Microbial interactions and community assembly at microscales

Abstract

In most environments, microbial interactions take place within microscale cell aggregates. At the scale of these aggregates (∼100μm), interactions are likely to be the dominant driver of population structure and dynamics. In particular, organisms that exploit interspecific interactions to increase ecological performance often co-aggregate. Conversely, organisms that antagonize each other will tend to spatially segregate, creating distinct micro-communities and increased diversity at larger length scales. We argue that, in order to understand the role that biological interactions play in microbial community function, it is necessary to study microscale spatial organization with enough throughput to measure statistical associations between taxa and possible alternative community states. We conclude by proposing strategies to tackle this challenge.

Figure 1

Multi-scale nature of microbial ecosystems. At the scale of meters or kilometers, microbial communities are driven by coarse-grained environmental parameters and may appear stable due to the averaging of multiple variable meso-environments and micro-environments. On the other end of the spectrum, at scales of 1–10 μm, we can measure the behavior and function of single cells. However, in between, there are multiple nested layers of ecological structure. At the scale of ∼cm-m, depending on the rate of mixing in the system, we are likely to sample meta-communities, most likely in the form of ensembles of microscale aggregates connected by dispersal. Community properties at these scales are likely driven by differences in dispersal and small-scale abiotic gradients. In environments like the ocean, dental plaques, sediments, and others, the cell aggregates that comprise local communities are found at the scale of ∼10–1000 μm, but often at the lower end of this range. It is at the scale of these cell aggregates (∼100 μm) that biological interactions between organisms are most likely to have a measurable effect on population dynamics and composition. However, current -omic techniques disrupt this structure and can only provide us with raw repertoires of taxa and genes, while imaging techniques are limited in throughput.

Figure 2

Ecological stability and community assembly. (a) A robust result of mathematical models of interacting populations is that interference competition by antagonistic interactions creates bistability, where one population outcompetes the other depending on initial conditions. This is shown in the upper panel, which depicts the phase space of a Lotka–Volterra (LK) model with interference competition. However, in stochastic cellular automata simulations where initial populations are randomly initialized at 50:50 ratios (lower panel) the same type of interactions lead to the emergence of large segregated patches dominated by one species or the other. (b) Mutualistic interactions lead to stable coexistence. Upper panel shows the result of a Lotka–Volterra model with mutualistic interactions. In stochastic cellular automata (lower panel) mutualisms manifests as strong mixing between species. (c) Extending these ideas to larger networks of potential interactions, antagonisms can create patterns of exclusion that segregate locally assembled communities across patches, provided that the scale of the patch is comparable to the length scale over which antagonistic effects manifest themselves. Thus, positive interactions like metabolic complementation should be more frequent within a patch than expected from null models without spatial structure.

Figure 3

Sampling microscale communities with synthetic particles. (a) We have developed the use of synthetic particles as community scaffolds to study microbial community structure and dynamics at the microscales where microbial interactions have the most significant impact on population dynamics [39••]. Microbial communities self-assemble on particles and are then sampled and sorted to reconstruct the spatial distribution of taxa and genes across micro-patches. This information can be used as input to network reconstruction algorithms, to get more accurate predictions of interactions, and to measure the probability distribution over possible communities and identify alternative states. (b) Example of a colonized alginate particle with magnetic cores. The particle was colonized by bacteria from the coastal ocean (Nahant Beach, MA), in incubation with untreated seawater with overhead rotation over a period of 24 hours. Green patches correspond to biofilms stained with Syto9.

Table 1

Examples of naturally occurring particulate microbial habitats. Besides the case of nutrient particles in aquatic environments, other types of particle structures can be found in a variety of other environments as well. These include (but are not limited to): colonic crypts in the human gut, trichomes and other surface structures on leaves, granules in activated sludge bioreactors, dental plaque, and many others [,–50]. Altogether, these particles represent discrete community units that can be individually sampled from the environment. Further work should be aimed at studying the structure and function of communities on these naturally occurring patches.