Proteome Analysis Is a Valuable Tool to Monitor Antigen Expression during Upstream Processing of Whole-Cell Pertussis Vaccines.
Bernard MetzMarieke HoonakkerJoost P UittenbogaardMichel WeytsGeert P M MommenHugo D MeiringWichard TilstraJeroen L A PenningsLeo A van der PolBetsy KuipersArjen SlootsJan van den IJsselBas van de WaterbeemdArno van der ArkPublished in: Journal of proteome research (2016)
Physicochemical and immunochemical assays were applied to substantiate the relation between upstream processing and the quality of whole-cell pertussis vaccines. Bordetella pertussis bacteria were cultured on a chemically defined medium using a continuous cultivation process in stirred tank reactors to obtain uniform protein expression. Continuous culture favors the consistent production of proteins known as virulence factors. Magnesium sulfate was added during the steady state of the culture in order to diminish the expression of virulence proteins. Changes in gene expression and antigen composition were measured by microarrays, mass spectrometry and ELISA. Transcriptome and proteome data revealed high similarity between the biological triplicates demonstrating consistent cultivation of B. pertussis. The addition of magnesium sulfate resulted in an instant downregulation of the virulence genes in B. pertussis, but a gradual decrease of virulence proteins. The quantity of virulence proteins concurred highly with the potency of the corresponding whole-cell pertussis vaccines, which were determined by the Kendrick test. In conclusion, proteome analysis provided detailed information on the composition and proportion of virulence proteins present in the whole-cell preparations of B. pertussis. Moreover, proteome analysis is a valuable method to monitor the production process of whole-cell biomass and predict the product quality of whole-cell pertussis vaccines.
Keyphrases
- single cell
- escherichia coli
- pseudomonas aeruginosa
- gene expression
- staphylococcus aureus
- mass spectrometry
- antimicrobial resistance
- cell therapy
- poor prognosis
- mesenchymal stem cells
- high throughput
- machine learning
- healthcare
- bone marrow
- cell proliferation
- deep learning
- genome wide
- binding protein
- electronic health record
- social media
- artificial intelligence
- wastewater treatment
- liquid chromatography
- capillary electrophoresis
- tandem mass spectrometry
- health information