4.6 Article

Indicative Marker Microbiome Structures Deduced from the Taxonomic Inventory of 67 Full-Scale Anaerobic Digesters of 49 Agricultural Biogas Plants

期刊

MICROORGANISMS
卷 9, 期 7, 页码 -

出版社

MDPI
DOI: 10.3390/microorganisms9071457

关键词

anaerobic digestion; biogas microbiome; taxonomic profiling; 16S rRNA gene amplicon sequencing; NMDS; indicative taxa; Pearson correlations

资金

  1. German Federal Ministry of Food and Agriculture (BMEL) via the Fachagentur fur Nachwachsende Rohstoffe e.V. (FNR) [FKZ 22403915, 22404015, 22404115, 22403515, 22403615, 22403715, 22403815]
  2. European Union [818431]
  3. BMBF within the German Network for Bioinformatics Infrastructure (de.NBI) [031A533, 031L0103]

向作者/读者索取更多资源

The study taxonomically investigated 67 microbiomes from 49 agricultural, full-scale biogas plants, revealing indicative microbial communities adapted to specific process conditions.
There are almost 9500 biogas plants in Germany, which are predominantly operated with energy crops and residues from livestock husbandry over the last two decades. In the future, biogas plants must be enabled to use a much broader range of input materials in a flexible and demand-oriented manner. Hence, the microbial communities will be exposed to frequently varying process conditions, while an overall stable process must be ensured. To accompany this transition, there is the need to better understand how biogas microbiomes respond to management measures and how these responses affect the process efficiency. Therefore, 67 microbiomes originating from 49 agricultural, full-scale biogas plants were taxonomically investigated by 16S rRNA gene amplicon sequencing. These microbiomes were separated into three distinct clusters and one group of outliers, which are characterized by a specific distribution of 253 indicative taxa and their relative abundances. These indicative taxa seem to be adapted to specific process conditions which result from a different biogas plant operation. Based on these results, it seems to be possible to deduce/assess the general process condition of a biogas digester based solely on the microbiome structure, in particular on the distribution of specific indicative taxa, and without knowing the corresponding operational and chemical process parameters. Perspectively, this could allow the development of detection systems and advanced process models considering the microbial diversity.

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