Category Archives: BIOETHANOL

Implications of amplifying EM6 flux

Comparison of the flux distributions between before (r) and after (r’) pathway modification suggests an approach to redirect flux distribution for increasing ethanol productivity. Fig. 3.3(a) shows r and r’ at a specific instant when cell density is 3g/L. Metabolic shift caused by the genetic change is also presented by displaying the difference between r and r’ (Fig. 3.3(b)). Then, amplification of the mode throughput flux could be translated as amplification of a set of reactions with positive values of r’- r which are highlighted in colors in Figs. 3.3(a) and (b). From this analysis, we obtain several interesting findings as follows:

i. First of all, it is observed that none of the reactions in the glycolytic pathway are amplified. It implies that amplification of the glycolytic enzymes may not be a key to increasing ethanol productivity. This is consistent with experimental findings reported in the literature. Overproduction of different glycolytic enzymes of S. cerevisiae showed no effect on the rate of ethanol formation (Schaaff et al., 1989). It is because flux control is not inside the glycolytic pathway. Understandably, past efforts for increasing the glycolytic flux by overproduction of glycolytic enzymes have been often unsuccessful (Koebmann et al., 2002). In the in silico analysis, flux control is found elsewhere (highlighted in color) which includes xylose utilization pathway, and pentose phosphate (PP) pathway.

ii. While recombinant strain 1400 (pLNH33) efficiently utilizes xylose through the pathway constructed by overexpressing exogenous genes (XR and XDH), as well as endogenous gene (XK), simulation shows that the increase of ethanol productivity requires further overexpression of not only xylose transport reactions (i. e., R31 and R32), but also xylitol conversion to X5P (i. e., R34 and R35).

iii. In addition, it is shown that four reactions in the PP pathway (R19 to R22), i. e., transaldolase (TAL1), transketolase (TKL1), ribulose-5-phosphate 4-epimerase (RPE1) and ribulokinase (RKI1), are possible targets for overexpression. The finding by Johansson & Hahn-Hagerdal (2002) that overexpression of all four genes resulted in better ethanol production than the overexpression of each gene individually is also consistent with the simulation result.

iv. Another interesting aspect that emerges from the model is as follows. Jeppsson et al. (2002) observed that deletion of ZWF1 (i. e., R17), coding for glucose-6-phosphate dehydrogenase, results in higher ethanol yield but lower productivity. Instead, the hybrid model shows the need to overexpress this oxidative PP pathway to increase ethanol productivity. The calculations show an increase in productivity though there is a small drop in the yield.


Fig. 3.3. Comparison between before and after amplifying the flux of the target mode (EM6). (a) Flux distributions within the network. The upper and lower numerical values along arrow denote the magnitude of fluxes before (r) and after (r’) the genetic change. The unit of flux is mmol/gDW/h. (b) Difference between r’and r.

Conclusions and future prospects

Studies of cell wall and CWDE presented in this chapter could be summarized by the same word: diversity. But how the previously mentioned studies inform us about the right strategy driving efficiently from biomass to ethanol? This will be discussed below.

We demonstrated for the first time that the exoproteome of Fusarium graminearum grown in presence of plant material was rich in various CWDE: more than 80 different proteins, half of them being putatively involved in cell wall digestion were recovered from culture supernatant (Phalip et al., 2005). It is noticeable that later, rather the same number of proteins was found to be secreted by Trichoderma reesei grown on corn cell wall (Nagendran et al., 2009). Commercial preparation Spezyme® used for biomass hydrolysis contains also more than 80 different proteins. All these data corroborate the concept of using complex
enzyme cocktails for complete biomass hydrolysis. A thorough analysis of Fusarium and Trichoderma proteomes reveals some differences between them. For instance, Fusarium secretes enzymes belonging to 29 different GH families and 6 pectate lyases whereas Trichoderma’s exoproteome exhibits 22 GH families but no pectate lyase. Furthermore, only 14 GH families are present in both proteomes (Fig 6), 15 are recovered in Fusarium but not in Trichoderma and 8 are only found in Trichoderma. These are convincing arguments that in a strategy meant to produce bioethanol from various sources, the design of a biomass to ethanol process should be optimized for each couple biomass/fungus.


Fig. 6. Nature of putative secreted CWDE found in Trichoderma reesei grown on corn cops (left; Nagendran et al., 2009) and in Fusarium graminearum grown on hop (right; Phalip et al., 2005).

We studied the patterns of enzymes produced by F. graminearum on four different lignocellulosic biomasses, two poorly lignified (wheat bran and corn cobs) and two highly lignified (hop and birch). Each enzyme cocktail was thereafter used for long-term hydrolyses of the ammonia pretreated four biomasses (16 combinations). The oligo — and mono-saccharides (end products) have been characterized. Their patterns showed variations depending on the nature of the biomass used for growth. Accordingly, different enzyme activities were measured on different culture supernatants. For example, enzymes produced when grown on birch, were efficient with pretreated birch and hop in a lesser extent but were also the most efficient for poorly lignified biomasses. Furthermore, the proteins produced in each condition were identified by mass spectrometry. As shown in Fig. 7, the number of unique proteins recovered in supernatants varies greatly from 25 on birch to 72 on hop (14 for the monosaccharide glucose). More interesting is that 80% (20/25) of the proteins recovered on birch corresponds to CWDE whereas only 19% of the proteins are putatively active on polysaccharides with corn cops for growth (0 on glucose). Finally,


Fig. 7. Number of unique proteins and CWDE identified by mass spectrometry in culture supernatants after growth on wheat bran, corn cops, hop, birch and glucose.

among the 43 CWDE identified during this experiment, not less than 32 were recovered in one culture condition only, 11 were produced in at least two conditions and only 2 were present for each growth conditions (hop, birch, corn cops and wheat bran). Therefore, the fine specificities of the CWDE towards polysaccharides are directly induced by the different lignocellulosic biomasses used for growth.

Of course, even if the couple biomass/fungus is rationally chosen, one could not exclude that the microorganism does not produce a set of keys enzyme(s) for the peculiar biomass to be completely split up. In this case, mixing two (or more) enzymes crude cocktails could be a very good alternative as illustrated by Gottschalk et al. (2010). Enzymes from Trichoderma reesei grown on corn steep liquor and Aspergillus awamori grown of wheat bran have been obtained. The cocktails display different hydrolytic activities and blends of both led to enhancement of some synergic activities up to a 2-fold factor. It is also observed that their association improved glucose release from steam-treated sugarcane. The authors notice that T. reesei cocktail was better for cellulose hydrolysis and A. awamori was better for xylan hydrolysis. This is obviously a very good argument for a fungal specific response towards a given biomass. The same kind of concept could also be extended by using one of the rare lignin-degrading fungi, Phanerochaete chrysosporium (Kersten & Cullen, 2007) in association with another one providing great amount of CWDE. Lynd et al. (2008) suggest that replacing chemical pretreatment by enzymatic one could be a way to explore to improve the process. Furthermore, proceeding this way is mimicking nature diversity since fungi are often associated in communities acting in synergy for the degradation of plants. Furthermore, Wei et al. (2009) claims rightly that "the plant-microbe-enzyme relationship is the foundation of plant biomass degradation in natural environments". They mean that plant cell wall is naturally degraded by a community of microorganisms. Efficient processes from "biomass to ethanol" could advantageously use this property, of course by means of accelerating the natural process.

Many studies have been performed aiming to get huge quantities of individual enzymes, mostly cellulases. Actually, high quantity of enzyme input is not the panacea since saturation of sugar yields due to increasing enzyme charge is often observed (see Sorensen et al., 2007 as an exemple). After reaching the plateau, adding more enzymes did not improve yields anymore. A real diversity of enzyme responding to that of cell wall is better to overcome cell wall recalcitrance for full degradation. Although fungi naturally secrete small amounts of enzymes in liquid cultures, solid state fermentation (SSF) is preferable as an accurate solution for increasing enzyme production yield at the industrial level. Furthermore, a lot of different biomasses, including wastes, have been proved to support fungal growth and to promote CWDE production. The "waste-to-energy" technology was recently reviewed (Bemirbas et al., 2011) and the authors underlined that as population and urbanization increase, the amount of wastes increased regularly. As described elsewhere in this volume (Verardi et al., 2011; Xavier et al., 2011), wastes included municipal solid waste, paper wastes and also agricultural and forestry by-products, all containing lignocellulosic material.

For sustainable development, we strongly encourage the concept of local small units of bioethanol production. Gnansounou & Dauriat (2010) evoke the necessity of "low-risk biorefineries" in opposition to "complex schemes" production units. Therefore biomass should be easily available, preferentially composed of wastes, transports as limited as possible, wastes almost totally used and co-products fully valorized.

As a conclusion, seeking a universal process for total hydrolysis of all kind of biomasses is utopian. Rather, there is an appropriate methodology to follow, described in this chapter, considering the biomass to be treated and the co-products to be valorized in the respect to sustainable development. This point is perfectly illustrated by Saxena et al., (2009). For every step of biomass conversion, starting by the biomass choice, there are multiple routes for hydrolysis technology, monomers produced, microorganism used for fermentation and by­product formed. In this volume, Xavier and al., 2011 underline the necessity of a specific


Fig. 8. Schematic representation of the whole process from "biomass to ethanol".

pretreatment for each biomass to be digested. This means that an industrial process should be developed by taking into account the nature of biomass, and consequently the enzymes necessary for its digestion and the down-stream processes.

Fig. 8 summarizes the views developed in this chapter. Taking into account fundamental research, a couple biomass / fungus is chosen. The fungus is grown on the biomass and produced a cocktail rich in CWDE (A). After adding maybe another cocktail (or individual enzymes; see also Verardi et al., 2011), the enzymes are used to digest the pretreated biomass (in a first approach, the same biomass that the one used for growth), yielding diverse fermentescible sugars in quantity (B). The latter are taken in charge by microorganisms to produce ethanol (C).

2. Acknowledgment

We are grateful to Marie-Laurence Phalip for language revision.