Microalgae growth-promoting bacteria for cultivation strategies: Recent updates and progress

Microalgae growth-promoting bacteria (MGPB), both actinobacteria and non-actinobacteria, have received considerable attention recently because of their potential to develop microalgae-bacteria co-culture strategies for improved efficiency and sustainability of the water-energy-environment nexus. Owing to their diverse metabolic pathways and ability to adapt to diverse conditions, microalgal-MGPB co-cultures could be promising biological systems under uncertain environmental and nutrient conditions. This review proposes the recent updates and progress on MGPB for microalgae cultivation through co-culture strategies. Firstly, potential MGPB strains for microalgae cultivation are introduced. Following, microalgal-MGPB interaction mechanisms and applications of their co-cultures for biomass production and wastewater treatment are reviewed. Moreover, state-of-the-art studies on synthetic biology and metabolic network analysis, along with the challenges and prospects of opting these approaches for microalgal-MGPB co-cultures are presented. It is anticipated that these strategies may significantly improve the sustainability of microalgal-MGPB co-cultures for wastewater treatment, biomass valorization, and bioproducts synthesis in a circular bioeconomy paradigm.

Diverse Combinatorial Biosynthesis Strategies for C-H Functionalization of Anthracyclinones

Streptomyces spp. are "nature's antibiotic factories" that produce valuable bioactive metabolites, such as the cytotoxic anthracycline polyketides. While the anthracyclines have hundreds of natural and chemically synthesized analogues, much of the chemical diversity stems from enzymatic modifications to the saccharide chains and, to a lesser extent, from alterations to the core scaffold. Previous work has resulted in the generation of a BioBricks synthetic biology toolbox in Streptomyces coelicolor M1152ΔmatAB that could produce aklavinone, 9-epi-aklavinone, auramycinone, and nogalamycinone. In this work, we extended the platform to generate oxidatively modified analogues via two crucial strategies. (i) We swapped the ketoreductase and first-ring cyclase enzymes for the aromatase cyclase from the mithramycin biosynthetic pathway in our polyketide synthase (PKS) cassettes to generate 2-hydroxylated analogues. (ii) Next, we engineered several multioxygenase cassettes to catalyze 11-hydroxylation, 1-hydroxylation, 10-hydroxylation, 10-decarboxylation, and 4-hydroxyl regioisomerization. We also developed improved plasmid vectors and S. coelicolor M1152ΔmatAB expression hosts to produce anthracyclinones. This work sets the stage for the combinatorial biosynthesis of bespoke anthracyclines using recombinant Streptomyces spp. hosts.

Heterologous overproduction of oviedomycin by refactoring biosynthetic gene cluster and metabolic engineering of host strain Streptomyces coelicolor

BACKGROUND

Oviedomycin is one among several polyketides known for their potential as anticancer agents. The biosynthetic gene cluster (BGC) for oviedomycin is primarily found in Streptomyces antibioticus. However, because this BGC is usually inactive under normal laboratory conditions, it is necessary to employ systematic metabolic engineering methods, such as heterologous expression, refactoring of BGCs, and optimization of precursor biosynthesis, to allow efficient production of these compounds.

RESULTS

Oviedomycin BGC was captured from the genome of Streptomyces antibioticus by a newly constructed plasmid, pCBA, and conjugated into the heterologous strain, S. coelicolor M1152. To increase the production of oviedomycin, clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9) system was utilized in an in vitro setting to refactor the native promoters within the ovm BGC. The target promoters of refactoring were selected based on examination of factors such as transcription levels and metabolite profiling. Furthermore, genome-scale metabolic simulation was applied to find overexpression targets that could enhance the biosynthesis of precursors or cofactors related to oviedomycin production. The combined approach led to a significant increase in oviedomycin production, reaching up to 670 mg/L, which is the highest titer reported to date. This demonstrates the potential of the approach undertaken in this study.

CONCLUSIONS

The metabolic engineering approach used in this study led to the successful production of a valuable polyketide, oviedomycin, via BGC cloning, promoter refactoring, and gene manipulation of host metabolism aided by genome-scale metabolic simulation. This approach can be also useful for the efficient production of other secondary molecules encoded by 'silent' BGCs.

Comparative genomic analysis of Streptomyces rapamycinicus NRRL 5491 and its mutant overproducing rapamycin

Streptomyces rapamycinicus NRRL 5491 is a well-known producer of rapamycin, a secondary metabolite with useful bioactivities, including antifungal, antitumor, and immunosuppressive functions. For the enhanced rapamycin production, a rapamycin-overproducing strain SRMK07 was previously obtained as a result of random mutagenesis. To identify genomic changes that allowed the SRMK07 strain’s enhanced rapamycin production, genomes of the NRRL 5491 and SRMK07 strains were newly sequenced in this study. The resulting genome sequences of the wild-type and SRMK07 strains showed the size of 12.47 Mbp and 9.56 Mbp, respectively. Large deletions were observed at both end regions of the SRMK07 strain’s genome, which cover 17 biosynthetic gene clusters (BGCs) encoding secondary metabolites. Also, genes in a genomic region containing the rapamycin BGC were shown to be duplicated. Finally, comparative metabolic network analysis using these two strains’ genome-scale metabolic models revealed biochemical reactions with different metabolic fluxes, which were all associated with NADPH generation. Taken together, the genomic and computational approaches undertaken in this study suggest biological clues for the enhanced rapamycin production of the SRMK07 strain. These clues can also serve as a basis for systematic engineering of a production host for further enhanced rapamycin production.

Reconstruction of the Genome-Scale Metabolic Model of Saccharopolyspora erythraea and Its Application in the Overproduction of Erythromycin

Saccharopolyspora erythraea is considered to be an effective host for erythromycin. However, little is known about the regulation in terms of its metabolism. To develop an accurate model-driven strategy for the efficient production of erythromycin, a genome-scale metabolic model (iJL1426) was reconstructed for the industrial strain. The final model included 1426 genes, 1858 reactions, and 1687 metabolites. The accurate rates of the growth predictions for the 27 carbon and 31 nitrogen sources available were 92.6% and 100%, respectively. Moreover, the simulation results were consistent with the physiological observation and ¹³C metabolic flux analysis obtained from the experimental data. Furthermore, by comparing the single knockout targets with earlier published results, four genes coincided within the range of successful knockouts. Finally, iJL1426 was used to guide the optimal addition strategy of n-propanol during industrial erythromycin fermentation to demonstrate its ability. The experimental results showed that the highest erythromycin titer was 1442.8 μg/mL at an n-propanol supplementation rate of 0.05 g/L/h, which was 45.0% higher than that without n-propanol supplementation, and the erythromycin-specific synthesis rate was also increased by 30.3%. Therefore, iJL1426 will lead to a better understanding of the metabolic capabilities and, thus, is helpful in a systematic metabolic engineering approach.

Systems metabolic engineering of Streptomyces venezuelae for the enhanced production of pikromycin

Pikromycin is an important precursor of drugs, for example, erythromycin. Hence, systems metabolic engineering for the enhanced pikromycin production can contribute to the development of pikromycin-related drugs. In this study, metabolic genes in Streptomyces venezuelae were systematically engineered for the enhanced pikromycin production. For this, a genome-scale metabolic model of S. venezuelae was reconstructed and simulated, which led to the selection of 11 metabolic gene targets. These metabolic genes, including four overexpression targets and seven knockdown targets, were individually engineered first. Next, two overexpression targets and two knockdown targets were selected based on the 11 strains' production performances in order to engineer two to four of these genes together for the potential synergistic effects on the pikromycin production. As a result, the NM1 strain with AQF52_RS24510 (methenyltetrahydrofolate cyclohydrolase/methylenetetrahydrofolate dehydrogenase) overexpression and AQF52_RS30320 (sulfite reductase) knockdown showed the best production performance among all the 22 strains constructed in this study. Fed-batch fermentation of the NM1 strain produced 295.25 mg/L of pikromycin, by far the best production titer using the native producer S. venezuelae, to the best of our knowledge. The systems metabolic engineering strategy demonstrated herein can also be applied to the overproduction of other secondary metabolites using S. venezuelae. This article is protected by copyright. All rights reserved.

A Computational Framework to Identify Metabolic Engineering Strategies for the Co-Production of Metabolites

Microbial production of chemicals is a more sustainable alternative to traditional chemical processes. However, the shift to bioprocess is usually accompanied by a drop in economic feasibility. Co-production of more than one chemical can improve the economy of bioprocesses, enhance carbon utilization and also ensure better exploitation of resources. While a number of tools exist for in silico metabolic engineering, there is a dearth of computational tools that can co-optimize the production of multiple metabolites. In this work, we propose co-FSEOF (co-production using Flux Scanning based on Enforced Objective Flux), an algorithm designed to identify intervention strategies to co-optimize the production of a set of metabolites. Co-FSEOF can be used to identify all pairs of products that can be co-optimized with ease using a single intervention. Beyond this, it can also identify higher-order intervention strategies for a given set of metabolites. We have employed this tool on the genome-scale metabolic models of Escherichia coli and Saccharomyces cerevisiae, and identified intervention targets that can co-optimize the production of pairs of metabolites under both aerobic and anaerobic conditions. Anaerobic conditions were found to support the co-production of a higher number of metabolites when compared to aerobic conditions in both organisms. The proposed computational framework will enhance the ease of study of metabolite co-production and thereby aid the design of better bioprocesses.

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.

The Design-Build-Test-Learn cycle for metabolic engineering of Streptomycetes

Streptomycetes are producers of a wide range of specialized metabolites of great medicinal and industrial importance, such as antibiotics, antifungals, or pesticides. Having been the drivers of the golden age of antibiotics in the 1950s and 1960s, technological advancements over the last two decades have revealed that very little of their biosynthetic potential has been exploited so far. Given the great need for new antibiotics due to the emerging antimicrobial resistance crisis, as well as the urgent need for sustainable biobased production of complex molecules, there is a great renewed interest in exploring and engineering the biosynthetic potential of streptomycetes. Here, we describe the Design-Build-Test-Learn (DBTL) cycle for metabolic engineering experiments in streptomycetes and how it can be used for the discovery and production of novel specialized metabolites.

Automatic reconstruction of metabolic pathways from identified biosynthetic gene clusters

Background

A wide range of bioactive compounds is produced by enzymes and enzymatic complexes encoded in biosynthetic gene clusters (BGCs). These BGCs can be identified and functionally annotated based on their DNA sequence. Candidates for further research and development may be prioritized based on properties such as their functional annotation, (dis)similarity to known BGCs, and bioactivity assays. Production of the target compound in the native strain is often not achievable, rendering heterologous expression in an optimized host strain as a promising alternative. Genome-scale metabolic models are frequently used to guide strain development, but large-scale incorporation and testing of heterologous production of complex natural products in this framework is hampered by the amount of manual work required to translate annotated BGCs to metabolic pathways. To this end, we have developed a pipeline for an automated reconstruction of BGC associated metabolic pathways responsible for the synthesis of non-ribosomal peptides and polyketides, two of the dominant classes of bioactive compounds.

Results

The developed pipeline correctly predicts 72.8% of the metabolic reactions in a detailed evaluation of 8 different BGCs comprising 228 functional domains. By introducing the reconstructed pathways into a genome-scale metabolic model we demonstrate that this level of accuracy is sufficient to make reliable in silico predictions with respect to production rate and gene knockout targets. Furthermore, we apply the pipeline to a large BGC database and reconstruct 943 metabolic pathways. We identify 17 enzymatic reactions using high-throughput assessment of potential knockout targets for increasing the production of any of the associated compounds. However, the targets only provide a relative increase of up to 6% compared to wild-type production rates.

Conclusion

With this pipeline we pave the way for an extended use of genome-scale metabolic models in strain design of heterologous expression hosts. In this context, we identified generic knockout targets for the increased production of heterologous compounds. However, as the predicted increase is minor for any of the single-reaction knockout targets, these results indicate that more sophisticated strain-engineering strategies are necessary for the development of efficient BGC expression hosts.

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.

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.

Current Status and Applications of Adaptive Laboratory Evolution in Industrial Microorganisms

Adaptive laboratory evolution (ALE) is an evolutionary engineering approach in artificial conditions that improves organisms through the imitation of natural evolution. Due to the development of multi-level omics technologies in recent decades, ALE can be performed for various purposes at the laboratory level. This review delineates the basics of the experimental design of ALE based on several ALE studies of industrial microbial strains and updates current strategies combined with progressed metabolic engineering, in silico modeling and automation to maximize the evolution efficiency. Moreover, the review sheds light on the applicability of ALE as a strain development approach that complies with non-recombinant preferences in various food industries. Overall, recent progress in the utilization of ALE for strain development leading to successful industrialization is discussed.

Construction, comparison and evolution of networks in life sciences and other disciplines

Network approaches have become pervasive in many research fields. They allow for a more comprehensive understanding of complex relationships between entities as well as their group-level properties and dynamics. Many networks change over time, be it within seconds or millions of years, depending on the nature of the network. Our focus will be on comparative network analyses in life sciences, where deciphering temporal network changes is a core interest of molecular, ecological, neuropsychological and evolutionary biologists. Further, we will take a journey through different disciplines, such as social sciences, finance and computational gastronomy, to present commonalities and differences in how networks change and can be analysed. Finally, we envision how borrowing ideas from these disciplines could enrich the future of life science research.