Uncovering Microbiome Adaptations in a Full-Scale Biogas Plant: Insights from MAG-Centric Metagenomics and Metaproteomics

Abstract

The current focus on renewable energy in global policy highlights the importance of methane production from biomass through anaerobic digestion (AD). To improve biomass digestion while ensuring overall process stability, microbiome-based management strategies become more important. In this study, metagenomes and metaproteomes were used for metagenomically assembled genome (MAG)-centric analyses to investigate a full-scale biogas plant consisting of three differentially operated digesters. Microbial communities were analyzed regarding their taxonomic composition, functional potential, as well as functions expressed on the proteome level. Different abundances of genes and enzymes related to the biogas process could be mostly attributed to different process parameters. Individual MAGs exhibiting different abundances in the digesters were studied in detail, and their roles in the hydrolysis, acidogenesis and acetogenesis steps of anaerobic digestion could be assigned. Methanoculleus thermohydrogenotrophicum was an active hydrogenotrophic methanogen in all three digesters, whereas Methanothermobacter wolfeii was more prevalent at higher process temperatures. Further analysis focused on MAGs, which were abundant in all digesters, indicating their potential to ensure biogas process stability. The most prevalent MAG belonged to the class Limnochordia; this MAG was ubiquitous in all three digesters and exhibited activity in numerous pathways related to different steps of AD.

Keywords: anaerobic digestion; biogas microbiome; biogas process chain; metagenome analyses; metagenomic binning; metaproteome analyses.

Figure 1

Schematic visualization of biogas plant 35 consisting of three separate lines. Indicated are the temperatures and volumes of the digesters. D1 = digester 1, SD1 = secondary digester 1, S1 = digestate storage 1; D2 = digester 2, SD2 = secondary digester 2, S2 = digestate storage 2; D3 = digester 3, S3 = digestate storage 3.

Figure 2

Normalized taxonomic profiles of the three digesters (D1–D3) of biogas plant 35 based on metagenomic single-read analyses. (A) Taxonomic profile of the most abundant taxa on order level, 42%, 44% and 54% of the reads remain unassigned for D1, D2 and D3, respectively. (B) Taxonomic profile of the most abundant taxa on genus level, 30%, 29% and 23% of the assigned taxa are below 1% relative abundance (marked in gray); 58%, 60% and 65% of the reads remain unassigned for D1, D2 and D3, respectively.

Figure 3

Clustered heatmap based on Pearson correlations of the twelve most abundant genera of biogas plant 35 based on metagenomic single-read analysis with the process and chemical analysis parameters.

Figure 4

Relative abundance of cluster of orthologous groups of proteins (COG) categories for the microbiomes residing in the three digesters D1, D2 and D3 of biogas plant 35 based on normalized single-read analyses calculated within the metagenomics platform MGX [28].

Figure 5

Average genomic copy numbers (black circles) and log-scaled proteome spectral counts (colored bars) of esterases (EC 3.1), glucosidases (EC 3.2.1) and peptidases (EC 3.4). Metrics for enzymes are calculated based on the result for related KEGG orthologies. Only enzymes exhibiting spectral counts ≥10 in at least one digester are shown. Colors of bars represent the maximum proteomic fold change that occurs between any of the three possible reactor pairings (D1/D2, D1/D3, D2/D3).

Figure 6

Average genomic copy numbers (black circles) and log-scaled proteome spectral counts (colored bars) of anaerobic digestion (AD) key enzymes in acido-, aceto- and methanogenesis [43] in the three digesters. KEGG orthology (KO) data were manually summarized at enzyme level by summing up AGCNs of KOs with similar functionality and taking the median AGCN where KOs represent individual subunits of an enzyme. Spectral counts were always summed up. Only entries exhibiting a spectral count of 10 or higher in at least one digester are shown. Colors of bars represent the maximum proteomic fold change that occurs between any of the three possible reactor pairings (D1 vs. D2, D1 vs. D3, and D2 vs. D3).

Figure 7

Relative genomic and proteomic abundances of the 46 high-quality MAGs in the three digesters of BP35 calculated based on mapped reads and assigned metaproteins. Taxonomic assignment of the MAGs is based on the GTDB taxonomy. Placement in the phylogenetic tree is based on GTDB-tk output. Branch lengths do not represent phylogenetic distance.

Figure 8

Proteomic expression of biogas process chain key enzymes and pathways for ten differentially abundant MAGs in the three biogas digesters. Key enzymes and pathways, as well as the categorization (A–F), were derived from Sikora et al. [43] and were marked when one key enzyme was present based on the metaproteome counts.

Figure 9

Overview of the main metabolic pathways of MAG 80. Amino acids were marked in orange. Metabolites and pathways were marked with an red asterisk when, for corresponding enzymes, expression was observed based on MAG-centric metaproteomics. CoA = coenzyme A, Fd = ferredoxin, GAP = glyceraldehyde 3-phosphate, Gcv = glycine cleavage system, PEP = phosphoenolpyruvate, PP pathway = pentose phosphate pathway, PPi = pyrophosphate, Rnf = ferredoxin:NAD${}^{+}$ oxidoreductase.

Figure 10

Proteomic expression of biogas process chain key enzymes and pathways for four in the three biogas digesters’ evenly abundant MAGs. Key enzymes and pathways, as well as the categorization (A–F) were derived from Sikora et al. [43] and were marked when one key enzyme was present based on the metaproteome counts.