Identification of transcriptome markers in blue catfish cryopreserved sperm to predict male reproductive performance

This study establishes a mechanistic link between sperm-specific transcriptomic profiles and hatch success, demonstrating that a six-gene panel can serve as a statistically robust predictor of hatch success in hybrid catfish aquaculture.

In hybrid catfish aquaculture, hatch success varies significantly using cryopreserved sperm from different males, but phenotypic assays only explain a fraction of observed variations. We profiled the sperm transcriptome of 30 blue catfish (Ictalurus furcatus) males, whose cryopreserved sperm yielded a hatch success from 14 to 63%. Our transcriptome study, through RNA sequencing, identified 3,850 differentially expressed genes (DEGs) between high hatch success and low hatch success groups (log2(fold change) > 2 and false discovery rate < 0.05). More than 95% of these DEGs were retained in fresh sperm, demonstrating that intrinsic molecular differences, rather than cryopreservation, drive fertility variation. In low hatch success sperm, upregulation of mitophagy, autophagy, RNA degradation, and cilium-assembly pathways were observed, consistent with cellular stress and motility deficiencies. Spearman correlation across all males identified 27 annotated DEGs strongly associated with hatch success, of which the positive marker trpm4a and negative markers kmt2b and zbtb33 were validated by qRT-PCR (P < 0.05). A random-forest regression model combining the six most informative transcriptomic markers with testosterone level and initial motility achieved high predictivity with ρ = 0.77 between predicted and observed hatch success (P < 0.001), outperforming models based on either data type alone. These findings establish a compact panel that can be implemented as a multiplex qPCR assay to guide broodstock selection, reduce gamete wastage, and enhance fry output. Our work continues to build the foundation to extend marker validation, explore molecular mechanisms, and incorporate epigenetic features to further refine predictive accuracy. Ultimately, this framework will support profitable and sustainable catfish aquaculture operations.