Physicochemical attributes, oxidative stability, and microbial profile of boneless sirloin and bone-in T-bone steaks from Hanwoo steer with reference to dry-aging

We investigated the comparative physicochemical attributes, oxidative stability, and microbial characteristics of 28 days dry-aged meat in between boneless sirloin (gluteus medius) and bone-in T-bone steaks (infraspinatus) muscles from Korean Native Hanwoo Steer (KNHS). Results reveal that regardless of the muscles, dry-aging increased protein content and water-holding capacity (WHC) (p < 0.05). Meat from infraspinatus-aged muscle led to darker meat with higher pH values than un-aged meat (p < 0.05). However, fat content, CIE a*, and CIE b* remained unchanged in both muscles at aging. At aged meat, thiobarbituric acid reactive substances (TBARS) values from bone-in infraspinatus muscle was 2.5-fold higher than boneless gluteus medius muscle (p < 0.05). Dry-aging led to an increase in the contents of total unsaturated fatty acids (UFAs), monounsaturated fatty acids (MUFAs), and UFA/saturated fatty acids (SFA) in both muscles (p < 0.05). Furthermore, gluteus medius aged muscle concentrated with olic acid (C18:1) compared to infraspinatus aged muscle. Irrespective of the muscles, dry-aging enhanced the total free amino acids (FAAs) as well as tasty, and bitter amino acid contents whereas decreased the tasty/bitter amino acids (p < 0.05). Aromatic amino acid, tryptophan that converted to serotonin was 2-fold higher in boneless gluteus medius muscle than bone-in infraspinatus muscle at pre and post aging processes (p < 0.05). Aged Infraspinatus muscle increased total bacteria (p < 0.05) while no salmonellaspp. was detected in both muscles. Taken together, our study confirms that 28 days dry-aging profiling the quality characteristics of boneless sirloin (gluteus medius) and bone-in T-bone steaks (infraspinatus) distinctly while gluteus medius aged steak performs better owing to oxidative stability and functional compounds than infraspinatus aged steak.

Genomic Approaches Reveal Pleiotropic Effects in Crossbred Beef Cattle

Carcass and meat quality are two important attributes for the beef industry because they drive profitability and consumer demand. These traits are of even greater importance in crossbred cattle used in subtropical and tropical regions for their superior adaptability because they tend to underperform compared to their purebred counterparts. Many of these traits are challenging and expensive to measure and unavailable until late in life or after the animal is harvested, hence unrealistic to improve through traditional phenotypic selection, but perfect candidates for genomic selection. Before genomic selection can be implemented in crossbred populations, it is important to explore if pleiotropic effects exist between carcass and meat quality traits. Therefore, the objective of this study was to identify genomic regions with pleiotropic effects on carcass and meat quality traits in a multibreed Angus-Brahman population that included purebred and crossbred animals. Data included phenotypes for 10 carcass and meat quality traits from 2,384 steers, of which 1,038 were genotyped with the GGP Bovine F-250. Single-trait genome-wide association studies were first used to investigate the relevance of direct additive genetic effects on each carcass, sensory and visual meat quality traits. A second analysis for each trait included all other phenotypes as covariates to correct for direct causal effects from identified genomic regions with pure direct effects on the trait under analysis. Five genomic windows on chromosomes BTA5, BTA7, BTA18, and BTA29 explained more than 1% of additive genetic variance of two or more traits. Moreover, three suggestive pleiotropic regions were identified on BTA10 and BTA19. The 317 genes uncovered in pleiotropic regions included anchoring and cytoskeletal proteins, key players in cell growth, muscle development, lipid metabolism and fat deposition, and important factors in muscle proteolysis. A functional analysis of these genes revealed GO terms directly related to carcass quality, meat quality, and tenderness in beef cattle, including calcium-related processes, cell signaling, and modulation of cell-cell adhesion. These results contribute with novel information about the complex genetic architecture and pleiotropic effects of carcass and meat quality traits in crossbred beef cattle.

Application of proteomics to understand the molecular mechanisms determining meat quality of beef muscles during postmortem aging

Proteomics profiling disclosed the molecular mechanism underlying beef poor meat quality. This study aimed to identify protein markers indicating the quality of beef during postmortem storage at 4°C. Beef longissimus dorsi samples were stored at 4°C. The meat water holding capacity (WHC), pH value and moisture content were determined at different time points during the storage period. The iTRAQ MS/MS approach was used to determine the proteomics profiling at 0, 3.5 and 7 d during storage at 4°C. Bioinformatics analysis was performed to investigate the potential correlated proteins associated with meat quality. Storage at 4°C gradually decreased the pH value, WHC, and hence the moisture content. The iTRAQ proteomic analysis revealed that a cluster of glycolytic enzymes including malate dehydrogenase, cytoplasmic, L-lactate dehydrogenase, phosphoglycerate mutase and pyruvate kinase, and another cluster of proteins involved in oxygen transport and binding (myoglobin) and hemoglobin complex (including Globin A1 and hemoglobin subunit alpha) were decreased during the postmortem storage. These results suggest that the decreased glycolysis, oxygen, and heme-binding activities might be associated with the beef muscle low quality and the decline of tenderness during postmortem storage at 4°C.

Aggregation of Omic Data and Secretome Prediction Enable the Discovery of Candidate Plasma Biomarkers for Beef Tenderness

Beef quality is a complex phenotype that can be evaluated only after animal slaughtering. Previous research has investigated the potential of genetic markers or muscle-derived proteins to assess beef tenderness. Thus, the use of low-invasive biomarkers in living animals is an issue for the beef sector. We hypothesized that publicly available data may help us discovering candidate plasma biomarkers. Thanks to a review of the literature, we built a corpus of articles on beef tenderness. Following data collection, aggregation, and computational reconstruction of the muscle secretome, the putative plasma proteins were searched by comparison with a bovine plasma proteome atlas and submitted to mining of biological information. Of the 44 publications included in the study, 469 unique gene names were extracted for aggregation. Seventy-one proteins putatively released in the plasma were revealed. Among them 13 proteins were predicted to be secreted in plasma, 44 proteins as hypothetically secreted in plasma, and 14 additional candidate proteins were detected thanks to network analysis. Among these 71 proteins, 24 were included in tenderness quantitative trait loci. The in-silico workflow enabled the discovery of candidate plasma biomarkers for beef tenderness from reconstruction of the secretome, to be examined in the cattle plasma proteome.

Statistical model choice including variable selection based on variable importance: A relevant way for biomarkers selection to predict meat tenderness

In this paper, we describe a new computational methodology to select the best regression model to predict a numerical variable of interest Y and to select simultaneously the most interesting numerical explanatory variables strongly linked to Y. Three regression models (parametric, semi-parametric and non-parametric) are considered and estimated by multiple linear regression, sliced inverse regression and random forests. Both the variables selection and the model choice are computational. A measure of importance based on random perturbations is calculated for each covariate. The variables above a threshold are selected. Then a learning/test samples approach is used to estimate the Mean Square Error and to determine which model (including variable selection) is the most accurate. The R package modvarsel (MODel and VARiable SELection) implements this computational approach and applies to any regression datasets. After checking the good behavior of the methodology on simulated data, the R package is used to select the proteins predictive of meat tenderness among a pool of 21 candidate proteins assayed in semitendinosus muscle from 71 young bulls. The biomarkers were selected by linear regression (the best regression model) to predict meat tenderness. These biomarkers, we confirm the predominant role of heat shock proteins and metabolic ones.

An Original Methodology for the Selection of Biomarkers of Tenderness in Five Different Muscles

For several years, studies conducted for discovering tenderness biomarkers have proposed a list of 20 candidates. The aim of the present work was to develop an innovative methodology to select the most predictive among this list. The relative abundance of the proteins was evaluated on five muscles of 10 Holstein cows: gluteobiceps, semimembranosus, semitendinosus, Triceps brachii and Vastus lateralis. To select the most predictive biomarkers, a multi-block model was used: The Data-Driven Sparse Partial Least Square. Semimembranosus and Vastus lateralis muscles tenderness could be well predicted (R² = 0.95 and 0.94 respectively) with a total of 7 out of the 5 times 20 biomarkers analyzed. An original result is that the predictive proteins were the same for these two muscles: µ-calpain, m-calpain, h2afx and Hsp40 measured in m. gluteobiceps and µ-calpain, m-calpain and Hsp70-8 measured in m. Triceps brachii. Thus, this method is well adapted to this set of data, making it possible to propose robust candidate biomarkers of tenderness that need to be validated on a larger population.

Imbalance of the redox system and quality of tilapia fillets subjected to pre-slaughter stress

The objective of this study was to evaluate the effect of oxidative stress on the instrumental and sensory quality of Nile tilapia fillets. The experiment was conducted in a 2x2 factorial arrangement, evaluating densities (60 and 300 kg m-3) and depuration times (1 and 24 hours) in a total of four treatments. The serum levels of cortisol and gene expression levels of catalase (CAT), glutathione peroxidase (GPx) and 70 kDa heat shock protein (HSP70) as well as the pH, color, tenderness, water-holding capacity and sensory analysis of the fillets were evaluated. High density (300 kg m-3) resulted in higher mean cortisol levels, lower expression of CAT and GPx enzymes as well as higher expression of HSP70. Fish under this treatment also exhibited fillets with greater tenderness, higher lightness, lower redness and lower sensory acceptance. The longer depuration time (24 hours) resulted in lower expression of the CAT and GPx enzymes and fillets with higher lightness. The water-holding capacity was not affected by the different treatments. Therefore, low density and longer depuration times are recommended for decreased stress and improved quality of fillets.

Data from the Farmgate-to-Meat Continuum Including Omics-Based Biomarkers to Better Understand the Variability of Beef Tenderness: An Integromics Approach

This study is based on an integromic approach of 71 young bulls' data from the farmgate-to-meat continuum including omics-based biomarkers, to understand beef tenderness variability in two muscle cuts that differ by their contractile and metabolic properties. By the means of chemometrics using partial least-squares (PLS) and principal component regressions (PCR), important variables from a list of 49 that characterize four levels of the continuum (rearing factors-carcass-muscle-meat) were identified to explain tenderness of Longissimus thoracis (LT) and Semitendinosus (ST) muscles evaluated by a sensory panel and instrumental Warner-Bratzler shear force (WBSF). The PLS and PCR analyses validated 16 and 15 variables for LT and 12 and 14 for ST from the whole continuum to explain sensory tenderness and WBSF, respectively. Among the explanatory variables in the four models and in line with the role of apoptosis in tenderness determinism, HSP70-1A/B (a heat shock protein) was retained to explain beef tenderness irrespective of muscle and evaluation method. Similarly, dressing percentage from the carcass level was another robust predictor but in a muscle-dependent direction manner. HSP20, ENO3, and MyHC-I as three muscle protein biomarkers and dry matter intake (DMI) as a rearing factor were involved in three models to explain beef tenderness. This study highlighted also that several variables were muscle-specific irrespective of the evaluation method of tenderness. For LT muscle, six variables including three carcass traits (fatness score, fat carcass %, and muscle carcass %), two muscle biomarkers (HSP70-8 and MyHC-IIx/b), and one meat quality trait (pH_3h) were found. For ST muscle, five variables were validated from two rearing factors (average daily gain and feed efficiency) and three structural protein biomarkers (α-actin, MyBP-H, and CapZ-β). Finally, for WBSF only, lactate dehydrogenase chain B (LDH-B) was retained positively for LT and negatively for ST muscles. Overall, this trial showed that tenderness of LT and ST muscle cuts is influenced by variables belonging to the whole continuum with relationships that depend on both the muscle type and the evaluation method. It further highlighted the potential of integromic/chemometric approaches on the farmgate-to-meat continuum data to better understand the sophisticated biological processes that orchestrate the conversion of muscle into meat and tenderness determinism.

The application of gene marker-assisted selection and proteomics for the best meat quality criteria and body measurements in Qinchuan cattle breed

In the past few decades, enhancement of animal productivity has been gaining increasing attention among decisions-makers, politicians, mangers, and breeders, because of the increasing of world population and shortage of natural resources. The selection of high productivity animals is the main goal, through the application of genetic improvement programs. The use of molecular genetics has conferred significant breeding advantages over conventional breeding techniques. In this regard, many economic characteristics are controlled by a small number of multiple gene loci, each of which is responsible for trait diversity and hence they are referred to as quantitative trait loci (QTL). Single-nucleotide polymorphisms (SNPs), which have recently been discovered through DNA sequencing, are considered one of the most useful types of genetic marker. SNPs are found where different nucleotides occur at the same position in the DNA sequence. They are found in both coding and noncoding regions of the genome and are present at one SNP in every 1000 b. Strategies for the identification and application of markers are based on reference to examples of loci that can control various traits. Furthermore, markers for growth, body measurements, and meat quality traits are preferred, because they can be used to predict the performance of animals, via blood samples, in the first few days of animal life. Marker-assisted selection using SNPs, such asSIRT1, SIRT2, LPL, CRTC2, SIX4, UCPs, and ZBTB38as selection criteria of body measurements and meat traits in beef cattle, will be beneficial in selection and breeding programs. The proteomic is a novel marker and a new approache of biotechnology which increases the understanding of the biological processes, besides being a remarkable biomarker that interrelated to growth and meat quality traits. Proteomics is a vigorous tool as usage for deduces molecular processes between quality traits and muscle proteins, which are helpful in analyzing the mechanisms of biochemistry that influence quality. So they could be potential biomarker for some meat quality traits. Among them, Actin, Myosin, Heat shock proteins are used a novel approaches in the field of biotechnology to understand the proteomics changes. This review article highlights the novel findings on the potential use of MAS and proteomics as biomarker for the selection for meat quality and carcass traits in Qinchuan cattle breed.

Network Analysis Reveals Putative Genes Affecting Meat Quality in Angus Cattle

Improvements in eating satisfaction will benefit consumers and should increase beef demand which is of interest to the beef industry. Tenderness, juiciness, and flavor are major determinants of the palatability of beef and are often used to reflect eating satisfaction. Carcass qualities are used as indicator traits for meat quality, with higher quality grade carcasses expected to relate to more tender and palatable meat. However, meat quality is a complex concept determined by many component traits making interpretation of genome-wide association studies (GWAS) on any one component challenging to interpret. Recent approaches combining traditional GWAS with gene network interactions theory could be more efficient in dissecting the genetic architecture of complex traits. Phenotypic measures of 23 traits reflecting carcass characteristics, components of meat quality, along with mineral and peptide concentrations were used along with Illumina 54k bovine SNP genotypes to derive an annotated gene network associated with meat quality in 2,110 Angus beef cattle. The efficient mixed model association (EMMAX) approach in combination with a genomic relationship matrix was used to directly estimate the associations between 54k SNP genotypes and each of the 23 component traits. Genomic correlated regions were identified by partial correlations which were further used along with an information theory algorithm to derive gene network clusters. Correlated SNP across 23 component traits were subjected to network scoring and visualization software to identify significant SNP. Significant pathways implicated in the meat quality complex through GO term enrichment analysis included angiogenesis, inflammation, transmembrane transporter activity, and receptor activity. These results suggest that network analysis using partial correlations and annotation of significant SNP can reveal the genetic architecture of complex traits and provide novel information regarding biological mechanisms and genes that lead to complex phenotypes, like meat quality, and the nutritional and healthfulness value of beef. Improvements in genome annotation and knowledge of gene function will contribute to more comprehensive analyses that will advance our ability to dissect the complex architecture of complex traits.

Muscle Protein Profiles Used for Prediction of Texture of Farmed Salmon (Salmo salar L.)

A soft texture is undesired in Atlantic salmon as it leads to downgrading and reduced yield, yet it is a factor for which the cause is not fully understood. This lack of understanding highlights the need for identifying the cause of the soft texture and developing solutions by which the processing industry can improve the yield. Changes in muscle protein profiles can occur both pre- and postharvest and constitute an overall characterization of the muscle properties including texture. The aim of this study was to investigate this relationship between specific muscle proteins and the texture of the salmon fillet. Samples for 2D-gel-based proteomics were taken from the fillet above the lateral line at the same position as where the texture had been measured. The resulting protein profiles were analyzed using multivariate data analysis. Sixteen proteins were found to correlate to the measured texture, showing that it is possible to predict peak force based on a small subset of proteins. Additionally, eight of the 16 proteins were identified by tandem mass spectrometry including serum albumin, dipeptidyl peptidase 3, heat shock protein 70, annexins, and a protein presumed to be a titin fragment. It is contemplated that the identification of these proteins and their significance for the measured texture will contribute to further understanding of the Atlantic salmon muscle texture.

Differences in Beef Quality between Angus (Bos taurus taurus) and Nellore (Bos taurus indicus) Cattle through a Proteomic and Phosphoproteomic Approach

Proteins are the major constituents of muscle and are key molecules regulating the metabolic changes during conversion of muscle to meat. Brazil is one of the largest exporters of beef and most Brazilian cattle are composed by zebu (Nellore) genotype. Bos indicus beef is generally leaner and tougher than Bos taurus such as Angus. The aim of this study was to compare the muscle proteomic and phosphoproteomic profile of Angus and Nellore. Seven animals of each breed previously subjected the same growth management were confined for 84 days. Proteins were extracted from Longissimus lumborum samples collected immediately after slaughter and separated by two-dimensional electrophoresis. Pro-Q Diamond stain was used in phosphoproteomics. Proteins identification was performed using matrix assisted laser desorption/ionization time-of-flight mass spectrometry. Tropomyosin alpha-1 chain, troponin-T, myosin light chain-1 fragment, cytoplasmic malate dehydrogenase, alpha-enolase and 78 kDa glucose-regulated protein were more abundant in Nellore, while myosin light chain 3, prohibitin, mitochondrial stress-70 protein and heat shock 70 kDa protein 6 were more abundant in Angus (P<0.05). Nellore had higher phosphorylation of myosin regulatory light chain-2, alpha actin-1, triosephosphate isomerase and 14-3-3 protein epsilon. However, Angus had greater phosphorylation of phosphoglucomutase-1 and troponin-T (P<0.05). Therefore, proteins involved in contraction and muscle organization, myofilaments expressed in fast or slow-twitch fibers and heat shock proteins localized in mitochondria or sarcoplasmic reticulum and involved in cell flux of calcium and apoptosis might be associated with differences in beef quality between Angus and Nellore. Furthermore, prohibitin appears to be a potential biomarker of intramuscular fat in cattle. Additionally, differences in phosphorylation of myofilaments and glycolytic enzymes could be involved with differences in muscle contraction force, susceptibility to calpain, apoptosis and postmortem glycolysis, which might also be related to differences in beef quality among Angus and Nellore.

Expression Marker-Based Strategy to Improve Beef Quality

For beef cattle research, a main objective is to control concomitantly the development of muscles and the qualities of beef cuts. Beef quality is a complex phenotype that is only detectable after slaughter and is highly variable. The beef industry is in need of tools to estimate beef quality of live cattle or online in abattoirs, with specific attention towards sensory attributes (tenderness, juiciness, flavour, and colour). Identification of relevant genetic and genomic markers is ongoing, especially for tenderness—a top priority quality attribute. In this paper, we describe the steps of an expression marker-based strategy to improve beef sensory quality, from the discovery of biomarkers that identify consistent beef and the biological functions governing beef tenderness to the integration of the knowledge into detection tests for desirable animals. These tools should soon be available for the management of sensory quality in the beef production chain for meeting market's demands and assuring good quality standards.

Recent advances in omic technologies for meat quality management

The knowledge of the molecular organization of living organisms evolved considerably during the last years. The methodologies associated also progressed with the development of the high-throughput sequencing (SNP array, RNAseq, etc.) and of genomic tools allowing the simultaneous analysis of hundreds or thousands of genes, proteins or metabolites. In farm animals, some proteins, mRNAs or metabolites whose abundance has been associated with meat quality traits have been detected in pig, cattle, chicken. They constitute biomarkers for the assessment and prediction of qualities of interest in each species, with potential biomarkers across species. The ongoing development of rapid methods will allow their use for decision-making and management tools in slaughterhouses, to better allocate carcasses or cuts to the appropriate markets. Besides, their application on living animals will help to improve genetic selection and to adapt a breeding system to fulfill expected quality level. The ultimate goal is to propose effective molecular tools for the management of product quality in meat production chains.

Inverse relationships between biomarkers and beef tenderness according to contractile and metabolic properties of the muscle

Previous proteomic analyses established a list of proteins biomarkers of beef tenderness. The present study quantified the relative abundance of 21 of these proteins by dot-blot technique in the Longissimus thoracis and Semitendinosus muscles of 71 young bulls from three breeds: Aberdeen Angus (AA), Limousin (LI), and Blond d'Aquitaine (BA). For both muscles overall tenderness was estimated by sensory analysis; shear force was measured with a Warner-Bratzler instrument, and an index combining sensory and mechanical measurements was calculated. Multiple regressions based on relative abundances of these proteins were used to propose equations of prediction of the three evaluations of tenderness. Hsp70-1B appeared to be a good biomarker of low tenderness in the three breeds and in the two muscles. Proteins such as lactate dehydrogenase-B, myosin heavy chain IIx, and small heat shock proteins (Hsp27, Hsp20, and αB-crystallin) were related to tenderness but inversely according to the muscle and breed. The results demonstrate that prediction of tenderness must take into account muscle characteristics and animal type.

A network-based approach for predicting Hsp27 knock-out targets in mouse skeletal muscles

Thanks to genomics, we have previously identified markers of beef tenderness, and computed a bioinformatic analysis that enabled us to build an interactome in which we found Hsp27 at a crucial node. Here, we have used a network-based approach for understanding the contribution of Hsp27 to tenderness through the prediction of its interactors related to tenderness. We have revealed the direct interactors of Hsp27. The predicted partners of Hsp27 included proteins involved in different functions, e.g. members of Hsp families (Hsp20, Cryab, Hsp70a1a, and Hsp90aa1), regulators of apoptosis (Fas, Chuk, and caspase-3), translation factors (Eif4E, and Eif4G1), cytoskeletal proteins (Desmin) and antioxidants (Sod1). The abundances of 15 proteins were quantified by Western blotting in two muscles of HspB1-null mice and their controls. We observed changes in the amount of most of the Hsp27 predicted targets in mice devoid of Hsp27 mainly in the most oxidative muscle. Our study demonstrates the functional links between Hsp27 and its predicted targets. It suggests that Hsp status, apoptotic processes and protection against oxidative stress are crucial for post-mortem muscle metabolism, subsequent proteolysis, and therefore for beef tenderness.