Selection and multi-environment yield stability analysis in a switchgrass (Panicum virgatum L.) half-sib panel
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09 BIOMASS FUELS↗
Engineering topics
Stewart, Charles Neal
Publications and source records attributed to Stewart, Charles Neal.
Plant synthetic biology innovations for biofuels and bioproducts
Plant-based biosynthesis of fuels, chemicals, and materials promotes environmental sustainability, which includes decreases in greenhouse gas emissions, water pollution, and loss of biodiversity. Advances in plant synthetic biology (synbio) should improve precision and efficacy of genetic engineering for sustainability. Applicable synbio innovations include genome editing, gene circuit design, synthetic promoter development, gene stacking technologies, and the design of environmental sensors. Moreover, recent advancements in developing spatially resolved and single-cell omics contribute to the discovery and characterization of cell-type-specific mechanisms and spatiotemporal gene regulations in distinct plant tissues for the expression of cell- and tissue-specific genes, resulting in improved bioproduction. In conclusion, this review highlights recent plant synbio progress and new single-cell molecular profiling towards sustainable biofuel and biomaterial production.
Yang, Yongil↗
Sustainability Trait Modeling of Field-Grown Switchgrass (Panicum virgatum) Using UAV-Based Imagery
Unmanned aerial vehicles (UAVs) provide an intermediate scale of spatial and spectral data collection that yields increased accuracy and consistency in data collection for morphological and physiological traits than satellites and expanded flexibility and high-throughput compared to ground-based data collection. In this project, we used UAV-based multispectral imagery collected from MicaSense RedEdge-M on a DJI Matrice 600 Pro for automated phenotyping of field-grown switchgrass (Panicum virgatum), a leading bioenergy feedstock. The raw images were processed with Pix4D Mapper to create the reflectance data, and vegetation indices were calculated from a UAV-based multispectral camera. Statistical models were developed for rust disease caused by Puccinia novopanici, leaf chlorophyll, nitrogen, and lignin contents. For the first time, UAV remote sensing technology was used to explore the potential for multiple traits associated with sustainable production of switchgrass. One statistical model was developed for each individual trait based on the statistical correlation between vegetation indices and the corresponding trait.
CBI↗
Sustainability Trait Modeling of Field-Grown Switchgrass ( Panicum virgatum ) Using UAV-Based Imagery
Unmanned aerial vehicles (UAVs) provide an intermediate scale of spatial and spectral data collection that yields increased accuracy and consistency in data collection for morphological and physiological traits than satellites and expanded flexibility and high-throughput compared to ground-based data collection. In this study, we used UAV-based remote sensing for automated phenotyping of field-grown switchgrass (Panicum virgatum), a leading bioenergy feedstock. Using vegetation indices calculated from a UAV-based multispectral camera, statistical models were developed for rust disease caused by Puccinia novopanici, leaf chlorophyll, nitrogen, and lignin contents. For the first time, UAV remote sensing technology was used to explore the potentials for multiple traits associated with sustainable production of switchgrass, and one statistical model was developed for each individual trait based on the statistical correlation between vegetation indices and the corresponding trait. Also, for the first time, lignin content was estimated in switchgrass shoots via UAV-based multispectral image analysis and statistical analysis. The UAV-based models were verified by ground-truthing via correlation analysis between the traits measured manually on the ground-based with UAV-based data. The normalized difference red edge (NDRE) vegetation index outperformed the normalized difference vegetation index (NDVI) for rust disease and nitrogen content, while NDVI performed better than NDRE for chlorophyll and lignin content. Overall, linear models were sufficient for rust disease and chlorophyll analysis, but for nitrogen and lignin contents, nonlinear models achieved better results. As the first comprehensive study to model switchgrass sustainability traits from UAV-based remote sensing, these results suggest that this methodology can be utilized for switchgrass high-throughput phenotyping in the field.
60 APPLIED LIFE SCIENCES↗
Silencing Folylpolyglutamate Synthetase1 (FPGS1) in Switchgrass (Panicum virgatum L.) Improves Lignocellulosic Biofuel Production
Switchgrass (Panicum virgatum L.) is a lignocellulosic perennial grass with great potential in bioenergy field. Lignocellulosic bioenergy crops are mostly resistant to cell wall deconstruction, and therefore yield suboptimal levels of biofuel. The one-carbon pathway (also known as C1 metabolism) is critical for polymer methylation, including that of lignin and hemicelluloses in cell walls. Folylpolyglutamate synthetase (FPGS) catalyzes a biochemical reaction that leads to the formation of folylpolyglutamate, an important cofactor for many enzymes in the C1 pathway. In this study, the putatively novel switchgrass PvFPGS1 gene was identified and its functional role in cell wall composition and biofuel production was examined by RNAi knockdown analysis. The PvFPGS1-downregulated plants were analyzed in the field over three growing seasons. Transgenic plants with the highest reduction in PvFPGS1 expression grew slower and produced lower end-of-season biomass. Transgenic plants with low-to-moderate reduction in PvFPGS1 transcript levels produced equivalent biomass as controls. There were no significant differences observed for lignin content and syringyl/guaiacyl lignin monomer ratio in the low-to-moderately reduced PvFPGS1 transgenic lines compared with the controls. Similarly, sugar release efficiency was also not significantly different in these transgenic lines compared with the control lines. However, transgenic plants produced up to 18% more ethanol while maintaining congruent growth and biomass as non-transgenic controls. Severity of rust disease among transgenic and control lines were not different during the time course of the field experiments. Altogether, the unchanged lignin content and composition in the low-to-moderate PvFPGS1-downregulated lines may suggest that partial downregulation of PvFPGS1 expression did not impact lignin biosynthesis in switchgrass. In conclusion, the manipulation of PvFPGS1 expression in bioenergy crops may be useful to increase biofuel potential with no growth penalty or increased susceptibility to rust in feedstock.
59 BASIC BIOLOGICAL SCIENCES↗