Biology of soft tissue repair: gingival epithelium in wound healing and attachment to the tooth and abutment surface.

Epithelium attachment to the tooth or abutment surface is necessary to form a biological seal preventing pathogens and irritants from penetrating the body and reaching the underlying soft tissues and bone, which in turn can lead to inflammation and subsequent bone resorption. The present review investigated oral wound closure and the role of micro-environment, saliva, crevicular fluid and microbiota in wound healing. The importance of the junctional epithelium (peri-implant epithelium) attachment to the abutment surface was investigated. Current research focuses on macro-design, surface-topography, surface-chemistry, materials, coatings and wettability to enhance attachment, since these optimised surface properties are expected to promote keratinocyte attachment and spreading through hemi-desmosome formation. Detailed studies describing the extent of junctional epithelium attachment - e.g. barrier function, hemi-desmosomes, epithelium quality, composition of the external basement membrane or ability of the epithelium to resist microbial penetration and colonisation - are not yet reported in animals due to ethical considerations, scalability, expense, technical challenges and limited availability of antibodies. In vitro studies generally include relatively simple 2D culture models, which lack the complexity required to draw relevant conclusions. Additionally, human organotypic 3D mucosa models are being developed. The present review concluded that more research using these organotypic mucosa models may identify relevant parameters involved in soft-tissue-abutment interactions, which could be used to study different macro-shapes and surface modifications. Such studies would bridge the gap between clinical, animal and traditional in vitro cell culture studies supporting development of abutments aiming at improved clinical performance.

Saliva-Derived Commensal and Pathogenic Biofilms in a Human Gingiva Model.

In vitro models that closely mimic human host-microbiome interactions can be a powerful screening tool for antimicrobials and will hold great potential for drug validation and discovery. The aim of this study was to develop an organotypic oral mucosa model that could be exposed to in vitro cultured commensal and pathogenic biofilms in a standardized and scalable manner. The oral mucosa model consisted of a tissue-engineered human gingiva equivalent containing a multilayered differentiated gingiva epithelium (keratinocytes) grown on a collagen hydrogel, containing gingiva fibroblasts, which represented the lamina propria. Keratinocyte and fibroblast telomerase reverse transcriptase-immortalized cell lines were used to overcome the limitations of isolating cells from small biopsies when scalable culture experiments were required. The oral biofilms were grown under defined conditions from human saliva to represent 3 distinct phenotypes: commensal, gingivitis, and cariogenic. The in vitro grown biofilms contained physiologic numbers of bacterial species, averaging >70 operational taxonomic units, including 20 differentiating operational taxonomic units. When the biofilms were applied topically to the gingiva equivalents for 24 h, the gingiva epithelium increased its expression of elafin, a protease inhibitor and antimicrobial protein. This increased elafin expression was observed as a response to all 3 biofilm types, commensal as well as pathogenic (gingivitis and cariogenic). Biofilm exposure also increased secretion of the antimicrobial cytokine CCL20 and inflammatory cytokines IL-6, CXCL8, and CCL2 from gingiva equivalents. This inflammatory response was far greater after commensal biofilm exposure than after pathogenic biofilm exposure. These results show that pathogenic oral biofilms have early immune evasion properties as compared with commensal oral biofilms. The novel host-microbiome model provides an ideal tool for future investigations of gingiva responses to commensal and pathogenic biofilms and for testing novel therapeutics.

Short communication: Protease activity measurement in milk as a diagnostic test for clinical mastitis in dairy cows.

Due to the increasing use of automated milking systems, automated detection of clinical mastitis is becoming more important. Various in- or on-line diagnostic tests are in use, but generally suffer from false mastitis alerts. In this study, we explored a new diagnostic approach based on measurement of protease activity using fluorogenic protease substrates, which can be performed on site, at high speed, and at low costs. Samples from cows with clinical mastitis submitted for bacteriological culture at the University Farm Animal Practice were collected during several months and kept at -20°C until protease activity measurement. A reference set of milk samples from clinically healthy cows were collected on 9 different farms and were tested for protease activity directly and after freezing at -20°C to allow for comparison with the samples from clinical cases. The protease activity in mastitic milk samples was significantly higher than in samples from healthy animals. Based on 71 clinical mastitis samples and 180 milk samples from clinically healthy quarters, the area under the receiver-operating characteristic curve was estimated to be between 0.88 and 0.90, and at a threshold of 38 fluorescence per minute the test had a specificity of 0.99 at a sensitivity of 0.58. Protease activity measured in fresh milk from clinically healthy cows was significantly associated with somatic cell count and parity, but not with electrical conductivity, whereas protease activity in milk that had been frozen was statistically significantly associated with all 3 parameters. This study indicates that protease activity measurement as a stand-alone test can be used for detecting mastitis samples, using milk samples that have been frozen. Because protease activity acts in part on a different biological mechanism than somatic cell count or electrical conductivity, this test may increase the accuracy of mastitis diagnosis in combination with currently available in- or on-line tests in automated milking systems.