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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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Local differences within metabolic models and their sub-type-specific pathways. The metabolic distance was determined according to the presence of specific reactions in the model (a). t-SNE was performed to obtain a low dimensional representation of the local differences within taxonomic groups which are represented by the different colors. Sub-types are defined based on hierarchical clustering of the reaction similarities. Members of one sub-type are connected with lines which originate from the cluster centroid. The ellipses represent confidence intervals of the clusters with a certainty of 95 %. Distinguished pathways within sub-types include the genera Lactobacillus (b), Bifidobacterium (c), and Bacteroides (d). The pathways occurring in only one of the sub-types are framed by boxes carrying the corresponding cluster name. Reactions within pathways are represented by black arrows. GAP glycerol-3-phosphate, PEPG peptidoglycan, PGP phosphatidylglycerophosphate, PG phosphatidylglycerol, APG 1-Acyl-sn-glycero-3-phosphoglycerol, TTDCA tetradecanoate, HDCA heptadecanoate, OCDCA octadecanoate
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Fig5: Local differences within metabolic models and their sub-type-specific pathways. The metabolic distance was determined according to the presence of specific reactions in the model (a). t-SNE was performed to obtain a low dimensional representation of the local differences within taxonomic groups which are represented by the different colors. Sub-types are defined based on hierarchical clustering of the reaction similarities. Members of one sub-type are connected with lines which originate from the cluster centroid. The ellipses represent confidence intervals of the clusters with a certainty of 95 %. Distinguished pathways within sub-types include the genera Lactobacillus (b), Bifidobacterium (c), and Bacteroides (d). The pathways occurring in only one of the sub-types are framed by boxes carrying the corresponding cluster name. Reactions within pathways are represented by black arrows. GAP glycerol-3-phosphate, PEPG peptidoglycan, PGP phosphatidylglycerophosphate, PG phosphatidylglycerol, APG 1-Acyl-sn-glycero-3-phosphoglycerol, TTDCA tetradecanoate, HDCA heptadecanoate, OCDCA octadecanoate

Mentions: To account for taxon-dependent differences between microorganisms (Table 1), we focused our analysis on model subsets of the five classes and the three genera with the highest number of representatives (Table 2). Additionally, this focus allows us to elucidate whether our results were dependent on our selection of microbes or could be expanded to other microbes not considered in this study. We found that the exponential relationship between phylogeny and metabolic repertoire as well as the linear relationship between nutrient essentiality and metabolic repertoire was apparent for most taxonomic groups (Table 2). However, we noticed differences within the taxa. In particular, there was a considerable exponential fit for all five major bacterial classes except for Clostridia, which could be explained by Clostridia’s broad metabolic versatility and the corresponding difficulties in the taxonomic assignment within this class [57]. Our result is in accordance with the observed cluster variability of Clostridia when comparing the clustering of the metabolic and phylogenetic distance (principal coordinate analysis, Fig. 3). When investigating individual genera, we detected a high correlation between essential nutrients and the metabolic repertoire of Bifidobacteria, whereas the correlation between their phylogeny and metabolic repertoire was less pronounced (Table 2, Fig. 3). For members of the genus Bacteroides, the metabolic repertoire correlated strongly with their phylogeny (Fig. 3), but only weakly with the essential nutrients (Table 2). Based on these results, we propose that the divergence within this genus can be explained by divergence in metabolic pathways relating to membrane synthesis (Fig. 5) rather than energy metabolism and thus nutrient essentiality. For the Lactobacillus genus, we found a strong correlation between metabolic potential with both, phylogeny and essential nutrients. Within this genus, energy metabolism explained particular phenotypic divergences of species (Fig. 5), which is consistent with the observed high correlation between reactions involved in nutrient uptake and the clustering of representatives of the Bacilli in the principal coordinate plot (Fig. 2). Taken together, our results show a generality of the observed relationships between phylogeny, metabolic repertoire, and nutrient essentiality within and between taxonomic groups (Fig. 4).Table 2


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

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

Local differences within metabolic models and their sub-type-specific pathways. The metabolic distance was determined according to the presence of specific reactions in the model (a). t-SNE was performed to obtain a low dimensional representation of the local differences within taxonomic groups which are represented by the different colors. Sub-types are defined based on hierarchical clustering of the reaction similarities. Members of one sub-type are connected with lines which originate from the cluster centroid. The ellipses represent confidence intervals of the clusters with a certainty of 95 %. Distinguished pathways within sub-types include the genera Lactobacillus (b), Bifidobacterium (c), and Bacteroides (d). The pathways occurring in only one of the sub-types are framed by boxes carrying the corresponding cluster name. Reactions within pathways are represented by black arrows. GAP glycerol-3-phosphate, PEPG peptidoglycan, PGP phosphatidylglycerophosphate, PG phosphatidylglycerol, APG 1-Acyl-sn-glycero-3-phosphoglycerol, TTDCA tetradecanoate, HDCA heptadecanoate, OCDCA octadecanoate
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4663747&req=5

Fig5: Local differences within metabolic models and their sub-type-specific pathways. The metabolic distance was determined according to the presence of specific reactions in the model (a). t-SNE was performed to obtain a low dimensional representation of the local differences within taxonomic groups which are represented by the different colors. Sub-types are defined based on hierarchical clustering of the reaction similarities. Members of one sub-type are connected with lines which originate from the cluster centroid. The ellipses represent confidence intervals of the clusters with a certainty of 95 %. Distinguished pathways within sub-types include the genera Lactobacillus (b), Bifidobacterium (c), and Bacteroides (d). The pathways occurring in only one of the sub-types are framed by boxes carrying the corresponding cluster name. Reactions within pathways are represented by black arrows. GAP glycerol-3-phosphate, PEPG peptidoglycan, PGP phosphatidylglycerophosphate, PG phosphatidylglycerol, APG 1-Acyl-sn-glycero-3-phosphoglycerol, TTDCA tetradecanoate, HDCA heptadecanoate, OCDCA octadecanoate
Mentions: To account for taxon-dependent differences between microorganisms (Table 1), we focused our analysis on model subsets of the five classes and the three genera with the highest number of representatives (Table 2). Additionally, this focus allows us to elucidate whether our results were dependent on our selection of microbes or could be expanded to other microbes not considered in this study. We found that the exponential relationship between phylogeny and metabolic repertoire as well as the linear relationship between nutrient essentiality and metabolic repertoire was apparent for most taxonomic groups (Table 2). However, we noticed differences within the taxa. In particular, there was a considerable exponential fit for all five major bacterial classes except for Clostridia, which could be explained by Clostridia’s broad metabolic versatility and the corresponding difficulties in the taxonomic assignment within this class [57]. Our result is in accordance with the observed cluster variability of Clostridia when comparing the clustering of the metabolic and phylogenetic distance (principal coordinate analysis, Fig. 3). When investigating individual genera, we detected a high correlation between essential nutrients and the metabolic repertoire of Bifidobacteria, whereas the correlation between their phylogeny and metabolic repertoire was less pronounced (Table 2, Fig. 3). For members of the genus Bacteroides, the metabolic repertoire correlated strongly with their phylogeny (Fig. 3), but only weakly with the essential nutrients (Table 2). Based on these results, we propose that the divergence within this genus can be explained by divergence in metabolic pathways relating to membrane synthesis (Fig. 5) rather than energy metabolism and thus nutrient essentiality. For the Lactobacillus genus, we found a strong correlation between metabolic potential with both, phylogeny and essential nutrients. Within this genus, energy metabolism explained particular phenotypic divergences of species (Fig. 5), which is consistent with the observed high correlation between reactions involved in nutrient uptake and the clustering of representatives of the Bacilli in the principal coordinate plot (Fig. 2). Taken together, our results show a generality of the observed relationships between phylogeny, metabolic repertoire, and nutrient essentiality within and between taxonomic groups (Fig. 4).Table 2

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