Metabolic pathway engineering towards enhancing microalgal lipidbiosynthesis for biofuel application—A review

Goldy De Bhowmick 1, Lokanand Koduru 1, Ramkrishna Sen n

Department of Biotechnology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India

a r t i c l e i n f o

Article history:Received 16 May 2014Received in revised form31 March 2015Accepted 23 April 2015Available online 12 June 2015

Keywords:Microalgal biodieselTriacylglycerol biosynthesisMetabolic pathway engineeringFlux analysisSystems biologyIntegrated approach

a b s t r a c t

Microalgae have recently emerged as the most favorite feedstock for triacylglycerol (TAG), the storageneutral lipid, for renewable and sustainable production of biodiesel, mainly due to their comparable lipidcontents, faster growth rates and lesser land requirements as compared to the non-conventional and non-edible oilseed crops. But the real technological challenge is to mass produce microalgae with much higherlipid content to make the production of a low-value-high-volume product like biodiesel economically viableand environmentally sustainable. Recent scientific achievements in TAG overproduction in higher eukaryoticsystems may be leveraged upon to enhance lipid synthesis by manifold in microalgae. Since the availablesequence homology information have been effectively used in case of the model unicellular green alga,Chlamydomonas reinhardtii to perform genome-scale metabolic reconstructions, the gained knowledge andthe well established genetic engineering tools and techniques coupled with the modern system biologyapproaches may well pave the way for delineating and deciphering the TAG biosynthetic pathways in lipidaccumulating microalgae as targets for metabolic pathway engineering. This review thus analyzes the trendsand developments in the area of metabolic engineering of lipid synthesis in microalgae and discusses thevision based on some of the possible strategies that could be adopted to reconstruct a stable modifiedengineered microalga with enhanced lipid producing capabilities. The strategies include flux balanceanalysis for target gene identification, over expression of the target enzymes involved in lipid biosynthesis,over expression of the target gene under specific inducible promoters, constitutive expression of transcrip-tion regulators, diverting the flux of key metabolites, and integrated in silico based approaches. An integratedapproach involving multiple gene targeting by applying the principles and knowledge of systems biologyand bioinformatics would provide us with a holistic view and help derive some feasible solutions.

& 2015 Elsevier Ltd. All rights reserved.

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12402. Algal lipid biochemistry: an overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12403. Conventional approaches towards enhancing storage lipid content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12414. Engineering lipid metabolism in microalgae at the molecular level: a new paradigm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1242

4.1. Current status of lipid engineering in microalgae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12434.2. Potential and future directions for lipid engineering in microalgae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1243

4.2.1. Single gene targeted approaches for modification of lipid biosynthesis pathway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12434.2.2. Systemic approaches for modification of lipid biosynthesis pathway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1244

5. Exploring the molecular mechanisms underlying lipid accumulation and future directions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12466. Improvement of fatty acid quality for biodiesel application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12477. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1249Acknowledgment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1250References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1250

Contents lists available at ScienceDirect

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Renewable and Sustainable Energy Reviews

http://dx.doi.org/10.1016/j.rser.2015.04.1311364-0321/& 2015 Elsevier Ltd. All rights reserved.

n Corresponding author.E-mail address: rksen@yahoo.com (R. Sen).1 The first two authors have contributed equally.

Renewable and Sustainable Energy Reviews 50 (2015) 1239–1253

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1. Introduction

The new millennium has ushered in an era of metabolic engi-neering that has created ample opportunities for custom designingand delivering novel molecules with diverse structures and proper-ties showing high degree of efficacy and specificity for commercial,environmental and health-care applications [1].

With the advent of high throughput platform technologies forgenomic, transcriptomic, proteomic and metabolomic research, scien-tific endeavors worldwide are currently focusing on delineatinggenetic networks governing biochemical pathway that are industriallydesirable. Metabolic engineering effectively produces new recombi-nant proteins biological systems include Pichia [2,3], other yeasts [4],bacteria [5], PHA (Bioplastic) in bacteria [6], polyketides in yeast [7],pyruvate in bacteria [8], bioethanol in yeast [9]. However, when animpending energy crisis is looming large all over the world due torapid depletion of fossil fuel resources on mass scale industrializationand environment friendly technologies for enhanced oil recovery likeMEOR are not yet fully operational [10], and as a fall out, unabatedenvironmental pollution is threatening our own existence; it is hightime that we need to put our best efforts to realize and harness thefull potential of metabolic engineering in the area of bioenergy,particularly in enhancing lipid and starch contents of microalgal cellfactories. Moreover in the 21st century, microalgae have gainedimportance as an attractive feedstock for the manufacture of valuablegreen products including biofuels in a biorefinery model, due to theirself-renewability by capturing solar energy, greater sustainability andrelatively higher economic feasibility [11]. Bioenergy offers one of themost potentially important solutions to mitigate green house gasemissions and boost de-carbonization of transportation fuels in orderto secure energy supply [12,13].

Microalgae have recently emerged as a better bioenergy feed-stock as compared to conventional energy crops [14]. These sun-light driven cell factories convert atmospheric CO2 into complexvalue added molecules and carbon rich lipids that, subsequentlytransform into biofuels, particularly biodiesel, renewable jet fuel(Bio-Synthetic Paraffinic Kerosene), bioethanol and biobutanolwithout the requirement of any arable land for its cultivation[11]. Though biodiesel production from microalgae has recentlybeen reported, the obsession for the usage of microalgae as afeedstock seems to be increasing day by day due to the escalatingprice of petroleum in the world market and global warmingassociated with the burning of fossil fuels [11,15,16]. Therefore,achieving high lipid content and high growth rates seems to beposing a technological challenge to the scientific community.Biochemical, genetic engineering and other approaches have beenemployed to enhance lipid accumulation in microalgae [17–19].Considering the immense future potential of metabolic pathwayengineering approaches in combination with systems biologytools, this review attempts to critically discuss the existing andemerging strategies towards achieving overproduction of lipid inmicroalgae.

2. Algal lipid biochemistry: an overview

To generate new knowledge gained out of “omics” research,understanding of fatty acid biosynthetic pathway for overproduc-tion of lipid is important from metabolic engineering standpoint.Key enzymes, mainly the rate limiting ones are integral factors tomanipulate the target genes responsible for desired fatty acidsynthesis. Although, major advances in understanding lipid meta-bolism and the regulatory factors in plants have progressed;molecular regulation of lipid in microalge are yet poorly defined.Algal lipid biochemistry therefore needs a special attention tounderstand lipid metabolism and its regulation.

Myriad of lipid content in terms of composition and positionaldistribution of fatty acids varies according to the species [20]. Lipidproduction in algae is primarily sourced from plasma membrane,endomembranes, chloroplast and lipid bodies (TAG and free fattyacids) [21]. Mostly, algal lipid occurs in the membrane bilayer inglycerinated forms, other than TAG, wax esters, hydrocarbons,sterols and prenyl derivatives [22]. Generally, the fatty acid meta-bolism in algae resembles with respect to model organism Arabi-dopsis thaliana. However, a unique plastidial pathway for TAGsynthesis was reported for Chlamydomonas reinhardtii, haploidgreen alga was used as a model organism to understand the lipidmetabolism in algae [23–26]. Algal lipid de novo biosynthesisoccurs in chloroplast. Atmospheric CO2 is fixed as an endogenoussource of acetyl-CoA producing all carbon atoms found in fatty acidchain, majorly derived from acetyl-coenzyme A (CoA) [20]. In algae,lipid biogenesis is eventually catalyzed by two evolutionarily-conserved enzymes such as acetyl-CoA carboxylase (ACCase) andtype-II fatty acid synthase [27,28]. Owing to the availability ofacetyl-Co Malonyl-coA (first product of lipid biosynthesis) thenforms. This acetyl-coA pool is either derived from cytosolic/plasti-dial glycolysis or directly from dihydroxyacetone phosphate duringCalvin cycle [29–31]. The next concomitant step is carried forwardby fatty acyl synthase (FAS), wherein Malonyl group from CoA istransferred to a protein cofactor placed on the acyl carrier protein[32]. Subsequently, the fatty acid from ACP complex is eithertransferred directly to glycerol-3-phosphate (G3P) by chloroplast-resident acyltransferases, or exported to cytosol for sequentialacylation in the E.R by E.R-resident acyltransferases [33,20,23].Phosphatidic acid (PA) and Diacyl glycerol (DAG) produced in thechloroplast acts as precursors for structural lipid of the photosyn-thetic membrane and PA and DAG generated in endoplasmicreticulum (E.R) are indulged in both membrane lipid and storageTAG synthesis [23].

In many plants biosynthesis of thylakoid lipids originates fromboth the plastid and E.R [34]. However, a unique plastidial path-way is observed for the denovo synthesis of thylakoid lipids inChlamydomonas. This could be due to the absence of plastidialcholine (PtdCho) in Chlamydomonas with a notable exception ofcontaining bataine lipid diacylglycerol-trimethyl-homoserine(DGTS) [34]. PtdCho is an intermediate in lipid trafficking betweenE.R and plastid [35]. Substrate specificity of acyltransferases at theE.R versus plastid envelopes is the most distinguished featurebetween lipids assembled by the E.R or plastid in plants. However,insufficient study on molecular trafficking of lipid in microalgederived from the above pathway restricts its universal applicationto microalgae [36].

In the kennedy pathway, first acylation occurs at the sn-1position of G3P by glycerol-3-phosphate acyltransferases (GPAT).Subsequently, the lysophosphatidic acid (LPA) is acylated to the sn-2position resulting in formation of phosphatidic acid (PA) by lyso-phosphatidate acyltranferase (LPAAT) [37]. PA is dephosphorylatedto produce diacylglycerol (DAG) [37] that can either be used asprimary precursor for structural lipid synthesis in the chloroplastfor the production of membrane and storage lipid (triacylglycerol:TAG) [37]. The TAG is then deposited in the cytosol as ER-derivedlipid droplets [38]. However, Fan et al. [23] reported chloroplastspecific TAG accumgulated as lipid droplets from DAG (precursor)containing exclusively C16 fatty acids, typical of glycerolipid assem-bly in Chlamydomonas reinhardtii. Therefore, chloroplast specificTAG biosynthesis was observed from its immediate precursor DAGby chloroplast specific acyltransferases [23]. TAG accumulation inalgal compartments is presented in Fig. 1.

Lipid droplets in microalgae have garnered increasing attentionas a storage organelle, while characterization and understandingof molecular dynamics of lipid droplets is still unclear [36]. Theselipid droplets connected with other organelles shuttle between

G. De Bhowmick et al. / Renewable and Sustainable Energy Reviews 50 (2015) 1239–12531240

TAG and membranes and helps in distribution and recirculation ofneutral lipids, phospholipids, lysophospholipids and acyl groups[39]. Formation of lipid droplets is triggered by cellular stress andcan be reversed by replenishing with required stress factor [36].Though structural information and correlative physiological datacan be found for Chlamydomonas [36], a detailed study on theintriguing pathway of lipid droplet dynamics would help inunderstanding of the storage lipid synthesis in microalgae.

3. Conventional approaches towards enhancing storage lipidcontent

The extent of lipid accumulation inmicroalgae is often influencedby the microenvironment the cells are exposed to. While mappingthe Sunderbans in the state of West Bengal, India for microalgalbiodiversity, we came across some marine and brackish watermicroalgae with variable lipid contents and fatty acid compositions[40]. These living microalgal cells react differently as conditions suchas medium components, environmental stress varies. For instance,cells exposed to a certain condition not optimum for growth, cellularmechanisms divert the metabolic fluxes towards the processesinvolving storage of energy rich compounds such as lipid. Lipid inmicroalgae is accumulated by optimizing the physical parametersinclude temperature, pH, light intensity, photo-oxidative stressand chemical parameters such as nutrient deprivation and salinity[41–43]. Among these parameters, nutrient depletion whichincludes nitrate, phosphate, and iron deficiency has been reportedthoroughly. While a nutrient deficiency ceases cell growth, it helpsin channeling metabolic fluxes towards fatty acid biosynthesiswhich results in accumulation of storage lipid in the form oftriacylglycerols (TAGs) [19,44]. Nutrient starvation-driven lipid accu-mulation has been reported for many microalgal species [20,43,45].Perhaps activation of diacylglycerol acyltransferase, which convertsacyl-CoA to triglyceride (TAG) results in lipid accumulation. Khozin-Goldberg and Cohen [50] mentioned three probable reasons for lipid

accumulation under nitrate starved condition: (i) decrease in cellularcontent of thylakoid membrane, (ii) activation of acyl hydrolase and(iii) stimulation of phospholipids hydrolysis [41,46]. Recently threeacyltransferases (DGAT, DGTT and PDAT) activated during nitrogenstarvation in order to catalyze lipid accumulation were identified inChlamydomonas reinhardtii [47]. Expression of few decarboxylatingenzymes such as glucose-6-phosphate-1-dehydrogenase, phospho-gluconate dehydrogenase and pyruvate decarboxylase wereobserved in C. reinhardtii [48]. Malic enzymes (ME) is a criticalsupplier of NADPH for intracellular fatty acid content formation.Three homologous genes for malic enzymes were expressed duringall nutrient deprivation in C. pyrenoidosa [49]. In case of phosphatestarvation Diacylglyceroltrimethylhomoserine (DGTS) and Digalac-tosyldiacylglycerol (DGDG) enzymes becomes activated instead ofmembrane phospholipid synthesizing enzymes, resulting in storagelipid accumulation [50]. Phosphate depletion also resulted inincreased expression of lecithin cholesterol acyltransferase (LRO1)and diacylglycerol acyltransferase (DGA1) in S.cerevisiae [51]. Jamesand Nachiappan [51], reported that during phosphate starvationphospholipids acts as a phosphate source, therefore, if the phosphatetransporters pho88 and pho86 are mutated then four acyltrasferasegenes get activated involved in neutral lipid formation in S.cerevisiae.Antioxidants such as epigallocatechin gallate and butylated hydro-xyanisole result in lipid induction via oxidative signaling [52].Possible mechanisms resulting in TAG formation on the basis ofbiochemical approaches are portrayed in Fig. 2 and brief discussionon the effect of nutrient deprivation on lipid accumulation isillustrated in Table 1.

Conventional process biochemistry approaches involving nutri-ent deprivation and/or application of physiological stress might beuseful for enhancing lipid content on dry weight basis, but oftenfail to enhance lipid productivity significantly as the increment inlipid content happens at the cost of biomass. Therefore, theproportion of lipid productivity vs biomass formation remainssame. Stress or starvation condition can be a useful strategy inaccumulating lipid if it is employed after attaining a significant

Acetate+CoA

PAP

DAGAT

DAG

GPAT

LPAAT Glycolysis

DHAP

LPA

PA

Acylation, sn-1

Acylation, sn-2

FatA/Fat B

Acetyl-CoAACCase

Malonyl-coAFASII ACP complex

6:0,8:0,10:0,12:0,14:0,16:0-ACP

FFAGPAT

LPAAT

PAP

DAGAT

G3P LPA

PA

DAG

TAG

Lipid granules

Phospholipids

TAG

Lipid Droplets

Channel protein

Channel protein

ACS

Calvin cycle

CO2 Plastidial Overexpression

ACS

inactive active

Mitochondrial Overexpression

inactive active

Fig. 1. Representative TAG accumulation pathway in specific algal cell compartment. ACCase¼acetyl-CoA carboxylase; FASII¼type-II fatty acid synthase; ACP¼acyl carrierprotein; FFA¼ free fatty acid; DHAP¼dihydroxyacetone phosphate; G3P¼glycerol-3-phosphate, GPDH¼glycerol-3-phosphate dehydrogenase; ACS¼acetyl-CoA synthetase;Chloroplast and Endoplasmic reticulum specific acyltransferases (GPAT¼glycerol-3-phosphate acyltransferase; LPAAT¼ lysophosphatidic acid acyltransferase; PAP¼pho-sphatidic acid phoaphatase; DAGAT¼diacylglyceryl hudroxymethyltrimethyl-β-alanine), cascade of enzymes can be over-expressed to accumulate lipid, indicated specificallyin figure1.

G. De Bhowmick et al. / Renewable and Sustainable Energy Reviews 50 (2015) 1239–1253 1241

quantity of algal biomass. However, the knowledge of the bio-chemical mechanisms and the molecular insights for lipid accu-mulation influenced by the microenvironments in microalgal cellscould be useful in inventing new strains, improving known strainsand methods for greater lipid productivities. Thus, metabolicengineering principles and tools that have successfully beenimplemented in photosynthetic plants as cell factories for bio-pharmaceuticals [1] can be effectively extended to photosyntheticmicroalgae.

4. Engineering lipid metabolism in microalgae at themolecular level: a new paradigm

Biochemical strategies such as nitrogen starvation [45], phos-phate limitation, high salinity induced stress [63] and the presenceof heavy metals such as cadmium [64], silicon deficiency [65] havebeen used to induce lipid accumulation in algae. However, thesestress conditions can reduce the growth rates, thereby diminishingbiomass yield and the overall lipid productivity [66]. Thus,

Table 1Effect of nutrient starvation on lipid accumulation in algae and other systems.

Strain System Nutrient Effect Reference

Anacystis nidulans Blue-greenAlgae

Nitrogen Accumulation of saturated, longer-chain (C18) fatty acids (nitrogen) [53]

Isochrysis galbana Marineprymnesiophyte

Nitrogen Reduction in the in-vitro activity of acetyl CoA carboxylase (nitrogen) [54]

Chlamydomonas reinhadittiCC124 and Chlorella vulgarisY019

Algae Nitrogen,phosphorus,sulfur, iron

Increase in lipid content (nitrogen, phosphorus, sulfur, iron) [55]

Arabidopsis lower plant Nitrogen Significant increase in TAG and free fatty acid (12:0,14:0) content with a concomitantincrease in digalactosyldiacylglycerol in the chloroplast membrane (nitrogen)

[56]

Scenedesmus sp. Algae Nitrogen,phosphorous

Nitrogen and phosphorus starvation leads to lipid accumulation [46]

Chlorella vulgaris Algae Iron High levels of chelated Fe3þ results in high levels of lipid accumulation [57]Chlorella pyrenoidosa Algae Nitrogen Lipid accumulation on nitrogen depletion [58]Dunaliella sp. Algae Nitrogen Accumulation of β-carotene and glycerol on nitrogen starvation [59]Porphyridium cruentum Red Algae Nitrogen Enhanced accumulation of TAGs on nitrogen starvation [60]Chlamydomonas reinhaditti Algae Phosphate, sulfur Phosphate and sulfur deprivation [61]Chlamydomonas reinhaditti Algae Sulfur Sulfur starvation leads to up-regulation of PG synthesis [62]

Acyltransferases

HeatPhysical parametersChemical parameters

Nitrate starvation:•Three genes (Acyltransferases)(i) DGAT1(ii) DGTT1(iii) PDAT1• Activation of

Acylhydrolase• Decrease in thylakoid

membrane celllularcontent

Glycerol

DAG

MAG

NO3

PO4

TAG

Acetyl CoApool

epigallocatechingallate

& butylatedhydroxyanisole

Neutral pHmore lipid ?

mechanism

increases

Polarlipid

Acylhydrolases

Salt?

mechanism

?mechanism

pho88

Activation of decarboxylatingenzymes

Fig. 2. Depiction of physico-chemical parameters leading towards lipid accumulations in a microalgal cell factory. (Probable reasons for lipid accumulation: (i) Nitratestarvation results in activation of acyltransferases, acylhydrolases and decrease in thylakoid membrane cellular content; (ii) Phosphate starvation results in storage lipidaccumulation by activation of enzymes namely DGTS and DGDG and alteration in phosphate transporter may also lead to TAG synthesis; (iii) Increase in salt and iron contentresults in TAG formation but the exact mechanism is unknown. In some cases, fluctuation in pH, photo-oxidative stress and fluctuations in light intensity also results in lipidaccumulation but the probable mechanism for lipid accumulation is not known thereby increasing the need for studying the in silico approaches leading towards lipidaccumulation).

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metabolic engineering strategies that address the problem ofenhancing lipid content and quality, without compromising thebiomass yield, may become useful and efficient.

The necessary insights of microalgal DNA sequences providedby the genomic databases and availability of powerful bioinfor-matics tools in public domain have enabled us to use microalgae asa valuable host for genetic manipulations. Microalgae are amen-able for genetic transformation with foreign genes [67], and theintroduction of exogenous DNA into algal cells for its transgenicexpression were achieved using methods like microprojectileparticle bombardment [68], electroporation [69], Agrobacteriummediated transformation [70], agitation in the presence of siliconcarbide whiskers [71] or glass beads [72]. Although there areunique pathways for TAG biosynthesis in microalgae such as thechloroplast pathway as reported by Fan et al. [23] in C. reinhardtii,majority of the pathway enzymes and components for the synth-esis of triacylglycerol (TAG) likely follow the similar pathway as inseed plants [73]. This indicates the potential for engineering thepathway in order to improve the TAG content in microalgae.

4.1. Current status of lipid engineering in microalgae

Identification and isolation of various genes and enzymesinvolved in the lipid biosynthesis have been recently carried outin the model green alga Chlamydomonas reinhardtii [21,47,74]. Theretrieved information can be strategically combined and used formanipulating various algal strains to achieve enhanced lipid pro-duction. Alteration of metabolic pathways involve different strate-gies such as over-expressing a rate limiting enzyme involved in thesynthesis of a desired product, down regulating or blocking thecompeting pathways and lipid catabolism via RNAi mediatedsilencing, over-expressing transcription factors controlling the keypathway enzymes, and site-directed mutagenesis to improve theperformance of key proteins/enzymes. In spite of having limitedknowledge on microalgal lipid metabolism, various attempts havebeen made to engineer microalgal fatty acid biosynthetic pathwayto enhance the lipid content. From the reports summarized inTable 2, it can be observed that some microalgae showed enhancedlipid content following genetic engineering but there are few which

did not show the same. Therefore, an effective approach would beto implement metabolic engineering strategies that are specific forparticular strains of oleaginous microalgae using advanced mole-cular tools. In future the knowledge gained from such strategiescould be used to create new domains of microalgal lipid productionwith a potential for industrial application.

4.2. Potential and future directions for lipid engineering inmicroalgae

Till date, at least 20 algal genomes have been sequenced,including the green algae – Chlamydomonas reinhardtii [81],Coccomyxa sp. C-169, Micromonas CCMP 1545 [82], Ostreococcuslucimarinus CCE9901 [83], Ostreococcus tauri [84] and Volvoxcarteri [85], red alga: Cyanidioschyzon merolae Strain:10D [86],brown alga: Ectocarpus siliculosus [87], diatoms: Phaeodactylumtricornutum [49,88] and Thalassiosira pseudonana [89]. Metabolicengineering has successfully been used in lipid pathway modifica-tion in many higher eukaryotic platforms. Genomic information ongenome sequences in general and expressed sequence tags inparticular unravel the molecular insights that might facilitate lipidengineering in microalgae. Since a considerably good homologybetween the sequenced algal genomes and the higher terrestrialplants exist [26,90] similar strategies used for lipid pathwaymodifications in higher plant can also be applicable in microalgae.

4.2.1. Single gene targeted approaches for modification of lipidbiosynthesis pathway4.2.1.1. Over-expression of enzymes involved in lipid biosynthesispathway. Flux towards a metabolite is directly dependant on theactivity of its preceding enzyme in the pathway provided thesubstrate supply is not limited. Over-expression of this enzymewould result in enhancement of the total activity of the enzyme andthereby increasing the flux towards the desired metabolite. Here,we emphasize on few examples of successful over-expressionstrategies.

TAG content in the amyloplast of potato tubers increasedfivefold when ACCase of Arabidopsis thaliana was over-expressed[91]. The results indicate that, malonyl-CoA, an immediate

Table 2Current trends on enhancement of lipid content via genetic engineering in microalgae.

Microalgaeused

Strategy employed Observed results References

Cyclotellacryptic;

Over expression of Acc1 2–3� ACC activity, no change in lipid content [67,75]

Naviculasaprophila

Chlamydomonasreinhardtii

sta6 and sta7 mutations have disruptions in the ADPglucosepyrophosphorylase or isoamylase genes, respectively.

Led to a 10-fold increase in TAG content as compared to the wild-type. In the nitrogen-starved sta6 mutant, the cellular content oflipid bodies increased 30-fold

[76]

Inactivation of AGPaseChlorellapyrenoidosa

Starchless mutant Elevated polyunsaturated fatty acid content [77]

Chlamydomonasreinhardtii

RNAi of major lipid droplet protein (MLDP) Led to increased lipid droplet size, but no change in triacylglycerolcontent or metabolism was observed.

[73]

Cyclotellacryptica

Antisense of upp1 gene coding for a fusion protein containing theactivities for UDP-glucosepyrophosphorylase (UGPase) andphosphoglucomutase, two key enzymes in the production ofchrysolaminarin.

Insertion of the ribozyme sequences did not result in detectabledecreases in UGPase expression.

[78]

Nannochloropsissp.

UV mutagenesis induced disruption of 20:4 desaturase activity Lipids analyzed by gas chromatography for any significantalteration in the proportion of fatty acids showed a mutant lackingin 20:5 fatty acids, apparently due to a mutation in a 20:4desaturase

[79]

Schizochytriumsp.

Over-expression of Escherichia coli acetyl-CoA synthetase (ACS) geneinto the marine microalga Schizochytrium sp. TIO1101

Biomass and fatty acid proportion of ACS transformants increasedby 29.9 and 11.3%

[80]

Thalassiosirapseudonana(diatom)

Targeted knockdown of lipase/phospholopase/acyltransferase 2–3 fold higher lipid content than the wild type [39]

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precursor of the fatty acid chain elongation can act as a limitingintermediate for lipid biosynthesis in the developing potatotubers. Acyltransferases of the Kennedy pathway including acyl-CoA: glycerol-3-phosphate acyltransferase (GPAT), the acyl-CoA:lysophosphatidicacyltransferase (LPAAT) and the acyl-CoA:diacyl-glycerolacyltransferase (DGAT) are important enzymes in theformation of fatty acid patterns of TAGs. These enzymes wereused in various over-expression studies and found to be worthytargets in lipid pathway engineering [90]. In algae two GPAT andone DGAT have been cloned and characterized based on thegenome information and annotation of the diatom Thalassiosirapseudonana [92] and the chlorophyte Ostreococcus tauri [93]. Forinstance, over-expression of GPAT in yeast gat1 mutant or twoisoforms of LPAAT from Brassica napus resulted in the increase ofthe TAG and phosphoinositol content [90] and increased seed oilcontent [94]. An increased transcript abundance of DGAT2 genewas also observed in the starchless mutant of C. reinhardtii,relative to the wild type, indicating the importance of DGAT2 inhyper-accumulation of TAG in the alga [90]. Satisfactory othersimilar strategies involving the over-expression of enzymesinvolved in TAG formation are shown in Fig. 1. Such successfulinstances exemplify the role of a single enzyme in controlling thewhole pathway fluxes and hence can present us with importantmetabolic engineering candidates for lipid over-production.

Many microalgal species accumulate lipids in their stationarygrowth phase as exposed to environmental stresses, e.g. lack ofnitrogen source. The lipid yields in this phase are often directlycorrelated with the extent of biomass present in the culture. Itwould therefore be advantageous to use microalgae-specific indu-cible promoters that control the over-expression to be operationalonly when there is sufficiently high biomass density. An examplefor such approach is the over-expression of DGAT under thecontrol of a P starvation–inducible promoter, sulphoquinovosyl-diacylglycerol 2 (SQD2) in C. reinhardtii. The engineered strainwhen compared to the control showed up to 2.5 fold increase inTAG levels [95]. Another example for an inducible promoter is thecopper-responsive elements (CuREs) in C. reinhardtii [96]. The twoCuREs present on the CYC6 gene responded to copper in themedium by means of switching off the downstream gene in thepresence of high concentrations of copper and activating themwhen the copper concentration goes below a certain level. In adifferent experiment a GFP reporter gene was expressed under thecontrol of promoter and terminator elements of the nitratereductase gene of the diatom, Cylindrotheca fusiformis. The GFPexpression was turned off when the cells were grown in a mediumcontaining ammonium ions and the expression was turned ontransferring the cells to nitrate containing medium [97].

4.2.1.2. Engineering extracellular secretion of lipid. Downstreamprocessing of microalgal biomass and lipid, involving the traditionaldewatering techniques and solvent extraction methods, accounts for70–80% of the total biofuel production cost [98]. We have recentlydeveloped and patented a low cost and low energy intensive processfor dewatering of thickened algal biomass slurry after removal ofbulk water using an efficient adsorbent [99]. However, due to thehigh costs involved, development of efficient technologies that aid inthe microalgal biomass harvesting and dewatering process stillremains a challenging area. An alternate way to deal with thisproblem is to engineer the microalgae to secrete the lipids directlyinto the medium. This approach would thus help us avoid the hasslesof dewatering, also thereby would result in significant reduction inthe cost and time of the downstream processing. In this regard, a freefatty acid (FFA) secreting strain of cyanobacterium Synechocystis sp.PCC6803 was developed [100] by introducing a codon-optimizedacyl carrier protein thioesterase, and mutated by deleting a possible

surface protein from the cell envelop (Sll1951) and a peptidoglycanassembly protein (PBP2). These modifications resulted in weakenedcell wall phospholipid layers through which the intracellular freefatty concentration and secretion increased. A green recovery processin which Synechocystis sp. PCC6803 was engineered by placingthe genes encoding lipolytic enzymes under the control of a CO2-limitation-inducible promoter was reported [101]. Fatty acids werereleased by the engineered strain upon the degradation ofdiacylglycerols caused during the induction of CO2 limitation.

Natural secretion of lipids can also be seen in higher plantswith the involvement of certain specialized membrane proteins,such as the ATP binding cassette subfamily protein, N. tabacumwhite-brown complex homolog 1(NtWBC1) involved in the exportof lipid in tobacco [102,103]. CER5 gene in Arabidopsis encodes anABC transporter, localized in plasma membrane of the epidermalcells. The transporter exports lipids from the epidermal cells to theplant surface for the formation of waxy cuticle [104]. For instance,a glycosylphosphatidylinositol- anchored lipid transfer protein(LTPG) is highly expressed in the epidermal cells of the Arabidopsisthaliana inflorescence stems and is involved in the formation ofwaxy cuticle [105]. These transporter proteins and their homologscan form potential targets for the induction of lipid secretion inmicroalgae.

Algae being evolutionarily diverse and representing non- uni-form class of lipid metabolism across the algal realm, it needs to beexplored and compared under the reflection of single genetargeted approach. The traditional method of identifying a singletargeted gene may be one of the viable approaches for increasinglipid in that specific algal strain with more refined analysis forindustrial exploitation in-terms of oil production. Consideringlipid as the final product, single gene targeted approach may notfulfill all the criteria to be an economically viable system. Never-theless, with the recent advances in molecular engineering inimproving lipid production, systemic approach offers a detailedand diverse alternative method in increasing lipid in microalgae.

4.2.2. Systemic approaches for modification of lipid biosynthesispathway

Many studies focused on single gene targeting approach thatwas usually directed to eliminate potential bottlenecks in the lipidbiosynthetic pathway. These studies also suggested the existenceof more than one rate determining steps in the pathways. It maytherefore be possible to employ multi-gene targeting strategies viametabolic engineering tools, where systemic properties of thepathway can be altered to facilitate the formation of a desiredproduct. Few such strategies, which could be potentially applicablefor whole pathway modifications are discussed below.

4.2.2.1. Diverting flux of key metabolic intermediates towards lipidbiosynthesis. Biochemical fluxes are often distributed into variousmetabolic subsystems through their respective enzymes com-peting for the same central substrate pool. The metabolic branchpoints from the central metabolism can thus be targeted to fine-tune and maximize the fluxes towards desirable pathway and tominimize fluxes towards less/un- desirable pathways. One suchbranch point is the breakdown of fructose 1,6-bis phosphate intothe glycolytic intermediates, dihydroxyacetone phosphate (DHAP)and glyceraldehyde 3-phosphate. Glycerol 3-phosphate dehydro-genase (Gly3PDH) reversibly converts DHAP to Glycerol 3-phos-phate (Gly3P), which allows further dephosphorylation of Gly3P toglycerol and, thereby diverting the flux from glycolysis to lipidbiosynthesis [106]. A yeast gene (GPD1) coding for cytosolicGly3DH was expressed in oil seed rape under the control of seedspecific napin promoter [107]. Enhanced GlyPDH activity of 2-foldin the transgenic plants led to three to a fourfold increase in Gly3P

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levels in developing seed and overall resulted in 40% increase intotal seed oil content. Lipid accumulation can also be induced bypartially blocking the fatty acid β-oxidation pathway that occurs inperoxisomes and/or mitochondrion [108]. For instance, asignificant increase in TAG levels was observed in the senescingleaves of Arabidopsis thaliana by blocking the fatty acid breakdownprocess [109]. However, complete blockage of the process can beharmful for the energy supply system [110].

4.2.2.2. Constitutive expression of transcription regulators involved inlipid biosynthesis. Over-expression of transcription factors that havemultiple regulatory points in lipid biosynthetic pathways can result ineffective redirection of flux towards lipid accumulation. Severaltranscription factors, such as Zinc-fingers (CHO cells), MYB(Arabidopsis) and ORCA2 (Catharanthus) that are known to enhancethe production of high-value products other than lipid were reported[111,112]. However, very few transcription factor mediated approacheshave been studied in microalgae towards lipid enhancement, e.g.,over-expression of stress induced lipid trigger (a transcription factor)resulted in up to 50% increase in the lipid production C. reinhardtii.[113]. To exploit the full potential of transcription factor targetedapproaches, it is worthy to consider the transcription regulators ofplant origin, in microalgal application for the regulation of lipidbiosynthetic pathway. For instance, a significant enhancement oflipid content in Chlorella ellipsoidea was observed when SoybeanGmDof4 transcription factor that affects lipid accumulation inArabidopsis was overexpressed in the alga [114]. Other similartranscription factors regulating lipid metabolism have been studiedand characterized in plants, few of which are described below.

The over-expression of LEC2 transcription factor from Arabi-dopsis in tobacco under the control of acetaldehyde induciblepromoter (Alc) resulted in the increase of lipid accumulation from2.9% to 6.8% of dry seed weight upon the induction of transgeniclines with 1% acetaldehyde [115]. LEC 2 transcription factorregulates the lipid metabolism by binding to RY repeats presentin the 5' UTR of target genes played a role during seed maturation.

Similarly, the ectopic over-expression of WRINKLED1 transcrip-tion factor of Arabidopsis in its vegetative tissues resulted in theover-accumulation of fatty acids [116,117]. Also, a homozygousmutant line of Arabidopsis for wrinkled1 (wri1) showed 80%reduction in the seed oil content [118], indicating a positivecorrelation between WRI1 and seed oil accumulation. Over-expression of WR1 gene is also known to up regulate the genesinvolved in lipid metabolism such as acetyl-CoA carboxylase(BCCP2), ketoacyl-acyl carrier protein synthase (KAS1) and acylcarrier protein (ACP1) [119]. WRI1 transcription factor regulates bybinding to AW box present in the 5' UTR the genes encoding theenzyme mentioned above. The presence of AP2 type transcriptionfactors with good sequence-homology to Arabidopsis WRI1 hasbeen reported in the model microalgae Chlamydomonas reinhardtii[26], suggesting that the over-expression of WRI1 has potential toeffectuate lipid accumulation in microalgae.

ABCISSIC ACID INSENSITIVE (ABI) transcription factors are alsoinvolved in regulating lipid metabolism in plants. TAG degradationis repressed by ABI3 and ABI5 that codes for B3 and basic region-leucine zipper type transcription factors respectively [120]. ABI3 onresponse to abcissic acid induces expression of oleosin, which is

required for the accumulation of TAG [121,122]. ABI4 binds to CE1-like elements present in the promoters of DGAT1 gene and,activation of DGAT1 is required for TAG accumulation duringnitrogen deficiency [123]. It appears that expression of ABI tran-scription can be promising factor for inducing lipid accumulation inmicroalgae. Different binding sites of WRI1, LEC2 and ABI4 presentupstream to their respective target genes are listed in Table 3.

Recently, at least 11 transcription factors having regulatoryroles in lipid metabolism were identified in Nannochloropsis, anoleaginous microalga, few of which were found to be orthologs ofthe transcription factors having lipid accumulating roles in higherplants [124]. In C. reinhardtii 147 putative transcription factors and87 putative transcription regulators were identified till 2008 [125],but the biological relevance of all these transcription factors areyet to be determined. The abovementioned transcription factorshelps to understand and categorize those factors as a pyramidalhierarchy, where the level of protein expression is equallybalanced and dependent on the level of hierarchy. However, it isof course possible that few of those low level transcription factorsfound in different systems other than algae may not be effective inalgal metabolic engineering. Possibly it is important to identify thespecific lipid regulating transcription factors starting from low tohigh-level and understand the influence of low-level transcriptionfactors on the high-level ones. Though transcription factor engi-neering in microalgae is still in its embryo, in contrast to geneticengineering approaches, it is perhaps the most breakthroughadvances in multi-gene targeted approach that has the ability toimprove lipid overproduction in microalgae.

4.2.2.3. Flux balance analysis for TAG biosynthesis to identify newgene targets for metabolic engineering in microalgae. Conventionalmetabolic engineering strategies rely upon the experimental data(such as mutagenesis, biochemical analysis and protein expression).These approaches, however, are time consuming and often notreliable as they do not necessarily take into consideration thecomplex nature of biochemical networks. Recently systems basedapproaches are incorporated to rationalize the metabolic engineeringprocesses. Modeling and simulation of biological systems in silicohelps us to predict biochemical network behaviors as they aresubjected to perturbations. While kinetic models seem to be usefulin understanding cellular functions, their use is often limited by theavailability of extensive experimental data. On the other handconstraint based approaches impose spatial constraints in ametabolic network, using basic biochemical information such asthe reaction stoichiometry and directionality [126] to find a set ofallowable predictions mathematically termed as solution space.

Flux balance analysis (FBA), a constraint-based modeling approachhas been developed aiming to use genome-scale metabolic networksto predict the steady-state metabolic fluxes and systemic phenotypes.[127]. Pool of information on pathway components, (i.e., metabolites,enzymes, reaction stoichiometry, gene-protein-reaction relationships,reversibility, cellular compartmentalization), available through thedatabases and bioinformatics analyses provides an excellent platformfor reconstruction of the metabolic network. Biochemical pathwaydata can be retrieved from the available databases such as MetaCycand KEGG [128,173]. Table 4 presents the results of FBA performed onvarious microalgae. Linear programming tools can be used to explore

Table 3Reports of various transcription factors with different binding domain types and binding sites present in their respective target genes.

Transcription factor (TF) Binding domain type Binding Site (BS) Target Gene (TG) References

WRI1 AP2 type AW Box:[CnTnG](n)7[CG] Genes of late glycolytic and lipid biosynthetic pathway [117]LEC2 B3 RY-repeat:CATGCA Gene involved in the regulation of seed maturation [115]ABI4 AP2 type CE1 like elements: CACCG DGAT1 [120]

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the metabolic capabilities of a cell such as biochemical productioncapabilities, prediction of metabolic responses, cellular behavior(batch, fed-batch and continuous mode), desired product yields. InFBA, metabolically interconnected network reactions are representedin the form of matrix with the necessary stoichiometry, energy andredox balance and the resulted stoichiometries are resolved usinglinear programming [135]. Primarily the metabolic fluxes used are attheir pseudo-steady state while the metabolic concentration remainsconstant. The steady-state of the metabolic network is described interms of mass balance equation. Based on the fluxes the equationsestablished represents the rate of change of concentration of meta-bolite production and consumption over time [136]. Mathematically,the mass balance constraints are represented in the form of astoichiometric matrix equation [136]:

S: v¼ 0Where, the stoichiometric matrix S is m x n matrix, m correspondingto the number of metabolites and n to the number of chemicalreactions or fluxes taking place within that metabolic network. Theflux vector (v) interprets all the possible fluxes in the metabolicnetworks. The matrix generated represents the relationship betweenthe metabolites and the products [136]. The metabolic flexibilities arerepresented in the form of null spaces which alters the metaboliccapabilities of a cell accordingly. Null space of S gives informationabout the linear combination of flux vectors that can balance thefluxes in biochemical network. As the model used in FBA is a steady-state function it indicates that all the concentrations of the metabo-lites used are constant i.e with time the change will be zero [137]. Incomparison to cellular growth rate and dynamic changes the meta-bolic transients are more rapid, this kind of assumptions madeencourages new opportunities to calculate the internal fluxes of thereactions involved. Also, the effects of gene insertion or deletion(knockout) on these fluxes of the pathway could be studied. There arevariations of FBA such as minimization of metabolic adjustment(MOMA), regulatory on/off minimization (ROOM) and dynamic FBA.MOMA is extremely useful in predicting the fluxes of knockoutmutants as it is assumed that the redistribution of fluxes in mutanttends to remain close to wild type strain; contrary to FBA whereoptimal metabolic states are assumed in created mutants [138].ROOM, also a constraint based algorithm predicts the flux distribu-tions in gene knockouts is based on an assumption that regulatorynetwork of cell possibly tries to minimize the number of flux changesfrom the wild type [139].

Microalgal metabolism can be modeled using FBA, and theoptimized set of perturbations required to maximize TAG formationcan be obtained through the analysis. However, the scarcity ofdatabases that provide biochemical information specific for micro-algae is limiting such analyses. ChlamCyc is one such database thatprovides the metabolic information exclusive to C. reinhardtii [140],although relevant information can also be found in other databases

such as KEGG, BioCyc and MetaCyc [129]. Although several genome-scale reconstructions have been constructed for organisms includ-ing Escherichia coli [141], Saccharomyces cerevisiae [142], Arabidopsisthaliana [143], mouse [144] and human [145], not many areavailable for various species of microalgae, which include those ofthe algal species C. reinhardtii [132,140],O. lucimarinus, and O. tauri[146] and Ectocarpus siliculosus [147]. The availability of genomesequence information for several microalgae, the continuousimprovements in the genome sequencing technologies along withthe development of automated model reconstruction tools wouldimply an accelerated increase of microalgal genome-scale metabolicreconstructions in the years to come. Hence, it is of utmostimportance to ensure formulation of effective ways to capitalizethe reconstructed genome-scale metabolic resources. Most of themicrobial models can be analyzed using FBA, where the objectivefunction is often set as the steady state biomass growth or steadystate production of valuable metabolites. However, the uniquefeature that distinguishes microalgae from bacteria being theirphotoautotrophic ability, where the cellular growth is accompaniedby a transition between light and dark phases of metabolism wouldindicate that the steady state assumption might not be universallyvalid. Attempts have been made to model microalgae using alter-native metabolic modeling frameworks such as the dynamic reduc-tion of unbalanced metabolism (DRUM) [133] that describes thenon-balanced growth and intracellular metabolite accumulation ina unicellular microalga, Tisochrysis lutea. In another study, dynamicFBA (dFBA) [148], a variant of FBA that can be applicable undertransient conditions accompanied with metabolic reprogrammingwas used to study the metabolism of Chlorella sp. under light- darkcycle [134]. This work succeeded in presenting several key findingsregarding the metabolic changes occurring in the microalgae duringthe light-dark transition cycles.

Thus, in a nut shell, application of in silico analyses to TAGmetabolism in microalgae for the design of lipid over-producingstrains could be situation and/or strain dependent. The accuracy ofmodel predictions can also be further improvement by theintegration of context specific omics data to the metabolic recon-structions. The drive for metabolic modeling and in silico analysesin microalgae should therefore be well directed and strategicallycombined with the advancing technologies to facilitate the yield ofmost appropriate predictions helpful in the development of lipidover-producing strains.

5. Exploring the molecular mechanisms underlying lipidaccumulation and future directions

Among 50,000 existing microalgal species around 30,000 havebeen studied and analyzed and in those 30,000 species very few areused for lipid over-production in biofuel [149]. Several researchers

Table 4Metabolic modeling frameworks employed in various microalgae.

Organism Type of metabolic modeling framework Target pathway/nature of analyses References

Chlorellapyrenoidosa

FBA Interplay between energy and carbon metabolism under light-dark cycle andautotrophic-heterotrophic conditions

[129]

Chlorellaprotothecoides

FBA coupled with C13 metabolic fluxanalysis

Carbon central metabolism and lipid biosynthesis [130]

Chlamydomonasreinhardtii

FBA Prediction of phosphoglycolate catabolism and pathways used for hydrogen production [131]

Chlamydomonasreinhardtii

FBA Quantitative growth prediction under the influence of different light sources [132]

Ostreococcus tauri FBA with integration of microarray data Optimal flux distribution and dynamics of starch content in light-dark cycle [132]Tisochrysis lutea Dynamic Reduction of Unbalanced

Metabolism (DRUM)Accumulation of lipids and carbohydrates under light-dark cycle [133]

Chlorella sp. Dynamic FBA Interplay between energy and carbon metabolism under light-dark cycle [134]

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suggested microalgae as a better feedstock for biofuel productiondue to its high intracellular lipid content [11,14,15]. Lipid contentalong with the biomass productivity of marine, freshwater andbrackish water microalgal species (including cynobacteria) arelisted in Table 5. A typical empirical elementary composition ofmicroalgae can be represented by C106H263O110N16P (N/Pratio¼7.2:1) and is varied with type of strain used and growthconditions [167]. As microalgae is exposed to limited nutrientcondition the N/P ratio changes dramatically, indicating a widerange of nutrient utilization and uptake capacity of microalgae. Forinstance, N/P ratio of a fresh water microalgae Scenedesmus sp. LX1fluctuated between 2:1 to 4:1 after 13 days of cultivation in growthmedium supplemented with low total nitrogen (2.5 mg L�1) andtotal phosphorus (0.2 mg L�1) [165]. Consequently, while themicroalgae species (Scenedesmus sp. LX1) accumulated high lipidcontent, the lipid productivity and accumulation rate suffered dueto relatively low biomass production [165]. Therefore, N/P ratio is animportant factor while considering environmental stress for lipidoverproduction in microalgae. Most of the nutrient limitation basedlipid accumulation studies discussed in Section 3 clearly demon-strates that high lipid content and high productivity often contra-dict each other. Keeping the N/P ratio constant (7:1 to 10:1)throughout the cultivation period is thus the key. Nonetheless,transient accumulation of starch concomitant with gradual accu-mulation of lipid was observed in Chlorella zofingiensis undernitrogen starvation [168]. Since starch and lipid biosynthesis sharesmetabolic intermediates and starch is considered as a primarycarbon and energy storage product, it can shift the carbon parti-tioning into neutral lipid [168]. The same phenomenon can beexplored for biofuel production. Taking cue from all the importantsubject matter discussed here, a comparison of various approachesis illustrated in Table 6. On an average 30–35% increment in TAGcontent was observed in various biochemical approaches (Section 3and Table 6). However, increase in TAG content often results in cellgrowth cessation. Therefore, application of new methods describedin this review for enhancement of TAG content can be a feasibleoption. A recent study by Fan et al. [49] described the relationshipbetween the genetic engineering and biochemical engineeringapproaches and the usefulness of their intermingling in achievinghigher lipid content. The authors studied the time-course transcriptpatterns of lipid biosynthesis related genes under biochemicalstress conditions in Chlorella pyrenoidosa [49]. This study provideda comprehensive picture of physiological attributes with an impli-cation of systemic metabolic engineering approaches. In anotherinstance, a combination of phosphate starvation and over-

expression of DGAT gene under inducible promoter system hasresulted in up to 29 fold increase in the total TAG content in themicroalga, C. reinhardtii when compared to the wild type strainexposed to a different set of biochemical conditions [95]. Based onthe studies reviewed here some future directions for lipid over-production in biofuel application are suggested:

� Under normal growth conditions if the starch content can beincreased during the log phase of the cell cycle, higher lipidcontents can possibly be produced in the stationary phase oftheir life cycle.

� From the metabolic engineering view: firstly, flux diversionfrom starch to lipid under nutrient deprivation should beidentified and calculated; whereas with the help of geneticengineering techniques those inter-molecular flux change ratesshould be manipulated for overproduction of the lipid. Sec-ondly, another approach could be identification of specifictranscription factors expressed during nutrient starvation andover-expressing them after determining the metabolic fluxrates for a particular microalgal strain under normal growthcondition.

Gap lies in understanding the metabolic molecular networkduring nutrient starvation and applying those data as a base tomanipulate a strain for overproduction of lipid. Furthermore, theincorporation of in silico approaches likely increase the successrate of metabolic engineering strategies through effective targetprediction capabilities. Focus therefore needs to integrate bio-chemical, genetic engineering and in silico approaches (Fig. 3) forproducing microalgal strains that are capable of lipid production atan industrial scale. Given that algal transgenesis coupled withbiochemical and metabolic engineering manipulations are still intheir infancy, a holistic understanding of all the approaches bothindividually and combined could be the key in substantial lipidenhancement in microalgae. In this context, investigators world-wide are not restricted to common method of lipid accumulationrather are involved in finding footprints in modern biotechnologyof biofuel.

6. Improvement of fatty acid quality for biodiesel application

Biodiesel and bioethanol, the most common biofuels, aremainly produced from biomass or renewable energy sources thatusually lower the combustion emission than fossil fuels per

Table 5Lipid content along with the biomass productivity of marine, freshwater and brackish water microalgal species (including cynobacteria).

Species Volumetric productivity of biomass (gL�1d�1) Lipid content (% DCW) Reference

Arthrospira platensis 0.91–2.7 13.0–30.0 [150,151]Botryococcus braunii 0.02 25.0–75.0 [13]Chlorella vulgaris 0.02–0.2 5.0–58.0 [13]Chlorella sp 0.17 9.0–50.0 [152]Chlorella protothecoides 51.2 (high yielding Photobioreactors) 51.3 [153]Chlorella sorokiniana 1.47–12.2 19–22 [154,155]Chlorococcum infusionum 0.28 19.3 [43]Dunaliella salina 0.22–0.34 6.0–25.0 [13]Haematococcus pluvialis 0.05–0.7 25.0 [156,157]Monoraphidium contortum 0.89 31.5 [158]Monoraphidium sp. FXY-10 0.68 56.8 [159]Nannochloropsis sp 0.051–0.27 20.0–53.0 [152,160]Phaeodactylum tricornutum 1.2–1.9 40 [161]Phaeodactylum tricornutum 0.3–1.4 18.0–57.0 [162]Spirulina platensis 0.046–0.4 4.0 [163,164]Scenedesmus sp. 0.09 19.0–22.0 [13]Scenedesmus sp. LX1 0.7 30 [165]Tetraselmis sp. 0.4–0.6 12.0–23.0 [166]

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equivalent power output [13, 171]. Based on the political andeconomical evaluation, biodiesel represents about 82% of the totalbiofuel production in EU and is still growing in other countriesincluding Brazil and United States [172]. While edible vegetable oilis banned as a feedstock for biodiesel production, low cost wastecooking oil is not sufficient to fulfill the target of producingcommercially viable biodiesel as a biofuel [13]. In the followingsection we review and analyze the quality of fatty acids required

for successful biodiesel production and propose that microalgaecan be a long term feasible substitute for biodiesel production.

Developments of a green process for biocatalytic production ofbiodiesel from vegetable oils and its accurate chromatographicestimation have recently been reported by us [173–176]. Biodieselusage in diesel engines following the norms of internationallyaccepted ASTM standards entirely depends upon the initial com-position of the feedstock [176]. Thermo-physical parameters

Table 6Comparison of various approaches with advantages and limitations.

Strain Stress factor Success rate effect and solution References

Conventionalapproaches

Chlorella kessleri Phosphorous limitation TAG production variedfrom 6.5% to 39.3%

Overall increment in TAG butgradual decrease in EPA

[169]

Chlorella vulgaris, Chlorellazofingiensis, Neochlorisoleoabundans, andScenedesmus obliquus

Nitrogen deprivation 35% dry cell weight as TAG Cell cycle cessation; solution:elevated concentration of inorganiccarbon, as carbon is required forlipid biosynthesis

[168]

Phaeodactylum tricornutum Iron deprivation 14 times slower comparedto cells grown in iron-replete condition

Resulted in decreased carbon-fixation and cellular growth

Advantages of Conventional approaches � Easy to use, no complicated techniques involved� Short time achievable target

Limitations of Conventional approaches � Cellular growth cessation, lipid productivity vs biomass formation proportion remains same� Similar stress condition may not be applicable across the microalgal realm� Often fails in understanding the reason for lipid accumulation hence better lipid productivity

Metabolicengineeringapproaches

Chlamydomonas reinhardtii sta6 and sta7 mutations 10-fold increase in TAGcontent as compared to thewild-type

Inactivation of AGPase [76]

Thalassiosira pseudonana(diatom)

Targeted knockdown of amultifunctional lipase/phospholipase/acyltransferase

2.4–3.3 fold higher lipidcontent than the wild typestrain without cell growthcessation

Plays a role in membrane lipidturnover and lipid homeostasis

[39]

Advantages of Metabolic engineeringapproaches

� Molecular knowhow of lipid metabolism, better understanding of techniques and strategies to enhance lipidproductivity

� Does not affect cellular growth, hence biomass yield� Quality and quantity of oil can be produced according to the need� Detailed diversified methods for increasing lipid content� Once established techniques in a specific strain provides to be promising on industrial front

Limitations of Metabolic engineeringapproaches

� Expensive techniques involved� Not yet established universal techniques across the microalgal realm� Requires long time and detailed analysis

Combination ofbiochemical andgeneticengineeringapproaches

Chlorella pyrenoidosa Nitrogen, Phosphorus and Irondeficiency and time-coursetranscription patterns of lipidbiosynthesis-related genes

34.29% dry cell weight asTAG

Identifying the correlation betweenlipid content upon nutrientstarvation and gene expression level

[170]

Advantages of Combination of biochemical andgenetic engineering approaches

� Molecular estimation ofquantity and quality of oil forbiofuel

� Comprehensive picture ofphysiological attributes withfeasibility of systemic metabolicengineering approaches

� Appropriate predictions withpractical implications

� Genomic understanding gives adirection in achieving thedesired target for a specificmicroalgal strain

Limitations of Combination of biochemical andgenetic engineering approaches

� Time consuming process� Restricted to a specific

microalgal strain� Need more refined analysis

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imparting to the quality of biodiesel includes chain length,branching and degree of saturation [175,177]. Among the otherparameters regarding combustion efficiency of biodiesel includescetane number (CN) of fatty acid methyl esters of oil, heat ofcombustion, oxidation stability viscosity, lubricity, iodine valueand saponification number [178]. Relatively higher concentrationsof free fatty acids (FFA's) in the feedstock may adversely affect thebiodiesel production yield because of soap formation. High FreeFatty Acids (FFA's) content necessitates the pretreatment stepprior to the transesterification as the allowable limit for FFAcontent is 2.5% [173,174]. Studies indicate that feedstock posses-sing high levels of oleic acid (C18:1) to be best suited for biodieselproduction as most conventional biodiesel contains oleic acid asits characteristic component [171]. Scarsella et al. [179] suggestedthat Chlorella sp is a potential candidate accumulating more than20–50% lipid of its dry cell weight with desirable fatty acid contentsuch as C 18:1, C 16:0 and C 18:3. De Bhowmick et al. [180] furtherindicated the suitability of fatty acid composition in Chlorellavaraibilis (an Indian marine origin) for biodiesel production. Asshown in Table 5, lipid content of the microalgal species can reachmore than 50% by its dry cell weight with higher biomassproductivity. In addition to achieve high biomass and lipid contentthe focus needs to increase the lipid productivity. Therefore, whileestablishing genetically modified alga one should keep in mindcertain integral aspects related to good quality biodiesel formationsuch as (i) increase in average chain length of the fatty acids ashigher chain length might lead to higher cetane number resultingin lower NOx exhaust emissions, (ii) degree of unsaturation in afatty acid chain as high unsaturation leads to lower cetane numberresulting in higher NOx exhaust emissions and oxidative

degradation (rancidity) resulting in gum formation in the engineby decreasing the lubricity of biodiesel [176,178]. It is not possiblefor a single fatty acid methyl ester (FAME) to fulfill all theproperties of an ideal biodiesel for CI engine application [176]. Aproportionate balance between higher amount of mono-unsaturated fatty acids such as oleic acid, and fewer saturatedand polyunsaturated fatty acids would suffice the purpose [169].

Moreover, Mata et al. [13] compared biodiesel productivity fromdifferent feedstocks and highlighted that microalgae even with lowoil content (30% DCW) was able to produce 51,917 kg biodiesel/hayear, which is on an average 300 times more than biodiesel producedfrom plant sources. Therefore, it is worth noting microalgae as thenext generation renewable feedstock for biofuel production, providedthe best possible solution depicted in Fig. 3 are considered.

7. Conclusion

The energy crisis coupled with environmental concerns hasthrown up new challenges and also opened up new opportunitiesbefore the world scientific community. While the challenges aremostly revolving around the issues of economic and environmen-tal sustainability of biofuels as alternative renewable energyresources, the opportunities stem from the urge and determina-tion of the scientists working in the field of energy and environ-ment worldwide to derive innovative solutions to this crisisthrough competitive research initiatives and endeavors. The firstand a major bottleneck in developing a sustainable solution forenergy and environment seem to be the feedstock. Out of manybiomass feedstocks for biodiesel tested so far, marine microalgae

Fig. 3. Conceptual diagram of multi-faceted integrated approaches: Rational combination of genetic engineering, in silico and biochemical engineering approaches (apotential few of which are shown in the above schematic diagram) is a requirement for effective enhancement of lipid in microalgal cell factories.

G. De Bhowmick et al. / Renewable and Sustainable Energy Reviews 50 (2015) 1239–1253 1249

have emerged as one of the front runners due to their fastergrowth rates, relatively higher lipid contents on unit dry weightbasis and minimum requirements of arable land and fresh water.Still the technological challenge remains as the neutral lipidcontent of the marine microalgae is not high enough to makebiodiesel production a cost-effective value proposition. The tradi-tional biochemical approaches did not result in significant lipidaccumulation so as to fulfill the need for biodiesel commercializa-tion. Hence, rational approaches of metabolic engineering incombination with systems and synthetic biology strategies couldpotentially bring about the necessary breakthrough to rendermicroalgae to be used as a commercial lipid feedstock. But thereremain certain constraints regarding the basic knowhow of micro-algal lipid metabolism. As algae belong to the lower phylogeneticechelons of the plant kingdom, the sequence homology resem-blances indicates that the enzymes involved in lipid biosynthesiswhen over-expressed resulted in lipid accumulation in higherplants might lead to similar kind of TAG formation if employedin microalgae. So, here we propose a few strategies recognized forTAG formation with new insights. Employment of similar kind ofgenetic modifications such as over-expression of enzymesinvolved in lipid biosynthesis pathway, over-expression of specialinducible promoters, redirection flux of key metabolites, transcrip-tion factor regulation might result in 30–40% lipid accumulationon dry weight basis. The pool of information available fromgenomic databases of microalgae creates new opportunities formetabolic engineering of microalgal cells in reconstructing thepathway for selectively redirecting metabolic fluxes towards lipidbiosynthesis. The reaction rates can be manipulated accordinglyfrom the reconstructed stiochiometric matrix for performingmetabolic flux or flux balance analysis towards identifying thetarget genes based on the enzymes responsible for lipid accumu-lation, in addition to discovering new branched pathways andcalculating the maximum theoretical lipid yield. Initial metabolicengineering approaches that were basically focused on targeting asingle pathway failed to result in significant increase in lipidaccumulation. The possible reasons could be the effect of theinterference of other pathways and processes associated withaccumulation of lipid or the regulatory factors involved in lipidaccumulation. Therefore, an innovative way of looking at it is todesign an integrated strategy combining in silico analyses, meta-bolic control analyses for better understanding of regulatorymechanisms for further genetic analyses coupled with biochemicaland bioprocess studies. This approach may be useful in offering apossible solution to the problem of enhancing neutral lipid contentby revolutionizing the way of microalgal metabolic manipulationto produce genetically stable and environmentally robust micro-algal cell lines. Thus, integrated approach involving the targetingof multiple pathways and regulatory networks (regulons) asdescribed in Fig. 3 could produce an optimal design for large scalelipid production. Then the real technological challenge will be inscaling up the process of cultivation of metabolically engineeredcell line in open raceway ponds or reactor systems for massproduction of microalgal biomass without compromising withthe environmental concerns of spreading antibiotic resistance orlosing recombined genetic traits. This would call for multidisci-plinary efforts for R&D with a holistic vision in this globallyemerging area of commercial importance and social significance.

Acknowledgment

The authors thankfully acknowledge their institute, IndianInstitute of Technology Kharagpur (IIT Kharagpur), for the com-puter & internet facilities and all subscribed online e-resources &full text databases.

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