Canadian Rivers Institute and the Center for Environmental and Human Toxicology, Department of Physiological Sciences, UF Genetics Institute, College of Veterinary Medicine, University of Florida, Gainesville, FL 32611 USA.
Environ Toxicol Pharmacol. 2018 Apr;59:87-93. doi: 10.1016/j.etap.2018.03.005. Epub 2018 Mar 8.
Environmental science has benefited a great deal from omics-based technologies. High-throughput toxicology has defined adverse outcome pathways (AOPs), prioritized chemicals of concern, and identified novel actions of environmental chemicals. While many of these approaches are conducted under rigorous laboratory conditions, a significant challenge has been the interpretation of omics data in "real-world" exposure scenarios. Clarity in the interpretation of these data limits their use in environmental monitoring programs. In recent years, one overarching objective of many has been to address fundamental questions concerning experimental design and the robustness of data collected under the broad umbrella of environmental genomics. These questions include: (1) the likelihood that molecular profiles return to a predefined baseline level following remediation efforts, (2) how reference site selection in an urban environment influences interpretation of omics data and (3) what is the most appropriate species to monitor in the environment from an omics point of view. In addition, inter-genomics studies have been conducted to assess transcriptome reproducibility in toxicology studies. One lesson learned from inter-genomics studies is that there are core molecular networks that can be identified by multiple laboratories using the same platform. This supports the idea that "omics-networks" defined a priori may be a viable approach moving forward for evaluating environmental impacts over time. Both spatial and temporal variability in ecosystem structure is expected to influence molecular responses to environmental stressors, and it is important to recognize how these variables, as well as individual factor (i.e. sex, age, maturation), may confound interpretation of network responses to chemicals. This mini-review synthesizes the progress made towards adopting these tools into environmental monitoring and identifies future challenges to be addressed, as we move into the next era of high throughput sequencing. A conceptual framework for validating and incorporating molecular networks into environmental monitoring programs is proposed. As AOPs become more defined and their potential in environmental monitoring assessments becomes more recognized, the AOP framework may prove to be the conduit between omics and penultimate ecological responses for environmental risk assessments.
环境科学从基于组学的技术中受益匪浅。高通量毒理学已经确定了不良结局途径(AOP),优先考虑了关注的化学物质,并确定了环境化学物质的新作用。虽然这些方法中的许多都是在严格的实验室条件下进行的,但一个重大挑战是在“真实世界”暴露场景中解释组学数据。这些数据的解释清晰度限制了它们在环境监测计划中的使用。近年来,许多人的一个总体目标是解决有关实验设计和在环境基因组学广泛领域下收集的数据稳健性的基本问题。这些问题包括:(1)在修复努力后分子谱是否有可能返回预定义的基线水平,(2)在城市环境中选择参考地点如何影响组学数据的解释,以及(3)从组学角度来看,最适合监测环境中的哪种物种。此外,还进行了跨组学研究,以评估毒理学研究中转录组的重现性。从跨组学研究中得出的一个教训是,存在可以通过多个实验室使用相同平台识别的核心分子网络。这支持了这样一种观点,即预先定义的“组学网络”可能是评估随时间推移的环境影响的一种可行方法。生态系统结构的空间和时间变异性预计会影响分子对环境胁迫的反应,重要的是要认识到这些变量以及个体因素(即性别、年龄、成熟度)如何混淆对化学物质网络反应的解释。这篇迷你综述总结了将这些工具应用于环境监测的进展,并确定了未来面临的挑战,因为我们进入了高通量测序的下一个时代。提出了一个验证和将分子网络纳入环境监测计划的概念框架。随着 AOP 变得更加明确,并且它们在环境监测评估中的潜力得到更多认可,AOP 框架可能被证明是组学和环境风险评估中最终生态反应之间的渠道。