Exploring biased activation characteristics by molecular dynamics simulation and machine learning for the µ-opioid receptor.

Biased ligands selectively activating specific downstream signaling pathways (termed as biased activation) exhibit significant therapeutic potential. However, the conformational characteristics revealed are very limited for the biased activation, which is not conducive to biased drug development. Motivated by the issue, we combine extensive accelerated molecular dynamics simulations and an interpretable deep learning model to probe the biased activation features for two complex systems constructed by the inactive µOR and two different biased agonists (G-protein-biased agonist TRV130 and ß-arrestin-biased agonist endomorphin2). The results indicate that TRV130 binds deeper into the receptor core compared to endomorphin2, located between W2936.48 and D1142.50, and forms hydrogen bonding with D1142.50, while endomorphin2 binds above W2936.48. The G protein-biased agonist induces greater outward movements of the TM6 intracellular end, forming a typical active conformation, while the ß-arrestin-biased agonist leads to a smaller extent of outward movements of TM6. Compared with TRV130, endomorphin2 causes more pronounced inward movements of the TM7 intracellular end and more complex conformational changes of H8 and ICL1. In addition, important residues determining the two different biased activation states were further identified by using an interpretable deep learning classification model, including some common biased activation residues across Class A GPCRs like some key residues on the TM2 extracellular end, ECL2, TM5 intracellular end, TM6 intracellular end, and TM7 intracellular end, and some specific important residues of ICL3 for µOR. The observations will provide valuable information for understanding the biased activation mechanism for GPCRs.

Exploring an accurate machine learning model to quickly estimate stability of diverse energetic materials.

High energy and low sensitivity have been the focus of developing new energetic materials (EMs). However, there has been a lack of a quick and accurate method for evaluating the stability of diverse EMs. Here, we develop a machine learning prediction model with high accuracy for bond dissociation energy (BDE) of EMs. A reliable and representative BDE dataset of EMs is constructed by collecting 778 experimental energetic compounds and quantum mechanics calculation. To sufficiently characterize the BDE of EMs, a hybrid feature representation is proposed by coupling the local target bond into the global structure characteristics. To alleviate the limitation of the low dataset, pairwise difference regression is utilized as a data augmentation with the advantage of reducing systematic errors and improving diversity. Benefiting from these improvements, the XGBoost model achieves the best prediction accuracy with R2 of 0.98 and MAE of 8.8 kJ mol-1, significantly outperforming competitive models.

Computational insights into diverse binding modes of the allosteric modulator and their regulation on dopamine D1 receptor.

Allosteric drugs hold the promise of addressing many challenges in the current drug development of GPCRs. However, the molecular mechanism underlying their allosteric modulations remain largely elusive. The dopamine D1 receptor (DRD1), a member of Class A GPCRs, is critical for treating psychiatric disorders, and LY3154207 serves as its promising positive allosteric modulator (PAM). In the work, we utilized extensive Gaussian-accelerated molecular dynamics simulations (a total of 41µs) for the first time probe the diverse binding modes of the allosteric modulator and their regulation effects, based on the DRD1 and LY3154207 as representative. Our simulations identify four binding modes of LY3154207 (one boat mode, two metastable vertical modes and a novel cleft-anchored mode), in which the boat mode is the most stable while there three modes are similar in the stability. However, it is interesting to observed that the most stable boat mode inversely exhibits the weakest positive allosteric effect on influencing the orthosteric ligand binding and maintaining the activity of the transducer binding site. It should result from its induced weaker correlation between the allosteric site and the orthosteric site, and between the orthosteric site and the transducer binding site than the other three binding modes, as well as its weakened interaction between a crucial activation-related residue (S2025.46) and the orthosteric ligand (dopamine). Overall, the work offers atomic-level information to advance our understanding of the complex allosteric regulation on GPCRs, which is beneficial to the allosteric modulator design and development.

MicroRNA-193a Promotes Apoptosis in Retinal Neuronal Cells in Early-Stage Diabetic (DM) Rat via Wilms' Tumor Gene 1.

OBJECTIVE: This study aimed to investigate the role and mechanism of microRNA (miR)-193a in promoting apoptosis of retinal neuronal cells in early diabetic (DM) rats. METHODS: Seventy-two male SD-grade rats were selected to establish a DM model by intraperitoneal injection of streptozotocin (STZ), and randomly divided into a control group (blank control group), a DM group (diabetic model group), a DM+miR-NC inhibitor group (miR-193a inhibition negative control group), a DM+miR-193a inhibitor group (miR-193a inhibitor group), DM+miR-NC mimic group (miR-193a overexpression negative control group), DM+miR-193a mimic group (miR-193a overexpression group), with12 rats in each group. RESULTS: The miR-193a expression, apoptosis rate, and Bax, Caspase3, and Caspase9 protein expression levels were elevated, and Bcl-2 protein expression was decreased in the retinal tissues of DM rats and high glucose-induced rat retinal neuronal cells, while miR-193a inhibitors reversed these processes. These dual luciferase reporter assay showed that WT1CDS, and WT1Mut were lower in the miR-193a group than in the miR-NC group (P<0.05); WT1 protein expression was reduced in the retinal tissues of DM rat and high glucose-induced rat retinal neuronal cells, and miR-193a inhibitors increased WT1 protein expression. Compared with cells co-transfected with miR-193a and WT1vector, miR-193a and WT1 cotransfection inhibited high glucose-induced apoptosis in retinal neuronal cells and regulated apoptotic protein expression. miR-193a was highly expressed and WT1 was lowly expressed in retinal tissues of DM rats and high glucose-induced rat retinal neuronal cells. CONCLUSION: miR-193a could inhibit early retinal neuronal cell apoptosis in DM rats by targeting and negatively regulating WT1 expression.

HOPEXGB: A Consensual Model for Predicting miRNA/lncRNA-Disease Associations Using a Heterogeneous Disease-miRNA-lncRNA Information Network.

Predicting disease-related microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) is crucial to find new biomarkers for the prevention, diagnosis, and treatment of complex human diseases. Computational predictions for miRNA/lncRNA-disease associations are of great practical significance, since traditional experimental detection is expensive and time-consuming. In this paper, we proposed a consensual machine-learning technique-based prediction approach to identify disease-related miRNAs and lncRNAs by high-order proximity preserved embedding (HOPE) and eXtreme Gradient Boosting (XGB), named HOPEXGB. By connecting lncRNA, miRNA, and disease nodes based on their correlations and relationships, we first created a heterogeneous disease-miRNA-lncRNA (DML) information network to achieve an effective fusion of information on similarities, correlations, and interactions among miRNAs, lncRNAs, and diseases. In addition, a more rational negative data set was generated based on the similarities of unknown associations with the known ones, so as to effectively reduce the false negative rate in the data set for model construction. By 10-fold cross-validation, HOPE shows better performance than other graph embedding methods. The final consensual HOPEXGB model yields robust performance with a mean prediction accuracy of 0.9569 and also demonstrates high sensitivity and specificity advantages compared to lncRNA/miRNA-specific predictions. Moreover, it is superior to other existing methods and gives promising performance on the external testing data, indicating that integrating the information on lncRNA-miRNA interactions and the similarities of lncRNAs/miRNAs is beneficial for improving the prediction performance of the model. Finally, case studies on lung, stomach, and breast cancers indicate that HOPEXGB could be a powerful tool for preclinical biomarker detection and bioexperiment preliminary screening for the diagnosis and prognosis of cancers. HOPEXGB is publicly available at https://github.com/airpamper/HOPEXGB.

Acquiring Charge-Transfer-Featured Single-Molecule Ultralong Organic Room Temperature Phosphorescence via Through-Space Electronic Coupling.

Although long-lived triplet charge-transfer (3 CT) state with high energy level has gained significant attention, the development of organic small molecules capable of achieving such states remains a major challenge. Herein, by using the through-space electronic coupling effect, we have developed a compound, namely NIC-DMAC, which has a long-lived 3 CT state at the single-molecule level with a lifetime of 210 ms and a high energy level of up to 2.50 eV. Through a combination of experimental and computational approaches, we have elucidated the photophysical processes of NIC-DMAC, which involve sequential transitions from the first singlet excited state (S1 ) that shows a 1 CT character to the first triplet excited state (T1 ) that exhibits a local excited state feature (3 LE), and then to the second triplet excited state (T2 ) that shows a 3 CT character (i.e., S1 (1 CT)→T1 (3 LE)→T2 (3 CT)). The long lifetime and high energy level of its 3 CT state have enabled NIC-DMAC as an initiator for photocuring in double patterning applications.

Attention Mechanism-Based Graph Neural Network Model for Effective Activity Prediction of SARS-CoV-2 Main Protease Inhibitors: Application to Drug Repurposing as Potential COVID-19 Therapy.

Compared to de novo drug discovery, drug repurposing provides a time-efficient way to treat coronavirus disease 19 (COVID-19) that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS-CoV-2 main protease (Mpro) has been proved to be an attractive drug target due to its pivotal involvement in viral replication and transcription. Here, we present a graph neural network-based deep-learning (DL) strategy to prioritize the existing drugs for their potential therapeutic effects against SARS-CoV-2 Mpro. Mpro inhibitors were represented as molecular graphs ready for graph attention network (GAT) and graph isomorphism network (GIN) modeling for predicting the inhibitory activities. The result shows that the GAT model outperforms the GIN and other competitive models and yields satisfactory predictions for unseen Mpro inhibitors, confirming its robustness and generalization. The attention mechanism of GAT enables to capture the dominant substructures and thus to realize the interpretability of the model. Finally, we applied the optimal GAT model in conjunction with molecular docking simulations to screen the Drug Repurposing Hub (DRH) database. As a result, 18 drug hits with best consensus prediction scores and binding affinity values were identified as the potential therapeutics against COVID-19. Both the extensive literature searching and evaluations on adsorption, distribution, metabolism, excretion, and toxicity (ADMET) illustrate the premium drug-likeness and pharmacokinetic properties of the drug candidates. Overall, our work not only provides an effective GAT-based DL prediction tool for inhibitory activity of SARS-CoV-2 Mpro inhibitors but also provides theoretical guidelines for drug discovery in the COVID-19 treatment.

Comparative Pollen Morphology of Selected Species of Blumea DC. and Cyathocline Cass. and Its Taxonomic Significance.

The pollen morphology of 20 species from Blumea and Cyathocline Cass. was investigated using a light microscope (LM) and scanning electron microscopy (SEM) to explore their taxonomic significance. This study showed that pollen grains of these species were usually tricolporate, rarely tetracolporate (B. sinuata). Nine pollen types were distinguishable through the exine sculpture characters and the number of apertures. It was easily distinguished Cyathocline from species of Blumea s. str. by its much smaller size (15.04 µm × 15.07 µm) and sparse and longer spines (24 spines, spine length 4.23 µm) with acute apex, which suggest that C. purpurea might not belong to the genus Blumea s. str. The palynological characteristics indicated that Section Macrophllae and Section Paniculatae of Blumea were not monophyletic groups. The pollen morphology differentiation of B. lacera clade is consistent with the interspecific relationship revealed by the molecular phylogenetic tree. However, the pollen morphology of the Blumea densiflora clade is inconsistent with the interspecific relationship based on molecular phylogenetic analysis. This palynology research can only partly support the previously published molecular phylogeny of Blumea s. str.

Corrigendum: Effects of Poria cocos extract on metabolic dysfunction-associated fatty liver disease via the FXR/PPARα-SREBPs pathway.

[This corrects the article DOI: 10.3389/fphar.2022.1007274.].

Multi-omics integration analysis of GPCRs in pan-cancer to uncover inter-omics relationships and potential driver genes.

G protein-coupled receptors (GPCRs) are the largest drug target family. Unfortunately, applications of GPCRs in cancer therapy are scarce due to very limited knowledge regarding their correlations with cancers. Multi-omics data enables systematic investigations of GPCRs, yet their effective integration remains a challenge due to the complexity of the data. Here, we adopt two types of integration strategies, multi-staged and meta-dimensional approaches, to fully characterize somatic mutations, somatic copy number alterations (SCNAs), DNA methylations, and mRNA expressions of GPCRs in 33 cancers. Results from the multi-staged integration reveal that GPCR mutations cannot well predict expression dysregulation. The correlations between expressions and SCNAs are primarily positive, while correlations of the methylations with expressions and SCNAs are bimodal with negative correlations predominating. Based on these correlations, 32 and 144 potential cancer-related GPCRs driven by aberrant SCNA and methylation are identified, respectively. In addition, the meta-dimensional integration analysis is carried out by using deep learning models, which predict more than one hundred GPCRs as potential oncogenes. When comparing results between the two integration strategies, 165 cancer-related GPCRs are common in both, suggesting that they should be prioritized in future studies. However, 172 GPCRs emerge in only one, indicating that the two integration strategies should be considered concurrently to complement the information missed by the other such that obtain a more comprehensive understanding. Finally, correlation analysis further reveals that GPCRs, in particular for the class A and adhesion receptors, are generally immune-related. In a whole, the work is for the first time to reveal the associations between different omics layers and highlight the necessity of combing the two strategies in identifying cancer-related GPCRs.

A consensual machine-learning-assisted QSAR model for effective bioactivity prediction of xanthine oxidase inhibitors using molecular fingerprints.

Xanthine oxidase inhibitors (XOIs) have been widely studied due to the promising potential as safe and effective therapeutics in hyperuricemia and gout. Currently, available XOI molecules have been developed from different experiments but they are with the wide structure diversity and significant varying bioactivities. So it is of great practical significance to present a consensual QSAR model for effective bioactivity prediction of XOIs based on a systematic compiling of these XOIs across different experiments. In this work, 249 XOIs belonging to 16 scaffolds were collected and were integrated into a consensual dataset by introducing the concept of IC50 values relative to allopurinol (RIC50). Here, extended connectivity fingerprints (ECFPs) were employed to represent XOI molecules. By performing effective feature selection by machine-learning method, 54 crucial fingerprints were indicated to be valuable for predicting the inhibitory potency (IP) of XOIs. The optimal predictor yields the promising performance by different cross-validation tests. Besides, an external validation of 43 XOIs and a case study on febuxostat also provide satisfactory results, indicating the powerful generalization of our predictor. Here, the predictor was interpreted by shapely additive explanation (SHAP) method which revealed several important substructures by mapping the featured fingerprints to molecular structures. Then, 15 new molecules were designed and predicted by our predictor to show superior IP than febuxostat. Finally, molecular docking simulation was performed to gain a deep insight into molecular binding mode with xanthine oxidase (XO) enzyme, showing that molecules with selenazole moiety, cyano group and isopropyl group tended to yield higher IP. The absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction results further enhanced the potential of these novel XOIs as drug candidates. Overall, this work presents a QSAR model for accurate prediction of IP of XOIs, and is expected to provide new insights for further structure-guided design of novel XOIs.

A new molecular subclassification and in silico predictions for diagnosis and prognosis of papillary thyroid cancer by alternative splicing profile.

Introduction: Papillary thyroid cancer (PTC) is the most common endocrine malignancy. However, different PTC variants reveal high heterogeneity at histological, cytological, molecular and clinicopathological levels, which complicates the precise diagnosis and management of PTC. Alternative splicing (AS) has been reported to be potential cancer biomarkers and therapeutic targets. Method: Here, we aim to find a more sophisticated molecular subclassification and characterization for PTC by integrating AS profiling. Based on six differentially expressed alternative splicing (DEAS) events, a new molecular subclassification was proposed to reclassify PTC into three new groups named as Cluster0, Cluster1 and Cluster2 respectively. Results: An in silico prediction was performed for accurate recognition of new groups with the average accuracy of 91.2%. Moreover, series of analyses were implemented to explore the differences of clinicopathology, molecular and immune characteristics across them. It suggests that there are remarkable differences among them, but Cluster2 was characterized by poor prognosis, higher immune heterogeneity and more sensitive to anti-PD1 therapy. The splicing correlation networks proved the complicated regulation relationships between AS events and splicing factors (SFs). An independent prognostic indicator for PTC overall survival (OS) was established. Finally, three compounds (orantinib, tyrphostin-AG-1295 and AG-370) were discovered to be the potential therapeutic agents. Discussion: Overall, the six DEAS events are not only potential biomarkers for precise diagnosis of PTC, but also the probable prognostic predictors. This research would be expected to highlight the effect of AS events on PTC characterization and also provide new insights into refining precise subclassification and improving medical therapy for PTC patients.

Protective effect and mechanism of Polygonatum kingianum against hypoxia-induced injury.

Background: Hypoxia is an essential cause of fatigue and aging, and is associated with the occurrence and development of many diseases. Polygonatum kingianum (PK) is a deficiency-nourishing Chinese herbal medicine utilized as both medicine and food, and it has long been used to ameliorate human conditions associated with fatigue and aging over 2000 years in China. PK is an important genuine-medicinal-materials cultivated in Yunnan, China, and is used by the Bai, Wa, and Zhuang nationalities as a traditional medicine for enhancing immunity, anti-fatigue, and anti-aging, while the preventive effect of PK on hypoxia-induced injury and the underlying mechanism are indefinite. Aim of the study: The present study aimed to evaluate the anti-hypoxia efficacy and understand the corresponding mechanism of PK water extract. Materials and methods: The main active ingredients and targets of PK were predicted using network pharmacology, and the anti-hypoxia activities of Gracillin and Liquiritigenin were verified by in vitro experiments. The pharmacodynamic experiments were conducted to evaluate the major signal pathways of PK for detecting anti-hypoxia activity. Results: Fifty active ingredients and 371 potential targets were screened by network pharmacology, then, we confirmed that Gracillin and Liquiritigenin were the main active components of PK to exert anti-hypoxia effect in vitro. The pharmacodynamic experiments revealed that PK enhanced the extension rate of the survival time (ERST) and regulated the targets-related biochemical parameters of rats under hypoxia, showing significant anti-hypoxia effects on rats. Conclusion: The network pharmacology results suggested that PK exerts its anti-hypoxia effect through a multi-component and multi-target manner. Simultaneously, we also observed that Gracillin (saponins) and Liquiritigenin (flavonoids) are the main active components of PK to play a role in anti-hypoxia. The anti-hypoxia effect of PK could be associated with scavenging excess free radicals, maintaining the activities of antioxidant enzymes, and inhibiting oxidative stress due to lipid peroxidation. These findings provide insight into the Polygonatum kingianum as promising medicines or healthcare products for preventing and treating hypoxia.

General Graph Neural Network-Based Model To Accurately Predict Cocrystal Density and Insight from Data Quality and Feature Representation.

Cocrystal engineering as an effective way to modify solid-state properties has inspired great interest from diverse material fields while cocrystal density is an important property closely correlated with the material function. In order to accurately predict the cocrystal density, we develop a graph neural network (GNN)-based deep learning framework by considering three key factors of machine learning (data quality, feature presentation, and model architecture). The result shows that different stoichiometric ratios of molecules in cocrystals can significantly influence the prediction performances, highlighting the importance of data quality. In addition, the feature complementary is not suitable for augmenting the molecular graph representation in the cocrystal density prediction, suggesting that the complementary strategy needs to consider whether extra features can sufficiently supplement the lacked information in the original representation. Based on these results, 4144 cocrystals with 1:1 stoichiometry ratio are selected as the dataset, supplemented by the data augmentation of exchanging a pair of coformers. The molecular graph is determined to learn feature representation to train the GNN-based model. Global attention is introduced to further optimize the feature space and identify important atoms to realize the interpretability of the model. Benefited from the advantages, our model significantly outperforms three competitive models and exhibits high prediction accuracy for unseen cocrystals, showcasing its robustness and generality. Overall, our work not only provides a general cocrystal density prediction tool for experimental investigations but also provides useful guidelines for the machine learning application. All source codes are freely available at https://github.com/Xiao-Gua00/CCPGraph.

Immune landscape-based machine-learning-assisted subclassification, prognosis, and immunotherapy prediction for glioblastoma.

Introduction: As a malignant brain tumor, glioblastoma (GBM) is characterized by intratumor heterogeneity, a worse prognosis, and highly invasive, lethal, and refractory natures. Immunotherapy has been becoming a promising strategy to treat diverse cancers. It has been known that there are highly heterogeneous immunosuppressive microenvironments among different GBM molecular subtypes that mainly include classical (CL), mesenchymal (MES), and proneural (PN), respectively. Therefore, an in-depth understanding of immune landscapes among them is essential for identifying novel immune markers of GBM. Methods and results: In the present study, based on collecting the largest number of 109 immune signatures, we aim to achieve a precise diagnosis, prognosis, and immunotherapy prediction for GBM by performing a comprehensive immunogenomic analysis. Firstly, machine-learning (ML) methods were proposed to evaluate the diagnostic values of these immune signatures, and the optimal classifier was constructed for accurate recognition of three GBM subtypes with robust and promising performance. The prognostic values of these signatures were then confirmed, and a risk score was established to divide all GBM patients into high-, medium-, and low-risk groups with a high predictive accuracy for overall survival (OS). Therefore, complete differential analysis across GBM subtypes was performed in terms of the immune characteristics along with clinicopathological and molecular features, which indicates that MES shows much higher immune heterogeneity compared to CL and PN but has significantly better immunotherapy responses, although MES patients may have an immunosuppressive microenvironment and be more proinflammatory and invasive. Finally, the MES subtype is proved to be more sensitive to 17-AAG, docetaxel, and erlotinib using drug sensitivity analysis and three compounds of AS-703026, PD-0325901, and MEK1-2-inhibitor might be potential therapeutic agents. Conclusion: Overall, the findings of this research could help enhance our understanding of the tumor immune microenvironment and provide new insights for improving the prognosis and immunotherapy of GBM patients.

Biased Activation Mechanism Induced by GPCR Heterodimerization: Observations from µOR/δOR Dimers.

GPCRs regulate multiple intracellular signaling cascades. Biasedly activating one signaling pathway over the others provides additional clinical utility to optimize GPCR-based therapies. GPCR heterodimers possess different functions from their monomeric states, including their selectivity to different transducers. However, the biased signaling mechanism induced by the heterodimerization remains unclear. Motivated by the issue, we select an important GPCR heterodimer (µOR/δOR heterodimer) as a case and use microsecond Gaussian accelerated molecular dynamics simulation coupled with potential of mean force and protein structure network (PSN) to probe mechanisms regarding the heterodimerization-induced constitutive ß-arrestin activity and efficacy change of the agonist DAMGO. The results show that only the lowest energy state of the µOR/δOR heterodimer, which adopts a slightly outward shift of TM6 and an ICL2 conformation close to the receptor core, can selectively accommodate ß-arrestins. PSN further reveals important roles of H8, ICL1, and ICL2 in regulating the constitutive ß-arrestin-biased activity for the apo µOR/δOR heterodimer. In addition, the heterodimerization can allosterically alter the binding mode of DAMGO mainly by means of W7.35. Consequently, DAMGO transmits the structural signal mainly through TM6 and TM7 in the dimer, rather than TM3 similar to the µOR monomer, thus changing the efficacy of DAMGO from a balanced agonist to the ß-arrestin-biased one. On the other side, the binding of DAMGO to the heterodimer can stabilize µOR/δOR heterodimers through a stronger interaction of TM1/TM1 and H8/H8, accordingly enhancing the interaction of µOR with δOR and the binding affinity of the dimer to the ß-arrestin. The agonist DAMGO does not change main compositions of the regulation network from the dimer interface to the transducer binding pocket of the µOR protomer, but induces an increase in the structural communication of the network, which should contribute to the enhanced ß-arrestin coupling. Our observations, for the first time, reveal the molecular mechanism of the biased signaling induced by the heterodimerization for GPCRs, which should be beneficial to more comprehensively understand the GPCR bias signaling.

Ultra-fast triplet-triplet-annihilation-mediated high-lying reverse intersystem crossing triggered by participation of nπ*-featured excited states.

The harvesting of 'hot' triplet excitons through high-lying reverse intersystem crossing mechanism has emerged as a hot research issue in the field of organic light-emitting diodes. However, if high-lying reverse intersystem crossing materials lack the capability to convert 'cold' T1 excitons into singlet ones, the actual maximum exciton utilization efficiency would generally deviate from 100%. Herein, through comparative studies on two naphthalimide-based compounds CzNI and TPANI, we revealed that the 'cold' T1 excitons in high-lying reverse intersystem crossing materials can be utilized effectively through the triplet-triplet annihilation-mediated high-lying reverse intersystem crossing process if they possess certain triplet-triplet upconversion capability. Especially, quite effective triplet-triplet annihilation-mediated high-lying reverse intersystem crossing can be triggered by endowing the high-lying reverse intersystem crossing process with a 3ππ*→1nπ* character. By taking advantage of the permanent orthogonal orbital transition effect of 3ππ*→1nπ*, spin-orbit coupling matrix elements of ca. 10 cm-1 can be acquired, and hence ultra-fast mediated high-lying reverse intersystem crossing process with rate constant over 109 s-1 can be realized.

Effects of Poria cocos extract on metabolic dysfunction-associated fatty liver disease via the FXR/PPARα-SREBPs pathway.

Despite the increase in the global prevalence of metabolic dysfunction-associated fatty liver disease (MAFLD), no approved drug currently exists for the disease. Poria cocos (Schw.) Wolf (P. cocos) is a medicinal mushroom belonging to a family of polyporaceae widely used in TCM clinics to protect the liver and treat obesity. However, its efficacy, practical components, and underlying mechanism against MAFLD are yet to be determined. In this study, we evaluated the effects of Poria cocos (P. cocos) ethanol extract (EPC) on hepatic dyslipidemia, steatosis, and inflammation by both bioinformatics analysis and MAFLD rats induced by HFD feeding. We found EPC treatment dramatically reduced lipid accumulation, inflammatory cell infiltration, and liver injury. EPC reduced serum TC, TG levels, and hepatic TG, TBA, and NEFA contents. UHPLC Q-Trap/MS examination of BA profiles in serum and feces showed that EPC increased fecal conjugated BAs, decreased free BAs, and improved BA metabolism in HFD-fed rats. Western blot and RT-qPCR analysis showed that EPC could activate hepatic FXR and PPARα expression and reduce CYP7A1 and SREBP-1c expression. Systemic pharmacology combined with molecular docking suggested that poricoic acid B and polyporenic acid C, the major active compounds in EPC, could ameliorate lipid homeostasis by activating the nuclear receptor PPARα. We further confirmed their inhibition effects of lipid droplet deposition in steatized L-02 hepatocytes. In summary, EPC alleviated HFD-induced MAFLD by regulating lipid homeostasis and BA metabolism via the FXR/PPARα-SREBPs signaling pathway. P. cocos triterpenes, such as poricoic acid B and polyporenic acid C, were the characteristic substances of P. cocos for the treatment of MAFLD.

Extracts of Knoxia roxburghii (Spreng.) M. A. Rau Induce Apoptosis in Human MCF-7 Breast Cancer Cells via Mitochondrial Pathways.

Knoxia roxburghii (Spreng.) M. A. Rau (KR) is a plant clinically used in traditional Chinese medicine (TCM) for the treatment of cancer. The study objectives were to examine the effects of KR extracts, petroleum ether (PET), ethyl acetate (EtoAc), butanol (n-BuOH), and H2O-soluble fractions (HSF) of the 75% EtOH extraction on A549 (non-small cell lung cancer), HepG2 (liver cancer), HeLa (cervical cancer), MCF-7 (breast cancer), and L02 (normal hepatocyte) cells. It was found that HSF exhibited the strongest cytotoxic activity against MCF-7 cells, and was accompanied by reduced mitochondrial transmembrane potential, increased levels of intra-cellular reactive oxygen species (ROS) and activated caspases, and upregulated pro-apoptotic and downregulated anti-apoptotic proteins. LC-MS analysis further showed that HSF primarily consisted of calycosin, aloe emodin, rein, maackiain, asperuloside, orientin, vicenin-2, and kaempferide, which have been mostly reported for anti-tumor activity in previous studies. In summary, the current study illustrated the effect, mechanism, and the potential major active components of KR against breast cancer.

Therapeutic and prognostic potential of GPCRs in prostate cancer from multi-omics landscape.

Prostate cancer (PRAD) is a common and fatal malignancy. It is difficult to manage clinically due to drug resistance and poor prognosis, thus creating an urgent need for novel therapeutic targets and prognostic biomarkers. Although G protein-coupled receptors (GPCRs) have been most attractive for drug development, there have been lack of an exhaustive assessment on GPCRs in PRAD like their molecular features, prognostic and therapeutic values. To close this gap, we herein systematically investigate multi-omics profiling for GPCRs in the primary PRAD by analyzing somatic mutations, somatic copy-number alterations (SCNAs), DNA methylation and mRNA expression. GPCRs exhibit low expression levels and mutation frequencies while SCNAs are more prevalent. 46 and 255 disease-related GPCRs are identified by the mRNA expression and DNA methylation analysis, respectively, complementing information lack in the genome analysis. In addition, the genomic alterations do not exhibit an observable correlation with the GPCR expression, reflecting the complex regulatory processes from DNA to RNA. Conversely, a tight association is observed between the DNA methylation and mRNA expression. The virtual screening and molecular dynamics simulation further identify four potential drugs in repositioning to PRAD. The combination of 3 clinical characteristics and 26 GPCR molecular features revealed by the transcriptome and genome exhibit good performance in predicting progression-free survival in patients with the primary PRAD, providing candidates as new biomarkers. These observations from the multi-omics analysis on GPCRs provide new insights into the underlying mechanism of primary PRAD and potential of GPCRs in developing therapeutic strategies on PRAD.