Photoperiodic and circadian clock mediated control of hydrocarbon production in botryococcus braunii.

Lead Research Organisation: University of Exeter
Department Name: Biosciences


In December 2008, the European Union agreed that, by 2020, member states must satisfy 10% of their transport fuel needs from renewable resources. Algae are a promising source of 'third generation' biofuel, not least because algae can be grown on marginal lands and do not compete for space with agricultural food crops. The planktonic alga Botryococcus braunii is particularly interesting because it synthesises and secretes up to 80% of its dry mass as long-chain (C30-C40) hydrocarbons (Banerjee et al. (2002) Crit. Rev. Biotechnol. 22, 245-79). However, the molecular control and timing of hydrocarbon (HC) production in B. braunii is not known. This knowledge is essential for a valid assessment of B. braunii as sustainable source of biofuel because in microalgae, as in higher plants, the timing of key metabolic processes such as sugar allocation or lipid production are controlled both by photoperiod and through cellular rhythms generated by the endogenous circadian clock (Johnson, C.H. (2001) Annu. Rev. Physiol. 63, 695-728; Mittag (2001) Int. Rev. Cytol. 206, 213-47). This project will investigate the hypothesis that HC production in B. braunii is modulated by photoperiod and the circadian clock via a dynamic collaboration between experts at the University of Exeter and the Plymouth Marine Laboratory (PML). The programme of work is intense and combines diverse and complex skills, including molecular physiology, bioinformatics, modelling and analytical chemistry. We anticipate that the student will spend at least 16 months seconded to the industrial partner. Consequently, the requested duration of the studentship is for 4 years. In year 1, the student will characterise growth and HC production of B. braunii, strain Guadeloupe, grown in batch culture, in different photoperiods; 8 h light, 16 h darkness (8L/16D), 12L/12D, 16L/8D, 20L/4D and in constant light. Samples will be harvested daily over a 30 day period. Growth will be quantified by dry biomass and chlorophyll content of the cultures and HC production monitored using a Nile red fluorescence assay. Every 5 days, total HC will be purified and analysed using gas chromatography (GC). The effect of photoperiod on B. braunii growth, HC yield and composition will be correlated and modelled to determine optimal culture conditions for a potential biofuel production stream. In years 2 & 3, the student will perform a detailed characterisation of HC production in B. braunii over an entire diurnal cycle. B. braunii will be grown to linear or stationary phase in 8L/16D, 12L/12D, 16L/8D and 20L/4D. HC yields will be monitored using the Nile red assay, every 30 min for at least 28 h and related to biomass. In addition, every 2 h, HC's will be extracted from B. braunii cells and the composition analysed by GC. To investigate whether HC production is controlled by the circadian clock, B. braunii will be entrained in the most appropriate photoperiod, transferred to circadian free-run (CFR; constant light or constant darkness) and HC production and composition monitored at selected time-points for at least 5 days. The free running period of the clock will be determined. Subjective night- and day-breaks will be used to determine the phase response curve of the circadian rhythm. In years 3 & 4, the student will construct and test a bioinformatic model of the B. braunii circadian clock. We have sequenced and are annotating the B. braunii transcriptome. The student will identify clock gene homologues by in silico comparison with identified circadian systems, and use that data to construct a molecular model of the B. braunii circadian clock. The functional relationships between the genes in the model will be empirically tested using qRT-PCR of RNA extracted from B. braunii grown in specified photoperiods and in CFR. This model will enable comparisons between the circadian system in B. braunii and other microalgae, and allow predictions of optimal yields in algal biofuel production.


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