Artemisia argyi essential oil (AAEO) in the treatment of pressure injuries (PIs), we analyzed the biological functions and pathways involved in the intersection targets of AAEO and PIs based on network pharmacology, and the affinity of AAEO active compounds and core targets was verified by molecular docking finally. In our study, we first screened 54 effective components according to the relative content and biological activity. In total, 103 targets related to active compounds of AAEO and 2760 targets associated with PIs were obtained, respectively, and 50 key targets were overlapped by Venny 2.1.0. The construction of key targets-compounds network was achieved by the STRING database and Cytoscape 3.7.2 software. GO analysis from Matespace shows that GO results are mainly enriched in biological processes, including adrenergic receptor activity, neurotransmitter clearance, and neurotransmitter metabolic process. KEGG analysis by the David and Kobas website shows that the key targets can achieve the treatment on PIs through a pathway in cancer, PI3K-Akt signaling pathway, human immunodeficiency virus 1 infection, MAPK signaling pathway, Wnt signaling pathway, etc. In addition, molecular docking results from the CB-Dock server indicated that active compounds of AAEO had good activity docking with the first 10 key targets. In conclusion, the potential targets and regulatory molecular mechanisms of AAEO in the treatment of PIs were analyzed by network pharmacology and molecular docking. AAEO can cure PIs through the synergistic effect of multicomponent, multitarget, and multipathway, providing a theoretical basis and new direction for further study.In order to comprehensively explore multitarget mechanism and key active compounds of  Pressure injuries (PIs), also named pressure ulcers, refer to localized injuries occurring in the skin and/or potential subcutaneous soft tissue, usually occurring in bone bulges or in contact with medical facilities . PIs havArtemisia argyi (AA), which is widely distributed in China and other Asian countries, has been used as traditional medicine or food supplement for hundreds of years [Artemisia argyi (Levl.) et Van., the herb with a spicy, bitter flavor and warm properties, enters into the channels of liver and kidney, and functions on resolving blood stasis, dispersing cold and relieving pain [The of years . AA is ting pain , 12. AA ing pain , resistiing pain , suppresing pain , and acting pain .Artemisia argyi is usually the main raw material [AA often treats diseases in the form of moxibustion; moxibustion is a critical intervention in traditional Chinese medicine (TCM). material . Althougmaterial , 19. TheIn view of the complex chemical compounds of AAEO, the chemical components and the corresponding mechanism of action that play the efficacy after entering the human body include a lot of unknown information. Therefore, it is necessary to comprehensively explore the mechanism of AAEO in the treatment of PIs.Network pharmacology is a new discipline emerging in recent years that combines the overall network analysis and pharmacological effects . With thβ, NR3C1, CASP3, TP53, PGR, REN, and NOS2 could be the potential receptor targets, involving many inflammatory proteins. The top three molecular docking points are PTGS2 (prostaglandin-endoperoxide synthase 2), TP53 (tumor protein p53), and PGR (progesterone receptor). PTGS2, also known as COX-2, as an important inflammatory mediator, exists in the early stage of inflammation to the whole process of inflammation formation [For all we know, our study is first time applied network pharmacology methods to explore the biological effect of active compounds in AAEO and the multitarget mechanism of active compounds in the treatment of PIs. In our study, TNF, PTGS2, IL6, IL1ormation . It is uormation , 27. TP5ormation –31. Receormation .https://tcmspw.com) and PubChem database [https://pubchem.ncbi.nlm.nih.gov/), and compounds with relevant targets.Over 200 components of AAEO can be detected by current technology, but more than 90 of them are common active, so we use 94 components as active compounds , 34. Fifdatabase  (https:/https://www.drugbank.ca) and Pharmmapper [https://www.lilab-ecust.cn/pharmmapper/). Next, the targets were entered into UniProt (https://www.uniprot.org/); the species selected was “Homo sapiens”; transformed gene symbols were obtained finally.The targets information identifying 54 potential compounds were attained on TCMSP and were reconfirmed by DrugBank  (https:/rmmapper  (https:/https://www.genecards.org/), OMIM (https://omim.org/) and DrugBank (https://go.drugbank.com/) database were used to screen relative targets of PIs. “Pressure Ulcers,” “Bedsore,” “Pressure Sore,” and “pressure injury” were keywords to search targets related to PIs. The obtained targets were integrated and eliminated duplication. Finally, the intersection targets were obtained on Venny 2.1.0 (https://bioinfogp.cnb.csic.es/tools/venny/). At last, 50 overlapping targets were obtained.GeneCard (https://www.string-db.org/). Protein with disconnected other protein and a combined score <0.4 was removed [K value was 2, and the maximum depth was 100 [PPI analysis of the overlapping targets was carried out in the STRING 11.0  to carry out GO analysis. The specific steps were as follows: input the gene ID, the parameter selected was “Homo sapiens,” click “custom analysis,” and click GO Molecular Functions, GO Biological Processes, and GO Cellular Components in turn for analysis [https://www.bioinformatics.com.cn/) was used to acquire the visualization of the results.The overlapping targets were imported into Matescape  , and the ENTRZ_GENE ID was input into Kobas (https://kobas.cbi.pku.edu.cn/) for KEGG pathways analysis [P values <0.01 were selected [50 overlapping targets were converted from gene symbol to ENTRZ_GENE ID in David Database  and ligand files in mol2 formats from PubChem (https://pubchem.ncbi.nlm.nih.gov/) [https://cao.labshare.cn/cb-dock/). CB-Dock server is a user-friendly blind docking network server developed by Dr. Liu's research team. It uses a novel curvature-based cavity detection approach, and Autodock Vina, the popular docking program, is used for docking [In silico methods are alternatives to experimental approaches to screen for potential bioactivity of compounds of essential oil compounds; for example, docking evaluated in silico the ability of EOs to interact with molecular targets with advantages of being less time-consuming and cheap. We selected the top 10 core targets and got the ligand with relative content of the first 7 for molecular docking; the PDB formats of proteins were obtained from the protein database (ih.gov/) ; both of docking . The suc docking . The RMS docking .A total of 54 active compounds that met the criteria were finally collected. The basic information of 54 obtained compounds is shown in 103 AAEO compound-related targets were retrieved from TCMSP and converted into official gene symbols according to the UniProt database. Moreover, 2760 PIs targets were searched by GeneCard, OMIM, and DrugBank databases. Finally, 50 targets were obtained by intersecting two parts of targets ; the PPICompounds-overlapping targets network involved 104 nodes and 441 edges. The results reflect the complex mechanism of multicomponent and multitarget treatment of diseases. Moreover, a core network was calculated by MCODE with 15 targets .GO analysis was mainly focused on the biological process, with a total of 3269 enrichment results, involving adrenergic receptor activity, nuclear receptor activity, and aspartic-type endopeptidase activity. The top 10 GO functional annotations of BP, CC, and MF are shown in The top 10 GO functional annotations of BP, CC, and MF are represented by green for biological process, orange for cellular component, light purple for molecular function, respectively.P-value ; when the color is redder, P value is smaller.Each bubble represents an enriched function, and the size of the bubble is from small to large. The bubble is colored according to its −log ,1,8-cineole (A1), terpinen-4-ol (A51), neointermedeol (A4), α-thujone (A11), borneol (A6), and camphor (A3). Generally, the Vina score is negative; the lower the score, the better the binding activity between ligand and protein. There will be top five Vina scores and docking cavity sizes from obtained results, which were first selected as representation [β-caryophyllene, and A3-camphor) and proteins  with better binding affinities are shown in Figures 6The 10 key targets, TNF, PTGS2, IL6, IL1entation . The resArtemisia argyi, a dried leaf of Ai Ye with multiple biological activities, is widely used to treat inflammatory diseases such as eczema, dermatitis, arthritis, allergic asthma, and colitis [ colitis . The phaβ-caryophyllene,1,8-cineole, terpinen-4-ol, neointermedeol, α-thujone, borneol, and camphor had a relative content of the first 7 [β-caryophyllene, and camphor. Neointermedeol has been shown to have antioxidant, antibacterial, and other biological activities [α, IL-1β, IL-6, and NF-κB, thereby improving the symptoms of inflammation and oxidative stress [Over 200 species of AAEO can be detected by gas chromatography-mass spectrometry (GC-MS) , mainly  first 7 , 34–36.  first 7 . In lipo first 7 . Borneol first 7 . Numerou first 7 –59. Also first 7 . More notivities , 62. Rece stress , 64.β, and PTGS2 participate in regulating inflammatory cascade reaction [β-catenin signaling pathway regulating the proliferation and differentiation of epithelial cells, hair follicles, and sebaceous glands [The core network calculated by MCODE had 15 targets, mostly related to inflammation, oxidative stress, and apoptosis. TNF, IL6, IL-1reaction –68 and creaction , 70. KEGreaction , and thereaction , providereaction . The prereaction . More sts glands , 76.The above arguments verify the accuracy of this network pharmacology prediction. Besides, the docking result showed that all selected core protein and ligand have a better affinity , and there were 15 docking scores ≥7 kcal/mol, indicating strong binding affinity of the compound to docking protein . The RMSα, IL-6, IL-10, IFN-β, and MCP-1 [In addition, study showed that AAEO dose-dependently inhibits inflammatory mediators, such as NO, PGE2, TNF-nd MCP-1 . In the nd MCP-1 . PTGS2 wnd MCP-1 –83. The Artemisia argyi in preventing and treating PIs by combining molecular biology and pathophysiology.The limitation of this study is that we have not conducted clinical or animal experiments as certification; further studies will validate the potential key targets and pathways predicted and explore the mechanism of effective components of the essential oil from In conclusion, in this study, the potential targets and regulatory molecular mechanisms of AAEO in the treatment of PIs were analyzed by network pharmacology and molecular docking. In total, 54 active components and 50 potential targets were screened, mainly involving PI3K-Akt signaling pathway, pathway in cancer, PI3K-Akt signaling pathway, human immunodeficiency virus 1 infection, MAPK signaling pathway, and Wnt signaling pathway, revealing that AAEO may play a role in the treatment of PIs by reducing inflammation, inhibiting apoptosis and oxidative stress, and showing the characteristics of multitarget and multipathway. Our study provides a basis for the mechanism and further research direction of AAEO in treating PIs by combining literature research, network analysis, and molecular docking.


WNT7B and PRKCA genes in the “melanogenesis” pathway and some likely involved in global aging such as the DDB1 gene in the “nucleotide excision repair” pathway, not picked up in the previously published GWAS. Overall, our results suggest that the four outcomes of skin aging possess specific molecular mechanisms such as the “proteasome” and “mTOR signaling pathway” but may also share common molecular mechanisms such as “nucleotide excision repair.”Skin aging is an ineluctable process leading to the progressive loss of tissue integrity and is characterized by various outcomes such as wrinkling and sagging. Researchers have identified impacting environmental factors  and several molecular mechanisms leading to skin aging. We have previously performed genome-wide association studies (GWAS) in 502 very-well characterized French women, looking for associations with four major outcomes of skin aging, namely, photoaging, solar lentigines, wrinkling, and sagging, and this has led to new insights into the molecular mechanisms of skin aging. Since individual SNP associations in GWAS explain only a small fraction of the genetic impact in complex polygenic phenotypes, we have made the integration of these genotypes into the reference Kegg biological pathways and looked for associations by the gene set enrichment analysis (GSEA) approach. 106 pathways were tested for association with the four outcomes of skin aging. This biological pathway analysis revealed new relevant pathways and genes, some likely specific of skin aging such as the  STXBP5L gene and facial photoaging . The study was conducted according to the Declaration of Helsinski Principles. Each participant completed a self-administered questionnaire related to lifetime sun exposure behavior, and three standardized high-resolution digital images  of the face were taken under normalized lighting conditions , using a Kodak DCS 760 digital camera with a 105 mm camera lens . Moreover, a blood sample was collected from each woman.This population was previously described in detail . BrieflyAfter image acquisition, several skin aging features were assessed by a dermatologist. The Photoaging was evaluated using a scale developed by −2), smoking habits , and hormonal status . BMI was categorized as underweight or normal (BMI < 25 kg m−2), overweight (25 ≤ BMI < 30 kg m−2), or obese (BMI ≥ 30 kg m−2) according to the World Health Organization (WHO) recommendations , body mass index , were rejected. Finally, we removed 191,123 SNPs with minor allele frequency <1% to avoid errors of genotyping, leaving a total of 795,063 SNPs.The quality control steps have been described in a previous study . BrieflyTo correct for possible population stratification, genotypes were analyzed using EIGENSTRAT utility of the EIGENSOFT package version 4.2 . The twoThe 795,063 SNPs were assigned to genes according to their physical position in the hg19 build using ANNOVAR . To takep-value permutations were performed for the normalization of the enrichment score. The significance and correction for multiple tests were based on the 5,000 permutations and finally assessed using the false discovery rate (FDR). Pathways with an FDR below 0.05 were considered as significantly associated with the outcome analyzed and pathways with an FDR between 0.05 and 0.25 were considered as of interest. Additionally, we investigated pathways having FDR <0.25 by looking at their top ranking genes, and for that, we picked up the genes of these pathways for which the associated p values were < 0.01.We first performed GWAS on each of the four outcomes as described in a previous work . BrieflyAccording to the classification of the KEGG pathways, the pathways are assigned to six categories: “environmental information processing,” “cellular processes,” “metabolism,” “organismal system,” “genetic information processing,” and “human disease.” For each outcome, we looked for an enrichment of significant pathways (FDR < 0.25) in the six categories compared to the distribution of these categories in the whole KEGG database. We used a Fisher’s exact test based on hypergeometric distributions to compare the distribution of the significant pathways in each category with the distribution of the pathways of the whole KEGG database in these categories.We looked for pathway associations using the GSEA pipeline based on 160 pathways selected from KEGG database  and the P = 2 × 10−4, FDR = 0.02) and “proteasome”  pathways. For these two pathways selected by GSEA, it was interesting to look at their top ranked genes , they were, respectively, XPC, MNAT1, and DDB1, and PSMB3, PSMA1, PSMC2, PSMD2, PSMB5, and PSMD4 , the “mTOR signaling pathway” , and the “nucleotide excision repair” pathway . The top ranked genes of these pathways are also presented in DDB1. No pathway association with an FDR below 0.05 was found for the photoaging or solar lentigine phenotypes.For the four phenotypes analyzed, five pathways reached the 0.05 FDR threshold. The wrinkling phenotype was associated with the “nucleotide excision repair”  of “primary immunodeficiency,” no gene was found in common for the three outcomes. In contrast, the DDB1 gene for “nucleotide excision repair” and the WNT7B and PRKCA genes for “melanogenesis” were associated with the three outcomes. We further investigated the top-ranked genes of all these pathways in common between phenotypes. Interestingly, for the melanogenesis pathway, the top-ranked genes encoded proteins having identical functions or interacting with each other . “Organismal system” was over-represented for solar lentigines with nearly half  in the six categories of KEGG  to the pathway distribution in the categories in the whole KEGG database. 1 × 10–3 .i.e., pathway). Therefore, by taking into account prior biological knowledge, this approach leads to a more comprehensive integration of underlying biological mechanisms than the usual GWAS single SNP approach. We first investigated the results for individual outcomes and then looked for shared pathways, in order to underline specific and shared mechanisms.We have performed a pathway-based genome-wide association study on four skin aging outcomes, namely, photoaging, solar lentigines, sagging, and wrinkling. The pathway-based approach looks for associations with a set of genes involved in the same biological function (XPC and DDB1 are involved in Xedoderma pigmentosum (XP), which is a pigmentary skin disorder characterized by solar hypersensitivity of the skin with dermal atrophy and high predisposition for developing cutaneous melanoma and squamous cell carcinoma of the skin  complex, which directly binds to UV-radiation-induced lesions and functions as an auxiliary damage-recognition factor . In contrast, we observed an over-representation of pathways from the “genetic information processing” category associated with wrinkles (compared with KEGG). This observation may recall the influence of UV exposure on aging for the induction of DNA damage and development of wrinkles . Finallyp value from the GWAS analysis) is chosen to represent a gene, and this approach is limitative knowing that several variants in the same gene may contribute to the overall association signal. However, some of these limitations have been overcome by several studies (The pathway-based approach is powerful but has a few limitations. For this analysis, we used the pathways from the KEGG database, and since the understanding of human gene function is incomplete and some genes are not yet characterized, the curated pathways of the database may not be fully representative of all the biological networks. Additionally, in GSEA, the best SNP (weaker  studies .In conclusion, this is the first pathway-based analysis on skin aging performed on a cohort of 502 very-well-characterized women. Our study points to specific and/or shared highly relevant pathways in photoaging, solar lentigines, wrinkling, and sagging phenotypes. Results of this work may help to further identify diagnostic or therapeutic targets for specific skin aging aspects. Interestingly, some of the identified pathways may be shared by aging features that involve other organs. As for any large-scale study, a replication in other cohorts will be needed to confirm these results.


This study suggests that luteolin has a promising potential for development into a drug and, moreover, it displays preventive effects against glioma by targeting various genes and pathways.Luteolin, a natural flavone compound, exists in a variety of fruits and vegetables, and its anticancer effect has been shown in many studies. However, its use in glioma treatment is hampered due to the fact that the underlying mechanism of action has not been fully explored. Therefore, we elucidated the potential antiglioma targets and pathways of luteolin systematically with the help of network pharmacology and molecular docking technology. The druggability of luteolin, including absorption, excretion, distribution, and metabolism, was assessed via the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). The potential targets of luteolin and glioma were extracted from public databases, and the intersecting targets between luteolin and glioma were integrated and visualized by a Venn diagram. In addition, GO and KEGG pathway analysis was engaged in Metascape. The network of the luteolin-target-pathway was visualized by Cytoscape. Ultimately, the interactions between luteolin and predicted key targets were confirmed by Discovery studio software. According to the ADME results, luteolin shows great potential for development into a drug. 4860 glioma-associated targets and 280 targets of luteolin were identified, of which 205 were intersection targets. 6 core targets of luteolin against glioma, including AKT1, JUN, ALB, MAPK3, MAPK1, and TNF, were identified via PPI network analysis of which AKT1, JUN, ALB, MAPK1, and TNF harbor diagnostic value. The biological processes of luteolin are mainly involved in the response to inorganic substances, response to oxidative stress, and apoptotic signaling pathway. The essential pathways of luteolin against glioma involve pathways in cancer, the PI3K-Akt signaling pathway, the TNF signaling pathway, and more. Meanwhile, luteolin's interaction with six core targets was verified by molecular docking simulation and its antiglioma effect was verified by  Glioma is the most common and malignant brain tumor, with more than 10,000 cases diagnosed per year and a five-year survival rate of only 5% , 3. HoweNatural products have shown a wide range of pharmacological or biological activities, rendering them potential candidates of treatment strategies against central nervous system diseases such as neurological disorders, neurodegenerative diseases, and tumors , 7. It hNetwork pharmacology can help to explore the systemic effects of TCMs by combining the methods of biology, pharmacology, and bioinformatics. These analyses provide the potential biological processes and pathways by which TCMs may work. Molecular docking is an emerging technology for drug design that can simulate the interaction among small molecules and their receptors. This technique has been widely used in pharmacological testing studies, thus providing a basis for drug design . In addihttps://tcmspw.com/tcmsp.php) is a unique systems pharmacology platform designed for TCMs. It provides information on the pharmacological properties of natural drugs including drug-likeness (DL), oral bioavailability (OB), intestinal epithelial permeability (Caco-2), blood-brain barrier (BBB), and more [Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP: and more . In thishttp://www.lilab-ecust.cn/pharmmapper/submitfile.html) [https://www.swisstargetprediction.ch) [http://ctdbase.org/) [https://pubchem.ncbi.nlm.nih.gov/). Glioma-related targets were identified via several public databases including Online Mendelian Inheritance in Man , Therapeutic Target Database 2020 (TTD) (http://db.idrblab.net/ttd/) [https://www.genecards.org/), and DisGeNET (v7.0) (http://www.disgenet.org/). All corresponding target gene symbols were derived from the UniProt database (http://www.uniprot.org/) [Luteolin-related targets were collected via PharmMapper using default values (le.html) , SwissTation.ch) , and these.org/) . The molet/ttd/) , GeneCarot.org/) .Draw Venn Diagram, http://bioinformatics.psb.ugent.be/webtools/Venn/). Based on these common targets, a PPI network was constructed via importing the luteolin against glioma target genes to STRING (11.0) database (https://string-db.org/) using a medium confidence of 0.4 [Draw Venn Diagram.The intersecting targets of luteolin and glioma represent the glioma targets of luteolin and were visualized with a web tool (e of 0.4 . This PPe of 0.4 . Hub tarhttps://metascape.org) and “P Value Cutoff” as well as “Min Overlap” was set to 0.01 and 3, respectively. GO functionally annotates key targets as cellular components (CCs), molecular functions (MFs), and biological processes (BPs). KEGG enrichment analysis reveals the potential biological process of hub targets. Eventually, the bubble chart of the KEGG enrichment analysis was displayed using the bioinformatics platform (http://www.bioinformatics.com.cn/).The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment of glioma were performed using the Funrich software (3.1.3) and the clusterProfiler package R (v3.14.3). The GO and KEGG enrichment analysis of luteolin against glioma was conducted using the functional annotation tool of Metascape (https://www.kegg.jp/) to provide a visualization of the possible complex interactions of luteolin in the glioma setting.The intersection targets of glioma and luteolin were uploaded to KEGG Mapper, an analysis tool of the KEGG website (https://xenabrowser.net/datapages/). Then, the diagnostic value of six core targets was assessed by employing pROC and ggplot2 R software packages.RNAseq data of glioma tissues (GBMLGG), extracted from TCGA, and corresponding normal tissue data originating from GTEx, were downloaded via UCSC XENA  and finally, sampling for 100 ns at constant pressure, saving one frame (conformation) per ps for subsequent analysis.The general AMBER force field was used to model luteolin, while the ff14SB force field was used to model proteins . Hydroge2. Luteolin was purchased from MedChemExpress (CAS No.:491-70-3). The cells were plated in 96-well plates (8000/well) for an MTT assay and in 6-well plates (3 × 105/well) for western blot (WB) and semiquantitative real-time-PCR (sqRT-PCR) analysis. After incubation with luteolin for 24 h, 10 μl MTT solution (5 mg/mL) was added to the 96-well plates for a 4 h incubation period and absorbance values at 490 nm were measured. The measured values were expressed as a ratio by comparing them to the control group values. More details for WB and sqRT-PCR were described previously [AKT1: 5′-AGCGACGTGGCTATTGTGAAG-3′ (forward) and 5′-GCCATCATTCTTGAGGAGGAAGT-3′ (reverse); ALB: 5′-TGCAACTCTTCGTGAAACCTATG-3′ (forward) and 5′-TGCAACTCTTCGTGAAACCTATG-3′ (reverse); JUN: 5′- TCCAAGTGCCGAAAAAGGAAG-3′ (forward) and 5′-CGAGTTCTGAGCTTTCAAGGT-3′ (reverse); TNF: 5′-GAGGCCAAGCCCTGGTATG-3′ (forward) and 5′- CGGGCCGATTGATCTCAGC-3′ (reverse); JUN: 5′- TCCAAGTGCCGAAAAAGGAAG-3′ (forward) and 5′-CGAGTTCTGAGCTTTCAAGGT-3′ (reverse); MAPK1: 5′- TACACCAACCTCTCGTACATCG-3′ (forward) and 5′-CATGTCTGAAGCGCAGTAAGATT-3′ (reverse); and MAPK3: 5′-ATGTCATCGGCATCCGAGAC-3′ (forward) and 5′- GGATCTGGTAGAGGAAGTAGCA-3′ (reverse). ACTB (B661102) was purchased from Sangon Biotech (Shanghai) Co., Ltd., and the antibodies were purchased from Santa Cruz Biotechnology (Shanghai) Co., Ltd.U87MG  and SHG4eviously . In briet-test using GraphPad Prism 8 , and P < 0.05 was considered as being statistically significantly different. All experiments were performed at least in triplicate.Data are expressed as the mean ± SD. Statistical analysis was performed by Student's Luteolin was studied via TCMSP to analyze its ADME characteristics . LuteoliTo reveal the potential targets of luteolin in glioma cells, all 275 targets of luteolin were identified with the help of Swiss Target Prediction (111), CTD (124), and PharmMapper (71) as shown in P < 0.05). The results suggest that the glioma-related targets could act either through identical protein binding, transcription factor binding, extracellular space, nucleoplasm, positive regulation of transcription by RNA polymerase II, or positive regulation of cell population proliferation. For the KEGG enrichment analysis, we used the clusterProfiler R package (v3.14.3). The results of the KEGG analysis included 56 signaling pathways (P < 0.05) .First, we performed a GO analysis of glioma-related targets through Funrich (3.1.3) to reveal the potentially involved therapeutic pathways. 10 significantly enriched BPs, CCs, and MFs terms are shown in  < 0.05) . The topP < 0.01) with regard to inorganic substance GO:0010035, protein kinase complex GO:1902911, phosphotransferase activity, and an alcohol group as acceptor GO:0016773 as the top ones, respectively. The result of GO enrichment analysis is summarized in P < 0.05), including 49 signaling pathways .To shed light on the potential biological function of luteolin in glioma cells, 205 intersection targets were submitted to Metascape to analyze their biological function. 15 significantly enriched BP, CC, and MF terms are shown in pathways . The KEGThe glioma pathway map and the signaling pathways of the luteolin-target-pathway network were combined into an integrated pathway map to provide a visualization of the complex interactions of luteolin in the glioma setting . As showTo clarify the mechanism of luteolin in the treatment of glioma through multiple pathways and targets, the luteolin-target-pathway network was visualized by Cytoscape 3.8.0 in The STRING database was used to further reveal the functional mechanisms of luteolin in glioma. The PPI network of luteolin in the glioma setting had 203 nodes and 3225 edges, in which nodes and edges meant targets and their interactions, respectively. An initial PPI network was established by Cytoscape 3.8.0 . Six hubP < 0.05), 0.727 , 0.764 , 0.815 , 0.562 , and 0.929 .We analyzed the diagnostic value of the six core targets through constructing ROC curves and their corresponding area under the curve (AUC) from TCGA and GTEx datasets. Based on the PPI network, six core targets were selected for molecular docking . The binTo analyze the fluctuation of the interfaces of luteolin-protein complexes, each complex was simulated for 100 ns. The results of the molecular dynamics simulations of luteolin with the six core proteins are shown in μM luteolin were 96.25%/95.65%, 88.45%/82.04%, 81.11%/81.66%, 69.95%/73.78%, 58.81%/69.52%, and 51.46%/59.14%, respectively. Thus, luteolin can effectively decrease the viability of glioma cells in a concentration-dependent manner.To validate the effect of luteolin on glioma cells, the classical glioma cell lines U87 and SHG44 were selected to perform cell-based experiments. The results of the MTT assay showed that the viability of the two cell lines decreased compared to control cells when the luteolin concentration was increased . Cell viμM incubation (μM incubation. Moreover, only U87 cells displayed a significantly decreased expression of MAPK3 (Western blotting and sqReal-Time PCR were used to verify the results of the molecular docking and molecular dynamics simulations of luteolin and its targets. After treatment with luteolin for 24 h, a significantly decreased expression of AKT1, TNF, and MAPK1 in the two cell lines was observed when compared to the control group, with JUN decreasing only after 160 cubation . We did of MAPK3 .Although the study of glioma treatment has come a long way, glioma still has a very high morbidity and mortality rate . This isPotential poor pharmacokinetics and toxicity impede drug discovery and development. Therefore, assessment of the ADME properties should be the first step in the drug discovery process, leading to a significantly accelerated drug discovery process and at the same time reducing side effects and costs . The twoμM. We could not detect ALB protein in western blots, but the results of sqRT-PCR showed that luteolin was able to inhibit its expression. Taken together, these outcomes suggest that the six core genes maybe potential targets of luteolin and may play an important role in the future treatment of glioma.In previous studies, luteolin has been utilized in the treatment of multiple cancer cell lines through multiple biological effects such as induction of apoptosis, cell cycle arrest, and inhibition of metastasis and angiogenesis . These rκB signaling pathway [Surprisingly, ALB was described as a potential novel protein marker for glioma , 47. It  pathway –58. JUN  pathway . It regu pathway . AP-1 is pathway . Small A pathway . These r pathway , 64. Thein vitro and in vivo.Taken together, we verified that luteolin is a prospective drug for the development of efficient multitargeted antiglioma TCMs with pharmacology, molecular docking technology, molecular dynamics simulation, and experimental verification. This study provides novel insights into luteolin in future clinical translational research. However, the database used in network pharmacology may omit some potential targets for difficulties in data acquisition. Next, we will validate the further antiglioma potency and mechanisms of luteolin


This research attempted to elucidate the molecular components are involved in the pathogenesis of recurrent implantation failure (RIF). We initially identified that 386 mRNAs, 144 miRNAs and 2548 circRNAs were differentially expressed (DE) in RIF and then investigated the genetic cause of the observed abnormal expression by constructing a circRNA‐miRNA‐mRNA network considering the competing endogenous RNA theory. We further analysed the upstream transcription factors and related kinases of DEmRNAs (DEMs) and demonstrated that SUZ12, AR, TP63, NANOG, and TCF3 were the top five TFs binding to these DEMs. Besides, protein‐protein interaction analysis disclosed that ACTB, CXCL10, PTGS2, CXCL12, GNG4, AGT, CXCL11, SST, PENK, and FOXM1 were the top 10 hub genes in the acquired network. Finally, we performed the functional enrichment analysis and found that arrhythmogenic right ventricular cardiomyopathy (ARVC), hypertrophic cardiomyopathy (HCM), pathways in cancer, TNF signalling pathway and steroid hormone biosynthesis were the potentially disrupted pathways in RIF patients. Optimistically, our findings may deepen our apprehensions about the underlying molecular and biological causes of RIF and provide vital clues for future laboratory and clinical experiments that will ultimately bring a better outcome for patients with RIF. Functionally, ncRNAs have been classified into two categories: housekeeping and regulatory ncRNAs. The first ones  play crucial roles in many cellular processes. The later ncRNAs  are tissue‐specific and work in response to various internal and environmental stimuli.In this research, first, we gathered the expression profiles of mRNAs, miRNAs and circRNAs in RIF patients from the Gene Expression Omnibus (GEO) database. Next, using GEO2R analysis, the Differentially Expressed mRNAs (DEMs), miRNAs (DEMIs) and circRNAs (DECs) were determined. Furthermore, CircRNA‐miRNA and miRNA‐mRNA axes were established. Then, by integrating them, a circRNA‐miRNA‐mRNA network was established. Moreover, an assessment of the upstream transcription factor (TFs) and Protein Kinases (PKs) of DEMs was performed. Besides, commonly used enrichment analysis approaches such as protein‐protein interactions (PPI), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were involved in the prediction of the probable mechanisms and tasks of dysregulated genes and pathways in RIF. The present work could advance our perception about the underlying molecular processes of RIF, providing novel diagnostic biomarkers, therapeutic targets and remarkable hints for further studies.22.1https://www.ncbi.nlm.nih.gov/geo/) , which included 48 samples (24 RIF samples and 24 fertile control patients’ samples). The GSE71332 data set contained the miRNA expression profile of seven RIF patients and five control endometria based on the GPL18402, Agilent‐046064 Unrestricted_Human_miRNA_V19.0_Microarray (miRNA ID version). The GSE147442 data set consisted of the circRNA expression profile of endometrium biopsies for eight RIF patients and eight fertile women based on the GPL21825, 074 301 Arraystar Human CircRNA microarray v2. All investigated samples in the microarray were obtained from Endometrial Tissue.First, a total of three microarray expression profile data sets were selected from the National Center of Biotechnology Information (NCBI) GEO (/) Table . The GSE2.2http://www.ncbi.nlm.nih.gov/geo/geo2r/), a web tool based on the R language limma package for comparing more than two groups of samples to obtain differentially expressed gene (DEGs) in almost any GEO series.GSE111974, GSE71332 and GSE147442 raw data. For this, we initially assessed the overall characteristics of value distributions. Usually, median‐centred values are representative that the data are normalized. If that was not our case, we used the force normalization option, which applies quantile normalization to the expression data forcing all selected samples to have identical value distribution. Then, we assigned samples from RIF patients and healthy women to ‘case group’ and ‘control group’, respectively. After that, we performed GEO2R analysis, and the results were downloaded and exported to Microsoft Excel 2019 software. Finally, we used a pre‐defined cut‐off criterion | > 1) and the Volcano Plot server (https://paolo.shinyapps.io/ShinyVolcanoPlot/), to identify DEGs in each data set and construct a volcano plot of DEGs.GEO2R (2.3http://163.172.134.150/miRNAmeConverter‐shiny/) https://david.ncifcrf.gov/conversion.jsp) and the circBase (http://www.circbase.org/cgi‐bin/webBlat) databases, respectively.GSE111974 annotated as any type of gene such as lncRNA, pseudogenes and uncharacterized genes except mRNA (protein‐coding) also were ruled out. We converted the multiple expression values referred to the same gene to a single value using the following approach: Genes with the same direction of expression were averaged and discarded if they had the reversed expression tendency.We used the miRNAmeConverter tool , as an integrated database of human microRNA‐target predictions across from 30 independent resources with an integrative score, was used to identify the possible interactions between DEMs and DEMIs via bidirectional mode search under a high confidence filter (score class 5% = high).The miRDIP v4.1 online tool (2.6http://www.circbank.cn/) database.GSE147442. As the expression levels of circRNAs typically do not alter the expression of corresponding miRNAs,Targeting circRNAs of DEMIs in miRNA‐mRNA network was predicted through the circBank  tool (https://amp.pharm.mssm.edu/X2K/).The upstream regulators and protein kinases related to 386 DEMs in 2.9GSE111974, the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database (https://string‐db.org/) Based on the 386 DEMs in 2.10GSE111974. The GO items involved in this study were as follows: molecular function (MF), cellular component (CC) and biological process (BP). The retrieved items were imported to Microsoft Excel 2019, and after removing those terms with a p‐value greater than 0.05, they were ranked based on the p‐value parameter. The bar chart was visualized utilizing Microsoft Excel 2019.The DAVID v6.8 database was applied to execute the GO and KEGG pathway analysis on 386 DEMs in 33.1GSE111974, GSE71332 and GSE147442, respectively. With the pre‐defined threshold (P <.05 and |logFC|>1) and using the Volcano Plot server and processing the acquired DEGs, we then obtained a total of 386 DEMs  was down‐regulated and MUC‐1  and COX2  were up‐regulated in GSE111974 (Table P = 4.46E‐02), hsa‐miR‐145‐5p , hsa‐miR‐145‐3p  and hsa‐miR‐29c‐5p  were overexpressed in GSE71332 (Table P = 4.00E‐03), hsa_circ_0000115  and hsa_circ_0091053  were decreased in GSE147442 , Androgen Receptor (AR), Tumor Protein P63 (TP63), Nanog Homeobox (NANOG) and Transcription Factor 3 (TCF3) were the top five TFs binding to DEMs , regulation of cell proliferation (GO:0 042 127), inflammatory response (GO:0 006 954), cell‐cell signalling (GO:0 007 267) and positive regulation of prostaglandin biosynthetic process (GO:0 031 394). With regard to CC, the top five enriched items were extracellular space (GO:0 005 615), membrane raft (GO:0 045 121), apical plasma membrane (GO:0 016 324), an integral component of the plasma membrane (GO:0 005 887) and plasma membrane (GO:0 005 886). In terms of MF, amino acid transmembrane transporter activity (GO:0 015 171), cAMP‐dependent protein kinase regulator activity (GO:0 008 603), retinoic acid binding (GO:0 001 972), calcium ion binding (GO:0 005 509) and serine‐type endopeptidase inhibitor activity (GO:0 004 867) were enriched for the first five places. Pathway enrichment analysis disclosed that the top five predominantly enriched pathways were hsa05412:Arrhythmogenic right ventricular cardiomyopathy (ARVC), hsa05410:Hypertrophic cardiomyopathy (HCM), hsa05200:Pathways in cancer, hsa04668:TNF signalling pathway and hsa00140:Steroid hormone biosynthesis , hypertrophic cardiomyopathy (HCM), pathways in cancer, TNF signalling pathway and steroid hormone biosynthesis. We speculated that dysregulated genes are likely exerting their roles in RIF progression through these potential pathways.Finally, we carried out the functional enrichment analysis for 386 DEMs in To the best of our knowledge, the present research was the first attempt to build a circRNA‐miRNA‐mRNA network and identify the most potential corresponding pathways in the pathogenesis of RIF. However, our study had some limitations. Firstly, due to the limited number of GEO data sets, we only have utilized the data obtained from three data sets deposited in the GEO database. Additionally, the sample size of these data sets was few. Therefore, it was hard to prevent random errors. Besides, our analyses' findings were not confirmed with reliable laboratory analysis, and we just focused on in‐silico assessments. Depending solely on bioinformatics investigations may trigger deviations. Hence, in vitro experiments remain to be carried out to validate the results of the present report.5In conclusion, the present research performed various bioinformatics analyses on the DEGs in the endometrium of RIF patients. These investigations demonstrated that the hub genes in RIF directly or indirectly affect cell cycle, apoptosis, angiogenesis and immune response‐related pathways that modulate the endometrial receptivity and embryo implantation. Hopefully, our study's findings may bear an increased knowledge about the underlying molecular and biological aetiologies of RIF and valuable clues for additional experiments that will ultimately lead to improved capacity for accelerated and accurate diagnosis and treatment of patients with RIF.The authors declare no competing interests.Mohsen Ahmadi: Conceptualization ; Formal analysis ; Investigation ; Methodology ; Visualization ; Writing‐original draft ; Writing‐review & editing . Salar Pashangzadeh: Writing‐original draft ; Writing‐review & editing . Mahta Moraghebi: Conceptualization ; Formal analysis ; Methodology ; Visualization . soudabeh Sabetian: Project administration ; Supervision ; Validation ; Writing‐review & editing . mohammad shekari: Writing‐original draft . Fatemeh Eini: Writing‐original draft . Ensieh Salehi: Writing‐original draft . Pegah Mousavi: Formal analysis ; Funding acquisition ; Methodology ; Project administration ; Validation ; Writing‐original draft ; Writing‐review & editing .Supporting InformationClick here for additional data file.


Diabetic nephropathy (DN) is a common and serious complication of diabetes, but without a satisfactory treatment strategy till now. Liuwei Dihuang pills (LDP), an effective Chinese medicinal formula, has been used to treat DN for more than 1000 years. However, its underlying mechanism of action is still vague.  Active compounds and corresponding targets of LDP were predicted from the TCMSP database. DN disease targets were extracted from the OMIM, GeneCards, TTD, DisGeNET, and DrugBank databases. Subsequently, the “herbal-compound-target” network and protein-protein interaction (PPI) network were constructed and analyzed via the STRING web platform and Cytoscape software. GO functional and KEGG pathway enrichment analyses were carried out on the Metascape web platform. Molecular docking utilized AutoDock Vina and PyMOL software.  41 active components and 186 corresponding targets of LDP were screened out. 131 common targets of LDP and DN were acquired. Quercetin, kaempferol, beta-sitosterol, diosgenin, and stigmasterol could be defined as five crucial compounds. JUN, MAPK8, AKT1, EGF, TP53, VEGFA, MMP9, MAPK1, and TNF might be the nine key targets. The enrichment analysis showed that common targets were mainly associated with inflammation reaction, oxidative stress, immune regulation, and cell apoptosis. AGE-RAGE and IL-17 were the suggested two significant signal pathways. Molecular docking revealed that the nine key targets could closely bind to their corresponding active compounds.  The present study fully reveals the multicompound's and multitarget's characteristics of LDP in DN treatment. Furthermore, this study provides valuable evidence for further scientific research of the pharmacological mechanisms and broader clinical application. Diabetes mellitus, like COVID-19, is a wicked problem . DiabeteCornus officinalis (Shanzhuyu), Alisma (Zexie), and Poria Cocos (Fuling). LDP is considered to have the efficacy of nourishing the yin and kidney. In recent years, more and more researchers' interest has been focused on LDP to treat DN. Xu et al. [LDP, a classical prescription first described in Xiaoer Yaozheng Zhijue, has been used to treat DN for more than 1000 years by Chinese people. Qian Yi formulated LDP during the Song dynasty. It is composed of 6 herbs, including Rehmanniae Radix Preparata (Shudihuang), Cortex Moutan (Mudanpi), Rhizoma Dioscoreae (Shanyao), u et al.  have repu et al.  have docu et al. . Howeveru et al. . Until nBecause of the holism concept of TCM, Chinese medicinal formulas are usually treated diseases through several components and targets rather than a single one . Networkhttps://tcmspw.com/tcmsp.php/), a systematic pharmacology database, provides information about the active herbal components and related targets [https://www.uniprot.org/) [Traditional Chinese Medicine System Pharmacology Database  . In thishttps://omim.org/) [https://www.genecards.org/) [http://db.idrblab.net/ttd/) [https://www.disgenet.org/home/) [https://go.drugbank.com/) [For the purpose of obtaining a target list associate with DN, “diabetic nephropathy” was set as a keyword to search five disease database: Online Mendelian Inheritance in Man (OMIM) database (im.org/) , GeneCards.org/) , Therapeet/ttd/) , DisGeNEg/home/) , and Drunk.com/) . After cAfter intersecting the acquired targets of formula and disease by using R language software, the common targets of LDP in treating DN were screened out. Then, we used Cytoscape 3.8.0 software to construct a “Herbal-compound-target” network . The relhttps://string-db.org/, version 11.0) is a data platform that collects almost all known and predicted interactions between the expressed proteins [The STRING  is an enrichment analysis platform updated monthly rather than longer to keep the data up to date [P value cutoff <0.01” were set as significant thresholds. Biological processes (BP), cellular components (CC), and molecular function (MF) are included in GO terms. After analysis, the top 10 terms of BP, CC, MF, and KEGG pathways were chosen to be visualized.Metascape ( to date . To furthttp://www.rcsb.org/) [https://pubchem.ncbi.nlm.nih.gov/) [The binding capability of nine key targets and their corresponding active compounds were evaluated by molecular docking. To ensure the accuracy of the data, we used the UniProt database to find the UniProt ID of the key targets. According to the Uniport ID, the 3-dimensional (3D) protein structures related to the nine key targets were downloaded from the RCSB PDB online tools (sb.org/) . The watih.gov/) . Then, tCornus officinalis (Shanzhuyu), 7 compounds in Alisma (Zexie), and 6 compounds in Poria cocos (Fuling).A total of 41 active compounds of 6 herbs in LDP were acquired from the TCMSP database after screening , includiCornus officinalis (Shanzhuyu), Alisma (Zexie), and Poria cocos (Fuling) was 27, 152, 62, 54, 5, and 19, respectively. As for the disease targets, 22 DN-related targets were acquired in TTD, 1189 in DisGeNET, 109 in DrugBank, 3319 in GeneCards, and 68 in OMIM. After removing duplicates, 3701 targets were identified  and their corresponding active compounds was performed. When the binding energy is <0 kJ mol, the small molecule ligand can spontaneously bind to the protein receptor. If the binding energy is <−5.0 kJ mol or lower, it indicates that the two have the better binding ability . ThroughDN is a common diabetic complication that threatens the health and lives of diabetic patients. Unfortunately, most diabetic patients cannot obtain good treatment effects with routine therapies. Growing evidence has suggested that LDP may be a potential adjuvant or alternative medicine for DN –11. HoweBased on the results of the network analysis, quercetin, kaempferol, beta-sitosterol, diosgenin, and stigmasterol can be defined as the crucial compounds of LDP in treating DN. It is reported that quercetin could inhibit inflammatory cell infiltration, alleviate renal oxidative stress injury, relieve the pathological damage of the kidney, and improve renal function in DN . Kaempfeβ1 via ross-activation and autoregulation during renal fibrosis in DN [Through the PPI network analysis, JUN, MAPK8, AKT1, EGF, TP53, VEGFA, MMP9, MAPK1, and TNF were the key targets of LDP in treating DN. These targets are mainly connected with inflammation, vascular permeability, and oxidative stress. In some ways, this is consistent with the disease characteristics and pathogenesis of DN. To further reveal LDP's possible anti-DN molecular mechanism, we conducted molecular docking of 9 key targets with their corresponding active compounds. Study results have shown that the nine key targets have an excellent ability to bind their related active compounds in LDP. Among them, JUN, MAKP1, and AKT1 had a more stable binding ability than others. A recent study showed that c-Jun could be progressively elevated, and it could activate the expression of TGFis in DN . MAPK1 cis in DN . AKT1 isis in DN .The GO functional enrichment analysis of the 131 common targets was carried out . The 10 β, and inflammatory cytokines [Associated with the GO enrichment analysis, we found out that the main pathways of LDP on DN might be the AGE-RAGE signaling pathway and IL-17 signaling pathway based on the enrichment results of KEGG. As we all know, the AGE-RAGE signaling pathway is of great significance to diabetic complications. The upregulation of AGEs levels and RAGE expression can aggravate the progression of DN . When thytokines . If the ytokines . Inflammytokines . Mohamedytokines .To some extent, our results are supported by the previous studies, which made them more reliable. However, there are also some limitations in this study. For example, it is difficult to ensure that the drug's active ingredients are identical to those absorbed into the patient's bloodstream; We are still vague about the interaction effects of different nodes in the network analysis. The functions and pathways highly researched may cause departures from expected results. Thus, further experimental and clinical studies are warranted to verify our theoretical prediction.This research proved that the therapeutic mechanism of LDP on DN might be realized by multitargets, multiactive compounds, and multipathways. We found that quercetin, kaempferol, beta-sitosterol, diosgenin, and stigmasterol can be defined as five crucial compounds. JUN, MAPK8, AKT1, EGF, TP53, VEGFA, MMP9, MAPK1, and TNF may be the nine most important therapeutic targets. AGE-RAGE and IL-17 are two key signal pathways of LDP for the treatment of DN. The potential pharmacological mechanism mainly associates with inflammation reaction, oxidative stress, immune regulation, and cell apoptosis. Also, some active compounds, target genes, and pathways in our study have few reports, which may be the clues for further research in the mechanism of LDP in treating DN. In summary, our study provides valuable evidence for further basic research and clinical applications.


Environmentally, bisphenol A (BPA) is a well-known pollutant caused human health risk, including osteosarcoma (OS). OS, a deadly bone neoplasia, may occur in children and adults. However, the anti-OS pharmacotherapy prescribes limitedly in clinical practice. Interestingly, previous experimental evidences indicate calycosin-exerting potential anti-OS actions. Thus, in this report, we aimed to further characterize and detail the therapeutic targets and molecular mechanisms of calycosin-anti-BPA-related OS by using network pharmacology and molecular docking analyses. In results, the bioinformatics data disclosed all mapped, core targets, biological functions, molecular pathways of calycosin to treat BPA-related OS. The computational analysis using molecular docking indicated that potential binding ability of core targets in calycosin to treat BPA-related OS was identified. Moreover, detailed biological functions and optimal pathways of calycosin-anti-BPA-related OS were revealed, as shown in integrated network maps. Taken together, these network pharmacology and structural biology findings illustrate the core biotargets, pharmacological functions and pathways of calycosin-anti-BPA-related OS. Potentially, these core targets identified by molecular docking may attribute to the potential clinical application of calycosin against BPA-related OS.  Statistin vitro, characterized with pharmacological actions of reducing cell proliferation, and promoting cell apoptosis [Calycosin, a naturally producing ingredient, is reported with functionally exerting effective antioxidation, neuroprotection, anti-cancers . Preclinpoptosis . Howeverpoptosis ,13. Our poptosis ,15. To a2.2.1A series of analytical tools, including Traditional Chinese Medicine Systems Pharmacology Database (TCMSP), SwissTargetPrediction, Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine (BATMAN-TCM), SuperPred were applied for obtaining anti-disease genes of calycosin, and BPA-related OS genes were harvested by using the databases of GeneCard, Online Mendelian Inheritance in Man (OMIM). In further determination, these isolated genes of calycosin and BPA-related OS were re-tested via online bioinformatics tool for drawing Venn diagram before all mapped targets of calycosin in the treatment of BPA-related OS were obtained ,17.2.2After assays, these mapped genes of calycosin and BPA-related OS were used to generate a protein–protein interaction (PPI) network of calycosin in the treatment of BPA-related OS by using String tool. And the raw data (saved as tsv. files) were determined via Cytoscape tool to identify core targets. All core targets were screened out according to the Degree value by using NetworkAnalyzer for topological analysis ,18.2.3By using FunRich analysis tool, the core targets of calycosin in the treatment of BPA-related OS were determined via Functional Annotation Bioinformatics Microarray Analysis (DAVID) database for revealing pharmacological processes and pathways of calycosin in the treatment of BPA-related OS. On the basis of the -Log p-value, top biological processes and signaling pathways of calycosin in the treatment of OS were created and demonstrated ,20.2.4By applying Cytoscape software for summarizing current bioinformatics data, the gene ontology (GO)-associated biological process and molecular pathway for calycosin in the treatment of BPA-related OS were identified accordingly. Furthermore, the visualization graph revealing drug-target-gene ontology-biological process-pathway-disease was constructed ,22.2.5As reported previously ,24, all 33.1As results, a number of 245 BPA-related OS genes were acquired, and other 138 anti-disease genes of calycosin were attained accordingly. As shown in Venn graph, a total of 20 mutual genes of calycosin and BPA-related OS were identified and highlighted in interaction network for connected visualization .Figure 13.2The mutual genes were further assayed by using Cytoscape software. The topological data showed that median degree of freedom was 3.667, and maximum degree of freedom was 10. Therefore, the core target screening range was set as 4–10. As a result, 9 core targets of calycosin in the treatment of BPA-related OS were identified totally, including Epidermal growth factor receptor erbB1 (EGFR), Estrogen receptor alpha (ESR1), Heat shock protein HSP 90-alpha (HSP90AA1), Mitogen-activated protein kinase 14 (MAPK14), Estrogen receptor beta (ESR2), Androgen receptor (AR), Breast cancer type 1 susceptibility protein (BRCA1), Prostaglandin G/H synthase 2 (PTGS2), Cytochrome P450 19A1 (CYP19A1). The findings were illustrated in an interaction network  and more3.3R language-related packages. As results, the histogram and Circos circle chart revealing GO-based biological processes were presented in P < 0.05) were identified with involvement of IL-17 signaling pathway, Th17 cell differentiation, Endocrine resistance, Estrogen signaling pathway, Prolactin signaling pathway, Ovarian steroidogenesis, Progesterone-mediated oocyte maturation, Breast cancer, Prostate cancer, Proteoglycans in cancer, MicroRNAs in cancer, PD-L1 expression and PD-1 checkpoint pathway in cancer, VEGF signaling pathway, GnRH signaling pathway, C-type lectin receptor signaling pathway, PI3K-Akt signaling pathway, TNF signaling pathway, Relaxin signaling pathway, FoxO signaling pathway, Oxytocin signaling pathway .All 9 core targets/genes were used to conduct GO-associated function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses through 3.4The network visualization of calycosin-target-BP-KEGG-BPA-related OS was determined and highlighted through Cytoscape software, as revealed in 3.5By using PDB-based analysis, BRCA1 protein/target was excluded as this protein failed to dock with calycosin-associated ligands. As shown in binding energy data , EGFR P. In ESR14in vitro and in vivo [in vitro indicates that BPA induces cell proliferation and growth in human choriocarcinoma cell line through affecting estradiol metabolism [in-silico findings using molecular docking analysis should be validated in clinical and experimental studies before the future clinical application of calycosin to treat BPA-related OS will be achieved.In current bioinformatics study, we identified and elucidated the pharmacological activity and mechanism of calycosin in the treatment of BPA-related OS. More interestingly, all core targets of calycosin in the treatment of BPA-related OS were revealed, comprising EGFR, ESR1, HSP90AA1, MAPK14, ESR2, AR, BRCA1, PTGS2, CYP19A1. In details, the biological functions enriched with core targets were identified with regulatory processes of bone, immunity, inflammation, hormone and cancer for revealing calycosin-exerted BPA-related OS activities. EGFR, if mutated, is reportedly correlated with the pathological development of tumorigenesis . Another in vivo . The tra in vivo . The cli in vivo . The stutabolism . As reve5In conclusion, our current computational findings identify detailed pharmacological biotargets, biological processes, molecular mechanisms of calycosin to treat BPA-related OS. In future medical application, naturally-isolating calycosin will be applied in clinical practice against osteosarcoma, including BPA-related OS.Click here for additional data file.


All cells have a complex internal network of ‘communication chains’ called signal transduction pathways (STPs). Through interaction of different proteins in STPs, they are partly responsible for the behavior of a cell. In our study, we investigated the activity of six STPs in 85 women with advanced stage high-grade serous ovarian cancer (HGSC). To investigate the relation between and differences in survival and STP activity, women with a short disease-free survival (below 12 months) and a long disease-free survival (above 24 months) were included. We found no differences in mean STP activity between short-term survivors (52 women) and long-term survivors (33 women). However, when we analyzed postmenopausal women, we found that both disease-free and overall survival were related to estrogen receptor (ER) pathway signaling. This indicates that a better survival outcome was related to a more active ER pathway in this subgroup.n = 52) or over 24 months (n = 33). There were no significant differences in median pathway activity between patients with a short and long DFS. In univariate Cox proportional hazards analysis, ER pathway activity was related to a favorable DFS and overall survival (OS) in postmenopausal women , but not in premenopausal women. We divided the postmenopausal group into subgroups based on ER pathway activity quartiles. Survival analysis revealed that postmenopausal women in the lowest ER quartile had a shorter DFS and OS . Furthermore, we were able to form subgroups of patients based on an inverse relation between ER and PI3K pathway activity. In conclusion, in postmenopausal patients with advanced stage HGSC, a poorer survival outcome was associated with low functional ER pathway activity.High-grade serous ovarian carcinoma (HGSC), the most common subtype of ovarian cancer, has a high mortality rate. Although there are some factors associated with survival, such as stage of disease, there are remarkable differences in survival among women diagnosed with advanced stage disease. In this study, we investigate possible relations between survival and signal transduction pathway (STP) activity. We assessed the functional activity of the androgen receptor (AR), estrogen receptor (ER), phosphoinositide-3-kinase (PI3K), Hedgehog (HH), transforming growth factor beta (TGF-β) and canonical wingless-type MMTV integration site (Wnt) pathway in 85 primary tumor samples of patients with FIGO stage IIIC to IVB HGSC and disease-free survival (DFS) below 12 ( Ovarian cancer is the most lethal gynecological malignancy and the fifth leading cause of cancer related death in women . High-grSeveral clinicopathological factors associated with improved survival have been established like stage of disease, CA125 concentration at diagnosis and after treatment, the possibility of primary debulking surgery and residual disease after surgery. Furthermore, immune factors such as tumor infiltrating lymphocytes have been identified as prognostic factors . HoweverIn 2010, Verhaegh et al. developed a technique to quantitatively measure functional signal transduction pathway (STP) activity, and therefore the functional phenotype, of cancer cells. With this assay, mRNA levels of target genes of major oncogenic STPs are used as input for knowledge-based Bayesian network models ,12,13. IIn this exploratory study, we use these pathway assays in patients diagnosed with advanced stage HGSC who achieved complete remission after treatment with debulking surgery and (neo-)adjuvant chemotherapy. To investigate disease recurrence and survival in relation to STP activity, we compare STP activity in patients with a short and long DFS as well as define interrelations between different pathways with regard to menopausal status and survival. We aim to explain the differences in survival and ultimately provide new leads for accurate selection of patients for targeted therapies.We retrospectively searched the Netherlands Cancer Registration (NCR) database for patients diagnosed with FIGO stage IIIC and IV HGSC between January 2000 and December 2016 in three Dutch hospitals . Patients were eligible for inclusion if histology of the primary tumor was taken prior to start of chemotherapy and available for analysis and if treatment with primary or interval debulking surgery combined with (neo-)adjuvant chemotherapy with carboplatin and paclitaxel resulted in complete remission. Complete remission was chosen as inclusion criterium as we aimed to compare DFS, which requires patients to achieve complete remission first. Patients were excluded if they objected to further use of pathology samples or if they were diagnosed with any other malignancy, either prior to or following HGSC diagnosis, with the exception of basal cell carcinoma as cases where this affects life expectancy are exceedingly rare. Based on reported median DFS for advanced stage HGSC of 16 and 19 months, we decided to exclude patients with a DFS between 12 and 24 months from our analysis to form two clearly defined groups . As suchThe following data were retrieved from the patients’ medical records: parity, menopausal status at diagnosis, age at diagnosis, CA125 concentrations at the time of diagnosis and at the end of treatment, number of chemotherapy cycles and response, type of debulking surgery, debulking outcome, number of recurrences, type of treatment for the recurrences, vital status at the end of follow-up, DFS and OS. DFS was defined as time between final day of primary treatment until histological confirmation of recurrent disease or start of second-line therapy. Patients were censored if they had no recurrence by the 1 May 2020, or if they were deceased prior to the end of follow-up without evidence of a recurrence. For OS, death was recorded as an event while patients were censored at the end of follow-up (1 May 2020) or on the date of last contact if they chose to continue follow-up in a different hospital. Debulking outcome was classified as either ‘complete’ , ‘optimal’  or ‘incomplete’  . Data on2 tumor surfaces. Total mRNA was isolated following the manufacturer’s protocol  and mRNA concentrations were measured using the Qubit® RNA HS Assay Kit and Qubit® Fluorometer . Pathway-specific target gene expression levels were measured by RT-qPCR using the SuperScriptTM III PlatinumTM One-Step qRT-PCR kit , PCR plates  and a CFX96 Real-Time PCR Detection System .Original histological sections of HGSC samples were reviewed by one of two expert gynecological pathologists (S.L.B. and M.H.F.M.L.-B.). Representative sections with sufficient tumor cells were annotated and HGSC samples containing < 40% tumor cells were excluded from analysis to minimize stromal contamination. Five-micrometer formalin-fixed paraffin-embedded (FFPE) sections of primary HGSC samples were cut with a microtome . Depending on total annotated tumor area, multiple sections were scraped manually resulting in at least 20 mmSOD2), a FOXO target gene, were used. There are a few important considerations for the interpretation of the generated pathway activity scores: 1. the pathway activity score range (minimum-maximum activity) on the normalized scale is unique for each cell or tissue type. Once the range has been defined using samples with known pathway activity, the absolute value for every new sample can be directly interpreted against that reference. If the range has not been defined, differences in pathway activity scores between samples can be interpreted; 2. in the same sample the pathway activity scores of different signaling pathways cannot be compared, since each of the signaling pathways has its own range of activity scores; and 3. pathway activity scores are highly quantitative, and even small differences can be reproducible and meaningful. In addition to pathway-specific target genes, we determined the expression levels of SOD2 and KI-67 as a marker for cellular oxidative stress and cell proliferation, respectively.Anonymized RT-qPCR data were provided to Philips Research to determine functional STP activity using previously described knowledge-based probabilistic Bayesian computational pathway models ,12,13. At-test. Skewed continuous variables are presented as the median with interquartile range (IQR) and compared with a Mann–Whitney U test or Kruskal–Wallis test. Categorical variables are presented as frequencies with percentage and compared with a Fisher’s exact test. STP activity per survival group is presented as boxplots displaying the median and IQR with overlying dot plots representing individual patient samples. Univariate Cox proportional hazards regression analysis was used to assess possible correlations between pathway activity and DFS and OS. Given the differences in hormonal status, we performed separate analyses for premenopausal and postmenopausal women. Subgroups were formed based on ER pathway activity, dividing the sample set in quartiles. Boxplots displaying pathway activity per ER subgroup were generated. DFS and OS of the newly formed subgroups were visualized in Kaplan–Meier curves and log-rank tests were used to test for differences. Subsequently, samples were divided over three subgroups containing: 1. Samples with ER pathway activity in the lowest quartile and PI3K pathway activity in the highest quartile; 2. Samples with ER pathway activity in the highest quartile and PI3K pathway activity in the lowest quartile; and 3. The remaining samples. Boxplots were created to visualize pathway activity per subgroup. p-values < 0.050 were considered statistically significant. Basic statistical analyses were performed using SPSS  and data visualization was conducted using Rstudio .For clinicopathological characteristics, normally distributed continuous variables are presented as mean values with standard deviation (SD) and compared with a Due to the retrospective nature of the study, the Medical Research Involving Human Subjects Act (Dutch: Wet Medisch-wetenschappelijk Onderzoek met Mensen) does not apply, which was confirmed by the Medical research Ethics Committees United . Given that a majority of patients had passed away by the time of inclusion and that our analysis would not yield any outcome of interest to either the patient or their families, patient approval was waived by local hospital committees under the condition, that prior to inclusion, medical files were checked for any signs that a patient would disapprove of the use of residual bodily tissue.p = 0.003) and after treatment (p = 0.027), as well as a higher number of primary debulking surgeries (p = 0.007) and complete debulking outcomes (p = 0.033). Furthermore, the number of recurrences was lower in the long DFS group (p < 0.001). An overview of clinicopathological characteristics per group is presented in We identified 580 patients with advanced stage HGSC in the NCR database treated in our region, 157 of which met our eligibility criteria. Thirty-five patients with a DFS between 12 and 24 months were excluded. For the remaining 122 patients, histological sections of the primary tumor were retrieved. Review of the histological sections resulted in the exclusion of 36 women as the samples contained less than 40% tumor cells. Thus, 86 primary tumor samples were available for analysis of STP activity. Internal quality control resulted in the exclusion of one more patient due to insufficient mRNA concentration. STP activity results of 85 patients were included in our analysis, of which 52 were in the short DFS group and 33 in the long DFS group. There were no differences between the two groups in age at diagnosis, parity, menopausal status at diagnosis or FIGO stage. In the long DFS group, we found lower CA125 concentrations at diagnosis , none of the pathways were significantly related to OS or DFS. In postmenopausal women (n = 67), ER pathway activity was associated with favorable DFS (Hazard Ratio (HR) = 0.943; 95% confidence interval (95%CI) = 0.894 to 0.995; p = 0.033) and OS . Results are visualized in Our cohort included both pre- and postmenopausal women. As menopausal status may affect the availability of androgens and estrogens and therefore AR and ER pathway activity, univariate Cox proportional hazards regression analysis were used to assess the effect of menopausal status on the relation between pathway activity and survival. In premenopausal women  with the shortest DFS and OS for patients with ER pathway activity in the lowest quartile. Kaplan–Meier curves of DFS and OS per quartile are shown in To investigate the relation between survival and ER pathway activity in postmenopausal women, this subgroup was divided into quartiles based on ER pathway activity. Samples in quartile 1 had an ER pathway activity score ranging from 0.12 to 4.80 (median 1.33), for quartile 2 ER scores ranged from 5.88 to 9.87 (median 9.23), for quartile 3 from 9.90 to 12.14 (median 11.46) and for quartile 4 from 12.23 to 27.94 (median 14.80). Survival analysis revealed a difference in both DFS and OS among the quartiles (log-rank n = 6), subgroup 2 contained all samples with ER pathway activity in the highest quartile and PI3K pathway activity in the lowest quartile (n = 6) and subgroup 3 contains all remaining samples (n = 55). For subgroup 1, ER pathway activity scores ranged from 0.36 to 4.80 (median 0.91) and PI3K pathway activity scores ranged from 62.83 to 89.17 (median 75.39). For subgroup 2, ER scores ranged from 12.86 to 27.94 (median 18.22) and PI3K scores from 33.28 to 36.82 (median 36.01) and for subgroup 3, ER scores ranged from 0.12 to 25.40 (median 9.87) and PI3K scores ranged from 19.44 to 84.23 (median 44.59). p = 0.009) and TGF-β pathway activity (p = 0.041). Subgroup 1 was associated with low AR and TGF-β pathway activity compared to the other subgroups, while subgroup 2 was characterized by higher TGF-β pathway activity. There was no statistically significant difference in expression levels of the KI-67 proliferation marker among the subgroups, neither were there differences in DFS or OS.To further investigate the inverse relation between ER and PI3K pathway activity, we divided samples of postmenopausal women in groups based on ER and PI3K pathway activity. Subgroup 1 contained all samples with ER pathway activity in the lowest quartile and PI3K pathway activity in the highest quartile (n = 9), high ER and low PI3K pathway activity  and the remaining samples . For subgroup A, ER pathway activity scores ranged from 0.22 to 4.80 (median 1.05) and PI3K pathway activity scores ranged from 60.61 to 89.17 (median 73.74). For subgroup B, ER scores ranged from 14.82 to 27.94 (median 21.16) and PI3K scores from 31.31 to 36.82 (median 34.51) and for subgroup C, ER scores ranged from 0.12 to 48.64 (median 10.72) and PI3K scores ranged from 19.44 to 84.23 (median 44.59). p = 0.001) and TGF-β (p = 0.001) was retained; furthermore, there was a difference in Wnt (p = 0.018) pathway activity between the subgroups.Subsequently we repeated the analysis including both pre- and postmenopausal women. Again, samples were divided based on low ER and high PI3K pathway activity  or over 24 months (long DFS). There were no differences between these two groups in median AR, ER, PI3K, HH, TGF-β and Wnt pathway activity. Since we observed a wide variety in activity of several STPs in both short and long DFS groups, our division of HGSC in two groups may have precluded the discovery of more subtle interactions between pathway activity and survival. The wide variety of STP activity may also indicate the existence of more specific subgroups. In univariate Cox proportional hazards analysis, stratification for menopausal status revealed a positive correlation between ER pathway activity and both DFS and OS in postmenopausal women. Moreover, Kaplan–Meier survival analysis demonstrated a difference in both DFS and OS among subgroups based on ER pathway activity quartiles in postmenopausal women. The difference is mostly due to the lowest quartile compared to the others as it was characterized by the shortest DFS and OS. Within each of the other quartiles, there were large differences in DFS and OS, for example in quartile 2 which had the second shortest DFS but the longest OS. The differences between DFS and OS within the clusters are illustrative of differences in intrinsic behavior. Alternatively, the low ER pathway activity in quartile 1, while there was no evident difference in survival among the second, third and fourth quartiles, may also suggest that an inactive pathway in particular is negatively related to survival. The comparability in survival among the higher quartiles may result from a lack of samples with a particularly active ER pathway or may indicate that whether or not the ER pathway is active is more important than the actual level of activity. While these samples may be active compared to other HGSC samples, ER pathway activity is still low to moderate when compared to healthy Fallopian tube tissue . Thus, nAlthough individual studies on the prognostic role of ER protein expression have previously resulted in conflicting outcomes, a recent meta-analysis concluded that OS was unrelated to ER protein expression in serous ovarian carcinoma  ,25,26,27When forming new subgroups based on ER and PI3K pathway activity, we found that subgroup 1 was also characterized by the lowest AR pathway activity, which further supports the loss of normal pathway activity in HGSC which is required for normal differentiated cell functions. An interplay between these pathways has been reported in ovarian cancer previously. In the study of Martins et al., immunohistochemical ER, phosphatase and tensin homologue (PTEN), and AR protein expression was found in 3244 HGSC samples . In our Another finding in our subgroups based on the inverse relation between ER and PI3K pathway activity is the low TGF-β pathway activity of subgroup 1. A crosstalk between the PI3K and TGF-β pathway has been described previously, in which the anti-proliferative effect of TGF-β signaling is decreased by PI3K pathway activation or even reversed to tumor promoting depending on the concomitant presence of an active MAPK-AP1 pathway . Analysip = 0.002); the subgroup with low ER pathway activity and high PI3K pathway activity had low AR pathway activity and the subgroup with high ER pathway activity and low PI3K pathway activity had high AR pathway activity.In a previously published study we used STP assays on a publicly available dataset of clinically annotated HGSC samples by Tothill et al. ,39. We aA major strength of our study is the clearly defined patient population. Ovarian carcinoma is a heterogeneous disease and a generally used term for several histotypes, each with their own distinct characteristics, course of disease and optimal treatment. To limit heterogeneity and treatment effects on our main outcome, DFS, we formulated concise in- and exclusion criteria. Although this approach has its limitations, as it results in the exclusion of a substantial number of patients, we feel it is justified and even necessary to answer our research question. As a result, the number of included patients is moderate to small, especially for the subgroup analysis. Future studies including larger groups of patients should be conducted to establish the translational value of our results. The effect of FIGO stage and treatment modalities are profound and thus may conceal the effects of differences in STP signaling. Another strength is the translation of real-time quantitative reverse transcription-PCR (RT-qPCR) results to pathway activity scores with potential clinical target and, in contrast to other tools to determine pathway activity, the STP assays used here can be used for individual patient samples and thus are suitable for use in daily clinical practice. This more personalized approach to determine tumor specific characteristics may also be beneficial for the selection of targeted therapies.n = 35), and it is unknown for how many of these patients’ primary tumor samples were available for PCR analysis, this may have concealed subtler differences. Furthermore, the use of achieving complete remission as inclusion criterium has resulted in a selection bias in which patients with the poorest outcome were excluded.A limitation of our study is the retrospective nature, as we were dependent on the quality and tumor percentage of readily available samples and on medical files for patient characteristics. The hospitals from which patients were included were large referral centers for gynecologic oncology. This generally means that patients are in care of a nearby hospital and are only referred after a first round of diagnostics indicated an ovarian malignancy, lowering the availability of primary tumor samples. Patients may also choose to return to the referring hospital for adjuvant treatment and follow-up, limiting the availability of follow-up data for a small number of patients. In addition, we chose to exclude women with a DFS between 12 and 24 months to maximize possible differences between short- and long-term survivors. Although the number of excluded patients was limited  targeted therapies. In our assessment of STP activity of short- and long-term disease-free survivors of HGSC, we were unable to identify a single pathway responsible for the differences in survival. However, we were able to identify subgroups of patients which were characterized by high ER and AR pathway activity and low PI3K pathway activity and conversely low ER and AR pathway activity and high PI3K pathway activity.


The shikimate pathway, the seven enzymatic steps that synthesize chorismate from phosphoenolpyruvate and erythrose 4-phosphate, produces the last common precursor of the three aromatic amino acids. It is firmly established that all seven enzymes are present in plastids, and it is generally accepted that this organelle is likely the sole location for production of chorismate in plants. However, recently a growing body of evidence has provided support for a previous proposal that at least portions of the pathway are duplicated in the cytosol, referred to as the Dual Pathway Hypothesis. Here I revisit this obscure hypothesis by reviewing the findings that provided the original basis for its formulation as well as more recent results that provide fresh support for a possible extra-plastidial shikimate pathway duplication. Similarities between this possible intercompartmental metabolic redundancy and that of terpenoid metabolism are used to discuss potential advantages of pathway duplication, and the translational implications of the Dual Pathway Hypothesis for metabolic engineering are noted. The shikimate pathway is ubiquitous across all plants, being necessary for the production of thousands of primary and specialized metabolites . CompounResearch and reviews to date have highlighted the genes and the encoded proteins responsible for the seven enzymatic steps of the shikimate pathway. All seven steps, which are identical to those observed in bacteria and fungi, have been unambiguously shown to function within the plastids . LikewisRecently, new evidence has lent fresh support to the presence of some, if not all, steps of the shikimate pathway and subsequent aromatic amino acid biosynthesis outside the plastids in at least some plant species, calling for a renewed look at the Dual Pathway Hypothesis in the genomic age. Here I review the original experimental basis of this hypothesis as it was first proposed, the recent findings that have prompted a revisit to the hypothesis,and the biological and practical implications of the potential complete or partial pathway duplication.Illicium religiosum) [Although the namesake intermediate of the shikimate pathway was first isolated from, and named after, the plant shikimi  , much ofigiosum) . After cigiosum) , 10, whiigiosum) , 12. HowVigna radiata, Rubin et al first reported the presence of two distinct isoforms of DAHPS [2+, and was thus designated DS-Mn, while the other was found to be apparently strictly dependent on Co2+, and was thus designated DS-Co, although this latter enzyme was also found to maintain high activity with Mg2+ in place of the Co2+. Subsequent work established that DS-Mn requires a reducing environment and a low pH, whereas Ds-Co maintains activity in absence of reducing agents and prefers an alkaline pH. Furthermore, DS-Co is larger than DS-Mn, with an apparent native molecular weight determined by gel filtration to be around 400 kDa [The most thoroughly studied of the extraplastidial shikimate pathway enzymes is that of the DAHP synthase (DAHPS). Using of DAHPS  that werof DAHPS , but alsof DAHPS , 15. The 400 kDa , compare 400 kDa . The lat 400 kDa , 18, whi 400 kDa .Nicotiana silvestris, multiple brassica species, and the algae Chlorella sorokiniana [in vitro DAHPS activity is relevant in vivo [Vitis cell suspension cultures correlated with production of Phe-derived anthocyanins [Isolation of the gene encoding Ds-Mn from potato  and lateokiniana ,19,23,24 in vivo . Howeverocyanins , consisten route to synthesis of Tyr and Phe [in vivo to synthesize prephenate for the production of Tyr or Phe was further demonstrated through the indirect confirmation that a pool of chorismate resides in the cytosol [The Dual Pathway hypothesis was also supported by the widespread occurrence of differentially localized isoforms of chorismate mutase (CM), which converts the product of the shikimate pathway, chorismate, to prephenate  and Phe . As with and Phe , though  and Phe . Cloning and Phe , 30, and cytosol .en route to synthesis of Trp. As with CM, two isoforms of AS were distinguishable by their localization and sensitivity to an inhibitor, in this case Trp, with only the feedback-sensitive isoform being present in the plastids and the insensitive isoform in the cytosol [A similar, though less well-studied, situation was reported for anthranilate synthase (AS), which also consumes chorismate, but for the synthesis of anthranilate  cytosol . Interes cytosol .Pisum sativum was reported as present in total shoot extracts, but absent in chloroplast preparations, suggesting a cytosolic localization [Although extra-plastidial activities for other enzymes of shikimate pathway and downstream AAA synthesizing enzymes were sporadically reported, the results were less clear. Whereas DS-Co and CM-2 had distinct biochemical properties to facilitate distinguishing them from their plastidial counterparts, no such differentiation was apparent for other enzymes of the network, and the mere presence of an enzymatic activity in a subcellular preparation may be an artifact of contamination by other cellular components. A chromatographically distinct EPSP synthase (EPSPS) isoform in lization . Howeverlization . In the lization , 34, thelization .en route to production of coniferyl and sinapyl alcohols revealed low activity with free coumaric acid as the substrate, or with the glucose and CoA-esters, yet high activity with the shikimate and quinate esters [Physcomitrella patens and Marchantia polymorpha, and phylogenetic analysis suggests the function emerged in a progenitor of embryophytes [de novo extra-plastidial shikimate biosynthesis.The first modern challenge to consensus view of the shikimate pathway being exclusively plastidial came with the discovery that cytosolic shikimate is necessary for efficient lignin synthesis. Characterization of the cytosolic enzyme responsible for hydroxylation of the 3-position of the phenyl ring e esters . The enze esters . A relate esters . Involveyophytes . The undyophytes . HoweverThe steady advance of genetic resources has also lent some support for the Dual Pathway Hypothesis. Analysis of genes encoding the bifunctional dehydroquinate dehydratase/shikimate dehydrogenase (DHD-SDH) in tobacco surprisingly revealed two copies that were highly conserved, with the exception that one copy lacks any apparent N-terminal plastidial transit peptide . ConfocaThe recent publication of a petunia genome enabled the identification of only a single copy of a chorismate synthase (CS)-encoding gene in that species . SubsequArabidopsis thaliana using a cytosolic 14–3-3 protein as bait revealed two of the three DAHPS isoforms encoded in the A. thaliana genome as potential interactors [in vivo confirmation of the potential for this interaction for one isoform, DAHPS2, with the resulting fluorescent signal evident in the cytosol [Similar dual localization has recently been observed for DAHPS in Arabidopsis. A high-throughput analysis for protein–protein interactions in eractors , though  cytosol . The autThe most comprehensive evidence to date supporting the Dual Pathway Hypothesis revolves around the need for a source of cytosolic chorismate , the endEvidence for cytosolic Tyr production is also mounting. Legumes have a non-plastidic, feedback-insensitive PDH , which iThe available data from two different eras support the plausibility of the Dual Pathway Hypothesis from a mechanistic standpoint: there are compartmentally-distinct, functionally-redundant enzymes catalyzing at least some of the intermittent steps between central carbon metabolites and chorismate  and subsMuch like AAA derivatives, terpenoids are a diverse class of thousands of primary and specialized metabolites that are involved in a variety of fundamental and specialized plant functions. As reviewed in , their sOne notable feature of the duplicated biosynthetic origin of DMAPP and IPP is that each pathway provides precursors for different downstream metabolites. For example, whereas the MEP pathway is necessary for production of monoterpenoids, chlorophylls, and carotenoids, the MVA pathway sustains production of di- and sesquiterpenoids, as well as sterols . The difBrassica juncea [Vitus suspension cultures, expression of DS-Co, but not DS-Mn, correlated with anthocyanin accumulation [The same principles of differential regulation of compartmentally-distinct pathways intrinsic to terpenoid metabolism may also underly production of pathway-specific shikimate-derived metabolites. Indeed, biochemical analysis of DS-Mn and DS-Co activities across different conditions are consistent with this. In suspension culture, DS-Mn activity rises quickly then peaks in early exponential phase, corresponding to active primary metabolism and protein synthesis, whereas Ds-Co activity peaks in later exponential phase, more consistent with a role in specialized metabolism . Similara juncea . In addia juncea , and a ca juncea . In Vitumulation , likely Differential regulation between compartments is readily apparent in the post-chorismate steps of this metabolic network. Unlike the plastidial CM1, which is subject to strict allosteric regulation, recombinantly-produced cytosolic CM2 appears completely devoid of such regulation , 57. LikResponse-specific needs of the two pathways may also explain the observation that the plastidial shikimate pathway is subject to redox regulation, with at least two steps, DAHPS and chorismate synthase, being redox sensitive , 59. Sinin vitro activity of all three known plastidial DAHPS isoforms was recently shown to be eliminated in the presence of caffeate [in vivo. As the potential for these interaction varies with alterations in the overall cellular metabolic landscape across conditions, growth state, and cell type, the ability to shift biosynthetic flux of a common product between pathways in separate compartments creates flexibility to avoid situation-specific detrimental inhibition in one organelle or the other.In addition to how the different pathway would derive precursors from, and provide substrate for, upstream and downstream metabolic processes, the two pathways would likely differ in their interactions with other co-localized pathways. This has already been observed with a metabolic interaction of the cytosolic Phe pathway with Trp-dependent auxin biosynthesis in which shared intermediates of the pathways create cross-talk , 66, 67.caffeate , a cytosFinally, the existence of two distinct, spatially separated and differentially regulated pathways creates evolutionary plasticity. Plants have intrinsic adaptive mechanisms that have facilitated evolution of the specialized metabolic diversity necessary for plants to survive and thrive in varied environments . RedundaThe possibility of a complete second, compartmentally-distinct pathway for production of chorismate and AAAs, as posited by the Dual Pathway Hypothesis, has dramatic implications for future research. First and foremost the hypothesis presents the necessity of identifying and characterizing any genes encoding cytosolic enzymes that catalyze shikimate pathway and AAA biosynthesis steps , which wDue to the plethora of metabolites derived from, and biological processes dependent on, the shikimate pathway, this pathway and its downstream branches are frequent targets for agricultural enhancement. Previous metabolic engineering strategies, which have almost exclusively targeted the plastidial pathways, have had mixed success in modulating phytochemical production , 67. An


Schizophrenia is a serious developmental psychiatric disorder with a neurodegenerative component that causes marked deterioration in social relationships and ability to work. Present treatments are not satisfactory. Meta-analysis of placebo-controlled studies in acute schizophrenia shows that only a minority of patients have a good response to current antipsychotic medications. Therefore, there is a need for more effective psychopharmacologic treatments for this disorder.The purpose of this paper is to provide new interpretations of existing data to provide a scaffolding for the development of novel drug targets for the treatment of schizophrenia. The causes of schizophrenia are most likely heterogeneous and involve both genetic and environmental factors. The authors examined a wide range of purported causes of schizophrenia to identify a common biochemical pathway that would contribute to this disorder. This review specifically did not consider pathways that supported the dopamine hypothesis of schizophrenia since historically drugs focused on dopaminergic mechanism, as noted in the aims, have not been successful for many patients with schizophrenia.Two prominent schizophrenia-associated factors that have been widely studied with significant supporting evidence are stress and inflammation. Stress and inflammation share a common biochemical pathway that converges on the kynurenine pathway of the metabolism of tryptophan, an essential amino acid. At one end of the pathway, recently hospitalized patients with schizophrenia have been found to have low plasma tryptophan levels, whereas chronic schizophrenics have not, suggesting stress- and/or inflammation-induced increased metabolism of tryptophan. At the other end of the pathway, there is increased level of cerebrospinal fluid kynurenic acid in patients with schizophrenia as compared with healthy controls. Salivary kynurenic acid is associated with stress intolerance in schizophrenia. Importantly, natural occurring compounds in this pathway have significant CNS effects that include neurotoxicity and altered neural transmitter behavior.Stress and inflammation, both associated as causes of schizophrenia, are linked by a common biochemical pathway involving kynurenine. Examination of specific elements of the kynurenine pathway may aid in the identification of drug targets for schizophrenia.