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Author Spotlight: Separation of Coral Host Tissues and Algal Symbionts and Analyzing Their Metabolites
Published on: October 13, 2023
INTERCOLONIAL VARIABILITY IN MACROMOLECULAR COMPOSITION IN P-STARVED AND P-REPLETE SCENEDESMUS POPULATIONS REVEALED
Philip Heraud1, Slobodanka Stojkovic1, John Beardall1
1Centre for Biospectroscopy and School of Chemistry, Monash University, 3800 Victoria, Australia School of Biological Sciences, Monash University, 3800 Victoria, AustraliaSchool of Biological Sciences, Monash University, 3800 Victoria, Australia Centre for Biospectroscopy and School of Chemistry, Monash University, 3800 Victoria, Australia School of Biological Sciences, Monash University, 3800 Victoria, AustraliaCentre for Biospectroscopy and School of Chemistry, Monash University, 3800 Victoria, Australia.
Abstract:
Macromolecular variability in microalgal populations subject to different nutrient environments was investigated, using the chlorophyte alga Scenedesmus quadricauda (Turpin) Bréb. as a model organism. The large size of the four-cell coenobia in the strain used in this study (∼35 μm diameter) conveniently allowed high quality spectra to be obtained from individual coenobia using a laboratory-based Fourier transform infrared (FTIR) microscope with a conventional globar source of IR. By drawing sizable subpopulations of coenobia from two Scenedesmus cultures grown under either nutrient-replete or P-starved conditions, the population variability in macromolecular composition, and the effects of nutrient change upon this, could be estimated. On average, P-starved coenobia had higher carbohydrate and lower protein absorbance compared with P-replete coenobia. These parameters varied between coenobia with histograms of the ratio of absorbance of the largest protein and carbohydrate bands being Gaussian distributed. Distributions for the P-replete and P-starved subpopulations were nonoverlapping, with the difference in mean ratios for the two populations being statistically significant. Greater variance was observed in the P-starved subpopulation. In addition, multivariate models were developed using the spectral data, which could accurately predict the nutrient status of an independent individual coenobium, based on its FTIR spectrum. Partial least squares discriminant analysis (PLS-DA) was a better prediction method compared with soft independent modeling by class analogy (SIMCA).
