Myogenesis in the Genomics Era

Review

Myogenesis in the Genomics Era

Alexandre Blais Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, K1H 8M5, Canada

Correspondence to Alexandre Blais: [email protected] http://dx.doi.org/10.1016/j.jmb.2015.02.009 Edited by M. Yaniv

Abstract Skeletal myogenesis is the process of formation of the muscles that enable movement and breathing. Muscles form after the fate determination and differentiation of precursor cells. Being an extraordinarily complex process, myogenesis is regulated at multiple levels, and transcriptional regulation naturally plays a big part in the making of muscle. A significant part of what we know today of the transcriptional regulatory networks overseeing myogenesis comes from large-scale functional genomics studies. The objective of this review is to provide an overview of the various genomics techniques that have been employed over the years to understand myogenic regulation, to give a sense of the degree of understanding they have provided us up to now, and to highlight the next challenges to be overcome. © 2015 Elsevier Ltd. All rights reserved.

Introduction At its simplest level, myogenesis is the formation of muscle precursor cells, often referred to as myoblasts, and their subsequent differentiation into contractile cells termed myofibers or myotubes. Two hallmarks of muscle differentiation are that it coincides with the fusion of several myoblasts together to form syncytial myofibers, as well as with an irreversible mitotic exit. In vertebrates, skeletal muscles of the trunk and limbs originate from mesodermal precursors arising in the dermomyotome, the dorsal part of the somites (reviewed in Refs. [1–3]). Under the influence of morphogens secreted by the surrounding tissues, dermomyotomal cells commit to the myogenic lineage and start their differentiation to acquire a muscle identity. Dermomyotomal precursors eventually migrate ventrally to populate the myotome, the site of primary myogenesis. Later, the dermomyotome undergoes de-epithelialization to allow a second wave of muscle fiber formation. Muscles of the limbs originate from cells at the ventrolateral region of the dermomyotome that delaminate and migrate to the limb buds, after which they undergo terminal differentiation. Myogenesis also occurs after birth, in postnatal growth, and during regeneration after damage. Muscle 0022-2836/© 2015 Elsevier Ltd. All rights reserved.

stem cells called satellite cells are responsible for both (recently reviewed in Refs. [4] and [5]). Satellite cells are undifferentiated precursors committed to the muscle lineage. They are wedged between the basal lamina and the sarcolemma of the myofibers [6]. During the first 3 weeks of a mouse postnatal life, muscle tissue growth occurs predominantly through the fusion of satellite-cell-derived myoblasts with existing fibers [7]. In the adult, after an injury involving myotrauma, satellite cells become activated and leave their state of profound quiescence. After extensive proliferation, the myoblasts produced engage in the differentiation route and fuse to damaged fibers for repair, or they fuse together to form new myofibers. The maintenance of the pool of satellite cells is thought to be enabled by the asymmetric divisions that satellite cells engage in, early in the activation process, or by a return to quiescence of actively proliferating cells. As in any other cellular system, the profound cell identity changes that occur during the prenatal or postnatal myogenesis programs are put in place by remodeling of the cellular transcriptome, such that the genes required by a given cell type to enable them to carry out their intended functions (e.g., alertness to muscle damage, proliferation, and contraction, to name just a few) are expressed at the appropriate levels. The J Mol Biol (2015) 427, 2023–2038

2024 changes to the complement of genes expressed by muscle cells are quite formidable, both in terms of the amplitude of the expression change that is often witnessed and in terms of the sheer number of genes being regulated concurrently. For example, hundreds of cell cycle effector genes simultaneously go from highly expressed to permanently silenced, when myoblasts differentiate into myotubes [8]. Gene expression changes require to be made in an orderly and concerted fashion and are thus orchestrated by an elaborate transcriptional regulatory network [9]. At its core are sequence-specific transcription factors that control which genes will be expressed and which ones should become or remain silent as cells progress through myogenesis. Because transcription factors perform their duties in the context of a chromatinized template, rather than naked DNA, their activity is tightly connected to the structure of that template: it is thought that transcription factor function can be both a cause and a consequence of specific chromatin states, often referred to as the epigenome [10]. Although a very large number of transcription factors are expressed in muscle cells at some point during development and/or in adulthood, some of them are in fact uniquely expressed, or strongly enriched, in skeletal muscle. The transcription factors most intimately connected to myogenesis are the myogenic regulatory factors (MRFs), a group of four related basic helix–loop–helix factors that are at the center of skeletal muscle formation (reviewed in Ref. [11]). The first member identified, MyoD, was identified by virtue of its ability to convert fibroblasts to the muscle lineage [12,13]. The MRFs do not act alone but cooperate with many other muscle-enriched factors, notably the homeodomain factors Six1, Six4, and Pbx1 [14,15]; the MADS box factors of the Mef2 family [16,17]; and the Rel-homology domain transcription factors of the NFATc family [18,19]. Other transcription factors are also involved in myogenesis, either by controlling the expression of the MRFs or by other means (reviewed in Ref. [20]). Notable cases include the paired-box transcription factors Pax3 and Pax7 [21,22], as well as the homeodomain factor Pitx2 [23] and Nfix [24]. Pax7 is especially important in the case of adult muscle myogenesis, as its expression is an essential and defining factor of satellite cells [25] and its absence, just like the absence of satellite cells themselves, abrogates regeneration after injury [26–29]. A detailed understanding of how myogenesis is regulated by these and other transcription factors, as well as by the epigenome, would be beneficial for multiple reasons, beyond simply improving our general knowledge: understanding of regulatory principles that may also prevail in other biological systems, elucidating the etiology of muscular diseases, and obtaining important clues on how we may be able to treat the afflicted patients. For instance, reprogramming of pluripotent stem cells (embryonic or induced) in view of cell therapy requires an in-depth knowledge of the

Review: Myogenesis in the Genomics Era

myogenic regulatory network [30–35]. Beyond the obvious involvement of skeletal muscle in breathing and movement, this tissue also plays an important role in energy metabolism due to its high total mass and it being responsible of about 20% of the body's resting energy expenditure. Skeletal muscle takes care of the bulk of glucose uptake in response to insulin, and deregulation in muscle composition or homeostasis is intimately connected to energy metabolism. For instance, obese insulin-resistant (type 2) diabetic patients tend to have a lower proportion of type I oxidative muscle fibers [36] and have malfunctioning skeletal muscle mitochondria [37]. Likewise, diet-resistant and diet-sensitive obese patients tend to have different muscle characteristics, with diet-sensitive patients having more type I oxidative muscle fibers compared to diet-resistant individuals [38]. Thyroid hormone signals to skeletal muscle to help establish the basal metabolic rate and to elicit adaptive thermogenesis (reviewed in Ref. [39]). Understanding the mechanisms controlling muscle formation, adaptation, and homeostasis can therefore inform us on the regulation of energy balance and on how metabolic changes are brought about by variations in physical activity, endocrine status, mode of life, or disease states. Thus, we can identify three broad goals to be achieved for an understanding of the myogenesis transcriptional regulatory network: (1) to obtain an accurate representation of transcriptomes of cells at the successive stages of myogenesis, (2) to identify the transcription factors and their target loci involved in establishing these changes, and (3) to determine the state of the epigenome at those stages and identify its relationship to transcription factor function. Over the last 10–15 years, one arguably successful way of achieving these goals has been the use of a systems biology approach, where genomics tools are employed to provide us with a global picture of the myogenic regulatory network. The next sections summarize the progress made and highlight areas for future research.

Genomics Phase I: Gene Expression Profiling The invention of gene expression profiling by DNA microarrays [40] revolutionized the way we study genome regulation, and in fact, it was instrumental to the emergence of systems biology as an experimental science [41]. The first microarray studies of gene expression in skeletal muscle were performed using the murine C2C12 myoblast cell line, an in vitro model of myogenesis. Derived from a hindlimb muscle, the cells proliferate rapidly under high-growth-factor conditions and differentiate when they reach confluence and are switched to low serum medium [42,43]. They are often used as a model of activated satellite cells, as they share many properties with the adult

Review: Myogenesis in the Genomics Era

muscle stem cells: the ability to proliferate, to fuse together and differentiate to give rise to multinucleated contractile myotubes, and to do so concomitantly with an irreversible mitotic exit. The adoption of C2C12 as a favorite model to study myogenesis has been facilitated by the facts that they can be cultured in large amounts indefinitely (being immortal at least due to their loss of tumor suppressor Arf [44]), that they are clonal and thus more homogeneous than primary myoblast cultures, and that they recapitulate many fundamental steps of myogenic differentiation. Two early microarray studies reported gene expression profiles of C2C12 myoblasts on a time course of differentiation [8,45]. The importance of this early work was in providing an idea of the scale of transcriptome changes that occur during myogenesis and revealing the identity of hundreds of differentially expressed genes in myoblasts and myotubes. The existence of large clusters of co-expressed genes was also put forth, confirming and reinforcing the notion that myogenic differentiation is a highly coordinated, and hence organized, process. A later study looked at gene expression changes that occur in the entire tibialis anterior muscle during regeneration following a massive necrotizing injury induced by the injection of the snake venom toxin cardiotoxin [46]. The results reaffirmed the notions that massive expression changes occur during myogenesis and that these transcriptome alterations are highly coordinated. This study also revealed the limits and challenges inherent to studying complex tissues as opposed to pure cell cultures. For instance, the massive infiltration of macrophages in the injured muscle was witnessed by the appearance of clusters of inflammatory function genes. However, this was resolved years later by the expression profiling of quiescent and activated satellite cells purified by virtue of their expression of specific cell surface markers [47]. Interestingly, clusters of genes associated to wound healing and chemoattraction were found to be specifically expressed in activated satellite cells, providing strong evidence that the muscle stem cells play an active role in the essential inflammatory process that follows injury. The advent of RNA sequencing (RNA-seq) to perform gene expression profiling has been a huge leap forward in terms of accuracy, sensitivity, and comprehensiveness of transcriptome surveys [48]. RNA-seq allows to discover unannotated transcripts and to look at alternative transcription start site usage and alternative splicing in an unbiased manner. Profiling of C2C12 myoblasts on a differentiation time course has revealed the extent by which alternative splicing occurs and changes with differentiation [49] and should help identifying the mechanisms behind these fluctuations. In particular, alternative splicing of transcripts from the Mef2D gene gives rise to two proteins with radically different transcriptional regulatory functions in myogenesis [50], and it is probably not exaggerated to believe that similar mechanisms control

2025 the function of other key players in the myogenic regulatory network. The increased sensitivity of RNA-seq has allowed the identification of important non-coding, enhancer-derived RNAs (eRNAs) implicated in myogenesis regulation [51,52]. They tend to be transcribed from loci neighboring highly expressed protein-coding genes that are associated to musclerelated functions. Two of these are transcribed from well-characterized enhancers of Myod, the core enhancer region and distal regulatory region. They are exclusively nuclear, unlike protein-coding transcripts, and knockdown experiments suggest that the eRNA derived from the core enhancer region is necessary for normal Myod transcription and might act in cis. The eRNA arising from MyoD's distal regulatory region has been termed MUNC, for “MyoD upstream non-coding RNA” [52]. There is conflicting evidence on whether MUNC is involved in the control of Myod expression, but it is clear that it is required for MyoD target gene activation (action in trans). Interestingly, MUNC also appears to play a transcriptional activation role independently of MyoD induction [52]. The two eRNAs would act by altering the opening of the structure of chromatin and influencing RNA pol II recruitment at proximal promoters. The discovery of these eRNAs adds to the growing list of non-coding RNAs that play a role in controlling myogenesis [53,54]. More recently, expression profiling has been applied to satellite cells purified by virtue of their expression of a fluorescent marker gene [55] or cell surface proteins [47,56]. One goal common to all studies was to identify genes that would be characteristic of the quiescent state and was achieved by comparing the transcriptome of quiescent satellite cells with that of activated satellite cells (isolated from actively regenerating muscle or cultured in vitro). This is something that could not be achieved using C2C12 cells, which mimic satellite cells in the active state and are grown outside of the muscle niche. In particular, these studies allowed us to identify negative regulators of the cell cycle and proteins involved in the interaction between these stem cells and their niche. Gene expression profiling can reveal the state of a cellular transcriptome. Bioinformatic algorithms to analyze transcriptome data on a time course of differentiation or on cells with gain or loss of function of a transcription factor can reveal the regulatory logic that exists in the system being studied. Segal et al. showed that, from expression profiling data, one can identify clusters of co-expressed genes that are likely under the control of the same, co-expressed transcription factors [57]. A similar strategy has been employed to various large expression datasets, incorporating or not knowledge of transcription factor DNA binding preferences [58–62]. However, these methods have important limits. For example, steady-state mRNA levels are not very accurate proxies to estimate transcription factor activity, since this does

2026 not take into account post-transcriptional levels of regulation (e.g., by alternative splicing, post-translational modifications, or interactions with co-regulators). They also cannot always resolve the contribution of highly related transcription factors, for example, those that would be co-expressed and expected to bind related or identical DNA sequences, as would be the case of MyoD and Myogenin. A second gene-expression-based approach used to identify regulatory relationships in the myogenesis control network is expression profiling after gene gain or loss of function. The assumption is simple: genes whose mRNA is deregulated by altered transcription factor activity might be under the control of that factor. One of the first studies was performed to identify MyoD-regulated genes in fibroblasts undergoing myogenic conversion [63], and the approach has been extended to many other transcriptional regulators. For example, profiling of wild-type and Six1/Six4 knockout embryos has revealed a basis for muscle-fiber-type development program [64], comparison of wild-type and Mef2c knockout mouse embryonic limbs has shown the importance of this factor in sarcomeric organization [65], the important role played by Runx1 in preventing muscle wasting after denervation was revealed by microarray profiling of muscle tissue of adult Runx1 knockout mice [66], putative Pax3 target genes in the developing somites were identified by their up-regulation in embryos expressing the transcriptionally potent Pax3-FKHR fusion protein [67], and possible genes mediating the proliferative effect of the Hippo-Yap pathway in satellite cells were uncovered by profiling the transcriptome of activated satellite cells with constitutively active Yap [68]. The approach has the power to reveal the identity of genes located downstream of the factors of interest, in the regulatory network. One drawback of the method is the difficulty to distinguish direct from indirect effects, which can occur any time a transcription factor regulates the expression of another protein acting in gene regulation; the regulation of Myog by MyoD is a prime example. While this problem can be circumvented by performing the expression profiling in the presence of the protein synthesis inhibitor cycloheximide, as was performed to identify direct targets of Myod [63], this proves impossible when work is being conducted in developing embryos.

Genomics Phase II: Genome-Wide Location Analysis of Transcription Factors The development of methods that allow us to identify the genomic sites of binding of transcription factors has been of tremendous accelerator of our understanding of myogenesis. Experiments using chromatin immunoprecipitation of transcription factors

Review: Myogenesis in the Genomics Era

followed by identification of the associated genomic DNA fragments by microarrays (ChIP-on-chip [69]) or by high-throughput sequencing (ChIP-seq [70,71]) tell investigators where their factor of interest locates in the genome, essentially revealing the identity of direct target genes. The first ChIP-on-chip studies performed in muscle looked at the binding sites of Myod, Myogenin, and Mef2c in C2C12 myoblasts and myotubes and in MRF-expressing fibroblasts [72,73]. The microarrays used for target DNA identification were limited to proximal regulatory regions of a subset of all known genes and therefore provided only a limited, biased view of the myogenic regulatory network. However, they revealed, for example, that Myod and Myogenin, despite having very similar DNA binding domains, do not have identical target gene sets. These studies also highlighted the existence of condition-specific targets of these transcription factors bound preferentially or more strongly in myoblasts or in myotubes. These are important considerations because they hint to the fact that DNA sequence is not the sole determinant of transcription factor binding. This has been echoed by the limited success of bioinformatic methods attempting to predict physiologically relevant transcription factor binding sites, even when parameters such as clustering into cis-regulatory modules and phylogenetic conservation are taken into account [74]. In these studies, the transcription factor binding profiles were combined to gene expression profiles of myoblasts prior to and after differentiation or MRF expression. What emerged is that the correlation between target gene binding by a transcription factor and transcriptional modulation of the genes is far from perfect: the expression of several direct targets of the MRFs is stable during myoblast differentiation. Beyond the trivial possibilities (e.g., false-positive binding sites and wrong gene annotations), it might be because the target genes are expressed equally in myoblasts and myotubes or because additional stimuli (or lifting of repressive activities) are necessary for transcriptional activation to occur. ChIP-seq mapping of MyoD targets in myoblasts and fibroblasts [75] generated quite a surprise because of the sheer number of binding sites discovered: the unbiased screened revealed that MyoD binds more than 20,000 genomic locations. Similar experiments performed by the laboratory of B. Wold (Caltech) under the ENCODE project or the laboratory of V. Sartorelli [51] came to comparable numbers. Again, most genes with neighboring MyoD binding sites turned out to have expression profiles that are unchanged during myogenic differentiation. However, analysis of covalent histone modification marks in fibroblasts expressing MyoD or not revealed that the majority of these binding events are at least partially functional: although most MyoD-bound regions do not display enhancer activity in transcriptional assays, the global trend is that MyoD binding correlates with acquisition of histone H4

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Review: Myogenesis in the Genomics Era

acetylation, a mark associated to transcribed loci. In contrast, there is no clear association between MyoD binding and trimethylation of H3K4, a mark associated mostly to the transcription start site of expressed genes. What then determines if MyoD will give rise to productive transcriptional activation? One possibility is that it must cooperate with other transcription factors that stimulate complementary essential steps of the transcription process. For instance, we found that binding sites for the homeodomain transcription factors of the Six family have a striking overlap with those of MyoD [14], and it was shown that Six4 helps recruit the H3K27me3 demethylase Utx to the Myog and Ckm loci for derepression in differentiating myoblasts [76]. Furthermore, Six1 is necessary for MyoD expression and for MyoD binding to DNA at its own core enhancer, in myoblasts [77]. Genome-wide binding profiles of Mef2c and Mef2D are also suggestive of a significant overlap with MyoD target sites [50,72], and Mef2D controls MyoD target gene transcription in at least two ways. First, Mef2D can repress MyoD target gene expression in proliferating myoblasts, through the recruitment of histone deacetylase Hdac9 [50]. Second, it participates in the recruitment of the Trithorax group MLL H3K4 methyltransferase, which contributes to the transcriptional activation of MyoD targets [78]. There is also a partial overlap between binding sites for the Tead4 transcription factor and those of MyoD [79], although the molecular basis for a possible cooperation between Tead and MyoD is not well understood, beyond the fact that it involves the vestigial-like 2 co-activator [80]. Other myogenesis regulators may function by antagonizing MyoD function. Such is the case for Snail1, which was shown to recruit the histone deacetylase Hdac1 to MyoD target sites in myoblasts and block MyoD binding to its targets before the onset of differentiation [81]. Down-regulation of Snail by microRNAs whose expression is induced by MyoD at the onset of differentiation would represent a mechanism that might render the differentiation process irreversible. Pax3 and Pax7 are two transcription factors essential for myogenesis. Although they are expressed in overlapping groups of cells and share significant structural homology, they do not carry out exactly the same function. In particular, Pax7 is essential in satellite cells while Pax3 is dispensable, and Pax3 cannot prevent satellite cell apoptosis triggered by the loss of Pax7 [22,82]. ChIP-seq analysis of TAP-tagged Pax3 and Pax7 in primary adult mouse myoblasts has shed light on the molecular basis of these functional differences by showing that Pax7 binds to a much larger number of genomic loci than Pax3 (more than 52,000 versus 4600) and the two overlap at only approximately 1200 loci [83]. This is reflected by apparent preferences of the two factors for binding DNA through the paired-box, in the case of Pax3, or the

homeodomain, for Pax7, and it is hypothesized that the dominance of Pax7 is due to its higher affinity for the homeodomain binding sequence motif “TAAT”.

Genomics Phase III: Contribution of the Epigenome Transcription factors function on a chromatin template. Histone proteins are subject to a myriad of covalent modifications that correlate with, and sometimes regulate, the transcriptional state of genes. The Dynlacht laboratory has used ChIP-seq to map in C2C12 myoblasts and myotubes an impressive number of histone marks associated to gene activity or silencing and to identify the binding sites of the protein complexes that write these marks to control myogenesis [84–87]. In particular, the profiling of multiple marks including H3K4me1 and H3K27ac and the recruitment of MyoD and the histone acetyltransferase p300 and RNA pol II allowed the identification of several thousands of active transcriptional enhancers, a large fraction of which are MyoD dependent in that the histone marks are greatly diminished in MyoD knockout myoblasts [86]. MyoD plays an active role in establishing the histone mark landscape of enhancers, in part by recruiting the H3K4 methyltransferase Setd7 and enzyme required for efficient myogenic differentiation [88]. It is interesting that although the H3K4me1 mark is found at enhancers independently of their activity state, the mark is also found at proximal promoters of silent but inducible genes [87]. The MLL3 and MLL4 methyltransferases are responsible for establishing this repressive chromatin environment at the proximal promoter of certain genes in myoblasts, and knockdown of MLL3/MLL4 in myoblasts leads to the up-regulation of a group of muscle genes. At the onset of differentiation, recruitment of the H3K4 demethylase Lsd1 is associated with the removal of the repressive mark and with the MLL1-dependent trimethylation of H3K4 (a mark that cannot be erased by Lsd1), allowing activation of the genes. H3K4me3 constitutes a recognition platform for ING1, which can recruit a Sin3a/Lsd1 complex that will further antagonize H3K4me1 once differentiation is initiated. Interestingly, MLL4 has been shown to be essential for skeletal myogenesis and adipogenesis, as mice without MLL4 in Myf5-expressing cells are characterized by deficient back muscle formation [89]. In the context of myogenic conversion of preadipocytes by MyoD overexpression, loss of MLL4 was associated to attenuated muscle gene induction, suggesting that the protein is involved in activating their expression during myogenesis. In concordance with that, ChIP-seq for MLL4 in MyoD-overexpressing preadipocytes revealed that the protein is strongly enriched at MyoD-bound enhancers categorized as “active” for bearing the H3K27ac mark. In fact, MLL4 was shown

2028 to be necessary not only for the monomethylation of H3K4 at muscle gene enhancers but also for their acetylation on H3K27. These two studies [87,89] on MLL4 are therefore in apparent contradiction since they propose that MLL4 functions at promoters or enhancers as inhibitor or activator of muscle gene expression, respectively. However, very different systems were used to look at MLL4 in myogenesis; thus, it is not inconceivable that the protein is involved in both aspects depending on the exact myogenesis context. One could imagine that MLL4 is required for myogenic determination, in the developing embryo or in heterologous cells with ectopic expression of MyoD, and that it is also needed to prevent the premature differentiation of committed myogenic progenitors that are already expressing MyoD. Another transcriptionally repressive histone mark that must be removed for myogenesis to occur is H3K27me3, a mark written by the polycomb repressive complex 2 (PRC2). The association of H3K27me3 with muscle gene repression was first revealed by Caretti et al., who showed that the activity of Ezh2, the catalytic subunit of PRC2, antagonizes muscle gene induction in order to prevent premature myogenic differentiation [90]. They showed that the Yy1 transcription factor is involved in recruiting Ezh2 to muscle genes in order to repress their expression by trimethylation of H3K27. The onset of differentiation coincides with dissociation of Yy1 and Ezh2 from these genes, lowered H3K27me3 levels, and gene activation. While Ezh2 and the rest of the PRC2 complex are present on the promoter of many muscle genes in myoblasts, more recent evidence suggests that, in most instances, Yy1 is not part of the recruitment mechanism since the genomic binding profiles of both proteins are very different [91]. The decrease in H3K27me3 levels when myoblasts engage in the differentiation path could occur through “passive dilution”, whereby H3K27me3-containing nucleosomes are replaced during cell division by nucleosomes devoid of this repressive mark. However, more recent work demonstrated the importance of an “active removal” mechanism. Demethylation of H3K27me3 by the Trithorax group protein Utx at muscle gene promoters is an important event for myogenesis [76]. Utx associates with the transcriptional elongation factor Spt6, a histone chaperone known to help the passage of RNA pol II through chromatin by destabilizing nucleosomes [92]. Spt6 knockdown is associated to a failure to undergo myogenesis and lower transcription of muscle genes. This is tied to higher H3K27me3 levels. The authors found that the two proteins are part of the same complex. In fact, using ChIP-seq allowed them to demonstrate a genome-wide correspondence between recruitment of Spt6 and the demethylase Utx and conversely absence of H3K27me3 signal. The proof that the role of Spt6-Utx in myogenesis is to erase the repressive H3K27me3 mark came from rescue experiments: knocking down the PRC2 component

Review: Myogenesis in the Genomics Era

Ezh2 rescues the effect of Spt6 knockdown on muscle gene expression. Genome-wide histone mark analyses are not limited to C2C12 cells. The Rando laboratory used ChIP-seq to look at the relationship between aging and epigenetic marks in satellite cells [47]. By profiling quiescent and activated young and old satellite cells for the presence of H3K4me3, H3K27me3, and H3K36me3, they found that quiescent and activated satellite cells have globally comparable profiles of the H3K4me3 mark, which suggests that genes induced during the transition from quiescence to activation are already marked for expression. Still, a small number of important myogenic regulators, such as Myogenin, acquire H3K4me3 only once cells become activated. The situation with H3K4me3 is in contrast to what happens to the repressive H3K27me3: the number of loci repressed by the H3K27me3 mark that greatly increase during activation. This brings up the question of how the repressive mark is removed from satellite cells that return to quiescence instead of differentiating, following activation. Presumably, a histone demethylase acting on H3K27me3, such as Utx or Jmjd3 [93], must come into play. In satellite cells from old mice, H3K4me3 is unaffected but there is an increase in the number of genomic loci marked by H3K27me3 in quiescent cells compared to young mice, suggesting that perhaps the demethylation mechanisms involved have lower activity with age or that PRC2 activity is increased. Access to DNA is controlled by the remodeling of chromatin, the addition or displacement of nucleosomes, or the replacement of canonical histones with variants. There are several protein complexes involved in chromatin remodeling and controlling access of transcription factors to DNA. One of the complexes shown to be important for myogenesis is mSWI/SNF, also called the BAF complex (reviewed in Refs. [94–96]). MyoD has been shown to participate in the recruitment of mSWI/SNF during myogenesis [97]. Interactions between MyoD and one of the subunits named Baf60c control recruitment of the rest of mSWI/ SNF in a manner that is dependent on phosphorylation of Baf60c by p38 [98]. It will be interesting to see how widespread this mechanism is and, more generally, to understand how mSWI/SNF and the other nucleosome-displacing complexes contribute to myogenesis. Several genome-scale methods exist to explore chromatin structure, such as DNAse-seq [99], FAIRE-seq [100], and ATAC-seq [101], and applying these techniques to the myogenesis system should reveal a great deal about its regulatory network. The ENCODE project includes FAIRE-seq and DNAseseq datasets from a large number of tissues or cell lines, including skeletal muscle, but an in-depth analysis of the observed patterns of DNA accessibility still remains to be conducted. There is ample evidence demonstrating that histone variants, loaded into chromatin independently of DNA

Review: Myogenesis in the Genomics Era

replication, contribute to regulating DNA accessibility. Genome-wide in vivo studies suggest that the presence of histone variants H3.3 and H2A.Z contributes to rendering nucleosomes more labile, which is postulated to facilitate access to DNA by transcription factors [102]. Loading of H3.3 is also associated with preventing the formation of higher-ordered chromatin, which could also help transcription factor activity [103]. Genome-wide maps of H3.3-containing nucleosomes in C2C12 myoblasts have been reported [104]. They reveal that this histone variant is loaded preferentially at genes to be induced during myogenesis and that this activity depends on MyoD's ability to recruit the SNF2-related enzyme Chd2. It has been shown that nucleosomes located immediately downstream of the transcription start site constitute a barrier to the progression of RNA polymerase II but that this block is easier to overcome when the nucleosomes contain H2A.Z in lieu of H2A [105]. Although both p400/NuA4 [106] and the SNF2-related complex SRCAP [107] can replace H2A with H2A.Z near transcription start sites, only SRCAP is necessary for the induction of Myog at the onset of C2C12 myoblast differentiation [108]. A genome-wide view of H2a.z location in the muscle system remains to be obtained and will be especially interesting to analyze in the context of the maps of H3.3 and histone marks mentioned above.

Challenges and Opportunities Ahead Despite the terrific progress that functional genomics approaches have collectively generated to understand the myogenic regulatory network, there is obviously a lot that remains to be discovered. Some areas of research, despite being challenging, have enormous potential. The vast amount of genomic data that have been garnered so far on the myogenic regulatory network is truly impressive; each study puts a new piece of the puzzle into place, such that we understand a little bit better every time how our regulatory network functions. One approach that holds great promise is the use of computational algorithms that integrate various data elements into descriptive and predictive models. Among others, it has been applied successfully to explore development regulatory pathways in the fruit fly [109–114], in Caenorhabditis elegans [115,116], in mouse heart formation [117], and in embryonic stem cells [118,119]. One benefit of these approaches is to process a collection of correlations (e.g., factor A binds gene set B, which carries histone mark C) into cause–effect relationships. The predictive nature of these models is also extremely useful because it helps to identify new regulatory nodes in the transcription network, which can then be studied re-iteratively using the array of techniques described above. Considering how much information has already been obtained

2029 from C2C12 myoblasts (see Table 1), there is little doubt that computational biologists in the muscle field have in hand a good starting collection of datasets. A second challenge resides in the fact that muscle tissue is very complex, being composed of a multitude of cell types, and that there is even heterogeneity within “defined” populations. Even within a single myofiber, not all nuclei transcribe the same genes at the same levels due to their varying distance from the neuromuscular junction (discussed in Ref. [120]). A lot of what we currently know about the myogenic regulatory network has been discovered using C2C12 or primary myoblasts in culture. C2C12 cells, being clonal, arose from a single, unidentified myogenic cell type out of the many known (reviewed in Ref. [121]). They spontaneously became immortal with serial passaging and have undergone poorly characterized chromosomal rearrangements. On the other hand, primary myoblast cultures used in genomics experiments can be considered genetically normal, but the issues reside elsewhere. Primary myoblasts are typically isolated in bulk from a mixture of limb muscle groups (e.g., see Ref. [122]) and used directly in genomic experiments (e.g., see Ref. [83]) or subjected first to enrichment based on cell surface (e.g., see Refs. [47] and [56]) or genetic markers (e.g., see Ref. [55]). Even with significant marker enrichment, the populations used can still be quite heterogeneous. Considering that satellite cells are profoundly affected by their niche [123] and that the niche may vary significantly according to anatomical location, slow- or fast-twitch myofiber association, proximity to blood vessels, or neuromuscular junctions, it seems logical to propose that not all satellite cells are equivalent. There is ample experimental evidence supporting the notion of satellite cell heterogeneity (reviewed in Refs. [124] and [125]). From there, it would make sense to assume that the myogenic regulatory network is not in quite the same state in all satellite cells either. Seemingly pure cell populations exhibit significant variability in the genes they express once individual cells are considered independently. This has been highlighted very clearly in a recent study where single-cell RNA-seq of primary human myoblasts was conducted [126]. Even in clonal cell lines, variability in gene expression is seen between individual cells [127]. Analysis of large cell pools provides a population average that can mask minor yet important trends in gene expression by groups of cells, for instance, bimodality of gene expression (i.e., the existence of subpopulations) and heterogeneity in gene expression levels and alternative splicing isoform selection [128,129]. Whole-transcript single-cell RNA-seq is now feasible [127] and could be applicable to isolated satellite cells as well; this should reveal the extent of transcriptome variability within this cell population. Another promising technique that may soon help deconvolute satellite cell subpopulations is fluorescent in situ RNA-seq (FISSEQ), which has the added benefit of functioning in the intact tissue [130].

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Table 1. A non-exhaustive list of genomics datasets relevant to myogenesis Description Expression profiling C2C12 differentiation time course

Cell type

Technology

References

Mapping of TF binding sites MyoD in myoblasts and myotubes

C2C12, growing and differentiated

ChIP-on-chip, proximal promoter array, ChIP-seq

Myogenin in myotubes

C2C12 myotubes

ChIP-on-chip, proximal promoter array, ChIP-seq

Mef2c in myotubes MyoD in myoblasts, fibroblasts

C2C12 myotubes C2C12, primary myoblasts, embryonic fibroblasts P19 murine embryonal carcinoma C2C12 myotubes Primary myoblasts, growing and differentiating C3H10T1/2 fibroblast Primary myoblasts Primary myoblasts

ChIP-on-chip, proximal promoter array, ChIP-seq

ENCODE (Caltech), [51,72,73,132] ENCODE (Caltech), [51,72] [72] [75,133]

ChIP-seq ChIP-seq ChIP-seq

[134] [132] [81]

ChIP-seq ChIP-seq ChIP-seq

[81] [81] [81]

Satellite cells, quiescent and in vitro activated

MyoD in pluripotent cells Smad3 in myotubes MyoD in myoblasts, TAP-tagged MyoD in fibroblasts, TAP-tagged Myf5 in myoblasts, TAP-tagged E47, TAP-tagged

Review: Myogenesis in the Genomics Era

Satellite cells from resting and regenerating tibialis anterior of young and aged mice Mouse embryos, wild-type and Six1/Six4 knockouts

Affymetrix microarrays; RNA-seq; Agilent microarrays, [8,14,45,49] Illumina Mouse WG-6 Expression Bead Arrays Mouse tissue cDNA microarray [46] C3H10T1/2 fibroblasts [63] Primary myoblasts Affymetrix microarrays [72] C2C12 myotubes Affymetrix microarrays [131] C2C12, growing and differentiating Agilent microarrays [14] C2C12, differentiating Agilent microarrays [14] C2C12 myotubes RNA-seq [79] C2C12, differentiating RNA-seq [50] Primary myoblasts RNA-seq [81] Primary myoblasts Affymetrix microarrays [83] Adult mouse satellite cells purified by FACS Affymetrix microarrays [56] (SM/C-2.6) Adult mouse satellite cells purified by FACS Affymetrix microarrays [55] (Pax3-GFP) [47] Adult mouse satellite cells purified by Affymetrix microarrays FACS (VCAM1+/CD31−/CD45−/Sca1−) E10.5 somites Affymetrix microarrays [64]

Tibialis anterior regeneration time course Fibroblast myogenic conversion Primary myoblasts, wild-type and Myod knockouts Myotubes after pRb knockdown Myoblasts after Six1 and Six4 knockdown Myoblasts after Myog knockdown Myoblasts after Tead4 knockdown Myoblasts after Mef2D knockdown Myoblasts after Snai1 or Snai2 knockdown Myoblasts with Pax3 or Pax7 overexpression Satellite cells, quiescent and in vitro activated

C2C12 mouse myoblasts

Mapping of co-regulators Utx in myoblasts and myotubes Spt6 in myoblasts and myotubes Ezh1 in myoblasts and myotubes Ezh2 in myoblasts and myotubes Suz12 in myoblasts and myotubes RNA pol II in differentiating myoblasts, all forms (8WG16) RNA pol II in differentiating myoblasts, CTD S5P (paused) MLL1 in myoblasts and myotubes MLL2 in myoblasts and myotubes MLL4 before and after myogenic conversion Ing1 in myoblasts and myotubes Lsd1 in myoblasts and myotubes Sin3a and Sin3b in myotubes p300 in myoblasts and myotubes Hdac1 in myoblasts, TAP-tagged Hdac2 in myoblasts, TAP-tagged Chromatin structure and modifications H3K4me3 and H4ac in fibroblasts, with and without MyoD H3K4me1, H3K4me2, H3K4me3, H3K9me3, H3K36me3, H3K27me3, H3K9ac, H4K12ac, H3K18ac, in myoblasts and myotubes H2bK120Ub, in myoblasts and myotubes H3K27me3 Histone variant H3.3 in myoblasts H3K4me3 H3K36me3 in myoblasts H3K4me3, H3K27me3, H3K36me3, in quiescent and activated satellite cells

Primary myoblasts Primary myoblasts Primary myoblasts C2C12 myotubes C2C12, growing and differentiating C2C12 myotubes C2C12, growing and differentiating C2C12, growing and differentiated C2C12, differentiating RD cells and normal human muscle tissue RD cells

ChIP-seq ChIP-seq ChIP-seq ChIP-seq ChIP-seq ChIP-on-chip, Agilent promoter ChIP-on-chip, Agilent extended promoters ChIP-on-chip, Agilent promoter microarrays ChIP-seq ChIP-seq ChIP-seq

[81] [83] [83] [135] [91] [79] [14] [136] [50] [140] [139]

C2C12, growing and differentiated ChIP-seq C2C12, growing and differentiated ChIP-seq C2C12, growing and differentiated ChIP-seq C2C12, growing ChIP-seq C2C12, growing and differentiated ChIP-seq C2C12, differentiating ChIP-seq C2C12, differentiating ChIP-seq C2C12, growing and differentiated ChIP-seq C2C12, growing and differentiated ChIP-seq Preadipocytes, preadipocytes with MyoD ChIP-seq overexpression C2C12, growing and differentiated ChIP-seq C2C12, growing and differentiated ChIP-seq C2C12 myotubes ChIP-seq C2C12, growing and differentiated ChIP-seq Primary myoblasts ChIP-seq Primary myoblasts ChIP-seq

[92] [92] [160] [91,139] [139] [139] [139] [87] [87] [89]

Embryonic fibroblasts C2C12, growing and differentiated

ChIP-seq ChIP-seq

[75] [84]

C2C12, growing and differentiated C2C12, growing C2C12, growing C2C12, growing C2C12, differentiating FACS-purified mouse satellite cells

ChIP-seq ChIP-seq ChIP-seq ChIP-seq ChIP-seq ChIP-seq

[85] [91,139] [104] [139] [139] [47]

Review: Myogenesis in the Genomics Era

Snai1 in myoblasts, TAP-tagged Pax3 in myoblasts, TAP-tagged Pax7 in myoblasts, TAP-tagged Msx1 in myotubes, Flag-tagged YY1 in myoblasts Tead4 in myotubes, Flag-HA-tagged Six1 in myoblasts and myotubes E2f3a and E2f3b in myoblasts Mef2D in myotubes MyoD in rhabdomyosarcoma cells Musculin in rhabdomyosarcoma cells

[87] [87] [140] [86,87] [81] [81]

2031

2032 Both approaches would lead to a better understanding of satellite cell gene and protein expression, which is essential if we want to devise strategies to separate them to a greater degree of purity for other genomic analyses. Genetic isolation of cells based on their expression of a specific gene is possible, and for instance, it is possible to isolate Pax3- or Pax7-expressing cells by virtue of their expression of fluorescent protein markers whose expression is controlled by the regulatory elements of the cognate genes [141–144]. However, pure populations cannot be isolated by virtue of their expression of a single gene. Strategies employing a combination of multiple genetic markers may be required but are currently limited by the number of fluorescent protein-coding genes that can be used concurrently in flow cytometry applications. Synthetic transcription-based circuits incorporating multi-input AND logic gates [145], possibly combined with split fluorescent proteins or enzymes as reporters [146], may one day be amenable to transgenic animal applications and would greatly increase the specificity of cell selection for genomic analyses. It should be noted that, for ChIP-seq studies, the BiTS-ChIP method makes it possible to isolate, from complex tissues that have been cross-linked with formaldehyde, only the nuclei of interest on the basis of reporter gene expression [147]. Single-cell ChIP-seq is not currently feasible, but for histone mark mapping, as low as 1000 cells can be used [148]. It should be possible to acquire this much satellite cells that are pure enough for such studies. Another avenue of great interest will be to explore, with a genomics perspective, the function of the various myogenic and non-myogenic cells that support muscle development and regeneration (e.g., infiltrating macrophages, PW1-expressing interstitial cells, fibroadipogenic progenitors, and fibroblasts). Some of these populations are becoming very well defined in terms of their surface markers, which should enable transcriptomic and various ChIP-seq analyses. Such work will allow us to compare the regulatory networks controlling myogenesis in these various cell types with myogenic potential and would help reveal how non-myogenic cells exert their influence on satellite cells and vice versa.

Conclusion From the discovery of MyoD to mapping of its transcriptional targets and to elucidation of epigenetic marks associated to muscle transcription factor function, the field of skeletal myogenesis research has made tremendous progress. Every novel finding sheds some new light on the transcriptional regulatory network that controls this process. The advent of new technologies, with increased analytical resolution, sensitivity, and/or precision has heightened the pace of discoveries, and there is little doubt that progress will

Review: Myogenesis in the Genomics Era

continue. Genomics studies in muscular disease models or clinical samples are also providing important insight about what is the diseased state precisely is or how it came to be (e.g., for rhabdomyosarcoma [137– 138], [149–151], muscular dystrophy [152–157], and muscle wasting [158–161]). Constantly improving our understanding of what keeps our muscles in good function and health and of what is deregulated during illnesses is the key to finding treatments to muscular diseases.

Acknowledgements The author would like to acknowledge the help of laboratory members for insightful discussions. Research in the author's laboratory is supported by an operating grant from the Canadian Institutes of Health Research (MOP 119458). Received 9 December 2014; Received in revised form 4 February 2015; Accepted 5 February 2015 Available online 14 February 2015 Keywords: myogenesis; transcriptional regulatory network; transcription factors; epigenetics; chromatin structure

Abbreviations used: MRF, myogenic regulatory factor; RNA-seq, RNA sequencing; PRC2, polycomb repressive complex 2.

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Review: Myogenesis in the Genomics Era

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