Reconstruction and exploitation of a dedicated Genome-Scale Metabolic Model of the human pathogen C. neoformans

C. neoformans is notorious for causing severe pulmonary and central nervous system infections, particularly in immunocompromised patients. High mortality rates, associated with its tropism and adaptation to the brain microenvironment and its drug resistance profile, makes this pathogen a public health threat and a World Health Organization (WHO) priority. In this study, we reconstructed GSMM iRV890 for C. neoformans var. grubii , providing a promising platform for the comprehensive understanding of the unique metabolic features of C. neoformans, and subsequently shedding light on its complex tropism for the brain microenvironment and potentially informing the discovery of new drug targets. The GSMM iRV890 model is openly available in the SBML format, and underwent validation using experimental d ata for nitrogen and carbon assimilation, as well as specific gro wth and glucose consumption rates. Based on the comparison with GSMMs available for other pathogenic yeasts, unique metabolic features were predicted for C. neoformans, .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 2 including key pathways shaping the dynamics between C. neoformans and the human host, and underlying its adaptation to the brain environment. Finally, predicted essential genes from the validated model are explored herein as potential novel antifungal drug targets.

C. neoformans, Global stoichiometric model, Drug targets, Metabolic features, Neurotropism. 1. Introduction Cryptococcal meningitis is a disease caused by a few pathogenic basidiomycetous yeast species, namely Cryptococcus neoformans (C. neoformans) and Cryptococcus gatii . Cryptococcosis is caused by three Cryptococcus species/variants, C. neoformans var. grubii (serotype A), responsible for 95% of Cryptococcus infections worldwide [1]; C. neoformans var. neoformans (serotype D) and Cryptococcus gattii (serotypes B and C) geographically restricted to tropical and/or subtropical regions [2]. These species are notorious for inducing severe pulmonary and central nervous system infections [3]. While these pathogens are harmless in healthy individuals, they poses a serious threat to immunocompromised patients, especially those with acquired immunodeficiency syndrome (AIDS) or those undergoing immunosuppressive therapies, causing severe meningoe ncephalitis and other serious neurological complications [4–6]. The latest systematic review, using data from more than 120 countries, estimates that cryptoc occal meningitis affects 190 000 people worldwide annually, being associated with a mortality rate of 76% [7]. Cryptococcal infections are commonly treated with combination therapy, usually flucytosine in combination with amphotericin B in a first induction stage, followed by consolidation and long -term maintenance with high dose .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 3 fluconazole [8]. Anti-cryptococcal monotherapy is not considered optimal, as it carries the risk of drug resistance [9]. Still, fluconazole monotherapy is still used, mostly due to limited drug access. An increase in fluconazole resistance among C. neoformans isolates was observed in past decades [10,11]. Fluconazole resistance is particularly notorious in isolates from relapse disease [12]. Despite verified in vitro susceptibility, echinocandins are not used clinically to treat cryptococcosis due to intrinsic resistance in vivo [13], attributed to echinocandins inability to penetrate the blood -brain barrier. Another possible contributor to echinocandin resistance in Cryptococcus species is fungal cell wall melanization, through the action of a fungal laccase, which uses the L -DOPA and dopamine found in the human brain as precursors [14]. Melanin is an important virulence factor in C. neoformans since it can neutralize oxidative stress radicals [15], as well as some toxic compounds, including some antifungal drugs, such as caspofungin and amphotericin B [16,17]. C. neoformans is widely spread in the environment, with worldwide distribution, in bird guano, soils and trees. Fungal particles are then inhaled by humans and other mammals [2]. This pathogen is characterized by their high resistance to harsh environments in nature and in mammalian hosts [18], and after inhalation into the host’s lungs, Cryptococccus can stay in a dormant latent granulomatous form for long periods of time [3]. However, tropism for the central nervous system is not yet fully understood [2,3]. Despite being a public health threat and a WHO priority pathogen [19], C. neoformans still has many aspects of its peculiar metabolism associated with the central nervous system and interactions with the host that remain poorly understood [20]. In this work, iRV890 the first reconstructed GSMM for the human pathogen C. neoformans var. grubii, a frequent variant of these pathogenic species, is presented. To facilitate usage by other researchers, the model is provided in the widely used SBML format. Model validation was conducted using experimental data for .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 4 nitrogen and carbon assimilation from phenotypic arrays covering 222 different sources [21]. Specific growth and glucose consumption rates were experimentally determined in order to quantitatively validate the model’s predictive power. A set of essential genes derived from the validated model is predicted and discussed in terms of their potential as novel antifungal drug targets. An additional comparison, with GSMM´s for other pathogenic yeast species and S. cerevisiae was performed regarding the gene essen tiality prediction and unique metabolic features of C. neoformans. Some peculiar characteristics and pathways of this fungus relevant to its pathogenicity are also discussed based on our findings. The iRV890 model provides a promising platform for global elucidation of the metabolic features of C. neoformans var. grubii , with expected impact in guiding the identification of new drug targets and understanding the complex metabolism of this pathogen in the context of the human brain. 2. Materials and Methods 2.1. Model Development The genome-scale metabolic model of C. neoformans var. grubii H99 , designated as iRV890, was reconstructed using merlin 4.0.5 [22] following the methodology described elsewhere [23] and OptFlux 3.0 [24], for curation and subsequent validation stages. All computational analyses were executed utilizing the IBM CPLEX 12.10 solver. 2.2. Genome Annotation and Assembling of the Metabolic Network The genome sequence of the C. neoformans var. grubii was retrieved from NCBI’s Assembly database, with the accession number ASM1180120v1 [25] and the .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 5 Taxonomy ID 235443 from NCBI´s Taxonomy database [26]. The genome-wide functional annotation was based on taxonomy and frequency of similar sequences through remote DIAMOND alignment [27] and similarity searches using the UniProtKB/Swiss -Prot database. Draft network assembly relied on protein-reaction associations available in the KEGG (Kyoto Encyclopedia of Genes and Genomes) database [28], with all reactions categorized as spontaneous or non-enzymatic also incorporated in the initial draft model. Hit sele ction was performed as described elsewhere [23] and phylogenetic proximity was implemented based on a phylogenetic tree from literature [22], this process automated via the “Automatic workflow” merlin tool and then integrated into the draft model [22]. 2.3. Reversibility, Directionality and Balancing Reaction reversibility and stoichiometry curation involved a multi -step process combining both automated and manual efforts. Initially, merlin was used to assist in correcting the direction and reversibility of reactions, utilizing references from remote databases like eQuilibrator [29] to predict reaction directionality, as described by Dias et al. [23]. This was followed by extensive manual curation, exploiting databases such as MetaCyc [30], Brenda [31], UniProt [32], FungiDB [33], RHEA [34], KEGG [30] and existing literature, in order to ensure that all reactions in the network are balanced, and with the correct directionality. All manually edited reactions can be found in Supplementary Data 1. 2.4. Compartmentalization and Transport reactions This model includes four compartments: extracellular, cytoplasm, mitochondrion, and peroxisome and one intercompartment, the cytoplasmic .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 6 membrane. The prediction of compartments for each enzyme was performed using the DeepLoc - 2.0 [35] and directly imported to merlin. The transport reactions were automatically generated by TranSyT [36], a tool integrated in merlin, based on the public database TCDB [37]. Additional transport reactions across internal and external membranes for common metabolites, such as H 2O, CO2, and NH3, often carried out without a transporter, were added to the model with no gene association. 2.5. Biomass Equation The biomass formation was depicted through an equation including proteins, DNA, RNA, lipids, carbohydrates, and cofactors, with detailed composition information for each macromolecule sourced from literature or experimental data. All calculations were perf ormed as in previously described methodology [38] and are detailed in Supplementary Data 1. ATP requirements for biomass production and growth -associated maintenance (GAM) were added to the biomass equation with a value of 25.65 mmol ATP/gDCW, based on the ATP requirements for the biosynthesis of cell polymers as reported in [39], and ATP requirements for non -growth-associated maintenance (NGAM) was inserted in the model by an equation with specific fixed flux boundaries inferred from Candida tropicalis [39]. The theoretical phosphorus -to-oxygen ratio used in the Saccharomyces cerevisiae iMM904 metabolic model was applied to our model adding three generic reactions contributing to this ratio: Reaction R00081: 1.0 Oxygenmito + 4.0 Ferrocytochrome cmito + 6.0 H+ mito ↔ 2.0 H2Omito + 4.0 Ferricytochrome cmito + 6.0 H+cyto, (1) Reaction R_Ubiquinol_Cytochrome_Reductase: 1.0 Ubiquinolmito + 2.0 Ferricytochrome cmito + 1.5 H+ mito ↔ 1.0 Ubiquinonemito + 2.0 Ferrocytochrome cmito + 1.5 H+cyto, (2) .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 7 Reaction T_ATP_Synthase: 1.0 Orthophosphatemito + 1.0 ADPmito + 3.0 H+ cyto ↔ 1.0 ATPmito + 1.0 H2Omito + 3.0 H+mito, (3) The final balance reaction: 3.0 Orthophosphatemito + 1.0 Oxygenmito + 3.0 ADPmito + 2.0 Ubiquinoolmito ↔ 3.0 ATPmito + 5.0 H2Omito + 2.0 Ubiquinonemito (4) 2.7 Network simulation and model curation During the model reconstruction process, an extensive manual curation was needed in order to correct gaps in some pathways, due to incorrect reversibility, incomplete reactions, annotation errors, and blocked metabolites. Each case was meticulously inspected and studied, and reactions were edited, manually added to, or removed from the model based on evidence from the literature or deposited on databases such as KEGG pathways, MetaCyc, FungiDB etc. The detailed list of all the performed alterations can be found in Supplementary Data 1. During this process, merlin’s “ Find blocked reactions ” was used to assist and accelerate the process. Additionally, BioISO, a tool based on the Constraints - Based Reconstruction and Analysis (COBRA) and Flux Balance Analysis (FBA) [40] frameworks, also integrated in merlin, assisted in the process of identifying potential errors in the network and accelerated the process of correcting the gaps. 2.8 Model Validation 2.8.1. Strains and Growth Media C. neoformans var. grubii H99E strain, from the laboratory of Jennifer Lodge was obtained from the Fungal Genomic Stock Center, and routinely maintained in .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 8 Yeast extract –Peptone–Dextrose (YPD), containing: 20 g/L glucose (Merck, Darmstadt, Germany), 20 g/L peptone (Merck, Darmstadt, Germany), and 10 g/L yeast extract (Merck, Darmstadt, Germany). The parental KN99 and derived KN99_ΔCNAG_02553 were obtained fro m the deletion library created by the Madhani laboratory [41], through Fungal Genetics Stock Center, and grown on YNB medium, containing 1.7 g/L Yeast Nitrogen Base, without amino acids (Difco BD, England, United Kingdom) and 20 g/L inositol, used as carbon source. Synthetic minimal media (SMM) was used for batch cultivation experiments used to validate model predictions, SMM including: 20g/L glucose (Merck, Darmstadt, Germany), 2.7 g/L ammonium sulphate (Merck, Darmstadt, Germany), 0.05 g/L magnesium sulphate (Riddle-de-Haen), 2 g/L potassium dihydrogen phosphate (Panreac, Barcelona, Spain), 0.5 g/L calcium chloride (Merck, Darmstadt, Germany), and 100 µg/L biotin (Sigma). 2.8.2. Aerobic Batch Cultivation C. neoformans var. grubii cells were batch cultivated in Erlenmeyer flasks containing 250ml of SMM or YNB medium, at 30 ºC (250 rpm). Exponential phase inocula, with an Optical Density (OD) (Hitachi u2001) at 600nm of 0.3, were prepared and cells were transferred to Erlenmeyer flas ks containing 250ml of fresh medium and cultivated at 30 ºC with orbital agitation (250 rpm) for the duration of the experiment. 2.8.3. Cell Density, Dry Weight, and Metabolite Concentration Assessment Throughout cell cultivation in SMM, 4 mL samples were collected every two hours for subsequent quantification of biomass and extracellular metabolites. Cell density was monitored by measuring OD600nm. For dry weight determination, culture samples were centrifuged at 13,000 rpm for 3 minutes, and the resulting .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 9 pellets were freeze -dried for 72 hours at -80 ◦C before being weighed. Extracellular metabolites, including glucose, ethanol, glycerol, and acetic acid, were identified and quantified by High -performance liquid chromatography (HPLC) on an Aminex HPX-87 H Ion Exchange chromatography column, eluted with 0.0005 M H2SO4 at a flow rate of 0.6 mL/min at room temperature. Samples were analyzed in triplicate, and concentrations were determined using appropriate calibration curves. During the exponential growth phase, the specific growth rate, specific glucose consumption rate, and specific production rates of ethanol, glycerol, and acetic acid were calculated as described elsewhere [42]. 2.8.4. Network simulation and analysis All the phenotype simulations were performed with Flux Balance Analysis (FBA) in OptFlux 3.0 using the IBM¨CPLEX solver, including: gene and reaction essentiality; growth assessment; metabolite production and consumption; and carbon and nitrogen source utilization. For gene and reaction essentiality, in silico growth was simulated in environmental conditions mimicking RPMI medium and a biomass flux lower than 5% of the wild -type strain, after the respective gene/reaction knockout, was considered the threshold for essentiality. Gene and reaction knockout was simulated by restraining its corresponding flux bounds to zero. 3. Results and Discussion 3.1. Model characteristics The C. neoformans var. grubii genome-scale metabolic model reconstructed herein, and denominated iRV890, comprises 890 genes associated with 2598 reactions, of .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 10 which 683 are transport reactions, and 2047 metabolites across four compartments (extracellular, cytoplasm, mitochondria, and peroxisome). The model can be found in SBML format in Supplementary Data 2. Among the 2598 reactions, 1747 are cytoplasmic, 351 mi tochondrial, 60 peroxisomal and 440 are drains (exchange constraints, used to simulate the import of media components or the leakage or export of extracellular metabolites). During the manual curation process, a total of 639 reactions/genes required alterations, including 80 who were mass balanced, 518 who were corrected for reversibility, directionality, or added or removed from the model, and 41 whose annotation was corrected, as detailed in Supplementary Data 1. The Biomass equation (Table 1) encompasses the cell's major components along with their respective and relative contributions, including DNA, RNA, lipids, carbohydrates, and cofactors. The equation's composition in carbohydrates [43], and lipids [44–46] was inferred from literature data for C. neoformans . The composition of Proteins, DNA and RNA was determined by the e -BiomassX where the whole genome sequence was used to estimate the amount of each deoxyribonucleotide as described in [47] mRNA, rRNA, and tRNA being used to estimate the total RNA in the cell as described in [47,48]. Table 1: Biomass composition used in the model iRV890. The full individual validated contributions of each of these metabolites are shown in Supplementary Data 1. Metabolite g/gDCW Metabolite g/gDCW Lipids Proteins Lanosterol 0.000122 L-Valine 0.019058 Zymosterol 0.000254 L-Tyrosine 0.020501 Squalene 0.000209 L-Tryptophan 0.006392 Ergosterol 0.000724 L-Threonine 0.022013 .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 11 Phosphatidylserine 0.005024 L-Serine 0.027696 Phosphatidylinositol 0.004638 L-Proline 0.015390 Phosphatidylcholine 0.031241 L-Phenylalanine 0.022928 Phosphatidylethanolamine 0.017714 L-Methionine 0.008274 Cardiolipin 0.002254 L-Lysine 0.033668 Phosphatidic acid 0.000644 L-Leucine 0.036895 Phosphatidylglycerol 0.000644 L-Isoleucine 0.028492 Tetradecanoic acid 0.000020 L-Histidine 0.010159 Hexadecanoic acid 0.000097 L-Glutamate 0.029371 Octadecanoic acid 0.000038 L-Cysteine 0.003902 Dodecanoic acid 0.000021 L-Aspartate 0.023883 Decanoic acid 0.000011 L-Asparagine 0.027060 Octanoic acid 0.000038 L-Arginine 0.020979 Octadecanoic acid 0.000038 L-Alanine 0.012706 (9Z)-Octadecenoic acid 0.000093 Glycine 0.010258 (9Z,12Z)-Octadecadienoic acid 0.000116 L-Glutamine 0.020550 (9Z,12Z,15Z)-Octadecatrienoic acid 0.000002 Triacylglycerol 0.032969 Soluble Pool Sterol esters 0.001127 Pyridoxine 5'-phosphate 0.000833 FAD 0.000833 Carbohydrates Thiamine(1+) diphosphate 0.000833 Chitin 0.005645 NAD 0.000833 Mannan 0.033956 Glutathione 0.000833 β (1,3)-Glucan 0.360399 Riboflavin 0.000833 Eumelanin 0.000833 Ribonucleotides Ubiquinone-6 0.000833 UTP 0.006713 NADP 0.000833 GTP 0.006806 COA 0.000833 CTP 0.005381 FMN 0.000833 ATP 0.007101 5-Methyltetrahydrofolate 0.000833 Deoxyribonucleotides dTTP 0.016718 dGTP 0.017029 dCTP 0.015059 dATP 0.017193 The translated genome sequence was used to calculate the amino acid composition using the percentage of each codon usage as described in [47]. Essential metabolites were included in the biomass composition to qualitatively account for the essentiality of their synthesis pathways [49,50]. The growth and non-growth ATP requirements were adopted from S. cerevisiae [51]. .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 12 3.2. Model validation 3.2.1 Carbon and nitrogen source utilization In silico simulations were conducted using 222 different compounds as the exclusive carbon or nitrogen sources, under conditions mimicking those of the minimal medium reported in [21]. The in silico growth was compared to publicly available phenotypic microarray (Biolog platform) data for C. neoformans var. grubii performed in [21]. A total of 155 sole carbon sources and 67 sole nitrogen sources were evaluated. For the analyses we used the data from stationary phase yeasts condition after calculating the di fference from the respective negative control group, without any carbon or nitrogen sources. iRV890 model correctly predicted growth in 85% (133/155) of the carbon sources tested and in 85% (57/67) of the nitrogen sources Supplementary Table 1. In some cases of failed predictions, such as L -ornithine and glycerol (carbon source) and amino acids and D -Glucosamine (nitrogen source), genetic information and the model include all the necessary steps to predict their assimilation as sole carbon/nitrogen sources, but no growth was experimentally observed. In such cases, the failed prediction may be related to non -metabolic factors that are not considered in model simulations, or to inaccuracies regarding the annotation of transporters, which is still a big challen ge in the current model development process [52]. In other cases, however, the prediction model failed because specific enzymes are not yet characterized for C. neoformans , despite growth in experimental conditions. The comparison between the model’s predi ction and experimental evidence suggests that the following enzyme activities are likely to be present in C. neoformans, although the underlying genes and proteins were not yet identified: 1.2.1.3 (aldehyde dehydrogenase), 1.1.1.21 (aldose reductase), 3.1.1.65 (L -rhamnono-1,4-lactonase), 1.1.1.56 (ribitol 2 -dehydrogenase), 5.1.3.30 .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 13 (D-psicose 3 -epimerase), 2.7.1.55 (allose kinase), 4.1.2.10 ((R) -mandelonitrile lyase), 5.3.1.3 (D -arabinose isomerase), 3.2.1.86 (6 -phospho-beta-glucosidase), 4.1.2.4 (deoxyribose-phosphate aldolase), 3.2.1.86 (6 -phospho-beta-glucosidase) and 1.1.1.16 (galactitol 2-dehydrogenase). The identification and characterization of these predicted functions and their underlying gene(s) will shed light on the specific pathways of carbon or nitrogen assimilation in this pathogen, potentially revealing new mechanisms of virulence related to adaptation to the host environment. Altogether, the model achieved 85% predictability which is a high value, especially considering that the extensive list of carbon and nitrogen sources tested includes many that are not commonly us ed in traditional metabolic and phenotypic experiments and thus lack biochemical characterization. 3.2.2 Growth parameters in batch culture To quantitatively validate the model, the specific growth rate, glucose consumption rate, and metabolite production rates were experimentally determined, and compared with in silico predicted values. For a glucose consumption rate of 1.72 mmol.gDCW-1.h-1, a specific growth rate of 0. 188 h-1 was experimentally determined, leading to no detectable production of ethanol, glycerol, or acetate. For comparison with in silico results, we simulated the system's behavior in SMM medium with a fixed glucose uptake fl ux of 1.72 mmol.g-1 dry weight.h -1. Other nutrient fluxes were left unconstrained, as the system was glucose-limited under these conditions. The simulation predicted a specific growth rate of 0.128 h -1, a difference of 0.06 h -1 to the experimentally determined value (Table 2). In these conditions, the model did not predict the formation of glycerol, acetic acid, or ethanol as by -products, consistent with the experimental data. Moreover, the model is accurate at predicting no growth of C. .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 14 neoformans under anaerobic conditions which is to be expected since this pathogen is an obligate aerobic fungus [53]. Table 2 - Growth parameter values predicted by the iRV890 model, in comparison with those determined experimentally. Specific growth rate (h-1) q (mmol g-1 dry weight h-1) Glucose Ethanol Glycerol Acetic acid In silico 0.128 1.72 0 0 0 In vivo 0.188 1.72 0 0 0 3.3. C. neoformans unique metabolic features To uncover unique metabolic features of this pathogen, a comparison was made between the C. neoformans GSMM with those previously built for C. glabrata [49], C. albicans [54], C. auris [55] and S. cerevisiae [56] by us and others. A comparison across the existing models was carried out based on shared EC numbers. After intersecting the EC numbers present in each of the five models, 40% (229/566) of the EC numbers were common among all the tested yeasts (Figure 1). Additionally, the remaining 17% (96/556) are exclusive to the C. neoformans model and may represent unique metabolic features of this species relative to the remaining. We confirmed none of these 96 EC numbers were associated with outdated, incomplete or incorrect reaction associations. However, a small subset of these 96 EC numbers may be present in other species included in the comparison, but not accounted for in their respective GSMMs during the process of reconstruction. .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 15 Figure 1: Multi-species comparison in terms of proteins with an associated EC number present in the C. neoformans iRV890, C. albicans iRV781, C. auris iRV973, S. cerevisiae iIN800 and C. glabrata iNX804 GSMMs. The multiple intersection was performed using jvenn [57]. From the list of 96 C. neoformans unique EC numbers (Supplementary Data 1), metabolic features or pathways relevant in the context of fungal infection in the host brain were searched manually for, and compared to extant knowledge of these pathways being defense mechanisms, or enabling host adaptation, through degradation or biosynthesis of specific metabolites. A few of these unique EC numbers with higher potential of impacting C. neoformans pathogenesis are discussed below: 1.1.1.12 and 1.1.1.287 - L-arabinitol 4 -dehydrogenase and D -arabinitol dehydrogenase are two enzymes that are required for L -arabinitol assimilation as carbon source, which is a particular metabolic feature of C. neoformans when .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 16 compared to other yeast species (Supplementary Table 1). Indeed, neither Candida species [54,58] nor S. cerevisiae [59], can assimilate L -arabitol, unless genetically engineered [60]. Interestingly, environment isolates containing SNPs in the PTP1 gene, encoding a C. neoformans arabitol transporter, were associated with increased patient survival, while a virulence defect was observed in BALB/c mice due to PTP1 gene deletion [61]. PTP1 expression was also found to be highly induced in macrophage and amoeba infection [62]. Since arabitol is present in the cerebrospinal fluid [63], it is possible this pathway may be used to feed from polyols in CNS and contributes to explain brain tropism of C. neoformans , compared to other fungal species. 1.1.3.8 and 3.1.1.17 - L-gulonolactone oxidase and gluconolactonase are two enzymes that participate in ascorbate metabolism, allowing the utilization of Inositol and D -glucuronate as source for L -ascorbate biosynthesis (Figure 2). Interestingly, it was reported by two independ ent studies that the presence of ascorbate, an antioxidant, lowers the susceptibility towards fluconazole in C. neoformans [64,65]. However, this effect seems to not be related to its antioxidant potential, but with ascorbate -induced up-regulation of Upc2, a transcriptional regulator of genes involved in ergosterol biosynthesis, as shown in C. albicans [66]. The ability of C. neoformans to synthesize ascorbate from inositol is particularly noteworthy, given the abundance of inositol in the human brain [20] and the widespread use of fluconazole in treating infections. Further it is possible that ascorbate contributes to resistance to ROS. Having a mechanism to produce a compound that mitigates the toxicity of fluconazole and ROS could co ntribute to a significant adaptive advantage for this species. .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 17 Figure 2 – C. neoformans pathway for ascorbate biosynthesis, with the respective C. neoformans var. grubii EC numbers present in the iRV890 model. The 1.1.3.8 and 3.1.1.17 enzymes, which are unique to C. neoformans among other pathogenic yeasts, are highlighted in purple. Additionally, the 1.1.3.8 and 3.1.1.17 enzymes are also important for inositol assimilation as a carbon source through a variation of the previous pathway. This pathway was suggested recently as an alternative pathway in fungi for inositol assimilation, and since inositol is highly abundant in the human brain, this may represent a very important metabolic feature for C. neoformans. In fact, in order to implement that pathway in the model, two of the reactions reported were recreated and attributed with the names R2_Inositol_Pathway and R1_Inositol_Pathway in the model, although the corresponding EC numbers and genes have not been identified in the annotated C. neoformans genome [67]. This pathway was recreated exclusively from literature, and while it lacks validation studies, two possible genes were hypothesized as probable candidates for .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 18 encoding the 1.1.1.69 enzyme, CNAG_02553 and CNAG_00126, predicted by OrthoMCL [68]. Additional pathways for inositol assimilation are reported for animal (Figure 3.B) and bacteria (Figure 3.C); however, since C. neoformans lacks almost all the enzymes in those pathways, we considered that the new pathway reported in fungi [67] was the most probable to occur in this pathogen. Taking advantage of the available ΔCNAG_02553 deletion strain, we tested whether a strain deleted for this putative enzyme could be g rown in inositol as a single carbon source, compared to parental strain. However, even in the absence of CNAG_02553 gene, C. neoformans is able to utilize inositol as the sole carbon source in SMM (YNB, supplemented with glucose or inositol, data not shown). Eventually, it would be necessary to knockout both CNAG_00126 and CNAG_02553 genes to obtain a strain unable to grow in media containing inositol as the sole carbon source. Further scrutiny is required to address this issue. 1.1.1.377 - L-rhamnose 1-dehydrogenase is required for L-rhamnose assimilation as sole carbon source. Rhamnose is used by some pathogens, for example Pseudomonas aeruginosa, to produce rhamnolipids, and constitutes an important virulence factor in those bacteria, with roles in biofilm formation, hydrophobic nutrient uptake, and host immunity evasion, characterized for increasing lung epithelial permeability [69,70] and inhib ition of macrophage phagocytosis [71]. Candida species [54,55,58] and S. cerevisiae (unless engineered) [72] cannot assimilate L -rhamnose, and thus assimilation of rhamnose is a particular metabolic feature of C. neoformans when compared to these yeast species (Supplementary Table 1). .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 19 Figure 3 - Metabolic pathways for inositol assimilation as carbon source, A - based on the proposed fungal inositol assimilation pathway reported in Kuivanen et al. 2016 [67], B – based on the animal inositol assimilation pathway, C- based on the bacterial inositol assimilation pathway, and D- the unknown, and apparently unique, pathway of inositol assimilation proposed for C. neoformans. The respective C. neoformans var. grubii genes present in iRV890 are highlighted in purple. The currently unknown genes are highlighted in red and the proposed reactions with an unknown EC number are represented as a question mark in red. .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 20 1.14.13.231 - tetracycline 11a-monooxygenase is an enzyme that allows the direct conversion of tetracycline into 11a -hydroxytetracycline, reported to confer resistance to all clinically relevant tetracyclines, by efficient degradation of a broad range of tetracycline an alogues. The hydroxylated product, 11a - hydroxytetracycline, is very unstable and leads to intramolecular cyclization and non-enzymic breakdown to undefined products, completely neutralizing the tetracycline effects [73,74]. Although tetracycl ines are generally used as antibacterial antibiotics, and have poor antifungal activity, the presence of this enzyme in C. neoformans should be taken into consideration when designing tetracyclines against fungi. 3.1.3.8 - 3-phytase is an enzyme involved in inositol metabolism that may be involved in the production of phytic acid from inositol, a primary storage molecule of phosphorus and inositol in fungi (although not in the pathogenic Candida species), bacteria and plants [75]. Interestingly, this pathway has been shown to play a key role in C. neoformans virulence. Indeed, it was previously reported that the deletion of the gene encoding the enzyme (EC number 2.7.1.158) that immediately precedes 3 -phytase leads to growth impairment and to attenuated virulence in C. neoformans, associated with failed dissemination into the brain [76]. 3.5.2.17 – hydroxyisourate hydrolase is an enzyme essential for the assimilation of uric acid as sole nitrogen source. Uric acid is a normal component of urine and bird guano. In bird guano, 70% of the nitrogen present is in the form of uric acid with the rest consis ting primarily of xanthine, urea, and creatinine [77] Additionally, uric acid enhances the production of key cryptococcal virulence factors, including capsule and urease, an enzyme required for full fitness at mammalian pH and dissemination to t he brain [78], C. neoformans capsule is induced in the presence of uric acid [79]. .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 21 4.1.1.105 – L-tryptophan decarboxylase catalyzes the conversion of L-tryptophan into Tryptamine, which can then be converted into serotonin, and shares structure with several aminergic neuromodulators. However, the reaction is bidirectional, and Tryptamine can also be converted into L-tryptophan. While it is unclear which may be the role of this enzyme in C. neoformans, it is potentially related to the brain environment, specifically in the utilization of serotonin as a nitrogen source, through its conversion to L-tryptophan. 4.1.1.28, 1.14.18.1, and 1.10.3.2 – DOPA decarboxylase, tyrosinase and laccase are particularly important in C. neoformans, as they are involved in the biosynthesis of melanin. Most fungi possess multiple melanin biosynthetic pathways, while Cryptococcus neoformans exclusively synthesizes melanin through the L -DOPA pathway. [80]. Melanin is able to neutralize oxidative stress radicals as well as protecting the pathogen against the host immune system and antifungal drugs, such as caspofungin and amphotericin B. L-DOPA and Dopamine are present in the human brain and serve as precursors for dopamine biosynthesis in this pathogen, but it is uncertain why C. neoformans exclusively uses this pathway, compared to other human pathogenic fungi. 3.4. Drug target analysis based on gene essentiality prediction Pathogen’s GSMM are particularly useful to identify potential new drug targets, among predicted essential genes. For that purpose, a list of all predicted essential genes and enzymes in C. neoformans was obtained through simulation of the system's behaviour in RPMI medium, which mimics the environmental conditions of human serum. A total of 157 enzymes and 101 genes were identified as essential in RPMI medium. Among these targets, some have been previ ously identified as essential genes in other pathogenic y easts (see Table 3), indicating potential drug targets common to all Candida species and C. neoformans. Notably, .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 22 Erg11 and Fks1 are already targets of currently used antifungals, fluconazole and echinocandins, respectively. Additionally, Erg26, Erg27, Erg24, Erg4, Erg7, Erg12, and Erg13 have all been identified herein as potential drug targets within the ergosterol biosynthetic pathway. Particularly interesting is Erg4, as it lacks a human ortholog, and may represent a superior candidate for designing compounds with enhanced selectivity and lower toxicity. Similarly to Candida, which lacks a Folate transporter [81] and relies on its de novo biosynthesis, C. neoformans seems to also lack a folate transporter, leading to the identification of Fol1, a multifunctional enzyme of the folic acid biosynthesis pathway, as a promising multi-yeast drug target. Furthermore, Fas1, a fatty acid synthase enzyme, and Chs1, a chitin sy nthase, also lack human orthologs and constitute promising alternative antifungal drug targets due to their important role for membrane and cell wall structure and integrity. Other noteworthy targets span various pathways, including purine metabolism, terpenoid backbone biosynthesis, pyrimidine metabolism, CoA biosynthesis, glycerophospholipid biosynthesis, and ubiquinone biosynthesis (Table 3). However, exploring these targets requires leveraging potential structural differences in the enzyme's active site compared to their human counterparts. Since C. neoformans colonizes a different host environment and is phylogenetically distant from Candida spp. our evaluation was extended to include potential new drug targets that are unique to this species, and not shared by Candida spp. We identified only two such targets: the 1.14.18.1 tyrosinase, encoded by the gene CNAG_03009, and the 2.5.1.83 hexaprenyl diphosphate synthase, encoded by the gene CNAG_04375. While tyrosinase, responsible for melanin production, has a human ortholo g (since humans also sy nthesize melanin via the L-DOPA), hexaprenyl diphosphate synthase (2.5.1.83) is fungal- specific and may represent an interesting target. This enzyme plays a crucial role .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 23 in terpenoid backbone biosynthesis, serving as a key contributor to the synthesis of precursors for ubiquinone biosynthesis. .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 24 Table 3 - Enzymes predicted to be essential in RPMI medium in 5 pathogenic fungal species, based on the screening of the genome - scale metabolic models of C. neoformans iRV890, C. auris IRV973, C. parapsilosis iDC1003, C. albicans iRV781, and C. glabrata iNX804. Grey rows highlight enzymes which are not encoded in the human genome. Data regarding the drug association retrieved from the DrugBank database; only drugs with known pharmacological action against pathogens were selected. C. neoformans C. albicans C. glabrata C. parapsilosis C. auris S. cerevisiae Human Pharmacological action EC Number Pathway/Target CNAG_04605 ERG26 CAGL0G00594g CPAR2_302110 CJI97_000938 ERG26 NSDHL 1.1.1.170 Steroid CNAG_00441 IMH3 CAGL0K10780g CPAR2_104580 CJI97_000080 IMD4 IMPDH - 1.1.1.205 Purine CNAG_07437 ERG27 CAGL0M11506g CPAR2_801560 CJI97_004310 ERG27 DHRS11 - 1.1.1.270 Steroid CNAG_06534 HMG1 CAGL0L11506g CPAR2_110330 CJI97_003299 HMG1 HMGCR - 1.1.1.34 Terpenoid backbone CNAG_00117 ERG24 CAGL0I02970g CPAR2_405900 CJI97_003097 ERG24 TM7SF2 - 1.3.1.70 Steroid CNAG_02830 ERG4 ERG4 ERG4 CJI97_002908 ERG4 - - 1.3.1.71 Steroid CNAG_04692 CDC21 CDC21 CPAR2_206550 CJI97_005101 TMP1 TYMS - 2.1.1.45 Pyrimidine CNAG_00700 ADE17 CAGL0A03366g CPAR2_202250 CJI97_002511 ADE17 ATIC - 2.1.2.3 Purine CNAG_07373 URA2 CAGL0L05676g CPAR2_203160 CJI97_002269 URA2 CAD - 2.1.3.2 Pyrimidine CNAG_06508 GSC1 FKS1 CPAR2_106400 FKS1 FKS1 - Echinocandins 2.4.1.34 1,3-beta-glucan CNAG_03196 URA5 URA5 CPAR2_802790 CJI97_002422 URA5 UMPS - 2.4.2.10 Pyrimidine CNAG_02853 ADE4 CAGL0M13717g CPAR2_208260 CJI97_001833 ADE4 PPAT - 2.4.2.14 Purine CNAG_02084 BTS1 CAGL0H05269g CPAR2_302840 CJI97_003197 BTS1 GGPS1 - 2.5.1.1 Terpenoid backbone CNAG_07780 ERG20 ERG20 CPAR2_103950 CJI97_001757 ERG20 FDPS - 2.5.1.10 Terpenoid backbone CNAG_02787 C5_05130C CAGL0F05555g CPAR2_502760 CJI97_003836 CAB5 COASY - 2.7.1.24 CoA CNAG_02976 CR_03740C CAGL0K11022g CPAR2_202590 CJI97_005311 FMN1 RFK - 2.7.1.26 Riboflavin CNAG_02866 C6_02980C CAGL0H01551g CPAR2_602050 CJI97_004586 CAB1 PANK - 2.7.1.33 CoA CNAG_05935 URA6 CAGL0L09867g CPAR2_105320 CJI97_000033 URA6 CMPK2 - 2.7.4.14 Pyrimidine CNAG_06001 ERG8 ERG8 CPAR2_400710 CJI97_001215 ERG8 PMVK - 2.7.4.2 Terpenoid backbone CNAG_03335 C5_00260W CAGL0D00550g CPAR2_304260 CJI97_000019 PRS1 PRPS1 - 2.7.6.1 Purine .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 25 CNAG_05384 C4_05210W CAGL0G03157g CPAR2_500260 CJI97_005306 PIS1 CDIPT - 2.7.8.11 Glycerophospholipid CNAG_02795 ADE8 CAGL0F02761g CPAR2_211620 CJI97_002826 ADE8 GART - 2.1.2.2 Purine CNAG_02609 COQ3 CAGL0I07601g CPAR2_602300 CJI97_005452 COQ3 COQ3 - 2.1.1.114 Ubiquinone CNAG_00138 COQ5 CAGL0J06710g CPAR2_209250 CJI97_003704 COQ5 COQ5 - 2.1.1.201 Ubiquinone CNAG_00040 ERG11 ERG11 ERG11 ERG11 ERG11 CYP51A 1 Azoles 1.14.14.154 Steroid CNAG_02844 PEL1 PGS1 CPAR2_805350 CJI97_000224 PEL1 PGS1 - 2.7.8.5 Glycerophospholipid CNAG_02878 C6_01340C CAGL0H04389g CPAR2_602700 CJI97_005490 GEP4 PTPMT1 - 3.1.3.27 Glycerophospholipid CNAG_00734 URA4 CAGL0J04598g CPAR2_100500 CJI97_002941 URA4 CAD - 3.5.2.3 Pyrimidine CNAG_02294 ADE2 ADE2 CPAR2_805940 CJI97_004071 ADE2 PAICS - 4.1.1.21 Purine CNAG_04961 URA3 URA3 URA3 CJI97_003384 URA3 UMPS - 4.1.1.23 Pyrimidine CNAG_02786 FOL1 CAGL0J07920g CPAR2_303390 CJI971_001274 FOL1 - Sulfacetamide 4.1.2.25 Folate biosynthesis CNAG_02786 FOL1 CAGL0J07920g CPAR2_303390 CJI971_001274 FOL1 - Sulfonamides 2.5.1.15 Folate biosynthesis CNAG_05125 MVD CAGL0C03630g CPAR2_109530 CJI97_001340 MVD1 MVD - 4.1.1.33 Terpenoid backbone CNAG_00909 CAB3 CAGL0L05302g CPAR2_800750 CJI97_003563 CAB3 PPCDC - 4.1.1.36 CoA CNAG_03270 ADE13 CAGL0B02794g CPAR2_204960 CJI97_000801 ADE13 ADSL - 4.3.2.2 Purine CNAG_00265 IDI1 CAGL0J06952g CPAR2_401630 CJI97_001183 IDI1 IDI1 - 5.3.3.2 Terpenoid backbone CNAG_01129 ERG7 CAGL0J10824g CPAR2_301800 CJI97_005090 ERG7 LSS Oxiconazole 5.4.99.7 Steroid CNAG_00143 ADE1 CAGL0I04444g CPAR2_500190 CJI97_003065 ADE1 PAICS - 6.3.2.6 Purine CNAG_06314 ADE5,7 CAGL0H07887g CPAR2_208400 CJI97_001704 ADE5,7 GART - 6.3.3.1 Purine CNAG_06314 ADE5,7 CAGL0H07887g CPAR2_208400 CJI97_001704 ADE5,7 GART - 6.3.4.13 Purine CNAG_04192 ADE6 CAGL0K04499g CPAR2_204070 CJI97_002160 ADE6 PFAS - 6.3.5.3 Purine CNAG_05759 ACC1 CAGL0L10780g CPAR2_804060 CJI97_001038 ACC1 ACACA - 6.4.1.2 Fatty acid CNAG_02686 ERG12 CAGL0F03861g CPAR2_803530 CJI97_005606 ERG12 MVK - 2.7.1.36 Terpenoid backbone CNAG_03311 ERG13 ERG13 CPAR2_701400 CJI97_004952 ERG13 HMGCS - 2.3.3.10 Terpenoid backbone CNAG_02099 FAS1 CAGL0D00528g FAS1 CJI97_001309 FAS1 - - 2.3.1.86 Fatty acid CNAG_01877 GUA1 CAGL0F03927g CPAR2_803560 CJI97_005609 GUA1 GMPS - 6.3.5.2 Pyrimidine CNAG_03099 CHS1 CAGL0I04818g CPAR2_805640 CHS2 CHS2 - - 2.4.1.16 Chitin .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 26 4. Conclusions The construction and validation of iRV890, the first genome-scale metabolic model for C. neoformans var. grubii is presented herein. iRV890 constitutes a robust platform for exploring and elucidating the metabolic features of this poorly understood pathogen, particularly concerning its interaction within the central nervous system and the human host. By encompassin g 890 genes associated with 1466 reactions, this model offers a comprehensive view of the metabolic landscape of the pathogen. Through in silico simulations, we predicted the use of more than 200 compounds as sole carbon or nitrogen sources, and after comp arison to experimental data from phenotypic microarrays we gained valuable insights into the metabolic capabilities of C. neoformans. The model correctly predicts 85% of the sole carbon and nitrogen sources tested. The model was able to accurately predict the organism’s specific growth rate and confirmed its inability to grow under anaerobic conditions or to accumulate glycerol, acetic acid, or ethanol as metabolic by -products during growth in synthetic minimal medium, with glucose as carbon source. Additio nally, we propose a list of yet unidentified enzymes expected to be present in C. neoformans, based on the carbon and nitrogen utilization and with potential to represent new host adaptation or virulence mechanisms, including new clues on the pathway for inositol utilization in C. neoformans. Our investigation into the unique metabolic features of C. neoformans has unveiled several pathways and enzymatic activities that are proposed to play pivotal roles in fungal infection within the host brain. Some enzymes constitute important virulence factors, such as Tyrosinase and laccase, enzymes responsible for productio n of melanin which has an important role in host immune evasion [15], infection proliferation and drug resistance [16,17]. Other enzymes are related to drug and stress resistance, such a s tetracycline 11a -monooxygenase, L -gulonolactone oxidase and gluconolactonase. The remaining enzymes are directed related to alternative carbon/nitrogen source utilization and are important for environmental adaptation. .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 27 For example, hydroxyisourate hydrolase is essential for the assimilation of uric acid as a nitrogen source, an important virulence factor mechanism, and 3 -phytase is involved in inositol metabolism and storage, important for brain dissemination. In this work we also propose several potential drug targets in C. neoformans. Notably, enzymes such as Erg4, Chs1, Fol1 and Fas1 present promising opportunities for targeted drug development, due to their absence in human cells, offering opportunities for the development of selective and low -toxicity compounds. The CNAG_03009 and CNAG_04375 genes, encoding a tyrosinase and a hexaprenyl diphosphate synthase, are presented as potential antifungal drug targets specific to C. neoformans. Our model contributes to a better understanding of C. neoformans metabolism, especially within the host environment. With this work, we not only propose new metabolic enzymes awaiting characterization but also offer insights into key pathways and interactions shaping the dynamics between host and pathogen and its adapt ive strategies. We also propose some potential antifungal targets for C. neoformans and confirmed the coverage of already identified targets also to that species. These results hold promise for the discovery of novel drug targets and for the full comprehension of this pathogen’s metabolic network with an expected impact in combating cryptococcosis. Author contributions R.V., C.C., and M.C.T. conceived and designed the study. R.V. performed the model construction & development, data analysis and curation. R.V., D.C., and W.N. performed the experiments with C. neoformans . O.D. contributed to model construction and data analysis. L.C. performed data analysis. R.V. wrote the original draft preparation. R.V., L.C., C.C., and M.C.T. reviewed and edited the final version. All authors have read and agreed to the published version of the manuscript. CRediT authorship contribution statement Romeu Viana: Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation. William Newton : .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 28 Methodology, Investigation. Diogo Couceiro: Methodology, Investigation. Oscar Dias: Methodology, Formal analysis. Luís Coutinho: Writing – review & editing, Formal analysis. Carolina Coelho: Writing – review & editing, Supervision, Methodology, Conceptualization. Miguel Cacho Teixeira: Writing – review & editing, Supervision, Methodology, Conceptualization, Funding. Declaration of Competing Interest The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

The authors acknowledge the OSCARS project, funded by the European Commission’s Horizon Europe Research and Innovation Programme under grant agreement No. 101129751. This work was further financed by national funds from “Fundação para a Ciência e a Tecnolo gia” (FCT) (AEM PhD grant to RV; projects UIDB/04565/2020 and UIDP/04565/2020 of the Research Unit Institute for Bioengineering and Biosciences —iBB; project UIDB/04469/2020 for the Centre of Biological Engineering —CEB; project LA/P/0029/2020 for LABBELS –Associate Laboratory in Biotechnology, Bioengineering and Microelectromechanical Systems ; and project LA/P/0140/2020 for the Associate Laboratory Institute for Health and Bioeconomy—i4HB). WN was funded by a DTP BRC Exeter NIHR203320. Fungal strain collection was funded via NIH funding (R01AI100272) led by Hiten Madhani, UCSF. This work was supported by AMS Springboard Award SBF006\1024 (UK), and Wellcome Trust Institutional Strategic Support Award (WT105618MA) to C.C. We acknowledge other funding from the MRC Centre for Medical Mycology at the University of Exeter (MR/N006364/2 and MR/V033417/1). This study/research is funded by the National Institute for Health and Care Research (NIHR) Exeter Biomedical Research Centre (BRC). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 29 Appendix A. Supplementary material .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 30

[1] Levitz SM. Cryptococcus neoformans by Casadevall, Arturo & Perfect, John R. (1998) ASM Press, Washington, DC. Hardcover. 542 pp. $89.95. (ASM Member price: $79.95). Med Mycol 2008;37. https://doi.org/10.1111/j.1365 - 280x.1999.00238.x. [2] Velagapudi R, Hsueh YP, Geunes -Boyer S, Wright JR, Heitman J. Spores as infectious propagules of Cryptococcus neoformans. Infect Immun 2009;77. https://doi.org/10.1128/IAI.00542-09. [3] Lin X, Heitman J. The biology of the Cryptococcus neoformans species complex. Annu Rev Microbiol 2006;60. https://doi.org/10.1146/annurev.micro.60.080805.142102. [4] Del Valle L, Piña -Oviedo S. HIV disorders of the brain; pathology and pathogenesis. Frontiers in Bioscience 2006;11. https://doi.org/10.2741/1830. [5] Park BJ, Wannemuehler KA, Marston BJ, Govender N, Pappas PG, et al. Estimation of the current global burden of cryptococcal meningitis among persons living with HIV/AIDS. AIDS 2009;23. https://doi.org/10.1097/QAD.0b013e328322ffac. [6] Ventura Aguiar P, Lopes V, Martins LS, Santos J, Almeida M, et al. Cryptococcal infection in non -HIV immunosuppressed patients - Three case reports in a nephrology setting. Med Mycol Case Rep 2014;3. https://doi.org/10.1016/j.mmcr.2013.11.003. [7] Rajasingham R, Govender NP, Jordan A, Loyse A, Shroufi A, et al. The global burden of HIV -associated cryptococcal infection in adults in 2020: a modelling analysis. Lancet Infect Dis 2022. https://doi.org/10.1016/S1473-3099(22)00499-6. [8] Bermas A, Geddes -McAlister J. Combatting the evolution of antifungal resistance in Cryptococcus neoformans. Mol Microbiol 2020;114. https://doi.org/10.1111/mmi.14565. [9] Hope W, Stone NRH, Johnson A, McEntee L, Farrington N, et al. Fluconazole monotherapy is a suboptimal option for initial treatment of cryptococcal meningitis because of emergence of resistance. MBio 2019;10. https://doi.org/10.1128/mBio.02575-19. .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 31 [10] Pfaller MA, Diekema DJ, Gibbs DL, Newell VA, Bijie H, et al. Results from the ARTEMIS DISK global antifungal surveillance study, 1997 to 2007: 10.5 -year analysis of susceptibilities of noncandidal yeast species to fluconazole and voriconazole determin ed by CLSI standardized disk diffusion testing. J Clin Microbiol 2009;47. https://doi.org/10.1128/JCM.01747-08. [11] Bongomin F, Oladele RO, Gago S, Moore CB, Richardson MD. A systematic review of fluconazole resistance in clinical isolates of Cryptococcus species. Mycoses 2018;61. https://doi.org/10.1111/myc.12747. [12] Zafar H, Altamirano S, Ballou ER, Nielsen K. A titanic drug resistance threat in Cryptococcus neoformans. Curr Opin Microbiol 2019;52. https://doi.org/10.1016/j.mib.2019.11.001. [13] Coelho C, Casadevall A. Cryptococcal therapies and drug targets: the old, the new and the promising. Cell Microbiol 2016;18. https://doi.org/10.1111/cmi.12590. [14] Williamson PR. Laccase and melanin in the pathogenesis of Cryptococcus neoformans. Front Biosci 1997;2. https://doi.org/10.2741/A231. [15] Wang Y, Aisen P, Casadevall A. Cryptococcus neoformans melanin and virulence: Mechanism of action. Infect Immun 1995;63. https://doi.org/10.1128/iai.63.8.3131-3136.1995. [16] Nosanchuk JD, Casadevall A. Impact of melanin on microbial virulence and clinical resistance to antimicrobial compounds. Antimicrob Agents Chemother 2006;50. https://doi.org/10.1128/AAC.00545-06. [17] Van Duin D, Casadevall A, Nosanchuk JD. Melanization of Cryptococcus neoformans and Histoplasma capsulatum reduces their susceptibilities to amphotericin B and caspofungin. Antimicrob Agents Chemother 2002;46. https://doi.org/10.1128/AAC.46.11.3394-3400.2002. [18] Botts MR, Hull CM. Dueling in the lung: How Cryptococcus spores race the host for survival. Curr Opin Microbiol 2010;13. https://doi.org/10.1016/j.mib.2010.05.003. [19] WHO. WHO fungal priority pathogens list to guide research, development and public health action. vol. 1. 2022. .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 32 [20] Wang Y, Wear M, Kohli G, Vij R, Giamberardino C, et al. Inositol Metabolism Regulates Capsule Structure and Virulence in the Human Pathogen Cryptococcus neoformans. MBio 2021;12. https://doi.org/10.1128/mBio.02790-21. [21] Hommel B, Sturny -Leclère A, Volant S, Veluppillai N, Duchateau M, et al. Cryptococcus neoformans resists to drastic conditions by switching to viable but non-culturable cell phenotype. PLoS Pathog 2019;15. https://doi.org/10.1371/journal.ppat.1007945. [22] Capela J, Lagoa D, Rodrigues R, Cunha E, Cruz F, et al. merlin, an improved framework for the reconstruction of high -quality genome -scale metabolic models. Nucleic Acids Res. 2022;11. https://doi.org/10.1093/nar/gkac459. [23] Dias O, Rocha M, Ferreira EC, Rocha I. Reconstructing high -quality large-scale metabolic models with merlin. Methods in Molecular Biology, vol. 1716, 2018. https://doi.org/10.1007/978-1-4939-7528-0_1. [24] Rocha I, Maia P, Evangelista P, Vilaça P, Soares S, et al. OptFlux: An open-source software platform for in silico metabolic engineering. BMC Syst Biol 2010;4. https://doi.org/10.1186/1752-0509-4-45. [25] Kitts PA, Church DM, Thibaud -Nissen F, Choi J, Hem V, et al. Assembly: A resource for assembled genomes at NCBI. Nucleic Acids Res 2016. https://doi.org/10.1093/nar/gkv1226. [26] Federhen S. The NCBI Taxonomy database. Nucleic Acids Res 2012. https://doi.org/10.1093/nar/gkr1178. [27] Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods 2014;12. https://doi.org/10.1038/nmeth.3176. [28] Kanehisa M, Goto S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res 2000;28. https://doi.org/10.1093/nar/28.1.27. [29] Flamholz A, Noor E, Bar -Even A, Milo R. EQuilibrator - The biochemical thermodynamics calculator. Nucleic Acids Res 2012;40. https://doi.org/10.1093/nar/gkr874. [30] Caspi R, Altman T, Billington R, Dreher K, Foerster H, et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 33 Pathway/Genome Databases. Nucleic Acids Res 2014. https://doi.org/10.1093/nar/gkt1103. [31] Schomburg I. BRENDA, enzyme data and metabolic information. Nucleic Acids Res 2002. https://doi.org/10.1093/nar/30.1.47. [32] Bateman A, Martin MJ, Orchard S, Magrane M, Ahmad S, et al. UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Res 2023;51. https://doi.org/10.1093/nar/gkac1052. [33] Amos B, Aurrecoechea C, Barba M, Barreto A, Basenko EY, et al. VEuPathDB: The eukaryotic pathogen, vector and host bioinformatics resource center. Nucleic Acids Res 2022;50. https://doi.org/10.1093/nar/gkab929. [34] Bansal P, Morgat A, Axelsen KB, Muthukrishnan V, Coudert E, et al. Rhea, the reaction knowledgebase in 2022. Nucleic Acids Res 2022;50. https://doi.org/10.1093/nar/gkab1016. [35] Thumuluri V, Almagro Armenteros JJ, Johansen AR, Nielsen H, Winther O. DeepLoc 2.0: multi -label subcellular localization prediction using protein language models. Nucleic Acids Res 2022;50. https://doi.org/10.1093/nar/gkac278. [36] Lagoa D, Faria JP, Liu F, Cunha E, Henry CS, et al. TranSyT, the Transport Systems Tracker. BioRxiv 2021. https://doi.org/10.1101/2021.04.29.441738. [37] Saier MH, Reddy VS, Moreno -Hagelsieb G, Hendargo KJ, Zhang Y, et al. The transporter classification database (TCDB): 2021 update. Nucleic Acids Res 2021;49. https://doi.org/10.1093/nar/gkaa1004. [38] Delmas G, Park S, Chen ZW, Tan F, Kashiwazaki R, et al. Efficacy of orally delivered cochleates containing amphotericin B in a murine model of aspergillosis. Antimicrob Agents Chemother 2002;46. https://doi.org/10.1128/AAC.46.8.2704-2707.2002. [39] Mishra P, Park GY, Lakshmanan M, Lee HS, Lee H, et al. Genome -scale metabolic modeling and in silico analysis of lipid accumulating yeast Candida tropicalis for dicarboxylic acid production. Biotechnol Bioeng 2016. https://doi.org/10.1002/bit.25955. .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 34 [40] Varma A, Palsson BO. Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl Environ Microbiol 1994. https://doi.org/10.1128/aem.60.10.3724-3731.1994. [41] Chun CD, Madhani HD. Applying genetics and molecular biology to the study of the human pathogen Cryptococcus neoformans. Methods Enzymol 2010;470:797-831. doi: 10.1016/S0076-6879(10)70033-1. [42] Sauer U, Lasko DR, Fiaux J, Hochuli M, Glaser R, et al. Metabolic flux ratio analysis of genetic and environmental modulations of Escherichia coli central carbon metabolism. J Bacteriol 1999;181:6679 –88. https://doi.org/10.1128/jb.181.21.6679-6688.1999. [43] Mukaremera L, Lee KK, Wagener J, Wiesner DL, Gow NAR, et al. Titan cell production in Cryptococcus neoformans reshapes the cell wall and capsule composition during infection. Cell Surface 2018;1. https://doi.org/10.1016/j.tcsw.2017.12.001. [44] Ghannoum MA, Spellberg BJ, Ibrahim AS, Ritchie JA, Currie B, et al. Sterol composition of Cryptococcus neoformans in the presence and absence of fluconazole. Antimicrob Agents Chemother 1994;38. https://doi.org/10.1128/AAC.38.9.2029. [45] Kaneko H, Hosohara M, Tanaka M, Itoh T. Lipid composition of 30 species of yeast. Lipids 1976;11. https://doi.org/10.1007/BF02532989. [46] Rawat DS, Upreti HB, Das SK. Lipid composition of Cryptococcus neoformans. Microbiologica 1984;7(4):299-307. PMID: 6392829. [47] Thiele I, Palsson B. A protocol for generating a high -quality genome -scale metabolic reconstruction. Nat Protoc 2010;5. https://doi.org/10.1038/nprot.2009.203. [48] Dias O, Pereira R, Gombert AK, Ferreira EC, Rocha I. iOD907, the first genome - scale metabolic model for the milk yeast Kluyveromyces lactis. Biotechnol J 2014;9. https://doi.org/10.1002/biot.201300242. [49] Xu N, Liu L, Zou W, Liu J, Hua Q, et al. Reconstruction and analysis of the genome-scale metabolic network of Candida glabrata. Mol Biosyst 2013;9. https://doi.org/10.1039/c2mb25311a. .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 35 [50] Xavier JC, Patil KR, Rocha I. Integration of Biomass Formulations of Genome - Scale Metabolic Models with Experimental Data Reveals Universally Essential Cofactors in Prokaryotes. Metab Eng 2017;39. https://doi.org/10.1016/j.ymben.2016.12.002. [51] Verduyn C, Postma E, Scheffers WA, Van Dijken JP. Physiology of Saccharomyces cerevisiae in anaerobic glucose-limited chemostat cultures. J Gen Microbiol 1990. https://doi.org/10.1099/00221287-136-3-395. [52] Casey J, Bennion B, D’haeseleer P, Kimbrel J, Marschmann G, et al. Transporter annotations are holding up progress in metabolic modeling. Front Syst Biol 2024;4:1394084. https://doi.org/10.3389/fsysb.2024.1394084. [53] Ingavale SS, Chang YC, Lee H, McClelland CM, Leong ML, et al. Importance of mitochondria in survival of Cryptococcus neoformans under low oxygen conditions and tolerance to cobalt chloride. PLoS Pathog 2008;4. https://doi.org/10.1371/journal.ppat.1000155. [54] Viana R, Dias O, Lagoa D, Galocha M, Rocha I, et al. Genome -Scale Metabolic Model of the Human Pathogen Candida albicans: A Promising Platform for Drug Target Prediction. J Fungi 2020;6. https://doi.org/10.3390/jof6030171. [55] Viana R, Carreiro T, Couceiro D, Dias O, Rocha I, et al. Metabolic reconstruction of the human pathogen Candida auris: using a cross -species approach for drug target prediction. FEMS Yeast Res 2023;23. https://doi.org/10.1093/femsyr/foad045. [56] Mo ML, Palsson B, Herrgård MJ. Connecting extracellular metabolomic measurements to intracellular flux states in yeast. BMC Syst Biol 2009. https://doi.org/10.1186/1752-0509-3-37. [57] Bardou P, Mariette J, Escudié F, Djemiel C, Klopp C. Jvenn: An interactive Venn diagram viewer. BMC Bioinformatics 2014;15. https://doi.org/10.1186/1471-2105- 15-293. [58] Viana R, Couceiro D, Carreiro T, Dias O, Rocha I, et al. A Genome -Scale Metabolic Model for the Human Pathogen Candida Parapsilosis and Early Identification of Putative Novel Antifungal Drug Targets. Genes (Basel) 2022;13(2). https://doi.org/DOI: 10.3390/genes13020303. .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 36 [59] Fonseca C, Romão R, Rodrigues De Sousa H, Hahn-Hägerdal B, Spencer-Martins I. L -Arabinose transport and catabolism in yeast. FEBS Journal 2007;274. https://doi.org/10.1111/j.1742-4658.2007.05892.x. [60] Bettiga M, Bengtsson O, Hahn -Hägerdal B, Gorwa -Grauslund MF. Arabinose and xylose fermentation by recombinant Saccharomyces cerevisiae expressing a fungal pentose utilization pathway. Microb Cell Fact 2009;8. https://doi.org/10.1186/1475-2859-8-40. [61] Gerstein AC, Jackson KM, McDonald TR, Wang Y, Lueck BD, et al. Identification of pathogen genomic differences that impact human immune response and disease during cryptococcus neoformans infection. MBio 2019;10. https://doi.org/10.1128/mBio.01440-19. [62] Paes HC, Albuquerque P, Tavares A, Fernandes L, Silva -Pereira I, et al. The transcriptional response of Cryptococcus neoformans to ingestion by Acanthamoeba castellanii and macrophages provides insights into the evolutionary adaptation to the mammalian host. Eukaryot Cell 2013;12. https://doi.org/10.1128/ec.00073- 13. [63] Christensson B, Roboz J. Arabinitol enantiomers in cerebrospinal fluid. J Neurol Sci 1991;105. https://doi.org/10.1016/0022-510X(91)90150-6. [64] Dbouk NH, Covington MB, Nguyen K, Chandrasekaran S. Increase of reactive oxygen species contributes to growth inhibition by fluconazole in Cryptococcus neoformans. BMC Microbiol 2019;19. https://doi.org/10.1186/s12866-019-1606-4. [65] Carlson T, Lupinacci E, Moseley K, Chandrasekaran S. Effects of environmental factors on sensitivity of Cryptococcus neoformans to fluconazole and amphotericin B. FEMS Microbiol Lett 2021;368. https://doi.org/10.1093/femsle/fnab040. [66] Van Hauwenhuyse F, Fiori A, Van Dijck P, Van Hauwenhuyse F, Fiori A, et al. Ascorbic acid inhibition of candida albicans Hsp90 -mediated morphogenesis occurs via the transcriptional regulator Upc2. Eukaryot Cell 2014;13. https://doi.org/10.1128/EC.00096-14. [67] Kuivanen J, Sugai-Guérios MH, Arvas M, Richard P. A novel pathway for fungal D-glucuronate catabolism contains an L -idonate forming 2 -keto-L-gulonate reductase. Sci Rep 2016;6. https://doi.org/10.1038/srep26329. .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 37 [68] Li L, Stoeckert CJ, Roos DS. OrthoMCL: Identification of ortholog groups for eukaryotic genomes. Genome Res 2003;13. https://doi.org/10.1101/gr.1224503. [69] Azghani AO, Miller EJ, Peterson BT. Virulence factors from Pseudomonas aeruginosa increase lung epithelial permeability. Lung 2000;178. https://doi.org/10.1007/s004080000031. [70] Zulianello L, Canard C, Köhler T, Caille D, Lacroix JS, et al. Rhamnolipids are virulence factors that promote early infiltration of primary human airway epithelia by Pseudomonas aeruginosa. Infect Immun 2006;74. https://doi.org/10.1128/IAI.01772-05. [71] McClure CD, Schiller NL. Inhibition of macrophage phagocytosis by Pseudomonas aeruginosa rhamnolipids in vitro and in vivo. Curr Microbiol 1996;33. https://doi.org/10.1007/s002849900084. [72] Bahia FM, De Almeida GC, De Andrade LP, Campos CG, Queiroz LR, et al. Rhamnolipids production from sucrose by engineered Saccharomyces cerevisiae. Sci Rep 2018;8. https://doi.org/10.1038/s41598-018-21230-2. [73] Volkers G, Palm GJ, Weiss MS, Wright GD, Hinrichs W. Structural basis for a new tetracycline resistance mechanism relying on the TetX monooxygenase. FEBS Lett 2011;585. https://doi.org/10.1016/j.febslet.2011.03.012. [74] Yang W, Moore IF, Koteva KP, Bareich DC, Hughes DW, et al. TetX is a flavin - dependent monooxygenase conferring resistance to tetracycline antibiotics. Journal of Biological Chemistry 2004;279. https://doi.org/10.1074/jbc.M409573200. [75] Kumar V, Sinha, AK. (2018). General aspects of phytases. Enzymes in Human and Animal Nutrition, 53 –72. https://doi.org/10.1016/b978-0-12-805419-2.00003- 4. [76] Li C, Lev S, Saiardi A, Desmarini D, Sorrell TC, et al. Identification of a major IP5 kinase in Cryptococcus neoformans confirms that PP-IP5/IP7, not IP6, is essential for virulence. Sci Rep 2016;6. https://doi.org/10.1038/srep23927. [77] Russel Lee I, Chow EWL, Morrow CA, Djordjevic JT, Fraser JA. Nitrogen metabolite repression of metabolism and virulence in the human fungal .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint 38 pathogen Cryptococcus neoformans. Genetics 2011;188. https://doi.org/10.1534/genetics.111.128538. [78] Fu MS, Coelho C, De Leon -Rodriguez CM, Rossi DCP, Camacho E, et al. Cryptococcus neoformans urease affects the outcome of intracellular pathogenesis by modulating phagolysosomal pH. PLoS Pathog 2018;14. https://doi.org/10.1371/journal.ppat.1007144. [79] Staib F, Mishra SK, Able T, Blisse A. Growth of Cryptococcus neoformans on uric acid agar. Zentralbl Bakteriol Orig A 1976;236:374-85. [80] Qin Y, Xia Y. Melanin in fungi: advances in structure, biosynthesis, regulation, and metabolic engineering. Microb Cell Fact 2024;23. https://doi.org/10.1186/s12934-024-02614-8. [81] DeJarnette C, Luna-Tapia A, Estredge LR, Palmer GE. Dihydrofolate Reductase Is a Valid Target for Antifungal Development in the Human Pathogen Candida albicans . MSphere 2020;5. https://doi.org/10.1128/msphere.00374-20. .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint