Epistasis × environment interactions among Arabidopsis thaliana glucosinolate genes impact complex traits and fitness in the field

Complementing model species with model clades

Model species continue to underpin groundbreaking plant science research. At the same time, the phylogenetic resolution of the land plant tree of life continues to improve. The intersection of these 2 research paths creates a unique opportunity to further extend the usefulness of model species across larger taxonomic groups. Here we promote the utility of the Arabidopsis thaliana model species, especially the ability to connect its genetic and functional resources, to species across the entire Brassicales order. We focus on the utility of using genomics and phylogenomics to bridge the evolution and diversification of several traits across the Brassicales to the resources in Arabidopsis, thereby extending scope from a model species by establishing a "model clade." These Brassicales-wide traits are discussed in the context of both the model species Arabidopsis and the family Brassicaceae. We promote the utility of such a "model clade" and make suggestions for building global networks to support future studies in the model order Brassicales.

UV RESISTANCE LOCUS 8-Mediated UV-B Response Is Required Alongside CRYPTOCHROME 1 for Plant Survival in Sunlight under Field Conditions

As sessile, photoautotrophic organisms, plants are subjected to fluctuating sunlight that includes potentially detrimental ultraviolet-B (UV-B) radiation. Experiments under controlled conditions have shown that the UV-B photoreceptor UV RESISTANCE LOCUS 8 (UVR8) controls acclimation and tolerance to UV-B in Arabidopsis thaliana; however, its long-term impact on plant fitness under naturally fluctuating environments remain poorly understood. Here, we quantified the survival and reproduction of different Arabidopsis mutant genotypes under diverse field and laboratory conditions. We found that uvr8 mutants produced more fruits than wild type when grown in growth chambers under artificial low-UV-B conditions but not under natural field conditions, indicating a fitness cost in the absence of UV-B stress. Importantly, independent double mutants of UVR8 and the blue light photoreceptor gene CRYPTOCHROME 1 (CRY1) in two genetic backgrounds showed a drastic reduction in fitness in the field. Experiments with UV-B attenuation in the field and with supplemental UV-B in growth chambers demonstrated that UV-B caused the cry1 uvr8 conditional lethal phenotype. Using RNA-seq data of field-grown single and double mutants, we explicitly identified genes showing significant statistical interaction of UVR8 and CRY1 mutations in the presence of UV-B in the field. They were enriched in Gene Ontology categories related to oxidative stress, photoprotection and DNA damage repair in addition to UV-B response. Our study demonstrates the functional importance of the UVR8-mediated response across life stages in natura, which is partially redundant with that of cry1. Moreover, these data provide an integral picture of gene expression associated with plant responses under field conditions.

Deconstructing taxa x taxa xenvironment interactions in the microbiota: A theoretical examination

A major objective of microbial ecology is to identify how the composition of microbial taxa shapes host phenotypes. However, most studies focus on pairwise interactions and ignore the potentially significant effects of higher-order microbial interactions.Here, we quantify the effects of higher-order interactions among taxa on host infection risk. We apply our approach to an in silico dataset that is built to resemble a population of insect hosts with gut-associated microbial communities at risk of infection from an intestinal parasite across a breadth of nutrient environmental contexts.We find that the effect of higher-order interactions is considerable and can change appreciably across environmental contexts. Furthermore, we show that higher-order interactions can stabilize community structure thereby reducing host susceptibility to parasite invasion.Our approach illustrates how incorporating the effects of higher-order interactions among gut microbiota across environments can be essential for understanding their effects on host phenotypes.

Plant biochemical genetics in the multiomics era

Our understanding of plant biology has been revolutionized by modern genetics and biochemistry. However, biochemical genetics can be traced back to the foundation of Mendelian genetics; indeed, one of Mendel's milestone discoveries of seven characteristics of pea plants later came to be ascribed to a mutation in a starch branching enzyme. Here, we review both current and historical strategies for the elucidation of plant metabolic pathways and the genes that encode their component enzymes and regulators. We use this historical review to discuss a range of classical genetic phenomena including epistasis, canalization, and heterosis as viewed through the lens of contemporary high-throughput data obtained via the array of approaches currently adopted in multiomics studies.

Improving genomic predictions with inbreeding and nonadditive effects in two admixed maize hybrid populations in single and multienvironment contexts

Genetic admixture, resulting from the recombination between structural groups, is frequently encountered in breeding populations. In hybrid breeding, crossing admixed lines can generate substantial nonadditive genetic variance and contrasted levels of inbreeding which can impact trait variation. This study aimed at testing recent methodological developments for the modeling of inbreeding and nonadditive effects in order to increase prediction accuracy in admixed populations. Using two maize (Zea mays L.) populations of hybrids admixed between dent and flint heterotic groups, we compared a suite of five genomic prediction models incorporating (or not) parameters accounting for inbreeding and nonadditive effects with the natural and orthogonal interaction approach in single and multienvironment contexts. In both populations, variance decompositions showed the strong impact of inbreeding on plant yield, height, and flowering time which was supported by the superiority of prediction models incorporating this effect (+0.038 in predictive ability for mean yield). In most cases dominance variance was reduced when inbreeding was accounted for. The model including additivity, dominance, epistasis, and inbreeding effects appeared to be the most robust for prediction across traits and populations (+0.054 in predictive ability for mean yield). In a multienvironment context, we found that the inclusion of nonadditive and inbreeding effects was advantageous when predicting hybrids not yet observed in any environment. Overall, comparing variance decompositions was helpful to guide model selection for genomic prediction. Finally, we recommend the use of models including inbreeding and nonadditive parameters following the natural and orthogonal interaction approach to increase prediction accuracy in admixed populations.

Genetic variation, environment and demography intersect to shape Arabidopsis defense metabolite variation across Europe

Plants produce diverse metabolites to cope with the challenges presented by complex and ever-changing environments. These challenges drive the diversification of specialized metabolites within and between plant species. However, we are just beginning to understand how frequently new alleles arise controlling specialized metabolite diversity and how the geographic distribution of these alleles may be structured by ecological and demographic pressures. Here, we measure the variation in specialized metabolites across a population of 797 natural Arabidopsis thaliana accessions. We show that a combination of geography, environmental parameters, demography and different genetic processes all combine to influence the specific chemotypes and their distribution. This showed that causal loci in specialized metabolism contain frequent independently generated alleles with patterns suggesting potential within-species convergence. This provides a new perspective about the complexity of the selective forces and mechanisms that shape the generation and distribution of allelic variation that may influence local adaptation.

Since plants cannot move, they have evolved chemical defenses to help them respond to changes in their surroundings. For example, where animals run from predators, plants may produce toxins to put predators off. This approach is why plants are such a rich source of drugs, poisons, dyes and other useful substances. The chemicals plants produce are known as specialized metabolites, and they can change a lot between, and even within, plant species. The variety of specialized metabolites is a result of genetic changes and evolution over millions of years.

Evolution is a slow process, yet plants are able to rapidly develop new specialized metabolites to protect them from new threats. Even different populations of the same species produce many distinct metabolites that help them survive in their surroundings. However, the factors that lead plants to produce new metabolites are not well understood, and it is not known how this affects genetic variation.

To gain a better understanding of this process, Katz et al. studied 797 European variants of a common weed species called Arabidopsis thaliana, which is widely studied. The investigation found that many factors affect the range of specialized metabolites in each variant. These included local geography and environment, as well as genetics and population history (demography). Katz et al. revealed a pattern of relationships between the variants that could mirror their evolutionary history as the species spread and adapted to new locations.

These results highlight the complex network of factors that affect plant evolution. Rapid diversification is key to plant survival in new and changing environments and has resulted in a wide range of specialized metabolites. As such they are of interest both for studying plant evolution and for understanding their ecology. Expanding similar work to more populations and other species will broaden the scope of our ability to understand how plants adapt to their surroundings.

Dynamic genetic architecture of yeast response to environmental perturbation shed light on origin of cryptic genetic variation

Cryptic genetic variation could arise from, for example, Gene-by-Gene (G-by-G) or Gene-by-Environment (G-by-E) interactions. The underlying molecular mechanisms and how they influence allelic effects and the genetic variance of complex traits is largely unclear. Here, we empirically explored the role of environmentally influenced epistasis on the suppression and release of cryptic variation by reanalysing a dataset of 4,390 haploid yeast segregants phenotyped on 20 different media. The focus was on 130 epistatic loci, each contributing to segregant growth in at least one environment and that together explained most (69–100%) of the narrow sense heritability of growth in the individual environments. We revealed that the epistatic growth network reorganised upon environmental changes to alter the estimated marginal (additive) effects of the individual loci, how multi-locus interactions contributed to individual segregant growth and the level of expressed genetic variance in growth. The estimated additive effects varied most across environments for loci that were highly interactive network hubs in some environments but had few or no interactors in other environments, resulting in changes in total genetic variance across environments. This environmentally dependent epistasis was thus an important mechanism for the suppression and release of cryptic variation in this population. Our findings increase the understanding of the complex genetic mechanisms leading to cryptic variation in populations, providing a basis for future studies on the genetic maintenance of trait robustness and development of genetic models for studying and predicting selection responses for quantitative traits in breeding and evolution.

Many biological traits are polygenic, with complex interplay between underlying genes and the surrounding environment. As a result, individuals with the same allele might have distinctive phenotypes due to differences in the polygenic background and/or the environment. Such differences often create additional genetic variation that is highly relevant to quantitative and evolutionary genetics by limiting our ability to accurately predict the phenotypes in medical or agricultural applications and providing opportunities for long term evolution. Previously, yeast growth regulating genes were found to be organised in large interacting networks. Here, we found that these networks were reorganised upon environmental changes, and that this resulted in altered effect sizes of individual genes, and how the whole network contributed to growth and the level of total genetic variance, providing a basis for future studies on the genetic maintenance of trait robustness and development of genetic models for studying and predicting selection responses for quantitative traits.

Using networks to identify and interpret natural variation

Studies on natural variation and network biology inherently work to summarize vast amounts of information and data. The combination of these two areas of study while creating datasets of immense complexity is critical to their mutual progress. Networks are necessary as a way to work to reduce the dimensionality inherent in natural variation with 100 s to 1000 s of genotypes. Correspondingly natural variation is essential for testing how networks may or may not be shared across individuals or species. Advances in this area of cross-fertilization including using networks directly as phenotypes and the use of networks to help in prioritizing candidate gene validation efforts. Interesting new observations on frequent presence-absence variation in gene content and adaptation is beginning to highlight the potential for natural variation in network presence-absence. This review attempts to delve into these new insights.

Epistatic Transcription Factor Networks Differentially Modulate Arabidopsis Growth and Defense

Plants integrate internal and external signals to finely coordinate growth and defense for maximal fitness within a complex environment. A common model suggests that growth and defense show a trade-offs relationship driven by energy costs. However, recent studies suggest that the coordination of growth and defense likely involves more conditional and intricate connections than implied by the trade-off model. To explore how a transcription factor (TF) network may coordinate growth and defense, we used a high-throughput phenotyping approach to measure growth and flowering in a set of single and pairwise mutants previously linked to the aliphatic glucosinolate (GLS) defense pathway. Supporting a link between growth and defense, 17 of the 20 tested defense-associated TFs significantly influenced plant growth and/or flowering time. The TFs' effects were conditional upon the environment and age of the plant, and more critically varied across the growth and defense phenotypes for a given genotype. In support of the coordination model of growth and defense, the TF mutant's effects on short-chain aliphatic GLS and growth did not display a simple correlation. We propose that large TF networks integrate internal and external signals and separately modulate growth and the accumulation of the defensive aliphatic GLS.

Transcriptional Variation in Glucosinolate Biosynthetic Genes and Inducible Responses to Aphid Herbivory on Field-Grown Arabidopsis thaliana

Recently, increasing attempts have been made to understand how plant genes function in natura. In this context, transcriptional profiles represent plant physiological status in response to environmental stimuli. Herein, we combined high-throughput RNA-Seq with insect survey data on 19 accessions of Arabidopsis thaliana grown at a field site in Switzerland. We found that genes with the gene ontology (GO) annotations of "glucosinolate biosynthetic process" and "response to insects" were most significantly enriched, and the expression of these genes was highly variable among plant accessions. Nearly half of the total expression variation in the glucosinolate biosynthetic genes (AOPs, ESM1, ESP, and TGG1) was explained by among-accession variation. Of these genes, the expression level of AOP3 differed among Col-0 accession individuals depending on the abundance of the mustard aphid (Lipaphis erysimi). We also found that the expression of the major cis-jasmone activated gene CYP81D11 was positively correlated with the number of flea beetles (Phyllotreta striolata and Phyllotreta atra). Combined with the field RNA-Seq data, bioassays confirmed that AOP3 was up-regulated in response to attack by mustard aphids. The combined results from RNA-Seq and our ecological survey illustrate the feasibility of using field transcriptomics to detect an inducible defense, providing a first step towards an in natura understanding of biotic interactions involving phenotypic plasticity.

Fast and flexible linear mixed models for genome-wide genetics

Linear mixed effect models are powerful tools used to account for population structure in genome-wide association studies (GWASs) and estimate the genetic architecture of complex traits. However, fully-specified models are computationally demanding and common simplifications often lead to reduced power or biased inference. We describe Grid-LMM (https://github.com/deruncie/GridLMM), an extendable algorithm for repeatedly fitting complex linear models that account for multiple sources of heterogeneity, such as additive and non-additive genetic variance, spatial heterogeneity, and genotype-environment interactions. Grid-LMM can compute approximate (yet highly accurate) frequentist test statistics or Bayesian posterior summaries at a genome-wide scale in a fraction of the time compared to existing general-purpose methods. We apply Grid-LMM to two types of quantitative genetic analyses. The first is focused on accounting for spatial variability and non-additive genetic variance while scanning for QTL; and the second aims to identify gene expression traits affected by non-additive genetic variation. In both cases, modeling multiple sources of heterogeneity leads to new discoveries.

The Role of the Glucosinolate-Myrosinase System in Mediating Greater Resistance of Barbarea verna than B. vulgaris to Mamestra brassicae Larvae

We investigated the influences of two structurally similar glucosinolates, phenethylglucosinolate (gluconasturtiin, NAS) and its (S)-2-hydroxyl derivative glucobarbarin (BAR), as well as their hydrolysis products on larvae of the generalist Mamestra brassicae (Lepidoptera: Noctuidae). Previous results suggested a higher defensive activity of BAR than NAS based on resistance toward M. brassicae larvae of natural plant genotypes of Barbarea vulgaris R. Br. (Brassicaceae) dominated by BAR. In the present study, the hypothesis of a higher defensive activity of BAR than NAS was tested by comparing two Barbarea species similarly dominated either by BAR or by NAS and by testing effects of isolated BAR and NAS on larval survival and feeding preferences. Larvae reared on leaf disks of B. verna (Mill.) Asch. had a lower survival than those reared on B. vulgaris P- and G-chemotypes. Leaves of B. verna were dominated by NAS, whereas B. vulgaris chemotypes were dominated by BAR or its epimer. In addition, B. verna leaves showed a threefold higher activity of the glucosinolate-activating myrosinase enzymes. The main product of NAS from breakdown by endogenous enzymes including myrosinases ("autolysis") in B. verna leaves was phenethyl isothiocyanate, while the main products of BAR in autolyzed B. vulgaris leaves were a cyclized isothiocyanate product, namely an oxazolidine-2-thione, and a downstream metabolite, an oxazolidin-2-one. The glucosinolates BAR and NAS were isolated and offered to larvae on disks of cabbage. Both glucosinolates exerted similar negative effects on larval survival but effects of NAS tended to be more detrimental. Low concentrations of BAR, but not of NAS, stimulated larval feeding, whereas high BAR concentrations acted deterrent. NAS only tended to be deterrent at the highest concentration, but the difference was not significant. Recoveries of NAS and BAR on cabbage leaf disks were similar, and when hydrolyzed by mechanical leaf damage, the same isothiocyanate-type products as in Barbarea plants were formed with further conversion of BAR to cyclic products, (R)-5-phenyloxazolidine-2-thione [(R)-barbarin] and (R)-5-phenyloxazolidin-2-one [(R)-resedine]. We conclude that a previously proposed generally higher defensive activity of BAR than NAS to M. brassicae larvae could not be confirmed. Indeed, the higher resistance of NAS-containing B. verna plants may be due to a combined effect of rather high concentrations of NAS and a relatively high myrosinase activity or other plant traits not investigated yet.

Comparison of the Relative Potential for Epigenetic and Genetic Variation To Contribute to Trait Stability

The theoretical ability of epigenetic variation to influence the heritable variation of complex traits is gaining traction in the study of adaptation. This theory posits that epigenetic marks can control adaptive phenotypes but the relative potential of epigenetic variation in comparison to genetic variation in these traits is not presently understood. To compare the potential of epigenetic and genetic variation in adaptive traits, we analyzed the influence of DNA methylation variation on the accumulation of chemical defense compounds glucosinolates from the order Brassicales. Several decades of work on glucosinolates has generated extensive knowledge about their synthesis, regulation, genetic variation and contribution to fitness establishing this pathway as a model pathway for complex adaptive traits. Using high-throughput phenotyping with a randomized block design of ddm1 derived Arabidopsis thaliana epigenetic Recombinant Inbred Lines, we measured the correlation between DNA methylation variation and mean glucosinolate variation and within line stochastic variation. Using this information, we identified epigenetic Quantitative Trait Loci that contained specific Differentially Methylated Regions associated with glucosinolate traits. This showed that variation in DNA methylation correlates both with levels and variance of glucosinolates and flowering time with trait-specific loci. By conducting a meta-analysis comparing the results to different genetically variable populations, we conclude that the influence of DNA methylation variation on these adaptive traits is much lower than the corresponding impact of standing genetic variation. As such, selective pressure on these traits should mainly affect standing genetic variation to lead to adaptation.