Data-driven transcriptomics analysis identifies PCSK9 as a novel key regulator in liver aging

Authors

• M Arif
• C Matyas
• P Mukhopadhyay
• B Yokus
• E Trojnar
• J Paloczi
• B Paes-Leme
• S Zhao
• FW Lohoff
• G Hasko
• P Pacher

Abstract

The liver, as a crucial metabolic organ, undergoes significant pathological changes during the aging process, which can have a profound impact on overall health. To gain a comprehensive understanding of these alterations, we employed data-driven approaches, along with biochemical methods, histology, and immunohistochemistry techniques, to systematically investigate the effects of aging on the liver. Our study utilized a well-established rat aging model provided by the National Institute of Aging. Systems biology approaches were used to analyze genome-wide transcriptomics data from liver samples obtained from young (4–5 months old) and aging (20–21 months old) Fischer 344 rats. Our findings revealed pathological changes occurring in various essential biological processes in aging livers. These included mitochondrial dysfunction, increased oxidative/nitrative stress, decreased NAD + content, impaired amino acid and protein synthesis, heightened inflammation, disrupted lipid metabolism, enhanced apoptosis, senescence, and fibrosis. These results were validated using independent datasets from both human and rat aging studies. Furthermore, by employing co-expression network analysis, we identified novel driver genes responsible for liver aging, confirmed our findings in human aging subjects, and pointed out the cellular localization of the driver genes using single-cell RNA-sequencing human data. Our study led to the discovery and validation of a liver-specific gene, proprotein convertase subtilisin/kexin type 9 (PCSK9), as a potential therapeutic target for mitigating the pathological processes associated with aging in the liver. This finding envisions new possibilities for developing interventions aimed at improving liver health during the aging process.

Scientific Focus Area: Systems Biology

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