Development of Synthetic Microbial Platforms to Convert Lignocellulosic Biomass to Biofuels

CHAPTER FIVE

Development of Synthetic Microbial Platforms to Convert Lignocellulosic Biomass to Biofuels Muhammad Aamer Mehmood*, x, Ayesha Shahidx, Liang Xiong{, Niaz Ahmadjj, Chenguang Liu*, Fengwu Bai*, { and Xinqing Zhao*, 1 *Shanghai Jiao Tong University, Shanghai, China x Government College University Faisalabad, Faisalabad, Pakistan { Dalian University of Technology, Dalian, China jj National Institute for Biotechnology & Genetic Engineering, Faisalabad, Pakistan 1 Corresponding author: E-mail: [email protected]

Contents 1. Biomass to Biofuels: An Overview 1.1 Composition of Biomass and Scheme for Biofuels Production 1.2 Pretreatment 1.3 Saccharification 1.4 Fermentation 2. Designing Robust Microbial Strains to Produce Biofuels 2.1 Role of Synthetic Biology 2.2 Strain Development Techniques 2.2.1 2.2.2 2.2.3 2.2.4 2.2.5

Classical Genetic Manipulation and Genome Editing Random Engineering Approaches Identification of Novel Targets for Strain Development Bioinformatics-Based Design of Metabolic Pathway Synthetic Microbes With Minimal Genomes

243 247 248 249 250

3. Yeast Strains for Robust Cellulosic Ethanol Production 3.1 Consolidated Bioprocessing 3.2 Development of Xylose Fermenting Yeast Strains 3.3 Development of Stress Tolerant Yeast Strains 3.4 Higher Alcohols-Producing Yeast Strains 4. Developing Bacterial Strains for Biofuels Production 4.1 Ethanol Production in Bacterial Hosts 4.2 Production of C3 and C6-Alcohols in Bacterial Hosts 5. Fine Tuning of Synthetic Microbial Factories 6. Conclusion and Future Prospects References

Advances in Bioenergy, Volume 2 ISSN 2468-0125 http://dx.doi.org/10.1016/bs.aibe.2016.12.001

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© 2017 Elsevier Inc. All rights reserved.

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Abstract The revolutionized economic growth, immense mobility and energy dependent lifestyles of the humankind have raised several new trends on the face of the earth. Exploration and development of renewable energy sources has become one of the leading research areas. Several attempts have been made to develop renewable energy sources, but high production cost of biomass-based biofuels is still a great challenge with issues, such as low yields, substantial feedback inhibition and limited stress tolerance. On the one hand, the inherent difficulty in conversion of lignocellulosic biomass into biofuels calls for development of robust biofuel-producing microbes. On the other hand, recent advances in genomics, transcriptomics, proteomics, metabolomics, bioinformatics and genome-editing tools have enabled us to develop deep understanding of microbial pathways followed by targeted engineering of the key genes involved. In this chapter, development of yeast strains of Saccharomyces cerevisiae and various bacteria for renewable energy production is summarized. Besides conventional metabolic engineering methods, we mainly focused on the employment of synthetic biologyebased genetic engineering strategies for the bioconversion of renewable feedstocks. The development of xylose assimilating and stress tolerant S. cerevisiae strains is discussed in particular. Moreover, the latest development of S. cerevisiae strains with CRISPR-Cas9 genome-editing tool is also summarized. This chapter is an effort to provide insights for further development of biofuels producers using advanced genome-editing technologies.

1. BIOMASS TO BIOFUELS: AN OVERVIEW It is believed that biofuels have potential to substitute fossil fuels, with additional benefits of mitigating global warming by reducing CO2 emission (Saqib et al., 2013; Skevas et al., 2014). At present, 10.0% of the global energy demand is being met through biofuels with an annual increasing rate of 2.5% (Edrisi and Abhilash, 2016). All over the world, governments have endowed heavy inputs, in terms of time and money, to accelerate research on production and commercialization of biofuels (Carriquiry et al., 2011; Mehmood et al., 2016). However, food-based biofuels may cause ecological and environmental problems along with food and landfor-food insecurity. Conventional biofuels produced from food crops directly contribute to deforestation and threaten biodiversity (Fargione et al., 2010; Pimentel et al., 2010). Lignocellulosic (LC) biomass is the most abundant biomass on Earth, which is produced at rate of 150170  109 tons per annum (Pauly and Keegstra, 2008) and is believed as a potential candidate for the production of bioalcohols (Unrean, 2016). Although it seems promising, development

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of biofuels has faced several challenges. There is no doubt that total calculated energy content of all the available biomass on Earth is inexplicably large, but unfortunately it is not easy to retrieve, and divert into the commercial ventures, because when it comes to the production of biofuels from biomass, problem is not to produce it, problem is produce it in cost-effective and energy efficient manner. Before we go into the synthetic biologyebased pathway design for the biological conversion of biomass to biofuels, we first present an overview of the whole process, and the challenges and prospects are further discussed.

1.1 Composition of Biomass and Scheme for Biofuels Production Commonly there are four major sources of LC biomass: (1) forest residues including foliage, woods and branches, (2) agricultural residues including sugarcane bagasse, wheat straw, rice straw, corn stover, cotton stalk, (3) energy crops including switchgrass, willow, poplar, oak etc. and (4) cellulosic components in food waste and municipal waste (Salehi Jouzani and Taherzadeh, 2015). LC biomass (Fig. 1 and Table 1) often comprised of cellulose microfibrils implanted in lignin, hemicellulose and pectin with an adjusted proportion during adaptation of each plant species in response to genetic and environmental factors (Nigam and Singh, 2011). Cellulose is a polysaccharide which is main component of LC feedstock. It consists of series of D-glucose linked by b-1,4-glycoside bonds. Hydrogen bonds link together cellulose fibrils to produce its crystalline structure. This structure of cellulose makes it insoluble in majority organic solvents as well as in water

Figure 1 General composition and structure of lignocellulosic compounds in biomass.

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Table 1 Biomass composition of various lignocellulosic feedstocks on dry mass basis Hemicellulose

Lignin

Biomass

Cellulose (%) Arabinan (%) Galactan (%) Xylan (%)

Acid-soluble Acid-insoluble (%) (%)

Soya stalk Cotton stalk Corn stover Rice straw Wheat straw Rye straw

34.5 14.4 38.3 31.1 30.2 30.9

e 14.4 2.7 3.6 2.8 e

e e 2.1 e 0.8 e

24.8 e 21.0 18.7 18.7 21.5

e e e e e 3.2

9.8 21.5 17.4 13.3 17 22.1

Sweet sorghum bagasse Sugarcane bagasse Switch grass Reed canary grass Miscanthus Oak Poplar

27.3

1.4

e

13.1

e

14.3

Nee’nigam et al. (2009) Nee’nigam et al. (2009) Li et al. (2010c) Chen et al. (2011a) Ballesteros et al. (2006) García-Cubero et al. (2009) Li et al. (2010a)

43.1

e

e

31.1

e

11.4

Martin et al. (2007)

39.5 24

2.1 e

2.6 e

20.3 36

4.0 e

17.8 e

Li et al. (2010b) Singh et al. (2014)

43 45.2 43.8

e e e

e e

24 20.3 14.8

e 3.3 e

e 21.0 29.1

Singh et al. (2014) Shafiei et al. (2010) Kumar et al. (2009)

References

Muhammad Aamer Mehmood et al.

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(Mood et al., 2013). Hemicellulose is a heterogeneous branched molecule that consists of pentose, hexose and organic acids (Gírio et al., 2010) and is the second major component of LC biomass. Unlike the cellulose, lower molecular weight as well as branched and amorphous structure of hemicellulose makes it easier to hydrolyze (Li et al., 2010b). Removal of hemicellulose from cellulose fibrils greatly enhances the digestibility of cellulose (Agbor et al., 2011). The third component lignin, however, is made up of complex molecules which are produced from phenyl-propanoid precursors (consist of guaiacyl, syringyl and p-hydroxy phenol) (Hutchison et al., 2016). Often, 50%e80% of total LC biomass consists of complex carbohydrates (mixture of pentose and hexose sugars). Lignin and cellulose together contributes towards recalcitrance nature of LC biomass making it a bottleneck challenge in the biological conversion of LC biomass to biofuels. Hence, LC biomass always requires pretreatments to open the structure and make it more accessible to enzymes (Salehi Jouzani and Taherzadeh, 2015). Despite of its difficult bioconversion, LC biomass has a number of advantages such as environmental sustainability, abundance, no direct competition with food and feed (Limayem and Ricke, 2012). The most obvious steps involved in the biomass conversion to biofuels are pretreatment, saccharification and fermentation (Fig. 2).

1.2 Pretreatment Pretreatment is the first step in the lignocellulose bioconversion, which facilitates to destroy the rigid structure of the feedstock and separating major

Figure 2 Systematic diagram of bioethanol production from the lignocellulosic (LC) biomass. LC biomass is subject to various pretreatments to open to compact structure of biomass / Pretreated biomass is subjected to enzyme hydrolysis which converts the cellulose and hemicellulose into simple sugars such as glucose, xylose etc., along with the release of small molecules which has inhibitory impact on the fermentation reaction / simple sugars released are utilized by various pathways of the genetically modified Saccharomyces cerevisiae strains to produce ethanol in fermenters / which is secreted out of the yeast cell.

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components cellulose, hemicelluloses and lignin from each other for more efficient enzymatic hydrolysis of the cellulose component (Nigam and Singh, 2011). It is reported that pretreatment contributes to 18%e20% of total production cost of bioalcohols from LC biomass (Yang and Wyman, 2008). Various pretreatment procedures have been grouped into four categories: biological, chemical, physical and physio-chemical (Rajendran and Taherzadeh, 2014). Each method has its own set of advantages but no single one is suitable for all biomasses (Salehi Jouzani and Taherzadeh, 2015). Development of cost-effective LC biomass pretreatment is major challenge in bioethanol technology (Singh et al., 2015). Pretreatments release several molecules from the cellulosic biomass which interfere with the fermentation process and cause inhibitory impact on the process. Therefore, a suitable pretreatment method should have following features: (1) high hydrogen bond disruption in cellulose, (2) breakdown of hemicellulose and lignin cross-linked matrix, (3) increasing the cellulose surface area for better enzymatic hydrolysis (Mood et al., 2013), (4) cost effectiveness, (5) low energy input and (6) high carbohydrate recovery rate with little or no lignin (Singh et al., 2014). The choice of the pretreatment method depends on the biomass itself, including (1) crystallinity index of cellulose, (2) hemicellulose covering of cellulose, (3) polymerization degree, (4) lignin content, (5) surface area and (6) acetyl content (Pan et al., 2006). Although the pretreatment benefits the following enzymatic hydrolysis, various toxic byproducts produced under harsh chemical and physical conditions hinder the viability of fermenting microbe to convert the sugar to biofuels. The inhibitors consist of three major groups: phenolic compounds, furan derivations and carboxylic acid (Thompson et al., 2016). Phenolic compounds released from lignin are complicated because of the heterogeneity of lignin, which cause membrane instability and induce reactive oxygen species damage (Larsson et al., 2000). Furan derivations such as furfural and 5-hydroxymethylfurfural (5-HMF) inhibit dehydrogenases, interfere membrane stability, consume reducing power and cause the disruption of DNA and mitochondria (Allen et al., 2010). Carboxylic acids are also released from cellulose and hemicellulose. They halt glycolytic enzymes; and aromatic amino acid import (Bauer et al., 2003) and uncouple the proton motive force and ATP reserves (Russell, 1992). Abundant research has been focused on inhibition mechanisms, but complex interactions of inhibitors from various feedstock and diverse pretreatments still result in synergistic effects that need to be analyzed case by case (Zhao et al., 2016).

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1.3 Saccharification The pretreated biomass is subjected to hydrolytic enzymes such as xylanases, cellulases and other carbohydrases (Khare et al., 2015) which hydrolyze the hemicellulose, cellulose and polymeric sugars into simple sugars. Some of these simple sugars can be converted into alcohols, which are often referred as fermentable sugars (Limayem and Ricke, 2012). Hydrolysis process is often categorized into two types (1) acid hydrolysis and (2) enzymatic hydrolysis. Dilute or concentrated acid may be used in acid hydrolysis. Higher reaction rate, sugar conversion efficiency and recovery rate are advantages of concentrated acid hydrolysis (Wijaya et al., 2014). However, high acid consumption (Moe et al., 2012), requirement of acid recovery and requirement of specialized reactors due to high corrosion and toxicity make this method economically unfavourable (Wijaya et al., 2014). These problems of acid hydrolysis moved the attention towards enzyme hydrolysis. Combination of cellulases (b-1,4-glucosidases, exo-b-1,4-glucanases, endo-b-1,4-glucanases) has been employed for breakdown of long chain glucose polymers to monomeric units from pretreated feedstock (Lamsal et al., 2010). Hemicellulose is hydrolyzed by xylanases or hemicellulases for the release of component sugars which then can be fermented through microbial catalyst (Khare et al., 2015). Some other enzymes, such as xyloglucanase, have been used for the degradation of secondary polysaccharides which cannot be converted into simple sugars by the activity of cellulases (Stickel et al., 2014). Enzymatic hydrolysis performed at high solid loadings is more economically feasible because of high sugar concentration at the completion of hydrolysis that can be converted into high ethanol concentrations resulting in reduced cost and energy demands for distillation (Modenbach and Nokes, 2013). Another saccharification method is termed as simultaneous saccharification and fermentation (SSF) in which fermentative microbes are used for simultaneous SSF of hemicellulose and cellulose (Mosier et al., 2005).

1.4 Fermentation Saccharomyces cerevisiae and some bacteria consume some selected sugars and convert them into CO2 and ethanol (Shah and Sen, 2011). Typically, S. cerevisiae is used during batch fermentation which converts usually hexoses (six carbon sugars, mainly glucose) into ethanol under controlled temperature and microaerobic (Fig. 3). In our recent work, flocculating Zymomonas mobilis was revealed to be a nice host for cellulosic ethanol production (Zhao et al., 2014). Genetically modified Z. mobilis has also

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Figure 3 An overview of ethanol producing pathway from cellulosic biomass.

been used for fermenting xylose which is the most prevalent five carbon sugar released by hemicellulose (Shah and Sen, 2011). During the pretreatment process of biomass, sugars and lignin are converted to degradation compounds which inhibit fermentation process (Talebnia et al., 2010). These inhibitory compounds often are yeast growth inhibitors that induce reduced productivity and yield of ethanol. Presence of these inhibitors in the hydrolysate is the major challenge in the bioethanol commercialization by the use of LC biomass (Pereira et al., 2014). Inhibitory compounds include aromatic and phenolic compounds, inorganic ions, furan aldehydes, aliphatic acids, bioalcohols and other fermentation products (J€ onsson et al., 2013). Pretreatment of lignin or sugar degradation produce aromatic/phenolic compounds(Annaluru et al. 2014) which interfere with the cell functionality

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by changing the lipid-to-protein ratio in cell membrane (J€ onsson et al., 2013) and also affect their ability as selective barriers, decrease growth of cell and sugar assimilation (Campos et al., 2009). It is reported that low molecular weight phenolic compounds are more inhibitory in nature (Behera et al., 2014). Furan aldehydes, such as 5-HMF, are produced by the pentose and hexose decomposition, respectively. These compounds inhibit yeast growth and decrease ethanol productivity and yield (J€ onsson et al., 2013). Formic acid, acetic acid and levulinic acid are called aliphatic acids and are produced by the breakdown of acetyl group which link sugar and hemicellulose backbone (Jovicevic et al., 2014) and they inhibit when their concentration exceeds 100 mM (J€ onsson et al., 2013). Similarly, chemicals used in pretreatment, hydrolysis conditions and process equipment cause the production of inorganic ions in LC hydrolysate which contribute to modify osmotic pressure resulting in inhibitory effect.

2. DESIGNING ROBUST MICROBIAL STRAINS TO PRODUCE BIOFUELS Developing high-yielding and robust microbial strains is required to address the current challenges in the microbial biofuel production. A variety of microbial strains have been developed through genome engineering and synthetic biology tools, which appeared to be fairly auspicious to improve yields of biofuel production including ethanol, butanol, biodiesel, terpenoids, syngas and hydrogen gas. Here wedescribe thegenome engineeringapproachesandtools at first, which are believed to derive the future of biofuel industry.

2.1 Role of Synthetic Biology Synthetic biology offers innovative approaches for a wide range of biotechnological applications ranging from sustainable bioenergy production through bioremediation and biopharmaceuticals. But the success of this technology depends on the availability of knowledge of genome sequences, metabolic engineering tools which have fortunately enabled us to engineer microbes for our desired purposes (Gomaa et al., 2016). Designing biofuelproducing cell factories with enhanced efficiency is now possible via designed engineering of biological systems in several steps (Fig. 4). Unlike high-value products (pharmaceuticals or enzymes), biofuels are cost sensitive and can only be competitive when their cost is comparable with the petroleum-based fuels. This goal can only be achieved through systematic design of robust strains which can efficiently convert renewable feedstocks into

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Figure 4 Schematic overview of synthetic bioengineering for strain development. (1) Identification and functional characterization of pathway genes through genomics, transcriptomics, metabolomics analyses to identify the key domains, active sites and the role of each gene in the target metabolic pathway via gene deletion of heterologous protein expression, and choosing a suitable host (Escherichia coli, Saccharomyces cerevisiae, Zymomona mobilis etc.) for the expression of metabolic pathway. (2) Synthetic pathway construction, which includes gene isolation, addition of suitable regulatory elements, codon optimization etc. (3) Choosing the suitable vectors or genome editing tools for the integration of designed pathway on the genome. (4) Analyses of the product and its impact on the synthesis of growth of the host for fine tuning of the host machinery for the enhance productivity and recovery of the biofuel. (5) Process optimization and scale for industry.

biofuels. Synthetic biology has capability to reduce the time required to make genetic modifications and enhance their reliability. The design of variation in many pathways can be more efficiently handled by assembly techniques rather than through synthesis of each variation. The assembly of smaller DNA fragments into large constructs has become of essential synthetic biology tool to design, construct and engineer metabolic pathways. Therefore, most of the work in synthetic biologyebased pathway construction involves intermediate joining of DNA fragments, encoding target proteins, using common restriction and ligation strategies. However, recent techniques employ standardized restriction enzyme assembly protocols such as BioBricks, Golden Gate methods, Gibson ligation (Ellis et al., 2011; Engler et al., 2014; Xu et al., 2013). There are techniques which are sequence independent, such as In-Fusion, SLIC and Gibson ligation which are becoming popular for combinatorial metabolic engineering, and in vivo DNA assembly in yeast (Ellis et al., 2011; Coussement et al., 2014; Chao et al., 2015). It is important to consider that how different techniques, for instance, ligation free techniques, can be utilized to design the required architecture of the metabolic pathway (Vroom and Wang, 2008). BioBrick methods (Weyman and Suzuki, 2016) have enabled rapid construction of

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pathways from existing genes in which each stage of assembly uses the enzymes which are identical to the previous stage. This cyclic nature of the method has enabled us to create large variation libraries in less time. Moreover, synthetic biology methods create reusable parts with predictable behaviour and can develop various regulatory expression systems (Mckeague et al., 2015; Williams et al., 2016), which subsequently offer a capability of fine-tuned expression to engineer metabolic pathways. Interestingly, synthetic biology has full potential to install plug-and-play systems with autoinduction switches in response to environmental changes; for instance, our designed microorganism can switch from a cellulose degradation mode to fuel production mode by sensing the surrounding environment (Berens and Suess, 2016). One of the challenges of metabolic engineering is the integration of multiple-DNA fragments into a host genome. Developing the regulatory networks of synthetic genes from scratch followed by in silico analyses are extremely encouraged in this regards. Synthetic biologists either modify existing pathways or design a completely new synthetic pathway. Designing several pathways and putting them together may lead to develop a completely synthetic organism with minimal genome having a minimal set of metabolic pathways (Gibson et al., 2008). However, transfer of one pathway from one organism to another is also a necessary move for higher productivity.

2.2 Strain Development Techniques To develop robust microbial strains for efficient biofuel production, we need to develop better understanding of cellular network of target strains in response to various environmental conditions and to identify essential and nonessential genes critical for cellular life and metabolism. Here we described the most common engineering strategies which have been employed to design biofuel-producing strains. Recent advances in synthetic biology have boosted the development of new tools which may be exploited to achieve set goals. Many of these techniques are readily adaptable to engineer the cellular metabolic networks of microorganisms for biofuels production. 2.2.1 Classical Genetic Manipulation and Genome Editing Overexpression and deletion of target genes are the most commonly used metabolic engineering approaches for strain development. To date, a wealth of gene disruption, deletion, replacement and integration systems have been

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developed for Escherichia coli and S. cerevisiae. Inactivation of target genes by simply replacing them through PCR (Datsenko and Wanner, 2000) has significantly facilitated us to generate specific mutants for the functional analysis of the genes. There are mobile-DNA elements distributed across the genome, which facilitate the recombination events to happen, such as transposition and horizontal gene transfer. These mobile elements are often named as jumping genes, which include insertion sequence (IS) elements, transposases, defective phages, integrases, and site-specific recombinases (Frost et al., 2005). As they are involved in auto-engineering of the genome, it is required to delete these elements to design a stable genome to avoid unwanted recombination. In addition, a Tn5-targeted Cre-IoxP excision system and Tn5-transposonebased high-throughput methods for systematic mutagenesis (Lee et al., 2014; Dohlemann et al., 2016) have enabled us to create deletion and insertion mutants without losing normal growth patterns. In particular, a powerful high-throughput technique, Multiplex Automated Genome Engineering (MAGE) (Wang et al., 2009) has potential to modify multiple loci at the same time in a single cell or across a population of cells using allelic replacement. This has allowed us to delete genes more extensively without risking the robustness. While homologous recombination is the basic rule for deletion or integration of specific fragments in the chromosome, S. cerevisiae has higher capacity for homologous recombination and so considered as a manageable organism when it comes to the deletion of target genes through homologous recombination. Taking the advantage of the Cre-loxP recombination system, deletion mutants of various yeast strains have been prepared by substituting the target genes with selectable markers (e.g., antibiotic) containing two repeated sequences (e.g., loxP) at the left and right arms of the marker gene (e.g., loxP-kanMX-loxP) (Gueldener et al., 2002). However, the homologous recombination-based techniques may cause unexpected/undesired deletion between the loxP sequences. A PCR-mediated seamless gene deletion technique permits recycling of URA3 selectable markers, which does not leave repeated sequences behind. This gene disruption technique is suitable for repeated use and is widely applicable to various yeast strains for gene disruption (Kondo et al., 2013). Moreover, genome reductions may improve metabolic efficiency and decrease the redundancy among microbial genes and regulatory circuits (Wohlbach et al., 2011). Therefore, a coherent design allows us to delete genes widely while keeping the robustness along, subsequently we can design biofuel-producing strains harbouring synthetic and/or engineered pathways.

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Unlike prokaryotic systems, one-promoter one-gene expression system exists in S. cerevisiae. To implement metabolic designs into S. cerevisiae, preparing sets of gene expression vectors to cover a wide variety of markers is essential (Ishii et al., 2009). It is also valuable to engineer multiple promoters into each vector. In addition, two proteins may be expressed together using dicistronic regulation system, which carry an internal ribosome entry site sequence (Joung and Sander, 2013). Yeast artificial chromosomes can also be used as a cloning system for DNA fragments larger than 100 kbp (Gibson, 2014) which has become a powerful tool for the bioengineering of yeast. The cocktail d-integration method is another unique technique for the chromosomal expression of multiple genes in S. cerevisiae (Kato et al., 2013). This approach has an advantage of single-step integration of multicopy gene expression cassettes, based on the integration of several copies of a particular gene cassette onto the d-sequences of the Ty-retrotransposon on the yeast chromosome (Shi et al., 2016a). Gap repair cloning technology is another way of gene integration into chromosomes, which is based on the indigenous ability of yeast to undergo efficient homologous recombination. In this technique, continuous assemblies of DNA fragments containing 25e30 bp of homologous sequences are integrated into the chromosome (Joska et al., 2014; Reddy et al., 2015). Gap repair cloning has previously been used for construction of libraries for two-hybrid systems (Haarer and Amberg, 2014), and it can be used to engineer proteins and to alter coenzyme specificities. Zinc-finger nucleases (ZFNs) (Gaj et al., 2013), transcription activatorelike effect or nucleases (TALENs) (Joung and Sander, 2013) and the RNA-guided CRISPR-Cas9 system (Doudna and Charpentier, 2014) have emerged as the popular genome-editing technologies in recent years. All these methods can generate double-strand breaks at specific genomic loci with the endonucleases. However, ZFN and TALEN-based targeting depends on customized DNA binding domain, while CRISPR-Cas9ebased cleavage is guided by small structured RNAs (tracrRNA and crRNA) (Jinek et al., 2012), making it highly specific, efficient, easier to design and the most suitable tool for genome editing in various hosts (Gaj et al., 2013; Ran et al., 2013). Chimeric single guide RNAs (sgRNAs) are designed for easier manipulation based on the structure of the original tracrRNA and crRNA (Cong et al., 2013; Jinek et al., 2012). The only requirement for Cas9 to recognize the target site is a Protospacer Adjacent Motif (PAM) sequence presenting directly at 30 of the 20-bp target sequence. Each Cas9 orthologue has a unique PAM sequence; for instance, the most commonly used Cas9 from Streptococcus pyogenes adopts an NGG

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PAM sequence, thus we can find a target sequence from an average 8-bp sequence in the genome. Besides, multiple gRNAs can be introduced into the host for multiple integrations or deletions (Jakoci unas et al., 2016). When the gRNAs sequence is flanked by self-cleaving hammer head and hepatitis delta virus ribozymes on the 5’- and 3’-ends, tandem gRNAs can be expressed in a single expressing plasmid to allow multiple knockouts and integrations (Gao and Zhao, 2014). Multiple sgRNA cassettes can also be integrated into a single expression plasmid to target multiple sites in S. cerevisiae (Ronda et al., 2015; Jessop-Fabre et al., 2016). It is also possible to obtain scarless constructs without any selection marker via this method (Jessop-Fabre et al., 2016). Beyond gene knockout and integration, the transcriptionally regulatory role of CRISPR-Cas9 system has also been investigated in other organisms by slightly modifying the gRNA-Cas9 structure (Doudna and Charpentier, 2014). CRISPRCas9 has been well developed and applied in the engineering industrial strains, including multiple gene knockout, integration and replacement of promoters (as reviewed by Jakoci unas et al., 2016) (Fig. 5).

Figure 5 An overview of the CRISPR-Cas9 based genome editing; (A) single plasmide based CRISPR-Cas9 system; (B) double plasmidebased CRISPR-Cas9 system; (C) diagram for recognition and cleavage of target sites by CRISPR-Cas9; (D) double stranded break mediated genome editing.

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During the past three years, the CRISPR-Cas9 system has been comprehensively optimized and employed for metabolic engineering of yeast strains. For example, multiple genes in b-carotenoids metabolic pathways were simultaneously integrated into the genome of S. cerevisiae strain (Ronda et al., 2015). The mevalonate level increased 41-folds when multiple genes in the metabolic pathway were simultaneous disrupted by CRISPR-Cas9 method (Jakoci unas et al., 2015). Recently, xylose-fermenting yeast strains were rapidly constructed with CRISPR-Cas9 by integrating the xyloseassimilating pathway into the genome of S. cerevisiae strains, two genes, PHO13 and ALD6, which hamper the assimilation of xylose, were disrupted at the same time, and no selection marker was integrated into the genome (Tsai et al., 2015). Cellobiose assimilating pathway was also introduced into S. cerevisiae strains (Ryan et al., 2014). Besides deletion or overexpression of certain genes, CRISPR-Cas9 was also employed for substitution of promoters; the promoter of TAL1 was replaced by a set of promoters with varied strengths for fine-tuned expression (Xu et al., 2016). In the future, more CRISPR-Cas9ebased genome-editing methods will emerge which will facilitate rapid strain development. 2.2.2 Random Engineering Approaches Along with the rational metabolic engineering methods, a set of random engineering approaches were also adopted for the development of microbial cell factories. Random mutagenesis and induced mutagenesis was applied to develop robust strains. An approach termed adaptive laboratory evolution was proved to be effective to develop S. cerevisiae strains with both improved inhibitor tolerance and xylose-assimilating abilities (also reviewed by Zhao et al., 2016).It is well known that the improvement of a single phenotype might involve a cluster of genes. Therefore, it is essential to explore the key genes which can control multiple genes. Interestingly, mutations of the TATA-binding protein improved cellular tolerance towards acetic acid (An et al., 2015). Moreover, artificial zinc finger proteins can be used to improve acetic acid tolerance (Ma et al., 2015). The performance of the indigenous proteins/enzymes is critically important for the success of bioengineering, which may be or may have to be modified as per requirement of the cellular reactions. For instance, protein engineering is often required to engineer the xylose utilization pathway in S. cerevisiae by changing the coenzyme specificity of either xylose reductase (XR) or xylitol dehydrogenase (XDH) to conquer the inherent coenzyme imbalance of heterologous XR-XDH pathway that form most species for

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efficient xylose assimilation by S. cerevisiae (reviewed by Kim et al., 2013). It is of importance to adopt protein engineering to change the coenzyme specificity (e.g., redox enzymes), activities (e.g., codon optimized enzymes) and substrate specificity (e.g., transporters) of specific enzymes. 2.2.3 Identification of Novel Targets for Strain Development Transcriptomics, proteomics, metabolomics and bioinformatics analyses together offer a comprehensive gene expression and global view of metabolisms which can be used to identify the genes responsible for various cellular activities including xylose fermentation, higher yield of alcohols and stress tolerance. Transcriptomics is the study of the complete set of RNA transcripts, the transcriptome, produced by a cell under specific environmental and growth conditions. The study involves high-throughput methods, such as microarray analysis and RNA-sequencing followed by computer aided analyses of molecular data. Comparative transcriptomics analyses can be used to identify the differentially expressed genes in discrete cell populations or in response to different environments provided. Therefore, comparative transcriptome analysis is a remarkable tool to reveal the specific genes which are responsive to specific conditions, which may lead us to select candidate genes for future manipulations. For instance, it was observed that deletion of PHO13 enhanced xylose assimilation in S. cerevisiae via expression of XR and xylose dehydrogenase (Van Vleet et al., 2008). To identify the underlying mechanism, a comparative expression profiling of PHO13 mutant and wild-type strain was performed. It was shown that key genes including ZWF1, SOL3 and ADH1 were upregulated in the PHO13 mutant and genes such as COX2 and CYC1 were down regulated (Fujitomi et al., 2012). Moreover, along with the overexpression of TAL1, the deletion of PHO13 improves ethanol production from LC hydrolysate in the presence of weak acids and furfural (Fujitomi et al., 2012; Wise et al., 2014). It was reported that the deletion of ALD6 (NADPþ-dependent aldehyde dehydrogenase gene) in the acetate biosynthesis pathway improved xylose fermentation (Lee et al., 2012; Vanholme et al., 2013). Moreover, 71% decrease in xylitol yield was observed in response to the deletion of FPS1 gene, and xylose fermentation was shown to be improved (Wei et al., 2013). Similarly, deletion of the YLR042c gene enhanced the specific xylose utilization rate and ethanol yield in strain TMB3057 (Parachin et al., 2010). Metabolomics is the methodical identification and quantification of the cellular metabolic products, the metabolome, of a single cell, a tissue, an organ or any biological fluid in response to any particular environment. Advanced

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spectroscopic techniques, including mass spectrometry and nuclear magnetic resonance, are most commonly used to profile the metabolome. Interestingly, comparative analyses of metabolome data also reveal the responsive pathways to various conditions, which may be targeted for future manipulations. For instance, a Capillary Electrophoresis Time-of-Flight Mass Spectrometry (CE-TOFMS) based metabolome profiling of yeast revealed that acetic acid addition may slow down the flux of the nonoxidative pentose phosphate pathway. In another study, comparative metabolome analyses of a wild-type and three deletion mutant strains was carried out, with higher intracellular glutathione concentration (Suzuki et al., 2011) which indicated that methionine synthesis activation may be used to enhance the intracellular glutathione concentration (Suzuki et al., 2011). Metabolomics has an advantage that it can be used to assess the posttranscriptional regulation of any target metabolic pathway by examining metabolic phenotypes (Yoshida et al., 2010). But unfortunately, the elucidation of the metabolome data is not easy when compared to the transcriptome data analyses, which itself is a tedious job. However, metabolome data cannot be used to unveil the true reasons that either the accumulation of intermediate molecule is due to its increased biosynthesis or decreased utilization. Hence, interpretation of metabolome data requires intensive literature and database search (Cherry et al., 2010). 2.2.4 Bioinformatics-Based Design of Metabolic Pathway In recent years, like any other field, computers have become an integral part of life sciences research too, which led towards the establishment of a new, but very important field called Bioinformatics. It is the application of computer to retrieve, store, analyze, process and predict the biological processes. Computerscanalsobeusedtodesignmetabolicpathwaysforbiofuelproduction. Bioinformatics-based tools can assist us to construct yeast metabolic model, to modify the metabolic model through simulations and to predict production of target molecules. However, a complete set of simulation comprising thermodynamics, reaction kinetics and stoichiometry is not easy to perform. A stoichiometry-based metabolic model is a set of metabolic reactions in the target organism, such as S. cerevisiae where a minimal model of central metabolism consists of only 50 reactions (Dobson et al., 2010; Matsuda et al., 2011) and the largest model constructed up to now contains 1412 reactions (Mo et al., 2009). In addition to stoichiometry-based simulation, Flux Balance Analysis (FBA) (Orth et al., 2010) had also been introduced in which an equilibrium condition is introduced as a constraint in the metabolic simulation and a balance between the synthesis and consumption of an

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intermediate molecule is studied. Although the FBA-based metabolic simulations undertake a prototypical metabolic condition and ignore regulation aspects such as gene expression and feedback mechanism, yet it can be used to evaluate the performance of a metabolic network (Shinfuku et al., 2009). The FBA-based simulations can be used to analyze the distribution of metabolic fluxes using a stoichiometric model without prior knowledge of concentrations and reaction kinetics of metabolic products. For instance, the impact of a gene deletion on the target metabolite synthesis can be simulated and evaluated using a modified stoichiometric model which lacks the corresponding reaction information. Previously, the in silico gene deletion strategy had been simulated to engineer indigenous metabolism in yeast using FBA, and it was shown that simultaneous deletion of five unrelated genes including ALT2, FDH1, FDH2, FUM1 and ZWF1 may increase formic acid secretion under aerobic environment (Kennedy et al., 2009) which was later demonstrated in the lab (Kennedy et al., 2009). Such studies endorsed that constraint-based simulation can help us to identify target genes for deletion and to design a metabolic pathway using synthetic biology techniques. The FBA-based simulation of yeast can also be used to elucidate and engineer the pathways for enhanced biosynthesis of sesquiterpenes, for analysis of the pentose utilization pathway (Ghosh et al., 2011), to cope stress tolerance and for enhanced production of short and long chain alcohols. 2.2.5 Synthetic Microbes With Minimal Genomes Deletion of alternative pathway genes, integration of desired pathway genes and engineering the indigenous proteins for the efficient balance of energy and the critical cellular reactions may help us design the synthetic microbes with minimal genomes. Reduced genomes would provide several associated benefits, such as higher electroporation efficiency, precise proliferation of recombinant plasmids, better adaptation to thermal and salt stress and utilization of unusual substrates (Trinh et al., 2008; Lee et al., 2014). Interestingly, selective sorting and deletion of aerobic or anaerobic reactions targeted to biomass and biofuel productions have shown the cells to have highest theoretical yields even with minimum metabolic functionality under anaerobic conditions (Lee et al., 2014). After deletion of the selective respiratory pathways, the remaining pathways showed nongrowth-associated conversion of sugars (C5 and C6) into ethanol. Catabolite repression was completely absent during anaerobic growth after the deletion of acetateproducing pathways with concurrent conversion of C5eC6 sugars into ethanol. It implies that removing the nonessential genes would be beneficial

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to achieve higher yields and for the economical production of desired molecules in synthetic microbes. Swift advances in gene synthesis have enabled us to assemble the complete microbial genomes (Gibson et al., 2008; Hutchison et al., 2016). Eukaryotic genomes are generally much larger and complex. The Chromosome III of S. cerevisiae was completely designed and synthesized recently, designated as synIII, which is approximately 14% smaller than its wild-type template and is fully functional with every gene tagged for easy removal (Annaluru et al., 2014). The bottom-up synthesis of yeast chromosome III represents part of the Sc2.0 Project, the synthesis of the other 15 chromosomes is ongoing. By introducing loxPsym sequences at sites downstream of nonessential genes, the SCRaMbLE system using an inducible Cre recombinase to shuffle-up regions of the genome was established in the synthetic genome, endowing the synthetic yeast a ‘hyperevolution’ capacity (Jovicevic et al., 2014, Annaluru et al., 2014). The landmark of the first synthesized designer eukaryote chromosome provides new perspectives on the future of synthetic biology and genome research.

3. YEAST STRAINS FOR ROBUST CELLULOSIC ETHANOL PRODUCTION Yeast strains are the leading industrial biocatalysts for the biological conversions of renewable feedstocks to biofuels (Table 2). However, the commercialization of cellulosic feedstock-based biofuels still has several technical issues to deal with. Here several strategies along with their challenges are discussed.

3.1 Consolidated Bioprocessing As discussed in the previous sections, LC biomass has recalcitrant nature which poses resistance to enzymatic hydrolysis. Moreover, presence of five carbon sugars is another technical issue (Lynd et al., 2005; Hasunuma and Kondo, 2012). Consolidated bioprocessing (CBP) is a promising strategy to overcome biomass recalcitrance by using cellulolytic microorganisms. Overall, the CBP technology is comprised of cellulase production, biomass hydrolysis and fermentation in single step. Cellulolytic microorganisms are being engineered to improve their hydrolysis capacity which can provide on-site low-cost enzymes. Among these, Clostridium thermocellum is being used for the production of ethanol through the CBP of plant biomass (Bayer et al., 2008). Genomic and proteomic analyses of several

Table 2 Metabolic pathway engineering in yeast for biofuel production Metabolic engineering Product description obtained Titre (g/L)

Xylose reductase (XR) and xylitol dehydrogenase (XDH) genes XR and XDH genes

XR (XYL1) and xylitol dehydrogenase (XYL2) from Pichia stipites Xylose utilization pathway from Scheffersomyces stipites into GLBRCY0 Addition of XR, XDH and b- glucosidase genes Overexpression of enzymes necessary for transhydrogenase like shunts and deletion of LPD1 gene Deletion of LPD1 gene and transhydrogenase like shunts activation Integration of butanol producing genes, coaA and adhE and deletion of ADH6 and GPD2 genes

Challenges/Limitations

References

Byproduct formation, toxic inhibition and difficulty in xylose utilization Biomass hydrolysate contain inhibitory compounds, reduced glucose utilization, cell growth and ethanol productivity under high salinity Inhibition due to toxic compounds

Erdei et al. (2013)

Ethanol

33

Ethanol

45

Ethanol

47

Ethanol

51.3

N/A

Ethanol

60

Iso-butanol

1.62

Laboratory strain and need to apply industrial strain for increased productivity and yield Reduced cell growth due to deletion of alcohol dehydrogenase and pyruvate decarboxylase genes

Iso-butanol

0.23

N/A

Kuroda and Ueda (2016)

n-butanol

0.13

Comparative low product yield specially by the presence of free Co-A

Schadeweg and Boles (2016)

Balan (2014); Casey et al. (2013) and Casey et al. (2010)

Moreno et al. (2013) and Nogué and Karhumaa (2015) Jin et al. (2013)

Balan (2014) and Ha et al. (2011)

Matsuda et al. (2013)

Development of synergistic pathway with overexpression of ADH and KDC enzymes

n-butanol

0.83 (Lab scale) 1.05 (Bioreactor)

High ethanol production as byproduct, instability in bioreactor performance, need to optimize dissolve oxygen requirement in process Low product yield due to glycerol production in large amount as side product

Shi et al. (2016b)

Deletion of GPD1 and GPD2 genes with addition of TER gene in place of ETFA, EFTB and BCD genes Novel pathway construction by expression of GOXB, MLS1, DAL7 and LEU2 genes Deletion of PDC1, PDC5, PDC6 genes with addition of MTH1T gene resulting in cofermentation of galactose and glucose molecules Blockage of degradation and activation ability of fatty acids with addition of optimized acetyl-CoA pathway and fatty acid synthase and overexpression of acetyl-CoA carboxylase hFAS mutation with overexpression of phosphorpantetheine transferase

1-butanol

0.014

1-butanol

0.092

Low yield, need to optimize pathway’s enzyme for better productivity

Branduardi et al. (2013) and Kuroda and Ueda (2016)

2,3-Butanediol

100

N/A

Lian et al. (2014)

FFAs, Fatty alcohols and Alkanes

10.4 (FFAs) 1.5 (Fatty alcohols) 0.0008 (Alkanes)

N/A

Zhou et al. (2016)

Short-chain Fatty Acid (SCFA)

0.11 (Total SCFAs) Improvements require for product selection and yield 0.08 (C8 FA)

Sakuragi et al. (2015)

Leber and Da Silva (2014)

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thermophilic microbes have shown significant number of hydrolytic enzymes which can be exploited in the future to further improve the cellulolytic potential of these microbes to bring robustness (Dam et al., 2011; Tolonen et al., 2011). Moreover, artificial enzymatic cellulosome complexes have been designed and evaluated for efficient degradation of crystalline cellulose, in vitro, ex vivo or in vivo (Vazana et al., 2012). Another approach of CBP technology is the expression of biomass degradation enzymes on the cell surface of yeast instead of using the cellulolytic microorganisms. Various amylolytic, cellulolytic and hemicellulolytic enzymes have been successfully expressed on the yeast cell surface by using the glycosyl phosphatidyl inositol, which enabled the S. cerevisiae to directly convert the biomass to biofuels or other bioproducts (Hara et al., 2012; Yoshida et al., 2011).

3.2 Development of Xylose Fermenting Yeast Strains Economic conversion of biomass to biofuels and chemicals requires efficient fermentation of all sugars present in cellulosic hydrolysates. After glucose, xylose is the most abundant sugar in the hydrolysates derived from LC biomass, which consists of up to 1/3 of the total sugar released from lignocellulose (Jin et al., 2004). However, the native strain of S. cerevisiae cannot ferment xylose into ethanol. Besides the introduction of the xylose-assimilating pathway into S. cerevisiae, numerous efforts have been made on the development of engineered S. cerevisiae strain to ferment xylose rapidly and efficiently, and genome-editing tools have been successfully employed to construct xyloseassimilating yeast, which have been reviewed previously (Jin et al., 2004). Although the XR/XDH pathway has intrinsic defect of cofactor imbalance, its thermodynamic advantage compared to the XI (xylose isomerase) pathway results in efficient xylose utilization and ethanol production (Karhumaa et al., 2007). Cofactor imbalance also acts as the main reason for xylitol accumulation in S. cerevisiae expressing XR-XDH pathway. Heterologous genes from different species with complementary cofactor specificity was also applied in the engineering of S. cerevisiae for efficient xylose fermentation, for example, csXR from Candida shehatae and ctXDH from Candida tropicalis was found to have the closest matched cofactor specificity among enzyme 20 XRs and 22 XDHs homologs tested (Du et al., 2012). Manipulating the cofactor levels by the overexpression and fine-tuning the expression of a water-forming NADH oxidase gene (noxE) from Lactococcus lactis resulted in reduced xylitol accumulation during xylose fermentation by engineered S. cerevisiae (Barrera et al., 2015; Li et al., 2012). Overexpression of all of the genes (RKI1, RPE1, TKL1,

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and TAL1) of the non-oxidative pentose phosphate pathway enhanced the growth of engineered yeast cells expressing either XI or XR-XDH pathway (Shen et al., 2012; Wu et al., 2012). As the metabolism of xylose been optimized by various strategies, engineered S. cerevisiae could convert xylose into ethanol much more efficiently, the bottleneck effect of xylose transport become more and more important (Young et al., 2012). Therefore it is of great importance to optimize the xylose transport in fast xylose-fermenting S. cerevisiae strains. Although expression of some of the individual transporters improved the xylose consumption ability of engineered S. cerevisiae; most original transporters showed either low efficiency or low specificity towards xylose. Hxt4p, Hxt5p, Hxt7p and Gal2p are important xylose-transporting proteins in S. cerevisiae cells (Hamacher et al., 2002), while Gal2p and Hxt7p showed higher specificity to xylose than any other HXTs (Young et al., 2012). GXS1 and GXF1 from C. intermedia, 2D01474 and XYLHP from D. hansenii and RGT2, XUT1 and XUT3 from S. stipitis demonstrated moderate transport efficiency and higher xylose preferences (Mehmood et al., 2013; Srirangan et al., 2014). Recently, directed evolution of the HXT11 resulted a mutant which reversed the transportation specificity from D-glucose into D-xylose, subsequently the mutant facilitated coconsumption of glucoseexylose in S. cerevisiae (Shin et al., 2015). Similarly, an N367A variant of Hxt36p can efficiently transport D-xylose subsequently enabling the cell to coconsume D-xylose and D-glucose (Nijland et al., 2015). By rational engineering of the conserved protein motifs, endogenous as well as heterogenous sugar transporters with reversed preference for glucose and xylose was obtained, and some of the variants showed negligible glucose inhibition (Srirangan et al., 2014; Farwick et al., 2014; Wang et al., 2015). Another variant, Gal2-N376F completely lost the ability to transport hexoses along with high xylose specificity (Farwick et al., 2014), while C. intermedia Gxs1 Phe38Ile39Met40, S. stipitis rgt2 Phe38Met40 and S. cerevisiae Hxt7 Ile39Met40Met340 were shown to be unable to grow on glucose but sustained growth on xylose (Young et al., 2014). Similarly, F432A and N360S mutations enhanced the D-xylose transport activities of Mgt05196p from M. guilliermondii, mutant N360 F specifically transported D-xylose without any glucose inhibition (Wang et al., 2015). Other than S. cerevisiae, the Scheffersomyces (Candida) shehatae, Pachysolen tannophilus, Scheffersomyces (Pichia) stipitis and Spathaspora passalidarum are native xylose-fermenting yeasts (Hazelwood et al., 2008). However, to achieve robustness for ethanol production from xylose, several improvements are required in future.

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3.3 Development of Stress Tolerant Yeast Strains Other than developing xylose-fermenting yeast strains, enhancing the stress tolerance of yeast is another key area. The biomass hydrolysate produced from pretreated biomass often contains various compounds, including formic acid, acetic acid and furfurals, which act as inhibitors during the fermentation reactions (Jonsson and Martin, 2016). To address the inhibitors issue, we need to uncover the underlying mechanism involved in adaptation to tolerate these inhibitors in yeast. So the functional genes involved in tolerance to various inhibitors may be elucidated using inhibitors-adapted S. cerevisiae strain with its parent by means of global transcript analyses. The latest research progress on development of stress tolerant yeast strains has been reviewed previously, where we emphasized the effect of cell flocculation and zinc supplementation on yeast stress tolerance (Zhao et al., 2016; Cheng et al., 2016a). We have been focussing on mechanisms of yeast stress tolerance and breeding of robust yeast strains for cellulosic ethanol production, and we have found that overexpression of histone H4 methyltransferase encoding gene SET5 and zinc finger protein PPR1 (Zhang et al., 2015) resulted in improved cell growth and ethanol fermentation in the presence of acetic acid stress. Furthermore, we found that absence of histone acetyltransferase Rtt109p (Cheng et al., 2016a), improved the tolerance of acetic acid of S. cerevisiae strains. In addition, we also revealed that deletion of the membrane transporter encoding gene QDR3 improved acetic acid tolerance (Ma et al., 2015). Most recently, overexpression of OLE1, which is responsible for fatty acid mechanisms, was reported to endow yeast strains with improved tolerance to multiple stressors (Nasutution et al., 2016). It was also revealed that overexpression of ARG4 or disruption of CAR1, which led to elevated intracellular arginine levels, enhances ethanol tolerance (Cheng et al., 2016b). From the studies mentioned earlier, it is clear that multiple genes can be manipulated to improve stress tolerance and biofuels production from cellulosic hydrolysates, but it should be emphasized that the genetic background of the different host strains should be considered, because even when the same gene is manipulated, improved stress tolerance can be only observed in specific yeast strains (our unpublished data). Therefore, it is also important to consider the host effect for the development of superior yeast cell factories using synthetic biology tools.

3.4 Higher Alcohols-Producing Yeast Strains Ethanol, though promising, yet has other technical problems in its storage and utilization as a sole fuel source due its hygroscopic nature. On the other

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hand, higher-chain alcohols are not hygroscopic and can provide energy densities equivalent to gasoline. For this reason, there is an increasing interest higher-chain (C3eC5) alcohols, such as 1-butanol and isobutanol (Connor and Liao, 2009; Weber et al., 2010). Isobutanol production has been attempted in S. cerevisiae using the Ehrlich pathway. The overexpression of genes responsible for valine biosynthesis increased isobutanol productivity by six-folds in S. cerevisiae (0.97 mg/g glucose) (Chen et al., 2011b). Moreover, isobutanol production in S. cerevisiae was further improved from 11 mg/L to 143 mg/L (6.6 mg/g glucose) via activation of valine biosynthesis by overexpressing ILV2, KIVD (from L. lactis) and ADH6, the PDC1gene which encodes a pyruvate decarboxylase (Kondo et al., 2012). In another study, KDC and ADH genes from different sources were overexpressed in yeast. The indigenous KDC, phenylpyruvate decarboxylase (ARO10), 2-ketoisocaproate decarboxylase (THI3) and 2KIV decarboxylase (KIVD) from L. lactis were overexpressed in S. cerevisiae to study their impact to enhance the butanol titre. Moreover, six genes including ADH1, ADH2, ADH5, ADH6, ADH7 and SFA1 were overexpressed in S. cerevisiae. Different KDCeADH combinations expressed and level of isobutanol was measured with additions of 8 g/L 2KIV, the isobutanol precursor. Interestingly, highest isobutanol (488 mg/L) was measured with KIVD-ADH6 combination. Furthermore, PDC1, which encodes an isozyme of the pyruvate dehydrogenase complex, was deleted to prevent acetaldehyde production. This strain was cultured in synthetic dextrose media supplemented with yeast nitrogen base and glucose and isobutanol titre was raised to 143 mg/L at a yield of 6.6 mg/g glucose (Kondo et al., 2012). However, the achieved titers indicate that engineering yeast for isobutanol production is still far from commercial requirements. So, it is suggested that novel approaches for the bioengineering of metabolic pathways are required to drastically improve production of C3eC5 alcohols by S. cerevisiae.

4. DEVELOPING BACTERIAL STRAINS FOR BIOFUELS PRODUCTION Due to various technical issues in the synthetic designs of yeast for biofuel production, several alternative strategies have come forward. One promising strategy is the use of nonnative and nonyeast hosts for the production of biofuels. Bacterial genomes are simpler and easy to modify when compared to yeast genome. Easy genetic modification is one of the various driving forces which can make the efforts fruitful in lesser time to alleviate

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negative impact in a synthetic strain. Because, altering a native pathway often play with growth and expression of several other genes which subsequently diminish cell fitness and create bottlenecks in the engineered system. A recurring problem is the pervasive occurrence of reversible reactions in the engineered metabolic pathways which slows down the production of key intermediate molecules and hence reduced the titre of our final product.

4.1 Ethanol Production in Bacterial Hosts Many microorganisms are being developed using synthetic biology tools for biofuel production (Table 3), but each microbial host has technical limitations and challenges to deal with to achieve the industrial robustness. Among these, yeast strains are the leading industrial biocatalysts for biofuel. However, genetically designed bacteria such as E. coli, Corynebacterium glutamicum, Z. mobilis, L. lactis and Bacillus subtilis have also been studied to address the industrial requirements. Previously, CBP technology has also been applied on genetically modified E. coli strains to produce biofuels from ionic liquid-pretreated switchgrass (Bokinsky et al., 2011). Macroalgae are being considered as feedstocks for biofuel production. A CBP-based microbial platform was developed for the bioconversion of macroalgae biomass to ethanol (Wargacki et al., 2012). A 36 kbp DNA fragment from Vibrio splendidus was installed into E. coli, with a capacity to encode enzymes required for alginate transport and metabolism. This fragment along with a preinstalled system for the extracellular degradation of alginate, generated a microbial platform with an installed capacity of simultaneous degradation, uptake and conversion of alginate to bioethanol directly from macroalgae biomass achieving w80% of the theoretical yield (Wargacki et al., 2012). Z. mobilis is a natural ethanol producer and encompasses many desirable features which make this microbe suitable for industrial biocatalysis. So, it has been used as a model system to study the various perspectives of substrate utilization and industrial robustness. Interestingly, Z. mobilis can utilize a broad range of carbon sources including biomass residues from industrial, agricultural and municipal wastes, so may be exploited for their bioconversion into valuable chemicals and biofuels (Yang et al., 2016b). Ethanol is the most established product produced by recombinant strains of Z. mobilis. In addition, its ethanol producing genes (PDC and ADH) have been expressed in various microbial hosts, including E. coli, to produce ethanol (Piriya et al., 2012). Moreover, its recombinant strain has demonstrated productive when used at pilot scale in combination with other commercial ethanol producers

Table 3 Metabolic pathway engineering of bacterial hosts for biofuel production Metabolic engineering Host description Product Titre (g/L)

Challenges/Limitations

References

Escherichia coliebased metabolic pathway engineering

LW06

KO11

Recombinant E. coli BuT-8

JCL260

TA76

CPC-PrOH3

LW02 strain with deletion of ADHE, ACKA, Frdabcd, and LDHA-FRT genes Alcohol dehydrogenase (ADHB) and pyruvate decarboxylase (PDC) from Zymomonas mobilis Overexpression of ATOB, HBD, CRT, TER, ADHE2 and FDH genes Butyrate conversion strain with deletion of undesired genes and addition of adhE2 and ATODA

Ethanol

>30

Ethanol

þ40

Low product production, Woodruff et al. (2013) efforts require for product tolerant traits Low product tolerance and Balan (2014) and Zhou instability of strain in et al. (2008) continuous system

1-butanol

30

Low titre

Shen et al. (2011)

n-butanol

6.2

Less efficiency due to toxic effect of product on strain, need to optimize the performance of coculture system 50 Byproduct formation, reduced cell densities and acetate accumulation 143 Lower isopropanol production rate resulting as hurdle in cost effectiveness 7 (1-propanol) Succinate accumulation 31 (Ethanol) due to limited NADH, presence of toxic metabolite

Saini et al. (2015)

Deletion of ADHE, FRDBC, Iso-butanol FNR, LDHA, PTA and PFLB genes Overexpression of THL, Isopropanol ATOAD, ADC, ADH

Activation of endogenous sleeping beauty mutase (Sbm) operon

1-propanol and Ethanol

Baez et al. (2011) and Lan and Liao (2013) Inokuma et al. (2010) and Zhang et al. (2011) Srirangan et al. (2014)

(Continued)

Table 3 Metabolic pathway engineering of bacterial hosts for biofuel productiondcont'd Metabolic engineering Host description Product Titre (g/L)

GAS3

Replacement of natural promoter with trc promoter, overexpression of FADD gene and introduction of ACR and CER1 genes in modified E. coli W3110 Modified GAS3 pTrcAtfA’TesA(L109P) and pTacAdhEmutFadD harbouring GAS3 strain Recombinant Engineering of 2 protein Escherichia coli lipoylation pathways Modified AL322 Introduction of Maqu_2220 gene in addition to modified FADD and TESA genes in E. coli ZF07/pZF15/ Addition of YBBO (aldehyde pKJ02 reductase) and AAR (acylACP reductase) and FADR genes with removal of PLSX gen in the native strain, modification of phospholipid and fatty acid synthesis pathways MGL2 Knockout the acyl-ACP thioesterases and competing genes (LDHA, PTA and ACKA) from other pathways

Hydrocarbons

0.58

Challenges/Limitations

References

Low strain activity under Choi and Lee (2013) aerobic conditions, need to enhance the activity of aldehyde decarbonylase

Short-chain Fatty 0.47 Acid Ethyl Ester (FAEE) Branched chain fatty 0.18 acid (BCFA) Fatty alcohols 1.72

Need to transfer the flux from C12 to C10 FAEE production N/A

Fatty alcohols

1.99

N/A

Fatma et al. (2016)

Fatty alcohols

6.33

N/A

Liu et al. (2016)

Choi and Lee (2013)

Bentley et al. (2016)

Low titre yields of product Liu et al. (2013) with toxic effect on producing cell

YJM33

Coexpression of MVAS and MVAE with OhyAEM (oleate hydratase) and OleTJE (fatty acid decarboxylase) for novel isoprene pathway

Isoprene

0.0022 (Lab scale) 0.62 (Fed-batch)

Low productivity, Yang et al. (2016a) economically unfeasible, need to enhance the pathway’s efficiency

Ethanol

þ42

Ethanol

136

Low cell performance Balan (2014) and Lau under toxic (acetic acid) et al. (2010) conditions N/A Wang et al. (2016)

Zymomonas mobilis and other microbial pathway engineering

Zymomonas mobilis AX101

Genetic engineering for glucose, xylose and arabinose fermentation Z. mobilis Integration of TESA, METB, TMY-FHPX YFDZ, AFTA, FAR, HSP and XYLA/XYLB/ TKTA/TALB genes Recombinant Overexpression of KIVD and Corynebacterium ADH3 genes with glutamicum (C12) inactivation of LDH and E1 subunit of ACEE genes Synechococcus elongatus Substitution of AdhE2 PCC 7942 (aldehyde dehydrogenase) with NAPH dependent YQHD (alcohol dehydrogenase) and BLDH (butyraldehyde dehydrogenase) Synechocystis sp. Deletion of PHB with PCC 6803 overexpression of PDC and adh2 genes under PRBC promoter S. elongatus Expression of KIVD/ALSSPCC 7942 ILVC-ILVD genes under PTRC/PLACO1 promoter

3-Methyl-1-butanol 0.49

N/A

Xiao et al. (2016)

1-butanol

0.0299

Malonyl-CoA synthesis which acts as limiting factor for synthesis of fatty acid

Lan and Liao (2012)

Ethanol

5.5

N/A

Lai and Lan (2015)

Iso-butyraldehyde

1.1

N/A

Lai and Lan (2015)

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including yeast. Furthermore, different metabolic pathways have been engineered in Z. mobilis to produce advanced biofuels or their intermediates. However, the low titre is a hindrance towards the commercialization of this technology in near future (Yang et al., 2016b). Although Z. mobilis can express cellulases and has potential to be an effective CBP strain, it still requires optimization of the metabolic pathways to meet the robustness required. Although, Z. mobilis has shown tolerance to ethanol and inhibitors yet synergistic action of several inhibitors can still have negative impact on its growth and ethanol productivity. L. lactis is famous for cheese production and has shown great potential for its use as cellular factory, owned to its higher glycolytic flux, metabolic potential to utilize various carbohydrates and ease of genetic manipulation (Kleerebezemab et al., 2000). Moreover, it can be successfully engineered to produce ethanol with heterologous expression of pyruvate decarboxylase and alcohol dehydrogenase with concomitant disruption of alternative product pathways (Solem et al., 2013). In an attempt to use L. lactis as an ethanol producing cell factory, its metabolic network was subjected to substantial rewiring. The ldh, pta, adhE genes were inactivated along with the heterologous expression of PDC and ADHB genes. The engineering resulted 41 g/L ethanol titre with 70% yield using a low-cost medium. This study revealed the potential of L. lactic cellular factory towards the bioconversion of dairy and corn milling industry waste into ethanol in a cost-effective manner (Liu et al., 2016). However, the fastidious nature and higher nutritional requirements make the L. lactis less attractive for industrial applications, where cost-effective production is the ultimate goal. However, use of cheaper fermentation media and the use of nutrient-rich waste substrates may help to circumvent this challenge which may allow us to use L. lactis as a cellular factory.

4.2 Production of C3 and C6-Alcohols in Bacterial Hosts Due to tremendous applications in plastics industry, in deicing and to prepare antifreeze fluids, and as additives in cosmetics, medicines, dyes, nutrition, liquid detergents and biofuels, annual consumption of 1,2-propanediol has reached to 1 billion pounds only in the United States (Saxena et al., 2010). Most of its demand is being met through petrochemical industry. However, this process produces toxic intermediates and side products which are not required under recent global scenario of cleaner production and consumption. So, more sustainable and environmental friendly routes are required to be found. Among these, fermentation-based conversion of renewable

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carbon sources to propane-diol has come forward. Various microorganisms including C. thermosaccharolyticum (Cameron and Cooney, 1986), S. cerevisiae (Jung and Lee, 2011), E. coli (Clomburg and Gonzalez, 2011), C. glutamicum (Niimi et al., 2011) and Synechococcus elongates (Li and Liao, 2013) have shown potential to produce 1,2-propanediol from renewable feedstocks. Among these, C. glutamicum have shown to be a better host for ethanol and butanol production, due to enhanced tolerance (Yamamoto et al., 2013) and higher isobutanol yields when compared to E. coli (Blombach et al., 2011). Moreover, C. glutamicum has shown higher tolerance ability to organic acids, furan and phenolic inhibitors present in lignocellulose hydrolysates (Sakai et al., 2007) which reflects the promising potential of C. glutamicum that is an alternative host for biofuel production. For sustainable production of biofuels using C. glutamicum as an alternative host, its substrate spectrum can be widened through metabolic engineering (Zahoor et al., 2012). For instance, deletion of the endogenous genes hdpA and ldh and the heterologous expression of mgsA, gldA and yqhD genes from E. coli, the C. glutamicum strain could produce 1,2-propanediol from glucose with a product yield of 0.343 mol/mol grown on a minimal salt medium (Siebert and Wendisch, 2015). Among various bacterial hosts, E. coli is the well-studied host for several purposes, and the situation is similar when it comes to the production of 1-butanol using E. coli as host. Interestingly, the production of 1-butanol from glucose by metabolically engineered E. coli has reached higher titers when compared to S. cerevisiae as a host (Atsumi et al., 2008a). The biological production of isobutanol in E. coli is achieved by the introduction of the Ehrlich pathway (Atsumi et al., 2008b), where robust synthesis of isobutanol is mediated by two genes including 2-keto acid decarboxylase (KDC) and alcohol dehydrogenase (ADH) using 2-ketoisovalerate as a substrate (Hazelwood et al., 2008). An isobutanol yield of 86% (22 g/L) was achieved by heterologous expression of KDC and ADH genes with concomitant deletion of competing pathways in E. coli. In addition to E. coli, bacterial hosts, such as C. glutamicum, Clostridium cellulolyticum and S. elongates have also been used for 1-butanol production (Atsumi et al., 2009; Higashide et al., 2012). The 1-butanol production pathway of Clostridium acetobutylicum was transferred into E. coli (Atsumi et al., 2008b) for the reason that Clostridium pathway can produce one 1-butanol molecule with the consumption of each glucose molecule and four NADH molecules. Using various engineering steps and with the deletion of several genes, the E. coli strain was shown to produce 1-butanol at the rate of 373 mg/L which was

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further increased to 552 mg/L when grown in Terrific Broth (TB)-enriched, glycerol-supplemented media. Understanding the chemical nature of enzymes involved in the 1-butanol production is very important to design a robust E. coli strains to produce 1-butanol. To get it done, an E. coli strain was double transformed with two compatible expression vectors, carrying phaA, phaB and adhE2 genes (Bond-Watts et al., 2011). The strain was shown to produce 1-butanol at the rate of 95 mg/L even after 6 days on glucose-supplemented TB-media. Upon further investigations, enzymes that generate stereo-specific products were found. Interestingly, replacing phaB with hbd (encoding hydroxybutyryl-CoA dehydrogenase from C. acetobutylicum), and crt with phaJ (encoding an R-specific enoyl-CoA hydratase from Aeromonas caviae), the butanol production was raised to 2.95 g/L. To enhance the availability and consumption of acetyl-CoA and NADH, aceEF-lpd (encoding the pyruvate dehydrogenase complex) was overexpressed, which subsequently provided two additional NADH molecules, and hence improved the production to a titre as high as 4.65 g/L. In another study, several genes from various genomes including atoB (E. coli), adhE2 (C. acetobutylicum), crt (C. acetobutylicum), hbd (C. acetobutylicum) and ter (T. denticola) were heterologously expressed in a special E. coli DadhEDldhADfrd strain (which cannot grow without an additional NADH-consuming pathway). The strain expressing the said genes could produce 1.8 g/L of butanoal under anaerobic conditions in glucosesupplemented TB-media. Later, the fdh gene encoding a formate dehydrogenase from Candida boidinii, was overexpressed to reduce excess pyruvate which caused the oxidation of formate into CO2 and NADH providing two additional NADH molecules required for butanol production. Another gene named pta, which encodes a phosphate acetyltransferase, was deleted to raise the intracellular acetyl-CoA, which dramatically raised the titre of butanol production to 15 g/L, only after 3 days achieving the 88% of the theoretical maximum yield (Shen et al., 2011). In another study, redox imbalance issue was addressed using a clostridial CoA-dependent synthetic pathway targeting three metabolite nodes including pyruvate, glucose-6-phosphate and acetyl-CoA. This engineering attempt was shown to exhibit the higher NADH level and butanol production titre of 6.1 g/L was obtained. It seems that production efficiency of fermentative products in microbes strongly depends on the intracellular redox balance and higher production of our desired products can be achieved by individual or coordinated modulation of these metabolite nodes (Saini et al., 2016). Other than E. coli, various bacterial hosts have been used for butanol production to see their potential,

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including C. acetobutylicum, Pseudomonas putida and B. subtilis (Nielsen et al., 2009). Interestingly, butanol pathway can be engineered to produce hexanol using E. coli as a host (Dekishima et al., 2011). Other than the modifying the butanol pathway, synthetic pathways can also be constructed to produce hexanol in E. coli. For instance, a synthetic pathway was constructed on two plasmids which were cotransformed into a modified E. coli DadhEDldhADfrdBC (Shen et al., 2011), and hexanol titre of 47 mg/L was achieved in 48 h. However, this titre is not comparable with butanol production in E. coli; therefore, further improvements are required to enhance the productivity and yield.

5. FINE TUNING OF SYNTHETIC MICROBIAL FACTORIES Once the metabolic pathways have been installed into industrial strains of yeast or bacteria, the last stage is the fine-tuning of the host to enhance the yield and productivity with reference to growth conditions and technicalities involved at the industry. Fine-tuning of the host metabolism, we need to understand that qualitative control of enzyme activities is based on the quantitative understanding of metabolic regulation. So to harness the maximum potential, slight upregulation and down regulation of enzyme activities are carefully studied on the biosynthesis of target molecules for each step of the reaction. The process is tedious and often assisted with modelling and simulation of the processes at first followed by the pilot scale optimization. For instance, a kinetic model, integrating the pentose phosphate and glycolysis pathways was constructed for Z. mobilis which revealed the TAL gene is very important to regulate the xylose fermentation (Altintas et al., 2006). Similarly, a xylose fermentation kinetic model was established (Parachin et al., 2011) using the experimentally determined kinetic parameters for the enzymes involved in xylose fermentation. The in silico analyses performed using this model were endorsed by the lab experiments to enhance xylose fermentation (Parachin et al., 2011). However, kinetic modelling is not sufficient and need detailed knowledge on the enzyme kinetics and their related parameters. For this reason, metabolic flux analysis, transcriptomics and metabolomics analyses are playing a critical role in synthetic bioengineering. Moreover, the pathway genes may be expressed under the regulation of various promoters, transcription factors and terminator sequences for enhanced expression of the target genes.

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The forced-evolution is being considered as a promising approach these days, because it has become possible to investigate that how evolution may lead the functional improvement. The prototype strains may be repeatedly subjected to subculturing under a particular selection pressure or stress to obtain a forcefully evolved strain with enhanced performance. Although the methodology is still infeasible, yet we need to focus on advancement of the key technologies for genome editing, promoter selection and de novo construction of pathways. Other than fine tuning of the synthetic microbial platform, modelling of bioprocess has been proven effective (Unrean, 2016) for achieving higher production, improved yield and productivity of desired product.

6. CONCLUSION AND FUTURE PROSPECTS Synthetic biology is a new hope for the construction of desired microbial cell factories using advanced techniques of analytical chemistry, biochemistry, bioinformatics and biotechnology. S. cerevisiae, E. coli, C. glutamicum, Z. mobilis and L. lactis have been the most desired host microorganisms to engineer for their versatile genetic capabilities, recycled fermentation, stress tolerance ability and suitability for biorefinery processes. However, the development of robust strains requires deletion and integration of genes along with a regulatory control over multiple genes. Fine-tuning of the installed pathway may require modification of promoters and/or deletions of several chromosomal genes. In near future, novel cellular factories may completely base on synthetic genomes with controlled regulation of the genes present on artificial chromosomes. The de novo synthesis of artificial chromosomes may be breakthrough in the future synthetic biologyebased engineering, where metabolic pathway engineering will no more require gene cloning and/or vector construction. Roles of suitable promoters, transcriptionally active elements and well-characterized terminator sequences cannot be ignored for the future synthetic designs of pathways. Though a long way to go, synthetic biology is the real hope of biotechnologists to construct robust microbial cell factories.

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