Genomic selection refers to the use of genotypic information for predicting breeding values of selection candidates. A prediction formula is calibrated with the genotypes and phenotypes of reference individuals constituting the calibration set. The size and the composition of this set are essential parameters affecting the prediction reliabilities. The objective of this study was to maximize reliabilities by optimizing the calibration set. Different criteria based on the diversity or on the prediction error variance (PEV) derived from the realized additive relationship matrix–best linear unbiased predictions model (RA–BLUP) were used to select the reference individuals. For the latter, we considered the mean of the PEV of the contrasts between each selection candidate and the mean of the population (PEVmean) and the mean of the expected reliabilities of the same contrasts (CDmean). These criteria were tested with phenotypic data collected on two diversity panels of maize (Zea mays L.) genotyped with a 50k SNPs array. In the two panels, samples chosen based on CDmean gave higher reliabilities than random samples for various calibration set sizes. CDmean also appeared superior to PEVmean, which can be explained by the fact that it takes into account the reduction of variance due to the relatedness between individuals. Selected samples were close to optimality for a wide range of trait heritabilities, which suggests that the strategy presented here can efficiently sample subsets in panels of inbred lines. A script to optimize reference samples based on CDmean is available on request.

The relatively new research discipline of Eco-Metabolomics is the application of metabolomics techniques to ecology with the aim to characterise biochemical interactions of organisms across different spatial and temporal scales. Metabolomics is an untargeted biochemical approach to measure many thousands of metabolites in different species, including plants and animals. Changes in metabolite concentrations can provide mechanistic evidence for biochemical processes that are relevant at ecological scales. These include physiological, phenotypic and morphological responses of plants and communities to environmental changes and also interactions with other organisms. Traditionally, research in biochemistry and ecology comes from two different directions and is performed at distinct spatiotemporal scales. Biochemical studies most often focus on intrinsic processes in individuals at physiological and cellular scales. Generally, they take a bottom-up approach scaling up cellular processes from spatiotemporally fine to coarser scales. Ecological studies usually focus on extrinsic processes acting upon organisms at population and community scales and typically study top-down and bottom-up processes in combination. Eco-Metabolomics is a transdisciplinary research discipline that links biochemistry and ecology and connects the distinct spatiotemporal scales. In this review, we focus on approaches to study chemical and biochemical interactions of plants at various ecological levels, mainly plant–organismal interactions, and discuss related examples from other domains. We present recent developments and highlight advancements in Eco-Metabolomics over the last decade from various angles. We further address the five key challenges: (1) complex experimental designs and large variation of metabolite profiles; (2) feature extraction; (3) metabolite identification; (4) statistical analyses; and (5) bioinformatics software tools and workflows. The presented solutions to these challenges will advance connecting the distinct spatiotemporal scales and bridging biochemistry and ecology.

Due to its biennual life cycle Brassica oleracea is especially exposed to seasonal changes in temperature that could limit its growth and fitness. Thermal stress could limit plant growth, leaf development and photosynthesis. We evaluated the performance of two local populations of B. oleracea: one population of cabbage (B. oleracea capitata group) and one population of kale (B. oleracea acephala group) under limiting low and high temperatures. There were differences between crops and how they responded to high and low temperature stress. Low temperatures especially affect photosynthesis and fresh weight. Stomatal conductance and the leaf water content were dramatically reduced and plants produce smaller and thicker leaves. Under high temperatures there was a reduction of the weight that could be associated to a general impairment of the photosynthetic activity. Although high temperatures significantly reduced the dry weight of seedlings, in general terms, low temperature had a higher impact in B. oleracea physiology than high temperature. Interestingly, our results suggest that the capitata population is less sensitive to changes in air temperature than the acephala population.

Glucosinolates are major secondary metabolites found in the Brassicaceae family. These compounds play an essential role in plant defense against biotic and abiotic stresses, but more interestingly they have beneficial effects on human health. We performed a genetic analysis in order to identify the genome regions regulating glucosinolates biosynthesis in a DH mapping population of Brassica oleracea. In order to obtain a general overview of regulation in the whole plant, analyses were performed in the three major organs where glucosinolates are synthesized (leaves, seeds and flower buds). Eighty two significant QTLs were detected, which explained a broad range of variability in terms of individual and total glucosinolate (GSL) content. A meta-analysis rendered eighteen consensus QTLs. Thirteen of them regulated more than one glucosinolate and its content. In spite of the considerable variability of glucosinolate content and profiles across the organ, some of these consensus QTLs were identified in more than one tissue. Consensus QTLs control the GSL content by interacting epistatically in complex networks. Based on in silico analysis within the B. oleracea genome along with synteny with Arabidopsis, we propose seven major candidate loci that regulate GSL biosynthesis in the Brassicaceae family. Three of these loci control the content of aliphatic GSL and four of them control the content of indolic glucosinolates. GSL-ALK plays a central role in determining aliphatic GSL variation directly and by interacting epistatically with other loci, thus suggesting its regulatory effect.

Levels of the enzymes that produce wound response mediators have to be controlled tightly in unwounded tissues. The Arabidopsis (Arabidopsis thaliana) fatty acid oxygenation up-regulated8 (fou8) mutant catalyzes high rates of α -linolenic acid oxygenation and has higher than wild-type levels of the α -linolenic acid-derived wound response mediator jasmonic acid (JA) in undamaged leaves. fou8 produces a null allele in the gene SAL1 (also known as FIERY1 or FRY1). Overexpression of the wild-type gene product had the opposite effect of the null allele, suggesting a regulatory role of SAL1 acting in JA synthesis. The biochemical phenotypes in fou8 were complemented when the yeast (Saccharomyces cerevisiae) sulfur metabolism 3′(2′), 5′-bisphosphate nucleotidase MET22 was targeted to chloroplasts in fou8. The data are consistent with a role of SAL1 in the chloroplast-localized dephosphorylation of 3′-phospho-5′-adenosine phosphosulfate to 5′-adenosine phosphosulfate or in a closely related reaction (e.g. 3′,5′-bisphosphate dephosphorylation). Furthermore, the fou8 phenotype was genetically suppressed in a triple mutant (fou8 apk1 apk2) affecting chloroplastic 3′-phospho-5′-adenosine phosphosulfate synthesis. These results show that a nucleotide component of the sulfur futile cycle regulates early steps of JA production and basal JA levels.