Identification of Biomarkers Associated with the Rearing Practices, Carcass Characteristics, and Beef Quality: An Integrative Approach

Abstract

Data from birth to slaughter of cull cows allowed using a PCA-based approach coupled with the iterative K-means algorithm the identification of three rearing practices classes. The classes were different in their carcass characteristics. Old cows raised mainly on pasture have better carcass characteristics, while having an equivalent tenderness, juiciness, flavor, intramuscular fat content, and pHu to those fattened with hay or haylage. The Longissimus thoracis muscle of the cows raised on pasture (with high physical activity) showed greater proportions of IIA fibers at the expense of the fast IIX ones. Accordingly, the meat of these animals have better color characteristics. Superoxide dismutase (SOD1) and αB-crystallin quantified by Dot-Blot were the only other biomarkers to be more abundant in "Grass" class compared to "Hay" and "Haylage" classes. The relationships between the biomarkers and the 6 carcass and 11 meat quality traits were investigated using multiple regression analyses per rearing practices. The associations were rearing practice class and phenotype trait-dependent. ICDH and TP53 were common for the three classes, but the direction of their entrance was different. In addition, rearing practices and carcass traits were not related with Hsp70-Grp75 and μ-calpain abundances. The other relationships were specific for two or one rearing practices class. The rearing practices dependency of the relationships was also found with meat quality traits. Certain proteins were for the first time related with some beef quality traits. MyHC-IIx, PGM1, Hsp40, ICDH, and Hsp70-Grp75 were common for the three rearing practices classes and retained to explain at list one beef quality trait. A positive relationship was found between PGM1 and hue angle irrespective of rearing practices class. This study confirms once again that production-related traits in livestock are the result of sophisticated biological processes finely orchestrated during the life of the animal and soon after slaughter.

Decision tree, a learning tool for the prediction of beef tenderness using rearing factors and carcass characteristics

“…Long fattening duration is not suitable because it leads to tough meat. This confirms our hypothesis dealing with the importance of the determination of the optimum thresholds of the factors governing the fattening period to achieve the best income and sustainability in cattle production . Recently, we pointed out this importance, and proposed to take in account the fattening duration in relation with further traits such as fat tissue and muscle percentages of young bulls .…”

“…This confirms our hypothesis dealing with the importance of the determination of the optimum thresholds of the factors governing the fattening period to achieve the best income and sustainability in cattle production . Recently, we pointed out this importance, and proposed to take in account the fattening duration in relation with further traits such as fat tissue and muscle percentages of young bulls . In cattle, several studies reported significant morphological changes that take place in the carcass, and hence on muscle, during the fattening period, as well as how these are to some extent confounded with age .…”

“…However, some of these systems are not completely accurate because they only reflect a compilation of traits that are indicators of tenderness, or eating quality is not yet part among them, namely for the EUROP system . Consequently, recent studies have proposed the inclusion of further variables that are related to the fattening period factors for the joint management of both carcass and meat qualities . Thus, based on those studies and in an attempt to use machine learning tools to sort beef carcasses into tenderness groups, we hypothesized that fattening period factors and carcass characteristics of young bulls would be accurate for the prediction of tenderness potential.…”

Contribution of connective tissue components, muscle fibres and marbling to beef tenderness variability in longissimus thoracis, rectus abdominis, semimembranosus and semitendinosus muscles

“…The distribution of the variables of interest to investigate the trend of differences among M and AT was illustrated by a principal component analysis (PCA), performed on raw data, by considering all the factors together including tenderness as a supplementary variable. Two factors with eigenvalues > 1.0 were considered in this PCA on the basis of the scree plot and evaluation of the factor loading matrix after orthogonal rotation following the procedure described by Gagaoua et al . An eigenvalue represents the amount of variance that is captured by a given component.…”

Assessment of cattle inter‐individual cluster variability: the potential of continuum data from the farm‐to‐fork for ultimate beef tenderness management

“…age at slaughter, carcass weight, CS and FS of the carcasses were centered and scaled by computing z ‐scores using PROC STANDARD of SAS. After that, principal component analysis (PCA) combined with agglomerative hierarchical cluster analysis (HCA) or k ‐means cluster analyses were undertaken to create animal groups . Two factors with eigenvalues > 1.0 were extracted on the factor loading matrix after orthogonal rotation.…”