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Recent Advances in OMICs Technologies and
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Mohammed Gagaoua, Brigitte Picard

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Mohammed Gagaoua, Brigitte Picard. Recent Advances in OMICs Technologies and Application for Ensuring Meat Quality, Safety and Authenticity. Mohammed Gagaoua et Brigitte Picard. MDPI, 2023, 978-3-0365-5665-9. 10.3390/books978-3-0365-5666-6. hal-03951791

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foods

Recent Advances in
OMICs Technologies
and Application
for Ensuring Meat
Quality, Safety and
Authenticity

Edited by

Mohammed Gagaoua and Brigitte Picard

Printed Edition of the Special Issue Published in *Foods*
Recent Advances in OMICs
Technologies and Application
for Ensuring Meat Quality, Safety
and Authenticity
<figure><img src="image_4.png" /></figure>
Recent Advances in OMICs
Technologies and Application
for Ensuring Meat Quality, Safety
and Authenticity

Editors

Mohammed Gagaoua

Brigitte Picard

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

<figure><img src="image_5.png" /></figure>
Editors

Mohammed Gagaoua

Teagasc Ashtown Food

Research Centre

Ireland

Brigitte Picard

UMR Herbivores

France

Editorial Office

MDPI

St. Alban-Anlage 66

4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Foods* (ISSN 2304-8158) (available at: https://www.mdpi.com/journal/foods/special_issues/omics\_meat).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

Last Name, A.A.; Last Name, B.B.; Last Name, C.C. Article Title. *Journal Name Year, Volume Number, Page Range*.

ISBN 978-3-0365-5665-9 (Hbk)

ISBN 978-3-0365-5666-6 (PDF)

© 2023 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications.

The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND.
# Contents

About the Editors vii

Preface to "Recent Advances in OMICs Technologies and Application for Ensuring Meat Quality, Safety and Authenticity" ix

## Mohammed Gagaoua

Recent Advances in OMICs Technologies and Application for Ensuring Meat Quality, Safety and Authenticity
Reprinted from: *Foods* **2022**, *11*, 2532, doi:10.3390/foods11162532 1

Laura González-Blanco, Yolanda Diñeiro, Andrea Díaz-Luis, Ana Coto-Montes, Mamen Oliván and Verónica Sierra
Impact of Extraction Method on the Detection of Quality Biomarkers in Normal vs. DFD Meat
Reprinted from: *Foods* **2021**, *10*, 1097, doi:10.3390/foods10051097 5

Verónica Sierra, Laura González-Blanco, Yolanda Diñeiro, Fernando Díaz, María Josefa García-Espina, Ana Coto-Montes, Mohammed Gagaoua and Mamen Oliván
New Insights on the Impact of Cattle Handling on Post-Mortem Myofibrillar Muscle Proteome and Meat Tenderization
Reprinted from: *Foods* **2021**, *10*, 3115, doi:10.3390/foods10123115 21

Yao Zhu, Mohammed Gagaoua, Anne Maria Mullen, Alan L. Kelly, Torres Sweeney, Jamie Cafferky, Didier Viala and Ruth M. Hamill
A Proteomic Study for the Discovery of Beef Tenderness Biomarkers and Prediction of Warner-Bratzler Shear Force Measured on *Longissimus thoracis* Muscles of Young Limousin-Sired Bulls
Reprinted from: *Foods* **2021**, *10*, 952, doi:10.3390/foods10050952 43

Mohammed Gagaoua, Muriel Bonnet and Brigitte Picard
Protein Array-Based Approach to Evaluate Biomarkers of Beef Tenderness and Marbling in Cows: Understanding of the Underlying Mechanisms and Prediction
Reprinted from: *Foods* **2020**, *9*, 1180, doi:10.3390/foods9091180 63

Xiaoguang Gao, Dandan Zhao, Lin Wang, Yue Cui, Shijie Wang, Meng Lv, Fangbo Zang and Ruitong Dai
Proteomic Changes in Sarcoplasmic and Myofibrillar Proteins Associated with Color Stability of Ovine Muscle during Post-Mortem Storage
Reprinted from: *Foods* **2021**, *10*, 2989, doi:10.3390/foods10122989 87

Zehu Yuan, Ling Ge, Weibo Zhang, Xiaoyang Lv, Shanhe Wang, Xiukai Cao and Wei Sun
Preliminary Results about Lamb Meat Tenderness Based on the Study of Novel Isoforms and Alternative Splicing Regulation Pathways Using Iso-seq, RNA-seq and CTCF ChIP-seq Data
Reprinted from: *Foods* **2022**, *11*, 1068, doi:10.3390/foods11081068 101

Zhendong Cai, Song Zhou, Qianqian Liu, Hui Ma, Xinyi Yuan, Jiaqi Gao, Jinxuan Cao and Daodong Pan
A Simple and Reliable Single Tube Septuple PCR Assay for Simultaneous Identification of Seven Meat Species
Reprinted from: *Foods* **2021**, *10*, 1083, doi:10.3390/foods10051083 115
Stefanie Dobrovolny, Steffen Uhlig, Kirstin Frost, Anja Schlierf, Kapil Nichani, Kirsten Simon, Margit Cichna-Markl and Rupert Hochegger
Interlaboratory Validation of a DNA Metabarcoding Assay for Mammalian and Poultry Species to Detect Food Adulteration
Reprinted from: *Foods* **2022**, *11*, 1108, doi:10.3390/foods11081108
129

Xiaoyun Chen, Yi Ji, Kai Li, Xiaofu Wang, Cheng Peng, Xiaoli Xu, Xinwu Pei, Junfeng Xu and Liang Li
Development of a Duck Genomic Reference Material by Digital PCR Platforms for the Detection of Meat Adulteration
Reprinted from: *Foods* **2021**, *10*, 1890, doi:10.3390/foods10081890
159

Athanasios Mallouchos, Theano Mikrou and Chrysavgi Gardeli
Gas Chromatography–Mass Spectrometry-Based Metabolite Profiling for the Assessment of Freshness in Gilthead Sea Bream (*Sparus aurata*)
Reprinted from: *Foods* **2020**, *9*, 464, doi:10.3390/foods9040464
171

Walter G. Bottje, Kentu R. Lassiter, Vivek A. Kuttappan, Nicholas J. Hudson, Casey M. Owens, Behnam Abasht, Sami Dridi and Byungwhi C. Kong
Upstream Regulator Analysis of Wooden Breast Myopathy Proteomics in Commercial Broilers and Comparison to Feed Efficiency Proteomics in Pedigree Male Broilers
Reprinted from: *Foods* **2021**, *10*, 104, doi:10.3390/foods10010104
183
# About the Editors

## Mohammed Gagaoua

Dr. Mohammed Gagaoua is a meat scientist at PEGASE research unit, INRAE, France who develops research activities on pork meat quality and production systems. Mohammed has expertise on muscle biology, meat biochemistry and the eating qualities of meat and meat products along their evaluation by consumers. He aims by his research a better understanding of the mechanisms underpinning meat quality development and determination. Mohammed has expertise in foodomics, mainly proteomics, and statistical approaches that consider environmental, production systems, and post-slaughter harvesting practices. He has respected knowledge in data integration in the context of farm-to-fork continuum and identification of the main drivers of the final eating qualities of meat. He is expert in MeatOmics and their use for the discovery of biomarkers of eating qualities of meat. Mohammed was runner-up in 2015 for the International Meat Secretariat Award; he received the ICoMST2018 award by Robin Shorthouse for outstanding contribution to advancing Meat Science and the ICoMST2018 Meat Science journal award for best presentation in Australia. He has published more than 100 peer-reviewed papers in international journals, edited 2 books; has 18 book chapters, 50 proceedings, along with over 150 contributions in national and international conferences. Dr. Mohammed Gagaoua is scientific editor and/or member of editorial boards of several journals including *Meat Science, Meat Technology, Scientific African, Chemical and Biological Technologies in Agriculture, Peer Community in Animal Science, Foods, Frontiers in Animal Science*.

## Brigitte Picard

Dr. Brigitte Picard is a Director of Research at the National Research Institute for Agriculture, Food and Environment (INRAE) in France. After a bachelor at the University of Clermont-Ferrand and a thesis on the baking quality of hybrid wheat, she was recruited by INRA in 1991. Her research topic at the Joint Unit of Research on Herbivores concerned skeletal muscle development and ruminants' meat quality. Her skills include skeletal muscle physiology, analysis of proteins (electrophoresis, Western blot, ELISA, and immunohistochemistry), primary cells cultures (myoblasts and satellite cells), and proteomic analysis of skeletal muscle. Her work focuses on the prediction of beef tenderness from proteomic biomarkers and the link between rearing practices and beef sensory qualities. Brigitte, with an h-index of 46, is the author of more than 230 peer-reviewed papers and several communications at national and international conferences. From 2004 to 2009, she was the French correspondent of the COST 925 action "The importance of prenatal events for postnatal muscle growth in relation to the quality of muscle-based foods". Then, she was a member of the COST FA1002 "Advances in Farm Animal Proteomics". From 1992 to 2016, she was the head of several INRA research teams. Since 2017, she has been co-director of the regional Ruminant Meat Consortium, comprising professionals in the meat sector and researchers.
<figure><img src="image_6.png" /></figure>
Preface to "Recent Advances in OMICs Technologies
and Application for Ensuring Meat Quality, Safety
and Authenticity"

OMICs-based approaches, also known as foodomics, are used for the in-depth characterization and better management of numerous food products, including muscle foods. In meat research, foodomics can be described as a discipline combining food and nutrition studies through the implementation of OMICs technologies to improve the quality and ensure the safety of fresh meat and processed meat products, with the objective of guaranteeing the health and well-being of consumers. OMICs technologies are further used to increase our knowledge of the pathways and biological mechanisms underlying the development and determination of intrinsic quality traits of muscle foods, hence advancing our understanding regarding variations in meat quality and the origin of certain meat quality defects and adulterations. Genomics, transcriptomics, proteomics, peptidomics, metabolomics and lipidomics, among other OMICs-based technologies, allow for comprehensive and high-throughput information to be obtained regarding the composition, safety, nutritional value, etc., of muscle foods thanks to the large amount of data that can be yielded before applying advanced statistical analyses and bioinformatics. This Special Issue aimed to gather the current advances in and applications of OMICs technologies to guarantee the quality, safety and authenticity of muscle foods.

Mohammed Gagaoua and Brigitte Picard
*Editors*
<figure><img src="image_7.png" /></figure>
Editorial

# Recent Advances in OMICs Technologies and Application for Ensuring Meat Quality, Safety and Authenticity

Mohammed Gagaoua

Food Quality and Sensory Science Department, Teagasc Food Research Centre, Ashtown,
D15 KN3K Dublin, Ireland; gmer2001@yahoo.fr or mohammed.gagaoua@teagasc.ie

Consumers and stakeholders are increasingly demanding that the meat industry guarantees high-quality meat products with stable and acceptable sensory and safety properties. To achieve this lofty goal, it is prerequisite for meat researchers to address current meat quality issues and consider certain important goals. First, it is essential to decipher the unknowns concerning the underlying mechanisms of meat quality determination and development. Second, we need a better understanding of the biochemical pathways behind the conversion of muscle into fresh meat and those related to the manufacturing steps and their impact on processed meat products. Third, it is more than necessary to refine our knowledge on the impact of pre- and post-harvest procedures on both the molecular aspects of muscle foods and the final quality and safety of meat products in order to develop management and decision tools. Over the last two decades, sophisticated OMICs technologies—genomics, transcriptomics, proteomics, peptidomics, metabolomics and lipidomics, also known as foodomics—have been powerful approaches that extended the scope of traditional methods and opened up impressive possibilities to explore the above objectives in significant ways [1–6]. Foodomics are used for in-depth characterization and better management of numerous food products including for muscle foods. Overall, these techniques aimed to study in a comprehensive manner the dynamic link(s) between the genome and the quality traits of the meat we eat compared to the traditional methods, hence improving both the accuracy and sensitivity thanks to the large quantities of data that can be generated. Accordingly, this Special Issue focused on cutting-edge research applications of OMICs tools to characterize or manage the quality of muscle foods. Eleven published papers applied transcriptomics, targeted and untargeted proteomics, metabolomics, and genomics, among others, to evaluate meat quality, to determine the molecular profiles of meat and meat products, to discover and/or evaluate biomarkers of meat quality traits, and to characterize the safety, the adulteration, and the authenticity of meat and meat products.

In the frame of the discovery and evaluation of beef quality biomarkers, González-Blanco et al. [7] assessed different extraction methods of the sarcoplasmic and myofibrillar sub-proteomes of the *Longissimus thoracis et lumborum* (LTL) to evaluate the most reliable protocol for the identification of biomarkers of dark-cutting beef condition, also known as dark, firm, and dry (DFD) meat [8]. By means of one-dimensional sodium dodecyl-sulfate polyacrylamide gel electrophoresis (SDS-PAGE), the authors investigated the protein fractions of each extraction protocol. Within the sarcoplasmic sub-proteome, the extraction buffers that contain Triton X-100 led to a higher protein extractability, while TES buffer containing Tris, EDTA, and Sucrose was effective to distinguish differences in the protein pattern between the normal and DFD meat. Within the myofibrillar sub-proteome, the non-denaturing buffer allowed higher intensity protein bands while the lysis buffer increased protein extractability with more sensitivity in the differences between the treatments. In a following paper, Sierra et al. [9] focused on the myofibrillar sub-proteome to explore the effects of production systems (intensive versus extensive) and transport and lairage (mixing versus non-mixing with unfamiliar animals) and the post-mortem time ageing (rate and extent of tenderization) of LTL muscle of yearling bulls. Twenty-one proteins

**Citation:** Gagaoua, M. Recent Advances in OMICs Technologies and Application for Ensuring Meat Quality, Safety and Authenticity. *Foods* **2022**, *11*, 2532.
https://doi.org/10.3390/foods11162532

Received: 13 August 2022

Accepted: 17 August 2022

Published: 22 August 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

<figure><img src="image_8.png" /></figure>

**Copyright:** © 2022 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
were differentially abundant due to any of the factors considered: farm, transport and lairage, and post-mortem time ageing. The proteins were from three major and interconnected pathways, such as muscle structure and associated proteins, energy metabolism and associated pathways, and heat shock proteins, of which several were known biomarkers of beef tenderness [4,10]. The study by Zhu et al. [11] applied a shotgun proteomics approach to identify biomarkers of beef tenderness evaluated using Warner–Bratzler shear force on young Limousin-sired bulls reared under an Irish production system. The authors revealed 34 putative protein biomarkers discriminating between the tender and tough meat groups. These proteins belong to biological pathways related to muscle structure, heat shock proteins, energy metabolism, response to oxidative stress, and apoptosis, from which 23 belong to the previous list of biomarkers of beef tenderness gathered by Gagaoua and co-workers in one repertoire by means of an integromics data mining approach [4]. Furthermore, Zhu et al. proposed a regression model using three proteins (Myozenin 3, Bridging Integrator-1, and Mimecan) that yielded a predictive power of 79%. Another study by Gagaoua et al. [12] aimed to evaluate, by means of Reverse Phase Protein Array (RPPA) quantification (a quantitative microformat Dot-Blot approach), a list of 20 protein biomarkers previously shortlisted to explain and predict both tenderness (evaluated by WBSF) and marbling (intramuscular fat (IMF) content) of 188 Protected Designation of Origin (PDO) Maine-Anjou cows. Using three statistical methods, namely, correlations analyses, clustering of WBSF and marbling into three quality clusters, and Partial Least Squares regressions (PLS-R), several biomarkers were selected. Whatever the statistical method, seven putative biomarkers for both WBSF values and marbling were qualified as being robust, hence allowing the authors to move forward in the pipeline of biomarker discovery for beef eating qualities. In this study, 10 and 9 proteins were qualified using a large database as significantly related to the determination of beef tenderness and marbling, respectively, in PDO Maine-Anjou cows.

In lamb research, two papers evaluated the variation in color [13] using proteomics and tenderness using a combination of Iso-seq, RNA-seq, and CTCF ChIP-seq data [14]. The first study by Gao et al. [13] investigated the sarcoplasmic and myofibrillar sub-proteomes of *Longissimus lumborum* (LL, color-stable) and *Psoas major* (PM, colour-labile) from Small-tailed Han sheep in relation to color stability during post-mortem storage (1, 3, and 5 days). The study revealed that the main differentially abundant proteins were from the glycolysis, others belong to the energy metabolism enzymes, chaperones and heat shock proteins, and proteins of structure. Thanks to correlation analyses, proteins such as adenylate kinase isoenzyme 1 (AK1), Pyruvate kinase (PKM), Carbonic anhydrase 3 (CA3), and Creatine kinase M-type (CKM) were significantly related to color stability in agreement with the available proteome repertoire of meat color [5]. This study allowed to validate predictors of color discoloration in sheep meat during storage. The second paper (a communication) by Yuan et al. [14] performed an experiment on sheep from two crossbred populations, Dorper x Hu x Hu (DHH) and Dorper x Dorper x Hu (DDH), with divergent meat tenderness. The authors aimed to identify key isoforms associated with this important quality and to better understand the underlying mechanisms of alternative splicing regulations leading to the production of isoforms. The authors revealed in this preliminary study 624 differentially expressed isoforms between DDH and DHH.

Meat and processed meat products have high nutritional value and economic importance, which makes them appealing commodities for fraudulent activities. Fraud activities associated with meat and meat products include addition (allergic proteins, preservatives), dilution (addition of water for yield increase and cost reduction), substitution, and mislabeling or misdescription, which are critical issues for economic, health, and religious reasons. Therefore, meat authentication is an important concern to protect consumers from illegal and unwanted ingredients. Accordingly, three papers dealing with meat authenticity, origin, and detection of meat adulteration using OMICs methods were published [15–17]. Cai et al. [15] proposed a simple and reliable single-tube septuple PCR assay based on mitochondrial DNA to simultaneously recognize seven meat species from pig, beef, sheep,
chicken, turkey, goose, and duck. Furthermore, the authors validated the method in terms of sensitivity, specificity, robustness, and low costs for broad application to detect the origin of meat in foodstuffs with suspected adulteration. Another interesting study by Dobrovolny et al. [16] consisted of a collaborative work among 15 laboratories (inter-laboratory ring trial) that aimed to harmonize an analytical method based on DNA metabarcoding assay to detect adulteration from poultry and mammalian species. In this European study, each research team received and analyzed 16 anonymously labeled samples (8 samples, 2 subsamples each) containing six mixtures of DNA extract, one DNA extract from a model sausage, and another from maize, considered in this trial as a negative control. The evaluation parameters of the method allowed the researchers to confirm the reliability of the DNA metabarcoding approach for meat species authentication in routine analysis. The third study by Chen et al. [17] developed a duck genomic reference material by means of digital PCR platforms to detect meat adulteration through the detection of the duck *interleukin 2 (IL2)* gene. Similarly, eight independent laboratories proceeded with the validation and certification of the proposed method

Two other research papers aiming to evaluate the freshness in gilthead sea bream (*Sparus aurata*) using metabolomics [18] and a better understanding of wooden breast myopathy in commercial broilers using proteomics [19] were published in this Special Issue.

In summary, the content of this Special Issue fits in with the current trend toward the use of foodomics to ensuring the quality, safety, and authenticity of meat and meat products. We hope that this Special Issue will attract the interest of the community of meat scientists, as well as students and scholars, by inspiring them to undertake more research in this emerging and important area of research towards the development of methods and decision tools to ensure more sustainable muscle foods. Special thanks go to the authors for their valuable contributions to this Special Issue and to our colleagues who devoted their time to review the papers. We sincerely hope that readers will find this Special Issue on meat OMICS-based approaches motivating and informative.

**Funding:** This research received no external funding.

**Acknowledgments:** The author acknowledges the support of the Marie Skłodowska-Curie grant agreement No. 713654 under the project number MF20180029.

**Conflicts of Interest:** The author declares no conflict of interest.

## References

1. Munekata, P.E.S.; Pateiro, M.; López-Pedrouso, M.; Gagaoua, M.; Lorenzo, J.M. Foodomics in meat quality. *Curr. Opin. Food Sci.* **2021**, *38*, 79–85. [CrossRef]

2. Purslow, P.P.; Gagaoua, M.; Warner, R.D. Insights on meat quality from combining traditional studies and proteomics. *Meat Sci.* **2021**, *174*, 108423. [CrossRef] [PubMed]

3. Valdés, A.; Álvarez-Rivera, G.; Socas-Rodríguez, B.; Herrero, M.; Ibañez, E.; Cifuentes, A. Foodomics: Analytical Opportunities and Challenges. *Anal. Chem.* **2022**, *94*, 366–381. [CrossRef] [PubMed]

4. Gagaoua, M.; Terlouw, E.M.C.; Mullen, A.M.; Franco, D.; Warner, R.D.; Lorenzo, J.M.; Purslow, P.P.; Gerrard, D.; Hopkins, D.L.; Troy, D.; et al. Molecular signatures of beef tenderness: Underlying mechanisms based on integromics of protein biomarkers from multi-platform proteomics studies. *Meat Sci.* **2021**, *172*, 108311. [CrossRef] [PubMed]

5. Gagaoua, M.; Hughes, J.; Terlouw, E.M.C.; Warner, R.D.; Purslow, P.P.; Lorenzo, J.M.; Picard, B. Proteomic biomarkers of beef colour. *Trends Food Sci. Technol.* **2020**, *101*, 234–252. [CrossRef]

6. Gagaoua, M.; Monteils, V.; Picard, B. Data from the farmgate-to-meat continuum including omics-based biomarkers to better understand the variability of beef tenderness: An integromics approach. *J. Agric. Food Chem.* **2018**, *66*, 13552–13563. [CrossRef] [PubMed]

7. González-Blanco, L.; Diñeiro, Y.; Díaz-Luis, A.; Coto-Montes, A.; Oliván, M.; Sierra, V. Impact of Extraction Method on the Detection of Quality Biomarkers in Normal vs. DFD Meat. *Foods* **2021**, *10*, 1097. [CrossRef] [PubMed]

8. Gagaoua, M.; Warner, R.D.; Purslow, P.; Ramanathan, R.; Mullen, A.M.; López-Pedrouso, M.; Franco, D.; Lorenzo, J.M.; Tomasevic, I.; Picard, B.; et al. Dark-cutting beef: A brief review and an integromics meta-analysis at the proteome level to decipher the underlying pathways. *Meat Sci.* **2021**, *181*, 108611. [CrossRef] [PubMed]

9. Sierra, V.; González-Blanco, L.; Diñeiro, Y.; Díaz, F.; García-Espina, M.J.; Coto-Montes, A.; Gagaoua, M.; Oliván, M. New Insights on the Impact of Cattle Handling on Post-Mortem Myofibrillar Muscle Proteome and Meat Tenderization. *Foods* **2021**, *10*, 3115. [CrossRef] [PubMed]
10. Picard, B.; Gagaoua, M. Meta-proteomics for the discovery of protein biomarkers of beef tenderness: An overview of integrated studies. *Food Res. Int.* **2020**, *127*, 108739. [CrossRef] [PubMed]

11. Zhu, Y.; Gagaoua, M.; Mullen, A.M.; Kelly, A.L.; Sweeney, T.; Cafferky, J.; Viala, D.; Hamill, R.M. A Proteomic Study for the Discovery of Beef Tenderness Biomarkers and Prediction of Warner–Bratzler Shear Force Measured on Longissimus thoracis Muscles of Young Limousin-Sired Bulls. *Foods* **2021**, *10*, 952. [CrossRef] [PubMed]

12. Gagaoua, M.; Bonnet, M.; Picard, B. Protein Array-Based Approach to Evaluate Biomarkers of Beef Tenderness and Marbling in Cows: Understanding of the Underlying Mechanisms and Prediction. *Foods* **2020**, *9*, 1180. [CrossRef] [PubMed]

13. Gao, X.; Zhao, D.; Wang, L.; Cui, Y.; Wang, S.; Lv, M.; Zang, F.; Dai, R. Proteomic Changes in Sarcoplasmic and Myofibrillar Proteins Associated with Color Stability of Ovine Muscle during Post-Mortem Storage. *Foods* **2021**, *10*, 2989. [CrossRef] [PubMed]

14. Yuan, Z.; Ge, L.; Zhang, W.; Lv, X.; Wang, S.; Cao, X.; Sun, W. Preliminary Results about Lamb Meat Tenderness Based on the Study of Novel Isoforms and Alternative Splicing Regulation Pathways Using Iso-seq, RNA-seq and CTCF ChIP-seq Data. *Foods* **2022**, *11*, 1068. [CrossRef] [PubMed]

15. Cai, Z.; Zhou, S.; Liu, Q.; Ma, H.; Yuan, X.; Gao, J.; Cao, J.; Pan, D. A Simple and Reliable Single Tube Septuple PCR Assay for Simultaneous Identification of Seven Meat Species. *Foods* **2021**, *10*, 1083. [CrossRef]

16. Dobrovolny, S.; Uhlig, S.; Frost, K.; Schlierf, A.; Nichani, K.; Simon, K.; Cichna-Markl, M.; Hochegger, R. Interlaboratory Validation of a DNA Metabarcoding Assay for Mammalian and Poultry Species to Detect Food Adulteration. *Foods* **2022**, *11*, 1108. [CrossRef] [PubMed]

17. Chen, X.; Ji, Y.; Li, K.; Wang, X.; Peng, C.; Xu, X.; Pei, X.; Xu, J.; Li, L. Development of a Duck Genomic Reference Material by Digital PCR Platforms for the Detection of Meat Adulteration. *Foods* **2021**, *10*, 1890. [CrossRef] [PubMed]

18. Mallouchos, A.; Mikrou, T.; Gardeli, C. Gas Chromatography–Mass Spectrometry-Based Metabolite Profiling for the Assessment of Freshness in Gilthead Sea Bream (*Sparus aurata*). *Foods* **2020**, *9*, 464. [CrossRef] [PubMed]

19. Bottje, W.G.; Lassiter, K.R.; Kuttappan, V.A.; Hudson, N.J.; Owens, C.M.; Abasht, B.; Dridi, S.; Kong, B.C. Upstream Regulator Analysis of Wooden Breast Myopathy Proteomics in Commercial Broilers and Comparison to Feed Efficiency Proteomics in Pedigree Male Broilers. *Foods* **2021**, *10*, 104. [CrossRef] [PubMed]
Article

# Impact of Extraction Method on the Detection of Quality Biomarkers in Normal vs. DFD Meat

Laura González-Blanco <fnref n="1" />,<fnref n="2" />, Yolanda Diñeiro <fnref n="1" />,<fnref n="2" />, Andrea Díaz-Luis <fnref n="3" />, Ana Coto-Montes <fnref n="2" />,<fnref n="4" />, Mamen Oliván <fnref n="1" />,<fnref n="2" /> and Verónica Sierra <fnref n="1" />,<fnref n="2" />,*

**Abstract:** The objective of this work was to demonstrate how the extraction method affects the reliability of biomarker detection and how this detection depends on the biomarker location within the cell compartment. Different extraction methods were used to study the sarcoplasmic and myofibrillar fractions of the *Longissimus thoracis et lumborum* muscle of young bulls of the Asturiana de los Valles breed in two quality grades, standard (Control) or dark, firm, and dry (DFD) meat. Protein extractability and the expression of some of the main meat quality biomarkers—oxidative status (lipoperoxidation (LPO) and catalase activity (CAT)), proteome (SDS-PAGE electrophoretic pattern), and cell stress protein (Hsp70)—were analyzed. In the sarcoplasmic fraction, buffers containing Triton X-100 showed significantly higher protein extractability, LPO, and higher intensity of high-molecular-weight protein bands, whereas the TES buffer was more sensitive to distinguishing differences in the protein pattern between the Control and DFD meat. In the myofibrillar fraction, samples extracted with the lysis buffer showed significantly higher protein extractability, whereas samples extracted with the non-denaturing buffer showed higher results for LPO, CAT, and Hsp70, and higher-intensity bands in the electrophoretic pattern. These findings highlight the need for the careful selection of the extraction method used to analyze the different biomarkers considering their cellular location to adapt the extractive process.

**Keywords:** cellular compartments; protein extractability; sarcoplasmic proteins; myofibrillar proteins; meat quality biomarkers; DFD meat; oxidative stress; proteomics

**Citation:** González-Blanco, L.; Diñeiro, Y.; Díaz-Luis, A.; Coto-Montes, A.; Oliván, M.; Sierra, V. Impact of Extraction Method on the Detection of Quality Biomarkers in Normal vs. DFD Meat. *Foods* **2021**, *10*, 1097. https://doi.org/10.3390/foods10051097

Academic Editor: Hanne Christine Bertram

Received: 6 April 2021

Accepted: 13 May 2021

Published: 15 May 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

<figure><img src="image_9.png" /></figure>

Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## 1. Introduction

Variations in meat quality depend on the specific changes that occur at the muscle cellular structure and metabolism levels, which rely on metabolic pathways triggered during the *post-mortem* conversion of muscle into meat. Changes in the protein profile of the muscle tissue can be key to understanding these processes; as such, proteomics has become a useful tool in this field [1].

Muscle is more complex than other tissues, as the subcellular architecture of skeletal muscle is different from that of mononucleated cells [2]. Therefore, the extraction of the meat proteome is influenced by the interaction of multiple factors such as the extraction method, the protein solubility, the protein location, and the *post-mortem* changes that occur during the transformation of muscle into meat [3]. To address this complexity meat scientists commonly divide the whole proteome in two fractions, sarcoplasmic and myofib-

<footnote n="1">¹ Área de Sistemas de Producción Animal, Servicio Regional de Investigación y Desarrollo Agroalimentario (SERIDA), Ctra. AS-267, PK 19, 33300 Villaviciosa, Asturias, Spain; lgblanco@serida.org (L.G.-B.); ydineiro@serida.org (Y.D.); mcolivan@serida.org (M.O.)</footnote>

<footnote n="2">² Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Av. del Hospital Universitario, s/n, 33011 Oviedo, Asturias, Spain; acoto@uniovi.es</footnote>

<footnote n="3">³ Department of Molecular Biology, Faculty of Medicine, University of Cantabria, Av. Herrera Oria, s/n, 39011 Santander, Cantabria, Spain; ANDYLUIS@hotmail.com</footnote>

<footnote n="4">⁴ Department of Morphology and Cell Biology, Faculty of Medicine, University of Oviedo, Av. Julián Clavería, 6, 33006 Oviedo, Asturias, Spain</footnote>

<footnote>* Correspondence: veroniss@serida.org; Tel.: +34-985-890-066</footnote>
rillar, which require different extraction methods due to their different extractabilities and water solubilities.

Sarcoplasmic proteins represent the 30%–35% of the total protein content of skeletal muscle and are mainly composed of metabolic proteins located in the sarcoplasm of the muscle fibers that are soluble in water or in low-ionic-strength solutions (<0.15 M). The myofibrillar proteins account for about 50% of total proteins and are mainly composed of contractile proteins that, because of their high molecular masses, structure, and being highly interconnected [4], require the use of denaturing solutions containing urea, thiourea, reducing agents (dithiothreitol (DTT) and beta-mercaptoethanol), detergents (sodium dodecyl sulfate (SDS)), and salts for their extraction and solubilization [5–7]. However, Chen et al. [5] reported the use of water or low-ionic-strength media for the extraction and solubilization of myofibrillar proteins from skeletal muscle.

Considering the above, we hypothesized that the analysis of biomarkers of the conversion of muscle into meat and the ultimate meat quality may be significantly affected by the muscle extraction method. Extraction conditions, such as buffer pH, ionic strength, type of salt, extraction volume, and homogenization, influence muscle protein extractability [8–10]. Furthermore, some of the extraction factors (reagents, pH, and ionic strength) may not be compatible with some of the analytical procedures used to determine the presence and/or abundance of the most common biomarkers. The structure of the muscle cells results in some portions of the sarcoplasm remaining between the myofibrils, complicating their protein extraction and, therefore, the analysis of some of these biomarkers. Finally, the same extractive method may perform differently depending on whether the muscle shows a compact or deteriorated structure.

To the best of our knowledge, no previous study has determined the effect of the ex-
traction method on the reliability of the determination of the main meat quality biomarkers
in different muscle cell fractions. Therefore, the objective of this work was to identify the
optimal methodology to be used for the extraction and detection of the main families of
meat quality biomarkers such as those related to oxidative status, metabolic and structural
proteins, and cell stress. We aimed to compare the reliability of protein extraction for a meat
of standard-quality grade (Control) with that for a type of defective meat (dark, firm, and
dry (DFD)), which exhibits alterations in the *post-mortem* muscle metabolism that produce
a dark color and poor processing characteristics, such as higher water-holding capacity,
unstructured texture, and higher spoilage [11–13].

## 2. Materials and Methods

2.1. *Animals*

A total of 80 young bulls from the autochthonous beef breed Asturiana de los Valles (AV) were included in this work. This breed is the second-most important in the Spanish market of protected geographical indication (PGI) fresh meat, both in production and economic value. Animals were slaughtered at 14-18 months of age, according to the commercial local market and PGI requirements, in two different slaughter batches (of 42 and 38 animals, respectively) with a one-week interval. Carcasses were transferred to a cold room at 3 °C within 2 h after slaughter. At 24 h *post-mortem*, the pH (pH24) was measured at the 13th, 10th, and 6th rib level of the *Longissimus thoracis et lumborum* (LTL) muscle of the left-half carcass using a penetration electrode coupled with a temperature probe (InLab Solids Go-ISM, Mettler-Toledo S.A.E., Barcelona, Spain). The average of the triplicate measurements was used to categorize the carcasses into two groups: Control (pH24 ≤ 6.2) and DFD (pH24 > 6.2). The pH24 threshold was set to 6.2 to ensure that the samples considered DFD were unambiguous [14]. DFD samples accounted for 9% of the total carcasses sampled. For each DFD carcass detected (n = 7), a carcass from the same farm, diet, transport, and weight but with a normal pH24 (5.4 to 5.5) was selected for the Control (n = 7) group.
## 2.2. Muscle Sample Collection

Muscle samples (20 g) were taken from the LTL at the 13th rib level at 24 h *post-mortem* for analysis of protein extractability and different biomarkers (oxidative status, sarcoplasmic and myofibrillar proteins, and stress protein). These samples were immediately snap-frozen in liquid nitrogen and stored at -80 °C until analysis.

At 24 h *post-mortem*, the LTL muscle was removed between the 6th and the 13th ribs and transported to the laboratory where it was divided into 2.5-cm steaks for the determination of the meat quality traits. The first steak was used for instrumental color and drip loss determination. The second steak was cut under sterile conditions and divided into three portions for subsequent microbiological analysis (mesophilic and *Enterobacteriaceae* total viable counts) at 3, 7, and 14 days *post-mortem*. The following three steaks were used for meat toughness measurement using the Warner–Bratzler shear force test at 3, 7, and 14 days *post-mortem*. Finally, the last steak was divided into three portions for subsequent proteomic analysis at 3, 7, and 14 days. The steaks intended for aging were vacuum-packed in 20 µm polyamide/70 µm polyethylene bags and aged in darkness under refrigerated conditions (4 ± 1 °C). After the corresponding aging period, the steaks were frozen and stored at -20 °C (-80 °C in the case of proteomics) for subsequent analysis.

## 2.3. Meat Quality Trait Measurements

Meat color was recorded on three 10 mm diameter spots on the exposed cut surface of the LTL muscle at the 7th rib level at 24 h *post-mortem*. Indicators of lightness (L*), redness (a*), and yellowness (b*) were taken after 60 min of blooming using a Minolta CM-2300d spectrophotometer, with a D65 illuminant, and a 10° standard observer in the CIE space (Konica Minolta Inc., Madrid, Spain), and the average value of the three determinations was used [15].

Meat drip loss (percent exudates) was determined by duplicates on 50 g of fresh samples taken 24 h *post-mortem* and placed in a container (Meat juice collector, Sarstedt, Germany) at 4 °C, according to the method of Honikel [16].

Meat toughness was measured on cooked meat using the Warner–Bratzler (WB) shear test as described by Diaz et al. [17]. Results are expressed as the mean WB shear force maximum load (kg) for each steak.

For microbiological analyses, meat samples were processed according to ISO 7218 (International Organization for Standardization, 2007). Firstly, each vacuum-packed portion of meat was opened (after 3, 7, and 14 days aging), a portion of 10 g was aseptically taken, and 90 mL of sterile buffered peptone water (PW, Oxoid, Basingstoke, UK) was added. The mixture was homogenized in a stomacher (IUL instruments, Barcelona, Spain) for 2 min. For microbial counts, the appropriate decimal dilutions of the samples were prepared and placed onto the corresponding medium Petri dishes. Total mesophilic aerobic microorganism counts were determined on plate count agar (PCA; Oxoid, Basingstoke, U.K.), incubated at 30 °C for 72 h; *Enterobacteriaceae* were determined on violet red bile glucose (VRBG; Oxoid, Basingstoke, UK), incubated at 37 °C for 24 h. After incubation, microbial counts were performed as described in ISO 7218:2007.

## 2.4. Muscle Extraction Methods

Figure S1 shows the flowchart of the extraction procedure for both sarcoplasmic and myofibrillar protein fractions from muscle.

### 2.4.1. Sarcoplasmic Protein Extraction

For each sample, eight different sarcoplasmic extraction methods, resulting from different combinations of four extraction buffers and two different centrifugation steps, were tested.

The four extraction buffers used were:

1. TES buffer (TES): 10 mM Tris (pH 7.6), 1 mM EDTA (pH 8.0), 0.25 M sucrose, and 0.6% protease inhibitor cocktail (P8340, Sigma-Aldrich Co., St. Louis, MO, USA) [18].
2. Sodium buffer (Na): 50 mM sodium phosphate buffer (pH 7.5) and 0.6% protease inhibitor cocktail (P8340, Sigma-Aldrich Co., St. Louis, MO, USA) [19].

3. Sodium with Triton buffer (Na + T): 50 mM sodium phosphate buffer (pH 7.5), 0.1% Triton X-100, and 0.6% protease inhibitor cocktail (P8340, Sigma-Aldrich Co., St. Louis, MO, USA) [20].

4. Potassium with Triton buffer (K + T): 10 mM potassium phosphate buffer (pH 7.4), 50 mM NaCl, 0.1% Triton X-100, and 0.6% protease inhibitor cocktail (P8340, Sigma-Aldrich Co., St. Louis, MO, USA) [21].

The homogenization of extracts was followed by two different speed centrifugation methods:

(a) $1000 \times g$, 6 min at 4 °C;

(b) $20,000 \times g$, 20 min at 4 °C.

For each meat sample and extraction method, 0.5 g of muscle was homogenized in 4 mL of the corresponding extraction buffer using a Polytron PT1200 E (Kinematica Inc., Luzern, Switzerland) two times for 15 s at maximum speed. The supernatants of the seven individuals of each sample group (Control and DFD) were collected and pooled (one pool for Control and one for DFD), aliquoted, and stored at -80 °C.

### 2.4.2. Myofibrillar Protein Extraction

For each sample, two different myofibrillar extraction methods were tested, using denaturing or non-denaturing solutions.

1. The denaturing extraction was performed on the sample residue after the extraction of sarcoplasmic proteins with the TES buffer and 20 min centrifugation at $20,000 \times g$ and 4 °C, as proposed by Bjarnadottir et al. [22]. The resulting pellet was homogenized into 4 mL of lysis buffer (10 mM Tris-HCl (pH 7.6), 7 M urea, 2 M thiourea, 2% CHAPS, and 10 mM DTT) with the polytron $2 \times 15$ s at 20,000 rpm. Subsequently, this solution was stirred for 1 h in a Multi Reax stirrer (Heidolph Instruments, Schwabach, Germany) and was centrifuged at 20,000 rpm for 20 min at 4 °C. The supernatant containing the myofibrillar proteins was collected and filtered through a nylon filter (5 mm), aliquoted, and stored at -80 °C.

2. The non-denaturing myofibrillar extraction was based on the method reported by Hashimoto et al. [23], with the following modifications: 0.5 g of muscle samples were homogenized in 4 mL of non-denaturing extraction buffer (30 mM of sodium phosphate buffer (pH 7)) and 0.6% protease inhibitor cocktail (Sigma-Aldrich Co., St. Louis, MO, USA) using a Polytron PT1200 E (Kinematica Inc., Luzern, Switzerland) two times for 15 s at maximum speed. The homogenates obtained were centrifuged at $8000 \times g$ for 20 min at 4 °C. The recovered pellet was resuspended in 4 mL of KCl phosphate buffer ((pH 7.5); 0.45 M KCl, 15.6 mM $Na_2PO_4$, and 3.5 mM $KH_2PO_4$) and vortexed. Subsequently, this solution was stirred for 30 min in a Multi Reax stirrer (Heidolph Instruments, Schwabach, Germany). The mixture was centrifuged twice at $5000 \times g$ for 15 min at 4 °C. After the centrifugation, the supernatant containing the myofibrillar proteins was recovered, aliquoted, and stored at -80 °C.

From now on, the eight different sarcoplasmic extracts are referred to as: TES 1000, TES 20,000, Na 1000, Na 20,000, Na + T 1000, Na + T 20,000, K + T 1000, and K + T 20,000, and the two myofibrillar extracts are referred to as “lysis” for the denaturing extraction and “ND” for the non-denaturing extraction.

### 2.5. Protein Extractability

The solubility of muscle proteins is the amount of protein remaining in a solution of defined characteristics after the application of a specific centrifugal force for a determined duration. The terms solubility and extractability are frequently interchanged, assuming that once the protein is solubilized, it can be readily extracted from muscle fibers or myofibrils into a solution [24]. The protein content of the different extracts was measured by the Bradford method [25].
## 2.6. Oxidative Stress

The oxidative status of the muscle tissue was assessed by the measurement of lipid oxidative damage (lipoperoxidation (LPO)) and catalase activity (CAT). LPO was analyzed by measuring the reactive aldehyde malondialdehyde (MDA) and 4-hydroxy-2-(E)-nonenal (4-HNE) using the LPO assay kit from Calbiochem (No.437634, San Diego, CA, USA) [26], which measures lipid hydroperoxides directly using redox reactions with ferrous ions, and the results are expressed as nmol MDA + 4-HNE/g protein.

Catalase activity (CAT; EC 1.11.1.6) was analyzed according to the method developed by Lubinsky and Bewley [27] using hydrogen peroxidase (H₂O₂) as the substrate. The results are expressed as µmol H₂O₂/min mg protein.

## 2.7. Sarcoplasmic and Myofibrillar Subproteome Analysis

The separation of proteins obtained with the different extraction buffers was performed using SDS-PAGE gels as described by Díaz et al. [17], with minor modifications. Sarcoplasmic (15 µg of protein) and myofibrillar (30 µg of protein) muscle extracts were denatured with sample buffer (65.8 mM Tris/HCl (pH 6.8), 2% SDS, 21% glycerol, 5% beta-mercaptoethanol, and 0.026% bromophenol blue) and boiled at 100 °C for 5 min. Samples were loaded into 1-mm dual vertical slab gels (Mini-PROTEAN® Tetra Cell, Bio-Rad Laboratories Inc., Hercules, CA, USA) and run for 2.50 h (sarcoplasmic extracts) or 2.20 h (myofibrillar extracts) at 150 V for one-dimensional electrophoresis (1D-SDS-PAGE). The resolving gel contained 12% and the stacking gel 4% of acrylamide/bis (30% acrylamide), 10% (w/v) SDS, 1.5 M Tris/HCl (pH 8.8), 0.5 M Tris/HCl (pH 6.8), 10% (w/v) ammonium persulphate, and 0.1% TEMED. Prestained molecular weight standards (Precision Plus Protein™ All Blue Standards, Bio-Rad Laboratories Inc., Hercules, CA, USA) were run on each gel to determine the protein band molecular weights. Gels were stained (50% methanol, 10% acetic acid, and QC Colloidal Coomassie from Bio-Rad) and afterward de-stained with distilled water. Three gels per sample were performed.

Stained-gel images were captured using the UMAX ImageScanner (Amersham Biosciences, Buckinghamshire, UK). SDS-PAGE densitometry analysis and band quantification were performed as described by Díaz et al. [17].

## 2.8. Stress Protein: Hsp70

Stressors, such as high temperature, hypoxia, ischemia, and oxidation, can induce the synthesis of stress proteins like the heat shock proteins (Hsps) to protect cellular proteins against denaturation [27]. Among the best-known and most-investigated Hsps is the Hsp70 family. Hsp70 is abundantly induced in the response to cellular stress in muscles [28] and it was proposed to be a key biomarker of the process of conversion of muscle into meat and, therefore, of the ultimate meat quality, as it can simultaneously indicate the tenderness, color, and WHC of meat [29], which are some of the quality traits that are more affected in DFD meat. Therefore, the expression of Hsp70-1A/B was measured by Western blotting. The homogenized tissue (90 µg protein per sample) was mixed with Laemmli sample buffer (65.8 mM Tris/HCl (pH 6.8)), 2% SDS, 21% glycerol, 5% beta-mercaptoethanol, and 0.026% bromophenol blue) and denatured by boiling at 100 °C for 5 min. The extracts were fractionated using SDS-PAGE at 200 V, and then proteins were transferred to polyvinylidene fluoride membranes (Immobilon TM-P; Millipore Corp., Burlington, MA, USA) at 350 mA. Once the membranes were blocked at 4 °C overnight with 10% (w/v) bovine seroalbumin (BSA) dissolved in Tris-buffered saline (TBS) (50 mM Tris-HCl and 150 mM NaCl, (pH 7.5)), they were incubated at 4 °C overnight with the primary antibody anti-Hsp70 (A5A) (ab2787, Abcam, Cambridge, U.K.), which detects Hsp70-1A/B (UniProtKB: P0DMV8/P0DMV9). The antibody was pre-diluted in TBS buffer containing 5% (w/v) BSA. After three washes in TBS-T (50 mM Tris-HCl (pH 7.5), 150 mM NaCl, and 0.05% Tween-20), the membranes were incubated with the corresponding horseradish-peroxidase-conjugated secondary antibody (Cell Signaling, Danvers, MA, USA) and diluted in TBS buffer with BSA 2% (w/v) for 1 h at room temperature. After
three washes in TBS-T, the immunoconjugates were detected using a chemiluminescent horseradish peroxidase substrate (WBKLS0500, Millipore Corp., Darmstadt, Germany) according to the manufacturer's instructions. The Image Studio Lite 5.2.5 program (LI-COR Biosciences, Lincoln, NE, USA) allowed us to quantify the optical density of the bands. The densitometry results are expressed as semi-quantitative optical density (in arbitrary units) of blot bands, normalized to Ponceau bands as the loading control. Three replicates per sample were performed.

## 2.9. Statistical Analysis

The normality of variables was tested using the Kolmogorov-Smirnoff test. The effect of sample type (Control vs. DFD) on the different quality traits was analyzed by a *t*-test of independent samples. For variables measured at different *post-mortem* times (WBSF, microbiological loads), the effect of aging time (with animal as the random factor) was tested. For the rest of the variables included in the study, the effect of extraction method E (eight different extraction methods for sarcoplasmic extracts and two for myofibrillar extracts) and the effect of sample type T (Control vs. DFD), and their interaction (E × T) were analyzed by ANOVA using the general linear model procedure in SPSS v. 22.0 (SPSS Inc., Chicago, IL, USA). Once the interaction between E and T was established, the effects of the extraction method and the type of sample were tested independently. When significant, differences between extraction methods were analyzed by means of Tukey's post hoc test, and the Games-Howell test when variances were not homogeneous.

# 3. Results and Discussion

## 3.1. Meat Quality Traits

As expected, DFD meat had a higher pH24 (*p* < 0.001), darker color (L *, *p* < 0.001), was less red (a *, *p* < 0.01) and less yellow (b *, *p* < 0.001), and had a higher growth of mesophilic (*p* < 0.001) and *Enterobacteriaceae* (*p* < 0.005) microorganisms at 14 days of aging (Table 1).

<table><caption>**Table 1.** Effect of quality grade (Control vs. DFD) on meat quality traits (mean ± standard deviation).</caption><thead><tr><th>Variable</th><th>Time<br>post-mortem</th><th>Control<br>(n = 7)</th><th>DFD<br>(n = 7)</th><th>Sig.</th></tr></thead><tbody><tr><td>pH</td><td>24 h</td><td>5.48 ± 0.05</td><td>6.49 ± 0.27</td><td>***</td></tr><tr><td>Drip loss (%)</td><td>48 h</td><td>1.19 ± 0.64</td><td>1.06 ± 0.31</td><td>NS</td></tr><tr><td>L*</td><td>48 h</td><td>34.35 ± 2.53</td><td>27.71 ± 2.34</td><td>***</td></tr><tr><td>a*</td><td>48 h</td><td>9.84 ± 2.82</td><td>5.83 ± 0.97</td><td>**</td></tr><tr><td>b*</td><td>48 h</td><td>11.87 ± 2.45</td><td>6.14 ± 2.38</td><td>***</td></tr><tr><td rowspan="3">Meat toughness<br>(WBSF, kg)</td><td>3 days</td><td>7.15 ± 1.74b</td><td>6.63 ± 2.50</td><td>NS</td></tr><tr><td>7 days</td><td>6.02 ± 1.31ab</td><td>5.56 ± 1.85</td><td>NS</td></tr><tr><td>14 days</td><td>4.97 ± 1.01a</td><td>5.33 ± 1.35</td><td>NS</td></tr><tr><td rowspan="3">Mesophilic<br>(log UFC/kg)</td><td>3 days</td><td>3.73 ± 0.37a</td><td>3.72 ± 1.06a</td><td>NS</td></tr><tr><td>7 days</td><td>4.31 ± 0.84a</td><td>4.42 ± 1.71a</td><td>NS</td></tr><tr><td>14 days</td><td>6.05 ± 0.37b</td><td>7.22 ± 0.64b</td><td>***</td></tr><tr><td rowspan="3">Enterobacteriaceae<br>(log UFC/kg)</td><td>3 days</td><td>1.26 ± 1.23</td><td>1.53 ± 1.32a</td><td>NS</td></tr><tr><td>7 days</td><td>1.45 ± 1.43</td><td>2.13 ± 1.64a</td><td>NS</td></tr><tr><td>14 days</td><td>3.08 ± 1.59</td><td>4.91 ± 0.83b</td><td>*</td></tr></tbody></table>

For variables measured at different times *post-mortem* (meat toughness, mesophilic, and *Enterobacteriaceae*), means in the same column followed by different letters differ statistically. DFD: dark, firm and dry; Sig.: Significance; NS: not significant; * *p* < 0.05; ** *p* < 0.01; *** *p* < 0.001.

Previous studies comparing meat from three different Spanish autochthonous breeds reported similar results for color traits with significantly higher values of L*, a*, and b* in high-pH (>6) meat from Asturiana de los Valles and Rubia Gallega [30]. Poleti et al. [31] reported lower values of the color parameters in high-pH meat when comparing beef from Nellore cattle classified into two different pH groups: high (≥6.0) and normal (<5.8). It
is known that due to the high pH, DFD meat is more prone to microbial spoilage than normal-pH meat [13]; accordingly, we found a faster reduction in the shelf life of the DFD meat at 14 days *post-mortem*. In agreement with our results, García-Torres et al. [30] found similar results with higher mesophlic loads in Rubia Gallega and Asturiana de los Valles breeds at 7 and 14 days of aging. No significant differences were found for WBSF between the Control and DFD samples in this study; however, an anomalous tenderization process was observed in DFD meat as meat toughness did not significantly decrease with aging.

Samples with a high pH24 (>6.2) in the present study were darker and their microbial spoilage was higher, so they were therefore of defective quality compared with those with a lower pH. These differences in quality traits may reflect differences at the muscle cell level (structure and metabolism), which have to be considered to understand the results obtained in this study.

### 3.2. Protein Extractability

The protein contents of the sarcoplasmic and myofibrillar fractions obtained by the different extraction methods tested are shown in Figure 1.

<figure><img src="image_10.png" /></figure>

**Figure 1.** Protein content (mean ± SEM) of the (A) sarcoplasmic and (B) myofibrillar fractions from Control (blue) and DFD (red) meat samples and the different extraction methods. Charts with different letters (blue for Control and red for DFD) were significantly different between extraction methods at *p* < 0.05. Asterisks indicate significant differences between Control and DFD samples within the same extraction method. \* *p* < 0.05; \*\*\* *p* < 0.001. TES 1000: TES buffer and 1000× g, 6 min; TES 20,000: TES buffer and 20,000× g, 20 min; Na 1000: sodium phosphate buffer and 1000× g, 6 min; Na 20,000: sodium phosphate buffer and 20,000× g, 20 min; Na + T 1000: sodium phosphate buffer with Triton X-100 and 1000× g, 6 min; Na + T 20,000: sodium phosphate buffer with Triton-X100 and 20,000× g, 20 min; K + T 1000: potassium phosphate buffer with Triton X-100 and 1000× g, 6 min; K + T 20,000: potassium phosphate buffer with Triton X-100 and 20,000× g, 20 min; Lysis: denaturing extraction with lysis buffer; ND: non-denaturing extraction.

In the sarcoplasmic fraction, higher extractability was obtained with buffers containing Triton X-100 (Na + T and K + T), which is a type of non-ionic detergent used for cell lysis, that is, for the disruption of cell membranes and the consequent release of intracellular materials that breaks protein-lipid and lipid-lipid associations, and generally does not denature proteins. The higher protein content in these extracts could be explained by Triton X-100 helping to solubilize most membrane proteins in their native and active form, retaining their protein interactors. In the sarcoplasmic fraction, the centrifugation speed only affected the protein solubility of some DFD extracts (TES, Na + T, and K + T), being significantly higher (*p* < 0.05) at 1000× g, whereas no significant differences were found for the Control samples. This could be related to a higher disintegration of the muscle structure in DFD meat, which resulted in the higher extraction capability of sarcoplasmic proteins retained within the sarcoplasm portions embedded between the myofibrils.
In the myofibrillar fraction, the lysis buffer showed significantly higher protein ex-
tractability than ND for both Control and DFD samples. Lysis buffer contains some agents
such as urea, thiourea, CHAPS, and DTT, which may have been responsible for these
extractability differences [32]. Urea is a chaotropic agent that denatures proteins by disrupt-
ing noncovalent and ionic links between amino acids [33], whereas thiourea improves the
solubilization of hydrophobic membrane proteins [34]; therefore, their combination is used
to extract proteins that are otherwise insoluble. CHAPS prevents hydrophobic interaction
and DTT aids in the solubilization of complex mixtures by reduction of disulfide bonds,
avoiding protein aggregation or precipitation [35]. The combination of these components
increases solubilization and proteins extractability [36,37].

3.3. *Oxidative Stress*

Figures 2 and 3 show the results for LPO and CAT in both the sarcoplasmic and myofibrillar fractions. The TES buffer was incompatible with some reagents present in the LPO assay kit (probably EDTA) and produced unstable results, so the results of the TES extracts were not considered for this assay.

DFD samples showed higher LPO values (Figure 2) in all extracts and in both cellular
fractions. This could be related to a higher pre-slaughter stress situation, which increases
the oxidative damage of lipids in the cells of the animals that finally produced DFD
carcasses. Within the sarcoplasmic fraction, the K + T 1000 extraction method showed
higher LPO values (p < 0.001) in both the Control and DFD samples, whereas in the
myofibrillar fraction, the ND buffer showed higher LPO values (p < 0.001).

CAT activity was higher in the Control samples in both the sarcoplasmic and myofib-
rillar extracts, which seems to be related to a higher antioxidant defense in the muscle of
standard-quality meat obtained in the absence of pre-slaughter stress. In the sarcoplasmic
extracts, higher CAT activity was found in the samples extracted with Na buffer inde-
pendent of the centrifugation speed. However, its determination in extracts containing
Triton X-100 was difficult due to the non-ionic detergents interfering with ultra-violet (UV)
spectrophotometry, thus producing unstable results.

<figure><img src="image_11.png" /></figure>

**Figure 2.** Lipoperoxidation (mean ± SEM) of (A) sarcoplasmic and (B) myofibrillar fractions from Control (blue) and DFD (red) meat samples. Charts with different letters (blue for and red for DFD) were significantly different between extraction methods at *p* < 0.05. Asterisks indicate significant differences between the Control and DFD samples within the same extraction procedure. ** *p* < 0.01; *** *p* < 0.001. Na 1000: sodium phosphate buffer and 1000× *g*, 6 min; Na 20,000: sodium phosphate buffer and 20,000× *g*, 20 min; Na + T 1000: sodium phosphate buffer with Triton X-100 and 1000× *g*, 6 min; Na + T 20,000: sodium phosphate buffer with Triton-X100 and 20,000× *g*, 20 min; K + T 1000: potassium phosphate buffer with Triton X-100 and 1000× *g*, 6 min; K + T 20,000: potassium phosphate buffer with Triton X-100 and 20,000× *g*, 20 min; Lysis: denaturing extraction with lysis buffer; ND: non-denaturing extraction.
<figure><img src="image_12.png" /><figcaption>**Figure 3.** Catalase activity (mean ± SEM) of the (A) sarcoplasmic and (B) myofibrillar fractions from Control (blue) and DFD (red) meat samples. Charts with different letters (blue for Control and red for DFD) were significantly different between extraction methods at *p* < 0.05. Asterisks indicate significant differences between the Control and DFD samples within the same extraction procedure. \* *p* < 0.05; \*\* *p* < 0.01; \*\*\* *p* < 0.001. TES 1000: TES buffer and 1000× g, 6 min; TES 20,000: TES buffer and 20,000× g, 20 min; Na 1000: sodium phosphate buffer and 1000× g; 6 min; Na 20,000: sodium phosphate buffer and 20,000× g, 20 min; Na + T 1000: sodium phosphate buffer with Triton X-100 and 1000× g, 6 min; Na + T 20,000: sodium phosphate buffer with Triton-X100 and 20,000× g, 20 min; K + T 1000: potassium phosphate buffer with Triton X-100 and 1000× g, 6 min; K + T 20,000: potassium phosphate buffer with Triton X-100 and 20,000× g, 20 min; Lysis: denaturing extraction with lysis buffer; ND: non-denaturing extraction.</figcaption></figure>

In the myofibrillar fraction, CAT activity was significantly higher in the extracts obtained by the non-denaturing method (*p* < 0.05).

### 3.4. Sarcoplasmic and Myofibrillar Subproteome

SDS-PAGE gels allowed for the separation of 26 protein bands (ranging from 15 to 200 kDa) from the muscle sarcoplasmic subproteome, as shown in Figure 4, which shows the protein pattern obtained with the different extraction methods at the maximum centrifugation speed tested (20,000× g) for both types of meat samples (Control and DFD).

<figure><img src="image_13.png" /><figcaption>**Figure 4.** The 1D-SDS-PAGE gel image of sarcoplasmic subproteome from the Control and DFD meat samples extracted with different buffers (TES, Na, Na + T, and K + T) at 20,000× g. Mk: prestained molecular weight marker (All Blue prestained, Biorad). Band names are denoted by S (sarcoplasmic protein) followed by a number.</figcaption></figure>
The complete details of the results (means ± SEM) for the significant sarcoplasmic bands obtained with the different extraction methods and type of samples are provided in Table S1. The analysis of the main factors studied (extraction method, type of sample, and their interaction) showed a significant interaction for four bands (S1, S4, S18, and S21). Once these bands were discarded, the differences between extraction methods were analyzed including all the samples regardless of being Control or DFD. Table 2 shows a total of 13 sarcoplasmic bands with significant differences in band intensity between extraction methods.

<table><caption>**Table 2.** Effect of the extraction method on the sarcoplasmic subproteome bands intensity (optical density in arbitrary units).</caption><thead><tr><th>Sarcoplasmic<br>Bands<br>(MWe<sup>1</sup>)</th><th>TES<br>1000</th><th>TES<br>20,000</th><th>Na<br>1000</th><th>Na<br>20,000</th><th>Na + T<br>1000</th><th>Na + T<br>20,000</th><th>K + T<br>1000</th><th>K + T<br>20,000</th><th>SEM</th><th>Sig.</th></tr></thead><tbody><tr><td>S2 (137.9 kDa)</td><td>0.188</td><td>0.211</td><td>0.246</td><td>0.262</td><td>0.431</td><td>0.446</td><td>0.42</td><td>0.316</td><td>0.061</td><td>**</td></tr><tr><td>S3 (115.8 kDa)</td><td>0.305a</td><td>0.249a</td><td>0.406ab</td><td>0.344a</td><td>0.753bc</td><td>0.911c</td><td>0.602abc</td><td>0.421ab</td><td>0.084</td><td>***</td></tr><tr><td>S6 (81.31 kDa)</td><td>0.55a</td><td>0.572a</td><td>1.556b</td><td>1.256b</td><td>1.442b</td><td>1.559b</td><td>1.705b</td><td>1.482b</td><td>0.146</td><td>***</td></tr><tr><td>S10 (53.60 kDa)</td><td>0.895a</td><td>0.992ab</td><td>0.998ab</td><td>1.031ab</td><td>1.336b</td><td>1.325b</td><td>1.211ab</td><td>1.197ab</td><td>0.087</td><td>**</td></tr><tr><td>S11 (50.70 kDa)</td><td>1.264abc</td><td>1.107a</td><td>1.244abc</td><td>1.223ab</td><td>1.438abcd</td><td>1.68d</td><td>1.603bcd</td><td>1.611cd</td><td>0.086</td><td>***</td></tr><tr><td>S12 (45.55 kDa)</td><td>8.244ab</td><td>8.814b</td><td>8.101ab</td><td>7.837ab</td><td>7.536ab</td><td>7.049a</td><td>7.474ab</td><td>6.874a</td><td>0.351</td><td>**</td></tr><tr><td>S13 (40.72 kDa)</td><td>10.805b</td><td>10.448ab</td><td>10.159ab</td><td>10.154ab</td><td>9.258a</td><td>9.34ab</td><td>8.513a</td><td>8.98a</td><td>0.427</td><td>**</td></tr><tr><td>S14 (37.6 kDa)</td><td>8.775ab</td><td>8.707ab</td><td>9.287b</td><td>9.16ab</td><td>8.524ab</td><td>8.413ab</td><td>8.18a</td><td>8.345ab</td><td>0.242</td><td>*</td></tr><tr><td>S15 (34.74 kDa)</td><td>10.859bcd</td><td>10.457abc</td><td>11.59d</td><td>11.376cd</td><td>10.74abcd</td><td>9.77a</td><td>10.341abc</td><td>9.857ab</td><td>0.241</td><td>***</td></tr><tr><td>S16 (32.14 kDa)</td><td>8.128b</td><td>8.079b</td><td>6.672a</td><td>6.878a</td><td>6.53a</td><td>6.136a</td><td>6.446a</td><td>6.38a</td><td>0.211</td><td>***</td></tr><tr><td>S17 (29.74 kDa)</td><td>1.576a</td><td>1.826a</td><td>2.399c</td><td>2.835cd</td><td>2.649cd</td><td>2.677cd</td><td>3.28d</td><td>3.179d</td><td>0.109</td><td>***</td></tr><tr><td>S19 (26.68 kDa)</td><td>2.889b</td><td>2.89b</td><td>2.258a</td><td>2.546ab</td><td>2.419ab</td><td>2.585ab</td><td>2.22a</td><td>2.521ab</td><td>0.128</td><td>***</td></tr><tr><td>S20 (25.76 kDa)</td><td>4.162b</td><td>4.103b</td><td>3.635ab</td><td>3.661ab</td><td>3.425a</td><td>3.329a</td><td>3.482a</td><td>3.483a</td><td>0.133</td><td>***</td></tr></tbody></table>

Means within a row followed by different letters were significantly different at * *p* < 0.05; ** *p* < 0.01; *** *p* < 0.001. ¹ MWe is the experimental molecular weight (kDa); SEM: standard error of the mean; Sig.: significance.

Bands of higher molecular weight (over 50 kDa) showed higher intensities when using the Na + T 20,000 method, whereas protein bands under 50 kDa showed higher intensities with the TES buffer. The majority of the sarcoplasmic bands separated by 1D SDS-PAGE fell into the <50 kDa molecular weight range; the TES buffer seems to be a better option for studying the sarcoplasmic subproteome.

When studying the effect of sample type, the extractions made with the TES buffer showed more protein bands (S1, S3, S4, S5, S9, S10, S15, S16, S18, S19, S20, and S21) with significant differences (*p* < 0.05) between the Control and DFD samples (Table S2), which reinforces the conclusion that this method of extraction is the most suitable for the electrophoretic analysis of the sarcoplasmic fraction of the muscle tissue. When skeletal muscle is homogenized in a sucrose medium, as in the case of TES buffer, it forms a gelatinous consistency that inhibits the disruption of the myofibrils; therefore, the differences found between the Control and DFD extracts reflect the differences in the *post-mortem* evolution of the myofibril disruption (faster in the defective and unstructured DFD meat), thus reporting an essential information of the differences in biomarker patterns between both meat types.

In the myofibrillar fraction, despite the large differences (*p* < 0.001) in protein extractability, both extracts provided a similar and adequate separation of 34 well-defined protein bands in the range of molecular weights from 15 to 250 kDa (Figure 5).

The complete details of the results (means ± SEM) for significant myofibrillar bands are provided in Table S3. The analysis of the main factors (extraction method, type of sample, and their interaction) showed a significant interaction of extraction method and sample type for three bands (M16, M17, and M26). Once these bands were discarded, differences between extraction methods were compared including all the samples regardless of being Control or DFD. Table 3 shows a total of 14 myofibrillar bands with significant differences in band intensity between the extraction methods.
<figure><img src="image_14.png" /><figcaption>**Figure 5.** The 1D-SDS-PAGE gel image of myofibrillar subproteome from Control and DFD meat samples extracted with different buffers: lysis and non-denaturing buffer (ND). Mk: prestained molecular weight marker (All Blue prestained, Biorad). Band names are denoted by M (myofibrillar protein) followed by a number.</figcaption></figure>

<table><caption>**Table 3.** Effect of extraction method on the myofibrillar subproteome band intensity (optical density in arbitrary units).</caption><thead><tr><th>Myofibrillar Bands (MWe <sup>1</sup>)</th><th>Lysis</th><th>ND</th><th>SEM</th><th>Sig.</th></tr></thead><tbody><tr><td>M2 (170.8 kDa)</td><td>1.667</td><td>2.464</td><td>0.142</td><td>**</td></tr><tr><td>M3 (143.58 kDa)</td><td>3.139</td><td>5.893</td><td>0.417</td><td>***</td></tr><tr><td>M6 (110.53 kDa)</td><td>0.719</td><td>1.066</td><td>0.096</td><td>***</td></tr><tr><td>M11 (74.77 kDa)</td><td>0.896</td><td>0.500</td><td>0.063</td><td>**</td></tr><tr><td>M18 (49.7 kDa)</td><td>0.698</td><td>1.245</td><td>0.113</td><td>***</td></tr><tr><td>M19 (47.58 kDa)</td><td>0.899</td><td>1.717</td><td>0.104</td><td>**</td></tr><tr><td>M20 (41.07 kDa)</td><td>14.276</td><td>8.959</td><td>1.033</td><td>**</td></tr><tr><td>M23 (34.80 kDa)</td><td>5.503</td><td>4.660</td><td>0.242</td><td>*</td></tr><tr><td>M24 (32.76 kDa)</td><td>4.874</td><td>7.404</td><td>0.303</td><td>***</td></tr><tr><td>M27 (26.31 kDa)</td><td>1.466</td><td>2.128</td><td>0.085</td><td>***</td></tr><tr><td>M30 (19.46 kDa)</td><td>3.033</td><td>2.388</td><td>0.128</td><td>***</td></tr><tr><td>M31 (18.40 kDa)</td><td>0.693</td><td>0.406</td><td>0.051</td><td>***</td></tr><tr><td>M32 (17.09 kDa)</td><td>2.254</td><td>3.100</td><td>0.117</td><td>***</td></tr><tr><td>M34 (14.94 kDa)</td><td>0.817</td><td>2.314</td><td>0.123</td><td>***</td></tr></tbody></table>

Means within a row were significantly different at * *p* < 0.05; ** *p* < 0.01; *** *p* < 0.001. ¹: Mwe: the experimental molecular weight (kDa); Sig.: significance; Lysis: denaturing extraction with lysis buffer; ND: non-denaturing extraction.

Overall, 10 bands (M2, M3, M6, M18, M19, M24, M27, M32, and M34) showed higher intensity in the non-denaturing extracts, which indicated that the ND method, even despite its lower protein extractability, adequately separated well-defined myofibrillar protein bands by SDS-PAGE. Lysis buffer resulted in major band intensity for M11, M20, M23, M30, and M31 bands.

The effect of sample type was also analyzed for each buffer independently. In the lysis extracts, seven bands (M10, M16, M17, M26, M31, M32, and M34) showed significant differences between the Control and DFD meat, whereas only two bands (M26 and M31)
showed significant differences in the ND extracts (Table S4). Therefore, it seems that the lysis buffer was more sensitive to changes in the muscle structure, probably due to the denaturing conditions increasing the extraction of proteins of a low molecular weight [32], leading to differences between samples of different muscle structure and compactness.

Considering the results for the sarcoplasmic and myofibrillar subproteome, it seems that some proteins were easily extracted with most of the buffers, whereas others remained linked to cellular organelles and membranes or in the sarcoplasm portions embedded within the myofibrils, which complicated their extraction. The intensity of these effects depends on the evolution of the muscle dismantlement in the process of conversion of muscle into meat and the resulting meat quality grade.

### 3.5. Stress Protein: Hsp70

Proteomic studies have reported the differential expression of Hsp70 in meat with variable quality traits [22,38]. Several studies have correlated meat quality with Hsp70 under stress situations [39–41].

Our results (Figure 6) showed clear differences in Hsp70 expression between the sarcoplasmic and myofibrillar fractions. For the sarcoplasmic fraction, higher Hsp70 expression was found in DFD samples, with these differences being significant for the TES 1000, TES 20,000, and Na 1000 extraction methods. However, the sarcoplasmic fraction of the Control samples showed higher Hsp70 expression when extracted with sodium buffers and higher centrifugal speed (Na 20,000 and Na + T 20,000).

For the myofibrillar fraction, higher Hsp70 expression was found for ND extracts, but contrary to what was observed for the sarcoplasmic fraction, the Control samples showed higher Hsp70 expression independent of the extractive method used (lysis and ND). This higher expression in Control samples from the myofibrillar extracts may have been due to the protective role that Hsp70 played against muscle dismantlement in the early *post-mortem* stages. Previous studies showed that the majority of Hsp70 is readily diffusible within the cytoplasm in non-stressed muscle fibers; after stress, Hsp70 primarily binds to and stabilizes the structure and function of cell membranes [42]. Furthermore, under stress situations such as during muscle-damaging exercise, Hsp70 translocates and accumulates to the cytoskeletal and myofibrillar proteins [43]. Xing et al. [44] found that Hsp70 was present in the cytoplasm and on the surface membranes of cells from the *Pectoralis major* muscle in normal-quality chicken meat following stress. Hsp70 was present on the surface membranes and extracellular matrix but was barely visible in the cytoplasm of the PSE-like samples, that is, low-quality meat due to stress. This diffusion capacity of Hsp70 may explain the differences found in this work between extracts. Under the stressful situation produced from slaughter, Hsp70 may translocate to the myofibrillar fraction in an attempt to stabilize the muscle structure, which would explain its increased expression in the myofibrillar fraction of the Control samples. However, in the DFD meat, more Hsp70 is easily removed and extracted from the sarcoplasmic fraction, showing the movement of Hsp70 from the inner myofibrillar compartment to the sarcoplasm due to the faster dismantlement of the muscle.
<figure><img src="image_15.png" /><figcaption>**Figure 6.** Hsp70 Western blotting results. (A) Representative immunoblot analyses of Hsp70 protein expression of sarcoplasmic extracts at (i) 1000× g and (ii) 20,000× g, and (iii) Hsp70 expression of myofibrillar extracts. Ponceau staining was used as a loading control. (B) Expression of Hsp70 (mean ± SEM from three independent experiments) of sarcoplasmic extracts (left) and (C) myofibrillar extracts (right) from Control (blue) and DFD (red) meat samples. Charts with different letters (blue for Control and red for DFD) were significantly different between extraction methods at *p* < 0.05. Asterisks indicate significant differences between the Control and DFD samples within the same extraction procedure. \* *p* < 0.05; \*\* *p* < 0.01; \*\*\* *p* < 0.001. TES 1000: TES buffer and 1000× g, 6 min; TES 20,000: TES buffer and 20,000× g, 20 min; Na 1000: sodium phosphate buffer and 1000× g, 6 min; Na 20,000: sodium phosphate buffer and 20,000× g, 20 min; Na + T 1000: sodium phosphate buffer with Triton X-100 and 1000× g, 6 min; Na + T 20,000: sodium phosphate buffer with Triton-X100 and 20,000× g, 20 min; K + T 1000: potassium phosphate buffer with Triton X-100 and 1000× g, 6 min; K + T 20,000: potassium phosphate buffer with Triton X-100 and 20,000× g, 20 min; Lysis: denaturing extraction with lysis buffer; ND: non-denaturing extraction.</figcaption></figure>

## 4. Conclusions

Within the sarcoplasmic fraction, buffers containing Triton X-100 led to a higher protein extractability, LPO detection, and determination of proteins with high molecular weight. However, TES buffer was more sensitive for the detection of Hsp70 expression and the electrophoretic bands of lower molecular weight, showing increased ability to discriminate between the meat samples with different metabolisms and the degree of cell dismantlement (Control vs. DFD). In the myofibrillar fraction, the non-denaturing buffer reported higher LPO, CAT activity, and Hsp70 expression, and showed higher intensity bands in the electrophoretic pattern; however, the lysis buffer increased protein
extractability and its electrophoretic pattern was more sensitive to differences between the Control and DFD samples.

These findings highlight the need to select the most appropriate extraction method for each biomarker family and muscle structure type, and the need to consider different cell fractions and the movements of proteins between cytoskeletal and myofibrillar structures, for an accurate and reliable study of the process of conversion of the muscle into meat.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10.3390/foods10051097/s1, Figure S1. Flowchart of the different extraction methods for sarcoplasmic and myofibrillar proteins from beef muscle. 1: TES buffer; 2: soidum phosphate buffer; 3: Naa+T: sodium phosphate buffer with triton X-100; K+T: potassium phosphate buffer with Triton X-100; ND: non-denaturing extraction. Table S1: Effect of extraction method (TES 1000, TES 20,000, Na 1000, Na 20,000, Na+T 1000, Na+T 20,000, K+T 1000 and K+T 20,000), type of sample (CONTROL vs. DFD) and their interaction on sarcoplasmic subproteome bands' intensity (optical density in arbitrary units). Table S2: The *p*-values for the effect of sample type (CONTROL vs. DFD) on the sarcoplasmic subproteome bands intensity (optical density in arbitrary units) obtained with the different extration methods. Table S3: Effect of extraction method (Lysis and Non-denaturant), type of sample (CONTROL vs. DFD) and their interaction on myofibrillar subproteome bands' intensity (optical density in arbitrary units). Table S4: Effect of meat type (CONTROL vs. DFD) within each extraction method (Lysis vs. Non-denaturant) on myofibrillar subproteome bands' intensity (optical density in arbitray units).

**Author Contributions:** Conceptualization: M.O., V.S., and A.C.-M.; methodology: L.G.-B., M.O., and V.S.; investigation: L.G.-B., A.D.-L., and Y.D.; formal analysis: V.S. data curation. L.G.-B.; writing—original draft preparation: V.S.; writing—review and editing: M.O. and A.C.-M.; visualization. L.G.-B.; supervision. M.O.; funding acquisition, V.S. and M.O. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Ministerio de Ciencia, Innovación y Universidades (MCIU) (Spain), Agencia Estatal de Investigación (Spain) and FEDER funds, under the project number RTI2018-096162-RC21. The APC was funded by RTI2018-096162-RC21. L.G-B acknowledges her contract to the MCIU (PRE2019-091053).

**Data Availability Statement:** Data are available upon request.

**Acknowledgments:** We thank the staff of the Area of Livestock Production Systems from SERIDA, Matadero Central de Asturias S. L., Alimerka S. L., and ASINCAR for their valuable help in this work. We also thank Vega-Naredo for his helpful suggestions on this work.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Article

# New Insights on the Impact of Cattle Handling on Post-Mortem Myofibrillar Muscle Proteome and Meat Tenderization

Verónica Sierra <fnref n="1" />,<fnref n="2" />, Laura González-Blanco <fnref n="1" />,<fnref n="2" />, Yolanda Diñeiro <fnref n="1" />,<fnref n="2" />, Fernando Díaz <fnref n="1" />, María Josefa García-Espina <fnref n="1" />, Ana Coto-Montes <fnref n="2" />,<fnref n="3" />, Mohammed Gagaoua <fnref n="4" />,* and Mamen Oliván <fnref n="1" />,<fnref n="2" />,*

**Abstract:** This study investigated the effect of different cattle management strategies at farm (Intensive vs. Extensive) and during transport and lairage (mixing vs. non-mixing with unfamiliar animals) on the myofibrillar subproteome of *Longissimus thoracis* et *lumborum* (LTL) muscle of "Asturiana de los Valles" yearling bulls. It further aimed to study the relationships with beef quality traits including pH, color, and tenderness evaluated by Warner-Bratzler shear force (WBSF). Thus, comparative proteomics of the myofibrillar fraction along meat maturation (from 2 h to 14 days *post-mortem*) and different quality traits were analyzed. A total of 23 protein fragments corresponding to 21 unique proteins showed significant differences among the treatments (*p* < 0.05) due to any of the factors considered (Farm, Transport and Lairage, and *post-mortem* time ageing). The proteins belong to several biological pathways including three structural proteins (MYBPC2, TNNT3, and MYL1) and one metabolic enzyme (ALDOA) that were affected by both Farm and Transport/Lairage factors. ACTA1, LDB3, and FHL2 were affected by Farm factors, while TNNI2 and MYLPF (structural proteins), PKM (metabolic enzyme), and HSPB1 (small Heat shock protein) were affected by Transport/Lairage factors. Several correlations were found between the changing proteins (PKM, ALDOA, TNNI2, TNNT3, ACTA1, MYL1, and CRYAB) and color and tenderness beef quality traits, indicating their importance in the determination of meat quality and their possible use as putative biomarkers.

**Keywords:** intensive management; extensive management; mixing unfamiliar animals; myofibrillar proteins; pre-slaughter stress; protein biomarkers

## 1. Introduction

Improving beef production and meat quality to cope with meet consumer demands is a major concern of the livestock production sector. It is well known that cattle intrinsic factors, such as breed and genetics, have a decisive influence on beef production and on the ultimate meat quality, therefore, different breeding strategies and meat maturation procedures must be adapted to the genetic diversity of the animals [1,2]. In this sense, there is great interest in promoting the development of native cattle breeds, as they seem to be more adapted to regional production systems and for promoting proximity trade as a sustainability strategy [3]. Apart from the intrinsic factors, there are also extrinsic factors, overall, from farm-to-fork, related to the routine handling of animals and animal-human interactions that must be considered to ensure beef quality [4]. Among them, production

**Citation:** Sierra, V.; González-Blanco, L.; Diñeiro, Y.; Díaz, F.; García-Espina, M.J.; Coto-Montes, A.; Gagaoua, M.; Oliván, M. New Insights on the Impact of Cattle Handling on Post-Mortem Myofibrillar Muscle Proteome and Meat Tenderization.
*Foods* **2021**, *10*, 3115. https://doi.org/10.3390/foods10123115

Academic Editor: Thierry Astruc

Received: 16 November 2021

Accepted: 13 December 2021

Published: 15 December 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

<figure><img src="image_16.png" /></figure>

Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

<footnote n="1">¹ Área de Sistemas de Producción Animal, Servicio Regional de Investigación y Desarrollo Agroalimentario (SERIDA), Ctra. AS-267, PK 19, 33300 Villaviciosa, Spain; veroniss@serida.org (V.S.); lgblanco@serida.org (L.G.-B.); ydineiro@serida.org (Y.D.); ferdm89@gmail.com (F.D.); mjgarcia@serida.org (M.J.G.-E.)</footnote>

<footnote n="2">² Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Av. del Hospital Universitario, s/n, 33011 Oviedo, Spain; acoto@uniovi.es</footnote>

<footnote n="3">³ Department of Morphology and Cell Biology, Faculty of Medicine, University of Oviedo, Av. Julián Claveria, 6, 33006 Oviedo, Spain</footnote>

<footnote n="4">⁴ Food Quality and Sensory Science Department, Teagasc Food Research Centre, Dublin 15, D15 KN3K Ashtown, Ireland</footnote>

<footnote>* Correspondence: Mohammed.Gagaoua@teagasc.ie or gmer2001@yahoo.fr (M.G.); mcolivan@serida.org (M.O.)</footnote>
system and feeding strategies play an important role, not only due to the effect that dietary
components may exert on the animal's growth rate, and muscle/meat characteristics [2,5,6],
but also due to their influence on the animal's physiology, social behavior, and reactivity to
stress [7–10]. Moreover, psychological, and physiological status of the animals can affect
final meat quality [10]. In fact, cattle are herd animals that establish social orders, so the
regrouping of animals or mixing with unfamiliar animals during transport and lairage,
despite being a common husbandry practice, can have a detrimental effect on animal
welfare, increasing animal stress [11,12].

On the other hand, animal handling may affect animal's emotional state, hence induc-
ing pre-slaughter stress (PSS), whose influence on the *post-mortem* process of muscle-to-meat
conversion has been shown in pigs [13,14] and in cattle [3,9,10,15-18]. Those biochemi-
cal changes were evidenced using several high-throughput OMICS methods, including
proteomics that revealed, for instance, that the meat tenderizing process involves myriad
pathways, such as the degradation of structural proteins, energy metabolism pathways,
response to stress, apoptosis, autophagy, and signaling pathways [19,20] as confirmed
recently by the integromics meta-analysis study of Gagaoua et al. [21]. Comparative pro-
teomics appeared to be a useful tool to study the biological pathways underpinning the
effect of PSS on the ultimate meat quality. In this context, proteomic approaches point
out the possible identification of putative biomarkers from the sarcoplasmic subproteome
fraction of the *post-mortem* muscle [9,17,21,22]. Since tenderness and color are considered as
important beef quality traits for consumers., the impact of PSS on these attributes is worthy
of investigation. Indeed, PSS is proven to have a detrimental effect due to the changes that
induces in the enzymatic processes that, for example, induce the breakdown of myofibrillar
structure mainly composed of structural and contractile proteins [23-26].

Based on the above, this study aimed to apply a proteomics approach to investigate the
effect of pre-slaughter factors such as mixing unfamiliar animals during the transport and
lairage period on the myofibrillar subproteome of young "Asturiana de los Valles" bulls
reared under two divergent rearing practices (intensive or extensive management systems).
This trial further provides an opportunity to identify putative protein biomarkers [27]
related to beef tenderization and PSS.

**2. Materials and Methods**

2.1. *Animals and Experimental Design*

This trial used 24 yearling bulls of "Asturiana de los Valles" (AV) breed that were slaughtered between 13 and 15 months of age. AV breed is a native breed from the north of Spain, with a high growth rate and low-fat content [28,29] and protected by the quality label "Ternera Asturiana", which is one of the most significant in terms of production and economic value [30]. Calves were managed with their mothers from birth to weaning, fed on concentrate and barley straw ad libitum during the winter, and assigned in spring to two different farm management systems:

(1) Intensive ("I") (n = 12), with animals managed indoors, in pens of 6 × 6 m (6 animals per pen) and finished for 100 days before slaughter with 8 kg/day of concentrate (84% barley meal, 10% soya meal, 3% fat, 3% minerals, vitamins and oligoelements) and 2 kg/day of barley straw

(2) Extensive ("E") (n = 12), with animals managed outdoors in two 1.5 ha plots (6 animals per plot) and finished for 100 days before slaughter grazing on ryegrass and clover pasture + 3.5 kg/day of supplementation with concentrate.

At an approximate slaughter weight of 500 kg, the animals were transported in groups of six to a commercial abattoir located at around 40 km from the farm where the animals were finished. Half of them from each rearing system (I and E) was mixed with unfamiliar animals from other pens/groups not belonging to the study (mixing treatment "M") and the other half (non-mixing treatment "NM") was maintained in their original group for transport and lairage. Thus, there were six animals assigned to each group (I-M, I-NM, E-M, E-NM).
The experimental procedures were in compliance with the RD 53/201, where no authorization is required for practices carried out for recognized zootechnical purposes (Art 2.5d) and those that do not cause more pain than the introduction of a needle (Art 2.5f).

The pre-slaughter management lasted 6 h from when the animals left the farm, including the process of loading, travelling, unloading, and lairage, and was in accordance with the Council Regulation (EU) Nr. 1/2005, which relates to protecting the welfare of animals during transport and related operations. Animals were stunned with a captive bolt, slaughtered by immediate exsanguination, and dressed according to the current EU regulations (Council Regulation (EC) No 1099/2009) in accredited abattoirs.

## 2.2. Muscle Sampling and Meat Quality Measurements

The carcasses were chilled at 3 °C within 2 h after slaughter. *Longissimus thoracis et lumborum* (LTL) muscle samples (20 g) were taken from the left-side carcass of each animal at the thirteenth rib level at 2 h, 8 h, and 24 h *post-mortem*. The muscle samples were immediately snap frozen in liquid nitrogen and stored at -80 °C until analysis.

At 24 h post-slaughter, the LTL muscle was removed from the left half carcass between the sixth and the tenth ribs, and transported to the laboratory. The LTL temperature and pH were recorded (pH24) at the sixth rib using a digital portable pH meter equipped with a penetration electrode coupled with a temperature probe (InLab Solids Go-ISM, Mettler-Toledo S.A.E., Barcelona, Spain).

Meat color was recorded at 24 h *post-mortem* on three 10 mm diameter spots on the exposed cut surface of the LTL muscle at the seventh rib level after 60 min blooming. The coordinates lightness (L*), redness (a*), and yellowness (b*) were obtained using a Minolta CM-2300d portable Spectrophotometer, with an illuminant C and D65 illuminant, 10° standard observer angle geometry and 8 mm aperture size in the CIE space (Konica Minolta Inc., Osaka, Japan), and the average value of the three spots was calculated. Further, both Chroma (C*) and Hue angle (h*) were calculated according to the next equations:
$$C^* = \sqrt{(a^{*2} + b^{*2})} \text{ and } h^* = \tan^{-1}(b^*/a^* [31].$$

The rest of the LTL striploin was sliced into 3.5 cm steaks that were vacuum packed in polyamide 20 µm/polyethylene 70 µm bags and aged in darkness under refrigerated conditions (4 °C ± 1 °C) at different *post-mortem* ageing times (3, 7, and 14 days). After the corresponding ageing period, steaks for comparative proteomics were frozen at -80 °C, while the steaks for meat toughness analysis were frozen and stored at -20 °C for subsequent analysis. Meat toughness was measured by the Warner-Bratzler (WB) shear test on meat cooked at 75 °C from 30 min by immersion in a water bath. After cooling, eight cores (1 cm² in cross-section) from each steak were subjected to a perpendicular cut by the WB blade set HDP/WBV with a "V" slot using the TA.XT Plus instrument (Stable Micro Systems, London, UK). The maximum load (kg) required for total split was recorded, the results were subjected to detection of outliers by box plot and the extreme values were deleted. Results were expressed as the mean WB shear force maximum load for each steak. Tenderization rate (TR, %) was calculated as the percentage of decrease in WB shear force in a given period of time (3 to 7 days, 7 to 14 days, 3 to 14 days).

## 2.3. Myofibrillar Protein Extraction

Proteomic analysis was performed on the muscle samples of the 24 animals. From each animal, muscle myofibrillar extracts were obtained at 2 h, 8 h, 24 h, 3 days, 7 days, and 14 days *post-mortem*, following the method described by Bjarnadottir et al. [32]. Briefly, 0.5 g muscle samples were homogenized in 4 mL of Tris-EDTA-Sucrose (TES) buffer containing 10 mM Tris [pH 7.6], 1 mM EDTA [pH 8.0], 0.25 M sucrose, and 0.6% protease inhibitor cocktail [P8340, Sigma-Aldrich Co., St. Louis, MO, USA], using a Polytron PT1200 E (Kinematica Inc., Luzern, Switzerland) two times for 15 s at maximum speed. The homogenate was centrifuged (20 min at 20,000 × g) at 4° C. The resulting pellet was homogenized into 4 mL of lysis buffer containing 10 mM Tris-HCl pH [7.6], 7 M urea, 2 M thiourea, 2% CHAPS, and 10 mM DTT with the polytron 2 × 15 s at maximum speed.
Subsequently, the solution was stirred at room temperature for 1 h in a Multi Reax stirrer (Heidolph Instruments, Schwabach, Germany) and was centrifuged at 20,000× g for 20 min at 4 °C. The supernatant containing the myofibrillar proteins was collected and filtered through a nylon filter (5 µm), aliquoted, and stored at -80 °C. The protein content of the extract was measured by the Bradford method [33].

## 2.4. Myofibrillar Subproteome Analysis (1D SDS-PAGE) and Protein Identification

The myofibrillar muscle extracts (30 µg) were prepared for SDS-PAGE as follows. First, they were denatured using a solution containing 65.8 mM Tris/HCl pH 6.8, 21% glycerol, 5% beta-mercaptoethanol, 2% SDS, 0.026% of bromophenol blue that were subsequently) heated at 100 °C for 5 min. Second, the denatured samples were loaded into 1 mm dual vertical slab gels (Mini-protean, Bio-Rad Laboratories Inc., Hercules, CA, USA) for separation using a 12% resolving gel and 4% stacking gel. Pre-stained molecular weight standards (Precision Plus Protein™ All Blue Standards, Bio-Rad Laboratories Inc., Hercules, CA, USA) were added on each gel.

Overall, three gels per sample were performed. The stained gel images were captured using the UMAX ImageScanner (Amersham Biosciences, Buckinghamshire, UK). The densitometry analysis and band quantification were carried out using Image Studio Lite 5.2.5 program (LI-COR Biosciences, Lincoln, NE, USA). To account for slight variations in protein loading, the optical density of protein bands was expressed as relative abundance (normalized volume) and expressed in arbitrary units.

Bands of interest (with significant differences among the groups) were manually excised from the gels and prepared for identification by MALDI-TOF/TOF mass spectrometry. The details of this procedure were previously described by Díaz et al. [9].

## 2.5. Statistical and Bioinformatics Analyses

Raw data were scrutinized for data entry errors and outliers by boxplot. Normality of variables was tested by a Kolmogorov-Smirnoff test. For meat quality traits (pH, meat color and WBSF), the effect of animal management at Farm "F" (I vs. E), during the Transport and Lairage "TL" (M vs. NM) and the interaction (F × TL) were analyzed using a General Linear model procedure (SPSSv22.0 SPSS Inc., Chicago, IL, USA). Further a repeated measure ANOVA was used to investigate the effect of Treatment (I-M, I-NM, E-M, E-NM) on WBSF measured at 3, 7, and 14 days post-mortem.

For myofibrillar bands intensities measured at different *post-mortem* times (2 h, 8 h, 24 h, 3 days, 7 days, and 14 days), the ANOVA model included Farm (F), Transport and Lairage (TL), ageing time (t), and their interactions as fixed factors and animal as covariate.

Significant differences among *post-mortem* times were studied by the Tukey's post-hoc test (Games-Howell when the variances were not homogeneous) at a significant level of $p \le 0.05$.

The relationships between meat quality traits and the myofibrillar subproteome at different *post-mortem* times were calculated by bivariate Pearson's correlations. Moreover, principal component analysis (PCA) was performed to study the relationships among the meat quality traits and the differential proteins along meat tenderization.

The significantly changing protein bands (differential proteins) were investigated using Metascape open-source tool to identify the main enriched Gene Ontology (GO) terms among the proteins following the procedures described by Gagaoua et al. [22,34]. The STRING database (Search Tool for Retrieval of Interacting Genes, ver. 11.0 at https://string-db.org/, accessed on 10 October 2021) was further used to construct the Protein-Protein Interactions (PPI) relating the differential proteins according to the pathways to which they belong. Moreover, the list of the identified proteins that differ among the groups were compared to the repertoire of Gagaoua et al. [21] to identify the extent of overlap with the previously identified beef tenderness biomarkers in LTL muscle.
## 3. Results

### 3.1. Meat Quality Attributes

The handling conditions (F and TL) had no significant effect on pH24 that showed normal values within the range 5.43–5.52, whatever the treatments. However, animals' farm management (F) affected meat color parameters (Table 1), being L* (*p* < 0.05) b*, C*, and h* (*p* < 0.001,) significantly lower in meat from the extensive system, that was brownish and darker, which agrees with previous literature that describes that meat from grass-fed animals is darker than meat from grain-fed animals, and attributes these differences to diet, physical activity, or a combination of both [35–37].

<table><caption>Table 1. Effect of Farm (F) and Transport and Lairage (TL) and their interaction on meat color parameters.</caption><thead><tr><th rowspan="2">Quality Traits</th><th colspan="3">Farm Management (F)</th><th colspan="3">Transport and Lairage (TL)</th><th colspan="3">Significance</th></tr><tr><th>I</th><th>E</th><th>SEM</th><th>NM</th><th>M</th><th>SEM</th><th>F</th><th>TL</th><th>F × TL</th></tr></thead><tbody><tr><td>pH</td><td>5.6</td><td>5.46</td><td>0.05</td><td>5.49</td><td>5.58</td><td>0.05</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>L*</td><td>41.86</td><td>38.20</td><td>0.99</td><td>38.73</td><td>41.33</td><td>0.99</td><td>*</td><td>NS</td><td>NS</td></tr><tr><td>a*</td><td>12.03</td><td>10.82</td><td>0.523</td><td>10.4</td><td>12.45</td><td>0.523</td><td>NS</td><td>*</td><td>*</td></tr><tr><td>b*</td><td>15.49</td><td>10.28</td><td>0.522</td><td>12.19</td><td>13.58</td><td>0.522</td><td>***</td><td>NS</td><td>NS</td></tr><tr><td>C*</td><td>19.64</td><td>14.97</td><td>0.681</td><td>16.13</td><td>18.49</td><td>0.681</td><td>***</td><td>*</td><td>*</td></tr><tr><td>h*</td><td>52.49</td><td>43.61</td><td>1.04</td><td>49.07</td><td>47.03</td><td>1.04</td><td>***</td><td>NS</td><td>NS</td></tr></tbody></table>

F: Management at farm; TL: Management during transport and lairage; NM: Non-mixing; M: Mixing; NS: not significant; * *p* < 0.05;

*** *p* < 0.001; SEM: standard error of the mean.

The effect of animal mixing during transport and lairage (TL) and the F × TL interaction were significant for a* and C* (*p* < 0.05). In both parameters, meat from mixed animals had higher values compared to the meat from non-mixed animals, being a* (12.45 vs. 10.4) and C* (18.49 vs. 16.13) for M and NM, respectively.

Farm rearing system were found to significantly affect meat toughness (*p* < 0.001) at 14 days *post-mortem* when meat from the intensive treatment showed lower values. These findings are in agreement to those of previous studies that found a negative effect of extensive treatment on tenderness [38,39].

When comparing the evolution of meat toughness in the different handling treatments (I-M, I-NM, E-M and E-NM) (Figure 1A), no significant differences were found at 3 days *post-mortem*; however lower values of WBSF were found at 7 (*p* < 0.05) and 14 days (*p* < 0.001) in meat from the I-NM animals. When looking to the tenderization rate (Figure 1B), a decrease in meat toughness along ageing, it can be seen that the meat from animals of the intensive treatment had a higher tenderization rate in the global studied period (3 to 14 days) being higher for I-NM animals (22%) in the earliest period from 3 to 7 days, and for I-M animals in the last period of the *post-mortem* ageing (22%) from 7 to 14 days.

Overall, meat from animals of the I-M group showed redder meat and a lower tenderization rate, which could together be related to higher PSS. In fact, previous studies of the serum biomarkers of stress (cortisol, lactate, glucose, amyloid A, and haptoglobin) in this group of animals [9,18] evidenced that I-M animals were the most sensitive to stress reactivity, as indicated by its high serum haptoglobin levels.

### 3.2. Separation and Identification of Myofibrillar Subproteome

1D SDS-PAGE of the myofibrillar proteins allowed the visualization of 36 protein bands (ranging from 15 to 200 kDa) from the muscle myofibrillar subproteome, as shown in Figure 2.
<figure><img src="image_17.png" /><figcaption>**Figure 1.** *Post-mortem* evolution of instrumental toughness for meat from the different handling treatments. (A) Warner-Braztler Shear force at 3, 7, and 14 days *post-mortem*. (B) Tenderization rate (%) calculated as the percentage of decrease in toughness in a given period of time (3 to 7 days, 7 to 14 days, 3 to 14 days). Different letters indicate significant differences (*p* < 0.05) among treatments (I-M, I-NM, E-M and E-NM) at 3, 7 or 14 days.</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>**Figure 2.** A representative 1D SDS-PAGE electrophoretic pattern of the myofibrillar subproteome profile of the LTL muscle of a yearling bull from "Asturiana de los Valles" at different *post-mortem* times (2 h, 8 h, 24 h, 3 days, 7 days, 14 days). MW marker: pre-stained molecular weight marker (All Blue pre-stained, Biorad). Band names are denoted by M (Myofibrillar proteins) followed by a number.</figcaption></figure>

Among the 36 bands analyzed in the myofibrillar subproteome, 23 bands were significantly affected by at least one of the factors analyzed in this study (F, TL, and t), as shown in Table 2.
<table><caption>**Table 2.** Effect of handling factors (animal management at Farm (F) and during Transport and Lairage (TL)) and the post-mortem time (t) and their interactions (F × TL, F × t, TL × t and F × TL × t) on the myofibrillar subproteome (arbitrary units).</caption><thead><tr><th rowspan="2">Band<br>[MWe]</th><th colspan="2">Farm<br>Management (F)</th><th colspan="2">Transport and<br>Lairage (TL)</th><th colspan="8">Post-Mortem Time (t)</th><th colspan="8">Significance</th></tr><tr><th>I</th><th>E</th><th>SEM</th><th>NM</th><th>M</th><th>SEM</th><th>2 h</th><th>8 h</th><th>24 h</th><th>3 d</th><th>7 d</th><th>14 d</th><th>SEM</th><th>F</th><th>TL</th><th>t</th><th>F × TL</th><th>F × t</th><th>TL × t</th><th>F × TL × t</th></tr></thead><tbody><tr><td>M1 (135.03)</td><td>4.66</td><td>3.67</td><td>0.25</td><td>4.76</td><td>3.87</td><td>0.15</td><td>4.08</td><td>4.39</td><td>4.22</td><td>4.4</td><td>4.36</td><td>3.57</td><td>0.24</td><td>*</td><td>**</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>M4 (69.16)</td><td>5.94</td><td>4.97</td><td>0.29</td><td>5.48</td><td>5.83</td><td>0.17</td><td>6.04 b</td><td>5.99 b</td><td>5.71 ab</td><td>5.03 ab</td><td>5.08 ab</td><td>4.93 a</td><td>0.28</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>M6 (83.66)</td><td>2.18</td><td>2.0</td><td>0.09</td><td>2.102</td><td>2.08</td><td>0.06</td><td>2.05 ab</td><td>2.24 b</td><td>2.28 b</td><td>2.13 b</td><td>2.07 ab</td><td>1.78 a</td><td>0.09</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>M9 (71.02)</td><td>1.23</td><td>1.36</td><td>0.06</td><td>1.27</td><td>1.52</td><td>0.04</td><td>1.57 b</td><td>1.43 b</td><td>1.33 b</td><td>1.37 b</td><td>1.21 a</td><td>1.05 a</td><td>0.06</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>M12 (59.93)</td><td>2.63</td><td>2.22</td><td>0.15</td><td>2.63</td><td>2.22</td><td>0.09</td><td>2.35</td><td>2.53</td><td>2.61</td><td>2.60</td><td>2.35</td><td>2.12</td><td>0.15</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>M13 (56.24)</td><td>2.42</td><td>2.49</td><td>0.09</td><td>2.47</td><td>2.44</td><td>0.05</td><td>2.47 ab</td><td>2.70 b</td><td>2.59 b</td><td>2.37 ab</td><td>2.37 ab</td><td>2.25 a</td><td>0.09</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>M15 (50.74)</td><td>1.5</td><td>1.57</td><td>0.06</td><td>1.49</td><td>1.58</td><td>0.04</td><td>1.47 ab</td><td>1.62 b</td><td>1.62 b</td><td>1.64 b</td><td>1.52 ab</td><td>1.34 a</td><td>0.06</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>M17 (40.53)</td><td>16.14</td><td>19.3</td><td>0.79</td><td>17.64</td><td>17.8</td><td>0.47</td><td>19.19 ab</td><td>16.23 a</td><td>17.08 a</td><td>16.93 a</td><td>16.91 a</td><td>19.98 b</td><td>0.76</td><td>*</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>M18 (37.64)</td><td>2.96</td><td>2.98</td><td>0.12</td><td>2.96</td><td>2.98</td><td>0.07</td><td>3.27 bc</td><td>3.49 c</td><td>3.38 c</td><td>2.82 ab</td><td>2.52 a</td><td>2.34 a</td><td>0.12</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>M19 (36.88)</td><td>2.34</td><td>1.94</td><td>0.07</td><td>2.24</td><td>2.05</td><td>0.04</td><td>2.61 c</td><td>2.73 c</td><td>2.67 c</td><td>1.99 b</td><td>1.49 a</td><td>1.38 a</td><td>0.07</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>M20 (35.03)</td><td>7.47</td><td>7.04</td><td>0.22</td><td>7.28</td><td>7.23</td><td>0.13</td><td>7.09 abc</td><td>7.58 bc</td><td>8.02 c</td><td>7.68 b</td><td>6.84 ab</td><td>6.33 a</td><td>0.21</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>M21 (32.95)</td><td>6.1</td><td>5.9</td><td>0.17</td><td>6.01</td><td>5.99</td><td>0.1</td><td>5.26 a</td><td>5.7 ab</td><td>5.74 ab</td><td>6.56 c</td><td>6.62 c</td><td>6.1 c</td><td>0.16</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>M23 (30.89)</td><td>1.03</td><td>1.25</td><td>0.05</td><td>1.15</td><td>1.13</td><td>0.03</td><td>1.05 a</td><td>1.04 a</td><td>1.04 a</td><td>1.06 a</td><td>1.21 ab</td><td>1.44 b</td><td>0.05</td><td>*</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>M24 (29.75)</td><td>2.4</td><td>2.6</td><td>0.1</td><td>2.49</td><td>2.51</td><td>0.06</td><td>3.04 d</td><td>2.89 d</td><td>2.74 cd</td><td>2.32 bc</td><td>2.17 ab</td><td>1.84 a</td><td>0.10</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>M25 (28.67)</td><td>1.58</td><td>1.71</td><td>0.08</td><td>1.61</td><td>1.68</td><td>0.05</td><td>0.89 a</td><td>0.92 a</td><td>0.97 a</td><td>1.68 b</td><td>2.51 c</td><td>2.89 d</td><td>0.08</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>*</td><td>NS</td></tr><tr><td>M26 (27.82)</td><td>0.65</td><td>0.8</td><td>0.03</td><td>0.71</td><td>0.73</td><td>0.02</td><td>0.54 a</td><td>0.53 a</td><td>0.51 a</td><td>0.73 b</td><td>0.95 c</td><td>1.08 c</td><td>0.03</td><td>*</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>M27 (27.25)</td><td>0.56</td><td>0.69</td><td>0.02</td><td>0.6</td><td>0.65</td><td>0.01</td><td>0.47 a</td><td>0.45 a</td><td>0.45 a</td><td>0.62 b</td><td>0.83 c</td><td>0.93 d</td><td>0.02</td><td>*</td><td>**</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>M30 (25.10)</td><td>1.22</td><td>1.33</td><td>0.06</td><td>1.35</td><td>1.2</td><td>0.04</td><td>1.33 bc</td><td>1.37 bc</td><td>1.42 c</td><td>1.24 abc</td><td>1.16 ab</td><td>1.11 a</td><td>0.06</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>M31 (23.20)</td><td>5.53</td><td>6.04</td><td>0.13</td><td>5.61</td><td>5.96</td><td>0.08</td><td>5.81</td><td>5.77</td><td>5.59</td><td>5.69</td><td>5.86</td><td>5.97</td><td>0.12</td><td>*</td><td>**</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>M32 (19.89)</td><td>3.83</td><td>3.7</td><td>0.13</td><td>3.66</td><td>3.86</td><td>0.07</td><td>4.11 bc</td><td>4.09 bc</td><td>3.98 bc</td><td>4.23 c</td><td>3.64 b</td><td>2.52 a</td><td>0.12</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>M33 (19.07)</td><td>1.17</td><td>1.22</td><td>0.08</td><td>1.2</td><td>1.18</td><td>0.05</td><td>0.79 a</td><td>0.77 a</td><td>0.79 a</td><td>1.05 a</td><td>1.54 b</td><td>2.21 c</td><td>0.08</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>M34 (17.54)</td><td>3.84</td><td>3.92</td><td>0.11</td><td>3.96</td><td>3.8</td><td>0.07</td><td>3.82</td><td>3.81</td><td>3.78</td><td>3.86</td><td>3.94</td><td>4.06</td><td>0.13</td><td>NS</td><td>NS</td><td>NS</td><td>*</td><td>NS</td><td>NS</td></tr><tr><td>M35 (16.05)</td><td>5.76</td><td>5.37</td><td>0.14</td><td>5.43</td><td>5.7</td><td>0.08</td><td>5.45</td><td>5.49</td><td>5.34</td><td>5.62</td><td>5.73</td><td>5.76</td><td>0.05</td><td>NS</td><td>*</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr></tbody></table>

Mwe: the experimental molecular weight (kDa). SEM: standard error of the mean; E: Management at farm; TL: Management during the transport and lairage; t: Post-mortem time; I: Intensive; E: Extensive; NM: Non-mixing with unfamiliar animals; M: Mixing with unfamiliar animals; Significance: NS: not significant; *, p < 0.001; **, p < 0.05; ***, p < 0.01. The variables not followed by the same letter in the same row (a, b, c, and d) are statistically different (p < 0.05).
It is important to note that these band intensity differences are due to the effect that handling factors may have in either the synthesis of a determined protein and/or to variations in the muscle post-mortem metabolism, which may result in increased proteolysis of that protein decreasing its relative abundance or causing its disappearance and the consequent increases of smaller protein fragments/peptides. Therefore, the relative abundance of a given (intact) protein is a balance between synthesis and degradation [22].

Table 3 shows the identification of protein bands with significant intensity differences among treatments. These proteins belong to three major biological pathways (Figure 3A):

➤ Muscle contraction, structure and associated proteins: M1 (Myosin-binding protein C, fast-type isoform X2 "MYBPC2"), M4 (Alpha-actinin-3 "ACTN3"), M13 (Desmin, partial "DES"), M17 (Actin, alpha skeletal muscle "ACTA1"), M21 (Tropomyosin alpha-1 chain "TPM1"), M23 (LIM domain-binding protein 3 isoform X5 "LDB3"), M24 (Four and a half LIM domains protein 1 isoform 1 "FHL1"), M25 and M27 (Troponin T, fast skeletal muscle isoform X31 "TNNT3"), M26 (Four and a half LIM domains protein 1 isoform 2 "FHL2"), M31 (Myosin light chain 1/3 skeletal muscle isoform "MYL1"), M32 (Troponin I, fast skeletal muscle "TNNT2"), M34 (Troponin C, skeletal muscle "TNNC1" and M35 (Myosin regulatory light chain 2, skeletal muscle isoform "MYLPF");

➤ Energy metabolism and associated pathways: M6 (ATP-dependent 6-phosphofructokinase, muscle type "PFKM"), M12 (Pyruvate kinase PKM isoform X1 "PKM"), M15 (ATP synthase subunit beta, mitochondrial precursor "ATP5F1B"), M18 y M19 (Fructose-biphosphate aldolase A "ALDOA") and M20 (Glyceraldehyde-3-phosphate dehydrogenase, "GAPDH");

➤ Heat shock proteins: M9 (Heat Shock 70 kDa protein 1A "HSPA1A"), M30 (Heat Shock protein family B member 1 variant 1 "HSPB1") and M33 (Alpha-crystallin B chain "CRYAB").

<table><caption>**Table 3.** Protein identification of myofibrillar bands separated by 1D-SDS-PAGE that showed significant differences with treatments (Farm, Transport and Lairage, and/or post-mortem time).</caption><thead><tr><th>Band: Gene Name</th><th>Identification</th><th>Accession Number</th><th>MOWSE Scores</th><th>Sequence Coverage (%)</th><th>Matched Queries</th><th>MWt</th></tr></thead><tbody><tr><td>M1: MYBPC2</td><td>Myosin-binding protein C, fast-type isoform X2</td><td>E1BNV1</td><td>295</td><td>24</td><td>25</td><td>128.5</td></tr><tr><td>M4: ACTN3</td><td>Alpha-actinin-3</td><td>Q0III9</td><td>506</td><td>38</td><td>35</td><td>103.7</td></tr><tr><td>M6: PFKM</td><td>ATP-dependent 6-phosphofructokinase, muscle type</td><td>Q0IIG5</td><td>352</td><td>40</td><td>42</td><td>86.1</td></tr><tr><td>M9: HSPA1A</td><td>Heat Shock 70 kDa protein 1A</td><td>Q27975</td><td>288</td><td>38</td><td>24</td><td>70.5</td></tr><tr><td>M12: PKM</td><td>Pyruvate kinase PKM, isoform X1</td><td>A5D984</td><td>822</td><td>66</td><td>46</td><td>58.5</td></tr><tr><td>M13: DES</td><td>Desmin, partial</td><td>O62654</td><td>246</td><td>54</td><td>21</td><td>52.6</td></tr><tr><td>M15: ATP5F1B</td><td>ATP synthase subunit beta, mitochondrial precursor</td><td>P00829</td><td>445</td><td>48</td><td>25</td><td>56.2</td></tr><tr><td>M17: ACTA1</td><td>Actin, alpha skeletal muscle</td><td>P68138</td><td>522</td><td>52</td><td>24</td><td>42.4</td></tr><tr><td>M18: ALDOA</td><td>Fructose-biphosphate aldolase A</td><td>A6QLL8</td><td>430</td><td>62</td><td>24</td><td>39.9</td></tr><tr><td>M19: ALDOA</td><td>Fructose-biphosphate aldolase A</td><td>A6QLL8</td><td>286</td><td>60</td><td>21</td><td>39.9</td></tr><tr><td>M20: GAPDH</td><td>Glyceraldehyde-3-phosphate dehydrogenase</td><td>P10096</td><td>394</td><td>45</td><td>20</td><td>36.1</td></tr><tr><td>M21: TPM1</td><td>Tropomyosin alpha-1 chain</td><td>Q5KR49</td><td>199</td><td>41</td><td>15</td><td>32.7</td></tr><tr><td>M23: LDB3</td><td>LIM domain-binding protein 3 isoform X5</td><td>G3N3C9</td><td>180</td><td>55</td><td>19</td><td>30.9</td></tr><tr><td>M24: FHL1</td><td>Four and a half LIM domains protein 1 isoform 1</td><td>G3MZ95</td><td>671</td><td>87</td><td>34</td><td>35.5</td></tr><tr><td>M25: TNNT3</td><td>Troponin T, fast skeletal muscle isoform X31</td><td>Q8MKI3</td><td>206</td><td>45</td><td>18</td><td>28.9</td></tr><tr><td>M26: FHL2</td><td>Four and a half LIM domains protein 1 isoform 2</td><td>Q2KI95</td><td>95</td><td>37</td><td>11</td><td>33.8</td></tr><tr><td>M27: TNNT3</td><td>Troponin T, fast skeletal muscle isoform X31</td><td>Q8MKI3</td><td>178</td><td>43</td><td>15</td><td>28.9</td></tr><tr><td>M30: HSPB1</td><td>Heat Shock protein family B member 1 variant 1</td><td>Q3T149</td><td>368</td><td>73</td><td>14</td><td>22.4</td></tr><tr><td>M31: MYL1</td><td>Myosin light chain 1/3 skeletal muscle isoform</td><td>A0JNJ5</td><td>425</td><td>77</td><td>18</td><td>21.1</td></tr><tr><td>M32: TNNI2</td><td>Troponin I, fast skeletal muscle</td><td>F6QIC1</td><td>194</td><td>70</td><td>23</td><td>21.6</td></tr><tr><td>M33: CRYAB</td><td>Alpha-crystallin B chain</td><td>P02510</td><td>163</td><td>69</td><td>13</td><td>20.1</td></tr><tr><td>M34: TNNC1</td><td>Troponin C, skeletal muscle</td><td>P63315</td><td>333</td><td>56</td><td>15</td><td>18.3</td></tr><tr><td>M35: MYLPF</td><td>Myosin regulatory light chain 2, skeletal muscle isoform</td><td>Q0P571</td><td>517</td><td>66</td><td>21</td><td>19.11</td></tr></tbody></table>

The MOWSE score: numeric descriptor of the likelihood that the identification is correct. Protein scores greater than 94 are significant (*p* < 0.05); Mwt: theoretical molecular weight (kDa).
<figure><img src="image_19.png" /></figure>

B

<figure><img src="image_20.png" /></figure>

C

<figure><img src="image_21.png" /><figcaption>**Figure 3.** Bioinformatics analyses of the differential proteins affected with the treatments (Farm, Transport and Lairage and/or post-mortem time). (A) Protein-Protein Interactions of the 21 proteins using String database, highlighting 3 major pathways these being Muscle contraction, structure & associated proteins, Energy metabolism & associated pathways and Heat Shock proteins. (B) Significant enriched Gene Ontology (GO) terms obtained using Metascape tool. (C) Overlap among the 21 proteins with the repertoire of beef tenderness biomarkers reported in the database of Gagaoua et al. [18]. The 15 common proteins were highlighted by the corresponding molecular pathway color as in (A).</figcaption></figure>
It is important to note that some proteins appeared in more than one band, as was the case of ALDOA (M18 and M19) and TNNT3 (M25 and M27), probably due to differences in the molecular weight or to conformational changes of the different proteolytic fragments originated from the same protein. The analyses of these differential proteins, which finally constitute 21 unique proteins (Table 3 and Figure 3A), allowed for the construction of an interconnected network (Figure 3A), highlighting the importance of muscle contraction and structure pathways. The Gene Ontology (GO) analysis allowed for the identification of seven enriched GO terms (Figure 3B), from which the top two enriched terms being GO:0006936: Muscle contraction and GO:0061061: Muscle structure development. These were followed by GO:0046034: ATP metabolic process, GO:0055001: Muscle cell development, GO:0006979: Response to oxidative stress, GO:1902532: Negative regulation of intracellular signal transduction and GO:0090130: Tissue migration.

It must be noted that most protein bands from the myofibrillar subproteome (61%) correspond to structural insoluble proteins, but soluble proteins such as glycolytic enzymes (26%) or HSPs (13%) were also found in the myofibrillar fraction (Figure 3A). This can be due to a decrease of solubility, maybe partly as a consequence of the early pH drop while the muscle temperature is still high, and also to the relationships that exist among the proteins [34], as confirmed in the network of Figure 3A. In fact, such conditions may cause denaturation of proteins which became insoluble, and their aggregation and precipitation onto myofibrils [24,40]. In support of this, the recent study by Gagaoua et al. showed that the maturation process involves interconnected molecular pathways in a pH-dependent manner leading, for instance, to the concomitant appearance of two major proteolytic fragments at 110 and 30 kDa, based on 1DE electrophoresis [34]. These two protein bands appearing during ageing, also observed in this study, increase in their intensity as a function of *post-mortem* time in a pH decline-dependent manner. LC-MS/MS analysis yielded 22 unique proteins for the 110 kDa fragment and 13 for the 30 kDa, with four common proteins related to both actin and fibrinogen complex. The Gene Ontology analysis revealed that a myriad of biological pathways are influential with many of them, as confirmed in the present study (Figure 3), and were related to proteins involved primarily in muscle contraction and structure. Other pathways were apoptotic mitochondrial changes, calcium and ion transport, energy metabolism, etc. Interestingly, most of the proteins composing these two fragments among others that appear or disappear during the tenderization process and in line to the results of this study have been so far identified as biomarkers of beef tenderness (Gagaoua et al., 2021) [21]. In addition, HSPs can translocate and accumulate in the cytoskeleton and myofibrillar proteins during early *post-mortem* stages, as they exert a protective role against muscle degradation [40]. These facts reinforce the need to consider different cell fractions and the movements of proteins between cytoskeletal and myofibrillar structures, for an accurate and reliable study of the process of conversion of the muscle into meat, as has been highlighted by previous studies [21,41].

### 3.2.1. Handling Effects on the Muscle Contraction, Structure and Associated Proteins

Among the protein bands with structural and contraction functions, only M27 (27.25 kDa), identified as Troponin T, fast skeletal muscle isoform X31 (TNNT3), was affected by the three factors analyzed in this study (F, TL, and t). Higher intensities of M27 were found in the muscle of the animals from the extensive treatment (*p* < 0.01) and mixed group animals during transport and lairage (*p* < 0.05). Further, a significant increase (*p* < 0.001) of TNNT3 band intensity with *post-mortem* time was found. Band M25 with a molecular weight of 28.67 kDa, was also identified as TNNT3, but it did not show significant differences with F or TL, but a significant increase with *post-mortem* time (*p* < 0.001). Troponin T is the most frequently identified differential biomarker of ongoing proteolysis and tenderization due to the appearance of degradation fragments of 30 kDa and 28 kDa correlated to meat tenderness [25,34,42–44]. TNNT3 is one of the proteins of the Troponin complex, composed of three regulatory proteins (Troponin T, C and I) that are integral to muscle contraction. It is well known that tenderization acts on all the proteins of the
complex and it was identified as a robust biomarker of beef tenderness in the study of Gagaoua et al. [21], as evidenced in the Venn diagram of Figure 3C. Accordingly, our results showed changes in all the proteins of the Troponin complex. Bands M32 and M34 were identified as TNNI2 and TNNC1 respectively. TNNI2 was affected by TL, with higher intensity (p < 0.01) in meat from the mixed animals, and also a significant effect of *post-mortem* time was observed with a significant decrease (p < 0.001) after 3 days *post-mortem* in meat from E-M, E-NM and I-NM treatments, but delayed for I-M, which seems to indicate slower tenderization rate.

Other bands with significant changes correspond to myosin related proteins M1 (Myosin-binding protein C, fast-type isoform X2 "MYBPC2") and M31 (Myosin light chain 1/3 skeletal muscle isoform "MYL1") were affected by both F and TL but not by ageing time. MYBPC2 showed higher intensity (p < 0.05) in meat from the indoor reared animals and in meat from the non-mixed group (p < 0.01), which could be related to differences in the level of physical exercise, as found for MYOM2, a major component of the myofibrillar subproteome that appeared in the sarcoplasmic subproteome of these animals, in agreement with the recent studies of Diaz et al., [9] and Gagaoua et al. [34]. MYBPC2 belongs to the Myosin Binding Proteins family formed by sarcomeric proteins located in the A-band in close association with the thick filaments that are known as regulators of the myofilament contractility [45]. MYBPC exists in three main isoforms: skeletal slow (MYBPC1), skeletal fast (MYBPC2), and cardiac (MYBPC3). MYPBC1 and MYBPC2 were recently identified to be the major components of the 110 kDa fragment appearing during the tenderization of beef in a pH dependent manner [34]. It is also important to note that a closely member, the myosin binding protein H (MYBPH) has been previously identified as a negative biomarker of color and beef tenderness, whatever the gender, due to its significant effect on length, thickness, and lateral alignment of myosin filaments [21,46,47]. In this work, MYBPC2 showed higher intensity levels in the I-NM meat, which was the tenderer one, hence confirming the findings by Gagaoua et al. [21,34] proposing this protein as a good marker of meat tenderization.

MYBPC has a theoretical molecular weight of approximately 130 kDa; however, the band identified as MYBPC2 in our study shows a higher experimental molecular weight (155 kDa) what could be indicative of aggregation of this protein or to its interaction with nebulin and other proteins as observed in earlier studies [34,48]. These modifications may explain the loss of its function in the alignment of myosin filaments, and therefore the positive role it might play on tenderness. On the other hand, MYL1 is a member of the Myosin light chains that are crucial for muscle function in terms of contraction velocity and power. In this work, MYL1 band showed higher intensities in the meat reared outdoors (p < 0.05) and in meat from mixed animals (p < 0.01). Apart from MYL1, another band (M35) from the same family was identified as Myosin regulatory light chain 2, skeletal muscle isoform "MYLPF" that was not different within farm management, but was significantly affected by TL with higher values at the M treatment (p < 0.05) in line with the findings for MYL1. *Post-mortem* disruption of myosin light and heavy chains and actin in the actomyosin complex plays a central role in the muscle to meat conversion and may have a direct effect on tenderness [49]. The *post-mortem* concentration of these proteins has been previously associated with pork and beef tenderness [50,51], and belong to the robust list of biomarkers of beef tenderness shortlisted by Gagaoua et al. [21]. Increased proteolysis of MYL1 has also been described in dark-cutting beef [22,26]. It is worthy to note that myosin light chains were also identified as biomarkers of several beef color traits [47].

Apart from the aforementioned, management at farms affected other three structural proteins: M17 (Actin, alpha skeletal muscle "ACTA1"), M23 (LIM domain-binding protein 3 isoform X5 "LDB3") and M26 (Four and a half LIM domains protein 1 isoform 2 "FHL2"). All of them were more intense (p < 0.05) in the meat from animals reared outdoors. Actin is the second most abundant myofibrillar protein, after myosin, and was described as the top biomarker of beef tenderness (Figure 3C) [21]. The breakdown of transverse cytoskeletal actin filaments can cause detachment of the sarcolemma from the basal lamina and the
extracellular matrix network, causing muscle cells degradation and hence increasing tenderness; therefore, actin has been found to be a good biomarker of tenderness [21,40,49,52]. The other two proteins affected by farm management were the LIM-domain containing proteins, LDB3 and FHL2. The LIM domain is a cysteine-histidine rich, zinc-coordinating domain, consisting of two tandemly repeated zinc fingers. The LIM domain-containing proteins are known to play critical roles in vertebrate development and cellular differentiation. The LDB3 protein, located in the sarcomere, is essential for maintaining Z line structure and muscle integrity [53]. FHL2 is a member of the four and a half LIM domain protein family (FHL), with an important role in muscle development [54]. To the best of our knowledge, this protein has never been related to meat quality before; however another protein from the FHL family, the Four and a half LIM domains protein 1 isoform 1 (FHL1,) also known as Cypher protein, has been related to the release of α-actinin and the weakening of the Z-disc during meat tenderization [55,56]. In the present study, band M24 was identified as FHL1 and, contrary to what was found for FHL2, only a significant decrease with *post-mortem* ageing and a significant interaction F × TL (E-NM > E-M = I-M > I-NM) was observed (*p* < 0.001). Previous studies found increased intensity of FHL1 in DFD meat [16,22,57].

The other structural protein bands found in the myofibrillar extract were affected only by *post-mortem* time, with a significant decrease (*p* < 0.01) of M4 (Alpha-actinin-3 “ACTN3”) and M13 (Desmin, partial “DES”) and a significant increase (*p* < 0.001) of M21 (Tropomyosin alpha-1 chain “TPM1”). It is important to note that the *post-mortem* ageing time is the factor that causes the greatest differences in the intensity of the muscle contraction and structural proteins, with 10 out of the 14 structural bands showing significant differences in agreement to the very recent findings of Gagaoua et al. [34]. Most of these band’s intensities remain similar at the earliest *post-mortem* period (from 0 h to 24 h), but then increase or decrease drastically as a consequence of proteolysis.

### 3.2.2. Handling Effects on the Energy Metabolism and Associated Pathways Proteins

Most of the bands related to energy metabolism found in the myofibrillar fraction were glycolytic enzymes (Figure 3), usually found in the sarcoplasmic fraction. Among them, only the band M19 (36.88 kDa), corresponding to Fructose-biphosphate aldolase A “ALDOA”, was affected by the three factors studied: F, TL, and ageing time, showing higher intensity in meat from the Intensive rearing system (*p* < 0.01) and from the NM animals (*p* < 0.01), and a significant decrease along *post-mortem* ageing (*p* < 0.001) that starts at 8 h *post-mortem* in the meat from the Intensive treatment and at 24 h from the Extensive system (significant interaction (*p* < 0.01) F × t). Band M18 (37.64 kDa), also identified as ALDOA, showed a significant *post-mortem* decrease. In contrast with these results in the myofibrillar fraction, previous studies in the same animals showed higher intensity of ALDOA (37.1 kDa) in the sarcoplasmic subproteome of meat from the extensive reared animals [9]. ALDOA catalyzes the conversion of fructose 1, 6-diphosphate to glyceraldehyde 3-phosphate during glycolysis, therefore its lower intensity in meat from Intensive reared animals in the sarcoplasmic fraction can be associated with a faster glycogenolysis exhaustion or degradation of the enzyme in these animals that were found to be more susceptible to pre-slaughter stress [9]. However, it is also known that ALDOA in association with other metabolic enzymes, assists in the creation of cross-links between adjacent actin filaments or in binding troponin to the thin filaments, to enhance energy provision, where it is actively needed during contraction, hence affecting the distance between myofibrils, and therefore light scattering and tenderness [57]. This could at least partially explain the differences found between sarcoplasmic and myofibrillar ALDOA contents, as, due to the higher glycolytic metabolism of Intensive reared animals at slaughter, more energy provision may be needed for contraction and more ALDOA can be retained within the interstitial spaces of the myofibrils during the extraction. It is worthy to note that ALDOA was identified as a robust biomarker of beef tenderness (Figure 3C) [18] and of color variation [47].
Apart from ALDOA, Farm management did not affect significantly any of the other proteins from the energy metabolic pathway, however TL affected significantly to the band M12 (Pyruvate kinase PKM isoform X1 "PKM") with increased intensity (*p* < 0.01) in the meat from the NM group under a significant F × TL interaction (*p* < 0.001). This band was the only one corresponding to metabolic enzymes that did not show significant differences with *post-mortem* time. PKM is a glycolytic enzyme implicated in the last phases of the glycolysis that catalyzes the dephosphorylation of phosphoenolpyruvate to pyruvate, yielding one molecule of pyruvic acid and one of ATP. Previous studies have shown that animals mixed during transport and lairage are more affected by pre-slaughter stress and they have also been related to higher *post-mortem* glycolytic metabolism [9,58]. Moreover, lower abundance of PKM was previously found in myofibrillar subproteome of high pH24 meat [26]. Thus, the lower levels of PKM found in the myofibrillar fraction of mixed animals in the present study can be explained by the glycogen depletion occurring due to PSS caused by the mixing procedure before slaughter. This can lead to a decrease in the glycolysis rate after slaughter due to the early depletion of glycogen and a downregulation of proteins involved in the glycolytic pathway in conjunction with oxidative pathways driven by mitochondria (for review see Gagaoua et al. [22]).

The other metabolic enzymes, PFKM, ATP5F1B, and GAPDH, showed significant differences (p < 0.01) only with *post-mortem* time, with increasing band intensities during the early *post-mortem* until 24 h or 3 days *post-mortem* and decreasing afterwards. This reflects the cell metabolism behavior, with high levels of this proteins at early *post-mortem* due to the trigger of the glycolytic metabolism but decreasing later due to the impact of pH that might desaturate them or significantly reduce their activity.

### 3.2.3. Handling Effects on the Heat Shock Proteins

Overall, three bands from the myofibrillar subproteome were identified as members of the Heat Shock proteins (HSPs) family: M9 was identified as the large Heat shock 70 kDa protein 1A "HSPA1A", and the other two (M30 and M33) as members of the small HSPs subfamily, identified as Heat Shock protein family B member 1 variant 1 "HSPB1" (also known as HSP27) and Alpha-crystallin B chain "CRYAB". Functional proteomic studies have confirmed differential expression of HSPs in meat from different breeds, handling systems and quality traits [16,21,32,47,59,60]. In the present study, all the HSPs bands showed significant changes along the *post-mortem* meat maturation, with a significant increase (*p* < 0.01) up to 24 h *post-mortem* followed by a progressive decrease afterwards in the case of HSPB1, a significant increase after three days (*p* < 0.001) for HSPA1A and a significant decrease (*p* < 0.001) after three days for CRYAB. Surprisingly, none of these bands were affected by management at farm, and the mixing treatment affected significantly only HSPB1 band that was more intense (*p* < 0.001) in meat from the non-Mixed group. Moreover, a significant interaction F × TL (*p* < 0.001) was found for this band.

Among the small HSPs, HSPB1 is one of the most frequently related protein to beef tenderness but in different directions (positive or negative manner) depending on the studied factors (PSS, breed, gender, post-translational modifications (isoform), rearing factor, etc.), as explained by Gagaoua et al. [21]. HSPs exert protective functions as chaperone proteins from proteases, therefore they could reduce degradation of myofibrillar proteins [61]. Several studies reported positive relationship between degradation of HSPB1 and meat tenderness improvements, as degraded HSPs may no longer prevent irreversible damage to myofibrillar proteins [1,60,62,63]. Accordingly, our results showed higher intensity of HSPB1 band in the myofibrillar subproteome of meat from the E-NM treatment, which was the treatment that showed higher WBSF, and thus a lower tenderization rate. Moreover, the evolution of HSPB1 along meat ageing is in agreement with previous studies in bovine muscles [50,64,65] that have demonstrated that muscle HSPB1 increases in abundance shortly after slaughter but decreases during meat storage. Similarly, our results showed a significant decrease (*p* < 0.001) of the large HSPA1A with *post-mortem* ageing. Members of the HSP70 family were previously found in the sarcoplasmic subproteome of
these animals with a significant effect of Farm management, showing higher intensity in meat from the extensive reared animals [9]. Under stress situations, HSPA1A, which is an inducible protein that translocates and accumulates in the cytoskeletal and myofibrillar proteins in an attempt of stabilizing the muscle structure [40]. This HSP diffusion capacity between cellular fractions may explain the differences found between rearing treatments. As aforementioned, animals from intensive rearing system are suggested to be more susceptible to handling and pre-slaughter stress [9,18] so that more HSP may translocate to myofibrils while in the extensively reared group more HSPA-A is probably easily removed and extracted from the sarcoplasmic fraction.

CRYAB was affected by *post-mortem* time showing a significant (*p* < 0.001) increase in intensity after seven days of storage. In contrast, previous studies demonstrate a decrease of intact CRYAB with ageing in total extracts from different muscles (Longissimus lumborum, Semimembranosus and Psoas major) of Angus x Simmental beef cattle [60]. Our results showing late *post-mortem* increases of this band could be explained by solubility changes due to protein modifications such as fragmentation, oxidation, precipitation, or aggregation thereby going from soluble to an insoluble state. This was previously pointed out by Bjarnadóttir et al. [32], who discovered that some of the small HSPs proteins increased their abundance in the insoluble protein fraction, possibly as a result of aggregation onto myofibrillar proteins, thereby following them during extraction. It is also important to note that CRYAB behaves also as a structural protein and therefore it can be susceptible of degradation along ageing.

### 3.3. Relationship between Meat Quality Traits and the Significantly Changing Myofibrillar Proteins

The correlations between the differential protein bands from the myofibrillar subpro-
teome and meat quality traits, measured at different *post-mortem* times were analyzed, and
significant correlations with color traits and meat toughness are shown in Tables 4 and 5
respectively.

In the present study beef color traits were correlated (Table 4) with two metabolic enzymes (PKM and ALDOA), two Heat Shock proteins (HSPA1A and HSPB1) and three structural proteins (DES, TNNI2 and MYLPF). This confirms the knowledge about the importance of cell glycolytic rate and its consequences on *post-mortem* modifications of proteins such as myosin, actin, troponin, and other metabolic proteins, particularly glycolytic enzymes in the sarcoplasm, and therefore influences the ultimate meat color.

A recent integromics study evidenced that both glycolytic enzymes and HSPs pathways have important roles in beef color determination where several relevant biomarkers were shortlisted [21]. Accordingly, PKM showed significant (*p* < 0.05) positive correlations with *L*\*, *b*\* and *h*\* and negative with *a*\* and *C*\*. The highest correlation coefficients were found at 8 h *post-mortem* with *a*\* (−0.850, *p* < 0.01) and *h*\* (0.800, *p* < 0.01). ALDOA showed the highest correlations with *a*\* and *h*\* at 7 days *post-mortem* (0.821, *p* < 0.01 and −0.695, *p* < 0.05, respectively). Other correlations were found between color traits and GAPDH (0.701, *p* < 0.05 with *a*\* and −0.705, *p* < 0.01 with *h*\*) at 7 days *post-mortem*. PKM and ALDOA may exert their effect on muscle color due to their involvement in the glycolytic pathway providing energy to the muscle contraction. They can also assist in the creation of crosslinks between actin filaments or in binding troponin to the thin filaments, hence affecting the distance between myofibrils and therefore light scattering [21,22,66–69]. In the case of HSPs, we found positive correlations between HSPA1A and *L*\* and *h*\* (+0.731, *p* < 0.05 at 8 h *post-mortem*) and negative between HSPB1 and *L*\* and *b*\* (−0.73, *p* < 0.01 at 7 days *post-mortem*). Many studies have related HSPs with color [70–72] probably due to their protective action against stress-induced denaturation of muscle proteins, that would affect reflectance, light scattering, and myoglobin, hence influencing color parameters [57,73–77].
<table><caption>Table 4. Significant Pearson correlations coefficients between myofibrillar subproteome bands and meat color traits.</caption><thead><tr><th></th><th>PFKM</th><th>HSPA1A</th><th>PKM</th><th>DES</th><th>ALDOA<br>M18</th><th>ALDOA<br>M19</th><th>GAPDH</th><th>TPM1</th><th>LDB3</th><th>FHL1</th><th>TNNT3<br>M25</th><th>TNNT3<br>M27</th><th>HSPB1</th><th>TNN12</th><th>CRYAB</th><th>MYLPF</th></tr></thead><tbody><tr><td rowspan="5">L*(60)</td><td>2 h</td><td>0.691 *</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.654 *</td><td></td><td></td><td></td><td>-0.651 *</td><td>0.624 *</td><td></td><td>0.702 *</td></tr><tr><td>8 h</td><td>0.731 **</td><td>0.613 *</td><td></td><td>0.608 *</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.581 *</td><td>0.604 *</td><td></td><td></td></tr><tr><td>24 h</td><td></td><td></td><td></td><td>0.615 *</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.687 *</td><td>0.697 *</td><td></td><td></td></tr><tr><td>3 d</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.777 **</td><td></td><td></td><td></td></tr><tr><td>7 d</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="5">a*(60)</td><td>2 h</td><td>-0.588 *</td><td></td><td></td><td>-0.695 *</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.593 *</td></tr><tr><td>8 h</td><td></td><td></td><td></td><td>-0.850 **</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>24 h</td><td>-0.694 *</td><td></td><td></td><td>-0.675 *</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>3 d</td><td></td><td></td><td></td><td>-0.726 *</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>7 d</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="5">b*(60)</td><td>2 h</td><td>0.645 *</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.633 *</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.608 *</td></tr><tr><td>8 h</td><td>0.612 *</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>24 h</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>3 d</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>7 d</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="5">C*(60)</td><td>2 h</td><td></td><td></td><td></td><td>-0.605 *</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>8 h</td><td></td><td></td><td></td><td>-0.613 *</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>24 h</td><td>-0.714 *</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>3 d</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>7 d</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="5">I*(60)</td><td>2 h</td><td>0.592 *</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>8 h</td><td>0.700 *</td><td></td><td></td><td>0.800 **</td><td>0.588 *</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.741 **</td></tr><tr><td>24 h</td><td></td><td></td><td></td><td>0.670 *</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>3 d</td><td></td><td></td><td></td><td>0.676 *</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>7 d</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>14 d</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>14 d</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

<footnote>* *p* < 0.05; ** *p* < 0.01; PFKM: ATP-dependent 6-phosphofructokinase; HSPA1A: Heat shock 70 kDa protein 1A; PKM: Pyruvate kinase muscle; DES: Desmin; ALDOA: Fructose-biphosphate aldolase A; GAPDH: Glyceraldehyde-3-phosphate dehydrogenase; TPM1: Tropomyosin alpha 1; LDB3: LIM domain-binding protein 3; FHL1: Four and a half LIM domains protein 1 isoform 1; TNNT3: Troponin T fast skeletal muscle isoform x31; HSPB1: Heat shock protein family B member 1 variant 1; TNN12: Troponin I fast skeletal muscle; CRYAB: Alpha-crystallin B chain; MYLPF: Myosin regulatory light chain 2. The higher and significant correlation coefficients are in bold.</footnote>
Finally, strong positive correlations were found between some structural proteins and color, such as between MYLPF with $L^*$ and $h^*$ at 2 h *post-mortem* (0.7, *p* < 0.01) and between TNNI2 and $L^*$ and $h^*$ at 3 days post-mortem (0.75, *p* < 0.01), while negative correlations were found between $a^*$ and DES at 2 h post-mortem (−0.683, *p* < 0.05) and MYLPF at 3 days post-mortem (−0.707, *p* < 0.01). Hughes et al. [76] found that meat color was not determined only by chromatic heme pigments, but also by the physical structure and achromatic light scattering properties of the muscle. Therefore, the effect of structural proteins in meat color is related to their denaturation and degradation during the *post-mortem* process that affect the protein density along the sarcomere, and therefore light scattering from the structural elements as evidenced in the recent integromics proteomics meta-analyses of beef color and dark-cutting beef by Gagaoua et al. [22,47]. The current insights as revealed by both proteomics and conventional biochemical studies were further recently discussed by Purslow et al. [77]. The authors stated that it is increasingly likely that omics techniques, including proteomics, will be used to discover more of the complex interactions between pathways behind the qualities of meat and their determination.

Regarding tenderness, a total of 18 bands showed significant correlations with WBSF and/or with meat tenderization rate (% of WBSF decrease from 3 days to 14 days (Table 5). ACTA1, TNNT3, MYL1, ALDOA, and CRYAB were correlated with tenderness. These proteins showed the highest correlations with WBSF during the first 24 h *post-mortem*, being negative in the case of ALDOA, mainly with WBSF 3 days and ALDOA measured at 24 h *post-mortem* (−0.549, *p* < 0.05), and positive in the case of TNNT3 and WBSF at 14 days *post-mortem* (0.625, *p* < 0.01). Thus, these findings support the previous knowledge of the importance of these proteins (Figure 3C) as early biomarkers of meat tenderization. In agreement with these results, ALDOA was positively associated with tenderness [18]. Finally, and with respect to tenderness, CRYAB, showed a positive correlation at 2 h *post-mortem* with the tenderization rate from 3 to 14 days *post-mortem* (0.592, *p* < 0.01). Increased CRYAB levels were associated with delayed myofibril degradation in beef with ultimate pH < 5.7 [61].

With the aim to summarize the complex relationships between meat quality traits and the myofibrillar subproteome, a PCA was performed including only the variables with higher correlation loadings (over 50% of explained variance). Figure 4 shows the biplot obtained by PCA between variables (loadings), individual meat samples (scores), and treatments (centroids). The first PC1 and PC2 explained 62% of the variability. The PC1 separated in the positive side the meat samples from the Extensive treatment, with higher meat WBSF values and overexpression of MYL1 at 2, 8, and 24 h, MYLPF at 24 h, LDB3 at 3 days, FHL2 at 7 and 14 days, TNNT3 (29 kDa) at 8 h, TNNT3 (27 kDa) at 3 days, TNNI2 at 24 h and CRYAB at 2 h. The meat samples from the Intensive treatment were located in the negative side, showing higher $b^*$ values, $h^*$ and $C^*$ and ACTA1 at 8 h *post-mortem*.

Multivariate analysis showed that the handling factors (F and TL) had a clear effect in the different variables analyzed. Thus, animal's farm management produced a clear separation between meat samples from animals reared under Intensive or Extensive systems. On the other hand, the effect of Mixing animals during Transport and Lairage was significant in the intensively reared animals with a clear separation in two groups: (1) meat of the I-NM animals with lower WBSF values and higher intensity of the ACTA1 band at 8 h *post-mortem*; and (2) meat of the I-M animals with higher $b^*$, $h^*$, and $C^*$. Our previous studies pointed out that these I-M animals suffered from higher PSS as they showed higher serum haptoglobin and glucose levels at slaughter and lower muscle ATP levels, thus resulting in the blockage of the muscle antioxidant defense and slower *post-mortem* autophagic rate [9,18]. In fact, response to oxidative stress was found as a significant enriched term in this study (Figure 3B), which need further studies in the future to better elucidate the underlying mechanisms about its role in relation to the factors we investigated in this study.
<figure><img src="image_22.png" /><figcaption>**Figure 4.** Biplot of variables and individuals (meat samples). The centroids of the animal treatments are shown in squares denoted with codes: I-M (Intensive-Mixed), I-NM (Intensive-Non-Mixed), E-M (Extensive Mixed), E-NM (Extensive-Non-Mixed). Individual samples are shown in yellow bullets (I-M), blue bullets (I-NM), green bullets (E-M) and grey bullets (E-NM). WB3 d: Warner–Bratzler Shear Force at 3 days, WB7 d: Warner–Bratzler Shear Force at 7 days, WB14 d: Warner–Bratzler Shear Force at 14 days; *b*#: yellowness; *C*#: Chroma; *h*#: hue angle; CRYAB: Alpha-crystallin B chain; MYL1: Myosin light chain 1/3 skeletal muscle isoform; MYLPF: Myosin regulatory light chain 2, skeletal muscle isoform; TNNT3: Troponin T, fast skeletal muscle isoform X31; TNNI2: Troponin I, fast skeletal muscle; LDB3: LIM domain-binding protein 3 isoform X5; FHL2: Four and a half LIM domains protein 1 isoform 2; ACTA1: Actin, alpha skeletal muscle. 2 h: 2 h *post-mortem*, 8 h: 8 h post-mortem; 24 h: 24 h *post-mortem*; 3 d: 3 days *post-mortem*; 7 d: 7 days *post-mortem*.</figcaption></figure>

Within the extensively reared animals, the effect of social mixing was not very clear as mixed and non-mixed animals were overlapped in the positive side of the PC1, indicating a lower effect of mixing unfamiliar animals, as previously described [9]. It is difficult to determine if these differences between treatments are due to differences in diet, physical activity or higher PSS derived from the different animal's handling treatments, but there are clear differences and the changes they produce in the myofibrillar subproteome at early *post-mortem* could provide putative biomarkers of the final meat quality.

**4. Conclusions**

The findings of this study confirmed that meat quality of young "Asturiana de los Valles" bulls is affected by handling practices at Farm and during Transport and Lairage before slaughter and during the tenderization of the meat. At the farm level, the production system (Intensive vs. Extensive) significantly affected meat color parameters (L*, b*, C*, and h*), highlighting that the meat from the Extensive treatment was brownish and darker. The Transport and Lairage factor (Mixed vs. Non-Mixed) affected also color traits mainly redness (a*) and Chroma (C*) leading to lower values in non-mixed animals. The tenderization rate of the meats of the investigated groups was higher but delayed in the meat samples from I-M animals. The comparative proteomics extended our knowledge and revealed
that Farm, Transport/Lairage and *post-mortem* ageing has huge but different effects on the *post-mortem* muscle myofibrillar subproteome. The major pathway that was impacted by this factor was related to muscle structure. In fact, farm management affected six structural proteins (MYBPC2, TNNT3, MYL1, ACTA1, LDB3, and FHL2) and one metabolic enzyme (ALDOA) while Transport and Lairage prior to slaughter induced changes in five structural protein bands (MYBPC2, TNNT3, TNNI2, MYL1, and MYLPF), two metabolic enzymes (PKM and ALDOA), and one Heat shock protein (HSPB1).

*Post-mortem* ageing was the most important factor affecting the myofibrillar proteome, among the different handling practices confirming the importance of monitoring subproteome changes along meat ageing for an accurate understanding of the effects. Several correlations were found between the protein changing in this trial at early *post-mortem* times with meat color and tenderness parameters (PKM, ALDOA, HSPA1A, HSPB1, CRYAB, DES, TNNT3, TNNI2, and MYLPF), confirming that they could be used as meat quality biomarkers in comparison to the largest and recent beef tenderness biomarkers database recently published by Gagaoua et al. [21].

**Author Contributions:** Conceptualization: M.O., V.S., M.G. and A.C.-M.; methodology: L.G.-B., F.D., M.O. and V.S.; investigation: L.G.-B., F.D., M.J.G.-E. and Y.D.; formal analysis: V.S. and M.G.; data curation: L.G.-B.; writing—original draft preparation: V.S.; writing—review and editing: M.O., M.G. and A.C.-M. visualization. L.G.-B. and M.G.; supervision. M.O.; funding acquisition V.S. and M.O. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) and FEDER funds under the project RTA2014-00034-C04-01, and grant RTI2018-096162-RC21 funded by MCIN/AEI/10.13039/501100011033 and by "ERDF a way making Europe". The APC was funded by RTI2018-096162-RC21. F.D. acknowledges the contract PEJ-2014-P-01094 awarded by MINECO (Spain). L.G.-B acknowledges her grant PRE2019-091053 funded by MCIN/AEI/10.13039/501100011033 and by ESF "investing in your future".

**Institutional Review Board Statement:** The consideration of the ethical and welfare aspects by the Animal Care & Ethics Committee (ACEC) was not required for the development of the current study, since the animals were subjected to standard production practices during growing and finishing phases in compliance with the Spanish Law 32/2007 for the care of animals during their exploitation, transport, experimentation, and sacrifice. No additional measures were required.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data available on request.

**Acknowledgments:** We thank the staff of the Area of Livestock Production Systems from SERIDA and the staff from ASEAVA for skilled management of Animals. Mohammed Gagaoua acknowledges the funding support of the Marie Skłodowska-Curie grant agreement No. 713654 under the project number MF20180029.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript or in the decision to publish the results.

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Article

# A Proteomic Study for the Discovery of Beef Tenderness Biomarkers and Prediction of Warner-Bratzler Shear Force Measured on *Longissimus thoracis* Muscles of Young Limousin-Sired Bulls

Yao Zhu<sup>1,2,†</sup>, Mohammed Gagaoua<sup>1,†</sup>, Anne Maria Mullen<sup>1</sup>, Alan L. Kelly<sup>2</sup>, Torres Sweeney<sup>3</sup>, Jamie Cafferky<sup>1</sup>, Didier Viala<sup>4</sup> and Ruth M. Hamill<sup>1,*</sup>

**Citation:** Zhu, Y.; Gagaoua, M.; Mullen, A.M.; Kelly, A.L.; Sweeney, T.; Cafferky, J.; Viala, D.; Hamill, R.M. A Proteomic Study for the Discovery of Beef Tenderness Biomarkers and Prediction of Warner-Bratzler Shear Force Measured on *Longissimus thoracis* Muscles of Young Limousin-Sired Bulls. *Foods* **2021**, *10*, 952. https://doi.org/10.3390/foods10050952

Academic Editor: Hanne Christine Bertram

Received: 14 April 2021

Accepted: 23 April 2021

Published: 27 April 2021

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**Abstract:** Beef tenderness is of central importance in determining consumers' overall liking. To better understand the underlying mechanisms of tenderness and be able to predict it, this study aimed to apply a proteomics approach on the *Longissimus thoracis* (LT) muscle of young Limousin-sired bulls to identify candidate protein biomarkers. A total of 34 proteins showed differential abundance between the tender and tough groups. These proteins belong to biological pathways related to muscle structure, energy metabolism, heat shock proteins, response to oxidative stress, and apoptosis. Twenty-three putative protein biomarkers or their isoforms had previously been identified as beef tenderness biomarkers, while eleven were novel. Using regression analysis to predict shear force values, MYOZ3 (Myozenin 3), BIN1 (Bridging Integrator-1), and OGN (Memoican) were the major proteins retained in the regression model, together explaining 79% of the variability. The results of this study confirmed the existing knowledge but also offered new insights enriching the previous biomarkers of tenderness proposed for *Longissimus* muscle.

**Keywords:** foodomics; beef tenderness; bovine biomarkers; muscle; proteome; liquid chromatography-tandem mass spectrometry (LC-MS/MS)

## 1. Introduction

Meat-eating quality consists of a complex set of sensory traits including tenderness, flavour, and juiciness, each of which plays an important role in defining the appeal of beef to consumers [1,2]. Amongst these quality attributes, however, tenderness is considered to be one of the most important factors in purchase decisions regarding beef, with negative experience on toughness contributing to a lower likelihood of repeat purchase [3]. To meet the expectations of consumers, beef producers must pursue the provision of consistent high-quality beef. The underlying mechanisms involved in dictating the final meat tenderness are intricate, with muscle biochemistry interacting with processing, influenced by several factors including breed [4,5], gender [6], age at slaughter [7], muscle type [8,9], cooking temperature [5], stress at slaughter [10], and post-slaughter management and many other factors from farm-to-fork [9,11].

There have been a number of studies using omics tools to, firstly, enhance our understanding of the pathways and processes contributing to beef tenderness variation [12,13] and secondly, to propose prediction equations to explain the observed variability in this

<footnote n="1">¹ Food Quality and Sensory Science Department, Teagasc Food Research Centre, Ashtown, D15 KN3K Dublin 15, Ireland; yao.zhu@teagasc.ie (Y.Z.); gmer2001@yahoo.fr (M.G.); AnneMaria.mullen@teagasc.ie (A.M.M.); Jamie.Cafferky@teagasc.ie (J.C.)</footnote>

<footnote n="2">² School of Food and Nutritional Sciences, University College Cork, T12 K8AF Cork, Ireland; a.kelly@ucc.ie</footnote>

<footnote n="3">³ School of Veterinary Sciences, University College Dublin, D04 V1W8 Dublin 4, Ireland; torres.sweeney@ucd.ie</footnote>

<footnote n="4">⁴ Metabolomic and Proteomic Exploration Facility (PFEM), INRAE, F-63122 Saint-Genés-Champanelle, France; didier.viala@inrae.fr</footnote>

<footnote>* Correspondence: ruth.hamill@teagasc.ie; Tel.: +353-(0)1-805-9933</footnote>

<footnote>† Both authors contributed equally to this work.</footnote>
important quality trait [14]. Thus, omics-related analytical technologies and bioinformatics tools have been applied in recent decades, resulting in a deeper understanding of gene expression, physiological responses, and other metabolic processes that are involved in meat quality determination, especially tenderness [2,12,15].

Foodomics is an emerging group of disciplines encompassing genomics, transcriptionics, proteomics, metabolomics, and lipidomics applied to food and parameters related to its quality and has been extensively used to study both fresh meat and meat products [16]. Among the many foodomics approaches, proteomics played an important role in the discovery of candidate biomarkers of several meat quality attributes [2,13,17]. A pipeline to search for proteomic biomarkers of beef tenderness was proposed [12,14]. Compared with traditional evaluation methods for beef tenderness using instrumental or sensory methods, an optimised protocol for quality monitoring using rapid methods to record the abundance of specific proteins of interest would offer an advantage to predict the meat quality before consumption. Moreover, these approaches have the potential to be developed further to allow advanced prediction of the future tenderness phenotype at a range of stages from farm-to-fork [11,15].

This study aimed to apply shotgun proteomics on muscle tissue of young Limousin-sired bulls to identify putative biomarkers of beef tenderness evaluated by Warner–Bratzler shear force (WBSF) [15]. We further aimed to propose regression models and identify the main biological interactions among the proteins underpinning WBSF variation to gain insights into the mechanisms of beef tenderness determination.

## 2. Materials and Methods

### 2.1. Meat Sample Collection

Eighteen young Limousin-sired bulls were obtained and finished at the Irish Cattle Breeders Federation Progeny Test Centre and slaughtered in an EU-licensed abattoir by electrical stunning (50 Hz) followed by exsanguination from the jugular vein. All 18 animals were finished to U- to E+ conformation score, 3- to 5= fat score and at an average age of 487 days (±24 days) and live weight of 678 kg (±58 kg) [6]. According to the muscle sampling method used by Zhu et al. [18], *Longissimus thoracis et lumborum* (LTL) samples from the 10th rib of each carcass were collected and finely macerated in 5 mL RNAlater® for 24 h. The RNAlater® was then removed, and the sample was subsequently transferred for storage at -80 °C until analysis. Loins were boned out at 48 h post-mortem, and steaks with a thickness of 2.54 cm were cut out from the right-side LTL of the carcass starting at the anterior end and packaged in vacuum bags. The steaks were then aged for 14 days and stored at -20 °C until Warner-Bratzler shear force (WBSF) analysis.

### 2.2. Warner-Bratzler Shear Force Measurement

Steaks were thawed at room temperature by immersion in a circulating water bath for 4 h. After that, external fat was trimmed from the steaks, and they were cooked in open bags in a circulating water bath (Grant Instruments Ltd., Cambridge, UK) set at 72 °C to reach an internal end-point cooking temperature of 71 °C. The cooked steaks were cooled down and stored in a refrigerator at 4 °C overnight. Shear force analysis was conducted following a modified version of the guideline of the American Meat Science Association (AMSA) [6]. For each steak, seven cores were taken with a 1.27 cm diameter parallel to the muscle fibre direction. The shear force was measured by an Instron 4464 Universal testing machine (Instron Ltd., Buckinghamshire, UK), and data analysed using Bluehill 2 Software (Instron Ltd., Buckinghamshire, UK). To reduce the standard deviation among the cores, the maximum and minimum shear values (Newton) were discarded, and the mean values of the remaining 5 cores were reported.

### 2.3. Muscle Protein Extraction

Frozen muscle tissue samples (80 mg) were first homogenised in 2 mL of 8.3 M urea, 2 M thiourea, 1% dithiothreitol, 2% 3-[(3-cholamidopropyl) dimethylammonio]-1-
propanesulfonate, 2% immobilised pH gradient (IPG) buffer pH 3–10 using a T 25 digital ULTRA-TURRAX® following the protocol of Bouley et al. [19]. To remove non-extracted cellular components, fat, insoluble proteins, the protein homogenates were incubated with shaking for 30 min at 4 °C followed by a 30 min centrifugation at 10,000× g. The supernatant was then transferred into Eppendorf tubes for protein quantification using the dye-binding protocol of Bradford [20].

## 2.4. Shotgun Proteomics

### 2.4.1. One Dimensional SDS-PAGE and Protein Bands Preparation

The protein extract was firstly mixed (1:1) with Laemmli sample buffer (Bio-Rad Laboratories, Deeside, UK), then concentrated on 1D stacking gel of sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE) using commercial Mini-PROTEAN® TGX™ precast gels of 8.6 × 6.7 × 0.1 cm and 12% polyacrylamide (Bio-Rad Laboratories, Deeside, UK). Twenty µg proteins were loaded in each gel lane, and the electrophoresis was run at 4 watts for about 15 min to concentrate the proteins in the stacking gel [21]. Subsequently, the gels were washed three times with Milli-Q water, stained with EZ Blue Gel staining reagent (Sigma, Saint Louis, MO, USA) with gentle shaking for 2 h, and then washed with Milli-Q water. The protein bands were excised from the washed gels using a sterile scalpel and immediately transferred into Eppendorf tubes containing 200 µL of 25 mM ammonium bicarbonate (Sigma, Saint Louis, MO, USA)-5% acetonitrile for 30 min. Then, bands were washed twice using 200 µL of 25 mM ammonium bicarbonate-50% acetonitrile for 30 min each. Finally, they were dehydrated with 100% acetonitrile for 10 min, and the liquid was discarded. Subsequently, the dried protein bands were stored at -80 °C until LC-MS/MS analysis. The immobilised proteins in the 1D gel bands were discoloured/reduced-alkylated, as described by Gagaoua et al. [22].

### 2.4.2. LC-MS/MS

The hydrolysis of the protein bands was carried out with 48 µL of a 25 mM ammonium bicarbonate buffer-12.5 ng/µL trypsin solution (Promega) per band for 5 h in an oven at 37 °C. Then, 30 µL buffer was added periodically during hydrolysis so that the bands were always covered with liquid. The extraction of the peptides was carried out under ultrasound (15 min) with acetonitrile and trifluoroacetic acid. Then, the supernatant was transferred into 500 µL Eppendorf tubes and dry concentrated using a Speedvac for 2 h. The volume was adjusted exactly to 20 µL with a solution of isotopologic peptides (50 pmol/µL) that was diluted 18 times in a 0.05% Trifluoroacetic acid (TFA) solution. After passing through the ultrasonic bath (10 min), the entire supernatant was transferred to the High performance liquid chromatography (HPLC) vial before LC-MS/MS analysis.

For the separation, the hydrolysate was injected into the nano-LC-MS/MS (Thermo Fisher Scientific) using an Ultimate 3000 system coupled to a QExactive HF-X mass spectrometer (MS) with a nanoelectrospray ion source. Briefly, 1 µL of hydrolysate was first preconcentrated and desalted at a flow rate of 30 µL/min on a C18 pre-column 5 cm length × 100 µm (Acclaim PepMap 100 C18, 5 µm, 100 Å nanoViper) equilibrated with trifluoroacetic acid 0.05% in water to remove contaminants that could potentially disrupt the efficiency of the mass spectrometry analysis. After 6 min, the concentration column was put in line with a nano debit analytical column operating at 400 nL/min. The peptides were then separated according to their hydrophobicity (column C18, length 25 cm, diameter 75 µm, SN 10711310), using a gradient of a solution of acetonitrile (ACN/FA-99.9/0.1) of 4 to 25% in 50 min.

### 2.4.3. LC-MS/MS Data Processing and Protein Identification

The raw files from the LC-MS/MS were aligned against the *Bos taurus* database (i.e., ref_bos_taurus, 23,970 sequences) with Mascot V.2.5.1 (http://www.matrixscience.com, accessed on 30 August 2020). The precursor and fragment mass tolerance were set up at 10 ppm and 0.02 Da, respectively. The variable modifications included carbamidomethy-