Feed Components and Timing to Improve the Feed Conversion Ratio for Sustainable Aquaculture Using Starch

被引:0
作者
Shima, Hideaki [1 ]
Asakura, Taiga [1 ]
Sakata, Kenji [1 ]
Koiso, Masahiko [2 ]
Kikuchi, Jun [1 ,3 ,4 ]
机构
[1] RIKEN Ctr Sustainable Resource Sci, 1-7-22,Suehiro Cho,Tsurumi Ku, Yokohama, Kanagawa 2300045, Japan
[2] Japan Fisheries Res & Educ Agcy, Seikai Natl Fisheries Res Inst, Res Ctr Subtrop Fisheries, 148 Fukaiota, Ishigaki, Okinawa 9070451, Japan
[3] Yokohama City Univ, Grad Sch Med Life Sci, 1-7-29 Suehiro Cho,Tsurumi Ku, Yokohama, Kanagawa 2300045, Japan
[4] Nagoya Univ, Grad Sch Bioagr Sci, 1 Furo Cho,Chikusa Ku, Nagoya, Aichi 4648601, Japan
关键词
aquaculture; carbohydrates; data-driven approach; machine learning; nuclear magnetic resonance; stable isotope labeling; FISH; METABOLOMICS; METABOLISM; SERRANIDAE;
D O I
10.3390/ijms25147921
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Aquaculture contributes to the sustainable development of food security, marine resource conservation, and economy. Shifting aquaculture feed from fish meal and oil to terrestrial plant derivatives may result in cost savings. However, many carnivorous fish cannot be sustained on plant-derived materials, necessitating the need for the identification of important factors for farmed fish growth and the identification of whether components derived from terrestrial plants can be used in feed. Herein, we focused on the carnivorous fish leopard coral grouper (P. leopardus) to identify the essential growth factors and clarify their intake timing from feeds. Furthermore, we evaluated the functionality of starch, which are easily produced by terrestrial plants. Results reveal that carbohydrates, which are not considered essential for carnivorous fish, can be introduced as a major part of an artificial diet. The development of artificial feed using starch offers the possibility of increasing the growth of carnivorous fish in aquaculture.
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页数:13
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共 48 条
[1]   Regional feature extraction of various fishes based on chemical and microbial variable selection using machine learning [J].
Asakura, Taiga ;
Sakata, Kenji ;
Date, Yasuhiro ;
Kikuchi, Jun .
ANALYTICAL METHODS, 2018, 10 (18) :2160-2168
[2]   Statistical Indices for Simultaneous Large-Scale Metabolite Detections for a Single NMR Spectrum [J].
Chikayama, Eisuke ;
Sekiyama, Yasuyo ;
Okamoto, Mami ;
Nakanishi, Yumiko ;
Tsuboi, Yuuri ;
Akiyama, Kenji ;
Saito, Kazuki ;
Shinozaki, Kazuo ;
Kikuchi, Jun .
ANALYTICAL CHEMISTRY, 2010, 82 (05) :1653-1658
[3]  
Craig S.R., 2017, Understanding fish nutrition, feeds, and feeding
[4]   Application of a Deep Neural Network to Metabolomics Studies and Its Performance in Determining Important Variables [J].
Date, Yasuhiro ;
Kikuchi, Jun .
ANALYTICAL CHEMISTRY, 2018, 90 (03) :1805-1810
[5]   Enzymatic digestion in stomachless fishes: how a simple gut accommodates both herbivory and carnivory [J].
Day, Ryan D. ;
German, Donovan P. ;
Manjakasy, Jennifer M. ;
Farr, Ingrid ;
Hansen, Mitchell Jay ;
Tibbetts, Ian R. .
JOURNAL OF COMPARATIVE PHYSIOLOGY B-BIOCHEMICAL SYSTEMS AND ENVIRONMENTAL PHYSIOLOGY, 2011, 181 (05) :603-613
[6]   Evaluation of Full-Resolution J-Resolved 1H NMR Projections of Biofluids for Metabonomics Information Retrieval and Biomarker Identification [J].
Fonville, Judith M. ;
Maher, Anthony D. ;
Coen, Muireann ;
Holmes, Elaine ;
Lindon, John C. ;
Nicholson, Jeremy K. .
ANALYTICAL CHEMISTRY, 2010, 82 (05) :1811-1821
[7]   Noninvasive fecal metabolic profiling for the evaluation of characteristics of thermostable lactic acid bacteria, Weizmannia coagulans SANK70258, for broiler chickens [J].
Ito, Kayo ;
Miyamoto, Hirokuni ;
Matsuura, Makiko ;
Ishii, Chitose ;
Tsuboi, Arisa ;
Tsuji, Naoko ;
Nakaguma, Teruno ;
Nakanishi, Yumiko ;
Kato, Tamotsu ;
Suda, Wataru ;
Honda, Fuyuko ;
Ito, Toshiyuki ;
Moriya, Shigeharu ;
Shima, Hideaki ;
Michibata, Ryounosuke ;
Yamada, Ryouichi ;
Takahashi, Yosuke ;
Koga, Hirohisa ;
Kodama, Hiroaki ;
Watanabe, Yuko ;
Kikuchi, Jun ;
Ohno, Hiroshi .
JOURNAL OF BIOSCIENCE AND BIOENGINEERING, 2022, 134 (02) :105-115
[8]  
Kamalam J., 2016, International Aquafeed, P20
[9]   Spin Couple: Development of a Web Tool for Analyzing Metabolite Mixtures via Two-Dimensional J-Resolved NMR Database [J].
Kikuchi, Jun ;
Tsuboi, Yuuri ;
Komatsu, Keiko ;
Gomi, Masahiro ;
Chikayama, Eisuke ;
Date, Yasuhiro .
ANALYTICAL CHEMISTRY, 2016, 88 (01) :659-665
[10]   A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury [J].
Kohonen, Pekka ;
Parkkinen, Juuso A. ;
Willighagen, Egon L. ;
Ceder, Rebecca ;
Wennerberg, Krister ;
Kaski, Samuel ;
Grafstrom, Roland C. .
NATURE COMMUNICATIONS, 2017, 8