Aldea, I. | Search bioRxiv

bioRxiv2026

Thirty years of fluconazole therapy selects an azole-resistant Candida albicans isolate with a pre-adapted physiological, metabolic and structural state

Azole resistance in Candida albicans is traditionally attributed to alterations in drug targets and efflux mechanisms; however, how long-term antifungal exposure reshapes fungal physiology remains incompletely understood. Here, we characterize a fluconazole-resistant C. albicans clinical isolate (PUJ256) recovered from a patient with chronic mucocutaneous candidiasis after more than 30 years of continuous fluconazole therapy. Compared with the reference strain SC5314, the resistant isolate exhibited a clear fitness trade-off, with reduced filamentation and increased membrane vulnerability under basal conditions, yet enhanced fitness in the presence of fluconazole. Integration of phenotypic analyses with label-free quantitative proteomics revealed extensive metabolic remodelling, including coordinated regulation of central carbon metabolism, ergosterol biosynthesis and redox homeostasis. Notably, mitochondrial membrane potential was preserved in the resistant isolate under fluconazole stress, whereas the susceptible strain exhibited mitochondrial depolarization together with activation of MAPK signalling pathways. In addition, the resistant isolate displayed reduced susceptibility to phagocytosis under antifungal exposure, consistent with an increased capacity for persistence in the context of ongoing treatment. Collectively, our findings indicate that long-term azole resistance in C. albicans PUJ256 is associated with a stable, pre-adapted physiological state characterized by metabolic reprogramming and mitochondrial resilience. The prolonged antifungal exposure of this isolate provides a valuable opportunity to explore adaptative strategies that extend beyond canonical mechanisms, pointing to mitochondrial function and metabolic plasticity as potential targets for therapeutic intervention.

microbiology↗

bioRxiv2026

'PePApipe': a complete bioinformatics analysis pipeline for African Swine Fever Virus genome

African Swine Fever Virus (ASFV) is of high concern in porcine livestock across the world due to both the high mortality rates and the trade restrictions imposed on affected regions. Viral genome is large and complex, but genomic analysis is essential for tracing its origin and evolution. Although several bioinformatics tools exist for genome assembly and analysis, no single platform integrates all necessary steps in an accessible and systematic way. In this study the authors developed PePApip, a custom-built, user-friendly pipeline that enables rapid, complete, and efficient ASFV genome analysis. It is specifically designed for laboratory professionals with limited bioinformatics experience, requiring only basic command-line knowledge. Starting from raw sequencing data, PePApipe integrates thirteen software tools into one automated workflow, covering quality control and pre-processing of raw reads, denovo genome assembly and variant calling. Programmed in Phyton, it can be executed locally through bash scripts, or using a SLURM protocol for batch processing of multiple samples. The main outputs are the ASFV consensus genome sequence and a file listing its putative variants compared to the selected reference genome. PePApipe classifies generated files into structures folders and produces intermediate files that can be used as inputs for further or parallel analyses; also users can enable or disable specific steps in each particular case. This pipeline is adaptable and complementary to downstream steps such as viral genome annotation or genome visualization. By consolidating all stages of viral genome analysis into a single automated workflow, PePApipe reduces the likelihood of user error and enhances reproducibility and efficiency. This easy-to-use pipeline will facilitate the transition from sequencing to assembly and analysis of viral genomes, ensuring a fast and reliable response to molecular analysis demands. Finally, the pipeline can be easily adapted to the study of other viral species, expanding its application in infectious diseases surveillance. Author summaryAfrican swine fever is a devastating viral disease that threatens pig production worldwide, causing severe economic losses and limiting international trade. Tracing and understanding how the virus spreads and evolves are key for preparedness and control of the disease, currently being a major sanitary challenge. However, viral genome analysis is often technically demanding and difficult to standardize, especially for laboratories without specialized bioinformatics expertise. In this study, we present PePApipe, an automated and user-friendly computational pipeline designed to simplify and standardize the complete genomic analysis of African swine fever virus. Starting directly from raw sequencing data, PePApipe guides users through all essential steps, from quality control to genome assembly and variant detection, producing reliable and reproducible results in a short time. By integrating multiple established tools into a single workflow and providing clear intermediate outputs, our approach reduces the risk of user error and increases transparency. Importantly, we designed PePApipe with accessibility in mind, enabling laboratory scientists to perform advanced genomic analyses with minimal computational background. While developed for African swine fever virus, the pipeline can be adapted to other viruses, making it a flexible resource for viral genomics, outbreak investigation, and future data-driven research.

bioinformatics↗