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The diversity of cyanobacterial metabolism: genome analysis of multiple phototrophic microorganisms.

Beck C, Knoop H, Axmann IM, Steuer R - BMC Genomics (2012)

Bottom Line: We describe genetic diversity found within cyanobacterial genomes, specifically with respect to metabolic functionality.Our results have direct implications for resource allocation and further sequencing projects.It can be extrapolated that the number of newly identified genes still significantly increases with increasing number of new sequenced genomes.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute for Theoretical Biology, Humboldt-University of Berlin, Invalidenstr, 43, D-10115 Berlin, Germany.

ABSTRACT

Background: Cyanobacteria are among the most abundant organisms on Earth and represent one of the oldest and most widespread clades known in modern phylogenetics. As the only known prokaryotes capable of oxygenic photosynthesis, cyanobacteria are considered to be a promising resource for renewable fuels and natural products. Our efforts to harness the sun's energy using cyanobacteria would greatly benefit from an increased understanding of the genomic diversity across multiple cyanobacterial strains. In this respect, the advent of novel sequencing techniques and the availability of several cyanobacterial genomes offers new opportunities for understanding microbial diversity and metabolic organization and evolution in diverse environments.

Results: Here, we report a whole genome comparison of multiple phototrophic cyanobacteria. We describe genetic diversity found within cyanobacterial genomes, specifically with respect to metabolic functionality. Our results are based on pair-wise comparison of protein sequences and concomitant construction of clusters of likely ortholog genes. We differentiate between core, shared and unique genes and show that the majority of genes are associated with a single genome. In contrast, genes with metabolic function are strongly overrepresented within the core genome that is common to all considered strains. The analysis of metabolic diversity within core carbon metabolism reveals parts of the metabolic networks that are highly conserved, as well as highly fragmented pathways.

Conclusions: Our results have direct implications for resource allocation and further sequencing projects. It can be extrapolated that the number of newly identified genes still significantly increases with increasing number of new sequenced genomes. Furthermore, genome analysis of multiple phototrophic strains allows us to obtain a detailed picture of metabolic diversity that can serve as a starting point for biotechnological applications and automated metabolic reconstructions.

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Phylogenetic analysis of cyanobacterial strains. A - Phylogenetic tree based on 16S rRNA comparison. B - Phylogenetic tree based on the number of shared CLOGs in common for pairs of strains. For the left figure, a tree in newick format was extracted from the Ribosomal Database Project web site (http://rdp.cme.msu.edu/) by selecting the 16 strains considered in this study and exporting the tree built with TREE BUILDER. The tree was plotted with DRAWGRAM of the phylogeny inference package (PHYLIP). For the right figure, a similarity matrix was calculated, such that the similarity between two strains was defined by the number of shared CLOGs divided by number of total CLOGs assigned to both strains. Subsequently, all entries in the matrix are substracted from the maximal entry.
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Figure 4: Phylogenetic analysis of cyanobacterial strains. A - Phylogenetic tree based on 16S rRNA comparison. B - Phylogenetic tree based on the number of shared CLOGs in common for pairs of strains. For the left figure, a tree in newick format was extracted from the Ribosomal Database Project web site (http://rdp.cme.msu.edu/) by selecting the 16 strains considered in this study and exporting the tree built with TREE BUILDER. The tree was plotted with DRAWGRAM of the phylogeny inference package (PHYLIP). For the right figure, a similarity matrix was calculated, such that the similarity between two strains was defined by the number of shared CLOGs divided by number of total CLOGs assigned to both strains. Subsequently, all entries in the matrix are substracted from the maximal entry.

Mentions: We are interested in the relationships between cyanobacterial species based on gene sharing, as compared to 16S rRNA analysis. Figure 4A shows a phylogenetic tree obtained from 16S rRNA, using PHYLIP (phylogeny inference package version 3.69) by Felsenstein [24]. Several options to estimate similarity based on assignment of CLOGs are available. Here, we use a simple measure based on the number of CLOGs common to two strains divided by the total number of CLOGs associated with both strains combined. The respective distance tree is shown in Figure 4B. Both trees exhibit a high degree of similarity, with only minor topological differences. In both cases, the Prochlorococcus strains form the closest related cluster. We note that we do not consider phylogenetic trees of individual gene families, where a higher degree of phylogenetic discordance must be expected [7]. Likewise any estimate of distance based on shared CLOGs is likely biased by genome size, which again reflects evolutionary distance as determined by 16S rRNA analysis. Table 2 gives a pair-wise comparison of shared CLOGs between all 16 cyanobacterial strains. The table confirms the close association of the three Prochlorococcus strains with Syc7803 with respect to shared genes.


The diversity of cyanobacterial metabolism: genome analysis of multiple phototrophic microorganisms.

Beck C, Knoop H, Axmann IM, Steuer R - BMC Genomics (2012)

Phylogenetic analysis of cyanobacterial strains. A - Phylogenetic tree based on 16S rRNA comparison. B - Phylogenetic tree based on the number of shared CLOGs in common for pairs of strains. For the left figure, a tree in newick format was extracted from the Ribosomal Database Project web site (http://rdp.cme.msu.edu/) by selecting the 16 strains considered in this study and exporting the tree built with TREE BUILDER. The tree was plotted with DRAWGRAM of the phylogeny inference package (PHYLIP). For the right figure, a similarity matrix was calculated, such that the similarity between two strains was defined by the number of shared CLOGs divided by number of total CLOGs assigned to both strains. Subsequently, all entries in the matrix are substracted from the maximal entry.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3369817&req=5

Figure 4: Phylogenetic analysis of cyanobacterial strains. A - Phylogenetic tree based on 16S rRNA comparison. B - Phylogenetic tree based on the number of shared CLOGs in common for pairs of strains. For the left figure, a tree in newick format was extracted from the Ribosomal Database Project web site (http://rdp.cme.msu.edu/) by selecting the 16 strains considered in this study and exporting the tree built with TREE BUILDER. The tree was plotted with DRAWGRAM of the phylogeny inference package (PHYLIP). For the right figure, a similarity matrix was calculated, such that the similarity between two strains was defined by the number of shared CLOGs divided by number of total CLOGs assigned to both strains. Subsequently, all entries in the matrix are substracted from the maximal entry.
Mentions: We are interested in the relationships between cyanobacterial species based on gene sharing, as compared to 16S rRNA analysis. Figure 4A shows a phylogenetic tree obtained from 16S rRNA, using PHYLIP (phylogeny inference package version 3.69) by Felsenstein [24]. Several options to estimate similarity based on assignment of CLOGs are available. Here, we use a simple measure based on the number of CLOGs common to two strains divided by the total number of CLOGs associated with both strains combined. The respective distance tree is shown in Figure 4B. Both trees exhibit a high degree of similarity, with only minor topological differences. In both cases, the Prochlorococcus strains form the closest related cluster. We note that we do not consider phylogenetic trees of individual gene families, where a higher degree of phylogenetic discordance must be expected [7]. Likewise any estimate of distance based on shared CLOGs is likely biased by genome size, which again reflects evolutionary distance as determined by 16S rRNA analysis. Table 2 gives a pair-wise comparison of shared CLOGs between all 16 cyanobacterial strains. The table confirms the close association of the three Prochlorococcus strains with Syc7803 with respect to shared genes.

Bottom Line: We describe genetic diversity found within cyanobacterial genomes, specifically with respect to metabolic functionality.Our results have direct implications for resource allocation and further sequencing projects.It can be extrapolated that the number of newly identified genes still significantly increases with increasing number of new sequenced genomes.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute for Theoretical Biology, Humboldt-University of Berlin, Invalidenstr, 43, D-10115 Berlin, Germany.

ABSTRACT

Background: Cyanobacteria are among the most abundant organisms on Earth and represent one of the oldest and most widespread clades known in modern phylogenetics. As the only known prokaryotes capable of oxygenic photosynthesis, cyanobacteria are considered to be a promising resource for renewable fuels and natural products. Our efforts to harness the sun's energy using cyanobacteria would greatly benefit from an increased understanding of the genomic diversity across multiple cyanobacterial strains. In this respect, the advent of novel sequencing techniques and the availability of several cyanobacterial genomes offers new opportunities for understanding microbial diversity and metabolic organization and evolution in diverse environments.

Results: Here, we report a whole genome comparison of multiple phototrophic cyanobacteria. We describe genetic diversity found within cyanobacterial genomes, specifically with respect to metabolic functionality. Our results are based on pair-wise comparison of protein sequences and concomitant construction of clusters of likely ortholog genes. We differentiate between core, shared and unique genes and show that the majority of genes are associated with a single genome. In contrast, genes with metabolic function are strongly overrepresented within the core genome that is common to all considered strains. The analysis of metabolic diversity within core carbon metabolism reveals parts of the metabolic networks that are highly conserved, as well as highly fragmented pathways.

Conclusions: Our results have direct implications for resource allocation and further sequencing projects. It can be extrapolated that the number of newly identified genes still significantly increases with increasing number of new sequenced genomes. Furthermore, genome analysis of multiple phototrophic strains allows us to obtain a detailed picture of metabolic diversity that can serve as a starting point for biotechnological applications and automated metabolic reconstructions.

Show MeSH
Related in: MedlinePlus