Mapping molar shapes on signaling pathways

Abstract

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.

Fig 1. Outline of procedures for morphometric mapping with data mining.

3D-reconstructions of diverse tooth shapes are turned into different morphometric maps that parameterize distinct aspects of the morphology, such as surface curvature, height from the cervix, radius from the center of the tooth crown, and vertex normal. All maps are converted with Fast Fourier Transformation (FFT) for low-pass filtering and finally analyzed by Principal Component Analysis (PCA). Two data mining schemes, machine learning and phylogenetic signal, are performed to select the combination of morphometric parameters and the size of low-pass filtering depending on the composition of the sample.

Fig 2. Molar shape variation in 22 mutant strains.

(A) Pseudo-genetic code for mutant strains with 3D models of the teeth (symbols correspond to PC plot in B). (B) Shape variation depicted on the first two principal axes of between-group PCA on vertex normal vector with the first six sets of Fourier coefficients. 3D model and corresponding vertex normal map of marked specimens are provided to illustrate shape variation. Red bidirectional arrow shows shape variation associated with the number of cusps. Blue arrow exhibits shape change towards parallel distributed cusps and increased lophedness. Green arrow represents shape change towards a fusion of two molar configurations. (C) The difference between the pseudo-genetic code from WT (x-axis) and phenetic distance from WT (y-axis) shows increasing phenotypic change with genetic change.

Fig 3. Molar shape variation of wild Murinae species.

(A) Phylogenetic tree of murines used for the analysis. Acomys wilsoni is included as an outgroup (symbols correspond to PC plot in B). Examples of a 3D model for each taxon are shown. (B) Shape variation depicted on the first two principal axes of between-group PCA on vertex normal vector and radius with the first two sets of Fourier coefficients. Morphometric map representation is the same as Fig 2. Blue−green arrow signifies shape change associated with a wedged outline and relatively large six cusps on a crown. Purple arrow captures shape change towards lamellation. Legends for mutants are the same as in Fig 2. Circled ‘a’ represents the estimated last common ancestor of murines analyzed.