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

Artificial-intelligence-driven innovations in mechanistic computational modeling and digital twins for biomedical applications

Understanding of complex biological systems remains a significant challenge due to their high dimensionality, nonlinearity, and context-specific behavior. Artificial intelligence (AI) and mechanistic modeling are becoming essential tools for studying such complex systems. Mechanistic modeling can facilitate the construction of simulatable models that are interpretable but often struggle with scalability and parameters estimation. AI can integrate multi-omics data to create predictive models, but it lacks interpretability. The gap between these two modeling methods limits our ability to develop comprehensive and predictive models for biomedical applications. This article reviews the most recent advancements in the integration of AI and mechanistic modeling to fill this gap. Recently, with omics availability, AI has led to new discoveries in mechanistic computational modeling. The mechanistic models can also help in getting insight into the mechanism for prediction made by AI models. This integration is helpful in modeling complex systems, estimating the parameters that are hard to capture in experiments, and creating surrogate models to reduce computational costs because of expensive mechanistic model simulations. This article focuses on advancements in mechanistic computational models and AI models and their integration for scientific discoveries in biology, pharmacology, drug discovery and diseases. The mechanistic models with AI integration can facilitate biological discoveries to advance our understanding of disease mechanisms, drug development, and personalized medicine. The article also highlights the role of AI and mechanistic model integration in the development of more advanced models in the biomedical domain, such as medical digital twins and virtual patients for pharmacological discoveries.

Enhancement of FK520 production in Streptomyces hygroscopicus var. ascomyceticus ATCC 14891 by overexpressing the regulatory gene fkbR2

Ascomycin (FK520) is a 23-membered macrolide antibiotic primarily produced by the Streptomyces hygroscopicus var. ascomyceticus. Structurally similar to tacrolimus and rapamycin, it serves as an effective immunosuppressant widely used in the treatment of rejection reactions after organ transplantation and certain autoimmune diseases. Currently, FK520 is mainly produced through microbial fermentation, but its yield remains low. Since the gene fkbR2 is a regulatory gene within the FK520 biosynthesis gene cluster that has not been studied, this paper focuses on the overexpression of the gene fkbR2 in Streptomyces hygroscopicus var. ascomyceticus ATCC 14891 (WT). By constructing a strain with overexpressed fkbR2 gene, we initially obtained a high-yield strain R2-17 through shake flask fermentation, with a 28% increase in yield compared to WT. In the process of further improving the stability of the high-yield strain, this paper defines two indices: high-yield index and stability index. After two consecutive rounds of natural breeding, strain R2-17 achieved a high-yield index of 100% and a stability index of 80%. Finally, the high-yield strain R2-17-3-10 was successfully screened, and the yield was increased by 34% compared with the strain WT, reaching 686.47 mg/L. A comparative analysis between the high-yield strain R2-17-3-10 and the original strain WT revealed differences in fermentation process parameters such as FK520 synthesis rate, pH, bacterial growth, glycerol consumption rate, ammonia nitrogen level, and ammonium ion concentration. In addition, the transcription levels of genes involved in the synthesis of precursors 4,5-dihydroxycyclohex-1-enecarboxylic acid (fkbO), ethylmalonyl-CoA (fkbE, fkbU, fkbS), and pipecolic acid (fkbL), as well as pathway-specific regulatory genes (fkbN, fkbR1), were significantly increased at different time points in the high-yield strain R2-17-3-10. EMSAs analysis showed that the FkbR2 protein could not bind to the promoter region of above genes. This suggests that the gene fkbR2 may enhance the supply of FK520 synthetic precursors by indirectly regulating the transcription levels of these genes, thereby promoting an increase in FK520 production. These results demonstrate that modifying genes within the biosynthetic gene clusters of natural products can be successfully applied to increase the yields of industrially and clinically important compounds. However, it is found that fkbR2 gene is a regulatory gene that has not been fully studied, and it is worth further studying its regulatory mechanism.

From waste to health-supporting molecules: biosynthesis of natural products from lignin-, plastic- and seaweed-based monomers using metabolically engineered Streptomyces lividans

BACKGROUND

Transforming waste and nonfood materials into bulk biofuels and chemicals represents a major stride in creating a sustainable bioindustry to optimize the use of resources while reducing environmental footprint. However, despite these advancements, the production of high-value natural products often continues to depend on the use of first-generation substrates, underscoring the intricate processes and specific requirements of their biosyntheses. This is also true for Streptomyces lividans, a renowned host organism celebrated for its capacity to produce a wide array of natural products, which is attributed to its genetic versatility and potent secondary metabolic activity. Given this context, it becomes imperative to assess and optimize this microorganism for the synthesis of natural products specifically from waste and nonfood substrates.

RESULTS

We metabolically engineered S. lividans to heterologously produce the ribosomally synthesized and posttranslationally modified peptide bottromycin, as well as the polyketide pamamycin. The modified strains successfully produced these compounds using waste and nonfood model substrates such as protocatechuate (derived from lignin), 4-hydroxybenzoate (sourced from plastic waste), and mannitol (from seaweed). Comprehensive transcriptomic and metabolomic analyses offered insights into how these substrates influenced the cellular metabolism of S. lividans. In terms of production efficiency, S. lividans showed remarkable tolerance, especially in a fed-batch process using a mineral medium containing the toxic aromatic 4-hydroxybenzoate, which led to enhanced and highly selective bottromycin production. Additionally, the strain generated a unique spectrum of pamamycins when cultured in mannitol-rich seaweed extract with no additional nutrients.

CONCLUSION

Our study showcases the successful production of high-value natural products based on the use of varied waste and nonfood raw materials, circumventing the reliance on costly, food-competing resources. S. lividans exhibited remarkable adaptability and resilience when grown on these diverse substrates. When cultured on aromatic compounds, it displayed a distinct array of intracellular CoA esters, presenting promising avenues for polyketide production. Future research could be focused on enhancing S. lividans substrate utilization pathways to process the intricate mixtures commonly found in waste and nonfood sources more efficiently.

Applications of genome-scale metabolic models to investigate microbial metabolic adaptations in response to genetic or environmental perturbations

Environmental perturbations are encountered by microorganisms regularly and will require metabolic adaptations to ensure an organism can survive in the newly presenting conditions. In order to study the mechanisms of metabolic adaptation in such conditions, various experimental and computational approaches have been used. Genome-scale metabolic models (GEMs) are one of the most powerful approaches to study metabolism, providing a platform to study the systems level adaptations of an organism to different environments which could otherwise be infeasible experimentally. In this review, we are describing the application of GEMs in understanding how microbes reprogram their metabolic system as a result of environmental variation. In particular, we provide the details of metabolic model reconstruction approaches, various algorithms and tools for model simulation, consequences of genetic perturbations, integration of '-omics' datasets for creating context-specific models and their application in studying metabolic adaptation due to the change in environmental conditions.

Use of elicitors to enhance or activate the antibiotic production in streptomyces

Streptomyces is the largest and most significant genus of Actinobacteria, comprising 961 species. These Gram-positive bacteria produce many versatile and important bioactive compounds; of these, antibiotics, specifically the enhancement or activation of their production, have received extensive research attention. Recently, various biotic and abiotic elicitors have been reported to modify the antibiotic metabolism of Streptomyces, which promotes the production of new antibiotics and bioactive metabolites for improvement in the yields of endogenous products. However, some elicitors that obviously contribute to secondary metabolite production have not yet received sufficient attention. In this study, we have reviewed the functions and mechanisms of chemicals, novel microbial metabolic elicitors, microbial interactions, enzymes, enzyme inhibitors, environmental factors, and novel combination methods regarding antibiotic production in Streptomyces. This review has aimed to identify potentially valuable elicitors for stimulating the production of latent antibiotics or enhancing the synthesis of subsistent antibiotics in Streptomyces. Future applications and challenges in the discovery of new antibiotics and enhancement of existing antibiotic production using elicitors are discussed.

Enhanced ascomycin production in Streptomyces hygroscopicus var. ascomyceticus by employing polyhydroxybutyrate as an intracellular carbon reservoir and optimizing carbon addition

BACKGROUND

Ascomycin is a multifunctional antibiotic produced by Streptomyces hygroscopicus var. ascomyceticus. As a secondary metabolite, the production of ascomycin is often limited by the shortage of precursors during the late fermentation phase. Polyhydroxybutyrate is an intracellular polymer accumulated by prokaryotic microorganisms. Developing polyhydroxybutyrate as an intracellular carbon reservoir for precursor synthesis is of great significance to improve the yield of ascomycin.

RESULTS

The fermentation characteristics of the parent strain S. hygroscopicus var. ascomyceticus FS35 showed that the accumulation and decomposition of polyhydroxybutyrate was respectively correlated with cell growth and ascomycin production. The co-overexpression of the exogenous polyhydroxybutyrate synthesis gene phaC and native polyhydroxybutyrate decomposition gene fkbU increased both the biomass and ascomycin yield. Comparative transcriptional analysis showed that the storage of polyhydroxybutyrate during the exponential phase accelerated biosynthesis processes by stimulating the utilization of carbon sources, while the decomposition of polyhydroxybutyrate during the stationary phase increased the biosynthesis of ascomycin precursors by enhancing the metabolic flux through primary pathways. The comparative analysis of cofactor concentrations confirmed that the biosynthesis of polyhydroxybutyrate depended on the supply of NADH. At low sugar concentrations found in the late exponential phase, the optimization of carbon source addition further strengthened the polyhydroxybutyrate metabolism by increasing the total concentration of cofactors. Finally, in the fermentation medium with 22 g/L starch and 52 g/L dextrin, the ascomycin yield of the co-overexpression strain was increased to 626.30 mg/L, which was 2.11-fold higher than that of the parent strain in the initial medium (296.29 mg/L).

CONCLUSIONS

Here we report for the first time that polyhydroxybutyrate metabolism is beneficial for cell growth and ascomycin production by acting as an intracellular carbon reservoir, stored as polymers when carbon sources are abundant and depolymerized into monomers for the biosynthesis of precursors when carbon sources are insufficient. The successful application of polyhydroxybutyrate in increasing the output of ascomycin provides a new strategy for improving the yields of other secondary metabolites.

Comparative metabolomics analysis of amphotericin B high-yield mechanism for metabolic engineering

BACKGROUND

The polyene macrocyclic compound amphotericin B (AmB) is an important antifungal antibiotic for the clinical treatment of invasive fungal infections. To rationally guide the improvement of AmB production in the main producing strain Streptomyces nodosus, comparative metabolomics analysis was performed to investigate the intracellular metabolic changes in wild-type S. nodosus ZJB20140315 with low-yield AmB production and mutant S. nodosus ZJB2016050 with high-yield AmB production, the latter of which reached industrial criteria on a pilot scale.

RESULTS

To investigate the relationship of intracellular metabolites, 7758 metabolites were identified in mutant S. nodosus and wildtype S. nodosus via LC-MS. Through analysis of metabolism, the level of 26 key metabolites that involved in carbon metabolism, fatty acids metabolism, amino acids metabolism, purine metabolism, folate biosynthesis and one carbon pool by folate were much higher in mutant S. nodosus. The enrichment of relevant metabolic pathways by gene overexpression strategy confirmed that one carbon pool by folate was the key metabolic pathway. Meanwhile, a recombinant strain with gene metH (methionine synthase) overexpressed showed 5.03 g/L AmB production within 120 h fermentation, which is 26.4% higher than that of the mutant strain.

CONCLUSIONS

These results demonstrated that comparative metabolomics analysis was an effective approach for the improvement of AmB production and could be applied for other industrially or clinically important compounds as well.

The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes

For over 10 years, ModelSEED has been a primary resource for the construction of draft genome-scale metabolic models based on annotated microbial or plant genomes. Now being released, the biochemistry database serves as the foundation of biochemical data underlying ModelSEED and KBase. The biochemistry database embodies several properties that, taken together, distinguish it from other published biochemistry resources by: (i) including compartmentalization, transport reactions, charged molecules and proton balancing on reactions; (ii) being extensible by the user community, with all data stored in GitHub; and (iii) design as a biochemical 'Rosetta Stone' to facilitate comparison and integration of annotations from many different tools and databases. The database was constructed by combining chemical data from many resources, applying standard transformations, identifying redundancies and computing thermodynamic properties. The ModelSEED biochemistry is continually tested using flux balance analysis to ensure the biochemical network is modeling-ready and capable of simulating diverse phenotypes. Ontologies can be designed to aid in comparing and reconciling metabolic reconstructions that differ in how they represent various metabolic pathways. ModelSEED now includes 33,978 compounds and 36,645 reactions, available as a set of extensible files on GitHub, and available to search at https://modelseed.org/biochem and KBase.

The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes

For over ten years, ModelSEED has been a primary resource for the construction of draft genome-scale metabolic models based on annotated microbial or plant genomes. Now being released, the biochemistry database serves as the foundation of biochemical data underlying ModelSEED and KBase. The biochemistry database embodies several properties that, taken together, distinguish it from other published biochemistry resources by: (i) including compartmentalization, transport reactions, charged molecules and proton balancing on reactions;; (ii) being extensible by the user community, with all data stored in GitHub; and (iii) design as a biochemical “Rosetta Stone” to facilitate comparison and integration of annotations from many different tools and databases. The database was constructed by combining chemical data from many resources, applying standard transformations, identifying redundancies, and computing thermodynamic properties. The ModelSEED biochemistry is continually tested using flux balance analysis to ensure the biochemical network is modeling-ready and capable of simulating diverse phenotypes. Ontologies can be designed to aid in comparing and reconciling metabolic reconstructions that differ in how they represent various metabolic pathways. ModelSEED now includes 33,978 compounds and 36,645 reactions, available as a set of extensible files on GitHub, and available to search at https://modelseed.org and KBase.

Bioprocess and genetic engineering aspects of ascomycin production: a review

BACKGROUND

Ascomycin is a highly valuable multifunctional drug which exhibits numerous biological properties. Being an immunosuppressant, it is known to prevent graft rejection in humans and has potential to treat varying skin ailments. Its derivatives represent a novel class of anti-inflammatory macrolactams. But the biosynthetic machinery of ascomycin is still unclear. Due to the structural complexity, there occurs difficulty in its chemical synthesis; therefore, microbial production has been preferred by using Streptomyces hygroscopicus subsp. ascomyceticus. Through several genetic manipulation and mutagenesis techniques, the yield can be increased by several folds without any difficulties. Genetic engineering has played a significant role in understanding the biosynthetic pathway of ascomycin.

SHORT CONCLUSION

Recently, many efforts have been made to utilize the therapeutic effects of ascomycin and its derivatives. This article covers concepts related to the production kinetics of ascomycin including an update of the ongoing yield improvement techniques as well as screening method of novel strains for ascomycin production.

A common approach for absolute quantification of short chain CoA thioesters in prokaryotic and eukaryotic microbes

BACKGROUND

Thioesters of coenzyme A participate in 5% of all enzymatic reactions. In microbial cell factories, they function as building blocks for products of recognized commercial value, including natural products such as polyketides, polyunsaturated fatty acids, biofuels, and biopolymers. A core spectrum of approximately 5-10 short chain thioesters is present in many microbes, as inferred from their genomic repertoire. The relevance of these metabolites explains the high interest to trace and quantify them in microbial cells.

RESULTS

Here, we describe a common workflow for extraction and absolute quantification of short chain CoA thioesters in different gram-positive and gram-negative bacteria and eukaryotic yeast, i.e. Corynebacterium glutamicum, Streptomyces albus, Pseudomonas putida, and Yarrowia lipolytica. The approach assessed intracellular CoA thioesters down to the picomolar level and exhibited high precision and reproducibility for all microbes, as shown by principal component analysis. Furthermore, it provided interesting insights into microbial CoA metabolism. A succinyl-CoA synthase defective mutant of C. glutamicum exhibited an unaffected level of succinyl-CoA that indicated a complete compensation by the L-lysine pathway to bypass the disrupted TCA cycle. Methylmalonyl-CoA, an important building block of high-value polyketides, was identified as dominant CoA thioester in the actinomycete S. albus. The microbe revealed a more than 10,000-fold difference in the abundance of intracellular CoA thioesters. A recombinant strain of S. albus, which produced different derivatives of the antituberculosis polyketide pamamycin, revealed a significant depletion of CoA thioesters of the ethylmalonyl CoA pathway, influencing product level and spectrum.

CONCLUSIONS

The high relevance of short chain CoA thioesters to synthetize industrial products and the interesting insights gained from the examples shown in this work, suggest analyzing these metabolites in microbial cell factories more routinely than done so far. Due to its broad application range, the developed approach appears useful to be applied this purpose. Hereby, the possibility to use one single protocol promises to facilitate automatized efforts, which rely on standardized workflows.

Eukaryotic phosphatase inhibitors enhance colistin efficacy in gram-negative bacteria

The mounting threat of multi-drug-resistant (MDR) bacteria places a tremendous strain on the antimicrobial clinical arsenal, forcing physicians to revert to near-obsolete antibiotics to treat otherwise intractable infections. Antibiotic adjuvant therapy has emerged as a viable alternative to the development of novel antimicrobial agents. This method uses combinations of an existing antibiotic and a non-antimicrobial small molecule, where the combination either breaks drug resistance or further potentiates antibiotic activity. Through a high-content screen of eukaryotic kinase inhibitors, our group previously identified two highly potent adjuvants that synergize with colistin, a cyclic, polycationic antimicrobial peptide that serves as a drug of last resort for the treatment of MDR Gram-negative bacterial infections. Cell signaling proteins implicated in colistin resistance mechanisms display both kinase and phosphatase activities. Herein, we explore the potential for eukaryotic phosphatase inhibitors to be repurposed as colistin adjuvants. From a panel of 48 unique structures, we discovered that the natural product kuwanon G breaks colistin resistance, while the non-antimicrobial macrolide ascomycin potentiates colistin in polymyxin-susceptible bacteria.

Enhancement of FK520 production in Streptomyces hygroscopicus by combining traditional mutagenesis with metabolic engineering

FK520 (ascomycin), a 23-membered macrolide with immunosuppressive activity, is produced by Streptomyces hygroscopicus. The problem of low yield and high impurities (mainly FK523) limits the industrialized production of FK520. In this study, the FK520 yield was significantly improved by strain mutagenesis and genetic engineering. First, a FK520 high-producing strain SFK-6-33 (2432.2 mg/L) was obtained from SFK-36 (1588.4 mg/L) through ultraviolet radiation mutation coupled with streptomycin resistance screening. The endogenous crotonyl-CoA carboxylase/reductase (FkbS) was found to play an important role in FK520 biosynthesis, identified with CRISPR/dCas9 inhibition system. FkbS was overexpressed in SFK-6-33 to obtain the engineered strain SFK-OfkbS, which produced 2817.0 mg/L of FK520 resulting from an increase in intracellular ethylmalonyl-CoA levels. In addition, the FK520 levels could be further increased with supplementation of crotonic acid in SFK-OfkbS. Overexpression of acetyl-CoA carboxylase (ACCase), used for the synthesis of malonyl-CoA, was also investigated in SFK-6-33, which improved the FK520 yield to 3320.1 mg/L but showed no significant inhibition in FK523 production. To further enhance FK520 production, FkbS and ACCase combinatorial overexpression strain SFK-OASN was constructed; the FK520 production increased by 44.4% to 3511.4 mg/L, and the FK523/FK520 ratio was reduced from 9.6 to 5.6% compared with that in SFK-6-33. Finally, a fed-batch culture was carried out in a 5-L fermenter, and the FK520 yield reached 3913.9 mg/L at 168 h by feeding glycerol, representing the highest FK520 yield reported thus far. These results demonstrated that traditional mutagenesis combined with metabolic engineering was an effective strategy to improve FK520 production.

Dynamic metabolic modelling of overproduced protein secretion in Streptomyces lividans using adaptive DFBA

BACKGROUND

Streptomyces lividans is an appealing host for the production of proteins of biotechnological interest due to its relaxed exogenous DNA restriction system and its ability to secrete proteins directly to the medium through the major Sec or the minor Tat routes. Often, protein secretion displays non-uniform time-dependent patterns. Understanding the associated metabolic changes is a crucial step to engineer protein production. Dynamic Flux Balance Analysis (DFBA) allows the study of the interactions between a modelled organism and its environment over time. Existing methods allow the specification of initial model and environment conditions, but do not allow introducing arbitrary modifications in the course of the simulation. Living organisms, however, display unexpected adaptive metabolic behaviours in response to unpredictable changes in their environment. Engineering the secretion of products of biotechnological interest has systematically proven especially difficult to model using DFBA. Accurate time-dependent modelling of complex and/or arbitrary, adaptive metabolic processes demands an extended approach to DFBA.

RESULTS

In this work, we introduce Adaptive DFBA, a novel, versatile simulation approach that permits inclusion of changes in the organism or the environment at any time in the simulation, either arbitrary or interactively responsive to environmental changes. This approach extends traditional DFBA to allow steering arbitrarily complex simulations of metabolic dynamics. When applied to Sec- or Tat-dependent secretion of overproduced proteins in S. lividans, Adaptive DFBA can overcome the limitations of traditional DFBA to reproduce experimental data on plasmid-free, plasmid bearing and secretory protein overproducing S. lividans TK24, and can yield useful insights on the behaviour of systems with limited experimental knowledge such as agarase or amylase overproduction in S. lividans TK21.

CONCLUSIONS

Adaptive DFBA has allowed us to overcome DFBA limitations and to generate more accurate models of the metabolism during the overproduction of secretory proteins in S. lividans, improving our understanding of the underlying processes. Adaptive DFBA is versatile enough to permit dynamical metabolic simulations of arbitrarily complex biotechnological processes.

Enhancement of ascomycin production via a combination of atmospheric and room temperature plasma mutagenesis in Streptomyces hygroscopicus and medium optimization

Ascomycin, a key intermediate for chemical synthesis of immunosuppressive drug pimecrolimus, is produced by Streptomyces hygroscopicus var. ascomyceticus. In order to improve the strain production, the original S. hygroscopicus ATCC 14891 strain was treated here with atmospheric and room temperature plasma to obtain a stable high-producing S. hygroscopicus SFK-36 strain which produced 495.3 mg/L ascomycin, a 32.5% increase in ascomycin compared to the ATCC 14891. Then, fermentation medium was optimized using response surface methodology to further enhance ascomycin production. In the optimized medium containing 81.0 g/L soluble starch, 57.4 g/L peanut meal, and 15.8 g/L soybean oil, the ascomycin yield reached 1466.3 mg/L in flask culture. Furthermore, the fermentation process was carried out in a 5 L fermenter, and the ascomycin yield reached 1476.9 mg/L, which is the highest ascomycin yield reported so far. Therefore, traditional mutagenesis breeding combined with medium optimization is an effective approach for the enhancement of ascomycin production.

Acyltransferases as Tools for Polyketide Synthase Engineering

Polyketides belong to the most valuable natural products, including diverse bioactive compounds, such as antibiotics, anticancer drugs, antifungal agents, immunosuppressants and others. Their structures are assembled by polyketide synthases (PKSs). Modular PKSs are composed of modules, which involve sets of domains catalysing the stepwise polyketide biosynthesis. The acyltransferase (AT) domains and their “partners”, the acyl carrier proteins (ACPs), thereby play an essential role. The AT loads the building blocks onto the “substrate acceptor”, the ACP. Thus, the AT dictates which building blocks are incorporated into the polyketide structure. The precursor- and occasionally the ACP-specificity of the ATs differ across the polyketide pathways and therefore, the ATs contribute to the structural diversity within this group of complex natural products. Those features make the AT enzymes one of the most promising tools for manipulation of polyketide assembly lines and generation of new polyketide compounds. However, the AT-based PKS engineering is still not straightforward and thus, rational design of functional PKSs requires detailed understanding of the complex machineries. This review summarizes the attempts of PKS engineering by exploiting the AT attributes for the modification of polyketide structures. The article includes 253 references and covers the most relevant literature published until May 2018.

Development and validation of an updated computational model of Streptomyces coelicolor primary and secondary metabolism

BACKGROUND

Streptomyces species produce a vast diversity of secondary metabolites of clinical and biotechnological importance, in particular antibiotics. Recent developments in metabolic engineering, synthetic and systems biology have opened new opportunities to exploit Streptomyces secondary metabolism, but achieving industry-level production without time-consuming optimization has remained challenging. Genome-scale metabolic modelling has been shown to be a powerful tool to guide metabolic engineering strategies for accelerated strain optimization, and several generations of models of Streptomyces metabolism have been developed for this purpose.

RESULTS

Here, we present the most recent update of a genome-scale stoichiometric constraint-based model of the metabolism of Streptomyces coelicolor, the major model organism for the production of antibiotics in the genus. We show that the updated model enables better metabolic flux and biomass predictions and facilitates the integrative analysis of multi-omics data such as transcriptomics, proteomics and metabolomics.

CONCLUSIONS

The updated model presented here provides an enhanced basis for the next generation of metabolic engineering attempts in Streptomyces.

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.