To promote the development and therapeutic application of new medications, it is crucial to conduct a thorough investigation into the mechanism by which the traditional Chinese herb pair of Haizao-Kunbu (HK) treats Graves’ disease (GD).Chemical ingredients of HK, putative target genes, and GD-associated genes were retrieved from online public databases. Using Cytoscape 3.9.1, a compound-gene target network was established to explore the association between prosperous ingredients and targets. STRING, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes pathway analyses visualized core targets and disease pathways. Additionally, we conducted a refined analysis of the binding interactions between active ingredients and their respective targets. To visualize these findings, we employed precise molecular docking techniques. Furthermore, we carried out molecular dynamics simulations to gain insights into the formation of more tightly bound complexes.We found that there were nine key active ingredients in HK, which mainly acted on 21 targets. These targets primarily regulated several biological processes such as cell population proliferation, protein phosphorylation, and regulation of kinase activity, and acted on PI3K-AKT and MAPK pathways to treat GD. Analysis of the molecular interaction simulation under computer technology revealed that the key targets exhibited strong binding activity to active ingredients, and Fucosterol-AKT1 and Isofucosterol-AKT1 complexes were highly stable in humans.This study demonstrates that HK exerts therapeutic effects on GD in a multi-component, multi-target, and multi-pathway manner by regulating cell proliferation, differentiation, inflammation, and immunomodulatory-related targets. This study provides a theoretical foundation for further investigation into GD. Graves’ Disease (GD) is an autoimmune illness characterized by an enlarged and hyperactive thyroid gland (Graves’ hyperthyroidism), ocular abnormalities (Graves’ orbitopathy), and regional dermopathy . After two decades of Universal Salt Iodization (USI), GD prevalence in China has gradually decreased, with the latest epidemiological survey results at 0.53% . HoweverControlling hyperthyroidism by restoring standard thyroid hormone concentrations is the principal objective of GD treatment. Some therapeutic options for Graves’ hyperthyroidism patients include Antithyroid Drugs (ATDs), radioiodine, and surgery. However, these treatment options have some drawbacks. Antithyroid medicines produce granulocyte count and liver enzyme abnormalities, and recurrence occurs in > 50% of cases after terminating ATDs . On the Zhu Bing Yuan Hou Lun, which focusses on diagnosis and management of a class of diseases manifested as goiter, and GD is one of them. According to Waike Zhengzong, Yingbing is “not swelling of yin and yang, but stasis of blood, turbidity, and phlegm in the five organs.” Consequently, doctors mostly use Chinese medications that reduce hardness and disperse stagnation for goiter treatment. From inception to the present time, the herb pair Haizao-Kunbu (HK), first appearing in Zhou Hou Bei Ji Fang, has been a common and effective combination therapy for goiter. According to Shennong′s Classic of the Materia Medica, Haizao (Sargassum) can “master goiter, tumor qi, and the nucleus of the neck.” Additionally, Mingyi Bielu reported that Kunbu (Laminaria japonica) could “treat twelve kinds of edema and goiter tumor coalescing gas.” Some TCM formulas containing HK for goiter treatment include the Sihai Shuyu pill from Yangyi Daquan, the Haizao Yuhu decoction from Waike Zhengzong, and the Huaying Micro pill from Rumen Shiqin. Previous pharmacological research has demonstrated that besides reducing autoimmune antibody levels in rats with thyroid disease  in Traditional Chinese Medicine (TCM). The records are derived from a Chinese medicine text,  disease –8, Haiza disease . Kunbu,  disease , 11. Fur disease , 13. Howin silico tool for exploring biological processes and molecular frameworks underlying linkages among macromolecules and ligands  and Drug-Likeness (DL). Subsequently, public databases were reviewed for target and GD-related genes. To predict core compositions and targets that probably participated in GD treatment, we created a network associating medicinal ingredients with target genes in HK. Additionally, Protein-Protein Interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were utilized to identify potential targets and pathways. The molecular docking and MD modeling techniques provide a foundation for further research on the HK molecular processes in GD treatment. http://tcmspw.com/) (www.swissadme.ch) was used to screen all ingredients  Database (pw.com/)  and reasredients .www.swisstargetprediction.ch) (https://pubchem.ncbi.nlm.nih.gov) (https://www.uniprot.org) (The TCMSP database and Swiss Target Prediction platform (tion.ch)  were usenih.gov)  in SDF frot.org) , we evenrot.org) .https://www.omim.org) (https://www.genecards.org) (https://go.drugbank.com) (https://www.disgenet.org)  , GeneCarrds.org) , Drugbanank.com) , and Disnet.org)  databasenet.org)  was evennet.org) . ObvolutAfter importing the common targets into STRING, we built a PPI network to further explore the role of crucial targets. In our investigation, we selected interactions with the highest confidence score (≥ 0.900) for ‘Homo sapiens’, among which we focused on the least demanding interrelation score. The unconnected nodes were concealed and the connection network was illustrated using Cytoscape 3.9.1. Additionally, the CytoNCA (Cytoscape software add-in)  was insthttps://metascape.org) (https://david.ncifcrf.gov) (https://www.bioinformatics.com.cn) was used to plot the bar and bubble charts with color gradients for data analysis and visualization. The FDR error control technique was used to establish whether biological processes differed significantly. After correcting the p-value, a significance threshold of p <0.05 was used.To further clarify the HK gene occupation and the role of intrinsic signaling pathways in GD, Metascape (ape.org)  and Davicrf.gov)  were usehttps://www.rcsb.org)  (csb.org)  and TCMScsb.org) . The Autcsb.org)  was usedcsb.org)  was utilLigand-receptor docked complex MD simulation was performed using GROMACS (version 2021.2) . WhereasWe selected 11 components by searching the literature and online platforms and qualified nine ingredients through the screening threshold. The OMIM, DrugBank, GeneCards, and Disgenet databases yielded 570, 12, 2470, and 585 GD targets, respectively. After collecting all the genes and removing duplicate data, 2,010 targets remained for further research Figure 3We imported 189 potential targets into STRING and obtained 165 potential targets through the screening threshold. Subsequently, we created a PPI network (165 nodes and 771 edges) after importing target genes into Cytoscape 3.9.1 software Figure 3We completed GO and KEGG enrichment studies to further demonstrate the proposed targets’ organic functions and prospective mechanisms. The analyses involved 189 HK potential target genes underlying GD. The results were obtained after analysis of the GO and KEGG enrichment results analyzed by Metascape and David databases Figure 4A KEGG pathway enrichment assessment was performed to further investigate the possible functions of the targets (P<0.05). The following 19 pathways appeared simultaneously in the top 20 critical pathways analyzed by the two databases: Cancer Pathways (hsa05200), the PI3K-AKT Signaling Pathway (hsa04151), Lipid and Atherosclerosis (hsa05417), Proteoglycans in Cancer (hsa05205), Human Cytomegalovirus (HCMV) Infection (hsa05163), Kaposi Sarcoma-associated Herpesvirus (KSHV) Infection (hsa05167), Fluid Shear Stress and Atherosclerosis (hsa05418), the AGE-RAGE Signaling Pathway in Diabetic Complications (hsa04933), Human Papillomavirus (HPV) Infection (hsa05165), Neurodegeneration-multiple Disease Pathways (hsa05022), the MAPK Signaling Pathway (hsa04010), Hepatitis B (hsa05161), Tuberculosis (hsa05152), Epstein-Barr Virus (EBV) Infection (hsa05169), Chemical Carcinogenesis-Reactive Oxygen Species (hsa05208), MicroRNAs in Cancer (hsa05206), Focal Adhesion (hsa04510), Alzheimer’s Disease (hsa05010), and Endocrine Resistance (hsa01522). Based on this outcome, we finally constructed a “KEGG pathway-gene target” network Figure 5We used molecular docking to identify chemicals from the PPI system based on GD-related targets. The relationships among essential active chemicals and crucial targets were examined using AutoDock4.2, Discovery Studio 2019 Client, and PyMOL software. Molecular structures of significant targets were obtained from RCSB PDB Table 2.We selected and implemented the top two compound-target dockings (Fucosterol-AKT1 and Isofucosterol-AKT1) for MD simulations. After 100 ns of MD simulations, the dynamic variations of the Fucosterol-AKT1 and Isofucosterol-AKT1 complexes were assessed. Subsequently, we computed the Root-Mean-Square Deviation (RMSD) to understand the complexes’ molecular configurations and the system’s stability during simulation. The RMSD curve represents the location deviations in the protein. Fucosterol-AKT1 and Isofucosterol-AKT1 had average RMSD values of 2.5 and 2.6 Å, respectively. The RMSD of the Fucosterol-AKT1 complex was less than that of the Isofucosterol-AKT1 complex between the 45-85 ns time interval, and the RMSD curves relatively stabilized after 85 ns  contains approximately 150 μg of iodine, while the iodine concentration of wild Sargassum is 58-629 μg/g . However-1.4 g cohibition . In thishibition . In our hibition . Furtherhibition , 55. Somin vitro experiments have demonstrated that it could act on the PI3K/AKT signaling pathway  family is required for the proliferation of blood vessels , and GD Additionally, KEGG enrichment analysis demonstrated that the PI3K-AKT and MAPK signaling pathways were involved in GD onset. Furthermore, some of the BP and MF items examined by GO, including protein phosphorylation, positive cell death regulation, cell activation, kinase activity regulation, MAPK cascade regulation, cell population proliferation, Transcription Factor (TF) binding, protein serine/threonine kinase activity, cytokine receptor binding, and Cell Adhesion Molecule (CAM) binding, revealed the significance of cell proliferation, differentiation, protein phosphorylation, and protein kinases in GD pathogenesis. Notably, GD patients have thicker, hypertrophied follicular cells in their thyroid glands that produce active thyroglobulin. The gland exhibits classic lymphocytic infiltrates considered to be principally connected with TSHR autoantibody secretion. Additionally, a histological examination showed occasional apoptotic cells and partial follicular wreckage . The preMD simulation is an effective technique for examining molecular mechanisms, especially in evaluating the binding stability  and the in-vivo and in-vitvo experiments should be conducted to accurately explore the therapeutic effects and associated mechanisms of the potential compound and target pairs. It is also necessary to identify the active ingredients with therapeutic effects in HK through basic experiments. These will be the focus of our future research.Although network pharmacology, molecular docking, and molecular dynamics simulation methods were implemented to characterize the potential chemicals and targets of HK, there are some several limitations that need to be acknowledged. First, to accurately comprehend the behavior of chemical elements functioning on disease targets, network pharmacology can be utilized to predict the up-regulation and down-regulation of targets. Second, in vitro and in vivo studies, our current findings suggest that HK holds significant promise as an effective herbal remedy for treating GD.GD is an autoimmune disease caused by genetic and environmental factors. The multifaceted nature of GD calls for a multifaceted approach to its treatment, often involving multiple targets. There is a growing interest in innovative treatment strategies that draw from the wisdom of traditional medicine and apply it to the clinical management of GD. These strategies have demonstrated favorable efficacy and safety profiles. Nine compounds including Fucosterol, Isofucosterol, Aurantiamide, Eicosapentaenoic Acid, quercetin and eckol of HK were combined to 21 core targets including AKT1, PIK3R1, MAPK1, MAPK8, MAPK14, VEGFA, EGFR, IL6, TNF, TP53, CAV1, JAK2, and STAT1, and by participating in biological processes such as cell population proliferation, protein phosphorylation, regulation of kinase activity and protein serine/threonine kinase activity and PI3K-AKT, MAPK, and other pathways play a crucial role in the treatment of GD. Molecular docking and molecular dynamics simulation demonstrated that the Fucosterol-AKT1 and Isofucosterol-AKT1 complexes exhibited the highest binding energy, indicating that HK contains key compounds and targets. While future research will necessitate further biological experiments, including additional The original contributions presented in the study are included in the article/Methodology: MY, YL, XH. Software: MY, XH. Validation: MY, XH. Formal analysis: MY, XH. Visualization: MY, YL, QL, YA. Conceptualization: YL, TG. Investigation: YL, DG, QL, YW, YA. Supervision: YL, TG. Resources: DG, QL, YW, YA. Data Curation: DG, QL, YW. Writing - Original Draft: MY. Writing - Review & Editing: YL, XH, TG. Project administration: YL, TG. Funding acquisition: YL. All authors contributed to the article and approved the submitted version.


Arsenic trioxide (ATO) is a promising anticancer drug for hematological malignancy. Given the dramatic efficacy of acute promyelocytic leukemia (APL), ATO has been utilized in other types of cancers, including solid tumors. Unfortunately, the results were not comparable with the effects on APL, and the resistance mechanism has not been clarified yet. This study intends to identify relevant genes and pathways affecting ATO drug sensitivity through genome-wide CRISPR-Cas9 knockdown screening to provide a panoramic view for further study of ATO targets and improved clinical outcomes.A genome-wide CRISPR-Cas9 knockdown screening system was constructed for ATO screening. The screening results were processed with MAGeCK, and the results were subjected to pathway enrichment analysis using WebGestalt and KOBAS. We also performed protein-protein interaction (PPI) network analysis using String and Cytoscape, followed by expression profiling and survival curve analysis of critical genes. Virtual screening was used to recognize drugs that may interact with the hub gene.We applied enrichment analysis and identified vital ATO-related pathways such as metabolism, chemokines and cytokines production and signaling, and immune system responses. In addition, we identified KEAP1 as the top gene relating to ATO resistance. We found that KEAP1 expression was higher in the pan-cancer, including ALL, than in normal tissue. Patients with acute myeloid leukemia (AML) with higher KEAP1 expression had worse overall survival (OS). A virtual screen showed that etoposide and eltrombopag could bind to KEAP1 and potentially interact with ATO.ATO is a multi-target anticancer drug, and the key pathways regulating its sensitivity include oxidative stress, metabolism, chemokines and cytokines, and the immune system. KEAP1 is the most critical gene regulating ATO drug sensitivity, which is related to AML prognosis and may bind to some clinical drugs leading to an interaction with ATO. These integrated results provided new insights into the pharmacological mechanism of ATO and potentiate for further applications in cancer treatments. ATO degGenome-wide CRISPR-Cas9 screening is a revolutionary method appearing in biomedicine in the last decade. Based on the CRISPR-Cas9 system, this screening system effectively creates a genome-wide knockout cell library, and the cells are further challenged with certain drugs, toxins, or virals. The depleted genes will contribute to a specific phenotype, such as cell death or survival, and thus are selected as essential targets of chemical and biological agents. Taking advantage of its high throughput  and unbiased nature, the CRISPR-Cas9 screening has emerged as a handy technique for identifying the genes or pathways relevant to the sensitivity or resistance of existing treatments. The determinants of cell response to a list of anticancer drugs have been identified successfully through this method, including traditional chemo agents  and identified a total of 151 genes with significance . We specp < 0.05)  patients compared with normal  can be found below: J-ZC and Y-LT designed the study. J-ZC performed the experiments. L-NW and X-QL contributed to the study design and scientific discussions. J-ZC drafted the manuscript. X-QL and Y-LT revised the manuscript critically. All authors contributed to the article and approved the submitted version.


This study aims to characterize differential expressed pathways (DEP) in subjects with preclinical systemic sclerosis (PreSSc) characterized uniquely by Raynaud phenomenon, specific autoantibodies, and/or capillaroscopy positive for scleroderma pattern.Whole-blood samples from 33 PreSSc with clinical prospective data (baseline and after 4 years of follow-up) and 16 matched healthy controls (HC) were analyzed for global gene expression transcriptome analysis via RNA sequencing. Functional Analysis of Individual Microarray Expression method annotated Reactome individualized pathways. ANOVA analysis identified DEP whose predictive capability were tested in logistic regression models after extensive internal validation.versus HC with an AUROC = 0.792 ± 0.242 in regression models. Four clinical groups were identified via unsupervised clustering . Biological signatures changed with disease progression while remaining unchanged in stable subjects. The magnitude of change was related to the baseline cluster, yet no DEP at baseline was predictive of progression. Disease progression was mostly related to changes in signal transduction pathways especially linked to calcium-related events and inositol 1,4,5-triphosphate metabolism.At 4 years, 42.4% subjects progressed (evolving PreSSc), while the others kept stable PreSSc clinical features (stable PreSSc). At baseline, out of 831 pathways, 541 DEP were significant at a false discovery rate <0.05, differentiating PreSSc PreSSc had distinguished Reactome pathway signatures compared to HC. Progression to definite SSc was characterized by a shift in biological fingertips. Calcium-related events promoting endothelial damage and vasculopathy may be relevant to disease progression. SSc usuTranscriptome sequencing, also called RNA sequencing (RNAseq), is an omic technique that allows the deep evaluation of the transcriptional status of biological samples to assess which genes are active under specific conditions. The evaluation of differentially expressed (DE) genes is usually the first step in RNAseq analysis, yet it often lacks adequate power or immediate interpretability. It has been extensively demonstrated that combining genes in a set of interest as, for instance, those converging to certain biological pathways may circumvent several problems related to single-gene analysis . The colIn the present work, we aim at analyzing DE pathways (DEP) obtained from whole-blood transcripts of PreSSc through the FAIME algorithm to characterize the biological alterations that may be relevant to the early development of SSc and progression.22.1Thirty-three PreSSc according to 2001 LeRoy and Medsger criteria  and prev2.2RNA was extracted according to the manufacturer’s protocol, and quality was assessed by using Agilent 2100 Bioanalyzer (Agilent Technologies). Globin genes were depleted using GLOBIN Clear kit. mRNA was enriched from total RNA using oligo(dt) beads (NEB Next Ultra II RNA Kit following the poly(A) enrichment workflow). The mRNA was fragmented randomly in fragmentation buffer and reverse-transcribed to cDNA. The cDNAs were converted to double-stranded cDNAs and then subjected to end-repair, A-tailing, adapter ligation, size selection, and PCR enrichment. The library concentration was first quantified using a Qubit 2.0 fluorometer (Life Technologies) and then diluted to 1 ng/uL before checking the insert size on Agilent 2100 and quantifying to greater accuracy by quantitative PCR (q-PCR) . The libraries were pooled into Novaseq6000 machines according to molarity and expected data volume. A paired-end 150-bp sequencing strategy was used to generate an average of 88 million reads per sample. Transcript data were normalized with DESeq2. The transcripts were filtered with count-per-million (CPM), keeping those genes which have at least two samples with CPM >1. A total of 17,746 transcripts out of 60,641 in raw count file passed the filtering criteria.2.3https://reactome.org); Entrez IDs without official gene symbols were dropped from the analysis. The FAIME scores were normalized in the  closed interval for visualization by heat maps. Data were aggregated via unsupervised clustering  and manually annotated.Individual functional annotations were performed with the FAIME algorithm  considert-test analysis. Annotations with a false discovery rate (FDR) p < 0.05 and with a |dR | > 0.62897, corresponding to a moderate effect size  and to perform the ect size , were coThe predictive capability of DEP was tested in logistic regression models with L2 penalty after 20 runs of 10-fold cross-validation, where pathways of interest were selected in training sets by applying the abovementioned criteria, and the results from independent test sets were averaged.Changes in FAIME Reactome scores between baseline and end of observation were determined via the ANCOVA change method .The capability of baseline Reactome scores to predict evolution was modeled via machine learning algorithms  via 20 runs of 10-fold cross-validation with univariate selection of relevant features  in each training set via nested cross-validation.https://scikit-learn.org/stable/index.html) were used.For all the analyses, custom codes written in python by LB on top of the Scikit-learn library . p < 0.05, chi-square test). Specifically, a number of pathways related to inositol 1,4,5-triphosphate (IP3) signaling, calcium-dependent mechanisms, and related metabolic functions were found among those positively associated with progression. Interestingly, two pathways were related specifically to the immune system biological function: the interleukin 37 (IL-37) signaling and the Fc gamma receptor. We observed that the IL-37 signaling scores decreased in evolving PreSSc , while they remained increased in stable subjects . Other pathways of interest with positive association included the semaphorins pathway.The Reactome pathways related to disease progression are detailed in 4PreSSc are SSc subjects at a preclinical stage whose unique clinical feature is the occurrence of RP and do not present any additional fibrotic feature or involvement of internal organs. As already described in a previous study of ours, the whole-blood transcriptome profile of these patients is different as compared to HCs . AccordiA remarkable finding of ours is that the signatures of PreSSc changed over time only in those subjects who did progress, while they remained unaltered in stable patients Figure 2While our study presents several strengths, including the careful characterization of PreSSc cohort as well as the longitudinal collection of samples, we shall acknowledge some limitations. These include the relatively small sample size due to the difficulties in intercepting SSc patients at a preclinical stage. Moreover, we experienced difficulties in building predictive models of disease progression that cannot thus be forecasted on the basis of clinical or biological characteristics. This is undoubtedly linked to the fact that many pathways do not change in non-progressing patients Figure 2In summary, we observed that differentially expressed pathways characterize subjects at a preclinical stage of SSc and that biomolecular changes are peculiar of PreSSc that over time clinically progressed into definite diseases. Furthers studies are worth to be conducted on PreSSc patient cohorts to detect and confirm the signatures that may herald possible therapeutic targets to prevent disease progression.The data presented in the study are deposited in the Gene Expression Omnibus (GEO) repository, accession number GSE224849.CB: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Methodology, Project administration, Visualization, Writing – original draft, Writing – review & editing. XW: Data curation, Formal Analysis, Investigation, Methodology, Software, Writing – review & editing. MAL: Data curation, Formal Analysis, Investigation, Methodology, Software, Writing – review & editing. MM: Data curation, Investigation, Methodology, Writing – review & editing. ML: Data curation, Investigation, Methodology, Writing – review & editing. VC: Data curation, Investigation, Methodology, Writing – review & editing. NM: Project administration, Supervision, Visualization, Writing – review & editing. SA: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Visualization, Writing – review & editing. LB: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Software, Supervision, Writing – original draft, Writing – review & editing.


Food-components-target-function (FCTF) is an evaluation and prediction model based on association rule mining (ARM) and network interaction analysis, which is an innovative exploration of interdisciplinary integration in the food field.Using the components as the basis, the targets and functions are comprehensively explored in various databases and platforms under the guidance of the ARM concept. The focused active components, key targets and preferred efficacy are then analyzed by different interaction calculations. The FCTF model is particularly suitable for preliminary studies of medicinal plants in remote and poor areas.The FCTF model of the local medicinal food Laoxianghuang focuses on the efficacy of digestive system cancers and neurological diseases, with key targets ACE, PTGS2, CYP2C19 and corresponding active components citronellal, trans-nerolidol, linalool, geraniol, α-terpineol, cadinene and α-pinene.Centuries of traditional experience point to the efficacy of Laoxianghuang in alleviating digestive disorders, and our established FCTF model of Laoxianghuang not only demonstrates this but also extends to its possible adjunctive efficacy in neurological diseases, which deserves later exploration. The FCTF model is based on the main line of components to target and efficacy and optimizes the research level from different dimensions and aspects of interaction analysis, hoping to make some contribution to the future development of the food discipline. UnfortuMost modern systematic and comprehensive food efficacy studies are based on genomics , proteomCitrus medica L. var. Sarcodactylis Swingle for more than several years through a complex process of salting, desalting, sugaring, cooking and drying. Compared to the bitter and spicy raw material Citrus medica L. var. Sarcodactylis Swingle, the fermented Laoxianghuang not only enhances its edibility but also expands its efficacy as revealed in empirical pharmacology  numbers on PubChem2.3.p < 0.01 were selected for advanced bubble mapping. GO analysis included molecular function (MF), cellular component (CC) and biological process (BP).Rough functional enrichment: Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by Metascapehttp://ctdbase.org/) (http://www.omim.org) (http://db.idrblab.net/ttd/)  , Online mim.org) , Therapeet/ttd/)  and Geneet/ttd/) . Accesseet/ttd/) , S4.2.4.http://www.rcsb.org/pdb/)     and PubCrg/pdb/) , respectrg/pdb/) . Applicah, 2023)  and Mculh, 2023)  for evalh, 2023) .2.5.Descriptive analysis: Microsoft Office Excel 2019 (Microsoft Corporation) was used to perform statistics on the frequency of components, targets and functions, including duplicate and position distribution.https://cn.string-db.org/) with confidence score >0.9  analysis of the above evaluation parameters based on the EWM was performed, and the performance of each target was ranked according to the distance from positive ideal solution, distance from negative ideal solution and the composite score index by the TOPSIS algorithm . SVM and3.3.1.We systematically studied and summarized the results of different methods to determine the components of Laoxianghuang . The res3.2.First, 35 components with higher relative content were screened out from numerous components of Laoxianghuang according to the screening rules. Then, their SMILES numbers were determined by PubChem, and the target genes corresponding to individual components were retrieved in the SwissTargetPrediction platform according to the SMILES numbers . A total3.3.GO and KEGG enrichment analyses were performed on the above predicted potential targets of Laoxianghuang to roughly describe their possible biological functions and signaling pathways. Biological processes (BP) in GO enrichment analysis are mainly involved in regulation of secretion, circulatory system process, response to hormone, etc., and molecular functions (MF) are associated with oxidoreductase activity, inorganic cation transmembrane transporter activity and hydrolase activity, acting on ester bond, etc. Cellular components (CC) are related to the synaptic membrane, membrane raft, receptor complex, etc. The KEGG enrichment results showed that Laoxianghuang was mainly associated with cancer, inflammation, immunity and nervous system, including neuroactive ligand–receptor interaction, glutamatergic synapse, pathways in cancer and steroid hormone biosynthesis .3.4.A deep mining of the efficacy corresponding to each target was conducted, identifying 1,976 relevant diseases with potential associations . One tarIn this work, the first five associated diseases were selected for further analysis, and each disease was deduplicated to reveal 123, 61, 140, 64, and 79 diseases under the categories of cancer, digestive system diseases, nervous system diseases, mental disorders and pathological processes, respectively. Of these, cancer overlaps with digestive system diseases to a high level, with 15 items belonging to both categories, including stomach neoplasms, colonic neoplasms and intestinal neoplasms. Nervous system diseases and mental disorders also share a high Degree of disease, with 19 diseases such as Alzheimer’s disease, learning disabilities and Tic disorders . Among a3.5.The Apriori correlation algorithm was used to analyze the crucial link targets using components and functions as index items. Thresholds were set according to support, confidence, lift, leverage and conviction, and a total of 279 sets of target sets were filtered, with a support interval of 20–45%, confidence interval of 35–90%, lift interval of 1.0–4.3, leverage interval of 0.01–0.19, and conviction interval of 1.0–6.6. (CNR2 → PPARA), (AR → (CYP19A1), and (ACHE → CYP19A1) performed better in items of support, all at 43%. (DRD2 → AR), (ESR1 → BCHE), and (PGR → DRD2) showed better confidence levels, all at 90%. (PARP1 → PPARG), (PARP1 → ACE), and (CYP17A1 → ESR2) showed higher lift, all at 4.23. (ESR1 → BCHE), (AR → ESR1) and (ACHE→ESR1) possessed better leverage, all at 0.18. (ESR1 → BCHE), (HMOX1 → TYK2), and (SLC6A4 → CHRM2) displayed better conviction, with (ESR1 → BCHE) at 6.6 and the other two at 6.13 . All the3.6.CT = 3, Degree TF = 108), HMOX1 , ESR1  were screened . Classification models were constructed for this target set, and the results showed that the RF model (AUC = 1.000) achieved higher separation accuracy compared to the SVM model (AUC = 0.856). The importance ranking of the targets in the RF model was filtered according to the Gini coefficient, and PRARA was particularly important, followed by PTGS2, CNR2, ACE, CYP2C19, etc.  in a large dataset. Finally, 80 bidirectional target sets such as PTGS2 , Scientific projects of key disciplines in Guangdong Province (grant numbers 2021ZDJS042 and 2022ZDJS070), Doctor Initiating Project of the Hanshan Normal University (grant numbers QD202125 and QD20190527), Special Focus Areas for General Universities of Guangdong Province (grant number 2022ZDZX2043), Education Department Project of Guangdong Province (grant numbers 2019-GDXK-0032 and 2020KZDZX1146), and Eastern Guangdong Technological Engineering Research Center (grant number P19004).The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The rat brains were assessed using immunohistochemical markers against demyelination, microglial activation, apoptosis, neurons, neurofilament, and reactive astrocytes. To evaluate the effect of VD on oxidative stress and the antioxidant capacity, we used two different oxidized lipid markers (anti- Cu++ and HOCl oxidized LDL antibodies) along with colorimetric methods for protective polyphenols (PP) and total antioxidative capacity (TAC). NfL serum levels of VD+ and VD− animals were analyzed by fourth generation single-molecule array (SIMOA) analysis. We found significant differences between the VD+ and VD− animals both in histopathology as well as in all serum markers. Myelin loss and microglial activation is lower in VD+ animals and the number of apoptotic cells is significantly reduced with a higher neuronal survival. VD+ animals show significantly lower NfL serum levels, a higher TAC, and more PP. Additionally, there is a significant reduction of oxidized lipid markers in animals under VD supplementation. Our data thus show a positive effect of VD on cellular features of cortical pathology in our animal model, presumably due to protection against reactive oxygen species. In this study, VD enhanced remyelination and prevented neuroaxonal and oxidative damage, such as demyelination and neurodegeneration. However, more studies on VD dose relations are required to establish an optimal response while avoiding overdosing.Vitamin D (VD) is the most discussed antioxidant supplement for multiple sclerosis (MS) patients and many studies suggest correlations between a low VD serum level and onset and progression of the disease. While many studies in animals as well as clinical studies focused on the role of VD in the relapsing-remitting MS, knowledge is rather sparse for the progressive phase of the disease and the development of cortical pathology. In this study, we used our established rat model of cortical inflammatory demyelination, resembling features seen in late progressive MS, to address the question about whether VD could have positive effects on reducing cortical pathology, oxidative stress, and neurofilament light chain (NfL) serum levels. For this purpose, we used male Dark Agouti (DA) rats, with one group being supplemented with VD (400 IE per week; VD Multiple sclerosis (MS) is a chronic disease of the central nervous system (CNS) that mainly affects young adults. It is caused by an autoimmune response to central nervous system structures, including both white and grey matter. After decades of research, many pathomechanisms behind this disease are still not fully understood. Despite the availability of immunomodulatory therapeutics, which effectively reduce inflammatory processes directed against the CNS, the symptoms still might worsen, which heavily impairs the quality of life of patients. This underlines the importance of identifying novel treatment targets, especially for the progressive disease phase (PMS), which is associated with the development of severe and irreversible disability. Vitamin D  is the most discussed antioxidant supplement for several autoimmune diseases, including MS. Its functions comprise regulation of calcium homeostasis as well as effects on immune response. VD is not only assigned to the environmental risk factors for developing MS, but is also part of a genetic factor and an epigenetic factor . Oxidati++ and HOCl oxidized LDL antibodies).Since cortical pathology is sparse or absent in common MS animal models, such as experimental autoimmune encephalomyelitis, our research team developed a rat model that reassembled cellular features of the PMS very well . In the Furthermore, we assessed the effect of VD on neurofilament light chain (NfL) serum levels as a marker of axonal damage in the rats’ sera through high-sensitivity single-molecule array (SIMOA) quantitation .+) and not supplemented (VD−) animals (controls).All analyses were correlated to thorough histopathological evaluation to assess differences between VD-supplemented (VDn = 22) and not supplemented (n = 23) groups. In total, 45 male Dark Agouti (DA) rats aged 10–12 weeks, obtained from Janvier, France, underwent the protocol described in detail in Ücal et al. 2017 . Animals+ group; n = 22). All other groups received standard rodent food only . Briefly, the animal model starts with the catheter implantation. After a healing period of two weeks, all rats are immunized with myelin oligodendrocyte glycoprotein (MOG) in incomplete Freund’s adjuvants. The MOG antibody titer is validated via ELISA after four weeks. Once the titer is sufficient, animals receive 2 µL of a cytokine mixture of interferon gamma (IFN-γ) and tumor necrosis factor alpha (TNF-α) via a programmable syringe pump through the catheter in order to open the blood–brain barrier. The peak of cortical pathology can be observed on day (d) 15, and on d30 the first traces of remyelination are detectable, and even a “second relapse” can be generated by an additional cytokine injection on d30—indicated as d45* [All animals underwent the experimental protocol described in detail in Ücal et al. 2017 to elicit cortical pathology. The VD animal group orally received one drop of VD solution  per week, starting from the weaning (at age of 3 weeks) until the end of the experiment (=VD as d45* .Blood sampling was performed before catheter implantation (HAC), after MOG immunization, and on d1, d3, d15, d30, and d45* after cytokine injection. NfL was additionally measured in the sera of animals one day after catheter implantation. Serum was harvested by centrifugation at 4600 rpm two times, one hour after blood sampling, and was stored at −70 °C until use according to guidelines . AnimalsAfter embedding in paraffin, brain tissue was cut into 1.5 µm sections. For immunohistochemical (IHC) staining, sections were dewaxed in xylene , rehydrated, and steamed for 1 h in citric acid (Merck) . After i2. Activated microglia (Iba1), apoptotic cells (Caspase-3), neurons (NeuN), and oxidative stress markers (Cu++ oxidized LDL and HOCl oxidized LDL) were assessed in three full optical grids in the cortex per hemisphere with the 20× objective. Average values were converted to cells/mm2.One investigator blinded for experimental groups assessed the quantification of demyelination, microglial activation, apoptotic cells, neuronal cell loss, and histological oxidative stress markers with an optical grid at a magnification of 20× on the microscope (Zeiss AXIO Imager.M2). Demyelination was assessed by counting the loss of proteolipid protein (PLP) immunoreactivity, and values were then transformed to PLP loss/mm®; Omnignostica Ltd., Höflein an der Donau, Austria) and Polyphenols  as described previously [To evaluate antioxidative effects, we used two different test systems, i.e., the total antioxidative capacity , based on single-molecule arrays and simultaneous counting of single captured microscopic beads carrying antibody complexes. The analytical sensitivity of this technique is manifold higher than that obtained with conventional photometric test systems, thus enabling a reliable measurement of low NfL concentrations in blood samples . Advancep < 0.05 was considered to be statistically significant. All statistical tests performed for each of the figures are summarized in Statistical analysis was performed using SPSS Statistics , and graphs were illustrated in Microsoft Excel 2010 as box-plots. The data was checked for normal distribution via the Kolmogorov–Smirnov test; we used non-parametric tests in all cases. The text body provides median values and interquartile range (IQR). We used the Kruskal–Wallis test followed by Mann–Whitney U test for statistical significance testing. A difference of + animals, although reaching statistical significance only on d30 on the ipsilateral side (p = 0.05). There is also a significant difference in microglial activation detectable on the ipsilateral side on d30 (p = 0.004) and d45* (p = 0.018) (p = 0.038). Furthermore, there is no significant difference between HAC and VD d30 animals on both the ipsilateral and contralateral sides. Apoptotic cells are significantly reduced on the ipsilateral side on d15 (p = 0.002), d30 (p = 0.004), and d45* (p = 0.023) (p = 0.019) and d30 between VD+ and VD− animals (p = 0.019), which is displayed in The quantification of PLP loss is displayed in = 0.018) c, and on= 0.018) d on d45*= 0.023) e. There + animals . Neurofilament structures are better preserved in VD-supplemented animals in comparison to normal d15 animals  d3 (0.033) and d15 (0.006) after cytokine injection  compared to the HAC with 9.0 IQR3.3 pg/mL. This value, caused by the mechanical trauma, alleviates during the healing period and reaches a value of 14.4 IQR 5.5 pg/mL after MOG immunization. There is a significantly lower NfL concentration detectable in VDnjection g. VD+ an+ animals detectable in both markers on d15 , d30 , and on d45* .Both markers for oxidative stress show a similar trend towards the experimental groups a,b with + and VD− (p < 0.001), also remaining on d3 (p < 0.001) and d15 (p < 0.001) after cytokine injection. On d30, there is no significant difference detectable anymore. This changes again on d45*, where the difference between VD+ and VD− becomes significant again (p < 0.025). In p < 0.001). On d1 and d3, there is no significant difference detectable in TAC; unlike d15, where the groups differ significantly from each other (p < 0.05). On d30, there is also a significant difference detectable (p < 0.040), which is no longer detectable on d45*.+ and the VD− group. Our finding of significantly more PLP preservation in VD+ animals on d30, where remyelination starts in our model, correlates with the conclusion of other studies that VD has a positive effect on remyelination [Overall, our data indicate a positive effect of VD supplementation in preserving cortical structures and regulating oxidative stress in our animal model of PMS. At least in our experiment, the most significant effect of VD supplementation in preventing cortical pathology is less pronounced microglial activation, fewer apoptotic cells, and increased preservation of neurons. Moreover, a tendency towards better preservation of PLP and neurofilament structures was observed in association with VD supplementation. On d30, at the start of remyelination in our animal model , all invlination .+ animals but at a significantly lower level on all investigated days. Since NfL rises upon neuroaxonal damage, we conclude that VD supplementation preserved neuroaxonal cell structures in our animal model, although not fully suppressing the pathology.Another marker investigated during this study is serum NfL, the most promising neurofilament subunit to track neuroaxonal damage . Our dat++-oxLDL and HOCl-oxLDL), and both were found in the tissue of our animals, with a much higher occurrence in VD− animals. Especially on d30, where the first remyelination events appear, there is a highly significant difference detectable between the VD+ and VD− groups. This again leads to the conclusion that there is a connection between oxidative stress and remyelination, and VD supplementation facilitates the latter.Precise mechanisms that drive the disease in patients with progressive MS are currently unknown, but demyelination may be triggered by mitochondrial injury from oxidative stress. In MS, it seems to be mainly driven by oxidative bursts in microglia ,24. Reac+ animals, polyphenols are significantly lower on d30 compared to VD+ animals on d1, d3, and d15, but increases again on d45* in the serum of animals that received a second cytokine injection on d30. The second cytokine injection on d30 could be a possible reason for the lowered polyphenols on this day.VD supplementation seems to also have positive effects on the preservation of neuroprotective PP. Since PP have immunomodulatory properties, they are more concentrated in animal serum on days right after the cytokine injection in both groups. In VD−- animals, the TAC stays approximately the same on d1 and starts to rise again during the experiment, with one decrease on d30. This drop can be prevented by supplementation with VD. Furthermore, at peak disease (d15), the TAC is significantly higher in VD-supplemented animals, suggesting an overall preservation of TAC by VD. ROS-mediated effects depend on fine-tuning a multicellular and multi-cascade network that is not yet fully understood. ROS-mediated pathways and cellular effects are involved in immune cell priming in the peripheral lymphoid organs. Since the oxidative brain environment is altered in MS patients [In addition, the TAC is increased by VD supplementation. Compared to PP data, the MOG immunization is detectable via determining TAC with a decreasing effect. In VDpatients , researcFurthermore, the measurements of antioxidative capacity and oxidative stress markers, and the direct ROS measurement post-treatment could be interesting additional information. Therefore, we used a biomarker induced by ROS in our study, i.e., we measured peroxides in the serum of our rodents. Although the method sensitively measures in the micromolar range, we did not obtain measurable concentrations, mainly since rodents synthesize their vitamin C autogenously. Furthermore, radicals are very short-lived and not easy to track during long-term experimental setup. Also, the handling during animal experiments could artificially increase ROS. Since we could not detect measurable peroxide levels in serum, we noticed oxidative stress ex vivo using antibodies against oxidatively modified molecules and measured the antioxidative capacity.+ animals appeared to have more pronounced astrocytic reactions than VD− animals. Since astrocytes have manifold functions and can either be protective or detrimental, we hypothesize that there are different phenotypes of astrocytes involved [Surprisingly, VDinvolved . A detriAlthough we of course cannot propose a definitive mechanism behind our findings, we can form hypotheses. Ample literature is available showing that VD protects the CNS from inflammation through the modulation at different levels, including cytokines, growth factors, cell signaling, response to oxidative stress, BBB integrity, and cellular trafficking  . ModifieDuring development, astrocytes play a major role in the maturation of the BBB and contribute to establishing the immune-privileged state of the CNS by forming a second barrier, called the glia limitans. This structure encapsulates the entire CNS parenchyma and—along with the BBB—prevents the unrestricted entrance of immune cells. As early responders, astrocytes can react promptly to inflammatory stimuli, such as cytokines. Since many studies have shown that astrocytes express VDR, they are most likely able to respond to VD in an autocrine or paracrine manner, and VD supplementation potentially reduces the pro-inflammatory response of astrocytes in the CNS, leading to the better preservation of the cellular architecture in our study ,30.Another possible mechanism may involve microglia as abundant immune cells present in active MS lesions playing a critical role in antigen presentation, recruitment of T cells, ROS production, and release of pro-inflammatory cytokines. Through all these actions they may further amplify neuroinflammation. It has been shown that microglia can synthetize calcitriol in vitro and that they express VDR on their surface ,31. In oIn summary, the possibilities of VD actions are manifold, acting on multiple sites of the inflammatory pathway, and most likely it is a combination of all these modes of action that lead to the overall beneficial effect we find in our animals. Our effect, however, may even be enhanced by the fact that the animals were supplemented with VD starting from when they were three-weeks old and were growing up with sufficient VD levels. It appears likely that the net effect may be less pronounced when VD supplementation is initiated at a later point in life.Even though a complete translation of the results obtained in animal studies to the pathophysiologic processes seen in humans is not possible, animal models can help overcome the limitations of clinical studies, thereby elucidating cellular mechanisms. Especially for VD studies, this is one crucial aspect due to the difficulties in clinical study design both for the relapsing-remitting MS and for the progressive disease phase, with much more literature being available for the former. Currently, only a few studies are available regarding VD and PMS with a long disease duration, with no detected connections between VD serum levels and clinical outcomes ,13. One To conclude, VD seems to have potential as a supplement in progressive MS, which is why much more research on VD in progressive MS and associated animal models is required.


Understanding the role of white adipose tissue (WAT) in the occurrence and progression of metabolic syndrome is of considerable interest; among the metabolic syndromes are obesity and type 2 diabetes (T2D). Insulin resistance is a key factor in the development of T2D. When the target cells become resistant to insulin, the pancreas responds by producing more insulin to try to lower blood glucose. Over time, this can lead to a state of hyperinsulinemia (high levels of insulin in the blood), which can further exacerbate insulin resistance and contribute to the development of T2D. In order to understand the difference between healthy and unhealthy obese individuals, we have used published transcriptomic profiling to compare differences between the WAT obtained from obese diabetics and subjects who are obese with normal glucose tolerance and insulin resistance. The identification of aberrantly expressed messenger RNA (mRNA) and the resulting molecular interactions and signaling networks is essential for a better understanding of the progression from normal glucose-tolerant obese individuals to obese diabetics. Computational analyses using Ingenuity Pathway Analysis (IPA) identified multiple activated signaling networks in obesity progression from insulin-resistant and normal glucose-tolerant (IR-NGT) individuals to those with T2D. The pathways affected are: Tumor Necrosis Factor (TNF), Extracellular signal-Regulated protein Kinase 1/2 ERK1/2, Interleukin 1 A (IL1A), Protein kinase C (Pkcs), Convertase C5, Vascular endothelial growth factor (Vegf), REL-associated protein (RELA), Interleukin1/1 B (IL1/1B), Triggering receptor expressed on myeloid cells (TREM1) and Nuclear factor KB1 (NFKB1) networks, while functional annotation highlighted Liver X Receptor (LXR) activation, phagosome formation, tumor microenvironment pathway, LPS/IL-1 mediated inhibition of RXR function, TREM1 signaling and IL-6 signaling. Together, by conducting a thorough bioinformatics study of protein-coding RNAs, prospective targets could be exploited to clarify the molecular pathways underlying the development of obesity-related type 2 diabetes. The worldwide obesity pandemic is driving a significant rise in the incidence of metabolic diseases, such as T2D . ObesityAdipose tissue is one of the most essential tissues contributing to obesity because of its fundamental roles of lipogenesis (fat storage) and lipolysis (fat mobilization) . The priThe NCBI-Gene Expression Omnibus (GEO) is a public database that contains a high quantity of gene expression data , includiFor that purpose, we found recent GEO gene datasets from human adipose tissues related to obesity and T2DM . We examp-value ≥ 0.05; p-value ≥ 0.05; The Sequence Read Archive (SRA) database was used to collect expression data for lean individuals, obese individuals with IR-NGT, and obese patients with T2D. The CLC Genomics Workbench 21 program was used to examine each Fastq file and determine the DEGs, followed by a comparison of Gene Expression (GE) tracks of the datasets relating to lean individuals and obese individuals with IR-NGT. The results of hierarchical clustering indicated two distinct DEGs in the obese IR-NGT and obese T2D groups when compared to lean subjects . Heat-maTo get a better understanding of the possible involvement of DEGs in obese IR-NGT pathology and obese T2D in WAT, obese patients with IR-NGT and obese patients with T2D datasets were submitted to IPA and downstream impact analysis (DEA). Affected functional categories are depicted as a heat tree map, which clusters functionally related categories together, resulting in a high-level view of enriched functional categories; the orange boxes represent activated functions, whereas the blue boxes represent suppressed functions. The data shown in  indicatep-value 0.05). The upstream regulators most inhibited by negative Z scores were TNF, ERK1/2, IL1A, Pkc(s), C5, Vegf, Oncostatin M (OSM), RELA, IL1/1B, TREM1 and NFKB1, while Nuclear Receptor Subfamily 1 Group H Member 3 (NR1H3), Retinoid X Receptor Alpha RXRA, Cbp/p300-interacting transactivator with Glu/Asp-rich carboxy-terminal domain 2 CITED2 and Nr1h are enriched in the obese patients in the IR-NGT group  and any gene or small molecule that has been observed experimentally to affect gene expression by analyzing linkage to DEGs through coordinated expression, we used the new feature in IPA, the upstream regulator analysis (URA) tool. IPA discovered 594 putative upstream regulators that have been detected , the following had absolute z-scores greater than 2.0, phagosome formation, CREB signaling in neurons, cardiac hypertrophy signaling, neuroinflammation signaling pathway and TREM signaling were enriched, indicating that they were strongly associated to T2D in the obese individuals with T2D  mass has been related to a variety of problems, including insulin resistance, type 2 diabetes and obesity . Recent The primary goal of this project was to identify adipocyte-specific mRNAs linked to obesity, insulin resistance and type 2 diabetes. We used 25 RNA-seq samples to characterize gene expression profiles and identify significant transcriptional regulators in this study. To avoid methodological inconsistencies in terms of data processing and bioinformatics pipelines among original research, we used a consistent bioinformatics workflow to handle the raw RNA-seq data (fastq files) of all datasets. As a result, we provide a transcriptome analysis study of white adipose tissue from lean subjects, obese subjects with IR-NGT and obese subjects with T2D. According to the results of the IPA pathway enrichment analysis, significant DEGs in obese subjects with IR-NGT were primarily involved in the activation of LXR/RXR activation, while the same pathway was inhibited in obese subjects with T2D. Liver X Receptors are integrators of metabolic and inflammatory signaling. LXRs play a central role in lipid metabolism regulation. In accordance with our results, previous studies showed that the activation of LXRs improves glucose tolerance in adipose tissue in the murine model .Additionally, for obese subjects with IR-NGT, the inhibited pathways were primarily involved in phagosome formation, TREM signaling, IL-6/8/17 signaling and leukocyte extravasation signaling. On the other hand, obese subjects with T2D showed a contrasting pattern of dysregulated canonical pathways. The latter group showed significant enrichment in phagosome formation and IL-6/8/17 signaling. These dysregulated pathways lie within the cellular immune response ,32,33. RStudies have well established that there is a link between obesity and T2D; however, not all obese patients develop T2D . The preLipid metabolism functions in Adipose tissue is a simple surrogate measure that represents pathophysiological changes in adipose tissue insulin sensitivity, with increasing change from normal weight to obesity and from NGT to type 2 diabetes . In our https://www.ncbi.nlm.nih.gov/geo/ (accessed on 15 May 2022)) using the keywords “Type 2 diabetes”, “obesity”, “insulin resistance” and “normal glucose tolerance”. We found one dataset (GSE141432) that described two studies: Measurements of adipose tissue lipolysis by microdialysis, a piece of case–control research examining the causes of obesity-driven insulin resistance, and adipose tissue genetics. We searched the National Center of Biotechnology Information (NCBI) GEO database  was used. Upstream regulator analysis (URA), downstream effects analysis (DEA), mechanistic networks (MN) and causal network analysis (CNA) prediction algorithms were used to perform functional annotations and regulatory network analysis [p-value is the negative log of P and indicates the likelihood that the network’s focal genes were discovered together by coincidence [To discover DEGs at the biologically functional level, Ingenuity Pathway Analysis IPA  that may play a role in the development of insulin resistance, obesity with normal glucose tolerance (NGT) and type 2 diabetes (T2D). The results obtained from this analysis offer a significant contribution to our understanding of the molecular mechanisms that underlie these complex metabolic disorders. The identification of these DEGs provides new avenues for future research, which may lead to the discovery of novel therapeutic targets. Despite the potential significance of these findings, further molecular biological tests are needed to confirm the specific roles of the identified genes. It is essential to validate these findings in experimental models, including cell cultures and animal models, to confirm the functional significance of these genes. Such studies may provide more detailed insights into the molecular mechanisms underlying insulin resistance and obesity with NGT and T2D and may ultimately lead to the development of more effective treatments for these disorders. Overall, the findings of this study highlight the potential of bioinformatic tools for identifying key genes involved in the development of metabolic diseases. However, it is important to recognize that these findings are preliminary and must be further validated through additional experimental investigations. With further research, it may be possible to develop targeted therapies that address the underlying molecular processes involved in these complex metabolic disorders.


Pathway Figure OCR (PFOCR) is a novel kind of pathway database approaching the breadth and depth of Gene Ontology while providing rich, mechanistic diagrams and direct literature support. Here, we highlight the utility of PFOCR in disease research in comparison with popular pathway databases through an assessment of disease coverage and analytical applications. In addition to common pathway analysis use cases, we present two advanced case studies demonstrating unique advantages of PFOCR in terms of cancer subtype and grade prediction analyses.The online version contains supplementary material available at 10.1186/s12864-023-09816-1. For the past few decades pathway databases have relied on the manual extraction of pathway knowledge from the literature by teams of biocurators ranging from small, centralized groups to communities of hundreds of contributors –6. Indivany pathway model. In terms of depth, pathway databases have traditionally focused on canonical pathways, for example having a single, generic representation of “apoptosis” or “hippo signaling”, despite the actual diversity of these biological processes across cell types, developmental stages, disease states and conditions. This oversimplification is understandable given the low throughput of pathway database content. Constructing a pathway model takes significant time and effort, including information gathering, synthesis, encoding and review [While GO Biological Process terms annotate 62% of human genes, pathway databases only cover up to 44% . This lid review , 11. Keed review , 12. SteThe Pathway Figure OCR (PFOCR) project takes a more direct approach to capturing pathway information in a pathway database . Collectutility of the PFOCR database in disease research in comparison to other popular pathway databases. We will begin with a detailed characterization of the PFOCR collection of pathways in terms of disease coverage. We will then demonstrate the utility of PFOCR in enrichment analyses and machine learning applications. While PFOCR is applicable to any analysis involving pre-defined gene sets, we will highlight specific types of enrichment analyses and machine learning where its unique breadth and depth are crucial to fruitful results and insightful interpretation.In this paper, we will highlight the A common goal of pathway analysis is to identify processes underlying a disease state. Thus, an important characteristic for a pathway database is its disease coverage, i.e., how many diseases are represented among its pathway-defined gene sets and how deep and diverse are the gene sets per disease. PFOCR has close to a hundred times as many pathways as a canonical pathway database, but how does their distribution across diseases compare to an intentionally curated database? In order to assess disease coverage, a standard set of 876 distinct disease names was compiled from the Comparative Toxicogenomics Database (CTD) . The titbreast cancer and offers 649 pathways on average among its top 20 diseases. Reactome holds 23 pathways relevant to leukemia and 7 pathways on average among its top 20 diseases. WikiPathways has 18 pathways relevant to SARS-CoV-2—which aligns with its unique, community-driven approach to collecting new content on emerging topics—and also has 7 pathways on average among its top 20 diseases. KEGG has a four-way tie with 5 pathways for each of lung cancer, insulin resistance, hepatitis, and cardiomyopathy, and only 3 pathways on average among its top 20 diseases. By contrast, PFOCR has 5 or more pathways for 351 diseases and at least 1 pathway for 555 diseases that are missing from the other pathway databases  diseases were represented by at least one pathway in PFOCR. Reactome, WikiPathways and KEGG represented 153 (17%), 127 (14%), and 94 (11%) diseases, respectively . Thus, PFOCR can fit into analytical pipelines involving any package supporting GMT files, including the top-ranked Bioconductor packages: fgsea , clusterhttps://maayanlab.cloud/Enrichr) provides quick and easy enrichment analysis against over 200 gene set databases simultaneously. Among the 27 databases in the pathway category, PFOCR has the greatest number of gene sets and the second largest coverage of human genes (the kinase co-expression gene sets by ARCHS4 has the largest). PFOCR ranks fourth among all 209 Enrichr databases in terms of gene set size. Among the suite of plots provided by the Enrichment Analysis Visualizer “appyter” for Enrichr databases [Enrichr  provides network and pathway gene set analysis using multiple methods simultaneously against PFOCR, WikiPathways, INDRA-connected GO terms [NDEx Integrated Query  to perform two types of enrichment analyses for one or more datasets simultaneously against GO, WikiPathways, and PFOCR [We also developed an R Shiny tool called Interactive Enrichment Analysis  and PCA-based quality control was performed. We then performed differentially expressed gene (DEG) analysis, where each mutation type was treated as a separate group and control samples were used as the reference, followed by gene set enrichment analysis (GSEA). Finally, hierarchical clustering was applied to the normalized enrichment scores (NES) from GSEA to cluster leukemia mutations in operational subtypes.A total of 705 leukemia-enriched pathways were investigated by hierarchical clustering analysis, including 673 PFOCR, 6 WikiPathways, 23 Reactome, and 3 KEGG pathways  was calculated as the average importance from the leave-one-out cross-validation iterations. The prediction accuracy rankings in Table To assess the overlap of gene content among the top 21 pathways from PFOCR and any of the results from the other databases that passed the minimum accuracy 0.55 threshold, we plotted the genes per pathway, ordered (and sized) by their importance scores Fig. . RemarkaCASP9, the gene with the second highest importance score in 21 PFOCR pathways, is an initiator of apoptosis in the mitochondrial apoptosis pathway. A study by Sharifi and Moridnia found an association of CASP9 expression with miR-182-5p . The breAmong the overlapping genes of PFOCR and REACTOME, HEY1, one of the well-known Notch target genes, had the highest shared gene importance score. Chen et al. investigated the expression of HEY1 in breast cancer cells and found that the HEY1 expression level increased under hypoxic conditions . In addiOn the other hand, NOTCH2 was the gene with the highest importance score overlapping between PFOCR and KEGG. The role of NOTCH2 was studied by Fu et al. by investigating NOTCH2 expression and polymorphisms of SNP rs11249433 in breast cancer patient data . That stUsing the Comparative Toxicogenomics Database (CTD)  for a coLike other pathway databases, PFOCR content is available in formats amenable to gene set enrichment analyses, such as GMT (see Data Availability), allowing bioinformaticians to include PFOCR in practically any pathway analysis workflow. PFOCR pathways have already been integrated into commonly used online tools for enrichment analyses, including Enrichr  and iQueAML subtype clustering analysis showed how PFOCR pathway information can be used to gain insights to understand differential regulatory mechanisms of disease subtypes at the pathway level. One example of a more general approach of enrichment analysis using canonical gene sets was demonstrated in the study performed by Asi et al. . In thisEach published pathway figure offers not only unique content, but also unique context based on the specific experiments and insights described in its parent article. As a collection, PFOCR offers a distinct advantage to researchers in providing diverse examples of a given biological process linked to specific experimental designs and analytical methods. For example, our case study on breast cancer grade prediction showed that PFOCR outperformed other pathway databases in terms of both rank and total number of results. In our breast cancer analysis results, the top 62 pathways that passed the accuracy threshold were all from PFOCR  includes figures published between 1995 and 2021 and was downloaded from the PFOCR data archive . The WikPFOCR, Reactome, WikiPathways, and KEGG pathways are filtered according to the criteria of having a minimum of 3 genes and a maximum of 500 genes. In order to best represent Reactome’s unique gene-level annotations, no gene minimum was applied for the disease gene coverage comparison Table . As a reDisease information was downloaded from the Comparative Toxicogenomics Database (CTD) . DiseaseTo investigate disease gene coverage of the pathway databases, the human disease gene file 'human_disease_knowledge_filtered.tsv' was downloaded from Jensen DISEASES . Jensen 2-counts per million (logCPM), and the mean–variance relationship and weights were estimated using voom in the R library limma [AML patient gene expression data was downloaded from GEO under accession number GSE108316. Samples with mutations in RUNX1, inv(16), t, CEBPA, SRSF2, FLT3-ITD, FLT3-ITD/NMP1, and inv(3)/RAS were selected for this study. Differentially expressed gene (DEG) analysis was performed using genes with two or more read counts in at least five samples using the limma pipeline . Gene cory limma . Then, lheatmap.2. At the top level of the sample hierarchy, there are two main clusters. The core leukemia gene set (CLGS) was defined by the hierarchical clustering and included 26 pathways with the greatest average NES differences.Based on the DEG results, GSEA was performed for each mutation type. First, leukemia related pathways were retrieved from PFOCR, WikiPathways, KEGG, and Reactome   was calcTo build random forest models for predicting breast cancer grade in patient data, first, breast cancer pathways were retrieved from PFOCR, Reactome, WikiPathways, and KEGG. Breast cancer pathways compiled from the disease coverage comparison analysis resulted in 1954 PFOCR, 20 Reactomes, 6 WikiPathways and 3 KEGG pathways . The “Top Gene” corresponds to the gene with the highest feature importance score in each pathway.


The use of deer antlers dates back thousands of years in Chinese history. Deer antlers have antitumor, anti-inflammatory, and immunomodulatory properties and can be used in treating neurological diseases. However, only a few studies have reported the immunomodulatory mechanism of deer antler active compounds. Using network pharmacology, molecular docking, and molecular dynamics simulation techniques, we analyzed the underlying mechanism by which deer antlers regulate the immune response. We identified 4 substances and 130 core targets that may play immunomodulatory roles, and the beneficial and non-beneficial effects in the process of immune regulation were analyzed. The targets were enriched in pathways related to cancer, human cytomegalovirus infection, the PI3K-Akt signaling pathway, human T cell leukemia virus 1 infection, and lipids and atherosclerosis. Molecular docking showed that AKT1, MAPK3, and SRC have good binding activity with 17 beta estradiol and estrone. Additionally, the molecular dynamics simulation of the molecular docking result using GROMACS software (version: 2021.2) was performed and we found that the AKT1–estrone complex, 17 beta estradiol–AKT1 complex, estrone–MAPK3 complex, and 17 beta estradiol–MAPK3 complex had relatively good binding stability. Our research sheds light on the immunomodulatory mechanism of deer antlers and provides a theoretical foundation for further exploration of their active compounds. Immunity refers to the ability of an organism to resist or defend against foreign invaders such as pathogens  or abnormal cells. The human body maintains health by destroying and rejecting antigens or self-produced damaged and tumor cells . Immune Deer antlers are the unossified, densely fluffy horns of sika or red deer and can periodically regenerate. Studies have shown that deer antlers can considerably improve immunity ,4, treatThe immunomodulatory effect of deer antlers has been reported by several scholars. In a study using a cyclophosphamide-induced immunosuppression mouse model, a deer antler aqueous extract ameliorated spleen damage and significantly promoted splenocyte proliferation . In cellGlycyrrhiza spp. [The emergence of network pharmacology aligns with the fundamental principles of Chinese medicine research, which involve the investigation of multiple components, targets, and pathways in a holistic approach to drug discovery and development. This study screened and predicted the possible targets of the active ingredients of the deer antler in the immunomodulatory process. Pharmacology was commonly used to investigate the immunomodulatory mechanisms of traditional medicines such as iza spp. , Yupingfiza spp. , and Qifiza spp. . MoleculA total of 15 immunomodulatory compounds  and 368 The PPI network findings are presented in The screening of core and central targets reflects the potential targets of deer antler involvement in immunomodulatory processes. A total of 130 core targets were obtained , and theWe conducted KEGG enrichment and GO functional annotation analyses to investigate the biological processes and metabolic pathways in which deer antler may be involved in immune regulation. We found that 2193 BPs, 163 MFs, 98 CCs, and 201 KEGG pathways were annotated. We ranked the top 25 items based on the number of annotations to a functional area . The mosWe selected the active compounds with degree values greater than 10 and the central targets for molecular docking. The immunoactive compounds included 17 beta estradiol, estrone, adenosine triphosphate and glucosamine. The central targets included TP53 (PDB ID:1GZH), AKT1 (PDB ID:1H10), JUN (PDB ID:1A02), STAT3 (PDB ID:5AX3), SRC (PDB ID:1A07), IL-6 (PDB ID:1ALU), TNF (PDB ID:1A8M), and MAPK3 (PDB ID:3FHR). The molecular docking results are shown in As is shown in To confirm the ligand–receptor binding stability, we performed MD simulations using the AKT1–estrone, AKT1–17 beta estradiol, MAPK3–estrone, and MAPK3–17 beta estradiol.We used the root-mean-square deviation (RMSD) to analyze the mobility of the receptor–ligand complex. The RMSD curve reflects the fluctuation in protein conformation . Figure Root mean square fluctuation (RMSF) can be used to indicate the fluctuation of the complex at the residue level . The resHydrogen bonding is a strong non-covalent interaction. The number of hydrogen bonds in the AKT1–estrone complex was 0–4 in 0–100 ns D; and thThe radius of gyration (Rg) reflects the tightness of binding and the degree of constraint of the system; it reflects the degree of protein folding . A higheThe Gibbs energy landscape shows the complex stability . RMSD anAfter MD simulation, we found that the structures of both AKT1–estrone and AKT1–17 beta estradiol complex had changed. As is shown in As is shown in After MD simulation, we found that the structures of both MAPK3–estrone and MAPK3–17 beta estradiol had changed. As is shown in The binding free energy was calculated using the final 50 ns stable RMSD trajectory, and the different types of energies that contributed to the binding, including van der Waals, electrostatic, polar solvation, and SASA energy, were calculated. We performed the binding free energy calculations for the AKT1–estrone, AKT1–17 beta estradiol, MAPK3–estrone, and MAPK3–17 beta estradiol complexes.The result revealed that the average binding free energy of the AKT1–estrone complex was −66.300 kJ/mol. The average van der Waals (−89.567 kJ/mol), electrostatic (−89.336 kJ/mol), and SASA energy (−13.140 kJ/mol) were favorable for the binding of the estrone to the AKT1, and polar solvation energy was not favorable for the binding of the complex . The binMeanwhile, the average binding free energy of the AKT1–17 beta estradiol complex was −60.261 kJ/mol . The aveAs is shown in The binding free energy of MAPK3–estrone complex ranged from −79.604 kJ/mol to −34.461 kJ/mol at 50–100 ns , and theAs a traditional medicine, deer antler has been proved to play an important role in immune regulation and anti-inflammation from in vitro and vivo studies . HoweverThe immunomodulatory active compounds of deer antlers were selected and screened using the TCMSP and BATMAN-TCM databases, but the relevant compounds could not be retrieved from the TCMSP database. This is because the TCMSP database does not include the active ingredient information of deer antler. Therefore, the immunomodulatory active compounds were all retrieved from the BATMAN-TCM database. The BATMAN-TCM database provides resources concerning the interactions between compounds used in Chinese medicine and their therapeutic targets, as well as the functional annotation of the targets. These methods aim to promote understanding of the combination therapeutic mechanisms of traditional Chinese medicine, which often involves multiple components, targets, and pathways. Furthermore, they can provide valuable clues for experimental validation . FinallyThe immunomodulatory active compounds of deer antler include hormone compounds and nucleoside compounds; this is the difference between deer antlers as animal medicine and Chinese herbal medicine. 17 beta estradiol and estrone are hormonal compounds. Estrogen is thought to enhance the body’s immunity  and has H) or regulatory T cells (Tregs) [To explore the central target of immune regulation was helpful to study the immune regulation mechanism of deer antler; we identified AKT1, TP53, JUN, STAT3, SRC, IL-6, TNF, and MAPK3 as the central targets involved in immune regulation. As a hub target of deer antler immunoreactive compounds, AKT1 plays an important role in regulating biological functions such as metabolism, cell proliferation, survival, and growth . AKT sig (Tregs) . JUN is  (Tregs) . SRC  and the lowest average binding free energy (−94.927 kJ/mol), which indicates that MAPK3–17 beta estradiol complex may play an indispensable role in immune regulation.It was indicated that these complexes may exert important roles in immune regulation. The potential immunomodulatory mechanisms of deer antler has been revealed by network pharmacology, molecular docking, and MD simulation. However, the in vivo and in vitro experiments are needed to verify the central targets and pathways.https://old.tcmsp-e.com/tcmsp.php, accessed on 11 December 2022) and BATMAN-TCM database  for the active compounds present in deer antlers [p-value were set to ≥20 and ≤0.05, respectively; this is an ideal and reasonable threshold for screening active compounds of deer antler [TCMSP and BATMAN-TCM databases are authoritative Chinese medicine databases for screening pharmacologically active substances and analyzing the relationships between drug targets and diseases. We searched the TCMSP database  and the OMIM database  to identify targets related to immune regulation. We then intersected the deer antler targets with the immunomodulatory targets.To screen targets related to immune regulation, the Genecards database, a searchable, comprehensive database was searched. The website offers extensive and user-friendly information for all annotated and predicted human genes . In addihttps://string-db.org/, accessed on 15 December 2022). The species selection was “Homo sapiens”, and the interaction network results were imported into Cytoscape 3.9.0 for analysis. The colors were adjusted according to the degree value. Finally, statistical analysis was performed based on the target degree value.We uploaded the deer antler immunomodulatory targets to the STRING database  to perform the GO functional annotation and KEGG pathway enrichment analyses [Homo sapiens” and set the cutoff p-value to ≤0.01 and the minimum overlap to 3. The results were visualized using imageGP .GO functional annotation involves assigning functional terms from the GO database to a set of genes based on their sequence similarity, experimental evidence, or curated literature . KEGG paanalyses . We limiThe selected core targets, immunomodulatory deer antler compounds, and the top 10 enriched KEGG pathways were used to construct the compounds–targets–pathways interaction network using Cytoscape 3.9.0.https://pubchem.ncbi.nlm.nih.gov/, accessed on 25 December 2022). The central targets were selected, and their structures were downloaded from the RCSB database  and saved as a PDB format file. Next, the receptor files were imported into AutoDockTools and converted to pdbqt format. After removing solvent and heteromolecules, hydrogen and Kollman charges were added to the receptor; the docking box information is shown in We docked the active compounds with the central targets to analyze their binding affinities. We used the compounds–targets–pathways network construction results to determine whether there is an interaction between each central target and each deer antler compound. This aimed to avoid false positive docking results. The structures of the active compounds were downloaded from the PubChem database (MD simulations were performed using GROMACS (version 5.1.5) . We usedCalculating binding free energy can verify the intermolecular interaction strength in receptor–ligand complexes. This parameter provides the contribution of various chemical energies to the stability of the complex. The molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) method provides a simple quantification of receptor–ligand binding free energy . The binThis research explored the potential immunomodulatory mechanisms of active compounds of deer antler. A total of four deer antler immunoactive compounds were screened. Metabolic pathway and target interactions analysis revealed that multiple metabolic networks were involved in the process of deer antler immunomodulation. Molecular docking and MD simulation results showed that the AKT1–estrone and MAPK3–17 beta estradiol complexes had stronger binding stabilities than the AKT1–17 beta estradiol and MAPK3–estrone complexes, respectively. Our results provide a theoretical basis for subsequent experimental verification.


Eucommia ulmoides Oliver bark, Eucommia ulmoides Oliver male flower, Panax notoginseng, and Syzygium aromaticum (EEPS), were selected to constitute the anti-fatigue herbal compound that may be suitable as functional food ingredients.Fatigue has attracted broad attention in recent years due to its high morbidity rates. The use of functional foods to relieve fatigue-associated symptoms is becoming increasingly popular and has achieved relatively good results. In this study, network pharmacology and molecular docking strategies were used to establish the material basis and mechanisms of Chinese herbal compounds in fatigue treatment. According to traditional medicine theories and relevant guidance documents published by the Chinese Ministry of Health, four herbal medicines, including The major active ingredients in EEPS were identified via comprehensive literature search and Traditional Chinese Medicine Systems Pharmacology database search. Corresponding targets for these ingredients were predicted using SwissTargetPrediction. The network was constructed using Cytoscape 3.9.1 to obtain key ingredients. Prediction of absorption, distribution, metabolism, excretion and toxicity properties was performed using the ADMETIab 2.0 database. The anti-fatigue targets were retrieved from GeneCards v5.13, OMIM, TTD and DisGeNET 7.0 databases. Then, the potential targets of EEPS in fatigue treatment were screened through a Venn diagram. A protein–protein interaction (PPI) network of these overlapping targets was constructed, and the hub targets in the network selected through topological screening. Gene Ontology and KEGG pathway enrichment analyses were performed using the DAVID database and the bioinformatics online platform. Finally, AutoDock tools were used to verify the binding capacity between the key active ingredients and the core targets.This study identified the active ingredients and potential molecular mechanisms of EEPS in fatigue treatment, which will provide a foundation for future research on applications of herbal medicines in the functional food industry. GivenSince fatigue is a chronic and complex disease, there is a need to exploit novel multitarget therapies for long-term use . FunctioEucommia ulmoides Oliver bark (EUOB), Eucommia ulmoides Oliver male flower (EUOF), Panax notoginseng (PN), and Syzygium aromaticum (SA), have attracted our research interests and attention (Panax notoginseng saponins (PNS) are the main active ingredients in PN  database and screened by oral bioavailability (OB ≥ 30%) and drug likeness (DL ≥ 0.18) –58. To c2.2.A comprehensive search was conducted in multiple databases [Online Mendelian Inheritance in Man (OMIM) , Therape2.3.To understand the complex interactions between herbal medicines, active compounds, and corresponding targets, the herbal medicine-active compound-targetnetwork (H-A-T) was constructed using the Cytoscape 3.9.1  software2.4.Structures (SMILES format) of active compounds were separately imported into the ADMETIab 2.0 database . Octanol2.5.Homo sapiens,” the minimum interaction threshold was set to “0.4,” while the rest of the parameters were kept at default to obtain the PPI network. Then, visualization and analysis of the PPI network were performed using Cytoscape 3.9.1. Ultimately, the top ranked targets by network degree were selected as the key anti-fatigue targets.The STRING 11.5 database  was used2.6.Homo sapiens, and “Gene list” was selected as the list type. Terms enriched with p < 0.01 and enrichment factor > 1.5 were considered significant. The top 10 most significantly enriched biological process (BP), cellular component (CC), and molecular function (MF) items and top 20 KEGG terms were selected. The bioinformatics online platform was used for data visualization (The DAVID v2022 q2 database was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses . The orglization .2.7.Crystal structures of key targets were obtained from the UniProt and RCSB PDB protein databases and saved in the PDB format . Small mEnzyme structure inputs were processed, and flexible ligand docking performed using the AUTOGRID program, with other values set as default parameters. The result of docking is the binding energy; the smaller the binding energy, the more stable the ligand and receptor are bound and the more likely the interactions are to occur .3.3.1.Sixty seven active compounds of EEPS were collected from the TCMSP v2.3 database and from other search methods . In Supp3.2.After searching the GeneCards v5.13, OMIM, TTD and DisGeNET 7.0 databases, 2,890 potential fatigue targets were obtained after removing duplicate targets. Then, the 817 predicted targets of the active compounds of EEPS and the 2,890 fatigue-related disease targets were imported into the EVenn online platform to construct the Venn diagram. Visual results are shown in 3.3.A total of 67 active components and 817 potential targets of EEPS were imported into the Cytoscape 3.9.1 software for construction of the H-A-T network. In 3.4.Eight key components were compiled using the ADMET screening function of the ADMETIab 2.0 database. Chemical structures of these components and predicted results are shown in 3.5.A total of 413 overlapping targets were imported into the STRING 11.5 database. After hiding the unrelated nodes, the exported data in TSV format were imported into the Cytoscape 3.9.1 software to establish the protein–protein interaction (PPI) network diagram. Analysis of network topology revealed that the network contained 411 nodes and 11,117 edges, while the average degree of nodes was 54.097. In 3.6.nd November 2022). In GO enrichment and KEGG pathway analyses were performed using the DAVID v2022q2 database to investigate the biological functions of the 413 overlapping targets. A total of 1,636 GO enrichment entries were obtained; including 1,232 biological process (BP) GO entries, 145 cellular component (CC) GO entries, 259 molecular function (MF) GO entries, and 189 KEGG signaling pathways. Bubble diagram of the GO enrichment results was visualized using the bioinformatics online platform. The enrichment degree is indicated by color. The darker the color, the more significant the enrichment. In 3.7.Molecular docking simulations were performed using the AutoDock Tool to verify the reliability of interactions between important proteins and key compounds. Crystal structures of AKT1, IL6, INS, EGFR, VEGFA, CTNNB1, IL1B, JUN CASP3 and SRC were compiled by searching against the UniProt and RCSB Protein Data Bank (PDB) databases. The chemical structures of eight key components, including MOL000098 (Quercetin), MOL000422 (Kaempferol), MOL009015 ((−)-Tabernemontanine), MOL009031 ((9R) Cinchonan-9-al, 6′-methoxy-, (9R)-), MOL004328 (Naringenin), MOL011604 (Syringetin), MOL009029 , and MOL001792 (DFV) were collated from the PubChem database. Then, molecular docking was performed to assess the binding between the top 10 targets and the key compounds through AutoDock 4.0 software. The lower the binding energy, the more stable the binding between the target and compound, and the more likely interactions will occur. The docking results shown in 4.Fatigue is a long protracted chronic disease that is cannot be easily cured within a short time . Currentin vitro results indicated that kaempferol and quercetin alone and in combination can induce antioxidant response elements (AREs) and increase Nrf2 protein levels, thereby exert antioxidant activity. It is worth mentioning that the free radical scavenging activities of kaempferol were enhanced when used in combination with quercetin (Panax notoginseng saponins (PNS) are widely considered to be the main anti-fatigue active compound of PN  can be found in the article/YW, WH, and YH: conceptualization. YW and WH: funding acquisition. YW, YM, WH, and YH: investigation. YW and YM: methodology and writing—original draft. YM: software. YM, JC, and YH: supervision. YW, YM, RX, and FC: validation. YW, YM, and YH: writing—review and editing. YW and YM: contributed equally to this work. All authors contributed to the article and approved the submitted version.The authors greatly appreciate the support for this research from National Science Foundation of China (Grant nos. 21602090 and 82160292) and the Open Project of Key Laboratory of Prevention and treatment of cardiovascular and cerebrovascular diseases Ministry of Education (Grant nos. XN202030 and XN201922).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.