Decoding the microbial connectivity of livestock manure and anaerobic digestion (biogas) microbiomes

目前，生物经济生产系统新概念的发展面临着重大挑战，其中包括发现可持续管理措施，以减少畜牧业的甲烷排放，特别是粪肥储存，以及提高厌氧消化系统的甲烷产量，以提供基本负荷电力和灵活的需求驱动型能源供应。这两个过程都是由数百到数千种不同的微生物类群介导的，这些微生物类群彼此之间复杂地相互联系。因此，该项目的目标是解码微生物组的连通性，特别强调发现指示性微生物关联，这些关联与减少粪便储存中的甲烷排放或提高厌氧消化系统中沼气产量有关。伴随着正在进行的项目，另外300个基于Illumina和Nanopore测序的宏基因组数据集将被进行。获得的数据集将用于全面破译微生物多样性，特别是通过确定宏基因组组装基因组（MAGs）。鉴定出的mag的连通性将通过共现网络分析进行调查，该分析允许估计积极或消极的相互作用，除此之外，还可以发现指示性微生物分类群及其关联。这些信息可用于评估与粪肥储存和厌氧消化系统内特定环境条件有关的微生物群落组合和微生物组连通性，并将有关微生物组的信息整合到过程和系统建模概念中。

In current times, the development of new concepts for bioeconomic production systems is faced with major challenges inter alia the discovery of sustainable management measures to reduce methane emissions from animal husbandry, particularly manure storage as well as to enhance the methane yield from anaerobic digestion systems to provide base load power and a flexible demand-driven energy supply. Both processes are mediated by hundreds to thousands of different microbial taxa which are complexly interconnected with each other. Therefore, the objective of this project is to decode the connectivity of microbiomes with special emphasis at the discovery of indicative microbial associations related either to the reduction of methane emissions from manure storage or an enhanced methane yield in anaerobic digestion systems for biogas production. Accompanying ongoing projects, additional 300 metagenome datasets based on Illumina and Nanopore sequencing will be conducted. The obtained datasets will be used to comprehensively decipher the microbial diversity, especially by determining metagenome-assembled genomes (MAGs). The connectivity of the identified MAGs will be investigated by co-occurrence network analyses which allow the estimation of positive or negative interactions and beyond that the discovery of indicative microbial taxa and their associations. This information can be used to assess the microbial community assemblies and microbiome connectivities in relation to specific environmental conditions within the manure storage and anaerobic digestion systems and to integrate information about the microbiome into process and system modelling concepts.