A multi-omics approach to understand toxic cyanobacterial bloom dynamics

Meta-omics technologies are becoming increasingly important for the discovery and characterisation of complex microbial communities. Cyanobacterial blooms have become attractive targets for these technologies due to their increased occurrence, intensity, and the production of secondary metabolites including toxins and taste and odour compounds. While initially thought to be dominated by a single taxon, cyanobacterial blooms are beginning to be seen as a complex consortium of microbes whose mutually beneficial interactions are thought to drive mechanisms for survival and bloom longevity. Increasing and improving tools for bloom detection, prevention and control requires a thorough understanding of these interactions, and how they might be exploited. Though metagenomics has been used previously to measure bloom composition and functions, interactions between nuisance cyanobacteria and co-occurring microbes remain poorly understood. Further, metaproteomics is still considered a fledgling technology and remains underutilised in environmental studies. This thesis employs both metagenomics and metaproteomics to study a real-world cyanobacterial bloom over the course of one year. It demonstrates a bias in metagenome-assembled genome recovery, a practice that is seeing exponential growth in publicly available genomes. Through correlative networks, a specific microbiome is identified whose growth is proposed to be driven by the polysaccharide exudates of nuisance cyanobacteria. Further, metaproteomics reveals the exceptionally complex functional interplay between three species of nuisance cyanobacteria and their respective microbiomes. The outcome is a deeper understanding of cyanobacterial bloom dynamics, which is critical in the development of new technologies required for their detection, prevention and control.