Large-volume fully automated cell reconstruction generates a cell atlas of plant tissues.

The geometric shape and arrangement of individual cells play a role in shaping organ functions. However, analyzing multicellular features and exploring their connectomes in centimeter-scale plant organs remain challenging. Here, we established a set of frameworks named Large-Volume Fully Automated Cell Reconstruction (LVACR), enabling the exploration of three-dimensional (3D) cytological features and cellular connectivity in plant tissues. Through benchmark testing, our framework demonstrated superior efficiency in cell segmentation and aggregation, successfully addressing the inherent challenges posed by light sheet fluorescence microscopy (LSFM) imaging. Using LVACR, we successfully established a cell atlas of different plant tissues. Cellular morphology analysis revealed differences of cell clusters and shapes in between different poplar (P. simonii Carr. and P. canadensis Moench.) seeds, whereas topological analysis revealed that they maintained conserved cellular connectivity. Furthermore, LVACR spatiotemporally demonstrated an initial burst of cell proliferation, accompanied by morphological transformations at an early stage in developing the shoot apical meristem. During subsequent development, cell differentiation produced anisotropic features, thereby resulting in various cell shapes. Overall, our findings provided valuable insights into the precise spatial arrangement and cellular behavior of multicellular organisms, thus enhancing our understanding of the complex processes underlying plant growth and differentiation.

TRAF2 decrease promotes the TGF-ß-mTORC1 signal in MAFLD-HCC through enhancing AXIN1-mediated Smad7 degradation.

According to recent research, metabolic-associated fatty liver disease (MAFLD) has emerged as an important underlying etiology of hepatocellular carcinoma (HCC). However, the molecular mechanism of MAFLD-HCC is still unclear. Tumor necrosis factor receptor-associated factor 2 (TRAF2) is the key molecule to mediate the signal of inflammatory NF-κB pathway. This study aims to investigate the potential dysregulation of TRAF2 and its biological function in MAFLD-HCC. Huh7 TRAF2-/- demonstrated increased tumor formation ability compared to huh7 TRAF2+/+ when stimulated with transforming growth factor-ß (TGF-ß). The decisive role of TGF-ß in the development of MAFLD-HCC was confirmed through the specific depletion of TGF-ß receptor II gene in the hepatocytes (Tgfbr2ΔHep) of mice. In TRAF2-/- cells treated with TGF-ß, both the glycolysis rate and lipid synthesis were enhanced. We proved the signal of the mechanistic target of rapamycin complex 1 (mTORC1) could be activated in the presence of TGF-ß, and was enhanced in TRAF2-/- cells. The coimmunoprecipitation (co-IP) experiments revealed that TRAF2 fortified the Smurf2-mediated ubiquitination degradation of AXIN1. Hence, TRAF2 depletion resulted in increased Smad7 degradation induced by AXIN1, thus promoting the TGF-ß signal. We also discovered that PLX-4720 could bind with AXIN1 and restrained the tumor proliferation of TRAF2-/- in mice fed with high-fat diet (HFD). Our findings indicate that TRAF2 plays a significant role in the pathogenesis of MAFLD-HCC. The reduction of TRAF2 expression leads to the enhancement of the TGF-ß-mTORC1 pathway by facilitating AXIN1-mediated Smad7 degradation.

NMR-Guided Isolation of Anti-inflammatory Carabranolides from the Fruits of Carpesium abrotanoides L.

Carabranolides present characteristic NMR resonances for the cyclopropane moiety, which distinctly differ from those of other compounds and were used for an NMR-guided isolation in this study. As a result, 11 undescribed carabranolides (1-11), along with five known ones (12-16), were isolated from the fruits of Carpesium abrotanoides L. Compounds 1-11 are new esters of carabrol at C-4 with different carboxylic acids. Their structures were elucidated by HRESIMS and NMR spectroscopic data analysis. The biological evaluation showed that compounds 2-4, 15, and 16 exhibited significant inhibitory activity against LPS-induced NO release with an IC50 value of 5.6-9.1 µM and dose-dependently decreased iNOS protein expression in RAW264.7 cells.

Three-dimensional reconstruction of rat sperm using volume electron microscopy.

Three-dimensional (3D) reconstruction serves as a crucial instrument for the analysis of biological structures. In particular, a comprehensive and accurate 3D ultrastructural examination of rat sperm is vital for understanding and diagnosing male fertility issues and the underlying causes of infertility. In this study, we utilize the automated tape-collecting ultramicrotome scanning electron microscopy (ATUM-SEM) imaging technique, which is a highly effective method for 3D cellular ultrastructural analysis. Our findings reveal that during spermiogenesis, the volume of the nucleus significantly decreases, shrinking to just 10% of its original size. The acrosomal vesicles derived from the Golgi apparatus converge and elongate along the spermatid nucleus. These vesicles then attach to the nucleus via a cap-like structure, thereby defining the head side of the spermatozoa. In the initial stages of spermiogenesis, the mitochondria in spermatids are distributed beneath the cell membrane. As the process progresses, these mitochondria gradually migrate to the sperm tail, where they form the mitochondrial sheath. This sheath plays a crucial role in providing the energy required for the movement of the sperm. In addition, we reconstruct the mRNA-stroring structure-chromatoid body in sperm cells, which are cloud-like or net-like structures in the cytoplasm. The precise and comprehensive nature of 3D ultrastructural examination allows for a deeper understanding of the morphological process of spermiogenesis, thereby contributing to our knowledge of male fertility and the causes of infertility. Our research has significantly advanced the understanding of the 3D ultrastructure of sperm more comprehensively than ever before.

PMSNet: Multiscale Partial-Discharge Signal Feature Recognition Model via a Spatial Interaction Attention Mechanism.

Partial discharge (PD) is a localized discharge phenomenon in the insulator of electrical equipment resulting from the electric field strength exceeding the local dielectric breakdown electric field. Partial-discharge signal identification is an important means of assessing the insulation status of electrical equipment and critical to the safe operation of electrical equipment. The identification effect of traditional methods is not ideal because the PD signal collected is subject to strong noise interference. To overcome noise interference, quickly and accurately identify PD signals, and eliminate potential safety hazards, this study proposes a PD signal identification method based on multiscale feature fusion. The method improves identification efficiency through the multiscale feature fusion and feature aggregation of phase-resolved partial-discharge (PRPD) diagrams by using PMSNet. The whole network consists of three parts: a CNN backbone composed of a multiscale feature fusion pyramid, a down-sampling feature enhancement (DSFB) module for each layer of the pyramid to acquire features from different layers, a Transformer encoder module dominated by a spatial interaction-attention mechanism to enhance subspace feature interactions, a final categorized feature recognition method for the PRPD maps and a final classification feature generation module (F-Collect). PMSNet improves recognition accuracy by 10% compared with traditional high-frequency current detection methods and current pulse detection methods. On the PRPD dataset, the validation accuracy of PMSNet is above 80%, the validation loss is about 0.3%, and the training accuracy exceeds 85%. Experimental results show that the use of PMSNet can greatly improve the recognition accuracy and robustness of PD signals and has good practicality and application prospects.

A new perspective on hippocampal synaptic plasticity and post-stroke depression.

Post-stroke depression, a common complication after stroke, severely affects the recovery and quality of life of patients with stroke. Owing to its complex mechanisms, post-stroke depression treatment remains highly challenging. Hippocampal synaptic plasticity is one of the key factors leading to post-stroke depression; however, the precise molecular mechanisms remain unclear. Numerous studies have found that neurotrophic factors, protein kinases and neurotransmitters influence depressive behaviour by modulating hippocampal synaptic plasticity. This review further elaborates on the role of hippocampal synaptic plasticity in post-stroke depression by summarizing recent research and analysing possible molecular mechanisms. Evidence for the correlation between hippocampal mechanisms and post-stroke depression helps to better understand the pathological process of post-stroke depression and improve its treatment.

Di- and Triterpenoids from the Rhizomes of Isodon amethystoides and Their Anti-inflammatory Activities.

Amethystoidesic acid (1), a triterpenoid with an unprecedented 5/6/6/6 tetracyclic skeleton, and six undescribed diterpenoids, amethystoidins A-F (2-7), were isolated from the rhizomes of Isodon amethystoides along with 31 known di- and triterpenoids (8-38). Their structures were fully elucidated via extensive spectroscopic analysis including 1D and 2D NMR, high-resolution electrospray ionization mass spectrometry (HRESIMS), and electronic circular dichroism (ECD) calculations. Compound 1 is the first example of a triterpenoid possessing a rare ring system (5/6/6/6) derived from a contracted A-ring and the 18,19-seco-E-ring of ursolic acid. Compounds 6, 16, 21, 22, 24, and 27 significantly inhibited nitric oxide (NO) production in lipopolysaccharide (LPS)-stimulated RAW264.7 cells, which could be partly mediated by the downregulation of LPS-induced inducible nitric oxide synthase (iNOS) protein expression.

Joint reconstruction of neuron and ultrastructure via connectivity consensus in electron microscope volumes.

BACKGROUND: Nanoscale connectomics, which aims to map the fine connections between neurons with synaptic-level detail, has attracted increasing attention in recent years. Currently, the automated reconstruction algorithms in electron microscope volumes are in great demand. Most existing reconstruction methodologies for cellular and subcellular structures are independent, and exploring the inter-relationships between structures will contribute to image analysis. The primary goal of this research is to construct a joint optimization framework to improve the accuracy and efficiency of neural structure reconstruction algorithms. RESULTS: In this investigation, we introduce the concept of connectivity consensus between cellular and subcellular structures based on biological domain knowledge for neural structure agglomeration problems. We propose a joint graph partitioning model for solving ultrastructural and neuronal connections to overcome the limitations of connectivity cues at different levels. The advantage of the optimization model is the simultaneous reconstruction of multiple structures in one optimization step. The experimental results on several public datasets demonstrate that the joint optimization model outperforms existing hierarchical agglomeration algorithms. CONCLUSIONS: We present a joint optimization model by connectivity consensus to solve the neural structure agglomeration problem and demonstrate its superiority to existing methods. The intention of introducing connectivity consensus between different structures is to build a suitable optimization model that makes the reconstruction goals more consistent with biological plausible and domain knowledge. This idea can inspire other researchers to optimize existing reconstruction algorithms and other areas of biological data analysis.

Discovery of honokiol thioethers containing 1,3,4-oxadiazole moieties as potential α-glucosidase and SARS-CoV-2 entry inhibitors.

Honokiol, isolated from a traditional Chinese medicine (TCM) Magnolia officinalis, is a biphenolic compound with several biological activities. To improve and broaden its biological activity, herein, two series of honokiol thioethers bearing 1,3,4-oxadiazole moieties were prepared and assessed for their α-glucosidase and SARS-CoV-2 entry inhibitory activities. Among all the honokiol thioethers, compound 7l exhibited the strongest α-glucosidase inhibitory effect with an IC50 value of 18.9 ± 2.3 µM, which was superior to the reference drug acarbose (IC50 = 24.4 ± 0.3 µM). Some interesting results of structure-activity relationships (SARs) have also been discussed. Enzyme kinetic study demonstrated that 7l was a noncompetitive α-glucosidase inhibitor, which was further supported by the results of molecular docking. Moreover, honokiol thioethers 7e, 9a, 9e, and 9r exhibited potent antiviral activity against SARS-CoV-2 pseudovirus entering into HEK-293 T-ACE2h. Especially 9a displayed the strongest inhibitory activity against SARS-CoV-2 pseudovirus entry with an IC50 value of 16.96 ± 2.45 µM, which was lower than the positive control Evans blue (21.98 ± 1.98 µM). Biolayer interferometry (BLI) binding and docking studies suggested that 9a and 9r may effectively block the binding of SARS-CoV-2 to the host ACE2 receptor through dual recognition of SARS-CoV-2 spike RBD and human ACE2. Additionally, the potent honokiol thioethers 7l, 9a, and 9r displayed relatively no cytotoxicity to normal cells (LO2). These findings will provide a theoretical basis for the discovery of honokiol derivatives as potential both α-glucosidase and SARS-CoV-2 entry inhibitors.

Novel 1,3,4-thiadiazole/oxadiazole-linked honokiol derivatives suppress cancer via inducing PI3K/Akt/mTOR-dependent autophagy.

Honokiol is a bioactive biphenolic component derived from Magnoliae officinalis Cortex (known as "Hou Po" in Chinese), a traditional Chinese herbal medicine. A series of novel 1,3,4-thiadiazole/oxadiazole-linked honokiol derivatives were synthesized and tested for anticancer activity against seven human cancer cell lines in this study. Among all derivatives, 8a had the most potent cytotoxic effect on all tested cancer cells, with IC50 values ranging from 1.62 ± 0.19 to 4.61 ± 0.51 µM, which were 10.38-34.36 folds more potent than the parental honokiol (IC50 values of 30.96 ± 1.81-55.67 ± 0.31 µM). On A549, HCT116, and MDA-MB-231 cell lines, 8a demonstrated 5.69-fold, 5.65-fold, and 4.83-fold greater cytotoxicity than cisplatin, respectively. Compound 8a also had higher selectivity (SI values of 8.41-49.38) towards seven cancer cell lines over the normal cell lines than cisplatin (SI values of 1.24-2.52). The analysis of structure-activity relationships (SARs) revealed that honokiol derivatives bearing 1,3,4-thiadiazoles (8a-j) possessed stronger anticancer activity than those containing 1,3,4-oxadiazoles. Further mechanistic investigation indicated that 8a induced cytotoxic autophagy in cancer cells in a time- and dose-independent manner via suppressing the PI3K/Akt/mTOR pathway. Molecular docking suggested that 8a could bind to the PI3Kα active sites. Additionally, 8a inhibited the migration and invasion of A549 and MDA-MB-231 cancer cells.

Behind the Non-Uniform Breakup of Bubble Slug in Y-Shaped Microchannel: Dynamics and Mechanisms.

Bubble flow in confined geometries is a problem of fundamental and technological significance. Among all the forms, bubble breakup in bifurcated microchannels is one of the most commonly encountered scenarios, where an in-depth understanding is necessary for better leveraging the process. This study numerically investigates the non-uniform breakup of a bubble slug in Y-shaped microchannels under different flow ratios, Reynolds numbers, and initial bubble volumes. Overall, the bubble can either breakup or non-breakup when passing through the bifurcation and shows different forms depending on flow regimes. The flow ratio-Reynolds number phase diagrams indicate a power-law transition line of breakup and non-breakup. The bubble takes longer to break up with rising flow ratios yet breaks earlier with higher Reynolds numbers and volumes. Non-breakup takes less time than the breakup patterns. Flow ratio is the origin of non-uniform breakup. Both the Reynolds number and initial volume influence the bubble states when reaching the bifurcation and thus affect subsequent processes. Bubble neck dynamics are analyzed to describe the breakup further. The volume distribution after breaking up is found to have a quadratic relation with the flow ratio. Our study is hoped to provide insights for practical applications related to non-uniform bubble breakups.

Deep Graph Reinforcement Learning for Solving Multicut Problem.

The multicut problem, also known as correlation clustering, is a classic combinatorial optimization problem that aims to optimize graph partitioning given only node (dis)similarities on edges. It serves as an elegant generalization for several graph partitioning problems and has found successful applications in various areas such as data mining and computer vision. However, the multicut problem with an exponentially large number of cycle constraints proves to be NP-hard, and existing solvers either suffer from exponential complexity or often give unsatisfactory solutions due to inflexible heuristics driven by hand-designed mechanisms. In this article, we propose a deep graph reinforcement learning method to solve the multicut problem within a combinatorial decision framework involving sequential edge contractions. The customized subgraph neural network adapts to the dynamically edge-contracted graph environment by extracting bilevel connected features from both contracted and original graphs. Our method can learn to infer feasible multicut solutions end-to-end toward optimization of the multicut objective in a data-driven manner. More specifically, by exploring the decision space adaptively, it implicitly gains heuristic knowledge from topological patterns of instances and thereby generates more targeted heuristics overcoming the short-sightedness inherent in the hand-designed ones. During testing, the learned heuristics iteratively contract graphs to construct high-quality solutions within polynomial time. Extensive experiments on synthetic and real-world multicut instances show the superiority of our method over existing combinatorial solvers, while also maintaining a certain level of out-of-distribution generalization ability.

A robust transformer-based pipeline of 3D cell alignment, denoise and instance segmentation on electron microscopy sequence images.

Germline cells are critical for transmitting genetic information to subsequent generations in biological organisms. While their differentiation from somatic cells during embryonic development is well-documented in most animals, the regulatory mechanisms initiating plant germline cells are not well understood. To thoroughly investigate the complex morphological transformations of their ultrastructure over developmental time, nanoscale 3D reconstruction of entire plant tissues is necessary, achievable exclusively through electron microscopy imaging. This paper presents a full-process framework designed for reconstructing large-volume plant tissue from serial electron microscopy images. The framework ensures end-to-end direct output of reconstruction results, including topological networks and morphological analysis. The proposed 3D cell alignment, denoise, and instance segmentation pipeline (3DCADS) leverages deep learning to provide a cell instance segmentation workflow for electron microscopy image series, ensuring accurate and robust 3D cell reconstructions with high computational efficiency. The pipeline involves five stages: the registration of electron microscopy serial images; image enhancement and denoising; semantic segmentation using a Transformer-based neural network; instance segmentation through a supervoxel-based clustering algorithm; and an automated analysis and statistical assessment of the reconstruction results, with the mapping of topological connections. The 3DCADS model's precision was validated on a plant tissue ground-truth dataset, outperforming traditional baseline models and deep learning baselines in overall accuracy. The framework was applied to the reconstruction of early meiosis stages in the anthers of Arabidopsis thaliana, resulting in a topological connectivity network and analysis of morphological parameters and characteristics of cell distribution. The experiment underscores the 3DCADS model's potential for biological tissue identification and its significance in quantitative analysis of plant cell development, crucial for examining samples across different genetic phenotypes and mutations in plant development. Additionally, the paper discusses the regulatory mechanisms of Arabidopsis thaliana's germline cells and the development of stamen cells before meiosis, offering new insights into the transition from somatic to germline cell fate in plants.

DeepMulticut: Deep Learning of Multicut Problem for Neuron Segmentation from Electron Microscopy Volume.

Superpixel aggregation is a powerful tool for automated neuron segmentation from electron microscopy (EM) volume. However, existing graph partitioning methods for superpixel aggregation still involve two separate stages-model estimation and model solving, and therefore model error is inherent. To address this issue, we integrate the two stages and propose an end-to-end aggregation framework based on deep learning of the minimum cost multicut problem called DeepMulticut. The core challenge lies in differentiating the NPhard multicut problem, whose constraint number is exponential in the problem size. With this in mind, we resort to relaxing the combinatorial solver-the greedy additive edge contraction (GAEC)-to a continuous Soft-GAEC algorithm, whose limit is shown to be the vanilla GAEC. Such relaxation thus allows the DeepMulticut to integrate edge cost estimators, Edge-CNNs, into a differentiable multicut optimization system and allows a decision-oriented loss to feed decision quality back to the Edge-CNNs for adaptive discriminative feature learning. Hence, the model estimators, Edge-CNNs, can be trained to improve partitioning decisions directly while beyond the NP-hardness. Also, we explain the rationale behind the DeepMulticut framework from the perspective of bi-level optimization. Extensive experiments on three public EM datasets demonstrate the effectiveness of the proposed DeepMulticut.

Cevanine-type alkaloids from the bulbs of Fritillaria unibracteata var. wabuensis and their antifibrotic activities in vitro.

Steroidal alkaloids are the main bioactive components of the bulbs of Fritillaria, which have been used as traditional Chinese medicine, known as "Beimu", for the treatment of cough for thousands of years in China. Cough and dyspnea are the most common symptoms observed in patients with pulmonary fibrosis. However, the antifibrotic activity of steroidal alkaloids has not been reported yet. In this study, two previously unreported cevanine-type steroidal alkaloids (1 and 2), four previously undescribed cevanine-type alkaloid glycosides (3-6), and 19 known steroidal alkaloids (7-25) were isolated from the bulbs of Fritillaria unibracteata var. wabuensis. The structures of these compounds were elucidated by comprehensive HRESIMS and NMR spectroscopic data analysis, as well as DP4+ NMR calculations. The biological evaluation showed that compounds 2, 7-10, 14, 15, and 17 downregulated fibrotic markers induced by transforming growth factor-ß (TGF-ß) in MRC-5 cells. Moreover, compounds 14 and 17 dose dependently inhibited TGF-ß-induced epithelial-mesenchymal transition in A549 cells, alleviated TGF-ß-induced migration and proliferation of fibroblasts, and decreased the expression of fibrotic markers, fibronectin, and N-cadherin in TGF-ß-induced MRC-5 cells. The research showed the potential of cevanine-type alkaloids as a class of natural antifibrotic agents.

Discovery of anti-melanogenic components in persimmon (Diospyros kaki) leaf using LC-MS/MS-MN, AlphaFold2-enabled virtual screening and biological validation.

Persimmon (Diospyros kaki) leaf is widely used as a tea substitute in East Asia, offering potential health benefits. Although studies have highlighted their effects on hyperpigmentation disorders, the active components remain unidentified. This study introduces a novel approach combining LC-MS/MS-based molecular networking with AlphaFold2-enabled virtual screening to expedite the identification of bioactive components in persimmon leaf. A total of 105 compounds were identified by MS/MS analysis. Further, virtual screening identified five flavonoids with potential anti-melanogenic properties. Bioassays confirmed myricetin, quercetin, and kaempferol inhibited melanogenesis in human melanocytes in a dose-dependent manner. Biolayer interferometry assays revealed strong binding affinity between these flavonols and hsTYR, with KD values of 23.26 ± 11.77 for myricetin, 12.43 ± 0.37 for quercetin, and 14.99 ± 3.80 µM for kaempferol. Molecular dynamics simulations provided insights into the binding interactions of these flavonols with hsTYR, particularly highlighting the essential role of the 3-OH group on the C-ring. This study elucidates the bioactive components responsible for the anti-melanogenic effects of persimmon leaf, supporting their use in product development.

Terahertz surface plasmon sensor for distinguishing gasolines.

Gasolines of two different octane numbers are experimentally distinguished using a thin metal sheet perforated with a periodic hole array terahertz surface plasmon (SP) sensor. This sensor is proved to be very sensitive to the change in permittivities of analytes. The differences between the gasolines 93# and 97# in composition lead to various refractive indices, permittivities, and absorption coefficients, thus varying their interactions with surface waves on the sensor, which enables a distinction of 6 GHz between the two octane numbers in the transmission peaks. The freestanding SP sensor is effective and reliable and can be simply employed in analyte distinction, which has potential applications in the petroleum industry.

Factors associated with gastrointestinal stromal tumor rupture and pathological risk: A single-center retrospective study.

BACKGROUND: Gastrointestinal stromal tumor (GIST) is a rare gastrointestinal mesenchymal tumor with potential malignancy. Once the tumor ruptures, regardless of tumor size and mitotic number, it can be identified into a high-risk group. It is of great significance for the diagnosis, treatment, and prognosis of GIST if non-invasive examination can be performed before surgery to accurately assess the risk of tumor. AIM: To identify the factors associated with GIST rupture and pathological risk. METHODS: A cohort of 50 patients with GISTs, as confirmed by postoperative pathology, was selected from our hospital. Clinicopathological and computed tomography data of the patients were collected. Logistic regression analysis was used to evaluate factors associated with GIST rupture and pathological risk grade. RESULTS: Pathological risk grade, tumor diameter, tumor morphology, internal necrosis, gas-liquid interface, and Ki-67 index exhibited significant associations with GIST rupture (P < 0.05). Gender, tumor diameter, tumor rupture, and Ki-67 index were found to be correlated with pathological risk grade of GIST (P < 0.05). Multifactorial logistic regression analysis revealed that male gender and tumor diameter ≥ 10 cm were independent predictors of a high pathological risk grade of GIST [odds ratio (OR) = 11.12, 95% confidence interval (95%CI): 1.81-68.52, P = 0.01; OR = 22.96, 95%CI: 2.19-240.93, P = 0.01]. Tumor diameter ≥ 10 cm, irregular shape, internal necrosis, gas-liquid interface, and Ki-67 index ≥ 10 were identified as independent predictors of a high risk of GIST rupture (OR = 9.67, 95%CI: 2.15-43.56, P = 0.01; OR = 35.44, 95%CI: 4.01-313.38, P < 0.01; OR = 18.75, 95%CI: 3.40-103.34, P < 0.01; OR = 27.00, 95%CI: 3.10-235.02, P < 0.01; OR = 4.43, 95%CI: 1.10-17.92, P = 0.04). CONCLUSION: Tumor diameter, tumor morphology, internal necrosis, gas-liquid, and Ki-67 index are associated with GIST rupture, while gender and tumor diameter are linked to the pathological risk of GIST. These findings contribute to our understanding of GIST and may inform non-invasive examination strategies and risk assessment for this condition.

In silico drug repurposing carvedilol and its metabolites against SARS-CoV-2 infection using molecular docking and molecular dynamic simulation approaches.

The pandemic of coronavirus disease 2019 (COVID-19) caused by the infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a significant impact on the economy and public health worldwide. Therapeutic options such as drugs and vaccines for this newly emerged disease are eagerly desired due to the high mortality. Using the U.S. Food and Drug Administration (FDA) approved drugs to treat a new disease or entirely different diseases, in terms of drug repurposing, minimizes the time and cost of drug development compared to the de novo design of a new drug. Drug repurposing also has some other advantages such as reducing safety evaluation to accelerate drug application on time. Carvedilol, a non-selective beta-adrenergic blocker originally designed to treat high blood pressure and manage heart disease, has been shown to impact SARS-CoV-2 infection in clinical observation and basic studies. Here, we applied computer-aided approaches to investigate the possibility of repurposing carvedilol to combat SARS-CoV-2 infection. The molecular mechanisms and potential molecular targets of carvedilol were identified by evaluating the interactions of carvedilol with viral proteins. Additionally, the binding affinities of in vivo metabolites of carvedilol with selected targets were evaluated. The docking scores for carvedilol and its metabolites with RdRp were - 10.0 kcal/mol, - 9.8 kcal/mol (1-hydroxyl carvedilol), - 9.7 kcal/mol (3-hydroxyl carvedilol), - 9.8 kcal/mol (4-hydroxyl carvedilol), - 9.7 kcal/mol (5-hydroxyl carvedilol), - 10.0 kcal/mol (8-hydroxyl carvedilol), and - 10.1 kcal/mol (O-desmethyl carvedilol), respectively. Using the molecular dynamics simulation (100 ns) method, we further confirmed the stability of formed complexes of RNA-dependent RNA polymerase (RdRp) and carvedilol or its metabolites. Finally, the drug-target interaction mechanisms that contribute to the complex were investigated. Overall, this study provides the molecular targets and mechanisms of carvedilol and its metabolites as repurposed drugs to fight against SARS-CoV-2 infection.

Anti-Entry Activity of Natural Flavonoids against SARS-CoV-2 by Targeting Spike RBD.

COVID-19 is still a global public health concern, and the SARS-CoV-2 mutations require more effective antiviral agents. In this study, the antiviral entry activity of thirty-one flavonoids was systematically evaluated by a SARS-CoV-2 pseudovirus model. Twenty-four flavonoids exhibited antiviral entry activity with IC50 values ranging from 10.27 to 172.63 µM and SI values ranging from 2.33 to 48.69. The structure-activity relationship of these flavonoids as SARS-CoV-2 entry inhibitors was comprehensively summarized. A subsequent biolayer interferometry assay indicated that flavonoids bind to viral spike RBD to block viral interaction with ACE2 receptor, and a molecular docking study also revealed that flavonols could bind to Pocket 3, the non-mutant regions of SARS-CoV-2 variants, suggesting that flavonols might be also active against virus variants. These natural flavonoids showed very low cytotoxic effects on human normal cell lines. Our findings suggested that natural flavonoids might be potential antiviral entry agents against SARS-CoV-2 via inactivating the viral spike. It is hoped that our study will provide some encouraging evidence for the use of natural flavonoids as disinfectants to prevent viral infections.