In order to increase productivity of biomass or a desired product, target genes have been selected and manipulated with success using transgenic approaches. These genes may comprise the metabolic pathways under consideration or be part of unrelated pathways that indirectly contribute to higher productivity. Highlighted below are some notable examples of target genes used for genetic manipulation along with examples of potential targets yet to be explored.

Much work has been done to genetically manipulate plants to produce more biomass or more of a specific tissue or compound. The Viridiplantae (green plants) include land plants and two lineages of algae, the chlorophytes and the charophytes (Finet et al. 2010). We therefore can look at gene targets in plants for possible research avenues when considering algae. A number of gene targets have already been picked and utilized with great success from previous knowledge of metabolic pathways. Fruit yield was increased in tomato (Solanum lycopersicum L.) when RNAi was used to decrease ascorbate oxidase activity (Garchery et al. 2013). Basically, the stress response to water or environment was down-regulated thus allowing more energy to be allocated for fruit production even in unfavorable conditions. Likewise, stress response genes in algae, such as those involved in oxidative stress (Perez-Martin et al. 2014) and light-related stress (Kukuczka et al.

2014) , might be down-regulated to allow for more energy to be allocated to biomass production. One strategy to increase biomass is to manipulate photosynthetic light capture. Internal shading is a limiting factor in biomass production in algae, so reducing the size of the light-harvesting antennae may increase biomass production. However, pigment mutants of Cyclotella sp., a diatom, failed to outperform their wild-type counterparts in biomass productivity (Huesemann et al. 2009). The authors note that the mutagenesis procedures (chemical and UV) may have affected other metabolic processes that contributed to the Cyclotella sp.’s unremarkable growth and found no difference in growth of wild and mutated strains of this alga when grown parallel in raceway ponds. Green algae (e. g., C. reinhardtii) have evolved genetic strategies to assemble large light-harvesting antenna complexes (LHC) to maximize light capture under low-light conditions, with the downside that under high solar irradiance, most of the absorbed photons are wasted as fluores­cence and heat generated by photoprotective mechanisms.

An insertional mutant was obtained with a disrupted gene for the antennae protein TLA1 (Polle et al. 2003). Biochemical analyses showed the TLA1 strain to be chlorophyll deficient, with a functional chlorophyll antenna size of photosystem I and II being about 50 and 65 % of that of the wild type, respectively. The TLA1 strain showed greater solar conversion efficiencies and a higher photosynthetic productivity than the wild type under mass culture conditions (Polle et al. 2003). Other researchers have used RNAi to knock down the entire family of LHCs (Mussgnug et al. 2007). The resultant mutant, Stm3LR3, exhibited reduced levels of fluorescence, a higher photosynthetic quantum yield and a reduced sensitivity to photoinhibition. Cultures with these mutants have higher light penetrance, which may lead to more efficient biomass production. Another strategy to increase bio­mass production is to focus on increasing the rate at which the alga assimilates CO2 from the atmosphere or through supplied gas, or to increase the efficiency of the carbon capture mechanism (CCM) (Stephenson et al. 2011). Genes that are involved in the CCM and are possible targets for genetic manipulation include an ABC transporter (Hla3/Mrp1 Cre02.g097800), two low-CO2-inducible chloroplast envelope proteins (Ccp1 Cre04.g223300), an anion transporter (LciA/Nar1.2 Cre06.g309000), and ten different carbonic anhydrases (Winck et al. 2013). When the desired outcome is increased production of a specific product, increased bio­mass yield may or may not be desirable. For example, in engineering algal cells to increase lipid yield, biomass productivity is only important so far as it increases total lipid yield. A variety of genes have been manipulated with this aim with varying degrees of success (Stephenson et al. 2011). One of the most remarkable achievements in lipid production from Chlamydomonas occurred when researchers showed that Chlamydomonas deprived of a nitrogen source accumulates a high degree of lipids (Wang et al. 2009b). This effect is much more pronounced in mutants lacking the small subunit of a heterotetrameric ADP-glucose pyrophos — phorylase (Zabawinski et al. 2001). The normal response of Chlamydomonas under nitrogen deprivation is to accumulate starch; however, the mutants, unable to accumulate starch, instead store energy as lipids (Wang et al. 2009b).

A recent study showed a 12 % increase in the total lipid content of the micro­algae D. salina by transforming it with a bioengineered plasmid comprising specific parts, genes, and inducible promoters, driving the cellular carbon flux into the fatty acid biosynthesis pathway (Talebi et al. 2014).

In addition to lipid production, hydrogen production is also seen as a possible route to biofuel production in Chlamydomonas (Lehr et al. 2012). Increasing hydrogen production does not necessarily follow an increase in biomass; instead, researchers usually aim to increase photosynthetic efficiency or force the cells to more readily assume an anaerobic state.

RNAi knockdown of light-harvesting proteins was found to increase H2 pro­duction in the high-H2-producing C. reinhardtii mutant Stm6Glc4 (Oey et al. 2013). Oey et al. (2013) also stated that the overall improved photon-to-H2 conversion efficiency is due to (1) reduced loss of absorbed energy by non-photochemical quenching (fluorescence and heat losses) near the photobioreactor surface, (2) improved light distribution in the reactor, (3) reduced photoinhibition, (4) early onset of HYDA expression, and (5) reduction of O2-induced inhibition of HYDA (Oey et al. 2013). Rubisco has also been used as a target for genetic manipulation to increase hydrogen production. The Rubisco mutant Y67A accounted for 10- to 15­fold higher hydrogen production than the wild type under the same conditions (Pinto et al. 2013). In conclusion, a variety of gene targets are available in algae that when manipulated may increase biomass and biofuel productivity.

A conventional microalgae production system consists of (a) growth and cultivation of microalgae, (b) biomass harvesting and dewatering and (c) extraction/conversion of the biomass to the product of interest (Fig. 1.1). It is to be noted that for this process to be energetically, environmentally, and economically sustainable, it is critical to recycle medium (water and fertilisers) in harvest and extraction stages of production (Fig. 1.1).

James Chukwuma Ogbonna and Mark P. McHenry

Abstract The feasibility of using various culture systems incorporating heterotro­phic metabolism for biodiesel oil production was compared. Heterotrophic culture can be used to achieve high cell concentration, and depending on the strain and organic carbon source employed, the introduction of light (mixotrophic culture) can enhance cell growth and oil accumulation. However, mixotrophic cultures also face the problem of light limitation, and depending on the relative concentrations of the organic carbon source and light intensity, the interaction between the heterotrophic and photoautotrophic metabolic activities can have negative effects on cell growth and oil accumulation. Systems that separate the two metabolic activities in time or space, such as cyclic photoautotrophic-heterotrophic cultures, sequential hetero­trophic-photoautotrophic cultures, and sequential photoautotrophic-mixotrophic cultures, can all be used to improve oil productivity. However, the effectiveness of each system depends on the strain of microalgae and other culture conditions.

4.1 Introduction

Requirement for light, and the technical problems and costs of light supply and distribution inside photobioreactors are the major challenges facing large-scale microaglae production in photoautotrophic cultures (Ogbonna et al. 1995, 1996). Much work has been carried out to optimize photoautotrophic cultures in large- scale outdoor cultures, yet it remains difficult to increase cell growth and final biomass concentrations in open pond cultures (Borowitzka and Moheimani 2013). Although higher productivities can be achieved in closed photobioreactors, such

J. C. Ogbonna (H)

Department of Microbiology, University of Nigeria, Nsukka, Nigeria e-mail: james. ogbonna@unn. edu. ng

M. P. McHenry

School of Engineering and Information Technology, Murdoch University, Murdoch, WA 6150, Australia

© Springer International Publishing Switzerland 2015

N. R. Moheimani et al. (eds.), Biomass and Biofuels from Microalgae,

Biofuel and Biorefinery Technologies 2, DOI 10.1007/978-3-319-16640-7_4 photobioreactors are highly complex, and technically difficult and very expensive to construct and operate (Ogbonna et al. 1996; Ogbonna 2003). Many strains of photosynthetic cells can metabolize various organic carbon sources in the presence of light (mixotrophy) or in its absence (heterotrophy). Various types of efficient large-scale heterotrophic bioreactors are available and are currently used for large — scale cultivation of fungi, bacteria, and yeasts for production of various metabolites. Such bioreactors are also used for large-scale heterotrophic cultivation of micro­algae. Even in mixotrophic cultures, the limitation of light is not as critical since the algae can still grow efficiently by metabolizing organic carbon sources. However, for many strains of microalgae in mixotrophic cultures, there is interaction between the photoautotrophic and heterotrophic metabolic activities (Ogbonna et al. 2002a). This interaction can positively or negatively affect both the cell growth and metabolite accumulation. Regulation of these metabolic activities is therefore required for the efficient accumulation of the desired metabolites (Ogbonna et al. 2002b). These metabolic activities can be separated in space (by employing a different bioreactor for each activity), or in time (by switching from one metabolic activity to another in the same bioreactor). Thus, many culture technical designs have been investigated to exploit heterotrophic metabolic activities in microalgae cultures. Such culture systems include heterotrophic, mixotrophic, cyclic photo­autotrophic-heterotrophic, sequential heterotrophic-photoautotrophic, and sequen­tial photoautotrophic-mixotrophic cultures. Each of these culture systems can be used for efficient biodiesel oil production, but their effectiveness is dependent on the nature of the microalgae used, the media components, and other culture conditions. The potentials and limitations of these culture systems are reviewed in this chapter.

Bicarbonate (HCO3) is the predominant form of dissolved inorganic carbon (DIC) in seawater (pH = 8). At this pH, only 10 pM (less than 1 %) CO2 is present as DIC, leading to low CO2 diffusion for microalgal photosynthesis. The growth of mic­roalgae can lead to alkalization of the growth medium and occurs as a result of CO2 and HCO3- uptake and OH- efflux. With an insufficient supplementation of DIC, microalgal photosynthesis may decrease (Moheimani 2013). To assist higher photosynthetic productivity, most microalgae species have a carbon concentrating mechanism (CCM) allowing them to not only use CO2, but uptake HCO3- as a carbon source (Huertas et al. 2000). For instance, culturing two strains, Chlorella sp. and Tetraselmis suecica CS-187, in 120 L hanging bag PBRs by Moheimani is an example of using various inorganic carbon sources (industrial CO2, flue gas, and sodium bicarbonate) to grow algae. The highest biomass and lipid productivities of T. suecica (51.45 ± 2.67 mg biomass L-1 day-1 and 14.8 ± 2.46 mg lipid L-1 day-1) and Chlorella sp. (60.00 ± 2.4 mg biomass L 1 day 1 and 13.70 ± 1.35 mg lipid L 1 day 1) were achieved using CO2 as inorganic carbon source. When using pure CO2 or flue gases as a source of inorganic carbon, the specific growth rate, biomass, and lipid productivities of T. suecica were 23, 10, and 22 % higher than those with NaHCO3, respectively. Using pure CO2 or flue gases as a source of inorganic carbon, the biomass yield and both biomass and lipid productivities of Chlorella sp. were 6, 7, and 8 % higher than those with NaHCO3, respectively (Moheimani 2013). Growth rate of Thermosynechococcus sp. (TCL-1), under various DIC concentrations at a constant pH of 9.5 and temperature of 50 °C, increased with an increase in initial DIC concentrations (Fig. 7.2) (Hsueh et al.

2009) . The effects of DIC concentrations on TCL-1 growth were investigated at four DIC levels (Su et al. 2012). Using steady-state conditions and 28, 57, 85, and 113 mM DIC, the cell mass productivities of 486, 620, 948, and 1056 mg L-1 d-1 were achieved, respectively. The cell mass productivity was enhanced with increasing DIC, and thus, DIC is considered a limiting production factor. However, as DIC increased from 85 to 113 mM, the increase in cell mass productivity was only from 950 to 1050 mg L-1 d-1 (Fig. 7.3). Furthermore, the uptake of CO2 from bicarbonate by photosynthesis will release hydroxyl anion and increase the pH that

can be used as an indicator to confirm the alkalization process as a result of CO2 and/or HCO3- uptake. The inverse correlation between DIC and pH may be due to the buffer capacities of bicarbonate, as it is achieved under higher DIC levels and lead to smaller changes in pH (Su et al. 2012).

In order to optimize strains for higher production yields of desired products or compounds, computational tools have been developed and employed to guide the design of knockout-strains with modified metabolic pathways leading to an increase in the bioproduct production. Two major examples of such tools are OptKnock (Burgard et al. 2003) and OptStrain (Pharkya et al. 2004). The OptKnock tool, for example, was used to identify gene knockout strategies leading to the optimization of bacterial strains for higher yields of lactate, succinate, and 1, 3-propanediol productions. In a similar approach, the Optknock tool has also allowed the

optimization of E. coli strains to achieve a higher production of several amino acids and has set in place a set of knockout strategies to achieve this goal (Pharkya et al. 2003). Pharkya et al. have established several different gene knockout combinations with each resulting in an increased production of a specific amino acid. For example, a knockout of three genes coding for three enzymes, namely pyruvate dehydrogenase, pyruvate formate lyase, and an ATPase, has allowed the cells to achieve a production of 14.95 mmol/g DW h of alanine. Alternatively, another strategy involving the knockout of a fourth enzyme, phosphofructokinase, on top of the previously mentioned genes, has increased the alanine production to 18.53 mmol/g DW h. Although the actual amino acid production has increased, the growth rate of the organism has sharply decreased and the choice of which strategy to employ remains subject to the investigator’s preferences and judgment, and is limited by the experimental setup limitations.

Lastly, OptStrain is a tool that can help identify the optimal strains producing the desired metabolite by determining the best substrate choices and genes that need to be deleted or over-expressed in order to achieve the increase in metabolite pro­duction. This strategy ensures high growth rates while optimizing the production of the desired bio-product.

A different approach, metabolic transformation algorithm (or MTA) (Yizhak et al. 2013), can potentially be used to shift the phenotypic state of algae from, e. g., growth with low lipid production, to a state at which lipids are produced at high rates with or without nutritional stress. Yizhak and colleagues introduced this algorithm that was used to predict gene knockouts that can shift metabolism from a given “source” state to a desired “target” state. The approach uses gene expression profiles of the two states in predicting gene deletions that forces changes in the flux distribution of source state to match the desired target state. While the authors used the yeast system to validate their algorithm, the approach is not system-, or state — specific, and is applicable to any organism, and many different phenotypic states. This algorithm presents a potentially exciting method for altering algal metabolism from a nonproductive to a productive state.

The requirements for microalgal dewatering, nutrient recycling and control of effluent wastewater are becoming major challenges to producers (Borowitzka and Moheimani 2010; Charcosset 2009; Clarens et al. 2010; Wyman and Goodman 1993b; Xiong et al. 2008). It is crucial for industrial microalgae production to avoid becoming a net energy-intensive process generating effluent waste at risk of vari­able profitability (Borowitzka et al. 2010; Borowitzka and Moheimani 2010; Charcosset 2009; Clarens et al. 2010; Wyman and Goodman 1993b; Xiong et al.

2008) . As freshwater and energy are essential commodities, finding low-cost and high energy-efficient means to process water and utilise both waste energy and wastewater streams is important (Gude et al. 2010; McHenry 2013). As open pond microalgae production can become expensive due to variable capital, operational, and down-stream processing costs derived from the low microalgae cell densities (Lee 2001; McHenry 2010), and if not optimised, industrial microalgae production will consume large volumes of water through evaporative loss (Chisti 2007; Clarens et al. 2010), generate effluent and become an energy-intensive process (Borowitzka and Moheimani 2010; Charcosset 2009; Clarens et al. 2010; McHenry 2013; Wyman and Goodman 1993b; Xiong et al. 2008). In parallel, conventional industrial and mining process contaminated wastewater streams are also utilising desalination technologies to reduce environmental contamination and associated costs. With new methods of desalting and water processing, including reverse osmosis (RO), forward osmosis (FO) and membrane distillation (MD), desalination technologies are decreasing water processing costs markedly (Bennett 2011; Bourcier and Bruton 2009; Nicoll et al. 2011). This nascent area will require advances in technical performance, reliability and cost of water processing tech­nology to become commercially viable (Banat and Jwaied 2008).

The introduction of microalgae to water processing is a major potential field of exploration in water processing and efficiency circles. As microalgae species can maintain high growth rates in poor-quality or contaminated water and salinities higher than sea water (Amin 2009; Beer et al. 2009; Hightower 2009), they are able to expand the existing tranche of development possibilities and enable new inten­sive production options (Cantrell et al. 2008; Gross 2007; Hankamer et al. 2007). Industrial-scale microalgae production will likely require large and intensive water processing technologies for both culturing and biomass recovery. Yet, achieving energy-efficient and cost-effective microalgae dewatering and water management are major challenges. Progressive vertical integration of energy and water-intensive technologies (including large-scale algae) may enable higher aggregated net industrial efficiencies and potentially create new major resources as by-products including minerals, animal feeds, fertilisers, freshwater, electricity, and biofuel. The integration and colocation of industrial microalgae production (e. g. photobioreac­tors and open ponds) with desalination and other industrial operations are a growing potential area. The theoretical basis behind higher aggregated efficiencies is essentially vertical integration of infrastructure, energy and material flows, reducing total costs, net waste and associated potential environmental contamination. In particular, the judicial use of fast advancing technical capabilities to process water at high efficiency or using waste heat and wastewater through colocating with other industrial facilities may effectively cross-subsidise microalgae energy and water use (McHenry 2013).

1.4 Conclusion

There is no doubt that finding alternative renewable sources of food and energy for future generations is needed. Algae may be a promising answer for the future of biomass production and a carbon-neutral fuel source, considering that microalgae produce significantly higher areal biomass than traditional terrestrial crops. Still, it is unreasonable to think that there is a ‘silver-bullet’ answer to microalgae large-scale biomass production. The conventional (mixed and unmixed open ponds) microalgae to biomass production systems have only been economical for high-value products. The cost of production of microalgae in closed photobioreactors is also most likely to be to very high and not sustainable. Some alternative cultivation methods such as biofilm (see Chap. 2) and mixotrophic growth (see Chaps. 3 and 4), milking (Moheimani et al. 2013 a, b) and combining solar panels with microalgae cultivation system (see Chap. 15) can bring down the cost of production. Combining microalgae cultivation with CO2 bioremediation (see Chap. 7) and wastewater treatment (see Chaps. 5 and 6) can also add value to the whole algae biomass production. Fur­thermore, developing novel and economical dewatering systems will positively reduce the overall cost of production (see Chaps. 12, 13 and 14). Last but not least, modifying the algal species of interest can also reduce the cost of production (see Chaps. 8, 9, 10 and 11). Chapter 17 summarises the technoeconomics of the microalgae to biofuel production.

An advantage of most wastewaters is that it contains N as well as P; the two most important nutrients required by microalgae. Microalgae, however, consume N and P in a more or less fixed ratio: the Redfield ratio; a molar ratio of N:P of 16:1. The ratio of N to P in wastewater is highly variable between wastewaters and even within a specific type of wastewater. If the N:P ratio in the wastewater exceeds 16:1, P will be limiting the growth of the microalgae, and vice versa. High or low N:P ratios may result in incomplete removal of N or P from the wastewater (Li and Brett 2013). However, studies on the stoichiometric composition of microalgae in the past two decades have indicated that the N:P Redfield ratio of 16 is not as fixed as it was once believed (Sterner and Elser 2002). When both the nutrient supply rate and microalgal growth rates are high, the N:P ratio in algae varies from 5 to 19. Conversely, when microalgal growth rates are limited by nutrients, this range is extended from less than 5 to more than 100; a strong deviation from the Redfield ratio (Geider and Roche 2002). Nutrient limitation influences the biochemical composition of the microalgae. Without nutrient limitation, microalgae tend to have a high content of proteins. When nutrients are limiting, the microalgae will accu­mulate carbohydrates or lipids (Smith et al. 2010; Markou et al. 2012a; Gonzalez — Fernandez and Ballesteros 2013). Whether carbohydrates or lipids are accumulated under nutrient starvation depends on the species of microalgae and the degree of nutrient limitation. Changes in the biochemical composition of the biomass as a result of variations in the N:P ratio of the wastewater may have important conse­quences for the valorization of the microalgal biomass. If N and P are balanced, the biomass will have a high protein content and will be attractive for animal feed production. When N or P are limiting, lipids or carbohydrates are accumulated and the biomass can be used for biodiesel, bioethanol, or biomethane production. When P concentrations in the medium are high, some microalgae can accumulate excess

P as polyphosphate granules, a phenomenon known as luxury uptake. Luxury uptake occurs when P-limited microalgae are suddenly supplied with P. This luxury uptake appears to be a transient phenomenon, as microalgae quickly convert their internal P-reserves into growth (Powell et al. 2009). The N:P ratio of microalgae is not only influenced by the N:P ratio of the culture medium, but it also differs between different species or groups of microalgae. P is mainly used in the micro­algal cells to produce rRNA, which is part of the ribosomes. Fast-growing mic­roalgae species have a high content of rRNA to produce new proteins and tend to have a high P content (Klausmeier et al. 2004). Cyanobacteria, on the contrary, have a high protein content and therefore have a higher N:P ratio than other mic­roalgae (Becker 2007; Lopez et al. 2010).

RNA and DNA are highly programmable polymers due to their ability to form specific Watson-Crick base pairing; a property that can be exploited to create well — defined 2D and 3D structures (Rothemund 2006; Seeman 2010). These structures can be thermodynamically stable, and formed via spontaneous self-assembly, a process that requires no catalytic co-factors. In addition to Watson-Crick base pairing, an increasing number of useful tertiary RNA motifs are being discovered in nature. These simple motifs, such as three-way junctions and interacting loops, can serve as diverse building blocks for construction of higher-order structures with increasing complexity.

Single-stranded RNA can be continuously expressed at high levels in live cells, and thus offers the opportunity to program cells to assemble designer nanostructures for specific cellular functions. One potential application of such intracellular nanostructures that is particularly relevant to algal biofuel optimization is the construction of RNA scaffolds (Fig. 8.3). The aim of such RNA scaffolds is to provide a spatially organized docking station where different proteins can ‘park’ at pre-determined distances and permutations. This higher level of organization can be designed to bring together a specific set of enzymes and organize them at fixed proximity and orientations. The configuration could be used to limit the diffusion of intermediate substrates, hence efficiently channel substrates to final products over several enzymatic steps leading to increased yields from sequential metabolic or cellular reactions (Dueber et al. 2009). An example of this utility in bacteria is demonstrated in a recent study where RNAs are engineered and expressed as 2D scaffolds to spatially organize enzymes that effectively lead to increased reaction output by 48-fold (Delebecque et al. 2011). This points to the possibility that RNA scaffolds could be used to engineer biosynthetic pathways to maximize algal biofuel production.

In general, constructing RNA scaffolds involves the following steps (Delebecque et al. 2012). The first is to design the overall RNA secondary structure with minimal predicted free energy. RNA folding software such as RNA Designer, mfold, and NUPACK can be used to design RNA sequences that fold into desired shapes. Considerations generally include the GC percentage, removal of problematic sequences that may cause alternative folding, and removal of motifs such as splice sites and poly(A) signals that may be processed by cells. The next step is choosing appropriate aptamers that allow the coupling of proteins to the RNA scaffolds. Aptamers are folded RNA structures that function as receptors with specific ligand binding properties. Selective aptamers can be incorporated into the RNA scaffold as the docking sites. Aptamers with high binding affinity and specificity are required to

Fig. 8.3 Conceptual illustration of optimizing algal metabolism using RNA scaffolds. RNA scaffold constructs are RNA structures introduced into algae by genetic transformation to form and guide higher order assembly of metabolic enzymes. These composite structures can optimize bioproduction of high value metabolites

achieve good protein docking interactions. RNA sequences can be designed to fold into either discrete self-standing scaffolds, or with additional polymerization between discrete scaffolds to form a more complex structure (Fig. 8.3). In a discrete RNA scaffold, the aptamer binding sites are located within the same RNA molecule separated by spacer sequences. In a polymerizing RNA scaffold, each discrete RNA within the polymer may carry a different aptamer to capture the desired enzyme. The resulting polymer structure is a higher-level one, two or even three-dimensional scaffold with aptamers placed in specific locations. In this case, secondary inter­actions, kinetic considerations, and non-canonical interactions among the
components of the RNA scaffold must all be considered in the secondary structure design process. Common examples of polymerizing RNA scaffolds are nanotubes and two-dimensional sheets. In such complicated cases, designing the RNA scaffold to be an assembly of modular parts might make the process simpler. Another strategy for simplifying polymerizing RNA scaffold design and assembly is the reliance on palindromic sequences as a design strategy. Palindromic sequences create a symmetrical structure that simplifies scaffold design and minimizes the interactions required to assemble the overall structure. Obtaining a desired RNA scaffold structure usually requires an iterative process that relies on experimental verifications. Finally, a possibly helpful design consideration in RNA scaffold sequences is the incorporation of restriction sites within the different relevant sequences to allow for interchangeability and modifications within the RNA scaffold structure.

The use of RNA scaffolds for the purpose of increasing metabolic reactions hinges on the ability to precisely engineer higher-order scaffolds in the complex cellular environment. There remain several challenges that may limit the general application of RNA nanostructure in the cellular environment. First, thousands of cellular RNAs and proteins that are present in cells may non-specifically interact with individual RNA building blocks and prevent the formation of higher-order structures. Second, RNA scaffolds may cause cellular stress and disrupt cellular functions leading to general toxicity. Additional concerns include the stability and half-life of RNA scaffolds, and whether the scaffolds could be targeted to the appropriate cellular compartment such as cytoplasm vs. nucleus. Nevertheless, the ever-increasing number of useful RNA motifs and knowledge of RNA biology at our disposal may help to solve these issues in the near term.

Some researchers have applied immobilization technologies to the stock culture management as an alternative to the common cryopreservation processes, since entrapment processes are cheaper and easier (Chen 2001; Faafeng et al. 1994; Hertzberg and Jensen 1989). Immobilization can also provide protection of the cells toward being consumed by any zooplankton present in the same aquatic ecosystems.

Chen (2001) observed that the immobilized Scenedesmus quadricauda cells in alginate beads can preserve their physiological activities for a long time, even after three years of storage in darkness at 4 °C. This observation was explained by the entrapped cells self-consuming their own pyrenoid reserves. Transmission electron microscopic images of immobilized S. quadricauda cells showed that they lose their pyrenoids after extended storage, which is then rebuilt when the cultures are placed back into their nutrient media under light conditions.

Lebeau et al. (1998) also established the ability to store marine diatom H. ostrearia cells for nearly 2 months after their entrapment in calcium alginate beads and later used them as a substrate source for the greening of oysters. As a follow-up study, the same group achieved a longer term storage when H. ostrearia diatom cells were entrapped in alginate beads and kept at 4 °C (Gaudin et al. 2006). Chen (2003) stored Isochrysis galbana marine microalgal cells for more than a year after immobilizing them in alginate at 4 °C in dark conditions, and the cells were then used for feeding clam cultures.

6.5.1 Cyanobacteria

Divisions of prokaryotic algae, the Cyanophyta and Prochlorophyta, include the cyanobacteria (blue-green algae) and the Prochlorophyta, which use a unique form of chlorophyll, divinyl-chlorophyll, lack red and blue phycobilin pigments, and have stacked thylakoids. These organisms are oxygen-evolving photosynthetic bacteria and have cell wall structures similar to gram-negative bacteria which include a cell membrane, a layer of peptidoglycan, and an outer membrane. Of all the microorganisms described in this chapter, the cyanobacteria pose the least difficulty in terms of cell lysis for lipid extraction. Although cyanobacteria including Spirulina and Nostoc have been used for food for centuries, they tend to have high protein concentrations but low levels of lipids (Becker 1994). However, the cyanobacteria may provide some significant advantages in biofuel production including bacterial cell walls that are easily lysed for lipid extraction and the ability to grow in extreme conditions allowing species control in outdoor ponds, and many have a filamentous morphology that enables facile harvest.

The genetics of cyanobacteria are now well developed (Park et al. 2013), and many strains are easily transformed. Another argument for the use of cyanobacteria is the production of compounds important to the chemical and nutraceutical industries whose current market value is far greater than generic algal oil. For example, Arthrospira platensis produces significant quantities of phycocyanin and gama linolenic acid (C18:3, ю6, GLA) (Colla et al. 2004).

Various groups have engineered cyanobacteria to overexpress either native alkane biosynthetic genes or regulatory genes (Rosgaard et al. 2012; Wang et al.

2013) and have determined compatibility issues between the host and high levels of alkanes synthesized through engineered pathways.