Cellulose hydrolysis requires enzymatic cleavage of p-1,4-glycosidic bonds between D-glucose units. GHs with this function are generally called cellulases, and can be divided into three classes based on their enzymatic activities (Lynd et al. 2002). The known classes of cellulose­degrading enzymes are summarized in Table 3 and illustrated in Fig. 6. Endoglucanases randomly cleave interior glycosidic bonds in cellulose, releasing oligosaccharides of varied lengths with new reducing and non­reducing ends. This function greatly contributes to solubilizing the cellulose polymer by reducing molecular size and creating accessible chain ends for further attack. Cel48F, CelC, Cel7B proteins are typical endoglucanses critical in cellulose degradation in Clostridium cellulolyticum (Perret et al. 2004), C. thermocellum (Wang et al. 1993) and Trichoderma reesei (Kleman-Leyer et al.1996), respectively. RNAi-based repression of Ce148F C. cellulolyticum resulted in a 30% decrease in the activity of the cellulolytic system on microcrystalline cellulose (Perret et al. 2004). In contrast, exoglucanases act from chain ends of cellulose oligosaccharides to processively chip off glucose or cellobiose (di-glucose) units (Lynd et al. 2002). Glucose- and cellobiose-releasing exoglucanases are also called exo-1,4-p-glucosidases and cellobiohydrolases, respectively. p-glucosidases typically split cellobiose dimers, or sometimes cellotrioses, into individual glucose units, thereby releasing the inhibitory effect of accumulated cellobiose on exo — and endo-glucanases (Gruno et al. 2004; Yue et al. 2004). These three classes of cellulases are critical to cellulose degradation and have been applied in different industries (Kuhad et al. 2011). In addition, some bacteria, like C. stercorarium and C. thermocellum, also encode other enzymes that act in cellulose degradation. Cellobiose phosphorylases are able to phosphorylate cellobiose to produce one glucose molecule and another activated glucose- 1-phosphate molecule without using ATP (Alexander 1968; Reichenbecher et al. 1997). Recently, a cellobiose dehydrogenase from Neurospora crassa was found to enhance cellulose degradation by coupling the oxidation of cellobiose to the reductive activation of a copper-dependent polysaccharide monooxygenase (Sygmund et al. 2012). Cellulolytic microorganisms produce a diversity of these enzymes for synergistic catalysis to significantly accelerate cellulose degradation (Doi 2008; Fontes et al. 2010).

The advancement of DNA sequencing technologies and the associated rapid decrease in sample prep and run costs has facilitated routine transcriptional analysis in non-model species, especially those with large genomes, such as switchgrass. Analyzing the transcribed fraction of a genome using Expressed Sequence Tags (ESTs) through RNAseq or Second-generation sequencing technologies allow researchers unprecedented resolution for gene mapping, annotation, functional analysis, discovery, and quantitation (Andersen and Lubberstedt 2003; Lister et al. 2009; Wang et al. 2009; Lu et al. 2010; Kaur et al. 2011). ESTs have been invaluable in the development of molecular markers (Gupta et al. 2003; Varshney et al. 2005; Barbazuk et al. 2007), comparative genomics (Fei et al. 2004; Ribichich et al. 2006; Tobias 2008; Mayer et al. 2011), and the elucidation of important traits (Moralejo et al. 2004; Shen et al. 2006). Without a complete reference genomic sequence, studies focused on unraveling the transcriptional complexity of switchgrass on a genomic scale have just begun to be presented and are still in their infancy. The earliest larger-scale efforts date back to 2005 (Tobias et al. 2005) where authors looked at a brief glimpse of the transcriptional landscape of 4 tissues (stem, callus, crown, and leaf). The authors identified 7,810 tentatively unique genes (TUGs) (Tobias et al. 2005) and focused analysis on gene targets with bioenergy conversion traits. In 2008, Tobias et al. (Tobias

2008) collected 61,585 EST sequences from three different cDNA libraries (crown, seedling, and callus tissue) of an elite high-yielding northern lowland ecotype that shows a high degree of phenotypic variation (Casler et al. 2007) to produce minimally redundant consensus sequences that were used for genome wide comparisons to other grasses, such as sorghum, to identify critical genes and gene families involved in photosynthetic efficiency, stress tolerance, and cell wall structure; as well as an EST-SSR marker set for genetic analysis (Tobias 2008). The sequences were produced using Sanger-style dye terminator sequencing techniques and a clone-based cDNA system. The result of a Sanger-style sequencing endeavor is long, high-quality reads (gold standard) that can be easily mapped to closely related reference genomes. These data were also used to assess the degree of subgenome similarity in this all suspected autopolyploid species, and search for genome duplication signatures (Tobias 2008). A more recent transcriptome study that employs the power of 2nd generation sequencing technologies (i. e., massively parallel sequencing), Roche 454, focuses on four different tissues that are underrepresented in the current EST databases (dormant seeds, germinating seedlings, emerging tillers, and flowers) (Wang et al. 2012). This study produced nearly 1 million pyrosequencing reads (approximately 360 Mbp of sequence) with an average cleaned read length of 367bp that represented the respective switchgrass transcriptome (Wang et al. 2012). A comprehensive dataset of 545,894 switchgrass ESTs obtained from Genbank was combined with this study and assembled using the foxtail millet genome sequence (Zhang et al. 2012) as a guide that resulted in 98,086 switchgrass EST contigs (Wang et al. 2012). The unigene assembly approach used is conservative because of the tetraploid genomic nature and the result is more contigs than traditional unigene assemblies, but intended to reduce erroneous assemblies of homologs, paralogs, and splice variants (Wang et al. 2012). In convergence with other functional studies, the authors focused the analysis on genes associated with C4 photosynthesis, a process that is critical to biomass accumulation, in addition to tillering and dormancy.

Genetic modification of endogenous biochemical and physiological pathways, along with traditional breeding methods, has the ability to improve the lignocellulosic feedstock quantity and quality of switchgrass. In order to decrease reliance on fossil fuels and efficiently utilize the renewable biomass produced by switchgrass, engineering new cultivars with value — added traits must be investigated. Value-added traits include, but are not limited to, enhanced taste, improved nutritional quality, or any features that would provide an additional benefit to consumers. Several value-added traits that are currently being studied include transforming switchgrass to produce bioplastics, as well as introducing cell wall degrading enzymes that will enhance conversion of the lignocellulosic feedstock into bioethanol.

Bioplastics

Bioplastics are currently being considered an alternative choice to petroleum — based polymers (Petrasovits et al. 2012). The most abundant bioplastic is polyhydroxyalanoate (PHA), a polyester that is naturally produced by microbial organisms as a reserve carbon nutrient source (Anderson and Dawes 1990). Polyhydroxybutyrate (PHB) is an extensively studied member of the PHA family that can be thermally altered to produce crotonic acid, a precursor for high demand chemicals such as propylene and butanol (Peterson and Fischer 2010; Coons 2010; Petrasovits et al. 2012).

The first report of PHB expressed in plants was published by Poirier et al. (1992). In this study, acetoacetyl-CoA reductase and PHB synthase, two enzymes from the bacterium Alicaligenes eutrophus, were expressed in Arabidopsis thaliana under the control of the cauliflower mosaic virus 35S promoter (Poirier et al. 1992). These enzymes, along with 3-ketothiolase, are essential in the conversion of acetoacetyl-CoA to PHB (Nawrath et al. 1994). In this experiment, PHB was expressed cytosolically and the plants produced 0.1 percent dry weight of PHB (Poirier et al. 1992). However, the plants displayed stunted growth, suggesting deleterious effects, along with erratic accumulation of PHB in unintended organelles, such as the nucleus and vacuole (Poirier et al. 1992). A couple of years later, Nawrath et al. (1994) expressed all three enzymes necessary for PHB production in Arabidopsis, but included a chloroplast transit peptide to target PHB production to the plastid. These plants were able to accumulate PHB up to 14 percent of their dry weight and displayed normal phenotypes (Nawrath et al. 1994). Collectively, these two studies created a platform for expressing PHB or other bioproducts, such as p-hydroxybenzoate (McQualter et al. 2005) and sorbitol (Chong et al. 2007), in plants.

Since the early 1990s, many studies have focused on engineering PHA-producing pathways into a plethora of crop species including cotton (Maliyakal and Keller 1996), tobacco (Lossl et al. 2003), maize (Poirier and Gruys 2001), sugarcane (Petrasovits et al. 2007), alfalfa (Saruul et al. 2002), and poplar (Dalton et al. 2011). As described earlier, PHB production in switchgrass was also investigated (Somleva et al. 2008).

Despite the occasional negative phenotype, this study has pioneered the way for genetically engineering switchgrass to produce functional multigene pathways. This innovation will ultimately aid in introducing value-added bioproducts in switchgrass that can be manufactured in correlation with biomass production. Further research will be necessary to optimize the expression and output of PHB without compromising plant health and viability. The next series of experiments should focus on optimizing transformation constructs (promoters, cis-acting elements, target peptide signals, etc.) with a target goal of obtaining high levels of PHB synthesis and accumulation in all tissues of the plant.

Cellulosic ethanol production process involves a series of steps including preprocessing of feedstock (transportation, grinding, sieving), pretreatment (chemical or biological to remove lignin), enzyme hydrolysis (to produce fermentable sugars), fermentation (for production of ethanol, biomass) and downstream processing (separation of biomass from product, distillation, purification). The cost of ethanol production using lignocellulosic feedstock is relatively high with lower yield based on the current technologies. The main challenges are with pretreatment, enzyme hydrolysis cost, fermentation and downstream processing of lignocellulosic feedstock.

In addition to considering environmental sustainability, long-term sustainable biofuel production from switchgrass requires that high levels of biomass production be maintained over time. Field trials have shown that switchgrass yields are sensitive to spatial variation in temperature and precipitation (Casler and Boe 2003; Casler et al. 2004). Future climate change may similarly alter the capacity of biofuel production. Therefore, modeling biomass production under future climatic conditions and elevated atmospheric carbon dioxide concentrations is necessary to ensure maintenance of high yields over time without supplemental nutrients and irrigation.

Efforts to estimate future biomass of switchgrass using mechanistic models have been limited. Brown et al. (2000) used EPIC to predict the future yield of swtichgrass in four states (MO, IN, NE, and KS) using future weather data from the NCAR-RegCM2 model. Their results predicted that switchgrass yields would increase in this region by more than 8 Mg ha1. Behrman et al. (2013) used ALMANAC to estimate future yields across the entire central and eastern U. S. for current climate conditions under two climate change scenarios for the ten-year interval from 2080 to 2090 using the CCCMA-CGCM2 model. The A2 scenario was chosen to represent a pessimistic future scenario that predicts a large increase in atmospheric carbon dioxide and large increases in temperature. The B2 scenario was chosen as a "middle of the road" scenario that corresponds to a moderate increase in carbon dioxide levels and a smaller increase in temperature. Similar to Brown et al. (2000) the ALMANAC model also predicts increased yield for MO, IN, NE, KS. Furthermore, regions in Eastern Texas are predicted to have large decreases in yield by 2080s, whereas ND and SD are predicted to have large increases in yield. Modeling of future climate change scenarios predicts warmer minimum temperatures that will shift the USDA Hardiness Zones northward and may make conditions suitable for lowland switchgrass types to thrive further north in upland regions.

Mechanistic models can be used to locate areas that produce relatively high yields over a time. Areas with high long-term potential were determined using yields reported from Behrman et al. (2013) for three climate scenarios. Areas that continually produce yields greater than 10 Mg ha1 for all three climates are labeled as high long-term potential and areas with low long­term potential have high yields for only one of the three climate scenarios (Fig. 1). Assessing the potential of an area to sustain high productivity has the potential benefit of minimizing the amount of land conversion needed to meet production demands. Land-use change increases greenhouse gas emissions and is the primary factor responsible for the loss of biodiversity (Searchinger et al. 2008; Fletcher et al. 2011).

Damaging population thresholds of plant-parasitic nematodes are currently unknown (Mekete et al. 2011). However, based on damage threshold value ranges for other monocotyledon hosts, several US states (e. g., Illinois, Iowa, Kentucky, Tennessee, and Georgia) have potential for to switchgrass yield losses due to plant-parasitic nematodes. Specifically, Helicotylenchus, Xiphinema, Pratylenchus, Hoplolaimus, Tylenchorhynchus, Criconemella, and Longidorus spp. were all found to have population densities within or above the threshold value ranges reported for other monocotyledon hosts (Mekete et al. 2011). Nematodes are most likely to damage either young or stressed plants (Griffin et al. 1996) by feeding on switchgrass roots. In many cases, nematode damage may be mistaken for environmental stress symptoms (Leath et al. 1996). Evers and Butler (2000) found that soil fumigation in Texas improved switchgrass establishment and seedling vigor compared to a weed-free check indicating soil borne pathogens were responsible for poor establishment. Cassida et al. (2005a) observed that in fully established (5 yr) switchgrass stands, nematode numbers were greater in higher rainfall regions such as Louisiana, Arkansas than in drier sites in Texas. They also reported that dry matter yield and persistence of switchgrass were reduced as nematode populations increased.

AM fungi are obligate and must be grown with their host plant. Soil collected or spores isolated from soil can be used as inoculum, which is called a soil trap culture. To create a plant trap culture, plants containing mycorrhizal fungi are collected from the field and are transplanted to a potting medium (sterile soil or soil-sand mixture, which should have low available P and not rich in organic matter) (http: //www. scribd. com/doc/58675784/4-3- Mycorrhiza0403). More details can be found in the International Culture Collection of (Vesicular) Arbuscular Mycorrhizal Fungi (INVAM) (http: // invam. caf. wvu. edu).

Identification

Several methods can be used to identify endophytic bacteria and fungi as well as AM fungi, such as morphological characterization, genomic sequencing, and staining (Zinniel et al. 2002).

Switchgrass is a native widely distributed species in North American continent, from southern provinces (i. e., Saskatchewan, Manitoba, Ontario, Quebec and New Brunswick) of Canada to a majority of the United States (except Washington, Oregon and California), to 15°N latitude in Mexico (Hitchcock and Chase 1950; USDA NRCS 2012). The broad geographic distribution of the species in extremely diverse climatic environments and edaphic conditions dictates its enormous genetic diversity through natural selection and evolution over a long period of time. Currently existing switchgrass germplasm has been classified into two groups: lowland ecotype and upland ecotype on the basis of morphology and habitat preference (Porter 1966). Lowland plants are generally larger than upland ones in most morphological characters (see an example in Fig. 5). Native

lowland plants occur on alluvial soils while native upland plants are found on typically drier and less fertile non-alluvial soils. The lowland switchgrass is largely distributed from the lower south to about 40° N latitudes while the upland ecotype is widely distributed throughout the range of switchgrass adaptation. Huang et al. (2003) reported switchgrass progenitor’s genomes may have been divergent from other species less than 2 million years ago (MYA) and the polyploidization events occurred within 1 MYA.

In recent years, especially after the selection of switchgrass as a model crop for feedstock development, molecular markers have been extensively used to quantify genetic differentiation and variation in switchgrass germplasm (Gunter et al. 1996; Hultquist et al. 1996; Huang et al. 2003; Narasimhamoorthy et al. 2008; Cortese et al. 2010; Todd et al. 2011; Zalapa et al. 2011). Combined use of nuclear DNA markers and chloroplast markers allow grouping switchgrass germplasm into two clusters, which correspond well with lowland and upland ecotypes (Todd et al. 2011; Zalapa et al. 2011). Within ecotypes, a large portion of the genetic diversity has been found to reside within populations and the remaining smaller part to be among populations (Gunter et al. 1996; Narasimhamoorthy et al. 2008; Cortese et al. 2010; Zalapa et al. 2011). Okada et al. (2010a) reported genetic diversity of octoploids is larger than that in tetraploid germplasm due to polysomic inheritance of octoploids. These results are well consistent with and reflect the outcrossing reproduction mechanisms in the species. Using SSR markers, Narasimhamoorthy et al. (2008), Cortese et al. (2010) and Zalapa et al. (2011) demonstrated genetic diversity is related to the geographic locations where the germplasms have been collected. Based on the results of Zalapa et al.

(2011), Casler et al. (2011) proposed the concept of regional gene pools, which can be used in developing complementary heterotic groups for breeding superior cultivars harnessing heterosis in biomass yield.

Although a large number of switchgrass accessions have been collected in public (USDA NRCS, ARS, and universities) and private research programs, it appears more collections are needed to fully sample naturally available genetic diversity. Large amounts of native germplasm are preserved in remnant prairie sites and fragmented habitats scattering across the species range. Individual programs, however, are not generally charged with or funded for collecting and maintaining large collections for a long term. Casler et al. (2011) correctly indicated concerted efforts are essential to coordinate effective collection and to ensure public availability of the germplasm in switchgrass. Full collection, characterization, long term preservation can be best accomplished by agencies like USDA NPGS, which has the dedicated responsibilities.

Hormones are important regulators in plant growth and development, playing pivotal roles not only in regulating plant cell division, elongation, and differentiation, but also in plant organ formation and responses to environmental stresses (Srivastava 2002; Woodward et al. 2005). In Arabidopsis, it was discovered that some transcription factor genes, Auxin Response Factors (ARFs) including ARF10, ARF16, ARF17, ARF6 and ARF8 have complementary sites for miRNAs (Bartel 2003; Wu et al. 2006). ARF10, ARF16 and ARF17 are the targets of miR160, and ARF6 and ARF8 are the targets of miR167 (Bartel 2003; Wu et al. 2006). GAMYB is a target of miR159 (Achard et al. 2004). GAMYB is regulated by GA and coordinates flower formation and floral organ development. miR159, also regulated by GA, is a negative regulator of GAMYB mRNA, adjusting flowering time in short — day photoperiod and anther development (Achard et al. 2004). NAC1 is a target of miR164 (Xie et al. 2002; Guo et al. 2005). The expression of NAC1 is positively regulated by auxin, controlling lateral root development. It was reported that miR164 is also induced by auxin. As it accumulates in response to auxin treatment, the NAC1 mRNAs decrease, exhibiting an auxin-induced, miR164-guided mRNA cleavage process in plants. This process indicates an intricate auto-regulatory loop in auxin signaling transduction pathways controlling lateral root development (Xie et al. 2002; Guo et al. 2005).

MiRNAs are Involved in Plant Biotic and Abiotic Stress Responses

Recent studies have demonstrated that miRNAs play an important role in mediating plant responses to many kinds of biotic and abiotic stresses (Kasschau et al. 2003; Navarro et al. 2006; Lu et al. 2007; Jung and Kang 2007; Pandey and Baldwin 2007; Liu et al. 2008; Li et al. 2010; Zhou et al.

2010) . Increasing evidence indicates that miRNAs are involved in plant response to viruses suggesting their possible role in virus-induced post­transcriptional gene silencing (PTGS) (Kasschau et al. 2003; Li et al. 2010). MiRNAs have also been implicated in plant responses to bacteria, fungi and insects (Navarro et al. 2006; Lu et al. 2007; Pandey and Baldwin 2007). Recently, it has been found that several miRNA families, such as miR1507, miR2109, miR482/2118, miR6019, and miR6020, target genes that encode nucleotide binding site-leucine rich repeat (NBS-LRR) plant innate immune receptors (Zhai et al. 2011; Li et al. 2012; Shivaprasad et al. 2012).

Plants have developed multiple mechanisms and strategies to cope with various abiotic stresses, in which miRNAs play an important role. MiRNAs can be induced by abiotic stresses, either up-regulated or down-regulated (Sunkar and Zhu 2004; Jung and Kang 2007; Sunkar et al. 2007; Liu et al. 2008; Zhou et al. 2010). For example, miR319 was up-regulated by cold stress (Sunkar and Zhu 2004); miR398 was down-regulated by oxidative stress (Sunkar et al. 2006); and miR171 was up-regulated by cold, drought and salinity stresses respectively (Liu et al. 2008). Sunkar et al. (2007) found that miR393 was up-regulated in response to cold stress, leading to the down-regulation of its targets encoding E3 ubiquitin ligase, which could degrade its target protein including some positive regulators for adapting cold stress. Thus, the up-regulation of miR393 positively contributed to plant response to cold stress.

Biomass generated syngas (or producer gas) contains many impurities, which must be removed through conditioning to a level acceptable to downstream applications for fuels, chemicals and power production. H2/CO ratio and compositions in the biomass-generated syngas are also generally lower than required for the syngas conversion into fuels and chemicals. The improvement in gas composition and reduction in impurities are accomplished through conditioning of syngas.