Project description:The airways of cystic fibrosis patients are chronically colonized by a diverse range of microbial pathogens, the composition of which changes throughout life Alteration to the pulmonary environment caused by inter-microbial interactions and pathogen-host interactions influence the type of microbes that can engage in sustained infection. The opportunistic bacterial pathogen Pseudomonas aeruginosa is the primary cause of morbidity and mortality amongst individuals with cystic fibrosis and it is estimated that 60 – 80 % of cystic fibrosis patients experience chronic P. aeruginosa infection by the age of 20 years Aspergillus fumigatus is the most prevalent fungal pathogen isolated from cystic fibrosis airways, affecting up to 58% of patients. It is the causative agent of allergic bronchopulmonary aspergillosis (ABPA), a hypersensitivity disorder resulting from the inhalation of fungal conidia. Although co-colonization of the cystic fibrosis airways by P. aeruginosa and A. fumigatus is rare (3.1 – 15.8 %) disease prognosis is poor when both are present. However, sequential infection is more common. It has recently been suggested that A. fumigatus is more prevalent in juvenile cystic fibrosis patients that has been initially reported due to inconsistencies in the culture methods used to detect A. fumigatus. Despite the prevalence and persistence of A. fumigatus, P. aeruginosa predominates as the primary pathogen in the cystic fibrosis lung, suggesting that interactions with other pathogens such as A. fumigatus may influence the pathogenicity of P. aeruginosa by altering its virulence. We report here an investigation of the effect of culturing P. aeruginosa in the presence of A. fumigatus by measuring differences in growth rate and the overall proteome of the bacteria. It was hypothesized that A. fumigatus creates an environment that promotes a metabolic-driven increase in P. aeruginosa that results in it outcompeting the fungus. The molecular basis of this increased proliferation was investigated further using Label-free quantitative (LFQ) proteomics to characterise the proteome changes in P. aeruginosa when exposed to the supernatant of i) A. fumigatus alone ii) the supernatant of an A. fumigatus/P. aeruginosa co-culture and iii) P. aeruginosa alone. LFQ proteomics involves the simultaneous identification and quantification of thousands of proteins (the ultimate determinants of phenotype) from a single sample has recently been employed to characterise the P. aeruginosa proteome in response to iron limiting conditions, resolving how how the bacteria survives and proliferates in such environments.

Project description:Backtround:Cystic fibrosis (CF) is an inherited genetic disorder caused by the cystic fibrosis transmembrane conductance regulator(CFTR) gene mutation, producing sticky and thick mucosal fluids. This leads to an environment that facilitates the colonization ofopportunistic microorganisms, causing progressive acute and chronic lung infections.Rothia mucilaginosa, an oral commensal, isrelatively abundant in the lungs of CF patients. Recent studies have unveiled the anti-inflammatory effects ofR. mucilaginosausinginvitro3D lung epithelial cell culture andin vivomouse models. Apart from its probiotic effect,R. mucilaginosacan be pathogenic,resulting in 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 intoR. mucilaginosa’s cellular metabolism and genetic alterations, we developed the first manually curatedgenome-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 growthbehaviors and utilizing multiple substrates. We used constraint-based modeling techniques to formulate novel hypotheses that couldexpedite the development of antimicrobial strategies. More specifically, we detected putative essential genes and assessed their effecton metabolism under varying nutritional conditions. These predictions could offer novel potential antimicrobial targets without laboriouslarge-scale screening of knock-outs and mutant transposon libraries. Finally, we examined the production levels of enterobactin undervarying nutritional conditions and suggested compounds that could alter its production.Conclusion:Overall,iRM23NL demonstrates a solid capability to predict cellular phenotypes and holds immense potential as avaluable resource for accurate predictions in advancing antimicrobial therapies. Moreover, it can guide the metabolic engineering totailorR. mucilaginosa’s metabolism for desired performance.Availability and implementation:Source code and model are freely accessible from GitHub and BioModels.

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