Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
, 83 (2)

Quantitative Metaproteomics Highlight the Metabolic Contributions of Uncultured Phylotypes in a Thermophilic Anaerobic Digester

Affiliations

Quantitative Metaproteomics Highlight the Metabolic Contributions of Uncultured Phylotypes in a Thermophilic Anaerobic Digester

Live H Hagen et al. Appl Environ Microbiol.

Abstract

In this study, we used multiple meta-omic approaches to characterize the microbial community and the active metabolic pathways of a stable industrial biogas reactor with food waste as the dominant feedstock, operating at thermophilic temperatures (60°C) and elevated levels of free ammonia (367 mg/liter NH3-N). The microbial community was strongly dominated (76% of all 16S rRNA amplicon sequences) by populations closely related to the proteolytic bacterium Coprothermobacter proteolyticus. Multiple Coprothermobacter-affiliated strains were detected, introducing an additional level of complexity seldom explored in biogas studies. Genome reconstructions provided metabolic insight into the microbes that performed biomass deconstruction and fermentation, including the deeply branching phyla Dictyoglomi and Planctomycetes and the candidate phylum "Atribacteria" These biomass degraders were complemented by a synergistic network of microorganisms that convert key fermentation intermediates (fatty acids) via syntrophic interactions with hydrogenotrophic methanogens to ultimately produce methane. Interpretation of the proteomics data also suggested activity of a Methanosaeta phylotype acclimatized to high ammonia levels. In particular, we report multiple novel phylotypes proposed as syntrophic acetate oxidizers, which also exert expression of enzymes needed for both the Wood-Ljungdahl pathway and β-oxidation of fatty acids to acetyl coenzyme A. Such an arrangement differs from known syntrophic oxidizing bacteria and presents an interesting hypothesis for future studies. Collectively, these findings provide increased insight into active metabolic roles of uncultured phylotypes and presents new synergistic relationships, both of which may contribute to the stability of the biogas reactor.

Importance: Biogas production through anaerobic digestion of organic waste provides an attractive source of renewable energy and a sustainable waste management strategy. A comprehensive understanding of the microbial community that drives anaerobic digesters is essential to ensure stable and efficient energy production. Here, we characterize the intricate microbial networks and metabolic pathways in a thermophilic biogas reactor. We discuss the impact of frequently encountered microbial populations as well as the metabolism of newly discovered novel phylotypes that seem to play distinct roles within key microbial stages of anaerobic digestion in this stable high-temperature system. In particular, we draft a metabolic scenario whereby multiple uncultured syntrophic acetate-oxidizing bacteria are capable of syntrophically oxidizing acetate as well as longer-chain fatty acids (via the β-oxidation and Wood-Ljundahl pathways) to hydrogen and carbon dioxide, which methanogens subsequently convert to methane.

Keywords: anaerobic digestion; metagenomics; metaproteomics; methane; microbial community.

Figures

FIG 1
FIG 1
Phylogenetic distribution of the most dominant 16S rRNA gene sequences in FrBGR. Details for the Euryarchaeota (not visible in the major plot because of low abundance) are provided in a separate plot to the lower left. Scattered areas contain two or more phylotypes with low abundance. The phylotype assigned to the order Natranaerobiales in the 16S rRNA data set corresponds to the population genome bin named unFirm02_FrBGR in the metagenomic data set, as indicated. The plot was generated using Krona and then simplified (i.e., removal of low-abundance phylotypes) in order to reduce size.
FIG 2
FIG 2
Methanogenesis pathways in FrBGR operating at high temperature and high ammonia concentration. Genes are shown as colored boxes, where the color indicates the protein abundance (MaxQuant LFQ values) ranging from high abundance (red) to low abundance (green). Acetoclastic methanogenesis is shown by light blue lines, where all proteins were mapped to the reference genome of Methanosaeta thermophila PT. Pathways illustrated by dark blue lines represent hydrogenotrophic methanogenesis, and the proteins were mapped to Methanothermobacter thermoautotrophicus supplemented by the population bins of Methanothermobacter, Methanobacteriales, and Archaea. Abbreviations used in this figure can be found in Table S3 in the supplemental material. Subunits of multimeric protein complexes are indicated, if detected (A, B, C, etc.).
FIG 3
FIG 3
Selected metabolic pathways of the putative novel SAOB unFirm02_FrBGR cluster 2. The pathways are proposed based on genome and proteome comparison, and protein abundances are indicated by color, ranging from high abundance (red) to low abundance (green). All enzymes needed for β-oxidation of fatty acids, in addition to most enzymes associated with the Wood-Ljungdahl pathway, were detected for unFirm02_FrBGR cluster 2 (unFi_c2). Parts of both pathways were also detected for unFirm02_FrBGR cluster 1 (unFi_c1), albeit at a lower detection level (see Fig. S4 in the supplemental material). Only proteins affiliated to the lower part of EMP were represented in the proteome. Acetate kinase (ack) was detected in the genome but not in the proteome. More details on the proteins detected can be found in Table S3, including abbreviations.
FIG 4
FIG 4
Hypothetical model of the carbon flux in FrBGR, with functional roles of dominant phylotypes inferred from comparison of metagenome and metaproteome data sets. Metabolic pathways showing the key stages (arrows) of acetogenesis (pink) and methanogenesis (blue), in addition to syntrophic metabolic processes (green). Only the most prominent (with regard to relative abundance in the 16S rRNA gene sequence inventory and protein abundance) phylotypes in the FrBGR microbial community were evaluated, and it should be noted that a rare portion of the population might account for underlying key metabolic pathways not shown here. Organism abbreviations used in this figure correspond to the population bin IDs listed in Table 2.

Similar articles

See all similar articles

Cited by 9 articles

See all "Cited by" articles

Publication types

LinkOut - more resources

Feedback