Bridging the gap between omics and earth system science to better understand how environmental change impacts marine microbes.
Bridging the gap between omics and earth system science to better understand how environmental change impacts marine microbes.
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DOI：
10.1111/gcb.12983
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发表时间：
2016-01
影响因子：
11.6
通讯作者：
Lenton TM
中科院分区：
文献类型：
--
作者：
Mock T;Daines SJ;Geider R;Collins S;Metodiev M;Millar AJ;Moulton V;Lenton TM
基于基因组学、转录组学和蛋白质组学的方法的出现彻底改变了我们描述海洋微生物群落的能力，包括生物地理学、代谢潜力和多样性、适应机制以及繁殖和进化历史。需要新的跨学科方法，从这种描述性的水平，以提高定量，过程水平的海洋微生物在海洋地球化学循环中的作用和环境变化对海洋微生物生态系统的影响的理解。将从基因组到生物体、生态战略以及生物体和生态系统反应等各个层面的研究联系起来，需要新的建模方法。这一点的关键将是建模规模的根本转变，从其大分子组分的水平代表微生物。这将使与组学数据集的联系，并允许在表型水平（即性状）的适应和适应性反应被模拟为适应性最大化和进化约束的组合。这种方法将建立在确定关键生物特征的生态学方法和将传统生理测量与组学新见解相结合的系统生物学方法的基础上。它将依赖于发展一个更好的了解生态生理学，了解定量环境控制微生物的生长策略。它还将把实验进化研究的结果纳入适应的表征中。由此产生的生态系统水平模型可以评估我们对生态系统结构和功能控制的理解水平，突出理解方面的主要差距，并帮助确定未来研究计划的优先领域。最终，这种大综合应提高生态系统对多种环境驱动因素的预测能力。
The advent of genomic‐, transcriptomic‐ and proteomic‐based approaches has revolutionized our ability to describe marine microbial communities, including biogeography, metabolic potential and diversity, mechanisms of adaptation, and phylogeny and evolutionary history. New interdisciplinary approaches are needed to move from this descriptive level to improved quantitative, process‐level understanding of the roles of marine microbes in biogeochemical cycles and of the impact of environmental change on the marine microbial ecosystem. Linking studies at levels from the genome to the organism, to ecological strategies and organism and ecosystem response, requires new modelling approaches. Key to this will be a fundamental shift in modelling scale that represents micro‐organisms from the level of their macromolecular components. This will enable contact with omics data sets and allow acclimation and adaptive response at the phenotype level (i.e. traits) to be simulated as a combination of fitness maximization and evolutionary constraints. This way forward will build on ecological approaches that identify key organism traits and systems biology approaches that integrate traditional physiological measurements with new insights from omics. It will rely on developing an improved understanding of ecophysiology to understand quantitatively environmental controls on microbial growth strategies. It will also incorporate results from experimental evolution studies in the representation of adaptation. The resulting ecosystem‐level models can then evaluate our level of understanding of controls on ecosystem structure and function, highlight major gaps in understanding and help prioritize areas for future research programs. Ultimately, this grand synthesis should improve predictive capability of the ecosystem response to multiple environmental drivers.