Mathematical modeling of Microcystis aeruginosa growth and [D-Leu1] microcystin-LR production in culture media at different temperatures

MELINA CELESTE, CRETTAZ MINAGLIA; LORENA, ROSSO; JORGE OSWALDO, ARANDA; SANDRO, GOÑI; DANIELA, SEDAN; DARIO, ANDRINOLO; LEDA, GIANNUZZI

HARMFUL ALGAE

ELSEVIER SCIENCE BV

Año: 2017 vol. 67 p. 13 - 25

1568-9883

The effect of temperature (26 °C, 28 °C, 30 °C and 35 °C) on the growth of native CAAT-3-2005 Microcystis aeruginosa and the production of Chlorophyll-a (Chl-a) and Microcystin-LR (MC-LR) were examined through laboratory studies. Kinetic parameters such as specific growth rate (μ), lag phase duration (LPD) and maximum population density (MPD) were determined by fitting the modified Gompertz equation to the M. aeruginosa strain cell count (cells mL−1). A 4.8-fold increase in μ values and a 10.8-fold decrease in the LPD values were found for M. aeruginosa growth when the temperature changed from 15 °C to 35 °C. The activation energy of the specific growth rate (Eμ) and of the adaptation rate (E1/LPD) were significantly correlated (R2 = 0.86). The cardinal temperatures estimated by the modified Ratkowsky model were minimum temperature = 8.58 ± 2.34 °C, maximum temperature = 45.04 ± 1.35 °C and optimum temperature = 33.39 ± 0.55 °C. Maximum MC-LR production decreased 9.5-fold when the temperature was increased from 26 °C to 35 °C. The maximum production values were obtained at 26° C and the maximum depletion rate of intracellular MC-LR was observed at 30?35 °C. The MC-LR cell quota was higher at 26 and 28 °C (83 and 80 fg cell−1, respectively) and the MC-LR Chl-a quota was similar at all the different temperatures (0.5?1.5 fg ng−1). The Gompertz equation and dynamic model were found to be the most appropriate approaches to calculate M. aeruginosa growth and production of MC-LR, respectively. Given that toxin production decreased with increasing temperatures but growth increased, this study demonstrates that growth and toxin production processes are uncoupled in M. aeruginosa. These data and models may be useful to predict M. aeruginosa bloom formation in the environment.