Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets

Functional genomic analysis of genes important for Candida albicans fitness in diverse environmental conditions

Fungal pathogens such as Candida albicans pose a significant threat to human health with limited treatment options available. One strategy to expand the therapeutic target space is to identify genes important for pathogen growth in host-relevant environments. Here, we leverage a pooled functional genomic screening strategy to identify genes important for fitness of C. albicans in diverse conditions. We identify an essential gene with no known Saccharomyces cerevisiae homolog, C1_09670C, and demonstrate that it encodes subunit 3 of replication factor A (Rfa3). Furthermore, we apply computational analyses to identify functionally coherent gene clusters and predict gene function. Through this approach, we predict the cell-cycle-associated function of C3_06880W, a previously uncharacterized gene required for fitness specifically at elevated temperatures, and follow-up assays confirm that C3_06880W encodes Iml3, a component of the C. albicans kinetochore with roles in virulence in vivo. Overall, this work reveals insights into the vulnerabilities of C. albicans.

The spliceosome impacts morphogenesis in the human fungal pathogen Candida albicans

At human body temperature, the fungal pathogen Candida albicans can transition from yeast to filamentous morphologies in response to host-relevant cues. Additionally, elevated temperatures encountered during febrile episodes can independently induce C. albicans filamentation. However, the underlying genetic pathways governing this developmental transition in response to elevated temperatures remain largely unexplored. Here, we conducted a functional genomic screen to unravel the genetic mechanisms orchestrating C. albicans filamentation specifically in response to elevated temperature, implicating 45% of genes associated with the spliceosome or pre-mRNA splicing in this process. Employing RNA-Seq to elucidate the relationship between mRNA splicing and filamentation, we identified greater levels of intron retention in filaments compared to yeast, which correlated with reduced expression of the affected genes. Intriguingly, homozygous deletion of a gene encoding a spliceosome component important for filamentation (PRP19) caused even greater levels of intron retention compared with wild type and displayed globally dysregulated gene expression. This suggests that intron retention is a mechanism for fine-tuning gene expression during filamentation, with perturbations of the spliceosome exacerbating this process and blocking filamentation. Overall, this study unveils a novel biological process governing C. albicans filamentation, providing new insights into the complex regulation of this key virulence trait.IMPORTANCEFungal pathogens such as Candida albicans can cause serious infections with high mortality rates in immunocompromised individuals. When C. albicans is grown at temperatures encountered during human febrile episodes, yeast cells undergo a transition to filamentous cells, and this process is key to its virulence. Here, we expanded our understanding of how C. albicans undergoes filamentation in response to elevated temperature and identified many genes involved in mRNA splicing that positively regulate filamentation. Through transcriptome analyses, we found that intron retention is a mechanism for fine-tuning gene expression in filaments, and perturbation of the spliceosome exacerbates intron retention and alters gene expression substantially, causing a block in filamentation. This work adds to the growing body of knowledge on the role of introns in fungi and provides new insights into the cellular processes that regulate a key virulence trait in C. albicans.

The putative prenyltransferase Nus1 is required for filamentation in the human fungal pathogen Candida albicans

Candida albicans is a major fungal pathogen of humans that can cause serious systemic infections in vulnerable immunocompromised populations. One of its virulence attributes is its capacity to transition between yeast and filamentous morphologies, but our understanding of this process remains incomplete. Here, we analyzed data from a functional genomic screen performed with the C. albicans Gene Replacement And Conditional Expression collection to identify genes crucial for morphogenesis in host-relevant conditions. Through manual scoring of microscopy images coupled with analysis of each image using a deep learning-based method termed Candescence, we identified 307 genes important for filamentation in tissue culture medium at 37°C with 5% CO2. One such factor was orf19.5963, which is predicted to encode the prenyltransferase Nus1 based on sequence homology to Saccharomyces cerevisiae. We further showed that Nus1 and its predicted interacting partner Rer2 are important for filamentation in multiple liquid filament-inducing conditions as well as for wrinkly colony formation on solid agar. Finally, we highlight that Nus1 and Rer2 likely govern C. albicans morphogenesis due to their importance in intracellular trafficking, as well as maintaining lipid homeostasis. Overall, this work identifies Nus1 and Rer2 as important regulators of C. albicans filamentation and highlights the power of functional genomic screens in advancing our understanding of gene function in human fungal pathogens.

Saturation transposon mutagenesis enables genome-wide identification of genes required for growth and fluconazole resistance in the human fungal pathogen Cryptococcus neoformans

Fungi can cause devastating invasive infections, typically in immunocompromised patients. Treatment is complicated both by the evolutionary similarity between humans and fungi and by the frequent emergence of drug resistance. Studies in fungal pathogens have long been slowed by a lack of high-throughput tools and community resources that are common in model organisms. Here we demonstrate a high-throughput transposon mutagenesis and sequencing (TN-seq) system in Cryptococcus neoformans that enables genome-wide determination of gene essentiality. We employed a random forest machine learning approach to classify the Cryptococcus neoformans genome as essential or nonessential, predicting 1,465 essential genes, including 302 that lack human orthologs. These genes are ideal targets for new antifungal drug development. TN-seq also enables genome-wide measurement of the fitness contribution of genes to phenotypes of interest. As proof of principle, we demonstrate the genome-wide contribution of genes to growth in fluconazole, a clinically used antifungal. We show a novel role for the well-studied RIM101 pathway in fluconazole susceptibility. We also show that 5' insertions of transposons can drive sensitization of essential genes, enabling screenlike assays of both essential and nonessential components of the genome. Using this approach, we demonstrate a role for mitochondrial function in fluconazole sensitivity, such that tuning down many essential mitochondrial genes via 5' insertions can drive resistance to fluconazole. Our assay system will be valuable in future studies of C. neoformans, particularly in examining the consequences of genotypic diversity.

Protocol to evaluate translation inhibition in Candida species using fluorescence microscopy and flow cytometry

Translation inhibitors have therapeutic potential against Candida species. Here, we present a protocol to measure translation inhibition in Candida spp. We describe steps for employing an alkynylated methionine analog, L-homopropargylglycine (HPG), that becomes incorporated into newly synthesized proteins. We then detail procedures to perform a click reaction of the alkyne with a fluorescent azide, which is visualized using fluorescence microscopy and quantified by flow cytometry. For complete details on the use and execution of this protocol, please refer to Puumala et al.,1 Fu et al.,2 and Iyer et al.3.

Going fishing: how to get what you want from a fungal genetic screen

Five years ago, as I was starting my lab, I wrote about two functional genomic screens in fungi that had inspired me (mSphere 4:e00299-19, https://doi.org/10.1128/mSphere.00299-19). Now, I want to discuss some of the principles and questions that I ask myself and my students as we embark on our own screens. A good screen, whether it is a genetic or chemical screen, can be the starting point for new discovery and an excellent basis for the beginning of a scientific research project. However, screens are often criticized for being "fishing expeditions." To stretch this metaphor to the extreme, this is because people are worried that we do not know how to fish, that we will come home without any fish, bring home the wrong fish, or not know what to do with a fish if we caught it. How you set up the screen and analyze the results determines whether the screen will be useful. In this mini-review, and in the spirit of teaching a scientist to fish, I will discuss recent excellent fungal genetic and chemical screens that illustrate some of the key aspects of a successful screen.

Allosteric inhibition of tRNA synthetase Gln4 by N-pyrimidinyl-β-thiophenylacrylamides exerts highly selective antifungal activity

Candida species are among the most prevalent causes of systemic fungal infections, which account for ∼1.5 million annual fatalities. Here, we build on a compound screen that identified the molecule N-pyrimidinyl-β-thiophenylacrylamide (NP-BTA), which strongly inhibits Candida albicans growth. NP-BTA was hypothesized to target C. albicans glutaminyl-tRNA synthetase, Gln4. Here, we confirmed through in vitro amino-acylation assays NP-BTA is a potent inhibitor of Gln4, and we defined how NP-BTA arrests Gln4's transferase activity using co-crystallography. This analysis also uncovered Met496 as a critical residue for the compound's species-selective target engagement and potency. Structure-activity relationship (SAR) studies demonstrated the NP-BTA scaffold is subject to oxidative and non-oxidative metabolism, making it unsuitable for systemic administration. In a mouse dermatomycosis model, however, topical application of the compound provided significant therapeutic benefit. This work expands the repertoire of antifungal protein synthesis target mechanisms and provides a path to develop Gln4 inhibitors.

Integration of graph neural networks and genome-scale metabolic models for predicting gene essentiality

Genome-scale metabolic models are powerful tools for understanding cellular physiology. Flux balance analysis (FBA), in particular, is an optimization-based approach widely employed for predicting metabolic phenotypes. In model microbes such as Escherichia coli, FBA has been successful at predicting essential genes, i.e. those genes that impair survival when deleted. A central assumption in this approach is that both wild type and deletion strains optimize the same fitness objective. Although the optimality assumption may hold for the wild type metabolic network, deletion strains are not subject to the same evolutionary pressures and knock-out mutants may steer their metabolism to meet other objectives for survival. Here, we present FlowGAT, a hybrid FBA-machine learning strategy for predicting essentiality directly from wild type metabolic phenotypes. The approach is based on graph-structured representation of metabolic fluxes predicted by FBA, where nodes correspond to enzymatic reactions and edges quantify the propagation of metabolite mass flow between a reaction and its neighbours. We integrate this information into a graph neural network that can be trained on knock-out fitness assay data. Comparisons across different model architectures reveal that FlowGAT predictions for E. coli are close to those of FBA for several growth conditions. This suggests that essentiality of enzymatic genes can be predicted by exploiting the inherent network structure of metabolism. Our approach demonstrates the benefits of combining the mechanistic insights afforded by genome-scale models with the ability of deep learning to infer patterns from complex datasets.

Artificial Intelligence: Exploring utility in detection and typing of fungus with futuristic application in fungal cytology

Artificial Intelligence (AI) is an emerging, transforming and revolutionary technology that has captured attention worldwide. It is translating research into precision oncology treatments. AI can analyse large or big data sets requiring high-speed specialized computing solutions. The data are big in terms of volume and multimodal with the amalgamation of images, text and structure. Machine learning has identified antifungal drug targets, and taxonomic and phylogenetic classification of fungi based on sequence analysis is now available. Real-time identification tools and user-friendly mobile applications for identifying fungi have been discovered. Akin to histopathology, AI can be applied to fungal cytology. AI has been fruitful in cytopathology of the thyroid gland, breast, urine and uterine cervical lesions. AI has a huge scope in fungal cytology and would certainly bear fruit with its accuracy, reproducibility and capacity for handling big data. The purpose of this systematic review was to highlight the AI's utility in detecting fungus and its typing with a special focus on future application in fungal cytology. We also touch upon the basics of AI in brief.

Bibliometric analysis and thematic review of Candida pathogenesis: Fundamental omics to applications as potential antifungal drugs and vaccines

Invasive candidiasis caused by the pathogenic Candida yeast species has resulted in elevating global mortality. The pathogenicity of Candida spp. is not only originated from its primary invasive yeast-to-hyphal transition; virulence factors (transcription factors, adhesins, invasins, and enzymes), biofilm, antifungal drug resistance, stress tolerance, and metabolic adaptation have also contributed to a greater clinical burden. However, the current research theme in fungal pathogenicity could hardly be delineated with the increasing research output. Therefore, our study analysed the research trends in Candida pathogenesis over the past 37 years via a bibliometric approach against the Scopus and Web of Science databases. Based on the 3993 unique documents retrieved, significant international collaborations among researchers were observed, especially between Germany (Bernhard Hube) and the UK (Julian Naglik), whose focuses are on Candida proteinases, adhesins, and candidalysin. The prominent researchers (Neils Gow, Alistair Brown, and Frank Odds) at the University of Exeter and the University of Aberdeen (second top performing affiliation) UK contribute significantly to the mechanisms of Candida adaptation, tolerance, and stress response. However, the science mapping of co-citation analysis performed herein could not identify a hub representative of subsequent work since the clusters were semi-redundant. The co-word analysis that was otherwise adopted, revealed three research clusters; the cluster-based thematic analyses indicated the severeness of Candida biofilm and antifungal resistance as well as the elevating trend on molecular mechanism elucidation for drug screening and repurposing. Importantly, the in vivo pathogen adaptation and interactions with hosts are crucial for potential vaccine development.

TTD: Therapeutic Target Database describing target druggability information

Target discovery is one of the essential steps in modern drug development, and the identification of promising targets is fundamental for developing first-in-class drug. A variety of methods have emerged for target assessment based on druggability analysis, which refers to the likelihood of a target being effectively modulated by drug-like agents. In the therapeutic target database (TTD), nine categories of established druggability characteristics were thus collected for 426 successful, 1014 clinical trial, 212 preclinical/patented, and 1479 literature-reported targets via systematic review. These characteristic categories were classified into three distinct perspectives: molecular interaction/regulation, human system profile and cell-based expression variation. With the rapid progression of technology and concerted effort in drug discovery, TTD and other databases were highly expected to facilitate the explorations of druggability characteristics for the discovery and validation of innovative drug target. TTD is now freely accessible at: https://idrblab.org/ttd/.

From beer to breadboards: yeast as a force for biological innovation

The history of yeast Saccharomyces cerevisiae, aka brewer's or baker's yeast, is intertwined with our own. Initially domesticated 8,000 years ago to provide sustenance to our ancestors, for the past 150 years, yeast has served as a model research subject and a platform for technology. In this review, we highlight many ways in which yeast has served to catalyze the fields of functional genomics, genome editing, gene-environment interaction investigation, proteomics, and bioinformatics-emphasizing how yeast has served as a catalyst for innovation. Several possible futures for this model organism in synthetic biology, drug personalization, and multi-omics research are also presented.

Respiration supports intraphagosomal filamentation and escape of Candida albicans from macrophages

IMPORTANCE

Candida albicans is a leading human fungal pathogen that often causes life-threatening infections in immunocompromised individuals. The ability of C. albicans to transition between yeast and filamentous forms is key to its virulence, and this occurs in response to many host-relevant cues, including engulfment by host macrophages. While previous efforts identified C. albicans genes required for filamentation in other conditions, the genes important for this morphological transition upon internalization by macrophages remained largely enigmatic. Here, we employed a functional genomic approach to identify genes that enable C. albicans filamentation within macrophages and uncovered a role for the mitochondrial ribosome, respiration, and the SNF1 AMP-activated kinase complex. Additionally, we showed that glucose uptake and glycolysis by macrophages support C. albicans filamentation. This work provides insights into the metabolic dueling that occurs during the interaction of C. albicans with macrophages and identifies vulnerabilities in C. albicans that could serve as promising therapeutic targets.

Predicting Chronic Hyperplastic Candidiasis Retro-Angular Mucosa Using Machine Learning

Chronic hyperplastic candidiasis (CHC) presents a distinctive and relatively rare form of oral candidal infection characterized by the presence of white or white-red patches on the oral mucosa. Often mistaken for leukoplakia or erythroleukoplakia due to their appearance, these lesions display nonhomogeneous textures featuring combinations of white and red hyperplastic or nodular surfaces. Predominant locations for such lesions include the tongue, retro-angular mucosa, and buccal mucosa. This paper aims to investigate the potential influence of specific anatomical locations, retro-angular mucosa, on the development and occurrence of CHC. By examining the relationship between risk factors, we present an approach based on machine learning (ML) to predict the location of CHC occurrence. In this way, we employ Gradient Boosting Regression (GBR) to classify CHC lesion locations based on important risk factors. This estimator can serve both research and diagnostic purposes effectively. The findings underscore that the proposed ML technique can be used to predict the occurrence of CHC in retro-angular mucosa compared to other locations. The results also show a high rate of accuracy in predicting lesion locations. Performance assessment relies on Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared (R2), and Mean Absolute Error (MAE), consistently revealing favorable results that underscore the robustness and dependability of our classification method. Our research contributes valuable insights to the field, enhancing diagnostic accuracy and informing treatment strategies.

Confronting antifungal resistance, tolerance, and persistence: Advances in drug target discovery and delivery systems

Human pathogenic fungi pose a serious threat to human health and safety. Unfortunately, the limited number of antifungal options is exacerbated by the continuous emergence of drug-resistant variants, leading to frequent drug treatment failures. Recent studies have also highlighted the clinical importance of other modes of fungal survival of antifungal treatment, including drug tolerance and persistence, pointing to the complexity of the fungal response to antifungal drugs. A lack of understanding of the fungal drug response has hampered the identification of new targets, the development of alternative antifungal strategies and the design of appropriate delivery systems. In this review we summarize recent advances in the study of antifungal resistance, tolerance and persistence, with an emphasis on promising drug targets and drug delivery systems that may yield important insights into the development of new or improved antifungal therapies against fungal infections.

The Ypk1 protein kinase signaling pathway is rewired and not essential for viability in Candida albicans

Protein kinases are central components of almost all signaling pathways that control cellular activities. In the model organism Saccharomyces cerevisiae, the paralogous protein kinases Ypk1 and Ypk2, which control membrane lipid homeostasis, are essential for viability, and previous studies strongly indicated that this is also the case for their single ortholog Ypk1 in the pathogenic yeast Candida albicans. Here, using FLP-mediated inducible gene deletion, we reveal that C. albicans ypk1Δ mutants are viable but slow-growing, explaining prior failures to obtain null mutants. Phenotypic analyses of the mutants showed that the functions of Ypk1 in regulating sphingolipid biosynthesis and cell membrane lipid asymmetry are conserved, but the consequences of YPK1 deletion are milder than in S. cerevisiae. Mutational studies demonstrated that the highly conserved PDK1 phosphorylation site T548 in its activation loop is essential for Ypk1 function, whereas the TORC2 phosphorylation sites S687 and T705 at the C-terminus are important for Ypk1-dependent resistance to membrane stress. Unexpectedly, Pkh1, the single C. albicans orthologue of Pkh1/Pkh2, which mediate Ypk1 phosphorylation at the PDK1 site in S. cerevisiae, was not required for normal growth of C. albicans under nonstressed conditions, and Ypk1 phosphorylation at T548 was only slightly reduced in pkh1Δ mutants. We found that another protein kinase, Pkh3, whose ortholog in S. cerevisiae cannot substitute Pkh1/2, acts redundantly with Pkh1 to activate Ypk1 in C. albicans. No phenotypic effects were observed in cells lacking Pkh3 alone, but pkh1Δ pkh3Δ double mutants had a severe growth defect and Ypk1 phosphorylation at T548 was completely abolished. These results establish that Ypk1 is not essential for viability in C. albicans and that, despite its generally conserved function, the Ypk1 signaling pathway is rewired in this pathogenic yeast and includes a novel upstream kinase to activate Ypk1 by phosphorylation at the PDK1 site.

Imaging-Based Screening Identifies Modulators of the eIF3 Translation Initiation Factor Complex in Candida albicans

Fungal pathogens like Candida albicans can cause devastating human disease. Treatment of candidemia is complicated by the high rate of resistance to common antifungal therapies. Additionally, there is host toxicity associated with many antifungal compounds due to the conservation between essential mammalian and fungal proteins. An attractive new approach for antimicrobial development is to target virulence factors: non-essential processes that are required for the organism to cause disease in human hosts. This approach expands the potential target space while reducing the selective pressure toward resistance, as these targets are not essential for viability. In C. albicans, a key virulence factor is the ability to transition to hyphal morphology. We developed a high-throughput image analysis pipeline to distinguish between yeast and filamentous growth in C. albicans at the single cell level. Based on this phenotypic assay, we screened the FDA drug repurposing library of 2,017 compounds for their ability to inhibit filamentation and identified 33 compounds that block the hyphal transition in C. albicans with IC50 values ranging from 0.2 to 150 μM. Multiple compounds showed a phenyl sulfone chemotype, prompting further analysis. Of these phenyl sulfones, NSC 697923 displayed the most efficacy, and by selecting for resistant mutants, we identified eIF3 as the target of NSC 697923 in C. albicans.

Leveraging artificial intelligence in the fight against infectious diseases

Despite advances in molecular biology, genetics, computation, and medicinal chemistry, infectious disease remains an ominous threat to public health. Addressing the challenges posed by pathogen outbreaks, pandemics, and antimicrobial resistance will require concerted interdisciplinary efforts. In conjunction with systems and synthetic biology, artificial intelligence (AI) is now leading to rapid progress, expanding anti-infective drug discovery, enhancing our understanding of infection biology, and accelerating the development of diagnostics. In this Review, we discuss approaches for detecting, treating, and understanding infectious diseases, underscoring the progress supported by AI in each case. We suggest future applications of AI and how it might be harnessed to help control infectious disease outbreaks and pandemics.

Imaging-based screening identifies modulators of the eIF3 translation initiation factor complex in Candida albicans

Fungal pathogens like Candida albicans can cause devastating human disease. Treatment of candidemia is complicated by the high rate of resistance to common antifungal therapies. Additionally, there is host toxicity associated with many antifungal compounds due to the conservation between essential mammalian and fungal proteins. An attractive new approach for antimicrobial development is to target virulence factors: non-essential processes that are required for the organism to cause disease in human hosts. This approach expands the potential target space while reducing the selective pressure towards resistance, as these targets are not essential for viability. In C. albicans, a key virulence factor is the ability to transition to hyphal morphology. We developed a high-throughput image analysis pipeline to distinguish between yeast and filamentous growth in C. albicans at the single cell level. Based on this phenotypic assay, we screened the FDA drug repurposing library of 2,017 compounds for their ability to inhibit filamentation and identified 33 compounds that block the hyphal transition in C. albicans with IC 50 values ranging from 0.2 to 150 µM. Multiple compounds showed a phenyl vinyl sulfone chemotype, prompting further analysis. Of these phenyl vinyl sulfones, NSC 697923 displayed the most efficacy, and by selecting for resistant mutants, we identified eIF3 as the target of NSC 697923 in C. albicans .

Functional genomic analysis of Candida albicans protein kinases reveals modulators of morphogenesis in diverse environments

Candida albicans is a leading cause of mycotic infection. The ability to transition between yeast and filamentous forms is critical to C. albicans virulence and complex signaling pathways regulate this process. Here, we screened a C. albicans protein kinase mutant library in six environmental conditions to identify regulators of morphogenesis. We identified the uncharacterized gene orf19.3751 as a negative regulator of filamentation and follow-up investigations implicated a role for orf19.3751 in cell cycle regulation. We also uncovered a dual role for the kinases Ire1 and protein kinase A (Tpk1 and Tpk2) in C. albicans morphogenesis, specifically as negative regulators of wrinkly colony formation on solid medium but positive regulators of filamentation in liquid medium. Further analyses suggested Ire1 modulates morphogenesis in both media states in part through the transcription factor Hac1 and in part through independent mechanisms. Overall, this work provides insights into the signaling governing morphogenesis in C. albicans.

Ent2 Governs Morphogenesis and Virulence in Part through Regulation of the Cdc42 Signaling Cascade in the Fungal Pathogen Candida albicans

The ability to transition between yeast and filamentous growth states is critical for virulence of the leading human fungal pathogen Candida albicans. Large-scale genetic screens have identified hundreds of genes required for this morphological switch, but the mechanisms by which many of these genes orchestrate this developmental transition remain largely elusive. In this study, we characterized the role of Ent2 in governing morphogenesis in C. albicans. We showed that Ent2 is required for filamentous growth under a wide range of inducing conditions and is also required for virulence in a mouse model of systemic candidiasis. We found that the epsin N-terminal homology (ENTH) domain of Ent2 enables morphogenesis and virulence and does so via a physical interaction with the Cdc42 GTPase-activating protein (GAP) Rga2 and regulation of its localization. Further analyses revealed that overexpression of the Cdc42 effector protein Cla4 can overcome the requirement for the ENTH-Rga2 physical interaction, indicating that Ent2 functions, at least in part, to enable proper activation of the Cdc42-Cla4 signaling pathway in the presence of a filament-inducing cue. Overall, this work characterizes the mechanism by which Ent2 regulates hyphal morphogenesis in C. albicans, unveils the importance of this factor in enabling virulence in an in vivo model of systemic candidiasis and adds to the growing understanding of the genetic control of a key virulence trait. IMPORTANCE Candida albicans is a leading human fungal pathogen that can cause life-threatening infections in immunocompromised individuals, with mortality rates of ~40%. The ability of this organism to grow in both yeast and filamentous forms is critical for the establishment of systemic infection. Genomic screens have identified many genes required for this morphological transition, yet our understanding of the mechanisms that regulate this key virulence trait remains incomplete. In this study, we characterized Ent2 as a core regulator of C. albicans morphogenesis. We show that Ent2 regulates hyphal morphogenesis through an interaction between its ENTH domain and the Cdc42 GAP, Rga2, which signals through the Cdc42-Cla4 signaling pathway. Finally, we show that the Ent2 protein, and specifically its ENTH domain, is required for virulence in a mouse model of systemic candidiasis. Overall, this work identifies Ent2 as a key regulator of filamentation and virulence in C. albicans.

Development and applications of a CRISPR activation system for facile genetic overexpression in Candida albicans

For the fungal pathogen Candida albicans, genetic overexpression readily occurs via a diversity of genomic alterations, such as aneuploidy and gain-of-function mutations, with important consequences for host adaptation, virulence, and evolution of antifungal drug resistance. Given the important role of overexpression on C. albicans biology, it is critical to develop and harness tools that enable the analysis of genes expressed at high levels in the fungal cell. Here, we describe the development, optimization, and application of a novel, single-plasmid-based CRISPR activation (CRISPRa) platform for targeted genetic overexpression in C. albicans, which employs a guide RNA to target an activator complex to the promoter region of a gene of interest, thus driving transcriptional expression of that gene. Using this system, we demonstrate the ability of CRISPRa to drive high levels of gene expression in C. albicans, and we assess optimal guide RNA targeting for robust and constitutive overexpression. We further demonstrate the specificity of the system via RNA sequencing. We highlight the application of CRISPR activation to overexpress genes involved in pathogenesis and drug susceptibility, and contribute toward the identification of novel phenotypes. Consequently, this tool will facilitate a broad range of applications for the study of C. albicans genetic overexpression.

InParanoiDB 9: Ortholog Groups for Protein Domains and Full-Length Proteins

Prediction of orthologs is an important bioinformatics pursuit that is frequently used for inferring protein function and evolutionary analyses. The InParanoid database is a well known resource of ortholog predictions between a wide variety of organisms. Although orthologs have historically been inferred at the level of full-length protein sequences, many proteins consist of several independent protein domains that may be orthologous to domains in other proteins in a way that differs from the full-length protein case. To be able to capture all types of orthologous relations, conventional full-length protein orthologs can be complemented with orthologs inferred at the domain level. We here present InParanoiDB 9, covering 640 species and providing orthologs for both protein domains and full-length proteins. InParanoiDB 9 was built using the faster InParanoid-DIAMOND algorithm for orthology analysis, as well as Domainoid and Pfam to infer orthologous domains. InParanoiDB 9 is based on proteomes from 447 eukaryotes, 158 bacteria and 35 archaea, and includes over one billion predicted ortholog groups. A new website has been built for the database, providing multiple search options as well as visualization of groups of orthologs and orthologous domains. This release constitutes a major upgrade of the InParanoid database in terms of the number of species as well as the new capability to operate on the domain level. InParanoiDB 9 is available at https://inparanoidb.sbc.su.se/.

Calcineurin Inhibitors Synergize with Manogepix to Kill Diverse Human Fungal Pathogens

Invasive fungal infections have mortality rates of 30–90%, depending on patient co-morbidities and the causative pathogen. The frequent emergence of drug resistance reduces the efficacy of currently approved treatment options, highlighting an urgent need for antifungals with new modes of action. Addressing this need, fosmanogepix (N-phosphonooxymethylene prodrug of manogepix; MGX) is the first in a new class of gepix drugs, and acts as a broad-spectrum, orally bioavailable inhibitor of the essential fungal glycosylphosphatidylinositol (GPI) acyltransferase Gwt1. MGX inhibits the growth of diverse fungal pathogens and causes accumulation of immature GPI-anchored proteins in the fungal endoplasmic reticulum. Relevant to the ongoing clinical development of fosmanogepix, we report a synergistic, fungicidal interaction between MGX and inhibitors of the protein phosphatase calcineurin against important human fungal pathogens. To investigate this synergy further, we evaluated a library of 124 conditional expression mutants covering 95% of the genes encoding proteins involved in GPI-anchor biosynthesis or proteins predicted to be GPI-anchored. Strong negative chemical-genetic interactions between the calcineurin inhibitor FK506 and eleven GPI-anchor biosynthesis genes were identified, indicating that calcineurin signalling is required for fungal tolerance to not only MGX, but to inhibition of the GPI-anchor biosynthesis pathway more broadly. Depletion of these GPI-anchor biosynthesis genes, like MGX treatment, also exposed fungal cell wall (1→3)-β-D-glucans. Taken together, these findings suggest the increased risk of invasive fungal infections associated with use of calcineurin inhibitors as immunosuppressants may be mitigated by their synergistic fungicidal interaction with (fos)manogepix and its ability to enhance exposure of immunostimulatory glucans.

Application of the Mutant Libraries for Candida albicans Functional Genomics

Candida albicans is a typical opportunistic pathogen in humans that causes serious health risks in clinical fungal infections. The construction of mutant libraries has made remarkable developments in the study of C. albicans molecular and cellular biology with the ongoing advancements of gene editing, which include the application of CRISPR-Cas9 and novel high-efficient transposon. Large-scale genetic screens and genome-wide functional analysis accelerated the investigation of new genetic regulatory mechanisms associated with the pathogenicity and resistance to environmental stress in C. albicans. More importantly, sensitivity screening based on C. albicans mutant libraries is critical for the target identification of novel antifungal compounds, which leads to the discovery of Sec7p, Tfp1p, Gwt1p, Gln4p, and Erg11p. This review summarizes the main types of C. albicans mutant libraries and interprets their applications in morphogenesis, biofilm formation, fungus-host interactions, antifungal drug resistance, and target identification.

Antifungal discovery

Fungi have a profound impact on human health, leading to billions of infections and millions of deaths worldwide each year. Exacerbating the public health burden is the continued emergence of drug-resistant fungal pathogens coupled with a dearth of treatment options to combat serious infections. Despite this health threat, scientific advances in chemistry, genetics, and biochemistry methodologies have enabled novel antifungal compounds to be discovered. Here, we describe current approaches for the discovery and characterization of novel antifungals, focusing on the identification of novel chemical matter and elucidation of the cellular target of bioactive compounds, followed by a review of the most promising emerging therapies in the antifungal-development pipeline.

The importance of antimicrobial resistance in medical mycology

Prior to the SARS-CoV-2 pandemic, antibiotic resistance was listed as the major global health care priority. Some analyses, including the O'Neill report, have predicted that deaths due to drug-resistant bacterial infections may eclipse the total number of cancer deaths by 2050. Although fungal infections remain in the shadow of public awareness, total attributable annual deaths are similar to, or exceeds, global mortalities due to malaria, tuberculosis or HIV. The impact of fungal infections has been exacerbated by the steady rise of antifungal drug resistant strains and species which reflects the widespread use of antifungals for prophylaxis and therapy, and in the case of azole resistance in Aspergillus, has been linked to the widespread agricultural use of antifungals. This review, based on a workshop hosted by the Medical Research Council and the University of Exeter, illuminates the problem of antifungal resistance and suggests how this growing threat might be mitigated.

Genomic Approaches to Antifungal Drug Target Identification and Validation

The last several decades have witnessed a surge in drug-resistant fungal infections that pose a serious threat to human health. While there is a limited arsenal of drugs that can be used to treat systemic infections, scientific advances have provided renewed optimism for the discovery of novel antifungals. The development of chemical-genomic assays using Saccharomyces cerevisiae has provided powerful methods to identify the mechanism of action of molecules in a living cell. Advances in molecular biology techniques have enabled complementary assays to be developed in fungal pathogens, including Candida albicans and Cryptococcus neoformans. These approaches enable the identification of target genes for drug candidates, as well as genes involved in buffering drug target pathways. Here, we examine yeast chemical-genomic assays and highlight how such resources can be utilized to predict the mechanisms of action of compounds, to study virulence attributes of diverse fungal pathogens, and to bolster the antifungal pipeline.

Molecular analysis and essentiality of Aro1 shikimate biosynthesis multi-enzyme in Candida albicans

In the human fungal pathogen Candida albicans, ARO1 encodes an essential multi-enzyme that catalyses consecutive steps in the shikimate pathway for biosynthesis of chorismate, a precursor to folate and the aromatic amino acids. We obtained the first molecular image of C. albicans Aro1 that reveals the architecture of all five enzymatic domains and their arrangement in the context of the full-length protein. Aro1 forms a flexible dimer allowing relative autonomy of enzymatic function of the individual domains. Our activity and in cellulo data suggest that only four of Aro1's enzymatic domains are functional and essential for viability of C. albicans, whereas the 3-dehydroquinate dehydratase (DHQase) domain is inactive because of active site substitutions. We further demonstrate that in C. albicans, the type II DHQase Dqd1 can compensate for the inactive DHQase domain of Aro1, suggesting an unrecognized essential role for this enzyme in shikimate biosynthesis. In contrast, in Candida glabrata and Candida parapsilosis, which do not encode a Dqd1 homolog, Aro1 DHQase domains are enzymatically active, highlighting diversity across Candida species.

Functional analysis of the Candida albicans kinome reveals Hrr25 as a regulator of antifungal susceptibility

Candida albicans is a leading cause of death due to systemic fungal infections. Poor patient outcomes are attributable to the limited number of antifungal classes and the increasing prevalence of drug resistance. Protein kinases have emerged as rewarding targets in the development of drugs for diverse diseases, yet kinases remain untapped in the quest for new antifungals. Here, we performed a comprehensive analysis of the C. albicans kinome to identify genes for which loss-of-function confers hypersensitivity to the two most widely deployed antifungals, echinocandins and azoles. Through this analysis, we found a role for the casein kinase 1 (CK1) homologue Hrr25 in regulating tolerance to both antifungals as well as target-mediated echinocandin resistance. Follow-up investigations established that Hrr25 regulates these responses through its interaction with the SBF transcription factor. Thus, we provide insights into the circuitry governing cellular responses to antifungals and implicate Hrr25 as a key mediator of drug resistance.

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Screening Candida albicans kinase mutants reveals 47 regulators of antifungal tolerance

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Hrr25 is important for growth and cell wall/membrane stress tolerance

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Hrr25 enables target-mediated echinocandin resistance

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Hrr25 interacts with the SBF transcription factor complex

Machine learning approaches for biomolecular, biophysical, and biomaterials research

A fluent conversation with a virtual assistant, person-tailored news feeds, and deep-fake images created within seconds-all those things that have been unthinkable for a long time are now a part of our everyday lives. What these examples have in common is that they are realized by different means of machine learning (ML), a technology that has fundamentally changed many aspects of the modern world. The possibility to process enormous amount of data in multi-hierarchical, digital constructs has paved the way not only for creating intelligent systems but also for obtaining surprising new insight into many scientific problems. However, in the different areas of biosciences, which typically rely heavily on the collection of time-consuming experimental data, applying ML methods is a bit more challenging: Here, difficulties can arise from small datasets and the inherent, broad variability, and complexity associated with studying biological objects and phenomena. In this Review, we give an overview of commonly used ML algorithms (which are often referred to as "machines") and learning strategies as well as their applications in different bio-disciplines such as molecular biology, drug development, biophysics, and biomaterials science. We highlight how selected research questions from those fields were successfully translated into machine readable formats, discuss typical problems that can arise in this context, and provide an overview of how to resolve those encountered difficulties.

The role of Candida albicans stress response pathways in antifungal tolerance and resistance

Human fungal pathogens are the causative agents of devastating diseases across the globe, and the increasing prevalence of drug resistance threatens to undermine the already limited treatment options. One prominent pathogen is the opportunistic fungus Candida albicans, which can cause both superficial and serious systemic infections in immunocompromised individuals. C. albicans antifungal drug resistance and antifungal tolerance are supported by diverse and expansive cellular stress response pathways. Some of the major players are the Ca²⁺-calmodulin-activated phosphatase calcineurin, the protein kinase C cell wall integrity pathway, and the molecular chaperone heat shock protein 90. Beyond these core signal transducers, several other enzymes and transcription factors have been implicated in both tolerance and resistance. Here, we highlight some of the major stress response pathways, key advances in identifying chemical matter to inhibit these pathways, and implications for C. albicans persistence in the host.

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Candida albicans can cause superficial and serious systemic infections in humans

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Stress response pathways regulate C. albicans antifungal resistance and tolerance

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Stress response regulators include calcineurin, Pkc1, Hsp90, and many others

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Stress response inhibitors could reduce the likelihood of fungi persisting in humans