2023 Fiscal Year Final Research Report
Development of a biological evaluation method for fattening status in beef cattle and elucidation of the molecular mechanisms controlling meat production
| Project/Area Number |
20H03134
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Review Section |
Basic Section 42010:Animal production science-related
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| Research Institution | Kindai University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
根本 充貴 近畿大学, 生物理工学部, 講師 (10451808)
松橋 珠子 近畿大学, 先端技術総合研究所, 講師 (60504355)
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| Project Period (FY) |
2020-04-01 – 2024-03-31
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| Keywords | バイオマーカータンパク質 / 枝肉形質 / 機械学習 / 肉用牛 / 生体評価 / 産肉形質 / プロテオミクス |
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| Outline of Final Research Achievements |
Using serum samples from Japanese Black cattle, we performed data analysis using quantitative 135 serum proteins data by SWATH-MS method. We have improved a predictive model for in vivo evaluation of meat production traits of cattle. The prediction model was constructed using carcass and meat quality traits (carcass weight, rib eye area, rib thickness, subcutaneous fat thickness, yield estimate, beef marbling standard, and oleic acid concentration) as objective variables. We performed regression analysis to build a prediction model, and also improved prediction accuracy through data preprocessing (missing value completion).
The physiological functions of the features (explanatory variables) (identified serum biomarker proteins) in the built prediction model are estimated by bioinformatics analysis using IPA software. We constructed a growth simulation model for meat production traits during the fattening period and considered its application to feeding management technology.
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| Free Research Field |
動物生産学
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| Academic Significance and Societal Importance of the Research Achievements |
研究代表者を中心とする研究グループでは、畜産業界の悩みの一つである「肉質は牛を出荷しないと分からない」の解決を目指して、採血検体を用いて肉用牛を生体のまま分子レベルで把握して個々の肉用牛の産肉形質を出荷前ｋに生体予測診断できる、リキッド・バイオプシー生体予測診断サービス技術 “AIビーフ（商標登録）”（（独）農畜産業振興機構「畜産の情報」2023年10月号）を開発してきた。本研究課題の成果は、その予測技術の精度向上と、予測に使用するバイオマーカータンパク質群の関係性に科学的根拠を示す内容であり、旧態依然とした畜産業界のデジタル化に寄与し、持続可能な畜産業の創出に貢献するものと判断される。
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