Multi-omics Comparative Analysis of Streptomyces Mutants Obtained by Iterative Atmosphere and Room-Temperature Plasma Mutagenesis

Front Microbiol. 2021 Jan 28:11:630309. doi: 10.3389/fmicb.2020.630309. eCollection 2020.

Abstract

Sponges, the most primitive multicellular animals, contain a large number of unique microbial communities. Sponge-associated microorganisms, particularly actinomyces, have the potential to produce diverse active natural products. However, a large number of silent secondary metabolic gene clusters have failed to be revived under laboratory culture conditions. In this study, iterative atmospheric room-temperature plasma. (ARTP) mutagenesis coupled with multi-omics conjoint analysis was adopted to activate the inactive wild Streptomyces strain. The desirable exposure time employed in this study was 75 s to obtain the appropriate lethality rate (94%) and mutation positive rate (40.94%). After three iterations of ARTP mutagenesis, the proportion of mutants exhibiting antibacterial activities significantly increased by 75%. Transcriptome analysis further demonstrated that the differential gene expression levels of encoding type I lasso peptide aborycin had a significant upward trend in active mutants compared with wild-type strains, which was confirmed by LC-MS results with a relative molecular mass of 1082.43 ([M + 2H]2+ at m/z = 2164.86). Moreover, metabolome comparative analysis of the mutant and wild-type strains showed that four spectra or mass peaks presented obvious differences in terms of the total ion count or extracting ion current profiles with each peak corresponding to a specific compound exhibiting moderate antibacterial activity against Gram-positive indicators. Taken together, our data suggest that the ARTP treatment method coupled with multi-omics profiling analysis could be used to estimate the valid active molecules of metabolites from microbial crudes without requiring a time-consuming isolation process.

Keywords: ARTP mutagenesis; antibacterial activity; awaken cryptic gene clusters; metabolome comparative analysis; transcriptome comparative analysis.