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

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".

Despite the importance of microbial communities (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.

The proposed research will focus on development of resources for collecting, sharing, and analyzing high- resolution temporal metagenomics data from MCs of AD reactors. In particular, we will achieve the most comprehensive, highest-resolution temporal data on MCs together with operational parameters from AD reactors. We will develop novel bioinformatics tools for analysing such data, and focusing on inferring both individual taxa functions and interactions from temporal microbiome data and then use these to develop predictive models of community dynamics. This models will focus on assembling genomes directly from metagenomics data, and predicting metabolic interaction partners and their metabolites based on temporal 16S rRNA data and metagenome assembled genomes.

We will also develop an operation model that will set a pathway towards converting this research platform into a self-sustaining enterprise that will monitor and analyse all AD reactors in the UK.

The proposed research will generate the most comprehensive, high time-resolution metagenomics data on functionally well-described microbial communities of Anaerobic Digestion (AD) reactors. 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.

AD is identified as a promising green-energy biotechnology that converts organic waste into energy-rich methane gas. The recent report on low carbon energy by the Department of Energy and Climate Change (DECC), states that the government aims to achieve about 0.3-0.4 GW of power from Anaerobic Digestion by 2020. Achieving such a target will require increased uptake of AD by end-users and improved understanding of the AD process. By analysing the microbial communities underpinning AD, this research will help develop the UK AD industry. In particular, monitoring microbial communities from many AD reactors through time and developing bioinformatics tools to convert this data into predictive models of reactor performance and stability will help this research to increase the efficiency and robustness of AD. This science will thus impact the government's goal towards achieving a more energy secure and environmentally friendly future where we use and manage resources more efficiently, prevent waste, and recycle or convert under-utilised wastes into high value products. The latter goal is expected to particularly contribute to the growth of the UK's bioeconomy as stated in the "BIS Building A High Value Bioeconomy" report.

More broadly, the proposed research will contribute to the UK's ability to scientifically lead the emerging research field of engineering MCs, linking up academic strengths in bioinformatics, synthetic biology, engineering, and microbial ecology. The outcome of this proposal will be novel bioinformatics tools for predicting interacting species from complex, temporal MC data and identification of possible "early warning" indicators for loss of stability. The remit of these tools can be extended to the study of other well- defined MCs, such as the soils utilised in precision-farming, or guts of farming animals.