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DOE OSTI2026

Mitigating the soil carbon deficit of annual agriculture with perennial bioenergy crops in the U.S. Midwest

Maize ( Zea mays L.) is the dominant bioenergy feedstock in the US Midwest but its cultivation since the early 1800s has incurred substantial losses in soil organic carbon (SOC). We quantified differences in SOC stocks under perennial bioenergy crops of Panicum virgatum L. (switchgrass) and Miscanthus x giganteus Greef et Deuter (miscanthus) planted on former maize and soybean fields relative to maize-based annual cropping and native prairie. Comparisons were made at seven locations across Illinois, USA, spanning a range of climate and soil types. Across sites, SOC stocks to 1-m depth on an equivalent soil mass basis were 146 Mg C ha −1 under prairie, 107 Mg C ha −1 under miscanthus, 97.9 Mg C ha −1 under switchgrass, and 87.7 Mg C ha −1 under maize. Higher SOC demonstrates the potential of perennial bioenergy crops to rebuild the SOC deficit accrued under nearly two centuries of maize-based annual cropping. SOC stock increased in the first 5 years under mature bioenergy crops at four out of seven sites. Carbon isotope (δ 13 C) analyses of surface depths confirmed short-term increases in SOC to be derived from miscanthus and switchgrass. Stocks of SOC could be increased over time under miscanthus or switchgrass cultivation even with annual harvesting, though our measured rates of SOC accumulation were lower than previous estimates for Illinois and varied by site.

bioenergy↗

DOE OSTI2021

Airborne hyperspectral imaging of nitrogen deficiency on crop traits and yield of maize by machine learning and radiative transfer modeling

Nitrogen is an essential nutrient that directly affects plant photosynthesis, crop yield, and biomass production for bioenergy crops, but excessive application of nitrogen fertilizers can cause environmental degradation. To achieve sustainable nitrogen fertilizer management for precision agriculture, there is an urgent need for nondestructive and high spatial resolution monitoring of crop nitrogen and its allocation to photosynthetic proteins as that changes over time. Here, we used visible to shortwave infrared (400–2400 nm) airborne hyperspectral imaging with high spatial (0.5 m) and spectral (3–5 nm) resolutions to accurately estimate critical crop traits, i.e., nitrogen, chlorophyll, and photosynthetic capacity (CO 2 -saturated photosynthesis rate, V max,27 ), at leaf and canopy scales, and to assess nitrogen deficiency on crop yield. We conducted three airborne campaigns over a maize (Zea mays L.) field during the growing season of 2019. Physically based soil-canopy Radiative Transfer Modeling (RTM) and data-driven approaches i.e. Partial-Least Squares Regression (PLSR) were used to retrieve crop traits from hyperspectral reflectance, with ground truth of leaf nitrogen, chlorophyll, V max,27 , Leaf Area Index (LAI), and harvested grain yield. To improve computational efficiency of RTMs, Random Forest (RF) was used to mimic RTM simulations to generate machine learning surrogate models RTM-RF. The results show that prior knowledge of soil background and leaf angle distribution can significantly reduce the ill-posed RTM retrieval. RTM-RF achieved a high accuracy to predict leaf chlorophyll content (R 2 = 0.73) and LAI (R 2 = 0.75). Meanwhile, PLSR exhibited better accuracy to predict leaf chlorophyll content (R 2 = 0.79), nitrogen concentration (R 2 = 0.83), nitrogen content (R 2 = 0.77), and V max,27 (R 2 = 0.69) but required measured traits for model training. We also found that canopy structure signals can enhance the use of spectral data to predict nitrogen related photosynthetic traits, as combining RTM-RF LAI and PLSR leaf traits well predicted canopy-level traits (leaf traits × LAI) including canopy chlorophyll (R 2 = 0.80), nitrogen (R 2 = 0.85) and V max,27 (R 2 = 0.82). Compared to leaf traits, we further found that canopy-level photosynthetic traits, particularly canopy V max,27 , have higher correlation with maize grain yield. This study highlights the potential for synergistic use of process-based and data-driven approaches of hyperspectral imaging to quantify crop traits that facilitate precision agricultural management to secure food and bioenergy production.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

scPlantAnnotate: an accurate and robust transformer-based model for plant cell type annotation

Accurate cell type annotation remains a major bottleneck in plant single-cell RNA sequencing (scRNA-seq), where existing tools are often adapted from animal studies and perform sub-optimally on plant data. The lack of plant-specific computational frameworks limits the construction of plant cell atlases and downstream biological discovery. We develop and evaluate scPlantAnnotate, a Transformer-based reference annotation framework tailored for plant scRNA-seq data, and benchmark it against state-of-the-art deep learning and conventional methods across multiple plant species. Species-specific scPlantAnnotate models were trained using curated datasets from Arabidopsis thaliana, Zea mays, Oryza sativa, and Glycine max. We compared scPlantAnnotate with leading baselines under both standard random-split evaluation and a more stringent leave-one-dataset-out setting, which tests robustness to completely unseen datasets and tissue types. scPlantAnnotate consistently outperforms existing approaches across all four species under random-split evaluation. In the leave-one-dataset-out setting for A. thaliana, where performance drops markedly for all methods due to strong batch effects and dataset heterogeneity, scPlantAnnotate nonetheless achieves the highest Accuracy, Macro-F1, Balanced Accuracy, and Macro-AUROC on average and ranks first on most held-out datasets. These results demonstrate improved robustness to dataset shifts, a critical yet underexplored challenge in plant scRNA-seq analysis. A freely accessible web server enables users to annotate their own datasets using pretrained models. scPlantAnnotate provides a plant-specific, Transformer-based framework for single-cell annotation that delivers state-of-the-art performance and enhanced robustness to unseen datasets. By addressing limitations of existing tools and enabling scalable reference-based annotation, scPlantAnnotate supports the development of comprehensive plant cell atlases and facilitates broader use of single-cell genomics in plant biology.

Bioinformatics↗

DOE OSTI2022

Structure of maize BZR1-type β-amylase BAM8 provides new insights into its noncatalytic adaptation

Plant β-amylase (BAM) proteins play an essential role in growth, development, stress response, and hormone regulation. Despite their typical (β/α) 8 barrel structure as active catalysts in starch breakdown, catalytically inactive BAMs are implicated in diverse yet elusive functions in plants. Further, the noncatalytic BAM7/8 contain N-terminal BZR1 domains and were shown to be involved in the regulation of brassinosteroid signaling and possibly serve as sensors of yet an uncharacterized metabolic signal. While the structures of several catalytically active BAMs have been reported, structural characterization of the catalytically inactive BZR1-type BAMs remain unknown. Here, we determine the crystal structure of β-amylase domain of Zea mays BAM8/BES1/BZR1-5 and provide comprehensive insights into its noncatalytic adaptation. Using structural-guided comparison combined with biochemical analysis and molecular dynamics simulations, we revealed conformational changes in multiple distinct highly conserved regions resulting in rearrangement of the binding pocket. Altogether, this study adds a new layer of understanding to starch breakdown mechanism and elucidates the acquired adjustments of noncatalytic BZR1-type BAMs as putative regulatory domains and/or metabolic sensors in plants.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Root exudate composition from different plant species influences the growth of rhizosphere bacteria

Plant roots release exudates that fuel microbial activities and can structure rhizosphere microbial communities, but how different plant species use their root exudate to potentially select for different soil microbes in the rhizosphere is not well understood. Here, we investigated how root exudate from plants of three diverging lineages, Lactuca sativa (lettuce), Brassica juncea (mustard cabbage), and Zea mays (maize) influence the growth of their own rhizosphere bacteria (host) and those from other plant species (non-host) in growth bioassays. We found that on the community level, lettuce rhizosphere bacteria grew better in non-host exudate, but mustard cabbage and maize rhizosphere bacteria grew similarly well in both host and non-host exudate. However, individual bacteria taxa showed strong preferences for exudate from different plant species. The bacterial growth patterns were independent of C and N quantity, suggesting that certain exometabolic compounds may drive the growth patterns. Our results demonstrate that root exudate from a given plant species have the potential to stimulate or suppress soil bacteria and hint at a mechanism that different plant species use to select for their specific suite of rhizosphere bacteria. Furthermore, these findings contribute to our broader understanding of how root exudate quality could be a mechanism that plants use to select for distinct microbial communities in the rhizosphere.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Integrating fine root diameter and watershed mapping to characterize rhizosphere hydrology

Root morphology and soil hydraulic characteristics were integrated using watershed distance mapping to show water distribution and uptake across the plant-soil interface. Poplar (Populus deltoides, P. trichocarpa), maize (Zea mays), juniper (Juniperus virginiana), grape (Vitis rotundifolia) and maple (Acer saccharum) seedlings were grown in sand, after which root diameter and soil water dynamics were assessed via sequential neutron radiography. Three local soil regions (root-soil interface or edge, rhizosphere, bulk soil) were classified based on both radial distance from the root surface and diameter of the nearest root, from which changes in water content and distribution were characterized using digital image processing. Water content dynamics across the rhizosphere showed two different species-independent processes: a consistently elevated water content at the root-soil edge interface which increased with root diameter, and hysteresis as the rhizosphere transitioned to bulk soil (~0.5 cm from the root), independent of root diameter. Water uptake per unit root surface area declined exponentially with root diameter, independent of species. Results highlight the species-independent hydrologic characteristics of the rhizosphere and the potential for evaluating them in a local spatially connected soil context. Avenues for improved integration of soil and root characteristics are discussed.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Soil oxidoreductase zymography: Visualizing spatial distributions of peroxidase and phenol oxidase activities at the root-soil interface

Decomposition of organic material in the rhizosphere – the most dynamic microbial habitat in soil – involves arrays of oxidoreductase and hydrolytic enzymes. Spatial distributions of various hydrolytic activities in soil have already been explored by zymographic techniques. However, the distribution of oxidative activity in the rhizosphere remains to be studied. Thus, we extended a Time-Lapse Zymography technique, using Amplex Red® reagent, to visualize and quantify distributions of phenol oxidase and peroxidase activities in the rhizosphere of Zea mays L. growing in a Haplic Phaeozem and the non-rhizospheric soil. The gross oxidative activity was greatest at the root surfaces, and fell to background soil levels 1.26 and 0.73 mm from seminal (>1 mm diameter) and lateral (<0.5 mm diameter) roots, respectively. The rhizosphere extent relative to the root radius was 59% broader around lateral than around seminal roots. The greatest activities, up to 30 nmol cm –2 min –1 , were peroxidase-dominated and closely associated with roots. Here, the results confirm the utility of the approach for studying spatio-temporal distributions of oxidative activities in soil. However, actual activity of oxidoreductases in the field will be strongly controlled by fluctuating environmental conditions such as soil aeration and the gradient of reactive oxygen species, which need to be considered especially in anoxic soils.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Plant litter traits control microbial decomposition and drive soil carbon stabilization

Efforts to manage soils for carbon (C) sequestration remain limited by our understanding of how differences in plant traits and microbial traits mechanistically drive soil organic C (SOC) storage. Addressing this uncertainty is particularly critical in bioenergy agriculture, due to its potential to enhance soil C and provide a C neutral fuel. As such, we examined differences between two contrasting feedstocks, Zea mays (corn) and Miscanthus x giganteus (miscanthus), in the ability of their litter to form new chemically resistant particulate SOC vs. physically protected mineral associated SOC and used this data to improve the parameterization of a microbial SOC model. We tested a hypothesized conceptual model whereby easy to decompose corn litters drive greater microbial carbon use efficiency (CUE) and the formation of more mineral associated SOC over particulate SOC than more complex miscanthus litters. To do this, we performed a soil microcosm experiment where we added 13 C enriched aboveground and belowground litters to soils and traced the fate of the 13 C into microbial respiration and SOC pools. We found that corn litters promoted higher microbial CUE (0.37) than miscanthus litters (0.24). In turn, corn litter formed approximately 50% more mineral associated SOC than miscanthus litters. Similarly, structurally complex root litters promoted a lower CUE and formed less mineral associated SOC than leaf and shoot litters for both crops. When we used our data to parameterize the SOC model, we found that modelling microbial trait differences uniquely allowed the model to capture the fate of litter C in SOC. Collectively, we found a robust link between litter quality, microbial efficiency, and the formation of SOC. This link bridges the empirical uncertainty in how different crops can form new soil C and provides an empirical basis for modelling SOC transformations.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Temporal variabilities of soil carbon dioxide fluxes from cornfield impacted by temperature and precipitation changes through high-frequent measurement and DAYCENT modelling

Soil carbon dioxide (CO 2 ) emissions from the field of corn (Zea mays L.) play an important role in global warming. This study investigated temporal variability of soil CO 2 fluxes (R s ) with soil temperature (T s ) and moisture (θ) and built DAYCENT models for predicting future impacts of climate changes on R s using the measured high-frequency data. R s trend was tested by Mann–Kendall and Sen Estimator. Predicted R s s under different climate scenarios were compared using Parallel-line Analysis. Herein, the findings indicated that daily R s exponentially increased with T s constrained by θ. During the θ of 27–31%, there was a strong exponential relationship between R s and T s , but the relationship was weaker for the θ of 38–41% and 22–26%. Soil environmental index (SEI, T s × θ) significantly impacted R s with linear regression R s 0.5 = 0.4599 + 0.002059 × SEI in 2008, 2009 and 2011. At the diurnal scale, there were different trends in R s s and relationships among R s and T s and θ in different years. Predicted yearly R s s, root R s s and corn yield in 2014–2049 increased with an increase in temperature scenarios, but the R s s significantly increased as temperature rose by 1°C or higher. Predicted yearly R s s, root R s s and yield reduced with precipitation scenario increase, and the root R s s and yield significantly diminished as precipitation increased by 15 and 30%. Predicted yearly R s from cornfields had a significantly increasing trend. Future research is needed to explore methods for mitigating cornfield R s and evaluating sensitivities of different cropland R s s to temperature changes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Measured and Simulated Carbon Dynamics in Midwestern U.S. Corn‐Soybean Rotations

Abstract Corn ( Zea mays L.) and soybean ( Glycine max [L.] Merr.) production dominate Midwestern U.S. agriculture and impact the regional carbon and nitrogen cycles. Sustaining soil carbon is important for corn‐soybean production (CS); however, quantifying soil carbon changes requires long‐term field measurements and/or model simulations. In this study, changes in soil organic (SOC), inorganic (SIC), and total (TC) carbon; pH; total nitrogen (TN); and net ecosystem production (NEP) were measured in a conventional corn‐soybean rotation in central Iowa. Soil samples ( n = 42; 0–120 cm depth) were collected from two adjacent fields in 2005 and 2016. Eddy‐flux stations set up in the fields continuously monitored NEP from 2005–2016, and net biome production (NBP) was calculated using yield records. The DayCENT model was used to simulate the effects of conventional management practices on soil carbon and calibrated with field‐measured NEP and SOC. Measured soil TC (0–120 cm) decreased by −14.19 ± 6.25 Mg ha −1 , with highest reductions in SOC and SIC ( p < 0.05) at 0–15 and 90–120 cm, respectively. Measured TN decreased by −0.7 ± 0.29 Mg ha −1 with N‐accumulation at 60–90 cm ( p < 0.05). Eddy‐flux NBP decreased by −13.19 ± 0.05 Mg ha −1 . Soil and eddy‐flux records show a carbon reduction by −1.14 ± 0.63 and −1.20 ± 0.06 Mg ha −1 yr −1 , respectively. The validated DayCENT model suggests that all SOC pools declined. We postulate that conventional CS production has adverse effects on C and N dynamics in Midwestern United States.

Dold, C.↗

DOE OSTI2021

Multiseriate cortical sclerenchyma enhance root penetration in compacted soils

Mechanical impedance limits soil exploration and resource capture by plant roots. We examine the role of root anatomy in regulating plant adaptation to mechanical impedance and identify a root anatomical phene in maize (Zea mays) and wheat (Triticum aestivum) associated with penetration of hard soil: Multiseriate cortical sclerenchyma (MCS). We characterize this trait and evaluate the utility of MCS for root penetration in compacted soils. Roots with MCS had a greater cell wall-to-lumen ratio and a distinct UV emission spectrum in outer cortical cells. Genome-wide association mapping revealed that MCS is heritable and genetically controlled. We identified a candidate gene associated with MCS. Across all root classes and nodal positions, maize genotypes with MCS had 13% greater root lignin concentration compared to genotypes without MCS. Genotypes without MCS formed MCS upon exogenous ethylene exposure. Genotypes with MCS had greater lignin concentration and bending strength at the root tip. In controlled environments, MCS in maize and wheat was associated improved root tensile strength and increased penetration ability in compacted soils. Maize genotypes with MCS had root systems with 22% greater depth and 49% greater shoot biomass in compacted soils in the field compared to lines without MCS. Of the lines we assessed, MCS was present in 30 to 50% of modern maize, wheat, and barley cultivars but was absent in teosinte and wild and landrace accessions of wheat and barley. MCS merits investigation as a trait for improving plant performance in maize, wheat, and other grasses under edaphic stress.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Investigating genomic prediction strategies for grain carotenoid traits in a tropical/subtropical maize panel

Abstract Vitamin A deficiency remains prevalent on a global scale, including in regions where maize constitutes a high percentage of human diets. One solution for alleviating this deficiency has been to increase grain concentrations of provitamin A carotenoids in maize (Zea mays ssp. mays L.)—an example of biofortification. The International Maize and Wheat Improvement Center (CIMMYT) developed a Carotenoid Association Mapping panel of 380 inbred lines adapted to tropical and subtropical environments that have varying grain concentrations of provitamin A and other health-beneficial carotenoids. Several major genes have been identified for these traits, 2 of which have particularly been leveraged in marker-assisted selection. This project assesses the predictive ability of several genomic prediction strategies for maize grain carotenoid traits within and between 4 environments in Mexico. Ridge Regression-Best Linear Unbiased Prediction, Elastic Net, and Reproducing Kernel Hilbert Spaces had high predictive abilities for all tested traits (β-carotene, β-cryptoxanthin, provitamin A, lutein, and zeaxanthin) and outperformed Least Absolute Shrinkage and Selection Operator. Furthermore, predictive abilities were higher when using genome-wide markers rather than only the markers proximal to 2 or 13 genes. These findings suggest that genomic prediction models using genome-wide markers (and assuming equal variance of marker effects) are worthwhile for these traits even though key genes have already been identified, especially if breeding for additional grain carotenoid traits alongside β-carotene. Predictive ability was maintained for all traits except lutein in between-environment prediction. The TASSEL (Trait Analysis by aSSociation, Evolution, and Linkage) Genomic Selection plugin performed as well as other more computationally intensive methods for within-environment prediction. The findings observed herein indicate the utility of genomic prediction methods for these traits and could inform their resource-efficient implementation in biofortification breeding programs.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

DCT4 —A New Member of the Dicarboxylate Transporter Family in C 4 Grasses

Malate transport shuttles atmospheric carbon into the Calvin–Benson cycle during NADP-ME C 4 photosynthesis. Previous characterizations of several plant dicarboxylate transporters (DCT) showed that they efficiently exchange malate across membranes. Here, we identify and characterize a previously unknown member of the DCT family, DCT4, in Sorghum bicolor. We show that SbDCT4 exchanges malate across membranes and its expression pattern is consistent with a role in malate transport during C 4 photosynthesis. SbDCT4 is not syntenic to the characterized photosynthetic gene ZmDCT2, and an ortholog is not detectable in the maize reference genome. We found that the expression patterns of DCT family genes in the leaves of Zea mays, and S. bicolor varied by cell type. Our results suggest that subfunctionalization, of members of the DCT family, for the transport of malate into the bundle sheath plastids, occurred during the process of independent recurrent evolution of C 4 photosynthesis in grasses of the PACMAD clade. We also show that this subfunctionalization is lineage independent. Our results challenge the dogma that key C 4 genes must be orthologues of one another among C 4 species, and shed new light on the evolution of C 4 photosynthesis.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Unique contributions of chlorophyll and nitrogen to predict crop photosynthetic capacity from leaf spectroscopy

The photosynthetic capacity or the CO 2 -saturated photosynthetic rate (Vmax), chlorophyll, and nitrogen are closely linked leaf traits that determine C 4 crop photosynthesis and yield. Accurate, timely, rapid, and non-destructive approaches to predict leaf photosynthetic traits from hyperspectral reflectance are urgently needed for high-throughput crop monitoring to ensure food and bioenergy security. Therefore, this study thoroughly evaluated the state-of-the-art physically based radiative transfer models (RTMs), data-driven partial least squares regression (PLSR), and generalized PLSR (gPLSR) models to estimate leaf traits from leaf-clip hyperspectral reflectance, which was collected from maize (Zea mays L.) bioenergy plots with diverse genotypes, growth stages, treatments with nitrogen fertilizers, and ozone stresses in three growing seasons. The results show that leaf RTMs considering bidirectional effects can give accurate estimates of chlorophyll content (Pearson correlation r=0.95), while gPLSR enabled retrieval of leaf nitrogen concentration (r=0.85). Using PLSR with field measurements for training, the cross-validation indicates that V max can be well predicted from spectra (r=0.81). Here, the integration of chlorophyll content (strongly related to visible spectra) and nitrogen concentration (linked to shortwave infrared signals) can provide better predictions of V max (r=0.71) than only using either chlorophyll or nitrogen individually. This study highlights that leaf chlorophyll content and nitrogen concentration have key and unique contributions to V max prediction.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

A Maize GT14 Family Glycosyltransferase Affects Cell Wall Composition and Carbohydrate Export from Source Leaves

Sucrose translocation from photosynthetic leaves to distant parts of a plant, such as seeds and roots, is a critical aspect of plant growth and development and a major determinant of crop yield. To identify genes contributing to this process in maize (Zea mays), we isolated four allelic mutants, carbohydrate partitioning defective7, 48, 49 (cpd7, cpd48, cpd49) and a UniformMu insertion (mu1049954), all of which exhibited reduced growth and fertility and hyperaccumulation of starch and soluble sugars in mature leaves. Consistent with carbohydrate accumulation, cpd7 mutants exhibited reduced sucrose export from mature leaves. Cpd7 encodes a Golgi-resident glucuronosyltransferase belonging to the Glycosyltransferase14 (GT14) family, which is involved in decoration of type II arabinogalactan proteins. No previously described GT14 mutants exhibit reduced sucrose transport or carbohydrate partitioning defects. Additionally, we show that mature leaves of cpd7 mutants have reduced cellulose content and an altered cell wall composition. Further, cpd7 mutants exhibit ectopic phloem lignification likely as a compensatory mechanism for reduced cell wall integrity. Collectively, our data suggest that Cpd7 functions to facilitate cell wall development in the phloem, which is required for efficient sucrose export from mature maize leaves.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Contrasting transcriptional responses to Fusarium virguliforme colonization in symptomatic and asymptomatic hosts

The broad host range of Fusarium virguliforme represents a unique comparative system to identify and define differentially induced responses between an asymptomatic monocot host, maize (Zea mays), and a symptomatic eudicot host, soybean (Glycine max). Using a temporal, comparative transcriptome-based approach, we observed that early gene expression profiles of root tissue from infected maize suggest that pathogen tolerance coincides with the rapid induction of senescence dampening transcriptional regulators, including ANACs (Arabidopsis thaliana NAM/ATAF/CUC protein) and Ethylene-Responsive Factors. In contrast, the expression of senescence-associated processes in soybean was coincident with the appearance of disease symptom development, suggesting pathogen-induced senescence as a key pathway driving pathogen susceptibility in soybean. Based on the analyses described herein, we posit that root senescence is a primary contributing factor underlying colonization and disease progression in symptomatic versus asymptomatic host–fungal interactions. This process also supports the lifestyle and virulence of F. virguliforme during biotrophy to necrotrophy transitions. Further support for this hypothesis lies in comprehensive co-expression and comparative transcriptome analyses, and in total, supports the emerging concept of necrotrophy-activated senescence. We propose that F. virguliforme conditions an environment within symptomatic hosts, which favors susceptibility through transcriptomic reprogramming, and as described herein, the induction of pathways associated with senescence during the necrotrophic stage of fungal development.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Bacterium-enabled transient gene activation by artificial transcription factors for resolving gene regulation in maize

Abstract Understanding gene regulatory networks is essential to elucidate developmental processes and environmental responses. Here, we studied regulation of a maize (Zea mays) transcription factor gene using designer transcription activator-like effectors (dTALes), which are synthetic Type III TALes of the bacterial genus Xanthomonas and serve as inducers of disease susceptibility gene transcription in host cells. The maize pathogen Xanthomonas vasicola pv. vasculorum was used to introduce 2 independent dTALes into maize cells to induced expression of the gene glossy3 (gl3), which encodes a MYB transcription factor involved in biosynthesis of cuticular wax. RNA-seq analysis of leaf samples identified, in addition to gl3, 146 genes altered in expression by the 2 dTALes. Nine of the 10 genes known to be involved in cuticular wax biosynthesis were upregulated by at least 1 of the 2 dTALes. A gene previously unknown to be associated with gl3, Zm00001d017418, which encodes aldehyde dehydrogenase, was also expressed in a dTALe-dependent manner. A chemically induced mutant and a CRISPR-Cas9 mutant of Zm00001d017418 both exhibited glossy leaf phenotypes, indicating that Zm00001d017418 is involved in biosynthesis of cuticular waxes. Bacterial protein delivery of dTALes proved to be a straightforward and practical approach for the analysis and discovery of pathway-specific genes in maize.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Enhancing lipid production in plant cells through automated high-throughput genome engineering and phenotyping

Plant bioengineering is a time-consuming and labor-intensive process with no guarantee of achieving desired traits. Here, we present a fast, automated, scalable, high-throughput pipeline for plant bioengineering (FAST-PB) in maize (Zea mays) and Nicotiana benthamiana. FAST-PB enables genome editing and product characterization by integrating automated biofoundry engineering of callus and protoplast cells with single-cell matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). We first demonstrated that FAST-PB could streamline Golden Gate cloning, with the capacity to construct 96 vectors in parallel. Using FAST-PB in protoplasts, we found that PEG2050 increased transfection efficiency by over 45%. For proof-of-concept, we established a reporter-gene-free method for CRISPR editing and phenotyping via mutation of high chlorophyll fluorescence 136. We show that diverse lipids were enhanced up to 6-fold using CRISPR activation of lipid controlling genes. In callus cells, an automated transformation platform was employed to regenerate plants with enhanced lipid traits through introducing multigene cassettes. Lastly, FAST-PB enabled high-throughput single-cell lipid profiling by integrating MALDI-MS with the biofoundry, protoplast, and callus cells, differentiating engineered and unengineered cells using single-cell lipidomics. Furthermore, these innovations massively increase the throughput of synthetic biology, genome editing, and metabolic engineering and change what is possible using single-cell metabolomics in plants.

59 BASIC BIOLOGICAL SCIENCES↗