Abstract
Genes involved in distinct diabetes types suggest shared disease mechanisms. Here we show that One Cut Homeobox 1 (ONECUT1) mutations cause monogenic recessive syndromic diabetes in two unrelated patients, characterized by intrauterine growth retardation, pancreas hypoplasia and gallbladder agenesis/hypoplasia, and early-onset diabetes in heterozygous relatives. Heterozygous carriers of rare coding variants of ONECUT1 define a distinctive subgroup of diabetic patients with early-onset, nonautoimmune diabetes, who respond well to diabetes treatment. In addition, common regulatory ONECUT1 variants are associated with multifactorial type 2 diabetes. Directed differentiation of human pluripotent stem cells revealed that loss of ONECUT1 impairs pancreatic progenitor formation and a subsequent endocrine program. Loss of ONECUT1 altered transcription factor binding and enhancer activity and NKX2.2/NKX6.1 expression in pancreatic progenitor cells. Collectively, we demonstrate that ONECUT1 controls a transcriptional and epigenetic machinery regulating endocrine development, involved in a spectrum of diabetes, encompassing monogenic (recessive and dominant) as well as multifactorial inheritance. Our findings highlight the broad contribution of ONECUT1 in diabetes pathogenesis, marking an important step toward precision diabetes medicine.
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Data availability
Sequencing data (RNA-seq, ATAC-seq and ChIP–seq) generated for this work have been deposited at the Gene Expression Omnibus (GEO) (GSE131817). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository75 with the dataset identifier PXD018887. Moreover, gene signatures obtained from publicly available data deposited at GEO GSE81547 were used. We re-analyzed public TF ChIP–seq data and activating histone ChIP–seq data deposited at GEO GSE54471 and promoter associated ChIP–seq histone marks deposited at ArrayExpress E-MTAB-1086 for pancreatic progenitors and E-MTAB-1919 for islets. Source data are provided with this paper.
Code availability
For raw data processing off the instruments, code for two custom programs based on Picard tools (2.19.2) is available at https://github.com/DanieleBarreca/picard/ and https://broadinstitute.github.io/picard/. Further programs used for transcriptome analysis are described in Methods section.
References
NCD Risk Factor collaboration (NCD-RisC). Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants. Lancet 387, 1513–1530 (2016).
Bansal, V. et al. Spectrum of mutations in monogenic diabetes genes identified from high-throughput DNA sequencing of 6888 individuals. BMC Med. 15, 213 (2017).
Shields, B. M. et al. Population-based assessment of a biomarker-based screening pathway to aid diagnosis of monogenic diabetes in young-onset patients. Diabetes Care 40, 1017–1025 (2017).
Hattersley, A. T. & Patel, K. A. Precision diabetes: learning from monogenic diabetes. Diabetologia 60, 769–777 (2017).
Mahajan, A. et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat. Genet. 50, 1505 (2018).
Flannick, J. et al. Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls. Nature 570, 71–76 (2019).
Heller, S., Melzer, M. K., Azoitei, N., Julier, C. & Kleger, A. Human pluripotent stem cells go diabetic: a glimpse on monogenic variants. Front. Endocrinol. (Lausanne) 12, 648284 (2021).
Breunig, M. et al. Modeling plasticity and dysplasia of pancreatic ductal organoids derived from human pluripotent stem cells. Cell Stem Cell 28, 1105–1124 e1119 (2021).
Wiedenmann, S. et al. Single-cell-resolved differentiation of human induced pluripotent stem cells into pancreatic duct-like organoids on a microwell chip. Nat. Biomed. Eng. 5, 897–913 (2021).
1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).
Oliver-Krasinski, J. M. & Stoffers, D. A. On the origin of the beta cell. Genes Dev. 22, 1998–2021 (2008).
Smith, S. B. et al. Rfx6 directs islet formation and insulin production in mice and humans. Nature 463, 775–780 (2010).
Zorn, A. M. & Wells, J. M. Vertebrate endoderm development and organ formation. Annu. Rev. Cell Dev. Biol. 25, 221–251 (2009).
Jacquemin, P. et al. Transcription factor hepatocyte nuclear factor 6 regulates pancreatic endocrine cell differentiation and controls expression of the proendocrine gene ngn3. Mol. Cell Biol. 20, 4445–4454 (2000).
Clotman, F. et al. The onecut transcription factor HNF6 is required for normal development of the biliary tract. Development 129, 1819–1828 (2002).
Jacquemin, P., Lemaigre, F. P. & Rousseau, G. G. The Onecut transcription factor HNF-6 (OC-1) is required for timely specification of the pancreas and acts upstream of Pdx-1 in the specification cascade. Dev. Biol. 258, 105–116 (2003).
Lannoy, V. J., Bürglin, T. R., Rousseau, G. G. & Lemaigre, F. P. Isoforms of hepatocyte nuclear factor-6 differ in DNA-binding properties, contain a bifunctional homeodomain, and define the new ONECUT class of homeodomain proteins. J. Biol. Chem. 273, 13552–13562 (1998).
Bonaldi, C. et al. A first national prevalence estimate of diagnosed and undiagnosed diabetes in France in 18- to 74-year-old individuals: the French Nutrition and Health Survey 2006/2007. Diabet. Med. 28, 583–589 (2011).
Møller, A. et al. Hepatocyte nuclear factor-6: associations between genetic variability and type II diabetes and between genetic variability and estimates of insulin secretion. Diabetologia 42, 1011–1016 (1999).
Zhu, Q. et al. Mutation screening of the hepatocyte nuclear factor (HNF)-6 gene in Japanese subjects with diabetes mellitus. Diabetes Res. Clin. Pract. 52, 171–174 (2001).
Machiela, M. J. & Chanock, S. J. LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants. Bioinformatics 31, 3555–3557 (2015).
Allada, R. & Bass, J. Circadian mechanisms in medicine. N. Engl. J. Med. 384, 550–561 (2021).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Cebola, I. et al. TEAD and YAP regulate the enhancer network of human embryonic pancreatic progenitors. Nat. Cell Biol. 17, 615–626 (2015).
Hrvatin, S. et al. Differentiated human stem cells resemble fetal, not adult, β cells. Proc. Natl Acad. Sci. USA 111, 3038–3043 (2014).
McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495 (2010).
Li, Z. et al. Identification of transcription factor binding sites using ATAC-seq. Genome Biol. 20, 45 (2019).
Schaffer, A. E. et al. Nkx6.1 controls a gene regulatory network required for establishing and maintaining pancreatic beta cell identity. PLoS Genet. 9, e1003274 (2013).
Pasquali, L. et al. Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants. Nat. Genet. 46, 136–143 (2014).
Oliver-Krasinski, J. M. et al. The diabetes gene Pdx1 regulates the transcriptional network of pancreatic endocrine progenitor cells in mice. J. Clin. Invest. 119, 1888–1898 (2009).
Kim, Y. S. et al. Glis3 regulates neurogenin 3 expression in pancreatic beta-cells and interacts with its activator, Hnf6. Mol. Cells 34, 193–200 (2012).
Jennings, R. E. et al. Development of the human pancreas from foregut to endocrine commitment. Diabetes 62, 3514–3522 (2013).
Thatava, T. et al. Indolactam V/GLP-1-mediated differentiation of human iPS cells into glucose-responsive insulin-secreting progeny. Gene Ther. 18, 283–293 (2011).
GTEx Consortium. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).
Yang, Y. & Chan, L. Monogenic diabetes: what it teaches us on the common forms of type 1 and type 2 diabetes. Endocr. Rev. 37, 190–222 (2016).
Fu, D. et al. Genetic polymorphism of glucokinase on the risk of type 2 diabetes and impaired glucose regulation: evidence based on 298,468 subjects. PloS ONE 8, e55727 (2013).
Njølstad, P. R. et al. Neonatal diabetes mellitus due to complete glucokinase deficiency. N. Engl. J. Med. 344, 1588–1592 (2001).
Vionnet, N. et al. Nonsense mutation in the glucokinase gene causes early-onset non-insulin-dependent diabetes mellitus. Nature 356, 721–722 (1992).
Stanger, B. Z., Tanaka, A. J. & Melton, D. A. Organ size is limited by the number of embryonic progenitor cells in the pancreas but not the liver. Nature 445, 886–891 (2007).
Churchill, Angela J. et al. Genetic evidence that Nkx2. 2 acts primarily downstream of Neurog3 in pancreatic endocrine lineage development. Elife 6, e20010 (2017).
Miguel-Escalada, I. et al. Human pancreatic islet three-dimensional chromatin architecture provides insights into the genetics of type 2 diabetes. Nat. Genet. 51, 1137–1148 (2019).
Schaffer, A. E., Freude, K. K., Nelson, S. B. & Sander, M. Nkx6 transcription factors and Ptf1a function as antagonistic lineage determinants in multipotent pancreatic progenitors. Dev. Cell 18, 1022–1029 (2010).
Chen, J., Bardes, E. E., Aronow, B. J. & Jegga, A. G. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 37, W305–W311 (2009).
Tweedie, E. et al. Maintenance of hepatic nuclear factor 6 in postnatal islets impairs terminal differentiation and function of β-cells. Diabetes 55, 3264–3270 (2006).
Zhang, H. et al. Multiple, temporal-specific roles for HNF6 in pancreatic endocrine and ductal differentiation. Mech. Dev. 126, 958–973 (2009).
Henley, K. D. et al. Threshold-dependent cooperativity of Pdx1 and Oc1 in pancreatic progenitors establishes competency for endocrine differentiation and β-cell function. Cell Rep. 15, 2637–2650 (2016).
Zhang, Y. et al. HNF6 and Rev-erbɑ integrate hepatic lipid metabolism by overlapping and distinct transcriptional mechanisms. Genes Dev. 30, 1636–1644 (2016).
Patel, K. A. et al. Heterozygous RFX6 protein truncating variants are associated with MODY with reduced penetrance. Nat. Commun. 8, 888 (2017).
Fuchsberger, C. et al. The genetic architecture of type 2 diabetes. Nature 536, 41–47 (2016).
Flannick, J. et al. Sequence data and association statistics from 12,940 type 2 diabetes cases and controls. Sci. Data 4, 170179 (2017).
Chung, W. K. et al. Precision medicine in diabetes: a consensus report from the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes Care 43, 1617–1635 (2020).
Smith, R. J. et al. Individualizing therapies in type 2 diabetes mellitus based on patient characteristics: what we know and what we need to know. J. Clin. Endocrinol. Metab. 95, 1566–1574 (2010).
Barker, J. M. et al. Two single nucleotide polymorphisms identify the highest-risk diabetes HLA genotype: potential for rapid screening. Diabetes 57, 3152–3155 (2008).
Oram, R. A. et al. A type 1 diabetes genetic risk score can aid discrimination between type 1 and type 2 diabetes in young adults. Diabetes Care 39, 337–344 (2016).
Johnson, M. B. et al. Trisomy 21 is a cause of permanent neonatal diabetes that is autoimmune but not HLA associated. Diabetes 68, 1528–1535 (2019).
Geusz, R. J. et al. Pancreatic progenitor epigenome maps prioritize type 2 diabetes risk genes with roles in development. eLife 10, e59067 (2021).
Lee, K. et al. FOXA2 is required for enhancer priming during pancreatic differentiation. Cell Rep. 28, 382–393.e387 (2019).
Howson, J. M. et al. Genetic analysis of adult-onset autoimmune diabetes. Diabetes 60, 2645–2653 (2011).
Zalloua, P. A. et al. WFS1 mutations are frequent monogenic causes of juvenile-onset diabetes mellitus in Lebanon. Hum. Mol. Genet. 17, 4012–4021 (2008).
Rong, E. et al. Heteroplasmy detection of mitochondrial DNA A3243G mutation using quantitative real-time PCR assay based on TaqMan-MGB probes. BioMed Res. Int. 2018, 1286480 (2018).
Hohwieler, M. et al. Human pluripotent stem cell-derived acinar/ductal organoids generate human pancreas upon orthotopic transplantation and allow disease modelling. Gut 66, 473–486 (2017).
Mali, P. et al. RNA-guided human genome engineering via Cas9. Science 339, 823–826 (2013).
Ding, Q. et al. Enhanced efficiency of human pluripotent stem cell genome editing through replacing TALENs with CRISPRs. Cell Stem Cell 12, 393–394 (2013).
Rezania, A. et al. Reversal of diabetes with insulin-producing cells derived in vitro from human pluripotent stem cells. Nat. Biotechnol. 32, 1121–1133 (2014).
Mahaddalkar, P. U. et al. Generation of pancreatic β cells from CD177+ anterior definitive endoderm. Nat. Biotechnol. 38, 1061–1072 (2020).
Salat, D., Liefke, R., Wiedenmann, J., Borggrefe, T. & Oswald, F. ETO, but not leukemogenic fusion protein AML1/ETO, augments RBP-Jκ/SHARP-mediated repression of Notch target genes. Mol. Cell. Biol. 28, 3502–3512 (2008).
Wang, A. et al. Epigenetic priming of enhancers predicts developmental competence of hESC-derived endodermal lineage intermediates. Cell Stem Cell 16, 386–399 (2015).
Allhoff, M., Seré, K., F. Pires, J., Zenke, M. & G. Costa, I. Differential peak calling of ChIP-seq signals with replicates with THOR. Nucleic Acids Res. 44, e153 (2016).
Rappsilber, J., Mann, M. & Ishihama, Y. Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips. Nat. Protoc. 2, 1896 (2007).
Zecha, J. et al. TMT labeling for the masses: a robust and cost-efficient, in-solution labeling approach. Mol. Cell. Proteomics 18, 1468–1478 (2019).
Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods 13, 731 (2016).
Tusher, V. G., Tibshirani, R. & Chu, G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl Acad. Sci. USA 98, 5116–5121 (2001).
Conforto, T. L., Steinhardt, G. F. IV & Waxman, D. J. Cross talk between GH-regulated transcription factors HNF6 and CUX2 in adult mouse liver. Mol. Endocrinol. 29, 1286–1302 (2015).
Wacker, S. A. et al. RITA, a novel modulator of Notch signalling, acts via nuclear export of RBP‐J. EMBO J. 30, 43–56 (2011).
Vizcaíno, J. A. et al. The PRoteomics IDEntifications (PRIDE) database and associated tools: status in 2013. Nucleic Acids Res. 41, D1063–D1069 (2012).
Acknowledgements
We thank R. Gowdru Bijegatte, K. Köhn, R. Köhntop, S. Schirmer, R. Rittelmann and J. Krüger from the Department of Internal Medicine I, Ulm University, Germany for their technical support. We thank S. Warth at the Core Facility Cytometry, Ulm University Medical Faculty, Germany for FACS-mediated cell sorting and M. Groth at Leibnitz Institute of Aging in Jena, Germany for performing RNA sequencing of our samples. We are also grateful to Kuhn Elektro-Technik GmbH for supporting our work. We thank S. Hays from the Neonatal Unit, Lyon, France for clinical management of the newborn patient and I. Plotton from the Biochemistry Laboratory, Lyon, France for her contribution to the hormonal evaluations. We thank C. Bonaldi and the Institut de Veille Sanitaire for providing data on IFG and T2D prevalence in the French population and the Centre National de Recherche en Génomique Humaine for providing access to their genomic platform. We thank the patients and their families for participating in this study. This project was funded by the Boehringer Ingelheim Ulm University BioCenter (BIU) as well as by the ANR-DFG collaborative research project (grant no. ANR-18-CE92-0031, DFG KL 2544/5-1) to C.J. and A.K. and via additional funding by the Deutsche Forschungsgemeinschaft (DFG) grant nos. KL 2544/6-1, KL 2544/7-1, ‘Fokus-Förderung COVID-19’ KL 2544/8-1 and KL 2544/1-2 to A.K.; by the Agence Nationale pour la Recherche (grant no. ANR-09-GENO-021), the European Foundation for the Study of Diabetes/JDRF/Novo Nordisk, the Assistance Publique-Hôpitaux de Paris Programme Hospitalier de Recherche Clinique (project DIAGENE) and France Génomique (project DIAPED) to C.J.; by a grant from the E: MED Consortia Fibromap funded by the German Ministry of Education and Science (BMBF) and by the DFG grant no. GE 2811/3-1 to I.G.C.; as well as grant no. SFB1074/A3, OS287/4-1 to F.O., NIH grants no. DK068471 and no. DK105541 as well as NIH T32 grant no. GM008666 to M. Sander and grant no. DFG-GrK1041, Centre of Excellence Metabolic Disorders Baden-Wuerttemberg, Germany as well as Ministry of Education, Singapore, MOE2018-T2-1-085 to B.O.B. Work in M. Hebrok’s laboratory was supported by a grant from the NIH (grant no. DK105831). This work was also supported by the France Génomique National infrastructure, funded as part of the Investissements d’Avenir program managed by the Agence Nationale pour la Recherche (grant no. ANR-10-INBS-09).
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Contributions
A.P., S.H., I.G.C. and V.S. contributed equally to this work. A.P., S.H., I.G.C. and V.S. acquired, analyzed and interpreted data, and drafted and revised the work. A.P. performed statistical, genetic and bioinformatics analysis of the human genetic part of the project. S.H. performed functional studies of PSCs and prepared samples for RNA-seq, ATAC-seq and mass spectrometry. I.G.C. performed and directed bioinformatics analysis of the project. V.S. performed sequencing and genotyping of diabetic patients and their families as well as of the German cohort. M.B., Z.L. and G.K. acquired data and performed data analysis. A.D., P.Z., H.N., E.S., T.K., M.W., C.B., R.O., J.-F.D., B.K. and C.D.-R. acquired data for the project. M.B., R.G., M. Hebrok and T.S. revised the manuscript. Specifically, M.B. and A.I. performed gene editing of hESCs and initial functional analysis. Z.L., G.K. and X.Z. did ChIP–seq; Z.L., G.K. and M. Schuster did ATAC-seq; and Z.L., G.K., M. Schuster and Q.L. did RNA-seq bioinformatics analysis. R.R. acquired data and performed substantial revision of the work. M. Hohwieler performed reprogramming of patient fibroblasts and iPSC analysis. A.D. performed bioinformatics analysis of WES data and P.Z. identified and clinically characterized the Lebanese patient and his family. S.L. interpreted data and revised the work. M. Sander interpreted data, provided materials and revised the work. J.K. acquired and analyzed mass spectrometry data. J.R.B. generated reporter ESC lines. A.W. acquired and R.G. provided ChIP–seq data. K.G. and J.C. interpreted genetics data and G.N. provided RNA from differentiated Mel1 hESCs. B.O.B., F.O., M.N., C.J. and A.K. were responsible for acquisition and analysis of data, and drafting and revision of the work. Also, B.O.B. directed studies regarding German patient cohorts and interpreted genetics data, F.O. expressed and analyzed TFs and ONECUT1 variants and M.N. identified and clinically characterized Patient 1 and his extended family and interpreted the human genetic and clinical data. In addition, C.J. and A.K. designed the work, interpreted data and drafted the manuscript with input from all authors. C.J. directed the genetic part of the project and performed human genetic analyses. A.K. directed the functional studies of the project.
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Extended data
Extended Data Fig. 1 Population ancestry studies of Family-1 and genetic analysis of ONECUT1 variants and T2D-associated SNPs.
a, Principal component analysis of Patient-1 and his parents compared to reference populations from the 1000 Genomes project10. b, Principal component analysis of Patient-1 compared to European subpopulations, showing that he clusters within the French subpopulation. c, Local ancestry analysis of parents from Patient-1 showing chromosome 15. The arrow shows the position of ONECUT1 locus on chromosome 15, estimated to be of European ancestry. Admixed American (AMR), East Asian (EAS), European (EUR), South Asian (SAS). d, Nuclear family tree of Patient-1 (Family-1), showing the genotypes of the protein truncated variant ONECUT1-p.E231X. e, Nuclear family tree of Patient-2 (Family-2), showing the genotype of the missense variant p.E231D. f, Schematic ONECUT1 protein representing rare coding variants identified in patients with neonatal diabetes (homozygous) and in patients with young-onset diabetes (heterozygous). Green: index patients with neonatal/very-early-onset diabetes (families 1 and 2), red: UDC-T2D population screening, blue: de novo, identified by WES in one patient. g, T2D and BMI association in ONECUT1 region. P-values for association are based on DIAMANTE GWAS for T2D and T2D adjusted for BMI (T2DadjBMI), and on GIANT-UK Biobank GWAS for BMI, all of which were available on the AMP-T2D site (www.type2diabetesgenetics.org; date 10/2020). Statistics are shown for the 4 credible SNPs for T2D (*) and for three representative SNPs for BMI association. Pairwise linkage disequilibrium between SNPs was estimated using LDLINK in the European population (https://ldlink.nci.nih.gov).
Extended Data Fig. 2 ONECUT1-depleted PSCs are defective in PP formation.
a, ONECUT1 sequence analysis of respective ONECUT1 mutated HUES8 and iPSC cells. b, Representative immunofluorescence stainings of pluripotency markers NANOG and OCT3/4 in ONECUT1 null and WT HUES8 ESCs as well as ONECUT1-p.E231X iPSCs. c, Western Blot analysis for ONECUT1 and β-Actin in ONECUT1 null and WT HUES8 as well as iPSC differentiated to pancreatic progenitor (PP) cells. Of note, HUES8 heterozygous ONECUT1 KO (het) was included and undifferentiated stem cells serve as control (ESC, PSC). d,e, Differentiation efficiency of HUES8 and iPSC ONECUT1 null and WT cells to definitive endoderm (DE) was analyzed by markers SOX17 or CXCR4 and c-Kit as shown by representative immunofluorescence images (d) and flow cytometry (e; HUES8: n = 4; iPSC: n = 3). f,g, Differentiation efficiency of ONECUT1 null iPSC cells and respective WT cells to pancreatic endoderm (PE) and pancreatic progenitors (PP) was analyzed by markers PDX1 and NKX6.1 as shown by representative immunofluorescence images and flow cytometry with 62% reduction of PP cells in iPSC ONECUT1 E231X (PE: n = 2, PP: n = 3; with 2 replicates; two-tailed, unpaired t-test).
Extended Data Fig. 3 Intrinsic defects in ONECUT1-depleted PP cells disturb launching of the β-cell program.
a,b, GSEA analysis of differentially expressed (DE) genes in HUES8 WT and ONECUT1 KO PP cells (a) as well as PDX1+/NKX6.1+ purified PP cells from HUES8 ONECUT1 KO and WT (b) using a specific gene set for pancreatic progenitors as well as genes important for endocrine development and β-cell function. c, GSEA enrichment scores contrasting HUES8 WT and KO (or trunc) at PP stage on gene expression signatures of pancreas cells obtained from a single cell RNA-seq study (GSE81547). d, Correlation of all significant differentially expressed genes (RNA-seq) and proteins (mass spectrometry, MS) in HUES8 ONECUT1 truncated (trunc) cells at the PP stage. e, Comparison of expression values for depicted genes in HUES8 edited (ONECUT1 truncated and KO) and WT PP cells. Bar graphs represent min and max values with indicated mean normalized to ONECUT1 WT cells (RNA-seq: n = 6; qPCR: WT n = 4, KO/trunc n = 4; MS: n = 3; one-way ANOVA with Tukey’s test).
Extended Data Fig. 4 ONECUT1 shapes chromatin accessibility during PE-PP transition.
a, PCA analysis of stage-specific ATAC-seq for differentiation of ONECUT1 null and WT HUES8 lines (left) as well as restricted to PE and PP stages (right). b, Genomic ___location of stage-specific ATAC peaks lost or gained in ONECUT1 null (KO) HUES8 line. TTS: transcriptional termination site. c, Heatmap depicting chromatin accessibility signals (+/- 2 kb of peak center) of OC peaks lost upon ONECUT1 KO in HUES8 and ordered by ONECUT1 ChIP-seq peak strength at the PE and PP stage. d, Enrichment analysis (GREAT) of OC peaks lost upon ONECUT1 KO at the PE stage. e, Significance of overlap (log10 p-value) of open chromatin (OC) for different tissues as well as OC lost in HUES8 KO at PE or PP stage and ONECUT1 ChIP-seq peaks. f, Scatter plot depicting the footprint-based activity score (strength of binding) of TFs in PE state (y-axis) versus the difference of the activity score upon ONECUT1 KO at the PE state (ATAC-seq, HUES8). g, HNF family factors have the highest loss in activity followed by PAX family, SOX9 and PDX1 factors upon ONECUT1 KO. Representative examples of footprints.
Extended Data Fig. 5 DNA binding capacity of distinct ONECUT1 variants with clinical relevance and control variants.
a, Overview of WT and ONECUT1 coding variants used in overexpression experiments. b, Representative images of ONECUT1 protein variants fused to GFP, overexpressed in HeLa cells. c-e, Electromobility shift assay (EMSA) and super shift assay of selected WT and ONECUT1 protein variants fused to a Flag-tag using a probe consisting of a ONECUT1 binding motif (label A). Additional Flag antibody binding the complex leads to a further shift (label B). Unspecific binding complexes are indicated with an asterisk. In addition, in vitro translated ONECUT1 proteins (TnT Transcription/Translation System) are detected by ONECUT1 or Flag antibody (WB: α-ONECUT1 control).
Extended Data Fig. 6 Physical interaction between ONECUT1 and pancreatic transcription factors.
a, Luciferase reporter assay (HeLa cells, n = 8) with WT and ONECUT1 coding variants fused to the transcriptional activator (transactivator) VP16 using a reporter construct consisting of six ONECUT1 binding motifs found in the human FOXA2 promoter region. After binding of ONECUT1 to its binding motif, VP16 is activating transcription independent of the transactivation activity of ONECUT1 variants. Statistical analysis was performed by one-way ANOVA with Dunnett’s test. b, Proportion of genes with or without restriction to endocrine lineage genes with overlapping binding by ONECUT1 (ChIP-seq, PP) with depicted TFs (ChIP-seq). c, Pearson correlation between genome-wide binding signals of depicted TFs. d,e, Co-immunoprecipitation of Flag- or GFP-tagged WT ONECUT1 protein and GFP- or Flag-tagged target proteins. Proteins co-immunoprecipitating with ONECUT1 are highlighted in green, others in orange. WBs on the bottom show successful overexpression of putative interaction partners in HEK293, while WBs on the top were performed after Flag immunoprecipitation. The heavy chain of the Flag-antibody is indicated with an asterisk.
Extended Data Fig. 7 ONECUT1 protein-protein interaction requires its N-terminal end.
a, Homo- and heterodimerization of ONECUT1 proteins was analyzed by co-immunoprecipitation of GFP-tagged ONECUT1 and Flag-tagged ONECUT1 WT or variant in HEK293. The heavy chain of the Flag-antibody is indicated with an asterisk. Note that the ONECUT1 PTV (p.E231X) did not bind to WT ONECUT1 protein. b,c, Co-immunoprecipitation (top) of NKX6.1 (b) and NKX2.2 (c) with ONECUT1 WT and E231X. Heterodimerization only in ONECUT1 WT and NKX6.1/NKX2.2. The asterisk on the blot shows the heavy chain of the Flag antibody. Bottom control western blots show successful overexpression of TFs in HEK293.
Extended Data Fig. 8 Cooperative ONECUT1 interaction at putative enhancers.
a, NKX6.2 expression in HUES8 WT and ONECUT1 KO PP bulk and PDX1 + /NKX6.1+ purified cells (RNA-seq, n = 6; two-tailed, unpaired t-test). b,c, ATAC-seq, histone modifications and ONECUT1 ChIP-seq signals around NKX6.1, NKX6.2, and NKX2.2 locus. Red traced squares indicate enhancer regions expanded in (c). Below, the region selected for luciferase assay and reporter assay are shown. d, Luciferase reporter assay with selected NKX6.2 enhancer region overexpressing WT or ONECUT1 variants alone or together with NKX2.2 in HeLa cells (n = 6; one-way ANOVA with Tukey’s test).
Extended Data Fig. 9 Patient variant ONECUT1-p.E231D impairs pancreatic differentiation in gene-edited HUES8 hESC.
a, Scheme of ONECUT1 variant E231D generated by targeted gene-editing in HUES8 hESCs. b, Sequence verification of ONECUT1-p.E231D edited HUES8 cells. Of note, sequencing was performed on reverse strand. c,d, Differentiation efficiency at the PE and PP stages in ONECUT1-p.E231D HUES8 cells. Of note, ILV was omitted after PE stage to better demonstrate small effects in differentiation efficiency of ONECUT1 variants. Representative images show immunofluorescence staining of PDX1 and PDX1/NKX6.1 at the PE and PP stage, respectively. Quantification of positive cells was performed by flow cytometry (PE: n = 4; PP: n = 3; two-tailed, unpaired t-test). e, Heatmap depicting relative marker expression in ONECUT1-p.E231D edited HUES8 cells at PP stage. Of note, ILV was omitted after PE stage compared to regular differentiation protocol. Expression values are normalized to HUES8 ONECUT1 WT (n = 4, 2 technical replicates) and scaled by the sum of each row. f, Co-immunoprecipitation of NKX2.2 with ONECUT1 E231D and WT. The asterisk on the blot shows the heavy chain of the Flag antibody. Bottom control western blots show successful overexpression of TFs in HEK293. g, Quantification relative to NKX2.2 input (n = 4; two-tailed, unpaired t-test) shows reduced heterodimerization for ONECUT1 E231D.
Extended Data Fig. 10 Fine-mapped T2D-associated variants reside at ONECUT1 locus.
a, Fine mapping of type II diabetes traits from DIAMANTE GWAS dataset. This region (chr15:53070141-53165681) corresponds to the 99% genetic credible set from Mahajan et al. and includes the first exon of ONECUT1 and the non-coding RNA RP11-209K10.2. IGV plot depicts ONECUT1, FOXA1/2, GATA6, PDX1, and NKX6.1 ChIP-seq peaks (PP), ATAC-seq signals and histone modifications. T2D-associated SNPs (‘T2D SNPs’) with a p-value < 10-5 are shown in pink, p-value <10-8 in blue. Of those, rs2440374 overlaps with both a ONECUT1 peak and a differential ATAC-seq peak in PE stage. This SNP is localized at the promoter region of the non-coding gene RP11-209K10.2 (ENSEMBL ID ENSG00000259203). b,c, Tissue-specific expression of ONECUT1 and RP11-209K10.2 obtained from GTEx database showing gene expression in top 10 tissues sorted by median expression. Both genes have high expression specific to pancreas, liver and testis. d, Motif analysis with RSAT-Var-tools indicates that the SNP disrupts a putative binding sequence of NKX2.2. e, eQTL analysis with GTEx indicates an association of rs2440374, rs2456530 and rs75332279 with the expression of lncRNA RP11-209K10.2 in pancreas. f, Expression of lncRNA RP11-209K10.2 in HUES8 WT and ONECUT1 KO PE and PP cells (RNA-seq, n = 6; two-tailed, unpaired t-test). g, Graphical illustration of the proposed mechanism how ONECUT1 loss impairs pancreatic development to cause diabetes.
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Supplementary Tables 1–17 and flow cytometry gating strategy
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Philippi, A., Heller, S., Costa, I.G. et al. Mutations and variants of ONECUT1 in diabetes. Nat Med 27, 1928–1940 (2021). https://doi.org/10.1038/s41591-021-01502-7
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DOI: https://doi.org/10.1038/s41591-021-01502-7
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