The milk of lactating cows presents a complex ecosystem of interconnected microbial communities which can influence the pathophysiology of mastitis. We hypothesized possible dynamic shifts of microbiome composition and genomic features with different pathological conditions of mastitis (Clinical Mastitis; CM, Recurrent CM; RCM, Subclinical Mastitis; SCM). To evaluate this hypothesis, we employed whole metagenome sequencing (WMS) in 20 milk samples (CM, 5; RCM, 6; SCM, 4; H, 5) to unravel the microbiome dynamics, interrelation, and relevant metabolic functions. The WMS data mapped to 442 bacterial, 58 archaeal and 48 viral genomes with distinct variation in microbiome composition (CM > H > RCM > SCM). Furthermore, we identified a number of microbial genomic features, including 333, 304, 183 and 50 virulence factors-associated genes (VFGs) and 48, 31, 11 and 6 antibiotic resistance genes (ARGs) in CM, RCM, SCM, and H-microbiomes, respectively. We also detected different metabolic pathway and functional genes associated with mastitis pathogenesis. Therefore, profiling microbiome dynamics in different conditions of mastitis and associated microbial genomic features contributes to developing microbiome-based diagnostics and therapeutics for bovine mastitis.

泌乳奶牛的牛奶呈现出一个由相互关联的微生物群落组成的复杂生态系统，可以影响乳腺炎的​​病理生理学。我们假设微生物组组成和基因组特征在乳腺炎的不同病理条件下可能发生动态变化（临床乳腺炎；CM，复发性 CM；RCM，亚临床乳腺炎；SCM）。为了评估这一假设，我们在 20 个牛奶样品（CM，5；RCM，6；SCM，4；H，5）中采用全宏基因组测序 (WMS) 来揭示微生物组动力学、相互关系和相关代谢功能。WMS 数据映射到 442 个细菌、58 个古细菌和 48 个病毒基因组，微生物组组成有明显差异（CM > H > RCM > SCM）。此外，我们确定了许多微生物基因组特征，包括 333、304、CM、RCM、SCM 和 H 微生物组中分别有 183 和 50 个毒力因子相关基因 (VFG) 和 48、31、11 和 6 个抗生素抗性基因 (ARG)。我们还检测到与乳腺炎发病机制相关的不同代谢途径和功能基因。因此，分析不同乳腺炎条件下的微生物组动力学和相关的微生物基因组特征有助于开发基于微生物组的牛乳腺炎诊断和治疗方法。