Prediction and characterization of small non-coding RNAs related to secondary metabolites in Saccharopolyspora erythraea.

• Main Authors: Wei-Bing Liu, Yang Shi, Li-Li Yao, Ying Zhou, Bang-Ce Ye
• Format: Article
• Language: English
• Published: Public Library of Science (PLoS) 2013-01-01
• Series: PLoS ONE
• Online Access: https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24236194/?tool=EBI

Saccharopolyspora erythraea produces a large number of secondary metabolites with biological activities, including erythromycin. Elucidation of the mechanisms through which the production of these secondary metabolites is regulated may help to identify new strategies for improved biosynthesis of erythromycin. In this paper, we describe the systematic prediction and analysis of small non-coding RNAs (sRNAs) in S. erythraea, with the aim to elucidate sRNA-mediated regulation of secondary metabolite biosynthesis. In silico and deep-sequencing technologies were applied to predict sRNAs in S. erythraea. Six hundred and forty-seven potential sRNA loci were identified, of which 382 cis-encoded antisense RNA are complementary to protein-coding regions and 265 predicted transcripts are located in intergenic regions. Six candidate sRNAs (sernc292, sernc293, sernc350, sernc351, sernc361, and sernc389) belong to four gene clusters (tpc3, pke, pks6, and nrps5) that are involved in secondary metabolite biosynthesis. Deep-sequencing data showed that the expression of all sRNAs in the strain HL3168 E3 (E3) was higher than that in NRRL23338 (M), except for sernc292 and sernc361 expression. The relative expression of six sRNAs in strain M and E3 were validated by qRT-PCR at three different time points (24, 48, and 72 h). The results showed that, at each time point, the transcription levels of sernc293, sernc350, sernc351, and sernc389 were higher in E3 than in M, with the largest difference observed at 72 h, whereas no signals for sernc292 and sernc361 were detected. sernc293, sernc350, sernc351, and sernc389 probably regulate iron transport, terpene metabolism, geosmin synthesis, and polyketide biosynthesis, respectively. The major significance of this study is the successful prediction and identification of sRNAs in genomic regions close to the secondary metabolism-related genes in S. erythraea. A better understanding of the sRNA-target interaction would help to elucidate the complete range of functions of sRNAs in S. erythraea, including sRNA-mediated regulation of erythromycin biosynthesis.