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. Author manuscript; available in PMC: 2014 Apr 15.
Published in final edited form as: Dev Cell. 2013 Apr 15;25(1):5–13. doi: 10.1016/j.devcel.2013.03.016

Gene regulatory networks governing pancreas development

H Efsun Arda 1, Cecil Benitez 1, Seung K Kim 1,2,3
PMCID: PMC3645877  NIHMSID: NIHMS462459  PMID: 23597482

Summary

Elucidation of cellular and gene regulatory networks (GRNs) governing organ development will accelerate progress toward tissue replacement. Here, we have compiled reference GRNs underlying pancreas development from data mining that integrates multiple approaches including mutant analysis, lineage tracing, cell purification, gene expression and enhancer analysis, and biochemical studies of gene regulation. Using established computational tools, we integrated and represented these networks into frameworks that should enhance understanding of the surging output of genomic-scale genetic and epigenetic studies of pancreas development and diseases like diabetes and pancreatic cancer. We envision similar approaches would be useful for understanding development of other organs.

Pancreas development and cell differentiation

The pancreas is an exocrine and endocrine organ. Its exocrine functions derive from acinar cells, which produce zymogens that aid with nutrient digestion, and from duct cells, which form branched tubules that secrete bicarbonate and deliver zymogens for activation in the duodenum. Pancreatic endocrine function derives from hormone-secreting epithelial clusters called Islets of Langerhans that include β-cells which produce insulin. Below, we briefly outline aspects of development relevant for our discussion of pancreas GRNs. Pancreas morphogenesis begins with evagination of embryonic endoderm to form dorsal or ventral ‘buds’, whose development is guided by distinct transcription programs (reviewed in Zaret, 2008). Pancreatic progenitor cells arise around embryonic day (E) 9.0, first expressing the homeodomain transcription factor Pdx1, then the basic helix-loop-helix (bHLH) factor Ptf1a (reviewed in Seymour and Sander, 2011; Benitez et al 2012). Early growth and branching of pancreatic epithelium is regulated by fibroblast growth factor signaling derived from surrounding mesenchyme tissue (Bhushan et al., 2001), to form defined cellular domains beginning after E11. This includes a ‘tip’ domain containing multipotent pancreatic progenitor cells harboring the potential to self renew or differentiate into pro-acinar cells, and a ‘trunk’ domain harboring ‘bipotent’ cells that give rise to endocrine islet cells or exocrine ducts (reviewed in Benitez et al., 2012). After E13, the tip domain loses its multipotency and becomes a pro-acinar region, which then gives rise to mature acinar cells (Zhou et al., 2007). Further development is accompanied by marked branching of pancreatic epithelial cells and islet formation.

Multipotent pancreatic progenitors express Sox9 (Seymour et al., 2007), and after E13.5, Sox9 expression is restricted to bipotent trunk cells (Lynn et al., 2007; Solar et al., 2009; Kopp et al., 2011). These bipotent epithelial cells generate duct cells or a transient population of endocrine precursor cells expressing the bHLH factor, Neurogenin3 (Neurog3). Neurog3+ endocrine precursors generate the principal islet endocrine cells: glucagon+ α-cells, insulin+ β-cells, somatostatin+ δ-cells, pancreatic polypeptide+ PP cells, and a transient fetal population expressing ghrelin, called ε-cells (Arnes et al., 2012). In mice, expression of Neurog3 in the developing pancreas is transient, detectable between E11.5 and E18, and restricted to developing hormoneneg cells, while in humans, Neurog3 expression is maintained for many weeks during pancreas development and readily detected in hormone+ cells (Lyttle et al., 2008; McDonald et al., 2012). Evidence suggests Neurog3+ cells are post-mitotic (Miyatsuka et al., 2011) and that a single Neurog3+ cell gives rise to a single type of hormone+ islet cell (termed ‘unipotency’ (Desgraz and Herrera, 2009). Thus, pancreas development and cell differentiation may be viewed as a series of morphological and cellular ‘transitions’ to generate several distinct types of differentiated functional epithelial cells (Figure 1A). Below we provide a coherent set of gene regulatory networks framing these transitions.

Figure 1. Pancreas cell lineage and gene regulatory motifs in development.

Figure 1

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(A) Current lineage model of pancreatic cell differentiation. Key factors that mark each lineage are: FoxA2 - definitive endoderm, Pdx1/Ptf1a/Sox9/Nkx6.1 - multipotent progenitor, Nkx6.1/Sox9 - bipotent progenitor, Ptf1a - pro-acinar and acinar cell, Neurog3 - endocrine progenitor, Sox9 - duct cell, Nfatc1/Ins - β-cells, Arx/Gcg - α-cell.

(B) Sub-circuits of pancreas gene regulatory network representing canonical network motifs.

(C) Pancreatic progenitor gene regulatory network. Pink nodes - transcription factors, yellow nodes - effector genes, black lines - positive/inductive relation, red lines - negative/inhibitory relation.

GRNs that control pancreas development

Cellular differentiation and organ morphogenesis in fetal development are orchestrated by coordinated interactions between diverse components including genes linked through regulatory networks known as GRNs (Davidson, 2006). Discovery of individual components and their network relationships is critical for predicting and manipulating the behavior of complex biological systems; GRNs provide testable predictions that are not resolvable using more simplistic views of gene regulation. Collectively the interactions that make up GRNs appear visually complex, but at their heart are simpler building blocks called ‘canonical sub-circuits’ or ‘network motifs’ (Davidson, 2006; Alon, 2007) (Figure 1B). These smaller circuits generally consist of two or three nodes and are defined by their unique topologies of positive or negative interactions (see below), which accomplish specialized developmental programs or tasks. Establishment of the animal body plan is a result of hierarchical and modular use of these sub-circuits, whose topology has been selected and conserved through evolution of different species. Our aim is to highlight pancreas development from the perspective of network architecture and discuss the implications for studies of tissue regeneration and cellular reprogramming.

To compile regulatory networks of mouse pancreas development, we manually curated interactions by data and literature mining. We focused on interactions involving transcription factors that establish, specify and maintain the development, fate and function of major pancreatic cell types in the mouse (Figure 1A). We assessed each reported interaction (network ‘edge’) between a regulator gene and a target gene (network ‘nodes’), and categorized these as ‘positive’ if the regulator enhanced the target expression, or ‘negative’ if the regulator was reported to diminish target gene expression. A connection by an edge simply denotes evidence for an interaction, not necessarily a physical interaction. The type of experimental evidence reported for each interaction, including biochemical evidence for direct interactions between regulator and target gene (accompanied by the NCBI PubMed citation number) is provided in Table S1. We displayed the network interactions using Cytoscape (version 2.8.1) with hierarchical layout options (Shannon et al., 2003).

GRNs for specifying the pancreas anlage and multipotent pancreatic progenitor cells

To initiate pancreas development, cells derived from posterior foregut endoderm commit to form the pancreatic anlage. How is this anlage selected? Prior studies implicate signaling mediated by FGF TGF-β, and other factors (Deutsch et al., 2001; Bort et al., 2004), as well as chromatin regulation by Ezh2 and histone acetyltransferase P300 (Xu et al., 2011). However, additional studies are needed to understand the gene regulatory network governing this early stage of pancreas development. Prior studies suggest that co-expression of Ptf1a and Pdx1 may subsequently commit gut endoderm to a pancreatic fate (Afelik et al., 2006; Wiebe et al., 2007), perhaps through a ‘coincidence detection’ mechanism, in which the cells of the prospective pancreatic anlage need to receive two different input signals simultaneously to define a new regulatory state. Once established, this new regulatory state is stabilized by positive feedback circuitries that involve both Ptf1a autoregulation and direct Ptf1a induction of Pdx1 expression in pancreatic progenitor cells (Wiebe et al., 2007; Masui et al., 2008). Deficiency of either factor leads to severe pancreas malformations, including agenesis in humans (Stoffers et al., 1997; Sellick et al., 2004). Likewise, loss of Hnf1b (Haumaitre et al., 2006) Mnx1 (also called Hb9 or Hlxb9; (Harrison et al., 1999; Li et al., 1999), Gata4 and Gata6 (Lango Allen et al., 2012; Xuan et al., 2012)also leads to partial or complete pancreas agenesis. Gata4, FoxA2 and Pdx1 might be operating in a coherent positive feed forward loop (Figures 1B and 1C) with so-called ‘AND logic’ (Alon, 2007). The coherent feed forward loop feature in developmental networks may enhance generation of a coordinated pulse of gene expression specifying competent late embryonic endoderm toward a pancreatic fate. We speculate that a requirement for rapid diversification of gastrointestinal tract epithelium in the latter half of development in mice (10 days) may have selected for this feature. Retinoic acid signaling pathways likely reinforce this loop (Martín et al., 2005).

Following commitment of dorsal and ventral endoderm to a pancreatic fate, self renewing multipotent progenitor cells expressing Ptf1a, Pdx1, Sox9, Hnf6 and Nkx6.1 emerge around E11.5 (Figure 1C) (Lynn et al., 2007; Seymour et al., 2007). Prior studies (Stanger et al., 2007; Zhou et al., 2007; T. Sugiyama and S.K., submitted) suggest that these multipotent cells represent a transient population ‘depleted’ by ~E14. In a current lineage model (Figure 1A), multipotent progenitors can self renew or differentiate to form exocrine or endocrine progenitors. Analysis of GRN features in progenitor cells, like Ptf1a autoregulation in a feed forward loop, suggests mechanisms for re-enforcing and sustaining proliferation and multipotency in this progenitor cell population. The natural transience of multipotent population in fetal pancreas may reflect cell intrinsic or non-autonomous mechanisms (or both). For example, the GRN of these progenitors contains several transcription factor cascades producing a sequential activation of factors (Figure 1B). In other developing organs this regulatory logic generates differentiated cells with increasingly restricted potential.

GRNs controlling pancreatic progenitor cell differentiation

Other than self-renewal, Sox9+ Pdx1+ Ptf1a+ Nkx6+ multipotent progenitor cells appear to have two principal fate choices, (1) differentiation to immature exocrine acinar (pro-acinar) cells expressing Ptf1a and Rpbj or (2) differentiation into bipotent Nkx6.1+ cells that generate exocrine duct cells expressing Sox9, or generate endocrine progenitor cells expressing Neurog3 (Figure 1A; Iype et al., 2004; Masui et al., 2008). Incisive analysis by Sander and colleagues (Schaffer et al., 2010) has revealed that a modified double negative feedback loop between Ptf1a and Nkx6.1/2 (Figures 1B and 1C) regulates differentiation of multipotent pancreatic progenitors to one of these two fates. In their model, based on gain- and loss-of-function studies, Ptf1a induces a pro-acinar cell program while the Nkx6.1/2 factors specify progenitors to an alternate fate that engenders Sox9+ Hnf1b+ ‘bipotential’ cells with the capacity to produce duct and endocrine islet progeny. The action of a regulated double negative feedback loop at this stage results in mutually exclusive expression of Ptf1a and Nkx6.1/2 and defines the boundaries of these two spatial domains (Davidson, 2010).

Reciprocal repression appears to be a general mechanism to regulate and stabilize lineage commitment choices in a variety of tissues (Briscoe et al., 2000). Thus the Ptf1a-Nkx6 bistable network structure in pancreatic progenitor cells may ensure that - once initiated - the cell fate is ‘locked’ into a transcriptional state that promotes establishment of chromatin-mediated cellular memory. Many aspects of this model warrant further investigation. For example, Ptf1a is thought to function principally in transcriptional activator complexes; however it is possible that the PTF1 complex might be interacting with yet unidentified co-repressor proteins. Recent studies modeling pancreas development with embryonic stem cell culture systems suggest that the Nkx6-Ptf1a motif may be regulated by extracellular signaling, including TGF-β pathways (Micallef et al., 2012; Guo, et al., 2013). In pro-acinar cells Ptf1a regulates maturation first by activating RBPJ, the vertebrate orthologue of Supressor of Hairless. Ptf1a and RBPJ function in a complex to induce expression of RBPJL, the constitutively active, pancreas-restricted paralog of RBPJ. As acinar cells mature, RBPJL replaces RBPJ in the PTF1 complex bound to Rbpjl and also associates with the promoters of other acinar-specific genes, including those encoding exocrine digestive enzymes (Beres et al., 2006; Fujikura et al., 2007; Masui et al., 2008). Thus acinar cell maturation is regulated by a positive feed forward loop governed by a dynamic Ptf1a transcriptional complex (Figure 2). There are intensive efforts underway to use ChIP-based approaches to identify Ptf1a-associated genetic targets in pancreatic progenitors, pro-acinar and acinar cells (Thompson et al., 2012). These - and related efforts to identify co-factors in Ptf1a transcription factor complexes - should eventually reveal the regulatory logic for producing and maintaining pro-acinar cells and acinar cells, and shed light on the GRN features related to pancreatic progenitor regulation by Ptf1a.

Figure 2. Gene regulatory network of differentiating acinar cells.

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GRN governing the transition to pancreatic endocrine progenitor cells

A crucial transition in pancreas development is the differentiation of bipotent Sox9+ Nkx6.1+ cells to generate ducts and Neurog3+ endocrine progenitors. One critical component of this fate choice appears to be the levels of Neurog3 expression. Recent studies suggest that Neurog3 expression must surpass a threshold in order to commit progenitors to an endocrine fate (Wang et al., 2010; Magenheim et al., 2011). Remarkably, these studies show that the development of Neurog3-expressing cells towards an endocrine fate is reversible. For example, progeny of bipotent progenitors that initiate transcription of a null Neurog3 allele lack Neurog3 protein but survive and remain within the epithelium, and later acquire a ductal or acinar cell phenotype fate (Wang et al., 2010; Magenheim et al., 2011). It should be useful to identify Neurog3 targets systematically at different stages of pancreatic epithelial development (E11 and later), to elucidate the GRN mechanisms underlying these striking examples of in vivo fate conversion in the pancreas. Likewise, elucidating mechanisms regulating Neurog3 expression (for example, see Johnson et al 2007) is a current focus of investigation by multiple groups. Neurog3 expression may be regulated by an incoherent positive feed-forward loop (Figure 1B, (Alon, 2007) in which Sox9 activates both Neurog3 and Hes1, a repressor of Neurog3 expression (Jensen et al., 2000). Prior studies suggest that Hes1 coordinates input from intercellular signaling, particularly the Notch pathway (Apelqvist et al., 1999; Jensen et al., 2000). Recent studies also revealed that Ptf1a controls Dll1 to restrain Neurog3 expression (Ahnfelt-Rønne et al., 2012).

Feed forward loop regulation can promote pulsatile gene expression and is associated with cell fate specification in several systems, but the features in the Neurog3 locus that control transient expression are poorly understood. There is evidence suggesting that Neurog3 may positively or negatively autoregulate its own gene expression (Smith et al., 2004; Wang et al., 2008; Ejarque et al., 2013). Building models that incorporate these types of autoregulation may reveal how cis-regulatory elements in the Neurog3 locus function. For example, low affinity enhancers that associate with Neurog3 to mediate repression may underlie both the rise and fall of Neurog3 expression. Alternately, positive autoregulatory elements could enhance initial increases of Neurog3 expression. Precedents for such models include studies of enhancer with distinct binding affinities for trans-acting factors in Drosophila (Jiang and Levine, 1993; Papatsenko and Levine, 2005).

Neurog3+ cells are post-mitotic and express Cdkn1a (Miyatsuka et al., 2011), and acquire features of mesenchyme, including the ability to migrate and form islets (Gouzi et al., 2011). Thus, GRNs describing development of pancreatic endocrine progenitors will likely incorporate modules governing cell cycle control, epithelial-mesenchymal transitions, and islet cell specification. How unipotent endocrine progenitors commit to specific islet cell fates also remains an open question. Prior studies have shown that the developmental stage when Neurog3 is active can influence the type of endocrine cell generated (Johansson et al., 2007). The authors suggested that Neurog3-expressing cells transit through different ‘competence’ periods that promote formation of α-cells, followed by β-cells then δ and PP-cells. The signaling-, genetic- or chromatin-regulated basis for this finding has not been elucidated, but likely connects with elements defining a double-negative feedback loop, including Neurog3, Nkx2.2, Pax4 and Arx (Figures 1B and 3; Collombat et al., 2003, 2005, 2009; Kordowich et al., 2011; Papizan et al., 2011), central to islet cell fate allocation. The core element in this loop is direct reciprocal repression by the homeodomain factors Pax4 and Arx (Collombat et al., 2003, 2005, 2009), a motif known to control and maintain lineage choices in development (Figure 1B). MafB, MafA, Is1 and Pdx1 are also important determinants of α-cell and β-cell fate (Artner et al., 2010; Hang and Stein, 2011; Yang et al., 2011) and networks linking the Pax4-Arx regulatory loop and these factors are being established (Liu et al., 2011; Hunter et al., 2012). However, our understanding is far from complete. For example, a direct reciprocal repression by Arx and Pax4 in an unmitigated double-negative feedback loop would produce an “ON-OFF” state for these factors. However, loss of Arx or Pax4 does not lead to complete loss of beta cells or alpha cells (Collombat et al., 2005). Thus, from GRN considerations, we predict discovery of additional regulators that modulate Arx or Pax4 expression to control apportioning of cell fates during islet cell differentiation (Figure 1B). With the advances of cell sorting followed by global chromatin and RNA analysis using small number of cells, and methods to assess gene function in a developmentally-relevant context, we anticipate rapid expansion and understanding of cell-specific, time-specific networks on a genome-wide scale.

Figure 3. Endocrine progenitor gene regulatory network.

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GRN governing pancreatic β-cell maturation and fate

Compared to knowledge about the genetic control of fetal pancreas development or adult β-cells (Figure 4), less is known about the GRNs governing late fetal and postnatal pancreas development. Once formed, islet β-cells proliferate and acquire physiological functions promoting their principal roles in nutrient sensing and insulin secretion. Knowledge about the GRNs governing adult β-cell function (Figure 4) is dominated by studies revealing the functions of transcription factors implicated in monogenic forms of diabetes, called maturity onset diabetes in the young (MODY), including Pdx1, Hnf1a, Hnf1b, Hnf4a, and NeuroD1. Recent studies show that β-cell ‘maturation’ and expansion is regulated by the Ca2+-regulated calcineurin/NFAT pathway (Figure 5; Heit et al., 2006; Goodyer et al., 2012; references therein). Cn/NFAT signaling is required for the appropriate post-natal expression of cell cycle regulators that promote β-cell growth, including CcnD2, FoxM1 and CcnA2, for expression of genes encoding MODY factors (Figure 5, Heit et al., 2006) and components crucial for insulin production, processing, storage and secretion (Goodyer et al., 2012). Cn/NFAT pathways also regulate ‘activity-dependent’ development, functional maturation and expansion in other tissues and cells, including neurons, muscle, bone and lymphocytes; thus knowledge from GRN investigations in these contexts may expand understanding of Cn/NFAT roles in β-cell maturation. The transcription factors MafB, MafA, and Glis3 are linked to this network and also regulate post-natal development of islet β-cells (Artner et al., 2008, 2010; Kang et al., 2009). The structure of this sub-circuit can be considered a ‘single input motif’ or ‘a gene battery’ (Figures 1B and 3). In these structures, the regulator has a high number of outgoing links, and may coordinately control a cohort of effector genes. This enables cells to coordinate the activity of a group of genes that function in the same or related pathways.

Figure 4. Immature β-cell gene regulatory network.

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Figure 5. Adult β-cell gene regulatory network.

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Information about GRNs controlling β-cell maturation in juvenile animals will grow in the near future, given the intensive worldwide efforts in this area, and enabled by knowledge about physiological and intercellular regulators of β-cell growth and maturation like glucose, incretins, Ca2+ and glucokinase (Puri and Hebrok, 2010; Seymour and Sander, 2011; Benitez et al., 2012; Goodyer et al., 2012). Studies of adult β-cell fate should also inform our understanding of β-cell maturation. Likewise, studies of cycling adult β-cells should demonstrate how regulatory networks change when β-cells proliferate. Do β-cells, perhaps similar to proliferating hepatocytes (Klochendler et al., 2012) have programs permitting controlled de-differentiation? If so, are the regulatory networks that govern β-cell development simply reestablished? For example, loss of Insulin expression has been repeatedly observed in β-cells driven to proliferate in culture (Chen et al., 2011; Figure 5), or deficient in mechanisms that re-enforce β-cell fate (Dhawan et al., 2011). Studies of Insulin chromatin and the GRN, including MODY factors, governing expression of β-cell Insulin might reveal that naturally cycling β-cells also transiently de-differentiate to accommodate the requirements of cell cycle progression and division. This possibility is underscored by recent studies, including those by Accili and colleagues (Talchai et al., 2012) providing evidence for de-differentiation of adult islet β-cells in animal models of diabetes. Identification of GRNs governing natural or pathological de-differentiation in β-cells might advance efforts to convert or re-program non-β cells toward a β-cell fate (Zhou et al., 2008). For instance, current reprogramming protocols involve constitutive over-expression of pancreatic factors, but GRN-guided reprogramming protocols, which would mimic the dosage, combinatorial logic, and temporal expression of normal pancreatic development, may generate functional β-cells with better efficiency.

Summary and prospects

For this perspective, we compiled a set of regulatory networks that represent development of major pancreatic cell types in the mouse based on literature mining. However, these networks remain far from being complete. A systematic catalog of transcription factors and cis-regulatory information that coordinates the regulation of target genes would provide essential building blocks of GRNs. Application of genome-scale assays to query gene expression profiles and chromatin states of purified pancreatic cell types at distinct developmental stages should provide an experimental strategy to expand the regulatory networks presented here. The insights obtained from studying developmental GRNs can have applications that range from building synthetic circuits in reprogrammed cells (Ruder et al., 2011) to finding pharmacological agents with minimal off-target effects. Despite some initial successes, cell reprogramming or conversion to generate medically-important cell types like functional pancreatic islet cells remains relatively inefficient and stochastic, likely reflecting an ignorance of important regulatory interactions that exist during pancreas development. Advances in developmental and stem cell biology also reveal an unexpected degree of flexibility in developing or diseased cells, and the regulatory networks that underlie acquisition or maintenance of cell fate and physiological functions. This may be particularly important for some adult organs, including the pancreas, which may lack a dedicated ‘stem cell’ and therefore require maintenance of cell and tissues function through self-renewal of differentiated cells instead of neogenesis.

Similar approaches should be possible for studies of human pancreas development and for building gene regulatory networks of human pancreas. For instance, using cell surface markers and flow cytometry it is possible to enrich for adult human β-cell, α-cell, acinar and ductal cell populations (Dorrell et al., 2011). Expanding this strategy to human juvenile and fetal pancreas would permit gene expression analysis that can ultimately guide mapping gene regulatory networks of human pancreas. In addition, integrating the data available from the ENCODE consortium regarding open chromatin sites, chromatin interactions, transcription factor motif information, and from gene-centered studies such as enhanced yeast one-hybrid assays (Reece-Hoyes et al., 2011) should prove fruitful for generating comprehensive maps of gene regulatory networks. Taken together, the analysis of these networks would provide insights beyond single-factor centered pathways, generate a more global view of pancreas development, and guide regenerative therapies by presenting alternative ways to reprogram cells into different pancreatic fates. The general points we discussed in this perspective should be applicable to other organ systems.

Supplementary Material

01

Table S1. List of interactions compiled to represent the gene regulatory networks of pancreas development.

Acknowledgments

We thank H. Chakravarthy, J. Lee, P. Pauerstein, T. Sugiyama, P. Wang for their contributions to construct the GRNs described here, and the members of the Kim group, and Drs. L. Sussel, R. MacDonald, J. Ferrer and R. Stein for discussions or critical reading of this manuscript. H.E.A. is supported by the Juvenile Diabetes Research Foundation (JDRF) postdoctoral fellowship. C.B. is supported by an NIH Developmental Genetics training grant. Work in the Kim group has been supported by the Snyder Foundation, the Elser Foundation, the Doolittle Charitable Trusts, the Helmsley Charitable Trust, JDRF, U.S. NIH Beta Cell Biology Consortium and the Howard Hughes Medical Institute (HHMI). S.K.K. is an Investigator of the HHMI. We apologize to colleagues whose work we could not cite because of space constraints.

Footnotes

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Supplementary Materials

01

Table S1. List of interactions compiled to represent the gene regulatory networks of pancreas development.

NIHMS462459-supplement-01.xls (83KB, xls)

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