Structural Characterization and Evaluation of an Epitope at the Tip of the A-Band Rhamnan Polysaccharide of Pseudomonas aeruginosa .
Chantelle M CairnsFrank St MichaelMohammad JamshidiHenk van FaassenQingling YangKevin A HenryGreg HussackJanelle SauvageauEvgeny V VinogradovAndrew D CoxPublished in: ACS infectious diseases (2022)
Pseudomonas aeruginosa produces a variety of cell surface glycans. Previous studies identified a common polysaccharide (PS) antigen often termed A-band PS that was composed of a neutral d-rhamnan trisaccharide repeating unit as a relatively conserved cell surface carbohydrate. However, nuclear magnetic resonance (NMR) spectra and chemical analysis of A-PS preparations showed the presence of several additional components. Here, we report the characterization of the carbohydrate component responsible for these signals. The carbohydrate antigen consists of an immunogenic methylated rhamnan oligosaccharide at the nonreducing end of the A-band PS. Initial studies performed with the isolated antigen permitted the production of conjugates that were used to immunize mice and rabbits and generate monoclonal and polyclonal antibodies. The polyclonal antibodies were able to recognize the majority of P. aeruginosa strains in our collection, and three monoclonal antibodies were generated, one of which was able to recognize and facilitate opsonophagocytic killing of a majority of P. aeruginosa strains. This monoclonal antibody was able to recognize all P. aeruginosa strains in our collection that includes clinical and serotype strains. Synthetic oligosaccharides (mono- to pentasaccharides) representing the terminal 3- O -methyl d-rhamnan were prepared, and the trisaccharide was identified as the antigenic determinant required to effectively mimic the natural antigen recognized by the broadly cross-reactive monoclonal antibody. These data suggest that there is considerable promise in this antigen as a vaccine or therapeutic target.
Keyphrases
- monoclonal antibody
- cell surface
- pseudomonas aeruginosa
- magnetic resonance
- escherichia coli
- cystic fibrosis
- electronic health record
- multidrug resistant
- magnetic resonance imaging
- acinetobacter baumannii
- machine learning
- type diabetes
- density functional theory
- transcription factor
- biofilm formation
- dengue virus
- computed tomography
- case control
- klebsiella pneumoniae
- drug resistant
- zika virus
- data analysis
- deep learning
- multiple myeloma