheimer’s disease. Mol Neurodegener 9:53–53. https:// doi.org/10.1186/1750-1326-9-53

INDEX A Affinity proteomics........................................................ 304 Alzheimer’s disease (AD)........................... 9, 28, 82, 156, 194, 234, 256, 292, 343–352, 378, 395, 396, 420 Antibodies ........................ 8, 9, 41, 42, 56–58, 122, 136, 196, 222, 233–243, 248, 276, 280, 292, 304–307, 309–316, 551 Antigen arrays.............................304, 305, 307, 309, 315 Antigens............................ 247–252, 304–307, 309, 310, 312–316, 344 Aptamers.................................................. 36, 41, 221–230 Autoantibodies ..................................................... 303–317 Autoantibody profiling ..............304, 306, 308, 309, 312 Automation ............................................................ 40, 214

B Biobanking ............................................................ v, 27–45 Bioinformatics ............. v, 9, 14, 113, 116, 267, 393–552 Biomarkers............................... v, 5, 27, 70, 82, 120, 130, 156, 171, 194, 221, 248, 260, 274, 321, 337, 344, 353, 377, 393 Blood contamination ........................... 14, 28–30, 33, 35, 65, 228, 285, 367

C Candidate markers ........................................................ 385 Cerebrospinal fluid (CSF) ........................v, 5, 28, 52, 61, 70, 82, 111, 120, 130, 156, 171, 194, 221, 234, 248, 256, 274, 292, 303, 321, 337, 344, 353, 365, 377, 393 Chick embryos ................................................... 53, 54, 56 ClueGO ...............................................394, 395, 423, 552 CSF proteomes.......................... v, 14, 28, 33, 36, 40, 43, 62, 130, 147, 152, 158, 196, 222, 274, 365–375 Cyanine dyes.................................................................. 229 Cystatin C ................................................ 40, 43, 291–301 Cytokines ........................................................14, 233–243

D Data dependent acquisition (DDA)................. 62, 69–71, 170, 174, 175, 322, 323 Data independent acquisition (DIA) ......................61–65, 70, 170

Data visualization ................................................. 331, 333 Deep proteome ............................................................. 130 Depletion ..................................... 62, 120–124, 126, 127, 131–133, 136–138, 145–147, 149, 152, 164, 171, 200–202, 205, 214, 275 Derivatization ...............................................265, 339–341 Development ..................... v, 5, 9–15, 27–45, 51, 53, 58, 119, 156, 235, 248, 255, 274, 357 Difference gel electrophoresis (DIGE).................. 72, 74, 84, 95, 107

E Embryonic cerebrospinal fluid (eCSF) ....................51–59 Endogenous peptides.................................................... 112 Endopeptidase digestion .............................................. 293 Exosomes.............................................................. 343–352

G Genomics ...............................................14, 130, 366, 369 Glucosamine-6-phosphate deaminase 2 (GNPDA2) ........................................................ 283 Guidelines..................................................... v, 27–45, 386

H High pH reversed-phase.......................70, 71, 73, 74, 76 Human brain .........................................v, 3–14, 275, 277, 284, 354 Human CSF ...............14, 123, 145–147, 185, 193–216, 354, 359, 365, 372

I Immune complex ................................................. 247–252 Immuno-affinity depletion ........................ 131, 137, 138, 145–147, 152, 275 Inflammation ....................... 42, 194, 234, 248, 291, 292 In-gel digest ..............................71, 86, 98, 99, 172, 173, 175, 284 In-solution digest.......................................................... 104 Internal standard (IS) ..........................92, 145, 172, 175, 180, 184, 185, 295, 297, 323–326, 328, 329, 332, 338, 341, 354–356 Isobaric labeling ..................... v, 134, 140, 149, 155–166

Enrique Santamarı´a and Joaquı´n Ferna´ndez-Irigoyen (eds.), Cerebrospinal Fluid (CSF) Proteomics: Methods and Protocols, Methods in Molecular Biology, vol. 2044, https://doi.org/10.1007/978-1-4939-9706-0, © Springer Science+Business Media, LLC, part of Springer Nature 2019

555

CEREBROSPINAL FLUID (CSF) PROTEOMICS: METHODS

PROTOCOLS

556 Index

AND

Isobaric tag for relative and absolute quantitation (iTRAQ)................................6, 81–109, 156–158, 160, 161, 163–165, 284

Neurology..................................................... v, 14, 42, 293 Neuronal growth regulator 1 (NEGR1) ..................... 283 N-glycans ................... 255–258, 260–262, 264–267, 269

L

O

Labeling .............84, 88, 89, 91, 92, 105–107, 109, 120, 124–126, 131, 140, 143, 148, 149, 156, 157, 160–162, 164, 228, 307, 310, 313 Large-scale ...........................................119, 145, 360, 378 Lipidomics ............................................................ 353–358 Liquid chromatography (LC)............................... 33, 112, 130, 173, 177, 195, 196, 199, 206, 293, 324, 356 Liquid chromatography-mass spectrometry (LC-MS) .......................... 61–63, 65, 86–88, 111, 113, 115–117, 173, 176, 185, 196, 199, 206, 208, 209, 293, 324, 327, 329, 330, 333, 334 Liquid chromatography tandem mass-spectrometry (LC-MS/MS) .............................14, 33, 125, 136, 142, 149, 156, 162, 176, 195, 199, 200, 206, 209, 211, 215, 275, 278, 284, 291–301, 323, 324, 327–333 Lysosomal storage diseases........................................... 256

18

M Mass spectrometric analysis ..............................70, 86, 88, 102, 107 Mass spectrometry (MS)............................ 4–6, 9, 12, 14, 119, 121, 122, 125, 126, 131, 132, 138, 143, 146–148, 150, 153, 155–166, 174, 177–183, 186, 187, 193, 222, 230, 234, 235, 247–252, 274, 277, 278, 285, 291–301, 321–335, 338, 353–360, 377, 378, 385, 393 Matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) ............162, 255–271 Mesencephalic explants...................................... 54, 56, 58 Metabolomics ............185, 321–335, 337–341, 353, 360 Methanol precipitation ..................... 164, 196, 215, 278, 325, 339 MicroRNAs (miRNAs) ........................................ 343–352 Missing proteins (MPs) ...................................9, 130, 152 Multiple sclerosis (MS) .......................120, 234, 354, 378 Multiplex ....................................... 29, 36, 157, 304, 306, 308, 313, 316

N Nano-liquid chromatography tandem mass spectrometry (nano-LC-MS/MS) ........................ 248, 249, 251 Neurobiology .................................................................... 9 Neurodegenerative/neurological disorders/diseases .v, 4, 7, 9, 14, 28, 29, 35, 45, 61, 82, 111, 130, 152, 155, 194, 233, 234, 255–271, 292, 303, 321, 337, 344, 354, 365, 394–422 Neurogenesis ................................................................. 531

O labeling.......................................................... 119–127 Olfactory bulb (OB) ..................8, 9, 274, 275, 278, 282

P Papain ................................................................... 248–251 Parkinson’s disease (PD) ............................. 9, 28, 29, 61, 82, 156, 194, 234, 273, 274, 281, 283, 321, 338, 378, 386, 395, 396, 419, 420 Peptide fractionation.............................70, 71, 73, 74, 76 Peptide fragments ......................111, 120, 157, 194, 294 Peptide isolation................................................... 112–115 Peptidome ..................................................................... 112 Polymorphisms.............................................................. 365 Posttranscriptional modification .................................. 299 Potential biomarkers ......................v, 120, 127, 156, 378, 386, 393, 421 Pre-analytical confounding factors.................... 28, 29, 43 Pre-fractionation .........................70, 126, 130, 131, 136, 145–147, 149, 157, 158, 195, 198–206, 214 Protein arrays................................................ v, 29, 36, 305 Protein interaction ......................................................8, 52 Protein profiling .........................304, 306, 308, 311, 316 Protein-protein interactions ....................... 431, 551, 552 Proteoforms................................... 9, 194, 197, 207, 209, 211, 213–215 Proteomics.........................v, 4, 28, 61, 69, 82, 111, 119, 130, 156, 169, 193, 221, 234, 248, 262, 274, 303, 323, 338, 377, 393

Q Quantitation ................................. 6, 9, 10, 149, 291–301 Quantitative trait locus (QTL)..................................... 370

R Rats ..................................... 8, 9, 14, 177, 179–181, 185, 186, 221–230 Reaction quenching ..................... 83, 106, 140, 205, 310 Reproducibility.................................. 9, 62, 82, 145, 149, 156, 185, 186, 196

S Sample fractionation ................69–76, 88, 106, 107, 149 Sample preparation ............................ v, 5, 10, 61–65, 82, 83, 86, 88, 90, 102, 123, 136, 145, 146, 149, 161, 166, 173, 176, 195, 198–206, 214, 215, 277, 284, 285, 295, 297, 322, 324, 327, 339, 340 SDB-RPS StageTips ............................................. 112–115

CEREBROSPINAL FLUID (CSF) PROTEOMICS: METHODS Search Tool for the Retrieval of Interacting Genes/ Proteins (STRING)........................ 394, 395, 431, 432, 552 SecretomeP................................................. 394, 395, 422, 423, 431, 551 Sequential window acquisition of all theoretical fragmention spectra acquisition-Mass Spectrometry (SWATH-MS).........................174, 177–183, 186, 187, 321–335 Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE).............................84, 86, 87, 93, 94, 98, 102, 104, 165, 203, 275 SOMAscan™ ............................................... 29, 36, 41–44, 221–230 Spectral library.................................................... 62, 69–76

AND

PROTOCOLS Index 557

Stability ...................................28, 29, 35, 36, 40–43, 267 SWATH-MS relative quantification ............171, 321–335

T Tandem mass tags (TMT) ..............................6, 131, 134, 136, 140, 142, 143, 148, 149, 151, 284, 385 Top-down ...............................................v, 9, 74, 193–216

U Untargeted metabolomics ................................... 321–335

V Variable Q1 windows ..........................179–181, 186, 187