Dynamic Metabolomics for Engineering Biology: Accelerating Learning Cycles for Bioproduction

Emerging trends in production of plant natural products and new-to-nature biopharmaceuticals in yeast

Natural products represent an inestimable source of valuable compounds for human health. Notably, those produced by plants remain challenging to access due to their low production. Potential shortages of plant-derived biopharmaceuticals caused by climate change or pandemics also regularly tense the market trends. Thus, biotechnological alternatives of supply based on synthetic biology have emerged. These innovative strategies mostly rely on the use of engineered microbial systems for compound synthesis. In this regard, yeasts remain the easiest-tractable eukaryotic models and a convenient chassis for reconstructing whole biosynthetic routes for the heterologous production of plant-derived metabolites. Here, we highlight the recent discoveries dedicated to the bioproduction of new-to-nature compounds in yeasts and provide an overview of emerging strategies for optimising bioproduction.

Progress in polystyrene biodegradation by insect gut microbiota

Polystyrene (PS) is frequently used in the plastics industry. However, its structural stability and difficulty to break down lead to an abundance of plastic waste in the environment, resulting in micro-nano plastics (MNPs). As MNPs are severe hazards to both human and environmental health, it is crucial to develop innovative treatment technologies to degrade plastic waste. The biodegradation of plastics by insect gut microorganisms has gained attention as it is environmentally friendly, efficient, and safe. However, our knowledge of the biodegradation of PS is still limited. This review summarizes recent research advances on PS biodegradation by gut microorganisms/enzymes from insect larvae of different species, and schematic pathways of the degradation process are discussed in depth. Additionally, the prospect of using modern biotechnology, such as genetic engineering and systems biology, to identify novel PS-degrading microbes/functional genes/enzymes and to realize new strategies for PS biodegradation is highlighted. Challenges and limitations faced by the application of genetically engineered microorganisms (GEMs) and multiomics technologies in the field of plastic pollution bioremediation are also discussed. This review encourages the further exploration of the biodegradation of PS by insect gut microbes/enzymes, offering a cutting-edge perspective to identify PS biodegradation pathways and create effective biodegradation strategies.

Metabolomics-based development of bioproduction processes toward industrial-scale production

Microbial biomanufacturing offers a promising, environment-friendly platform for next-generation chemical production. However, its limited industrial implementation is attributed to the slow production rates of target compounds and the time-intensive engineering of high-yield strains. This review highlights how metabolomics expedites bioproduction development, as demonstrated through case studies of its integration into microbial strain engineering, culture optimization, and model construction. The Design-Build-Test-Learn (DBTL) cycle serves as a standard workflow for strain engineering. Process development, including the optimization of culture conditions and scale-up, is crucial for industrial production. In silico models facilitate the development of strains and processes. Metabolomics is a powerful driver of the DBTL framework, process development, and model construction.

Engineering of increased L-Threonine production in bacteria by combinatorial cloning and machine learning

The goal of this study is to develop a general strategy for bacterial engineering using an integrated synthetic biology and machine learning (ML) approach. This strategy was developed in the context of increasing L-threonine production in Escherichia coli ATCC 21277. A set of 16 genes was initially selected based on metabolic pathway relevance to threonine biosynthesis and used for combinatorial cloning to construct a set of 385 strains to generate training data (i.e., a range of L-threonine titers linked to each of the specific gene combinations). Hybrid (regression/classification) deep learning (DL) models were developed and used to predict additional gene combinations in subsequent rounds of combinatorial cloning for increased L-threonine production based on the training data. As a result, E. coli strains built after just three rounds of iterative combinatorial cloning and model prediction generated higher L-threonine titers (from 2.7 g/L to 8.4 g/L) than those of patented L-threonine strains being used as controls (4-5 g/L). Interesting combinations of genes in L-threonine production included deletions of the tdh, metL, dapA, and dhaM genes as well as overexpression of the pntAB, ppc, and aspC genes. Mechanistic analysis of the metabolic system constraints for the best performing constructs offers ways to improve the models by adjusting weights for specific gene combinations. Graph theory analysis of pairwise gene modifications and corresponding levels of L-threonine production also suggests additional rules that can be incorporated into future ML models.

Untargeted metabolomics elucidated biosynthesis of polyhydroxyalkanoate by mixed microbial cultures from waste activated sludge under different pH values

Waste activated sludge has been frequently used as mixed substrate to produce polyhydroxyalkanoate (PHA). However, insufficient research on microbial metabolism has led to difficulties in regulating PHA accumulation in mixed microbial cultures (MMCs). To explore the variation of functional genes during domestication and the effect of different pH conditions on metabolic pathways during PHA accumulation, MMCs were domesticated by adding acetate and propionate with aerobic dynamic feeding strategy for 60 days. As the domestication progressed, the microbial community diversity declined and PHA-producing bacteria, Brevundimonas, Dechloromonas and Hyphomonas, were enriched. Through bacterial function prediction by PICRUSt the gene rpoE involved in starvation resistance of bacteria was enriched after the domestication. The pH value of 8.5 was the best condition for PHA accumulation in MMCs, under which a maximum PHA content reached 23.50% and hydroxybutyric (HB)/hydroxyvaleric (HV) reached 2.22. Untargeted metabolomics analysis exhibited that pH conditions of 7 and 8.5 could promote the up-regulation of significant differential metabolites, while higher alkaline conditions caused the inhibition of metabolic activity. Functional annotation showed that pH condition of 8.5 significantly affected Pyrimidine metabolism, resulting in an increase in PHA production. Regarding the pathways of PHA biosynthesis, acetoacetate was found to be significant in the metabolism of hydroxybutyric, and the alkaline condition could restrain the conversion from hydroxybutyric (HB) to the acetoacetate to protect PHB accumulation in MMCs compared with neutral condition. Taken together, the present results can advance the fundamental understanding of metabolic function in PHA accumulation under different pH conditions.

Design and construction of microbial cell factories based on systems biology

Environmental sustainability is an increasingly important issue in industry. As an environmentally friendly and sustainable way, constructing microbial cell factories to produce all kinds of valuable products has attracted more and more attention. In the process of constructing microbial cell factories, systems biology plays a crucial role. This review summarizes the recent applications of systems biology in the design and construction of microbial cell factories from four perspectives, including functional genes/enzymes discovery, bottleneck pathways identification, strains tolerance improvement and design and construction of synthetic microbial consortia. Systems biology tools can be employed to identify functional genes/enzymes involved in the biosynthetic pathways of products. These discovered genes are introduced into appropriate chassis strains to build engineering microorganisms capable of producing products. Subsequently, systems biology tools are used to identify bottleneck pathways, improve strains tolerance and guide design and construction of synthetic microbial consortia, resulting in increasing the yield of engineered strains and constructing microbial cell factories successfully.

Metabolomics and modelling approaches for systems metabolic engineering

Metabolic engineering involves the manipulation of microbes to produce desirable compounds through genetic engineering or synthetic biology approaches. Metabolomics involves the quantitation of intracellular and extracellular metabolites, where mass spectrometry and nuclear magnetic resonance based analytical instrumentation are often used. Here, the experimental designs, sample preparations, metabolite quenching and extraction are essential to the quantitative metabolomics workflow. The resultant metabolomics data can then be used with computational modelling approaches, such as kinetic and constraint-based modelling, to better understand underlying mechanisms and bottlenecks in the synthesis of desired compounds, thereby accelerating research through systems metabolic engineering. Constraint-based models, such as genome scale models, have been used successfully to enhance the yield of desired compounds from engineered microbes, however, unlike kinetic or dynamic models, constraint-based models do not incorporate regulatory effects. Nevertheless, the lack of time-series metabolomic data generation has hindered the usefulness of dynamic models till today. In this review, we show that improvements in automation, dynamic real-time analysis and high throughput workflows can drive the generation of more quality data for dynamic models through time-series metabolomics data generation. Spatial metabolomics also has the potential to be used as a complementary approach to conventional metabolomics, as it provides information on the localization of metabolites. However, more effort must be undertaken to identify metabolites from spatial metabolomics data derived through imaging mass spectrometry, where machine learning approaches could prove useful. On the other hand, single-cell metabolomics has also seen rapid growth, where understanding cell-cell heterogeneity can provide more insights into efficient metabolic engineering of microbes. Moving forward, with potential improvements in automation, dynamic real-time analysis, high throughput workflows, and spatial metabolomics, more data can be produced and studied using machine learning algorithms, in conjunction with dynamic models, to generate qualitative and quantitative predictions to advance metabolic engineering efforts.

Metabolomics and Biomarkers in Retinal and Choroidal Vascular Diseases

The retina is one of the most important structures in the eye, and the vascular health of the retina and choroid is critical to visual function. Metabolomics provides an analytical approach to endogenous small molecule metabolites in organisms, summarizes the results of “gene-environment interactions”, and is an ideal analytical tool to obtain “biomarkers” related to disease information. This study discusses the metabolic changes in neovascular diseases involving the retina and discusses the progress of the study from the perspective of metabolomics design and analysis. This study advocates a comparative strategy based on existing studies, which encompasses optimization of the performance of newly identified biomarkers and the consideration of the basis of existing studies, which facilitates quality control of newly discovered biomarkers and is recommended as an additional reference strategy for new biomarker discovery. Finally, by describing the metabolic mechanisms of retinal and choroidal neovascularization, based on the results of existing studies, this study provides potential opportunities to find new therapeutic approaches.

Emerging Trends in Genetic Engineering of Microalgae for Commercial Applications

Recently, microalgal biotechnology has received increasing interests in producing valuable, sustainable and environmentally friendly bioproducts. The development of economically viable production processes entails resolving certain limitations of microalgal biotechnology, and fast evolving genetic engineering technologies have emerged as new tools to overcome these limitations. This review provides a synopsis of recent progress, current trends and emerging approaches of genetic engineering of microalgae for commercial applications, including production of pharmaceutical protein, lipid, carotenoids and biohydrogen, etc. Photochemistry improvement in microalgae and CO2 sequestration by microalgae via genetic engineering were also discussed since these subjects are closely entangled with commercial production of the above mentioned products. Although genetic engineering of microalgae is proved to be very effective in boosting performance of production in laboratory conditions, only limited success was achieved to be applicable to industry so far. With genetic engineering technologies advancing rapidly and intensive investigations going on, more bioproducts are expected to be produced by genetically modified microalgae and even much more to be prospected.

Machine learning discovery of missing links that mediate alternative branches to plant alkaloids

Engineering the microbial production of secondary metabolites is limited by the known reactions of correctly annotated enzymes. Therefore, the machine learning discovery of specialized enzymes offers great potential to expand the range of biosynthesis pathways. Benzylisoquinoline alkaloid production is a model example of metabolic engineering with potential to revolutionize the paradigm of sustainable biomanufacturing. Existing bacterial studies utilize a norlaudanosoline pathway, whereas plants contain a more stable norcoclaurine pathway, which is exploited in yeast. However, committed aromatic precursors are still produced using microbial enzymes that remain elusive in plants, and additional downstream missing links remain hidden within highly duplicated plant gene families. In the current study, machine learning is applied to predict and select plant missing link enzymes from homologous candidate sequences. Metabolomics-based characterization of the selected sequences reveals potential aromatic acetaldehyde synthases and phenylpyruvate decarboxylases in reconstructed plant gene-only benzylisoquinoline alkaloid pathways from tyrosine. Synergistic application of the aryl acetaldehyde producing enzymes results in enhanced benzylisoquinoline alkaloid production through hybrid norcoclaurine and norlaudanosoline pathways.

Producing plant secondary metabolites by microbes is limited by the known enzymatic reactions. Here, the authors apply machine learning to predict missing link enzymes of benzylisoquinoline alkaloid (BIA) biosynthesis in Papaver somniferum, and validate the specialized activities through heterologous production.

Titer improvement of milbemycins via coordinating metabolic competition and transcriptional co-activation controlled by SARP family regulator in Streptomyces bingchenggensis

Streptomyces bingchenggensis is a promising producer of milbemycins (MILs), the macrolide pesticide used widely in agriculture. The relationship between different biosynthetic gene clusters (BGCs) and the MIL BGC remains unclear, which hinders the precise metabolic engineering of S. bingchenggensis for titer improvement. To address this issue, this study discovered the regulatory function of a previously unidentified regulator KelR on a type-II polyketide BGC, MIL BGC and two other BGCs, and caused titer improvement. First, a type II polyketide synthase (PKS) gene cluster kel with a bidirectional effect on MIL biosynthesis was found using transcriptome analysis. A Streptomyces antibiotic regulatory protein (SARP) family regulator KelR from the kel cluster was then characterized as an activator of several BGCs including mil and kel clusters. Metabolic competition between mil and kel clusters at the late fermentation stage was confirmed. Finally, KelR and those BGCs were manipulated in S. bingchenggensis, which led to a 71.7% titer improvement of MIL A3/A4 to 4058.2±71.0 mg/L. This research deciphered the regulatory function of a previously unidentified regulatory protein KelR on several BGCs including mil in S. bingchenggensis and provided an example of coordinating metabolic competition and co-regulation for titer improvement of secondary metabolites. This article is protected by copyright. All rights reserved.

Metabolomics-based engineering for biofuel and bio-based chemical production in microalgae and cyanobacteria: A review

Metabolomics, an essential tool in modern synthetic biology based on the design-build-test-learn platform, is useful for obtaining a detailed understanding of cellular metabolic mechanisms through comprehensive analyses of the metabolite pool size and its dynamic changes. Metabolomics is critical to the design of a rational metabolic engineering strategy by determining the rate-limiting reaction and assimilated carbon distribution in a biosynthetic pathway of interest. Microalgae and cyanobacteria are promising photosynthetic producers of biofuels and bio-based chemicals, with high potential for developing a bioeconomic society through bio-based carbon neutral manufacturing. Metabolomics technologies optimized for photosynthetic organisms have been developed and utilized in various microalgal and cyanobacterial species. This review provides a concise overview of recent achievements in photosynthetic metabolomics, emphasizing the importance of microalgal and cyanobacterial cell factories that satisfy industrial requirements.

Omics-Driven Biotechnology for Industrial Applications

Biomanufacturing is a key component of biotechnology that uses biological systems to produce bioproducts of commercial relevance, which are of great interest to the energy, material, pharmaceutical, food, and agriculture industries. Biotechnology-based approaches, such as synthetic biology and metabolic engineering are heavily reliant on "omics" driven systems biology to characterize and understand metabolic networks. Knowledge gained from systems biology experiments aid the development of synthetic biology tools and the advancement of metabolic engineering studies toward establishing robust industrial biomanufacturing platforms. In this review, we discuss recent advances in "omics" technologies, compare the pros and cons of the different "omics" technologies, and discuss the necessary requirements for carrying out multi-omics experiments. We highlight the influence of "omics" technologies on the production of biofuels and bioproducts by metabolic engineering. Finally, we discuss the application of "omics" technologies to agricultural and food biotechnology, and review the impact of "omics" on current COVID-19 research.

Regulatory molecule cAMP changes cell fitness of the engineered Escherichia coli for terpenoids production

Terpenoids are a class of natural compounds with many important functions and applications. They are synthesized from a long synthetic pathway of isoprenyl unit coupling with the myriads of terpene synthases. Owing to the catalytic divergence of terpenoids synthesis, microbial production of terpenoids is compromised to the complexity of pathway engineering and suffers from the metabolic engineering burden. In this work, the adaptive Escherichia coli HP variant exhibited a general cell fitness in terpenoid synthesis. Especially, it could yield taxadiene of 193.2 mg/L in a test tube culture, which is a five-fold increase over the production in the wild type E. coli DH5α. Mutational analyses indicated that IS10 insertion in adenylate cyclase CyaA (CyaA_HP) resulted in lowering intracellular cyclic AMP (cAMP), which could regulate its receptor protein CRP to rewire cell metabolism and contributed to the improved cell fitness. Our results suggested a way to manipulate cell fitness for terpenoids production and other products.

Single-Stage Astaxanthin Production Enhances the Nonmevalonate Pathway and Photosynthetic Central Metabolism in Synechococcus sp. PCC 7002

The natural pigment astaxanthin is widely used in aquaculture, pharmaceutical, nutraceutical, and cosmetic industries due to superior antioxidant properties. The green alga Haematococcus pluvialis is currently used for commercial production of astaxanthin pigment. However, slow growing H. pluvialis requires a complex two-stage stress-induced process with high light intensity leading to increased contamination risks. In contrast, the fast-growing euryhaline cyanobacterium Synechococcus sp. PCC 7002 (Synechococcus 7002) is able to reach high density under stress-free phototrophic conditions, and is therefore a promising metabolic engineering platform for astaxanthin production. In the present study, genes encoding β-carotene hydroxylase and β-carotene ketolase, from the marine bacterium Brevundimonas sp. SD212, are integrated into the endogenous plasmid of Synechococcus 7002, and then expressed to biosynthesize astaxanthin. Although Synechococcus 7002 does not inherently produce astaxanthin, the recombinant ZW strain yields 3 mg/g dry cell weight astaxanthin from CO₂ as the sole carbon source, with significantly higher astaxanthin content than previous cyanobacteria reports. Synechococcus 7002 astaxanthin productivity reached 3.35 mg/L/day after just 2 days in a continuous autotrophic process, which is comparable to the best H. pluvialis astaxanthin productivities when factoring in growth times. Metabolomics analysis reveals increases in fractions of hexose-, pentose-, and triose phosphates along with intermediates involved in the nonmevalonate pathway. Dynamic metabolomics analysis of ¹³C labeled metabolites clearly indicates flux enhancements in the Calvin cycle and glycolysis resulting from the overexpression of astaxanthin biosynthetic genes. This study suggests that cyanobacteria may enhance central metabolism as well as the nonmevalonate pathway in an attempt to replenish depleted pigments such as β-carotene and zeaxanthin.