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Nile, Mustafa; Folwaczny, Matthias; Wichelhaus, Andrea; Baumert, Uwe; Janjic Rankovic, Mila (2023): Fluid flow shear stress and tissue remodeling—an orthodontic perspective: evidence synthesis and differential gene expression network analysis. Frontiers in Bioengineering and Biotechnology, 11: 1256825. ISSN 2296-4185

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Introduction: This study aimed to identify and analyze in vitro studies investigating the biological effect of fluid-flow shear stress (FSS) on cells found in the periodontal ligament and bone tissue.

Method: We followed the PRISMA guideline for systematic reviews. A PubMed search strategy was developed, studies were selected according to predefined eligibility criteria, and the risk of bias was assessed. Relevant data related to cell source, applied FSS, and locus-specific expression were extracted. Based on this evidence synthesis and, as an original part of this work, analysis of differential gene expression using over-representation and network-analysis was performed. Five relevant publicly available gene expression datasets were analyzed using gene set enrichment analysis (GSEA).

Result: A total of 6,974 articles were identified. Titles and abstracts were screened, and 218 articles were selected for full-text assessment. Finally, 120 articles were included in this study. Sample size determination and statistical analysis related to methodological quality and the ethical statement item in reporting quality were most frequently identified as high risk of bias. The analyzed studies mostly used custom-made fluid-flow apparatuses (61.7%). FSS was most frequently applied for 0.5 h, 1 h, or 2 h, whereas FSS magnitudes ranged from 6 to 20 dyn/cm 2 depending on cell type and flow profile. Fluid-flow frequencies of 1 Hz in human cells and 1 and 5 Hz in mouse cells were mostly applied. FSS upregulated genes/metabolites responsible for tissue formation (AKT1, alkaline phosphatase, BGLAP, BMP2, Ca 2+ , COL1A1, CTNNB1, GJA1, MAPK1/MAPK3, PDPN, RUNX2, SPP1, TNFRSF11B, VEGFA, WNT3A) and inflammation (nitric oxide, PGE-2, PGI-2, PTGS1, PTGS2). Protein-protein interaction networks were constructed and analyzed using over-representation analysis and GSEA to identify shared signaling pathways.

Conclusion: To our knowledge, this is the first review giving a comprehensive overview and discussion of methodological technical details regarding fluid flow application in 2D cell culture in vitro experimental conditions. Therefore, it is not only providing valuable information about cellular molecular events and their quantitative and qualitative analysis, but also confirming the reproducibility of previously published results.

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