Maize anthracnose, caused by the fungal pathogen Colletotrichum graminicola, is among the most devastating diseases affecting maize production. Homeobox transcription factors (HTFs) regulate key developmental and physiological processes in eukaryotes, including fungal pathogenesis. In this study, we identified two HTFs, CgrHtf1 and CgrAfh1, in C. graminicola. Both CgrHtf1 and CgrAfh1 contain a conserved homeobox domain and exhibit distinct nuclear localization, consistent with their predicted roles as transcriptional regulators. Disruption of Cgrhtf1 sharply reduced conidial production while increasing melanin biosynthesis. In contrast, deletion of Cgrafh1 not only impaired conidiation but also abolished the formation of functional appressoria and hyphopodia. Notably, overexpression of Cgrafh1 enhanced appressorium formation compared to the wild-type strain, suggesting its crucial role in the morphogenesis of appressoria. Transcriptome analysis revealed that CgrHtf1 regulates many genes associated with melanin biosynthesis, fungal development and cell cycle control, while CgrAfh1 predominantly modulates the expression of genes linked to signal transduction, cell cycle progression and autophagy processes. Collectively, we demonstrate that CgrHtf1 controls conidiation and melanin biosynthesis, whereas CgrAfh1 governs appressorium development, revealing hierarchical regulation of infection-related morphogenesis in C. graminicola.
The spatial prediction of edible fungi is essential for the conservation and sustainable use of non-wood forest products (NWFPs) and contributes to the understanding of fungal biodiversity in forest ecosystems. This study compares multiple species distribution modeling (SDM) techniques to predict the spatial distribution of Lactarius deliciosus (L.) Gray in the Refahiye and Tekçam Forest Planning Units (FPUs) in Türkiye. Using the Biomod2 platform, we implemented five modeling algorithms: generalized linear models (GLM), multivariate adaptive regression splines (MARS), classification tree analysis (CTA), boosted regression trees (BRT), and random forests (RF). Among these, the RF model outperformed the others, demonstrating superior accuracy across all performance metrics, likely due to its ability to handle non-linear relationships, categorical predictor variables, and complex interactions without requiring extensive parameter tuning. The resulting RF-based suitability map provides valuable guidance for sustainable mushroom harvesting, forest management planning, and the conservation of mycological resources.

