Specificity and Plasticity of the Functional Ionome of Brassica napus and Triticum aestivum Exposed to Micronutrient or Beneficial Nutrient Deprivation and Predictive Sensitivity of the Ionomic Signatures

Silicon modifies leaf nutriome and improves growth of oak seedlings exposed to phosphorus deficiency and Phytophthora plurivora infection

Beneficial effects of silicon (Si) on plants have primarily been studied in crop species under single stress. Moreover, nutrient acquisition-based responses to combination of biotic and abiotic stresses (a common situation in natural habitats) have rarely been reported, in particular in conjunction with soil amendments with Si. Pedunculate oak (Quercus robur L.), one of the ecologically and economically most important tree species in Europe, is facing a severe decline due to combined stresses, but also problems in assisted regeneration in nurseries. Here, we studied the effect of Si supply on the leaf nutriome, root traits and overall growth of 12-weeks-old oak seedlings exposed to abiotic stress [low phosphorus (P) supply], biotic stress (Phytophthora plurivora root infection), and their combination. The application of Si had the strongest ameliorative effect on growth, root health and root phenome under the most severe stress conditions (i.e., combination of P deficiency and P. plurivora root infection), where it differentially affected the uptake and leaf accumulation in 11 out of 13 analysed nutrients. Silicon supply tended to reverse the pattern of change of some, but not all, leaf nutrients affected by stresses: P, boron (B) and magnesium (Mg) under P deficiency, and P, B and sulphur (S) under pathogen attack, but also nickel (Ni) and molybdenum (Mo) under all three stresses. Surprisingly, Si affected some nutrients that were not changed by a particular stress itself and decreased leaf Mg levels under all the stresses. On the other hand, pathogen attack increased leaf accumulation of Si. This exploratory work presents the complexity of nutrient crosstalk under three stresses, and opens more questions about genetic networks that control plant physiological responses. Practically, we show a potential of Si application to improve P status and root health in oak seedlings, particularly in nurseries.

Drought specifically downregulates mineral nutrition: Plant ionomic content and associated gene expression

One of the main limiting factors of plant yield is drought, and while the physiological responses to this environmental stress have been broadly described, research addressing its impact on mineral nutrition is scarce. Brassica napus and Triticum aestivum were subjected to moderate or severe water deficit, and their responses to drought were assessed by functional ionomic analysis, and derived calculation of the net uptake of 20 nutrients. While the uptake of most mineral nutrients decreased, Fe, Zn, Mn, and Mo uptake were impacted earlier and at a larger scale than most physiological parameters assessed (growth, ABA concentration, gas exchanges and photosynthetic activity). Additionally, in B. napus, the patterns of 183 differentially expressed genes in leaves related to the ionome (known ionomic genes, KIGs) or assumed to be involved in transport of a given nutrient were analyzed. This revealed three patterns of gene expression under drought consisting of up (transport of Cl and Co), down (transport of N, P, B, Mo, and Ni), or mixed levels (transport of S, Mg, K, Zn, Fe, Cu, or Mn) of regulation. The three patterns of gene regulations are discussed in relation to specific gene functions, changes of leaf ionomic composition and with consideration of the crosstalks that have been established between elements. It is suggested that the observed reduction in Fe uptake occurred via a specific response to drought, leading indirectly to reduced uptake of Zn and Mn, and these may be taken up by common transporters encoded by genes that were downregulated.

Humic Acid Alleviates Fe Chlorosis in Graminaceous Plants Through Coordinated Fe-Dependent and Fe-Independent Mechanisms

Many studies have shown the close relationship between the beneficial action of soil and sedimentary humic acids on the growth of plants cultivated in calcareous soils and their ability to improve Fe plant nutrition. These results have been ascribed to the humic acid (HA) capability to improve Fe solubility and bioavailability. However, other effects more related to a humic acid action on the specific mechanisms activated in roots of plants under Fe deficiency cannot be ruled out. Although this question has been studied in dicotyledonous plants, in graminaceous plants there are no specific studies. Here we investigate the ability of a humic acid extracted from peat (HA) to improve Fe nutrition in wheat plants cultivated under Fe deficient and sufficient conditions. The results show that HA can improve the physiological status of Fe deficient wheat plants by alleviating some of the deleterious consequences of Fe deficiency on plant development and increasing the plant ability to secrete phytosiderophores to the nutrient solution. This action of HA is associated with increases in the Fe-active pool in leaves that might be related to the mobilization of the Fe complexed by HA resulting from the interaction of HA with the phytosiderophores in the nutrient solution. The Fe translocation from the root to the shoot may be favored by the action of trans-Zeatin Riboside (tZR) since the leaf concentration of this phytohormone was enhanced by HA in Fe deficient plants.

Transcriptomic, Metabolomic and Ionomic Analyses Reveal Early Modulation of Leaf Mineral Content in Brassica napus under Mild or Severe Drought

While it is generally acknowledged that drought is one of the main abiotic factors affecting plant growth, how mineral nutrition is specifically and negatively affected by water deficit has received very little attention, other than being analyzed as a consequence of reduced growth. Therefore, Brassica napus plants were subjected to a gradual onset of water deficits (mild, severe, or severe extended), and leaves were analyzed at the ionomic, transcriptomic and metabolic levels. The number of Differentially Expressed Genes (DEGs) and of the most differentially accumulated metabolites increased from mild (525 DEGs, 57 metabolites) to severe (5454 DEGs, 78 metabolites) and severe extended (9346 DEGs, 95 metabolites) water deficit. Gene ontology enrichment analysis of the 11,747 DEGs identified revealed that ion transport was one of the most significant processes affected, even under mild water deficit, and this was also confirmed by the shift in ionomic composition (mostly micronutrients with a strong decrease in Mo, Fe, Zn, and Mn in leaves) that occurred well before growth reduction. The metabolomic data and most of the transcriptomic data suggested that well-known early leaf responses to drought such as phytohormone metabolism (ABA and JA), proline accumulation, and oxidative stress defense were induced later than repression of genes related to nutrient transport.

Comparative Omics Analysis of Brassica napus Roots Subjected to Six Individual Macronutrient Deprivations Reveals Deficiency-Specific Genes and Metabolomic Profiles

The early and specific diagnosis of a macronutrient deficiency is challenging when seeking to better manage fertilizer inputs in the context of sustainable agriculture. Consequently, this study explored the potential for transcriptomic and metabolomic analysis of Brassica napus roots to characterize the effects of six individual macronutrient deprivations (N, Mg, P, S, K, and Ca). Our results showed that before any visual phenotypic response, all macronutrient deprivations led to a large modulation of the transcriptome and metabolome involved in various metabolic pathways, and some were common to all macronutrient deprivations. Significantly, comparative transcriptomic analysis allowed the definition of a subset of 3282, 2011, 6325, 1384, 439, and 5157 differentially expressed genes (DEGs) specific to N, Mg, P, S, K, and Ca deprivations, respectively. Surprisingly, gene ontology term enrichment analysis performed on this subset of specific DEGs highlighted biological processes that are common to a number of these macronutrient deprivations, illustrating the complexity of nutrient interactions. In addition, a set of 38 biochemical compounds that discriminated the macronutrient deprivations was identified using a metabolic approach. The opportunity to use these specific DEGs and/or biochemical compounds as potential molecular indicators to diagnose macronutrient deficiency is discussed.

Current and next-year cranberry yields predicted from local features and carryover effects

Wisconsin and Quebec are the world leading cranberry-producing regions. Cranberries are grown in acidic, naturally low-fertility sandy beds. Cranberry fertilization is guided by general soil and tissue nutrient tests in addition to yield target and vegetative biomass. However, other factors such as cultivar, location, and carbon and nutrient storage impact cranberry nutrition and yield. The objective of this study was to customize nutrient diagnosis and fertilizer recommendation at local scale and for next-year cranberry production after accounting for local factors and carbon and nutrient carryover effects. We collected 1768 observations from on-farm surveys and fertilizer trials in Quebec and Wisconsin to elaborate a machine learning model using minimum datasets. We tested carryover effects in a 5-year Quebec fertilizer experiment established on permanent plots. Micronutrients contributed more than macronutrients to variation in tissue compositions. Random Forest model related accurately current-year berry yield to location, cultivars, climatic indices, fertilization, and tissue and soil tests as features (classification accuracy of 0.83). Comparing compositions of defective and successful tissue compositions in the Euclidean space of tissue compositions, the general across-factor diagnosis differed from the local factor-specific diagnosis. Nutrient standards elaborated in one region could hardly be transposed to another and, within the same region, from one bed to another due to site-specific characteristics. Next-year yield and nutrient adjustment could be predicted accurately from current-year yield and tissue composition and other features, with R² value of 0.73 in regression mode and classification accuracy of 0.85. Compositional and machine learning methods proved to be effective to customize nutrient diagnosis and predict site-specific measures for nutrient management of cranberry stands. This study emphasized the need to acquire large experimental and observational datasets to capture the numerous factor combinations impacting current and next-year cranberry yields at local scale.