Peptide-based biopesticides represent a promising strategy for sustainable disease control in agriculture. Synthetic antifungal peptides incorporating the γ-core motif of plant defensins offer multiple modes of action (MoAs) and potential as biofungicides. We investigated a synthetic short-chain variant of the olive defensin OefDef1.1 for antifungal activity, structure-function relationships, and MoAs against Botrytis cinerea, a necrotrophic pathogen causing gray mold disease in fruits and vegetables. A disulfide-bridged peptide, GMAOe1C_V1*, derived from OefDef1.1 (G32-Y53) modified with hydrophobic amino acid substitutions inhibited B. cinerea growth in vitro and reduced lesion formation in detached leaves. Foliar application of GMAOe1C_V1* suppressed disease symptoms in pepper plants. Mechanistically, GMAOe1C_V1* rapidly permeabilized the fungal plasma membrane and accumulated in vacuole, triggering vacuolar expansion and cell death. It also inhibited protein synthesis in vitro and in vivo, suggesting a role as a translation inhibitor. Alanine scanning mutagenesis of the non-disulfide-bridged variant identified the 7RHSKH11 motif as essential for antifungal activity. Circular dichroism revealed an unstructured conformation with minimal secondary structure. Transcriptomic analysis of GMAOe1C_V1*-treated B. cinerea showed downregulation of genes involved in mitochondrial function and amino acid biosynthesis. These findings demonstrate the potential of an olive defensin-derived peptide as a bio-inspired antifungal agent with multifaceted MoAs for crop protection. [Formula: see text] Copyright © 2026 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.
As part of two open international core facility surveys focused on microscopy and flow cytometry, we obtained an in-depth snapshot of their state-of-play. Here we focus on microscopy cores across 'the Americas', 'Asia Pacific' and 'Europe and Africa' with responses from 241 facilities. We compared instrument types and numbers, staffing, user fees, funding, etc. This revealed both remarkable similarities and notable differences. For example, the staff/instrument ratio in Europe and Africa was smaller compared to other regions World View and Asia Pacific had a larger proportion of electron microscopes. Importantly, we want to emphasise that the anonymised raw data is made available for anyone to do their own further mining and analysis, including cross-correlations and constrained groupings. This survey will serve as an essential baseline for future longitudinal studies following trends across all metrics as our facilities evolve.
Introduction:Sorghum (Sorghum bicolor (L.) Moench) is a vital cereal crop for food, feed, and biofuel production. Accurate estimation of grain biochemical composition, crude protein (CP), lysine from grain (LysG) and protein (LysP), starch (SC), amylose from grain (AMLG) and starch (AMLS), and crude fat (CF), is crucial for improving breeding and management strategies. Our aim is not pre-harvest forecasting but reducing laboratory cost by identifying a minimal set of post-harvest measurements required to estimate other grain composition traits accurately. Methods:We used machine learning (ML) models to predict grain quality traits in commercial sorghum hybrids under different management practices, including precision nitrogen application, cover cropping, and no-till methods. Multi-year field trials (2023-2024) in Saint Charles, Missouri, integrated agronomic, physiological, UAV-based, and environmental data for model training and validation. Results:Phenotypic analysis showed that grain composition traits varied significantly by year and management practices. Among ML models, LASSO and ElasticNet achieved the highest predictive accuracy for crude protein (R² = 0.90) and amylose content (AMLS, R² = 0.99; AMLG, R² = 0.92). Bayesian Ridge was most effective for lysine from protein (R² = 0.64), while Partial Least Squares (PLS) excelled in starch content prediction (R² = 0.80). The correlation between grain composition (LysP, CF) and photosystem II efficiency (PhiPS2) indicated that enhanced photosynthesis and yield promote their accumulation. However, Partial Dependence Plots (PDPs) revealed strong non-linear effects, where slight variations in leaf temperature (Tleaf) and stomatal conductance (gsw) were associated with significant shifts in amylose content. Discussion:This study highlights the role of genotype × management interactions in sorghum breeding and demonstrates the value of integrating ML-driven models to enhance grain quality and precision agriculture strategies.
Industrial hemp (Cannabis sativa L.) is emerging as a compelling crop for climate change mitigation and economic development, given its rapid growth, substantial biomass yield, significant carbon sequestration capacity, and diverse industrial applications. Estimating total plant carbon (TPC), particularly the below-ground biomass (BGB) component, is challenging due to the complexity of capturing root biomass. To address this challenge, this study presents a methodology for estimating TPC, encompassing both above-ground biomass (AGB) and BGB, by integrating UAV-based Light Detection and Ranging (LiDAR) and hyperspectral data. We derived structural metrics from LiDAR, indicative of canopy complexity, and salient hyperspectral bands, reflective of plant biochemistry. In situ destructive sampling provided AGB and BGB measurements, while laboratory analyses provided %C values that served as a conversion factor for biomass estimates. We used these datasets to validate the accuracy of the remote sensing-based models. We applied two distinct modeling approaches: statistical machine learning models and a knowledge distillation framework utilizing a teacher-student paradigm. Under our initial modeling approach, the SVR model achieved R & sup2; values of 0.817 for AGB and 0.869 for BGB, which improved to 0.915 and 0.926, respectively, under the knowledge distillation framework. Strong correlations (R > 0.93) between estimated AGB and BGB were observed, reflecting the inherent linear trend in root-shoot interrelation. Moreover, TPC varied substantially across study areas, reaching 6.176 kg/plot. Our findings highlight the advancement of using remote sensing to estimate TPC, offering a more comprehensive and accurate assessment than previous methods, which often overlooked BGB.
Although the green revolution adapted a handful of crops to homogeneous and high-input industrialized agriculture, much of the global population still relies on the local production of variable crop cultivars by low-input smallholder farms. This diversity of unhomogenized crops1, like that of the grain and bioenergy crop sorghum2-5, offers raw materials for genetic gain and cultivar improvement. However, breeding efforts can be constrained by highly specialized traits and breeding targets6. Here, to bridge this diversity, we constructed a 33-member pangenome reference and a diversity panel across 1,984 cultivars and landraces. We leveraged these resources to explore the complex interplay among historical contingency, ongoing adaptation and previously uncharacterized structural diversity. Specifically, our analyses conclusively demonstrated multiple nested and deeply diverged structural variants in the domestication gene SHATTERING1, which distinguish the previously established multicentric origin of sorghum. We then applied landscape genomics to reveal how gene flow and secondary contact created the complex genetic mosaic in contemporary breeding networks. As proof of concept for pangenome-accelerated trait discovery, we connected biosynthetic gene cluster structural variation to phenotypic leaf concentration of the cyanogenic glucoside dhurrin. Combined, these approaches will accelerate breeding and trait discovery and provide a framework for similar applications in other crops.
