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

Genomic analysis of lignocellulolytic enzyme producing novel Streptomyces sp.MS2A for the bioethanol applications

The conversion of lignocellulosic waste to energy offers a cost-effective biofuel. The current study discusses the utilization of cellulose in rice husks by lichen-associated Streptomyces sp. MS2A via carbohydrate metabolism. Out of 39 actinobacteria, one actinobacterial strain MS2A, showed CMCase, FPase, and cellobiohydrolase activity. The whole genome analysis of Streptomyces sp. MS2A showed maximum similarity with Streptomyces sp. CCM_MD2014. The genome analysis confirmed the presence of cellulose-degrading genes along with xylan-degrading genes that code for GH3, GH6, GH9, GH11, GH43, GH51, and 15 other GH families with glycosyl transferase, carbohydrate-binding modules, and energy metabolism groups. Protein family analysis corroborates the enzyme family. Among the 19,402 genes of Streptomyces sp. MS2A, approximately 70 GH family codes for lignocellulose degradation enzymes. The structure of cellulase was modeled and validated. Scanning electron microscopy and gas chromatography-mass spectrometry (GCMS) was performed to analyze the lignocellulosic degradation of rice husk and the end product bioethanol.

Flux balance analysis-based metabolic modeling of microbial secondary metabolism: Current status and outlook

In microorganisms, different from primary metabolism for cellular growth, secondary metabolism is for ecological interactions and stress responses and an important source of natural products widely used in various areas such as pharmaceutics and food additives. With advancements of sequencing technologies and bioinformatics tools, a large number of biosynthetic gene clusters of secondary metabolites have been discovered from microbial genomes. However, due to challenges from the difficulty of genome-scale pathway reconstruction and the limitation of conventional flux balance analysis (FBA) on secondary metabolism, the quantitative modeling of secondary metabolism is poorly established, in contrast to that of primary metabolism. This review first discusses current efforts on the reconstruction of secondary metabolic pathways in genome-scale metabolic models (GSMMs), as well as related FBA-based modeling techniques. Additionally, potential extensions of FBA are suggested to improve the prediction accuracy of secondary metabolite production. As this review posits, biosynthetic pathway reconstruction for various secondary metabolites will become automated and a modeling framework capturing secondary metabolism onset will enhance the predictive power. Expectedly, an improved FBA-based modeling workflow will facilitate quantitative study of secondary metabolism and in silico design of engineering strategies for natural product production.

A Holistic Approach from Systems Biology Reveals the Direct Influence of the Quorum-Sensing Phenomenon on Pseudomonas aeruginosa Metabolism to Pyoverdine Biosynthesis

Computational modeling and simulation of biological systems have become valuable tools for understanding and predicting cellular performance and phenotype generation. This work aimed to construct, model, and dynamically simulate the virulence factor pyoverdine (PVD) biosynthesis in Pseudomonas aeruginosa through a systemic approach, considering that the metabolic pathway of PVD synthesis is regulated by the quorum-sensing (QS) phenomenon. The methodology comprised three main stages: (i) Construction, modeling, and validation of the QS gene regulatory network that controls PVD synthesis in P. aeruginosa strain PAO1; (ii) construction, curating, and modeling of the metabolic network of P. aeruginosa using the flux balance analysis (FBA) approach; (iii) integration and modeling of these two networks into an integrative model using the dynamic flux balance analysis (DFBA) approximation, followed, finally, by an in vitro validation of the integrated model for PVD synthesis in P. aeruginosa as a function of QS signaling. The QS gene network, constructed using the standard System Biology Markup Language, comprised 114 chemical species and 103 reactions and was modeled as a deterministic system following the kinetic based on mass action law. This model showed that the higher the bacterial growth, the higher the extracellular concentration of QS signal molecules, thus emulating the natural behavior of P. aeruginosa PAO1. The P. aeruginosa metabolic network model was constructed based on the iMO1056 model, the P. aeruginosa PAO1 strain genomic annotation, and the metabolic pathway of PVD synthesis. The metabolic network model included the PVD synthesis, transport, exchange reactions, and the QS signal molecules. This metabolic network model was curated and then modeled under the FBA approximation, using biomass maximization as the objective function (optimization problem, a term borrowed from the engineering field). Next, chemical reactions shared by both network models were chosen to combine them into an integrative model. To this end, the fluxes of these reactions, obtained from the QS network model, were fixed in the metabolic network model as constraints of the optimization problem using the DFBA approximation. Finally, simulations of the integrative model (CCBM1146, comprising 1123 reactions and 880 metabolites) were run using the DFBA approximation to get (i) the flux profile for each reaction, (ii) the bacterial growth profile, (iii) the biomass profile, and (iv) the concentration profiles of metabolites of interest such as glucose, PVD, and QS signal molecules. The CCBM1146 model showed that the QS phenomenon directly influences the P. aeruginosa metabolism to PVD biosynthesis as a function of the change in QS signal intensity. The CCBM1146 model made it possible to characterize and explain the complex and emergent behavior generated by the interactions between the two networks, which would have been impossible to do by studying each system's individual components or scales separately. This work is the first in silico report of an integrative model comprising the QS gene regulatory network and the metabolic network of P. aeruginosa.

Construction and application of the genome-scale metabolic model of Streptomyces radiopugnans

Geosmin is one of the most common earthy-musty odor compounds, which is mainly produced by Streptomyces. Streptomyces radiopugnans was screened in radiation-polluted soil, which has the potential to overproduce geosmin. However, due to the complex cellular metabolism and regulation mechanism, the phenotypes of S. radiopugnans were hard to investigate. A genome-scale metabolic model of S. radiopugnans named iZDZ767 was constructed. Model iZDZ767 involved 1,411 reactions, 1,399 metabolites, and 767 genes; its gene coverage was 14.1%. Model iZDZ767 could grow on 23 carbon sources and five nitrogen sources, which achieved 82.1% and 83.3% prediction accuracy, respectively. For the essential gene prediction, the accuracy was 97.6%. According to the simulation of model iZDZ767, D-glucose and urea were the best for geosmin fermentation. The culture condition optimization experiments proved that with D-glucose as the carbon source and urea as the nitrogen source (4 g/L), geosmin production could reach 581.6 ng/L. Using the OptForce algorithm, 29 genes were identified as the targets of metabolic engineering modification. With the help of model iZDZ767, the phenotypes of S. radiopugnans could be well resolved. The key targets for geosmin overproduction could also be identified efficiently.

Nutrient-depended metabolic switching during batch cultivation of Streptomyces coelicolor explored with absolute quantitative mass spectrometry-based metabolite profiling

The well-known secondary metabolite-producing bacterium Streptomyces coelicolor is a natural choice for the development of super-hosts optimized for the heterologous expression of antibiotic biosynthetic gene clusters (BGCs). In this study, we used S. coelicolor M145 and its derivative strain M1146 where all active BGCs have been deleted and generated high-resolution quantitative time series metabolite profiles under two cultivation conditions (phosphate and nitrogen limitation to cease growth and trigger secondary metabolism). Five targeted LC-MS/MS-based methods were used to quantify intracellular primary metabolites covering phosphorylated metabolites, amino acids, organic acids, (deoxy) nucleoside/sugar phosphates, Nicotinamide adenine dinucleotide (NAD), and Coenzyme A (CoA). The nitrogen limitation resulted in a sharp decline in respiration and an immediate drop in the cell mass concentration. Intracellularly, a reduction in the level of the metabolites next to α-ketoglutarate in the tricarboxylic acid cycle and a decrease in the NADH pool were among the most prominent adaptation to this nutrient limitation. Phosphate limitation evoked a different adaptation of the metabolite pools as most of the phosphorylated metabolite pools except 6-phosphogluconic acid (6PG) pool were downregulated. ¹³C-isotope-labeling experiments revealed the simultaneous activity of both glycolysis and gluconeogenesis during the co-utilization of glucose and glutamate. The S. coelicolor M1146 strain had similar time-series metabolite profile dynamics as the parent M145 strain, except for a visibly increased 6PG pool in the stationary phase. In general, the nutrient limitation had a larger effect on the metabolite pool levels than the absence of secondary metabolite production in M1146. This study provides new insight into the primary carbon metabolism and its link to the secondary metabolism which is needed for further optimization of both super-host genotype and cultivation conditions.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-022-03146-x.

Multi-omics Study of Planobispora rosea, Producer of the Thiopeptide Antibiotic GE2270A

Planobispora rosea is the natural producer of the potent thiopeptide antibiotic GE2270A. Here, we present the results of a metabolomics and transcriptomics analysis of P. rosea during production of GE2270A. The data generated provides useful insights into the biology of this genetically intractable bacterium. We characterize the details of the shutdown of protein biosynthesis and the respiratory chain associated with the end of the exponential growth phase. We also provide the first description of the phosphate regulon in P. rosea. Based on the transcriptomics data, we show that both phosphate and iron are limiting P. rosea growth in our experimental conditions. Additionally, we identified and validated a new biosynthetic gene cluster associated with the production of the siderophores benarthin and dibenarthin in P. rosea. Together, the metabolomics and transcriptomics data are used to inform and refine the very first genome-scale metabolic model for P. rosea, which will be a valuable framework for the interpretation of future studies of the biology of this interesting but poorly characterized species.

IMPORTANCEPlanobispora rosea is a genetically intractable bacterium used for the production of GE2270A on an industrial scale. GE2270A is a potent thiopeptide antibiotic currently used as a precursor for the synthesis of two compounds under clinical studies for the treatment of Clostridium difficile infection and acne. Here, we present the very first systematic multi-omics investigation of this important bacterium, which provides a much-needed detailed picture of the dynamics of metabolism of P. rosea while producing GE2270A.

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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.

Comparative Transcriptome Analysis of Streptomyces nodosus Mutant With a High-Yield Amphotericin B

Antibiotics play an important role in human health. Most antibiotics are derived from microbial secondary metabolites. Amphotericin is a polyene macrolide antibiotic synthesized by Streptomyces nodosus. S. nodosus ZJB2016050 with high-yield amphotericin B (AmB) was obtained by traditional mutagenesis using S. nodosus ATCC14899 as the original strain. The differences in the characterization of the two strains were found in color, mycelium morphology, and AmB yield. Subsequent comparative transcriptome explained the yield differences between the two strains. Pathways including the carbohydrate metabolic pathway and the secondary product synthesis pathway were targeted. The upregulation of glucokinase, phosphoglycerate mutase, and pyruvate dehydrogenase accelerates the consumption of glucose and has great effects on the accumulation of precursors. One of the competitive secondary metabolites of the polyketone synthetase (PKS) II type sapromomycin analog synthesis gene cluster was downregulated, which competes for malonyl-CoA. Five PKS modules (except for the first module amphA) of the amphotericin synthetic gene cluster in the high-yielding strain were downregulated, which resulted in the total amphotericin A (AmA) and AmB of S. nodosus ZJB2016050 being less than that of the wild-type S. nodosus ATCC14899. Combined with gene differential expression in the pentose phosphate pathway and the reaction mechanism of the ER5 domain, the reason that S. nodosus ZJB2016050 preferred to synthesize AmB was probably related to intracellular reduction.

Clavulanic Acid Production by Streptomyces clavuligerus: Insights from Systems Biology, Strain Engineering, and Downstream Processing

Clavulanic acid (CA) is an irreversible β-lactamase enzyme inhibitor with a weak antibacterial activity produced by Streptomyces clavuligerus (S. clavuligerus). CA is typically co-formulated with broad-spectrum β‑lactam antibiotics such as amoxicillin, conferring them high potential to treat diseases caused by bacteria that possess β‑lactam resistance. The clinical importance of CA and the complexity of the production process motivate improvements from an interdisciplinary standpoint by integrating metabolic engineering strategies and knowledge on metabolic and regulatory events through systems biology and multi-omics approaches. In the large-scale bioprocessing, optimization of culture conditions, bioreactor design, agitation regime, as well as advances in CA separation and purification are required to improve the cost structure associated to CA production. This review presents the recent insights in CA production by S. clavuligerus, emphasizing on systems biology approaches, strain engineering, and downstream processing.

Systems and synthetic biology to elucidate secondary metabolite biosynthetic gene clusters encoded in Streptomyces genomes

Covering: 2010 to 2020 Over the last few decades, Streptomyces have been extensively investigated for their ability to produce diverse bioactive secondary metabolites. Recent advances in Streptomyces research have been largely supported by improvements in high-throughput technology 'omics'. From genomics, numerous secondary metabolite biosynthetic gene clusters were predicted, increasing their genomic potential for novel bioactive compound discovery. Additional omics, including transcriptomics, translatomics, interactomics, proteomics and metabolomics, have been applied to obtain a system-level understanding spanning entire bioprocesses of Streptomyces, revealing highly interconnected and multi-layered regulatory networks for secondary metabolism. The comprehensive understanding derived from this systematic information accelerates the rational engineering of Streptomyces to enhance secondary metabolite production, integrated with the exploitation of the highly efficient 'Design-Build-Test-Learn' cycle in synthetic biology. In this review, we describe the current status of omics applications in Streptomyces research to better understand the organism and exploit its genetic potential for higher production of valuable secondary metabolites and novel secondary metabolite discovery.

Enzyme-Constrained Models and Omics Analysis of Streptomyces coelicolor Reveal Metabolic Changes that Enhance Heterologous Production

Many biosynthetic gene clusters (BGCs) require heterologous expression to realize their genetic potential, including silent and metagenomic BGCs. Although the engineered Streptomyces coelicolor M1152 is a widely used host for heterologous expression of BGCs, a systemic understanding of how its genetic modifications affect the metabolism is lacking and limiting further development. We performed a comparative analysis of M1152 and its ancestor M145, connecting information from proteomics, transcriptomics, and cultivation data into a comprehensive picture of the metabolic differences between these strains. Instrumental to this comparison was the application of an improved consensus genome-scale metabolic model (GEM) of S. coelicolor. Although many metabolic patterns are retained in M1152, we find that this strain suffers from oxidative stress, possibly caused by increased oxidative metabolism. Furthermore, precursor availability is likely not limiting polyketide production, implying that other strategies could be beneficial for further development of S. coelicolor for heterologous production of novel compounds.