Motivation and overview of the analytical framework

BiomeNet is a hierarchical mixed-membership model, where metabolic reactions are mixed to form subnetworks, subnetworks are mixed to form metabosystems, and environmental samples are treated as potential mixtures of metabosystems. We explicitly model metabolism as a hierarchy because biochemical networks are widely considered to be organized in this way. For example, the KEGG pathways database arranges biochemical reactions into sub-pathways (consecutive reaction steps within curated pathways), which are arranged into “pathway modules” that are intended to represent functional units [23]. Alternatively, analyses of metabolic interactions from a purely topological perspective also support the view of hierarchical modularity [24]–[26]. We differ, however, in desiring a probabilistic framework for discriminating the structural components of a full biochemical network. Our mixed-membership approach permits the components of one level (e.g., reactions) to contribute to other structures to different degrees (e.g., to different subnetworks, metabosystems and samples). Aside from avoiding the need to place arbitrary boundaries on the components of a biochemical network, this framework reflects how community-wide metabolic activities of a microbiome arise from mixtures of organisms having different metabolic repertoires [12], [14], and how microbiome samples are often comprised of mixtures of different communities (e.g., stool samples are comprised of mixtures of the epithelium and luminal niche communities [27]).

Within BiomeNet, each enzymatic reaction is decomposed into substrate-product pairs (Figure 1a). Dependence among pairs is modeled through shared membership in a subnetwork comprised of substrates and products related by reactions. Because compounds can serve as both substrate and product, reaction pairs within a subnetwork are non-independent (Figure 1b), they are only independent once conditioned on their subnetwork assignment. Subnetworks are not rigidly defined in the model. Each reaction could have some membership to any of the subnetworks. However, for model identifiability and interpretability, we want subnetworks to be defined by a relatively few major reactions, with all other reactions having a negligible membership. Therefore, we model membership in subnetworks as sparse probability vectors. This also permits subnetworks to partially overlap, with some reactions participating in several subnetworks with different degrees.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Figure 1. Enzymatic reactions are decomposed into substrate-product pairs that link reactions within metabolic subnetworks.

(a) An enzymatic reaction is broken down into pairwise relations between its substrates and products. These reactions are more complex than pairwise edges studied in most types of network analysis. Here, reactions are “hyper-edges”, meaning that the relations are between two sets of nodes instead of a pair of nodes. For an undirected reaction, one can consider both directions when breaking the reaction into substrate-product pairs. (b) A subnetwork is composed of a set of reactions. In the model, different subnetworks can potentially share reactions and therefore have overlapping regions.

https://doi.org/10.1371/journal.pcbi.1003918.g001

Metabosystems are modeled as a mixture of L subnetworks each contributing to different degrees. We model their composition with a mixture variable φ. For example, if we assume that we have 10 subnetworks (L = 10), we can look at metabosystem k and see it has mixing probabilities for each subnetwork φ_k  =  (≪0.001, ≪0.001, 0.2, ≪0.001, ≪0.001, 0.1, ≪0.001, ≪0.001, 0.7, ≪0.001). We can see from this example that metabosystem k is mainly comprised of 20% subnetwork 3, 10% subnetwork 6 and 70% subnetwork 9.

Finally, each microbiome sample is modeled as a mixture of metabosystems. These metabosystems can be thought of as different facets of community-level metabolic activities. Considering the case of gut microbiome samples from diseased and healthy individuals, it is unlikely that the community metabolism of all diseased individuals will be exactly the same. Individuals with a more severe case of the disease might have a larger contribution from a “dysbiotic metabosystem” to their microbiome. Thus, we do not assume that samples are necessarily comprised of just one metabosystem. We denote the metabosystem mixture for a sample as θ. For example, if we have 3 metabosystems (K = 3), and a microbiome sample has a metabosystem mixture of θ = (0.2, ≪0.001, 0.8), then θ indicates that this sample consists of 20% metabosystem 1 and 80% metabosystem 3.

Our model is completely unsupervised; the membership of reactions to subnetworks, the contribution of subnetworks to metabosystems, and the mixture of metabosystems within a sample are learned from the data. Collapsed Gibbs sampling [28], [29] is used to infer the posterior distributions of the metabosystem and subnetwork assignments.

A hierarchical mixed-membership model for BiomeNet

Suppose we have a total of N microbiome samples in the data, and those data are comprised of a total of I_n reactions in the n^th microbiome sample, and a total of J_ni substrate-product pairs in the i^th reaction of the n^th sample. Because we expect that a microbiome sample could be a mixture of partially overlapping assemblages of microbes with varying types of ecological interaction, we model each sample as a mixture of K metabosystems, where K is assumed to be known and fixed in advance. The relative contribution of each metabosystem to the microbiome associated with the n^th sample is modeled through latent variable θ_n, a probability vector of K values summing to one. Thus, we have

Where, Z_ni denotes the metabosystem assignment for the i^th reaction in the n^th sample. The θ variables will be inferred from the data. We assume an independent and identical (iid) sparse symmetric Dirichlet prior on θ_n.

A symmetric Dirichlet is appropriate because we have no prior preference for any of the metabosystems. The probability function of this sparse symmetric Dirichlet distribution is given bywhere α_θ is a positive parameter. The mean of is given by 1/K, for i = 1,…, K, and the variance of is given by . Thus the variance is larger when α_θ is closer to 0, in which case, the probability that a randomly drawn point from this distribution is a sparse vector is larger. The sparsity introduced by a prior can be viewed as equivalent to the penalty added to the likelihood in the penalized likelihood method [30], which here solves the model identifiability issues, reduces the model variance and also improves the model interpretability.

Next, we assume each metabosystem is comprised of a fixed number (L) of metabolic subnetworks. Therefore, metabosystems differ with respect to their mixture probabilities of the different subnetworks. The contribution of each subnetwork to the k^th metabosystem, φ_k, is modeled by a vector of L mixing probabilities that sum to one. For K metabosystems, there will be K probability vectors of L mixing probabilities. Thus element φ_kl in K × L matrix φ represents the relative contribution of subnetwork l in metabosystem k. Let Y_ni denote the subnetwork assignment for the i^th reaction in the n^th sample. Then we model Y_ni as:

As above, we assume an iid sparse symmetric Dirichlet prior on rows of φ.

The choice of prior is motivated by the idea that each metabosystem should be mostly a mixture of relatively few subnetworks. For a given metabosystem, subnetworks contributing significantly more to this metabosystem in comparison to the other metabosystems will differentiate this metabosystem from the others and are considered “discriminatory” subnetworks.

Finally, we assume each subnetwork is comprised of a mixture of reactions. This implies that subnetworks differ according to their particular mixture of reactions. One of the goals of the model is to find connected subnetworks of reactions carrying out a function or a set of functions. Therefore, reactions contributing to a subnetwork cannot be considered independent.

Reactions within a subnetwork are linked through their shared chemical compounds. We model subnetworks as a subset of compounds that are converted to another subset of compounds. It is reasonable to assume that the substrate and product sets cannot be arbitrary sets of compounds. Therefore, we assume that compounds are grouped together and make up substrate groups and product groups. Each subnetwork has its own substrate group (S) and product group (R). The compounds in the substrate group associated with a subnetwork are used as substrates in reactions that belong to that subnetwork. The product group associated with a subnetwork consists of products that are produced by at least one of the reactions in the subnetwork. Note that a compound can belong to both the substrate and the product group. Such a compound will be involved in the intermediary reactions of the subnetwork. In general, membership of a compound to a substrate or product group is considered to be a “soft” (i.e., probabilistic) membership.

For L subnetworks, we have L substrate and L product groups modeled as probability vectors over all compounds. For subnetwork l, we denote the substrate group as δ_l and the product group as γ_l, each a vector of C probability values summing to one, where C is the number of compounds. With L subnetworks, there are two L × C matrices δ and γ, one for substrates and one for products respectively. The value in row l and column c of matrix δ represents the relative contribution of compound c in the substrate group of subnetwork l. A similar definition applies for product groups in matrix γ.

We assume iid sparse symmetric Dirichlet priors for the rows of both matrices. We expect to have a relatively small number of compounds in each subnetwork and therefore, a Dirichlet prior with a small α value serves as a proper candidate.

We induce the dependencies between substrates and products by conditioning on their subnetwork membership. Specifically, the substrate-product pairs are considered conditionally independent given the subnetwork membership of the corresponding reaction.

Each reaction i in sample n is broken down into J_ni substrate-product pairs (Figure 1), with each pair denoted as S_nij → R_nij. Conditioning on the membership of its reaction in the l^th subnetwork, the probability of each substrate and product is:

The substrate-product pairs in a reaction are linked through the subnetwork assigned to the reaction.

BiomeNet is a generative model, and a full description of how to generate a network from it is provided in Text S1. A plate diagram of the model is provided in Figure 2.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Figure 2. Plate diagram for the BiomeNet model.

θ is the probability distribution of possible metabosystems in a sample. Z represents metabosystems and Y represents subnetworks. φ is the prior distribution of subnetworks in metabosystems. δ is the prior distribution of substrate compounds in subnetworks. γ is the prior distribution of product compounds in subnetworks. α is the concentration parameter of the Dirichlet distribution. K is the number of metabosystems. L is the number of subnetworks. C is the number of compounds. To indicate relationships, n indexes a sample, i indexes a reaction, and j indexes substrate-product pairs. This model specifies a generative process; coupling between substrate-product pairs is enforced by conditioning their generation on a single subnetwork membership.

https://doi.org/10.1371/journal.pcbi.1003918.g002

Model inference

The complete likelihood of the data given the hyper-parameters of the prior distributions is:where Z, Y, θ, φ, δ, and γ are latent variables in our model. Z and Y collectively represent the metabosystem and subnetwork assignments for all reactions in all samples. Finally, S → R represent the set of all substrate-product pairs observed in our dataset for all reactions in all samples. To infer the model framework, we need to sample from the posterior distribution of latent variables given the data:This is a high dimensional distribution having conditional distributions that can be sampled relatively easily. Therefore, we use collapsed Gibbs sampling [29] by integrating out the other latent variables θ, φ, δ, γ and sample from the posterior distributions of the metabosystem (Z) and subnetwork (Y) assignments for each reaction conditional on the assignments of all other reactions.

More specifically, each iteration of Gibbs sampling will provide one sampling point from the joint posterior distribution . We use the following conditional probability to sample the subnetwork and metabosystem assignment of one reaction in a microbiome sample given we know the subnetwork and metabosystem assignments of all other reactions in every microbiome sample:where Z_-ni and Y_-ni denote the metabosystem and subnetwork assignment for all reactions in all samples except only reaction i in sample n. The individual terms in the above equation can be analytically derived (Text S2). Each iteration of Gibbs sampling cycles through all reactions in all microbiome samples. Thus it will not only provide one sampling point from the joint posterior distribution , it also provides a sampling point from all marginal distributions .

We can infer the posterior distributions of θ and φ based on the sampling results for the posterior distribution of Z and Y. From each iteration of the Gibbs sampling, we get one sampling point of , which permits estimation of the θ value; i.e. for the n^th microbiome sample, the estimate of θ_n will be the relative frequencies among all reactions of this microbiome that were assigned to different metabosystems. This estimated θ_n is approximately a sampling point from the posterior distribution . If we take many iteration of the Gibbs sampling results, and estimate θ_n from each iteration, this will provide a sampling distribution for the posterior distribution of θ_n.

For φ_k, the estimate will be relative frequencies of subnetwork assignments among all the reactions that are assigned to the k^th metabosystem, where the frequency is taken across all microbiome samples. Similarly, each iteration of Gibbs sampling provides one approximate sampling point from the posterior distribution of φ_k. From the above, the posterior mean of θ_n and φ_k can be directly calculated as the mean of the estimated θ_n and φ_k from many iterations of Gibbs sampling.

BiomeNet's MCMC sampler is implemented in R and C++. Source codes are available from http://sourceforge.net/projects/biomenet/. For both datasets analyzed below, the first 100 samples were considered “burn-in” and were discarded. Following the burn-in, 500 samples were retained, with a lag of 20 iterations of the MCMC between samples.

Choice of K, L and hyper-parameters

The purpose of BiomeNet is to learn the structure of the data. It is an unsupervised learning method. As with most other unsupervised learning methods, the validity of the inference is mainly judged through the scientific background of the problem and the interpretability of the results [31]. Here, K, is the preset number of “prototypical” structures (metabosystems for reaction data) within the model. Thus, each microbiome sample is interpreted as a mixture of such structures. In unsupervised applications such as those presented in this paper, trying several different K values to find the value that shows separation of classes is often considered a natural way to choose K. Although this tactic requires some degree of heuristic judgment, typically it is difficult to avoid at least some heuristics in the context of unsupervised learning methods [31].

Ideally, inference under BiomeNet should be largely robust to the choice of L as long as L is not too small. If L is larger than needed, there will be redundant subnetworks in BiomeNet, but they will carry very little weight for any of the metabosystems, and their reactions will have only a trivial impact on the reaction composition of each metabosystem. Alternatively, the informative subnetworks should make consistent contributions to each metabosystem, and their reactions should be highly influential on the reaction composition of each metabosystem. Thus, BiomeNet should be able to infer a consistent reaction profile for each of the K metabosystems, as long as the L value is not too small. This can be visually verified by using a heat map that portrays differences in reaction composition among metabosystems. The details for computing the reaction composition of a metabosystem, and for comparing the composition of different metabosystems are given in Text S3. In addition to assessing the robustness of reaction composition to the L value, the approach can be used to identify a minimum value for L.

In our analyses of real data, we chose a value of K according to biological criteria. This approach avoids the computational burden associated with assessing many values of both K and L. However, we can use the same heat map described above to check for divergence among the K metabosystems. If there is no signal for divergent metabosystems (e.g., all the samples have similar, or the same, reaction composition), differences between metabosystems will be similar to those observed for the same metabosystem over different values of L. If at least some of the metabosystems are divergent, differences in reaction composition will be large as compared to those within the same metabosystem. In this way, we can assess the chosen value of K. Further details are given in Text S3.

The hyper-parameters of our model are the concentration parameters of the symmetric Dirichlet distributions (i.e., α_θ, α_φ, α_δ, and α_γ). The values of α_θ and α_φ control the extent by which subnetworks and metabosystems are mixed, while α_δ and α_γ control the size of the subnetworks. If very small values are chosen for α_δ and α_γ, then a relatively larger value of L should be chosen. Because we want (i) only a few subnetworks to contribute significantly to each metabosystem, and (ii) each subnetwork to consist of relatively few reactions, we chose a value for the concentration parameters close to zero (0.01). Users can control the extent of mixing for their data by resetting the concentration parameter values.

Simulation and validation of the inference procedure

It is straightforward to simulate data from the model (Text S1). To simulate data, the generative process is repeated for the desired number of microbiome samples. We simulated datasets of different size, with number of samples varying from 40 to 100 by increments of 20. Each sample is associated with a metabolic network generated by the above process. The number of nodes (chemical compounds) for each network varied between 100, 500, and 1000. The number of reactions in each network was assumed to follow a Poisson distribution with mean 1000. The number of substrate-product pairs for each reaction was drawn from a Poisson distribution with mean 2 (plus one to avoid zero). The mixing distribution of metabosystems for samples was drawn from a symmetric Dirichlet distribution with α parameter equal to 0.05, 0.10, and 0.20. We simulated datasets with 3 and 5 metabosystems. The subnetwork mixture for each metabosystem was drawn from a symmetric Dirichlet distribution with α parameter equal to 0.05, 0.10, and 0.20. This allowed metabosystems to have overlapping subnetworks to different degrees (Text S1). The number of subnetworks varied between 10, 20 and 50.

We fitted BiomeNet using our preferred value for the concentration parameters of the Dirichlet priors of the model (0.01). We compared the estimated mixture weights for metabosystems in each sample with the corresponding weights used for simulation. For each simulation, the inference algorithm was able to recover mixture weights that were very close to the actual metabosystem and subnetwork composition despite fixing value of the concentration parameters at 0.01 (Text S1). Although discrepancies were larger when the misspecification of the concentration parameter was larger, the closeness of the inferred mixture weight indicates robustness of the inference algorithm to the choice of hyper-parameter value within the range examined (0.05 through 0.2).

Gut communities associated with different mammalian dietary niches are metabolically divergent

Carnivorous, omnivorous and herbivorous mammals are well known for diverse digestive physiologies. Metagenome sequencing of fecal samples from 33 mammal species revealed that 16S communities and metagenomes within the mammalian gut differ according to dietary niche of the host [13]. Based on one-by-one testing of metagenomic sequences, Muegge et al. [13] detected differences in the relative abundance of 495 enzymes between the communities of herbivores and carnivores. However, this approach, in addition to aforementioned issues with one-by-one analyses, provides no information about differences in metabolic subnetworks without post hoc mapping to human-annotated databases. Although applying PCoA reveals separation between carnivore and herbivore gut microbiome (Text S4), PCoA is not equipped with a means to interpret the nature of these differences. We applied BiomeNet to this data, to (i) verify that observed differences in gut communities of carnivores and herbivores indeed reflects differences in community metabolic function, and (ii) gain insights into the differences in metabolic functions of those communities.

We obtained metagenomic data for 38 samples of mammals [13] as deposited in MGRAST [19] (MGRAST project #116, with the subsystem hierarchical classification). Each sample comprises a separate gut microbiome sample. We extracted gene sequences with an Enzyme Commission (EC) number and recorded their abundance within each sample. The EC number designates the chemical reactions catalyzed by the encoded enzyme, and the reactions were then converted into substrate-product pairs. The fully processed data, formatted for input to BiomeNet, are available with the source code at http://sourceforge.net/projects/biomenet/. This dataset yielded 2,824 unique reactions between 2713 compounds. Reaction abundances for samples, as input into BiomeNet, range from 12,626 to 174,103 (see Text S5 for further detail about how these reaction abundances are obtained from raw reads). We applied BiomeNet to these data assuming that samples could be mixtures of as many as three metabosystems (K = 3). The number of metabosystems was initially chosen to match the number of categories for dietary niche (carnivore, herbivore & omnivore). However, because the model does not assign a diet status to a particular metabosystem, they are simply designated as metabosystems 1, 2 and 3, and the uniqueness of their reaction composition was assessed for these data.

Using BiomeNet with L = 100 subnetworks, we obtained an estimate of the contribution of different metabosystems to each gut community sample. As seen in the simplex plot within Figure 3, carnivore gut communities (magenta dots) tend to have a high membership to metabosystem 1 whereas herbivore gut communities (green dots) tend to have a low membership (θ₁<15%). Interestingly, omnivores did not form a separate cluster; some omnivore communities (black dots) had mixtures more similar to carnivores and others more similar to herbivores (Figure 3). This highlights the benefits of an unsupervised analysis, as compared to an a priori (and possibly incorrect) assignment of one metabosystem as unique to omnivores.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Figure 3. Inferred metabolic composition of carnivore, omnivore and herbivore gut-microbiomes.

Mammalian microbiome samples are mapped to three metabosystems and plotted on a simplex (de Finetti diagram). Each point within the simplex represents a sample having a unique set of proportions for the 3 metabosystems. The proportions for each sample sum to one. Lower left, lower right and top corners of the simplex plot indicate 100% membership to metabosystems 1, 2 and 3 respectively. Carnivore (magenta) and herbivore (green) samples are plotted in the large simplex, and omnivore (black) samples are plotted in the small simplex. Each metabosystem is represented in terms of 19 principal subnetworks by using a “composition ribbon” along the sides of the large simplex. Metabosystem 1 and its principal subnetworks are plotted along the horizontal side of the large plot. The colored bars represent the membership of the subnetworks to this metabosystem. The other two sets of grey bars represent the membership of the same subnetworks in the other two metabosystems for comparison. Bold labels indicate the discriminatory subnetworks. One subnetwork, 49, was highly discriminatory for metabosystem 1. It is easy to see from the composition ribbon that subnetwork 49 is substantially more abundant in metabosystem 1 compared to the other two metabosystems. The criteria for selecting principal subnetworks and discriminatory subnetworks are provided in Text S3. Composition ribbon plots for all 100 subnetworks can be found in Text S7.

https://doi.org/10.1371/journal.pcbi.1003918.g003

We investigated the sensitivity of these results to changes in the number of subnetworks (i.e., L = 50, 100, 150 & 200). Even though the labeling of a metabosystem is arbitrary, they can be distinguished if they have a characteristic composition of reactions. For these data, the metabosystems identified by BiomeNet had different reaction compositions that were largely robust to L. This is illustrated by the heat map in Figure 4, which shows the Jensen-Shannon divergence (JSD) [32] between metabosystems according to their reaction composition (see Text S3 for additional details). Within the heat map, divergence matrices along the diagonal represent comparisons within the same metabosystems, and the off-diagonal matrices are for comparisons between metabosystems. Very low JSD (dark blue) along the diagonal matrices in Figure 4 indicates highly similar reaction composition within each of three metabosystem. Higher JSD (pink to red) within both of the off-diagonal divergence matrices that involve metabosystem 1 indicate that there is very strong signal for its uniqueness. JSD scores in the remaining divergence matrix suggest that metabosystems 2 and 3 are also divergent from each other, but not as much as they are from metabosystem 1. When L = 50, JSD is consistently lower in the diagonal matrices as compared to L>50. Also when L = 50, some comparisons between metabosystems 2 and 3 have JSD scores similar to those observed for comparison within the same metabosystem. Taken together, the results indicate that these metabosystems have characteristic reaction compositions, with stable mixture weights in each sample when K = 3 and L≥100. Consequently, it was easy to coordinate metabosystems across the different analyses (Text S6 and Figures 1 and 2 in Text S6). Thus, all results are hereafter derived from a model with K = 3 and L = 100, and the mixture probabilities of reactions in all subnetworks are provided in Data File S1 for K = 3 and L = 100. Note that we are able to separate carnivores and herbivores under a model having K = 2, but employ K = 3 because there is evidence for samples being a mixture of at least 3 reaction profiles (Figure 4).

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Figure 4. Divergence of reaction composition within and between the three metabosystems inferred from the gut microbiomes of carnivores, omnivores and herbivores.

Divergence between metabosystems is measured at the level of their reaction composition by using the Jensen-Shannon divergence (JSD), and presented as a heat map. Further details about computing the reaction composition of a metabosystem are given in Text S3. The JSD score is used here to provide a symmetric measure of the difference between the composition of all 2,824 reactions in these data. The heat map is comprised of six divergence matrices, one for each of the possible pairwise comparisons between the metabosystems. Because the metabosystems had characteristic reaction compositions, it was easy to coordinate metabosystems across different analyses (Text S6). The three matrices along the diagonal represent comparisons within the same metabosystem for different numbers of subnetworks in the model (L = 50, 100, 150, 200 and 250); the dominance of blue (low JSD) in those matrices indicates that reaction composition is robust to the L value. This result also validates the coordination of those metabosystems into 3 different groups. The three off-diagonal matrices represent comparisons between metabosystems for different numbers of subnetworks (L); larger JSD scores here indicate greater divergence between metabosystems.

https://doi.org/10.1371/journal.pcbi.1003918.g004

Next we used the model to investigate the differences between carnivore and herbivore microbiomes by identifying those subnetworks most diagnostic of metabosystem 1. Recall that metabosystem 1 tended to make a high contribution to carnivore gut communities and low contribution to herbivore gut communities. Rather than attempt to summarize the contribution of all 100 subnetworks to each metabosystem, we show the composition of their “principal subnetworks” via the composition ribbon plots along each side of the simplex (Figure 3). The principal subnetworks are defined as those with a membership>2/L to at least one metabosystem (Text S3), and there were 19 principal subnetworks for this dataset. The information is repeated in each ribbon in Figure 3, however using color and increasing the width of the frequency bar emphasizes the contribution of the subnetworks to the selected metabosystem. The grey bars in each ribbon give the relative contribution of subnetworks to the non-selected metabosystems. Among the principal subnetworks, 10 had probability differences large enough to be discriminatory for the metabosystems (Text S3 and S7). Among those 10, subnetwork 49 was highly discriminatory for metabosystem 1 (metabosystem 1 is emphasized in the ribbon along the bottom of the simplex in Figure 3). The ribbon shows how subnetwork 49 makes a large contribution to metabosystem 1 (having a large green bar), but very little contribution to the other two (very small grey bars adjacent to the large green bar). Other discriminatory subnetworks can be seen within the composition ribbon; e.g., subnetwork 11 makes a larger contribution to metabotye 2 than to metabosystem 1, and subnetwork 17 makes a larger contribution to metabosystem 3 than to metabosystem 1. Although subnetwork 49 is highly discriminatory, it should not be viewed as a presence-absence polymorphism; each sample is a mixture of metabosystems, with herbivores characterized as having a low, but not zero, contribution of subnetwork 49 to their samples.

Subnetwork 49 stands out because it contributes to metabosystem 1 nearly as much as all other subnetworks combined (Figures 3 & 5 and Text S7). The KEGG reaction numbers, reactant numbers and pathways, as well as mixing probabilities, are given for the principal reactions in Table S1. This subnetwork is dominated by a particular metabolic function: reactions related to importation of extracellular saccharides (N-acetylmuramic acid, N-acetylglucosamine, fucose, glucose and mannose). This suggests that community metabolic function within the carnivore gut might be impacted by their low carbohydrate diet. In particular, the carnivore community appears to be exploiting alternative carbohydrate sources, such as of the cell walls of the gut bacteria themselves, whose outer membranes are composed of alternating molecules of N-acetylmuramic acid and N-acetylglucosamine [33], [34]. Presumably, the dead bacteria in the large intestine comprise a good source of these two compounds. Another nutrient source is the fucosylated mucins secreted by the host's large intestine [35], [36]. Indeed, experimental studies demonstrate that fucose can serve as an important carbon source for at least some species within the mammalian gut, especially under nutrient deprivation [e.g., 37]. Taken together, our results support the recent finding of Koropatkin and co-authors [27] that a high protein diet, such as that found in carnivores, could select for species exploiting the alternative nutrient source represented by mucus glycans. Whatever the source, the carnivore community appears to be exploiting input nutrients that are less important to the gut community of herbivores.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Figure 5. Discriminatory subnetworks for the carnivore associated metabosystem.

The histogram in (a) gives the relative membership score of subnetworks in metabosystem 1 relative to metabosystems 2 and 3. Membership scores are for metabosystem 1 because it makes a high contribution to carnivore gut communities and a low contribution to herbivore gut communities. The membership score for a subnetwork in a selected metabosystem is based on the ratio of the probability of membership in this metabosystem to its largest probability membership in the other two metabosystems. The score is the absolute value of the logarithm of this ratio. Only the “principal” subnetworks are plotted for carnivores and herbivores. Principal subnetworks for this dataset are defined as subnetworks with a membership>2/L to at least one metabosystem. A plot of all 100 subnetworks is provided in Text S7. Note that relative membership scores are computed solely for the purpose of visual assessment of metabosystem composition; the individual value of this score should not be attributed any additional meaning. (b) Discriminatory subnetwork 49 is depicted as a network. This network illustrates a high degree of connectivity. Reactions are shown as square nodes and compounds are plotted as circular nodes. Links originating or ending in so-called currency compounds are represented with dashed lines.

https://doi.org/10.1371/journal.pcbi.1003918.g005

IBD is associated with a metabosystem having greater capacity to exploit host-associated glycans and interfere with the host capacity to manage oxidative stress

We illustrate the value of our approach to human microbiomics by applying it to a sample of adults who are classified either as healthy or as having IBD. The dataset consists of N = 124 adult human gut microbiome samples compiled and sequenced by Qin and co-authors [20]. They found that IBD patients (Crohn's and ulcerative colitis) could be differentiated from healthy individuals according to differences in the abundance of microbial species, providing further support for the notion that gut community can have a profound effect on human gut function and dysfunction. Here, our analytical objectives were to (i) determine if the species-level differentiation between these healthy and IBD samples represents community-level divergence in the prevalence of metabolic networks, and (ii) gain insights into the metabolic basis of the differences between those gut environments. We processed the data to obtain EC assignments and reaction abundances (Text S5). Reactions were converted into substrate-product pairs and analyzed using BiomeNet. The processed data, formatted for input to BiomeNet are available with the source code at: http://sourceforge.net/projects/biomenet/. Reaction abundances, as input into BiomeNet, ranged from 5,091 to 374,945 counts per sample (see Text S5 for further detail about how reaction counts are obtained from raw reads). We initially evaluated K = 3 metabosystems based on categories for health status (healthy, Crohn's disease & ulcerative colitis). Recall that the model will not assign a health status to a particular metabosystem.

Again, we used JSD scores to measure divergence between metabosystems according to their reaction composition, and we visualized the divergence patterns by using a heat map (Figure 6). As with the mammal dataset above, the diagonal matrices (within metabosystem comparisons) are characterized by low JSD scores and the off-diagonal matrices (between metabosystem comparisons) are characterized by higher JSD scores. This confirms that characteristic reaction compositions can be identified when K = 3. Again, the three metabosystems are not equally divergent; while metabosystems 2 and 3 are divergent from each other, they are even more divergent from metabosystem 1. We also evaluated a model having K = 2 metabosystems, and found that no separation of samples was possible. Because each metabosystem has a characteristic reaction composition, it was easy to coordinate metabosystems across the different analyses when K = 3 (Text S6 and Figures 1and 2 in Text S6). Although L = 50 appears adequate for these data (Figure 6), for consistency we present the results derived from a model with L = 100 (and we provide the mixture probabilities of reactions in those subnetworks in Data File S1).

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Figure 6. Divergence of reaction composition within and between the three metabosystems inferred from the gut-microbiomes of healthy humans and IBD patients.

Divergence between metabosystems is measured at the level of their reaction composition by using the Jensen-Shannon divergence (JSD), and presented as a heat map. Further details about computing the reaction composition of a metabosystem are given in Text S3. The JSD score is used here to provide a symmetric measure of the difference between the composition of all 3,433 reactions in these data. The heat map is comprised of six divergence matrices, one for each of the possible pairwise comparisons between the metabosystems. The three matrices along the diagonal represent comparisons within the same metabosystem for different numbers of subnetworks in the model (L = 50, 100, 150 and 200); the dominance of blue (low JSD) in those matrices indicates that reaction composition is robust. The three off-diagonal matrices represent comparisons between metabosystems for different numbers of subnetworks (L); larger JSD scores here indicate greater divergence between metabosystems.

https://doi.org/10.1371/journal.pcbi.1003918.g006

Figure 7 presents the contribution of different metabosystems to each sample. Note that there are 41 obese individuals (body mass index>30) among the samples plotted in Figure 7, but we found no evidence for obesity-related metabolic systems. Neither could we separate the Crohn's and ulcerative colitis patients by using these data; however, only 4 of the 25 IBD samples were from Crohn's patients. Given that inferences must be made according to 3,433 reactions spread among 100 subnetworks, those 4 samples may have contained insufficient signal to characterize the Crohn's patients. Expanded sampling of Crohn's patients could be more informative. However, we did discover that the gut samples of IBD patients (red points) had a generally larger contribution from metabosystem 2, as compared to the healthy individuals (green points) who have a consistently low contribution (θ₂<20%). We found that 15 of the 22 principal subnetworks (mixture weight>2/L) in these data make a large contribution to the divergence among metabosystems. Three of those 15 subnetworks (38, 64, and 73) are diagnostic of metabosystem 2 due to their very large relative contribution to that metabosystem (Figures 7 & 8 and Text S8). Reactions within those subnetworks are involved in amino sugar metabolism, ascorbate metabolism, fructose and mannose metabolism, aminobenzoate degradation, glycolysis and gluconeogenesis, as well as additional pathways (Tables S2, S3 & S4). Those subnetworks do not correspond to full KEGG pathways, or even contiguous subsets of those pathways. However, inspection of Figure 8 reveals that their reactions are indeed connected. Interestingly, reactions belonging to the non-discriminatory, or “core”, subnetworks do tend to comprise contiguous subsets of the main KEGG pathways (Text S9).

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Figure 7. Inferred metabolic composition of the gut-microbiomes of healthy humans and IBD patients.

Human microbiome samples are mapped to 3 metabosystems and plotted on a simplex (de Finetti diagram). Each point in the simplex is a different human sample. The metabosystem proportions for each sample sum to one. Lower left, lower right and top corners of the simplex plot indicate 100% membership to metabosystems 1, 2 and 3 respectively. Samples with IBD are represented as red dots and healthy samples are in green. Each metabosystem is represented in terms of 22 principal subnetworks by using a composition ribbon plotted along each side of the plot. Metabosystem 1 and its principal subnetworks are plotted along the horizontal side of the simplex plot. The colored bars represent the membership of the subnetworks corresponding to the selected metabosystem (e.g., metabosystem 1 is selected in the horizontal ribbon plot). The other two sets of grey bars represent the membership of the same subnetworks in the other two metabosystems for comparison. Bold labels indicate the discriminatory subnetworks. For example, subnetwork 25 is substantially more abundant in metabosystem 1 compared to the other two metabosystems. The criteria for selecting principal subnetworks and discriminatory subnetworks are provided in Text S3. The composition plot for all 100 subnetworks can be found as Text S8.

https://doi.org/10.1371/journal.pcbi.1003918.g007

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Figure 8. Discriminatory subnetworks for IBD patients.

This histogram gives the relative membership score of subnetworks in metabosystem 2, which is found in higher proportion in IBD patients, compared to the other two metabosystems. The membership score for a subnetwork in a selected metabosystem is based on the ratio of its membership probability in this metabosystem to its largest membership probability in the other two metabosystems. The score is the absolute value of the logarithm of this ratio. Scores are plotted for principal subnetworks. Principal subnetworks for this dataset are defined as subnetworks with a membership greater than 2/L to at least one metabosystem. A similar plot for all subnetworks is provided as Text S8. Note that relative membership scores are computed solely for the purpose of visual assessment of metabosystem composition; the individual value of this score should not be attributed any additional meaning. It is easy to see that subnetworks 38, 64 and 73 are discriminatory for metabosystem 2. The discriminatory subnetworks also are depicted as networks. These networks illustrate a high degree of connectivity. Reactions are shown as square nodes and compounds are plotted as circular nodes. Links originating or ending in so-called currency compounds are represented with dashed lines.

https://doi.org/10.1371/journal.pcbi.1003918.g008

As the contribution of metabosystem 2 is elevated in IBD patients, we assessed the functional implications of their principal reactions (mixing probabilities>2/R) within subnetworks 38, 64 and 73. Subnetworks 38, 64 and 73 were comprised of 29, 19 and 18 principal reactions, respectively. Within each subnetwork, the principal reactions had a cumulative probability density>0.99 (Data File S1). The KEGG reaction numbers, reactant numbers and pathways, as well as mixing probabilities, are given for the principal reactions in Tables S2 to S4. Subnetworks 38, 64 and 73 contained reactions that are relevant to the phenotype of IBD.

One way to validate the IBD associated signal is to seek concordance with results obtained from independent data. Although there are no other metagenome datasets that are suitable for a similar investigation under BiomeNet, two different taxonomic studies [38], [39] attempted “indirect inference” of metabolic capacity in different sets of IBD samples. Those studies mapped 16S reads to the genomes of reference species, applied an algorithm to predict metagenome composition, and then used the metabolic capacity of the predict metagenomes to make inferences about the IBD phenotype. For certain metabolic systems, we observed a remarkable degree of concordance with thier findings. We found an enrichment of genes involved in the phosphotransferase (PTS) system (within subnetwork 38), and Morgan et al. [38] inferred that this system was more abundant in their IBD samples. We found an enrichment of genes associated with the ability to deal with oxidative stress (within subnetwork 64), and Morgan et al. [38] inferred a major shift in oxidative stress pathways with IBD. We found an enrichment of genes involved in the aminobenzoate degradation (within subnetwork 73), and Gevers et al. [39] inferred an association between this pathway and their IBD samples. The similarity of the IBD-related signal in these different datasets represents an important cross-validation for all the studies. At the individual reaction level, our results do differ somewhat from theirs; however this is expected, at least in part, because community metabolic function can only be indirectly assessed via 16S. Below we discuss in detail the key reactions that we have identified and, where possible, we derived hypotheses having explicitly testable predictions.

PTS reactions for various sugars (D-mannitiol, D-fructose, D-mannose) are abundant in subnetwork 38, and hence metabosystem 2. Reactions responsible for transport of N-acetylglucosamine and fucose are potentially meaningful because they suggest that metabosystem 2 is more dependent on glycans derived from host-associated substances, like mucin and shed epithelial cells, as a source of energy. Although these transporters do not point directly at the cause of gut inflammation associated with IBD, it does suggest that establishment of community metabosystem 2 could explain the resistance of IBD to moderate dietary intervention because the endogenous glycans provide a persistent source of nutrients for the community. Interestingly, extreme intervention whereby an exclusive liquid diet is administered up to 12 weeks (called EEN treatment) can sometimes induce remission of IBD in pediatric cases [40]. Taxonomic surveys confirm that EEN alters gut bacterial composition in those cases [41], [42], supporting the hypothesis that IBD may be associated with a dysfunctional community [43]. The hypothesis that IBD is associated with a community having the capacity of subnetwork 38 could be tested if future clinical investigation of EEN were to include sampling of the gut microbiome. The first prediction is that subnetwork 38 should be prevalent in the IBD samples prior to EEN treatment. Second, if remission is associated with displacing a dysfunctional community, then we predict that subnetwork 38 (and, more broadly, metabosystem 2) should exhibit a significant decline in those cases that responded to EEN.

Subnetwork 64 contains reactions associated with ascorbate metabolism. Two reactions that convert ascorbate to dehydro-gulonate-6P have high relative-abundance in IBD patients suggesting the potential for reduced ascorbate levels within the gut due to microbiome metabolic activity. Remarkably, direct measurement reveals reduced ascorbate levels in IBD patients [44]. Since ascorbate absorption in the gut depends, in part, on a localized concentration gradient, its decomposition by gut bacteria could interfere with the human cells capacity to absorb it. This represents a critical link between metabosystem 2 and IBD. The chronic intestinal pathophysiology of IBD patients is related to the increased production of reactive oxygen and nitrogen species, leading to oxidative stress within the intestinal mucosa [45]–[47]. IBD patients have reduced antioxidants levels within the intestinal mucosa [48], and the severity of the disease is correlated with antioxidant levels and oxidative stress markers [48]. Ascorbate is an antioxidant that is reduced within the inflamed tissues of IBD patients [44], and the association of metabosystem 2 with IBD suggest that this metabolic phenotype could be interfering with the human cells capacity to absorb ascorbate.

Subnetwork 73 is enriched in genes involved in the modification and transport of sugars and the metabolism of benzoate (compound C00180 in Table S4). The direct relationship of subnetwork 73 to the IBD phenotype is unclear. However, the reactions involving benzoate suggest that the microbiota in IBD patients might differ from healthy individuals in how they influence the metabolic processing of dietary aromatic compounds. Metabolism of such compounds typically occurs within the gut through several intermediates to benzoate, which can be subsequently converted to hippurate [49]. Williams et al. [50] found that urinary hippurate excretion was significantly reduced in IBD cohorts, and that it was not due to any intrinsic metabolic deficiencies, nor to diet. Indeed, this hippurate deficiency is so reliable that it is now considered an important criterion for diagnosing IBD via urinary metabolic profiling [51]. The critical role of the microbiota in the metabolism of dietary aromatic compounds is supported by experimentation on mice and rats. Germ free animals do not excrete hippurate, but it comes to dominate 2–3 weeks after exposure to environmental microbes [52]. Conversely, treatment with antibiotics eliminates the production of hippurate in mice and rats [53]. It is interesting that the enzyme hippurate hydrolase is also enriched in subnetwork 73. Given a possible connection to IBD, the reactions of subnetwork 73 are good candidates for further study.

Bacteria inhabiting the luminal side of the mucosa are thought to play key role in human intestinal health and disease [e.g., 27; 54; 55]. Although colonic mucus is an effective barrier against infection of the epithelium, bacteria do colonize and persist within the outer mucosal layer, and explicitly utilize it as an energy source [27], [43]. The colon is characterized by invaginations within the epithelium (called enfolding crypts) that are laden with mucosal gel [56]. Recent work combining culture-independent qPCR and laser capture microdissection confirmed that the mucos gel within the crypts represent an exploitable niche in both healthy and diseased hosts [57]. Further, 16S-based comparison of mucosal biopsies and faecal samples indicate that their community composition is different [58]. As this is such a close association, the varied effects of the mucosal bacteria are believed to be elicited via the by-products of their metabolism [results from experimental models reviewed in 43]. Interestingly, individual bacteria do not appear to possess the enzymes necessary to cleave all mucin linkages; thus, the ability to colonize and persist within the mucosal gel appears to require community metabolic activities [43]. The capacity of community metabosystem 2 to exploit host-derived glycans suggests a close association with the host mucosa. If this notion is correct, then the ascorbate metabolic phenotype of this community could impact on the state of human health. Unfortunately working with stool samples limits our ability to attribute results to the colonic mucosal community, as such samples are comprised of a mixture of luminal and colonic mucosal communities [27], [58]. However, our results do lead to a testable hypothesis; if community metabosystems 2 is associated with the mucosal gel, then there should be a higher prevalence of metabosystem 2 in mucosal biopsy samples, as compared to stool samples, taken from the same individual having IBD.

Because members of the gut bacterial community provide real benefits to the host (digestion and metabolism, resistance of pathogen colonization, immune maturation [4], [5], [59]), selection of beneficial species by the host (partner selection) is often assumed to be an important part of the host-microbiome interaction. Recent work modeling microbial bioconversion [60] illustrated how host secretion of nutrients at the epithelium-microbiota interface (in addition to antimicrobial factors) could act as a powerful mechanism for partner selectivity [60]. The models also reveal that in the absence of nutrient-based partner selection, the slower growing strains will be lost, along with any beneficial effects they might provide to the host [60]. Interestingly, mucosal glycans secreted by the host are known to impact the attachment and growth of bacteria within the gut [61]. Thus the increased prevalence of community metabosystem 2 in IBD samples could be due to the growth of opportunistic bacteria on the epithelium that can utilize, or even circumvent, the host's nutrient-based mechanisms for partner selection. Intriguing work in a mouse model support the notion that IBD might be related to failure of an alternative mechanism of partner selection [62]. Garret et al. [62] showed that mice defective in T-bet (a transcription factor that regulates immune system cells) will spontaneously develop ulcerative colitis. The disease state in these mice is due to a community, rather than a single transmissible agent. Although this community does not arise spontaneously in wild type mice, the colitis phenotype is inducible in wild type mice via the “transmission” of the community [62]. T-bet represents another potential mechanism of partner selection. The challenge will be to understand how partner selection might be weakened within the human gut, and if this can lead to the establishment of communities with metabolic interactions like those of metabosystem 2.