Sequential window acquisition of all theoretical mass spectra (SWATH-MS) as an emerging proteomics approach for the discovery of dark-cutting beef biomarkers

Recent advances in “omics” technologies have enabled the identification of new beef quality biomarkers and have also allowed for the early detection of quality defects such as dark-cutting beef, also known as DFD (dark, firm, and dry) beef. However, most of the studies conducted were carried out on...

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Veröffentlicht in:Meat science 2024-11, Vol.217, p.109618, Article 109618
Hauptverfasser: González-Blanco, Laura, Oliván, Mamen, Diñeiro, Yolanda, Bravo, Susana B., Sierra, Verónica, Gagaoua, Mohammed
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container_start_page 109618
container_title Meat science
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creator González-Blanco, Laura
Oliván, Mamen
Diñeiro, Yolanda
Bravo, Susana B.
Sierra, Verónica
Gagaoua, Mohammed
description Recent advances in “omics” technologies have enabled the identification of new beef quality biomarkers and have also allowed for the early detection of quality defects such as dark-cutting beef, also known as DFD (dark, firm, and dry) beef. However, most of the studies conducted were carried out on a small number of animals and mostly applied gel-based proteomics. The present study proposes for the first time a Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH-MS) proteomics approach to characterize and comprehensively quantify the post-mortem muscle proteome of DFD (pH24 ≥ 6.2) and CONTROL (5.4 ≤ pH24 ≤ 5.6) beef samples within the largest database of DFD/CONTROL beef samples to date (26 pairs of the Longissimus thoracis muscle samples of young bulls from Asturiana de los Valles breed, n = 52). The pairwise comparison yielded 35 proteins that significantly differed in their abundances between the DFD and CONTROL samples. Chemometrics methods using both PLS-DA and OPLS-DA revealed 31 and 36 proteins with VIP > 2.0, respectively. The combination of different statistical methods these being Volcano plot, PLS-DA and OPLS-DA allowed us to propose 16 proteins as good candidate biomarkers of DFD beef. These proteins are associated with interconnected biochemical pathways related to energy metabolism (DHRS7B and CYB5R3), binding and signaling (RABGGTA, MIA3, BPIFA2B, CAP2, APOBEC2, UBE2V1, KIR2DL1), muscle contraction, structure and associated proteins (DMD, PFN2), proteolysis, hydrolases, and activity regulation (AGT, C4A, GLB1, CAND2), and calcium homeostasis (ANXA6). These results evidenced the potential of SWATH-MS and chemometrics to accurately identify novel biomarkers for meat quality defects, providing a deeper understanding of the molecular mechanisms underlying dark-cutting beef condition. •SWATH-MS is a powerful proteomics approach for the discovery of dark-cutting beef biomarkers.•Comparison of proteome of DFD and normal-pH beef samples within the largest existing database of to date.•First study of dark-cutting beef condition combining multivariate and chemometrics with SWATH-MS proteomics.•DFD beef condition involves a myriad of interconnected biochemical pathways such as muscle structure, energy metabolism, binding and signaling mechanisms, calcium homeostasis, apoptosis and proteolysis.
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These proteins are associated with interconnected biochemical pathways related to energy metabolism (DHRS7B and CYB5R3), binding and signaling (RABGGTA, MIA3, BPIFA2B, CAP2, APOBEC2, UBE2V1, KIR2DL1), muscle contraction, structure and associated proteins (DMD, PFN2), proteolysis, hydrolases, and activity regulation (AGT, C4A, GLB1, CAND2), and calcium homeostasis (ANXA6). 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However, most of the studies conducted were carried out on a small number of animals and mostly applied gel-based proteomics. The present study proposes for the first time a Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH-MS) proteomics approach to characterize and comprehensively quantify the post-mortem muscle proteome of DFD (pH24 ≥ 6.2) and CONTROL (5.4 ≤ pH24 ≤ 5.6) beef samples within the largest database of DFD/CONTROL beef samples to date (26 pairs of the Longissimus thoracis muscle samples of young bulls from Asturiana de los Valles breed, n = 52). The pairwise comparison yielded 35 proteins that significantly differed in their abundances between the DFD and CONTROL samples. Chemometrics methods using both PLS-DA and OPLS-DA revealed 31 and 36 proteins with VIP &gt; 2.0, respectively. The combination of different statistical methods these being Volcano plot, PLS-DA and OPLS-DA allowed us to propose 16 proteins as good candidate biomarkers of DFD beef. These proteins are associated with interconnected biochemical pathways related to energy metabolism (DHRS7B and CYB5R3), binding and signaling (RABGGTA, MIA3, BPIFA2B, CAP2, APOBEC2, UBE2V1, KIR2DL1), muscle contraction, structure and associated proteins (DMD, PFN2), proteolysis, hydrolases, and activity regulation (AGT, C4A, GLB1, CAND2), and calcium homeostasis (ANXA6). 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source MEDLINE; Access via ScienceDirect (Elsevier)
subjects Agricultural sciences
Animal production studies
Animals
Beef quality defects
Biological mechanisms
Biomarkers - analysis
Cattle
Dark-cutting
Life Sciences
Male
Mass Spectrometry - methods
Muscle Proteins - analysis
Muscle proteome
Muscle, Skeletal - chemistry
Protein biomarkers
Proteome - analysis
Proteomics - methods
Red Meat - analysis
Shotgun proteomics
title Sequential window acquisition of all theoretical mass spectra (SWATH-MS) as an emerging proteomics approach for the discovery of dark-cutting beef biomarkers
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