Sea cucumber genome provides insights into saponin biosynthesis and aestivation regulation

Triterpenoids in Echinoderms: Fundamental Differences in Diversity and Biosynthetic Pathways

Echinoderms form a remarkable phylum of marine invertebrates that present specific chemical signatures unique in the animal kingdom. It is particularly the case for essential triterpenoids that evolved separately in each of the five echinoderm classes. Indeed, while most animals have Δ5-sterols, sea cucumbers (Holothuroidea) and sea stars (Asteroidea) also possess Δ7 and Δ9(11)-sterols, a characteristic not shared with brittle stars (Ophiuroidea), sea urchins (Echinoidea), and crinoids (Crinoidea). These particular Δ7 and Δ9(11) sterols emerged as a self-protection against membranolytic saponins that only sea cucumbers and sea stars produce as a defense mechanism. The diversity of saponins is large; several hundred molecules have been described in the two classes of these saponins (i.e., triterpenoid or steroid saponins). This review aims to highlight the diversity of triterpenoids in echinoderms by focusing on sterols and triterpenoid glycosides, but more importantly to provide an updated view of the biosynthesis of these molecules in echinoderms.

Predicting Growth Traits with Genomic Selection Methods in Zhikong Scallop (Chlamys farreri)

Selective breeding is a common and effective approach for genetic improvement of aquaculture stocks with parental selection as the key factor. Genomic selection (GS) has been proposed as a promising tool to facilitate selective breeding. Here, we evaluated the predictability of four GS methods in Zhikong scallop (Chlamys farreri) through real dataset analyses of four economical traits (e.g., shell length, shell height, shell width, and whole weight). Our analysis revealed that different GS models exhibited variable performance in prediction accuracy depending on genetic and statistical factors, but non-parametric method, including reproducing kernel Hilbert spaces regression (RKHS) and sparse neural networks (SNN), generally outperformed parametric linear method, such as genomic best linear unbiased prediction (GBLUP) and BayesB. Furthermore, we demonstrated that the predictability relied mainly on the heritability regardless of GS methods. The size of training population and marker density also had considerable effects on the predictive performance. In practice, increasing the training population size could better improve the genomic prediction than raising the marker density. This study is the first to apply non-linear model and neural networks for GS in scallop and should be valuable to help develop strategies for aquaculture breeding programs.