4.7 Article

Insights on meat quality from combining traditional studies and proteomics

期刊

MEAT SCIENCE
卷 174, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.meatsci.2020.108423

关键词

Beef; Colour; Tenderness; Proteomics; Meat quality; Muscle to meat conversion; Glycolysis; Mitochondria; Omics; Data integration

资金

  1. Marie Sklodowska-Curie grant [713654]
  2. Marie Curie Actions (MSCA) [713654] Funding Source: Marie Curie Actions (MSCA)

向作者/读者索取更多资源

This review explores how proteomics studies can confirm and extend knowledge of the mechanisms and factors underlying variations in beef colour and tenderness. While proteomics may overlook some sources of variations in beef toughness, it highlights the role of post-mortem energy metabolism in setting the conditions for development of meat colour and tenderness. Further experimental studies are needed to confirm the findings from data-driven proteomics analyses.
Following a century of major discoveries on the mechanisms determining meat colour and tenderness using traditional scientific methods, further research into complex and interactive factors contributing to variations in meat quality is increasingly being based on data-driven omics approaches such as proteomics. Using two recent meta-analyses of proteomics studies on beef colour and tenderness, this review examines how knowledge of the mechanisms and factors underlying variations in these meat qualities can be both confirmed and extended by data-driven approaches. While proteomics seems to overlook some sources of variations in beef toughness, it highlights the role of post-mortem energy metabolism in setting the conditions for development of meat colour and tenderness, and also points to the complex interplay of energy metabolism, calcium regulation and mitochondrial metabolism. In using proteomics as a future tool for explaining variations in meat quality, the need for confirmation by further hypothesis-driven experimental studies of post-hoc explanations of why certain proteins are biomarkers of beef quality in data-driven studies is emphasised.

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