Analyzing microbial communities using metagenomes is a powerful approach to understand compositional structures and functional connections in anaerobic digestion (AD) microbiomes. Whereas short-read sequencing approaches based on the Illumina platform result in highly fragmented metagenomes, long-read sequencing leads to more contiguous assemblies. To evaluate the performance of a hybrid approach of these two sequencing approaches we compared the metagenome-assembled genomes (MAGs) resulting from five AD microbiome samples. The samples were taken from reactors fed with short-chain fatty acids at different feeding regimes (continuous and discontinuous) and organic loading rates (OLR). Methanothrix showed a high relative abundance at all feeding regimes but was strongly reduced in abundance at higher OLR, when Methanosarcina took over. The bacterial community composition differed strongly between reactors of different feeding regimes and OLRs. However, the functional potential was similar regardless of feeding regime and OLR. The hybrid sequencing approach using Nanopore long-reads and Illumina MiSeq reads improved assembly statistics, including an increase of the N50 value (on average from 32 to 1740 kbp) and an increased length of the longest contig (on average from 94 to 1898 kbp). The hybrid approach also led to a higher share of high-quality MAGs and generated five potentially circular genomes while none were generated using MiSeq-based contigs only. Finally, 27 hybrid MAGs were reconstructed of which 18 represent potentially new species-15 of them bacterial species. During pathway analysis, selected MAGs revealed similar gene patterns of butyrate degradation and might represent new butyrate-degrading bacteria. The demonstrated advantages of adding long reads to metagenomic analyses make the hybrid approach the preferable option when dealing with complex microbiomes.

Metagenomics analysis revealing the composition and functional repertoire of complex microbial communities typically relies on large amounts of sequence data. Numerous analysis strategies and computational tools are available for their analysis. Fully integrated automated analysis pipelines such as MG-RAST or MEGAN6 are user-friendly but not designed for integrating specific knowledge on the biological system under study. In order to facilitate the consideration of such knowledge, we introduce a modular, adaptable analysis pipeline combining existing tools. We applied the novel pipeline to simulated mock data sets focusing on anaerobic digestion microbiomes and compare results to those obtained with established automated analysis pipelines. We find that the analysis strategy and choice of tools and parameters have a strong effect on the inferred taxonomic community composition, but not on the inferred functional profile. By including prior knowledge, computational costs can be decreased while improving result accuracy. While automated off-the-shelf analysis pipelines are easy to apply and require no knowledge on the microbial system under study, custom-made pipelines require more preparation time and bioinformatics expertise. This extra effort is minimized by our modular, flexible, custom-made pipeline, which can be adapted to different scenarios and can take available knowledge on the microbial system under study into account.

For biogas-producing continuous stirred tank reactors, an increase in dilution rate increases the methane production rate as long as substrate input can be converted fully. However, higher dilution rates necessitate higher specific microbial growth rates, which are assumed to have a strong impact on the apparent microbial biomass yield due to cellular maintenance. To test this, we operated two reactors at 37°C in parallel at dilution rates of 0.18 and 0.07 days-1 (hydraulic retention times of 5.5 and 14 days, doubling times of 3.9 and 9.9 days in steady state) with identical inoculum and a mixture of volatile fatty acids as sole carbon sources. We evaluated the performance of the Anaerobic Digestion Model No. 1 (ADM1), a thermodynamic black box approach (TBA), and dynamic flux balance analysis (dFBA), to describe the experimental observations. All models overestimated the impact of dilution rate on the apparent microbial biomass yield when using default parameter values. Based on our analysis, a maintenance coefficient value below 0.2 kJ per carbon mole of microbial biomass per hour should be used for the TBA, corresponding to 0.12 mmol ATP per gram dry weight per hour for dFBA, which strongly deviates from the value of 9.8 kJ Cmol h-1 that has been suggested to apply to all anaerobic microorganisms at 37°C. We hypothesized that a decrease in dilution rate might select taxa with minimized maintenance expenditure. However, no major differences in the dominating taxa between the reactors were observed based on amplicon sequencing of 16S rRNA genes and terminal restriction fragment length polymorphism analysis of mcrA genes. Surprisingly, Methanosaeta dominated over Methanosarcina even at a dilution rate of 0.18 days-1, which contradicts previous model expectations. Furthermore, only 23-49% of the bacterial reads could be assigned to known syntrophic fatty acid oxidizers, indicating that unknown members of this functional group remain to be discovered. In conclusion, microbial maintenance was found to be much lower for acetogenesis and methanogenesis than previously assumed, likely due to the exceptionally low growth rates in anaerobic digestion. This finding might also be relevant for other microbial systems operating at similarly low growth rates.

Generating chemical energy carriers and bulk chemicals from solar energy by microbial metabolic capacities is a promising technology. In this long-term study of over 500 days, methane was produced by a microbial community that was fed by the mono-substrate glycolate, which was derived from engineered algae. The microbial community structure was measured on the single cell level using flow cytometry. Abiotic and operational reactor parameters were analyzed in parallel. The R-based tool flowCyBar facilitated visualization of community dynamics and indicated sub-communities involved in glycolate fermentation and methanogenesis. Cell sorting and amplicon sequencing of 16S rRNA and mcrA genes were used to identify the key organisms involved in the anaerobic conversion process. The microbial community allowed a constant fermentation, although it was sensitive to high glycolate concentrations in the feed. A linear correlation between glycolate loading rate and biogas amount was observed (R2 = 0.99) for glycolate loading rates up to 1.81 g L−1 day−1 with a maximum in biogas amount of 3635 mL day−1 encompassing 45% methane. The cytometric diversity remained high during the whole cultivation period. The dominating bacterial genera were Syntrophobotulus, Clostridia genus B55_F, Aminobacterium, and Petrimonas. Methanogenesis was almost exclusively performed by the hydrogenotrophic genus Methanobacterium.