Fungal morphology can be observed under a microscope, and chitin, a specific fungal cell wall component, can be stained with dyes to generally identify fungal species. For a more specific identification, fungal genomic DNA can be isolated using a standard bacterial DNA isolation protocol (Sambrook et al. 1989) or commercial kits, such as the DNeasy Plant Mini Kit (Qiagen).

For identification, the internal transcribed spacer (ITS) regions of fungal ribosomal DNA are widely used because the regions are highly variable (Ghimire et al. 2011). The specific primers ITS1 (5′-TCC GTA GGT GAA CCT TGC GG-3′) and ITS4 (5′-TCC TCC GCT TAT TGA TAT GC-3′) are used to PCR-amplify highly variable ITS1 and ITS2 regions surrounding the 5.8S coding region (Martin and Rygiewicz 2005). However, the primers do not effectively exclude host plant sequences in mixed samples so ITS1-F (5′-CTT GGT CAT TTA GAG GAA GTA A-3′) and ITS4-B (5′-CAG GAG ACT TGT ACA CGG TCC AG-3′) were designed to amplify fungal ITS regions, and a pair of ITS1F and ITS4 resulted in strong PCR amplification from both ascomycetes and basidiomycetes (Gardes and Bruns 1993).

In general, PCR reactions should include 1X reaction buffer containing Mg++, 1 gl of 10 gM of each primer, 1 gl of 10 mM dNTPs, 0.5-1.0 unit Taq DNA polymerase, and 5O-20o ng genomic DNA to total 25-50 gl. PCR can be performed in any thermal cycler with program like 95°C for 2-4 min, then 95°C for 30 sec, 55°C for 30 sec, 72°C for 45 sec for 35 cycles, finally 72°C for 10 min. PCR products are checked in agarose gel first to make sure only one clear band exists, then the product is either cloned into pGEM-T vector (Promega) or similar kits, such as TA cloning kits for sequencing. Direct sequencing of the PCR product after purification using Qiagen’s PCR purification kit is also an option.

Once PCR product sequences are obtained, BLASTN searches can be performed to compare similar sequences from gene bank to identify the species of the target microorganism. A phylogenetic tree can also be constructed to further clarify its evolutionary relationship among other species. In addition, PCR-RFLP, Length Heterogeneity PCR, and Terminal Restriction Fragment Length Polymorphism can be used to characterize microbial communities (Martin and Rygiewicz 2005).

Selection and hybridization have long been used separately or combinationally in the development of cultivars and release of germplasm in switchgrass. Switchgrass is so broadly distributed that enormous genetic variation exists in nature as discussed above. These locally adapted germplasm provide precious raw materials for initial releases of "natural — track" cultivars and named germplasms. Casler (2011) recently provided a comprehensive list of "natural-track" cultivars and germplasms. The Plant Material Centers of the USDA Natural Resource Conservation Service (NRCS) (formerly Soil Conservation Service), State Agricultural Experiment Stations (SAES) and USDA Agricultural Research Service (ARS) laboratories have released most, if not all, of the "natural-track" cultivars and germplasm. Basically, local strains from target geographic regions are collected. Collected accessions are grown in screening nurseries with other available cultivars and germplasm for evaluating important agronomic and adaptive traits. Desirable accessions are further tested in additional environments. Seed of best accessions are increased and released as a new cultivar or a named germplasm. As described by Vogel (2004), release of "natural-track" cultivars and germplasm utilized the natural variation between native ecotypic populations. Selection within individual populations may be applied. For example, ‘Kanlow’ switchgrass was developed using germplasm collected by the Soil Conservation Service at a lowland site near Wetumka, OK. The collection was grown and plants were selected for leafiness, vigor, and retention of green late in season near Manhattan, KS. Seeds of 200 selected plants were increased and released as ‘Kanlow’ by Kansas AES and ARS (Alderson and Sharp 1994). Some other releases are direct seed increase of original collections. ‘Penn Center’ switchgrass was released as a source identified germplasm (USDA NRCS 2010). ‘Southlow Michigan’ switchgrass is a source identified germplasm released by the USDA-NRCS, Michigan Association of Conservation Districts, and the Michigan Department of Natural Resources (Durling et al. 2008). Although "natural-track" releases are genetically similar to or the same as wild plants of the original sources, they provide premier germplasm or populations for developing genetically improved cultivars in breeding programs.

Strong outcrossing sexual behavior and relatively weak asexual reproduction capability of switchgrass require typical recurrent selection procedures used for genetic improvement through hybridization. Vogel and Pedersen (1993) described breeding protocols for outcrossing perennial species. Phenotypic selection within a population consists of preparing seedlings in the greenhouse, transplanting the seedlings into a space-planted nursery, and performing visual selections in year 2 and/or year 3. The selected plants can be used to make new synthetics by poly­crossing with replicated ramets of the selected ones. Seeds of synthetics are grown along with commercial standards for performance and persistence testing at multiple environments (years and locations). ‘Trailblazer’ is an outstanding synthetic cultivar developed by the USDA-ARS and the Nebraska Agricultural Research Division, Department of Agronomy, University of Nebraska (Vogel et al. 1991). It was improved for in vitro dry matter digestibility (IVDMD) by 6% and for beef production by 23% over its source population (Vogel et al. 1991). The success of Trailblazer was evidenced as it was grown over 63,000 ha from 1986 to 1997 (Vogel 2004). Similarly, ‘Shawnee’ was developed with phenotypic selection for IVDMD from a released cultivar ‘Cave-in-Rock’ (Vogel et al. 1996). After two cycles of phenotypic selection, half-sib progenies of 10 superior plants were cross-pollinated to produce Syn 1 seed of ‘Sunburst’ (Boe and Ross 1998). ‘Sunburst’ was improved for seed weight, resulting in superior seedling vigor over other cultivars adapted to the northern Great Plains. Phenotypic recurrent selection with one or more cycles was used to develop ‘Dacotah’, ‘TEM-SLC’, ‘TEM-SEC’, ‘WS4U’and ‘WS8U’ switchgrass (Barker et al. 1990; Tischler et al. 2001; Casler et al. 2006). ‘TEM-LoDorm’ switchgrass was improved through four cycles of phenotypic recurrent selection for reduced seed dormancy (Burson et al. 2009).

Although phenotypic selection within populations was successful for improving IVDMD, reducing seed dormancy, increasing seed weight, and some other traits, the procedure has not been much effective for enhancing biomass yield in switchgrass. Field trials repeatedly indicated narrow-sense heritabilities for biomass yield were low, indicating phenotypic selection is not reliable (Hopkins et al. 1993; Rose et al. 2008; Bhandari et al. 2010 and

2011) . To improve biomass yield, genotypic recurrent selection procedures involving half-sib progeny testing has been used to develop recently released lowland cultivars, ‘Performer’, ‘BoMaster’ and ‘Colony’ by the USDA-ARS and the North Carolina Agricultural Research Service (Burns et al. 2008a, b, and 2010) and ‘Cimarron’ by Oklahoma AES (Wu and Taliaferro

2009) . ‘Cimarron’ switchgrass was developed by poly-crossing seven parents, which were selected from a South Lowland breeding population with one cycle of recurrent selection for general combining ability (RSGCA) after two cycles of restricted recurrent phenotypic selection (RRPS). Details of RSGCA and RRPS used at the Oklahoma State University switchgrass breeding program have been described by Taliaferro (2002). Evidently, half — sib progeny testing is effective in the selection of elite parents for synthetic cultivar development in switchgrass.

Up to now, 21264 hairpins and 25141 mature miRNAs in 193 species have been deposited in miRBase (miRBase release 19, 2012, http: //www. mirbase. org/) (Griffiths-Jones et al. 2008), and 9277 mature plant miRNAs have been deposited in Plant MicroRNA Database, PMRD (http: //bioinformatics. cau. edu. cn/PMRD/) (Zhang et al. 2010). A variety of strategies and approaches have been developed to identify novel miRNAs in diverse plant species (Bonnet et al. 2004; Jones-Rhoades and Bartel 2004; Wang et al. 2004; Adai et al. 2005; Zhang et al. 2005; Zhao et al. 2007; Meyers et al. 2008; Zhang et al. 2008; Zhou et al. 2010; Wang et al. 2011). This increasing knowledge of plant miRNAs and well-developed common tools have provided great opportunities for identification of miRNAs in switchgrass.

MiRNAs have been identified by three common approaches: direct cloning, forward genetics and bioinformatics predication followed by experimental validation (Jones-Rhoades et al. 2006). Forward genetics is rarely used for plant microRNA discovery (Jones-Rhoades et al. 2006). Cloning is the most direct and initial method for large-scale miRNA discovery (Reinhart et al. 2002; Jones-Rhoades et al. 2006). It includes isolation of small RNAs, ligation of small RNAs to adaptor oligonucleotides, reverse transcription, amplification and sequencing (Jones-Rhoades et al. 2006). The early sequencing method is conventional Sanger sequencing, which was successful in identifying some conserved miRNAs (Reinhart et al. 2002; Sunkar and Zhu 2004; Axtell and Bartel 2005), but is relatively in low-depth and not ideal for discovering evolutionarily young miRNAs with low abundance (Moxon et al. 2008). The next generation sequencing (NGS), such as the 454 technology and the Solexa platform, provides a powerful high throughput tool, which has greatly facilitated the identification of novel miRNAs (Lu et al. 2005; Rajagopalan et al. 2006; Fahlgren et al. 2007; Yao et al. 2007; Moxon et al. 2008; Sunkar et al. 2008; Zhao et al. 2010; Chi et al. 2011).

However, cloning method suffers from several limitations such as sequence-based biases during the cloning procedures, and difficulty in detecting miRNAs expressed in low levels or only in response to certain stressors (Jones-Rhoades et al. 2006). Bioinformatics approaches can be a great complement to overcoming these limitations (Jones-Rhoades and Bartel 2004; Jones-Rhoades et al. 2006; Gebelin et al. 2012). Thus, a combination of bioinformatics prediction and experimental validation approaches is often used to identify plant miRNAs.

With the access of the complete genome sequencing data, it is possible to predict and identify a complete set of conserved miRNAs using bioinformatics approaches (Matts et al. 2010; Thakur et al. 2011). However, currently there have been neither complete genome sequencing data, nor large genomic fragmented data (genomic survey sequences, whole-genome shortgun reads and high throughput genomic sequences) available for switchgrass (Matts et al. 2010). Although lack of genomic resources can be a challenge for identifying the complete set of conserved switchgrass miRNAs, the availability of expressed sequence tags (ESTs) deposits (currently 720,590 ESTs deposited in NCBI database) can be a viable source for miRNA discovery. Moreover, previous research has already led to discovery of conserved miRNAs from diverse plant species by EST database mining (Zhang et al. 2006; Sunkar and Jagadeeswaran 2008; Matts et al. 2010; Gebelin et al. 2012).

Particulates of the syngas include ash and char. Ash is composed of minerals such as metal oxides, whereas char is composed of carbon. The most commonly used technology for particulate removal is cyclone separator. Cyclone separator removes particulate by applying centrifugal force on the particle and letting it move downward for collection. Several designs such as 1D-2D (1 dimension width and 2 dimension height), 1D-3D (1 dimension width and 2 dimension height) and 2D-2D (1 dimension width and 2 dimension height) have been described by Parnell and others for removing these particulates (Parnell 1982, 1990). Particulate with smaller size (fine particulates) can be removed by electrostatic precipitator (ESP) and other technologies if downstream syngas applications require syngas to have lower particulate content.

Cellulose is a long chain of polyglycans linked by P(1^-4)-glycosidic linkages. The synergetic action of cellulase consists of endoglucanase, exoglucanase (cellobiohydrolases) and p-glucosidase (cellobiase) acting together to depolymerize cellulose into glucose molecules with liberated water molecules. The hydrolysis of cellulose involves three different synergetic stages. The following steps are summarized:

• Endoglucanase hydrolyzes inner polyglucan molecules linkages into small polysaccharides chain.

• Exoglucanase (CBHI and CBHII) remove the terminal disaccharide glucan units of cellulose.

• CBHI degrades terminal of cellulose polymer.

• CBHII releases cellobiose units from the endoglucanase degraded

cellulose.

• p-glucosidase hydrolyzes cellobiose units into glucose.

P-glucosidase prevents feedback substrate inhibition of endo — and exgo — glucanases by hydrolyzing cellobiose molecules into glucose. The maximum enzymatic hydrolysis rates using cellulase mixtures are generally carried out at 50 ± 5°C between pH 4.8 and 5.5 (Galbe and Zacchi 2002). Other factors that govern the hydrolysis of cellulose include lignin content, crystallinity, hemicellulose content, particle size and surface area of lignocellulosic biomass (Sun and Cheng 2002; Pan 2008; Zhu et al. 2008; Hendriks and Zeeman 2009; Harmsen et al. 2010).

Charles Kwit, h* Madhugiri Nageswara-Raoha and
C. Neal Stewart Jr. U2

Introduction

Providing food and energy to an ever-increasing world population is arguably the most challenging issue we face today. Exactly how to proceed on the agricultural side of this challenge, in conjunction with worldwide economic growth forecasts, is the subject of intense interest (see Tilman et al. 2011). Indeed, food and energy production comprise top priorities in the U. S. President’s Council of Advisors on Science and Technology’s recent report on agricultural research (Executive Office of the President 2012), and they are primary components of the U. S. Department of Agriculture’s current priority areas and challenges (through Authorization—7 U. S.C. 450i). One seemingly simple way to successfully address this challenge is through increases in agricultural crop biomass. Historically speaking, these challenges, or at least portions of them, are not new. The ‘Green

department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USA. “Email: mnrao@utk. edu; mnrbhav@yahoo. com

2BioEnergy Science Center, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA. Email: nealstewart@utk. edu ^Corresponding author: ckwit@utk. edu

Revolution’ of the 1960s is often heralded for its resultant increases in agricultural biomass and crop yields through traditional and technological improvements in breeding, mechanization, and promotion of irrigation, fertilization, and pest control (see Borlaug et al. 1969; Hesser 2006). At its core, the Green Revolution was primarily focused on food (particularly grain) production, and though its efforts did result in increased yields, biomass for energy production was not emphasized.

The current push for the use of biomass (and other non-biomass renewable energy sources) for energy production is being driven by desires to lessen dependence on foreign oil, and to promote rural development and climate change mitigation. Interest in this area can be found worldwide, as bioenergy crop production comprises a top priority of numerous countries and governments (Sang and Zhu 2011; Nijsen et al. 2012). In the U. S., in the liquid transportation fuel sector alone, recent government mandates put forward in the Energy Policy Act of 2005, the Energy Independence and Security Act of 2007, and the Food, Conservation, and Energy Act of 2008 call for up to 36 billion gallons (> 136 billion liters) of fuel being produced from domestic biomass sources by 2022. This amount would displace 30% of current demands from foreign petroleum sources (U. S. Department of Energy 2011), and may indeed promote rural economic development in certain geographic locations (Leistritz and Hodur 2008). Exactly how to sustainably accomplish such lofty energy-related goals and still provide food and fiber for an ever-increasing human population will indeed prove challenging. Many would agree that one place to start is to ensure that biomass for energy does not compete with biomass for food. This concern is complemented by the need to ensure sustainable practices in bioenergy crop production to reduce carbon emissions, promote environmental stewardship, and conserve biodiversity.

The wide geographic distribution of switchgrass provides clear evidence of the species’ large genetic variation and broad adaptability. Indeed, this was a key attribute in the selection of switchgrass as a model species for energy production studies (McLaughlin and Kszos 2005; Wright and Turhollow

2010) . Within regions, switchgrass’ suitability to widely ranging edaphic and fertility environments is largely a function of ecotypic adaptation. Mesic, upland sites most often are occupied by upland ecotypes, which have lower sensitivity to moisture stress, whereas hydric bottomlands are the typical habitat for lowlands.

Although soils for these landscape positions may be quite distinct, soil type per se does not appear to have a particularly strong effect on switchgrass production (Sanderson et al. 1999). However, soil texture—and thus, soil water holding capacity—can have strong effects on switchgrass establishment and yield. Both excessively drained sandy soils and soils with poor internal drainage (as found on depositional sites—e. g., see Thelemann et al. (2010)) can limit switchgrass productivity.

Within the same soil type, slope may be a significant variable for switchgrass production. For example, Fike et al. (2006a) reported that yields on two sites with the same soil type differed by about 40% although the plots were only about 100 m apart. Aspect may also have played a role in these results as the higher yielding plots were more south facing. However, reductions in water infiltration and availability would be expected with increased slope.

Typical switchgrass production guides advise amending soil pH to 6.0 or higher for planting (e. g., Teel et al. 2003). However, switchgrass can tolerate a wide pH range (5-8) for germination (Hanson and Johnson 2005) and soil acidity is rarely a limiting factor for switchgrass in normal production settings. In fact, root growth at pH 3.7 was observed for switchgrass grown on a mine land reclamation site (Stucky et al. 1980). While reclaimed mine lands would be outside the typical boundaries for growing agronomic crops, switchgrass’ adaptability to harsh conditions, its tolerance of soil contaminants, and its capacity to grow on poorly structured soils makes it a useful crop for such difficult-to-crop sites.

Cultivar Selection

It may seem surprising—or even disconcerting—to note that as of this writing (2012), no registered cultivars specifically bred for biomass production have been released. But the reader should not be alarmed; switchgrass, relative to traditional row and forage crops, has been under investigation for only a relatively short period of time—and particularly so for bioenergy purposes. Historically, nearly all cultivars were selected for forage and conservation uses; only recently have registrations included descriptions of switchgrass cultivars as suited for bioenergy production. Such "multitasking" will likely be the norm for switchgrass for the foreseeable future, although these different purposes (forage and biofuels) require rather disparate management practices.

For any site, switchgrass cultivar selection should be driven by the need to match the plant materials to the extremes of the local growing conditions, regardless of end use. Adaptation to seasonal moisture regimes and temperature maxima and minima would be of primary consideration for choice of cultivars. With these factors in mind, we reconsider the value of moving switchgrass of "low-latitude" origin to greater latitudes. As we have noted above, such a strategy can increase yields by delaying inflorescence development, but this approach has bounds (and risks) because switchgrass needs adequate time to develop freeze resistance going into winter (Hope and McElroy 1990; Casler et al. 2004). Thus, moving low-latitude lines too far north (or to too high elevation) may be akin to imposing a death sentence on the crop. The general guideline of Moser and Vogel (1995) is to choose cultivars that are not more than 500 km from their latitude of origin, although we note exceptions where conditions are warmer than would be predicted based on latitude alone (e. g., Christian et al. 2001).

The ability of some endophytes to colonize the xylem provides the opportunity for their systemic spread throughout the rest of the plant, via the transpirational stream in the xylem lumen. However, not all endophytes are capable of colonizing the aerial parts of plants. This may reflect the inability of some to adapt and survive the different niches represented by aerial tissues and organs (Compant et al. 2010). In switchgrass, B. phytofirmans strain PsJN titers were higher in the root than in the leaves 7 days post­inoculation of the roots. However, by 14 days post-inoculation, titers were higher in leaves and sheaths than in the roots, indicating translocation to these tissues (Kim et al. 2012). Generally, bacterial endophyte titers in the aerial plant tissues are reported to be lower than in the root (Rosenbleuth and Martinez-Romero 2006; Compant et al. 2008). In addition, a fair amount of variation can be observed in these tissues. Compant et al. (2008) reported that PsJN could be found in only 10-60% of grape inflorescence stalks and grape berries following initial inoculation of roots. These were localized to xylem vessels, and only a single or few cells were observed. These results further indicated the importance of the xylem for systemic spread of endophytes, allowing them to reach as far as the reproductive tissues. However, this spread was very slow, taking 5 weeks to reach inflorescence tissues. The very low titers of PsJN that ended up in these tissues was attributed to competition with other co-localized endophytes, which can inhabit different tissues and organs, reflecting different niches of colonization (Compant et al. 2011). This report of endophytic bacteria being low or absent in flowers and fruits echoes other comments (Hallman 2001), suggesting low vertical transmission. Bacterial colonization, in general, varies from one cultivar to another and depends on many factors. For example, in soybean, plant genotype, tissue age, season of isolation, and herbicide application, all affected colonization (Kuklinsky-Sobral et al. 2004).

In grasses, the most abundant matrix polysaccharide (i. e., hemicellulose) is xylan. In the last five years, several GTs and other enzymes from Arabidopsis and grasses that likely function in xylan biosynthesis have been characterized (Fig. 2). A complete list of the proteins that function in and the mechanism of xylan synthesis in the Golgi and subsequent release into the cell wall is still being unraveled. Here, we will discuss recent progress with an emphasis on results in grasses. Table 2 lists enzymes implicated in synthesis of the xylan backbone, reducing end oligosaccharide, and sidechains. The reader is referred for additional information to other recent reviews on the topic (Buanafina 2009; Faik 2010; Scheller et al. 2010; Carpita

2012) . When it has been assayed, cell wall material from loss-of-function xylan mutants all exhibit improved digestibility (Mortimer et al. 2010; Brown et al. 2011; Chen et al. 2013; Chiniquy et al. 2012), consistent with the role of this polysaccharide in stabilizing cell walls.

At least two Carbohydrate-Active enZyme (CAZy) database GT families are implicated in the backbone synthesis of xylans, namely GT43s and GT47s (Table 2). Studies in Arabidopsis showed that GT43s are responsible for xylan backbone synthesis since irx9 and irx!4 have drastically shorter xylan chains and reduced ability to transfer Xyl from UDP-Xyl onto xylo-oligosaccharide acceptors (Brown et al. 2007; Pena et al. 2007). Similarly, double mutation of two GT47 genes, irxl0 and irxl0-L severely reduced xylan length, but without affecting the reducing end of xylan. The importance of GT47 family enzymes in xylan synthesis has recently been extended to rice. The Osirx10 mutant has greatly reduced xylan amounts in stems without showing a reduction in xylan chain length, suggesting a somewhat different xylan synthesis mechanism in grasses compared with dicots (Chen et al. 2012). A biochemical study in wheat (Triticum aestivum) suggested proteins from the GT43, GT47, and GT75 families are promising candidates for members of the gluronoarabinoxylan synthetase. Coimmunoprecipitation indicated these three GT proteins interact with each other to form a complex exhibiting xylan synthesis activity (Zeng et al. 2010).

Other studies in Arabidopsis have identified proteins that function in synthesis of the xylan reducing-end oligosaccharide (Fig. 2B), which has been found in several dicots and conifers but not detected in grasses (York et al. 2008; Scheller et al. 2010). The sequence of this reducing end oligosaccharide, or "primer", is 4-p-D-Xylp-(1—>4)-p-D-Xylp-(1—>3)-a- L-Rhap-(1—>2)-a-D-GalpA-(1—>4)-D-Xylp. As summarized in Table 2, the mutants irx7/fra8, irx8, and parvus are depleted for the reducing-end oligosaccharide (Pena et al. 2007). IRX8 and PARVUS, both GT8s, are implicated in adding the galacturonic acid and a a-xylose residue to the primer (Lee et al. 2007; Pena et al. 2007). IRX7, and its close homolog F8H, have been implicated as Rha-specific xylosyltransferases, because they act on a diversity of sugars (Rennie et al. 2012). Despite the absence of the reducing-end primer in experiments in grasses, enzymes with sequence similarity to those implicated in its synthesis have been retained in the rice genome (Scheller et al. 2010).

Other recent work has revealed enzymes that likely function to attach the xylan side chains, glucuronic acid and, in grasses, arabinose. Mortimer et al. (2010) identified mutants in two GT8 family genes, gux1 and gux2. The proteins encoded by these genes are Golgi-localized and required for the addition of both glucuronic acid and 4-O-methylglucuronic acid branches to xylan in Arabidopsis stem cell walls (Mortimer et al. 2010). Recently, another double mutant, irx15 and irx15L, was also found to be involved in xylan synthesis (Brown et al. 2011). These two genes, which belong to the domain of unknown function 579 family, might also be glucuronic acid transferases because they exhibited similar mutant features to gux1 and gux2 (Brown et al. 2011). For addition of the side chains of grass xylan, studies have focused on GT61 family members, which are much more highly expressed in grasses than in dicots (Mitchell et al. 2007). Repression of expression of a GT61 encoding gene, TaXat1, in wheat endosperm and its heterologous overexpression in Arabidopsis provided strong evidence that TaXAT1 possesses a-(1,3)-arabinosyltransferase activity (Anders et al.

2012) . Disruption in rice of another GT61 encoding gene, called Xax1, has also been published recently (Chiniquy et al. 2012). This mutant is lacking a previously observed but poorly characterized xylan substitution, an arabinofuranose residue substituted at the O-2 position of a xylosyl residues, in the structure p-Xylp-(1^-2)-a-Araf-(1^-3) (Fig. 2A). Based on H1-NMR and glycosidic linkage analysis, XAX1 possess a xylosyl transferase activity that can attach (P-1,4-Xylp)4 onto the acceptor p-Xylp-(1^2)-a-Araf-(1^3) (Chiniquy et al. 2012).

Other work has provided important insight into synthesis of the arabinofuranose (Araf) nucleotide sugar precursor of grass cell wall glucuronoarabinoxylan. A recent study of reversibly glycosylated proteins (RGPs) in Arabidopsis showed that the conversion of UDP-p-L — arabinopyranose (UDP-Arap) to UDP-p-L-Araf is indispensable for cell wall synthesis (Rautengarten et al. 2011). The rGps are in the GT75 family. The knockout mutants, rgp1 and rgp2 significantly reduced the total L-Ara content relative to the wild type and showed reduced UDP-Ara mutase (UAM) activity. UAM activity has been identified in rice, as well (Konishi et al. 2007; Konishi et al. 2011). Three rice genes with close sequence similarity to RGP-encoding genes were predicted to be UAM candidates (Konishi et al.

2007) . Later, knock-down of one of these three genes suppressed the UAM activity and reduced UDP-Araf amounts in mutant rice plants (Konishi et al. 2011). The mutant also decreased the incorporation of ferulic acid and p-coumaric acid to the cell wall and presented dwarfed and infertile phenotypes (Konishi et al. 2011). One of the GT75 family members in wheat mentioned above has been inferred to have the UAM activity but needs to be further studied (Zeng et al. 2010).

The final set of recent advances in our understanding of xylan synthesis in grasses relates to acylation of grass xylan arabinose residues by the hydroxycinnamates, ferulic acid and p-coumaric acid (Buanafina 2009). Consistent with expectations, in two studies of rice mentioned above, mutants with reduced Araf substituted xylan had reduced cell wall content of ferulic acid and p-coumaric acid (Konishi et al. 2011; Chiniquy et al. 2012). Mitchell and colleagues developed the hypothesis that 12 members of the BAHD family of acyl-CoA acyltransferases that were much more highly expressed in grasses than dicots might act as arabinofuranose feruloyl transferases (Mitchell et al. 2007). Later, silencing of four members of this family in rice (LOC_Os05g08640, LOC_Os01g09010, LOC_Os06g39470 and LOC_Os06g39390) reduced the ferulic acid content in young leaves by about 20%, leading to the refined hypothesis that one or more of the targeted genes acts as the feruloyl transferase (Piston et al. 2010). Recently, the overexpression of one of the same genes examined by Piston et al., LOC_Os06g39390, dubbed OsAT10, has led to the hypothesis that this protein functions as a p-coumaroyl transferase, as the overexpression plants have increased levels of p-coumaroyl esters bound to arabinose in the cell wall (Bartley et al. 2013a). The plants also exhibit a reduction in the level of polysaccharide-linked ferulic acid and show a concomitant improvement in digestibility (Bartley et al. 2013a), consistent with the model that ferulate — mediated crosslinking is important for grass cell wall digestibility.

Target Traits

Unlike food crops, for which much of the yield gain during the past century was accomplished by minimizing the amount of biomass in the leaves, stalks and roots in favor of grain yield, the opposite strategy is desirable for energy crops, such as switchgrass. To effective increase cellulosic biofuels from switchgrass, two aspects need to be considered. The first goal is the direct elevation of biomass yield. The direct traits related to biomass yield include flowering time, plant height, leaf size and orientation, stem thickness, tiller number and spread, etc. Van Esbroeck et al. (1998) showed there was considerable genetic variation for flowering time in ‘Alamo’ and late flowering types was associated with the production of more leaves due to extended period of vegetative growth. Casler et al. (2004) reported higher biomass yield potential of southern-origin ecotypes (mainly lowland cytotypes) than northern — origin ones (mainly upland cytotypes) was attributed to their later maturity and more rapid stem elongation rate. Das et al. (2004) used 11 lowland switchgrass population and found tiller number per plant had the highest positive direct effect in biomass yield at both test locations over other traits such as tiller length, stem width, node number per tiller, internode length, leaf blade length, and width. Boe and Casler (2005) used six upland cultivars and summarized cultivar differences for biomass production were due to variation at tiller (phytomers tiller -1) and phytomer (weight phytomer -1) level. Boe (2007) further showed reproductive tillers per square meter and seed mass per panicle could be used as accurate predictors of vegetative and seed biomass, respectively, in swards of two cultivars (i. e., ‘Summer’ and ‘Sunburst’). While frequency of reproductive tillers, number of phytomers per tiller, and rate of phytomer could be potential selection criteria for improving biomass yield for switchgrass adapted to the northern Great Plains (Boe 2007). Later, Boe and Beck (2008) used sward plants from ‘Cave-In-Rock’, ‘Nebraska 28’, and ‘Sunburst’, and further demonstrated tiller density, mass per phytomer, and number of phytomers per tiller could be used as indirect selection criteria for improving biomass.

Besides morphological traits, increased stress tolerances (such as drought, disease, pest, etc.) can boost the biomass yield (Gravert and Munkvold 2002). Stroup et al. (2003) examined the differences of two lowland (‘Alamo’ and ‘Kanlow’) and two upland (‘Blackwell’ and ‘Caddo’) switchgrass cultivars in response to water deficit conditions. They found that, although lowland cultivars produced greater biomass yields than upland cultivars, upland cultivars showed a smaller response to drought stress (Stroup et al. 2003). Wullschleger et al. (2010) used 25 upland and 14 lowland switchgrass cultivars, and revealed that water use efficiency (WUE) was higher for lowland than for upland cultivars. WUE was 25.6 for lowland cultivars whereas 16.2 kg ha1 mm1 for upland cultivars. Thus breeding improvement of biomass yield in drought condition is possible by crossing of two switchgrass ecotypes. In other case for abiotic tolerance, Vogel et al. (2002) used 48 half-sib families from a high in vitro dry matter digestibility (IVDMD) population, and found there were significant differences among the families for winter survival. Casler (2012) suggested improving cold resistance is necessary for lowland ecotypes if they will be bred for increased biomass yield at northern latitudes.

Once harvested, plant biomass needs to be broken down to monosaccharides, which are further converted to fuels. In plant cell wall, the major components of carbohydrates are cellulose, hemicellulose and lignin. The cellulose microfibrils are linked via hemicellulose to form the cellulose-hemicellulose network, which is copolymerized with lignin. This complex structure inhibits the saccharification of cell wall polysaccharides by cell wall degrading enzymes (Sarath et al. 2008; Li et al. 2010). Therefore, improving saccharification or conversion efficiency is the other major goal in developing an efficient, cost-effective biofuel switchgrass (Chuck et al. 2011; Youngs and Somerville 2012). Because lignin is the chief limited factors for ethanol production in grasses (Vogel and Jung 2001; Chen and Dixon

2007) , and conversion efficiency of the biomass to ethanol was inversely correlated to lignin content in switchgrass (Dien et al. 2006), low-lignin genotypes are preferred in switchgrass breeding. Casler (2005) used 49 populations including 38 collections from 33 prairie-remnant sites and 11 switchgrss cultivars and evaluated nine variables (including biomass yield, survival, dry matter, lodging, maturity, plant height, holocellulose, lignin and ash) for two years at two locations. They found genotypic variability in lignin content was significant among populations, and the values ranged from 91 to 112 g per kg neutral detergent fiber (NDF) (Casler 2005). Sarath et al. (2011) reported field grown low lignin plants significantly out-yielded high lignin plants for conversion to ethanol by 39.1% and extraction of xylans by 12%.

Besides lignin, there were significant differences among switchgrass cultivars for biomass conversion traits (such as dry matter, in vitro dry matter digestibility, total soluble carbohydrates, starch, crude protein, etc.). Cassida et al. (2005) investigated biofuel components of switchgrass using nine genotypes from four combinations of ecotypes and morphological types at five locations for three years. They found that, compared with upland genotypes across all site-years, lowland genotypes had greater lignocellulose yields, greater removal rates of soil N and P, greater concentrations of moisture and cellulose, and lower concentrations of N and ash. Compared with northern ecotypes, southern ecotypes had greater lignocellulose yields, greater removal rates of soil N and P, greater moisture concentrations, and lower ash concentrations. Vogel et al. (2011) reported ‘Kanlow’ had the highest biomass yield per hectare but lowest simultaneous saccharification and fermentation (SSF) conversion efficiency, comparing with ‘Summer’ ‘Trailblazer’ and ‘Shawnee’. Through breeding improvements, it would be feasible to increase both biomass yield and conversion efficiency (Vogel et al. 2011). Generally, detecting biomass conversion efficiency by classical wet chemistry methods requires expensive equipments, complex fermentation assays, and extensive data analysis. Recently a near-infrared reflectance spectroscopy (NIRS) method was established, and actual and theoretical ethanol yields for switchgrass were quantified, which provided a rapid and economical testing of switchgrass for biomass conversion to biofuel (Vogel et al. 2011; Schmer et al. 2012).