Improving seed quality is amongst the most important challenges of contemporary agriculture. In fact, using plant varieties with better germination rates that are more tolerant to stress during seedling establishment may improve crop yield considerably.Therefore, intense efforts are currently being devoted to improve seed quality in many species, mostly using genomics tools. However, despite its considerable importance during seed imbibition and germination processes, primary carbon metabolism in seeds is less studied. Our knowledge of the physiology of seed respiration and energy generation and the impact of these processes on seed performance have made limited progress over the past three decades. In particular, (isotope-assisted) metabolomics of seeds has only been assessed occasionally, and there is limited information on possible quantitative relationships between metabolic fluxes and seed quality. Here, we review the recent literature and provide an overview of potential links between metabolic efficiency, metabolic biomarkers, and seed quality and discuss implications for future research, including a climate change context.

Climate change reshapes the physiology and development of organisms through phenotypic plasticity, epigenetic modifications, and genetic adaptation. Under evolutionary pressures of the sessile lifestyle, plants possess efficient systems of phenotypic plasticity and acclimation to environmental conditions. Molecular analysis, especially through omics approaches, of these primary lines of environmental adjustment in the context of climate change has revealed the underlying biochemical and physiological mechanisms, thus characterizing the links between phenotypic plasticity and climate change responses. The efficiency of adaptive plasticity under climate change indeed depends on the realization of such biochemical and physiological mechanisms, but the importance of sensing and signaling mechanisms that can integrate perception of environmental cues and transduction into physiological responses is often overlooked. Recent progress opens the possibility of considering plant phenotypic plasticity and responses to climate change through the perspective of environmental sensing and signaling. This review aims to analyze present knowledge on plant sensing and signaling mechanisms and discuss how their structural and functional characteristics lead to resilience or hypersensitivity under conditions of climate change. Plant cells are endowed with arrays of environmental and stress sensors and with internal signals that act as molecular integrators of the multiple constraints of climate change, thus giving rise to potential mechanisms of climate change sensing. Moreover, mechanisms of stress-related information propagation lead to stress memory and acquired stress tolerance that could withstand different scenarios of modifications of stress frequency and intensity. However, optimal functioning of existing sensors, optimal integration of additive constraints and signals, or memory processes can be hampered by conflicting interferences between novel combinations and novel changes in intensity and duration of climate change-related factors. Analysis of these contrasted situations emphasizes the need for future research on the diversity and robustness of plant signaling mechanisms under climate change conditions.

The natural 13 C abundance (δ 13 C) in plant leaves has been used for decades with great success in agronomy to monitor water-use efficiency and select modern cultivars adapted to dry conditions. However, in wheat, it is also important to find genotypes with high carbon allocation to spikes and grains, and thus with a high harvest index (HI) and/or low carbon losses via respiration. Finding isotope-based markers of carbon partitioning to grains would be extremely useful since isotope analyses are inexpensive and can be performed routinely at high throughput. Here, we took the advantage of a set of field trials made of more than 600 plots with several wheat cultivars and measured agronomic parameters as well as δ 13 C values in leaves and grains. We find a linear relationship between the apparent isotope discrimination between leaves and grain (denoted as Δδ corr ), and the respiration use efficiency-to-HI ratio. It means that overall, efficient carbon allocation to grains is

Isotopic analyses of plant samples are now of considerable importance for food certification and plant physiology. In fact, the natural nitrogen isotope composition (δ15N) is extremely useful to examine metabolic pathways of N nutrition involving isotope fractionations. However, δ15N analysis of amino acids is not straightforward and involves specific derivatization procedures to yield volatile derivatives that can be analysed by gas chromatography coupled to isotope ratio mass spectrometry (GC-C-IRMS). Derivatizations other than trimethylsilylation are commonly used since they are believed to be more reliable and accurate. Their major drawback is that they are not associated with metabolite databases allowing identification of derivatives and by-products. Here, we revisit the potential of trimethylsilylated derivatives via concurrent analysis of δ15N and exact mass GC-MS of plant seed protein samples, allowing facile identification of derivatives using a database used for metabolomics. When multiple silylated derivatives of several amino acids are accounted for, there is a good agreement between theoretical and observed N mole fractions, and δ15N values are satisfactory, with little fractionation during derivatization. Overall, this technique may be suitable for compound-specific δ15N analysis, with pros and cons.