More than the sum of its parts: uncovering emerging effects of microbial interactions in complex communities

Abstract

Microbial communities are not only shaped by the diversity of microorganisms and their individual metabolic potential, but also by the vast amount of intra- and interspecies interactions that can occur pairwise interactions among microorganisms, we suggest that more attention should be drawn towards the effects on the entire microbiome that emerge from individual interactions between community members. The production of certain metabolites that can be tied to a specific microbe-microbe interaction might subsequently influence the physicochemical parameters of the habitat, stimulate a change in the trophic network of the community or create new micro-habitats through the formation of biofilms, similar to the production of antimicrobial substances which might negatively affect only one microorganism but cause a ripple effect on the abundance of other community members. Here, we argue that combining established as well as innovative laboratory and computational methods is needed to predict novel interactions and assess their secondary effects. Such efforts will enable future microbiome studies to expand our knowledge on the dynamics of complex microbial communities.

Keywords: community dynamics; microbial interactions; microbiome research; secondary effects.

Figure 1.

Microbial interactions at different scales. (A) Microbial communities are not only shaped by the metabolic potential of their members as well as individual microbial interactions but also the emerging effects that these interactions have on other community members and their environment. The manifold of positive and negative interactions taking place on a species-species level can cause further effects on the community level, such as a change in the net-productivity of communities, the ecosystem functions they provide as well as their resilience towards disturbances. (B) An overview of different interaction types and emerging effects of microbial interactions as well as examples for underlying mechanisms.

Figure 2.

Examples of techniques that enable to study individual microbial interactions and their emerging effects. (A) Prediction of metabolic interactions. Protein-SIP: Communities are incubated with labelled substrates after which all proteins are extracted and analysed with mass spectrometry. Trophic interactions can be identified by following the labelled molecules. BONCAT: Communities are incubated (in situ or ex situ) with non-canonical amino acids homologues which will be built into active microorganisms. Via a bioortogonal click reaction the active cells are labelled fluorescently and identified via microscopy or cell-sorting. Active cells can be identified after different incubation time intervals to identify interactions such as predation, cross-feeding and by-product degradation. Metabolic network modelling: Metabolic network models can predict microbial interactions in communities when high quality meta-omic data combined with environmental data and data gathered from labelling experiments is used as input for metabolic network models. (B) Prediction of physical interaction via co-cultivation techniques or based on molecular signals. Reverse Genomics: Targeted microorganisms are labelled with specifically designed antibodies. Labelled cells and physically attached cells can be sorted from their community and co-cultivated together. Droplet-Microfluidics: Microorganisms in a community are encapsulated in droplets. Cells that physically interact are encapsulated conjointly. Microorganisms are (co)-cultivated within their droplet and the droplets can be separated from the other encapsulated community members for further (co)-cultivation. epicPCR: Microorganisms in a community are encapsulated in polyacrylamide beads. Cells that physically interact are encapsulated conjointly. Fusion PCR is performed on the cells in the beads and physically attached microorganisms can be identified. Hi-C metagenomics: Co-localized DNA from endo (and epi)-symbionts in a community is crosslinked, isolated and sequenced with long-read sequencing platforms. Both endo- and episymbionts can then be identified.

Figure 3.

Suggested roadmap towards a better understanding of the impact of microbial interactions and their emerging effects on complex communities. To better understand the dynamics within complex microbial communities in situ, an improved understanding of individual microbial interactions using innovative approaches (see Fig. 2) as well as efforts to quantify and qualify the emerging effects that these interactions have on the entire community are needed.