Iron homeostasis and secondary metabolism are controlled by the basic leucine zipper (bZIP) transcription factor HapX in many fungi. In this study, AflHapX was identified through homology research of the Aspergillus flavus genome with the bZIP protein HapX in Aspergillus fumigatus and subsequent gene deletion. We established that AflHapX functions as a regulator for adaptation to iron-excess and iron-starvation conditions. The AflhapX deletion strain (ΔAflhapX) produces less aflatoxin B1 (AFB1) compared to the wild type (WT) and complementary strain (ΔAflhapXC) on YES medium. While iron typically represses AFB1 biosynthesis in A. flavus, the deletion of AflhapX restores iron’s inhibition of AFB1 production. Furthermore, compared to WT and ΔAflhapXC, ΔAflhapX produced more sclerotia and displayed greater sensitivity to the oxidative reagents H2O2, menadione sodium bisulfite (MSB), and diamide. On peanuts, the ΔAflhapX strain produced fewer conidia and less AFB1 than WT and ΔAflhapXC. Transcriptome analysis data provided evidence that AflHapX globally regulates genes involved in aflatoxin biosynthesis, oxidative stress response, and iron homeostasis. Our results suggest that AflHapX may regulate the effect of iron on aflatoxin production.
Fungi are important chassis organisms for sustainable biomanufacturing owing to their strong secretion capacity, metabolic diversity, and adaptability to industrial processes. Driven by the rapid development of Artificial Intelligence (AI), computational approaches that enable machines to learn patterns from data and make predictions, together with omics technologies and synthetic biology, data- and model-driven strategies are increasingly reshaping fungal cell factory design and optimization. This review summarizes recent advances in AI-enabled fungal biomanufacturing, covering AI-assisted genome mining, enzyme and pathway analysis, genome editing, metabolic network modeling, and intelligent fermentation control. The review further highlights the emerging concept of the artificial intelligence virtual cell (AIVC), which integrates multi-omics data, mechanistic modeling, and machine learning-based approaches into a unified multiscale framework for simulating dynamic cellular behaviors. The technical foundations and potential applications of AIVC in fungal metabolic engineering, drug discovery, agriculture, and environmental biotechnology are reviewed. Finally, key challenges-data quality and standardization, cross-scale model integration and interpretability, and the gap between in silico prediction and experimental validation-are highlighted. This review provides a systematic perspective on the role of AI and virtual cell technologies in advancing next generation fungal biotechnology.
Verticillium dahliae is a widely distributed vascular wilt pathogen with a broad host range, infecting over 200 plant species. Nitrogen metabolism is closely associated with fungal growth and pathogenicity, but the role of glutamate synthase-mediated nitrogen assimilation in V. dahliae has not been fully clarified. Here, we examined the functions of two glutamate synthase-encoding genes, VdGogat1 and VdGogat2, in nitrogen metabolism, virulence, and stress adaptation. Deletion of VdGogat1 (ΔVdGogat1) or VdGogat2 (ΔVdGogat2) reduced host colonization and compromised penetration peg formation. The ΔVdGogat1, ΔVdGogat2, and double mutant strain (ΔVdGogat1/2) showed defects in utilizing specific nitrogen sources. VdGogat1 played a major role in ammonium utilization, whereas VdGogat2 contributed more specifically to glutamine metabolism; accordingly, the ΔVdGogat1/2 strain showed a significantly reduced capacity to utilize ammonium. In addition, loss of VdGogat1 enhanced sensitivity to nitrosative stress. Collectively, these findings indicate that VdGogat1 and VdGogat2 contribute to nitrogen utilization, penetration, and full virulence in V. dahliae.
Dermatophytosis, or tinea, is a common superficial fungal infection affecting people of all ages worldwide. It is caused by keratinophilic dermatophytes that invade the skin barrier, colonize keratinized tissues, and activate host immune responses, leading to inflammation and tissue damage. Understanding the molecular pathogenesis is critical for standardizing treatment and developing targeted therapies. Key aspects include fungal invasion mechanisms, adhesion and proliferation on skin, secreted molecules, virulence factors, colonization patterns, and host-pathogen interactions. As new dermatophyte species emerge, advanced techniques are needed to study gene expression regulation during infection, disease progression, and how fungal factors modulate the host immune response, particularly in keratinocytes. Functional annotation of dermatophyte genes and their human counterparts is essential to map these interactions. This review summarizes the most common clinical forms of dermatophytosis, discusses selected host-dermatophyte interactions and evolutionary adaptations, and highlights current strategies for diagnosis and therapy. A deeper comprehension of these mechanisms will help in designing precise antifungal drugs and improving disease management.
Morphological development in filamentous fungi critically impacts nutrient and oxygen transfer, broth rheology, and metabolite production during submerged fermentation. However, life-cycle-resolved quantitative analysis remains technically challenging because fungal morphology dynamically spans multiple spatial scales from germinating spores and branching hyphae to mycelial pellets. Here, we developed PAMP, Python-based Automated Morphology analysis Pipeline, integrating skeletonization with quantitative image analysis for high-resolution morphological profiling throughout the fungal life cycle. Using two Aspergillus niger strains, MA70.15 and D353.8, PAMP accurately captures distinct morphogenetic trajectories associated with protein secretion and citric acid production. Application to pkaC conditional-expression mutant further reveals that graded PkaC activity modulates hyphal extension, branching frequency and pellet porosity, thereby improving citric acid production. Correlation analysis identifies peripheral hyphae and pellet porosity as major morphology determinants of productivity, which were not detectable by conventional pellet-analysis tools due to their limited resolution of peripheral hyphae. PAMP aslo demonstrates robust performance across multiple filamentous fungi, including Fusarium venenatum and Acremonium chrysogenum. Overall, PAMP provides a robust platform for morphology-guided strain engineering and fermentation optimization in fungal biotechnology.
