Schematic representation of experimental setup.

Study groups included the healthy control group received tap water, the sucrose group received 10% sucrose water, the sucrose+JNK_D1 group received 10% sucrose and 30 mg/kg/day JNK-IN-5A, and the sucrose+JNK_D2 group received 10% sucrose and 60 mg/kg/day JNK-IN-5A (N = 44, n = 11 per group).

Sucrose consumption and JNK-IN-5A treatment exhibit distinct effects on the mRNA expression of genes encoding JNK isoforms in the major metabolic tissues.

(a) The acute effect of JNK-IN-5A in rats (n = 3/Sex/Group). Statistically significant changed clinical chemistry parameters were presented as mean ± standard deviation (SD), see also Data S1. ALB: albumin; ALP: alkaline phosphatase; BUN: blood urea nitrogen; PHOS: phosphate; GLU: glucose; K+: potassium; Na+: sodium; TP: total Protein; GLOB: globulin. Statistical significance was assessed by one-way ANOVA testing followed by Dunn’s multiple comparisons post-test, p-value < 0.05 is considered as statistical significance. (b) Boxplot showing the levels of plasma triglycerides in control, sucrose, sucrose+JNK_D1, and sucrose+JNK_D2 rats. Statistical significance was assessed by one-way ANOVA testing followed by Dunn’s multiple comparisons post-test, p-value < 0.05 is considered as statistical significance. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001. (c) Boxplot showing the relative mRNA expression of Mapk8, Mapk9, and Mapk10 in the studied tissues. The count-based abundance of genes was transformed using the vst function and the adjusted p-value (p.adj) is derived from DESeq2 (Love et al., 2014). * p.adj < 0.05, ** p.adj < 0.01, *** p.adj < 0.001, **** p.adj < 0.0001. (d) Correlation between the relative mRNA expression of Mapk8 and (e) Mapk9 in skeletal muscle tissue (SkM) and plasma triglycerides level.

System-wide transcriptomics profiling revealed tissue-specific metabolic rewiring by sucrose consumption and JNK-IN-5A treatment

(a) Principal component analysis (PCA) of liver, vWAT, SkM, and brain transcriptome data showing global gene expression profiles in control (n = 9), sucrose (n = 11), sucrose+JNK_D1 and sucrose+JNK_D2 (n = 10/per group). Each data point represents a sample in the respective coloured group. (b) Bar graphs represent the percentage of differential expressed genes (or regulated genes) associated with sucrose (sucrose vs. control), sucrose+JNK_D1 (sucrose+JNK_D1 vs. sucrose), or sucrose+JNK_D2 (sucrose+JNK_D2 vs. sucrose) in all protein-coding genes (N = 19,780) for each tissue. (c) Heatmap of all genes differentially upregulated (red) and downregulated (blue) in three pairwise comparisons for sucrose (sucrose vs. control), sucrose+JNK_D1 (sucrose+JNK_D1 vs. sucrose), and sucrose+JNK_D2 (sucrose+JNK_D2 vs. sucrose) (adjusted p-value, p.adj < 0.01) for each tissue. Column annotation represents tissues. (d) Venn diagram showing the overlap between significantly regulated genes response to sucrose consumption and JNK_D1 treatment, see also Fig. S1.

JNK inhibition in SkM correlated with the regulation of energy metabolism in the liver and adipose tissues.

(a-b) Venn diagram showing the overlap between significantly regulated genes response to sucrose consumption and INK_D1 treatment in SkM, see also Fig. S1&Fig. S5. (c-d) Functional annotation of up-(red) and down-regulated (blue) genes in JNK_D1-treated SkM with a Benjamini Hochberg adjusted p-value < 0.05. (e) Correlation of log2(Fold change) for shared DEGs between the SkM and Liver, vWAT, or Brain after JNK_D1 treatment.

JNK inhibition regulates insulin signalling-related genes in SkM

(a) A representative diagram of metabolic pathways associated with insulin resistance and insulin signalling was found to be largely inhibited by JNK_D1 treatment. (b) Correlation between the relative mRNA expression of Phkg1, Phkb, and Ppp1r3f (those three genes are also significantly upregulated in sucrose-feeding only group) in SkM and plasma triglycerides level. (c) Relative changes of genes involved in insulin resistance and insulin signalling pathways in response to sucrose consumption and JNK-IN-5A treatment in the tissues. The adjusted p-value (p.adj) is derived from DESeq2 (Love et al., 2014). * p.adj < 0.05, ** p.adj < 0.01, *** p.adj < 0.001, **** p.adj < 0.0001.

Inter-tissue network analysis identifies JNK-IN-5A inhibition-associated molecular mechanism.

(a) Significantly enriched modules (hypergeometric test, p < 0.05) by the differentially expressed genes related to sucrose, sucrose+JNK_D1 and sccrose+JNK_D1 in each tissue. (b) Dot-plot showing the significant correlation among inter-tissue module pairs. The size and colour of the connected line are proportional to the correlation coefficient and statistical significance indicated by the adjusted p-value. The module pairs with Benjamini Hochberg adjusted p-value (p.adj) < 0.05 are presented. (c) Spearman coefficient correlation of the first component from PCA analyses on gene expression of module pairs. (d) Significantly enriched MSigDB hallmark gene sets by each module.

Metabolic modelling highlights metabolic reprogramming linked to JNK-IN-5A inhibition

(a) The numbers of differentially altered metabolic reactions in response to sucrose, sucrose+JNK_D1, and sucrose+JNK_D2, respectively, see also Data S13. (b) Distribution of Cohen’s d statistic for differential metabolic reactions in response to sucrose, sucrose+JNK_D1 and sucrose+JNK_D2 in each tissue, see also Data S13. (c) Principal component analysis of the Compass score of metabolic reaction potential activity scores showing the metabolic heterogeneity in response to sucrose ingestion and JNK-IN-5A treatment at the tissue levels, for principal components 1 and 2. (d) Heatmap showing the tissue-level differential activity of metabolic reactions in response to sucrose intake and JNK-IN-5A treatment. Reactions are partitioned by Recon3 pathways (Brunk et al., 2018) and coloured by the sign of their Cohen’s d statistic derived from different contrasts: sucrose vs control, sucrose+JNK_D1 vs sucrose, or sucrose+JNK_D2 vs sucrose, respectively. Abbreviations: vWAT, visceral white adipose tissue; SkM, skeletal muscle.

(a) Gene expression pattern of JNK isoforms (b-e) UpSet plot showing the number of shared differentially expressed genes (DEGs) (adjusted-p < 0.01) associated with sucrose consumption and JNK-IN-5A treatment in the liver, visceral white adipose tissue (vWAT), skeletal muscle tissue (SkM) and brain tissues in the experimental rats. The histogram bar graph (right) shows the total number of DEGs in each comparison corresponding. The histogram bar graph (top) shows the number of DEGs shared among pairwise comparisons. Connected lines indicate what comparisons are sharing DEGs.

(a) Venn diagram showing the overlap between significantly regulated genes response to sucrose consumption and JNK_D2 treatment in liver and (b) in vWAT, related to Fig. 3. (c) Top-10 enriched KEGG pathways enriched by reversed genes after JNK_D1 treatment in liver and (d) in vWAT.

Relative changes of genes involved in fatty acid degradation, elongation, biosynthesis, and metabolism in response to sucrose consumption and JNK-IN-5A treatment in the tissues, related to Fig. 2.

Relative changes of genes involved in fatty acid uptake in response to sucrose consumption and JNK-in-5A treatment in the tissues, related to Fig. 2.

Relative changes of genes involved in redox homeostasis in response to sucrose consumption and JNK-in-5A treatment in the tissues, related to Fig. 2.

(a) Venn diagram showing the overlap between significantly regulated genes response to sucrose consumption and JNK_D2 treatment in SkM, see also Fig. S1&Fig. S5. (b) Correlation of log2(Fold change) for shared DEGs between the SkM and Liver, vWAT, or Brain after JNK_D2 treatment. (c) Venn diagram showing the overlap between significantly regulated genes response to sucrose consumption and JNK_D1 treatment as well as (d) JNK_D2 treatment in the Brain, respectively.

(a) The number of gene modules are identified from the tissue-specific co-expression networks. The text on the module indicates the module name and the number of genes of individual modules in each tissue. The edges between the clusters were aggregations of the inter-cluster edges. (b) PCA of Liver.M0, vWAT.M3, and SkM.M0 gene expression data. (c) Module neighbors of Mapk9 in SkM.M0.