Control for unwelcome blooms
Cyanobacterial or blue-green algal blooms are a worldwide issue, resulting from human activities such as agriculture and climate change. A threat to freshwater ecosystems, they can also affect human health and tourism as cyanobacteria produce cyanotoxins that lead to the death of many organisms or even ecosystems. Particularly toxic is the hepatotoxin produced by Microcystis, often a dominant member of a bloom. As little is known about population diversity in Microcystis, the EU Marie Skłodowska-Curie Individual Fellowship-funded MicroEcoEvol project investigated how a Microcystis population responded to environmental and biological factors in Lake Champlain, North America in real time. In parallel, the researchers used mesocosm studies to observe the bloom dynamics in laboratory settings where certain variables were controlled. Reverse ecology for bloom prediction Using a reverse ecology approach – extracting genomic information from environments to obtain novel insights into ecological processes – the team determined for the first time the potential for blooms to be predicted by biological factors. “From an applied/academic perspective, we used a machine learning approach to find biomarkers of the blooms,” outlines Dr Nicolas Tromas, fellow and lead researcher with MicroEcoEvol. Results showed that bloom events significantly alter the bacterial community without reducing overall diversity, suggesting that a distinct microbial community – including non-cyanobacteria – 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,” he emphasises. This signifies that data collected from these aquatic microbial communities can be used to classify blooms and improve predictions such as composition and repeatability. Significantly, the researchers also investigated the impact of cyanophages on Microcystis populations using the CRISPR-Cas system to determine the role of phage therapy in bloom termination. Applying cyanophages, naturally occurring viruses that infect cyanobacteria, could be a promising way to control cyanobacterial bloom. Data for other important bloom projects Toxic bloom outbreaks, in addition to posing a threat to humans, livestock, fish and wildlife, are extremely costly, estimated at USD825 million in the United States. Worryingly, a growing number of drinking water treatment facilities in Canada fed by the Great Lakes are now considered at risk. MicroEcoEvol have supplied the bulk of preliminary data for a USD12 million project, ATRAPP, to find solutions to the toxic bloom problem in Canada. “We also developed a new approach to analyse cyanobacterial colonies, which allows us to improve our understanding of the cyanobacteria’s microbiome,” Dr Tromas adds. Learning from challenges and on to the future From Dr Tromas’ perspective, the Marie Curie fellowship opened up both personal and professional opportunities. “Developing local and international collaborations were essential in the success of this project. I also improved my leadership skills by supervising several undergraduate students and organising several conferences and workshops in Montreal and Exeter,” he adds. The MicroEcoEvol team met several technical challenges, especially with cyanobacterial isolation and culture for the mesocosm studies in situ. “You have to adapt and find a solution to these technical issues, be patient and sometimes even accept that you won’t fulfil the deadline. The answer is to accept a temporary fail, understand why, generate new ideas and test again,” Dr Tromas concludes. In this spirit, even though the funding is finished, the project work continues with new research roads that have opened up.
Keywords
MicroEcoEvol, cyanobacteria, ecosystem, cyanophages, algal blooms, machine learning, biomarker, phage therapy
