Category Archives: Switchgrass

Primary Cell Wall Composition

Cellulose is the predominant polymer in both primary and secondary land plant cell walls and is thought to bare most of the load in supporting the mass of a plant (Cosgrove 2005; Carpita 2011). Higher plant cellulose consists of long chains of 500 to 15000 fi-(1-4)-covalently bonded glucose residues that hydrogen bond with approximately 36 other chains to form compact crystalline microfibrils that exclude internal water solvation and form crystalline surfaces that are inaccessible to enzymatic hydrolysis (Somerville

2006) . Primary walls have shorter, less crystalline microfibrils, while those of secondary walls are longer and more crystalline. This variation may contribute to the varied functions of the wall throughout development.

From the perspective of optimizing biological conversion to biofuels, cellulose is the most important polymer, since it is abundant and fermenting organisms readily metabolize its constituent glucose. This is also the case for the other, scarcer 6-carbon sugars present in cell walls, including mannose, galactose, and the sugar acids, glucuronic acid and galacturonic acid. However, that laboratory strains of yeast and E. coli that also make efficient use of 5-carbon sugars like xylose have been developed should eventually alleviate this need (Jeffries et al. 2004).

In contrast to cellulose, other specific polymers present in grass cell walls are different in abundance and often structure compared with dicotyledenous plants. These differences are most pronounced in the matrix polysaccharides of primary cell walls (Vogel 2008). For example, primary cell walls of dicots consist of ~30% pectin and ~20% xyloglucan (Vogel

2008) . In contrast, grass primary walls contain very little pectin (~5%) and xyloglucan (~1%), but instead consist of the grass-specific polymer, mixed linkage glucan (5-10%) and glucuronoarabinoxylan (~30%) (Scheller et al.

2010) . Mixed-linkage glucan, which consists of fi-(1-4) and fi-(1-3) linked glucose residues, hydrogen bonds to cellulose and other polymers and
plays a role in strengthening and increasing the flexibility of grass cell walls (Vega-Sanchez et al. 2012).

image008In grass cell walls, glucuronoarabinoxylan, or xylan for short, consists of a backbone of fi-(1-4) linked xylose residues (Fig. 2). Grass xylose is periodically linked at the O-3 position to the 1-carbon of arabinose residues. The arabinose residues are in the furanose f) form with five atoms in the sugar ring, (Araf rather than in the pyranose form (p), which has a 6-membered ring. Of apparent importance to the structure and recalcitrance of grass cell walls, some of the arabinose residues of xylan are modified at the 5-carbon by acylation with hydroxycinnamic acids, especially, ferulic acid and to a lesser extent, p-coumaric acid (Buanafina 2009; Bartley et al. 2013a). Ferulate residues are especially important as they undergo oxidative coupling reactions to form dehydrodimers, i. e., diferulates. Consistent with a role in cross-linking and rigidifying cell walls, the concentration of diferulates in grass cell walls correlates with reduced digestibility (Casler et al. 2006).

Подпись: GT43, GT47, GT75(?)Xylan backbone: B-(l,4)-D-Xylopyranose

Arabino furanose Ую

B-(l,4)-D-Xylopyranose a-(l,3)-D-Arabinofuranose

Ferulic Acid

a-(l,2)-D-Glucuromc Acid

Reducing End: GT47, GT8

GT8

Xylan backbone: GT43 GT47

Подпись: B-( 1,4)-D-Xylopyranose

(1,2)-4-0-Methyl-a-D-Glucuromc Acid

Figure 2. Structure of glucuronoarabinoxylan in (A) grasses and (B) dicots. The glycosytransferase (GT) enzyme families implicated in the synthesis of each bond are noted when known. See text and Table 2 for references.

JoinMap

JoinMap is one of the most widely used software for the construction of genetic maps. It is commercially available and benefits from a highly advanced MS-Windows user interface for data management and analysis, professional support and continued development. JoinMap has two distinct marker order search strategies: the regression mapping and maximum likelihood mapping algorithms. The original regression mapping approach uses a goodness-of-fit statistic strategy to judge, at each step, whether the addition of a marker should be accepted. The maximum likelihood approach was developed to deal with larger datasets and is much faster than the regression mapping approach (Jansen et al. 2001). It maximizes a multipoint likelihood function using Gibbs sampling to estimate multipoint recombination frequencies, and simulated annealing to search marker orders. JoinMap also has a range of other functionality such as testing segregation distortion, analyzing similarity of loci and individuals, comparing two maps with homologous markers, testing heterogeneity, etc. (Stam 1993). Among those a powerful capability of JoinMap is able to integrate data from multiple populations to construct consensus maps. The current software version (V4.1) was released in July 2011.

In switchgrass, JoinMap has been used to construct linkage maps in two studies (Okada et al. 2010; Liu et al. 2012). In both examples, linkage maps were constructed in two steps. Initially, using strict LOD thresholds and a maximum-likelihood module, the markers with high fidelity were grouped into linkage groups to construct a framework map. Secondly, those markers on framework map were fixed to allow more markers to be added on the linkage map by reducing the LOD thresholds. This strategy guaranteed both the accuracy and high coverage of the final full maps (Okada et al. 2010; Liu et al. 2012, 2013b).

Recently, Yang et al. (2013) used the same data as Okada et al. (2010), and reconstructed linkage maps of female and male parents, respectively. The maps included 24 linkage groups for female parent and 21 linkage groups for male parent. They claimed the quality of map construction could be improved by using a new algorithm, which allowed simultaneous estimation of the linkage, genetic interference and preferential pairing factors (Yang et al. 2013).

Strategies for Improving Switchgrass Stress Tolerance

Stresses have been the major limiting factors in plant growth and reproduction. Although switchgrass is one of the toughest plant species which can thrive on marginal lands and tolerate adverse circumstances, increasing stress tolerance will further improve plant growth and is one of the important goals in switchgrass breeding programs. A number of extensive studies have revealed that the expression levels of certain miRNAs are regulated in plants exposed to various stresses and suggested that miRNAs are an integral part of plant stress regulatory networks (Jones — Rhoades and Bartel 2004; Sunkar and Zhu 2004; Sunkar et al. 2007, 2012; Zhao et al. 2007; Shukla et al. 2008; Wang et al. 2011; Khraiwesh et al. 2012; Zhou et al. 2012), suggesting that regulating the expressions of miRNAs could be one of the effective strategies to genetically improve plant stress tolerance in switchgrass.

The roles miRNAs play in plant stress responses have recently been studied in switchgrass. Sun et al. (2012) investigated how drought and salinity alter the expression of miRNAs. Using real-time RT-PCR, they analyzed 12 conserved miRNAs in switchgrass, which have been implicated in salt and/or drought stress in other plant species, and found that both salt and drought stresses could impact the expression pattern of many miRNAs. Under high drought stress, the expression levels of miR156 and miR162 changed significantly suggesting that miRNAs may contribute to plant adaptation to stress and are potential candidates for improving switchgrass (Sun et al. 2012). We have explored the potential of manipulating miRNAs in transgenics for improving plant resistance to environmental stress. Transgenic creeping bentgrass (Agrostis stolonifera L.) plants overexpressing a rice miR319 gene, Osa-miR319a, were generated and found to exhibit enhanced drought and salt tolerance. The enhanced abiotic stress tolerance in transgenic plants was related to significant down-regulation of miR319 target genes, and associated with increased leaf wax content and water retention, but reduced sodium uptake (Zhou et al. 2013). Similar strategy can also be applied in other crop species, including switchgrass to genetically engineer plants for enhanced resistance to environmental stress.

Pyrolysis Conditions

Primary operating conditions affecting pyrolysis (shown in Fig. 3) are biomass flow rate, flow rate of purge gas, reactor temperature profile, heating rate, and residence time. Biomass properties, such as composition and particle size as well as reactor configuration, also affect the reaction conditions (Fig. 4).

image044

Size reduction Drying

___ J

Heating

Chemical

reactions

Catalysis

Quenching

C J

Removal of impurities, e. g.. Char, H2S & NH3

Improving bio-oil composition and properties

Catalysis

Combined Heat and Power (CHP)

Chemicals

Liquid fuels e. g. ethanol, green diesel and gasoline

Y

Y

Upstream

Pyrolysis

Downstream processing

processing

Figure 3. Operations in biomass conversion through pyrolysis.

Подпись: Product properties • Bio-oil, gas and char yields • Bio-oil composition •Carbon conversion efficiency •Overall energy efficiency •Yields of char and tar •Yields of other contaminants

Подпись: Properties of input streams and the reactor • Biomass properties • Particle size and density • Density • Proximate analysis • Ultimate analysis • Energy content • Biochemical composition • Biomass flowrate •Temperature and flowrate of inert purging agent •Pyrolyzer type and configuration • Rate and quantity of heat addition; »Type and quantity of catalyst у
Подпись: Pyrolysis I Heating ^ Chemical reactions L Catalysis Dependent process variables S N •Temperature profile •Heating rate • Purge gas/biomass • Residence time

Figure 4. Pyrolysis process variables.

Process Integration for Cellulosic Ethanol Production Using Switchgrass as a Feedstock

Switchgrass holds great promise as a valuable fuel crop for cellulosic ethanol production with pretreatments discussed earlier such as dilute sulfuric acid, sodium hydroxide, soaking in aqueous ammonia, ammonia fiber explosion, hot water, and lime pretreatment, etc. (Yang et al. 2009; Digman et al. 2010; Xu et al. 2010; Tao et al. 2011). Based on the discussed requirements for cellulosic ethanol process integration, soaking in aqueous ammonia (SAA) pretreatment may be the most feasible for lignocellulosic feedstock such as switchgrass (Isci et al. 2008; Isci et al. 2009). However, for the SAA pretreatment, a pressure vessel is required. The design of the pressure vessel is dependent on the concentration of aqueous ammonia, operating temperature, switchgrass loading, and a ratio of switchgrass to aqueous ammonia. The vapor pressure exerted by 15% (w/w) aqueous ammonium hydroxide at 80°C is approximately 31.5 psi (absolute). After the pretreatment, ammonium hydroxide may be recovered through condensation followed by lignin separation in the pretreated solvent using a filter press. The recovered lignin may then be used for power generation or have industrial importance in making biomaterials and paints (Gargulak and Lebo 1999; Lora and Glasser 2002; Keshwani and Cheng 2009; Laser et al. 2009). After recovering the lignin, the filtered water should be recycled for use in both washing SAA-treated solids after the pretreatment or to make up the ammonium hydroxide concentration after the condensation of recovered ammonium hydroxide. Recovered ammonium hydroxide concentration could be maintained up to 35% (w/v) in the separate vessel.

After SAA pretreatment, further processes could be approached in two different ways using either SHCF or SSCF for cellulosic ethanol production. As mentioned earlier, SSCF has an advantage of requiring a minimal number of vessels compared to SHCF. However, the consideration of downstream processing and energy requirements during the process would help in economical process integration for the cellulosic ethanol production. The addition of reverse osmosis between the hydrolysis step and the fermentation step would be beneficial in the concentration of the hydrolyzed sugar slurry thus minimizing the energy requirement for both ethanol fermentation and distillation. Moreover, the reverse osmosis is often more energy efficient when compared to conventional evaporation techniques (Madaeni et al. 2004). Gul and sek (2009) have mentioned that the concentration of 15% (w/v) sugar syrup to 65% (w/v) requires 86% less energy using reverse osmosis and evaporation technique compared to evaporation technique alone. The enzyme hydrolysis yields up to 10% (w/v) sugars syrup using SAA-treated lignocellulosic biomass. The addition of a reverse osmosis step following enzyme hydrolysis would allow increasing the concentration of hydrolyzed lignocellulosic sugar syrup from 10% (w/v) to 20% (w/v). The concentrated sugar syrup would influence both the fermentation and distillation steps in terms of energy saving and increasing ethanol productivity.

Figure 6 shows the overall process scheme for ethanol fermentation of glucose and xylose illustrated in separate fermenters using S. cerevisiae and P. stipitis, respectively. The dissolved chemicals in the spent broth after the ethanol distillation could be recovered using evaporation. The evaporated water vapor could be condensed and recycled to the enzyme hydrolysis step.

Future Prospects

The future widespread planting of switchgrass as a bioenergy crop is a highly multidimensional and complex issue. Apart from the highly important social and economic components, which this chapter does little more than make mention of their existence, there is room for further work on a number of aspects that may strengthen the argument for its incorporation into the landscape and the fabric of near-future bioenergy portfolios. Critical pressing and future research can easily be tied to target areas discussed in this chapter. More specifically, improvements in (a) biomass and ethanol outputs, (b) other growth aspects and plant protection, and (c) multi-use possibilities, will simultaneously require recognition of environmentally sustainable outcomes (Box 1). The breeding and biotechnological programs essential to these endeavors (see Aguirre et al. 2012) will benefit from concurrent genomics efforts (e. g., Palmer et al. 2012; Wang et al. 2012).

Seed Inoculants and Beneficial Microbes

Switchgrass seems to benefit from a number of interactions with soil bacteria and fungal mutualisms. Switchgrass forms essentially symbiotic relationships with arbuscular mycorrhizal fungi, which grow into the plant’s roots. This relationship enhances nutrient and water uptake, drought tolerance, and protection against pathogens and toxic contaminants and can lead to greater plant growth (Koslowsky and Boerner 1989; Brejda et al. 1998; Clark 2002; Clark et al. 2005; Ghimire et al. 2009; Ghimire and Craven 2011) although the success of these relationships can vary by strain and source (Koslowsky and Boerner 1989; Clark 2002). These associations may play an important role in switchgrass’ adaptation to marginal sites, as Clark (2002) reported that switchgrass plants grown in acidic soils (pHCa 4 and 5) with mycorrhizal fungus Glomus etunicatum had greater P, N, S, K, Mg, Zn, and Cu uptake with reduced uptake of toxic minerals such as Al.

Work by Brejda et al. (1998) showed that rhizosphere microflora from native prairies in Nebraska, Kansas, Iowa, Missouri, Virginia, and North Carolina were effective in enhancing (up to 15-fold increase) switchgrass seedling shoot and root growth, as well as up to 6- and 36-fold increases in N and P recoveries. Switchgrass also appears to be somewhat indiscriminant as a fungal host. For example, Ghimire et al. (2009) reported that switchgrass roots formed association with the ectomycorrhizal fungus Sebacina vermifera [Serendipita vermifera (Oberw.) P. Roberts, comb. nov]. This association enhanced germination of Kanlow switchgrass seed by 52%. In three harvests, S. vermifera increased shoot biomass of NF/GA-993 (EG1101) by 75, 113, and 18% over that of un-inoculated control plants, with no consequent reduction in root biomass. Ghimire and Craven (2011) have also reported large increases in shoot length and shoot and root mass when inoculated with strains of S. vermifera under both stressed (drought) and unstressed growing conditions. Associations of these ectomycorrhizal fungi also altered root architecture. Intriguingly, these fungi can have bifunctional lifestyles, acting as insect pathogens as well as endophytes (Sasan and Bidochka 2012).

While more effort has been given to studying grass-fungal associations with switchgrass, there are ongoing efforts to improve production with bacterial endophytes. Kim et al. (2012) reported greater root and shoot length, increased tillering, and greater mass (about 50%) of lowland (cv. Alamo) switchgrass seedlings when seed were first inoculated with Burkholderia phytofirmans (strain PsJN). Success (as greater plant growth) occurred under both normal and drought-stressed conditions, but was cultivar-specific.

Ker et al. (2012) isolated bacterial strains from the roots of Cave-In­Rock switchgrass that had grown for several years without fertilization. The isolated bacteria included a strain of Paenibacillus polymyxa, a N2- fixing bacterium, as well as bacteria capable of solubilizing phosphate or producing plant hormones (auxins) or both. When tested in field studies, seeds treated with the inoculum "cocktail" produced more tillers and about 40% greater total biomass.

These results suggest there are significant potential opportunities to improve establishment success and yields using various types of fungal or bacterial inocula. Greater understanding of the interactions among host plants and their microbial colonizers may lead to ways that further improve the adaptability of switchgrass to marginal sites or low input systems. For now, however, we are aware of no commercial inocula produced to capitalize on this potential.

Biotic Stress Tolerance

Endophytes inhibit plant pathogen growth and prevent or reduce disease development through the production of toxic alkaloids or by occupying the same ecological niche as the pathogen (Clay 1990). Studies found that three Bacillus strains and two Pseudomonas fluorescens strains decreased up to 60% of the disease symptoms caused by Pseudomonas syringae, a powdery mildew and angular leaf spot, and increased the fresh weight of inoculated melon plants compared with non-inoculated controls (Garcia-Gutierrez et al. 2012). In tomato plants, bio-control of Bacillus subtillis S499 was tested for antagonism against Fusarium spp. by treating the seeds with a formulated powder containing different concentrations of viable spores of B. subtillis S499, and results showed that all treatments significantly reduced disease severity up to 65-70% compared with control plants (non-inoculated seeds) (Nihorimbere et al. 2010).

Since endophytes have the ability to inhibit or prevent pathogen growth, they have been considered as biological control agents. In the interaction of Italian ryegrass (Lolium multiflorum Lam) with the fungal endophyte Neotyphodium, the ryegrass exhibited increased resistance to Trigonotylus caelestialium (Shiba et al. 2011). Additionally, the bird cherry oat-aphid (Rhopalosiphum padi), a notorious pest of forage and cereal grasses, showed a preference to non-infected plants of Alpine timothy (Pleum alpinum) over the plants infected with Neotyphodium spp. (Clement et al. 2011). Perennial ryegrass (L. perenne) plants colonized by N. lolii exhibited reduced aphid populations and in some cases the aphids exhibited reduced adult life­span and fecundity (Meister et al. 2006). Tall fescue plants inoculated with Neotyphodium coenophialum decreased the survival rate and feeding of the corn flea beetle, Chaetocnema pilucaria (Ball et al. 2011). Similar preferences were observed in Achnatherum inebrians (drunken horse grass) where Neotyphodium gansuense-infected plants decreased the preference of herbivores such as bird cherry-oat aphid, carmine spider mite (Tetranychus cinnabarius), grasshopper (Oedaleus decorus) and seed-harvesting ant (Messor aciculatus) due to high levels of ergine, ergonovine and ergoit alkaloids produced by the fungal endophyte (Zhang et al. 2011). Recently, endophytic bacteria isolated from root tissue of six plants growing in a tidal flat area of Korea showed antagonistic potential toward the pathogenic oomycete fungi Phytophtore capsici and Pythium ultimim, and some of them were able to degrade biopolymers, such as cellulose and p-1,3-glucan, which are major components of the cell wall of oomycetes (Bibi et al. 2012).

In switchgrass production, it was found that large-scale planting of switchgrass could be devastated by Puccinia emaculata Schwein, a rust fungus (Zhao B. http: //hayandforage. com/biofuels/rust-resistant — switchgrass-research-goal-0323). In the future, it may be possible to identify endophytes which produce antifungal compounds to help offset losses caused by the rust fungus.

Biological Catalysts for Biomass Deconstruction

Lignocellulolytic microorganisms produce diverse enzymes to degrade cellulose, matrix polysaccharides (i. e., hemicellulose) and even lignin, into soluble carbons to support cellular metabolism (Lynd et al. 2002; Doi 2008). Extensive examination of these degraders and active enzymes has uncovered a wide variety of biological mechanisms in lignocellulose hydrolysis. By definition, biochemical deconstruction relies on biological catalysts. In the first three conversion platforms described above, i. e., SHF, SSF, and SSCF, the enzymes are heterologously expressed, purified and added to pretreated, neutralized biomass. A long-standing goal has been to reduce costs and increase efficiency by exploiting multifunctional hydrolase complexes, or as for CBP, using organisms whose suite of enzymes provide them with hyper-degrading abilities. To give an overview of how lignocellulose is decomposed for conversion to biofuels, here we discuss the diversity and discovery, classification and action, and engineering strategies of lignocellulolytic enzymes, with a focus on cellulases, xylanases, and ligninases, and complexes of these enzymes. Throughout, barriers to efficient deconstruction will be discussed along with potential strategies to overcome them.

Decomposition of most lignocellulose biomass requires the cleavage of O-glycosidic bonds, which link sugar units to form large polysaccharides. Glycosyl hydrolases (GHs) acting on these bonds are roughly classified into endo-acting and exo-acting enzymes (Naumoff 2011). Endo-acting glycosidases cleave the internal glycosidic linkages of polymers; Exo-acting ones act on the bond between the sugar residue at the end of the chain and the rest of the polymer. GHs have versatile enzymatic properties, in terms of substrate specificity, product diversity and catalytic efficiency. Table 3 summarizes the known enzyme families that function in cellulose, xlyan and lignin hydrolysis. In addition to possessing a single hydrolase catalytic

Table 3. Classification of lignocellulolytic enzymes.

Class

Enzyme

EC number

GH families

Mode of action

References

Cellulase

Endoglucanase

(endo-l,4-p-glucanase or 1,4-p-D — glucan 4-glucanohydrolase

EC 3.2.1.4

GH5-10, GH12, GH18, GH19, GH26, GH44, GH45, GH48, GH51, GH61, GH74, and GH124

Hydrolyzes interior 1,4-p-D — glucosidic linkages

(Lynd et al. 2002; Cantarel et al. 2009)

Exo-l,4-p-glucosidase (1, 4-p-D — glucan glucanohydrolase)

EC 3.2.1.74

GH1, GH3, GH5 and GH9

Hydrolyzes terminal 1,4-p — linkages to release one glucose

(Lynd et al. 2002; Cantarel et al. 2009)

Cellobiohydrolase

(exoglucanase or 1,4-p-D-glucan cellobiohydrolase)

EC 3.2.1.91

GH5-7 and GH9

Hydrolyzes 1,4-p-D-glucosidic linkages to release cellobiose

(Lynd et al. 2002; Cantarel et al. 2009)

p-glucosidase

(cellobiase or p-D-glucoside glucohydrolase)

EC 3.2.1.21

GH1, GH3, GH5, GH9, GH30 and GHU6

Hydrolyzes terminal p-D — glucose residues to yield one glucose

(Lynd et al. 2002; Cantarel et al. 2009)

Cellobiose phosphorylase

EC 2.4.1.20

GH94

Phosphorylates cellobiose to yield glucose and glucose 1-phosphate

(Cantarel et al. 2009)

Cellobiose dehydrogenase

EC 1.1.99.18

Oxidizes cellobiose into cellobiono-lactone

(Henriksson et al. 2000; Cantarel et al. 2009; Phillips et al. 2011)

Hemicellulase

Endo-xylanase

(1,4-p-D-xylan xylanohydrolase)

EC 3.2.1.8

GH5, GH7-12, GH16, GH26, GH30, GH43, GH44, GH51 and GH62

Hydrolyzes mainly interior p-1,4-xylose linkages of the xylan backbone

(Gilbert et al. 2008; Cantarel et al. 2009)

 

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Exo-xylanase

(Exo-l,4-p-D-xylanase)

EC 3.2.1.37

GH1, GH3, GH30, GH39, GH43, GH51, GH52, GH54, GH116 and GH120

Hydrolyzes p-1,4-xylose linkages to release xylobioses

(Gilbert et al. 2008; Cantarel et al. 2009)

p-xylosidase

(1,4-p-D-xylan xylohydrolase)

EC 3.2.1.37

GH1, GH3, GH30, GH39, GH43, GH51, GH52, GH54, GH116 and GH120

Releases xylose from xylobiose and short chain xylooligosaccharides

(Gilbert et al. 2008; Cantarel et al. 2009)

a-arabinofuranosidase

EC3.2.1.55

GH3, GH10, GH43, GH51, GH54 and GH62

Hydrolyzes terminal nonreducing a-arabinofuranose from arabinoxylans

(Gilbert et al. 2008; Cantarel et al. 2009)

a-glucuronidase

EC 3.2.1.139

GH4 and GH67

Releases glucuronic acid from glucuronoxylans

(Cantarel et al. 2009)

Acetylxylan esterase

EC 3.1.1.72

GH5 and GH11

Hydrolyzes acetylester bonds in acetyl xylans

(Cantarel et al. 2009)

Feruloyl-/p-coumaroyl — esterase

EC 3.1.1.73

GH10 and GH78

Hydrolyzes feruloyl or p-coumaroyl ester bonds in xylans

(Cantarel et al. 2009)

Endomananase

(p-mannanase)

EC 3.2.1.78

GH5, GH9, GH26, GH44 and GH113

Hydrolyzes interior mannoglycosidic bonds in mannan-based polysaccharides

(Gilbert et al. 2008; Cantarel et al. 2009)

p-mannosidase

(p-l,4-D-mannoside

mannohydrolase)

EC 3.2.1.25

GH1, GH2and GH5

Hydrolyzes and releases mannose units from the non­reducing end of mannosides

(Gilbert et al. 2008; Cantarel et al. 2009; Lundell et al. 2010)

Table 3. contd….

 

Подпись: Switchgrass Biomass Content, Synthesis, and Biochemical Conversion to Biofuels 139

Table 3. contd.

Class

Enzyme

EC number

GH families

Mode of action

References

Ligninase

Lignin peroxidase

(LiP, ligninase)

EC 1.11.1.14

Catalyzes the one-electron oxidation of various aromatic compounds

(Koua et al. 2009; Lundell et al. 2010)

Manganese peroxidase

(MnP, Mn-dependent peroxidase)

EC 1.11.1.13

Catalyzes the oxidation of Mn(II) to Mn(III), which in turn can oxidize several phenolic substrates

(Koua et al. 2009; Lundell et al. 2010)

Laccase

(benzenedioLoxygen oxidoreductase)

EC 1.10.3.2

Catalyzes the oxidation of phenols, polyphenols and anilines by one-electron abstraction

(Wong 2009; Arora et al. 2010)

Versatile peroxidase

(VP)

EC 1.11.1.16

Catalyzes the oxidation of various aromatic compounds

(Koua et al. 2009; Lundell et al. 2010)

Oxidases

(e. g., glucose oxidase, aryl alcohol oxidase and dehydrogenase)

EC 1.1.3.4 EC 1.1.3.7 EC 1.1 91

Generates H202 or conducts aldehyde-alcohol transformation

(Leonowicz et al. 2001)

 

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domain, many GHs are linked by flexible amino acid chains to an additional catalytic domain, a carbohydrate-binding module (CBM), and/or, a type I dockerin domain (Fontes et al. 2010; Naumoff 2011). CBMs bring catalytic domains close to specific substrates and increase catalytic processivity. Type I dockerin domains mediate binding to a complex of cellulose degradation proteins, called a cellulosome, which will be further discussed below (Fontes et al. 2010). Figure 6 summarizes the various enzymes that participate in cellulose and matrix polysaccharide digestion.

Подпись: Non-cellulosome-generating microorganism Figure 6. Model of cellulose degradation with complexed and non-complexed cellulase systems. Components in the model are not drawn to scale. Adapted from Wu et al. (1998) and Lynd et al. (2002). • Glucose •« Cellobiose Celloderxtrin Endoglucanase (2) Cellobiohydrolase m Exo-l,4-|3-glucosidase CT 3-glucosidase & Cellobiose phosphorylase a Carbohydrate-binding module (CBM)

■—a Cohesin

®l¥ Endoglucanase with dockerin

Cellobiohydrolase with dockerin 0* Exo-l,4-|3-glucosidase with dockerin Гр Other hydrolases Q/b Other dockerin-carrying components

eg. Hydrolases, protease and protease inhibitor

Hydrolase Discovery and Diversity

Extensive studies on lignocellulosic degraders and their hydrolytic mechanisms have uncovered a vast diversity of hydrolases from isolated microbes and microbial communities. In the most recent update of the CAZy database, GHs are classified into 131 families and 14 different clans, A to N, based on amino acid sequence similarities and structural folds, respectively (Cantarel et al. 2009; Naumoff 2011). Cellulases are spread across at least 12 different GH families, seven of which can be distributed into four different clans; xylanases are classified into 12 GH families (King et al. 2011). Some GH families contain both cellulases and xylanases (e. g., GH5) while others contain cellulases but no xylanases (e. g., GH7) or vice versa (e. g., GH11). Additionally, a single clan, the GH-As, contains a GH5 endoglucanase, GH26 mannanase and a GH53 endo-p-1,4-galactanase (Gilbert 2010). These observations suggest that GHs show a large diversity in structure and enzymatic activity as the result of convergent and divergent evolution.

High-throughput techniques for DNA sequencing, activity measurements, and proteomics accelerate opportunities to understand the diversity of degradation mechanisms of widely distributed lignocellulose-
degrading microorganisms. Cow rumen microbes specialize in degrading ligoncellulosic biomass, but most members of this complex community resist cultivation. To characterize biomass-degrading genes and genomes, Hess et al. (2011) sequenced metagenomic DNA from microbes adhering to plant fiber incubated in the rumen of a cow. These researchers identified 27,755 putative carbohydrate-active genes and expressed 90 candidate proteins, of which 57% were enzymatically active against cellulosic substrates (Hess et al. 2011). They also assembled 15 uncultured microbial genomes (Hess et al.

2011) . The metagenomics approach also has been used to isolate cellulolytic and xylanolytic genes from such sources as rice straw compost (Yeh et al.

2012) and the hindgut paunch of a wood-feeding ‘higher’ termite species (Warnecke et al. 2007). Metagenomic approaches have also successfully mined for genes encoding enzymes responsible for cellular tolerance to the biomass inhibitors, syringaldehyde and 2-furoic acid (Sommer et al.

2010) . Similarly, Beloqui et al. (2006) identified a novel polyphenol oxidase through activity screening of a metagenome expression library from bovine rumen microflora. Proteome-wide systems analysis of a cellulosic biofuel — producing microbes has also been conducted. For example, quantitative mass spectrometry integrated with physiological characterization revealed proteome-wide expression changes and more than 100 CAZy family proteins expressed in C. phytofermentans (Tolonen et al. 2011).

Structural Genomic Resources BAC Library Resources

Bacterial artificial chromosome (BAC) libraries are often the starting point for genomic enablement of any given species. BACs are the vector of choice for cloning and exploiting long stretches of genomic DNA ranging from 120­350kb in size and are relatively stable with low levels of chimeric content and can be safely stored and accessed in a standard molecular biology laboratory. A deep coverage BAC library with at least 10X redundancy produced from multiple restriction enzymes can serve an essential role in whole genome physical mapping, positional cloning, integration of genetic markers, and through the extraction of a minimal tiling path (MTP) a template for accurate genome reconstruction and targeted genome finishing. Currently, there are two reports for available BAC resources for switchgrass; an EcoRI restriction derived BAC library from the SL93 2001-1 Alamo genotype (Saski et al. 2011) and two complimentary restriction derived BACs created with HindIII and BstYI, respectively, of the AP13 genotype (Sharma et al.

2012) . The first BAC efforts by Saski et al. represents approximately 10 haploid genome equivalents based on a 3.2 Gbp estimated genome size. The BAC was constructed by partial digestion with EcoRI and contains 147,456 clones with an average insert size of 120kb, and is publicly available through the Clemson University Genomics Institute (www. genome. clemson. edu) (Saski et al. 2011). Saski et al. screened the BAC library with a rice brassinosteroid insensitive homolog, OsBRIl, which exhibits a dwarf phenotype when knocked out in rice and maize to assess the feasibility of sub-genome organization and potential for homeologous resolution through High Information Content Fingerprinting (HICF). Through this study, the authors point out that orthologous BACs containing the OsBRI1 locus were identified on homeologous BACs that can be distinguished through HICF. Two BACs were sequenced to completion and a comparison of the suspected homeologous regions suggest preservation of gene order to closely related grasses, yet significant fractionation. These data suggest the switchgrass sub-genomes are similar enough to discover homeologous segments, yet divergent enough to allow for sub-genome placement (Saski et al. 2011). As a follow up study and additional BAC effort, Sharma et al. (2012) produced two additional deep coverage BAC libraries from the AP13 clone (Missaoui et al. 2005), which has been widely used for mapping efforts (Missaoui et al. 2005). The AP13 BACs, PV_ABa and PV_ABb were constructed by partial digestion using complimentary restriction enzymes, HindIII and BstYI, respectively. The average insert sizes are 144kb and 110kb for PV_Aba and PV_ABb, respectively. Combined, these BAC resources represent 16 haploid genome equivalents and are publicly available through the Clemson University Genomics Institute (www. genome. clemson. edu). The authors exploit these BAC libraries through BAC-end, and full-BAC sequencing and comparative mapping in an effort to characterize genome structure and composition, and provide long-range connectivity to the ongoing switchgrass reference genome activities (Sharma et al. 2012). The results of sequencing 47 full-length BACs and close to 330k BAC-end sequences and aligning these resources with available grass genome sequences such as rice, sorghum, maize, and Brachypodium suggest that switchgrass retains a higher degree of microsynteny with sorghum and high gene order and conservation with rice, but is largely collinear with these grass genomes in general (Sharma et al. 2012). These resources present a valuable framework for functional, comparative, and genome reconstruction efforts.