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DOE OSTI2024

Genomic prediction of regional-scale performance in switchgrass ( Panicum virgatum ) by accounting for genotype-by-environment variation and yield surrogate traits

Switchgrass is a potential crop for bioenergy or carbon capture schemes, but further yield improvements through selective breeding are needed to encourage commercialization. To identify promising switchgrass germplasm for future breeding efforts, we conducted multisite and multitrait genomic prediction with a diversity panel of 630 genotypes from 4 switchgrass subpopulations (Gulf, Midwest, Coastal, and Texas), which were measured for spaced plant biomass yield across 10 sites. Our study focused on the use of genomic prediction to share information among traits and environments. Specifically, we evaluated the predictive ability of cross-validation (CV) schemes using only genetic data and the training set (cross-validation 1: CV1), a subset of the sites (cross-validation 2: CV2), and/or with 2 yield surrogates (flowering time and fall plant height). We found that genotype-by-environment interactions were largely due to the north–south distribution of sites. The genetic correlations between the yield surrogates and the biomass yield were generally positive (mean height r = 0.85; mean flowering time r = 0.45) and did not vary due to subpopulation or growing region (North, Middle, or South). Genomic prediction models had CV predictive abilities of –0.02 for individuals using only genetic data (CV1), but 0.55, 0.69, 0.76, 0.81, and 0.84 for individuals with biomass performance data from 1, 2, 3, 4, and 5 sites included in the training data (CV2), respectively. To simulate a resource-limited breeding program, we determined the predictive ability of models provided with the following: 1 site observation of flowering time (0.39); 1 site observation of flowering time and fall height (0.51); 1 site observation of fall height (0.52); 1 site observation of biomass (0.55); and 5 site observations of biomass yield (0.84). The ability to share information at a regional scale is very encouraging, but further research is required to accurately translate spaced plant biomass to commercial-scale sward biomass performance.

09 BIOMASS FUELS↗

DOE OSTI2025

Soft X-ray tomography reveals variations in B. subtilis biofilm structure upon tasA deletion

Bacterial biofilms are complex cell communities within a self-produced extracellular matrix, crucial in various fields but challenging to analyze in 3D. We developed a "biofilm-in-capillary" growth method compatible with full-rotation soft X-ray tomography, enabling high-resolution 3D imaging of bacterial cells and their matrix during biofilm formation. This approach offers 50 nm isotropic spatial resolution, rapid imaging, and quantitative native analysis of biofilm structure. Using Bacillus subtilis biofilms, we detected coherent alignment and chaining of wild-type cells towards the oxygen-rich capillary tip. In contrast, the ΔtasA genetic knock-out showed a loss of cellular orientation and changes in the extracellular matrix. Adding TasA protein to the ΔtasA strain restored matrix density and led to cell assembly compaction, but without the chaining observed in wild-type biofilms. This scalable and transferable approach opens new avenues for examining biofilm structure and function across various species, including mixed biofilms, and response to genetic and environmental factors.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Automated segmentation of soft X-ray tomography: Native cellular structure with submicron resolution at high-throughput for whole-cell quantitative imaging in yeast

Soft X-ray tomography (SXT) is an invaluable tool for quantitatively analyzing cellular structures at suboptical isotropic resolution. However, it has traditionally depended on manual segmentation, limiting its scalability for large datasets. Here, we leverage a deep learning-based autosegmentation pipeline to segment and label cellular structures in hundreds of cells across three Saccharomyces cerevisiae strains. This task-based pipeline uses manual iterative refinement to improve segmentation accuracy for key structures, including the cell body, nucleus, vacuole, and lipid droplets, enabling high-throughput and precise phenotypic analysis. Using this approach, we quantitatively compared the three-dimensional (3D) whole-cell morphometric characteristics of wild-type, VPH1-GFP, and vac14 strains, uncovering detailed strain-specific cell and organelle size and shape variations. We show the utility of SXT data for precise 3D curvature analysis of entire organelles and cells and detection of fine morphological features using surface meshes. Our approach facilitates comparative analyses with high spatial precision and statistical throughput, uncovering subtle morphological features at the single-cell and population level. This workflow significantly enhances our ability to characterize cell anatomy and supports scalable studies on the mesoscale, with applications in investigating cellular architecture, organelle biology, and genetic research across diverse biological contexts.

Chen, Jianhua [Lawrence Berkeley National Laborato↗

DOE OSTI2024

Exon disruptive variants in Populus trichocarpa associated with wood properties exhibit distinct gene expression patterns

Abstract Forest trees may harbor naturally occurring exon disruptive variants (DVs) in their gene sequences, which potentially impact important ecological and economic phenotypic traits. However, the abundance and molecular regulation of these variants remain largely unexplored. Here, 24,420 DVs were identified by screening 1014Populus trichocarpafull genomes. The identified DVs were predominantly heterozygous with allelic frequencies below 5% (only 26% of DVs had frequencies greater than 5%). Using common garden‐grown trees, DVs were assessed for gene expression variation in the developing xylem, revealing that their gene expression can be significantly altered, particularly for homozygous DVs (in the range of 27%–38% of cases depending on the studied common garden). DVs were further investigated for their correlations with 13 wood quality traits, revealing that, among the 148 discovered DV associations, 15 correlated with more than one wood property and six genes had more than one DV in their coding sequences associated with wood traits. Approximately one‐third of DVs correlated with wood property variation also showed significant gene expression variation, confirming their non‐spurious impact. These findings offer potential avenues for targeted introduction of homozygous mutations using tree biotechnology, and while the exact mechanisms by which DVs may directly influence wood formation remain to be unraveled, this study lays the groundwork for further investigation.

Genetics & Heredity↗

DOE OSTI2025

Using leaf and stomatal traits to predict biomass production and water use efficiency in Populus

Climate change is reshaping ecosystems, driving plants to adapt through leaf-trait plasticity that reflects strategies for growth and water use. Predicting biomass production and intrinsic water use efficiency (iWUE) remains challenging because of genetic, taxonomic, and environmental variability. Here, we used eastern cottonwood and Populus hybrids as a model system to test whether easily measurable leaf traits can serve as reliable predictors of performance, and whether adding stomatal and biochemical traits improves predictive power. Across two field sites in Mississippi, leaf mass per area (LMA), biomass production, iWUE, leaf area, and foliar nitrogen ( N %) differed significantly among taxa and sites, while other traits were conserved. Factorial analysis of mixed data (FAMD) revealed distinct clustering of taxa and sites, indicating coordinated variation among leaf and stomatal traits. Pairwise correlations highlighted fundamental trade-offs, with biomass positively related to LMA and petiole length but negatively associated with iWUE, N %, and carbon isotopic ratios (δ 13 C). Leaf temperature and leaf angle varied among taxa and were significantly correlated with LMA and petiole length, suggesting mechanisms of heat dissipation and leaf movability that link simple traits to gas exchange and productivity. Weighted multiple linear regression models explained 80%–91% of variation in biomass production and iWUE. Models using only LMA, petiole length, and stomatal metrics performed nearly as well as those incorporating N %, and δ 13 C, with complex traits adding approximately 10% explanatory power. These results demonstrate that simple morphological traits capture integrated functional trade-offs, while complex traits refine predictions. This tiered approach provides an efficient framework for selecting high-yielding, water-efficient genotypes of Populus and other hardwood species, offering practical pathways to enhance carbon uptake and iWUE under climate change.

biomass production↗

DOE OSTI2024

Patterns of genomic variation reveal a single evolutionary origin of the wild allotetraploid Mimulus sookensis

Abstract Polyploidy occurs across the tree of life and is especially common in plants. Because newly formed cytotypes are often incompatible with their progenitors, polyploidy is also said to trigger “instantaneous” speciation. If a polyploid can self-fertilize or reproduce asexually, it is even possible for one individual to produce an entirely new lineage, but how often this scenario occurs is unclear. Here, we investigate the evolutionary history of the wild allotetraploid Mimulus sookensis, which was formed through hybridization between self-compatible, diploid species in the Mimulus guttatus complex. We generate a chromosome-scale reference assembly for M. sookensis and define its distinct subgenomes. Despite previous reports suggesting multiple origins of this highly selfing polyploid, we discover patterns of population genomic variation that provide unambiguous support for a single origin. One M. sookensis subgenome is clearly derived from the selfer Mimulus nasutus, which organellar variation suggests is the maternal progenitor. The ancestor of the other subgenome is less certain, but it shares variation with both Mimulus decorus and M. guttatus, two outcrossing diploids with geographic ranges that overlap broadly with M. sookensis. This study establishes M. sookensis as an example of instantaneous speciation, likely facilitated by the polyploid’s predisposition to self-fertilize.

Environmental Sciences & Ecology↗

DOE OSTI2024

Multicellular magnetotactic bacteria are genetically heterogeneous consortia with metabolically differentiated cells

Consortia of multicellular magnetotactic bacteria (MMB) are currently the only known example of bacteria without a unicellular stage in their life cycle. Because of their recalcitrance to cultivation, most previous studies of MMB have been limited to microscopic observations. To study the biology of these unique organisms in more detail, we use multiple culture-independent approaches to analyze the genomics and physiology of MMB consortia at single-cell resolution. We separately sequenced the metagenomes of 22 individual MMB consortia, representing 8 new species, and quantified the genetic diversity within each MMB consortium. This revealed that, counter to conventional views, cells within MMB consortia are not clonal. Single consortia metagenomes were then used to reconstruct the species-specific metabolic potential and infer the physiological capabilities of MMB. To validate genomic predictions, we performed stable isotope probing (SIP) experiments and interrogated MMB consortia using fluorescence in situ hybridization (FISH) combined with nanoscale secondary ion mass spectrometry (NanoSIMS). By coupling FISH with bioorthogonal noncanonical amino acid tagging (BONCAT), we explored their in situ activity as well as variation of protein synthesis within cells. We demonstrate that MMB consortia are mixotrophic sulfate reducers and that they exhibit metabolic differentiation between individual cells, suggesting that MMB consortia are more complex than previously thought. These findings expand our understanding of MMB diversity, ecology, genomics, and physiology, as well as offer insights into the mechanisms underpinning the multicellular nature of their unique lifestyle.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

A haplotype-resolved, chromosome-scale genome assembly for the southern live oak, Quercus virginiana

Hybridization is a major force driving diversification, migration, and adaptation in Quercus species. While population genetics and phylogenetics have traditionally been used for studying these processes, advances in sequencing technology now enable us to incorporate comparative and pan-genomic approaches as well. Here, we present a highly contiguous, chromosome-scale and haplotype-resolved genome assembly for the southern live oak, Quercus virginiana, the first reference genome for section Virentes, as part of the American Campus Tree Genomes program. Originating from a clone of Auburn University's historic “Toomer's Oak,” this assembly contributes to the pool of genomic resources for investigating recombination, haplotype variation, and structural genomic changes influencing hybridization potential in this clade and across Quercus. It also provides insights into the architecture of the putative centromeric regions within the genus. Alongside other oak references, the Q. virginiana genome will support research into the evolution and adaptation of the Quercus genus.

Quercus virginiana↗

DOE OSTI2025

eDNAjoint: An R package for interpreting paired or semi‐paired environmental DNA and traditional survey data in a Bayesian framework

Abstract Environmental DNA (eDNA) sampling is increasingly used in surveys of species distribution as a potentially sensitive and efficient monitoring method. Yet access to modelling tools designed specifically for interpreting this new data type lags behind its ubiquity. While occupancy modelling software has dominated the analytical landscape for eDNA data analysis of single species, this type of model may not always be the most appropriate. The rate of eDNA detection often corresponds to species density, rather than just occupancy, and researchers often have access to observations from non‐genetic sampling methods at the same sites. To provide users access to a modelling framework designed to maximize the use of all available data, we developed an R package, eDNAjoint . The package provides an easy‐to‐use interface for fitting a ‘joint’ model that integrates data from paired or semi‐paired eDNA and traditional surveys in a Bayesian framework. The model can be used to estimate parameters like the probability of a false positive eDNA detection and mean catch rate at a site, and the package allows access to multiple model variations and Bayesian prior customization. Additional functionality can be used for model selection, summarising posteriors and comparing the relative sensitivities of the two survey methods. We demonstrate the use of eDNAjoint by fitting a variation of the model with site‐level covariates that scale the sensitivity of eDNA sampling relative to traditional sampling. The example workflow uses binary eDNA and seine count data for the endangered tidewater goby ( Eucyclogobius newberryi ) from a study by Schmelzle and Kinziger (2016). This use case includes a prior sensitivity analysis and an evaluation of the relationship between detection rates and environmental variables. eDNAjoint has the potential to greatly increase the range of users who will be able to rigorously analyse eDNA and traditional survey data in a Bayesian framework, understand if and how eDNA can improve monitoring practices, and gain confidence in the interpretability of eDNA data.

Keller, Abigail G. [Department of Environment Scie↗

DOE OSTI2026

Transcriptomic and functional analyses uncover a conserved effector driving genotype-dependent virulence in the Sphaerulina musiva-Populus trichocarpa interaction

The introduction of invasive microbes compromises the structure, biodiversity, and function of naïve ecosystems. Sphaerulina musiva, a hemibiotrophic pathogen that causes leaf spot and stem cankers in Populus species, exemplifies an invasive fungal pathogen spread by human activities. However, the genetic mechanisms of pathogenicity and virulence are poorly understood, impeding mitigation strategies. We utilized RNA sequencing to identify fungal effectors linked to stem canker formation, informing the development of future strategies for effective disease management. Our analysis revealed 70 genes differentially expressed at 2 weeks and 110 genes at 3 weeks between inoculated trees and controls. Notably, the gene with the highest expression at 2 weeks and the second highest at 3 weeks was homologous to Extracellular protein 2 (Ecp2). Complementary genome-wide association studies linked sequence polymorphisms in this locus to phenotypic variation in disease severity. Infiltration of S. musiva Ecp2 into Populus trichocarpa leaves induced necrosis in susceptible genotypes. Gene disruption using a CRISPR-Cas9 RNP system resulted in a genotype-dependent reduction of stem canker and disease severity. Tracing the evolutionary history of this effector across the fungal kingdom, we uncovered clade-specific gene-family expansions and orthologs in new species. These findings raise questions about the function and adaptive significance of these gene families in fungal lifestyles. Our study provides the first tractable target for breeding resistant poplar genotypes, addressing the challenges of managing S. musiva and uncovering mechanisms that drive its virulence, and provides deeper insights into the evolutionary dynamics of a conserved small-secreted protein with a diversity of functions.

Sondreli, Kelsey L [Oregon State University]↗

DOE OSTI2024

Intraspecific variability in plant and soil chemical properties in a common garden plantation of the energy crop Populus

Optimizing crops for synergistic soil carbon (C) sequestration can enhance CO 2 removal in food and bioenergy production systems. Yet, in bioenergy systems, we lack an understanding of how intraspecies variation in plant traits correlates with variation in soil biogeochemistry. This knowledge gap is exacerbated by both the heterogeneity and difficulty of measuring belowground traits. Here, we provide initial observations of C and nutrients in soil and root and stem tissues from a common garden field site of diverse, natural variant, Populus trichocarpa genotypes—established for aboveground biomass-to-biofuels research. Our goal was to explore the value of such field sites for evaluating genotype-specific effects on soil C, which ultimately informs the potential for optimizing bioenergy systems for both aboveground productivity and belowground C storage. To do this, we investigated variation in chemical traits at the scale of individual trees and genotypes and we explored correlations among stem, root, and soil samples. We observed substantial variation in soil chemical properties at the scale of individual trees and specific genotypes. While correlations among elements were observed both within and among sample types (soil, stem, root), above-belowground correlations were generally poor. We did not observe genotype-specific patterns in soil C in the top 10 cm, but we did observe genotype associations with soil acid-base chemistry (soil pH and base cations) and bulk density. Finally, a specific phenotype of interest (high vs low lignin) was unrelated to soil biogeochemistry. Our pilot study supports the usefulness of decade-old, genetically-variable, Populus bioenergy field test plots for understanding plant genotype effects on soil properties. Finally, this study contributes to the advancement of sampling methods and baseline data for Populus systems in the Pacific Northwest, USA. Further species- and region-specific efforts will enhance C predictability across scales in bioenergy systems and, ultimately, accelerate the identification of genotypes that optimize yield and carbon storage.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Spatiotemporal Dynamics of the Relative Abundance of Soil Nutrient‐Degrading Enzyme‐Encoding Genes Across Continental US Ecoregions

Understanding the spatiotemporal patterns in the relative abundance of soil extracellular enzyme‐encoding genes is critical for predicting microbial responses to environmental change and their potential role in nutrient cycling. Yet, integrating novel metagenomic observations with spatiotemporal environmental gradients to infer regional patterns and future trajectories has remained unclear. To address this gap, we applied a machine learning (ML) approach, integrating soil metagenomic data with environmental variables—soil properties, topography, vegetation, and climate—to predict the relative abundance of enzyme‐encoding genes for soil carbon (C), nitrogen (N), and phosphorus (P) across surface soils of the continental United States. We assessed potential responses under future emission scenarios (SSP2‐4.5 and SSP5‐8.5) by comparing a baseline (1985–2014) to a future period (2071–2100). The ML model explained 57%–63% of baseline variation. Precipitation was identified as the most influential factor for the relative abundance of C‐ and N‐degrading enzyme‐encoding genes, while slope length, representing horizontal distance that water can travel downslope, was the primary driver for P‐degrading enzyme‐encoding genes abundance. Projections revealed spatially heterogeneous shifts across continental US ecoregions: the relative abundance of C‐ and N‐degrading enzyme‐encoding genes decreased in wetter ecoregions and increased in drier ecoregions under future climate, while P‐degrading enzyme‐encoding genes abundance decreased significantly in semiarid and Mediterranean ecoregions. This study demonstrates the utility of metagenomic data for mapping soil genetic potential and predicting its regional response to environmental change, to inform ecosystem management strategies.

extracellular enzyme-encoding genes↗

DOE OSTI2024

Anaerobic fungi contain abundant, diverse, and transcriptionally active Long Terminal Repeat retrotransposons

Long Terminal Repeat (LTR) retrotransposons are a class of repetitive elements that are widespread in the genomes of plants and many fungi. LTR retrotransposons have been associated with rapidly evolving gene clusters in plants and virulence factor transfer in fungal-plant parasite-host interactions. We report here the abundance and transcriptional activity of LTR retrotransposons across several species of the early-branching Neocallimastigomycota, otherwise known as the anaerobic gut fungi (AGF). The ubiquity of LTR retrotransposons in these genomes suggests key evolutionary roles in these rumen-dwelling biomass degraders, whose genomes also contain many enzymes that are horizontally transferred from other rumen-dwelling prokaryotes. Up to 10% of anaerobic fungal genomes consist of LTR retrotransposons, and the mapping of sequences from LTR retrotransposons to transcriptomes shows that the majority of clusters are transcribed, with some exhibiting expression greater than 104 reads per kilobase million mapped reads (rpkm). Many LTR retrotransposons are strongly differentially expressed upon heat stress during fungal cultivation, with several exhibiting a nearly three-log10 fold increase in expression, whereas growth substrate variation modulated transcription to a lesser extent. We show that some LTR retrotransposons contain carbohydrate-active enzymes (CAZymes), and the expansion of CAZymes within genomes and among anaerobic fungal species may be linked to retrotransposon activity. We further discuss how these widespread sequences may be a source of promoters and other parts towards the bioengineering of anaerobic fungi.

Genetics & Heredity↗

DOE OSTI2024

The ancestral environment of teosinte populations shapes their root microbiome

Summary Background The composition of the root microbiome affects the host’s growth, with variation in the host genome associated with microbiome variation. However, it is not known whether this intra-specific variation of root microbiomes is a consequence of plants performing targeted manipulations of them to adapt to their local environment or varying passively with other traits. To explore the relationship between the genome, environment and microbiome, we sampled seeds from teosinte populations across its native range in Mexico. We then grew teosinte accessions alongside two modern maize lines in a common garden experiment. Metabarcoding was performed using universal bacterial and fungal primers to profile their root microbiomes. Results The root microbiome varied between the two modern maize lines and the teosinte accessions. We further found that variation of the teosinte genome, the ancestral environment (temperature/elevation) and root microbiome were all correlated. Multiple microbial groups significantly varied in relative abundance with temperature/elevation, with an increased abundance of bacteria associated with cold tolerance found in teosinte accessions taken from high elevations. Conclusions Our results suggest that variation in the root microbiome is pre-conditioned by the genome for the local environment (i.e. non-random). Ultimately, these claims would be strengthened by confirming that these differences in the root microbiome impact host phenotype, for example, by confirming that the root microbiomes of high-elevation teosinte populations enhance cold tolerance.

Genetics & Heredity↗

DOE OSTI2026

Telomere-to-telomere assemblies of chromosome 10 reveal complex adaptive variation of 3-ketoacyl-CoA-synthases in Populus trichocarpa likely driven by Helitrons

The model woody plant Populus trichocarpa displays an atypical alkene-diverse wax cuticle likely driven by copy number variation (CNV) of 3-ketoacyl-CoA synthases ( KCS ), which has been difficult to confirm with short-read assemblies. Long-read sequencing enables the development of telomere-to-telomere resources to detect cryptic variation, including CNVs, which are currently missed. Integrating this information can improve genomic prediction for breeding and provide insights into the evolutionary basis of important traits. Our analysis of 78 long-read haplotypes from chromosome 10 identified more than twice as many KCS genes as previously reported, and numerous intragenic non-synonymous substitutions. Random Forest predictive models highlighted the importance of Potri.010G079500 in producing very long chain alkenes; however, its absence did not predict previously reported alkene-deficient phenotypes. Instead, alkene levels are best predicted by the combinations of KCS copies. Additionally, amino acid substitutions clustered around ligand and donor binding pockets, suggesting they contribute to differing wax cuticle composition. Finally, each KCS gene and copy was linked to a Helitron transposon. A phylogenetic analysis suggests Helitrons are the evolutionary mechanism for generating KCS tandem arrays. Long-read generated telomere-to-telomere assemblies of P. trichocarpa chromosome 10 revealed large-effect loci critical to genetic studies that are unattainable from short-reads. This new resource produced novel insights into genome structure and function, and a novel mechanism for generating tandem gene duplication. Our results highlight that, given current challenges in annotation and assembly, detailed and focused long-read sequences are key to interpreting complex genomic regions that contain tandem copy number variants.

09 BIOMASS FUELS↗

DOE OSTI2025

Comparison of the earliest NC and CC planetesimals: Evidence from ungrouped iron meteorites

Isotope anomalies in meteorites reveal a fundamental dichotomy between Non-Carbonaceous- (NC) and Carbonaceous-type (CC) planetary bodies. Until now, this dichotomy is established for the major meteorite groups, representing about 36 distinct parent bodies. Ungrouped meteorites represent an even larger number of additional parent bodies, but whether they conform to the overall NC-CC dichotomy is unknown. Here, the genetics and chronology of 26 ungrouped iron meteorites are considered through nucleosynthetic Mo and radiogenic W isotopic compositions. Secondary cosmic ray-induced modifications of these isotope compositions are corrected using Pt isotope measurements on the same samples. We find that all of the ungrouped irons have Mo isotope anomalies within the range of the major meteorite groups and confirm the NC-CC dichotomy for Mo, where NC and CC meteorites define two distinct, subparallel s-process mixing lines. All ungrouped NC irons fall on the NC-line, which is now precisely defined for 41 distinct parent bodies. The ungrouped CC irons show scatter around the CC-line indicative of small r-process Mo heterogeneities among these samples. These r-process Mo isotope variations correlate with O isotope anomalies, most likely reflecting mixing of CI chondrite-like matrix, chondrule precursors, and Ca-Al-rich inclusions. This implies that CC iron meteorite parent bodies accreted the same nebular components as the later-formed carbonaceous chondrites. The Hf-W model ages of core formation for the ungrouped irons overlap with those of the iron meteorite groups from each reservoir and reveal a narrow age peak at ∼3.3 Ma after Ca-Al-rich inclusions for the CC irons. By contrast, the NC irons display more variable ages, including younger ages indicative of impact-induced melting events, which seem absent among the CC irons. This is attributed to the more fragile and porous nature of the CC bodies, making impact-induced melting on their surfaces difficult. The chemical characteristics of all iron meteorites together reveal slightly more oxidizing conditions during core formation for CC compared to NC irons. More strikingly, strong depletions in moderately volatile elements, typical of many iron meteorite parent bodies, predominantly occur among CC irons, for reasons that remain unclear at present.

Chondrite components↗

DOE OSTI2026

Spatial analysis of cell patterning to aid genetic and phenotypic understanding of grass stomatal density: A case study in maize

Biological processes involve complex hierarchies where composite traits result from multiple component traits. However, holistically understanding of how sets of component traits interact to underpin genotype-to-phenotype relationships is generally lacking. Stomatal density (SD) is a tractable model system for exploring how high-throughput phenotyping (HTP) data could be exploited by a new spatial analysis approach to better understand a developmentally and functionally important trait. SD is a composite trait, resulting from various components related to cell identity and size, which are themselves governed by a series of spatio-developmental processes. Data from 192 recombinant inbred lines of maize [Zea mays (L.)] were analyzed by a new stomatal patterning phenotype (SPP) to (1) describe the average spatial probability distribution of the nearest neighboring stomata; (2) derive a core set of component traits related to cell size, cell packing, and positional probabilities; (3) build a structural equation model of component traits underlying SD; and (4) identify stomatal patterning quantitative trait loci (QTL). The core set of SPP-derived traits explained 74% of the variation in SD. Analyzing SPP component traits allowed some loci previously identified as generic SD QTL to be recognized as specific to lateral versus longitudinal elements of stomatal patterning. Therefore, this study highlights how novel insights can be gained by decomposing a composite trait (e.g., SD) into a set of component traits that were present in HTP data but not previously exploited.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Correlated Anion Disorder in Heteroanionic Cubic TiOF 2

Resolving anion configurations in heteroanionic materials is crucial for understanding and controlling their properties. For anion-disordered oxyfluorides, conventional Bragg diffraction cannot fully resolve the anionic structure, necessitating alternative structure determination methods. We have investigated the anionic structure of anion-disordered cubic (ReO 3 -type) TiOF 2 using X-ray pair distribution function (PDF), 19 F MAS NMR analysis, density functional theory (DFT), cluster expansion modeling, and genetic-algorithm structure prediction. Our computational data predict short-range anion ordering in TiOF 2 , characterized by predominant cis-[O 2 F 4 ] titanium coordination, resulting in correlated anion disorder at longer ranges. To validate our predictions, we generated partially disordered supercells using genetic-algorithm structure prediction and computed simulated X-ray PDF data and 19 F MAS NMR spectra, which we compared directly to experimental data. To construct our simulated 19 F NMR spectra, we derived new transformation functions for mapping calculated magnetic shieldings to predicted magnetic chemical shifts in titanium (oxy)fluorides, obtained by fitting DFT-calculated magnetic shieldings to previously published experimental chemical shift data for TiF 4 . We find good agreement between our simulated and experimental data, which supports our computationally predicted structural model and demonstrates the effectiveness of complementary experimental and computational techniques in resolving anionic structure in anion-disordered oxyfluorides. From additional DFT calculations, we predict that increasing anion disorder makes lithium intercalation more favorable by, on average, up to 2 eV, highlighting the significant effect of variations in short-range order on the intercalation properties of anion-disordered materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗