Classic and temporal mixture synergism in terrestrial ecosystems: Prevalence, mechanisms and impacts

陆地生态系统中的经典和时间混合协同作用：普遍性、机制和影响

• 批准号: NE/S000194/1
• 负责人: Peter Kille
• 金额: $ 56.54万
• 依托单位: CARDIFF UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FS000194%2F1
• 关键词: Classic; temporal; mixture; synergism; terrestrial

Invertebrate species living above and below ground are central to terrestrial food webs and key contributors to carbon cycling, soil fertility and pest control. Many of these important species are highly vulnerable to chemical pollution. The range of chemicals these species are regularly exposed to is becoming increasingly complex. For example, farmers now use 50% more types of pesticide on arable crops than they did 15 years ago and an ever-increasing diversity of chemicals enter ecosystems from our domestic and industrial wastes. A challenge for chemical producers, users and regulators is to find ways to maximise the benefits of chemical use, while minimising any negative effects. Scientific research to support better 'ecological risk assessment' of chemicals is central to meeting this challenge. Many of the chemicals we use today come from new, less well studied, compound classes that can affect biological processes, in diverse ways, in different species. Our current lack of knowledge about these chemicals makes their ecological effects difficult to assess. Things become more complicated when we realise that pollutants almost always occur as mixtures. If we want to properly to address and avoid unwanted chemical impacts, we need to better understand and take account of chemical mixtures. The most commonly used way to predict the likely effects of pollutant mixtures on invertebrates and ecosystems assumes that chemicals do not interact with each other and that, therefore, their toxicities can be added together. This relatively simple 'additive' approach has been shown to work most of the time. However, for a substantial proportion of mixtures (up to 20% depending on chemical classes included), the observed effects are worse than expected based on addition. Where such 'synergy' occurs, environmental protection policies for mixtures based on additivity will underestimate actual effects (see Fig. 1, Case for Support). Clearly this is a problem. To address it, we need to identify interactive chemical mixtures and predict the most likely causes of synergy. In turn, this requires us to understand how the mechanisms of toxicity of different chemicals in a mixture interplay with the different biochemical, physiological and ecological traits of exposed species to cause synergy. The main aim of this project is to gain and apply this knowledge. Our own research has identified some chemical mixtures that are more likely to show synergy, with higher levels of toxicity to exposed invertebrates. For example, where: (a) a chemical affects the way that another is detoxified or activated; or (b) a chemical increases the biological uptake of another chemical; or (c) prior exposure to a chemical changes the biological toxicity of another chemical, depending on the timing of exposure. However, we are very far from understanding all cases. Thus, this project aims to transform our ability to identify, quantify and predict the potential for synergy in common terrestrial pollution scenarios (agrochemical use, waste inputs). Working with partner agencies, we will identify potentially synergistic chemical pollutant mixtures, relevant to terrestrial ecosystems, and conduct experiments to test their effects on a range of invertebrate species. When we observe synergy in one species, other species will be tested to discover if this is a general effect. Biochemical and genetic methods will be used to identify mechanisms of toxicity and species traits associated with synergism, integrating this information to develop models and new predictive tools. To ensure the effects we see in the laboratory are relevant to the field, we will conduct studies in outdoor systems to test for the presence of synergism in natural communities. Ultimately, we will use our findings to produce a POSTnote 'White paper' detailing how future risk assessment policies can explicitly consider synergism to support environmental protection.

生活在地上和地下的无脊椎动物物种是陆地食物网的核心，也是碳循环、土壤肥力和害虫控制的关键贡献者。这些重要物种中有许多非常容易受到化学污染的影响。这些物种经常接触的化学物质的范围变得越来越复杂。例如，农民现在在可耕地作物上使用的杀虫剂种类比15年前多了50%，越来越多的化学品从我们的家庭和工业废物中进入生态系统。化学品生产商、使用者和监管机构面临的一个挑战是，如何最大限度地发挥化学品使用的效益，同时最大限度地减少任何负面影响。支持更好地对化学品进行"生态风险评估"的科学研究是应对这一挑战的核心。我们今天使用的许多化学物质来自新的、研究较少的化合物类别，它们可以以不同的方式影响不同物种的生物过程。我们目前对这些化学品缺乏了解，因此难以评估其生态影响。当我们意识到污染物几乎总是以混合物的形式出现时，事情变得更加复杂。如果我们想适当地处理和避免不必要的化学影响，我们就需要更好地了解和考虑化学混合物。预测污染物混合物对无脊椎动物和生态系统可能产生的影响的最常用方法是假定化学品之间不会相互作用，因此，它们的毒性可以相加。这种相对简单的"加法"方法已被证明在大多数情况下有效。然而，对于相当大比例的混合物（取决于所包括的化学类别，最多可达20%），观察到的影响比基于添加的预期更差。在出现这种"协同作用"的情况下，基于加和性的混合物环境保护政策将低估实际效果（见图1，支持案例）。显然这是个问题。为了解决这个问题，我们需要确定相互作用的化学混合物，并预测协同作用的最可能原因。反过来，这就要求我们了解混合物中不同化学品的毒性机制如何与接触物种的不同生化、生理和生态特征相互作用，从而产生协同作用。该项目的主要目的是获得和应用这些知识。我们自己的研究已经确定了一些更有可能显示协同作用的化学混合物，对暴露的无脊椎动物具有更高的毒性。例如，其中：（a）一种化学品影响了另一种化学品的解毒或活化方式;或（B）一种化学品增加了另一种化学品的生物吸收;或（c）在接触一种化学品之前改变了另一种化学品的生物毒性，这取决于接触的时间。然而，我们离了解所有情况还很远。因此，该项目旨在改变我们的能力，以确定，量化和预测在常见的陆地污染情景（农用化学品的使用，废物投入）的协同作用的潜力。我们将与伙伴机构合作，确定与陆地生态系统相关的潜在协同化学污染物混合物，并进行实验，以测试其对一系列无脊椎动物物种的影响。当我们在一个物种中观察到协同作用时，其他物种将被测试以发现这是否是一种普遍效应。生物化学和遗传学方法将用于确定与协同作用相关的毒性机制和物种性状，整合这些信息以开发模型和新的预测工具。为了确保我们在实验室中看到的效果与现场相关，我们将在户外系统中进行研究，以测试自然群落中协同作用的存在。最终，我们将利用我们的研究结果制作一份POSTnote "白色文件"，详细说明未来的风险评估政策如何明确考虑协同作用，以支持环境保护。

项目成果

Harmonised risk assessment for human health, animal health and ecological risk assessment of combined exposure to multiple chemicals: a food and feed safety perspective

人类健康、动物健康的统一风险评估以及多种化学品联合暴露的生态风险评估：食品和饲料安全视角

• DOI: 10.1016/j.toxlet.2018.06.1155
• 发表时间: 2018
• 期刊: Toxicology Letters
• 影响因子: 3.5
• 作者: Dorne J

Off-Target Stoichiometric Binding Identified from Toxicogenomics Explains Why Some Species Are More Sensitive than Others to a Widely Used Neonicotinoid.

• DOI: 10.1021/acs.est.0c05125
• 发表时间: 2021-02
• 期刊: Environmental science & technology
• 影响因子: 11.4
• 作者: S. Short;A. Robinson;E. Lahive;A. Green Etxabe;Szabolcs Hernádi;M. Pereira;P. Kille;D. Spurgeon