搜索结果：Plant disease

Neurodegenerative diseases, including Alzheimer's disease (AD), Parkinson's disease (PD), and Huntington's disease (HD), are progressive disorders with limited therapeutic options. Centella asiatica (C. asiatica), a medicinal and edible plant, has been reported to exert neuroprotective and anti-neuroinflammatory properties. Yet, the mechanisms underlying its effects against neurodegenerative diseases remain largely unclear. We employed an integrative strategy combining network pharmacology, transcriptomic analyses, machine learning and molecular docking to prioritize disease-associated molecular networks and candidate compound-target relationships in AD, PD and HD. Sixteen candidate constituents of C. asiatica met the predefined drug-likeness, gastrointestinal absorption and blood-brain barrier permeability criteria, yielding 370 unique predicted targets. Disease-gene mining identified 983 AD-associated genes, 1,103 PD-associated genes, and 3,316 HD-associated genes. Integration of compound targets, disease-associated genes, and transcriptomic profiles prioritized five hub genes in PD (CCKAR, MAPK8, PSEN2, SLC6A3, and TH), four in AD (APP, PGK1, PIK3CA, and TTR), and four in HD (CHRND, HSP90AA1, PRKCQ, and TH). Enrichment analyses highlighted disease-relevant processes involving neurotransmitter signalling, cAMP and calcium pathways, MAPK-related responses and inflammatory regulation. ROC analyses provided additional support for the discriminatory performance of the prioritized genes in independent datasets, whereas molecular docking identified favourable predicted Vina docking scores and structurally plausible interactions between selected compounds and hub targets. This integrative computational analysis prioritizes candidate C. asiatica constituents, putative disease-associated targets, and molecular pathways in AD, PD, and HD. The findings provide a foundation for subsequent biochemical, cellular, and in vivo validation.

Cardiovascular diseases (CVDs) are closely associated with mitochondrial dysfunction, including impaired mitochondrial biogenesis, abnormal mitochondrial dynamics, excessive oxidative stress, dysregulated mitophagy, and disorders of energy metabolism. Accumulating evidence indicates that natural products exert significant cardioprotective effects by coordinately regulating multiple mitochondrial pathways, thereby alleviating myocardial injury and slowing disease progression. This review systematically summarises recent advances in natural metabolites and their rational combination strategies for cardiovascular diseases, with a particular focus on mitochondrial regulation. Representative metabolites, including flavonoids, alkaloids, saponins, and polyphenols, regulate key signalling pathways such as SIRT1/PGC-1α, AMPK, Nrf2, PINK1/Parkin, and Drp1/MFN2 to restore mitochondrial homeostasis. Importantly, natural metabolite combinations further demonstrate synergistic and complementary effects through coordinated regulation of distinct mitochondrial pathways. For example, EGCG and Rhein cooperatively alleviate myocardial ischemia/reperfusion injury by simultaneously suppressing oxidative stress and TLR4-mediated inflammatory signalling. Collectively, this review highlights the considerable potential of natural products and their rational combinations as therapeutic strategies for cardiovascular diseases through the modulation of mitochondrial function.

Exogenously applied nucleic acid-based agents are emerging as a promising strategy in agriculture for highly selective crop protection and plant trait modulation; however, their practical deployment remains constrained by inefficient delivery, rapid environmental degradation, and poor robustness under field conditions. Lipid-based nanocarriers, long established in pharmaceutical science as non-viral delivery systems, offer a versatile platform to address these challenges but require substantial adaptation to function effectively in both plants and open-environment agricultural conditions. This review critically examines lipid-based nanocarrier platforms, including liposomes, solid lipid nanoparticles, and oil-in-water nanoemulsions, for nucleic acid delivery in plant systems. Fundamental differences between mammalian and plant biology, such as the presence of the cell wall, apoplastic transport pathways, extracellular nucleases, and continuous exposure to environmental stressors represent key determinants of nanocarrier performance. Drawing on principles from nanomedicine, we analyse how nanocarrier size, surface chemistry, charge regulation, and deformability govern transport across major plant barriers, including mucilage layers, cuticles, cell walls, and intracellular membranes. Beyond direct plant delivery, the review also highlights the growing use of lipid-based nanocarriers in plant protection, summarizing applications targeting fungal pathogens, bacterial and viral diseases, nematodes, and insect pests. By integrating pharmaceutical nanotechnology concepts with agricultural constraints, this review highlights both the opportunities and limitations of lipid-based nanocarriers for nucleic acid-enabled crop technologies.

This study evaluated in vitro antigiardial activity in 16 Indonesian plants extracted in methanol, methanol-tetrahydrofuran (1:1), and water. These plants exhibiting promising antiparasitic activity were selected on the basis of collected behavioral data and the ability of these plants to decrease parasite load in Sumatran orangutans. Plant extracts of different concentrations (0.15625-10 mg/mL) and metronidazole (100 μg/mL), a standard antigiardial drug, were incubated with 105 trophozoites per milliliter of growth medium in 96-well tissue culture plates under anaerobic conditions for 24 h. Cultures were counted in a hemocytometer using a light microscope and then statistically evaluated. All tested plants were found to be effective to some extent against Giardia intestinalis trophozoites, with eight of them having never been previously tested for any biological activity nor probably used in ethnomedicine (as far as we know). Our results show that these extracts have potential as an alternative treatment of enteric diseases caused by G. intestinalis and confirm the assumption that orangutans use these plants for self-medication.

Uric acid is the end product of purine metabolism and plays a dichotomous role in the human body. On one hand, it exerts antioxidant and neuroprotective effects; on the other hand, chronic hyperuricemia has been strongly associated with diseases beyond gout, affecting the cardiovascular, renal, metabolic, autoimmune, and central nervous systems (CNS). Excess uric acid promotes oxidative stress, endothelial damage, neurodegeneration, inflammasome activation, and impairs energy metabolism. It exacerbates autoimmune diseases, such as Systemic Lupus Erythematosus (SLE) and antiphospholipid syndrome (APS), by increasing inflammatory and oxidative damage, leading to greater end-organ damage. The British Society for Rheumatology, European League Against Rheumatism, American College of Rheumatology, and National Institute for Health and Care Excellence (NICE) have all established a "treat-to-target" approach for hyperuricemia with serum urate levels below 6 mg/dL and below 5 mg/dL in severe cases. Allopurinol and Febuxostat, xanthine oxidase inhibitors, are used as first-line pharmacological therapies for the treatment of hyperuricemia, whereas uricosurics and Interleukin-1 (IL-1) inhibitors are preferred in cases of refractory hyperuricemia. Lifestyle modifications, such as the Dietary Approaches to Stop Hypertension (DASH) diet, weight reduction, and smoking cessation, are also recommended for the long-term management of the disease. Sodium-Glucose Cotransporter 2 (SGLT2) inhibitors, selective urate transport inhibitors, and plant-derived anti-inflammatory compounds have emerged as new treatments with promising responses. This review synthesizes the current literature on the multifaceted role of uric acid and emphasizes its systemic implications in chronic diseases. It also outlines the already established management options and new innovative therapies for managing this disease. Understanding this dichotomous role is essential for adopting a precise management approach that balances the protective and pathological effects of uric acid.

The outcomes of pathogen infection can be sensitive to temperature, interactions with other pathogen species, and host age. Yet few studies have experimentally tested how warming alters infection outcomes for multiple, potentially interacting, pathogen species across host ages. In this study, we conducted a factorial experiment to test how plant age (7-week vs. 13-week plants) and temperature (21°C vs. 29°C) influence infection outcomes of two foliar fungal pathogens with contrasting feeding strategies-Rhizoctonia solani (a necrotroph) and Colletotrichum cereale (a hemibiotroph)-in the grass species tall fescue (Lolium arundinaceum). Contrary to expectations of coinfection with pathogens of opposing life-history strategies leading to increased disease symptoms, coinfection had relatively minor effects across disease metrics. Instead, infection outcomes were driven by host age, pathogen identity and temperature. In plants inoculated with R. solani, higher temperature reduced lesion severity, and independently, severity was less in older plants. C. cereale lesion development showed a strong age × temperature interaction, with older plants being more resistant to disease in cooler conditions but not under warming. In plants co-inoculated with both pathogens, elevated temperature reduced disease severity, and this effect was stronger in older plants. These findings demonstrate that environmental conditions and host age can both interact and outweigh within-host pathogen interactions, highlighting the importance of incorporating host demographic structure in predicting disease responses to climate warming.

Prospective cohort studies analyzing the effect of plant versus animal protein intake on mortality in relatively young individuals from Mediterranean populations are limited. Moreover, a limited number of studies have performed repeated measurements of protein intake. We evaluated the relationship between plant versus animal protein intake (at baseline and at the ten-year follow-up) and mortality in the "Seguimiento Universidad de Navarra" (SUN) project. The SUN project is a prospective, multi-purpose, dynamic cohort study of Spanish university graduates. Plant and animal protein intake were assessed using a semi-quantitative food frequency questionnaire (FFQ) previously validated in Spain. Participants were divided into quartiles based on their intake of protein. Cox regression models were used, with the first quartile serving as the reference category. A total of 17,989 subjects (10,961 women and 7028 men) were included in the analysis. During 251.363 person-years of follow-up (median follow-up time: 12 years), 460 deaths were identified. The mean age at baseline was 38 years with a standard deviation of 12 years. Participants in the highest quartile of plant protein intake had a 35% lower risk of all-cause mortality compared to those in the lowest quartile after adjusting for potential confounders [hazard ratio (HR): 0.65 (95% CI: 0.45-0.93), p for trend = 0.009]. No significant association was found between animal protein intake and mortality [HR: 0.94 (95% CI: 0.70-1.26); p for trend = 0.555] nor between plant protein intake and cardiovascular or cancer mortality. Plant protein intake was inversely associated with all-cause mortality in a Mediterranean population. Animal protein intake was not associated with total mortality.

Rhizoctonia solani AGI-IA is a polyphagous necrotrophic fungal pathogen that causes sheath blight disease in rice. Efforts are being made to identify pathogenicity-associated genes in R. solani and modulate them to develop a disease control strategy. Here, we investigate the roles of some predicted pathogenicity-associated genes of R. solani that have previously been reported to be upregulated during infection in rice. The tobacco rattle virus-based host-induced gene silencing of the selected pathogenicity-associated genes revealed that silencing of Rs_MEP1, a zinc-containing Peptidase_M43 domain-metalloprotease, severely compromises R. solani infection in tomato. Moreover, double-stranded RNA-mediated silencing of Rs_MEP1 prevented R. solani infection in rice. The signal sequence trap assay indicated the secretory nature of Rs_MEP1, while the reporter assay suggested its localization in the plant apoplast. Notably, agrobacterium-mediated transient overexpression of Rs_MEP1 induces necrotic cell death responses in plants. We provide evidence that Rs_MEP1 interacts with GH19 family of rice chitinases and potentially modulates their functions. Overall, our study emphasizes that Rs_MEP1 facilitates R. solani in promoting necrotic responses and targets rice GH19 chitinases to impart disease susceptibility in plants.

Grape diseases cause substantial economic losses worldwide, making accurate detection critical for effective control. UAV imagery offers a promising solution for automated disease surveillance, but detecting grape diseases from UAV images remains challenging due to high lesion variability, complex backgrounds (e.g., soil, shadows, overlapping canopy), and scale variation caused by changing flight altitudes. To address these challenges, we propose AWAVM-UNet (Adaptive-Weighted Attention VM-UNet), which integrates feature aggregation and channel-spatial attention. The model has three key components: (1) an AWA module that redesigns skip connections to fuse multi-scale encoder features using spatial and channel attention; (2) an MSCSA module that performs local multi-scale feature extraction via parallel group convolutions to handle altitude-induced scale variation; and (3) a CSA module with a learnable attention matrix that adaptively weights encoder-decoder features while suppressing background clutter. Experiments on a UAV-captured grape disease dataset show that AWAVM-UNet achieves state-of-the-art performance: 88.12% OA, 86.76% DSC, 76.58% IoU, and 88.39% Precision, outperforming CNN-based, Transformer-based, and Mamba-based methods. Ablation studies confirm the positive contribution of each component, and qualitative results demonstrate cleaner boundaries and fewer false positives, especially for small lesions at higher altitudes and in complex backgrounds. The proposed method provides an effective foundation for automated UAV-based vineyard disease monitoring.

Based on a strategy integrating topological feature identification and path priority assessment, this study systematically explored the efficacy targets and differentiated mechanisms of Colquhounia Root Tablets(CRT) in the "homotherapy for heteropathy" of rheumatoid arthritis(RA) and diabetic kidney disease(DKD). Firstly, candidate targets of CRT and disease-specific genes were retrieved by integrating multi-source databases and transcriptomic data. Protein-protein interaction(PPI) networks were constructed to identify key targets shared by or specific to RA and DKD via topological feature identification. Subsequently, a path priority assessment was introduced to calculate the average shortest path(ASP) values from drug targets to various pathological segments, thereby quantifying intervention efficacy to lock onto "dominant pharmacodynamic links". The predicted key functional axes were validated through animal experiments. The results indicated that the core targets of CRT for both diseases involved shared pathways such as phosphatidylinositol 3-kinase(PI3K)-protein kinase B(Akt), tumor necrosis factor(TNF), and glycolysis/gluconeogenesis. Notably, path priority assessment revealed distinct dominant intervention links: for RA, CRT preferentially targeted "fibroblast-like synoviocyte(FLS) activation and invasion"(ASP=2.361), which mapped to the TNF-p38 mitogen-activated protein kinase(p38)-LIM domain kinase 1(LIMK1)-Cofilin1 axis to regulate cytoskeleton remodeling; for DKD, the dominant link was "filtration barrier injury and interstitial fibrosis"(ASP=2.295), converging on the TNF-poly(ADP-ribose) polymerase 1(PARP1)-signal transducer and activator of transcription 1(STAT1)-matrix metallopeptidase 9(MMP9) axis to mediate cellular senescence and senescence-associated secretory phenotype(SASP) secretion. Animal experiments confirmed that CRT significantly alleviated RA synovial invasion and DKD renal fibrosis by inhibiting these two differentiated signaling axes, respectively. By employing topological feature identification and path priority assessment, this study elucidates the scientific connotation of "homotherapy for heteropathy" of RA and DKD with CRT through both shared network regulation and intervention in disease-specific differential signaling axes associated with pharmacodynamic links.

Shifting to sustainable dietary patterns is essential for improving human and planetary health. Current dietary habits substantially contribute to non-communicable diseases and environmental impacts, particularly due to high consumption of animal-based foods. Food-based dietary guidelines (FBDGs) that integrate nutritional, health, and environmental dimensions are therefore increasingly important. While global frameworks highlight the need for sustainable diets, national guidelines must reflect cultural and regional contexts to ensure feasibility and acceptance. Mathematical optimization has emerged as a promising approach to develop such guidelines. This study aimed to update the Austrian FBDGs using mathematical optimization, incorporating nutritional adequacy, burden of disease expressed as disability-adjusted life years (DALYs), cultural acceptability, greenhouse gas emissions (GHGE), and land use (LU). Linear programming was applied to identify dietary patterns that meet nutritional requirements, minimize environmental and health impacts, and remain close to observed Austrian dietary intake. The optimized model outputs formed the scientific foundation for translating results into consumer-oriented national dietary guidelines. The optimized diet met all nutrient recommendations and macronutrient distribution ranges. The resulting FBDGs emphasize higher intakes of plant-based foods, particularly vegetables, fruits, grains, and legumes, alongside substantially lower amounts of animal-based products. Meat intake should not exceed 300 g per week, while legumes are recommended at least three times per week. Compared with the current Austrian diet, the optimized dietary pattern suggests a potential reduction in diet-related health burden and environmental impacts (DALYs -43%, GHGE -58%, LU -61%), highlighting its potential to improve public health and reduce environmental pressures. The updated Austrian FBDGs provide clear, quantitative recommendations that simultaneously ensure nutritional adequacy, reduce disease burden, and support environmental sustainability. As current meat consumption substantially exceeds recommendations, gradual dietary shifts are essential. Increasing legumes and minimally processed legume-based products can facilitate this transition. Effective implementation will require broad communication efforts and supportive food environments to the enable adoption of healthier and more sustainable dietary patterns.

Dictating cell growth and morphology, cellulose biosynthesis is intrinsic to plant cell biology. Accordingly, cellulose biosynthesis inhibitors (CBIs) are important herbicides, toxins, and experimental tools. We currently lack mechanistic understanding of CBI activity, preventing engineering of herbicide selectivity and disease immunity. Contrasting classical inhibitors, we unexpectedly identify the unusual Streptomyces phytotoxin thaxtomin A as the only in vitro -active CBI, with unprecedentedly broad-spectrum activity against various cellulose synthase enzymes. High-resolution cryo-electron microscopy reveals that thaxtomin A leverages exotic nitroaromatic chemistry to target a strictly conserved site in cellulose synthase's polysaccharide secretion channel. Strikingly, in vitro biosynthesis and biophysical assays demonstrate thaxtomin A's near-picomolar efficacy. Plant and algal systems reveal that its global arrest of cellulose biosynthesis produces an osmotically driven crisis in expanding cells. Finally, site-directed mutagenesis generates the first toxin-resistant cellulose synthase. Our results underscore cellulose's critical function in plant lifeforms and inform efforts to inhibit related enzymes across kingdoms.

Metabolic dysfunction-associated fatty liver disease (MAFLD) is recognized as the hepatic manifestation of metabolic syndrome. Hepatic steatosis resulting from impaired energy metabolism constitutes the core of its pathogenesis, and this disease has become a global public health concern. AMP-activated protein kinase (AMPK) is widely expressed in high-energy-consuming organs and functions as a vital energy sensor that maintains systemic energy homeostasis. Current preclinical in vitro and in vivo evidence demonstrates that AMPK activation modulates multiple MAFLD-related pathological processes, including enhancing cellular autophagy, regulating lipid metabolism, reducing inflammatory and oxidative damage, improving IR, and mitigating mitochondrial dysfunction. Plant metabolites have attracted increasing research attention for targeting the AMPK pathway in MAFLD basic research, due to their multi-target regulatory characteristics and low adverse reaction profiles. Accordingly, based on the intrinsic connection between the AMPK signaling pathway and MAFLD, this review summarizes the research progress regarding the pharmacological mechanisms of plant metabolites acting on the AMPK pathway in MAFLD intervention. Notably, most studies included in this review are preclinical experiments conducted in vitro and in vivo, with scarce supporting clinical data. Further efforts are still needed to advance the translation of these findings and confirm their clinical potential. This review systematically summarizes recent basic research progress and identifies unresolved issues, so as to offer a theoretical basis for subsequent mechanistic studies and translational exploration of traditional Chinese medicine against MAFLD.

Accurate and reliable diagnosis of grape leaf diseases is essential for sustainable viticulture, enabling timely intervention, reducing economic losses, and supporting precision crop management. Although deep learning (DL) models have demonstrated remarkable classification performance, their reliability under real-world field conditions remains insufficiently explored. In particular, confidence estimates often fail to reflect true predictive correctness when models are exposed to distributional shifts, limiting their practical applicability. To address this challenge, this study proposes a confidence- and uncertainty-aware DL framework for grape leaf disease diagnosis that extends evaluation beyond conventional accuracy-based metrics. Two publicly available grape leaf datasets were employed for stratified five-fold cross-validation in binary and multiclass classification tasks, while a third independently collected dataset was reserved exclusively for leakage-free external validation. EfficientNet-B0 and MobileNetV3-Large were evaluated under four inference configurations: raw inference, temperature scaling (TempScaling), Monte Carlo dropout, and an ensemble strategy combining uncertainty estimation with calibration. External validation demonstrated that both architectures maintained discriminative capability under domain shift. Under ensemble inference, EfficientNet-B0 achieved an accuracy of 73.8%, a macro-F1 score of 73.3%, a Matthews correlation coefficient (MCC) of 0.468, and a receiver operating characteristic area under the curve (ROC-AUC) of 0.804, while MobileNetV3-Large achieved 71.8% accuracy, 71.2% macro-F1, an MCC of 0.429, and a ROC-AUC of 0.787. Despite these promising results, raw predictions exhibited substantial overconfidence, with Expected Calibration Error (ECE) values of 0.287 and 0.312 for EfficientNet-B0 and MobileNetV3-Large, respectively. TempScaling markedly improved calibration quality, reducing ECE to 0.038 and 0.042 without affecting classification performance. Ensemble inference further enhanced the balance between predictive discrimination and reliability. The results demonstrate that strong classification performance alone is insufficient for trustworthy deployment in agricultural environments. Confidence calibration and uncertainty quantification provide complementary information for identifying overconfident predictions and improving decision reliability under field variability. The proposed framework offers a reliability-oriented approach for developing trustworthy artificial intelligence systems for grape leaf disease diagnosis in precision agriculture.

Alzheimer's disease(AD) is a highly prevalent neurodegenerative disorder with complex pathogenesis. Currently available mainstream drugs offer limited efficacy and often cause significant side effects. The herb pair of Astragali Radix and Acori Tatarinowii Rhizoma, known for its Qi-tonifying and orifice-opening properties in TCM, has demonstrated advantages in multi-target and holistic regulation in anti-AD research. This review systematically summarizes the synergistic mechanisms of active ingredients such as astragaloside Ⅳ, calycosin, and β-asarone against AD through multiple pathways, including peroxisome proliferator-activated receptor γ(PPARγ)/brain-derived neurotrophic factor(BDNF) pathway, phosphatidylinositol 3-kinase(PI3K)/protein kinase B(Akt) pathway, and gut-brain axis. It also points out that current studies remain largely confined to in vitro and animal experiments, with insufficient evidence for clinical translation. Building on this, the review further proposes innovative research directions, such as constructing astragaloside Ⅳ-β-asarone co-delivery nanosystems, optimizing the compatibility ratio of the herb pair, and combining with fecal microbiota transplantation to validate causal mechanisms via microbiota-gut-brain axis. These proposals aim to provide a systematic theoretical framework and experimental pathway for the in-depth development and clinical translation of the herb pair of Astragali Radix and Acori Tatarinowii Rhizoma.

Respiratory diseases, such as lung injury and chronic obstructive pulmonary disease (COPD), significantly impact the quality of life and socio-economics of patients, and there is an urgent need for new therapeutic approaches and preventive measures. This study aimed to systematically analyze the toxic constituents of Kusnezoff Monkshood Root and their effects on the respiratory system through network toxicology and molecular docking techniques. We screened the active ingredients of Kusnezoff Monkshood Root from the TCMSP database and selected the active ingredient with the strongest toxic effect as the core research object. The TargetNet, SEA, and SwissTargetPrediction databases were used to collect potential targets of this active ingredient; GeneCards, TTD, and OMIM databases were used to search for respiratory toxic effect targets; and the intersection targets of the two were taken. The intersection targets were then used to construct a protein-protein interaction network through the STRING database, and then imported into Cytoscape software for visualization, and CytoNCA plug-in was used to find out the core targets, and finally the protein-protein interaction network analysis was carried out for the 5 core targets with the highest Degree values; and then the David database was used for the GO and OMIM analysis. Then, GO and KEGG analyses were carried out in David's database; finally, the molecular docking technique was used to predict the relationship between the active ingredients and the core targets. Eight active ingredients with respiratory toxicity in Kusnezoff Monkshood Root were screened by the TCMSP database, and the most respiratory toxic component of Izoteolin was selected as the core research object. Finally, molecular docking analyses showed a strong affinity between Izoteolin and these core targets (binding energies ranging from -7.4 to -8.1 kcal/mol), suggesting a potential mechanism of action in respiratory toxicity and lung injury. In summary, this study reveals the toxic effects of Kusnezoff Monkshood Root on the respiratory system and provides a theoretical basis for future safety assessment of Kusnezoff Monkshood Root and the rules of dispensing, emphasizing the need for an in-depth understanding of the mechanisms of toxicity in the use of Chinese and Mongolian medicines.

Magnaporthe oryzae is a devastating fungal pathogen causing blast disease in rice and other crops, threatening global grain production and food security. Phenamacril (PHA) effectively inhibits Fusarium graminearum by targeting F. graminearum myosin I (FgMyoI). However, PHA shows limited activity against M. oryzae, despite the high sequence similarity between FgMyoI and M. oryzae myosin I (MoMyoI). Using our published PHA-FgMyoI complex structure as a template, we identified K378 in MoMyoI as a key determinant of insensitivity to PHA. Substitution of K378 with methionine, the corresponding residue in FgMyoI, markedly enhanced PHA-mediated inhibition of MoMyoI ATPase activity and improved PHA efficacy against M. oryzae. Guided by structure-based design, we synthesized PHA derivatives targeting MoMyoI. Among them, NJY-10 showed improved MoMyoI binding, ATPase inhibition, and protective efficacy against rice blast disease.

Rising atmospheric CO2 has significant implications for crop productivity and food security. Based on studies in C3 plants, elevated CO2 (eCO2) can shape plant-pathogen interactions, although the outcomes are often variable. The question of how eCO2 influences immunity and disease development in C4 plants, such as the globally important cereal crop maize (Zea mays L.), has not been systematically examined. We challenged maize plants grown under ambient CO2 (aCO2, 420 ppm) and eCO2 (550 ppm) with bacterial, viral, fungal, and oomycete pathogens. Plants grown in eCO2 were more susceptible to sugarcane mosaic virus, suggesting compromised antiviral defenses, less susceptible to Clavibacter nebraskensis, Exserohilum turcicum, and Colletotrichum graminicola, and susceptibility to Puccinia sorghi and Pythium sylvaticum was unchanged. Reduced susceptibility to C. nebraskensis was associated with enhanced basal immune responses. These results establish a foundation for dissecting eCO2-responsive defense mechanisms, and they highlight a critical need to understand how eCO2 will impact plant responses to microbes, pests, and abiotic stresses under future conditions.

Bacterial blight, caused by Xanthomonas axonopodis pv. punicae, poses a significant threat to pomegranate (Punica granatum L.) cultivation, resulting in considerable yield losses. The disease exhibits pronounced seasonal variability, which is largely influenced by environmental and climatic factors. Understanding the temporal behaviour of disease severity and its relationship with meteorological factors is essential for effective disease surveillance, forecasting, and management. This study analysed long-term surveillance data on bacterial blight severity from pomegranate-growing regions in Maharashtra, India (2013-2024), along with meteorological parameters. This study employed statistical methods, time-series models (ARIMA, SARIMA, and VAR), and Machine learning (ML) based regression models using seasonal assessments of disease severity based on Standard Meteorological Weeks (SMWs). The results indicated weak associations between disease severity and weather variables, with temperature showing a weak positive correlation (r = 0.18), while relative humidity exhibited a moderate inverse relationship (r = -0.33). Time-series analysis revealed a clear temporal dependence in disease progression, with the non-seasonal ARIMA (2,1,1) model providing the best fit (R² = 0.691; RMSE = 0.085), whereas seasonal components were not retained in the final model. The multivariate VAR model further enhanced biological interpretability by integrating weather variables, achieving a comparable accuracy (R² = 0.732; RMSE = 0.239). Among ML-based regression models, LightGBM achieved the best prediction (R² = 0.776; RMSE = 0.566). The explainable ML analysis consistently identified temperature as the dominant driver of bacterial blight severity. Overall, the integrated analytical framework provides a robust understanding of bacterial blight dynamics by combining surveillance data, temporal modelling, and predictive analytics. These findings provide a basis for data-driven forecasting systems, enabling timely interventions and improved disease management under varying climatic conditions.