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DOE OSTI2023

Summary Data for paper titled: Influence of soil depth, irrigation, and plant genotype on the soil microbiome, metaphenome, and carbon chemistry

Climate change is causing an increase in drought in many soil ecosystems and a loss of soil organic carbon. Calcareous soils may partially mitigate these losses via carbon capture and storage. Here, we aimed to determine how irrigation-supplied soil moisture and perennial plants impact biotic and abiotic soil properties that underpin deep soil carbon chemistry in an unfertilized calcareous soil. Soil was sampled up to one meter in depth from irrigated and planted field treatments and analyzed using a suite of omics and chemical analyses. Carbon cycling processes in the surface soil were dominated by plant-microbe interactions that drive organic carbon cycling, whereas inorganic carbon chemistry dominated in deeper soil layers. Both irrigation and plant cover impacted organic and inorganic carbon pools in the soil profiles. This study reveals the complex interactions between water, plants, minerals, and microorganisms that govern organic and inorganic pools of soil carbon at different depths.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Data for Influence of soil depth, irrigation, and plant genotype on the soil microbiome, metaphenome, and carbon chemistry: Summary Data

Naasko, Katherine I↗

DOE OSTI2023

Data for Influence of soil depth, irrigation, and plant genotype on the soil microbiome, metaphenome, and carbon chemistry: Sequence Data

Naasko, Katherine I↗

DOE OSTI2021

A KBase Case Study on Genome-wide Transcriptomics and Plant Primary Metabolism in Sorghum

A better understanding of the genetic and metabolic mechanisms that confer stress resistance and tolerance in plants is key to engineering new crops through advanced breeding technologies. This requires a systems biology approach that builds on a genome-wide understanding of the regulation of gene expression, plant metabolism, physiology and growth. In this study, we examine the response to drought stress in Sorghum, as we leverage the tools for transcriptomics and plant metabolic modeling we have implemented at the U.S. Department of Energy Systems Biology Knowledgebase (KBase). KBase enables researchers worldwide to collaborate and advance research by allowing them to upload private or public data into the KBase Narrative Interface, empowering them to analyze this data using a rich, extensible array of computational and data-analytics tools, and allows them to securely share scientific workflows and conclusions. We demonstrate how to use the current RNA-seq tools in KBase, applicable to both plants and microbes, to assemble and quantify long transcripts and identify differentially expressed genes effectively. More specifically, we demonstrate the utility of the platform by identifying key genes that are differentially expressed during drought-stress in Sorghum bicolor, which is an important sustainable production crop plant. We then show how to use KBase tools to predict the membership of genes in metabolic pathways and examine expression data in the context of metabolic subsystems. We demonstrate the power of the platform by making the data analysis and interpretation available to the biologists in the reproducible, re-usable, point-and-click format of a KBase Narrative thus promoting FAIR (Findable, Accessible, Interoperable and Reusable) guiding principles for scientific data management and stewardship.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Plant sulfate transporter protein sequences for phylogenetic analysis

Sulfur is an essential macronutrient that supports plant growth, development, and responses to environmental stress. Sulfate is the predominant inorganic form of sulfur in soils, and its uptake by roots and translocation to shoots are facilitated by the sulfate transporter (SULTR) family of proteins. Although the first plant SULTR gene was identified nearly three decades ago, several subfamily members, particularly those in the expansive and angiosperm-specific SULTR3 group, remain poorly characterized. To support comprehensive phylogenetic and sequence-based analyses, we compiled a curated dataset of 262 SULTR protein sequences from 22 plant species spanning the evolutionary breadth of land plants. This collection includes representatives from two basal lineages, two early-divergent angiosperms, six monocots, and ten dicots. All sequences were extracted from genome assemblies available in Phytozome v13 (Joint Genome Institute) and manually curated, with cross-referencing to additional databases such as NCBI when needed. This dataset provides a valuable resource for reconstructing the evolutionary history of the SULTR family, with particular emphasis on the diversification of SULTR3 transporters in flowering plants. This resource may also support functional annotation, comparative genomics, and structural modeling of sulfate transport proteins.

CBI↗

DOE OSTI2021

Transcriptional Regulation of Drought Response in Arabidopsis and Woody Plants

Within the context of global warming, long-living plants such as perennial woody species endure adverse conditions. Among all of the abiotic stresses, drought stress is one of the most detrimental stresses that inhibit plant growth and productivity. Plants have evolved multiple mechanisms to respond to drought stress, among which transcriptional regulation is one of the key mechanisms. In this review, we summarize recent progress on the regulation of drought response by transcription factor (TF) families, which include abscisic acid (ABA)-dependent ABA-responsive element/ABRE-binding factors (ABRE/ABF), WRKY, and Nuclear Factor Y families, as well as ABA-independent AP2/ERF and NAC families, in the model plant Arabidopsis. We also review what is known in woody species, particularly Populus, due to its importance and relevance in economic and ecological processes. We discuss opportunities for a deeper understanding of drought response in woody plants with the development of high-throughput omics analyses and advanced genome editing techniques.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Overcoming the Challenges to Enhancing Experimental Plant Biology With Computational Modeling

The study of complex biological systems necessitates computational modeling approaches that are currently underutilized in plant biology. Many plant biologists have trouble identifying or adopting modeling methods to their research, particularly mechanistic mathematical modeling. Here we address challenges that limit the use of computational modeling methods, particularly mechanistic mathematical modeling. We divide computational modeling techniques into either pattern models (e.g., bioinformatics, machine learning, or morphology) or mechanistic mathematical models (e.g., biochemical reactions, biophysics, or population models), which both contribute to plant biology research at different scales to answer different research questions. We present arguments and recommendations for the increased adoption of modeling by plant biologists interested in incorporating more modeling into their research programs. As some researchers find math and quantitative methods to be an obstacle to modeling, we provide suggestions for easy-to-use tools for non-specialists and for collaboration with specialists. This may especially be the case for mechanistic mathematical modeling, and we spend some extra time discussing this. Through a more thorough appreciation and awareness of the power of different kinds of modeling in plant biology, we hope to facilitate interdisciplinary, transformative research.

58 GEOSCIENCES↗

DOE OSTI2022

A toolkit for plant lipid engineering: surveying the efficacies of lipogenic factors for accumulating specialty lipids

Plants produce energy-dense lipids from carbohydrates using energy acquired via photosynthesis, making plant oils an economically and sustainably attractive feedstock for conversion to biofuels and value-added bioproducts. A growing number of strategies have been developed and optimized in model plants, oilseed crops and high-biomass crops to enhance the accumulation of storage lipids (mostly triacylglycerols, TAGs) for bioenergy applications and to produce specialty lipids with increased uses and value for chemical feedstock and nutritional applications. Most successful metabolic engineering strategies involve heterologous expression of lipogenic factors that outperform those from other sources or exhibit specialized functionality. In this review, we summarize recent progress in engineering the accumulation of triacylglycerols containing - specialized fatty acids in various plant species and tissues. We also provide an inventory of specific lipogenic factors derived from a wide variety of organisms, along with their reported efficacy in supporting the accumulation of desired lipids. A review of previously obtained results serves as a foundation to guide future efforts to optimize combinations of factors to achieve further enhancements to the production and accumulation of desired lipids in a variety of plant tissues and species.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Field performance of switchgrass plants engineered for reduced recalcitrance

Switchgrass ( Panicum virgatum L.) is a promising perennial bioenergy crop that achieves high yields with relatively low nutrient and energy inputs. Modification of cell wall composition for reduced recalcitrance can lower the costs of deconstructing biomass to fermentable sugars and other intermediates. We have engineered overexpression of OsAT10 , encoding a rice BAHD acyltransferase and QsuB , encoding dehydroshikimate dehydratase from Corynebacterium glutamicum , to enhance saccharification efficiency in switchgrass. These engineering strategies demonstrated low lignin content, low ferulic acid esters, and increased saccharification yield during greenhouse studies in switchgrass and other plant species. In this work, transgenic switchgrass plants overexpressing either OsAT10 or QsuB were tested in the field in Davis, California, USA for three growing seasons. No significant differences in the content of lignin and cell wall-bound p -coumaric acid or ferulic acid were detected in transgenic OsAT10 lines compared with the untransformed Alamo control variety. However, the transgenic overexpressing QsuB lines had increased biomass yield and slightly increased biomass saccharification properties compared to the control plants. This work demonstrates good performance of engineered plants in the field, and also shows that the cell wall changes in the greenhouse were not replicated in the field, emphasizing the need to validate engineered plants under relevant field conditions.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

A multi-omic survey of black cottonwood tissues highlights coordinated transcriptomic and metabolomic mechanisms for plant adaptation to phosphorus deficiency

Phosphorus (P) deficiency in plants creates a variety of metabolic perturbations that decrease photosynthesis and growth. Phosphorus deficiency is especially challenging for the production of bioenergy feedstock plantation species, such as poplars (Populus spp.), where fertilization may not be practically or economically feasible. While the phenotypic effects of P deficiency are well known, the molecular mechanisms underlying whole-plant and tissue-specific responses to P deficiency, and in particular the responses of commercially valuable hardwoods, are less studied. We used a multi-tissue and multi-omics approach using transcriptomic, proteomic, and metabolomic analyses of the leaves and roots of black cottonwood (Populus trichocarpa) seedlings grown under P-deficient (5 µM P) and replete (100 µM P) conditions to assess this knowledge gap and to identify potential gene targets for selection for P efficiency. In comparison to seedlings grown at 100 µM P, P-deficient seedlings exhibited reduced dry biomass, altered chlorophyll fluorescence, and reduced tissue P concentrations. In line with these observations, growth, C metabolism, and photosynthesis pathways were downregulated in the transcriptome of the P-deficient plants. Additionally, we found evidence of strong lipid remodeling in the leaves. Metabolomic data showed that the roots of P-deficient plants had a greater relative abundance of phosphate ion, which may reflect extensive degradation of P-rich metabolites in plants exposed to long-term P-deficiency. With the notable exception of the KEGG pathway for Starch and Sucrose Metabolism (map00500), the responses of the transcriptome and the metabolome to P deficiency were consistent with one another. No significant changes in the proteome were detected in response to P deficiency. Collectively, our multi-omic and multi-tissue approach enabled the identification of important metabolic and regulatory pathways regulated across tissues at the molecular level that will be important avenues to further evaluate for P efficiency. These included stress-mediating systems associated with reactive oxygen species maintenance, lipid remodeling within tissues, and systems involved in P scavenging from the rhizosphere.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Editorial: Artificial Intelligence and Machine Learning Applications in Plant Genomics and Genetics

In plants, as in other species, genotypic variation in combination with environmental variation leads to differences in the biochemical makeup of cells, measurable as molecular phenotypes. These in turn influence physiological and developmental traits such as organ formation and plant growth, and eventually traits relevant in agriculture, such as yield and stress tolerance. Thus, relating genotypes to phenotypes yields fundamental insights into the regulation of important processes in plant development and physiology, but also provides the ability to predict yield and quality traits in specific environments, which is essential in basic plant science and molecular breeding of resilient plants in changing environments.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Harnessing Plant Sugar Metabolism for Glycoengineering

Plants possess an innate ability to generate vast amounts of sugar and produce a range of sugar-derived compounds that can be utilized for applications in industry, health, and agriculture. Nucleotide sugars lie at the unique intersection of primary and specialized metabolism, enabling the biosynthesis of numerous molecules ranging from small glycosides to complex polysaccharides. Plants are tolerant to perturbations to their balance of nucleotide sugars, allowing for the overproduction of endogenous nucleotide sugars to push flux towards a particular product without necessitating the re-engineering of upstream pathways. Pathways to produce even non-native nucleotide sugars may be introduced to synthesize entirely novel products. Heterologously expressed glycosyltransferases capable of unique sugar chemistries can further widen the synthetic repertoire of a plant, and transporters can increase the amount of nucleotide sugars available to glycosyltransferases. In this opinion piece, we examine recent successes and potential future uses of engineered nucleotide sugar biosynthetic, transport, and utilization pathways to improve the production of target compounds. Additionally, we highlight current efforts to engineer glycosyltransferases. Ultimately, the robust nature of plant sugar biochemistry renders plants a powerful chassis for the production of target glycoconjugates and glycans.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Nitrogen-Fixing Gamma Proteobacteria Azotobacter vinelandii—A Blueprint for Nitrogen-Fixing Plants?

The availability of fixed nitrogen limits overall agricultural crop production worldwide. The so-called modern “green revolution” catalyzed by the widespread application of nitrogenous fertilizer has propelled global population growth. It has led to imbalances in global biogeochemical nitrogen cycling, resulting in a “nitrogen problem” that is growing at a similar trajectory to the “carbon problem”. As a result of the increasing imbalances in nitrogen cycling and additional environmental problems such as soil acidification, there is renewed and increasing interest in increasing the contributions of biological nitrogen fixation to reduce the inputs of nitrogenous fertilizers in agriculture. Interestingly, biological nitrogen fixation, or life’s ability to convert atmospheric dinitrogen to ammonia, is restricted to microbial life and not associated with any known eukaryotes. It is not clear why plants never evolved the ability to fix nitrogen and rather form associations with nitrogen-fixing microorganisms. Perhaps it is because of the large energy demand of the process, the oxygen sensitivity of the enzymatic apparatus, or simply failure to encounter the appropriate selective pressure. Whatever the reason, it is clear that this ability of crop plants, especially cereals, would transform modern agriculture once again. Successfully engineering plants will require creating an oxygen-free niche that can supply ample energy in a tightly regulated manner to minimize energy waste and ensure the ammonia produced is assimilated. Nitrogen-fixing aerobic bacteria can perhaps provide a blueprint for engineering nitrogen-fixing plants. This short review discusses the key features of robust nitrogen fixation in the model nitrogen-fixing aerobe, gamma proteobacteria Azotobacter vinelandii, in the context of the basic requirements for engineering nitrogen-fixing plants.

Microbiology↗

DOE OSTI2021

Biogeochemical and plant trait mechanisms drive enhanced methane emissions in response to whole-ecosystem warming

Abstract. Climate warming perturbs ecosystem carbon (C) cycling, causing both positive and negative feedbacks on greenhouse gas emissions. In 2016, we began a tidal marsh field experiment in two vegetation communities to investigate the mechanisms by which whole-ecosystem warming alters C gain, via plant-driven sequestration in soils, and C loss, primarily via methane (CH4) emissions. Here, we report the results from the first 4 years. As expected, warming of 5.1 ∘C more than doubled CH4 emissions in both plant communities. We propose this was caused by a combination of four mechanisms: (i) a decrease in the proportion of CH4 consumed by CH4 oxidation, (ii) more C substrates available for methanogenesis, (iii) reduced competition between methanogens and sulfate-reducing bacteria, and (iv) indirect effects of plant traits. Plots dominated by Spartina patens consistently emitted more CH4 than plots dominated by Schoenoplectus americanus, indicating key differences in the roles these common wetland plants play in affecting anaerobic soil biogeochemistry and suggesting that plant composition can modulate coastal wetland responses to climate change.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

The curled wake model: a three-dimensional and extremely fast steady-state wake solver for wind plant flows

Abstract. Wind turbine wake models typically require approximations, such as wake superposition and deflection models, to accurately describe wake physics. However, capturing the phenomena of interest, such as the curled wake and interaction of multiple wakes, in wind power plant flows comes with an increased computational cost. To address this, we propose a new hybrid method that uses analytical solutions with an approximate form of the Reynolds-averaged Navier–Stokes equations to solve the time-averaged flow over a wind plant. We compare results from the solver to supervisory control and data acquisition data from the Lillgrund wind plant obtaining wake model predictions which are generally within 1 standard deviation of the mean power data. We perform simulations of flow over the Columbia River Gorge to demonstrate the capabilities of the model in complex terrain. We also apply the solver to a case with wake steering, which agreed well with large-eddy simulations. This new solver reduces the time – and therefore the related cost – it takes to simulate a steady-state wind plant flow (on the order of seconds using one core). Because the model is computationally efficient, it can also be used for different applications including wake steering for wind power plants and layout optimization.

17 WIND ENERGY↗

DOE OSTI2022

Fast yaw optimization for wind plant wake steering using Boolean yaw angles

Abstract. In wind plants, turbines can be yawed into the wind to steer their wakes away from downstream turbines and achieve an overall increase in plant power. Mathematical optimization is typically used to determine the best yaw angles at which to operate the turbines in a plant. In this paper, we present a new heuristic to rapidly determine the yaw angles in a wind plant. In this method, we define the turbine yaw angles as Boolean – either yawed at a predefined angle or nonyawed – as opposed to the typical methods of defining yaw angles as continuous or with fine discretizations. We then optimize which turbines should be yawed with an algorithm that sweeps through the turbines from the most upstream to the most downstream. We demonstrate that our new Boolean optimization method can find turbine yaw angles that perform well compared to a traditionally used gradient-based optimizer for which the yaw angles are defined as continuous. There is less than 0.6 % difference in the optimized power between the two optimization methods for randomly placed turbine layouts and less than a 0.6 % difference in the optimal annual energy production between the two optimization methods for a real wind farm. Additionally, we show that our new method is much more computationally efficient than the traditional method. For plants with nonzero optimal yaw angles, our new method is generally able to solve for the turbine yaw angles 50–150 times faster, and in some extreme cases up to 500 times faster, than the traditional method.

17 WIND ENERGY↗

DOE OSTI2021

Integrating New Arctic Plant Functional Types in a Land Surface Model Using Above- and Belowground Field Observations: Modeling Archive

This Modeling Archive is in support of an NGEE Arctic publication "Integrating New Arctic Plant Functional Types in a Land Surface Model Using Above- and Belowground Field Observations". We used field observations of biomass and leaf traits (Salmon et al., 2019b) across a gradient of plant communities on the Seward Peninsula in northwest Alaska (Breen et al., 2020; Iversen et al., 2017) to replace the original ELM configuration with nine, arctic-specific PFTs. Original arctic PFTs in the model were (1) broadleaf deciduous boreal shrub and (2) C3 arctic grass. New PFTs were (1) lichen, (2) bryophyte, (3) evergreen dwarf shrub, (4) deciduous dwarf shrub, (5) deciduous low shrub, (6) deciduous low to tall shrub, (7) alder shrub, (8) forb, and (9) graminoid. New PFTs were parameterized and evaluated using site-level trait and biomass measurements (Salmon et al., 2019b), root C:N ratios and rooting depth patterns (Salmon et al., 2019c), and root biomass (Salmon et al., 2020). This archive contains forcing data and model output from ELM simulations conducted at the Kougarok Hillslope site (Kougarok Road Mile Marker 64). Simulations were conducted for the Kougarok Hillslope site using meteorological driving data from the Scenarios Network for Alaska and Arctic Planning (SNAP) downscaled climate projection dataset using NCAR-CCSM forcing (Bieniek et al., 2020 dataset; Walsh et al., 2018). The archive includes three versions of model simulations representing a progression from the original model configuration to an Arctic-specific configuration (see Output section below). Simulation 1 used grid cell data from a global E3SM configuration, including the fractional area of E3SM PFTs assigned to the grid cell containing the Kougarok Hillslope site in global simulations. This simulation did not distinguish between different plant communities on the Kougarok Hillslope, but instead used a single point simulation to represent the entire area. Simulation 2 used default E3SM PFT definitions combined with adjusted depth to bedrock for plant communities with shallow rocky layers to represent the role of abiotic soil factors in driving site differences (based on depth measurements of Iversen et al., 2019a), and adjusted the relative areas of the broadleaf deciduous boreal shrub and C3 arctic grass PFTs to reflect the observed spatial coverage of shrub and non-shrub PFTs across Kougarok Hillslope plant communities. Simulation 3 used the new arctic PFT definitions and parameterizations based on the vegetation types present at the site, along with the adjusted soil depths used in Simulation 2. Included are *.pdf, *.nc (NetCDF), *tar.gz, *.py, and *.bash files. We would like to thank Mary's Igloo Native Corporation for allowing us to perform this research on their land and giving us the opportunity to share this research with their community. Thanks to Shawn Serbin and Alistair Rogers for helpful discussions. Holly Vander Stel, Joanne Childs, and Stan Wullschleger helped with collection of field data. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Heat Pump Retrofits for Central Plant Hydronic Heating Systems: A Software Toolkit for Screening and Design

Retrofitting existing central plants with high-efficiency heat pump technologies can play a crucial role in achieving long-term planning goals. Modern heat pump technologies are able to use waste heat recovery to meet a building's heating demand, but there is a lack of accessible tools designed for non-HVAC experts, such as building owners, to quickly and easily conduct what-if analysis, e.g., estimating retrofit costs and payback period for their partial or full equipment replacement. This paper introduces an open-source software toolkit designed to facilitate the initial screening and decision-making of heat pump retrofits in existing central plants using a building's yearly load profile from metered or utility bill data. The toolkit evaluates the technical and economic viability of replacing traditional central plant equipment with various options including water-to-water or air-to-water heat pumps, which can provide efficient and lower-cost heating and cooling. It allows users to compare current central plant configurations with retrofit scenarios, assessing energy consumption, life-cycle costs, and environmental impact. The toolkit offers (1) a web-based tool designed for user-friendly access by a broad audience and (2) Python-based source code for researchers and engineers conducting parametric studies and design parameter optimization. The toolkit compares a typical central plant configuration to a configuration that uses a heat pump to supply hydronic heating and cooling. The output metrics include energy consumption and output of each equipment, life-cycle cost analyses and metrics, and environmental impact of the system.

Excell, L↗