Blaustein Ryan A, Smith Jaclyn E, Toro Magaly, Pachepsky Yakov, Stocker Matthew D
Department of Nutrition and Food Science, University of Maryland, College Park, MD, United States.
Oak Ridge Institutes for Science and Education, Oak Ridge, TN, United States.
Front Microbiol. 2025 Jun 4;16:1535096. doi: 10.3389/fmicb.2025.1535096. eCollection 2025.
Agricultural ponds are essential irrigation resources, though may also serve as reservoirs for pathogens and antimicrobial resistance (AMR) genes. While monitoring microbiological water quality is critical for food safety, the influence of sampling factors (e.g., when and where to collect samples) in making risk assessments and potential applications for using environmental covariates as indicators remain unclear. Here, we explored the hypothesis that metagenomes of agricultural waters change with spatiotemporal shifts in physicochemical water quality, i.e., across water depths over time. Water samples and underlying sediments were collected at a model pond at the surface and within the water column (0, 1, 2 m depths) throughout one day (i.e., 9:00, 12:00, 15:00). All samples were processed for shotgun metagenomic sequencing analysis and enumeration of various water quality parameters (e.g., temperature, nutrient concentrations, turbidity, pH, culturable ). At the pond surface, and members of Cyanobacteria, along with genes encoding pathways related to photosynthesis and nucleotide biosynthesis, were enriched throughout the day. In contrast, within the water column (1-2 m depths) and sediments, diverse members of Proteobacteria and Actinobacteria were more dominant, along with encoded pathways related to respiration and amino acid biosynthesis. Various aspects of water quality (i.e., chlorophyll dissolved organic matter, ammonia, concentrations) correlated with water metagenome diversity, albeit not with any specific AMR genes or virulence factors. Nevertheless, assembly of sequenced reads uncovered 22 unique strains encoding several AMR, virulence, or stress response genetic elements, thus linking metagenome functional potential to key taxa. Overall, our findings highlight distinctions in agricultural pond water metagenomes at the surface and in the water column and demonstrate the potential for metagenomic surveillance in water quality monitoring to support food safety.
农业池塘是重要的灌溉资源，尽管它们也可能成为病原体和抗微生物耐药性（AMR）基因的储存库。虽然监测微生物水质对食品安全至关重要，但采样因素（例如何时何地采集样本）在进行风险评估中的影响以及将环境协变量用作指标的潜在应用仍不明确。在这里，我们探讨了这样一个假设，即农业水体的宏基因组会随着理化水质的时空变化而变化，即随时间在不同水深发生变化。在一个典型池塘的水面以及水柱内（0、1、2米深度），于一天内（即9:00、12:00、15:00）采集水样和底层沉积物。所有样本都进行了鸟枪法宏基因组测序分析，并对各种水质参数（例如温度、营养物浓度、浊度、pH值、可培养物）进行了计数。在池塘水面，蓝细菌及其光合和核苷酸生物合成相关途径的编码基因在一天中均有富集。相比之下，在水柱内（1 - 2米深度）和沉积物中，变形菌门和放线菌门的多种成员更为占主导地位，同时还有与呼吸作用和氨基酸生物合成相关的编码途径。水质的各个方面（即叶绿素、溶解有机物、氨、浓度）与水体宏基因组多样性相关，尽管与任何特定的AMR基因或毒力因子无关。然而，对测序读数的组装发现了22种独特菌株，它们编码了几种AMR、毒力或应激反应遗传元件，从而将宏基因组功能潜力与关键分类群联系起来。总体而言，我们的研究结果突出了农业池塘水体表面和水柱中宏基因组的差异，并证明了宏基因组监测在水质监测中支持食品安全的潜力。
