The significance of gut microbiota in human health has gained increasing attention. Accordingly, metabolomics has been used to elucidate host-microbiota interactions. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is an ideal choice for metabolome analysis of gut microbiota due to its quantitative capabilities. However, conventional LC-MS/MS requires multiple columns, multiple mobile phases, and complex procedures to optimize conditions for each target metabolite. To address these limitations, we developed a quantitative serial LC-MS/MS method, termed the Kobe University Serial LC-MS/MS Analysis using Multiple columns with a Single mobile phase (KUSLAMS). This platform integrates two columns (PFPP and C18) and a derivatization method for seamless, high-throughput quantification of 215 metabolites, including amino acids, nucleotides, carboxylic acids, amines, and fatty acids. Reproducibility for repeated analysis was assessed using 82 intracellular gut microbiota metabolites, for which new analytical methods were developed. Among these, 64 metabolites were detected with coefficients of variation (CV) below 15%. The application of KUSLAMS to an in vitro gut microbiota culture system with and without inulin revealed differences in the concentrations of 21 intracellular and 14 extracellular metabolites. Notably, several metabolites exhibited increased intracellular and decreased extracellular concentrations, suggesting a possible link between intracellular accumulation and extracellular depletion, although this interpretation is exploratory. These results indicate that KUSLAMS allows for the simultaneous monitoring of intra- and extracellular metabolite dynamics. Together, these findings demonstrate that KUSLAMS is a robust and versatile platform for the exploration of microbiota-derived metabolites relevant to human health.

Abstract The meta-selective C–H functionalization of aromatic substrates lacking meta-directing electron-withdrawing groups remains challenging. To engineer the regioselective enzymatic conversion of biphenyl (BP) to 3,3′-dihydroxybiphenyl (33DHBP), an industrial precursor to high-performance polymers, substrate positioning within the active site must be precisely controlled. Here, we report the regioselective catalytic promiscuity engineering of toluene/o-xylene monooxygenase (ToMO) to achieve sequential, meta-selective hydroxylation of the non-natural biaryl BP, where distinct ToMO variants were engineered to catalyze meta-selective hydroxylation of BP, 3-hydroxybiphenyl (3HBP), or both. Variants to catalyze the meta-selective hydroxylation of BP were designed using an automated docking workflow, with predicted binding poses consistent with the observed regioselectivity; this resulted in the development of the I100V-E103V-F205G variant that hydroxylated BP with 100% apparent meta-selectivity. Additional substitutions, especially L268A and L402A, were introduced to widen the long hydrophobic active-site access channel, further improving meta-selective BP hydroxylation in Pseudomonas putida. Variants to catalyze the meta-selective hydroxylation of 3HBP were selected through a structure-based residue scan of 437 active-site substitutions, resulting in the identification of the I100V-E103V-F176H variant that hydroxylated 3HBP with over 90% apparent meta-selectivity and no activity toward BP. In addition, the combined I100V-E103V-I162Y-F205G variant hydroxylated 3HBP with over 90% apparent meta-selectivity and improved hydroxylation of BP. This study demonstrates structural and computational monooxygenase engineering for the regioselective hydroxylation of non-natural biaryls, enabling the production of 33DHBP as a valuable precursor to specialized polymers.

Enzyme selection is an essential process in the biobased production of chemicals. It is essential to develop a method to extract yet unknown useful enzymes from protein databases. Enzymes that exhibit substrate promiscuity and high activity hold the potential to access unknown reactions and mediate known reactions with a higher performance. Herein, we propose and validate a principal component analysis (PCA)-based classification method, termed MUSASHI (MUltiple-Sequence Alignment-based protein Selection via clustering using HIgh-dimensional analysis), to identify subfamily-specific residues that are highly conserved among promiscuous alcohol dehydrogenase (ADH). Specifically, zinc-dependent ADH homologues retrieved from the protein database were classified into 9 groups, and according to PCA-based clustering, the activities of 18 ADHs, with representative enzymes from each group, were characterized. As a result, we identified two promiscuous ADH groups: Group 1 ADH, efficient with short-chain and aromatic aldehydes, and Group 3 ADH, efficient with aliphatic and aromatic ketones. Sequence feature analysis then revealed subfamily-specific residues, which are highly conserved only in promiscuous ADH Groups 1 and 3, with the potential to biosynthesize a wide spectrum of target compounds. Tatumella ptyseos ADH, identified from Group 1 of this study, showed higher isobutanol and 2-phenylethanol bioconversions than that of a conventional ADH (Ahr). These results indicate that the MUSASHI method for subfamily-specific residue identification can enable optimal enzyme selection from protein databases.

Mass-based fingerprinting can characterize microorganisms; however, expansion of these methods to predict specific gene functions is lacking. Therefore, mass fingerprinting was developed to functionally profile a yeast knockout library. Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) fingerprints of 3,238 Saccharomyces cerevisiae knockouts were digitized for correlation with gene ontology (GO). Random forests and support vector machine (SVM) algorithms assigned GO terms with average AUC values of 0.994 and 0.980, respectively. SVM was the best predictor with average true-positive and true-negative rates of 0.983 and 0.993, respectively. To test predictions of unknown gene functions, the dataset of uncharacterized yeast gene knockouts was evaluated based on SVM scores, and new functions were suggested for 28 corresponding genes. Metabolomics analysis of two knockouts (YDR215C and YLR122C) of uncharacterized genes predicted to be involved in methylation-related metabolism showed altered intracellular contents of methionine-related metabolites. Increased S-adenosylmethionine in YDR215C indicated that this strain shows potential as a chassis for bioproduction of methylated compounds. This study demonstrates that fingerprinting can generate large functional datasets for improved machine learning-based gene function prediction.

3,4-Dihydroxyphenylacetaldehyde synthase (DHPAAS) catalyzes oxygen-dependent conversion of 3,4-dihydroxyphenylalanine (dopa) to 3,4-dihydroxyphenylacetaldehyde (DHPAA), a likely cross-linking agent precursor of the insect cuticle. In the current study, extensive in vivo experiments in Aedes aegypti show that DHPAAS is essential for abdominal integrity, egg development and cuticle structure formation. Solid-state 13C nuclear magnetic resonance analysis of the Ae. aegypti cuticle molecular structure shows chemical shifts of 115 to 145 ppm, suggesting the presence of catechols derived from DHPAA. The crystal structure of insect DHPAAS was then solved, revealing an active site that is divergent from that of the homologous enzyme dopa decarboxylase. In the DHPAAS crystal structure, stabilization of the flexible 320-350 region accompanies the positioning of the 350-360 loop relatively close to the catalytic Asn192 residue while the conserved active site residue Phe103 adopts an open conformation away from the active center; these distinct features participate in the formation of a specific hydrophobic tunnel which potentially facilitates delivery of oxygen to pyridoxal 5'-phosphate in the conversion of dopa to DHPAA.

Insect phenoloxidase, presented as an inactive precursor prophenoloxidase (PPO) in hemolymph, catalyzes melanin formation, which is involved in wound healing, pathogen killing, reversible oxygen collection during insect respiration, and cuticle and eggshell formation. Mosquitoes possess 9 to 16 PPO members across different genera, a number that is more than that found in other dipteran insects. However, the reasons for the redundancy of these PPOs and whether they have distinct biochemical properties and physiological functions remain unclear. Phylogenetic analysis confirmed that Aedes aegypti PPO6 (Aea-PPO6) is an ortholog to PPOs in other insect species, classified as the classical insect type, while other Aea-PPOs are unique to Diptera, herein referred to as the dipteran type here. We characterized two Aea-PPO members, Aea-PPO6, the classical insect type, and Aea-PPO10, a dipteran type, which exhibit distinct substrate specificities. By resolving Aea-PPO6’s crystal structure and creating a chimera protein (Aea-PPO6-cm) with Motif 1 ( 217 GDGPDSVVR 225 ) from Aea-PPO10, we identified the motif that determines PPO substrate specificity. In vivo, loss of Aea-PPO6 led to larval lethality, while Aea-PPO10 was involved in development, pigmentation, and immunity. Our results enhance the understanding of the functional diversification of mosquito PPOs.

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 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.

Maoecrystal V is a highly oxidized and rearranged norterpenoid isolated from the Chinese herb, Isodon eriocalyx. A synthetic route for the C/D/E-ring core compound of maoecrystal V has been developed. Introduction of the C1 fragment to the D/E-ring compound was achieved by lithito dithiane attack, and the C-ring was formed by deprotective intramolecular hemiacetalization of the dithiane-alcohol precursor.

Abstract3,4-Dihydroxyphenylacetaldehyde synthase (DHPAAS) catalyzes the direct conversion of 3,4-dihydroxyphenylalanine to 3,4-dihydroxyphenylacetaldehyde (DHPAA), an important intermediate in the formation of flexible insect cuticle. In order to clarify the precise roles DHPAAS plays in insect development and survival, DHPAAS was characterized throughout the physiological to the molecular levels. Extensive in vivo experiments inAedes aegypticonfirm that DHPAAS is essential for blood feeding, egg development and cuticle structure formation. The crystal structure of insect DHPAAS was then solved to reveal the structural basis underlying the catalytic production of the key cuticle intermediate DHPAA. The molecular view shows a DHPAAS active site that is distinct from that of the homologous enzyme 3,4-dihydroxyphenylalanine decarboxylase. Stabilization of the flexible 320–350 region is observed to position the 350–360 loop towards the catalytic asparagine residue, and these distinct features are suggested to promote pyridoxal 5'-phosphate-dependent amine oxidation. Additional molecular dynamics simulations further support the involvement of Phe82, Tyr83 and Asn195 in substrate binding and catalysis, and also shows increased fluctuations limited to loop residues 330–345 inAedes aegyptiDHPAAS.

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.

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.

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.

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-β-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.

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.

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 F-1 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.

Thelepamide, an unique ketide-amino acid isolated from a marine annelid wormThelepus 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-catalysedN,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.

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.

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.

Aromatic amino acids (AAAs) are parts of the building blocks for protein synthesis. They are also precursors in the synthesis of many other important compounds that play some key roles in the biochemistry and physiology of living organisms. All living species have evolved AAA metabolic pathways to best fit their physiological needs and to adapt to their specific living environments, environmental stresses, adverse conditions, and pathogen infections. This article discusses the metabolism of AAAs in living organisms in general. Major emphases are placed on specific metabolic pathways in different species with mammals, invertebrates and plants as typical models.