Temperature influences the physiological processes and ecology of both hosts and endophytes; however, it remains unclear how long noncoding RNAs (lncRNAs) modulate the consequences of temperature-dependent changes in host–pathogen interactions. To explore the role of lncRNAs in culm gall formation induced by the smut fungus Ustilago esculenta in Zizania latifolia, we employed RNA sequencing to identify lncRNAs and their potential cis-targets in Z. latifolia and U. esculenta under different temperatures. In Z. latifolia and U. esculenta, we identified 3194 and 173 lncRNAs as well as 126 and four potential target genes for differentially expressed lncRNAs, respectively. Further function and expression analysis revealed that lncRNA ZlMSTRG.11348 regulates amino acid metabolism in Z. latifolia and lncRNA UeMSTRG.02678 regulates amino acid transport in U. esculenta. The plant defence response was also found to be regulated by lncRNAs and suppressed in Z. latifolia infected with U. esculenta grown at 25 °C, which may result from the expression of effector genes in U. esculenta. Moreover, in Z. latifolia infected with U. esculenta, the expression of genes related to phytohormones was altered under different temperatures. Our results demonstrate that lncRNAs are important components of the regulatory networks in plant-microbe-environment interactions, and may play a part in regulating culm swelling in Z. latifolia plants.

Modulating nutrient-solution electrical conductivity (EC) is an important strategy for improving the fruit flavor of tomato (Solanum lycopersicum L.) in soilless culture. Previous salinity studies have typically elevated EC through NaCl supplementation, which is physiologically distinct from raising EC via basal nutrient concentrations. Moreover, whether the quality-improving effect of moderately high electrical conductivity (HEC) arises from passive osmotic concentration or active metabolic reprogramming remains unclear. Tomato cultivar ‘Hangza 602’ was grown under CK (2.0 mS cm⁻¹) and HEC (3.0 mS cm⁻¹). Fruits were sampled at mature-green, breaker, pink and red-ripe stages for integrated transcriptomic, enzymatic and metabolomic analyses. HEC increased total soluble solids across ripening stages and promoted sugar and organic acid accumulation at pink and red-ripe stages, with a greater organic-acid increment reducing the sugar–acid ratio. Sugar transporter genes were induced at the breaker stage. At the pink stage, HEC elevated the activities of phosphofructokinase, citrate synthase and NADP–malate dehydrogenase without altering sucrose-phosphate synthase, indicating enhanced catabolic flux rather than sucrose biosynthesis. Sugars and citric acid accumulated persistently, whereas malic acid rose transiently then declined at red-ripe. Multiple downstream pathways were altered in HEC-treated red-ripe fruits. Collectively, HEC initiates stage-specific carbon-metabolic reprogramming combining osmotic and active effects, providing experimental insights for precision EC management in protected tomato production. Future work will further identify key regulatory genes governing this stage-specific metabolic response and validate the combined effects of EC and other environmental factors to develop robust soilless culture strategies for flavor improvement.

Phytophthora blight, caused by Phytophthora capsici, is difficult to control after symptoms appear, motivating rapid, non-destructive detection during the asymptomatic stage. This study evaluated hyperspectral imaging (HSI) for early disease detection in chili pepper seedlings. Stem and leaf spectra were monitored during disease progression, and the basal stem was selected for its disease-related spectral changes before visible symptoms appeared. A genetic algorithm coupled with partial least squares and correlation analysis identified five characteristic wavelengths (550, 670, 722, 760, and 800 nm). Among the six machine-learning algorithms evaluated on day 5 using five-fold cross-validation, linear discriminant analysis (LDA) and support vector machine (SVM) achieved classification accuracies of 90.7% and 90.1%, respectively. Detection before symptom onset was feasible by day 5. Healthy and asymptomatic infected samples collected on that day were therefore evaluated using 20 vegetation indices to identify simpler diagnostic predictors. Single-index support vector machine (SVM) prediction accuracies ranged from 60.00% to 91.25%. The photochemical reflectance index (PRI) achieved the highest accuracy (91.25%), followed by the enhanced vegetation index (EVI) and triangular vegetation index (TVI) (90.00% each). These findings support basal-stem HSI and selected vegetation indices as candidate approaches for early P. capsici detection, subject to independent validation.