A framework to analyze multiple time series data: A case study with Streptomyces coelicolor

Sarika Mehra, Wei Lian, Karithik P. Jayapal, Salim P. Charaniya, David H. Sherman, Wei Shou Hu

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

Abstract

Transcriptional regulation in differentiating microorganisms is highly dynamic involving multiple and interwinding circuits consisted of many regulatory genes. Elucidation of these networks may provide the key to harness the full capacity of many organisms that produce natural products. A powerful tool evolved in the past decade is global transcriptional study of mutants in which one or more key regulatory genes of interest have been deleted. To study regulatory mutants of Streptomyces coelicolor, we developed a framework of systematic analysis of gene expression dynamics. Instead of pair-wise comparison of samples in different combinations, genomic DNA was used as a common reference for all samples in microarray assays, thus, enabling direct comparison of gene transcription dynamics across different isogenic mutants. As growth and various differentiation events may unfold at different rates in different mutants, the global transcription profiles of each mutant were first aligned computationally to those of the wild type, with respect to the corresponding growth and differentiation stages, prior to identification of kinetically differentially expressed genes. The genome scale transcriptome data from wild type and a ΔabsA1 mutant of Streptomyces coelicolor were analyzed within this framework, and the regulatory elements affected by the gene knockout were identified. This methodology should find general applications in the analysis of other mutants in our repertoire and in other biological systems.

Original languageEnglish (US)
Pages (from-to)159-172
Number of pages14
JournalJournal of Industrial Microbiology and Biotechnology
Volume33
Issue number2
DOIs
StatePublished - Feb 2006

Keywords

  • Antibiotics
  • DNA microarray
  • Streptomyces
  • Time Series
  • Transcriptome analysis

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