Alcoholism Clinical and Experimental ResearchBioinformaticsPeer-reviewed journal article
Chronic alcohol intake elicits distinct multi-omic profiles in the liver versus skeletal muscle of mice
Craig R. G. WillisiD, Muni Swamy GanjayiiD, Austin M. BrowniD, Samantha E. Moser, Nathaniel J. SzewczykiD, Brian C. ClarkiD, Cory W. BaumanniD
Abstract
Alcohol-related liver disease and alcohol-related myopathy are widespread consequences of chronic alcohol use. However, understanding of the associated molecular mechanisms and effective treatments remains limited. To address this, we employed multi-omics to uncover molecular blueprints of liver versus skeletal muscle responses to chronic alcohol exposure, using a pre-clinical mouse model showing signs of alcohol-related liver dysregulation (diminished liver phosphatidylcholine-to-phosphatidylethanolamine lipid ratio) and alcohol-related myopathy (reduced muscle mass and strength). We found that the liver was more sensitive to chronic alcohol than muscle across the transcriptome, proteome and metabolome levels, but both tissues were equally sensitive at the lipidome level. The liver displayed an extensive and multi-layered metabolic molecular profile, while muscle was associated with upregulated inflammatory and matrisome responses and impaired mitochondrial energetics. Lipidome analyses also revealed a novel potential role for altered phospholipid remodelling in the aetiology of alcohol-related myopathy. Finally, computational drug repurposing identified several compounds for therapeutic targeting of alcohol-induced liver (e.g., saracatinib, GSK126) and muscle (e.g., metformin, trichostatin A) pathophysiology, perhaps working partly to counter metabolic dysregulation. Overall, our study provides a tractable list of therapeutic targets and treatments to help expedite the understanding of and countermeasures against alcohol-related liver disease and alcohol-related myopathy in humans.
- organism: mouse
- assay: bulk RNA-seq
- assay: proteomics
- assay: metabolomics
- research focus: differential expression
- research focus: drug response / drug discovery
- approach: statistical model
- Citations
- 2
- FWCI
- 9.33
- Field percentile
- top 4%
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