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The circadian clock genes in tomato are key regulators of cold stress adaptation. However, the low-temperature regulatory mechanism of the circadian clock gene SlPCL1 remains unclear. In this study, we evaluated the role of SlPCL1 in cold tolerance through low-temperature treatment of transgenic plants. Downstream target genes were identified using RNA-seq, RT-qPCR, yeast-one-hybrid (Y1H), dual-luciferase assays, and electrophoretic mobility shift assay (EMSA), while interacting proteins were characterized using yeast-two-hybrid (Y2H), luciferase complementation imaging (LCI), and pull-down assays, thereby elucidating the molecular mechanism underlying SlPCL1-mediated low-temperature regulation. We identified SlPCL1 as a nuclear-localized circadian clock gene with transcriptional repressor activity. Overexpression of SlPCL1 resulted in a cold-sensitive phenotype, whereas virus-induced gene silencing (VIGS)-mediated silencing of SlPCL1 enhanced cold tolerance. SlNPF4.6 functions as an abscisic acid (ABA) transporter involved in ABA transport. RNA-seq and RT-qPCR identified the ABA transporter SlNPF4.6 as a downstream target. Functional assays confirmed that SlPCL1 binds to the MYB element in the SlNPF4.6 promoter to repress its expression. Meanwhile, VIGS-mediated silencing of SlNPF4.6 decreased cold tolerance. Furthermore, the expression levels of the ABA receptor SlPYLs in the silenced lines were significantly reduced, confirming the decrease in intracellular ABA content. SlSUMO1, a ubiquitin-like protein, can influence gene transcription through noncovalent interactions. In addition, SlSUMO1 was found to interact with the SlPCL1 protein, attenuating SlPCL1 transcriptional repression activity. Together, these findings establish an SlSUMO1-mediated fine control mechanism of the SlPCL1-SlNPF4.6 regulatory module. This integration of circadian clock regulation uncovers new molecular mechanisms of cold tolerance and supports the development of cold-resistant breeding materials.

Plant protein phosphatase 2C (PP2C) is recognized as one of the most critical protein family in plants and plays a pivotal role in disease resistance responses. However, the involvement of tomato PP2C family members in resistance to bacterial wilt caused by Ralstonia solanacearum remains poorly understood. In this study, we found that silencing SlPP2C28 increased tomato susceptibility to R. solanacearum; then, we introduced the tomato gene SlPP2C28, which exhibits a strong response to R. solanacearum, into the Nicotiana benthamiana genome via Agrobacterium-mediated transformation, generating high-expression transgenic lines OE-2 and OE-3. Following inoculation with R. solanacearum, the transgenic tobacco plants displayed reduced wilting symptoms, delayed disease onset, lower disease index, reduced stem cross-section damage, decreased internal bacterial colonization, diminished accumulation of reactive oxygen species in leaves, enhanced expression of SlPP2C28, up-regulated expression of defense-related genes NbSOD, NbPOD, and NbPAL along with an increase in the activities of their corresponding enzymes, and elevated expression levels of pathogenesis-related genes NbPR1a, NbPR2, NbPR4, and NbPR10. Collectively, these findings demonstrate that the SlPP2C28 gene has the function of enhancing resistance to bacterial wilt disease.

Jasmonates (JAs), including jasmonic acid (JA), its precursor 12-oxo-phytodienoic acid (OPDA) and its derivatives jasmonoyl-isoleucine (JA-Ile), methyl jasmonate (MeJA), cis-jasmone (CJ) and other oxylipins, are important in the regulation of a range of ecological interactions of plants with their abiotic and particularly their biotic environments. Plant secondary/specialized metabolites play critical roles in implementing these ecological functions of JAs. Pathway and transcriptional regulation analyses have established a central role of JA-Ile-mediated core signaling in promoting the biosynthesis of a great diversity of secondary metabolites. Here, we summarized the advances in JAs-induced secondary metabolites, particularly in secondary metabolites induced by OPDA and volatile organic compounds (VOCs) induced by CJ through signaling independent of JA-Ile. The roles of JAs in integrating and coordinating the primary and secondary metabolism, thereby orchestrating plant growth–defense tradeoffs, were highlighted and discussed. Finally, we provided perspectives on the improvement of the adaptability and resilience of plants to changing environments and the production of valuable phytochemicals by exploiting JAs-regulated secondary metabolites.

The plant protein phosphatase 2C (PP2C) plays an irreplaceable role in phytohormone signaling, developmental processes, and manifold stresses. However, information about the PP2C gene family in tomato (Solanum lycopersicum) is relatively restricted. In this study, a genome-wide investigation of the SlPP2C gene family was performed. A total of 92 SlPP2C genes were identified, they were distributed on 11 chromosomes, and all the SlPP2C proteins have the type 2C phosphatase domains. Based on phylogenetic analysis of PP2C genes in Arabidopsis, rice, and tomato, SlPP2C genes were divided into eight groups, designated A–H, which is also supported by the analyses of gene structures and protein motifs. Gene duplication analysis revealed that the duplication of whole genome and chromosome segments was the main cause of SLPP2Cs expansion. A total of 26 cis-elements related to stress, hormones, and development were identified in the 3 kb upstream region of these SlPP2C genes. Expression profile analysis revealed that the SlPP2C genes display diverse expression patterns in various tomato tissues. Furthermore, we investigated the expression patterns of SlPP2C genes in response to Ralstonia solanacearum infection. RNA-seq and qRT-PCR data reveal that nine SlPP2Cs are correlated with R. solanacearum. The above evidence hinted that SlPP2C genes play multiple roles in tomato and may contribute to tomato resistance to bacterial wilt. This study obtained here will give an impetus to the understanding of the potential function of SlPP2Cs and lay a solid foundation for tomato breeding and transgenic resistance to plant pathogens.

Self-incompatibility and recurrent transitions to self-compatibility have shaped the extant mating systems underlying the nonrandom mating critical for speciation in angiosperms. Linkage between self-incompatibility and speciation is illustrated by the shared pollen rejection pathway between self-incompatibility and interspecific unilateral incompatibility (UI) in the Brassicaceae. However, the pollen discrimination system that activates this shared pathway for heterospecific pollen rejection remains unknown. Here we show that Stigma UI3.1 , the genetically identified stigma determinant of UI in Arabidopsis lyrata × Arabidopsis arenosa crosses, encodes the S-locus-related glycoprotein 1 (SLR1). Heterologous expression of A. lyrata or Capsella grandiflora SLR1 confers on some Arabidopsis thaliana accessions the ability to discriminate against heterospecific pollen. Acquisition of this ability also requires a functional S-locus receptor kinase (SRK), whose ligand-induced dimerization activates the self-pollen rejection pathway in the stigma. SLR1 interacts with SRK and interferes with SRK homomer formation. We propose a pollen discrimination system based on competition between basal or ligand-induced SLR1–SRK and SRK–SRK complex formation. The resulting SRK homomer levels would be sensed by the common pollen rejection pathway, allowing discrimination among conspecific self- and cross-pollen as well as heterospecific pollen. Our results establish a mechanistic link at the pollen recognition phase between self-incompatibility and interspecific incompatibility. Through genetic and molecular analyses of interspecific stigma–pollen interactions, the authors show that Brassicaceae plants use an integrated pollen discrimination system and a shared pollen rejection pathway to reject conspecific self-pollen and heterospecific pollen. This establishes a mechanistic link between self-incompatibility and speciation in this clade.

The aim of this paper is to establish an analytical method for simultaneously determining the alisol C 23-acetate, Alisol A, alisol B, alisol B 23-acetate in RHIZOMA ALISMATIS. The assay was performed on an Ultimate XB - C18 colunm with the mobile phase consisting of acetonitrile-water at a flow rate of 1.0 mL/min. The column temperature was at 30 °C and the optimum detection wavelength of DAD was 245 nm The drift tube and atomizer temperatures of ELSD were 60 and 55 °C, respectively, with a carrier g~s (N2) pressure of 20 psi. The HPLC-DAD-ELSD quantitative analysis method was simple, rapid and accurate, and could be used for the quantitative analysis of multi-component of RHIZOMA ALISMATIS, which provided a novel approach for the comprehensively evaluation of the quality of RHIZOMA ALISMATIS.

Ferroptosis, a regulated form of cell death, has emerged as a critical modulator of melanoma's tumor progression and immune evasion. However, its integration with the tumor immune microenvironment (TME) and clinical prognostication remains underexplored. This study aims to construct a multi-omics framework combining ferroptosis-related signatures, immune infiltration patterns, and machine-learning approaches to stratify melanoma patients and guide therapeutic decision-making. We developed a multi-omics framework integrating bulk transcriptomics (TCGA/GEO), single-cell RNA sequencing, and machine learning to decode melanoma's ferroptosis-immune axis. Ferroptosis-immune subtypes were identified through consensus clustering and immune profiling, while prognostic models were constructed via LASSO/stepwise Cox regression and machine learning optimization. Three ferroptosis-immune subtypes exhibiting distinct survival outcomes and immune phenotypes were identified. A 40-gene prognostic signature (externally validated) effectively stratified patient survival risk and predicted chemotherapy sensitivity. Single-cell analysis revealed elevated ferroptosis activity within an immunosuppressive microenvironment, specifically implicating POSTN-ITGB5 signaling in fibroblast-immune cell crosstalk. A clinically applicable nomogram integrating risk scores and clinical factors demonstrated robust predictive accuracy (AUC 0.829-0.845). Machine learning refined a 4-gene prognostic signature (CLN6, GMPR, AP1S2, ITGA6), with functional validation confirming the role of CLN6 in proliferation and migration. This study establishes a prognostic framework and therapeutic roadmap for precision immuno-oncology in melanoma, bridging multi-omics discovery with clinical translation.

Ulcerative colitis (UC), a chronic and complex inflammatory bowel disorder, presents ongoing therapeutic challenges. Although multi-tiered anti-inflammatory strategies represent significant advances, issues like treatment resistance and adverse effects persist. Consequently, identifying more effective therapeutic targets and potentially curative strategies remains imperative. Emerging evidence underscores neutrophils, particularly through neutrophil extracellular trap (NET) formation, as pivotal contributors to UC pathogenesis. In affected individuals, excessive NET accumulation exacerbates intestinal inflammation, compromises the epithelial barrier, activates coagulation pathways, promotes resistance to biologic therapies, and may even facilitate malignant transformation. Critically, a bidirectional interplay exists between NETs and the gut microbiota (GM) in this disease. Recent research indicates that certain traditional Chinese medicine (TCM) herbal extracts and formulas hold promise for modulating aberrant NET generation and GM composition. This review examines the roles of NETs and GM in UC pathogenesis and synthesizes evidence on potential TCM-based interventions targeting these pathways, offering novel perspectives for future therapeutic development.

Mesenchymal stem cells (MSC) are not universal and may be subject to dynamic changes upon local milieus in vivo and after isolation and cultivation in vitro. Here, we demonstrate that MSC derived from murine pericardial adipose tissue (pMSC) constitute two cohorts of population distinguished by the level of CD73 expression (termed as CD73high and CD73low pMSC). Transplantation of two types of cells into mouse hearts after myocardial infarction (MI) revealed that the CD73high pMSC preferentially brought about structural and functional repair in comparison to the PBS control and CD73low pMSC. Furthermore, the CD73high pMSC displayed a pronounced anti-inflammatory activity by attenuating CCR2+ macrophage infiltration and upregulating several anti-inflammatory genes 5 days after in vivo transplantation and ex vivo cocultivation with peritoneal macrophages. The immunomodulatory effect was not seen in cocultivation experiments with pMSC derived from CD73 knockout mice (CD73-/-) but was partially blocked by pretreatment of the A2b receptor antagonist, PSB603. The results highlight a heterogeneity of the CD73 expression that may be related to its catalytic products on the modulation of the local immune response and thus provide a possible explanation to the inconsistency of the regenerative results when different sources of donor cells were used in stem cell-based therapy.

Sea ice is an important part of the global cryosphere and an important variable in the global climate system. Sea ice also presents one of the major natural disasters in the world. The automatic and accurate extraction of sea ice extent is of great significance for the study of climate change and disaster prevention. The accuracy of sea ice extraction in the Yellow River Estuary is low due to the large dynamic changes in the suspended particulate matter (SPM). In this study, a set of sea ice automatic extraction method systems combining image spectral information and textural information is developed. First, a sea ice spectral information index that can adapt to sea areas with different turbidity levels is developed to mine the spectral information of different types of sea ice. In addition, the image’s textural feature parameters and edge point density map are extracted to mine the spatial information concerning the sea ice. Then, multi-scale segmentation is performed on the image. Finally, the OTSU algorithm is used to determine the threshold to achieve automatic sea ice extraction. The method was successfully applied to Gaofen-1 (GF1), Sentinel-2, and Landsat 8 images, where the extraction accuracy of sea ice was over 93%, which was more than 5% higher than that of SVM and K-Means. At the same time, the method was applied to the Liaodong Bay area, and the extraction accuracy reached 99%. These findings reveal that the method exhibits good reliability and robustness.