Murphy Claire M, Weller Daniel L, Love Tanzy M T, Danyluk Michelle D, Strawn Laura K
School of Food Science, Washington State University Irrigated Agriculture Research and Extension Center, Prosser, Washington, USA.
Department of Food Science and Technology, Virginia Tech, Blacksburg, Virginia, USA.
Microbiol Spectr. 2025 Feb 4;13(2):e0197224. doi: 10.1128/spectrum.01972-24. Epub 2024 Dec 17.
Fecal contamination of surface waters presents significant human health and environmental risks. While many methods for understanding fecal contamination cannot differentiate between human and animal sources, microbial source tracking (MST) marker methods are used to identify fecal sources. To understand how to best employ MST marker data for managing fecal contamination risks, the present study compiled previously collected MST marker data ( = 12,878 samples) from across North America; each sample in the data set had been tested for fecal contamination from one or more of five sources (avian, canine, human, ruminant, swine). Using these data, this study aimed to characterize associations between non-methodological and methodological factors and detection of host-specific MST markers and determine how methodological differences may complicate the interpretation of these associations between studies. Variance partitioning analysis was used to quantify the variance in host-specific MST marker detection attributable to non-methodological and methodological factors. Conditional forest and regression analysis were utilized to assess the association between detection and select non-methodological and methodological factors. Between 10% (canine) and 38% (human) of total variance was uniquely attributable to non-methodological factors for any of the fecal sources considered, while between 50% (human) and 84% (canine) of variance could not be attributed to either methodological or non-methodological factors. This highlights the need for standardization of methods for MST marker detection across studies but also suggests that other factors, beyond those non-methodological variables considered here, influenced variation in the likelihood of MST marker detection.IMPORTANCEThis study underscores complications associated with comparing findings from studies that used different methodologies to detect the same fecal targets and highlights the difficulties associated with using non-comparable data to generalize findings and develop science-based risk management plans. These findings highlight the need for standardization of sampling and laboratory methods across microbial source tracking marker studies. Our findings build on previous research to suggest that one-size-fits-all approaches to managing fecal hazards in surface waterways may not be appropriate; instead, strategies tailored to specific water sources and conditions at the time of water use may be more effective.
地表水体的粪便污染会带来重大的人类健康和环境风险。虽然许多了解粪便污染的方法无法区分人类和动物来源，但微生物源追踪（MST）标记方法可用于识别粪便来源。为了了解如何最好地利用MST标记数据来管理粪便污染风险，本研究汇总了此前在北美各地收集的MST标记数据（n = 12,878个样本）；数据集中的每个样本都针对来自五个来源（禽类、犬类、人类、反刍动物、猪）中一个或多个的粪便污染进行了检测。利用这些数据，本研究旨在描述非方法学和方法学因素与宿主特异性MST标记物检测之间的关联，并确定方法学差异如何使这些研究之间关联的解释变得复杂。方差分解分析用于量化宿主特异性MST标记物检测中可归因于非方法学和方法学因素的方差。条件森林和回归分析用于评估检测与选定的非方法学和方法学因素之间的关联。在所考虑的任何粪便来源中。总方差的10%（犬类）至38%（人类）可唯一归因于非方法学因素，而50%（人类）至84%（犬类）的方差既不能归因于方法学因素也不能归因于非方法学因素。这凸显了跨研究对MST标记物检测方法进行标准化的必要性，但也表明，除了这里考虑的那些非方法学变量之外，其他因素也会影响MST标记物检测可能性的变化。重要性本研究强调了比较使用不同方法检测相同粪便目标的研究结果时所涉及的复杂性，并突出了使用不可比数据来概括研究结果和制定基于科学的风险管理计划的困难。这些发现凸显了跨微生物源追踪标记物研究对采样和实验室方法进行标准化的必要性。我们的发现基于先前的研究，表明采用一刀切的方法来管理地表水道中的粪便危害可能并不合适；相反，针对特定水源和用水时的条件量身定制的策略可能更有效。
