Molecular cloning and functional characterization of 2,3-oxidosqualene cyclases from Artemisia argyi.

Artemisia argyi is a traditional medicinal and edible plant, generating various triterpenoids with pharmacological activities, such as anti-virus, anti-cancer, and anti-oxidant. The 2,3-oxidosqualene cyclase family of A. argyi offers novel insights into the triterpenoid pathway, which might contribute to the medicinal value of its tissue extracts. Nevertheless, the biosynthesis of active triterpenoids in Artemisia argyi is still uncertain. In this study, four putative OSC (2,3-oxidosqualene cyclase) genes (AaOSC1-4) were first isolated and identified from A. argyi. Through the yeast heterologous expression system, three AaOSCs were characterized for the biosynthesis of diverse triterpenoids including cycloartenol, ß-amyrin, (3S,13R)-malabarica-14(27),17,21-trien-3ß-ol, and dammara-20,24-dien-3ß-ol. AaOSC1 was a multifunctional dammara-20,24-dien-3ß-ol synthase, which yielded 8 different triterpenoids, including tricyclic, and tetracyclic products. AaOSC2 and AaOSC3 were cycloartenol, and ß-amyrin synthases, respectively. As a result, these findings provide a deeper understanding of the biosynthesis pathway of triterpenes in A. argyi.

Metabolic engineering of Saccharomyces cerevisiae for high-level production of (+)-ambrein from glucose.

(+)-Ambrein is the primary component of ambergris, a rare product found in sperm whales (Physeter microcephalus). Microbial production using sustainable resources is a promising way to replace animal extraction and chemical synthesis. We constructed an engineered yeast strain to produce (+)-ambrein de novo. Squalene is a substrate for the biosynthesis of (+)-ambrein. Firstly, strain LQ2, with a squalene yield of 384.4 mg/L was obtained by optimizing the mevalonate pathway. Then we engineered a method for the de novo production of (+)-ambrein using glucose as a carbon source by overexpressing codon-optimized tetraprenyl-ß-curcumene cyclase (BmeTC) and its double mutant enzyme (BmeTCY167A/D373C), evaluating different promoters, knocking out GAL80, and fusing the protein with BmeTC and squalene synthase (AtSQS2). Nevertheless, the synthesis of (+)-ambrein is still limited, causing low catalytic activity in BmeTC. We carried out a protein surface amino acid modification of BmeTC. The dominant mutant BmeTCK6A/Q9E/N454A for the first step was obtained to improve its catalytic activity. The yield of (+)-ambrein increased from 35.2 to 59.0 mg/L in the shake flask and finally reached 457.4 mg/L in the 2 L fermenter, the highest titer currently available for yeast. Efficiently engineered strains and inexpensive fermentation conditions for the industrial production of (+)-ambrein. The metabolic engineering tools provide directions for optimizing the biosynthesis of other high-value triterpenes.

Molecular recognition between volatile molecules and odorant binding proteins 7 by homology modeling, molecular docking and molecular dynamics simulation.

BACKGROUND: Odorant-binding proteins (OBPs) in insects are key to detection and recognition of external chemical signals associated with survival. OBP7 in Spodoptera frugiperda's larval stage (SfruOBP7) may search for host plants by sensing plant volatiles, which are important sources of pest attractants and repellents. However, the atomic-level basis of binding modes remains elusive. RESULTS: SfruOBP7 structure was constructed through homology modeling, and complex models of six plant volatiles ((E)-2-hexenol, α-pinene, (Z)-3-hexenyl acetate, lauric acid, O-cymene and 1-octanol) and SfruOBP7 were obtained through molecular docking. To study the detailed interactions between the six plant volatile molecules and SfruOBP7, we conducted three 300 ns molecular dynamics simulations for each study object. The correlation coefficients between binding free energy obtained by molecular mechanics/generalized Born surface area together with solvated interaction energy methods and experimental values are 0.90 and 0.88, respectively, showing a good correlation. By comparing binding free energy along with interaction patterns between SfruOBP7 and the six volatile molecules, hotspot residues of SfruOBP7 when binding with different volatile molecules were determined. Hydrophobic interactions stemming from van der Waals interactions play a significant role in SfruOBP7 and these plant volatile systems. CONCLUSION: The optimized three-dimensional structure of SfruOBP7 and its binding modes with six plant volatiles revealed their interactions, thus providing a means for estimating the binding energies of other plant volatiles. Our study will help to guide the rational design of effective and selective insect attractants. © 2024 Society of Chemical Industry.

Adjusting Catalytic Activity of ß-Amyrin Synthase GgBAS by Utilizing the Plasticity Residues of an Active Site.

ß-Amyrin synthase (bAS) is a representative plant oxidosqualene cyclase (OSC), and previous studies have identified many functional residues and mutants that can alter its catalytic activity. However, the regulatory mechanism of the active site architecture for adjusting the catalytic activity remains unclear. In this study, we investigate the function of key residues and their regulatory effects on the catalytic activity of Glycyrrhiza glabra ß-amyrin synthase (GgbAS) through molecular dynamics simulations and site-directed mutagenesis experiments. We identified the plasticity residues located in two active site regions and explored the interactions between these residues and tetracyclic/pentacyclic intermediates. Based on computational and experimental results, we further categorize these plasticity residues into three types: effector, adjuster, and supporter residues, according to their functions in the catalytic process. This study provides valuable insights into the catalytic mechanism and active site plasticity of GgbAS, offering important references for the rational enzyme engineering of other OSC enzyme.

Calibrate the Inter-observer Segmentation Uncertainty via Diagnosis-first Principle.

Many of the tissues/lesions in the medical images may be ambiguous. Therefore, medical segmentation is typically annotated by a group of clinical experts to mitigate personal bias. A common solution to fuse different annotations is the majority vote, e.g., taking the average of multiple labels. However, such a strategy ignores the difference between the grader expertness. Inspired by the observation that medical image segmentation is usually used to assist the disease diagnosis in clinical practice, we propose the diagnosis-first principle, which is to take disease diagnosis as the criterion to calibrate the inter-observer segmentation uncertainty. Following this idea, a framework named Diagnosis-First segmentation Framework (DiFF) is proposed. Specifically, DiFF will first learn to fuse the multi-rater segmentation labels to a single ground-truth which could maximize the disease diagnosis performance. We dubbed the fused ground-truth as Diagnosis-First Ground-truth (DF-GT). Then, the Take and Give Model (T&G Model) to segment DF-GT from the raw image is proposed. With the T&G Model, DiFF can learn the segmentation with the calibrated uncertainty that facilitate the disease diagnosis. We verify the effectiveness of DiFF on three different medical segmentation tasks: optic-disc/optic-cup (OD/OC) segmentation on fundus images, thyroid nodule segmentation on ultrasound images, and skin lesion segmentation on dermoscopic images. Experimental results show that the proposed DiFF can effectively calibrate the segmentation uncertainty, and thus significantly facilitate the corresponding disease diagnosis, which outperforms previous state-of-the-art multi-rater learning methods.

Machine learning in predicting T-score in the Oxford classification system of IgA nephropathy.

Background: Immunoglobulin A nephropathy (IgAN) is one of the leading causes of end-stage kidney disease (ESKD). Many studies have shown the significance of pathological manifestations in predicting the outcome of patients with IgAN, especially T-score of Oxford classification. Evaluating prognosis may be hampered in patients without renal biopsy. Methods: A baseline dataset of 690 patients with IgAN and an independent follow-up dataset of 1,168 patients were used as training and testing sets to develop the pathology T-score prediction (T pre) model based on the stacking algorithm, respectively. The 5-year ESKD prediction models using clinical variables (base model), clinical variables and real pathological T-score (base model plus T bio), and clinical variables and T pre (base model plus T pre) were developed separately in 1,168 patients with regular follow-up to evaluate whether T pre could assist in predicting ESKD. In addition, an external validation set consisting of 355 patients was used to evaluate the performance of the 5-year ESKD prediction model using T pre. Results: The features selected by AUCRF for the T pre model included age, systolic arterial pressure, diastolic arterial pressure, proteinuria, eGFR, serum IgA, and uric acid. The AUC of the T pre was 0.82 (95% CI: 0.80-0.85) in an independent testing set. For the 5-year ESKD prediction model, the AUC of the base model was 0.86 (95% CI: 0.75-0.97). When the T bio was added to the base model, there was an increase in AUC [from 0.86 (95% CI: 0.75-0.97) to 0.92 (95% CI: 0.85-0.98); P = 0.03]. There was no difference in AUC between the base model plus T pre and the base model plus T bio [0.90 (95% CI: 0.82-0.99) vs. 0.92 (95% CI: 0.85-0.98), P = 0.52]. The AUC of the 5-year ESKD prediction model using T pre was 0.93 (95% CI: 0.87-0.99) in the external validation set. Conclusion: A pathology T-score prediction (T pre) model using routine clinical characteristics was constructed, which could predict the pathological severity and assist clinicians to predict the prognosis of IgAN patients lacking kidney pathology scores.

Noisy Label Learning With Provable Consistency for a Wider Family of Losses.

Deep models have achieved state-of-the-art performance on a broad range of visual recognition tasks. Nevertheless, the generalization ability of deep models is seriously affected by noisy labels. Though deep learning packages have different losses, this is not transparent for users to choose consistent losses. This paper addresses the problem of how to use abundant loss functions designed for the traditional classification problem in the presence of label noise. We present a dynamic label learning (DLL) algorithm for noisy label learning and then prove that any surrogate loss function can be used for classification with noisy labels by using our proposed algorithm, with a consistency guarantee that the label noise does not ultimately hinder the search for the optimal classifier of the noise-free sample. In addition, we provide a depth theoretical analysis of our algorithm to verify the justifies' correctness and explain the powerful robustness. Finally, experimental results on synthetic and real datasets confirm the efficiency of our algorithm and the correctness of our justifies and show that our proposed algorithm significantly outperforms or is comparable to current state-of-the-art counterparts.

Phloroglucinol derivatives, coumarins and an alkaloid from the roots of Evodia lepta Merr.

Two previously undescribed phloroglucinol derivatives [(±) evolephloroglucinols A and B], five unusual coumarins [evolecoumarins A and B and (±) evolecoumarins C-E], and one novel enantiomeric quinoline-type alkaloid [(±) evolealkaloid A], along with 20 known compounds, were isolated from the EtOH extract of the roots of Evodia lepta Merr. Their structures were elucidated by extensive spectroscopic analyses. The absolute configurations of the undescribed compounds were determined by X-ray diffraction or computational calculations. Their anti-neuroinflammatory effects were assayed. Among the identified compounds, compound 5a effectively reduced nitric oxide (NO) production with an EC50 value of 22.08 ± 0.46 µM. Hence, it could indeed inhibit the lipopolysaccharide (LPS)-induced Nod-like receptor family, pyrin domain containing 3 (NLRP3) inflammasome.

Functional characterization of triterpene synthases in Cibotium barometz.

Cibotium barometz (Linn.) J. Sm., a tree fern in the Dicksoniaceae family, is an economically important industrial exported plant in China and widely used in Traditional Chinese Medicine. C. barometz produces a range of bioactive triterpenes and their metabolites. However, the biosynthetic pathway of triterpenes in C. barometz remains unknown. To clarify the origin of diverse triterpenes in C. barometz, we conducted de novo transcriptome sequencing and analysis of C. barometz rhizomes and leaves to identify the candidate genes involved in C. barometz triterpene biosynthesis. Three C. barometz triterpene synthases (CbTSs) candidate genes were obtained. All of them were highly expressed in C. barometz rhizomes, consisting of the accumulation pattern of triterpenes in C. barometz. To characterize the function of these CbTSs, we constructed a squalene- and oxidosqualene-overproducing yeast chassis by overexpressing all the enzymes in the MVA pathway under the control of GAL-regulated promoter and disrupted the GAL80 gene in Saccharomyces cerevisiae simultaneously. Heterologous expressing CbTS1, CbTS2, and CbTS3 in engineering yeast strain produced cycloartenol, dammaradiene, and diploptene, respectively. Phylogenetic analysis revealed that CbTS1 belongs to oxidosqualene cyclase, while CbTS2 and CbTS3 belong to squalene cyclase. These results decipher enzymatic mechanisms underlying the origin of diverse triterpene in C. barometz.

Discovering a uniform functional trade-off of the CBC-type 2,3-oxidosqualene cyclases and deciphering its chemical logic.

Many functionally promiscuous plant 2,3-oxidosqualene cyclases (OSCs) have been found, but complete functional reshaping is rarely reported. In this study, we have identified two new plant OSCs: a unique protostadienol synthase (AoPDS) and a common cycloartenol synthase (AoCAS) from Alisma orientale (Sam.) Juzep. Multiscale simulations and mutagenesis experiments revealed that threonine-727 is an essential residue responsible for protosta-13 (17),24-dienol biosynthesis in AoPDS and that the F726T mutant completely reshapes the native function of AoCAS into a PDS function to yield almost exclusively protosta-13 (17),24-dienol. Unexpectedly, various native functions were uniformly reshaped into a PDS function by introducing the phenylalanine → threonine substitution at this conserved position in other plant and non-plant chair-boat-chair-type OSCs. Further computational modeling elaborated the trade-off mechanisms of the phenylalanine → threonine substitution that leads to the PDS activity. This study demonstrates a general strategy for functional reshaping by using a plastic residue based on the decipherment of the catalytic mechanism.

Characterization of a Group of 2,3-Oxidosqualene Cyclase Genes Involved in the Biosynthesis of Diverse Triterpenoids of Perilla frutescens.

Perilla frutescens (L.), a traditional edible and medicinal crop, contains diverse triterpenes with multiple pharmacological properties. However, the biosynthesis of triterpenes in perilla remains rarely revelation. In this study, nine putative 2,3-oxidosqualene cyclase (OSC) genes (PfOSC1-9) were screened from the P. frutescens genome and functionally characterized by heterologous expression. Camelliol C, a triterpenol with pharmacological effect, was first identified as abundant in perilla seeds, and the camelliol C synthase (PfOSC7) was first identified in P. frutescens utilizing a yeast system. In addition, PfOSC2, PfOSC4, and PfOSC9 were identified as cycloartenol, lupeol, and ß-amyrin synthase, respectively. Molecular docking and site-directed mutagenesis revealed that changes in Leu253 of PfOSC4, Ala480 of PfOSC7, and Trp257 of PfOSC9 might lead to variations of catalytic specificity or efficiency. These results will provide key insights into the biosynthetic pathways of triterpenoids and have great significance for germplasm breeding in P. frutescens.

Open Set Domain Adaptation With Soft Unknown-Class Rejection.

The goal of domain adaptation (DA) is to train a good model for a target domain, with a large amount of labeled data in a source domain but only limited labeled data in the target domain. Conventional closed set domain adaptation (CSDA) assumes source and target label spaces are the same. However, this is not quite practical in real-world applications. In this work, we study the problem of open set domain adaptation (OSDA), which only requires the target label space to partially overlap with the source label space. Consequently, the solution to OSDA requires unknown classes detection and separation, which is normally achieved by introducing a threshold for the prediction of target unknown classes; however, the performance can be quite sensitive to that threshold. In this article, we tackle the above issues by proposing a novel OSDA method to perform soft rejection of unknown target classes and simultaneously match the source and target domains. Extensive experiments on three standard datasets validate the effectiveness of the proposed method over the state-of-the-art competitors.

Reproducibility of deep learning based scleral spur localisation and anterior chamber angle measurements from anterior segment optical coherence tomography images.

AIMS: To apply a deep learning model for automatic localisation of the scleral spur (SS) in anterior segment optical coherence tomography (AS-OCT) images and compare the reproducibility of anterior chamber angle (ACA) width between deep learning located SS (DLLSS) and manually plotted SS (MPSS). METHODS: In this multicentre, cross-sectional study, a test dataset comprising 5166 AS-OCT images from 287 eyes (116 healthy eyes with open angles and 171 eyes with primary angle-closure disease (PACD)) of 287 subjects were recruited from four ophthalmology clinics. Each eye was imaged twice by a swept-source AS-OCT (CASIA2, Tomey, Nagoya, Japan) in the same visit and one eye of each patient was randomly selected for measurements of ACA. The agreement between DLLSS and MPSS was assessed using the Euclidean distance (ED). The angle opening distance (AOD) of 750 µm (AOD750) and trabecular-iris space area (TISA) of 750 µm (TISA750) were calculated using the CASIA2 embedded software. The repeatability of ACA width was measured. RESULTS: The mean age was 60.8±12.3 years (range: 30-85 years) for the normal group and 63.4±10.6 years (range: 40-91 years) for the PACD group. The mean difference in ED for SS localisation between DLLSS and MPSS was 66.50±20.54 µm and 84.78±28.33 µm for the normal group and the PACD group, respectively. The span of 95% limits of agreement between DLLSS and MPSS was 0.064 mm for AOD750 and 0.034 mm2 for TISA750. The respective repeatability coefficients of AOD750 and TISA750 were 0.049 mm and 0.026 mm2 for DLLSS, and 0.058 mm and 0.030 mm2 for MPSS. CONCLUSION: DLLSS achieved comparable repeatability compared with MPSS for measurement of ACA.

Integration of artificial intelligence and multi-omics in kidney diseases.

Kidney disease is a leading cause of death worldwide. Currently, the diagnosis of kidney diseases and the grading of their severity are mainly based on clinical features, which do not reveal the underlying molecular pathways. More recent surge of ∼omics studies has greatly catalyzed disease research. The advent of artificial intelligence (AI) has opened the avenue for the efficient integration and interpretation of big datasets for discovering clinically actionable knowledge. This review discusses how AI and multi-omics can be applied and integrated, to offer opportunities to develop novel diagnostic and therapeutic means in kidney diseases. The combination of new technology and novel analysis pipelines can lead to breakthroughs in expanding our understanding of disease pathogenesis, shedding new light on biomarkers and disease classification, as well as providing possibilities of precise treatment.

Genomic characterization of bZIP transcription factors related to andrographolide biosynthesis in Andrographis paniculata.

The basic leucine zipper (bZIP) transcription factor family plays an important role in various biological processes in plants. Andrographis paniculata (Burm.f) Nees, belonging to the family Acanthaceae, has been widely used as an important traditional herb with a wide range of pharmacological activities, such as antivenom, antiretroviral, anticancer and so on. However, there was no comprehensive analysis of bZIP gene family in the Andrographis paniculata been reported. In this study, we identified 62 bZIPs in Andrographis paniculata and grouped them into 12 subfamilies through the phylogenetic tree analysis. The bZIPs in the same groups have similar motif composition, exon-intron structure and domain distribution. In addition, the RNA-seq data gave a reference for selecting candidate bZIPs to make further function verification. Lastly, qRT-PCR analyses revealed seven ApbZIPs (ApbZIP4, ApbZIP19, ApbZIP30, ApbZIP42, ApbZIP50, ApbZIP52, ApbZIP62) were the most highly expressed in leaf and significantly up-regulated with MeJA and ABA treatment which may be involved in biosynthesis regulation of andrographolide. These data pave the way for further revealing the function of the bZIPs in Andrographis paniculata.

Instantaneous High-Resolution Visual Imaging of Latent Fingerprints in Water Using Color-Tunable AIE Pincer Complexes.

Instant visualization of latent fingerprints is developed by using a series of water-soluble terpyridine zinc complexes as aggregation-induced emission probes in pure water, under UV light or ambient sunlight. By simply soaking, or spraying with an aqueous solution of the probe, bright yellow fluorescence images with high contrast and resolution are readily developed on various surfaces including tinfoil, glass, paper, steel, leather, and ceramic tile. Remarkably, latent fingerprints can be visualized within seconds including details of whorl and sweat pores. The color of emission can be tuned from blue to orange by modifying the pincer ligands, allowing direct imaging under sunlight. These inexpensive, water-resistant, and color-tunable probes provide a practical approach for latent fingerprints recording and analysis, security protection, as well as criminal investigation in different scenarios.

Oxidosqualene Cyclases Involved in the Biosynthesis of Diverse Triterpenes in Camellia sasanqua.

Camellia sasanqua is an important economic plant that is rich in lipophilic triterpenols with pharmacological activities including antiallergic, anti-inflammatory, and anticancer activities. However, the key enzymes related to triterpene biosynthesis have seldom been studied in C. sasanqua. Oxidosqualene cyclases (OSCs) are the rate-limiting enzymes related to triterpene biosynthesis. In this study, seven putative OSC genes (CsOSC1-7) were mined from the C. sasanqua transcriptome. Six CsOSCs were characterized for the biosynthesis of diverse triterpene skeletons, including α-amyrin, ß-amyrin, δ-amyrin, dammarenediol-II, ψ-taraxasterol, taraxasterol, and cycloartenol by the heterologous expression system. CsOSC3 was a multiple functional α-amyrin synthase. Three key residues, Trp260, Tyr262, and Phe415, are critical to the catalytic performance of CsOSC3 judging from the results of molecular docking and site-directed mutagenesis. These findings provide important insights into the biosynthesis pathway of triterpenes in C. sasanqua.

Transcriptome Analysis and Identification of the Cholesterol Side Chain Cleavage Enzyme BbgCYP11A1 From Bufo bufo gargarizans.

Bufo bufo gargarizans Cantor are precious medicinal animals in traditional Chinese medicine (TCM). Bufadienolides as the major pharmacological components are generated from the venomous glands of B. bufo gargarizans. Bufadienolides are one type of cardiac aglycone with a six-member lactone ring and have properties of antitumor, cardiotonic, tonsillitis, and anti-inflammatory. The biosynthesis of bufadienolides is complex and unclear. This study explored the transcriptome of three different tissues (skin glands, venom glands, and muscles) of B. bufo gargarizans by high-throughput sequencing. According to the gene tissue-specific expression profile, 389 candidate genes were predicted possibly participating in the bufadienolides biosynthesis pathway. Then, BbgCYP11A1 was identified as a cholesterol side chain cleaving the enzyme in engineering yeast producing cholesterol. Furthermore, the catalytic activity of BbgCYP11A1 was studied with various redox partners. Interestingly, a plant NADPH-cytochrome P450 reductase (CPR) from Anemarrhena asphodeloides showed notably higher production than BbgAdx-2A-BbgAdR from B. bufo gargarizans. These results will provide certainly molecular research to reveal the bufadienolides biosynthesis pathway in B. bufo gargarizans.

Six C21 steroidal glycosides from Cynanchum wallichii Wight roots and their multidrug resistance reversal activities.

Six unidentified C21 steroidal glycosides, cynwallosides A-F, as well as twenty-two known compounds, were isolated from the roots of Cynanchum wallichii Wight. The structures of cynwallosides A-F were determined by spectroscopic analysis and acidic hydrolysis. Most of these twenty-eight compounds were found to significantly reverse drug resistance in both the MCF-7/ADR and HepG2/ADM cell lines by suppressing P-gp protein expression. Further investigation revealed that three compounds suppressed P-gp expression by significantly inactivating the JNK and NF-κB pathways.

Feature Re-Representation and Reliable Pseudo Label Retraining for Cross-Domain Semantic Segmentation.

This paper presents a novel unsupervised domain adaptation method for semantic segmentation. We argue that a good representation of the target-domain data should keep both the knowledge from the source domain and the target-domain-specific information. To obtain the knowledge from the source domain, we first learn a set of bases to characterize the feature distribution of the source domain, then features from both the source and the target domain are re-represented as a weighted summation of the source bases. A discriminator is additionally introduced to make the re-representation responsibilities of both domain features under the same bases indistinguishable. In this way, the domain gap between the source re-representation and target re-representation is minimized, and the re-represented target domain features contain the source domain information. Then we combine the feature re-representation with the original domain-specific feature together for subsequent pixel-wise classification. To further make the re-represented target features semantically meaningful, a Reliable Pseudo Label Retraining (RPLR) strategy is proposed, which utilizes the consistency of the prediction by the networks trained with multi-view source images to select the clean pseudo labels on unlabeled target images for re-training. Extensive experiments demonstrate the competitive performance of our approach for unsupervised domain adaptation on the semantic segmentation benchmarks.