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. Author manuscript; available in PMC: 2009 Dec 1.
Published in final edited form as: Curr Opin Genet Dev. 2008 Oct 9;18(6):521–529. doi: 10.1016/j.gde.2008.09.003

Toward a Systems-Level Understanding of Developmental Regulatory Networks

Brian W Busser 1, Martha L Bulyk 2,3,4,*, Alan M Michelson 1,*
PMCID: PMC2704888  NIHMSID: NIHMS92976  PMID: 18848887

Abstract

Developmental regulatory networks constitute all the interconnections among molecular components that guide embryonic development. Developmental transcriptional regulatory networks are circuits of transcription factors and cis-acting DNA elements that control expression of downstream regulatory and effector genes. Developmental networks comprise functional subnetworks that are deployed sequentially in requisite spatiotemporal patterns. Here we discuss integrative genomics approaches for elucidating transcriptional regulatory networks, with an emphasis on those involved in Drosophila mesoderm development and mammalian embryonic stem cell maintenance and differentiation. As examples of regulatory subnetworks, we consider the transcriptional and signaling regulation of genes that interact to control cell morphology and migration. Finally, we describe integrative experimental and computational strategies for defining the entirety of molecular interactions underlying developmental regulatory networks.

Introduction

Developmental regulatory networks comprise the complete set of molecular components and their functional interactions that guide the progressive determination of pluripotent cells, thereby allowing cell fates to become sequentially restricted and differentiation programs to be properly executed during embryonic development. Both intracellular signaling and intrinsic control mechanisms that reflect the developmental histories of cells contribute to such developmental networks. Transcriptional regulatory networks (TRNs) act within and contribute to the more global effects of developmental networks by orchestrating embryonic gene expression patterns by controlling whether a gene will be expressed and at what level in a particular place and time within the embryo. TRNs operate through cis regulatory modules (CRMs), stretches of DNA composed of short DNA subsequences that are recognized by sequence-specific DNA binding proteins that in many cases integrate the activity of tissue-specific, cell-specific and signal-activated transcription factors (TFs) to guide gene expression programs [1,2]. That is, CRMs are responsive to specific combinations of TFs, and TRNs comprise networks of TFs, CRMs, and co-regulated genes. A TRN that orchestrates the spatiotemporal gene expression programs specifying a given developmental process, i.e., a developmental TRN, is one part of a larger developmental regulatory network. The biochemical functions, posttranslational modifications and molecular interactions of the gene products or effectors that define specific cellular behaviors within a developing organism complete the regulatory network. Here, we summarize recent work aimed at deciphering selected developmental TRNs, and consider current efforts directed toward the more challenging problem of elucidating predictive models that account for the complete architecture and function of developmental regulatory networks.

Historically, molecular and genetic methods have been used to define TFs, effector genes and CRMs involved in orchestrating a developmental process. Building on extensive gene perturbation studies, the TRN controlling sea urchin endomesoderm specification was the first developmental TRN to be described in detail [3]. More recently, inroads have been made in dissecting the TRNs regulating patterning and cell fate specification in numerous plant and invertebrate and vertebrate animal model systems [2,4]. Interestingly, this work has shown that a network can be subdivided into subnetworks of interconnected genes, each of which performs a particular developmental function. Each developmental subnetwork acts at a specific time and place to induce characteristic changes in cell division, movement, size, shape and a variety of specialized functions specific to particular cell types (such as myoblast fusion, neuronal synapse formation or hormone secretion). Thus, development can be viewed as being controlled by a sequence of subnetworks arising at particular places and times, with spatiotemporally coincident subnetworks defining distinct cell states (Figure 1). To illustrate this view, we discuss recent studies that have focused on understanding the developmental networks controlling organogenesis in selected model systems, starting with the specification of individual cell fates and progressing to the regulatory circuits that execute unique cellular differentiation programs.

Figure 1.

Figure 1

A developmental regulatory network controlling Drosophila embryonic somatic muscle formation. The specification and differentiation of somatic muscle cells in the Drosophila embryo is shown here as an example of how distinct developmental subnetworks direct the progressive determination of initially pluripotent cells. The fundamental themes that are highlighted are likely to be similar for a diverse array of other cell types. Maternal factors first activate expression of a TF (diamond) which subdivides the Drosophila embryo into distinct tissue domains by activating and repressing (not shown) distinct sets of zygotic target genes. The zygotically expressed TF specific for the mesodermal germ layer (oval) is able to autoregulate its own expression to amplify the maternal response and to promote stability in the transcriptional network governing mesoderm formation. The germ layer-specific TF in association with the maternal factor activate subnetworks of genes important for different processes such as those governing cell migration. The germ layer-specific TF—along with signal-activated TFs (pentagons)—also activates batteries of genes that include additional tissue-specific TFs (squares) that serve to subdivide the mesoderm into distinct mesodermal tissues (including heart, fat body, visceral and somatic muscle). Focusing on the somatic mesodermal subdivision, germ layer-specific, tissue-specific and signal-activated TFs cooperate in activating distinct sets of genes, including cell type-specific or “identity” TFs (light blue-green and dark blue triangles). Repressive and autoregulatory interactions amongst the cell type-specific TFs contribute to the pattern of individual cellular identities. Coincident with or shortly after cell fate specification, sets of genes will be activated by the germ layer-, tissue-, signal-activated and cell type-specific TFs to form subnetworks of effectors important for a myriad of developmental processes including cell fusion and adhesion. The products encoded by these genes may work together with constitutively expressed components that also are involved in the same process. In addition, different subnetworks can overlap as the components from one subnetwork can be utilized in other subnetworks and can be affected by the activity of multiple inputs. The final stages of differentiation are driven by tissue-specific and cell type-specific TFs which activate additional subnetworks of genes, such as those important for sarcomere structure and high rates of metabolism. Cell type-specific effectors—regulated by the corresponding identity TFs—contribute to the unique functional and structural features of individual myotubes (represented by the light blue-green and dark blue nuclei in the differentiated muscles shown at the upper right of the figure). The myogenic regulatory network depicted here incorporates the collective findings of numerous independent studies [6,7,11-13,19-21].

Developmental Networks for Cell Fate Specification and Differentiation

Dorsal, an NF-κb homolog, along with two of its targets, Twist, a basic helix-loop-helix TF required to initiate mesoderm formation [5] and Snail, a TF involved in activating ectodermal and repressing mesodermal target genes, comprise a set of TFs specifying distinct tissue domains in the Drosophila embryo. Recently, two independent groups, using a combination of chromatin immunoprecipitation followed by microarray analysis (so-called ChIP-chip), genome-wide expression profiling, and integrative bioinformatics approaches, investigated how these three TFs cooperate to regulate a developmental program [••6,••7]. Interestingly, computational scans for Dorsal binding sites revealed an enrichment of such sites associated with Twist-bound CRMs [••6], which agrees with the extensive co-clustering of Twist and Dorsal DNA binding site motifs [••7]. Twist and Dorsal proteins are known to physically interact [8] and this interaction could be dependent on the number, spacing, order and orientation of their respective binding sites. However, whether such architectural features of a CRM, or binding site “grammar” [9,10], apply to these two TFs is a possibility that remains to be examined. This idea could be tested by altering motifs within authentic CRMs or by creating entirely synthetic CRMs comprising combinations of TF binding sites of interest. Similar integrative genomics approaches combining ChIP-chip, gene expression profiling, and bioinformatics have been applied to investigate the role of Mef2 in muscle gene transcription and the role of Forkhead and NK homeobox TFs in Drosophila visceral muscle gene regulation [11-13]. Underscoring the utility of the ChIP-chip approach for characterizing regulatory networks in distinct tissue types in other organisms, including mammals, is the recent description of the role of different TFs orchestrating human liver [14], mouse muscle [15-17], and mouse liver and pancreas [18] cellular determination and differentiation.

As development progresses following the specification and separation of embryonic germ layers, distinct developmental circuits program the progressive determination of individual cells within these tissues. Some recent studies have explored the regulatory networks specifying individual cells in the Drosophila mesoderm. Jagla and his colleagues undertook an integrative approach to examine the role of the NK homeobox TF Ladybird (Lb) in the specification of a subset of Drosophila heart and muscle cells [19]. They combined ChIP-chip with gene expression profiling of Lb mutants to generate a genome-scale view of cell fate determination. With this approach, it was established that Lb is involved not only in the initial specification but also in the later differentiation program of Lb-expressing cells.

We have recently combined genetic, genomic and computational methodologies to elucidate the transcriptional codes and downstream effectors directing Drosophila muscle development [••20,••21,22-24]. Specifically, we compiled a compendium of genome-wide gene expression profiles from flow-sorted primary mesodermal cells derived from flies of multiple different genotypes that perturb muscle development in known ways [••20]. A statistical meta-analysis of the cumulative microarray data resulted in the identification of hundreds of previously uncharacterized genes with myoblast subtype expression patterns. Computational methods were then used to investigate if these muscle genes are subject to a common cis regulatory logic. To this end, the expression data affecting muscle specification were clustered and then analyzed with a novel computational algorithm that evaluates the likelihood that a specific combination of TF binding site motifs is enriched among a set of co-expressed genes [••21]. The results of this analysis showed that only a subset of known myoblast subtype genes is targeted by a subset of the predicted regulating TFs. Of critical importance in these studies was the use of empirical tests to establish the validity of the statistical predictions of both gene expression and gene regulation.

More recently, these computational algorithms were refined and extended to decipher matrices of cis regulatory codes for hundreds of putative mammalian regulatory motifs and gene sets [25]. These programs significantly advance the goal of ascertaining relevant TRNs directly from genomic sequence and gene expression data. As a complement to these studies, catalogs of TF binding site motifs (‘motif dictionaries’) are being systematically compiled using protein binding microarray, one-hybrid, and comparative genomics approaches [••26,27,••28,29,30]. In an important application of these approaches to one large family of TFs, Berger et al. and Noyes et al. highlighted differences in binding sequence preferences associated with different classes of homeodomain (HD) proteins [••26,••28], thereby increasing the utility of information about such motifs for genome-wide computational scans. Such DNA binding profiles or motif dictionaries can be used to computationally predict combinations of TFs that may co-regulate particular gene sets [25]. Hypotheses derived from such bioinformatics approaches must then be independently validated using appropriate transcriptional reporter assays.

As comprehensive in situ hybridization atlases of gene expression are compiled for various tissues, cis regulatory codes for co-expressed genes can be predicted using similar methods. Recently, Malik and his colleagues combined in situ hybridization with the embryonic position of gene expression and morphological “landmarks” to describe an atlas of co-expressed genes along the anteroposterior axis in Drosophila [31]. Similarly, the Allen Brain Atlas combined serial sectioning and anatomical registering to elucidate expression of transcripts in the adult mouse brain [32]. Although a significant first step, it is likely that more sophisticated approaches and technological advances will be required to resolve the expression patterns of the full complement of genes expressed by an individual cell in more complex tissues. Toward this end, individual cells can now be purified by laser capture microdissection [33] or by fluorescence-activated cell sorting [20,••34,35,•36], and significant increases in the sensitivity of transcript detection can be obtained by massively parallel sequencing technologies [37]. Combining these approaches should lead to the identification of co-expressed genes at single cell resolution. Together, experimental and computational strategies that refine gene expression profiling and CRM prediction will facilitate the delineation of the complete genetic programs of individual cells, and will contribute to our understanding of the molecular basis of cellular and organismal phenotypes.

An emerging view is that key regulatory TFs controlling organogenesis exert their influence upon many downstream targets throughout a developmental process. In further support of this mechanism, we described a role for Twist in regulating gene expression within a subset of Drosophila embryonic myoblasts, including genes that have either early or late developmental functions as these cells differentiate [••21]. In addition, Twist activates the expression of numerous TFs (about 25% of all annotated TFs) and potentially functions together with these factors in regulating downstream target genes [••6]. Such feed-forward mechanisms—in which one TF regulates the expression of a second TF and then both factors regulate expression of a third gene—are quite prevalent in developmental regulatory networks, serving to generate combinatorial specificity and to lock in a new cellular state [2,14,18,38,39]. In addition, combinatorial regulation of target genes involving the cooperative binding of multiple TFs is widespread throughout development [1,2,••6,••7,11,16-18,••21,24]. Related work has further shown that there is significant cross- and auto-regulation of key regulatory TFs, which has been suggested to promote stability in transcriptional networks [2,••6,••7,13,14,16-18]. Finally, these studies highlight the importance of integrating numerous genome-scale datasets to dissect and model TRNs. Integration of various data types can provide novel insights, in particular because setting empirical thresholds set on a single data type can be challenging and can lead to erroneous conclusions [40]. Combining multiple, independently generated observations (such as gene expression, in vivo TF binding, physical and genetic interactions, and the clustering and evolutionary conservation of DNA motifs) to infer network structure can strengthen the resulting model [••41,42].

Developmental Subnetworks

Transcriptional mechanisms drive the co-expression of functionally related genes which interact within subnetworks that govern how cells undergo their unique developmental behaviors. An “interactome”—defined here as the complete set of molecular associations in a cell—comprise not only physical interactions but also functional relationships which do not require direct contacts such as protein-protein, protein-DNA or mRNA-microRNA interactions. Given their functional significance, defining cell type-specific interactomes can be even more important than identifying gene co-expression and co-regulation for acquiring a systems-level view of development.

In addition, a comprehensive understanding of the subnetworks that underlie development can provide mechanistic insight into a mutant phenotype or drug action. For example, a network analysis approach has been successfully applied in yeast by clustering gene expression data for approximately 300 deletion mutants to predict and verify the mechanism of action of an antifungal drug [43]. A similar perturbation-based molecular signature strategy has been applied to a mammalian cell line to infer strong connectivities among related drugs which act at points distal to transcriptional regulation, to generate testable hypotheses about how uncharacterized small molecules act based on their related effects on gene expression, and to identify unanticipated relationships between known drugs that can be exploited in developing new combination chemotherapies [44]. The utility of these systematic perturbation strategies will be greatly enhanced by efforts to create predictive models of biological networks in many different contexts, including development.

Interactome data can be derived from various sources (gene co-expression, co-regulation, literature mining, etc.) with many interactions being identified by high-throughput technologies such as yeast two-hybrid assays [45-47], protein affinity purification followed by mass spectrometry [48,49], and synthetic lethal/sickness screens [50-52]. Fraser and his colleagues have undertaken a synthetic lethal screen to catalog genetic interactions affecting C. elegans vulva development. They identified a small group of genes, or hubs, that interact with many other genes in the genetic network [52]. Integrating this information with gene expression profiles, physical and genetic interactions, and literature mining, Marcotte, Fraser and their colleagues built a proteome-scale probabilistic network of genetic interactions for C. elegans [••53]. This network successfully predicted tissue-specific phenotypes and identified novel genes involved in disparate pathways. Similarly, Zhong and Sternberg integrated interactome, expression, and annotation data sets for C. elegans and comparable data from other species to fill in “holes” in any one data set to predict novel genetic interactions [••54]. Finally, a study by Zhu et al. combined gene expression, protein-protein interaction and TF binding site data to build a network capable of predicting systems-level behavior in yeast [••41]. Importantly, they identified and empirically verified subnetworks of genes which are coordinately controlled by factors associated with particular expression quantitative trait loci. Taken together, these studies establish that a comprehensive ‘circuit diagram’ can be delineated for complicated biological processes, and suggest the potential to similarly elucidate even more complex developmental networks.

The previously described work involved organism-scale interactome studies. However, developmental subnetworks are largely operative at the level of individual cells, each of which expresses a unique combination of proteins. Thus, recent studies have moved toward a systems-level understanding of the composition of the signaling and transcriptional networks regulating the morphology and migration of a uniform population of single cells. Bakal et al. sought a quantitative understanding of the signaling networks regulating cell morphology by manipulating gene expression in fluorescently labeled cultured Drosophila neuronal cells [••55]. Image analysis algorithms and hierarchical clustering were used to define groups of genes which, when perturbed, exhibit phenotypes with similar morphological features (“phenoclusters”). This strategy allowed the authors to map the local signaling networks that control cell adhesion and membrane protrusion and tension.

A logical extension of this work would involve proceeding from cellular phenotypes to phenoclustering genes orchestrating more complex developmental functions. As genes performing a similar function tend to be co-expressed and transcriptionally co-regulated, it is likely that these developmental phenoclusters are under common transcriptional regulation. Along these lines, a recent study sought to understand how transcriptional networks control the generation of morphological form during heart cell migration in Ciona intestinalis [••34]. The major finding was that distinct effector genes important for directed cell migration were transcriptionally regulated by the cardiogenic network. Interestingly, these effector genes worked in concert with other constitutively expressed cellular components involved in the same process. Thus, these two sets of genes together coded for the cellular machinery enabling proper cell migration. This study highlights the modular nature of regulatory networks, in which transcriptional regulation of a small set of effector genes can dramatically influence a developmental process.

Developmental Networks Controlling Embryonic Stem Cells

Embryonic stem (ES) cells depend on an extensive network of TFs and external signals to maintain their undifferentiated state and their ability to self-renew and differentiate [56]. There have been extensive efforts to study these cells’ regulatory networks not only to understand development and disease but also with the goal of reprogramming somatic cells to acquire a stem cell fate [57]. Because of their prevalence and ability to be cultured and differentiated in vitro, ES cell lines provide a facile system for investigating developmental regulatory networks and subnetworks. In order to avoid the pitfalls of working in vitro, additional genome-scale studies will be required to dissect the cellular and molecular events directing self-renewal and differentiation of ES cells in systems where these cells can be tracked in vivo [58].

Two recent studies used loss- and gain-of-function approaches to define genes important for ES cell maintenance [59,60]. Lemischka and his colleagues used loss-of-function RNA interference to define three previously unrecognized genes that regulate ES cell maintenance [59]. In contrast, Takahashi and Yamanaka used a gain-of-function approach to define genes important for reprogramming mouse somatic cells to an ES-like cell state [60]. They showed that the over-expression of just four TFs induced reprogramming of embryonic and adult mouse fibroblasts to an ES cell-like state (so-called induced pluripotent stem (iPS) cells). Similar combinations of TFs are sufficient to convert human somatic cells into iPS cells [61,62].

Our understanding of somatic cell reprogramming is increasing as the regulatory network used by ES cells is interrogated on the genome-scale with ChIP-chip or ChIP-Seq (which uses high-throughput sequencing to detect and quantify immunoprecipitated DNA fragments bound by TFs) [63,•64,65,66]. In fact, with such extensive whole-genome data sets—including ChIP-chip and ChIP-Seq of multiple TFs, maps of epigenetic states, expression profiling of genetically manipulated cells, protein-protein interactions, and complementary studies in more than one mammalian species [59,63,•64,65-68,•69,70-72]—it should be possible to construct comprehensive developmental ‘wiring diagrams’ similar to those previously described for yeast, sea urchin and C. elegans [2,3,••41,••53,••54]. Finally, reprogramming somatic cells into iPS cells is progressing at an unprecedented pace, and recent protocols have removed the tumor-inducing c-myc gene from the cocktail of inducing factors [62,73]. However, this manipulation led to a lowering of the reprogramming efficiency [73] which could be circumvented by adding chemical inhibitors of histone deacetylase and DNA methyltransferase to cell cultures [•69,74]. A more thorough understanding of the ES cell network should suggest additional rational perturbations that may avoid the use of inhibitors which alter genome-wide patterns of epigenetic modifications, thereby preventing potentially unintended consequences.

Conclusions and Future Directions

In this review, we have described examples of regulatory networks that direct organogenesis through their effects on cell fate specification and differentiation, and we have highlighted integrative systems-level approaches that are being used to analyze the organization and functions of such networks. The emerging view is that subnetworks within larger cellular regulatory networks drive specific aspects of the progressive determination and subsequent differentiation of individual cells. Recent work has also demonstrated the importance of integrating multiple genome- and proteome-scale data sets to increase the power of network models to predict the functions of interacting genes involved in a particular biological process. As Davidson and his colleagues have recently shown for skeletogenesis in the sea urchin embryo [•75], it is now possible to reconstruct regulatory networks that are sufficiently comprehensive as to explain the transcriptional control of genes encoding all TFs and signals affecting a developmental process. Combining genomic and other systems-level data with loss-of-function studies should be an essential part of a platform for understanding and modeling other developmental regulatory networks. Indeed, whole-genome RNAi-based loss-of-function studies are now approachable in cultured mammalian cells [76,77], paving the way for rapid genome-wide perturbation analysis of numerous cellular and developmental mechanisms. The challenge here will be to relate cell culture findings to in vivo events where cell-cell and cell-matrix interactions play an important role.

While genomic tools are currently in place for identifying components of biological regulatory networks through systematic perturbation studies, a major limitation to applying these methods for the dissection of functional protein interactions is the availability of informative high-throughput assays. In this regard, it should be possible to adapt a recently described method, protein fragment complementation assays, to directly measure protein-protein interactions in living cells, thereby raising the possibility of describing large-scale, cell type-specific interactomes in the very near future [78]. This approach can also be employed in a more targeted manner to test specific predictions of regulatory network models derived from other findings. Finally, it must be recognized that studies of developmental regulatory networks to date have largely emphasized qualitative assessments of network components and their functional interactions. However, it must be kept in mind that a regulatory model is only as complete and accurate as its ability to explain all aspects of development—from the integration of upstream signaling and transcriptional networks, to the downstream effector functions of target gene products—in a predictive, quantitative manner that can account for all cellular behaviors and interactions within the embryo. The construction of such models will require the generation of quantitative and dynamic datasets—including, for example, developmental time course measurements of protein concentrations in relevant subcellular compartments and affinity constants for molecular interactions—as well as the development of new computational tools for the analysis and integration of such information. Achieving these goals will be a major challenge for developmental systems biology in the coming years.

Acknowledgments

B.W.B. and A.M.M. were supported by the Division of Intramural Research of the National, Heart, Lung and Blood Institute, National Institutes of Health. M.L.B. was supported in part by grants U01 GM076603 from NIH/NIGMS and U01 HL081755 from NIH/NHLBI.

Footnotes

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