methodology open access protein co-expression network

JOURNAL OF CLINICAL BIOINFORMATICS

Gibbs et al Journal of Clinical Bioinformatics 2013 311httpwwwjclinbioinformaticscomcontent3111

METHODOLOGY Open Access

Protein co-expression network analysis (ProCoNA)David L Gibbs1 Arie Baratt2 Ralph S Baric3 Yoshihiro Kawaoka4 Richard D Smith5 Eric S Orwoll2Michael G Katze6 and Shannon K McWeeney127

Abstract

Background Biological networks are important for elucidating disease etiology due to their ability to modelcomplex high dimensional data and biological systems Proteomics provides a critical data source for such modelsbut currently lacks robust de novo methods for network construction which could bring important insights insystems biology

Results We have evaluated the construction of network models using methods derived from weighted geneco-expression network analysis (WGCNA) We show that approximately scale-free peptide networks composed ofstatistically significant modules are feasible and biologically meaningful using two mouse lung experiments andone human plasma experiment Within each network peptides derived from the same protein are shown to have astatistically higher topological overlap and concordance in abundance which is potentially important for inferringprotein abundance The module representatives called eigenpeptides correlate significantly with biologicalphenotypes Furthermore within modules we find significant enrichment for biological function and knowninteractions (gene ontology and protein-protein interactions)

Conclusions Biological networks are important tools in the analysis of complex systems In this paper we evaluatethe application of weighted co-expression network analysis to quantitative proteomics data Protein co-expressionnetworks allow novel approaches for biological interpretation quality control inference of protein abundance aframework for potentially resolving degenerate peptide-protein mappings and a biomarker signature discovery

Keywords Biomarkers Biological networks Networks Systems biology Virology Sarcopenia LC-MS Proteomics

BackgroundSystems biology embraces the complexity found in bio-logical networks by taking a holistic view of the cell[12] As systems biology moves forward models makinguse of quantitative proteomic data will become increas-ingly necessary since this information is not accessibleusing other analytical methods [34]Large-scale quantitative proteomics however is still de-

veloping and can be challenging and complex in practice[56] In order to boost throughput and ease computationtag-based approaches are used [7] Briefly proteins aredigested enzymatically producing a multitude of peptidefragments Using liquid chromatography coupled to massspectroscopy (referred to as LC-MS) the digested mixtureis quantified resulting in a set of features containing bothmass and net elution time measurements Peptides are

Correspondence gibbsdohsuedu1Division of Bioinformatics and Computational Biology Oregon Health ampScience University 3181 SW Sam Jackson Park Rd Portland OR 97239 USAFull list of author information is available at the end of the article

copy 2013 Gibbs et al licensee BioMed Central LCommons Attribution License (httpcreativecreproduction in any medium provided the or

identified by mapping features to entries in an accuratemass and time tag (AMT) database Tag databases areconstructed using pooled samples processed on a tandemMSMS platform [8]Currently a majority of protein networks are cons-

tructed using protein-protein interaction (PPI) data-bases However manually curated PPI databases areregularly revised as our understanding of biology growsPPI databases are typically heterogeneous containingdifferent experiment types and model organisms leadingto sparse annotation and a lack of experimental con-cordance [9] In addition interaction temporality andcontextual information is lacking Coverage selectionbias and detection bias all remain problems [910]De novo approaches based on observed data offer an

alternative under which prior knowledge of proteininteraction is eliminated and replaced by direct measure-ments of abundance In this paper we evaluate a novelapproach to proteomic network analysis that is applic-able to peptide and protein level data (see Figure 1) By

td This is an Open Access article distributed under the terms of the Creativeommonsorglicensesby20) which permits unrestricted use distribution andiginal work is properly cited

Figure 1 Detail of the peptide network (A) The peptide network is decomposed into a set of subnetworks or modules Module members thepeptide nodes are more connected to peptides within the module than across modules (B) Singular value decomposition on the peptideabundance module matrix produces module summaries called eigenpeptides Module prioritization is accomplished by correlating theeigenpeptide with sample phenotypes By correlating peptides to the module summary relative peptide importance is determined (C) Takingpeptides that map to a given protein defines a protein subnetwork Peptides tend to be strongly connected within a protein subnetwork(D) Difficulties arise when peptide nodes map to multiple proteins rendering them degenerate

Gibbs et al Journal of Clinical Bioinformatics 2013 311 Page 2 of 10httpwwwjclinbioinformaticscomcontent3111

using methods derived from weighted gene co-expressionnetwork analysis (WGCNA) [1112] we show that un-biased de novo protein co-expression networks can beconstructed and used for determining potential bio-markers functional module prediction and the discoveryof important elements of human disease We evaluatethese methods utilizing data from two mouse infectiousdisease studies for SARS and Influenza as well as a hu-man population proteomics study for sarcopenia

MethodsMouse and human proteomic data sourcesThree quantitative LC-MS data sets including both humanand mouse disease studies were used The Thermo ElectronExactive platform was used to generate data The PacificNorthwest National Labs (PNNL httpomicspnlgov) de-veloped the Accurate Mass and Time (AMT) tag databasesVIPER (v348) is used to align individual samples and iden-tify peptides using an AMT database [13] Identificationshave confidence metrics the probability for a correctmatch the STAC score and the probability for a uniquedatabase match the uniqueness probability (UP) [14]Peptides with STAC scores gt 0 and UP gt 0 were used Pep-tide abundances were normalized by total ion count persample and log10 scaled Missing data are encountered

when peptides are identified in a subset of samplesldquoMissingness filtrationrdquo involves removing any peptide withgreater than X missing data across samples In this casepeptides with greater than 10 missing data were removedThe infectious disease data came from the NIAID

Systems Virology project (publically available data isfound at httpwwwsystemsvirologyorg) We utilizedboth longitudinal SARS-CoV and influenza mouse stud-ies These data are generated using C57BL6J mice ex-posed to either a mouse adapted SARS-CoV (MA-15) oravian influenza virus (AVietnam12032004 (H5N1VN1203)) [1516] Measurements took place on post in-fection days 1 2 4 and 7SARS control samples include three technical replicates

per day Infected samples are five technical replicates withviral dosages of 102 103 104 and 105 PFU per dayAbundance measurements for 16890 peptides mappingto 3277 proteins were recorded After missingness filtra-tion 2008 peptides mapping to 707 proteins remained352 proteins were associated with a single peptide while355 proteins had two or more peptides associatedInfluenza control samples include three technical rep-

licates per day Infected samples include five technicalreplicates with dosages of 102 103 and 104 PFU per dayAbundances for 10285 peptides mapping to 2661


proteins were recorded After missingness filtration 989peptides associated with 493 proteins remained 274 pro-teins were associated with a single peptide while 219proteins had at least 2 peptides associatedThe human proteomics data (currently unpublished)

comes from a sub-cohort of participants selected from alarge (N=6000) longitudinal study of musculoskeletalhealth in older (ge 65 years) men (from the OsteoporoticFractures in Men (MrOS) Study) [1718] The protocolwas approved by the local institutional review boards Allparticipants provided written informed consent The sub-cohort is focused on the sarcopenia phenotype which isrelated to loss of lean mass and muscle performance [19]A subset of 68 samples from two phenotyped groups(sarcopenic (N=38) and non-sarcopenic (N=30)) based onlean mass and leg power are used Abundances for 10679peptides mapping to 1868 proteins were recorded Aftermissingness filtration 2845 peptides mapping to 685 pro-teins remained 505 proteins were associated with a singlepeptide and 180 were associated with at least twopeptides

Protein co-expression network constructionProtein co-expression networks contain nodes repre-senting peptides connected with edges weighted by simi-larity in abundance profile Edge weights are calculatedusing peptide intensity measurements Although not al-ways representative of absolute abundance intensity isfrequently used to track relative peptide abundance andto infer protein abundance [20] In this work we did notattempt to rectify situations where proteins were repre-sented by a single peptide or where degenerate peptidesmapped to multiple proteins See Additional file 1 for adescription of the software used in this workConstruction of the network follows the WGCNA

method [21-24] Pearsonrsquos correlations are computedpairwise between all peptides retaining the sign as inMason et al resulting in a signed similarity matrix [25]According to the scale-free criterion a power (beta) is se-lected that transforms the distribution of node degrees inthe similarity matrix to log-linear producing the appropri-ate adjacency matrix Topological overlap is a similaritymetric that incorporates information from neighboringnodes making it robust to noisy correlations The TOM iscomputed as TOMij = (lij + aij) [min (ki kj) + 1 - aij]where lij is defined as the dot product on row i and col-umn j in adjacency matrix [a] and ki (the connectivity) isthe summation of row i in adjacency matrix [a] Modulesor subnetworks are composed of strongly connected pep-tides Modules are discovered by hierarchical clustering ofthe distance matrix 1-TOM using the ldquoaveragerdquo agglom-eration method followed by branch cutting with the dy-namic hybrid treecut algorithm [21] The followingparameters were used after visualization and exploratory

analysis deepSplit = 2 minModuleSize = 30 merge-Threshold = 01

Calculating module significance using permutationtestingSimilar to Iancu et al [2627] module significance wasexamined using permutation testing Empirical p-valuesare computed by comparing the mean topological over-lap of peptides within a module to a similarly sizedrandom peptide sample These samples are taken fromthe total set of peptides used in the network For a givenmodule with size n mean edge weights are computed Fora number of trials t a sample of peptides is drawn withsize equal to n and the mean edge weight computed Ifthis value is equal to or higher than the observed modulemean a count is incremented The p-value is equal to(countst) In this work 10000 random samples weredrawn

Summarizing modules with eigenpeptidesAfter assigning peptides to modules an aggregate mod-ule signature is computed The first right-singular vectoror eigenpeptide is computed from a singular valuedecomposition of the standardized abundance modulematrix The eigenpeptide has length equal to the numberof samples This vector acts as an overall summary ofthe module Modules can be prioritized according tocorrelations between the eigenpeptide and biologicalphenotypes Additionally the relative centrality orldquoimportancerdquo of any given peptide within a module isfound by computing a Pearsonrsquos correlation to theeigenpeptide (called the Kme) [2829] Peptides with astrong correlation to the eigenpeptide are said to bemore central and important within the module allowingprioritization on peptides

Describing concordance of peptides within modulesConcordance among a set of peptides relates to theshared sign of the slope when regressed against a givenvariable such as time or infection status Our approachto this problem involves constructing protein sub-networks initially as ldquoall-to-allrdquo networks After applyinga topological overlap threshold edges start to fall awayThis results in a disjoint set of connected componentsTwo methods were used to examine whether concord-

ant peptides are connected in the network First a linearmodel is constructed for each peptide using a referencevariable such as time or a phenotypic trait Peptides areclassified as increasing (+1) decreasing (minus1) or no-slope(0) depending on the adjusted p-value If a connected sub-graph contains both increasing and decreasing peptides itis considered a discordant component (see Figure 2)Alternatively the expression fold change (infected vs

non-infected) of peptides can be compared within a


protein to determine discordance A fold change cut-offof 15 was used to define peptides as up or down regu-lated If both up and down labeled peptides mapped to aprotein then the protein was counted as discordant

Testing for strong peptide connectivity by proteinSimilar to testing for module significance the connectivityamong peptides mapping to a given protein can be testedby permutation For each protein with greater than twopeptides the pairwise topological overlaps are averagedThen for a set number of trials (10000) the same numberof peptides is randomly sampled from all peptides in thenetwork and the mean pair wise topological overlaprecorded The empirical p-value is taken as the number oftimes the random sample has values equal or greater thanthe observed case divided by the number of trials Thistest can also be applied using correlations between thepeptides

Protein-protein interaction enrichment within modulesCo-expression modules are thought to reflect to some de-gree true protein interactions To examine this we com-pare the contents of modules with known PPIs Aspreviously done permutation testing was used to deter-mine whether a significant amount of PPI edges existwithin a module Within each module peptides with weakconnections to the module eigenpeptide were removed(Kme lt 0333) Centrality filtration is performed to focusthe analysis on peptides associated with overall modulefunction The remaining peptides are mapped to proteinsProteins with any number of mapping peptides are in-cluded The number of observed PPIs within a module isrecorded and compared to the number of PPIs in a ran-dom module for a set number of trials P-values are com-puted using permutation testing as before The PPIdatabases HPRD [30] and MPPI [31] were used for humanand mouse data respectively

Figure 2 Utility of de novo network inference in resolving peptide levtopological overlap show correlated clusters Taken together some proteinsuch as time some peptides are increasing in abundance and other decreapeptides were used A protein sub-network is constructed by taking associspecified upper quantile (eg the upper 20 of all topological overlaps forthe number of connected components with discordant peptides dramaticacan be guided by network topology

Pathway enrichment within modulesAfter PPI enrichment tests significant sets of proteinswere collected by module Querying KEGG [3233] usingthe R package KEGGSOAP [34] with these proteins pro-vided a list of potential pathways to investigate by moduleFor each pathway returned a hypergeometric test wasperformed using significant PPIs from the module andother proteins taking part in the pathway The universe isdefined as the subset of proteins in the mass tag databasewith known roles in KEGG pathways P-values are ad-justed using the Benjamini and Yekutieli method [35]

Gene ontology functional enrichment within modulesFunctional enrichment on modules was computed usingthe R package GOstats [36] Peptides are mapped to pro-teins and counted once in any module The universe isdefined as all proteins found in the AMT mass tag data-base (similar to microarray studies) Annotation data-bases ldquoorgMmegdbrdquo and ldquoorgHsegdbrdquo (Bioconductor28) are used for mouse and human annotations Condi-tional hypergeometric testing is used to account for cor-related GO terms

Results and discussionPeptide networks were approximately scale-freeScale-free network topologies have node degree distribu-tions following the power law [3738] There is a continu-ous range of node degrees with the fewest nodes havingthe greatest number of connections [3738] We foundthat peptide networks share this topology (Figure 3) andhave biologically informative graph properties similar tothose found in gene co-expression networks (See descrip-tive network statistics in Table 1)With regard to distinct and significant modules the

SARS network contained 14 modules ranging from 65 to369 peptides with a mean size of 1339 peptides The In-fluenza network contained 6 modules with sizes ranging

el discordance Protein sub-networks constructed using peptides show conflicts between constituent peptides where given a variablesing (A) To examine this only proteins with uniquely mappingated peptides and keeping only edges with topological overlaps in athe protein) In all three data sources as the edge threshold is raisedlly decreases (B C) This suggests that inference of protein abundance

Figure 3 Protein co-expression networks are shown to be approximately scale-free As the soft thresholding power β grows the resultingadjacency matrix increasingly fits the scale-free model This trend is robust to missing data shown here with networks constructed using signedsimilarity matrices Subsets of peptide data were taken to eliminate missing data (dark lines) Then incrementally additional peptides wereincluded containing between one to ten missing data points shown here with lighter broken lines For network analysis it is strongly in ourinterest to incorporate peptides with missing data since it increases the proteome coverage without weakening the model


from 56 to 327 peptides and a mean size of 1413 pep-tides The sarcopenia network contained 19 modulesranging in size from 36 to 477 peptides with a mean sizeof 14225 peptides An initial examination indicates thatlow to moderate levels of missing data did not negativelyimpact the model fit (Figure 3) Importantly we notethat all of the identified de-novo modules have signifi-cant connectivity with the exception of the sarcopenianetwork which contained one module without signifi-cant connectivity (p-value 033)

Significant modules were correlated with phenotypeUsing module summaries (ie eigenpeptide) correlationwith biological phenotypes can guide the discovery of

Table 1 Co-expression network construction methods are app

Data Peptides Proteins Power

Sarcopenia 2845 685 15

SARS 2008 707 16

Influenza 989 493 15

Power is the parameter used to scale the adjacency matrix R2 and Slope describe thadjacency matrix

biomarkers and aid in prioritization of modules for val-idation and perturbation experiments (see Figure 4)In the SARS network strong correlations with disease-

related pathological features were observed including dif-fuse alveolar damage tissue inflammation and alveoliparenchyma pneumonia (Figure 5) The strongest correla-tions were found with time (module 3 Pearson correlation08 p-value 1e-22) potentially relating to progression ofinfectionThe influenza network showed strong correlations with

average weight loss an important indicator of infection se-verity Two modules showed positive correlation (p-values2e-10 and 2e-6) and two modules showed negative correl-ation (p-values 8e-10 and 2e-15)The sarcopenia network showed the weakest correla-

tions with sample phenotypes Several modules correlate

licable to proteomics

R^2 Slope Mean K Modules

081 minus155 2522 19

076 minus167 108 14

082 minus131 700 6

e scale-free topology fit Definition of mean K network connectivity using the

Figure 4 Correlation with biological phenotypes can aid in prioritization of modules and proteins In modules where the eigenpeptide isstrongly correlated with a biological phenotype an upward trend is observed between the Kme of a peptide and the correlation with the givenphenotype An illustration from the Influenza data is shown This demonstrates structural order within the module After sorting along thesedimensions top peptides suggest further experiments


with technical variables indicating that the normalizationmethod did not completely remove systematic effectsThis finding guided the re-evaluation of data processingand motivated new methods in normalization which is inpreparation by Baraff et al

Peptide modules had significant protein-levelconnectivityGiven a complex biological mixture a significant problemin quantitative proteomics remains in confidently identify-ing the protein component This problem is made worseby the existence of degenerate peptides which can lead tomultiple solutions for peptide to protein mapping Wefind that the connectivity of a proteinrsquos constituent pep-tides is far from random This is potentially useful forresolving cases of degenerate peptide mapping increasingconfidence in protein identification

Topological overlap can be utilized with a threshold toidentify high confidence edges between peptides Uponexamination we found that with a topological overlapthreshold of 80 (keeping only edges in the top 20 of allweights) the majority of proteins remained connected(Sarcopenia 84 SARS 72 influenza 63)

To test for significant protein connectivity we com-pared the mean topological overlap between constituentpeptides and similar numbers of randomly selected pep-tides In this evaluation of mean protein connectivitiesand random connectivities we found significant connec-tions between constituent peptides compared to thoseseen at random (Table 2) This suggests that the networkstructure should be helpful in resolving degenerate map-pings by comparing network graphs and connectivitiesfor alternative peptide-to-protein attributions

Strongly connected peptides were concordantDiscordance observed among related peptides (ie thosefrom the same protein) might reflect the activities of pro-teins with differing post-translational modifications or dif-ferent isoforms First we assessed the relationship betweenconnectivity and peptide discordance with respect toabundance over time (Figure 6) In the influenza data setas the edge weight threshold increases the number of dis-cordant edges quickly drops to zero far before the lineartrend of concordant edgesExamining discordance in the fold change of peptide

expression and using proteins with unique peptide map-pings 48 of 218 proteins in the SARS data were

Figure 5 The de novo modules (represented by module eigenpeptide ME) are highly correlated to pathologically associatedphenotypes An illustration from the SARS dataset is shown Clear patterns emerge showing positive and negative correlation clusters Asexpected related phenotypes such as airspace inflammation interstitial septum inflammation and diffuse alveolar damage tend to be correlatedin the same direction showing an overarching biological process at work Label Key AlvParPneumonia alveolar parenchyma pneumonia DADdiffuse alveolar damage OverallTotalScore cumulative score calculated by a pathologist

Table 2 Network topology may be useful for resolvingdegenerate peptide mappings

Data Peptides Proteins Mean TO RandomTO p-value

Sarcopenia 2845 685 0089 0004 209e-14

SARS 2008 707 0025 0004 22e-16

Influenza 989 493 0028 0005 899e-16

Results from a two-sample t-test between topological overlap (TO) of peptidesderived from the same protein versus peptides selected at random This resultstatistically shows that the connectivity for a proteinrsquos constituent peptides isfar from random Network topology may be useful for resolving cases ofdegenerate peptide mapping increasing confidence in protein identification


discordant After applying a topological overlap thresh-old of 08 (as above) the number of discordant compo-nents dropped to 24 and with a threshold of 09dropped to 12 In the sarcopenia network peptides thatwere modeled against leg strength provided the mostdiscordant proteins resulting in 11 of 139 discordantproteins After applying the topological overlap thresh-olds of 08 and 09 these dropped to 4 and 3 respect-ively In influenza there were 15 (out of 115 proteins)classified as discordant this reduced to 7 and 3 respect-ively after applying the 08 and 09 thresholds It appearsthat edge strength is informative with respect to con-cordance among constituent peptides for any given

Figure 6 Strongly connected peptides are concordant when regressed against time An illustration from the influenza dataset is shown Aprotein sub-network consists of peptides mapping to a given protein forming a all-to-all network with weighted edges Applying an edgethreshold decreases the number of ldquostrongrdquo peptide connections An edge is counted as concordant if the two connected peptides havesignificant slopes in the same direction after linear regression against infection day The discordant edges are clearly differentiated byedge thresholding


protein showing potential utility for both protein infer-ence and quantification

Modules had significant enrichment for known PPIinteractionsDe novo co-expression modules are thought to be usefulfor detecting new interactions andor functional pathwaymembers We first evaluate however whether the denovo modules are enriched for known interactions toaid in assessing the utility of this approach Using theHURD and MPPI protein-protein interaction databasessignificant interactions were identified in all three exper-iments After adjusting for multiple testing the influenzanetwork had 56 modules with significant PPI enrich-ment the SARS network had 1214 modules signifi-cantly enriched and the MrOS network had 1019 Somemodules overlapped in terms of mapped proteins whichis typically the result of highly similar or related proteinsequences such as sets of histonesWe then examined whether modules with significant

PPIs were also enriched for known pathways similar towhat has been seen in de-novo gene expression studiesFor the influenza modules with significant PPI

enrichment we examined known pathways in the KEGGdatabase When defining the universe (or background forcomparison) as all proteins contained in the mass tagdatabase (5521 proteins) a range of significant pathwayswere found including ldquoregulation of actin cytoskeletonrdquo(mmu04810) the ldquotight junctionsrdquo pathway (mmu04530)and the ldquoantigen presentation and processingrdquo pathway(mmu04612) However if the universe is restricted to onlythose proteins with KEGG annotations (2539 proteins)the antigen presentation pathway alone remained signifi-cant in two modules These pathways are important in thepathological progression of influenza highlighting therelationship between network structure and biology Iden-tification of enriched pathways in these unbiased modulescould potentially aid discovery of novel interactions orpathway members

Modules had significant gene ontology functionalenrichmentGiven the significant numbers of PPI interactions mod-ules may have overarching functional organization Tostudy this Gene Ontology enrichment by module wasevaluated using the GOstats package [36] All three data


sources showed GO term enrichment with highly signifi-cant Bonferroni adjusted p-values (Additional file 2Table S1) In the SARS and influenza networks enrich-ment for biological processes such as DNA packagingcellular component assembly and cellular complexassembly was observed The sarcopenia network mod-ules also showed significant functional enrichment in-cluding immune response and blood processes Thisfurther reiterates the non-random biological compos-ition of these modules and provides support for the useof this approach to network inference in proteomics Wenote that this framework is generalizable to many datatypes and is not limited to proteomics

ConclusionsWe have demonstrated the feasibility of constructing denovo peptide co-expression networks We show that thesenetworks have a biologically meaningful and approxi-mately scale-free topology and contain statistically signifi-cant modules We also noted that the network structureand connectivity of the modules are potentially useful forresolution of degenerate peptides and inference of proteinabundance Across three distinct experiments we have il-lustrated how module summaries significantly correlatewith clinically relevant phenotypes In addition we haveshown how de novo modules show significant enrichmentfor known PPI and biological function Peptides can beranked according to their module centrality and relation-ship to phenotypic traits allowing researchers to prioritizetargets for further research Finally modules can provide anatural aggregate representation for composite biomarkerdiscovery

Additional files

Additional file 1 The ProCoNA Software Supplemental (added toBioconductor) describes the R package developed as part of thiswork along with the relevant functions and descriptions of theiruse

Additional file 2 Table S1 Protein Co-expression Network Analysis(ProCoNA) GO enrichment summary results and examples These tablesgive in-depth examples of the results from gene ontology enrichment foreach experiment

Competing interestsThe authors declare that they have no competing interests

Authorsrsquo contributionsDLG designed and programmed the methods interpreted the results andwrote the manuscript AB maintained the proteomics pipeline andperformed the peptide identification RB provided the SARS-CoV virus andphenotype data YK provided the influenza virus and phenotype data RDSprovided the proteomics data and software EO provided data andinterpretation of the human cohort MK provided gene expression data SMdesigned methods and made significant contributions to the writing Allauthors read and approved the final manuscript

AcknowledgementsThis work was supported by the National Institute of Allergy and InfectiousDiseases National Institutes of Health Department of Health and HumanServices [HHSN272200800060C 5U54AI081680] National Institutes of HealthNational Center for Advancing Translational Sciences [5UL1RR024140]National Library of Medicine [3T15LM7088-18S1] and National CancerInstitute [5P30CA069533]The Osteoporotic Fractures in Men (MrOS) Study is supported by NationalInstitutes of Health funding The following institutes provide support theNational Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)the National Institute on Aging (NIA) the National Center for ResearchResources (NCRR) and NIH Roadmap for Medical Research under thefollowing grant numbers U01 AR45580 U01 AR45614 U01 AR45632 U01AR45647 U01 AR45654 U01 AR45583 U01 AG18197 U01 AG027810 andUL1 RR024140 The National Institute for Dental and Craniofacial Research(NIDCR) provides funding for the MrOS Dental ancillary study ldquoOral andSkeletal Bone Loss in Older Menrdquo under the grant number R01 DE014386The experimental proteomics measurements described herein weresupported in part by the NIH NIGMS P41 BTRC [RR185220 and GM103493-10]and was performed in the Environmental Molecular Sciences LaboratoryEMSL a national scientific user facility sponsored by DOEBiological andEnvironmental Research (BER) and located at Pacific Northwest NationalLaboratory which is operated by the Battelle Memorial Institute for the DOEunder Contract DE-AC05-76RL0 1830 All authors read and approved the finalmanuscript

Author details1Division of Bioinformatics and Computational Biology Oregon Health ampScience University 3181 SW Sam Jackson Park Rd Portland OR 97239 USA2Oregon Clinical amp Translational Research Institute Oregon Health amp ScienceUniversity 3181 SW Sam Jackson Park Rd Portland OR 97239 USA3Department of Microbiology and Immunology University of North Carolinaat Chapel Hill 220 E Cameron Ave Chapel Hill NC 27514 USA 4Departmentof Pathobiological Sciences University of Wisconsin-Madison 2015 LindenDr Madison WI 53706 USA 5Biological Sciences Division Pacific NorthwestNational Laboratory Richland WA USA 6Department of MicrobiologySchool of Medicine Box 357735 University of Washington Seattle WA98195 USA 7OHSU Knight Cancer Institute Oregon Health amp ScienceUniversity 3181 SW Sam Jackson Park Rd Portland OR 97239 USA

Received 22 February 2013 Accepted 23 May 2013Published 1 June 2013

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10 Figeys D Mapping the human protein interactome Cell Res 200818716ndash724

Gibbs et al Journal of Clinical Bioinformatics 2013 311 0 of 10httpwwwjclinbioinformaticscomcontent3111

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16 Barnard DL Animal models for the study of influenza pathogenesis andtherapy Antiviral Res 2009 82A110ndash22

17 Orwoll E Blank JB Barrett-Connor E Cauley J Cummings S Ensrud K LewisC Cawthon PM Marcus R Marshall LM McGowan J Phipps K Sherman SStefanick ML Stone K Design and baseline characteristics of theosteoporotic fractures in men (MrOS) studyndasha large observational studyof the determinants of fracture in older men Contemp Clin Trials 200526569ndash585

18 Cawthon PM Marshall LM Michael Y Dam T-T Ensrud KE Barrett-Connor EOrwoll ES Osteoporotic Fractures in Men Research Group Frailty in oldermen prevalence progression and relationship with mortalityJ Am Geriatr Soc 2007 551216ndash1223

19 Morley JE Baumgartner RN Roubenoff R Mayer J Nair KS SarcopeniaJ Lab Clin Med 2001 137231ndash243

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21 Langfelder P Zhang B Horvath S Defining clusters from a hierarchicalcluster tree the Dynamic Tree Cut package for R Bioinformatics 200824719ndash720

22 Langfelder P Horvath S Fast R Functions for Robust Correlations andHierarchical Clustering J Stat Softw 2012 461ndash17

23 Langfelder P Luo R Oldham MC Horvath S Is my network modulepreserved and reproducible PLoS Comput Biol 2011 7e1001057

24 Langfelder P Tutorial for the WGCNA package for R [httplabsgeneticsuclaedu horvathCoexpressionNetworkRpackagesWGCNATutorialsindexhtml]

25 Mason MJ Fan G Plath K Zhou Q Horvath S Signed weighted geneco-expression network analysis of transcriptional regulation in murineembryonic stem cells BMC Genomics 2009 10327

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27 Iancu OD Kawane S Bottomly D Searles R Hitzemann R McWeeney SUtilizing RNA-Seq data for de novo coexpression network inferenceBioinformatics 2012 281592ndash1597

28 Oldham MC Konopka G Iwamoto K Langfelder P Kato T Horvath SGeschwind DH Functional organization of the transcriptome in humanbrain Nat Neurosci 2008 111271ndash1282

29 Oldham MC Horvath S Geschwind DH Conservation and evolution ofgene coexpression networks in human and chimpanzee brains Proc NatlAcad Sci 2006 10317973ndash17978

30 Keshava Prasad TS Goel R Kandasamy K Keerthikumar S Kumar SMathivanan S Telikicherla D Raju R Shafreen B Venugopal A BalakrishnanL Marimuthu A Banerjee S Somanathan DS Sebastian A Rani S Ray SHarrys Kishore CJ Kanth S Ahmed M Kashyap MK Mohmood RRamachandra YL Krishna V Rahiman BA Mohan S Ranganathan PRamabadran S Chaerkady R Pandey A Human Protein ReferenceDatabasendash2009 update Nucleic Acids Res 2009 37D767ndashD772

31 Yellaboina S Dudekula DB Ko MS Prediction of evolutionarily conservedinterologs in Mus musculus BMC Genomics 2008 9465

32 Kanehisa M The KEGG resource for deciphering the genome NucleicAcids Res 2004 32277Dndash280

33 Kawashima S Katayama T Sato Y Kanehisa M KEGG API A web serviceusing SOAPWSDL to access the KEGG system In International Conferenceon Genome Informatics December 14-17 2003 Pacifico Yokohama Japan2003 14673ndash674

34 Zhang J Gentleman R KEGGSOAP Client-side SOAP access KEGG[httpwwwbioconductororgpackagesreleasebiochtmlKEGGSOAPhtml]

35 Benjamini Y Yekutieli D The control of the false discovery rate inmultiple testing under dependency Ann Stat 2001 291165ndash1188

36 Falcon S Gentleman R Using GOstats to test gene lists for GO termassociation Bioinformatics 2007 23257ndash258

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doi1011862043-9113-3-11Cite this article as Gibbs et al Protein co-expression network analysis(ProCoNA) Journal of Clinical Bioinformatics 2013 311

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Abstract

Background

Results

Conclusions

Background

Methods

Mouse and human proteomic data sources

Protein co-expression network construction

Calculating module significance using permutation testing

Summarizing modules with eigenpeptides

Describing concordance of peptides within modules

Testing for strong peptide connectivity by protein

Protein-protein interaction enrichment within modules

Pathway enrichment within modules

Gene ontology functional enrichment within modules

Results and discussion

Peptide networks were approximately scale-free

Significant modules were correlated with phenotype

Peptide modules had significant protein-level connectivity

Strongly connected peptides were concordant

Modules had significant enrichment for known PPI interactions

Modules had significant gene ontology functional enrichment

Conclusions

Additional files

Competing interests

Authorsrsquo contributions

Acknowledgements

Author details

References

Figure 1 Detail of the peptide network (A) The peptide network is decomposed into a set of subnetworks or modules Module members thepeptide nodes are more connected to peptides within the module than across modules (B) Singular value decomposition on the peptideabundance module matrix produces module summaries called eigenpeptides Module prioritization is accomplished by correlating theeigenpeptide with sample phenotypes By correlating peptides to the module summary relative peptide importance is determined (C) Takingpeptides that map to a given protein defines a protein subnetwork Peptides tend to be strongly connected within a protein subnetwork(D) Difficulties arise when peptide nodes map to multiple proteins rendering them degenerate

Gibbs et al Journal of Clinical Bioinformatics 2013 311 of 10httpwwwjclinbioinformaticscomcontent3111

using methods derived from weighted gene co-expressionnetwork analysis (WGCNA) [1112] we show that un-biased de novo protein co-expression networks can beconstructed and used for determining potential bio-markers functional module prediction and the discoveryof important elements of human disease We evaluatethese methods utilizing data from two mouse infectiousdisease studies for SARS and Influenza as well as a hu-man population proteomics study for sarcopenia

MethodsMouse and human proteomic data sourcesThree quantitative LC-MS data sets including both humanand mouse disease studies were used The Thermo ElectronExactive platform was used to generate data The PacificNorthwest National Labs (PNNL httpomicspnlgov) de-veloped the Accurate Mass and Time (AMT) tag databasesVIPER (v348) is used to align individual samples and iden-tify peptides using an AMT database [13] Identificationshave confidence metrics the probability for a correctmatch the STAC score and the probability for a uniquedatabase match the uniqueness probability (UP) [14]Peptides with STAC scores gt 0 and UP gt 0 were used Pep-tide abundances were normalized by total ion count persample and log10 scaled Missing data are encountered

when peptides are identified in a subset of samplesldquoMissingness filtrationrdquo involves removing any peptide withgreater than X missing data across samples In this casepeptides with greater than 10 missing data were removedThe infectious disease data came from the NIAID

Systems Virology project (publically available data isfound at httpwwwsystemsvirologyorg) We utilizedboth longitudinal SARS-CoV and influenza mouse stud-ies These data are generated using C57BL6J mice ex-posed to either a mouse adapted SARS-CoV (MA-15) oravian influenza virus (AVietnam12032004 (H5N1VN1203)) [1516] Measurements took place on post in-fection days 1 2 4 and 7SARS control samples include three technical replicates

per day Infected samples are five technical replicates withviral dosages of 102 103 104 and 105 PFU per dayAbundance measurements for 16890 peptides mappingto 3277 proteins were recorded After missingness filtra-tion 2008 peptides mapping to 707 proteins remained352 proteins were associated with a single peptide while355 proteins had two or more peptides associatedInfluenza control samples include three technical rep-

licates per day Infected samples include five technicalreplicates with dosages of 102 103 and 104 PFU per dayAbundances for 10285 peptides mapping to 2661





























SARS 2008 707 16









081 minus155 2522 19

076 minus167 108 14

082 minus131 700 6













Sarcopenia 2845 685 0089 0004 209e-14

SARS 2008 707 0025 0004 22e-16

Influenza 989 493 0028 0005 899e-16














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Abstract

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Results

Conclusions

Background

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SARS 2008 707 16









081 minus155 2522 19

076 minus167 108 14

082 minus131 700 6













Sarcopenia 2845 685 0089 0004 209e-14

SARS 2008 707 0025 0004 22e-16

Influenza 989 493 0028 0005 899e-16














Additional files

























































Abstract

Background

Results

Conclusions

Background

Methods

















Conclusions

Additional files

Competing interests


Acknowledgements

Author details

References












Sarcopenia 2845 685 0089 0004 209e-14

SARS 2008 707 0025 0004 22e-16

Influenza 989 493 0028 0005 899e-16














Additional files

























































Abstract

Background

Results

Conclusions

Background

Methods

















Conclusions

Additional files

Competing interests


Acknowledgements

Author details

References











Additional files

























































Abstract

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Results

Conclusions

Background

Methods

















Conclusions

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Competing interests


Acknowledgements

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Abstract

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Results

Conclusions

Background

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Competing interests


Acknowledgements

Author details

References

methodology open access protein co-expression network

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