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不同虾类商业品种的基因组规模代谢网络模型重建及营养需求分析

Reconstruction of Genome-Scale Metabolic Network Model and Nutritional Requirements Analysis of Different Shrimp Commercial Varieties.

作者信息

Gao Chenchen, Yang Jiarui, Hao Tong, Li Jingjing, Sun Jinsheng

机构信息

Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China.

Tianjin Fisheries Research Institute, Tianjin, China.

出版信息

Front Genet. 2021 May 12;12:658109. doi: 10.3389/fgene.2021.658109. eCollection 2021.

Abstract

As an important tool for systematic analysis, genome-scale metabolic network (GSMN) model has been widely used in various organisms. However, there are few reports on the GSMNs of aquatic crustaceans. is the largest and most productive shrimp species. Feed improvement is one of the important methods to improve the yield of and control water pollution caused by the inadequate absorption of feed. In this work, the first GSMN named GH3005 was reconstructed and applied to the optimization of feed. GH3005 was reconstructed based on the genomic data. The model includes 2,292 reactions and 3,005 genes. GH3005 was used to analyze the nutritional requirements of five different commercial varieties and the genes influencing the metabolism of the nutrients. Based on the simulation, we found that tyrosine-protein kinase src64b like may catalyze different reactions in different commercial varieties. The preference of carbohydrate utilization is different in various commercial varieties, which may due to the different expressions of some genes. In addition, this investigation suggests that a rational and targeted modification in the macronutrient content of shrimp feed would lead to an increase in growth and feed conversion rate. The feed for different commercial varieties should be adjusted accordingly, and possible adjustment schemes were provided. The results of this work provided important information for physiological research and optimization of the components in feed of .

摘要

作为系统分析的重要工具,基因组尺度代谢网络(GSMN)模型已在各种生物中广泛应用。然而,关于水生甲壳类动物的GSMN报道较少。[具体虾种名称]是最大且产量最高的虾类品种。饲料改良是提高[具体虾种名称]产量以及控制因饲料吸收不充分导致水污染的重要方法之一。在本研究中,构建了首个名为GH3005的[具体虾种名称]GSMN并将其应用于饲料优化。GH3005基于基因组数据构建。该模型包含2292个反应和3005个基因。GH3005用于分析五个不同[具体虾种名称]商业品种的营养需求以及影响营养物质代谢的基因。基于模拟,我们发现类酪氨酸蛋白激酶src64b在不同商业品种中可能催化不同反应。不同商业品种对碳水化合物利用的偏好不同,这可能是由于某些基因表达不同所致。此外,本研究表明对虾饲料中大量营养素含量进行合理且有针对性的调整将导致生长和饲料转化率提高。应相应调整不同商业品种的饲料,并提供了可能的调整方案。本研究结果为[具体虾种名称]的生理研究和饲料成分优化提供了重要信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ab5/8149995/e065fc97787e/fgene-12-658109-g001.jpg

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