GEOapplication/xmlftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE310nnn/GSE310771/primaryOK200GenomicsDanio rerioGenome binding/occupancy profiling by high throughput sequencinghttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE310771GEOGSEfalseIntegrating AI in characterization receptor and construction signaling network for beta-glucan-engaged trained immunity in zebrafish [ChIP-seq]Trained immunity, a form of innate immune memory, involves the functional reprogramming of innate immune cells, enabling an enhanced nonspecific response to subsequent challenges. While beta-glucan, a fungal cell wall component, is a known inducer of this process in zebrafish, the specific receptor responsible remains unidentified. Here, we identify C-type lectin domain-containing 1 (CLDC1), designated DrDectin-1, as the pivotal beta-glucan receptor in zebrafish through AI-driven bioinformatic screening based on mammalian Dectin-1. Structural analysis suggests key beta-glucan binding residues (D182, Y183, H184). Using cldc1 knockout zebrafish in beta-glucan training and secondary infection models, combined with RNA-seq, H3K4me3 ChIP-seq, and virtual cell modeling, we demonstrate that CLDC1 mediates trained immunity via the Syk-Raf signaling pathways. Our findings identify the long-sought beta-glucan receptor in zebrafish and provide a comprehensive mechanistic framework for innate immune memory in teleosts, with implications for evolutionary immunology and disease management.2026/06/01GSE310771GSM9308631GSM9308630GSM9308633GSM9308632GSM9308635GSM930863424995310771Danio rerio