Improving lipid production by strain development in microalgae: Strategies, challenges and perspectives

Bioresource Technology 292 (2019) 121953

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Review

Improving lipid production by strain development in microalgae: Strategies, challenges and perspectives Seunghye Park, Thu Ha Thi Nguyen, EonSeon Jin

T

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Department of Life Science, Research Institute for Natural Sciences, Hanyang University, Seoul, Republic of Korea

ARTICLE INFO

ABSTRACT

Keywords: Microalgae Strain development Genome editing Lipid productivity

Over the past decade, the number of original articles and reviews presenting microalgae as a promising feedstock for biodiesel has increased tremendously. Many improvements of microalgae have been achieved through selection and strain development for industrial applications. However, the large-scale production of lipids for commercialization is not yet realistic because the production is still much more expensive than that of agricultural products. This review summarizes recent research on the induction of lipid biosynthesis in microalgae and the various strategies of genetic and metabolic engineering for enhancing lipid production. Strain engineering targets are proposed based on these strategies. To address current limitations of strain engineering for lipid production, this review provides insights on recent engineering strategies based on molecular tools and methods, and also discusses further perspectives.

1. Introduction Nowadays, humans are faced with a lack of necessary resources to maintain or improve the quality of life. In addition to population growth, the development of our culture implies increasing demand for energy. However, the amount of energy available on the Earth is limited, and therefore our future development will depend on how we use it. As industrialization progresses and quality of life improves, global energy consumption continues to increase. Fossil fuels are now our core energy source, and the consequent CO2 emissions have been recognized as the cause of global climate change (Shields-Menard et al., 2018). Hence, renewable sources of energy such as biofuels receive more attention. Microalgae are aquatic photosynthetic organisms capable of producing starch, oils and other biochemicals (Tao et al., 2015). Microalgae grow faster than land plants because they better utilize solar energy. Thus, microalgae are likely to be able to provide enormous amounts of starch and oil for conversion to various types of biofuels, such as bioethanol, biodiesel, or biogas (Silva and Bertucco, 2016). Microalgae have received the most attention as lipid producers (Han et al., 2015). The oil productivity of microalgae is much greater than that of the most commercially productive plant, oil palm (Chisti, 2007). In addition, microalgae can grow in sea water or salt water and therefore they can be grown on land that is not suitable for agriculture. Despite their many advantages, commercially feasible lipid production by microalgae faces tough challenges that cannot be overcome in the

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short term (Majidian et al., 2018; Remmers et al., 2018a) because biomass production and downstream processing costs are higher than those of land plants. The yield of lipid production in microalgae is species-dependent and various physio-chemical properties such as light, carbon dioxide, temperature, salinity and cultivation conditions affect lipid productivity. Increasing the overall lipid productivity is hampered by trade-off between lipid accumulation and cell growth. Wide-ranging efforts are being made to solve these challenges, from selecting oleaginous strains and strain development to different modes of cultivation in conjunction with induction of lipid production (Remmers et al., 2018b). Key enzymes and metabolic pathways involved in lipid biosynthesis have been elucidated (Chen et al., 2017; Zhao et al., 2019) and could be manipulated by genetic engineering to improve lipid production (Sun et al., 2019). This review primarily summarizes strategies for enhancing lipid production of eukaryotic microalgae. In particular, random mutagenesis, genetic engineering including genome editing, and metabolic pathway engineering to improve lipid-producing microalgae are surveyed. We propose a schematic diagram that includes the lipid biosynthesis pathway and supply of carbon precursors and/or reducing equivalents, and also discuss blocking pathways competing with lipid biosynthesis. We also elucidate several challenges and other strain engineering strategies to improve lipid production.

Corresponding author. E-mail address: [email protected] (E. Jin).

https://doi.org/10.1016/j.biortech.2019.121953 Received 31 May 2019; Received in revised form 31 July 2019; Accepted 1 August 2019 Available online 02 August 2019 0960-8524/ © 2019 Elsevier Ltd. All rights reserved.

2

12 6

14

7

20

10

Heavy ion beam ( C +, N +, Ne + (135 MeV/u), 40Ar17+ (95 MeV/u), or 56 Fe24+ (90 MeV/u) ions)

UV

Irradiations γ-rays, X-rays

• MNU • 4-NQO • DEB • 2AP • FdUMP

• MNNG • EMS

Chemicals

Mutagen

High linear energy transfer (LET) of heavy-ion beam causes double-strand breaks on DNA molecules

Photochemical reactions resulting in cyclobutanepyridine dimers and A/T to C/G conversions

Energy produced by disintegration of radioisotopes (60Co or 137Cs)/Ionization of DNA molecules causing double-stranded breaks and deletions

Base analog

DNA adducts

Alkylation of DNA base, particularly guanine point mutation

Mode of action

Iodine evaporation

Chlorella sorokiniana

Chlorophyll fluorescence Nile Red stain under N limitation or replacement

Parachlorella kessleri

High salinity adaptation screening, iodine Desmodesmus sp.

Chlorella vulgaris (local) Chlamydomonas sp.

Scenedesmus obliquus

Iodine evaporation after N deprivation Antibiotics and biocides

Gradual increase in medium salinity

Nitzschia sp.

Scenedesmus sp.

Nile red

Biocides

FACS

Nile red/Microplate reader BODIPY/FACS

Screening strategy

Scenedesmus dimorphus

Chlorella vulgaris

Nannochloropsis sp.

Nannochloropsis salina

Desmodesmus sp.

Species

Published examples

Lipid productivity increase by 20.6%, higher photosynthetic efficiency (FV/FM increase) Increase in lipid accumulation to 66%, and biomass productivity to 0.82 g/L/d

Increase in lipid content from 11.9% to 27.2% (Further increase up to 51.2% upon N and Si starvation) Starchless mutants, but no difference in lipid contents Lipid content increase from 40% to 50% (further increase by oxidative stress up to 1.63 g/L) TAG accumulation up to 45% under N starvation lipid content from 15% to 17.4%∼26.9%

25% increased lipid content, morphological change with higher growth rate

Lipid content up to 23.4% in FdUMP induced mutant

Increase in lipid productivity from 19.83 to 778.10 mg/L Increase in PUFA and FAME to 156% (Due to decrease in growth, increase of lipid productivity was 76%) 30% increase in palmitoleic acid, 45% decrease in eicosapentaenoic acid

Mutant phenotype

Takeshita et al. (2018)

Hu et al. (2013)

Kato et al. (2017)

Anthony et al. (2015)

de Jaeger et al. (2014)

Sivaramakrishnan and Incharoensakdi (2017)

Vonlanthen et al. (2015)

Cheng et al. (2014)

Choi et al. (2014)

Anthony et al. (2015) Zhang et al. (2016)

Doan and Obbard (2012)

Beacham et al. (2015)

Zhang et al. (2016)

Reference

Table 1 Overview of random mutagenesis of microalgae to increase lipid contents. (Abbreviations: MNNG, N-Methyl- N′-nitro-N-nitrosoguanidine; EMS, Ethyl methanesulfonate; MNU, N-Methyl-N-nitrosourea; BODIPY, Borondipyrromethene (4,4-difluoro-1,3,5,7-tetramethyl-4-bora-3a,4a-diaza-s-indacene); 4-NQO, 4-Nitroquinoline 1-oxide; DEB, Diepoxybutane; 2-AP, 2-Amino purine; FdUMP, 5-Fluorodeoxyuridine monophosphate; PUFA, Poly unsaturated fatty acids; FAME, Fatty acid methyl esters; TAG, Triacyl glycerol).

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2. Limitation of the conventional strategy, lipid induction through environmental stimuli

However, because of the randomness of mutagenesis, many seemingly meaningless mutations occur alongside the causal mutations, which hinders the efficient use of this method. For example, Takeshita et al. (2018) reported three point mutations in a mutant strain generated by carbon-ion beam irradiation. Only one of them, in the endo-1,4β-mannanase gene, is presumed to increase lipid productivity through a mechanism that is not yet clear. Development of new tools and strategies for mutant screening and gene identification will improve the applicability of this method. In Table 1, we listed some examples of mutagenesis used to increase lipid content in microalgae.

Environmental stresses such as light, temperature, and pH, induce lipid synthesis and accumulation in microalgae. Nutrient deprivation is efficient for uniform lipid induction within the culture batch and is a popular subject of studies on lipid synthesis induction. The mechanism behind lipid induction by nutrient starvation is not yet fully understood; however, nitrogen limitation stimulates acyl hydrolase and phospholipid hydrolysis, resulting in a decrease in the cellular content of thylakoid membranes and in protein synthesis, eventually leading to a decrease in cell growth (Ajjawi et al., 2017; Corteggiani Carpinelli et al., 2014; Khozin-Goldberg and Cohen, 2006; Li et al., 2014). Although nitrogen limitation is the most effective leverage to induce lipid synthesis, it does so at the expense of growth, therefore leading to a reduction of overall productivity. This is a biological limitation; this trade-off is an evolutionarily built-in trait and is therefore difficult to break. To achieve the necessary production scale at an economically feasible cost, it is reasonable to attempt to develop new strains through genetic engineering that would overcome this evolutionary limitation.

3.2. Nuclear gene transformation for insertional mutagenesis or/and the expression of transgenes Nuclear gene transformation is used to introduce gene segments into the nucleus either for expression or repression (in the case of knockdown by RNA interference). This method is the primary technique for heterologous gene expression in the host organism. Technologically, synthetic gene fragments can be introduced into the cell either by a physical approach (e.g. glass beads, electroporation, particle bombardment) or via a bacterial vector such as Agrobacterium. The underlying mechanisms and technical characteristics of these approaches were reviewed by Gong et al., (2011). Genetic transformation is complicated and combines several different processes: DNA penetration into the cell and nucleus, integration into a chromosome, and its expression of the transgene. Failure in any subprocess produces no transgenic strain at all. For example, Muñoz et al. (2018) reported optimal conditions for DNA penetration into the cell using a fluorescent signal from the tagged DNA as readout, however, no transformants could be produced under these optimal conditions. The success of genetic transformation itself depends on efficient expression of a selection marker gene. In many cases, genetic transformation efforts failed to yield transformants due to poor expression of selection marker genes. Therefore, the success of transformation depends on efficient transgene expression. However, even in the model species in which transformation methods are well established, expression of a transgene is inhibited through unknown mechanisms (Cerutti et al., 1997; Schroda, 2006) making biggest obstacle in applying this method to microalgal strain development. Strategies that have been tried to solve this problem are codon optimization, adding and optimizing promoters, intron within the transgene, or changing traits of the host strain, as discussed below.

3. Technical strategy: strain improvement through genetic manipulation for lipid production Increasing lipid production by manipulating culture conditions is achievable only within the biological limits of a particular species or strain. Even oleaginous algal strains require a breakthrough technology that would help overcome natural barriers. Now researchers are exploring possibilities for strain improvement at the level of the genome. Conceptually, genetic improvement is to change a genetic trait by (over-)expression or repression of the gene of interest. Technically, these favorable changes can be designed through molecular tools, or occur randomly and be selected. Here, we divided the genetic engineering strategies into three categories according to their technology: i) random mutagenesis; ii) nuclear transformation for transgene expression; iii) genome editing for precise target gene modification. 3.1. Strain improvement through random mutagenesis This technique relies on a chemical or radiation that induces lesions in the chromosomes, called a mutagen; treatments with the mutagen result in randomly mutated strains. Because the mutation point cannot be pre-designed, mutagenesis is followed by high-throughput screening for desired traits, such as staining with lipophilic fluorescent dyes and fluorescence-activated cell sorting (FACS) for high–lipid-content mutants. This seemingly primitive method is still a powerful tool in the genome editing era for several reasons. First, treatment with a mutagen changes the genome efficiently without any prior knowledge needed, and provides an excellent platform for functional genomic studies. Second, through proper screening, this directly provides a genetically engineered strain, and more importantly, the mutagenesis mechanism applies to all living organisms, which renders this method the only practical genetic engineering tool for recalcitrant organisms. The advent of high-throughput sequencing technology has almost solved the problem of identifying the mutation point, and has helped to identify the key enzymes for lipid productivity. For example, Ma et al. (2019) identified mutations in two key genes of the lipid biosynthesis pathway in a lipid-rich mutant of Scenedesmus sp. by sequencing the whole genomes of this γ-ray irradiated mutant strain (Liu et al., 2015) and the corresponding wild type. By expressing these two mutated genes in a lipid-poor mutant strain, Ma et al. (2019) even further increased lipid productivity than that in the lipid-rich mutant strain. This is an excellent example of strain improvement through random mutagenesis, because, even if the key enzymes can be predicted in silico, increasing their efficiency is hardly achieved through genetic engineering.

3.2.1. Codon optimization Microalgae are evolutionarily diverse, with GC contents ranging from 47% (model diatom Thalassiosira pseudonana) to 71% (Monoraphidium sp.) (Jarvis et al., 1992); therefore, considering codon usage in transgene design is of critical importance. Barahimipour et al. (2015) performed a GC content–based codon optimization test and reported that codon usage is a critical determinant of translation efficiency, along with the epigenetic status of the host strain. 3.2.2. Promoters For transgene expression, Schroda et al. (2002) reported a chimeric promoter consisting of endogenous hsp70A and rbcS2 promoters, which are widely used in transformation of Chlamydomonas reinhardtii (Kong et al., 2017; Zauner et al., 2012). Efficient transformation using endogenous promoters has been reported for other host strains were reported (Seo et al., 2015). With the help of bioinformatic analysis, Scranton et al. (2016) designed synthetic algal promoters (saps) to mimic native cis-motif elements and other characteristics of the top expressed genes in Chlamydomonas reinhardtii. Among 25 saps, 7 were more efficient than the chimeric hsp70/rbs2 promoter in expressing a transgenic fluorescent reporter. A new DNA motif has been found to be essential for promoter function in Chlamydomonas reinhardtii. 3

Bioresource Technology 292 (2019) 121953

Kovar et al. (2002)

Baier et al. (2018)

Rasala et al. (2012)

Four copies of the intron within the HSP70A/RBCS promoter increased gene expression (endogenous) Different levels of gene expression according to location, frequency and flanking exon length (endogenous) Optimal transformation rate obtained when all introns were present in the coding region (endogenous)

3.2.3. Introns The presence of introns in the expression cassette has a positive effect on transgene expression. Fischer and Rochaix (2001) reported that the promoter of PsbD, which has no introns, increased the expression of an intron-less transgene. However, on average the Chlamydomonas genome contains 8.5 introns per gene, and only 9% of genes are intron-less (Jakalski et al., 2016), which implies that we cannot overlook the role of introns in gene expression regulation. Studies on introns are much more limited compared to that of promoters. In an early study, Lumbreras et al. (1998) reported positive effects of an endogenous promoter and an endogenous intron on transgene expression. Kovar et al. (2002) reported that efficient expression of the acetolactate synthase gene in C. reinhardtii required the entire set of endogenous UTRs and introns. Recently, Baier et al (2018) systematically changed insertion site of the first intron of RbcS2 within a selection marker gene, and found differences in gene expression ranging from 3% to 132%. However, the optimal exon length, intron frequency, and intron insertion site tested in their study might be specific to the RbcS2 intron or the gene to be expressed. Kovar et al. (2002) reported efficient transformation of C. reinhardtii with the ALS gene with all introns in the coding region, but not with the cDNA. It is likely that the study of introns will be one of the research areas that will benefit most from bioinformatics combined with high-throughput experiments. 3.2.4. Other approaches Another strategy to tackle low transgene expression while developing a mutant strain was attempted by Neupert et al. (2009) and Kurniasih et al. (2016). Although the mechanism underlying poor transgene expression is still not understood clearly, their results suggest that epigenetic suppression or compact chromatin structure are responsible. In the study by Neupert et al. (2009), a UV-induced mutant strain expressed a transgene at a high level, despite the fully functional the post-transcriptional gene silencing pathway. Expressing the desired gene product fused with a selection marker protein is also a feasible scheme, because the selection marker is highly expressed in surviving clones. Using this scheme, Rasala et al. (2012) reported a 100-fold increase in transgene expression compared to the unlinked construct; to separate the two parts of the fusion protein after translation, the authors used the 2A peptide sequence from foot-andmouth disease virus, which mediates self-cleavage. The use of this sequence to fuse a signal peptide to ensure secretion would facilitate harvesting of the desired gene product. Doron et al. (2016) reviewed the selection markers, promoters and delivery methods used for the last three decades, however, their practical applications and successes are limited to several model organisms. Therefore, further efforts are needed for extending the options and applications to non-model organisms.

Chlamydomonas reinhardtii/Acetolactate synthase

Inducible by phosphorus limitation Chlamydomonas reinhardtii/Sulphoquinovosyl transferase 2

Chlamydomonas reinhardtii/First intron of the RBCS2 gene

Induced chloramphenicol acetyltransferase gene by nitrate (Phaeodactylum tricornutum) Transcription of bar gene was induced by nitrate and repressed by ammonium (Dunaliella salina) Overexpression of diacylglycerol acyl-CoA acyltransferase transgene leading to an increase in TAG accumulation (endogenous)

Iwai et al. (2014)

Niu et al. (2012)

Induced transcription of a miRNA coding gene by nitrate (endogenous)

Inducible, switched on in the absence of ammonium ion Chlamydomonas reinhardtii/Nitrate reductase

Endogenous promoters of non-model microalgae are also being studied, and Vieler et al. (2012) reported successful genetic transformation of Nannochloropsis oceanica by using an endogenous promoter resulting in a 10-fold increase in efficiency compared to that of pHyg3 plasmid. Several examples of promoters that have improved transgene expression are listed in Table 2. It is also important to find an effective combination of a transcription factor (TF) and promoter. Anderson et al. (2017) generated a TF library based on a TF database (PlnTFDB, http://plntfdb.bio.unipotsdam.de/v3.0/), and carried out yeast one hybrid screening to find TF-promoter combinations for transgene expression.

In genome editing, a nuclease breaks the DNA double strand in a sequence-specific manner. For transcription activator-like effector nucleases (TALENs), a specific nuclease needs to be designed for each specific target DNA sequence, which is a challenging work. On the

ALS

Introns RBCS2

SQD2

NIT1

3.3. Genome editing for targeted gene modification

LIP

Selection marker expression (endogenous) Constitutive gene expression GAPDH promoter

Strong, constitutive gene expression FCP promoter

Li et al. (2007)

Baek et al. (2016b); Park et al. (2013) Schmollinger et al. (2010) Light dose-dependent transgene expression (Chlamydomonas reinhardtii) High light inducible

Jia et al. (2012)

Poulsen et al. (2006)

Schroda et al. (2002)

Reduction in the silencing of transgene expression from 80% to 36% (endogenous) Selection marker expression (endogenous) Strong, constitutive gene expression

Chlamydomonas reinhardtii/Fusion of HSP70A and RBCS2 promoters Thalassiosira pseudonana/Fucoxanthin-chlorophyll a/ c binding protein Dunaliella salina/Glyceraldehyde-3-phosphate dehydrogenase Dunaliella sp./Light inducible protein Promoters AR

Effect (in the expression host) Characteristics (in the species of origin) Origin (Species/gene) Gene segment

Table 2 Examples of promoters and introns used to enhance transgene expression in microalgal transformation studies.

Reference

S. Park, et al.

4

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contrary, Cas endonuclease cuts DNA strand where the guide RNA binds, and the latter can be easily made to be specific to the target. Many reviews describe the mechanisms of action of various types of endonucleases in detail (e.g., Adli, 2018; Naduthodi et al., 2018). Early studies on gene editing were based on the TALEN method, and successful gene editing was reported in model diatoms (Weyman et al., 2015); however, for the reasons mentioned above, gene editing tends to shift to the use of the CRISPR/Cas system. Gene knock-out using the CRISPR/Cas system also has advantages over the use of RNAi because it completely abolishes the gene product due to a target-specific mutation in the gene. This result can be achieved without introducing undesirable DNA fragments such as selection markers or a gene encoding microRNA. For example, Baek et al. (2016a) obtained Chlamydomonas reinhardtii clones with target-specific mutations by using ribonucleoprotein complexes leading to direct target gene editing. By carefully designing the guide RNA (Banerjee et al., 2018) or by applying multiple guide RNAs simultaneously (Serif et al., 2018), one can mutate multiple genes of a gene family in a single experiment. However, in spite of this “ideal” gene-editing mechanism, there are two main obstacles to applying this system to the strain improvement of microalgae. One is the requirement for markers for clone selection. Baek et al. (2016a) were able to visually screen their mutants thanks to the traits of their target gene. But surely this is not the case for many other genes, and it is almost impossible to find the target mutated clones without the help of a selection marker. Serif et al. (2018) pointed out this problem and developed two endogenous counter-selectable markers, PtUMPS and PtAPT, which confer resistance to 5-FOA (with uracil auxotrophy) and 2-FA (with adenine auxotrophy), respectively. Using a selection marker as a knock-in mutation is a feasible alternative. Another obstacle is the lack of efficient delivery technology. The molecular scissors and the guide RNA need an appropriate vector for delivery into the cell, and the same delivery system has been used as in nuclear gene transformation. Therefore, the efficiency of mutagenesis using the CRISPR/Cas-system is currently no better than that of genetic transformation. For this reason, some researchers have introduced genes encoding the Cas9 enzyme and guide RNA into microalgal cells so that they produce these molecular tools. Among such trials, using the episome is promising (Poliner et al., 2018; Sharma et al., 2018). Improved versions of Cas enzymes with lower off-target effects and higher efficiency have also been reported (Adli, 2018). In addition, highthroughput bioinformatics tools are helping with finding proper targets and confirming mutations (Ajjawi et al., 2017). As to the delivery system, the development of microfluidic technology might bring changes. Researchers are improving exogenous gene delivery efficiency into a single cell using nanowire (Bae et al., 2015) or small cell suspension droplet (Kim et al., 2019b); these approaches require only small amounts of material. These technologies will improve gene editing efficiency in a wide range of host algal strains including industry-relevant species, and also allow high-throughput genetic engineering experiments as well.

4.1. Enhancing fatty acid and TAG synthesis Acetyl-CoA carboxylase (ACCase) mediates the key rate-limiting step in fatty acid (FA) synthesis and has been the primary target. However, this strategy has not proved very successful in microalgae. For instance, although Cyclotella cryptica and Navicula saprophila transformants exhibited a 2- to 3-fold increase in ACCase activity, it was not accompanied by an increase in FA content (Sheehan et al., 2009). Among enzymes involved in TAG synthesis, the overexpression of diacylglycerol acyltransferase is the most often attempted, and in most cases increases of lipid synthesis (Iwai et al., 2014; Li et al., 2016; Niu et al., 2013). Other reports on enzymes in the TAG synthesis pathway are listed in Table 3. Enzymes involved in the Kennedy pathway, which takes place in the endoplasmic reticulum, are also major targets for increasing lipid productivity. Previous efforts and achievements through manipulation of these enzymes are listed on Table 3. One of the remarkable improvements in lipid production was achieved through co-expression of five acyltransferases from yeast (Saccharomyces cerevisiae and Yarrowia lipolytica) in Chlorella minutissima, which increased lipid accumulation up to 2-fold (Hsieh et al., 2012). In another case, simultaneous overexpression of a subunit of ACCase (accD) and malic enzyme (ME), increased total lipid content in Dunaliella salina (Talebi et al., 2014). These studies show that the multigene approach opens the possibility of positive results by overexpressing enzymes that were seemingly ineffective in previous studies. 4.2. Reducing the contribution of competitive pathways Another promising strategy is blocking metabolic pathways that compete against lipogenesis over the same metabolic resources, particularly starch biogenesis and lipid catabolism. Several strains of microalgae utilize carbon sources to synthesize starch as the primary storage metabolite and inhibiting starch biosynthesis can drive the carbon flux toward lipid production. A strategy based on the knockdown of key enzymes involved in the starch synthesis pathway has proved efficient in increasing lipid accumulation. The starchless mutants of C. reinhardtii (sta-6 and sta7-10 in which ADP-glucose pyrophosphorylase (AGPase) and isoamylase genes are mutated, respectively) have increased total lipid accumulation per cell during nitrogen deprivation (Work et al., 2010). UDP-glucose pyrophosphorylase (UGPase) plays an important role in carbon allocation in algal cell. Silencing UGPase in a diatom, increased lipid synthesis while decreasing the content of chrysolaminarin, which is a polysaccharide reservoir in diatoms (Zhu et al., 2016). Another option is to reduce the rate of lipid catabolism. In T. pseudonana, the suppression of multifunctional lipase/acyltransferase/ phospholipase resulted in a 3.3-fold higher lipid content compared to the wild type (Trentacoste et al., 2013). However, several studies have indicated that disrupting lipid catabolism and starch generation, which are vital cellular metabolic pathways, may decrease microalgal growth and biomass production (Chu, 2017; Radakovits et al., 2010). This issue can be overcome by using alternative strategies such as reduction of target gene expression through RNA interference, or editing only the untranslated regulatory region to maintain basal level of target gene expression. The degradation of synthesized lipids should also be prevented in order to improve yield. In C. reinhardtii, a knockout mutant of the phospholipase A2 gene had the total lipid content increased up to 64.25% (Shin et al., 2019), whereas another study reported that a 10fold increase in TAG was achieved when the cht7 gene (encoding a TAG lipase) was silenced (Tsai et al., 2014). As an alternative, inhibition of β-oxidation is a promising target. The mutation in ACX gene encoding acyl‐CoA oxidase which has a peroxisomal targeting signal, resulted in 20% increase of oil contents in Chlamydomonas under nitrogen limited condition (Kong et al., 2017).

4. Genetic strategy: promising targets for genetic engineering of microalgae In addition to the technology explained above, improving a strain through genetic engineering requires strategic designing of the target and directing metabolic flux. The rational choice of targets is of critical importance, and there are many metabolic pathway databases available to the public such as KEGG, dEMBF, and MetaCyc (Caspi et al., 2014; Misra et al., 2016; Ogata et al., 1999). Here, we categorized the strategic targets for genetic engineering that have been used to increase lipid content in diatoms. 5

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Table 3 Overviews of molecular approaches to enhance improve lipid accumulation productivity studied in microalgae. The list is categorized by strategic approach. “Heterologous expression” and “overexpression” denote transgene expression of either exogenous gene (originate from other organism/species) or endogenous gene (originate from the host itself), respectively. Gene

Approach

Fatty acid and TAG biosynthesis ACCase Heterologous expression Overexpression Heterologous expression DGAT Five TAG synthesis enzymes GPAT1 & LPAT1

Overexpression Overexpression Heterologous expression Overexpression Heterologous gene expression Overexpression

Supply of reducing equivalent and precursors ME Overexpression Overexpression Heterologous expression

Species

Phenotypic changes

Reference

Dunaliella salina Cyclotella cryptica Schizochytrium sp.

1.4-fold increase in total lipid content 2–3-fold increase in lipid expected Increase in biomass by 29.9% and fatty acid by 11.3% 2.4-fold increase in TAG content Total lipid increase by 69% 2-fold increase in TAG content Increase of neutral lipid by 35% 30–40% TAG content increase TAG increase by 2.3-fold under nitrogen depletion

Talebi et al. (2014) Dunahay (1993) Yan et al. (2013)

2.5-fold increase in lipid content 2.8-fold increase in total lipid content 23.4% fatty acid, 19.9% lipid content increase

Xue et al. (2015) Yan et al. (2019) Kim et al. (2019a)

1.6-fold increase in G3P content 1.9-fold increase in neutral lipid content, slight decline of growth 2.7-fold increase in lipid content

Gomma et al. (2015) Yao et al. (2014)

10-fold increase in TAG content 45-fold increase in TAG Significant decrease in chrysolaminarin and slight decrease in total lipids 3-fold increase in TAG content 20% increase in TAG content Increase in neutral lipid from 23.1% to 42.1%, slight decrease in growth

Li et al. (2010) Daboussi et al. (2014) Zhu et al. (2016)

Chlamydomonas reinhardtii Nannochloropsis oceanica Chlamydomonas reinhardtii Phaeodactylum tricornutum Chlorella minutissima Phaeodactylum tricornutum

Rengel et al. (2018) Li et al. (2016) Ahmad et al. (2015) Niu et al. (2013) Hsieh et al. (2012) Wang et al. (2018)

Glycerol kinase G3PDH

Heterologous expression Overexpression

Phaeodactylum tricornutum Chlorella protothecoides Chlamydomonas reinhardtii PTS42 Scenedesmus quadricauda Phaeodactylum tricornutum

G6PD

Overexpression

Phaeodactylum tricornutum

Knock-out/mutation Knock-out/TALEN Knock-down/RNAi

Chlamydomonas Phaeodactylum tricornutum Phaeodactylum tricornutum

CIS PEPC1 PDK

Knock-down/RNAi Knock-down/RNAi Knock-down/RNAi

Chlamydomonas reinhardtii Chlamydomonas reinhardtii Phaeodactylum tricornutum

Lipid body formation StLDP AtOLEO3

Overexpression Heterologous expression

Phaeodactylum tricornutum Phaeodactylum tricornutum

Increased lipid droplet accumulation 1.4 fold increase in TAG content

Yoneda et al. (2018) Zulu et al. (2017)

Phaeodactylum tricornutum Chlamydomonas reinhardtii Chlamydomonas reinhardtii

Increased TAG level in lipid extracts 64% increase in total lipid content 20% increase in oil content under N starvation

Barka et al. (2016) Shin et al. (2019) Kong et al. (2017)

multifunctional lipase

Knock-down/RNAi Knock-out/CRISPR/Cas9 Knock-out/random insertional mutagenesis Knock-down/RNAi

Thalassiosira pseudonana

4.1-fold increase in total lipids under Si starvation

Trentacoste et al. (2013)

Transcription regulators bHLH2 WRI1 Dof-type TF

Overexpression Heterologous expression Heterologous expression

Nannochloropsis salina Nannochloropsis salina Chlamydomonas reinhardtii

33% FAME yield increase 64% FAME yield increase 2 fold increase in total lipid production

ZnCys TF bZIP PSR1

Heterologous expression Knock-down/CRISPR/Cas9 Overexpression Overexpression

Chlorella ellipsoidea Nannochloropsis gaditana Nannochloropsis salina Chlamydomonas reinhardtii

Knock-out/random insertional mutagenesis Knock-out/random insertional mutagenesis Knock-out/random insertional mutagenesis

Chlamydomonas reinhardtii

46.4–52.9% increase in total lipids 2-fold increase in lipid productivity Both growth and lipid production were improved Increase in starch granules, decrease in neutral lipid content 90% reduction in TAG and lipid droplet accumulation Unable to resume growth and recover from S and N deprivation Reduction in TAG accumulation in defective mutant

Kang et al. (2015) Kang et al. (2017) Ibáñez-Salazar et al. (2014) Zhang et al. (2014) Ajjawi et al. (2017) Kwon et al. (2018) Bajhaiya et al. (2016)

Competitive pathways AGPase UGPase

TAG and lipid catabolism TGL1 PLA2 CrACX2

CHT7 TAR1 NRR1

Enhancing photosynthetic efficiency LHC isoforms Knock-down/RNAi CpSRP54 Knock out/CRISPR/Cas9 NAB1 Overexpression RuBisCO activase Overexpression

Chlamydomonas reinhardtii Chlamydomonas reinhardtii

Chlamydomonas reinhardtii Chlamydomonas reinhardtii Chlamydomonas reinhardtii Nannochloropsis oceanica

65% faster growth rate 80% increase of Pmax 53% increase of growth rate More than 40% increase in biomass and lipid productivity

Xue et al. (2018)

Deng et al. (2013) Deng et al. (2014) Ma et al. (2014)

Tsai et al. (2014) Kajikawa et al. (2015) Boyle et al. (2012)

Mussgnug et al. (2007) Jeong et al. (2017) Beckmann et al. (2009) Wei et al. (2017)

Asbbreviations: ACCase, Acetyl CoA carboxylase; DGAT, Diacylglycerol acyltransferase; GPAT, Acyl-CoA:glycerol-sn-3-phosphate acyltransferase; LPAT, Lysophosphatidate acyltransferase; ME, Malic enzyme; G3PDH, Glycerol-3-phosphate dehydrogenase; G6PDH, Glucose-6-phosphate dehydrogenase; AGPase, ADPglucose pyrophosphorylase; UGPase, UDP-glucose pyrophosphorylase; CIS, Citrate synthase; PEPC, Phosphoenolpyruvate carboxylase; PDK, Pyruvate dehydrogenase kinase; LDP, Lipid droplet protein; OLEO, Oleosin; TGL, Triglyceride lipase; PLA, Phospholipase; ACX, Acyl-CoA oxidase/dehydrogenase; bHLH, basic Helix-loophelix; PSR1, Pi Starvation Response1; CHT7, Compromised Hydrolysis of Triacylglycerols 7; TAR, Triacylglycerol Accumulation Regulator1; RGQ, RING-GAF-Glncontaining protein; NRR, Nitrogen-responsive regulator; SRP, Signal Recognition Particle; NAB, Nucleic acid binding protein. 6

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This proves that β-oxidation occurs in microalgal peroxisome affecting lipid contents and need to be studied further.

Increasing photosynthetic efficiency is one of the indirect approaches, because photosynthesis provides both assimilated carbon sources for lipid biosynthesis and reducing energy to power the synthesis pathways. Light-harvesting antenna complexes are highly efficient in capturing and transferring light energy to the reaction center, on the other hand, highly labile under light intensity beyond the downstream capacity. It is speculated that the evolutionarily developed photoprotective mechanisms allowed photosynthesizing cells to survive, however, hamper the productivity by dissipating excessively absorbed light energy rather than using it in mass culture (Melis, 2009). Inspired by this theory, many study generated microalgal strain with reduced light harvesting antenna, and demonstrated an increase in photosynthetic efficiency (Jeong et al., 2017; Kirst et al., 2012). This is a promising platform to be used in combination with other promising targets, especially decreased energy input in culture (de Mooij et al., 2015). Increasing RuBisCO activity is a straightforward strategy to increase carbon assimilation through the Calvin cycle. Overexpression of RuBisCO activase in Nannochloropsis oceanica increased biomass and lipid content (Wei et al., 2017). Overexpression of ME is another approach. ME catalyzes the conversion of malate to pyruvate and releases NAD(P)H in the process, supplying reducing energy for lipid synthesis. However, overexpression of this enzyme yielded different results in different experiments. Kim et al. (2019a) reported an increase in total fatty acid (FA) content in Chlamydomonas reinhardtii by overexpression of ME2. In Nannochloropsis sp., ME appeared to preferentially channel NADPH toward FA desaturation rather than lipid biosynthesis, on the contrary, FA desaturation level in Haematococcus pluvialis remained low, showing different regulation of metabolism in the two species under the same conditions (Recht et al., 2012). These data show that in-depth study of metabolism and regulatory pathways of each strain is critical for designing rational strategy. The above-mentioned pathways and candidate target genes for genetic engineering for strain improvement are schematized in Fig. 1, and the published attempts are listed in Table 3.

4.3. Redirecting metabolic flux through transcription factors In the previous sections, we discussed some evidence that manipulating individual genes is not a very effective strategy to increase lipid productivity, leading some researchers to introduce multiple genes in a pathway simultaneously. Therefore, manipulating the regulatory signaling pathway to shift the balance toward lipid synthesis is an insightful strategy, and transcription factors are good candidates. Various transcriptional regulators and transcription-related proteins have been identified under conditions that induce lipid accumulation and tried for manipulation to divert metabolic flux toward lipid production. The transcription factor bHLH is crucial for regulating cell growth, development, and stress-related behaviors (Pireyre and Burow, 2015). In Nannochloropsis salina, overexpression of the bHLH gene increased biomass and fatty acid methyl ester (FAME) productivity and, moreover, it increased growth rate and nutrient uptake (Kang et al., 2015). These authors also expressed the WRI1 gene from Arabidopsis thaliana in N. salina and the transformants exhibited a 64% increase in lipid productivity in comparison with the wild type (Kang et al., 2017). Overexpression of a Dof-type TF in C. reinhardtii doubled total lipid accumulation, which indicated that that the Dof gene may play a role in lipogenesis (Ibáñez-Salazar et al., 2014). Overexpression of a Dof-type TF followed by nutrient stress increased both total lipid and the desired specific FA for biodiesel production (Salas-Montantes et al., 2018). An up to 2-fold increase in lipid productivity was achieved by attenuating the expression of a negative regulator of lipid biosynthesis in Nannochloropsis gaditana. Using CRISPR/Cas9-mediated editing, the hygromycin resistance cassette was inserted into 5′ and 3′ UTRs of the target transcription factor encoding gene, then resulted in expression of Zn (II)2Cys6 transcription factor, which reduced its expression, and increased lipid synthesis; the complete knock-out of this gene resulted in growth inhibition, therefore, reduced overall lipid productivity, in spite of increase of lipid synthesis (Ajjawi et al., 2017). Li et al. (2019) overexpressed a TF named NobZIP1, which is a key metabolic node that redirects precursors of energy and carbon from protein and carbohydrate metabolism toward lipid biosynthesis; overexpression of NobZIP1 up-regulated the expression of enzymes essential for TAG biosynthesis such as 3-ketoacyl-acyl-carrier protein synthase (KAS), acyl-CoA binding protein, long-chain acyl-CoA synthetase, and lysophosphatidate acyl-transferase (LPAAT). Overall, transcription factor engineering has provided advantages over the traditional manipulation of metabolic enzymes and can reveal novel patterns of regulation of metabolic processes and stress-responsive pathways. There are other transcription factors that might help to understand the regulatory mechanisms of lipid synthesis: PSR1 regulates starch granule accumulation and lipid catabolism in response to Pi availability, CHT7 is responsible for TAG and lipid droplet accumulation (Tsai et al., 2014), TAR1 is responsible for resuming growth at the recover from nutrient deprivation (Kajikawa et al., 2015), RGQ1 is a RING-domain protein regulating responses to N deprivation (Matthijs et al., 2016), and NRR1 regulates TAG accumulation in response to N deprivation (Boyle et al., 2012; Wei et al., 2017). They have multiple regulatory targets, and their interaction partners are not known; therefore, more intensive and comprehensive studies are needed to apply these TFs for strain improvement.

5. Challenges and perspectives Microalgae are attracting attention as the most promising lipid producers, and they have many advantages over land plants, but lipid yield is highly species dependent. Moreover, various environmental factors such as light, carbon dioxide, temperature, salinity and nutritional availability affect lipid productivity. As we discussed in this review, very limited information is available on how microalgae respond to physiological stress at the molecular level, and the relevant pathways in microalgae have not been fully revealed. Transcriptomic analysis can be an efficient approach for obtaining functional genomic information for microalgae; this technique targets only coding DNA sequences and thus helps to reduce the required amount of sequencing and allows to rapidly achieve high transcriptome coverage depth (Parchman et al., 2010; Rismani-Yazdi et al., 2011). Thanks to systems biology based on large-scale high-throughput omics technologies, a broader understanding of how metabolism varies under different environmental conditions is possible; hence, comprehensive omics data can enable strategies for optimization of microalgal growth to increase lipid production (Salama et al., 2019). In some cases, strains developed through genetic engineering technology do not show the expected phenotype (Vonlanthen et al., 2015) or undesirable consequences are found (Kao and Ng, 2017). Therefore, genetically engineered strains should be subjected to multifaceted analysis, which can be used to improve strategy for the next round of engineering. Synthetic biology can also be implemented for the development of microalgal strains (Crozet et al., 2018). This approach enables us to redesign and construct de novo biological circuits and networks in a rational way for producing the desired target products

4.4. Other approaches, including increase in supply of precursors and reducing equivalent, and lipid droplets Another strategy is to reinforce the supply of reducing equivalents, precursors, and available pool of carbon assimilates. These approaches do not directly regulate enzymes related to lipid biosynthesis but rely on indirect positive effects on lipid synthesis and accumulation. 7

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A

ER

B

Glycolysis

LD CP

P

N

bZIP Dof1 ZnCys

NADH ME

malate

ATP NADPH

Calvin cycle

GPAT

Glyox.

PDH

Free FA

PA

PAP

PUFA

DAG

DGAT

Elongation/ Desaturation

TAG MT

PE P G6 P sugar

6P G

DAG

Acyl-CoA

LPAAT

A-CoA PDC

CP TAG pyruvate PEP Calvin cycle

LPA

TCA cycle

pyruvat OAA e

DHAP

G3P

Fatty acid

β-oxid. A-CoA

PSR1

GPDH

ER

PA LPA AcylACP

M-CoA MAT

M-ACP

G6P

PGM

Glucose-1P

AGPase

ADP-Glucose starch synthase

Starch

Dof1

ZnCys

Protein synthesis

3PGA

ACCase

FAS

PSR1

bZIP

A-CoA

Lipid synthesis N deprivation

Fig. 1. Simplified overview of lipid biosynthesis and the regulatory pathways as reported target sites for the genetic manipulation of microalgae to increase lipid accumulation (summarized in Table 3). (Abbreviations) • Cellular organelles: ER, Endoplasmic reticulum; LD, Lipid droplets; CP, Chloroplasts; P, Peroxisome; MT, Mitochondria; N, Nucleus • Enzymes and intermediates: β-oxid, β-oxidation cycle; Glyox, Glyoxylation cycle; A-CoA, Acetyl-CoA; PEP, Phosphoenol pyruvate; 6PG, 6Phosphogluconate; G6P, Glucose-6-phosphate; PDC, Pyruvate dehydrogenase complex; ME, Malic enzyme; OAA, Oxaloacetate; G3P, Glycerol-3-phosphate; GPAT, Acyl-CoA:glycerol-sn-3-phosphate acyltransferase; LPA, Lysophosphatidic acid; LPAAT, Lysophosphatidate acyltransferase; PA, Phosphatidic acid; PAP, Phosphatidic acid phosphatase; DAG, Diacylglycerol; DGAT, diacylglycerol acyltransferase; TAG, Triacylglycerol; DHAP, Dihydroxyacetone phosphate; GPDH, Glycerol-3-phosphate dehydrogenase; PUFA, Polyunsaturated fatty acid; 3PGA, 3-phosphoglycerate; M-CoA, Malonyl-CoA; MAT, Malonyl-CoA ACP transacylase; M-ACP, Malonyl acyl-carrier protein; ACCase, Acetyl-CoA carboxylase; PDH, Pyruvate dehydrogenase; PGM, Phosphoglucomutase; AGPase, ADP-glucose pyrophosphorylase.

(Brodie et al., 2017a). As Brodie et al. (2017b) suggested, a design–build–test cycle should be coupled with a high-throughput method to build a comprehensive regulatory and metabolic network that could eventually lead to a systems-level understanding of microalgal metabolism. So far, genetically engineered strains with improved lipid metabolism have mostly been developed in model microalgae (Table 3). However, the recent advent of genome-editing technologies, in particular, CRISPR/Cas9, allows to rapidly implement different metabolic engineering strategies (Baek et al., 2016a), including those to engineer oleaginous microalgae (Ajjawi et al., 2017). Non-model microalgae, which are relevant to the industrial production of lipids, could be manipulated using advanced genome-editing tools as soon as their genome sequences become available. Manipulating the aforementioned key genes involved in lipid synthesis and accumulation has to meet the right molecular engineering tools to improve microalgal strains. A combination of the right targets and right tools can help develop candidate microalgal strains that would produce sufficiently large amounts of lipids to make their use economically feasible. Recent developments in systems biology, synthetic biology, and genome editing will facilitate the development of essential tools and help to design and develop microalgal stains for lipid production. In addition, integrating the major goals of microalgaebased biofuel production with co-production of high-value products such as natural pigments, nutrients and pharmaceuticals can help to achieve economic benefits and economic equivalence between biofuels and fossil fuels.

the tuning of lipid biosynthesis, engineered strains could also be subjected to multifaceted analyses such as the design–build–test cycle based on systems biology. These efforts should be further focused on oleaginous microalgae and their successful use would be a step toward microalgae-based biofuel. Acknowledgments This research was supported by the Korea CCS R&D Center (KCRC) Korea CCS 2020 Project) grant funded the Korean Government (Ministry of Science and ICT) in 2017 (KCRC-2014M1A8A1049273). And also supported by the Basic Core Technology Development Program for the Oceans and the Polar Regions of the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (2015M1A5A1037053). References Adli, M., 2018. The CRISPR tool kit for genome editing and beyond. Nat. Commun. 9. Ahmad, I., Sharma, A.K., Daniell, H., Kumar, S., 2015. Altered lipid composition and enhanced lipid production in green microalga by introduction of brassica diacylglycerol acyltransferase 2. Plant Biotechnol. J. 13 (4), 540–550. Ajjawi, I., Verruto, J., Aqui, M., Soriaga, L.B., Coppersmith, J., Kwok, K., Peach, L., Orchard, E., Kalb, R., Xu, W., Carlson, T.J., Francis, K., Konigsfeld, K., Bartalis, J., Schultz, A., Lambert, W., Schwartz, A.S., Brown, R., Moellering, E.R., 2017. Lipid production in Nannochloropsis gaditana is doubled by decreasing expression of a single transcriptional regulator. Nat. Biotechnol. 35 (7), 647–652. Anderson, M.S., Muff, T.J., Georgianna, D.R., Mayfield, S.P., 2017. Towards a synthetic nuclear transcription system in green algae: Characterization of Chlamydomonas reinhardtii nuclear transcription factors and identification of targeted promoters. Algal Res.-Biomass Biofuels Bioprod. 22, 47–55. Anthony, J., Rangamaran, V.R., Gopal, D., Shivasankarasubbiah, K.T., Thilagam, M.L.J., Dhassiah, M.P., Padinjattayil, D.S.M., Valsalan, V.N., Manambrakat, V., Dakshinamurthy, S., Thirunavukkarasu, S., Ramalingam, K., 2015. Ultraviolet and 5'Fluorodeoxyuridine Induced Random Mutagenesis in Chlorella vulgaris and Its Impact on Fatty Acid Profile: a New Insight on Lipid-Metabolizing Genes and Structural Characterization of Related Proteins. Mar. Biotechnol. 17 (1), 66–80. Bae, S., Park, S., Kim, J., Choi, J.S., Kim, K.H., Kwon, D., Jin, E., Park, I., Kim, D.H., Seo, T.S., 2015. Exogenous gene integration for microalgal cell transformation using a nanowire-incorporated microdevice. ACS Appl. Mater. Interfaces 7 (49),

6. Conclusion We have comprehensively overviewed microalgae with improved lipid productivity developed via various approaches. Molecular techniques and tools for microalgal strain development have been extensively applied to increase lipid production but are still limited. Here, we proposed strategic targets for increasing lipid production. Besides 8

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