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Gibbin mesodermal regulation patterns epithelial development

Abstract

Proper ectodermal patterning during human development requires previously identified transcription factors such as GATA3 and p63, as well as positional signalling from regional mesoderm1,2,3,4,5,6. However, the mechanism by which ectoderm and mesoderm factors act to stably pattern gene expression and lineage commitment remains unclear. Here we identify the protein Gibbin, encoded by the Xia–Gibbs AT-hook DNA-binding-motif-containing 1 (AHDC1) disease gene7,8,9, as a key regulator of early epithelial morphogenesis. We find that enhancer- or promoter-bound Gibbin interacts with dozens of sequence-specific zinc-finger transcription factors and methyl-CpG-binding proteins to regulate the expression of mesoderm genes. The loss of Gibbin causes an increase in DNA methylation at GATA3-dependent mesodermal genes, resulting in a loss of signalling between developing dermal and epidermal cell types. Notably, Gibbin-mutant human embryonic stem-cell-derived skin organoids lack dermal maturation, resulting in p63-expressing basal cells that possess defective keratinocyte stratification. In vivo chimeric CRISPR mouse mutants reveal a spectrum of Gibbin-dependent developmental patterning defects affecting craniofacial structure, abdominal wall closure and epidermal stratification that mirror patient phenotypes. Our results indicate that the patterning phenotypes seen in Xia–Gibbs and related syndromes derive from abnormal mesoderm maturation as a result of gene-specific DNA methylation decisions.

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Fig. 1: Gibbin regulates mesoderm gene expression during development.
Fig. 2: Gibbin interacts with zinc-finger transcription factors and DNA methylation regulators.
Fig. 3: Loss of Gibbin causes DNA hypermethylation and decreased CTCF deposition.
Fig. 4: Gibbin regulates mesoderm maturation to pattern skin development.
Fig. 5: Loss of Gibbin in vivo causes global development defects.

Data availability

Deep-sequencing and array data generated in this paper have been deposited at the Gene Expression Omnibus (GEO: GSE180495). Previously published datasets analysed in this study are available at the GEO (GSE114846) or through ENCODE (https://www.encodeproject.org/experiments/ENCSR168AUX/). Raw data from BASU experiments are provided in Supplementary Table 1. Uncropped immunoblots and FACS gating controls are provided in Supplementary Figs. 1 and 2. The human reference genome hg38 was downloaded from the UCSC genome browser. The GRCh38 reference genome for single-cell mapping can be downloaded from 10x genomics (https://support.10xgenomics.com/single-cell-gene-expression/software/downloads/latest). There are no restrictions on data availability.

Code availability

The manifest and annotation files for methylation analysis are available at GitHub (https://github.com/achilleasNP/IlluminaHumanMethylationEPICanno.ilm10b5.hg38). Previously published custom code used in this study is available at GitHub (https://github.com/OroLabStanford).

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Acknowledgements

We thank the members of the Oro laboratory for comments on this manuscript; S. Yamanaka for sharing the PiggyBac inducible expression plasmid and P. Khavari for sharing the BASU cDNA. This work was supported by R. L. Kirchstein NRSA (F32AR074221 to A.C.), the Stanford Dean’s Fellowship (to A.C.), the Stanford Maternal and Childhood Health Research Institute (to A.C.), NIH (R01ARO73170 to A.O.) and the California Institute of Regenerative Medicine (RT3-07796 to A.O.). Mass spectrometry was performed at the Vincent Coates Foundation Mass Spectrometry Laboratory, Stanford University Mass Spectrometry and supported in part by NIH P30 CA124435 using the Stanford Cancer Institute Proteomics/Mass Spectrometry Shared Resource. FACS analysis was performed at the Stanford Shared FACS Facility using an instrument obtained with an NIH S10 Shared Instrument Grant (S10RR025518-01).

Author information

Authors and Affiliations

Authors

Contributions

A.C. and A.O. conceptualized the study and prepared the manuscript. A.C. generated all GKO and GATA3-knockout lines, performed single-cell and bulk RNA-seq, cohesin HiChIP, ATAC-seq, BASU, FACS, IF staining, immunoblotting, qPCR and methylation arrays. A.L. performed Gibbin ChIP–seq and FACS. J.T. assisted with the creation of BASU lines and performed keratinocyte sorting with K.M. J.P. generated the AP2a knockout line and performed RNA-seq. H.Z. and T.P. performed mouse grafting and assisted with embryo extraction and phenotype assessment. H.Z. performed 3D organoid and imaging experiments. H.G. performed DNA methylation colorimetric and enzyme assays. A.C. performed all bioinformatic analyses with assistance from S.G. and A.L.

Corresponding author

Correspondence to Anthony E. Oro.

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The authors declare no competing interests.

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Nature thanks Ya-Chieh Hsu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Gibbin regulates mesoderm gene expression in response to RA/BMP4.

(a) Genotyping evidence from two different GKO hES cell clones. (b) Gene expression comparing WT to each GKO clone. Clone 2 was used for the remainder of the study. Error bars are mean +/− s.d., n = 4 biologically independent samples averaged from 2 technical replicates repeated across 2 different runs. * Indicates p-value < 0.05 calculated by two-tailed t-test. (c) Proportions of Gibbin-regulated genes that are also altered by RA/BMP4. (d) Representative RPKM values from RNA-seq differential expression after 7 days RA/BMP4. Error bars are mean +/− s.d., n = 2 biologically independent samples, adjusted p-values were measured by DESeq2. (e) Expression patterns of ectoderm (purple, red) or mesoderm (blue) transcripts after RA/BMP4 treatment. (f) UMAP of PDGFRA or (g) EPCAM expression at day 7, marking the mesoderm or ectoderm clusters, respectively. (h) Representative expression plots and adjusted p-values from differential expression testing of scRNA-seq subsets as measured by DESeq2. (i) UMAP of hESCs after 3 days of RA/BMP4. (j) Integrated UMAP and (k) cluster quantification comparing WT and GKO after 3 days of RA/BMP4.

Extended Data Fig. 2 Gibbin ChIP–seq QC.

(a) Western blot of doxycycline inducible HA-Gibbin used for ChIP–seq. Immunoblotting was repeated experimentally twice and GAPDH was run on the same gel as a loading control. For gel source data, see Supplementary Figure 1. (b) Gibbin ChIP–seq binding profile in hESCs or after 7 days of RA/BMP4 at Gibbin binding sites or (c) at all gene transcription start sites. n = 2 biologically independent samples. (d) Representative Gibbin ChIP–seq tracks at example target genes. (e) Differential expression analysis from untreated hESC RNA-seq between WT and GKO cells. Gene expression is either unchanged (grey), increased (blue, >2-fold), or decreased (red, <2-fold), with an adjusted p-value cutoff < 0.05 as measured by DESeq2. n = 2 biologically independent samples. (f) Number of Gibbin-regulated genes with a nearby Gibbin ChIP–seq peak and number of mesoderm, ectoderm, or neuroectoderm genes (identified by scRNA-seq) that are bound by Gibbin. (g) Expression of Gibbin-bound genes across scRNA-seq clusters. (h) ChromHMM analysis of Gibbin binding sites in HEPG2 cells (from ENCODE26), showing enrichment of various chromatin states in relation to Gibbin ChIP–seq peaks. (i) Numbers of peaks and (j) overlap of genes identified in our Gibbin ChIP–seq dataset or the ENCODE HEPG2 dataset. (k) GO term enrichment for direct, Gibbin-bound target genes as measured using a two-sided Fisher’s Exact Test (via EnrichR).

Extended Data Fig. 3 Gibbin and GATA3 are co-expressed.

(a) Principal component analysis (PCA) of RNA-seq from WT, GKO, TFAP2A KO, GATA3 KO, and p63 KO cells after 7 days of RA/BMP4 treatment. n = 2 biologically independent samples. (b) Violin plot of AHDC1/Gibbin or (b) GATA3 expression in each scRNA-seq cluster after 7 days of RA/BMP4. (c) Percent of cells after 7 days of RA/BMP4 which express AHDC1/Gibbin and/or GATA3. (d) qPCR of transcripts induced by GATA3 overexpression in WT or GKO backgrounds. Error bars are mean +/− s.d., n = 3 biologically independent samples averaged from 2 technical replicates. * indicates p-value < 0.05 by two-tailed Student’s t-test. (e) ChromHMM analysis of GATA3 binding sites showing enrichment of various chromatin states in relation to GATA3 ChIP–seq peaks. (f) Average distances between Gibbin and GATA3 ChIP–seq peaks.

Extended Data Fig. 4 Gibbin does not alter chromatin accessibility or GATA3 DNA binding.

(a) ATAC-seq signal heatmaps in WT or GKO cells at all gene transcription start sites, or those subset by mesoderm, ectoderm, or neuroectoderm. n = 2 biologically independent samples. (b) ATAC-seq differential expression analysis comparing WT to Gibbin or (c) GATA3 KO cells after 7 days of RA/BMP4. Differential chromatin accessibility is either unchanged (grey), increased (blue, >2-fold), or decreased (red, <2-fold), with an adjusted p-value cutoff <0.05 measured by DESeq2. (d) Chromatin accessibility at GATA3 binding sites in WT or GKO cells. (e) RPKM values for GATA3 or AHDC1/Gibbin in reciprocal mutant cell lines. Error bars are mean +/− s.d., n = 2 biologically independent samples, adjusted p-value <0.05 as measured by DESeq2. (f) Known motif enrichment at GATA3 ChIP–seq peaks in WT and GKO cells, calculated by Homer. (g) GATA3 ChIP–seq differential binding analysis comparing WT and GKO cells. GATA3 DNA binding is either unchanged (grey), increased (blue, >2-fold), or decreased (red, <2-fold), with an adjusted p-value cutoff <0.05 as measured by DESeq2.

Extended Data Fig. 5 Gibbin regulates gene expression by maintaining local enhancer–promoter chromatin architecture.

(a) Sequencing depth, PCR duplicates, and (b) valid interaction characterization of cohesin HiChIP libraries. (c) Correlation of insulation scores between WT and GKO. (d) A and B compartment scores for chromosome 1, which are representative of the entire genome. (e) Contact probability relative to contact distance for each cell type. (f) Length of contacts called by FitHiChIP in each cell type. (g) Chromatin contact matrix comparing WT and GKO cohesin HiChIP, overlaid with domains called by insulation score. (h) Contact matrix displaying the difference between GKO and WT cohesin HiChIP signal. Blue indicates greater contact in the WT, while red suggests more contact in the GKO. All matrices display chr4:110,000,000-120,000,000. (i) Overlap of contact coordinates called by FitHiChiP in either the WT and GKO. (j) Number of Gibbin-dependent chromatin contacts that are anchored in Gibbin target gene TSS’s. (k) Number of cell-type specific contacts that have one end anchored in a gene TSS (l) APA showing contact strength difference between WT and GKO cohesin Hi-ChIP at Gibbin-independent genes.

Extended Data Fig. 6 Validation of the BASU proximal proteomics system.

(a) Validation of BASU system from whole cell lysate western blots of hESCs stably expressing doxycycline-inducible, HA-tagged BASU control or Gibbin-BASU fusion proteins. The N-terminal version is shown here. Biotinylated protein was measured using dye-labelled streptavidin (Strep). Signal was only detected in the presence of both doxycycline (DOX) and biotin (BIO). (b) SAINT scores plotted against fold change over the BASU control for all proteins detected by Gibbin-BASU N-terminal or C-terminal mass spectrometry. SAINT scores > 0.9 are indicated in blue. (d) Representative immunoblot of biotinylated extracts from Gibbin-BASU and negative control cells after 24 h DOX and 2 h BIO. (d) GO term or Pfam domain enrichment of Gibbin interacting proteins as measured using a two-sided Fisher’s Exact Test (via EnrichR). (e) Overlap of N and C terminal Gibbin interacting proteins identified by BASU. (f) Expression of Gibbin interacting transcription factors across clusters identified by scRNA-seq. (g) Validation of GATA3-BASU system by western blot. (h) SAINT scores and fold change over negative BASU control for all proteins detected by GATA3-BASU mass spectrometry. (i) Overlap of Gibbin and GATA3 interacting proteins from BASU datasets. (j) Differential binding analysis from H3K9me3 ChIP–seq between WT and GKO cells. Signal is either unchanged (grey), increased (blue, >2-fold), or decreased (red, <2-fold), with an adjusted p-value cutoff < 0.05 as measured by DESeq2, n = 2 biologically independent samples. (k) Number, size and intensity measurements of HP1α foci in WT vs GKO cells, quantified from four representative immunofluorescent images for each cell type. All immunoblots were repeated experimentally twice. GAPDH was used as a processing control. For gel source data, see Supplementary Figure 1.

Extended Data Fig. 7 Gibbin loss causes DNA hypermethylation independent of DNMT expression.

(a) DNA methylation signal in WT and GKO cells as measured by a colorimetric assay. Error bars are mean +/− s.d., n = 4 biologically independent samples averaged from 2 technical replicates repeated across 2 different runs. * indicates p-value <0.001 as calculated by two-tailed t-test (p-value = 0.0003). (b) Overlap of RA/BMP4-dependent demethylation with Gibbin-dependent hypermethylation. (c) Number of Gibbin-dependent methylation sites that are demethylated during differentiation with RA/BMP4. (d) Volcano plot of differentially methylated probes (DMPs) between WT and GKO hESCs. Signal at each probe is either unchanged (grey), hypermethylated (red, >2-fold), or hypomethylated (blue, <2-fold), with an adjusted p-value cutoff <0.01 as measured by limma. n = 2 biologically independent samples. (e) ChromHMM analysis of the DMPs. (f) RPKM and adjusted p-values values for known DNMTs and effectors as measured by differential RNA-seq. Error bars are mean +/− s.d., n = 2 biologically independent samples, and adjusted p-values were calculated with DESeq2. (g) Differential binding analysis from CTCF ChIP–seq between WT and GKO cells. CTCF DNA binding is either unchanged (grey), increased (blue, >2-fold), or decreased (red, <2-fold), with an adjusted p-value cutoff <0.05 as measured by DESeq2. n = 2 biologically independent samples. (h) Overlap of genes anchored in WT chromatin contacts, GKO chromatin contacts, and sites of differential CTCF binding. (i) Overlap of genes that are bound by Gibbin and have Gibbin-dependent methylated DNA or CTCF binding sites. (j) Virtual 4C plot of normalized cohesin HiChIP signal for the normally repressed ATF3 locus, overlayed with a WashU Genome Browser screenshot depicting Gibbin, GATA3, and WT/GKO CTCF ChIP–seq peaks, DMP pileups, and FitHiChIP contacts. The ATF3 TSS was used as an anchor point in constructing the 4C plot.

Extended Data Fig. 8 Aberrant dermal maturation results in keratinocyte dysfunction.

Violin plot of (a) AHDC1/Gibbin or (b) GATA3 expression after 50 days of differentiation. (c) Monocle pseudotime trajectory of the EPCAM+/KRT5+ ectodermal and epidermal clusters from WT or (d) GKO cells. (e) RNA-seq differential expression analysis comparing WT and GKO day 50 cells. Gene expression is either unchanged (grey), increased (blue, >2-fold), or decreased (red, <2-fold), with an adjusted p-value cutoff <0.05 as measured by DESeq2. n = 2 biologically independent samples. (f) Representative RPKM and adjusted p-values from day 50 RNA-seq. Error bars are mean +/− standard deviation. (g) RNA-seq differential expression analysis between WT and GKO keratinocytes, n = 2 biologically independent samples. (h) CellChat circle plot showing the number of cell signalling interactions between each identity. (i) Number of cell–cell signalling interactions in WT and GKO day 50 cells as measured by CellChat. (j) H&E and immunofluorescent images of 3D skin organoid cultures generated from primary normal human keratinocyte (NHKs). NHKs were nucleofected with control or Gibbin targeting sgRNAs prior to stratification. Organoids were made in biological triplicate and repeated experimentally twice. All imaging was repeated in biological duplicate across 2 independent experiments.

Extended Data Fig. 9 FACS sorting strategy.

(a) Surface marker rankings identified by COMET for the EPCAM+ ectoderm or the (b) PDGFRA+ mesoderm after 7 days of RA/BMP4. Markers used for further analyses are noted in red, and markers that were tested but did not work well by FACS are noted in blue. (c) Expression patterns and fold changes in day 7 scRNA-seq of markers used for cell sorting including VTCN1 (ectoderm), (d) ABCG2 (ectoderm), and (e) PDGFRA (mesoderm). (f) Fluorescence associated cell sorting (FACS) strategy used to separate day 7 cells into ectoderm and mesoderm. Plots show a distinction between ABCG2+ or PDGFRA+ populations or (g) overlapping ABCG2+ and VTCN1+ populations. Populations were gated on unstained control cells, and the gating strategy can be found in the Supplementary Figure 2.

Extended Data Fig. 10 Pronuclear CRISPR Injections Targeting Gibbin Cause a Spectrum of Phenotypes in Mice.

(a) Quantification of three phenotype classes observed following Gibbin depletion in vivo. Mosaic genotypes exhibited CRISPR cutting on at least one allele. (b) Images of mouse embryos collected 18 days following pronuclear CRISPR injection to deplete Gibbin. Embryos were organized into four phenotypic classes. Injections were performed in duplicate across 6 different litters totaling n = 52 collected embryos. (c) Adult mice 5 weeks after receiving grafts from E18 mice. The GKO graft was taken from a more moderate mutant. n = 3 for each genotype, and grafting was repeated in two separate experiments. (d) H&E images depicting hair follicle cyst phenotypes from grafted mice. Images are representative of at least 3 biological and 3 technical replicates.

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2 and legends for Supplementary Tables 1–3.

Reporting Summary

Supplementary Table 1

A list of differentially expressed genes and adjusted P values from RNA-seq experiments, as well as a list of nearest genes from Gibbin ChIP–seq.

Supplementary Table 2

A list of SAINT scores for all Gibbin and GATA3 BASU experiments.

Supplementary Table 3

PCR primers and sgRNA sequences used in this study.

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Collier, A., Liu, A., Torkelson, J. et al. Gibbin mesodermal regulation patterns epithelial development. Nature 606, 188–196 (2022). https://doi.org/10.1038/s41586-022-04727-9

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