Dermatophytes are challenging to treat because they have developed many strategies to neutralize the stress triggered by antifungals. Drug tolerance is achieved by mechanisms such as drug efflux and biofilm formation, and cellular efflux is a consequence of the synergistic and compensatory regulation of efflux pumps. Alternative splicing (AS) has also been considered as a mechanism that enhances fungal adaptive responses. We used RNA-seq data from the dermatophyte Trichophyton rubrum exposed to undecanoic acid (UDA) to search for and validate AS in genes encoding efflux pumps. The magnitude of this phenomenon was evaluated using UDA and other antifungals (caspofungin, itraconazole, and terbinafine) in planktonic and biofilm cultures. In addition to the conventional isoforms, the efflux pump encoded by TERG_04309 presented two intron-retained isoforms. Biofilms trigger the simultaneous production of at least two isoforms. The intron-retained isoforms showed short lengths and topologically different organization. Furthermore, we identified the putative interaction of efflux pumps (TERG_04309 and TERG_04224). Co-expression of these genes suggests a synergistic action in antifungal resistance. Our data provide new insights into drug tolerance related to differential isoform usage and the co-expression of stress-responsive genes, which may lead to higher antifungal resistance, mainly in biofilms.

Opportunistic infections are a growing concern in medical mycology. In that context, Mucor circinelloides is recognized as an etiological agent of emerging life-threatening infections in humans. Furthermore, this biomass-degrading species is important in the biotechnological context due to its potential for enzyme discovery. In this work, we describe an in silico pipeline for the comprehensive characterization of the secretome of this species. Our analyses suggest that M. circinelloides encodes a diverse enzymatic repertoire, including multiple carbohydrate-active enzymes (CAZymes). Functional characterization of this secreted protein set identified candidate proteins with potential relevance for industrial application and virulence-related processes. Importantly, we extended this framework by integrating a sequence-based machine-learning (ML) approach for the prediction of extracellular vesicle (EV)-associated proteins. Using a Random Forest model trained on high-confidence EV datasets, we predicted a subset of candidate proteins in M. circinelloides and explored their functional distribution. The predicted EV proteome displayed a degree of similarity to experimentally characterized human EV proteomes, suggesting that the model is able to capture sequence-derived predictive signals. Overall, our results support the potential of ML as a scalable strategy to be integrated into the study of fungal EVs. This approach provided a foundation for research aimed at experimentally validating EV cargo and refining predictive models. Beyond M. circinelloides, we provide a framework that may be useful for future studies on EV biology across diverse fungi of medical and biotechnological interest.