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DOE OSTI2024

Neurospora intermedia from a traditional fermented food enables waste-to-food conversion

AbstractFungal fermentation of food and agricultural by-products holds promise for improving food sustainability and security. However, the molecular basis of fungal waste-to-food upcycling remains poorly understood. Here we use a multi-omics approach to characterize oncom, a fermented food traditionally produced from soymilk by-products in Java, Indonesia. Metagenomic sequencing of samples from small-scale producers in Western Java indicated that the fungus Neurospora intermedia dominates oncom. Further transcriptomic, metabolomic and phylogenomic analysis revealed that oncom-derived N. intermedia utilizes pectin and cellulose degradation during fermentation and belongs to a genetically distinct subpopulation associated with human-generated by-products. Finally, we found that N. intermedia grew on diverse by-products such as fruit and vegetable pomace and plant-based milk waste, did not encode mycotoxins, and could create foods that were positively perceived by consumers outside Indonesia. These results showcase the traditional significance and future potential of fungal fermentation for creating delicious and nutritious foods from readily available by-products.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Optimizing resource allocation in Miscanthus breeding via sparse testing designs for genomic prediction

Phenotyping high-biomass perennial crops is laborious and the rate of genetic gain in conventional perennial crop breeding programs is typically low. So, it is especially important to identify methods that produce efficiency gains in the breeding process. Miscanthus is a C4 perennial grass with favorable characteristics for producing biomass as a feedstock for biofuels and diverse bio-based products. Increasing biomass yield will increase profitability and environmental benefits, so it is a key target for Miscanthus breeding. In addition, the identification of well-adapted genotypes across a wide range of environmental conditions requires the establishment of multi-environment trials (METs). Sparse testing is a genomic prediction-based strategy that reduces the phenotyping costs in METs by selecting a subset of genotypes to evaluate in a subset of environments and then predicts the performance of the unobserved genotype-environment combinations. A Miscanthus sacchariflorus (MSA) population comprising 336 genotypes observed across three environments was analyzed implementing sparse testing designs. Three prediction models considering main effects (environments, genotypes, genomic) and interaction effects (genotype-by-environment; G×E interaction) were implemented for forecasting dry biomass yield (YDY), total culm (TCM), average internode length (AIL), and culm node number (CNN). Multiple calibration sets based on different compositions and sizes were considered to evaluate performance in terms of the predictive ability (PA) and the mean square error (MSE) for a fixed testing set size. The training set size ranged from 52 to 112 to predict a fixed set of 224 unobserved genotypes across all three environments. The results showed that the model accounting for G×E interaction consistently presented the highest PA and the lowest MSE: for CNN (PA: ~0.77, MSE: ~0.5) and YDY (PA: ~0.70, MSE: ~1.3) while for TCM and AIL these ranged from ~0.28 to 0.41 and ~1.3 to 4.3, respectively. Overall, varying training sets and allocation strategies did not affect PA and MSE, with 52 non-overlapping and 0 overlapping genotypes per environment as the optimal cost-effective allocation framework. This suggests that implementing sparse testing designs could significantly reduce phenotyping costs by fivefold, without compromising PA in breeding programs for perennial crops such as Miscanthus.

Miscanthus sacchariflorus (MSA)↗

DOE OSTI2024

Xylem-dwelling pathogen unaffected by local xylem vessel network properties in grapevines ( Vitis spp.)

Abstract Background and aims Xylella fastidiosa (Xf) is the xylem-dwelling bacterium associated with Pierce’s disease (PD), which causes mortality in agriculturally important species, such as grapevine (Vitis vinifera). The development of PD symptoms in grapevines depends on the ability of Xf to produce cell-wall-degrading enzymes to break up intervessel pit membranes and systematically spread through the xylem vessel network. Our objective here was to investigate whether PD resistance could be mechanistically linked to xylem vessel network local connectivity. Methods We used high-resolution X-ray micro-computed tomography (microCT) imaging to identify and describe the type, area and spatial distribution of intervessel connections for six different grapevine genotypes from three genetic backgrounds, with varying resistance to PD (four PD resistant and two PD susceptible). Key results Our results suggest that PD resistance is unlikely to derive from local xylem network connectivity. The intervessel pit area (Ai) varied from 0.07 ± 0.01 mm2 mm−3 in Lenoir to 0.17 ± 0.03 mm2 mm−3 in Blanc do Bois, both PD resistant. Intervessel contact fraction (Cp) was not statically significant, but the two PD-susceptible genotypes, Syrah (0.056 ± 0.015) and Chardonnay (0.041 ± 0.013), were among the most highly connected vessel networks. Neither Ai nor Cp explained differences in PD resistance among the six genotypes. Bayesian re-analysis of our data shows moderate evidence against the effects of the traits analysed: Ai (BF01 = 4.88), mean vessel density (4.86), relay diameter (4.30), relay density (3.31) and solitary vessel proportion (3.19). Conclusions Our results show that radial and tangential xylem network connectivity is highly conserved within the six different Vitis genotypes we sampled. The way that Xf traverses the vessel network may limit the importance of local network properties to its spread and may confer greater importance on host biochemical responses.

Plant Sciences↗

DOE OSTI2025

Optimizing genomic prediction for complex traits via investigating multiple factors in switchgrass

Genomic prediction has accelerated breeding processes and provided mechanistic insights into the genetic bases of complex traits. To further optimize genomic prediction, we assess the impact of genome assemblies, genotyping approaches, variant types, allelic complexities, polyploidy levels, and population structures on the prediction of 20 complex traits in switchgrass (Panicum virgatum L.), a perennial biofuel feedstock. Surprisingly, short read-based genome assembly performs comparably to or even better than long read-based assembly. Due to higher gene coverage, exome capture and multi-allelic variants outperform genotyping-by-sequencing and bi-allelic variants, respectively. Tetraploid models show higher prediction accuracy than octoploid models for most traits, likely due to the greater genetic distances among tetraploids. Depending on the trait in question, different types of variants need to be integrated for optimal predictions. Furthermore, our study provides insights into the factors influencing genomic prediction outcomes, guiding best practices for future studies and for improving agronomic traits in switchgrass and other species through selective breeding.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

ER stress and viral defense: Advances and future perspectives on plant unfolded protein response in pathogenesis

Viral infections pose significant threats to crop productivity and agricultural sustainability. The frequency and severity of these infections are increasing, and pathogens are evolving rapidly under the influence of climate change. This underscores the importance of exploring the fundamental mechanisms by which plants defend themselves against dynamic viral threats. One such mechanism is the unfolded protein response (UPR), which is activated when the protein folding demand exceeds the capacity of the endoplasmic reticulum, particularly under adverse environmental conditions. While the key regulators of the UPR in response to viral infections have been identified, our understanding of how they modulate the UPR to suppress plant viral infections at the molecular and genetic levels is still in its infancy. Recent findings have shown that, in response to plant viral infections, the UPR swiftly reprograms transcriptional changes to support cellular, metabolic, and physiological processes associated with cell viability. However, the underlying mechanisms and functional outcomes of these changes remain largely unexplored. Here, we highlight recent advances in plant UPR research and summarize key findings related to viral infection–induced UPR, focusing on the balance between prosurvival and prodeath strategies. We also discuss the potential of systems-level approaches to uncover the full extent of the functional link between the UPR and plant responses to viral infections.

ER stress↗

DOE OSTI2020

Population Genomic Analyses Reveal Connectivity via Human-Mediated Transport across Populus Plantations in North America and an Undescribed Subpopulation of Sphaerulina musiva

Domestication of plant species has affected the evolutionary dynamics of plant pathogens in agriculture and forestry. A model system for studying the consequences of plant domestication on the evolution of an emergent plant disease is the fungal pathogen Sphaerulina musiva. This ascomycete causes leaf spot and stem canker disease of Populus spp. and their hybrids. A population genomics approach was used to determine the degree of population structure and evidence for selection on the North American population of S. musiva. In total, 122 samples of the fungus were genotyped identifying 120,016 single-nucleotide polymorphisms after quality filtering. In North America, S. musiva has low to moderate degrees of differentiation among locations. Three main genetic clusters were detected: southeastern United States, midwestern United States and Canada, and a new British Columbia cluster (BC2). Population genomics suggest that BC2 is a novel genetic cluster from central British Columbia, clearly differentiated from previously reported S. musiva from coastal British Columbia, and the product of a single migration event. Phenotypic measurements from greenhouse experiments indicate lower aggressiveness of BC2 on Populus trichocarpa. In summary, S. musiva has geographic structure across broad regions indicative of gene flow among clusters. The interconnectedness of the North American S. musiva populations across large geographic distances further supports the hypothesis of anthropogenic-facilitated transport of the pathogen.

Biochemistry & Molecular Biology↗

DOE OSTI2024

Integrating multi-modal remote sensing, deep learning, and attention mechanisms for yield prediction in plant breeding experiments

In both plant breeding and crop management, interpretability plays a crucial role in instilling trust in AI-driven approaches and enabling the provision of actionable insights. The primary objective of this research is to explore and evaluate the potential contributions of deep learning network architectures that employ stacked LSTM for end-of-season maize grain yield prediction. A secondary aim is to expand the capabilities of these networks by adapting them to better accommodate and leverage the multi-modality properties of remote sensing data. In this study, a multi-modal deep learning architecture that assimilates inputs from heterogeneous data streams, including high-resolution hyperspectral imagery, LiDAR point clouds, and environmental data, is proposed to forecast maize crop yields. The architecture includes attention mechanisms that assign varying levels of importance to different modalities and temporal features that, reflect the dynamics of plant growth and environmental interactions. The interpretability of the attention weights is investigated in multi-modal networks that seek to both improve predictions and attribute crop yield outcomes to genetic and environmental variables. This approach also contributes to increased interpretability of the model's predictions. The temporal attention weight distributions highlighted relevant factors and critical growth stages that contribute to the predictions. The results of this study affirm that the attention weights are consistent with recognized biological growth stages, thereby substantiating the network's capability to learn biologically interpretable features. Accuracies of the model's predictions of yield ranged from 0.82-0.93 R 2 ref in this genetics-focused study, further highlighting the potential of attention-based models. Further, this research facilitates understanding of how multi-modality remote sensing aligns with the physiological stages of maize. The proposed architecture shows promise in improving predictions and offering interpretable insights into the factors affecting maize crop yields, while demonstrating the impact of data collection by different modalities through the growing season. By identifying relevant factors and critical growth stages, the model's attention weights provide valuable information that can be used in both plant breeding and crop management. The consistency of attention weights with biological growth stages reinforces the potential of deep learning networks in agricultural applications, particularly in leveraging remote sensing data for yield prediction. To the best of our knowledge, this is the first study that investigates the use of hyperspectral and LiDAR UAV time series data for explaining/interpreting plant growth stages within deep learning networks and forecasting plot-level maize grain yield using late fusion modalities with attention mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Ancient co-option of LTR retrotransposons as yeast centromeres

Centromeres ensure accurate chromosome segregation, yet their DNA evolves rapidly across eukaryotes leaving the origins of new centromere architectures unclear. The brewer’s yeast Saccharomyces cerevisiae exemplifies this long-standing puzzle. Its centromeres shifted ancestrally from large, repeat-rich, epigenetically specified forms to the compact, genetically defined ‘point’ centromeres. How this transition occurred has remained unresolved6. Here we identify evolutionarily related ‘proto-point’ centromeres that provide a resolution to the evolutionary origins of point centromeres. Proto-point centromeres contain a single centromeric nucleosome positioned over an AT-rich core, accompanied by relaxed organization and sequence variability of flanking cis-elements. In two species, these proto-point centromeres lie within retrotransposon-derived repeat clusters, linking ancestral repeat-rich centromeres to genetically encoded ones. Comparative and phylogenetic analyses indicate that proto-point and point centromeres evolved in an ancestor with retrotransposon-rich centromeres. These results identify long-terminal-repeat retrotransposons, specifically Ty5 sequences, as the genetic substrate for point-centromere evolution and provide a mechanistic route by which an epigenetic centromere can become genetically specified. More broadly, they show how selfish elements can be co-opted to perform essential chromosomal functions.

Haase, Max A. B. [Max Planck Institute of Molecula↗

DOE OSTI2025

Time‐series multi‐omics analysis of micronutrient stress in Sorghum bicolor reveals iron and zinc crosstalk and regulatory network conservation

Micronutrient stress impacts growth, biomass production, and grain yield in crops. Multi-omics studies are valuable resources in identifying genes for functional studies and trait improvement, such as accumulation of Fe or Zn under deficient or excess conditions for bioenergy or grain agriculture. We conducted transcriptomics and ionomics analyses on Sorghum bicolor BTx623, grown under Fe and Zn limited and excess conditions over a 21-day period. To identify early and late transcriptional response in roots and leaves, 180 RNAseq libraries were sequenced for differential expression and co-expression network analyses. Fe and Zn accumulation was measured using ICP-MS at each time point, and a fluorometer was used to estimate chlorophyll content in leaves. Among the four treatments, Fe limitation and Zn excess resulted in the largest phenotypic effects and transcriptional response in roots and leaves. Several of the reduction (Strategy I) and chelation (Strategy II) strategy genes that improve bioavailability of Fe and Zn in plant roots often used by non-grass and grass species, respectively, were differentially expressed. Gene regulatory network (GRN) analysis of roots revealed enrichment of genes from Fe limiting and Zn excess which strongly connect to homologues of SbFIT, SbPYE, and SbBTS as hub genes. The GRN for leaf responses showed homologues of SbPYE and SbBTS as hubs connecting genes for chloroplast biosynthesis, Fe-S cluster assembly, photosynthesis, and ROS scavenging. Expression analyses suggest sorghum uses Strategy II genes for Fe and Zn uptake, as expected, but can also utilize Strategy I genes, which may be advantageous in variable moisture environments. We found strong overlap between Fe and Zn responsive GRNs, indicative of micronutrient crosstalk. We also found conservation of root and leaf GRNs, and known homologous genes suggest strong constraints on homeostasis networks in plants. These data will provide a resource for functional genetics to enhance micronutrient transport in sorghum, and opportunities to conduct further comparative GRN analysis across diverse crops species.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Protecting honey bees through microbiome engineering

Honey bees are indispensable insects. Their pollination services support modern agriculture and natural ecosystems. Managed honey bee colonies face increasing threats to their survival, ranging from environmental stressors that include agrochemicals to infestations of arthropod pests and infections with microbial pathogens. Like humans, honey bees have a native gut microbiome that supports their health. However, the bee gut microbiome has a simpler composition than the gut microbiome of mammals, and its main constituent bacterial species can be easily cultured outside of the host. Furthermore, this experimental tractability and the need for new methods for protecting hive health have made honey bees a testbed for synthetic microbiomes augmented with probiotic bacteria and engineered DNA. Here, we discuss the natural benefits of bee gut bacteria, recent progress in genetically modifying these bacteria, and how symbiont-mediated RNA interference and other microbiome engineering approaches can boost bee immunity and suppress bee pathogens and parasites. Finally, we discuss how emerging methods for microbiome engineering and biocontainment could be applied to honey bees and used to address challenges in translating these proof-of-principle achievements into safe and effective technologies for field applications at scale.

Biological and medical sciences↗

DOE OSTI2021

A new primer set for Clade I nosZ that recovers genes from a broader range of taxa

Denitrification is an important global N cycle process. The gene encoding NosZ that converts nitrous oxide (N 2 O) to N 2 has been widely used as a biomarker to study denitrifying communities. However, conventional PCR primers target a limited range of the genetically diverse Clade I nosZ, and the amplicons are too long for sequencing on current NGS platforms. To address these issues, here we developed a new PCR primer set that amplifies a 355-bp region of Clade I nosZ and captures broader taxonomic coverage than conventional primers in in silico tests. When compared with the widely used nosZF_nosZR_Rich_2003 set using the same soil samples and the same sequencing depth, the new set retrieved genes from four times more unique species, with consistently higher general diversity-based metrics. The new primer set performed well with different sequencing platforms (Ion Torrent and Illumina), and among a wide variety of soils from polar to tropical, desert to agricultural, and surface to a very low biomass subsoil, with significant differences in denitrifying community diversity and composition. This new primer set for Clade I together with the primers recently reported for Clade II by Chee-Sanford et al. provides a more comprehensive assessment of denitrifier gene hosts, their ecological patterns, and the degree of novelty in retrieved gene sequences.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

VA EDH Data Curation Documentation FY22-Q2, Rev. 2

The health and well-being of the Nation’s men and women who have served in uniform is the highest priority for the U.S. Department of Veterans Affairs (VA). VA is committed to providing timely access to high-quality, recovery-oriented, evidence-based mental health care that anticipates and responds to Veterans’ needs and supports the reintegration of returning Service members into their communities. VA is working to eliminate suicide among all Veterans by developing and implementing innovative suicide prevention approaches and resources. Health outcomes, such as suicide are typically modeled as a function of genetics and environment, where environment refers to factors beyond medical, e.g., air quality, access to transportation and food, homelessness status, etc. Mental health outcomes for each individual are considered to be associated with multiple stressors that fall under a variety of categories socioeconomic, economic, physical environment. Understanding the relationships between these stressors, covariates and health outcomes, requires curated, standardized data that can be input into the VA’s Recovery Engagement and Coordination for Health - Veterans Enhanced Treatment (REACH VET) or other health outcomes model. Environmental Determinants of Health (EDH) as defined by the World Health Organization (WHO) is clean air, stable climate, adequate water, sanitation and hygiene, safe use of chemicals, protection from radiation, healthy and safe workplaces, sound agricultural practices, health-supportive cities and built environments, and a preserved nature are all prerequisites for good health.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

VA EDH Data Curation Documentation (FY22-Q3, Rev.2)

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

VA EDH Data Curation Documentation FY22-Q4

The health and well-being of the Nation’s men and women who have served in uniform is the highest priority for the U.S. Department of Veterans Affairs (VA). VA is committed to providing timely access to high-quality, recovery-oriented, evidence-based mental health care that anticipates and responds to Veterans’ needs and supports the reintegration of returning Service members into their communities. VA is working to eliminate suicide among all Veterans by developing and implementing innovative suicide prevention approaches and resources. Health outcomes, such as suicide are typically modeled as a function of genetics and environment, where environment refers to factors beyond medical, e.g., air quality, access to transportation and food, homelessness status, etc. Mental health outcomes for each individual are considered to be associated with multiple stressors that fall under a variety of categories socioeconomic, economic, physical environment. Understanding the relationships between these stressors, covariates, and health outcomes, requires curated, standardized data that can be input into the VA’s Recovery Engagement and Coordination for Health - Veterans Enhanced Treatment (REACH VET) or other health outcomes model. Environmental Determinants of Health (EDH) as defined by the World Health Organization (WHO) is clean air, stable climate, adequate water, sanitation and hygiene, safe use of chemicals, protection from radiation, healthy and safe workplaces, sound agricultural practices, health-supportive cities and built environments, and a preserved nature are all prerequisites for good health.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

VA EDH Data Curation Documentation FY23-Q2

The health and well-being of the Nation’s men and women who have served in uniform is the highest priority for the U.S. Department of Veterans Affairs (VA). VA is committed to providing timely access to high-quality, recovery-oriented, evidence-based mental health care that anticipates and responds to Veterans’ needs and supports the reintegration of returning Service members into their communities. Since its creation, VA has been working to eliminate suicide among all veterans by developing and implementing innovative suicide prevention approaches and resources. Health outcomes, such as suicide are typically modeled as a function of genetics and environment, where environment refers to factors beyond medical, e.g., air quality, access to transportation and food, homelessness status, etc. Mental health outcomes for each individual are considered to be associated with multiple stressors that fall under a variety of categories including socioeconomic, economic, physical environment. Understanding the relationships between these stressors, covariates and health outcomes requires curated, standardized data that can be input into the VA’s Recovery Engagement and Coordination for Health, Veterans Enhanced Treatment (REACH VET) or other health outcomes model. Environmental Determinants of Health (EDH) as defined by the World Health Organization (WHO) is clean air, stable climate, adequate water, sanitation and hygiene, safe use of chemicals, protection from radiation, healthy and safe workplaces, sound agricultural practices, health-supportive cities and built environments, and a preserved nature are all prerequisites for good health.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

VA EDH Data Curation Documentation FY23-Q3

The health and well-being of the Nation’s men and women who have served in uniform is the highest priority for the U.S. Department of Veterans Affairs (VA). VA is committed to providing timely access to high-quality, recovery-oriented, evidence-based mental health care that anticipates and responds to Veterans’ needs and supports the reintegration of returning Service members into their communities. Since its creation, VA has been working to eliminate suicide among all veterans by developing and implementing innovative suicide prevention approaches and resources. Health outcomes, such as suicide are typically modeled as a function of genetics and environment, where environment refers to factors beyond medical, e.g., air quality, access to transportation and food, homelessness status, etc. Mental health outcomes for each individual are considered to be associated with multiple stressors that fall under a variety of categories including socioeconomic, economic, physical environment. Understanding the relationships between these stressors, covariates and health outcomes requires curated, standardized data that can be input into the VA’s Recovery Engagement and Coordination for Health, Veterans Enhanced Treatment (REACH VET) or other health outcomes model. Environmental Determinants of Health (EDH) as defined by the World Health Organization (WHO) refers to clean air, stable climate, adequate water, sanitation and hygiene, safe use of chemicals, protection from radiation, healthy and safe workplaces, sound agricultural practices, health-supportive cities and built environments, and a preserved nature are all prerequisites for good health.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Genome-wide Association Study Identifies Candidate Loci with Major Contributions to the Genetic Control of Pod Morphological Traits in Snap Bean

Snap beans are cultivars of common bean (Phaseolus vulgaris) that are cultivated for their fleshy immature pods that exhibit a wide diversity of pod shapes and sizes. The genetic basis of the snap bean pod shape is complex and involves the interaction of multiple genes. This study used a snap bean diversity panel composed of heirloom and improved cultivars used in North America and genome-wide association studies (GWAS) to investigate the genetic basis of pod morphological characteristics, including length, width, height, width/height ratio, and coefficients of variation (CVs). The GWAS detected multiple genomic regions associated with each pod trait, with a total of 20 quantitative trait loci (QTLs) for pod length, 9 for pod width, 14 for pod height, and 10 for pod width/height ratio. Regarding theCVof each pod trait, genome-wide association analyses detected six QTL for lengthCVs, five for widthCVs, 15 for heightCVs, and six for width/height ratioCVs. Thirteen regions in seven chromosomes were associated with two or more pod traits. Eighteen QTLs for pod traits in this study colocated with previously reported QTLs for pod and seed traits. The QTL intervals encompass gene models with homologues in other species that are involved in the control of developmental processes. These results capture the complex nature of the genetic control of snap bean pod traits and confirm the significance of genomic regions harboring overlapping QTLs identified in this and other studies. The phenotypic expression of pod traits in snap bean appears to be under the control of a few genomic regions with a strong effect with additional contributions of multiple small-effect regions. Validation of the function of the candidate genes identified in associated regions will contribute to our understanding of legume pod development.

Agriculture↗

DOE OSTI2024

Phylogenetically diverse wild plant species use common biochemical strategies to thrive in the Atacama Desert

The best ideotypes are under mounting pressure due to increased aridity. Understanding the conserved molecular mechanisms that evolve in wild plants adapted to harsh environments is crucial in developing new strategies for agriculture. Yet our knowledge of such mechanisms in wild species is scant. We performed metabolic pathway reconstruction using transcriptome information from 32 Atacama and phylogenetically related species that do not live in Atacama (sister species). We analyzed reaction enrichment to understand the commonalities and differences of Atacama plants. To gain insights into the mechanisms that ensure survival, we compared expressed gene isoform numbers and gene expression patterns between the annotated biochemical reactions from 32 Atacama and sister species. We found biochemical convergences characterized by reactions enriched in at least 50% of the Atacama species, pointing to potential advantages against drought and nitrogen starvation, for instance. Furthermore, these findings suggest that the adaptation in the Atacama Desert may result in part from shared genetic legacies governing the expression of key metabolic pathways to face harsh conditions. Enriched reactions corresponded to ubiquitous compounds common to extreme and agronomic species and were congruent with our previous metabolomic analyses. Convergent adaptive traits offer promising candidates for improving abiotic stress resilience in crop species.

59 BASIC BIOLOGICAL SCIENCES↗