Reconstruction and Analysis of a Genome-Scale Metabolic Network for Eriocheir Sinensis Hepatopancreas

The application of big data in biology has been a hot trend in the research of bioinformatics. High throughput sequencing and large-scale biology network contribute massive valuable data for the complex biology analysis, in which genome-scale metabolic network (GSMN) has become an important tool for the systematic analysis of metabolism in various organisms. We reconstructed a GSMN model based on the high-throughput transcriptome sequencing of Eriocheir sinensis hepatopancreas. The model contains 1471 reactions, 1882 unigenes, and 1400 metabolites distributed in 101 pathways and 11 subsystems. Functional module analysis indicates that the metabolic modules in the network is in consistence with the functions of hepatopancreas for lipid metabolism, amino acid metabolism, and other related metabolic processes. Differential analysis of the hepatopancreas reveals that the eyestalk-ablation has the greatest effect on the lipid metabolism pathway, with a total of 66 related unigenes differentially expressed, which agrees with the functions of the hepatopancreas for nutrient absorption, storage, and utilization. The GSMN supplies a novel dataset of the metabolic information in E.sinensis to facilitate the further analysis of the metabolic mechanism of E.sinensis and other aquatic crustaceans. Besides, it also provides a valuable reference for the studies on regulation mechanism of eyestalk on hepatopancreas in E.sinensis.