Investigating the active compounds and mechanism of HuaShi XuanFei formula for prevention and treatment of COVID-19 based on network pharmacology and molecular docking analysis.

Traditional Chinese medicine (TCM) has exerted positive effects in controlling the COVID-19 pandemic. HuaShi XuanFei Formula (HSXFF) was developed to treat patients with mild and general COVID-19 in Zhejiang Province, China. The present study seeks to explore its potentially active compounds and pharmacological mechanisms against COVID-19 based on network pharmacology, molecular docking, and molecular dynamics (MD) simulation. All components of HSXFF were harvested from the pharmacology database of the TCMSP system. COVID-19-related targets were retrieved from using OMIM and GeneCards databases. The herb-compound-targets network was constructed by Cytoscape. The target protein-protein interaction (PPI) network, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed to discover the potential key target genes and mechanism. The main active compounds of HSXFF were docked with 3C-like (3CL) protease hydrolase and angiotensin-converting enzyme 2 (ACE2). The MD simulation confirmed the binding stability of docking results. The herbs-targets network mainly contained 52 compounds and 70 corresponding targets, including key targets such as RELA, TNF, TP53, IL6, MAPK1, CXCL8, IL-1ß, and MAPK14. The GO and KEGG indicated that HSXFF may be mainly acting on the IL-17 signaling pathway, TNF signaling pathway, NF-κB signaling pathway, etc. The molecular docking results indicated that isovitexin and procyanidin B1 showed the highest affinity with 3CL and ACE2, respectively, which were confirmed by MD simulation. These findings suggested HSXFF exerted therapeutic effects involving "multi-compounds and multi-targets." It might be working through directly inhibiting the virus, improving immune function, and reducing the inflammatory in response to anti-COVID-19. In summary, the present study would provide a valuable direction for further research of HSXFF.

Soil bacterial community composition and diversity respond to soil environment in the Ebinur Lake Wetland.

To understand the relationship between the community structure of bacteria and soil environment, the diversity and composition of soil bacterial communities were investigated, based on 16S rRNA gene clone library, in three different sampling sites (SP1, SP2 and SP3) in the Bortala and Jinghe River basins of Ebinur Lake Wetland. The results showed that the diversity of bacteria among plots was SP2 > SP3 > SP1, and the richness was SP3 > SP2 > SP1. Community composition analysis of bacteria showed that Proteobacteria and Bacteroides accounted for 49.7% and 53.7%, respectively, making them the most dominant phyla observed. In SP1, Proteobacteria was the most dominant phylum, followed by Bacteroides. In SP2 and SP3, Bacteroides was the most dominant phylum, followed by Proteobacteria. At subphyla level, Gammaproteobacteria, Alphaproteobacteria and Deltaproteobacteria accounted for 50%, 51.0%, and 42.2% of the Proteobacteria of SP1, SP2, and SP3, respectively. Betaproteobacteria and Epsilonproteobacteria were found only in SP3. RDA results showed that SOM, SM and EC were the main soil environmental factors affecting bacterial community structure.

Diversity and community structure of ammonia-oxidizing archaea in rhizosphere soil of four plant groups in Ebinur Lake wetland.

The aim of this study was to reveal the differences in the community structure of ammonia-oxidizing archaea (AOA) between rhizosphere and non-rhizosphere soil, to provide a theoretical basis for further study on the relationship between halophyte rhizosphere soil microorganisms and salt tolerance. The results of diversity and community structure showed that the diversity of the AOA community in rhizosphere soil of Reeds was higher than that in non-rhizosphere soil in spring and lower than that in non-rhizosphere soil in summer and autumn. In summer, the diversity of rhizosphere soil of Karelinia caspica was higher than that of non-rhizosphere soil and lower than that of non-rhizosphere soil in spring and autumn. The diversity of rhizosphere soil of Halocnemum strobilaceum in 3 seasons was lower than that in non-rhizosphere soil. The diversity of rhizosphere soil of Salicornia was higher than that in non-rhizosphere soil in 3 seasons. In addition, the relative abundance of AOA in rhizosphere soil of 4 plants was higher than that in non-rhizosphere soil. The AOA community in all soil samples was mainly concentrated in Crenarchaeota and Thaumarchaeota. Redundancy analysis results showed salinity, soil water moisture, pH, and soil organic matter were important factors affecting the differentiation of AOA communities.

Diversity of Archaea and Its Correlation with Environmental Factors in the Ebinur Lake Wetland.

The diversity and community composition of archaea in soil samples from three wetlands (SP1, SP2, and SP3) of Ebinur Lake were studied by constructing 16S rDNA cloning library. The correlation between the diversity of archaea and soil environmental factors was analyzed by CANOCO software. The aim of this study was to reveal the differences of community structures of archaea in different sample sites, to provide a theoretical basis for further study on degradation and restoration of Ebinur Lake wetland. The results showed that Euryarchaeota accounted for 57.1% was the most dominant phylum observed, followed by Thaumarchaeota and Crenarchaeota for the three wetland soil analyzed. Compared with SP3 site, the proportions of Euryarchaeota were decreased by 16.70% and 31.78%, while Thaumarchaeota increased by 7.26% and 17.64% in the SP1 and SP2, respectively. Crenarchaeota was found only in SP3. Shannon-wiener diversity indices in SP1, SP2, and SP3 sites were 3.44, 3.87, and 3.94, respectively, indicating that the diversity of archaea in three plots was: SP3 > SP2 > SP1. Redundancy analysis (RDA) showed that electrical conductivity (EC), soil moisture (SM), hydrogen potential (pH), and soil organic matter content (SOM) may affect archaeal communities. Compared to EC and pH, SM and SOM may have a greater impact on the community composition of archaea.

An In Silico Model for Predicting Drug-Induced Hepatotoxicity.

As one of the leading causes of drug failure in clinical trials, drug-induced liver injury (DILI) seriously impeded the development of new drugs. Assessing the DILI risk of drug candidates in advance has been considered as an effective strategy to decrease the rate of attrition in drug discovery. Recently, there have been continuous attempts in the prediction of DILI. However, it indeed remains a huge challenge to predict DILI successfully. There is an urgent need to develop a quantitative structure-activity relationship (QSAR) model for predicting DILI with satisfactory performance. In this work, we reported a high-quality QSAR model for predicting the DILI risk of xenobiotics by incorporating the use of eight effective classifiers and molecular descriptors provided by Marvin. In model development, a large-scale and diverse dataset consisting of 1254 compounds for DILI was built through a comprehensive literature retrieval. The optimal model was attained by an ensemble method, averaging the probabilities from eight classifiers, with accuracy (ACC) of 0.783, sensitivity (SE) of 0.818, specificity (SP) of 0.748, and area under the receiver operating characteristic curve (AUC) of 0.859. For further validation, three external test sets and a large negative dataset were utilized. Consequently, both the internal and external validation indicated that our model outperformed prior studies significantly. Data provided by the current study will also be a valuable source for modeling/data mining in the future.

Herb-Induced Liver Injury: Phylogenetic Relationship, Structure-Toxicity Relationship, and Herb-Ingredient Network Analysis.

Currently, hundreds of herbal products with potential hepatotoxicity were available in the literature. A comprehensive summary and analysis focused on these potential hepatotoxic herbal products may assist in understanding herb-induced liver injury (HILI). In this work, we collected 335 hepatotoxic medicinal plants, 296 hepatotoxic ingredients, and 584 hepatoprotective ingredients through a systematic literature retrieval. Then we analyzed these data from the perspectives of phylogenetic relationship and structure-toxicity relationship. Phylogenetic analysis indicated that hepatotoxic medicinal plants tended to have a closer taxonomic relationship. By investigating the structures of the hepatotoxic ingredients, we found that alkaloids and terpenoids were the two major groups of hepatotoxicity. We also identified eight major skeletons of hepatotoxicity and reviewed their hepatotoxic mechanisms. Additionally, 15 structural alerts (SAs) for hepatotoxicity were identified based on SARpy software. These SAs will help to estimate the hepatotoxic risk of ingredients from herbs. Finally, a herb-ingredient network was constructed by integrating multiple datasets, which will assist to identify the hepatotoxic ingredients of herb/herb-formula quickly. In summary, a systemic analysis focused on HILI was conducted which will not only assist to identify the toxic molecular basis of hepatotoxic herbs but also contribute to decipher the mechanisms of HILI.

Site of metabolism prediction for oxidation reactions mediated by oxidoreductases based on chemical bond.

Motivation: The metabolites of exogenous and endogenous compounds play a pivotal role in the domain of metabolism research. However, they are still unclear for most chemicals in our environment. The in silico methods for predicting the site of metabolism (SOM) are considered to be efficient and low-cost in SOM discovery. However, many in silico methods are focused on metabolism processes catalyzed by several specified Cytochromes P450s, and only apply to substrates with special skeleton. A SOM prediction model always deserves more attention, which demands no special requirements to structures of substrates and applies to more metabolic enzymes. Results: By incorporating the use of hybrid feature selection techniques (CHI, IG, GR, Relief) and multiple classification procedures (KStar, BN, IBK, J48, RF, SVM, AdaBoostM1, Bagging), SOM prediction models for six oxidation reactions mediated by oxidoreductases were established by the integration of enzyme data and chemical bond information. The advantage of the method is the introduction of unlabeled SOM. We defined the SOM which not reported in the literature as unlabeled SOM, where negative SOM was filtered. Consequently, for each type of reaction, a series of SOM prediction models were built based on information about metabolism of 1237 heterogeneous chemicals. Then optimal models were attained through comparisons among these models. Finally, independent test set was used to validate optimal models. It demonstrated that all models gave accuracies above 0.90. For receiver operating characteristic analysis, the area under curve values of all these models over 0.906. The results suggested that these models showed good predicting power. Availability and implementation: All the models will be available when contact with wangyun@bucm.edu.cn. Contacts: wangyun@bucm.edu.cn or yjqiao@yahoo.com. Supplementary information: Supplementary data are available at Bioinformatics online.

Sequencing, Characterization, and Comparative Analyses of the Plastome of Caragana rosea var. rosea.

To exploit the drought-resistant Caragana species, we performed a comparative study of the plastomes from four species: Caragana rosea, C. microphylla, C. kozlowii, and C. Korshinskii. The complete plastome sequence of the C. rosea was obtained using the next generation DNA sequencing technology. The genome is a circular structure of 133,122 bases and it lacks inverted repeat. It contains 111 unique genes, including 76 protein-coding, 30 tRNA, and four rRNA genes. Repeat analyses obtained 239, 244, 258, and 246 simple sequence repeats in C. rosea, C. microphylla, C. kozlowii, and C. korshinskii, respectively. Analyses of sequence divergence found two intergenic regions: trnI-CAU-ycf2 and trnN-GUU-ycf1, exhibiting a high degree of variations. Phylogenetic analyses showed that the four Caragana species belong to a monophyletic clade. Analyses of Ka/Ks ratios revealed that five genes: rpl16, rpl20, rps11, rps7, and ycf1 and several sites having undergone strong positive selection in the Caragana branch. The results lay the foundation for the development of molecular markers and the understanding of the evolutionary process for drought-resistant characteristics.

Gastrodin Alleviates Cognitive Dysfunction and Depressive-Like Behaviors by Inhibiting ER Stress and NLRP3 Inflammasome Activation in db/db Mice.

Patients with diabetes mellitus (DM) suffer more risks from diabetic encephalopathy such as cognitive dysfunction and depressive-like behaviors. Numerous studies show that ER (endoplasmic reticulum) stress and inflammation play important roles in the development of diabetic encephalopathy. Gastrodin (Gas), one major component of Gastrodia elata, is traditionally used in central nervous system disorders and is believed to possess anti-inflammatory, anti-apoptotic, and other neuroprotective effects. This present study aims to explore the protective effects of Gas on diabetic encephalopathy. Gas was administrated daily (70 and 140 mg/Kg) for 12 weeks. Meanwhile, the fasting blood glucose and body weight of db/db mice were measured every two weeks. After Gas treatment, the Morris water maze (MWM) test and novel object recognition (NOR) test were performed to assess the learning and memory functions of db/db mice, and the forced swim test was performed to evaluate depressive-like behaviors of db/db mice. Additionally, the expression of ER stress and Nucleotide binding and oligomerization domain-like (Nod) receptor family pyrin domain-containing 3 (NLRP3) inflammasome related proteins were evaluated by using Western blot. Our study suggested that Gas attenuated blood glucose levels and dyslipidemia of db/db mice. It has been shown that Gas could improve learning and memory function and depressive-like behaviors of db/db mice. Moreover, Gas inhibited ER stress and NLRP3 inflammasome activation in the hippocampus. Taken together, this study demonstrates that Gas attenuates the diabetic encephalopathy by inhibiting ER stress and NLRP3 inflammasome activation.

Construction of Metabolism Prediction Models for CYP450 3A4, 2D6, and 2C9 Based on Microsomal Metabolic Reaction System.

During the past decades, there have been continuous attempts in the prediction of metabolism mediated by cytochrome P450s (CYP450s) 3A4, 2D6, and 2C9. However, it has indeed remained a huge challenge to accurately predict the metabolism of xenobiotics mediated by these enzymes. To address this issue, microsomal metabolic reaction system (MMRS)-a novel concept, which integrates information about site of metabolism (SOM) and enzyme-was introduced. By incorporating the use of multiple feature selection (FS) techniques (ChiSquared (CHI), InfoGain (IG), GainRatio (GR), Relief) and hybrid classification procedures (Kstar, Bayes (BN), K-nearest neighbours (IBK), C4.5 decision tree (J48), RandomForest (RF), Support vector machines (SVM), AdaBoostM1, Bagging), metabolism prediction models were established based on metabolism data released by Sheridan et al. Four major biotransformations, including aliphatic C-hydroxylation, aromatic C-hydroxylation, N-dealkylation and O-dealkylation, were involved. For validation, the overall accuracies of all four biotransformations exceeded 0.95. For receiver operating characteristic (ROC) analysis, each of these models gave a significant area under curve (AUC) value >0.98. In addition, an external test was performed based on dataset published previously. As a result, 87.7% of the potential SOMs were correctly identified by our four models. In summary, four MMRS-based models were established, which can be used to predict the metabolism mediated by CYP3A4, 2D6, and 2C9 with high accuracy.

[Mechanism of anti-aging therapy on Parkinson's disease].

Many studies have shown that anti-aging treatment has value to prevention and treatment of some diseases. For the treatment of Parkinson' s disease, clinical and experimental researches have proved the potential value of anti-aging treatment, yet the mechanism remains unclear. For this reason, this work used the anti-aging prescriptions of Buyang Huanwu decoction in traditional Chinese medicines example to discover the anti-aging treatment mechanism on Parkinson's disease. The results showed that the mechanism of mitochondrial damage, apoptosis, free radicals and oxidative stress could contribute to the treatment of Parkinson' s disease. Buyang Huanwu decoction is more than as the carrier in this article, the discovered anti-aging treatment mechanism Parkinson's disease is not confined to Buyang Huanwu decoction, could also be used to understand the anti-aging treatment mechanism using other prescription. The main contribution of this paper is to clarify the mechanism of anti-aging treatment of Parkinson's disease, and provide a new strategy for the treatment and prevention of Parkinson's disease.

[Elucidating hypoglycemic mechanism of Dendrobium nobile through auxiliary elucidation system for traditional Chinese medicine mechanism].

To build the Dendrobium nobile -T2DM network, and elucidate the molecular mechanism of D. nobile to type 2 diabetes (T2DM). Collect the chemical composition of D. nobile and the targets on T2DM by retrieving database and documents, build the network of D. nobile to T2DM using the entity grammar systems inference rules. The molecular mechanism of D. nobile to T2DM includes: (1) regulating lipid metabolism by lowering triglyceride; (2) reducing insulin resistance; (3) protecting islet cells; (4) promoting the glucose-dependent insulin tropic peptide (GIP) secretion; (5) inhibiting calcium channel. Under the guidance of network pharmacology, through entity grammar systems inference rules we elucidate the molecular mechanism of D. nobile to T2DM, and provide the basis for the further development of health care products based on D. nobile.

[Study on mechanism of Salvia miltiorrhiza treating cardiovascular disease through auxiliary mechanism elucidation system for Chinese medicine].

Salvia miltiorrhiza is a traditional Chinese medicine (TCM) and is widely used as a clinically medication for its efficiency in treating cardiovascular disease. Due to TCM is a comprehensive system, the mechanism of S. miltiorrhiza treating cardiovascular disease through integrated multiple pathways are still unclear in some aspects. With the rapid progress of bioinformatics and systems biology, network pharmacology is considered as a promising approach toward reveal the underlying complex relationship between an herb and the disease. In order to discover the mechanism of S. miltiorrhiza treating cardiovascular disease systematically, we use the auxiliary mechanism elucidation system for Chinese medicine, built up a molecule interaction network on the active component targets of S. miltiorrhiza and the therapeutic targets of cardiovascular disease to offer an opportunity for deep understanding the mechanism of S. miltiorrhiza treating cardiovascular disease from the perspective of network pharmacology. The results showed that S. miltiorrhiza treating cardiovascular disease through ten pathways as follows: improve lipid metabolism, anti-inflammation, regulate blood pressure, negatively regulates blood coagulation factor and antithrombotic, regulate cell proliferation, anti-stress injury, promoting angiogenesis, inhibited apoptosis, adjust vascular systolic and diastolic, promoting wound repair. The results of this paper provide theoretical guidance for the development of new drugs to treat cardiovascular disease and the discovery of new drugs through component compatibility.

[Study on mechanism for anti-hypertension efficacy of Eucommiae Cortex through assistant analysis systems for acting mechanisms of traditional Chinese medicine].

Elucidate the mechanism of Eucommiae Cortex in treatment of hypertension, to provide the basis for further research and development of Eucommiae Cortex. Our study using the entity grammar systems inference rules to analyse the interactions of chemical constituents of Eucommiae Cortex and disease target proteins at the molecular level, and got a biological network of Eucommiae Cortex anti-hypertension which inciude 602 nodes and 2 354 edges. We got 3 treatment of hypertension pathways of Eucommiae Cortex by analyzing biological network, that is, by inhibition of vascular remodeling to improve the deterioration of hypertension, reduce activity of polymorphism genetic genes related to essential hypertension, inhibition of carbonic anhydrase 1 to maintain the osmotic pressure, Eucommiae Cortex play the role of anti-hypertension.

[Study on mechanism for anti-depression efficacy of Sini San through auxiliary mechanism elucidation system for Chinese medicine].

As regulating the function of the liver and spleen of the famous traditional formula, Sini San is widely used in the treatment of various diseases caused by liver depression and Qi stagnation, and its efficacy is significant clinically. Recently it is discovered that Sini San is effective in the treatment of nervous system diseases such as depression. Furthermore, there is a lot of literature about the effect of Sini San on the molecular mechanism of antidepressant. However, the anti-depression mechanism of Sini San is not very clear, in our present study, based on the auxiliary mechanism elucidation system for Chinese medicine and network pharmacology system to construct the chemical ingredients of the target interactions and disease-related protein of the interaction network. Results show that there are 263 chemical ingredients and 19 corresponding targets of depression in Sini San network. Sini San can anti-depressant effect through G-protein coupled receptor protein signaling pathway, cAMP system, neurological system process and neurotransmitter secretion, inflammatory response, neuroendocrine, metal ion transport and so on. These studies provided valuable clues for the mechanism and treatment of anti-depressant.

A novel bioinformatics strategy to uncover the active ingredients and molecular mechanisms of Bai Shao in the treatment of non-alcoholic fatty liver disease.

Introduction: As a new discipline, network pharmacology has been widely used to disclose the material basis and mechanism of Traditional Chinese Medicine in recent years. However, numerous researches indicated that the material basis of TCMs identified based on network pharmacology was the mixtures of beneficial and harmful substances rather than the real material basis. In this work, taking the anti-NAFLD (non-alcoholic fatty liver disease) effect of Bai Shao (BS) as a case, we attempted to propose a novel bioinformatics strategy to uncover the material basis and mechanism of TCMs in a precise manner. Methods: In our previous studies, we have done a lot work to explore TCM-induced hepatoprotection. Here, by integrating our previous studies, we developed a novel computational pharmacology method to identify hepatoprotective ingredients from TCMs. Then the developed method was used to discover the material basis and mechanism of Bai Shao against Non-alcoholic fatty liver disease by combining with the techniques of molecular network, microarray data analysis, molecular docking, and molecular dynamics simulation. Finally, literature verification method was utilized to validate the findings. Results: A total of 12 ingredients were found to be associated with the anti-NAFLD effect of BS, including monoterpene glucosides, flavonoids, triterpenes, and phenolic acids. Further analysis found that IL1-ß, IL6, and JUN would be the key targets. Interestingly, molecular docking and molecular dynamics simulation analysis showed that there indeed existed strong and stable binding affinity between the active ingredients and the key targets. In addition, a total of 23 NAFLD-related KEGG pathways were enriched. The major biological processes involved by these pathways including inflammation, apoptosis, lipid metabolism, and glucose metabolism. Of note, there was a great deal of evidence available in the literature to support the findings mentioned above, indicating that our method was reliable. Discussion: In summary, the contributions of this work can be summarized as two aspects as follows. Firstly, we systematically elucidated the material basis and mechanism of BS against NAFLD from multiple perspectives. These findings further enhanced the theoretical foundation of BS on NAFLD. Secondly, a novel computational pharmacology research strategy was proposed, which would assist network pharmacology to uncover the scientific connotation TCMs in a more precise manner.

The Influences of Rainfall Intensity and Timing on the Assemblage of Dung Beetles and the Rate of Dung Removal in an Alpine Meadow.

Changes in precipitation patterns, including rainfall intensity and rainfall timing, have been extensively demonstrated to impact biological processes and associated ecosystem functions. However, less attention has been paid to the effects of rainfall intensity and rainfall timing on the assembly of detritivore communities and the decomposition rate of detritus such as animal dung. In a grazed alpine meadow on the eastern Qinghai-Tibet Plateau, we conducted a manipulative experiment involving two levels of rainfall intensity (heavy rainfall, 1000 mL/5 min; light rainfall, 100 mL/5 min) and five levels of rainfall timing (0, 2, 4, 24, and 48 h after yak dung deposition). The aim was to determine the effects of rainfall intensity, timing, and their interaction on the assemblage of dung beetles and dung removal rate during the early stage (i.e., 96 h after yak dung deposition) of dung decomposition. Light rainfall significantly increased species richness in the treatment of 48 h after dung pats were deposited. Heavy rainfall significantly decreased beetle abundance in both the 0 h and 48 h treatments while light rainfall had no effect on beetle abundance. Dung mass loss was significant lower in the 2 h treatment compared to other treatments regardless of rainfall intensity. The structural equation model further revealed that the species richness of dung beetles and dung mass loss were significantly affected by rainfall timing but not by rainfall intensity. However, no significant relationships were observed between any variables examined. These findings suggest that changes in precipitation patterns can influence both the structure of dung beetles and the rate of dung decomposition but may also decouple their relationship under a certain circumstance. Therefore, it is crucial to pay greater attention to fully understand local variability between the biological processes and ecosystem functions within a global climate change scenario.

The efficacy of therapies for post-stroke depression in aging: An umbrella review.

Post-stroke depression (PSD) is a common complication after stroke. PSD is associated with emotional disorders and psychological dependence, which are potential risk factors for stroke recurrence and suicidality. This study aimed to perform an umbrella review of therapies for PSD through a comprehensive literature search. A systematic search was conducted in the PubMed and Web of Science by two independent authors. We examined the Hamilton Depression Scale (HAMD), Activities of daily living (ADL), Neurologic function as efficacy endpoints, and the incidence of adverse events as safety profiles. Seventeen eligible studies, including 267 clinical trials were included in this study. The results showed that High-Frequency Repetitive Transcranial Magnetic Stimulation (HfrTMS), Acupuncture/EA+conventional treatment, Escitalopram, Modified Sini San, Moxibustion, Xiaoyao Formula, Paroxetine, Chinese herbal medicine, Exercise, Citalopram, and Cognitive behavioral therapy are beneficial for improving the depression symptoms of patients with PSD. HfrTMS and Sertraline may have an impact on slowing the scores of activities of daily living or neurologic function. In addition, Acupuncture/EA+conventional, Escitalopram, Citalopram, Sertraline, and Fluoxetine showed no serious adverse events in PSD patients. Our study demonstrated that 11 treatment methods can effectively improve the condition of PSD patients.

Identification of hepatoprotective traditional Chinese medicines based on the structure-activity relationship, molecular network, and machine learning techniques.

The efforts focused on discovering potential hepatoprotective drugs are critical for relieving the burdens caused by liver diseases. Traditional Chinese medicine (TCM) is an important resource for discovering hepatoprotective agents. Currently, there are hundreds of hepatoprotective products derived from TCM available in the literature, providing crucial clues to discover novel potential hepatoprotectants from TCMs based on predictive research. In the current study, a large-scale dataset focused on TCM-induced hepatoprotection was established, including 676 hepatoprotective ingredients and 205 hepatoprotective TCMs. Then, a comprehensive analysis based on the structure-activity relationship, molecular network, and machine learning techniques was performed at molecular and holistic TCM levels, respectively. As a result, we developed an in silico model for predicting the hepatoprotective activity of ingredients derived from TCMs, in which the accuracy exceeded 85%. In addition, we originally proposed a material basis and a drug property-based approach to identify potential hepatoprotective TCMs. Consequently, a total of 12 TCMs were predicted to hold potential hepatoprotective activity, nine of which have been proven to be beneficial to the liver in previous publications. The high rate of consistency between our predictive results and the literature reports demonstrated that our methods were technically sound and reliable. In summary, systematical predictive research focused on the hepatoprotection of TCM was conducted in this work, which would not only assist screening of potential hepatoprotectants from TCMs but also provide a novel research mode for discovering the potential activities of TCMs.

Licoflavone B, an isoprene flavonoid derived from licorice residue, relieves dextran sodium sulfate-induced ulcerative colitis by rebuilding the gut barrier and regulating intestinal microflora.

Ulcerative colitis (UC) is a major inflammatory disease worldwide. We previously demonstrated that licorice residue flavones (LFs) showed satisfactory efficacy in the treatment of UC. Therefore, research into the ingredients of LFs may lead to the discovery of novel anti-UC targets. In the current study, we separated licoflavone B (LB) from LFs and administered it to dextran sodium sulfate (DSS)-exposed C57BL/6 mice for 14 days. Our results demonstrated that high dose LB (120 mg/kg) significantly prevented DSS-induced weight loss, disease activity index (DAI) increase, histological damage, and colonic inflammation, indicating that LB has ameliorative effects on UC. We also investigated the composition of the intestinal barrier and microflora in an attempt to explore the mechanisms of LB against UC. As a result, we found that LB preserved the integrity of the colonic barrier by inhibiting colonic cell apoptosis and protecting the expression of occludin, claudin-1, and ZO-1. Moreover, LB reshaped the microflora composition by suppressing harmful bacteria (Enterococcus et al.) and boosting beneficial microorganisms (Bacteroides et al.). Further molecular exploration implied that LB exerted anti-UC activity through blocking the MAPK pathway. Here, we explored anti-UC activity of LB for the first time and clarified its mechanisms. These results will provide valuable clues for the discovery of novel anti-UC agents.