The landscape of toxic intermediates in the metabolic networks of pathogenic fungi reveals targets for antifungal drugs

Hbr2 is part of the more complex glyoxylate detoxification in C. albicans , which 210 provides new avenues for drug target search. Overall, we show that toxic intermediates are an untapped resource for the development of antifungal drugs by the characterization of enzymes as well as the underlying transcriptional regulation, which control accumulation of pathway intermediates during host-pathogen interactions.

[1]  R. Guthke,et al.  Survival Strategies of Pathogenic Candida Species in Human Blood Show Independent and Specific Adaptations , 2020, mBio.

[2]  M. Hoenigl.  Invasive Fungal Disease Complicating Coronavirus Disease 2019: When It Rains, It Spores , 2020, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[3]  J. Lopez-Ribot,et al.  Current Antimycotics, New Prospects, and Future Approaches to Antifungal Therapy , 2020, Antibiotics.

[4]  Dohoon Kim,et al.  Endogenous toxic metabolites and implications in cancer therapy , 2020, Oncogene.

[5]  O. Tenaillon,et al.  Flux, toxicity, and expression costs generate complex genetic interactions in a metabolic pathway , 2020, Science Advances.

[6]  Bashar Ibrahim,et al.  Trends in mathematical modeling of host–pathogen interactions , 2019, Cellular and Molecular Life Sciences.

[7]  I. Spriet,et al.  Antifungal drugs: What brings the future? , 2019, Medical mycology.

[8]  L. Salusjärvi,et al.  Biotechnological production of glycolic acid and ethylene glycol: current state and perspectives , 2019, Applied Microbiology and Biotechnology.

[9]  Di Liu,et al.  Dynamic metabolic control: towards precision engineering of metabolism , 2018, Journal of Industrial Microbiology & Biotechnology.

[10]  M. Hickey,et al.  Glucose Homeostasis Is Important for Immune Cell Viability during Candida Challenge and Host Survival of Systemic Fungal Infection. , 2018, Cell metabolism.

[11]  T. Walsh,et al.  Amino Acid Metabolism and Transport Mechanisms as Potential Antifungal Targets , 2018, International journal of molecular sciences.

[12]  Tatsuya Takagi,et al.  Mordred: a molecular descriptor calculator , 2018, Journal of Cheminformatics.

[13]  L. Ries,et al.  Overview of carbon and nitrogen catabolite metabolism in the virulence of human pathogenic fungi , 2018, Molecular microbiology.

[14]  Hong Liu,et al.  Comparative transcriptomics of Aspergillus fumigatus strains upon exposure to human airway epithelial cells , 2018, Microbial genomics.

[15]  M. Shinohara,et al.  Tissue-Resident Macrophages in Fungal Infections , 2017, Front. Immunol..

[16]  Suzanne M. Paley,et al.  The MetaCyc database of metabolic pathways and enzymes , 2017, Nucleic Acids Res..

[17]  J. Sacchettini,et al.  Glyoxylate detoxification is an essential function of malate synthase required for carbon assimilation in Mycobacterium tuberculosis , 2017, Proceedings of the National Academy of Sciences.

[18]  M. Lorenz,et al.  Adaptation of Candida albicans to Reactive Sulfur Species , 2017, Genetics.

[19]  Christoph Kaleta,et al.  Optimality principles reveal a complex interplay of intermediate toxicity and kinetic efficiency in the regulation of prokaryotic metabolism , 2017, PLoS Comput. Biol..

[20]  M. Lorenz,et al.  A feast for Candida: Metabolic plasticity confers an edge for virulence , 2017, PLoS pathogens.

[21]  D. Denning,et al.  An estimation of burden of serious fungal infections in France. , 2016, Journal de mycologie medicale.

[22]  Minoru Kanehisa,et al.  KEGG: new perspectives on genomes, pathways, diseases and drugs , 2016, Nucleic Acids Res..

[23]  Nir Osherov,et al.  Systematic Identification of Anti-Fungal Drug Targets by a Metabolic Network Approach , 2016, Front. Mol. Biosci..

[24]  Yovani Marrero-Ponce,et al.  Multi-output Model with Box-Jenkins Operators of Quadratic Indices for Prediction of Malaria and Cancer Inhibitors Targeting Ubiquitin- Proteasome Pathway (UPP) Proteins. , 2016, Current protein & peptide science.

[25]  F. Baquero,et al.  The refusal of the Society to accept antibiotic toxicity: missing opportunities for therapy of severe infections. , 2016, Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases.

[26]  H. Flores-Rozas,et al.  The Mechanistic Targets of Antifungal Agents: An Overview. , 2016, Mini reviews in medicinal chemistry.

[27]  Humberto González-Díaz,et al.  Brain-inspired cheminformatics of drug-target brain interactome, synthesis, and assay of TVP1022 derivatives , 2016, Neuropharmacology.

[28]  S. Doyle,et al.  Exploration of Sulfur Assimilation of Aspergillus fumigatus Reveals Biosynthesis of Sulfur-Containing Amino Acids as a Virulence Determinant , 2016, Infection and Immunity.

[29]  Vladimir B Bajic,et al.  In silico toxicology: computational methods for the prediction of chemical toxicity , 2016, Wiley interdisciplinary reviews. Computational molecular science.

[30]  Canglin Wu,et al.  RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse , 2015, Database J. Biol. Databases Curation.

[31]  Andreas Ziegler,et al.  ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R , 2015, 1508.04409.

[32]  Christian W. Remmele,et al.  Integrated inference and evaluation of host–fungi interaction networks , 2015, Front. Microbiol..

[33]  Pu Li,et al.  Optimal programs of pathway control: dissecting the influence of pathway topology and feedback inhibition on pathway regulation , 2015, BMC Bioinformatics.

[34]  Zunyao Wang,et al.  Assessment of bromide-based ionic liquid toxicity toward aquatic organisms and QSAR analysis. , 2015, Ecotoxicology and environmental safety.

[35]  Christoph Kaleta,et al.  Footprints of Optimal Protein Assembly Strategies in the Operonic Structure of Prokaryotes , 2015, Metabolites.

[36]  P. Magistretti,et al.  Methylglyoxal, the dark side of glycolysis , 2015, Front. Neurosci..

[37]  T. C. White,et al.  The evolution of drug resistance in clinical isolates of Candida albicans , 2015, eLife.

[38]  N. Pavelka,et al.  The Transcriptional Stress Response of Candida albicans to Weak Organic Acids , 2015, G3: Genes, Genomes, Genetics.

[39]  Erik L. L. Sonnhammer,et al.  InParanoid 8: orthology analysis between 273 proteomes, mostly eukaryotic , 2014, Nucleic Acids Res..

[40]  I. Gabriel,et al.  Inhibitors of amino acids biosynthesis as antifungal agents , 2014, Amino Acids.

[41]  Alistair J. P. Brown,et al.  Metabolism in fungal pathogenesis. , 2014, Cold Spring Harbor perspectives in medicine.

[42]  Vuanghao Lim,et al.  Inhibitors of the Glyoxylate Cycle Enzyme ICL1 in Candida albicans for Potential Use as Antifungal Agents , 2014, PloS one.

[43]  V. Kvedarienė,et al.  Formic Acid and Acetic Acid Induce a Programmed Cell Death in Pathogenic Candida Species , 2014, Current Microbiology.

[44]  George Papadatos,et al.  The ChEMBL bioactivity database: an update , 2013, Nucleic Acids Res..

[45]  Marek S. Skrzypek,et al.  The Aspergillus Genome Database: multispecies curation and incorporation of RNA-Seq data to improve structural gene annotations , 2013, Nucleic Acids Res..

[46]  J. Keasling,et al.  Engineering dynamic pathway regulation using stress-response promoters , 2013, Nature Biotechnology.

[47]  Catarina Costa,et al.  The YEASTRACT database: an upgraded information system for the analysis of gene and genomic transcription regulation in Saccharomyces cerevisiae , 2013, Nucleic Acids Res..

[48]  M. Penttilä,et al.  Glycolic acid production in the engineered yeasts Saccharomyces cerevisiae and Kluyveromyces lactis , 2013, Microbial Cell Factories.

[49]  Stefan Schuster,et al.  Dynamic optimization identifies optimal programmes for pathway regulation in prokaryotes , 2013, Nature Communications.

[50]  A. Andrianopoulos,et al.  Fungal Genes in Context: Genome Architecture Reflects Regulatory Complexity and Function , 2013, Genome biology and evolution.

[51]  Stephen R. Heller,et al.  InChI - the worldwide chemical structure identifier standard , 2013, Journal of Cheminformatics.

[52]  David W. Denning,et al.  Hidden Killers: Human Fungal Infections , 2012, Science Translational Medicine.

[53]  José Augusto Baranauskas,et al.  How Many Trees in a Random Forest? , 2012, MLDM.

[54]  J. Brownstein,et al.  Emerging fungal threats to animal, plant and ecosystem health , 2012, Nature.

[55]  Johannes Goll,et al.  Protein interaction data curation: the International Molecular Exchange (IMEx) consortium , 2012, Nature Methods.

[56]  Robert P. Sheridan,et al.  Three Useful Dimensions for Domain Applicability in QSAR Models Using Random Forest , 2012, J. Chem. Inf. Model..

[57]  Pablo Carbonell,et al.  Compound toxicity screening and structure-activity relationship modeling in Escherichia coli. , 2012, Biotechnology and bioengineering.

[58]  Gos Micklem,et al.  YeastMine—an integrated data warehouse for Saccharomyces cerevisiae data as a multipurpose tool-kit , 2012, Database J. Biol. Databases Curation.

[59]  P. D. Rogers,et al.  The Transcription Factor Ndt80 Does Not Contribute to Mrr1-, Tac1-, and Upc2-Mediated Fluconazole Resistance in Candida albicans , 2011, PloS one.

[60]  Pablo Carbonell,et al.  A retrosynthetic biology approach to metabolic pathway design for therapeutic production , 2011, BMC Systems Biology.

[61]  Reinhard Guthke,et al.  Optimal regulatory strategies for metabolic pathways in Escherichia coli depending on protein costs , 2011, Molecular Systems Biology.

[62]  C. Rotstein,et al.  Emerging fungal infections in immunocompromised patients , 2011, F1000 medicine reports.

[63]  Jitender Verma,et al.  3D-QSAR in drug design--a review. , 2010, Current topics in medicinal chemistry.

[64]  Humberto González-Díaz,et al.  Multi-target spectral moment: QSAR for antifungal drugs vs. different fungi species. , 2009, European journal of medicinal chemistry.

[65]  Roberto Todeschini,et al.  Comments on the Definition of the Q2 Parameter for QSAR Validation , 2009, J. Chem. Inf. Model..

[66]  M. Friedrich,et al.  Epidemiological trends in skin mycoses worldwide , 2008, Mycoses.

[67]  J. Robertus,et al.  The gene for cobalamin-independent methionine synthase is essential in Candida albicans: a potential antifungal target. , 2007, Archives of biochemistry and biophysics.

[68]  Lourdes Santana,et al.  A QSAR model for in silico screening of MAO-A inhibitors. Prediction, synthesis, and biological assay of novel coumarins. , 2006, Journal of medicinal chemistry.

[69]  Sanjay Kumar,et al.  Free heme toxicity and its detoxification systems in human. , 2005, Toxicology letters.

[70]  Marek S. Skrzypek,et al.  The Candida Genome Database (CGD), a community resource for Candida albicans gene and protein information , 2004, Nucleic Acids Res..

[71]  C. Gätgens,et al.  Alanine : glyoxylate aminotransferase of Saccharomyces cerevisiae–encoding gene AGX1 and metabolic significance , 2004, Yeast.

[72]  J. Wendland,et al.  New modules for PCR‐based gene targeting in Candida albicans: rapid and efficient gene targeting using 100 bp of flanking homology region , 2003, Yeast.

[73]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[74]  J. M. Palma,et al.  Reactive oxygen species, antioxidant systems and nitric oxide in peroxisomes. , 2002, Journal of experimental botany.

[75]  L. Breiman.  Random Forests , 2001, Machine Learning.

[76]  Gerald R. Fink,et al.  The glyoxylate cycle is required for fungal virulence , 2001, Nature.

[77]  D. Kontoyiannis.  Modulation of fluconazole sensitivity by the interaction of mitochondria and erg3p in Saccharomyces cerevisiae. , 2000, The Journal of antimicrobial chemotherapy.

[78]  A. Mitchell,et al.  Rapid Hypothesis Testing with Candida albicans through Gene Disruption with Short Homology Regions , 1999, Journal of bacteriology.

[79]  E. Brass.  Overview of coenzyme A metabolism and its role in cellular toxicity. , 1994, Chemico-biological interactions.

[80]  David Weininger,et al.  SMILES. 2. Algorithm for generation of unique SMILES notation , 1989, J. Chem. Inf. Comput. Sci..

[81]  D. Enoch,et al.  The Changing Epidemiology of Invasive Fungal Infections. , 2017, Methods in molecular biology.

[82]  M. Roberts.  Antibiotic toxicity, interactions and resistance development. , 2002, Periodontology 2000.

[83]  M. Richardson.  Opportunistic and pathogenic fungi. , 1991, The Journal of antimicrobial chemotherapy.

[84]  S. Kanaya,et al.  KEGGscape: a Cytoscape app for pathway data integration , 2014, F1000Research.