Aeration is a key parameter in mass transfer of CO2. Flue treatment productivity in particular will be decreased in low aeration rates, and adequate CO2 concentrations will be required. However, simply increasing CO2 aeration rates does not neces­sarily lead to a higher CO2 fixation efficiency (Li et al. 2011). For example, Fig. 7.5 shows that increasing aeration rates from 0.1 vvm (volume of gas per volume of culture per min) to 0.5 vvm in a S. obliquus WUST4 culture medium resulted in a decreasing CO2 removal efficiency from 67 to 20 % (Li et al. 2011). A comparable result was obtained for C. vulgaris (Fig. 7.6), with the capability of CO2 fixation and O2 evolution decreasing with increasing feed gas flow rates (Fan et al. 2007). Therefore, in low aeration rates, gas retention time increases leading to an increased interface between CO2 and microalgal cells (Fan et al. 2007). One factor may be the influence of bubble coalescence; as it increases with increased flow rates, larger bubbles rise to the surface at a faster rate than smaller bubbles and the bubble surface area per unit of gas volume declines. This leads to decrease in CO2 absorption (Chiu et al. 2009). However, this is far from consistent across the literature, as the opposite can result in which increasing aeration rates improves

CO2 removal rates (Ong et al. 2010). For example, the effect of aeration rates from 0.25 to 0.5 vvm on CO2 fixation rate of Chlorella sp. MT-7 and MT-15 is signif­icantly higher than CO2 fixation rate at 0.5 vvm as compared with 0.25 vvm (Table 7.6) (Ong et al. 2010). Furthermore, the effect of different aeration rates (0.001, 0.002, and 0.005 ms-1) and CO2 fixation rates (1.5 g d-1) on the dry weight of C. vulgaris and D. tertiolecta was studied (Hulatt and Thomas 2011). The maximum biomass concentration for D. tertiolecta was obtained at the 0.005 ms-1 gas flow rate and at 12 % CO2 (1.5 g d-1), and 0.005 ms-1 and at 12 % CO2 (1.12 g d-1) for C. vulgaris (Table 7.6) (Hulatt and Thomas 2011). This indicates that improved mass transfer occurs at higher gas flow rates. Therefore, coming to an overall conclusion on the effect of flow gas rates on CO2 fixation rates is compli­cated due to opposite results being reported (see Table 7.6). While it is true that normally decreasing aeration rates lead to CO2 fixation efficiency increase, the opposite results have been obtained. This might arise as a consequence of the various production parameters [biomass concentration, light regime, nutrients, and types of PBRs (Hulatt and Thomas 2011)], and how the individual microalgae species affect each system. Nonetheless, increasing or decreasing aeration rate effectively determines whether CO2 fixation rates will increase or decrease in a microalgal system.

7.3 Conclusion

To achieve economical bioremediation of CO2 emitted from power stations using microalgae requires much research in order to maximize its efficiency and at the same time improve the microalgal biomass productivity at larger scales. Further­more, various microalgae and cyanobacteria species exhibit very different CO2 bioremediation rates and potentials for large-scale production. Results presented in this chapter demonstrated that the most attractive species for environmental CO2 mitigation include S. obliquss, D. tertiolecta, C. vulgaris, Phormidium sp., A. microscopic negeli, and C. littorale. The CO2 removal rate by the aforementioned species will require customization and optimization to meet each system-specific requirements. This chapter has reported on the initial cell concentrations, initial input CO2 concentrations, and aeration rates impact on CO2 bioremediation. In general, increasing initial cell concentrations and decreasing aeration rates lead to increasing CO2 fixation efficiency. It is to be noted that lowering aeration rates lead to a higher CO2 biofixation efficiency because of improved CO2 mass transfer between microalgal cells and the culture medium. Moreover, the input CO2 con­centration influences removal efficiency of CO2, however, providing high levels of CO2 into culture mediums leads to acidification. In contrast, the consumption of CO2 by microalgae through photosynthesis results in pH increase that may impact growth rates of some microalgae species.

11.2.1 Genomics and Molecular Biology of Microalgae

Microalgae-based biocrude production is an established technology, but compared to conventional fossil fuel extraction, it is energetically unattractive and the chemistry poorly understood. Improvements in process chemistry are necessary for microalgae biocrude to compete successfully with fossil fuels and non-algal biofuel technologies and to reach its full potential. While conventional strategies for strain development can yield significant improvements, genetic modification (GM) has the potential to improve aspects of biocrude production more rapidly and poten­tially to greater effect. As the primary aim of HTL is to generate more biocrude product per unit biomass with reduced energy costs, the manipulation of the initial biomass quality and yield, as well as aspects of the HTL chemistry (e. g. N & S content), may be amenable to GM strategies. The first step is to determine what traits would be helpful for HTL processing; the second is to identify how manip­ulating algal genetics can produce those traits.

Genetic Research in Microalgae To engineer beneficial traits into production strains, sufficient knowledge of algal biology is required to conduct targeted opti­misation. This is embodied primarily in both the understanding of the most appropriate effects to target and of the methods to enable their engineering. Just as bacterial engineering rests upon a deep knowledge of bacterial biochemistry and genetics, algal GM biotechnology needs to rest upon a firm foundation of funda­mental research into the way that algal genomes work. Despite the commonality of fundamental genetic mechanisms across the span of life on Earth, great variety is also present, and a consistent lesson is that ‘the devil is in the detail’ with respect to individual organisms. Consequently, the specifics matter greatly. Further, much biological variability will have accumulated from the ancient origins of algal phyla and their early divergence from plants and animals. Much specific knowledge of algal gene regulation will therefore be required before skilful, efficient and routine genetic manipulation will be possible. The recently expanded library of available algal genomes is a welcome advance but is of limited utility until these genomes are systematically mapped, curated, annotated and understood, a much more time­consuming task than the actual sequencing. Systematic approaches such as the generation of knockout mutants of all Chlamydomonas genes at Stanford University (Zhang et al. 2014) and the transcriptomic (FANTOM) approaches pioneered at RIKEN in Japan (Forrest et al. 2014) are needed to provide the ability to quickly and with certainty assign biological functions to specific genes and curate algal genomes similarly to those of mammals. While microalgal genomes are undoubt­edly simpler than the human genome, the resources allocated to studying them are miniscule by comparison, and the molecular toolkit is sparse, especially the lack of specific antibodies.

Advancements in Genomics Genome sequencing and sequence analysis is an important first step in deepening our understanding of microalgal systems and ultimately developing improved engineering processes. Only a very small number of genomes are available particularly when considered against the huge microalgal species diversity; however, the number of genome sequencing programs is steadily increasing (see Table 11.1). The National Centre for Biotechnology Information (NCBI) now contains 25 green algae genomes either in full, as scaffolds, or for which sequencing is currently underway (www. ncbi. nlm. nih. gov/genomes). Fur­thermore, there are novel bioinformatic tools (e. g. KEGG assignments accessible at www. genome. jp/kegg), and as BioModels databases accessible at www. ebi. ac. uk/ biomodels-mainwww. ebi. ac. uk/biomodels-mainwww. ebi. ac. uk/biomodels- mainwww. ebi. ac. uk/biomodels-main) become available online, they will enable researchers to predict and characterise gene regulatory pathways, forecast outcomes of metabolic shifts and functionally annotate de novo genomes of diverse algal species.

Genetic Mechanisms The existence of functional microRNAs in Chlamydomonas (Molnar et al. 2007) demonstrates that much of the convoluted genomic biology being revealed in mammals can also be expected in these simple organisms. The general schema of molecular pattern receptors, signal transduction mechanisms, and complex transcription factor-mediated feedback control of nuclear genes is to be expected, and many of the protein motifs will be familiar (e. g. helix-loop-helix transcription factors). However, given the evolutionary distance between different algal clades and between algae and land plants, it is to be expected that apart from highly conserved central mechanisms (core metabolism, cell replication, and mitochondrial and photosynthetic machinery), many baroque variations remain to be discovered. Algal genetics lags far behind algal physiology, much of which is common to plants in specific detail as well as general principles. To fill this gap, high-throughput gene analysis and bioinformatics will be critical for rapid mapping of the overall territory, even if painstaking molecular analysis is still needed for final validation of proposed biochemical and information pathways.

The algal genes that have so far been studied in detail illustrate this need. Significant changes to cell status, such as nutrient limitation (sulphate, nitrogen, iron, copper), lead not to up-regulation of a few receptors or import proteins, but to coordinated changes of thousands of genes, which resemble those waves of altered

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gene expression seen in multicellular organisms. Only high-throughput mapping can provide the necessary background to support the efficient dissection of these biological responses. Apart from nutrient limitation, the kinds of coordinated responses which might be expected include photoacclimation, responses to pre­dators and pathogens, differentiation-like developmental programs and adaptions to environmental niches. Fortunately, many of the tools developed for the study of other organisms can readily be adapted for algal biology. These include powerful genome-editing platforms either developed [zinc finger nucleases, TALENs (Gao et al. 2014; Sizova et al. 2013)] or under-development [CRISPR/Cas (Sander and Joung 2014)]. Although not yet routine, the ability to conduct precise genome engineering will greatly advance the speed and scope of algal GM production.

Case Studies A number of genetic responses in algae have been described, mainly in response to key physiological processes such as photosynthesis, nutrient limi­tation and circadian rhythm. These include the analysis of the transcriptional responses of the light-harvesting complex (LHC) genes to light and circadian signals, the carbon concentrating mechanism (CCM) in response to CO2 limitation, and responses to iron, copper and sulphur limitation.

Few of the estimated 234 transcription factors and regulators initially identified bioinformatically in the Chlamydomonas genome (Riano-Pachon et al. 2008) have even tentative roles assigned to them. Although no promoters have been compre­hensively analysed, several have been cloned and their behaviours studied and utilised for experimental systems. The best examples are the light-harvesting antenna genes which are regulated both by light and by circadian mechanisms. In addition to promoter regulation, post-transcriptional regulation has been demon­strated by an mRNA binding protein CHLAMY1, composed of two subunits (C1 and C3). In turn, an E-box-like promoter element has been shown to be involved in the regulation of the circadian rhythm protein C3 (Seitz et al. 2010) and some binding factors isolated. Regulatory factors controlling the CCM have been iden­tified [CCMl (Fang et al. 2012; Fukuzawa et al. 2001); LCR1 (Ohnishi et al. 2010; Yoshioka et al. 2004)]. Iron-responsive elements have been identified in several genes [Fox1 (Allen et al. 2007; Fei et al. 2009), Atx1, Fbp1, Fld, Fea1], while the copper response regulator CRR1 has been shown to mediate copper and zinc responses (Malasarn et al. 2013; Sommer et al. 2010) and anaerobiosis [HydA1 (Lambertz et al. 2010; Pape et al. 2012) and Fdx5]; other nutrient uptake regulatory genes include those for sulphur SAC1 (Davies et al. 1996; Moseley et al. 2009) and phosphate PSR1 (Moseley et al. 2009; Wykoff et al. 1999). Although this represents a beginning, it pales in comparison with the extensive analyses of animal genomes, and when contrasted to the * 15,000 genes of Chlamydomonas, it is unlikely that this subset will provide an adequate basis for modelling promoter function in algae in general.

Some analysis has started in species other than Chlamydomonas including Dunaliella (Jia et al. 2012; Lu et al. 2011; Park et al. 2013), and some crossover is expected from plant gene analysis especially in Arabidopsis. A start has also been made in understanding the role of mRNA regulation (Schulze et al. 2010; Wobbe et al. 2009) and chromatin remodelling in Chlamydomonas (Strenkert et al. 2011, 2013). While miRNA regulation has been demonstrated (Molnar et al. 2007; Yamasaki et al. 2013), little detail is available, nor is epigenetics well understood. In summary, the detail and breadth of examples typical of the regulation of mammalian promoters and their resultant mRNAs is sorely lacking for algal gen­omes. Consequently, close study of a set of promoter control mechanisms as models is badly needed and will greatly advance the level of understanding in this area, enabling much more sophisticated photosynthetic engineering, including the discovery of useful inducible/repressible promoters, and the ability to manipulate metabolic pathways and cellular strategies which are normally tightly regulated by photosynthesis. Lipid and starch accumulation, photoprotection and cellular repli­cation, for example, are all cellular functions which are desirable to control for biotechnology applications. Abundant proteins including rubisco and LHC proteins represent substantial cellular resources. Some LHC adaptive functions are important to retain or enhance; others are potentially dispensable under bioreactor conditions or can even reduce biomass growth if allowed to operate naturally. Resource allocation within a cell is complex (Pahlow and Oschlies 2013) and only partly within our control as over — or under-production of specific metabolites can be detrimental to the fitness of the organism and feedback regulation in algae is incompletely understood. Therefore, opportunities exist for the development of the excretion of the end product (e. g. H2 produced from water via the photosynthetic machinery; volatile metabolic intermediates (Melis 2013); specific secretion mechanisms for proteins and lipids).

The study of gene regulation has traditionally proceeded through intensive analysis of specific cases. As broad understanding evolves of the kinds of mech­anisms that are present in biological systems, the emphasis has shifted to high — throughput analyses starting with microarrays and mass mutant libraries, and it is to be expected that this will quickly generate large amounts of data once algal genomics matures. Nonetheless, there are very few case studies of algal genetic mechanisms, and the study of particular cases will still be vital to anchor, interpret and calibrate the results of mass data acquisition.

One of the most straightforward methods for harvesting algal cultures is to let the cells settle naturally. This has been done for many different algal strains and has broad applicability in wastewater treatment facilities where the high bacterial load and nutrient levels tend to favor clumping and settling. Cell density and the radius of the algal cells influence its utility as well as flow rate of the system, which has been enhanced by lamellar separators and sedimentation tanks. Settling is frequently deployed with added flocculant/coagulants (Chen et al. 2011). Reliance on settling is time-consuming and is less useful in situations where one would like the harvested algal biomass or its cellular components to remain intact and/or to maintain cellular function or product integrity for further downstream processing. While it is also the slowest separation option, it is also the one with the lowest energy requirement. Therefore, settling/sedimentation is not employed for algal biofuel application without at least some type of flocculation or settling accelerant.

One of the focuses of microalgal lipid biofuel research is high lipid-containing algae. As has been reported in the past, low-density cells tend to not settle well (Edzwald 1993). The best oleaginous algae used for biofuel feedstock have between 37 and 70 % of their biomass by weight as lipid under induction. High lipid content translates into cells of low density (Eroglu and Melis 2009) that can remain sus­pended and make settling not useful. This type of buoyant biomass can also complicate other methods that rely on gravity for separation.

The proximate composition of microalgae can have a major influence on the biogas productivity. For instance, lipid and protein play an important role in anaerobic digestion as they breakdown during the hydrolysis stage (Chen et al. 2008). High protein concentrations lead to the formation of ammonia compounds within the digester (Chen et al. 2008; McCarty 1964). Ammonia is produced from the bio­logical breakdown of nitrogenous matter (i. e. protein). High concentrations of ammonia being formed within the digester can lead to inhibition of the bacterial community (Buswell and Boruff 1932). Ammonia toxicity has been shown to affect the methanogenic bacteria in two separate ways. The first is by the ammonium ion directly inhibiting the methane synthesising enzyme. The second is by the hydro­phobic ammonia nitrogen (NH3-N) molecule diffusing passively into the bacterial cell causing an imbalance and/or a potassium deficiency within the microbial cell (Kayhanian 1999; Sialve et al. 2009; Ward et al. 2014). Furthermore, NH3-N in the gaseous form has been shown to be more toxic at lower concentrations than the aqueous ionised form (McCarty 1964). Ammonia is extremely toxic at levels above 3000 mg/L and can be moderately inhibitory at level of 1500-3000 mg/L (McCarty 1964). On the other hand, maintaining NH3-N concentrations between 50 and 200 mg/L have been shown to be beneficial for the bacterial population as ammonia nitrogen is an essential nutrient required by the microbial community (Parkin and Owen 1986). As the utilisation of volatile fatty acids must balance the production of volatile fatty acids by hydrolytic and acetogenic bacteria to maintain digester sta­bility, efficient digester performance is therefore dependant on maintaining the

NH3-N concentration below the inhibitory limits for all associated anaerobic digestion bacteria (McCarty 1964; Ward et al. 2014).

Several studies have demonstrated the potential of microalgae for the removal of nitrogen and phosphorus elements from wastewater effluents, with cells taking them up as their nutrient sources. Some of those studies are listed in Table 2.1, while some are further described in the following text. It should be noted that the direct comparison of the nutrient removal efficiencies from various experiments is inherently difficult, because of variations in the initial nutrient concentration, duration of the experiment, pH of the working solution, selected algal species, and the type of immobilization matrix.

The most common algal species used for the removal of nitrates and phosphates are Chlorella, Scenedesmus, and Spirulina. Various open and closed bioreactors have been used for the removal of nutrients by algae, ranging from tubular photobioreactors to corrugated raceways and high-rate algae ponds (Borowitzka 1999; Cromar et al. 1996; Olguin et al. 2003). Increased nutrient removal efficiencies with immobilized algae are usually related, with the dual effect of the enhanced photosynthetic rate of the cells and the ionic exchange between the nutrient ions and the immobilization matrix. Gels which are anionic in nature, such as carrageenan, are usually associated with the adsorption of cations (such as ammonium (NH4+)), while cationic gels such as chitosan yield adsorption of anions (phosphate (PO-3), nitrate (NO3-), nitrite (NO2-)) with higher efficiencies (Mallick and Rai 1994). Moreover, calcium ions of the alginate or chitosan gels are particularly efficient for the precipitation of PO-3 ions from wastewaters (Lau et al. 1997).

Immobilization of C. vulgaris cells within sodium alginate beads showed higher nutrient removal efficiencies from sewage wastewater compared to their externally immobilized counterparts on polyurethane foam (Travieso et al. 1996). de-Bashan et al. (2002b) obtained higher ammonium and phosphate removal efficiencies after co-immobilization of C. vulgaris microalgae with plant growth-promoting bacte­rium Azospirillum brasilense in alginate beads, relative to immobilized C. vulgaris cells alone. Tam and Wong (2000) obtained 78 % ammonium and 94 % phosphate removal efficiencies with immobilized C. vulgaris, entrapped in calcium alginate beads, compared to the 40 % ammonium and 59 % phosphate removal with free cells. Lau et al. (1997) also observed significantly higher ammonium (95 %) and phosphate (99 %) removal efficiencies for C. vulgaris cells immobilized in alginate beads relative to their free counterparts, resulting in only 50 % nitrogen and 50 % phosphate removal. In contrast, free cells of Nannochloropsis sp. cells yielded higher total phosphorus removal with respect to their immobilized cells within calcium alginate beads (Jimenez-Perez et al. 2004).

Pretreatment of the cells by starving them in a saline solution for three days was found to increase the cellular growth and phosphate removal efficiencies of the independently co-immobilized Chlorella sorokiniana & A. brasilense and C. vul­garis & A. brasilense pairs entrapped in alginate beads (Hernandez et al. 2006). Kaya et al. (1995) observed higher nutrient removal rates using S. bicellularis cells when they were immobilized on flat-surface alginate screens compared to their encapsulated form inside alginate beads.

Canizares et al. (1993) used immobilized Spirulina maxima cells in kappa — carrageenan gel beads for nutrient removal from swine waste. This immobilized system achieved around 90 % total phosphorus and ammonium-nitrogen removal, while it also allowed processing swine waste at higher concentrations. Chevalier

and de la Notie (1985) investigated Scenedesmus acutus and Scenedesmus obliquus cells individually immobilized in kappa-carrageenan beads for nutrient removal from a secondary effluent. Immobilized cells showed similar cellular growth and ammonium or phosphate uptake rates compared to their free-living cell counter­parts. They observed around 90 % ammonium removal within the first 4 h, while all traces of phosphate were removed within 2 h (Chevalier and de la Notie 1985).

C. vulgaris and Anabaena doliolum cells immobilized in chitosan have higher phosphate, nitrate, and nitrite removal efficiencies than when they were immobi­lized within agar, alginate, or carrageenan (Mallick and Rai 1994). In addition, the phosphate removal capacity of the immobilization process was increased when phosphate-deprived cells were initially entrapped within chitosan. Fierro et al. (2008) investigated the nitrate and phosphate removal efficiencies of individually entrapped Scenedesmus sp. cells within chitosan beads. Immobilized cells achieved approximately 94 % phosphate and 70 % nitrate removal within the first 12 h after incubation, whereas by themselves chitosan beads removed 60 % phosphate and 20 % nitrate by the end of the experiment. The reason for yielding a significant phosphate removal rate (60 %) by chitosan beads alone was explained by the increased pH values, which eventually triggered the release of some calcium ions from chitosan polymer, resulting in the precipitation of phosphate ions (Fierro et al. 2008; Tam and Wong 2000).

Other immobilization matrices have also been proposed as alternatives to the gel beads. Immobilized cells of Trentepohlia aurea microalgal cells on a filter paper formed a biofilm layer that reduced the concentration of ammonium, nitrate, and nitrite ions, for around 40 days (Abe et al. 2003). Shi et al. (2007) proposed a twin- layer system, where the microalgal cells are attached on an ultrathin and micropo­rous “substrate layer” composed of a nitrocellulose membrane, which is surrounded by a “source layer” of macroporous glass fiber providing the growth medium (Shi et al. 2007). They observed phosphate, ammonium, and nitrate removal when C. vulgaris and Scenedesmus rubescens microalgal cells were entrapped in this twin — layer system.

In a recent study, C. vulgaris cells immobilized on electrospun chitosan nano­fiber mats yielded an efficient nitrate removal rate (87 %) as a result of the dual action of nitrate removal by the microalgal cells and electrostatic binding of the nitrate ions on chitosan nanofibers (Eroglu et al. 2012). In other studies from the authors’ laboratories, the resulting microalgal composites with multilayer graphene (Wahid et al. 2013b) or graphene oxide sheets (Wahid et al. 2013a) also achieved significant nitrate uptake rates, without being toxic for the microalgal cells.

Contamination of cultures by “weed” microalgae or predators is emerging as a major issue in large-scale cultivation of microalgae (Kazamia et al. 2012; Shurin et al. 2013). Although little is known about dispersal strategies of microorganisms, it is likely that wastewater contains spores of weed microalgae and predators. Contamination will therefore be more difficult to avoid when wastewater is used as a source of nutrients than when a pure culture medium is used. The risk of contamination can be limited by sterilizing the wastewater by micro — or ultrafiltration or by chemical disinfection. The cost of disinfection, however, will probably be too high when microalgae are pro­duced for low-value products such as fuel or animal feed (Wang et al. 2013). Wastewater will likely contain spores or dispersal stages of herbivores of microalgae such as microcrustaceans, rotifers, or ciliates. The microcrustacean Daphnia or the water flea is often an important herbivore in microalgae-based wastewater treatment systems. If Daphnia invades the system, it can reduce microalgal biomass by two orders of magnitude within a few days (Cauchie et al. 2000). Due to its large size, Daphnia can be relatively easily removed by simple screening using a nylon mesh (Borowitzka et al. 1985). Smaller herbivores like rotifers or ciliates can also decimate microalgal biomass within a few days once they have invaded the culture (Schltiter et al. 1987; Moreno-Garrido and Canavate 2001). However, these smaller herbivores cannot be so easily controlled by simple screening.

Some authors have proposed to use a wild consortium of microalgae rather than monospecific cultures for biofuel production. Today, most microalgae-based wastewater treatment systems use such wild consortia rather than monospecific microalgal cultures. Consortia of microalgae may be more resistant to the impact of small herbivores than monospecific microalgal cultures. If a small herbivore invades the culture, small microalgae will be consumed by large microalgal species that cannot be ingested by the herbivore may take over the culture and maintain a high productivity (Shurin et al. 2013). Consortia of microalgae are not only more resistant to herbivores, but may also be more efficient converting nutrients into biomass than monospecific cultures (Ptacnik et al. 2008; Kazamia et al. 2012; Shurin et al. 2013). However, the use of consortia rather than pure algal cultures may pose a problem for the valorization of the resultant biomass. First, if the consortia contain toxic species such as cyanobacteria, the biomass cannot be used for food or animal feed. Second, it is more difficult to control the biochemical composition of the biomass in mixed consortia than in pure cultures. Some species in the consortium may produce carbohydrates, for instance, while others produce lipids. The consortia that occur in wastewater treatment systems are often domi­nated by a few freshwater microalgal species, very often chlorophytes (Chlorella, Scenedesmus, Micractinium, Pediastrum) (Pittman et al. 2011). Some control over community composition is possible by recycling part of the harvested biomass. In a long-term study in a HRAP, recycling of the harvested biomass resulted in a 90 % dominance of the community by the large species Pediastrum, which improved the harvestability of the biomass (Park et al. 2011b).

Kenan Jijakli, Rasha Abdrabu, Basel Khraiwesh, David R. Nelson, Joseph Koussa and Kourosh Salehi-Ashtiani

Abstract The uniquely diverse metabolism of algae can make this group of organisms a prime target for biotechnological purposes and applications. To fully reap their biotechnological potential, molecular genetic techniques for manipulating algae must gain track and become more reliable. To this end, this chapter describes the currently available molecular genetic techniques and resources, as well as a number of relevant computational tools that can facilitate genetic manipulation of algae. Genetic transformation is perhaps the most elemental of such techniques and has become a well-established approach in algal-based genetic experiments. The utility of genetic transformations and other molecular genetic techniques is guided by phenotypic insights resulting from forward and reverse genetic analysis. As such, genetic transformations can form the building blocks for more complex genic manipulations. Herein, we describe currently available engineered homologous recombination or recombineering approaches, which allow for substitutions, inser­tions, and deletions of larger DNA segments, as well as manipulation of endogenous DNA. In addition, as reagent resources in the form of cloned open reading frames (ORFs) of transcription factors (TFs) and metabolic enzymes become more readily available, algal genetic manipulations can greatly increase the range of obtainable phenotypes for biotechnological applications. Such resources and a few case studies are highlighted in the context of candidate genes for algal bioengineering. On a final note, tools for computer-aided design (CAD) to prototype molecular genetic tech­niques and protocols are described. Such tools could greatly increase the reliability and efficiency of genetic molecular techniques for algal bioengineering.

Kenan Jijakli and Rasha Abdrabu contributed equally to this work.

K. Jijakli

Division of Engineering, New York University Abu Dhabi,

P. O. Box 129188, Abu Dhabi, United Arab Emirates

R. Abdrabu • B. Khraiwesh • D. R. Nelson • J. Koussa • K. Salehi-Ashtiani (H) Division of Science and Math, Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi, P. O. Box 129188, Abu Dhabi, United Arab Emirates e-mail: ksa3@nyu. edu

© 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_9

9.1 Introduction

Microalgae have been described as nature’s very own power cells and can provide alternatives to petroleum-based fuels without competing with food crops (Dismukes et al. 2008; Singh et al. 2011). The heterogeneity and diversity that algae evolved make the molecular mechanisms that different algae have adopted, along with manipulating those mechanisms of tremendous interest. Currently, research is being conducted to develop methods for genetic modification to introduce desirable traits into algae and to develop synthetic biology approaches to re-engineer algal cells (Ferry et al. 2011; Gimpel et al. 2013; Rabinovitch-Deere et al. 2013). The crux of this research is to advance the molecular biology techniques utilized for algae and to ease the modification of the molecular systems of the species of interest.

One powerful example is the alga Chlamydomonas reinhardtii. As a single — celled alga containing a single large chloroplast, C. reinhardtii represents typical soil green algae. Moreover, Chlamydomonas combines powerful genetics with the availability of unique genetic and genomic resources. All three genomes (nuclear, plastid, and mitochondrial) have been fully sequenced (Merchant et al. 2007); large mutant collections have been established; and all three genomes are amenable to genetic manipulation by transformation (Hippler et al. 1998; Neupert et al. 2009). Most tools required for systematic functional genomics studies are available in Chlamydomonas, including high-frequency transformation protocols (Kindle 1990), efficient methods for chemical and insertional mutagenesis (Dent et al. 2005), and workable protocols for RNA interference (RNAi) (Arif et al. 2013; Molnar et al. 2007; Zhao et al. 2007). Overall, this represents a great advance in the molecular techniques and methods, especially with their applications to algae.

In the absence of cell differentiation, some algae such as Chlamydomonas can provide a much simpler system for genetic manipulations compared with higher plants. Manipulation of microalgae by metabolic and genetic methods would both permit (1) selection of beneficial pathways redirecting cellular functions toward the synthesis of preferred products and (2) introduction of non-algal genes for the generation of algal recombinant proteins. The selection of favorable pathways may include increased resistance to environmental or stress changes on the culturing and life cycle of the algae, expedited biomass production, and excretion of valuable products. The potential of such system remains to be optimized as an alternative protein expression system.

In light of all these potentials, and particularly during the past two or three decades, algal biotechnology grew steadily into an important global industry with new entrepreneurs realizing the potential of algae. However, creating profitable industries out of microalgae still remains challenging, and perhaps the development of new molecular techniques might expedite microalgae’s full industrial develop­ment, especially that some microalgal classes have highly complex genetic compositions rendering their modification arduous: Microalgal genome sizes range from 12.6 Mbp in Ostreococcus tauri, a Chlorophyta member, to 168 Mbp in the

Haptophyta Emiliania huxleyi and up to an impressive 10,000 Mbp for the Dinophyta Karenia brevis (Cadoret et al. 2012). Currently, a genome size of 10 Gbp precludes full genome sequencing, and as such, a lesser extent of knowl­edge would be available rendering the modification of such organisms a highly demanding task.

Koenraad Muylaert, Dries Vandamme, Imogen Foubert and Patrick V. Brady

Abstract Large-scale production of microalgae for biofuels is still facing several major challenges to become competitive with other forms of renewable and non­renewable energy. A major challenge is harvesting, which requires the separation of a low amount of biomass consisting of small individual cells from a large volume of culture medium. Flocculation is seen as a promising low-cost harvesting method for microalgae biomass. In this chapter, the challenges and potential advantages of using flocculation as a harvesting method for microalgae are reviewed.

12.1 Introduction

Microalgae have attracted attention in recent years as a promising new source of biofuels (e. g., Chisti 2007). In addition to biofuels, microalgae have a high potential for other innovative applications. They have a high content of proteins with a favorable amino acid composition and are potentially interesting for use as animal feed (Draaisma et al. 2013). Microalgae can absorb nitrogen and phosphorus from wastewater and used in wastewater treatment (e. g., Park et al. 2011a, b). Microalgae [3] [4]

can also produce a range of high-value chemicals that cannot be derived from conventional crops (e. g., specific fatty acids, carotenoids, or natural pigments) (Pulz and Gross 2004). Despite this enormous potential, commercial applications of microalgae are today still limited to production of value-added biochemicals or nutritional supplements. To use microalgae for low-value applications such as biofuels, animal feed, or wastewater treatment, the cost and energy inputs in the production process need to be decreased by at least an order of magnitude (Wijffels and Barbosa 2010; Christenson and Sims 2011). One of the major challenges is to develop a low-cost and energy-efficient harvesting method (Mata et al. 2010; Rawat et al. 2012).

Microalgae are generally small (2-20 pm) and have a specific density that is close to that of their culture medium. This precludes the use of simple screening or sedimentation methods for harvesting microalgae. Similar challenges exist with harvesting of other microorganisms, such as yeasts or bacteria produced hetero — trophically in fermentors. An important difference is that biomass concentrations of phototrophically cultivated microalgae are more than an order of magnitude lower than those of bacteria or yeasts. This is because high biomass concentrations in microalgal cultures result in self-shading and light limitation of photosynthesis. Microalgal biomass concentrations range from 5 g L-1 dry weight in closed pho­tobioreactors to only 0.5 g L 1 in open raceway ponds. The low biomass con­centration implies that large volumes of culture medium need to be processed to harvest the biomass. For example, a 10-ha facility that produces microalgae in raceway ponds and that produces 30 tons of dry microalgal biomass ha-1 year-1 should process about 2000 m3 of microalgal broth per day. This is in the same order of magnitude as a water treatment plant processing wastewater of 10,000 people. To realize large-scale production of microalgae, a harvesting technology is required that is capable of processing large volumes of culture medium at a minimal cost (Molina Grima et al. 2003; Uduman et al. 2010; Christenson and Sims 2011; Benemann et al. 2012; Vandamme et al. 2013).

Magnetic separation of algae has been proposed in the past and is currently being investigated. The concept is to pass magnetized algal cells (or magnetic aggregates) past a magnet and remove them directly from the culture medium.

The original methods for magnetic separation used addition of magnetite and aluminum sulfate to the culture that are bound to the algal cells and were subse­quently removed with an electromagnet. This was very successful at neutral and slightly acid pH waters (79-94 % removal) but less effective with water at higher pH (55-64 %) (Bitton et al. 1975). This approach has the problem of pH sensitivity as well as the dependence on flocculant and added magnetic materials.

There are other current approaches for magnetic separation of algae. One company, Advantageous Systems LLC, specializes in functionalized nanomaterials and is marketing a system for the separation of algae. Public details of their methods are not available, but it appears that they have used functionalized nanomaterials added to the algal culture that is then passed through magnetic plates to separate the algae in a rapid, continuous, and efficient manner (http://www. adsalgae. com/). Another similar approach used chitosan-Fe3O4 nanoparticle composites to effec­tively (99 % efficiency) remove Chlorella sp. KR-1 from the culture medium using a permanent NdFeB magnet in laboratory scale (Lee et al. 2013).

Using molecular engineering, there are efforts underway to produce algae with improved iron uptake and storage as well as being more magnetically susceptible, so that they can be separated magnetically (Sayre and Postier 2013). These authors engineered iron uptake and storage proteins into Chlamydomonas reinhardtii and Auxenochlorella protothecoides and found improved iron utilization but, to this point, have been unable to definitively show improved magnetic moment (personal communication).

Magnetic separation systems that recognize only production strains of algae and harvest them specifically while excluding bacteria and other contaminants would be advantageous in that they could provide a clean separation of suitable feedstock for the downstream processing and a cleaner final product. However, addition of magnetic materials to the system would impact downstream processes as well as add OpEx for the materials used. Currently, there are no scaled-up systems for magnetically separating algae. However, large magnetic separators are already in use in the mining industry should technological and cost advantages be overcome.

In addition to being economically viable, biofuels from microalgae must also meet life cycle targets to provide quantitative improvements to current fuels. The key elements typically considered in life cycle assessment of biofuels include:

• Energy—Usually the net energy ratio (NER), that is, does it require more energy to produce the fuel than is available in the fuel.

• Greenhouse gas—Are the net greenhouse gas emissions lower than fossil fuels or current biofuels.

• Water use—How many litres of water are consumed to produce a litre of biofuel.

The key motivation for asking these questions is whether or not the proposed process is sustainable. A range of LCA is reviewed in de Boer et al. (2012) with a focus on energy consumption. These are provided in Table 17.5.

The motivation for analysing energy is that the energetic viability is very closely linked with the economic viability. That is, it is almost impossible to have a process which is economically viable when the process uses more energy than it produces. The review of the LCA studies shown in Table 17.5 leads to the following conclusions:

• IF PBR’s are used, then cultivation is typically the major energy user

• If raceway ponds are used, the major energy user is either dewatering, cell disruption or solvent extraction.

These conclusions suggest that an energetically viable process must use raceway ponds, process wet biomass (avoid drying), minimise energy required for cell disruption and minimise solvent recovery. Evaluation of different approaches by de Boer et al. (2012) indicated that hydrothermal liquefaction and wet processing methods with limited cell disruption were energetically feasible. This aligns with the processing focuses of the American laboratories as they process wet (fermen­tation and hydrothermal liquefaction) and do not use cell disruption.

As a final note, it is important to continually evaluate the key life cycle criteria (greenhouse gas emissions, net energy ratio and specific water consumption) in addition to the techno-economic analysis. This is simply because the lowest costs solution is not always the most sustainable.

W

17.3 Conclusion

This work provides a review of the economic and energy attributes of microalgae biofuels. The clear outcome of this analysis is that the economics of microalgae biofuels need to improve substantially before they can compete with current fuels. In addition to this, the challenges faced in reaching the metrics are productivity and capital cost and there are other barriers, including:

• The instability of algae monocultures and exposure to pests and viruses.

• The limited number of sites with access to the optimum climate, available land, low-cost CO2 source and abundant water.

• Competition from other options including biofuels from terrestrial crops, electric cars and unconventional fossil fuels.

Despite these substantial challenges, there is a very strong chance that micro­algae will become a source of liquid fuels into the future; however, this will be at a time when oil prices are higher, further development has driven costs down to suitable levels and there is strong markets for co-products. That is, microalgae biofuels are likely to form part of a solution to liquid transport fuels rather than being the ultimate solution.

K. Muylaert (H) • A. Beuckels • O. Depraetere • D. Vandamme Laboratory of Aquatic Biology, KU Leuven Kulak—University of Leuven,

Etienne Sabbelaan 53, BE8500 Kortrijk, Belgium e-mail: Koenraad. muylaert@kuleuven-kulak. be

I. Foubert

Research Unit Food and Lipids, Department of Molecular and Microbial Systems Kulak, KU Leuven Kulak, Etienne Sabbelaan 53, 8500 Kortrijk, Belgium

[2] Foubert

Leuven Food Science and Nutrition Research Centre (LFoRCe), KU Leuven, Kasteelpark Arenberg 20, 3001 Heverlee, Belgium

G. Markou

Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece

© 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_5

K. Muylaert (H) • D. Vandamme

Laboratory of Aquatic Biology, KU Leuven Kulak—University of Leuven,

Etienne Sabbelaan 53, 8500 Kortrijk, Belgium e-mail: Koenraad. muylaert@kuleuven-kulak. be

I. Foubert

Research Unit Food and Lipids, Department of Molecular and Microbial Systems Kulak, KU Leuven Kulak, Etienne Sabbelaan 53, 8500 Kortrijk, Belgium

[4] Foubert

Leuven Food Science and Nutrition Research Centre (LFoRCe), KU Leuven,

Kasteelpark Arenberg 20, 3001 Heverlee, Belgium

P. V. Brady

Sandia National Laboratories, Geoscience Research and Applications Group,

P. O. Box 5800, Albuquerque, NM 87185, USA © 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_12

[5]The information is summarised in DOE (2014); however, process economics data are drawn from Davis et al. (2014) for the ALU process and Jones et al. (2014) for the AHLT process. Harvest and dewatering numbers were taken from further work conducted on the original harmonisation report (ANL et al. 2012).