Hepatic kinome atlas: An in-depth identification of kinase pathways in liver fibrosis of humans and rodents

Abstract

Background and aims: Resolution of pathways that converge to induce deleterious effects in hepatic diseases, such as in the later stages, have potential antifibrotic effects that may improve outcomes. We aimed to explore whether humans and rodents display similar fibrotic signaling networks.

Approach and results: We assiduously mapped kinase pathways using 340 substrate targets, upstream bioinformatic analysis of kinase pathways, and over 2000 random sampling iterations using the PamGene PamStation kinome microarray chip technology. Using this technology, we characterized a large number of kinases with altered activity in liver fibrosis of both species. Gene expression and immunostaining analyses validated many of these kinases as bona fide signaling events. Surprisingly, the insulin receptor emerged as a considerable protein tyrosine kinase that is hyperactive in fibrotic liver disease in humans and rodents. Discoidin domain receptor tyrosine kinase, activated by collagen that increases during fibrosis, was another hyperactive protein tyrosine kinase in humans and rodents with fibrosis. The serine/threonine kinases found to be the most active in fibrosis were dystrophy type 1 protein kinase and members of the protein kinase family of kinases. We compared the fibrotic events over four models: humans with cirrhosis and three murine models with differing levels of fibrosis, including two models of fatty liver disease with emerging fibrosis. The data demonstrate a high concordance between human and rodent hepatic kinome signaling that focalizes, as shown by our network analysis of detrimental pathways.

Conclusions: Our findings establish a comprehensive kinase atlas for liver fibrosis, which identifies analogous signaling events conserved among humans and rodents.

FIGURE 1

Paralogous phylogenic relationships between differentially altered kinases in humans and rodents. (A) Human liver fibrosis compared with normal control samples. (B) mouse fibrosis compared with normal control samples. Node color and size are the mean final kinase score that corresponds to the bubble plot on the paralogous phylogenetic trees. Abbreviations: TK, tyrosine kinase; TKL, Tyrosine Kinase‐Like.

FIGURE 2

Data concordance heatmap. (A) Sample–sample correlation heatmap of analytical replicate samples from aggregate human and mouse control and fibrotic liver samples. Diagonal entries represent the percentage of non‐missing values in each analytical sample. Correlations were calculated using Information‐Content‐Informed Kendall‐tau Correlation (ICI‐Kt) (available online: https://github.com/MoseleyBioinformaticsLab/ICIKendallTau) using features that had nonzero values in at least one sample. Feature values < 30 were treated as missing. (B) Median ICI‐Kt correlations among samples within a type (diagonal) and between types (off‐diagonal). (C,D) Sirius red and trichrome blue staining (scale bar = 100 μm) in the livers from human cirrhotic and healthy control and chronic CCl₄ and vehicle control–treated mice, fibrosis score, and TGF‐B1 or Tgf‐b1 mRNA expression

FIGURE 3

Identification of tyrosine kinase families changed in fibrosis of humans and rodents. Heatmap of substrate phosphorylation levels for tyrosine kinases and waterfall plot of tyrosine family of kinases in humans (A) and mice (B) for fibrotic samples compared with their control. Upstream tyrosine kinase families were identified by peptide substrate phosphorylation in human (C), mice fibrotic (D), and normal samples. Quantification of discoidin domain receptor (DDR) and insulin receptor (INSR; Z > 2) by histogram peacock plots, mRNA expression measured via real‐time PCR, and immunostaining with antibodies for phospho‐DDR1 (pDDR1), phospho‐INSR (pINSR), or α‐smooth muscle actin (αSMA) (scale bar = 50 μm) in humans (E) and mice (F) for fibrotic samples compared with their control. Real‐time PCR measurement of mRNA expression of midlevel differentially changed (Z = 2–1.5) tyrosine kinases in humans (G) and mice (H)

FIGURE 4

Interrogation of individual tyrosine kinases in fibrosis of humans and rodents. Paralogous phylogenetic comparisons of differentially active (red text) tyrosine kinases in human liver fibrosis versus normal control samples (A) or mouse fibrosis versus normal control samples (B). The red dots of the log₂ fold‐change images indicate increased or decreased activity for each individual kinase in fibrosis compared with control. Differentially active individual kinases in human (C) or mouse (D) fibrosis compared with normal samples. Red coloring indicates higher specificity. Quantification of differentially changed individual kinases is shown as histogram peacock plots and mRNA expression measured via real‐time PCR in humans (E) or mice (F)

FIGURE 5

Assessment of serine/threonine kinase families changed in fibrosis of humans and rodents. Heatmap of substrate phosphorylation levels for serine/threonine kinases and waterfall plot of serine/threonine family of kinases in humans (A) and mice (B) for fibrotic samples compared with their control. Upstream serine/threonine kinase families identified by peptide substrate phosphorylation in humans (C) or mice (D) fibrotic and normal samples. Quantification of DM1 protein kinase (DMPK) and PKA (Z > 2) by histogram peacock plots and mRNA expression measured via real‐time PCR and immunostaining with antibodies for DMPK, phospho‐PKA (pPKA), or αSMA (scale bar = 50 μm) in humans (E) and mice (F) for fibrotic samples compared with their control. Real‐time PCR measurement of mRNA expression of differentially changed (Z < 2) serine/threonine kinases (DRYK1A, PDK1, mitogen‐activated protein kinase 1 [MAPK1] [ERK], and MAPK8 [JNK]) in humans (G) and mice (H)

FIGURE 6

Characterization of individual serine/threonine kinases in fibrosis of humans and rodents. Paralogous phylogenetic comparisons of differentially active (red text) protein tyrosine kinase (PTK) in human liver fibrosis samples versus normal control samples (A) or mouse fibrosis versus normal control samples (B). The red dots of the log₂ fold‐change images indicate increased or decreased activity for each individual kinase in fibrosis samples compared with control. Differentially active individual kinases in human (C) or mouse (D) fibrosis samples compared with normal samples. Red coloring indicates higher specificity. Quantification of differentially changed individual kinases is shown as histogram peacock plots and mRNA expression measured via real‐time PCR in humans (E) or mice (F)

FIGURE 7

Histological and kinomic analysis of additional animal models with fatty liver and emerging fibrosis. Sirius red staining (scale bar = 100 μm), fibrosis score, and Tgf‐b1 mRNA expression in the livers of mice fed a choline‐deficient (CD) diet and chow control (A) or a high‐fat plus fructose (HFD+F) diet and chow control (B). Heatmap of substrate phosphorylation levels for tyrosine kinases, waterfall plot of tyrosine families, peacock plots, and mRNA expression for DDR and INSR in mice fed CD diet and chow control (C) or mice fed HFD+F diet and chow control (D). Heatmap of substrate phosphorylation levels for serine/threonine kinases, waterfall plot, peacock plots, and mRNA expression for DMPK and PKA in mice fed CD diet and chow control (E) or mice fed HFD+F diet and chow control (F)

FIGURE 8

Human fibrosis molecular interaction network. Molecular interactions network of human kinomic data to indicate pathways that are changed in fibrosis samples versus control samples. Combined Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways from MAPK, phosphatidylinositol‐3‐kinase (PI3K), RAS, and EGF receptor (EGFR). Gene nodes were kept if they formed edges with genes in at least three of the four pathways and genes that were noted as differential peptides from humans and mice. Genes from complexes or where multiple genes have very similar symbols were collapsed to single entries, noted with an asterisk (*). Genes from the differential peptides list are green. The network was generated in R using ggraph, visualized in Cytoscape, and laid out using yfiles layouts