Flavonoid synthesis is crucial for Trichoderma asperellum-induced systemic resistance to root-knot nematodes in tomato plants.

Trichoderma spp. can enhance plant resistance against a wide range of biotic stressors. However, the fundamental mechanisms by which Trichoderma enhances plant resistance against Meloidogyne incognita, known as root-knot nematodes (RKNs), are still unclear. Here, we identified a strain of Trichoderma asperellum (T141) that could effectively suppress RKN infestation in tomato (Solanum lycopersicum L.). Nematode infestation led to an increase in the concentrations of reactive oxygen species (ROS) and malondialdehyde (MDA) in roots but pre-inoculation with T141 significantly decreased oxidative stress. The reduction in ROS and MDA was accompanied by an increase in the activity of antioxidant enzymes and the accumulation of flavonoids and phenols. Moreover, split root test-based analysis showed that T141 inoculation in local roots before RKN inoculation increased the concentration of phytohormone jasmonate (JA) and the transcripts of JA synthesis and signaling-related genes in distant roots. UPLC-MS/MS-based metabolomics analysis identified 1051 differentially accumulated metabolites (DAMs) across 4 pairwise comparisons in root division test, including 81 flavonoids. Notably, 180 DAMs were found in comparison between RKN and T141-RKN, whereas KEGG annotation and enrichment analysis showed that the secondary metabolic pathways, especially the flavonoid biosynthesis, played a key role in the T141-induced systemic resistance to RKNs. The role of up-regulated flavonoids in RKN mortality was further verified by in vitro experiments with the exogenous treatment of kaempferol, hesperidin and rutin on J2-stage RKNs. Our results revealed a critical mechanism by which T141 induced resistance of tomato plants against the RKNs by systemically promoting secondary metabolism in distant roots.

A carrier-free tri-component nanoreactor for multi-pronged synergistic cancer therapy.

Non-invasive therapies such as photodynamic therapy (PDT) and chemodynamic therapy (CDT) have received wide attention due to their low toxicity and side effects, but their efficacy is limited by the tumor microenvironment (TME), and monotherapy cannot achieve satisfactory efficacy. In this work, a multifunctional nanoparticle co-assembled from oleanolic acid (OA), chlorin e6 (Ce6) and hemin was developed. The as-constructed nanoparticle named OCH with diameters of around 130 nm possessed good biostability, pH/GSH dual-responsive drug release properties, and remarkable cellular internalization and tumor accumulation capabilities. OCH exhibited prominent catalytic activities to generate •OH, deplete GSH, and produce O2 to overcome the hypoxia TME, thus potentiating the photodynamic and chemodynamic effect. In addition, OCH can induce the occurrence of ferroptosis in both ferroptosis-sensitive and ferroptosis-resistant cancer cells. The multi-pronged effects of OCH including hypoxia alleviation, GSH depletion, ferroptosis induction, CDT and PDT effects jointly facilitate excellent anticancer effects in vitro and in vivo. Hence, this work will advance the development of safe and effective clinically transformable nanomedicine by employing clinically-applied agents to form drug combinations for efficient multi-pronged combination cancer therapy.

Nonsmooth Optimization-Based Model and Algorithm for Semisupervised Clustering.

Using a nonconvex nonsmooth optimization approach, we introduce a model for semisupervised clustering (SSC) with pairwise constraints. In this model, the objective function is represented as a sum of three terms: the first term reflects the clustering error for unlabeled data points, the second term expresses the error for data points with must-link (ML) constraints, and the third term represents the error for data points with cannot-link (CL) constraints. This function is nonconvex and nonsmooth. To find its optimal solutions, we introduce an adaptive SSC (A-SSC) algorithm. This algorithm is based on the combination of the nonsmooth optimization method and an incremental approach, which involves the auxiliary SSC problem. The algorithm constructs clusters incrementally starting from one cluster and gradually adding one cluster center at each iteration. The solutions to the auxiliary SSC problem are utilized as starting points for solving the nonconvex SSC problem. The discrete gradient method (DGM) of nonsmooth optimization is applied to solve the underlying nonsmooth optimization problems. This method does not require subgradient evaluations and uses only function values. The performance of the A-SSC algorithm is evaluated and compared with four benchmarking SSC algorithms on one synthetic and 12 real-world datasets. Results demonstrate that the proposed algorithm outperforms the other four algorithms in identifying compact and well-separated clusters while satisfying most constraints.