Cyanobacterial Algal Bloom Monitoring: Molecular Methods and Technologies for Freshwater Ecosystems

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.

Precision early detection of invasive and toxic cyanobacteria: A case study of Raphidiopsis raciborskii

Blooms of the toxic cyanobacterium, Raphidiopsis raciborskii (basionym Cylindrospermopsis raciborskii), are becoming a major environmental issue in freshwater ecosystems globally. Our precision prevention and early detection of R. raciborskii blooms rely upon the accuracy and speed of the monitoring method. A duplex digital PCR (dPCR) monitoring approach was developed and validated to detect the abundance and toxin-producing potential of R. raciborskii simultaneously in both laboratory spiked and environmental samples. Results of dPCR were strongly correlated with traditional real time quantitative PCR (qPCR) and microscopy for both laboratory and environmental samples. However, discrepancies between methods were observed when measuring R. raciborskii at low abundance (1 - 10⁵ cells L ^- 1), with dPCR showing a higher precision compared to qPCR at low cell concentration. Furthermore, the dPCR assay had the highest detection rate for over two hundred environmental samples especially under low abundance conditions, followed by microscopy and qPCR. dPCR assay had the advantages of simple operation, time-saving, high sensitivity and excellent reproducibility. Therefore, dPCR would be a fast and precise monitoring method for the early warning of toxic bloom-forming cyanobacterial species and assessment of water quality risks, which can improve prediction and prevention of the impacts of harmful cyanobacterial bloom events in inland waters.

Long-term seasonal and temporal changes of hydrogen peroxide from cyanobacterial blooms in fresh waters

In water, hydrogen peroxide (H₂O₂) is produced through abiotic and biotic reactions with organic matter, including algal cells. The production of H₂O₂ is influenced by harmful algal cell communities and toxicity. However, only a few studies have been conducted on H₂O₂ concentrations in natural water. Particularly, the seasonal and temporal patterns of H₂O₂ concentration suggest that H₂O₂ generation from aquatic microorganisms could be identified to compare of photochemical production from dissolved organic matter. Study area is a source of raw water and is a large artificial lake located near a metropolitan city. Due to various environmental conditions, harmful algal blooms frequently occur in summer. The purpose of this study was to trace the H₂O₂ concentration and water quality parameters of study area where algal bloom occurs and what factors directly affect the H₂O₂ concentration. Experiments were performed on the influencing factors via water samples from study area and lab-scale culture tank. The lake produces an average of 553 nM H₂O₂, which increases by more than three times (1460 nM) in summer compared the winter. The lake (18.6-23.8 nMh^-1) produced more H₂O₂ than streams (7.4-9.0 nMh^-1) during daylight hours. All water sites presented the lowest production rates in dark conditions (1.1-1.5 nMh^-1). Daytime environment increased the generation rate more than the nighttime. The trend of H₂O₂ produced by algal cells was similar to that of the growth of algal cells. The exposure to external substances (heavy metals and antibiotics) increased the incidence by approximately five times; antibiotics were more influential than heavy metals.

Phytoremediation of CYN, MC-LR and ANTX-a from Water by the Submerged Macrophyte Lemna trisulca

Cyanotoxins are harmful to aquatic and water-related organisms. In this study, Lemna trisulca was tested as a phytoremediation agent for three common cyanotoxins produced by bloom-forming cyanobacteria. Cocultivation of L. trisulca with Dolichospermum flos-aquae in BG11 medium caused a release of the intracellular pool of anatoxin-a into the medium and the adsorption of 92% of the toxin by the plant-after 14 days, the total amount of toxin decreased 3.17 times. Cocultivation with Raphidopsis raciborskii caused a 2.77-time reduction in the concentration of cylindrospermopsin (CYN) in comparison to the control (62% of the total pool of CYN was associated with the plant). The greatest toxin limitation was noted for cocultivation with Microcystis aeruginosa. After two weeks, the microcystin-LR (MC-LR) concentration decreased more than 310 times. The macrophyte also influenced the growth and development of cyanobacteria cells. Overall, 14 days of cocultivation reduced the biomass of D. flos-aquae, M. aeruginosa, and R. raciborskii by 8, 12, and 3 times, and chlorophyll a concentration in comparison to the control decreased by 17.5, 4.3, and 32.6 times, respectively. Additionally, the macrophyte stabilized the electrical conductivity (EC) and pH values of the water and affected the even uptake of cations and anions from the medium. The obtained results indicate the biotechnological potential of L. trisulca for limiting the development of harmful cyanobacterial blooms and their toxicity.

Use of qPCR and RT-qPCR for monitoring variations of microcystin producers and as an early warning system to predict toxin production in an Ohio inland lake

Public concern over cyanobacterial blooms has increased due to their higher frequency of occurrence and their potential ecological and health impacts. Detection of microcystin (MC) producers (MCPs) using qPCR and RT-qPCR allows for the rapid identification of blooms by combining specificity and sensitivity with a relatively high throughput capability. Investigation of MCP population composition (correlation, dominance), toxin gene expression, and relationship to MC concentration was conducted using a panel of qPCR assays targeting mcyA, E and G on weekly and daily water samples collected from an Ohio inland reservoir lake. Further, these data were used to develop early warning thresholds for prediction of MC concentrations exceeding the US EPA Health Advisory cutoff value (>0.3 μg L-1) using receiver operating characteristic curves and tobit regression. MCP Microcystis genomic copy number made up approximately 35% of the total Microcystis spp. and was the dominant toxic subpopulation of MCPs. The expressed MCPs were 0.2% of the extant genomic copy numbers, while toxic Microcystis had higher expressed proportion (0.5%) than that of toxic Planktothrix (0.04%). Microcystis toxin genes increased in June and July but decreased in August and September along with similar trends of cell replication. Quantities of both RT-qPCR and qPCR followed the same trend and were highly correlated with MC-ADDA, while RT-qPCR not only reflected the active toxin genes or toxic species, but also indicated the beginning and ending of toxin production. A one-week early warning of MC exceedance over the EPA Health Advisory was based on signaling of qPCR and RT-qPCR using receiver operating characteristic curves. This study illustrates the potential use of qPCR or RT-qPCR as an early warning system of extant and MC producing potentials during a toxic algal bloom, with predictive powers of 50%-60% and 30%-40% (p < 0.001), respectively, and false positive rates of about 70% for both LC-MS/MS or ELISA.