There are several studies on the presence of cyanobacteria and the correlation between environmental factors that may influence or trigger blooms. However, information concerning the physiological and biochemical control of the production of secondary metabolites, toxins and volatile organic compounds (VOC) by cyanobacteria is poorly understood. Geosmin and 2-methyl-isoborneolare are commonly found VOC in cyanobacteria, they resist to conventional water treatment and can cause bad smell and taste in the final water. In addition, VOC can bioaccumulate in fish and shellfish. Studies on possible competition systems (allelopathy) either among strains of cyanobacteria or among them and other organisms such as green microalgae, may help to elucidate the role of VOC production by cyanobacteria. Thus, the main objectives of this study are: (i) prospect the production of VOC by cyanobacteria by GC-MS; (ii) verify their effects on the self-regulation and physiological features in some cyanobacterial strains cultivated in lab conditions; (iii) analyze the production of VOC by different cyanobacterial strains isolated from Brazilian environments, as well as (iv) develop effective analytical methods for the effective determination of these compounds and (v) find out possible biomarkers that can predict bloom formation. Tests will be carried out on the effect of different environmental variables (light, temperature and pH) on the production of VOC by cyanobacteria. To evaluate their allelopathic effects, exudates from different cultures will be tested in either mixed cultures or mono-specific cultures. The production of VOC will be monitored and determined by GC-MS (with head space and/or ether extracts) and data will be compared by software based on metabolomic analyses. These studies can contribute to a better understanding of VOC production and its role in the growth and physiology of cyanobacteria as well as its relationship to environmental stimuli. It is expected that some VOC may play an important role on bloom formation and some biomarkers might be useful to predict blooms.
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