Persistent poor egg quality is a major problem in finfish aquaculture that has remained intractable and of unknown cause(s) despite decades of attention by researchers around the world. In the national breeding program for the striped bass, which supports a major fish-farming industry in the Carolinas and nationwide as well as important recreational fisheries, half of females fail to reproduce even though their oocytes/eggs are morphologically indistinguishable from those of good spawners. Our recent research has strongly implicated dysfunction of the ovarian transcriptome as a root cause of the problem. We developed a microarray to simultaneously measure expression of virtually all of the thousands of genes present in ovarian biopsies taken from female striped bass before the breeding season. Using a novel approach to analysis of microarray data based on artificial neural networks and machine learning tools, we then modeled the profiles of gene expression and their relationship to egg quality. This research could lead to more efficient production of hybrids for growout facilities and have important implications for fisheries management policies.
We aim to discover patterns of ovarian gene expression associated with high and low egg quality in striped bass. This will identify the specific physiological functions that are impaired and point the way toward changes in husbandry practices that can optimize egg quality. We will extend the analysis to wild caught individuals and evaluate the capacity of gene expression profiling as a tool for assessing the egg quality among age cohorts.