Enhanced recombination empowers the detection and mapping of Quantitative Trait Loci.

Modern plant breeding, such as genomic selection and gene editing, is based on the knowledge of the genetic architecture of desired traits. Quantitative trait loci (QTL) analysis, which combines high throughput phenotyping and genotyping of segregating populations, is a powerful tool to identify these genetic determinants and to decipher the underlying mechanisms. However, meiotic recombination, which shuffles genetic information between generations, is limited: Typically only one to two exchange points, called crossovers, occur between a pair of homologous chromosomes. Here we test the effect on QTL analysis of boosting recombination, by mutating the anti-crossover factors RECQ4 and FIGL1 in Arabidopsis thaliana full hybrids and lines in which a single chromosome is hybrid. We show that increasing recombination ~6-fold empowers the detection and resolution of QTLs, reaching the gene scale with only a few hundred plants. Further, enhanced recombination unmasks some secondary QTLs undetected under normal recombination. These results show the benefits of enhanced recombination to decipher the genetic bases of traits.

Natural variation in response to combined water and nitrogen deficiencies in Arabidopsis.

Understanding plant responses to individual stresses does not mean that we understand real world situations, where stresses usually combine and interact. These interactions arise at different levels, from stress exposure to the molecular networks of the stress response. Here, we built an in-depth multi-omics description of plant responses to mild water (W) and nitrogen (N) limitations, either individually or combined, among five genetically different Arabidopsis (Arabidopsis thaliana) accessions. We highlight the different dynamics in stress response through integrative traits such as rosette growth and the physiological status of the plants. We also used transcriptomics and metabolomics profiling during a stage when the plant response was stabilized to determine the wide diversity in stress-induced changes among accessions, highlighting the limited reality of a 'universal' stress response. The main effect of the WxN interaction was an attenuation of the N-deficiency syndrome when combined with mild drought, but to a variable extent depending on the accession. Other traits subject to WxN interactions are often accession-specific. Multi-omics analyses identified a subset of transcript-metabolite clusters that are critical to stress responses but essentially variable according to the genotype factor. Including intra-specific diversity in our descriptions of plant stress response places our findings in perspective.

DiffSegR: an RNA-seq data driven method for differential expression analysis using changepoint detection.

To fully understand gene regulation, it is necessary to have a thorough understanding of both the transcriptome and the enzymatic and RNA-binding activities that shape it. While many RNA-Seq-based tools have been developed to analyze the transcriptome, most only consider the abundance of sequencing reads along annotated patterns (such as genes). These annotations are typically incomplete, leading to errors in the differential expression analysis. To address this issue, we present DiffSegR - an R package that enables the discovery of transcriptome-wide expression differences between two biological conditions using RNA-Seq data. DiffSegR does not require prior annotation and uses a multiple changepoints detection algorithm to identify the boundaries of differentially expressed regions in the per-base log2 fold change. In a few minutes of computation, DiffSegR could rightfully predict the role of chloroplast ribonuclease Mini-III in rRNA maturation and chloroplast ribonuclease PNPase in (3'/5')-degradation of rRNA, mRNA and tRNA precursors as well as intron accumulation. We believe DiffSegR will benefit biologists working on transcriptomics as it allows access to information from a layer of the transcriptome overlooked by the classical differential expression analysis pipelines widely used today. DiffSegR is available at https://aliehrmann.github.io/DiffSegR/index.html.

Genomic Basis of Adaptation to a Novel Precipitation Regime.

Energy production and metabolism are intimately linked to ecological and environmental constraints across the tree of life. In plants, which depend on sunlight to produce energy, the link between primary metabolism and the environment is especially strong. By governing CO2 uptake for photosynthesis and transpiration, leaf pores, or stomata, couple energy metabolism to the environment and determine productivity and water-use efficiency (WUE). Although evolution is known to tune physiological traits to the local environment, we lack knowledge of the specific links between molecular and evolutionary mechanisms that shape this process in nature. Here, we investigate the evolution of stomatal conductance and WUE in an Arabidopsis population that colonized an island with a montane cloud scrubland ecosystem characterized by seasonal drought and fog-based precipitation. We find that stomatal conductance increases and WUE decreases in the colonizing population relative to its closest outgroup population from temperate North Africa. Genome-wide association mapping reveals a polygenic basis of trait variation, with a substantial contribution from a nonsynonymous single-nucleotide polymorphism in MAP KINASE 12 (MPK12 G53R), which explains 35% of the phenotypic variance in WUE in the island population. We reconstruct the spatially explicit evolutionary history of MPK12 53R on the island and find that this allele increased in frequency in the population due to positive selection as Arabidopsis expanded into the harsher regions of the island. Overall, these findings show how adaptation shaped quantitative eco-physiological traits in a new precipitation regime defined by low rainfall and high humidity.

The sugar-responsive circadian clock regulator bZIP63 modulates plant growth.

Adjustment to energy starvation is crucial to ensure growth and survival. In Arabidopsis thaliana (Arabidopsis), this process relies in part on the phosphorylation of the circadian clock regulator bZIP63 by SUCROSE non-fermenting RELATED KINASE1 (SnRK1), a key mediator of responses to low energy. We investigated the effects of mutations in bZIP63 on plant carbon (C) metabolism and growth. Results from phenotypic, transcriptomic and metabolomic analysis of bZIP63 mutants prompted us to investigate the starch accumulation pattern and the expression of genes involved in starch degradation and in the circadian oscillator. bZIP63 mutation impairs growth under light-dark cycles, but not under constant light. The reduced growth likely results from the accentuated C depletion towards the end of the night, which is caused by the accelerated starch degradation of bZIP63 mutants. The diel expression pattern of bZIP63 is dictated by both the circadian clock and energy levels, which could determine the changes in the circadian expression of clock and starch metabolic genes observed in bZIP63 mutants. We conclude that bZIP63 composes a regulatory interface between the metabolic and circadian control of starch breakdown to optimize C usage and plant growth.

Natural variation at FLM splicing has pleiotropic effects modulating ecological strategies in Arabidopsis thaliana.

Investigating the evolution of complex phenotypes and the underlying molecular bases of their variation is critical to understand how organisms adapt to their environment. Applying classical quantitative genetics on a segregating population derived from a Can-0xCol-0 cross, we identify the MADS-box transcription factor FLOWERING LOCUS M (FLM) as a player of the phenotypic variation in plant growth and color. We show that allelic variation at FLM modulates plant growth strategy along the leaf economics spectrum, a trade-off between resource acquisition and resource conservation, observable across thousands of plant species. Functional differences at FLM rely on a single intronic substitution, disturbing transcript splicing and leading to the accumulation of non-functional FLM transcripts. Associations between this substitution and phenotypic and climatic data across Arabidopsis natural populations, show how noncoding genetic variation at a single gene might be adaptive through pleiotropic effects.

Mild drought in the vegetative stage induces phenotypic, gene expression, and DNA methylation plasticity in Arabidopsis but no transgenerational effects.

There is renewed interest in whether environmentally induced changes in phenotypes can be heritable. In plants, heritable trait variation can occur without DNA sequence mutations through epigenetic mechanisms involving DNA methylation. However, it remains unknown whether this alternative system of inheritance responds to environmental changes and if it can provide a rapid way for plants to generate adaptive heritable phenotypic variation. To assess potential transgenerational effects induced by the environment, we subjected four natural accessions of Arabidopsis thaliana together with the reference accession Col-0 to mild drought in a multi-generational experiment. As expected, plastic responses to drought were observed in each accession, as well as a number of intergenerational effects of the parental environments. However, after an intervening generation without stress, except for a very few trait-based parental effects, descendants of stressed and non-stressed plants were phenotypically indistinguishable irrespective of whether they were grown in control conditions or under water deficit. In addition, genome-wide analysis of DNA methylation and gene expression in Col-0 demonstrated that, while mild drought induced changes in the DNA methylome of exposed plants, these variants were not inherited. We conclude that mild drought stress does not induce transgenerational epigenetic effects.

The complex genetic architecture of shoot growth natural variation in Arabidopsis thaliana.

One of the main outcomes of quantitative genetics approaches to natural variation is to reveal the genetic architecture underlying the phenotypic space. Complex genetic architectures are described as including numerous loci (or alleles) with small-effect and/or low-frequency in the populations, interactions with the genetic background, environment or age. Linkage or association mapping strategies will be more or less sensitive to this complexity, so that we still have an unclear picture of its extent. By combining high-throughput phenotyping under two environmental conditions with classical QTL mapping approaches in multiple Arabidopsis thaliana segregating populations as well as advanced near isogenic lines construction and survey, we have attempted to improve our understanding of quantitative phenotypic variation. Integrative traits such as those related to vegetative growth used in this work (highlighting either cumulative growth, growth rate or morphology) all showed complex and dynamic genetic architecture with respect to the segregating population and condition. The more resolutive our mapping approach, the more complexity we uncover, with several instances of QTLs visible in near isogenic lines but not detected with the initial QTL mapping, indicating that our phenotyping accuracy was less limiting than the mapping resolution with respect to the underlying genetic architecture. In an ultimate approach to resolve this complexity, we intensified our phenotyping effort to target specifically a 3Mb-region known to segregate for a major quantitative trait gene, using a series of selected lines recombined every 100kb. We discovered that at least 3 other independent QTLs had remained hidden in this region, some with trait- or condition-specific effects, or opposite allelic effects. If we were to extrapolate the figures obtained on this specific region in this particular cross to the genome- and species-scale, we would predict hundreds of causative loci of detectable phenotypic effect controlling these growth-related phenotypes.

Phenoscope: an automated large-scale phenotyping platform offering high spatial homogeneity.

Increased phenotyping accuracy and throughput are necessary to improve our understanding of quantitative variation and to be able to deconstruct complex traits such as those involved in growth responses to the environment. Still, only a few facilities are known to handle individual plants of small stature for non-destructive, real-time phenotype acquisition from plants grown in precisely adjusted and variable experimental conditions. Here, we describe Phenoscope, a high-throughput phenotyping platform that has the unique feature of continuously rotating 735 individual pots over a table. It automatically adjusts watering and is equipped with a zenithal imaging system to monitor rosette size and expansion rate during the vegetative stage, with automatic image analysis allowing manual correction. When applied to Arabidopsis thaliana, we show that rotating the pots strongly reduced micro-environmental disparity: heterogeneity in evaporation was cut by a factor of 2.5 and the number of replicates needed to detect a specific mild genotypic effect was reduced by a factor of 3. In addition, by controlling a large proportion of the micro-environmental variance, other tangible sources of variance become noticeable. Overall, Phenoscope makes it possible to perform large-scale experiments that would not be possible or reproducible by hand. When applied to a typical quantitative trait loci (QTL) mapping experiment, we show that mapping power is more limited by genetic complexity than phenotyping accuracy. This will help to draw a more general picture as to how genetic diversity shapes phenotypic variation.