In this study, we predicted the core active compounds of rhubarb used in the treatment of diabetic kidney disease (DKD) and the related core gene targets and pathways using network pharmacological approaches.The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform was used to identify active compounds of rhubarb. PharmMapper was used to predict the gene targets of active compounds, which were subsequently provided a standard nomenclature using the UniProt database. In addition, DKD-related target genes were predicted using GeneCards, Online Mendelian Inheritance in Man, and Therapeutic Target Database. The genes that were targeted both by rhubarb active compounds and implicated in DKD (hereafter referred to as overlapping target genes) were identified using Venny 2.1. A drug–target–disease network diagram was obtained using Cytoscape and a protein–protein interaction network diagram was constructed using the STRING database. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses of overlapping target proteins were performed using the Database for Annotation, Visualization and Integrated Discovery Bioinformatics Resources 6.8.Eighteen core active compounds of rhubarb were extracted, and 136 target genes of rhubarb were identified. Some of the active compounds revealed by the network pharmacological analysis were catechin, aloe-emodin, rhein, and emodin; certain core target proteins suggested by the protein–protein interaction network analysis were AKT1, PIK3R1, and SRC. The overlapping target genes were primarily involved in apoptosis and proteolysis, with the PI3K/Akt signaling pathway identified as significantly enriched.Network pharmacological strategies were used to identify core active compounds of rhubarb and their related pathways. We believe that our study will provide potential and effective novel targets to identify active compounds of rhubarb for treating DKD. According to a study, 30% to 40% of individuals with diabetes tend to develop DKD despite good control of blood pressure and blood sugar. The China Kidney Disease Network Annual Data Report mentioned DKD as the top-most risk factor for chronic kidney diseases in urban residents in China in 2016.Diabetes is a chronic, metabolic hyperglycemic disorder and occurs due to impaired secretion or function of insulin, or both. The global prevalence of diabetes was about 8.8% in 2017, with about 7.3% of individuals having impaired glucose tolerance. The highest prevalence of diabetes is reported in China, India, and the United States.–3 Rhubarb, a widely used component of TCM, has several medicinal properties. For example, rhubarb extracted from the rhizomes of Rheum officinale Baill, Rheum palmatum L., and Rheum tanguticum Maxim. ex Balf. is known for its medicinal benefits. The primary active components of rhubarb include aloe-emodin, chrysaron, emodin, chrysophanol, rhaponiticin, and D-catechin, which have the main effects of cooling and detoxification, in addition to removing stagnation, improving blood circulation, and preventing blood stasis. Clinically, rhubarb has been used to treat diabetes, DKD, and other metabolic syndromes.–6 For example, emodin, a major active component of rhubarb, efficiently prevents oxidative damage to the kidneys of mice with DKD. However, the molecular mechanism contributing to the beneficial effects of rhubarb in treating DKD remains unclear.The Traditional Chinese medicine (TCM) has been used for more than 2,000 years; moreover, it is currently being widely used in clinical practice to treat several diseases including diabetes.–9; Ginkgo biloba L. leaves extract prevents brain damage after ischemic stroke by regulating the expression of Bax/Bcl-2 and Caspase-3. Network pharmacology, a pharmacological branch to study the interactions of “drug–target–disease,” was first proposed in 2007 by Andrew L. Hopkins, a pharmacologist from Dundee University, UK. Therefore, the concept of network pharmacology deviates from “one drug, one gene, one disease” principle used in earlier studies. Thus, this concept can be applied to predict the underlying mechanism of decoction pieces of TCM involving multiple targets and pathways.Several drugs function by regulating the expression of multiple genes and proteins. For example, mulberry leaves alleviate diabetes by activating the p38 MAPK/NF-κB pathway and inducing autophagyWe used network pharmacological approaches to identify the active components of rhubarb. In addition, we predicted the related target genes and key pathways to decipher the underlying mechanism of rhubarb in treating DKD and provide a scientific rationale for further in-depth studies on the therapeutic effects of rhubarb on DKD. Figure 22.1 It contains details of 499 herbal medicines, included in the 2010 edition of Chinese Pharmacopoeia, and their active compounds . The active compounds were screened for several pharmacological properties including oral bioavailability (OB), drug-like (DL) properties, intestinal epithelial permeability, blood–brain barrier permeability, and half-life.The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) provides information on active compounds, targets, and drug–target networks.2.2 DL reflects how similar a compound is to the known drugs, that is, DL compounds have the potential to behave like drugs. These compounds are known as drug-like molecules or drug analogs. We used “rhubarb” as the keyword to search the TCMSP database using the screening criteria of DL ≥ 0.18 and OB > 30%.We selected OB and DL as the primary parameters to screen active compounds of rhubarb using the TCMSP database. Bioavailability refers to the rate and extent to which a drug is absorbed into circulation. Oral bioavailability reflects the amount of a given drug that enters the circulation, that is, the percentage of oral drug absorbed by the gastrointestinal tract and reaching the blood circulation via the liver.2.3https://pubchem.ncbi.nlm.nih.gov/) and submitted to the PharmMapper service platform. We selected only “human protein targets ” for “select target set.” The results were organized according to the fit value, and the top 50 targets were selected. Finally, the names of the retrieved target genes were replaced with their corresponding official symbols given in the UniProt database (https://www.uniprot.org/).To predict the active compounds of rhubarb, their two-dimensional (2D) structures were retrieved from PubChem (2.4https://www.genecards.org/), Online Mendelian Inheritance in Man , and Therapeutic Target Database (http://db.idrblab.net/ttd/) for DKD-related genes. The results were integrated, and duplicated entries were removed.We used the “diabetic kidney disease” as the keyword to search the GeneCards (2.5https://bioinfogp.cnb.csic.es/tools/venny/). Finally, a Venn diagram was drawn.The genes that were simultaneously targeted by rhubarb active compounds and implicated in DKD (hereafter referred to as overlapping target genes) were identified using Venny 2.1 . Next, network topological parameters were obtained using the “network analyzer” function. A core architecture network was constructed using Cytoscape and the degree value of a node was represented as the number of edges connected to that node2.7https://string-db.org/cgi/input?sessionId=bR2DXqok3ofm) and rhubarb–target interactions were identified. We restricted our research in the STRING database to “Homo sapiens” with a high confidence level of > 0.7. The results from Cytoscape analysis were calculated and the core proteins were identified using the degree value.Next, a PPI network was constructed for overlapping target proteins using the STRING database (2.8https://david.ncifcrf.gov/).–24 A P < .05 was used as the threshold, with a smaller value of P denoting significant enrichment of a GO or a KEGG term.We next performed GO and KEGG enrichment analyses of overlapping target proteins using the Database for Annotation, Visualization and Integrated Discovery Bioinformatics Resources 6.8  . Based on the degree values and betweenness centrality of network nodes, the top five active compounds of rhubarb identified were mutatochrome , 10beta-hydroxy-6beta-isobutyrylfuranoeremophilane , toralactone , palmidin A , and cinnamic acid  , CASP3 , INSR , CRABP2 , and MAP2K1 . The majority of the target genes were associated with more than one compound. For example, NR3C1 was associated with 10 compounds, and CASP3 was associated with 9 compounds.The top five target genes identified were 3.4We used a high confidence level of > 0.7 and eliminated discrete targets to obtain a PPI network diagram for overlapping targets using the STRING database Fig. . The PPI3.5P value of < .05. The 20 GO annotation terms with the lowest P values are shown in Figure To identify the functions of overlapping targets, we performed GO and KEGG pathway enrichment analyses of overlapping targets. We obtained 352 GO annotated enriched terms by setting a threshold P values are shown in Figure The KEGG pathway enrichment analysis resulted in 93 signaling pathways. The 20 signaling pathways with the lowest 4Diabetic kidney disease, one of the microvascular complications associated with diabetes, is characterized by damage to small blood vessels. DKD can affect different kidney cells including glomerular epithelial cells, distal renal tubular cells, mesangial cells, and glomerular podocytes.–28Rhubarb, a herb, has been widely used in TCM. It is bitter in taste and cold in nature; raw rhubarb is known for its cooling effects. In addition, it detoxifies the blood, removes stagnation, increases blood circulation, and prevents blood stasis. Wine-processed rhubarb is used to detoxify the body and restore homeostasis. Recent studies have reported the use of major components and several prescriptions of rhubarb in the treatment of DKD.We extracted 18 rhubarb core active compounds and identified 136 target genes of rhubarb that could be used as potential therapeutic targets for DKD.–32 For instance, Zhu et al reported that (–)–catechin blocked the inflammatory response pathways by scavenging methylglyoxal, thereby inhibiting the formation of advanced glycation end-products and improving the renal function in DKD mice. Similarly, Li et al stated that aloe-emodin effectively improved the renal function in DKD rats and exerted certain therapeutic and preventive effects on DKD. Another study demonstrated that rhein alleviated the autophagy of renal tubular cells in DKD mice by regulating the AMPK/mTOR pathway, thereby delaying the process of renal fibrosis. A study by Qi et al found that emodin reduced cell autophagy and alleviated oxidative damage in the kidneys of DKD mice by downregulating the expression of the mouse miR-21 gene.The primary active compounds of rhubarb included (–)–catechin, aloe-emodin, rhein, and emodin. Their mechanisms of intervention in treating DKD have previously been confirmed to a certain extent.CASP3 gene, which encodes Caspase-3, has been previously reported as a target gene of rhubarb in DKD treatment. Caspase-3 has been implicated in normal brain development. In addition, it is implicated in the execution stage of apoptosis, that is, chromosome condensation and DNA fragmentation. Apoptosis refers to autonomous programmed death of cells; abnormal cell apoptosis can lead to several diseases. For example, studies have reported the involvement of renal cell apoptosis in DKD.–40 Caspase-3 can be activated through both extrinsic and intrinsic pathways of apoptosis.–43 Both in vivo and in vitro studies have demonstrated that the inhibition of Caspase-3 activity by Z-DEVD-FMK effectively reduced the degree of renal tubular cell apoptosis. A previous study proved that Danggui Buxue decoction reduced the apoptosis of kidney cells in DKD rats by downregulating the expression of ATF6, CHOP, and Caspase-3. Steroid hormones can regulate inflammatory responses and immune and reproductive functions in the body. Natural steroid hormones are lipids that can easily pass through the cell membranes to bind to steroid hormone receptors, thereby causing cellular alterations. Insulin combined with steroid therapy can partially rescue diabetes-induced imbalance in hormones and angiogenesis.The results of our PPI network analysis revealed the involvement of AKT1, PIK3R1, SRC, HRAS, IGF1, GRB2, and MMP9 in the mechanism underlying the therapeutic effects of rhubarb in DKD treatment. The GO enrichment analysis resulted in 352 enriched GO annotations that corresponded to oxidation, cell apoptosis, and proteolysis, which were implicated in the treatment of DKD with rhubarb. Moreover, the involvement of multiple proteins and pathways reflects the diversity in the mechanism underlying the therapeutic effects of rhubarb in DKD treatment and provides a scientific rationale for future studies. The steroid hormone-mediated transduction is executed via several molecular signals generated by the binding between intracellular steroid hormone receptors and their physiological ligands. Proteolysis, a process of degrading proteins into shorter polypeptides or amino acids, maintains protein homeostasis. Therefore, abnormalities in proteolysis can cause diseases. For example, a previous study demonstrated that the abnormal activation of matriptase caused hydrolysis of podocin, thereby damaging podocytes.PIK3R1, IGF1R, KDR, AKT1, RAC1, JAK2, PCK1, HRAS, JAK3, MAP2K1, HSP90AA1, SYK, NOS3, PDPK1, INSR, IGF1, IL2, PGF, CDK6, KIT, CDK2, MDM2, GRB2, TEK, RAF1, MET, and FGFR1, were found to be significantly enriched . The PI3K/Akt signaling pathway transduces extracellular signals to promote cell metabolism, cell proliferation, cell growth, and angiogenesis. The major effector molecules involved in this pathway include receptor tyrosine kinases (RTKs), phosphatidylinositol-4,5-bisphosphate (PIP2), phosphatidylinositol 3-kinase (PI3K), AKT/protein kinase B, and phosphatidylinositol-3,4,5-bisphosphate. A study demonstrated that PAQR3 knockout inhibited the activation of the PI3K/Akt signaling pathway in glomerular mesangial cells following hyperglycemia, consequently inhibiting the deposition of extracellular matrix in glomerular mesangial cells. Animal experiments indicated that histone deacetylase (HDAC) inhibitors reduced the extracellular matrix deposition in the kidneys of diabetic mice and downregulated the expression of HDACs, TGF-β1, and α-SMA. In addition, METL14-mediated regulation of PTEN-induced activation of this pathway significantly affected the HDAC-induced epithelial–mesenchymal transition (EMT) of renal tubular cells in DKD. In addition, this pathway is related to the degree of renal fibrosis and glomerular podocyte damage.–52The KEGG pathway enrichment analysis revealed multiple signaling pathways to be implicated in the therapeutic effects exerted by rhubarb on DKD. The analysis indicated that rhubarb could be exerting its therapeutic effect on DKD by regulating the PI3K/Akt signaling pathway. Twenty-seven PI3K/Akt signaling pathway-related genes, namely This study has the following limitations. First of all, network pharmacology research is based on databases and software. At present, the existing databases and software still have certain imperfections that may lead to imprecision of our conclusions, for example,the effect of different decoction time of Rhubarb on the active ingredients was not recorded in the database. Second, in experiments and clinical applications, different processing methods, decoction methods, and decoction time may cause changes in active ingredients and therapeutic targets of single Chinese medicines. These changes were not taken into consideration in our study and may thus cause imprecision of our conclusions. Third, our analysis of rhubarb active compounds was based on only TCMSP, which may result in selection bias. Fourth, this study only predicted the potential active ingredients, therapeutic targets and related signaling pathways of rhubarb in the treatment of DKD. The exact pharmacological effects of rhubarb on DKD need to be further validated through animal experiments and even clinical trials. Finally, the efficacy of drugs is based on medicinal substances, which must reach certain doses or concentrations to be effective. Therefore, cell or animal experiments are needed to test the efficacy of rhubarb in the treatment of DKD.5In this study, we adopted network pharmacological strategies to identify the active compounds and core target genes of rhubarb for treating DKD. A PPI network was constructed for the overlapping target proteins, followed by GO and KEGG pathway enrichment analyses of overlapping targets. Our results showed PI3K/Akt signaling pathway as the primary contributor to the mechanism underlying the therapeutic effect of rhubarb in treating DKD. In addition, we predicted other potential active components, core gene targets, and signaling pathways implicated in DKD treatment by rhubarb. Being a bioinformatic study, the active compounds, gene targets, and pathways identified and proposed in this study warrant further verification and validation by follow-up studies.Conceptualization: Zheng Nan.Data curation: Yingyuan Gao.Formal analysis: Yingyuan Gao.Funding acquisition: Zheng Nan.Investigation: Yingyuan Gao, Zheng Nan.Methodology: Yingyuan Gao, Zheng Nan.Project administration: Yingyuan Gao, Zheng Nan.Resources: Yingyuan Gao, Zheng Nan.Software: Yingyuan Gao.Supervision: Zheng Nan.Validation: Zheng Nan.Visualization: Yingyuan Gao.Writing – original draft: Yingyuan Gao.Writing – review & editing: Zheng Nan.


Since these three biological processes are frequently implicated in ASD, our findings support the hypothesis that cell death processes and specific metabolic pathways, both of which appear to be targeted by APAP and pesticide exposure, may be involved in the etiology of ASD. This novel exposures-gene-disease database mining might inspire future work on understanding the biological underpinnings of various ASD risk factors.Recent epidemiological studies suggest that prenatal exposure to acetaminophen (APAP) is associated with increased risk of Autism Spectrum Disorder (ASD), a neurodevelopmental disorder affecting 1 in 59 children in the US. Maternal and prenatal exposure to pesticides from food and environmental sources have also been implicated to affect fetal neurodevelopment. However, the underlying mechanisms for ASD are so far unknown, likely with complex and multifactorial etiology. The aim of this study was to explore the potential effects of APAP and pesticide exposure on development with regards to the etiology of ASD by highlighting common genes and biological pathways. Genes associated with APAP, pesticides, and ASD through human research were retrieved from molecular and biomedical literature databases. The interaction network of overlapping genetic associations was subjected to network topology analysis and functional annotation of the resulting clusters. These genes were over-represented in pathways and biological processes (FDR  According to the Center for Disease Control and Prevention (CDC), 1 in 59 children born in 2008 in the United States have been diagnosed with Autism Spectrum Disorder (ASD), and the prevalence of ASD has increased in recent decades .Based on recent evidence, two prenatal exposures of importance to the risk of developing ASD are the common analgesic, APAP, and pesticides, which are highly present in our current environment and food . APAP isRecent epidemiological cohort studies conducted in various countries have associated maternal use of APAP with increased risk of ASD and ASD-linked symptoms, such as internalizing and communication behavioral problems, in offspring and raise concerns that APAP may interfere with optimal fetal brain development ,14,15,16Widespread environmental chemicals might modify the effects and toxicity of pharmaceutical agents, a concept known as drug-exposome interactions, resulting in reduced drug efficacy, increased drug resistance, or adverse effects ,22. CrosAnother possible environmental agent that could target similar pathways or modify the effects of APAP would be neurotoxic pesticide compounds, which have also been linked to ASD risk ,28. SomeThe work presented here is a novel, first attempt to explore whether there are shared underlying biological pathways connecting genes associated with these two external exposure risk factors for the development of ASD. Both APAP and numerous pesticide compounds can function as oxidative stressors and subsequently affect neurodevelopment . AlthougStudies have shown that prenatal exposure to pesticides may result in the occurrence of general neurobehavioral disorders . Of the To explore whether APAP and pesticide exposure might influence ASD risk through similar underlying biological mechanisms, we conducted this study to perform database mining of the genetic associations of ASD in combination with network and functional analysis of candidate genes. Computer-based text mining methods have been used before to generate biomedical hypotheses on the impact of multiple exposures by examining novel associations between genes and diseases . For exahttps://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis, accessed in May 2020) [Finding and retrieving genetics associations: Lists of genes associated with the exposures and disease of interest  were extracted from the public databases: Comparative Toxicogenomics Database (CTD) , PubChemay 2020)  and Metahttp://www.interactivenn.net/, accessed in May 2020), which revealed overlapping genes between lists. The genes in each category that were identified in at least two databases, or the same database but with two independent search criteria, were used for further analysis.The PubChem, DrugBank, CTD, Open Targets, and IPA databases were searched with keywords such as “Acetaminophen”, “Tylenol”, and “APAP” to identify genes in the human genome associated with APAP. Each database then matched the search term to existing concepts before executing the search. The search term “Tylenol”, for example, yielded results related to terms such as “Paracetamol”. For identifying genes associated with pesticides, the Chemicals and Diseases tabs were selected in independent searches of the term “Pesticides” using CTD. Each independent search of CTD employed different combinations of disease category, set to either Autism Disorder or Nervous System disease, and association type, set to curated or inferred. Finally, independent searches of the six molecular databases  were performed to identify genes related to ASD using the search terms “Autism”, “Autism Spectrum Disorder”, or “Autism or Intellectual Disability” and find genes associated with ASD. The lists of genes generated by these searches were sorted into their respective categories  and used to create Venn diagrams using InteractiVenn , a biomedical literature search engine, to retrieve genetic associations for APAP, pesticides and ASD. This is a mining tool that utilizes various ontologies  to map terms  from the MEDLINE database. When any two terms are both found in any particular record, they become connected in the network. This tool also calculates a statistical score for the association between the two terms using a function that considers the frequency with which the entities co-occur versus their independent occurrence in the corpus as a whole . Genes significantly associated (p < 0.05) with the search terms  were used for further analysis. Both molecular database and literature methods are necessary as the former is more sensitive, while the latter is more specific to the search terms. Thus, incorporating both leads to more comprehensive results.To complement the results from the molecular databases, we used Coremine Medical  of these genes in pathways, diseases, and biological functions. We used the Comparison Analysis function in IPA to compare the pathways overrepresented in the genetic associations found in the molecular database and literature analyses. IPA was also used to find the associations of overlapping genes with neurodevelopment functions. This knowledgebase contains associations between molecules, diseases and functions manually curated from the biomedical literature. It also calculates the significance of the overlap using Fisher’s Exact test.Pathway and network analysis: Ingenuity Pathway Analysis  was used to carry out analyses for pathway, network, and molecular and cellular functions for found genetic associations. Each gene symbol was mapped onto its corresponding gene object in the Ingenuity Pathways Knowledge Base. This tool provides information about the overrepresentation  database [For network analysis, we used the STRING database to retrieve the gene interaction networks , CYP1A2, CYP3A4, FAS Cell Surface Death Receptor (FAS), Insulin like Growth Factor Receptor 1 (IGF1R).The Venn diagram in p-value of the overrepresentation analysis, and the overlapping genes. Results from this table are reassuring as pathways related to metabolism of foreign substances are present, as well as pathways that have been targeted in treatment efforts relating to ASD.p = 1.41 × 10−11). Green nodes comprise a cluster that is significantly related to carbohydrate metabolism (FDR p = 3.39 × 10−12).The interaction network in p < 0.05) frequencies of co-occurrence between the searched terms . In order to complement the results from different molecular databases, literature mining analysis was carried out using Coremine Medical. p = 9.10 × 10−7), ERK1 and ERK2 cascade (FDR p = 9.20 × 10−7), and cell growth (FDR p = 1.40 × 10−7).The 16 genes found in the center node of p-value of 2.02 × 10−7. Such an enrichment p-value indicates that the proteins are at least partially biologically connected, as a group. STRING database analysis also showed interesting biological processes in the functional enrichment of the 16-gene network. Of note, regulation of phosphate metabolic process (FDR p = 3.19 × 10−12), regulation of cell death (FDR p = 6.25 × 10−9), regulation of ROS metabolic process (FDR p = 2.00 × 10−9), regulation of ERK1 and ERK2 cascade (FDR p = 1.02 × 10−9), and regulation of apoptotic process (FDR p = 4.76 × 10−8) were all processes related to the interactions between these genes.Over representation analysis using the STRING database of the 16 genes resulted in a protein–protein interaction enrichment p-values and respective genes. Some relevant pathways include Telomerase Signaling (p = 5.45 × 10−5), FXR/RXR activation (p = 8.86 × 10−5), Type I Diabetes Mellitus (p = 6.08 × 10−5), as well as several neurological pathways, such as Hypoxia-inducible factor 1α (HIF1α) signaling (p = 2.82 × 10−3), Agrin Interactions at Neuromuscular Junction (p = 1.39 × 10−3), Neuregulin Signaling (p = 2.04 × 10−3), and Neuroprotective Role of THOP1 in Alzheimer’s Disease (p = 3.02 × 10−3).IPA pathway analysis results are shown in We compared the results from molecular datasets with those obtained with literature mining at the pathway level. Overrepresented common pathways are shown in p = 2.91 × 10−11), in the IPA knowledgebase.IPA knowledgebase showing the overlap between the 6 and 16 genes found via molecular database and literature mining approaches, respectively, as they relate to neurodevelopmental functions . Each arTo the best of our knowledge, this is the first study to explore the potential effects of two common prenatal exposures, APAP and pesticides, on development with regards to the increasingly prevalent ASD. We found overlapping genes associated with APAP, pesticides, and ASD to be overrepresented in several biological pathways relating to apoptosis, metabolism of ROS, and carbohydrate metabolism. These pathways, many of which relate to the nervous system and xenobiotic metabolism, could elucidate the shared biology of APAP and pesticide exposures on ASD risk through the lens of genetic functions. Our results show that the considered co-exposures have many biological and mechanistic overlaps that connect them with the disease of interest and support the hypothesis that APAP and pesticides may target the same genes and molecular pathways.The functions for each of the six genes uncovered through the molecular database associated with APAP, pesticides, and ASD, as well as the 16 genes elucidated through the literature mining approach, are presented in HIF1α leads to increased cell death and decreased proliferation of neuronal progenitor cells within the sympathetic nervous system [HIF1α increased necrotic and apoptotic cell death [p = 0.017) in HIF1α in the cerebellum [HIF1α expression [HIF1α was induced even prior to the onset of any hepatoxic effects and contributed to oxidative stress, which also links it to ROS metabolism [HIF1a expression in a manner that would result in the activation of apoptosis.Various biological pathways identified here, such as ERK1 and ERK2 cascade, as well as telomerase signaling, play a direct role in the signaling pathway of apoptosis ,51 and ps system . Anotherll death . After erebellum . Followipression . Neuropapression . In micetabolism . Thus, aAn established connection between ROS and apoptosis is further supported by our finding of AHR signaling. AHR is a ligand-induced receptor involved in interactions between an individual and its environment . After cROS metabolism has been implicated in perinatal mechanisms potentially leading to ASD’s clinical symptoms and its pathogenesis ,31,32,65IGF1R (Proper metabolism of carbohydrates is essential for normal neurodevelopment in order to supply the developing brain with the necessary energy demands needed for such dramatic changes in brain function and structure ,73. StudIGF1R , which hAPAP and pesticides may not always affect the same genes, apart from those identified in the center of the presented Venn diagrams, but they are associated with genes related to the same pathways, which is relevant because no single gene or region of the genome is responsible for ASD . If the This novel network analysis reveals patterns that add to the evidence in favor of studying a mixture of APAP and pesticides in relation to ASD. A major strength of this study’s exploratory, hypothesis-generating approach is the ability of in silico methods to relate and consolidate genetic associations of APAP, a widely used medication in pregnancy, with pesticides, a broad class of chemicals, and with ASD, a heterogeneous disorder with varying impacts on the lives of affected individuals. The use of broad inclusion criteria for pesticides in the methods may appear too heterogeneous; however, because pesticide exposures among pregnant women are common and variable, a strength of the present approach is the general inclusion criteria that capture all pesticide exposures, which might vary by location, age, occupation and socioeconomic status. One limitation of this work is its reliance on public databases. The quality of such databases cannot always be controlled because the extent of their reporting excludes effect size and effect direction, and they lack comprehensive lists of genetic associations with pesticides. Additionally, these methods do not enable us to differentiate between exposure timing. Thus, database gene-exposure associations are not specific to perinatal exposure but instead represent associations among the adult population; however, a literature review reveals that the 22 genes listed in APAP and pesticides are widespread exposures that have been implicated to affect risk of ASD, but the exposure-disease mechanisms were unknown. We presented a novel in silico analysis by mining literature and molecular databases to elucidate the potential role of these exposures in the etiology of ASD. Our findings suggest apoptosis, ROS metabolism, and carbohydrate metabolism as biological pathways intertwined in a possible mechanism of ASD’s etiology. Further, our results reveal the potential for intra-pathway interactions regarding genes and biological processes as a possible means by which APAP and pesticide co-exposure may modify the adverse exposure effects of one another. Future biological tests are needed to show the interactions between these genes of interest as they relate to the considered exposures and their larger roles in affecting apoptosis, the metabolism of ROS, and carbohydrate metabolism during development. Such work would involve in vivo or in vitro models and should consider testing the exposure mixtures of APAP and select pesticides that have been shown to affect neurodevelopment via developmental neurotoxicity studies and are evidenced to target the three biological processes identified here.


Pediatric sepsis is a great threat to death worldwide. However, the pathogenesis has not been clearly understood until now in sepsis. This study identified differentially expressed mRNAs and lncRNAs based on Gene Expression Omnibus (GEO) database. And the weighted gene co-expression network analysis (WGCNA) was performed to explore co-expression modules associated with pediatric sepsis. Then, Gene Ontology (GO), KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway, mRNA‑lncRNA and mRNA‑lncRNA-pathway co-expression network analysis was conducted in selected significant module. A total of 1941 mRNAs and 225 lncRNAs were used to conduct WGCNA. And turquoise module was selected as a significant module that was associated with particular traits. The mRNAs functions associated with many vital processes were also shown by GO and KEGG pathway analysis in the turquoise module. Finally, 15 mRNAs  and 4 lncRNAs  were selected as hub genes in mRNA‑lncRNA-Pathway co-expression network. We identified 15 mRNAs and 4 lncRNAs as diagnostic markers, which have potential functions in pediatric sepsis. Our study provides more directions to study the molecular mechanism of pediatric sepsis.Abbreviations: mRNA: messenger RNA; lncRNA: long noncoding RNAs; GEO: Gene Expression Omnibus; WGCNA: weighted gene co-expression network analysis; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; SIRS: systemic inflammatory response syndrome; TOM: topological overlap measure; BP: biological process; MF: molecular function; CC: cellular component; ROC: receiver operating characteristic curve; AUC: area under curve; MAPK14: Mitogen-activated protein kinase 14; ALI: acute lung injury; ITGAM: Integrin subunit alpha M; HK3: Hexokinase 3; LPS: lipopolysaccharide; 5-LO: 5-lipoxygenase; LTs: leukotrienes; LTB4R: leukotriene B4 receptor.  There are about 1.2 million childhood sepsis cases to be diagnosed globally per year . TherefoLong noncoding RNAs (lncRNAs) are a type of non-protein transcripts that exceed 200 nucleotides in length . LncRNAsTo explore the interaction mRNA-lncRNA and diagnostic markers in pediatric sepsis, we first identified mRNAs and lncRNAs based on RNA-seq datasets of the whole spectrum of systemic inflammatory response syndrome (SIRS), sepsis, septic shock, SIRS resolved and control group at day 1 and day 3 from Gene Expression Omnibus (GEO) database. Then, weighted gene co-expression network analysis (WGCNA) and module–trait relationships were conducted to select important modules. Subsequently, function and pathway enrichment analyses of module were conducted by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). mRNA‑lncRNA and mRNA‑lncRNA-Pathway networks also were constructed in significant modules. No study performed WGCNA to systematically construct mRNA‑lncRNA-Pathway co-expression networks concerning pediatric sepsis, which may provide a better understanding for lncRNA biological function and to find related biomarkers.Expression dataset of sepsis (GSE13904) were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13904) and then collated into standardized raw data for subsequent analysis. The mRNAs and lncRNAs expression data of 227 children included 18 control samples, 32 sepsis samples, 67 septic shock samples, and 22 SIRS samples on day 1. The day 3 samples consisted of 20 sepsis patients, 39 septic shock patients, 24 patients meeting SIRS resolved and patients had paired day 1 and day 3.mRNA and lncRNA screeningP < 0.05 & false discovery rate (FDR) <0.05 & fold change (FC) >2 to obtain the mRNA and lncRNA for WGCNA analysis.Limma package in R was used to find mRNA and lncRNA between the four related symptoms  and control samples at two different time points . The differential screening parameter was set as: ij was calculated as follows: aij = ))β. β was a soft threshold power, which mostly be concerned with the independence and the average connectivity degree in co-expression modules. The topological overlap measure (TOM) represented the overlap of network neighbors, and (1-TOM) retrieved a pairwise distance to identify hierarchical clustering nodes and modules [WGCNA is a method that summarized gene expression data into co-expression modules. It has an obvious advantage to find the complex relationships between relating modules and associated with traits . The 194 modules . ClusterP < 0.05 was defined significant correlation.The correlation between co-expression modules and clinical traits was estimated based on the phenotypic information of pediatric sepsis, septic shock, SIRS, SIRS resolved at day 1 and day 3 and control group. A significant co-expression module highly related to traits was identified. Module–trait relationships were computed by Pearson’s correlation tests, and p-value), and p-value was corrected by FDR. Thus, the function and pathway of mRNAs is identiﬁed in the significant module.DAVID (http://david.abcc.ncifcrf.gov/) was a database for annotation, visualization, and integrated discovery. GO and KEGG pathway analysis of mRNAs were conducted by DAVID. The ontology contained three classes: biological process (BP), molecular function (MF), and cellular component (CC). Fisher exact test, and multiple comparison test were used to calculate the significance level .p < 0.05 was supposed to be significant.Statistical analysis was conducted using R software 3.4.0 and SPSS 19.0. Fisher exact and multiple comparison tests were used to find differences between traits. The receiver operating characteristic (ROC) curve was conducted (glmnet package in R software) to estimate the specificity and sensitivity of potential biomarkers. 1941 mRNAs and 225 lncRNAs were identified among the four related symptoms  and control group. Then they were performed to build the co-expression modules using WGCNA analysis. And turquoise module was selected as a significant module correlated with pediatric sepsis. mRNA‑lncRNA co-expression networks also was conducted, and we found 15 mRNAs  and four lncRNAs , which were regarded as hub genes. Therefore, the four lncRNAs were speculated to have common functions on complement and coagulation cascades, phagosome, leukocyte trans-endothelial migration, metabolic pathways, and Insulin signaling pathways in pediatric sepsis. The area under the curve of ROC was above 0.88, indicating that 15 mRNAs and four lncRNAs are likely to have good diagnostic value for pediatric sepsis.Identification of mRNAs and lncRNAsOur analysis identified 1110 mRNAs and 111 lncRNAs in the comparison groups control vs. pediatric sepsis day 1, 435 mRNAs and 37 lncRNAs in the groups control vs pediatric sepsis day 3, 1278 mRNAs and 134 lncRNAs in the groups control vs pediatric septic shock day1, 1565 mRNAs and 172 lncRNAs in the groups control vs pediatric septic shock day 3, 760 mRNAs and 77 lncRNAs in the groups control vs pediatric SIRS day 1, 824 mRNAs and 112 lncRNAs in the groups control vs pediatric SIRS day 3, 613 mRNAs and 56 lncRNAs in the groups control vs. pediatric SIRS resolved day 3. Totally, we obtained 1941 mRNAs including 927 (47.8%) up-regulated and 1014 (52.2%) down-regulated mRNAs, and 225 lncRNAs containing 132 (58.7%) up-regulated and 93 (41.3%) down-regulated lncRNAs between the four related symptoms  and control group. And these mRNAs and lncRNAs were performed by the hierarchical cluster analysis .1941 mRNAs and 225 lncRNAs were performed to build the co-expression modules using WGCNA. The cluster analysis was shown that all the samples were in the clusters, so it is not necessary to remove the outlier. The soft threshold power β was set at 16, which was more consistent with scale-free networks and had a more biological significance . The indp value. There were 468 mRNAs and 26 lncRNAs in the turquoise module.The relationship between co-expression modules and clinical traits is identiﬁed in The mRNAs in the turquoise module were conducted by GO analysis and KEGG pathway enrichment analysis. The results showed that neutrophil degranulation, inflammatory response, innate immune response, and cytokine-mediated signaling pathway were enriched in BP, protein binding, protein homodimerization activity, and ATP binding were mostly involved in MF, plasma membrane, extracellular exosome, and cytosol were enriched in CC. KEGG pathway enrichment analysis suggested that metabolic pathway, TNF signaling pathway and cytokine-cytokine receptor interaction were the most significant pathways .Figure 3mRNA‑lncRNA co-expression networks were built to understand the roles and functional mechanisms of lncRNA in turquoise module . We founAccording to the interaction network of mRNA‑lncRNA and mRNA-Pathway, we obtained mRNA‑lncRNA-Pathway networks . In our XR_926068.1 (a lncRNA) was related to seven mRNAs  and abundant in MAPK signaling pathway, IL-17 signaling pathway, leukocyte trans-endothelial migration, cellular senescence, VEGF signaling pathway, TNF signaling pathway, platelet activation, endocrine resistance, toll-like receptor signaling pathway, inflammatory mediator regulation of TRP channels, metabolic pathways, phagosome, insulin signaling pathway, metabolic pathways, NOD-like receptor signaling pathway, and autophagy.RARA-AS1 (a lncRNA) was connected with six mRNAs  enriched in Complement and coagulation cascades, Phagosome, leukocyte trans-endothelial migration, metabolic pathways, and insulin signaling pathways. These four lncRNAs have common function on complement and coagulation cascades, phagosome, leukocyte trans-endothelial migration, metabolic pathways, and insulin signaling pathways in pediatric sepsis.Using the identified 15 mRNAs  and four lncRNAs , the ROC curves were constructed to estimate their specificity and sensitivity. The area under the curve (AUC) was above 0.88 between sepsis VS. control and septic shock VS. control groups, indicating that 15 mRNAs and four lncRNAs are likely to have good diagnostic value for pediatric sepsis .Figure 6Early diagnosis of pediatric sepsis is a challenge but important for the effective treatment. And the mortality rate sharply increased when pediatric sepsis progressed to severe sepsis and septic shock. Many studies displayed the essential role of genetic and epigenetic alterations as it is related to sepsis diagnosis and treatment . TherefoP value. The mRNA‑lncRNA co‑expression network of the turquoise module provided an insight into the correlation between mRNA and lncRNA. And we also conducted mRNA‑lncRNA-Pathway co‑expression network and found 15 mRNAs and four lncRNAs were significantly up-regulated, which had positive function on inflammation and enriched in Complement and coagulation cascades, Phagosome, leukocyte trans-endothelial migration, metabolic pathways, Insulin signaling pathways, and other Inflammation or immune pathways. It indicated that pediatric sepsis was due to immune disorders caused by multiple genes, and also revealed the signal pathway that the four lncRNAs participate in. In addition, 15 mRNAs and four lncRNAs expected to be good diagnostic markers, because the AUC was all more than 0.88.In our study, 1941 mRNAs and 225 lncRNAs were identified based on GEO database. After WGCNA analysis, the turquoise module was significantly correlated with pediatric sepsis because of the maximum number of genes and significant LncRNA participates in some important functions of cells and plays an important role in the pathogenesis of various diseases ,19. In tXR_926068.1 was related to hub mRNAs . NLRC4 (NLR family CARD domain containing 4) was found to be up-regulated by MAPK pathway in pediatric sepsis, which also inhibited IL‑1β and IL‑18 production to contribute to the anti-inflammatory response . SH3GLB1RARA-AS1 was connected with hub mRNAs . NCF4 promoted intracellular ROS production and played a role in immune and inflammatory diseases ,36. FLOTIn conclusion, our study highlights the important roles of turquoise module in the progression of pediatric sepsis. This article comprehensively expounds the mRNA and lncRNA differences between pediatric sepsis and normal group. Fifteen mRNAs  and four lncRNAs  were found play important roles on pediatric sepsis by WGCNA analysis. And they all have good diagnostic efficiency in pediatric sepsis and septic shock. It provides more directions to study the useful diagnostic markers and molecular mechanism of pediatric sepsis. Several limitations still existed in this study. Large numbers of pediatric sepsis samples are needed for further researches and more experiments are required to understand the biological function of key lncRNAs.Click here for additional data file.


Carotenoids represent a class of pigmented terpenoids. They are distributed in all taxonomic groups of fungi. Most of the fungal carotenoids differ in their chemical structures to those from other organisms. The general function of carotenoids in heterotrophic organisms is protection as antioxidants against reactive oxygen species generated by photosensitized reactions. Furthermore, carotenoids are metabolized to apocarotenoids by oxidative cleavage. This review presents the current knowledge on fungal-specific carotenoids, their occurrence in different taxonomic groups, and their biosynthesis and conversion into trisporic acids. The outline of the different pathways was focused on the reactions and genes involved in not only the known pathways, but also suggested the possible mechanisms of reactions, which may occur in several non-characterized pathways in different fungi. Finally, efforts and strategies for genetic engineering to enhance or establish pathways for the production of various carotenoids in carotenogenic or non-carotenogenic yeasts were highlighted, addressing the most-advanced producers of each engineered yeast, which offered the highest biotechnological potentials as production systems. Microbotryum violaceum (formerly Ustilago violacea) and Neurospora crassa [Carotenoids are terpenoid pigments of yellow, orange, and red color. They are synthesized in species from all taxa with the exception of animals . In conta crassa ,6. In sea crassa . This lia crassa . More dea crassa .14C-labelled at different positions [First attempts to elucidate the biosynthesis pathway were with mucoraceous fungi. Radioactivity incorporated by feeding acetate and mevalonate, both ositions , were reositions . The resositions  and withositions .The review provides an updated knowledge of developments in the fungal carotenoid field. The focus is on the carotenoid structures found in fungi, their biosynthesis pathways, and considerations on the reaction mechanisms for insertion of oxy groups. This also includes distribution of carotenoids within the different fungal groups. Furthermore, the potential of fungi for the production of commercially interesting carotenoids, including their formation by pathway engineering of yeasts, was addressed.crtYB fusion gene [crtY and crtB in Archaea where they are organized in a gene cluster, as exemplified for Sulfolobus solfaticus [Xanthophyllomyces dendrorhous (see below). In several fungi, carotenoid synthesis is photo-regulated. Transcriptional regulation of carotenoid pathways by light and other factors in Fusarium (formerly Gibberella) fujikuroi, N. crassa [Phycomyces blakesleanus [As part of the Archaea-Eukarya lineage , fungi iion gene . It evollfaticus . Due to lfaticus  and are lfaticus . In cont. crassa , and Phyesleanus  were revAcyrthosiphon pisum, the synthesis of torulene, which is a typical fungal carotenoid, was discovered [crtYB gene, which can be regarded as a fungal signature, is present and involved in aphid carotenoid biosynthesis, it is obvious that this pathway was acquired by horizontal gene transfer from fungi.Along the eukaryotic lineage, animals lost the potential of carotenoid biosynthesis. However, in aphids, such as varieties of scovered . Since tal-2, crtYB, or carRA [al-1, crtI, or carB [al-2 gene was first cloned from N. crassa as a phytoene synthase gene. For the homolog crtYB from X. dendrorhous, functional analysis revealed that the expressed gene product catalyzed not only phytoene synthesis, but also lycopene cyclization. Both functional domains could be located in the gene sequence [Blakeslea trispora, indicating a posttranslational protein cleavage [al-1 was also cloned from N. crassa [carB from P. blakesleanus encodes a 4-step desaturase which produces lycopene, as demonstrated by heterologous expression [al-1 from N. crassa inserts a fifth double bond at C3-C4 [In β-carotene synthesizing fungi, the pathway proceeds in three steps . After tor carRA ,22,23 de or carB ,25. The sequence . Both docleavage . The fir. crassa . Dependipression  and by lpression . In contat C3-C4 . This prN. crassa as 4′-apo-β,ψ-caroten-4′-oate [N. crassa based on the genes involved and the carotenoid composition of the yellow YLO mutants. Carotenoids in different YLO strains were identified as apo-4′-lycopenal and β-apo-4′- carotenal [cao-2 gene of N. crassa [carT in the genus Fusarium [ylo-1 from N. crassa [carD in Fusarium) [F. fujikuroi [Gibberella zeae [Podospora anserina [The end product of carotenoid synthesis of Sordariomycetes is neurosporaxanthin. Its structure was elucidated from -4′-oate . Other a-4′-oate ,31. The arotenal  or theirarotenal . In caro. crassa , also asFusarium , and the. crassa  (as carDusarium) . For theujikuroi , Gibberella zeae , and Podanserina , only thX. denrorhous (anamorph Phaffia rhodozyma) is the only fungus which is able to synthesize the 3,3′-dihydroxy-4,4′-diketo β-carotene derivative astaxanthin [asy gene [crtS in another publication [crtR encoded cytochrome P450 reductase [X. denrorhous is 3-HO-4-ketotorulene. Its synthesis diverts from the main pathway by a fifth desaturation of lycopene to 3,4,-didehydrolycopene in competition to lycopene cyclization  strain in the formation of trisporin [B. trispora [Mucor mucedo as the product of the tsp1 gene [The residual pathway continuing with the metabolization of 4-dihydrotrisporin relies on chemical interactions between the two mating partners . Individrisporin . It was trispora   and (−) strains. Highest yields of 39 mg/g dw were obtained with improved mutants or intersexual heterokaryons with nuclei of both mating types [B. trispora is at the moment the only large-scale industrial process for fungal carotenoids. This carotenoid was recovered by extraction and supplied in a crystalline form as a colorant or as provitamin A [Mutagenesis for alteration of the carotenoid pathway including the generation of high-yield strains was first successfully carried out with sleeanus . The carormation . Consideadiation . Severaladiation . An estang types . The samitamin A .Candida utilis with bacterial genes to establish the pathway to astaxanthin. The genes used were crtB for phytoene synthesis, crtI for phytoene desaturation to lycopene, crtY for cyclisation to β-carotene, and crtW together with crtZ for β-carotene ketolation and hydroxylation to astaxanthin (C. utilis strains were modified in key metabolic steps by over expression of the gene for HMG-CoA reductase, HMG, which is the limiting enzyme for the entire terpenoid pathway [crtE to re-direct metabolite flow from the sterol pathway into carotenoids (Lipomyces starkeyi [Pichia pastoris [C. utilis, the carotenoid pathway was also extended to astaxanthin in P. pastoris, but only 0.004 mg/g dw were obtained [Different non-carotenogenic yeasts were genetically engineered for carotenoid biosynthesis . This staxanthin A, which  pathway . In addiotenoids A. This rstarkeyi  and Pichpastoris , reachinobtained .Yarrowia lipolytica, including the establishment of carotenoid pathways [crtB for phytoene synthesis, and crtI for lycopene formation and enhancement of the terpenoid pathway by additional expression of the genes of HMG, phosphomevalonate kinate ERG8, and crtE led to the formation of 3.4 mg/g dw lycopene [ERG8 and replacement of the phytoene synthase gene crtB by the bifunctional phytoene synthase/lycopene cyclase gene crtYB, all conventionally genome-integrated, the pathway was extended to β-carotene (crtZ extended the pathway to zeaxanthin reaching 3.2 mg/g dw of this carotenoid [crtZ by an algal hydroxylase gene and the use of the algal β-carotene ketolase gene bkt, about half the synthesized carotenoids were converted to astaxanthin at a yield of 5.1 mg/g dw, as calculated from fermenter cultures [Considerable progress has been made with metabolic engineering of pathways . First, lycopene . Using acarotene B, and a rotenoid . There icultures .Rhodotorula glutinis with enhanced pathway by over-expression of limiting and pathway genes including crtYB (crtYB gene. It favors the formation of bicyclic β-carotene over monocyclic γ-carotene, which is part of the competing pathway to torularhodin in R. glutinis [X. denrorhous [HMG, crtI, and crtYB genes (asy was effective. This led to a strain with 9.0 mg/dw astaxanthin [crtI (X. denrorhous nor of any other fungus, asy was inactivated (crtZ [crtZ into the genome for substantial metabolization of β-carotene to zeaxanthin reaching up to 5.2 mg/g dw. X. denrorhous is also useful for building novel structures of carotenoid. By a combination of bacterial genes encoding β-carotene 2-hydroxylase, 3-hydroxylase, and 4-ketolase, the novel structures 2,2′-dihydroxy-4,4′-diketo-zeaxanthin, and 2,2′-dihydroxy-4-monoketo-zeaxanthin were generated [X. denrorhous as a synthesis platform was applicable for the biosynthesis of novel structures.Carotenogenic yeasts offer the advantage of an already existing terpenoid pathway, which is directed towards carotenoid biosynthesis and a carotenoid storage system. A β-carotene accumulating strain of ng crtYB C resulteglutinis . A systenrorhous . StartinYB genes D. For moaxanthin . The samed (crtZ . It was enerated . For furY. lipolytica for transformation [Y. lipolytica, as well as 10.0 mg/g dw phytoene [X. denrorhous. Many yeast are able to grow on agricultural waste materials. For example, enhanced zeaxanthin synthesis was obtained with engineered X. denrorhous growing on wheat straw hemicellulose hydrolysate [Y. lipolytica was improved by metabolic engineering to utilize of a wide range of substrates [The carotenoid concentrations of the transformants in ormation , optimizormation  and 90 mormation  were reaphytoene  in fermerolysate . Furtherbstrates .Saccharomyces cerevisiae is an advanced system for metabolic pathway construction. Therefore, this yeast is not only a prominent system for establishing carotenoid pathways with similar genes used for other yeasts shown in S. cerevisiae were engineered to intensify the conversion of ethanol into acetyl-CoA [OLE1 [In addition to the establishment of the lycopene pathway and the search for best possible gene combinations , the cenetyl-CoA ,107 and etyl-CoA ,106. AnoS. cerevisiae is less advanced. A transformant carrying the genes from X. dendrorhous for β-carotene synthesis [2O2 tolerance were identified, which were also accountable for higher β-carotene synthesis of up to 16.4 mg/g dw. A straightforward strategy for enhancing the carotenoid pathway is the adaptation of S. cerevisiae substrate utilization. Transformation with extracellular and cell-bound lipases enabled a β-carotene producing strain to grow on olive oil exclusively as a way to use olive mill waste as a substrate [The development of a β-carotene producing ynthesis  was subjynthesis . By sequubstrate . SupplemCCS1 gene, transcriptional profiles indicated an impact on sterol biosynthesis. Higher formation of sterols may also suggest better metabolite supply for carotenogensis. In fermenter cultures of this mutant, the astaxanthin yield was 13.8 mg/g dw, accounting for a remarkable 89.4% of total carotenoids, and a titer of 0.22 mg/L.For the synthesis of astaxanthin from β-carotene, additional ketolation and hydroxylation steps are necessary. After screening for optimum ketolase and hydroxylase gene combinations, random mutagenesis resulted in a library including several mutants with increased astaxanthin formation . For oneFungi are of biotechnological interest as biotechnological production platforms for commercially important metabolites. Furthermore, they have a high potential for genetic pathway engineering. In fungi, special carotenoids of unique structures exist. For some of them, the biosynthesis pathways have been established by mutant analysis and functionality studies of the pathway genes. However, for the majority of fungal carotenoids, very little is known about the reaction sequences, the enzymes, and the genes involved in their biosynthesis. By comparison to well-known bacterial carotenogenic reactions, mechanisms for the insertion of oxy groups into carotenoid molecules were proposed. They may give a clue on the expected types of genes involved in fungal carotenogenesis and may be helpful for their identification in the future.B. trispora which already made it into the market, astaxanthin from engineered X. dendrorhous, with a content of almost 1% of cell mass in non-optimized laboratory cultures [Haematococcus species, especially as it is accumulating in a non-esterified form [Y. lipolytica [X. dendrorhous [Dunaliella salina [Metabolic engineering of carotenoid pathway especially in yeasts, either carotenogenic or non-carotenogenic, has made considerable progress in recent years. Combination of mutagenesis, adaptive laboratory evolution of carotenogenic lines, genetic engineering of the central carbon metabolism to enhance acetyl-CoA supply, and modification of the triacylglycerol lipid pathway to improve carotenoid storage capacity led to the construction of high-yield carotenoid-producing strains. Apart from mass produced β-carotene with cultures , can comied form . Metabolpolytica , and pardrorhous , resultea salina .Microorganisms such as yeasts offer the advantage of a controlled fermentation process. Development of advancing conditions for customized fermentation and application of selected substrates can make these transgenic yeasts attractive for commercial utilization. This includes production of carotenoids by growing on agroindustrial waste materials. This recycling process may work with the original strains or with those further engineered for the utilization of a specific substrate.


The purpose of this work is to study the mechanism of action of Duhuo Jisheng Decoction (DHJSD) in the treatment of osteoporosis based on the methods of bioinformatics and network pharmacology.  In this study, the active compounds of each medicinal ingredient of DHJSD and their corresponding targets were obtained from TCMSP database. Osteoporosis was treated as search query in GeneCards, MalaCards, DisGeNET, Therapeutic Target Database (TTD), Comparative Toxicogenomics Database (CTD), and OMIM databases to obtain disease-related genes. The overlapping targets of DHJSD and osteoporosis were identified, and then GO and KEGG enrichment analysis were performed. Cytoscape was employed to construct DHJSD-compounds-target genes-osteoporosis network and protein-protein interaction (PPI) network. CytoHubba was utilized to select the hub genes. The activities of binding of hub genes and key components were confirmed by molecular docking.  174 active compounds and their 205 related potential targets were identified in DHJSD for the treatment of osteoporosis, including 10 hub genes . Pathway enrichment analysis of target proteins indicated that osteoclast differentiation, AGE-RAGE signaling pathway in diabetic complications, Wnt signaling pathway, MAPK signaling pathway, PI3K-Akt signaling pathway, JAK-STAT signaling pathway, calcium signaling pathway, and TNF signaling pathway were the specifically major pathways regulated by DHJSD against osteoporosis. Further verification based on molecular docking results showed that the small molecule compounds  contained in DHJSD generally have excellent binding affinity to the macromolecular target proteins encoded by the top 10 genes.  This study reveals the characteristics of multi-component, multi-target, and multi-pathway of DHJSD against osteoporosis and provides novel insights for verifying the mechanism of DHJSD in the treatment of osteoporosis. Osteoporosis is characterized by low bone mass, impaired bone microstructure, increased bone fragility, and susceptibility to fracture and is also a systemic bone disease . OsteopoAs we all know, traditional Chinese medicine (TCM) plays an important role in health maintenance in China and other Asian countries . For a lRadix Angelicae Pubescentis), Sang Ji Sheng (Herba Taxilli), Qin Jiao (Radix Gentianae Macrophyllae), Fang Feng (Radix Saposhnikoviae), Xi Xin (Herba Asari), Fu Ling (Poria Cocos), Chuan Xiong (Rhizoma Chuanxiong), Bai Shao (Radix Paeoniae Alba), Du Zhong (Cortex Eucommiae Ulmoidis), Ren Shen (Panax Ginseng), Gan Cao (Radix Glycyrrhizae), Dang Gui (Radix Angelicae Sinensis), Niu Xi (Radix Achyranthis Bidentatae), Shu Di Huang (Radix Rehmanniae Preparata), and Rou Gui (Cortex Cinnamomi) [DHJSD is a TCM recorded in Bei Ji Qian Jin Yao Fang of the Tang Dynasty for the treatment of “Bi Zheng,” usually consisting of the following 15 herbs, including Du Huo (nnamomi) . Among tnnamomi) . The comnnamomi) . Accordinnamomi) . Osteoponnamomi) . And thennamomi) . Therefonnamomi) , 14–17. The network pharmacology of TCM is a part of bioinformatics, and it is still a priori analysis method for studying the relationship between drugs, compounds, diseases, and targets . Due to https://tcmspw.com/tcmsp.php) database and the analysis platform [https://www.uniprot.org/) was used to obtain the unique corresponding gene names and UniProt ID of drug or disease-related targets [Homo sapiens.”We used the Traditional Chinese Medicine Systems Pharmacology  [https://www.malacards.org/) [https://www.disgenet.org/, v7.0) [http://db.idrblab.net/ttd/) [http://ctdbase.org/, Last update by June, 2020) [https://omim.org/, updated November 25, 2020) [Osteoporosis-related targets were obtained through retrieving GeneCards (ds.org/) , MalaCards.org/) , DisGeNe/, v7.0) , Therapeet/ttd/) , Comparae, 2020) , and Onl5, 2020)  using th5, 2020) . DisGeNE5, 2020) . Finallyhttp://www.bioinformatics.com.cn/) for common targets, which were candidate targets of DHJSD against osteoporosis. The interaction between “DHJSD-active compounds-target genes-osteoporosis” was constructed by Cytoscape software (version 3.7.2) [DHJSD-related targets and osteoporosis-related targets were entered separately into the Venn online tool (n 3.7.2) .http://string-db.org/; version 11) with the species limited to “Homo sapiens” and a confidence score >0.4 [PPI underlies most biological processes in living cells and is essential for understanding cell physiology in normal and disease states . In thisore >0.4 . The PPIore >0.4 , 32.R (R 4.0.2 for Windows) to identify the biological processes and signaling pathways involved in DHJSD in treating osteoporosis [P value of <0.05 was considered to identify the enriched terms.We performed GO and KEGG pathway enrichment analysis using the clusterProfiler package in oporosis –35. An ahttp://sankeymatic.com/) was built with top 10 hub genes and relative active ingredients of DHJSD to find out key active ingredients. Sankey diagram discloses the relationship among herb, ingredients, and targets. Subsequently, molecular docking between top 10 hub genes and key active ingredients was carried out to predict the accuracy of the pivotal components and prediction targets using AutoDock Vina [https://pubchem.ncbi.nlm.nih.gov/) and RCSB protein data (http://www.rcsb.org/) were selected to download the MOL2 format of ligands and PDB format of proteins [https://pymol.org/2/; version 2.4.1) to conduct dehydration and separation of ligands [The plug-in CytoHubba of Cytoscape software was used to screen top 10 hub genes . In addiock Vina . PubChemproteins . Crystal ligands . Subsequ ligands . The low ligands . EventuaA total of 1939 compounds in DHJSD were retrieved through the TCMSP database, of which 99 were from Du Huo, 46 were from Sang Ji Sheng, 27 were from Qin Jiao, 173 were from Fang Feng, 192 were from Xi Xin, 34 were from Fu Ling, 189 were from Chuan Xiong, 85 from Bai Shao, 147 from Du Zhong 190 from Ren Shen, 280 from Gan Cao, 125 from Dang Gui, 176 from Niu Xi, 76 from Shu Di Huang, and 100 from Rou Gui. According to the criteria of OB ≥30% and DL ≥0.18, a total of 240 (after removing duplication: 209) active compounds of DHJSD were screened, of which 9 were from Du Huo, 2 were from Sang Ji Sheng, 2 were from Qin Jiao, 18 were from Fang Feng, and 8 were from Xi Xin, 15 from Fu Ling, 7 from Chuan Xiong, 13 from Bai Shao, 28 from Du Zhong, 22 from Ren Shen, 92 from Gan Cao, 2 from Dang Gui, 20 from Niu Xi, and 2 from Shu Di Huang. However, there is no compound in Rou Gui that meets the screening criteria. By using the compound-target search function of TCMSP database, it was confirmed that 179 of the 209 active compounds in DHJSD possess target proteins. Subsequently, UniProt database was used to convert the target protein predicted by the biologically active compound of DHJSD into gene name. Finally, 267 putative target genes were identified.A total of 3131 potential therapeutic targets of osteoporosis were obtained by searching GeneCards, MalaCards, DisGeNET, TTD, CTD, and OMIM databases. The Venn diagram was drawn by using Venn online platform, which is derived from the targets regulated by the active ingredients of DHJSD and the potential targets of osteoporosis. Subsequently, a total of 205 core targets were obtained, which were the potential therapeutic targets of DHJSD in the treatment of osteoporosis, as shown in A total of 205 potential target genes of DHJSD in the treatment of osteoporosis were input into STRING database to obtain PPI network. The network involves 205 nodes and 4078 edges. Then, the obtained data was imported into Cytoscape 3.7.2 version for further visualization . SubsequR, and a total of 2590 GO terms  were obtained, including 2320 biological process (BP) terms and 179 molecular function (MF) terms and 91 cellular component (CC) terms. Based on the adjusted P value from small to large, the top 10 GO-BP terms were mainly enriched in cellular response to chemical stress (GO:0062197), response to metal ion (GO:0010038), response to antibiotic (GO:0046677), response to lipopolysaccharide (GO:0032496), response to alcohol (GO:0097305), response to steroid hormone (GO:0048545), response to molecule of bacterial origin (GO:0002237), response to oxidative stress (GO:0006979), response to reactive oxygen species (GO:0000302), and response to nutrient levels (GO:0031667). The top 10 GO-CC terms were mainly enriched in membrane raft (GO:0045121), membrane microdomain (GO:0098857), membrane region (GO:0098589), transcription regulator complex (GO:0005667), vesicle lumen (GO:0031983), RNA polymerase II transcription regulator complex (GO:0090575), cyclin-dependent protein kinase holoenzyme complex (GO:0000307), serine/threonine protein kinase complex (GO:1902554), mitochondrial outer membrane (GO:0005741), and cytoplasmic vesicle lumen (GO:0060205). The top 10 GO-MF terms were mainly enriched in nuclear receptor activity (GO:0004879), ligand-activated transcription factor activity (GO:0098531), steroid hormone receptor activity (GO:0003707), RNA polymerase II-specific DNA-binding transcription factor binding (GO:0061629), DNA-binding transcription factor binding (GO:0140297), “DNA-binding transcription activator activity, RNA polymerase II-specific” (GO:0001228), DNA-binding transcription activator activity (GO:0001216), phosphatase binding (GO:0019902), heme binding (GO:0020037), and NADP binding (GO:0050661). Finally, the top 10 GO enrichment terms were selected and visualized using bar diagram, as shown in In order to elucidate the biological mechanisms of DHJSD against osteoporosis, GO and KEGG pathway enrichment analysis was performed by using clusterProfiler in R. The 205 potential targets of DHJSD for treating osteoporosis were input into the P < 0.05) were obtained, the top 10 including AGE-RAGE signaling pathway in diabetic complications (hsa04933), Kaposi sarcoma-associated herpesvirus infection (hsa05167), fluid shear stress and atherosclerosis (hsa05418), hepatitis B (hsa05161), prostate cancer (hsa05215), hepatitis C (hsa05160), pancreatic cancer (hsa05212), human cytomegalovirus infection (hsa05163), IL-17 signaling pathway (hsa04657), and TNF signaling pathway (hsa04668). The top 20 KEGG pathway enrichment terms were selected and visualized using bar diagram, as shown in Additionally, a total of 170 enriched KEGG pathways  and key active compounds  was carried out using AutoDock Vina. The docking scores of the strongest affinity of 10 core target proteins and 11 key active compounds were visualized using the heatmap, as shown in th Pymol .Osteoporosis is a degenerative disease, which increases with age. With the prolongation of human life and the coming of aging society, osteoporosis has become an important health problem . Conside-target databases , a total of 3131 potential osteoporosis treatment targets were obtained. Finally, we obtained 205 potential target genes of DHJSD in the treatment of osteoporosis. The network of DHJSD against osteoporosis was built, which was involved in 394 nodes and 2265 interactions. Furthermore, PPI network has been constructed involved in 205 nodes and 4078 edges. Top 10 hub genes were revealed by weighing the CytoHubba 12 algorithms of Cytoscape software , including AKT1, ALB, IL6, MAPK3, VEGFA, JUN, CASP3, EGFR, MYC, and EGF.According to the principle of ADME (setting OB ≥30 and DL ≥0.18) , we searα reduced bone loss [AKT1 is one of three closely related serine/threonine protein kinases , which regulates many physiological processes including metabolism, proliferation, cell survival, and angiogenesis . A studyone loss . The maiα-induced impairment of BMSC osteogenic function [The one-to-one correspondence between the top 10 hub genes and the corresponding active compounds contained in DHJSD had been shown using the Sankey diagram. The relative active compounds include MOL000098 (Quercetin), MOL000422 (Kaempferol), MOL000358 (Beta-sitosterol), MOL000173 (Wogonin), MOL002773 (Beta-carotene), MOL002714 , MOL004328 (Naringenin), MOL000392 (Formononetin), MOL001924 (Paeoniflorin), MOL005344 (Ginsenoside Rh2), and MOL009031 (Epiquinidine). Among them, Quercetin targets most central genes. Quercetin is a member of the flavonoid family isolated from onion, apple, grape, tea, and many kinds of Chinese herbal medicines, which seems to have obvious anti-osteoporosis properties . Studiesfunction .P value from small to large, the top 3 GO-BP terms were mainly enriched in cellular response to chemical stress, response to metal ion, and response to antibiotic. The top 3 GO-CC terms were mainly enriched in membrane raft, membrane microdomain, and membrane region. The top 3 GO-MF terms were mainly enriched in nuclear receptor activity, ligand-activated transcription factor activity, and steroid hormone receptor activity. Based on the adjusted P value from small to large, the top 10 KEGG pathways were mainly enriched in AGE-RAGE signaling pathway in diabetic complications, Kaposi sarcoma-associated herpesvirus infection, fluid shear stress, and atherosclerosis, hepatitis B, prostate cancer, hepatitis C, pancreatic cancer, human cytomegalovirus infection, and IL-17 signaling pathway. We searched for osteoporosis in the KEGG pathway database and found that there are ten pathways directly related to osteoporosis and related diseases, including osteoclast differentiation (hsa04380), AGE-RAGE signaling pathway in diabetic complications (hsa04933), Wnt signaling pathway (hsa04310), MAPK signaling pathway (hsa04010), apoptosis (hsa04210), chemokine signaling pathway (hsa04062), T cell receptor signaling pathway (hsa04660), B cell receptor signaling pathway (hsa04662), endocrine and other factor-regulated calcium reabsorption (hsa04961), and mineral absorption (hsa04978). The first eight pathways are in the list of KEGG pathway enrichment analysis we have done. Six of the top 10 hub genes  are enriched in AGE-AGE signaling pathway in diabetic complications, three of the top 10 hub genes  are enriched in osteoclast differentiation, eight of the top 10 hub genes  are enriched in MAPK signaling pathway, two of the top 10 hub genes  are enriched in Wnt signaling pathway, four of the top 10 hub genes  are enriched in Apoptosis, two of the top 10 hub genes  are enriched in Chemokine signaling pathway, and three of the top 10 hub genes  are enriched in T cell receptor signaling pathway and B cell receptor signaling pathway.Then, we performed the GO and KEGG pathway enrichment analysis of genes that DHJSD against osteoporosis. Based on the adjusted β signaling pathway [κB and MAPK/ERK signaling pathways and inhibits osteoclast production in vitro. Zha et al. [In addition, the enriched pathways related to the above pathways in the KEGG database include PI3K-Akt signaling pathway , JAK-STAT signaling pathway , calcium signaling pathway , TNF signaling pathway , and NF-kappa B signaling pathway (hsa04064). Li et al.  found th pathway . In addi pathway . Jin et  pathway  found tha et al.  found tha et al. .β-catenin signaling pathway to promote bone formation [γ pathway [By mining the database, we obtained the compounds contained in each herb in DHJSD, and then we predicted the potential targets of DHJSD by these compounds, and then we performed KEGG pathway enrichment analysis to obtain the potential pathways of action of DHJSD based on these targets. Therefore, the above obtained targets and signaling pathways should be relevant to the functions of the herbs in DHJSD. It has been shown that the therapeutic effect of the herb Du Huo in osteoporotic rats is associated with the activation of Wnt/ormation . Qin Jiaormation . Fu Ling pathway . These rBy using the network pharmacology approach, we have studied the potential targets of DHJSD and the underlying mechanism of its anti-osteoporosis effect, which has the characteristics of multi-component, multi-target, and multi-pathway. AKT1, ALB, IL6, MAPK3, VEGFA, JUN, CASP3, EGFR, MYC, and EGF may be potential targets of DHJSD in treating osteoporosis. According to the results of enrichment analysis of KEGG pathway, we found pathways closely related to the pathological process of osteoporosis, mainly including AGE-RAGE signaling pathway in diabetic complications, osteoclast differentiation, MAPK signaling pathway, Wnt signaling pathway, PI3K-Akt signaling pathway, JAK-STAT signaling pathway, and TNF signaling pathway. Therefore, this study reveals that the anti-osteoporosis effect of DHJSD may be based on its direct or indirect regulation of the above-mentioned potential targets and pathways. DHJSD may provide a promising direction for future research, so enough relevant experimental research verification is needed, which is very important for revealing its exact regulatory mechanism.


The purpose of this study was to investigate the mechanism of action of the Chinese herbal formula Buyang Huanwu Decoction (BYHWD), which is commonly used to treat nerve injuries, in the treatment of spinal cord injury (SCI) using a network pharmacology method.  BYHWD-related targets were obtained by mining the TCMSP and BATMAN-TCM databases, and SCI-related targets were obtained by mining the DisGeNET, TTD, CTD, GeneCards, and MalaCards databases. The overlapping targets of the abovementioned targets may be potential therapeutic targets for BYHWD anti-SCI. Subsequently, we performed protein-protein interaction (PPI) analysis, screened the hub genes using Cytoscape software, performed Gene Ontology (GO) annotation and KEGG pathway enrichment analysis, and finally achieved molecular docking between the hub proteins and key active compounds.  The 189 potential therapeutic targets for BYHWD anti-SCI were overlapping targets of 744 BYHWD-related targets and 923 SCI-related targets. The top 10 genes obtained subsequently included AKT1, IL6, MAPK1, TNF, TP53, VEGFA, CASP3, ALB, MAPK8, and JUN. Fifteen signaling pathways were also screened out after enrichment analysis and literature search. The results of molecular docking of key active compounds and hub target proteins showed a good binding affinity for both.  This study shows that BYHWD anti-SCI is characterized by a multicomponent, multitarget, and multipathway synergy and provides new insights to explore the specific mechanisms of BYHWD against SCI. Accordi19.3 CNY . There iRadix Paeoniae Rubra), Chuanxiong (Rhizoma Chuanxiong), Danggui (Radix Angelicae Sinensis), Dilong (Pheretima), Huangqi , Honghua (Flos Carthami), and Taoren (Semen Persicae) [Modern Chinese medicine scholars classify SCI as “Ti Duo” and “Wei Zheng” based on the TCM classics “Ling Shu Jing-Han Re Bing” and “Su Wen-Wei Lun” . Based oersicae) . Studiesersicae) , and BYHersicae) . AlthougIn the last decade, with the development of modern multiomics theories such as genomics, proteomics, and metabolomics, the introduction of a systems biology perspective, and the application of bioinformatics, the concept of network pharmacology has emerged. According to the network basis of drug action, network pharmacology methods, including protein-protein interaction (PPI) network, Gene Ontology (GO) annotation, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, are applied to infer the specific pathways of multimolecular drug action in the human body, which is consistent with the multicomponent, multitarget, and multipathway synergy of Chinese herbal medicine and helps to clarify the mechanism of action of BYHWD .Radix Paeoniae Rubra,” “Chuanxiong or CHUAN XIONG or Rhizoma Chuanxiong,” “Danggui or DANG GUI or Radix Angelicae Sinensis,” “Dilong or DI LONG or Pheretima,” “Huangqi or HUANG QI or Radix Astragali,” “Honghua or HONG HUA or Flos Carthami,” and “Taoren or TAO REN or Semen Persicae” in Traditional Chinese Medicine Systems Pharmacology  database [https://www.uniprot.org/) (species should be set to “Homo sapiens”), the target protein name is converted into the corresponding gene name and UniProt ID [http://bionet.ncpsb.org/batman-tcm/) according to the set criteria  [The compounds related to BYHWD were obtained by searching “Chishao or CHI SHAO or database , and thedatabase , 9, and iProt ID . The com < 0.05) . Finallyhttps://www.disgenet.org/, v7.0) (score ≥ 0.01) [https://www.genecards.org/) (relevance score ≥ 20) [https://www.malacards.org/) [http://ctdbase.org/, last update by June 2020) (inference score ≥ 20) [http://db.idrblab.net/ttd/, last update by June 1, 2020) [DisGeNet database ( ≥ 0.01) , GeneCarre ≥ 20) , MalaCards.org/) , Comparare ≥ 20) , and The1, 2020)  were usehttp://www.bioinformatics.com.cn/). Subsequently, we used Cytoscape software (version 3.7.2) to construct a network diagram of “BYHWD-active compounds-target genes-SCI” [Potential therapeutic targets of BYHWD in the treatment of SCI should be derived from the overlapping targets of BYHWD-related targets and SCI-related targets obtained from the Venn online tool , BottleNeck, Betweenness, ClusteringCoefficient, Closeness, Density of Maximum Neighborhood Component (DMNC), EcCentricity, Edge Percolated Component (EPC), Maximum Neighborhood Component (MNC), Radiality, and Stress , 21.P value < 0.05) to explore the biological processes (BP), cellular components (CC), molecular functions (MF), and signaling pathways associated with BYHWD anti-SCI.R is commonly used for statistical analysis and plotting software, and its open-source and free features have been favored by many programming enthusiasts, resulting in the birth of many convenient and efficient packages, such as the ClusterProfiler package for GO annotation and KEGG pathway enrichment analysis –24. In thttp://sankeymatic.com/). In the following, we will select key active compounds as small molecule ligands and corresponding large molecule receptors (proteins encoded by the top 10 hub genes) for molecular docking analysis. The raw files of key active compounds (MOL2 format) can be downloaded from PubChem database (https://pubchem.ncbi.nlm.nih.gov/), and the raw files of the top 10 core genes (PDB format) can be downloaded from the RCSB protein data (http://www.rcsb.org/), and after corresponding processing by AutoDock Tool [Based on the previously mined BYHWD-related targets and related active compounds, the active compounds and herbs corresponding to the top 10 hub genes need to be sorted out. The interrelationship between “herbs in BYHWD-active compounds-top 10 hub genes” was visualized by a Sankey diagram , of which 119 (BATMAN-TCM: 8) were from Chishao, 189 (87) were from Chuanxiong, 125 (77) were from Danggui, 87 (23) were from Huangqi, 189 (22) were from Honghua, and 66 (2) were from Taoren. Relevant compounds and targets' information for Dilong was not retrieved in either database. After setting the ADME criteria (OB ≥ 30% and DL ≥ 0.18), a total of 103 active compounds (after removing duplicates and nontarget compounds: 60) were screened from the TCMSP database, of which 29 (After removing nontarget compounds: 14) were from Chishao, 7 (6) were from Chuanxiong, 2 (2) were from Danggui, 20 (17) were from Huangqi, 22 (17) were from Honghua, and 23 (19) were from Taoren. The BATMAN-TCM platform provides the gene names directly, while the TCMSP database first provides the target protein names of the active compounds and therefore needs to be converted to gene names via the UniProt database. Finally, after integrating the results of the two databases, we obtained 244 BYHWD-related active compounds and 744 BYHWD-related targets.A total of 775 compounds  in BYHWD were preliminarily retrieved from the TCMSP database and BATMAN-TCM platform , TTD (number: 5), CTD (number: 553), GeneCards (number: 432), and MalaCards (number: 19) databases.The overlapping targets of BYHWD-related targets and SCI-related targets were considered as potential therapeutic targets for BYHWD anti-SCI. As shown in Homo sapiens” and then entered 189 potential therapeutic targets for BYHWD anti-SCI to obtain the PPI network , including 2567 BP items, 99 CC items, and 176 MF items. We combined the top 10 enrichment results for each of GO-BP, GO-CC, and GO-MF by a bar graph . The top 20 KEGG enrichment pathways are listed below, including AGE-RAGE signaling pathway in diabetic complications (hsa04933), Fluid shear stress and atherosclerosis (hsa05418), Prostate cancer (hsa05215), IL-17 signaling pathway (hsa04657), Hepatitis B (hsa05161), TNF signaling pathway (hsa04668), Kaposi sarcoma-associated herpesvirus infection (hsa05167), Human cytomegalovirus infection (hsa05163), Hepatitis C (hsa05160), Human T-cell leukemia virus 1 infection (hsa05166), Small cell lung cancer (hsa05222), Pancreatic cancer (hsa05212), Measles (hsa05162), Epstein-Barr virus infection (hsa05169), Nonalcoholic fatty liver disease (hsa04932), Influenza A (hsa05164), Endocrine resistance (hsa01522), Bladder cancer (hsa05219), Platinum drug resistance (hsa01524), and Apoptosis (hsa04210). The visualization of the abovementioned KEGG enrichment pathway is realized in In the same way, through the ClusterProfiler package in R, we performed KEGG pathway enrichment analysis on the 189 potential therapeutic targets for BYHWD anti-SCI and obtained 177 KEGG pathways  that binds well to the small molecule ligand (key active compounds). As shown in Sankey diagram was used to establish one-to-one correspondence between the top 10 hub genes  for BYHWD anti-SCI and BYHWD-related compounds, providing the basis for the next molecular docking analysis. Among them, the gene that targets the most active compounds is TNF, and the key active compounds that target the most hub genes are quercetin and luteolin . As seenAccording to the report of the National SCI Statistics Center, there are about 17,000 new cases of SCI in the United States each year, and the annual incidence of SCI is about 54 cases per million people . Among tBYHWD, which comes from Qing Dynasty medical classics, consists of seven herbs: Chishao, Chuanxiong, Danggui, Dilong, Huangqi, Honghua, and Taoren . Based oP value <0.05) directly obtains BYHWD-related compounds and target genes. The screening process of TCMSP database is divided into five steps: (1) the relevant compounds of each component of BYHWD are directly obtained ; (2) the relevant compounds of each component are screened by ADME criteria ; (3) the above compounds are screened again by removing the null targets ; (4) the above compounds are finally screened by removing duplicates ; and (5) the target proteins corresponding to the obtained active compounds are converted into gene names . And the screening process of BATMAN-TCM platform is divided into two steps: (1) the relevant compounds and targets of each component of BYHWD are directly obtained , and (2) the relevant compounds of each component are screened after deduplication . The results of the two databases were integrated and deduplicated to finally obtain 244 BYHWD-related active compounds and 744 BYHWD-related targets. In the second step, a total of 923 SCI-related targets were acquired by mining 5 disease databases, including DisGeNET (number: 84), TTD (number: 5), CTD (number: 553), GeneCards (number: 432), and MalaCards (number: 19). Next, the Venn diagram of BYHWD-related targets and SCI-related targets was plotted by the website online tool to obtain 189 potential therapeutic targets of BYHWD against SCI.In the first step, BYHWD-related compounds and related targets were obtained. We obtained relevant compounds and targets by mining two herbal-related databases, TCMSP and BATMAN-TCM. The mining methods of these two databases are different: TCMSP database (ADME principle: OB ≥ 30 and DL ≥ 0.18) first obtains BYHWD-related compounds, then obtains related target proteins, and finally needs to be converted into standard gene names by UniProt database; BATMAN-TCM platform  the “BYHWD-active compounds-target genes-SCI” network was constructed, involving 410 nodes and 1571 interactions; (2) the PPI network obtained from the STRING database was reconstructed here, involving 189 nodes and 3717 interactions; (3) the top 10 hub genes  of BYHWD against SCI were obtained based on 12 topological analysis methods  contained in the CytoHubba plug-in; (4) the “potential therapeutic targets-pathways” network was constructed, involving 347 nodes and 2650 interactions. Studies have shown that the protein encoded by TP53 and other factors regulate the regeneration, germination, and functional recovery of axons after central nervous system injury ; inflammy in SCI , 37; by y in SCI .κB pathway activation [The Sankey diagram was used once, mainly to show the correspondence between the top 10 hub genes and the corresponding active compounds contained in BYHWD -quinolone, 4-ethylresorcinol, angelicin, astragaloside I, astragaloside II, caffeic acid dimethyl ether, cibarian, cordycepin, dihydropinosylvin, dodecenoic acid, ellagic acid, ethyl-P-methoxycinnamate, formononetin, linoleic acid, M-cresol, M-ethylphenol, O-ethylphenol, oleic acid, paeoniflorin, P-cresol, P-ethylphenol, sebiferic acid, sucrose, and thymol). Among them, quercetin targeted six hub genes, luteolin targeted six hub genes, beta-carotene targeted five hub genes, kaempferol targeted four hub genes, baicalein targeted three hub genes, beta-sitosterol targeted two hub genes, anisic acid targeted two hub genes, and the others targeted one hub gene. The experimental results showed that quercetin exerts a protective effect on the spinal cord by inhibiting the activation of the p38MAPK/iNOS signaling pathway in SCI rats and subsequently regulating secondary oxidative stress ; the comtivation ; baicaletivation .Based on the results of the Sankey diagram, we performed molecular docking of the top 10 genes and the top 6 compounds in terms of the number of targeted genes . The results of molecular docking of 60 cohorts of hub target proteins and key active compounds showed a good binding affinity for both. We used R software twice, once to present the scores of the above molecular docking as a heat map, and another time to perform GO and KEGG pathway enrichment analysis using the ClusterProfiler package.P value , are as follows: response to metal ion (GO-BP:0010038), response to nutrient levels (GO-BP:0031667), response to lipopolysaccharide (GO-BP:0032496), response to molecule of bacterial origin (GO-BP:0002237), cellular response to chemical stress (GO-BP: 0062197), response to antibiotic (GO-BP:0046677), response to oxidative stress (GO-BP: 0006979), response to reactive oxygen species (GO-BP:0000302), response to nutrient (GO-BP:0007584), cellular response to oxidative stress (GO-BP: 0034599), membrane raft (GO-CC:0045121), membrane microdomain (GO-CC:0098857), membrane region (GO-CC:0098589), vesicle lumen (GO-CC: 0031983), protein kinase complex (GO-CC: 1902911), RNA polymerase II transcription regulator complex (GO-CC: 0090575), serine/threonine-protein kinase complex (GO-CC:1902554), secretory granule lumen (GO-CC: 0034774), cytoplasmic vesicle lumen (GO-CC: 0060205), transcription regulator complex (GO-CC: 0005667), RNA polymerase II-specific DNA-binding transcription factor binding (GO-MF:0061629), DNA-binding transcription factor binding (GO-MF:0140297), tetrapyrrole binding (GO-MF:0046906), heme binding (GO-MF:0020037), electron transfer activity (GO-MF: 0009055), nuclear receptor activity (GO-MF:0004879), ligand-activated transcription factor activity (GO-MF:0098531), ubiquitin-like protein ligase binding (GO-MF: 0044389), steroid hormone receptor activity (GO-MF:0003707), and scaffold protein binding (GO-MF:0097110). The first 20 results of the  KEGG pathway enrichment analysis of 189 potential therapeutic targets of BYHWD against SCI have been presented, and we screened the enriched pathways again to find possible relevant pathways for SCI. By searching the relevant literature, we screened a total of 15 pathways with strong relevance, as follows: AGE-RAGE signaling pathway in diabetic complications (hsa04933) [The top 10 results of the GO enrichment analysis of 189 potential therapeutic targets for BYHWD anti-SCI, based on the adjusted GO-MF: 00389, ster GO-BP:00677, respGO-MF:00079, ligansa04933) , JAK-STAGO-CC:00421, membrGO-MF: 00389, ster GO-BP:00677, respThis study still has some limitations. First, the corresponding experimental validation is lacking. Second, the compounds, targets, and pathways contained in these databases may not be exhaustive. Finally, compounds screened based on ADME principles may be missing other important compounds.This study uncovered the multicompound, multitarget, and multipathway characteristics of BYHWD through a network pharmacology approach. Among them, the key compounds include quercetin, luteolin, beta-carotene, kaempferol, baicalein, and beta-sitosterol; the hub genes include AKT1, IL6, MAPK1, TNF, TP53, VEGFA, CASP3, ALB, MAPK8, and JUN; and the important pathways are the 15 related pathways mentioned above. These results clearly give a general direction for SCI-related researchers, and there is no doubt that the anti-SCI mechanism of BYHWD is much more than the above-mentioned research directions, and we will find more accurate targets and pathways while exploring them one by one.Using a network pharmacology approach, we explored the potential mechanism of BYHWD as a classical herbal formula for treating nerve injury in the treatment of SCI. The top 10 core genes among the potential therapeutic targets for BYHWD anti-SCI include AKT1, IL6, MAPK1, TNF, TP53, VEGFA, CASP3, ALB, MAPK8, and JUN. Key active compounds of BYHWD against SCI include quercetin, luteolin, beta-carotene, kaempferol, baicalein, and beta-sitosterol. In addition, combining the KEGG pathway enrichment results and reviewing SCI-related literature, we obtained 15 SCI potentially relevant pathways, mainly including PI3K-Akt signaling pathway, MAPK signaling pathway, NF-kappa B signaling pathway, TNF signaling pathway, Apoptosis, Toll-like receptor signaling pathway, HIF-1 signaling pathway, JAK-STAT signaling pathway, Wnt signaling pathway, AMPK signaling pathway, and mTOR signaling pathway. The above analysis results demonstrated the multitarget, multicomponent, and multipathway characteristics of BYHWD in treating SCI, which laid the foundation for us to carry out specific experimental validation in the next step, and provided new ideas for researchers dedicated to the treatment of SCI with herbal extracts.