Genome-scale metabolic flux analysis of Streptomyces lividans growing on a complex medium

3-Amino-4-hydroxybenzoic acid production from glucose and/or xylose via recombinant Streptomyces lividans

The aromatic compound 3-amino-4-hydroxybenzoic acid (3,4-AHBA) can be employed as a raw material for high-performance industrial plastics. The aim of this study is to produce 3,4-AHBA via a recombinant Streptomyces lividans strain containing griI and griH genes derived from Streptomyces griseus using culture medium with glucose and/or xylose, which are the main components in lignocellulosic biomass. Production of 3,4-AHBA by the recombinant S. lividans strain was successful, and the productivity was affected by the kind of sugar used as an additional carbon source. Metabolic profiles revealed that L aspartate-4-semialdehyde (ASA), a precursor of 3,4-AHBA, and coenzyme NADPH were supplied in greater amounts in xylose medium than in glucose medium. Moreover, cultivation in TSB medium with a mixed sugar (glucose/xylose) was found to be effective for 3,4-AHBA production, and optimal conditions for efficient production were designed by changing the ratio of glucose to xylose. The best productivity of 2.70 g/L was achieved using a sugar mixture of 25 g/L glucose and 25 g/L xylose, which was 1.5 times higher than the result using 50 g/L glucose alone. These results suggest that Streptomyces is a suitable candidate platform for 3,4-AHBA production from lignocellulosic biomass-derived sugars under appropriate culture conditions.

Knowledge extraction from literature and enzyme sequences complements FBA analysis in metabolic engineering

Flux balance analysis (FBA) using genome-scale metabolic model (GSM) is a useful method for improving the bio-production of useful compounds. However, FBA often does not impose important constraints such as nutrients uptakes, by-products excretions and gases (oxygen and carbon dioxide) transfers. Furthermore, important information on metabolic engineering such as enzyme amounts, activities, and characteristics caused by gene expression and enzyme sequences is basically not included in GSM. Therefore, simple FBA is often not sufficient to search for metabolic manipulation strategies that are useful for improving the production of target compounds. In this study, we proposed a method using literature and enzyme search to complement the FBA-based metabolic manipulation strategies. As a case study, this method was applied to shikimic acid production by Corynebacterium glutamicum to verify its usefulness. As unique strategies in literature-mining, overexpression of the transcriptional regulator SugR and gene disruption related to by-products productions were complemented. In the search for alternative enzyme sequences, it was suggested that those candidates are searched for from various species based on features captured by deep learning, which are not simply homologous to amino acid sequences of the base enzymes.

Synthetic biology approaches to actinomycete strain improvement

Their biochemical versatility and biotechnological importance make actinomycete bacteria attractive targets for ambitious genetic engineering using the toolkit of synthetic biology. But their complex biology also poses unique challenges. This mini review discusses some of the recent advances in synthetic biology approaches from an actinomycete perspective and presents examples of their application to the rational improvement of industrially relevant strains.

Transcriptomic and fluxomic changes in Streptomyces lividans producing heterologous protein

BACKGROUND

The Gram-positive Streptomyces lividans TK24 is an attractive host for heterologous protein production because of its high capability to secrete proteins-which favors correct folding and facilitates downstream processing-as well as its acceptance of methylated DNA and its low endogeneous protease activity. However, current inconsistencies in protein yields urge for a deeper understanding of the burden of heterologous protein production on the cell. In the current study, transcriptomics and [Formula: see text]-based fluxomics were exploited to uncover gene expression and metabolic flux changes associated with heterologous protein production. The Rhodothermus marinus thermostable cellulase A (CelA)-previously shown to be successfully overexpressed in S. lividans-was taken as an example protein.

RESULTS

RNA-seq and [Formula: see text]-based metabolic flux analysis were performed on a CelA-producing and an empty-plasmid strain under the same conditions. Differential gene expression, followed by cluster analysis based on co-expression and co-localization, identified transcriptomic responses related to secretion-induced stress and DNA damage. Furthermore, the OsdR regulon (previously associated with hypoxia, oxidative stress, intercellular signaling, and morphological development) was consistently upregulated in the CelA-producing strain and exhibited co-expression with isoenzymes from the pentose phosphate pathway linked to secondary metabolism. Increased expression of these isoenzymes matches to increased fluxes in the pentose phosphate pathway. Additionally, flux maps of the central carbon metabolism show increased flux through the tricarboxylic acid cycle in the CelA-producing strain. Redirection of fluxes in the CelA-producing strain leads to higher production of NADPH, which can only partly be attributed to increased secretion.

CONCLUSIONS

Transcriptomic and fluxomic changes uncover potential new leads for targeted strain improvement strategies which may ease the secretion stress and metabolic burden associated with heterologous protein synthesis and secretion, and may help create a more consistently performing S. lividans strain. Yet, links to secondary metabolism and redox balancing should be further investigated to fully understand the S. lividans metabolome under heterologous protein production.

Modelling the metabolism of protein secretion through the Tat route in Streptomyces lividans

Background

Streptomyces lividans has demonstrated its value as an efficient host for protein production due to its ability to secrete functional proteins directly to the media. Secretory proteins that use the major Sec route need to be properly folded outside the cell, whereas secretory proteins using the Tat route appear outside the cell correctly folded. This feature makes the Tat system very attractive for the production of natural or engineered Tat secretory proteins. S. lividans cells are known to respond differently to overproduction and secretion of Tat versus Sec proteins. Increased understanding of the impact of protein secretion through the Tat route can be obtained by a deeper analysis of the metabolic impact associated with protein production, and its dependence on protein origin, composition, secretion mechanisms, growth phases and nutrients. Flux Balance Analysis of Genome-Scale Metabolic Network models provides a theoretical framework to investigate cell metabolism under different constraints.

Results

We have built new models for various S. lividans strains to better understand the mechanisms associated with overproduction of proteins secreted through the Tat route. We compare models of an S. lividans Tat-dependent agarase overproducing strain with those of the S. lividans wild-type, an S. lividans strain carrying the multi-copy plasmid vector and an α-amylase Sec-dependent overproducing strain. Using updated genomic, transcriptomic and experimental data we could extend existing S. lividans models and produce a new model which produces improved results largely extending the coverage of S. lividans strains, the number of genes and reactions being considered, the predictive behaviour and the dependence on specification of exchange constraints. Comparison of the optimized solutions obtained highlights numerous changes between Tat- and Sec-dependent protein secreting strains affecting the metabolism of carbon, amino acids, nucleotides, lipids and cofactors, and variability analysis predicts a large potential for protein overproduction.

Conclusions

This work provides a detailed look to metabolic changes associated to Tat-dependent protein secretion reproducing experimental observations and identifying changes that are specific to each secretory route, presenting a novel, improved, more accurate and strain-independent model of S. lividans, thus opening the way for enhanced metabolic engineering of protein overproduction in S. lividans.

Electronic supplementary material

The online version of this article (10.1186/s12866-018-1199-3) contains supplementary material, which is available to authorized users.

Analysis of metabolic networks of Streptomyces leeuwenhoekii C34 by means of a genome scale model: Prediction of modifications that enhance the production of specialized metabolites

The first genome scale model (GSM) for Streptomyces leeuwenhoekii C34 was developed to study the biosynthesis pathways of specialized metabolites and to find metabolic engineering targets for enhancing their production. The model, iVR1007, consists of 1,722 reactions, 1,463 metabolites, and 1,007 genes, it includes the biosynthesis pathways of chaxamycins, chaxalactins, desferrioxamines, ectoine, and other specialized metabolites. iVR1007 was validated using experimental information of growth on 166 different sources of carbon, nitrogen and phosphorous, showing an 83.7% accuracy. The model was used to predict metabolic engineering targets for enhancing the biosynthesis of chaxamycins and chaxalactins. Gene knockouts, such as sle03600 (L-homoserine O-acetyltransferase), and sle39090 (trehalose-phosphate synthase), that enhance the production of the specialized metabolites by increasing the pool of precursors were identified. Using the algorithm of flux scanning based on enforced objective flux (FSEOF) implemented in python, 35 and 25 over-expression targets for increasing the production of chaxamycin A and chaxalactin A, respectively, that were not directly associated with their biosynthesis routes were identified. Nineteen over-expression targets that were common to the two specialized metabolites studied, like the over-expression of the acetyl carboxylase complex (sle47660 (accA) and any of the following genes: sle44630 (accA_1) or sle39830 (accA_2) or sle27560 (bccA) or sle59710) were identified. The predicted knockouts and over-expression targets will be used to perform metabolic engineering of S. leeuwenhoekii C34 and obtain overproducer strains.

Streptomyces clavuligerus shows a strong association between TCA cycle intermediate accumulation and clavulanic acid biosynthesis

Clavulanic acid (CA) is produced by Streptomyces clavuligerus (S. clavuligerus) as a secondary metabolite. Knowledge about the carbon flux distribution along the various routes that supply CA precursors would certainly provide insights about metabolic performance. In order to evaluate metabolic patterns and the possible accumulation of tricarboxylic acid (TCA) cycle intermediates during CA biosynthesis, batch and subsequent continuous cultures with steadily declining feed rates were performed with glycerol as the main substrate. The data were used to in silico explore the metabolic capabilities and the accumulation of metabolic intermediates in S. clavuligerus. While clavulanic acid accumulated at glycerol excess, it steadily decreased at declining dilution rates; CA synthesis stopped when glycerol became the limiting substrate. A strong association of succinate, oxaloacetate, malate, and acetate accumulation with CA production in S. clavuligerus was observed, and flux balance analysis (FBA) was used to describe the carbon flux distribution in the network. This combined experimental and numerical approach also identified bottlenecks during the synthesis of CA in a batch and subsequent continuous cultivation and demonstrated the importance of this type of methodologies for a more advanced understanding of metabolism; this potentially derives valuable insights for future successful metabolic engineering studies in S. clavuligerus.

Metabolic adaptation of two in silico mutants of Mycobacterium tuberculosis during infection

BACKGROUND

Up to date, Mycobacterium tuberculosis (Mtb) remains as the worst intracellular killer pathogen. To establish infection, inside the granuloma, Mtb reprograms its metabolism to support both growth and survival, keeping a balance between catabolism, anabolism and energy supply. Mtb knockouts with the faculty of being essential on a wide range of nutritional conditions are deemed as target candidates for tuberculosis (TB) treatment. Constraint-based genome-scale modeling is considered as a promising tool for evaluating genetic and nutritional perturbations on Mtb metabolic reprogramming. Nonetheless, few in silico assessments of the effect of nutritional conditions on Mtb's vulnerability and metabolic adaptation have been carried out.

RESULTS

A genome-scale model (GEM) of Mtb, modified from the H37Rv iOSDD890, was used to explore the metabolic reprogramming of two Mtb knockout mutants (pfkA- and icl-mutants), lacking key enzymes of central carbon metabolism, while exposed to changing nutritional conditions (oxygen, and carbon and nitrogen sources). A combination of shadow pricing, sensitivity analysis, and flux distributions patterns allowed us to identify metabolic behaviors that are in agreement with phenotypes reported in the literature. During hypoxia, at high glucose consumption, the Mtb pfkA-mutant showed a detrimental growth effect derived from the accumulation of toxic sugar phosphate intermediates (glucose-6-phosphate and fructose-6-phosphate) along with an increment of carbon fluxes towards the reductive direction of the tricarboxylic acid cycle (TCA). Furthermore, metabolic reprogramming of the icl-mutant (icl1&icl2) showed the importance of the methylmalonyl pathway for the detoxification of propionyl-CoA, during growth at high fatty acid consumption rates and aerobic conditions. At elevated levels of fatty acid uptake and hypoxia, we found a drop in TCA cycle intermediate accumulation that might create redox imbalance. Finally, findings regarding Mtb-mutant metabolic adaptation associated with asparagine consumption and acetate, succinate and alanine production, were in agreement with literature reports.

CONCLUSIONS

This study demonstrates the potential application of genome-scale modeling, flux balance analysis (FBA), phenotypic phase plane (PhPP) analysis and shadow pricing to generate valuable insights about Mtb metabolic reprogramming in the context of human granulomas.

Metabolic network model guided engineering ethylmalonyl-CoA pathway to improve ascomycin production in Streptomyces hygroscopicus var. ascomyceticus

Background

Ascomycin is a 23-membered polyketide macrolide with high immunosuppressant and antifungal activity. As the lower production in bio-fermentation, global metabolic analysis is required to further explore its biosynthetic network and determine the key limiting steps for rationally engineering. To achieve this goal, an engineering approach guided by a metabolic network model was implemented to better understand ascomycin biosynthesis and improve its production.

Results

The metabolic conservation of Streptomyces species was first investigated by comparing the metabolic enzymes of Streptomyces coelicolor A3(2) with those of 31 Streptomyces strains, the results showed that more than 72% of the examined proteins had high sequence similarity with counterparts in every surveyed strain. And it was found that metabolic reactions are more highly conserved than the enzymes themselves because of its lower diversity of metabolic functions than that of genes. The main source of the observed metabolic differences was from the diversity of secondary metabolism. According to the high conservation of primary metabolic reactions in Streptomyces species, the metabolic network model of Streptomyces hygroscopicus var. ascomyceticus was constructed based on the latest reported metabolic model of S. coelicolor A3(2) and validated experimentally. By coupling with flux balance analysis and using minimization of metabolic adjustment algorithm, potential targets for ascomycin overproduction were predicted. Since several of the preferred targets were highly associated with ethylmalonyl-CoA biosynthesis, two target genes hcd (encoding 3-hydroxybutyryl-CoA dehydrogenase) and ccr (encoding crotonyl-CoA carboxylase/reductase) were selected for overexpression in S. hygroscopicus var. ascomyceticus FS35. Both the mutants HA-Hcd and HA-Ccr showed higher ascomycin titer, which was consistent with the model predictions. Furthermore, the combined effects of the two genes were evaluated and the strain HA-Hcd-Ccr with hcd and ccr overexpression exhibited the highest ascomycin production (up to 438.95 mg/L), 1.43-folds improvement than that of the parent strain FS35 (305.56 mg/L).

Conclusions

The successful constructing and experimental validation of the metabolic model of S. hygroscopicus var. ascomyceticus showed that the general metabolic network model of Streptomyces species could be used to analyze the intracellular metabolism and predict the potential key limiting steps for target metabolites overproduction. The corresponding overexpression strains of the two identified genes (hcd and ccr) using the constructed model all displayed higher ascomycin titer. The strategy for yield improvement developed here could also be extended to the improvement of other secondary metabolites in Streptomyces species.

Electronic supplementary material

The online version of this article (doi:10.1186/s12934-017-0787-5) contains supplementary material, which is available to authorized users.

Protein Secretion in Gram-Positive Bacteria: From Multiple Pathways to Biotechnology

A number of Gram-positive bacteria are important players in industry as producers of a diverse array of economically interesting metabolites and proteins. As discussed in this overview, several Gram-positive bacteria are valuable hosts for the production of heterologous proteins. In contrast to Gram-negative bacteria, proteins secreted by Gram-positive bacteria are released into the culture medium where conditions for correct folding are more appropriate, thus facilitating the isolation and purification of active proteins. Although seven different protein secretion pathways have been identified in Gram-positive bacteria, the majority of heterologous proteins are produced via the general secretion or Sec pathway. Not all proteins are equally well secreted, because heterologous protein production often faces bottlenecks including hampered secretion, susceptibility to proteases, secretion stress, and metabolic burden. These bottlenecks are associated with reduced yields leading to non-marketable products. In this chapter, besides a general overview of the different protein secretion pathways, possible hurdles that may hinder efficient protein secretion are described and attempts to improve yield are discussed including modification of components of the Sec pathway. Attention is also paid to omics-based approaches that may offer a more rational approach to optimize production of heterologous proteins.

Time-Resolved Transcriptomics and Constraint-Based Modeling Identify System-Level Metabolic Features and Overexpression Targets to Increase Spiramycin Production in Streptomyces ambofaciens

In this study we have applied an integrated system biology approach to characterize the metabolic landscape of Streptomyces ambofaciens and to identify a list of potential metabolic engineering targets for the overproduction of the secondary metabolites in this microorganism. We focused on an often overlooked growth period (i.e., post-first rapid growth phase) and, by integrating constraint-based metabolic modeling with time resolved RNA-seq data, we depicted the main effects of changes in gene expression on the overall metabolic reprogramming occurring in S. ambofaciens. Moreover, through metabolic modeling, we unraveled a set of candidate overexpression gene targets hypothetically leading to spiramycin overproduction. Model predictions were experimentally validated by genetic manipulation of the recently described ethylmalonyl-CoA metabolic node, providing evidence that spiramycin productivity may be increased by enhancing the carbon flow through this pathway. The goal was achieved by over-expressing the ccr paralog srm4 in an ad hoc engineered plasmid. This work embeds the first metabolic reconstruction of S. ambofaciens and the successful experimental validation of model predictions and demonstrates the validity and the importance of in silico modeling tools for the overproduction of molecules with a biotechnological interest. Finally, the proposed metabolic reconstruction, which includes manually refined pathways for several secondary metabolites with antimicrobial activity, represents a solid platform for the future exploitation of S. ambofaciens biotechnological potential.

Development of fungal cell factories for the production of secondary metabolites: Linking genomics and metabolism

The genomic era has revolutionized research on secondary metabolites and bioinformatics methods have in recent years revived the antibiotic discovery process after decades with only few new active molecules being identified. New computational tools are driven by genomics and metabolomics analysis, and enables rapid identification of novel secondary metabolites. To translate this increased discovery rate into industrial exploitation, it is necessary to integrate secondary metabolite pathways in the metabolic engineering process. In this review, we will describe the novel advances in discovery of secondary metabolites produced by filamentous fungi, highlight the utilization of genome-scale metabolic models (GEMs) in the design of fungal cell factories for the production of secondary metabolites and review strategies for optimizing secondary metabolite production through the construction of high yielding platform cell factories.

The secondary metabolite bioinformatics portal: Computational tools to facilitate synthetic biology of secondary metabolite production

Natural products are among the most important sources of lead molecules for drug discovery. With the development of affordable whole-genome sequencing technologies and other ‘omics tools, the field of natural products research is currently undergoing a shift in paradigms. While, for decades, mainly analytical and chemical methods gave access to this group of compounds, nowadays genomics-based methods offer complementary approaches to find, identify and characterize such molecules. This paradigm shift also resulted in a high demand for computational tools to assist researchers in their daily work. In this context, this review gives a summary of tools and databases that currently are available to mine, identify and characterize natural product biosynthesis pathways and their producers based on ‘omics data. A web portal called Secondary Metabolite Bioinformatics Portal (SMBP at http://www.secondarymetabolites.org) is introduced to provide a one-stop catalog and links to these bioinformatics resources. In addition, an outlook is presented how the existing tools and those to be developed will influence synthetic biology approaches in the natural products field.

Deciphering the streamlined genome of Streptomyces xiamenensis 318 as the producer of the anti-fibrotic drug candidate xiamenmycin

Streptomyces xiamenensis 318, a moderate halophile isolated from a mangrove sediment, produces the anti-fibrotic compound xiamenmycin. The whole genome sequence of strain 318 was obtained through long-read single-molecule real-time (SMRT) sequencing, high-throughput Illumina HiSeq and 454 pyrosequencing technologies. The assembled genome comprises a linear chromosome as a single contig of 5,961,401-bp, which is considerably smaller than other reported complete genomes of the genus Streptomyces. Based on the antiSMASH pipeline, a total of 21 gene clusters were predicted to be involved in secondary metabolism. The gene cluster responsible for the biosynthesis of xiamenmycin resides in a strain-specific 61,387-bp genomic island belonging to the left-arm region. A core metabolic network consisting of 104 reactions that supports xiamenmycin biosynthesis was constructed to illustrate the necessary precursors derived from the central metabolic pathway. In accordance with the finding of a putative ikarugamycin gene cluster in the genome, the targeted chemical profiling of polycyclic tetramate macrolactams (PTMs) resulted in the identification of ikarugamycin. A successful genome mining for bioactive molecules with different skeletons suggests that the naturally minimized genome of S. xiamenensis 318 could be used as a blueprint for constructing a chassis cell with versatile biosynthetic capabilities for the production of secondary metabolites.

Metabolic engineering with systems biology tools to optimize production of prokaryotic secondary metabolites

This Highlight examines current status of metabolic engineering and systems biology tools deployed for the optimal production of prokaryotic secondary metabolites.

Cell scale host-pathogen modeling: another branch in the evolution of constraint-based methods

Constraint-based models have become popular methods for systems biology as they enable the integration of complex, disparate datasets in a biologically cohesive framework that also supports the description of biological processes in terms of basic physicochemical constraints and relationships. The scope, scale, and application of genome scale models have grown from single cell bacteria to multi-cellular interaction modeling; host-pathogen modeling represents one of these examples at the current horizon of constraint-based methods. There are now a small number of examples of host-pathogen constraint-based models in the literature, however there has not yet been a definitive description of the methodology required for the functional integration of genome scale models in order to generate simulation capable host-pathogen models. Herein we outline a systematic procedure to produce functional host-pathogen models, highlighting steps which require debugging and iterative revisions in order to successfully build a functional model. The construction of such models will enable the exploration of host-pathogen interactions by leveraging the growing wealth of omic data in order to better understand mechanism of infection and identify novel therapeutic strategies.

Flux balance analysis in the production of clavulanic acid by Streptomyces clavuligerus

In this work, in silico flux balance analysis is used for predicting the metabolic behavior of Streptomyces clavuligerus during clavulanic acid production. To choose the best objective function for use in the analysis, three different optimization problems are evaluated inside the flux balance analysis formulation: (i) maximization of the specific growth rate, (ii) maximization of the ATP yield, and (iii) maximization of clavulanic acid production. Maximization of ATP yield showed the best predictions for the cellular behavior. Therefore, flux balance analysis using ATP as objective function was used for analyzing different scenarios of nutrient limitations toward establishing the effect of limiting the carbon, nitrogen, phosphorous, and oxygen sources on the growth and clavulanic acid production rates. Obtained results showed that ammonia and phosphate limitations are the ones most strongly affecting clavulanic acid biosynthesis. Furthermore, it was possible to identify the ornithine flux from the urea cycle and the α-ketoglutarate flux from the TCA cycle as the most determinant internal fluxes for promoting clavulanic acid production.

Sampling with poling-based flux balance analysis: optimal versus sub-optimal flux space analysis of Actinobacillus succinogenes

BACKGROUND

Flux balance analysis is traditionally implemented to identify the maximum theoretical flux for some specified reaction and a single distribution of flux values for all the reactions present which achieve this maximum value. However it is well known that the uncertainty in reaction networks due to branches, cycles and experimental errors results in a large number of combinations of internal reaction fluxes which can achieve the same optimal flux value.

RESULTS

In this work, we have modified the applied linear objective of flux balance analysis to include a poling penalty function, which pushes each new set of reaction fluxes away from previous solutions generated. Repeated poling-based flux balance analysis generates a sample of different solutions (a characteristic set), which represents all the possible functionality of the reaction network. Compared to existing sampling methods, for the purpose of generating a relatively "small" characteristic set, our new method is shown to obtain a higher coverage than competing methods under most conditions. The influence of the linear objective function on the sampling (the linear bias) constrains optimisation results to a subspace of optimal solutions all producing the same maximal fluxes. Visualisation of reaction fluxes plotted against each other in 2 dimensions with and without the linear bias indicates the existence of correlations between fluxes. This method of sampling is applied to the organism Actinobacillus succinogenes for the production of succinic acid from glycerol.

CONCLUSIONS

A new method of sampling for the generation of different flux distributions (sets of individual fluxes satisfying constraints on the steady-state mass balances of intermediates) has been developed using a relatively simple modification of flux balance analysis to include a poling penalty function inside the resulting optimisation objective function. This new methodology can achieve a high coverage of the possible flux space and can be used with and without linear bias to show optimal versus sub-optimal solution spaces. Basic analysis of the Actinobacillus succinogenes system using sampling shows that in order to achieve the maximal succinic acid production CO₂ must be taken into the system. Solutions involving release of CO₂ all give sub-optimal succinic acid production.

Reduced quenching and extraction time for mammalian cells using filtration and syringe extraction

In order to preserve the in vivo metabolite levels of cells, a quenching protocol must be quickly executed to avoid degradation of labile metabolites either chemically or biologically. In the case of mammalian cell cultures cultivated in complex media, a wash step previous to quenching is necessary to avoid contamination of the cell pellet with extracellular metabolites, which could distort the real intracellular concentration of metabolites. This is typically achieved either by one or multiple centrifugation/wash steps which delay the time until quenching (even harsh centrifugation requires several minutes for processing until the cells are quenched) or filtration. In this article, we describe and evaluate a two-step optimized protocol based on fast filtration by use of a vacuum pump for quenching and subsequent extraction of intracellular metabolites from CHO (Chinese hamster ovary) suspension cells, which uses commercially available components. The method allows transfer of washed cells into liquid nitrogen within 10-15s of sampling and recovers the entire extraction solution volume. It also has the advantage to remove residual filter filaments in the final sample, thus preventing damage to separation columns during subsequent MS analysis. Relative to other methods currently used in the literature, the resulting energy charge of intracellular adenosine nucleotides was increased to 0.94 compared to 0.90 with cold PBS quenching or 0.82 with cold methanol/AMBIC quenching.

Synthetic biology of avermectin for production improvement and structure diversification

Natural products are still key sources of current clinical drugs and innovative therapeutic agents. Since wild-type microorganisms only produce natural products in very small quantities, yields of production strains need to be improved by breaking down the precise genetic and biochemical circuitry. Herein, we use avermectins as an example of production improvement and chemical structure diversification by synthetic biology. Avermectins are macrocyclic lactones produced by Streptomyces avermitilis and are well known and widely used for antiparasitic therapy. Given the importance of this molecule and its derivatives, many efforts and strategies were employed to improve avermectin production and generate new active analogues. This review describes the current status of synthetic strategies successfully applied for developing natural-product-producing strains and discusses future prospects for the application of enhanced avermectin production.

The genome sequence of Streptomyces lividans 66 reveals a novel tRNA-dependent peptide biosynthetic system within a metal-related genomic island

The complete genome sequence of the original isolate of the model actinomycete Streptomyces lividans 66, also referred to as 1326, was deciphered after a combination of next-generation sequencing platforms and a hybrid assembly pipeline. Comparative analysis of the genomes of S. lividans 66 and closely related strains, including S. coelicolor M145 and S. lividans TK24, was used to identify strain-specific genes. The genetic diversity identified included a large genomic island with a mosaic structure, present in S. lividans 66 but not in the strain TK24. Sequence analyses showed that this genomic island has an anomalous (G + C) content, suggesting recent acquisition and that it is rich in metal-related genes. Sequences previously linked to a mobile conjugative element, termed plasmid SLP3 and defined here as a 94 kb region, could also be identified within this locus. Transcriptional analysis of the response of S. lividans 66 to copper was used to corroborate a role of this large genomic island, including two SLP3-borne “cryptic” peptide biosynthetic gene clusters, in metal homeostasis. Notably, one of these predicted biosynthetic systems includes an unprecedented nonribosomal peptide synthetase—tRNA-dependent transferase biosynthetic hybrid organization. This observation implies the recruitment of members of the leucyl/phenylalanyl-tRNA-protein transferase family to catalyze peptide bond formation within the biosynthesis of natural products. Thus, the genome sequence of S. lividans 66 not only explains long-standing genetic and phenotypic differences but also opens the door for further in-depth comparative genomic analyses of model Streptomyces strains, as well as for the discovery of novel natural products following genome-mining approaches.

Systems biology and biotechnology of Streptomyces species for the production of secondary metabolites

Streptomyces species continue to attract attention as a source of novel medicinal compounds. Despite a long history of studies on these microorganisms, they still have many biochemical mysteries to be elucidated. Investigations of novel secondary metabolites and their biosynthetic gene clusters have been more systematized with high-throughput techniques through inspections of correlations among components of the primary and secondary metabolisms at the genome scale. Moreover, up-to-date information on the genome of Streptomyces species with emphasis on their secondary metabolism has been collected in the form of databases and knowledgebases, providing predictive information and enabling one to explore experimentally unrecognized biological spaces of secondary metabolism. Herein, we review recent trends in the systems biology and biotechnology of Streptomyces species.

Insights into the roles of exogenous glutamate and proline in improving streptolydigin production of Streptomyces lydicus with metabolomic analysis

The addition of precursors was one strategy to improve antibiotic production. The exogenous proline and glutamate, as precursors of streptolydigin, could significantly improve the streptolydigin production, but their underlying molecular mechanisms remain unknown. Herein, metabolomic analysis was carried out to explore the metabolic responses of Streptomyces lydicus to the additions of proline and glutamine. The significant differences in the quantified 53 metabolites after adding the exogenous proline and glutamate were enunciated by gas chromatography coupled to time-of-flight mass spectrometry. Among them, the levels of some fatty acids (e.g., dodecanoic acid, octadecanoic acid, hexadecanoic acid) were significantly decreased after adding glutamate and proline, indicating that the inhibition of fatty acid synthesis might be benefit for the accumulation of streptolydigin. Particularly, the dramatic changes of the identified metabolites, which are involved in glycolysis, the tricarboxylic acid cycle, and the amino acid and fatty acid metabolism, revealed that the additions of glutamate and proline possibly caused the metabolic cross-talk in S. lydicus. Additionally, the level of intracellular glutamate dramatically enhanced at 12 h after adding proline, showing that exogenous proline may be firstly convert into glutamate and consequently result in crease of the streptolydigin production. The high levels of streptolydigin at 12 and 24 h after adding glutamate unveiled that part glutamate were rapidly used to synthesize the streptolydigin. Furthermore, there is the significant difference in metabolomic characteristics of S. lydicus after adding glutamate and proline, uncovering that multiple regulatory pathways are involved in responses to the additions of exogenous glutamate and proline. Taken together, exogenous glutamate and proline not only directly provided the precursors of streptolydigin biosynthesis, but also might alter the metabolic homeostasis of S. lydicus E9 during improving the production of streptolydigin.

Metabolic impact assessment for heterologous protein production in Streptomyces lividans based on genome-scale metabolic network modeling

The metabolic impact exerted on a microorganism due to heterologous protein production is still poorly understood in Streptomyces lividans. In this present paper, based on exometabolomic data, a proposed genome-scale metabolic network model is used to assess this metabolic impact in S. lividans. Constraint-based modeling results obtained in this work revealed that the metabolic impact due to heterologous protein production is widely distributed in the genome of S. lividans, causing both slow substrate assimilation and a shift in active pathways. Exchange fluxes that are critical for model performance have been identified for metabolites of mouse tumor necrosis factor, histidine, valine and lysine, as well as biomass. Our results unravel the interaction of heterologous protein production with intracellular metabolism of S. lividans, thus, a possible basis for further studies in relieving the metabolic burden via metabolic or bioprocess engineering.