Identification and Metabolite Profiling of Chemical Activators of Lipid Accumulation in Green Algae

Abstract

Microalgae are proposed as feedstock organisms useful for producing biofuels and coproducts. However, several limitations must be overcome before algae-based production is economically feasible. Among these is the ability to induce lipid accumulation and storage without affecting biomass yield. To overcome this barrier, a chemical genetics approach was employed in which 43,783 compounds were screened against Chlamydomonas reinhardtii, and 243 compounds were identified that increase triacylglyceride (TAG) accumulation without terminating growth. Identified compounds were classified by structural similarity, and 15 were selected for secondary analyses addressing impacts on growth fitness, photosynthetic pigments, and total cellular protein and starch concentrations. TAG accumulation was verified using gas chromatography-mass spectrometry quantification of total fatty acids, and targeted TAG and galactolipid measurements were performed using liquid chromatography-multiple reaction monitoring/mass spectrometry. These results demonstrated that TAG accumulation does not necessarily proceed at the expense of galactolipid. Untargeted metabolite profiling provided important insights into pathway shifts due to five different compound treatments and verified the anabolic state of the cells with regard to the oxidative pentose phosphate pathway, Calvin cycle, tricarboxylic acid cycle, and amino acid biosynthetic pathways. Metabolite patterns were distinct from nitrogen starvation and other abiotic stresses commonly used to induce oil accumulation in algae. The efficacy of these compounds also was demonstrated in three other algal species. These lipid-inducing compounds offer a valuable set of tools for delving into the biochemical mechanisms of lipid accumulation in algae and a direct means to improve algal oil content independent of the severe growth limitations associated with nutrient deprivation.

Figure 1.

Summary of results of high-throughput screening. A, Z-factor calculation for each of 124 plates totaling 43,736 compounds. The average Z′ value was 0.78 ± 0.08 with a coefficient of variation of 14.4%. B, Growth measured as OD₆₀₀ in the presence of compound after 72 h. The average of the N+ control cells was 0.41 ± 0.04. C, Lipid accumulation measured as relative fluorescence after Nile Red (NR) staining of cells treated with compound relative to cells treated with vehicle (DMSO). D, Confirmation of hits and dose response. Data for 243 compounds are shown fitting the concentration-response curve (from 0.25 to 30 μm) to lipid accumulation. The scale bar represents the relative fold change of treatment compared with control (N+).

Figure 2.

Structural comparisons of hits from the primary screen. A, Network view of lipid-accumulating small molecules. All small molecules identified through the primary screen and verified using an eight-point dose-response curve were clustered according to their Tanimoto similarity score. Each node represents a unique small molecule. Edges represent the structural similarities at a Tanimoto score cutoff of 0.7. Data for the relevant compound at 30 μm (red), 15 μm (green), and 10 μm (blue) are mapped in a pie chart. The node size represents the fold change of each chemical at the 30 μm concentration. A small portion of the network is magnified to show clustered compounds having structural similarities. B, Clustering analysis of active compounds using Ward’s linkage method. Distance was calculated based on Tanimoto coefficient, and Estate Bit fingerprints were used for similarity calculations. One of the clusters was highlighted showing the admantane moiety. Note that some of the compounds are presented as salts of HCl; two HCl molecules indicate chiral enantiomers.

Figure 3.

Lipid body accumulation in C. reinhardtii induced by diverse compounds. Compounds are grouped according to their structural similarities as described in the text. Cultures were treated with 10 μm compound, and the corresponding lipid accumulation was visualized using confocal microscopy after 72 h in culture.

Figure 4.

Growth of cells and accumulation of protein during compound treatment. A, Cells were treated with 30 µm of the specified compounds as indicated, and OD₆₀₀ was monitored over 72 h (n = 3; ±sd). B, After harvesting, total protein levels were measured per 10⁶ cells. Bar height indicates the mean of three biological replicates (n = 3; ±sd). The significance of differences in the levels of total protein was assessed using ANOVA to compare the treated samples with controls (*, P < 0.05 and **, P < 0.01).

Figure 5.

Assessment of cellular macromolecule accumulation after treatment with selected compounds for 72 h. A, Total starch. B, Total carotenoids. C, Chlorophyll a. D, Chlorophyll b. Bar height indicates the mean of three independent experiments (±sd). Controls were values obtained for cultures treated with the vehicle, DMSO. ANOVA (JMP version 11) was applied to determine the significance of differences in the levels of total protein as compared with untreated control cells (N+; *, P < 0.05; **, P < 0.01; and ***, P < 0.001).

Figure 6.

Identification and quantification of complex lipids by liquid chromatography-tandem mass spectrometry. A, TAG. B, Galactolipids (GL). C, Relative quantities of TAG and galactolipids as indicated. The height of the bar is the mean of the absolute quantity of the measured lipid species, and error bars give the se (*, P < 0.05 relative to control; n = 3). The relative fold change compared with control values is listed below each bar for A and B. DW, Dry weight.

Figure 7.

Univariate and multivariate analyses of the GC-MS metabolites. A, PCA of primary metabolites/features of compound-treated and untreated control samples. Control (black), WD30030 (red), compound WD20542 (cyan), compound WD10461 (blue), compound WD20067 (green), and compound WD10784 (pink) are indicated. B, PLS-DA of the data for better separation of the samples to identify features that are responsible for differentiation in the treatment. C, Top 20 metabolites with significantly different abundance between compound treatments based on the VIP deduced using the Metaboanalyst Web tool (http://www.metaboanalyst.ca).

Figure 8.

Summary of metabolite profiling experiments. A, Heat map showing the metabolite abundance profiles of compound-treated versus control cells. The expression levels of the top 50 metabolites selected after applying ANOVA P < 0.05 are illustrated. B, Venn diagram showing the unique and common differentially changed features/metabolites in different compound-treated metabolomes. The number of peaks that were not significantly changed (33) is shown at bottom right. C, Metabolite peaks generated after peak picking and deconvolution were identified using the MassBank and Golm metabolome libraries. For each identified feature, a KEGG compound code was assigned according to KEGG brite and classified according to its biological role.

Figure 9.

Pathway map representing the impacts of various compounds on carbon metabolism. Red bars indicate significantly increased levels of metabolites in compound-treated samples relative to controls; blue bars indicate significantly decreased levels of metabolites in compound-treated samples relative to controls; and white bars indicate no significant differences between treated and control samples. For the quantitation of changes, see Table IV and Supplemental Table S4, A and B.

Figure 10.

Pathway map indicating the impacts of various compounds on amino acid biosynthesis. Red bars indicate significantly increased levels of metabolites in compound-treated samples relative to controls; blue bars indicate significantly decreased levels; and white bars indicate no significant differences between treated and control samples. For the quantitation of changes, see Table IV.