Limits...
High-throughput metabolic screening of microalgae genetic variation in response to nutrient limitation.

Bajhaiya AK, Dean AP, Driver T, Trivedi DK, Rattray NJ, Allwood JW, Goodacre R, Pittman JK - Metabolomics (2015)

Bottom Line: Limitation of nutrients including nitrogen and phosphorus can induce metabolic changes in microalgae, including the accumulation of glycerolipids and starch.These results demonstrate that the PSR1 gene is an important determinant of lipid and starch accumulation in response to phosphorus starvation but not nitrogen starvation.However, the SNRK2.1 and SNRK2.2 genes are not as important for determining the metabolic response to either nutrient stress.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Life Sciences, The University of Manchester, Michael Smith Building, Oxford Road, Manchester, M13 9PT UK.

ABSTRACT

Microalgae produce metabolites that could be useful for applications in food, biofuel or fine chemical production. The identification and development of suitable strains require analytical methods that are accurate and allow rapid screening of strains or cultivation conditions. We demonstrate the use of Fourier transform infrared (FT-IR) spectroscopy to screen mutant strains of Chlamydomonas reinhardtii. These mutants have knockdowns for one or more nutrient starvation response genes, namely PSR1, SNRK2.1 and SNRK2.2. Limitation of nutrients including nitrogen and phosphorus can induce metabolic changes in microalgae, including the accumulation of glycerolipids and starch. By performing multivariate statistical analysis of FT-IR spectra, metabolic variation between different nutrient limitation and non-stressed conditions could be differentiated. A number of mutant strains with similar genetic backgrounds could be distinguished from wild type when grown under specific nutrient limited and replete conditions, demonstrating the sensitivity of FT-IR spectroscopy to detect specific genetic traits. Changes in lipid and carbohydrate between strains and specific nutrient stress treatments were validated by other analytical methods, including liquid chromatography-mass spectrometry for lipidomics. These results demonstrate that the PSR1 gene is an important determinant of lipid and starch accumulation in response to phosphorus starvation but not nitrogen starvation. However, the SNRK2.1 and SNRK2.2 genes are not as important for determining the metabolic response to either nutrient stress. We conclude that FT-IR spectroscopy and chemometric approaches provide a robust method for microalgae screening.

No MeSH data available.


Related in: MedlinePlus

LC–MS analysis of wild type and mutant strains in response to P limitation. Principal component analysis (PCA) of UHPLC–MS spectra derived from wild type and mutant strains cultured in replete concentrations of P (1 mM), indicated by blue symbols, and in limited concentrations of P (10 µM), indicated by red symbols. Different symbols represent the different wild type and mutant strains. For this plot all snrk2.1 and snrk2.2 have been categorized together as ‘snrk’. PCA loading plots of PC1 and PC2 are shown below the scores plot. Peaks have been categorized into one of five lipid classes as indicated by peak colour, and within each class are arranged in ascending m/z value. Multiple phospholipid types (described in Supplementary Fig. 7) are grouped together as phospholipids. Peaks with a PC loading value greater than 0.2 are highlighted and m/z value indicated. Lipid peak definitions are shown in Supplementary Table 2 (Color figure online)
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Fig5: LC–MS analysis of wild type and mutant strains in response to P limitation. Principal component analysis (PCA) of UHPLC–MS spectra derived from wild type and mutant strains cultured in replete concentrations of P (1 mM), indicated by blue symbols, and in limited concentrations of P (10 µM), indicated by red symbols. Different symbols represent the different wild type and mutant strains. For this plot all snrk2.1 and snrk2.2 have been categorized together as ‘snrk’. PCA loading plots of PC1 and PC2 are shown below the scores plot. Peaks have been categorized into one of five lipid classes as indicated by peak colour, and within each class are arranged in ascending m/z value. Multiple phospholipid types (described in Supplementary Fig. 7) are grouped together as phospholipids. Peaks with a PC loading value greater than 0.2 are highlighted and m/z value indicated. Lipid peak definitions are shown in Supplementary Table 2 (Color figure online)

Mentions: To compare the high-throughput FT-IR spectroscopy profiling of the strains with an alternative analytical method, UHPLC–MS was performed on a non-polar lipophilic fraction isolated from wild type and mutant strains grown under high P and low P conditions until day seven. PCA of the UHPLC–MS data showed some similarity with the profile seen by PCA of the FT-IR spectra. There was clear separation of the high P and low P treated strains on the basis of PC1 (determined largely by increases in peaks classified as diglycerides and triglycerides), but for either treatment group, further clear separation on the basis of genetic background was lacking (Fig. 5). Even treatment of the data by PC-DFA was unable to distinguish many of the mutant lines apart from discrimination of snrk2.1 and psr1 snrk2.2-2 from the other strains on the basis of DF2, determined in part by changes in peaks classified as triglycerides (Supplementary Fig. 5). In particular, it was not possible to distinguish psr1 mutants from wild type under P limitation conditions, which was very different from the outcome using FT-IR data. The explanation for this is likely to be due to the more limited metabolic information gained from the UHPLC–MS analysis of the non-polar fractions in contrast to the macromolecular quantification of whole cells by FT-IR spectroscopy, which identified significant changes in carbohydrates (Fig. 6b), particularly starch (Supplementary Fig. 6). This difference in the information content between different metabolomics methods has been recently illustrated for a set of bacteria that displayed different metabolic diversities and virulence factors (AlRabiah et al. 2014). Furthermore, the majority of the metabolites determined by UHPLC–MS are lipids (Supplementary Fig. 7; Supplementary Table 2), and while there were some fairly subtle differences in total lipid accumulation between wild type and psr1 in response to P limitation, the most marked change was for the accumulation of starch (see below).Fig. 5


High-throughput metabolic screening of microalgae genetic variation in response to nutrient limitation.

Bajhaiya AK, Dean AP, Driver T, Trivedi DK, Rattray NJ, Allwood JW, Goodacre R, Pittman JK - Metabolomics (2015)

LC–MS analysis of wild type and mutant strains in response to P limitation. Principal component analysis (PCA) of UHPLC–MS spectra derived from wild type and mutant strains cultured in replete concentrations of P (1 mM), indicated by blue symbols, and in limited concentrations of P (10 µM), indicated by red symbols. Different symbols represent the different wild type and mutant strains. For this plot all snrk2.1 and snrk2.2 have been categorized together as ‘snrk’. PCA loading plots of PC1 and PC2 are shown below the scores plot. Peaks have been categorized into one of five lipid classes as indicated by peak colour, and within each class are arranged in ascending m/z value. Multiple phospholipid types (described in Supplementary Fig. 7) are grouped together as phospholipids. Peaks with a PC loading value greater than 0.2 are highlighted and m/z value indicated. Lipid peak definitions are shown in Supplementary Table 2 (Color figure online)
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Fig5: LC–MS analysis of wild type and mutant strains in response to P limitation. Principal component analysis (PCA) of UHPLC–MS spectra derived from wild type and mutant strains cultured in replete concentrations of P (1 mM), indicated by blue symbols, and in limited concentrations of P (10 µM), indicated by red symbols. Different symbols represent the different wild type and mutant strains. For this plot all snrk2.1 and snrk2.2 have been categorized together as ‘snrk’. PCA loading plots of PC1 and PC2 are shown below the scores plot. Peaks have been categorized into one of five lipid classes as indicated by peak colour, and within each class are arranged in ascending m/z value. Multiple phospholipid types (described in Supplementary Fig. 7) are grouped together as phospholipids. Peaks with a PC loading value greater than 0.2 are highlighted and m/z value indicated. Lipid peak definitions are shown in Supplementary Table 2 (Color figure online)
Mentions: To compare the high-throughput FT-IR spectroscopy profiling of the strains with an alternative analytical method, UHPLC–MS was performed on a non-polar lipophilic fraction isolated from wild type and mutant strains grown under high P and low P conditions until day seven. PCA of the UHPLC–MS data showed some similarity with the profile seen by PCA of the FT-IR spectra. There was clear separation of the high P and low P treated strains on the basis of PC1 (determined largely by increases in peaks classified as diglycerides and triglycerides), but for either treatment group, further clear separation on the basis of genetic background was lacking (Fig. 5). Even treatment of the data by PC-DFA was unable to distinguish many of the mutant lines apart from discrimination of snrk2.1 and psr1 snrk2.2-2 from the other strains on the basis of DF2, determined in part by changes in peaks classified as triglycerides (Supplementary Fig. 5). In particular, it was not possible to distinguish psr1 mutants from wild type under P limitation conditions, which was very different from the outcome using FT-IR data. The explanation for this is likely to be due to the more limited metabolic information gained from the UHPLC–MS analysis of the non-polar fractions in contrast to the macromolecular quantification of whole cells by FT-IR spectroscopy, which identified significant changes in carbohydrates (Fig. 6b), particularly starch (Supplementary Fig. 6). This difference in the information content between different metabolomics methods has been recently illustrated for a set of bacteria that displayed different metabolic diversities and virulence factors (AlRabiah et al. 2014). Furthermore, the majority of the metabolites determined by UHPLC–MS are lipids (Supplementary Fig. 7; Supplementary Table 2), and while there were some fairly subtle differences in total lipid accumulation between wild type and psr1 in response to P limitation, the most marked change was for the accumulation of starch (see below).Fig. 5

Bottom Line: Limitation of nutrients including nitrogen and phosphorus can induce metabolic changes in microalgae, including the accumulation of glycerolipids and starch.These results demonstrate that the PSR1 gene is an important determinant of lipid and starch accumulation in response to phosphorus starvation but not nitrogen starvation.However, the SNRK2.1 and SNRK2.2 genes are not as important for determining the metabolic response to either nutrient stress.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Life Sciences, The University of Manchester, Michael Smith Building, Oxford Road, Manchester, M13 9PT UK.

ABSTRACT

Microalgae produce metabolites that could be useful for applications in food, biofuel or fine chemical production. The identification and development of suitable strains require analytical methods that are accurate and allow rapid screening of strains or cultivation conditions. We demonstrate the use of Fourier transform infrared (FT-IR) spectroscopy to screen mutant strains of Chlamydomonas reinhardtii. These mutants have knockdowns for one or more nutrient starvation response genes, namely PSR1, SNRK2.1 and SNRK2.2. Limitation of nutrients including nitrogen and phosphorus can induce metabolic changes in microalgae, including the accumulation of glycerolipids and starch. By performing multivariate statistical analysis of FT-IR spectra, metabolic variation between different nutrient limitation and non-stressed conditions could be differentiated. A number of mutant strains with similar genetic backgrounds could be distinguished from wild type when grown under specific nutrient limited and replete conditions, demonstrating the sensitivity of FT-IR spectroscopy to detect specific genetic traits. Changes in lipid and carbohydrate between strains and specific nutrient stress treatments were validated by other analytical methods, including liquid chromatography-mass spectrometry for lipidomics. These results demonstrate that the PSR1 gene is an important determinant of lipid and starch accumulation in response to phosphorus starvation but not nitrogen starvation. However, the SNRK2.1 and SNRK2.2 genes are not as important for determining the metabolic response to either nutrient stress. We conclude that FT-IR spectroscopy and chemometric approaches provide a robust method for microalgae screening.

No MeSH data available.


Related in: MedlinePlus