S3-10 Saturday, Jan. 4 14:30 - 15:00 Finding new rules for the patterning and shape of mammalian dentition: insights from Noctilionoid bats SADIER, A*; DESSALES, R; SANTANA, S; SEARS, K; UCLA; UCLA; University of Washington; UCLA email@example.com
Teeth are ones of the most diverse organs in term of morphology. However, most of the extensive developmental work that has been done in mammals is based on mouse which exhibit a very derived dentition. Here, we take the advantage of the ~200 species of noctilionoids bats that encompass nearly all possible mammalian diets. In consequence, noctilionoids have evolved a wide diversity of post-canine dentition morphologies providing a natural experiment with which to investigate the developmental basis of morphological diversification. We will present a new model for the patterning of the mammalian post-canine dentition using this group as a reference. By combining morphometric and quantitative data from 117 adult species, we showed that the number of post-canine teeth is related to the length of the jaw and that premolar and molar proportions are independent, suggesting distinct developmental mechanisms for their formation. To get insight into these underlying mechanisms, we analyzed the development of 12 species across 8 developmental stages by µCT scan and tested markers. We also injected pregnant bats with EdU to link teeth formation to the growth rate of the jaw. Finally, we proposed a new Turing-based model to explain the development of premolars and molars rows. Our data reveal that the premolar and molar rows are established by two independent signaling mechanisms and that teeth number and size is linked to the local growth rate of the jaw. We believe that this work provides a testable framework for other bats and mammals. Then, we present new data on the relationship between molar traits and the underlying gene regulatory networks (GRNs) and pathways, using bat molar as a foundation to test the existence of developmental modules in GRNs that control shape variation. We will present morphological and computational models using machine learning as well as experimental data.