Unlocking nature's treasure-chest: screening for oleaginous algae

Abstract

Micro-algae synthesize high levels of lipids, carbohydrates and proteins photoautotrophically, thus attracting considerable interest for the biotechnological production of fuels, environmental remediation, functional foods and nutraceuticals. Currently, only a few micro-algae species are grown commercially at large-scale, primarily for "health-foods" and pigments. For a range of potential products (fuel to pharma), high lipid productivity strains are required to mitigate the economic costs of mass culture. Here we present a screen concentrating on marine micro-algal strains, which if suitable for scale-up would minimise competition with agriculture for water. Mass-Spectrophotometric analysis (MS) of nitrogen (N) and carbon (C) was subsequently validated by measurement of total fatty acids (TFA) by Gas-Chromatography (GC). This identified a rapid and accurate screening strategy based on elemental analysis. The screen identified Nannochloropsis oceanica CCAP 849/10 and a marine isolate of Chlorella vulgaris CCAP 211/21A as the best lipid producers. Analysis of C, N, protein, carbohydrate and Fatty Acid (FA) composition identified a suite of strains for further biotechnological applications e.g. Dunaliella polymorpha CCAP 19/14, significantly the most productive for carbohydrates, and Cyclotella cryptica CCAP 1070/2, with utility for EPA production and N-assimilation.

Figure 1. Taxonomic distribution of micro-algal strains.

The 175 strains subjected to the primary screen for growth under standard conditions and the 117 selected for the secondary screen for composition and yield measurements are indicated by pi-charts. Colour-coding designates taxa to 21 protistan classes covering 7 phyla. Number of strains per class is indicated on the pi-chart. Data tabulated in Supplementary Dataset S1-3 online.

Figure 2. Molecular phylogeny of the screened algae.

This was inferred from a comparison of 18S rDNA sequences from the micro-algal strains studied. The resultant tree was generated from a maximum likelihood analysis with Bootstrap percentage values indicated where N = 1000. Strains are labelled according to 18S GenBank accession; name and CCAP culture collection accession. Where denoted (*) the sequence was derived from the same strain held in other collections.

Figure 3. Biomass and TFA production levels of the algae screened.

Biomass productivity and yields are shown in terms of (a) C content and (b) DW. (c) Analysis of N and C content depicted in relation to TFA content, which is indicated by colour coding. (d) Stationary and log phase TFA content in DW biomass (ratios 4:1, 2:1 and 1:1 indicated by diagonals). Data points in (a), (b), (d) are colour coded by class as defined in Fig. 1 (2° screen) and error bars (SD) are depicted for key algae labelled according to CCAP strain accession no. Data are derived from replicate batch cultures (tabulated in Supplementary Datasets S4-5 online).

Figure 4. C and N resource partitioning in the algal screen.

C productivities are depicted in comparison with assimilation of supplied N in terms of (a) N culture yield (b) protein. Comparison of TFA production levels with (c) carbohydrate and (d) protein. Data points are colour coded by class as defined in Fig. 1 (2° screen) and error bars (SD) are depicted for key algae labelled according to CCAP strain accession number (red text indicates strains grown in 3NBBM+V). Data are from 117 strains and are derived from replicate batch cultures (tabulated in Supplementary Dataset S4 online).

Figure 5. Cluster analysis of FA compositional data.

A data cut-off of 0.1% was applied and data (mol%) clustered using a PAST algorithm employing Rho parameters (bootstrap value N = 1000). Micro-algal classes were defined by the colour-coding scheme in Fig. 1 (2° screen). Strains undergoing taxonomic review are indicated (*); see Supplementary Text S1 online. Data tabulated in Supplementary Dataset S6 online.