Patterns of gene expression in larvae of the giant grouper.

Abstract

The giant grouper Epinephelus lanceolatus (Bloch 1790) is the largest bony fish found on coral reefs, achieving lengths of over 2 m. It has delicious flesh that is highly prized by the live reef fish trade. Consequently, declining wild populations have been listed as Vulnerable by IUCN. There is now aquaculture of E. lanceolatus but, as is often the case for marine fishes, larval feeding is a major production bottleneck. In particular, knowing how the digestive system develops and how this might determine the feed that larvae can digest to grow satisfactorily. In this issue, Anderson et al. (2018) used a transcriptomic approach to explore molecular processes that underpin development of appetite, feeding and digestion in E. lanceolatus larvae. They focussed on the first two weeks of life, a critical period for larval husbandry during which the larvae first open their mouth and start exogenous feeding. Because transcriptomics typically yield a very large quantity of information, the challenge is to reveal patterns. The authors used Self Organising Maps to hone in on genes that contribute to specific developmental milestones. They developed a conceptual model to show patterns of expression of transcripts in relation to major developmental stages, such as mouth-opening, onset of exogenous feeding, swim bladder development. There were clear trends for digestive enzyme transcripts, which tended to increase their expression in concert with mouth-opening and onset of exogenous feeding. Gene expression for various appetite-regulating factors was highly variable although, in some cases, this could be linked to important developmental milestones. Although such studies are inductive, they provide an essential knowledge base for development of targeted hypothesis-driven research. A major objective would be to find means of improving growth and survival of larvae without having to rely on feeding with copepods, which are expensive to produce in large quantities. Such improvements in methods for larval husbandry, in the farming of this highly-prized species, will take pressure off vulnerable wild stocks. The authors commented that Self Organising Maps can be applied to any project generating high volumes of multi-dimensional data, and are especially useful for time-course studies and pattern visualisation. As such, fish biologists are able to use this approach to more easily hone in on genes that contribute to specific developmental milestones, phenotype, mortality event, whose expression changes in response to particular stimuli.