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.