Integrated pathway mining and selection of an artificial CYP79-mediated bypass to improve benzylisoquinoline alkaloid biosynthesis.

BACKGROUND: Computational mining of useful enzymes and biosynthesis pathways is a powerful strategy for metabolic engineering. Through systematic exploration of all conceivable combinations of enzyme reactions, including both known compounds and those inferred from the chemical structures of established reactions, we can uncover previously undiscovered enzymatic processes. The application of the novel alternative pathways enables us to improve microbial bioproduction by bypassing or reinforcing metabolic bottlenecks. Benzylisoquinoline alkaloids (BIAs) are a diverse group of plant-derived compounds with important pharmaceutical properties. BIA biosynthesis has developed into a prime example of metabolic engineering and microbial bioproduction. The early bottleneck of BIA production in Escherichia coli consists of 3,4-dihydroxyphenylacetaldehyde (DHPAA) production and conversion to tetrahydropapaveroline (THP). Previous studies have selected monoamine oxidase (MAO) and DHPAA synthase (DHPAAS) to produce DHPAA from dopamine and oxygen; however, both of these enzymes produce toxic hydrogen peroxide as a byproduct. RESULTS: In the current study, in silico pathway design is applied to relieve the bottleneck of DHPAA production in the synthetic BIA pathway. Specifically, the cytochrome P450 enzyme, tyrosine N-monooxygenase (CYP79), is identified to bypass the established MAO- and DHPAAS-mediated pathways in an alternative arylacetaldoxime route to DHPAA with a peroxide-independent mechanism. The application of this pathway is proposed to result in less formation of toxic byproducts, leading to improved production of reticuline (up to 60 mg/L at the flask scale) when compared with that from the conventional MAO pathway. CONCLUSIONS: This study showed improved reticuline production using the bypass pathway predicted by the M-path computational platform. Reticuline production in E. coli exceeded that of the conventional MAO-mediated pathway. The study provides a clear example of the integration of pathway mining and enzyme design in creating artificial metabolic pathways and suggests further potential applications of this strategy in metabolic engineering.

Synthesis of the A/D/E-ring Core Compounds of Maoecrystal V†.

Synthesis of the A/D/E-ring core compounds of maoecrystal V was achieved. The key Diels-Alder reactions between tricyclic α-methylene lactones and Kitahara-Danishefsky dienes afforded the spirocyclic core compounds in a regioselective and stereoselective manner.

Online SFE-SFC-MS/MS colony screening: A high-throughput approach for optimizing (-)-limonene production.

Conventional analysis of microbial bioproducers requires the extraction of metabolites from liquid cultures, where the culturing steps are time consuming and greatly limit throughput. To break through this barrier, the current study aims to directly evaluate microbial bioproduction colonies by way of supercritical fluid extraction-supercritical fluid chromatography-triple quadrupole mass spectrometry (SFE-SFC-MS/MS). The online SFE-SFC-MS/MS system offers great potential for high-throughput analysis due to automated metabolite extraction without any need for pretreatment. This is the first report of SFE-SFC-MS/MS as a method for direct colony screening, as demonstrated in the high-throughput screening of (-)-limonene bioproducers. Compared with conventional analysis, the SFE-SFC-MS/MS system enables faster and more convenient screening of highly productive strains.

Dark accumulation of downstream glycolytic intermediates initiates robust photosynthesis in cyanobacteria.

Photosynthesis must maintain stability and robustness throughout fluctuating natural environments. In cyanobacteria, dark-to-light transition leads to drastic metabolic changes from dark respiratory metabolism to CO2 fixation through the Calvin-Benson-Bassham (CBB) cycle using energy and redox equivalents provided by photosynthetic electron transfer. Previous studies have shown that catabolic metabolism supports the smooth transition into CBB cycle metabolism. However, metabolic mechanisms for robust initiation of photosynthesis are poorly understood due to lack of dynamic metabolic characterizations of dark-to-light transitions. Here, we show rapid dynamic changes (on a time scale of seconds) in absolute metabolite concentrations and 13C tracer incorporation after strong or weak light irradiation in the cyanobacterium Synechocystis sp. PCC 6803. Integration of this data enabled estimation of time-resolved nonstationary metabolic flux underlying CBB cycle activation. This dynamic metabolic analysis indicated that downstream glycolytic intermediates, including phosphoglycerate and phosphoenolpyruvate, accumulate under dark conditions as major substrates for initial CO2 fixation. Compared with wild-type Synechocystis, significant decreases in the initial oxygen evolution rate were observed in 12 h dark preincubated mutants deficient in glycogen degradation or oxidative pentose phosphate pathways. Accordingly, the degree of decrease in the initial oxygen evolution rate was proportional to the accumulated pool size of glycolytic intermediates. These observations indicate that the accumulation of glycolytic intermediates is essential for efficient metabolism switching under fluctuating light environments.

Simple and Rapid Non-ribosomal Peptide Synthetase Gene Assembly Using the SEAM-OGAB Method.

Recombination of biosynthetic gene clusters including those of non-ribosomal peptide synthetases (NRPSs) is essential for understanding the mechanisms of biosynthesis. Due to relatively huge gene cluster sizes ranging from 10 to 150 kb, the prevalence of sequence repeats, and inability to clearly define optimal points for manipulation, functional characterization of recombinant NRPSs with maintained activity has been hindered. In this study, we introduce a simple yet rapid approach named "Seamed Express Assembly Method (SEAM)" coupled with Ordered Gene Assembly in Bacillus subtilis (OGAB) to reconstruct fully functional plipastatin NRPS. This approach is enabled by the introduction of restriction enzyme sites as seams at module borders. SEAM-OGAB is then first demonstrated by constructing the ppsABCDE NRPS (38.4 kb) to produce plipastatin, a cyclic decapeptide in B. subtilis. The introduced amino acid level seams do not hinder the NRPS function and enable successful production of plipastatin at a commensurable titer. It is challenging to modify the plipastatin NRPS gene cluster due to the presence of three long direct-repeat sequences; therefore, this study demonstrates that SEAM-OGAB can be readily applied towards the recombination of various NRPSs. Compared to previous NRPS gene assembly methods, the advantage of SEAM-OGAB is that it readily enables the shuffling of NRPS gene modules, and therefore, chimeric NRPSs can be rapidly constructed for the production of novel peptides. This chimeric assembly application of SEAM-OGAB is demonstrated by swapping plipastatin NRPS and surfactin NRPS modules to produce two novel lipopeptides in B. subtilis.

Comprehensive Machine Learning Prediction of Extensive Enzymatic Reactions.

New enzyme functions exist within the increasing number of unannotated protein sequences. Novel enzyme discovery is necessary to expand the pathways that can be accessed by metabolic engineering for the biosynthesis of functional compounds. Accordingly, various machine learning models have been developed to predict enzymatic reactions. However, the ability to predict unknown reactions that are not included in the training data has not been clarified. In order to cover uncertain and unknown reactions, a wider range of reaction types must be demonstrated by the models. Here, we establish 16 expanded enzymatic reaction prediction models developed using various machine learning algorithms, including deep neural network. Improvements in prediction performances over that of our previous study indicate that the updated methods are more effective for the prediction of enzymatic reactions. Overall, the deep neural network model trained with combined substrate-enzyme-product information exhibits the highest prediction accuracy with Macro F1 scores up to 0.966 and with robust prediction of unknown enzymatic reactions that are not included in the training data. This model can predict more extensive enzymatic reactions in comparison to previously reported models. This study will facilitate the discovery of new enzymes for the production of useful substances.

Synthesis and Neuraminidase Inhibitory Activity of Sialic Acid Analogues with Fluoro, Phosphono, and Sulfo Functionalities.

Methods to synthesize influenza virus inhibitors with fluoro, phosphono, and/or sulfo functional groups are described. The resulting sialic acid analogues are produced from the natural substrate N-acetylneuraminic acid as starting material. Fluorescent assay methods for inhibition of influenza neuraminidase and virus proliferation are also provided.

Influenza A Virus Neuraminidase Inhibitors.

Depending on the strain, influenza A virus causes animal, zoonotic, pandemic, or seasonal influenza with varying degrees of severity. Two surface glycoprotein spikes, hemagglutinin (HA) and neuraminidase (NA), are the most important influenza A virus antigens. NA plays an important role in the propagation of influenza virus by removing terminal sialic acid from sialyl decoy receptors and thereby facilitating the release of viruses from traps such as in mucus and on infected cells. Some NA inhibitors have become widely used drugs for treatment of influenza. However, attempts to develop effective and safe NA inhibitors that can be used for treatment of anti-NA drugs-resistant influenza viruses have continued. In this chapter, we describe the following updates on influenza A NA inhibitor development: (i) N-acetylneuraminic acid (Neu5Ac)-based derivatives, (ii) covalent NA inhibitors, (iii) sulfo-sialic acid analogs, (iv) N-acetyl-6-sulfo-ß-D-glucosaminide-based inhibitors, (v) inhibitors targeting the 150-loop of group 1 NAs, (vi) conjugation inhibitors, (vii) acylhydrazone derivatives, (viii) monoclonal antibodies, (ix) PVP-I, and (x) natural products. Finally, we provide future perspectives on the next-generation anti-NA drugs.

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.

Synthesis of the oxazolidinone fragment of thelepamide.

Thelepamide, an unique ketide-amino acid isolated from a marine annelid worm Thelepus crispus, has a unique oxazolidinone ring derived from cysteine, glycine and valine. Rareness in nature as well as promising bioactive possibility make the oxazolidinone ring an attractive synthetic target. The hydroxy oxazolidinone fragment of thelepamide was prepared by acid-catalysed N,O-acetal formation between a ketoamide and formaldehyde. Lactone-carbonyl selective isopropyl addition to an oxazilidine-dione under Grignard conditions also forms the target compound.

Enhancing carbohydrate repartitioning into lipid and carotenoid by disruption of microalgae starch debranching enzyme.

Light/dark cycling is an inherent condition of outdoor microalgae cultivation, but is often unfavorable for lipid accumulation. This study aims to identify promising targets for metabolic engineering of improved lipid accumulation under outdoor conditions. Consequently, the lipid-rich mutant Chlamydomonas sp. KOR1 was developed through light/dark-conditioned screening. During dark periods with depressed CO2 fixation, KOR1 shows rapid carbohydrate degradation together with increased lipid and carotenoid contents. KOR1 was subsequently characterized with extensive mutation of the ISA1 gene encoding a starch debranching enzyme (DBE). Dynamic time-course profiling and metabolomics reveal dramatic changes in KOR1 metabolism throughout light/dark cycles. During light periods, increased flux from CO2 through glycolytic intermediates is directly observed to accompany enhanced formation of small starch-like particles, which are then efficiently repartitioned in the next dark cycle. This study demonstrates that disruption of DBE can improve biofuel production under light/dark conditions, through accelerated carbohydrate repartitioning into lipid and carotenoid.

An ion-pair free LC-MS/MS method for quantitative metabolite profiling of microbial bioproduction systems.

Data-driven engineering of microbes has been demonstrated for the sustainable production of high-performance chemicals. Metabolic profiling analysis is essential to increase the productivity of target compounds. However, improvement of comprehensive analysis methodologies is required for the high demands of metabolic engineering. Therefore, a liquid chromatography-tandem mass spectrometry (LC-MS/MS) based methodology was designed and applied to cover a wide target range with high precision. Ion-pair free separation of metabolites on a pentafluorophenyl propyl column enabled high-precision quantification of 113 metabolites. The method was further evaluated for high reproducibility and robustness. Target analytes consisted of primary metabolites and intermediate metabolites for microbial production of high-performance chemicals. 95 metabolites could be detected with high reproducibility of peak area (intraday data: CV<15%), and 53 metabolites could be sensitively determined within a wide dynamic linear range (3-4 orders of magnitude). The developed system was further applied to the metabolomic analysis of various prokaryotic and eukaryotic microorganisms. Differences due to culture media and metabolic phenotypes could be observed when comparing the metabolomes of conventional and non-conventional yeast. Furthermore, almost all Kluyveromyces marxianus metabolites could be detected with moderate reproducibility (CV<40%, among independent extractions), where 41 metabolites were detected with very high reproducibility (CV<15%). In addition, the accuracy was validated via a spike-and-recovery test,and 78 metabolites were detected with analyte recovery in the 80-120% range. Together these results establish ion-pair free metabolic profiling as a comprehensive and precise tool for data-driven bioengineering applications.

Exploration and Evaluation of Machine Learning-Based Models for Predicting Enzymatic Reactions.

Unannotated gene sequences in databases are increasing due to sequencing advances. Therefore, computational methods to predict functions of unannotated genes are needed. Moreover, novel enzyme discovery for metabolic engineering applications further encourages annotation of sequences. Here, enzyme functions are predicted using two general approaches, each including several machine learning algorithms. First, Enzyme-models (E-models) predict Enzyme Commission (EC) numbers from amino acid sequence information. Second, Substrate-Enzyme models (SE-models) are built to predict substrates of enzymatic reactions together with EC numbers, and Substrate-Enzyme-Product models (SEP-models) are built to predict substrates, products, and EC numbers. While accuracy of E-models is not optimal, SE-models and SEP-models predict EC numbers and reactions with high accuracy using all tested machine learning-based methods. For example, a single Random Forests-based SEP-model predicts EC first digits with an Average AUC score of over 0.94. Various metrics indicate that the current strategy of combining sequence and chemical structure information is effective at improving enzyme reaction prediction.

Dynamic Metabolomics for Engineering Biology: Accelerating Learning Cycles for Bioproduction.

Metabolomics is a powerful tool to rationally guide the metabolic engineering of synthetic bioproduction pathways. Current reports indicate great potential to further develop metabolomics-directed synthetic bioproduction. Advanced mass metabolomics methods including isotope flux analysis, untargeted metabolomics, and system-wide approaches are assisting the characterization of metabolic pathways and enabling the biosynthesis of more complex products. More importantly, a design, build, test, and learn (DBTL) cycle is accelerating synthetic biology research and is highly compatible with metabolomics data to further expand bioproduction capability. However, learning processes are currently the weakest link in this workflow. Therefore, guidelines for the development of metabolic learning processes are proposed based on bioproduction examples. Linking dynamic mass spectrometry (MS) methodologies together with automated learning workflows is encouraged.

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 ß-carotene hydroxylase and ß-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 ß-carotene and zeaxanthin.

Mechanism-based tuning of insect 3,4-dihydroxyphenylacetaldehyde synthase for synthetic bioproduction of benzylisoquinoline alkaloids.

Previous studies have utilized monoamine oxidase (MAO) and L-3,4-dihydroxyphenylalanine decarboxylase (DDC) for microbe-based production of tetrahydropapaveroline (THP), a benzylisoquinoline alkaloid (BIA) precursor to opioid analgesics. In the current study, a phylogenetically distinct Bombyx mori 3,4-dihydroxyphenylacetaldehyde synthase (DHPAAS) is identified to bypass MAO and DDC for direct production of 3,4-dihydroxyphenylacetaldehyde (DHPAA) from L-3,4-dihydroxyphenylalanine (L-DOPA). Structure-based enzyme engineering of DHPAAS results in bifunctional switching between aldehyde synthase and decarboxylase activities. Output of dopamine and DHPAA products is fine-tuned by engineered DHPAAS variants with Phe79Tyr, Tyr80Phe and Asn192His catalytic substitutions. Balance of dopamine and DHPAA products enables improved THP biosynthesis via a symmetrical pathway in Escherichia coli. Rationally engineered insect DHPAAS produces (R,S)-THP in a single enzyme system directly from L-DOPA both in vitro and in vivo, at higher yields than that of the wild-type enzyme. However, DHPAAS-mediated downstream BIA production requires further improvement.

Author Correction: Mechanism-based tuning of insect 3,4-dihydroxyphenylacetaldehyde synthase for synthetic bioproduction of benzylisoquinoline alkaloids.

In the original version of this Article, the abbreviation of 3,4-dihydroxyphenylacetaldehyde synthase presented in the first paragraph of the Discussion section was given incorrectly as DYPAA. The correct abbreviation for this enzyme is DHPAAS. This error has been corrected in both the PDF and HTML versions of the Article.

Light/dark cycling causes delayed lipid accumulation and increased photoperiod-based biomass yield by altering metabolic flux in oleaginous Chlamydomonas sp.

BACKGROUND: Light/dark cycling is an inevitable outdoor culture condition for microalgal biofuel production; however, the influence of this cycling on cellular lipid production has not been clearly established. The general aim of this study was to determine the influence of light/dark cycling on microalgal biomass production and lipid accumulation. To achieve this goal, specific causative mechanisms were investigated using a metabolomics approach. Laboratory scale photoautotrophic cultivations of the oleaginous green microalga Chlamydomonas sp. JSC4 were performed under continuous light (LL) and light/dark (LD) conditions. RESULTS: Lipid accumulation and carbohydrate degradation were delayed under the LD condition compared with that under the LL condition. Metabolomic analysis revealed accumulation of phosphoenolpyruvate and decrease of glycerol 3-phosphate under the LD condition, suggesting that the imbalance of these metabolites is a source of delayed lipid accumulation. When accounting for light dosage, biomass yield under the LD condition was significantly higher than that under the LL condition. Dynamic metabolic profiling showed higher levels of lipid/carbohydrate anabolism (including production of 3-phosphoglycerate, fructose 6-phosphate, glucose 6-phosphate, phosphoenolpyruvate and acetyl-CoA) from CO2 under the LD condition, indicating higher CO2 fixation than that of the LL condition. CONCLUSIONS: Photoperiods define lipid accumulation and biomass production, and light/dark cycling was determined as a critical obstacle for lipid production in JSC4. Conversions of phosphoenolpyruvate to pyruvate and 3-phosphoglycerate to glycerol 3-phosphate are the candidate rate-limiting steps responsible for delayed lipid accumulation. The accumulation of substrates including ribulose 5-phosphate could be explained by the close relationship of increased biomass yield with enhanced CO2 fixation. The present study investigated the influence of light/dark cycling on lipid production by direct comparison with continuous illumination for the first time, and revealed underlying metabolic mechanisms and candidate metabolic rate-limiting steps during light/dark cycling. These findings suggest promising targets to metabolically engineer improved lipid production.

Chemo-enzymatic synthesis of p-nitrophenyl ß-D-galactofuranosyl disaccharides from Aspergillus sp. fungal-type galactomannan.

ß-d-Galactofuranose (Galf) is a component of polysaccharides and glycoconjugates. There are few reports about the involvement of galactofuranosyltransferases and galactofuranosidases (Galf-ases) in the synthesis and degradation of galactofuranose-containing glycans. The cell walls of filamentous fungi in the genus Aspergillus include galactofuranose-containing polysaccharides and glycoconjugates, such as O-glycans, N-glycans, and fungal-type galactomannan, which are important for cell wall integrity. In this study, we investigated the synthesis of p-nitrophenyl ß-d-galactofuranoside and its disaccharides by chemo-enzymatic methods including use of galactosidase. The key step was selective removal of the concomitant pyranoside by enzymatic hydrolysis to purify p-nitrophenyl ß-d-galactofuranoside, a promising substrate for ß-d-galactofuranosidase from Streptomyces species.

Xylomexicanins I and J: Limonoids with Unusual B/C Rings from Xylocarpus granatum.

Two tetranortriterpenoids with new skeletons, xylomexicanins I and J (1 and 2), were isolated during the investigation of chemical constituents from seeds of the Chinese mangrove, Xylocarpus granatum. Xylomexicanin I (1) is an unprecedented limonoid with bridged B- and C-rings. A biosynthesis pathway for 1 from xylomexicanin F is proposed.