Comparative metabolomics analysis shows key metabolites as potential biomarkers for selection of beef fat colour
Rugang Tian![https://orcid.org/0000-0002-1648-7226](/media/client/orcid_16x16.png)
![https://orcid.org/0000-0003-2819-7637](/media/client/orcid_16x16.png)
A Inner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot, Inner Mongolia, China.
B Institute of Biotechnology, Shiraz University, Shiraz, Iran.
C Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran.
D School of Animal & Veterinary Sciences, University of Adelaide, Adelaide, SA, Australia.
Animal Production Science 63(11) 1063-1067 https://doi.org/10.1071/AN22476
Submitted: 5 January 2023 Accepted: 10 April 2023 Published: 8 May 2023
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing
Abstract
Context: Fat colour is one of the most important economic traits in the marketing of beef. There are many factors that affect fat colour, such as breed, age, diet and gender. Fat colour is observed in different ranges of colours, including white, yellow and brown. The main issue with improving fat colour is that consumer preferences of fat colour vary across the globe. Therefore, investigating the metabolic mechanisms of fat colour may provide new biomarkers for phenotyping, so as to develop effective selection strategies to achieve the locally desired fat colour.
Aims: This study aimed to perform a comparative metabolic analysis between white and yellow fat from crossbred cattle so as to identify potential biomarkers for the selection of fat colour and to better understand the metabolism of white and yellow fat depots.
Methods: Carcass samples of subcutaneous fat were collected from crossbred cattle (Simmental × Mongolian cattle) and scored for fat colour. Liquid chromatography–mass spectrophotometry analysis of extracted metabolites from the subcutaneous fat of six animals with white fat and six animals with yellow fat was performed.
Key results: The comparison between metabolites of white and yellow fat colour samples indicated that there were five categories of 235 significant metabolites, which included hydrocarbons, lipids and lipid-like molecules, organic acids and their derivatives, organic oxygen compounds and organoheterocyclic compounds. The principal-component analysis illustrated that yellow and white fat samples can be classified in groups; however, the metabolites of white fat samples showed greater variation than those in the yellow fat. In the white fat, there were 163 metabolites that had a higher relative abundance than in yellow fat and 72 that had a lower relative abundance than in yellow fat. 3-Hydroxyoctanoic acid, anethofuran, 9,10-DiHODE, furanoeremophilane, pregeijerene, N-glycolylneuraminic acid, and glycocholic acid were identified as the metabolites that differed the most in abundance between the white and yellow fat samples.
Conclusions: This study has provided insights into the metabolic differences between white and yellow fat depots and identified key metabolites associated with beef fat colour.
Implications: This study has provided potential biomarkers that may be useful for selection of beef fat colour in live animals.
Keywords: adipose, beef, bovine, cattle, fat color, meat, metabolites, phenotype.
References
Cambiaghi A, Ferrario M, Masseroli M (2017) Analysis of metabolomic data: tools, current strategies and future challenges for omics data integration. Briefings in Bioinformatics 18, 498–510.| Analysis of metabolomic data: tools, current strategies and future challenges for omics data integration.Crossref | GoogleScholarGoogle Scholar |
Darvin ME, Gersonde I, Ey S, Brandt NN, Albrecht H, Gonchukov SA, Sterry W, Lademann J (2004) Noninvasive detection of beta-carotene and lycopene in human skin using raman spectroscopy. Laser Physiology 14, 231–233.
da Silva Martins T, de Lemos MVA, Mueller LF, Baldi F, de Amorim TR, Ferrinho AM, Muñoz JA, de Souza Fuzikowa IH, de Moura GV, Gemelli JL, Pereira ASC (2018) Fat deposition, fatty acid composition, and its relationship with meat quality and human health. In ‘Meat science and nutrition’. (Ed. MS Arshad) pp. 17–37. (IntechOpen: London, UK)
Dieterle F, Ross A, Schlotterbeck G, Senn H (2006) Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Analytical Chemistry 78, 4281–4290.
| Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics.Crossref | GoogleScholarGoogle Scholar |
Dunne PG, Monahan FJ, O’Mara FP, Moloney AP (2009) Colour of bovine subcutaneous adipose tissue: a review of contributory factors, associations with carcass and meat quality and its potential utility in authentication of dietary history. Meat Science 81, 28–45.
| Colour of bovine subcutaneous adipose tissue: a review of contributory factors, associations with carcass and meat quality and its potential utility in authentication of dietary history.Crossref | GoogleScholarGoogle Scholar |
Hayes BJ, Burrow HM, Hunter RA (1995) Genetic and environmental factors affecting fat colour in carcasses of beef cattle grazing at pasture. Proceedings of Australian Association of Animal Breeding and Genetics 11, 550–553.
Jeong JY, Kim M, Ji S-Y, Baek Y-C, Lee S, Oh YK, Reddy KE, Seo H-W, Cho S, Lee H-J (2020) Metabolomics analysis of the beef samples with different meat qualities and tastes. Food Science of Animal Resources 40, 924–937.
| Metabolomics analysis of the beef samples with different meat qualities and tastes.Crossref | GoogleScholarGoogle Scholar |
Jin Q, Cheng H, Wan F, Bi Y, Liu G, Liu X, Zhao H, You W, Liu Y, Tan X (2015) Effects of feeding β-carotene on levels of β-carotene and vitamin A in blood and tissues of beef cattle and the effects on beef quality. Meat Science 110, 293–301.
| Effects of feeding β-carotene on levels of β-carotene and vitamin A in blood and tissues of beef cattle and the effects on beef quality.Crossref | GoogleScholarGoogle Scholar |
Knight TW, Death AF, Lambert MG, McDougall DB (2001) The rate of reduction in carotenoid concentration in fat of steers fed a low carotenoid ration, and the role of increasing carcass fatness. Australian Journal of Agricultural Research 52, 1023–1032.
| The rate of reduction in carotenoid concentration in fat of steers fed a low carotenoid ration, and the role of increasing carcass fatness.Crossref | GoogleScholarGoogle Scholar |
Lamichhane S, Sen P, Dickens AM, Orešič M, Bertram HC (2018) Gut metabolome meets microbiome: a methodological perspective to understand the relationship between host and microbe. Methods 149, 3–12.
| Gut metabolome meets microbiome: a methodological perspective to understand the relationship between host and microbe.Crossref | GoogleScholarGoogle Scholar |
Maoka T (2020) Carotenoids as natural functional pigments. Journal of Natural Medicines 74, 1–16.
| Carotenoids as natural functional pigments.Crossref | GoogleScholarGoogle Scholar |
Muroya S, Ueda S, Komatsu T, Miyakawa T, Ertbjerg P (2020) MEATabolomics: muscle and meat metabolomics in domestic animals. Metabolites 10, 188
| MEATabolomics: muscle and meat metabolomics in domestic animals.Crossref | GoogleScholarGoogle Scholar |
Park SJ, Beak S-H, Jung DJS, Kim SY, Jeong IH, Piao MY, Kang HJ, Fassah DM, Na SW, Yoo SP, Baik M (2018) Genetic, management, and nutritional factors affecting intramuscular fat deposition in beef cattle: a review. Asian–Australasian Journal of Animal Sciences 31, 1043–1061.
| Genetic, management, and nutritional factors affecting intramuscular fat deposition in beef cattle: a review.Crossref | GoogleScholarGoogle Scholar |
Tian R (2012) Genomic approach to understanding variation in bovine fat colour. PhD thesis, University of Adelaide, SA, Australia.
Tian R, Pitchford WS, Bottema CDK (2009) QTL analysis of beef fat colour and the effect of BCDO2. Proceedings of the Association for the Advancement of Animal Breeding and Genetics 18, 656–659.
Tian R, Cullen NG, Morris CA, Fisher PJ, Pitchford WS, Bottema CDK (2012) Major effect of retinal short-chain dehydrogenase reductase (RDHE2) on bovine fat colour. Mammalian Genome 23, 378–386.
| Major effect of retinal short-chain dehydrogenase reductase (RDHE2) on bovine fat colour.Crossref | GoogleScholarGoogle Scholar |
Våge DI, Boman IA (2010) A nonsense mutation in the beta-carotene oxygenase 2 (BCO2) gene is tightly associated with accumulation of carotenoids in adipose tissue in sheep (Ovis aries). BMC Genetics 11, 10
| A nonsense mutation in the beta-carotene oxygenase 2 (BCO2) gene is tightly associated with accumulation of carotenoids in adipose tissue in sheep (Ovis aries).Crossref | GoogleScholarGoogle Scholar |
Verbeke W, Van Wezemael L, de Barcellos MD, Kügler JO, Hocquette J-F, Ueland Ø, Grunert KG (2010) European beef consumers’ interest in a beef eating-quality guarantee: insights from a qualitative study in four EU countries. Appetite 54, 289–296.
| European beef consumers’ interest in a beef eating-quality guarantee: insights from a qualitative study in four EU countries.Crossref | GoogleScholarGoogle Scholar |
Wen B, Mei Z, Zeng C, Liu S (2017) metaX: a flexible and comprehensive software for processing metabolomics data. BMC Bioinformatics 18, 183
| metaX: a flexible and comprehensive software for processing metabolomics data.Crossref | GoogleScholarGoogle Scholar |
Wickham H (2016) ‘ggplot2: elegant graphics for data analysis.’ (Springer-Verlag: New York, NY, USA)
Wood JD, Enser M, Fisher AV, Nute GR, Sheard PR, Richardson RI, Hughes SI, Whittington FM (2008) Fat deposition, fatty acid composition and meat quality: a review. Meat Science 78, 343–358.
| Fat deposition, fatty acid composition and meat quality: a review.Crossref | GoogleScholarGoogle Scholar |
Zhou GH, Yang A, Tume RK (1993) A relationship between bovine fat colour and fatty acid composition. Meat Science 35, 205–212.
| A relationship between bovine fat colour and fatty acid composition.Crossref | GoogleScholarGoogle Scholar |