Browsing by Subject "TOOTH DEVELOPMENT"

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  • Krivanek, Jan; Soldatov, Ruslan A.; Kastriti, Maria Eleni; Chontorotzea, Tatiana; Herdina, Anna Nele; Petersen, Julian; Szarowska, Bara; Landova, Marie; Matejova, Veronika Kovar; Holla, Lydie Izakovicova; Kuchler, Ulrike; Zdrilic, Ivana Vidovic; Vijaykumar, Anushree; Balic, Anamaria; Marangoni, Pauline; Klein, Ophir D.; Yianni, Val; Neves, Vitor C. M.; Sharpe, Paul T.; Harkany, Tibor; Metscher, Brian D.; Bajenoff, Marc; Mina, Mina; Fried, Kaj; Kharchenko, Peter V.; Adameyko, Igor (2020)
    Understanding cell types and mechanisms of dental growth is essential for reconstruction and engineering of teeth. Therefore, we investigated cellular composition of growing and non-growing mouse and human teeth. As a result, we report an unappreciated cellular complexity of the continuously-growing mouse incisor, which suggests a coherent model of cell dynamics enabling unarrested growth. This model relies on spatially-restricted stem, progenitor and differentiated populations in the epithelial and mesenchymal compartments underlying the coordinated expansion of two major branches of pulpal cells and diverse epithelial subtypes. Further comparisons of human and mouse teeth yield both parallelisms and differences in tissue heterogeneity and highlight the specifics behind growing and non-growing modes. Despite being similar at a coarse level, mouse and human teeth reveal molecular differences and species-specific cell subtypes suggesting possible evolutionary divergence. Overall, here we provide an atlas of human and mouse teeth with a focus on growth and differentiation. Unlike human teeth, mouse incisors grow throughout life, based on stem and progenitor cell activity. Here the authors generate single cell RNA-seq comparative maps of continuously-growing mouse incisor, non-growing mouse molar and human teeth, combined with lineage tracing to reveal dental cell complexity.
  • Morita, Wataru; Morimoto, Naoki; Jernvall, Jukka (2020)
    A major challenge in evolutionary developmental biology is to understand how genetic mutations underlie phenotypic changes. In principle, selective pressures on the phenotype screen the gene pool of the population. Teeth are an excellent model for understanding evolutionary changes in the genotype-phenotype relationship since they exist throughout vertebrates. Genetically modified mice (mutants) with abnormalities in teeth have been used to explore tooth development. The relationship between signaling pathways and molar shape, however, remains elusive due to the high intrinsic complexity of tooth crowns. This hampers our understanding of the extent to which developmental factors explored in mutants explain developmental and phenotypic variation in natural species that represent the consequence of natural selection. Here we combine a novel morphometric method with two kinds of data mining techniques to extract data sets from the three-dimensional surface models of lower first molars: i) machine learning to maximize classification accuracy of 22 mutants, and ii) phylogenetic signal for 31 Murinae species. Major shape variation among mutants is explained by the number of cusps and cusp distribution on a tooth crown. The distribution of mutant mice in morphospace suggests a nonlinear relationship between the signaling pathways and molar shape variation. Comparative analysis of mutants and wild murines reveals that mutant variation overlaps naturally occurring diversity, including more ancestral and derived morphologies. However, taxa with transverse lophs are not fully covered by mutant variation, suggesting experimentally unexplored developmental factors in the evolutionary radiation of Murines. Author summary Teeth are found in almost all vertebrates, and they show many different morphologies. In mammals, especially the cheek teeth or molars are highly diverse in shape, reflecting a vast range of dietary habits and efficiency of occlusion. As teeth are the most durable part of the body, they preserve well in the fossil record. The diversity of molar fossils has been useful in reconstructing the diet and phylogeny of extinct mammals. Genetically modified mice (mutants) show diverse modifications of their molar morphology, but we lack computational tools to test to what extent mutant morphologies account for the natural diversity found in the wild. We developed data mining using machine learning and phylogeny-based methods to analyze three-dimensional molar shapes in mouse mutants and natural species. Although many mutants and species have comparable features, most of the mutant molar variation covers the more evolutionarily ancestral than the more evolutionary derived shapes. Yet to be explored developmental factors may underly the more extreme shapes.