Poster abstracts
Poster number 20 submitted by Abigail Leggett
Unique metabolite and pathway differences between planktonic and biofilm states in Pseudomonas aeruginosa by NMR-based metabolomics
Abigail Leggett (Ohio State Biochemistry Program), Devin Sindeldecker (Department of Microbial Infection and Immunity), Lei Bruschweiler-Li (Campus Chemical Instrument Center), Paul Stoodley (Department of Microbial Infection and Immunity), Rafael Brschweiler (Department of Chemistry and Biochemistry, Department of Biological Chemistry and Pharmacology, Campus Chemical Instrument Center)
Abstract:
P. aeruginosa is a gram-negative, opportunistic pathogen that exhibits resistance to most antibiotics leading to acute and chronic infections in immunocompromised patients. P. aeruginosa readily forms biofilms in diverse environments such as the diseased lung, which are difficult to detect and eradicate, greatly contributing to the persistence of infection 1,2. Therefore, there is a critical need for new approaches to accurately identify, regulate, and prevent biofilm formation. Biofilm development is a highly complex and regulated process resulting in multiple changes in phenotype, therefore changes in gene expression and metabolic pathways are likely essential. However, the underlying biological mechanisms that allow the transition from planktonic to attached biofilm communities are not fully understood. Metabolomics provides a global analysis of metabolites from many pathways, giving an unbiased view of cellular activity 3. The metabolome is acted upon by endogenous factors such as genome-encoded enzymes and influenced by the environment, thus it is reflective of the phenotypic state 4. We hypothesize that the metabolic profiles of cells in the planktonic and biofilm states will reveal differences in pathways linked to coordinating the change in phenotype, which could help increase the understanding of mechanisms required for biofilm formation and point to pathways or enzymes that could be targeted to slow or prevent biofilm formation. In addition, metabolic signatures may provide diagnostic biomarkers for biofilm. Our metabolomics approach is to utilize NMR, which has the ability to reproducibly detect, quantify, and reveal molecular structural information of all abundant known/unknown metabolites in complex mixtures in a single set of measurements 5,6. Our NMR data analysis including metabolite identification, quantification, and uni- and multi-variate statistical analysis is completed using our COLMAR suite of webservers 7. We report newly discovered metabolite differences between PAO1 planktonic and biofilm states. Of the 71 metabolites identified so far, 28 have a significant difference (p<0.01 and fold change >2) between planktonic and biofilm sample cohorts. Several of these metabolites are specific carbohydrates and metabolites found along amino acid degradation pathways.
References:
1. Hall-Stoodley, L., et al. (2004) Bacterial biofilms: from the natural environment to infectious diseases, Nat Rev Microbiol 2, 95-108.
2. Mulcahy, L. R., et al. (2014) Pseudomonas aeruginosa biofilms in disease, Microb Ecol 68, 1-12.
3. Cheng, J., et al. (2018) Metabolomics: A High-Throughput Platform for Metabolite Profile Exploration. Comp. Systems Bio, 265-292.
4. Wishart, D.S. (2016) Emerging applications of metabolomics in drug discovery and precision medicine. Nat Rev Drug Discov., 15, 473-484.
5. Silvestre, R., et al. (2018) Metabolic Interaction in Infection, Springer, 14.
6. Markley J.L., et al. (2017) The future of NMR-based metabolomics. Curr. Opin. biotech, 43:34-40.
7. Bingol K, et al. (2016) Comprehensive metabolite identification strategy using multiple two-dimensional NMR spectra of a complex mixture implemented in the COLMARm web server. Analytical chem, 88(24):12411-8.
Keywords: metabolomics, NMR, biofilm