In a first study we used a deep 16S amplicon sequencing approach to profile the bacterial community in eutrophic Lake Champlain over time, to characterise the composition and repeatability of cyanobacterial blooms, and to determine the potential for blooms to be predicted based on time course sequence data. Our analysis, based on 135 samples between 2006 and 2013, spans multiple bloom events. We found that bloom events significantly alter the bacterial community without reducing overall diversity, suggesting that a non-cyanobacterial community prospers during the bloom. We also observed that the community changes cyclically over the course of a year, with a repeatable pattern from year to year. This suggests that, in principle, bloom events are predictable. By using symbolic regression, we were able to predict the start date of a bloom with 78–92% accuracy (depending on the data used for model training), and found that sequence data was a better predictor than environmental variables (Tromas et al., 2017).
Two cyanobacterial genera, Microcystis (M) and Dolichospermum (D), were frequently observed simultaneously (during bloom events in lake Champlain) and might have partially overlapping niches. In a second study, we determined the intra-genus variability and the ecological niche of the different strains. Within each genus, we identified strains differentially associated with specific environmental conditions. In general, we found that niche similarity between strains (as measured by co-occurrence over time) declined with genetic distance. This pattern is consistent with habitat filtering – in which closely related taxa are ecologically similar, and therefore tend to co-occur under similar environmental conditions. However, in contrast with this general pattern, we found that similarity in certain niche dimensions (notably nutrient) did not decline linearly with genetic distance, and instead showed a complex polynomial relationship. This observation suggests the importance of processes other than habitat filtering – such as competition between closely related taxa, or convergent trait evolution in distantly related taxa – in shaping particular traits in microbial communities (Tromas et al., 2018).
In a third study, we analyzed 76 Microcystis genomes from cultures to investigate the Microcystis population structure; i.e whether Microcystis consists of a single recombining population, or whether there are ecologically-specialized sub-populations. We found that indeed several genomic clusters correspond to named species and monophyletic groups whereas others are paraphyletic, distributed across genomic clusters. We also observed a higher recombination rate within clusters than between clusters supporting the species coherence of monophyletic groups. This work is in progress.
In a fourth study, we analyzed the Microcystis /cyanophage infection dynamic and the role of phages (virus infecting bacteria) in terminating blooms using CRISPR array information. In this analysis, based on 62 metagenomic samples between 2006 and 2016, we found evidence in arms race dynamic between phages, especially during the intensive sampling (2015-2016) where samples were taken every 1-3 days. However, the overall pattern showed that Microcystis population remains abundant over time, which could be explained by the maintenance of a highly diverse CRISPR arrays within Microcystis genomes. This work is in progress.
In this project, we used a reverse ecology approach, i.e extracting genomic information from natural environments and obtain novel comprehension of an organism’s ecology. We improved our understanding of a complex freshwater microbial community. We used for example the microbial genomic information with machine learning approaches to develop predictive tools and define cyanobacterial sub-population ecological niches. Finally, this project lead to a better understanding of Microcystis population structure and cyanobacterial predators (bacteria and phages), leading to a better understanding of how these predators impact bloom termination.
