Selection and phenotypic characterization of a core collection of Brachypodium distachyon inbred lines
© Tyler et al.; licensee BioMed Central Ltd. 2014
Received: 9 July 2013
Accepted: 2 January 2014
Published: 14 January 2014
The model grass Brachypodium distachyon is increasingly used to study various aspects of grass biology. A large and genotypically diverse collection of B. distachyon germplasm has been assembled by the research community. The natural variation in this collection can serve as a powerful experimental tool for many areas of inquiry, including investigating biomass traits.
We surveyed the phenotypic diversity in a large collection of inbred lines and then selected a core collection of lines for more detailed analysis with an emphasis on traits relevant to the use of grasses as biofuel and grain crops. Phenotypic characters examined included plant height, growth habit, stem density, flowering time, and seed weight. We also surveyed differences in cell wall composition using near infrared spectroscopy (NIR) and comprehensive microarray polymer profiling (CoMPP). In all cases, we observed extensive natural variation including a two-fold variation in stem density, four-fold variation in ferulic acid bound to hemicellulose, and 1.7-fold variation in seed mass.
These characterizations can provide the criteria for selecting diverse lines for future investigations of the genetic basis of the observed phenotypic variation.
KeywordsBrachypodium distachyon Cell wall NIR Seed Biofuel
The investigation of natural variation is arguably one of the oldest fields in modern biology, and innumerable discoveries have been made by studying a wide variety of organisms. The advent of next generation technologies for whole-genome sequencing and the development of powerful genotyping techniques (e.g. genotyping by sequencing) enable researchers to saturate the genome of any organism with genetic markers. These new tools are especially powerful for associating natural phenotypic variation to specific DNA sequences and are leading to increased interest in natural diversity.
A relative of wheat, oat, and barley, Brachypodium distachyon was suggested as a model for the grasses in 2001 . During the ensuing years, rapid progress has been made in developing research tools for this small annual grass, including efficient transformation methods [2–4]; a high-quality whole genome sequence ; large germplasm collections [4, 6–8]; T-DNA mutant resources [9, 10]; and more. (For a recent review see ). In addition, 54 diverse accessions have been resequenced (unpublished). Building on this foundation, the goal of this study is to gain an overview of natural diversity in the available B. distachyon germplasm and then to identify a core collection of lines for the further investigation of bioenergy and grain traits.
As a genetically tractable model, Brachypodium can be used to increase our understanding of the genes that control grass growth and cell wall composition. Biomass yield is a function of numerous factors including plant height, which is often positively correlated with biomass accumulation [12, 13], and growth habit, which impacts the amount of space required between plants in the field. The density of the plant material is also a consideration, because denser biomass can be more efficiently transported to biorefineries  and might lead to higher biomass yield. Cell walls comprise the bulk of the plant biomass, and their composition determines the efficiency with which biomass can be converted into biofuel .
Although much of our knowledge about cell walls is derived from studies of the eudicotyledonous plant Arabidopsis thaliana, the composition of grass cell walls is quite distinct from that of dicot cell walls [17, 18]. Major differences in the carbohydrate polymers of the primary cell walls include the type of hemicellulose (arabinoxylans in grasses and xyloglucans in dicots), the level of pectin (low in grasses and high in dicots), and the presence of mixed-linkage glucans (present in grasses and absent in dicots). In addition, grass primary cell walls contain significant amounts of phenolic compounds, some of which cross-link carbohydrate polymers, while the majority of dicot primary cell walls contain few or no phenolic compounds. Likewise, grass secondary cell walls contain relatively high levels of the phenolic compounds ferulic acid and ρ-coumaric acid . Illustrating these compositional differences, quantification of the non-cellulosic monosaccharides extracted from mature stems revealed that grasses, including Brachypodium and the bioenergy species Miscanthus, had higher amounts of arabinose, but lower amounts of galacturonic acid and rhamnose when compared to Arabidopsis. In addition to their relevance for biofuel production and animal feed, grass cell walls also play a major role in human nutrition, because they can be a large component of grains and have health benefits as the fiber fraction of grains such as oat and wheat .
Despite the biological and commercial importance of plant cell walls, it is difficult to precisely determine their composition [21, 22]. Much of this difficulty is due to the extremely complex composite polymer nature of the cell wall . Large spatial and temporal variation in cell wall composition further complicates our ability to reproducibly characterize this trait. In this context, spectroscopic techniques have been useful for surveying differences in cell wall composition, because many of the linkages and chemical groups contained in the cell wall contribute to the net signal. Near infrared (NIR) spectroscopy can be particularly useful because it is fast and requires little or no sample preparation [23, 24]. A significant limitation of NIR analysis is that without samples of known composition to serve as calibration standards, it is impossible to identify specific compositional differences between samples. However, NIR spectroscopy can readily be used to determine if unknown samples differ in composition without identifying the exact chemical differences. Thus, NIR has been employed to identify plant cell wall mutants and to predict digestibility of forage grasses [25–27]. Another method to assess cell wall composition is to measure the intensity with which antibodies that recognize specific epitopes within cell wall polymers bind to cell wall samples. By combining the specificity of monoclonal antibodies (mAbs) with the high-throughput capacity of microarrays, it is possible to rapidly analyze large numbers of cell wall samples. This approach, known as comprehensive microarray polymer profiling or CoMPP, has been successfully applied to many diverse plants including grasses [28–30]. The primary limitations of this technique are that it is semi-quantitative, and mAbs are not available to study all epitopes. Nevertheless, CoMPP is a powerful tool for the high-throughput comparative analysis of large numbers of cell wall samples
In this paper we characterize several phenotypes in a large collection of B. distachyon germplasm and then select a core collection of 17 diverse lines for more extensive characterization. We observed significant variation in plant height, growth habit, flowering time, cell wall composition, and seed size. Our results demonstrate that the phenotypic diversity in the current B. distachyon germplasm collection is sufficient to allow researchers to better understand the genetic basis of traits relevant to the development of superior crops.
The full collection of lines contained 166 lines from Turkey, four lines from Iraq and one line from Spain. Inbred lines Bd1-1, Bd2-3, Bd3-1, Bd18-1, Bd21, Bd21-3 and the Turkish lines were described previously [3, 4, 6, 7]. Line Bd30-1 was developed by Dr. David Garvin (USDA-ARS, St. Paul, MN, USA), from material collected in Spain by Dr. Antonio Manzaneda (University of Jaén, Spain).
Plant growth conditions
Summary of experiments conducted for phenotypic characterizations
Parameters measured (No. of lines examined)
Preliminary phenotypic survey
Outside, winter of 2008-2009
Seed detachment (171)
Stem density (46)1
Synchronization of flowering time
Growth chamber, with varying vernalization periods
Flowering time (16)2
Repeat of outdoor growing conditions
Outside, winter of 2010-2011, three trials planted over 24 days
Flowering time (17)
Flowering-time matched plants
Growth chamber with staggered vernalization times
Stem density (17)
Flowering-time matched plants for seed measurements
Growth chamber with staggered vernalization times
Seed size (16)2
Plants in growth chambers were grown as previously described  (experiments 2, 4 and 5, Table 1). Briefly, the potting soil consisted of a 1:2:3:3 mix of sandy loam, sand, peat moss and #3 vermiculite plus a time-release fertilizer with micronutrients (Osmocote Plus 15-9-12, Scotts Co., Marysville, OH). Growth chambers had 20 hours of illumination (150 μEm-2s-1) from fluorescent lights. The temperature regime was 24°C in the day and 18°C at night.
Plants grown outside in the winter of 2010-2011 (experiment 3, Table 1) were grown in the same soil as growth-chamber-grown plants. Weather data for the 2010-2011 outdoor trial were obtained from the Oakland International Airport weather station located 22 km from the lab in a similar microclimate (http://www.wunderground.com/history/airport/KOAK/2008/12/10/MonthlyHistory.html).
Vernalization was conducted by incubating imbibed seeds at 4°C for the required amount of time. Initially, seeds were planted in damp soil, and the pots were placed in the cold (experiments 1,2,4; Table 1). After noticing low germination rates for some lines, particularly BdTR2g, BdTR5i, and BdTR11i, we began removing the lemmas from seeds and sterilizing the seeds. Sterilization was accomplished by washing the seeds in 15% bleach plus 0.1% Triton-X 100 (Sigma-Aldrich, St. Louis, MO, USA) for 4 minutes, followed by two rinses in water (experiments 3,5; Table 1). The sterilized seeds were placed on moist paper towels in the cold, before being transferred to soil. This treatment improved overall germination. For vernalization periods longer than 3 weeks, pots were placed under continuous weak fluorescent lighting because seedlings emerged after approximately 3 weeks.
Morphological measurements of plants
For plants grown outside in the winter of 2008-2009, the length of the longest culm in each pot was measured from the soil to the top of the seed head, omitting lemma hairs. The height of plants grown in the growth chamber was measured by uprooting the plants and measuring the length of the longest culm from the soil to the top of the seed head, excluding lemma hairs. Average heights of growth-chamber-grown plants were based on measurements of 3 to 24 individuals per line, with an average sample size of 16. Three lines had poor germination and were represented by three (BdTR2g and BdTR5i) or five individuals (BdTR11i). All height measurements were determined by straightening the stem at the time of seed harvest.
Stem density was determined by dividing the mass by the volume of the plant’s longest, intact, undamaged internode – usually the uppermost internode in the primary tiller. Internodes were photographed, and the width was measured at six points along the length of the internode using ImageJ software . The average width was used to calculate the volume of the cylindrical internode.
Near infrared spectroscopy
We used NIR to help us select lines that varied in cell wall composition. The uppermost two internodes (not including seed heads, leaf sheaths or nodes) of stems from fully senesced plants were harvested, cut into ~5 mm long pieces and placed into 2 ml impact-resistant tubes (#1420-9600, USA Scientific, Ocala, FL) containing one 6.2 mm and two 3.2 mm chrome steel grinding beads. The larger bead was placed between the smaller beads to ensure thorough grinding. Very small stems (<5 cm) were not used. The tubes were only filled about three-quarters full to allow for free movement of the steel balls. The stem segments in open tubes were oven-dried overnight at 70°C. After drying, the tubes were immediately capped and placed in a ball mill (MM400, Retsch, Haan, Germany) and ground for 12 minutes at 30 cycles per second. The ground stem material was then transferred to a glass slide and another glass slide was placed on top such that the powder was spread in a thin layer between the slides. A Field Spec Pro spectrometer equipped with the plant probe attachment (ASD, Boulder, CO) was then used to obtain an average spectrum from 35 readings. The spectra were then converted to a .dx format. Principal component analysis (PCA) was conducted using Win-DAS software . The spectra were baseline-corrected and only the region from 1000 to 2400 nm was used for PCA.
Four groups of 25 seeds each were weighed separately and the mass divided by 25 to determine the average seed mass. Seeds were photographed, and seed length and width were measured using ImageJ software . Ten seeds were measured for each parameter.
We analyzed stem samples from the core collection grown in the growth chamber with staggered vernalization (experiment 4, Table 1). However, BdTR2g and BdTR5i were omitted from the CoMPP analysis because insufficient material was available. CoMPP analysis was conducted as previously described . Briefly, alcohol-insoluble reside was prepared by grinding stem samples to a fine powder in liquid nitrogen prior to extraction with ethanol and acetone. Initial trials using three previously used polysaccharide extraction solvents (cyclohexylenedinitrilotetraacetate (CDTA), NaOH and cadoxen ) indicated that the cadoxen step did not result in appreciable release of additional cell wall material after the NaOH extraction (not shown). Therefore, only CDTA and NaOH extractions were used for subsequent experiments. These extractions were printed at three dilutions (2-, 5-, and 25-fold). The microarrays were then probed separately with panels of monoclonal antibodies (mAbs); the resulting spot signals were quantified as described in . All samples were run with four biological replicates and three dilutions.
To avoid artifacts due to signal saturation and zero values, one dilution was selected for each antibody for each extraction method. The appropriate dilution was selected by examining the raw data and finding the dilution that gave a strong yet unsaturated signal for most of the samples. The raw numbers were multiplied by the appropriate dilution factor and averages calculated for each point. The values were then normalized by assigning the highest individual reading a value of 1,000 and setting all negative values to zero.
Morphological characterization of the full collection
Stem density of plants grown outside in experiment 1
Stem density (mg/mm3)1
Near infrared spectroscopy
Selection of a core collection
For many applications it is impractical to use the full collection of 171 lines. Thus, it is desirable to select a small, highly diverse core collection. We used the phenotypic data described above, together with previously published genotypic and geographic data , to select a core collection of 17 lines. Twelve Turkish lines were chosen based on phenotypic information summarized in Additional file 3: Table S2. BdTR3c was included, even though we did not analyze it by NIR spectroscopy, because it had a maximum height of 59 cm – well above the average height of 45 cm for the 171 lines taken together. Bd30-1 is an inbred Spanish line which became available after the initial survey was finished; Bd30-1 was included to broaden the geographic distribution of the collection. Four well-characterized lines –Bd3-1, Bd21, and Bd21-3 from Iraq and Bd1-1 from Turkey – complete the core collection. Bd3-1 is commonly used for mapping, while Bd1-1 represents a distinctive clade of late-flowering lines . Despite their similarity in many respects, both Bd21 and Bd21-3 were included, because Bd21 is the source of the reference genome and a parent of several recombinant inbred line (RIL) populations, and Bd21-3 is the parent of >20,000 T-DNA lines ; http://brachypodium.pw.usda.gov/TDNA/.
Synchronization of flowering time in the core collection
B. distachyon lines differ considerably in flowering time when grown under the same conditions [6, 34]. When grown outside without controlled vernalization, the earliest-flowering lines (Bd3-1, Bd21, and Bd21-3) flowered up to three months earlier than the latest flowering lines (e.g.Bd1-1 and Tek lines). These differences complicate the interpretation of experiments focused on fully mature plants for two reasons. First, late-flowering plants typically achieve a much larger biomass because many additional leaves, tillers and roots grow during the extended juvenile period. Second, since much of the lifecycle will have been completed at different times and plants in pots become pot-bound, plants with different flowering times may be subjected to significantly different environmental conditions over the course of development.
Fortunately, B. distachyon responds to vernalization at the seedling stage by accelerating flowering. Initial vernalization experiments conducted while creating inbred lines divided the lines into three broad groups . The first group consists of four lines from Iraq (Bd21, Bd21-3, Bd2-3, and Bd3-1) that require a short vernalization of 3 weeks or less and require no vernalization at all under long-day conditions (>16 hrs. light) . The second group of lines requires 3-5 weeks of vernalization and needs vernalization even under long days. The third group consists of lines that require very long vernalization (≥6 weeks) to initiate flowering. Most of these late-flowering lines form a genotypically distinct group based on SSR markers .
Length of vernalization periods used to synchronize flowering of the core collection when grown under 20-hour days
Effect of environment on plant height and stem density in the core collection
Without controlled vernalization, plants were substantially taller: The core collection of lines attained heights of 28 to 59 cm outside versus average heights of 15 to 31 cm following staggered vernalization and cultivation in a growth chamber (Figure 5A, B). Thus, accelerating flowering through vernalization shortened the vegetative growth phase and resulted in less stem elongation prior to seed set. Bd1-1 provides a clear example of this general trend. Although a few centimeters taller than Bd21-3 when grown outside, Bd1-1 was 45% shorter than Bd21-3 when flowering was synchronized. This result suggests that the growth rate of the late-flowering line Bd1-1 is relatively slow and that, outdoors, it achieved a slightly greater height by undergoing stem elongation over a protracted period of time compared to the early flowering Bd21-3 line. For Bd21, Bd21-3, and Bd3-1, which have similar flowering times under various conditions (Figures 3 and 4), Bd21 was consistently the shortest of the three lines, Bd21-3 was intermediate in height, and Bd3-1 was the tallest. Both without and with controlled vernalization, BdTR3c was the tallest in the core set of lines, indicating that this height difference is at least partially under genetic regulation, rather than being simply the secondary effect of flowering time or environmental differences.
As observed for height, there was variation in the stem density of flowering-time-matched lines, although the magnitude of variation was smaller under the controlled conditions (Figure 5C, D). For plants with synchronized flowering times, the densest stems (0.43 mg/mm3) were only about 30% denser that the least dense stems (0.33 mg/mm3), compared to a difference of 230% for plants grown outdoors. Importantly, however, some lines showed similar trends under both conditions. For example, whether or not vernalization was staggered, BdTR2g was the least dense line, and Koz-3 was the second densest line.
Comprehensive microarray polymer profiling in the core collection
To gain more molecular information about the differences between the stems of our core lines, we performed comprehensive microarray polymer profiling (CoMPP). The CoMPP technique provides information about the relative abundance of polysaccharide-borne epitopes across plant sample sets. Unlike glycosidic linkages and NIR spectra, epitopes can almost always be assigned with confidence to particular polysaccharide structures. However, it is important to note that the CoMPP spot signal values do not necessarily reflect the absolute amount of epitope present, because polysaccharide extractability may vary across the samples.
Grain characteristics in the core collection
As a step toward characterizing phenotypic variation in B. distachyon, we examined several traits in a diverse B. distachyon germplasm collection. We conducted an initial survey of growth habit, height, and seed shattering for 171 inbred lines (Figure 1). We also examined the density of stems and used NIR spectroscopy to infer cell wall differences in a smaller subset of the lines (Figures 2 and 3). Considerable variation existed for all traits examined. Using these results and previous genotypic data  we selected a core collection of 17 lines for more detailed characterization. Significantly, we have resequenced the genomes of all the lines in the core collection, and those sequences plus many more will be released shortly.
In order to conduct a robust phenotypic comparison of the lines in the core collection, we identified vernalization times that triggered nearly simultaneous flowering of the lines (Figure 3 and Table 3). This allowed us to remove flowering time as a variable and minimize the contribution of environmental differences toward phenotypic differences. Using the flowering-time-matched plants (experiment 4, Table 1), we again detected significant variation in all traits examined. Our assessment of height and stem density after senescence is relevant to the end-of-season harvesting employed for dedicated biofuel grasses and stover. By identifying lines that are both tall and dense, e.g. Koz-3, we are taking the first step toward identifying genotypes that lead to a favorable combination of these key traits.
Seed size is an important trait for grain crops. The largest average seed mass was 70% larger than the smallest (Figure 7). This variation in seed size is less than the 2.4-fold difference previously reported , because we did not include any lines from the group known to have small seed size (e.g. Bd1-1) due to their long vernalization requirement . The fact that Bd21-3 and Bd30-1 represent the extremes for seed mass in the core collection, while having only slightly different vernalization requirements, makes these two lines good candidates for crossing in order to map the genetic basis of seed size.
Additionally, the use of NIR spectroscopy successfully allowed us to capture cell wall diversity when selecting the core collection of lines: In the CoMPP analysis, flowering-time-matched plants exhibited many differences in cell wall composition as measured by antibody binding (Figure 6). Differences detected by CoMPP included up to two-fold variation in signal intensity for all five antibodies that bound to various epitopes in pectin. While pectins are a small component of the grass cell wall [17, 18], they are present in the middle lamella and play a key role in cell adhesion and division [35, 36]. Thus, these observed compositional differences may result in developmental differences. Similarly, we observed a four-fold variation in the signal from an antibody that recognized ferulic acid bound to hemicellulose. Since ferulic acid contributes to the recalcitrance of biomass toward saccharification and fermentation to ethanol , this variation may be useful in tailoring the cell wall for use as a biomass feedstock.
The appearance of plants grown outside differs substantially from plants grown in greenhouses or growth chambers. This is not surprising because plants grown outside are subjected to higher light intensity, wind, and much greater environmental variability. In fact, it is difficult to observe differences in growth habit from plants grown in greenhouses and growth chambers. Thus, it is not surprising that the growth habit we observed in plants grown outside differs from a previous report on the growth habit of greenhouse-grown plants . While scoring some phenotypes outside may be more agriculturally relevant, the environmental variability can also complicate efforts to identify genes controlling particular traits. Thus, lines with phenotypes that remain constant under varied environmental conditions are of particular interest. We measured height and stem density under two distinct conditions: outside without controlled vernalization and in growth chambers with staggered vernalization to synchronize flowering time. While the phenotypic differences were less dramatic in growth-chamber-grown plants, several lines followed the same trends under both conditions. For example, BdTR3c was consistently tall; BdTR2g had the least dense stems, and Koz-3 had the second densest stems under both conditions. Overall, cataloging phenotypes and especially identifying lines with contrasting phenotypes provide a foundation for further studies investigating the genetic factors regulating these phenotypes. Whether the trait is growth habit, flowering time, seed size, or abundance of cell wall epitopes, phenotypic data of the type presented here can, for example, inform the choice of lines for the generation of RIL populations, genome resequencing, and genome-wide association studies.
In summary, we observed a significant amount of natural variation in the wild grass B. distachyon in traits relevant to grain and biomass crops. Since B. distachyon is amenable to experimental manipulation and genetic analysis, relatively rapid identification of the genes controlling this variation is feasible. These genes can then be used to improve crops through biotechnology, as well as by guiding the mining and deployment of natural variation in the crops themselves. In this context, it is important to note that B. distachyon has not experienced a population bottleneck as have the species grown for grain in the course of their domestication. Thus, in addition to identifying genes more rapidly, B. distachyon may contain genes and natural variation that are simply not present in domesticated cereals. Although a population bottleneck is not a problem with most of the grasses likely to be deployed as the first large-scale biomass crops (e.g. switchgrass), their large size, complex genetics and long generation times make identifying the responsible genes extremely difficult. Thus, for both grains and biomass crops, the identification of genes that control natural diversity in B. distachyon could prove extremely useful.
We would like to thank Matthew De La Housaye for technical assistance. This work was supported by the Office of Science (BER), US Department of Energy, and by USDA CRIS project 5325-21000-017-00.
- Draper J, Mur LAJ, Jenkins G, Ghosh-Biswas GC, Bablak P, Hasterok R, Routledge APM: Brachypodium distachyon: a new model system for functional genomics in grasses. Plant Physiol. 2001, 127 (4): 1539-1555. 10.1104/pp.010196.PubMed CentralView ArticlePubMedGoogle Scholar
- Vain P, Worland B, Thole V, McKenzie N, Alves SC, Opanowicz M, Fish LJ, Bevan MW, Snape JW: Agrobacterium-mediated transformation of the temperate grass Brachypodium distachyon (genotype Bd21) for T-DNA insertional mutagenesis. Plant Biotechnol J. 2008, 6: 236-245. 10.1111/j.1467-7652.2007.00308.x.View ArticlePubMedGoogle Scholar
- Vogel J, Hill T: High-efficiency Agrobacterium-mediated transformation of Brachypodium distachyon inbred line Bd21-3. Plant Cell Rep. 2008, 27 (3): 471-478. 10.1007/s00299-007-0472-y.View ArticlePubMedGoogle Scholar
- Vogel JP, Garvin DF, Leong OM, Hayden DM: Agrobacterium-mediated transformation and inbred line development in the model grass Brachypodium distachyon. Plant Cell Tiss Org Cult. 2006, 85: 199-211.View ArticleGoogle Scholar
- IBI: Genome sequencing and analysis of the model grass Brachypodium distachyon. Nature. 2010, 463: 763-768. 10.1038/nature08747.View ArticleGoogle Scholar
- Filiz E, Ozdemir BS, Budak F, Vogel JP, Tuna M, Budak H: Molecular, morphological, and cytological analysis of diverse Brachypodium distachyon inbred lines. Genome. 2009, 52 (10): 876-890. 10.1139/G09-062.View ArticlePubMedGoogle Scholar
- Vogel JP, Tuna M, Budak H, Huo N, Gu YQ, Steinwand MA: Development of SSR markers and analysis of diversity in Turkish populations of Brachypodium distachyon. BMC Plant Biol. 2009, 9: 88-10.1186/1471-2229-9-88.PubMed CentralView ArticlePubMedGoogle Scholar
- Mur LAJ, Allainguillaume J, CatalÃ¡n P, Hasterok R, Jenkins G, Lesniewska K, Thomas I, Vogel J: Exploiting the Brachypodium tool box in cereal and grass research. New Phytol. 2011, 191: 334-347. 10.1111/j.1469-8137.2011.03748.x.View ArticlePubMedGoogle Scholar
- Bragg JN, Wu J, Gordon SP, Guttman ME, Thilmony R, Lazo GR, Gu YQ, Vogel JP: Generation and characterization of the Western Regional Research Center Brachypodium T-DNA insertional mutant collection. PLoS One. 2012, 7 (9): e41916-10.1371/journal.pone.0041916.PubMed CentralView ArticlePubMedGoogle Scholar
- Thole V, Worland B, Wright J, Bevan MW, Vain P: Distribution and characterization of more than 1000 T-DNA tags in the genome of Brachypodium distachyon community standard line Bd21. Plant Biotechnol J. 2012, 8 (6): 734-747.View ArticleGoogle Scholar
- Brkljacic J, Grotewold E, Scholl S, Mockler T, Garvin D, Vain P, Brutnell T, Sibout R, Bevan M, Budak H, et al: Brachypodium as a model for the grasses: today and the future. Plant Physiol. 2011, 157: 3-13. 10.1104/pp.111.179531.PubMed CentralView ArticlePubMedGoogle Scholar
- Salas Fernandez MG, Becraft PW, Yin Y, Lübberstedt T: From dwarves to giants? Plant height manipulation for biomass yield. Trends Plant Sci. 2009, 14 (8): 454-461. 10.1016/j.tplants.2009.06.005.View ArticlePubMedGoogle Scholar
- Murray SC, Rooney WL, Mitchell SE, Sharma A, Klein PE, Mullet JE, Kresovich S: Genetic improvement of sorghum as a biofuel feedstock: II QTL for stem and leaf structural carbohydrates. Crop Sci. 2008, 48 (6): 2180-2193. 10.2135/cropsci2008.01.0068.View ArticleGoogle Scholar
- Richard TL: Challenges in scaling up biofuels infrastructure. Science. 2012, 329 (5993): 793-796.View ArticleGoogle Scholar
- Somerville C, Bauer S, Brininstool G, Facette M, Hamann T, Milne J, Osborne E, Paredez A, Persson S, Raab T, et al: Toward a systems approach to understanding plant cell walls. Science. 2004, 306 (5705): 2206-2211. 10.1126/science.1102765.View ArticlePubMedGoogle Scholar
- Liepman AH, Wightman R, Geshi N, Turner SR, Scheller HV: Arabidopsis - a powerful model system for plant cell wall research. Plant J. 2010, 61 (6): 1107-1121. 10.1111/j.1365-313X.2010.04161.x.View ArticlePubMedGoogle Scholar
- Carpita NC: Structure and biogenesis of the cell walls of grasses. Annu Rev Plant Physiol Plant Mol Biol. 1996, 47: 445-476. 10.1146/annurev.arplant.47.1.445.View ArticlePubMedGoogle Scholar
- Vogel J: Unique aspects of the grass cell wall. Curr Opin Plant Biol. 2008, 11 (3): 301-307. 10.1016/j.pbi.2008.03.002.View ArticlePubMedGoogle Scholar
- Gomez LD, Bristow JK, Statham ER, McQueen-Mason SJ: Analysis of saccharification in Brachypodium distachyon stems under mild conditions of hydrolysis. Biotechnol for Biofuels. 2008, 1: 15-10.1186/1754-6834-1-15.View ArticleGoogle Scholar
- Anderson JW, Baird P, Davis RH, Ferreri S, Knudtson M, Koraym A, Waters V, Williams CL: Health benefits of dietary fiber. Nutr Rev. 2009, 67 (4): 188-205. 10.1111/j.1753-4887.2009.00189.x.View ArticlePubMedGoogle Scholar
- Foster CE, Martin TM, Pauly M: Comprehensive compositional analysis of plant cell walls (lignocellulosic biomass) part II: carbohydrates. J Vis Exp. 2010, 37: 10521577.Google Scholar
- Brinkmann K, Blaschke L, Polle A: Comparison of different methods for lignin determination as a basis for calibration of near-infrared reflectance spectroscopy and implications of lignoproteins. J Chem Ecol. 2002, 28 (12): 2483-2501. 10.1023/A:1021484002582.View ArticlePubMedGoogle Scholar
- Himmelsbach DS, Akin DE: Near-infrared fourier-transform Raman spectroscopy of flax (linum usitatissimum L.) stems. J Agric Food Chem. 1998, 46 (3): 991-998. 10.1021/jf970656k.View ArticleGoogle Scholar
- Chen LM, Carpita NC, Reiter WD, Wilson RH, Jeffries C, McCann MC: A rapid method to screen for cell-wall mutants using discriminant analysis of Fourier transform infrared spectra. Plant J. 1998, 16 (3): 385-392. 10.1046/j.1365-313x.1998.00301.x.View ArticlePubMedGoogle Scholar
- Penning BW, Hunter CT, Tayengwa R, Eveland AL, Dugard CK, Olek AT, Vermerris W, Koch KE, McCarty DR, Davis MF, et al: Genetic resources for maize cell wall biology. Plant Physiol. 2009, 151 (4): 1703-1728. 10.1104/pp.109.136804.PubMed CentralView ArticlePubMedGoogle Scholar
- Ruiter JM, Burns JC FMW, TD H: Prediction of cell wall carbohydrates and quality in panicum species by near infrared reflectance spectroscopy. Crop Sci. 1988, 28: 348-353. 10.2135/cropsci1988.0011183X002800020035x.View ArticleGoogle Scholar
- Casler MD: Phenotypic recurrent selection methodology for reducing fiber concentration in smooth bromegrass. Crop Sci. 1999, 39: 381-390. 10.2135/cropsci1999.0011183X0039000200014x.View ArticleGoogle Scholar
- Moller IE, Pettolino FA, Hart C, Lampugnani ER, Willats WG, Bacic A: Glycan profiling of plant cell wall polymers using microarrays. J Vis Exp. 2012, 70.
- Moller I, Sørensen I, Bernal AJ, Blaukopf C, Lee K, Øbro J, Pettolino F, Roberts A, Mikkelsen JD, Knox JP, et al: High-throughput mapping of cell-wall polymers within and between plants using novel microarrays. Plant J. 2007, 50 (6): 1118-1128. 10.1111/j.1365-313X.2007.03114.x.View ArticlePubMedGoogle Scholar
- Alonso-Simón A, Kristensen JB, Øbro J, Felby C, Willats WGT, Jørgensen H: High-throughput microarray profiling of cell wall polymers during hydrothermal pre-treatment of wheat straw. Biotechnol Bioeng. 2010, 105 (3): 509-514. 10.1002/bit.22546.View ArticlePubMedGoogle Scholar
- Schneider C, Rasband W, Eliceiri K: NIH image to Image J: 25 years of image analysis. Nat Methods. 2012, 9: 671-675. 10.1038/nmeth.2089.View ArticlePubMedGoogle Scholar
- Kemsley EK: Discriminant Analysis and Class Modeling of Spectroscopic Data.New York: John Wiley & Sons; 1998.Google Scholar
- Fuller DQ, Allaby R: Seed dispersal and crop domestication: shattering, germination and seasonality in evolution under cultivation. Annu Plant Rev. 2009, 38: 238-232.Google Scholar
- Schwartz C, Doyle M, Manzaneda AJ, Rey PJ, Mitchell-Olds T, Amasino R: Natural variation of flowering time and vernalization responsiveness in Brachypodium distachyon. Bioenergy Res. 2010, in pressGoogle Scholar
- Caffall KH, Mohnen D: The structure, function, and biosynthesis of plant cell wall pectic polysaccharides. Carbohydr Res. 2009, 344 (14): 1879-1900. 10.1016/j.carres.2009.05.021.View ArticlePubMedGoogle Scholar
- Palin R, Geitmann A: The role of pectin in plant morphogenesis. Biosyst. 2012, 109 (3): 397-402. 10.1016/j.biosystems.2012.04.006.View ArticleGoogle Scholar
- Akin DE: Plant cell wall aromatics: influence on degradation of biomass. Biofuels Bioprod Biorefin. 2008, 2 (4): 288-303. 10.1002/bbb.76.View ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.