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Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), stands out as one of the most devastating epidemics impacting wheat production worldwide. Resistant wheat varieties had swiftly been overcome due to the emergence of new virulent Pst strains. Effectors secreted by Pst interfere with plant immunity, and verification of their biological function is extremely important for controlling wheat stripe rust. In this study, we identified an effector, Pst-18220, from Puccinia striiformis f. sp. tritici (Pst), which was induced during the early infection stage of Pst. Silencing the expression of Pst-18220 through virus-mediated host-induced gene silencing (HIGS) resulted in a decreased number of rust pustules. In Nicotiana benthamiana, it significantly suppressed cell death induced by Pseudomonas syringae pv. tomato (Pto) DC3000. In Arabidopsis, plants with stable overexpression of Pst-18220 showed increased susceptibility to Pto DC3000, accompanied by a decrease in the expression level of pattern-triggered immunity (PTI)/effector-triggered immunity (ETI)-related genes, namely, AtPCRK1, AtPCRK2, and AtBIK1. These results emphasize the significant role of the Pst candidate effector, Pst-18220, in rust pathogenicity and the suppression of plant defense mechanisms. This broadens our understanding of effectors without any known motif.

Magnaporthe oryzae, the pathogen responsible for rice blast disease, utilizes specialized infection structures known as appressoria to breach the leaf cuticle and establish intracellular, infectious hyphae. Our study demonstrates that the peroxin MoPex22 is crucial for appressorium function, specifically for the development of primary penetration hyphae. The ∆Mopex22 mutant exhibited slow growth, reduced aerial hyphae, and almost complete loss of virulence. Specifically, despite the mutant’s capability to form appressoria, it showed abnormalities during appressorium development, including reduced turgor, increased permeability of the appressorium wall, failure to form septin rings, and significantly decreased ability to penetrate host cells. Additionally, there was a delay in the degradation of lipid droplets during conidial germination and appressorium development. Consistent with these findings, the ΔMopex22 mutant showed an inefficient utilization of long-chain fatty acids and defects in cell wall integrity. Moreover, our findings indicate that MoPex22 acts as an anchor for MoPex4, facilitating the localization of MoPex4 to peroxisomes. Together with MoPex4, it affects the function of MoPex5, thus regulating the import of peroxisomal matrix proteins. Overall, these results highlight the essential role of MoPex22 in regulating the transport of peroxisomal matrix proteins, which affect fatty acid metabolism, glycerol accumulation, cell wall integrity, growth, appressorium development, and the pathogenicity of M. oryzae. This study provides valuable insights into the significance of peroxin functions in fungal biology and appressorium-mediated plant infection.

Ceratocystis fimbriata (C. fimbriata) is a notorious pathogenic fungus that causes sweet potato black rot disease. The APSES transcription factor Swi6 in fungi is located downstream of the cell wall integrity (CWI)-mitogen-activated protein kinase (MAPK) signaling pathway and has been identified to be involved in cell wall integrity and virulence in several filamentous pathogenic fungi. However, the specific mechanisms by which Swi6 regulates the growth and pathogenicity of plant pathogenic fungi remain elusive. In this study, the SWI6 deletion mutants and complemented strains of C. fimbriata were generated. Deletion of Swi6 in C. fimbriata resulted in aberrant growth patterns. Pathogenicity assays on sweet potato storage roots revealed a significant decrease in virulence in the mutant. Non-targeted metabolomic analysis using LC-MS identified a total of 692 potential differentially accumulated metabolites (PDAMs) in the ∆Cfswi6 mutant compared to the wild type, and the results of KEGG enrichment analysis demonstrated significant enrichment of PDAMs within various metabolic pathways, including amino acid metabolism, lipid metabolism, nucleotide metabolism, GPI-anchored protein synthesis, and ABC transporter metabolism. These metabolic pathways were believed to play a crucial role in mediating the growth and pathogenicity of C. fimbriata through the regulation of CWI. Firstly, the deletion of the SWI6 gene led to abnormal amino acid and lipid metabolism, potentially exacerbating energy storage imbalance. Secondly, significant enrichment of metabolites related to GPI-anchored protein biosynthesis implied compromised cell wall integrity. Lastly, disruption of ABC transport protein metabolism may hinder intracellular transmembrane transport. Importantly, this study represents the first investigation into the potential regulatory mechanisms of SWI6 in plant filamentous pathogenic fungi from a metabolic perspective. The findings provide novel insights into the role of SWI6 in the growth and virulence of C. fimbriata, highlighting its potential as a target for controlling this pathogen.

Precipitation extremes driven by the El Niño–Southern Oscillation (ENSO) are one of the critical ways in which the ENSO impacts the global climate, specifically in the tropical Pacific, where they have the potential to cause extreme weather conditions. However, existing approaches struggle to effectively identify the evolution of ENSO-related precipitation anomalies that change rapidly in spatial distribution. To address this challenge, we propose the object-oriented spatiotemporal clustering approach using remote sensing products (OSCAR) for detecting evolutions of ENSO-related precipitation anomalies. The OSCAR was validated using simulated datasets and applied to precipitation anomalies over the tropical Pacific. The simulation experiment demonstrates that the OSCAR outperforms the dual-constraint spatiotemporal clustering approach (DcSTCA) in accuracy, particularly for rapidly evolving precipitation anomaly variations. The application of the OSCAR demonstrates its ability to recognize the evolution of ENSO-related precipitation anomalies over the tropical Pacific, which may offer valuable references for global climate change research.

Many effective and advanced methods have been developed to explore oceanic dynamics using time series of raster-formatted datasets; however, they have generally been designed at a scale suitable for data observation and used independently of each other, despite the potential advantages of combining different modules into an integrated system at a scale suited for dynamic evolution. From raster-formatted datasets to marine knowledge, we developed and integrated several mining algorithms at a dynamic evolutionary scale and combined them into six modules: a module of raster-formatted dataset pretreatment; a module of process-oriented object extraction; a module of process-oriented representation and management (process-oriented graph database); a module of process-oriented clustering; a module of process-oriented association rule mining; and a module of process-oriented visualization. On the basis of such modules, we developed a process-oriented spatiotemporal dynamic mining system named PoSDMS (Process-oriented Spatiotemporal Dynamics Mining System). PoSDMS was designed to have the capacity to deal with at least six environments of marine anomalies with 40 years of raster-formatted datasets, including their extraction, representation, storage, clustering, association and visualization. The effectiveness of the integrated system was evaluated in a case study of sea surface temperature datasets during the period from January 1982 to December 2021 in global oceans. The main contribution of this study was the development of a mining system at a scale suited for dynamic evolution, providing an analyzing platform or tool to deal with time series of raster-formatted datasets to aid in obtaining marine knowledge.

It is important to consider where, when, and how the evolution of sea surface temperature anomalies (SSTA) plays significant roles in regional or global climate changes. In the comparison of where and when, there is a great challenge in clearly describing how SSTA evolves in space and time. In light of the evolution from generation, through development, and to the dissipation of SSTA, this paper proposes a novel approach to identifying an evolution of SSTA in space and time from a time-series of a raster dataset. This method, called PoAIES, includes three key steps. Firstly, a cluster-based method is enhanced to explore spatiotemporal clusters of SSTA, and each cluster of SSTA at a time snapshot is taken as a snapshot object of SSTA. Secondly, the spatiotemporal topologies of snapshot objects of SSTA at successive time snapshots are used to link snapshot objects of SSTA into an evolution object of SSTA, which is called a process object. Here, a linking threshold is automatically determined according to the overlapped areas of the snapshot objects, and only those snapshot objects that meet the specified linking threshold are linked together into a process object. Thirdly, we use a graph-based model to represent a process object of SSTA. A node represents a snapshot object of SSTA, and an edge represents an evolution between two snapshot objects. Using a number of child nodes from an edge’s parent node and a number of parent nodes from the edge’s child node, a type of edge (an evolution relationship) is identified, which shows its development, splitting, merging, or splitting/merging. Finally, an experiment on a simulated dataset is used to demonstrate the effectiveness and the advantages of PoAIES, and a real dataset of satellite-SSTA is used to verify the rationality of PoAIES with the help of ENSO’s relevant knowledge, which may provide new references for global change research.