Abstract Tobacco (Nicotiana tabacum L.) is a major economic crop and a model for plant–pathogen interactions, yet the spatiotemporal dynamics of defense metabolism during infection remain poorly characterized. Here, we used MALDI-MSI-based spatial metabolomics to systematically profile tobacco leaves during Pseudomonas syringae infection. Multidimensional analysis of 1,399 annotated metabolites revealed distinct spatiotemporal regulation patterns. Temporally, early infection (12 h postinfection (hpi)) was characterized by increased organic acids and terpenoids, followed by a mid-stage shift toward phenolic acids and quinones (24 hpi) and a late-stage enrichment of alkaloids by 60 hpi. Spatially, constrained clustering produced anatomy-aligned segmentation maps and revealed cell type preferences across epidermal, mesophyll, and vascular regions, with directional redistribution of differentially expressed metabolites as infection progressed. Defense hormones, including salicylic acid (SA) and jasmonic acid (JA), preferentially accumulated in vascular bundles and varied dynamically over time. Functional validation through exogenous application of representative metabolites (eg calystegine C1 and L-phenylalanine) and hormones (JA and SA), together with genetic manipulation of JA-biosynthetic genes, confirmed their roles in reducing lesion development and suppressing bacterial proliferation. Notably, epidermal enrichment of alkaloids—especially nicotine—and amino acid derivatives showed a decrease-then-increase pattern consistent with early consumption and later replenishment; nicotine's defensive contribution was further supported using a low-nicotine mutant. Collectively, P. syringae infection orchestrates a coordinated, cell type-compartmentalized defense metabolic program in tobacco, providing a resource for mechanistic studies and metabolic engineering of disease resistance. This time- and tissue-resolved atlas links metabolite remodeling to hormone-associated signaling and chemical barrier formation during wildfire disease progression.
Xin-Hua Tian, Ze-Chao Qu, Jia-Qi Wang et al.· Plant Physiology· 0 citations
In Industrial Internet of Things (IIoT) deployments, mobile edge computing (MEC) offloads computation-intensive tasks, a constrained biobjective problem trading time delay against energy consumption (MTOP). We show that this benchmark is exactly separable across micro base-station (MiBS) regions: its delay and energy objectives are additive over regions, and the only coupling, intra-cell interference, stays within a region. Exploiting this, we propose CR-MTMEMTO-D, a structure-aware decomposition multitasking method that treats each region as an independent subtask, solves it with a feasibility-repaired NSGA-II, and reconstructs the global feasible Pareto front as the non-dominated subset of the Minkowski sum of the regional fronts, an exact composition that adds no global evaluations. Across 12 instances (45–432 variables, 20 seeds), it attains the best hypervolume and IGD on every instance (mean HV 0.9340 vs. 0.8021 for a plain NSGA-II baseline; average rank 1.00), with the margin widening as the problem scales, and it is unchanged under total-evaluation matching because every evaluation is a regional main task. A feasibility-priority acceptance gate keeps the population fully feasible. Under matched budgets, a prior cheap-task pool with bandit-controlled transfer adds no significant gain, which motivates the structural approach.