Predictive Temporal Analysis of Functional Microbiomes in UK's Anerobic Digestion Reactors

微生物群落（MC）的科学和实践重要性怎么强调都不过分。它们支撑着地球土壤、海洋和大气的生物地球化学循环，并通过肠道和皮肤为植物、动物和人类提供生态功能。尽管MC的重要性，我们的理解的结构功能关系和动力学在这些复杂的系统是非常有限的。造成这种限制的原因是难以定义自然MC的明确功能，并进行受控的时间实验。相比之下，微生物技术的应用提供了良好的模型系统，具有明确的功能参数和可控的环境。厌氧消化（AD）是一种关键的绿色能源技术，它利用复杂的微生物技术将有机废物转化为甲烷。在撰写本文时，有250多个AD反应堆，总容量约为1000吨。英国有240，000 kWe，以有机废物进料，并以受控方式运行，定期记录功能参数。在这里，我们将开发实验和生物信息学资源，用于收集、共享和分析AD反应器MC的高分辨率时间宏基因组学数据。该方法将获得迄今为止最全面、最高分辨率的MCs时间数据，这些数据与开发的分析工具相结合，将大大增加我们对AD微生物组结构-功能关系的理解。同样重要的是，我们的方法有可能改变英国AD技术的稳健性和生产力。同样的资源和见解也将转移到其他MC，例如用于精准农业的土壤或养殖动物的内脏。因此，我们希望这个示范性项目能够产生一个自我维持的在线知识库，使英国生物经济和科学同样受益。因此，这个建议与BBSRC支持基础科学的核心目标完全一致。该项目的重点完全符合BBSRC的战略，该战略将MC确定为优先领域。在TRDF的呼吁中，该提案符合“分析和解释生物科学研究数据的新方法”，“管理，共享和重新使用/重新利用生物科学研究数据的新框架”以及“共享研究数据的社区方法”。

The scientific and practical importance of microbial communities (MCs) cannot be overstated. They underpin the biogeochemical cycles of the earth's soil, oceans, and the atmosphere, and provide eco-functions to plants, animals, and humans through the gut and skin. Despite the importance of MCs, our understanding of the structure-function relation and dynamics in these complex systems is highly limited. Contributing to this limitation is the difficulty of defining a clear function of natural MCs and conducting controlled, temporal experiments. In contrast, biotechnological applications of MCs provide excellent model systems that have clear functional parameters and offer controlled environments.Anaerobic digestion (AD) is a key green-energy technology that makes use of complex MCs for the conversion of organic waste into methane. At the time of writing, there are more than 250 AD reactors with a total capacity of approx. 240,000 kWe in the UK that are fed with organic wastes and run in a controlled manner with regular recording of functional parameters. Here, we will develop the experimental and bioinformatics resources for collecting, sharing, and analyzing high- resolution temporal metagenomics data from MCs of AD reactors. The proposed approach will achieve the most comprehensive, highest-resolution temporal data on MCs available to date.This data, combined with the developed analysis tools will greatly increase our understanding of the structure-function relation in AD microbiomes. Equally importantly, our approach holds the potential to transform the robustness and productivity of the AD technology in the UK. The same resources and insights will also be transferable to other MCs, such as the soils utilised in precision- farming, or guts of farming animals. We, thus, expect this demonstrative project to result in a self- sustaining, online knowledge base that will equally benefit UK bioeconomy and science.As such, this proposal is in perfect alignment with the BBSRCs core aim to support enabling basic science. The project's focus fits squarely within BBSRC strategy, which identifies MCs as a priority area. Within the TRDF call, this proposal fits with the enabling of "new approaches to the analysis and interpretation of research data in the biological sciences", "new frameworks for the curation, sharing, and re-use/re-purposing of research data in the biological sciences", and "community approaches to the sharing of research data".