Transcriptome analysis and machine learning methods reveal potential mechanisms of zebrafish muscle aging

Abstract

Muscle is one of the most abundant tissues in the human body, and its aging usually leads to many adverse consequences. Zebrafish is a powerful model used to study human muscle diseases, yet we know little about the molecular mechanisms of muscle aging in zebrafish. In this study, we determined the gene expression profiles of muscle tissues from male zebrafish of four different ages. Through differential expression analysis and expression pattern analysis, we identified a set of genes associated with muscle aging in zebrafish. Functional enrichment analysis revealed that several biological changes accompanied zebrafish muscle aging, including chronic inflammation, accumulation of sphingolipids, reduction of autophagy, and activation of the ferroptosis pathway. H&E staining showed that zebrafish muscle senescence leads to myofibrillar interstitial expansion and inflammatory cell infiltration. Furthermore, we screened zebrafish muscle aging related biomarkers by machine learning and verified the expression levels of some biomarkers by RT-qPCR. Based on these biomarkers, we constructed a zebrafish muscle aging clock that can predict muscle age based on transcriptomic data. This study provides us with a new perspective to understand the molecular mechanism of muscle aging and a new tool for zebrafish-based anti-aging research.