Transcriptomics, metabolomics, and in-silico drug predictions for liver damage in young and aged burn victims

Abstract

Burn induces a systemic response affecting multiple organs, including the liver. Since the liver plays a critical role in metabolic, inflammatory, and immune events, a patient with impaired liver often exhibits poor outcomes. The mortality rate after burns in the elderly population is higher than in any other age group, and studies show that the liver of aged animals is more susceptible to injury after burns. Understanding the aged-specific liver response to burns is fundamental to improving health care. Furthermore, no liver-specific therapy exists to treat burn-induced liver damage highlighting a critical gap in burn injury therapeutics. In this study, we analyzed transcriptomics and metabolomics data from the liver of young and aged mice to identify mechanistic pathways and in-silico predict therapeutic targets to prevent or reverse burn-induced liver damage. Our study highlights pathway interactions and master regulators that underlie the differential liver response to burn injury in young and aged animals.

Fig. 1. Experiments overview.

a Experimental mice model. b PCA (Principal Component Analysis) overview of sample clustering. c Volcano plot showing differential liver gene expression between aged and young mice in sham conditions (without burn). d KEGG pathway overrepresentation analysis of genes up-regulated in aged mice. N = 3 per group.

Fig. 2. RNA-seq analysis of liver burn response in young and old mice.

a Volcano plot showing genes responding to burn stimuli in young mice. b Volcano plot showing genes responding to burn stimuli in aged mice. c Venn diagram showing overlap of genes with up-regulated expression by burn stimuli in both aged and young mice. d Venn diagram showing overlap of genes with down-regulated expression by burn stimuli in both aged and young mice. e Sankey diagram of KEGG pathway from overrepresentation analysis based on overlapping up-regulated genes (panel C). f Sankey diagram of KEGG pathway from overrepresentation analysis based on overlapping down-regulated genes (panel D). g Volcano plot showing differential liver gene expression between aged and young mice after burn stimuli. h KEGG pathway overrepresentation analysis of genes down-regulated in aged burn mice vs young burn mice. For panels e, f and h, only pathways meeting the following criteria were shown. Size term < 200, intersection > 3, Benjamini–Hochberg adj. p-value < 0.05. N = 3 per group.

Fig. 3. Metabolomic analysis of liver response to burn stimuli in aged and young mice.

a A heatmap of differentially expressed metabolites in both aged burn versus aged sham and young burn mice versus young sham ones (p < 0.05 for each comparison). The blue cluster shows metabolites with a lower level in aged burn mice, and the red cluster shows metabolites with a higher level in aged burn mice. b Network of differentially expressed genes and metabolites in both young and aged mice after burn injury (only nodes having any connections were shown). c Upstream regulators of metabolic and transcriptomic changes after the burn injury are common for both aged and young mice. Only experimentally proven data for hepatocytes and/or liver was used for the prediction. d A heatmap of differentially expressed metabolites between aged burn and young burn mice (p < 0.05). In network, red and green colors mark up- and down-regulated nodes found in either metabolomic or transcriptomic data. N = 5 per group.

Fig. 4. In silico drug predictions.

a Predictions of drugs able to cause similar (right panel, red columns) or reverse effect (left panel, blue columns) in HepG2 cell line as common transcriptional changes observed after burn injury in the liver in both aged and young mice. b Predictions of drugs performed for the transcriptional difference observed after burn injury between aged and young mice. Only drugs with HepG2 tau score > |95| are shown.