Unraveling the dynamic transcriptomic changes during the dimorphic transition of Talaromyces marneffei through time-course analysis

Systemic dimorphic fungi pose a significant public health challenge, causing over one million new infections annually. The dimorphic transition between saprophytic mycelia and pathogenic yeasts is strongly associated with the pathogenesis of dimorphic fungi. However, despite the dynamic nature of dimorphic transition, the current omics studies focused on dimorphic transition primarily employ static strategies, partly due to the lack of suitable dynamic analytical methods. We conducted time-course transcriptional profiling during the dimorphic transition of , a model organism for thermally dimorphic fungi. To capture non-uniform and nonlinear transcriptional changes, we developed DyGAM-NS (dynamic optimized generalized additive model with natural cubic smoothing). The performance of DyGAM-NS was evaluated by comparison with seven other commonly used time-course analysis methods. Based on dimorphic transition induced genes (DTIGs) identified by DyGAM-NS, cluster analysis was utilized to discern distinct gene expression patterns throughout dimorphic transitions of . Simultaneously, a gene expression regulatory network was constructed to probe pivotal regulatory elements governing the dimorphic transitions. By using DyGAM-NS, model, we identified 5,223 DTIGs of . Notably, the DyGAM-NS model showcases performance on par with or superior to other commonly used models, achieving the highest F1 score in our assessment. Moreover, the DyGAM-NS model also demonstrates potential in predicting gene expression levels throughout temporal processes. The cluster analysis of DTIGs suggests divergent gene expression patterns between mycelium-to-yeast and yeast-to-mycelium transitions, indicating the asymmetrical nature of two transition directions. Additionally, leveraging the identified DTIGs, we constructed a regulatory network for the dimorphic transition and identified two zinc finger-containing transcription factors that potentially regulate dimorphic transition in . Our study elucidates the dynamic transcriptional profile changes during the dimorphic transition of . Furthermore, it offers a novel perspective for unraveling the underlying mechanisms of fungal dimorphism, emphasizing the importance of dynamic analytical methods in understanding complex biological processes.