Abstract: Cadmium (Cd) pollution is an important environmental problem. Quercus variabilis has stronger tolerance to heavy metals than other tree species. To clarify the molecular response of Q. variabilis to Cd stress, high-throughput sequencing of the transcriptome was performed on leaves of current-year seedlings subjected to 15, 30, and 90 days of stress with CdCl₂ solutions at concentrations of 50, 100, and 150 mg·L^-1. Differentially expressed genes (DEGs) were identified. Subsequent analyses of DEGs included gene annotation, gene enrichment analysis, weighted gene co-expression network analysis (WGCNA), and transcriptional regulatory network analysis, aimed to identify key genes associated with Cd tolerance in Q. variabilis. The results showed that a total of 23930 DEGs were identified after 15 days of Cd stress. Gene Ontology (GO) enrichment analysis indicated that these genes were predominantly enriched in terms related to “chromosome regulation activity” and “energy metabolism process”. A total of 15675 DEGs were identified after 30 days of Cd stress. GO enrichment analysis demonstrated that these genes were primarily enriched in “ion channel activity”, “cell division regulation”, and “enzyme activity”. A total of 12701 DEGs were identified after 90 days of Cd stress. GO enrichment analysis indicated that these genes were predominantly enriched in “transmembrane transporter activity”. The WGCNA revealed that the blue and yellow modules exhibited the strongest correlation with Cd stress. Glycine Cleavage System P Protein (GCSPP), Caffeoyl-CoA O-methyltransferase (CCOM), and DNA Damage Repair/Tolerance Protein DRT102-like (DDRP) played a key role in the blue module. The ribosome biogenesis protein WDR12 homolog (RBP) played a key role in the yellow module. This study screened DEGs related to Cd tolerance in Q. variabilis, among which four genes play a pivotal role in Cd tolerance of Q. variabilis, laying a foundation for elucidating the molecular mechanisms of Cd tolerance and detoxification in Q. variabilis.

Key words: Quercus variabilis, transcriptome, differentially expressed genes (DEGs), gene enrichment, weighted gene co-expression network analysis (WGCNA)