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Genome-scale screens identify JNK–JUN signaling as a barrier for pluripotency exit and endoderm differentiation

Nature Genetics volume 51, pages 999–1010 (2019)Cite this article

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Abstract

Human embryonic stem cells (ESCs) and human induced pluripotent stem cells hold great promise for cell-based therapies and drug discovery. However, homogeneous differentiation remains a major challenge, highlighting the need for understanding developmental mechanisms. We performed genome-scale CRISPR screens to uncover regulators of definitive endoderm (DE) differentiation, which unexpectedly uncovered five Jun N-terminal kinase (JNK)–JUN family genes as key barriers of DE differentiation. The JNK–JUN pathway does not act through directly inhibiting the DE enhancers. Instead, JUN co-occupies ESC enhancers with OCT4, NANOG, SMAD2 and SMAD3, and specifically inhibits the exit from the pluripotent state by impeding the decommissioning of ESC enhancers and inhibiting the reconfiguration of SMAD2 and SMAD3 chromatin binding from ESC to DE enhancers. Therefore, the JNK–JUN pathway safeguards pluripotency from precocious DE differentiation. Direct pharmacological inhibition of JNK significantly improves the efficiencies of generating DE and DE-derived pancreatic and lung progenitor cells, highlighting the potential of harnessing the knowledge from developmental studies for regenerative medicine.

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Fig. 1: Genome-scale screens identify regulators of DE differentiation.
Fig. 2: Genetic inactivation of the JNK pathway improves DE differentiation.
Fig. 3: Pharmacological JNK inhibition improves endoderm differentiation.
Fig. 4: JUN impedes chromatin landscape remodeling during ESC-DE differentiation.
Fig. 5: JUN impedes SMAD2/3 reconfiguration during ESC-DE differentiation.
Fig. 6: Modeling the effect of JNK inhibition on ESC-DE transition.

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Data availability

Sequencing data are available under NCBI GEO accession number GSE109524. H3K27ac ChIP-seq data are from GSM733718. p300 ChIP-seq data are from GSM803542. Codes used in this study are available from the corresponding authors on request.

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Acknowledgements

The CV iPSC line was a gift from L. Goldstein. We thank Z. Zhu, Z.-D. Shi, I. Caspi and T. Leonardo for technical assistance; S. Mehta for assistance with CRISPR screen HiSeq; Y. Zou for assistance with ChIP-seq analysis; A.-K. Hadjantonakis and S. Tao for assisting with additional experiments not included in the manuscript; and K.V. Anderson, L. Studer, W. Guo, L. Dow and members of D.H.’s laboratory for insightful discussions. This study was supported in part by New York State Stem Cell Science (NYSTEM; grant nos. C029156 and C32593GG); NIH (grant no. R01DK096239); the MSKCC Cancer Center Support Grant (grant no. P30CA008748); NIH grant no. U01HG009380 (to M.A.B.); NIH grant no. R01HG007348 (to M.A.B.); an NIH T32 Training Grant in Developmental and Stem Cell Biology (grant no. T32HD060600 to G.D.); an NIH T32 Training Grant in Molecular and Cellular Biology (grant no. T32GM008539 to B.P.R.); a Howard Hughes Medical Institute Medical Research Fellowship (to N.V.); and a postdoctoral fellowship from a NYSTEM grant (no. C026879 to Q.W.) to the Center for Stem Cell Biology of the Sloan Kettering Institute and the National Natural Science Foundation of China (grant no. 31771512 to Q.W.).

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Authors and Affiliations

  1. Sloan Kettering Institute, New York, NY, USA

    Qing V. Li, Gary Dixon, Nipun Verma, Bess P. Rosen, Renhe Luo, Chunlong Xu, Qiong Wang, Chew-Li Soh, Dapeng Yang, Abhijit Shukla, Qing Xiang, Matthew Witkin, Richard Koche, Joan Massagué, Ralph Garippa & Danwei Huangfu

  2. Louis V. Gerstner Jr. Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Qing V. Li & Renhe Luo

  3. Weill Graduate School of Medical Sciences at Cornell University, New York, NY, USA

    Gary Dixon & Bess P. Rosen

  4. Weill Graduate School of Medical Sciences at Cornell University/The Rockefeller University/Sloan Kettering Institute Tri-Institutional MD–PhD Program, New York, NY, USA

    Nipun Verma

  5. Department of Surgery, Weill Cornell Medical College, New York, NY, USA

    Miriam Gordillo, Miguel Crespo, Shuibing Chen & Todd Evans

  6. Department of Histo-Embryology, Genetics and Developmental Biology, Shanghai Key Laboratory of Reproductive Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Qiong Wang

  7. Applied Bioinformatics Core, Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY, USA

    Friederike Dündar & Paul Zumbo

  8. Department of Medicine and Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY, USA

    Doron Betel

  9. Department of Biomedical Engineering and McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA

    Michael A. Beer

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  1. Qing V. Li
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Contributions

Q.V.L. and D.H. designed experiments and analyzed and interpreted results. Q.V.L. performed most experiments. M.A.B. performed the mathematical modeling and computational analysis. G.D., B.P.R., R.L. and A.S. assisted with the screen and the validation experiments. N.V. performed neuroectoderm differentiation experiments. Q.X. and R.G. provided assistance related to the CRISPR library construction and sequencing. Q.W. and J.M. provided protocols, reagents and technical support on experiments related to ChIP-seq and TGF-β signaling. M.W. and R.K. assisted with ATAC-seq and ChIP-seq experiments. C.-L.S., D.Y., M.G., C.X., M.C., S.C. and T.E. tested JNKis and assisted with additional experiments and data analyses. F.D., P.Z. and D.B. performed Drop-Seq data analysis. Q.V.L., M.A.B. and D.H. wrote the manuscript. All other authors provided editorial advice.

Corresponding authors

Correspondence to Michael A. Beer or Danwei Huangfu.

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Competing interests

D.H. and Q.V.L. are listed as inventors for a patent application (publication no. WO/2018/035454) relating to this work for the application of JNKi in endoderm differentiation. J.M. is an advisor and stockholder of Scholar Rock.

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Integrated supplementary information

Supplementary Fig. 1 Generation of the HUES8 SOX17GFP/+ iCas9 line.

a, Schematic shows the iCRISPR platform used for efficient genome editing. Doxycycline inducible Cas9 cells were transduced with lentiviruses expressing gRNAs for genome editing. b, Knock-in strategy for generating the SOX17GFP/+ reporter line. The PAM sequence is labeled in purple, the gRNA sequence is labeled in green. c, Southern blotting analysis. 5′ external probe was used to detect the integration of the GFP cassette at one of the SOX17 alleles. Uncropped image from one single experiment is shown in Supplementary Fig. 10. d, Immunostaining of DE cells differentiated from the SOX17GFP/+ cell line for GFP, SOX17, and FOXA2 expression. Scale bar, 100 μm. Representative images are from 2 independent experiments. e, Representative flow cytometry dot plots of DE cells differentiated from the SOX17GFP/+ cell line co-stained for SOX17 and GFP. Representative images are from 2 independent experiments. f, Representative flow cytometry dot plots of DE cells differentiated from SOX17GFP/+ cells that were infected with the EOMES-gRNA lentivirus (at MOI 0.36) with puromycin selection. The control sample (Ctrl) represents DE cells differentiated from uninfected WT SOX17GFP/+ cells. Representative images are from 3 independent experiments. g, Karyotyping result of the HUES8 SOX17GFP/+ line. h, Flow cytometry gating strategy. The FSC-A/SSC-A gate identifies DE cells based on the size and granularity. The FSC-H/FSC-W and SSC-H/SSC-W gates identify single cells. Live-dead staining distinguishes live cells from dead cells.

Supplementary Fig. 2 Genome-scale screen results.

a, Method of calculating the Z-score of each gRNA from raw read counts. b, Method of calculating and ranking the Z-score of each hit gene. c, A scatter plot of the gRNA distribution from Brunello screen. y axis: Z-score of log2 fold change of SOX17– / SOX17+. x axis: the mean abundance of gRNA reads in the SOX17– and SOX17+ populations. Each grey dot represents an individual targeting gRNA. Each black dot represents a non-targeting control gRNA (1,000 total). Selected positive and negative regulator hits are labeled in green and red, respectively. d, Theoretical FDR calculated for GeCKO screen based on MAGeCK v.0.5.7. For each theoretical FDR, the replicated fraction (frepl), is the fraction of genes below that FDR which are also below that FDR in the Brunello screen. Experimental FDR is 1-frepl.

Supplementary Fig. 3 Validation of gene hits identified from the genome-scale screens.

a, Histograms showing SOX17 expression from flow cytometry analysis. The black lines represent results from control cells infected with non-targeting gRNA lentiviruses. The red lines represent results from cells infected with targeting gRNA lentiviruses. Each plot shows a representative result from 2 non-targeting gRNAs and 2 different targeting gRNAs. n = 2 independent experiments. b, Flow cytometry quantification of differentiation efficiency based on the percentage SOX17+ DE cells in H1 and HUES8 SOX17GFP/+ reporter line after gRNA targeting (2 gRNAs/gene). Statistical analysis was performed by unpaired two-tailed Student’s t test. P < 0.05 for one of the MLL2 gRNAs (indicated by one asterisk), while results from all other gRNAs were not significantly (P ≥ 0.05) different from the control non-targeting gRNAs. n = 2 independent experiments. Error bars indicate SD. c, RNA-seq gene expression profiles of 24 verified screen hits in log2 (fold change DE/ESC). Positive regulators are labeled in green and negative regulators are labeled in red. Dashed lines indicate the 2-fold change.

Supplementary Fig. 4 Phenotypes of MKK7 and JUN KO hESC lines.

a, CRISPR/Cas9 gene targeting strategy used to generate MKK7 and JUN KO lines. Two homozygous KO cells were picked for further analysis. The blue bars indicate the exons of the gene. JUN is an intronless gene. CRISPR target and PAM sequences are shown in green and purple, respectively. b, Growth curve of WT, MKK7 KO and JUN KO cells cultured in the self-renewing condition (ESC) or DE differentiation condition. For the ESC condition, cell numbers are normalized to D0 (1 day after splitting). For the DE condition, cell numbers are normalized to D0 (1 day after splitting and the day when DE differentiation was initiated). n = 3 independent experiments. c, Flow cytometry quantification of the proliferation marker phospho-histone H3 and the apoptosis marker cleaved caspase-3 from WT, MKK7 KO and JUN KO DE D3 cells. n = 3 independent experiments. d, Immunostaining of ESC markers OCT4, NANOG and SOX2 in WT, MKK7 KO and JUN KO ESCs. Scale bar, 100 μm. e, Immunostaining of ESC markers OCT4, NANOG and SOX2 in WT, MKK7 KO and JUN KO ESCs. Scale bar, 100 μm. Representative images in (d) and (e) are from 2 independent experiments. f, RT-qPCR analysis of DE marker genes in undifferentiated WT, MKK7 KO and JUN KO ESCs. The relative expression level is normalized to the housekeeping gene GAPDH. n = 3 independent experiments. g, RT-qPCR analysis of DE (SOX17, FOXA2) and ESC (OCT4, NANOG, SOX2) marker genes in WT, MKK7 KO and JUN KO DE cells. The relative expression levels were normalized to the housekeeping gene GAPDH. n = 3 independent experiments. Error bars indicate SD. Statistical analysis was performed by unpaired two-tailed Student’s t test. Exact P values are shown. NS, not significant (P ≥ 0.05).

Supplementary Fig. 5 Neuroectoderm (NE) differentiation of MKK7 and JUN KO hESC lines.

a, NE differentiation schematic. The day when NE differentiation was initiated is designated as D0. Cells were examined on D4, 6, 8 and 10 of NE differentiation. b, Immunostaining of PAX6, SOX1 and OCT4 on D10 of NE differentiation from WT, MKK7 KO and JUN KO cell lines. Images are from one experiment. c, Representative flow cytometry dot plots of D10 NE cells stained for PAX6 (WT, MKK7 KO and JUN KO cell lines). d, Flow cytometry quantification of differentiation efficiency based on the percentage of PAX6+ NE cells from D4, D6, D8, D10 (WT, MKK7 KO and JUN KO cell lines). n = 3 independent experiments (D6-10), n = 2 independent experiments (D4). Error bars indicate SD. Statistical analysis was performed by unpaired two-tailed Student’s t test. NS, not significant (P ≥ 0.05).

Supplementary Fig. 6 Transcriptional profiling of JNKi-treated DE cells.

a, Gene expression analysis by RT-qPCR of DE marker genes in DE cells differentiated from control and JNKi treated condition, n = 3 independent experiments. b, Flow cytometry quantification of differentiation efficiency based on the percentage of CXCR4+SOX17+ DE D3 cells from untreated control and JNKi treated condition, including HUES8 SOX17GFP/+ line, HUES6 hESC line, CV hiPSC, and BJ hiPSC lines. n = 3 independent experiments. Error bars indicate SD. Statistical analysis was performed by unpaired two-tailed Student’s t test. Exact P values are shown. c, Representative flow cytometry dot plots of DE D3 cells co-stained for SOX17 and CXCR4 from untreated control and JNKi treated condition, including HUES8 SOX17GFP/+ line, HUES6 hESC line, CV hiPSC, and BJ hiPSC lines. d, Unsupervised hierarchical clustering of the expression values from individual JNKi treated and control DE cells for the 1,000 most variably expressed genes. Each vertical line represents the gene expression profile for each single cell (orange bars label control DE cells; blue bars label JNKi treated DE cells). The ESC and DE signature genes that were shown in Fig. 3d are indicated by the dark grey (DE) and pink (ESC) bars. The color gradient of the heatmap reflects the row-normalized z-score of expression where dark blue indicates the lowest expression value for a particular gene. The gaps in the heatmap serve as a visual separator of the top two branches of the dendrograms for the clustering of both cells and genes, respectively. e, Expression patterns of selected DE genes in individual cells following DE differentiation. Every dot represents a single cell. Their positions are determined by the expression levels for the genes indicated on the x and y axes. The color gradient reflects OCT4 expression.

Supplementary Fig. 7 JNKi treatment improves DE and subsequent endoderm lineage differentiation.

a, Schematic of DE differentiation for endoderm derivative differentiation. JNKi was only added during the first day of differentiation. b, Representative flow cytometry dot plots of DE D3 cells stained for CXCR4 and SOX17-GFP (FITC-gating) from untreated control and JNKi (1 day) treated condition, HUES8 SOX17GFP/+ line differentiated from high (100 ng/ml) and low (20 ng/ml) Activin A. c, Flow cytometry quantification of differentiation efficiency based on the percentage of CXCR4+SOX17-GFP+ DE D3 cells from (b). n = 3 independent experiments. d, Representative Flow cytometry dot plots of DE D3 cells stained for CXCR4 and SOX17-GFP from untreated control and JNKi (1 day) treated condition, including HUES8 (non-reporter), H1, HUES6 hESC lines, CV hiPSC, and BJ hiPSC lines. Quantification is shown in Fig. 3g. e-f. RT-qPCR analysis of pancreatic progenitor (e) and lung progenitor (f) markers gene expression. The relative expression levels were normalized to the housekeeping gene GAPDH, and further normalized to the expression level in hESC. n = 3 independent experiments. Error bars indicate SD. Statistical analysis was performed by unpaired two-tailed Student’s t test. Exact P values are shown.

Supplementary Fig. 8 The effects of JNK inhibition on chromatin accessibility and transcription factors binding in ESC-DE lineage transition.

a, Motif analysis at regions of differential SMAD2/3 ChIP-seq binding signals in ESCs and DE cells. Beeswarm plot shows change in SMAD2/3 binding signal ESC vs DE, motifs and gkm-SVM are trained on genomic intervals in the tails of this distribution. b, Heatmaps of TF binding intensities at differentially bound SMAD2/3 sites in ESC and DE D3 stage. c, The Venn diagram shows overlapping OCT4 and JUN binding sites at the ESC stage using the standard peak calling criteria (q = 0.05) (P < 3.89 × 10−224) for OCT4. d, The boxplots show the average ATAC-seq, SMAD2/3, OCT4, H3K27ac (GSM733718), and p300 (GSM803542) ChIP-seq signals over OCT4+JUN+ and OCT4+JUN- regions at the ESC stage. On these and following boxplots, boxes show interquartile range, whiskers show fixed multiples of interquartile range, and solid line shows median. e, Average ChIP-seq signals of OCT4, NANOG, SMAD2/3 and JUN at regions of OCT4+JUNhigh ESC enhancers and OCT4+JUNlow ESC enhancers. f, Boxplots show the normalized read counts of ATAC-seq and SMAD2/3 ChIP-seq of DE D1 Ctrl and JNKi condition in OCT4+SMAD2/3+ ESC sites, GATA6+SMAD2/3+ DE sites (left 2 panels) and in the top SMAD2/3+ESC, top SMAD2/3+ DE sites (right 2 panels). g, Western blotting analysis of protein expression at 15 minutes, 1 hour, 4 hours and 1 day after initiating DE differentiation showed that Activin A treatment induces C-terminal SMAD2 phosphorylation (at Ser465/467) as expected, an effect blocked by SB431542, a selective inhibitor of ACVR1B/ALK4, TGFBR1/ALK5 and ACVR1C/ALK7. JNKi treatment inhibited JUN phosphorylation, but did not change the level of C-terminal SMAD2 phosphorylation (S465/467) and linker SMAD2 phosphorylation (S245/250/255). Conversely SB431542 treatment also did not affect the level of JUN phosphorylation. Uncropped images are shown in Supplementary Fig. 11. Representative images are from two independent experiments. h, Average ATAC-seq signals at regions of decreased (red) or increased (blue) SMAD2/3 binding upon JNKi treatment at DE D1. i, SMAD2/3 binding intensity (number of reads per 300 bp genomic interval) at ESC and DE associated gene enhancers based on SMAD2/3 ChIP-seq analysis of DE D1 control and DE D1 JNKi. j, Venn diagram of GATA6 and SMAD2/3 unique and common binding sites in DE D1 Ctrl and DE D1 JNKi. k, ChIP-seq peaks of increased JUN binding in ESC relative to DE D1 are preferentially located near the top 200 ESC-expressed genes (blue) and farther from the top 200 DE-expressed genes (red) compared to random gene sets (grey). 142 are located within 100 kb of the transcription start site (TSS) of one of the 200 genes with the greatest fold decrease in expression during DE differentiation, which is a significant enrichment compared to the expected overlap of 107.6 ± 11.3 binding sites by chance from sampling ten random sets of 200 genes (P < 0.0012). Error bars indicate SD.

Supplementary Fig. 9 JNK inhibition lowers the threshold of Activin A doses for efficient endoderm differentiation.

a, Representative flow cytometry dot plots of DE D3 cells stained for CXCR4 and SOX17, GATA4 and GATA6 from untreated control and JNKi treated condition (H1 hESC line, 20 ng/ml Activin A). b, Flow cytometry quantification of differentiation efficiency based on the percentage of CXCR4+SOX17+, GATA6+GATA4+ DE D3 cells from (a). n = 3 independent experiments. c, Representative flow cytometry dot plots of DE D3 cells stained for CXCR4 and SOX17, GATA6 and GATA4 (H1 hESC line, WT vs MKK7 and JUN KO, treated with 20 ng/ml Activin A). d, Flow cytometry quantification of differentiation efficiency based on the percentage of CXCR4+SOX17+, GATA6+GATA4+ DE D3 cells from (c). n = 3 independent experiments. Error bars indicate SD. Statistical analysis was performed by unpaired two-tailed Student’s t test. Exact P values are shown. e, Immunostaining of DE markers SOX17 and FOXA2 in WT, MKK7 KO and JUN KO DE cells differentiated from 20 ng/ml Activin A condition. Scale bar, 100 μm. Representative images are from two independent experiments.

Supplementary Fig. 10 Uncropped images for Fig. 2c and Supplementary Fig. 1c.

Protein ladder: Precision Plus Protein Dual Color Standards from Bio-Rad #1610374. DNA ladder: 1 kb Plus DNA Ladder from New England Biolabs # N3232. Boxed areas with red dashed borders were shown in the corresponding figures.

Supplementary Fig. 11 Uncropped images for Fig. 3b and Supplementary Fig. 8f.

Protein ladder: Precision Plus Protein Dual Color Standards from Bio-Rad #1610374. Boxed areas with red dashed borders were shown in the corresponding figures.

Supplementary information

Supplementary Information

Supplementary Figs. 1–11, Supplementary Notes 1 and 2, Supplementary Tables 1–5

Reporting Summary

Supplementary Data Set 1

Comprehensive analyses of GeCKO and Brunello screens by MAGeCK and Z-score methods, related to Fig. 1d–f and Supplementary Fig. 2

Supplementary Data Set 2

RNA-seq analysis of differentially expressed genes in DE and ESC, related to Supplementary Fig. 3c

Supplementary Data Set 3

RNA-seq analysis of MKK7 KO and WT cells (DE stage and ESC stage), related to Fig. 2f

Supplementary Data Set 4

Summary of motif analysis, related to Figs. 4a, 5f and Supplementary Fig. 8a

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Li, Q.V., Dixon, G., Verma, N. et al. Genome-scale screens identify JNK–JUN signaling as a barrier for pluripotency exit and endoderm differentiation. Nat Genet 51, 999–1010 (2019). https://doi.org/10.1038/s41588-019-0408-9

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