Discovery of a Fungal P450 with an Unusual Two-Step Mechanism for Constructing a Bicyclo[3.2.2]nonane Skeleton.

The successful biomimetic or chemoenzymatic synthesis of target natural products (NPs) and their derivatives relies on enzyme discovery. Herein, we discover a fungal P450 BTG5 that can catalyze the formation of a bicyclo[3.2.2]nonane structure through an unusual two-step mechanism of dimerization and cyclization in the biosynthesis of beticolin 1, whose bicyclo[3.2.2]nonane skeleton connects an anthraquinone moiety and a xanthone moiety. Further investigation reveals that BTG5-T318 not only determines the substrate selectivity but also alters the catalytic reactions, which allows the separation of the reaction to two individual steps, thereby understanding its catalytic mechanism. It reveals that the first heterodimerization undergoes the common oxidation process for P450s, while the second uncommon formal redox-neutral cyclization step is proved as a redox-mediated reaction, which has never been reported. Therefore, this work advances our understanding of P450-catalyzed reactions and paves the way for expansion of the diversity of this class of NPs through synthetic biology.

Fluorophore Label-Free Light-up Near Infrared Deoxyribonucleic Acid Nanosensor for Monitoring Extracellular Potassium Levels.

Fluorescent DNA nanosensors have been widely used due to their unique advantages, among which the near-infrared (NIR) imaging mode can provide deeper penetration depth and lower biological background for the nanosensors. However, efficient NIR quenchers require ingenious design, complex synthesis, and modification, which severely limit the development of NIR DNA nanosensors. Label-free strategies based on G-quadruplex (G4) and NIR G4 dyes were first introduced into in situ extracellular imaging, and a novel NIR sensing strategy for the specific detection of extracellular targets is proposed. The strategy avoids complex synthesis and site-specific modification by controlling the change of the NIR signal through the formation of a G4 nanostructure. A light-up NIR DNA nanosensor based on potassium ion (K+)-sensitive G4 chain PS2.M was constructed to verify the strategy. PS2.M forms a stable G4 nanostructure in the presence of K+ and activates the NIR G4 dye CSTS, thus outputting NIR signals. The nanosensor can rapidly respond to K+ with a linear range of 5-50 mM and has good resistance to interference. The nanosensor with cholesterol can provide feedback on the changes in extracellular K+ concentration in many kinds of cells, serving as a potential tool for the study of diseases such as epilepsy and cancer, as well as the development of related drugs. The strategy can be potentially applied to the NIR detection of a variety of extracellular targets with the help of functional DNAs such as aptamer and DNAzyme.

Synthetic Biology-based Construction of Unnatural Perylenequinones with Improved Photodynamic Anticancer Activities.

The construction of structural complexity and diversity of natural products is crucial for drug discovery and development. To overcome high dark toxicity and poor photostability of natural photosensitizer perylenequinones (PQs) for photodynamic therapy, herein, we aim to introduce the structural complexity and diversity to biosynthesize the desired unnatural PQs in fungus Cercospora through synthetic biology-based strategy. Thus, we first elucidate the intricate biosynthetic pathways of class B PQs and reveal how the branching enzymes create their structural complexity and diversity from a common ancestor. This enables the rational reprogramming of cercosporin biosynthetic pathway in Cercospora to generate diverse unnatural PQs without chemical modification. Among them, unnatural cercosporin A displays remarkably low dark toxicity and high photostability with retention of great photodynamic anticancer and antimicrobial activities. Moreover, it is found that, unlike cercosporin, unnatural cercosporin A could be selectively accumulated in cancer cells, providing potential targets for drug development. Therefore, this work provides a comprehensive foundation for preparing unnatural products with customized functions through synthetic biology-based strategies, thus facilitating drug discovery pipelines from nature.

Modular assembly of an artificially concise biocatalytic cascade for the manufacture of phenethylisoquinoline alkaloids.

Plant-derived alkaloids are an important class of pharmaceuticals. However, they still rely on phytoextraction to meet their diverse market demands. Since multistep biocatalytic cascades have begun to revolutionize the manufacture of natural or unnatural products, to address the synthetic challenges of alkaloids, herein we establish an artificially concise four-enzyme biocatalytic cascade with avoiding plant-derived P450 modification for synthesizing phenethylisoquinoline alkaloids (PEIAs) after enzyme discovery and enzyme engineering. Efficient biosynthesis of diverse natural and unnatural PEIAs is realized from readily available substrates. Most importantly, the scale-up preparation of the colchicine precursor (S)-autumnaline with a high titer is achieved after replacing the rate-limiting O-methylation by the plug-and-play strategy. This study not only streamlines future engineering endeavors for colchicine biosynthesis, but also provides a paradigm for constructing more artificial biocatalytic cascades for the manufacture of diverse alkaloids through synthetic biology.

Highly Efficient Biosynthesis of Rebaudioside M8 through Structure-Guided Engineering of Glycosyltransferase UGT94E13.

Given the low-calorie, high-sweetness characteristics of steviol glycosides (SGs), developing SGs with improved taste profiles is a key focus. Rebaudioside M8 (Reb M8), a novel non-natural SG derivative obtained through glycosylation at the C-13 position of rebaudioside D (Reb D) using glycosyltransferase UGT94E13, holds promise for further development due to its enhanced sweetness. However, the low catalytic activity of UGT94E13 hampers further research and commercialization. This study aimed to improve the enzymatic activity of UGT94E13 through semirational design, and a variant UGT94E13-F169G/I185G was obtained with the catalytic activity improved by 13.90 times. A cascade reaction involving UGT94E13-F169G/I185G and sucrose synthase AtSuSy was established to recycle uridine diphosphate glucose, resulting in an efficient preparation of Reb M8 with a yield of 98%. Moreover, according to the analysis of the distances between the substrate Reb D and enzymes as well as between Reb D and the glucose donor through molecular dynamics simulations, it is found that the positive effect of shortening the distance on glycosylation reaction activity accounts for the improved catalytic activity of UGT94E13-F169G/I185G. Therefore, this study addresses the bottleneck in the efficient production of Reb M8 and provides a foundation for its widespread application in the food industry.

[Determination of 12 prohibited veterinary drug residues in pig urine by ultra high performance liquid chromatography-tandem mass spectrometry].

A method was established for the simultaneous detection of 12 prohibited veterinary drugs, including ß2-receptor agonists, nitrofuran metabolites, nitroimidazoles, chlorpromazine, and chloramphenicol, in pig urine. The sample was pretreated by enzymolysis, acid hydrolysis/derivatization, and liquid-liquid extraction combined with solid-phase extraction. Detection was performed using ultra high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). Ammonium acetate solution (0.2 mol/L, 4.5 mL) and ß-glucuronidase/aryl sulfatase (40 µL) were added to the sample, which was subsequently enzymolized at 37 ℃ for 2 h. Then, 1.5 mL of 1.0 mol/L hydrochloric acid solution and 100 µL of 0.1 mol/L o-nitrobenzaldehyde solution were added to the sample. The mixture was incubated at 37 ℃ for 16 h, and the analytes were extracted with 8 mL of ethyl acetate by liquid-liquid extraction. The lower aqueous phase obtained after extraction was extracted and purified using a mixed cation-exchange solid-phase extraction column. The extracts were combined, the extraction solution was blow-dried with nitrogen, and the residue was redissolved for determination. The samples were analyzed under multiple-reaction monitoring mode with both positive and negative electrospray ionization, and quantified using an isotope internal standard method. The correlation coefficients (r) of the 12 compounds were >0.99. The limits of detection (LODs) and quantification (LOQs) of chloramphenicol were 0.05 and 0.1 µg/L, respectively, and the LODs and LOQs of the other compounds were 0.25 and 0.5 µg/L, respectively. The mean recoveries and RSDs at 1, 2, and 10 times the LOQ were 83.6%-115.3% and 2.20%-12.34%, respectively. The proposed method has the advantages of high sensitivity, good stability, and accurate quantification; thus, it is suitable for the simultaneous determination of the 12 prohibited veterinary drug residues in pig urine.

Selective synthesis of rebaudioside M2 through structure-guided engineering of glycosyltransferase UGT94D1.

Rebaudioside M2 (Reb M2), a novel steviol glycoside derivative, has limited industrial applications due to its low synthetic yield and selectivity. Herein, we identify UGT94D1 as a selective glycosyltransferase for rebaudioside D (Reb D), leading to the production of a mono ß-1,6-glycosylated derivative, Reb M2. A variant UGT94D1-F119I/D188P was developed through protein engineering. This mutant exhibited a 6.33-fold improvement in catalytic efficiency, and produced Reb M2 with 92% yield. Moreover, molecular dynamics simulations demonstrated that UGT94D1-F119I/D188P exhibited a shorter distance between the nucleophilic oxygen (OH6) of the substrate Reb D and uridine diphosphate glucose, along with an increased Ophosphate-C1-Oacceptor angle, thus improving the catalytic activity of the enzyme. Therefore, this study provides an efficient method for the selective synthesis of Reb M2 and paves the way for its applications in various fields.

Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report.

Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density data crucial for accurate dose calculations. However, accurately representing patient anatomy is challenging, especially in adaptive radiotherapy, where CT is not acquired daily. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast. Still, it lacks electron density information, while cone beam CT (CBCT) lacks direct electron density calibration and is mainly used for patient positioning. Adopting MRI-only or CBCT-based adaptive radiotherapy eliminates the need for CT planning but presents challenges. Synthetic CT (sCT) generation techniques aim to address these challenges by using image synthesis to bridge the gap between MRI, CBCT, and CT. The SynthRAD2023 challenge was organized to compare synthetic CT generation methods using multi-center ground truth data from 1080 patients, divided into two tasks: (1) MRI-to-CT and (2) CBCT-to-CT. The evaluation included image similarity and dose-based metrics from proton and photon plans. The challenge attracted significant participation, with 617 registrations and 22/17 valid submissions for tasks 1/2. Top-performing teams achieved high structural similarity indices (≥0.87/0.90) and gamma pass rates for photon (≥98.1%/99.0%) and proton (≥97.3%/97.0%) plans. However, no significant correlation was found between image similarity metrics and dose accuracy, emphasizing the need for dose evaluation when assessing the clinical applicability of sCT. SynthRAD2023 facilitated the investigation and benchmarking of sCT generation techniques, providing insights for developing MRI-only and CBCT-based adaptive radiotherapy. It showcased the growing capacity of deep learning to produce high-quality sCT, reducing reliance on conventional CT for treatment planning.

Shape-Aware 3D Small Vessel Segmentation with Local Contrast Guided Attention.

The automated segmentation and analysis of small vessels from in vivo imaging data is an important task for many clinical applications. While current filtering and learning methods have achieved good performance on the segmentation of large vessels, they are sub-optimal for small vessel detection due to their apparent geometric irregularity and weak contrast given the relatively limited resolution of existing imaging techniques. In addition, for supervised learning approaches, the acquisition of accurate pixel-wise annotations in these small vascular regions heavily relies on skilled experts. In this work, we propose a novel self-supervised network to tackle these challenges and improve the detection of small vessels from 3D imaging data. First, our network maximizes a novel shape-aware flux-based measure to enhance the estimation of small vasculature with non-circular and irregular appearances. Then, we develop novel local contrast guided attention(LCA) and enhancement(LCE) modules to boost the vesselness responses of vascular regions of low contrast. In our experiments, we compare with four filtering-based methods and a state-of-the-art self-supervised deep learning method in multiple 3D datasets to demonstrate that our method achieves significant improvement in all datasets. Further analysis and ablation studies have also been performed to assess the contributions of various modules to the improved performance in 3D small vessel segmentation. Our code is available at https://github.com/dengchihwei/LCNetVesselSeg.