Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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S. coelicolor YSK4425 Time-course Study in R5- medium (Culture#2)


ABSTRACT: Antibiotic biosynthesis in Streptomyces species is controlled by a complex genetic and biochemical network of global and pathway specific regulators. Details of their precise interactions in mediating temporal and spatial expression of secondary metabolite genes remain poorly defined. In this study, we employed whole-genome microarrays to investigate the temporal transcriptome profiles of S. coelicolor A3(2) afsS::apr mutant strain (YSK4425) and compare it to wild-type M145 strain. The regulatory protein encoded by afsS is known to affect antibiotic biosynthesis (Floriano, B., Bibb, M. 1996. afsR is a pleiotropic but conditionally required regulatory gene for antibiotic production in Streptomyces coelicolor A3(2). Mol Microbiol, 21, 385-96). Keywords: Time course The time-course study involved analysis of 14 samples (15h, 17h, 19h, 21h, 23h, 25h, 28h, 31h, 32h, 35h, 37h, 38h, 41h, 45h). Of these, 15h, 17h, 19h, 21h, 23h, 25h, 32h, 41h, and 45h samples were analyzed on duplicate arrays (2 technical replicates). Genomic DNA of S. coelicolor wild type (M145) was used as a reference for all the arrays. cDNA was labeled with Alexa647 and genomic DNA was labeled with Cy3.

ORGANISM(S): Streptomyces coelicolor

SUBMITTER: Karthik Jayapal 

PROVIDER: E-GEOD-8160 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Transcriptome dynamics-based operon prediction and verification in Streptomyces coelicolor.

Charaniya Salim S   Mehra Sarika S   Lian Wei W   Jayapal Karthik P KP   Karypis George G   Hu Wei-Shou WS  

Nucleic acids research 20071024 21


Streptomyces spp. produce a variety of valuable secondary metabolites, which are regulated in a spatio-temporal manner by a complex network of inter-connected gene products. Using a compilation of genome-scale temporal transcriptome data for the model organism, Streptomyces coelicolor, under different environmental and genetic perturbations, we have developed a supervised machine-learning method for operon prediction in this microorganism. We demonstrate that, using features dependent on transcr  ...[more]

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