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

Abstract

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.

Figure 1. Flowchart of the procedures for identifying sRNA genes.

Figure 2. Schematic representation of the genomic positions of sRNA genes.

Circles 1 and 2 (from the outside in), all genes represented through reverse and forward strand, respectively; circle 3, the core (red) and noncore (blue) genome regions; circle 4, cis-encoded antisense sRNA predicted in this study, in which, the sRNA related to secondary metabolism colored by red; circle 5, trans-encoded antisense sRNA predicted in this work, in which, the sRNA overlapped with previous study (Marcellin et al., 2013) colored by yellow, and the sRNA related to secondary metabolism colored by red; circle 6, gene clusters correlated with the sRNA correlated with secondary metabolism.

Figure 3. RNA-RNA interactions of the six sRNAs and their targets.

The sRNA and its corresponding target are shown for the following sRNA-target pairs: (A) sernc292 and SACE_7029, (B) sernc293 and SACE_3034, (C) sernc350 and SACE_3976, (D) sernc351 and SACE_3977, (E) sernc361 and SACE_4129, and (F) sernc389 and SACE_4573. Black line indicates the local opening energies for both molecules and the red line shows interaction energies. The best interaction site of two molecules could be analyzed by computing the interaction energies.

Figure 4. Relative expression analysis of sRNAs by real-time RT-PCR.

The differential expression of the six sRNAs was analyzed using qRT-PCR in strains M and E3 at 24 (blue), 48 (yellow), and 72 h (gray).