The T-2 toxin-related genes were obtained from the GeneCards database. An online tool (https://www.bioinformatics.com.cn) was used for enrichment analysis. Hematoxylin and eosin (H&E) staining was used to observe damaged articular cartilage, and immunohistochemical (IHC) staining was used to validate differentially expressed proteins. The H&E staining shows the number of cells decreased significantly, and the arrangement of chondrocytes became disordered in the T-2 toxin group. RNA-seq analysis identified 195 upregulated and 89 downregulated mRNAs in the T-2 toxin group. The top immune-related biological processes (Gene Ontology) were regulation of hormone secretion, regulation of peptide hormone secretion, and regulation of transcription involved in cell fate commitment. KEGG pathway enrichment analysis revealed that the IL-17 and tumor necrosis factor signaling pathways were significantly expressed, and the IL-17 signaling pathway was also identified in the enrichment analysis of T-2 toxin-related genes. Also, Mmp3, Tnf, Mapk10, Ccl11, Creb5, Cxcl2, and Cebpb were significantly enriched in the two pathways. The immunohistochemical staining showed that the levels of Mmp3 and Tnf proteins were significantly increased in the T-2 toxin group, which was consistent with the RNA-seq results. This study revealed the critical roles of IL-17 and TNF signaling pathways in damaged cartilage induced by T-2 toxin.Many studies have shown that ingestion of the T-2 toxin is harmful to articular cartilage. However, the mechanisms underlying damaged articular cartilage induced by T-2 toxin have not been elucidated. Twenty-four SD rats were randomly divided into T-2 toxin and control groups. In the control group, the 12 rats were administered 4% absolute ethanol by gavage, and in the T-2 toxin group, the 12 rats were administered T-2 toxin  by gavage. After the rats were sacrificed, the knee joints were collected, and RNA was extracted using TRIzol reagent for RNA sequencing (RNA-seq). Differentially expressed mRNA was identified based on  Fusarium. It is widely distributed in nature and mainly contaminates cereals and grains, such as wheat, barley, oats, rice, maize, and sorghum, and is harmful to humans and poultry. At present, it is believed that the T-2 toxin can cause a variety of human and animal diseases such as alimentary toxic aleukia and Kashin–Beck disease (KBD)  . The Sovae Bilai . The ratnormally . In 1990normally . In 1995normally . TherefoAccording to the food mycotoxin contamination hypothesis, the grain harvest season is rainy, and thus, fungi contaminate the grain during harvesting, drying, and storage. As healthy children in the KBD areas consumed fungus-contaminated grains, the T-2 toxin selectively damaged the developing articular cartilage, resulting in damage to the membrane system of chondrocytes, inhibition of DNA synthesis, and induction of chondrocytes apoptosis . When chA previous study examined the DNA methylation dynamics of human chondrocytes treated with T-2 toxin and found that the dysfunction of the MAPK pathway was involved in the damaged human chondrocyte . Howeverad libitum. The feed and ultrapure water were kept for 1 week, and after weighing, the rats were randomly divided into the control and T-2 toxin groups. The 12 rats were administered 4% absolute ethanol by gavage in the control group, and the 12 rats were administered T-2 toxin  by gavage in the T-2 toxin group  were purchased from the Henan Provincial Laboratory Animal Center. They were maintained in a spacious animal house and fed a standard mouse diet and water in group . During After the rats were sacrificed, the knee joints were collected, fixed in 4% paraformaldehyde for 24 h, and decalcified with 10% EDTA for 1 month. The decalcified knee joints were embedded in paraffin and sectioned. The tissue sections were stained with H&E. Finally, the stained tissue sections were dehydrated and sealed with neutral gum, and the collected images were observed under a microscope.260/280 and OD260/230 were detected using a Nanodrop 2000 spectrophotometer . Then, the degree of RNA degradation was detected by agarose gel electrophoresis and an Agilent 2100 Bioanalyzer .RNA was extracted using the TRIzol reagent  for RNA sequencing (RNA-seq). After collecting the RNA samples, the concentration was accurately quantified using Qubit, and the ratios of ODAn appropriate amount of total RNA was used to remove rRNA, and the RNA was purified and fragmented. Reverse transcription was used to synthesize the first strand cDNA and second strand cDNA. Next, a single adenylate “A” was added to the 3′ ends on both sides of the modified double-stranded cDNA to prevent self-ligation of the flat ends between the cDNA fragments. Ligation buffer and a double-stranded sequencing linker were added to the aforementioned reaction system, and the Illumina sequencing linker was connected to both ends of the DNA library using T4 DNA ligase . The Agencourt SPRI select Nucleic Acid Fragment Screening kit was used to screen the fragment size while the library was being purified, and a two-step screening method was used to select the original library with a fragment peak of 300 bp. In a 50-μl reaction system, high fidelity polymerase was used to amplify the original library to ensure a sufficient total number of libraries. The constructed library was qualified using Qubit and an Agilent 2100 Bioanalyzer  and then sequenced using an Illumina HiSeq 2500 System .p-values <0.05 and | log2 (fold change) | > 1 were considered differentially expressed mRNAs, log2 (fold change) > 1 was labeled as an upregulated mRNA, and log2 (fold change) < −1 was labeled as a downregulated mRNA.Deseq2 software was used to analyze differentially expressed mRNA in the T-2 toxin and control groups. https://www.bioinformatics.com.cn) was used to perform GO and KEGG enrichment analyses (Gene Ontology (GO) is an international standard classification system for gene functions. Through GO enrichment analysis, significantly differentially expressed genes were classified according to cellular components, molecular functions, and biological processes. KEGG (Kyoto Encyclopedia of Genes and Genomes) is a vital public database of classical pathways for experimental verification. Pathway enrichment analysis takes the KEGG pathway as the unit and applies a hypergeometric test to determine which pathways are significantly enriched in a specific gene set compared to the whole genome background. The analysis was based on differentially expressed genes marked as up and down, and an online tool  database. The relevance scores of >5 were used as the screening criteria (https://www.bioinformatics.com.cn).The T-2 toxin-related genes were obtained from the GeneCards (criteria . KEGG paThe dewaxed and rehydrated tissue sections were placed in a repair box filled with citric acid (pH 6.0) antigen repair buffer. Next, 3% hydrogen peroxide was used to inhibit the endogenous peroxidase activity. After blocking with 3% bovine serum albumin for 30 min at room temperature, the slices were incubated with primary anti-Mmp and anti-Tnf antibodies  overnight at 4°C and then incubated with horseradish peroxidase–conjugated secondary antibodies for 50 min at room temperature. Next, the slices were washed with phosphate buffered saline, developed, and sealed after re-staining the nuclei with hematoxylin. Semi-quantitative analysis of the acquired images was performed using Image Pro Plus 6.0.The H&E staining results show the histopathologic changes of rat cartilage tissues in each group . In the Analyses of differentially expressed mRNAs showed that 284 significant differentially expressed mRNAs were identified in the T-2 group compared to the control group. Of these, 195 mRNAs were significantly upregulated, and 89 mRNAs were downregulated . ClusterThe GO results were divided into three categories . As shown in The KEGG pathway enrichment analysis identified significant pathways. According to the enrichment score, the top 10 significant pathways were the adipocytokine signaling pathway, the PPAR signaling pathway, the IL-17 signaling pathway, alcoholic liver disease, protein digestion and absorption, proximal tubule bicarbonate reclamation, insulin resistance, the TNF signaling pathway, type II diabetes mellitus, and cholesterol metabolism . Figure https://www.bioinformatics.com.cn). The IL-17 signaling pathway was significantly correlated with T-2 toxin .The expression levels of Mmp3 and Tnf proteins were determined by IHC staining. As shown in It is well known that T-2 toxin is harmful to human health. T-2 toxins can cause cytotoxicity, genotoxicity, immunotoxicity, hepatotoxicity, gastrointestinal toxicity, skeletal toxicity, nephrotoxicity, reproductive toxicity, and neurotoxicity. It damages the immune organs, such as the liver, digestive tract, bones, and kidneys, leading to pathological changes and impaired physiological functions of these organs . FurtherOur results demonstrated T-2 toxin-induced pathological changes in the articular cartilage of rats. The KEGG pathway enrichment results showed that IL-17 and TNF signaling pathways were related to inflammation. In addition, T-2 toxin-related genes obtained from GeneCards were used for enrichment analysis, and it was found that the IL-17 signaling pathway was also significantly expressed. These results indicated that the inflammatory reaction may play an important role in the damaged joint cartilage of rats induced by T-2 toxin. In addition, Mmp3, Tnf, Mapk10, Ccl11, Creb5, Cxcl2, and Cebpb were significantly enriched in the IL-17 and TNF signaling pathways.Interleukin-17 (IL-17) is a member of a new family of inflammatory cytokines . IL-17 iIn addition, the IHC analysis results showed that the expression levels of Mmp3 and Tnf proteins in the T-2 toxin group were significantly higher than those in the control group. This indicated that T-2 toxin can activate the expression of Mmp3 and Tnf, which is consistent with the RNA-seq results. Generally, TNF first binds to its receptors and then transmits molecular signals for biological functions such as inflammation and cell death. It has also been shown that T-2 toxin can increase the level of TNF . TNF canIn this study, RNA-seq was used to explore the mechanism of damaged cartilage induced by T-2 toxin, which may enhance the inflammatory response by activating the IL-17 and TNF signaling pathways, resulting in an abnormal inflammatory response in chondrocytes and cartilage injury. However, several limitations need to be addressed. First, an animal model was used to study the inflammatory response involved in damaged articular cartilage, and this needs to be validated in the cartilage of KBD patients. Second, further molecular experiments should be performed to validate the IL-17 and TNF signaling pathways. Despite these limitations, our results provided new insight for further understanding the mechanism of damaged articular cartilage induced by T-2 toxin.


Hedyotis diffusa Willd (HDW) and its main components have been widely used to treat a variety of tumors and inflammatory diseases. The present study utilized a network pharmacology approach, microarray data analysis and molecular docking to predict the key active ingredients and mechanisms of HDW against RA. Eleven active ingredients in HDW and 180 potential anti-RA targets were identified. The ingredients-targets-RA network showed that stigmasterol, beta-sitosterol, quercetin, kaempferol, and 2-methoxy-3-methyl-9,10-anthraquinone were key components for RA treatment. KEGG pathway results revealed that the 180 potential targets were inflammatory-related pathways with predominant enrichment of the AGE-RAGE, TNF, IL17, and PI3K-Akt signaling pathways. Screened through the PPI network and with Cytoscape software, RELA, TNF, IL6, TP53, MAPK1, AKT1, IL10, and ESR1 were identified as the hub targets in the HDW for RA treatment. Molecular docking was used to identify the binding of 5 key components and the 8 related-RA hub targets. Moreover, the results of network pharmacology were verified by vitro experiments. HDW inhibits cell proliferation in MH7A cells in a dose and time-dependent manner. RT-qPCR and WB results suggest that HDW may affect hub targets through PI3K/AKT signaling pathway, thereby exerting anti-RA effect. This study provides evidence for a clinical effect of HDW on RA and a research basis for further investigation into the active ingredients and mechanisms of HDW against RA.Rheumatoid arthritis (RA) is a chronic, systemic, autoimmune disease that may lead to joint damage, deformity, and disability, if not treated effectively.  RA occurs in 0.5 to 1.0% of the population worldwide3. In China, up to 5 million people suffer from pain and recurrence of RA4. Due to its high prevalence, debilitating nature, and disabling consequences, RA has generated a substantial clinical, economic, and social burden. The pathogenesis of RA is complicated, incompletely understood, and considered to be mediated by various mechanisms. At present, the typical events in the pathogenesis of RA are considered to be hyperplasia of cells in the synovial membrane consisting of synovial fibroblasts, macrophages, and lymphocytes5. Some studies have shown that fibroblast-like synoviocytes (FLS) are the dominant cell type and are considered to play a critical role in the pathogenesis of RA7. Disease-modifying antirheumatic drugs (DMARDs) and non-steroidal anti-inflammatory medications (NSAIDs) have been widely used in RA therapy8. Conventional DMARDs, including hydroxychloroquine, methotrexate (MTX), sulfasalazine, and leflunomide, have been approved by the US Food and Drug Administration (FDA) as a first-line therapy for RA patients9; however, there are no truly effective pharmacotherapies for the treatment of RA. Most of these drugs have frequent side effects, including gastrointestinal irritation, kidney injury, and cardiovascular risk10. There is therefore an urgent need for safe and effective medical treatments for RA.Rheumatoid arthritis (RA), one of the most common autoimmune diseases, is characterized by invasive joint synovitis, pannus formation, deterioration of joints, and loss of joint functionHedyotis Diffusa Willd (HDW) is a member of the Rubiaceae family of Chinese herbal remedies and is mainly found in the southeastern provinces of China11. Modern pharmacological studies have shown that HDW exhibits multiple pharmacological effects, including anti-tumor, anti-inflammatory, anti-oxidation, anti-fibroblastic, hepatoprotective and immunomodulatory13. It has been widely studied as a potential therapeutic drug for treatment of malignant tumors of the breast, stomach, colon, rectum, cervix, and ovary16. It has also been used in the treatment of inflammation-related diseases, including urinary tract infection, colitis, tonsillitis, appendicitis, pharyngitis, hepatitis, dysentery, diarrhea, and snake bites18. Studies have shown that the certain chemical constituents of HDW  exerted an anti-inflammatory effect on LPS-induced RAW 264.7 macrophages by suppressing the NF-κB and MAPK signaling pathways20. In a complete Freund's adjuvant (CFA)-induced arthritis model in rats, 12 days of oral treatment with HDW extract ursolic acid (50 mg/kg/day) was demonstrated to suppress paw swelling, plasma PGE (2) production, spinal Fos expression, and arthritis-induced mechanical and thermal hyperalgesia21. Zhu et al.23 also revealed that HDW compounds ferulic acid and p-coumaric acid demonstrated an anti-inflammatory effect on collagen-induced arthritis as indicated by decreased numbers of inflammatory cells and reduced levels of IL-1β and TNF-α. Intriguingly, unfractionated HDW had a better therapeutic outcome than ferulic acid, although a poorer one than p-coumaric acid alone. A recent study has shown that HDW effectively suppressed the progression of disease in a collagen-induced arthritis (CIA) model by reducing the arthritis index, by reducing levels of IL-lβ, TNF-α, PGE2, RANKL, OPG, and RANKL/OPG, and by increasing the pain threshold24. Nevertheless, despite extensive studies on the pharmacological effect of HDW, the potential targets and underlying molecular mechanism(s) of HDW in RA remain unclear.In recent years, with the understanding of pathogenesis of RA in Chinese medicine, some progress has been made in the treatment of RA with traditional Chinese medicine. 25. Through network pharmacological analyses, we can investigate TCM systematically, identify the active components, predict potential targets and mechanisms, and provide the opportunity for modernization of TCM26. Therefore, our study aimed to investigate the active ingredients, potential targets, and the underlying mechanism of HDW for the treatment of RA by adopting a network pharmacology approach, molecular docking and cell experiments. The main scheme of this study is presented in Fig. Network pharmacology is a novel approach that combines system network analysis and pharmacology27. A total of 142 related components of HDW was retrieved from TCMSP and the published literature. According to pharmacokinetic characteristics (OB ≥ 30% and DL ≥ 0.18) and ADME information, 11 active components were selected from 142 ingredients of HDW. The TCMSP and Swiss Target Prediction databases were used to determine the pharmacological targets of the HDW components. Table Our study performed network pharmacology prediction based on network pharmacology evaluation method guidance‐DraftUsing “Rheumatoid Arthritis” as the search term, 42, 192, 141, 174, and 623 disease-targets were obtained from the OMIM, DrugBank, TTD, GeneCards, and DisGeNET databases, respectively. Merging all results from the five databases and removing duplicates, 942 related-RA potential targets were finally collected.Applying a Venn diagram, 85 common targets were found overlapped between HDW compound targets and RA-related targets Fig. A. We impIn total, GO analysis identified 1542 significantly enriched GO terms (P.adjusted < 0.01 adjusted with Benjamini–Hochberg), consisting mainly of 1459 biological processes, 18 cellular components, and 65 molecular functions. We screened the top 10 ranked GO terms shown in Fig. Next, we analyzed 85 potential therapeutic targets by using the STRING database to obtain a PPI network to explore the relationship between RA-related targets. The PPI relationship network, with a total of 85 nodes, 238 edges and an average node degree of 5.6 was generated with a confidence of 0.9  in the compounds-targets-RA network. The hub targets, RELA, TNF, IL6, TP53, MAPK1, AKT1, IL10, and ESR1, play a significant role in the action of HDW against RA. Molecular docking of the 5 compounds and 8 hub genes revealed binding energies shown in Fig. P < 0.05, Fig. Overproliferation of fibroblast-like synoviocytes (FLS) are important pathogenesis of RA. Therefore, we investigated the effect of HDW at different concentrations  on the proliferation of MH7A cells after 48 h. CCK-8 assay showed that HDW inhibited the proliferation of MH7A cells in a dose-dependent  were core target genes in the network. GO enrichment analysis indicated that numerous targets are involved in response to lipopolysaccharides and molecules of bacterial origin in BP, are localized to membrane rafts and membrane microdomains in CC, and are associated with nuclear receptor and transcription factor activities in MF. KEGG pathway analysis indicated that numerous targets are associated with certain inflammatory events and cancer. Molecular docking showed that stigmasterol, β-sitosterol, quercetin, kaempferol, and 2-methoxy-3-methyl-9,10-anthraquinone have good binding activity with RELA, TNF, IL6, TP53, MAPK1, AKT1, IL10, and ESR1 targets. Finally, the molecular mechanisms of HDW predicted by network pharmacology approach against RA were validated by in vitro experiments.Given their potent anti-inflammatory, anti-fibroblastic, and immunomodulatory actions, HDW and its components have been widely used in animal models over the last several years as an intervention for RA30. β-sitosterol exerts an inhibitory influence on synovial angiogenesis by suppressing endothelial cell proliferation and migration, thereby alleviating joint swelling and bone destruction in CIA mice31, and quercetin inhibits the release of proinflammatory cytokines  by activating SIRT1, thereby becoming a potential effector of RA33. Kaempferol inhibits the proliferation and migration of RA-FLSs and the release of activated T-cell-mediated inflammatory cytokines by suppressing fibroblast growth factor receptor 3-ribosomal S6 kinase 2 (FGFR3-RSK2) signaling34. Collectively, these active components exhibit antirheumatic activity by various mechanisms, including anti-inflammatory, immunoregulatory, and reduction of bone destruction. Notably, however, there have been few previous studies on the treatment of RA with stigmasterol and β-sitosterol.A potential components-targets-RA target network indicated that stigmasterol, β-sitosterol, quercetin, kaempferol, and 2-methoxy-3-methyl-9,10-anthraquinone, are likely to play vital roles in the process of RA treatment , TNF (tumor necrosis factor), IL6 (interleukin-6), TP53 (cellular tumor antigen p53), MAPK1 (mitogen-activated protein kinase 1), AKT1(RAC-α serine/threonine-protein kinase), IL10 (interleukin-10), and ESR1(estrogen receptor). RELA, ranking first with the highest connection in the PPI network, is a subunit of nuclear factor (NF)-κB and is regarded as an important member of the NF-κB pathwayAs noted above, RELA, TNF, IL6, TP53, MAPK1, AKT1, IL10, and ESR1 are the top 8 hub genes in the PPI network. Additionally, however, potential targets were found to be involved in multiple pathways in the KEGG enrichment analysis. This indicates that HDW may exert its anti-RA effects by the combined interaction of multi-pathways and multi-targets. Also, the molecular docking analysis was carried out to investigate the interaction of 5 compounds and 8 targets. For example, the absolute value of binding energy about β-sitosterol and ESR1 is the highest in each group, indicating that β-sitosterol has a higher binding affinity than other target genes. Although there have been relatively few studies with stigmasterol and RA, the docking results indicated that stigmasterol performed good binding activity with TP53, ESR1, and IL10. In brief, the higher the binding affinity of components and targets, the more likely HDW treatment of RA will be achieved by modulating several related targets. However, further experiments are needed to clarify active compounds and mechanisms of action analysis of HDW for RA.https://old.tcmsp-e.com/tcmsp.php)55. As recommended by TCMSP, the compounds with OB ≥ 30% have good absorption and slow metabolism after oral administration, while the compounds with DL ≥ 0.18 were chemically suitable for drug development. We therefore used OB ≥ 30% and DL ≥ 0.18 as filtering for thresholds56. An exhaustive search for components was performed using PubMed (https://pubmed.ncbi.nlm.nih.gov/) and CNKI (https://www.cnki.net/). The active components of HDW were determined by SwissADME database (http://www.swissadme.ch/). Afterward, the targets (probability ≥ 0.9) corresponding to the components were screened from the SwissTargetPrediction database (http://swisstargetprediction.ch/)57. Duplicates were deleted to obtain potential targets for further analysis.Active components and potential targets of HDW were selected from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform 58, DrugBank (https://go.drugbank.com/)59, TTD (http://db.idrblab.net/ttd/)60, GeneCards (https://www.genecards.org/)61 and DisGeNET (https://www.disgenet.org/) databases62. All acquired targets were imported into UniProt (https://www.uniprot.org/) for normalization and removal of duplicate and erroneous targets.Potential RA-related therapeutic targets were collected using keywords “rheumatoid arthritis” as a query from the OMIM  method, P.adjusted values < 0.01 were considered to be statistically significant.Gene Ontology (GO) analysis and KEGG pathway analysis were performed using the R 3.6.3 and related R packages https://cn.string-db.org/)65. To ensure reliability, we used a cutoff ≥ 0.9 (high-confidence interaction score) to obtain the significant PPIs in network visualization. The CytoNCA plug-in was used to explore hub targets, and the top 8 were generated using Betweenness (BC), Closeness (CC), Degree (DC), and Network (NC) methods.To further identify interactive relationships among the common target proteins, the protein list was mapped to the STRING database (https://www.rcsb.org/). Molecular docking studies were carried out with AutoDock 4.0 software. After docking, the results were carried out by sorting the binding energy predicted by docking conformations. The lower the affinity score is, the better the binding effect with a binding energy of less than − 5.0 kcal/mol defined as dependable binding. Binding modes were visualized with the program Pymol.To investigate the associations between the Active Components and hub targets, we applied molecular docking analysis. The mol2 structure files of the 5 main components were downloaded from TCMSP. The crystal structure of the 8 hub targets were obtained from the Protein Data Bank . The MH7A cell was maintained in DMEM with 10% fetal bovine serum , supplemented with 1% penicillin-streptomycin . HDW (Lot. Number: R02S11Y123184) was purchased from Shanghai Yuanye Bio-Technology.3 cells/well and incubated in a 5% CO2 incubator at 37 °C. After adhering to the wall, cells were treated with Bavachinin of different concentrations  for 48 h. Optical density at 450 nm was measured with a microplate reader after adding the CCK-8 solution and incubated for additional 3 h.CCK‐8  assay was used to measure cell proliferation. MH7A cells were seeded into 96-well plates at a cell density of 5 × 106 cells/well. After 48 h of treatment, cells were collected by trypsinization. Total RNA from MH7A cells and ankle joint tissues were extracted by employing the TRIzol reagent, and cDNA was synthesized using a reverse transcription kit. qPCR was performed using SYBR Green PCR Master Mix . Finally, the relative quantity of mRNA was calculated using the 2−∆∆Ct method. The primers  used are listed in the Supplementary Table MH7A cells were seeded in 6-well plates at 5 × 10MH7A cells were cultured in T75 cell culture flask. After 48 h of treatment, cells were lysed with 1 × RIPA lysis buffer containing 1% of PMSF and 1% of phosphotransferase inhibitor. Protein concentration was detected by the BCA Protein Quantitative Kit . Subsequently, proteins (25 μg) were denatured by employing heating followed by electrophoresis with 10% SDS-PAGE, and were transferred to polyvinylidene fluoride membranes . The membranes were sealed with 5% skimmed milk (or 5% BSA) for 2 h at 37 °C, and incubated at 4 °C overnight with the following primary antibodies: protein kinase B (AKT), phospho-Akt (p‐AKT) and β-actin. After incubation, the membrane was further incubated with horseradish peroxidase (HRP)-conjugated secondary antibodies for 1.5 h at 37 °C. Lastly, specific bands were detected using enhanced chemiluminescence reagent (Thermo Fisher Scientific) and calculated by Image J software for the quantitative analysis of proteins.In summary, this study explored the therapeutic effect and mechanism of HDW on RA through a strategy combining network pharmacology and in vivo experiment verification. Five active components and 8 core targets of HDW were finally screened. PI3K/AKT signaling pathways were enriched by KEGG analysis in addition to virus and cancer-related pathways. Molecular docking showed that the active ingredients successfully docked with related targets. Through experimental validation, we found that HDW may affect target genes RELA, TNF, IL6, and IL10 through multiple signaling pathways PI3K/AKT signaling pathways, thereby affecting the pathological process of RA and ultimately inhibiting the occurrence and development of RA. Our findings provide a theoretical basis for the use of HDW as a therapeutic for RA.Supplementary Information.


Non-small-cell lung cancer (NSCLC) is one of the most prevalent cancers worldwide. A Yi-Fei-San-Jie formula (YFSJF), widely used in NSCLC treatment in south China, has been validated in clinical studies. However, the pharmacological mechanism behind it remains unclear. In this study, 73 compounds were identified using ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), with 58 enrolled in network pharmacology. The protein-protein interaction network, functional enrichment analysis, and compound-target-pathway network were constructed using 74 overlapping targets from 58 drugs and NSCLC. YFSJF has many targets and pathways in the fight against NSCLC. PIK3R1, PIK3CA, and AKT1 were identified as key targets, and the PI3K/AKT pathway was identified as the key pathway. According to the Human Protein Atlas (THPA) database and the Kaplan–Meier Online website, the three key targets had varying expression levels in normal and abnormal tissues and were linked to prognosis. Molecular docking and dynamics simulations verified that hub compounds have a strong affinity with three critical targets. This study revealed multiple compounds, targets, and pathways for YFSJF against NSCLC and suggested that YFSJF might inhibit PIK3R1, PIK3CA, and AKT1 to suppress the PI3K/AKT pathway and play its pharmacological role. Lung cancer is the most frequent and leading cause of cancer death, with NSCLC accounting for most cases. Fighting against lung cancer is still a major issue. According to the World Cancer Report 2020 published by the International Agency for Research on Cancer (IARC), 2.1 million new lung cancer cases and 1.8 million deaths worldwide were reported in 2018, accounting for 11.6% of cancer cases and 18.4% of cancer deaths [The most common treatments for lung cancer include surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy . With thin silico methods to investigate the mechanism of YFSJF against NSCLC. Scientists are working on multiple aspects of studying TCM, including clinical efficacy, pharmaceutical mechanisms, effective components, extraction separation, and preparation development. The multiple components and targets of TCM make it challenging to figure out its mechanism. With the development of analysis technology, a combination of chromatography and mass spectrometry was invented and applied in various fields, providing the opportunity to separate and analyze complex compositions simultaneously. UPLC-MS/MS has been extensively applied in TCM for chemical analysis, quality control, pharmacokinetics, serum pharmacochemistry, and rapid screening of active components –21. In rAll herbs were purchased from Kangmei pharmacy . HPLC-grade acetonitrile and ethanol were purchased from Fisher Scientific , and HPLC-grade formic acid was purchased from Sigma . Water was purified via a purification system .μm membrane.The prescribed amount of YFSJF was soaked for 30 min, followed by the addition of 8 and 6 times the volume of water, respectively, for reflux extraction (twice), each for one hour. The filtrate was concentrated to 100 mL, and ethanol was added to the cooled filtrate for a final concentration of 85%. The liquid was filtered, concentrated, and diluted in 100 mL water after 24 hours. The final crude drug concentration was 1.45 g/mL. The sample used in high-performance liquid chromatography-mass spectrometry (HPLC-MS/MS) was a tenfold diluent with ethanol after filtering with a 0.22 μm 4.6 ∗ 250 mm) ; column temperature, 30°C; flow rate, 1 ml/min; mobile phase, acetonitrile (A) and 0.1% aqueous formic acid (v/v) (B); and elution gradient, 5–60% A at 0–60 min, 60–95% at 60–90 min, 95–5% at 90–95 min, and 95–95% at 95–100 min.The Shimadzu UPLC XR instrument  was employed for chromatographic separation. The following were the chromatographic conditions: chromatographic column, Acclaim 120 C18  was used to evaluate the data. By comparing the mass fragments in the database and analyzing the fragmentation pattern, the ingredients of YFSJF were identified.A triple TOF-TM 5600 plus hybrid triple quadrupole time-of-flight mass spectrometer  equipped with Analyst®TF 1.6 software was utilized for mass spectrometry. The following were the mass spectrometry conditions: electrospray ionization (ESI) source; ion spray voltage, 5500 V in a positive mode and −4500 V in a negative mode; ion source gas 1/2, 55 psi; curtain gas, 35 psi; temperature, 550°C; collision energy, 10 eV; collision energy spread, 20 eV; declustering potential, 80 eV; and scanning range, 50–1500 https://tcmspw.com/tcmsp.php). Molecules with anticancer effects or oral bioavailability (OB) less than 20% and drug-likeness (DL) less than 0.15 were enrolled for target screening [https://pubchem.ncbi.nlm.nih.gov/). The canonical SMILES strings of each selected molecule were imported into the Swiss Target Prediction database to obtain related targets when species was set as Homo sapiens (https://www.swisstargetprediction.ch/). The target with a probability of 0.1 or higher was maintained. UniProt was used to correct the target name (https://www.uniprot.org/).Molecules identified in UPLC-MS/MS were searched in the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform , Therapeutic Target Database , and Drug Bank (https://go.drugbank.com/) were used to search for “non-small-cell lung cancer” and “NSCLC” and get NSCLC targets. The target names were corrected in UniProt.Databases such as the GeneCards , with a required confidence level >0.9. The Cytoscape software  was used to examine the network. The key treatment targets were those whose betweenness, closeness, and degree were larger or equivalent to the median, and they were preserved for future visualization. The top 10 degrees in the PPI network were hub targets.A PPI network without independent nodes was used to elucidate the molecular mechanisms of YFSJF's anti-NSCLC activities by the STRING database  was employed for functional analysis of the key prospective therapeutic targets. The DAVID online tool analyzed target functions using gene ontology (GO) analysis and the Kyoto Encyclopedia of Genes and Genomics (KEGG) pathway analysis.The Database for Annotation, Visualization, and Integrated Discovery . Hub compounds were among the top ten compounds.The compounds, main therapeutic targets, and top 20 KEGG pathways were collected in an MS Excel file as a backup. The C-T-P network was created using the Cytoscape software . The Kaplan–Meier Online website  was used to investigate the connection between the expression level of hub genes and prognosis in NSCLC. The result was depicted through a survival curve with a hazard ratio (HR).The expression of hub targets in normal and NSCLC tissues was compared using the THPA database (https://pubchem.ncbi.nlm.nih.gov/) and the PDB website (https://www.rcsb.org/), respectively. The AutoDock software was used to remove water molecules from the protein structure and hydrogenate it, after which docking pockets were constructed using the protein's ligand. The CB-Dock website (https://cao.labshare.cn/cb-dock/) was used to predict docking pockets for proteins without their ligand. After determining docking pocket parameters, the AutoDock Vina software (1.1.2 version) was used for molecular docking and conformation grading. A 2D combination conformation was generated through Discovery Studio (2019 version).Molecular docking technology was used to study the binding interaction between hub compounds and objective hub targets. Compounds and targets were constructed using the PubChem database (https://cgenff.umaryland.edu/initguess), and then, str files were converted to topology files through Python 2.7 (script: cgenff_charmm2gmx.py) (https://mackerell.umaryland.edu/charmm_ff.shtml#gromacs). The simulating system employed a dodecahedral solvent box with a 1 ns periodic boundary condition. To balance the system, 500 ps NVT and 1000 ps NPT were used, with a temperature of 300 K and a pressure of 1 bar, followed by a 20∼30 ns simulation. The time increment was 2 fs, and the confirmation was saved every 10 ps.A molecular dynamics simulation through GROMACS 2021 was used to verify the interaction between hub compounds and objective hub targets. The unconstrained dynamics simulation was run with the CHARMM36 force field and the TIP3P water model. Molecular MOL2 files were converted to str files through the CHARMM General Force Field (CGenFF) website  of UPLC-MS/MS is shown in According to the Swiss Target Prediction database, 522 targets were predicted for 58 compounds. NSCLC was estimated to have 1668 targets according to various disease target databases. As shown in After eliminating independent nodes, the Cytoscape software created a PPI network with 223 nodes and 1288 edges based on 254 overlapping targets. To obtain additional parameters, CytoNCA was used to analyze the network. We screened out 74 main targets whose betweenness, closeness, and degree were greater than or equal to the median . Based on key 74 targets, a PPI network with 74 nodes and 640 edges was generated, as shown in p values in the biological process (BP), cellular component (CC), and molecular function (MF). The major BPs were protein phosphorylation, peptidyl-tyrosine autophosphorylation, response to a drug, platelet activation, and peptidyl-serine phosphorylation. The key CCs were cytosol, nucleus, nucleoplasm, plasma membrane, and the extrinsic components of the plasma membrane. Enzyme binding, protein binding, ATP binding, protein tyrosine kinase activity, and kinase activity were the key MFs. The top 20 p values in the KEGG signaling pathway are depicted in Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomics (KEGG) pathway analysis based on 74 main targets were used to further explore the mechanism of YFJSF for NSCLC. A C-T-P network  was estaThe expression of the three major targets in tissue was investigated using the Human Protein Atlas (THPA) database. As shown in p < 0.05). The mOS of the group with high PIK3R1 and PIK3CA expression was better than that of the group with low PIK3R1 and PIK3CA . The mOS result was reversed for different expressions of AKT1 (HR = 1.67). In the case of lung squamous cell carcinoma, the curves for different expressions in 3 key targets appeared to separate, particularly for AKT1. The separating tendency indicated a correlation between the target and the mOS in lung squamous cell carcinoma, but there was no statistical significance.The Kaplan–Meier plotter online database was used to analyze the correlation between three key targets and the prognosis of NSCLC . The surThe binding affinity of ten hub compounds and three major targets was investigated using molecular docking. Generally, binding affinities of less than −5 kcal/mol are considered, indicating a good confirmation intersection. The result was visualized through a heatmap , in whicTCM's abundance of herbs and diverse formulas create a blue ocean for medical study, attracting an increasing number of researchers. This study discovered multiple ingredients, targets, and signal pathways for YFSJF against NSCLC.The discovery of 73 YFSJF components using UPLC-MS/MS established the groundwork for additional research. Fifty-eight filtered compounds were enrolled in network pharmacology analysis. The PPI network was built using the intersection of compound targets and NSCLC. PTPN11, FYN, AKT1, PIK3CA, PIK3R1, MAPK3, STAT3, MAPK1, HSP90AA1, and Src were promising targets for YFSJF against NSCLC by the PPI. They are mainly involved in PI3K-AKT signaling and MAPK signaling. Src and Fyn are members of the Src family kinases of nonreceptor tyrosine kinases, mediating different intracellular signaling pathways. Src family kinases have been identified as important in terms of progression, invasion, metastasis, bone pain, and drug resistance in human cancer, including NSCLC –26. Src The gene ontology biological process (GO-BP) analysis showed that YFSJF is primarily involved in protein/amino acid phosphorylation and autophosphorylation. Protein phosphorylation is one of the most prevalent post-translational modifications. It regulates protein function and various pathways. Enzymes catalyzing protein phosphorylation are called protein kinases. Protein kinases are the principal hub targets of YFSJF, including Src, Fyn, PI3K, AKT, and ERK1/2. They can phosphorylate downstream signals to activate appropriate pathways . The GO-β-Glycyrrhetinic acid can induce apoptosis and G2/M cell cycle arrest and inhibit migration via ROS/MAPK/STAT3/NF-κB signaling pathways in A549 lung cancer cells [Ten YFSJF hub chemicals were identified using the C-T-P network. Combining PPI and KEGG pathway results, we infer that YFSJF may mainly treat NSCLC by targeting PIK3R1, PIK3CA, and AKT1 and regulating the PI3K/AKT pathway. As a result, prognostic analysis and molecular docking were applied to the three primary targets. The results showed that PIK3R1, PIK3CA, and AKT1 expressions could distinguish between normal and NSCLC tissues and might be used as prognostic markers in NSCLC. Overexpression of PIK3R1 was shown to be significantly associated with a poor prognosis in adenocarcinoma . Studieser cells .The study verified the characteristics of multiple ingredients, targets, and pathways for YFSJF against NSCLC, with PIK3R1, PIK3CA, and AKT1 identified as the major targets associated with NSCLC patients' prognosis. The PI3K/AKT pathway is identified as the key pathway. Hub compounds demonstrated a good affinity for the three key targets. To combat NSCLC, YFSJF may inhibit PIK3R1, PIK3CA, and AKT1 to suppress the PI3K/AKT pathway. This study provides a solid foundation for future YFSJF mechanism research.


This paper introduces a pathway expression framework as an approach for constructing derived biomarkers. The pathway expression framework incorporates the biological connections of genes leading to a biologically relevant model. Using this framework, we distinguish between shedding subjects post-infection and all subjects pre-infection in human blood transcriptomic samples challenged with various respiratory viruses: H1N1, H3N2, HRV (Human Rhinoviruses), and RSV . Additionally, pathway expression data is used for selecting discriminatory pathways from these experiments. The classification results and selected pathways are benchmarked against standard gene expression based classification and pathway ranking methodologies. We find that using the pathway expression data along with selected pathways, which have minimal overlap with high ranking pathways found by traditional methods, improves classification rates across experiments. Accurate detection of these types of viruses is necessary to isolate infected individuals and consequently slow the spread throughout the population. Moreover, some infected individuals can be shedding the virus without showing symptoms. It is advantageous to have methods of detecting shedding that are robust to symptoms to avoid asymptomatic super spreader scenarios2. Previous work on these respiratory virus data ran various machine learning (ML) models, e.g., neural networks, support vector machines (SVM), centroid encoders (CE), and spectral gene network analysis, to identify discriminatory biomarkers within early shedders challenged with influenza and subsequently classify those subjects6.Analyses of respiratory illnesses aid in understanding the mechanisms of shedding and in developing methodologies that succeed across multiple infectious diseases. Understanding the imprint of viral shedding on human gene expression may uncover latent effects which are beyond disease symptoms. In this paper we will run our experiments on a human microarray data set with multiple respiratory viruses and studies, the GSE73072 data set7. Given the numerous definitions of pathway membership and number of ways to relate genes within a pathway, many of these pathway analyses require an a priori set of “important” genes (like those found using ML on gene expression data3) to determine significant pathways9. Standard tools that rely on an a priori gene set include: over representation analysis (ORA)10, gene set enrichment analysis (GSEA)14, Centrality-based pathway enrichment (CePa)15 and more16. ORA determines the statistical significance of the overlap between an a priori set of genes and a given pathway. GSEA improves upon ORA by accounting for the expression levels of the genes. CePa is a further improvement on the ORA method which uses the biological connections between genes in a pathway. Specifically, it uses the centrality of genes in the network generated by a pathway as part of its ranking. Beyond simply ranking pathways by statistical significance, Maglietta et al. offer a method that ranks functional groups of genes, specifically genes associated with the same GO term17, to predict a phenotype16. All of these pathway methods rely on a pathway database which is used to determine pathway membership. There are a plethora of pathway databases including KEGG18, MetaCyc19, Reactome10, Wikipathways20, BioCarta21, InnateDB22 and many more. Most of these databases contain essentially the same information regarding pathway membership. We use the Reactome database, along with their built in tool for ORA in order to produce pathway rankings to compare against our novel pathway expression analysis rankings.Rather than just select significant genes, a biological pathway analysis uses biological relationships between genes, usually by grouping related genes, to build a biologically informed modelpathway expression data. This allows for the determination of pathway significance and subject classification using pathways expression levels rather than genes expression levels. This is not the first time such a translation has been considered. Some of the following works use GO terms or other biologically known collections of genes rather than pathways. For the sake of simplicity, we will use the term pathway loosely in this paragraph to refer to a biologically known collection of genes. Between the early 2000’s and 2012, various authors have explored classification and pathway ranking using statistical techniques. Pathway expressions, often referred to as pathway activities, have been calculated using means and medians23, metagenes (aka. eigengenes)24, t-scores25, log-likelihood ratios (LLRs)26, and FAIME 27. Some of these methods do not use all the genes in the pathway to calculate pathway expression. For example Guo et al. only calculate the average expression level of the differentially expressed genes in a pathway. Pathway selection in these papers is not always done using pathway expression levels. Su et al.26 use gene expression levels to select pathways. On the other hand, some works do indeed use their pathway expression data to determine significant pathways and to classify between phenotype. Lee et al. determine significant pathways for classification using the AUC (area under the ROC curve) on a validation data set25. Classification and regression of changes in phenotype with pathway expression data has been done using simple models like decision trees23, linear discriminant analysis26, logistic regression26, clustering27 and other statistical models27.In this paper, we formalize a method called pathway expression analysis which transforms gene expression data into 27. Within the last 10 years, the field of machine learning in biology has boomed in popularity and there’s a need for formalization of these previous pathway expression methods. Our work fills this gap by defining pathway expression data as the result of a simple linear transformation of gene expression data. We offer two forms of pathway expression data: Linear Pathway Expression (LPE) and Centrality Pathway Expression (CPE). Methods like those presented by Guo et al. fall easily into the pathway expression framework as a special case of LPE23. CPE, on the other hand, is a novel method for pathway expression that ranks genes based on networks of known and inferred gene interactions rather than statistically inferring a gene ranking using differences between phenotype like the FAIME method27. Unlike other previous pathway expression methods , CPE and LPE can be thought of as ’unsupervised’ pathway expression methods since they do not require phenotype labels for their computation. Due to their simplicity, CPE and LPE use less computational resources than most previous pathway expression methods since our methodology involves a linear transformation rather than more complicated non-linear mappings. Rather than use one of the classifiers from the previous works we choose to use Sparse Support Vector Machines (SSVMs) for our classifier. This sparse classification scheme allows us to preform our pathway selection and classification using pathway expression data in one step, without any parameter tuning.The last known work in this field is FAIME in 20126. This work compliments the early detection analysis done by Aminian et al.3 by utilizing “pathways expression” with a simple feature selector and classifier in place of gene expression with optimal feature selection and classification techniques. By using pathway expression as an alternative to gene expression, we extract pathways as features using SSVMs and subsequently rank pathways by their weight in the SSVM model. We do this by extracting the top pathways for each pathway expression method on training data, restricting the test pathway expression data to those top pathways, and finally classifying controls vs. shedders (from 4 evenly spaced time bins within 32 hours after infection) using the test pathway expression data using SVM. We also benchmark these selected pathways against two known pathway ranking methodologies ORA and CePa. As an aside, our implementation of CePa in Python, to the best of our knowledge, is one of the first Python implementations of such a workflow.We use our pathway expression methods (LPE and CPE with SSVM) to select pathways which discriminate between uninfected subjects (controls) and eventual shedders infected with various respiratory viruses: H1N1, H3N2, HRV (Human Rhinoviruses), RSV , in the early stages of infection using the GSE73072 data set. O’Hara et al. found that gene expression data from some pathways like B Cell Maturation and Activation + Cell Adhesion Molecules were used to produce 100% classification accuracy for certain experiments with this data set but analyses with pathway expression has never been donehttps://github.com/nmank/PathwayAnalysis. This work is meant to revive work in pathway expression analysis by adding a simple framework for generating pathway expression data, introducing a new method for pathway expression, CPE, which leverages gene networks to produce pathway expression data and finally implementing a simple sparse classifier to select pathways and discriminates between phenotype at the same time.In our experiments on the GSE73072 dataset we find that pathway expression methods generally produce higher classification rates than gene expression methods with the same type of SSVM feature selector and classifier. We also find that using CPE, which adds gene network information, increases classification rates over simple LPE. Classification rates with CPE and LPE are found to be robust LIMMA normalization of the gene expression data and the pathways selected by these methods prove to be appropriately robust across training dataset partitions. The pathways which are selected using CPE and LPE methods tend to be distinct from the pathways selected using CePa and ORA. Hence, pathway expression is a useful method for discovering biological pathways which are not traditionally associated with a disease of interest. The code for LPE and CPE is implemented in Python and available at In this section we provide a number of results using pathway expression on GSE73072. Specifically, we investigate two types of pathway expression: Centrality Pathway Expression (CPE) and Linear Pathway Expression (LPE) and compare these methods to Gene Expression (GE) methods. We provide a visualization of pathway expression data in Supplementary Information. This section is broken down into Classification Results, Comparing Pathway Selection Methodologies and Top CPE Pathways. In Classification Results we provide classification statistics including Balanced Success Rates (BSRs) for experiments using the features selected on pathway expression on test studies. We also pose an argument for the best CPE method in Classification Results. In Comparing Pathway Selection Methodologies, we compare the pathways selected using pathway expression to those selected using CePa and ORA. Lastly, in Top CPE Pathways, we list the top pathways found using the best CPE method.In our classification experiments we draw samples from the GSE73072 data set and distinguish between controls and shedders within 32 hours after infection. For our experiments, we separate these data into 4 evenly sized time bins. Controls are all subjects at times before infection. We perform 3 different data set splits. For two of these splits, we find features using data from 4 studies and on the remaining 2 or 3 studies. For the third split, we find features using data from 6 studies then test on 1 study. The details of the data sets used for these experiments are in We use these classification experiments to compare LPE and CPE methods to each other and benchmark these pathway expression methods against GE methods. Classification results on the test studies from the 4 to 2, 4 to 3 and 6 to 1 experiments with LIMMA batch correction by subject ID are reported Table CPE requires two parameters to be set: a pathway gene network edge type and a centrality measure. We test CPE by using either correlation edges or pre-computed (from Reactome) edges, which may be directed or undirected. For centrality measures, we use either PageRank or out-degree centrality. Supplementary Information is a table of the CPE configuration which produces the highest test BSR. Using this table, we observe that pre-computed edges with PageRank centrality is the most common method across all experiments and time bins to produce the highest BSR.Finding the “best” CPE configurations by using the configurations which produce the maximum test BSR is not the best way to select the “best” CPE configuration since it doesn’t take into account the performance of each CPE configuration across all experiments. So, in order to choose the best CPE edge type and centrality ranking, we look at the distributions of test BSRs for each edge type and centrality ranking across experiments. The box plot from this investigation is in Fig. We perform two pathway selection experiments using two data sets: 1) 4 studies (the training data for the 4 to 2 and the 4 to 3 experiments) and 2) 6 studies (the training data for the 6 to 1 experiments).In these experiments, we compare the pathways that are selected by the pathway expression methods to the pathways selected by standard pathway ranking algorithms, ORA and CePA, as well as a list of influenza related pathways (labeled Flu) from Reactome. We find this list of influenza related pathways by simply searching for influenza on the Reactome website. We do not expect high overlap with these influenza pathways since they are likely only activated at later time points during infection. For methodological consistency in these Jaccard plots, we use the same edge and centrality methods for CePa and CPE, namely pre-computed, directed edges with PageRank centrality.For comparison, we use the Jaccard/Tanimoto similarity coefficient as a measure of overlap between these two sets of pathways. These comparisons for the 6 study features are shown in Fig. Over all methods and experiments, we notice LPE and CPE have a high Jaccard similarity, and CePa and ORA have a relatively high Jaccard similarity. CePa and ORA both have a small Jaccard similarity with LPE and CPE. We generally see low overlap between all methods and the influenza pathways. Generally, ORA is the method with the highest overlap with the influenza pathways since it detects at least twice as many pathways as each of the other pathway selection methods. In fact, CPE has absolutely no overlap with influenza pathways for the 4 study experiments. In contrast, at the 6 study experiments, we see that CPE has a higher overlap with the flu pathways than all other methods at the 9 to 16 and 17 to 24 time bins even though it detects less than half as many pathways as ORA. LPE has the highest overlap with the flu pathways at the 1 to 8 hour time bin for the 4 study experiment even though there are only 60 LPE pathways in contrast to the 254 ORA pathways.Next, we investigate the robustness of these selected pathways across different studies in Table So far, we have seen that CPE generally produces the highest classification rates in our experiments over all methods examined in Table The top pathways from CPE across all experiments are found by adding the SSVM weights across all experiments and time bins are in Table Now we present the top pathways from CPE for each experiment in Table In the results we provided a comparison between pathway expression and gene expression methods with the same linear feature selection and classification methodology on train/test partitions of the GSE73072 data set. Both methods selected features (pathways or genes) using influenza training data then use these selected features in a LOSO (Leave One Subject Out) cross validation experiment with an SVM classifier on testing data. In these experiments, we found that pathway expression (CPE and LPE) methods generally produce higher test BSRs than gene expression methods. Specifically, we found that pathway expression produced higher test BSR than gene expression on 10 out of 12 classification experiments.We used the distributions of test BSRs across experiments and time bins to conclude that pre-computed, directed edges with PageRank centrality is the “best” centrality configuration. We found that CPE with pre-computed edges and PageRank centrality produced the highest test BSR out of all CPE configurations for most experiments and time bins. Consequently, we reported the pre-computed, directed edges with PageRank centrality CPE configuration in all our classification rates and pathway ranking comparisons.In addition to the feature selection and classification experiments, we compared these selected pathways from pathway expression methods to two standard gene expression pathway analysis methods: CePa and ORA. We found that the pathways selected from pathway expression methods generally have little similarity to pathways from these gene expression methods. This suggests that the pathway expression methods along with SSVM feature selection provides a unique pipeline that selects discriminatory pathways which are not detected by standard pathway analyses on gene expression data.Pathway expression methods are also pulling out some respiratory virus signal since LPE produced a non-zero overlap with influenza pathways on 6 out of 8 experiments and time bins and CPE  had a non-zero overlap with the influenza pathways for each time bin in the 6 study experiment while sporting the highest overlap in 2 out of these 4 time bins. Since CPE has a non-zero overlap with the influenza pathways for the each of the time bins in the 6 study experiments, we suggest investigation into the links between respiratory viruses and the top pathways from this method, R-HSA-2485179, R-HSA-8939242, R-HSA-9694631 and R-HSA-5218921. The pathways R-HSA-8939242 and R-HSA-9694631 have the most promise to be interesting since they were the top pathways at the time bins where CPE had the highest overlap with the influenza pathways out each one of the tested methods.29. Additionally, R-HSA-3595177 is involved in the synthesis of chondroitin sulfate which has been shown to be involved with pulmonary immune response to influenza infection by Brune et al.30.When we look at both the highest magnitude SVM weight pathways from CPE by experiment and overall, we see that R-HSA-74713 and R-HSA-3595177 appear in both lists. Therefore these are the most discriminatory of the pathways listed in the Results section. In fact, the R-HSA-74713 pathway appeared in more than one experiment and time bin. Our methods detected two insulin-related pathways: R-HSA-74713 and R-HSA-264876. R-HSA-74713 is a mediator of insulin signaling events and R-HSA-264876 is an insulin processing pathway. Insulin signaling is related to the influenza virus because the influenza virus impairs insulin signaling and down-regulates the expression of genes in the insulin pathway which supports our detection of the31. Aryl hydrocarbon receptor (ARH) signaling (the R-HSA-8937144 pathway) is important part of the immune system, and ARH specifically regulates the immune response which is directly related to infection33. We find that the R-HSA-5676594 pathway is related to NF-35. The R-HSA-9634635 pathway involves signaling through PRKCZ and it was found in one study that suppressing the expression of PRKCZ reduces RSV infection which suggests that the two are linked to some extent36. The R-HSA-5603037 pathway is involved with IRAK4 deficiency. Kim et al. found that IRAK4 kinase activity is suggested be involved with TLR-dependent immune responses and influenza virus is dependent on IRAK4 kinase activity37.Many of the other pathways found to have the highest total SSVM weights across all experiments and time bins have links to the influenza virus. The R-HSA-163680 pathway involves AMPK signaling which is known to be significant in the modulation of viral infectionsNot only does our implementation of pathway expression produce interesting pathways, it also is robust to perturbation in theory and in practice. Linear pathway expression for a given pathway, is robust to perturbation of the gene expression levels in a pathway by mean 0 noise because it is a mean of gene expression levels in a pathway. Centrality pathway expression is only approximately robust to such an addition of noise since it is a weighted mean of gene expression levels. In our experiments we found pathway expression methods proved to be more robust to subject batch effect than gene expression methods. Linear pathway expression was the pathway expression method that was the least affected by subject batch affect. The plot to support this claim is in the Results section in the We also examine the robustness of pathway expression with regards to the pathways selected using SSVM feature selection on pathway expression data. We do this by comparing these selected pathways to those selected using gene expression features along with standard pathway ranking methods. Specifically, we look at the overlap between the pathways found with 4 studies and those found with 6 studies. We find that the pathways selected by ORA have the highest overlap, the pathway expression method have the second highest and CePa has the lowest overlap. It is expected that this overlap is somewhat proportional to the number of pathways that were selected by each method since we are sampling from a background set of pathways. In an extreme example, if the each of the two pathway sets have more than half of the total pathways, then they must overlap. We see this proportionality of the number of pathways selected and the overlap for each of the methods. Additionally, the viruses in the 4 study training dataset are H3N2 and H1N1), and the viruses in the 6 training dataset are H3N2, H1N1, RSV and HRV. We note that the relatively high overlap in the pathways detected by ORA indicates that this method pulls out the general respiratory virus signal but does not detect the differences between the different viruses in the training datasets. On the other hand, the low overlap for CePa suggests that this method detects the difference between the datasets but does not capture the overall respiratory virus signal. Pathway expression methods take the middle ground since their overlap is between the standard pathway analysis methods (ORA and CePa). That is to say, the pathways found using pathway expression encapsulate the difference between the viruses in the training datasets while still maintaining the overall respiratory virus signal.Returning to analysing our methodologies, CPE and LPE are simple linear models for translating gene expression data to pathway expression data which result in improved BSR of our tested machine learning models. The linear nature of our implementation of pathway expression, especially LPE, makes it possibly one of the simplest methods available for transforming vectors from the gene space to the pathway space. A different non-linear pathway expression formulation may improve upon the results with pathway expression presented in this paper. One non-linear modification of LPE has been done by using the absolute values of the entries in the gene expression matrix instead. This modification collapses the gene expression data into the positive hyper-octant and results in a loss of information. However, there are many possible variants of non-linear pathway expression that require further investigation.The pathway expression methods in this paper are meant to re-inspire a wide variety of investigations into the pathway expression pipeline including batch correction, pathway expression generation and downstream pathway expression analyses. Investigation into the robustness of pathway expression methods to batch effects could be done by running experiments with corrections for study or strain effects. In this paper, we applied batch normalization to gene expression vectors before calculating pathway expression. Another interesting experiment in future work could be to apply batch normalization to the pathway expression vectors themselves. Within the framework of CPE, different methods for network generation, and centrality measures still need to be tested. LPE and CPE are arguably the simplest pathway expression methods since they are effectively an averaging of gene expression levels. From a bird’s eye view, pathway expression methods are any method to transform a gene expression matrix to a pathway expression matrix. Therefore any type of pathway transition matrix, as well as a non-linear transform, can be applied to gene expression data to produce pathway expression data. This leaves the door wide open to the use of other central prototypes for generating pathway expression matrices. Pathway expression data can be seen as just a pre-processing step. Hence, any machine learning algorithm that has been used on gene expression data can be used on pathway expression data to select pathways, classify pathways, cluster pathways, etc. Hence, the opportunity for novel work with pathway expression can include an investigation into pathway expression pre-processing, batch correction techniques, methods for pathway expression generation, and downstream pathway expression analyses using statistics, machine learning algorithms and more. The bottom line is, creativity and/or applying sound biological principles while designing a pathway expression workflow will be key in selecting meaningful pathways and perhaps improve classification rates.3. For a direct comparison of best CPE results between this paper to the gene expression results using SVM from Aminian et al. see Table and GE generally produce lower SVM BSRs than those from the same experiments in Aminian et al. This is not surprising since the feature selection technique in this paper is far more simple and less robust than what was done by Aminian et al. However, at the 25 to 32 hour time bin, the pathway expression methods produce higher BSR than the classification results in Aminian et al. by 1 to 4 percent in BSR in two out of three experiments. The higher classification BSRs at the latest time bin are consistent with the concept that genes generally don’t work in concert in pathways during the early hours after infection. A PCA of all the probes related to genes in immune response pathway, Our 4 to 2, 4 to 3 and 6 to 1 classification experiments in the results are designed to mimic the experiments that were done by Aminian et al.https://github.com/nmank/PathwayAnalysis.Overall, the pathway expression analysis framework developed in this work provides a concise approach for characterizing the biological processes associated with the host response to infection. Although we produce lower classification rates than those in Aminian et al, in a head to head comparison with gene expression, we see that CPE and LPE improve classification rates. Moreover, we envision that pathway selection using this approach may provide additional insights into biological mechanisms associated with the host response to infection. The pathways detected using CPE are connected to the immune response and to some specific respiratory viruses. Our preliminary experiments addressing time-evolving human clinical data provide some insight into the pathway activity for humans infected with respiratory viruses. Finally, we offer a python package for computing CPE, LPE and CePa In this section we detail the models, metrics, experiments, and data sets used in this paper. We run two mirrored classification and pathway selection pipelines on pathway expression data and gene expression data.Batch normalize via LIMMA each train and test partition separately by subject ID.Select pathways, viewed as features, in pathway expression data using the training data (LPE-SSVM or CPE-SSVM).Run a LOSO classification experiment using SVM on test pathway expression data restricted to the selected pathways (generated by LPE-SSVM or CPE-SSVM) and record mean test statistics (eg. BSR).For pathway selection using pathway expression we simply use the pathways selected by SSVM on the training data and rank these selected pathways by their SSVM weights.Our pipeline used in the pathway expression (CPE and LPE) classification experiments is the following: Batch normalize via LIMMA each train and test partition separately by subject ID.Select genes, viewed as features, using the training data using SSVM.Run a LOSO classification experiment using SVM on test gene expression data restricted to the selected genes and record the mean test statistics (eg. BSR).For pathway selection using gene expression data we use the genes selected by SSVM on the training data as input ORA or CePa pathway selection. ORA is implemented using the p-value from the analysis.identifiers function reactome2py, (https://github.com/reactome/reactome2py). CePa is implemented in the GLPE.simple_transform function in the PathwayAnalysis package . Details on the ORA and CePa methodologies are provided in Pathway Ranking Using Gene Feature Sets in the Methods section of the Supplementary Information.The pipeline for the GE classification experiments is: 2 from the NCBI Gene Expression Omnibus (GEO). This data set is a microarray gene expression data set for human subjects challenged with the influenza virus. These data were collected from 7 studies by Duke, UVA and hVIVO and was funded by the Defense Advanced Research Projects Agency (DARPA). The entire data set consists of 22277 probe identifiers and 148 human subjects infected with four different types of respiratory viruses: HRV, RSV, H1N1 and H3N2. The data samples are collected at irregular time intervals from 38 hours before infection to 680 hours after infection. The data can be found here https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE73072.In this paper we perform experiments on the GSE73072 data set3. In these experiments we classify between control subjects and shedding subjects. Our control group consists of all subjects between, and including, 38 to 0 hours before infection. We define (early) shedders as pre-symptomatic subjects within 32 hours after infection who will eventually be characterized as shedders over the course of the immune response. We break our data sets down further into 4 sets of shedders from evenly spaced time bins within 32 hours after infection. Our train/test splits are described in Supplementary Information using the format: study identifier (virus).We run two binary classification experiments with GSE73072 data set that are copies of experiments by Aminian et al.3. First, we normalize the entire data set using the robust multi–array average (RMA)38 method. We then correct for subject differences by applying normalization across subject identifier using the LIMMA package39 to each train/test partition separately.For data pre-processing, we perform two steps which follow the experimental design of Aminian et al. so that we can make a faithful comparison of classification rates9 to determine pathway membership and networks. We use the R package graphite40 to generate pathway networks with edges. The graphite package, using the information from Reactome, gives protein-based edges which are then translated using Entrez gene identifiers. We also directly use the Reactome database information, available on the Reactome website, to ensure genes are in appropriate pathways and edges are within Reactome pathways only .We use the Reactome Pathway Databasewe retain all the probe IDs that correspond to Entrez IDs from in affymetrix platform file. The edges between probe IDs derived from this method are used for the edges and edge weights for the pre-computed directed and undirected edges for CPE and CePa workflows. For every probe ID in affymetrix platform file, map to the first Entrez ID it its associated list of Entrez IDs (in the event of multiple maps). Since some probe IDs are not mapped to Entrez IDs via the platform file, we loose Use the pathway network information to draw an Entrez ID networkNote: this means some nodes are mapped to multiple probe IDs. Assign edges in the pathway networks with probe ID nodes according to the edges between Entrez IDs. Multiple probe IDs that map to the same Entrez ID will have no edges between them. Probe IDs with no Entrez ID to map to are dropped. Entrez IDs with no probe IDs mapping to them are dropped.Map all the Entrez ID nodes from the pathway networks to probe IDs using this mapping. For a visual of this mapping see Fig. The GSE73072 data set is a microarray data set with features given as microarray probe identifiers (probe IDs). We choose to do all our analyses on the probe IDs rather than determine a mapping to Entrez identifiers (Entrez IDs) in order to retain as much feature information as possible. However, this means that the pathway membership and network edge information needs to be converted to probe IDs. We use the following mapping from Entrez ID pathway networks to probe ID pathway networks. Although this method results in a loss of some information, Pathway expression is a method to represent biological data as a pathway expression matrix with pathway expression levels as features. In this section we develop the mathematical underpinnings of the pathway expression methods used in this paper.n gene expression levels for p subjects. In a gene expression matrix each gene is assigned a gene index and each subject is assigned a subject index. We can map m pathway expression levels for p subjects. In a pathway expression matrix each pathway is assigned a pathway index and each subject is assigned a subject index. We define such a linear mapping in Let mentclass2pt{minimith row and jth column of Let entclass1pt{minimai in the jth pathway network A slightly more involved definition of pathway expression is centrality pathway expression (CPE) where we weight gene expression levels by their centrality within a pathway network. This pathway transition matrix is denoted ) in Eq.  where \dThe full CPE algorithm is in Fig. j. Analogously, let i. We claimNow we will investigate CPE further by formulating the calculation of pathway expression as an optimization problem. Let The obvious solution to this problem is the scaled average of the set j we recover Eq.  from O’Hara et al.m features Our feature selection methodology starts with all the P and For comparison between sets of pathways, we use the Jaccard/Tanimoto similarity coefficient. Given two sets of pathways, We evaluate the novel linear pathway expression data by using SVM to classify between controls and shedders in a LOSO experiment on the test data set using the only the pathways that were selected on the training data. We use the mean test BSR, precision, recall and accuracy of these SVMs to determine the “best” method.Note: that standard deviation for these statistics is not available since we are computing them from a sum of the confusion matrices across the LOSO experiments.We compute mean test BSR for these LOSO experiments on test data in three steps. (1) Compute a confusion matrix for each subject’s SVM experiment on the test data. (2) Sum all the subject confusion matrices. (3) Compute the average of the true positive rate and the true negative rate. This metric provides a better model assessment than accuracy on data sets with imbalanced class sizes. Precision, recall and accuracy are computed in a similar manner leveraging the sum of all the confusion matrices across experiments. These pathway expression results are compared to results using the same workflow on gene expression data. We run an SSVM feature selection on the training gene expression matrices to select discriminatory genes. Then we restrict the gene expression matrices of the test data to the discriminatory genes and run a LOSO SVM experiment on the test data set and use the BSR to compare against the BSRs from the LPE methods.Supplementary Information.


To investigate the main pharmacological basis and mechanism of action of Gujiansan in the treatment of steroid-induced avascular necrosis of the femoral head (SANFH).  The active constituents and targets of Gujiansan were screened by using TCMSP and other databases, and relevant disease targets were obtained by analyzing the microarray of SANFH in the GEO database. The intersection of the two was taken to obtain the potential targets of Gujiansan for the treatment of SANFH, and key active constituents were screened with the “active constituent-target” network constructed by the Cytoscape software; then, the STRING database was used to construct the protein interaction network to screen the key targets. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses of key targets were performed by the DAVID database, and the relationship between the “key active constituent-key target-key signaling pathway” was explored. Finally, the molecular docking between key active constituents and key targets was verified. In addition, qPCR detection technology was used to evaluate the preventive and therapeutic effects of key active constituents of Gujiansan in a rat osteoblast model of SANFH to verify the possible mechanism of the effect of Gujiansan in the treatment of SANFH.  (1) 106 active constituents and 55 targets were obtained for the treatment of SANFH. (2) Quercetin, luteolin, kaempferol, cryptotanshinone, and naringenin were the key active constituents for the treatment of SANFH. (3) IL1B, STAT3, CAT, PTGS2, and MAPK3 were the key targets for the treatment of SANFH. (4) IL1B, STAT3, CAT, PTGS2, MAPK3, and HMOX1 are key targets in the protein interaction network. (5) DAVID enrichment analysis mainly covers the regulation of DNA-binding transcription factor activity, positive regulation of cytokine production, and response to oxidative stress and other biological processes, involving IL-17, AGE-RAGE, C-type lectin receptor, and other signaling pathways. (6) Gujiansan is a multitarget and multisignaling pathway for the treatment of SANFH. (7) Good binding activity exists between key active constituents and key targets.  This study analyzes the potential mechanism of action of Gujiansan in the treatment of SANFH with network pharmacology, which can provide a reference for the further study of its pharmacological basis and targets. Avascular necrosis of the femoral head (ANFH), also known as osteonecrosis of the femoral head (ONFH), is a disabling disease that occurs in young and middle-aged people. According to statistics, the total number of diagnosed patients worldwide has exceeded 30 million, including more than 8 million in China, and about 75,000 to 150,000 new cases are added every year . The etiα, autophagy-associated factors , and bone formation markers , bone-specific alkaline phosphatase (BAP)) and decreasing the levels of proinflammatory cytokines  . Activated Th17 mediates multiple downstream signals such as NF-κB and MAP3K  by secreting IL-17 and ultimately promotes the gene expression of TNF-α, AP-1, and PTGS2/COX-2 [α-subunits and constitutively expressed β-subunits, such as activating HMOX1 to promote heme catabolism, activating VEGF to promote angiogenesis, and activating the BNIP3 autophagy factor to reduce apoptosis. HIF-1α expression was decreased in the necrotic bone tissue of SANFH, and HIF-1α compensatory expression was upregulated in early bone tissue under hypoxic or anoxic conditions. However, with the progression of the disease, the imbalance of energy metabolism in necrotic bone tissue was aggravated, and the expression levels of HIF-1α, VEGF, HMOX1, and BNIP3 were decreased, resulting in the decreased differentiation and apoptosis of vascular endothelial cells [The six core target genes  in the network were screened out by constructing PPIs. GO enrichment analysis indicated that many targets are involved in the regulation of DNA-binding transcription factor activity, positive regulation of cytokine production, and response to oxidative stress. The results of the KEGG pathway analysis suggested that several signaling pathways such as IL-17, AGE-RAGE, C-type lectin receptor, HIF-1, and TNF are mainly involved. (1) For the IL-17 signaling pathway, studies have shown that the secretion levels of IL-6, IL-17, IL-1appeared –43. CD4+S2/COX-2 –45 al cells . In vitral cells , 57. (5)al cells . The anaMolecular docking results showed that quercetin, luteolin, kaempferol, cryptotanshinone, and naringenin interacted with the six core target genes . The six core target genes also participated in the conduction of the five signaling pathways. Therefore, this may indicate the overall effectiveness and diversity of constituents of Gujiansan in intervening with SANFH and offer insights into the occurrence and development of other osteoarticular-related inflammatory diseases.β gene expression in SANFH osteoblasts; studies also showed that IL-1β could inhibit the production of DKK1, thereby promoting the activation of Wnt signaling [β gene expression in osteoblasts and finally promote cartilage formation. Quercetin, kaempferol, and cryptotanshinone could also significantly promote the upregulation of COX-2 gene expression in SANFH osteoblasts; COX-2 was essential for the osteogenic differentiation of MSCs. Studies have shown that COX-2 was induced in the early stages of bone repair and was produced in large amounts in local areas. COX-2 could induce and/or synergize with BMPs to increase RUNX2 and osterix, while these two factors were two important transcription factors required for endochondral and intramembranous bone formation [In the osteoblast experiments in this study, the results of qRT-PCR indicated that quercetin could significantly promote the upregulation of IL-1ignaling . The actignaling –62. Therormation . Quercetormation , which wormation   was a key antioxidant enzyme in the body's defense against oxidative stress, which could convert reactive oxygen species Htoxicity , 67. Lowtoxicity . Fernandtoxicity . Naringetoxicity , 71. Howtoxicity . The resα and IL-6) in the blood of SANFH rats in in vitro expression and elevated some inflammatory factors (such as IL-1β and COX-2) of osteoblasts to promote osteoblast differentiation. This antagonistic dual regulatory effect was the great potential of traditional Chinese medicine in the treatment of SANFH.It was found that Gujiansan could promote osteoblast differentiation by regulating multiple genes, and an interesting phenomenon was observed that the Chinese herbal compound reduced the inflammatory factors (such as TNF-The insufficiency of this study was that its prediction of the pharmacological mechanism of Gujiansan in the intervention of SANFH was based on the previous research findings of the team and the existing database. In order to ensure the reliability and rationality of the prediction results, further experimental verification was required. Certain limitations exist though the results of network pharmacology analysis and molecular docking analysis indicated the characteristics of Gujiansan in the treatment of SANFH. This study further elaborated on the pathogenesis of SANFH and brought new treatment strategies for the prevention and treatment of SANFH.


In central nervous system, axons fail to regenerate after injury while in peripheral nervous system, axons retain certain regenerative ability. Dorsal root ganglion (DRG) neuron has an ascending central axon branch and a descending peripheral axon branch stemming from one single axon and serves as a suitable model for the comparison of growth competence following central and peripheral axon injuries. Molecular alterations underpin different injury responses of DRG branches have been investigated from many aspects, such as coding gene expression, chromatin accessibility, and histone acetylation. However, changes of circular RNAs are poorly characterized. In the present study, we comprehensively investigate circular RNA expressions in DRGs after rat central and peripheral axon injuries using sequencing analysis and identify a total of 33 differentially expressed circular RNAs after central branch injury as well as 55 differentially expressed circular RNAs after peripheral branch injury. Functional enrichment of host genes of differentially expressed circular RNAs demonstrate the participation of Hippo signaling pathway and Notch signaling pathway after both central and peripheral axon injuries. Circular RNA changes after central axon injury are also linked with apoptosis and cellular junction while changes after peripheral axon injury are associated with metabolism and PTEN-related pathways. Altogether, the present study offers a systematic evaluation of alterations of circular RNAs in rat DRGs following injuries to the central and peripheral axon branches and contributes to the deciphering of essential biological activities and mechanisms behind successful nerve regeneration. Axon injury elicits distal nerve degeneration, impairs neuronal functions, and causes motor incapacity, sensation loss, and neuropathic pain . The recDorsal root ganglion (DRG) neurons are pseudounipolar neurons that have two separate axonal branches stemming from one single axon. The central branch extends into the central nervous system and is generally incapable of regeneration after axon injury while the peripheral branch projects to peripheral target and often obtains a remarkable growth capacity . DespiteRecently, the application of high-throughput RNA sequencing discriminates genetic changes in DRGs after central and peripheral axon injury and reveals the essential roles of reactive oxygen species in axon regeneration by comparing changes between non-regenerative central axon injury and regenerative peripheral axon injury . ATAC anCircular RNAs are endogenous non-coding RNAs with covalently closed loop structures formed by transcript back splicing . AlthougA total of 56 8-week-old male Sprague Dawley rats (∼200 g) were obtained from the Animal Center of Nantong University. Rats were randomly divided into central axon branch injury group, central axon branch sham surgery group, peripheral axon branch injury group, and peripheral axon branch sham surgery group, with 14 rats in each group.Animal work was carried out in accordance with the guidelines of Nantong University Institutional Animal Care and Ethical approved by the Administration Committee of Experimental Animals, Nantong University, Jiangsu Province, China.Central and peripheral axon injuries were performed according to a previous publication with modifications . BrieflyCRA006070.Total RNAs was extracted from rat L4-L5 DRGs using TRIzol reagent kit  and subjected to RNA quality check using an Agilent Bioanalyzer . mRNA was enriched, fragmented into short fragments, and reverse transcripted to cDNA. cDNA fragments were ligated to Illumina sequencing adapters and sequencing was performed using HiSeq™ 4000 platform. RNA libraries were sequenced on the Illumina sequencing platform by Genedenovo Biotechnology Co., Ltd. . Sequencing data were stored in Genome Sequence Archive database with accession number p-value calculation. Circular RNAs with log2 (fold change) > 1 or < −1 and p-value < 0.05 were screened and considered as differentially expressed. The functions of host genes of differentially expressed circular RNAs were discovered using kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis  to obtain high quality clean reads . Clean ranalysis , reactomanalysis . The sigThe competing endogenous RNA (ceRNA) network was constructed by assembling all co-expression competing circular RNAs, microRNAs, and mRNAs. Expression correlations between circular RNAs and microRNAs as well as microRNAs and mRNAs were analyzed using the Spearman Rank correlation coefficient (SCC) to screen negatively co-expressed circular RNA-microRNA pairs or microRNA-mRNA pairs. Expression correlations between circular RNAs and mRNAs were analyzed using Pearson correlation coefficient (PCC) to screen positively co-expressed circular RNA-mRNA pairs. The interactions between circular RNAs, microRNAs, and mRNAs in the ceRNA network were displayed using Cytoscape software (v3.6.0).–ΔΔCt method with GAPDH as the reference gene. Primer sequences were as follows (5′−3′): novel_circ_000290-F TGACTCCCTCTCTGGTGACA, novel_circ_000290-R GCTTCCTCAACACCATCACC, novel_circ_001817-F AGGCTATTCGCTTAGGATTCCA, novel_circ_001817-R CCAGGTAGTTGTGTCCGATGTA, novel_circ_000991-F CATCCACGTCATGAGGAACAG, novel_circ_000991-R TACAAGACACTCGGTCTACCAA, GAPDH-F ACAGCAACAGGGTGGTGGAC, and GAPDH-R TTTGAGGGTGCAGCGAACTT.Total RNA extracted from rat L4-L5 DRG was reversely transcribed to cDNA using PrimeScript RT Reagent Kit . The characterization of circular RNA was determined using the divergent primers annealing at the distal end. PCR product was subjected to agarose gel electrophoresis and Sanger sequencing . The relative abundance of novel_circ_000290, novel_circ_001817 and novel_circ_000991 was determined using the comparative 2t-test . Significantly difference was set at a p-value < 0.05.Numerical data were expressed as mean with SEM. Comparisons between DR-Exp and DR-sham as well as SN-Exp and SN-sham were generated using unpaired Dorsal root ganglions of rats underwent central axon branch injury, peripheral axon branch injury, or corresponding sham surgery were collected at 24 h after surgery and subjected to sequencing to obtain a global view of circular RNAs in rat DRGs after central and peripheral axon injuries . SequencBioinformatic analysis identified a total of 2,310 circular RNAs in rat DRGs. These circular RNAs showed similar sample expression distributions after central and peripheral axon injuries . The locThe expression levels of circular RNAs after central and peripheral axon injuries were determined to screen differentially expressed circular RNAs. A total of 33 circular RNAs were found to be differentially expressed after central branch injury versus central branch sham surgery, with 9 circular RNAs up-regulated and 24 circular RNAs down-regulated . A largeDetailed information of all differentially expressed circular RNAs, including their chromosome localizations, genomic start/end sites, spliced lengths, annotation types , and host genes, was provided in Among these differentially expressed circular RNAs, circular RNA novel_circ_000991 exhibited relative high abundances in all groups and thus was further examined for its characterization and expression. Consistent with RNA sequencing outcome, Sanger sequencing recognized Mtmr7 (NM_001107312.3) as the host gene of novel_circ_000991 . The cycTo discover biological implications of differentially expressed circular RNAs in DRGs after central axon injury, host genes of these differentially expressed circular RNAs were annotated with KEGG database. Top enrich KEGG pathways were listed in Reactome analysis further demonstrated the considerable participation of Hippo signaling  and apoptosis  by caspase 3, R-RNO-2028697) in DRGs after central axon injury. The enrichment of reactome pathways WWTR1 (TAZ) binds to ZO-1 (TJP1) (R-RNO-2064417), phosphorylation of Cx43 by c-src (R-RNO-191636), c-src mediated regulation of Cx43 function and closure of gap junctions (R-RNO-191647), and regulation of gap junction activity (R-RNO-191654) implied changes of cellular junction. The identification of significantly involved reactome pathway Caspase-mediated cleavage of TJP1 (R-RNO-351913) further indicated the association of apoptosis and cellular junction regulation. Moreover, the recognition of reactome pathways IL6 binds IL6R-2 (R-RNO-1067640), IL6 binds IL6R (R-RNO-1067667), and IL6:IL6R-2 binds IL6ST-2 (R-RNO-1067676) illuminated the involvement of immune responses in central axon injury-mediated molecular features .Gene ontology term enrichment revealed the involvement of GO biological processes biological regulation, cellular process, regulation of biological process, GO cellular components cell, cell part, and GO molecular function binding .Host genes of differentially expressed circular RNAs in DRGs after peripheral axon injury were subjected to functional enrichment as well. Similar as changes after central axon injury, Hippo signaling pathway and Notch signaling pathway were found to be enriched after peripheral axon injury. A metabolism-related pathway, Inositol phosphate metabolism (ko00562), was also discovered as a commonly involved KEGG pathway in host genes of differentially expressed circular RNAs after both central and peripheral axon injuries. Besides Inositol phosphate metabolism, many other metabolism-related pathways, i.e., Glycosphingolipid biosynthesis-ganglio series (ko00604), Mucin type O-glycan biosynthesis (ko00512), Fatty acid degradation (ko00071), and Fatty acid metabolism (ko01212) were also significantly changed in DRGs after peripheral axon injury .Reactome enrichment analysis showed the essential involvement of PTEN in peripheral axon injury-induced changes in DRGs as many significant reactome pathways were related to PTEN, including Regulation of PTEN gene transcription (R-RNO-8943724), SALL4 recruits NuRD to PTEN gene (R-RNO-8943780), and PTEN Regulation (R-RNO-6807070). Furthermore, TP53-related reactome pathways MTA2-NuRD complex deacetylates TP53 (R-RNO-6805650) and Regulation of TP53 activity through acetylation (R-RNO-6804758) as well as ATF-associated pathway MSK1 activates ATF1 (R-RNO-6804758) were also identified as top enriched reactome pathways .Gene ontology analysis revealed cellular process as the most significantly enriched GO biological process, cell and cell part as the most significantly enriched GO cellular components, and binding as the most significantly enriched GO molecular function .Given the essential participation of PTEN-related pathways in peripheral axon injury-induced changes, circular RNAs sourced from genes involved in PTEN pathways are worthy of note. Novel_circ_002033, a circular RNA cyclizated from Gatad2b, was found to be up-regulated after peripheral axon branch injury versus sham surgery but not significantly altered after central axon branch injury. Therefore, besides the function of the host gene of novel_circ_002033, the regulatory roles of novel_circ_002033 was also explored by generating the ceRNA network of novel_circ_002033. Regulatory network showed that novel_circ_002033 might function as a microRNA sponge and regulate the target genes of miR-6331-z  and miR-129-x  .Dorsal root ganglion neurons are extensively used to investigate axon regeneration as the central and peripheral axon branches of DRG neurons have different axon regrowth abilities . IdentifPreviously, our laboratory has investigated changes in DRGs at a series of time points, i.e., 0 h, 3 h, 9 h, 24 h, 4, and 7 days after rat sciatic nerve injury and found that many genes exhibited altered expression levels at 24 h post peripheral axon injury . To examFunctional analyses of host genes of circular RNAs provide important biological implications as circular RNAs regulate their host genes . Hippo svia PTEN oxidation and inactivation  can be found in the article/The animal study was reviewed and approved by Nantong University Institutional Animal Care and Ethical approved by the Administration Committee of Experimental Animals, Nantong University, Jiangsu Province, China.H-JC, LH, M-RZ, and TZ performed the experiment. L-CX designed the experiment. All authors wrote the manuscript.