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<label for="study_grid_item434" href="#ST004753" class="study__grid__item">ST004753: Crop-specific vs. generalized effects of biostimulants - Untargeted Metabolomics for Pepper - Catholic University of the Sacred Heart - Salehi, Hajar</label>
<div class="desc__grid__item" style="white-space:nowrap;overflow:hidden;text-overflow:ellipsis;width:calc(100%);">Crop-specific vs. generalized effects of biostimulants - Untargeted Metabolomics for Pepper</div>
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<div class="desc__grid__item">STUDY_SUMMARY</div>
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<div class="desc__grid__item" style="white-space:nowrap;overflow:hidden;text-overflow:ellipsis;width:calc(100%);">In this study, the untargeted metabolomics was performed for pepper (Capsicum annuum L.). The experiment was conducted under tunnel conditions to simulate a controlled environment. A randomized block design (RBD) method was adopted, designating specific areas for each crop. The biostimulants used in this study were representative commercial formulations provided by Sofbey S.A., differing in their bioactive components: proline (Pro), melatonin (Mel), trehalose (Tre), organic matter (Org), seaweed extract (Swe), and the multi-component O+Z. This latter formulation is composed of osmolytes and includes the cytokinin phytohormone zeatin. Each plant was exposed to six different biostimulant treatments plus a control, resulting in a total of 21 treatments. For each condition, four individual plants were used. The biostimulants were diluted in water to achieve application rates of 2 L/ha for Pro, 7 g/ha for Mel, 400 g/ha for Tre, 1.5 L/ha for Org, 1 L/ha for Swe, and 1,500 g/ha for O+Z. The total spray volume for each biostimulant and the control treatment was 300 L/ha. According to the manufacturers’ instructions, the biostimulants were foliar applied. Distilled water was used for the control plants. The experiment, conducted from August to November, encountered natural suboptimal conditions due to ambient temperature variations in the field, which resulted from seasonal fluctuations rather than controlled, artificial conditions. The temperature and humidity data throughout the experiment are provided as supplementary material (Figure S1). Biostimulants were applied to the plants at six different stages: A) 7 days after transplanting (DAT); B) at BBCH 59 (Biologische Bundesanstalt, Bundessortenamt and CHemical industry), during the formation of the first bunch; C) at BBCH 59, during the formation of the second bunch; D) 14 days after stage C; E) 14 days after stage D; and F) 14 days after stage E. Sampling for metabolomics analysis was conducted 3 days after the second biostimulant application. This coincided with the period during which the plants experienced the highest temperature range, beyond optimal conditions, from August to early September. For metabolomics, leaves were collected from the upper one-third part of the plants and immediately frozen at -20 °C until the extraction. summary of key findings: This study provides novel insights into how large metabolomics datasets can be handled to go beyond the crop-specific mode of action of biostimulants. Unlike previous approaches, metabolomic datasets from three different crops were jointly analyzed. Despite the small sample size, clear themes emerged from combining high-resolution metabolomics with advanced multivariate data integration approaches. The Venn diagram, which was applied to the machine-learning-derived most discriminant features, indicated that three compounds were consistently present in all three crops and were associated with the multi-component biostimulant O+Z. The identified metabolites included betaine, N-caffeoylputrescine, and 2-amino-4-hydroxypyrimidine-5-carboxylic acid, which could potentially be involved in defence responses when plants face detrimental conditions. The convergence of these metabolites may underlie a conserved and reproducible mode of action of O+Z, clearly distinguishing it from single-component biostimulants, which typically showed crop-specific metabolic signatures. Indeed, this consistent behaviour can likely be attributed to the composition of the O+Z biostimulant, especially due to the presence of the phytohormone zeatin. Generally, our research showed that integrating omics data offers advances beyond evaluating crops individually and offers avenues to reduce complexity and inconsistency in biostimulant investigations. Therefore, the analytical and chemometric procedures performed in this study could potentially be used to predict and fine-tune the efficiency of a given biostimulant across different plants, thus providing guidelines for developing new products for specific targets. Still, to deeply validate this approach, future studies are necessary to be performed on a broader range of crop plants and with a larger sample size.</div>
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<div class="desc__grid__item">INSTITUTE</div>
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<div class="desc__grid__item" style="white-space:nowrap;overflow:hidden;text-overflow:ellipsis;width:calc(100%);">Catholic University of the Sacred Heart</div>
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