Alterations in Whey Protein Abundance Correlated with the Somatic Cell Count Identified via Label-Free and Selected Reaction Monitoring Proteomic Approaches

The somatic cell count (SCC) is widely used to assess milk quality and diagnose intramammary infections. Several whey proteins have been shown to correlate significantly with SCC and are considered potential indicators of udder health. However, the relationship between milk whey proteins and SCC has not been fully elucidated. In this study, milk samples were grouped into five categories based on SCC levels. Subsequently, whey proteins were identified using a label-free proteomics approach, and the differential abundance of proteins was validated through a selected reaction monitoring (SRM) method. The levels of various proteins, including azurocidin 1 and kininogen-2, exhibited an increase, whereas topoisomerase I, tropomyosin-1, and desmin showed a significant decrease depending on the SCCs. Principal component analysis unveiled that these proteins contributed to the developmental alterations in milk proteins. A majority of these differentially abundant proteins were associated with response to stimulus, localization, and defense response. Our results provide fundamental information on the SCC that can be utilized for evaluating milk quality and serve as potential indicators for detecting intramammary infections.

Authors

Institutions

Publication Details

Journal
Animals
Published
2025-02-26
DOI
https://doi.org/10.3390/ani15050675
Primary Topic
Milk Quality and Mastitis in Dairy Cows
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Alterations in Whey Protein Abundance Correlated with the Somatic Cell Count Identified via Label-Free and Selected Reaction Monitoring Proteomic Approaches

Zhiqiang Cheng, Kaixu Chen, Xiaowei Zhao, Kailun Yang et al.
Animals
Milk Quality and Mastitis in Dairy Cows
article

Alterations in Whey Protein Abundance Correlated with the Somatic Cell Count Identified via Label-Free and Selected Reaction Monitoring Proteomic Approaches

Zhiqiang Cheng, Kaixu Chen, Xiaowei Zhao, Kailun Yang, Changjiang Zang, Yong Chen, Caidie Wang, Xiaobin Li, Jing Li
article en

Abstract

The somatic cell count (SCC) is widely used to assess milk quality and diagnose intramammary infections. Several whey proteins have been shown to correlate significantly with SCC and are considered potential indicators of udder health. However, the relationship between milk whey proteins and SCC has not been fully elucidated. In this study, milk samples were grouped into five categories based on SCC levels. Subsequently, whey proteins were identified using a label-free proteomics approach, and the differential abundance of proteins was validated through a selected reaction monitoring (SRM) method. The levels of various proteins, including azurocidin 1 and kininogen-2, exhibited an increase, whereas topoisomerase I, tropomyosin-1, and desmin showed a significant decrease depending on the SCCs. Principal component analysis unveiled that these proteins contributed to the developmental alterations in milk proteins. A majority of these differentially abundant proteins were associated with response to stimulus, localization, and defense response. Our results provide fundamental information on the SCC that can be utilized for evaluating milk quality and serve as potential indicators for detecting intramammary infections.

AnimalsVol. 15(5)
Xinjiang Agricultural University (CN), Anhui Academy of Agricultural Sciences (CN)
Openalex Percentile: Top 96%
Milk Quality and Mastitis in Dairy Cows
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.