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

微生物群落（MCs）的科学和实践重要性怎么强调都不为过。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MCs很重要，但我们对这些复杂系统的结构-功能关系和动力学的理解非常有限。造成这种限制的原因是难以明确定义天然MCs的功能并进行受控的临时实验。相比之下，MCs的生物技术应用提供了具有明确功能参数和可控环境的优秀模型系统。厌氧消化（AD）是一项关键的绿色能源技术，它利用复杂的MCs将有机废物转化为甲烷。在撰写本文时，有250多个AD反应器，总容量约为。在英国有24万千瓦，用有机废物喂养，在受控的方式下运行，定期记录功能参数。在此，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反应器中MCs的高分辨率时间宏基因组数据。拟议的方法将获得迄今为止可获得的关于MCs的最全面、最高分辨率的时间数据。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被转移到其他MCs，例如精确农业中使用的土壤，或农业动物的内脏。因此，我们期望这个示范项目能够形成一个自我维持的在线知识库，这将同样有利于英国的生物经济和科学。因此，这一提议完全符合bbsrc支持基础科学的核心目标。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“生物科学研究数据分析和解释的新方法”、“生物科学研究数据管理、共享和再利用/再利用的新框架”和“研究数据共享的社区方法”的实现。

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".