Heat shock proteins (HSPs) are involved in critical processes like host tissue invasion, resistance, and pathogenicity in dermatophytes. RNA-Seq analysis of Trichophyton rubrum exposed to undecanoic acid (UDA) revealed intron retention events in HSP transcripts. Because HSPs are modulated in response to various stimuli and as alternative splicing (AS) can result in a broad diversity in the proteome of eukaryotic cells, our objective was to confirm the aforementioned retention events, investigating their consequences and extent. Furthermore, we aimed to determine: (1) the expression profile of HSP genes in an infection-like scenario and (2) the importance of Hsp90 for the keratinolytic potential of T. rubrum. RT and qPCR analyses comparing the exposure to UDA and terbinafine (TRB) confirmed the presence of two mRNA isoforms of the hsp7-like gene, with distinct expression patterns in response to UDA and TRB. The HSP expression profile revealed two upregulated, three downregulated, and four unmodulated transcripts; Hsp90 inhibition by 17-AAG resulted in a significant decrease in keratinolytic potential at 37 °C. Altogether, these results broaden the current knowledge on the importance of HSP-mediated pathways for cell adaptation and other aspects of dermatophyte biology, indicating that HSP network proteins can be potential targets for antifungal therapy.

Fungi are a diverse group of eukaryotic organisms that infect humans, animals, and plants. To successfully colonize their hosts, pathogenic fungi must continuously adapt to the host’s unique environment, e.g., changes in temperature, pH, and nutrient availability. Appropriate protein folding, assembly, and degradation are essential for maintaining cellular homeostasis and survival under stressful conditions. Therefore, the regulation of proteostasis is crucial for fungal pathogenesis. The heat shock response (HSR) is one of the most important cellular mechanisms for maintaining proteostasis. It is activated by various stresses and regulates the activity of heat shock proteins (HSPs). As molecular chaperones, HSPs participate in the proteostatic network to control cellular protein levels by affecting their conformation, location, and degradation. In recent years, a growing body of evidence has highlighted the crucial yet understudied role of stress response circuits in fungal infections. This review explores the role of protein homeostasis and HSPs in fungal pathogenicity, including their contributions to virulence and host–pathogen interactions, as well as the concerted effects between HSPs and the main proteostasis circuits in the cell. Furthermore, we discuss perspectives in the field and the potential for targeting the components of these circuits to develop novel antifungal therapies.

• AS expansion in fungi correlates with cellular complexity and pathogenic traits. • Intron retention dominates fungal splicing and challenges regulatory interpretations. • Splicing signals define a leverage point that shifts stochastic noise to regulation. • We propose a practical roadmap to detect regulated splicing events in RNA-Seq data.

Sertraline, a selective serotonin reuptake inhibitor, is a widely used antidepressant. In recent decades, research has revealed its potential for drug repurposing, demonstrating various biological activities beyond psychiatric applications, including antifungal, antibacterial, antitumor, anthelmintic, and leishmanicidal effects. This review explores the antifungal potential of sertraline and its possible mechanisms of action against fungal cells, highlighting similarities with its effects in higher eukaryotes. We examine the effects of sertraline on (1) gene expression pathways, (2) lipid and carbohydrate metabolism, (3) membrane function, (4) stress response, and (5) alternative splicing modulation. Finally, we discuss future perspectives on the use of sertraline as an antifungal agent.

Rapid responses to environmental changes are essential for maintaining fitness. In pathogenic fungi such as the dermatophyte Trichophyton interdigitale, appropriate responses to environmental shifts determine successful infection. Transcriptional regulation and alternative splicing (AS) are key modulators of fungal adaptation and pathogenesis. Here, we validated the role of the transcription factor PacC in coordinating AS in T. interdigitale grown in infection-mimicking medium. RNA-seq analysis of a ΔpacC mutant revealed a predominance of intron retention events, mainly involving introns 1 and 2, indicating defective splicing and potential nonsense-mediated decay of genes related to ion transport, metabolism, and genome maintenance. These alterations compromised energy balance, ergosterol biosynthesis, and cellular homeostasis. PacC-dependent AS generated alternative isoforms of cytoskeletal and metabolic proteins, including myosin-1 and a GH3 β-glucosidase, potentially modulating enzymatic activity, metabolic burden, and cell wall remodeling during infection. Exon-skipping in the chromatin remodeler RSC1 suggests PacC involvement in epigenetic regulation under host-mimicking conditions. Transmission electron microscopy revealed possible Woronin bodies, cytoplasmic disruption, and cell wall thinning in the mutant. Overall, PacC integrates transcriptional and post-transcriptional regulation to promote adaptation, survival, and virulence, highlighting AS as a regulatory layer linking environmental sensing to metabolic and epigenetic plasticity in pathogenic fungi.

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.