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Kim, Hyojin

Publications and source records attributed to Kim, Hyojin.

6 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Data for Multisite Field Evaluation of Oil Accumulation and Agronomic Performance in Grain and Sweet Sorghums Engineered for Lipid Hyperaccumulation

Oil sorghum (OS) has been developed by engineering grain (TX430) and sweet (Ramada) genetic backgrounds to accumulate triacylglycerols (TAG) in vegetative tissues as an energy-dense feedstock for sustainable aviation fuel (SAF) and other biofuels. This study evaluated two TX430 OS lines (TxHO-2, TxHO-3) and two Ramada OS lines (RmHO-1, RmHO-2) alongside wild-type (WT) lines in NE and IL over 2 years (2023–2024) to quantify genotype × environment effects on agronomic performance and TAG accumulation. Across four environments, TX430 OS lines showed average TAG concentrations of 15.0 g kg−1 in leaves and 12.8 g kg−1 in stems, approximately 19-fold higher than WT. Ramada OS lines accumulated 26.1 g kg−1 in leaves and 12.3 g kg−1 in stems, approximately 25-fold and 13-fold increases over WT, respectively. OS lines in TX430 exhibited an 18% reduction in biomass (8.4 vs. 9.9 Mg ha−1 for WT), while Ramada OS lines had similar WT biomass (18.3 vs. 19.9 Mg ha−1 for WT). Among TX430 OS lines, TxHO-2 achieved the highest TAG yield (190 kg ha−1), while RmHO-1 led the Ramada lines (335 kg ha−1) due to higher biomass and similar TAG concentration. Enhanced TAG accumulation increased N, P, and K removal in TX430 lines but not in Ramada lines. Structural carbohydrate and ash concentration were unaffected. Overall, results confirm vegetative lipid accumulation as a viable strategy for high-biomass sorghum, supporting its potential as a dual-purpose feedstock for SAF. Future work should focus on minimizing biomass yield penalties and improving nutrient use efficiency in oil sorghum systems.

Agronomy↗
DOE OSTI2026

Data for Development of Vegetative Oil Sorghum: From Lab-to-Field

Biomass crops engineered to accumulate energy-dense triacylglycerols (TAG or ‘vegetable oils’) in their vegetative tissues have emerged as potential feedstocks to meet the growing demand for renewable diesel and sustainable aviation fuel (SAF). Unlike oil palm and oilseed crops, the current commercial sources of TAG, vegetative tissues, such as leaves and stems, only transiently accumulate TAG. In this report, we used grain (Texas430 or TX430) and sugar-accumulating ‘sweet’ (Ramada) genotypes of sorghum, a high-yielding, environmentally resilient biomass crop, to accumulate TAG in leaves and stems. We initially tested several gene combinations for a ‘push-pull-protect’ strategy. The top TAG-yielding constructs contained five oil transgenes for a sorghum WRINKLED1 transcription factor (‘push’), a Cuphea viscosissima diacylglycerol acyltransferase (DGAT; ‘pull’), a modified sesame oleosin (‘protect’) and two combinations of specialized Cuphea lysophosphatidic acid acyltransferases and medium-chain acyl-acyl carrier protein thioesterases. Though intended to generate oils with medium-chain fatty acids, engineered lines accumulated oleic acid-rich oil to amounts of up to 2.5% DW in leaves and 2.0% DW in stems in the greenhouse, 36-fold and 49-fold increases relative to wild-type (WT) plants, respectively. Under field conditions, the top-performing event accumulated TAG to amount to 5.5% DW in leaves and 3.5% DW in stems, 78-fold and 58-fold increases, respectively, relative to WT TX430. Transcriptomic and fluxomic analyses revealed potential bottlenecks for increased TAG accumulation. Overall, our studies highlight the utility of a lab-to-field pipeline coupled with systems biology studies to deliver high vegetative oil sorghum for SAF and renewable diesel production.

Biofuels↗
DOE OSTI2024

Development of vegetative oil sorghum: From lab‐to‐field

Summary Biomass crops engineered to accumulate energy‐dense triacylglycerols (TAG or ‘vegetable oils’) in their vegetative tissues have emerged as potential feedstocks to meet the growing demand for renewable diesel and sustainable aviation fuel (SAF). Unlike oil palm and oilseed crops, the current commercial sources of TAG, vegetative tissues, such as leaves and stems, only transiently accumulate TAG. In this report, we used grain (Texas430 or TX430) and sugar‐accumulating ‘sweet’ (Ramada) genotypes of sorghum, a high‐yielding, environmentally resilient biomass crop, to accumulate TAG in leaves and stems. We initially tested several gene combinations for a ‘push‐pull‐protect’ strategy. The top TAG‐yielding constructs contained five oil transgenes for a sorghum WRINKLED1 transcription factor (‘push’), a Cuphea viscosissima diacylglycerol acyltransferase (DGAT; ‘pull’), a modified sesame oleosin (‘protect’) and two combinations of specialized Cuphea lysophosphatidic acid acyltransferases and medium‐chain acyl‐acyl carrier protein thioesterases. Though intended to generate oils with medium‐chain fatty acids, engineered lines accumulated oleic acid‐rich oil to amounts of up to 2.5% DW in leaves and 2.0% DW in stems in the greenhouse, 36‐fold and 49‐fold increases relative to wild‐type (WT) plants, respectively. Under field conditions, the top‐performing event accumulated TAG to amount to 5.5% DW in leaves and 3.5% DW in stems, 78‐fold and 58‐fold increases, respectively, relative to WT TX430. Transcriptomic and fluxomic analyses revealed potential bottlenecks for increased TAG accumulation. Overall, our studies highlight the utility of a lab‐to‐field pipeline coupled with systems biology studies to deliver high vegetative oil sorghum for SAF and renewable diesel production.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Few-view computed tomography reconstruction using deep neural network inference

A system for generating 2D slices of a 3D image of a target volume is provided. The system receives a target sinogram collected during a computed tomography scan of the target volume. The system inputs the target sinogram to a convolutional neural network (CNN) to generate predicted 2D slices of the 3D image. The CNN is trained using training 2D slices of training 3D images. The system initializes 2D slices to the predicted 2D slices. The system reconstructs 2D slices of the 3D image from the target sinogram and the initialized 2D slices.

Kim, Hyojin↗