Extracellular ATP (eATP) and L-Glutamic acid (L-Glu) are important damage-associated molecular pattern (DAMP) molecules released from cells during injury. Both molecules trigger wound-associated signal transduction pathways, as well as the enhanced production of reactive oxygen species (ROS) by the RESPIRATORY BURST OXIDASE HOMOLOG D (RBOHD) protein. However, whether eATP and L-Glu trigger overlapping or distinct pathways is mostly unknown. Here, we report that Arabidopsis (Arabidopsis thaliana) responses to eATP or L-Glu are distinct from each other in terms of tissue specificity and transcriptomic responses. Thus, although both DAMPs trigger the expression of multiple wounding and hormone response transcripts in systemic tissues, eATP and L-Glu-induced transcripts have little overlap between them. We further show that wounding of different tissues may result in ROS responses that are controlled by different DAMP receptors. Thus, activation of ROS production following injury of non-vascular tissues primarily depended on the eATP receptors PURINORECEPTOR 2 KINASE 1 and 2 (P2K1P2K2), while activation of ROS responses in vascular tissues following injury primarily depended on the L-Glu receptors GLU-LIKE RECEPTORS 3.3 and 3.6 (GLR3.3GLR3.6). Interestingly, we found that in the absence of the GLR3.3GLR3.6 receptors (ie, in the glr3.3glr3.6 double mutant), the ROS response to eATP application is enhanced. This finding suggests that the L-Glu pathway may suppress the eATP pathway during wounding. Taken together, our findings suggest that the DAMP molecules eATP and L-Glu have complex interactions that appear to be both partly complementary and partly antagonistic, as well as tissue dependent.

Accurate cell type annotation remains a major bottleneck in plant single-cell RNA sequencing (scRNA-seq), where existing tools are often adapted from animal studies and perform sub-optimally on plant data. The lack of plant-specific computational frameworks limits the construction of plant cell atlases and downstream biological discovery. We develop and evaluate scPlantAnnotate, a Transformer-based reference annotation framework tailored for plant scRNA-seq data, and benchmark it against state-of-the-art deep learning and conventional methods across multiple plant species. Species-specific scPlantAnnotate models were trained using curated datasets from Arabidopsis thaliana, Zea mays, Oryza sativa, and Glycine max. We compared scPlantAnnotate with leading baselines under both standard random-split evaluation and a more stringent leave-one-dataset-out setting, which tests robustness to completely unseen datasets and tissue types. scPlantAnnotate consistently outperforms existing approaches across all four species under random-split evaluation. In the leave-one-dataset-out setting for A. thaliana, where performance drops markedly for all methods due to strong batch effects and dataset heterogeneity, scPlantAnnotate nonetheless achieves the highest Accuracy, Macro-F1, Balanced Accuracy, and Macro-AUROC on average and ranks first on most held-out datasets. These results demonstrate improved robustness to dataset shifts, a critical yet underexplored challenge in plant scRNA-seq analysis. A freely accessible web server enables users to annotate their own datasets using pretrained models. scPlantAnnotate provides a plant-specific, Transformer-based framework for single-cell annotation that delivers state-of-the-art performance and enhanced robustness to unseen datasets. By addressing limitations of existing tools and enabling scalable reference-based annotation, scPlantAnnotate supports the development of comprehensive plant cell atlases and facilitates broader use of single-cell genomics in plant biology.

Cancer immunoprevention leverages the immune system’s surveillance mechanisms to mitigate tumor development. Vaccines that constitute a tumor antigen and an immune adjuvant are perceived as immunoprevention modalities. However, relevant tumor antigens are unknown for non-viral cancers, which constitute most human cancers. Our group has recently shown that SA–4–1BBL, a novel agonist of CD137 receptor, but not antibodies, shows immunoprevention efficacy against various tumors. Advanced bioinformatics analyses of bulk RNA-seq data were conducted to elucidate mechanisms underlying cancer immunoprevention. Mice received subcutaneous injections of SA–4–1BBL or agonistic 3H3 antibody, and the injection-site tissue (IS) and draining lymph nodes (LN) were analyzed for differential gene expression. SA–4–1BBL induced a compartmentalized and temporally dynamic immune program characterized by early effector activation at IS and sustained immune regulation in draining LN. K-means clustering of 4564 DEGs identified eight functionally distinct clusters. IS-enriched clusters contained activation genes for CD4⁺ T and NK cells, including Cd28, Klra1, Cd4, Cd40, and Cd40l, while LN clusters were enriched for regulatory genes (Tnfaip3, Irf5, Col1a2) that ensure immune priming and homeostatic restraint for a balanced response. SA–4–1BBL generated a more selective and durable activation of adaptive immunity, TCR signaling, Th1/Th2 differentiation, and NK cytotoxicity. 3H3 activated broader innate inflammatory programs, including Toll-like receptor and neurodegeneration-linked pathways. IMPRes analysis showed that SA–4–1BBL activates sequential immune-regulatory circuits centered on Stat1, Cd247, and Ifng and modulates the CD151–TGF-β axis. These findings demonstrate that SA–4–1BBL elicits a balanced immune response, ensuring both safety and efficacy in preventing cancer development.