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Wiker, H. G.

Publications and source records attributed to Wiker, H. G..

2 recordsLinked to original sources

Protein interactions between the oral bacteria Fusobacterium nucleatum and Porphyromonas gingivalis in biofilm and planktonic culture

The opportunistic pathogens Fusobacterium nucleatum and Porphyromonas gingivalis are Gram-negative bacteria associated with oral biofilm and periodontal disease. Although liquid cultures are often the preferred cultivation method in microbiology, bacterial cells in biofilm adopt a profoundly different phenotype reflecting the close cell-to-cell contact compare to their planktonic counterparts. To investigate F. nucleatum and P. gingivalis interactions relevant in biofilm formation, we applied liquid chromatography-tandem mass spectrometry to determine the expressed proteome of F. nucleatum and P. gingivalis cells that were grown either as biofilm or in planktonic culture, and individually or together. The proteomic analyses detected 1,322 F. nucleatum and 966 P. gingivalis proteins. We statistically compared the proteins label-free quantitative (LFQ) intensities between biofilm and planktonic culture and identified significant changes (p-value [≤]0.05) in 0,4% F. nucleatum proteins, 7% P. gingivalis proteins, and more than 14% of all proteins in the dual-species model. For both species, proteins involved in vitamin B2 (riboflavin) metabolic process had significantly increased levels in the biofilm condition. In both mono- and dual-species biofilm models, P. gingivalis increased the production of proteins functional in translation, oxidation-reduction, and amino acid metabolism, when compared to planktonic cultures. However, when we compared LFQ intensities between mono- and dual-species models, over 90% of the significantly changed P. gingivalis proteins had their levels reduced in biofilm and planktonic settings of the dual-species model. Our findings suggest that the two bacteria interact with each other at the protein level and indicate that P. gingivalis reduces the production of multiple proteins because of more favourable growth conditions provided by F. nucleatum presence. The results highlight the complex interactions of bacteria contributing to oral biofilm, which need to be considered in the design of future prevention strategies.

microbiology↗

Building comprehensive MS-friendly databases for proteomic analysis of bacterial species of unknown genetic background

In proteomics, peptide information within mass spectrometry data from a specific organism sample is routinely challenged against a protein sequence database that best represent such organism. However, if the species/strain in the sample is unknown or poorly genetically characterized, it becomes challenging to determine a database which can represent such sample. Building customized protein sequence databases merging multiple strains for a given species has become a strategy to overcome such restrictions. However, as more genetic information is publicly available and interesting genetic features such as the existence of pan- and core genes within a species are revealed, we questioned how efficient such merging strategies are to report relevant information. To test this assumption, we constructed databases containing conserved and unique sequences for ten different species. Features that are relevant for probabilistic-based protein identification by proteomics were then monitored. As expected, increase in database complexity correlates with pangenomic complexity. However, Mycobacterium tuberculosis and Bortedella pertusis generated very complex databases even having low pangenomic complexity or no pangenome at all. This suggests that discrepancies in gene annotation is higher than average between strains of those species. We further tested database performance by using mass spectrometry data from eight clinical strains from Mycobacterium tuberculosis, and from two published datasets from Staphylococcus aureus. We show that by using an approach where database size is controlled by removing repeated identical tryptic sequences across strains/species, computational time can be reduced drastically as database complexity increases.

bioinformatics↗