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Phenotypic differentiation of gastrointestinal microbes is reflected in their encoded metabolic repertoires.

Bauer E, Laczny CC, Magnusdottir S, Wilmes P, Thiele I - Microbiome (2015)

Bottom Line: Based on the global metabolic differences, we found that energy metabolism and membrane synthesis play important roles in delineating different taxonomic groups.Furthermore, we found an exponential relationship between phylogeny and the reaction composition, meaning that closely related microbes of the same genus can exhibit pronounced differences with respect to their metabolic capabilities while at the family level only marginal metabolic differences can be observed.These results have important implications in our understanding of the ecological and evolutionary complexity of the human gastrointestinal microbiome.

View Article: PubMed Central - PubMed

Affiliation: Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg. eugen.bauer@uni.lu.

ABSTRACT

Background: The human gastrointestinal tract harbors a diverse microbial community, in which metabolic phenotypes play important roles for the human host. Recent developments in meta-omics attempt to unravel metabolic roles of microbes by linking genotypic and phenotypic characteristics. This connection, however, still remains poorly understood with respect to its evolutionary and ecological context.

Results: We generated automatically refined draft genome-scale metabolic models of 301 representative intestinal microbes in silico. We applied a combination of unsupervised machine-learning and systems biology techniques to study individual and global differences in genomic content and inferred metabolic capabilities. Based on the global metabolic differences, we found that energy metabolism and membrane synthesis play important roles in delineating different taxonomic groups. Furthermore, we found an exponential relationship between phylogeny and the reaction composition, meaning that closely related microbes of the same genus can exhibit pronounced differences with respect to their metabolic capabilities while at the family level only marginal metabolic differences can be observed. This finding was further substantiated by the metabolic divergence within different genera. In particular, we could distinguish three sub-type clusters based on membrane and energy metabolism within the Lactobacilli as well as two clusters within the Bifidobacteria and Bacteroides.

Conclusions: We demonstrate that phenotypic differentiation within closely related species could be explained by their metabolic repertoire rather than their phylogenetic relationships. These results have important implications in our understanding of the ecological and evolutionary complexity of the human gastrointestinal microbiome.

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Related in: MedlinePlus

Tanglegram between the hierarchical clustering of the phylogenetic and metabolic distance. Tanglegram between the dendrograms of the reaction distance according to the presence of specific reactions and the phylogenetic distance according to the cophenetic distance of the maximum likelihood tree (rooted with two methanogenic archea) calculated from the sequence similarity of 400 selected essential genes. The dendrograms were calculated using hierarchical clustering with complete linkage. Lines connecting the same microbe are colored according to the taxonomic class
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Fig3: Tanglegram between the hierarchical clustering of the phylogenetic and metabolic distance. Tanglegram between the dendrograms of the reaction distance according to the presence of specific reactions and the phylogenetic distance according to the cophenetic distance of the maximum likelihood tree (rooted with two methanogenic archea) calculated from the sequence similarity of 400 selected essential genes. The dendrograms were calculated using hierarchical clustering with complete linkage. Lines connecting the same microbe are colored according to the taxonomic class

Mentions: To further investigate the observed metabolic diversity (Fig. 2) and its evolutionary basis, we computed the phylogenetic relationship between the 301 bacteria based on 400 protein-coding metabolic genes [48] using two methanogenic archaea as outgroups (Additional file 5: Figure S2). On the basis of this rooted phylogenetic tree, we computed pairwise phylogenetic distances from the heights within the tree using the cophenetic distance [49]. While the clustering of this phylogenetic distance (Fig. 3) recapitulated the original phylogeny (Additional file 5: Figure S2), we additionally computed a genetic distance based on the 16S rRNA gene similarity of the microbes (Additional file 6: Figure S3), to ensure that our observations were reproducible with other methods or markers. The pairwise distance based on the phylogenetic tree and the inferred presence of distinct reactions were overall congruent with each other (Fig. 3). Interestingly, we identified an exponential relationship between phylogeny and metabolic repertoire (Fig. 4), which is in accordance to a previous study based on genomic measures [50]. To exclude potential artifacts resulting from homology-based annotation methods (Model SEED) used for the generation of the metabolic reconstructions, we also determined the distance based on the presence of detected clusters of orthologous groups (COGs) [51] and Pfam protein domains [52]. These two measures also exhibited the same exponential relationship between metabolic repertoire and phylogeny (Fig. 4). Importantly, this relationship indicates that closely related species can have an extremely divergent set of metabolic reactions, while at taxonomic ranks above the family level, only limited amounts of additional emergent features were observed. Since COG annotations and Pfam domains are prone to misclassification, we also included annotation measures with a higher quality, such as MetaCyc functionalities [53] as well as EC numbers (Additional file 7: Figure S4) and observed a comparable exponential trend. Similar observations have been obtained in published experimental studies based on the phenotypic properties of different strains from the same genus or species [6, 8], underlining the biological relevance of our observations. In the context of a microbial community or biofilm, our observed relationship explains why closely related taxonomic groups (e.g., species of the same genus) are able to co-exist, while the overall consortium is limited in its metabolic potential [54]. In addition to this result, we identified a linear relationship between the metabolic repertoire and the similarity of essential nutrients, which we calculated using flux balance analysis as a proxy for the metabolic phenotype (Fig. 4b). These findings complement previous knowledge about the relationship between genotype and phenotype by Plata et al. [55]. Here, a similar exponential relation was observed between microbial phylogeny and varying growth conditions in selected genome-scale metabolic models, which were not directly associated with a specific habitat [55]. Additionally, this relationship has also been found with respect to the phenotypic similarity based on gene essentiality and synthetic lethal genes [55]. Taking into account that these latter measures have been based on flux balance analysis and are thus analogous to our results, we conclude that the observed patterns are generally applicable to bacteria. Furthermore, we argue that the metabolic network constituting of a set of reactions is appropriate to represent and explain a phenotype (Fig. 4b). Assuming the metabolic repertoire as one of the major factors for the evolution of intestinal microbes, transfer of metabolic traits within different taxa may account for fast metabolic diversification of species and strains leading to niche partitioning. In fact, horizontal gene transfer has been shown to be enriched within organisms inhabiting the same environment, particularly, the human gut [56]. In addition to the results of Plata et al. [55], we propose the metabolic repertoire as one of the major factors influencing the phenotypic differentiation of human gut microbial communities. Still, the clear separation of taxonomic groups noted above (Fig. 2) suggests that exchange of functionalities is limited to ensure a certain metabolic divergence within the whole microbiota to maintain functional diversity and limit competition between closely related organisms.Fig. 3


Phenotypic differentiation of gastrointestinal microbes is reflected in their encoded metabolic repertoires.

Bauer E, Laczny CC, Magnusdottir S, Wilmes P, Thiele I - Microbiome (2015)

Tanglegram between the hierarchical clustering of the phylogenetic and metabolic distance. Tanglegram between the dendrograms of the reaction distance according to the presence of specific reactions and the phylogenetic distance according to the cophenetic distance of the maximum likelihood tree (rooted with two methanogenic archea) calculated from the sequence similarity of 400 selected essential genes. The dendrograms were calculated using hierarchical clustering with complete linkage. Lines connecting the same microbe are colored according to the taxonomic class
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4663747&req=5

Fig3: Tanglegram between the hierarchical clustering of the phylogenetic and metabolic distance. Tanglegram between the dendrograms of the reaction distance according to the presence of specific reactions and the phylogenetic distance according to the cophenetic distance of the maximum likelihood tree (rooted with two methanogenic archea) calculated from the sequence similarity of 400 selected essential genes. The dendrograms were calculated using hierarchical clustering with complete linkage. Lines connecting the same microbe are colored according to the taxonomic class
Mentions: To further investigate the observed metabolic diversity (Fig. 2) and its evolutionary basis, we computed the phylogenetic relationship between the 301 bacteria based on 400 protein-coding metabolic genes [48] using two methanogenic archaea as outgroups (Additional file 5: Figure S2). On the basis of this rooted phylogenetic tree, we computed pairwise phylogenetic distances from the heights within the tree using the cophenetic distance [49]. While the clustering of this phylogenetic distance (Fig. 3) recapitulated the original phylogeny (Additional file 5: Figure S2), we additionally computed a genetic distance based on the 16S rRNA gene similarity of the microbes (Additional file 6: Figure S3), to ensure that our observations were reproducible with other methods or markers. The pairwise distance based on the phylogenetic tree and the inferred presence of distinct reactions were overall congruent with each other (Fig. 3). Interestingly, we identified an exponential relationship between phylogeny and metabolic repertoire (Fig. 4), which is in accordance to a previous study based on genomic measures [50]. To exclude potential artifacts resulting from homology-based annotation methods (Model SEED) used for the generation of the metabolic reconstructions, we also determined the distance based on the presence of detected clusters of orthologous groups (COGs) [51] and Pfam protein domains [52]. These two measures also exhibited the same exponential relationship between metabolic repertoire and phylogeny (Fig. 4). Importantly, this relationship indicates that closely related species can have an extremely divergent set of metabolic reactions, while at taxonomic ranks above the family level, only limited amounts of additional emergent features were observed. Since COG annotations and Pfam domains are prone to misclassification, we also included annotation measures with a higher quality, such as MetaCyc functionalities [53] as well as EC numbers (Additional file 7: Figure S4) and observed a comparable exponential trend. Similar observations have been obtained in published experimental studies based on the phenotypic properties of different strains from the same genus or species [6, 8], underlining the biological relevance of our observations. In the context of a microbial community or biofilm, our observed relationship explains why closely related taxonomic groups (e.g., species of the same genus) are able to co-exist, while the overall consortium is limited in its metabolic potential [54]. In addition to this result, we identified a linear relationship between the metabolic repertoire and the similarity of essential nutrients, which we calculated using flux balance analysis as a proxy for the metabolic phenotype (Fig. 4b). These findings complement previous knowledge about the relationship between genotype and phenotype by Plata et al. [55]. Here, a similar exponential relation was observed between microbial phylogeny and varying growth conditions in selected genome-scale metabolic models, which were not directly associated with a specific habitat [55]. Additionally, this relationship has also been found with respect to the phenotypic similarity based on gene essentiality and synthetic lethal genes [55]. Taking into account that these latter measures have been based on flux balance analysis and are thus analogous to our results, we conclude that the observed patterns are generally applicable to bacteria. Furthermore, we argue that the metabolic network constituting of a set of reactions is appropriate to represent and explain a phenotype (Fig. 4b). Assuming the metabolic repertoire as one of the major factors for the evolution of intestinal microbes, transfer of metabolic traits within different taxa may account for fast metabolic diversification of species and strains leading to niche partitioning. In fact, horizontal gene transfer has been shown to be enriched within organisms inhabiting the same environment, particularly, the human gut [56]. In addition to the results of Plata et al. [55], we propose the metabolic repertoire as one of the major factors influencing the phenotypic differentiation of human gut microbial communities. Still, the clear separation of taxonomic groups noted above (Fig. 2) suggests that exchange of functionalities is limited to ensure a certain metabolic divergence within the whole microbiota to maintain functional diversity and limit competition between closely related organisms.Fig. 3

Bottom Line: Based on the global metabolic differences, we found that energy metabolism and membrane synthesis play important roles in delineating different taxonomic groups.Furthermore, we found an exponential relationship between phylogeny and the reaction composition, meaning that closely related microbes of the same genus can exhibit pronounced differences with respect to their metabolic capabilities while at the family level only marginal metabolic differences can be observed.These results have important implications in our understanding of the ecological and evolutionary complexity of the human gastrointestinal microbiome.

View Article: PubMed Central - PubMed

Affiliation: Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg. eugen.bauer@uni.lu.

ABSTRACT

Background: The human gastrointestinal tract harbors a diverse microbial community, in which metabolic phenotypes play important roles for the human host. Recent developments in meta-omics attempt to unravel metabolic roles of microbes by linking genotypic and phenotypic characteristics. This connection, however, still remains poorly understood with respect to its evolutionary and ecological context.

Results: We generated automatically refined draft genome-scale metabolic models of 301 representative intestinal microbes in silico. We applied a combination of unsupervised machine-learning and systems biology techniques to study individual and global differences in genomic content and inferred metabolic capabilities. Based on the global metabolic differences, we found that energy metabolism and membrane synthesis play important roles in delineating different taxonomic groups. Furthermore, we found an exponential relationship between phylogeny and the reaction composition, meaning that closely related microbes of the same genus can exhibit pronounced differences with respect to their metabolic capabilities while at the family level only marginal metabolic differences can be observed. This finding was further substantiated by the metabolic divergence within different genera. In particular, we could distinguish three sub-type clusters based on membrane and energy metabolism within the Lactobacilli as well as two clusters within the Bifidobacteria and Bacteroides.

Conclusions: We demonstrate that phenotypic differentiation within closely related species could be explained by their metabolic repertoire rather than their phylogenetic relationships. These results have important implications in our understanding of the ecological and evolutionary complexity of the human gastrointestinal microbiome.

Show MeSH
Related in: MedlinePlus