Biology subjects

Pageau, A.

Publications and source records attributed to Pageau, A..

9 recordsLinked to original sources

bioRxiv2025

Making deep mutational scanning accessible: a cost-efficient approach to construct barcoded libraries for genes of any length

Recent developments in DNA synthesis and sequencing have allowed the construction of comprehensive gene variant libraries and their functional analysis. Achieving high-replication and thorough mutation characterization remains technically and financially challenging for long genes. Here, we developed an efficient, affordable and scalable library construction approach that relies on low-cost DNA synthesis and standard cloning technologies, which will increase accessibility to systematic mutational studies and help advance the field of protein science.

synthetic biology↗

bioRxiv2025

Epistasis at the cell surface: what is the role of Erg3 loss-of-function in acquired echinocandin resistance?

Echinocandins, which target the fungal {beta}-1,3-glucan synthase (Fks), are essential for treating invasive fungal infections, yet resistance is increasingly reported. While resistance typically arises through mutations in Fks hotspots, emerging evidence suggests a contributing role of changes in membrane sterol composition due to ERG3 mutations. Here, we present a clinical case of Nakaseomyces glabratus (Candida glabrata) in which combined mutations in ERG3 and FKS2, but not FKS2 alone, appear to confer echinocandin resistance. Integrated analyses reveal a recurrent association between Erg3 loss-of-function and echinocandin resistance mediated by Fks variation across Candida species, but exclude ERG3 loss-of-function as an independent resistance mechanism. Advances in Fks structural biology and insights into echinocandin-Fks interactions support a model of epistatic crosstalk between membrane sterols and Fks function. Understanding this interaction is crucial, as it may underlie not only acquired echinocandin resistance but also the broader development of multidrug resistance across major antifungal drug classes.

molecular biology↗

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↗

bioRxiv2025

gyoza: a Snakemake workflow for modular analysis of deep-mutational scanning data

Deep-mutational scanning (DMS) is a powerful technique that allows screening large libraries of mutants at high throughput. It has been used in many applications, including to estimate the fitness impact of all single mutants of entire proteins, to catalog drug resistance mutations and even to predict protein structures. Here, we present gy[o]za, a Snakemake-based workflow to analyze DMS data. gy[o]za requires little programming knowledge and comes with comprehensive documentation to help the user go from raw sequencing data to functional impact scores. Complete with quality control and an automatically generated HTML report, this new pipeline should facilitate the analysis of time-series DMS experiments. gy[o]za is freely available on GitHub (https://github.com/durr1602/gyoza). Article summaryDeep-mutational scanning (DMS) refers to molecular biology methods used to generate many genetic variants and evaluate their effect on adaptive fitness. It is used both in fundamental and applied research, with implications in genomic medicine. Many data processing steps are needed to transform the output of DMS, high-throughput sequencing data, into scores that quantify the fitness effect of each variant. The analysis is tailored to the type of experimental design, which can vary a lot. To facilitate such analysis and make it accessible to people with limited knowledge in bioinformatics, we have developed gy[o]za, a free and easy-to-use program to analyze DMS data.

bioinformatics↗

bioRxiv2024

FungAMR: A comprehensive portrait of antimicrobial resistance mutations in fungi

Antimicrobial resistance (AMR) is a global threat. To optimize the use of our antifungal arsenal, we need rapid detection and monitoring tools that rely on high-quality AMR mutation data. Here, we performed a thorough manual curation of published AMR mutations in fungal pathogens to produce the FungAMR reference dataset. A total of 501 papers were curated, leading to 35,792 mutation entries all classified with the degree of evidence that supports their role in resistance. FungAMR covers 95 species, 246 genes and 208 drugs. We combined variant effect predictors with FungAMR resistance mutations and showed that these tools could be used to help predict the potential impact of mutations on AMR. Additionally, a comparative analysis among species revealed a high level of convergence in the molecular basis of resistance, highlighting some potentially universal resistance mutations. The analysis also showed that a significant number of resistance mutations lead to cross-resistance within antifungals of a class, as well as between classes for certain mutated genes. The acquisition of fungal resistance in the clinic and the field is an urging concern. Finally, we provide a computational tool, ChroQueTas, that leverages FungAMR to screen fungal genomes for AMR mutations. These resources are anticipated to have great utility for researchers in the fight against antifungal resistance.

microbiology↗

bioRxiv2024

Mutational landscape and molecular bases of echinocandin resistance

One of the front-line drug classes used to treat invasive fungal infections is echinocandins, which target the fungal-specific beta-glucan synthase (Fks). Treatment failure due to resistance often coincides with mutations in three protein regions defined as hotspots. Unfortunately, the scarcity of the mutational data reported, combined with the large size and membrane-embedded nature of the enzyme hinder any effort to characterize genotype-phenotype links. Recent advances in solving the structure of Fks bring us one step closer to reliable predictions of the binding modes of each echinocandin. To help with that endeavor, we used molecular dynamics simulations to develop a membrane-embedded model of Fks that captures key structural and environmental features. Our results show that the three hotspots shape a single solvent-exposed binding cavity, hinting at the orientation and positioning of echinocandins. This structural framework is integrated with deep-mutational scanning to comprehensively assess the impact of mutations across the three hotspots in the model yeast Saccharomyces cerevisiae. We elucidate several key molecular bases of resistance to the three most widely used echinocandins; anidulafungin, caspofungin and micafungin and provide clues to better understand intrinsic resistance of critical fungal pathogens. One sentence summaryKey residues at specific positions in Fks hotspots lead to echinocandin-specific resistance.

molecular biology↗

bioRxiv2023

Most azole antifungal resistance mutations in the drug target provide cross-resistance and carry no intrinsic fitness cost

Azole antifungals are among the most frequently used drugs to treat fungal infections. Amino acid substitutions in and around the binding site of the azole target Erg11 (Cyp51) are a common resistance mechanism in pathogenic yeasts such as Candida albicans. How many and which mutations confer resistance, and at what cost, is however largely unknown. Here, we measure the impact of nearly 4,000 amino acid variants of the Erg11 ligand binding pocket on the susceptibility to six medical azoles. We find that a large fraction of amino acid substitutions lead to resistance (33%), most resistance mutations confer cross-resistance to two or more azoles (88%) and most importantly, only a handful of resistance mutations show a significant fitness cost in the absence of drug (9%). Our results reveal that resistance to azoles can arise through a large set of mutations and this will likely lead to azole pan-resistance, with very little evolutionary compromise. Such a resource will help inform treatment choices in clinical settings and guide the development of new drugs.

microbiology↗

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

Coherent activity at three major lateral hypothalamic neural outputs controls the onset of motivated behavior responses

The lateral hypothalamus (LH) plays an important role in motivated behavior. However, it is not known how LH neural outputs dynamically signal to major downstream targets to organize behavior. We used multi-fiber photometry to show that three major LH neural outputs projecting to the dorsal raphe nucleus (DRN), ventral tegmental area (VTA), and lateral habenula (LHb) exhibit significant coherent activity in mice engaging motivated responses, which decrease during immobility. Mice engaging active coping responses exhibit increased activity at LH axon terminals that precedes an increase in the activity of serotonin neurons and dopamine neurons, indicating that they may play a role in initiating active responses stemming from LH signal transmissions. The optogenetic activation of LH axon terminals in either the DRN, VTA, or LHb was sufficient to increase mobility but had different effects on passive avoidance and sucrose consumption, suggesting that LH outputs use complementary mechanisms to control behavioral responses. Our results support the notion that the three LH neural outputs play complementary roles in initiating motivated behaviors.

neuroscience↗