Comparative analysis of deep sequenced methanogenic communities: identification of microorganisms responsible for methane production

Abstract

Background Although interactions between microorganisms involved in biogas production are largely uncharted, it is commonly accepted that methanogenic Archaea are essential for the process. Methanogens thrive in various environments, but the most extensively studied communities come from biogas plants. In this study, we employed a metagenomic analysis of deeply sequenced methanogenic communities, which allowed for comparison of taxonomic and functional diversity as well as identification of microorganisms directly involved in various stages of methanogenesis pathways. Results A comprehensive metagenomic approach was used to compare seven environmental communities, originating from an agricultural biogas plant, cattle-associated samples, a lowland bog, sewage sludge from a wastewater treatment plant and sediments from an ancient gold mine. In addition to the native consortia, two laboratory communities cultivated on maize silage as the sole substrate were also analyzed. Results showed that all anaerobic communities harbored genes of all known methanogenesis pathways, but their abundance varied greatly between environments and that genes were encoded by different methanogens. Identification of microorganisms directly involved in different stages of methane production revealed that hydrogenotrophic methanogens, such as Methanoculleus, Methanobacterium, Methanobrevibacter, Methanocorpusculum or Methanoregula, predominated in most native communities, whereas acetoclastic Methanosaeta seemed to be the key methanogen in the wastewater treatment plant. Furthermore, in many environments, the methylotrophic pathway carried out by representatives of Methanomassiliicoccales, such as Candidatus Methanomethylophilus and Candidatus Methanoplasma, seemed to play an important role in methane production. In contrast, in stable laboratory reactors substrate versatile Methanosarcina predominated. Conclusions The metagenomic approach presented in this study allowed for deep exploration and comparison of nine environments in which methane production occurs. Different abundance of methanogenesis-related functions was observed and the functions were analyzed in the phylogenetic context in order to identify microbes directly involved in methane production. In addition, a comparison of two metagenomic analytical tools, MG-RAST and MetAnnotate, revealed that combination of both allows for a precise characterization of methanogenic communities.