Quantifying environmental adaptation of metabolic pathways in metagenomics - PubMed (original) (raw)
. 2009 Feb 3;106(5):1374-9.
doi: 10.1073/pnas.0808022106. Epub 2009 Jan 22.
Jeroen Raes, Prianka V Patel, Robert Bjornson, Jan O Korbel, Ivica Letunic, Takuji Yamada, Alberto Paccanaro, Lars J Jensen, Michael Snyder, Peer Bork, Mark B Gerstein
Affiliations
- PMID: 19164758
- PMCID: PMC2629784
- DOI: 10.1073/pnas.0808022106
Quantifying environmental adaptation of metabolic pathways in metagenomics
Tara A Gianoulis et al. Proc Natl Acad Sci U S A. 2009.
Abstract
Recently, approaches have been developed to sample the genetic content of heterogeneous environments (metagenomics). However, by what means these sequences link distinct environmental conditions with specific biological processes is not well understood. Thus, a major challenge is how the usage of particular pathways and subnetworks reflects the adaptation of microbial communities across environments and habitats-i.e., how network dynamics relates to environmental features. Previous research has treated environments as discrete, somewhat simplified classes (e.g., terrestrial vs. marine), and searched for obvious metabolic differences among them (i.e., treating the analysis as a typical classification problem). However, environmental differences result from combinations of many factors, which often vary only slightly. Therefore, we introduce an approach that employs correlation and regression to relate multiple, continuously varying factors defining an environment to the extent of particular microbial pathways present in a geographic site. Moreover, rather than looking only at individual correlations (one-to-one), we adapted canonical correlation analysis and related techniques to define an ensemble of weighted pathways that maximally covaries with a combination of environmental variables (many-to-many), which we term a metabolic footprint. Applied to available aquatic datasets, we identified footprints predictive of their environment that can potentially be used as biosensors. For example, we show a strong multivariate correlation between the energy-conversion strategies of a community and multiple environmental gradients (e.g., temperature). Moreover, we identified covariation in amino acid transport and cofactor synthesis, suggesting that limiting amounts of cofactor can (partially) explain increased import of amino acids in nutrient-limited conditions.
Conflict of interest statement
The authors declare no conflict of interest.
Figures
Fig. 1.
Schematic representation of approach. The large squares labeled B1, B2, etc. represent the geographic sites (buckets). Each bucket has sequence and environmental feature data associated with it. (A) Mapping quantitative environmental features [salinity (ppt), sample depth (position in water column from which the sample was collected), water column depth (measured from surface to floor), and chlorophyll]. (B) Metagenome-derived metabolism at different levels of resolution (see Materials and Methods). Reads are color-coded according to their corresponding pathway elements (shapes). Different pathways are represented by different shapes (square, circle, etc.). All of the instances of a particular pathway are summed and normalized to compute the pathway score. (C) Schematic representation of DPM (see details in text). (D) Schematic representation of CCA (see details in text).
Fig. 2.
Predicting specific environmental parameters from subsets of metabolic pathways. Linear model for temperature built from subsets of highly correlated pathways, including _N_-acetylglucosamine biosynthesis, many components of amino acid metabolism, and fatty acid biosynthesis (for full list and coefficients, see
Table S4
). Axes are normalized actual and predicted temperature for x and y, respectively.
Fig. 3.
Metabolic map of structural correlations at 2 resolutions. Central panel is a plot of the environmental features (triangles) and pathways (circles), where the x axis and y axis are the structural correlation coefficients (normalized weights) derived from CCA for the first and second dimension, respectively (see Fig. 1_D_). The remainder of the figure indicates the strength of both the environmental covariation of the pathways (KEGG, Right) and of the sections of pathways (modules, Left), as measured by the absolute value of normalized weights (color-coded yellow strongest to blue weakest) (see interactive version of this map in
http://pathways.embl.de/metagenomics
). Nodes symbolize compounds, and lines connecting nodes are enzymes. All enzymes (lines) corresponding to a single KEGG map or a single module will have the same color. Shaded gray Insets (A–G) for pathways and corresponding insets for modules (no modules available for C and D) indicate examples from the text: energy conversion (A–E), amino acid metabolism (G), and lipid synthesis and glycan metabolism (F). Photosystem I and II modules (A, bright yellow to green) show significant covariation with the environment, but the ATPase is invariant (blue). (B) A similar pattern was observed for oxidative phosphorylation (see text for more details). (C) Pieces of the photosynthetic machinery (including heme/porphyrin synthesis). (D) Carbon fixation. (F) Glycerophospholipid pathways show that only the “pipe” leading to or from the citrate acid cycle covaries. (G) Amino acid metabolic pathways discussed in the text. For map generation, see ref. .
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