Complete biosynthesis of the bisbenzylisoquinoline alkaloids guattegaumerine and berbamunine in yeast

Benzylisoquinoline alkaloids (BIAs) are a diverse class of medicinal plant natural products. Nearly 500 dimeric bisbenzylisoquinoline alkaloids (bisBIAs), produced by the coupling of two BIA monomers, have been characterized and display a range of pharmacological properties, including anti-inflammatory, antitumor, and antiarrhythmic activities. In recent years, microbial platforms have been engineered to produce several classes of BIAs, which are rare or difficult to obtain from natural plant hosts, including protoberberines, morphinans, and phthalideisoquinolines. However, the heterologous biosyntheses of bisBIAs have thus far been largely unexplored. Here, we describe the engineering of yeast strains that produce the Type I bisBIAs guattegaumerine and berbamunine de novo. Through strain engineering, protein engineering, and optimization of growth conditions, a 10,000-fold improvement in the production of guattegaumerine, the major bisBIA pathway product, was observed. By replacing the cytochrome P450 used in the final coupling reaction with a chimeric variant, the product profile was inverted to instead produce solely berbamunine. Our highest titer engineered yeast strains produced 108 and 25 mg/L of guattegaumerine and berbamunine, respectively. Finally, the inclusion of two additional putative BIA biosynthesis enzymes, SiCNMT2 and NnOMT5, into our bisBIA biosynthetic strains enabled the production of two derivatives of bisBIA pathway intermediates de novo: magnocurarine and armepavine. The de novo heterologous biosyntheses of bisBIAs presented here provide the foundation for the production of additional medicinal bisBIAs in yeast.

Benzylisoquinoline alkaloids (BIAs) are a class of plant natural products (PNPs) that includes numerous medicinally important compounds, including the analgesic morphine, the histological stain berberine, and the antitussive noscapine. Included within the BIA family are the bisbenzylisoquinoline alkaloids (bisBIAs), which are produced through the coupling of two 1-benzylisoquinoline monomers, of which nearly 500 examples are known (1). Several natural and semisynthetic bisBIAs have been historically important, including tubocurarine, a natural BIA from Chondrodendron tomentosum used as an arrow poison (2), and cisatracurium, a semisynthetic derivative of the BIA tetrahydropapaverine from Papaver somniferum (opium poppy) used as a muscle relaxant during surgery and mechanical ventilation (3).Due to the chemical complexity of BIAs, commercially viable chemical syntheses have yet to be developed, and this class of compounds is sourced primarily from plant biomass. However, this supply is hampered by low yield and purity, requiring laborious extraction and purification procedures, and faces variability caused by weather and climate change. Furthermore, harvesting of wild medicinal plants can damage their populations and the ecosystems of which they are a part, as has occurred with the bisBIA-producing plant Stephania tetrandra (4 –6). To overcome these limitations, engineered Saccharomyces cerevisiae strains have recently been reported that heterologously biosynthesize several BIAs de novo, including hydrocodone (7), berberine (8), and noscapine (9). These strains have served as valuable platforms for the production of additional pathway derivatives that would be challenging to produce synthetically (10, 11). However, to date, no microbial strains have been reported that produce any of the bisBIAs de novo.One group of bisBIAs has drawn interest for the reported medicinal properties of both the parent plant species and the isolated compounds. This group of compounds is formed by the 3''–4′ coupling of two N-methylcoclaurine (NMC) monomers in either the (R) or (S) configuration, forming four structurally identical, diastereomeric linear bisBIAs. One of these diastereomers has only been prepared synthetically (12), while the other three have been identified in and isolated from plant sources: magnoline from Magnolia fuscata (13); berbamunine from a variety of Berberis species (14); and guattegaumerine from Mosannona depressa (15). Several Berberis species have been reported to have medicinal effects (16), most notably antidiabetic properties (17), and berbamunine has been shown to have dopaminergic properties (18). G. gaumeri has been used to treat hypercholesterolemia (19), and guattegaumerine has been shown to have antitumor (20) and neuroprotectant (21) properties. While synthetic strategies toward these compounds have been developed (22 –25), these procedures involve numerous steps, chiral resolutions, and often rely on BIAs isolated from plant biomass as starting materials.To develop a more efficient platform for the synthesis of bisBIAs and their derivatives, we engineered S. cerevisiae strains that produce guattegaumerine and berbamunine de novo. To accomplish this, we first generated a strain that produces high titers of (S)-NMC de novo, which we used as a platform to determine the activities of downstream enzymes in vivo. Through strain engineering, optimization of fermentation conditions, and protein engineering, we were able to increase the total bisBIA titer over 10,000-fold to over 100 mg/L. By altering the cytochrome P450 that performs the final coupling reaction in the biosynthesis of our bisBIAs, we were able to effect a nearly complete switch in the product ratio from >98% guattegaumerine to >99% berbamunine. Finally, we leveraged our de novo bisBIA strains to produce two additional pathway derivatives, magnocurarine and armepavine, de novo.     

Silencing of tryptamine biosynthesis for production of nonnatural alkaloids in plant culture

Natural products have long served as both a source and inspiration for pharmaceuticals. Modifying the structure of a natural product often improves the biological activity of the compound. Metabolic engineering strategies to ferment “unnatural” products have been enormously successful in microbial organisms. However, despite the importance of plant derived natural products, metabolic engineering strategies to yield unnatural products from complex, lengthy plant pathways have not been widely explored. Here, we show that RNA mediated suppression of tryptamine biosynthesis in Catharanthus roseus hairy root culture eliminates all production of monoterpene indole alkaloids, a class of natural products derived from two starting substrates, tryptamine and secologanin. To exploit this chemically silent background, we introduced an unnatural tryptamine analog to the production media and demonstrated that the silenced plant culture could produce a variety of novel products derived from this unnatural starting substrate. The novel alkaloids were not contaminated by the presence of the natural alkaloids normally present in C. roseus. Suppression of tryptamine biosynthesis therefore did not appear to adversely affect expression of downstream biosynthetic enzymes. Targeted suppression of substrate biosynthesis therefore appears to be a viable strategy for programming a plant alkaloid pathway to more effectively produce desirable unnatural products. Moreover, although tryptamine is widely found among plants, this silenced line demonstrates that tryptamine does not play an essential role in growth or development in C. roseus root culture. Silencing the biosynthesis of an early starting substrate enhances our ability to harness the rich diversity of plant based natural products.     

Nonuniform distribution of glucosinolates in Arabidopsis thaliana leaves has important consequences for plant defense

The spatial distribution of plant defenses within a leaf may be critical in explaining patterns of herbivory. The generalist lepidopteran larvae, Helicoverpa armigera (the cotton bollworm), avoided the midvein and periphery of Arabidopsis thaliana rosette leaves and fed almost exclusively on the inner lamina. This feeding pattern was attributed to glucosinolates because it was not evident in a myrosinase mutant that lacks the ability to activate glucosinolate defenses by hydrolysis. To measure the spatial distribution of glucosinolates in A. thaliana leaves at a fine scale, we constructed ion intensity maps from MALDI-TOF (matrix assisted laser desorption/ionization-time of flight) mass spectra. The major glucosinolates were found to be more abundant in tissues of the midvein and the periphery of the leaf than the inner lamina, patterns that were validated by HPLC analyses of dissected leaves. In addition, there were differences in the proportions of the three major glucosinolates in different leaf regions. Hence, the distribution of glucosinolates within the leaf appears to control the feeding preference of H. armigera larvae. The preferential allocation of glucosinolates to the periphery may play a key role in the defense of leaves by creating a barrier to the feeding of chewing herbivores that frequently approach leaves from the edge.     

Combinatorial genetic transformation generates a library of metabolic phenotypes for the carotenoid pathway in maize

Combinatorial nuclear transformation is a novel method for the rapid production of multiplex-transgenic plants, which we have used to dissect and modify a complex metabolic pathway. To demonstrate the principle, we transferred 5 carotenogenic genes controlled by different endosperm-specific promoters into a white maize variety deficient for endosperm carotenoid synthesis. We recovered a diverse population of transgenic plants expressing different enzyme combinations and showing distinct metabolic phenotypes that allowed us to identify and complement rate-limiting steps in the pathway and to demonstrate competition between β-carotene hydroxylase and bacterial β-carotene ketolase for substrates in 4 sequential steps of the extended pathway. Importantly, this process allowed us to generate plants with extraordinary levels of β-carotene and other carotenoids, including complex mixtures of hydroxycarotenoids and ketocarotenoids. Combinatorial transformation is a versatile approach that could be used to modify any metabolic pathway and pathways controlling other biochemical, physiological, or developmental processes.     

A molecularly enhanced proof of concept for targeting cocrystals at molecular scale in continuous pharmaceuticals cocrystallization

It is impossible to optimize a process for a target drug product with the desired profile without a proper understanding of the interplay among the material attributes, the process parameters, and the attributes of the drug product. There is a particular need to bridge the micro- and mesoscale events that occur during this process. Here, we propose а molecular engineering methodology for the continuous cocrystallization process, based on Raman spectra measured experimentally with a probe and from quantum mechanical calculations. Using molecular dynamics simulations, the theoretical Raman spectra were calculated from first principles for local mixture structures under an external shear force at various temperatures. A proof of concept is developed to build the process design space from the computed data. We show that the determined process design space provides valuable insight for optimizing the cocrystallization process at the nanoscale, where experimental measurements are difficult and/or inapplicable. The results suggest that our method may be used to target cocrystallization processes at the molecular scale for improved pharmaceutical synthesis.

Many drugs discovered in the past few decades are low in aqueous solubility (1), which is a very important indicator of bioavailability (2). After oral administration, drugs enter the stomach with an acidic aqueous environment, in which most active pharmaceutical ingredients show very poor solubility (3). Among many techniques developed to improve the solubility of drugs (4), cocrystal formation has become very common because it does not negatively impact the drug’s pharmacological properties (5). Besides better bioavailability, cocrystals have improved physicochemical properties including tabletability, stability, and permeability (6). The formed cocrystals usually consist of an active pharmaceutical ingredient and an approved component (known as a coformer) in stoichiometric ratio (7). There is a strong interest in cocrystals because they reduce the time and therefore the cost of drug development (8).Among various developed cocrystallization processes (9, 10), solid-state synthesis is superior due to its high efficiency, low level of by-products, and no need for solvents (6). For continuous processing of pharmaceutical formulations using solid-state synthesis, twin-screw granulation is considered an excellent and promising technology (11, 12) that combines cocrystallization and granulation, with a short residence time and the possibility of conducting chemical reactions (13–15). Unfortunately, this technique is yet to be implemented on an industrial scale (16), essentially due to the lack of micro-/macroscopic insight into the compounds’ behavior and the proper process control strategies to optimize the formulations (17, 18).Many researchers have investigated continuous cocrystallization via twin-screw granulators (19), as reviewed elsewhere (20–23). However, information from the experimental studies tends to be very limited and empirical because those studies often focus on analyzing the individual operating parameters in a trial-and-error approach (24, 25). Other problems with the experimental approach include material cost, implementation and reconfiguration of the twin-screw granulator, training human resources, and time consumption. On the other hand, the most sophisticated theoretical models currently available are practically top-to-bottom approaches and hence require the input of experimentally correlated parameters, such as particle size distribution (26, 27). Therefore, such models fail to bridge the gap between the micro- and mesoscales of continuous cocrystallization processing (28–30). Consequently, it is hardly possible to robustly synthesize an optimization procedure for a drug product to achieve the desired target product profile (31).Solving the above problem requires reliable insight into the interplay of 1) the critical raw material attributes, 2) the critical process parameters, and 3) the drug product’s critical quality attributes. This in turn necessitates models that utilize a bottom-up approach (32, 33) [where the material properties are calculated from scratch using, e.g., density functional theory [DFT] and molecular dynamics [MD] (34)] to establish a process design space without prior experimental information. After producing this design space, a process optimization strategy can be synthesized for any specific operational parameters.Here, we chose the cocrystallization process of ibuprofen (IBF) and nicotinamide (NCTA) for case study. Ibuprofen is a drug widely used to treat pain and fever (2, 35). Since it has very poor solubility in the stomach environment (3, 36), nicotinamide was used as a coformer for cocrystal formation (37, 38) via twin-screw granulator. The cocrystal structure is usually studied through spectroscopic techniques (39–41), mainly Raman spectroscopy (42). Examples include cocrystallization via twin-screw granulation (43) and in aqueous media during slurry conversion (44). Analysis of the Raman spectra can reveal whether interactions between the compounds are chemical or physical in nature (45). However, Raman spectroscopy in this context tends to be used as a tool for a product (end) quality check (46–48). In contrast, in the current study we used signals from the Raman spectrometer equipped on the twin-screw granulator to quantify interactions between compounds throughout the granulator. Depending on the identified interactions, the intensity of a specific interaction affecting the target cocrystal in formulation can be controlled, provided that one knows how to affect the stability and kinetics of that interaction through macroscopic processing parameters (such as the temperature and screw rotation speed) (49). This molecular-level information can bridge the gap between the micro- and mesoscales of continuous cocrystallization processing. Instead of exhaustive empirical experimentation, we determined the process design space from scratch through quantum mechanical methods, resulting in a protocol that requires no experiments, is generic, and can be applied to any system of interest. For the three considered processing parameters (temperature, shear rate as exerted by screw rotation speed, and residency time) in wide practical value ranges, we performed DFT and MD calculations to determine the possible interactions between ibuprofen and nicotinamide, as well as changes in their stability and kinetics. In particular, we calculated the Raman intensities as described by Porezag and Pederson (50). The computed Raman patterns were correlated with the three processing parameters using the proposed proof of concept, resulting in a process design space. This design space was compared for the target interaction, set as input, with the signals from the Raman spectrometer to estimate the proper temperature, shear rate, and residency time and therefore gauge the twin-screw granulator. The following sections discuss our developed approach and its implementation.     

A review of approaches for analysing obstructive sleep apnoea‐related patterns in pulse oximetry data

Overnight pulse oximetry allows the relatively non‐invasive estimation of peripheral blood haemoglobin oxygen saturations (SpO₂), and forms part of the typical polysomnogram (PSG) for investigation of obstructive sleep apnoea (OSA). While the raw SpO₂ signal can provide detailed information about OSA‐related pathophysiology, this information is typically summarized with simple statistics such as the oxygen desaturation index (ODI, number of desaturations per hour). As such, this study reviews the technical methods for quantifying OSA‐related patterns in oximetry data. The technical methods described in literature can be broadly grouped into four categories: (i) Describing the detailed characteristics of desaturations events; (ii) Time series statistics; (iii) Analysis of power spectral distribution (i.e. frequency domain analysis); and (d) Non‐linear analysis. These are described and illustrated with examples of oximetry traces. The utilization of these techniques is then described in two applications. First, the application of detailed oximetry analysis allows the accurate automated classification of PSG‐defined OSA. Second, quantifications which better characterize the severity of desaturation events are better predictors of OSA‐related epidemiological outcomes than standard clinical metrics. Finally, methodological considerations and further applications and opportunities are considered.