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Fig. 6 Zebrafish Transcriptome Validation of Machine Learning Results on ROS-Mediated Circadian Rhythm Regulation (A) GEO datasets were randomly divided into two groups, and their average expression levels were compared with those of zebrafish transcriptome samples through Pearson correlation coefficient analysis, which revealed similar expression patterns between the zebrafish and GEO datasets. (B) Volcano plot illustrating the distribution of upregulated and downregulated genes in the zebrafish transcriptome. (C–D) KEGG and GO enrichment analysis results of differentially expressed genes (DEGs). (E) GSEA enrichment analysis using the zebrafish circadian clock gene set from the Misdb database as the background gene set. (F) Heatmap showing the expression levels of core circadian clock-related genes among the DEGs. (G–H) Under LD and DD conditions, zebrafish larvae at 5 dpf were treated with different drugs for 3 h, with samples collected every 4 h (30 larvae per sample). RT‒qPCR was performed to analyse the expression levels of ezh2, and the experiments were repeated three times. JTK-Cycle was used to analyse mRNA rhythmicity, period, phase, and amplitude. Two-way ANOVA and multiple t tests were performed for statistical analysis (∗, p < 0.05; ∗∗, p < 0.01; ∗∗∗, p < 0.001).