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Diabetologia

, Volume 61, Issue 7, pp 1614–1622 | Cite as

Patterns of differential gene expression in a cellular model of human islet development, and relationship to type 2 diabetes predisposition

  • Marta Perez-Alcantara
  • Christian Honoré
  • Agata Wesolowska-Andersen
  • Anna L. Gloyn
  • Mark I. McCarthy
  • Mattias Hansson
  • Nicola L. Beer
  • Martijn van de Bunt
Open Access
Article

Abstract

Aims/hypothesis

Most type 2 diabetes-associated genetic variants identified via genome-wide association studies (GWASs) appear to act via the pancreatic islet. Observed defects in insulin secretion could result from an impact of these variants on islet development and/or the function of mature islets. Most functional studies have focused on the latter, given limitations regarding access to human fetal islet tissue. Capitalising upon advances in in vitro differentiation, we characterised the transcriptomes of human induced pluripotent stem cell (iPSC) lines differentiated along the pancreatic endocrine lineage, and explored the contribution of altered islet development to the pathogenesis of type 2 diabetes.

Methods

We performed whole-transcriptome RNA sequencing of human iPSC lines from three independent donors, at baseline and at seven subsequent stages during in vitro islet differentiation. Differentially expressed genes (q < 0.01, log2 fold change [FC] > 1) were assigned to the stages at which they were most markedly upregulated. We used these data to characterise upstream transcription factors directing different stages of development, and to explore the relationship between RNA expression profiles and genes mapping to type 2 diabetes GWAS signals.

Results

We identified 9409 differentially expressed genes across all stages, including many known markers of islet development. Integration of differential expression data with information on transcription factor motifs highlighted the potential contribution of REST to islet development. Over 70% of genes mapping within type 2 diabetes-associated credible intervals showed peak differential expression during islet development, and type 2 diabetes GWAS loci of largest effect (including TCF7L2; log2FC = 1.2; q = 8.5 × 10−10) were notably enriched in genes differentially expressed at the posterior foregut stage (q = 0.002), as calculated by gene set enrichment analyses. In a complementary analysis of enrichment, genes differentially expressed in the final, beta-like cell stage of in vitro differentiation were significantly enriched (hypergeometric test, permuted p value <0.05) for genes within the credible intervals of type 2 diabetes GWAS loci.

Conclusions/interpretation

The present study characterises RNA expression profiles during human islet differentiation, identifies potential transcriptional regulators of the differentiation process, and suggests that the inherited predisposition to type 2 diabetes is partly mediated through modulation of islet development.

Data availability

Sequence data for this study has been deposited at the European Genome-phenome Archive (EGA), under accession number EGAS00001002721.

Keywords

Diabetes Endocrine pancreas In vitro differentiation Islets Stem cells Transcriptome 

Abbreviations

FC

Fold change

GSEA

Gene set enrichment analysis

GWAS

Genome-wide association study

iPSC

Induced pluripotent stem cell

NES

Normalised enrichment score

NKX6-1

NK6 homeobox 1

WGCNA

Weighted gene co-expression network analysis

Introduction

Our understanding of the genetic contribution to pathogenesis of type 2 diabetes has been greatly facilitated by genome-wide association studies (GWASs). These have identified over 100 genomic regions showing a robust association to disease risk [1]. However, teasing out the biological mechanisms underlying these disease associations continues to prove difficult, as most GWAS signals fall outside coding sequences. Broad inference across loci has been more successful, demonstrating from both phenotypic and genomic perspectives the importance of the pancreatic islet in risk of type 2 diabetes [2, 3].

Most functional follow-up of GWAS signals has involved studies in adult islets and/or a variety of beta cell lines, but there is mounting evidence that some of the implicated genetic variants influence islet development [4]. For example, many of the monogenic diabetes genes—most of which impact on islet development [5]—are also found in or near type 2 diabetes-associated loci [1]. Changes in the composition or number of islets as a result of events during development could lead to an altered functional islet mass in later life, increasing risk of type 2 diabetes.

Until recently, restricted access to human fetal material constrained the study of islet development to murine models. However, key differences between human and murine islet development [6], together with the potential of stem cell regenerative approaches to the treatment of diabetes, have motivated recent endeavours to differentiate human stem cells into pancreatic islet-like cells [7, 8, 9].

Islet differentiation protocols are rapidly improving [7, 10] and are now able to generate functional insulin-producing, although still somewhat immature, islet-like cells [8, 9]. In this study, we demonstrate how such cellular models of human pancreatic islet development can provide insights into the role of monogenic diabetes and type 2 diabetes-associated genes in islet development, and highlight the cellular pathways and mechanisms through which they act.

Methods

Generation of human induced pluripotent stem cells

Human induced pluripotent stem cell (iPSC) lines from three independent individuals without diabetes were obtained from the StemBANCC consortium (www.stembancc.org) (see ESM Methods). The generation of lines SB Ad2 and SB Ad3 has previously been described [10]. A third line, SB Neo1, was generated from commercial fibroblasts obtained from a neonatal donor of European descent with no reported diagnosis of diabetes (CC-2509, tissue acquisition number 15819; Lonza, Walkersville, MD, USA). Characterisation of all three lines has been reported elsewhere [10, 11]. All lines were free of mycoplasma.

Ethics

All tissue samples for reprogramming were collected with full informed consent. Ethical approval for the StemBANCC study (UK) was received from the National Research Ethics Service South Central Hampshire A research ethics committee (REC 13/SC/0179).

In vitro differentiation of iPSCs towards beta-like cells

The iPSC lines were cultured in mTeSR1 medium (StemCell Technologies, Vancouver, BC, Canada) at 37°C under 5% CO2, and passaged as single cells every 3–4 days or when confluent. In vitro differentiation involved the timely addition of recombinant growth factors and small molecules to sequentially generate cells representing key developmental stages of the endocrine pancreas: definitive endoderm, primitive gut tube, posterior foregut, pancreatic endoderm, endocrine progenitors, endocrine-like cells and beta-like cells. The differentiation protocol was carried out as described by Rezania and colleagues [9] with some modifications (ESM Tables 1, 2). All three iPSC lines were differentiated once, in parallel, using the same culture and differentiation media (ESM Methods).

Flow cytometry

The efficiency of in vitro differentiation was evaluated by measuring the expression of stage-specific markers indicative of the development of the endocrine pancreas. For each specific stage, these were: definitive endoderm (SRY-box 17 [SOX17] and octamer-binding transcription factor 4 [OCT4, also known as POU5F1]); pancreatic endoderm (NK6 homeobox 1 [NKX6-1] and pancreas/duodenum homeobox protein 1 [PDX1]); and endocrine-like cells (NKX6-1, insulin [INS] and glucagon [GCG]) (ESM Fig. 1). Methods for flow cytometry were as previously described [10], and details of antibodies are listed in ESM Table 3.

RNA extraction, sequencing and quantification

Cells were harvested and RNA extracted using TRIzol Reagent (ThermoFisher Scientific, Paisley, UK) as per the manufacturer’s guidelines. Library preparation and sequencing was performed at the Oxford Genomics Centre (Wellcome Centre for Human Genetics, Oxford, UK) as previously described [10]. RNA sequencing libraries were sequenced to a mean read depth of 148 (±12) million reads per sample. Reads were mapped to human genome build hg19, with GENCODE v19 (https://www.gencodegenes.org/releases/19.html) as the transcriptome reference, using STAR v.2.5 [12], followed by gene-level quantification with featureCounts from the Subread package v.1.5 (http://subread.sourceforge.net/) [13] (ESM Methods).

Principal component analysis was used to cluster samples with those from previously published studies [10, 14]. Correlation of gene expression patterns across all stages was calculated using the weighted gene co-expression network analysis (WGCNA) package (v.1.51) in R (v.3.3.2) (ESM Methods) [15, 16].

Differential expression analysis

Analysis was performed on 15,221 autosomal protein-coding and long intergenic non-coding RNA (lincRNA) genes present in Ensembl Genes v88 (http://mar2017.archive.ensembl.org/index.html) with more than one count per million in all donors of at least one differentiation stage (ESM Table 4). Genes were normalised using the voom function within the limma package (v.3.32.5) in R [17]. The eBayes function in limma was used for differential expression analysis, comparing all the differentiation stages with iPSC as the baseline, and adjusting for donor effects. We adjusted p values for multiple testing (q values) using the Benjamini–Hochberg method [18].

To define stage-specific marker genes, differentially expressed genes (q < 0.01) with an absolute log2 fold change (FC) > 1 were assigned to the stage in which they were most upregulated compared with the baseline iPSC profile. When the log2FC was negative for all contrasted stages, the gene was assigned to iPSCs (ESM Table 5). For comparison with the previously reported protocol [10], published data were reprocessed in an analogous manner for the stages shared between the protocols (ESM Methods; ESM Tables 6, 7).

Gene ontology and transcription factor binding motif enrichment

Differentially expressed genes in each stage were tested for enrichment in gene ontology terms for biological processes using the GOstats package (v. 2.40.0) in R [19]. All genes tested for differential expression were used as background. Significant gene ontology terms (q < 0.05) were retained (ESM Table 8).

For transcription factor enrichment, upstream regulators for the differentially expressed genes were predicted using the iRegulon (v. 1.3) Cytoscape plugin (ESM Methods) [20]. Motifs and chromatin immunoprecipitation (ChIP) sequencing tracks were ranked based on the normalised enrichment score (NES), with only those with an NES > 3 (corresponding to a false discovery rate (FDR) of 3–9%) being considered. Enriched motifs were then matched to transcription factors known to bind them (ESM Table 9).

Type 2 diabetes and fasting glucose gene enrichment

Enrichment analysis was implemented in two ways: as a hypergeometric test in R (using all genes tested for differential expression as background) or using the gene-scoring function in MAGENTA [21] followed by a gene set enrichment analysis (GSEA) [22, 23] (ESM Methods).

For the hypergeometric test, we analysed the differentially expressed genes from each differentiation stage for enrichment in genes mapping to type 2 diabetes or fasting glucose GWAS signals, which were defined as protein-coding and lincRNA genes located within specified distance bins (0, 50, 100, 200 or 500 kb) surrounding the credible intervals for trait-associated loci. Credible intervals were defined by the boundaries of the 99% credible sets of variants [24] from DIAGRAM (96 loci) [25] and ENGAGE (16 loci) [26] consortium data, respectively (ESM Table 10). A subset of 15 loci was considered to influence type 2 diabetes via beta cell dysfunction; these loci included ones causing hyperglycaemia, reduced insulin processing and secretion, and reduced fasting proinsulin levels [27, 28] (ESM Table 11, ESM Methods).

For the analysis with MAGENTA and GSEA, we mapped SNPs from the type 2 diabetes GWAS meta-analysis from DIAGRAM (96 loci) [25], and the ranked list of p values for each gene was tested in GSEA (ESM Methods).

Results and discussion

Characterising an in vitro-derived model of human beta-like cells

To determine whether the differentiated cells followed normal islet development, we profiled gene expression patterns across iPSC and seven subsequent developmental stages in lines from three independent donors (SB Ad2, SB Ad3 and SB Neo1) differentiated in parallel. Each iPSC line successfully generated cells recapitulating key developmental stages of the endocrine pancreas as confirmed by the expression of known marker genes from developing and adult beta cells (ESM Fig. 2) [10].

Principal component analysis of the transcriptome showed that the beta-like cells generated in the current study clustered more closely with in vivo-matured islet-like cells [14] than cells from earlier differentiations [10] (Fig. 1, ESM Fig. 3). Differential expression analysis comparing transcriptomic profiles obtained from differentiations under current and previous protocols (see Methods) showed increasing divergence with differentiation stage (from 17 genes showing differential expression in iPSCs to 2095 at the endocrine-like cell stage) (ESM Table 7). Gene ontology analysis indicated that genes displaying increased expression at the endocrine-like cell stage (in comparisons of the current vs previous protocols) were enriched for terms including ‘regulation of insulin secretion’ (q = 2.3 × 10−4) and ‘hormone transport’ (q = 2.0 × 10−5).
Fig. 1

Principal component analysis of whole-transcriptome data derived from multiple differentiated human islet-like cell models. Data include all stages from our current differentiation protocol (Current), the most mature stage of a previously published differentiation protocol (Previous) [10], and cells derived via in vivo maturation by Xie and colleagues (Xie) [14]. The first two principal components (PC1, PC2) have been calculated using normalised gene counts for all stages of all three studies and corrected for batch effects. DE, definitive endoderm; GT, primitive gut tube; PF, posterior foregut; PE, pancreatic endoderm; EP, endocrine precursor; EN, endocrine-like cells; BLC, beta-like cells. Stages shown from the current study are iPSC, DE, GT, PF, PE, EP, EN and BLC. The stage shown from the previously reported study [10] is EN. The stage shown from Xie and colleagues’ in vivo maturation study [14] is ‘Matured in vivo’

Overall, cells generated in this study, compared with those previously reported [10], are more aligned to cells that have been further matured in vivo [14] (the current benchmark for most functionally mature endocrine pancreas-like cells). This reveals how advances in differentiation protocols are reflected in the transcriptome, particularly in the later stages of differentiation where there is a clear increase in the expression of genes essential for beta cell function and identity. This is the case for MAFA, which was completely absent in our previous differentiation protocol, and INS, whose high expression indicates the correct differentiation towards the last stage of beta cell development.

Identifying transcriptional networks underlying islet development and diabetes

To characterise the transcriptomic landscape of each developmental stage in the in vitro-differentiated cells produced in this study, we assigned significantly differentially expressed genes to the stage at which they were most upregulated: if expression peaked in iPSCs, the gene was assigned to that stage (see Methods). We detected 9409 significantly differentially expressed genes (q < 0.01, absolute log2FC > 1) across all stages, ranging in number from 623 in the primitive gut tube stage to 2773 in iPSCs (ESM Table 5). Known developmental marker genes, such as NEUROG3 in endocrine progenitors and INS in beta-like cells, were correctly assigned to their canonical stages. Gene ontology analysis of the sets of differentially expressed genes (ESM Table 8) showed enrichment in biological terms such as ‘hormone transport’ in endocrine-like cells (q = 0.047) and ‘regulation of insulin secretion’ in beta-like cells (q = 2.0 × 10−4).

The expression patterns of monogenic diabetes genes can point towards stages at which disruption of islet development has long-term consequences for glucose homeostasis. Of 28 genes implicated in monogenic or syndromic diabetes [1], 24 were differentially expressed in at least one stage of the in vitro-differentiated model. Nine mapped to the latest beta-like cell stage, but the other 15 showed significant upregulation earlier in differentiation (ESM Table 12). GATA6, for example, was differentially expressed at the definitive endoderm stage (log2FC = 9.5, q = 7.6 × 10−11), whereas GATA4 was differentially expressed in posterior foregut cells (log2FC = 8.2, q = 1.9 × 10−11); the later expression of GATA4 could contribute to the less severe phenotype of individuals carrying GATA4 vs GATA6 mutations [29, 30].

The differentiation model used in this study also sheds light on the developmental role of monogenic diabetes genes with lesser described roles. LMNA, for example, encodes a nuclear membrane protein involved in chromatin structure and nuclear stability; it has been implicated in the function and development of many tissues [31]. The diabetes in carriers of the LMNA mutation is mostly driven by altered adipose tissue deposition and insulin resistance [32]. However, the profile of LMNA expression during in vitro islet differentiation (peaking in pancreatic endoderm; log2FC = 1.1, q = 3.1 × 10−3) may indicate an additional impact on islet development [33].

The developmental competence of differentiating cells is in part driven by a subset of transcription factors that initiate and regulate changes in response to external stimuli, as highlighted by the many monogenic diabetes genes that are also transcription factors. To identify potential upstream transcriptional regulators active at each stage of islet development, we performed a WGCNA and determined the enrichment of transcription factor binding motifs and ChIP sequencing signals near differentially expressed genes using iRegulon (see Methods; ESM Table 9). This analysis confirmed the impact of well-established developmental transcriptional regulators such as the monogenic diabetes gene HNF1B, which showed iRegulon enrichment of its targets at the primitive gut tube stage (NES 3.0–5.7 [see Methods]). Some of these HNF1B targets also have known effects on pancreas development (SMAD7 [34], ID2 [35]), on mature islet function and on the development of other tissues that also arise from the gut tube (GGCX) [36].

Analysis of the sets of stage-specific differentially expressed genes also highlighted the targets of transcription factors with less-well studied roles in human islet development. For example, expression of the transcriptional repressor REST peaks in the intermediate steps of in vitro differentiation and declines at the endocrine-like cell and beta-like cell stages, with reciprocal expression patterns seen among its predicted targets. These targets include genes encoding neurexins (NRXN1, NRXN2) and subunits of the glutamate receptor channels (GRIA1, GRIA2, GRID1, GRIK2) implicated in insulin exocytosis [37, 38]. Correlation of gene expression with WGCNA assigns REST to the same cluster as TCF7L2 and other genes from the Wnt signalling pathway, such as TCF7, TCF3 and TCF12 [39]. This pathway is important for islet development and is targeted in many in vitro differentiation protocols [8, 9]. These data therefore indicate that REST is likely to be an important transcriptional regulator of human islet development, both in intermediate (pancreatic endoderm, endocrine progenitor) and later (endocrine-like cell, beta-like cell) [40] stages of differentiation, as has also been recently suggested by studies in mice and humans [41, 42].

TCF7L2 maps to the type 2 diabetes-associated locus with the largest common effect on disease risk [1]. Analysis of TCF7L2 targets (as assessed by ChIP sequencing with iRegulon) shows marked enrichment at the posterior foregut stage (NES = 3.4) that mirrors that of TCF7L2 expression (log2FC = 1.2; q = 8.5 × 10−10). The expression of several other Wnt family members also peaks at the posterior foregut stage; these include the coactivator CREBBP, the binding sites of which are significantly enriched in type 2 diabetes-associated loci [43], and HHEX, which maps to a prominent type 2 diabetes-risk locus and is implicated in foregut development [44]. In the developing embryo, cells of the posterior foregut can differentiate into liver as well as endocrine pancreas [45]. Alleles associated with risk of type 2 diabetes within the TCF7L2 and HHEX loci may influence early expression of these genes, which could affect development in multiple metabolic tissues. This view is supported by cellular and murine studies indicating that TCF7L2 regulates beta cell development and function [46], including via indirect effects in supporting tissues [47], as well as affecting hepatic function [48]. Similarly, Hhex is essential for the differentiation of the posterior foregut into the liver in mice [44], yet is also thought to regulate delta cell identity and function in islets [49].

Thus, several key functional candidates mapping within type 2 diabetes GWAS signals, in addition to those which overlap known monogenic diabetes genes, appear to be active during this early critical window of pancreatic development. Studying these and other diabetes-relevant genes in stem cell-derived models can help to decipher the role of multiorgan developmental effects on pathogenesis of diabetes. By integrating the differential expression data with genomic annotations on transcription factor binding and clustering of longitudinal expression, we identified novel potential regulators orchestrating gene expression patterns within the different developmental stages. Such transcriptomic analysis can also illuminate the mechanisms of action for monogenic diabetes genes and inform the search for novel MODY genes that influence the same pathways.

Developing and mature cells are enriched in genes within type 2 diabetes-associated loci

Most of the more than 100 type 2 diabetes susceptibility loci identified to date [1] map to non-coding regions of the genome and are likely to exert their effects through altered regulation of nearby genes. We examined the transcriptomic data for evidence of developmental stage-specific enrichment of genes near these loci.

We first concentrated on genes whose coding sequence was at least partly contained within 99% credible intervals from type 2 diabetes GWAS fine-mapping efforts on the basis that these represented a set of genes likely to be substantially enriched for type 2 diabetes effector transcripts (see Methods). Of the 117 genes so defined, most (86; 73%) showed differential expression that peaked before the final beta-like cell stage (ESM Table 13); the stages of maximal differential expression were widely distributed. GSEA, which considers the strength of association at type 2 diabetes GWAS signals (see Methods), demonstrated enrichment of the type 2 diabetes GWAS loci with largest effect for differentially expressed genes at the posterior foregut stage (q = 0.002, Fig. 2a). This enrichment remained significant (q = 0.001) if the GWAS genes also implicated in monogenic diabetes (ESM Table 12) were excluded. Using a complementary GSEA approach that ranked the strength of differential expression of each gene (in q value) per stage, we compared the most differentially expressed genes at each stage for enrichment among type 2 diabetes GWAS loci; this analysis highlighted the beta-like cell stage (q = 0.033, Fig. 2a). This enrichment was no longer significant (q = 0.151) after monogenic diabetes genes had been excluded.
Fig. 2

Both developing and mature islet-like cells are enriched for genes within type 2 diabetes-associated loci. (a) Results from the GSEA. SNPs from the type 2 diabetes GWAS meta-analysis from DIAGRAM (96 loci) [24] were mapped to genes, and type 2 diabetes association scores were calculated for each gene using MAGENTA. Two complementary analyses were performed: enrichment of all genes ordered by their MAGENTA scores in sets of differentially expressed genes for each stage (Ranked T2D GWAS list), and enrichment of differentially expressed genes per stage (ordered by q value) in significant (p < 0.05 by MAGENTA) gene scores (ranked differentially expressed genes). The y-axis represents the results of the GSEA in FDR-adjusted p values (q values, −log10). The horizontal grey dashed line marks the 5% significance threshold. (b) Results for the hypergeometric enrichment analysis. Enrichment was tested for all differentially expressed genes per stage in the 96 type 2 diabetes credible intervals [T2D (all)] from DIAGRAM [24] and the 16 fasting glucose credible intervals (Fasting glucose) from ENGAGE [25] (ESM Table 10), and for all differentially expressed genes in only physiological type 2 diabetes loci [T2D (beta cell)] (ESM Table 11). We consider beta cell function loci as 15 loci influencing hyperglycaemia, beta cell function and insulin processing [26, 27]. The y-axis represents the results of the hypergeometric test in permuted p values (−log10). The horizontal grey dashed line marks the 5% significance threshold. T2D, type 2 diabetes; DE, definitive endoderm; GT, primitive gut tube; PF, posterior foregut; PE, pancreatic endoderm; EP, endocrine precursor; EN, endocrine-like cells; BLC, beta-like cells

As an additional analytical approach, we performed a hypergeometric test for enrichment in the same set of 117 type 2 diabetes credible interval genes (see Methods). As opposed to the GSEA method above, this analysis does not consider the strength of differential expression (or of association with type 2 diabetes) above the significance threshold. This test again demonstrated that genes showing differential expression at the beta-like cell stage were enriched (compared with background) for location within type 2 diabetes credible intervals (permuted p value =0.049; Fig. 2b). Excluding the monogenic diabetes genes, and those that fell in the same credible interval, from the differentially expressed genes at each stage removed the significance of the beta-like cells (permuted p value =0.302). We repeated the enrichment test using a subset of 15 type 2 diabetes GWAS loci for which the evidence from physiological studies points most emphatically to risk of type 2 diabetes mediated via reduced insulin secretion (ESM Table 11) [27, 28]. In this analysis, enrichment for genes differentially expressed at the beta-like cell stage became more significant (permuted p value =0.007; Fig. 2b). This enrichment was reduced (but not eliminated; permuted p value =0.03) after excluding the monogenic diabetes genes and those within the same credible interval. Using the same approach of sampling from the hypergeometric distribution, we also detected enrichment for genes mapping to credible intervals for 16 loci significantly associated with fasting glucose (permuted p value =0.0002; Fig. 2b). Earlier stages of differentiation did not show significant enrichment for genes within type 2 diabetes or fasting glucose credible intervals. Nevertheless, the assignment of differentially expressed genes to a specific stage may lead to a wide distribution of signal that dilutes the power to detect significant enrichment at stages before the beta-like cell stage.

Type 2 diabetes-associated signals falling in non-coding regions have a presumed regulatory function: some may map to tissue-specific enhancers acting some distance away from their effector transcripts [50]. However, consistent with observations that most regulatory GWAS effects operate at relatively short distances [3], we found attenuation of these enrichment signals as we extended the analyses to include genes mapping at increasing distance from the credible intervals (see Methods), both for genes in all type 2 diabetes credible intervals and for the subset implicated in beta cell function (ESM Fig. 4).

The notable overlap between monogenic diabetes genes and those mapping within type 2 diabetes-associated loci supports the hypothesis that some component of type 2 diabetes susceptibility arises through impairment of islet development [1], concretely in the posterior foregut stage. The final stage in the islet development model (featuring cells expressing genes encoding the machinery to support glucose-stimulated insulin secretion) is also enriched for genes mapping to GWAS signals for both type 2 diabetes and fasting glucose. These data are consistent with the concept that type 2 diabetes-associated loci act both on the adult islet and during earlier developmental stages.

In summary, this study demonstrates how characterisation of gene expression during human islet differentiation can identify potential novel transcriptional regulators of the differentiation process, and provide insights into developmental aspects underlying inherited predisposition to type 2 diabetes. Further refinement of in vitro models of endocrine pancreas development will allow more detailed interrogation of the genes and pathways influencing islet development and function in humans. Mechanistic analyses of the contribution of candidate regulators of islet development to long-term islet function is enhanced by recent advances in clustered regularly interspaced short palindromic repeats- (CRISPR-) based approaches that allow their experimental manipulation in in vitro systems [51]. Stem cell-derived islets may also serve as a cost-effective platform for drug screening in research into treatment of diabetes, and could provide material for transplant into individuals with diabetes [8, 9].

Notes

Acknowledgements

We thank the High-Throughput Genomics Group at the Wellcome Centre for Human Genetics (University of Oxford, UK) for generation of the sequencing data.

Contribution statement

NLB, CH and MvdB conceived the study. CH and NLB designed and performed the differentiation experiments. MvdB and MPA designed and performed the data analyses. ALG and MH gave conceptual advice and edited the manuscript. All the authors interpreted the data. MPA, NLB, MvdB, MIM, AWA and CH wrote the manuscript. All the authors revised the manuscript and approved the final version submitted for publication. NLB and MvdB are the guarantors of this work.

Funding

The research leading to these results has received funding from the Innovative Medicines Initiative Joint Undertaking (IMI JU) under Grant Agreement number 115439 (StemBANCC), resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies in kind contribution. This publication reflects only the authors’ views, and neither the IMI JU, the EFPIA nor the European Commission is liable for any use that may be made of the information contained therein. This work was also supported by the Wellcome Trust (098381, 106130, 090532, 203141), Medical Research Council (MR/L020149/1, BRR00030) and National Institute for Health Research (NIHR) Oxford Biomedical Research Centre Programme. ALG is a Wellcome Trust Senior Fellow in Basic Biomedical Research (95101 and 200837). MIM is a Wellcome Trust Senior Investigator. NLB was a Naomi Berrie Fellow in Diabetes Research. MvdB was supported by a Novo Nordisk postdoctoral fellowship run in partnership with the University of Oxford. NLB and MvdB are now employees of Novo Nordisk (although all experimental work was carried out under employment at the University of Oxford). MPA is supported by a Wellcome Trust PhD studentship (H5R00430). The study sponsor was not involved in the design of the study; the collection, analysis and interpretation of data; the writing of the report; or the decision to submit the report for publication.

Duality of interest

MH and CH are employees of, and shareholders in, Novo Nordisk. The remaining authors declare that there is no duality of interest associated with this manuscript.

Supplementary material

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References

  1. 1.
    Fuchsberger C, Flannick J, Teslovich TM et al (2016) The genetic architecture of type 2 diabetes. Nature 536:41–47CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Benner C, van der Meulen T, Cacéres E, Tigyi K, Donaldson CJ, Huising MO (2014) The transcriptional landscape of mouse beta cells compared to human beta cells reveals notable species differences in long non-coding RNA and protein-coding gene expression. BMC Genomics 15:620CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    van De Bunt M, Manning Fox JE, Dai X et al (2015) Transcript expression data from human islets links regulatory signals from genome-wide association studies for type 2 diabetes and glycemic traits to their downstream effectors. PLoS Genet 11:e1005694CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Travers ME, Mackay DJG, Nitert MD et al (2013) Insights into the molecular mechanism for type 2 diabetes susceptibility at the KCNQ1 locus from temporal changes in imprinting status in human islets. Diabetes 62:987–992CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Schwitzgebel VM (2014) Many faces of monogenic diabetes. J Diabetes Investig 5:121–133CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Jennings RE, Berry AA, Strutt JP et al (2015) Human pancreas development. Development 142:3126–3137CrossRefPubMedGoogle Scholar
  7. 7.
    Bruin JE, Erener S, Vela J et al (2014) Characterization of polyhormonal insulin-producing cells derived in vitro from human embryonic stem cells. Stem Cell Res 12:194–208CrossRefPubMedGoogle Scholar
  8. 8.
    Pagliuca FW, Millman JR, Gürtler M et al (2014) Generation of functional human pancreatic β cells in vitro. Cell 159:428–439CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Rezania A, Bruin JE, Arora P et al (2014) Reversal of diabetes with insulin-producing cells derived in vitro from human pluripotent stem cells. Nat Biotechnol 32:1121–1133CrossRefPubMedGoogle Scholar
  10. 10.
    van de Bunt M, Lako M, Barrett A et al (2016) Insights into islet development and biology through characterization of a human iPSC-derived endocrine pancreas model. Islets 8:83–95CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Yang C, Xu Y, Yu M et al (2017) Induced pluripotent stem cell modelling of HLHS underlines the contribution of dysfunctional NOTCH signalling to impaired cardiogenesis. Hum Mol Genet 26:3031–3045CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Dobin A, Gingeras TR (2015) Mapping RNA-seq reads with STAR. Curr Protoc Bioinformatics 51:11.14.1–11.14.19CrossRefGoogle Scholar
  13. 13.
    Liao Y, Smyth GK, Shi W (2014) featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30:923–930CrossRefPubMedGoogle Scholar
  14. 14.
    Xie R, Everett LJ, Lim H-W et al (2013) Dynamic chromatin remodeling mediated by Polycomb proteins orchestrates pancreatic differentiation of human embryonic stem cells. Cell Stem Cell 12:224–237CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9:559CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    R Core Team (2017) R: A language and environment for statistical computing. Vienna, Austria. www.r-project.org
  17. 17.
    Ritchie ME, Phipson B, Wu D et al (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43:e47CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. Source J R Stat Soc Ser B 57:289–300Google Scholar
  19. 19.
    Falcon S, Gentleman R (2007) Using GOstats to test gene lists for GO term association. Bioinformatics 23:257–258CrossRefPubMedGoogle Scholar
  20. 20.
    Verfaillie A, Imrichová H, Van de Sande B et al (2014) iRegulon: from a gene list to a gene regulatory network using large motif and track collections. PLoS Comput Biol 10:e1003731CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Segrè AV, Groop L, Mootha VK et al (2010) Common inherited variation in mitochondrial genes is not enriched for associations with type 2 diabetes or related glycemic traits. PLoS Genet 6:e1001058CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Subramanian A, Tamayo P, Mootha VK et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102:15545–15550CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Mootha VK, Lindgren CM, Eriksson K-F et al (2003) PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet 34:267–273CrossRefPubMedGoogle Scholar
  24. 24.
    Maller JB, McVean G, Byrnes J et al (2012) Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat Genet 44:1294–1301CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Scott RA, Scott LJ, Mägi R et al (2017) An expanded genome-wide association study of type 2 diabetes in Europeans. Diabetes 66:2888–2902CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Horikoshi M, Mӓgi R, van de Bunt M et al (2015) Discovery and fine-mapping of glycaemic and obesity-related trait loci using high-density imputation. PLoS Genet 11:e1005230CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Dimas AS, Lagou V, Barker A et al (2014) Impact of type 2 diabetes susceptibility variants on quantitative glycemic traits reveals mechanistic heterogeneity. Diabetes 63:2158–2171CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Wood AR, Jonsson A, Jackson AU et al (2017) A genome-wide association study of IVGTT-based measures of first phase insulin secretion refines the underlying physiology of type 2 diabetes variants. Diabetes 66:2296–2309CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Lango Allen H, Flanagan SE, Shaw-Smith C et al (2011) GATA6 haploinsufficiency causes pancreatic agenesis in humans. Nat Genet 44:20–22CrossRefGoogle Scholar
  30. 30.
    Shaw-Smith C, De Franco E, Lango Allen H et al (2014) GATA4 mutations are a cause of neonatal and childhood-onset diabetes. Diabetes 63:2888–2894CrossRefPubMedGoogle Scholar
  31. 31.
    Zuela N, Bar DZ, Gruenbaum Y (2012) Lamins in development, tissue maintenance and stress. EMBO Rep 13:1070–1078CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Boschmann J, Engeli JM, Engeli S et al (2010) LMNA mutations, skeletal muscle lipid metabolism, and insulin resistance. J Clin Endocrinol Metab 95:1634–1643CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Kowluru A (2000) Evidence for the carboxyl methylation of nuclear lamin-B in the pancreatic beta cell. Biochem Biophys Res Commun 268:249–254CrossRefPubMedGoogle Scholar
  34. 34.
    Smart NG, Apelqvist SA, Gu X et al (2006) Conditional expression of Smad7 in pancreatic beta cells disrupts TGF-beta signaling and induces reversible diabetes mellitus. PLoS Biol 4:e39CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Hua H, Zhang Y-Q, Dabernat S et al (2006) BMP4 regulates pancreatic progenitor cell expansion through Id2. J Biol Chem 281:13574–13580CrossRefPubMedGoogle Scholar
  36. 36.
    Ferron M, Lacombe J, Germain A, Oury F, Karsenty G (2015) GGCX and VKORC1 inhibit osteocalcin endocrine functions. J Cell Biol 208:761–776CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Mosedale M, Egodage S, Calma RC, Chi N-W, Chessler SD (2012) Neurexin-1α contributes to insulin-containing secretory granule docking. J Biol Chem 287:6350–6361CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Wu Z-Y, Zhu L-J, Zou N et al (2012) AMPA receptors regulate exocytosis and insulin release in pancreatic β cells. Traffic 13:1124–1139CrossRefPubMedGoogle Scholar
  39. 39.
    Cadigan KM, Waterman ML (2012) TCF/LEFs and Wnt signaling in the nucleus. Cold Spring Harb Perspect Biol 4:a007906CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Atouf F, Czernichow P, Scharfmann R (1997) Expression of neuronal traits in pancreatic beta cells. J Biol Chem 272:1929CrossRefPubMedGoogle Scholar
  41. 41.
    Martin D, Kim Y-H, Sever D, Mao C-A, Haefliger J-A, Grapin-Botton A (2015) REST represses a subset of the pancreatic endocrine differentiation program. Dev Biol 405:316–327CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Solimena M, Schulte AM, Marselli L et al (2018) Systems biology of the IMIDIA biobank from organ donors and pancreatectomised patients defines a novel transcriptomic signature of islets from individuals with type 2 diabetes. Diabetologia 61:641–657CrossRefPubMedGoogle Scholar
  43. 43.
    Morris A, Voight B, Teslovich T (2012) Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet 44:981–990CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Arterbery AS, Bogue CW (2016) Hhex is necessary for the hepatic differentiation of mouse ES cells and acts via Vegf signaling. PLoS One 11:e0146806CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Gittes GK (2009) Developmental biology of the pancreas: a comprehensive review. Dev Biol 326:4–35CrossRefPubMedGoogle Scholar
  46. 46.
    Le Bacquer O, Shu L, Marchand M et al (2011) TCF7L2 splice variants have distinct effects on β-cell turnover and function. Hum Mol Genet 20:1906–1915CrossRefPubMedGoogle Scholar
  47. 47.
    Sakhneny L, Rachi E, Epshtein A et al (2018) Pancreatic pericytes support beta-cell function in a Tcf7l2-dependent manner. Diabetes 67:437–447CrossRefPubMedGoogle Scholar
  48. 48.
    Boj SF, Van Es JH, Huch M et al (2012) Diabetes risk gene and Wnt effector Tcf7l2/TCF4 controls hepatic response to perinatal and adult metabolic demand. Cell 151:1595–1607CrossRefPubMedGoogle Scholar
  49. 49.
    Zhang J, McKenna LB, Bogue CW, Kaestner KH (2014) The diabetes gene Hhex maintains δ-cell differentiation and islet function. Genes Dev 28:829–834CrossRefPubMedPubMedCentralGoogle Scholar
  50. 50.
    Gaulton KJ, Ferreira T, Lee Y et al (2015) Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci. Nat Genet 47:1415–1425CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Beer NL, Gloyn AL (2016) Genome-edited human stem cell-derived beta cells: a powerful tool for drilling down on type 2 diabetes GWAS biology. F1000Res 5:F1000 Faculty Rev-1711Google Scholar

Copyright information

© The Author(s) 2018

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Marta Perez-Alcantara
    • 1
  • Christian Honoré
    • 2
  • Agata Wesolowska-Andersen
    • 1
  • Anna L. Gloyn
    • 1
    • 3
    • 4
  • Mark I. McCarthy
    • 1
    • 3
    • 4
  • Mattias Hansson
    • 5
  • Nicola L. Beer
    • 3
  • Martijn van de Bunt
    • 1
    • 3
  1. 1.Wellcome Centre for Human GeneticsUniversity of OxfordOxfordUK
  2. 2.Department of Stem Cell BiologyNovo Nordisk A/SMaaloevDenmark
  3. 3.Oxford Centre for Diabetes, Endocrinology & MetabolismUniversity of OxfordOxfordUK
  4. 4.Oxford NIHR Biomedical Research CentreChurchill HospitalOxfordUK
  5. 5.Department of Stem Cell ResearchNovo Nordisk A/SMaaloevDenmark

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