Interactions in the microbiome: communities of organisms and communities of genes

Abstract

A central challenge in microbial community ecology is the delineation of appropriate units of biodiversity, which can be taxonomic, phylogenetic, or functional in nature. The term 'community' is applied ambiguously; in some cases, the term refers simply to a set of observed entities, while in other cases, it requires that these entities interact with one another. Microorganisms can rapidly gain and lose genes, potentially decoupling community roles from taxonomic and phylogenetic groupings. Trait-based approaches offer a useful alternative, but many traits can be defined based on gene functions, metabolic modules, and genomic properties, and the optimal set of traits to choose is often not obvious. An analysis that considers taxon assignment and traits in concert may be ideal, with the strengths of each approach offsetting the weaknesses of the other. Individual genes also merit consideration as entities in an ecological analysis, with characteristics such as diversity, turnover, and interactions modeled using genes rather than organisms as entities. We identify some promising avenues of research that are likely to yield a deeper understanding of microbial communities that shift from observation-based questions of 'Who is there?' and 'What are they doing?' to the mechanistically driven question of 'How will they respond?'

Keywords: Black Queen Hypothesis; Public Goods Hypothesis; genome evolution; metagenomics; microbial communities; trait-based ecology.

Figure 1

Conceptual representation of communities: (a) no interactions (i.e. a neutral community model), (b) indirect interactions (competition for a resource), (c) direct interactions (cross-feeding and targeted killing). Circles represent individuals, squares indicate a resource, and diamonds indicate a toxic substance.

Figure 2

Interactions supporting the growth and metabolism of the key dehalogenating organisms Dehalococcoides and Geobacter via metabolite provision (solid arrows) and detoxification via oxygen scavenging (dashed arrows) in the KB-1 mixed culture. Key metabolites and functions provided by other members of the community are underlined. Met, methionine, PCE, perchlorinated ethene.

Figure 3

The application of nonphylogenetic and phylogenetic diversity measures to two samples of microorganisms. OTUs at 97% present in sample A and sample B are shown with red and blue circles, respectively. OTUs absent from samples are shown with white circles. Black edges in the tree have leaves from only one of the two samples as descendants, while green edges cover both samples. The calculation of two unweighted (qualitative) measures of community dissimilarity is indicated at the bottom.

Figure 4

Contrasting two modes of bacterial evolution that modify the genotype and ecological role of a microorganism. The top shows an assemblage of three organisms colored green, blue, and red. Squares indicate a resource that is taken up and metabolized by the cell (yellow bars), and diamonds indicate a toxic substance that is metabolized by the secretion of enzymes from producing cells (black bars). (a) Gene loss via the BQH: Because the red organism can metabolize the toxic substance, the blue organism gains an energetic advantage, by not expressing (and eventually, no longer encoding, due to gene loss) the detoxification pathway. The blue organism then becomes dependent on other members of the community to carry out this process. (b) Gain of function according to the Public Goods Hypothesis: The blue organism acquires a gene or pathway from the green organism via LGT and emerges as a competitor for the resource.

Figure 5

Metacommunity approaches in microbial community analysis. (a) Metacommunity of organisms, with locations as encompassing units, lines indicating migration pathways and different taxa indicated with color. (b) Metacommunity of genes, with organisms as units. Gray clouds represent the core genome, while colored circles indicate the presence or absence of different genes of different functional classes in the pan-genome. Lines indicate sharing of genes; gray lines connecting taxon 6 with other taxa represent reduced levels of LGT due to decreased efficiency of homologous recombination. The phylogenetic tree indicates the relationships between taxa based on a marker gene such as 16S.

Figure 6

Computing diversity (expressed here as the dissimilarity between samples A and B) with multiple types of data. (a) A phylogeny of marker genes, which serves as the basis for most studies of microbial beta-diversity. (b) The distribution (i.e. phylogenetic profile) of different classes of genes can highlight associations that do not necessarily coincide with the phylogeny in (a), suggesting evolutionary and possibly functional connections between more distant taxa. (c) Co-occurrence networks display positively and negatively correlated sets of taxa, highlighting possible species sorting effects and functionally equivalent or similar taxa. Such taxa could contribute relatively little to overall beta-diversity. (d) Like the distributions in (b), phylogenies of nonmarker genes can recapitulate the dispersal of genes across a set of taxa in a nonvertical manner and identify taxa that are more functionally similar than their marker gene phylogeny would suggest.

Figure 7

Identifying handoff points in metagenome samples. Steps in a directed, branching metabolic pathway are shown, with colored squares indicating the presence of a given reaction in different members of a microbial community. Some organisms such as the blue individuals in both communities encode only the first few steps of the pathway and do not require the products of later steps. However, handoff points (indicated with ‘*’) are steps where an organism depends on other members of the community for synthesis of a particular metabolite. The handoff point locations differ for the orange taxon in (a) and the pink taxon in (b), possibly due to different combinations of LGT and gene loss in the impacted organisms.