Evidence suggests that hesperidin may have a therapeutic impact on osteoporosis. Nevertheless, little is known about the role of hesperidin in the development of osteoporosis. Bioinformatics analyses were carried out to explore the functions and possible molecular mechanisms by which hesperidin regulates osteogenic differentiation. In the present study, we screened and harvested 12 KEGG pathways that were shared by hesperidin-targeted genes and osteoporosis. The p53 signaling pathway was considered to be a key mechanism. Our  Millions of individuals suffer from skeletal disorders each year, which result in severe morbidity and mortality in the elderly . Osteopoin vitro and in vivo (Hesperidin (hesperetin-7-O-rutinoside), a flavanone glycoside highly abundant in citrus fruits, particularly in oranges, has gained considerable attention in recent years due to its diverse bioactivities . Hesperi in vivo . In an e in vivo . Howeverin vitro experiments to further investigate the potential molecular mechanisms underlying hesperidin’s anti-osteoporosis effects. The schematic flow chart of this work is shown in In the present study, we identified the targets genes of hesperidin using the STITCH database, followed by the construction of protein–protein interaction (PPI) network to investigate the protein interactions. In addition, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was carried out to identify pathways that are involved in hesperidin targeted genes and osteoporosis. According to the bioinformatics analyses results, we finally conducted Hesperidin was purchased from Herbpurify  and dissolved in dimethyl sulphoxide (DMSO). Ascorbic acid phosphate, β-glycerophosphate, and dexamethasone used in this study were purchased from Sigma-Aldrich.Bone marrow mesenchymal stem cells (BMSCs) were harvested from C57BL/6 mice and were kept in α-MEM supplemented with 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin according to a previous work . The thiBMSCs were seeded and cultured in OIM on indicated days, then washed with PBS and fixed with 4% paraformaldehyde for 15 min. After 7 days of osteogenic differentiation, alkaline phosphatase (ALP) staining was performed using a BCIP/NBT Kit  following the manufacturer’s instructions. After 14 days of osteogenic differentiation, alizarin red S (ARS) staining was performed to evaluate the mineralized matrix formation.® II 1st Strand cDNA Synthesis SuperMix for qPCR . Quantitative real-time polymerase chain reaction (qRT-PCR) was performed on an ABI Stepone plus real-time PCR system  with Hieff® qPCR SYBR Green Master Mix . Relative expression of each gene was analyzed using the 2−ΔΔCt method. The primer sequences were listed in The BMSCs were seeded into and cultured in 6-well plates, co-incubated with DEX and different concentrations of hesperidin for 7 days. Total RNA was extracted from the cells using the NucleoZOL Reagent . Next, 1 μg of total RNA from each sample was reverse transcribed into cDNA using the HifairThe BMSCs were seeded into and cultured in 6-well plates, co-incubated with DEX and different concentrations of hesperidin for 7 days. Total cell lysates from BMSCs were extracted using RIPA lysis buffer supplemented with protease inhibitors. The protein concentrations were measured with the use of a BCA protein assay kit  according to the manufacturer’s protocol. Equal amounts of protein from each sample were then separated by SDS-PAGE gel and subjected to standard western blot procedures. Protein bands were visualized using an ECL kit  and quantitatively analyzed with Image Lab 3.0 software (Bio-Rad). The antibodies against P53 and GAPDH were purchased from Proteintech .For immunofluorescence, BMSCs were washed and fixed with 4% paraformaldehyde at room temperature for 30 min, followed by permeabilization with 0.3% Triton X-100 for 10 min. After blocking with 3% BSA for 30 min, the cells were incubated with anti-Runx2 antibody overnight at 4°C. Subsequently, the cells were washed with PBS and incubated with corresponding secondary antibody for 1 h at room temperature, then mounted with DAPI contained fluorescent mounting solution . All images were viewed and photoed with a fluorescence microscope (Olympus).http://stitch.embl.de/) based on the following settings: three shells with a maximum interaction number of 10 for each shell, while the parameter organism was limited to “Homo sapiens”  .Hesperidin-targeted genes were imported into DAVID database (rf.gov/) , and theoporosis . Subsequp values calculated in the KEGG pathway analysis were screened out, followed by graphically visualizing the enrichment information using the bioinformatics platform (http://www.bioinformatics.com.cn/). Hub genes were defined as those genes included in each of the top five shared KEGG pathways.The top five shared KEGG pathways ranked by the smallest p-value of less than 0.05 indicated statistical significance.The data was displayed as mean ± standard deviation (SD) and statistically analyzed with GraphPad Prism 8.0 (GraphPad). Statistically differences between two groups were evaluated by Student’s t-test, while comparisons among multiple groups were estimated by one-way analyses of variance (ANOVA). A Based on a three shell limit screening criteria, a total of 30 hesperidin-targeted genes were obtained from online database STITCH. Subsequently, a network map of the interactions between hesperidin and the targeted genes was constructed the STITCH online tool . MembersA visualized PPI network of hesperidin-targeted genes was constructed by using Cytoscape . Subsequp-value < 0.05 were finally selected. Additionally, 110 KEGG pathways associated with osteoporosis were identified using the miRwalk database. With the help of a Venn Diagram, we were able to identify 12 KEGG pathways that were shared by hesperidin-targeted genes and osteoporosis (We used DAVID to perform KEGG pathway enrichment analysis. 39 hesperidin-related KEGG pathways were obtained, and 33 KEGG pathways with oporosis . The enrAmong the 30 hesperidin-targeted genes, TP53, CDKN1A, BCL2, EP300, MDM2, ATM, HSP90AA1, CDKN2A, CREBBP, CHEK2, PRKDC, MAPK8, CASP3, MDM4, MMP9, and RCHY1 were involved in the top five shared KEGG pathways. The enrichment analysis results of these genes were shown in ALP staining and ARS staining results showed that dexamethasone exposure significantly inhibited the osteogenic differentiation of BMSCs, whereas hesperidin partially promoted nodule formation against dexamethasone treatment . FurtherImmunofluorescence staining results demonstrated that dexamethasone dramatically inhibited the protein levels of Runx2 during the osteogenic differentiation of BMSCs, while higher levels of Runx2 protein expression were observed after hesperidin treatment . Besidesin vitro studies.Osteoporosis is a chronic metabolic bone disorder associated with aging, resulting in functional disability and a decrease in quality of life. As the population ages, a rising number of people currently suffer from osteoporosis substantially. Up to date, there is still no effective treatment for osteoporosis. Furthermore, the mechanisms responsible for osteoporosis remain largely unknown. In the present study, the molecular mechanisms behind hesperidin’s anti-osteoporosis benefits were explored using bioinformatics analyses and Hesperidin, a flavanone glycoside with a wide range of biological activities, is widely used in the treatment of various diseases. In recent years, emerging studies highlight the importance of hesperidin in the regulation and bone metabolism. Hesperidin has been shown to protect male mice against androgen deficiency-induced bone loss by inhibiting bone resorption and hyperlipidemia . It has p-values were the p53 signaling pathway, Cell cycle signaling pathway, Prostate cancer pathway, Pathways in cancer, and Bladder cancer. The hub genes involved in all five KEGG pathways were TP53, CDKN1A, and MDM2. These findings suggested that hesperidin may exert its biological activity by regulating the p53 signaling pathway.In this study, 110 KEGG pathways associated with osteoporosis and 33 KEGG pathways associated with hesperidin-targeted genes were screened out by KEGG pathway enrichment analysis. A total of 13 KEGG pathways were commonly shared by these two groups. Among them, the top five KEGG pathways with the smallest in vitro and in vivo studies shows that glucocorticoids can inhibit osteoblast proliferation and promote its apoptosis, subsequently resulting in the suppression of bone production and growth (Glucocorticoids, most commonly dexamethasone, have been widely used to treat a variety of diseases due to their significant anti-inflammatory, immunosuppressive, and metabolic regulator effects . It has d growth .in vitro. Notably, hesperidin treatment partially alleviated the dexamethasone-induced suppression of osteogenic differentiation, showing the positive effect of hesperidin on dexamethasone-induced bone deterioration.Consistent with previous findings, the results of the present study suggest that dexamethasone treatment significantly inhibited the osteogenic differentiation of BMSCs following ALP Staining and ARS Staining, as well as downregulated the expression of osteogenic genes such as ALP and Runx2, implying that dexamethasone inhibited osteogenic differentiation of BMSCs in vitro have demonstrated that p53 plays a negative role in the differentiation of MSCs(in vitro. However, treated with hesperidin partially inhibited both the mRNA and protein level of p53. Thus, when combined with our findings, this evidence suggests that hesperidin may protect against dexamethasone-induced osteoporosis by inhibiting the p53 signaling pathway.The p53 tumor suppressor has long been recognized as critical in cancer prevention . In recen of MSCs. It was n of MSCs. Emerginn of MSCs. Therefoin vivo evidence was presented regarding the beneficial effects of hesperidin on osteoporosis.Some concerns and limitations in this study should be acknowledged. Firstly, we did not perform quantitative analysis of the ALP and ARS staining results. Second, the effects of hesperidin on osteogenic differentiation were not investigated while activating or inhibiting the p53 signaling pathway. Furthermore, no In summary, our study determined that dexamethasone activated the p53 signaling pathway in BMSCs, causing downregulation of osteogenic markers and suppression of extracellular matrix mineralization during osteogenic differentiation, while hesperidin exerted anti-osteoporosis effects by inhibiting the p53 signaling pathway. Collectively, our study demonstrated that hesperidin could be a potential candidate for the treatment against dexamethasone-induced osteoporosis.


Datasets were downloaded from the Gene Expression Omnibus online database. Differential expression analysis was first performed using the blood exosome dataset GSE39833 from colorectal cancer and normal humans to identify differentially expressed (DE) miRNAs, and then, transcriptional enrichment analysis was performed to identify important enriched genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by FunRich software. Using the muscle atrophy sample GSE34111, the DE mRNAs in the muscle atrophy sample were analyzed, a regulatory network map was established based on miRNA‒mRNA regulatory mechanisms, further GO and KEGG enrichment analyses were performed for the DE genes in muscle atrophy via Cytoscape’s ClueGO plug-in, and the network pharmacology pharmacophore analysis method was used to analyze the celastrol therapeutic targets, taking intersections to find the therapeutic targets of celastrol, using the artificial intelligence AlphaFold2 to predict the protein structures of the key targets, and finally using molecular docking to verify whether celastrol and the target proteins can be successfully docked. A total of 82 DE miRNAs were obtained, and the top 10 enriched target genes were identified. The enrichment of the 82 miRNAs showed a close correlation with muscle atrophy, and 332 DE mRNAs were found by differential expression analysis in muscle atrophy samples, among which 44 mRNA genes were involved in miRNA‒mRNA networks. The DE genes in muscle atrophy were enriched for 30 signaling pathways, and 228 target genes were annotated after pharmacophore target analysis. The NR1D2 gene, the target of treatment, was found by taking intersections, the protein structure of this target was predicted by AlphaFold2, and the structure was successfully docked and validated using molecular docking. In our present study, colorectal cancer likely enters the muscle from blood exosomes and regulates skeletal muscle atrophy through miRNA‒mRNA regulatory network mechanisms, and celastrol treats muscle through NR1D2 in the miRNA‒mRNA regulatory network.Muscle atrophy due to colorectal cancer severely reduces the quality of life and survival time of patients. However, the underlying causative mechanisms and therapeutic agents are not well understood. The aim of this study was to screen and identify the microRNA (miRNA)–mRNA regulatory network and therapeutic targets of celastrol in colorectal cancer causing muscle atrophy  Cachexia is a severe atrophy syndrome ; in manyMany factors produced by the body and cancer cells, including inflammatory cytokines, contribute to the cachexia of tumors . The recChinese medicine has been in clinical practice in China for thousands of years . NumerouIn this study, blood exosome data from colorectal cancer were used to analyze differentially expressed (DE) miRNAs, and then, DE mRNAs were analyzed in muscle atrophy samples to model the miRNA‒mRNA regulatory network and to analyze the potential network targets of celastrol for the treatment of colorectal cancer-related muscle atrophy based on network pharmacology using AlphaFold2 and molecular docking for molecular dynamics validation. The overall goal was to provide new insights into the potential miRNA‒mRNA regulatory mechanisms of colorectal cancer muscle atrophy and the targets of celastrol therapy and explore the role of the AI AlphaFold2 in drug development.First, the keywords “colorectal cancer” and “muscular atrophy” were retrieved from the Gene Expression Omnibus (GEO) database , and thep < 0.05 were used as screening conditions to identify significant DE miRNAs.The samples were grouped according to the two groups of the GSE39833 dataset and analyzed by the “limma” package  in R , TFs enriched with DE miRNAs were determined, and the top 10 significant TFs were graphically displayed. Gene Ontology (GO) terms were annotated and mapped by FunRich software, including biological processes, cell composition, and molecular function, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were identified.p < 0.05. DE mRNAs with significant expression were screened out.In the GSE39833 dataset, we selected samples from six healthy controls and 12 patients with muscle atrophy in the R software package “limma” for differential expression analysis, with the filtering conditions |log2 FC (fold change)| > 0.5 and The miRNA target genes were predicted within three databases: miRTarBase, miRDB, and TargetScan. Subsequently, we constructed miRNA‒mRNA gene regulatory networks for the screened DE miRNAs with DE mRNAs based on miRNA‒mRNA regulatory mechanisms. Cytoscape (version 3.9.0) was used to visualize miRNA‒mRNA gene regulatory networks.Homo sapiens” was selected for GO enrichment analysis, including the biological process, cell composition, and molecular function, as well as KEGG signaling pathway enrichment analysis, and the map was drawn.DE mRNAs were imported into Cytoscape’s ClueGO plug-in , and “Hohttp://www.lilab-ecust.cn/pharmmapper/) database was used to select the top 600 ranked targets for pharmacophore target prediction. The names of the analyzed target genes were annotated in the UniProt (https://www.uniprot.org/) database by selecting the species “Homo sapiens,” and the network was constructed in Cytoscape based on the drug–target relationships.PharmMapper is an online platform for pharmacophore matching and potential identification of targets. The PharmMapper , and downloaded a PDB format file of the corresponding NR1D2 protein structure, a display map of the protein structure, the protein sequence map used, and the predicted confidence map.AlphaFold2 is an artificial intelligence program that predicts protein structure online. We selected the target gene The 2D structure of the small-molecule ligand celastrol was downloaded from the PubChem database. NR1D2 was selected as the protein receptor, and the PDB file predicted by the AI AlphaFold2 was used. AutoDockTools was used to read the protein receptor file, which was converted into the PDBQT format after hydrotreating ion modification. This structure was then converted to a 2D structure to draw active pockets. Finally, AutoDock Vina was used for molecular docking, and the model with the lowest free energy was selected for visualization.p < 0.05 was considered statistically significant.R (4.1.0) was used for bioinformatics analysis, and the R package was used for statistical analysis. p < 0.05, a total of 82 DE miRNAs were obtained in the colorectal cancer group compared to the healthy group  were graphically displayed   enrichmep < 0.05 , ascorbate and aldarate metabolism, drug metabolism, TGF-beta signaling pathway, tyrosine metabolism, sphingolipid metabolism, fatty acid degradation, pyruvate metabolism, TNF signaling pathway, relaxin signaling pathway, and fluid shear stress and atherosclerosis. The citrate cycle (TCA cycle), pyruvate metabolism, and fatty acid degradation have also been predicted to be important signaling pathways in muscle atrophy in spinal cord injury . The TGFNR1D2 for drug treatment. In GeneCards, NR1D2 is presented as a transcriptional repressor that coordinates circadian rhythms and metabolic pathways in a hemoglobin-dependent manner. NR1D2 is a component of complex transcriptional machinery that controls circadian rhythms and forms a key pathway in the biological clock by directly repressing the expression of the core clock components ARNTL/BMAL1 and CLOCK. It also regulates genes involved in metabolic functions, including lipid metabolism and inflammatory responses. In the previous literature, NR1D2 was shown to regulate mitochondrial function by regulating the circadian rhythm, and impaired mitochondrial function leads to Duchenne muscular dystrophy. Many important signaling pathways in the KEGG enrichment of DE mRNAs in muscle atrophy are also associated with hypo, including lipid metabolism and inflammatory responses.The therapeutic targets of celastrol were analyzed by pharmacophore target analysis of network pharmacology, and 228 genes were successfully annotated and then intersected with mRNAs of the miRNA‒mRNA regulatory network to find the disease target gene We then used the AI AlphaFold2 on the protein structure of NR1D2 to predict the human protein structure of the gene, successfully molecularly docking it to celastrol.AlphaFold2 is a revolutionary change in the field of biology, demonstrating the power of AI, which is changing the way drugs are developed, greatly accelerating the development of new drugs, and helping millions of patients around the world who are unable to develop drugs because of unknown protein structures. AlphaFold2 will be a powerful AI weapon in the fight against various diseases. We offer a new strategy for drug development by combining bioinformatics, network pharmacology, and artificial intelligence.In this study, we identified a potential miRNA‒mRNA regulatory network of colorectal cancer regulating muscle atrophy through exosomes in a bioinformatics approach. There are 22 miRNAs and 44 mRNAs in this network. Molecular docking validation by the AI AlphaFold2 predicted the structure of the NR1D2 protein and revealed the molecular target of celastrol for the treatment of colorectal muscular atrophy. AlphaFold2 will help develop potential therapeutic drugs for more diseases.


Using a multi-omic consensus representation,padma quantifies and characterizes individualizedpathway-specific multi-omic deviations and their underlying drivers, withrespect to the sampled population. We demonstrate the utility ofpadma to correlate patient outcomes with complex genetic,epigenetic, and transcriptomic perturbations in clinically actionable pathwaysin breast and lung cancer.Malignant progression of normal tissue is typically driven by complex networks ofsomatic changes, including genetic mutations, copy number aberrations,epigenetic changes, and transcriptional reprogramming. To delineate aberrantmulti-omic tumor features that correlate with clinical outcomes, we present anovel pathway-centric tool based on the multiple factor analysis frameworkcalled  With these goals in mind, many multivariate approaches havethe advantage of being unsupervised, using matched or partially matched omics dataacross genes, obviating the need for predefined groups for comparison as in theframework of standard differential analyses. A variety of such approaches has beenproposed in recent years. For example, Multi-omics Factor Analysis(MOFA) uses group factor analysis to infer sets of hidden factors that capturebiological and technical variability for downstream use in sample clustering, dataimputation, and sample outlier detection , to quantify individualized pathway deviation scores. In particular, wepropose an approach called padma  to characterize individuals with aberrantmulti-omic profiles for a given pathway of interest and to quantify this deviationwith respect to the sampled population using a multi-omic consensus representation.We further investigate the following succession of questions. In which pathways arehigh deviation scores strongly associated with measures of poor prognosis? For suchpathways, which specific individuals are characterized by the most highly aberrantmulti-omic profile? And for such individuals, which specific genes and omics drivelarge pathway deviation scores? By providing graphical and numerical outputs toaddress these questions, padma represents both an approach forgenerating hypotheses as well as an exploratory data analysis tool for identifyingindividuals and genes/omics of potential interest for a given pathway. There isalready some precedent for using MFA to integrate multi-omic data, although existingapproaches differ from that proposed here. For instance, padmashares some similarities with a recently proposed integrative multi-omicsunsupervised gene set analysis called mogsa, which is similarlybased on a MFA . Next, an individual PCA is performed using singular valuedecomposition for each gene table lculated B. Note tocument}C. Using mogsaapproach, supplementary binary variables representing gene membership in genesets are projected onto a transcriptome-wide multiple factor analysis tocalculate gene set scores  in the observation and variable space.Optionally, an independent set of supplementary individuals (or supplementaryvariables) can then be projected onto the original representation; this isperformed by centering and scaling variables for the supplementary individuals to the same scaleas for the reference individuals, and projecting these rescaled variables intothe reference PCA space. Note that in the related Biostatisticsonline.The MFA thus provides a consensus across-gene representation of the individualsfor a given pathway, and the global PCA performed on the weighted gene tablesdecomposes the consensus variance into orthogonal variables  that are ordered by the proportion of variance explained by each.The coordinates of each individual on these components, also referred to asfactor scores, can be used to produce factor maps to represent individuals inthis consensus space such that smaller distances reflect greater similaritiesamong individuals. In addition, partial factor scores, which represent theposition of individuals in the consensus for a given gene, can also berepresented in the consensus factor map; the average of partial factor scoresacross all dimensions and genes for a given individual corresponds to the factorscore D. A moreIn the consensus space obtained from the MFA, the origin represents the“average” pathway behavior across genes, omics, and individuals;individuals that are projected to increasingly distant points in the factor maprepresent those with increasingly aberrant values, with respect to this average,for one or more of the omics measures for one or more genes in the pathway. Toquantify these aberrant individuals, we propose an individualized pathwaydeviation score padma, there is no dimension reduction and thecalculation of the pathway deviation score (and the corresponding gene-levelcontributions) is equivalent to that calculated in the original space of theconcatenated set of weighted, standardized variables. However, if desired, theuser can calculate the pathway deviation score on a subset of components, forexample after removing one or more components that are correlated with batcheffects or those that explain little variability. Finally, to facilitatecomparisons of scores calculated for pathways of differing sizes , deviation scores with respect to the origin are normalized forthe pathway size by dividing them by the number of genes in the pathway.Note that the full set of components is used for this deviation calculation,rather than subsetting to an optimal number of components; we remark that due totheir small variance relative to lower components, higher components contributerelatively little to the overall pathway deviation scores. When all componentsare used in In order to quantify the role played by each gene for each individual, wedecompose the individualized pathway deviation scores into gene-levelcontributions. Recall that the average of partial factor scores across all MFAdimensions corresponds to each individual’s factor score. We define thegene-level deviation for a given individual as follows: Biostatistics online formore details.Interestingly, it is possible to quantify the contribution of any single variableof any arbitrary grouping of variables  to the individualized pathway deviation score; see theSupplementary Methods available at The richness of MFA outputs also includes various decompositions of the totalvariance  ofthe multi-omic data for a given pathway. Similarly to a standard PCA, thepercent contribution of each axis of the MFA can be calculated as the ratiobetween the variance of the corresponding MFA component and the total variance;by construction, the fraction of explained variance explained decreases as theMFA dimension increases. Similarly, the percent contribution to the inertia ofeach axis for a given omic, gene, or individual can be quantified as the ratiobetween the inertia of its respective partial projection in the consensus spaceand the inertia of the full data projection for that axis. These per-gene,per-omic, and per-individual contributions can be quantified for a subset ofcomponents  or for the entire set of components;here, as we calculate individualized pathway deviation scores using the full setof dimensions, we also calculated a weighted per-omic contribution, whichcorresponds to the average contribution across all dimensions, weighted by thecorresponding eigenvalue.padma, freely available athttps://bioconductor.org/packages/padma. Padmanotably makes use FactoMineR , which was chosen as individuals have previously beenclassified , and microRNA (miRNA) abundance forWe illustrate the utility of assified into onepadma approach integrates multi-omic data by mapping omicsmeasures to genes in a given pathway. Although this assignment of values togenes is straightforward for RNA-seq, CNA, and methylation data, a definitivemapping of miRNA-to-gene relationships does not exist, as miRNAs can eachpotentially target multiple genes. Many methods and databases based ontext-mining or bioinformatics-driven approaches exist to predict miRNA-targetpairs ; the number of MFA componentsneeded to explain 80% of the total variability was strongly associated with thetotal number of features in each pathway (Biostatistics online).The  instead . After rhttp://legacy.dx.ai/tcga_breast on March 7, 2019), which is anestablished cancer hallmark of cellular de-differentiation and poor prognosis(Biostatistics online).As a measure of patient prognosis, we focused on two different metrics. First, weused the standardized and curated clinical data included in the TCGA Pan-CancerClinical Resource . These multi-omic healthy tissue samples were subsequentlybatch-corrected in the same way as the tumor samples. Next, to create amulti-omic data set with a set of “true positives,” we randomlyselected five tumor samples to include with the 70 healthy samples; for eachpathway, this random sampling was repeated 20 independent times. Using theRNA-seq, miRNA-seq, and methylation data for the full set of 75 samples, we thenevaluated whether the padma pathway deviation scores were ableto successfully identify the five tumor samples by calculating the Area Underthe Curve (AUC) of the Receiver Operating Characteristic (ROC) curve for each ofthe 1136 pathways. Mean AUC values across the 20 repetitions were very high fornearly all pathways considered . As a whole, this suggests that the padma deviationscores indeed reflect true biological signal, as the tumor controls added tohealthy samples nearly always had the largest deviation scores acrosspathways.Before exploring in detail the results of padma pathway deviationscores can correctly identify aberrant individuals. We compared the AUC valuesof padma with those of pathway deviation scores calculatedusing two alternatives: a PCA on all concatenated data tables  and a padma single-omicsapproach. Full details of the simulation study may be found in the SupplementaryMethods available at Biostatistics online, and results areshown in Biostatisticsonline. Overall, we confirm the solid performance of padmaacross a wide range of settings, as well as an advantage in identifying aberrantindividuals for padma compared to a PCA-based alternative,particularly in cases with smaller sample sizes and fewer driver genes and omics. Finally, we also confirmed that the per-genecontributions to the individualized padma pathway deviationscores successfully recover the true gene drivers in all scenariosconsidered.In addition, we conducted a simulation study to investigate the conditions  under which n = 80; Stage II, n = 33; Stage III+,n = 31). Using the Benjamini–Hochberg ; for all of these, higher pathway scores corresponded to a worsesurvival outcome. Although overlaps in gene lists between pairs of pathwayscreate a positive dependency structure among the deviation scores (Biostatistics online), the BH correction methodhas been shown to control the FDR in such families of tests (Biostatistics online) have been univariatelyassociated with patient outcome  we consider only thosewith the most highly positively skewed distribution of deviation scores. Foreach of the top 5% of pathways ; further, after BHcorrection none of the 57 pathways were significantly associated with survival, as well as cell cycle and apoptosisrelated pathways, prompted us to consider whether these pathway deviation scoresare simply acting as proxies for the tumor mutational burden  for each individual. To investigate this, weestimated the mutational burden for each individual by counting the number ofsomatic nonsynonymous mutations in a set of cancer-specific driver genessignaling pathway. D4-GDI is a negative regulator of the ras-related Rho Familyof GTPases, and it has been suggested that it may promote breast cancer cellproliferation and invasiveness  for which large deviation scores were observed for the two highlyaberrant individuals (TCGA-78-7536 and TCGA-78-7155) in the D4-GDI signalingpathway. We plot boxplots of the Z-scores for each available omic for the threegenes across all 144 individuals with lung cancer .When examining the partial factor maps for this individual over the first threeMFA dimensions C,D, we npadma insuch a case, we focus on histological grade for breast cancer. To quantifywhether pathway deviation scores tend to be associated with histological gradein breast cancer, we performed a one-way ANOVA on the three measures thatcomprise histological grade for each of the 1136 pathways. Based on theBH-adjusted p-values from an F-test (FDR Biostatistics online). In allbut two cases, higher deviation pathway scores corresponded to the higher gradesfor these two measures, corresponding to more aggressive tumors; the twoexceptions were the presynaptic nicotinic acetylcholine receptor and highlycalcium permeable postsynaptic nicotinic acetylcholine receptor pathways (bothfrom Reactome), for which the largest pathway deviation scores were associatedwith grade II, rather than grade III, of the mitotic index.For some cancers, additional clinical phenotypes beyond survival information maybe of particular interest; to illustrate the use of Biostatistics online (see Table S5 of the Biostatistics online for the full gene lists ineach pathway). The Wnt signaling pathway, which is made up of 63 genes, had thehighest combined ranking for these two histological measures. Of this set ofgenes, all had RNA-seq, methylation, and CNA measures available, with theexception of FAM123B and PSMD10 (no CNA measures with nonzero variance) andPSMB1 to PSMB10, PSMC2, PSMC3, PSMC5, PSMC6, PSME1, and PSME2 (no promotermethylation measures). miRNA-seq data were included for only two predictedtarget pairs: hsa-mir-375 To prioritize pathways among this list, we calculated the rank product of theindividual rankings by p-value for mitosis and nuclear pleomorphism; the top 10pathways according to this joint ranking are shown in Table S2 of the Similarly to the distribution of D4-GDI pathway scores in lung adenocarcinomas, asmall number of breast cancer patients are characterized by highly aberrantscores in the Wnt signaling pathway, including TCGA-BH-A1FM, TCGA-E9-A22G, andTCGA-EW-A1PH, and the number of pathway-specific nonsynonymous somatic mutationsdoes not appear to be related to this score. The associated factor map on thefirst two dimensions of the MFA AclearlyBiostatistics online), miRNA expressioncontributed relatively little to the overall variation (1.2%). We do need to becautious in the interpretation of this phenomenon, as it may be due to realbiology or to the mapping uncertainty and much smaller number (with respect tothe other omics) of miRNA features (Biostatistics online). Because we have structured the datainto gene-tables (and not omics-tables) in padma, the MFAweighting leads to a balancing of contributions among genes (and not omics); assuch, the drastically smaller number of miRNA features is likely directly linkedto the overall smaller contributions to the variance explained.When examining the percent contribution of each omic to the axes of the MFA forthe Wnt signaling pathway B, werempadmaapproach, which is used in an unsupervised manner on multi-omic cancer data fora given pathway, is able to recapitulate known sample structure in the form ofintrinsic tumor subtypes as well as relevant prognostic factors such ashistological grade.Taken together, these results illustrate that the padma accommodates multipleomics-sources which, for some sample sets and pathways, account for more than 50% ofthe overall variation (Unsupervised dimension reduction approaches (such as PCA) have been widely used ingenetics and genomics for many years, both to identify sample structure and batcheffects  to identify any potential technical outliersbefore running padma. Here, we chose to remove known batch effectsprior to the padma analysis, but in principle the method could berun on uncorrected data and MFA components correlated with undesired batch effectscould be identified and removed prior to computing the pathway deviation score. Forlarge, multi-omic data such as TCGA, padma allows investigators tosummarize overall variation and assist in generating hypotheses for more targetedanalyses and follow-up studies. As a case in point, we identified two lung cancerpatients with aberrant multi-omic profiles at three CASP genes. With access to thetumor samples and more fine-grained clinical data, future molecular experimentscould help to clarify the role (if any) that these genes play in contributing tolung cancer mortality. Although the multi-omic TCGA data considered here were quitelarge  identify overall sources ofvariation (such as batch effects); (ii) prioritize high variance pathways defined byvariability across subjects; (iii) identify aberrant observations  within a given pathway; and (iv) identify the genes and omics sourcesthat drive these aberrant observations. As with any analysis of omic data wegenerally recommend that standard quality control analyses be performed  are of interest, the MFA can be based on one set of samples , and the other set of samples  can be projected onto this originalrepresentation. This is accomplished by centering and scaling supplementaryindividuals to the same scale as the reference individuals, and projecting theserescaled variables into the reference MFA space. In this setting, the interpretationof pathway deviation scores would no longer correspond to the identification of“aberrant” individuals compared to an overall average, but ratherindividuals that are most different from the reference set ; this strategy would besimilar in spirit to the individualized pathway aberrance score (iPAS) approach,which proposed using accumulated (unmatched) normal samples as a reference set(kxaa029_Supplementary_DataClick here for additional data file.


Age-related changes in functional ability, particularly among people aging with long-term disabilities, can impact safe performance of toilet and bathtub transfers and severely limit their ability to successfully age-in-place. The SmartBathroom is a state-of-the-art bathroom laboratory that features mechanically adjustable features and an array of sensors that can measure walking performance, location of feet and hands, and forces applied to the bathroom surfaces and fixtures. To complement the SmartToilet system, TechSAge recently developed the SmartBathing Transfer Testbed prototype that will enable us to study how different bathing environment configurations can impact transfer performance. The testbed consists of a height adjustable tub wall and three-wall, height and angle adjustable grab bars with integrated sensors to measure hand location and grip and load forces. This session will describe the SmartBathing Testbed and present results of studies with individuals both with and without ambulatory impairments performing simulated transfers within the testbed.


The expression of core targets was quantified using real-time polymerase chain reaction and western blotting. A total of 139 common targets for DHHQD and RIF in chronic kidney disease were detected. Compared with the untreated UUO rats, the DHHQD-treated rats showed reductions in the following: blood urea nitrogen and serum creatinine levels, kidney tubular atrophy and necrosis, interstitial fibrosis, hyperplasia and abnormal deposition of extracellular matrix, and microstructural changes in the mesangial matrix and glomerular basement membrane. DHHQD treatment significantly regulated the levels of renal core proteins, such as eNOS, IL-6, EGFR, and VEGF and reduced the mRNA and protein expression of the core targets involved in inflammation pathways, such as PI3K/AKT and TLR4/NF-κB. DHHQD treatment ameliorated the severity of RIF by potentially regulating the AKT/PI3K and TLR4/NF-κB signaling pathways. Our study findings provide insights into the mechanisms associated with DHHQD action and essential data for future research.Dahuang and Huangqi are the most frequently prescribed treatment methods for chronic kidney disease in China. Our study aimed to clarify the pharmacological mechanism of action of Dahuang-Huangqi decoction (DHHQD) in renal interstitial fibrosis (RIF). The intersection of genes targeted by DHHQD active ingredients and RIF target genes was searched using network pharmacology to build a chemical ingredient and disease target network. For  Chronic kidney disease (CKD) is currently a prominent global health challenge , with a Traditional Chinese medicine (TCM) maintains that after a long illness, blood stasis is inevitable. In his book “Correcting the Errors in The Forest of Medicine,” Wang Qingren  referredRheum palmatum L., root and rhizome, raw. RL) and Huangqi  Bge., root, raw. AM) were the most frequently prescribed treatments [Astragalus polysaccharide can enhance cell proliferation and inhibit megakaryocyte apoptosis [Data mining of more than 70 popular TCM prescriptions for CKD patients in China revealed that Dahuang  can reduce urinary protein, serum creatinine (SCr), and blood urea nitrogen (BUN) levels to mitigate renal pathological changes, protect renal function, and delay renal failure. The DHHQD is also used for the treatment of CKD, and its effects have been verified pharmacologically. Specifically, administration of DH and HQ is reportedly associated with lower risks of end-stage renal disease and death among advanced CKD patients . AdditioNetwork pharmacology can be used to analyze traditional Chinese formulas in TCM using multiple databases  to detecIn this study, we analyzed (i) the effectiveness and mechanism underlying DHHQD action in RIF treatment and (ii) the potential chemically active components and key signaling pathways of DHHQD in RIF.Rheum officinale Baill., root and rhizome, raw. RL and Huangqi  Bge., root, raw. AM) herbs in a 1 : 1 ratio. All herbs were purchased from Chongqing Traditional Chinese Medicine Hospital. The herbs were extracted twice using distilled water and concentrated to 1 g/mL.DHHQD is a widely used TCM prescription and consists of the Dahuang μL aliquot of the DHHQD supernatant was passed through a 0.22 μm filter membrane and subsequently transferred to an autosampler vial for UHPLC-MS/MS analysis. LC-MS/MS analysis was performed on an Agilent ultra-high performance liquid chromatography 1290 UPLC system with a Waters UPLC BEH C18 column . The following conditions were applied: column temperature, 55°C; sample injection volume, 5 μL; flow rate, 0.5 mL/min; and mobile phase, 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B). A multistep linear elution gradient program was implemented as follows: 0–11 min, 85%–25% A; 11–12 min, 25%–2% A; 12–14 min, 2% A; 14–14.1 min, 2%–85% A; 14.1–15 min, 85% A; and 15–16 min, 85% A. A Q Exactive Focus mass spectrometer coupled with Xcalibur software was employed to obtain the MS and MS/MS data based on the IDA acquisition mode. During each acquisition cycle, the mass range was 100–1,500. The top three-parent ions of each cycle were screened, and the corresponding MS/MS data were acquired. The parameters were as follows: sheath gas flow rate, 45 Arb; aux gas flow rate, 15 Arb; capillary temperature, 400°C; full ms resolution, 70,000; MS/MS resolution, 17,500; collision energy, 15/30/45 in NCE mode; and spray voltage, 4.0 kV (positive) or −3.6 kV (negative). Material identification from peaks containing MS/MS data was performed using the secondary mass spectrometry database provided by the Shanghai BIOTREE Biotech Co., Ltd. and the corresponding cleavage law matching method [A 400 https://tcmspw.com/tcmsp.php) and the encyclopedia of traditional Chinese medicine (https://www.tcmip.cn/ETCM) [https://pubmed.ncbi.nlm.nih.gov) was searched for literature related to the use of Dahuang and Huangqi for CKD-RIF to extract the effective components and their roles, which were combined with the identified active components of DHHQD using MS/MS. Further, the corresponding targets of DHHQD from the TCMSP database and SymMap database (https://www.symmap.org) were screened [The chemical ingredients of DHHQD were screened from the TCM systems pharmacology database and analysis platform (cn/ETCM) , and canscreened .https://omim.org/) and GeneCards (https://www.genecards.org/) databases were utilized to screen the known RIF-related potential targets. These databases predict human genes and their related diseases. We searched for intersection genes of the RIF and DHHQD target genes.The Online Mendelian Inheritance in Man . (iii) The topological features of each node in the PPI network were analyzed [Network construction was performed as follows: (i) A network between the chemical components of the DHHQD and CKD-RIF targets was constructed using Cytoscape 3.5.1 software, and the core decoction components and intermolecular interactions were analyzed by visualizing networks. (ii) The PPI network of core protein target genes was constructed using the online STRING database , and eight typical attributes, such as intersection genes, coexpression, gene fusion events, protein complexes, and genomic neighborhood from curated databases and automated text-mining, of each node were assessed. We retained PPIs with comprehensive scores >0.4, and the remaining parameters were set to the default values in STRING [The analysis of the PPI network helps to study the molecular mechanism of a disease and discover new drug targets from a systematic perspective. Here, the PPI network was forecast using the STRING database (n STRING .Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses were performed for the target genes. The GO database was used to classify the differentially expressed genes into biological process (BP), cellular component (CC), and molecular function (MF) categories. Target genes were enriched in KEGG and the 20 most enriched pathways were visualized. Combined with research references , the tar−1·d−1 and 10 mL·kg−1·d−1 DHHQD, respectively, through intragastric gavage for 14 d. The DHHQD-H group dosage was calculated based on body surface area to approximate human equivalence according to previously described methods [−1·d−1) for 14 d. After 2 weeks, all rats were anesthetized with 2% pentobarbital sodium and euthanized. Blood and tissues were harvested from rats that were fasted for 12 h.Forty male 7-week-old Sprague–Dawley rats (body weight 200 ± 20 g) were obtained from the Laboratory Animal Center of the Chongqing Medical University. All animal experiments were approved by the Animal Experimental Ethics Committee of Chongqing Medical University  and performed according to the guidelines of the Animal Care Committee of Chongqing Medical University. All rats were housed in a thermostatic-control facility at 22°C with a 12 h light/dark cycle. After 1 week of acclimatization, they were divided randomly into three groups of ten rats each: sham (sham operation rats), model , DHHQD-H (UUO + DHHQD high-dose-administered rats), and DHHQD-L (UUO + DHHQD low-dose-administered rats) groups. First, the rats in the sham group were anesthetized with an intraperitoneal injection of 2% pentobarbital sodium (30 mg/kg body weight). Second, their abdominal cavities were incised to expose the left ureter. Blunt dissection was performed on the left ureter before the closure of the abdominal cavity using stratified suturing. Apart from the sham group, all other groups underwent the same surgical procedure to establish the UUO model rats. Specifically, after anesthesia with 2% pentobarbital sodium (30 mg/kg body weight), the left ureters of all rats were exposed and bluntly dissected. A double ligature with a 4-0 surgical suture on the upper 1/3 portion of the ureters was applied to block flow through the ureters before closing the abdominal cavities. The DHHQD-H and DHHQD-L group rats were administered 20 mL·kgThe blood samples from the rats were centrifuged at 3,500 rpm  for 5 min. The clear supernatant was used for the analysis of SCr and BUN levels using an automatic biochemical analyzer. Subsequently, the renal index of each rat was calculated as the kidney weight/body weight (g/kg). The hydroxyproline (Hyp) content of the kidney was measured using HYP Colorimetric Assay kits .μm-thick sections obtained were dewaxed, treated with gradient ethanol, and stained with hematoxylin-eosin (HE), Masson's trichrome, and periodic acid-Schiff (PAS) stains. Pathological analysis of glomeruli and tubules was performed using an Olympus BX53 microscope .Rat renal tissue was first fixed in 4% paraformaldehyde for 48 h, embedded in paraffin, and further sectioned. The 3 The interstitial score of the renal tubules was analyzed at a 200× magnification; ten tubules per visual field were observed. According to the range of renal tubular atrophy, tubular type, stromal cell infiltration, and fibrosis, scores from 0 to 3 were assigned as follows: 0 for none; 1 for less than 25%; 2 for 25%–50%; and 3 for 50%–75% . Photo iThe morphological and microstructural structures of the kidneys, such as the glomerular basement membrane, mesangial cells, podocytes, endothelial cells, and collagen fibers in the interstitial tissue were examined using transmission electron microscopy (TEM). The renal tissues from the rats were fixed in 2.5% glutaraldehyde and embedded in paraffin. The 70 nm-thick tissue sections were stained and observed using TEM .μm-thick renal tissue sections were incubated with citrate antigen retrieval solution for 20 min at 95°C. Thereafter, the sections were stained with a monoclonal antibody against transforming growth factor-β1  and α-smooth muscle actin . The sections were incubated with goat antirabbit antibodies overnight and then with a secondary antibody for 50 min. The positive areas were visualized using a DAB Kit (Servicebio). The integrated optical density of protein expression was calculated using ImageJ software .The 3 t-tests after logarithmic transformation.Rat IL-6, epidermal growth factor receptor 2 (EGFR2), vascular endothelial growth factor (VEGF), and eNOS enzyme-linked immunosorbent assay (ELISA) kits  were used to detect protein levels in the kidney tissues. The protein concentration analysis of each group was performed according to the manufacturer's instructions. The concentrations in each group were compared with Student's GAPDH) gene as a reference. The following primers were used: phosphatidylinositol 3-kinase (PI3K): sense, 5ʹ-AGAGCTTGGAGGACGATGACG-3ʹ, antisense, 5ʹ-TGGACTGGGCTATCTCACTTCG-3ʹ; protein kinase B (AKT): sense, 5ʹ-TCTCAGTGGCACAATGTCAGC-3ʹ, antisense, 5ʹ-TGGGTGAACCTGACCGGAAG-3ʹ; toll-like receptor 4 (TLR4): sense, 5ʹ-AGTTTAGAGAATCTGGTGGCTGTG-3ʹ, antisense, 5ʹ-TTCCCTGAAAGGCTTGGTCT-3ʹ; nuclear factor kappa-B (NF-κB), sense, 5ʹ-GCTCCTTTTCTCAAGCCGATGT-3ʹ, antisense, 5ʹ-CGTAGGTCCTTTTGCGTTTTTC-3ʹ; and GAPDH: forward, 5′-CTGGAGAAACCTGCCAAGTATG-3′, reverse, 5′-GGTGGAAGAATGGGAGTTGCT-3′.According to the manufacturers' protocols, total RNA was extracted from renal tissue using TRIzol reagent . Furthermore, the RNA was reverse transcribed into cDNA . The PCR cycling conditions were as follows: 95°C for 10 min, followed by 95°C for 15 s and 60°C for 60 s for 40 cycles. Gene expression levels were calculated from the standard curve using the expression of the glyceraldehyde 3-phosphate dehydrogenase  were used, and the membranes were incubated overnight at 4°C on a shaking bed. After washing the membranes, the secondary antibody  was added and incubated at 37°C for 2 h. After applying the ECL color reagent and performing dark chamber exposure imaging, the gray value of the images was analyzed using ImageJ software.Renal tissue proteins were extracted, quantified, and visualized using western blotting. First, target proteins were separated by sodium dodecyl sulfate polyacrylamide gel electrophoresis and transferred to polyvinylidene fluoride  membranes. The PVDF membranes with bound target proteins were then blocked with 5% skim milk for 2 h. Primary antibodies (dilution 1 : 1000) against p-PI3K , PI3K , p-AKT , AKT , TLR4 , and NF-p < 0.05.Data analyses were performed using SPSS ver.17.0 . Data are presented as mean ± standard deviation (SD). Variables in each group were tested to determine if they were normally distributed. One-way analysis of variance (ANOVA) was then performed, followed by a least significant difference (LSD) test or Dunnett's T3 test. Skewed data were presented as median with range or transformed to rank cases for normalization and statistically analyzed. Statistical significance was set at To identify the major chemical components, the DHHQD samples were analyzed using UHPLC-MS/MS. The positive  and negaMAPK, NOS, MMP, STAT, PPAR, SOD, EGFR, and IL6.The UHPLC-MS/MS analysis was combined with the TCMSP database. A total of 631 potential pathophysiological targets were collected from the TCMSP database, including frequently studied genes, such as We obtained 2,490 CKD-RIF-related disease targets from GeneCards and 139 CKD-RIF-related disease targets from OMIM. After removing duplicates, 2,351 disease targets and 197 chemical targets remained. Following the intersection of DHHQD and RIF, we observed 139 common targets. A new Venn diagram was constructed to identify intersection genes between those targeted by the active components of DHHQD and those involved in RIF to identify genes potentially targeted by DHHQD in RIF .According to the intersection genes of DHHQD and CKD-RIF identified using the Venn diagram, we further constructed a DHHQD components-potential targets-diseases macro network diagram . The netTo explore the core pharmacological mechanism of DHHQD action against RIF and the antifibrotic properties of DHHQD, we evaluated the core network using topological methods. By analyzing the PPI network, we determined how protein targets interact with each other to exert pharmacological effects. Therefore, we constructed a macro network of DHHQD action against RIF and identified 139 interacting protein targets and 1,775 interactions. The protein target level of each node was equal to the number of edges it was connected to , cellular responses to oxidative stress (GO:0034599), and reactive oxygen species (GO:0034614) were closely related to inflammation. Similarly, response to mechanical stimulus (GO:0009612), regulation of apoptotic signaling pathway (GO:2001233), response to radiation (GO:0009314), and cellular response to external stimulus (GO:0071496) were positively related to fibrosis. Most of the targets identified in the PPI analysis were also associated with inflammation  and apoptosis .To explore the anti-RIF mechanism of DHHQD, the core proteins were enriched in KEGG analysis. The top 20 signaling pathways were obtained according to the KEGG enrichment analysis results. The results indicated that 131 targets were mapped into 123 KEGG pathways, including the AKT/PI3K signaling pathway, IL-17 signaling pathway, TNF signaling pathway, apoptosis, HIF-1 signaling pathway, EGFR tyrosine kinase inhibitor resistance, and toll-like receptor signaling pathway. Based on analysis of the data and relevant biological processes, the 20 highest significant KEGG signaling pathways were screened out Figures .κB signaling pathway (hsa04064). The data revealed that PI3K-AKT was the most enriched signaling pathway  was the most enriched signaling pathway. Additionally, the most potential core targets were enriched in signaling pathways such as EGFR tyrosine kinase inhibitor resistance (hsa01521), MAPK signaling pathway (hsa04010), ErbB signaling pathway (hsa04012), Ras signaling pathway (hsa04014), HIF-1 signaling pathway (hsa04066), FOXO signaling pathway (hsa04068), toll-like receptor signaling pathway (hsa04620), JAK-STAT signaling pathway (hsa04630), and NF- pathway .κB, FOXO, and JAK-STAT signaling pathway appear in the KEGG analysis, which are directly relevant to the PI3K-AKT signaling pathway [κB and toll-like receptor signaling pathways [Notably, numerous targets such as MAPK, toll-like receptor, NF- pathway . Most of pathway , and oxi pathway . Consist pathway . Once th pathway . Consistpathways .p < 0.01). Moreover, DHHQD-H and DHHQD-L treatment significantly alleviated the renal changes observed in the untreated UUO rats (p < 0.01), and DHHQD-H treatment significantly alleviated the renal changes observed in the DHHQD-L rats (p < 0.05). The renal collagen content was assessed by Hyp quantification. The level of Hyp in the renal tissue was significantly increased (p < 0.01) in each group compared with that in the sham group, whereas the Hyp content was significantly decreased (p < 0.01) in the DHHQD groups compared with that in the UUO group (The levels of SCr  and BUN UO group .p < 0.01) showed that increased fibrosis was present in the renal interstitium (p < 0.01). The area in the DHHQD-H group was also significantly lower than that in the DHHQD-L group . In addition, the renal tubular injury and renal fibrosis area in the DHHQD-H group were significantly lower than those in the DHHQD-L group  and α-SMA (p < 0.01) were significantly reduced after DHHQD delivery   deposition, causing glomerulosclerosis, hardening, and scarring. RIF is a pathological process that induces an inflammatory reaction, ECM deposition, interstitial scar formation, and stress interaction, resulting in ischemic injury to the nephron's glomerulus. In this process, immune cells migrate into the damaged glomerulus and secrete hypoxia-oxidative stress growth factors, such as TGF-mal loss . These genhanced . This exβ1 expression levels were previously shown to be high in both the cytoplasm and ECM in scar tissue [β1 deposition measured via immunohistochemistry in the cytoplasm and nucleus of renal interstitial lesion cells was suppressed in the DHHQD group compared to that in the untreated UUO group. Our results also showed that DHHQD could reduce the pathological changes caused by RIF. Therefore, to establish whether our findings regarding improvement in renal function and pathological changes after DHHQD administration were consistent with our network pharmacology data, we conducted molecular biology experiments on the expression of core genes and proteins observed in the study.RIF is a scar formation process induced by an inflammatory reaction, interstitial scar formation, or hypoxia-oxidative stress interaction. Overexpression of cytokines, renal interstitial cell proliferation disorder, excessive accumulation of ECM, and injury, activation, proliferation, and apoptosis of renal intrinsic cells owing to various reasons ultimately lead to structural deterioration and functional loss in renal tissue . Renal fr tissue . Our datκB) expression in the intestinal TLR4 signaling pathway [κB activation [κB [β-catenin pathway [β1 and the release of inflammatory cytokines like IL-6 [in vivo hypoxia with HIF-1α activation, HIF-1α can increase VEGF expression through gene regulation, increasing the production of VEGF by the body. After VEGF binds to its receptor, the endothelial cell structure changes, and vascular permeability increases, which eventually causes tissue edema [Based on the experience of TCM practitioners, we analyzed the DHHQD prescription using UHPLC-MS/MS and network pharmacology and observed sufficient evidence for the treatment of CKD with the main components of this decoction, such as rhein, emodin, gallic acid, epicatechin, quercetin, apigenin, and formononetin. Previous reports have presented that rhein may decrease interstitial fibrosis through antioxidants via activating the SIRT3/FOXO3a signaling pathway . Researc pathway . Peng et pathway . One stutivation . Quercettivation . Quercettion [κB . Apigeni pathway . Formono pathway . However pathway , 42. Bas pathway . Obstrucike IL-6 . IL-6 isike IL-6 . As an eike IL-6 . It prodike IL-6 . In in vue edema . With thue edema . VEGF isue edema . Our datpvalues are present. Phosphatidylinositol 3-kinase (PI3K) has Ser/Thr and phosphatidylinositol kinase activities. Tyrosine kinase receptors expressed on the cell surface, such as PDGF receptor and EGFR, bind with their corresponding ligands to activate PI3K, act on phosphatidylinositol 4,5-bisphosphate (PIP2) to generate phosphatidylinositol 3,4,5-trisphosphate (PIP3), bind and activate AKT, and then activate downstream proteins such as CHK1, BAD, FOXO, and other target proteins that regulate cell proliferation and apoptosis. Based on network pharmacology and experimental pharmacology, the PI3K-AKT signaling pathway as a molecular switch for signal pathway regulation is involved in antifibrotic effects, inflammation, oxidative stress resistance, degradation, cell cycle control, and apoptosis [κB exists in the cytoplasm and plays a role in transcription and activation. Under normal conditions, NF-κB and its inhibitor protein IκB are bound and inactive. During inflammation, IκB phosphorylation leads to NF-κB dissociation and activation followed by its translocation to the nucleus, where it promotes ECM generation by modulating gene expression. Previous network pharmacology and experimental pharmacology [κB or PI3K/AKT signaling pathway can be inhibited by gut microbial-derived metabolites and can exert anti-RIF and anti-inflammatory effects [κB in UUO rat kidneys showed reduced expression in response to DHHQD. Therefore, we speculate that DHHQD inhibits PI3K/AKT and TLR4/NF-κB pathway activity and ultimately, inflammation.Based on our GO enrichment analysis, the critical mechanisms of DHHQD action in RIF include the regulation of apoptosis and anti-inflammatory effects. Combined with KEGG analysis, PI3K/AKT signaling pathways were the most relevant signaling pathways affected by DHHQD in RIF treatment. TLR4, NF-кB, and eNOS are high-frequency research targets, enriched in the TLR4/NF-кB signaling pathway. These signaling pathways lie upstream of the enrichment sequence, wherein the enriched genes with significant poptosis . Once thpoptosis . NF-κB emacology , 53 stud effects , 55. HerOwing to the complex composition of herbs, this study verified the effectiveness of DHHQD in treating RIF through animal experiments after UHPLC-MS/MS and network pharmacological analysis. However, based on the results of the DHHQD analysis, we intend to conduct animal and cell experiments on the monomer ingredients with high levels or those known to play key roles in the treatment of RIF. Although the initial screening required for such experiments will be a monumental task, it will provide a scientific basis for elucidating the therapeutic effect of DHHQD against RIF and provide new directions for further TCM research and development.κB pathways; hence, further investigations are warranted.Our network pharmacology analysis and experimental data demonstrated that ingestion of DHHQD can modulate the clinical and pathological changes observed in UUO rats. DHHQD also reduced inflammatory cytokine levels and attenuated collagen deposition, which alleviated renal fibrosis in DHHQD-treated UUO rats. Additionally, the renal protective effect may be related to the downregulation of PI3K/AKT and TLR4/NF-


V2R, also called OlfC) multigene family is found in a broad range of jawed vertebrates from cartilaginous fish to tetrapods. V2Rs encode receptors for food-related amino acids in teleost fish, whereas for peptide pheromones in mammals. In addition, V2Rs of teleost fish are phylogenetically distinct from those of tetrapods, implying a drastic change in the V2R repertoire during terrestrial adaptation. To understand the process of diversification of V2Rs in vertebrates from “fish-type” to “tetrapod-type”, we conducted an exhaustive search for V2Rs in cartilaginous fish  and basal ray-finned fish , and compared them with those of teleost, coelacanth, and tetrapods. Phylogenetic and synteny analyses on 1897 V2Rs revealed that basal ray-finned fish possess unexpectedly higher number of V2Rs compared with cartilaginous fish, implying that V2R gene repertoires expanded in the common ancestor of Osteichthyes. Furthermore, reedfish and sterlet possessed various V2Rs that belonged to both “fish-type” and “tetrapod-type”, suggesting that the common ancestor of Osteichthyes possess “tetrapod-type” V2Rs although they inhabited underwater environments. Thus, the unexpected diversity of V2Rs in basal ray-finned fish may provide insight into how the olfaction of osteichthyan ancestors adapt from water to land.The vomeronasal type 2 receptor ( Olfaction is a critical chemosensory system for eliciting social behaviors in vertebrates, including reproduction, kin recognition, aggression, and feeding. The vertebrate olfaction system has experienced drastic changes in anatomy and neurophysiology during adaptation from water to land, one of the most important events is vertebrate evolution. Specifically, aquatic vertebrates detect water-soluble chemicals using their olfactory epithelium (OE), whereas terrestrial vertebrates detect both volatile and nonvolatile chemicals by differentiating their OE into main olfactory epithelium (MOE) and vomeronasal organ (VNO). Accompanied by the terrestrial adaptation of olfaction, the chemosensory receptors are proposed to have undergone major innovative diversification, although the detailed evolutionary process of diversification remains to be poorly understood at the DNA level.OR), vomeronasal receptor type I (V1R), vomeronasal receptor type II (V2R), and trace amine-associated receptor (TAAR), all forming multigene families1. In teleost fish, V2Rs, also referred to as OlfCs (olfactory receptors classified as type C GPCRs)3, are expressed in OE of the nasal cavity. Several independent studies have shown that teleost V2Rs detect amino acids that elicit certain feeding behaviors. For example, V2Rs are expressed in microvillous sensory neurons of zebrafish and respond to amino acids, but not bile acids or sex pheromones4. In addition, the genetic blockage of neural transmission in the V2R-expressing neurons abolishes the attractive response to a mixture of amino acids5. However, given that Yang et al.6 have proposed a possible contribution of some V2Rs to elicit fright reactions, it is premature to rule out the possibility that V2Rs detect some chemicals other than amino acids. In tetrapods, VNO is a specific organ that mainly detect pheromones1. V2Rs are specifically expressed in the VNO of mice7, frogs8, and reptiles9. Hence, they are believed to encode pheromone receptors. Indeed, it has been shown that V2Rs detect peptide pheromones11 and peptides for the major histocompatibility complex12.Olfaction of vertebrates is mainly composed of four types of G protein-coupled receptors (GPCRs), namely, the olfactory receptor , which are genetically closely related to each other16. In the phylogenetic tree, V2Rs of teleost fish are distinct from those of tetrapods, which were described as “fish-like” and “tetrapod-like”17. Similar distinctive evolution was also observed between V1Rs of teleost fish and tetrapods18, which were named “fish-type” (f-V1Rs) and “tetrapod-type” (t-V1Rs), respectively21. In accordance with previous studies, we hereafter use the name of "fish-type" V2Rs (f-V2Rs) for the putative amino acid receptor in teleost fish and "tetrapod-type" V2Rs (t-V2Rs) for the putative peptide receptor in tetrapods. The f-V2Rs and t-V2Rs are also found to be distinct in their synteny relationships in that the f-V2Rs form single large cluster in particular chromosomes16 and t-V2Rs are scattered on several chromosomes22. It is notable that the coelacanth, which is the close relatives of tetrapods, have both f-V2Rs and t-V2Rs17, showing that the coelacanth is an important organism as it serves as a missing link to fill an evolutionary gap between vertebrates under water and land21. Recent studies on several shark genomes have shown that olfaction in cartilaginous fish is dominated by f-V2Rs rather than conventional ORs25, which is consistent with the ultrastructural observation that the presence of only microvillous sensory neurons in OE26 and with the immunohistochemical observations that most neurons in OE are positive for Go antibodies27. In addition, a previous study has shown that V2Rs were absent in lamprey genomes28. Based on the above findings, V2Rs are considered to have originated in the jawed vertebrate ancestor before the split between the two extant descendant lineages, that is, cartilaginous fish and Osteichthyes (ray-finned and lobed-finned fish including tetrapods).Until now, phylogenetic analyses have revealed that teleost fish possess 20–60 V2Rs in nine cartilaginous fish species , three basal ray-finned fish , two teleost fish , a coelacanth, two amphibians (western clawed frog and caecilian), an anole lizard, and a mouse to elucidate the process of diversification of f-V2Rs and t-V2Rs in vertebrates. Especially, focusing on basal ray-finned fish, which retain many ancestral characters of Osteichthyes, is of primary importance in understanding the evolution and diversification of V2Rs from fish to tetrapods29. As a result, we have characterized 9–42 V2Rs in cartilaginous fish, and a large amount (47–189) of V2Rs in basal ray-finned fish. Phylogenetic analyses of V2Rs of 19 various vertebrate species have revealed the existence of t-V2Rs in the genomes of reedfish and sterlet, implying the presence of an evolutionary seed for mammalian peptide pheromone receptors in the basal ray-finned fish. The results of this study including; (1) genomic survey and identification of V2Rs, (2) fine-scale phylogenetic and synteny analyses, and (3) mRNA expression profiles on the OE, would provide important insights into understanding the process of diversification of V2Rs during vertebrate evolution.In this study, we have performed a comprehensive exploration and phylogenetic analyses for V2R gene repertoires from the genomes of nine cartilaginous fish , three basal ray-finned fish , two teleost fish , a coelacanth, two amphibians , an anole lizard, and a mouse using a software “fate”30. The copy numbers of V2Rs in individual species are summarized and provided in Table V2Rs in teleost fish was mostly comparable to previous studies with some update. For example, zebrafish that possessed 72 V2Rs, which was larger than in previous studies13. The numbers of V2Rs in cartilaginous fish ranged from 8 (rays) to 41 (chimera), which were found to be smaller than those of the teleost fish. In contrast, basal ray-finned fish possessed an unexpectedly large number of V2Rs. In reedfish, we identified 188 intact V2Rs, which was the largest in all ray-finned fish studied until now. The numbers of V2Rs in sterlet and spotted gars were also high . The copy number of V2Rs in the western clawed frog (691), which far exceeded that previous studies15, was the largest among vertebrates studied so far; it is much higher than those of other tetrapods, such as caecilian (275), anole lizard (64), and mouse (154). It is noteworthy that we found no V2Rs in the genomes of agnathans and amphioxus.We explored V2Rs in vertebrates, we constructed a phylogenetic tree of all V2Rs identified in the study (1897 from 19 species) using maximum likelihood algorithm in RAxML31. The phylogenetic tree with all sequences shows a clear separation of the two V2R clades, each of which consists of the sequences of f-V2Rs of teleost fish and t-V2Rs of tetrapods, respectively  were classified into t-V2Rs. Indeed, in the phylogenetic tree, these V2Rs were sister group of the coelacanth V2R cluster  of the basal ray-finned fish is meaningful in discussing vertebrate evolution (see discussion).The t-ter Fig. . AlthougV2Rs were divided into three well-supported clades, namely, the V2R2s, f-V2Rs, and t-V2Rs28. The orthologous V2R2s were shared in all vertebrates with a few species-specific duplications in mice32. In this study, V2R2 ortholog of all vertebrates was located at the basal position of the V2R tree  V2R. In this study, highly conserved ancV2R sequences were found in the genomes of cartilaginous fish to amphibians, but not in teleost fish, coelacanth, and mammals  eta1 and neprilysin16. In contrast, no V2Rs were found between these two genes in tetrapods, and t-V2Rs were scattered into several chromosomes22. Therefore, to ascertain if the V2Rs are f-V2Rs or t-V2Rs, we examined the synteny relationships, in which the gene arrangements in the genomic region of two landmark genes in various vertebrates from cartilaginous fish to mammals were summarized , which is a basal ray-finned fish closely related to reedfish . First, we precisely examined the presence/absence of 2:1 relationship for Western clawed frog, in which V2Rs were expanded  to mammals were shown in Fig. V2R clades in extant species and two ancestral nodes. In addition, the character of the four clades of V2Rs were summarized in Table V2Rs in cartilaginous fish, basal ray-finned fish and caecilian provided new insights into V2R evolution in vertebrates. No V2R was found in the agnathans, implying the acquisition of a V2R-mediated olfaction as a common ancestor of jawed vertebrates (cartilaginous fish and Osteichthyes). Given that agnathans have three other types of chemoreceptor genes, namely, ORs, TAARs, and V1Rs, it is likely that V2Rs were originated later than these three receptors42. V2R2 was conserved in all extant jawed vertebrates with no exceptions, which suggests the highly important and fundamental role of V2R2 in the detection and subsequent signaling pathway for olfactory substances in both underwater and terrestrial environments. Importantly, except for V2R2, mammal and teleost fish possessed only specific clades of V2Rs, namely, t-V2Rs and f-V2Rs, respectively. In contrast, basal ray-finned fish and one amphibian (caecilian) possessed both t-V2Rs and f-V2Rs. This finding suggests that all four clades of V2Rs were present at least in the common ancestor of Osteichthyes, or even earlier in the common ancestor of jawed vertebrates. However, subsequently, the repertoires of V2Rs were lost in teleost fish and mammals during adaptation to their specific environments. It is interesting to note that one copy of f-V2R is exceptionally retained in caecilian. This may reflect its ancestral position in Amphibia, its water-dependent habitat, and/or subterranean lifestyle, but further studies are needed to examine these possibilities. Because the previous studies of V2Rs have been limited to teleost fish and mammalians, the diversity of V2Rs was underestimated. Thus, the genome sequences of non-model animals, which become available due to the recent advancement of sequence technique, allowed us to uncover the hidden and remarkable diversity in the genomes of vertebrates.The previous (A) and present (B) hypotheses for evolutionary scenarios of the four clades of V2Rs diversification. The repertoires of the subfamily of the f-V2Rs are abundant in ray-finned fish and coelacanth compared with those in cartilaginous fish , it might be possible that an innovative evolution of the olfactory system occurred in this timing.One of the important discussions is the timing of sh Table . In addi45. Similar spiracle-like structures were observed in the fossil records of stem tetrapods46. Thus, breathing air using spiracles may have been an important respiratory strategy in the stem Osteichthyes, which inhabit shallow freshwater environments and use lungs in addition to gills for respiration47. Specifically, the evolution of air-breathing by spiracles may increase the opportunity to raise their head above water, which led to the acquisition of the primitive capabilities of detecting airborne chemicals before terrestrial adaptation. Thus, such dual functional roles of the olfactory system in stem Osteichthyes were related to the diversification of V2Rs, including f-V2Rs and t-V2Rs. However, at this moment, both types of V2Rs are expected to detect non-volatile ligands such as amino acids and peptides  possesses large paired openings (spiracles) on top of their head, in which they use for breathing airV2Rs, we conducted a comprehensive exploration of V2R sequences in the genome assemblies of a broad range of vertebrates, including nine cartilaginous fishes and three basal ray-finned fishes. In addition to two teleost fishes (Japanese eel and zebrafish), coelacanth, two amphibians (caecilian and western clawed frog), anole lizard, and mouse were explored  domain of V2Rs as queries against all of the genomic sequences. The query sequences were generated by aligning V2R sequences of mouse, anole lizard, tropical clawed frog, coelacanth, spotted gar, and zebrafish deposited in the Ensembl database and of elephant shark and cloudy catshark24 using the MA–T –dash option48. Then, the TM domains, from 50-aa sequences 7n1upstream of the 1st TM region to 50-aa downstream of the 7th TM region, were extracted using a protein structure of the glutamate receptor (PDB: 4OR2)49 as reference.To estimate the evolutionary history of ed Table . To idenV2R-coding sequences were predicted for tBLASTn hit regions using GeneWise v.2.4.150. The above procedures were conducted by using a software “fate”, which is developed specifically for the identification of multiple gene families30. Sequences shorter than 600 nucleotides were discarded. The homology of intact outputs was then determined by the next phylogenetic analysis. The queries used in the BLAST search were critical to the estimated number of V2Rs. Indeed, the number of V2Rs identified in this study was consistent with previous studies, whereas a few differences were observed in some cases. For example, the numbers of V2Rs in elephant shark and catshark identified in this study (29 and 24 copies) were slightly smaller than those reported by Sharma et al.24. The difference in query sequences used for the BLAST search reflects results. Specifically, we used the seven-transmembrane regions of the V2R, whereas Sharma et al.24 used the entire region of V2R, including the extracellular Venus flytrap module region (VFTM)51 The VFTM region of the V2R protein has been determined to be highly diverse and difficult in predicting the exon–intron structure of the gene. This result affects the judgment of intact or pseudogenes. Therefore, we compared the number of V2Rs estimated under a unified condition using the seven-transmembrane region as a query.Next, the 52 and default parameters. The sites with < 50% coverage among all sequences were removed. Maximum likelihood trees for V2R genes were then inferred using RAxML-NG v.1.0.131 with the JTT + G + F amino acid substitution model. This result was the best fitting model selected by the ModelTest-NG54 based on AIC scores. Rapid bootstrap analyses were performed using 100 replicates to assess the reliability of nodes. The V2R sequences for coelacanth, western clawed frog, and mouse were then clustered based on the criteria of 80% similarity at the amino acid level to save computational costs. To avoid false positives in identification of the V2R genes and prevent long branch attractions, we obtained other GPCR family C sequences from GenBank or Ensembl for use as outgroups, including CaSR (NM_013803.3), Tas1r1 to Tas1r3 , GPCR6 (NM_153071.1), GRM1a to GRM8 , and GABA B1 to GABA B2 (NM_019439.3 and NM_001081141.2), for constructing an initial gene tree. All genes in the sister clade to CaSR were named homologs of V2R (including V2R2). Using these genes, we constructed the V2R gene tree again (CaSR was used as an outgroup). We also included sequences of all 16 teleost fish subfamilies classified in previous studies14, as markers to indicate V2R subfamilies.Sequences were translated into amino acid sequences and aligned using MAFFT with the -ginsi optionV2Rs of vertebrates were then illustrated based on the genomic location of the identified V2Rs  individuals of 11–25 cm long, which were used for the preparation of frozen sections and the extraction of total RNA from the olfactory organs, were purchased from a commercial supplier; they were kept under standard conditions suitable for tropical fish breeding until experimental manipulations. All experimental studies using the animals were approved by the Institutional Animal Experiment Committee of the Tokyo Institute of Technology were performed in accordance with the institutional, governmental ARRIVE guidelines. In addition, all methods were performed in accordance with the relevant guidelines and regulations. TRIzol (Invitrogen) was then used for total RNA extraction from the olfactory organs of the bichir. Using the total RNA extracted from the olfactory organs of the bichir, cDNA was synthesized by reverse transcription reaction using SuperScript III RTase (Invitrogen). Coding regions of V2R were amplified by PCR using the primer sets, which were designed on the basis of V2R sequences of reedfish, as has been summarized in Supplementary Table E. coli. Digoxigenin-labeled RNA probes were synthesized using the plasmid vector as a template using T7 or SP6 RNA Polymerase (Roche) and DIG RNA labeling mix (Roche) as well. The olfactory organs of the bichir were then fixed with 4% PFA, replaced with sucrose, and embedded in an O.C.T compound (Sakura Finetek). In situ hybridization was performed according to the method as previously described44. Briefly, hybridization was performed using DIG-labeled RNA probes. The antibody reaction was conducted using anti-digoxigenin-POD, Fab fragments (Sigma-Aldrich), or anti-fluorescein-POD, Fab fragments (PerkinElmer). The signal was then amplified with Tyramide Signal Amplification Plus Biotin kit (Kiko Tech), and detected by streptavidin, Alexa Fluor 488 conjugate (Thermo Fisher). Finally, the sections were sealed using a VECTASHIELD Mounting Medium with DAPI (VECTOR). The sealed sections were observed using a fluorescence microscope Axioplan (Carl Zeiss). All fluorescence photographs were taken using an Axiocam 503 color (Carl Zeiss) and optimized for brightness and contrast in Adobe Photoshop.Bichir .Supplementary Information.


Scutellaria baicalensis Georgi (SBG) has significant anti-inflammatory and immune-modulating activities and is widely used in the treatment of inflammatory and autoimmune diseases. However, the mechanism of SBG in the treatment of ankylosing spondylitis (AS) remains to be elucidated.  Differentially expressed genes (DEGs) related to AS were analyzed based on two GEO gene chips. The DEGs were merged with the data derived from OMIM, GeneCards, and PharmGKB databases to ascertain AS-related targets. Active components of SBG and their targets were acquired from the TCMSP database. After overlapping the targets of AS and SBG, the action targets were acquired. Subsequently, protein-protein interaction (PPI) network and core target screening were conducted using the STRING database and Cytoscape software. Moreover, the DAVID platform was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of action targets. Finally, the affinity of major active components and core targets was validated with molecular docking. κB signaling pathways. Molecular docking results confirmed a strong binding activity between the main active components and the core targets.  A total of 36 active components of SBG were acquired from TCMSP database. Among these, the main active components were baicalein, wogonin, and oroxylin A. The PPI network and screening showed TNF, IL-6, CXCL8, PTGS2, and VEGFA as core targets associated SBG against AS. GO and KEGG analyses indicated that SBG participated in various biological processes, via regulating IL-17, TNF, and NF- The therapeutic mechanism of SBG associated with AS can be characterized as a multicomponent, multitarget, and multipathway mechanism. SBG may be a promising therapeutic candidate for AS. Ankylosing spondylitis (AS), an autoimmune disorder, primarily afflicts the axial joint. Pathological changes mainly include inflammation, bone destruction, and new bone formation , 2. ThisTCM mainly treats AS by mediating the regulation of the immune inflammatory response. Scutellaria baicalensis Georgi (SBG) is the dry root of the Lamiaceae plant Huang-Qin . Modern In this context, network pharmacology was utilized in our study to predict the active components, action targets, and pathways of SBG in AS treatment, thereby ascertaining the related mechanism. These results were preliminarily validated using molecular docking technology.The workflow of the current research is presented in Homo sapiens and the gene microarray data depicting gene expression profiles. Finally, two datasets  and GSE73754 ) were selected for further analyses, and their corresponding matrices and platform annotation files were downloaded.AS-related gene expression profile data were searched in the GEO database with the keyword “ankylosing spondylitis.” Data were included with the following criteria: datasets that included AS and normal samples and datasets with samples derived from https://www.r-project.org) was applied for systematic analyses. After the probes of raw data in AS-related microarray data were annotated and filtered, these raw data were subjected to probe averaging and background correction, and the samples were normalized using the normalize function. Subsequently, the combined data set was subjected to further analyses with the limma package, and the differentially expressed genes (DEGs) were retrieved under the conditions of P < 0.05 and |logFC| ≥ 1.3. The volcano plots and heat maps were drawn.R (https://omim.org/), GeneCards (https://www.genecards.org/), and PharmGKB (https://www.pharmgkb.org/) databases, which were intersected with the DEGs screened by gene microarray analyses to acquire the targets of AS. Thereafter, a Venn diagram of AS targets was drawn using the online platform jvenn [Using “ankylosing spondylitis” as the keyword, AS-related targets were searched in OMIM (rm jvenn .https://www.uniprot.org/) for standardization of their names. jvenn was employed to match the AS targets with the targets of SBG to acquire the potential targets of SBG in AS treatment.The active components of SBG were searched in Traditional Chinese Medicine System Pharmacology (TCMSP) database . Oral bihttps://www.perl.org/). Next, a network of “active component-action target” was constructed by importing the above components and targets of SBG into Cytoscape (version 3.9.1). Here, a node referred to the active components and action targets, and an edge marked the interaction of the active components with the action targets.The active components corresponding to the aforementioned action targets were mapped with Perl (https://STRING-db.org/) is used for searching protein interactions and for predicting protein interactions from many organisms [Homo sapiens, and the minimum interaction score was set as 0.4. The isolated targets were hidden. A tsv format file of the protein interaction was acquired and exported. This file was then imported into Cytoscape for visualizing the PPI network, which was then topologically analyzed with the CytoNCA plug-in. The screening criteria were as follows: degree centrality and betweenness centrality higher than or equal to its median value. The screening was performed twice to obtain the core targets of SBG for AS treatment.The STRING database (rganisms . Interachttps://david.ncifcrf.gov/), which is widely utilized in gene annotation. Specifically, the action targets were inputted into the DAVID for GO and KEGG analyses. GO functional annotation was applied to annotate mechanisms associated with the targets with respect to biological process (BP), cellular components (CC), and molecular function (MF) [The biological function and specific pathways of potential targets were analyzed with the DAVID database (ion (MF)  and analion (MF)  and selehttps://www.rcsb.org/). Thereafter, AutoDock  was used for molecular docking, and the results were drawn with PyMOL . The binding affinity was calculated, thereby verifying the accuracy of the network pharmacological results. An affinity of <0 kcal/mol indicated that the ligand and the receptor could bind spontaneously, and a lower affinity represented a stronger binding force between the ligand and the receptor [To verify whether the active components and action targets exhibit better binding activity, the top three major active components acquired from the active component-action target network were considered ligands, and the core targets obtained from the PPI network screening were considered receptors for molecular docking. The “mol2” format files of the major active components were downloaded from the TCMSP database. Likewise, the structure of the core targets was obtained from the PDB database , 26 CCs , and 74 MFs . The top 10 targets of each entry are demonstrated in We obtained three major active components  from the active component-action target network and five core targets  from PPI network screening for molecular docking. And the docking affinity is presented in AS is an autoimmune disease characterized by inflammation, and its pathogenesis has not yet been fully clarified . At presκB, thereby reducing the expression of iNOS and COX-2 [Inflammation, bone destruction, and new bone formation are the main pathological characteristics of AS, which are closely related to abnormal immune inflammatory reactions. Therefore, inhibiting immune inflammatory reaction is an important strategy to control the progression of AS. Interestingly, in this study, baicalein, wogonin, and oroxylin A were discovered as the major active components of SBG in AS treatment after searching for all the active components and analyzing the active component-action target network. The aforementioned main active components had anti-inflammatory and immunomodulatory activities. Baicalein and wogonin are the principal flavonoid active compounds present in SBG . Baicalend COX-2 . Therefoα, a classic proinflammatory factor, can mediate an inflammatory cascade and promote the upregulation of various inflammatory factors, such as IL-6, IL-1β, and IL-8 [α are important drugs used in western medicine for the treatment of AS. These drugs primarily alleviate AS by inhibiting inflammation and slowing the progression of spinal imaging. As a pleiotropic cytokine with multiple biological functions, IL-6 participates in the pathogenesis of various rheumatic disorders [The study revealed that TNF, IL-6, CXCL8, PTGS2, and VEGFA may be core targets of SBG involved in the treatment of AS. These targets are closely associated with immune inflammation. In particular, TNF-and IL-8 . It alsoand IL-8 , 37, whiisorders . It promisorders . CXCL8, isorders . It is aisorders . PTGS2, isorders . Interesisorders . Thus, tκB signaling pathways. Lipopolysaccharide, which is a component of the bacterial cell wall, can stimulate the immune inflammatory response of the body and mediate the aggregation of adhesion molecules, cell chemokines, and inflammatory cytokines in local joints. It has been used to induce AS in animal models [κB pathway is involved in the formation of a hypercoagulable state in AS patients [α [κB promotes inflammation and immune response and participates in apoptosis [GO and KEGG analyses showed that the therapeutic effect of SBG may be related to responses to lipopolysaccharides, responses to molecules of bacterial origin, and IL-17, TNF, and NF-l models . A previl models . Hence, l models  and thatpatients . The proients [α . NF-κB ppoptosis , playingBased on network pharmacology, this study analyzed the molecular mechanism of SBG in AS treatment, demonstrated the complex molecular network associated with SBG in AS treatment, and the characteristics of “multicomponent, multitarget, and multipathway” of SBG, providing a scientific basis for in-depth research and development of SBG as a drug for the treatment of AS. However, one of the main limitations of the study is that our findings are based on existing databases. Therefore, our results need to be further verified in cell experiments and clinical trials. In future research, we aim to group different active components of SBG, screen the active components with good therapeutic effects, and actively explore the optimal dosage of SBG for the treatment of AS along with possible adverse reactions. We aim to find a single component or a combination for the optimum treatment of AS and clarify the mechanism of action so that it can be applied to clinical practice.κB signaling pathways, assuming a vital role in AS treatment. As a medicinal plant, SBG is hopeful to be developed as an effective drug for the treatment of AS.The study suggests that the therapeutic mechanism of SBG associated with AS can be characterized as a multicomponent, multitarget, and multipathway mechanism. Baicalein, wogonin, and oroxylin A may be the major active components of SBG in AS treatment. These components primarily act on the core targets of TNF, IL-6, CXCL8, PTGS2, and VEGFA and regulate the IL-17, TNF, and NF-