Dufresne, P. J. | Search bioRxiv

bioRxiv2026

Population genomics of Candida albicans reveals genetic footprints that allow the prediction of antifungal resistance

Rapid detection of drug resistance in fungi is becoming increasingly urgent. While multiple loci and mutations were linked to antifungal resistance in Candida albicans, the utility of whole genome sequencing in predicting resistance has not been tested thoroughly. We address this by integrating more than 1000 C. albicans genomes, with associated resistance phenotypes to six antifungals, a curated repository of resistance-causing mutations and variant effect predictors. We used Genome Wide Association Studies and Machine Learning approaches to associate genetic variants to resistance phenotypes. We found that well curated databases like FungAMR are able to accurately identify markers predictive antifungal resistance. Curated mutations were detected in 56 to 74% of isolates with reduced azole susceptibility and 57 to 90% of those with reduced echinocandin susceptibility, revealing incomplete coverage of resistance across both drug classes. Complementary variant- and gene-level association analyses recovered established resistance determinants and uncovered additional candidate loci, whereas copy-number changes provided limited independent explanatory power. Genomic prediction declined when genetic backgrounds were absent from training, and when leading predictive features differed among backgrounds. We also found that models trained on a curated list of mutations and variant effect predictors performed well when classifying drug-susceptible strains, but the drug-resistance trait was predicted with lower accuracy. These findings support the potential of genome-based resistance prediction while highlighting the need for broader population sampling, functional validation of candidate variants and expanded resistance catalogues to improve prediction across genetic backgrounds.

microbiology↗

bioRxiv2025

Predicting antifolate resistance in the unculturable fungal pathogen Pneumocystis jirovecii

Pneumocystis jirovecii is a fungal pathogen causing Pneumocystis pneumonia in humans, mainly in immunocompromised individuals. Infections by P. jirovecii are treated using the antifolate combination drug trimethoprim-sulfamethoxazole (TMP-SMX), targeting the dihydrofolate reductase (DHFR) and the dihydropteroate synthase (DHPS). In recent years, there has been an increase of treatment failure, with no mutations observed in the DHPS, implying the potential evolution of resistance through this pathogens DHFR (PjDHFR). Experimental methods to study this pathogen are limited, as it cannot be grown in vitro. Model fungi are insensitive to TMP-SMX due to unknown mechanisms, preventing the use of functional complementation to study mutations causing resistance to this specific drug combination. In a previous study, we conducted deep mutational scanning (DMS) on PjDHFR to identify resistance mutations to methotrexate (MTX), another antifolate drug. Here, by leveraging this data, as well as computational data modeling aspects of protein function and stability in the PjDHFR-MTX complex, we train a machine learning model to predict the effect of mutations on MTX resistance. We find that the model can predict the effect of mutations outside of its training dataset (balanced accuracy on training set: 98.3%, and 88.3% on testing set). We also find that the best predictors of resistance, such as distance to ligand and effect on region flexibility, are coherent with previously established models, and that experimental data about the effect of mutations on protein function is critical to optimize model performance. Using this model on computational data generated using the PjDHFR-TMP complex, we predict the effect of mutations on resistance to TMP. We predict TMP resistance mutations in PjDHFR that did not confer resistance to MTX, one of which had been characterized in vitro as reducing affinity to TMP by 100-folds. We compare the predictions from this model to PjDHFR sequences from previously and newly sequenced clinical samples. Our results offer a resource to interpret the impact of amino acid variants in PjDHFR on TMP resistance, as well as methods to predict resistance in hard-to-study organisms. Author summaryPneumocystis jirovecii is a fungal pathogen causing pneumonia in immunocompromised humans. Infections by P. jirovecii are treated using drugs that prevent this pathogen from making folate, an essential component of many cellular mechanisms. In recent years, this treatment has been failing in an increasing number of cases, implying the evolution of resistance to this treatment. As P. jirovecii does not grow in the lab, the investigation of this resistance has been difficult, and common lab models do not respond to the drugs used to treat it. To overcome these limitations, we use a combination of experimental data and computer modeling to train a machine learning model to predict how genetic changes in one of the drug targets might cause drug resistance in this pathogen. The presented model predicts mutations in the drug target that may make this pathogen resistant to treatment, including mutations that have been previously characterized in vitro as drastically reducing drug binding. To investigate if our model predicted mutations that accrued in nature, we also sequenced the largest number of this pathogens drug target to date. Our study provides new tools to predict drug resistance in hard-to-study pathogens, helping to understand and potentially respond to treatment failure.

biochemistry↗