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

在当今时代，生物经济生产系统新概念的发展面临重大挑战，除其他外，发现可持续管理措施，以减少畜牧业，特别是粪便储存的甲烷排放，并提高厌氧消化系统的甲烷产量，以提供基本负荷动力和灵活的需求驱动的能源供应。这两个过程都是由成百上千个相互复杂相互联系的不同微生物类群介导的。因此，该项目的目标是破译微生物群的连通性，特别强调发现与减少粪便储存的甲烷排放或提高用于沼气生产的厌氧消化系统的甲烷产量有关的指示性微生物组合。在进行中的项目的同时，还将根据Illumina和纳米孔测序进行另外300个元基因组数据集。获得的数据集将被用来全面破译微生物多样性，特别是通过确定元基因组组装基因组(MAG)。将通过共生网络分析来调查已确定的MAG的连通性，从而能够估计积极或消极的相互作用，并在此基础上发现指示性微生物分类群及其联系。这些信息可用于评估与粪便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接性，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.