{"paper_id":"2c8b15f8-d120-4f8d-8bdf-70fd9d6d89d4","body_text":"1 \nReconstruction and exploitation of a dedicated Genome -Scale Metabolic \nModel of the human pathogen C. neoformans \nRomeu Viana a,b, Diogo Couceiro a,b,c, William Newton d, Luís Coutinho a,b, Oscar \nDias e, Carolina Coelho d, Miguel Cacho Teixeira a,b \nAffiliations \naDepartment of Bioengineering, Instituto Superior Técnico, University of Lisbon, 1049 -001 \nLisboa, Portugal.  \nbiBB - Institute for Bioengineering and Biosciences, Associate Laboratory Institute for Health \nand Bioeconomy - i4HB, 1049-001 Lisboa, Portugal.  \nc INESC-ID, R. Alves Redol, 9, 1000-029 Lisbon, Portugal \ndMRC Centre for Medical Mycology at University of Exeter, University of Exeter, Exeter, United \nKingdom \neCEB - Centre of Biological Engineering, Universidade do Minho, 4710-057 Braga, Portugal. \nCorresponding author: Miguel Cacho Teixeira – email: miguel.cacho.teixeira@tecnico.ulisboa.pt; \nPhone number: +351 218417772 \n \n \nAbstract \nC. neoformans  is notorious for causing severe pulmonary and central nervous \nsystem infections, particularly in immunocompromised patients. High mortality \nrates, associated with its tropism and adaptation to the brain microenvironment \nand its drug resistance profile, makes this pathogen a public health threat and a \nWorld Health Organization (WHO) priority. \nIn this study, we reconstructed GSMM iRV890 for C. neoformans var. grubii , \nproviding a promising platform for the comprehensive understanding of the \nunique metabolic features of C. neoformans, and subsequently shedding light on \nits complex tropism for the brain microenvironment and potentially informing \nthe discovery of new drug targets. The GSMM iRV890 model is openly available \nin the SBML format, and underwent validation using experimental d ata for \nnitrogen and carbon assimilation, as well as specific gro wth and glucose \nconsumption rates. Based on the comparison with GSMMs available for other \npathogenic yeasts, unique metabolic features were predicted for C. neoformans, \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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 \nThe copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint \n\n2 \nincluding key pathways shaping the dynamics between C. neoformans and the \nhuman host, and underlying its adaptation to the brain environment. Finally, \npredicted essential genes from the validated model are explored herein as \npotential novel antifungal drug targets. \n \nKeywords: C. neoformans, Global stoichiometric model, Drug targets, Metabolic \nfeatures, Neurotropism. \n \n1. Introduction \nCryptococcal meningitis is a disease caused by a few pathogenic \nbasidiomycetous yeast species, namely Cryptococcus neoformans  (C. neoformans) \nand Cryptococcus gatii . Cryptococcosis is caused by three Cryptococcus \nspecies/variants, C. neoformans var. grubii  (serotype A), responsible for 95% of \nCryptococcus infections worldwide [1]; C. neoformans var. neoformans (serotype D) \nand Cryptococcus gattii (serotypes B and C) geographically restricted to tropical \nand/or subtropical regions [2]. \nThese species are notorious for inducing severe pulmonary and central nervous \nsystem infections [3]. While these pathogens are harmless in healthy individuals, \nthey poses a serious threat to immunocompromised patients, especially those \nwith acquired immunodeficiency syndrome (AIDS) or those undergoing \nimmunosuppressive therapies, causing severe meningoe ncephalitis and other \nserious neurological complications [4–6]. The latest systematic review, using data \nfrom more than 120 countries, estimates that cryptoc occal meningitis affects 190 \n000 people worldwide annually, being associated with a mortality rate of 76% \n[7]. Cryptococcal infections are commonly treated with combination therapy, \nusually flucytosine in combination with amphotericin B in a first induction stage, \nfollowed by consolidation and long -term maintenance with high dose \n.CC-BY 4.0 International licensemade available under a \n(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 \nThe copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint \n\n3 \nfluconazole [8]. Anti-cryptococcal monotherapy is not considered optimal, as it \ncarries the risk of drug resistance [9]. Still, fluconazole monotherapy is still used, \nmostly due to limited drug access. An increase in fluconazole resistance among \nC. neoformans  isolates was observed in past decades [10,11]. Fluconazole \nresistance is particularly notorious in isolates from relapse disease [12]. Despite \nverified in vitro  susceptibility, echinocandins are not used clinically to treat \ncryptococcosis due to intrinsic resistance in vivo [13], attributed to echinocandins \ninability to penetrate the blood -brain barrier. Another possible contributor to \nechinocandin resistance in Cryptococcus species is fungal cell wall melanization, \nthrough the action of a fungal laccase, which uses the L -DOPA and dopamine \nfound in the human brain as precursors [14]. Melanin is an important virulence \nfactor in C. neoformans since it can neutralize oxidative stress radicals [15], as well \nas some toxic compounds, including some antifungal drugs, such as caspofungin \nand amphotericin B [16,17].  \nC. neoformans is widely spread in the environment, with worldwide distribution, \nin bird guano, soils and trees. Fungal particles are then inhaled by humans and \nother mammals [2]. This pathogen is characterized by their high resistance to \nharsh environments in nature and in mammalian hosts [18], and after inhalation \ninto the host’s lungs, Cryptococccus  can stay in a dormant latent granulomatous \nform for long periods of time [3]. However, tropism for the central nervous \nsystem is not yet fully understood [2,3]. Despite being a public health threat and \na WHO priority pathogen [19], C. neoformans still has many aspects of its peculiar \nmetabolism associated with the central nervous system and interactions with the \nhost that remain poorly understood [20]. \n In this work, iRV890 the first reconstructed GSMM for the human pathogen C. \nneoformans var. grubii, a frequent variant of these pathogenic species, is presented. \nTo facilitate usage by other researchers, the model is provided in the widely used \nSBML format. Model validation was conducted using experimental data for \n.CC-BY 4.0 International licensemade available under a \n(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 \nThe copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint \n\n4 \nnitrogen and carbon assimilation from phenotypic arrays covering 222 different \nsources [21]. Specific growth and glucose consumption rates were experimentally \ndetermined in order to quantitatively validate the model’s predictive power. A \nset of essential genes derived from the validated model is predicted and \ndiscussed in terms of their potential as novel antifungal drug targets. An \nadditional comparison, with GSMM´s for other pathogenic yeast species and S. \ncerevisiae was performed regarding the gene essen tiality prediction and unique \nmetabolic features of C. neoformans. Some peculiar characteristics and pathways \nof this fungus relevant to its pathogenicity are also discussed based on our \nfindings. The iRV890 model provides a promising platform for global elucidation \nof the metabolic features of C. neoformans var. grubii , with expected impact in \nguiding the identification of new drug targets and understanding the complex \nmetabolism of this pathogen in the context of the human brain. \n \n2. Materials and Methods \n \n2.1. Model Development \nThe genome-scale metabolic model of C. neoformans var. grubii H99 , designated \nas iRV890, was reconstructed using merlin 4.0.5 [22] following the methodology \ndescribed elsewhere [23] and OptFlux 3.0 [24], for curation and subsequent \nvalidation stages. All computational analyses were executed utilizing the IBM \nCPLEX 12.10 solver. \n \n2.2. Genome Annotation and Assembling of the Metabolic Network \nThe genome sequence of the C. neoformans var. grubii was retrieved from NCBI’s \nAssembly database, with the accession number ASM1180120v1 [25] and the \n.CC-BY 4.0 International licensemade available under a \n(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 \nThe copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint \n\n5 \nTaxonomy ID 235443 from NCBI´s Taxonomy database [26]. The genome-wide \nfunctional annotation was based on taxonomy and frequency of similar \nsequences through remote DIAMOND alignment [27] and similarity searches \nusing the UniProtKB/Swiss -Prot database. Draft network assembly relied on \nprotein-reaction associations available in the KEGG (Kyoto Encyclopedia of \nGenes and Genomes) database [28], with all reactions categorized as spontaneous \nor non-enzymatic also incorporated in the initial draft model. Hit sele ction was \nperformed as described elsewhere [23] and phylogenetic proximity was \nimplemented based on a phylogenetic tree from literature [22], this process \nautomated via the “Automatic workflow” merlin tool and then integrated into \nthe draft model [22]. \n \n2.3. Reversibility, Directionality and Balancing \nReaction reversibility and stoichiometry curation involved a multi -step process \ncombining both automated and manual efforts. Initially, merlin was used to assist \nin correcting the direction and reversibility of reactions, utilizing references from \nremote databases like eQuilibrator [29] to predict reaction directionality, as \ndescribed by Dias et al. [23]. This was followed by extensive manual curation, \nexploiting databases such as MetaCyc [30], Brenda [31], UniProt [32], FungiDB \n[33], RHEA [34], KEGG [30] and existing literature, in order to ensure that all \nreactions in the network are balanced, and with the correct directionality. All \nmanually edited reactions can be found in Supplementary Data 1. \n \n2.4. Compartmentalization and Transport reactions \nThis model includes four compartments: extracellular, cytoplasm, \nmitochondrion, and peroxisome and one intercompartment, the cytoplasmic \n.CC-BY 4.0 International licensemade available under a \n(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 \nThe copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint \n\n6 \nmembrane. The prediction of compartments for each enzyme was performed \nusing the DeepLoc - 2.0 [35] and directly imported to merlin. The transport \nreactions were automatically generated by TranSyT [36], a tool integrated in \nmerlin, based on the public database TCDB [37]. Additional transport reactions \nacross internal and external membranes for common metabolites, such as H 2O, \nCO2, and NH3, often carried out without a transporter, were added to the model \nwith no gene association. \n \n2.5. Biomass Equation \nThe biomass formation was depicted through an equation including proteins, \nDNA, RNA, lipids, carbohydrates, and cofactors, with detailed composition \ninformation for each macromolecule sourced from literature or experimental \ndata. All calculations were perf ormed as in previously described methodology \n[38] and are detailed in Supplementary Data 1. ATP requirements for biomass \nproduction and growth -associated maintenance (GAM) were added to the \nbiomass equation with a value of 25.65 mmol ATP/gDCW, based on the  ATP \nrequirements for the biosynthesis of cell polymers as reported in [39], and ATP \nrequirements for non -growth-associated maintenance (NGAM) was inserted in \nthe model by an equation with specific fixed flux boundaries inferred from \nCandida tropicalis  [39]. The theoretical phosphorus -to-oxygen ratio used in the \nSaccharomyces cerevisiae  iMM904 metabolic model was applied to our model \nadding three generic reactions contributing to this ratio: \nReaction R00081: \n1.0 Oxygenmito + 4.0 Ferrocytochrome cmito + 6.0 H+ mito ↔ 2.0 H2Omito + 4.0 Ferricytochrome cmito + \n6.0 H+cyto,                                                                                                                                                                                                                                                        (1) \n \nReaction R_Ubiquinol_Cytochrome_Reductase: \n1.0 Ubiquinolmito + 2.0 Ferricytochrome cmito + 1.5 H+ mito ↔ 1.0 Ubiquinonemito + 2.0 \nFerrocytochrome cmito + 1.5 H+cyto,                                                                                                                                                                                   (2) \n.CC-BY 4.0 International licensemade available under a \n(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 \nThe copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint \n\n7 \n \nReaction T_ATP_Synthase: \n1.0 Orthophosphatemito + 1.0 ADPmito + 3.0 H+ cyto ↔ 1.0 ATPmito + 1.0 H2Omito + 3.0 H+mito,                       (3) \n \nThe final balance reaction: \n3.0 Orthophosphatemito + 1.0 Oxygenmito + 3.0 ADPmito + 2.0 Ubiquinoolmito ↔ 3.0 ATPmito + 5.0 \nH2Omito + 2.0 Ubiquinonemito                                                                                                                    (4)                                                                                                                        \n \n2.7 Network simulation and model curation \nDuring the model reconstruction process, an extensive manual curation was \nneeded in order to correct gaps in some pathways, due to incorrect reversibility, \nincomplete reactions, annotation errors, and blocked metabolites. Each case was \nmeticulously inspected and studied, and reactions were edited, manually added \nto, or removed from the model based on evidence from the literature or deposited \non databases such as KEGG pathways, MetaCyc, FungiDB etc. The detailed list \nof all the performed alterations can be found in Supplementary Data 1. \nDuring this process, merlin’s “ Find blocked reactions ” was used to assist and \naccelerate the process. Additionally, BioISO, a tool based on the Constraints -\nBased Reconstruction and Analysis (COBRA) and Flux Balance Analysis (FBA) \n[40] frameworks, also integrated in merlin, assisted in the process of identifying \npotential errors in the network and accelerated the process of correcting the gaps.  \n \n2.8 Model Validation \n \n2.8.1. Strains and Growth Media \nC. neoformans var. grubii H99E strain, from the laboratory of Jennifer Lodge was \nobtained from the Fungal Genomic Stock Center, and routinely maintained in \n.CC-BY 4.0 International licensemade available under a \n(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 \nThe copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint \n\n8 \nYeast extract –Peptone–Dextrose (YPD), containing: 20 g/L glucose (Merck, \nDarmstadt, Germany), 20 g/L peptone (Merck, Darmstadt, Germany), and 10 g/L \nyeast extract (Merck, Darmstadt, Germany). The parental KN99 and derived \nKN99_ΔCNAG_02553 were obtained fro m the deletion library created by the \nMadhani laboratory [41], through Fungal Genetics Stock Center, and grown on \nYNB medium, containing 1.7 g/L Yeast Nitrogen Base, without amino acids (Difco \nBD, England, United Kingdom) and 20 g/L inositol, used as carbon source. Synthetic \nminimal media (SMM) was used for batch cultivation experiments used to \nvalidate model predictions, SMM including: 20g/L glucose (Merck, Darmstadt, \nGermany), 2.7 g/L ammonium sulphate (Merck, Darmstadt, Germany), 0.05 g/L \nmagnesium sulphate (Riddle-de-Haen), 2 g/L potassium dihydrogen phosphate \n(Panreac, Barcelona, Spain), 0.5 g/L calcium chloride (Merck, Darmstadt, \nGermany), and 100 µg/L biotin (Sigma). \n \n2.8.2. Aerobic Batch Cultivation \nC. neoformans var. grubii cells were batch cultivated in Erlenmeyer flasks \ncontaining 250ml of SMM or YNB medium, at 30 ºC (250 rpm). Exponential phase \ninocula, with an Optical Density (OD) (Hitachi u2001) at 600nm of 0.3, were \nprepared and cells were transferred to Erlenmeyer flas ks containing 250ml of \nfresh medium and cultivated at 30 ºC with orbital agitation (250 rpm) for the \nduration of the experiment. \n \n2.8.3. Cell Density, Dry Weight, and Metabolite Concentration Assessment \nThroughout cell cultivation in SMM, 4 mL samples were collected every two \nhours for subsequent quantification of biomass and extracellular metabolites. \nCell density was monitored by measuring OD600nm. For dry weight determination, \nculture samples were centrifuged at 13,000 rpm for 3 minutes, and the resulting \n.CC-BY 4.0 International licensemade available under a \n(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 \nThe copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint \n\n9 \npellets were freeze -dried for 72 hours at -80 ◦C before being weighed. \nExtracellular metabolites, including glucose, ethanol, glycerol, and acetic acid, \nwere identified and quantified by High -performance liquid chromatography \n(HPLC) on an Aminex HPX-87 H Ion Exchange chromatography column, eluted \nwith 0.0005 M H2SO4 at a flow rate of 0.6 mL/min at room temperature. Samples \nwere analyzed in triplicate, and concentrations were determined using \nappropriate calibration curves. During the exponential growth phase, the specific \ngrowth rate, specific glucose consumption rate, and specific production rates of \nethanol, glycerol, and acetic acid were calculated as described elsewhere [42]. \n \n2.8.4. Network simulation and analysis \nAll the phenotype simulations were performed with Flux Balance Analysis (FBA) \nin OptFlux 3.0  using the IBM¨CPLEX solver, including: gene and reaction \nessentiality; growth assessment; metabolite production and consumption; and \ncarbon and nitrogen source utilization. For gene and reaction essentiality, in silico \ngrowth was simulated in environmental conditions mimicking RPMI medium \nand a biomass flux lower than 5% of the wild -type strain, after the respective \ngene/reaction knockout, was considered the threshold for essentiality. Gene and \nreaction knockout was simulated by restraining its corresponding flux bounds to \nzero. \n \n3. Results and Discussion \n \n3.1. Model characteristics \nThe C. neoformans var. grubii genome-scale metabolic model reconstructed herein, \nand denominated iRV890, comprises 890 genes associated with 2598 reactions, of \n.CC-BY 4.0 International licensemade available under a \n(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 \nThe copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint \n\n10 \nwhich 683 are transport reactions, and 2047 metabolites across four \ncompartments (extracellular, cytoplasm, mitochondria, and peroxisome). The \nmodel can be found in SBML format in Supplementary Data 2. Among the 2598 \nreactions, 1747 are cytoplasmic, 351 mi tochondrial, 60 peroxisomal and 440 are \ndrains (exchange constraints, used to simulate the import of media components \nor the leakage or export of extracellular metabolites). \nDuring the manual curation process, a total of 639 reactions/genes required \nalterations, including 80 who were mass balanced, 518 who were corrected for \nreversibility, directionality, or added or removed from the model, and 41 whose \nannotation was corrected, as detailed in Supplementary Data 1. \nThe Biomass equation (Table 1) encompasses the cell's major components along \nwith their respective and relative contributions, including DNA, RNA, lipids, \ncarbohydrates, and cofactors. The equation's composition in carbohydrates [43], \nand lipids [44–46] was inferred from literature data for C. neoformans . The \ncomposition of Proteins, DNA and RNA was determined by the e -BiomassX \nwhere the whole genome sequence was used to estimate the amount of each \ndeoxyribonucleotide as described in [47] mRNA, rRNA, and tRNA being used to \nestimate the total RNA in the cell as described in [47,48]. \n \nTable 1: Biomass composition used in the model iRV890. The full individual \nvalidated contributions of each of these metabolites are shown in Supplementary \nData 1. \n          \nMetabolite g/gDCW   Metabolite g/gDCW \n          \nLipids     Proteins   \nLanosterol 0.000122   L-Valine 0.019058 \nZymosterol 0.000254   L-Tyrosine 0.020501 \nSqualene 0.000209   L-Tryptophan 0.006392 \nErgosterol 0.000724   L-Threonine 0.022013 \n.CC-BY 4.0 International licensemade available under a \n(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 \nThe copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint \n\n11 \nPhosphatidylserine 0.005024   L-Serine 0.027696 \nPhosphatidylinositol 0.004638   L-Proline 0.015390 \nPhosphatidylcholine 0.031241   L-Phenylalanine 0.022928 \nPhosphatidylethanolamine 0.017714   L-Methionine 0.008274 \nCardiolipin 0.002254   L-Lysine 0.033668 \nPhosphatidic acid 0.000644   L-Leucine 0.036895 \nPhosphatidylglycerol 0.000644   L-Isoleucine 0.028492 \nTetradecanoic acid 0.000020   L-Histidine 0.010159 \nHexadecanoic acid 0.000097   L-Glutamate 0.029371 \nOctadecanoic acid 0.000038   L-Cysteine 0.003902 \nDodecanoic acid 0.000021   L-Aspartate 0.023883 \nDecanoic acid 0.000011   L-Asparagine 0.027060 \nOctanoic acid 0.000038   L-Arginine 0.020979 \nOctadecanoic acid 0.000038   L-Alanine 0.012706 \n(9Z)-Octadecenoic acid 0.000093   Glycine 0.010258 \n(9Z,12Z)-Octadecadienoic acid 0.000116   L-Glutamine 0.020550 \n(9Z,12Z,15Z)-Octadecatrienoic \nacid 0.000002       \nTriacylglycerol 0.032969   Soluble Pool   \nSterol esters 0.001127   Pyridoxine 5'-phosphate 0.000833 \n      FAD 0.000833 \nCarbohydrates     Thiamine(1+) diphosphate 0.000833 \nChitin 0.005645   NAD 0.000833 \nMannan 0.033956   Glutathione 0.000833 \n β (1,3)-Glucan 0.360399   Riboflavin 0.000833 \n      Eumelanin 0.000833 \nRibonucleotides     Ubiquinone-6 0.000833 \nUTP 0.006713   NADP 0.000833 \nGTP 0.006806   COA 0.000833 \nCTP 0.005381   FMN 0.000833 \nATP 0.007101   5-Methyltetrahydrofolate 0.000833 \n          \nDeoxyribonucleotides         \ndTTP 0.016718       \ndGTP 0.017029       \ndCTP 0.015059       \ndATP 0.017193       \n \nThe translated genome sequence was used to calculate the amino acid \ncomposition using the percentage of each codon usage as described in [47]. \nEssential metabolites were included in the biomass composition to qualitatively \naccount for the essentiality of their synthesis pathways [49,50]. The growth and \nnon-growth ATP requirements were adopted from S. cerevisiae [51]. \n.CC-BY 4.0 International licensemade available under a \n(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 \nThe copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint \n\n12 \n \n3.2. Model validation \n \n3.2.1 Carbon and nitrogen source utilization \nIn silico  simulations were conducted using 222 different compounds as the \nexclusive carbon or nitrogen sources, under conditions mimicking those of the \nminimal medium reported in [21]. The in silico growth was compared to publicly \navailable phenotypic microarray (Biolog platform) data for C. neoformans var. \ngrubii performed in [21]. A total of 155 sole carbon sources and 67 sole nitrogen \nsources were evaluated. For the analyses we used the data from stationary phase \nyeasts condition after calculating the di fference from the respective negative \ncontrol group, without any carbon or nitrogen sources.  iRV890 model correctly \npredicted growth in 85% (133/155) of the carbon sources tested and in 85% (57/67) \nof the nitrogen sources Supplementary Table 1. In some cases of failed \npredictions, such as L -ornithine and glycerol (carbon source) and amino acids \nand D -Glucosamine (nitrogen source), genetic information and the model \ninclude all the necessary steps to predict their assimilation as sole \ncarbon/nitrogen sources, but no growth was experimentally observed. In such \ncases, the failed prediction may be related to non -metabolic factors that are not \nconsidered in model simulations, or to inaccuracies regarding the annotation of \ntransporters, which is still a big challen ge in the current model development \nprocess [52]. In other cases, however, the prediction model failed because specific \nenzymes are not yet characterized for C. neoformans , despite growth in \nexperimental conditions. The comparison between the model’s predi ction and \nexperimental evidence suggests that the following enzyme activities are likely to \nbe present in C. neoformans, although the underlying genes and proteins were not \nyet identified:  1.2.1.3 (aldehyde dehydrogenase), 1.1.1.21 (aldose reductase), \n3.1.1.65 (L -rhamnono-1,4-lactonase), 1.1.1.56 (ribitol 2 -dehydrogenase), 5.1.3.30 \n.CC-BY 4.0 International licensemade available under a \n(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 \nThe copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint \n\n13 \n(D-psicose 3 -epimerase), 2.7.1.55 (allose kinase), 4.1.2.10 ((R) -mandelonitrile \nlyase), 5.3.1.3 (D -arabinose isomerase), 3.2.1.86 (6 -phospho-beta-glucosidase), \n4.1.2.4 (deoxyribose-phosphate aldolase), 3.2.1.86 (6 -phospho-beta-glucosidase) \nand 1.1.1.16 (galactitol 2-dehydrogenase). The identification and characterization \nof these predicted functions and their underlying gene(s) will shed light on the \nspecific pathways of carbon or nitrogen assimilation in this pathogen, potentially \nrevealing new mechanisms of virulence related to adaptation to the host \nenvironment. Altogether, the model achieved 85% predictability which is a high \nvalue, especially considering that the extensive list of carbon and nitrogen \nsources tested includes many that are not commonly us ed in traditional \nmetabolic and phenotypic experiments and thus lack biochemical \ncharacterization. \n \n3.2.2 Growth parameters in batch culture \nTo quantitatively validate the model, the specific growth rate, glucose \nconsumption rate, and metabolite production rates were experimentally \ndetermined, and compared with in silico  predicted values. For a glucose \nconsumption rate of 1.72 mmol.gDCW-1.h-1, a specific growth rate of 0. 188 h-1 was \nexperimentally determined, leading to no detectable production of ethanol, \nglycerol, or acetate. For comparison with in silico  results, we simulated the \nsystem's behavior in SMM medium with a fixed glucose uptake fl ux of 1.72 \nmmol.g-1 dry weight.h -1. Other nutrient fluxes were left unconstrained, as the \nsystem was glucose-limited under these conditions. The simulation predicted a \nspecific growth rate of 0.128 h -1, a difference of 0.06 h -1 to the experimentally \ndetermined value (Table 2). In these conditions, the model did not predict the \nformation of glycerol, acetic acid, or ethanol as by -products, consistent with the \nexperimental data. Moreover, the model is accurate at predicting no growth of C. \n.CC-BY 4.0 International licensemade available under a \n(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 \nThe copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint \n\n14 \nneoformans under anaerobic conditions which is to be expected since this \npathogen is an obligate aerobic fungus [53]. \nTable 2 - Growth parameter values predicted by the iRV890 model, in \ncomparison with those determined experimentally. \n  Specific growth  \nrate (h-1) \nq (mmol g-1 dry weight h-1) \n  Glucose Ethanol Glycerol Acetic acid \nIn silico 0.128 1.72 0        0 0 \nIn vivo 0.188 1.72 0        0 0 \n \n \n \n3.3. C. neoformans unique metabolic features \nTo uncover unique metabolic features of this pathogen, a comparison was made \nbetween the C. neoformans GSMM with those previously built for C. glabrata [49], \nC. albicans [54], C. auris [55] and S. cerevisiae [56] by us and others. A comparison \nacross the existing models was carried out based on shared EC numbers. After \nintersecting the EC numbers present in each of the five models, 40% (229/566) of \nthe EC numbers were common among all the tested yeasts (Figure 1). \nAdditionally, the remaining 17% (96/556) are exclusive to the C. neoformans model \nand may represent unique metabolic features of this species relative to the \nremaining. We confirmed none of these 96 EC numbers were associated with \noutdated, incomplete or incorrect reaction associations. However, a small subset \nof these 96 EC numbers may be present in other species included in the \ncomparison, but not accounted for in their respective GSMMs during the process \nof reconstruction. \n.CC-BY 4.0 International licensemade available under a \n(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 \nThe copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint \n\n15 \n \nFigure 1: Multi-species comparison in terms of proteins with an associated EC \nnumber present in the C. neoformans iRV890, C. albicans iRV781, C. auris iRV973, \nS. cerevisiae iIN800 and C. glabrata iNX804 GSMMs. The multiple intersection was \nperformed using jvenn [57]. \nFrom the list of 96 C. neoformans unique EC numbers (Supplementary Data 1), \nmetabolic features or pathways relevant in the context of fungal infection in the \nhost brain were searched manually for, and compared to extant knowledge of \nthese pathways being defense mechanisms, or enabling host adaptation, through \ndegradation or biosynthesis of specific metabolites. A few of these unique EC \nnumbers with higher potential of impacting C. neoformans  pathogenesis are \ndiscussed below: \n1.1.1.12 and 1.1.1.287 - L-arabinitol 4 -dehydrogenase and D -arabinitol \ndehydrogenase are two enzymes that are required for L -arabinitol assimilation \nas carbon source, which is a particular metabolic feature of C. neoformans when \n.CC-BY 4.0 International licensemade available under a \n(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 \nThe copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint \n\n16 \ncompared to other yeast species (Supplementary Table 1). Indeed, neither \nCandida species [54,58] nor S. cerevisiae [59], can assimilate L -arabitol, unless \ngenetically engineered [60]. Interestingly, environment isolates containing SNPs \nin the PTP1 gene, encoding a C. neoformans arabitol transporter, were associated \nwith increased patient survival, while a virulence defect was observed in BALB/c \nmice due to PTP1 gene deletion [61]. PTP1 expression was also found to be highly \ninduced in macrophage and amoeba infection [62]. Since arabitol is present in the \ncerebrospinal fluid [63], it is possible this pathway may be used to feed from \npolyols in CNS and contributes to explain brain tropism of C. neoformans , \ncompared to other fungal species. \n1.1.3.8 and 3.1.1.17 - L-gulonolactone oxidase and gluconolactonase are two \nenzymes that participate in ascorbate metabolism, allowing the utilization of \nInositol and D -glucuronate as source for L -ascorbate biosynthesis (Figure 2). \nInterestingly, it was reported by two independ ent studies that the presence of \nascorbate, an antioxidant, lowers the susceptibility towards fluconazole in C. \nneoformans [64,65]. However, this effect seems to not be related to its antioxidant \npotential, but with ascorbate -induced up-regulation of Upc2, a transcriptional \nregulator of genes involved in ergosterol biosynthesis, as shown in C. albicans \n[66]. The ability of C. neoformans to synthesize ascorbate from inositol is \nparticularly noteworthy, given the abundance of inositol in the human brain [20] \nand the widespread use of fluconazole in treating infections. Further it is possible \nthat ascorbate contributes to resistance to ROS. Having a mechanism to produce \na compound that mitigates the toxicity of fluconazole and ROS could co ntribute \nto a significant adaptive advantage for this species. \n \n \n.CC-BY 4.0 International licensemade available under a \n(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 \nThe copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint \n\n17 \nFigure 2 – C. neoformans  pathway for \nascorbate biosynthesis, with the respective \nC. neoformans var. grubii  EC numbers \npresent in the iRV890 model. The 1.1.3.8 \nand 3.1.1.17 enzymes, which are unique to \nC. neoformans  among other pathogenic \nyeasts, are highlighted in purple. \n \n \n \n \n \n \n \n \nAdditionally, the 1.1.3.8 and 3.1.1.17 enzymes are also important for inositol \nassimilation as a carbon source through a variation of the previous pathway. This \npathway was suggested recently as an alternative pathway in fungi for inositol \nassimilation, and since inositol is highly abundant in the human brain, this may \nrepresent a very important metabolic feature for C. neoformans. In fact, in order \nto implement that pathway in the model, two of the reactions reported were \nrecreated and attributed with the names R2_Inositol_Pathway and \nR1_Inositol_Pathway in the model, although the corresponding EC numbers and \ngenes have not been identified in the annotated C. neoformans genome [67]. This \npathway was recreated exclusively from literature, and while it lacks validation \nstudies, two possible genes were hypothesized as probable candidates for \n.CC-BY 4.0 International licensemade available under a \n(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 \nThe copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint \n\n18 \nencoding the 1.1.1.69 enzyme, CNAG_02553 and CNAG_00126, predicted by \nOrthoMCL [68]. Additional pathways for inositol assimilation are reported for \nanimal (Figure 3.B) and bacteria (Figure 3.C); however, since C. neoformans lacks \nalmost all the enzymes in those pathways, we considered that the new pathway \nreported in fungi [67] was the most probable to occur in this pathogen. Taking \nadvantage of the available ΔCNAG_02553 deletion strain, we tested whether a \nstrain deleted for this putative enzyme could be g rown in inositol as a single \ncarbon source, compared to parental strain. However, even in the absence of \nCNAG_02553 gene, C. neoformans  is able to utilize inositol as the sole carbon \nsource in SMM (YNB, supplemented with glucose or inositol, data not shown). \nEventually, it would be necessary to knockout both CNAG_00126 and \nCNAG_02553 genes to obtain a strain unable to grow in media containing inositol \nas the sole carbon source. Further scrutiny is required to address this issue. \n1.1.1.377 - L-rhamnose 1-dehydrogenase is required for L-rhamnose assimilation \nas sole carbon source. Rhamnose is used by some pathogens, for example \nPseudomonas aeruginosa, to produce rhamnolipids, and constitutes an important \nvirulence factor in those bacteria, with roles in biofilm formation, hydrophobic \nnutrient uptake, and host immunity evasion, characterized for increasing lung \nepithelial permeability [69,70] and inhib ition of macrophage phagocytosis [71]. \nCandida species [54,55,58] and S. cerevisiae (unless engineered) [72] cannot \nassimilate L -rhamnose, and thus  assimilation of rhamnose is a particular \nmetabolic feature of C. neoformans  when compared to these yeast species \n(Supplementary Table 1). \n.CC-BY 4.0 International licensemade available under a \n(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 \nThe copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint \n\n19 \n \nFigure 3 - Metabolic pathways for inositol assimilation as carbon source, A - \nbased on the proposed fungal inositol assimilation pathway reported in \nKuivanen et al. 2016 [67], B – based on the animal inositol assimilation pathway, \nC- based on the bacterial inositol assimilation pathway, and D- the unknown, and \napparently unique, pathway of inositol assimilation proposed for C. neoformans. \nThe respective C. neoformans var. grubii genes present in iRV890 are highlighted \nin purple. The currently unknown genes are highlighted in red and the proposed \nreactions with an unknown EC number are represented as a question mark in \nred. \n.CC-BY 4.0 International licensemade available under a \n(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 \nThe copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint \n\n20 \n1.14.13.231 - tetracycline 11a-monooxygenase is an enzyme that allows the direct \nconversion of tetracycline into 11a -hydroxytetracycline, reported to confer \nresistance to all clinically relevant tetracyclines, by efficient degradation of a \nbroad range of tetracycline an alogues. The hydroxylated product, 11a -\nhydroxytetracycline, is very unstable and leads to intramolecular cyclization and \nnon-enzymic breakdown to undefined products, completely neutralizing the \ntetracycline effects [73,74]. Although tetracycl ines are generally used as \nantibacterial antibiotics, and have poor antifungal activity, the presence of this \nenzyme in C. neoformans  should be taken into consideration when designing \ntetracyclines against fungi. \n3.1.3.8 - 3-phytase is an enzyme involved in inositol metabolism that may be \ninvolved in the production of phytic acid from inositol, a primary storage \nmolecule of phosphorus and inositol in fungi (although not in the pathogenic \nCandida species), bacteria and plants [75]. Interestingly, this pathway has been \nshown to play a key role in C. neoformans virulence. Indeed, it was previously \nreported that the deletion of the gene encoding the enzyme (EC number 2.7.1.158) \nthat immediately precedes 3 -phytase leads to growth impairment and to \nattenuated virulence in C. neoformans, associated with failed dissemination into \nthe brain [76]. \n3.5.2.17 – hydroxyisourate hydrolase is an enzyme essential for the assimilation \nof uric acid as sole nitrogen source. Uric acid is a normal component of urine and \nbird guano. In bird guano, 70% of the nitrogen present is in the form of uric acid \nwith the rest consis ting primarily of xanthine, urea, and creatinine [77] \nAdditionally, uric acid enhances the production of key cryptococcal virulence \nfactors, including capsule and urease, an enzyme required for full fitness at \nmammalian pH and dissemination to t he brain [78], C. neoformans capsule is \ninduced in the presence of uric acid [79]. \n.CC-BY 4.0 International licensemade available under a \n(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 \nThe copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint \n\n21 \n4.1.1.105 – L-tryptophan decarboxylase catalyzes the conversion of L-tryptophan \ninto Tryptamine, which can then be converted into serotonin, and shares \nstructure with several aminergic neuromodulators. However, the reaction is \nbidirectional, and Tryptamine can also be converted into L-tryptophan. While it \nis unclear which may be the role of this enzyme in C. neoformans, it is potentially \nrelated to the brain environment, specifically in the utilization of serotonin as a \nnitrogen source, through its conversion to L-tryptophan.  \n4.1.1.28, 1.14.18.1, and 1.10.3.2 – DOPA decarboxylase, tyrosinase and laccase are \nparticularly important in C. neoformans, as they are involved in the biosynthesis \nof melanin. Most fungi possess multiple melanin biosynthetic pathways, while \nCryptococcus neoformans  exclusively synthesizes melanin through the L -DOPA \npathway. [80]. Melanin is able to neutralize oxidative stress radicals as well as \nprotecting the pathogen against the host immune system and antifungal drugs, \nsuch as caspofungin and amphotericin B. L-DOPA and Dopamine are present in \nthe human brain and serve as precursors for dopamine biosynthesis in this \npathogen, but it is uncertain why C. neoformans exclusively uses this pathway, \ncompared to other human pathogenic fungi. \n \n3.4. Drug target analysis based on gene essentiality prediction \nPathogen’s GSMM are particularly useful to identify potential new drug targets, \namong predicted essential genes. For that purpose, a list of all predicted essential \ngenes and enzymes in C. neoformans  was obtained through simulation of the \nsystem's behaviour in RPMI medium, which mimics the environmental \nconditions of human serum. A total of 157 enzymes and 101 genes were identified \nas essential in RPMI medium. Among these targets, some have been previ ously \nidentified as essential genes in other pathogenic y easts (see Table 3), indicating \npotential drug targets common to all Candida species and C. neoformans. Notably, \n.CC-BY 4.0 International licensemade available under a \n(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 \nThe copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint \n\n22 \nErg11 and Fks1 are already targets of currently used antifungals, fluconazole and \nechinocandins, respectively. Additionally, Erg26, Erg27, Erg24, Erg4, Erg7, \nErg12, and Erg13 have all been identified herein as potential drug targets within \nthe ergosterol biosynthetic pathway. Particularly interesting is Erg4, as it lacks a \nhuman ortholog, and may represent a superior candidate for designing \ncompounds with enhanced selectivity and lower toxicity.  \nSimilarly to Candida, which lacks a Folate transporter [81] and relies on its de novo \nbiosynthesis, C. neoformans seems to also lack a folate transporter, leading to the \nidentification of Fol1, a multifunctional enzyme of the folic acid biosynthesis \npathway, as a promising multi-yeast drug target. Furthermore, Fas1, a fatty acid \nsynthase enzyme, and Chs1, a chitin sy nthase, also lack human orthologs and \nconstitute promising alternative antifungal drug targets due to their important \nrole for membrane and cell wall structure and integrity. Other noteworthy targets \nspan various pathways, including purine metabolism, terpenoid backbone \nbiosynthesis, pyrimidine metabolism, CoA biosynthesis, glycerophospholipid \nbiosynthesis, and ubiquinone biosynthesis (Table 3). However, exploring these \ntargets requires leveraging potential structural differences in the enzyme's active \nsite compared to their human counterparts.  \nSince C. neoformans  colonizes a different host environment and is \nphylogenetically distant from Candida spp. our evaluation was extended to \ninclude potential new drug targets that are unique to this species, and not shared \nby Candida spp. We identified only two such targets: the 1.14.18.1 tyrosinase, \nencoded by the gene CNAG_03009, and the 2.5.1.83 hexaprenyl diphosphate \nsynthase, encoded by the gene CNAG_04375. While tyrosinase, responsible for \nmelanin production, has a human ortholo g (since humans also sy nthesize \nmelanin via the L-DOPA), hexaprenyl diphosphate synthase (2.5.1.83) is fungal-\nspecific and may represent an interesting target. This enzyme plays a crucial role \n.CC-BY 4.0 International licensemade available under a \n(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 \nThe copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint \n\n23 \nin terpenoid backbone biosynthesis, serving as a key contributor to the synthesis \nof precursors for ubiquinone biosynthesis.\n.CC-BY 4.0 International licensemade available under a \n(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 \nThe copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint \n\n24 \nTable 3 - Enzymes predicted to be essential in RPMI medium in 5 pathogenic fungal species, based on the screening of the genome -\nscale metabolic models of C. neoformans iRV890, C. auris IRV973, C. parapsilosis iDC1003, C. albicans iRV781, and C. glabrata iNX804. \nGrey rows highlight enzymes which are not encoded in the human genome. Data regarding the drug association retrieved from the \nDrugBank database; only drugs with known pharmacological action against pathogens were selected. \nC. neoformans C. albicans C. glabrata C. parapsilosis C. auris S. cerevisiae Human \nPharmacological \naction EC Number Pathway/Target \n                    \nCNAG_04605 ERG26 CAGL0G00594g CPAR2_302110 CJI97_000938 ERG26 NSDHL   1.1.1.170 Steroid  \nCNAG_00441 IMH3 CAGL0K10780g CPAR2_104580 CJI97_000080 IMD4 IMPDH - 1.1.1.205 Purine  \nCNAG_07437 ERG27 CAGL0M11506g CPAR2_801560 CJI97_004310 ERG27 DHRS11 - 1.1.1.270 Steroid  \nCNAG_06534 HMG1 CAGL0L11506g CPAR2_110330 CJI97_003299 HMG1 HMGCR - 1.1.1.34 Terpenoid backbone  \nCNAG_00117 ERG24 CAGL0I02970g CPAR2_405900 CJI97_003097 ERG24 TM7SF2 - 1.3.1.70 Steroid  \nCNAG_02830 ERG4 ERG4 ERG4 CJI97_002908 ERG4 - - 1.3.1.71 Steroid  \nCNAG_04692 CDC21 CDC21 CPAR2_206550 CJI97_005101 TMP1 TYMS - 2.1.1.45 Pyrimidine  \nCNAG_00700 ADE17 CAGL0A03366g CPAR2_202250 CJI97_002511 ADE17 ATIC  - 2.1.2.3 Purine  \nCNAG_07373 URA2 CAGL0L05676g CPAR2_203160 CJI97_002269 URA2 CAD - 2.1.3.2 Pyrimidine  \nCNAG_06508 GSC1 FKS1 CPAR2_106400 FKS1 FKS1 - Echinocandins 2.4.1.34 1,3-beta-glucan  \nCNAG_03196 URA5 URA5 CPAR2_802790 CJI97_002422 URA5 UMPS - 2.4.2.10 Pyrimidine  \nCNAG_02853 ADE4 CAGL0M13717g CPAR2_208260 CJI97_001833 ADE4 PPAT - 2.4.2.14 Purine  \nCNAG_02084 BTS1 CAGL0H05269g CPAR2_302840 CJI97_003197 BTS1 GGPS1 - 2.5.1.1 Terpenoid backbone  \nCNAG_07780 ERG20 ERG20 CPAR2_103950 CJI97_001757 ERG20 FDPS  - 2.5.1.10 Terpenoid backbone  \nCNAG_02787 C5_05130C CAGL0F05555g CPAR2_502760 CJI97_003836 CAB5 COASY - 2.7.1.24 CoA  \nCNAG_02976 CR_03740C CAGL0K11022g CPAR2_202590 CJI97_005311 FMN1 RFK - 2.7.1.26 Riboflavin  \nCNAG_02866 C6_02980C CAGL0H01551g CPAR2_602050 CJI97_004586 CAB1 PANK - 2.7.1.33 CoA  \nCNAG_05935 URA6 CAGL0L09867g CPAR2_105320 CJI97_000033 URA6 CMPK2 - 2.7.4.14 Pyrimidine  \nCNAG_06001 ERG8 ERG8 CPAR2_400710 CJI97_001215 ERG8 PMVK - 2.7.4.2 Terpenoid backbone  \nCNAG_03335 C5_00260W CAGL0D00550g CPAR2_304260 CJI97_000019 PRS1 PRPS1 - 2.7.6.1 Purine  \n.CC-BY 4.0 International licensemade available under a \n(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 \nThe copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint \n\n25 \nCNAG_05384 C4_05210W CAGL0G03157g CPAR2_500260 CJI97_005306 PIS1 CDIPT  - 2.7.8.11 Glycerophospholipid  \nCNAG_02795 ADE8 CAGL0F02761g CPAR2_211620 CJI97_002826 ADE8 GART - 2.1.2.2 Purine  \nCNAG_02609 COQ3 CAGL0I07601g CPAR2_602300 CJI97_005452 COQ3 COQ3 - 2.1.1.114 Ubiquinone  \nCNAG_00138 COQ5 CAGL0J06710g CPAR2_209250 CJI97_003704 COQ5 COQ5 - 2.1.1.201 Ubiquinone  \nCNAG_00040 ERG11 ERG11 ERG11 ERG11 ERG11 \nCYP51A\n1 Azoles 1.14.14.154 Steroid  \nCNAG_02844 PEL1 PGS1 CPAR2_805350 CJI97_000224 PEL1 PGS1 - 2.7.8.5 Glycerophospholipid  \nCNAG_02878 C6_01340C CAGL0H04389g CPAR2_602700 CJI97_005490 GEP4 PTPMT1 - 3.1.3.27 Glycerophospholipid  \nCNAG_00734 URA4 CAGL0J04598g CPAR2_100500 CJI97_002941 URA4 CAD - 3.5.2.3 Pyrimidine  \nCNAG_02294 ADE2 ADE2 CPAR2_805940 CJI97_004071 ADE2 PAICS - 4.1.1.21 Purine  \nCNAG_04961 URA3 URA3 URA3 CJI97_003384 URA3 UMPS - 4.1.1.23 Pyrimidine  \nCNAG_02786 FOL1 CAGL0J07920g CPAR2_303390 CJI971_001274 FOL1 - Sulfacetamide 4.1.2.25 Folate biosynthesis \nCNAG_02786 FOL1 CAGL0J07920g CPAR2_303390 CJI971_001274 FOL1 - Sulfonamides 2.5.1.15 Folate biosynthesis \nCNAG_05125 MVD CAGL0C03630g CPAR2_109530 CJI97_001340 MVD1 MVD - 4.1.1.33 Terpenoid backbone  \nCNAG_00909 CAB3 CAGL0L05302g CPAR2_800750 CJI97_003563 CAB3 PPCDC - 4.1.1.36 CoA  \nCNAG_03270 ADE13 CAGL0B02794g CPAR2_204960 CJI97_000801 ADE13 ADSL - 4.3.2.2 Purine  \nCNAG_00265 IDI1 CAGL0J06952g CPAR2_401630 CJI97_001183 IDI1 IDI1 - 5.3.3.2 Terpenoid backbone  \nCNAG_01129 ERG7 CAGL0J10824g CPAR2_301800 CJI97_005090 ERG7 LSS Oxiconazole 5.4.99.7 Steroid  \nCNAG_00143 ADE1 CAGL0I04444g CPAR2_500190 CJI97_003065 ADE1 PAICS - 6.3.2.6 Purine  \nCNAG_06314 ADE5,7 CAGL0H07887g CPAR2_208400 CJI97_001704 ADE5,7 GART - 6.3.3.1 Purine  \nCNAG_06314 ADE5,7 CAGL0H07887g CPAR2_208400 CJI97_001704 ADE5,7 GART - 6.3.4.13 Purine  \nCNAG_04192 ADE6 CAGL0K04499g CPAR2_204070 CJI97_002160 ADE6 PFAS - 6.3.5.3 Purine  \nCNAG_05759 ACC1 CAGL0L10780g CPAR2_804060 CJI97_001038 ACC1 ACACA - 6.4.1.2 Fatty acid  \nCNAG_02686 ERG12 CAGL0F03861g CPAR2_803530 CJI97_005606 ERG12 MVK - 2.7.1.36 Terpenoid backbone  \nCNAG_03311 ERG13 ERG13 CPAR2_701400 CJI97_004952 ERG13 HMGCS - 2.3.3.10 Terpenoid backbone  \nCNAG_02099 FAS1 CAGL0D00528g FAS1 CJI97_001309 FAS1 - - 2.3.1.86 Fatty acid  \nCNAG_01877 GUA1 CAGL0F03927g CPAR2_803560 CJI97_005609 GUA1 GMPS - 6.3.5.2 Pyrimidine  \nCNAG_03099 CHS1 CAGL0I04818g CPAR2_805640 CHS2 CHS2 - - 2.4.1.16 Chitin  \n                    \n.CC-BY 4.0 International licensemade available under a \n(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 \nThe copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint \n\n26 \n4. Conclusions \nThe construction and validation of iRV890, the first genome-scale metabolic model for \nC. neoformans var. grubii is presented herein. iRV890 constitutes a robust platform for \nexploring and elucidating the metabolic features of this poorly understood pathogen, \nparticularly concerning its interaction within the central nervous system and the \nhuman host. By encompassin g 890 genes associated with 1466 reactions, this model \noffers a comprehensive view of the metabolic landscape of the pathogen. Through in \nsilico simulations, we predicted the use of more than 200 compounds as sole carbon or \nnitrogen sources, and after comp arison to experimental data from phenotypic \nmicroarrays we gained valuable insights into the metabolic capabilities of C. \nneoformans. The model correctly predicts 85% of the sole carbon and nitrogen sources \ntested. The model was able to accurately predict the organism’s specific growth rate \nand confirmed its inability to grow under anaerobic conditions or to accumulate \nglycerol, acetic acid, or ethanol as metabolic by -products during growth in synthetic \nminimal medium, with glucose as carbon source. Additio nally, we propose a list of \nyet unidentified enzymes expected to be present in C. neoformans, based on the carbon \nand nitrogen utilization and with potential to represent new host adaptation or \nvirulence mechanisms, including new clues on the pathway for inositol utilization in \nC. neoformans. \nOur investigation into the unique metabolic features of C. neoformans has unveiled \nseveral pathways and enzymatic activities that are proposed to play pivotal roles in \nfungal infection within the host brain. Some enzymes constitute important virulence \nfactors, such as Tyrosinase and laccase, enzymes responsible for productio n of \nmelanin which has an important role in host immune evasion [15], infection \nproliferation and drug resistance [16,17]. Other enzymes are related to drug and stress \nresistance, such a s tetracycline 11a -monooxygenase, L -gulonolactone oxidase and \ngluconolactonase. The remaining enzymes are directed related to alternative \ncarbon/nitrogen source utilization and are important for environmental adaptation. \n.CC-BY 4.0 International licensemade available under a \n(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 \nThe copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint \n\n27 \nFor example, hydroxyisourate hydrolase is essential for the assimilation of uric acid \nas a nitrogen source, an important virulence factor mechanism, and 3 -phytase is \ninvolved in inositol metabolism and storage, important for brain dissemination. \nIn this work we also propose several potential drug targets in C. neoformans. Notably, \nenzymes such as Erg4, Chs1, Fol1 and Fas1 present promising opportunities for \ntargeted drug development, due to their absence in human cells, offering \nopportunities for the development of selective and low -toxicity compounds. The \nCNAG_03009 and CNAG_04375 genes, encoding a tyrosinase and a hexaprenyl \ndiphosphate synthase, are presented as potential antifungal drug targets specific to C. \nneoformans. \nOur model contributes to a better understanding of C. neoformans  metabolism, \nespecially within the host environment. With this work, we not only propose new \nmetabolic enzymes awaiting characterization but also offer insights into key pathways \nand interactions shaping the dynamics between host and pathogen and its adapt ive \nstrategies. We also propose some potential antifungal targets for C. neoformans and \nconfirmed the coverage of already identified targets also to that species.  These results \nhold promise for the discovery of novel drug targets and for the full comprehension \nof this pathogen’s metabolic network with an expected impact in combating \ncryptococcosis. \nAuthor contributions \nR.V., C.C., and M.C.T. conceived and designed the study. R.V. performed the model \nconstruction & development, data analysis and curation. R.V., D.C., and W.N. \nperformed the experiments with C. neoformans . O.D. contributed to model \nconstruction and data analysis. L.C. performed data analysis. R.V. wrote the original \ndraft preparation. R.V., L.C., C.C., and M.C.T. reviewed and edited the final version. \nAll authors have read and agreed to the published version of the manuscript. \nCRediT authorship contribution statement \nRomeu Viana:  Writing – review & editing, Writing – original draft, Visualization, \nMethodology, Investigation, Formal analysis, Data curation. William Newton : \n.CC-BY 4.0 International licensemade available under a \n(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 \nThe copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint \n\n28 \nMethodology, Investigation. Diogo Couceiro:  Methodology, Investigation. Oscar \nDias: Methodology, Formal analysis. Luís Coutinho:  Writing – review & editing, \nFormal analysis. Carolina Coelho:  Writing – review & editing, Supervision, \nMethodology, Conceptualization. Miguel Cacho Teixeira: Writing – review & editing, \nSupervision, Methodology, Conceptualization, Funding. \nDeclaration of Competing Interest \nThe authors declare no conflict of interest. The funders had no role in the design of the \nstudy; in the collection, analyses, or interpretation of data; in the writing of the \nmanuscript, or in the decision to publish the results. \nAcknowledgements \nThe authors acknowledge the OSCARS project, funded by the European \nCommission’s Horizon Europe Research and Innovation Programme under grant \nagreement No. 101129751. This work was further financed by national funds from \n“Fundação para a Ciência e a Tecnolo gia” (FCT) (AEM PhD grant to RV; projects \nUIDB/04565/2020 and UIDP/04565/2020 of the Research Unit Institute for \nBioengineering and Biosciences —iBB; project UIDB/04469/2020 for the Centre of \nBiological Engineering —CEB; project LA/P/0029/2020 for LABBELS –Associate \nLaboratory in Biotechnology, Bioengineering and Microelectromechanical Systems ; \nand project LA/P/0140/2020 for the Associate Laboratory Institute for Health and \nBioeconomy—i4HB). WN was funded by a DTP BRC Exeter NIHR203320. Fungal \nstrain collection was funded via NIH funding (R01AI100272) led by Hiten Madhani, \nUCSF. This work was supported by AMS Springboard Award SBF006\\1024 (UK), and \nWellcome Trust Institutional Strategic Support Award (WT105618MA) to C.C. We \nacknowledge other funding from the MRC Centre for Medical Mycology  at the \nUniversity of Exeter (MR/N006364/2 and MR/V033417/1). This study/research is \nfunded by the National Institute for Health and Care Research (NIHR) Exeter \nBiomedical Research Centre (BRC). The views expressed are those of the author(s) and \nnot necessarily those of the NIHR or the Department of Health and Social Care. \n.CC-BY 4.0 International licensemade available under a \n(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 \nThe copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint \n\n29 \n \nAppendix A. Supplementary material \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. 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It is \nThe copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint \n\n.CC-BY 4.0 International licensemade available under a \n(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 \nThe copyright holder for this preprintthis version posted April 8, 2025. ; https://doi.org/10.1101/2025.04.02.646762doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}