Proteomic biomarkers of beef colour
Graphical abstract
Introduction
Meat colour is critical to fresh beef marketability as it influences consumer purchase decisions and attractiveness at the point-of-sale. Historically, the role of muscle proteins in meat colour have been identified including the important role of fibre type (Klont, Brocks, & Eikelenboom, 1998), glycolysis and sarcoplasmic proteins (Nair, Li, Beach, Rentfrow, & Suman, 2018b), oxidation and myofibrillar structure (Gagaoua, Terlouw, & Picard, 2017c; Hughes, Oiseth, Purslow, & Warner, 2014). During the past two decades, sophisticated –OMICs technologies within a Foodomics approach have been applied by meat scientists to elucidate the biological basis/mechanisms of meat quality traits including colour, with varying success (Nair, Costa-Lima, Wes Schilling, & Suman, 2017). Proteomics can be an efficient tool to study the dynamic biochemical changes taking place in the post-mortem muscle (Jia et al. 2007; Nair et al. 2018b). Proteomics combined with mass spectrometry (MS) or proteomic-based techniques, were able to offer increased resolving power and capability to separate and identify a great number of muscle proteins, allowing a more in-depth study of the conversion of muscle to meat and associated eating qualities (Picard & Gagaoua, 2020).
Proteomics is a quantitative analysis technique involving large-scale and systematic characterization of the whole protein content (proteome) present in a cell, tissue, or organism at given moment and environmental conditions. Proteome analysis depends on five major steps; protein separation, identification, characterization, quantification and functional characterization, allowing the study of interactions between the proteins. The muscle proteome can be studied at the level of proteins or at the peptide level after protein digestion, referred to as “top-down” or “bottom-up” approach, respectively. In the former approach, one- (1DE) or two-dimensional (2DE) gel electrophoresis coupled to MS is the most common technique for both separation and identification of the proteins (Ohlendieck, 2011; Picard & Gagaoua, 2020). As an alternative to this time-consuming approach, new “bottom-up” versatile and cost-effective MS technologies with much better sensitivity and resolution have been proposed (Moradian, Kalli, Sweredoski, & Hess, 2014) and have been recently applied to study meat discoloration and stability (Yu et al. 2017a, 2017b). These advancements have allowed for the identification of new potential protein biomarkers, which may explain the large variation in, and underlying mechanisms of meat colour other than myoglobin chemistry (Gagaoua et al. 2017c, 2018; Joseph, Suman, Rentfrow, Li, & Beach, 2012; Nair et al. 2017; Purslow, Warner, Clarke, & Hughes, 2020; Sayd et al. 2006). These potential biomarkers have previously been used to explain different meat qualities such as tenderness (Bjarnadottir et al. 2012; Picard & Gagaoua, 2020), pH (Huang et al. 2011; Kwasiborski et al. 2008), water-holding capacity (Di Luca, Hamill, Mullen, Slavov, & Elia, 2016), marbling and adipose tissue content (Mao et al. 2016) as well as protein oxidation and other modifications occurring in post-mortem muscle (Lametsch et al. 2003).
Proteomics was first used to investigate fresh meat colour in pigs (Hwang, 2004) and more recently for beef colour (Kim et al. 2008). The proteomic approach can be used to identify the biochemical basis of pre- and post-harvest aspects affecting colour at the point of sale and identify predictive candidate protein biomarkers for colour stability. In view of the vast amount of information generated by subsequent beef colour proteomics trials, and the need for deciphering this information, this integromics gathers 79 putative protein biomarkers correlated with beef colour traits (lightness (L*), redness (a*), yellowness (b*), among others) irrespective of muscle and proteomic platform. This was generated from published lists of differentially expressed proteins that were significantly correlated with beef colour traits from 13 recent, independent proteomic-based studies. Therefore, this review aims to generate a comprehensive ranked list of candidate biomarkers and attempts to distinguish key candidate beef colour biomarkers from spurious proteins. Consequently, these key biomarkers are discussed in relation to the mechanistic biological processes and pathways involved in beef colour determination and stability.
Section snippets
Meat colour definition and measurement
The colour of meat is dependent on both the chromatic attributes and on the achromatic (without colour) attributes. Chromatic and achromatic contributions to meat colour can be derived from measurements of reflectance on a meat surface and are described by the absorption (K) and scattering (S) coefficients, respectively for each attribute, or K/S ratio as a combined trait for overall colour perceived (Macdougall, 1970). Each attribute contributes to the overall colour of the meat, and together
Database creation: literature search strategy, inclusion criteria and data collection
A computerized search using Pubmed.gov (NCBI), Google Scholar, Web of Science (Clarivate Analytics) and Scopus databases was performed, attempting to identify all relevant published proteomics studies dealing with meat colour. Databases were searched from August 2018 to January 2020 for studies published from 2000 to 2020. The keywords used were “proteomic”, “omic”, “proteome”, “protein”, “biomarker” and “colour”, in combination with “meat” or “muscle”. There were no language or data
Database description
The database created includes the following details, for each reference; study number (from 1, the oldest to 13, the newest publication), author's name, publication year, country of the authors, the breed of the animals used and when possible the gender and type, muscle, number of animals included, colour traits/instrument/conditions of measurement and the proteomics platform used (Table S1). The data collected included five muscles which are known to differ in their contractile and metabolic
Mining the putative protein biomarkers of beef colour – database analysis
A computational workflow allowed aggregation of the data from the 13 publications and creation of the first list of putative protein biomarkers of beef colour that was subsequently mined using web service bioinformatics tools. The list of 79 proteins was submitted to a custom analysis using ProteINSIDE (http://www.proteinside.org/). ProteINSIDE obtains results from several software and databases with a single query. Using this tool, Gene Ontology (GO) enrichment tests (P-value, Benjamini
Six main biological pathways associated with beef colour
The GO analyses showed that the 79 proteins clustered into 6 distinct but strongly interconnected biological pathways (Table 1, Fig. S2 and Fig. S3). These pathways are known to be related to beef tenderness (Guillemin, Bonnet, Jurie, & Picard, 2011; Ouali et al. 2013; Picard & Gagaoua, 2020). This suggests that the biological pathways associated with variations in meat tenderness and meat colour are related. The main pathways and related proteins are (see Table 1 for full names of each
Putative protein biomarkers most frequently correlated with beef colour traits
Of the 79 protein biomarkers, 27 were reported 3 to 8 times in independent studies, generally as being correlated with beef colour traits. These similarities ranged from one protein common to 8 studies to 13 proteins identified in at least 3 studies (Table 1 and Fig. S3). At the top of this list, β-enolase (ENO3) was correlated with colour traits in 8 studies (Gagaoua et al. 2015, 2017a, 2017b, 2017c; Joseph et al. 2012; Nair et al. 2016; Wu et al. 2016; Yu et al. 2017b); Peroxiredoxin 6
Conclusion and future perspectives
It is a challenging task to improve the beef colour and colour stability during post-mortem storage and retail display. This integrative work reviewed the several biological pathways that are involved in meat colour development, including energy metabolism, heat shock proteins and oxidative stress, myofibril structure, signalling, proteolysis and apoptosis. The pathways underpinning meat colour are similar to those very recently described for beef tenderness, but with differences in the extent
Declaration of competing interest
The authors declared that there is no conflict of interest.
Acknowledgements
Dr. Mohammed Gagaoua is a Marie Skłodowska-Curie Career-FIT Fellow under the number MF20180029. He is grateful to the funding received from the Marie Skłodowska-Curie grant agreement No. 713654 and support of Meat Technology Ireland (MTI) a co-funded industry/Enterprise Ireland project (TC 2016 002).
References (87)
- et al.
Differences in phosphorylation of phosphoglucomutase 1 in beef steaks from the longissimus dorsi with high or low star probe values
Meat Science
(2014) - et al.
Differential abundance of sarcoplasmic proteome explains animal effect on beef Longissimus lumborum color stability
Meat Science
(2015) - et al.
The small heat shock protein, HSPB6, in muscle function and disease
Cell Stress & Chaperones
(2010) - et al.
Cooperation of molecular chaperones with the ubiquitin/proteasome system
Biochimica et Biophysica Acta (BBA) - Molecular Cell Research
(2004) - et al.
Myoglobin and lipid oxidation interactions: Mechanistic bases and control
Meat Science
(2010) Peroxiredoxin 6 in the repair of peroxidized cell membranes and cell signaling
Archives of Biochemistry and Biophysics
(2017)- et al.
Reverse Phase Protein array for the quantification and validation of protein biomarkers of beef qualities: The case of meat color from Charolais breed
Meat Science
(2018) - et al.
The study of protein biomarkers to understand the biochemical processes underlying beef color development in young bulls
Meat Science
(2017) - et al.
Functional analysis of beef tenderness
J Proteomics
(2011) - et al.
The phosphatidylethanolamine-binding protein is the prototype of a novel family of serine protease inhibitors
Journal of Biological Chemistry
(2001)