Inference of Expanded Lrp-Like Feast/Famine Transcription Factor Targets in a Non-Model Organism Using Protein Structure-Based Prediction [electronic resource]

• Ngôn ngữ: eng
• Ký hiệu phân loại: 574.5 [Unassigned]
• Thông tin xuất bản: Washington, D.C. : Oak Ridge, Tenn. : United States. Dept. of Energy. Office of Science ; Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2014
• Mô tả vật lý: Size: Article No. e107863 : , digital, PDF file.
• Bộ sưu tập: Metadata
• ID: 261362
• Chủ đề: 1. 59 basic biological sciences; 2. 60 applied life sciences; 3. Dna transcription; 4. Dna-binding proteins; 5. Gene regulation; 6. Halobacterium; 7. Sequence motif analysis; 8. Transcription factors

Tóm tắt

Widespread microbial genome sequencing presents an opportunity to understand the gene regulatory networks of non-model organisms. This requires knowledge of the binding sites for transcription factors whose DNA-binding properties are unknown or difficult to infer. We adapted a protein structure-based method to predict the specificities and putative regulons of homologous transcription factors across diverse species. As a proof-of-concept we predicted the specificities and transcriptional target genes of divergent archaeal feast/famine regulatory proteins, several of which are encoded in the genome of Halobacterium salinarum. This was validated by comparison to experimentally determined specificities for transcription factors in distantly related extremophiles, chromatin immunoprecipitation experiments, and cis-regulatory sequence conservation across eighteen related species of halobacteria. Through this analysis we were able to infer that Halobacterium salinarum employs a divergent local trans-regulatory strategy to regulate genes (carA and carB) involved in arginine and pyrimidine metabolism, whereas Escherichia coli employs an operon. The prediction of gene regulatory binding sites using structure-based methods is useful for the inference of gene regulatory relationships in new species that are otherwise difficult to infer.