Sorghum mutant RG displays antithetic leaf shoot lignin accumulation resulting in improved stem saccharification properties

BACKGROUND: Improving saccharification efficiency in bioenergy crop species remains an important challenge. Here, we report the characterization of a Sorghum (Sorghum bicolor L.) mutant, named REDforGREEN (RG), as a bioenergy feedstock. RESULTS: It was found that RG displayed increased accumulation of lignin in leaves and depletion in the stems, antithetic to the trend observed in wild type. Consistent with these measurements, the RG leaf tissue displayed reduced saccharification efficiency whereas the stem saccharification efficiency increased relative to wild type. Reduced lignin was linked to improved saccharification in RG stems, but a chemical shift to greater S:G ratios in RG stem lignin was also observed. Similarities in cellulose content and structure by XRD-analysis support the correlation between increased saccharification properties and reduced lignin instead of changes in the cellulose composition and/or structure. CONCLUSION: Antithetic lignin accumulation was observed in the RG mutant leaf-and stem-tissue, which resulted in greater saccharification efficiency in the RG stem and differential thermochemical product yield in high lignin leaves. Thus, the red leaf coloration of the RG mutant represents a potential marker for improved conversion of stem cellulose to fermentable sugars in the C4 grass Sorghum

Selective One-Dimensional \u3csup\u3e13\u3c/sup\u3eC-\u3csup\u3e13\u3c/sup\u3eC Spin-Diffusion Solid-State Nuclear Magnetic Resonance Methods to Probe Spatial Arrangements in Biopolymers including Plant Cell Walls, Peptides, and Spider Silk

© 2020 American Chemical Society. All rights reserved. Two-dimensional (2D) and 3D through-space 13C-13C homonuclear spin-diffusion techniques are powerful solid-state nuclear magnetic resonance (NMR) tools for extracting structural information from 13C-enriched biomolecules, but necessarily long acquisition times restrict their applications. In this work, we explore the broad utility and underutilized power of a chemical shift-selective one-dimensional (1D) version of a 2D 13C-13C spin-diffusion solid-state NMR technique. The method, which is called 1D dipolar-assisted rotational resonance (DARR) difference, is applied to a variety of biomaterials including lignocellulosic plant cell walls, microcrystalline peptide fMLF, and black widow dragline spider silk. 1D 13C-13C spin-diffusion methods described here apply in select cases in which the 1D 13C solid-state NMR spectrum displays chemical shift-resolved moieties. This is analogous to the selective 1D nuclear Overhauser effect spectroscopy (NOESY) experiment utilized in liquid-state NMR as a faster (1D instead of 2D) and often less ambiguous (direct sampling of the time domain data, coupled with increased signal averaging) alternative to 2D NOESY. Selective 1D 13C-13C spin-diffusion methods are more time-efficient than their 2D counterparts such as proton-driven spin diffusion (PDSD) and dipolar-assisted rotational resonance. The additional time gained enables measurements of 13C-13C spin-diffusion buildup curves and extraction of spin-diffusion time constants TSD, yielding detailed structural information. Specifically, selective 1D DARR difference buildup curves applied to 13C-enriched hybrid poplar woody stems confirm strong spatial interaction between lignin and acetylated xylan polymers within poplar plant secondary cell walls, and an interpolymer distance of ∼0.45-0.5 nm was estimated. Additionally, Tyr/Gly long-range correlations were observed on isotopically enriched black widow spider dragline silks

Genetic variation of biomass recalcitrance in a natural Salix viminalis (L.) population

Background: Salix spp. are high-productivity crops potentially used for lignocellulosic biofuels such as bioethanol. In general, pretreatment is needed to facilitate the enzymatic depolymerization process. Biomass resistance to degradation, i.e., biomass recalcitrance, is a trait which can be assessed by measuring the sugar released after combined pretreatment and enzymatic hydrolysis. We have examined genetic parameters of enzymatic sugar release and other traits related to biorefnery use in a population of 286 natural Salix viminalis clones. Furthermore, we have evaluated phenotypic and genetic correlations between these traits and performed a genomewide association mapping analysis using a set of 19,411 markers. Results: Sugar release (glucose and xylose) after pretreatment and enzymatic saccharifcation proved highly variable with large genetic and phenotypic variations, and chip heritability estimates (h2 ) of 0.23–0.29. Lignin syringyl/guaiacyl (S/G) ratio and wood density were the most heritable traits (h2=0.42 and 0.59, respectively). Sugar release traits were positively correlated, phenotypically and genetically, with biomass yield and lignin S/G ratio. Association mapping revealed seven marker–trait associations below a suggestive signifcance threshold, including one marker associated with glucose release. Conclusions: We identifed lignin S/G ratio and shoot diameter as heritable traits that could be relatively easily evaluated by breeders, making them suitable proxy traits for developing low-recalcitrance varieties. One marker below the suggestive threshold for marker associations was identifed for sugar release, meriting further investigation while also highlighting the difculties in employing genomewide association mapping for complex trait

High Throughput Screening Technologies in Biomass Characterization

Biomass analysis is a slow and tedious process and not solely due to the long generation time for most plant species. Screening large numbers of plant variants for various geno-, pheno-, and chemo-types, whether naturally occurring or engineered in the lab, has multiple challenges. Plant cell walls are complex, heterogeneous networks that are difficult to deconstruct and analyze. Macroheterogeneity from tissue types, age, and environmental factors makes representative sampling a challenge and natural variability generates a significant range in data. Using high throughput (HTP) methodologies allows for large sample sets and replicates to be examined, narrowing in on more precise data for various analyses. This review provides a comprehensive survey of high throughput screening as applied to biomass characterization, from compositional analysis of cell walls by NIR, NMR, mass spectrometry, and wet chemistry to functional screening of changes in recalcitrance via HTP thermochemical pretreatment coupled to enzyme hydrolysis and microscale fermentation. The advancements and development of most high-throughput methods have been achieved through utilization of state-of-the art equipment and robotics, rapid detection methods, as well as reduction in sample size and preparation procedures. The computational analysis of the large amount of data generated using high throughput analytical techniques has recently become more sophisticated, faster and economically viable, enabling a more comprehensive understanding of biomass genomics, structure, composition, and properties. Therefore, methodology for analyzing large datasets generated by the various analytical techniques is also covered

Identification and thermochemical analysis of high-lignin feedstocks for biofuel and biochemical production

Background - Lignin is a highly abundant biopolymer synthesized by plants as a complex component of plant secondary cell walls. Efforts to utilize lignin-based bioproducts are needed. Results - Herein we identify and characterize the composition and pyrolytic deconstruction characteristics of high-lignin feedstocks. Feedstocks displaying the highest levels of lignin were identified as drupe endocarp biomass arising as agricultural waste from horticultural crops. By performing pyrolysis coupled to gas chromatography-mass spectrometry, we characterized lignin-derived deconstruction products from endocarp biomass and compared these with switchgrass. By comparing individual pyrolytic products, we document higher amounts of acetic acid, 1-hydroxy-2-propanone, acetone and furfural in switchgrass compared to endocarp tissue, which is consistent with high holocellulose relative to lignin. By contrast, greater yields of lignin-based pyrolytic products such as phenol, 2-methoxyphenol, 2-methylphenol, 2-methoxy-4-methylphenol and 4-ethyl-2-methoxyphenol arising from drupe endocarp tissue are documented. Conclusions - Differences in product yield, thermal decomposition rates and molecular species distribution among the feedstocks illustrate the potential of high-lignin endocarp feedstocks to generate valuable chemicals by thermochemical deconstruction

Biomass Recalcitrance in Willow Under Two Biological Conversion Paradigms: Enzymatic Hydrolysis and Anaerobic Digestion

Biomass recalcitrance, the inherent resistance of plants towards deconstruction, negatively affects the viability of biorefineries. This trait is not only dictated by the properties of the biomass but also by the conversion system used and its interactions with specific features of the biomass. Here, biomass recalcitrance to anaerobic digestion (AD) was assessed using a biomethanation potential (BMP) assay. Plant material (n = 94) was selected from a large population of natural Salix viminalis accessions, previously evaluated for biomass recalcitrance using hydrothermal pretreatment-enzymatic hydrolysis. Correlations between yields from the two biological conversion systems were evaluated, as well as the influence of biomass compositional features, analyzed by pyrolysis-molecular beam mass spectrometry (py-MBMS), and other biomass physical properties on conversion performance. BMP values averaged 198.0 Nml CH4/g biomass after 94 days, ranging from 28.6 to 245.9. S lignin and carbohydrate-derived spectral features were positively correlated with performance under both systems, whereas G lignin, p-coumaric acid, and ferulic acid-derived ions were negatively correlated with yields and rates. Most spectral features were more strongly correlated with enzymatic hydrolysis yields compared to methane production. For early-stage methane production and rate, recalcitrance factors were similar compared to enzymatic hydrolysis, with weaker correlations observed at later timepoints. The results suggest that although variation in methane potential was considerably lower than enzymatic hydrolysis yields, a reduced recalcitrance under this system will still be of importance to improve early conversion rates. Spectral features of low methane-producing samples indicate the presence of inhibitory substances, warranting further study

Machine Learning-Based Classification of Lignocellulosic Biomass from Pyrolysis-Molecular Beam Mass Spectrometry Data

High-throughput analysis of biomass is necessary to ensure consistent and uniform feedstocks for agricultural and bioenergy applications and is needed to inform genomics and systems biology models. Pyrolysis followed by mass spectrometry such as molecular beam mass spectrometry (py-MBMS) analyses are becoming increasingly popular for the rapid analysis of biomass cell wall composition and typically require the use of different data analysis tools depending on the need and application. Here, the authors report the py-MBMS analysis of several types of lignocellulosic biomass to gain an understanding of spectral patterns and variation with associated biomass composition and use machine learning approaches to classify, differentiate, and predict biomass types on the basis of py-MBMS spectra. Py-MBMS spectra were also corrected for instrumental variance using generalized linear modeling (GLM) based on the use of select ions relative abundances as spike-in controls. Machine learning classification algorithms e.g., random forest, k-nearest neighbor, decision tree, Gaussian Naïve Bayes, gradient boosting, and multilayer perceptron classifiers were used. The k-nearest neighbors (k-NN) classifier generally performed the best for classifications using raw spectral data, and the decision tree classifier performed the worst. After normalization of spectra to account for instrumental variance, all the classifiers had comparable and generally acceptable performance for predicting the biomass types, although the k-NN and decision tree classifiers were not as accurate for prediction of specific sample types. Gaussian Naïve Bayes (GNB) and extreme gradient boosting (XGB) classifiers performed better than the k-NN and the decision tree classifiers for the prediction of biomass mixtures. The data analysis workflow reported here could be applied and extended for comparison of biomass samples of varying types, species, phenotypes, and/or genotypes or subjected to different treatments, environments, etc. to further elucidate the sources of spectral variance, patterns, and to infer compositional information based on spectral analysis, particularly for analysis of data without a priori knowledge of the feedstock composition or identity

The effect of coumaryl alcohol incorporation on the structure and composition of lignin dehydrogenation polymers

Abstract Background Lignin dehydrogenation polymers (DHPs) are polymers generated from phenolic precursors for the purpose of studying lignin structure and polymerization processes Methods Here, DHPs were synthesized using a Zutropfverfahren method with horseradish peroxidase and three lignin monomers, sinapyl (S), coumaryl (H), and coniferyl (G) alcohols, in the presence of hydrogen peroxide. The H monomer was reacted with G and a 1:1 molar mixture of S:G monomers at H molar compositions of 0, 5, 10, and 20 mol% to study how the presence of the H monomer affected the structure and composition of the recovered polymers. Results At low H concentrations, solid-state NMR spectra suggest that the H and G monomers interact to form G:H polymers that have a lower average molecular weight than the solely G-based polymer or the G:H polymer produced at higher H concentrations. Solid-state NMR and pyrolysis–MBMS analyses suggest that at higher H concentrations, the H monomer primarily self-polymerizes to produce clusters of H-based polymer that are segregated from clusters of G- or S:G-based polymers. Thioacidolysis generally showed higher recoveries of thioethylated products from S:G or S:G:H polymers made with higher H content, indicating an increase in the linear ether linkages. Conclusions Overall, the experimental results support theoretical predictions for the reactivity and structural influences of the H monomer on the formation of lignin-like polymers

Genetic Modification of KNAT7 Transcription Factor Expression Enhances Saccharification and Reduces Recalcitrance of Woody Biomass in Poplars

The precise role of KNAT7 transcription factors (TFs) in regulating secondary cell wall (SCW) biosynthesis in poplars has remained unknown, while our understanding of KNAT7 functions in other plants is continuously evolving. To study the impact of genetic modifications of homologous and heterologous KNAT7 gene expression on SCW formation in transgenic poplars, we prepared poplar KNAT7 (PtKNAT7) overexpression (PtKNAT7-OE) and antisense suppression (PtKNAT7-AS) vector constructs for the generation of transgenic poplar lines via Agrobacterium-mediated transformation. Since the overexpression of homologous genes can sometimes result in co-suppression, we also overexpressed Arabidopsis KNAT7 (AtKNAT7-OE) in transgenic poplars. In all these constructs, the expression of KNAT7 transgenes was driven by developing xylem (DX)-specific promoter, DX15. Compared to wild-type (WT) controls, many SCW biosynthesis genes downstream of KNAT7 were highly expressed in poplar PtKNAT7-OE and AtKNAT7-OE lines. Yet, no significant increase in lignin content of woody biomass of these transgenic lines was observed. PtKNAT7-AS lines, however, showed reduced expression of many SCW biosynthesis genes downstream of KNAT7 accompanied by a reduction in lignin content of wood compared to WT controls. Syringyl to Guaiacyl lignin (S/G) ratios were significantly increased in all three KNAT7 knockdown and overexpression transgenic lines than WT controls. These transgenic lines were essentially indistinguishable from WT controls in terms of their growth phenotype. Saccharification efficiency of woody biomass was significantly increased in all transgenic lines than WT controls. Overall, our results demonstrated that developing xylem-specific alteration of KNAT7 expression affects the expression of SCW biosynthesis genes, impacting at least the lignification process and improving saccharification efficiency, hence providing one of the powerful tools for improving bioethanol production from woody biomass of bioenergy crops and trees