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At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Xylanolytic metabolism is regulated by coordination of transcription factors XynR and XylR in extremely thermophilic Caldicellulosiruptorales

ABSTRACT Global transcription factors (TFs) control metabolic processes in bacteria to efficiently utilize available carbon. The orderCaldicellulosiruptoraleshas drawn interest due to the ability of its members to degrade components of lignocellulosic biomass. Regulatory reconstruction ofAnaerocellum (f. Caldicellulosiruptor) besciiidentified two major global transcription factors for xylan utilization, XynR and XylR, and the corresponding putative transcription factor binding sites. Recombinant versions of XynR (LacI family) and XylR (ROK family) were subjected to fluorescence polarization (FP) and biolayer interferometry (BLI) analysis to confirm the predicted binding sites. Four XynR sites and two XylR sites were validated, accounting for 20 of 26 genes regulated by XynR and six of seven genes regulated by XylR. Bioinformatic analysis of the individual genes controlled by the two regulators showed an inter-dependent scheme for xylan conversion; the transport of xylooligosaccharides (XOS) is dependent on XylR, while enzymes responsible for hydrolysis are controlled by both regulators. For xylose catabolism by the xylose isomerase-xylulose kinase pathway, regulation is also split, with XylR controlling xylose isomerase and XynR controlling xylokinase. The XynR/XylR regulator pair withinA. besciiis conserved in all sequenced species ofCaldicellulosiruptorales, suggesting similarities in regulating linear xylan conversion. In other xylanolytic thermophiles, XylR homologs control xylan degradation, compared to just 6 out of 26 genes forA. bescii. These results show that two separate regulatory schemes (dual repression) are coordinated byA. besciito effectively regulate the hemicellulose inventory and xylan catabolism. IMPORTANCE To take full advantage of extreme thermophiles as platform metabolic engineering microorganisms, the tools for genetic manipulation must be further developed, and strategies that exploit a better understanding of metabolic regulation need to be discerned.Anaerocellum bescii, the most studied of the extremely thermophilic fermentative anaerobic bacteria that can utilize microcrystalline cellulose, can degrade microcrystalline cellulose and hemicellulose and has been metabolically engineered to convert the resulting sugars to products such as ethanol and acetone. For xylan, in particular, two major global transcription factors (TFs), XynR and XylR, play a role in sugar metabolism, although their predicted regulatory interdependence from bioinformatics analysis has not been elucidated experimentally. Here, fluorescence polarization (FP) and biolayer interferometry (BLI) were used to explore this issue to support metabolic engineering efforts aimed at improving carbohydrate processing to industrial chemicals.

Biotechnology & Applied Microbiology↗
DOE OSTI2021

Comparative Virulence and Genomic Analysis of Streptococcus suis Isolates

Streptococcus suis is a zoonotic bacterial swine pathogen causing substantial economic and health burdens to the pork industry. Mechanisms used by S. suis to colonize and cause disease remain unknown and vaccines and/or intervention strategies currently do not exist. Studies addressing virulence mechanisms used by S. suis have been complicated because different isolates can cause a spectrum of disease outcomes ranging from lethal systemic disease to asymptomatic carriage. The objectives of this study were to evaluate the virulence capacity of nine United States S. suis isolates following intranasal challenge in swine and then perform comparative genomic analyses to identify genomic attributes associated with swine-virulent phenotypes. No correlation was found between the capacity to cause disease in swine and the functional characteristics of genome size, serotype, sequence type (ST), or in vitro virulence-associated phenotypes. A search for orthologs found in highly virulent isolates and not found in non-virulent isolates revealed numerous predicted protein coding sequences specific to each category. While none of these predicted protein coding sequences have been previously characterized as potential virulence factors, this analysis does provide a reliable one-to-one assignment of specific genes of interest that could prove useful in future allelic replacement and/or functional genomic studies. Collectively, this report provides a framework for future allelic replacement and/or functional genomic studies investigating genetic characteristics underlying the spectrum of disease outcomes caused by S. suis isolates.

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 OSTI2025

Testing for the Genomic Footprint of Conflict Between Life Stages in an Angiosperm and Moss Species

Abstract The maintenance of genetic variation by balancing selection is of considerable interest to evolutionary biologists. An important but understudied potential driver of balancing selection is antagonistic pleiotropy between diploid and haploid stages of the plant life cycle. Despite sharing a common genome, sporophytes (2n) and gametophytes (n) may undergo differential or even opposing selection. Theoretical work suggests antagonistic pleiotropy between life stages can generate balancing selection and maintain genetic variation. Despite the potential for far-reaching consequences of gametophytic selection, empirical tests of its pleiotropic effects (neutral, synergistic, or antagonistic) on sporophytes are generally lacking. Here, we examined the population genomic signals of selection across life stages in the angiosperm Rumex hastatulus and the moss Ceratodon purpureus. We compared gene expression between life stages and sexes, combined with neutral diversity statistics and the analysis of the distribution of fitness effects. In contrast to what would be predicted under balancing selection due to antagonistic pleiotropy, we found that unbiased genes between life stages were under stronger purifying selection, likely explained by a predominance of synergistic pleiotropy between life stages and strong purifying selection on broadly expressed genes. In addition, we found that 30% of candidate genes under balancing selection in R. hastatulus were located within inversion polymorphisms. Our findings provide novel insights into the genome-wide characteristics and consequences of plant gametophytic selection.

Evolutionary Biology↗
DOE OSTI2020

Local-scale Arctic tundra heterogeneity affects regional-scale carbon dynamics

In northern Alaska nearly 65% of the terrestrial surface is composed of polygonal ground, where geomorphic tundra landforms disproportionately influence carbon and nutrient cycling over fine spatial scales. Process-based biogeochemical models used for local to Pan-Arctic projections of ecological responses to climate change typically operate at coarse-scales (1km 2 –0.5°) at which fine-scale (<1km 2 ) tundra heterogeneity is often aggregated to the dominant land cover unit. Here, we evaluate the importance of tundra heterogeneity for representing soil carbon dynamics at fine to coarse spatial scales. We leveraged the legacy of data collected near Utqiagvik, Alaska between 1973 and 2016 for model initiation, parameterization, and validation. Simulation uncertainty increased with a reduced representation of tundra heterogeneity and coarsening of spatial scale. Hierarchical cluster analysis of an ensemble of 21 st -century simulations reveals that a minimum of two tundra landforms (dry and wet) and a maximum of 4km 2 spatial scale is necessary for minimizing uncertainties (<10%) in regional to Pan-Arctic modeling applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Prediction of crystal structures and motifs in the Fe–Mg–O system at Earth’s core pressures

Abstract Fe, Mg, and O are among the most abundant elements in terrestrial planets. While the behavior of the Fe–O, Mg–O, and Fe–Mg binary systems under pressure have been investigated, there are still very few studies of the Fe–Mg–O ternary system at relevant Earth’s core and super-Earth’s mantle pressures. Here, we use the adaptive genetic algorithm (AGA) to study ternary Fe x Mg y O z phases in a wide range of stoichiometries at 200 GPa and 350 GPa. We discovered three dynamically stable phases with stoichiometries FeMg 2 O 4 , Fe 2 MgO 4, and FeMg 3 O 4 with lower enthalpy than any known combination of Fe–Mg–O high-pressure compounds at 350 GPa. With the discovery of these phases, we construct the Fe–Mg–O ternary convex hull. We further clarify the composition- and pressure-dependence of structural motifs with the analysis of the AGA-found stable and metastable structures. Analysis of binary and ternary stable phases suggest that O, Mg, or both could stabilize a BCC iron alloy at inner core pressures.

Wang, Renhai↗
DOE OSTI2022

Basin-scale biogeography of marine phytoplankton reflects cellular-scale optimization of metabolism and physiology

Extensive microdiversity within Prochlorococcus, the most abundant marine cyanobacterium, occurs at scales from a single droplet of seawater to ocean basins. To interpret the structuring role of variations in genetic potential, as well as metabolic and physiological acclimation, we developed a mechanistic constraint-based modeling framework that incorporates the full suite of genes, proteins, metabolic reactions, pigments, and biochemical compositions of 69 sequenced isolates spanning the Prochlorococcus pangenome. Optimizing each strain to the local, observed physical and chemical environment along an Atlantic Ocean transect, we predicted variations in strain-specific patterns of growth rate, metabolic configuration, and physiological state, defining subtle niche subspaces directly attributable to differences in their encoded metabolic potential. Predicted growth rates covaried with observed ecotype abundances, affirming their significance as a measure of fitness and inferring a nonlinear density dependence of mortality. Our study demonstrates the potential to interpret global-scale ecosystem organization in terms of cellular-scale processes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Expanding the genetic toolkit: adenine and cytosine base editors for gene disruption in Aspergillus niger

Despite revolutionizing fungal genetic engineering, conventional CRISPR/Cas9-mediated knockouts rely on DNA double-strand breaks (DSBs), which can cause unwanted insertions and deletions, chromosomal abnormalities, and cytotoxicity. Base editors such as adenine base editors (ABEs), which convert A‧T to G‧C, and cytosine base editors (CBEs), which convert C‧G to T‧A, offer a safer alternative by enabling predictable, target-specific single-nucleotide changes without introducing DSBs. To overcome the limitations of traditional genome editing in filamentous fungi, we developed efficient base-editing systems in Aspergillus niger . For the first time, we constructed an ABE in A. niger , achieving up to 80% editing efficiency and inducing predictable A-to-G mutations at the intended intron sites, disrupting gene function through mRNA mis-splicing. We also developed a highly efficient CBE system, capable of introducing premature stop codons with 50–100% efficiency. To broaden the editing scope, we implemented a Cas9-NG variant recognizing a relaxed PAM sequence requiring only a single guanine (G), enabling editing at start codons and splice sites. Leveraging this expanded scope, we established gene disruption approaches by targeting start codons via ABE-mediated A-to-G conversions (ATG-to-GTG and ATG-to-ACG) and CBE-mediated C-to-T conversion (ATG-to-ATA). Additionally, our base-editing systems enable multiplex gRNA delivery and marker-free editing of multiple genes. Collectively, the scope-expanding strategies increase the number of genes targetable for disruption by base-editing in A. niger by 26.3% and enable near-complete coverage of 96% of the coding genes. Overall, this work demonstrates the potential of ABE and CBE systems as versatile, efficient, and safer alternatives to DSBs-based gene disruption in filamentous fungi.

Aspergillus↗
DOE OSTI2023

Optimizing Fast Charging and Wetting in Lithium-Ion Batteries with Optimal Microstructure Patterns Identified by Genetic Algorithm

To sustain the high-rate current required for fast charging electric vehicle batteries, electrodes must exhibit sufficiently high effective ionic diffusion. Additionally, to reduce battery manufacturing costs, wetting time must decrease. Both of these issues can be addressed by structuring the electrodes with mesoscale pore channels. However, their optimal spatial distribution, or patterns, is unknown. Herein, a genetic algorithm has been developed to identify these optimal patterns using a CPU-cheap proxy distance-based model to evaluate the impact of the added pore networks. Both coin-cell and pouch cell form factors have been considered for the wetting analysis, with their respective electrolyte infiltration mode. Regular hexagonal and mud-crack-like patterns, respectively, for fast charging and fast wetting were found to be optimal and have been compared with pre-determined, easier to manufacture, patterns. The model predicts that using cylindrical channels arranged in a regular hexagonal pattern is ∼6.25 times more efficient for fast charging as compared to grooved lines with both structuring strategies being restricted to a 5% electrode total volume loss. The model also shows that only a very limited electrode volume loss (1%–2%) is required to dramatically improve the wetting (5–20 times) compared to an unstructured electrode.

25 ENERGY STORAGE↗
DOE OSTI2023

Climate lags and genetics determine phenology in quaking aspen ( Populus tremuloides )

Spatiotemporal patterns of phenology may be affected by mosaics of environmental and genetic variation. Environmental drivers may have temporally lagged impacts, but patterns and mechanisms remain poorly known. Here, we combine multiple genomic, remotely sensed, and physically modeled datasets to determine the spatiotemporal patterns and drivers of canopy phenology in quaking aspen, a widespread clonal dioecious tree species with diploid and triploid cytotypes. We show that over 391 km 2 of southwestern Colorado: greenup date, greendown date, and growing season length vary by weeks and differ across sexes, cytotypes, and genotypes; phenology has high phenotypic plasticity and heritabilities of 31–61% (interquartile range); and snowmelt date, soil moisture, and air temperature predict phenology, at temporal lags of up to 3 yr. Our study shows that lagged environmental effects are needed to explain phenological variation and that the effect of cytotype on phenology is obscured by its correlation with topography. Phenological patterns are consistent with responses to multiyear accumulation of carbon deficit or hydraulic damage.

carbon allocation↗
DOE OSTI2026

MAL33 drives natural variation in maltose metabolism in Saccharomyces eubayanus

Maltose is one of the most abundant sugars in brewer’s wort, and its efficient utilization is critical for successful fermentation. However, maltose consumption varies naturally among Saccharomyces eubayanus strains isolated from different host trees, such as Quercus and Nothofagus. To identify the genetic determinants underlying these phenotypic differences, we performed bulk segregant analysis (BSA) and quantitative trait loci (QTL) mapping using an F 2 offspring derived from QC18 (Quercus-associated) and CL467.1 (Nothofagus-associated) strains. QTL mapping identified two significant genomic regions on subtelomeric loci of chromosomes V-R and XVI-L, each containing complete MAL loci composed of MAL32 (encoding maltase), MAL31 (transporter), and MAL33 (transcriptional activator) genes. Comparative polymorphism analyses identified mutations in MAL32 and MAL33 of QC18, including frameshift mutations resulting in premature stop codons. Functional validation demonstrated that the heterologous expression of MAL33 ChrV from CL467.1 fully restored maltose utilization in QC18, indicating the functional presence of MAL33 cis-regulatory sequences and MAL32 and MAL31 genes in QC18. While structural protein predictions identified truncation and impaired functionality in the maltose-responsive activation domain of Mal33p from QC18, overexpression of QC18’s own MAL33 ChrV allele also improved maltose metabolism, suggesting dosage-dependent transcriptional limitations rather than complete functional loss. These results indicate that allelic variations in the maltose-responsive activation domain of Mal33p result in differences in maltose consumption between strains. Here, we hypothesized that reduced maltose metabolism in QC18 is an adaptive response to the distinct sugar composition in Quercus robur bark, contrasting with the starch-rich environment of Nothofagus pumilio. These findings highlight subtelomeric MAL gene diversity as a reservoir of genetic variation, representing a key evolutionary mechanism that influences maltose adaptation among natural Saccharomyces isolates.

evolutionary plasticity↗
DOE OSTI2024

Tree drought physiology: critical research questions and strategies for mitigating climate change effects on forests

Droughts of increasing severity and frequency are a primary cause of forest mortality associated with climate change. Yet, fundamental knowledge gaps regarding the complex physiology of trees limit the development of more effective management strategies to mitigate drought effects on forests. Here, in this work, we highlight some of the basic research needed to better understand tree drought physiology and how new technologies and interdisciplinary approaches can be used to address them. Our discussion focuses on how trees change wood development to mitigate water stress, hormonal responses to drought, genetic variation underlying adaptive drought phenotypes, how trees ‘remember’ prior stress exposure, and how symbiotic soil microbes affect drought response. Next, we identify opportunities for using research findings to enhance or develop new strategies for managing drought effects on forests, ranging from matching genotypes to environments, to enhancing seedling resilience through nursery treatments, to landscape-scale monitoring and predictions. We conclude with a discussion of the need for co-producing research with land managers and extending research to forests in critical ecological regions beyond the temperate zone.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Climate adaptation and sustainability in switchgrass: exploring plant-microbe-soil interactions across continental scale environmental gradients

Less carbon-intensive energy sources are needed to reduce greenhouse gas emissions and their predicted role in climate change. There is growing interest in the potential of biofuels for meeting this need. A critical question is whether large-scale biofuel production can be sustainable over the time scales needed to mitigate our carbon debt from fossil fuel consumption. The carbon balance and ultimately the sustainability of biofuel feedstock production is the result of complex climate-coupled interactions between carbon fixation, sequestration, and release through combustion. Similarly, the long-term productivity of biofuels depends on the environmental factors limiting plant growth. These factors are often related to soil resources which involve complex interactions at the plant-microbe-soil interface impacting their availability and cycling. Our collaborative project addressed sustainable switchgrass (Panicum virgatum) production by exploring Plant Systems, Plant-Microbiome Interactions, and Ecosystem Processes through the integrating lens of Multi-Scale Modeling. Our research was based on detailed characterization of genetically diverse switchgrass genotypes planted in common gardens across a continental latitudinal gradient. The underlying theme of our Plant Systems research was the use of locally adapted plant material to explore plant function, to understand the mechanistic basis of environmental interactions, and to discover the plant genes important for adaptation and sustainability in the face of climate change. Our Plant-Microbiome Interaction project characterized the microbial communities associated with switchgrass using genomic tools. Our Ecosystem Processes research focused on carbon cycle responses at the ecosystem level using stand level plantings. Finally, our Multi-Scale Modeling helped to define conditions of a sustainable biofuel system and identify key tradeoffs between genetic diversity, productivity, and ecosystem services. Genome-wide association analyses were used to identify alleles that contribute to successful establishment and biomass production across North America. Together, our work provided a baseline analyses of the potential of switchgrass as a biofuel feedstock. Our project resulted in a number of successful outcomes. First, we were successful in collecting switchgrass germplasm across the species range, propagating the material, and establishing common garden experiments across the species range. In collaboration with DOE JGI, we successfully assembled the first tetraploid switchgrass genome and published this resource with an analyses of the genetic basis local adaptation from our gardens (Lowry et al. 2019, Lovell et al. 2021). The gardens were used to characterize the genetic architecture for a number of important plant phenotypes. Our project also conducted extensive sampling and sequencing to characterize the bacterial and fungal associates of switchgrass roots and leaves. We showed that host genotype, location, and harvesting practices can play a role in microbiome assembly (Singer et al. 2019 & 2022, Van Wallendael et al. 2020 & 2022, Edwards et al. 2023). Our ecosystem processes work created baseline dataset of carbon and nutrient cycling in realistic stand plantings of switchgrass. Data from this experiment provided new insight into the role of plant traits, phenology, and local environments in ecosystem processes like soil respiration, net-ecosystem exchange, and dynamics of soil and plant nutrients (Ricketts et al. 2023). Finally, our crop modelling experiments help to characterize the sensitivity of common modeling frameworks to parameters, identify key limiters of productivity across large geographic scales, and leverage patterns of local adaptation in prediction. Ultimately, these studies help to identify critical plant-microbe-soil traits that may be manipulated, through breeding or agronomic management, to improve the sustainability of biofuel feedstocks.

09 BIOMASS FUELS↗
DOE OSTI2026

Knowledge-guided learning with curated prior genetic biomarkers for robust model interpretation

Abstract Motivation Knowledge-guided learning offers effective and robust model training strategies in data-scarce settings by incorporating established domain knowledge, thereby enhancing generalization, robustness, and interpretability. By contrast, conventional deep learning approaches rely purely on data-driven learning, which can limit robust model interpretability, particularly in high-dimensional settings with limited size samples. In computational biology, knowledge-guided learning has primarily leveraged network- and structural-based knowledge, leading to biologically interpretable representations and enhanced predictive performance compared to conventional approaches. However, curated biomarkers, one of the most accessible forms of biological knowledge, remain largely unexplored within knowledge-guided paradigms. Results In this study, we propose a model-agnostic training paradigm, Biomarker-driven Explainable Prior-guided Learning (BioExPL), that can be applied to any neural networks that incorporates curated prior knowledge. BioExPL enforces neural networks to reflect curated biomarker priors in their latent representations through a novel knowledge-alignment loss. BioExPL consistently demonstrated significantly improved predictive performance and enhanced model interpretability with minimized computational overhead in simulation studies and intensive experiments on multiple cancer datasets. BioExPL not only integrates prior curated knowledge into the model but also accurately identifies unknown associated signals additionally. BioExPL is model-agnostic and domain-independent, enabling its integration into diverse neural network architectures. Availability and implementation The open-source is publicly available at: https://github.com/datax-lab/BioExPL.

Baek, Beomsu [Department of Computer Science, Univ↗
DOE OSTI2020

Thermonuclear Reaction Rates and Primordial Nucleosynthesis

Assuming the best numerical value for the cosmic baryonic density and the existence of three neutrino flavors, standard Big Bang nucleosynthesis is a parameter-free model. It is important to assess if the observed primordial abundances can be reproduced by simulations. Numerous studies have shown that the simulations overpredict the primordial 7 Li abundance by a factor of ≈3 compared to the observations. The discrepancy may be caused by unknown systematics in 7 Li observations, poorly understood depletion of lithium in stars, errors in thermonuclear rates that take part in the lithium and beryllium synthesis, or physics beyond the standard model. Here, we focus on the likelihood of a nuclear physics solution. The status of the key nuclear reaction rates is summarized. Big Bang nucleosynthesis simulations are performed with the most recent reaction rates, and the uncertainties of the predicted abundances are established using a Monte Carlo technique. Correlations between abundances and reaction rates are investigated based on the metric of mutual information. The rates of four reactions impact the primordial 7 Li abundance: 3 He(α,γ) 7 Be, d(p,γ) 3 He, 7 Be(d,p)2α, and 7 Be(n,p) 7 Li. We employ a genetic algorithm to search for simultaneous rate changes in these four reactions that may account for all observed primordial abundances. Furthermore, when the search is performed for reaction rate ranges that are much wider than recently reported uncertainties, no acceptable solutions are found. Based on the currently available evidence, we conclude that it is highly unlikely for the cosmological lithium problem to have a nuclear physics solution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

A single genomic region controls primocane fruiting in tetraploid blackberry

The fresh-market blackberry ( Rubus subgenus Rubus ) industry has expanded dramatically in the past 2 decades, driven in part by improved cultivars. Introgression of the primocane-fruiting (PF; annual flowering) trait into elite germplasm has enabled dual cropping in a single year, season extension, and cultivation in tropical and subtropical regions. Despite its economic performance, the genetic basis of PF is not well understood. It has been proposed that the PF trait is controlled by a major recessive locus, but its genomic location is unclear. Here, a genome-wide association study (GWAS) of 365 tetraploid blackberry genotypes identified a single genomic region on chromosome Ra03 (∼33 Mb) strongly associated with PF. Genetic linkage analysis in a biparental population confirmed that the same interval (32–35 Mb) was linked to the PF phenotype. Ten putative candidate genes were identified in this region. Allele mining using whole-genome resequencing of 17 genotypes highlighted 2 high-priority candidates: a CCCH-type zinc finger gene and an ubiquitin-specific protease gene. Use of an improved Rubus argutus “Hillquist” genome annotation (v1.2) enabled refined variant interpretation, including identification of regulatory 3′ UTR polymorphisms in the zinc finger homolog. Two diagnostic KASP markers (PF1 and PF2), designed from the most significant GWAS SNPs, predicted the PF phenotype with over 96% accuracy in a validation panel of 494 tetraploid blackberries from multiple breeding programs. Together, these results provide the first high-resolution mapping of the PF locus in blackberry, identify candidate genes for flowering regulation in Rubus , and deliver diagnostic markers that can be immediately deployed in breeding programs.

GWAS↗
DOE OSTI2024

Data driven discovery and quantification of hyperspectral leaf reflectance phenotypes across a maize diversity panel

Abstract Estimates of plant traits derived from hyperspectral reflectance data have the potential to efficiently substitute for traits, which are time or labor intensive to manually score. Typical workflows for estimating plant traits from hyperspectral reflectance data employ supervised classification models that can require substantial ground truth datasets for training. We explore the potential of an unsupervised approach, autoencoders, to extract meaningful traits from plant hyperspectral reflectance data using measurements of the reflectance of 2151 individual wavelengths of light from the leaves of maize ( Zea mays ) plants harvested from 1658 field plots in a replicated field trial. A subset of autoencoder‐derived variables exhibited significant repeatability, indicating that a substantial proportion of the total variance in these variables was explained by difference between maize genotypes, while other autoencoder variables appear to capture variation resulting from changes in leaf reflectance between different batches of data collection. Several of the repeatable latent variables were significantly correlated with other traits scored from the same maize field experiment, including one autoencoder‐derived latent variable (LV8) that predicted plant chlorophyll content modestly better than a supervised model trained on the same data. In at least one case, genome‐wide association study hits for variation in autoencoder‐derived variables were proximal to genes with known or plausible links to leaf phenotypes expected to alter hyperspectral reflectance. In aggregate, these results suggest that an unsupervised, autoencoder‐based approach can identify meaningful and genetically controlled variation in high‐dimensional, high‐throughput phenotyping data and link identified variables back to known plant traits of interest.

Tross, Michael C.↗
DOE OSTI2024

Viral niche-partitioning: comparative genomics of giant viruses across environmental gradients in a high Arctic freshwater-saltwater lake

Giant viruses (GVs; Nucleocytoviricota) impact the biology and ecology of a wide range of eukaryotic hosts, with implications for global biogeochemical cycles. Here, we investigated GV niche separation in highly stratified Lake A at the northern coast of Ellesmere Island, Nunavut, Canada. This lake is composed of a layer of ice-covered freshwater that overlies saltwater derived from the ancient Arctic Ocean, and it therefore provides a broad gradient of environmental conditions and ecological habitats, each with a distinct protist community and rich assemblages of associated GVs. The upper layer (mixolimnion) had measurable light and oxygen, and contained diverse GVs linked to photosynthetic protists, indicating adaptation to surface biotic and abiotic conditions. In contrast, the saline lower layer (monimolimnion), lacking oxygen and light, hosted GVs associated with predicted heterotrophic protists, some of which are known for a predatory lifestyle, and with several viral genes suggesting adaptation to deep-water anaerobic conditions. Our observations underscore the coupling between physical and chemical gradients, microeukaryotes and their associated GVs in Lake A, and provide insight into the potential for GVs to directly and indirectly impact host metabolism. There were similarities between the genetic composition of GVs and the metabolic processes of their potential hosts, implying co-evolution and niche-adaptation within the lake habitats. Notably, we found a greater presence of viral rhodopsins in deeper water layers, suggesting an evolutionary relationship with potential hosts capable of supplementing their energetic needs to thrive in low energy, anoxic conditions.

59 BASIC BIOLOGICAL SCIENCES↗