Periodic Reporting for period 2 - MicroEcoEvol (Ecological and evolutionary forces shaping microbial diversity in freshwater blooms)
Période du rapport: 2017-06-01 au 2018-05-31
Cyanobacterial bloom is a worldwide issue, resulting from human activities (e.g agriculture) and one of its major consequences: climate change. These blooms are a threat to freshwater ecosystems, human health (e.g consumption of fish from aquaculture) and tourism as cyanobacteria produce cyanotoxins leading to the death of many organisms or even ecosystems. The cyanobacterial genus Microcystis is often the dominant member of bloom, especially in eutrophic lakes, producing potentially an hepatoxin that is toxic to animals including humans. Predicting toxin production and bloom severity is still an unsolved problem, due in part to our lack of knowledge about population diversity among cyanobacteria taxa, particularly Microcystis. Evolutionary dynamics of Microcystis populations are poorly understood – specifically how genetic diversity is selected and maintained by natural selection in the population according to environmental and ecological factors.
In this project we attempted to (i) characterize the evolutionary dynamics of natural Microcystis populations by investigating both ecological and evolutionary responses to selective pressure and (ii) dissect the impact of the different selective pressures (environmental and biological factors) that may shape Microcystis populations. The aims of this project will be achieved using a unique multidisciplinary approach mixing ecology and evolution, and combining observations from natural time-courses in lakes, in situ experiments in microcosms within lakes, and in vitro experiments in the lab. This project will be the first to comprehensively and simultaneously quantify both ecological and evolutionary responses of a bacterial population in real time, and in a natural setting.
In this project we attempted to (i) characterize the evolutionary dynamics of natural Microcystis populations by investigating both ecological and evolutionary responses to selective pressure and (ii) dissect the impact of the different selective pressures (environmental and biological factors) that may shape Microcystis populations. The aims of this project will be achieved using a unique multidisciplinary approach mixing ecology and evolution, and combining observations from natural time-courses in lakes, in situ experiments in microcosms within lakes, and in vitro experiments in the lab. This project will be the first to comprehensively and simultaneously quantify both ecological and evolutionary responses of a bacterial population in real time, and in a natural setting.
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.
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.
Overall, the different publications coming out of this project will be of great interest to evolutionary biologists and microbial ecologists but also for a wider range of scientists including limnologists and ecologists. Moreover, the different research outcomes from these studies will impact the community of scientists studying cyanobacterial as well as the broader scientific community working on harmful algal blooms. The project, as predicted, combines multiples disciplines like microbial evolutionary genomic, limnology and computational biology, inducing new collaborations and the development of multidisciplinary tools (computational and experimental). The project also got a special attention, for example, the recent ISMEJ (2017) and Frontiers in Microbiology (2018) papers are highly read and already cited. Moreover, different local media has mentioned my talks for the Baie Missisquoi conservation association where multiples mayors were present to discuss about how to improve cyanobacterial bloom prevention and information, in Lake Champlain. Furthermore, I have presented my work in four national/international conferences (Canada, US), I have organized and lead a session in an international conference (ASLO 2017) and I have organized two international workshops (“ANVI'O workshop” Montreal 2016; “Resolving Microbial Communities At Strain-Level Resolution”, Exeter 2018). Finally, this project furnished the bulk of preliminary results for a $12M Genome Canada Large-Scale Applied Project on predicting and preventing toxic freshwater cyanobacterial blooms.