Fig 2: Human adhesion fibroblasts are heterogeneous and JUN dependent.a Uniform manifold approximation and projection (UMAP) plots showing single-cell (sc)RNA-seq data from human adhesion fibroblasts FACS-isolated using an unbiased, lineage-negative sort strategy from three unique human specimens. Four unique clusters are identified. b UMAP plot showing representation of individual patient samples (arbitrarily numbered) across the cluster presented in 1. Human hashtag labels indicated at right, 2 hashtag antibodies were used for patient 33 as an internal control, as noted in the figure panel legend. c Pseudotime analysis of human abdominal adhesion fibroblast scRNA-seq data. Colors match cluster colors assigned in panel a. d Quantitation of qPCR analysis for JUN, STAT5, STAT3, and SPP1 of vehicle control versus JUN CRISPR Cas9-knockdown human abdominal adhesions fibroblasts. P-values noted in figure. n = 3 replicates per condition, datapoints represent average of technical replicates. e Quantitation of Ki67 expression using ICC of primary human abdominal adhesions fibroblasts treated with CRISPR Cas9 JUN knockdown compared with vehicle control. n = 4 biological replicates assessed per condition. f Quantitation of collagen type 1 expression using ICC of primary human abdominal adhesions fibroblasts treated with virally mediated JUN overexpression, vehicle control, or CRISPR Cas9 JUN knockdown. n = 3 biological replicates assessed per condition. g Schematic (based on published KEGG pathways) displays proposed JUN-relevant signaling pathways identified in this study. The left panel shows the acute phase response following tissue injury by which JUN is initially activated. The right panel shows the chronic profibrotic state that is established in adhesion fibroblasts. Blue circle highlights AP-1, red indicates the role of JUN inhibitor. Colors as labelled in the figure. ECM extracellular matrix. Data and error bars represent means ± SD. HPF high power field. A.U. arbitrary units. *P = 0.04, **P = 0.03, ***P = 0.03, unpaired two-tailed t-test. Source data are provided as a Source Data file.

Fig 3: STAT5 activation enhances IESC niche differentiation by regulating the IESC transcriptome.(A) Hierarchical clustering of 691 genes with ANOVA P < 0.05 and fold change > 2 in at least one pairwise comparison between STAT5+/+, STAT5ΔIEC−/−, and STAT5ΔIEC+++ mice, using Pearson’s centered distance metric and average linkage rule. (B) Scatterplots of log (FPKM) of differentially regulated genes in STAT5ΔIEC−/− and STAT5ΔIEC+++ mice when compared to STAT5+/++ mice. (C) β-catenin was determined by IH and immunoblotting with NE from intestinal crypts. Results are expressed as mean ± SEM, n ≥ 3 mice per groups, and t tests were used to compare the significant difference. (D) Sox9 was immunostained and quantitated in the 200 crypts. Results are expressed as mean ± SEM, n ≥ 3 mice per groups, and t tests were used to compare the significant difference. (E) icS5-ER-transduced HIOs were in vitro matured for 35 d. icS5 activation in HIOs was induced with different doses of 4HT (0, 10, 50, 100, or 200 nM or 1 mM) for 72 h. pYSTAT5, STAT5A, and β-catenin were determined by immunoblotting. This experiment was repeated three times. (F) Hypothesis model: pYSTAT5 amplifies Lgr5hi →to Lgr5LowKi67+IESCs; Ca-pYSTAT5 promotes Lgr5lowKi67+ IESCs to give rise to a sublineage of crypt cells, intestinal pYSTAT5+Lgr5−CD24+Lyso+ Paneth cells by activating Wnt/β-catenin/Sox9 pathway.

Fig 4: STAT5 activation regulates Wnt-β-Catenin-Sox9 signaling axis to sustain niche differentiation.(A) Venn diagram to identify differentially regulated the genes shared between and specific to STAT5ΔIEC−/− and STAT5ΔIEC+++ as compared to STAT5+/+. (B, C) Barchart of significance of pathways and biological processes enriched in genes significantly up- and down-regulated in STAT5ΔIEC+++ and STAT5ΔIEC−/− as compared to STAT5+/+. Ontologies were identified through ToppGene.cchmc.org. (D) Diagram of ontological enrichments associated with gene lists built through comparisons between STAT5ΔIEC−/− and STAT5ΔIEC+++, when compared to STAT5+/+, showing shared and specific ontologies connected to up- and down-regulated genes, generated by Toppcluster.cchmc.org and cytoscape. (E) Hierarchical clustering of averaged normalized expression values of 13 genes in STAT5ΔIEC−/− and STAT5ΔIEC+++ mice compared to STAT5+/+ mice identified significantly enriched canonical Paneth cell genes regulated by STAT5 protein. (F) Representative images of colonic crypts used for RNA-seq.

Fig 5: SKLB-677 inhibited FLT3 activation and induced cell cycle arrest and apoptosis.(a) MV4-11 cells were treated with various concentrations of SKLB-677 or 0.1% DMSO for 1 h. The phosphorylation levels of FLT3 and downstream signal proteins were assessed using antibodies against total FLT3 (FLT3), phosphorylated Y589/591 FLT3 (p-FLT3), total STAT5 (STAT5), phosphorylated Y694 STAT5 (p-STAT5), total p44/42 MAPK (Erk1/2), and phosphorylated Y202/204 p44/42 MAPK (p-Erk1/2). β-actin was used as a loading control. (b) Primary PBMCs isolated from AML patients (FLT3-ITD positive) were treated with various concentrations of SKLB-677 or 0.1% DMSO for 6 h. The phosphorylation levels of FLT3 and downstream signal proteins were detected used the same method as that in panel a. (c) MV4-11 cells were treated with various concentrations of SKLB-677 or 0.1% DMSO for 24 h. Cell cycle analysis was performed using propidium iodide staining by flow cytometry. Data shown in the histogram are means ± SD from three independent experiments. (d) MV4-11 cells were cultured in the presence of SKLB-677 or 0.1% DMSO, followed by the detection of apoptosis by using Annexin-V and PI co-staining. The early and late apoptotic cells are shown in the right lower and right upper quadrant, respectively. The assays were performed in triplicate, and the data in the histogram represent average percentage of AnnexinV-positive cells ± SD. ns, not statistically significant, ***P < 0.005 vs. vehicle.