Abstract
Efforts to map gingival tissue proteomes and microbiomes have been hampered by lack of sufficient tissue extraction methods. The pressure cycling technology (PCT) is an emerging platform for reproducible tissue homogenisation and improved sequence retrieval coverage. Therefore, we employed PCT to characterise the proteome and microbiome profiles in healthy and diseased gingival tissue. Healthy and diseased contralateral gingival tissue samples (total n = 10) were collected from five systemically healthy individuals (51.6 ± 4.3 years) with generalised chronic periodontitis. The tissues were then lysed and digested using a Barocycler, proteins were prepared and submitted for mass spectrometric analysis and microbiome DNA for 16S rRNA profiling analysis. Overall, 1,366 human proteins were quantified (false discovery rate 0.22%), of which 69 proteins were differentially expressed (≥2 peptides and p < 0.05, 62 up, 7 down) in periodontally diseased sites, compared to healthy sites. These were primarily extracellular or vesicle-associated proteins, with functions in molecular transport. On the microbiome level, 362 species-level operational taxonomic units were identified. Of those, 14 predominant species accounted for >80% of the total relative abundance, whereas 11 proved to be significantly different between healthy and diseased sites. Among them, Treponema sp. HMT253 and Fusobacterium naviforme and were associated with disease sites and strongly interacted (r > 0.7) with 30 and 6 up-regulated proteins, respectively. Healthy-site associated strains Streptococcus vestibularis, Veillonella dispar, Selenomonas sp. HMT478 and Leptotrichia sp. HMT417 showed strong negative interactions (r < -0.7) with 31, 21, 9, and 18 up-regulated proteins, respectively. In contrast the down-regulated proteins did not show strong interactions with the regulated bacteria. The present study identified the proteomic and intra-tissue microbiome profile of human gingiva by employing a PCT-assisted workflow. This is the first report demonstrating the feasibility to analyse full proteome profiles of gingival tissues in both healthy and disease sites, while deciphering the tissue site-specific microbiome signatures.