RT Journal Article SR Electronic T1 Genome-Scale Modeling of Rothia mucilaginosa Reveals Insights into Metabolic Capabilities and Therapeutic Strategies for Cystic Fibrosis JF bioRxiv FD Cold Spring Harbor Laboratory SP 2023.11.20.567620 DO 10.1101/2023.11.20.567620 A1 Leonidou, Nantia A1 Ostyn, Lisa A1 Coenye, Tom A1 Crabbé, Aurélie A1 Dräger, Andreas YR 2023 UL http://biorxiv.org/content/early/2023/11/21/2023.11.20.567620.abstract AB Background Cystic fibrosis (CF) is an inherited genetic disorder caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene, resulting in the production of sticky and thick mucosal fluids. This leads to an environment that facilitates the colonization of various microorganisms, some of which can cause acute and chronic lung infections, while others may have a positive influence on the disease process. Rothia mucilaginosa, an oral commensal, is relatively abundant in the lungs of CF patients. Recent studies have unveiled the anti-inflammatory properties of R. mucilaginosa using in vitro three-dimensional (3-D) lung epithelial cell cultures and in vivo mouse models relevant to chronic lung diseases. Apart from a potentially beneficial anti-inflammatory role in chronic lung diseases, R. mucilaginosa has been associated with severe infections. This dual nature highlights the bacterium’s complexity and diverse impact on health and disease. However, its metabolic capabilities and genotype-phenotype relationships remain largely unknown.Results To gain insights into the cellular metabolism and genetic content of R. mucilaginosa, we developed the first manually curated genome-scale metabolic model, iRM23NL. Through growth kinetic experiments and high-throughput phenotypic microarray testings, we defined its complete catabolic phenome. Subsequently, we assessed the model’s effectiveness in accurately predicting growth behaviors and utilizing multiple substrates. We used constraint-based modeling techniques to formulate novel hypotheses that could expedite the development of antimicrobial strategies. More specifically, we detected putative essential genes and assessed their effect on metabolism under varying nutritional conditions. These predictions could offer novel potential antimicrobial targets without laborious large-scale screening of knock-outs and mutant transposon libraries.Conclusion Overall, iRM23NL demonstrates a solid capability to predict cellular phenotypes and holds immense potential as a valuable resource for accurate predictions in advancing antimicrobial therapies. Moreover, it can guide metabolic engineering to tailor R. mucilaginosa’s metabolism for desired performance.Competing Interest StatementThe authors have declared no competing interest.ANIaverage nucleotide identityATPadenosine triphosphateAUCarea under curveBHIbrain heart infusionBiGGBiochemical, Genetical, and GenomicalBLASTBasic Local Alignment Search ToolBMBFFederal Ministry of Education and Research (Bundesministerium fur Bildung und Forschung)BOFbiomass objective functionCFcystic fibrosisCFTRcystic fibrosis transmembrane conductance regulatorCMFIControlling Microbes to Fight InfectionscNMPcyclic nucleoside monophosphateCOBRApyConstraints-Based Reconstruction and Analysis for PythonDFGDeutsche ForschungsgemeinschaftDZIFGerman Center for Infection ResearchECOEvidence and Conclusion OntologyEGCenergy generating cycleETCelectron transport chainFBAflux balance analysis fbc flux balance constraintsFCfold changeFNfalse negativeFPfalse positiveGEMgenome-scale metabolic modelGFFGeneral Feature FormatGOGene OntologyGPRgene-protein-reaction associationIFinoculating fluidIPinorganic phosphorusJSONJavaScript Object NotationKEGGKyoto Encyclopedia of Genes and GenomesLBLuria-BertaniM9M9 minimal mediumMEMOTEMetabolic Model TestingMILPmixed-integer linear programmingMQmilliQ waterNAnutrient agarNCBINational Centre for Biotechnology InformationNMPnucleoside monophosphateODoptical densityOMEXOpen Modelling EXchange formatOPorganic phosphoruspFBAparsimonious enzyme usage flux balance analysisPGAPProkaryotic Genome Annotation PipelinePMphenotype microarrayROSreactive oxygen speciesRPMrevolutions per minuteRPMIRoswell Park Memorial InstituteSBMLSystems Biology Markup LanguageSBOSystems Biology OntologySCFMsynthetic cystic fibrosis sputum mediumSNMsynthetic nasal mediumTNtrue negativeTPtrue positiveTSBtryptic soy broth