Diversity and Habitat Preferences of Cultivated and Uncultivated Aerobic Methanotrophic Bacteria Evaluated Based on pmoA as Molecular Marker

Abstract

Methane-oxidizing bacteria are characterized by their capability to grow on methane as sole source of carbon and energy. Cultivation-dependent and -independent methods have revealed that this functional guild of bacteria comprises a substantial diversity of organisms. In particular the use of cultivation-independent methods targeting a subunit of the particulate methane monooxygenase (pmoA) as functional marker for the detection of aerobic methanotrophs has resulted in thousands of sequences representing "unknown methanotrophic bacteria." This limits data interpretation due to restricted information about these uncultured methanotrophs. A few groups of uncultivated methanotrophs are assumed to play important roles in methane oxidation in specific habitats, while the biology behind other sequence clusters remains still largely unknown. The discovery of evolutionary related monooxygenases in non-methanotrophic bacteria and of pmoA paralogs in methanotrophs requires that sequence clusters of uncultivated organisms have to be interpreted with care. This review article describes the present diversity of cultivated and uncultivated aerobic methanotrophic bacteria based on pmoA gene sequence diversity. It summarizes current knowledge about cultivated and major clusters of uncultivated methanotrophic bacteria and evaluates habitat specificity of these bacteria at different levels of taxonomic resolution. Habitat specificity exists for diverse lineages and at different taxonomic levels. Methanotrophic genera such as Methylocystis and Methylocaldum are identified as generalists, but they harbor habitat specific methanotrophs at species level. This finding implies that future studies should consider these diverging preferences at different taxonomic levels when analyzing methanotrophic communities.

Keywords: diversity; ecological niche; habitat specificity; methanotrophic bacteria; phylogeny; pmoA.

Figure 1

Phylogenetic trees showing the phylogeny of methanotrophic type strains based on 16S rRNA gene sequences (left tree) and PmoA sequences (right tree). The neighbor joining trees were calculated using the ARB software package (Ludwig et al., 2004) based on 1556 nucleotide positions with Jukes Cantor correction or 160 amino acid positions with Kimura correction, respectively. PmoA sequences of Methylobacter luteus, Methylobacter whittenburyi, and Methylomicrobium pelagicum are not available from the type strains, but were taken from a different strain representing the species. The 16S rRNA gene based tree was rooted with sequences of methanogenic Archaea (AB301476, M60880, AB065296, AM114193, AB196288), the PmoA tree with AmoA sequences of ammonia-oxidizing bacteria (NC_004757, X90822). Dots label branch points that were confirmed in maximum likelihood trees. The scale bars display 0.10 changes per nucleotide or amino acid position.

Figure 2

Minimum and maximum pmoA sequence dissimilarity at DNA (upper panel) and protein level (lower panel) between a type strain and its most closely and most distantly related type strain within the same species, as well as to the most closely and distantly related type strain of a different genus within the same family or type (according to Table 1). DNA and protein distance matrices were calculated in ARB based on 480 aligned nucleotide positions or 160 deduced amino acid positions. Methylomicrobium album and Methylomicrobium agile were not included, due to the very distant clustering from the other Methylomicrobium strains (Figure 1), while “Candidatus Crenothrix polyspora” was excluded due to the fact that it contains a highly divergent pmoA sequence compared to all other Gammaproteobacteria.

Figure 3

Number of OTUs in dependence on the cut-off value applied for OTU differentiation. The number of OTUs containing type strains of different genera or species are displayed on the left axis, the number of OTUs formed based on all high quality sequences (= total) is presented on the right axis at logarithmic scale. Clustering was performed with 12502 high quality pmoA sequences (upper panel) or the deduced amino acid sequences (lower panel) available from Genbank. Sequences with at least 400 bp sequence length and without accumulation of sequencing errors were included. Distance matrices were calculated in ARB based on 480 aligned nucleotide positions or 160 deduced amino acid positions. OTU clustering was done using Mothur by applying the average neighbor algorithm. Orange stars denote the cut-off values applied in this review.

Figure 4

Neighbor joining tree showing the phylogeny of representative pmoA sequences and the habitats in which they were detected. For each OTU₁₂ one representative sequence was included. The dots on the rings around the tree display detection in different habitat types. The size of the dots corresponds to the relative frequency with which sequences of an OTU₁₂ were detected in the seven habitats. The diagram was set up using the iTOL online package (Letunic and Bork, 2011). The phylogenetic tree was calculated in the ARB software package based on 480 nucleotide positions and a Jukes-Cantor correction.

Figure 5

Number of habitats that were analyzed in research studies (upper left) and grouping of pmoA sequences from the NCBI database according to the habitats in which they were detected. The upper right diagram is based on all available high quality sequences, while the lower diagrams include only non-redundant sequence reads. Redundant reads are those that were detected in the same study and fall within the same OTU. Arrows denote the position of the group that is shown as first entry in the legend.

Figure 6

Neighbor joining tree showing the phylogeny of uncultivated clusters in relation to methanotrophic type strains. The tree includes pmoA sequences from all OTUs that were assigned to uncultivated clusters. It was calculated based on 480 nucleotide positions with Jukes Cantor correction. The scale bars display 0.10 changes per nucleotide or amino acid position.

Figure 7

Habitat specificity of methanotrophic bacteria evaluated in non-metric multidimensional scaling (NMDS) plots. Non-redundant sequence reads were used to calculate the relative detection frequency of all OTUs in a habitat. The upper plots show differences between all 18 different habitats, while the lower plots focus on the 13 most similar habitats. OTU clustering was done using 12% (left panels) and 4% dissimilarity cut-off (right panels). The NMDS plots were set up based on Bray-Curtis dissimilarities calculated from Hellinger transformed data using the online tool GUSTA ME (Buttigieg and Ramette, 2014).

Figure 8

Relative detection frequency of OTUs across habitats. Non-redundant reads were normalized by the number of studies available for each habitat and the relative frequency with which each OTU was detected across the different habitats was calculated. The upper panel shows the results at genus level resolution (OTU₁₂), the lower panel at species level resolution (OTU₄). OTUs displayed in red are highly specific for a certain type of habitat. OTUs that were detected in less then five studies were set to zero. The identity of the most habitat-specific and most common OTUs is given in Tables 8, 9. A list including detailed information about all OTUs is provided as Supplementary Material.