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a University of Florida Genetics Institute and Agronomy Dep., Gainesville, FL 32610; Dep. of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907; Laboratory of Renewable Resources Engineering, Purdue University, West Lafayette, IN 47907
b Dep. of Agronomy, Purdue University, West Lafayette, IN 47907
c Dep. of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907; Laboratory of Renewable Resources Engineering, Purdue University, West Lafayette, IN 47907
d Dep. of Botany and Plant Pathology, Purdue University, West Lafayette, IN 47907
* Corresponding author (wev{at}ufl.edu).
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONConclusionREFERENCES |
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Abbreviations: LAP, laboratory analytical procedure • NREL, National Renewable Energy Laboratory • NIR, near infrared reflectance • PC, principal component • Py-GC-MS, pyrolysis–gas chromatography–mass spectrometry
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ACKNOWLEDGMENTS |
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Received for publication April 7, 2007.
a University of Florida Genetics Institute and Agronomy Dep., Gainesville, FL 32610; Dep. of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907; Laboratory of Renewable Resources Engineering, Purdue University, West Lafayette, IN 47907
b Dep. of Agronomy, Purdue University, West Lafayette, IN 47907
c Dep. of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907; Laboratory of Renewable Resources Engineering, Purdue University, West Lafayette, IN 47907
d Dep. of Botany and Plant Pathology, Purdue University, West Lafayette, IN 47907
* Corresponding author (wev{at}ufl.edu).
Political and environmental concerns have resulted in a growing interest in renewable energy, especially transportation fuels. In the United States the majority of fuel ethanol is currently produced from corn (Zea mays L.) starch, but grain supplies will be insufficient to meet anticipated demands. Enzymatic hydrolysis of lignocellulosic biomass such as corn and sorghum [Sorghum bicolor (L.) Moench] stover can provide an abundant alternative source of fermentable sugars. While production of cellulosic ethanol from stover is feasible from an energy-balance perspective, its production is currently not economically competitive. Along with improvements in bioprocessing, enhancing the yield and composition of the biomass has the potential to make ethanol production considerably more cost effective. This requires (i) a better understanding of how cell wall composition and structure affect the efficiency of enzymatic hydrolysis, (ii) the development of traits that enhance biomass conversion efficiency and increase biomass yield, and (iii) the development of rapid screening protocols to evaluate biomass conversion efficiency. Several genetic resources are available to improve maize and sorghum as sources of lignocellulosic biomass. This includes the use of existing mutants, forward and reverse genetics to obtain novel mutants, and transgenic approaches in which the expression of genes of interest is modified. Plant breeding can be implemented to improve biomass yield, biomass quality, and biomass conversion efficiency, either through selection among progeny obtained by crossing parents with desirable traits, or as a way to enhance the agronomic performance of promising mutants and transgenics. Examples from current research will be used to illustrate progress in these different areas.
Abbreviations: LAP, laboratory analytical procedure • NREL, National Renewable Energy Laboratory • NIR, near infrared reflectance • PC, principal component • Py-GC-MS, pyrolysis–gas chromatography–mass spectrometry
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONConclusionREFERENCES |
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Renewable energy refers to sources of energy that do not irreversibly exhaust and deplete the source. It includes wind and solar energy and bio-based fuels such as ethanol, biodiesel, and hydrogen. Wind and solar energy will be primarily of use in generating electricity for households and industry, whereas ethanol, biodiesel, and hydrogen can be used as transportation fuel. While hydrogen is considered the ultimate green fuel, the technology and infrastructure to power cars on hydrogen are still in their infancy. In the short term ethanol and biodiesel are considered the most promising alternative sources of fuel. Biodiesel is produced from plant-based oils and fat, either produced directly from oil-containing seeds {e.g., soybean [Glycine max (L.) Merr.], sunflower [Helianthus annuus L.], canola [Brassica napus L.]} or from waste products of the food industry, such as oil used for the production of deep-fried foods. As the name implies, biodiesel can only be used in diesel engines. Ethanol can be used as a substitute for gasoline, as long as so-called flexible fuel vehicles, now offered by many car manufacturers, are used. The focus here will be on the production of ethanol from plants.
The majority of ethanol in the United States is produced from corn (Zea mays L.) starch, through wet- or dry-milling processes. In both cases corn starch is enzymatically hydrolyzed to fermentable sugars that are fed to yeast in large fermenters. Pure (95%) ethanol is then obtained via distillation. In 2006 the production of ethanol in the United States was 18.4 x 109 L. Global production of ethanol is increasing. Brazil is the other large producer of ethanol (18.2 x 109 L in 2006), primarily derived from sugarcane. Brazil has sustained production of ethanol for fuel since the oil crisis in the 1970s, realizing energy self-sufficiency in 2005.
The U.S. government has set as a target that 30% of the transportation fuel used in the United States has to come from renewable resources by the year 2030 (US DOE, 2006). Given that the annual consumption of gasoline in the United States is currently 542 x 109 L per year and still rising, this will require drastic increases in the total production of ethanol from both grain and plant biomass. Lignocellulosic biomass such as stover (the residues remaining on the field after grain harvest) from corn and sorghum [Sorghum bicolor (L.) Moench]; straw from wheat (Triticum aestivum L.); bagasse (the residues remaining after extraction of the sugar-rich juice) from sugarcane (Saccharum spp.); switchgrass (Panicum virgatum L.), elephant grass (Miscanthus x giganteus), and trees such as poplar (Populus spp.), eucalyptus (Eucalyptus spp.), willow (Salix spp.), and spruce (Picea abies L.) offer an abundant and inexpensive source of fermentable sugars. In this case the sugars are obtained from cell wall polysaccharides—cellulose and hemicellulose—through hydrolysis with (hemi)cellulose-degrading enzymes. This process is referred to as enzymatic saccharification. The monosaccharides thus obtained are fed to microorganisms in fermenters similar to the systems used with starch- or sugarcane-derived sugars.
Although the energy balance of the stover-to-ethanol process has been debated (Pimentel and Patzek, 2005), technical and economic analyses have shown that the production of ethanol from lignocellulosics results in a net gain of energy (Shapouri et al., 2002; Shapouri and McAloon, 2004), and that compared to gasoline and ethanol derived from starch, ethanol produced from lignocellulosic biomass is projected to have the smallest contribution to the emission of CO2 and the largest net energy production (Farrell et al., 2006).
Nevertheless, the production of ethanol from lignocellulosic biomass will need to be considerably more cost effective than is possible with the current technologies before fuel ethanol is economically competitive. Improvements to make this process economically viable are necessary on all fronts, as outlined by Ragauskas et al. (2006). Cost-effective and efficient means of transportation and storage of the biomass need to be developed (Kumar et al., 2004). Also needed is the development of efficient and cost-effective pretreatment strategies. Pretreatment is a process during which the stover is subjected to chemical and/or physical agents with the aim of improving the rate and the extent of cellulose hydrolysis. This is achieved by an overall loosening of the cell wall structure, solubilization of hemicellulose in the cell walls of the stover, and reduction in the crystallinity of the cellulose, while minimizing the formation of degradation products that could interfere with the microorganisms used during fermentation. Pretreatment can consist of acid- or base-catalyzed hydrolysis, steam explosion, ammonia fiber explosion, and liquid hot water pretreatment (Yang and Wyman, 2004; Mosier et al., 2005a, 2005b). Equally important innovations will be needed in the development of more efficient and cheaper cellulolytic enzymes (Escovar-Kousen et al., 2004) as well as yeast strains that can coferment hexose and pentose sugars (Ho et al., 1998). More efficient recovery of ethanol from the fermentation broth (O'Brien and Craig, 1996) at the fermentation plant, and production of valuable by-products that can off-set some of the cost associated with the production of ethanol will likely be developed as technology advances. One area that offers tremendous potential, but that has not been explored very much, is the development of crop plants with enhanced bioprocessing characteristics. This paper describes critical elements required for successful exploitation of plant biomass and presents alternative genetic strategies drawn from an array of collaborative research projects currently underway.
Essentials for Successful Exploitation of Plant Biomass for Fuel
Understanding Physical and Chemical Properties of the Plant Cell Wall
Stover, straw, bagasse, and wood have in common that they consist largely of cell walls. The plant cell wall is a complex structure in which cellulose microfibrils are embedded in a matrix of hemicellulose, pectin, cell wall proteins, and phenolic compounds. Carpita and Gibeaut (1993) defined two types of cell walls. The Type I wall is common in the dicots and the noncommelinoid monocots, and contains a relatively high proportion of pectin and protein, and a hemicellulose fraction consisting of (substituted) xyloglucans. In contrast, the Type II wall of the commelinoid monocots, which include the grasses, contains relatively low amounts of pectin and protein, and a hemicellulose fraction consisting primarily of glucurono-arabinoxylans. Thus, the principal monosaccharides released from the cell walls of grasses are glucose, xylose, and arabinose. Arabinose levels decline markedly during plant maturity, leaving glucose and xylose as the major sugars. In addition, the Type II cell wall contains substantial amounts of the hydroxycinnamic acids ferulic acid and p-coumaric acid that can cross-link glucurono-arabinoxylans and lignin in both primary and secondary walls.
All plant cells have a primary cell wall, with secondary cell walls limited to specialized parts such as the vascular tissue (needed for water transport) and support tissue. Compared to primary cell walls, secondary cell walls are generally rich in lignin, a complex hydrophobic polymer that plays a significant biological role in providing rigidity and offering defense against pests and pathogens. The hydrophobic nature of lignin facilitates water transport through the vasculature. The lignin monomers—p-coumaryl alcohol, coniferyl alcohol, and sinapyl alcohol—are synthesized via the concerted action of the shikimic acid and phenylpropanoid pathways, and polymerize via an oxidative coupling mechanism (reviewed by Hatfield and Vermerris, 2001; Ralph et al., 2004), giving rise in the lignin to p-hydroxyphenyl, guaiacyl, and syringyl residues, respectively. The structural genes and some regulatory genes involved in lignin biosynthesis have been identified in a number of species, notably Arabidopsis (Raes et al., 2003; Rogers and Campbell, 2004; Rogers et al., 2005).
The complex heterogeneous structure of the plant cell wall can influence the ability of cellulolytic enzyme complexes to digest plant biomass to fermentable sugars. Lignin in particular appears to be very significant. Chang and Holtzapple (2000) identified lignin removal as the dominant factor improving enzyme digestibility. Draude et al. (2001) showed that a removal of 67% of the lignin from softwood pulp resulted in a nearly threefold increase in the yield of reducing sugars, an 88% increase in the ultimate yield of glucose, and a twofold increase in the initial hydrolysis rate (over the first hour of hydrolysis). Similar results were reproduced in softwood pulps (Charles et al., 2003) as well as in corn stover (Yang and Wyman, 2004). Lignin appears to have two primary effects on the enzymatic hydrolysis of cellulose within this matrix: it prohibits cellulose fiber swelling, which reduces surface area access to the enzyme (Mooney et al., 1998), and cellulases irreversibly adsorb to lignin, thus preventing their action on the cellulose (Chernoglazov et al., 1988; Converse, 1993; Palonen et al., 2004). This "titration" effect necessitates the use of more enzymes to saturate these nonproductive adsorption sites on the surface of the biomass. This leads to prohibitively high enzyme costs for processing purposes.
Genetic Modification and Selection of Plant Germplasm Resources
To render it more amenable to bioprocessing, modification of plant biomass through the application of genetic, genomic, and plant breeding approaches is very likely to pay great dividends. With current genomic technology, both intraspecific and interspecific variations can be readily exploited to develop crops with a suite of desirable physical and chemical attributes for high biofuel yield and increased biomass production. Genetic enhancement of plants for the production of renewable resources can be achieved through (i) altering plant biochemical composition to improve the processing characteristics, (ii) modifying of plant stature and architecture, (iii) improving resistance to biotic and abiotic challenges, and (iv) making potential overall biomass yield a target for crop improvement.
Below follows a summary of genetic strategies that can be implemented to improve corn and sorghum specifically for the production of biomass for energy production. A focus on corn is warranted because over 37 million hectares of corn are grown throughout the United States and the first lignocellulosic substrate for large-scale fuel ethanol production will likely be corn stover. The majority of the corn grown in the United States, however, is based on hybrid cultivars intended for grain production, and stover composition was not one of the selection criteria during the breeding process. Consequently, hybrid corn stover does not necessarily have a composition that is optimal for biomass conversion purposes, and this offers major opportunities for plant breeding, whether conventional or through the application of transgenic approaches.
We also focus on sorghum because it has several inherent attributes that make it a highly promising, yet often overlooked biomass crop. Sorghum possesses great genetic diversity for high biomass production, and has a high tolerance to abiotic stresses such as drought and heat. Sorghum tends to arrest growth during periods of drought and grows rapidly when water is available, avoiding yield losses. This is in part due to an extensive root system that can penetrate 1.5 to 2.5 m into the soil and extend 1 m away from the stem. In contrast, corn roots typically extend only 0.8 m into the soil and extend 0.5 from the stalk (Pellerin and Pagès, 1996). Thus, the large amount of sorghum root material contributes to the build-up of soil organic C after removal of the aerial parts of the plant, and would thus alleviate concerns about depletion of soil organic matter resulting from the removal of stover (Wilhelm et al., 2004). Furthermore, sorghum requires less fertilizer than corn to achieve high yield (Lipinsky and Kresovich, 1980) and can tolerate a wider range of soil conditions, from heavy clay soils to light sand, with a pH from 5.0 to 8.5 (Smith and Frederiksen, 2000), and dry as well as poorly drained soils. This makes it suitable for cultivation as a crop in optimal conditions, as well as in marginal lands where other crops cannot be produced. Finally, cultural practices for corn and sorghum are similar, thus requiring only limited adaptations on the part of the producer.
The genetic strategies available to enhance biomass conversion properties of corn and sorghum are outlined in a general sense, and will be illustrated with some specific examples.
Development of Rapid Screening Protocols
Plant breeding is based on selection of recombinants derived from parents with largely nonoverlapping desirable traits. To improve bioprocessing characteristics, a selection method needs to be available. The National Renewable Energy Laboratory (NREL) in Golden, CO, has developed a series of laboratory analytical procedures (LAPs) for the determination of biomass quality and biomass conversion properties. These methods are available via NREL's Internet site (http://www.nrel.gov/biomass/analytical_procedures.html). LAP009 is used to determine biomass conversion properties. This assay is based on hydrolysis of 300 mg dried and ground stover (the equivalent of 100 mg cellulose) with 6 filter paper units of a cocktail of commercially available cellulolytic enzymes. The assay is performed at 50°C for an incubation period of 72 to 168 h (3–7 d), followed by quantification of the glucose yield with a urine analyzer. The urine analyzer is a device used in medical settings and can be replaced by the more universal high performance liquid chromatography. To save time, the incubation time can be reduced to 24 to 48 h, as long as the same incubation time is used consistently among different samples.
In the context of selecting for biomass conversion properties, it will be necessary to evaluate large numbers of stover samples, first to select suitable parents, and then to select desirable offspring in subsequent generations. Consequently, a fast and inexpensive screening method would be of interest to the breeder, and ideally one that does not involve the need for large capital investments. One modification of the NREL protocol described above could be a microtiter plate–based assay. This has the advantage of being small-scale, and amenable to automated sample analysis. The preparation of the stover sample can be challenging when such small sample cups are used, especially given that dry stover is somewhat static. An alternative screening protocol was developed based on commercially available blood glucose meters (FitzGerald and Vermerris, 2005). Blood glucose meters rely on a colorimetric or electrochemical reaction in which glucose is converted by enzymes immobilized on small sample strips. The cost of a glucose meter is approximately US$60, and the cost per sample strip is approximately US$0.80. Our initial evaluations indicated significant variation among different brands of glucose meters, with variation in specificity and dynamic range (FitzGerald and Vermerris, 2005). Figure 1 shows that the OneTouch UltraSmart blood glucose meter (LifeScan, Milpitas, CA), which is specific for glucose, also has a large dynamic range. A similarly convenient assay for the determination of xylose concentration in stover hydrolysates has yet to be developed.
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Analytical pyrolysis relies on the thermal degradation of the sample at temperatures above 500°C under anoxic conditions. When stover is subjected to pyrolysis, a vapor is generated that contains fragments representative of the different cell wall constituents. Lignin and hydroxycinnamic acids retain the substitution pattern on the benzene ring, so that the origin (p-hydroxyphenyl, guaiacyl, and syringyl residues; p-coumarate; ferulate) can easily be ascertained. In contrast, the breakdown products from cell wall polysaccharides tend to undergo rearrangements, so that their exact origin is often hard to establish, although hexoses and pentoses can typically be identified easily (Boon, 1989; Ralph and Hatfield, 1991). While pyrolysis–gas chromatography–mass spectrometry (Py-GC-MS), whereby the pyrolysis fragments are first separated on a GC column and then identified via mass spectrometry, has the advantage of being very informative, the analysis time per sample (>45 min) tends to limit its use as a screening tool. In contrast, pyrolysis–mass spectrometry (Py-MS), coupled with multivariate statistical analysis of the resultant mass spectra is a promising technique, especially when used in combination with an autosampler. A recent development in Py-MS is the use of metastable atom bombardment. Compared to the more traditional electron impact ionization to ionize the pyrolysis fragments, metastable atom bombardment offers softer ionization conditions, and as a consequence, reduced fragmentation (Boutin et al., 2004; Wilkes et al., 2005).
Near infrared reflectance spectroscopy is a vibrational spectroscopic technique in which the reflectance (R) of light in the near-infrared range of the spectrum (800–2500 nm) is quantified. Absorbance [defined as log (1/R)] of light of a given wavelength confers information on the chemical composition of the sample. In order for a chemical bond to absorb energy in the infrared region of the spectrum, the light energy (calculated by multiplying Planck's constant [h] with the speed of light, and dividing the product by the wavelength [
]) has to match the energy associated with bend or stretch vibrations in molecular bonds, and in addition, these vibrations have to result in a change in the dipole moment (Siesler et al., 2002). The interpretation of NIR spectra is not unambiguous, since multiple chemical moieties can absorb light in the same region of the spectrum. An NIR spectrum can be divided in different regions, referred to as the first, second, and third overtone regions, and a combination region (Fig. 2
). The term overtone is used here in the same manner as in harmonic vibrations. While different chemical moieties may absorb light of the same energy (wavelength) in a specific region of the spectrum, most of the time it is possible to distinguish the different moieties based on absorbances in a different region of the spectrum, representative of a different overtone. A major advantage of NIR spectroscopy as a screening tool is that spectral acquisition is fast and easy. The challenge of this technique is the interpretation of the data. The most common strategy is to develop a calibration in which spectral features that are correlated with the parameter of interest are identified through multivariate statistical techniques, such as principal component analysis. This requires analysis of a training set that is analyzed both with NIR spectroscopy and, in this case, enzymatic saccharification. After the model has been developed, the model needs to be tested with a set of samples that has also been analyzed by both techniques, but that were not part of the model development. If the model performs adequately (for example, >80% correct prediction), unknown samples can be analyzed and their biomass conversion potential can be analyzed. Many plant breeders will be familiar with this approach when selecting for forage quality or seed composition (cf. Jung et al., 1998).
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Genetic Strategies to Improve Biomass Conversion Properties in Corn and Sorghum
Several different genetic strategies can be employed to improve biomass conversion properties. The choice of the method depends in part on the trait of interest or the biochemical process that is being targeted, as well as the plant species employed. Biomass quality is heavily influenced by cell wall composition and structure (determined by content and composition of lignin, cellulose, hemicellulose, and the way they are cross-linked), whereas biomass yield is determined by agronomic traits such as plant height, stalk diameter, number of leaves, disease and pest resistance, and lodging susceptibility and can be manipulated through crop management practices.
Characterization of Existing Mutants
The number of existing mutants that can be explored is relatively small. The most promising class of mutants are the brown midrib mutants in which cell wall composition is altered. These mutants are available in both maize and sorghum and are easily recognized by the reddish-brown coloration of the vascular tissue in the leaf blade and sheath. The genetic mutations are designated as bm in maize and bmr in sorghum. There are four independent maize mutants, all spontaneous, that were discovered as early as 1924. The cell wall composition of the maize bm mutants has been characterized in detail (Chabbert et al., 1994a, 1994b; Marita et al., 2003; Barrière et al., 2004). The Bm3 gene was shown to encode the enzyme caffeic acid O-methyltransferase (COMT) (Vignols et al., 1995), which is more accurately referred to as 5-hydroxyconiferaldehyde/5-hydroxyconiferyl alcohol O-methyltransferase (Humphreys et al., 1999). The bm1 mutation has been shown to affect the lignin biosynthetic enzyme cinnamyl alcohol dehydrogenase (CAD), but it is not clear whether the mutation is actually in the CAD gene (Halpin et al., 1998). The lignin of the bm2 mutant contains fewer guaiacyl and syringyl residues and the developmental gradient in lignin content that is normally present is essentially lacking in this mutant (Vermerris and Boon, 2001). The chemical composition of the bm4 mutant resembles that of the bm2 mutant (Barrière et al., 2004).
Brown midrib (bmr) mutants of sorghum were first developed at Purdue University via chemical mutagenesis (Porter et al., 1978). Since then, additional spontaneous brown midrib mutants have been identified (Vogler et al., 1994). Both groups of bmr mutants, numbered consecutively 1 though 28, show altered cell wall composition, particularly relative to lignin subunit composition, and some have superior forage quality. Allelism tests were performed to establish the number of independent loci (Bittinger et al., 1981), but these tests did not include the spontaneous mutants and also contained some ambiguities, presumably resulting from accidental self-pollination. The allelism tests have been redone for the entire collection with male-sterile testers and the results will be published separately.
The molecular basis of the sorghum bmr mutations is largely unknown. Bout and Vermerris (2003) reported that the bmr12, bmr18, and bmr26 alleles contained mutations that created premature stop codons in the COMT gene, after chemical analyses of the cell walls indicated that their composition was similar to the cell wall of the maize bm3 mutant. Allele-specific molecular markers were developed to aid breeding efforts with these mutants. Efforts are currently underway to establish the genetic basis of the other bmr mutations. The implementation of a candidate gene approach (Pflieger et al., 2001) is becoming more feasible now that the genomes of rice (Oryza sativa L.) and Arabidopsis have been sequenced. A preliminary assembly of the sorghum genome has been released in the spring of 2007 (http://www.phytozome.net/sorghum; verified 13 Nov. 2007), and the fully annotated sequence is expected in the near future (Kresovich et al., 2005).
Preliminary experiments with enzymatic saccharification of stover obtained from maize and sorghum near-isogenic brown midrib mutants and the corresponding wild-type controls revealed increases in the yield of fermentable sugars as a result of certain mutations. Hydrolysis of stover from the maize mutants bm1 and bm3 in an inbred A619 background resulted in 144 ± 21 and 152 ± 20 mg glucose g–1 stover (dry weight), respectively. This represents an average 40 to 50% increase relative to stover from the wild-type control, which yielded 98 ± 9 mg g–1. In contrast, glucose yields obtained after hydrolysis of bm2 and bm4 stover did not differ from the wild-type. In the case of sorghum, enzymatic saccharification of stover from the bmr12 mutant resulted in 76 mg glucose g–1 stover, whereas the wild-type control (same genetic background) yielded 60 mg g–1. The percent increase in xylose was even higher, 14 mg g–1 from bmr12 stover, versus 8 mg g–1 from the wild-type. Thus, the combined maize and sorghum data suggest that biomass conversion efficiencies can improve significantly by making specific modifications to the lignin content and lignin subunit composition.
Employment of Reverse Genetics to Generate New Variants
Reverse genetics can be used when there is good reason to believe that the inactivation of a specific gene will lead to enhanced biomass conversion characteristics. The use of reverse genetics requires a sizeable population of mutants generated with transposable elements. Such populations exist in maize, but not (yet) in sorghum. There are four maize populations available for reverse genetics: at Pioneer Hi-Bred (a DuPont company; Johnston, IA), the University of Florida, Stanford University, and Cold Spring Harbor. The Pioneer system is known as Trait Utility System for Corn (TUSC) and may be accessed for research purposes through a collaborative arrangement. After identification of a Mutator insertion in the target gene, F2 progeny is supplied in which the mutation is typically segregating as a Mendelian recessive trait. The genetic background of the TUSC population is mixed, so that there can be considerable phenotypic variation within the F2 family, often necessitating some backcrosses to establish the phenotype associated with the mutation. The population developed at the University of Florida is called the UniformMu population (www.uniformmu.org). As the name implies, it was generated by backcrossing a Mutator-active line at least seven times with inbred line W22 to ensure uniformity in the genetic background. This makes it possible to attribute phenotypic variation to novel mutations. The development of the UniformMu population has enabled two National Science Foundation–funded genome projects, one focused on endosperm development (http://currant.hos.ufl.edu/mutail/mutant.htm; verified 13 Nov. 2007) and one on cell walls (http://cellwall.genomics.purdue.edu; verified 13 Nov. 2007). The Cold Spring Harbor collection is referred to as the Maize Targeted Mutagenesis Database (http://mtm.cshl.org; verified 13 Nov. 2007) and is also based on insertional mutagenesis by Mutator elements. The Stanford population (www.mutransposon.org/project/RescueMu; verified 13 Nov. 2007) is based on the RescueMu element, which is a genetically engineered Mutator element that allows facile cloning through the use of plasmid rescue. The availability of the genome sequence of rice and Arabidopsis has made these populations even more valuable. Sequence data generated from the three populations developed at the academic institutions can be accessed via the Internet. This allows one to determine if there is a Mutator insertion in a gene of interest. Seed from the corresponding mutant can then be obtained from the Maize Genetics Cooperation Stock Center (http://maizecoop.cropsci.uiuc.edu; verified 13 Nov. 2007) or through an arrangement with the respective investigators.
The cell wall genomics project has resulted in the identification of a set of mutants in which cell wall biogenesis-related genes are disrupted by a Mutator element. This is an ideal way to dissect the role of individual members of gene families.
Generating Novel Mutants via a Variety of Approaches
The identification of novel mutants with improved biomass conversion properties will require the generation of a sizeable mutant population and an efficient screening protocol. The generation of mutants can be accomplished with established methods, including chemical mutagenesis, radiation, or transposable elements. As discussed above, there is no established screening protocol to evaluate biomass conversion properties. The options include enzymatic saccharification assay, development of an NIR calibration model, or some other (high-throughput) chemical analysis.
Given that cell wall composition is a major determinant of biomass conversion efficiency, novel cell wall mutants can potentially be used for breeding purposes. One of the goals of the multi-institutional cell wall genomics project (http://cellwall.genomics.purdue.edu) is the identification of novel maize mutants with altered cell wall composition by screening the UniformMu population with NIR spectroscopy (Yong et al., 2005). Since the mutations are the result of Mutator insertions, it is relatively easy to obtain the sequence of the gene underlying the mutant phenotype. To identify cell wall mutants, 2200 F2 families (
40,000 plants tagged with weatherproof barcodes) were screened. The central 5- to 8-cm-long section of the blade of the fifth mature leaf of 6- to 8-wk-old plants was removed with scissors, placed in glassine envelopes, and dried for at least 2 d in a forced-air dryer set at 45 to 50°C. To accommodate environmental and soil variation in the field, W22 controls were planted along the sides and through the middle of the field. Spectral acquisition appeared to be very sensitive to operator effects. To minimize operator effects in the subsequent analysis, each operator included the leaves from the W22 controls that were in the same range of the field as the F2 families under investigation. The NIR spectra were acquired using a FieldSpec Pro NIR spectrometer (Analytical Spectral Devices, Inc., Boulder, CO). We used a handheld contact probe. Each leaf sample was placed between a piece of quartz glass and a GoreTex disk. The same GoreTex disk also served as the white reference (=100% reflectance).
The cell wall genomics group at Purdue University has shown the use of discriminant analysis of Fourier transform infrared spectra as a high-throughput screen for cell wall mutants (Chen et al., 1998). The objective of the data analysis was to modify these algorithms to identify mutants based on their altered NIR spectra. This required a multivariate statistical analysis of 20,000 spectra per year. The WinDAS software package (Kemsley, 1998) was selected for the data analysis because of well-documented procedures and ease of use. This required, however, the development of a custom-designed conversion computer program that could convert the spectral data into a format that WinDAS accepted. After importing the spectra, the region between 1000 and 2400 nm was selected, the spectra were baseline corrected, and then area-normalized to reduce noise (Fig. 2). Given that there is a large degree of correlation between the individual data points, a data reduction was performed. We selected 3 to 5 principal components (PCs), capturing 90 to 95% of the variance present in the original spectra to build a class model for the W22 wild-type controls from the same area of the field as the putative mutants of interest. The spectra of putative mutants were then evaluated against this class model of the wild-type. An F2 family in which there were no mutations affecting cell wall composition will produce NIR spectra that are indistinguishable from the W22 control, whereas spectra from cell wall mutants are expected to be different. The statistical analysis is based on 
2 statistics (Kemsley, 1998), and the WinDAS software displays the decision as to whether an individual spectrum is consistent with the W22 control spectra or not. An additional constraint, based on the assumption that the vast majority of the mutations would be segregating in a Mendelian recessive manner, was placed on the decision to consider spectra representative of a putative cell wall mutant: there had to be at least two outlier spectra (out of 15 to 20 spectra per family) that were different from W22 but similar to each other. To evaluate this, we generated PC score plots displaying the spectra from the putative mutants. In the ideal case, several outlier spectra consistently clustered together along all axes of the PC score plot (Fig. 3
).
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Assessing Transgenic Approaches
Transgenic strategies can be applied to both maize and sorghum. The gene of interest can be up-regulated via introduction of a transgene under control of a strong promoter. Alternatively, the gene can be downregulated or knocked out through the use of cosuppression, the introduction of an antisense construct, or the introduction of an RNA interference construct. In the latter case, two short fragments of the target gene are cloned in opposite orientation from each other separated by a spacer sequence. Transcription of such a transgene results in the formation of a dsRNA molecule that is targeted for destruction. As a consequence of this, the native mRNA from the target gene is also degraded (reviewed by Baulcombe, 2004).
The use of transgenic strategies also offers opportunities to introduce novel genes in the plant's genome. In the context of biomass processing, genes encoding various hydrolytic enzymes may be of interest. Given the expense of the cellulolytic enzymes, production of cellulases in planta may offer opportunities to generate enzymes at a reduced cost. The production of enzymes would have to withstand processing conditions involving high temperatures and/or high or low pH. Enzymes that degrade the plant cell wall at the end of the season may also improve biomass conversion efficiency. An example is the introduction of a fungal ferulic acid esterase that accumulates in the vacuole of transgenic Italian ryegrass (Lolium multiflorum Lam.) plants and that significantly enhanced cell wall digestibility in mature leaves (Buanafina et al., 2006).
The implementation of transgenic strategies requires appropriate facilities and equipment, permits for testing, and ultimately approval by government authorities. The use of transgenic approaches to enhance biomass conversion properties may encounter less resistance from the general public than has been seen, as long as any grain from these plants does not enter the food chain.
Increasing Efficiency of Plant Breeding in Product Development
Plant breeding strategies can be implemented to improve biomass yield, biomass quality, and biomass conversion efficiency. This includes conventional selection of progeny with desired traits following cross-fertilization of two or more parent lines, and the use of new molecular tools to increase efficiency of selection for traits of interest. Since plant traits that enhance quality parameters are often linked with undesirable agronomic characteristics (Pedersen et al., 2005), newly derived mutants or transgenics can be selected for improved bioprocessing characteristics or to enhance desired agronomic traits that include optimal height, maturity, stalk strength, as well as resistance to diseases and pests.
Compared to maize, sorghum has a wider genetic base of readily available traits, allowing for the development of some truly promising lines for bioenergy production. Traits of interest include high-biomass producing lines (plants that are up to 6 m tall), and the sweet sorghum, staygreen, and brown midrib traits. Sweet sorghums contain phloem sap with elevated levels of sucrose and/or reducing sugars, similar to sugarcane. In some parts of the world sweet sorghums are used in a manner similar to sugarcane, whereby the phloem sap (the "juice") is collected and used as a source of sugar. Sweet sorghums were commonly used in the United States to produce molasses, and this still occurs on a small scale in the southeastern United States. Sweet sorghums have also been of interest as a source of silage due to their high energy. Although grain sorghum is cultivated as an annual crop, there is significant genetic variation for residual perenniality in the sorghum crop, resulting in the so-called staygreen trait. This trait confers higher-than-normal levels of photosynthate in the stalk and offers drought tolerance and stalk strength. Thus, this is a favorable trait for stress tolerance as well as for enhanced biomass quality (Tuinstra et al., 1997).
We are in the process of combining these traits to develop sorghum germplasm with enhanced biomass conversion properties. The availability of sweet sorghum and brown midrib traits introduced in a high biomass, staygreen sorghum germplasm background are expected to significantly enhance the yield, quality, and bioprocessing efficiency of sorghum stover. The availability of molecular markers associated with the bmr mutations is expected to expedite the selection.
In maize we initiated a small-scale "proof-of-concept" breeding effort aimed at developing new inbred lines for biomass production. A number of F2 populations were generated by crossing several different standard inbred lines. The most successful inbred lines were F8 progeny from a cross between A619 and B52 selected with the pedigree method. Selection of families and individual plants within families was based on good resistance to late-season lodging (evaluated visually), plant height (evaluated visually), and in the later generations (F5 and onward) on biomass conversion efficiency using the lab-scale enzymatic saccharification protocol described in FitzGerald and Vermerris (2005). As a result of this selection strategy we have three tall maize inbred lines that have yields of fermentable sugars that equal or exceed the best brown midrib mutants, combined with excellent late-season standability. Hydrolysis of stover from the two parents, A619 and B52, and the bm3 mutant in an inbred A619 inbred background resulted in 110 ± 30, 126 ± 30, and 171 ± 34 mg glucose g–1 stover (dry weight), respectively. Hydrolysis of stover from the best three inbred lines, designated 37, 42, and 45, yielded 229 ± 49, 155 ± 29, and 195 ± 37 mg g–1, respectively. We were also able to evaluate the susceptibility to fall armyworm [Spodoptera frugiperda (J.E. Smith)] during the 2005–2006 winter season in Puerto Rico, where these lines were being propagated. The heavy worm infestation, occurring naturally, resulted in only limited damage compared to most standard research inbred lines. A detailed chemical analysis of these new inbred lines may reveal the basis of the enhanced yields of fermentable sugars. This information could then be used in future selection strategies. Since the majority of the maize crop will continue to be grown for grain production in the foreseeable future, the new inbred lines can also be of value in the development of new maize hybrids that combine good grain yield with good stover yield and good stover quality.
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The authors would like to acknowledge the assistance of Bill Foster, Terry Lemming, and Phil Devillez with the management of field plots, the assistance of Javier Campos, Cecile Grenier, Joan Goetz, and Anna Olek with managing our respective laboratories and coordinating experiments, and Alma Armenta for generating the sorghum hydrolysis data. We would also like to acknowledge the participation of our colleagues from the cell wall genomics project, Karen Koch and Don McCarty, for development of the maize UniformMu collection from which cell wall mutants were screened; Maureen McCann and Steven Thomas for development of high through-put screening of maize by Fourier transform infrared and NIR; Mark Davis for pyrolysis-MBMS analyses; Sara Patterson, Bryan Penning, and Wolf-Dieter Reiter for expanding the annotations of cell wall–related genes in maize, rice, and Arabidopsis; and Reuben Tayengwa for characterization of selected cell wall mutants. We are grateful for funding from the U.S. National Science Foundation Plant Genome Research Program (DBI-0217552) and the Consortium for Plant Biotechnology Research, Inc. (CPBR), U.S. Department of Energy (DOE) Prime Agreement no. DEFG36-02GO12026. This support does not constitute and endorsement by DOE or CPBR of the views expressed in this publication. Additional financial support from Dow AgroSciences, Purdue University, Purdue Research Foundation, and Purdue Agricultural Research Programs is gratefully acknowledged.
Received for publication April 7, 2007.
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