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Predicting future biomass yield in Miscanthus using the carbohydrate metabolic profile as a biomarker.

ABSTRACT: In perennial energy crop breeding programmes, it can take several years before a mature yield is reached when potential new varieties can be scored. Modern plant breeding technologies have focussed on molecular markers, but for many crop species, this technology is unavailable. Therefore, prematurity predictors of harvestable yield would accelerate the release of new varieties. Metabolic biomarkers are routinely used in medicine, but they have been largely overlooked as predictive tools in plant science. We aimed to identify biomarkers of productivity in the bioenergy crop, Miscanthus, that could be used prognostically to predict future yields. This study identified a metabolic profile reflecting productivity in Miscanthus by correlating the summer carbohydrate composition of multiple genotypes with final yield 6 months later. Consistent and strong, significant correlations were observed between carbohydrate metrics and biomass traits at two separate field sites over 2 years. Machine-learning feature selection was used to optimize carbohydrate metrics for support vector regression models, which were able to predict interyear biomass traits with a correlation (R) of >0.67 between predicted and actual values. To identify a causal basis for the relationships between the glycome profile and biomass, a ¹³C-labelling experiment compared carbohydrate partitioning between high- and low-yielding genotypes. A lower yielding and slower growing genotype partitioned a greater percentage of the ¹³C pulse into starch compared to a faster growing genotype where a greater percentage was located in the structural biomass. These results supported a link between plant performance and carbon flow through two rival pathways (starch vs. sucrose), with higher yielding plants exhibiting greater partitioning into structural biomass, via sucrose metabolism, rather than starch. Our results demonstrate that the plant metabolome can be used prognostically to anticipate future yields and this is a method that could be used to accelerate selection in perennial energy crop breeding programmes.

SUBMITTER: Maddison AL

PROVIDER: S-EPMC5488626 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Predicting future biomass yield in <i>Miscanthus</i> using the carbohydrate metabolic profile as a biomarker.

Maddison Anne L AL Camargo-Rodriguez Anyela A Scott Ian M IM Jones Charlotte M CM Elias Dafydd M O DMO Hawkins Sarah S Massey Alice A Clifton-Brown John J McNamara Niall P NP Donnison Iain S IS Purdy Sarah J SJ

Global change biology. Bioenergy 20170121 7

In perennial energy crop breeding programmes, it can take several years before a mature yield is reached when potential new varieties can be scored. Modern plant breeding technologies have focussed on molecular markers, but for many crop species, this technology is unavailable. Therefore, prematurity predictors of harvestable yield would accelerate the release of new varieties. Metabolic biomarkers are routinely used in medicine, but they have been largely overlooked as predictive tools in plant ...[more]

PMID: 28713439

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Project description:As a great potential bio-fuel feedstock, the genus Miscanthus has been widely studied around the world, especially Miscanthus × giganteus owing to its high biomass yield in Europe and North America. However, the narrow genetic basis and sterile characteristics of M. × giganteus have become a limitation for utilization and adaptation to extreme climate conditions. In this study, we focused on one of the progenitors of M. × giganteus, Miscanthus sinensis, which was originally distributed in East Asia with abundant genetic resources and comparable biomass yield potential to M. × giganteus in some areas. A collection of 138 individuals was selected for conducting a 3-year trial of biomass production and analyzed by using 104 pairs of SRAP, ISAP, and SSR primers for genetic diversity as well as marker-trait association. Significant differences in biomass yield and related traits were observed among individuals. Tiller number, fresh biomass yield per plant and dry biomass yield per plant had a high level of phenotypic variation among individuals and the coefficient of variation were all above 40% in 2011, 2012, and 2013. The majority of the traits had a significant correlation with the biomass yield except for the length and width of flag leaves. Plant height was a highly stable trait correlated with biomass yield. A total of 1059 discernible loci were detected by markers across individuals. The population structure (Q) and cluster analyses identified three subpopulations in the collection and family relative kinship (K) represented high gene flow among M. sinensis populations from Southwest China. Model testing identified that Q+K was the best model for describing the associations between the markers and traits, compared to the simple linear, Q or K model. Using the Q+K model, 12 significant associations (P < 0.001) were identified including four markers with plant height and one with biomass yield. Such associations would serve an efficient tool for an early selection of M. sinensis and facilitate a genetic improvement of biomass yield for this species.

Project description:BackgroundThe course of Crohn's disease (CD) varies substantially between individuals, but reliable prognostic markers do not exist. This hinders disease management because patients with aggressive disease are undertreated by conventional 'step-up' therapy (in which treatment is gradually escalated in response to refractory or relapsing disease) while those with more indolent disease would be exposed to unnecessary treatment-related toxicity if a more aggressive 'top-down' approach was indiscriminately used. The Predicting outcomes for Crohn's disease using a molecular biomarker trial will assess whether a prognostic transcriptional biomarker, that we have developed and validated, can improve clinical outcomes by facilitating personalised therapy in CD. This represents the first the biomarker-stratified trial in inflammatory bowel disease.Methods and analysisThis biomarker-stratified trial will compare the relative efficacy of 'top-down' and 'accelerated step-up' therapy between biomarker-defined subgroups of patients with newly diagnosed CD. 400 participants from ~50 UK centres will be recruited. Subjects within each biomarker subgroup (IBDhi or IBDlo) will be randomised (1:1) to receive one of the treatment strategies until trial completion (48 weeks). The primary outcome is the incidence of sustained surgery and steroid-free remission from the completion of induction treatment through to week 48. Secondary outcomes include mucosal healing, quality-of-life assessments and surrogate measures of disease burden including number of flares, cumulative steroid exposure, number of hospital admissions and number of Crohn's-related surgeries (assessed hierarchically). Analyses will compare the relative benefit of the treatment strategies in each biomarker-defined subgroup, powered as an interaction analysis, to determine whether the biomarker can accurately match patients to the most appropriate therapy.Ethics and disseminationEthical approval has been obtained and recruitment is under way at sites around the UK. Following trial completion and data analysis, the results of the trial will be submitted for publication in peer-reviewed journals and presented at international conferences.Trial registration numberISRCTN11808228; Pre-results.

Project description:Background:Lignocellulosic biomass will progressively become the main source of carbon for a number of products as the Earth's oil reservoirs disappear. Technology for conversion of wood fiber into bioproducts (wood biorefining) continues to flourish, and access to reliable methods for monitoring modification of such fibers is becoming an important issue. Recently, we developed a simple, rapid approach for detecting four different types of polymer on the surface of wood fibers. Named fluorescent-tagged carbohydrate-binding module (FTCM), this method is based on the fluorescence signal from carbohydrate-binding modules-based probes designed to recognize specific polymers such as crystalline cellulose, amorphous cellulose, xylan, and mannan. Results:Here we used FTCM to characterize pulps made from softwood and hardwood that were prepared using Kraft or chemical-thermo-mechanical pulping. Comparison of chemical analysis (NREL protocol) and FTCM revealed that FTCM results were consistent with chemical analysis of the hemicellulose composition of both hardwood and softwood samples. Kraft pulping increased the difference between softwood and hardwood surface mannans, and increased xylan exposure. This suggests that Kraft pulping leads to exposure of xylan after removal of both lignin and mannan. Impact of enzyme cocktails from Trichoderma reesei (Celluclast 1.5L) and from Aspergillus sp. (Carezyme 1000L) was investigated by analysis of hydrolyzed sugars and by FTCM. Both enzymes preparations released cellobiose and glucose from pulps, with the cocktail from Trichoderma being the most efficient. Enzymatic treatments were not as effective at converting chemical-thermomechanical pulps to simple sugars, regardless of wood type. FTCM revealed that amorphous cellulose was the primary target of either enzyme preparation, which resulted in a higher proportion of crystalline cellulose on the surface after enzymatic treatment. FTCM confirmed that enzymes from Aspergillus had little impact on exposed hemicelluloses, but that enzymes from the more aggressive Trichoderma cocktail reduced hemicelluloses at the surface. Conclusions:Overall, this study indicates that treatment with enzymes from Trichoderma is appropriate for generating crystalline cellulose at fiber surface. Applications such as nanocellulose or composites requiring chemical resistance would benefit from this enzymatic treatment. The milder enzyme mixture from Aspergillus allowed for removal of amorphous cellulose while preserving hemicelluloses at fiber surface, which makes this treatment appropriate for new paper products where surface chemical responsiveness is required.

Dataset Information

Predicting future biomass yield in Miscanthus using the carbohydrate metabolic profile as a biomarker.

Publications

Predicting future biomass yield in <i>Miscanthus</i> using the carbohydrate metabolic profile as a biomarker.

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