Comparative and joint analysis of two metagenomic datasets from a biogas fermenter obtained by 454-pyrosequencing

Abstract

Biogas production from renewable resources is attracting increased attention as an alternative energy source due to the limited availability of traditional fossil fuels. Many countries are promoting the use of alternative energy sources for sustainable energy production. In this study, a metagenome from a production-scale biogas fermenter was analysed employing Roche's GS FLX Titanium technology and compared to a previous dataset obtained from the same community DNA sample that was sequenced on the GS FLX platform. Taxonomic profiling based on 16S rRNA-specific sequences and an Environmental Gene Tag (EGT) analysis employing CARMA demonstrated that both approaches benefit from the longer read lengths obtained on the Titanium platform. Results confirmed Clostridia as the most prevalent taxonomic class, whereas species of the order Methanomicrobiales are dominant among methanogenic Archaea. However, the analyses also identified additional taxa that were missed by the previous study, including members of the genera Streptococcus, Acetivibrio, Garciella, Tissierella, and Gelria, which might also play a role in the fermentation process leading to the formation of methane. Taking advantage of the CARMA feature to correlate taxonomic information of sequences with their assigned functions, it appeared that Firmicutes, followed by Bacteroidetes and Proteobacteria, dominate within the functional context of polysaccharide degradation whereas Methanomicrobiales represent the most abundant taxonomic group responsible for methane production. Clostridia is the most important class involved in the reductive CoA pathway (Wood-Ljungdahl pathway) that is characteristic for acetogenesis. Based on binning of 16S rRNA-specific sequences allocated to the dominant genus Methanoculleus, it could be shown that this genus is represented by several different species. Phylogenetic analysis of these sequences placed them in close proximity to the hydrogenotrophic methanogen Methanoculleus bourgensis. While rarefaction analyses still indicate incomplete coverage, examination of the GS FLX Titanium dataset resulted in the identification of additional genera and functional elements, providing a far more complete coverage of the community involved in anaerobic fermentative pathways leading to methane formation.

Figure 1. Read length and average GC content of pyrosequencing reads.

A sharp decline in GC content can be seen once the read length exceeds a certain value. The vertical bars indicate the computed filtering thresholds for the GS FLX and the Titanium dataset, respectively.

Figure 2. Characterization of the GS FLX and Titanium datasets based on the taxonomic classification of Environmental Gene Tags (EGTs).

Displayed are only the most abundant taxa among Bacteria and Archaea lineages at various taxonomic ranks. For each group, the first number represents the number of assigned EGTs from the GS FLX dataset, the second number the EGTs from the Titanium dataset, respectively. Due to the different number of reads obtained from each sequencing platform, the amount of EGTs listed for the Titanium dataset is typically the fourfold of the number listed for the GS FLX dataset.

Figure 3. Comparison of taxonomic profiles on rank genus.

The taxonomic profiles for the GS FLX (black bars) and the Titanium (lightgray bars) datasets were computed employing the CARMA pipeline. The percentage values correspond to the total amount of reads in the filtered datasets; included are the ten most abundant genera.

Figure 4. Taxonomic and physiological overview of relevant members in ‘polysaccharide degradation’ (a) and ‘acetogenesis/methanogenesis’ (b).

The trees are based on reads classified into the NCBI taxonomy by the CARMA software. For each taxonomic group the underlying number of reads is given. Numbers in brackets refer to the amount of reads which could not be classified at corresponding lower taxonomic ranks. Associated COG entries are depicted as pie charts, where the interpretation of the numbers is equivalent.

Figure 5. Identification of Methanoculleus variants.

Partial view (A) of pyrosequencing reads aligned to the 16S rRNA sequence of Methanoculleus bourgensis (Genbank accession AY196674). Colored bases indicate differences between reads and the reference (shown in the bottom line). In the depicted part, two of the seven different variants are visible. To characterize the variants, a phylogenetic tree (B) was constructed together with various reference sequences. Most variants show close relationship to M. bourgensis; only variant VAR2 was placed in another branch formed by M. marisnigri, M. palmolei, M. chikugoensis and M. thermophilus. Several 16S rRNA sequences from the genus Methanoculleus were used: M. olentangyi (AF095270), M. bourgensis (AY196674), M. palmaeoli (Y16382), M. thermophilus (AB065297), M. chikugoensis MG62 (AB038795) and M. marisnigri JR1 (CP000562 (Memar_R0043)). Additional sequences in increasing taxonomic distance were included as outgroups: Methanosarcina mazeii (MMU20151), Methanococcus vannielii SB (CP000742 (Mevan_R0025)), Clavibacter michiganensis ssp. michiganensis NCPPB 382 (AM711867 (CMM_RNA_0001)) and two sequences from Escherichia coli K12 DH10B (NC_010473 (ECDH10B_3945 and ECDH10B_2759)).

Figure 6. Comparison of kernel density estimates for metagenome reads mapped to coding regions of the Methanoculleus marisnigri JR1 genome.

The figure shows the mapped bases per position in the genome separated into different groups: genes that have potentially been acquired by horizontal gene transfer (HGT, depicted as dot-dashed line) and all other genes without a prediction for horizontal gene transfer (CDS, shown as dashed line). For comparison, the density function for all genes combined is shown as well (solid line). Poor coverage of HGT genes hints at genomic features probably missing in the Methanoculleus species present in the studied biogas fermenter, further supporting their difference to Methanoculleus marisnigri JR1.

Figure 7. Rarefaction analysis of observed genera.

The rarefaction curves represent the estimated number of genera that would be observed in biogas fermenter metagenomes of different sizes. The values were determined based on 16S rRNA fragments classified at rank genus identified in the entire Titanium and GS FLX datasets, respectively.

Figure 8. Rarefaction analysis of Pfam families.

The estimated number of Pfam protein families that would be identified in biogas fermenter metagenomes of different sizes is shown. The values were computed based on the number of protein families identified in the entire Titanium and FLX metagenomes, respectively.