Integrated Transcriptomic and Metabolomic Analyses for Circadian Disruption biomarker identification
Although the impact of circadian disruption is well recognized, definitive indicators and time windows for its detection are currently lacking. Here, by conducting transcriptomic, metabolomic, and integrated analysis, we comprehensively investigated the gene and metabolite changes on the 7th day after a single 6-hour phase advance jet lag in mice. Our findings revealed significant alterations during this post-jet lag window, especially in core clock genes Bmal1 and Cry1, and metabolites L-Arginine and SM(d18:1/18:1(11Z)), with notable differences at Zeitgeber Time 0 (ZT0), suggesting ZT0 as a key diagnostic time point. Additionally, we identified L-Serine as a potential biomarker for indexing circadian disruption irrespective of time points. Our study provides new insights into potential biomarkers for detecting circadian clock disruption. Overall design: Experimental Design: Species/Strain: Male C57BL/6 mice (8-10 weeks old). Treatment: A single 6-hour phase advance light cycle (simulated jet lag) vs. stable light-dark controls. Time Points: Liver and serum samples collected on day 7 post-jet lag at ZT0 (lights-on) and ZT12 (lights-off). Groups: Control (n=24): Stable 12:12 light-dark cycle. Jet Lag (n=24): Phase-advanced light cycle. Omics Data: Transcriptomics: RNA-seq (Illumina NovaSeq) of liver tissue, focusing on core clock genes (Bmal1, Cry1) and circadian pathways. Metabolomics: LC-MS/MS analysis of serum metabolites, including L-Arginine, SM(d18:1/18:1(11Z)), and L-Serine. Integrated Analysis: Multi-omics network and pathway enrichment (KEGG, Reactome) to identify biomarkers.