Fourier transform infrared (FTIR) spectra were measured from cells of Microcystis aeruginosa and Protoceratium reticulatum, whose growth rates were manipulated by the availability of nutrients or light. As expected, the macromolecular composition changed in response to the treatments. These changes were species-specific and depended on the type of perturbation applied to the growth regime. Microcystis aeruginosa showed an increase in the carbohydrate-to-protein ratio with decreased growth rates, under nutrient limitation, whereas light limitation induced a decrease of the carbohydrate-to-protein ratio with decreasing proliferation rates. The macromolecular pools of P. reticulatum showed a higher degree of compositional homeostasis. Only when the lowest light irradiance and nutrient availability were supplied, an increase of the carbohydrate-to-protein FTIR absorbance ratio was observed. A species-specific partial least squares (PLS) model was developed using the whole FTIR spectra. This model afforded a very high correlation between the predicted and the measured growth rates, regardless of the growth conditions. On the contrary, the prediction based on absorption band ratios generally used in FTIR studies would strongly depend on growth conditions. This new computational method could constitute a substantial improvement in the early warning systems of algal blooms and, in general, for the study of algal growth, e.g. in biotechnology. Furthermore, these results confirm the suitability of FTIR spectroscopy as a tool to map complex biological processes like growth under different environmental conditions.
Statistical growth rate modelling can be applied in a variety of ecological and biotechnological applications. Such models are frequently based on Monod or Droop equations and, especially for the latter, require reliable determination of model input parameters such as C:N quotas. Besides growth rate modelling, a C:N quota quantification can be useful for monitoring and interpretation of physiological acclimation to abiotic and biotic disturbances (e.g., nutrient limitations). However, as high throughput C:N quota determination is difficult to perform, alternatives need to be established. Fourier‐transformed infrared (FTIR) spectroscopy is used to analyze a variety of biochemical, chemical, and physiological parameters in phytoplankton. Hence, a quantification of the C:N quota should also be feasible. Therefore, using FTIR spectroscopy, six phytoplankton species from among different phylogenetic groups have been analyzed to determine the effect of nutrient limitation on C:N quota patterns. The typical species‐specific response to increasing nitrogen limitation was an increase in the C:N quota. Irrespective of this species specificity, we were able to develop a reliable multi‐species C:N quota prediction model based on FTIR spectroscopy using the partial least square regression (PLSR) algorithm. Our data demonstrate that the PLSR approach is more robust in C:N quota quantification (R2 = 0.93) than linear correlation of C:N quota versus growth rate (R2 ranges from 0.74 to 0.86) or biochemical information based on FTIR spectra (R2 ranges from 0.82 to 0.89). This accurate prediction of C:N values may support high throughput measurements in a broad range of future approaches.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.