Streptococcus salivarius MS-oral-D6 promotes gingival re-epithelialization in vitro through a secreted serine protease

Abstract

Gingival re-epithelialization represents an essential phase of oral wound healing in which epithelial integrity is re-establish. We developed an automated high-throughput re-epithelialization kinetic model, using the gingival epithelial cell line Ca9–22. The model was employed to screen 39 lactic acid bacteria, predominantly including oral isolates, for their capacity to accelerate gingival re-epithelialization. This screen identified several strains of Streptococcus salivarius that stimulated re-epithelialization. Further analysis revealed that S. salivarius strain MS-oral-D6 significantly promoted re-epithelialization through a secreted proteinaceous compound and subsequent experiments identified a secreted serine protease as the most likely candidate to be involved in re-epithelialization stimulation. The identification of bacteria or their products that stimulate gingival wound repair may inspire novel strategies for the maintenance of oral health.

KREAP: an automated Galaxy platform to quantify in vitro re-epithelialization kinetics

“…The enumeration of cells infiltrating the scratched area over time consistently results in a sigmoidal curve similar to the ones obtained with bacterial growth curves that are characterized by a lag phase, an exponential phase, and a stationary phase (Fig. 3 ) [ 18 ]. The modified Gompertz function has been successfully used to model bacterial growth and estimate three biologically relevant parameters that mathematically describe the different phases of growth [ 29 ].…”

“…The scratch assay analysis workflow was developed within our own laboratory [ 18 ] (also see Methods) and involved a multi-software approach to acquire images, perform image analysis, visualize extracted data, and model re-epithelialization kinetics based on the enumeration of cells migrating into the scratch area over time. CellProfiler [ 19 ] was used in the original workflow and implemented in the KREAP toolbox (version 2.2.0) to perform automated segmentation and feature extraction of image series.…”

“…CellProfiler [ 19 ] was used in the original workflow and implemented in the KREAP toolbox (version 2.2.0) to perform automated segmentation and feature extraction of image series. FCS Express 4 Plus (De Novo Software, CA, USA) was originally used to relate the features extracted by CellProfiler back to the raw images and to enumerate the cells infiltrating the scratched area over time [ 18 ]. Since FCS Express 4 Plus requires the purchase of a license, we developed and implemented an R script [ 20 ], now part of the KREAP toolbox, that can automatically recognize the scratch boundaries and determine the number of cells inside and outside of the scratch over time.…”

High-Throughput Screening Model to Quantify Re-Epithelialization Kinetics

“…Despite all these improvements, data analysis has been usually limited to the calculation of wound closure 17,18 , disregarding the kinetic information inherent to the healing process. In addition, effective processing and quantification of high-throughput image-based assays involve the integration of multiple image analysis and data extraction software tools that often require computer programming skills and/or purchase of a commercial license, increasing total experimental costs 2 . Here we present the procedure of an optimized high-throughput scratch assay using high-content microscopy and a mathematical model to quantify the kinetics of epithelial wound repair in individual wells through the calculation of biologically relevant parameters.…”

“…Here we present the procedure of an optimized high-throughput scratch assay using high-content microscopy and a mathematical model to quantify the kinetics of epithelial wound repair in individual wells through the calculation of biologically relevant parameters. The assay can be used for screening chemical compounds, bacterial suspensions 2 , and other bioactive substances for their effect on re-epithelialization kinetics. Furthermore, we developed the Kinetic Re-Epithelialization Analysis Pipeline (KREAP) that we implemented in Galaxy, thus providing an end-to-end workflow integrating different validated tools for reproducible image analysis, cell enumeration, reepithelialization modeling, and HTML reporting 19 .…”