on november 26, 2014 bcatenin is central to dux4-driven ...ucapsse/fshd.pdf · facioscapulohumeral...

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ResearchCite this article: Banerji CRS, Knopp P, MoyleLA, Severini S, Orrell RW, Teschendorff AE,

Zammit PS. 2015 b-catenin is central to

DUX4-driven network rewiring in

facioscapulohumeral muscular dystrophy.

J. R. Soc. Interface 12: 20140797.http://dx.doi.org/10.1098/rsif.2014.0797

Received: 21 July 2014

Accepted: 27 October 2014

Subject Areas:bioinformatics, systems biology, biochemistry

Keywords:facioscapulohumeral muscular dystrophy,

meta-analysis, b-catenin, differential

networks, canonical Wnt signalling, DUX4

Authors for correspondence:Christopher R. S. Banerji

e-mail: [email protected]

Peter S. Zammit

e-mail: [email protected]

†These authors contributed equally to this

work.

Electronic supplementary material is available

at http://dx.doi.org/10.1098/rsif.2014.0797 or

via http://rsif.royalsocietypublishing.org.

& 2014 The Author(s) Published by the Royal Society. All rights reserved.

b-catenin is central to DUX4-drivennetwork rewiring in facioscapulohumeralmuscular dystrophy

Christopher R. S. Banerji1,2,3,4,†, Paul Knopp4,†, Louise A. Moyle4,Simone Severini2, Richard W. Orrell5, Andrew E. Teschendorff1,6

and Peter S. Zammit4

1Statistical Cancer Genomics, Paul O’Gorman Building, UCL Cancer Institute,2Department of Computer Science, and 3Centre of Mathematics and Physics in the Life Sciences andExperimental Biology, University College London, London WC1E 6BT, UK4Randall Division of Cell and Molecular Biophysics, King’s College London, New Hunt’s House, Guy’s Campus,London SE1 1UL, UK5Department of Clinical Neuroscience, Institute of Neurology, University College London, Rowland Hill St.,London NW3 2PF, UK6CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences,Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, People’s Republic of China

Facioscapulohumeral muscular dystrophy (FSHD) is an incurable disease,characterized by skeletal muscle weakness and wasting. Genetically, FSHD ischaracterized by contraction or hypomethylation of repeat D4Z4 units onchromosome 4, which causes aberrant expression of the transcription factorDUX4 from the last repeat. Many genes have been implicated in FSHD patho-physiology, but an integrated molecular model is currently lacking. Wedeveloped a novel differential network methodology, Interactome Sparsificationand Rewiring (InSpiRe), which detects network rewiring between phenotypesby integrating gene expression data with known protein interactions. UsingInSpiRe, we performed a meta-analysis of multiple microarray datasets fromFSHD muscle biopsies, then removed secondary rewiring using non-FSHDdatasets, to construct a unified network of rewired interactions. Our analysisidentified b-catenin as the main coordinator of FSHD-associated protein inter-action signalling, with pathways including canonical Wnt, HIF1-a and TNF-aclearly perturbed. To detect transcriptional changes directly elicited by DUX4,gene expression profiling was performed using microarrays on murine myo-blasts. This revealed that DUX4 significantly modified expression of the genesin our FSHD network. Furthermore, we experimentally confirmed that Wnt/b-catenin signalling is affected by DUX4 in murine myoblasts. Thus, we providethe first unified molecular map of FSHD signalling, capable of uncoveringpathomechanisms and guiding therapeutic development.

on November 26, 2014//rsif.royalsocietypublishing.org/

1. IntroductionFacioscapulohumeral muscular dystrophy (FSHD) is the third most commoninheritable disease of skeletal muscle, yet, due to the relative longevity ofpatients, it is the most prevalent muscular dystrophy (approx. 12/100 000[1,2]). Despite such prevalence, no curative therapy exists. Clinically, FSHD ischaracterized by asymmetric, skeletal muscle atrophy affecting specificmuscle groups, often associated with features including retinal vasculatureabnormalities and sensorineural hearing loss [1,3–5]. Approximately 95% ofcases (FSHD1; OMIM158900) are associated with deletion of a number of3.3 kb D4Z4 macrosatellite repeats on chromosome 4q35. Healthy individualstypically have between 11 and 100 such repeats, whereas FSHD1 patientshave 1–10. Importantly, complete loss of D4Z4 units is not associated withFSHD. Each D4Z4 repeat contains an open reading frame for a transcriptionfactor, double homeobox 4 (DUX4) [6,7]. Reduced D4Z4 repeat number leads

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to hypomethylation, which on a permissive genetic back-ground supplying a polyadenylation signal, stabilizesDUX4 transcripts from the last D4Z4 repeat. In the remain-ing 5% of cases (FSHD2; OMIM158901), D4Z4 repeats arenot contracted, but become hypomethylated, likely throughmutations in chromatin-modifying genes such as SMCHD1[8]. Again on the permissive genetic background with a poly-adenylation signal, this leads to DUX4 transcription from thelast D4Z4 repeat. Thus, aberrant DUX4 expression underliesFSHD pathogenesis in both FSHD1 and FSHD2 [9]. HowDUX4 perturbs pathway interactions to produce the complexFSHD phenotype is unclear, with many genes and pathwaysimplicated in FSHD. Indeed, the FSH Society FSHD Inter-national Research Consortium has annually set uncoveringFSHD molecular networks as a top research priority.

Complex diseases are frequently examined by geneexpression profiling using an arrayed platform of cDNA probes(microarray) and many datasets are published each year. Thesedescribe healthy and diseased biological processes, includingFSHD and other muscular dystrophies and myopathies. How-ever, their use is often limited to selecting genes for furtherexamination based on differential expression, evidence fromthe literature or intuition. Systems biology and network concepts,particularly the conceptual framework of differential networksand network rewiring, allow higher order, unbiased analysis ofsuch data [10,11]. These approaches involve integration ofmolecular profiles (especially gene or protein expression) withnetwork models of protein interactions [12,13], to identifyeither hotspots of differential expression, or a change in localinteraction patterns-network rewiring. Although manydifferential network algorithms exist [13,14], each has limitations.

Here, we developed a novel differential network algor-ithm based on information theoretic principles that we callInteractome Sparsification and Rewiring (InSpiRe). InSpiRedetects shifts in active pathway regimes, identifying proteinsfrom the human interactome displaying a wide class of rewir-ing events between two phenotypes. This protein set isconstructed into a sub-network, which is sparsified todescribe pathways altered between phenotypes.

Here, we performed a meta-analysis with InSpiRe on multi-ple FSHD and healthy human control datasets to identifynetwork rewiring. Rewiring associated with ageing, disuseatrophy, inflammation and muscle wasting was then subtractedto construct a unified FSHD-specific disease network. Our FSHDnetwork is significantly more connected than expected by chance(99.5% of the network forms a single maximally connected com-ponent) demonstrating that the disease phenotype arises via ahighly coordinated perturbation of signalling pathways.

Our analysis confirms previous findings on processes andsignalling pathways perturbed in FSHD, such as myogenesis,oxidative stress sensitivity, actin cytoskeletal signalling, Wnt/b-catenin signalling and p53-mediated apoptosis [4,15–17].Importantly, we also describe novel FSHD molecular mech-anisms. Notably, local network measures revealed b-cateninat the centre of our network, as the main coordinator ofFSHD-associated protein interaction signalling, and providesinsight into the precise molecular determinants of oxidativestress sensitivity. To examine how much of our FSHD net-work can be explained by DUX4, we performed expressionprofiling using microarrays of primary satellite cell-derivedmurine myoblasts expressing DUX4. This revealed thatexpression of genes in our FSHD network is directly attribu-ted to DUX4. A recent single dataset study demonstrated that

DUX4 expression in human myoblasts perturbs a large pro-portion of genes which are differentially expressed in FSHD[18]. Our analysis further demonstrates in a meta-analysis set-ting that signalling network rewiring in FSHD, independentof atrophy, ageing and inflammation, can also be explainedby DUX4, lending more support to the hypothesis thatDUX4 expression drives pathology.

To determine whether DUX4 expression results in b-cateninsignalling, we assayed readouts of the Wnt pathway and foundsignificant DUX4-mediated activation of b-catenin signalling inmouse myoblasts. Thus, we provide the first integrated FSHDnetwork, capable of explaining DUX4-driven pathomechanismsand informing development of therapeutics.

2. Results2.1. Meta-analysis of facioscapulohumeral muscular

dystrophy datasets using InSpiReInSpiRe is a differential network methodology we designed toextract a subset of the human protein interaction networkcontaining proteins and interactions that are altered betweentwo phenotypes described by expression data. The three stepsof InSpiRe are summarized in figure 1, and explained in detailin Material and methods.

Four human FSHD datasets were obtained from the GEODatabase. GSE3307 [17,19] consisted of gene expression datafrom 14 skeletal muscle (nine biceps, five deltoid) biopsiesfrom FSHD patients, and 14 skeletal muscle biopsies fromhealthy, matched control individuals. GSE10760 [5] containsgene expression data from vastus lateralis biopsies from 19FSHD patients and 30 control individuals. Both GSE3307 andGSE10760 were profiled on the Affymetrix Human GenomeU133A Array platform. GSE26145 [20] and GSE26061 [21] areexon array studies using the Affymetrix Human Exon 1.0 STArray each profiling three myoblast and three myotube samplesfrom either quadriceps, rhomboid or deltoid muscles of threeFSHD patients and three control individuals. As only isolatedcells were arrayed in GSE26145 [20] and GSE26061 [21], non-muscle gene expression was assumed to be low/negligible.These studies were employed in our meta-analysis to refinethe larger datasets of primary muscle biopsies of GSE3307[17,19] and GSE10760 [5]. All datasets were pre-processed andnormalized as described in Material and methods.

Approximately 3500 genes were implicated per set byInSpiRe as rewiring between FSHD and control samples.Considering all FSHD data, the intersection of rewired setsconsisted of a significantly large overlap of 829 genes( p , 10� 1025, based on randomly selecting genes from eachdataset and assessing the size of overlap). Many genes in thisintersection have been associated with FSHD, e.g. TP53 [16],JUNB [20,17], HIF1A [20], WNT3 [4], LMO3 [20], ANXA4 [5]and HSPB1 [22]. Gene Set Enrichment Analysis (GSEA) [23] onthe intersection also implicated many FSHD associated pro-cesses, such as myogenesis [17] and regulation of the actincytoskeleton [15], and pathways, including, p53 [16], Wnt [4],and VEGF [5] signalling.

2.2. Gene expression changes specific tofacioscapulohumeral muscular dystrophy

Superimposed on network rewiring due to FSHD molecularmechanisms, are rewiring events due to non-specific changes.

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primary muscle biopsieshealthy and pathological

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protein A protein B

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Figure 1. An overview of the InSpiRe algorithm. Public databases are interrogated for expression data and protein – protein interaction data, as inputs to thealgorithm. (a) Step 1 of InSpiRe: integration of expression data with the protein interaction network, via Pearson correlations, results in a weighted networkfor each phenotype. (b) Step 2 of InSpiRe: information theoretic measures detect rewiring hotspots between the two phenotypes. Differential (local) flux entropydetects shifts in active pathway regimes, Kullback – Leibler (KL) divergence detects rewiring events to which differential entropy is blind. (c) Step 3 of InSpiRe:sparsification of the neighbourhood of rewiring proteins, through consideration of differential correlations across phenotypes, results in a sparse networksubset, containing proteins and interactions that are significantly rewiring between the two phenotypes.


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To identify genes implicated as rewiring specifically in FSHD,we also ran InSpiRe on two datasets each describing skeletalmuscle gene expression during ageing (GSE5086 [24] andGSE9676 [25]), disuse atrophy (GSE5110 [26] and GSE8872[27]) and other muscle diseases involving inflammation andwasting (GSE3307 [19], where juvenile dermatomyositisand limb-girdle muscular dystrophy type 2A datasets wereindependently analysed). Genes rewiring in these non-FSHD datasets were considered as secondary rewiring. Ofthe 829 InSpiRe identified genes, 273 were associated withageing, 364 with disuse atrophy and 394 with other musclediseases, identifying a core set of 164 genes specifically rewir-ing in FSHD (electronic supplementary material, table S1).For the final stage of InSpiRe, we considered the 164 high con-fidence genes and their direct neighbours in the proteininteraction network (a complete network of 2866 genes). Stat-istical sparsification on the largest FSHD dataset (GSE10760)was performed to eliminate interactions that were not signifi-cantly altered between FSHD and control phenotypes, togenerate the FSHD network.

2.3. The facioscapulohumeral muscular dystrophynetwork

Our FSHD network consists of 2616 proteins and 15 972 inter-actions, the majority of which form a single maximallyconnected component (2603/2616 genes). To evaluate the sig-nificance of certain properties of the FSHD network, wecomputed a distribution of random networks by performing1000 random selections of 164 core genes and re-running thestatistical sparsification on GSE10760 each time. This demon-strated that the FSHD network has significantly moreinteractions and genes than one would expect by chance( p ¼ 0.04 and p ¼ 0.034, respectively). Such network densityimplies that signalling dysregulation underlying FSHD is acoordinated perturbation of a large number of intersectingsignalling pathways.

2.4. Dysregulation of b-catenin signalling is central torewiring in facioscapulohumeral musculardystrophy

To identify critical genes and pathways in our FSHD net-work, we employed local network measures. Betweennesscentrality measures the number of shortest paths betweenany two genes passing through a given gene and can identifysignalling bottlenecks. Genes in our network demonstratinghigh betweenness centrality are important for coordinationof signal dysregulation in FSHD: the gene with the highestbetweenness centrality is CTNNB1, encoding b-catenin.CTNNB1 is also highly connected in our network, with adegree of 73, supporting a role for this gene in numerous dys-regulated interactions. To determine whether an increase inb-catenin activity is occurring in FSHD muscle, we con-sidered the neighbourhood of CTNNB1 in the FSHDnetwork (figure 2). b-catenin is highly correlated with itsneighbours in the FSHD network across FSHD samples, butnot across control samples, implying an increase in b-cateninactivation in FSHD (figure 2).

b-catenin is fundamental to canonical Wnt signalling,initiated when Wnt ligands interact with a heterodimericFrizzled/LRP5/6 complex, which signals to DVL1/2, leading

to stabilization of b-catenin and its nuclear translocation. Inthe nucleus, b-catenin interacts with TCF/LEF transcriptionfactors that normally interact with Groucho, a transcriptionalrepressor. This interaction with b-catenin allows TCF/LEF toact as transcriptional activators to induce expression of targetgenes. To determine whether b-catenin is acting via its rolein transcription, we queried our network for downstream tar-gets of b-catenin signalling, i.e. TCF/LEF genes. Importantly,all members of the TCF/LEF family (TCF7, TCF7L1 (TCF-3),TCF7L2 (TCF-4) and LEF1) were involved in network rewiringin FSHD.

To analyse control of b-catenin activity via canonicalWNT signalling, we queried our network for WNT, DVLand FZD family members [28]. This revealed WNT16,DVL1, DVL2 and FZD1 were rewired (figure 3).

These genes are connected in the FSHD network (exceptWNT16), indicating dysregulation of the b-catenin signall-ing pathway is contributing to FSHD pathogenesis. Thereis a significantly increased positive correlation in geneexpression along the chain: FZD1! DVL1! CTNNB1!TCF-3 (TCFL1)! c-Myc (MYC) in FSHD samples, implyingan increased activation of this pathway (figure 3). Increa-sed negative correlation between b-catenin expression andthat of PITX2 and increased positive correlation betweengene expression of PITX2 and LEF1 also occurred. b-cateninis also involved in numerous other processes includingsubstantially altered correlations with CASP3 and CASP8(interactions associated with apoptosis), and hypoxiainducible factor 1-a (HIF1-a) (figure 2).

2.5. Activation of HIF1-a signalling infacioscapulohumeral muscular dystrophy

HIF1-a is one of the most rewired of the 164 genes andincreases in activity in FSHD (electronic supplementarymaterial, figure S1), with many genes associated with HIF1-asignalling in the FSHD network, including, VHL, HSP90AA1,RBX1, RRAS, VEGFA, MAPK8, NCOA1, PIK3R3, SLC2A4,HIF1AN and TCEB2. HIF1-a signalling has recently been impli-cated in FSHD, due to identification of several downstreamcomponents of the pathway being differentially expressed [20].

2.6. TNF-a over-activation of reactive oxygen speciesinduced JNK cell death pathways

Many genes involved in TNF-a over-activation of reactiveoxygen species (ROS) induced JNK cell death pathwayswere in our FSHD network. These included MAP4K5,PARP2, JUNB (electronic supplementary material, figuresS2–S4) TNFA, JUN, JUND, JNK1 and JNK3. MAP4K5 is ahighly specific activator of JNK signalling [29] and displaysa significantly increased ( p ¼ 0.0035) negative expression cor-relation with TNF-receptor-associated factor 2 (TRAF2) inFSHD samples. PARP2 is necessary for activating TNF-a-induced necrosis [30] and displays increased positiveexpression correlation with BRCA1 across FSHD samples, inan interaction associated with cell death [31]. JUNB displayssignificantly increased positive expression correlation withFOS ( p ¼ 1.5 � 1028) and JUN ( p ¼ 0.044). JNK1 and JNK3display clear shifts from predominantly uncorrelated inexpression with neighbours in control samples, to highly cor-related in FSHD samples, implying their increased activity inFSHD muscle.


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Figure 2. The neighbourhood of CTNNB1 in the FSHD network. Interactions are coloured proportional to the Pearson correlation in gene expression between connectedgenes across control samples (a) and FSHD samples (b). Red edges are negatively correlated, grey edges uncorrelated and green edges positively correlated. The thicknessof edges is proportional to 1 2 p, where p [ (0, 0:05] is the p-value of the statistical analysis performed to determine whether correlation in gene expression betweenconnected edges is different between FSHD and control. Large nodes belong to the core set of 164 high confidence FSHD-specific rewiring genes. Large circles indicateproteins significantly rewiring between FSHD and control phenotypes, identified in the second stage of InSpiRe. There is a clear shift from predominantly uncorrelated tohighly correlated between FSHD and controls, with an increased correlation between CTNNB1 and its interaction partners across FSHD samples as compared with controls.This is indicative of increased b-catenin activity. Note the increased positive correlation between CTNNB1 and HIF1A, CASP3 and CASP8.


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2.7. DUX4-driven gene expression mirrorsfacioscapulohumeral muscular dystrophy

We next determined how much network rewiring in FSHD isdirectly due to DUX4. We generated a panel of DUX4constructs in the pMSCV-IRES-eGFP retroviral backbone [32].In addition to full-length DUX4, we also analysedtMALDUX4—the putative splice variant initiating with theamino acids MAL and lacking the C-terminal domains [33].We also fused tMALDUX4 to a VP16 transactivation domainfrom the human herpes simplex virus 1 VP16 protein, to gen-erate tMALDUX4-VP16, a transcriptional activator of DUX4

targets. Conversely, to repress transcriptional targets of DUX4via recruitment of a repressive complex, tMALDUX4 wasalso fused to the N-terminal of Drosophila melanogaster engrailed(residues 2–298) to create tMALDUX4-ERD. Also includedwas the single inverted truncated D4Z4 repeat centromeric tothe D4Z4 arrays that encode DUX4c [34]. In all DUX4 con-structs, DNA binding, and so target gene selection, isdictated by the homeodomains, which are common to all con-structs. Altering the C-terminal region should only affect thedegree of target gene activation, allowing both cross-validationand detection of target genes that are only weakly activated byDUX4. Thus, multiple DUX4 constructs containing the same


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Figure 3. The Wnt pathway in the FSHD network. Interactions are coloured proportional to the Pearson correlation in gene expression between connected genesacross control samples (a) and FSHD samples (b). Note the shift from predominantly uncorrelated links in controls to highly correlated links in FSHD, implying anactivation of this Wnt pathway in FSHD.


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DNA-binding domain robustly and independently confirmtarget gene selection.

To determine the confluence between our FSHD networkand gene expression changes generated by DUX4, we per-formed a microarray to detect transcriptional changes inducedby the DUX4 constructs compared to control retrovirus. Primarymouse satellite cells from three adult C57BL10 male mice wereexpanded, passaged and infected in parallel. RNAwas prepared20 h later to assay early gene expression changes caused byDUX4, before more non-specific changes associated with inhi-bition of myogenesis or cell death occur. Gene expression wasanalysed using Affymetrix GeneChip Mouse Gene 1.0 STArrays and was quality controlled and log-normalized asdescribed in Material and methods.

Hierarchical clustering and principal component analysis(PCA) on the 1000 most variable probes demonstrated thatthe different constructs clustered as expected. Full-lengthDUX4 clustered with the strong transcriptional activatortMALDUX4-VP16, while DUX4c and tMALDUX4 displayedsimilar transcriptional profiles, distinct from those obtainedfor DUX4 and tMALDUX4-VP16 (figure 4). tMALDUX4-ERD, designed to negatively regulate DUX4 transcriptional tar-gets, did not cluster with the other DUX4 constructs. All DUX4constructs were distinct from control retrovirus. To determine

whether DUX4 modifies pathways identified in our FSHDnetwork, multivariate analysis was used to identify geneexpression significantly perturbed by one or more DUX4construct, using the limma package in R [35].

Enrichment analysis was performed using Metacore(Genego), to identify biological processes and pathways (net-works) modulated by genes perturbed by DUX4 constructs.This revealed considerable enrichment for Wnt signalling( p ¼ 1 � 1028, figure 5).

Individual DUX4 constructs were also independentlycompared to control retrovirus and lists of significantlyaltered genes generated. As expected, Metacore (Genego)also identified significant enrichment of Wnt signalling inDUX4 perturbed genes ( p ¼ 7.99 � 1029), which matchesidentification of b-catenin as critically rewiring in ourFSHD network.

As several pathways identified as rewiring in our FSHD net-work involved transcription factors, we next determined whichtranscription factor targets were overrepresented among genessignificantly perturbed by the DUX4 constructs. This analysisrevealed several enriched transcriptional hubs perturbedby DUX4, of which nine of the top 11 matched to genes in ourFSHD network. Consistent with our network identifiedpathways, among enriched hubs were HIF1-a, JUN and FOS.


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Figure 4. Clustering of the DUX4 construct expressing mouse satellite cell-derived myoblast samples. PCA (a) and hierarchical clustering (b) on the 1000 most variableprobes across the five DUX4 constructs and control RV show clustering of technical replicates, demonstrating reproducibility. Note also clustering of tMALDUX4-VP16(black) with DUX4 (red); DUX4c (green) with tMALDUX4 (yellow) but separation of tMALDUX4-ERD ( purple) and control (blue) from other DUX4 constructs.


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To establish whether genes in the FSHD network are per-turbed as a result of DUX4, we extracted all microarrayprobes mapping to genes with direct human orthologues inour FSHD network, creating a network probeset consistingof 1866 genes. We then performed a re-sampling procedure(see Material and methods) to determine whether expressionof the network probeset was a significant biomarker of DUX4expression. This analysis confirmed that mouse orthologuesof genes in our human FSHD network are significantly

modified by DUX4 (p ¼ 1 � 1025), and can be used as abiomarker of DUX4 expression.

2.8. Perturbed Wnt/b-catenin signalling in DUX4-expressing satellite cell-derived myoblasts

Several Wnt/b-catenin targets were identified as beingaltered in both the human FSHD network and theDUX4 microarray. These included Lgr5/6, Tcf3/4, Myf5


Biological processes enriched among DUX4 construct perturbed genes

Biological pathways enriched among DUX4 construct perturbed genes

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cell adhesion: attractive and repulsive receptorsdevelopmental ossification and bone remodelling

developmental Hedgehog signallingcell adhesion amyloid proteins

cardiac development Wnt/b-catenin, Notch, Vegf, lp3 and integrin signallingdevelopmental regulation of epithelial to mesenchyme transition

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strong enrichmentfor Wnt signalling

Figure 5. Enrichment analysis of genes perturbed by DUX4 constructs in mouse satellite cell-derived myobalsts. (a) Biological processes enriched among genesperturbed by DUX4 constructs. (b) Biological pathways enriched among genes perturbed by DUX4 constructs. Note the significant enrichment for Wnt/b-cateninsignalling among genes perturbed by DUX4 constructs.


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and Lef1. To validate this, we performed quantitative PCR(QPCR) on satellite cell-derived myoblasts from threeC57Bl10 mice retrovirally infected with either DUX4 orcontrol retroviral constructs and cultured for 24 or 48 h.At one or both time points, Lef1, Tcf4, Lgr5 and Myf5were significantly increased in DUX4-infected samples,whereas Tcf3 and Lgr6 were significantly decreased byDUX4 (figure 6a), confirming that DUX4 alters thissignalling cascade at a transcriptional level.

2.9. DUX4 induces Wnt/b-catenin signalling in murinemyoblasts

Wnt/b-catenin signalling involves the physical interaction ofseveral proteins, and so transcriptional disruption of targetsdoes not necessarily mean that Wnt/b-catenin signalling isaltered. To confirm activation of Wnt/b-catenin signalling inDUX4-expressing myoblasts, we performed a TopFLASH/Fop-FLASH assay. The TopFLASH Tcf reporter plasmid contains sixTcf-binding sites, whereas in the FopFLASH reporter, these sitesare mutated, upstream of a luciferase reporter. The relative ratiois a widely used assay for measuring canonical Wnt signalling[36]. Primary satellite cell-derived myoblasts can be difficult totransfect with high efficiency, so we switched to murineC2C12 myoblasts. These were co-transfected with either Top-FLASH or FopFLASH plus Renilla, then passaged andtransfected with DUX4 or control vector for 24 h. Relative lucifer-ase activity measured from four independent experimentsshowed that DUX4 expression significantly increased the Top-FLASH/FopFLASH ratio 2.36-fold ( p ¼ 0.04) and hence DUX4expression activates Wnt/b-catenin signalling in myoblasts(figure 6b).

3. DiscussionWe have constructed the FSHD network: the first unified,unbiased map of network rewiring underlying FSHD.DUX4-driven transcriptional changes demonstrated thatexpression of genes in our FSHD network was significantlyaltered by DUX4. This is consistent with the growing consen-sus that FSHD1/2 is caused by aberrant DUX4 expression[9,18]. Importantly, our FSHD network is significantly moreconnected than a random network. Such connectivity allowsanalysis of crosstalk in FSHD pathological signalling, essentialto guide development of therapeutics targeting the multiplesymptoms of FSHD. Our network is derived from geneexpression in muscle biopsies and myogenic cells, and thusprovides clear insight into pathways perturbed in FSHDmuscle. In addition however, we have also identified path-ways which influence vasculogenesis and neurogenesis ingeneral, suggesting that our results can also inform on extra-muscular symptoms of FSHD, including retinal telangiectasiaand sensorineural hearing loss. Differential gene expressionstudies have generally implicated disjointed collections ofgenes associated with these symptoms (e.g. myogenesis [17]and vasculature growth factors [5]), and hypotheses havebeen proposed [4,37]. However, there has been no unbiased,data driven insight into how these pathways relate, let aloneactually coordinate. Our results emphasize the importance ofthe development and application of network theoretic tools,such as InSpiRe, to identify pathomechanisms and therapeutictargets in complex diseases.

We compared InSpiRe to other commonly used networkmethodologies (electronic supplementary material): Net-Walk [13] and GSEA on differentially expressed genes.NetWalk also uses a protein interaction network to identify


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Figure 6. DUX4 perturbs Wnt/b-catenin signalling. (a) QPCR of downstream targets of Wnt/b-catenin signalling Lef1, Tcf3, Tcf4, Lgr5, Lgr6 and Myf5 in mousesatellite cell-derived myoblasts, 24 or 48 h after infection with either DUX4 or control retroviral constructs. Data are mean+ s.e.m. from three mice, where anasterisk denotes a significant difference ( p , 0.05) from infection with control retrovirus using a paired two-tailed Student’s t-test. (b) TopFLASH/FopFLASH assay ofimmortalized C2C12 murine myoblasts transfected with either DUX4 or control plasmid, shows a significant increase in TopFLASH/FopFLASH luciferase activity withDUX4 expression (n ¼ 4). Data are mean+ s.e.m. from four independent experiments where an asterisk denotes significance ( p , 0.05) from control using atwo-tailed Student’s t-test.


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candidate genes, but unlike InSpiRe, uses weighted randomwalks. Both NetWalk and InSpiRe displayed a significant con-sensus in the features identified across the four FSHDdatasets ( p ¼ 1 � 1025). This is in contrast to differentialexpression, which lacks consistency in the features identifiedacross these datasets. Thus demonstrating the power ofnetwork-based methodologies over conventional differentialexpression. NetWalk performed similarly to InSpiRe in identi-fying genes which proved a significant classifier of the DUX4construct microarray samples (p ¼ 1 � 1025) but generated anetwork with more genes and fewer interactions (3363genes and 5651 interactions) than InSpiRe (2616 genes and15 972 interactions). However, both NetWalk and GSEA ondifferentially expressed genes were inferior to InSpiRe inidentifying functional annotations previously associatedwith FSHD (electronic supplementary material, figure S5).

There is increasing evidence that aberrant DUX4 expressiondue to changes in the number or methylation state of D4Z4repeats on chromosome 4 underlies FSHD pathology [9].Indeed, it was recently demonstrated that DUX4 drives overhalf the differentially expressed transcripts in FSHD [18]. How-ever, the number of differentially expressed transcripts is verylow in FSHD as compared with other pathologies [18,5,15] andit has been shown that a more subtle transcriptional dysregula-tion may better represent unifying features of FSHD [15]. OurFSHD network captures such subtle dysregulation and it wasimportant to determine whether the network, derived fromunbiased meta-analysis of multiple muscle biopsies fromFSHD patients, could be controlled by DUX4. Performingmicroarray analysis to detect changes in gene expression afterDUX4 constructs were expressed in muirne myoblasts demon-strated that DUX4 modifies expression of genes in our FSHD



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network. Moreover, we found significant enrichment of DUX4-mediated transcriptional changes for critical pathways ident-ified in our network, including Wnt/b-catenin, HIF1-a andJNK signalling. This also indicates that many DUX4-mediatedsignalling changes are conserved between man and mouse,making mouse models useful for understanding FSHDpathology and as platforms for testing therapeutics [38].

CTNNB1 has the highest betweenness centrality in ourFSHD network, identifying b-catenin as a bottleneck of FSHD-associated protein interaction signalling. Gene expressioncorrelation between b-catenin and its interaction partners isalso significantly increased in FSHD, implying increasedactivation. Moreover, the TCF/LEF transcription factorsare present in our network, as well as several upstreamcomponents of the canonical Wnt pathway. Involvement ofWnt signalling in tissue-specific myogenesis and in retinalangiogenesis led to a recent hypothesis that Wnt/b-cateninsignalling was important for FSHD pathomechanisms [4]. Arole of Wnt/b-catenin signalling in controlling DUX4expression and FSHD muscle cell apoptosis was also recentlyproposed [39]. Moreover, enhancing expression of b-cateninvia administration of LiCl2 reduces apoptosis in a model ofocculopharyngeal muscular dystrophy [40]. Our FSHD net-work, compiled algorithmically from multiple independentdatasets, provides the detailed interactions of dysregulatedb-catenin signalling. Furthermore, Wnt/b-catenin-relatedgenes were enriched in our microarray of DUX4-driven tran-scriptional changes in mouse myoblasts and validated viaQPCR. DUX4 also drives activation of the canonical Wntpathway, as shown by the TopFLASH assay.

Interaction partners of b-catenin in our FSHD networkreveal mechanisms by which Wnt/b-catenin signalling isinvolved in certain hallmark phenotypes of FSHD throughpathway crosstalk. Among these is the characteristic oxi-dative stress sensitivity of FSHD muscle/myoblasts [41,42].Our work implicates HIF1-a signalling as critically perturbedin FSHD and shows that HIF1A displays strong positivecorrelation with b-catenin in FSHD samples, providingmechanism for previous observations of involvement ofdownstream components of the HIF1-a pathway in FSHD[20]. HIF1-a binds b-catenin during hypoxia competitivelywith TCF-4 [43], which may both inhibit TCF4/b-catenin-driven cell proliferation and increase transcription of hypoxicresponse genes, including VEGF. We found that in FSHD,correlation in gene expression between b-catenin, and bothHIF1A and TCF-4, significantly increases, resulting inelevated angiogenic genes such as VEGF, providing an expla-nation for the hallmark oxidative stress sensitivity in FSHD,as well as retinal vasculature abnormalities [4,5].

Actin cytoskeleletal signalling has also been implicated inFSHD pathology [15]. Our work shows perturbed crosstalkbetween such signalling and HIF1-a may contribute toFSHD. MAP2 is associated with microtubule stability andrewired in FSHD. In hypoxic cardiomyocytes, MAP2 isrequired for stabilization of the microtubule network, leadingto suppression of pVHL and increased HIF1-a [44]. Interest-ingly via this mechanism, HIF1-a was upregulated at theprotein level but not the mRNA level (in early stages ofhypoxia) [44], emphasizing the power of our method fordetecting events invisible to differential expression analysisof microarrays.

Another notable pathway perturbed in FSHD was ROS.The role of ROS in FSHD is well reported, and our FSHD

network contains many genes in the ROS-mediated pathway,including TNF-a. DUX4 represses genes of the glutathioneredox pathway, likely causing ROS accumulation in FSHDmuscle [45], which may lead to increased TNF-a as part ofa pro-inflammatory response [46]. Additionally, levels ofTNF-a are negatively correlated with muscle endurance[42]. FSHD myoblasts undergo cell death in response tonon-pathological levels of hydrogen peroxide [41] and otheroxidative stress-inducing factors; while antioxidants inhibitDUX4-induced toxicity in FSHD myoblasts [45]. Thus, over-activation of ROS-mediated TNF-a-induced cell death path-ways are potentially important pathomechanism in FSHD.TNF-a also stimulates ROS production via interaction withNADPH-oxidase, in a positive feedback loop [30]. Ourresults support this occurring in FSHD as RAC1 is a well-connected member of our FSHD network and a criticalcomponent of the NADPH complex, also capable of activat-ing JNK cell death signalling. RAC1 is also regulated bynon-canonical Wnt signalling in a manner dependent onMAP4K5 [47]. Our network unifies TNF-a, Wnt and JNKsignalling in FSHD.

JNK signalling also plays an important role in oxidativestress-induced cell death, and we found that JNK signallingis more active in FSHD. JNK signalling can be controlled inmany ways, including via TNF-a signalling. In this scenario,the AP-1 transcription factor target genes JUN, JUNB andFOS are specifically upregulated downstream of JNK. Thesegenes are present in our FSHD network, moreover, all displaya significant increase in positive gene expression correlationwith one another in FSHD samples, indicating their co-regulation. This result is evidence of TNF-a-mediated JNKsignalling in FSHD.

In the absence of NF-kB activity, prolonged JNK activationby TNF-a leads to apoptosis [48]. Owing to the increasedapoptosis of FSHD muscle cells, we postulate that NF-kBmay be less active. In addition to activating JNK cell deathpathways in response to ROS, TNF-a also activates NF-kB sur-vival signalling causing the production of the antioxidantMnSOD to suppress ROS and minimize JNK cell death signal-ling [30]. MnSOD is the only antioxidant downregulated inFSHD [42], suggesting that NF-kB may be less active inFSHD muscle. Our results provide evidence for this theory:NFKB1 encodes the DNA-binding subunit of the NF-kB tran-scription factor, which though present in our FSHD network,is relatively uncorrelated with its interaction partners inFSHD samples. This implies NFKB1 is inactive in FSHDmuscle, and thus unable to repress the increased cell deathvia overactive JNK. Finally, we find crosstalk between JNK sig-nalling and Wnt, in that all members of the PAR-1 gene familyare in our FSHD network. This family was identified asdishevelled kinases, capable of simultaneously regulatingWnt activation of b-catenin signalling and JNK signalling[49]. Overactive JNK signalling has also been implicated insensorineural hearing loss, an FSHD clinical phenotype [50].Inhibitors of JNK signalling could partially mitigate the oxi-dative stress sensitivity of FSHD muscle cells, and D-JNKI-1is currently in clinical trials for treatment of strokes [51].

In conclusion, we have developed a novel, general algor-ithm, InSpiRe, to detect network rewiring from multiplegene expression datasets describing two phenotypes. UsingInSpiRe, we performed the first meta-analysis of the FSHDtranscriptome, creating an integrated FSHD network. Wealso assayed DUX4-driven transcriptional changes and


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demonstrated that expression of genes in our network is sig-nificantly altered by DUX4. Our FSHD network provides anunbiased, unifying molecular map of FSHD-associatedprotein interaction signalling, elucidating perturbed genesand pathways critical to pathomechanisms. Importantly, weidentify and validate b-catenin as central to FSHD patho-logy. Our network provides insight into the crucial steps ofdysregulated signalling in FSHD and so will inform designof well-targeted therapeutics: currently lacking for FSHD.We provide the FSHD network as a queryable interface(electronic supplementary material).

J.R.Soc.Interface12:20140797

4. Material and methods4.1. Construction of the protein interaction networkThe protein interaction network was constructed as previouslydescribed [52] from integration of the Human Protein InteractionNetwork (www.pathwaycommons.org) [53], the Human ProteinReference Database [54], the National Cancer InstitutePathway Interaction Database, the Interactome (www.ebi.ac.uk/intact/) and the Molecular Interaction Database [55]. Redun-dant interactions were removed leaving only genes with a uniqueEntrezGene ID, within the maximally connected component. Theprotein interaction network contains 10 720 proteins and 152 889documented interactions and post-translational modifications,including protein binding, complex formation, phosphorylation,ubiquitylation and sumoylation.

4.2. Public mRNA expression datasetsWe ran InSpiRe on the following four datasets from the GEODatabase [56]: GSE3307 [19] (14 FSHD samples, 14 controls),GSE10760 [5] (19 FSHD samples, 30 controls), both profiled onthe Affymetrix Human Genome U133A Array platform.GSE26145 [20] and GSE26061 [21] expression studies of FSHDand control myoblasts and myotubes profiled on the AffymetrixHuman Exon 1.0 ST Array, were also analysed.

A recent study GSE36398 [15] was excluded from our analy-sis as it reported substantially weaker gene expression changesbetween FSHD and control muscle biopsies compared withmost previous studies [15]. It was considerably discordant withthe other datasets analysed by InSpiRe in identification of rewiredgenes. Thus, given that the other studies displayed significantagreement, GSE36398 was omitted.

We also used human skeletal muscle studies into geneexpression in muscle diseases other than FSHD (GSE3307 [19]),ageing (GSE5086 [24] and GSE9676 [25]) and atrophy (GSE5110[26] and GSE8872 [27]).

4.3. Pre-processing and normalization of public datasetsExpression datasets underwent quality control using the ArrayQualityMetrics package in R [57]. This included analysis of RNAdegradation and elimination of samples displaying clear signal sat-uration [58]. Datasets were log-normalized using robust multi-arrayaverage (RMA) and PCA performed to determine whether thedominant principal component correlated with condition beingcompared (e.g. disease status). PCA performed on the two largerFSHD studies (GSE3307 and GSE10760) revealed that the dominantprincipal component correlated with both disease status and ageof patients sampled. For this reason, we additionally analyseddatasets associated with age-dependent gene expression.

PCA on the age-dependent study, GSE5086 revealed that thedominant principal component correlated with age and separ-ated into two groups corresponding to young and oldersamples. We used this clustering to classify samples as either

younger or older patients. Non-FSHD muscle diseases analysedwere selected from GSE3307 due to the large number ofhigh-quality samples available.

GSE2614 and GSE26061 were suitable for integration by theComBat function [59] in R which employs an empirical Bayesapproach to eliminate batch effects and was recently demonstratedas superior to other microarray data integration methods [60]. Theintegrated dataset contained six FSHD myoblasts, six FSHD myo-tubes, six control myobalsts and six control myotubes. PCA wasperformed on the integrated dataset and the dominant principalcomponent correlated with cell type and disease status but notwith batch, implying a successful integration.

We extracted from datasets probes mapping to genescorresponding to proteins in our interaction network. For all micro-array platforms considered, there were cases where multiple probesmapped to a single gene. As our protein interaction network doesnot take into account alternative splice variants of a given gene,we must assign a single value to each protein based on theexpression data. There are multiple ways of achieving this, andfor our methodology we computed the average expression acrossprobes mapping to a single gene for each sample, assigning theresulting value to the gene. This approach has proved successfulin our previous studies for identification of network rewiring incancer [52] and cell differentiation [61]. For each dataset, proteinsin the interaction network with no corresponding probe in themicroarrays were deleted from the network, proteins with adegree of zero following this deletion were also removed. Thisresulted in a reduced protein interaction network for each dataset.

4.4. The interactome sparsification and rewiringalgorithm

InSpiRe is a differential network methodology, consisting of threemain steps (figure 1). An R-script for implementation of InSpiReis provided in the electronic supplementary material.

4.5. Integration of mRNA expression data with theprotein interaction network

The first step of InSpiRe is integration of expression data withinteraction data (figure 1a). For each phenotype, we integrateexpression data with the protein interaction network by assign-ing each interaction connecting two proteins, i and j, with atransformed Pearson correlation in gene expression profiles ofproteins i and j across the samples corresponding to the givenphenotype (denoted Cij). This results in a weighted network wijwhich is then transformed into a stochastic matrix Pij

Pij ¼wijP

k[N(i) wik, (4:1)

where N(i) denotes the set of neighbours of protein i in theinteractome.

We interpret row k in the matrix Pij as a probability distributiondescribing the interaction preferences of protein k. Note thatP

j[N(i) Pij ¼ 1 and Pij ¼ 0 whenever i and j are not connected inthe interaction network. For (Pij) j[N(i) to be a probability distri-bution, we require that Pij � 0, which is guaranteed if wij� 0. Thechoice of the transformation of the Pearson correlation wij must bemade carefully to ensure non-negativity and that interpretation ofa high edge weight, indicative of an increased likelihood of inter-action between connected proteins, is valid. We considered twopossible transformations of which wij ¼ jCijj was superior (see theelectronic supplementary material).

4.6. Detecting rewiring hotspotsThe second stage of InSpiRe is utilization of information theoreticmeasures to detect rewiring between two phenotypes

http://www.pathwaycommons.orghttp://www.ebi.ac.uk/intact/http://www.ebi.ac.uk/intact/http://rsif.royalsocietypublishing.org/


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described by weighted networks constructed by stage 1 ofInSpiRe (figure 1b). Two measures are employed to detect signifi-cant rewiring between two phenotypes: differential local fluxentropy and Kullback–Leibler divergence.

Local flux entropy quantifies the disorder of a protein interactiondistribution in a given phenotype and is based on Shannon entropy.

Given a stochastic matrix Pij describing a given phenotype,the local flux entropy of protein i is defined as

Si :¼ �1

log ki

X

j[N(i)

Pij log Pij, (4:2)

where ki is the degree of protein i in the interaction network.Si [ [0, 1] is a measure of how close a protein’s interaction distri-bution is to uniform. Values close to 0 imply protein i has adeterministic interaction distribution and values close to 1suggest a uniform profile.

We compute vectors describing local flux entropy of eachprotein in the interactome for two phenotypes. These arestatistically analysed using the jack-knife technique (electronicsupplementary material) to identify proteins with significantlydifferent local flux entropies between phenotypes. Such proteinscan be considered as altering heterogeneity in their interactiondistributions across phenotypes.

Our previous studies on cancer and cell differentiationhave revealed that proteins with lower entropy in a givenphenotype can be interpreted as being more active in thatphenotype [14,61,52]. Examples of interaction distributionchanges which lead to either an increase or a decrease in localflux entropy are displayed in figure 1b.

It is possible for a protein interaction distribution to berewired without changing uniformity (figure 1b). Consequen-tially, we introduce a novel measure based upon Kullback–Leibler divergence, which though lacking the functionalinterpretation of local flux entropy, is sensitive to such rewiring.

The Kullback–Leibler divergence is defined as follows:Given two probability distributions P :X ! [0, 1] and

Q :X ! [0, 1], provided {x [ X : Q(x) . 0} , {x [ X : P(x) . 0}the Kullback–Leibler divergence between P and Q is given by

DKL(PjjQ) :¼X

x[XP(x) log

P(x)Q(x)

: (4:3)

Note that DKL(PjjQ) = DKL(QjjP) as DKL(PjjQ) quantifies theexpected number of bits required to describe a sample from theprobability distribution P given one incorrectly assumes thesample follows the distribution Q. To compare distributionsdescribing the interactions of proteins in control and pathologicalsamples, choice of direction of Kullback–Leibler divergence isambiguous, we will therefore use symmetrized Kullback–Leiblerdivergence defined by

DS(P, Q) :¼ DKL(PjjQ)þDKL(QjjP): (4:4)

Given two phenotypes A and B described by stochastic matricesPAij , P

Bij , the local symmetrized Kullback–Leibler divergence of

protein i between phenotypes is

Ki(A, B) :¼ DS((PAij ) j[N(i), (PBij ) j[N(i)) (4:5)

and

¼X

j[N(i)

(PAij � PBij ) logPAijPBij

: (4:6)

Ki(A, B) [ [0, 1), values near 0 indicate that interaction distri-bution of protein i is similar across phenotypes, large valuesimply rewiring of protein i between phenotypes.

We employ the jack-knife technique (electronic supplemen-tary material) to determine which proteins have a significantlynon-zero Kullback–Leibler divergence (and therefore arerewiring) between two phenotypes.

4.7. Sparsification of relevant subset of proteininteraction network

The final step of InSpiRe is sparsification of the rewiring subsetof protein interactions (figure 1c). To create the relevant subsetof the protein interaction network, proteins significantly rewir-ing between two phenotypes, as identified in step 2 ofInSpiRe, are connected to their neighbours in the interactome.Interactions in this sub-network connecting proteins whoseexpression is not significantly differently correlated (assessedagain via the jack-knife technique) between two phenotypesare deleted, sparsifying the network to leave only rewiredinteractions.

4.8. DUX4 constructsCoding sequences for DUX4 and DUX4c were kindly receivedfrom Dr Stephen Tapscott and Dr Alexandra Belayew. tMAL-DUX4 was created by removing 75 amino acids from theC-terminus of DUX4 [33]. The stop codon of tMALDUX4 wasremoved and ligated to either a VP16 transactivation domainor engrailed repressor domain (ERD) to create tMALDUX4-VP16 and tMALDUX4-ERD, respectively. All inserts wereligated into the pMSCV-IRES-eGFP vector (Clonetech) [32].

Retroviruses-encoding DUX4 constructs and controlpMSCV-IRES-eGFP were produced by transfecting HEK293Tpackaging cells with DUX4/pMSCV-IRES-eGFP constructs anda retroviral helper plasmid using Lipofectamine (Invitrogen).

4.9. MicroarraySatellite cells from three male eight-week-old C57BL/10 micewere isolated and cultured [62]. Satellite cell-derived myoblastswere expanded on Matrigel-coated plates for 6 days in DMEM-Glutamax (Invitrogen) with 30% fetal bovine serum (PAA), 10%horse serum (Gibco), 1% chick embryo extract (ICN Flow),10 ng ml21 bFGF (Peprotech) and 1% penicillin/streptomycin(Sigma) at 378C 5% CO2. Myoblasts were infected withDUX4, DUX4c, tMALDUX4, tMALDUX4-VP16, tMALDUX4-ERD or control pMSCV-IRES-eGFP retrovirus with 4 mg ml21

Polybrene for 20 h, before RNA extraction using QiagenRNeasy Kit and quantification on a Nanodrop ND-1000 spec-trophotometer (Labtech). Gene expression analysis wasperformed using GeneChip Mouse Gene 1.0ST Array andGCS3000 microarray system (Affymetrix) by the King’s Geno-mic Centre. Data were quality controlled and log-normalizedusing RMA, and processed in three independent batches, withbatch effects compensated using the ComBat function in R cite-combat. PCA on the 1000 most variable probes confirmedcompensation, with the top principal components uncorrelatedwith batch (figure 4).

4.10. Re-sampling procedure to assess concordancebetween microarray and facioscapulohumeralmuscular dystrophy network

To determine whether expression of genes in our FSHD networkwas significantly altered by DUX4, we first identified a probesetof mouse orthologues to 1866 genes in the FSHD network, topermit comparison with our murine satellite cell microarray ofDUX4 construct expression. Our objective was to assess whetherexpression of genes in the network probeset was able to dis-tinguish between the five DUX4 retroviral constructs betterthan would be expected by chance. If this is the case, then clus-tering of DUX4 constructs based on expression of the networkprobeset should be significantly better than that based on arandom probeset of equivalent size.


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We therefore performed a re-sampling procedure, evaluating10 000 random probesets of 1866 genes from our microarray. Foreach random probeset, we performed a hierarchical clusteringand enforced a six cluster solution, which we then compared tothe optimal six cluster solution corresponding to the perfect sep-aration of the six retroviral constructs. To compare clusterdistributions, we used the Rand index, a function which assessesclassification similarity on a unit scale. In this manner, weobtained a null distribution of Rand indices describing howwell a random probeset may be expected to cluster the fiveDUX4 constructs. This null distribution then allowed us to calcu-late a p-value evaluating the hypothesis that expression of thenetwork probeset clustered the DUX4 constructs better thancould be expected by chance.
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4.11. TopFLASH/FopFLASHTopFLASH and FopFLASH Tcf reporter constructs (Millipore)contain either six complete (TopFLASH) or mutant (FopFLASH)Tcf-binding sites, upstream of a luciferase reporter gene [63].C2C12 myoblasts cultured in DMEM þ 10% FBS þ 1% penicil-lin/streptomycin were transfected with either construct plusRenilla (transfection control) for 24 h before trypsinizing andtransfected a second time in triplicates with either 100 ngDUX4 or control pMSCV-IRES-eGFP using Lipofectamine (Invi-trogen). Myoblasts were then incubated in DMEM Glutamax(Invitrogen) þ 2% horse serum (Gibco) þ 1% penicillin/strepto-mycin (Sigma) for 24 h. Relative luciferase was measured usingthe Dual Luciferase Kit (Promega), b-catenin activity was calcu-lated as luciferase/renilla, then TopFLASH/FopFLASH, andplotted as a fold change of the control. The experiment was per-formed on four independent occasions with statistics based onthe combined average of each experiment.

4.12. Quantitative PCRSatellite cell-derived myoblasts were infected with DUX4 andcontrol retroviral constructs and RNA extracted as per the micro-array samples at 24 and 48 h after infection. RNA was thenreverse-transcribed using the Reverse Transcription Kit withgenomic DNA wipe-out (Qiagen) and QPCR was performedon an Mx3005P QPCR system (Stratagene) with MESA BlueQPCR MasterMix Plus and ROX reference dye (Eurogentec).Expression was normalized relative to Tbp expression. Primersused were as follows: Lef1; F-TCATCACCTACAGCGACGAG,R-TGATGGGAAAACCTGGACAT. Lgr5; F-CCGCCAGTCTCCTACATCGCC, R-GCATTGTCATCTAGCCACAGGTGCC. Lgr6;F-CACACATCCCGGGACAGGCAT, R-GGGAGGAGAGCCCCTCAAGC. Tcf3; F-TCTCAAGCCGGTTCCCACAC, R-TTTCCGGGCAAGCTCATAGTATTT. Tcf4; F-TGCCGACTACAACAGGGACT, R-TGCTGGACTGTGGGATATGA. Myf5 F-TGAGGGAACAGGTGGAGAAC, R-AGCTGGACACGGAGCTTTTA. TbpF-ATCCCAAGCGATTTGCTG, R-CCTGTGCACACCA.

Acknowledgements. Statistical analysis was performed by C.R.S.B. withguidance from A.E.T. and S.S. The study was conceived byC.R.S.B., P.K. and P.S.Z. Experiments were performed by P.K., L.M.and C.R.S.B. C.R.S.B. and P.S.Z wrote the manuscript with contri-butions from L.M., A.E.T., S.S., R.O., and P.K.Funding statement. C.R.S.B. is supported by a CoMPLEX PhD student-ship, the British Heart Foundation (SP/08/004) and the FSHSociety Shack Family and Friends research grant (FSHS-82013–06).L.M. is funded by a Muscular Dystrophy Campaign studentship(RA4/817). S.S. is supported by the Royal Society. A.E.T. is sup-ported by a Heller Research Fellowship. The Zammit laboratory isadditionally supported by the Medical Research Council and Associ-ation Française contre les Myopathies, together with BIODESIGN(262948–2) from EU FP7.Conflict of interests. The authors declare that they have no conflict ofinterest.

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&bgr;-catenin is central to DUX4-driven network rewiring in facioscapulohumeral muscular dystrophyIntroductionResultsMeta-analysis of facioscapulohumeral muscular dystrophy datasets using InSpiReGene expression changes specific to facioscapulohumeral muscular dystrophyThe facioscapulohumeral muscular dystrophy networkDysregulation of &bgr;-catenin signalling is central to rewiring in facioscapulohumeral muscular dystrophyActivation of HIF1-&agr; signalling in facioscapulohumeral muscular dystrophyTNF-&agr; over-activation of reactive oxygen species induced JNK cell death pathwaysDUX4-driven gene expression mirrors facioscapulohumeral muscular dystrophyPerturbed Wnt/&bgr;-catenin signalling in DUX4-expressing satellite cell-derived myoblastsDUX4 induces Wnt/&bgr;-catenin signalling in murine myoblasts

DiscussionMaterial and methodsConstruction of the protein interaction networkPublic mRNA expression datasetsPre-processing and normalization of public datasetsThe interactome sparsification and rewiring algorithmIntegration of mRNA expression data with the protein interaction networkDetecting rewiring hotspotsSparsification of relevant subset of protein interaction networkDUX4 constructsMicroarrayRe-sampling procedure to assess concordance between microarray and facioscapulohumeral muscular dystrophy networkTopFLASH/FopFLASHQuantitative PCR

AcknowledgementsFunding statementConflict of interests

References

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