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. Published by Elsevier Ltd.