Next-generation sequencing based multi-omic analysis of Streptomyces genome for deciphering secondary metabolism

Abstract

In this thesis, applications using next-generation sequencing (NGS) technology were employed to obtain genome-wide data, elucidating diverse cellular events of Streptomyces genome. First, comparative genomic analysis using 17 completely sequenced genome of Streptomyces revealed that 2018 gene families constitute core genome of this genus, including 15 ortholog clusters of sigma factors, 22 ortholog clusters involved in cell division category and secondary metabolite genes related to stress protection. Next, genome-wide binding of NdgR, a common transcriptional regulator involved in the biosynthesis of amino acids in S. coelicolor, was discovered by using chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP- seq). The study showed that NdgR binds 19 genomic loci including upstream regions of most genes involved in branched-chain and sulfur-containing amino acids biosynthesis. For this experiment, tandem epitope tagging systems for Streptomyces genome engineering was developed, which can be applied to other transcription factors in Streptomyces. Further study revealed that NdgR maintains homeostasis of sulfur assimilation under thiol oxidative stress conditions. In addition, genome architecture and dynamic expressions of mRNA and protein were uncovered by using multiple NGS tools, including TSS-seq, RNA-seq and ribosome profiling. Total 3926 transcription start sites were identified, indicating the length of 5 untranslated region of mRNA. This revealed that abundant existence of leaderless genes (~20%) and many of them were involved in transcription category. In particular, dynamic change of RNA and ribosome protected mRNA fragment (RPF) level showed disparity between transcription and translation, indicating the existence of translational control. With the integration of multiple NGS data, the single-based resolution map of genome architecture and expression profiles of each secondary metabolite clusters were examined, which provides valuable information for manipulating secondary metabolite production. The enormous data generated in this thesis and methodologies can be applied to engineering of genetic circuits for the antibiotics synthesis in S. coelicolor.