PLoS ONEGenomicsPeer-reviewed journal article
Genome-wide analysis of the role of the antibiotic biosynthesis regulator AbsA2 in Streptomyces coelicolor A3(2)
Richard A. Lewis, Abdul Wahab, Giselda BuccaiD, Emma LaingiD, Carla S. Möller‐LevetiD, Andrzej M. KierzekiD, Colin P. Sambrook SmithiD
Abstract
The AbsA1-AbsA2 two component signalling system of Streptomyces coelicolor has long been known to exert a powerful negative influence on the production of the antibiotics actinorhodin, undecylprodiginine and the Calcium-Dependent Antibiotic (CDA). Here we report the analysis of a{Delta} absA2 deletion strain, which exhibits the classic precocious antibiotic hyper-production phenotype, and its complementation by an N-terminal triple-FLAG-tagged version of AbsA2. The complemented and non-complemented{Delta} absA2 mutant strains were used in large-scale microarray-based time-course experiments to investigate the effect of deleting absA2 on gene expression and to identify the in vivo AbsA2 DNA-binding target sites using ChIP-on chip. We show that in addition to binding to the promoter regions of redZ and actII-orfIV AbsA2 binds to several previously unidentified sites within the cda biosynthetic gene cluster within and/or upstream of SCO3215 - SCO3216, SCO3217, SCO3229 - SCO3230, and SCO3226, and we relate the pattern of AbsA2 binding to the results of the transcriptomic study and antibiotic phenotypic assays. Interestingly, dual biphasic ChIP peaks were observed with AbsA2 binding across the regulatory genes actII-orfIV and redZ and the absA2 gene itself, while more conventional single promoter-proximal peaks were seen at the CDA biosynthetic genes suggesting a different mechanism of regulation of the former loci. Taken together the results shed light on the complex mechanism of regulation of antibiotic biosynthesis in Streptomyces coelicolor and the important role of AbsA2 in controlling the expression of three antibiotic biosynthetic gene clusters.
- organism: bacteria (other)
- assay: ChIP-seq
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 17
- FWCI
- 1.86
- Field percentile
- top 17%
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