Generation of Chlorella vulgaris starch mutants and their biomass and lipid productivities under different culture media
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Abstract
Background
Microalgae are an important feedstock for the production of a wide variety of products, including biodiesel. Biodiesel, composed of fatty acid alkyl esters, is produced through the transesterification reaction of triacylglycerol (TAG). Microalgae store their energy reserves primarily as starch and TAGs. Therefore, several studies have focused on understanding the partitioning of carbon precursors between starch and TAG biosynthetic pathways. In this study, 5 starch mutants of Chlorella vulgaris were developed and cultured on different culture media.
Results
Chlorella vulgaris starch mutants were generated through UV-random mutagenesis. Five starch mutants were selected for this study: four low-starch producing mutants (st27, st29, st43 and st54) and one high-starch producing mutant (st80). The starch mutants were cultured on media with different organic carbon sources, and lipid and biomass productivity were measured. Mixotrophic growth on glucose resulted in the highest lipid productivity in all the mutants, including st80, without compromising growth, whereas photoautotrophic growth generally did not result in changes in lipid productivity of the starch mutants. The highest increase in lipid productivity was observed for st27, with a 3.8-fold higher lipid productivity than wildtype.
Conclusions
All starch mutants increased their lipid productivities when grown mixotrophically on glucose, suggesting the overflow hypothesis could explain the partitioning of carbon between starch and TAGs. Out of the mutants generated in this work, st27 resulted in the highest increases in lipid productivities, reaching an increase of 380% when grown mixotrophically on glucose, without compromising growth. The high-starch producing mutant st80 provides insight into a possibility to develop starch- and TAG-rich microalgal biomass.
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The mutant lines generated in this study had different responses in the culture media tested, suggesting that the carbon source available in the culture medium has an effect on TAG production
Because these mutants were generated via random mutagenesis, it's difficult to interpret phenotypic variability of each mutant's lipid metabolism in the context of their varied genetic backgrounds. It may be worth showing lipid-productivity data in mutants that have similar levels of starch content to wild-type to show a clear baseline for interpretation.
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Five starch mutants were generated in this study: four low-starch producing mutants (st27, st29, st43 and st54) and one high-starch producing mutant (st80). All starch mutants increased their lipid productivities when grown mixotrophically on glucose, suggesting the overflow hypothesis could explain the partitioning of carbon between starch and TAGs.
It would help to show more of the screening process that led to the five chosen lines. For example, reporting how many colonies were screened, the growth conditions used during screening (e.g., BBM vs BBM−N, duration, light), how starch phenotypes were called from iodine staining, and how many candidates fell into each class (low starch / high starch). A semi-quantitative summary of iodine phenotypes would let readers estimate the probability of generating starch phenotypes via this …
Five starch mutants were generated in this study: four low-starch producing mutants (st27, st29, st43 and st54) and one high-starch producing mutant (st80). All starch mutants increased their lipid productivities when grown mixotrophically on glucose, suggesting the overflow hypothesis could explain the partitioning of carbon between starch and TAGs.
It would help to show more of the screening process that led to the five chosen lines. For example, reporting how many colonies were screened, the growth conditions used during screening (e.g., BBM vs BBM−N, duration, light), how starch phenotypes were called from iodine staining, and how many candidates fell into each class (low starch / high starch). A semi-quantitative summary of iodine phenotypes would let readers estimate the probability of generating starch phenotypes via this mutagenesis protocol (an informative result on its own).
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Growth of the mutant lines and wildtype is presented in terms of biomass productivity (BP, gDW L−1 day−1), calculated as the product of their specific growth rate (µ, day−1) and biomass concentration (B, gDW L−1) at the end of the cultivation period, following Equation 1
Here biomass productivity is calculated as u (growth rate) x B_end, but the more common way to calculate BP is B_end - B_start / time. u x B could overstate productivity and/or make comparisons phase-sensitive. Sometimes those early timepoints may be harder to collect/quantify, but is it known that the cultures are in exponential phase for the duration of the experiment?
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The five generated mutants presented differential starch accumulation, as revealed by their coloration patterns after iodine staining
How many mutants did you have to screen to find five starch high/low phenotypes? Did you also consider mutants that had spatially abnormal starch distribution?
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