Surveys, simulation and single-cell assays relate function and phylogeny in a lake ecosystem

Abstract

Much remains unknown about what drives microbial community structure and diversity. Highly structured environments might offer clues. For example, it may be possible to identify metabolically similar species as groups of organisms that correlate spatially with the geochemical processes they carry out. Here, we use a 16S ribosomal RNA gene survey in a lake that has chemical gradients across its depth to identify groups of spatially correlated but phylogenetically diverse organisms. Some groups had distributions across depth that aligned with the distributions of metabolic processes predicted by a biogeochemical model, suggesting that these groups performed biogeochemical functions. A single-cell genetic assay showed, however, that the groups associated with one biogeochemical process, sulfate reduction, contained only a few organisms that have the genes required to reduce sulfate. These results raise the possibility that some of these spatially correlated groups are consortia of phylogenetically diverse and metabolically different microbes that cooperate to carry out geochemical functions. Explaining the vast diversity of microbes found in many ecosystems is a challenge for microbial ecology. Environments with chemical or other abiotic gradients such as temperature have been a key resource for studying microbial ecology. For example, studies in Winogradsky columns, microbial mats, mine drainage sites, hydrothermal vents and dimictic lakes have provided insight about the relationships between environmental parameters, microbial diversity and ecosystem functions. Microbial surveys with spatial scales comparable to those of the ecosystem gradients can identify groups of spatially correlated organisms and relate the distribution of those organisms to the environmental gradients.

There are challenges to interpreting the relationship between organisms in spatially correlated groups and environmental information. First, the relationship between an organism's spatial distribution and environmental parameters can be complicated. For example, a naive expectation might be that sulfate-reducing organisms are abundant where sulfate concentrations are highest. In fact, the distribution of sulfate-reducing organisms also depends on the distribution of more favourable electron acceptors and the transport of sulfur compounds around the ecosystem. Even more subtly, bacterial populations may be capable of performing multiple metabolisms and they can even be simply inactive. Thus, there is a need to develop techniques that provide quantitative expectations about factors that shape organismal distributions given observed environmental information.

A second challenge is that there are multiple experimental methods that can verify the relationships between function and phylogeny, but most of these methods are in vitro or perturb the environment. A method that relates phylogeny and function without perturbing the natural ecosystem would clarify the in situ functional relationships between organisms in a spatially correlated group. Deep metagenomic sequencing along with differential genome binning techniques can produce draft genomes from complex communities, but this is expensive and cannot target specific functions.

A third challenge to studying spatially correlated organisms in ecosystems with gradients is relating the groups' diversities to their environmental functions, especially if these organisms are unrelated. Organisms in these groups could use similar resources, as it is know that many traits are widespread in the tree of life, or could have recently exchanged genes through horizontal gene transfer. Unrelated organisms with similar distributions could also be found together because they are part of multispecies, symbiotic associations. The challenge lies in differentiating between these or other possibilities.

In this Article, we investigate spatially correlated organisms in an ecosystem with gradients. First, we conducted a microbial survey of a dimictic lake. Second, we constructed a quantitative, dynamic biogeochemical model that shows how bacteria can drive the creation of chemical gradients. Third, we show that there are many groups of spatially correlated organisms in this lake and relate those groups to the biogeochemical model. Finally, we use a single-cell assay to investigate the functional capabilities of the groups of spatially correlated bacteria related to one modelled process, sulfate reduction. We show that, taken together, these results raise the possibility that these spatially correlated groups are multispecies, symbiotic associations of microbes, that is, consortia.