Rik Kooke

Epigenetic Basis of Morphological Variation and Phenotypic Plasticity in Arabidopsis thaliana

Differences in epigenetic marks can explain substantial levels of variation in plant performance and often exert pleiotropic effects. Epigenetics is receiving growing attention in the plant science community. Epigenetic modifications are thought to play a particularly important role in fluctuating environments. It is hypothesized that epigenetics contributes to plant phenotypic plasticity because epigenetic modifications, in contrast to DNA sequence variation, are more likely to be reversible. The population of decrease in DNA methylation 1-2 (ddm1-2)-derived epigenetic recombinant inbred lines (epiRILs) in Arabidopsis thaliana is well suited for studying this hypothesis, as DNA methylation differences are maximized and DNA sequence variation is minimized. Here, we report on the extensive heritable epigenetic variation in plant growth and morphology in neutral and saline conditions detected among the epiRILs. Plant performance, in terms of branching and leaf area, was both reduced and enhanced by different quantitative trait loci (QTLs) in the ddm1-2 inherited epigenotypes. The variation in plasticity associated significantly with certain genomic regions in which the ddm1-2 inherited epigenotypes caused an increased sensitivity to environmental changes, probably due to impaired genetic regulation in the epiRILs. Many of the QTLs for morphology and plasticity overlapped, suggesting major pleiotropic effects. These findings indicate that epigenetics contributes substantially to variation in plant growth, morphology, and plasticity, especially under stress conditions.

Marker-Based Estimation of Heritability in Immortal Populations

Heritability is a central parameter in quantitative genetics, from both an evolutionary and a breeding perspective. For plant traits heritability is traditionally estimated by comparing within- and between-genotype variability. This approach estimates broad-sense heritability and does not account for different genetic relatedness. With the availability of high-density markers there is growing interest in marker-based estimates of narrow-sense heritability, using mixed models in which genetic relatedness is estimated from genetic markers. Such estimates have received much attention in human genetics but are rarely reported for plant traits. A major obstacle is that current methodology and software assume a single phenotypic value per genotype, hence requiring genotypic means. An alternative that we propose here is to use mixed models at the individual plant or plot level. Using statistical arguments, simulations, and real data we investigate the feasibility of both approaches and how these affect genomic prediction with the best linear unbiased predictor and genome-wide association studies. Heritability estimates obtained from genotypic means had very large standard errors and were sometimes biologically unrealistic. Mixed models at the individual plant or plot level produced more realistic estimates, and for simulated traits standard errors were up to 13 times smaller. Genomic prediction was also improved by using these mixed models, with up to a 49% increase in accuracy. For genome-wide association studies on simulated traits, the use of individual plant data gave almost no increase in power. The new methodology is applicable to any complex trait where multiple replicates of individual genotypes can be scored. This includes important agronomic crops, as well as bacteria and fungi.

Arabidopsis semidwarfs evolved from independent mutations in GA20ox1, ortholog to green revolution dwarf alleles in rice and barley.

Significance Semidwarf accessions occur at low frequency across the distribution range of Arabidopsis thaliana and are mainly mutants of the GA5 (GA20ox1) gene, mutations of which originate from wild-type alleles still present in the regions where the mutants were found. We identified the causal mutations by allelism tests and sequencing and performed a detailed population genetics analysis of this variation. Using Fay and Wu’s H statistics, we obtained indications for local selection of the dwarf alleles. Mutants of functional orthologs of this gene have been selected as the so-called “green revolution genes” in rice and barley, thus indicating that Arabidopsis natural variation can be a source for the identification of useful genes for plant breeding. Understanding the genetic bases of natural variation for developmental and stress-related traits is a major goal of current plant biology. Variation in plant hormone levels and signaling might underlie such phenotypic variation occurring even within the same species. Here we report the genetic and molecular basis of semidwarf individuals found in natural Arabidopsis thaliana populations. Allelism tests demonstrate that independent loss-of-function mutations at GA locus 5 (GA5), which encodes gibberellin 20-oxidase 1 (GA20ox1) involved in the last steps of gibberellin biosynthesis, are found in different populations from southern, western, and northern Europe; central Asia; and Japan. Sequencing of GA5 identified 21 different loss-of-function alleles causing semidwarfness without any obvious general tradeoff affecting plant performance traits. GA5 shows signatures of purifying selection, whereas GA5 loss-of-function alleles can also exhibit patterns of positive selection in specific populations as shown by Fay and Wu’s H statistics. These results suggest that antagonistic pleiotropy might underlie the occurrence of GA5 loss-of-function mutations in nature. Furthermore, because GA5 is the ortholog of rice SD1 and barley Sdw1/Denso green revolution genes, this study illustrates the occurrence of conserved adaptive evolution between wild A.thaliana and domesticated plants.

Improved batch correction in untargeted MS-based metabolomics.

AbstractIntroductionBatch effects in large untargeted metabolomics experiments are almost unavoidable, especially when sensitive detection techniques like mass spectrometry (MS) are employed. In order to obtain peak intensities that are comparable across all batches, corrections need to be performed. Since non-detects, i.e., signals with an intensity too low to be detected with certainty, are common in metabolomics studies, the batch correction methods need to take these into account. ObjectivesThis paper aims to compare several batch correction methods, and investigates the effect of different strategies for handling non-detects.MethodsBatch correction methods usually consist of regression models, possibly also accounting for trends within batches. To fit these models quality control samples (QCs), injected at regular intervals, can be used. Also study samples can be used, provided that the injection order is properly randomized. Normalization methods, not using information on batch labels or injection order, can correct for batch effects as well. Introducing two easy-to-use quality criteria, we assess the merits of these batch correction strategies using three large LC–MS and GC–MS data sets of samples from Arabidopsis thaliana.ResultsThe three data sets have very different characteristics, leading to clearly distinct behaviour of the batch correction strategies studied. Explicit inclusion of information on batch and injection order in general leads to very good corrections; when enough QCs are available, also general normalization approaches perform well. Several approaches are shown to be able to handle non-detects—replacing them with very small numbers such as zero seems the worst of the approaches considered.ConclusionThe use of quality control samples for batch correction leads to good results when enough QCs are available. If an experiment is properly set up, batch correction using the study samples usually leads to a similar high-quality correction, but has the advantage that more metabolites are corrected. The strategy for handling non-detects is important: choosing small values like zero can lead to suboptimal batch corrections.

Multi-dimensional regulation of metabolic networks shaping plant development and performance

The metabolome is an integral part of a plants life cycle and determines for a large part its external phenotype. It is the final, internal product of chemical interactions, obtained through developmental, genetic, and environmental inputs, and as such, it defines the state of a plant in terms of development and performance. Understanding its regulation will provide knowledge and new insights into the biochemical pathways and genetic interactions that shape the plant and its surroundings. In this review, we will focus on four dimensions that contribute to the huge diversity of metabolomes and we will illustrate how this diversity shapes the plant in terms of development and performance: (i) temporal regulation: the metabolome is extremely dynamic and temporal changes in the environment can have an immense impact on its composition; (ii) spatial regulation: metabolites can be very specific, in both quantitative and qualitative terms, to specialized organs, tissues, and cell types; (iii) environmental regulation: the metabolic profile of plants is highly dependent on environmental signals, such as light, temperature, and nutrients, and very susceptible to biotic and abiotic stresses; and (iv) genetic regulation: the biosynthesis, structure, and accumulation of metabolites have a genetic origin, and there is quantitative and qualitative variation for metabolomes within a species. We will address the contribution of these dimensions to the wide diversity of metabolomes and highlight how the multi-dimensional regulation of metabolism defines the plants phenotype.

Effects of Multi-Generational Stress Exposure and Offspring Environment on the Expression and Persistence of Transgenerational Effects in Arabidopsis thaliana

Plant phenotypes can be affected by environments experienced by their parents. Parental environmental effects are reported for the first offspring generation and some studies showed persisting environmental effects in second and further offspring generations. However, the expression of these transgenerational effects proved context-dependent and their reproducibility can be low. Here we study the context-dependency of transgenerational effects by evaluating parental and transgenerational effects under a range of parental induction and offspring evaluation conditions. We systematically evaluated two factors that can influence the expression of transgenerational effects: single- versus multiple-generation exposure and offspring environment. For this purpose, we exposed a single homozygous Arabidopsis thaliana Col-0 line to salt stress for up to three generations and evaluated offspring performance under control and salt conditions in a climate chamber and in a natural environment. Parental as well as transgenerational effects were observed in almost all traits and all environments and traced back as far as great-grandparental environments. The length of exposure exerted strong effects; multiple-generation exposure often reduced the expression of the parental effect compared to single-generation exposure. Furthermore, the expression of transgenerational effects strongly depended on offspring environment for rosette diameter and flowering time, with opposite effects observed in field and greenhouse evaluation environments. Our results provide important new insights into the occurrence of transgenerational effects and contribute to a better understanding of the context-dependency of these effects.

Genome-Wide Association Mapping and Genomic Prediction Elucidate the Genetic Architecture of Morphological Traits in Arabidopsis

Integration of genome wide association mapping and genomic prediction increases resolution of genetic architecture and uncovers missing heritability of quantitative traits in plants. Quantitative traits in plants are controlled by a large number of genes and their interaction with the environment. To disentangle the genetic architecture of such traits, natural variation within species can be explored by studying genotype-phenotype relationships. Genome-wide association studies that link phenotypes to thousands of single nucleotide polymorphism markers are nowadays common practice for such analyses. In many cases, however, the identified individual loci cannot fully explain the heritability estimates, suggesting missing heritability. We analyzed 349 Arabidopsis accessions and found extensive variation and high heritabilities for different morphological traits. The number of significant genome-wide associations was, however, very low. The application of genomic prediction models that take into account the effects of all individual loci may greatly enhance the elucidation of the genetic architecture of quantitative traits in plants. Here, genomic prediction models revealed different genetic architectures for the morphological traits. Integrating genomic prediction and association mapping enabled the assignment of many plausible candidate genes explaining the observed variation. These genes were analyzed for functional and sequence diversity, and good indications that natural allelic variation in many of these genes contributes to phenotypic variation were obtained. For ACS11, an ethylene biosynthesis gene, haplotype differences explaining variation in the ratio of petiole and leaf length could be identified.

Combined Use of Genome-Wide Association Data and Correlation Networks Unravels Key Regulators of Primary Metabolism in Arabidopsis thaliana

Plant primary metabolism is a highly coordinated, central, and complex network of biochemical processes regulated at both the genetic and post-translational levels. The genetic basis of this network can be explored by analyzing the metabolic composition of genetically diverse genotypes in a given plant species. Here, we report an integrative strategy combining quantitative genetic mapping and metabolite‒transcript correlation networks to identify functional associations between genes and primary metabolites in Arabidopsis thaliana. Genome-wide association study (GWAS) was used to identify metabolic quantitative trait loci (mQTL). Correlation networks built using metabolite and transcript data derived from a previously published time-course stress study yielded metabolite‒transcript correlations identified by covariation. Finally, results obtained in this study were compared with mQTL previously described. We applied a statistical framework to test and compare the performance of different single methods (network approach and quantitative genetics methods, representing the two orthogonal approaches combined in our strategy) with that of the combined strategy. We show that the combined strategy has improved performance manifested by increased sensitivity and accuracy. This combined strategy allowed the identification of 92 candidate associations between structural genes and primary metabolites, which not only included previously well-characterized gene‒metabolite associations, but also revealed novel associations. Using loss-of-function mutants, we validated two of the novel associations with genes involved in tyrosine degradation and in β-alanine metabolism. In conclusion, we demonstrate that applying our integrative strategy to the largely untapped resource of metabolite–transcript associations can facilitate the discovery of novel metabolite-related genes. This integrative strategy is not limited to A. thaliana, but generally applicable to other plant species.

Backcross Populations and Near Isogenic Lines

The development of near isogenic lines (NILs) through repeated backcrossing of genetically distinct parental lines is rather straightforward. Nonetheless, depending on the available resources and the purpose of the lines to be generated, several choices can be made to guide the design of such inbred populations. Here we outline the implications of these choices and provide recommendations for the efficient and proper development of NILs for a number of common scenarios.

Epigenetic variation contributes to environmental adaptation of Arabidopsis thaliana

Epigenetic variation is frequently observed in plants and direct relationships between differences in DNA methylation and phenotypic responses to changing environments have often been described. The identification of contributing genetic loci, however, was until recently hampered by the lack of suitable genome wide mapping resources that specifically segregate for epigenetic marks. The development of epi-RIL populations in the model species Arabidopsis thaliana has alleviated this obstacle, enabling the accurate genetic analysis of epigenetic variation. Comprehensive morphological phenotyping of a ddm1 derived epi-RIL population in different environments and subsequent epi-QTL mapping revealed a high number of epi-QTLs and pleiotropic effects of several DMRs on numerous traits. For a number of these epi-QTLs epistatic interactions could be observed, further adding to the complexity of epigenetic regulation. Moreover, linkage to epigenetic marks indicated a specific role for DNA-methylation variation, rather than TE transposition, in plastic responses to changing environments. These findings provide supportive evidence for a role of epigenetic regulation in evolutionary and adaptive processes.