Expression of Extracellular Matrix Proteins in Human Periodontal Ligament Cells During Mineralization In Vitro

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142249/1/jper0320.pd

Current concepts in periodontal bioengineering

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73112/1/j.1601-6343.2005.00352.x.pd

Exploring hypotheses of the actions of TGF-beta 1 in epidermal wound healing using a 3D computational multiscale model of the human epidermis

In vivo and in vitro studies give a paradoxical picture of the actions of the key regulatory factor TGF-beta 1 in epidermal wound healing with it stimulating migration of keratinocytes but also inhibiting their proliferation. To try to reconcile these into an easily visualized 3D model of wound healing amenable for experimentation by cell biologists, a multiscale model of the formation of a 3D skin epithelium was established with TGF-beta 1 literature-derived rule sets and equations embedded within it. At the cellular level, an agent-based bottom-up model that focuses on individual interacting units ( keratinocytes) was used. This was based on literature-derived rules governing keratinocyte behavior and keratinocyte/ECM interactions. The selection of these rule sets is described in detail in this paper. The agent-based model was then linked with a subcellular model of TGF-beta 1 production and its action on keratinocytes simulated with a complex pathway simulator. This multiscale model can be run at a cellular level only or at a combined cellular/subcellular level. It was then initially challenged ( by wounding) to investigate the behavior of keratinocytes in wound healing at the cellular level. To investigate the possible actions of TGF-beta 1, several hypotheses were then explored by deliberately manipulating some of these rule sets at subcellular levels. This exercise readily eliminated some hypotheses and identified a sequence of spatial-temporal actions of TGF-beta 1 for normal successful wound healing in an easy-to-follow 3D model. We suggest this multiscale model offers a valuable, easy-to-visualize aid to our understanding of the actions of this key regulator in wound healing, and provides a model that can now be used to explore pathologies of wound healing

Development of a Three Dimensional Multiscale Computational Model of the Human Epidermis

Transforming Growth Factor (TGF-β1) is a member of the TGF-beta superfamily ligand-receptor network. and plays a crucial role in tissue regeneration. The extensive in vitro and in vivo experimental literature describing its actions nevertheless describe an apparent paradox in that during re-epithelialisation it acts as proliferation inhibitor for keratinocytes. The majority of biological models focus on certain aspects of TGF-β1 behaviour and no one model provides a comprehensive story of this regulatory factor's action. Accordingly our aim was to develop a computational model to act as a complementary approach to improve our understanding of TGF-β1. In our previous study, an agent-based model of keratinocyte colony formation in 2D culture was developed. In this study this model was extensively developed into a three dimensional multiscale model of the human epidermis which is comprised of three interacting and integrated layers: (1) an agent-based model which captures the biological rules governing the cells in the human epidermis at the cellular level and includes the rules for injury induced emergent behaviours, (2) a COmplex PAthway SImulator (COPASI) model which simulates the expression and signalling of TGF-β1 at the sub-cellular level and (3) a mechanical layer embodied by a numerical physical solver responsible for resolving the forces exerted between cells at the multi-cellular level. The integrated model was initially validated by using it to grow a piece of virtual epidermis in 3D and comparing the in virtuo simulations of keratinocyte behaviour and of TGF-β1 signalling with the extensive research literature describing this key regulatory protein. This research reinforces the idea that computational modelling can be an effective additional tool to aid our understanding of complex systems. In the accompanying paper the model is used to explore hypotheses of the functions of TGF-β1 at the cellular and subcellular level on different keratinocyte populations during epidermal wound healing

Grade C molar-incisor pattern periodontitis subgingival microbial profile before and after treatment

AIM: This study evaluated the influence of periodontal therapy on the microbiological profile of individuals with Grade C Molar-Incisor Pattern Periodontitis (C/MIP). METHODS: Fifty-three African-American participants between the ages of 5–25, diagnosed with C/MIP were included. Patients underwent full mouth mechanical debridement with systemic antibiotics (metronidazole 250 mg + amoxicillin 500 mg, tid, 7 days). Subgingival samples were collected from a diseased and a healthy site from each individual prior to treatment and at 3, 6, 12, 18 and 24 months after therapy from the same sites. Samples were subjected to a 16S rRNA gene based-microarray. RESULTS: Treatment was effective in reducing the main clinical parameters of disease. Aggregatibacter actinomycetemcomitans (A.a.) was the strongest species associated with diseased sites. Other species associated with diseased sites were Treponema lecithinolyticum and Tannerella forsythia. Species associated with healthy sites were Rothia dentocariosa/mucilaginosa, Eubacterium yurii, Parvimonas micra, Veillonella spp., Selenomonas spp., and Streptococcus spp. Overall, treatment was effective in strongly reducing A.a. and other key pathogens, as well as increasing health-associated species. These changes were maintained for at least 6 months. CONCLUSIONS:Treatment reduced putative disease-associated species, particularly A.a., and shifted the microbial profile to more closely resemble a healthy-site profile. (Clinicaltrials.gov registration #NCT01330719).Introduction Methods - Demographics of the study population - Clinical measurements - Collection of bacterial subgingival biofilm - Periodontal therapy - DNA isolation and microarray analysis - Statistical analyses Results Discussio