Biology subjects

Rouleau, F. D.

Publications and source records attributed to Rouleau, F. D..

4 recordsLinked to original sources

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↗

bioRxiv2023

Deep mutational scanning of Pneumocystis jirovecii dihydrofolate reductase reveals allosteric mechanism of resistance to an antifolate

Pneumocystis jirovecii is a fungal pathogen that causes pneumocystis pneumonia, a disease that mainly affects immunocompromised individuals. This fungus has historically been hard to study because of our inability to grow it in vitro. One of the main drug targets in P. jirovecii is its dihydrofolate reductase (PjDHFR). Here, by using functional complementation of the bakers yeast ortholog, we show that PjDHFR can be inhibited by the antifolate methotrexate in a dose-dependent manner. Using deep mutational scanning of PjDHFR, we identify mutations conferring resistance to methotrexate. Thirty-one sites spanning the protein have at least one mutation that leads to resistance, for a total of 355 high-confidence resistance mutations. Most resistance-inducing mutations are found inside the active site, and many are structurally equivalent to mutations known to lead to resistance to different antifolates in other organisms. Some sites show specific resistance mutations, where only a single substitution confers resistance, whereas others are more permissive, as several substitutions at these sites confer resistance. Surprisingly, one of the permissive sites (F199) is without direct contact to either ligand or cofactor, suggesting that it acts through an allosteric mechanism. Modeling changes in binding energy between F199 mutants and drug shows that most mutations destabilize interactions between the protein and the drug. This evidence points towards a more important role of this position in resistance than previously estimated and highlights potential unknown allosteric mechanisms of resistance to antifolate in DHFRs. Our results offer unprecedented resources for the interpretation of mutation effects in the main drug target of an uncultivable fungal pathogen. Author summaryThe study of uncultivable microorganisms has always been a challenge. Such is the case of the human-specific pathogen Pneumocystis jirovecii, the causative agent of pneumocystis pneumonia. P. jirovecii is insensitive to classical antifungal drugs, making options for treatment and prophylaxis limited. In recent years, more and more cases of P. jirovecii infections have become resistant to treatment, highlighting the need to study and understand this pathogens mechanisms of resistance. Here, we use a yeast strain expressing P. jiroveciis DHFR as a reporter for resistance to an antifolate, one of the drug families used to treat infections. We observed that this DHFR was sensitive to methotrexate, a powerful antifolate, in a quantitative manner. Then, by using a large-scale mutational assay, we identified virtually all single mutations that confer this protein resistance to methotrexate. While any of them have also been reported in other eukaryotes, we find new mutations at positions of the protein not previously known to confer resistance or to be in contact with this competitive inhibitor. Overall, our results are a comprehensive portrait of this DHFRs resistance to methotrexate.

biochemistry↗

bioRxiv2021

Toeholder: a software for automated design and in silico validation of toehold riboswitches

Synthetic biology aims to engineer biological circuits, which often involve gene expression. A particularly promising group of regulatory elements are riboswitches because of their versatility with respect to their targets, but early synthetic designs were not as attractive because of a reduced dynamic range with respect to protein regulators. Only recently, the creation of toehold switches helped overcome this obstacle by also providing an unprecedented degree of orthogonality. However, a lack of automated design and optimization tools prevents the widespread and effective use of toehold switches in high throughput experiments. To address this, we developed Toeholder, a comprehensive open-source software for toehold design and in silico comparison. Toeholder takes into consideration sequence constraints from experimentally tested switches, as well as data derived from molecular dynamics simulations of a toehold switch. We describe the software and its in silico validation results, as well as its potential applications and impacts on the management and design of toehold switches.

bioinformatics↗

bioRxiv2021

BFG-PCA: tools and resources that expand the potential for binary protein interaction discovery

Barcode fusion genetics (BFG) utilizes deep sequencing to improve the throughput of protein-protein interaction (PPI) screening in pools. BFG has been implemented in Yeast two-hybrid (Y2H) screens (BFG-Y2H). While Y2H requires test protein pairs to localize in the nucleus for reporter reconstruction, Dihydrofolate Reductase Protein-Fragment Complementation Assay (DHFR-PCA) allows proteins to localize in broader subcellular contexts and proves to be largely orthogonal to Y2H. Here, we implemented BFG to DHFR-PCA (BFG-PCA). This plasmid-based system can leverage ORF collections across model organisms to perform comparative analysis, unlike the original DHFR-PCA that requires yeast genomic integration. The scalability and quality of BFG-PCA were demonstrated by screening human and yeast interactions for >11,000 protein pairs. BFG-PCA showed high-sensitivity and high-specificity for capturing known interactions for both species. BFG-Y2H and BFG-PCA capture distinct sets of PPIs, which can partially be explained based on the domain orientation of the reporter tags. BFG-PCA is a high-throughput protein interaction technology to interrogate binary PPIs that exploits clone collections from any species of interest, expanding the scope of PPI assays.

systems biology↗