Integrating omics into the cardiac differentiation of human pluripotent stem cells

Focus Article

Integrating omics into the cardiacdifferentiation of humanpluripotent stem cellsAgustin Rojas-Muñoz,1,2,† Mano R. Maurya,2,† Frederick Lo1 andErik Willems1∗

Time-dependent extracellular manipulations of human pluripotent stem cells canyield as much as 90% pure populations of cardiomyocytes. While the extracellularcontrol of differentiation generally entails dynamic regulation of well-known path-ways such as Wnt, BMP, and Nodal signaling, the underlying genetic networks arefar more complex and are poorly understood. Notably, the identification of thesenetworks holds promise for understanding heart disease and regeneration. Theavailability of genome-wide experimentation, such as RNA and DNA sequencing,as well as high throughput surveying with small molecule and small interferingRNA libraries, now enables us to map the genetic interactions underlying car-diac differentiation on a global scale. Initial studies demonstrate the complexityof the genetic regulation of cardiac differentiation, exposing unanticipated novelmechanisms. However, the large datasets generated tend to be overwhelming andsystematic approaches are needed to process the vast amount of data to improveour mechanistic understanding of the complex biology. Systems biology methodsspur high hopes for parsing vast amounts of data into genetic interaction modelsthat can be verified experimentally and ultimately yield functional networks thatexpose the genetic connections underlying biological processes. © 2014 Wiley Periodi-cals, Inc.

How to cite this article:WIREs Syst Biol Med 2014. doi: 10.1002/wsbm.1268

INTRODUCTION

Since their derivation from the human embryoin the late nineties, embryonic stem cells (ESC)

have transformed studies of human biology.1 ESC arepluripotent, i.e., they can be expanded indefinitelyand have the ability to form most cell types ofthe body, both in a petri dish as well as in vivounder the form of teratoma tumors.1,2 These featureshave enabled us to study human development invitro and have contributed to important discoveriesrelevant to congenital disease and regeneration. With

∗Correspondence to: [email protected] Development and Regeneration Program, Sanford-Burnham Medical Research Institute, La Jolla, CA, USA2Department of Bioengineering, UC San Diego, La Jolla, CA, USA†These authors contributed equally to this work.Conflict of interest: The authors have declared no conflicts of interestfor this article.

the identification of key transcription factors thatreprogram somatic cells to pluripotency, the humanstem cell landscape was transformed as so-calledinduced pluripotent stem cells (iPSC) could now begenerated from patient-derived cells, allowing us toprobe human disease mechanisms in a dish.3

For the cardiac field, ESC/IPSC advances haveallowed the bulk generation of human cardiomy-ocytes, which are implementable in transplantationstudies and drug toxicity screening.4–6 Moreover, car-diomyocytes generated from patient-derived IPSC areimplementable for in vitro studies of heart disease.7

The major challenge over the past 5 years has been toimprove the ability to drive cardiac differentiation effi-ciently from a variety of human and mouse pluripotentstem cell (PSC) lines. Through mimicking steps knownfrom mouse development in combination with smallmolecules identified in phenotypic screening, hPSCcan now be directed to cardiomyocytes efficiently

© 2014 Wiley Per iodica ls, Inc.

Focus Article wires.wiley.com/sysbio

through manipulation of the extracellular environ-ment, but cardiomyocyte purity varies greatly betweencell lines and differentiation methods used.4,8–12 Inter-estingly, the underlying genetic cascades that drivecardiac fate are largely unknown, even though severalimportant cardiac genes have been identified. Theregulation by extracellular cues, the time-dependentalterations and the genetic interactions are poorlycharacterized. There is a need to fill the gaps betweenextracellular control and the activation or silencingof key genes as the identification of such cardiac net-works are essential to improve our understanding ofmechanisms underlying heart disease and regenerationor direct reprogramming.13,14 With the advent of highthroughput screening and next generation sequencingmethods, appropriate resources to investigate cellbiology on a genome-wide scale are finally available.Here, we discuss the use and limitations of wholegenome or so-called ‘omics’ or ‘big data’ methodsin exposing the underlying genetic mechanisms ofcardiac differentiation. We moreover describe howsystems biology approaches will be instrumental inthe integration of large amounts of data into networksof genetic connections that control cardiac fate.

DRIVING CARDIACDIFFERENTIATION: EXTRACELLULARCONTROL IS KEY

A variety of culture conditions and differentiationprotocols have been described to drive cardiac fatefrom PSC, and not always employ the same extracellu-lar cues to drive cardiac fate.4,8–12 As a result, cardiacdifferentiation efficiency may differ greatly from cellline to cell line and laboratory to laboratory. Whileoptimal culture conditions are undoubtedly importantto achieve consistency (reviewed in Burridge et al. 12),the overall differentiation process and the expressionof marker genes are largely conserved. In general,differentiation toward cardiomyocytes is a multistepprocess, which requires four main steps that can beidentified through the dynamic expression of a seriesof key transcription factors, receptors and/or struc-tural proteins (Figure 1).11,12 (1) Initially, mesodermis induced, which in PSC is marked by the increasedexpression of the BRACHYURY/T gene. The sig-nals driving mesoderm induction in PSC, originallyidentified in the developing mouse embryo, includeWnt, BMP, and Nodal (Figure 1).4,15–17 A carefullytitrated dose of Activin A (a Nodal mimic) and BMP4at this stage is key for optimal cardiac induction,4

and more recently, small molecules activating theWnt pathway via inhibition of GSK3𝛽 were shownto have the ability to replace both Activin A and

BMP4 for efficient induction of mesoderm and sub-sequently cardiac fate.6 FGF2 is needed in mesodermdifferentiation as well, where it appears to maintainembryonic mesoderm induction by BMP4, therebylimiting the formation of extra-embryonic cell typeswhich are also induced by BMP4.4,18 (2) Mesoderm isthen further specified to cardiogenic mesoderm, whichcan be identified by the presence of the transcriptionfactor MESP1 and by the expression of KDR andPDGFRA receptors.4 Early titration of Activin Aand BMP4 are important here (or their replacementwith a Wnt agonist), and are sufficient for expressionof MESP1, KDR, and PDGFRA.4 We moreoverfound that selective inhibition of TGF𝛽 during thesestages enhances MESP1 expression (Figure 1).19 Ofinterest, cells at this stage do not have ability todifferentiate toward cardiomyocytes in the absenceof crucial growth factors or small molecules.10 (3)Cardiogenic mesoderm thus needs to be convertedinto cardiac mesoderm, which represents the earliestappearance of cardiomyocyte capable progenitorcells, and can be identified through the expressionof, e.g., NKX2-5, TBX5, GATA4, HAND2, ISL1,and MEF2C. Mesoderm-inducing signals includingNodal, BMP4, and Wnt need to be repressed inMESP1+ cells to drive the activation of NKX2-5 andother cardiac genes (Figure 1).4,10,20 (4) Once theNKX2-5+ stage is established, ESC-derived cardiacprogenitors easily differentiate into cardiomyocytes,which are characterized by their spontaneous con-traction and expression of cardiac structural proteinssuch as MYH6, TNNT2, or ACTN2. Cardiomy-ocyte containing cultures generally comprise differentcardiomyocyte subtypes, including atrial and ven-tricular cardiomyocytes as well as nodal-like cellsthat act as pacemaker cells.21 These subtypes canbe easily identified by the expression of MLC2v orIRX4 for ventricular myocytes and MLC2a for atrialcells.22 Nodal-like cells can be readily identified byGATA6 expression or the presence of the ion channelHCN4.23 Furthermore, these subtypes are distin-guishable electrophysiologically, through comparisonof their action potentials.21 Extracellular control ofthe enrichment of one subtype over the other duringdifferentiation from hPSC was recently described,as retinoic acid signaling governs a switch betweenatrial and ventricular cardiomyocytes.22 More-over, hPSC cardiomyocyte cultures can be stronglyenriched for nodal-like cells through inhibition ofthe NRG-1𝛽/ErbB signaling pathway.23 A commonissue for hPSC-derived cardiomyocyte applicationsis that the formed cardiomyocytes typically resemblean immature, embryonic-like stage and require fur-ther maturation to reach the true adult phenotype.


WIREs Systems Biology and Medicine Integrating omics into human cardiogenesis

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FIGURE 1 | Overview of cardiac differentiation in human pluripotent stem cells (PSC). Schematic representation of the sequential steps and theirassociated markers during PSC differentiation to cardiomyocytes. Pathways that need to be modulated to increase cardiac yields are indicated.

Maturation progresses spontaneously over the courseof months in human PSC-derived cardiomyocytes,but can be accelerated artificially through for exampleculture in 3D systems or extracellular treatment withtriiodothyronine.10,24,25

The extracellular signals that control the var-ious steps of cardiac differentiation are thus wellestablished, and enable PSC to cardiomyocyte con-versions with over 90% purity,4–6 yet the geneticsteps underlying the multiphasic titration of Wnt,BMP, and Nodal signaling and especially the spon-taneous differentiation of NKX2-5+ progenitorstoward cardiomyocytes and the differentiation tocardiomyocyte sub-populations are poorly charac-terized. Key transcription factors such as MESP1,MEF2C, or NKX2-5 are well established, yet recentresults from direct reprogramming efforts indicatethat neither factor alone is sufficient to drive thegeneration of cardiomyocytes through reprogram-ming of human fibroblasts, suggesting that geneticregulation of cardiac fate may be far more complex

than anticipated.26–28 Indeed, coexpression of at leastfive cardiac transcription factors, including GATA4,MEF2C, TBX5, HAND2, MESP1, and MYOCD, arerequired to drive a cardiac-like program in humanfibroblasts.27,29,30

Tremendous progress in mass spectrometry andnext generation sequencing (NGS) platforms over thelast few years now allows us to look into the trans-lation of an extracellular signal toward the geneticregulation of cardiac differentiation.31,32 We can studyphosphorylation on a global proteome scale, followtranscriptome-wide changes over time and assess howthat transcriptome is altered at the chromatin levelor through the action of noncoding RNAs. More-over, through the ability of miniaturization of humanPSC cultures into 384-wells or even to the singlecell level,10,33 the cardiogenic transcriptome can nowbe explored functionally. Integration of such largedatasets through systems biology will then producefunctional interaction networks that map the geneticcascades controlling cardiac fate.



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FIGURE 2 | Overview of extracellular inputs and their intracellular effectors that regulate cardiac differentiation. Most known extracellularfactors at the different stages of cardiac differentiation are mapped into one diagram, and their receptors are located on the cell membrane (red line).The arrows indicate connections to the intracellular signaling proteins that are the effectors of the induced signals (key indicates the type ofregulation for each connection). Key cardiac genes downstream (directly or indirectly) are also indicated.

TRANSMISSION OF THEEXTRACELLULAR SIGNAL TO THENUCLEUS: PHOSPHOPROTEOMICS

The signaling proteins that transmit cardiogenic extra-cellular signals to the nucleus are well described(Figure 2). Both the activation of the Nodal/ActivinA pathway and BMP4 pathway results in the nucleartranslocation of Smad transcription factors, throughSmad2/3 and Smad1/5, respectively.34 Both Smadclasses then feed into the common Smad4 to regulategene transcription. For the Wnt pathway, activationof the Frizzled receptor results in the nuclear translo-cation of 𝛽-catenin, which binds to Tcf/Lef transcrip-tion factors to activate gene transcription.35 Althoughthe immediate responses to the extracellular signalare thus rather well defined for cardiac specification,

the use of phosphoproteomics can potentially revealpreviously uncharacterized components and identifycross-talk between the pathways that transmit theextracellular signal to the nucleus.

An examination of rapid phosphorylationchanges induced by extracellular signals is veryimportant, since protein phosphorylation is the mostimportant molecular event in cell signaling that con-verts the extracellular signal to a nuclear signal.31

By using a kinase phosphorylation immunoblot-ting system (Kinexus) or a multi-dimensional liquidchromatography- (MDLC, comprising prefractiona-tions through cation exchange chromatography anda subsequent phospho-peptide enrichment throughimmobilized metal affinity chromatography) tandemmass spectrometry (MS/MS) approach, affected path-ways can be identified by detection of alterations in



protein phosphorylation. The Kinexus system screensa portion of the approximately 520 known humanprotein kinases.36,37 However, such methods requireknown target proteins with unknown relevance to thesignaling events in question and this is rarely the casewhen identifying new pathways or mechanisms. Incontrast, MDLC-MS/MS can overcome these prob-lems by detecting and quantifying, in an unbiasedfashion, known and novel phosphoproteins withina sample.38,39 The mapping of dynamic changes inprotein phosphorylation can then form the basisfor reconstruction of signaling networks inducedby various stimuli. Moreover, the identification ofphosphorylated proteins and the kinases catalyz-ing their phosphorylation in particular will unravelthe pathways affected during differentiation and canpotentially reveal cross-talk between pathways.40 Onebeautiful example of the power of phosphoproteomicsis the recent mapping of the entire phosphoproteomein hESC and their differentiated derivatives usingMDLC-MS/MS.38 This study identified over 2500phosphorylation sites in about 1600 proteins involvedin pluripotency and differentiation. Roughly 400 ofthose were enriched in undifferentiated hESC and500 were enriched in their differentiated progeny. Inundifferentiated cells, these phosphorylated proteinscomprised numerous receptor tyrosine kinases andsignaling kinases, corresponding to previously estab-lished pathways such as FGF, but also to unexpectedcascaded such as EGF, VEGF, and PDGF. Functionalconfirmation indeed corroborated that these pathwayswere maintaining pluripotency of hESC.38 Moreover,numerous transcription factor regulators such asEZH2 and DNMT3B were found phosphorylated,indicating connections between extracellular factorsand transcriptional regulation. Phosphoproteomicsthus identifies the first line of intracellular cascadestriggered by an extracellular signal, which will thenresult in the initiation of a complex interaction net-work of genes, regulated by transcriptional regulatorsand noncoding RNAs. Furthermore, phosphopro-teomics may also be predictive of transcriptionalregulation downstream through the identification ofphosphorylated transcriptional regulators.

EXTRACELLULAR SIGNAL TONUCLEUS: FOLLOWING CHANGES INTHE TRANSCRIPTOME

Gene expression is one of the most widely used meth-ods to study cellular behavior and fate. As discussedabove, the different stages of cardiac differentiationcan be identified by the expression of certain genes.These factors were initially identified as markers for

the cardiac differentiation stages by following theirmRNA level over time, and they each have a very spe-cific temporal expression pattern under the control ofextracellular signals (or the absence thereof). Some ofthese genes drive cardiac fate when overexpressed ina time-dependent manner in PSC. MESP1 has beendescribed as one of the master switches of cardiac fate,and lineage tracing of Mesp1 expressing cells in themouse demonstrates that most cells in the heart orig-inate from a Mesp1+ cell.41,42 Evidently, MESP1 isimportant for heart generation, yet MESP1 also con-tributes to skeletal muscle or hematopoietic lineagesand introduction of MESP1 into fibroblasts does notresult in the reprogramming toward cardiomyocytes,suggesting that MESP1 alone is not sufficient to drivea cardiac program28,41 and other factors must thusbe at play. Similarly, NKX2-5, one of the first car-diac transcription factors described, has an importantrole in cardiac development and differentiation, butNKX2-5 knockouts in the mouse form a heart, indi-cating NKX2-5 alone is not essential or that othergenes may compensate for the loss of NKX2-5.43,44

These examples clearly demonstrate that the geneticcascades underlying cardiac development are far morecomplex than ever anticipated.

Advances in microarray and RNA sequencing(RNAseq) technologies contributed to genome-wideassessments of transcriptional changes during cardiacspecification and now allow us to map the dynamicbehavior of genetic factors during fate specification,downstream of signaling events.45,46 Through its sin-gle base resolution, major advantages of RNAseqover microarray technology include the higher sen-sitivity, the ability to detect different isoforms ofa gene and the discovery of novel uncharacterizedgenes.47 Recent improvements in RNAseq amplifi-cation based library preparation furthermore pro-vide means to run RNAseq analyses on single cells,which brings the opportunity to study differentia-tion at single cell resolution.33,48,49 Moreover, inclu-sion of a systems biology powered primer designfor amplification-mediated library preparation per-mits focused sequencing reactions by for exampleexcluding amplification of all ribosomal proteins,thereby freeing up sequencing capacity for low abun-dant transcripts, which typically contain the tran-scription factors.50 Despite issues including technicalnoise and bioinformatics processing, RNAseq, andmicroarray experiments have lived up to their expec-tation and exposed the high level of complexity ofthe transcriptome in response to cardiogenic extra-cellular signals.45,46 A RNAseq study in mESC offour stages of cardiac differentiation (as discussedabove) has identified four main classes of genes,



with over 3000 genes uniquely present in pluripo-tent cells (e.g., Nanog, Essrb, and Syce2), about 1000genes exclusively expressed in mesoderm stages (e.g.,Mesp1, Wnt2b, and Cer1), and cardiac progenitors(e.g., Slit2, Meis1, and Isl1), respectively and another2700 are found only in ESC-derived cardiomyocytes(e.g., Actc1, Tnni3, and Ryr2). Evidently, these are avast collection of novel genes that may be crucial inthe regulation of cardiac differentiation and clearlydemonstrate that the transcriptional network is farmore complex than exposed through classical genet-ics experiments. Clustering analysis of these genes hasrevealed more subgroups of genes that are dynamicallyregulated over time,45,46 and while genetic relation-ships within these classes and clusters are assumed,functional relevance of the novel genes and their inter-actions still need to be mapped out to understand howthese coordinated transcriptome changes relate to thenetworks of genes that mediate the biological effectsof cardiac differentiation (discussed below).

TRANSCRIPTOME REGULATIONAT THE CHROMATIN LEVEL

Transcriptome analyses are however, rather descrip-tive, and aside from identification of crucial genesand/or isoforms, very little information about func-tion, interaction or regulation is gathered. A first levelof gene regulation occurs at the promoters of the var-ious genes. Dynamic processes regulate the chromatinstatus at specific sites in a particular promoter, pro-viding or blocking access to RNA polymerases and/orcertain transcription factors to, respectively activateor repress transcription of a gene. Key mediators oftranscriptional regulation include histone deacetylases(HDAC), polycomb repressor complexes (PRC), theNuRD complex, SWI/SNF complexes, and histonedemethylases (KDM).51–53 These complexes place orremove methyl or acetyl groups at particular residuesin the chromatin and control the binding of transcrip-tion factors or extension by RNA polymerases.51,52

Selectivity of these complexes for promoters is mostlikely achieved through association with transcriptionfactors, which bind to DNA enhancer regions ofspecific promoters.54–56 Moreover, recent examples inthe context of cardiac differentiation in PSC suggestfurther regulation by extracellular signaling cascadesthat alter the activity of these transcriptional reg-ulation complexes. For example, Nodal is able todrive expression of Brachyury/T through recruit-ment of Jmjd3, a KDM that counteracts PRC and isshown to be essential for cardiac differentiation57,58

(Figure 3). In later stages of PSC differentiation,another connection between extracellular cues and

chromatin regulation has been described, whereNodal/TGF𝛽 represses BAF60C expression, which isa component of the cardiac SWI/SNF complex.20,59

BAF60C opens chromatin at the cardiac locus of theNKX2-5 promoter to drive cardiac differentiation.While these are important examples of extracellularcontrol regulating chromatin status, the vast amountof chromatin regulators suggests a far more complexand context dependent regulation of specific pro-moters. The global regulation of chromatin status ispoorly understood in the cardiac system, but throughcoupling chromatin immunoprecipitation with nextgeneration sequencing (ChIPseq) we are beginning tounderstand how alterations in chromatin status areimportant to regulate mRNA expression.45,46

ChIPseq can be exploited in two different waysto expose interactions between transcription fac-tors. Pull-down of DNA associated with a specifictranscription factor allows identification of all the pro-moter regions that are associated with that transcrip-tion factor. Mapping the pulled down DNA regions togenomic DNA sequences then facilitates the identifi-cation of genes that are regulated by the transcriptionfactor of interest.60 Limited by the understanding ofthe regulation by one factor only, a second more globalChIPseq approach has been described: rather thanrelying on pull-downs of specific transcription factors,pull-downs with antibodies for specific histone markssurvey the entire genetic landscape.61,62 Dependingon which mark is selected, promoter, enhancer, orpolymerase activity can be followed on theoreticallyevery promoter in the genome. Common histonemarks used include H3K27me3 (repressed sites),H3K4me3 (activated sites), H3K4me1, and H3K27ac(enriched in active promoters and enhancers). More-over, immunoprecipitation with RNA polymeraseshave been employed to identify genes that are activelytranscribed.62,63 Combinatorial analyses of several ofthese approaches result in interesting deductions suchas the distinction of activity in enhancers and tran-scriptional start sites by comparison of H3K4me1 andRNA polymerase II ChIPseq datasets.46 Transcriptionfactor binding motif analysis of sequenced sites canthen predict which factors are actually regulatingthese enhancers and/or promoters.46

Such ChIPseq experiments relevant to the car-diac differentiation paradigm have already exposednovel levels of regulation, some even completely unex-pected. Nodal for example induces mesoderm or endo-derm depending on the amount of signal exposed tothe cell.15,64 ChIPseq in mESC for promoters boundby the Nodal effector Smad2 under graded concen-trations of Nodal/Activin A signaling demonstratedthat Smad2 binds to the promoter of pluripotency



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FIGURE 3 | Example of extracellular control of transcriptional regulation. Jmjd3 is a histone demethylase enzyme that removes PRC2-placedmethyl groups in histones to allow transcription. The example illustrates how active Nodal signaling controls transcriptional regulation by theinduction of Jmjd3, which then requires interaction with a transcription factor (T-box) to achieve specificity for certain promoters (Brachyury/T in thisexample). An extra level of complexity is present as indicated by the need of Tcf (downstream of a Wnt signal) to be bound to the promoter as well toallow transcription.57

(e.g., Oct4), mesoderm (e.g., Brachyury/T), or endo-derm (e.g., Eomes) genes respectively and activatestheir transcription, depending on the level of Nodalsignaling, again indicating that extracellular controlof chromatin regulation is important for cell fate.65

Others have described the use of ChIPseq for sev-eral histone marks (see above) to identify activeand repressed genes during a time course of car-diac differentiation.45,46 These analyses have demon-strated a dynamic regulation of above-mentioned car-diac genes at the promoter level as cardiac differen-tiation progresses. Through stage-specific analysis ofthese chromatin landmarks, new regulators of cardiacfate could be predicted through methylation activityin the promoters, which has resulted in the identifica-tion of novel genes important for cardiac differentia-tion such as MEIS2 and TMEM88.45,66 A more unex-pected finding from these analyses was that differentfunctional groups within one transcriptionally activegene cluster were identified since they were controlledby unique histone mark signatures.45,46 For example,the pluripotency genes Nanog and Oct4 have differ-ent chromatin patterns, indicating that they may be

regulated differently on the epigenetic level. Such find-ings are in a way transformative as it was largelyassumed that coinduced genes are regulated similarly,which through genome-wide promoter activity anal-ysis now appears to be at least partially incorrect.Another exciting finding is the discovery that cer-tain fate specific promoters are poised to be activewell before cellular fate is actually established in acell.46 Wamstad et al. describe the example of theActc1 (or 𝛼-actinin, a cardiomyocyte-specific struc-tural protein) gene, for which activating H3K4me1marks are detected in its promoter during the specifi-cation of mesoderm.46 Indeed, RNA polymerase II atthe Actc1 promoter and thus mRNA is only detectedin much later stages of differentiation. Actc1 is not theonly gene that shows such early epigenetic modifica-tions; about 20 similar genes were identified, includingwell-known cardiac genes Myh6 and Ttn. It is unclearwhy these genes become poised so early in the devel-opment, but it is evidently an important step to furtherdifferentiation to the cardiac lineage. Further analysisof the chromatin regulators driving these early changesas well as the factors controlling them will expose



how this unprecedented mechanism drives cardiacfate.

ChIPseq experiments thus not only exposeunprecedented mechanisms of fate regulation,they also allow a global survey of promoter andenhancer activity, which in combination with othergenome-wide methods provides powerful connec-tions between inputs for systems biology basedreconstruction of interaction networks.

TRANSCRIPTOME REGULATION BYNONCODING RNAS

More recently, microRNAs (miRNAs) have emergedas a novel class of genes that are important for awide range of developmental and pathological pro-cesses. Notably, current research on miRNA biologyindicates that their mechanisms of action and the reg-ulatory networks they modulate may provide mecha-nistic insights into developmental processes.67,68 miR-NAs are 19–22 nucleotide single-stranded noncodingRNAs that modulate gene expression posttranscrip-tionally through direct interaction with complemen-tary sequences usually located in the 3′ untranslatedregions (UTRs) of protein-coding transcripts.69,70 Thisinteraction often results in reduced mRNA levels andtranslational repression. Over 2500 mature miRNAsof human origin have been identified as of June 2013,many of which are evolutionarily conserved.71 Com-putational predictions indicate that individual miR-NAs modulate the expression of several hundreds ofmRNAs that might interact at a level not evident bycurrent experimental approaches. Both the number ofmiRNAs expressed in the differentiating ESC systemand their predicted targets indicate that many pro-cesses during cardiac differentiation are likely to becontrolled by miRNAs.46

Evidence supporting the involvement of miR-NAs in cardiovascular development initially camefrom tissue and stage-specific genetic ablation of sev-eral proteins necessary for miRNA biogenesis. Thetemporal removal of the general miRNA popula-tion through knock out of the miRNA biogenesisgenes Ago2, Dicer, or Drosha resulted in early lethal-ity associated to strong cardiovascular phenotypes.72

Interestingly, genetic deletion of individual miRNAsmostly generated subtle postnatal phenotypes of vari-able penetrance.73 These observations suggested thatmiRNAs with similar seed sequences might performredundant roles and/or that residual miRNA activityremained due to incomplete ablation of the targetedmiRNA loci. Similarly, the role of miRNAs in cardiacdifferentiation from ESC remains largely uncharac-terized and only few examples have been described

spanning the sequential stages cardiac differentiation.This is in part due to the absence of suitable in vitrofunctional assays and systematic protocols to eval-uate the resulting phenotypes.74–78 Nevertheless, afew miRNAs have been studied extensively in thecardiac differentiation system. miR-1 and miR-133expression is for example temporally regulated dur-ing PSC differentiation to cardiomyocytes, with highlevels seen during mesoderm formation and later dur-ing cardiomyocyte maturation stages.76,77 Both miRsdrive mesoderm differentiation at the expense of endo-derm and neuroectoderm, with miR-1 having thiseffect through controlling Dll1, a Notch pathwaycomponent.77 miR-125b and miR-499 then regulatethe expression of NKX2.5 and GATA4 and MEF2C,and GATA4, respectively.74,75 Interestingly, two ofthese miRs, namely miR-1 and miR-499, are alsoinvolved in the ventricular specification and matura-tion of hPSC-derived cardiomyocytes, respectively.76

Clearly miRNAs have important functionsthroughout cardiac development, yet a major issuewith miRNA biology is the identification of rele-vant miRNA targets, as some miRNAs target over200 distinct mRNAs.79 Initially, the necessity tocharacterize the miRNA interactome resulted in thedevelopment of numerous miRNA target predictionalgorithms. Most algorithms rely on the comple-mentarity between the 5′ of the miRNA and thebases in 3′UTR of an mRNA. Popular tools such asTargetScan, miRanda, or PicTar provide high sen-sitivity but low specificity and thus result in a highrate of false positive targets.80–83 The sensitivity andprecision of each algorithm varies but compared toexperimental datasets (e.g., PAR-CLIP, CLASH, targetsensors) the best algorithm barely approaches 65%precision (DIANA-microT-CDS)(Figure 4(d)).84–86

This means that only in the best case 2 of 3 pre-dicted miRNA targets are real, and thus advocates forexperimental methods to identify context dependentmiRNA targets. While miRNA targets could be pre-dicted by overlay with transcriptome data, accuracyis questionable. One approach that holds promise foridentification of relevant miRNA targets are targetsensors85(Figure 4). They allow the identificationof miRNAs regulating a target of interest throughusually coupling the 3′UTR with a reporter gene,which would be degraded if a particular miRNA isactive (Figure 4(a) and (b)). Context dependency is amajor issue here, since a miRNA most likely regulatesdifferent targets depending on the biological system.Moreover, whole miRNAome screening against targetsensors typically yield too many hits to follow up andthere is thus a need to develop assays that functionallyprioritize biologically relevant miRNAs (Figure 4(c)).



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368 3250

Identified from target sensor60% predicted

FIGURE 4 | microRNA (miRNA) target identification using target sensors. (a) Schematic illustration of a miRNA target sensor (SERCA2a is shownas an example). The 3′ untranslated region (3′UTR) of the gene of interest is cloned downstream of the eGFP sequence under the control of the PGKpromoter. Upon miRNA binding the hybrid GFP-3′UTR RNA strand is degraded, resulting in the loss of GFP. (b) Workflow of a miRNA target sensorscreen and examples of a GFP-based miRNA target sensor in the presence of negative controls and active miRNAs. GFP intensity and area arequantified through a GFP thresholding algorithm to convert images into data. (c) Example result of a whole genome miRNA screen run against atarget sensor for Serca2a. It plots significance versus GFP fold change for each of the 875 miRNAs tested. (d) Venn diagram indicating the overlapbetween miRNA target prediction algorithms and experimental data from a target sensor screen for SERCA2a.

This could be achieved by placing target sensors forthe gene(s) of interest into a biologically relevantcontext, in combination with libraries of antagomiRsor miRNA sponges to identify functionally impor-tant miRNAs in a certain biological context. Analternative to target sensors is the recently describedCLASH method, which relies on the crosslinking andligation of a miRNA to its target mRNA, and hybridsare then analyzed by next generation sequencing.86

The main advantage of such a method is that itwill identify all active miRNAs and their targetsin any biological system of interest. The functionalneed of each of these interactions is however notprobed with CLASH and further experimentationsuch as, e.g., target protection assays are requiredto prioritize functionally important miRNAs.87

Clearly, target identification of miRNAs is takingleaps forward and it will not take long until miR-NAs can be included in the generation of interactionnetworks.

A second class of noncoding RNAs, long non-coding RNAs (lncRNA), has been identified in thepast few years, and while poorly characterized todate, emerging results have already shed light onhow these lncRNAs may be functionally important.88

LncRNAs do not get translated into proteins, yet theyappear to be important for gene regulation beforeand after transcription. Mechanisms of action includechromatin modification through interaction withproteins in chromatin remodeling complexes suchas PRC2 and activation of transcriptional initiationthrough interaction with TFIIH, a transcriptionalinitiation factor.89,90 LncRNAs can furthermore regu-late genes posttranscriptionally by acting as antisenseRNAs to functionally prevent transcription or transla-tion of mRNA or by preventing the binding of certainmiRNAs to their target mRNA, thereby increasingthe number of available transcripts.91 The importanceof lncRNAs in cardiac development was recentlysupported by the fact that the lncRNAs Braveheart



and Fendrr are essential for the formation of cardiactissue from mesoderm.92,93 Braveheart controls thecardiac phenotype by interacting with SUZ12, a com-ponent of the PRC2 complex, which appears to beimportant for the regulation of a cardiac core networkincluding MESP1, and this at the expense of othermesendoderm lineages.92 Fendrr similarly interactswith the PRC2 complex at a later stage of cardiacdifferentiation, where it controls the expression ofNKX2-5 and GATA6.93 Many other lncRNAs arealso dynamically expressed during cardiac differen-tiation in mouse PSC, suggesting important roles forthese noncoding RNAs.46 Incorporation of lncRNAsinto networks is somewhat difficult at this point,mainly because of our limited understanding of theirmechanisms and targets. Functional perturbationsand correlations with mRNA in cis are some of thefew options to identify connections between lncRNAsand the genes they regulate.46 Noncoding RNAs havethus become important mediators of gene regulationand as their target identification is facilitated, theirinclusion into generated networks will be of highvalue.

INTEGRATION INTO MEANINGFULNETWORKS: SYSTEMS BIOLOGY TOTHE RESCUE

As discussed, many reports have recently documentedthe use of next generation sequencing to map tran-scripts, noncoding RNA or DNA sequence asso-ciated with proteins to dissect cellular changes atmultiple levels (Figure 5(a)). Most studies howeveridentify very little systems-wide information fromthe above-discussed methodologies. A straightforwardapproach is to manually select one or more genesbased on criteria of interest, such as an expectedexpression pattern, its regulation at the chromatinlevel, or even a response to a certain signaling path-way. Prioritizing genes through the use of avail-able tools, including clustering algorithms, pathwayenrichment or GO term enrichment generally facili-tates manual selection, and is heavily suffering fromuser bias. Such analyses can even be taken a step fur-ther by examining correlative patterns between severalof these criteria. For example, whole genome tran-scriptome analysis of human cardiac differentiationwas performed and correlated with ChIPseq experi-ments for repressive and activating histone marks.45,66

This correlation identified a number of novel genessuch as MEIS2 and TMEM88, and yielded elegantfollow-up studies that document the functional impor-tance of these genes in cardiac development.45,66

The reprogramming field however demonstratesthat single genes are not able to mitigate fate con-versions, indicating that complex regulatory net-works control the genetic cascades underlying cell fatedecisions. Genome-wide sequencing datasets containmuch more information than the regulation of sin-gle genes or the coregulation of gene clusters, yet itwould require a village to manually mine such datasetson a systems level to extract genome-wide regulatorynetworks. Integration of data, generated from severalomics methods discussed above, will allow insights inthe behavior and interaction of genes at the globallevel and would drive exposure of large functionalnetworks that in our case mediate cell fate. A recentreport reveals a first glimpse of how such analysescould contribute to exposing the genetic cascades driv-ing cardiac fate in PSC.46 Through generation of tran-scriptome, miRNA and lncRNA profiles and ChIPseqfor activating and repressive histone marks over time,the genetic dynamics of cardiac differentiation weremapped on a systems-wide level.46 The main chal-lenge of such a tour de force is to then parse thesedata to map systems-wide changes and to eventuallybuild interaction network models. This is where thediscipline of systems biology is crucial, which aims atparsing large datasets by bringing together the iden-tification of key players, their dynamic behavior andtheir interaction to build a network model that pro-vides new insights (Figure 5).

Networks are built up from nodes and edges.Nodes consist of the molecular components of the cellsuch as specific proteins (signaling proteins includingkinases and phosphatases, transcription factors andother gene regulatory components) and metabolites.The edges represent the interactions between and/oreffect of one node to another. Typically, nodes consistof only those proteins that show significant phos-phorylation (proteomics), differential transcription(RNAseq), or differential binding (ChIPseq or DNAmodifications) upon application of a stimulus or otherperturbation, as compared to an untreated or wildtype context. Nodes are thus easily identified fromRNAseq or ChIPseq experiments, but their edges (orfunctional interactions), are far more challenging tomap. Edges identifiable by omics methods include reg-ulation of expression (transcription factors, chromatinremodeling), direct interaction (protein–protein inter-actions, complex formation) or posttranscriptionalmodifications (noncoding RNAs). The node connec-tivity information or edges in the network are thusprovided directly by, e.g., ChIPseq data for transcrip-tion factor to mRNA connections and miRNA orlncRNA data for noncoding RNA to mRNA connec-tions. ChIPseq data for specific transcription factors



Growthfactor

SP

ASPP

GENECRCASPP

PROTEIN

Phosphoproteomics

Transcription factor ChIP

Histone mark ChIP

Transcriptomics

miRNA profilingData integrationnetwork building

functional validation

Key:Multiple steps

Single step

Histone mark

mRNA

SP/ASPSignaling proteinActive signaling protein

P Phosphrylation

CRCChromatin remodeling complex

miRNA

Model confirmationby perturbing pathways with siRNAs

(functional genomics)

Transcriptome analysis using high throughput mRNA sequencing at different time points

(normalization, t-test, ANOVA)

Chromatin IP analysis using high throughput sequencing (Peak detection and t-test)

Integrative data analysis for reconstruction and modeling of networks

(correlation analysis, TF to target mapping)

miRNA profiling and target identification(Sensor screens, CLASH)

Inclusion of edges between nodes builds the interactome model (TRANSFAC, PPI, own data)

Identification of key signaling and genetic nodes(gene set/function/pathway enrichment)

Addition of extracellular signals that activate cardiac differentiation

Phosphoproteome analysis using immunoblotand mass spec approach at different time points

Reconstruction and modeling of networks downstream of extracellular signals

(a)

(b)

FIGURE 5 | Integration of omics data into functional networks. (a) Signaling cascades and the downstream genetic events can be profiled atseveral levels on a genome-wide scale (indicated with red dashed lines). The various datasets can then be integrated into functional networks thatcan be validated experimentally. (b) Flow chart of the steps needed to reconstruct the genetic interactions downstream of extracellular signalsthrough integration of omics data.



provides direct information of the genes regulated bythat particular transcription factor. Moreover, histonemark ChIPseq can provide such information on agenome-wide level. Regulated target gene promotersites can be analyzed for transcription factor bindingmotifs, which are predicted through the TRANSFACdatabase94. ChIPseq data can thus be used to build atranscription factor to target gene interaction networkby identifying the direct target gene promoter bindingsites. In the case of miRNAs, if a miRNA is induced bya protein (or gene-product) x and that miRNA resultsin differential expression of another gene y (in a tem-poral manner) then one could connect node x directlyto node y by attaching the miRNA up/down regulationas the edge property. Alternatively, a miRNA itself canbe implemented as a node in between gene x and geney. The latter form is likely preferable as it maintainsthe simplicity of the meaning of the edge. Similararguments hold true for other regulatory connectionssuch as those representing transcription factor to tar-get(s) connections inferred from ChIPseq data. Oncethe nodes and edges are identified, networks can bebuilt using a variety of tools including GeneWeaver,Ingenuity Pathway Analysis (IPA) from IngenuitySystems, DAVID, and Cytoscape.95–98 These toolsfacilitate integration and analysis of omics data, andtypically rely on publicly available databases to buildinteraction networks between the nodes identified.From a network building point of view, Cytoscape islikely the most thorough as the user can upload a listof self-identified edges in addition to nodes. This is animportant advantage over other tools, which identifyedges based on interaction databases that are limitedto a small number of biological contexts.

The overall network will consist of nodesrepresenting genes/proteins, transcription factorto target gene edges, miRNA to target gene edges(directed) and protein–protein interaction (PPI) edges(undirected). As a first pass, legacy knowledge canbe used to identify proteins directly interactingwith the nodes or interaction between the nodes toexpand the network. PPI information can be obtainedfrom several resources such as the National Insti-tute of Aging (NIA) PPI database for mouse, andfor humans several are available including HumanProtein Reference Database (http://www.hprd.org),99

BioGRID database (thebiogrid.org), MINT (Molec-ular INTeraction),100 and INTACT.101 A morecontextualized/refined network can then be obtainedby focusing on the targets belonging to a specificKEGG pathway, GO term, or functional/diseaseannotation. When mRNA data is also availablealong with miRNA phenotypic data, one can excludethe miRNA to target connections for which the

target gene does not show differential expression.An example of such network building, reported incardiac specification in mouse ESC, identified 441target genes of Meis1 and 327 of Gata4.46 Thesetargets exhibit a strong interconnection when placedin a predicted network, and GO term enrichment ofGata4 targets clearly exposes a cardiac developmentconnection, while for Meis1 only the common targetswith Gata4 are relevant for cardiac developmentwhile other genes function more generally in organregeneration.

Integration of experimental data with legacyknowledge is one of the strengths of systems biol-ogy, yet at the same time it is one of its weaknesses.The interaction databases for network generation arebased on a most likely context irrelevant biologicalsystem and the accuracy for a biological context ofchoice is therefore questionable. As a result, the gen-erated models include inaccurate predictions of nodesand edges in the network and thus require furtherfunctional validation, which improve the interactionmaps through the inclusion of iterations of functionalperturbations.

ANALYSIS OF THE NETWORKS:FUNCTIONAL REFINEMENT OFPREDICTED NETWORK MODELS

Integration of omics data through systems analysiswill provide predicted dynamic genetic network mod-els that regulate a biological process. Before assum-ing the importance of these theoretical models, func-tional data is required not only to validate predictednodes and edges, but also to understand the func-tional importance of activated cascades. Functionalperturbation of individual or multiple nodes is there-fore an essential step in a systems biology pipelineto improve accuracy of the edges between the differ-ent nodes (Figure 5(b)). Functional validation throughsmall interfering RNA (siRNA)-mediated knockdownis a common approach, and results are then portedback into the network model to refine it. Such an itera-tive process takes time to refine the predicted network,and inclusion of a functional genomics approach upfront may facilitate network building by filtering outfunctionally irrelevant genes.

Through miniaturization of cell cultures to a 384well format, larger throughput functional genomicsapproaches can be implemented to functionally studygenes and signaling pathways (Figure 6(a)).10,19 Usingphenotypic readouts of cell fate, thousands of condi-tions can be probed simultaneously, greatly improvingthe scale of functional validation. Through screeningsmall molecule inhibitors and siRNAs we have been


http://www.hprd.org


NANOGOCT4

T/BRACH KDR

ESCCardiovascular

ProgenitorMesoderm

KDRMESP1

CardiogenicMesoderm

MEF2C

NKX2.5

CardiacMesoderm

MYH6TNNT2

Cardiomyocytes

siRNA

Smallmolecules

Regulation by signaling

miRNA/siRNASmall molecules

Screening with functional readout

Target Functional Interaction Network

(a)

(b)

Regulation at the genetic level

FIGURE 6 | Functional genomics to validate network interactions. (a) High throughput screening allows large scale functional testing of nodesand edges identified through integration of the various omics datasets. microRNAs (miRNA), siRNA, or small molecule pathway modulators can beimplemented in assays to functionally validate or identify nodes and edges in an assay of choice. We use cardiac specific reporters (illustrated) toidentify functional nodes that are important for cardiac differentiation. In a screening campaign, small RNAs or small molecules that downregulatecardiac differentiation, are identified as key nodes (indicated in red, green indicates increase in cardiac differentiation), which are then incorporatedin the interaction network to yield functionally validated genetic cascades. (b) Schematic overview of functional genomics results during cardiacdifferentiation. Functional screens have indicated that early differentiation steps are dependent on signaling factors to drive fate specification, whilelater stages are not. Genetic interactions are, however, important throughout the differentiation process, initially downstream of extracellular factors,and later downstream of other transcription factors.

able to identify key factors that control different stagesof cardiac differentiation.10,19 For example, duringcell fate specification, our experiments with treat-ment of kinase inhibitors over time suggest that earlystages of differentiation are much more dependent onsignaling factors that drive genetic networks, whilelater stages are exclusively controlled by genetic cas-cades (Figure 6(b)). Indeed, during earlier stages ofcardiac differentiation well-known pathways suchas TGF𝛽 and Wnt are needed, while during thelater stages we only identified kinases regulating celldivision, mainly from the cyclin dependent kinase fam-ily. Such findings suggest that mostly transcriptionalnetworks are at play in these later stages and thereforeimmediately expose a major caveat of using path-way modulator libraries in functional validations, asgenetic events are not functionally probed. siRNAsthen present an important tool to probe the functionsof individual genes. Unbiased genome-wide screen-ing can be performed using commercially available

siRNA collections, or more focused screening canbe performed with pathway, kinome, or transcrip-tion factor siRNA collections. For network building,incorporation of all small molecule and siRNA data,negative as well as positive, can act as a functional fil-ter for genes identified through RNAseq.

A major limitation of functional genomicsscreening as a filter for network building is themassive scale whole genome siRNA screeningrequires, yet several alternatives exist to keep thescale to a minimum. One approach reviewed else-where is the use of miRNA mimics, which should intheory functionally cover relevant genes in the entiregenome.102 While promising, an important drawbackis the current lack of methods to systematically iden-tify miRNA targets (see above). A second approachthrough biased screening of manually selected siR-NAs from a RNAseq or ChIPseq experiment or evena predicted network reduces the scale of functionalscreening. Unbiased screens up front or functional



assessment of predicted nodes after modeling willboth strengthen the validity of generated models.However, due to the fact that screening only relieson a distant readout to make functional calls, a vastof amount of information is lost on the immediateresponses of gene knockdown, thereby excluding anydirect edges or interactions between the nodes in anetwork. Ideally, the knockdown of a single geneshould be correlated with the changes in the entiretranscriptome at any given time, allowing incorpo-ration of genome-wide functional data into networkbuilding. While technically feasible, the relativelyhigh cost of next generation sequencing currentlyhinders such an endeavor, but this could be resolvedin the future as technologies evolve and costs fall.Currently, detailed functional validation would thusonly be possible for a key set of nodes, but it is only amatter of time until genome-wide functional data canbe integrated up front to build functional interactionnetworks.

CONCLUSIONS/PERSPECTIVES

Omics methods have been around for several decades,mostly covering transcriptome surveys throughmicroarray and later ChIP arrays. Initially, data wascurated manually and a biased list of a few highlyregulated genes were selected for follow up, whichwas very similar to the way random mutagenesisscreens were analyzed. Even more, somewhat surpris-ingly, researchers tend to select recognizable genes orpathways, limiting the discovery of novel networks.Systems-wide analyses were, however, unmanageabledue to the absence of technology or lack of resourcesand as a result, the complexity of genetic interactionscould not be assessed at that time. For an accuratesystems level understanding information on as manygenes as possible is needed, especially avoiding userbias (which would occur from manually selectinggenes). The emergence of massive parallel sequencingmethods in the early 2000s has definitely contributedto the rise of systems biology due to an increasing needfor systematic data analysis. Systems-based integra-tion allows genome-wide analysis of gene functionsand interactions and will undoubtedly advance thebiological characterization of many processes, yetseveral technical hurdles hinder full exploitation ofthe power of data integration for building complexregulatory networks.

Identification of nodes and predicted edges iscurrently easily attained with the tools discussed

above, yet the resulting interactome usually becomesfar too complex to understand and often conceals howgenes are functioning together. Furthermore, sincesystems approaches rely on interaction databases,which are typically context dependent, we may befeeding models with inaccurate data. Consequently,filters establishing edges and the functional impor-tance of nodes and edges are essential to build reliableinteraction maps. Several of the discussed omicsmethods hold promise for tackling this issue, andare starting to be implemented to improve networkbuilding. One important tool to establish directedges between two nodes is transcription factorspecific ChIPseq. However, implementing such anidea is currently unthinkable, as over 1000 ChIPexperiments and thus sequencing reactions per timepoint or condition would have to be run. Cleverapproaches combining sequencing of active enhancersand promoters with transcription factor biding motifanalysis provide alternatives and while remarkablyaccurate, they typically lack full genome coverage.Another crucial tool in the establishment of edgesis functional perturbation by loss and/or gain offunction approaches. The accuracy of the networksbuilt, however, currently suffer from the inability tofunctionally test and integrate them on a large scale.As described above, whole genome perturbationsthrough siRNA/miRNA knockdown should ideallybe followed up with RNAseq and/or ChIPseq for eachsingle siRNA/miRNA to allow immediate identifica-tion of nodes and edges to build networks that do notrequire iterative refinements.

Despite their limitations, the availability of thediscussed genome-wide omics tools has permitted sys-tematic surveying of genetic cascades during biolog-ical processes such as cardiac differentiation. Thesesets hold vast amounts of data, and initial efforts tobuild interaction networks around them are under-going. With new technologies being developed theextent of data used for modeling will increase, yieldingeven more complex interaction networks. The majorchallenge in systems biology remains to establish anunbiased strategy to identify biologically meaningfulinformation from genome-wide datasets. In the car-diac differentiation field early work has demonstratedthe integrative power of systems biology to exposepreviously unknown regulatory networks, which mayprove relevant for better understanding cardiac devel-opment, reprogramming, and physiology and thuseffectively expand the therapeutic landscape to treatheart disease.



ACKNOWLEDGMENTS

The authors would like to thank Vipul Bhargava, Laurence Brill, Shankar Subramaniam, and Mark Mercola foruseful discussions during the writing of this review.

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Integrating omics into the cardiac differentiation of human pluripotent stem cells

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