β-catenin mediates endodermal commitment of human ES cells via distinct transactivation functions

Background β-catenin, acting as the core effector of canonical Wnt signaling pathway, plays a pivotal role in controlling lineage commitment and the formation of definitive endoderm (DE) during early embryonic development. Despite extensive studies using various animal and cell models, the β-catenin-centered regulatory mechanisms underlying DE formation remain incompletely understood, partly due to the rapid and complex cell fate transitions during early differentiation. Results In this study, we generated new CTNNB1-/- human ES cells (hESCs) using CRISPR-based insertional gene disruption approach and systematically rescued the DE defect in these cells by introducing various truncated or mutant forms of β-catenin. Our analysis showed that a truncated β-catenin lacking both N- and C-terminal domains (ΔN148C) could robustly rescue the DE formation, whereas hyperactive β-catenin mutants with S33Y mutation or N-terminal deletion (ΔN90) had limited ability to induce DE lineage. Notably, the ΔN148C mutant exhibited significant nuclear translocation that was positively correlated with successful DE rescue. Transcriptomic analysis further uncovered that two weak β-catenin mutants lacking the C-terminal transactivation domain (CTD) activated primitive streak (PS) genes, whereas the hyperactive β-catenin mutants activated mesoderm genes. Conclusion Our study uncovered an unconventional regulatory function of β-catenin through weak transactivation, indicating that the levels of β-catenin activity determine the lineage bifurcation from mesendoderm into endoderm and mesoderm. Supplementary Information The online version contains supplementary material available at 10.1186/s13578-024-01279-5.


qRT-PCR
Total RNA from cell samples was extracted using TRIzol reagent (Lige Technologies).500-1000 ng RNA was reverse transcribed into cDNA using High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems).Quantitative real-time PCR (qRT-PCR) was performed using Power SYBR Green PCR Master Mix (Applied Biosystems) in QuantStudio™ 7 Flex Real-Time PCR system (Applied Biosystems).The primers used for qRT-PCR in this study were listed in Supplementary Table 1.

Flow cytometry
Fluorescence-activated cell sorting (FACS) analysis and cell sorting were performed using BD FACSAria™ II system (BD Biosciences), based on GFP or TdTomato expression.Undifferentiated hESCs (wt H1, CTNNB1-/-cells, or rescue clones) were dissociated into single cells using TrypLE (Gibco).The cells were then resuspended in fresh mTeSR1 medium supplemented with 10 µM Y-

Western blot
Cells were collected and washed with cold PBS, followed by lysis in cold lysis buffer containing 20mM Tris, 137mM NaCl, 1% Triton X-100, 5 mM EDTA, and Protease Inhibitor Cocktail (Roche) on ice for 20 min.After centrifugation at 4°C for 15 min, protein concentration in supernatants were determined using Pierce™ BCA Protein Assay Reagent (Thermo Scientific™).10μg protein from each sample was resolved by SDS/PAGE and subsequently transferred to polyvinylidene difluoride (PVDF) membranes (Bio-Rad).The PVDF membranes were blocked with 5% non-fat dry milk in TBST buffer for 1 hr at room temperature and then incubated with primary antibodies for overnight.In next day, the membranes were washed three times with TBST buffer and incubated with HRP-conjugated secondary antibodies at room temperature for 2 -4 hours.Luminescence signals were detected using Amersham ECL select western blotting detection kit (GE Health Care Life Sciences) and exposed to Super RX-N film (Fuji).The antibodies used were anti-E-Cadherin (Cell Signaling Technology, 3195S), anti-β-catenin (Santa Cruz, sc-7963), anti-α-catenin (ENZO, ALX-804-101-C100), anti-JUP (BD Biosciences, 610254), anti-Flag (sigma, F1804), anti-β-actin (Santa Cruz, sc-47778), and HRP-conjugated anti-mouse (Cell Signaling Technology, 7076) and HRP-conjugated anti-rabbit (Cell Signaling Technology, 7074).

RNA-seq & Bioinformatics analysis
Total RNA was extracted from wt hESC (H1), CTNNB1-/-KO#7 and various rescue clones after Dox treatment for 24 hrs or DE induction for one day (Supplementary Table 2) using TRIzol reagent (Thermo Fisher Scientific) and sent to Beijing Genomics Institute (BGI) for RNA sequencing.Fragment Analyzer and Standard Sensitivity RNA Analysis Kit (15 nt) (DNF-471) were used to verify the RNA quality (RIN/RQN > 8.0, 28S/18S > 2.2) before the construction of cDNA library.RNA sequencing was then performed using DNBSEQ platform (https://www.bgi.com/us/dnbseq-ngstechnology/).SOAPnuke software developed by BGI (Version v1.5.2, https://github.com/BGIflexlab/SOAPnuke)(2, 3) was used for data filtering to remove the reads containing adaptors or with "N" greater than 5%.Hierarchical Indexing for Spliced Alignment of Transcripts (HISTAT) is used for mapping RNA-seq reads to human genome GRCh38.p12(4).Bowtie2 (5) was used to map the clean reads to the reference gene sequence (transcriptome), and RSEM (6) was used to calculate the gene expression levels in each sample.The Dr. Tom online platform (https://www.bgi.com/global/dr-tom/)developed by BGI and R package was used for bioinformatics analysis and visualization.The normalized data of filtered genes in all 18 samples, presented with FPKM (Fragments Per Kilobase Million), were used for principal component analysis (PCA) with the 'ggplot2' package in R. To control the data quality, the genes showing FPKM value lower than 5 in all samples were excluded before further analysis.P-value was corrected by multiple hypothesis test and determined the domain value by controlling the FDR (False Discovery Rate).Differentially expressed genes (DEGs), with an FDR ≤ 0.001 and fold change ≥ 2, were determined using R package DEGseq based on the FPKM values.The R package heatmap was used for hierarchical clustering analysis on the union set differential genes.The R package corrplot was used for sample correlation analysis.Venn diagrams were generated via VENN/UpSetR.The DEGs from selected samples were further examined by gene ontology (GO) enrichment and KEGG pathway enrichment.The Gene Set Enrichment Analysis (GSEA) was performed using Molecular Signatures Database v6.2 (MSigDB).For Filtering threshold, we set the Max size (the maximum number of genes included in a pathway) as 500 and the Min size (the minimum number of genes included in a pathway) as 15.The differentiation outcomes observed were defined as DE high and DE low to represent the biological phenotypes.The CTNNB1-/-KO#7 sample was defined as non-DE control in this analysis.In Fig. 5B and Supplementary Fig. S10, we compared the transcriptomes of individual DE high or DE low clones to KO#7.

Statistical analysis
All statistical analyses were performed using Excel or GraphPad Prism v9 software.All data (luciferase activity, mRNA expression levels, etc.) were reported as mean ± standard deviation (SD).Student's t-test were used to compare data from two groups.p < 0.05 was considered to be statistically significant.*, p≤0.05; **, p≤0.01; ***, p≤0.001.