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Mapping molar shapes on signaling pathways
PLOS Computational Biology ( IF 4.3 ) Pub Date : 2020-12-14 , DOI: 10.1371/journal.pcbi.1008436
Wataru Morita , Naoki Morimoto , Jukka Jernvall

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.



中文翻译:

在信号传导途径上绘制磨牙形状

进化发育生物学的一个主要挑战是要了解遗传突变是表型变化的基础。原则上,对表型的选择性压力会筛选种群的基因库。牙齿是了解基因型与表型关系的进化变化的绝佳模型,因为它们遍布整个脊椎动物。具有牙齿异常的转基因小鼠(突变体)已用于探索牙齿发育。然而,由于牙冠的固有高度复杂性,信号传导途径与磨牙形状之间的关系仍然难以捉摸。这阻碍了我们对突变体中探索的发育因子在多大程度上解释了代表自然选择结果的自然物种的发育和表型变异的理解。在这里,我们将一种新颖的形态计量学方法与两种数据挖掘技术相结合,以从较低的第一磨牙的三维表面模型中提取数据集:i)机器学习以最大程度地提高22个突变体的分类准确度,ii)31个鼠科的系统发生信号种类。突变体之间的主要形状变化由齿冠上的齿尖数量和齿尖分布来解释。突变小鼠在形态空间中的分布表明信号通路与磨牙形状变化之间存在非线性关系。对突变体和野生鼠类的比较分析表明,突变体变异与自然发生的多样性重叠,包括更多的祖先和衍生形态。但是,带有横l的类群并没有完全被突变变异所覆盖,

更新日期:2020-12-14
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