Metabolic complexity drives divergence in microbial communities

Abstract

Microbial communities are shaped by the metabolites available in their environment, but the principles that govern whether different communities will converge or diverge in any given condition remain unknown, posing fundamental questions about the feasibility of microbiome engineering. To this end, we studied the longitudinal assembly dynamics of a set of natural microbial communities grown in laboratory conditions of increasing metabolic complexity. We found that different microbial communities tend to become similar to each other when grown in metabolically simple conditions, but diverge in composition as the metabolic complexity of the environment increases, a phenomenon we refer to as the divergence-complexity effect. A comparative analysis of these communities revealed that this divergence is driven by community diversity and by the diverse assortment of specialist taxa capable of degrading complex metabolites. An ecological model of community dynamics indicates that the hierarchical structure of metabolism itself, where complex molecules are enzymatically degraded into progressively smaller ones, is necessary and sufficient to recapitulate all of our experimental observations. In addition to pointing to a fundamental principle of community assembly, the divergence-complexity effect has important implications for microbiome engineering applications, as it can provide insight into which environments support multiple community states, enabling the search for desired ecosystem functions.

Fig. 1 |. Microbial communities may diverge in environments with increasing metabolic complexity.

a-d, Hypothesis of microbial community divergence in theoretical simple (b) and complex (c) metabolic conditions. Microbial communities A, B, and C are initially composed of different compositions of the same three microbial species (a; blue, red, and yellow). Over time, communities grown on a simple substrate (b) converge, while these same communities grown on a complex substrate (c) diverge. d, Quantification of divergence at the final time point for hypothetical scenarios in a and b. e-g, Divergence observed in two independent experimental studies where microbial communities were sourced from soils or leaves and grown on glucose (e; a relatively simple metabolic environment from Goldford et al.) and communities were sourced from pitcher plants and grown on acidified cricket media (f; a more complex metabolic environment from Bittleston et al.). Each colored line in e and f represents the trajectory of a community’s composition over time in separately computed multidimensional scaling (MDS) projections. g, The divergence for each metabolic environment, calculated as the pairwise distances between all communities within a given condition at each time point. Each point is the mean pairwise distance within condition at each time point and shading represents the 95% confidence interval over all pairwise distances within each environment at each timepoint.

Fig. 2 |. Divergence of microbial communities increases in environments of increasing metabolic complexity.

a, Study design: microbial communities were extracted from six forest soils and were then grown in nine conditions (citrate, glucose, cellobiose, cellulose, lignin, citrate + glucose, citrate + glucose + cellobiose, citrate + glucose + cellobiose + cellulose, and citrate + glucose + cellobiose + cellulose + lignin). Communities were passaged ten times once every three days and sequenced on days 0, 3, 6, 9, 12, and 33. b-c, MDS projections of community trajectories over time in each single-metabolite condition (b) and mixed-metabolite condition (c). MDS was calculated on all samples together for ease of visually comparing trajectories between conditions. d, Divergence of communities within each condition over time from day 3 onwards. Initial communities are a distance of 58.1+/−3.5 (not shown for clarity). Single metabolite conditions are in blue, mixed conditions are in orange, and colors darken with complexity. Points on each line represent the mean divergence and the shaded region represents the 95% confidence interval for pairwise distances between all six communities within each condition. e, Distribution of divergence for the final time point where divergence increases with metabolic complexity for single and mixed-metabolite conditions (same colors as d). f, Metabolic complexity effect by condition type (slopes shown in e) for all time points. P-values computed on significance of effect (slope) > 0 (*: p<.05, ***: p<1e-3).

Fig. 3 |. Divergence dynamically correlates with diversity.

a, The slope of the relationship between community alpha diversity and divergence (red) and the mean community alpha diversity (gray) over time. Shaded areas around each regression line represents the 95% confidence interval. b, The data underlying the relationship in a over time. Each point is the diversity of a community in a condition (x-axis) and the divergence of that community from all others within a condition. While diversity is expected to increase with metabolic complexity, it is not clear if different communities will increase in diversity in the same ways.

Fig. 4 |. Endemic taxa are enriched and unevenly distributed in complex conditions.

a, The distribution of condition-specificity per condition for day 33. Condition-specificity is calculated as the fraction of occurrences of a taxon that is attributed to a particular condition, such that a specificity of 1 means that taxon occurs in only one condition (a specialist). b, The number of specialists per condition. c, Taxon occurrence by number of conditions and number of source communities. ASVs found in fewer conditions are less evenly distributed across source communities (found in fewer source communities) and taxa found in more conditions are more evenly distributed across source communities. d, Two hypotheses for single metabolite conditions following from a and b, where H2 is supported and H1 is not. H1: more complex conditions are enriched for specialists and when those taxa are evenly distributed across source communities, it results in similar divergence for complex and simple metabolic conditions. H2: when more complex conditions are enriched for specialists and these taxa are less evenly distributed across communities, more complex conditions result in greater divergence.

Fig. 5 |. Trophic resource transformations reproduce divergence with consumer resource model simulations.

The distribution of divergence for communities with simulated consumer resource models with and without trophic structure in resource transformations and consumer preferences. Mimicking our experiment, community growth was simulated in single metabolite (blue) and mixed metabolite (orange) conditions of increasing complexity (darker). a-d, Divergence for communities simulated with trophic resource transformations consumer preferences (fully structured; a), trophic resource transformations and random consumer preferences (resource structured; b), random resource transformations and trophic consumer preferences (consumer structured; c), and random resource transformations and consumer preferences (fully random; d). e-f, The effect of metabolic complexity on divergence for single and mixed metabolite conditions with trophic resource transformations (e) and random transformations (f; ***: p<1e-6). g-h, Using the fully structured configuration, the relationship between diversity and mean divergence (g) and the relationship between occupancy in conditions and number of source communities (h).