Reconstructing the evolution of transcription factor DNA binding specificity across living species and their common ancestors
Reconstructing the evolution of transcription factor DNA binding specificity across living species and their common ancestors
批准号:
407463262
负责人:
Dr. Jose M. Muino, Ph.D.
金额:
$0.0万
依托单位国家:
德国
项目类别:
Research Grants
财政年份:
2018
资助国家:
德国
项目状态:
已结题
起止时间:
2017-12-31 至 2023-12-31
中文摘要
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英文摘要
Transcription factors (TFs) are major regulators of developmental processes and environmental adaptation in plants and animals. They achieve their function by binding to a specific set of DNA regions. However, it is not well understood how their DNA-binding specificity evolves associated with protein sequence evolution. This knowledge is important to understand their functional evolutionary diversification: how they acquire new target genes and new biological functions, and how they diverge after gene duplication events. Therefore, the aim of this proposal is to develop new computational methods to characterize the evolution of DNA binding specificities in a TF family across species. This is: 1) to phylogenetically reconstruct the protein sequence of the TF family at study at different evolutionary time scales and 2) characterize and compare the DNA binding specificities of reconstructed ancestral TFs using high-throughput in vitro SELEX-seq experiments. The importance of the novel computational methods to determine evolutionary changes in DNA-binding specificity will be exemplified by the precise characterization of the AP1/FUL lineage of the plant MADS-box TF genes, whose duplication at the origin of the core eudicots and subsequent diversification gave rise to three paralogous TF lineages with very different biological functions in flowering time control, cauline leaf and fruit development. Thus, the project will answer the question of how changes in DNA binding specificity have contributed to the functional diversification of these important developmental regulators. In addition, the combination of experimental and computational methods established here also provides a starting point for comparable analyzes in other TF families and model systems.
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