Mixtures of selected thermostable enzymes (Table 2) were first evaluated for their hydrolytic efficiency by measuring the FPU activities at different tem­peratures (Fig. 3). The temperature optima of the new thermostable mixtures in the FPU activity assay were 5-10 °C higher than those of the commercial enzyme mixtures when a relatively short reaction time (60 min) in this assay was used. The relative FPU activity was set at the value of 100 at the refer­ence point at 50 °C. The maximum FPU activity of the novel enzyme mixture was about 25% higher at the optimum temperature at 65 °C as compared with the highest activity of the commercial reference enzyme at 60 °C. As could be expected, at lower temperature (35 °C), corresponding to the fermentation temperature of traditional yeasts in a simultaneous saccharification and fer­mentation process (SSF), the FPU activities of the thermostable preparations were slightly lower than those of the commercial T. reesei enzymes.

The thermostable enzyme mixture without added xylanase activity (TM 1) was evaluated on pure cellulose (Avicel) and compared with the commercial enzyme preparations (Celluclast supplemented with в-glucosidase) at 45 °C, 55 °C and 60 °C in a 48 h hydrolysis (Fig. 4). On pure cellulose, the mixture of thermostable enzymes gave nearly similar hydrolysis results at 60 °C as the T. reesei enzymes at 45 °C, i. e. thus enabling an increase of temperature of about 15 °C. At 60 °C, the hydrolysis yield of Avicel was about three — to four­fold better with the thermostable enzymes than with the commercial fungal enzymes. The highest hydrolysis yield was about 90% of the theoretical.

On the spruce substrate, the thermostable enzyme mixture resulted in an even more significant improvement in the performance at higher hydrolysis temperature as compared with the commercial enzymes. Thus, the hydrolysis yield was about threefold better at 55 °C and about fivefold better at 60 °C
using the thermostable enzyme mixture (Fig. 5). The hydrolysis was, however, also decreased with the thermoenzyme mixture at 60 °C. When comparing the hydrolytic performance of the commercial enzymes by increasing the temperature from 45 °C to 60 °C on Avicel and on spruce, it can be observed that the increased hydrolysis temperature decreased the performance on the natural lignocellulose substrate significantly more: from 70-10% on spruce, as compared with 90-30% on Avicel within 48 h. Obviously, the spruce sub­strate, even washed, contained compounds that, with increasing temperature, inhibited or inactivated not only the T. reesei enzymes, but also the ther­mostable enzymes.

High temperature enzyme mixtures suitable for hemicellulose-containing raw materials were evaluated in the hydrolysis of steam pretreated corn stover substrate (Fig. 6). With this raw material, the hydrolysis by the thermostable enzyme mixture at 45 °C was better than with the commercial preparation. The hydrolysis was still efficient at 55 °C and only slightly decreased at 60 °C with the thermostable enzyme mixture. The relative decrease of the hy­drolytic performance of both enzyme preparations was less pronounced on the corn stover substrate than with the spruce substrate at elevated tempera­tures. Based on HPLC analysis (Table 4) of the corn stover hydrolysates, the yield of glucose was around 90-95% of the theoretical after 72 h. The cor­responding yield of xylose was about 80-90% at temperatures up to 60 °C. The hydrolysis yields of the minor monosaccharide sugars, arabinose and galactose, were not significantly improved by the thermophilic enzyme mix­tures, indicating the absence of corresponding thermostable enzymes, i. e. arabinosidases and galactanases in the mixtures. In the hydrolysis of the

Table 4 Sugars released from steam pretreated spruce and corn stover (% of the initial sugar component in the substrate), analysed by HPLC

Enzymes Hydrolysis Sugars released Sugars released from corn stover

temp. from spruce

% of theoretical % of theoretical

(°C) Glucose Glucose Xylose Arabinose Galactose

Enzyme dosage was for reference enzymes: Celluclast 5 FPU g-1 substrate supplemented with 100 nkatg-1 Novozym 188; and for thermostable enzyme (TM 3) 5 FPU g-1 substrate. Hydrolysis time 72 h at pH 5, triplicates with mixing. Release of xylose, mannose and ara — binose from spruce substrate was below the reliable detection limit (less than 0.1% of the substrate) spruce substrate (Fig. 5, Table 4), only glucose was released. The individual sugar analyses corresponded well with the measured values of the reducing sugars.

9

Xylose isomerase (XI, D-xylose ketol isomerase, EC 5.3.1.5) catalyses the re­versible isomerisation of D-xylose to D-xylulose. This enzyme has been the subject of much applied research because it also catalyses the isomerisation of D-glucose and D-fructose. In this role of “glucose isomerase”, xylose iso — merase is applied on a huge scale for the production of high-fructose corn syrup and continues to be one of the most abundantly applied industrial en­zymes. The high-fructose syrup application has led to intensive screening and protein engineering studies, with increased activity and stability of XIs at el­evated temperature as a priority target [11,23]. For excellent reviews on the molecular and industrial aspects of XI, the reader is referred to a number of specialised reviews [4,11,12].

In the context of the present paper, several characteristics of XIs are note­worthy. First and foremost, and in contrast to the xylose reductase/xylitol dehydrogenase pathway, the XI reaction does not involve pyridine nucleotide cofactors. As this will entirely circumvent the cofactor regeneration chal­lenges associated with the xylose reductase/xylose dehydrogenase pathway, functional expression of a XI in S. cerevisiae has long been regarded the most promising approach to engineering S. cerevisiae for alcoholic fermentation of D-xylose [14].

XIs generally require divalent cations, but the specificity of the metal re­quirement is strongly dependent on the source of the enzyme, with many enzymes requiring Co2+, but others Mn2+ or Mg2+ [11]. Although S. cere­visiae has been demonstrated to accumulate cobalt intracellularly [18], it is not clear whether this metal is available in the cytosol or sequestered in, for example, the vacuole. Other aspects with potential relevance for yeast metabolic engineering include the high temperature optimum (60-80 °C) and the relatively high pH optimum (7.0-9.0) of many of the XIs that have been characterised [11]. As S. cerevisiae is a mesophilic micro-organism with a cytosolic pH slightly below 7, intracellular expression of heterologous struc­tural genes for XIs may not always lead to optimal activity.

Even in the pre-genomics era, it was clear that XIs are widespread among prokaryotic micro-organisms, and also occur in several plants [11]. Figure 3 shows a phylogenetic tree of XI gene sequences based on an October 2006 GenBank database search. This phylogenetic tree gives a good indication of the diversity of XI genes and the phylogenetic relationships between se­quences from related organisms. With respect to eukaryotes, the tree contains four sequenced XI sequences from the plants Hordeum vulgare, Arabidop — sis thaliana, Oryza sativa and Medicago truncatula, which cluster together (Fig. 3). The phylogenetic tree contains only one other eukaryotic XI se­quence, namely that of the anaerobic fungus Piromyces sp. E2 [28]. Inter­estingly, this eukaryotic XI sequence clusters with those of the prokaryotic phylum Bacteroidetes, which has led to the suggestion that the fungus may have acquired XI via horizontal gene transfer [28], as previously suggested for other enzymes in anaerobic fungi [20].

3

Proteomics is the quantitative study of all proteins expressed in a cell under defined conditions. Proteomics represents one of the more challeng­ing x-omes given that analytical methods enabling measurement of all pro­teins with the sensitivity, accuracy, and precision required have only recently been developed [62,72]. Rapid advances in protein analytical technologies, fueled by the addition of mass spectrometry (MS), liquid chromatography (LC), sequence databases, and data handling methods, have made it possible for protein chemists to identify and examine the expression of many pro­teins resolvable by 2DE (two-dimensional gel electrophoresis). The possibility for large-scale protein studies seemed attainable [97]. It was in this context that in 1994, at the first 2DE meeting in Siena, Italy, the term “proteome” was coined [98]. Methods employed in proteomics have since gone on to include two-dimensional differential gel-electropheresis (DiGE), multidimen­sional protein identification technology (MuDPiT), isotope-coded affinity tag technology (ICAT), and quantitative proteome analysis based on MS-MS spe — tra and a multiplexed set of chemical reagents referred to as iTRAQ [99]. Although still slowly emerging, there are clear examples of where proteome analysis has resulted in strain improvement and successful metabolic engin­eering strategies [62,100].

In line with industrial biotechnology applications, results of 2DE analy­sis can identify targets for strain improvement, such as target gene dele­tions [101] or co-expression for product enhancement [102]. Proteome an­alysis may also improve the design and control of industrial fermentation processes. In such a study, the dynamics of the E. coli proteome were recorded during an industrial fermentation process with and without in­duction of recombinant antibody synthesis [103]. The recombinant anti­body fragment CD18 F(ab’)2 was developed as a biopharmaceutical for the treatment of acute myocardial infarction. Proteomic analysis of the above fermentation process suggested co-expression of Phage shock pro­tein A (PspA) with a recombinant antibody fragment in E. coli resulted in improved yields. Further investigation is required to understand why PspA addition resulted in improved yield [104]. Another example, more relevant to bulk chemical manufacturing, is the metabolic engineering of E. coli to pro­duce the biodegradable and biocompatible thermoplastic polymer, poly-(3- hydroxybutyrate), often referred to as PHB, which has numerous applications including serving as a primary feedstock for synthesis of enantiomerically pure chemicals. Specifically, the proteome of the metabolically engineered E. coli XL-1 Blue for PHB intracellular accumulation was compared to the reference strain, noting that PHB accumulation is not observed in the refer­ence strain. It was revealed that 2-keto-3-deoxy-6-phosphogluconate adolase (Eda) plays a pivotal role in supplying glycerol-3-phosphate and pyruvate to further increase the flux to acetyl-CoA. A larger acetyl-CoA and NADPH de­mand is consistent with cells that produce a large amount of PHB. These conclusions were based on identification of protein spots on 2DE using matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry [105].

Among the most recent examples of proteomics applied to industrial biotechnology process development is the recent reporting of the com­plete proteome of Mannheimia succiniciproducens [100]. M. succinicipro — ducens MBEL55E is a capnophilic Gram-negative bacterium isolated from bovine rumen, which produces large amounts of succinic acid under anaer­obic conditions (0.68 g-succinic acid/g-glucose), and was first reported in 2002 [106]. Succinic acid is a C4 organic acid, traditionally produced via petrochemical conversion of maleic anhydride. It promises to be a strategic building block chemical to be produced by industrial biotechnology, due to its use as the primary feedstock in the synthesis of key products including bu — tanediol, tetrahydrofuran, y-butyrolactone, and poly-amides [107,108]. Nu­merous groups are exploring production of succinic acid in different host or­ganisms, including E. coli [109], Anaerobiospirillum succiniciproducens [110], Actinobacillus succinogenes [110,111], Aspergillus niger, and Saccharomyces cerevisiae. In M. succiniciproducens using 2DE coupled with MS-MS identi­fication and characterization lead to identification of 200 proteins, with 129 proteins from the whole cell proteome, 48 proteins from the membrane pro — teome, and 30 proteins from the secreted proteome. Characterization of cell growth and metabolite levels in conjunction with proteome measurements during the transition from exponential to stationary growth was carried out.

Two interesting conclusions could be drawn from such analysis that was not possible a priori. First, a gene locus previously annotated as the succinate dehydrogenase subunit A (sdhA) is likely to be the fumarate reductase sub­unit A (frdA), based on comparative proteome analysis supported by physi­ological data. Second, two novel enzymes were identified as likely metabolic engineering targets for future improvements in succinic acid production. PutA and OadA are enzymes responsible for acetate formation and conver­sion of oxaloacetate to pyruvate, respectively, and their deletion is likely to induce higher flux towards succinic acid through minimization of byprod­uct formation [100]. This is a clear example of where proteome measurement and analysis not only provided novel information for future metabolic engin­eering strategies, but also served as a quality-control check for two critical assumptions: (i) that genome annotation is error-free, and (ii) that mRNA expression directly correlates with protein expression and activity.

As discussed previously, acquisition of large bodies of genomic sequences has prompted development and application of tools such as cDNA/oligo — nucleotide microarrays, which in turn has made possible global analysis of cellular processes. As powerful as this approach is proving to be, much of the regulation of physiological processes occurs post-transcriptionally. Thus, measurement of mRNA levels provides an incomplete picture of cellular ac­tivity and regulatory control points that may yield themselves as preferred metabolic engineering targets. Methods and techniques developed to meas­ure the global expression, localization, and interaction of proteins fall within the domain of proteomics. By integrating various data sources with known biological function about individual genes and proteins, one starts uncover­ing underlying mechanisms leading to the creation and analysis of static and dynamic models of regulatory networks and pathways.

A recent study has shown the value of this union of data as an experimen­tal strategy to gain insights into cellular physiology [87]. In this study, both transcriptional and proteomic data were collected from S. cerevisiae and all of the known components of the galactose induction pathway were systemati­cally perturbed. The different data were integrated into a mathematical model that included enzymatic reactions, membrane transport, transcriptional acti­vation, protein activation, and protein inhibition. The model predicted pre­viously unknown intra-pathway interactions, and inter-pathway interactions of the galactose induction pathway and other cellular processes. Several of these predictions were then verified experimentally [87]. The galactose sig­naling pathway is of particular industrial relevance as one of the classical and best-understood promoter and induction systems used for protein expres­sion. This example further highlights that even such an extensively studied pathway will manifest new mechanisms for control and manipulation using x-omic approaches.

Related directly to bioethanol process development, several groups are evaluating proteomes of production organisms under defined environments that are of immediate industrial relevance. For example, Salusjarv et al. (2003) performed a proteome analysis of metabolically engineered S. cerevisiae strains cultured on xylose as compared to glucose under aerobic and anaero­bic carbon-limited chemostats [113]. Lignocellulosic feedstocks are abundant and renewable; however, are also composed of xylose — the most abundant pentose sugar in hemicellulose, hardwoods, and crop residues, and the sec­ond most abundant monosaccharide after glucose [114]. S. cerevisiae fails to consume pentose sugars efficiently, compared to glucose, and therefore sig­nificant research has occurred in metabolically engineering such strains (see Sect. 3.5 for further discussion). Proteome analysis of xylose fermentations revealed 22 proteins that were found in significantly higher concentrations relative to glucose fermentations. Such proteins included alcohol dehydroge­nase 2 (Adh2p), acetaldehyde dehydrogenases 4 and 6 (Ald4p and Ald6p), and DL-glycerol-3-phosphatase (Gpp1p) [113]. As will be revealed in the fluxome discussion, this protein expression profile is indicative of the redi­rection of metabolic fluxes believed to occur under xylose fermentation. Pro — teome analysis bridges the gap between genetic engineering, transcription profiles, and observed metabolism by identifying that over — or underexpres­sion of specific proteins (i. e., enzymes) are pushing targeted (or untargeted) metabolic fluxes in desired (or undesired) directions.

Proteomics is a rapidly developing area of research, whereby new technolo­gies are often developed and validated in model systems such as S. cerevisiae. Compared with genomics, however, proteomics is still limited because it is strongly biased towards highly abundant proteins and, therefore, does not yet provide the genome-wide coverage obtained by other x-ome technologies. Additionally, the proteome world is possibly the most complex of all x-omes because of its highly dynamic nature and complexity resulting from splice — variants, isoforms, and protein post-translational modifications. For some proteins, in excess of 1000 variants have been described [104]. It is evident that there is an ongoing need for improvement in (quantitative) proteomics technologies, whereby yeast will likely have its role again as the benchmark model system. Proteomics, largely absent in bioethanol development, is at the infancy of finding key roles in industrial products. Those products are likely to be targeted as co-products for bioethanol-based biorefineries. Succinic acid has already been considered as a potential added value co-product that could diversify the product portfolio of a biorefinery where the high-volume, low — value product will be bioethanol [115,116].

3.4

The amount and types of enzymes required for the saccharification of cel­lulose and hemicellulose are strongly dependent on the biomass being hy­drolyzed and the type and severity of pretreatment. Ultimately the selection of biomass feedstock will be based on local availability and economy of sup­ply. In the early stages of commercial development, feedstocks with the great­est potential for demonstrating economic viability of an integrated process are likely to be developed first. These likely will include processing residues that are already available at processing plants such as corn fiber, soybean hulls, sugarcane bagasse, wood waste, and paper mill waste. The selection of both desizing and pretreatment processes may also be strongly influ­enced by local economics, especially with regard to co-location with existing wood, coal, or municipal solid waste-burning power plants, where inexpen­sive power and steam are available. With a diversity of potential substrates, different thermochemical pretreatments will be utilized to balance accessibil­ity to enzymatic attack with destruction of valuable sugars. Variations in the severity of the pretreatment (pretreatment severity is defined as the combined effect of temperature, acidity, and duration of treatment) must also be varied to maximize both sugar and fermentation compatibility. For example, an acid pretreatment, run at high temperature, high pressure and for long periods of time is considered more severe than a neutral pH water pretreatment run under the same temperature and pressure conditions. A low severity pretreat­ment will solubilize less of the hemicellulose fraction, increasing the amount of hemicellulase enzymes required, but may also reduce the production of by-products toxic to the fermentation, increasing the ethanol yield from the fermentation.

3.1

The transport of pentose sugars in S. cerevisiae occurs through hexose trans­porters [80,81], albeit with an affinity one to two orders of magnitude lower than for hexose sugars [47,82]. Therefore, pentose transport was early con­sidered a rate-controlling step for ethanolic pentose fermentation [47]. Nev­ertheless, a metabolic control analysis study demonstrated that transport controlled xylose conversion only in strains with high XR activity, and only at low xylose concentrations [83]. Few reports exist on expression of pen­tose transporters in pentose-utilizing S. cerevisiae strains [80,84]. The most effective approach has been the overexpression of the galactose permease Gal2 in recombinant arabinose-fermenting S. cerevisiae [71]. This is in part due to the difficulty in actively expressing heterologous membrane proteins. In a recent breakthrough, the first active heterologous expression of a glu — cose/xylose facilitated diffusion transporter and a glucose/xylose symporter from Candida intermedia [85] in S. cerevisiae [86] was reported. So far the fermentation performance of these strains has not been reported. Never­theless, the expression of heterologous xylose transporters opens up new metabolic engineering strategies to further increase the rate of xylose utiliza­tion in xylose-fermenting S. cerevisiae strains.

4.2

Despite the status of S. cerevisiae as a proven industrial microorganism, con­ferring the ability to rapidly convert pretreated cellulose to ethanol is a daunt­ing proposition. Apart from essential traits, such as high ethanol yield and productivity, industrial strains need to concurrently ferment both hexoses and pentoses under robust industrial conditions that require minimum nutri­ent requirements and high ethanol and inhibitor tolerance. In addition, these strains also have to hydrolyze cellulosics and thus need to produce and se­crete heterologous hydrolases at high enough levels to sustain hydrolysis and fermentation of cellulosics to ethanol (Table 1). Before contemplating these

challenges, it is worth considering the evolutionary development of S. cere­visiae as microorganism of choice for ethanol production.

Through the serendipitous duplication of its entire genome about 100 mil­lion years ago, followed by the further duplication of the alcohol dehydroge­nase (ADH) genes < 80 million years ago, the S. cerevisiae sensu stricto yeast (comprised of 14 Saccharomyces species) adapted the “make-accumulate — consumption strategy” for ethanol production [27,28]. This ability is largely attributed to its overriding glucose repression circuit that suppresses respi­ration of glucose and other C6 sugars above 20-40 mM threshold concentra­tions in the presence of oxygen, a characteristic feature of Crabtree-positive yeasts [29]. This strategy provided the ancestor of S. cerevisiae with an ad­vantage over its competitors because high ethanol levels (concentrations ex­ceeding 4% v/v) are toxic to most other microbes. Once S. cerevisiae has colonized a niche by producing ethanol levels often exceeding 10% v/v from readily available hexoses, the produced ethanol is reconsumed if oxygen is present. These yeasts therefore developed two distinct alcohol dehydrogenase enzymes through the duplication of the ADH genes for the production and

Table 1 Features required from S. cerevisiae as successful CBP microorganism (modified from [2,26])

Suitability of currently available strains of S. cerevisiae

Essential traits:

Only hexoses by native industrial strains. Partial pentose utilization has been engineered in some laboratory and industrial strains Most industrial strains Most industrial strains Most industrial strains

Primarily multicopy expression in laboratory strains

Laboratory and some industrial strains

Manipulated laboratory and some industrial

strains (maltose and glucose utilization)

Most laboratory and industrial strains

Most industrial strains

Some industrial strains, particularly wine

strains

Laboratory and some industrial strains

subsequent utilization of the ethanol: Adh1 that is constitutively produced and is required for ethanol production, and Adh2 that is only induced in the absence of C6 sugars and is necessary for ethanol consumption.

Regardless of the processes used for biomass hydrolysis, CBP-enabling microorganisms may encounter a variety of toxic compounds derived from biomass pretreatment and hydrolysis that could inhibit microbial growth, particularly in the presence of ethanol [30]. However, industrial strains of S. cerevisiae have been adapted to handle stress conditions, such as high ethanol and sugar concentrations (hence osmotolerance), in fermenting sim­ple hexoses (glucose, fructose, galactose, and mannose) or disaccharide (su­crose and maltose) streams. It also has a natural hardiness against inhibitors and has the ability to grow at low oxygen levels. These features confer to S. cerevisiae a general robustness in industrial process conditions [28]. S. cere­visiae has proven itself as a robust ethanol producer in traditional large — scale processes, and therefore presents itself as platform organism for plant biomass conversion to products such as ethanol [2].

3