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All sourcesNASA NTRSDOE OSTI
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 OSTI2025

Which Plant Traits Increase Soil Carbon Sequestration? Empirical Evidence From a Long‐Term Poplar Genetic Diversity Trial

Plants play a key role in mediating soil response to global change, and breeding or engineering crops to increase soil organic carbon (SOC) storage is a potential route to land-based carbon dioxide removal in agricultural systems. However, due to limited observational datasets plus shifting paradigms of SOC stabilization, it is unclear which plant traits are most important for enhancing different types of soil organic matter. Existing long-term common gardens of genetically diverse plant populations may provide an opportunity to evaluate biological controls on SOC, separate from environmental or management variability. Here we report on soil and root chemical data collected for 24 genotypes within a 13-year-old common garden in northwestern Oregon planted with a large natural variant population of Populus trichocarpa. Fractionating surface soil (0–15 cm) revealed substantial variation in stocks of mineral-associated organic matter (MAOM; 18–67 t C/ha) and particulate organic matter (POM; 2–22 t C/ha). Tree genotype explained 24% and 26% of the MAOM and POM stock variability, respectively, after controlling for background variability. We found minimal association between SOC concentration and either aboveground tree productivity or root biomass recalcitrance (C/N ratios and lignin content). In contrast, root elemental content appeared influential for MAOM-C concentration, which showed a strong positive association with root aluminum (Al) and a strong negative association with root boron (B) and magnesium (Mg). Furthermore, root concentrations of these elements were highly heritable (57%–78%) and not simply a reflection of background variation in soil elemental concentrations. We estimate that surface SOC stocks under these 24 genotypes have diverged at rates of up to 1.2–4.3 t C/ha/year. These results suggest that long-term genetic diversity trials have value for elucidating biological controls on soil organic matter dynamics, and that traits associated with root elemental content may be a useful target for enhancing biosequestration.

biomass recalcitrance↗
DOE OSTI2023

Transcriptomic Analysis Reveals Novel Regulators of the Scots Pine Stilbene Pathway

Stilbenes accumulate in Scots pine heartwood where they have important roles in protecting wood from decaying fungi. They are also part of active defense responses, and their production is induced by different (a)biotic stressors. The specific transcriptional regulators as well as the enzyme responsible for activating the stilbene precursor cinnamate in the pathway are still unknown. UV-C radiation was the first discovered artificial stress activator of the pathway. Here, we describe a large-scale transcriptomic analysis of pine needles in response to UV-C and treatment with translational inhibitors, both activating the transcription of stilbene pathway genes. We used the data to identify putative candidates for the missing CoA ligase and for pathway regulators. We further showed that the pathway is transcriptionally activated by phosphatase inhibitor, ethylene and jasmonate treatments, as in grapevine, and that the stilbene synthase promoter retains its inducibility in some of the tested conditions in Arabidopsis, a species that normally does not synthesize stilbenes. Shared features between gymnosperm and angiosperm regulation and partially retained inducibility in Arabidopsis suggest that pathway regulation occurs not only via ancient stress-response pathway(s) but also via species-specific regulators. Understanding which genes control the biosynthesis of stilbenes in Scots pine aids breeding of more resistant trees.

4-Coumarate-CoA ligase↗
DOE OSTI2022

Performance of abiotic stress-inducible synthetic promoters in genetically engineered hybrid poplar (Populus tremula × Populus alba)

Abiotic stresses can cause significant damage to plants. For sustainable bioenergy crop production, it is critical to generate resistant crops to such stress. Engineering promoters to control the precise expression of stress resistance genes is a very effective way to address the problem. Here we developed stably transformed Populus tremula × Populus alba hybrid poplar (INRA 717-1B4) containing one-of-six s ynthetic d rought stress-inducible promoters (SDs; SD9-1, SD9-2, SD9-3, SD13-1, SD18-1, and SD18-3) identified previously by transient transformation assays. We screened green fluorescent protein (GFP) induction in poplar under osmotic stress conditions. Of six transgenic lines containing synthetic promoter, three lines (SD18-1, 9-2, and 9-3) had significant GFP expression in both salt and osmotic stress treatments. Each synthetic promoter employed heptamerized repeats of specific and short cis -regulatory elements (7 repeats of 7-8 bases). To verify whether the repeats of longer sequences can improve osmotic stress responsiveness, a transgenic poplar containing the synthetic promoter of the heptamerized entire SD9 motif (20 bases, containing all partial SD9 motifs) was generated and measured for GFP induction under osmotic stress. The heptamerized entire SD9 motif did not result in higher GFP expression than the shorter promoters consisting of heptamerized SD9-1, 9-2, and 9-3 (partial SD9) motifs. This result indicates that shorter synthetic promoters (~50 bp) can be used for versatile control of gene expression in transgenic poplar. These synthetic promoters will be useful tools to engineer stress-resilient bioenergy tree crops in the future.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Gray matter volume drives the brain age gap in schizophrenia: a SHAP study

Neuroimaging-based brain age is a biomarker that is generated by machine learning (ML) predictions. The brain age gap (BAG) is typically defined as the difference between the predicted brain age and chronological age. Studies have consistently reported a positive BAG in individuals with schizophrenia (SCZ). However, there is little understanding of which specific factors drive the ML-based brain age predictions, leading to limited biological interpretations of the BAG. We gathered data from three publicly available databases - COBRE, MCIC, and UCLA - and an additional dataset (TOPSY) of early-stage schizophrenia (82.5% untreated first-episode sample) and calculated brain age with pre-trained gradient-boosted trees. Then, we applied SHapley Additive Explanations (SHAP) to identify which brain features influence brain age predictions. We investigated the interaction between the SHAP score for each feature and group as a function of the BAG. These analyses identified total gray matter volume (group × SHAP interaction term β = 1.71 [0.53; 3.23]; p corr < 0.03) as the feature that influences the BAG observed in SCZ among the brain features that are most predictive of brain age. Other brain features also presented differences in SHAP values between SCZ and HC, but they were not significantly associated with the BAG. We compared the findings with a non-psychotic depression dataset (CAN-BIND), where the interaction was not significant. This study has important implications for the understanding of brain age prediction models and the BAG in SCZ and, potentially, in other psychiatric disorders.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

JGI-Trichoderma v1.0

There is a series of Python and bash scripts to parse genomics datasets used to evaluate the coevolution of gene families and the feature importance of gene families using an SVM classifier. - Cover analysis: takes a list of single-copy genes in a set of genomes, aligns and builds the gene trees to determine if two gene families have a signature of covariation with one another. It parses the files to run phykit cover script described here: https://jlsteenwyk.com/PhyKIT/usage/index.html - SVM-classifier: This Python script is an SVM-based genomic classifier designed for biological data analysis. It combines machine learning with feature selection to identify important genomic markers and classify biological samples. Core Functionality: The script uses Support Vector Machines from scikit-learn to classify genomic data, incorporating SelectKBest for automated feature selection and leave-one-out cross-validation for performance assessment. It operates in multiple modes: feature ranking, optimal combination discovery, and sample prediction. Primary Applications: Genomic sample classification and biomarker discovery Feature importance analysis in high-dimensional biological datasets Prediction of sample categories based on genomic profiles Research applications requiring robust classification of biological data Key Advantages: High-dimensional handling: SVMs excel with genomic data's typical high feature-to-sample ratios Integrated feature selection: Reduces noise and computational overhead while identifying key markers Probability estimation: Provides confidence scores essential for biological interpretation Validation robustness: Leave-one-out cross-validation ensures reliable performance metrics Operational flexibility: Multiple analysis modes support different research phases from exploration to prediction

Stecca Steindorff, Andrei [Lawrence Berkeley Natio↗
DOE OSTI2025

COMPASS-FME Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) Experiment Tree Inventory

This is the tree inventory (diameter, species, and live/dead status) data from the Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experimental site. This manipulative, ecosystem-scale TEMPEST experiment is part of the COMPASS-FME (Coastal Observations, Mechanisms, and Predictions Across Systems and Scales: Field Measurements and Experiments; see https://compass.pnnl.gov/FME/COMPASSFME) project. It addresses the potential for freshwater and estuarine-water disturbance events to alter tree function, species composition, and ecosystem processes in a deciduous coastal forest in eastern Maryland, USA. The experiment uses a large-unit (2000 m2), un-replicated experimental design, with three 50 m × 40 m plots serving as control, freshwater, and estuarine-water treatments.This dataset includes:- An overall dataset README file.- The tree inventory data in both "wide" and "long" forms. These contain the same information but are structured differently, with the former more useful for human viewers and the latter more amenable for programmatic analyses.- A key to the species/genus codes used, which follow the U.S. Department of Agriculture's PLANTS schema (https://plants.usda.gov/).- A copy of the R code used to generate the wide- and long-form data files.All files are comma-separated value (CSV) and no special software is required to read them.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Tonoplast Sucrose Trafficking Modulates Starch Utilization and Water Deficit Behavior in Poplar Leaves

Leaf osmotic adjustment by the active accrual of compatible organic solutes (e.g. sucrose) contributes to drought tolerance throughout the plant kingdom. In Populus tremula x alba, PtaSUT4 encodes a tonoplast sucrose–proton symporter, whose downregulation by chronic mild drought or transgenic manipulation is known to increase leaf sucrose and turgor. While this may constitute a single drought tolerance mechanism, we now report that other adjustments which can occur during a worsening water deficit are damped when PtaSUT4 is constitutively downregulated. Specifically, we report that starch use and leaf relative water content (RWC) dynamics were compromised when plants with constitutively downregulated PtaSUT4 were subjected to a water deficit. Leaf RWC decreased more in wild-type and vector control lines than in transgenic PtaSUT4-RNAi (RNA-interference) or CRISPR (clustered regularly interspersed short palindromic repeats) knockout (KO) lines. The control line RWC decrease was accompanied by increased PtaSUT4 transcript levels and a mobilization of sucrose from the mesophyll-enriched leaf lamina into the midvein. The findings suggest that changes in SUT4 expression can increase turgor or decrease RWC as different tolerance mechanisms to reduced water availability. Evidence is presented that PtaSUT4-mediated sucrose partitioning between the vacuole and the cytosol is important not only for overall sucrose abundance and turgor, but also for reactive oxygen species (ROS) and antioxidant dynamics. Interestingly, the reduced capacity for accelerated starch breakdown under worsening water-deficit conditions was correlated with reduced ROS in the RNAi and KO lines. A role for PtaSUT4 in the orchestration of ROS, antioxidant, starch utilization and RWC dynamics during water stress and its importance in trees especially, with their high hydraulic resistances, is considered.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Suillus : an emerging model for the study of ectomycorrhizal ecology and evolution

Research on mycorrhizal symbiosis has been slowed by a lack of established study systems. To address this challenge, we have been developing Suillus, a widespread ecologically and economically relevant fungal genus primarily associated with the plant family Pinaceae, into a model system for studying ectomycorrhizal (ECM) associations. Over the last decade, we have compiled extensive genomic resources, culture libraries, a phenotype database, and protocols for manipulating Suillus fungi with and without their tree partners. Our efforts have already resulted in a large number of publicly available genomes, transcriptomes, and respective annotations, as well as advances in our understanding of mycorrhizal partner specificity and host communication, fungal and plant nutrition, environmental adaptation, soil nutrient cycling, interspecific competition, and biological invasions. Here, we highlight the most significant recent findings enabled by Suillus, present a suite of protocols for working with the genus, and discuss how Suillus is emerging as an important model to elucidate the ecology and evolution of ECM interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Metatranscriptomic dynamics after Verticillium dahliae infection and root damage in Olea europaea

Background: The olive tree is of particular economic interest in the Mediterranean basin. Researchers have conducted several studies on one of the most devastating disorders affecting this tree, the Verticillium wilt, which causes substantial economic losses in numerous areas. We analyzed metatranscriptomic samples taken from a previous study conducted on leaves and roots of Olea europaea that were infected with Verticillium dahliae. In addition, we also analyzed mechanically damaged roots. The aim of our approach is to describe the dynamics of the root microbiome after severe perturbations. Results: Our results not only describe the dynamics of the microbial community associated with the disturbance, but also show the high complexity of these systems and explain how this can lead to a conflicting assignment of the various types of parasitism observed in a specific organism. Conclusions: Our findings indicate that this infection, although led by Verticillium, is driven not by a single species, but by a polymicrobial consortium that also includes natural endophytes of the olive tree. This community contains both biotrophic and necrotrophic organisms that alternate and live together during the infection. In addition, opportunistic organisms appear that take profit not from plant tissues, but from new emerging populations of microorganisms. Therefore, this system can be described as a complex biological system composed of different interacting communities. Notably, our work has important considerations when it comes to classifying the type of parasitism of a given species.

Verticillium dahliae↗
DOE OSTI2024

Divide and conquer: using RhizoVision Explorer to aggregate data from multiple root scans using image concatenation and statistical methods

Roots are important in agricultural and natural systems for determining plant productivity and soil carbon inputs. Sometimes, the amount of roots in a sample is too much to fit into a single scanned image, so the sample is divided among several scans, and there is no standard method to aggregate the data. Here, we describe and validate two methods for standardizing measurements across multiple scans: image concatenation and statistical aggregation. We developed a Python script that identifies which images belong to the same sample and returns a single, larger concatenated image. These concatenated images and the original images were processed with RhizoVision Explorer, a free and open-source software. An R script was developed, which identifies rows of data belonging to the same sample and applies correct statistical methods to return a single data row for each sample. These two methods were compared using example images from switchgrass, poplar, and various tree and ericaceous shrub species from a northern peatland and the Arctic. Most root measurements were nearly identical between the two methods except median diameter, which cannot be accurately computed by statistical aggregation. We believe the availability of these methods will be useful to the root biology community.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Effects of feruloyl-CoA 6'-hydroxylase 1 overexpression on lignin and cell wall characteristics in transgenic hybrid aspen

In plant cell walls, lignin, cellulose, and the hemicelluloses form intricate three-dimensional structures. Owing to its complexity, lignin often acts as a bottleneck for the efficient utilization of polysaccharide components as biochemicals and functional materials. A promising approach to mitigate and/or overcome lignin recalcitrance is the qualitative and quantitative modification of lignin by genetic engineering. Feruloyl-CoA 6'-hydroxylase (F6'H1) is a 2-oxoglutarate-dependent dioxygenase that catalyzes the conversion of feruloyl-CoA, one of the intermediates of the lignin biosynthetic pathway, into 6'-hydroxyferuloyl-CoA, the precursor of scopoletin (7-hydroxy-6-methoxycoumarin). In a previous study with Arabidopsis thaliana, we demonstrated that overexpression of F6'H1 under a xylem-preferential promoter led to scopoletin incorporation into the cell wall. This altered the chemical structure of lignin without affecting lignin content or saccharification efficiency. In the present study, the same F6'H1 construct was introduced into hybrid aspen (Populus tremula × tremuloides T89), a model woody plant, and its effects on plant morphology, lignin chemical structure, global gene expression, and phenolic metabolism were examined. The transgenic plants successfully overproduced scopoletin while exhibiting severe growth retardation, a phenotype not previously observed in Arabidopsis. Scopoletin accumulation was most pronounced in the secondary walls of tracheary elements and the compound middle lamella, with low levels in the fiber cell walls. Overexpression of F6'H1 also affected the metabolism of aromatics, including lignin precursors. Heteronuclear single-quantum coherence (HSQC) NMR spectroscopy revealed that scopoletin in cell walls was bound to lignin, leading to a reduction in lignin content and changes in its monomeric composition and molar mass distribution. Furthermore, the enzymatic saccharification efficiency of the transgenic cell walls was more than three times higher than that of the wild-type plants, even without pretreatment. Although addressing growth inhibition remains a priority, incorporating scopoletin into lignin demonstrates significant potential for improving woody biomass utilization.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Tree-inspired lignin microrods-based composite heterogeneous nanochannels for ion transport and osmotic energy harvesting

One of the key processes of tree lignification is that lignin penetrates into the cell wall and fills in the cell wall framework, thereby increasing the hardness and hydrophobic of the tree channels, which is beneficial to consolidate and support the tree cell wall and water transport. Inspired by this natural process, we demonstrated a lignin-based nanofluidic heterogeneous membrane that closely mimics the channels in tree, which can realize ion transport function and effectively capture reverse electrodialysis. The membrane was synthesized by heating dealkaline lignin and PVA at 200 °C and this formed fusiform microrods and a closed-packed membrane. Simultaneously, this membrane composited with anodized aluminum (AAO) channels membrane at 200 °C to form asymmetric heterogeneous nanochannels membrane, which can transport counter-ions and harvest osmotic energy. This membrane implements ion current rectification in 0.1 M KCl electrolyte solution at pH 3 due to the confinement of pores and opposite surface charges in lignin-based heterogeneous nanochannel. An output power density of 0.97 W m- 2 is obtained under a 50-fold salinity gradient, which can be further improved to 1.19 W m -2 by increasing the salinity gradient from 50-fold to 500-fold. Accordingly, this nanofluidic membranes were prepared by using lignin, the key component in tree, which not only mimicked a crucial process of the water and ionic transport process of channels in tree, but also had the prospect in the field of osmotic energy harvesting.

09 BIOMASS FUELS↗
DOE OSTI2020

Complete Genome Sequence of the Novel Roseimicrobium sp. Strain ORNL1, a Verrucomicrobium Isolated from the Populus deltoides Rhizosphere

Roseimicrobium sp. strain ORNL1 is a soil bacterium that belongs to the phylum Verrucomicrobia and was isolated from the rhizosphere of a forest Eastern cottonwood tree, Populus deltoides, in Tennessee. Its 7.9-Mb chromosome was completely sequenced using PacBio long reads and is predicted to encode 6,288 proteins and 76 RNAs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Forest residue harvest optimization: spanning the bridge between plant biology and biorefinery performance

Forestry residues have immense potential as alternative feedstocks to petroleum, yet their inherent complexity remains a major challenge to widespread use. Pairing the temporal rhythms of plant biology with biorefinery performance is critical to industrial-scale biorefinery development. Here, we provide the first report of a techno-economic analysis (TEA) and life cycle assessment (LCA) for a model integrated reductive catalytic fractionation (RCF)–molten salt hydrolysis process for forestry residues varying in tree part, species, and phenophase. All forestry residues resulted in net-negative greenhouse gas (GHG) emissions vs. comparable petroleum feedstocks, with GHG emissions potentially reduced >4.0× through composition-based feedstock selection (e.g., harvesting American beech bark in spring vs. summer). Moreover, American beech twigs/branchlets and bark in leafed and emergence phenophases, respectively, had 7.9× lower predicted phenolic minimum selling prices (MSPs) vs. other feedstocks and MSPs within the current global phenolic market range. Hemicellulose content and RCF yield emerged as key parameters impacting GHG emissions and biorefinery revenue, identifying hardwood twigs/branchlets in the leafed phenophase as optimal biofeedstocks. Biorefinery expenses were dominated by purchased equipment, raw materials, and utility costs, highlighting essential areas for future study. Notably, RCF reactor pressures drove 85–90% of equipment costs, but sensitivity analysis revealed that decreasing the pressure 20% could reduce the phenol MSP 4-fold. Structural carbohydrate dynamics were also investigated using a two-step acid hydrolysis method to resolve tissue- and species-level patterns in biomass composition throughout the year to enable harvest optimization based on TEA/LCA findings. Ultimately, elucidating the impact of biofeedstock dynamics on biorefinery performance enables harvest optimization, informed engineering design, and progress towards an expanded bioeconomy.

Shapiro, Alison J. [University of Delaware, Newark↗
DOE OSTI2022

Mycorrhizal nutrient acquisition strategies shape tree competition and coexistence dynamics

Mycorrhizal fungi with different nutrient acquisition strategies influence plant species performance and physiology, thereby defining their trophic niche. This might drive resource competition dynamics that cumulatively impact tree species coexistence, but few manipulative experiments have directly tested this. Combining surveys and experiments in a modern coexistence theory framework, we tested how variation in mycorrhizal strategies and nutrient conditions affects plant competitive outcomes. We focused on two genera of co-occurring tree species with different mycorrhizal states: Acer (arbuscular mycorrhizal, AM) and Populus (dual mycorrhizal, but often considered predominantly ectomycorrhizal, EM). The EM and AM fungal responsiveness in Populus species varied with latitude and nitrogen (N) limitation. Host-specific soil microbiome conditioning and inorganic N fertilization combined to qualitatively affect coexistence outcomes. Lower N conditions favoured Populus over Acer trees, and N fertilization reversed this outcome for southern species, aligning with regional-scale forest mycorrhizal transitions. Results from the coexistence experiment also predict competitive exclusion between the tree species pairs, which could arise, in part, from their mycorrhizal differences and is consistent with alternative stable states in dominant forest mycorrhizal strategies. Such bistability appears in natural systems as a bimodal distribution of Populus vs. Acer tree species dominance using long-term forest inventory data. Synthesis: The magnitude and outcome of microbially mediated competition between Populus and Acer depends on soil nutrient availability, which likely relates to their mycorrhizal differentiation. These findings support the importance of mycorrhizal symbioses for contributing to large-scale biogeographical patterns of tree species trophic niche separation across soil resource gradients and bistability in forest mycorrhizal structure.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Development of DNA methylation‐based epigenetic age predictors in loblolly pine ( Pinus taeda )

Abstract Biological ageing is connected to life history variation across ecological scales and informs a basic understanding of age‐related declines in organismal function. Altered DNA methylation dynamics are a conserved aspect of biological ageing and have recently been modelled to predict chronological age among vertebrate species. In addition to their utility in estimating individual age, differences between chronological and predicted ages arise due to acceleration or deceleration of epigenetic ageing, and these discrepancies are linked to disease risk and multiple life history traits. Although evidence suggests that patterns of DNA methylation can describe ageing in plants, predictions with epigenetic clocks have yet to be performed. Here, we resolve the DNA methylome across CpG, CHG, and CHH‐methylation contexts in the loblolly pine tree ( Pinus taeda ) and construct epigenetic clocks capable of predicting ages in this species within 6% of its maximum lifespan. Although patterns of CHH‐methylation showed little association with age, both CpG and CHG‐methylation contexts were strongly associated with ageing, largely becoming hypomethylated with age. Among age‐associated loci were those in close proximity to malate dehydrogenase, NADH dehydrogenase, and 18S and 26S ribosomal RNA genes. This study reports one of the first epigenetic clocks in plants and demonstrates the universality of age‐associated DNA methylation dynamics which can inform conservation and management practices, as well as our ecological and evolutionary understanding of biological ageing in plants.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Increasing the Scale of the Mass Spectrometry Query Language Compendium with Explainable AI

A significant bottleneck in metabolomics data interpretation is the effective use of domain knowledge to assign structural information based on fragmentation patterns. The mass spectrometry query language (MassQL) aims to make this process accessible and applicable across multiple analysis platforms. While advanced computational methods are capable of predicting compound structures from fragmentation data, AI/ML approaches often rely on complex, opaque criteria that are difficult to interpret or modify. As a result, their predictive patterns cannot be readily translated into human-readable rules, such as those used in MassQL. Here, in this study, we introduce ChemEcho, a machine learning embedding method that converts tandem mass spectrometry data into sparse feature vectors containing peak and neutral mass subformulae to enhance explainable AI/ML-based methods. An advantage of this approach is that decision trees trained using these feature vectors can be directly translated to MassQL. Using a battery of decision trees trained using ChemEcho embeddings to predict molecular attributes, we generated over 1500 MassQL queries for 765 molecular features and evaluated their precision and recall. From these queries, the 50 highest-performing queries were integrated into the MassQL compendium. This set of generated MassQL queries included environmentally and biologically relevant classes such as PFAS and molecules containing phosphate or sulfate substructures. To illustrate the impact these queries would have on a typical metabolomics experiment, these MassQL queries were applied to a public metabolomics data set─resulting in a marked increase in the structural information derived from tandem mass spectra. Access and reuse of these queries is expected to enhance structural annotation in untargeted experiments, leading to more specific claims and advancing many applications in metabolomics.

Harwood, Thomas V. [USDOE Joint Genome Institute (↗