This project investigated Metabolomics-based applications (LC-MS and GC-MS) to advance cyanobacterial toxin detection and analysis in water to:
• Understand the biochemical triggers for cyanobacterial blooms.
• Identification of any new potential toxins produced from blooms.
• Assess the application of metabolomics  for the rapid detection of cyanobacterial toxins and identification of unknown toxins.
• Asses the cost effectiveness and time efficient technique.
• Characterise the biomolecules present in bloom as toxins are produced.

Thesis underway.

This project investigated the effect of environmental parameters on the regulation of cylindrospermopsin biosynthesis in cyanobacteria inhabiting Australian drinking water supplies.

Honours Thesis completed by Melissa Rapadas in November 2012.

This project increased understanding of how cyanobacteria adapt and function in today’s environment. It provided insight to the key factors that contribute to growth of Microcystis and Anabaena in relation to physical conditions of the reservoir and reduced as well as predicted the occurrence and toxicity of blooms by understanding toxin regulation.

PhD Thesis completed by Anna Chi Ying Yeung in August 2016.

This project developed novel sampling tools to allow time integrated passive sampling of existing and emerging chemicals pollutants. Sampling devices developed through this work assisted with more comprehensive and cost-effective monitoring of newly recognised potential toxicants in surface and treated water across Australia.

Honours Thesis completed by Sarit Kaserzon in 2014.

This project established a bioassay using mouse embryonic stem cells to assess the effects of the cyanobacterial toxin, cylindrospermopsin, on early embryonic development. Using embryonic stem cells cultured in vitro as a model for embryos in vivo, the project will analysed cytotoxicity of cylindrospermopsin, its effects on cell morphology, and on gene expression.

Blue-green algae reduce water quality, especially when they produce toxins. Each algal cell can grow, reproduce all its DNA, and split into two ‘daughter’ cells, then those two ‘daughter’ cells produce four more until the numbers of algal cells bloom to extremely high numbers. High algal growth rates are associated with favourable environmental conditions (for the algae), stationary growth rates occur when the production of new cells is about the same as the number of dying cells, and if more cells die than are reproduced, the growth rate declines. The ability to predict or measure which of these three population growth rates is prevalent, and how much toxin is being produced, is information that the water industry needs to select the best methods for treating water. This project analysed the amount of DNA, and some specific sequences of DNA which correspond to the genes coding for toxins; and related the DNA analysis to actual counts of cells and measurements of toxin in water samples. This allowed the development of an improved and more informative technique for forecasting and monitoring toxic blue-green algae blooms.

The environmental conditions which cause blue-green algae (cyanobacteria) blooms vary according to location, the climate, and other attributes of aquatic ecosystems. This variety has made it difficult to develop one broadly applicable predictive model for cyanobacterial blooms. Water utilities monitor source waters to implement cyanobacterial risk management programmes but there are no standard protocols while limited information transfer between utilities has prevented the identification of management strategies that do or do not work. This research reviewed literature about early warning systems (Almuhtaram et al., 2021) and source control strategies, conducted a survey of 35 utilities in America and Canada (74%) and Australia (Kibuye et al., 2021) and evaluated selected control strategies. These different types of information were synthesised into decision trees within an overarching guidance document. It was concluded that a 3-tier framework to detect algal blooms which monitored biological activity, then confirmed the identification of cyanobacterial genes and associated metabolites gave sufficient early warning, while multi-barrier control strategies gave field-scale efficacy and enabled timely responses.

There are many species of blue-green algae (cyanobacteria), and each species can have a number of genotypes. Water utilities routinely monitor reservoirs and lagoons for the harmful toxin-producing species, and when they find a threshold number of cells, proceed to test for toxins. The problem is that not all genotypes of known toxin-producing species produce toxins. There is already a well-established quantitative PCR method that detects the genes responsible for making toxins, but the relationship between occurrence of these toxic genotypes and the amount of toxin in one water sample is not straightforward.

This project will create a modified version of the existing regulatory protocols for managing toxic blue-green algal blooms by adding the toxic gene qPCR test to the current tests for toxin and species identification. This modified protocol will then be used to assess samples for which there are already results from all three tests. The original management costs will be compared to the desktop analysis of hypothetical costs that would have been incurred if genotoxicity testing had been included in the protocol. Could gene testing reduce overall cyanobacteria management costs and confer operational benefits?

Several cyanobacteria species are well known for their potential to produce cyanotoxins. However, not all genotypes of known toxin producing species produce cyanotoxins and of these there is significant variation in the spatial and temporal dynamics of toxin production. The water industry currently relies of observational measurement of the presence of ‘potentially toxic species’, toxin gene and toxin presence to inform management of cyanobacteria blooms in water supply storages. Predictive tools and preventative management are limited by a lack of simple environmental predictors to predict toxin production events. Understanding the drivers for toxin production that inform risk management frameworks would be of great benefit to water supply managers and to inform alternate management options. These tools would enable better responses to bloom events and allowing for the establishment of pre-emptive measures to minimize cyanotoxin production by targeted manipulation of environmental drivers.

Blue-green algae (cyanobacteria) can bloom in marine and freshwater and cause additional problems for water utilities when they produce toxins and taste and odour compounds. This project consolidated a wealth of knowledge and experience in the management of cyanobacteria into an electronic / online international, practical, and user-friendly manual. It includes information about conducting risk assessments, developing monitoring programmes and incident management strategies, and management procedures to mitigate the risk posed by cyanotoxins.