1,135 Genomes Reveal the Global Pattern of Polymorphism in Arabidopsis thaliana

PanVA: Pangenomic Variant Analysis

Genomics researchers increasingly use multiple reference genomes to comprehensively explore genetic variants underlying differences in detectable characteristics between organisms. Pangenomes allow for an efficient data representation of multiple related genomes and their associated metadata. However, current visual analysis approaches for exploring these complex genotype-phenotype relationships are often based on single reference approaches or lack adequate support for interpreting the variants in the genomic context with heterogeneous (meta)data. This design study introduces PanVA, a visual analytics design for pangenomic variant analysis developed with the active participation of genomics researchers. The design uniquely combines tailored visual representations with interactions such as sorting, grouping, and aggregation, allowing users to navigate and explore different perspectives on complex genotype-phenotype relations. Through evaluation in the context of plants and pathogen research, we show that PanVA helps researchers explore variants in genes and generate hypotheses about their role in phenotypic variation.

Plant pangenomes for crop improvement, biodiversity and evolution

Plant genome sequences catalogue genes and the genetic elements that regulate their expression. Such inventories further research aims as diverse as mapping the molecular basis of trait diversity in domesticated plants or inquiries into the origin of evolutionary innovations in flowering plants millions of years ago. The transformative technological progress of DNA sequencing in the past two decades has enabled researchers to sequence ever more genomes with greater ease. Pangenomes - complete sequences of multiple individuals of a species or higher taxonomic unit - have now entered the geneticists' toolkit. The genomes of crop plants and their wild relatives are being studied with translational applications in breeding in mind. But pangenomes are applicable also in ecological and evolutionary studies, as they help classify and monitor biodiversity across the tree of life, deepen our understanding of how plant species diverged and show how plants adapt to changing environments or new selection pressures exerted by human beings.

Substrate specificity and protein stability drive the divergence of plant-specific DNA methyltransferases

DNA methylation is an important epigenetic mechanism essential for transposon silencing and genome integrity. Across evolution, the substrates of DNA methylation have diversified between kingdoms to account for genome complexity. In plants, Chromomethylase3 (CMT3) and CMT2 are the major methyltransferases mediating CHG and CHH methylation, respectively. However, how these two enzymes diverge on substrate specificities during evolution remains unknown. Here, we reveal that CMT2 originates from a duplication of the evolutionarily more ancient CMT3 in flowering plants. Lacking a key arginine residue recognizing CHG in CMT2 impairs its CHG methylation activity in most flowering plants. An engineered V1200R mutation empowers CMT2 to restore both CHG and CHH methylation in Arabidopsis cmt2cmt3 mutant, testifying a loss-of-function effect for CMT2 after ∼200 million years of evolution. Interestingly, CMT2 has evolved a long and unstructured N-terminus critical for balancing protein stability, especially under heat stress. Furthermore, CMT2 N-terminus is plastic and can be tolerant to various natural mutations. Together, this study reveals the mechanism of chromomethylase divergence for context-specific DNA methylation in plants and sheds important lights on DNA methylation evolution and function.

GeCKO: user-friendly workflows for genotyping complex genomes using target enrichment capture. A use case on the large tetraploid durum wheat genome

BACKGROUND

Genotyping of individuals plays a pivotal role in various biological analyses, with technology choice influenced by multiple factors including genomic constraints, number of targeted loci and individuals, cost considerations, and the ease of sample preparation and data processing. Target enrichment capture of specific polymorphic regions has emerged as a flexible and cost-effective genomic reduction method for genotyping, especially adapted to the case of very large genomes. However, this approach necessitates complex bioinformatics treatment to extract genotyping data from raw reads. Existing workflows predominantly cater to phylogenetic inference, leaving a gap in user-friendly tools for genotyping analysis based on capture methods. In response to these challenges, we have developed GeCKO (Genotyping Complexity Knocked-Out). To assess the effectiveness of combining target enrichment capture with GeCKO, we conducted a case study on durum wheat domestication history, involving sequencing, processing, and analyzing variants in four relevant durum wheat groups.

RESULTS

GeCKO encompasses four distinct workflows, each designed for specific steps of genomic data processing: (i) read demultiplexing and trimming for data cleaning, (ii) read mapping to align sequences to a reference genome, (iii) variant calling to identify genetic variants, and (iv) variant filtering. Each workflow in GeCKO can be easily configured and is executable across diverse computational environments. The workflows generate comprehensive HTML reports including key summary statistics and illustrative graphs, ensuring traceable, reproducible results and facilitating straightforward quality assessment. A specific innovation within GeCKO is its 'targeted remapping' feature, specifically designed for efficient treatment of targeted enrichment capture data. This process consists of extracting reads mapped to the targeted regions, constructing a smaller sub-reference genome, and remapping the reads to this sub-reference, thereby enhancing the efficiency of subsequent steps.

CONCLUSIONS

The case study results showed the expected intra-group diversity and inter-group differentiation levels, confirming the method's effectiveness for genotyping and analyzing genetic diversity in species with complex genomes. GeCKO streamlined the data processing, significantly improving computational performance and efficiency. The targeted remapping enabled straightforward SNP calling in durum wheat, a task otherwise complicated by the species' large genome size. This illustrates its potential applications in various biological research contexts.

Natural variation of TBR confers plant zinc toxicity tolerance through root cell wall pectin methylesterification

Zinc (Zn) is an essential micronutrient but can be cytotoxic when present in excess. Plants have evolved mechanisms to tolerate Zn toxicity. To identify genetic loci responsible for natural variation of plant tolerance to Zn toxicity, we conduct genome-wide association studies for root growth responses to high Zn and identify 21 significant associated loci. Among these loci, we identify Trichome Birefringence (TBR) allelic variation determining root growth variation in high Zn conditions. Natural alleles of TBR determine TBR transcript and protein levels which affect pectin methylesterification in root cell walls. Together with previously published data showing that pectin methylesterification increase goes along with decreased Zn binding to cell walls in TBR mutants, our findings lead to a model in which TBR allelic variation enables Zn tolerance through modulating root cell wall pectin methylesterification. The role of TBR in Zn tolerance is conserved across dicot and monocot plant species.

Programmed cell death regulator BAP2 is required for IRE1-mediated unfolded protein response in Arabidopsis

Environmental and physiological situations can challenge the balance between protein synthesis and folding capacity of the endoplasmic reticulum (ER) and cause ER stress, a potentially lethal condition. The unfolded protein response (UPR) restores ER homeostasis or actuates programmed cell death (PCD) when ER stress is unresolved. The cell fate determination mechanisms of the UPR are not well understood, especially in plants. Here, we integrate genetics and ER stress profiling with natural variation and quantitative trait locus analysis of 350 natural accessions of the model species Arabidopsis thaliana. Our analyses implicate a single nucleotide polymorphism to the loss of function of the general PCD regulator BON-ASSOCIATED PROTEIN2 (BAP2) in UPR outcomes. We establish that ER stress-induced BAP2 expression is antagonistically regulated by the UPR master regulator, inositol-requiring enzyme 1 (IRE1), and that BAP2 controls adaptive UPR amplitude in ER stress and ignites pro-death mechanisms in conditions of UPR insufficiency.

A foundational large language model for edible plant genomes

Significant progress has been made in the field of plant genomics, as demonstrated by the increased use of high-throughput methodologies that enable the characterization of multiple genome-wide molecular phenotypes. These findings have provided valuable insights into plant traits and their underlying genetic mechanisms, particularly in model plant species. Nonetheless, effectively leveraging them to make accurate predictions represents a critical step in crop genomic improvement. We present AgroNT, a foundational large language model trained on genomes from 48 plant species with a predominant focus on crop species. We show that AgroNT can obtain state-of-the-art predictions for regulatory annotations, promoter/terminator strength, tissue-specific gene expression, and prioritize functional variants. We conduct a large-scale in silico saturation mutagenesis analysis on cassava to evaluate the regulatory impact of over 10 million mutations and provide their predicted effects as a resource for variant characterization. Finally, we propose the use of the diverse datasets compiled here as the Plants Genomic Benchmark (PGB), providing a comprehensive benchmark for deep learning-based methods in plant genomic research. The pre-trained AgroNT model is publicly available on HuggingFace at https://huggingface.co/InstaDeepAI/agro-nucleotide-transformer-1b  for future research purposes.

Estimating scale-specific and localized spatial patterns in allele frequency

Characterizing spatial patterns in allele frequencies is fundamental to evolutionary biology because these patterns contain evidence of underlying processes. However, the spatial scales at which gene flow, changing selection, and drift act are often unknown. Many of these processes can operate inconsistently across space, causing nonstationary patterns. We present a wavelet approach to characterize spatial pattern in allele frequency that helps solve these problems. We show how our approach can characterize spatial patterns in relatedness at multiple spatial scales, i.e. a multilocus wavelet genetic dissimilarity. We also develop wavelet tests of spatial differentiation in allele frequency and quantitative trait loci (QTL). With simulation, we illustrate these methods under different scenarios. We also apply our approach to natural populations of Arabidopsis thaliana to characterize population structure and identify locally adapted loci across scales. We find, for example, that Arabidopsis flowering time QTL show significantly elevated genetic differentiation at 300-1,300 km scales. Wavelet transforms of allele frequencies offer a flexible way to reveal geographic patterns and underlying evolutionary processes.

Using Runs of Homozygosity and Machine Learning to Disentangle Sources of Inbreeding and Infer Self-Fertilization Rates

Runs of homozygosity (ROHs) are indicative of elevated homozygosity and inbreeding due to mating of closely related individuals. Self-fertilization can be a major source of inbreeding which elevates genome-wide homozygosity and thus should also create long ROHs. While ROHs are frequently used to understand inbreeding in the context of conservation and selective breeding, as well as for consanguinity of populations and their demographic history, it remains unclear how ROH characteristics are altered by selfing and if this confounds expected signatures of inbreeding due to demographic change. Using simulations, we study the impact of the mode of reproduction and demographic history on ROHs. We apply random forests to identify unique characteristics of ROHs, indicative of different sources of inbreeding. We pinpoint distinct features of ROHs that can be used to better characterize the type of inbreeding the population was subjected to and to predict outcrossing rates and complex demographic histories. Using additional simulations and four empirical datasets, two from highly selfing species and two from mixed-maters, we predict the selfing rate and validate our estimations. We find that self-fertilization rates are successfully identified even with complex demography. Population genetic summary statistics improve algorithm accuracy particularly in the presence of additional inbreeding, e.g. from population bottlenecks. Our findings highlight the importance of ROHs in disentangling confounding factors related to various sources of inbreeding and demonstrate situations where such sources cannot be differentiated. Additionally, our random forest models provide a novel tool to the community for inferring selfing rates using genomic data.

Can plants build their niche through modulation of soil microbial activities linked with nitrogen cycling? A test with Arabidopsis thaliana

In natural systems, different plant species have been shown to modulate specific nitrogen (N) cycling processes so as to meet their N demand, thereby potentially influencing their own niche. This phenomenon might go beyond plant interactions with symbiotic microorganisms and affect the much less explored plant interactions with free-living microorganisms involved in soil N cycling, such as nitrifiers and denitrifiers. Here, we investigated variability in the modulation of soil nitrifying and denitrifying enzyme activities (NEA and DEA, respectively), and their ratio (NEA : DEA), across 193 Arabidopsis thaliana accessions. We studied the genetic and environmental determinants of such plant-soil interactions, and effects on plant biomass production in the next generation. We found that NEA, DEA, and NEA : DEA varied c. 30-, 15- and 60-fold, respectively, among A. thaliana genotypes and were related to genes linked with stress response, flowering, and nitrate nutrition, as well as to soil parameters at the geographic origin of the analysed genotypes. Moreover, plant-mediated N cycling activities correlated with the aboveground biomass of next-generation plants in home vs away nonautoclaved soil, suggesting a transgenerational impact of soil biotic conditioning on plant performance. Altogether, these findings suggest that nutrient-based plant niche construction may be much more widespread than previously thought.

The great tit HapMap project: A continental-scale analysis of genomic variation in a songbird

A major aim of evolutionary biology is to understand why patterns of genomic diversity vary within taxa and space. Large-scale genomic studies of widespread species are useful for studying how environment and demography shape patterns of genomic divergence. Here, we describe one of the most geographically comprehensive surveys of genomic variation in a wild vertebrate to date; the great tit (Parus major) HapMap project. We screened ca 500,000 SNP markers across 647 individuals from 29 populations, spanning ~30 degrees of latitude and 40 degrees of longitude - almost the entire geographical range of the European subspecies. Genome-wide variation was consistent with a recent colonisation across Europe from a South-East European refugium, with bottlenecks and reduced genetic diversity in island populations. Differentiation across the genome was highly heterogeneous, with clear 'islands of differentiation', even among populations with very low levels of genome-wide differentiation. Low local recombination rates were a strong predictor of high local genomic differentiation (FST), especially in island and peripheral mainland populations, suggesting that the interplay between genetic drift and recombination causes highly heterogeneous differentiation landscapes. We also detected genomic outlier regions that were confined to one or more peripheral great tit populations, probably as a result of recent directional selection at the species' range edges. Haplotype-based measures of selection were related to recombination rate, albeit less strongly, and highlighted population-specific sweeps that likely resulted from positive selection. Our study highlights how comprehensive screens of genomic variation in wild organisms can provide unique insights into spatio-temporal evolutionary dynamics.

Polygenic selection to a changing optimum under self-fertilisation

Many traits are polygenic, affected by multiple genetic variants throughout the genome. Selection acting on these traits involves co-ordinated allele-frequency changes at these underlying variants, and this process has been extensively studied in random-mating populations. Yet many species self-fertilise to some degree, which incurs changes to genetic diversity, recombination and genome segregation. These factors cumulatively influence how polygenic selection is realised in nature. Here, we use analytical modelling and stochastic simulations to investigate to what extent self-fertilisation affects polygenic adaptation to a new environment. Our analytical solutions show that while selfing can increase adaptation to an optimum, it incurs linkage disequilibrium that can slow down the initial spread of favoured mutations due to selection interference, and favours the fixation of alleles with opposing trait effects. Simulations show that while selection interference is present, high levels of selfing (at least 90%) aids adaptation to a new optimum, showing a higher long-term fitness. If mutations are pleiotropic then only a few major-effect variants fix along with many neutral hitchhikers, with a transient increase in linkage disequilibrium. These results show potential advantages to self-fertilisation when adapting to a new environment, and how the mating system affects the genetic composition of polygenic selection.

Dual-trait genomic analysis in highly stratified Arabidopsis thaliana populations using genome-wide association summary statistics

Genome-wide association study (GWAS) is a powerful tool to identify genomic loci underlying complex traits. However, the application in natural populations comes with challenges, especially power loss due to population stratification. Here, we introduce a bivariate analysis approach to a GWAS dataset of Arabidopsis thaliana. We demonstrate the efficiency of dual-phenotype analysis to uncover hidden genetic loci masked by population structure via a series of simulations. In real data analysis, a common allele, strongly confounded with population structure, is discovered to be associated with late flowering and slow maturation of the plant. The discovered genetic effect on flowering time is further replicated in independent datasets. Using Mendelian randomization analysis based on summary statistics from our GWAS and expression QTL scans, we predicted and replicated a candidate gene AT1G11560 that potentially causes this association. Further analysis indicates that this locus is co-selected with flowering-time-related genes. The discovered pleiotropic genotype-phenotype map provides new insights into understanding the genetic correlation of complex traits.

Dynamic evolution of the heterochromatin sensing histone demethylase IBM1

Heterochromatin is critical for maintaining genome stability, especially in flowering plants, where it relies on a feedback loop involving the H3K9 methyltransferase, KRYPTONITE (KYP), and the DNA methyltransferase CHROMOMETHYLASE3 (CMT3). The H3K9 demethylase INCREASED IN BONSAI METHYLATION 1 (IBM1) counteracts the detrimental consequences of KYP-CMT3 activity in transcribed genes. IBM1 expression in Arabidopsis is uniquely regulated by methylation of the 7th intron, allowing it to monitor global H3K9me2 levels. We show the methylated intron is prevalent across flowering plants and its underlying sequence exhibits dynamic evolution. We also find extensive genetic and expression variations in KYP, CMT3, and IBM1 across flowering plants. We identify Arabidopsis accessions resembling weak ibm1 mutants and Brassicaceae species with reduced IBM1 expression or deletions. Evolution towards reduced IBM1 activity in some flowering plants could explain the frequent natural occurrence of diminished or lost CMT3 activity and loss of gene body DNA methylation, as cmt3 mutants in A. thaliana mitigate the deleterious effects of IBM1.

Wild grass-derived alleles represent a genetic architecture for the resilience of modern common wheat to stresses

This review explores the integration of wild grass-derived alleles into modern bread wheat breeding to tackle the challenges of climate change and increasing food demand. With a focus on synthetic hexaploid wheat, this review highlights the potential of genetic variability in wheat wild relatives, particularly Aegilops tauschii, for improving resilience to multifactorial stresses like drought, heat, and salinity. The evolutionary journey of wheat (Triticum spp.) from diploid to hexaploid species is examined, revealing significant genetic contributions from wild grasses. We also emphasize the importance of understanding incomplete lineage sorting in the genomic evolution of wheat. Grasping this information is crucial as it can guide breeders in selecting the appropriate alleles from the gene pool of wild relatives to incorporate into modern wheat varieties. This approach improves the precision of phylogenetic relationships and increases the overall effectiveness of breeding strategies. This review also addresses the challenges in utilizing the wheat wild genetic resources, such as the linkage drag and cross-compatibility issues. Finally, we culminate the review with future perspectives, advocating for a combined approach of high-throughput phenotyping tools and advanced genomic techniques to comprehensively understand the genetic and regulatory architectures of wheat under stress conditions, paving the way for more precise and efficient breeding strategies.

Transmembrane formins as active cargoes of membrane trafficking

Formins are a large, evolutionarily old family of cytoskeletal regulators whose roles include actin capping and nucleation, as well as modulation of microtubule dynamics. The plant class I formin clade is characterized by a unique domain organization, as most of its members are transmembrane proteins with possible cell wall-binding motifs exposed to the extracytoplasmic space-a structure that appears to be a synapomorphy of the plant kingdom. While such transmembrane formins are traditionally considered mainly as plasmalemma-localized proteins contributing to the organization of the cell cortex, we review, from a cell biology perspective, the growing evidence that they can also, at least temporarily, reside (and in some cases also function) in endomembranes including secretory and endocytotic pathway compartments, the endoplasmic reticulum, the nuclear envelope, and the tonoplast. Based on this evidence, we propose that class I formins may thus serve as 'active cargoes' of membrane trafficking-membrane-embedded proteins that modulate the fate of endo- or exocytotic compartments while being transported by them.

Ecological trade-offs drive phenotypic and genetic differentiation of Arabidopsis thaliana in Europe

Plant diversity is shaped by trade-offs between traits related to competitive ability, propagule dispersal, and stress resistance. However, we still lack a clear understanding of how these trade-offs influence species distribution and population dynamics. In Arabidopsis thaliana, recent genetic analyses revealed a group of cosmopolitan genotypes that successfully recolonized Europe from its center after the last glaciation, excluding older (relict) lineages from the distribution except for their north and south margins. Here, we tested the hypothesis that cosmopolitans expanded due to higher colonization ability, while relicts persisted at the margins due to higher tolerance to competition and/or stress. We compared the phenotypic and genetic differentiation between 71 European genotypes originating from the center, and the south and north margins. We showed that a trade-off between plant fecundity and seed mass shapes the differentiation of A. thaliana in Europe, suggesting that the success of the cosmopolitan groups could be explained by their high dispersal ability. However, at both north and south margins, we found evidence of selection for alleles conferring low dispersal but highly competitive and stress-resistance abilities. This study sheds light on the role of ecological trade-offs as evolutionary drivers of the distribution and dynamics of plant populations.

A synchronized, large-scale field experiment using Arabidopsis thaliana reveals the significance of the UV-B photoreceptor UVR8 under natural conditions

This study determines the functional role of the plant ultraviolet-B radiation (UV-B) photoreceptor, UV RESISTANCE LOCUS 8 (UVR8) under natural conditions using a large-scale 'synchronized-genetic-perturbation-field-experiment'. Laboratory experiments have demonstrated a role for UVR8 in UV-B responses but do not reflect the complexity of outdoor conditions where 'genotype × environment' interactions can mask laboratory-observed responses. Arabidopsis thaliana knockout mutant, uvr8-7, and the corresponding Wassilewskija wild type, were sown outdoors on the same date at 21 locations across Europe, ranging from 39°N to 67°N latitude. Growth and climatic data were monitored until bolting. At the onset of bolting, rosette size, dry weight, and phenolics and glucosinolates were quantified. The uvr8-7 mutant developed a larger rosette and contained less kaempferol glycosides, quercetin glycosides and hydroxycinnamic acid derivatives than the wild type across all locations, demonstrating a role for UVR8 under field conditions. UV effects on rosette size and kaempferol glycoside content were UVR8 dependent, but independent of latitude. In contrast, differences between wild type and uvr8-7 in total quercetin glycosides, and the quercetin-to-kaempferol ratio decreased with increasing latitude, that is, a more variable UV response. Thus, the large-scale synchronized approach applied demonstrates a location-dependent functional role of UVR8 under natural conditions.

The Roles of Drift and Selection on Short Stamen Loss in Arabidopsis thaliana along an Elevational Gradient in the Spanish Pyrenees

Traits that have lost function sometimes persist through evolutionary time. These traits may be maintained by a lack of standing genetic variation for the trait, if selection against the trait is weak relative to drift, or if they have a residual function. To determine the evolutionary processes shaping whether nonfunctional traits are retained or lost, we investigated short stamens in 16 populations of Arabidopsis thaliana along an elevational cline in the Spanish Pyrenees. We found a cline in short stamen number from retention of short stamens in high elevation populations to incomplete loss in low elevation populations. We did not find evidence that limited genetic variation constrains the loss of short stamens at high elevations nor evidence for divergent selection on short stamens between high and low elevations. Finally, we identified loci associated with short stamens in the Spanish Pyrenees that are different from loci associated with variation in short stamen number across latitudes from a previous study. Overall, we did not identify the evolutionary mechanisms maintaining an elevational cline in short stamen number but did identify different genetic loci underlying the variation in short stamen along similar phenotypic clines.

Genome-wide association studies meta-analysis uncovers NOJO and SGS3 novel genes involved in Arabidopsis thaliana primary root development and plasticity

BACKGROUND

Arabidopsis thaliana primary root growth has become a model for evo-devo studies due to its simplicity and facility to record cell proliferation and differentiation. To identify new genetic components relevant to primary root growth, we used a Genome-Wide Association Studies (GWAS) meta-analysis approach using data published in the last decade. In this work, we performed intra and inter-studies analyses to discover new genetic components that could participate in primary root growth.

METHODS AND RESULTS

We used 639 accessions from nine different studies under control conditions and performed different GWAS tests. We found that primary root growth changes were associated with 41 genes, of which six (14.6%) have been previously described as inhibitors or promoters of primary root growth. The knockdown lines of two genes, Suppressor of Gene Silencing (SGS3), involved in tasiRNA processing, and a gene with a Sterile Alpha Motif (SAM) motif named NOJOCH MOOTS (NOJO), confirmed their role as repressors of primary root growth, none has been shown to participate in this developmental process before.

CONCLUSIONS

In summary, our GWAS analysis of different available studies identified new genes that participate in primary root growth; two of them were identified as repressors of primary root growth.

Next-generation data filtering in the genomics era

Genomic data are ubiquitous across disciplines, from agriculture to biodiversity, ecology, evolution and human health. However, these datasets often contain noise or errors and are missing information that can affect the accuracy and reliability of subsequent computational analyses and conclusions. A key step in genomic data analysis is filtering - removing sequencing bases, reads, genetic variants and/or individuals from a dataset - to improve data quality for downstream analyses. Researchers are confronted with a multitude of choices when filtering genomic data; they must choose which filters to apply and select appropriate thresholds. To help usher in the next generation of genomic data filtering, we review and suggest best practices to improve the implementation, reproducibility and reporting standards for filter types and thresholds commonly applied to genomic datasets. We focus mainly on filters for minor allele frequency, missing data per individual or per locus, linkage disequilibrium and Hardy-Weinberg deviations. Using simulated and empirical datasets, we illustrate the large effects of different filtering thresholds on common population genetics statistics, such as Tajima's D value, population differentiation (FST), nucleotide diversity (π) and effective population size (Ne).

Cross-species modeling of plant genomes at single nucleotide resolution using a pre-trained DNA language model

Understanding the function and fitness effects of diverse plant genomes requires transferable models. Language models (LMs) pre-trained on large-scale biological sequences can learn evolutionary conservation, thus expected to offer better cross-species prediction through fine-tuning on limited labeled data compared to supervised deep learning models. We introduce PlantCaduceus, a plant DNA LM based on the Caduceus and Mamba architectures, pre-trained on a carefully curated dataset consisting of 16 diverse Angiosperm genomes. Fine-tuning PlantCaduceus on limited labeled Arabidopsis data for four tasks involving transcription and translation modeling demonstrated high transferability to maize that diverged 160 million years ago, outperforming the best baseline model by 1.45-fold to 7.23-fold. PlantCaduceus also enables genome-wide deleterious mutation identification without multiple sequence alignment (MSA). PlantCaduceus demonstrated a threefold enrichment of rare alleles in prioritized deleterious mutations compared to MSA-based methods and matched state-of-the-art protein LMs. PlantCaduceus is a versatile pre-trained DNA LM expected to accelerate plant genomics and crop breeding applications.

Biologically meaningful genome interpretation models to address data underdetermination for the leaf and seed ionome prediction in Arabidopsis thaliana

Genome interpretation (GI) encompasses the computational attempts to model the relationship between genotype and phenotype with the goal of understanding how the first leads to the second. While traditional approaches have focused on sub-problems such as predicting the effect of single nucleotide variants or finding genetic associations, recent advances in neural networks (NNs) have made it possible to develop end-to-end GI models that take genomic data as input and predict phenotypes as output. However, technical and modeling issues still need to be fixed for these models to be effective, including the widespread underdetermination of genomic datasets, making them unsuitable for training large, overfitting-prone, NNs. Here we propose novel GI models to address this issue, exploring the use of two types of transfer learning approaches and proposing a novel Biologically Meaningful Sparse NN layer specifically designed for end-to-end GI. Our models predict the leaf and seed ionome in A.thaliana, obtaining comparable results to our previous over-parameterized model while reducing the number of parameters by 8.8 folds. We also investigate how the effect of population stratification influences the evaluation of the performances, highlighting how it leads to (1) an instance of the Simpson's Paradox, and (2) model generalization limitations.

Evolutionary patterns of DNA base composition at polymorphic sites highlight the role of the environment in shaping barley and rice genomes

Insights into changes in genome base composition underlying crop domestication can be gained by using comparative genomics. With this approach, previous studies have reported that crop genomes during domestication accumulate more nucleotides adenine (A) and thymine (T) (termed as [AT]-increase) across polymorphic sites. However, the potential influence of the environment or its factors, for example, solar ultraviolet (UV) radiation and temperature, on the [AT]-increase has not been well elucidated. Here, we investigated the [AT]-increase in barley (Hordeum vulgare L.) and rice (Oryza sativa L.) and the association with natural environments, where accessions are distributed. With 12,798,376 and 2,861,535 single-nucleotide polymorphisms from 368 barley and 1375 rice accessions, respectively, we discovered that [AT] increases from wild accessions to improved cultivars, and genomic regions with larger [AT]-increase tend to have higher UV-related motif frequencies, suggesting solar UV radiation as a potential factor in driving genome variation. To link [AT] change with the geographic distribution, we gathered georeferenced accessions and examined their local environments. Interestingly, negative correlations between [AT] and environmental factors were observed (r = -0.39 ∼ -0.75) and modern accessions with higher [AT] values, as compared with wild relatives, are from the environments with lower solar UV radiation or lower temperature. With [AT] and environmental factors as phenotypes, genome-wide association mapping identified three candidate genes that have the potential to contribute to [AT] variation under the effect of environmental conditions. Our findings provide genomic and environmental insights into evolutionary pattern of DNA base composition and underlying mechanisms.

H3K4me1 recruits DNA repair proteins in plants

DNA repair proteins can be recruited by their histone reader domains to specific epigenomic features, with consequences on intragenomic mutation rate variation. Here, we investigated H3K4me1-associated hypomutation in plants. We first examined 2 proteins which, in plants, contain Tudor histone reader domains: PRECOCIOUS DISSOCIATION OF SISTERS 5 (PDS5C), involved in homology-directed repair, and MUTS HOMOLOG 6 (MSH6), a mismatch repair protein. The MSH6 Tudor domain of Arabidopsis (Arabidopsis thaliana) binds to H3K4me1 as previously demonstrated for PDS5C, which localizes to H3K4me1-rich gene bodies and essential genes. Mutations revealed by ultradeep sequencing of wild-type and msh6 knockout lines in Arabidopsis show that functional MSH6 is critical for the reduced rate of single-base substitution (SBS) mutations in gene bodies and H3K4me1-rich regions. We explored the breadth of these mechanisms among plants by examining a large rice (Oryza sativa) mutation data set. H3K4me1-associated hypomutation is conserved in rice as are the H3K4me1-binding residues of MSH6 and PDS5C Tudor domains. Recruitment of DNA repair proteins by H3K4me1 in plants reveals convergent, but distinct, epigenome-recruited DNA repair mechanisms from those well described in humans. The emergent model of H3K4me1-recruited repair in plants is consistent with evolutionary theory regarding mutation modifier systems and offers mechanistic insight into intragenomic mutation rate variation in plants.

Natural variation in SSW1 coordinates seed growth and nitrogen use efficiency in Arabidopsis

Seed size is controlled not only by intrinsic genetic factors but also by external environmental signals. Here, we report a major quantitative trait locus (QTL) gene for seed size and weight on chromosome 1 (SSW1) in Arabidopsis, and we found SSW1 acts maternally to positively regulate seed size. Natural variation in SSW1 contains three types of alleles. The SSW1Cvi allele produces larger seeds with more amino acid and storage protein contents than the SSW1Ler allele. SSW1Cvi displays higher capacity for amino acid transport than SSW1Ler due to the differences in transport efficiency. Under low nitrogen supply, the SSW1Cvi allele exhibits increased seed yield and nitrogen use efficiency (NUE). Locations of natural variation alleles of SSW1 are associated with local soil nitrogen contents, suggesting that SSW1 might contribute to geographical adaptation in Arabidopsis. Thus, our findings reveal a mechanism that coordinates seed growth and NUE, suggesting a potential target for improving seed yield and NUE in crops.

Current status of community resources and priorities for weed genomics research

Weeds are attractive models for basic and applied research due to their impacts on agricultural systems and capacity to swiftly adapt in response to anthropogenic selection pressures. Currently, a lack of genomic information precludes research to elucidate the genetic basis of rapid adaptation for important traits like herbicide resistance and stress tolerance and the effect of evolutionary mechanisms on wild populations. The International Weed Genomics Consortium is a collaborative group of scientists focused on developing genomic resources to impact research into sustainable, effective weed control methods and to provide insights about stress tolerance and adaptation to assist crop breeding.

ACMGA: a reference-free multiple-genome alignment pipeline for plant species

BACKGROUND

The short-read whole-genome sequencing (WGS) approach has been widely applied to investigate the genomic variation in the natural populations of many plant species. With the rapid advancements in long-read sequencing and genome assembly technologies, high-quality genome sequences are available for a group of varieties for many plant species. These genome sequences are expected to help researchers comprehensively investigate any type of genomic variants that are missed by the WGS technology. However, multiple genome alignment (MGA) tools designed by the human genome research community might be unsuitable for plant genomes.

RESULTS

To fill this gap, we developed the AnchorWave-Cactus Multiple Genome Alignment (ACMGA) pipeline, which improved the alignment of repeat elements and could identify long (> 50 bp) deletions or insertions (INDELs). We conducted MGA using ACMGA and Cactus for 8 Arabidopsis (Arabidopsis thaliana) and 26 Maize (Zea mays) de novo assembled genome sequences and compared them with the previously published short-read variant calling results. MGA identified more single nucleotide variants (SNVs) and long INDELs than did previously published WGS variant callings. Additionally, ACMGA detected significantly more SNVs and long INDELs in repetitive regions and the whole genome than did Cactus. Compared with the results of Cactus, the results of ACMGA were more similar to the previously published variants called using short-read. These two MGA pipelines identified numerous multi-allelic variants that were missed by the WGS variant calling pipeline.

CONCLUSIONS

Aligning de novo assembled genome sequences could identify more SNVs and INDELs than mapping short-read. ACMGA combines the advantages of AnchorWave and Cactus and offers a practical solution for plant MGA by integrating global alignment, a 2-piece-affine-gap cost strategy, and the progressive MGA algorithm.

Specialized metabolite modifications in Brassicaceae seeds and plants: diversity, functions and related enzymes

Covering: up to 2023Specialized metabolite (SM) modifications and/or decorations, corresponding to the addition or removal of functional groups (e.g. hydroxyl, methyl, glycosyl or acyl group) to SM structures, contribute to the huge diversity of structures, activities and functions of seed and plant SMs. This review summarizes available knowledge (up to 2023) on SM modifications in Brassicaceae and their contribution to SM plasticity. We give a comprehensive overview on enzymes involved in the addition or removal of these functional groups. Brassicaceae, including model (Arabidopsis thaliana) and crop (Brassica napus, Camelina sativa) plant species, present a large diversity of plant and seed SMs, which makes them valuable models to study SM modifications. In this review, particular attention is given to the environmental plasticity of SM and relative modification and/or decoration enzymes. Furthermore, a spotlight is given to SMs and related modification enzymes in seeds of Brassicaceae species. Seeds constitute a large reservoir of beneficial SMs and are one of the most important dietary sources, providing more than half of the world's intake of dietary proteins, oil and starch. The seed tissue- and stage-specific expressions of A. thaliana genes involved in SM modification are presented and discussed in the context of available literature. Given the major role in plant phytochemistry, biology and ecology, SM modifications constitute a subject of study contributing to the research and development in agroecology, pharmaceutical, cosmetics and food industrial sectors.

Two adjacent NLR genes conferring quantitative resistance to clubroot disease in Arabidopsis are regulated by a stably inherited epiallelic variation

Clubroot caused by the protist Plasmodiophora brassicae is a major disease affecting cultivated Brassicaceae. Using a combination of quantitative trait locus (QTL) fine mapping, CRISPR-Cas9 validation, and extensive analyses of DNA sequence and methylation patterns, we revealed that the two adjacent neighboring NLR (nucleotide-binding and leucine-rich repeat) genes AT5G47260 and AT5G47280 cooperate in controlling broad-spectrum quantitative partial resistance to the root pathogen P. brassicae in Arabidopsis and that they are epigenetically regulated. The variation in DNA methylation is not associated with any nucleotide variation or any transposable element presence/absence variants and is stably inherited. Variations in DNA methylation at the Pb-At5.2 QTL are widespread across Arabidopsis accessions and correlate negatively with variations in expression of the two genes. Our study demonstrates that natural, stable, and transgenerationally inherited epigenetic variations can play an important role in shaping resistance to plant pathogens by modulating the expression of immune receptors.

Natural variation in CYCLIC NUCLEOTIDE-GATED ION CHANNEL 4 reveals a novel role of calcium signaling in vegetative phase change in Arabidopsis

The timing of vegetative phase change (VPC) in plants is regulated by a temporal decline in the expression of miR156. Both exogenous cues and endogenous factors, such as temperature, light, sugar, nutrients, and epigenetic regulators, have been shown to affect VPC by altering miR156 expression. However, the genetic basis of natural variation in VPC remains largely unexplored. Here, we conducted a genome-wide association study on the variation of the timing of VPC in Arabidopsis. We identified CYCLIC NUCLEOTIDE-GATED ION CHANNEL 4 (CNGC4) as a significant locus associated with the diversity of VPC. Mutations in CNGC4 delayed VPC, accompanied by an increased expression level of miR156 and a corresponding decrease in SQUAMOSA PROMOTER BINDING-LIKE (SPL) gene expression. Furthermore, mutations in CNGC2 and CATION EXCHANGER 1/3 (CAX1/3) also led to a delay in VPC. Polymorphisms in the CNGC4 promoter contribute to the natural variation in CNGC4 expression and the diversity of VPC. Specifically, the early CNGC4 variant promotes VPC and enhances plant adaptation to local environments. In summary, our findings offer genetic insights into the natural variation in VPC in Arabidopsis, and reveal a previously unidentified role of calcium signaling in the regulation of VPC.

A pan-genome of 69 Arabidopsis thaliana accessions reveals a conserved genome structure throughout the global species range

Although originally primarily a system for functional biology, Arabidopsis thaliana has, owing to its broad geographical distribution and adaptation to diverse environments, developed into a powerful model in population genomics. Here we present chromosome-level genome assemblies of 69 accessions from a global species range. We found that genomic colinearity is very conserved, even among geographically and genetically distant accessions. Along chromosome arms, megabase-scale rearrangements are rare and typically present only in a single accession. This indicates that the karyotype is quasi-fixed and that rearrangements in chromosome arms are counter-selected. Centromeric regions display higher structural dynamics, and divergences in core centromeres account for most of the genome size variations. Pan-genome analyses uncovered 32,986 distinct gene families, 60% being present in all accessions and 40% appearing to be dispensable, including 18% private to a single accession, indicating unexplored genic diversity. These 69 new Arabidopsis thaliana genome assemblies will empower future genetic research.

High allelic diversity in Arabidopsis NLRs is associated with distinct genomic features

Plants rely on Nucleotide-binding, Leucine-rich repeat Receptors (NLRs) for pathogen recognition. Highly variable NLRs (hvNLRs) show remarkable intraspecies diversity, while their low-variability paralogs (non-hvNLRs) are conserved between ecotypes. At a population level, hvNLRs provide new pathogen-recognition specificities, but the association between allelic diversity and genomic and epigenomic features has not been established. Our investigation of NLRs in Arabidopsis Col-0 has revealed that hvNLRs show higher expression, less gene body cytosine methylation, and closer proximity to transposable elements than non-hvNLRs. hvNLRs show elevated synonymous and nonsynonymous nucleotide diversity and are in chromatin states associated with an increased probability of mutation. Diversifying selection maintains variability at a subset of codons of hvNLRs, while purifying selection maintains conservation at non-hvNLRs. How these features are established and maintained, and whether they contribute to the observed diversity of hvNLRs is key to understanding the evolution of plant innate immune receptors.

Flowering time: From physiology, through genetics to mechanism

Plant species have evolved different requirements for environmental/endogenous cues to induce flowering. Originally, these varying requirements were thought to reflect the action of different molecular mechanisms. Thinking changed when genetic and molecular analysis in Arabidopsis thaliana revealed that a network of environmental and endogenous signaling input pathways converge to regulate a common set of "floral pathway integrators." Variation in the predominance of the different input pathways within a network can generate the diversity of requirements observed in different species. Many genes identified by flowering time mutants were found to encode general developmental and gene regulators, with their targets having a specific flowering function. Studies of natural variation in flowering were more successful at identifying genes acting as nodes in the network central to adaptation and domestication. Attention has now turned to mechanistic dissection of flowering time gene function and how that has changed during adaptation. This will inform breeding strategies for climate-proof crops and help define which genes act as critical flowering nodes in many other species.

From sub-Saharan Africa to China: Evolutionary history and adaptation of Drosophila melanogaster revealed by population genomics

Drosophila melanogaster is a widely used model organism for studying environmental adaptation. However, the genetic diversity of populations in Asia is poorly understood, leaving a notable gap in our knowledge of the global evolution and adaptation of this species. We sequenced genomes of 292 D. melanogaster strains from various ecological settings in China and analyzed them along with previously published genome sequences. We have identified six global genetic ancestry groups, despite the presence of widespread genetic admixture. The strains from China represent a unique ancestry group, although detectable differentiation exists among populations within China. We deciphered the global migration and demography of D. melanogaster, and identified widespread signals of adaptation, including genetic changes in response to insecticides. We validated the effects of insecticide resistance variants using population cage trials and deep sequencing. This work highlights the importance of population genomics in understanding the genetic underpinnings of adaptation, an effort that is particularly relevant given the deterioration of ecosystems.

Climate-related naturally occurring epimutation and their roles in plant adaptation in A. thaliana

DNA methylation has been proposed to be an important mechanism that allows plants to respond to their environments sometimes entirely uncoupled from genetic variation. To understand the genetic basis, biological functions and climatic relationships of DNA methylation at a population scale in Arabidopsis thaliana, we performed a genome-wide association analysis with high-quality single nucleotide polymorphisms (SNPs), and found that ~56% on average, especially in the CHH sequence context (71%), of the differentially methylated regions (DMRs) are not tagged by SNPs. Among them, a total of 3235 DMRs are significantly associated with gene expressions and potentially heritable. 655 of the 3235 DMRs are associated with climatic variables, and we experimentally verified one of them, HEI10 (HUMAN ENHANCER OF CELL INVASION NO.10). Such epigenetic loci could be subjected to natural selection thereby affecting plant adaptation, and would be expected to be an indicator of accessions at risk. We therefore incorporated these climate-related DMRs into a gradient forest model, and found that the natural A. thaliana accessions in Southern Europe that may be most at risk under future climate change. Our findings highlight the importance of integrating DNA methylation that is independent of genetic variations, and climatic data to predict plants' vulnerability to future climate change.

A comprehensive benchmark of graph-based genetic variant genotyping algorithms on plant genomes for creating an accurate ensemble pipeline

BACKGROUND

Although sequencing technologies have boosted the measurement of the genomic diversity of plant crops, it remains challenging to accurately genotype millions of genetic variants, especially structural variations, with only short reads. In recent years, many graph-based variation genotyping methods have been developed to address this issue and tested for human genomes. However, their performance in plant genomes remains largely elusive. Furthermore, pipelines integrating the advantages of current genotyping methods might be required, considering the different complexity of plant genomes.

RESULTS

Here we comprehensively evaluate eight such genotypers in different scenarios in terms of variant type and size, sequencing parameters, genomic context, and complexity, as well as graph size, using both simulated and real data sets from representative plant genomes. Our evaluation reveals that there are still great challenges to applying existing methods to plants, such as excessive repeats and variants or high resource consumption. Therefore, we propose a pipeline called Ensemble Variant Genotyper (EVG) that can achieve better genotyping performance in almost all experimental scenarios and comparably higher genotyping recall and precision even using 5× reads. Furthermore, we demonstrate that EVG is more robust with an increasing number of graphed genomes, especially for insertions and deletions.

CONCLUSIONS

Our study will provide new insights into the development and application of graph-based genotyping algorithms. We conclude that EVG provides an accurate, unbiased, and cost-effective way for genotyping both small and large variations and will be potentially used in population-scale genotyping for large, repetitive, and heterozygous plant genomes.

Pleiotropy, epistasis and the genetic architecture of quantitative traits

Pleiotropy (whereby one genetic polymorphism affects multiple traits) and epistasis (whereby non-linear interactions between genetic polymorphisms affect the same trait) are fundamental aspects of the genetic architecture of quantitative traits. Recent advances in the ability to characterize the effects of polymorphic variants on molecular and organismal phenotypes in human and model organism populations have revealed the prevalence of pleiotropy and unexpected shared molecular genetic bases among quantitative traits, including diseases. By contrast, epistasis is common between polymorphic loci associated with quantitative traits in model organisms, such that alleles at one locus have different effects in different genetic backgrounds, but is rarely observed for human quantitative traits and common diseases. Here, we review the concepts and recent inferences about pleiotropy and epistasis, and discuss factors that contribute to similarities and differences between the genetic architecture of quantitative traits in model organisms and humans.

Climate biogeography of Arabidopsis thaliana: linking distribution models and individual variation

AIM

Patterns of individual variation are key to testing hypotheses about the mechanisms underlying biogeographic patterns. If species distributions are determined by environmental constraints, then populations near range margins may have reduced performance and be adapted to harsher environments. Model organisms are potentially important systems for biogeographical studies, given the available range-wide natural history collections, and the importance of providing biogeographical context to their genetic and phenotypic diversity.

LOCATION

Global.

TAXON

Arabidopsis thaliana ("Arabidopsis").

METHODS

We fit occurrence records to climate data, and then projected the distribution of Arabidopsis under last glacial maximum, current, and future climates. We confronted model predictions with individual performance measured on 2,194 herbarium specimens, and we asked whether predicted suitability was associated with life-history and genomic variation measured on ~900 natural accessions.

RESULTS

The most important climate variables constraining the Arabidopsis distribution were winter cold in northern and high elevation regions and summer heat in southern regions. Herbarium specimens from regions with lower habitat suitability in both northern and southern regions were smaller, supporting the hypothesis that the distribution of Arabidopsis is constrained by climate-associated factors. Climate anomalies partly explained interannual variation in herbarium specimen size, but these did not closely correspond to local limiting factors identified in the distribution model. Late-flowering genotypes were absent from the lowest suitability regions, suggesting slower life histories are only viable closer to the center of the realized niche. We identified glacial refugia farther north than previously recognized, as well as refugia concordant with previous population genetic findings. Lower latitude populations, known to be genetically distinct, are most threatened by future climate change. The recently colonized range of Arabidopsis was well-predicted by our native-range model applied to certain regions but not others, suggesting it has colonized novel climates.

MAIN CONCLUSIONS

Integration of distribution models with performance data from vast natural history collections is a route forward for testing biogeographical hypotheses about species distributions and their relationship with evolutionary fitness across large scales.

Forces driving transposable element load variation during Arabidopsis range expansion

Genetic load refers to the accumulated and potentially life-threatening deleterious mutations in populations. Understanding the mechanisms underlying genetic load variation of transposable element (TE) insertion, a major large-effect mutation, during range expansion is an intriguing question in biology. Here, we used 1,115 global natural accessions of Arabidopsis (Arabidopsis thaliana) to study the driving forces of TE load variation during its range expansion. TE load increased with range expansion, especially in the recently established Yangtze River basin population. Effective population size, which explains 62.0% of the variance in TE load, high transposition rate, and selective sweeps contributed to TE accumulation in the expanded populations. We genetically mapped and identified multiple candidate causal genes and TEs, and revealed the genetic architecture of TE load variation. Overall, this study reveals the variation in TE genetic load during Arabidopsis expansion and highlights the causes of TE load variation from the perspectives of both population genetics and quantitative genetics.

PAT (Periderm Assessment Toolkit): A Quantitative and Large-Scale Screening Method for Periderm Measurements

The periderm is a vital protective tissue found in the roots, stems, and woody elements of diverse plant species. It plays an important function in these plants by assuming the role of the epidermis as the outermost layer. Despite its critical role for protecting plants from environmental stresses and pathogens, research on root periderm development has been limited due to its late formation during root development, its presence only in mature root regions, and its impermeability. One of the most straightforward measurements for comparing periderm formation between different genotypes and treatments is periderm (phellem) length. We have developed PAT (Periderm Assessment Toolkit), a high-throughput user-friendly pipeline that integrates an efficient staining protocol, automated imaging, and a deep-learning-based image analysis approach to accurately detect and measure periderm length in the roots of Arabidopsis thaliana. The reliability and reproducibility of our method was evaluated using a diverse set of 20 Arabidopsis natural accessions. Our automated measurements exhibited a strong correlation with human-expert-generated measurements, achieving a 94% efficiency in periderm length quantification. This robust PAT pipeline streamlines large-scale periderm measurements, thereby being able to facilitate comprehensive genetic studies and screens. Although PAT proves highly effective with automated digital microscopes in Arabidopsis roots, its application may pose challenges with nonautomated microscopy. Although the workflow and principles could be adapted for other plant species, additional optimization would be necessary. While we show that periderm length can be used to distinguish a mutant impaired in periderm development from wild type, we also find it is a plastic trait. Therefore, care must be taken to include sufficient repeats and controls, to minimize variation, and to ensure comparability of periderm length measurements between different genotypes and growth conditions.

Next-Gen GWAS: full 2D epistatic interaction maps retrieve part of missing heritability and improve phenotypic prediction

The problem of missing heritability requires the consideration of genetic interactions among different loci, called epistasis. Current GWAS statistical models require years to assess the entire combinatorial epistatic space for a single phenotype. We propose Next-Gen GWAS (NGG) that evaluates over 60 billion single nucleotide polymorphism combinatorial first-order interactions within hours. We apply NGG to Arabidopsis thaliana providing two-dimensional epistatic maps at gene resolution. We demonstrate on several phenotypes that a large proportion of the missing heritability can be retrieved, that it indeed lies in epistatic interactions, and that it can be used to improve phenotype prediction.

Coordination between two cis-elements of WRKY33, bound by the same transcription factor, confers humid adaption in Arabidopsis thaliana

To cope with flooding-induced hypoxia, plants have evolved different strategies. Molecular strategies, such as the N-degron pathway and transcriptional regulation, are known to be crucial for Arabidopsis thaliana's hypoxia response. Our study uncovered a novel molecular strategy that involves a single transcription factor interacting with two identical cis-elements, one located in the promoter region and the other within the intron. This unique double-element adjustment mechanism has seldom been reported in previous studies. In humid areas, WRKY70 plays a crucial role in A. thaliana's adaptation to submergence-induced hypoxia by binding to identical cis-elements in both the promoter and intron regions of WRKY33. This dual binding enhances WRKY33 expression and the activation of hypoxia-related genes. Conversely, in arid regions lacking the promoter cis-element, WRKY70 only binds to the intron cis-element, resulting in limited WRKY33 expression during submergence stress. The presence of a critical promoter cis-element in humid accessions, but not in dry accessions, indicates a coordinated regulation enabling A. thaliana to adapt and thrive in humid habitats.

Phase separation of GRP7 facilitated by FERONIA-mediated phosphorylation inhibits mRNA translation to modulate plant temperature resilience

Changes in ambient temperature profoundly affect plant growth and performance. Therefore, the molecular basis of plant acclimation to temperature fluctuation is of great interest. In this study, we discovered that GLYCINE-RICH RNA-BINDING PROTEIN 7 (GRP7) contributes to cold and heat tolerance in Arabidopsis thaliana. We found that exposure to a warm temperature rapidly induces GRP7 condensates in planta, which can be reversed by transfer to a lower temperature. Cell biology and biochemical assays revealed that GRP7 undergoes liquid-liquid phase separation (LLPS) in vivo and in vitro. LLPS of GRP7 in the cytoplasm contributes to the formation of stress granules that recruit RNA, along with the translation machinery component eukaryotic initiation factor 4E1 (eIF4E1) and the mRNA chaperones COLD SHOCK PROTEIN 1 (CSP1) and CSP3, to inhibit translation. Moreover, natural variations in GRP7 affecting the residue phosphorylated by the receptor kinase FERONIA alter its capacity to undergo LLPS and correlate with the adaptation of some Arabidopsis accessions to a wider temperature range. Taken together, our findings illustrate the role of translational control mediated by GRP7 LLPS to confer plants with temperature resilience.

Strong prevalence of light regime-specific QTL in Arabidopsis detected using automated high-throughput phenotyping in fluctuating or constant light

Plants have evolved and adapted under dynamic environmental conditions, particularly to fluctuating light, but plant research has often focused on constant growth conditions. To quantitatively asses the adaptation to fluctuating light, a panel of 384 natural Arabidopsis thaliana accessions was analyzed in two parallel independent experiments under fluctuating and constant light conditions in an automated high-throughput phenotyping system upgraded with supplemental LEDs. While the integrated daily photosynthetically active radiation was the same under both light regimes, plants in fluctuating light conditions accumulated significantly less biomass and had lower leaf area during their measured vegetative growth than plants in constant light. A total of 282 image-derived architectural and/or color-related traits at six common time points, and 77 photosynthesis-related traits from one common time point were used to assess their associations with genome-wide natural variation for both light regimes. Out of the 3000 significant marker-trait associations (MTAs) detected, only 183 (6.1%) were common for fluctuating and constant light conditions. The prevalence of light regime-specific QTL indicates a complex adaptation. Genes in linkage disequilibrium with fluctuating light-specific MTAs with an adjusted repeatability value >0.5 were filtered for gene ontology terms containing "photo" or "light", yielding 15 selected candidates. The candidate genes are involved in photoprotection, PSII maintenance and repair, maintenance of linear electron flow, photorespiration, phytochrome signaling, and cell wall expansion, providing a promising starting point for further investigations into the response of Arabidopsis thaliana to fluctuating light conditions.

PDR9 allelic variation and MYB63 modulate nutrient-dependent coumarin homeostasis in Arabidopsis

Plant roots release phytochemicals into the soil environment to influence nutrient availability and uptake. Arabidopsis thaliana roots release phenylpropanoid coumarins in response to iron (Fe) deficiency, likely to enhance Fe uptake and improve plant health. This response requires sufficient phosphorus (P) in the root environment. Nonetheless, the regulatory interplay influencing coumarin production under varying availabilities of Fe and P is not known. Through genome-wide association studies, we have pinpointed the influence of the ABC transporter G family member, PDR9, on coumarin accumulation and trafficking (homeostasis) under combined Fe and P deficiency. We show that genetic variation in the promoter of PDR9 regulates its expression in a manner associated with coumarin production. Furthermore, we find that MYB63 transcription factor controls dedicated coumarin production by regulating both COUMARIN SYNTHASE (COSY) and FERULOYL-CoA 6'-HYDROXYLASE 1 (F6'H1) expression while orchestrating secretion through PDR9 genes under Fe and P combined deficiency. This integrated approach illuminates the intricate connections between nutrient signaling pathways in coumarin response mechanisms.

Genome-wide association studies identify loci controlling specialized seed metabolites in Arabidopsis

Plants synthesize specialized metabolites to facilitate environmental and ecological interactions. During evolution, plants diversified in their potential to synthesize these metabolites. Quantitative differences in metabolite levels of natural Arabidopsis (Arabidopsis thaliana) accessions can be employed to unravel the genetic basis for metabolic traits using genome-wide association studies (GWAS). Here, we performed metabolic GWAS on seeds of a panel of 315 A. thaliana natural accessions, including the reference genotypes C24 and Col-0, for polar and semi-polar seed metabolites using untargeted ultra-performance liquid chromatography-mass spectrometry. As a complementary approach, we performed quantitative trait locus (QTL) mapping of near-isogenic introgression lines between C24 and Col-0 for specific seed specialized metabolites. Besides common QTL between seeds and leaves, GWAS revealed seed-specific QTL for specialized metabolites, indicating differences in the genetic architecture of seeds and leaves. In seeds, aliphatic methylsulfinylalkyl and methylthioalkyl glucosinolates associated with the ALKENYL HYDROXYALKYL PRODUCING loci (GS-ALK and GS-OHP) on chromosome 4 containing alkenyl hydroxyalkyl producing 2 (AOP2) and 3 (AOP3) or with the GS-ELONG locus on chromosome 5 containing methylthioalkyl malate synthase (MAM1) and MAM3. We detected two unknown sulfur-containing compounds that were also mapped to these loci. In GWAS, some of the annotated flavonoids (kaempferol 3-O-rhamnoside-7-O-rhamnoside, quercetin 3-O-rhamnoside-7-O-rhamnoside) were mapped to transparent testa 7 (AT5G07990), encoding a cytochrome P450 75B1 monooxygenase. Three additional mass signals corresponding to quercetin-containing flavonols were mapped to UGT78D2 (AT5G17050). The association of the loci and associating metabolic features were functionally verified in knockdown mutant lines. By performing GWAS and QTL mapping, we were able to leverage variation of natural populations and parental lines to study seed specialized metabolism. The GWAS data set generated here is a high-quality resource that can be investigated in further studies.

Arabidopsis as a model for translational research

Arabidopsis thaliana is currently the most-studied plant species on earth, with an unprecedented number of genetic, genomic, and molecular resources having been generated in this plant model. In the era of translating foundational discoveries to crops and beyond, we aimed to highlight the utility and challenges of using Arabidopsis as a reference for applied plant biology research, agricultural innovation, biotechnology, and medicine. We hope that this review will inspire the next generation of plant biologists to continue leveraging Arabidopsis as a robust and convenient experimental system to address fundamental and applied questions in biology. We aim to encourage lab and field scientists alike to take advantage of the vast Arabidopsis datasets, annotations, germplasm, constructs, methods, molecular and computational tools in our pursuit to advance understanding of plant biology and help feed the world's growing population. We envision that the power of Arabidopsis-inspired biotechnologies and foundational discoveries will continue to fuel the development of resilient, high-yielding, nutritious plants for the betterment of plant and animal health and greater environmental sustainability.

Overcoming the "feast or famine" effect: improved interaction testing in genome-wide association studies

In genetic association analysis of complex traits, detection of interaction (either GxG or GxE) can help to elucidate the genetic architecture and biological mechanisms underlying the trait. Detection of interaction in a genome-wide association study (GWAS) can be methodologically challenging for various reasons, including a high burden of multiple comparisons when testing for epistasis between all possible pairs of a set of genomewide variants, as well as heteroscedasticity effects occurring in the presence of GxG or GxE interaction. In this paper, we address the problem of an even more striking phenomenon that we call the "feast or famine" effect that occurs when testing interaction in a genomewide context. As we verify, even in a simplified setting in which there is no interaction at all (and so no heteroscedasticity), in a GWAS to detect GxG or GxE interaction with a fixed genetic variant or environmental factor, the distribution of the genome-wide p-values under the null hypothesis is not the i.i.d. uniform one that is commonly assumed. Using standard methods, even if all SNPs are independent, some GWASs will have systematically underinflated p-values ("feast"), and others will have systematically overinflated p-values ("famine"), which can lead to false detection of interaction, reduced power, inconsistent results across studies, and failure to replicate true signal. This startling phenomenon is specific to detection of interaction in a GWAS, and it may partly explain why such detection has so far proved challenging and difficult to replicate. We show theoretically that the key cause of this phenomenon is which variables are conditioned on in the analysis, and this suggests an approach to correct the problem by changing the way the conditioning is done. Using this insight, we have developed the TINGA method to adjust the interaction test statistics to make their p-values closer to uniform under the null hypothesis. In simulations we show that TINGA both controls type 1 error and improves power. TINGA allows for covariates and population structure through use of a linear mixed model and accounts for heteroscedasticity. We apply TINGA to detection of epistasis in a study of flowering time in Arabidopsis thaliana.