Stroke is a leading cause of morbidity and mortality worldwide. As the most common type of stroke cases, treatment effectiveness is still limited despite intensive research. Recently, traditional Chinese medicine has attracted attention because of potential benefits for stroke treatment. Among these, luteolin, a natural plant flavonoid compound, offers neuroprotection following against ischemic stroke, although the specific mechanisms are unknown. Here we used network pharmacology, molecular docking, and experimental verification to explore the mechanisms whereby luteolin can benefit stroke recovery. The pharmacological and molecular properties of luteolin were obtained from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. The potential targets of luteolin and ischemic stroke were collected from interrogating public databases. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed by Funrich and Database for Annotation, Visualization and Integrated Discovery respectively, a luteolin-target-pathway network constructed using Cytoscape, Autodock vina was used for molecular docking simulation with Discovery Studio was used to visualize and analyze the docked conformations. Lastly, we employed an in vitro model of stroke injury to evaluate the effects of luteolin on cell survival and expression of the putative targets. From 95 candidate luteolin target genes, our analysis identified six core targets . KEGG analysis of the candidate targets identified that luteolin provides therapeutic effects on stroke through TNF signaling and other pathways. Our experimental analyses confirmed the conclusions analyzed above. In summary, the molecular and pharmacological mechanisms of luteolin against stroke are indicated in our study from a systematic perspective. Indeed, a number of clinical trials have explored the efficacy of Chinese medicine with results showing these agents can complement traditional Western treatment strategies and benefit patient recoveries after stroke ,15. PosiNetwork pharmacology is a systematic approach to discover relationships between diseases, targets and drugs, helping to understand the pharmacology of drugs and their impact on biological networks . To expl2.2.1https://tcmspw.com/tsps) is a unique systemic pharmacology platform used to capture the relationships between herbal medicines, targets and diseases, which provides pharmacokinetic characteristics of natural mixtures, such as oral bioavailability (OB), drug-likeness (DL), intestinal epithelial permeability (Caco-2), water-solubility, etc [We determined the ADME-related properties of luteolin. The traditional Chinese medicine systems pharmacology database and analysis platform TCMSP , a comprehensive compendium of human genes and genetic phenotypes, which focuses on the relationship between phenotype and genotype; DisGeNET (v7.0) (http://www.disgenet.org/), a platform containing publicly available collections of genes associated to human diseases that used to validate computationally predicted disease genes; DrugBank (https://go.drugbank.com/), a comprehensive database to combine detailed drug data with comprehensive drug target information [http://db.idrblab.net/ttd/), a database to provide information about the targeted disease, pathway information [http://www.uniprot.org/), a globally recognized knowledgebase used to access protein sequence and annotation data [https://pubchem.ncbi.nlm.nih.gov/), an authoritative web-based resource providing chemical information. The targets of luteolin were gathered from 3 public databases: PharmMapper (http://www.lilab-ecust.cn/pharmmapper/submitfile.html) used to identify potential target candidates of small molecules using a pharmacophore mapping approach [www.swisstargetprediction.ch) used to perform ligand-based target prediction for bioactive small molecules [http://ctdbase.org/) used to curate and infer gene-disease associations [http://bioinformatics.psb.ugent.be/webtools/Venn/) representing the overlapping genes. The potential target genes were entered into the STRING database(https://string-db.org/) which incorporates > 52 million proteins from > 1100 species, to derive the associated protein-protein interactions [Then we collected the targets of ischemic stroke and luteolin associated with ischemic stroke via different databases in order to prepare for the subsequent analysis. Ischemic-related targets were determined by searching public databases, including Online Mendelian Inheritance in Man  to construct and visualize PPI networks with a medium confidence level (0.4) . Cytohub2.4GO enrichment and KEGG pathway enrichment analysis were used to explore the potential functions of luteolin against complications of ischemic stroke. The targets of luteolin for treatment of ischemic stroke were calculated by FunRich software for GO analysis to derive statistically significant  enrichments .https://david.ncifcrf.gov/), which was widely used for gene function annotation and enrichment, to obtain the KEGG pathway analysis results [http://www.bioinformatics.com.cn/) was used to draw the bubble chart of KEGG enrichment analysis.KEGG pathway analysis can give functional meaning to genes at the molecular level, and provide a systematic and comprehensive analysis of a defined list of target genes that are significantly enriched in defined biological pathways . All lut results ,36. Addi2.5https://pubchem.ncbi.nlm.nih.gov/) was converted from the SDF to the mol2 format using Chem 3D software. PDB format structures of the target proteins interleukins-1β(IL1B) (PDB ID: 5R85), JUN (PDB ID: 2G01), Mitogen-activated protein kinase 3 (MAPK3) (PDB ID: 6GES), Matrix metalloproteinase 9 (MMP9) (PDB ID: 4JIJ), Prostaglandin H synthase 2 (PTGS2) (PDB ID:5F19) and tumor necrosis factor (TNF) (PDB ID: 2AZ5) were downloaded from the RCSB (https: //www.rcsb.org/), a database which is established as the digital data resource in all of biology and medicine and provides access to 3D structure data for large biological molecules. The solvent molecules and ligands were removed by Pymol software, performed to pre-dock small molecule components and proteins. After using AutoDock Tools 1.5.6, a software for molecule docking based on the semi-flexible principle, to add hydrogen, calculate charges, assign atomic types and carry out other operations, the processed structures were saved as PDBQT format. Finally, Autodock vina 1.1.2 was run for molecular docking simulation to analyze the binding properties of the luteolin ligand to the target proteins, and Discovery Studio 2020 was used to visualize and analyze the docked conformations.Molecular docking techniques between small molecules and relevant targets can, to a certain degree, predict binding mechanisms and activity between active ingredients and putative target proteins . Toward 2.6PC-12 cells (Cell Bank of the Chinese Academy of Sciences) were employed as an in vitro model to simulate ischemic stroke using oxygen and glucose deprivation (OGD) treatment. Cells were maintained in Dulbecco’s Modified Eagle Medium  containing 10% FBS and 1% antibiotics  at 37°C in 5% CO2 atmospheric conditions. To verify the neuroprotective effects of luteolin, PC-12 cells were pretreated with gradually increasing concentrations of luteolin  for 24 hours prior to an 6-hour OGD treatment ,39, cons2.7CCK-8 assays were used to evaluate cell viability according to the manufacturer’s instructions. Briefly, cells were seeded in 96-well plates at a density of 8000 cells per well. After treatment with the indicated combinations of luteolin and OGD, 10 μl CCK-8 reagent was added to each well and the cells were further incubated for 2 h at 37°C. Finally, the absorbance was read at 450 nm using Microplate Reader  and cell viability was expressed as percentages relative to the control treatment.2.8 qRT-PCR assaysThe mRNA expression levels of MMP9, MAPK3, PTGS2, JUN, IL1B and TNF were determined using quantitative reverse-transcription polymerase chain reaction (qRT-PCR) analysis. Briefly, total cellular RNA was extracted using the Trizol reagent  and reverse transcribed to cDNA using reverse transcription kit  according to the manufacturer’s instructions under the following conditions: 65°C (5 min), 37°C (15 min), and a final step of 98°C (5 min). qRT-PCR reactions were performed with 2X SYBR green qPCR master mix  on a Bio-rad CFX-96 real-time PCR system . The speTNFα: forward(TCAAGAGCCCTTGCCCTAAG) and reverse (TGGAAGACTCCTCCCAGGTA)MMP9: forward(CCTGGAACTCACACAACGTC) and reverse (TGCAGGAGGTCATAGGTCAC)JUN: forward(GAGTCTCAGGAGCGGATCAA) and reverse (CTGTTCCCTGAGCATGTTGG)MAPK3: forward(AAGCGCATCACAGTAGAGGA) and reverse (TCAGCCACTGGTTCATCTGT)IL1B: forward(GGGATGATGACGACCTGCTA) and reverse (TGTCGTTGCTTGTCTCTCCT)PTGS2: forward(TCCAAACCAGCAGGCTCATA) and reverse (ATTCAGAGGCAATGCGGTTC)2.9The influence of luteolin on the expressions of MMP9, MAPK3, PTGS2, JUN, IL1B and TNF in protein level was assessed by Western blot. PC-12 cells were lysed before measuring the protein concentrations of the cell lysates using the BCA protein assay kit . Thereafter, 20 ug of protein/sample was separated using 12% SDS-PAGE gels  before electrophoretic transferred to the PVDF membranes. The membranes were then blocked with 5% skim milk  for 2 h at room temperature and followed by incubation overnight at 4°C with primary antibodies directed against MMP9, MAPK3, PTGS2, JUN , IL1B and TNF . After washing with TBST for 30 minutes at room temperature, we incubated the immune blots with secondary antibodies for 2 h. Finally, proteins were visualized by Yeasen super ECL detection reagent Kit  using Syngene Bio Imaging instrument  and were quantified by Image J [2.10t-test used to perform statistical comparisons between groups. P < 0.05 was considered to indicate a statistically significant difference.All values are expressed as mean ± SD with the student’s 3.ResultsIn order to verify the neuroprotective effects of luteolin against ischemic stroke and to explore the mechanisms of neuroprotection, we first employed network pharmacology analysis. This was followed by experimental verification methods, including PPI networks, GO analysis, KEGG pathway enrichment analysis, molecular docking, and in vitro culture models of ischemic stroke assessed by cell survival assays, qRT-PCR and Western blotting.3.1ADME parameters represent important steps in the drug discovery process. We obtained theADME characteristics of luteolin by interrogating the TCMSP database , includi3.2This list was about 1200 stroke-related genes, among which 1159 genes were from the DisGeNET database, 77 from Drugbank, 12 from TTD, and 180 from OMIM . The PPI3.3GO is a comprehensive resource that classifies gene functions into 3 main terms: biological processes (BPs), cellular components (CCs), and molecular functions (MFs) .The resu3.4Using DAVID 6.8, we performed KEGG enrichment analysis for the targets related to the treatment of ischemic stroke. This analysis retrieved a total of 38 significantly associated signaling pathways . Of these, the top 20 enriched pathways in stroke are illustrated in 3.5We compiled a total of 275 luteolin target genes from different sources including 111 from the Swiss target prediction, 124 from the CTD database, and 71 from Pharm-Mapper  that wer3.6Targets and interactions between targets are represented respectively by nodes and edges in a PPI network. Interrogation of the STRING database with the 95 luteolin stroke target genes produced a PPI network with 95 nodes and 1397 edges. The initial PPI network was built by Cytoscape 3.8.0 where the circles represent targets of luteolin against stroke, and the edges represent interactions between targets . Analysi3.7The luteolin-target-pathway network was then visualized using Cytoscape 3.8.0 to derive the key biological mechanisms . This an3.8The input of the 95 potential luteolin stroke target genes derived 6 CC enrichment items including extracellular space, extracellular, microsome, cell surface, extracellular region and cytosol . MF enri3.9KEGG analysis against the 95 common gene targets between luteolin and stroke showed enrichments in 28 signaling pathways  suggesting these pathways are likely targeted by luteolin to perform a therapeutic role in stroke. The top 30 significantly enriched KEGG pathways are shown in 3.10Based on the luteolin-targeting-pathway network, we constructed the integrated pathway map by integrating the stroke pathway map and signaling pathways obtained from the assessment of the targeted pathway network to visualize the complex mechanism of luteolin anti-stroke . As showIn terms of key signaling pathways , based o3.11Based on the PPI network, six core target proteins were selected for molecular docking experiments against luteolin . The bin3.12Ischemic conditions in the brain can be modeled in vitro through OGD. We assessed the effects of luteolin on OGD-induced injury in PC-12 cells, a cell line that closely resembles neurons. We first pretreated PC-12 cells with 5, 10 or 20 μM luteolin for 24 hours prior to conducting OGD treatment for 6 hours . AssessmNext, we sought to establish whether luteolin pretreatment could decrease the expression levels of MMP9, MAPK3, PTGS2, JUN, IL1B and TNF after OGD-induced injury. Measurement of their mRNA and protein expressions levels by qRT-PCR and Western blotting, respectively, showed that OGD treatment significantly increased the expression of MMP9, MAPK3, PTGS2, JUN, IL1B and TNF compared with the control group , C and D4.Despite years of intensive research, effective treatment options for stroke are still limited. Progress has been made in reducing the rates of acute deaths but nevertheless, the incidence of serious long-term disability resulting from stroke places an enormous strain on family members and society in general. Therefore, searching for effective treatments that reduce the morbidity and mortality of stroke remains a pressing issue. As a promising and cutting-edge approach to drug discovery, network pharmacology has been developed to predict new drugs or disease-specific targets ,44. IndeBased on PPI network analysis of stroke targets, we obtained 10 targets with the highest degree values which included TP53, TNF, CASP3, STAT3, MAPK3, CXCL3, FN1, IL6, VEGFA and ALB that have been established to be involved in the development of stroke. Thereafter we selected the top three clusters from the PPI network via the MCODE plugin and performed GO functional enrichment and KEGG pathway analysis. The genes derived from GO analysis showed enrichment in relevant pathways including cytokine-mediated signaling pathway, inflammatory response, positive regulation of gene expression, aging, response to hypoxia, positive proliferation response to drug, and positive regulation of cell. Moreover, both the TNF signaling pathway and the cytokine-cytokine receptor interaction were the two highly ranked pathways associated with stroke based on the results of KEGG analysis, indicating these pathways as crucial targets for the treatment of stroke. In addition, the 10 core targets mentioned above were exactly covered by the TNF signaling pathway and the cytokine-cytokine receptor interaction pathways, which verified the correctness of our analysis.The next step involved determining the intersection between the 275 putative genes targeted by luteolin with the 1200 genes related to stroke. Based on the 95 crossover genes identified, PPI network analysis uncovered six core genes including MMP9, MAPK3, PTGS2, JUN, IL1B and TNF likely linking the activity of luteolin to effective stroke control. Moreover, according to GO analysis, the candidate target genes were found to be associated with energy pathways, metabolism and immune responses. Furthermore, based on KEGG analysis, the therapeutic effects of luteolin on stroke may have through various pathways like the TNF signaling pathway, which overlapped with the results of KEGG analysis of stroke targets. Finally, we verified the binding ability of the six core genes of stroke and luteolin through molecular docking simulation, with the results indicating stable ligand-protein interactions. Moreover, in the case of the key targets MMP9 and MAPK3, the results in vitro model using PC-12 cells showed that luteolin treatment could alleviate their induction following OGD treatment.According to the above analysis, the potential therapeutic mechanism of luteolin in treating stroke with luteolin could be summarized as the following four major points.4.1The cytokines IL-1β and TNF-α play an important role in regulating immune responses after ischemic stroke and are stroke potential therapeutic targets . MicroglThe MAPK signaling pathway involves can be grouped into three main families of intracellular serine/threonine protein kinases that include extracellular signal-regulated kinase (ERK), p38, and c-Jun NH2-terminal kinase (JNK) . MAPK siPTGS2, also called cyclooxygenase (COX) 2, is the key rate-limiting enzyme that converts arachidonic acid into prostaglandins ,57, such4.2MMPs are members of the zinc-dependent proteolytic enzymes family and are essential for the process of blood vessel formation as well as vascular remodeling . HoweverAdditionally, TNF-α secreted from M1 phenotype microglia can cause endothelial necroptosis after MCAO, which also promotes vascular permeability and BBB breakdown . ILs can4.3Oxidative stress decreases neuronal cell death by associating it with activation of the intracellular JNK signaling pathway . Stress The expression of COX-2 has been reported to increase sharply in the brain of ischemic rats from 30 minutes onwards and persisted until 15 days after stroke ,79. In a4.4It is known that the sensitivity of COX-2 knockout (COX-2-/-) mice to ischemic brain injury is sharply reduced . ConversOurs results demonstrated that luteolin can decrease the expression of MMP9, MAPK3, PTGS2, JUN, IL1B and TNF to maintain neuronal cell viability following an ischemic insult. We related the neuroprotective effects of luteolin to reductions in neuroinflammatory responses, endothelial cell damage, BBB breakdown, apoptosis, oxidative stress, thrombus formation and infarct volume. Together our findings propose the utility of luteolin as a therapeutic drug for ischemic stroke. And an important point is that, compared with other studies, our study abandoned the classical onset and injury mechanism to explore the possible mechanism of luteolin against ischemic stroke. Instead, we directly captured the rich representation of stroke-related targets from extensively annotated databases and combined these with the targets of luteolin. The intersection of these data enables candidate target selection in a less biased and visual way. Streamlining the understanding of the neuroprotective effects of luteolin on ischemic stroke provides several key benefits, not only reducing the time, financial and human costs required for trials and errors, but also providing new therapeutic ideas that undoubtedly will be of great value for the treatment of ischemic stroke.5.From a systematic perspective, our study explored the molecular and pharmacological mechanisms of luteolin as a therapeutic agent to promote stroke recovery. However, there are still several limitations to network pharmacology and the companion methods used in our study. Ischemic stroke is a multifaceted process and in vitro experiments such as those used here are unlikely to fully capture this complexity. Furthermore, the research of new effective treatment opinions should not be limited in neuroprotection, but rather also for related processes including neuroregeneration, enhancement of repair and recovery, and maintenance of long-term neuroplasticity. Consequently, additional in vivo and clinical studies experiments are needed to verify the mechanisms disclosed by our research. Nonetheless, our study provides new directions for stroke patient recovery through promoting innovative research involving anti-stroke drugs.


Escherichia coli (E. coli) strain CFT073 was applied to establish in vivo and in vitro models. In vivo results revealed that the key targets, IL6 and FOS, are significantly upregulated in rat bladder tissues of UTIs, whereas the expression of MYC and EGFR remained steady. Last, in vitro results further confirmed the therapeutic potential of CCF by reducing the expression of IL6 and FOS. In conclusion, IL6 and FOS were generally upregulated in the progression of E. coli–induced UTIs, whereas the CCF intervention exerted a preventive role in host cells stimulated by E. coli, partially due to inhibiting the expression of IL6 and FOS.Coptis chinensis Franch (CCF) is extensively used in the treatment of inflammatory-related diseases. Accumulating studies have previously demonstrated the anti-inflammatory properties of CCF, yet data on its exact targets against urinary tract infections (UTIs) remain largely unknown. Therefore, the present study decodes the potential targets of action of CCF against UTIs by network pharmacology combined with experiment evaluations. Based on the pharmacology network analysis, the current study yielded six core ingredients: quercetin, palmatine (R)-canadine, berlambine, berberine, and berberrubine. The protein–protein interaction network (PPI) was generated by the string database, and then, four targets  were perceived as the major CCF targets using the CytoNCA plug-in. The results of molecular docking showed that the six core constituents of CCF had strong binding affinities toward the four key targets of UTIs after docking into the crystal structure. The enrichment analysis indicated that the possible regulatory mechanisms of CCF against UTIs were based on the modules of inflammation, immune responses, and apoptosis among others. Experimentally, the  Escherichia coli (E. coli) infections typically occur in the bladder (cystitis) and may ascend to the kidney, resulting in pyelonephritis  are regarded to be among the most common bacterial infections, affecting 150 million people around the world annually . Data esephritis . In somebe fatal . Virulenbe fatal . Recentlbe fatal . Howeverountries . Our pre limited . Moreovefor UTIs . TherefoTraditional Chinese medicine (TCM) has unparalleled advantages of possessing the synergistic action of multiingredient, multitarget, and multichannel medicines, as compared with the single-target drugs, which hence contributes to the further resource of novel therapies against UTIs . In ChinE. coli–induced UTIs in rats and human uroepithelial cells (HUCs). The obtained data will provide evidence for the therapeutic potential and targets of CCF against UTIs and enrich the medicinal uses of CCF.In the current research, for the first time, the pharmacological investigation and experimental evaluations on CCF against UTIs were carried out to encode the pivotal targets of action of CCF itself, as shown in http://lsp.nwu.edu.cn/tcmsp.php), a database of systems pharmacology for drug discovery from 499 Chinese herbal medicines. The keyword “Coptis chinensis Franch” was utilized for collecting the candidate chemical ingredients. The oral availability (OB) and drug-likeness (DL) were regarded as the core indicators in drug screen, and the constituents with the established criteria of OB ≥ 30% and DL > 0.18 were selected as active components for further study. The chemical structures of the selected active compounds were prepared using ChemDraw.The active CCF components and targets were retrieved from the traditional Chinese medicine systems pharmacology (TCMSP) (http://db.idrblab.net/ttd/), the drug bank database (https://go.drugbank.com/), the online Mendelian inheritance in man database , the PharmGKB database (https://www.PharmGkb.org/), and the microarray dataset GSE43790 which was downloaded from the GEO database on the basis of the screening criteria of p < 0.05. The overlapping disease and drug targets inferred the CCF targets in the treatment of UTIs.Using the keyword, urinary tract infection, we searched for UTI-specific union target genes using the therapeutic target database . We eliminated the disconnected targets and then maintained the remaining parameters at default settings. We then used topological analysis plug-in CytoNCA to filter out the key targets based on betweenness centrality (BC) ≥ median BC, closeness centrality (CC) ≥ median CC, degree centrality (DC) ≥ median DC, eigenvector centrality (EC) ≥ median EC, network centrality (NC) ≥ median NC, and local average connectivity–based centrality (LAC) ≥ median LAC .https://www.uniprot.org/) and then were regarded as receptors. Besides, the crystal structures of these targets were screened at the RCSB PDB database (https://www.rcsb.org/). We removed the solvent and organic molecules using Pymol software, added nonpolar hydrogen through AutoDock 1.5.6 software, and then saved the structures as PDBQT files  structures of the core effective CCF ingredients were downloaded from the PubChem database and saved in the SDF format. The 2D structures were transformed into the mol2 format via Chem3D software and then saved in the PDBQT format as docking ligands using the AutoDock Tool. On the other hand, the core proteins of the overlapping genes with the highest resolution were searched at the Uniport database  were used to analyze gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), and then, the bubble plot was applied to visualize the enrichment results. The GO enrichment analysis analyzed the biological process (BP), cellular component (CC), and molecular function (MF). The threshold for analysis was an adjusted Escherichia coli strains and human uroepithelial cell (HUC) lines were purchased from the American Type Culture Collection . All E. coli strains were grown in a terrific broth (TB) medium for 8 h overnight. The required suspension was prepared with PBS for further experiments. HUCs were incubated with WT-CFT073 at a ratio of 1:10.Wild-type urinary tract pathogenic Coptis chinensis Franch (CCF), a commercial extract, was purchased from Yuanye . The various concentrations of CCF were prepared according to the corresponding instructions. The CCF was dissolved in DMSO. After the solutions were ultrasonicated and filtered, the filtered solutions underwent further experiments. The final concentrations of CCF were 0, 3, 10, 30, 100, and 300 mg/L.E. coli to induce the UTI model was performed as previously described  per rat. Urine samples of rats were serially diluted and plated onto blood agar plates at 37°C for 12 h.All animal experiments were consistent with guidelines approved by the Institutional Animal Care and Use Committee at the Second Affiliated Hospital of Guangzhou Medical University (China). A total of 12 female 6-week-old Sprague–Dawley (SD) rats, weighing from 160 to 180 g, were purchased from Guangdong Medical Laboratory Animal Center  and lived in a specific pathogen-free environment (SPF) room. The body weights were recorded on the first and third days. The transurethral injection of escribed . The 12 E. coli. The collection of total RNA, primer-specific reverse transcription (RT), and PCR were performed according to the corresponding instructions . RocheLightCycler480 was used for all data analysis. Based on the 2−ΔΔCt method, the relative expression of IL6, FOS, MYC, and EGFR mRNA was calculated. The detailed primer sequences are shown in RT-PCR was then applied to evaluate the expression of predicted hub genes in rat bladder tissues that occur after the infection of To observe the histological changes of bladder tissues, all fixed bladder samples were embedded in paraffin and cut into 6 μm sections for hematoxylin and eosin staining. For each HE-stained bladder section, five random fields were scored according to the previous criteria . IL6 and3 cells/well at 37°C and cultured for 48 h. Then, the HUCs were washed with PBS thrice, followed by incubation with 0.1% DMSO and various concentrations of CCF ranging from 0 mg/L to 300 mg/L  for 3 h. Afterward, a CCK8 working solution was added. After incubation for an additional 4 h at 37°C, a hybrid multimode microplate reader  was used for detecting the OD values at 450 nm.A CCK8 Kit  was applied to evaluate the cell viability rates. The cells were seeded in 96-well plates at a density of 4*10E. coli in the presence or absence of CCF (3 mg/L). Cell samples were washed by PBS and lysed in RIPA buffer. After the lysates were ultrasonicated and centrifuged, the supernatants were retained. The protein concentrations were evaluated by using a bicinchoninic acid assay (BCA) protein assay kit . All proteins were denatured by heating before equal amounts of protein were added to the chamber for electrophoresis and transferred to an NC membrane (Millipore). After being blocked by 5% skimmed milk for 1–2 h, the membranes were incubated at 4 °C overnight with primary antibodies: IL6 , FOS , and GAPDH . After being washed in TBST three times, the membranes were incubated with the secondary antibodies (Santa Cruz Biotechnology) for 1–2 h at room temperature. The ImageJ software  was applied to quantify the relative band density.HUCs were collected after incubation for 3 h with or without p < 0.05 was considered statistically significant. The CCK8 assay was conducted in quintuplicate and repeated three times. The entire experiment of PCR, WB, H&E-staining inflammatory scores, and IHC were performed in six biological and technical replicates. The statistical significance of bacterial burden in urine, H&E-staining inflammatory scores, and IHC was assessed by t-tests. The statistical significance of CCK8 and WB were assessed by one-way ANOVA with LSD post hoc comparisons or Dunnett’s T3 post hoc tests. The statistical significance of PCR was assessed by t-tests or one-way ANOVA with LSD post hoc comparisons or Dunnett’s T3 post hoc tests.All results were analyzed by using SPSS software version 20.0. The value of To sieve key ingredients, we first obtained a total of 48 candidate compounds from the TCMSP database based on the absorption, distribution, metabolism, excretion, and toxicity (ADME/T) values. Using an oral bioavailability (OB) ≥ 30% and a drug-likeness (DL) ≥ 0.18, we selected 14 components as vital effective ingredients . Subsequp-value <0.05), which yielded a total of 14600 UTI-related GEO genes. Meanwhile, a volcano plot , protein-oncogene c-FOS (2WT7), MYC proto-oncogene (5I50), and the epidermal growth factor receptor (5LV6) from the RCSB PDB database. In the process of preparing proteins, we removed the water molecules and irrelevant ligands, followed by the addition of hydrogen atoms. Subsequently, AutoDock Vina was applied to calculate the docking scores representing the potential affinity. The lower the energy, the more stable the interaction. The potential affinities for the six ingredients -canadine, berlambine, berberine, and berberrubine) with the IL6 crystal structure were −7.2, −6.9, −6.9, −7.6, −7.4, and −7.6 kcal/mol, respectively. The docking scores for the four ingredients with the FOS crystal structure were −9.9, −8.1, −8.2, −8.2, −8.2, and −8.2 kcal/mol, respectively. The docking scores for the four ingredients with the EGFR crystal structure were −5.4, −5.4, −5.8, −5.8, −5.9, and −5.9 kcal/mol, respectively. The docking scores for the four ingredients with the MYC crystal structure were −6.1, −6.2, −6.3, −7.0, −7.0, and −7.0 kcal/mol, respectively. Our results indicated that the whole four key proteins had potential affinity to the core components .To identify the differentially affected functions by the 68 overlapping proteins, we performed GO enrichment analysis through R (Version 3.6.2) . After ep-value  and FDR . We excluded unrelated signaling pathways such as “chemical carcinogenesis—receptor activation,” “Proteoglycans in cancer,” and “Fluid shear stress and atherosclerosis”. The overlapping proteins were closely associated with pathways such as the PI3K-AKT signaling pathway (hsa:04151), MAPK signaling pathway (hsa:04010), AGE-RAGE signaling pathway in diabetic complications (hsa:04933), TNF signaling pathway (hsa:04668), HIF-1 signaling pathway (hsa:04066), Apoptosis (hsa:04210), NF-kappa B signaling pathway (hsa:04064), p53 signaling pathway (hsa:04115), and IL-17 signaling pathway -canadine, berlambine, berberine, and berberrubine among others. An interesting study observed that the antiadhesive properties of urine samples were obviously increased after quercetin consumption . These rE. coli . PalmatiE. coli . FurtherE. coli . BerberiE. coli . MoreoveE. coli . Up to nE. coli–induced UTIs, which partially supported the predicted results of network pharmacology analysis. IL6 has been regarded as a pleiotropic proinflammatory cytokine, which is involved in several functions such as maturation of B cells, immunity, inflammation, etc.  secreted by the host. Thus, it might provide a new insight to address the current plight of emerging resistance as a result of antibiotic treatment.The in vivo experiments, it was found that IL6 and FOS were significantly regulated in UTIs in rats infected by E. coli, whereas MYC and EGFR showed no significant difference. Last, it was evident that after treatment with CCF, the disorder of IL6 and FOS can be recovered in E. coli–stimulated HUCs. Therefore, the findings of the present study not only revealed the therapeutic targets of CCF but also provided a theoretical basis for CCF against UTIs.Based on network pharmacology, six core ingredients and four core hub genes of CCF in UTIs were identified in the first phase of the current study. In the next phase, by using


Nonalcoholic fatty liver disease (NAFLD) has become the most prevalent chronic liver disease. Recent technological advances, combined with OMICs experiments and explorations involving different biological samples, have uncovered vital aspects of NAFLD biology. In this review, we summarize recent work by our group and others that expands what is known about the role of lipidome in NAFLD pathogenesis. We discuss how pathway and enrichment analyses were performed by integrating a list of query metabolites derived from text-mining existing NAFLD-lipidomics studies, resulting in the identification of nine Kyoto Encyclopedia of Genes and Genomes dysregulated pathways, including biosynthesis of unsaturated fatty acids, butanoate metabolism, synthesis and degradation of ketone bodies, sphingolipid, arachidonic acid and pyruvate metabolism, and numerous nonsteroidal antiinflammatory drug pathways predicted from The Small Molecule Pathway Database. We also summarize an integrated pathway-level analysis of genes and lipid-related metabolites associated with NAFLD, which shows overrepresentation of signal transduction, selenium micronutrient network, Class A/1Rhodopsin-like receptors and G protein-coupled receptor ligand binding, and G protein-coupled receptor downstream signaling. Generated gene-metabolite-disease interaction networks indicate that NAFLD and arterial hypertension are interlinked by molecular signatures. Finally, we discuss how mining pathways and associations among metabolites, lipids, genes, and proteins can be exploited to infer networks and potential pharmacological targets and how lipidomic studies may provide insight into the interrelationships among metabolite clusters that modify NAFLD biology, genetic susceptibility, diet, and the gut microbiome. Nonalcoholic fatty liver disease (NAFLD) has become the most common chronic liver disease with global prevalence in the 20–50% range , 2. ThisTechnological advances that have occurred in the last decade, combined with OMICs experiments and high-throughput explorations across different biological samples, have revealed key aspects of NAFLD biology as well as mechanisms that explain the disease severity.However, while this field is continuously evolving, how this knowledge can be integrated and applied to biomarker and drug discovery to treat nonalcoholic steatohepatitis (NASH)—the severe histological form of the disease—is less clear. In this review, we explored the potential use of biological data mining and system biology approaches to expand the existing knowledge on the lipidome's role in NAFLD and NASH pathogenesis. Ultimately, mining pathways and associations between metabolites, lipids, genes, and proteins can be exploited to infer networks, interactions, and potential pharmacological targets.Lipidomics approaches have been used in several human studies to explore the mechanisms that may explain the progression of NAFLD into severe histologic disease stages. A summary of the main studies, including their key findings, is shown in et al.  AND (NAFLD OR NASH) AND human∗” search string, while adopting the disease term “fatty liverMeSH ID D005234” for searching the LipidPedia.For this purpose, we text-mined lipidomics studies in NAFLD and NASH by using the PubTator Central search engine available at https://www.metaboanalyst.ca. The complete list of metabolites, including the HMDB (Human Metabolome Database) ID and PubChem number is shown in To ensure proper name entity recognition of the retrieved metabolites, including abbreviation and synonym recognition, we manually curated the list of terms. A final list of 70 metabolites, mostly lipids, and a list of 61 genes were used to perform further pathway and enrichment analyses provided at the MetaboAnalyst—a web-based platform freely available at P < 0.05) were found, including biosynthesis of unsaturated fatty acids, butanoate metabolism, and synthesis and degradation of ketone bodies that had the highest P values, along with sphingolipid metabolism, arachidonic acid metabolism, and pyruvate metabolism . In addition, for inferring butyric acid levels, daily total energy and nutrient intake from food and beverages was obtained for each participant through dietary recall surveys. Butyric acid (variable ID: DR1TS040) was obtained by extrapolating dietary intake data pertaining to 7,484 individuals.To understand the potential clinical associations of butanoate metabolism and NAFLD, we analyzed the epidemiological data retrieved from the National Health and Nutrition Examination Surveys (NHANES) 2017–2018 database. This data set is freely available online at P = 0.035). We thus modeled the relationship between NAFLD and butyric acid by linear logistic regression with an interaction term for butyric acid intake and waist circumference while adjusting for subjects' demographic and clinical characteristics. We used waist circumference as main covariate because we previously found that excess of abdominal adipose tissue is a common risk factor for NAFLD not only in obese but also in lean subjects  independently of gender, waist circumference, and type 2 diabetes or glycohemoglobin  and frailty and mortality risk conducted as a part of the NHANES population study showed that after adjusting for potential covariates and the higher total FAs, saturated FAs and butanoic acid intake were associated with a higher degree of frailty not only among older but also among middle-aged individuals . This fiTogether, these data suggest that a metabolite imbalance in the butanoate metabolism pathway may play a significant role in NAFLD pathogenesis and might have adverse systemic consequences that differ from the local protective effects against intestinal injury.Tributyrin—also known as 1,2,3-tributyrylglycerol, glycerol tributyrate, or glyceryl tributyrate—is a triglyceride obtained by formal acylation of the three hydroxy groups of glycerol by butyric acid. Tributyrin is naturally present in butter and is used as an ingredient in making margarine. Most importantly, tributyrin must undergo chemical conversion by metabolic processes before becoming the pharmacologically active substance for which it is a prodrug.PNPLA3, particularly rs738409 that is the major genetic modifier of NAFLD  to butanoate . Still, of NAFLD  and the of NAFLD , 46, hasThe text-mined metabolite list linked to the NAFLD lipidome was used to perform enrichment analyses based on chemical structures and drug pathways. These analyses were based on several libraries assembled in the MetaboAnalyst platform containing ∼9,000 biologically meaningful metabolites primarily identified through human studies and including >1,500 chemical classes , 27.The enrichment analysis based on chemical structures revealed high representation of fatty acids and conjugates, eicosanoids, and short-chain acids and derivatives A, whereaAnalyses focusing specifically on drug pathways from The Small Molecule Pathway Database showed enrichment on rofecoxib, acetaminophen, acetylsalicylic, antipyrine, and antrafenine action pathways, among many other related nonsteroidal antiinflammatory drug pathways . These fGenome-wide association studies with high-throughput metabolic profiling showed high heritability among components of the human metabolome .http://impala.molgen.mpg.de/, which allows combining data sets and analyzing both types of data simultaneously  resource freely available at aneously . Pathwayaneously  and web ormation .When seaP-value  for genes and metabolites. The analysis was narrowed to including at least 10 genes and 10 shared metabolites from the input lists in the enrichment sets and was restricted to pathways showing both Q-values ≤0.05. The top five pathways are based on the overrepresentation of genes and lipids/metabolites involved in signal transduction, selenium micronutrient network, metabolism of lipids, Class A/1 (Rhodopsin-like receptors), GPCR (G protein-coupled receptors) ligand binding, and GPCR downstream signaling, among other relevant pathways. GPCRs involve ∼103 druggable targets for the treatment of many common diseases (https://www.proteinatlas.org) and represent ∼34% of the marketed drugs  that joined at the main node of the network are shown in PNPLA3  and response to bacterium —emphasize the relevance of environmental factors in the biology of NAFLD and its interrelationship with the lipidome, both of which are plausible intervention targets.Hence, we created a metabolite-gene-phenotype interaction network by integrating the list of 70 metabolites and 61 genes generated by mining lipidomic studies on NAFLD from the PubTator Central, as outlined in previous sections. This analysis allows the identification of connections that cross pathway boundaries  and provides a global view of the potential functional relationships among metabolites, connected genes, and target diseases. The network is indeed an integration of gene-metabolite, metabolite-disease, and gene-disease interaction networks. The analyses revealed 120 enriched pathways , whereby IL6 and IL-8), APOB (apolipoprotein B), and PRKAA2 —an important energy-sensing enzyme that monitors cellular energy status, which is activated in response to cellular metabolic stresses and is a metformin's target  was highly connected with the (R)-3-hydroxybutyric acid node, as well as oxoglutaric and pyruvic acid; schizophrenia with oleic and palmitic acid nodes, as well as with acetoacetic acid; and arterial hypertension with arachidonic, docosahexaenoic, eicosapentanoic, and linoleic acid pathways. s target . Peroxiss target , which rPNPLA3 (TM6SF2 (HSD17B13 (https://newdrugtargets.org/. TIN-X is a web-based tool that explores the relationships between proteins and diseases extracted from scientific literature (The druggable-NAFLD genome/proteome is overrepresented by genes and proteins linked to lipid metabolism. PNPLA3 , 45, TM6 (TM6SF2 , 57, 58,HSD17B13 , 60, 61,terature . By harmInterestingly, proteins predicted by the Tdark resource , includiIntegration of new knowledge through systems biology approach, including large-scale OMICs data, is a robust and useful strategy to explore the molecular signatures associated with NAFLD disease severity. Pathway analysis highlighted butanoate metabolism as one of the most dysregulated lipidome-related signatures in NAFLD and NASH. Hence, while further research on this topic is certainly needed, it appears that butyric acid may be regarded as a novel lipidome-related NAFLD phenotype that might explain disease mechanisms linking NAFLD susceptibility, diet, and the gut microbiome. Nevertheless, butyric acid might exhibit differential local effects in the intestine and the liver that could be either beneficial or detrimental. Additional evidence is therefore required to disentangle the putative positive and negative impact of butanoate metabolism, including its participation in translocation of intestinal bacteria to the liver, the putative involvement in liver fat accumulation, and NAFLD-fetal programming. Drug pathways prediction and integrated pathway-level analysis of genes and lipid-related metabolites highlighted nonsteroidal antiinflammatory drug pathways and selenium micronutrient network, respectively. Integration of gene-metabolite, metabolite-disease, and gene-disease interaction networks with a specific focus on nutrition and the microbiome suggests that NAFLD, type 2 diabetes, schizophrenia, and arterial hypertension are highly interconnected by molecular signatures.However, potential bias in the data extracted through data mining suggests that further research is needed to confirm and validate the molecular signatures and pathway predictions yielded by the present work. Despite these limitations, the findings presented here strongly suggest that novel and unanticipated associations derived from system biology have the potential to open new research opportunities, including biomarker and drug discovery.This article contains The authors declare that they have no conflicts of interest with the contents of this article.


Sedum sarmentosum Bunge (SSBE).  Using network pharmacology and in vivo experiments, we investigated the antidrug-induced liver injury components and functional processes of  The effective components, primary active ingredients, and possible target in the therapy of DILI were predicted using network pharmacology and bioinformatics. APAP was inducing the DILI model. In vivo testing of the pharmacodynamic foundation of SSBE in the treatment of DILI was performed. β, MARK14, SSP1, and MMP9. These genes are closely related to the Nrf2/ARE signaling pathway, and they may play a key role in the hepatoprotective effect of SSBE. Verification experiment results showed that, in the DILI mouse model, SSBE promoted inflammation diminution and regulation of Nrf2-ARE cascade. SSBE protected normal hepatocyte growth and inhibited apoptosis of normal liver cells induced by APAP. SSBE inhibited the expression of Nrf2 and ARE proteins in the liver tissue of the DILI mouse model in vivo.  The TCMSP database evaluated five main active components and 299 related targets. In addition, 707 differential genes for DILI were obtained from the DisGeNET database, DigSee database, and OMIM database. 61 related targets were mapped to predict the targets of SSBE acting on DILI. The protein-protein interaction (PPI) core network contained 59 proteins, including IL- By modulating the Nrf2 signaling pathway, the active components in SSBE may protect against drug-induced liver damage. Injury to the liver caused by drugs (drug-induced liver injury (DILI)) refers to abnormalities in liver function tests related to the intake of medicinal compounds, medications, aerobatics, and herbs and is attributed to a significant percentage of patient morbidity and mortality, with an annual incidence rate between 1 and 20 per 100,000 people [Sedum sarmentosum Bunge (SSBE) has been recorded by Chinese Pharmacopoeia (2010), and various preparations have been developed from it [Sedum sarmentosum total flavonoids (SSTF), and other compounds, which have various biological functions such as liver protection, antitumor, anti-inflammatory, antioxidation, antikidney fibrosis, and strengthening muscle strength [Traditional Chinese medicine (TCM) offers unique benefits for treating DILI and has been shown to have hepatoprotective effects. As a traditional medicinal plant,  from it . It is wstrength , 5.It has been previously verified that SSBE has a protective effect on drug-induced liver injury, but few research studies have been done on the primary active ingredients and possible molecular mechanisms of action. The pathogenesis and pathological factors of drug-induced liver injury are relatively complex, with complex components and numerous targets, so how to interpret the mechanism of drug-induced liver injury more comprehensively is an urgent problem to be solved. However, single component-single target-single disease is not suitable for the study of Chinese herbal compound.Sedum sarmentosum Bunge against drug-induced liver injury, so as to explore theoretical origin towards subsequent basic research on pharmacodynamic substances and their mechanism exploration. In the current investigation, network pharmacology was used to screen SSBE constituents that were effective in the treatment of DILI, and molecular biology tools were used to evaluate the potential molecular mechanism in a mouse model to provide a foundation for prospective clinical studies on the effectiveness of SSBE in the treatment of DILI.Using a variety of databases, pathway analysis, and network analysis, network pharmacology, as a new era in contemporary traditional Chinese medicine (TCM) pharmacological investigations, can be used to envisage the targets of action with TCM, discover the active components of TCM, and explore the mechanism of TCM as disease treatments . Networkhttp://lsp.nwu.edu.cn/tcmsp.php,Version:2.3) that offers various databases on plant components and their chemical structures [Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) (ructures  was usedructures . After dructures . DL is aructures . It can https://cytoscape.org/). The following two features of the database are used to obtain drug-target interactions: (1) drug targets from the HIT database that have been experimentally verified and (2) SysDT models that were mostly utilised for the prediction of drug combinations without experimental data support. Obtaining a target necessitates a more succinct statement of the connection between the active component and the target, as it involves numerous species targets. As a result, we utilised the UniProt database (http://www.uniprot.org/) to find the gene names of all targets, using humans as the chosen species.To determine the link between each chemical component and its possible targets, we searched through the TCMSP database and Cytoscape 3.7.1 software (http://www.disgenet.org/); (2) the DigSee database (http://210.107.182.61/geneSearch/); and (3) the Online Mendelian Inheritance in Man (OMIM) (http://omim.org/). Our investigation searched these human illness target databases for targets associated with DILI using the keywords “drug-induced liver damage” and the species “homo species.” The targets linked to DILI were found by searching for the term “drug-induced liver damage.” The website http://www.uniprot.org/was used to bring the targets together.The DILI targets were obtained from three different sources: (1) the DisGeNET database (http://bioinforgp.cnb.csic.es/tools/venny/index.html set the intersection between medication active component of compound targets and illness targets. UniProt (https://www.uniprot.org/) moved all of the targets to “ENTRY” before the network was established. Cytoscape 3.7.1, an open-source software project, was used to create the networks. It integrates biomolecular interaction networks with high-throughput expression data and other molecular states into a coherent conceptual framework.Common targets linked to DILI and potential targets of bioactive substances were chosen as SSBE's targets against DILI in order to study the possible mechanisms of SSBE on DILI. Wayne at the website https://david.ncifcrf.gov/home.jsp, Vision 6.8) and KEGG . The results of GO and KEGG pathway enrichment were considered to have statistically significant and necessary functional mechanisms of DILI, when P < 0.05. Search Tool for the Retrieval of Interacting Genes  was used to collect possible protein-protein interactions (PPI) by uploading 61 common targets that related to DILI and putative targets of active compounds. Species were limited to “Homo sapiens” with a confidence score >0.4.The SSBE pathways associated with DILI were investigated using the Database for Annotation, Visualization, and Integrated Discovery  were randomly assigned to 5 groups: PBS control group, APAP model group, and total flavonoids drug preconditioning group . The rats were given intraperitoneal injection of APAP 1 h after the last administration of SCF extract. At the end of the experiment, all animals were sacrificed for subsequent analysis.The tissue samples of mice liver were preserved in 4% neutral formaldehyde, dried, and paraffin-embedded. The fossils were cut into four-meter thick slabs and dyed with HE. The pathological alterations in the pancreas following HE staining were examined under a light microscope and scored using the METAVIR pathological score as a guide.The concentrations of amylase, ALT, and AST in animal serum or cell culture medium were detected using an ELISA kit according to the manufacturer's instructions. GST/SOD activities and GSH/MDA contents were detected by the biochemical method.The expression of Nrf2/ARE protein in each group was determined by Western blotting. The changes of Nrf2/ARE mRNA levels in normal liver cells treated by APAP were determined by QT-PCR. To separate the proteins, a suitable lysis solution was employed, and the protein content was determined using a BCA assay kit. The protein sample was separated on a 10% SDS-polyacrylamide gel (SDS-PAGE), and the proteins were then transferred to a PVDF membrane. The membrane was blocked with 10% BSA for 120 minutes before being incubated at 4°C overnight with the target antibodies and the GAPDH antibody as the reference protein. The membrane was rinsed three times in TBST for 10 minutes before being incubated for two hours at room temperature with sheep anti-rabbit IgG secondary antibody. The membrane was rinsed three times with TBST for ten minutes before being imaged with ECL photoluminescence solution. The test data were analysed using ImageJ software. Total RNA was isolated from single E10.5 embryos using the TRIZOL reagent (Life Technologies) and the guanidine isothiocyanate technique for quantitative PCR (QT-PCR).The CCK8 test was used to determine cell viability. Cell Counting Kit 8  was used for the CCK8 test according to the manufacturer's instructions. In all groups, flow cytometry was utilised to identify LO2 apoptosis in normal hepatocytes. The FITC Annexin V Apoptosis Detection Kit  was used to perform the apoptosis test, which was followed by cell cycle analysis using a flow cytometer.Isolation and purification of Nf2 were done using an antibody to assess its activity. The Nf2 activity measurements were carried out according to the directions included in the cell activity test kit. To begin an enzymatic reaction, 30 uL of enzyme was introduced to 30 uL of substrate. Standard samples were used to determine the OD value at 450 nm. The relative Nf2 activity in the sample was calculated using the standard curve.P value of <0.05, the differences were considered statistically significant.The experimental data were analysed using the statistical programme SPSS 24.0. The results were stated as TCMSP yielded a total of 5 different compounds. Following ADME screening with OB ≥ 30% and DL ≥ 0.18, 20 compounds were identified as SSBE bioactive substances. All five compounds were identified as promising bioactive molecules for future research after HL ≥4 h was optimised. The results of selected 5 compounds from SSBE are presented in 185 putative targets linked to 5 compounds of SSBE were collected.A total of 707 genes related to DILI were obtained . Through“Homo sapiens”. After that, PPI network was established including 59 nodes and 1466 edges by Cytoscape. A larger node and darker color represent the protein that has a greater degree in this network. In addition, a greater combined score of the edge was symbolized by thicker and darker line (β (IL-1β).61 common targets were uploaded to the STRING database and the PPI network was generated, with the following conditions: combined score (≥0.4) and species limited to ker line . We seleker line . The keyAiming to explore the underlying mechanism of SSBE against DILI, we analysed the enrichment results of the GO term and KEGG pathway executed by DAVID. We divided GO term enrichment results into three parts including biological process (BP), cell compound (CC), and molecular function (MF). The top 10 significant GO terms  are chosen according to the enrichment score .P values and enrichment factors, which we utilised to filter the data. The remaining relevant words were then hierarchically grouped into a tree based on their gene memberships' kappa-statistical similarity. The tree is divided into word clusters using a 0.3 kappa score as a cutoff. The terms within each cluster are exported in the Excel spreadsheet named “Enrichment Analysis.” Cytoscape (v3.1.2) is used to display the network, with a “force-directed” structure with edge bundling for clarity. One phrase from each cluster is chosen as the label for its term description.After identifying all statistically enriched words, we produced accumulative hypergeometric Finally, 20 possible signaling pathways are identified, including the IL-17 signaling route, the MAPK, PI3K-Akt, NF-kappa B, and PPAR signaling pathway .β found are thought to have a close association with oxidative stress [Sedum sarmentosum total flavonoids, quercetin, and hyperoside are effective in alleviating liver damage [The core targets of the PPI network consisting of MMP9, SSP1, MARK14, IL-10, IFNG, and IL-1e stress –18. The r damage . They mar damage . Nrf2 cor damage . Nrf2-ARr damage .The effect of antioxidant stress is achieved mainly by the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway that plays a central regulating effect in the process of oxidative stress injury concerning cells and the body. In addition, its specific receptor called cytoplasmic protein Kelch-like ECH-associated protein 1 (Keap1) is dissociated first, and then the oxidative stress or electrophilic substances are activated following the stimulation of Keap1. After that, the activated Nrf2 interacts with antioxidant reaction elements (ARE) when it translocates into the nucleus. Moreover, the downstream gene expression was upregulated and thus increased the antioxidant stress ability in cells . We assuThe distribution of interlobar space of the hepatic lobule was homogeneous and compact in the control group, and there was no edema or inflammatory exudation. The gap between liver cells was dilated in the DILI model group, leukocytes were infiltrated in or around the lobule, and the levels of edema and inflammation were considerably higher than those in the normal group. The SSBE therapy group decreased pancreatic pathological injury to varying degrees. The shift in liver function tests corresponded to the shift in pancreatic pathology. The DILI model group's antioxidation function indexes  were substantially lower. In terms of the inflammatory response, the DILI model group had substantially higher levels of AST, ALT, and MDA. P < 0.05)  . CCK8 as < 0.05) . As showIn the animal model experiment, the mRNA level and the expressions of Nrf2 which were proteins in the liver tissue of mice in each group were detected. When compared with the normal group, the mRNA level and the expressions of Nrf2 and ARE proteins in the DILI model group were significantly lower than those in the normal group, and the protein expression and mRNA level improved as the concentration of SSBE increased .In Chinese herbal formulae, network pharmacology can discover potentially active components, targets, and pharmacological processes of complicated substances , 25. A sδ-amyrone, was identified as the efficacious component with hepatoprotective action by boosting Nrf2 antioxidant defense and inhibiting the NF-B inflammatory response [P38, thus activating AREs and upregulating HO-1 to resist oxidative stress injury of human hepatocytes to LO2 caused by H2O2 [Traditional Chinese medicine plays a certain role in promoting regeneration and repair of drug-induced liver injury and has unique advantages in DILI treatment. It can play a protective role with multiple components and targets. it has fewer side effects than chemotherapy. SSBE mainly contains flavonoids, cyanosides, alkaloids, triterpenes, sterols, and other compounds, which have anti-inflammatory, liver protection, antirenal fibrosis, antitumor, antioxidant, and other activities. The multicomponent and multitarget network showed the antidrug liver injury effect of SSBE. It shows that there is an interrelation between the constituent targets of SSBE, which is a complex interactive network rather than acting alone. Wang et al. discovered that SSBE had a clear influence on AST, ALT, ALP, GGT/-GT, DBiL, and TBiL levels, as well as increases in ALB and TP levels in serum and ANIT-induced bile flow slowdown for liver damage. The major component of SSBE, response . Quercetresponse . Hyperin by H2O2 .ß-sitosterol, and DFV make up the majority of the chemical makeup of SSBE. Biological activities such as cellular functions, metabolic processes, and oxidative stress responses are among the possible active pathways of SSBE, according to GO word enrichment analysis and KEGG target pathway analysis. Organelles, membranes, and cytoplasm are among the cellular components, as are tiny molecules, cations, and metal ions. It involves a variety of substances such as signal molecules and transcription factors, which is a complex process. And we predicted 7 core targets and the Nrf2 signaling pathway as the main pharmacological mechanisms of SSBE against DILI. The core target of the PPI network is MMP9, SSP1, MARK14, IL-10, IFNG, and IL-1B, which is closely related to oxidative stress [By network pharmacology analysis and experimental validation, this study looked at the pharmacological mechanisms of SSBE in DILI. According to our findings, luteolin, quercetin, isorhamnetin, e stress –18. Nrf2Sedum sarmentosum had an effect on improving liver injury. Furthermore, the mRNA level and the expressions of Nrf2 and ARE proteins in liver tissue of mice in the DILI model made by APAP were significantly lower than those in the normal group. As the concentration of SSBE increased, the protein expression and mRNA level increased as the concentration of SSBE improved (The Nrf2 signaling pathway is a key endogenous antioxidant stress mechanism, and reactive oxygen species and the oxidative stress response are crucial in the development of liver disorders. Oxidative stress may harm a range of cellular components such as DNA, proteins, and lipids, impacting a variety of organs and systems throughout the body. With numerous targets, the Nrf2 pathway can have a favourable preventive function in the incidence and development of liver disease and postpone the blockage of liver disease progression. Therefore, it is necessary to actively and deeply study the mechanism of the Nrf2 pathway in liver diseases. Our results showed that SSBE was involved in the process of drug-resistant liver injury through the Nrf2 signaling pathway. We also found that SSBE reduced significantly pathological injury and inflammation in the liver. SSBE reduced liver cell necrosis, rescued the cell viability of liver cells, and inhibited the autophagy of liver cells, in which the active compounds of improved . We can Furthermore, KEGG target pathways reveal that possible signaling pathways such as the IL-17 signaling route, MAPK signaling pathway, PI3K-Akt signaling pathway, NF-kappa B signaling network, and PPAR signaling pathway are linked to the creation and progression of liver damage. The key targets of SSBE antiliver injury active components are distributed in different pathways, which can coordinate each pathway to play an antiliver injury role. These pathways can all serve as directions for further research in the future.There were some flaws in this study, as well as certain areas that need more research. These include the need for a more thorough investigation of protein interactions and predicted monomers, as well as an increase in the DILI chip's sample size from the database. Despite this, the possibility of investigating DILI therapy with traditional SSBE prescription based on network pharmacology was verified in this investigation. This reduces the amount of time and money spent on exploration as well as the expense of scientific study. This research suggests that more research on monomers and associated transcription factors with potential therapeutic effects on DILI might be done in the future. This might pave the way for a breakthrough in the treatment of DILI with traditional Chinese medicine.In summary, the network pharmacology and in vivo experimental results in this study indicated that SSBE reduced the inflammatory response caused by APAP in a DLII model and protected liver tissue. 61 potentially active components of SSBE in DILI treatment were discovered using network pharmacology techniques and drug toxicity risk assessment. Furthermore, the Nrf2 signaling pathway was found to play an important regulatory role in the mechanism of DILI in the treatment of SSBE.


Milk is an excellent source of nutrients for humans. Therefore, in order to enhance the quality and production of milk in cattle, it is interesting to examine the underlying mechanisms. A number of new investigations and research have found that, circRNA; a specific class of non-coding RNAs, is linked with the development of mammary gland and lactation. In the present study, genome wide identification and expression of the circRNAs in mammary epithelial cells of two distinct cattle breeds viz Jersey and Kashmiri at peak lactation was conducted. We reported 1554 and 1286 circRNA in Jersey and Kashmiri cattle, respectively, with 21 circRNAs being differentially expressed in the two breeds. The developmental genes of the established differentially expressed circRNAs were found to be largely enriched in antioxidant activity, progesterone, estradiol, lipid, growth hormone, and drug response. Certain pathways like MAPK, IP3K and immune response pathways were found significantly enriched in KEGG analysis. These results add to our understanding of the controlling mechanisms connected with the lactation process, as well as the function of circRNAs in bovine milk synthesis. Additionally, the comparative analysis of differentially expressed circRNAs showed significant conservation across different species. Milk is a highly nutritious liquid produced in mammary glands of mammals to support and feed their new born babies during their initial months of life. Milk is secreted by epithelial alveolar cells that are organized into isolated pockets called lobules. Nutritional value of milk is very high and contains almost every nutrient that our body requires. Cow’s milk is a rich and economical source of proteins (casein and whey proteins), casein alone accounts for approximately 80% of milk protein. It is also a good source of fat, carbohydrates, hormones, vitamins and minerals .As milk production is an economic trait in dairy animals, an increase in the milk synthesis is a desirable goal for the dairy industry . A deepeCircular RNAs (circRNAs), discovered more than three decades ago , have gai.e., high lactating Jersey and low lactating Kashmiri cattle. We describe the type, length and chromosomal distribution of the circRNAs and their functional enrichment analysis with the aim to understand their role in lactation of dairy cows.Accounting the universal expression characteristics of circRNAs, these molecules have been found to be involved in the milk synthesis as well as mammary gland development of various species, like sheep , cattle As a global trend, we concluded that the larger replicate numbers increase the power of differential expression analysis, except for low-expressed genes. We utilized RnaSeqSampleSize v2.0.0  for the SRR6324372), including one sample from each breed as technical replicate. The details of experiments followed are mentioned below.In this study, a total of eight samples (four Jersey and four Kashmiri cows) at the peak lactation was collected from our published data in the SRA database (accession ID. Clean and fresh milk samples (1.5 l/cow) were collected from healthy and unrelated cows of each cattle breed by hand milking. The animals were kept in free stall sheds, provided with a complete healthy nutritious and balanced diet, and had unrestricted access to clean water and grass. The detailed sample collection including sample preparation and RNA sequencing were explained in the previous study .A total of eight samples (4 Jersey and 4 Kashmiri) at peak stage of lactation were selected for the identification of circRNAs mediating different milk traits in both breeds. The FASTQC program v0.11.2 was used to perform quality control on all raw reads . The CutTwo-way approach was employed for the identification of differentially expressed (DE) circRNAs. At first seekCRIT tool v1.0.0.b  were runP-value <0.05 and logFC >1 threshold. The final set of DE circRNAs contains circRNAs common between seekCRIT dataset and edgeR dataset. CircRNAs whose log2FC >1 and p-value <0.05 were considered as DE in two breeds. To identify the conserved circRNAs, all DE circRNAs were compared against the known circRNAs from Homo sapiens, Mus musculus, Sus scrofa, Gallus gallus and Canis lupus.Based on the expression of circular - RNAs, the quasi-likelihood F-test method of the edgeR program  was usedCt−ΔΔ method .The circRNA enriched samples with depleted ribosomal RNA were reverse transcribed into cDNA with random hexamer primers using iScript RT Supermix Kit (Bio-Rad) following the manufacturer’s procedure. cDNA synthesized using specific divergent sets of primers were subjected to RT-qPCR. Expermient was performed in triplicate for RT-qPCR. A total reaction of 20 µl was made with 0.5 µl (10 µM) of each forward and backward divergent primer, 10 µl of SYBR Green PCR master mix (Roche), 1 µl (70 ng/  µl) of cDNA and 8 µl of nuclease free water. Glyceraldehyde-3-Phosphate (GAPDH) was used as an internal control. The experimental validation was selected for the group of five circRNAs, including circ_03409, circ_87295, circ_03409a, circ_10119b and circ_25279. For the selected circRNAs, five divergent primers were designed using Primer3 Plus software. The details of the primers are given in . The relt method .Bos taurus gene-set were used as background  and pathways enrichment analysis. The enrichment analysis was performed utilizing KOBAS 3 server . Bos tauckground .An average of 2986 circRNAs were reported from the samples analyzed, majority of which were ecircRNAs (≈ 72.16%/2154) whereas ciRNAs were less common (≈ 27.83%/831). Most of the circRNAs were distributed on chromosome (chr) 5 (≈ 20.26% /605 and chr 6 (≈7.23%/ 216) while fewest circRNA were distributed on chr 12, 24, 26 and 27. The number of circRNAs obtained from mammary glands of Jersey and Kashmiri were 1554  and 1286  respectively. In Jersey, a higher number of circRNAs were derived from chr 4 (125 circRNAs) followed by chr 3 (105 circRNAs), chr 7 (104) and chr 19 (103). While as in Kashmiri cattle 518 circRNAs were derived from chr 1 followed by chr 2 (333), chr 6 (155), chr 3 (153) and chr 7 .The most abundant and highly expressed circRNAs in both Kashmiri and Jersey cattle were circ“-_003409, circ_010119, circ_081923, circ_006590, circ_021268, circ”-_023981 and circ_013402 which were derived from CSN2, CSN1S1, PAEP, CSN1S2, RPS5, NUPR1 and TPT1 respectively. A total of 150 circRNAs were obtained from casein protein that includes 87 CSN1S1, 31 CSN1S2, 29 CSN2, and 03 CSN3 circRNAs. Two circRNAs, LALBA and BLG were derived from whey protein coding genes.P-value <0.05, with 14 up-regulated circRNAs in Kashmiri cattle and 7 up-regulated circRNAs in Jersey. All differentially expressed circRNAs were found to be exonic, only one circ_60634 derived from RHBDD2 gene  present on chr 25 and upregulated in Jersey breed was intronic. The DE circRNA were compared against the known circRNAs from Homo sapiens, Mus musculus, Sus scrofa, Gallus gallus and Canis lupus. A total of 8 out of 21 DE circRNAs were mapped with the threshold of e-value <0.00001 and similarity >85%  and 02 DE circRNAs from jersey cattle (circ_87295 and circ_25279). The different suffixes indicate the circRNAs derived from same genes. The findings demonstrated that the head-to-tail backsplicing of circRNAs was consistent with results of RNA-Seq, verifying the reliability and integrity of the circRNAs discovered in the analysis.There were 21 differentially expressed circRNAs between Kashmiri and Jersey breeds with log2FC >1 and ity >85% . Seven Dity >85% . The cirWe analyzed the cluster analysis of gene ontology (GO) and KEGG pathway enrichment of differentially expressed circRNAs to further investigate the relationship between circRNAs and regulation of gene expression. The parental genes for 21 differentially expressed circRNAs were primarily enriched in 09 biological processes including response to hormones like progesterone, estradiol, dehydroepiandrosterone,11- deoxycorticosterone and growth hormone, response to lipids, drugs, cellular oxidant detoxification, 01 molecular function showing antioxidant activity and 05 cellular components showing strong enrichment for terms related to golgi lumen, extracellular region, golgi apparatus, extracellular space and endomembrane system. The KEGG pathway revealed that the developmental genes of circRNAs were concentrated in a variety of pathways including intestinal immune network for IgA production, pertussis pathway, prolactin pathway, salmonella infection, Toll like receptor pathway, ECM receptor interaction, MAPK signalling pathway and PI3K-Akt signaling pathway, NF kappa B signalling pathway, and cell adhesion molecules  and S5.Among various DE circRNAs, RT-qPCR of five differentially expressed circRNA from Kashmiri and Jersey breed was performed to confirm the reliability as well as accuracy and reliability of RNA-Seq data. From Kashmiri cattle we selected 03 DE circRNAs , deoxycorticosterone, oxygen containing compounds, drug response and cellular oxygen detoxification. Previous studies have already shown that progesterone alone with prolactin has a role in generating milk alveoli cells which secrete milk during lactation . FurtherκB signalling pathway, the intestinal immune network for IgA development and production and Toll-like receptor pathway were related to immunity. On the basis of the study done, circRNAs identified in our study might have the capability to act as regulators of milk synthesis and stimulate growth and development of mammary glands.The host genes of circRNAs were abundantly expressed in the prolactin pathway, Ras, Mitogen Activated Protein Kinase signalling pathways (MAPK), Phosphatidylinositol 3-Kinase (PI3K) and Akt Kinase signalling pathway in KEGG pathway enrichment. In the epithelial cells of mammary gland, the PI3K-Akt signalling pathway as well as MAPK through Stat5 and mTOR and also prolactin-mediated activation of the Jak2-Stat5 pathway, regulate the majority of metabolic mechanisms and biological pathways including transcriptional activation of genes for proliferation as well as cell cycle and synthesis of milk protein . Other s2448 in milk protein synthesis  compared to Kashmiri cattle . The results also reveal that among the total circRNAs identified, 21 circRNAs were expressed differentially between Jersey and Kashmiri cattle. Most of the DE circRNAs are derived from casein genes. This signifies the role of circRNAs in lactation of animals. The chromosomal distrubition revealed most of circRNAs were assembled on chr 4 in Jersey, while as in Kashmiri cattle most circRNAs were gathered on chr 1. These results suggest breed specific distribution and variation of circRNAs in cattle. The pathway analysis shows that most enriched pathways like MAPK, IP3K and prolactin pathway are involved in milk synthesis. These findings add to our understanding of the functional role of circRNAs in the production of bovine milk. Further anlysis revealed that identified circRNAs were highly conserved across various species like 10.7717/peerj.13029/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.13029/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.13029/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.13029/supp-4Supplemental Information 4Click here for additional data file.10.7717/peerj.13029/supp-5Supplemental Information 5Click here for additional data file.10.7717/peerj.13029/supp-6Supplemental Information 6Click here for additional data file.10.7717/peerj.13029/supp-7Supplemental Information 7Click here for additional data file.


Acute myocardial infarction (AMI) is a common disease leading threat to human health around the world. Here we aimed to explore new biomarkers and potential therapeutic targets in AMI through adopting integrated bioinformatics tools.The gene expression Omnibus (GEO) database was used to obtain genes data of AMI and no-AMI whole blood. Furthermore, differentially expressed genes (DEGs) were screened using the “Limma” package in R 3.6.1 software. Functional and pathway enrichment analyses of DEGs were performed via “Bioconductor” and “GOplot” package in R 3.6.1 software. In order to screen hub DEGs, the STRING version 11.0 database, Cytoscape and molecular complex detection (MCODE) were applied. Correlation among the hub DEGs was evaluated using Pearson's correlation analysis.By performing DEGs analysis, 289 upregulated and 62 downregulated DEGs were successfully identified from GSE66360, respectively. And they were mainly enriched in the terms of neutrophil activation, immune response, cytokine, nuclear factor kappa-B (NF-κB) signaling pathway, IL-17 signaling pathway, and tumor necrosis factor (TNF) signaling pathway. Based on the data of protein–protein interaction (PPI), the top 10 hub genes were ranked, including interleukin-8 (CXCL8), TNF, N-formyl peptide receptor 2 (FPR2), growth-regulated alpha protein (CXCL1), transcription factor AP-1 (JUN), interleukin-1 beta (IL1B), platelet basic protein (PPBP), matrix metalloproteinase-9 (MMP9), toll-like receptor 2 (TLR2), and high affinity immunoglobulin epsilon receptor subunit gamma (FCER1G). What's more, the results of correlation analysis demonstrated that there was positive correlation between the 10 hub DEGs.Ten DEGs were identified as potential candidate diagnostic biomarkers for patients with AMI in present study. However, further experiments are needed to confirm the functional pathways and hub genes associated with AMI. The risk factors associated with AMI are mainly caused by the interaction between genetic and environmental factors, including diabetes mellitus, hypercholesterolemia, obesity, and hypertension. Therefore, an in-depth understanding of AMI is great significance.According to the report of World Health Organization (WHO), cardiovascular diseases (CVDs) are modern epidemic, and it accounts for most noncommunicable diseases death. However, these treatment methods may cause some adverse effects. For example, combination of various antithrombotic drugs will increase the incidence of bleeding complications. Furthermore, compared with thrombolytic therapy, primary PCI seems to be more effective for acute ST-segment elevation myocardial infarction. Overall, in order for AMI patients to be treated as soon as possible, early diagnosis is essential.At present, the key treatment strategies for AMI include reducing myocardial oxygen demand, pharmacotherapy, thrombolysis, and percutaneous coronary intervention. Although CK-MB and cTn are 2 major biomarkers for early diagnosis of AMI, the false positive rates and the risks associated with detecting a single biomarker have also become troubling problems.,15 In order to better assist in the diagnosis of AMI, it is also necessary to explore new biomarkers in the blood.In recent years, several serum biomarkers were widely used to identify AMI patients, such as creatine kinase myocardial band (CK-MB), cardiac troponin (cTn) isoforms I and T. Zou et al clarified that zinc finger protein 566 (ZNF566), zinc finger homeobox protein 3 (ZFHX3), PDZK1-interacting protein 1 (PDZK1IP1), and pituitary homeobox 2 (PITX2) are significantly associated with new biomarkers of atrial fibrillation-related stroke through the bioinformatics analysis of gene expression Omnibus (GEO) dataset. Besides, Qian et al analyzed the microarray data of GSE59867 and found that proto-oncogene c-Fos (FOS), integrin alpha-IIb (ITGA2B), thrombospondin-1 (THBS1), and interleukin-8 (CXCL8) may play a vital role in development of heart failure after acute ST-segment elevation myocardial infarction. However, there is still a large amount of bioinformatics data related to AMI to be explored.With the advancement of high-throughput gene chip and transcriptome sequencing technology, integrated bioinformatics provides an effective tool for discovering valuable new biomarker targets. Using bioinformatics strategy, Shen et al found that fibronectin (FN1), neuromedin-U (NMU), chordin-like protein (CHRDL1), guanine nucleotide-binding protein subunit alpha-14 (GNA14), vasopressin V1a receptor (AVPR1A), guanine nucleotide-binding protein G(i) subunit alpha-1 (GNAI1), and integrin alpha-2 (ITGA2) may be potential biomarkers for the treatment of thyroid cancer.In this study, the microarray data of GSE66360 were applied to identify the differentially expressed genes (DEGs) between AMI and non-AMI whole blood utilizing an integrated bioinformatics. Afterwards, in order to analyze the main biological functions regulated by DEGs, enrichment analysis was conducted. Meanwhile, through correlation analysis, protein–protein interaction (PPI) and modular analysis of upregulated DEGs, key genes related to the diagnosis and treatment of AMI were identified. The detailed workflow is shown in Figure 22.1https://www.ncbi.nlm.nih.gov/gds/), were used to screen DEGs in AMI. Ninety-nine specimens (49 AMI samples and 50 control samples) were included in the data series of GSE66360. The blood collection time was shortly after the AMI patients arrived at the acute care facility, and their arterial blood was collected. In addition, the age of the research population was 18 to 80 years old, both genders. And the gene expression was detected by GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array.The gene expression profile data of GSE66360, downloaded from GEO database  function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses.,28 Adjust P-value < .05 was regarded as the cut-off criteria.The statistically significant DEGs were further analyzed in R 3.6.1 software with the “Bioconductor” and “GOplot” package,2.4https://string-db.org/) was used. At the same time, the confidence score was set as >0.7 and species limited to “Homo sapiens.” Subsequently, the Cytoscape version 3.7.1 (http://www.cytoscape.org/) plug-in the molecular complex detection (MCODE) was used to identify the key modules from PPI network. And modules with MCODE score >4.5 were presented.In order to obtain directly or indirectly interacting proteins related to DEGs, the STRING version 11.0 database (2.5http://www.ehbio.com/ImageGP/) to show the difference in expression of hub genes in AMI and no-AMI whole blood.By using the boxplot tool in ImageGP  (Supplementary file 1). As shown in Figure GSE66360 was selected and underwent DEGs analysis using “Limma” package in R 3.6.1 software. Three hundred fifty-one DEGs were identified either up- or downregulated in all, including 289 up- and 62 downregulated genes . Figure r), the absolute value of 0 to 0.30, 0.30 to 0.50, 0.50 to 0.70, and 0.70 to 1.00 represented “poor” correlation, “fair” or “moderate” correlation, “good” correlation, and “strong” correlation, respectively. Furthermore, “r = 0” indicated “no correlation at all” and “r = 1.00” represented “perfect correlation.” As listed in Figure r = 0.96). However, XIST had highly negative correlation with NFIL3 (r = −0.28). In addition, a stronger negative correlation existed between PTX3 and CRTAM (r = −0.12). TREM1 and S100A12 also had relatively obvious positive correlation with interleukin-1 beta (IL1B) (r = 0.74 and r = 0.75).The top 10 up- or downregulated DEGs were selected for correlation analysis  were extracted from the PPI network. Module 1 contained 39 gene nodes, including FPR2, CXCL1, PPBP, CXCL8, and CC motif chemokine 20 (CCL20) with 279 edges Fig. B. Module3.5r = 0.81).The interaction network between the 10 hub DEGs was constructed by Cytoscape version 3.7.1 plug-in “cytoHubba” based on their degree , respectively. To obtain a deeper insight into the biological functions of DEGs, GO annotation and KEGG pathway enrichment analyses were performed. Based on the data of PPI, the top 10 hub genes were ranked, including CXCL8, TNF, FPR2, CXCL1, JUN, IL1B, PPBP, MMP9, TLR2, and FCER1G. Notably, 6 hub DEGs namely CXCL8, FPR2, CXCL1, PPBP, MMP9, and FCER1G were found in module 1. Two hub DEGs of JUN and IL1B were enriched in module 2. However, TNF and TLR2 were not shown in significant modules.Despite advances in adjunctive pharmacotherapy and revascularization strategies of AMI, it is still the leading threat to human health in CVDs.r = 0.81).Accordingly, the expression level and correlation of 10 hub genes were analyzed. The 10 hub genes expression levels were markedly upregulated in AMI whole blood compared to those in non-AMI whole blood. Obviously, TLR2 had a notable positive correlation with FCER1G  plays a crucial role in inflammatory process by promoting the expression of pro-inflammatory factors. In this study, 4 hub DEGs, namely IL1B, CXCL1, CXCL8, and TNF, enriched in NF-κB signaling pathway.GO analysis showed that the DEGs were enriched in neutrophil activation, immune response, cytokine, secretory granule, and receptor ligand activity. KEGG pathway annotation analysis revealed that DEGs were mainly involved in NF-κB signaling pathway, IL-17 signaling pathway, and TNF signaling pathway. However, the NF-κB signaling pathway was the most significantly enriched pathway. NF-κB was regarded as a crucial transcription factor in plaque destabilization.,36 And IL-1B has been considered as an important pro-inflammatory cytokine.,38 Previous animal studies have demonstrated that IL-1 is linked with the development mechanisms of atherosclerosis. In arterial lesions, IL-1 stimulates vascular smooth muscle cells through transforming adherence molecules and growth factor-β, leading to coagulation and thrombosis. What's more, levels of IL-1B are increased in patients with AMI. Furthermore, the IL1B-3737T polymorphism modifies a binding site of the anti-inflammatory p50 subunit, which belongs to the NF-κB transcription factor family. IL-1B also induces the synthesis of inflammatory mediators associated with atherosclerotic plaque formation, such as C-reactive protein and interleukin-6. Activation of the NLRP3-inflammasome pathway and production of IL-1B after cellular damage caused by infarct is a key process in AMI. Veltman predicted IL-1B activation kinetics based on Ordinary Differential Equation model, and the study found that by attenuating the production of IL-1B proline, more upstream regulation was more potent in attenuating active IL-1B production than direct inhibition of the NLRP3-inflammasome.IL-1B, which is mainly produced by the activation of innate immune cells, is one of the classical interleukin-1 (IL-1) cytokines.–46 Pordel et al conducted that the plasma levels of CXCL1 chemokine were notably increased in AMI patients compared with healthy participants. And the plasma levels of CXCL1 were significantly correlated with TT genotype of TRAF3IP2 (rs33980500) in myocardial infarction patients. Whereas, CXCL8 may mediate the recruitment and activation of neutrophils, and promote the formation of new blood vessels. In addition, CXCL8 is upregulated in the infarct area inducing polymorphonuclear leukocyte infiltration. It was reported that CXCL1 and CXCL8 participate in pathogenic processes of several cancers, such as colorectal cancer, non-small cell lung cancer, and gastric carcinoma.–52Recent evidence indicates that CXC chemokine is not only the key to inflammation, but also mediates the recruitment of inflammatory leukocytes. TNF can promote inflammation damage, inducing the synthesis of chemokines, and adhesion molecules in the infarcted myocardium. It is worth noting that TNF signaling gives rise to cytoprotective signals that delay or prevent the development of cardiac myocyte apoptosis. Previous research suggested some plasma-induced cardiac biomarkers, such as TNF, IL6, MMP9, and cell-adhesion molecules could be valuable in the diagnosis and prognosis of AMI. The NF-κB transcriptional activation pathway is regarded as the “master regulator” of inflammation. Activated NF-κB increases the expression of TNF-α, IL-6, and IL-1β, thereby activating collagen deposition and myocardial fibrosis, leading to heart failure and myocardial remodeling.,59 Similarly, FPR2, JUN, PPBP, MMP9, TLR2, and FCER1G expression has significant diagnosis value in AMI patients and acts as potential targets for AMI-targeted therapy.–63 Consistent with these results, the present study indicated that IL1B, CXCL1, CXCL8, TNF, FPR2, JUN, PPBP, MMP9, TLR2, and FCER1G were upregulated hub DEGs.In the experimental model of animal myocardial infarction, expression of TNF is markedly and consistently increased.Some limitations exist in this study. First, the sample size of the microarray data set used in this study is relatively small. Second, although we have identified some enriched pathways and core DEGs, the hierarchical processes between them have not yet been fully elucidated. However, in order to identify potential biomarkers associated with AMI, further studies with larger sample sizes are still warranted.5In conclusion, the present study identified IL1B, CXCL1, CXCL8, TNF, FPR2, JUN, PPBP, MMP9, TLR2, and FCER1 as key genes in the pathogenesis of AMI by integrated analysis of microarray datasets. The results of this study further provide useful evidence for investigation into molecular mechanisms, selection of biomarkers, and treatment targets exploration of AMI. However, further vitro and in vivo analyses experiments are needed to confirm the functional pathways and hub genes associated with AMI.GSY and WJR conceived and designed the study. ZW, LXK, and JSS collected the data. LYY, ZJY, LJL, and WHJ performed the data analysis, and GSY wrote the manuscript. All authors were responsible for reviewing data. All authors read and approved the final manuscript.Conceptualization: Siyu Guo, Jiarui Wu.Data curation: Jiarui Wu, Wei Zhou, Jialin Li, Haojia Wang.Formal analysis: Wei Zhou.Methodology: Xinkui Liu.Validation: Jingyuan Zhang, Shanshan Jia.Visualization: Jingyuan Zhang, Shanshan Jia.Writing – original draft: Siyu Guo.Writing – review & editing: Yingying Liu.