The term ‘endophyte’ is derived from the Greek term ‘endo’ (within) and ‘phyte’ (plant), and may apply to both fungi and bacteria that reside in plant tissues during all or part of their life cycle and cause no apparent harm (Wilson 1995). It is estimated that every plant species has at least one associated bacterial endophyte (Strobel et al. 2004), and they belong to diverse classes of bacteria including alpha, beta, and gamma subdivisions of Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria (Rosenblueth and Martinez-Romero 2006). These bacteria thrive within plants where they successfully colonize roots, translocate to leaves, stems, and even to reproductive organs where they may be vertically transmitted to the next generation, ensuring a stable interaction with its host plant.

The number of microorganisms present in natural ecosystems is tremendous. In fact, estimates of the number of bacterial endophytes in the Brazilian Atlantic forest indicate the possibility of 2-13 million species in the aboveground plant parts alone (Lambais et al. 2006). Of the bacterial species identified, 97% were previously not described. A single plant species may also have a wide range of different bacterial genera associated. In wheat, culture based studies have shown that 88 bacterial species representing 37 genera inhabit the aboveground plant tissue (Legard et al. 1994), which likely underestimate the number of microorganisms as molecular studies yield much larger population numbers (Rasche et al. 2006). Both culture based and molecular based analyses indicate that alpha and beta Proteobacteria are the most numerous colonizers of the phyllosphere (Thompson et al. 1993). In total, 853 bacterial endophytes were isolated from aboveground parts of four agronomic crops and 27 prairie plants including switchgrass. Cellulomonas, Clavibacter, Curtobacterium, and Microbacterium isolates showed high levels of colonization and had the ability to persist in host plants (Zinniel et al. 2002).

Diazotrophs, or atmospheric nitrogen-fixing bacteria have been isolated from bioenergy crops, including Miscanthus spp. and Pennisetum purpureum, where Herbaspirillum frisingense sp. nov. (Kirchhof et al. 2001), Azospirillum doebereinerae (Eckert et al. 2001), and Herbaspirillum frisingense (Rothballer et al. 2008) were found. Similarly, different nitrogen-fixing bacteria belonging to genera Stenotrophomona, Pseudomonas and Burkholderia were isolated from sand dune grasses (Ammophila arenaria and Elymus mollis) in Oregon, which may biologically fix nitrogen and promote the growth of these plants under poor soil conditions (Dalton et al. 2004). Nitrogen-fixing bacteria have also been isolated from different plant species, such as Kallar grass (Leptochoa fusa) (Reinhold-Hurek et al. 1993), lodgepole pine (Pinus contorta), western red cedar (Thuja plicata) (Bal et al. 2012), and hybrid poplar (Populous trichocarpa) (Taghavi et al. 2010). While general surveys of endophytic populations in switchgrass have been undertaken (Zinniel et al. 2002), there are no detailed analysis on native bacterial endophytic interactions in switchgrass.

Fungal endophytic populations may also be substantial, particularly in longer lived plants, as 340 genetically distinct taxa were recovered from two tropical understory plant species (Arnold et al. 2000). Endophytic fungi can also have a significant beneficial impact on switchgrass performance (Kleczewski et al. 2012). While much emphasis has been placed on the study of clavicipitaceous fungal endophytes (Neotyphodium/Epichloe) with cool — and warm-season grasses (Rodriguez et al. 2009), two recent surveys of switchgrass endophytes have failed to identify members of the Clavicipitaceae family (Ghimire et al. 2011; Kleczewski et al. 2012), suggesting that the major endophytic fungi inhabiting switchgrass are of the non-clavicipitaceous type, representing primarily ascomycetous fungi (Kleczewski et al. 2012). These endophytes may be found colonizing tissues above — and/or below-ground (Rodriguez et al. 2009). Recently, 18 taxonomic orders of fungal endophytes were isolated from switchgrass plants in northern Oklahoma belonging to the genera Alternaria, Codinaeopsis, Fusarium, Gibberella, Hypoerea and Periconia, and switchgrass shoot tissues showed a significantly higher diversity of fungal endophytic species compared to the root tissues (Ghimire et al. 2011). Similar fungal endophytic genera were isolated from switchgrass plants growing in a range of habitats across Indiana and Illinois, such as Alternaria, Epicoccum, Phoma, Phaeosphaeria and Stagonospora (Kleczewski et al. 2012). Since switchgrass is one of the most promising bioenergy crops, several laboratories in the US have been working on isolation and characterization of bacterial and fungal endophytes from switchgrass. Identifying and harnessing beneficial endophytic microorganisms that have a broad spectrum of plant growth promotion traits and possess various mechanisms for stress tolerance may aid in the development of a low input and sustainable switchgrass feedstock production system, particularly on marginal land.

Mycorrhizae are symbiotic fungi that interact with the roots of vascular plants. These fungi are typically divided into two groups: ectomycorrhizas which have hyphae that do not penetrate individual cells within the root and endomycorrhizas which, as the name implies, have hyphae that penetrate the cell wall and invaginate the cell membrane. Eighty to 92% of land plant species surveyed are associated with mycorrhizal fungi, among them, arbuscular mycorrhizal (AM) fungi are the predominant type (Wang and Qiu 2006), and are placed in the phylum (division) Glomeromycota. AM fungus is characterized by highly branched fungal structures located within the plant root cortical cells. Generally, AM fungi comprise 130 species of fungi classified as Zygomycotina (Simon et al. 1993). AM fungi from the order Glomales (Glomeromycota) are associated with most plant species including angiosperms, gymnosperms, pteridophytes, lycopods, and mosses (reviewed in Hause and Fester 2005). The fungi involved in the AM interaction are obligate biotrophs and reproduce asexually. As obligate biotrophs, AM fungi are not culturable without their host plant, making the study of these organisms difficult. AM fungal associations are important to help switchgrass tolerate unfavorable soil conditions (Parrish and Fike 2005). It has been reported that AM fungi play essential roles in switchgrass growth in acidic soil, which has high levels of exchangeable aluminum and immobile minerals, such as phosphorus (Koslowsky and Boerner 1989; Brejda et al. 1993; Johnson 1998). AM fungal associations may be more critical in warm-season grasses, such as switchgrass because from an evolutionary perspective, both are of tropical origin (Hetrick et al. 1988).

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).

In 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).

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

(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 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).

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.

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.

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.

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)

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….

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)

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.

• 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).

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.

While insects can impede switchgrass establishment and may affect productivity, little information is available in the literature regarding suitable and efficacious insecticides. McKenna and Wolf (1990) reported greater stand density and yield when carbofuran (2,3-dihydro-2,2- dimethyl-7-benzofuranyl methylcarbamate) was applied in the row with seeds at establishment. While variable responses to carbofuran treatment have been reported (Bryan et al. 1984), the compound has been used as a matter of course for establishment in other studies (e. g., Stout and Jung 1995); however, this insecticide is now banned by the US Environmental Protection Agency. Some have suggested that switchgrass seedlings may be particularly vulnerable to injury by herbivory (Hartnett 1989), but others have reported no advantage to using insecticides for establishment (Cassida et al. 2000). This should not be surprising given that insect injury is a function of many factors including climatic conditions and weather, seeding dates and seedling development, and weed competition (Boerner and Harris 1991; Parrish and Fike 2005).

Arbuscular mycorrhizal (AM) fungi are the majority of mycorrhizal fungi and are the focus of this section. AM fungi penetrate the cortical cells of roots and form arbuscules (tree-like structures) and vesicles within host plant cells, and their hyphae penetrate into the soil to aid in absorption of nutrients, extending the area of nutrient acquisition. AM spores can be isolated from soil samples containing roots by the wet sieving method, which is widely used and works well with sandy soil samples (Utobo et al.

2011) . Briefly, after soil samples are collected, they are suspended in water (approximately 15-30 ml/g), and mixed vigorously. If spores form in the interior of roots, soil and root samples are blended and the suspension solution is left to settle for a while, and then the supernatant is decanted through standard sieves, which should capture the spores of interest. The procedure should then be repeated, particularly with soil containing large amounts of clay. Spores isolated can be further purified by sucrose centrifugation particularly if the soil is rich in organic debris because it may be difficult to isolate spores hidden in organic matter (Utobo et al. 2011).