发表机构
University of California, Davis; USDA/NSF AI Institute for Next Generation Food Systems (AIFS); Innovation Institute for Food and Health; Process Integration and Predictive Analytics, PIPA LLC; USDA ARS Western Human Nutrition Research Center; Graduate School of Agricultural Science, Tohoku University（加州大学戴维斯分校; 美国农业部/国家科学基金会人工智能下一代食品系统研究所; 食品与健康创新研究所; 流程整合与预测分析，PIPA有限责任公司; 美国农业部农业研究局西方人类营养研究中心; 东北大学农学研究科）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过七层组学分析60份美国牛奶样本，构建乳制品分子数据库，发现抗菌肽活性及生物活性与品种、区域等因素相关，为优化牛奶质量提供依据。
AI 中文摘要
尽管牛奶是全球性的饮食 staple，但其与健康相关的生物活性的分子基础以及控制精细组成变异的因素仍知之甚少。在此，我们对来自十个地理区域的60份美国零售牛奶样本进行了整合的七层组学表征，在同一批样本上结合了基因组学、转录组学、肽组学、蛋白质组学、脂质组学、代谢组学和糖组学。我们还将已鉴定和定量的化合物整理到乳制品分子数据库（DMD）中，这是一个网络可访问的资源，用于将牛奶化合物浓度与SNP、miRNA和产品级因素联系起来，以支持未来的牛奶质量优化。总共，我们鉴定了6,714种化合物，并对其中5,288种进行了绝对浓度定量，包括5,220种在现有牛奶化合物数据库中未曾提供绝对浓度估计的化合物。这些谱图使得能够进行生物活性功效评估以及与牛奶化合物变异相关因素的关联分析。我们测试了54种计算预测的抗菌候选肽；其中35种显示出活性，6种对鲍曼不动杆菌（一种ESKAPE病原体）的IC50值低于256 μg/mL。我们进一步确定了下游生物活性化合物浓度或估计的生物活性功效与产品级因素（包括购买区域、购买环境温度和包装不透明度）以及SNP、估计的娟姗牛品种比例和miRNA丰度之间的关联。总体而言，生物活性功效谱与购买区域、选定的SNP、娟姗牛品种比例和选定的miRNA相关。这些发现表明，选择性育种和供应链优化可能有助于改善商业牛奶的生物活性质量。
英文摘要
Despite milk being a global dietary staple, the molecular basis of its health-relevant bioactivity and the factors governing fine-scale compositional variation remain poorly understood. Here, we present an integrated seven-layer omics characterization of 60 US retail milk samples from ten geographic regions, combining genomics, transcriptomics, peptidomics, proteomics, lipidomics, metabolomics, and glycomics on the same samples. We also organized the identified and quantified compounds into the Dairy Molecule Database (DMD), a web-accessible resource for linking milk compound concentrations with SNPs, miRNAs, and product-level factors to support future milk quality optimization. In total, we identified 6,714 compounds and quantified 5,288 with absolute concentrations, including 5,220 compounds not previously available with absolute concentration estimates in existing milk compound databases. These profiles enabled bioactivity efficacy estimation and association analyses of factors linked to milk compound variation. We tested 54 computationally predicted antimicrobial candidate peptides; 35 showed activity and 6 had IC50 values below 256 μg/mL against A. baumannii, an ESKAPE pathogen. We further identified associations between downstream bioactive compound concentrations or estimated bioactivity efficacy and product-level factors, including purchase region, purchase ambient temperature, and packaging opacity, as well as SNPs, estimated Jersey breed proportion, and miRNA abundance. Overall, bioactivity efficacy profiles were associated with purchase region, selected SNPs, Jersey breed proportion, and selected miRNAs. These findings suggest that selective breeding and supply-chain optimization could help improve the bioactive quality of commercial milk.
