Quantitative Comparison of the Anterior-Posterior Patterning System in the Embryos of FiveDrosophilaSpecies
Author(s) -
Zeba Wunderlich,
Charless C. Fowlkes,
Kelly B. Eckenrode,
Meghan D. J. Bragdon,
Arash Abiri,
Angela H. DePace
Publication year - 2019
Publication title -
g3 genes genomes genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.468
H-Index - 66
ISSN - 2160-1836
DOI - 10.1534/g3.118.200953
Subject(s) - biology , drosophila virilis , gene , gene expression , evolutionary biology , phenotype , drosophila (subgenus) , regulatory sequence , regulation of gene expression , developmental biology , genetics , gene regulatory network , computational biology
Complex spatiotemporal gene expression patterns direct the development of the fertilized egg into an adult animal. Comparisons across species show that, in spite of changes in the underlying regulatory DNA sequence, developmental programs can be maintained across millions of years of evolution. Reciprocally, changes in gene expression can be used to generate morphological novelty. Distinguishing between changes in regulatory DNA that lead to changes in gene expression and those that do not is therefore a central goal of evolutionary developmental biology. Quantitative, spatially-resolved measurements of developmental gene expression patterns play a crucial role in this goal, enabling the detection of subtle phenotypic differences between species and the development of computations models that link the sequence of regulatory DNA to expression patterns. Here we report the generation of two atlases of cellular resolution gene expression measurements for the primary anterior-posterior patterning genes in Drosophila simulans and Drosophila virilis By combining these data sets with existing atlases for three other Drosophila species, we detect subtle differences in the gene expression patterns and dynamics driving the highly conserved axis patterning system and delineate inter-species differences in the embryonic morphology. These data sets will be a resource for future modeling studies of the evolution of developmental gene regulatory networks.
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