Obesity continues to rise in the juveniles and obese children are more likely to develop metabolic syndrome (MetS) and related cardiovascular disease. Unfortunately, effective prevention and long‐term treatment options remain limited. We determined the juvenile cardiac response to MetS in a swine model. Juvenile male swine were fed either an obesogenic diet, to induce MetS, or a lean diet, as a control (LD). Myocardial ischemia was induced with surgically placed ameroid constrictor on the left circumflex artery. Physiological data were recorded and at 22 weeks of age the animals underwent a terminal harvest procedure and myocardial tissue was extracted for total metabolic and proteomic LC/MS–MS, RNA‐seq analysis, and data underwent nonnegative matrix factorization for metabolic signatures. Significantly altered in MetS versus. LD were the glycolysis‐related metabolites and enzymes. In MetS compared with LD Glycogen synthase 1 (GYS1)‐glycogen phosphorylases (PYGM/PYGL) expression disbalance resulted in a loss of myocardial glycogen. Our findings are consistent with the concept that transcriptionally driven myocardial changes in glycogen and glucose metabolism‐related enzymes lead to a deficiency of their metabolite products in MetS. This abnormal energy metabolism provides insight into the pathogenesis of the juvenile heart in MetS. This study reveals that MetS and ischemia diminishes ATP availability in the myocardium via altering the glucose‐G6P‐pyruvate axis at the level of metabolites and gene expression of related enzymes. The observed severe glycogen depletion in MetS coincides with disbalance in expression of GYS1 and both PYGM and PYGL. This altered energy substrate metabolism is a potential target of pharmacological agents for improving juvenile myocardial function in MetS and ischemia. While previous research has identified a number of signaling pathways, individual proteins, and metabolites involved in the myocardial response to MetS, these discoveries are limited to correlations and mostly reflect reactive processes rather than causative factors. Although MetS is strongly associated with the development of coronary artery disease (CAD), the true nature of this relationship is not yet fully understood. Unfortunately, effective prevention and long‐term treatment options for CVD are limited. As a result, the risk of long‐term cardiovascular damage remains high in obese children.Analyses of blood or blood components are unlikely to capture all of the metabolic alterations that the myocardium undergoes in MetS due to the myocardium's specific gene expression pattern and metabolic characteristics , which we have previously shown induces effects analogous to MetS in humans (hyperlipidemia and impaired glucose tolerance), or a control normoglycemic lean diet  for 11 weeks, as previously described  male Ossabaw swine aged 4 weeks were fed either an obesogenic diet  methanol and 0.6 mL acetonitrile. Samples were analyzed using a 5500 QTRAP hybrid triple quadrupole mass spectrometer (AB/SCIEX) coupled to a Prominence UFLC HPLC system (Shimadzu) with SRM, as previously reported . Hierarchical clustering was performed  using the Sequest algorithm within Proteome Discoverer v 2.3 software (Thermo Fisher Scientific). The Sequest database search was performed with the following parameters: trypsin enzyme cleavage specificity, two possible missed cleavages, 10 ppm mass tolerance for precursor ions. Peptide assignments from the database search were filtered down to a 1% FDR. The relative label‐free quantitative and comparative among the samples were performed using the Minora algorithm and the adjoining bioinformatics tools of the Proteome Discoverer 2.3 software. To select proteins that show a statistically significant change in abundance between two groups, a threshold of 1.5‐fold change with p‐value (0.05) were selected . 200 μg of protein per sample was subjected for trypsin digestion. LC/MS–MS was conducted on a fully automated proteomic technology platform that includes an Agilent 1200 Series Quaternary HPLC system (Agilent Technologies) connected to a Q Exactive Plus mass spectrometer (Thermo Fisher Scientific). (Agilent Technologies) and Q Exactive Plus mass spectrometer (Thermo Fisher Scientific). The MS/MS spectra were acquired at a resolution of 17,500, with a targeted value of 2 × 104 ions or maximum integration time of 200 ms. The ion selection abundance threshold was set at 8.0 × 102 with charge state exclusion of unassigned and 2.5Sample lysates were prepared by homogenizing tissue samples with RIPA buffer (Boston BioProducts). Following lysate preparation, Western blots were conducted (50 μg of protein/lane), as previously reported, using Glycogenin‐1 antibody  and goat‐anti‐Rabbit IgG, Alexa Fluor 594   staining was conducted on deparaffinized and rehydrated to water tissue sections, as suggested by the kit supplier . Following PAS staining, tissue sections underwent α‐amylase treatment as a control.2.7t‐test was used to compare differences between 2 datasets. Data are presented as means ± SD. In all cases, p < 0.05 was considered to be a statistically significant difference. Functional biological data analysis was conducted with the list of genes that were differentially expressed (p < 0.05) in response to diet using the g:Profiler approach . For statistical analysis unpaired two‐tail Student's 33.1n = 6, 193 ± 18 mg/dL vs. 94 ± 12 mg/dL, p = 0.02), triglycerides (1.78 ± 0.7 mmol/L vs. 0.71 ± 0.27 mmol/L), plasma LDL , total cholesterol , increased weight gain (59 ± 3.1 kg vs. 22 ± 2.5 kg), and systolic  and diastolic  blood pressure versus. swine in the LD group  exhibited higher fasting plasma glucose , Warburg effect (p = 3.4e‐5), glycolysis, and butyrate metabolism (p = 1.1e‐4). Also overrepresented in MetS are processes related to the urea cycle (p = 0.004). NMF identified 27 metabolites as being overrepresented in LD versus. MetS based on the weight of the metabolite with a higher probability in LD than in MetS  for polar metabolites. For each myocardial sample, 283 metabolites were identified and quantitatively compared. Six MetS and six LD samples were analyzed in a single experiment to prevent batch effects. The obtained mass spec data are challenging to statistically analyze since there are 283 metabolites and less replicates (12 pigs). Because of this, we used an unsupervised learning method based on NMF to compare variations in the metabolite profiles in MetS versus. LD based on the specific proportion of the 283 metabolites in each swine , nucleotide binding (padj = 1.07e‐4), glucose binding (padj = 2.97e‐4), carbohydrate derivative binding (padj = 1.44e‐3), and glycogen phosphorylase activity (padj = 2.74e‐3). We confirmed specific transcriptomic changes for individual swine using data from parallel LC–MS/MS proteomics and targeted LC–MS/MS metabolomics.RNA‐Seq was performed on the same swine tissues and the results were correlated with the expression of genes involved in sugar and glycolysis metabolism. To validate the RNA‐Seq results at the protein level, mass spectroscopy was performed on three LD and three MetS parallel samples. From the RNA‐Seq and proteomics LC–MS/MS data, genes and proteins that showed a threshold of a 1.5‐fold change were selected for analysis. To perform pathway‐related and biological process‐related analyses, we used g:Profiler Figure . The g:P3.3n = 12, p = 0.001) and fructose‐6‐phosphate , both of which are glycolysis entry metabolites. Less abundant in MetS is fructose 1,6‐bisphosphate  from the ATP reproductive glycolysis step. The juvenile MetS myocardium displayed a significantly diminished presence of pyruvate , which represents glycolysis completion. Lactate availability was reduced to a lesser extent . Hydroxyphenylpyruvate HPP  and N‐acetyl‐L‐alanine  were used as a control for the equal loading content of metabolites as the change in tissue content are not significant  and pyruvate  in MetS swine compared to LD swine with no diet‐related difference , the insulin‐responsive glucose transporter 4 , and the mitochondrial hexokinase 1 , which catalyzes the rate‐limiting and first obligatory step of glucose metabolism. The protein level of the glucose‐6‐phosphate isomerase , which catalyzes the reversible isomerization of G6P to F6P, was also significantly reduced. It is noteworthy that MetS had no effect on the PGI mRNA , a key regulatory enzyme of glycolysis that catalyzes the phosphorylation of F6P to FBP , P2Y purinoceptor 6, P2RY6 , P2Y purinoceptor 12, P2RY12  and desmin  expression levels did not show diet‐related mRNA or protein content differences. Desmin was used as internal controls for the above observations and have traditionally been used as such in cardiac literature ‐)‐methyltransferase, METTL1 . Western blots with total cardiac tissue lysates (50 μg protein) from three MetS and three LD pigs using a specific antibody against GYS1 supported the RNA‐seq and protein mass spectroscopy observations  and liver glycogen phosphorylase PYGL  are elevated in MetS versus LD. The glycogen phosphorylases' main function is to degrade glycogen, and their upregulation corresponds to the observed glycogen depletion in the MetS myocardium. Thus, the balance of GYS1 and glycogen phosphorylase expression and activity represent a powerful mechanism that ensures glycogen availability as a source of energy in the myocardium.Besides glucose, endogenous glycogen can be catabolized via glycolysis after enzymatic conversion to a glycolytic intermediate. The periodic acid Schiff (PAS) staining of tissue sections revealed that glycogen was significantly less abundant in MetS versus LD. Figure  and thats Figure . HoweverNeither glycogen synthase kinase 3 beta (GSK3ß), glycogen synthase kinase 3 alpha (GSK3α), (both key regulators of the function of glycogen synthase and thus cell glycogen levels), nor the phosphohexose isomerase (GDI), which catalyzes the reversible isomerization of GDP and GTP, exhibited diet‐related mRNA and protein variations in content. These enzymes served as internal controls for the observations made above Figure .When taken together, these findings point to impaired glycolytic flux and loss of glycogen in the juvenile myocardium under conditions of hyperglycemia, obesity, hypertension, and ischemia which result in dysregulated glucose metabolism homeostasis and ATP availability Figure .4Obesity is becoming more prevalent in children and adolescents, coinciding with rising MetS rates. Obesity in children is associated with abnormal glucose metabolism, dyslipidemia, inflammation, and compromised vascular function, all of which are components of MetS reproduced in our juvenile swine model. Given the role of MetS in metabolic changes the alteration in cardiac tissue metabolism in Mets has been an area of intense investigation. Previous studies have investigated various MetS‐related processes including abnormal gene expression, mitochondrial function, ion channel regulation, hemodynamics, and electrophysiology. Despite this research, the complexity of MetS has left a myriad of metabolic alterations that are not yet fully explored or even discovered. Few researchers have looked at energy substrate metabolism and the myocardial function as paradigm for the treatment of heart failure despite the fact that metabolism and myocardial function are inextricably linked.To bridge gaps in our understanding of the myocardial response to MetS, and further unravel the molecular basis of predisposition to cardiovascular dysfunction in children, we adopted a multiomics approach that includes metabolomics, transcriptomics, and proteomics combined with physiologic data. By utilizing the computational NMF approach, we were able to integrate the various layers of information derived from the multiomics approach, resulting in identification of new key regulatory nodes in the control of the juvenile myocardial response to MetS.In addition to the detrimental effects of the proinflammatory state associated with MetS, hyperglycemia also adversely affects myocardial glucose uptake. Our findings indicate that MetS animals have impaired myocardial glucose absorption; specifically, we noticed significantly lower protein levels of GLUT1 and GLUT4, reducing the transport of glucose across cellular plasma membranes. The low levels of mitochondrial HK1, which is required for the first mandatory step of glucose metabolism, resulted in low G6P availability in MetS swine. The role of G6P in glucose metabolism has been well‐established as the metabolic hub connecting glycolysis, glycogen synthesis, and the pentose phosphate pathway (PPP). G6P and its isomerization product, F6P, were both significantly reduced in the MetS heart. The decreased availability of F6P in MetS swine might reflect the limited availability of the glycolytic isomerase, PGI. In addition to its function as a glycolytic enzyme, PGI is a significant angiogenic factor that promotes endothelial cell motility . It should also be noted that FBP alone has functions that are unrelated to the glycolysis pathway. It acts as an antioxidant by binding and sequestering soluble Fe(II) preventing its oxidation to insoluble Fe(III), which is a generator of reactive oxygen species via the Fenton reaction , RO1 HL46716(FWS), NIH T32HL160517 to DH and MB; B.S.A. is supported by The Laboratory Directed Research and Development (LDRD) program of Los Alamos National Laboratory under project 20230067DR. Los Alamos National Laboratory is operated by Triad National Security, LLC, for the National Nuclear Security Administration of U.S. Department of Energy under Contract No. 89233218CNA000001.The authors declare that they have no competing interests.Figure S1Click here for additional data file.Figure S2Click here for additional data file.Figure S3Click here for additional data file.Table S1Click here for additional data file.


Fraxini Cortex (FC) has been widely used as a medicinal herb to treat some diseases. However, the pharmacological mechanisms of FC for UC treatment are still unclear.Ulcerative colitis (UC) is a common type of inflammatory bowel disease. Due to the elusive pathogenesis, safe and effective treatment strategies are still lacking. An integrated platform combining network pharmacology and experimental studies was introduced to decipher the mechanism of FC against UC. The active compounds, therapeutic targets, and the molecular mechanism of action were acquired by network pharmacology, and the interaction between the compounds and target proteins were verified by molecular docking. Dextran sulfate sodium (DSS)-induced colitis model was employed to assess the therapeutic effect of FC on UC, and validate the molecular mechanisms of action predicted by network pharmacology.A total of 20 bioactive compounds were retrieved, and 115 targets were predicted by using the online databases. Ursolic acid, fraxetin, beta-sitosterol, and esculetin were identified as the main active compounds of FC against UC. PPI network analysis identified 28 FC-UC hub genes that were mainly enriched in the IL-17 signaling pathway, the TNF signaling pathway, and pathways in cancer. Molecular docking confirmed that the active compounds had high binding affinities to the predicted target proteins. GEO dataset analysis showed that these target genes were highly expressed in the UC clinical samples compared with that in the healthy controls. Experimental studies showed that FC alleviated DSS-induced colitis symptoms, reduced inflammatory cytokines release, and suppressed the expression levels of IL1β, COX2, MMP3, IL-17 and RORγt in colon tissues.In vivo results demonstrated that FC alleviated DSS-induced colitis.FC exhibits anti-UC properties through regulating multi-targets and multi-pathways with multi-components. The online version contains supplementary material available at 10.1186/s12906-023-03983-0. Ulcerative colitis (UC) is a chronic and recurrent bowel disease, seriously impairs the quality of life, and aggravates the economic burden on patients . In the Fraxini Cortex (FC) is the dry barks of Fraxinus rhynchophylla Hance, Fraxinus chinensis Roxb, Fraxinus szaboana Lingelsh, or Fraxinus stylosa Lingelsh [Lingelsh , 8. It hLingelsh , 10, antLingelsh  and antiLingelsh . AccumulLingelsh . In a prLingelsh . FraxineLingelsh . BesidesLingelsh . MoreoveLingelsh , 17. HowThis study aimed to uncover the pharmacological mechanism of FC for UC treatment through a systems approach. Firstly, the active ingredients of FC were screened out based on the existing databases and pharmacokinetic characteristics. Then, the targets of the compounds, and the compound-target interactions were identified using comprehensive methods. The hub targets of FC against UC were obtained through the PPI network analysis. GO and KEGG pathway enrichment analyses were performed to predict the potential function of hub genes. Molecular docking and GEO microarray dataset were further used to identify the core targets, and in vivo studies were performed to assess the therapeutic effect of FC on DSS-induced colitis and validate the results of network pharmacology. The schematic overview of the process is summarized in Fig. A Traditional Chinese Medicine Systems Pharmacology Database  was used to collect the compounds contained in FC. The name and molecular structure of the compounds were verified by PubChem database. Predicted targets of all compounds were collected from Swiss Target Prediction and TCMSP databases. The official target names were standardized by the UniProt database (release 2022_02), and duplicates were excluded to obtain potential targets of FC against UC.UC-related genes were collected from DisGeNET database (version 7.0) and GeneCards database (Version 5.8) with the keyword “ulcerative colitis, UC”, as our previous studies , 19. ThePPI network of the FC-UC targets was built via STRING database (version 11.5), and visualized by Cytoscape software (version 3.8.0). The topology analyses of the networks were conducted using CytoNCA plugin. The hub targets were obtained based on three topological parameters, including degree centrality (DC), betweenness centrality (BC) and closeness centrality (CC), according to network pharmacology evaluation method guidance .https://david.ncifcrf.gov/home.jsp) to uncover the detailed events of these genes involved in UC pathogenesis. The inclusion criteria was set as p-value < 0.05.GO function and KEGG pathway enrichment analyses of hub genes were conducted on the online platform of DAVID 2021. The SDF format of compounds were obtained from the PubChem database (2021 update), and the target molecular structures of the proteins were derived from the RCSB protein database. The docking study was employed with a semi-flexible docking system, and conducted via AutoDock v4.2.6. PyMOL (version 2.5.4) was used to generate docking conformation, and analyze the lowest binding energy.p-value< 0.05. The expression distributions of DEGs in different tissue were showed as box plots. Statistical difference of two groups was analyzed by Wilcox test, and a p-value< 0.05 was considered statistically significant.To study the expression levels of FC targeted UC genes, the expression profiling data from GSE22619  and GSE3Fraxini cortex extract was provided by Xi’an Quanao Biotech Co., Ltd. , and prepared as follows: Fraxini Cortex was chopped and grinded into small pieces, then extracted in 50% ethanol (v/v) with a ratio of 1/3 (g/mL) at 80 °C for 30 min twice. The extracts were centrifuged at 3500 rpm for 20 min at 4 °C, and concentrated and dried at 65 °C for 8 h to obtain powder. The contents of esculin and esculetin were 46.87 mg/g and 16.05 mg/g, respectively, according to the quality control report from the manufacturer. The power was redissolved in distilled water at various concentrations for animal experiments. The drug concentration was set based on a previous study [us study  and our C57BL/6 male mice 20 ~ 25 g) were purchased from Hangzhou Ziyuan Experimental Animal Corporation  2019–0004). DSS was purchased from MP Biomedicals Inc. . MES was obtained from Ethypharm pharmaceutical company . After adaptive feeding for 1 week, the mice were randomized into six groups of 10 each, control, model, FC low dosage , FC median dosage , FC high dosage , and mesalazine (MES) groups. The mice in model, FC, and MES group were given 3.0% (w/v) DSS for consecutive 7 days, respectively , 25. The g were pBlood samples were collected and serum were isolated as in our previous reports , 26, 27.The tissues were collected and fixed in 10% neutral-buffered formalin for over 24 hours. Then, the tissues were embedded in paraffin and cut into 4 μm thick sections for hematoxylin and eosin (H&E) staining. Histopathological score (HS) was scored using colon histopathology criteria .Total proteins of colon tissues were extracted using Protein Prep Kit , and quantified by BCA Protein Assay Kits . Proteins were then resolved in a 10% SDS–polyacrylamide gel electrophoresis (SDS–PAGE) gel, and transferred onto a polyvinylidene fluoride (PVDF) membrane. After blocked with skimmed milk for 2 h, the membranes were incubated with primary antibodies overnight at 4 °C. The primary antibodies, IL-1β (AF510), MMP1 (DF6325), MPP3 (AF0217), MMP9 (AF5228), COX2 (AF7003), IL-17A (DF6127) and β-actin (AF7018) were obtained from Affinity Biosciences , and anti-RORγt (14–6988-82) from Thermo Fisher Scientific . After washing with PBST, the membranes were incubated in horseradish peroxidase-conjugated secondary antibodies , and the bands were visualized with enhanced chemiluminescence (ECL) reagents. Signal intensities were quantified using Image J software .t test was used for comparison of two groups, and one-way analysis of variance (ANOVA) was used for comparison among multiple groups. Statistical significance was defined as p-values less than 0.05.All data were presented as the mean ± standard deviation (SD), and the results were analyzed with SPSS 22.0 software . Student’s A total of 20 FC active compounds were obtained from TCMSP database Table S. AlthougThe UC-related genes were retrieved from the GeneCards database and DisGeNet database. Then, the FC potential targets were matched with these UC-related genes via the Venny software, and a total of 43 FC-UC common targets were acquired Fig. B.A network with 43 nodes (FC-UC common genes) and 459 edges was constructed basing on the results of the protein-protein interactions (PPI) analyzed by STRING database. According to topological analysis and the criteria of 2-fold median of DC, CC and BC, a core network with 28 nodes and 318 edges was generated , 15 items to cellular components (CC), and 40 items to molecular function (MF) Table S-S5. In tA compound-target-pathway network was built by matching the nine key inflammatory and immune response-related pathways with the hub genes and FC components. This network consists of 35 nodes  and 117 edges  were analyzed after normalization and batch removal  were highly expressed in UC clinical samples validated by analysis of GEO expression datasets. Previous studies also demonstrated that THMs conquer UC via interfering IL-17 signaling pathway. For example, Qing Chang Suppository Powder improved DSS-induced colitis, and modulated the expression of mediators in IL-17 signaling pathway . As the Recently, network pharmacology has been widely applied to explore the action mechanism of THMs on various disease. This approach can be used to screen active compounds, describe the interaction between compounds and targets, and predict the molecular mechanism of THMs . In addiIn this research, ursolic acid, fraxetin, beta-sitosterol, and esculetin were identified as the main compounds, and MAPK8, IL6, RELA, TNF, IL1B, FOS, PTGS2, and MMP3 were considered as major targets of FC in UC treatment. Molecular docking verified that these compounds showed good binding interaction with these target proteins. FC exerted its therapeutic effect on UC primarily through IL-17 signaling pathway, TNF signaling pathway and Pathways in cancer. In vivo studies demonstrated that FC exerted its therapeutic effect on UC by inhibiting inflammatory cytokines release, and modulated IL-17 signaling pathway. Future studies are needed to determine the detailed molecular mechanism in mammals and human cell lines.Additional file 1: Figure S1. Therapeutic effects of FC on DSS-induced colitis. A. HE staining of colon tissues ; B. Histological score. Data are presented as the mean ± SD (n = 10). *P < 0.05 versus model group. Figure S2. Schematic graph of animal experiment. Figure S3. GEO datasets processing. (A) The boxplot of the normalized data. The x-axis represents samples and the y-axis represents the expression values. (B) PCA results before batch removal for multiple datasets. (C) PCA results after batch removal. Figure S4. Representative picture of the colon (n = 10).Additional file 2: Table S1. Active compounds of Fraxini Cortex. Table S2. Predicted targets of the Fraxini Cortex compounds. Table S3. The hub genes enriched in biological processes. Table S4. The hub genes enriched in cellular components. Table S5. The hub genes enriched in molecular function. Table S6. KEGG pathway enrichment analysis of the hub genes. Table S7. Molecular docking of four compounds for UC targets. Table S8. The mRNA expression profile of FC targets in IL-17 signaling pathway.Additional file 3.


Treatment of plants with pathway-specific inhibitors and analysis of the effects of various light conditions indicated distinct biosynthetic origin of Prens and Dols. Feeding with deuteriated, pathway-specific precursors revealed that Dols, present in leaves and roots, were derived from both MEP and MVA pathways and their relative contributions were modulated in response to precursor availability. In contrast, Prens, present in leaves, were almost exclusively synthesized via the MEP pathway. Furthermore, results obtained using a newly introduced here ‘competitive’ labeling method, designed so as to neutralize the imbalance of metabolic flow resulting from feeding with a single pathway-specific precursor, suggest that under these experimental conditions one fraction of Prens and Dols is synthesized solely from endogenous precursors , while the other fraction is synthesized concomitantly from endogenous and exogenous precursors. Additionally, this report describes a novel methodology for quantitative separation of 2H and 13C distributions observed for isotopologues of metabolically labeled isoprenoids. Collectively, these in planta results show that Dol biosynthesis, which uses both pathways, is significantly modulated depending on pathway productivity, while Prens are consistently derived from the MEP pathway.Isoprenoids, including dolichols (Dols) and polyprenols (Prens), are ubiquitous components of eukaryotic cells. In plant cells, there are two pathways that produce precursors utilized for isoprenoid biosynthesis: the mevalonate (MVA) pathway and the methylerythritol phosphate (MEP) pathway. In this work, the contribution of these two pathways to the biosynthesis of Prens and Dols was addressed using an  Isoprenoids constitute a very large and diverse group of natural compounds and their structural and functional diversity is especially astonishing in plants. They include representatives of both primary  and secondary  metabolites  which play vital roles in basic physiological processes in plants as well as in the interactions of plants with the environment ,2. The ucis-prenyltransferases (CPTs) + or ammoniated [M + NH4]+) were used for the calculations.Mass spectra of a quality allowing for the calculations of deuterium incorporation  were obtained only for dominating polyisoprenoids  and phytosterols . For polyisoprenoid alcohols, signals comprising the isotopic envelope of the most abundant adducts in the mass spectra , where p13C — the natural 13C abundance estimated independently from MS spectra recorded for the native products, k — number of 13C atoms out of n carbon atoms present in the molecule; n = 5·N for polyisoprenoids (N — number of isoprene units in Pren and Dol molecules), n = 29 for sitosterol and stigmasterol, and n = 28 for campesterol.Natural 2H enrichment due to the substrate used is described by the binomial distribution B, where d = 2 for 2H2-DX and 3 for 2H3-MVL, p2H — the relative contribution of unlabeled (i.e. 1H) molecules in 2H-labeled precursors (estimated independently for both precursors), k — number of 2H atoms out of d·N locations at which the hydrogen atoms in the molecule can be replaced with 2H, N — number of isoprene units.z(i = 0–N) — unknown distribution describing the substrate-dependent cross-talk between the MEP and MVA pathways, where i — number of isoprene units derived from the MEP pathway, while the remaining N-i units are derived from the MVA pathway.For native  polyisoprenoids, their isotopologue distribution associated with the natural 13C abundance was modeled according to the binomial distribution B. The appropriate model was implemented in Origin Pro (ver. 9.6.0). The natural 13C abundance (p13C) was estimated as 1.02 ± 0.01%  using a global fit applied to the isotopic envelopes determined for Dol-16 and Pren-11.The proposed procedure for analyzing the MS data was aimed at separating the three independent phenomena which contribute to the mass of a given molecule. Thus, it was assumed that the experimentally observed distribution of isotopologues is the convolution of three independent distributions describing the following features:2H and N + 1 values z(0..N)) were estimated using an appropriate written ad hoc function with a standard non-linear fitting procedure (nls) in the R package (R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ version 3.6.1).For each experiment performed with deuteriated precursors, the values of 13 (Pren-11) or 18 (Dol-16) parameters (pk = 0–N).The obtained data were analyzed regarding either the deuteriation level or the deuteriation pattern. The deuteriation level is the efficiency of deuterium incorporation into a given metabolite. It was calculated as the fraction (%) of the total area of the signals assigned to deuteriated isotopologues relative to the area of all isotopologues, both deuteriated and non-deuteriated, identified in the MS spectrum of a given compound. The deuteriation pattern describes the profile of isotopologues as a function of the number of deuteriated isoprene units in the polyisoprenoid alcohol (13C abundance was assumed for both native (synthesized from photosynthesis-derived precursors) and deuteriated molecules of phytosterols of a particular type to be identical with that estimated for Pren-11 and Dol-16.The MS spectra of phytosterols were analyzed using a similar methodology as described above for polyisoprenoids. For phytosterols, signals of molecular ions were taken into account. 12C Pren-11 or Dol-16, i.e. corresponding to k = 0, were excluded from the analysis.The deuteriation patterns for Pren-11 and Dol-16 were normalized relative to the total population of a given molecule . A generk and N–k are the total numbers of isoprene units derived from MVA and MEP pathway, respectively, N is the total number of isoprene units in the molecule.where a and b stand for asymptotic populations of natural isotopic abundance and fully deuteriated isoprene units in a macromolecule, p represents the probability of a subsequent step of polyisoprenoid chain elongation to occur via the assumed pathway, t test (α = 0.01).Statistical significance of the effects of specific inhibitors and light conditions was determined by Student's HPLC/UV analysis of the lipid fraction isolated from leaves of hydroponically grown Arabidopsis plants revealed the presence of a single family of Prens composed of 9, 10, 11 and 12 isoprene units (i.u.) accompanied by a single family of Dols built of 15, 16 and 17 i.u. . The rooNext, we estimated the content of polyisoprenoids together with phytosterols and plastidial pigments  in the leaves and roots of inhibitor-treated  and contQuantitative analysis of the selected isoprenoids revealed inhibitor-induced changes in both tissues. In the roots, LOV treatment resulted in a considerably reduced content of Dols and phytosterols   while FSIn the leaves, LOV treatment resulted in a slight decrease in the content of Dols (∼80% of control) while that of phytosterols remained unchanged (∼98% of control). In contrast with Dols, LOV did not affect the content of Prens (∼95% of control). Surprisingly, LOV induced an increase in the content of pigments . FSM treatment did not alter the content of Dols while that of phytosterols was slightly decreased (∼86% of control). Most interestingly, FSM caused a considerable decrease in the content of Prens (61% of control) while the level of plastidial pigments was decreased only a little (∼90% of control). Nevertheless, when only young rosette leaves were analyzed, a prominent reduction in pigment content  was noted  indicatiThe changed levels of the isoprenoid pathway end-products noted both in leaves and in roots fully confirmed that the inhibitors were taken up from the medium by the root system and transported to the leaves. The observed changes of the content of ‘marker’ isoprenoids in response to the inhibitors suggest tight interconnection and reciprocal dynamic regulation of the activity of the MEP and MVA pathways. Furthermore, although the opposing effects of LOV and FSM treatment on Dol and Pren levels suggest divergent mechanisms of their formation, the biosynthetic origin of polyisoprenoids cannot be unambiguously deduced basing on this data.To further investigate the involvement of the MVA and MEP pathways in Pren and Dol biosynthesis we were looking for a stimulus that could have contradictory impact on the flux through both pathways toward their end products. Light seemed a good candidate since it down-regulates the MVA pathway and induces the MEP pathway ,45. ThusThe opposite effects of light on the accumulation of plastidial pigments and phytosterols in leaves are in agreement with the influence of light on the MEP and MVA pathway activity  reported earlier ,47. ImpoIn planta, metabolic labeling experiments were designed to address the role of the MVA and MEP pathways in polyisoprenoid biosynthesis in leaves and roots. To diminish metabolic perturbations exerted by exogenous sugars [2H3)MVL (D-MVL) and 1-deoxy-D-xylulose (D-DX) are employed, specifically deuteriated D-IPP and D-DMAPP are formed  while Ds sugars  to form e formed  and thene formed  and for e formed .Two sets of labeling experiments were performed. During a ‘single precursor’ labeling experiment plants were grown in the presence of a single labeled precursor (D-DX or D-MVL). During a ‘competitive’ labeling experiment plants were simultaneously fed with both precursors . This second type of experiment was introduced in order to minimize the perturbations of metabolic fluxes caused by feeding with a single precursor. In a single-precursor labeling experiment deuteriated IPP/DMAPP are shuffled with natural isotopic abundance counterparts stemming from photosynthesis, while in a competitive labeling experiment they are additionally shuffled with a pool of exogenous unlabeled precursors. Consequently, the mass spectra of the labeled isoprenoid compounds comprise complex sets of signals derived both from natural isotopic abundance  and from13C (∼1.1%) and (ii) deuterium abundance in the applied substrate (see below), and (iii) the unknown distribution of labeled isoprene units resulting from cross-talk between the MEP and MVA pathways. Every deuterium-enriched molecule is supposed to replicate the 13C isotopic envelope of the compound of natural isotopic abundance  , i.e. two binomial distributions associated with (i) the natural abundance of bundance . As a reC, etc.) . Consequ3C atoms .13C abundance (∼1.1%). The outcome is described below for the analyzed groups of isoprenoids. We present the results in terms of ‘deuteriation level’ and ‘deuteriation pattern’.Therefore, to quantify deuterium incorporation the raw mass spectra of metabolically deuteriated isoprenoids were numerically processed to dissect the effects of the metabolic feeding with deuteriated precursors from that resulting from natural Deuteriation level of the compound of interest corresponds to the fraction (%) of deuteriated isotopologues possessing at least one labeled isoprene unit and is used as an indicator of the efficiency of incorporation of deuterium   presents the distribution of isotopologues with an indicated number of deuteriated isoprene units  its effect on the isotopic envelopes of the analyzed isoprenoids is negligible.2H enrichment of the deuteriated precursors was evaluated by fitting an appropriate model to the experimental distributions of Pren-11 and Dol-16 isotopologues upon labeling with a single precursor. The level of non-deuteriated precursors was estimated at 2.9 ± 0.9% for 2H2-DX and 0.7 ± 0.1% for 2H3-MVL, which corresponds to the enrichment of 97.1 ± 0.9% for 2H2-DX and 99.3 ± 0.1% for 2H3-MVL.Supplementation of Arabidopsis growth medium with exogenous D-MVL or D-DX resulted in the appearance of additional signals in the mass spectra of the analyzed polyisoprenoids. It indicated the incorporation of labeled precursors  . SignalsAnalysis of MS spectra allowed us to estimate the number of MEP-derived isoprene units in Dol-16 molecules .When D-DX was used as a single precursor, Dol-16 labeling reached 13 i.u. while the D-DX/MVL competitive experiment resulted in labeling of 4–5 i.u. . Upon apn > 6) of deuteriated i.u. was decreased upon competitive labeling conditions. Moreover, we observed a local maximum at 8–10 Da for D-DX used as a single precursor which might suggest that preferably ∼4–5 i.u. are derived from the MEP pathway (n > 13) was much larger for the D-MVL/DX experiment than for the D-DX/MVL experiment , revealed that all 16 i.u. were labeled, i.e. derived from the MEP pathway, although the population of isotopologues containing high numbers  remains constant independently of the type of precursor used and the labeling conditions employed (P > 1), which suggests that the probability of elongation exceeds 1, are apparently illogical and indicate that the tested model is not applicable for Pren-11 upon D-MVL labeling; it also suggests that access of the MVA-derived precursors to the site of Pren-11 biosynthesis remains limited which again confirms the predominance of the MEP pathway in Pren-11 synthesis. It should be kept in mind however that due to the limited deuteriation level observed for Pren-11 (e.g. 25% for D-MVL/DX experiment) and resulting data noise these results require careful interpretation. In addition during D-MVL/DX competitive labeling experiments when both precursors are administered to plants the conversion of MVL to IPP used to synthesize might not follow the classical isoprenoid pathway  the presence of exogenous MVL or DX of natural isotopic abundance increases the contribution to the final product of exogenous deuteriated D-DX or D-MVL, respectively.Taken together the application of specific precursors suggests that Pren-11 is favorably derived from MEP pathway precursors, while Dol-16 is derived — both in roots and in leaves — concomitantly from MEP and MVA pathway precursors with a consistently stronger contribution of the MVA pathway .To get a more in-depth understanding of the mechanism of polyisoprenoid biosynthesis, modeling of their MS spectra was employed. This analysis, described in detail in the Supplemental Material , shows tThe model shows that Pren-11 is preferentially synthesized from endogenous MEP precursors derived from photosynthesis. At the same time, the presence of exogenous DX activates an additional minor route that leads to Pren molecules of a ‘mosaic’ character. Thus, some molecules consist of units derived from exogenous MEP precursors and endogenous precursors  with a m13C)glucose suggesting that the initial isoprene units of Dol molecules are of MEP-pathway origin [The model indicates that upon precursor feeding the Dol-16 produced in the leaves is of mixed origin, and it can be expected that the same is valid under native conditions. The model suggests that the enzymatic machinery responsible for the formation of Dol can use both exogenous and endogenous substrates . When apy origin . So far,in vivo labeling of Dol-16 in roots with pathway-specific precursors are in line with our data for Dols isolated from the hairy roots after labeling with (13C)glucose.Dol-16 in roots, similarly to that in leaves, is formed with the contribution of both the MEP and MVA pathways. However, contrary to the situation in the leaves, in the roots, another pool of Dol-16 molecules originating solely from the MEP pathway can be detected . The exiLabeled molecules of the analyzed phytosterol species  were detected both in the leaves and in the roots of plants grown in media supplemented with either type of deuteriated isoprenoid precursor .In single-precursor labeling experiments, phytosterols were more efficiently labeled with D-MVL  than with D-DX . Still, these results indicate that the MEP pathway could, to some extent, contribute to the biosynthesis of phytosterols under the tested conditions. Not only did the labeling levels of individual phytosterols differ, but dissimilar deuteriation profiles were observed for stigmasterol and sitosterol in most of the labeling experiments. This observation is, at first glance, surprising since stigmasterol is synthesized from sitosterol through C-22 desaturation . It shouIn the competitive labeling experiments, the presence of MVL of natural isotopic abundance (D-DX/MVL labeling) increased the incorporation of deuterium (∼28–38% of molecules deuteriated). In contrast, the addition of natural isotopic abundance DX (D-MVL/DX labeling) caused a decrease in the deuteriation for sitosterol and stigmasterol  and an increase for campesterol (∼45%).The incorporation of D-MVL into phytosterols was low (∼10–20%) and so was the incorporation of D-DX (∼20%). In the competitive labeling experiments the presence of natural isotopic abundance DX (D-MVL/DX experiment) substantially increased the incorporation of deuterium , similarly as was the case for polyisoprenoid alcohols. The presence of MVL of natural isotopic abundance (D-DX/MVL experiment) slightly decreased the deuteriation of campesterol and sitosterol (∼10%) but increased that of stigmasterol (∼35%).Among all tested conditions the highest level of deuterium incorporation was found for D-MVL/DX competitive labeling in the leaves. The relatively low level of D-MVL incorporation prompted us to analyze the profiles of phytosterols and intermediates of the phytosterol biosynthetic pathway. For plants grown in media supplemented with exogenous MVL, both labeled or unlabeled, the amount of these intermediates was considerably elevated compared with control plants, e.g. after D-DX/MVL labeling we observed a 64- and 12-fold increase in 24-methylene cycloartanol levels in leaves and roots, respectively . At the in vitro studies of Arabidopsis sterol methyltransferase 2 (SMT2) revealed its potential to form end products with M + 10 Da [The calculated distribution of deuteriated isotopologues was not in full accordance with the pattern predicted for labeling via the classical (cycloartenol-mediated) phytosterol biosynthetic route  i.e. the + 10 Da . Additio + 10 Da . Taken tm/z higher than those found in samples from control plants. This shows successful albeit low incorporation of labeled precursors of both pathways. A qualitative assessment of these spectra suggests that feeding with D-DX was more efficient for carotenoid labeling than D-DX/MVL feeding, which resulted in the lowest labeling efficiency of all tested variants. The incorporation of D-MVL into carotenoids was marginal in both tested variants (D-MVL and D-MVL/DX). A qualitative assessment of these spectra suggests that feeding with D-DX was more efficient for carotenoid labeling than D-DX/MVL feeding. The incorporation of D-MVL into carotenoids was very low in both tested variants (D-MVL and D-MVL/DX).Analysis of labeling was performed exclusively for pigments isolated from leaves. Although the presence of carotenoids (lutein and β-carotene) was confirmed in the roots of hydroponically grown Arabidopsis plants by HPLC/UV (data not shown), their content in the samples obtained from metabolically labeled plants was too low to permit mass spectrometry analysis. Molecular ions, convenient for the analysis of deuterium incorporation, were detected in LC/APCI mass spectra of lutein and β-carotene . HoweverThe contributions of the MVA and MEP pathways to Pren synthesis have never been documented. In contrast, the biosynthetic origin of Dols has so far been investigated only in hairy root cultures, indicating the involvement of both pathways ,32. ThusThe first approach we took was to evaluate changes in the accumulation of dolichols and Prens upon differentiated metabolic flow through the pathways induced by photoperiod or by the application of inhibitors. These experiments indicated that the biosynthetic origin of Prens and Dols is divergent. The accumulation of Prens in long-day conditions (together with chlorophylls and carotenoids), as well as the decrease in their content after application of fosmidomycin clearly show that they are derived from the MEP pathway, while changes in Dol content revealed a pattern similar to that noted for phytosterols, connecting these compounds with the MVA pathway. Divergent regulation of the MVA and MEP pathways by light is well documented . Light sNicotiana benthamiana leaves, the activity of the key MVA pathway enzymes HMGR (HMG-CoA reductase) and HMGS (HMG-CoA synthase) was substantially lower than in the roots, and it was further decreasing upon plant senescence [However, inhibitor-triggered changes in the accumulation of phytosterols and plastidial pigments differed from those anticipated for ‘marker’ compounds. For phytosterols, the changes were organ-dependent and in the leaves differed from those expected for MVA-end products. The significant inhibitory effect of lovastatin treatment towards phytosterols in the roots but not in the leaves might reflect different biosynthesis rates in these organs. This supposition is supported by a previous study, which showed that in nescence . Furthernescence . It shounescence . Thus thnescence ,65. Addinescence ,44 that per se toxic to plants unless used in high concentrations, it still may alter the intermediate exchange rate between the MVA and MEP [in planta. Although at first glance, the ‘mosaic' character of Dols seems not fully consistent with the results of the inhibitor studies, the observed inconsistencies might be the effect of fluctuations resulting from the metabolic cross-talk between the MVA and MEP pathways throughout plant growth and development. The results might also reflect a potential for pathway cross-flow, which is attained due to the increased availability of the specific precursors. Interestingly, either of the pathways alone seems sufficient to provide the IPP needed for Dol chain biosynthesis. However, overall labeling was more efficient after supplementation with the deuteriated precursor of the MVA pathway, both in leaves and roots  after feeding young (two-week old) cotton seedlings with labeled specific precursors. Interestingly, the analysis of labeled isoprenoids indicated a more intensive flux of labeled MVA into the specific MEP end products than in the opposite direction. Additionally the labeling efficiency was tissue-dependent [In contrast with the generally accepted opinion that phytosterols are MVA pathway products, metabolic labeling also revealed some MEP pathway contribution to their formation, again indicating pathway cross-talk. In line with this, substantial cross-talk between pathways has been recently observed in the ependent . Numerouependent ,66. Furtependent .LOI1) and CHLOROPLAST BIOGENESIS6 (CLB6) genes control the posttranscriptional regulation of the MEP and MVA pathways [Moreover, elucidations of the mechanisms regulating the cross-talk led to interesting conclusions reported in the literature. Hence upon treatment of Arabidopsis seedlings with pathway-specific inhibitors a cross-talk between MEP and MVA pathway was documented at the metabolite level, but lack of correlation between gene expression patterns indicated the role of posttranscriptional processes in regulating flux through isoprenoid metabolic pathways . Subsequpathways ,69. Collpathways . Moreovepathways .In our study incorporation of labeled precursors into chlorophylls and carotenoids in mature leaves was generally very low. This might suggest that the developmental stage of the plant, with its particular biochemical demands, might define the potential for cross-talk between pathways and thus the labeling outcome. Another important factor affecting the labeling results might be the duration of the feeding with specific precursors due to the divergent turnover rate of isoprenoids. Chlorophylls and carotenoids were shown to undergo constant turnover in mature Arabidopsis leaves even in non-stress conditions , which mIt is worth noting that polyisoprenoids appear to be attractive model compounds for labeling studies aimed at the elucidation of isoprenoid biosynthesis. Formation of the polyisoprenoid chain directly from IPP minimalizes the possibility of miscalculating label incorporation due to the presence of enzymatic regulatory steps and the accumulation of labeled intermediates as was observed for phytosterols.To counteract the possible metabolic disturbances triggered by fueling up of one of the pathways through the application of a single precursor (for references see ) we came(i) one uses predominantly endogenous precursors and has very limited access to exogenous DX and/or its metabolites and is simultaneously blocked for exogenous MVL-derived metabolites,(ii) the other one uses both exogenous and endogenous precursors and/or their metabolites and receives influx from both pathways.Any of the methods commonly used to analyze the cross-talk of the MEP and MVA pathway, whether the genetic or chemical blockade of the pathway or supplementation with pathway specific precursors affects the homeostasis of the plant cell and perturb the metabolic flow. It should be kept in mind however, that in contrast with the former methods which lead to visible phenotypic changes/lethality [Finally, the intriguing conclusion from analysis of labeling data that under the conditions of the competitive labeling experiments two subpopulations of differentially labeled Pren-11 and Dol-16 were detected deserves special attention. It might suggest that differentially labeled Pren molecules are formed in the two functionally or perhaps even spatially separated putative plastidial sub-compartments comprising the plastidial isoprenoid-generating pathway:ethality  feeding ad hoc formation of the complex using these DXP molecules to form isoprenoids.The methodology used in this report to analyze polyisoprenoid biosynthetic pathway assumes that exogenous precursors, DX and MVL, are accessible to the synthetic machinery of both isoprenoid generating systems. Indeed, the results obtained here and manifold studies using metabolic labeling, confirm this assumption. Still, the possible effect of metabolite channeling, which might control the influx of precursors, cannot be neglected and should be addressed in future projects. It is an open question whether polyisoprenoid biosynthetic route comprises a metabolon — ‘supramolecular complex of sequential metabolic enzymes and cellular structural elements'  or rathecis-prenyltransferase(s), CPT(s), sequentially elongating the growing polyisoprenoid chain, has independent access to these two pools of differentially labeled oligoprenyl precursors. Such a model of spatial regulation of Dol biosynthesis could be accomplished by spatially- and/or temporally -regulated export of oligoprenyl precursors from plastids towards cytoplasmic CPT(s) via yet unknown transporters (Catharantus roseus [Since our previous reports suggested that the synthesis of Dols is initiated in plastids and finalized in the cytoplasm , the modsporters . Althougs roseus ; the exps roseus .An alternative explanation of how Dol is synthesized might come from the suggested ability of cytoplasmic/ER enzymes to access the pool of non-polar plastidial metabolites, most likely through plastid:ER membrane interaction domains . This trWe documented for the first time that in intact plants, the biosynthetic routes leading to Prens and Dols differ in terms of MEP- and MVA-pathway contributions. We show that Prens are derived mainly from the MEP pathway; however, marginal contributions from the MVA pathway are observed under specific conditions. In contrast, Dols are ‘mosaic’ isoprenoids, and the relative input of the MVA and MEP pathways to their synthesis is modulated by the current productivity of these routes.The model of Dol biosynthesis built on these data is in line with our previous observations noted for hairy root cultures ,32. The The incorporation of deuterium from specific deuteriated precursors, DX or MVL, documents their effective uptake from the medium and transport towards the cellular compartments capable of converting them to IPP, i.e. the plastids and the cytoplasm/ER, respectively. The data presented here show that a limited bi-directional exchange of intermediates between the cytoplasm and the plastids is plausible, as suggested earlier ,80; the The competitive labeling approach, presented here for the first time, provides additional information on the cross-talk between the MVA and MEP pathways.


The boundary conditions are crucial for numerical methods. This study aims to contribute to this growing area of research by exploring boundary conditions for the discrete unified gas kinetic scheme (DUGKS). The importance and originality of this study are that it assesses and validates the novel schemes of the bounce back (BB), non-equilibrium bounce back (NEBB), and Moment-based boundary conditions for the DUGKS, which translate boundary conditions into constraints on the transformed distribution functions at a half time step based on the moment constraints. A theoretical assessment shows that both present NEBB and Moment-based schemes for the DUGKS can implement a no-slip condition at the wall boundary without slip error. The present schemes are validated by numerical simulations of Couette flow, Poiseuille flow, Lid-driven cavity flow, dipole–wall collision, and Rayleigh–Taylor instability. The present schemes of second-order accuracy are more accurate than the original schemes. Both present NEBB and Moment-based schemes are more accurate than the present BB scheme in most cases and have higher computational efficiency than the present BB scheme in the simulation of Couette flow at high Re. The present Moment-based scheme is more accurate than the present BB, NEBB schemes, and reference schemes in the simulation of Poiseuille flow and dipole–wall collision, compared to the analytical solution and reference data. Good agreement with reference data in the numerical simulation of Rayleigh–Taylor instability shows that they are also of use to the multiphase flow. The present Moment-based scheme is more competitive in boundary conditions for the DUGKS. Discrete Unified Gas Kinetic Scheme (DUGKS) for all Knudsen Number Flows was proposed , which ct + 0.5Δt in the DUGKS but at time t in the LBM. Yang et al. )xw, ξα, t + h) at the cell interface center xw and at time nt+1/2 = nt + 0.5Δt (h = 0.5Δt). If the grid mesh is fine enough, it is easy and efficient to treat the curved boundaries by adopting the current boundary treatment  schemes are the conditional scheme proposed by Bouzidi et al.  and the q < 0.5 and q ≥ 0.5 according to the scaled distance.Inspired by the conditional scheme , the IBBTaking the boundary configuration in q = ½, the conditional interpolation formulas are reduced to the “bounce-back” scheme. Since a distribution function travels precisely one grid spacing from t to t + ∆t, particles that start from xf can end precisely at the same location only when xf is half a grid spacing from the wall.When ➀xw, t + h) is interpolated from the distribution functions at xf, xff and xfff. We use quadratic interpolation to obtain,A virtual distribution function ➁xw, t + h) is obtained by using the known distribution function xw, t + h). All the present BB, NEBB, and Moment-based schemes .According to the current boundary treatment, the unknown distribution function ➂xf, t + h), which is interpolated from xw, t + h), xff, t + h), xfff, t + h).At last, we need to calculate the known distribution function Inspired by the unified scheme , the prexf, xff, and xfff. It is inevitable to undermine the local computation property and further the parallel performance of LBM . Later, it was reported that the free parameter γ can be arbitrarily chosen within the range of  in a general single-node second-order scheme proposed by Chen et al. (2021)  × . The present boundary conditions are applied to the no-slip walls. The initial vortex is located at positions  and the total enstrophy Ω(t) are calculatedω is the vorticity.The symbol Clercx and Bruneau  simulatex1, y1) =  and  =  initially. Then they are propelled towards the right wall. To test the effect of the present schemes on the vortices after the dipole collides, vorticity contour plots are present in In the dipole–wall collision benchmark test for the normal case, two monopoles are located at positions  and enstrophy Ω(t) at different times, as shown in For further comparison with reference data , we presRayleigh–Taylor instability can occur when a layer of heavy fluid descends as light fluid below it rises. We perform a simulation using the same parameters as the first case of Re = 256, as shown in Figure 7.4 in Ref. . The lefy = 0.5Y + 0.1Xcos(2πx/X), where x, y, X, and Y are all in lattice units. We set the densities of heavy fluid and light fluid to be ρh = 0.12 and ρl = 0.04, respectively, so that At = (ρh − ρl)/(ρh + ρl) = 0.5. We set U = 0.04, and the gravitational acceleration is g = U2/X = 1.25 × 10−5 in the -y direction.Initially, there is a zero-velocity field, and the location of the perturbed interface is set as In this study, the HCZ model  is introg and f, corresponding to the density ρ and phase order ϕ, denote the particle distribution function in terms of position x, discrete particle velocity ξα, and time t. The relaxation times gτ and fτ are related to the viscosity and the mobility coefficient in the Cahn–Hilliard equation. Usually, we set gτ = fτ.The distribution functions gS and fS represent the source terms, which can be written asFs represents the force associated with surface tension,κ determines the strength of surface tension and κ = 0.01 in our simulations.ψ represents the function of the density ρ or phase order ϕ. mined as (72)ψ(ρ)a = 12RT, b = 4.In our simulations, eqg and eqf can be calculated asThe equilibrium distributions Since Equations (66) and (67) share the same format as Equation (1), we will not repeat the detailed depiction of evolution in the DUGKS. In this subsection, we study the effect of varied boundary conditions on the numerical simulation results of Rayleigh–Taylor instability.ρ = 0.045, 0.05, 0.055, …, 0.115 are drawn in each figure. In this subsection, it is noted that the “Original” scheme represents the boundary condition with xa = ya = 0 in Equation (37) or Equation (39), and the “Present” scheme represents the proposed scheme of the boundary condition with xa = 0, ya = -g in Equation (37) or Equation (39).The results of varied boundary conditions at Re = 256 are shown in As shown in error and CPU time of the present Moment-based scheme are minimal, although they are almost indistinguishable.The simulation is executed on a Win10 X64 system with Intel(R) Core(TM) i5-8250U CPU (1.60 GHz)s. For comparison, the convergence error and CPU time at the last time step are recorded in t + 0.5Δt). The present work tests and analyzes, for the first time, the Moment-based scheme for the DUGKS. The mentioned boundary conditions are evaluated through theoretical analysis and numerical tests.Owing to the DUGKS with transformed distribution functions and force terms, we should convert the original boundary conditions into a new format. In this study, the proposed schemes of the BB, NEBB, and Moment-based boundary conditions are proposed for the DUGKS. The boundary conditions will be translated into constraints on the unknown transformed distribution functions at a half time step Couette flowThe accuracy and stability of the present schemes are validated by numerical tests of Couette flow, Poiseuille flow, Lid-driven cavity flow, normal dipole–wall collision, and Rayleigh–Taylor instability. The following conclusions are obtained:6). The L2 errors in both NEBB and Moment-based schemes are equal, which are a little less than those in the BB scheme at Re = 100 under different meshes. Both the NEBB and Moment-based schemes are more accurate and converge to the steady state faster than the BB scheme at high Re .The present schemes can predict accurate results of simulating the Couette flow, even with a coarse mesh for a large Reynolds number (Re = 10(2)Lid-driven cavity flowThe present schemes can predict accurate results by simulating the lid-driven cavity flow. It is found that the results of the BB scheme are in better agreement with the reference data than those of Moment and NEBB schemes at Re = 10,000.(3)Poiseuille flowL2 errors of the present NEBB and Moment-based schemes are less than the original NEBB and Moment-based schemes, respectively, which shows the present schemes are more accurate than the original schemes. It is found that the present schemes for the DUGKS are second-order accurate. The original Moment scheme is more accurate than the original NEBB scheme under different meshes. The present Moment scheme is more accurate than the present NEBB scheme with N = 16 and 32, in contrast to the cases with N = 64 and 128. Compared with the BB and NEBB schemes in Ref. [L2 errors of the present Moment-based scheme are minimal.The results of the present schemes agree very well with the analytical solution under different meshes  at Re = 100, 1000, and 10,000. The  in Ref. , the L2 (4)Normal dipole–wall collisionWith the present moment-based scheme, the DUGKS appears to be more accurate than the TRT-LBM. The data obtained by using the present moment-based scheme appears to be more accurate than the data obtained by using the present BB and NEBB schemes in the sense that they are in closer agreement with the benchmark data. These show that the proposed moment-based scheme can be a competitive method.(5)Rayleigh–Taylor instabilityThe present results agree well with the reference results, which show the present schemes can effectively capture the evolution of the interface. It is found that the error and CPU time of the present Moment-based scheme are minimal among the mentioned boundary conditions. The Rayleigh–Taylor instability simulation shows that choosing a wall boundary condition can influence the density and velocity near the interface slightly.


The Radix Bupleuri and Radix Paeoniae Alba herb-pair (RRH) are the most classic compatible drug pair for the treatment of hepatitis. However, the underlying mechanism remains unclear. Therefore, network pharmacology and molecular docking were conducted to investigate the prospective therapeutic constituents, targets, and pharmacological mechanisms of RRH in the treatment of hepatitis. The active components of RRH from the TCMSP database and disease-related targets from the OMIM, PharmGkb, GeneCards, TTD, and DrugBank databases were identified. The “drug-target-disease” network diagram and protein–protein interaction (PPI) network were constructed using Cytoscape (v3.8.0) and Online STRING 11.0. GO and KEGG pathway enrichment analyses were performed using R version 4.1.2, and molecular docking was performed to verify the results. We placed 176 overlapping cross genes into Online STRING 11.0 and obtained 14 core targets. A “Component-Target-GO-KEGG” network diagram was constructed, which was composed of 7 components, 14 targets, 10 biological processes, and 10 signal pathways. A total of 2413 GO biological processes and 174 KEGG pathways were explored for hepatitis treatment. Quercetin, kaempferol, isorhamnetin, and beta-sitosterol, which are the main bioactive components, were employed to bind the disease's hub targets, ensuring fulfillment of spatial and energy matching. The anti-hepatitis mechanism of RRH may be associated with several targets including RELA, AKT1, JUN, MAPK1, TP53, CCND1, MYC, NFKBIA, CDKN1A, and their respective signaling pathways. The main bioactive components in RRH, including quercetin, kaempferol, isorhamnetin, and beta-sitosterol, were used to bind the hub targets of the disease, which may provide insights into drug development for hepatitis. China is the world largest burden of HBV infection, about 7 million people develop advanced liver disease or liver cancer every year, of which 0.38 million people die of liver cancer. Given the seriousness of the epidemic of hepatitis virus infection, despite the continuous development of new drugs and the continuous improvement of antiviral drugs in recent years, the complete cure of chronic hepatitis remains a clinical problem and other more effective drug treatments need to be found. There are various causes of hepatitis, including hepatitis virus infection, alcoholic hepatitis, nonalcoholic fatty liver disease, autoimmune hepatitis, and chemical drugs. Despite the availability of diverse antiviral and liver-protective drugs, the majority of them are designed to target only one specific therapeutic site. As a result, the development of effective liver-protective drugs with multiple components, targets, and pathways remains insufficient. Drug treatments for various types of hepatitis are still in the exploration stage. Therefore, it is of great significance for human health care to develop new drugs and explore new therapeutic targets for hepatitis treatment. This article aims to identify effective components and specific targets for hepatitis treatment using network pharmacology in RRH. Additionally, it aims to establish a theoretical basis for the development of multi-component, multi-target, and multi-path liver protection drugs through molecular docking validation.Hepatitis is an important public health problem worldwide and its harm to the world is obvious. According to the statistics of the World Health Organization, the number of chronic hepatitis B patients worldwide is as high as 250 million, accounting for approximately 3.5% of the world total population. Since liver fibrosis is a manifestation of hepatitis, the primary objective of this study is to further investigate the important constituents of traditional Chinese medicine that possess anti-hepatitis properties, through in silico modeling validation. Radix Bupleuri and Radix Paeoniae Alba herb-pair (RRH) are the most classic compatible drug pairs for the treatment of chronic liver disease.,5 It is also the core drug of many classic and famous prescriptions for the treatment of liver disease, such as Xiaoyao Powder, Chaihu-Shugan-San, and Sini Powder, which have many functions, such as restoring liver function and inhibiting liver fibrosis.–8 In these formulas, Radix Bupleuri plays a major role, and Radix Paeoniae Alba plays an auxiliary role. However, the mechanism of RRH in the anti-hepatitis effect of RRH remains unclear. Therefore, it is important to clarify the effective components of RRH for the treatment of hepatitis and explore new therapeutic targets using network pharmacology and molecular docking technology.Previous studies have investigated the mechanisms of peach kernel and safflower in the prevention of liver fibrosis, utilizing network pharmacology and molecular docking methods. Furthermore, the binding mode and intensity of drug ligands and targets can be predicted using molecular docking technology. In this study, we conducted a comprehensive network pharmacology study based on network pharmacology and molecular docking technology to explore the possible pharmacological mechanism of RRH in the treatment of hepatitis, including screening of key ingredients and exploring new therapeutic targets, to lay a theoretical foundation for the development of new drugs for hepatitis treatment.Network pharmacology systematically predicts the possible mechanism of drug treatment of diseases by building a “component-gene target-disease” network diagram based on gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses.http://tcmspw.com/tcmsp.php) was used to screen for key ingredients and targets of RRH. Five databases, including GeneCards Human Gene database , online mendelian inheritance in man database , therapeutic target database , DrugBank Database (https://go.drugbank.com/), and pharmacogenetics and pharmacogenomics knowledge base , were used to search for disease-related targets. We used Cytoscape 3.8.0, STRING Databases 11.5, AutoDockTools version 1.5.6 for network construction, protein-protein interaction (PPI) information collection, and molecular docking verification. R version 4.1.2 was used to explore the mechanism of RRH in treating hepatitis based on GO and KEGG enrichment analysis.The traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) (https://www.uniprot.org/). The active components and related targets in RRH were screened from TCMSP, and the targets were annotated using the UniPort and GeneCards databases.The active components and drug targets of RRH were explored using TCMSP. By screening the drug components of Radix Bupleuri and Radix Paeoniae Alba, we defined drug molecules with oral bioavailability ≥ 30% and drug similarity ≥ 0.18 as active molecules. The corresponding target gene information was searched for and annotated using the UniProt database , and signaling pathways, we built a “Components-Core Targets-GO-KEGG” network diagram concurrently. Target molecules, including components, BPs, signaling pathways, and disease-related target genes, are represented as nodes. The interactions between the nodes are represented as edges.The components and disease-related targets of RRH in the treatment of hepatitis were imported into Cytoscape 3.8.0, and then a “component-target-disease” network was constructed and visualized. The amplified target proteins of RRH in the STRING database were input into Cytoscape software V3.8.0, and then the 6 parameters of “Betweenness,” “Closeness,” “Degree,” “Eigenvector,” “Local Average Connectivity-based method,” and “Network” were obtained by CytoNCA tool. The median values of these parameters were calculated, and all nodes whose 6 parameters were greater than the median values were selected as hub nodes.The overlapping intersection genes between the active component targets of RRH and target genes in hepatitis were imported into Online STRING 11.0 to construct a visualized PPI network diagram. The confidence score in PPI was set to 0.9 for the highest confidence from the STRING database.We repeated the screening of these 6 parameters to obtain the core target of the disease. Cytoscape was used to construct a visual network map between core targets, key ingredients, BPs, and signal pathways. The GO database, including BP, molecular function, and cellular component, was used to explore the potential biological molecular mechanisms. The KEGG database has also been used to identify biological functions and candidate targets.R version 4.1.2 was used for GO functional annotation and KEGG pathway analysis of RRH in the treatment of hepatitis, and the enrichment analysis results were visualized.https://pubchem.ncbi.nlm.nih.gov/) and the protein database database (http://www.rcsb.org/pdb/home/home.do), respectively. The molecular docking protocol was performed by comparing the binding positions and binding energies of each ligand. The planar structure of the 4 ligands, namely kaempferol, quercetin, beta-sitosterol, and isorhamnetin, was converted into a 3-dimensional structure using ChemBio3D software (Version 14.0.0.117). Subsequently, the MM2 function optimized the force field of each ligand and saved the processed ligands in *.mol2 format. Finally, AutoDockTools Version 1.5.6 converted each ligand to *.pdbqt format. After removing water molecules and original ligand molecules, as well as undergoing hydrogenation using PyMOL Version 2.4.0, the key target proteins were converted to *.pdbqt format using AutoDockTools Version 1.5.6. The Ligand Docking module was used to verify the reliability of the results using AutoDockTools Version 1.5.6, and the bonding activity of the component to the key targets was evaluated using the binding energy. The grid size (60 Å × 60 Å × 60 Å) was set for ligand molecules and receptor molecules, with the center of the grid aligned with the ligand. The molecular docking employed a semi-flexible docking method, allowing for ligand conformational changes. The Lamarckian genetic algorithm was utilized with the number of docking rounds set to 100, while all other parameters were maintained at their default values. Subsequently, the Autodock docking program was executed for molecular docking. The binding energy was used as a reference indicator to obtain the docking values between each ligand and receptor protein. The optimal docking result with the lowest binding energy was then screened out for further analysis.The structures of the RRH components and key targets were downloaded from the PubChem database  to build a visual PPI network diagram, with a confidence score of 0.9. The PPI network diagram was composed of 175 nodes and 556 edges . To further investigate 2413 GO BPs, 2142 BPs, 61 cellular component terms, and 209 molecular functions were found to be enriched in GO enrichment analysis (P < .01). The top 10 GO BPs are listed as follows: (GO:0009410) response to xenobiotic stimulus, (GO:0032496) response to lipopolysaccharide, (GO:0002237) response to molecule of bacterial origin, (GO:0010038) response to metal ion, (GO:0031667)response to nutrient levels, (GO:0070482) response to oxygen levels, (GO:0001666) response to hypoxia, (GO:0062197) cellular response to chemical stress, (GO:0036293) response to decreased oxygen levels, and (GO:0000302) response to reactive oxygen species , Fluid shear stress and atherosclerosis (hsa05418), AGE-RAGE signaling pathway in diabetic complications (hsa04933), Prostate cancer (hsa05215), Hepatitis B (hsa05161), Chemical carcinogenesis-receptor activation (hsa05207), Bladder cancer (hsa05219), Human cytomegalovirus infection (hsa05163), Kaposi sarcoma-associated herpesvirus infection (hsa05167), and Hepatitis C (hsa05160) , kaempferol (MOL000422), isorhamnetin (MOL000354), and beta-sitosterol (MOL000358), for binding to the hub targets of the disease. The lowest binding energies of CDKN1A, MAPK1, RB1, TP53, NFKBIA, and MYC for quercetin were −6.9, −5.5, −8.0, −7.5, −7.9, and −7.2 kcal/mol, respectively. Meanwhile, the docking results showed that there were 1, 2, 3, 2, 3, and 2 hydrogen bonds between quercetin and the proteins mentioned above, respectively. The binding energy of AKT1 for kaempferol was −6.1 kcal/mol with 2 hydrogen bonds. JUN for beta-sitosterol was −7.1 kcal/mol with 0 hydrogen bonds, and RELA for isorhamnetin was −6.1 kcal/mol with 4 hydrogen bonds. The affinities of the combinations of bioactive components and hub targets are shown in Table RRH, which is the core medicine of many famous prescriptions including Xiaoyao Powder and Chaihu Shugan Powder, is the classic compatible drug for the treatment of chronic hepatitis. However, the role of RRH in the treatment of chronic hepatitis remains unclear. In this study, we constructed a “components-core gene targets-GO-KEGG” network diagram by screening the core targets of RRH to explore the mechanism of RRH in treating chronic hepatitis. Through a network pharmacological study of RRH in the treatment of chronic hepatitis, we explored the main active ingredients and pharmacological mechanism of RRH, providing a theoretical basis and new targets for follow-up drug research and development.Topological analysis of the core component network diagram shows the mechanism and function of the core components. According to the degree of composition, the results suggested that quercetin (MOL000098), kaempferol (MOL000422), isorhamnetin (MOL000354), beta-sitosterol (MOL000358), (+)-catechin (MOL000492), 3,5,6,7-tetramethoxy-2- chromone (MOL004598), and petunidin (MOL000490) in RRH may play important roles in the treatment of chronic hepatitis based on our results from network pharmacology. We obtained 14 core targets in the PPI network using the CytoNCA tool in Cytoscape software, including AKT1, MYC, RELA, JUN, MAPK1, FOS, TP53, HIF1A, CCND1, NFKBIA, ESR1, MAPK14, RB1, and CDKN1A. Moreover, the molecular docking results verified the combination of core targets and key ingredients, including quercetin (MOL000098), kaempferol (MOL000422), isorhamnetin (MOL000354), and beta-sitosterol (MOL000358).By constructing a “components-core targets-GO-KEGG” network diagram, we have visualized the core therapeutic targets of multiple components in RRH, and explored the potential BPs and signaling pathways for the treatment of chronic hepatitis. Through GO enrichment analysis, we found that the top GO BPs, including response to lipopolysaccharide (GO:0032496), response to molecule of bacterial origin (GO:0002237), response to nutrient levels (GO:0031667), response to oxygen levels (GO:0070482), response to hypoxia (GO:0001666), cellular response to chemical stress (GO:0062197), response to decreased oxygen levels (GO:0036293), and response to reactive oxygen species (GO:0000302), may be the main BPs involved in the treatment of chronic hepatitis. At the same time, KEGG enrichment analysis revealed the potential signaling pathways of RRH in the treatment of chronic hepatitis, including Hepatitis B (hsa05161), Hepatitis C (hsa05160), Chemical carcinogenesis-receptor activation (hsa05207), Lipid and atherosclerosis signaling pathway (hsa05417).Among the classic famous prescriptions for the treatment of chronic hepatitis, Radix Bupleuri plays a major role, and Radix Paeoniae Alba plays an auxiliary role, which was verified by our research results. Among the 7 core components of RRH, Radix Bupleuri contains 4 components, whereas Radix Paeoniae Alba has 2 components, both of which share 1 component. The 2 components of Radix Paeoniae Alba play an auxiliary role in treating hepatitis by binding targets AKT1 and JUN and activating Hepatitis B, Hepatitis C, and other pathways. Furthermore, the 4 components of Radix Bupleuri can combine with most of the core targets to activate most pathways related to the disease and play a vital role in the treatment of hepatitis. Recent studies have shown that quercetin has direct and host-mediated antiviral effects on HCV and can also inhibit drug-mediated hepatitis. Recent studies have shown that quercetin, kaempferol and isorhamnetin can interfere with viral Pol/RT and core proteins, thus exerting an anti-HBV therapeutic effect.,24 The binding energy of molecular docking is the most direct means to evaluate its outcomes. A lower binding energy corresponds to a stronger binding between the ligand and the receptor. Our molecular docking results demonstrated consistently lower binding energies, indicating stable conformations and strong ligand-receptor interactions. Our molecular docking analysis also showed that quercetin can combine with CDKN1A, MAPK1, RB1, TP53, NFKBIA, and MYC to activate the Hepatitis B and Hepatitis C pathways for anti-hepatitis. Briefly, the minimum binding energies of CDKN1A, MAPK1, RB1, TP53, NFKBIA, and MYC for quercetin were −6.9, −5.5, −8.0, −7.5, −7.9, and −7.2 kcal/mol, respectively. Meanwhile, the docking results showed that there were 1, 2, 3, 2, 3, and 2 hydrogen bonds between quercetin and the proteins mentioned above, respectively, which suggested that quercetin may have a better binding activity to RB1 and NFKBIA. Beta-sitosterol is the main phytosterol found in plants and has extensive protective effects against various chronic diseases. The binding energy of JUN for beta-sitosterol were −7.1 kcal/mol. As an important component of RRH, beta-sitosterol can combine with JUN to play an anti-hepatitis role. Furthermore, beta-sitosterol may play an effective protective role against nonalcoholic fatty liver disease through lipid and atherosclerosis, fluid shear stress, and atherosclerosis signaling pathways. Natural flavonoids (catechins) have been shown to have various pharmacological activities for liver protection. Catechin has potent antioxidant activity and a protective effect on the plasma membrane structure of hepatocytes, thus exerting an inhibitory effect on hepatitis.,27 3,5,6,7-tetramethoxy-2- chromone, which contains high levels of chromones, is known to have anti-inflammatory and anti-hepatoma effects. Moreover, it has been reported that chromone has strong free radical scavenging activity in vitro, which could alleviate hepatic inflammation, inhibit hepatocyte apoptosis, and reduce alcohol-induced histological alteration and lipid accumulation in the liver tissues, thus having an anti-hepatitis effect.,29 Our results also confirmed that chromone has a significant anti-hepatitis effect; therefore, 3,5,6,7-tetramethoxy-2- chromone may be a novel agent for hepatitis therapy. Petunidin, a member of the anthocyanin family, is considered to be an effective anti-oxidant. The protective effect of petunidin on liver may be achieved by reducing oxidative stress and inhibiting ROS production, thus treating chronic hepatitis.KEGG enrichment analysis revealed that the key components of RRH, including quercetin, kaempferol, isorhamnetin, beta-sitosterol, (+)-catechin, 3,5,6,7-tetramethoxy-2- chromone, and petunidin, could play an anti-hepatic fibrosis role through several key BPs. Quercetin is a natural flavonoid with multiple functions and targets, including antibacterial, anti-inflammatory and immune-regulatory effects. Quercetin has been proven to be used for the treatment of hepatitis B for many years. The development of chronic hepatitis is related to a few BPs. The results of GO enrichment analysis showed that the drug plays a role in the treatment of hepatitis in BPs involving response to lipopolysaccharide, response to nutrient levels, response to oxidative stress, and cellular response to chemical stress, etc. In short, RRH may treat hepatitis by inhibiting oxidative stress, reducing chemical damage to the liver, regulating lipid metabolism, and supplying oxygen and nutrition to the liver. KEGG pathway analysis revealed that exerts its effects via multiple signaling pathways, including Hepatitis B, Hepatitis C, Chemical carcinogenesis-receptor activation, lipid and atherosclerosis, and other disease pathways. The results of PPI network topology analysis showed that the therapeutic effect of this drug on hepatitis was mainly achieved by regulating RELA, AKT1, JUN, MAPK1, TP53, CCND1, MYC, RB1, MAPK14, NFKBIA, CDKN1A, HIF1A, ESR1, and FOS. Our results show that RRH has a significant improved liver protection. Network pharmacology research revealed that the effects on liver protection of RRH are characterized by multi-component, multi-target, and multi-path mechanisms of action. The results showed that RRH screened a total of 19 active ingredients and 176 targets, participated in inhibiting oxidative stress, reducing chemical damage to the liver, regulating lipid metabolism, and supplying oxygen and nutrition to the liver and other BPs, and mainly involved multiple signaling pathways to jointly exert liver protection effects.The formation and progression mechanisms of chronic hepatitis, which are caused by various factors including hepatitis virus, autoimmune factors, and lipid disorders, etc, are complicated. By screening the TCMSP database, we were able to identify various pharmacological components with anti-hepatitis activity. Subsequently, we conducted further analysis on the binding mode of core compounds using molecular docking. Research has shown that piperine antimicrobial activity and cytotoxicity effects can be verified through molecular modeling.,34 Additionally, molecular modeling can also confirm the clinical therapeutic effects of different components such as geissoschizoline N4-methylchlorine and Eleutherine plicata by simulating molecular docking.,36 Consequently, in silico modeling offers the potential to develop new drug candidate ingredients with targeted effects.Molecular modeling proves to be an efficient method for screening potential new compounds in the search for anti-hepatitis capabilities.Although network pharmacology offers a visual network diagram of the mechanism of RRH in the treatment of chronic hepatitis, it still has some limitations. First, the authenticity and reliability of network pharmacology need to be verified by animals and experiments because network pharmacology is based on database analysis. Second, the active ingredients of the drug are affected by the dose, and the key ingredients shown in network pharmacology may not play an important role in clinical practice. To solve these problems, network pharmacology results of chronic hepatitis need be carried out to verify the mechanism of action of effective ingredients on chronic hepatitis and provide a theoretical basis for new drug research and development.In conclusion, our results revealed that RRH in the treatment of hepatitis was characterized by multi-component, multi-target, and multi-path mechanisms of action. Although our results are based on database analysis and lack the influence of drug dosage, network visualization shows the anti-hepatitis effect of key components. Furthermore, verification is needed through animal experiments in the future. The main bioactive components in RRH, including quercetin, kaempferol, isorhamnetin, and beta-sitosterol, were used to bind the hub targets of the disease, which may provide a theoretical basis for the development of multi-component, multi-target, and multi-path drugs for hepatitis.Conceptualization: Long Huang.Data curation: Long Huang, Qingsheng Yu, Zhou Zhen.Formal analysis: Long Huang.Funding acquisition: Long Huang, Qingsheng Yu.Methodology: Hui Peng.Project administration: Long Huang, Qingsheng Yu, Zhou Zhen.Resources: Long Huang, Hui Peng, Zhou Zhen.Software: Long Huang, Hui Peng, Zhou Zhen.Supervision: Qingsheng Yu.Validation: Long Huang, Qingsheng Yu, Zhou Zhen.Visualization: Long Huang, Hui Peng.Writing – original draft: Long Huang.Writing – review & editing: Qingsheng Yu, Hui Peng, Zhou Zhen.