Metabolic engineering for enhancing microbial biosynthesis of advanced biofuels

Renewable and Sustainable Energy Reviews 119 (2020) 109562

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Metabolic engineering for enhancing microbial biosynthesis of advanced biofuels Manali Das a, 1, Pradipta Patra b, 1, Amit Ghosh b, c, * a

School of Bioscience, Indian Institute of Technology Kharagpur, West Bengal, 721302, India School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal, 721302, India c P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal, 721302, India b

A R T I C L E I N F O

A B S T R A C T

Keywords: Butanol Fatty-acid based biofuel Isoprenoids Genome-scale model CRISPR/Cas9 13 CMFA

Increasing global energy demand and environmental concerns associated with petroleum have raised interest in biofuels for reducing dependency on crude oil and promote carbon-neutral energy generation. Although ethanol is a well-established biofuel, properties like low energy density, hygroscopicity and corrosiveness limit their usage in existing transportation sectors. Need for more energy-dense fuel similar to conventional oil has moti­ vated research on advanced biofuels like butanol, isobutanol, fatty-acid and isoprenoid-derivatives. These fuels not only have very similar energy content and combustion properties to existing fuels but also their storage and transportation properties are compatible with the current infrastructure. Microbes have the native pathway for the synthesis of these molecules, but natural titer is significantly low for commercialization. Metabolic engi­ neering approaches can help in redirecting the cellular fluxes towards these pathways thus improving the titer for microbial synthesis of advanced biofuels. This review provides a comprehensive outlook on the trends and de­ velopments in metabolic engineering strategies for advanced biofuel production using different hosts. Possible strategies include protein engineering, co-factor balancing using rapid genome engineering tools like CRISPR/ Cas9, MAGE/eMAGE, RNAi. Additionally, in silico approaches like flux balance analysis and 13C metabolic flux

Abbreviations: ACC1, Acetyl CoA carboxylase; ACP, Acyl carrier protein; ACR, acyl-CoA reductase; adh2, alcohol dehydrogenase; adhE2, Aldehyde-alcohol-de­ hydrogenase; ADO, aldehyde decarbonylase; alsS, acetolactate synthase; AHR, aldehyde reductase; atoB/thl, acetyl-CoA acetyltransferase; AtfA, Acyltransferase; Bcd, Etf, genes encoding butyryl-CoA dehydrogenase; BnFatA, Brassica napus fatty acyl-ACP thioesterase; budA, α-acetolactate decarboxylase; budB, Acetolactate synthase; BdhB, butanol dehydrogenase; CAR, carboxylic acid reductase; Cas9, CRISPR associated protein 9; CnFatB2, Cocos nucifera fatty acyl-ACP thioesterase; crt, 3hydroxybutyryl-CoA dehydratase (crotonase); CDPME, 4-(Cytidine 50 diphospho)-2-C-methyl-D-erythritol; CDPMEP, 2-Phospho-4-(Cytidine 50 diphospho)-2-Cmethyl-D-erythritol; CRISPR, clustered regularly interspaced short palindromic repeats; ctp1, Citrate Transport Protein; DGAT, Dga1, Diacylglycerolacyltransferase; DMAPP, Dimethylallyl pyrophosphate; dxs, 1-deoxy-D-xylulose 5-phosphate synthase; DXP, deoxyxylulose-5-phosphate; EgTE, Elaeis guineensis fatty acyl-ACP thioesterase; eMAGE, eukaryotic multiplex automated genome engineering; EutE, CoA-acylating aldehyde dehydrogenase; FAA1, FAA4, fatty acyl CoA synthe­ tases; FFA, Free fatty acids; FAEE, Fatty acid ethyl esters; FOH, Fatty alcohols; FAS1, Fatty Acid Synthetase 1; FAS2, Fatty Acid Synthetase 2; FAR, fatty acyl reductase; fabH, 3-oxoacyl-[acyl-carrier-protein] synthase 3; fabF/B, 3-oxoacyl-[acyl-carrier-protein] synthase 1; fabG, 3-oxoacyl-[acyl-carrier-protein] reductase; fabA/Z, 3-hydroxydecanoyl-[acyl-carrier-protein] dehydratase; fabI, Enoyl-[acyl-carrier-protein] reductase; fadD, Acyl CoA ligase; fadE, Acyl-coenzyme A dehy­ drogenase; fadA, β-ketoacyl CoA thiolase; fni, Isopentenyl-diphosphate delta-isomerase; FPP, Farnesyl Pyrophosphate; GPP, Geranyl Pyrophosphate; hbd, β-hydroxybutyryl -CoA dehydrogenase; HFD1, hexadecanal dehydrogenase; HMGS, Hydroxymethylglutaryl-CoA synthase; HMGR, Hydroxymethylglutaryl-CoA reductase; HMBPP, 1-Hydroxy-2-methyl-2-butenyl-4-diphosphate; HMG-CoA, 3-hydroxy-3-methylglutaryl-coenzyme A; IL, Ionic Liquid; IPP, Isopentanyl Pyro­ phosphate; ilvC, acetohydroxy acid isomeroreductase; ilvD, dihydroxy acid dehydratase; ispA, Farnesyl diphosphate synthase; ispC/dxr, 1-deoxy-D-xylulose 5-phos­ phate reductoisomerase; ispD, 2-C-methyl-D-erythritol 4-phosphate cytidylyltransferase; ispE, 4-diphosphocytidyl-2-C-methyl-D-erythritol kinase; ispF, 2-C-methylD-erythritol 2,4-cyclodiphosphate synthase; ispG, 4-hydroxy-3-methylbut-2-en-1-yl diphosphate synthase (flavodoxin); ispH, -hydroxy-3-methylbut-2-enyl diphos­ phate reductase; ispS, Isoprene synthase; idi, IPP isomerase; kivD, 2-ketoisovalerate; ldhA, lactate dehydrogenase A; MAGE, multiplex automated genome engineering; MevP, Mevalonate 5-phosphate; MevPP, Mevalonate 5-pyrophosphate; MEcPP, 2-C-methyl-D-erythritol-2,4-cyclodiphosphate; MmCAR, Mus musculus carboxylic acid reductase; MEP, methylerythritol phosphate; MK, mevalonate kinase; MVA, mevalonate; PMK, phosphomevalonate kinase; pflB, Formate acetyltransferase; TAG, Triacylglycerides; TALEN, Transcription activator-like effector nucleases; TEA, Techno-economic Analysis; ‘tesA, thioesterase; ter, trans-enoyl-CoA reductase; SCD, Stearoyl-CoA Desaturase; ZFN, Zinc finger nucleases. * Corresponding author. School of Energy Science and Engineering, Sir J.C. Bose Laboratory Complex, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, 721302, India. E-mail address: [email protected] (A. Ghosh). 1 Both authors contributed equally to this manuscript. https://doi.org/10.1016/j.rser.2019.109562 Received 13 February 2019; Received in revised form 14 October 2019; Accepted 3 November 2019 Available online 9 November 2019 1364-0321/© 2019 Published by Elsevier Ltd.

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analysis can help in the further improvisation and optimization of designed pathways to maximize carbon-flux towards desired pathways. However, the techno-economic analysis predicts that commercialization of biofuels is highly influenced by feedstock and productivity. Still, several countries have adopted energy mandates to incorporate these fuels in transportation sector for a greener and cost-effective energy supply. An integrated approach involving metabolic engineering and systems biology would help in improving titer of advanced biofuels.

1. Introduction

cerevisiae are mainly preferred for metabolic engineering approaches, as they have easy genetic tractability with available ‘omics’-databases. Also, accessible sophisticated genome editing techniques for these or­ ganisms facilitate their targeted engineering. However, non-conventional organisms having properties like high natural titer of biofuels, increased tolerance to these metabolites and ability to use non-sugar substrates are also being employed in advanced biofuel pro­ duction [9]. The development of cutting-edge research tools and tech­ nologies like CRISPR/Cas9, MAGE/eMAGE for both model and non-conventional hosts can speed up genetic engineering. Particularly, CRISPR/Cas9 is advantageous over other genome engineering tools like ZFN and TALEN as it depends on RNA–DNA recognition using highly specific 20-nucleotides guide RNA for directing the Cas9 towards the target site. This minimizes the off-target effects along with making the process easily programmable and user-friendly [10]. CRISPR/Cas9 tool is also markerless and scarless, unlike other conventional gene editing systems that leaves a scar in the genome hindering rapid metabolic engineering. In fact, CRISPR-based methods have already been used for the production of both biofuels and other commercial compounds [11, 12]. Recent breakthrough in ‘omics’-data generation and reduced cost of sequencing have led to generation of enormous data that offers new opportunities for systems and synthetic biologists aiming to rational designing of naturally evolved systems and to build new phenotypes thorough reverse genetics [13]. In silico approaches like genome-scale models and flux analyses can also help in the improvisation and opti­ mization of designed pathways. Model-based gene interventions have gained popularity in recent years for rational metabolic engineering of microbes. In this review, we have highlighted the current state of metabolic engineering for advanced biofuel production from different microbes. It describes the need for advanced biofuels to reduce the dependency on fossil fuels and its advantages over ethanol with respect to present automobile infrastructure. To increase the native titer of advanced biofuels from microbes, various metabolic engineering approaches have been extensively reviewed. With the emergence of rapid and directed genome editing tools like CRISPR/Cas9 and MAGE/eMAGE, researchers have found a necessity for incorporation of these methods to improve the yield of target molecules. Furthermore, the use of combinatorial metabolic engineering approaches using CRISPRi/CRISPRa (interfer­ ence/activation) has found new applicability in fine-tuning of the engineered pathways. Additionally, the use of in silico approaches has greatly speeded up the process of metabolic engineering by rapid pre­ diction of appropriate gene targets. Application of emerging technolo­ gies like flux balance analysis and 13C metabolic flux analysis for pathway engineering to produce advanced biofuel have also been focused on. This review also deliberates direction on the current technoeconomic state of biofuels as well as potential environmental policies across different countries to use biofuels in a more sustainable and costeffective manner in future. Although research is in pipeline to establish the high-energy advanced biofuels to a commercial level, the produc­ tivity and fine-tuning of pathways in host still pose a major challenge. In short, this review will ensure to provide a comprehensive overview on different advanced biofuel molecules and their corresponding metabolic pathways, current applications of these advanced biofuels, metabolic engineering approaches and tools, present economic status, respective governmental policies, existing research challenges and research impact

A major part of the current energy and consumer market is heavily dependent on the petrochemical industry. However, the unrestrained use of fossil fuels over the last century has resulted in the addition of excess carbon from underground to the atmosphere thereby causing an imbalance in the carbon cycle. With an expanding worldwide human population and subsequent increase in consumer-demand, there is a growing concern over energy security along with the issue of global warming and climate change [1]. One solution to the impending energy demand and environmental issues is to enhance the use of renewable energy sources and technologies. The International Energy Agency (IEA) � has proposed the 2 C scenario, which stated that CO2 emissions in 2060 should be reduced by 70% relative to 2017 CO2 emissions to avoid in­ crease in global temperature [2]. One way this target can be achieved is by the reduction of CO2 emissions in the transport sector while focusing on carbon neutrality. Biofuel for transportation can serve as key alter­ native energy towards this goal. Thus, minimizing the economic and environmental threats associated with fossil fuel combustion. Ethanol is a well-established biofuel that has already reached commercialization. However, it has several drawbacks in terms of physical properties like low energy density (over 30% less energy than gasoline), low flame luminosity, corrosiveness, lower vapor pressure than gasoline which makes cold-start difficult and relative toxicity to the ecosystem, thus limiting its use as biofuels. Additionally, since ethanol is hygroscopic, it’s blending to gasoline must be done right before use. This creates storage and distribution issues that make it expensive in trans­ portation sectors. Furthermore, existing automobile infrastructures need to be modified to meet the blending regulations like EN228 and ASTM D5798 that mandate ethanol blends of 5% (E5) to 10% (E10) in Europe and North America, respectively [3]. These issues have necessitated the demand for more energy-dense molecules over ethanol that are similar to existing oils. Advanced biofuels like butanol, isobutanol, fatty-acid and isoprenoid-derivatives are comparable to fossil fuels (gasoline, diesel, jet fuels) as they have similar energy content as well as storage and transportation properties [4]. Although microbes have the inherent metabolic pathways for syn­ thesis of these valuable molecules, the natural titer is significantly low that limits its industrial production and commercialization. Various factors like low level of secondary metabolite production, loss of metabolic abilities due to evolution, inefficient genetic parts and regu­ lators, inhibitors like glycerol, ethanol tolerance, balance of enzyme activities and carbon flow through metabolic flux, competitive pathways for intermediates and redox potential, cofactor imbalance, transcrip­ tional regulations are the major contributors for low titer which de­ mands metabolic engineering approaches to overcome these challenges [3,5–7]. Metabolic engineering is an emerging research area that helps in the targeted rewiring of cellular metabolism to improve titer, rate, and yield (TRY) of target metabolite. It has found broad applications in cell factory development and has been considered imperative for designing meta­ bolic chassis to produce advanced fuel from targeted hosts [7]. Meta­ bolic engineering approaches like overexpression of target enzymes, gene parts engineering, co-factor engineering, heterologous gene expression, orthogonal pathway construction, blocking of competitor pathways are the key solutions for increasing yield [8]. Established microbial industrial hosts like Escherichia coli and Saccharomyces 2

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30 g/L n-Butanol [22]. Flux through the glycerol pathway was increased by rewiring central metabolism for enhancing NADH level in E. coli resulted in the maximum n-butanol production of 6.9 g/L [21,23]. Metabolomics studies suggested that CoA imbalance can be overcome by overexpressing alcohol dehydrogenase (adhE2) in E. coli, improving n-butanol production to 18.3 g/L [24]. S. cerevisiae naturally produces n-butanol through the keto-acid pathway in small titers [25]. To increase the titer of n-butanol in S. cerevisiae, crt from Streptomyces collinus along with hbd, crt, and adhE2 from C. beijerinckii were heterologously expressed, resulting in 2.5 mg/L n-butanol [16]. In another study, ter from Treponema denticola was expressed in S. cerevisiae and the titer was improved to 6.6 mg/L [26]. To increase CoA level in the host, pantothenate kinase CoA gene from E. coli was overexpressed and alcohol dehydrogenase genes (adh1-5) were deleted in S. cerevisiae leading to 71 mg/L titer of n-butanol [25]. Furthermore, this strain was engineered to produce 235 mg/L n-butanol in aerobic conditions when bacterial water-forming NADH oxidase (nox) was expressed [27]. Co-culture of C. acetobutylicum and S. cerevisiae increased n-butanol concentrations to 16.3 g/L due to extensive secre­ tion of amino acids by S. cerevisiae that reduced ‘acid-crash’ and stim­ ulated high butanol synthesis by C. acetobutylicum [28]. Currently companies like Butamax, Green Biologics are producing n-butanol using the biorefinery technologies established for ethanol in commercial scale [29,30]. However, further optimization of hosts is still needed for cost-effective butanol production in large-scale.

which can accelerate the global biofuel research. 2. Metabolic engineering for biofuel production Metabolic engineering involves modifying the genetics and meta­ bolism of microbes through the deregulation of cellular metabolism for enhancing the production of a metabolite of our choice [14] and has been successfully applied in several sectors like fuels, pharmaceuticals, and fine chemicals [15]. In this section, the different metabolic engi­ neering approaches for advanced biofuel production have been discussed. 2.1. n-Butanol n-Butanol is an advanced biofuel having energy content comparable to gasoline which can be transported easily through existing pipelines and blended with gasoline in any ratio [16]. n-Butanol is produced through the keto-acid pathway where acetoacetyl-CoA is reduced and dehydrated to Crotonyl-CoA. Crotonyl-CoA is further reduced to butyryl-CoA which is finally converted to n-butanol by alcohol dehy­ drogenase (Fig. 1). n-Butanol is naturally produced in high titers (20 g/L) by Clostridium acetobutylicum through native acetone-butanol-ethanol (ABE) fermentation [17]. However, its slow growth rate and sporulation cycle make it relatively unfavorable for the industrial production process [18]. Metabolic engineering attempts such as insertion of C. acetobutylicum n-butanol biosynthesis pathway or reversal of the β-oxidation pathway have yielded n-butanol in E. coli [17, 19,20]. Since the insertion of the non-native n-butanol pathway in E. coli possess an imbalance in NADH levels [17,21] an irreversible reaction catalyzed by trans-enoyl-CoA reductase (ter) was used to improve NADH pool and redirect more flux from acetyl-CoA to n-butanol which yielded

2.2. Isobutanol Isobutanol is an important molecule that has gained importance as a biofuel due to properties like decreased corrosiveness and aqueous miscibility compared to ethanol [31–34]. Like n-butanol, isobutanol is Fig. 1. Keto-acid pathway for n-butanol and isobutanol production in E. coli. The black lines represent the native pathway and blue represents heterologous pathways. Py­ ruvate is converted to acetoacetate by alsS, followed by subsequent hydration and oxidation to produce n-butanol. The enzymes involved are acetyl transferase (atoB), 3hydroxybutyryl-CoA dehydrogenase (hbd), crotonase (crt), butyryl CoA dehydrogenase (bcd) and aldehydealcohol-dehydrogenase (adhE2). Isobutanol is produced from a modified fermentative pathway in E. coli involving enzymes ketol-acid reducto isomerase (ilvC), dihydroxyacid dehydratase (ilvD), alpha-keto-acid-decarboxylase (kivD) and alcohol dehydrogenase (adh). Acetoacetate is converted to isobutanol by the sequential action of these enzymes. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

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also derived from the keto-acid pathway (Fig. 1) [35]. To improve iso­ butanol production in E. coli, overexpression of isobutanol pathway genes alsS (from Bacillus subtilis), kivD (from Lactobacillus lactis) and adh2 (from S. cerevisiae) were done which directed the conversion of keto-acids to isobutanol giving a final yield of 22 g/L isobutanol [17]. However, it was found that elevated isobutanol levels were toxic to the host. Hence, another strain was developed which could tolerate higher levels of isobutanol by modifying cAMP receptor protein (CRP) pro­ ducing 12 g/L isobutanol without appreciable cell loss [36]. Recently, the Entner-Doudoroff (ED) pathway in E. coli was engineered along with deletions in Embden–Meyerhof (EM) pathway to direct carbon towards isobutanol pathway yielding 15 g/L isobutanol [37]. Other bacterial species such as Klebsiella pneumoniae and Enterobacter aerogenes have also been modified to improve isobutanol production. K. pneumoniae has a dormant isobutanol pathway which was activated by turning off the lactate pathway associated genes, resulting in a yield of 2.45 g/L [38]. On the other hand, the anoxic pathway of anaerobic bacteria, E. aerogenes was used for the production of isobutanol from the formate and nitrate pathways. Deletion of ldhA, budA, and pflB genes and het­ erologous expression of isobutanol genes kivD, adhA, ilvC, ilvD, and budB have improved the yield to 4.4 g/L [39]. In S. cerevisiae, isobutanol is produced in very less amount as a byproduct of valine and isoleucine synthesis pathways (Ehrlich pathway) [40,41]. Engineering of S. cerevisiae generally involved the deletion of the competing threonine pathway [41] and/or corresponding overexpression of valine pathway genes [40,41]. Modifications also involved editing genes in ED pathways [42] or pyruvate shunts [43]. Although isobutanol has good fuel prop­ erties, isobutanol-blended fuel is costly. Further engineering and opti­ mization of hosts is necessary to make isobutanol cost-effective for use as an alternative fuel. Isobutanol is currently produced by industries like BASF SE (Germany), Gevo and Butamax (the U.S.) to market this molecule as gasoline blend [44].

acetyl-CoA into malonyl CoA via ATP-dependent acetyl-CoA carbox­ ylase (ACC), and (ii) the incorporation of malonyl-CoA into growing fatty acid chain [46]. In bacteria, the reaction is catalyzed by Fatty-Acid-Synthase Type II (FAS II) represented by discrete, mono­ functional enzymes encoded by genes fabH, fabF/fabB, fabG, fabA/fabZ, and fabI (Fig. 2). The same reaction in yeasts is performed by Fatty-Acid-Synthase Type I (FAS I), which is a 2.6-MDa protein encoded by genes FAS1 (β-subunit) and FAS2 (α-subunit) represented in Fig. 3. Since fatty acids are naturally produced in low quantities, metabolic engineering offers scope to increase productivity. Additionally, the commercially valuable derivatives of free fatty acids (FFA) such as fatty acid ethyl esters (FAEE), fatty alcohols (FOH) and methyl ketones can be produced microbially through introduction of necessary terminal genes. FAEE is produced by esterification of FFA/fatty acyl product with ethanol by wax ester synthase (WS) [47]. The fatty aldehyde is syn­ thesized by reduction of FFA/fatty acyl product catalyzed by different reductases (Fig. 2). On the other hand, alkanes/alkenes are produced by the reduction of FFA into fatty aldehydes followed by decarbonylation of fatty aldehydes via aldehyde decarbonylase (ADO). Methyl Ketones, however, are produced from a modified β-oxidation pathway (Fig. 4). FFA, FAEE, and FOH are mainly involved in biodiesel and detergent industries, while alkane(ene) and methyl ketones have a usage in fragrance, artificial flavor as well as biofuel industries. 2.3.1. Fatty-acid based biofuels from bacteria Metabolic engineering of bacteria for FFA production has gained attention recently because of their relatively simple metabolic machin­ ery and harvesting costs. Common methods that have been employed to overproduce fatty acids in E. coli include increasing the pool of fatty acid precursors like acetyl-CoA, malonyl-CoA, and ACC expression and blocking the β-oxidation pathway by deleting the initiator enzymes (fadD or fadE genes). A breakthrough work for FFA production in E. coli was done by eliminating fadD, fadE and overexpressing ‘tesA resulting in ~1.2 g/L FFA [47]. Upregulation of fabZ, ‘tesA along with the deletion of fadD were done based on OptForce predictions which resulted in an engineered E. coli strain that could produce 1.7 g/L FFA [45]. An E. coli strain producing 8.6 g/L FFA in fed-batch was developed to eliminate the metabolic pathway bottlenecks by deleting fadD gene and

2.3. Fatty acid-based biofuels Fatty acids are class of promising molecules in biofuel research due to the presence of high energy long carbon-chains [45]. Naturally, fatty acid biosynthesis involves two major steps: (i) carboxylation of

Fig. 2. Diagrammatic representation of fatty acid biosynthesis and β-oxidation pathways in E. coli. The native pathway is represented in black while the heterologous pathway is represented in blue. Acetyl CoA is converted to malonyl CoA by acetyl-CoA carboxylase (ACC) which in turn is converted to malonyl ACP by fabD. Malonyl ACP enters the fatty acid elongation where it is iteratively added to each cycle catalyzed by FASII (fabH, fabF, fabB, fabG, fabA, fabZ, and fabI). Fatty acids are released as fatty acyl ACP which is either converted to triacylglycerol (TAG) or into free fatty acids by thiolase (‘tesA). Free fatty acid (FFA) produced is modified to fatty acyl CoA by acyl CoA ligase (ACL). The FFA or acyl CoA gets converted to different de­ rivatives such as FAEE, Fatty alcohol and alkane(ene) depending on the terminal enzyme acting on it (WS/DGAT, FAR, ACR, AHR, CAR, ADO, OleTJE). Acyl CoA also en­ ters the β-oxidation pathway where it is degraded by fadE, fadB, and fadA to release acetyl CoA. (For interpretation of the refer­ ences to colour in this figure legend, the reader is referred to the Web version of this article.)

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Fig. 3. Schematic representation of fatty acid biosynthesis pathway for biofuel pro­ duction and β-oxidation pathway in S. cerevisiae. The native pathway is presented in black and heterologous pathway in blue. Acetyl CoA is produced in mitochondria is transported to the cytosol and converted to malonyl CoA by acetyl CoA carboxylase (ACC1). Acetyl CoA and malonyl CoA then enter fatty acid synthase complex (FASI) to produce fatty acyl CoA in a cyclic manner. Fatty acyl CoA is directly converted to FFA by heterologous’tesA. FFA can also be con­ verted back to fatty acyl CoA by synthetases (FAA1-4). The fatty acids are either directed to the endoplasmic reticulum for phospho­ lipid synthesis or enter peroxisomes for β-oxidation to finally release acetyl CoA. FFA is converted to FAEE and alcohol/alkane by heterologous expression of WS/DGAT and FAR respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 4. A modified β-oxidation pathway in E. coli for methyl ketone production. The native pathway is denoted by black, heterologous genes by blue, upregulated genes by green and deletion by red. FFA from fatty acid biosynthesis pathway enters β-oxidation by fadD in the form of fatty acyl CoA. The membrane-bound fadE is replaced with a soluble acyl-CoA oxidase from M. luteus to produce 2,3-Enoyl CoA from fatty acyl CoA. Upregulated fadB enzymes help to produce higher titers of β-ketoacyl CoA. The native fadA thioesterase was replaced by upregulated native thioesterase fadM to produce a β-ketoacid, which is spontaneously converted to methyl ketones. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

upregulating different fatty acid pathway genes (ACC, fabD, fabH, fabG, fabA, fabI) along with three plant thioesterases (BnFatA, CnFatB2, and EgTE) [48]. A detailed list of the several strategies adopted for fatty-acid based compounds that have been produced from E. coli has been docu­ mented in Table 1 [7,45,47–67]. Although E. coli is one of the preferred hosts for fatty-acid based oleochemical production, overproduction of fatty acids in E. coli is naturally difficult because of a tight transcriptional and post-transcriptional regulation of fatty acid biosynthesis including strong feedback inhibition [45]. Moreover, bacteria release the final product as fatty acyl-ACP that needs to be hydrolyzed to FFA before any

modifications can be done [47]. To avoid these steps, eukaryotic systems like yeast are being preferred over prokaryotes for fatty acid production as these are directly released as fatty acyl-CoA. 2.3.2. Fatty-acid based biofuels from yeast Since yeast FAS is encoded by two genes unlike bacteria (10 separate genes), it enables overexpressing the entire pathway in a more effortless manner [68]. Although S. cerevisiae is not an oleaginous organism (3.5%–10.7% fatty acids of dry cell weight), it has still been engineered to improve the fatty acid production due to its easy manipulation and 5

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Table 1 Metabolic engineering for fatty acid-based biofuels from E. coli. Target Molecule

Manipulation

Yield

Year

References

FFA FFA FFA FFA FFA FFA

Δ fadD, ΔfadE and overexpressing ‘tesA ΔfadD gene, overexpressed ACC, Cinnamomum camphorum ‘tesA ΔfadD gene, overexpressed ACC, Umbellularia californica ‘tesA ‘tesA from Ricinus communis and Jatropha curcas. Upregulation of fabZ, ’tesA, deletion of fadD based on OptForce predictions ΔfadD gene; upregulating ACC, fabD, fabH, fabG, fabA, fabI; inserted BnFatA, CnFatB2 and EgTE Expressing FASI from Corynebacterium glutamicum Development of FFA biosensor mediated PopQC method to select the high-performing cells. Overexpressed fadD, tesA and atfA; inserted Z. mobilis pdc and adhB Expressed fadD, pdc, adhB, ‘tesA, atfA, ΔfadE

1.2 g/L 2 g/L 1.7 g/L 8.6 g/L

2010 2010 2010 2011 2012 2013

[46] [49] [50] [51] [45] [52]

400 mg/L 71 mg/L

2015 2016 2010 2011

[53] [54] [47] [56]

500 mg/L

2012 2013 2015

[55] [57] [58]

2010.

[46]

2014 2015 2015

[59] [53] [60]

2016 2010 2013 2010 2014 2012 2014 2015

[61] [62] [63] [64] [65] [66] [67] [53]

FFA FFA FAEE FAEE from lignocellulosic biomass FAEE FAEE FAEE Fatty alcohols

A dynamic sensor-regulator system (DSRS) was developed to regulate FAEE production Thioesterase gene CcFatB1 from Cinnamomum camphorum Expressed plsB, pgpB, fadD, atfA; overexpressed native fadD, fadR, and ‘tesA; inserted mutated atfA from Acinetobacter baylyi Eliminating fadE, and overexpressing ‘tesA, fadD, and acr1 genes

Fatty alcohols Fatty alcohols Fatty alcohols

Synechococcus elongatus acyl-ACP reductase (AAR); upregulated AHR (AdhP) C. glutamicum FAS1A into E. coli and Marinobacter aquaeolei ACP/CoA-reductase Oryza sativa αDOX; overexpressed ‘tesA, AHR, and fadR

Fatty alcohols Alkanes Alkanes Alkenes Alkenes Methyl Ketones Methyl Ketones Methyl Ketones

Δ‘tesA, heterologous FAR Inserted cyanobacterial acyl-ACP reductase and aldehyde carbonylase Upregulated fadD; inserted C. acetobutylicum FAR and Arabidopsis thaliana ADO (cer1) Expressed three-gene cluster from Micrococcus luteus Expressing a fatty acid hydroxylases OleTJE(P450 family) from Jeotgalicoccus spp. Overexpressed ACO and fadM from M. luteus; ΔfadA Balancing the overexpression of fadR and fadD genes Upregulation of ‘tesA, fadD, fadM, fadB, and mlut_11700 genes

higher genetic tractability makes it advantageous for biofuel production. On over-expressing FAS1, FAS2, ACC1, and DGAT in S. cerevisiae, it was found that TAG production increased, resulting in lipid accumulation up to 17% of dry cell weight. This strain was further engineered by deleting FAA1, FAA4, POX and expressing E. coli ‘tesA, to produce 400 mg/L of FFA [68]. In another study, overexpression of acyltransferases and TAG-degrading lipases were done along with the deletion of PXA1, POX1, FAA1, FAA4, and FAT1, resulting in 2.2 g/L of FFA [69]. A very high FFA yielding cell factory was developed by expressing Rhodo­ sporidium toruloides FAS-ACP domains, deleting FAA1, FAA4, POX1, HFD1 (hexadecanal dehydrogenase) and upregulating ATP-citrate lyase, citrate transporter Ctp1 and E. coli ‘tesA genes, producing 10.4 g/L of FFA in fed-batch culture [70]. A detailed tabulation of the different modifi­ cations for fatty acid oleochemical production in S. cerevisiae has been presented in Table 2 [11,61,68,69,71–78]. In addition to S. cerevisiae, oleaginous yeasts have been a target of many researchers due to their high lipogenesis ability, potential to grow in different carbon sources (including lignocellulosic biomass), ease in large-scale cultivation and growing knowledge on their molecular biology. The oleaginous yeast Yarrowia lipolytica is considered as a good model for fatty acid biosynthesis and biofuel production due to its high acetyl-CoA flux and associated oil sequestration mechanism. Manipu­ lations such as over-expressing ACC1 and DGA1 in Y. lipolytica increased lipids synthesis up to 62% of dry cell weight (yield 0.270 g/g) [79]. Y. lipolytica was also engineered to produce short-chain alkane (pentane) from linoleic acid by insertion of soybean lipoxygenase and deletion of the β-oxidation pathway [80]. Also, to avoid the tight metabolic regu­ lation controlling fatty acid homeostasis and increase flux through fatty acid biosynthesis, Y. lipolytica was engineered by simultaneous over­ expression of ACC1, stearoyl-CoA desaturase (SCD), and DGA1 finally producing a titer of ~55 g/L lipids [81]. Additionally, Y. lipolytica strains were developed to produce FFA, FAEE, FOH, alkanes, and TAG by engineering different metabolic pathways (native and heterologous) in the host. Targeting acyl-CoA/acyl-ACP processing enzymes and het­ erologous AtfA and MmCAR to different cellular compartments pro­ duced 142.5 mg/L of FAEE and alkanes of 23.3 mg/L. Furthermore, FFA

~60 mg/L medium-chain fatty alcohol 750 mg/L ~3.5 g/L 1.95 g/L Odd-chain fatty alcohols 6.33 g/L in fed-batch culture 580 mg/L alkanes 97.6 mg/L α –alkenes 174 mg/L

and TAG were produced in a higher amount to 9.67 g/L and 66.4 g/L respectively, by decoupling its production from nitrogen starvation through engineering alternative cytosolic acetyl-CoA pathways. In the same study, FOH was produced to 2.15 g/L by reduction of fatty acyl-CoAs catalyzed by heterologously expressed M. aquaeolei FAR and E. coli fadD [82]. Methyl ketones (314.8 mg/L) were produced from Y. lipolytica using lignocellulosic matter as a carbon source by chromo­ somal deletion of POT1 and peroxisomal localization of bacterial methyl ketone pathway enzymes (FadB, ACO, and FadM) [83]. 2.4. Isoprenoid based biofuels Isoprenoids or terpenoids are a group of diverse long-chain mole­ cules having attractive fuel properties such as low freezing point (methyl branches), high energy density (cyclic structures), high octane and ce­ tane numbers which makes it a suitable natural compound for biofuel research [84,85]. Based on the number of carbon atoms present, ter­ penoids are grouped as hemiterpenoids (C5), monoterpenoids (C10), and sesquiterpenoids (C15). Terpenoid synthesis involves the condensation of a C5 precursor, isopentenyl diphosphate (IPP) with its isomer dime­ thylallyl diphosphate (DMAPP). Both DMAPP and IPP can be synthe­ sized by two pathways: the 2-methyl-d-erythrito-4-phosphate (MEP) or 1-deoxy-D-xylulose-5-phosphate (DXP) pathway and the mevalonate (MVA) pathway (Fig. 5). Hemiterpenes are derived only from DMAPP whereas monoterpenes and sesquiterpenes are derived by the conden­ sation of DMAPP with one and two molecules of IPP respectively [84]. In native state, DMAPP and IPP ratio is low (<1) that can limit isoprenoid synthesis in vivo because DMAPP solely plays the role of reactant in isoprenoid synthesis [86]. Hence, for industrial-scale production of terpenoids from microbes for biofuel production, overproduction of DMAPP and IPP are generally targeted [4]. Isoprene is a simple hemiterpene that is extensively used in rubber production, pesticides, fragrances, adhesives, and considered as the liquid fuel due to high energy density [1]. Approximately 95% of isoprene is used for rubber synthesis, and approximately 20 million tons of rubber is produced annually [87]. Engineered E. coli, Synechocystis 6

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importance in perfumes, flavors, pharmaceuticals, solvents and poten­ tially biofuels [7,90]. E. coli and S. cerevisiae have a unique monoterpene pathway where their substrate geranyl pyrophosphate (GPP) is pro­ duced as the only intermediate for sesquiterpene precursor farnesyl pyrophosphate (FPP). Hence, monoterpene (pinene/limonene) syn­ thases compete for the sesquiterpene precursor FPP synthases (FPPS) for GPP [91]. Hence, to increase monoterpene production from these hosts, strategies like overexpression of GPP synthases (GPPS) with moderately decreased expression of FPPS are considered [92]. Additionally, at­ tempts like fusing GPPS with monoterpene synthases or combinatorial screening for high-flux pinene synthase and GPPS enzymes to address these issue [93]. Sesquiterpenes are commercially used in personal care, food industries with potential as bio-jet fuels [94]. They are synthesized by condensation of two IPP molecules with one DMAPP catalyzed by farnesyl-diphosphate-isomerase to produce FPP [94,95]. Sesquiterpene synthesis attempts using E. coli and S. cerevisiae have involved increasing DMAPP level in cytosol and overexpressing FPPS or bisabolene synthase (BIS) [94,96]. A detailed study of different metabolic engineering strategies per­ formed on various bacterial, cyanobacterial and yeast species for pro­ duction of isoprene, pinene, limonene, farnesene, and bisabolene are provided in Table 3 [12,56,86,88,89,93–116]. The isoprenoid market was $510 Million in 2017 and is expected to increase to $730 Million by 2025. Since isoprenoids have huge potential as bio-jet fuels as well as other commercial applications, metabolic engineering of microbes for the synthesis of these compounds have huge future prospects. In fact, companies like Amyris, Evolva, Isobionics have units for commercial terpenoids synthesis using bacterial and yeast hosts [117]. However, factors such as low precursor level in cell as well as cytotoxicity are a major drawback limiting commercial scale-up of terpenoids [93]. Further advancement of research in this area can be done by identifying stronger promoter for isoprenoid-compound synthesis or by balancing pathway expression and cellular metabolism for improving the titer.

Table 2 Metabolic engineering for fatty acid-based biofuels from S. cerevisiae. Target Molecule

Manipulation

Yield

Year

Publication

FFA

Overexpressed FAS1, FAS2, ACC1; ΔFAA1, ΔFAA4, ΔPOX; E. coli ‘tesA ΔADH1, ΔPOX, ΔFAA1, ΔFAA4; overexpressed ACC1 Truncated Mus musculus thioesterase (Acot5s) Overexpressed acyltransferases and lipases, ΔPXA1, ΔPOX1, ΔFAA1, ΔFAA4, and ΔFAT1 Expressing Rhodosporidium toruloides FAS-ACP domains, ΔFAA1, ΔFAA4, ΔPOX1, ΔHFD1; upregulating ACL, Ctp1 and E. coli ‘tesA genes Mutations in the active sites of keto synthase and malonylpalmitoyltransferase subunits of FASI Insertion of a codonoptimized atfA Deletion of genes involved in competing pathways like TAG, steryl ester and β-oxidation Chromosomal integration of WS2, ADH2, ADH6, ACC1 and mutated acetyl CoA synthetase Insertion of Brevibacterium ammoniagenes FASI and bacterial WS2 Overexpressing mouse FAR (mFAR1) ΔFAA1, ΔFAA4, ΔHFD1; Mycobacterium marinum MmCAR and M. aquaeolei FAR M. aquaeolei FAR (FaCoAR); upregulated Adh5 and MmCAR Upregulation of MmFAR, mutated ACC1, Ole1; ΔADH6, ΔDGA1, and ΔHFD1 FAR and FADO from Synechococcus elongates; ΔHFD1 Expressing MmCAR and Aspergillus nidulans 4/phosphopantetheinyl transferase (NpgA)

400 mg/L

2014

[68]

140 mg/L

2014

[71]

500ug/mL extracellular FFA 2.2 g/L

2014

[72]

2015

[69]

10.4 g/L in fedbatch culture

2016

[70]

hexanoic acid (72 mg/L) and octanoic acid (245 mg/L)

2017

[73]

5 mg/L

2014

[68]

17.2 mg/L

2013

[74]

4.4 mg/L

2015

[75]

10mg/gCDW

2015

[76]

100 mg/L

2014

[68]

1.5 g/L

2016

[70]

43.6 mg/L

2017

[77]

1.2 g/L

2017

[11]

2015

[78]

2016

[68]

FFA FFA FFA

FFA

FFA

FAEE FAEE

FAEE

FAEE Fatty alcohol Fatty alcohol Fatty alcohol Fatty alcohol Alkane Alkane

0.82 mg/L

2.5. β-oxidation pathway mediated biofuel: n-alcohol and fatty acids The β-oxidation cycle is a natural fatty acid degradation process in bacteria and eukaryotes which is regulated by many transcription fac­ tors and genes for the cyclic degradation of fatty acyl CoA, releasing acetyl CoA into the cytosol. In bacteria, the β-oxidation cycle involves the breakdown of fatty acids by cyclic removal of two carbon atoms (as acetyl CoA) catalyzed by acyl ACP synthase (fadD), acyl-CoA dehydro­ genase (fadE), β-hydroxylacyl-CoA (fadB), and thiolase (fadA) (Fig. 6). The β-oxidation pathway has been modified in the reverse biosyn­ thetic direction for producing n-alcohols and fatty acids [118,119]. Several engineering strategies are being used for reversal of the β-oxidation pathway which ultimately leads to fuels and chemical syn­ thesis in an ATP independent manner. Metabolic engineering of E. coli and Clostridium species by imposing a reverse beta-oxidation pathway enables to produce 1-butanol, butyrate, 1-hexanol, and several other C6– C10 long-chain alcohols [1]. A reverse β-oxidation cycle has been engi­ neered for E. coli to produce different biofuel-molecules to improve fatty-acid biosynthesis by converting acetyl CoA to acyl CoA without ATP consumption. This required the operation of the pathway with some other non-fatty-acid carbon source as a substrate in place of fatty acids. Furthermore, rewiring of the regulatory network was done by activation of β-oxidation enzymes by cyclic AMP (cAMP) receptor protein (CRP)– cAMP complex, repression of genes regulating the catalytic action of β-oxidation enzymes and replacement of fadE with enoyl-CoA reductase (ydiO; ter) that converts enoyl-CoA to acyl CoA. The fatty acyl CoA produced could then be directed for the production of long-chain alco­ hols [20]. Another E. coli strain was developed that could produce fatty alcohols from the reverse β-oxidation pathway under anaerobic condi­ tions. The genes for the reverse β-oxidation pathway were placed under Vitreoscilla anaerobic auto inducible promoter up to 1.8 g/L of fatty al­ cohols [120]. Both β-oxidation and reverse β-oxidation pathways have

and S. cerevisiae strains are capable of producing isoprene, although the yield is not significant enough for commercialization of the microbial synthesis. Bottlenecks such as low cellular DMAPP:IPP ratio [86] and poor expression of isoprene synthase (IspS) [88] have been identified in Synechocystis as rate-limiting factors in isoprene synthesis. Addressing these bottlenecks by heterologous expression of Streptococcus pneumo­ niae fni gene along with fused ISPS-cpcB protein in Synechocystis could increase isoprene synthesis by 62-fold [89]. In addition to hemiterpenes, certain monoterpenes such as pinene, limonene, and sesquiterpenes like farnesene, bisabolene have already been targeted as biofuel-precursors for production of advanced biofuels from engineered microbes. Monoterpenes have huge commercial 7

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Renewable and Sustainable Energy Reviews 119 (2020) 109562

Fig. 5. Isoprenoid biosynthetic pathways via the mevalonate (MVA) pathway and DXP/MEP pathway. In the MVA pathway, three acetyl CoA molecules condense to form HMG-CoA which is then converted to mevalonate. Mevalonate is then converted to IPP via Mev-P and MevPP. In the DXP pathway, pyruvate condenses with glyceraldehyde-3-phosphate (G3P) to produce DXP. IPP isomerizes to DMAPP by Idi. DMAPP produces hemiterpenes, while monoterpenes and sesquiterpenes are formed by condensation of DMAPP with one and two IPP molecules respectively.

Similarly, fatty alcohol production in S. cerevisiae was also done using CRISPR/Cas9 through the deletion of competing genes and the insertion of heterologous genes in targeted loci [11]. Parallel to single loci tar­ geting using CRISPR/Cas9, CRISPR mediated multiplex genome inte­ gration has also been demonstrated by multi-copy integration of metabolic pathways [126,127]. Multiplex CRISPR/Cas9 was used for up to five metabolic targets, resulting in a 41-fold increase in mevalonate accumulation in S. cerevisiae strain [128]. In another case, CRISPR/Cas9 mediated multiplexing was used for genomic integration of both xylose utilization and 2,3-butanediol (BDO) pathways into the yeast genome, leading to direct BDO production from xylose [127].

the potential for ubiquitous production of different biofuel compounds in engineered microbial hosts. Improvement of the production of fine chemicals using this pathway can be done by identifying more targets and balancing regulatory networks. 3. Genome engineering tools for biofuels For improved biofuel production, increasing the concentration of precursors such as acetyl CoA, malonyl CoA, and mevalonate is a pre­ requisite. Genome engineering tools are indispensable for rearranging the complex metabolic interactions through different methods such as introducing heterologous genes and pathways, overexpression the ratelimiting enzymes, down-regulating genes, and blocking the competing pathways [8]. Some of the important tools and techniques that allow the directed engineering of pathways for biofuel production are discussed below.

3.1.2. MAGE/eMAGE Multiplex automated genome engineering (MAGE) has been described in prokaryotes where many chromosomal loci are simulta­ neously targeted to produce combinatorial genomic diversity by modi­ fication in a single cell or multiple cells. MAGE technology was used in E. coli where mutations were inserted into the ribosome binding sites of up to 20 genes involved in DXP pathway, resulting in the enhancement of their translational efficiency for lycopene production [129]. MAGE was later developed for S. cerevisiae called eMAGE (eukaryotic multiplex automated genome engineering) where synthetic oligonucleotides were used to insert mutations in the genome. This provides an advantage over genome editing tools because it does not create double-strand DNA breaks and is homologous recombination independent. It uses oligonu­ cleotides containing necessary mutations that anneal at the lagging strand of DNA replication fork to obtain specific genetic manipulations without unintended mutagenic changes and with high efficiency (~40%) (Fig. 7b). This method was used to diversify a heterologous β-carotene biosynthetic pathway in S. cerevisiae through the introduc­ tion of specific mutations in promoters, genes, and terminators [130].

3.1. Genome editing and transcriptional regulation tools 3.1.1. CRISPR/Cas9 CRISPR/Cas9 system is a recently developed versatile and userfriendly molecular biology tool that has found immense importance as a customizable and invaluable system for fast genome engineering of different organisms. It uses highly specific 20-nucleotides guide RNA that forms a complex with the Cas9 endonuclease and guides it to target areas for cleavage (Fig. 7a). Since its early demonstrations for genome engineering purposes [121,122], its use has expanded to multiple or­ ganisms for myriad applications. Successful genome engineering in the host can be done by repairing the CRISPR/Cas9 induced double-strand break with non-homologous end joining (NHEJ) or homology-directed repair (HDR). CRISPR/Cas9 tool was used for metabolic engineering of E. coli for fine-tuning the β-carotene [123] and the n-butanol [124] pathways that produced 2 g/L and ~5 g/L of the respective molecules in fed-batch fermentation. Multiplexed CRISPR/Cas9 tool was also used in Clostridium tyrobutyricum to fine-tune the butanol pathway resulting in extremely high butanol yield of 26.2 g/L in batch fermentation [125].

3.1.3. CRISPRi/CRISPRa and RNAi for transcriptional regulation Transcriptional regulation is another efficient approach for genetic manipulation that is often adopted in metabolic engineering. Directed knock-down of genes can be achieved through methods like CRISPR 8

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Renewable and Sustainable Energy Reviews 119 (2020) 109562

Table 3 Metabolic engineering for isoprenoid biofuel production from different microbes. Type of terpenoid

Molecule

Microbial Host

Genetic interventions

Yield

Reference

Hemitepene (C5)

Isoprene Isoprene Isoprene

E. coli E. coli E. coli

314 mg/L 620 mg/L

[97] [98] [99]

Isoprene Isoprene Isoprene

1.434 mg/L

[100] [101] [102]

Isoprene Isoprene

B. subtilis Synechocystis Synechocystis PCC6803 Synechocystis Synechocystis

ispS from Populus nigra; dxs and dxr from B. subtilis. OleTJE from Jeotgalicoccus sp.; OhyAEM from Elizabethkingia meningoseptica. Identify isoprene synthases from Ipomoea batatas, Populus alba, and Pueraria montana that could be used in E. coli. ispS gene from Pueraria spp. Kudzu ispS was expressed under photosynthetic psbA2 promoter. Plasmid-based system containing kudzu ispS expressed in low NaCl concentrations. fni gene from Streptococcus pneumoniae to increase the cellular DMAPP:IPP ratio. Fusion of the IspS gene to the β-subunit of phycocyanin.

Pinene Pinene Pinene

E. coli E. coli E.coli

Pinene

E.coli

Pinene Limonene Limonene

S. cerevisiae E. coli E. coli

Limonene Limonene

S. cerevisiae S. cerevisiae

Limonene

Synechocystis sp. PCC 6803

Farnesene

E. coli

Farnesene

E. coli

Farnesene Farnesene

E. coli S. cerevisiae

Farnesene

S. cerevisiae

Farnesene

S. cerevisiae

Farnesene Bisabolene

Y. lipolytica E. coli and S. cerevisiae

Monoterpene (C10)

Sesquiterpene (C15)

GPPS2 from Artemisia grandis and pinene synthase Pt30 from Pinus taeda. PS and GPPS from A. grandis in an IPP-DMAPP overproducing strain. Integrative screening of high-fidelity GPPS and PS enzymes from different plant conifers. Mutated Abies grandis GPPS through error-prone PCR and DNA shuffling; insertion of a tunable intergenic region GPPS and PS genes. Geraniol synthase from Ocimum basilicum. Heterologous mevalonate pathway and limonene synthase. Heterologous expression of mutated Enterococcus faecalis MvaS, Methanosarcina mazei MK, Solanum lycopersicum SlNPPS (neryl pyrophosphate synthase) and Mentha spicata MsLS. Citrus limon limonene synthase in a supplemented YP medium. Orthogonal limonene biosynthetic pathway containing exogenous Solanum lycopersicum SlNDPS1 (catalyzes IPP and DMAPP to cis-GPP) and C. limon limonene synthase. Overexpression of ribose 5-phosphate isomerase and ribulose 5-phosphate 3epimerase gene in pentose phosphate pathway; limonene synthase from M. spicata and geranyl diphosphate synthase from Abies grandis. Exogenous MVA pathway and a codon-optimized farnesene synthase fused with FPP protein. Information obtained by in vitro reconstitution of MVA pathway aided in targeted metabolic engineering. Over-expression of IPP isomerase and FPP synthase. Engineering the MVA pathway with Artemisia annua and Picea abies farnesene synthase (by Amyris company). Formerly engineered for artemisinic acid production further modified to produce farnesene by allowing biosynthesis of cytosolic acetyl-CoA with reduced ATP requirement. nphT7 from Streptomyces sp. to catalyze irreversible condensation of acetyl CoA and Malonyl CoA to produce acetoacetyl CoA hence directing the flux to the MVA pathway. Overexpression of HMG1, idi, ERG20 and codon-optimized farnesene synthase Codon-optimized A. grandis bisabolene synthase (AgBIS).

interference (CRISPRi) (Fig. 7c) [131] and RNA interference (RNAi) (Fig. 7d) [132]. CRISPRi is a CRISPR/Cas9-based interference system where gRNA-bound nuclease deficient CRISPR protein (dCas) acts as a physical block towards transcription initiation and elongation [131]. CRISPRi was applied in E. coli for enhanced flavonoid production through fine-tuning of regulatory networks of central metabolic path­ ways like fatty acid biosynthesis, TCA cycle [133]. RNAi is another transcriptional regulatory mechanism naturally present in most eu­ karyotes. However, it has recently been applied in S. cerevisiae through heterologous expression of Dicer and Argonaute and further optimiza­ tion was done to adopt this technique for metabolic engineering. Syn­ thetic RNAi method for S. cerevisiae was used to test putative genetic targets for improved itaconic acid production [134] and acetic acid tolerance [132]. Corresponding to CRISPRi, nuclease deficient CRISPR protein tagged with an activator domain can also be employed for tar­ geted activation of genes (CRISPRa) (Fig. 7c) [135]. CRISPRa has been demonstrated to activate reporter genes in E. coli [136] and S. cerevisiae [135] through the designer fusion of dCas9 with ω subunit of RNA po­ lymerase and VP64 activator respectively and has potential for upre­ gulation of genes for biofuel production.

increased yield of up to a 27-fold 970 mg/L 28 mg/L 11.2–27.9 mg/L

[86] [88] [103] [93] [56]

166.5 mg/L

[12]

400 mg/L 1.29 g/L

[105] [107] [106]

1.48 mg/L 917.7 mg/L

[108] [109]

6.7 mg/L

[110]

380 mg/L

[95]

1.1 g/L

[111]

8.74 g/L 728 mg/L

[96] [112, 113] [114] [115]

259.98 mg/L 389 mg/L and 994 mg/ L respectively

[116] [94]

3.2. Combinatorial metabolic engineering A growing interest in combining gain- and loss-of-function genome engineering modules has enabled combinatorial optimization for ge­ netic manipulation of many targets simultaneously. Recently, an orthogonal tri-functional CRISPR system was developed that combined transcriptional activation, transcriptional interference, and gene dele­ tion (CRISPR-AID) in the same cell. In this system, Cas9 orthologs were expressed along with gRNA cassettes for directed activation, deactiva­ tion, and deletion. The gRNAs for CRISPRa and CRISPRi contained aptamers that recruited either an activator or a repressor to the dCas9, allowing targeted transcriptional regulation. This method was used to increase the β-carotene production via simultaneous downregulation of ERG9 gene, upregulation of HMG1, and deletion of ROX1 through engineered Cas9 [137]. Combinatorial CRISPRa and CRISPRi were also applied to investigate pinene synthesis pathway in E. coli capable of overproducing and tolerating high levels of pinene [138]. 3.3. Adaptive evolution based metabolic engineering Adaptive evolution is known to improve the growth of host strains under harsh industrial conditions on non-preferred carbon sources. This method generally results in random mutagenesis that plays an important 9

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Renewable and Sustainable Energy Reviews 119 (2020) 109562

Fig. 6. β-oxidation and reverse the β-oxida­ tion pathway in E. coli. In β-oxidation, the Cn fatty acyl CoA from fatty-acid biosynthesis pathway is converted to Cn enoyl CoA by fadE. This is an irreversible process and en­ sures the complete oxidation for one cycle to release acetyl CoA. fadE converts enoyl-CoA to β-ketoacyl-CoA via β-hydroxy acyl-CoA (fadB). Finally, β-ketoacyl CoA is cleaved by β-ketoacyl CoA thiolase (fadA) to release one molecule of acetyl CoA from the Cn-2 acyl CoA. In this process the reactions cata­ lyzed by fadB and fadA are reversible. In reverse β-oxidation pathway, fatty acyl CoA is produced by adding acetyl CoA to the growing fatty acid chain without the utili­ zation of ATP. The fadE gene is replaced with enoyl-CoA reductase (ter, represented in blue) that converts enoyl-CoA to acyl CoA. Flux is directed towards acyl-CoA formation rather than degradation by manipulation of the regulatory network. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 7. Genome editing and transcriptional regulation tools for biofuel production: (A) CRISPR/Cas9-mediated genome editing. A highly specific 20-nucleotides spacer sequence in guide RNA is used for targeted cleavage of specific loci; (B) Eukaryotic Multiplex Automated genome engineering (eMAGE) uses oligonucleotides containing necessary mutations that anneal at the lag­ ging strand of DNA replication fork to ach­ ieve precise chromosome modifications with high efficiency; (C) CRISPRi/CRISPRa mediated transcriptional regulation of genes. For CRISPRi/CRISPRa, a dCas9 associated with an effector domain sits on target pro­ moter sequence and either downregulate (interference) or upregulate (activation) genes. (D) RNAi mediated transcriptional regulation can also be done where the Dicer associates with Argonaute and siRNA to cleave specific mRNA sequences.

role in the enhancement of growth-coupled complex phenotypes. Ex­ amples include the evolution of S. cerevisiae strains to utilize xylose [139] and cellobiose [140] from lignocellulose and co-utilization of hexose and pentose [141]. Adaptive laboratory evolution was also performed in E. coli to enable tolerance to pinene such that the final strain was tolerant to 2% [138]. Nevertheless, due to the presence of several mutations, the molecular machinery of the evolved phenotypes are still difficult to understand [142]. The available tools that have been discussed were used to engineer microbes for different purposes and have scope for application in bio­ fuels too. Although CRISPR-based tools have been used for certain

biofuel production, its use can be broadened for fine-tuning of pathways for optimal synthesis of desired metabolites. 4. Systems biology approaches in metabolic engineering In silico approaches for genome-scale modeling serves a potential resource to guide rational engineering of biological systems and quan­ titatively simulate fluxes through chemical reactions within the host metabolism [143,144]. A reliable genome-scale model (GEM) can evaluate the system-wide effect of environmental and genetic pertur­ bations in different conditions of an organism [145] and are generally 10

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Renewable and Sustainable Energy Reviews 119 (2020) 109562

constructed using stoichiometric methods based on annotated se­ quences. Constraint-based approaches like flux balance analysis (FBA) [146] can be applied to a genome-scale biochemical network that re­ quires stoichiometry matrix (S) of metabolic reactions and mass/charge balances of the metabolites in biochemical pathways by assuming it under pseudo-steady-state [147]. Currently, genome-scale metabolic modeling is considered as a potential technique in the study of metabolic pathways in chemical [148], pathogens [149] and environmental research areas.

reformulated model by applying 1/6 the number of free variables and solving in less than 10 min which was a significant improvement over the 6 h solve time of previous ME-model formulations [174]. Step 2: Curating the draft reconstruction. Curation of the draft model is done to establish certain organismspecific features such as substrate or cofactor specificity and subcellu­ lar localization. A well-curated model effectively generates a biochem­ ically, genomically and genetically (BiGG) structured knowledge base that is organism-specific and the reactions present in this database forms a genome-scale network reconstruction, GENRE [175]. Step 3: Conversion of the genome-scale reconstructed model to a computational model. The mathematical representation of a reconstructed model is necessary for the computational study of the network [176,177]. It translates a GENRE into a mathematical expression to make it a basis for a genome-scale model (GEM). Multiple computational platforms have been designed that apply constraint-based methods to metabolic GEMs [155,178,179]. Determination of a biomass objective function along with the mathematical expression and the computational platform is necessary to compute a model’s ability to support growth. A useful consistent screening of reconstructed networks is performed to use them for computing growth rates under a given condition. Step 4: Application of Reconstruction and high-throughput data integration. Refinement and expansion of the metabolic content of a network is an important part of GEM which is usually done by high-throughput data sets that predict various interactions for different growth or ge­ netic conditions. These analyses and comparisons enable the evaluation of genome-scale omics data in a structured and functional context in an integrated fashion [180].

4.1. Genome-scale reconstruction Metabolic model reconstruction generally requires knowledge on biochemical reactions (S matrix), species-specific information from genome annotations, high throughput experimental data which can be obtained from publicly available database like EcoCyc [150], KEGG [151], BRENDA [152], BKM-react [153]. Nowadays metabolic models are available in systems biology markup language (SMBL) [154] which can be accessed by importing into the FBA software application like COBRA Toolbox [155,156]. Gap-filling methods are implemented in the newly reconstructed model to make it more accurate which subse­ quently improves connectivity to the point where the model can simu­ late phenotypes. After construction of an upgraded working model, in silico experiment can be done for prediction of flux distribution in different metabolic pathways and phenotypic behavior under various conditions of user’s interest. This model can also help to target the possible gene targets for improving the strains efficiency as well as the calculation of knockout lethality or growth rates which can be further validated with experimental data. Computational FBA simulator like COBRA Toolbox has been developed for manipulation of metabolic models and phenotypic simulations. This modeling process is done in four steps which are discussed below. Step 1: Automated genome-based reconstruction. A specific draft model can be built by using and integrating the available information of a specific organism in various biological data­ bases in an automated method. Annotated sequences are necessary and can be available in organism-specific databases, like EcoCyc for E. coli [157] and SGD [158] or CYGD for S. cerevisiae, or in databases with various genome annotations, such as Comprehensive Microbial Resource (CMR) [159], EntrezGene [160], and the Integrated Microbial Genomes (IMG) [161]. Metabolic databases like MetaCyc, SEED [162], KEGG, BRENDA, and Transport DB [163] contain various metabolic and transport reactions which are present in different organisms. Numerous programs and tools like Model SEED (fully automated genome annota­ tion program), RAST (model reconstruction program), BioNetBuilder (a Cytoscape plugin) [164], ReMatch (web-based framework) [165], MicrobesFlux [166], COPABI [167] are available for automated genome reconstruction. The Flux Analysis and Modeling Environment (FAME) and CARMEN [168] are also modeling tools that help to reconstruct metabolic models by translating genomic data into functional ones. Fully automated software program like metabolic SearcH And Recon­ struction Kit (metaSHARK) [169,170] are used for the detecting and visualization of enzyme-encoding genes within unannotated genome data with reference to the associated metabolic network. Another recently proposed software KBase has been designed for reconstruction, optimization, and analysis of genome-scale metabolic models by using Knowledgebase tools provided by the DOE Systems Biology [171]. Raven (Reconstruction, Analysis, and Visualization of Metabolic Net­ works) [172] is a semi-automatic reconstruction tool of GEMs based on protein orthology (KEGG ID) and previously reported genome-scale metabolic models followed by extensive gap-filling and quality control of the metabolic model [173]. Recently, a software called COBRAme has been developed for building and simulating metabolic reactions and associated macromolecular expression (ME models). COBRAme was used to reconstruct an E. coli condensed ME-model. Functionally similar solutions to earlier E. coli ME-models was obtained from this

4.2. Application of genome-scale modeling in metabolic engineering Constraints-based flux balance analysis is used to determine the gene overexpression and knockout targets to enhance a strain’s potential to synthesize a molecule of interest. Various computational approach for successful applications of genome-scale modeling have been reported where production level of molecules like succinic acid [181], lycopene [182], threonine [183], ethanol [184], butanol, propanol, propanediol [185], terpenoid [186], 3 hydroxy propionic acids [187] has been improved. Researchers have shown that the production level of L-butanol, L-propanol, 1,3-propanediol can be improved using a gene knockout strategy. Based on the iAF1260 genome-scale model, in silico analysis was performed on a reduced metabolic model of E. coli for possible knockout and evaluation of their metabolic potential. Multiple deletion strategies were determined from the in silico metabolic model. Thus, genome-scale model-based study and prediction of genetic manipulation are indicative of the possibility to design highly efficient strains for biosynthesis of the desired molecule on an industrial scale [185]. Fatty acid bio-production from microbial and plants hosts depends upon the manipulation of tightly regulated metabolic networks where precursors for fatty acids are often redirected towards other central metabolic pathways. Based on the OptForce predictions, E. coli strain engineering was done by upregulation of fabZ and acyl-ACP thioesterase and deletion of fadD that produced 1.70 g/L of C14-16 fatty acids in minimal media [174]. Recently, FBA based single-gene knockout studies were done in E. coli for improved alkane and fatty alcohol by identifying important genes in central carbon metabolism. Iterative FBA simulations were performed for identification of gene deletion targets and were experimentally verified by the production of 2.54 g/L alkane (~34% of theoretical yield) and 12.5 g/L long-chain fatty alcohol (86% of theo­ retical yield) [188]. Similarly, in silico identification of gene knockout targets for improvement of amorphadiene synthesis in S. cerevisiae was done using FBA and MOMA resulting in an 8–10-fold increase in het­ erologous terpenoid production [186]. 11

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using MFA with the help of the stoichiometric model of elementary biochemical equations. MFA study also helps to understand the control mechanism in the cellular system like gene knockouts or integration of heterologous genes. For example, MFA studies were conducted to investigate and quantify the roles played by the acid formation path­ ways in the metabolism of wild-type and acid/acetone over-producing C. acetobutylicum strains [200]. Moreover, MFA study suggests for the improvement of yield through metabolic engineering by understanding the control of pathway nodes and information of important reactions. MFA studies have been used to investigate the role of the main reaction steps in C. acetobutylicum metabolic pathway for identifying targets to convert glucose and xylose from hydrolyzed lignocellulosic biomass into butanol [201]. Various types of MFA approaches like (a) classical stoi­ chiometric MFA, (b) FBA, and (c) 13C MFA are described below and represented in Fig. 8. In the case of underdetermined systems like simple parallel pathways, ordinary MFA can be applied to determine the branch fluxes i.e. v1and v2 (Fig. 8a). If v1 is associated with NADPH production, the branch fluxes can then be obtained by FBA applying the objective function for maximization of NADPH production (Fig. 8b). Whereas, if v1 and v2 eliminate one carbon atom from different positions in B, the branch fluxes can then be estimated by 13C MFA, using labeling of substrate A, and by calculating the labeling patterns of E or F (Fig. 8c).

A genetic algorithm-based framework like OptKnock, OptGene, OptORF [189], RobustKnock [190] has been designed which can rapidly target knocked out strategies for optimization of engineered strains. However, they suffer some limitation in the selection of reactions included in the metabolic reconstruction. Incorporation of new reactions that are not part of the original metabolic framework is not considered in these in silico frameworks. OptStrain [191] satisfies these limitations by using a database of known biotransformations to maximize the yield of a target product in the metabolic pathway considering incorporation of selected heterologous reactions. OptForce is another method that pre­ dicts all possible engineering perturbations by classifying biochemical reactions in the metabolic model depending upon whether their flux values should increase, decrease or must be zero to satisfy a pre-determined overproduction yield [192]. 5. Metabolic flux analysis Comprehensive understanding of complex cellular metabolism is necessary for metabolic engineering and systems biology. This includes high throughput omics approaches [193,194], advanced in silico methods for metabolic network prediction [180,195–197], and under­ standing cum evolution of metabolic network properties by elementary mode and extreme pathway analyses. In the field of bioengineering metabolic flux analysis (MFA) is a valuable approach for the quantitative estimation of intracellular metabolic flows through metabolic pathways. The flux determination strategies help to understand the regulatory machinery at the transcription, translation, and metabolism level of a cell [198,199].

5.2. Flux balance analysis (FBA) Flux balance analysis is a constraint-based optimization approach used to simulate various ranges of achievable reaction rates [202,203]. This method starts with the generation of one stoichiometric matrix S, which contains all the available stoichiometric information for a meta­ bolic network. Initially, the network is assumed to exist in a quasi-steady state; i.e. S.v ¼ 0 where v denotes the fluxes through each reaction. The directionality of the reactions and the capacity requirements are

5.1. Quantitative analysis of metabolic pathways Intracellular metabolic fluxes for each reaction step can be calculated

Fig. 8. Various MFA approaches: (a) classical stoichiometric MFA. In the case of underdetermined systems like simple parallel pathways, ordinary MFA can be applied to determine the branch fluxes i.e v1and v2 (b) FBA. If v1 is associated with NADPH production, the branch fluxes can then be obtained by FBA applying the objective function for maximization of NADPH production and (c) 13C MFA. v1 and v2 eliminate one carbon atom from different positions in B, the branch fluxes can then be estimated by 13C MFA, using labeling of substrate A, and by calculating the labeling patterns of E or F. The graphs represent the solution space of v1 and v2 flux. A closed circle represents a labeled carbon atom. The red line and points in the right graphs represent the solutions for v1 and v2. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.) 12

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[1-13C] and [U–13C] glucose is used. The cellular metabolism is studied until the proteins get labeled by 13C because 13C-labeled proteogenic amino acids are usually used for flux determination in a biochemical reaction. Finally, the optimization of flux distribution is done by determining the labeling patterns that iteratively computed from the predicted flux distribution as the best fit to the measured labeling pattern of amino acids using nonlinear optimized algorithms. Typically, 13 C MFA is done in four steps: (i) Analytical pathway determination followed by specific substrate labeling; (ii) Conducting a 13C-labeling experiment under steady-state condition followed by measuring the la­ beling patterns of proteogenic amino acids using NMR or GC-MS [215]; (iii) preparing a stoichiometric model followed by generating iso­ topomer balance equations for intracellular metabolites based on the previously determined network; and (iv) Optimization of flux distribu­ tion by estimating the labelling patterns of proteogenic amino acids computed from the predicted fluxes. For isotope labeling pattern, the isotopomer method is mainly preferred in metabolic flux analysis [216]. Recently, researchers have been developed a detailed guide for per­ forming metabolic modeling and flux analysis using the open source JBEI Quantitative Metabolic Modeling (jQMM) library. This source al­ lows users to convert metabolomics data into an isotope-labeled data from a 13C labeling experiment for determination of cellular fluxes which can be applied in developing genetic engineering strategies for metabolic engineering [217]. Owing to its simple metabolism and fast cellular growth, E. coli has been employed for synthetic biology and metabolic engineering to obtain various products, including fatty acids. S. cerevisiae is another important model eukaryotic organism, widely engineered for various biofuel production. Although its physiology is well documented, the complexity of its cellular metabolism still hampers the engineered chemical productivity. 13C MFA was performed in a high fatty-acid yielding strain of E. coli, DH1 (overexpressed ‘tesA and fadR genes, the knocked-out fadE gene) to analyses its metabolism. When compared to the wild-type strain, it was found that the metabolic flux was diverted towards the fatty acid biosynthesis pathway from the original acetate pathway. This suggested that an increase in the fatty acid precursor, acetyl-CoA, and NADPH significantly diverted the fluxes to our desired pathway [218]. In an attempt to improve fuel production in S. cerevisiae, increased availability of cytosolic acetyl-CoA (key precursor for fuel-based pathways) and further metabolic manipulation based on 13C MFA has been done for synthesis free fatty acids [219]. Furthermore, fatty alcohol titer and yield were improved by using advanced ap­ proaches involving quantitative analysis of protein levels and metabolic flux, engineering enzyme level and localization, pull-push-block engi­ neering of carbon flux, and cofactor balancing. This successful strategy produced 1.2 g/L fatty alcohols in shake flasks, and 6.0 g/L in fed-batch, (~20% of the maximum theoretical yield from glucose) [11].

introduced for some or all the reactions by applying upper and lower bounds. Usually, the system is underdetermined with many solutions for flux distributions in the constraints-based model. Maximization of biomass production is described by an optimized objective function. A typical FBA formulation is characterized by multiple equivalents and non-unique optimal solutions to the problem and it maximizes specific objective function subjected to stoichiometric constraints with neces­ sary upper and lower bounds on system fluxes. FBA tries to minimize or maximize an objective function Z ¼ cTv which can be any linear com­ bination of fluxes. c represents a vector of weights and suggests how much each reaction contributes to the objective function. So, FBA can be defined as the process of solving equation S.v ¼ 0 using linear pro­ gramming; where a set of upper and lower bounds on v is provided and a linear combination of fluxes is set as an objective function. The FBA output represents a particular flux distribution {vj}, that minimizes or maximizes the objective function [146]. The objective function and mass balance constraints are applied in this method where ATP yield or maximization of biomass is mainly considered as the objective functions. By considering the maximization of target production rate as an objec­ tive function it is possible to evaluate the maximum yield of any com­ pound of interest. Flux balance analysis approach uses a genome-scale metabolic network rather than any small pathway alone like central carbon metabolism. This method can run without the enzyme kinetics data as well. A stoichiometric model with constraints like upper and lower bound of each flux are considered for Flux analysis which predicts a unique flux distribution through incorporating necessary objective functions. Finally, the genome-scale model from the knowledge database is simulated using MATLAB with the COBRA Toolbox to get the solutions. Although FBA is a very robust and powerful approach for solving the underdetermined system, determination of proper objective function is subjective as well as needs careful consideration. For a single knock out mutant prediction, it is not certain whether the single objective function should be considered or not for targeting biomass yield maximization. To address this problem, advanced FBA approaches like Minimization of Metabolic Adjustment (MOMA) [204], and Regulatory on/off Minimi­ zation (ROOM) [205] are being used for designing gene knock out mutants. In MOMA, the objective function is to minimize the Euclidean distance of the flux differences between the mutant and the wild-type whereas for ROOM it is to minimize the number of significant flux changes from the wild-type. However, all of these approaches are based on assumption, therefore the prediction and flux determination are usually less accurate. Therefore, to obtain more reliable fluxes 13C la­ beling method has been introduced for indirectly measuring metabolic fluxes for various reactions [206]. Stoichiometric network analysis in butanol-producing C. acetobutylicum showed that NADPH redox imbal­ ance limits butanol yield due to loss of Hþ ions as H2. The problem was remedied by the use of methyl viologen to prevent electron loss to H2 production and reinforce NAPDH supply, resulting in up to 37.8% in­ crease in butanol yield [207]. 5.3.

13

6. Current trends and future Perspectives 6.1. Techno-economic analysis

C metabolic flux analysis (13C MFA)

The techno-economic analysis (TEA) of the biofuels production process for a successful transition from laboratory scale to commercial production mainly considers feedstock and final productivity [220]. Appropriate feedstocks are an important parameter as the deconstruc­ tion of biomass to release fermentable sugars contribute towards final cost. The annual operating costs in biofuels plants reveal raw feedstock and facility-dependent costs (insurance, maintenance, and overhead) as the most significant cost contributors [221]. A TEA comparing the total production costs required for ethanol, butanol and isobutanol produc­ tion showed that feedstock contributed the maximum (~32%) of the total expense [222]. Lignocellulosic biomass is an abundant and cheap renewable resource for biofuel production in various commercial plants [223]. It was estimated that an average of 30 million gallons/year fermentable sugars can be produced from 349.47, 354.36, and 398.38

13

C MFA deals with complex biological networks that involve par­ allel, cyclic, and reversible biochemical reaction and has been used extensively to elucidate the metabolic properties of OptKnock based knockout strains [208], identify metabolic bottlenecks [209] and even confirm the activity of various pathways [210,211]. As this method uses cellular metabolite labeling, it can easily provide necessary valuable data regarding the metabolism of the 13C labeled carbon source through intracellular metabolic pathways [212–214]. This labeling pattern of cellular compounds can estimate the flux distribution of the metabolic reactions. These labeling experiments are done for continuous culture under steady-state condition. The experimental medium is replaced with a similar medium with the 13C labeled substrate at a specific position. Usually, for studying the central carbon metabolism, a combination of 13

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million t/year of corn stover, poplar, and switchgrass respectively [224]. In addition to raw biomass itself, the cost of breakdown processes such as acids, ionic liquids (IL) and enzymes are also significant. Enzyme cost is a variable factor dependent on the initial feedstock load. Using acid-treated poplar as feedstock for ethanol production, it was found that cost contribution of enzyme ranged from $0.68-$1.47/gal based on saccharification and fermentation yields [225]. A TEA was performed for comparing the economic potential of IL-based processes in ‘water-­ wash’ (WW) and ‘one-pot’ (OP) by fixing IL price at $0.75/kg, with 99.6% recovery and $10.14/kg enzyme price. The study suggested that while both processes exhibited comparable economic performance on higher feedstock loading, OP was more cost-effective with minimum ethanol selling price of $4.5/gallon [221]. In addition, algal biomass for biodiesel or ethanol production has been shown to be economically viable feedstock option with selling prices below $5/gallon and $2.95/gallon respectively [226]. Several companies like Sapphire En­ ergy, Algenol and Seambiotic are involved in commercial-scale pro­ duction of bioethanol from algal biomass with output of 1 billion gallons/year costing at 85 cents/L [227]. The fermentable sugars released during the deconstruction process are utilized by either wild type or engineered microbes for the produc­ tion of various biofuel molecules. The productivity of biofuels is another critical issue for competing with traditional fuels. Fed-batch based industrial-scale fermentation requires minimum productivity of 2–5 g/ L/hr depending on the product [228]. TEA of butanol production in a plant from acid-treated lignocellulosic biomass using metabolically engineered C. acetobutylicum showed that at productivity of 2.8 g/L/hr, the operational costs needed to be reduced from $1693/t to $770/t for economic butanol production [229]. In addition to the biofuel mole­ cules, the by-products of the deconstruction process can be utilized to increase the total revenue. It has been reported that selling price of unprocessed lignin, a deconstruction by-product, ranges from $200-1000/MT [221] which can be used to manufacture value-added chemicals [230]. It can be concluded that for the establishment of bio­ fuel into commercial scale, optimization of factors such as feedstock logistics, deconstruction, fermentation, and biofuels extraction need to be considered to estimate the viability of the process. Application of metabolically engineered microbes with higher tolerance to inhibitors and higher productivity is necessary along with appropriate utilization of by-products to reduce the final cost of biofuels.

Brazil became economically competitive to the international prices of gasoline at that time [235]. In Europe, biofuel consumption and blending mandates are mostly issued in both France and Germany. The European Commission issued a directive in 2003 to blend minimum 10% biofuel with traditional fuels. In 2010, a revised directive was issued that mandated member nations of the European Commission to derive 10% of their transportation energy from biofuels by 2020 [236]. The revised directive also specified reduction in GHG emissions by a minimum of 50% starting from 2017 [237]. The France-based biofuels company Total has been producing hydrotreated vegetable oil biodiesels as diesel additives with an aim for reduction of GHG emissions. In fact, they are Europe’s leading retailer of biofuels, with more than 2.4 million metric tons biofuels incorporated into gasoline and diesel in 2018 [238]. Among the Asian countries, the Chinese government’s biofuel policy focuses on ethanol production issuing standards for denatured fuel ethanol and for bioethanol gasoline for automobiles in 2001. In 2002, the Ethanol Promotion Program was launched in an attempt to use the excessive maize stockpiles for ethanol production. The government and CNCP tightly regulated the production and distribution of bioethanol, whereas Sinopec were given the distribution rights. By early 2006, pilot plants in 27 cities across five provinces had achieved the 10% blending target [239]. In India, a National Policy on Biofuels in 2018 encouraged setting up of supply chain mechanisms for biodiesel production from non-edible oilseeds and used cooking oil, along with ethanol from sug­ arcane to promote biofuels for a cleaner environment and reduce import dependency on foreign imports of oil [240]. Research on biofuel compatible engine infrastructure design is of primary importance to increase the current blending levels. Since cost of biofuels is another rate limiting factor, research on improved carbon capture and biofuel pro­ duction is necessary. 6.3. Practical implications of biofuels The current biofuel production from lignocellulosic biomass is two to three times more expensive than fossil fuels on an energy equivalent basis [241]. Challenges in the areas of feedstock production, logistics, pretreatment, enzyme hydrolysis, microbial fermentation, and biofuel distribution need to be addressed to bring down the production cost. All of these challenging areas require expertise in agronomy, feedstock improvement, biomass logistics and conversion, genetic engineering, process engineering, microbial fermentation, and economics [242]. In addition to feedstock availability, strain improvement for higher pro­ ductivity is necessary. Industrial biotechnology can transform the pre­ sent energy scenario due to progress made in the field of re-engineering cellular machinery [243]. However, one of the major challenges is the scaling-up of the TRY at the industrial level. With the present production rate and titer, very few biofuel molecules have actually reached commercialization. Advancement in biofuel production technology, in­ vestments in infrastructure, energy, and feedstock are required so that final cost can compete with present fuels. Rapid metabolic engineering of microbes along with the optimiza­ tion of fermentation processes is potential solutions towards process scale-up [244]. However, one main challenge that still remains is that most of the tools available for metabolic engineering are not applicable to different hosts. In fact, many tools developed for one organism cannot be readily utilized in another due to differences in metabolism and regulatory mechanisms [243]. Generalization of tools and techniques is necessary for characterization and manipulation of the regulatory pro­ cesses within a host for the heterologous synthesis of important me­ tabolites. The declining costs of gene synthesis, improved use of bioinformatics tools for data mining, sorting, analyzing and sophisti­ cated investigational tools with high sensitivity have made the genetic or metabolic manipulation of organisms easier to researchers. This is also expected to improve the experimental and analytical techniques for over-production of value-added chemicals [243]. Furthermore, ’omics’-data obtained from transcriptome, proteome, and metabolome,

6.2. Environmental policies Over the last decade, the production and use of different biofuels have experienced remarkable growth, resulting in the need for appro­ priate environmental policies across different nations. The major guidelines for policymakers include a reduction in GHG emission levels, recycling of CO2, and use of biowastes for fuel production in a low-cost manner [231]. Particularly ethanol and biodiesel have reached commercialization, resulting in different policy mandates for these molecules in different countries. In the U.S., a Renewable Fuel Standard (RFS) was introduced by the Energy Policy Act of 2005 that governs quantitative mandates for the minimum amount of biofuel, particularly ethanol, to be included in transportation fuel. These mandates were further expanded by the Energy Independence and Security Act (EISA) of 2007 [232]. Together these mandates aimed at increasing use total amount of biofuel up to 36 billion gallons by 2022 [233]. Biofuel companies like ExxonMobil have designed their environmental policies that comply with these government regulations while undertaking additional safety standards [234]. Brazil has a well developed and in­ tegrated biofuels program which can be dated back to the oil crisis in the 1970s. To address the rising fuel prices during 1970s, the Brazilian government introduced the National Alcohol Program Pro� alcool in 1975 that focused on bioethanol production from sugarcane. The aim of this program was to limit external dependency on oil, provide a stable in­ ternal demand for the major crop. In fact, by 2004 the price of ethanol in 14

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can help in designing modules in an objective-oriented manner.

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7. Conclusion Compared to bioethanol, advanced biofuels such as butanol, iso­ butanol, fatty acid derivatives, and isoprenoids have been investigated with an aim to compensate the dependency on fossil fuels. Even though microbes have the native pathway for the synthesis of these molecules, the natural titer is significantly low for commercialization. Application of metabolic engineering approaches can help in improving the titer for microbial synthesis of advanced biofuels using cheap carbon sources. Currently, E. coli and S. cerevisiae are the major hosts-of-choice for bio­ production, but the presence of weak metabolic pathways and issues in gene regulatory parts limit their performance as industrial workhorse. Use of non-conventional hosts like Clostridia, Yarrowia is also preferred due to the presence of inherently strong pathways like ABE fermentation and fatty acid production that allows high yield of target molecules like butanol and fatty acids respectively. Hence, appropriate choice of host is an important factor to be considered for improving the TRY of specific metabolites. However, unavailability of universal genetic tools for use across a wide range of microbial species still remains a challenge in metabolic engineering. Integration of cutting-edge research tools like genome-scale metabolic models, FBA, CRISPR/Cas9, and MAGE/ eMAGE, can assist in designing and building the metabolic chassis for rapid metabolic engineering in a cost-effective and less labor-intensive manner. For cheaper and sustainable microbial synthesis of advanced bio­ fuels, choice of feedstock is a prerequisite as the costs involved in pro­ duction, logistics, deconstruction, and processing are the major contributors to the final selling price of the fuel. It is also necessary to avert contamination during large scale fermentation, optimize culture conditions and develop efficient separation technique to maximize the final biofuel yield. Therefore, extensive research is still needed to address these issues for increasing the marketability of microbe based advanced biofuel in future. Huge investment in technologies and infra­ structure is needed to achieve these goals along with the support of government policies to induce biofuels in transportation sectors. Relentless efforts and innovations of the synthetic biologists and meta­ bolic engineers worldwide have put the technology in the pipeline which may result in the boom in biofuels market over the upcoming decades. Declaration of competing interest No potential conflict of interest was reported by the authors. Acknowledgment This work was supported by a research grant from the Department of Science and Technology (No. ECR/2016/001096) and Department of Biotechnology (DBT)-Ramalingaswami fellowship (No. BT/RLF/Reentry/06/2013) to A.G. M.D. acknowledges Council of Scientific and Industrial Research (CSIR), India and P.P. thanks the Department of Science and Technology (-INSPIRE, India for the award of fellowships DST). The authors would also like to thank Ms. Kheerthana Duraivelan (Senior Research Fellow, IIT Kharagpur) and Mr. Pritam Kundu (Junior Research Fellow, IIT Kharagpur) for critically reviewing the manuscript. References [1] Rabinovitch-Deere CA, Oliver JWK, Rodriguez GM, Atsumi S. Synthetic biology and metabolic engineering approaches to produce biofuels. Chem Rev 2013;113: 4611–32. https://doi.org/10.1021/cr300361t. [2] Data Visualisation. n.d. https://www.iea.org/etp/explore/. [Accessed 30 January 2019].

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