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Genome-scale metabolic network model of Eriocheir sinensis icrab4665 and nutritional requirement analysis

BACKGROUND: Genome-scale metabolic network models (GEMs) provide an efficient platform for the comprehensive analysis the physical and biochemical functions of organisms due to their systematic perspective on the study of metabolic processes. Eriocheir sinensis is an important economic species culti...

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Autores principales: Li, Jingjing, Gou, Yifei, Yang, Jiarui, Zhao, Lingxuan, Wang, Bin, Hao, Tong, Sun, Jinsheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9238104/
https://www.ncbi.nlm.nih.gov/pubmed/35764922
http://dx.doi.org/10.1186/s12864-022-08698-z
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author Li, Jingjing
Gou, Yifei
Yang, Jiarui
Zhao, Lingxuan
Wang, Bin
Hao, Tong
Sun, Jinsheng
author_facet Li, Jingjing
Gou, Yifei
Yang, Jiarui
Zhao, Lingxuan
Wang, Bin
Hao, Tong
Sun, Jinsheng
author_sort Li, Jingjing
collection PubMed
description BACKGROUND: Genome-scale metabolic network models (GEMs) provide an efficient platform for the comprehensive analysis the physical and biochemical functions of organisms due to their systematic perspective on the study of metabolic processes. Eriocheir sinensis is an important economic species cultivated on a large scale because it is delicious and nutritious and has a high economic value. Feed improvement is one of the important methods to improve the yield of E. sinensis and control water pollution caused by the inadequate absorption of feed. RESULTS: In this study, a GEM of E. sinensis, icrab4665, was reconstructed based on the transcriptome sequencing, combined with KEGG database, literature and experimental data. The icrab4665 comprised 4665 unigenes, 2060 reactions and 1891 metabolites, which were distributed in 12 metabolic subsystems and 113 metabolic pathways. The model was used to predict the optimal nutrient requirements of E. sinensis in feed, and suggestions for feed improvement were put forward based on the simulation results. The simulation results showed that arginine, methionine, isoleucine and phenylalanine had more active metabolism in E. sinensis. It was suggested that the amount of these essential amino acids should be proportionally higher than that of other amino acids in the feed to ensure the amino acid metabolism of E. sinensis. On the basis of the simulation results, we further suggested increasing the amount of linoleic acid, EPA and DHA in the feed to ensure the intake of essential fatty acids for the growth of E. sinensis and promote the accumulation of cell substances. In addition, the amounts of zinc and selenium in the feed were also suggested to be properly increased to ensure the basic metabolism and growth demand of E. sinensis. CONCLUSION: The largest GEM of E. sinensis was reconstructed and suggestions were provide for the improvement of feed contents based on the model simulation. This study promoted the exploration of feed optimization for aquatic crustaceans from in vivo and in silico. The results provided guidance for improving the feed proportion for E. sinensis, which is of great significance to improve its yield and economic value. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-022-08698-z.
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spelling pubmed-92381042022-06-29 Genome-scale metabolic network model of Eriocheir sinensis icrab4665 and nutritional requirement analysis Li, Jingjing Gou, Yifei Yang, Jiarui Zhao, Lingxuan Wang, Bin Hao, Tong Sun, Jinsheng BMC Genomics Research BACKGROUND: Genome-scale metabolic network models (GEMs) provide an efficient platform for the comprehensive analysis the physical and biochemical functions of organisms due to their systematic perspective on the study of metabolic processes. Eriocheir sinensis is an important economic species cultivated on a large scale because it is delicious and nutritious and has a high economic value. Feed improvement is one of the important methods to improve the yield of E. sinensis and control water pollution caused by the inadequate absorption of feed. RESULTS: In this study, a GEM of E. sinensis, icrab4665, was reconstructed based on the transcriptome sequencing, combined with KEGG database, literature and experimental data. The icrab4665 comprised 4665 unigenes, 2060 reactions and 1891 metabolites, which were distributed in 12 metabolic subsystems and 113 metabolic pathways. The model was used to predict the optimal nutrient requirements of E. sinensis in feed, and suggestions for feed improvement were put forward based on the simulation results. The simulation results showed that arginine, methionine, isoleucine and phenylalanine had more active metabolism in E. sinensis. It was suggested that the amount of these essential amino acids should be proportionally higher than that of other amino acids in the feed to ensure the amino acid metabolism of E. sinensis. On the basis of the simulation results, we further suggested increasing the amount of linoleic acid, EPA and DHA in the feed to ensure the intake of essential fatty acids for the growth of E. sinensis and promote the accumulation of cell substances. In addition, the amounts of zinc and selenium in the feed were also suggested to be properly increased to ensure the basic metabolism and growth demand of E. sinensis. CONCLUSION: The largest GEM of E. sinensis was reconstructed and suggestions were provide for the improvement of feed contents based on the model simulation. This study promoted the exploration of feed optimization for aquatic crustaceans from in vivo and in silico. The results provided guidance for improving the feed proportion for E. sinensis, which is of great significance to improve its yield and economic value. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-022-08698-z. BioMed Central 2022-06-28 /pmc/articles/PMC9238104/ /pubmed/35764922 http://dx.doi.org/10.1186/s12864-022-08698-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Li, Jingjing
Gou, Yifei
Yang, Jiarui
Zhao, Lingxuan
Wang, Bin
Hao, Tong
Sun, Jinsheng
Genome-scale metabolic network model of Eriocheir sinensis icrab4665 and nutritional requirement analysis
title Genome-scale metabolic network model of Eriocheir sinensis icrab4665 and nutritional requirement analysis
title_full Genome-scale metabolic network model of Eriocheir sinensis icrab4665 and nutritional requirement analysis
title_fullStr Genome-scale metabolic network model of Eriocheir sinensis icrab4665 and nutritional requirement analysis
title_full_unstemmed Genome-scale metabolic network model of Eriocheir sinensis icrab4665 and nutritional requirement analysis
title_short Genome-scale metabolic network model of Eriocheir sinensis icrab4665 and nutritional requirement analysis
title_sort genome-scale metabolic network model of eriocheir sinensis icrab4665 and nutritional requirement analysis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9238104/
https://www.ncbi.nlm.nih.gov/pubmed/35764922
http://dx.doi.org/10.1186/s12864-022-08698-z
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