Introduction
As one of the largest bacterial orders, actinobacteria are well known as prolific producers of numerous antibiotics, biofuels, materials, and commodity chemicals (Sayed, Abdel-Wahab, Hassan, & Abdelmohsen, 2019). Increasing numbers of new natural bioactive products are discovered in both actinomycetes inhabiting hostile environments or well-known production/laboratory strains (Newman & Cragg, 2016; Weber, Blin, et al., 2015). However, in most cases wild type strains isolated from nature only produce limited amounts of the desired compounds. To meet the industrial demand, several rounds of random mutagenesis and screening with subsequent optimization of cultivation process are usually required. Although many cases have shown the success of mutate-and-screen methods, this classical breeding strategy requires intensive labor and challenges the physiological stability of the strain, as various subculturing steps involving different cultivation conditions are required (Zeng, Guo, Xu, Chen, & Zhou, 2020). To some extent, the widely applied strategy of mutate-and-screen in industry is inevitable due to the lack of either genome/transcriptome information or feasible genetic tools for specific strains. On the other hand, most industrial mutants are likely over-mutagenized after several rounds of mutate-and-screen, resulting in a large number of unnecessary genomic variations relative to the beneficial mutations (Peano et al., 2014). The underlying correlation between genomic variations and phenotypic changes is still obscure in most cases. With the great strides made in recent years on DNA and RNA sequencing, in combination with the development of efficient genetic tools for actinomycetes (Tong, Charusanti, Zhang, Weber, & Lee, 2015), rational metabolic engineering approaches provide faster and more sparking ways to improve the production of desired products (Y. Liu et al., 2019). For instance, reverse engineering combined with omics analysis has been used to identify key factors responsible for high production of secondary metabolites and to further boost the biosynthesis of desired metabolites (Lum, Huang, Hutchinson, & Kao, 2004; Peano et al., 2014; Zhuo et al., 2010; wang et al., 2019).
Even though, most mechanisms by which industrial actinomycetes increase yield or production of desired metabolites remain ambiguous. The desired products from actinomycetes are usually derived from secondary metabolism, which is interconnected tightly with primary metabolism, morphological differentiation, transcriptional regulation and post-translational modification (Butler et al., 2002; Chng, Lum, Vroom, & Kao, 2008; Manteca & Yague, 2018). Furthermore, actinomycetes have larger genomes compared to E. coli , Corynebacterium glutamicum or some other widely-used industrial strains (Redenbach, Scheel, & Schmidt, 2000). Larger genomes usually imply more complex regulatory networks for the biosynthesis of desired secondary metabolites, more genes with unknown functions and more dependence of products profile on cultivation conditions. All these factors make the rational engineering approaches in actinomycetes difficult to enhance production of desired secondary metabolites.
Saccharopolyspora erythraea is of particularly interest as it is the producer of erythromycin in industry. S. erythraea has a large circular genome with 8 Mb (Oliynyk et al., 2007), which is also a host of several kinds of novel enzyme or natural products (Sayed et al., 2019). Erythromycin serves as a broad-spectrum macrolide antibiotic against pathogenic gram-positive bacteria. A series of derivatives of erythromycin, such as azithromycin, roxithromycin and clarithromycin, have been sought out for antiparasitic, antineoplastic, immunosuppressant, neurotrophic, antiinflammatory and gastroenteric therapeutic activities (Mironov, Sergienko, Nastasyak, & Danilenko, 2004). For instance, azithromycin combined with hydroxychloroquine served as a drug candidate to treat the pandemic COVID-19 (Andreani et al., 2020). However, the industrial production of erythromycin remains lower titer compared to some other antibiotics, e.g. penicillin (Y. Chen et al., 2013) and there is still large economic drive to improve the microbial production of erythromycin. S. erythraea is also considered as a model microorganism for investigating the production of antibiotics. The biosynthesis of erythromycin starts with the assembly of six methylmalonyl-CoA molecule and one propionyl-CoA molecules, and methylmalonyl/propionyl-CoA are common precursors of some other bioactive compounds. In addition, S. erythraea and other actinomycetes, e.g., Streptomyces coelicolor andStreptomyces avermitilis share high similarities regarding life cycle and regulation networks (Liao et al., 2015; Zhuo et al., 2010).
Hitherto, most previous attempts for improving the production of erythromycin by rational engineering were realized by genetic manipulations predominantly focused on stimulating the accumulation of erythromycin precursors (Reeves et al., 2006, 2007) or by optimizing the cultivation process (Y. Wang et al., 2007). Studies on transcriptional regulation of erythromycin biosynthesis gene cluster (BGC) also managed to boost the production of erythromycin through global or pathway-specific regulators (Chng et al., 2008; J. Liu et al., 2017; Z. Xu, Liu, & Ye, 2018). With fast advances of nucleotide sequencing, omics analysis could further provide more rational engineering strategies to enhance the production of erythromycin. The whole genome of S. erythraea NRRL23338 (the wild type, WT) was first reported in 2007 and initiated comparative omics analysis of S. erythraea (Oliynyk et al., 2007). Genomic and transcriptomic comparisons have been conducted between NRRL23338 and several high-erythromycin producing strains, which were isolated using classical mutagenesis and screening (Carata et al., 2009; Karnicar et al., 2016; Y. Li et al., 2013; Peano et al., 2012). Omics comparison illustrated underlying key features potentially associated with the phenotype of high-erythromycin production at the specific omics level. However, only few engineering strategies were proposed and validated. Particularly noteworthy is that all published transcriptomic data ofS. erythraea were collected by using microarrays (Carata et al., 2009; Karnicar et al., 2016; Y. Li et al., 2013; Peano et al., 2007; Peano et al., 2012), which has inevitable disadvantages compared to the high throughput RNA sequencing. As considerable amount of information about S. erythraea can be obtained after DNA or RNA sequencing, more rational engineering strategies would be proposed and deserve implementation.
In the present study, a high-erythromycin producing mutant S. erythraea HL3168 E3 was subjected to high throughput sequencing followed by comparative omics analysis. First, the whole genome was re-sequenced with Illumina Hiseq2000 platform (C. Chen et al., 2017; Y. Li et al., 2013). Then, time-series transcriptomic profiles of E3 and the wild type NRRL23338 were obtained by RNAseq. We identified more details about genomic variations relative to the first genome sequence of E3, which was determined by pyrosequencing (Y. Li et al., 2013). By integrating genomic and transcriptomic analysis, we proposed promising molecular targets to further boost the production of erythromycin by E3. Based on the comparative omics analysis, the node of 2-oxoglutarate was manipulated in E3, which contributed to a further enhancement of erythromycin production by 71%. This work showed that comparative omics analysis can provide readily available strategies to further enhance the production of secondary metabolites even in the overproducer.