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SWATH-MS proteomics data on differentially abundant proteins between normal and dark-cutting beef.

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Data in Brief
|October 20, 2025
PubMed
Summary
This summary is machine-generated.

Dark, firm, and dry (DFD) beef, a costly quality defect, was analyzed using SWATH-MS proteomics. Researchers identified 35 proteins that may serve as biomarkers for predicting DFD beef, aiding in early detection and quality improvement.

Keywords:
Beef quality defectsBovine muscleHigh muscle pH beefMuscle proteomePost-mortemProtein biomarkers

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Area of Science:

  • Food Science
  • Proteomics
  • Animal Science

Background:

  • Dark, firm, and dry (DFD) beef causes significant economic losses and consumer rejection due to its undesirable appearance and increased spoilage.
  • The underlying mechanisms of DFD beef are complex and multifactorial, with pre-slaughter factors playing a role but not fully elucidating the condition.
  • Proteomic approaches are valuable for understanding the molecular basis of DFD beef and identifying potential biomarkers.

Purpose of the Study:

  • To characterize and quantify post-mortem muscle proteomes in DFD beef using SWATH-MS proteomics.
  • To identify protein expression differences associated with the DFD beef condition.
  • To discover candidate protein biomarkers for early prediction and better characterization of DFD beef.

Main Methods:

  • SWATH-MS (Sequential Window Acquisition of All Theoretical Mass Spectra) proteomics was employed for in-depth proteome analysis.
  • A comprehensive dataset was analyzed, including 26 DFD beef samples (pH24 ≥ 6.2) and 26 control samples (5.4 ≤ pH24 ≤ 5.6).
  • Muscle samples from the Longissimus thoracis et lumborum of Asturiana de los Valles yearling bulls were collected 24 hours post-mortem.

Main Results:

  • A total of 735 proteins were quantified in the muscle samples.
  • Significant differences in protein abundance were observed between DFD and control samples.
  • 35 proteins were identified with significantly different abundances, suggesting their potential as biomarkers for DFD beef.

Conclusions:

  • The study provides a comprehensive proteomic dataset for DFD beef, facilitating further research.
  • The identified 35 proteins represent potential biomarkers for the early prediction of DFD beef.
  • This research contributes to integromics approaches for refining DFD beef biomarker selection and understanding underlying biological mechanisms.