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Cyanobacteria (blue-green algae) are a diverse group of prokaryotic microorganisms that impact global biogeochemical cycles. Under eutrophic conditions, cyanobacterial species can produce cyanotoxins, resulting in harmful algal blooms (cHABs) that degrade water quality and result in economic and recreational losses. The Laurentian Great Lakes, a key global freshwater source, are increasingly affected by these blooms. To understand the underlying mechanisms in cHAB formation, we investigated microcystin levels, cyanotoxin genes/transcripts, and taxonomic/microcystin metabarcoding across three sampling locations in the Canadian Great Lakes region, including Hamilton Harbour, Bay of Quinte, and Three Mile Lake (Muskoka), to observe the regional and longitudinal cyanobacterial dynamics. The results revealed a positive correlation between microcystin levels, the occurrence of cyanobacterial taxonomic/cyanotoxin molecular markers, and the relative widespread abundance of specific dominant cyanobacterial taxa, including Planktothrix, Microcystis, and Dolichospermum. The Cyanobium genus was not observed in Hamilton Harbor samples during late summer (August to September), while it was consistently observed in the Three Mile Lake and Bay of Quinte samples. Notably, Dolichospermum and saxitoxin genes were predominantly higher in Three Mile Lake (an inland lake), suggesting site-specific characteristics influencing saxitoxin production. Additionally, among the potential microcystin producers, in addition to Microcystis, Hamilton Harbour and Bay of Quinte samples showed consistent presence of less dominant microcystin-producing taxa, including Phormidium and Dolichospermum. This study highlights the complexity of cHAB formation and the variability in cyanotoxin production in specific environments. The findings highlight regional and site-specific factors that can influence cyanobacterial taxonomic and molecular profiles, necessitating the integration of advanced molecular technologies for effective monitoring and targeted management strategies.

Harmful algal bloom (HAB) formation leads to the eutrophication of water ecosystems and may render recreational lakes unsuitable for human use. We evaluated the applicability and comparison of metabarcoding, metagenomics, qPCR, and ELISA-based methods for cyanobacteria/cyanotoxin detection in bloom and non-bloom sites for the Great Lakes region. DNA sequencing-based methods robustly identified differences between bloom and non-bloom samples (e.g., the relative prominence of Anabaena and Planktothrix). Shotgun sequencing strategies also identified the enrichment of metabolic genes typical of cyanobacteria in bloom samples, though toxin genes were not detected, suggesting deeper sequencing or PCR methods may be needed to detect low-abundance toxin genes. PCR and ELISA indicated microcystin levels and microcystin gene copies were significantly more abundant in bloom sites. However, not all bloom samples were positive for microcystin, possibly due to bloom development by non-toxin-producing species. Additionally, microcystin levels were significantly correlated (positively) with microcystin gene copy number but not with total cyanobacterial 16S gene copies. In summary, next-generation sequencing-based methods can identify specific taxonomic and functional targets, which can be used for absolute quantification methods (qPCR and ELISA) to augment conventional water monitoring strategies.

Cyanobacteria (blue-green algae) can accumulate to form harmful algal blooms (HABs) on the surface of freshwater ecosystems under eutrophic conditions. Extensive HAB events can threaten local wildlife, public health, and the utilization of recreational waters. For the detection/quantification of cyanobacteria and cyanotoxins, both the United States Environmental Protection Agency (USEPA) and Health Canada increasingly indicate that molecular methods can be useful. However, each molecular detection method has specific advantages and limitations for monitoring HABs in recreational water ecosystems. Rapidly developing modern technologies, including satellite imaging, biosensors, and machine learning/artificial intelligence, can be integrated with standard/conventional methods to overcome the limitations associated with traditional cyanobacterial detection methodology. We examine advances in cyanobacterial cell lysis methodology and conventional/modern molecular detection methods, including imaging techniques, polymerase chain reaction (PCR)/DNA sequencing, enzyme-linked immunosorbent assays (ELISA), mass spectrometry, remote sensing, and machine learning/AI-based prediction models. This review focuses specifically on methodologies likely to be employed for recreational water ecosystems, especially in the Great Lakes region of North America.