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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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. |
doi_str_mv | 10.1016/j.meatsci.2024.109618 |
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•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.</description><identifier>ISSN: 0309-1740</identifier><identifier>ISSN: 1873-4138</identifier><identifier>EISSN: 1873-4138</identifier><identifier>EISSN: 0309-1740</identifier><identifier>DOI: 10.1016/j.meatsci.2024.109618</identifier><identifier>PMID: 39096797</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>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</subject><ispartof>Meat science, 2024-11, Vol.217, p.109618, Article 109618</ispartof><rights>2024</rights><rights>Copyright © 2024. Published by Elsevier Ltd.</rights><rights>Attribution</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c277t-6c0cebd92df561068898a09649e2d99d2b2db7faf8e785bc3aa536f77a521f2a3</cites><orcidid>0000-0001-6913-3379</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.meatsci.2024.109618$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>230,315,782,786,887,3552,27931,27932,46002</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39096797$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://hal.inrae.fr/hal-04666195$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>González-Blanco, Laura</creatorcontrib><creatorcontrib>Oliván, Mamen</creatorcontrib><creatorcontrib>Diñeiro, Yolanda</creatorcontrib><creatorcontrib>Bravo, Susana B.</creatorcontrib><creatorcontrib>Sierra, Verónica</creatorcontrib><creatorcontrib>Gagaoua, Mohammed</creatorcontrib><title>Sequential window acquisition of all theoretical mass spectra (SWATH-MS) as an emerging proteomics approach for the discovery of dark-cutting beef biomarkers</title><title>Meat science</title><addtitle>Meat Sci</addtitle><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.</description><subject>Agricultural sciences</subject><subject>Animal production studies</subject><subject>Animals</subject><subject>Beef quality defects</subject><subject>Biological mechanisms</subject><subject>Biomarkers - analysis</subject><subject>Cattle</subject><subject>Dark-cutting</subject><subject>Life Sciences</subject><subject>Male</subject><subject>Mass Spectrometry - methods</subject><subject>Muscle Proteins - analysis</subject><subject>Muscle proteome</subject><subject>Muscle, Skeletal - chemistry</subject><subject>Protein biomarkers</subject><subject>Proteome - analysis</subject><subject>Proteomics - methods</subject><subject>Red Meat - analysis</subject><subject>Shotgun proteomics</subject><issn>0309-1740</issn><issn>1873-4138</issn><issn>1873-4138</issn><issn>0309-1740</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkc1uEzEUhS0EomnhEUBetosJ9vzZXqGoggYpiEWKWFoe-7pxmBmntpOqD8O74tGEbllZPvruuTr3IPSBkiUltP20Xw6gUtRuWZKyzppoKX-FFpSzqqhpxV-jBamIKCiryQW6jHFPCKFVyd-ii0pknAm2QH-28HiEMTnV4yc3Gv-ElX48uuiS8yP2Fqu-x2kHPkByOlODihHHA-gUFL7e_lrdr4vv2xusIlYjhgHCgxsf8CH4BH5wOsuH_FF6h60PkxU2Lmp_gvA8-RsVfhf6mNI01QFY3Dk_ZBFCfIfeWNVHeH9-r9DPr1_ub9fF5sfdt9vVptAlY6loNdHQGVEa27SUtJwLrnLCWkBphDBlV5qOWWU5MN50ulKqqVrLmGpKaktVXaGb2XenenkILq9_ll45uV5t5KSRum1bKpoTzez1zOZQ-XQxySHHgb5XI_hjlBXhrBW85jyjzYzq4GMMYF-8KZFTi3Ivzy3KqUU5t5jnPp5XHLsBzMvUv9oy8HkGIB_l5CDIbAGjBuNCLkYa7_6z4i-1PLLg</recordid><startdate>20241101</startdate><enddate>20241101</enddate><creator>González-Blanco, Laura</creator><creator>Oliván, Mamen</creator><creator>Diñeiro, Yolanda</creator><creator>Bravo, Susana B.</creator><creator>Sierra, Verónica</creator><creator>Gagaoua, Mohammed</creator><general>Elsevier Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0001-6913-3379</orcidid></search><sort><creationdate>20241101</creationdate><title>Sequential window acquisition of all theoretical mass spectra (SWATH-MS) as an emerging proteomics approach for the discovery of dark-cutting beef biomarkers</title><author>González-Blanco, Laura ; Oliván, Mamen ; Diñeiro, Yolanda ; Bravo, Susana B. ; Sierra, Verónica ; Gagaoua, Mohammed</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c277t-6c0cebd92df561068898a09649e2d99d2b2db7faf8e785bc3aa536f77a521f2a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Agricultural sciences</topic><topic>Animal production studies</topic><topic>Animals</topic><topic>Beef quality defects</topic><topic>Biological mechanisms</topic><topic>Biomarkers - analysis</topic><topic>Cattle</topic><topic>Dark-cutting</topic><topic>Life Sciences</topic><topic>Male</topic><topic>Mass Spectrometry - methods</topic><topic>Muscle Proteins - analysis</topic><topic>Muscle proteome</topic><topic>Muscle, Skeletal - chemistry</topic><topic>Protein biomarkers</topic><topic>Proteome - analysis</topic><topic>Proteomics - methods</topic><topic>Red Meat - analysis</topic><topic>Shotgun proteomics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>González-Blanco, Laura</creatorcontrib><creatorcontrib>Oliván, Mamen</creatorcontrib><creatorcontrib>Diñeiro, Yolanda</creatorcontrib><creatorcontrib>Bravo, Susana B.</creatorcontrib><creatorcontrib>Sierra, Verónica</creatorcontrib><creatorcontrib>Gagaoua, Mohammed</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>Meat science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>González-Blanco, Laura</au><au>Oliván, Mamen</au><au>Diñeiro, Yolanda</au><au>Bravo, Susana B.</au><au>Sierra, Verónica</au><au>Gagaoua, Mohammed</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Sequential window acquisition of all theoretical mass spectra (SWATH-MS) as an emerging proteomics approach for the discovery of dark-cutting beef biomarkers</atitle><jtitle>Meat science</jtitle><addtitle>Meat Sci</addtitle><date>2024-11-01</date><risdate>2024</risdate><volume>217</volume><spage>109618</spage><pages>109618-</pages><artnum>109618</artnum><issn>0309-1740</issn><issn>1873-4138</issn><eissn>1873-4138</eissn><eissn>0309-1740</eissn><abstract>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.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>39096797</pmid><doi>10.1016/j.meatsci.2024.109618</doi><orcidid>https://orcid.org/0000-0001-6913-3379</orcidid><oa>free_for_read</oa></addata></record> |
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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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