Detection of Antimicrobial Proteins/Peptides and Bacterial Proteins Involved in Antimicrobial Resistance in Raw Cow’s Milk from Different Breeds

Cristian Piras, Rosario De Fazio, Antonella Di Francesco, Francesca Oppedisano, Anna Antonella Spina, Vincenzo Cunsolo, Paola Roncada, Rainer Cramer, Domenico Britti
{"title":"Detection of Antimicrobial Proteins/Peptides and Bacterial Proteins Involved in Antimicrobial Resistance in Raw Cow’s Milk from Different Breeds","authors":"Cristian Piras, Rosario De Fazio, Antonella Di Francesco, Francesca Oppedisano, Anna Antonella Spina, Vincenzo Cunsolo, Paola Roncada, Rainer Cramer, Domenico Britti","doi":"10.3390/antibiotics13090838","DOIUrl":null,"url":null,"abstract":"Proteins involved in antibiotic resistance (resistome) and with antimicrobial activity are present in biological specimens. This study aims to explore the presence and abundance of antimicrobial peptides (AMPs) and resistome proteins in bovine milk from diverse breeds and from intensive (Pezzata rossa, Bruna alpina, and Frisona) and non-intensive farming (Podolica breeds). Liquid atmospheric pressure matrix-assisted laser desorption/ionization (LAP-MALDI) mass spectrometry (MS) profiling, bottom-up proteomics, and metaproteomics were used to comprehensively analyze milk samples from various bovine breeds in order to identify and characterize AMPs and to investigate resistome proteins. LAP-MALDI MS coupled with linear discriminant analysis (LDA) machine learning was employed as a rapid classification method for Podolica milk recognition against the milk of other bovine species. The results of the LAP-MALDI MS analysis of milk coupled with the linear discriminant analysis (LDA) demonstrate the potential of distinguishing between Podolica and control milk samples based on MS profiles. The classification accuracy achieved in the training set is 86% while it reaches 98.4% in the test set. Bottom-up proteomics revealed approximately 220 quantified bovine proteins (identified using the Bos taurus database), with cathelicidins and annexins exhibiting higher abundance levels in control cows (intensive farming breeds). On the other hand, the metaproteomics analysis highlighted the diversity within the milk’s microbial ecosystem with interesting results that may reflect the diverse environmental variables. The bottom-up proteomics data analysis using the Comprehensive Antibiotic Resistance Database (CARD) revealed beta-lactamases and tetracycline resistance proteins in both control and Podolica milk samples, with no relevant breed-specific differences observed.","PeriodicalId":8151,"journal":{"name":"Antibiotics","volume":"26 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Antibiotics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/antibiotics13090838","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Proteins involved in antibiotic resistance (resistome) and with antimicrobial activity are present in biological specimens. This study aims to explore the presence and abundance of antimicrobial peptides (AMPs) and resistome proteins in bovine milk from diverse breeds and from intensive (Pezzata rossa, Bruna alpina, and Frisona) and non-intensive farming (Podolica breeds). Liquid atmospheric pressure matrix-assisted laser desorption/ionization (LAP-MALDI) mass spectrometry (MS) profiling, bottom-up proteomics, and metaproteomics were used to comprehensively analyze milk samples from various bovine breeds in order to identify and characterize AMPs and to investigate resistome proteins. LAP-MALDI MS coupled with linear discriminant analysis (LDA) machine learning was employed as a rapid classification method for Podolica milk recognition against the milk of other bovine species. The results of the LAP-MALDI MS analysis of milk coupled with the linear discriminant analysis (LDA) demonstrate the potential of distinguishing between Podolica and control milk samples based on MS profiles. The classification accuracy achieved in the training set is 86% while it reaches 98.4% in the test set. Bottom-up proteomics revealed approximately 220 quantified bovine proteins (identified using the Bos taurus database), with cathelicidins and annexins exhibiting higher abundance levels in control cows (intensive farming breeds). On the other hand, the metaproteomics analysis highlighted the diversity within the milk’s microbial ecosystem with interesting results that may reflect the diverse environmental variables. The bottom-up proteomics data analysis using the Comprehensive Antibiotic Resistance Database (CARD) revealed beta-lactamases and tetracycline resistance proteins in both control and Podolica milk samples, with no relevant breed-specific differences observed.
检测不同品种生牛乳中的抗菌蛋白/肽和涉及抗菌素耐药性的细菌蛋白
生物样本中存在涉及抗生素耐药性(耐药性组)和具有抗菌活性的蛋白质。本研究旨在探讨不同品种、集约化养殖(Pezzata rossa、Bruna alpina 和 Frisona)和非集约化养殖(Podolica 品种)的牛乳中抗菌肽(AMPs)和抗生素组蛋白质的存在和丰度。研究人员利用液体常压基质辅助激光解吸/电离(LAP-MALDI)质谱(MS)分析、自下而上的蛋白质组学和元蛋白质组学对来自不同牛种的牛奶样本进行了全面分析,以确定AMPs的特征并研究抗性组蛋白质。将 LAP-MALDI MS 与线性判别分析 (LDA) 机器学习相结合,作为一种快速分类方法,用于识别 Podolica 牛奶与其他牛种牛奶的区别。对牛奶进行 LAP-MALDI MS 分析和线性判别分析(LDA)的结果表明,根据 MS 图谱区分 Podolica 牛奶样本和对照牛奶样本的潜力巨大。训练集的分类准确率为 86%,而测试集的准确率则达到 98.4%。自下而上的蛋白质组学发现了约 220 种量化的牛蛋白质(使用金牛数据库进行鉴定),对照组奶牛(集约化养殖品种)中的柔毛素和附件素含量较高。另一方面,元蛋白质组学分析强调了牛奶微生物生态系统的多样性,其有趣的结果可能反映了环境变量的多样性。使用抗生素耐药性综合数据库(CARD)进行的自下而上的蛋白质组学数据分析显示,对照组和 Podolica 牛奶样本中都有β-内酰胺酶和四环素耐药性蛋白质,没有观察到相关的品种特异性差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信