Integrating omics into the cardiac differentiation of human pluripotent stem cells.

Focus Article

Integrating omics into the cardiac differentiation of human pluripotent stem cells Agustin Rojas-Muñoz,1,2,† Mano R. Maurya,2,† Frederick Lo1 and Erik Willems1∗ Time-dependent extracellular manipulations of human pluripotent stem cells can yield as much as 90% pure populations of cardiomyocytes. While the extracellular control of differentiation generally entails dynamic regulation of well-known pathways such as Wnt, BMP, and Nodal signaling, the underlying genetic networks are far more complex and are poorly understood. Notably, the identification of these networks holds promise for understanding heart disease and regeneration. The availability of genome-wide experimentation, such as RNA and DNA sequencing, as well as high throughput surveying with small molecule and small interfering RNA libraries, now enables us to map the genetic interactions underlying cardiac differentiation on a global scale. Initial studies demonstrate the complexity of the genetic regulation of cardiac differentiation, exposing unanticipated novel mechanisms. However, the large datasets generated tend to be overwhelming and systematic approaches are needed to process the vast amount of data to improve our mechanistic understanding of the complex biology. Systems biology methods spur high hopes for parsing vast amounts of data into genetic interaction models that can be verified experimentally and ultimately yield functional networks that expose the genetic connections underlying biological processes. © 2014 Wiley Periodicals, Inc.

How to cite this article:

WIREs Syst Biol Med 2014, 6:311–328. doi: 10.1002/wsbm.1268

INTRODUCTION

S

ince their derivation from the human embryo in the late nineties, embryonic stem cells (ESC) have transformed studies of human biology.1 ESC are pluripotent, i.e., they can be expanded indefinitely and have the ability to form most cell types of the body, both in a petri dish as well as in vivo under the form of teratoma tumors.1,2 These features have enabled us to study human development in vitro and have contributed to important discoveries relevant to congenital disease and regeneration. With ∗ Correspondence

to: [email protected]

1 Muscle

Development and Regeneration Program, SanfordBurnham Medical Research Institute, La Jolla, CA, USA

2 Department

of Bioengineering, UC San Diego, La Jolla, CA, USA authors contributed equally to this work. Conflict of interest: The authors have declared no conflicts of interest for this article. † These

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the identification of key transcription factors that reprogram somatic cells to pluripotency, the human stem cell landscape was transformed as so-called induced pluripotent stem cells (iPSC) could now be generated from patient-derived cells, allowing us to probe human disease mechanisms in a dish.3 For the cardiac field, ESC/IPSC advances have allowed the bulk generation of human cardiomyocytes, which are implementable in transplantation studies and drug toxicity screening.4–6 Moreover, cardiomyocytes generated from patient-derived IPSC are implementable for in vitro studies of heart disease.7 The major challenge over the past 5 years has been to improve the ability to drive cardiac differentiation efficiently from a variety of human and mouse pluripotent stem cell (PSC) lines. Through mimicking steps known from mouse development in combination with small molecules identified in phenotypic screening, hPSC can now be directed to cardiomyocytes efficiently

© 2014 Wiley Periodicals, Inc.

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through manipulation of the extracellular environment, but cardiomyocyte purity varies greatly between cell lines and differentiation methods used.4,8–12 Interestingly, the underlying genetic cascades that drive cardiac fate are largely unknown, even though several important cardiac genes have been identified. The regulation by extracellular cues, the time-dependent alterations and the genetic interactions are poorly characterized. There is a need to fill the gaps between extracellular control and the activation or silencing of key genes as the identification of such cardiac networks are essential to improve our understanding of mechanisms underlying heart disease and regeneration or direct reprogramming.13,14 With the advent of high throughput screening and next generation sequencing methods, appropriate resources to investigate cell biology on a genome-wide scale are finally available. Here, we discuss the use and limitations of whole genome or so-called ‘omics’ or ‘big data’ methods in exposing the underlying genetic mechanisms of cardiac differentiation. We moreover describe how systems biology approaches will be instrumental in the integration of large amounts of data into networks of genetic connections that control cardiac fate.

DRIVING CARDIAC DIFFERENTIATION: EXTRACELLULAR CONTROL IS KEY A variety of culture conditions and differentiation protocols have been described to drive cardiac fate from PSC, and not always employ the same extracellular cues to drive cardiac fate.4,8–12 As a result, cardiac differentiation efficiency may differ greatly from cell line to cell line and laboratory to laboratory. While optimal culture conditions are undoubtedly important to achieve consistency (reviewed in Burridge et al. 12), the overall differentiation process and the expression of marker genes are largely conserved. In general, differentiation toward cardiomyocytes is a multistep process, which requires four main steps that can be identified through the dynamic expression of a series of key transcription factors, receptors and/or structural proteins (Figure 1).11,12 (1) Initially, mesoderm is induced, which in PSC is marked by the increased expression of the BRACHYURY/T gene. The signals driving mesoderm induction in PSC, originally identified in the developing mouse embryo, include Wnt, BMP, and Nodal (Figure 1).4,15–17 A carefully titrated dose of Activin A (a Nodal mimic) and BMP4 at this stage is key for optimal cardiac induction,4 and more recently, small molecules activating the Wnt pathway via inhibition of GSK3𝛽 were shown to have the ability to replace both Activin A and 312

BMP4 for efficient induction of mesoderm and subsequently cardiac fate.6 FGF2 is needed in mesoderm differentiation as well, where it appears to maintain embryonic mesoderm induction by BMP4, thereby limiting the formation of extra-embryonic cell types which are also induced by BMP4.4,18 (2) Mesoderm is then further specified to cardiogenic mesoderm, which can be identified by the presence of the transcription factor MESP1 and by the expression of KDR and PDGFRA receptors.4 Early titration of Activin A and BMP4 are important here (or their replacement with a Wnt agonist), and are sufficient for expression of MESP1, KDR, and PDGFRA.4 We moreover found that selective inhibition of TGF𝛽 during these stages enhances MESP1 expression (Figure 1).19 Of interest, cells at this stage do not have ability to differentiate toward cardiomyocytes in the absence of crucial growth factors or small molecules.10 (3) Cardiogenic mesoderm thus needs to be converted into cardiac mesoderm, which represents the earliest appearance of cardiomyocyte capable progenitor cells, and can be identified through the expression of, e.g., NKX2-5, TBX5, GATA4, HAND2, ISL1, and MEF2C. Mesoderm-inducing signals including Nodal, BMP4, and Wnt need to be repressed in MESP1+ cells to drive the activation of NKX2-5 and other cardiac genes (Figure 1).4,10,20 (4) Once the NKX2-5+ stage is established, ESC-derived cardiac progenitors easily differentiate into cardiomyocytes, which are characterized by their spontaneous contraction and expression of cardiac structural proteins such as MYH6, TNNT2, or ACTN2. Cardiomyocyte containing cultures generally comprise different cardiomyocyte subtypes, including atrial and ventricular cardiomyocytes as well as nodal-like cells that act as pacemaker cells.21 These subtypes can be easily identified by the expression of MLC2v or IRX4 for ventricular myocytes and MLC2a for atrial cells.22 Nodal-like cells can be readily identified by GATA6 expression or the presence of the ion channel HCN4.23 Furthermore, these subtypes are distinguishable electrophysiologically, through comparison of their action potentials.21 Extracellular control of the enrichment of one subtype over the other during differentiation from hPSC was recently described, as retinoic acid signaling governs a switch between atrial and ventricular cardiomyocytes.22 Moreover, hPSC cardiomyocyte cultures can be strongly enriched for nodal-like cells through inhibition of the NRG-1𝛽/ErbB signaling pathway.23 A common issue for hPSC-derived cardiomyocyte applications is that the formed cardiomyocytes typically resemble an immature, embryonic-like stage and require further 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 their associated markers during PSC differentiation to cardiomyocytes. Pathways that need to be modulated to increase cardiac yields are indicated.

Maturation progresses spontaneously over the course of months in human PSC-derived cardiomyocytes, but can be accelerated artificially through for example culture in 3D systems or extracellular treatment with triiodothyronine.10,24,25 The extracellular signals that control the various steps of cardiac differentiation are thus well established, and enable PSC to cardiomyocyte conversions with over 90% purity,4–6 yet the genetic steps underlying the multiphasic titration of Wnt, BMP, and Nodal signaling and especially the spontaneous differentiation of NKX2-5+ progenitors toward cardiomyocytes and the differentiation to cardiomyocyte sub-populations are poorly characterized. Key transcription factors such as MESP1, MEF2C, or NKX2-5 are well established, yet recent results from direct reprogramming efforts indicate that neither factor alone is sufficient to drive the generation of cardiomyocytes through reprogramming of human fibroblasts, suggesting that genetic regulation of cardiac fate may be far more complex Volume 6, July/August 2014

than anticipated.26–28 Indeed, coexpression of at least five cardiac transcription factors, including GATA4, MEF2C, TBX5, HAND2, MESP1, and MYOCD, are required to drive a cardiac-like program in human fibroblasts.27,29,30 Tremendous progress in mass spectrometry and next generation sequencing (NGS) platforms over the last few years now allows us to look into the translation of an extracellular signal toward the genetic regulation of cardiac differentiation.31,32 We can study phosphorylation on a global proteome scale, follow transcriptome-wide changes over time and assess how that transcriptome is altered at the chromatin level or through the action of noncoding RNAs. Moreover, through the ability of miniaturization of human PSC cultures into 384-wells or even to the single cell level,10,33 the cardiogenic transcriptome can now be explored functionally. Integration of such large datasets through systems biology will then produce functional interaction networks that map the genetic cascades controlling cardiac fate.


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FIGURE 2 | Overview of extracellular inputs and their intracellular effectors that regulate cardiac differentiation. Most known extracellular factors 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 of regulation for each connection). Key cardiac genes downstream (directly or indirectly) are also indicated.

TRANSMISSION OF THE EXTRACELLULAR SIGNAL TO THE NUCLEUS: PHOSPHOPROTEOMICS The signaling proteins that transmit cardiogenic extracellular signals to the nucleus are well described (Figure 2). Both the activation of the Nodal/Activin A pathway and BMP4 pathway results in the nuclear translocation of Smad transcription factors, through Smad2/3 and Smad1/5, respectively.34 Both Smad classes then feed into the common Smad4 to regulate gene transcription. For the Wnt pathway, activation of the Frizzled receptor results in the nuclear translocation of 𝛽-catenin, which binds to Tcf/Lef transcription factors to activate gene transcription.35 Although the immediate responses to the extracellular signal are thus rather well defined for cardiac specification, 314

the use of phosphoproteomics can potentially reveal previously uncharacterized components and identify cross-talk between the pathways that transmit the extracellular signal to the nucleus. An examination of rapid phosphorylation changes induced by extracellular signals is very important, since protein phosphorylation is the most important molecular event in cell signaling that converts the extracellular signal to a nuclear signal.31 By using a kinase phosphorylation immunoblotting system (Kinexus) or a multi-dimensional liquid chromatography- (MDLC, comprising prefractionations through cation exchange chromatography and a subsequent phospho-peptide enrichment through immobilized metal affinity chromatography) tandem mass spectrometry (MS/MS) approach, affected pathways can be identified by detection of alterations in





protein phosphorylation. The Kinexus system screens a portion of the approximately 520 known human protein kinases.36,37 However, such methods require known target proteins with unknown relevance to the signaling events in question and this is rarely the case when identifying new pathways or mechanisms. In contrast, MDLC-MS/MS can overcome these problems by detecting and quantifying, in an unbiased fashion, known and novel phosphoproteins within a sample.38,39 The mapping of dynamic changes in protein phosphorylation can then form the basis for reconstruction of signaling networks induced by various stimuli. Moreover, the identification of phosphorylated proteins and the kinases catalyzing their phosphorylation in particular will unravel the pathways affected during differentiation and can potentially reveal cross-talk between pathways.40 One beautiful example of the power of phosphoproteomics is the recent mapping of the entire phosphoproteome in hESC and their differentiated derivatives using MDLC-MS/MS.38 This study identified over 2500 phosphorylation sites in about 1600 proteins involved in pluripotency and differentiation. Roughly 400 of those were enriched in undifferentiated hESC and 500 were enriched in their differentiated progeny. In undifferentiated cells, these phosphorylated proteins comprised numerous receptor tyrosine kinases and signaling kinases, corresponding to previously established pathways such as FGF, but also to unexpected cascaded such as EGF, VEGF, and PDGF. Functional confirmation indeed corroborated that these pathways were maintaining pluripotency of hESC.38 Moreover, numerous transcription factor regulators such as EZH2 and DNMT3B were found phosphorylated, indicating connections between extracellular factors and transcriptional regulation. Phosphoproteomics thus identifies the first line of intracellular cascades triggered by an extracellular signal, which will then result in the initiation of a complex interaction network of genes, regulated by transcriptional regulators and noncoding RNAs. Furthermore, phosphoproteomics may also be predictive of transcriptional regulation downstream through the identification of phosphorylated transcriptional regulators.

EXTRACELLULAR SIGNAL TO NUCLEUS: FOLLOWING CHANGES IN THE TRANSCRIPTOME Gene expression is one of the most widely used methods to study cellular behavior and fate. As discussed above, the different stages of cardiac differentiation can be identified by the expression of certain genes. These factors were initially identified as markers for Volume 6, July/August 2014

the cardiac differentiation stages by following their mRNA level over time, and they each have a very specific temporal expression pattern under the control of extracellular signals (or the absence thereof). Some of these genes drive cardiac fate when overexpressed in a time-dependent manner in PSC. MESP1 has been described as one of the master switches of cardiac fate, and lineage tracing of Mesp1 expressing cells in the mouse demonstrates that most cells in the heart originate from a Mesp1+ cell.41,42 Evidently, MESP1 is important for heart generation, yet MESP1 also contributes to skeletal muscle or hematopoietic lineages and introduction of MESP1 into fibroblasts does not result in the reprogramming toward cardiomyocytes, suggesting that MESP1 alone is not sufficient to drive a cardiac program28,41 and other factors must thus be at play. Similarly, NKX2-5, one of the first cardiac transcription factors described, has an important role in cardiac development and differentiation, but NKX2-5 knockouts in the mouse form a heart, indicating NKX2-5 alone is not essential or that other genes may compensate for the loss of NKX2-5.43,44 These examples clearly demonstrate that the genetic cascades underlying cardiac development are far more complex than ever anticipated. Advances in microarray and RNA sequencing (RNAseq) technologies contributed to genome-wide assessments of transcriptional changes during cardiac specification and now allow us to map the dynamic behavior of genetic factors during fate specification, downstream of signaling events.45,46 Through its single base resolution, major advantages of RNAseq over microarray technology include the higher sensitivity, the ability to detect different isoforms of a gene and the discovery of novel uncharacterized genes.47 Recent improvements in RNAseq amplification based library preparation furthermore provide means to run RNAseq analyses on single cells, which brings the opportunity to study differentiation at single cell resolution.33,48,49 Moreover, inclusion of a systems biology powered primer design for amplification-mediated library preparation permits focused sequencing reactions by for example excluding amplification of all ribosomal proteins, thereby freeing up sequencing capacity for low abundant transcripts, which typically contain the transcription factors.50 Despite issues including technical noise and bioinformatics processing, RNAseq, and microarray experiments have lived up to their expectation and exposed the high level of complexity of the transcriptome in response to cardiogenic extracellular signals.45,46 A RNAseq study in mESC of four stages of cardiac differentiation (as discussed above) has identified four main classes of genes,


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with over 3000 genes uniquely present in pluripotent cells (e.g., Nanog, Essrb, and Syce2), about 1000 genes exclusively expressed in mesoderm stages (e.g., Mesp1, Wnt2b, and Cer1), and cardiac progenitors (e.g., Slit2, Meis1, and Isl1), respectively and another 2700 are found only in ESC-derived cardiomyocytes (e.g., Actc1, Tnni3, and Ryr2). Evidently, these are a vast collection of novel genes that may be crucial in the regulation of cardiac differentiation and clearly demonstrate that the transcriptional network is far more complex than exposed through classical genetics experiments. Clustering analysis of these genes has revealed more subgroups of genes that are dynamically regulated over time,45,46 and while genetic relationships within these classes and clusters are assumed, functional relevance of the novel genes and their interactions still need to be mapped out to understand how these coordinated transcriptome changes relate to the networks of genes that mediate the biological effects of cardiac differentiation (discussed below).

TRANSCRIPTOME REGULATION AT THE CHROMATIN LEVEL Transcriptome analyses are however, rather descriptive, and aside from identification of crucial genes and/or isoforms, very little information about function, interaction or regulation is gathered. A first level of gene regulation occurs at the promoters of the various genes. Dynamic processes regulate the chromatin status at specific sites in a particular promoter, providing or blocking access to RNA polymerases and/or certain transcription factors to, respectively activate or repress transcription of a gene. Key mediators of transcriptional regulation include histone deacetylases (HDAC), polycomb repressor complexes (PRC), the NuRD complex, SWI/SNF complexes, and histone demethylases (KDM).51–53 These complexes place or remove methyl or acetyl groups at particular residues in the chromatin and control the binding of transcription factors or extension by RNA polymerases.51,52 Selectivity of these complexes for promoters is most likely achieved through association with transcription factors, which bind to DNA enhancer regions of specific promoters.54–56 Moreover, recent examples in the context of cardiac differentiation in PSC suggest further regulation by extracellular signaling cascades that alter the activity of these transcriptional regulation complexes. For example, Nodal is able to drive expression of Brachyury/T through recruitment of Jmjd3, a KDM that counteracts PRC and is shown to be essential for cardiac differentiation57,58 (Figure 3). In later stages of PSC differentiation, another connection between extracellular cues and 316

chromatin regulation has been described, where Nodal/TGF𝛽 represses BAF60C expression, which is a component of the cardiac SWI/SNF complex.20,59 BAF60C opens chromatin at the cardiac locus of the NKX2-5 promoter to drive cardiac differentiation. While these are important examples of extracellular control regulating chromatin status, the vast amount of chromatin regulators suggests a far more complex and context dependent regulation of specific promoters. The global regulation of chromatin status is poorly understood in the cardiac system, but through coupling chromatin immunoprecipitation with next generation sequencing (ChIPseq) we are beginning to understand how alterations in chromatin status are important to regulate mRNA expression.45,46 ChIPseq can be exploited in two different ways to expose interactions between transcription factors. Pull-down of DNA associated with a specific transcription factor allows identification of all the promoter regions that are associated with that transcription factor. Mapping the pulled down DNA regions to genomic DNA sequences then facilitates the identification of genes that are regulated by the transcription factor of interest.60 Limited by the understanding of the regulation by one factor only, a second more global ChIPseq approach has been described: rather than relying on pull-downs of specific transcription factors, pull-downs with antibodies for specific histone marks survey the entire genetic landscape.61,62 Depending on which mark is selected, promoter, enhancer, or polymerase activity can be followed on theoretically every promoter in the genome. Common histone marks used include H3K27me3 (repressed sites), H3K4me3 (activated sites), H3K4me1, and H3K27ac (enriched in active promoters and enhancers). Moreover, immunoprecipitation with RNA polymerases have been employed to identify genes that are actively transcribed.62,63 Combinatorial analyses of several of these approaches result in interesting deductions such as the distinction of activity in enhancers and transcriptional start sites by comparison of H3K4me1 and RNA polymerase II ChIPseq datasets.46 Transcription factor binding motif analysis of sequenced sites can then predict which factors are actually regulating these enhancers and/or promoters.46 Such ChIPseq experiments relevant to the cardiac differentiation paradigm have already exposed novel levels of regulation, some even completely unexpected. Nodal for example induces mesoderm or endoderm depending on the amount of signal exposed to the cell.15,64 ChIPseq in mESC for promoters bound by the Nodal effector Smad2 under graded concentrations of Nodal/Activin A signaling demonstrated that 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-placed methyl groups in histones to allow transcription. The example illustrates how active Nodal signaling controls transcriptional regulation by the induction of Jmjd3, which then requires interaction with a transcription factor (T-box) to achieve specificity for certain promoters (Brachyury/T in this example). 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 to allow transcription.57

(e.g., Oct4), mesoderm (e.g., Brachyury/T), or endoderm (e.g., Eomes) genes respectively and activates their transcription, depending on the level of Nodal signaling, again indicating that extracellular control of chromatin regulation is important for cell fate.65 Others have described the use of ChIPseq for several histone marks (see above) to identify active and repressed genes during a time course of cardiac differentiation.45,46 These analyses have demonstrated a dynamic regulation of above-mentioned cardiac genes at the promoter level as cardiac differentiation progresses. Through stage-specific analysis of these chromatin landmarks, new regulators of cardiac fate could be predicted through methylation activity in the promoters, which has resulted in the identification of novel genes important for cardiac differentiation such as MEIS2 and TMEM88.45,66 A more unexpected finding from these analyses was that different functional groups within one transcriptionally active gene cluster were identified since they were controlled by unique histone mark signatures.45,46 For example, the pluripotency genes Nanog and Oct4 have different chromatin patterns, indicating that they may be Volume 6, July/August 2014

regulated differently on the epigenetic level. Such findings are in a way transformative as it was largely assumed that coinduced genes are regulated similarly, which through genome-wide promoter activity analysis now appears to be at least partially incorrect. Another exciting finding is the discovery that certain fate specific promoters are poised to be active well before cellular fate is actually established in a cell.46 Wamstad et al. describe the example of the Actc1 (or 𝛼-actinin, a cardiomyocyte-specific structural protein) gene, for which activating H3K4me1 marks are detected in its promoter during the specification of mesoderm.46 Indeed, RNA polymerase II at the Actc1 promoter and thus mRNA is only detected in much later stages of differentiation. Actc1 is not the only gene that shows such early epigenetic modifications; about 20 similar genes were identified, including well-known cardiac genes Myh6 and Ttn. It is unclear why these genes become poised so early in the development, but it is evidently an important step to further differentiation to the cardiac lineage. Further analysis of the chromatin regulators driving these early changes as well as the factors controlling them will expose


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how this unprecedented mechanism drives cardiac fate. ChIPseq experiments thus not only expose unprecedented mechanisms of fate regulation, they also allow a global survey of promoter and enhancer activity, which in combination with other genome-wide methods provides powerful connections between inputs for systems biology based reconstruction of interaction networks.

TRANSCRIPTOME REGULATION BY NONCODING RNAS More recently, microRNAs (miRNAs) have emerged as a novel class of genes that are important for a wide range of developmental and pathological processes. Notably, current research on miRNA biology indicates that their mechanisms of action and the regulatory networks they modulate may provide mechanistic insights into developmental processes.67,68 miRNAs are 19–22 nucleotide single-stranded noncoding RNAs that modulate gene expression posttranscriptionally through direct interaction with complementary sequences usually located in the 3′ untranslated regions (UTRs) of protein-coding transcripts.69,70 This interaction often results in reduced mRNA levels and translational repression. Over 2500 mature miRNAs of human origin have been identified as of June 2013, many of which are evolutionarily conserved.71 Computational predictions indicate that individual miRNAs modulate the expression of several hundreds of mRNAs that might interact at a level not evident by current experimental approaches. Both the number of miRNAs expressed in the differentiating ESC system and their predicted targets indicate that many processes during cardiac differentiation are likely to be controlled by miRNAs.46 Evidence supporting the involvement of miRNAs in cardiovascular development initially came from tissue and stage-specific genetic ablation of several proteins necessary for miRNA biogenesis. The temporal removal of the general miRNA population through knock out of the miRNA biogenesis genes Ago2, Dicer, or Drosha resulted in early lethality associated to strong cardiovascular phenotypes.72 Interestingly, genetic deletion of individual miRNAs mostly generated subtle postnatal phenotypes of variable penetrance.73 These observations suggested that miRNAs with similar seed sequences might perform redundant roles and/or that residual miRNA activity remained due to incomplete ablation of the targeted miRNA loci. Similarly, the role of miRNAs in cardiac differentiation from ESC remains largely uncharacterized and only few examples have been described 318

spanning the sequential stages cardiac differentiation. This is in part due to the absence of suitable in vitro functional assays and systematic protocols to evaluate the resulting phenotypes.74–78 Nevertheless, a few miRNAs have been studied extensively in the cardiac differentiation system. miR-1 and miR-133 expression is for example temporally regulated during PSC differentiation to cardiomyocytes, with high levels seen during mesoderm formation and later during cardiomyocyte maturation stages.76,77 Both miRs drive mesoderm differentiation at the expense of endoderm and neuroectoderm, with miR-1 having this effect through controlling Dll1, a Notch pathway component.77 miR-125b and miR-499 then regulate the expression of NKX2.5 and GATA4 and MEF2C, and GATA4, respectively.74,75 Interestingly, two of these miRs, namely miR-1 and miR-499, are also involved in the ventricular specification and maturation of hPSC-derived cardiomyocytes, respectively.76 Clearly miRNAs have important functions throughout cardiac development, yet a major issue with miRNA biology is the identification of relevant miRNA targets, as some miRNAs target over 200 distinct mRNAs.79 Initially, the necessity to characterize the miRNA interactome resulted in the development of numerous miRNA target prediction algorithms. Most algorithms rely on the complementarity between the 5′ of the miRNA and the bases in 3′ UTR of an mRNA. Popular tools such as TargetScan, miRanda, or PicTar provide high sensitivity but low specificity and thus result in a high rate of false positive targets.80–83 The sensitivity and precision of each algorithm varies but compared to experimental datasets (e.g., PAR-CLIP, CLASH, target sensors) 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 predicted miRNA targets are real, and thus advocates for experimental methods to identify context dependent miRNA targets. While miRNA targets could be predicted by overlay with transcriptome data, accuracy is questionable. One approach that holds promise for identification of relevant miRNA targets are target sensors85 (Figure 4). They allow the identification of miRNAs regulating a target of interest through usually coupling the 3′ UTR with a reporter gene, which would be degraded if a particular miRNA is active (Figure 4(a) and (b)). Context dependency is a major issue here, since a miRNA most likely regulates different targets depending on the biological system. Moreover, whole miRNAome screening against target sensors typically yield too many hits to follow up and there is thus a need to develop assays that functionally prioritize biologically relevant miRNAs (Figure 4(c)).




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FIGURE 4 | microRNA (miRNA) target identification using target sensors. (a) Schematic illustration of a miRNA target sensor (SERCA2a is shown as 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 PGK promoter. Upon miRNA binding the hybrid GFP-3′ UTR RNA strand is degraded, resulting in the loss of GFP. (b) Workflow of a miRNA target sensor screen and examples of a GFP-based miRNA target sensor in the presence of negative controls and active miRNAs. GFP intensity and area are quantified through a GFP thresholding algorithm to convert images into data. (c) Example result of a whole genome miRNA screen run against a target sensor for Serca2a. It plots significance versus GFP fold change for each of the 875 miRNAs tested. (d) Venn diagram indicating the overlap between miRNA target prediction algorithms and experimental data from a target sensor screen for SERCA2a.

This could be achieved by placing target sensors for the gene(s) of interest into a biologically relevant context, in combination with libraries of antagomiRs or miRNA sponges to identify functionally important miRNAs in a certain biological context. An alternative to target sensors is the recently described CLASH method, which relies on the crosslinking and ligation of a miRNA to its target mRNA, and hybrids are then analyzed by next generation sequencing.86 The main advantage of such a method is that it will identify all active miRNAs and their targets in any biological system of interest. The functional need of each of these interactions is however not probed with CLASH and further experimentation such as, e.g., target protection assays are required to prioritize functionally important miRNAs.87 Clearly, target identification of miRNAs is taking leaps forward and it will not take long until miRNAs can be included in the generation of interaction networks. Volume 6, July/August 2014

A second class of noncoding RNAs, long noncoding RNAs (lncRNA), has been identified in the past few years, and while poorly characterized to date, emerging results have already shed light on how these lncRNAs may be functionally important.88 LncRNAs do not get translated into proteins, yet they appear to be important for gene regulation before and after transcription. Mechanisms of action include chromatin modification through interaction with proteins in chromatin remodeling complexes such as PRC2 and activation of transcriptional initiation through interaction with TFIIH, a transcriptional initiation factor.89,90 LncRNAs can furthermore regulate genes posttranscriptionally by acting as antisense RNAs to functionally prevent transcription or translation of mRNA or by preventing the binding of certain miRNAs to their target mRNA, thereby increasing the number of available transcripts.91 The importance of lncRNAs in cardiac development was recently supported by the fact that the lncRNAs Braveheart


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and Fendrr are essential for the formation of cardiac tissue from mesoderm.92,93 Braveheart controls the cardiac phenotype by interacting with SUZ12, a component of the PRC2 complex, which appears to be important for the regulation of a cardiac core network including MESP1, and this at the expense of other mesendoderm lineages.92 Fendrr similarly interacts with the PRC2 complex at a later stage of cardiac differentiation, where it controls the expression of NKX2-5 and GATA6.93 Many other lncRNAs are also dynamically expressed during cardiac differentiation in mouse PSC, suggesting important roles for these noncoding RNAs.46 Incorporation of lncRNAs into networks is somewhat difficult at this point, mainly because of our limited understanding of their mechanisms and targets. Functional perturbations and correlations with mRNA in cis are some of the few options to identify connections between lncRNAs and the genes they regulate.46 Noncoding RNAs have thus become important mediators of gene regulation and as their target identification is facilitated, their inclusion into generated networks will be of high value.

INTEGRATION INTO MEANINGFUL NETWORKS: SYSTEMS BIOLOGY TO THE RESCUE As discussed, many reports have recently documented the use of next generation sequencing to map transcripts, noncoding RNA or DNA sequence associated with proteins to dissect cellular changes at multiple levels (Figure 5(a)). Most studies however identify very little systems-wide information from the above-discussed methodologies. A straightforward approach is to manually select one or more genes based on criteria of interest, such as an expected expression pattern, its regulation at the chromatin level, or even a response to a certain signaling pathway. Prioritizing genes through the use of available tools, including clustering algorithms, pathway enrichment or GO term enrichment generally facilitates manual selection, and is heavily suffering from user bias. Such analyses can even be taken a step further by examining correlative patterns between several of these criteria. For example, whole genome transcriptome analysis of human cardiac differentiation was performed and correlated with ChIPseq experiments for repressive and activating histone marks.45,66 This correlation identified a number of novel genes such as MEIS2 and TMEM88, and yielded elegant follow-up studies that document the functional importance of these genes in cardiac development.45,66 320

The reprogramming field however demonstrates that single genes are not able to mitigate fate conversions, indicating that complex regulatory networks control the genetic cascades underlying cell fate decisions. Genome-wide sequencing datasets contain much more information than the regulation of single genes or the coregulation of gene clusters, yet it would require a village to manually mine such datasets on a systems level to extract genome-wide regulatory networks. Integration of data, generated from several omics methods discussed above, will allow insights in the behavior and interaction of genes at the global level and would drive exposure of large functional networks that in our case mediate cell fate. A recent report reveals a first glimpse of how such analyses could contribute to exposing the genetic cascades driving cardiac fate in PSC.46 Through generation of transcriptome, miRNA and lncRNA profiles and ChIPseq for activating and repressive histone marks over time, the genetic dynamics of cardiac differentiation were mapped on a systems-wide level.46 The main challenge of such a tour de force is to then parse these data to map systems-wide changes and to eventually build interaction network models. This is where the discipline of systems biology is crucial, which aims at parsing large datasets by bringing together the identification of key players, their dynamic behavior and their interaction to build a network model that provides new insights (Figure 5). Networks are built up from nodes and edges. Nodes consist of the molecular components of the cell such as specific proteins (signaling proteins including kinases and phosphatases, transcription factors and other gene regulatory components) and metabolites. The edges represent the interactions between and/or effect of one node to another. Typically, nodes consist of only those proteins that show significant phosphorylation (proteomics), differential transcription (RNAseq), or differential binding (ChIPseq or DNA modifications) upon application of a stimulus or other perturbation, as compared to an untreated or wild type context. Nodes are thus easily identified from RNAseq or ChIPseq experiments, but their edges (or functional interactions), are far more challenging to map. Edges identifiable by omics methods include regulation of expression (transcription factors, chromatin remodeling), direct interaction (protein–protein interactions, complex formation) or posttranscriptional modifications (noncoding RNAs). The node connectivity information or edges in the network are thus provided directly by, e.g., ChIPseq data for transcription factor to mRNA connections and miRNA or lncRNA data for noncoding RNA to mRNA connections. ChIPseq data for specific transcription factors





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Reconstruction and modeling of networks downstream of extracellular signals

Addition of extracellular signals that activate cardiac differentiation

Phosphoproteome analysis using immunoblot and mass spec approach at different time points

Transcriptome analysis using high throughput mRNA sequencing at different time points (normalization, t-test, ANOVA)

miRNA profiling and target identification (Sensor screens, CLASH)

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)

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

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

Model confirmation by perturbing pathways with siRNAs (functional genomics)

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



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provides direct information of the genes regulated by that particular transcription factor. Moreover, histone mark ChIPseq can provide such information on a genome-wide level. Regulated target gene promoter sites can be analyzed for transcription factor binding motifs, which are predicted through the TRANSFAC database94 . ChIPseq data can thus be used to build a transcription factor to target gene interaction network by identifying the direct target gene promoter binding sites. In the case of miRNAs, if a miRNA is induced by a protein (or gene-product) x and that miRNA results in differential expression of another gene y (in a temporal manner) then one could connect node x directly to node y by attaching the miRNA up/down regulation as the edge property. Alternatively, a miRNA itself can be implemented as a node in between gene x and gene y. The latter form is likely preferable as it maintains the simplicity of the meaning of the edge. Similar arguments hold true for other regulatory connections such as those representing transcription factor to target(s) connections inferred from ChIPseq data. Once the nodes and edges are identified, networks can be built using a variety of tools including GeneWeaver, Ingenuity Pathway Analysis (IPA) from Ingenuity Systems, DAVID, and Cytoscape.95–98 These tools facilitate integration and analysis of omics data, and typically rely on publicly available databases to build interaction networks between the nodes identified. From a network building point of view, Cytoscape is likely the most thorough as the user can upload a list of self-identified edges in addition to nodes. This is an important advantage over other tools, which identify edges based on interaction databases that are limited to a small number of biological contexts. The overall network will consist of nodes representing genes/proteins, transcription factor to target gene edges, miRNA to target gene edges (directed) and protein–protein interaction (PPI) edges (undirected). As a first pass, legacy knowledge can be used to identify proteins directly interacting with the nodes or interaction between the nodes to expand the network. PPI information can be obtained from several resources such as the National Institute of Aging (NIA) PPI database for mouse, and for humans several are available including Human Protein Reference Database (http://www.hprd.org),99 BioGRID database (thebiogrid.org), MINT (Molecular INTeraction),100 and INTACT.101 A more contextualized/refined network can then be obtained by focusing on the targets belonging to a specific KEGG pathway, GO term, or functional/disease annotation. When mRNA data is also available along with miRNA phenotypic data, one can exclude the miRNA to target connections for which the 322

target gene does not show differential expression. An example of such network building, reported in cardiac specification in mouse ESC, identified 441 target genes of Meis1 and 327 of Gata4.46 These targets exhibit a strong interconnection when placed in a predicted network, and GO term enrichment of Gata4 targets clearly exposes a cardiac development connection, while for Meis1 only the common targets with Gata4 are relevant for cardiac development while other genes function more generally in organ regeneration. Integration of experimental data with legacy knowledge is one of the strengths of systems biology, yet at the same time it is one of its weaknesses. The interaction databases for network generation are based on a most likely context irrelevant biological system and the accuracy for a biological context of choice is therefore questionable. As a result, the generated models include inaccurate predictions of nodes and edges in the network and thus require further functional validation, which improve the interaction maps through the inclusion of iterations of functional perturbations.

ANALYSIS OF THE NETWORKS: FUNCTIONAL REFINEMENT OF PREDICTED NETWORK MODELS Integration of omics data through systems analysis will provide predicted dynamic genetic network models that regulate a biological process. Before assuming the importance of these theoretical models, functional data is required not only to validate predicted nodes and edges, but also to understand the functional importance of activated cascades. Functional perturbation of individual or multiple nodes is therefore an essential step in a systems biology pipeline to improve accuracy of the edges between the different nodes (Figure 5(b)). Functional validation through small interfering RNA (siRNA)-mediated knockdown is a common approach, and results are then ported back into the network model to refine it. Such an iterative process takes time to refine the predicted network, and inclusion of a functional genomics approach up front may facilitate network building by filtering out functionally irrelevant genes. Through miniaturization of cell cultures to a 384 well format, larger throughput functional genomics approaches can be implemented to functionally study genes and signaling pathways (Figure 6(a)).10,19 Using phenotypic readouts of cell fate, thousands of conditions can be probed simultaneously, greatly improving the scale of functional validation. Through screening small molecule inhibitors and siRNAs we have been





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Cardiomyocytes MYH6 TNNT2

Small Regulation by signaling molecules Regulation at the genetic level

siRNA

FIGURE 6 | Functional genomics to validate network interactions. (a) High throughput screening allows large scale functional testing of nodes and edges identified through integration of the various omics datasets. microRNAs (miRNA), siRNA, or small molecule pathway modulators can be implemented in assays to functionally validate or identify nodes and edges in an assay of choice. We use cardiac specific reporters (illustrated) to identify functional nodes that are important for cardiac differentiation. In a screening campaign, small RNAs or small molecules that downregulate cardiac differentiation, are identified as key nodes (indicated in red, green indicates increase in cardiac differentiation), which are then incorporated in the interaction network to yield functionally validated genetic cascades. (b) Schematic overview of functional genomics results during cardiac differentiation. Functional screens have indicated that early differentiation steps are dependent on signaling factors to drive fate specification, while later 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 stages of cardiac differentiation.10,19 For example, during cell fate specification, our experiments with treatment of kinase inhibitors over time suggest that early stages of differentiation are much more dependent on signaling factors that drive genetic networks, while later stages are exclusively controlled by genetic cascades (Figure 6(b)). Indeed, during earlier stages of cardiac differentiation well-known pathways such as TGF𝛽 and Wnt are needed, while during the later stages we only identified kinases regulating cell division, mainly from the cyclin dependent kinase family. Such findings suggest that mostly transcriptional networks are at play in these later stages and therefore immediately expose a major caveat of using pathway modulator libraries in functional validations, as genetic events are not functionally probed. siRNAs then present an important tool to probe the functions of individual genes. Unbiased genome-wide screening can be performed using commercially available Volume 6, July/August 2014

siRNA collections, or more focused screening can be performed with pathway, kinome, or transcription factor siRNA collections. For network building, incorporation of all small molecule and siRNA data, negative as well as positive, can act as a functional filter for genes identified through RNAseq. A major limitation of functional genomics screening as a filter for network building is the massive scale whole genome siRNA screening requires, yet several alternatives exist to keep the scale to a minimum. One approach reviewed elsewhere is the use of miRNA mimics, which should in theory functionally cover relevant genes in the entire genome.102 While promising, an important drawback is the current lack of methods to systematically identify miRNA targets (see above). A second approach through biased screening of manually selected siRNAs from a RNAseq or ChIPseq experiment or even a predicted network reduces the scale of functional screening. Unbiased screens up front or functional


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assessment of predicted nodes after modeling will both strengthen the validity of generated models. However, due to the fact that screening only relies on a distant readout to make functional calls, a vast of amount of information is lost on the immediate responses of gene knockdown, thereby excluding any direct edges or interactions between the nodes in a network. Ideally, the knockdown of a single gene should be correlated with the changes in the entire transcriptome at any given time, allowing incorporation of genome-wide functional data into network building. While technically feasible, the relatively high cost of next generation sequencing currently hinders such an endeavor, but this could be resolved in the future as technologies evolve and costs fall. Currently, detailed functional validation would thus only be possible for a key set of nodes, but it is only a matter of time until genome-wide functional data can be integrated up front to build functional interaction networks.

CONCLUSIONS/PERSPECTIVES Omics methods have been around for several decades, mostly covering transcriptome surveys through microarray and later ChIP arrays. Initially, data was curated manually and a biased list of a few highly regulated genes were selected for follow up, which was very similar to the way random mutagenesis screens were analyzed. Even more, somewhat surprisingly, researchers tend to select recognizable genes or pathways, limiting the discovery of novel networks. Systems-wide analyses were, however, unmanageable due to the absence of technology or lack of resources and as a result, the complexity of genetic interactions could not be assessed at that time. For an accurate systems level understanding information on as many genes as possible is needed, especially avoiding user bias (which would occur from manually selecting genes). The emergence of massive parallel sequencing methods in the early 2000s has definitely contributed to the rise of systems biology due to an increasing need for systematic data analysis. Systems-based integration allows genome-wide analysis of gene functions and interactions and will undoubtedly advance the biological characterization of many processes, yet several technical hurdles hinder full exploitation of the power of data integration for building complex regulatory networks. Identification of nodes and predicted edges is currently easily attained with the tools discussed

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above, yet the resulting interactome usually becomes far too complex to understand and often conceals how genes are functioning together. Furthermore, since systems approaches rely on interaction databases, which are typically context dependent, we may be feeding models with inaccurate data. Consequently, filters establishing edges and the functional importance of nodes and edges are essential to build reliable interaction maps. Several of the discussed omics methods hold promise for tackling this issue, and are starting to be implemented to improve network building. One important tool to establish direct edges between two nodes is transcription factor specific ChIPseq. However, implementing such an idea is currently unthinkable, as over 1000 ChIP experiments and thus sequencing reactions per time point or condition would have to be run. Clever approaches combining sequencing of active enhancers and promoters with transcription factor biding motif analysis provide alternatives and while remarkably accurate, they typically lack full genome coverage. Another crucial tool in the establishment of edges is functional perturbation by loss and/or gain of function approaches. The accuracy of the networks built, however, currently suffer from the inability to functionally test and integrate them on a large scale. As described above, whole genome perturbations through siRNA/miRNA knockdown should ideally be followed up with RNAseq and/or ChIPseq for each single siRNA/miRNA to allow immediate identification of nodes and edges to build networks that do not require iterative refinements. Despite their limitations, the availability of the discussed genome-wide omics tools has permitted systematic surveying of genetic cascades during biological processes such as cardiac differentiation. These sets hold vast amounts of data, and initial efforts to build interaction networks around them are undergoing. With new technologies being developed the extent of data used for modeling will increase, yielding even more complex interaction networks. The major challenge in systems biology remains to establish an unbiased strategy to identify biologically meaningful information from genome-wide datasets. In the cardiac differentiation field early work has demonstrated the integrative power of systems biology to expose previously unknown regulatory networks, which may prove relevant for better understanding cardiac development, reprogramming, and physiology and thus effectively expand the therapeutic landscape to treat heart disease.





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

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Differentiation of pluripotent stem cells into hypothalamic and pituitary cells.

Differentiation of pluripotent stem cells into endothelial cells.

Differentiation potential of o bombay human-induced pluripotent stem cells and human embryonic stem cells into fetal erythroid-like cells.

Directed Differentiation of Pluripotent Stem Cells into Kidney.

Differentiation of pluripotent stem cells into retinal pigmented epithelium.

Differentiation of human pluripotent stem cells into Medial Ganglionic Eminence vs. Caudal Ganglionic Eminence cells.

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Proneural transcription factor Atoh1 drives highly efficient differentiation of human pluripotent stem cells into dopaminergic neurons.

Differentiation of human induced pluripotent stem cells into a keratinocyte lineage.

Directed Differentiation of Human Induced Pluripotent Stem Cells into Fallopian Tube Epithelium.

Differentiation of human pluripotent stem cells into highly functional classical brown adipocytes.

High content screening for modulators of cardiac differentiation in human pluripotent stem cells.

A systemic evaluation of cardiac differentiation from mRNA reprogrammed human induced pluripotent stem cells.

Development of a scalable suspension culture for cardiac differentiation from human pluripotent stem cells.

Aggregate Size Optimization in Microwells for Suspension-based Cardiac Differentiation of Human Pluripotent Stem Cells.

Electrical Stimulation Promotes Cardiac Differentiation of Human Induced Pluripotent Stem Cells.

Functional differentiation of human pluripotent stem cells on a chip.

Directed Differentiation of Human Pluripotent Stem Cells to Microglia.