Spanish Human Proteome Project: Dissection of Chromosome 16V. Segura,† J. A. Medina-Aunon,‡ E. Guruceaga,† S. I. Gharbi,‡ C. Gonzalez-Tejedo,‡
M. M. Sanchez del Pino,§ F. Canals,∥ M. Fuentes,⊥ J. Ignacio Casal,# S. Martínez-Bartolome,‡ F. Elortza,¶
J. M. Mato,¶ J. M. Arizmendi,□ J. Abian,● E. Oliveira,△ C. Gil,▼ F. Vivanco,○ F. Blanco,& J. P. Albar,‡,$
and F. J. Corrales†,$,#
†ProteoRed-ISCIII, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain‡ProteoRed-ISCIII, Centro Nacional de Biotecnología - CSIC, Madrid, Spain§ProteoRed-ISCIII. Biochemistry Department, University of Valencia, Valencia, Spain∥ProteoRed-ISCIII, Proteomics Laboratory and Medical Oncology Research Program, Vall d’Hebron Institute of Oncology,Vall d’Hebron University Hospital Research Institute, Barcelona, Spain
⊥ProteoRed-ISCIII, Centro de Investigacion del Cancer/IBMCC (USAL/CSIC), Departamento de Medicina and Servicio General deCitometría, University of Salamanca, IBSAL, 37007 Salamanca, Spain
#ProteoRed-ISCIII, Functional Proteomics, Department of Cellular and Molecular Medicine, Centro de Investigaciones Biologicas(CIB-CSIC), Madrid, Spain¶ProteoRed-ISCIII, Proteomics Platform, CIC bioGUNE, CIBERehd, ProteoRed, Bizkaia Technology Park, Derio, Spain□ProteoRed-ISCIII, Department of Biochemistry and Molecular Biology, University of the Basque Country, UPV/EHU, Spain●ProteoRed-ISCIII, CSIC/UAB Proteomics Laboratory, Instituto de Investigaciones Biomedicas de Barcelona-CSIC/IDIBAPS,Bellaterra, Spain
△ProteoRed-ISCIII, Plataforma de Proteomica, Parc Cientifıc de Barcelona, Universitat de Barcelona, Barcelona, Spain▼ProteoRed-ISCIII, Departamento de Microbiología II, Facultad de Farmacia, Universidad Complutense de Madrid, Madrid, Spain○ProteoRed-ISCIII, Department of Immunology, IIS-Fundacion Jimenez Diaz, Madrid, Spain&ProteoRed-ISCIII, Osteoarticular and Aging Research Lab, Proteomics Unit, ProteoRed/ISCIII, Rheumatology Division,INIBIC−CHU A Coruna, As Xubias 84, 15006 A Coruna, Spain
*S Supporting Information
ABSTRACT: The Chromosome 16 Consortium forms part of the HumanProteome Project that aims to develop an entire map of the proteins encoded bythe human genome following a chromosome-centric strategy (C-HPP) to makeprogress in the understanding of human biology in health and disease (B/D-HPP).A Spanish consortium of 16 laboratories was organized into five working groups:Protein/Antibody microarrays, protein expression and Peptide Standard, S/MRM,Protein Sequencing, Bioinformatics and Clinical healthcare, and Biobanking. Theproject is conceived on a multicenter configuration, assuming the standards andintegration procedures already available in ProteoRed-ISCIII, which isencompassed within HUPO initiatives. The products of the 870 protein codinggenes in chromosome 16 were analyzed in Jurkat T lymphocyte cells, MCF-7epithelial cells, and the CCD18 fibroblast cell line as it is theoretically expected thatmost chromosome 16 protein coding genes are expressed in at least one of these.The transcriptome and proteome of these cell lines was studied using gene expression microarray and shotgun proteomicsapproaches, indicating an ample coverage of chromosome 16. With regard to the B/D section, the main research areas have beenadopted and a biobanking initiative has been designed to optimize methods for sample collection, management, and storageunder normalized conditions and to define QC standards. The general strategy of the Chr-16 HPP and the current state of thedifferent initiatives are discussed.
KEYWORDS: Human Proteome Project, chromosome 16, proteomics, transcriptomics
■ INTRODUCTION
The sequencing of the human genome1,2 has provided the first
level of complexity of human biology. Despite this undoubted
Special Issue: Chromosome-centric Human Proteome Project
Received: September 24, 2012Published: December 12, 2012
success, there is still a vast territory to be explored before acomplete understanding of our own biology is achieved. Proteinsare the tools used by the cells to perform most of their processes:gene expression, splicing events, metabolic reactions, signalingpathways, cell shape, differentiation. Proteins therefore decidethe cell fate. The knowledge of their specific functions, regulatorymechanisms, networks of interaction, abundance, isoformpatterns, thus constitutes an essential issue for the understandingof human physiology in health and disease. Unravelling thehuman proteome is a project that, despite the obvious a priorianalogies with the sequencing of the human genome, representsa task that is far more challenging and whose boundaries stillremain to be defined. As Amos Bairoch stated in a recentinterview, the proteome is a fractal system; the deeper you go, themore you have to do.3 The proteomic universe generated fromthe information encrypted in the genome is massive as the20300 protein-coding human genes comprise up to an estimated1million different protein species derived fromDNA recombina-tion, alternative splicing of mRNAs, processing events and amyriad of covalent modifications of many types that display adynamic behavior resulting in different profiles with time, loca-tion, association with other proteins and biological, pathological andpharmacological perturbations. These observations must also bemade considering that the human body comprises about 230 celltypes4 with different gene expression profiles and, therefore, dif-ferent proteomes. Moreover, the vast heterogeneity, wide dynamicrange and different ionization efficiencies of proteins, among otherreasons, restrict detection and quantification capacity on a large-scale omics level even using state of the art technology. Accordingly,it has been estimated that close to 35% of predicted proteins haveyet to be observed reliably by mass spectrometry.5
The Human Proteome Organization (HUPO) has coordi-nated the efforts of the international community promotingseveral initiatives6−11 to describe the human proteome in asystematic manner during the last twelve years (http://www.hupo.org). In September 2010, during the annual HUPOconference in Sydney, Australia, the Human Proteome Project(HPP) was officially launched.4 The HPP is designed to map theentire human proteome in a systematic effort using currentlyavailable and emerging techniques. With the aim of providing acomprehensive map of human proteins in their biologicalcontext, the HPP rests on three pillars: shotgun and targetedmass spectrometry (MS), polyclonal and monoclonal antibodies(Ab), and integrated knowledge base (KB: Ensembl, neXtProt(gold), GPMDB (green), and Peptide Atlas (1%FDR at proteinlevel)). The project is organized according to a chromosome-centric strategy (C-HPP) where scientific groups from differentnationalities agree to characterize the proteome of a selectedchromosome following the guidelines of the internationalconsortium and an open access policy.12,13 All 24 chromosomeshave already been adopted by as many teams from 21 differentcountries. Knowledge and technical resources generated withinthe C-HPP initiative are expected to contribute to progress in theunderstanding and treatment of diseases by the integration andcoordination of specific research initiatives in the Biology andDisease (B/D) − HPP initiative.12
Chromosome 16 (Chr16) has been adopted by a Spanishconsortium belonging to the Spanish Proteomics Institute,ProteoRed-ISCIII. These teams combine scientists withrecognized research and clinical skills which ensures an efficientC-HPP and B/D-HPP joint development and integration. Themain general objectives of the Spanish HPP (Chr-16 SpHPP)project are: (1) development of analytical methods based on MS
and protein capture reagents to detect and quantify chromosome16 proteins, with special interest in those proteins with weakexperimental evidence; (2) definition of changes in the levels ofspecific proteins that may explain pathogenic mechanisms andprovide novel diagnostic, prognostic and therapeutic approachesto improving the management of patients afflicted with diseasesthat represent a social burden worldwide and particularly inSpain. These include cancer, obesity, neurologic, rheumatoid,cardiovascular, and infectious disorders; (3) to create acomputational environment to analyze, integrate and sharedata in line with the C-HPP consortium; and (4) to promote thedevelopment of prototypic devices as precursors of preclinicaland clinical instruments, by collaborative efforts with industrialstakeholders.In the present manuscript we describe the progress in Chr16
investigation. Annotations and data analysis of Chr16 genes,selection of cell lines to cover the chromosome 16 proteome,transcriptomic and proteomic shotgun characterization of theselected cell lines, progress in informatics resources, B/D designand working plan for SRM/MRM analysis are discussed.
■ MATERIALS AND METHODS
Chromosome 16 Annotation
The information about genes, transcripts and proteins and therelationship between accession numbers of most of the databasesused for description of Chr16 have been extracted from Ensembldatabase (http://www.ensembl.org) release 68. We used thebiomaRt package of Bioconductor to query the database, and Rfunctions for processing and graphical representations of theresults. The retrieved information from Ensembl includes datafrom eGenetics, GNF Gene Expression Atlas, OMIM andUniprot public repositories. In addition, we considered theGPMDB (01−10−2012 release), HPA (version 10.0) and ourprotein expression vector database as additional sources ofbiological and experimental knowledge.Microarray Hybridization and Data Analysis
Experiments were performed in triplicate with three selected celllines, MCF7, CCD18 and Jurkat. Cells were harvested in TRIzolReagent (Invitrogen) and the RNA was extracted according tothe manufacturer’s instructions. As a last step of the extractionprocedure, the RNA was purified with the RNeasy Mini-kit(Qiagen, Hilden, Germany). Prior to cDNA synthesis, RNAintegrity from each sample was confirmed on Agilent RNANanoLabChips (Agilent Technologies). The sense cDNA wasprepared from 300 ng of total RNA using the Ambion WTExpression Kit. The sense strand cDNA was then fragmentedand biotinylated with the Affymetrix GeneChip WT TerminalLabeling Kit (PN 900671). Labeled sense cDNA was hybridizedto the Affymetrix HuGene 1.0 ST array according to themanufacturer’s protocols and using the GeneChipHybridization,Wash and Stain Kit. Genechips were scanned with the AffymetrixGeneChip Scanner 3000. Both background correction andnormalization were performed using the RMA (Robust Multi-chip Average) algorithm.14,15 R/Bioconductor14 was used forpreprocessing and statistical analysis. After normalization, anexpression threshold for each cell line was calculated to eliminatelow intensity probe sets that can be considered technical noise.First, probe sets were sorted by increasing expression value. Foreach probe set a t test was performed to evaluate the differentialexpression between this probe set and the median value of theprobe sets with lower expression levels. The p-values obtainedwere corrected for multiple hypothesis testing using FDR
Journal of Proteome Research Article
dx.doi.org/10.1021/pr300898u | J. Proteome Res. 2013, 12, 112−122113
method16 and FDR > 0.95 (background signal) was consideredas the criterion to calculate the corresponding intensitythreshold. Microarray data files were submitted to the GEO(Gene Expression Omnibus) database and are available underaccession number GSE40168.
Protein Sample Preparation
Cell growth was carried out between three laboratories from theChr-16 HPP consortium, following standard growth conditions.At exponential growth, cells were collected and lysed in aCHAPS/Urea lysis buffer (7 M urea, 2 M thiourea, 4% CHAPS,protease and phosphatase inhibitors). 100 μg of each cell linewere digested in-solution. Briefly, cell lysates were precipitatedwith methanol/chloroform, as described elsewhere,17 and precipi-tated proteins were resuspended in denaturing and reducingbuffer (8MUrea, 25mM ammonium bicarbonate, 10mMDTT)for 1 h at 37 °C and cysteine residues were alkylated with 50 mMiodoacetamide for 45 min in the dark. Samples were diluted with25 mM ammonium bicarbonate to a final concentration of 2 MUrea and Proteomics grade Trypsin (Sigma Aldrich) was thenadded at a 1:50 w:w ratio (protein:enzyme) and the reaction wasleft for 18 h at 37 °C. Samples were dried in a vacuum centrifuge(SpeedVac, Savant, Inc.) and stored at −20 °C until off-linepeptide fractionation.
Basic pH-RP-HPLC
Tryptic peptides were fractionated off-line on a 2.1 × 100 mmC18, 5 μm XBridge column (BEH Technology, Waters),connected to a Smartline HPLC system (KNAUER). SolventA was 10 mM NH4OH, pH 9.4 and solvent B was 10 mMNH4OH, pH 9.4, 80% Methanol. Peptides were separated at aflow rate of 150 μL/min following isocratic conditions on a lineargradient from 2 to 25% solvent B in 15 min; from 25 to 70% B in40 min and 70 to 100% B in 5 min and maintained at 100% B for5 min; 15 min equilibration was allowed (98% A:2% B) prior tonext sample injection. Blanks were run between samples to avoidcarry over. Thirty fractions were collected during the 80 min totalchromatographic run. To maximize orthogonal separation,fractions were mixed throughout the gradient (e.g., FR 1 withFR 16, FR 2 with FR 17, and so on or FR 1 with FR 11 and FR 21,FR 2 with FR 12 and FR 22, etc.). After pooling, we were left with10 or 15 fractions respectively that were dried in a speed-vacdryer and stored at −20 °C until LC−MS/MS acquisition.
Liquid Chromatography and Mass Spectrometry Analysis
The second dimension of the 2D-LC tandem MS analysis wasperformed using a nano liquid chromatography system (EksigentTechnologies nanoLC Ultra 1D plus, AB SCIEX, Foster City,CA) coupled to a TripleTOF 5600 mass spectrometer (ABSCIEX, Foster City, CA) with a nanospray ionization source.The analytical column was a silica-based reversed phase columnC18ChromXP 75 μm× 15 cm, 3 μmparticle size and 120 Å poresize (Eksigent Technologies, AB SCIEX, Foster City, CA). Thetrap column was a C18 ChromXP, 3 μm particle diameter, 120 Åpore size (Eksigent Technologies, AB SCIEX, Foster City, CA),switched online with the analytical column. The loading pumpdelivered a solution of 0.1% formic acid in water at 2 μL/min.The nanopump provided a flow-rate of 300 nL/min and wasoperated under gradient elution conditions, using 0.1% formicacid in water as mobile phase A, and 0.1% formic acid inacetonitrile as mobile phase B. Gradient elution was performedaccording to the following scheme: isocratic conditions of 98%A: 2% B for 1 min, a linear increase to 30% B in 110 min, a linearincrease to 40% B in 10 min, a linear increase to 90% B in 5 min,
isocratic conditions of 90% B for 5 min and return to initialconditions in 2 min. Generally, 1/5th of the sample was run bynanoLC−MS, injection volume was 5 μL.Data acquisition was performed with a TripleTOF 5600
System (AB SCIEX, Foster City, CA). Data was acquired usingan ionspray voltage floating (ISVF) 2800 V, curtain gas (CUR)20, interface heater temperature (IHT) 150, ion source gas1 (GS1) 30, declustering potential (DP) 85 V. All data wereacquired using an information-dependent acquisition (IDA)mode with Analyst TF 1.5 software (AB SCIEX, Foster City,CA). For IDA parameters, 0.25 s MS survey scan in the massrange of 350−1250 m/z was followed by 50 MS/MS scans of50 ms in the mass range of 100−1500m/z (total cycle time: 2.8 s).Switching criteria were set to ions greater than mass to chargeratio (m/z) 350 and smaller than m/z 1250 with charge state of2−5 and an abundance threshold of more than 90 counts (cps).Former target ions were excluded for 20 s. The IDA rollingcollision energy (CE) parameters script was used for automati-cally controlling the CE.Data Analysis
MS and MS/MS data obtained for each sample fraction wereprocessed using Analyst TF 1.5.1 Software (AB SCIEX, FosterCity, CA). Raw data were translated to mascot general file (mgf)format and searched against the UniProtKB/Swiss-Prot humandatabase (release 2012_06, June 13) that contains 36852proteins and their corresponding reversed sequences, using anin-house Mascot Server v. 2.4 (Matrix Science, London, U.K.).Search parameters were set as follows: carbamidomethyl cysteineas fixedmodification, oxidizedmethionines and acetylation of thepeptide amino termini as variable ones. Peptide mass tolerancewas set to 50 ppm, both in MS and MS/MS mode, and 2 missedcleavages were allowed. Typically, an accuracy of ±10 ppm wasfound both for MS and MS/MS spectra. False Discovery Rates(FDR ≤ 1% at the protein level) for protein identification weremanually calculated.18
For standard reporting and comparison analysis, first, MS mgffiles and their corresponding Mascot results, formatted asmzIdentML were submitted to the ProteoRed MIAPE webrepository19 to create both the MIAPE MS and MSI reports asthe ProteoRed MIAPE web toolkit20 usage guide recommends.Afterward, the MIAPEs were compared through the MIAPEExtractor Software v. 2.92 (http://www.proteored.org/miape-extractor).Finally, to adhere to the C-HPP reporting guidelines, MIAPE
MS and MSI compliant reports were translated to PRIDE XMLand, together with the raw MS file for each sample fraction, weresubmitted to the ProteomeXchange repository (http://www.proteomexchange.org/) following the ProteomeXchange sub-mission guidelines.
■ RESULTS AND DISCUSSION
Structure of the Consortium and Main Goals
The Spanish Chr16 Consortium form part of the global initiativeChromosome-based Human Proteome Project (C-HPP) thataims to develop an entire map of the proteins encoded followinga chromosome-centric strategy to make progress in the under-standing of human biology in health and disease (B/D-HPP).After several preliminary meetings, the kick-off workshop washeld in Madrid, Spain, on the second of April 2012. Adopting thegeneral rules established for HPP,12 the Spanish initiative isconstructed on a multidisciplinary basis with 16 scientific groupsorganized into five working sections namely, Protein/Antibody
Journal of Proteome Research Article
dx.doi.org/10.1021/pr300898u | J. Proteome Res. 2013, 12, 112−122114
microarrays, protein expression and Peptide Standard, S/MRM,Protein Sequencing, Bioinformatics and Clinical healthcare andBiobanking.. The C-HPP initiative is based on the ProteoRed-ISCIII platform, a proteomics consortium integrating 21proteomics laboratories with more than 7 years of experiencein the coordination of multicenter activities,21 sharing state of theart technology, data standardization,20,22 bioinformatics19 andresearch.23−27 Our experience in these areas paves the way for theefficient progress of the Chr-16 HPP in the short term. The B/D-Chr-16 HPP has been initially launched focusing on theparticular areas of expertise of the participating laboratoriesalthough the initiative has been conceived as being open in twodifferent directions. First, to collaboration with other chromo-some initiatives in order to cope with the biological complexity ofhuman diseases and also to the involvement of the researchactivities of scientists interested in joining the project and whocan benefit from the knowledge and tools generated by the HPPcommunity.The main structure and principal end points of the Chr-16
HPP initiative are summarized in Figure 1. The specific aims
include: (1) Annotation and data analysis of Chr16 to generate atheoretical definition of the Chr16 proteome according to
Ensembl, UniprotKB and GPMDB, as well as a tissue geneexpression pattern of Chr16 genes using the eGenetics and GNFAtlas (these databases are from Ensembl Web site). Thisinformation will allow selection of tissues/cell types for optimumChr16 coverage and may prove its value mostly in the case ofthose gene products with no or faint experimental observations.(2) Development of SRM/MRM assays for the quantification ofChr16 proteins. The setting up of quantitative SRM/MRMassays is the core of the project and requires attention to all geneproducts of Chr16 and their variants, especially for those proteinswhich as yet remain elusive. Protein expression systems arealready on hand to produce light and heavy versions of some ofthe challenging proteins, largely enhancing our capacity for thedevelopment of efficient SRM assays with optimal results inbiological matrices. A library with more than 11000 expressionvectors is currently available (M. Fuentes, personal communi-cation, www.dnasu.org, www.plasmid.med.harvard.edu) thatmight be also of interest for other chromosome initiatives. Asrecommended in the HPP guidelines, detection and quantifica-tion of Chr16 proteins will be initially performed in 3 differentcell lines that were selected according to the results provided bythe bioinformatics analysis performed on the existing annota-tions and public data related to the Chr16. (3) Quantification ofChr16 proteins in control and disease samples. The definition ofquantitative alterations of specific proteins related to pathogenicprocesses is of major interest and hence a priority for this project.Although special attention will be dedicated to Chr16 proteins,the study will not be restricted solely to these proteins as most ofthe diseases also involve genes and/or gene products located indifferent chromosomes. Of special interest are proteinsmeasurable in biofluids as the consortium is aware of therelevance of developing diagnostic tests based on noninvasiveprocedures. These research avenues will be explored in closecoordination with national clinical and biomedical researchinitiatives, including the Spanish National Biobank Network,CIBER, RETICS and other networked entities from the CarlosIII National Health Institute (ISCIII) and MINECO as well asother International Biobank Platforms (BBMRI). (4) Develop-ment of antibody-based protein measurement procedures. Thisobjective will be pursued in close collaboration with the HumanProtein Atlas initiative that currently accounts for more than14079 genes with protein expression profiles based on 17,298specific antibodies (HPA V10.0). The combination of theseresources with our expertise on protein arrays28 ensures the rapidprogress of this objective and the capacity for the construction ofprototypic devices for preliminary verification of potentialbiomarkers. Moreover, capture reagents will complement massspectrometry data relative to protein abundance and tissue andsubcellular distribution. (5) Definition of standardized protocols(SOPs), data formats and bioinformatics pipelines for datasubmission to the public repositories. This is a central issue thatmust be carefully considered to enable data sharing under acommon quality criterion. Samples must be collected, stored andanalyzed following a common protocol that ensures thetraceability and the reliable comparison of the results fromdifferent laboratories. Moreover, data require complex statisticalanalysis, integration with other sources of biological information(transcriptomics, metabolomics, etc.) and generation of curateddata sets in standardized formats that allow deposition in publicrepositories. It is worth mentioning that ProteoRed-ISCIII hasbeen deeply involved in the development of methods for datastandardization within the Proteomics Standards Initiative ofHUPO (HUPO-PSI) with significant contributions to the
Figure 1. Executive diagram of Chr16 SpHPP. The SpHPP is governedby a steering committee and rests on three main pillars, analyticalresources, bioinformatics and research, under the supervision of theSpanish Ministry for Innovation and Competitivity (MINECO) and theNational Institute of Health Carlos III (ISCIII). Protein mapping andthe quantitative methods developed will promote a better understandingof human biology in health and disease, leading to the discovery of novelbiomarkers and therapeutic targets and to the development of deviceswith clinical applications for the benefit of patients. In addition ofMINECO and ISCIII, Biotech, Pharmaceutical companies and otherstakeholders have already enrolled in the project. The researchinstitutions currently participating in the SpHPP are shown: CIB (Centrode Investigaciones Biologicas), CicBiogune, CIMA (Centro para laInvestigacionMedica Aplicada), CNB (CentroNacional de Biotecnologia),FJD (Fundacion Jimenez Diaz), INIBIC (Instituto de InvestigacionBiomedica A Coruna), PCB (Parc Cientific de Barcelona), UAB(Universidad Autonoma de Barcelona), UCM (Universidad Complutensede Madrid), USAL (Universidad de Salamanca), UV (Universidad deValencia), VHIO (Vall D’Hebron Instituto de Oncologia).
Journal of Proteome Research Article
dx.doi.org/10.1021/pr300898u | J. Proteome Res. 2013, 12, 112−122115
definition of the Minimum Definition About a ProteomicsExperiment (MIAPE) documents29 and the PSI-XML formats(http://www.psidev.info/). (6) Implementation of prototypicdevices as precursors of preclinical and clinical instruments. Theidentification of biomarker panels for stratification of diseasedpopulations will bring clear benefits for society and for thebiotech sector in particular. In a preliminary preclinical phase,taking advantage of our own experience, we propose the designof testing devices based on antibodies (ELISA, arrays) for a proofof concept; however, wide population screening and follow-upwill require the participation of industrial partners with thecapacity to transform the developed prototypes into commercialproducts with applications for patient care.
Bioinformatics
An essential element within the Chr-16 SpHPP consortium isbioinformatics. The combination of well-known and traditionalissues regarding protein and peptide identification, statisticalmeaning or data analysis with current trends in datasynchronization and public deposition are pivotal to establishinga comprehensive environment where the resulting data can beshared and studied in depth by the proteomics community. Toaccomplish this goal, a cross-sectional workgroup has beenfounded recruiting experienced computational scientists fromacross the different laboratories that currently make up theChr-16 SpHPP consortium. This working group started from theHPP bioinformatics initiative launched in Beijing May 2012 andsince then maintains telecommeetings every fifteen days to trackprogress in the different lines of work.In addition to the leading focus of this bioinformatics team, the
use of proteomics data standards for analyzing, storing andreporting the experimental data, the following items summarizethe main activities developed so far. (1) Central database ofproteomics associated with Chromosome 16. In accordance with theC-HPP’s aims, the information regarding the detection andcharacterization of the proteins of Chr16 has been deposited in acentralized database. A safe connection was provided to theparticipating laboratories to both check the reported data andintroduce new experimental evidence of this subset of proteins.The stored data will be also linked to the C-HPP’s referenceresources such as NextProt (http://www.nextprot.org), Pepti-deAtlas (http://www.peptideatlas.org/) or GPMDB (http://www.thegpm.org) through the existing Application Program-ming Interfaces (APIs) or public URLs. This database alsocontains protein data and annotations regarding biologicalfunction, cellular location, metabolic and cellular pathways ordisease relationships. (2) MIAPE-compliant repository forexperimental data. The storage of data under consensus formatsensuring traceability and compliance with QC rules is pivotal incollaborative projects, most importantly if massive amounts ofdata are being generated, to guarantee efficient global analysisand biological outcomes. In this sense the HUPO-PSI’s standarddata formats and MIAPE guidelines29 are strictly followed andaccordingly, the ProteoRed MIAPE web repository19 is one ofthe mainstays of the project. Starting from the PSI’s XML-basedstandards mzML30 and mzIdentML31 and through theProteoRed MIAPE web toolkit20 users are able to store all theinformation derived from the experiments in a straightforwardmanner, ensuring the compliance of the deposited data with thewidely accepted principles gathered in the MIAPE MassSpectrometry32 and Protein Informatics guidelines.33 (Figure 2a).(3) Global analysis of the experimental data. From the datadeposited in the ProteoRed MIAPE web repository a global
analysis will be performed with special emphasis on the biologicalinterpretation (Figure 2b). As part of the ProteoRedMIAPE webtoolkit, the ProteoRed MIAPE extractor has been developed toprovide the users with a friendly graphic-based environmentwhere all generated data can be analyzed and integrated from adifferent experimental and functional perspectives. This softwareis freely available at http://proteored.org/miape-extractor. (4)Data sharing and reporting. Reporting the results is another keyissue in the project. In this regard, assuming that all data arecompliant with the MIAPE reporting guidelines, this softwareenvironment allows the generation of Mass Spectrometry andProtein and Peptide identification files in formats recommendedin the EBI PRIDE XML specifications. As an example, data canbe exported using this XML format and stored in the centralizedpublic data repository for protein and peptide identificationsEMBL-EBI PRIDE database34 (Figure 2c), and consequentlyenable their sharing through the ProteomeXchange Web site(http://www.proteomexchange.org/).35
Annotation of Chr16
The gene and protein sets of chromosome 16 were analyzedusing the information available in ENSEMBL, UniprotKB andGPMDB (Figure 3) and the results are summarized inSupplementary Table 1 (Supporting Information). The workplans of both C-Chr-16 HPP and B/D-Chr-16 HPP are designedaccording to the biochemical information resulting from thisanalysis. Chromosome 16 spans about 89 million base pairs,representing almost 3% of the total DNA in human cells. Morethan 2300 genes (Ensembl V68) have so far been identified,although the actual figure is still under debate as is reflected bythe differences found in different databases. A total of 870protein-coding genes have been proposed so far on Chr16, 866among them with Uniprot reference. In light of massspectrometry data on GPMDB, the figure of unknown proteinswas defined as 305 proteins as we decided arbitrarily to include allproteins with log(e) values above −15 assuming that theirobservation might have some constraints in complex matrices.
Figure 2. Data management flow-chart. Data will be uploaded in theProteoRed MIAPE web repository. The ProteoRed MIAPE extractorwill allow data calculations, data set comparisons and general analyses ina friendly and efficient way, a pivotal aspect to integrate and evaluate theinformation provided by different laboratories. Moreover, through theProteoRed MIAPE web toolkit, the MIAPE compliant data can beexported into different formats, including those compatible withongoing HUPO PSI initiatives, and finally deposited in the Pridedatabase.
Journal of Proteome Research Article
dx.doi.org/10.1021/pr300898u | J. Proteome Res. 2013, 12, 112−122116
This threshold provides a slightly larger cluster of missingproteins than the EC < 4 (nongreen coded) proteins in GPMDB(250 missing proteins).On the other hand, to define three cell lines to identify and
quantify proteins from chromosome 16, tissue and cell specificexpression patterns were evaluated using eGenetics and the GNFAtlas. A combination of fibroblasts, lymphoid and epithelial cellsmight provide theoretically up to 71% coverage of chromosome16 proteins and 39.7% of the missing species. Finally, availabilityof HPA antibodies and protein expression resources for studyingChr16 proteins were evaluated. HPA antibodies (612, 512among them considered as high quality or supportive at least forone application) are already available for 486 Chr16 genes,including 121 for proteins within the unknown group. Moreover,expression vectors for 260 proteins, 58 included in the unknowngroup, are available. On the one hand, these tools guarantee theavailability of methods for protein detection and quantificationand on the other, the ability to produce nonobserved proteins tooptimize mass spectrometry methods.Functional analysis with Gene Ontology (GO) and Ingenuity
Pathway Analysis (IPA), revealed implication of chromosome 16genes in most of the principal cell functions, as might be expectedfrom a search with a large number of genes, including, amongothers, metabolism, cell proliferation, cell signaling or cell death.It is hardly surprising that 110 of these genes are involved inhuman diseases (OMIM) such as cancers, neurodegenerativesyndromes, obesity and inflammation, a pathological conditioncommonly involved in the onset of many diseases. Proteinsencoded by chromosome 16 genes have already been identifiedin this context such as cardiotrophin 1, a protein involved in themaintenance of the cellular energy balance36 and liverprotection37−39 that is located in 16p11.2 locus, which has
been associated with obesity and cachexia.40,41 Nevertheless, weare aware that the multigenic nature of these diseases will requirethe collaboration with other chromosome initiatives for acomplete understanding of their molecular pathogenesis.
Transcriptomics and Proteomics Profiling of MCF7, CCD18and Jurkat Cell Lines
To characterize the proteome of Chr16, three cell lines wereselected, MCF7 breast cancer human epithelial cells, CCD18human colon fibroblasts, and Jurkat human T lymphocytes.Transcriptomic and shotgun proteomics experiments wereconducted to define in detail the molecular background ofthese cell lines. As for transcriptomics, the cluster of expressinggenes was selected by defining intensity thresholds for each cellline under study, to eliminate low signal probe sets that areconsidered as technical noise (Supporting Information Figure 1).The resulting values were 5.10, 4.83, and 5.37 forMCF7, CCD18and Jurkat cells, respectively. Upon filtering, 81.01, 78.54, and83.03% of the microarray probe sets were considered for furtheranalysis on MCF7, CCD18 and Jurkat cell lines respectively.Accordingly, a total of 19878 genes were expressed in at least one ofthe three cell lines, 86.13% were common, 0.75% were specificallydetected in MCF7 cells, 2.06% in CCD18 and 1.23% in Jurkat cells(Figure 4A). Up to 1533 genes fromChr16were not detected, mostof these being nonprotein coding genes (Figure 4B). However,84.6% (736) of the Chr16 protein coding genes were detectedamong a total number of 18465 genes homogenously distributedacross chromosomeswith roughly 75% coverage in all cases with theonly exception of chromosome Y (Supporting Information Figure 2),likely due to the low proportion of protein coding genes in thisparticular chromosome. The high coverage of protein codinggenes was expected as they are preferentially represented in thecDNA microarray used in our analysis. This may also explain theundetected 1533 genes of Chr16, most of which (91%) correspondto nonprotein coding genes.Shotgun proteomic analyses were also conducted for MCF7,
CCD18 and Jurkat cell lines. Assuming a FDR below 1% at theprotein level, 6608 proteins were identified, 3355, 3156, and5892 in MCF7, CCD18 and Jurkat cells (Supp Table 2)respectively, 29% commonly found in the three cell lines(Supporting Information Table 2). The distribution of identifiedproteins across chromosomes is very dissimilar with chromo-some coverages that were about 30% (Supporting InformationFigure2), in clear contrast with the transcriptomic results thatshowed coverages above 75%, with the exception of ChrY(Supporting Information Figure 3A). This discrepancy mostlikely results from the limitations of proteomics technology tocope with the vast complexity and very high dynamic range of theproteome compared with gene expression microarray technol-ogy. This pilot study is being used to determine protein detectionthresholds that will provide important hints for our proteomicworkflows, particularly to establish SRM/MRM methods.Optimization of sample preparation to enhance extraction ofmembrane proteins, fractionation procedures to enrich lowabundance proteins and definition of additional cell lines andenvironmental conditions to detect proteins specifically ex-pressed under certain stimuli or pathological conditions arecentral aspects that will extend our proteome coverage. It isworth mentioning that these ongoing shotgun analyses are beingperformed on a multicenter basis encompassing 9 laboratoriesand that data will be analyzed following the proceduresmentioned above. This will allow not only the generation of afull collection of MIAPE compliant results but also inter- and
Figure 3. Bioinformatic analysis of Chr16. Biological information ofENSEMBL, SWISSPROT and GPMwas integrated to define the clusterof genes and protein coding genes of Chr16. Disease-related informationwas extracted from OMIM. HPA antibodies for Chr16 gene productswere estimated including those for unknown proteins (log(e) > −15 inGPMDB), as well as the availability of expression vectors. MCF7, CCD18 and Jurkat cell lines are estimated to cover theoretically about 80% ofthe Chr16 proteome. Transcriptomic and proteomic analysis weredesigned to define in detail their biological background and plan thestrategy for the SRM/MRM experiments.
Journal of Proteome Research Article
dx.doi.org/10.1021/pr300898u | J. Proteome Res. 2013, 12, 112−122117
intralaboratory evaluations using the different tools included inthe ProteoRed MIAPE web tool kit. These MIAPE compliantresults are available in PRIDE (accessions 27330−27369).Among the identified proteins, 292 are encoded by Chr16 genes,82 were common to the three cell lines while 22 were only foundin MCF7, 10 in CCD18 and 100 in Jurkat cells and 160 wereidentified in at least two cell lines (Figure 5). Noteworthy, 9 ofthe identified proteins are among the group of missing proteins,including Putative 3-phosphoinositide-dependent protein kinase2, Pyridoxal-dependent decarboxylase domain-containing pro-tein 2, Putative Rab-43-like protein, Calpain small subunit 2,Nodal modulator 3, Chromosome transmission fidelity protein 8homologue isoform 2, L-fucose kinase, Uncharacterized proteinC16orf59, tRNA-specific adenosine deaminase 1. The identifiedproteins represent a 33.56% coverage of Chr16 protein codinggenes and correspond in all cell lines to proteins encoded by highexpression genes with some exceptions (Figure 6A), as might beexpected. It is worth noting that 17 proteins were detected at theprotein level, 13 in Jurkat, 7 in CCD18 and 10 in MCF7 cells
while the transcript signal was below the accepted threshold(Figure 6B). Whether these findings indicate different half-livesof mRNA-protein, the limited detection ability of the particulartranscripts due to the design of the gene expression array or if,alternatively, the expression threshold defined in this studyrequires revision, are open questions that should be investigatedfurther. All the protein and gene expression results aresummarized in Supporting Information Table 3 and a heatmap representation of the specific data for Chr16 is provided(Supporting Information Figure 4). Additionally, functionalenrichment analysis of Gene Ontology (GO) categories wascarried out using standard hypergeometric tests.16,42 The set ofprotein coding genes of chromosome 16 was considered the geneuniverse and the proteins identified in this chromosome in thethree cell lines (292 genes) the selected genes (SupportingInformation Figure 4).
B/D-SpHPP
The Biology and disease section of theHPPwas first conceived ina meeting held in June 2012 and the structure of the project and
Figure 5. Venn diagram representing shotgun proteomic results. Protein identifications from MCF7, CCD18 and Jurkat experiments and theintersection with Chr16 genes are represented. Taking together results from the three cell lines, 292 proteins encoded by Chr16 genes (31.9%) weredetected. Ensembl version 68 was used as the reference database.
Figure 4. (A) Venn diagrams representing transcriptomics data. Gene expression data fromMCF7, CCD18 and Jurkat cells, and their intersection withChr16 genes is represented. Different subgroups can be easily followed using the distinctive colored lines under each corresponding figure. From 2316genes on chromosome 16, expression of 783 (33.8%) was detected, 95% among them commonly found in the three cell lines. (B) Intersection ofexpressed genes in all cell lines with total and Chr16 specific protein coding genes. Most Chr16 genes whose expression is detected are protein codinggenes, 736 (93.99%) and represent a 85% of the protein coding genes of this chromosome. Ensembl version 68 was used as the reference database.
Journal of Proteome Research Article
dx.doi.org/10.1021/pr300898u | J. Proteome Res. 2013, 12, 112−122118
the areas of interest, cancer, obesity, infectious, neurodegener-ative, cardiovascular and rheumatoid disorders, were agreed on ina second meeting in June (Supporting Information Figure 5).Three main driving criteria for the selection process wereestablished: social impact in Spain, the experience of theparticipating groups, and the involvement of Chr16 proteins.The initial objective is to launch the initiative taking advantage ofthe valuable experience of the scientists in the consortium andthe financial support already available for their own researchactivities in the above-mentioned areas, to integrate thecontributions from the rest of the scientific community in asecond phase, in coordination with National Institutes and
Organizations for clinical/biomedical research (Spanish Bio-banking Network, CIBER, RETICS) that have already expressedtheir interest in participating in the HPP project. Although thefinal structure will be decided in a meeting to be held in the lastquarter of 2012, initial proposals have already been elaboratedmostly focusing on the application of the C-HPP developedresources for investigation of acute coronary syndrome andaortic stenosis, Parkinson’s disease and muscular dystrophies, theinvolvement of cardiotrophin-1 and prohibitin-1 in obesity andnonalcoholic liver diseases, osteoarthritis and rheumatoidarthritis and characterization of the innate and adaptive immuneresponse to Candida albicans infection.
Figure 6. (A) Comparison of transcriptomic and proteomic data relative to Chr16 protein coding genes. Genes and proteins were ranked according totheir expression level and then plotted against the gene expression values for both the genes (blue) and the proteins (red). The gene expressionthreshold is represented by the blue line. (B) Overlapping of proteins and transcripts in MCF7, CCD18 and Jurkat cell lines. The total number ofproteins from shotgun experiments (open bars) and the corresponding transcripts detected by gene expression microarray analysis (gray bars) arerepresented.
Journal of Proteome Research Article
dx.doi.org/10.1021/pr300898u | J. Proteome Res. 2013, 12, 112−122119
Conclusion and Perspectives
The C-HPP Chr16 program is fully active in all the activitiesdescribed above and a first deliverable time line has beenproposed (Supporting Information Figure 6). SOP andbioinformatics plan to complete the process from the generationof raw data to their upload to open access repositories by the endof 2013. SRM/MRM experiments (assuming one gene- oneprotein) are expected to extend up to the end of 2014,programming the assays for roughly one-third of Chr16 proteinsyearly (including known and unknown proteins). Each assay willbe validated by three independent laboratories and by alternativeanalysis with capture reagents when available. Intervalidationexperiments among other European Chromosome centric-HPPteams are under evaluation. This activity will span up to the endof 2016. The first phase is already on going and a follow-upmeeting will take place in December. Isoform detection and post-translationally modified species will be tackled in parallel, oncethe assay for the most predominant species is optimized. As forthe B/D-SpHPP, collaboration has been started with the SpanishNational Biobank Network to define the SOPs on samplecollection and storing.The proposed time frame fits well with the schedule of the
global C-HPP project with a particular milestone in 2014, whenthe HUPO congress will be hosted in Madrid and the outcomesconcerning protein mapping and the contribution to theunderstanding human biology in health and disease will bediscussed.
■ ASSOCIATED CONTENT*S Supporting Information
Supplemental tables and figures. This material is available free ofcharge via the Internet at http://pubs.acs.org.
■ AUTHOR INFORMATIONCorresponding Author
#Corresponding author: Prof. Fernando J. Corrales Center forApplied Medical Research (CIMA), University of Navarra. PioXII, 55. 31008 Pamplona, Spain. Tel: +34948194700 Fax:+34948194717 e-mail: [email protected] Contributions$These authors contributed equally to this manuscript.Notes
The authors declare no competing financial interest.
■ ACKNOWLEDGMENTSAll participating laboratories are members of ProteoRed-ISCIII.This work was supported by: ProteoRed and the Carlos IIINational Health Institute Agreement, ProteoRed-ISCIII; theagreement between FIMA and the “UTE project CIMA”; grantsSAF2011-29312 from Ministerio de Ciencia e Innovacion andISCIII-RETIC RD06/0020 to F.J.C. and EU FP7 grantProteomeXchange (grant number 260558); BBVA Foundationfor its support to HUPO initiatives.
■ REFERENCES(1) Lander, E. S.; Linton, L. M.; Birren, B.; Nusbaum, C.; Zody, M. C.;Baldwin, J.; Devon, K.; Dewar, K.; Doyle, M.; FitzHugh, W.; Funke, R.;Gage, D.; Harris, K.; Heaford, A.; Howland, J.; Kann, L.; Lehoczky, J.;LeVine, R.; McEwan, P.; McKernan, K.; Meldrim, J.; Mesirov, J. P.;Miranda, C.; Morris, W.; Naylor, J.; Raymond, C.; Rosetti, M.; Santos,R.; Sheridan, A.; Sougnez, C.; Stange-Thomann, N.; Stojanovic, N.;
Subramanian, A.; Wyman, D.; Rogers, J.; Sulston, J.; Ainscough, R.;Beck, S.; Bentley, D.; Burton, J.; Clee, C.; Carter, N.; Coulson, A.;Deadman, R.; Deloukas, P.; Dunham, A.; Dunham, I.; Durbin, R.;French, L.; Grafham, D.; Gregory, S.; Hubbard, T.; Humphray, S.; Hunt,A.; Jones, M.; Lloyd, C.; McMurray, A.; Matthews, L.; Mercer, S.; Milne,S.; Mullikin, J. C.; Mungall, A.; Plumb, R.; Ross, M.; Shownkeen, R.;Sims, S.; Waterston, R. H.; Wilson, R. K.; Hillier, L. W.; McPherson, J.D.; Marra, M. A.; Mardis, E. R.; Fulton, L. A.; Chinwalla, A. T.; Pepin, K.H.; Gish, W. R.; Chissoe, S. L.; Wendl, M. C.; Delehaunty, K. D.; Miner,T. L.; Delehaunty, A.; Kramer, J. B.; Cook, L. L.; Fulton, R. S.; Johnson,D. L.; Minx, P. J.; Clifton, S. W.; Hawkins, T.; Branscomb, E.; Predki, P.;Richardson, P.;Wenning, S.; Slezak, T.; Doggett, N.; Cheng, J. F.; Olsen,A.; Lucas, S.; Elkin, C.; Uberbacher, E.; Frazier, M.; Gibbs, R. A.; Muzny,D. M.; Scherer, S. E.; Bouck, J. B.; Sodergren, E. J.; Worley, K. C.; Rives,C. M.; Gorrell, J. H.; Metzker, M. L.; Naylor, S. L.; Kucherlapati, R. S.;Nelson, D. L.; Weinstock, G. M.; Sakaki, Y.; Fujiyama, A.; Hattori, M.;Yada, T.; Toyoda, A.; Itoh, T.; Kawagoe, C.; Watanabe, H.; Totoki, Y.;Taylor, T.; Weissenbach, J.; Heilig, R.; Saurin, W.; Artiguenave, F.;Brottier, P.; Bruls, T.; Pelletier, E.; Robert, C.; Wincker, P.; Smith, D. R.;Doucette-Stamm, L.; Rubenfield, M.; Weinstock, K.; Lee, H. M.;Dubois, J.; Rosenthal, A.; Platzer, M.; Nyakatura, G.; Taudien, S.; Rump,A.; Yang, H.; Yu, J.; Wang, J.; Huang, G.; Gu, J.; Hood, L.; Rowen, L.;Madan, A.; Qin, S.; Davis, R. W.; Federspiel, N. A.; Abola, A. P.; Proctor,M. J.; Myers, R. M.; Schmutz, J.; Dickson, M.; Grimwood, J.; Cox, D. R.;Olson, M. V.; Kaul, R.; Raymond, C.; Shimizu, N.; Kawasaki, K.;Minoshima, S.; Evans, G. A.; Athanasiou, M.; Schultz, R.; Roe, B. A.;Chen, F.; Pan, H.; Ramser, J.; Lehrach, H.; Reinhardt, R.; McCombie,W. R.; la Bastide, de, M.; Dedhia, N.; Blocker, H.; Hornischer, K.;Nordsiek, G.; Agarwala, R.; Aravind, L.; Bailey, J. A.; Bateman, A.;Batzoglou, S.; Birney, E.; Bork, P.; Brown, D. G.; Burge, C. B.; Cerutti,L.; Chen, H. C.; Church, D.; Clamp, M.; Copley, R. R.; Doerks, T.;Eddy, S. R.; Eichler, E. E.; Furey, T. S.; Galagan, J.; Gilbert, J. G.;Harmon, C.; Hayashizaki, Y.; Haussler, D.; Hermjakob, H.; Hokamp, K.;Jang, W.; Johnson, L. S.; Jones, T. A.; Kasif, S.; Kaspryzk, A.; Kennedy,S.; Kent, W. J.; Kitts, P.; Koonin, E. V.; Korf, I.; Kulp, D.; Lancet, D.;Lowe, T. M.; McLysaght, A.; Mikkelsen, T.; Moran, J. V.; Mulder, N.;Pollara, V. J.; Ponting, C. P.; Schuler, G.; Schultz, J.; Slater, G.; Smit, A.F.; Stupka, E.; Szustakowski, J.; Thierry-Mieg, D.; Thierry-Mieg, J.;Wagner, L.;Wallis, J.; Wheeler, R.;Williams, A.;Wolf, Y. I.; Wolfe, K. H.;Yang, S. P.; Yeh, R. F.; Collins, F.; Guyer, M. S.; Peterson, J.; Felsenfeld,A.; Wetterstrand, K. A.; Patrinos, A.; Morgan, M. J.; de Jong, P.;Catanese, J. J.; Osoegawa, K.; Shizuya, H.; Choi, S.; Chen, Y. J.;Szustakowki, J.; International Human Genome SequencingConsortium. Initial sequencing and analysis of the human genome.Nature 2001, 409, 860−921.(2) Venter, J. C.; Adams, M. D.; Myers, E. W.; Li, P. W.; Mural, R. J.;Sutton, G. G.; Smith, H. O.; Yandell, M.; Evans, C. A.; Holt, R. A.;Gocayne, J. D.; Amanatides, P.; Ballew, R. M.; Huson, D. H.; Wortman,J. R.; Zhang, Q.; Kodira, C. D.; Zheng, X. H.; Chen, L.; Skupski, M.;Subramanian, G.; Thomas, P. D.; Zhang, J.; GaborMiklos, G. L.; Nelson,C.; Broder, S.; Clark, A. G.; Nadeau, J.; McKusick, V. A.; Zinder, N.;Levine, A. J.; Roberts, R. J.; Simon, M.; Slayman, C.; Hunkapiller, M.;Bolanos, R.; Delcher, A.; Dew, I.; Fasulo, D.; Flanigan, M.; Florea, L.;Halpern, A.; Hannenhalli, S.; Kravitz, S.; Levy, S.; Mobarry, C.; Reinert,K.; Remington, K.; Abu-Threideh, J.; Beasley, E.; Biddick, K.; Bonazzi,V.; Brandon, R.; Cargill, M.; Chandramouliswaran, I.; Charlab, R.;Chaturvedi, K.; Deng, Z.; Di Francesco, V.; Dunn, P.; Eilbeck, K.;Evangelista, C.; Gabrielian, A. E.; Gan, W.; Ge, W.; Gong, F.; Gu, Z.;Guan, P.; Heiman, T. J.; Higgins, M. E.; Ji, R. R.; Ke, Z.; Ketchum, K. A.;Lai, Z.; Lei, Y.; Li, Z.; Li, J.; Liang, Y.; Lin, X.; Lu, F.; Merkulov, G. V.;Milshina, N.; Moore, H. M.; Naik, A. K.; Narayan, V. A.; Neelam, B.;Nusskern, D.; Rusch, D. B.; Salzberg, S.; Shao, W.; Shue, B.; Sun, J.;Wang, Z.; Wang, A.; Wang, X.; Wang, J.; Wei, M.; Wides, R.; Xiao, C.;Yan, C.; Yao, A.; Ye, J.; Zhan, M.; Zhang, W.; Zhang, H.; Zhao, Q.;Zheng, L.; Zhong, F.; Zhong, W.; Zhu, S.; Zhao, S.; Gilbert, D.;Baumhueter, S.; Spier, G.; Carter, C.; Cravchik, A.; Woodage, T.; Ali, F.;An, H.; Awe, A.; Baldwin, D.; Baden, H.; Barnstead, M.; Barrow, I.;Beeson, K.; Busam, D.; Carver, A.; Center, A.; Cheng, M. L.; Curry, L.;Danaher, S.; Davenport, L.; Desilets, R.; Dietz, S.; Dodson, K.; Doup, L.;
Journal of Proteome Research Article
dx.doi.org/10.1021/pr300898u | J. Proteome Res. 2013, 12, 112−122120
Ferriera, S.; Garg, N.; Gluecksmann, A.; Hart, B.; Haynes, J.; Haynes, C.;Heiner, C.; Hladun, S.; Hostin, D.; Houck, J.; Howland, T.; Ibegwam,C.; Johnson, J.; Kalush, F.; Kline, L.; Koduru, S.; Love, A.; Mann, F.;May, D.; McCawley, S.; McIntosh, T.; McMullen, I.; Moy, M.; Moy, L.;Murphy, B.; Nelson, K.; Pfannkoch, C.; Pratts, E.; Puri, V.; Qureshi, H.;Reardon, M.; Rodriguez, R.; Rogers, Y. H.; Romblad, D.; Ruhfel, B.;Scott, R.; Sitter, C.; Smallwood, M.; Stewart, E.; Strong, R.; Suh, E.;Thomas, R.; Tint, N. N.; Tse, S.; Vech, C.; Wang, G.; Wetter, J.;Williams, S.; Williams, M.; Windsor, S.; Winn-Deen, E.; Wolfe, K.;Zaveri, J.; Zaveri, K.; Abril, J. F.; Guigo, R.; Campbell, M. J.; Sjolander, K.V.; Karlak, B.; Kejariwal, A.; Mi, H.; Lazareva, B.; Hatton, T.;Narechania, A.; Diemer, K.; Muruganujan, A.; Guo, N.; Sato, S.;Bafna, V.; Istrail, S.; Lippert, R.; Schwartz, R.; Walenz, B.; Yooseph, S.;Allen, D.; Basu, A.; Baxendale, J.; Blick, L.; Caminha, M.; Carnes-Stine,J.; Caulk, P.; Chiang, Y. H.; Coyne, M.; Dahlke, C.; Mays, A.;Dombroski, M.; Donnelly, M.; Ely, D.; Esparham, S.; Fosler, C.; Gire,H.; Glanowski, S.; Glasser, K.; Glodek, A.; Gorokhov, M.; Graham, K.;Gropman, B.; Harris, M.; Heil, J.; Henderson, S.; Hoover, J.; Jennings,D.; Jordan, C.; Jordan, J.; Kasha, J.; Kagan, L.; Kraft, C.; Levitsky, A.;Lewis,M.; Liu, X.; Lopez, J.; Ma, D.;Majoros,W.;McDaniel, J.; Murphy,S.; Newman, M.; Nguyen, T.; Nguyen, N.; Nodell, M.; Pan, S.; Peck, J.;Peterson, M.; Rowe, W.; Sanders, R.; Scott, J.; Simpson, M.; Smith, T.;Sprague, A.; Stockwell, T.; Turner, R.; Venter, E.; Wang, M.; Wen, M.;Wu, D.; Wu, M.; Xia, A.; Zandieh, A.; Zhu, X. The sequence of thehuman genome. Science 2001, 291, 1304−1351.(3) Perkel, J. M. The human proteome project takes shape down under.BioTechniques 2011, 50, 149−155.(4) Legrain, P.; Aebersold, R.; Archakov, A.; Bairoch, A.; Bala, K.;Beretta, L.; Bergeron, J.; Borchers, C. H.; Corthals, G. L.; Costello, C. E.;Deutsch, E. W.; Domon, B.; Hancock, W.; He, F.; Hochstrasser, D.;Marko-Varga, G.; Salekdeh, G. H.; Sechi, S.; Snyder, M.; Srivastava, S.;Uhlen, M.; Wu, C. H.; Yamamoto, T.; Paik, Y.-K.; Omenn, G. S. TheHuman Proteome Project: current state and future direction. Mol. Cell.Proteomics 2011, 10, M111.009993.(5) Nilsson, T.; Mann, M.; Aebersold, R.; Yates, J. R.; Bairoch, A.;Bergeron, J. J. M. Mass spectrometry in high-throughput proteomics:ready for the big time. Nat. Methods 2010, 7, 681−685.(6) Orchard, S.; Hermjakob, H.; Taylor, C. F.; Potthast, F.; Jones, P.;Zhu, W.; Julian, R. K.; Apweiler, R. Second proteomics standardsinitiative spring workshop. Expert Rev. Proteomics 2005, 2, 287−289.(7) Omenn, G. S. Exploring the human plasma proteome. Proteomics2005, 5, 3223−3225.(8) He, F. Human liver proteome project: plan, progress, andperspectives. Mol. Cell. Proteomics 2005, 4, 1841−1848.(9) Uhlen, M.; Bjorling, E.; Agaton, C.; Szigyarto, C. A.-K.; Amini, B.;Andersen, E.; Andersson, A.-C.; Angelidou, P.; Asplund, A.; Asplund, C.;Berglund, L.; Bergstrom, K.; Brumer, H.; Cerjan, D.; Ekstrom, M.;Elobeid, A.; Eriksson, C.; Fagerberg, L.; Falk, R.; Fall, J.; Forsberg, M.;Bjorklund, M. G.; Gumbel, K.; Halimi, A.; Hallin, I.; Hamsten, C.;Hansson, M.; Hedhammar, M.; Hercules, G.; Kampf, C.; Larsson, K.;Lindskog, M.; Lodewyckx, W.; Lund, J.; Lundeberg, J.; Magnusson, K.;Malm, E.; Nilsson, P.; Odling, J.; Oksvold, P.; Olsson, I.; Oster, E.;Ottosson, J.; Paavilainen, L.; Persson, A.; Rimini, R.; Rockberg, J.;Runeson, M.; Sivertsson, A.; Skollermo, A.; Steen, J.; Stenvall, M.;Sterky, F.; Stromberg, S.; Sundberg, M.; Tegel, H.; Tourle, S.; Wahlund,E.; Walden, A.; Wan, J.; Wernerus, H.; Westberg, J.; Wester, K.;Wrethagen, U.; Xu, L. L.; Hober, S.; Ponten, F. A human protein atlas fornormal and cancer tissues based on antibody proteomics. Mol. Cell.Proteomics 2005, 4, 1920−1932.(10) Hamacher, M.; Marcus, K.; Stephan, C.; Klose, J.; Park, Y. M.;Meyer, H. E. HUPO Brain Proteome Project: toward a code of conduct.Mol. Cell. Proteomics 2008, 7, 457.(11) Yamamoto, T.; Langham, R. G.; Ronco, P.; Knepper, M. A.;Thongboonkerd, V. Towards standard protocols and guidelines forurine proteomics: a report on the Human Kidney and Urine ProteomeProject (HKUPP) symposium and workshop, 6 October 2007, Seoul,Korea and 1 November 2007, San Francisco, CA, USA. Proteomics 2008,8, 2156−2159.
(12) Paik, Y.-K.; Omenn, G. S.; Uhlen, M.; Hanash, S.; Marko-Varga,G.; Aebersold, R.; Bairoch, A.; Yamamoto, T.; Legrain, P.; Lee, H.-J.; Na,K.; Jeong, S.-K.; He, F.; Binz, P.-A.; Nishimura, T.; Keown, P.; Baker, M.S.; Yoo, J. S.; Garin, J.; Archakov, A.; Bergeron, J.; Salekdeh, G. H.;Hancock, W. S. Standard guidelines for the Chromosome-centricHuman Proteome Project. J. Proteome Res. 2012, 11 (4), 2005−2013.(13) Paik, Y.-K.; Jeong, S.-K.; Omenn, G. S.; Uhlen, M.; Hanash, S.;Cho, S. Y.; Lee, H.-J.; Na, K.; Choi, E.-Y.; Yan, F.; Zhang, F.; Zhang, Y.;Snyder, M.; Cheng, Y.; Chen, R.; Marko-Varga, G.; Deutsch, E.W.; Kim,H.; Kwon, J.-Y.; Aebersold, R.; Bairoch, A.; Taylor, A. D.; Kim, K. Y.;Lee, E.-Y.; Hochstrasser, D.; Legrain, P.; Hancock, W. S. TheChromosome-centric Human Proteome Project for cataloging proteinsencoded in the genome. Nat. Biotechnol. 2012, 30, 221−223.(14) Gentleman, R. C.; Carey, V. J.; Bates, D. M.; Bolstad, B.; Dettling,M.; Dudoit, S.; Ellis, B.; Gautier, L.; Ge, Y.; Gentry, J.; Hornik, K.;Hothorn, T.; Huber, W.; Iacus, S.; Irizarry, R.; Leisch, F.; Li, C.;Maechler, M.; Rossini, A. J.; Sawitzki, G.; Smith, C.; Smyth, G.; Tierney,L.; Yang, J. Y.; Zhang, J. Bioconductor: open software development forcomputational biology and bioinformatics. Genome Biol. 2004, 5, R80.(15) Irizarry, R. A.; Bolstad, B. M.; Collin, F.; Cope, L. M.; Hobbs, B.;Speed, T. P. Summaries of Affymetrix GeneChip probe level data.Nucleic Acids Res. 2003, 31, e15.(16) Storey, J. D.; Tibshirani, R. Statistical significance for genomewidestudies. Proc. Natl. Acad. Sci. U.S.A. 2003, 100, 9440−9445.(17) Marcilla, M.; Alpizar, A.; Paradela, A.; Albar, J.-P. A systematicapproach to assess amino acid conversions in SILAC experiments.Talanta 2011, 84, 430−436.(18) Elias, J. E.; Gygi, S. P. Target-decoy search strategy for increasedconfidence in large-scale protein identifications by mass spectrometry.Nat. Methods 2007, 4, 207−214.(19) Martínez-Bartolome, S.; Medina-Aunon, J. A.; Jones, A. R.; Albar,J. P. Semi-automatic tool to describe, store and compare proteomicsexperiments based on MIAPE compliant reports. Proteomics 2010, 10,1256−1260.(20) Medina-Aunon, J. A.; Martínez-Bartolome, S.; Lopez-Garcia, M.A.; Salazar, E.; Navajas, R.; Jones, A. R.; Paradela, A.; Albar, J. P. TheProteoRed MIAPE web toolkit: a user-friendly framework to connectand share proteomics standards. Mol. Cell. Proteomics 2011, 10,M111.008334.(21) Bech-Serra, J.-J.; Borthwick, A.; Colome, N.; ProteoRedConsortium; Albar, J.-P.; Wells, M.; Sanchez del Pino, M.; Canals, F.A multi-laboratory study assessing reproducibility of a 2D-DIGEdifferential proteomic experiment. J. Biomol. Tech. 2009, 20, 293−296.(22) Martínez-Bartolome, S.; Blanco, F.; Albar, J.-P. Relevance ofproteomics standards for the ProteoRed Spanish organization. J.Proteomics 2010, 73, 1061−1066.(23) Babel, I.; Barderas, R.; Diaz-Uriarte, R.; Moreno, V.; Suarez, A.;Fernandez-Acenero, M. J.; Salazar, R.; Capella, G.; Casal, J. I.Identification of MST1/STK4 and SULF1 proteins as autoantibodytargets for the diagnosis of colorectal cancer by using phage microarrays.Mol. Cell. Proteomics 2011, 10, M110.001784.(24) Pitarch, A.; Nombela, C.; Gil, C. Prediction of the clinicaloutcome in invasive candidiasis patients based on molecular fingerprintsof five anti-Candida antibodies in serum.Mol. Cell. Proteomics 2011, 10,M110.004010.(25) Calamia, V.; Fernandez-Puente, P.;Mateos, J.; Lourido, L.; Rocha,B.; Montell, E.; Verges, J.; Ruiz-Romero, C.; Blanco, F. J.Pharmacoproteomic study of three different chondroitin sulfatecompounds on intracellular and extracellular human chondrocyteproteomes. Mol. Cell. Proteomics 2012, 11, M111.013417.(26) la Cuesta, de, F.; Alvarez-Llamas, G.; Maroto, A. S.; Donado, A.;Zubiri, I.; Posada, M.; Padial, L. R.; Pinto, A. G.; Barderas, M. G.;Vivanco, F. A proteomic focus on the alterations occurring at the humanatherosclerotic coronary intima. Mol. Cell. Proteomics 2011, 10,M110.003517.(27) Sanchez-Quiles, V.; Mora, M. I.; Segura, V.; Greco, A.; Epstein, A.L.; Foschini, M. G.; Dayon, L.; Sanchez, J.-C.; Prieto, J.; Corrales, F. J.;Santamaria, E. HSV-1 Cgal+ infection promotes quaking RNA bindingprotein production and induces nuclear-cytoplasmic shuttling of
Journal of Proteome Research Article
dx.doi.org/10.1021/pr300898u | J. Proteome Res. 2013, 12, 112−122121
quaking I-5 isoform in human hepatoma cells. Mol. Cell. Proteomics2011, 10, M111.009126.(28) Ramachandran, N.; Raphael, J. V.; Hainsworth, E.; Demirkan, G.;Fuentes, M. G.; Rolfs, A.; Hu, Y.; LaBaer, J. Next-generation high-density self-assembling functional protein arrays. Nat. Methods 2008, 5,535−538.(29) Taylor, C. F.; Paton, N. W.; Lilley, K. S.; Binz, P.-A.; Julian, R. K.;Jones, A. R.; Zhu,W.; Apweiler, R.; Aebersold, R.; Deutsch, E.W.; Dunn,M. J.; Heck, A. J. R.; Leitner, A.; Macht, M.; Mann, M.; Martens, L.;Neubert, T. A.; Patterson, S. D.; Ping, P.; Seymour, S. L.; Souda, P.;Tsugita, A.; Vandekerckhove, J.; Vondriska, T. M.; Whitelegge, J. P.;Wilkins, M. R.; Xenarios, I.; Yates, J. R.; Hermjakob, H. The minimuminformation about a proteomics experiment (MIAPE). Nat. Biotechnol.2007, 25, 887−893.(30) Martens, L.; Chambers, M.; Sturm, M.; Kessner, D.; Levander, F.;Shofstahl, J.; Tang, W. H.; Rompp, A.; Neumann, S.; Pizarro, A. D.;Montecchi-Palazzi, L.; Tasman, N.; Coleman, M.; Reisinger, F.; Souda,P.; Hermjakob, H.; Binz, P.-A.; Deutsch, E. W. mzML–a communitystandard for mass spectrometry data. Mol. Cell. Proteomics 2011, 10,R110.000133.(31) Jones, A. R.; Eisenacher, M.; Mayer, G.; Kohlbacher, O.; Siepen,J.; Hubbard, S. J.; Selley, J. N.; Searle, B. C.; Shofstahl, J.; Seymour, S. L.;Julian, R.; Binz, P.-A.; Deutsch, E. W.; Hermjakob, H.; Reisinger, F.;Griss, J.; Vizcaíno, J. A.; Chambers, M.; Pizarro, A.; Creasy, D. ThemzIdentML Data Standard for Mass Spectrometry-Based ProteomicsResults. Mol. Cell. Proteomics 2012, 11, M111.014381.(32) Binz, P.-A.; Barkovich, R.; Beavis, R. C.; Creasy, D.; Horn, D. M.;Julian, R. K.; Seymour, S. L.; Taylor, C. F.; Vandenbrouck, Y. Guidelinesfor reporting the use of mass spectrometry informatics in proteomics.Nat. Biotechnol. 2008, 26, 862.(33) Taylor, C. F.; Binz, P.-A.; Aebersold, R.; Affolter, M.; Barkovich,R.; Deutsch, E. W.; Horn, D. M.; Huhmer, A.; Kussmann, M.; Lilley, K.;Macht, M.; Mann, M.; Muller, D.; Neubert, T. A.; Nickson, J.; Patterson,S. D.; Raso, R.; Resing, K.; Seymour, S. L.; Tsugita, A.; Xenarios, I.; Zeng,R.; Julian, R. K. Guidelines for reporting the use of mass spectrometry inproteomics. Nat. Biotechnol. 2008, 26, 860−861.(34) Martens, L.; Hermjakob, H.; Jones, P.; Adamski, M.; Taylor, C.;States, D.; Gevaert, K.; Vandekerckhove, J.; Apweiler, R. PRIDE: theproteomics identifications database. Proteomics 2005, 5, 3537−3545.(35) Hermjakob, H.; Apweiler, R. The Proteomics IdentificationsDatabase (PRIDE) and the ProteomExchange Consortium: makingproteomics data accessible. Expert Rev. Proteomics 2006, 3, 1−3.(36) Moreno-Aliaga, M. J.; Perez-Echarri, N.; Marcos-Gomez, B.;Larequi, E.; Gil-Bea, F. J.; Viollet, B.; Gimenez, I.; Martínez, J. A.; Prieto,J.; Bustos, M. Cardiotrophin-1 is a key regulator of glucose and lipidmetabolism. Cell Metab. 2011, 14, 242−253.(37) Bustos, M.; Beraza, N.; Lasarte, J.-J.; Baixeras, E.; Alzuguren, P.;Bordet, T.; Prieto, J. Protection against liver damage by cardiotrophin-1:a hepatocyte survival factor up-regulated in the regenerating liver in rats.Gastroenterology 2003, 125, 192−201.(38) Iniguez, M.; Berasain, C.; Martinez-Anso, E.; Bustos, M.; Fortes,P.; Pennica, D.; Avila, M. A.; Prieto, J. Cardiotrophin-1 defends the liveragainst ischemia-reperfusion injury and mediates the protective effect ofischemic preconditioning. J. Exp. Med. 2006, 203, 2809−2815.(39) Marques, J. M.; Belza, I.; Holtmann, B.; Pennica, D.; Prieto, J.;Bustos, M. Cardiotrophin-1 is an essential factor in the natural defenseof the liver against apoptosis. Hepatology 2007, 45, 639−648.(40) Walters, R. G.; Jacquemont, S.; Valsesia, A.; de Smith, A. J.;Martinet, D.; Andersson, J.; Falchi, M.; Chen, F.; Andrieux, J.; Lobbens,S.; Delobel, B.; Stutzmann, F.; El-Sayed Moustafa, J. S.; Chevre, J.-C.;Lecoeur, C.; Vatin, V.; Bouquillon, S.; Buxton, J. L.; Boute, O.; Holder-Espinasse, M.; Cuisset, J.-M.; Lemaitre, M.-P.; Ambresin, A.-E.; Brioschi,A.; Gaillard, M.; Giusti, V.; Fellmann, F.; Ferrarini, A.; Hadjikhani, N.;Campion, D.; Guilmatre, A.; Goldenberg, A.; Calmels, N.; Mandel, J.-L.;Le Caignec, C.; David, A.; Isidor, B.; Cordier, M.-P.; Dupuis-Girod, S.;Labalme, A.; Sanlaville, D.; Beri-Dexheimer, M.; Jonveaux, P.; Leheup,B.; Ounap, K.; Bochukova, E. G.; Henning, E.; Keogh, J.; Ellis, R. J.;Macdermot, K. D.; van Haelst, M. M.; Vincent-Delorme, C.; Plessis, G.;Touraine, R.; Philippe, A.; Malan, V.; Mathieu-Dramard, M.; Chiesa, J.;
Blaumeiser, B.; Kooy, R. F.; Caiazzo, R.; Pigeyre, M.; Balkau, B.; Sladek,R.; Bergmann, S.; Mooser, V.; Waterworth, D.; Reymond, A.;Vollenweider, P.; Waeber, G.; Kurg, A.; Palta, P.; Esko, T.; Metspalu,A.; Nelis, M.; Elliott, P.; Hartikainen, A.-L.; McCarthy, M. I.; Peltonen,L.; Carlsson, L.; Jacobson, P.; Sjostrom, L.; Huang, N.; Hurles, M. E.;O’Rahilly, S.; Farooqi, I. S.; Mannik, K.; Jarvelin, M.-R.; Pattou, F.;Meyre, D.; Walley, A. J.; Coin, L. J. M.; Blakemore, A. I. F.; Froguel, P.;Beckmann, J. S. A new highly penetrant form of obesity due to deletionson chromosome 16p11.2. Nature 2010, 463, 671−675.(41) Jacquemont, S.; Reymond, A.; Zufferey, F.; Harewood, L.;WaClters, R. G.; Kutalik, Z.; Martinet, D.; Shen, Y.; Valsesia, A.;Beckmann, N. D.; Thorleifsson, G.; Belfiore, M.; Bouquillon, S.;Campion, D.; de Leeuw, N.; de Vries, B. B. A.; Esko, T.; Fernandez, B.A.; Fernandez-Aranda, F.; Fernandez-Real, J. M.; Grataco s, M.;Guilmatre, A.; Hoyer, J.; Jarvelin, M.-R.; Kooy, R. F.; Kurg, A.; LeCaignec, C.; Mannik, K.; Platt, O. S.; Sanlaville, D.; Van Haelst, M. M.;Villatoro Gomez, S.; Walha, F.; Wu, B.-L.; Yu, Y.; Aboura, A.; Addor, M.-C.; Alembik, Y.; Antonarakis, S. E.; Arveiler, B.; Barth, M.; Bednarek, N.;Bena, F.; Bergmann, S.; Beri, M.; Bernardini, L.; Blaumeiser, B.;Bonneau, D.; Bottani, A.; Boute, O.; Brunner, H. G.; Cailley, D.; Callier,P.; Chiesa, J.; Chrast, J.; Coin, L.; Coutton, C.; Cuisset, J.-M.; Cuvellier,J.-C.; David, A.; de Freminville, B.; Delobel, B.; Delrue, M.-A.; Demeer,B.; Descamps, D.; Didelot, G.; Dieterich, K.; Disciglio, V.; Doco-Fenzy,M.; Drunat, S.; Duban-Bedu, B.; Dubourg, C.; El-Sayed Moustafa, J. S.;Elliott, P.; Faas, B. H. W.; Faivre, L.; Faudet, A.; Fellmann, F.; Ferrarini,A.; Fisher, R.; Flori, E.; Forer, L.; Gaillard, D.; Gerard, M.; Gieger, C.;Gimelli, S.; Gimelli, G.; Grabe, H. J.; Guichet, A.; Guillin, O.;Hartikainen, A.-L.; Heron, D.; Hippolyte, L.; Holder, M.; Homuth,G.; Isidor, B.; Jaillard, S.; Jaros, Z.; Jimenez-Murcia, S.; Helas, G. J.;Jonveaux, P.; Kaksonen, S.; Keren, B.; Kloss-Brandstatter, A.; Knoers, N.V. A. M.; Koolen, D. A.; Kroisel, P. M.; Kronenberg, F.; Labalme, A.;Landais, E.; Lapi, E.; Layet, V.; Legallic, S.; Leheup, B.; Leube, B.; Lewis,S.; Lucas, J.; MacDermot, K. D.; Magnusson, P.; Marshall, C.; Mathieu-Dramard, M.; McCarthy, M. I.; Meitinger, T.; Mencarelli, M. A.; Merla,G.; Moerman, A.; Mooser, V.; Morice-Picard, F.; Mucciolo, M.; Nauck,M.; Ndiaye, N. C.; Nordgren, A.; Pasquier, L.; Petit, F.; Pfundt, R.;Plessis, G.; Rajcan-Separovic, E.; Ramelli, G. P.; Rauch, A.; Ravazzolo,R.; Reis, A.; Renieri, A.; Richart, C.; Ried, J. S.; Rieubland, C.; Roberts,W.; Roetzer, K. M.; Rooryck, C.; Rossi, M.; Saemundsen, E.; Satre, V.;Schurmann, C.; Sigurdsson, E.; Stavropoulos, D. J.; Stefansson, H.;Tengstrom, C.; Thorsteinsdottir, U.; Tinahones, F. J.; Touraine, R.;Vallee, L.; van Binsbergen, E.; Van der Aa, N.; Vincent-Delorme, C.;Visvikis-Siest, S.; Vollenweider, P.; Volzke, H.; Vulto-van Silfhout, A. T.;Waeber, G.; Wallgren-Pettersson, C.; Witwicki, R. M.; Zwolinksi, S.;Andrieux, J.; Estivill, X.; Gusella, J. F.; Gustafsson, O.; Metspalu, A.;Scherer, S. W.; Stefansson, K.; Blakemore, A. I. F.; Beckmann, J. S.;Froguel, P. Mirror extreme BMI phenotypes associated with genedosage at the chromosome 16p11.2 locus. Nature 2011, 478, 97−102.(42) Draghici, S.Data Analysis Tools for DNAMicroarrays; Chapman&Hall/CRC: Boca Raton, FL, 2003.
Journal of Proteome Research Article
dx.doi.org/10.1021/pr300898u | J. Proteome Res. 2013, 12, 112−122122
