VEP1  and its paralogue gene on locus At5g58750 (AtStR2) from Arabidopsis to study this issue. The enzymes encoded by VEP1-like genes were clustered under the term PRISEs (progesterone 5β-reductase/iridoid synthase-like enzymes) as it was previously demonstrated that they are involved in cardenolide and/or iridoid biosynthesis in other plants. In order to further understand the general role of PRISEs and to detect additional more “accidental” roles we herein characterized A. thaliana steroid reductase 1 (AtStR1) and compared it to A. thaliana steroid reductase 2 (AtStR2). We used A. thaliana Col-0 wildtype plants as well as VEP1 knockout mutants and VEP1 knockout mutants overexpressing either AtStR1 or AtStR2 to investigate the effects on vein patterning and on the stress response after treatment with methyl vinyl ketone (MVK). Our results added evidence to the assumption that AtStR1 and AtStR2, as well as PRISEs in general, play specific roles in stress and defense situations and may be responsible for sudden metabolic shifts.Small or specialized natural products (SNAPs) produced by plants vary greatly in structure and function, leading to selective advantages during evolution. With a limited number of genes available, a high promiscuity of the enzymes involved allows the generation of a broad range of SNAPs in complex metabolic networks. Comparative metabolic studies may help to understand why—or why not—certain SNAPs are produced in plants. Here, we used the wound-induced, vein patterning regulating  Specialized metabolism, sometimes still termed “secondary metabolism”, has been studied extensively in plants and microorganisms. In the twentieth century, the vast chemical diversity of the natural products formed by plants attracted organic chemists even more than plant biologists. A lot of the complex natural products appearing in certain taxa during different developmental stages were labelled as “secondary” metabolites because a fundamental role in cell metabolism and growth was not seen. Today, several small  natural plant products (SNAPs), such as jasmonates, brassinosteroids, and strigolactones have lost their stigma of being secondary metabolites. They are now regarded as important regulatory elements of plant development and growth. In some instances, it is difficult to determine which metabolite is “secondary” and which is “primary”, particularly when they share a common biosynthetic route and the same structural scaffold. Examples are psilocybin  and indole-3-acetic acid (a phytohormone), both derived from tryptophan, or β-sitosterol (a regulator of membrane fluidity in plants) and digitonin (a toxic saponin) both formed via the mevalonic acid pathway. A “secondary” pathway leading to just a single specific natural product therefore does not seem very probable. SNAP formation depends on the availability of suitable substrates and enzymes able to modify them. A relaxed substrate specificity of many enzymes enhances a plant’s options to develop complex metabolic grids, because a limited number of genes is available in a plant’s genome to encode the respective enzymes. This is also the basis for substrate–enzyme coevolution, which has been demonstrated as the fundamental principle leading to the formation of SNAPs ,2. EcoloArabidopsis thaliana gene VEP1 (At4g24220) [AWI 31 [VEP1 expression in A. thaliana [in silico analyses only one other locus (At5g58750) was identified within the genome of A. thaliana encoding a related protein [AtStR1  and AtStR2 , because they reduced the double bond of Δ4,5-steroids and other α,β-unsaturated ketones [VEP1-like genes were grouped under the name PRISEs (progesterone 5β-reductase/ iridoid synthase-like enzymes) [The 4g24220) , also de [AWI 31 , is ideathaliana . Gene knthaliana . Based ous At5g5850 was id ketones . All enzenzymes) . In the us At5g5850 was idRNAi-mediated gene knockdown of PRISEs led to reduced cardenolide contents in Digitalis lanata [Catharanthus roseus [PRISEs came into focus in specialized plant metabolism because they act as progesterone 5β-reductases in 5β-cardenolide biosynthesis ,12,13,14s roseus . It was s roseus .VEP1-like genes appeared early in plant evolution, and it was assumed that a gene transfer from α-proteobacteria ultimately helped early plants to adapt to life on land [VEP1-like genes in 198 species of seed plants, most of them containing neither cardenolides nor iridoids, including A. thaliana [VEP1-encoded AtStR1 converted progesterone, a precursor of cardenolides, 20 times more efficiently than the corresponding Digitalis lanata enzyme [ on land . A recena enzyme ,19, and a enzyme ,9.VEP1 gene products may have enhanced the pathogenicity of α-proteobacteria by interrupting defense-related signal cascades involving RES. This may have assisted the plants conquering land to survive in an oxygen-containing atmosphere that promoted lipid peroxidation without a pathogen attack [PRISEs can detoxify reactive electrophile species (RES), such as methyl vinyl ketone (MVK) , a produn attack . MVK is a powerful inducer of defense-related gene expression  and PRISAtStR1 and compared it to AtStR2. We used A. thaliana Col-0 wildtype (wt) plants as well as AtStR1 knockout mutants (AtStR1-) and knockout mutants overexpressing either AtStR1 (35S:AtStR1/AtStR1-) or AtStR2 (35S:AtStR2/AtStR1-) to investigate the effects of MVK treatment.In order to investigate the general role of PRISEs and to elucidate their “accidental” roles we characterized AtStR2 is encoded by locus At5g58750. An appropriate cDNA was deduced and cloned into a pDEST17 expression vector and then functionally expressed in E. coli. The recombinant enzyme was termed rAtStR2. It was purified . This high interspecific similarity and the much lower intraspecific similarity  led to the assumption that at least two clusters (cluster I and cluster II) of PRISEs needed to be differentiated [PRISE genes were identified in the genome of A. thaliana, whereas up to seven VEP1-like genes were reported in other plant genomes [AtStR1 and other cluster I PRISEs were studied, it was demonstrated that these enzymes converted small RES more efficiently [P/M) of the catalytic efficiencies shown in P/M .entiated . Only twiciently ,9,19. MeAtStR1 muteins that had almost or completely lost their capacity to convert MVK and small cyclic 1,4-enones, such as cyclocexen-1-one. These muteins preferred larger substrates such as progesterone, a precursor of 5β-cardenolides  cocrystallized with NADP+ [AtStR1 and AtStR2, respectively.To analyze this in more detail, we here modeled th NADP+ , which ith NADP+ . The modth NADP+  and progth NADP+ . The resth NADP+  and demoAtStR2, as well as in other cluster II PRISEs, the phenylalanine adjacent to motif 5 was substituted for other amino acids, hence a phenylalanine clamp could not be formed. As a consequence, the active site could accommodate larger molecules such as progesterone , (ii) homozygous SALK 097798 knockout plants (AtStR1-) and (iii) SALK 097798 expressing either AtStR1 (35S:AtStR1/AtStR1-) or AtStR2 (35S:AtStR2/AtStR1-) under the control of the CaMV 35S promotor. Homozygous knockout plants were identified using primers designed by the “SALK T-DNA verification primer design”.Here, we established and analyzed three systems: (i) AtStR1 was expressed in Ath Col-0 wt but not in AtStR1- mutant plants, verifying the stable knockout of AtStR1 in the homozygous plants used here , AtStR2 activity did not interfere with the determination of AtStR1 activities in the enzyme assays of leaf extracts (sed here . AtStR2 eedlings b and coleedlings  but poorextracts . Other eextracts .35S:AtStR1/AtStR1- and 35S:AtStR2/AtStR1-) were analyzed. The two lines of each approach showing the highest expression rates were chosen for further experiments. These lines were termed 35S:AtStR1/AtStR1-1, 35S:AtStR1/AtStR1-2, 35S:AtStR2/AtStR1-1 and 35S:AtStR2/AtStR1-2  expression. Glutathione reductase (GR1) is an enzyme influencing the redox state of a cell by releasing reduced glutathione (GSH) from oxidized glutathione (GSSG) [GR1 expression [We investigated the effects of MVK treatment on pression  and PRISe (GSSG) . Redox spression .tStR2 was not induced under MVK stress and rAtStR2 only poorly accepted MVK  formed during stress reactions, have already been identified as putative substrates for PRISEs. Examples are small 1,4-enones such as methyl vinyl ketone (MVK) or 1-cyclohexen-2-one which are converted by iciently ,9,10,19.iciently . MVK acciciently ,37,38 aniciently . It is kpersicum  leaves. persicum . RES, spVEP1, AER and AOR are differentially expressed in A. thaliana. In the absence of stress, AER is expressed only weakly, whereas AOR is strongly expressed in young and VEP1 in mature leaves (Arabidopsis eFP browser 2.0). The respective enzymes differ in their substrate preferences, especially the size of potential substrates. For example, an AOR of Cucumis sativa reduced 4-hexen-3-one about 100 times more efficiently than MVK [AtAOR characterized in the same paper was equally active towards these two substrates. The authors discussed that AOR together with other enzymes might cooperatively detoxify reactive carbonyls. We here provide evidence that VEP1-encoded PRISEs may also be important players in this scenario.than MVK . On the AtAER (At5g16970) encodes a cytosolic alkenal reductase in A. thaliana. As far as the kinetic constants are concerned AtStRs and AtAER have similar Km values but differ in their catalytic efficiency, AtAER being about 200 times more efficient than AtStR1. Expression of AtAER and AtAOR was equally high in Ath Col-0 wt and in AtStR1- mutants and could be amplified by MVK, in this way paralleling enhanced AtStR1 expression , in contrast to AtAER, is strongly and constitutively expressed in the chloroplast and is assumed to be involved in the general elimination of reactive carbonyls [AtStR1 expression in Ath Col-0 wt  has successfully been used as a surrogate to estimate MVK effects [AtPR4 encodes a PR (pathogen-related) protein. PR proteins are induced in response to pathogen attacks and other stresses. When treated with MVK, AtPR4 was expressed around five times higher in AtStR1- mutants than in Ath Col-0 wt. This difference was taken as an indication for impaired MVK detoxification in AtStR1- mutants. Support for this conclusion was obtained from experiments with AtStR1- mutants overexpressing 35S:AtStR1, where AtPR4 expression levels were again comparable with those of Ath Col-0 wt  displayed aberrations in vein patterning, whereas 50% of the cotyledons of non-treated controls looked like the one shown in PIN1 expression (about 70%) in MVK treated Ath Col-0 wt plants.All AtStR1- mutant overexpressing 35S:AtStR1 or 35S:AtStR2 could not cure aberrant vein patterning  to reduce small reactive 1,4-enones, as demonstrated here for MVK released in stress reactions. Other PRISEs, like AtStR2 in this study, may play completely different roles considering their individual expression patterns, their inability to cure symptoms caused by AtStR1 deficiency and their different substrate specificities with a tendency to discriminate for larger molecules. RES are cytotoxic substances causing cell damage [AtStR1 gene knockout led to stronger responses of defense-related genes after exposure to MVK  was used to investigate the effects of AtStR1 knockout. Plants derived from ordered seeds  were tested for homozygous T-DNA insertion by PCR (TA = 50 °C). Primers were derived by the online-tool “SALK T-DNA verification primer design” ; LBp1.3 ATT TTG CCG ATT TCG GAA C; LP097798 TTA TCG CCG TAA CCA CTT TTG; RP097798: AAA CAA CAA CGG AGA CCC TTC). Seeds from homozygous plants were stratified, germinated and cultivated as wild type (wt) plants in the greenhouse. Coding sequence of AtStR1 (At2g24220) or AtStR2 (At5g58750), respectively, was amplified using primers: JK_pEG100_StR1_for: TAT ACT CGA GAT GAA ACA TCA CCA TCA CCA TCA C; JK_pEG100_StR1_rev: AAT TTC TAG ATC AAG GTA CGA TCT TGA ACG C; JK_pEG100_StR2_for: TAT ACT CGA GAT GGG GTC TGA AAA TGG CAG; JK_pEG100_StR2_rev: AAT TTC TAG ATT ACA AAG GAA TGA GTT TTT CAT CT. AtStR1 and AtStR2 coding sequences were then cloned into pEarleyGate 100 (pEG100) using restriction enzymes XhoI and XbaI  and transformed into E. coli DH5α cells. Plasmids isolated from these cells were sequenced and those with verified constructs were used for the transformation of the Agrobacterium tumefaciens strain GV3101 (GV).An −1). This was repeated 3 times within 10 days. Surviving plants were tested for the integration of T-DNA by PCR with primers against bar gene . Plants with integrated T-DNA were transferred into the greenhouse for propagation. Seeds of these plants were stratified, germinated and cultivated as described above. All plants were tested for the integration of T-DNA before their use in experiments.Transformed GVs carrying the cloned constructs were used for the transformation of SALK_097798 by floral-dip transformation . Seeds oAtStR2 (At5g58750) into expression vector pDEST17, the following primer pairs were used: forward primer: 5′TCGTACTACCATCACCATCACCATCACATGGGGTCTGAAAATGGCA′3. reverse primer: 5′GCCCCAAGGGGTTATGCTAGTTATTACAAAGGAATGAGTTTTTCATCT ′3.In the restriction-free cloning of E. coli DH5α cells for the primary transformation and E. coli soluBL21 for the recombinant protein expression. Correctly integrated coding sequence (in frame with His-Tag of pDEST17 vector) was confirmed by sequencing  using standard primers against T7 promoter and T7 terminator. AtStR2 expression was induced in 1 L E. coli soluBL21 cultures  by adding IPTG . To avoid inclusion bodies [AtStR2 by immobilized metal-chelate chromatography, as described by [AtStR2 was detected using mouse anti-His  and anti-mouse IgG-peroxidase antibodies . As a result, we detected recombinant AtStR2 as the chemiluminescence of 3-aminophtalate released from luminol.Primer pairs were derived using rf-cloning.org . The rf-n bodies  the cultn bodies . This wan bodies . An ÄKTAribed by . SDS-PAGAtStR2 was measured spectrophotometrically by following the conversion of NADPH/H+ at 340 nm [AtStR2 , 0.2 mM NADPH/H+ and varying concentrations of the different substrates (0.05 to 1.6 mM) [The reductase activity of recombinant t 340 nm ,14,32. EAtStR2 was built on the crystal structure of AtStR1  with Swissmodel [The sequence alignment was generated using ClustalW . The homissmodel . Dockingissmodel . The seaissmodel  was used−1 air volume and the chamber sealed during a 3 h incubation. Control plants were treated with tap water. Plant material was harvested after a 3 h treatment [A total of 16 µL of a MVK solution  was diluted with 50 mL of water. Plants were transferred into a 1 L gas chamber. Diluted MVK was added to a total concentration of 2 µmol Lreatment .AtStR1, AtStR2 and AtAct2 were shown on agarose gels of both control and stressed plants . Control plants were cultivated in a climate chamber at 22 °C under long day conditions (16 h light/8 h dark). mRNA values of d plants . Primersd plants .® Total RNA Miniprep Kit . gDNA was digested with a DNase I  treatment. A total of 1500 µg of the resulting RNA was transcribed into cDNA by using a RevertAid H Minus First Strand cDNA Synthesis Kit . cDNA was diluted 3:1 and used for quantitative real-time PCR.For RNA isolation, the 4 youngest leaves and the shoot apical meristem or 7 days old seedlings were used. Isolation was done with the use of MonarchA 40 s) and Brilliant III Ultra-Fast SYBR® Green QPCR Master Mix . Quantification of gene expression was calculated using the 2−ΔΔct-method [Taq DNA Polymerase  was used for semiquantitative PCR. PCR products were separated on 1% agarose gels.All quantitative real-time PCRs (qPCR) were realized in a Rotor-Gene Q thermocycler  using the following program  as described by [−1) and DB-5ms columns (JW GC column) from Agilent (30 m × 0.25 mm × 0.25 µm). Progesterone and testosterone  were from Steraloids Inc. . The program used is described by [Total protein concentrations were determined according to . Progestribed by ,52. To mribed by . Additioribed by .Plant tissue was fixed by immersion in a mixture of ethanol and acetic acid (3:1) overnight to remove chlorophyll and subsequently washed with water. The vein patterning was analyzed with an Axioskop HBO 50 (Zeiss).p values ≤ 0.05 were considered as statistically significant and indicated by an asterisk. GraphPad Prism 8 Software (GraphPad) was used for data analysis and graph design.Experiments were conducted as three or more independent biological replicates with three technical replicates each. One-way analyses of variance (ANOVA followed by Tukey’s post hoc test) were used to compare the means of various groups. A post hoc Bonferroni correction was applied for multiple comparisons. AtStR1, AtStR2 and PRISEs of different clusters in general play different roles in stress and defense mechanisms. Based on our findings, we conclude that PRISEs are involved in the detoxification of small reactive 1,4-enones and are therefore part of the complex detoxification machinery of RES in plants. In the context of comparative metabolism and the evolution of specialized natural products (SNAPs), promiscuous enzymes, such as PRISEs, can provide important elements in pathway evolution. A comparison of closely related plant species, accumulating different sets of SNAPs but sharing homologues sets of genes/enzymes, adds a valuable new way of looking at comparative metabolism and “silent” or “underground” metabolism.Our results added evidence that


Scutellariae barbatae Herba (SBH) is one of the most frequently used traditional Chinese medicine (TCM) in the treatment of CRC. Although many experiments have been carried out to explain the mechanisms of SBH, the mechanisms of SBH have not been illuminated fully. Thus, we constructed a network pharmacology and molecular docking to investigate the mechanisms of SBH.  Colorectal cancer (CRC) is one of the most common gastrointestinal tumors, which accounts for approximately 10% of all diagnosed cancers and cancer deaths worldwide per year.  We adopted active constituent prescreening, target predicting, protein-protein interaction (PPI) analysis, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, differentially expressed gene analysis, and molecular docking to establish a system pharmacology database of SBH against CRC.  A total of 64 active constituents of SBH were obtained and 377 targets were predicted, and the result indicated that quercetin, luteolin, wogonin, and apigenin were the main active constituents of SBH. Glucocorticoid receptor (NR3C1), pPhosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform (PIK3CA), cellular tumor antigen p53 (TP53), transcription factor AP-1 (JUN), mitogen-activated protein kinase 1 (MAPK1), Myc protooncogene protein (MYC), cyclin-dependent kinase 1 (CDK1), and broad substrate specificity ATP-binding cassette transporter ABCG2 (ABCG2) were the major targets of SBH in the treatment of CRC. GO analysis illustrated that the core biological process regulated by SBH was the regulation of the cell cycle. Thirty pathways were presented and 8 pathways related to CRC were involved. Molecular docking presented the binding details of 3 key targets with 6 active constituents.  The mechanisms of SBH against CRC depend on the synergistic effect of multiple active constituents, multiple targets, and multiple pathways. Colorectal cancer (CRC) is one of the most common gastrointestinal tumors. Nowadays, CRC accounts for approximately 10% of all diagnosed cancers and cancer deaths worldwide per year . PreviouScutellariae barbatae Herba  is the dried full plant of Scutellaria barbata D. Don in the Lamiaceae family, which has been used for thousands of years in China as a “heat-clearing and detoxifying” drug [Traditional Chinese medicine (TCM) has a 5000-year history in China, and it is presently regarded as a useful complementary and alternative medication worldwide . Many hing” drug , 18. Morng” drug . AlthougThe traditional drug discovery paradigm focuses on screening exquisite ligands, and the presupposition of “one drug for one target for one disease” is accepted by the majority of researchers . HoweverMolecular docking is an essential tool in computer-assisted drug design, which was firstly proposed in the mid-80s with the purpose of predicting mode of ligand and protein and virtually screening digital compound libraries to reduce expense and speed up drug discovery. The accuracy of molecular docking keeps increasing with the development of computing power and hardware capability.In this study, we adopted network pharmacology and molecular docking to investigate the mechanisms of SBH in the treatment of CRC. Our work involved 6 parts as follows: (1) collecting active constituents of SBH from online TCM databases and literatures; (2) predicting the targets of SBH and disease targets of CRC; (3) constructing network to show the interaction between SBH and CRC; (4) conducting function analysis including Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis; (5) seeking differentially expressed genes of CRC with a bioinformatics analysis; (6) implementing molecular docking after a literature retrieval.A detailed diagram used to describe the overall design of this study is shown in http://lsp.nwu.edu.cn/tcmsp.php) [http://www.nrc.ac.cn:9090/ETCM/) [http://www.megabionet.org/tcmid/) [http://bidd.nus.edu.sg/group/TCMsite/) [http://mesh.tcm.microbioinformatics.org/) [http://tcm.lifescience.ntu.edu.tw/index.html) [http://bionet.ncpsb.org/batman-tcm/) [http://lsp.nwsuaf.edu.cn/CancerHSP.php) [The active constituents of SBH were obtained in 3 steps. Step 1: data retrieval was performed with 7 databases: Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform  , The Enc0/ETCM/) , Traditi/tcmid/) , TraditiCMsite/) , TCM-Mescs.org/) , TCMGeneex.html) , and Bioan-tcm/) . The benan-tcm/) , and actan-tcm/) . Step 2:HSP.php)  was retrHSP.php) , barbatiHSP.php) , SPS2p [HSP.php) , and SBPHSP.php)  were suphttps://www.drugbank.ca/) [http://www.swisstargetprediction.ch), and the top 15 predicted targets were selected for each result of the predicted target classes [Target protein prediction was based on TCMSP database and DrugBank database (ank.ca/) . Active  classes , 37.http://www.cytoscape.org/) [n is the number of target proteins linked to n. The betweenness centrality reflects the extent to which a node acts as a communication intermediate in the network.To better visualize the relationship between active constituents and target proteins, we coded each active constituent and the SBH active constituent-target protein network was generated using Cytoscape (v3.7.1) software (pe.org/) . We usedhttps://www.genecards.org/), which integrates more than 190 data source about gene, disease, pathway, and compound [https://omim.org/), which contains information on all known Mendelian disorders and over 15000 genes [http://db.idrblab.net/ttd/), which is a database to provide therapeutic proteins and nucleic acid targets, the targeted disease, and the correspondence between drugs and these targets [http://ctdbase.org/), which supplies information of 3 interactions: chemical-gene/protein, chemical-disease, and gene-disease [Homo sapiens. The proteins corresponding to the CRC-related genes were standardized using UniProt database for subsequent analysis.CRC-related genes were obtained from 4 databases: GeneCards: the Human Gene Database  to construct the PPI network [Homo sapiens, and we selected confidence score as 0.95 in the minimum . The PPI data was exported as a.tsv file for further analysis. Afterward, we used CytoNCA (a Cytoscape plugin) to evaluate the PPI network [To figure out the interactions between PATPs, we used the STRING database  analysis using MCODE (a Cytoscape plugin)  and ClueClusterProfiler package of R language [Homo sapiens.Traditionally, one of the most well-known methods of exploring biological pathways and potential biological functions is KEGG pathway enrichment analysis. In our study, KEGG pathway analysis was performed with language , and thehttps://www.ncbi.nlm.nih.gov/geo/) with the following limits: “colorectal cancer” (keyword), “Homo sapiens” (study organism), and “tissue” (attribute name). After collection, the GSE32323 dataset, which contained 17 pairs of matched CRC samples based on the GPL570 platform, and the GSE44076 dataset, which contained 98 pairs of matched CRC samples based on the GPL13667 platform, were selected for seeking differentially expressed genes of CRC.Microarray data was collected from the GEO database (http://www.ncbi.nlm.nih.gov/geo/geo2r/), an online web tool based on limma package of R language, is one of the most common tools of determining differentially expressed genes (DEGs) of upregulation or downregulation. In our study, we used GEO2R to finish this procedure, and the results were presented as a table of genes. We set adjusted p value <0.05 and |log  2 fold change (FC)| ≥ 2 as a criterion to screen out DEGs. The proteins corresponding to the DEGs are standardized using UniProt database for subsequent molecular docking.GEO2R  [https://pubchem.ncbi.nlm.nih.gov/) [First, a suitable protein structure of each target was obtained from the RCSB Protein Data Bank (sb.org/) , and theih.gov/) . AutoDocih.gov/) . The resIn our study, we obtained 64 active constituents in total. All active constituents were ranked and detailed information of each active constituent is shown in Homo sapiens and setting a minimum confidence score as 0.95, 150 proteins were retained as shown in A total of 377 targets of SBH and 6390 target genes of CRC were obtained, and the intersection of the two groups was regarded as PATPs . A totalWe constructed a cluster analysis using MCODE before GO enrichment analysis, and 9 clusters were obtained altogether . AfterwaClusterProfiler package of R language, and 174 significant pathways  were identified. We sorted them with adjusted p value and the top 30 terms are shown in p value < 0.05.KEGG pathway analysis was conducted with the In this section, we did an analysis of DEGs in CRC firstly using GEO2R. Two datasets including GSE32323 and GSE44076 were screened from the GEO database. A total of 369 DEGs were obtained from the GSE32323 dataset and 541 DEGs were obtained from the GSE44076. We normalized the proteins corresponding to these DEGs by the STRING database. Seventeen key target proteins were remaining after intersecting with PATPs : ABCG2, To verify how an active constituent binds to target as previously referred to a molecular docking using Autodock Vina was developed in this section. We predicted whether an active constituent could enter the active pocket of the target protein successfully and calculated the affinities between them. We summarized the affinities of the selected active constituents with interactive residues and the count of hydrogen bonds formed between interactive residues, which are set out in As shown in As presented in The binding affinity of 2 active constituents on CDK1 was −8 kcal/mol. From Chinese Pharmacopoeia 2015. In spite of that, SBH has been used in various cancers, specially treated with CRC [SBH has been used in TCM for thousands of years, the function of which is described as “heat-clearing and detoxifying” in TCM therapeutic principle and the with CRC .α and the catalytic subunit of PI3K [neo-clerodane diterpenoid alkaloid extracted from SBH, and an in vitro experiment has shown significant cytotoxic activities against HT29 CRC cells [In our study, we investigated the potential mechanisms of SBH on CRC and predicted the pivotal active constituents and target proteins. The SBH active constituent-target network consisted of 64 active constituents and 441 targets, revealing the pharmacological foundation of SBH. The potential active constituents and targets were deduced in this section. The majority of potential active constituents of SBH are flavonoids. Quercetin (degree = 147) is a polyphenolic flavonoid with underlying anticancer activity, which exists ubiquitously in the vegetal food source, especially in various traditional Chinese medicine . Accumul of PI3K , was fou of PI3K , which i of PI3K –74. PI3KRC cells . ScutebaRC cells . HoweverWe defined the intersection of SBH target proteins and CRC-related proteins as the PATPs. In order to describe the target protein's function fully, a PPI network was constructed using the PATPs, as could be seen in R package ClusterProfiler, and the most significant 30 of 174 pathways are set out in p value: 4.89E − 15) is significant, and the results also indicate that SBH has the potential to treat diverse cancers such as prostate cancer [p value: 1.46E − 12), ErbB signaling pathway , cell cycle pathway , p53 signaling pathway , mTOR signaling pathway , PI3K-Akt signaling pathway , apoptosis pathway , and Wnt signaling pathway . It indicates that the therapeutical effects of TCM depend on the cooperation of multiple pathways. We speculated the PI3K-AKT signaling pathway was the most important pathway, which was the most significant among them with a total of 52 genes gathered. With many fundamental cellular functions such as proliferation, growth, and survival identified [KEGG pathway analysis was accomplished using e cancer , 86, lune cancer , 88, bree cancer , and pane cancer , which hentified , 93, theentified , 94. Inhentified . Interesentified . In addientified , and appentified , which ientified . Therefoentified . Our finThree key target proteins were selected for molecular docking. MYC is a protooncogene encoding nuclear transcription factor and regulates tumorigenesis , which aTraditional Chinese medicine is characterized by multiple components, multiple targets, and multiple pathways. Network pharmacology, along with molecular docking, bioinformation, and system biology, provides an available methodology to uncover the complex therapeutical effects of TCM. Prior studies have confirmed that SBH presents noticeable antitumor effects. We set out to investigate the potential mechanisms of SBH and hunt pivotal active constituents, targets, and pathways.In this study, a total of 64 active constituents of SBH were obtained from 7 TCM databases and literature, and these active constituents were associated with 377 targets, which were mapped to predicted targets of CRC to get 297 common targets treated as PATPs. After that, a PPI network was constructed to demonstrate the interactions between PATPs. The second major investigation was that we did a GO and KEGG analysis using PATPs. Finally, we did a differentially expressed gene analysis of CRC and 229 DEGs were obtained. After the DEGs related proteins were mapped to PATPs, seventeen key target proteins were remaining for molecular docking. The result indicated that quercetin, luteolin, wogonin, and apigenin were the effective active constituents of SBH. NR3C1, PIK3CA, TP53, JUN, MAPK1, MYC, CDK1, and ABCG2 were the major targets of SBH in the treatment of CRC. The most obvious finding emerging from GO analysis was that the core biological process regulated by SBH was the regulation of cell cycle. One of the most significant findings from KEGG analysis was that pathways were significantly enriched in CRC and its related pathways. Molecular docking results reveal that SBH's active constituents have an acceptable binding affinity with MYC, CDK1, and ABCG2, all of which have shown the potential to treat with CRC. Interestingly, we found that targeting MYC and ABCG2 could contribute to enhancing the efficacy of chemotherapy. A limitation of this study is that further experiments are necessary to demonstrate our findings.


Gan-Mai-Da-Zao Decoction (GMDZD) is effective for PSD, but its mechanism of action in PSD is unknown. In this study, we explored the mechanism of action of GMDZD in PSD treatment using network pharmacology and molecular docking. Material and methods. We obtained the active components of all drugs and their targets from the public database TCMSP and published articles. Then, we collected PSD-related targets from the GeneCards and OMIM databases. Cytoscape 3.8.2 was applied to construct PPI and composite target disease networks. In parallel, the DAVID database was used to perform GO and KEGG enrichment analyses to determine the biological processes enriched in the treatment-related drugs in vivo. Finally, molecular docking was used to verify the association between the main active ingredients and their targets.  Poststroke depression (PSD) is the most common and serious neuropsychiatric complication occurring after cerebrovascular accidents, seriously endangering human health while also imposing a heavy burden on society. Nevertheless, it is difficult to control disease progression.  The network pharmacological analysis of GMDZD in PSD revealed 107 active ingredients with important biological effects, including quercetin, luteolin, kaempferol, naringenin, and isorhamnetin. In total, 203 potential targets for the treatment of this disease were screened, including STAT3, JUN, TNF, TPT53, AKT1, and EGFR. These drugs are widely enriched in a series of signaling pathways, such as TNF, HIF-1, and toll-like receptor. Moreover, molecular docking analysis showed that the core active components were tightly bound to their core targets, further confirming their anti-PSD effects.  This prospective study was based on the integrated analysis of large data using network pharmacology technology to explore the feasibility of GMDZD for PSD treatment that was successfully validated by molecular docking. It reflects the multicomponent and multitarget characteristics of Chinese medicine and, more importantly, brings hope for the clinical treatment of PSD. Poststroke depression (PSD) is one of the most common and severe neuropsychiatric complications after stroke , 2; it oGan-Mai-Da-Zao Decoction (GMDZD) is derived from Jingui Yaolue and is composed of three Chinese herbal medicines: Glycyrrhiza uralensis Fisch. (gancao in Chinese), Triticum aestivum L. (xiaomai in Chinese), and Ziziphus jujuba Mill. (dazao in Chinese); it is mainly used for sleep disorders and depression-related psychiatric disorders [Traditional Chinese medicine (TCM) for PSD is characterized by few side effects and individualized treatment . Therefoisorders . A randoisorders . Modern isorders –16. HoweTCM with multiple components, multiple targets, and multiple pathways can bring very many new possibilities for clinical treatment. Network pharmacology is the result of the integration of multidisciplinary basic theories, such as those from biology, computer science, multidirectional pharmacology, molecular pharmacology, and medicine, and research tools that can systematically and comprehensively reflect the intervention mechanisms of drugs through disease networks. This has strong convergence with the principle of the overall dynamics of TCM treatment for diseases and the characteristics of multicomponent, multitarget, and multipathway interactions. Therefore, network pharmacology can provide new and powerful technical support for studying the mechanism of action of TCM compounding, which can help to reveal the scientific connotation of TCM compounding, discover drug targets, and develop TCM theories . This sthttps://tcmspw.com/tcmsp.php), an authoritative public database that contains a large number of active ingredients, their related targets, and pharmacokinetic information [https://www.uniprot.org/) was utilized to standardize the protein targets on which compounds act, resulting in more comprehensive target information such as gene IDs and gene symbols.Through the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform  and the OMIM database (https://omim.org/) for gene targets related to PSD [http://bioinformatics.psb.ugent.be/webtools/Venn/), and the common targets were defined as the potential targets of GMDZD in PSD.Using “poststroke depression” as the keyword, we mined the GeneCards database (d to PSD . The tarhttps://string-db.org/) [Homo sapiens,” and the confidence score with a correlation degree was set as ≥0.950; meanwhile, disconnected nodes were hidden. Then, the interaction information was further visually analysed by Cytoscape 3.8.2. The CytoNCA plug-in was used to analyse the topological attributes of the data submitted to Cytoscape [A PPI network was constructed by submitting common disease-drug targets to the Search Tool for the Retrieval of Interacting Genes database  . The orgytoscape , 27. In https://david.ncifcrf.gov/summary.jsp), a repository with comprehensive annotation capability that is updated monthly with gene annotation data to analyse the biological processes and metabolic pathways in which they are enriched. We chose “Homo sapiens” for the species, and terms with a p value <0.05 were considered significant. The results were saved and visualized using Bioinformatics (http://www.bioinformatics.com.cn/) to obtain a bubble map of the results of the GO and KEGG enrichment analyses. GO enrichment analysis covers three aspects of biology: cellular component, molecular function, and biological process. KEGG enrichment analysis can also suggest the biological mechanisms of action of drugs in the human body and the pathways involved in their regulation.The previously obtained GMDZD targets for the treatment of PSD were imported into the DAVID database ((1) Cytoscape 3.8.2 was implemented to draw the disease-herb-component-target (D-H-C-T) network to visualize the relationship between GMDZD and PSD. The different colours and shapes in the diagram represent the disease, drug, component, and target, and the “edges” represent the correlation between these parameters. (2) To more visually reflect the relationships of the target genes and the enriched pathways, a more complex but more intuitive network was constructed by combining the previous network, which includes more comprehensive information on the drugs and their active ingredients, targets, and pathways.sdf format through the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) and then converting the 2D structures into 3D structures in the mol2 format through ChemOffice software. Finally, the results were imported into AutoDockTools to convert to files into the pdbqt format [To better evaluate the reliability of the network predictions, the core active ingredients were then molecularly docked to the core gene targets. First, small-molecule ligands were prepared by obtaining the 2D structures of their active ingredients in the t format .http://www.rcsb.org/) in the pdb format. PyMOL 2.3.4 software was used to dewater and deligand the proteins, the core gene targets were hydrogenated and charge calculated using AutoDockTools [pdbqt format. Upon completion, ligand-receptor molecular docking was performed using AutoDockVina. Among the existing drugs that have been used to treat PSD, SSRIs have shown high efficacy and few side effects, with sertraline and escitalopram being the representative drugs [First, the core gene targets were entered into the UniProt database to obtain their UniProt IDs, and the 3D structures of the core gene targets were retrieved and downloaded from the Protein Data Bank database , response to lipopolysaccharide (GO: 0032496), positive regulation of transcription from RNA polymerase II promoter (GO: 0045944), response to ethanol (GO: 0045471), and response to estradiol (GO: 0032355) . The maip value and was also identified as an important pathway , pathways in cancer (hsa05200), pancreatic cancer (hsa05212), bladder cancer (hsa05219), and the TNF signaling pathway (hsa04668). Among these pathways, “pathways in cancer” had the highest degree of target enrichment and were, thus, identified as an important critical pathway; similarly, “hepatitis B” had the lowest  pathway . DetailsThere were 314 nodes and 1825 edges in the D-H-C-T network, among which the potential active ingredients icos-5-enoic acid (MOL004985) and gadelaidic acid (MOL004996) were hidden because the corresponding gene targets did not overlap with the disease targets . Details−1 indicates some binding activity, a value less than −5.0 kcal·mol−1 indicates good binding activity, and a value less than −7.0 kcal·mol−1 indicates strong binding activity [The nodes with high degree values in the PPI network were considered core gene targets, and the five core gene targets with the highest degree values in the PPI network, namely, STAT3 (PDB ID: 6NJS), JUN (PDB ID: 5T01), TP53 (PDB ID: 6WQX), AKT1 (PDB ID: 5WBL), and TNF (PDB ID: 2E7A), were correlated with the 10 active ingredients with the highest degree in the “D-H-C-T” network (quercetin (MOL000098), luteolin (MOL000006), kaempferol (MOL000422), 7-methoxy-2-methyl isoflavone (MOL003896), naringenin (MOL004328), isorhamnetin (MOL000354), formononetin (MOL000392), licochalcone a (MOL000497), beta-sitosterol (MOL000358), and medicarpin (MOL002565)). These compounds were molecularly docked, and their binding energies were calculated. On this basis, we molecularly docked sertraline and escitalopram to the core genes to refine the comparative analysis . Moreoveactivity . A totalNetwork pharmacology is a method used to predict the ability of a drug to treat a certain disease by searching for shared genes and identifying enriched pathways, which are then confirmed through available experimental evidence. Molecular docking can be used to predict the binding activity of active ingredients to their target proteins, which further confirms the therapeutic effect of the drugs. The use of these techniques has largely solved the great challenges posed to research due to the multicomponent and multitarget nature of TCM. Therefore, we used network pharmacology and molecular docking to reveal the possible mechanism of action of GMDZD in PSD.Glycyrrhiza uralensis Fisch. and Ziziphus jujuba Mill. A relevant animal study confirmed that quercetin could reverse stress-induced depression and anxiety in mice [Triticum aestivum L. It has been reported that luteolin exerts an antidepressant effect by suppressing the endoplasmic reticulum [Glycyrrhiza uralensis Fisch. Kaempferol promotes the protein expression of brain-derived neurotrophic factor (BDNF) and nerve growth factor (NGF) in hippocampal tissue from aged rats with chronic stress/depression, and these changes resulted in neuroprotection and improved depression-like behaviour [In our study, the core active ingredients in the “D-H-C-T” network were identified as quercetin, luteolin, kaempferol, naringenin, and isorhamnetin. In recent years, flavonoids have been found to have significant effects on the central nervous system, with neuroprotective, antidepressant, and anxiolytic effects . In our  in mice . In addi in mice . Luteolieticulum  and inhieticulum . It has eticulum . Kaempfeehaviour . Naringeehaviour . The appehaviour . These iIn addition, a total of 19 core targets of GMDZD in PSD were screened in the PPI network: AKT1, STAT3, TP53, CTNNB1, CDKN1A, ESR1, VEGFA, MAPK1, MAPK3, CASP8, CCND1, MAPK14, RELA, TNF, EGFR, FOS, JUN, CXCL8, and STAT1. Among them, EGFR is highly expressed in a variety of malignancies, and depression is common in oncology patients  , 42. Morα, and increased high-sensitivity C-reactive protein (CRP) concentrations were found to be present in patients with mild to moderate depression six months following stroke [Among the 20 pathways screened by KEGG enrichment analysis, some are closely related to PSD, including the TNF and toll-like receptor signaling pathways. The immune-inflammatory response is an important pathogenic mechanism of PSD. Elevated levels of various inflammatory biomarkers, such as IL-6 and TNF-g stroke , 49. It g stroke . Additiog stroke . Furtherg stroke .In summary, network pharmacological analysis showed that there are as many as 203 possible targets of GMDZD in the treatment of PSD. Several pathways may be very closely related to the treatment of PSD, including the TNF and toll-like receptor signaling pathways, and the 19 core gene targets screened from the PPI network are also enriched in these important pathways. Therefore, the results of this study provide evidence for follow-up research and a basis for the clinical application of GMDZD in the treatment of PSD .


Scutellaria baicalensis Georgi (SBG) has been widely shown to induce apoptosis and inhibit invasion and migration of various cancer cells. Increased evidence shows that SBG may be useful to treat oral squamous cell carcinoma (OSCC). However, the biological activity and possible mechanisms of SBG in the treatment of OSCC have not been fully elucidated. This study aimed to clarify the bioactive component and multitarget mechanisms of SBG against OSCC using network pharmacology and molecular docking.  Traditional Chinese Medicine Systems Pharmacology (TCMSP) database was used to predict the active components in SBG, and putative molecular targets of SBG were identified using the Swiss Target Prediction database. OSCC-related targets were screened by GeneCards, Online Mendelian Inheritance in Man (OMIM), and Therapeutic Target Database (TTD). Then, we established protein-protein interaction (PPI), compound-target-disease (C-T-D), and compound-target-pathway (C-T-P) networks by Cytoscape to identify the main components, core targets, and pharmacological pathways of SBG against OSCC via applying data mining techniques and topological parameters. Metascape database was utilized for Gene Ontology (GO) and pathway enrichment analysis. The potential interaction of the main components with core targets was revealed by molecular docking simulation, and for the correlation between core targets and OSCC prognosis analysis, the Kaplan–Meier Plotter online database was used.  There were 25 active compounds in SBG and 86 genes targeted by OSCC. A total of 141 signaling pathways were identified, and it was found that the PI3K-Akt signaling pathway may occupy core status in the anti-OSCC system. GO analysis revealed that the primary biological processes were related to apoptosis, proliferation, and migration. Molecular docking results confirmed that core targets of OSCC had a high affinity with the main compounds of SBG.  Our study demonstrated multicomponent, multitarget, and multipathway characteristics of SBG in the treatment of OSCC and provided a foundation for further drug development research. Oral squamous cell carcinoma (OSCC) is one of the most ubiquitous head and neck cancers and is prevalent worldwide, with about 350,000 newly diagnosed patients in 2018 . TherefoScutellaria baicalensis Georgi (SBG) belongs to the Lamiaceae herb family with extensive biological and pharmacological activities, whose desiccative radix is an important component of the fundamental herb used in Traditional Chinese medicine (TCM) [ne (TCM) . The actne (TCM) , 6. Recene (TCM) . A wealtne (TCM) –10. HoweTCM including SBG has the characteristics of “multicomponent, multitarget, and multipathway system.”  The recehttps://tcmspw.com/tcmsp.php) database was used to collect the effective components of SBG [https://pubchem.ncbi.nlm.nih.gov/) [http://www.swisstargetprediction.ch/) [Traditional Chinese Medicine Systems Pharmacology   and wereion.ch/) ; click “https://www.genecards.org Version 5.0.0) [https://omim.org/, updated March 21, 2020) [http://db.idrblab.org/ttd/, updated June 1, 2020) [OSCC-related target genes were mined using GeneCards (n 5.0.0) , Online http://bioinformatics.psb.ugent.be/webtools/Venn/) platform for analysis [To collect candidate targets for OSCC treatment, the presumed targets of SBG were mapped to OSCC-related targets to obtain common targets. The screened chemical targets and disease targets are imported into the Draw Venn Diagram  was used to visualize the component-target-disease network to reflect the complex links among active constituents, potential targets, and diseases [Cytoscape  database was used to derive the PPI network [Search Tool for the Retrieval of Interacting Genes/Proteins  to complete GO functional analysis and KEGG pathways analysis [P < 0.01.Further analysis is required to reveal the specific mechanism of SBG in OSCC treatment. The common targets were uploaded into the Metascape system  and both the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used to analyze the relationship between the expression level of core targets and cancer prognosis [P < 0.05 was statistically significant.The Kaplan–Meier Online website  [A molecular docking tool (Systems Dock) was used to determine if the main components of SBG have the binding capacity with core targets. We selected core targets in the PPI and Component-Targets-Pathways networks as potential receptors based on the degree value. The PubChem database was used to obtain 3D structures of the main active components of SBG, and the crystal structures of core target genes were directly obtained from the RCSB PDB database (sb.org/) . AutoDocsb.org/) . FinallyA total of 25 active chemical ingredients of SBG were obtained through the screening criteria properties (OB ≥30% and DL ≥0.18) based on TCMSP . The 2D GeneCards, OMIM, and TTD were used to collect OSCC-related targets. By using the keywords “oral squamous cell carcinoma” in sequence, 4,586 OSCC-related targets were obtained from GeneCards, and then we chose 1,147 targets two times greater than the median of relevance score from GeneCards, 469 OSCC-related genes from OMIM, and 1 OSCC-related target from TTD, respectively , molecular functions (MF), and cellular components (CC) related to the GO results revealed the functions of these potential targets. A total of 1,846 significant enriched GO entries were obtained from Metascape, including 1,653 BP, 115 MF, and 78 CC entries based on  Figures .The compound-target-pathway network analysis is shown in P < 0.05). The median survival time of the patients with high expression of PIK3R1 and MAPK3 was better than that of the low expression group, and increased expression of SRC, AKT1, and VEGFA was worse than low SRC, AKT1, and VEGFA expression as shown in We used the Kaplan–Meier Plotter online database to analyze the correlation between five identified genes  and OSCC prognosis. Correlation analysis showed that the expressions of PIK3R1, SRC, AKT1, MAPK3, and VEGFA were all related to the median survival time of OSCC patients  from complex networks through the integration of network pharmacology were predicted to be critical targets of SBG in treating OSCC. Besides, we found that the PI3K-Akt signaling pathway may occupy core status in the anti-OSCC system. Results from molecular docking further revealed that several SBG compounds exhibited an excellent affinity for their core targets. These results elucidate the potentially effective mechanisms of SBG to treat OSCC on multiple targets and multiple pathways, which also shed a light on the development of the new drug for OSCC.


Cutaneous melanoma (CM) is the most aggressive form of skin cancer, and its worldwide incidence is rapidly increasing. Early stages can be successfully treated by surgery, but once metastasis has occurred, the prognosis is poor. However, some 5–10% of thick (≥2 mm) melanomas do not follow this scenario and run an unpredictable course. Little is known about the factors that contribute to metastasis in some patient with thick melanomas and the lack thereof in thick melanoma patients who never develop metastatic disease. We were therefore interested to study differential gene expression and pathway analysis and compare non-metastatic and metastatic thick melanomas. We found that the TNF-like weak inducer of apoptosis (TWEAK) pathway was upregulated in thick non-metastasizing melanomas. MAP3K14 (NIK1), BIRC2 (cIAP1), RIPK1, CASP7, CASP8, and TNF play an important role in inhibiting proliferation and invasion of tumor cells via the activation of the non-canonical NF-κB signaling pathway. In particular, this pathway sensitizes melanoma cells to TNF-alpha and activates the apoptosis module of the TWEAK pathway in thick non-metastasizing melanomas. Hence, our study suggests a potential role of the TWEAK pathway in inhibiting thick melanoma from metastasis. Exploitation of these genes and the pathway they control may open future therapeutic avenues. Cutaneous melanoma (CM) is a tumor that originates from melanocytes of the skin, and continues to carry the potential to be a deadly disease worldwide . While eThe prognosis of primary cutaneous melanoma is correlated with several clinical and histological variables including Breslow-thickness, ulceration, mitotic rate, pattern of tumor-infiltrating lymphocytes, age and gender of the patients and the tumor site. According to Breslow, the thickness of the primary melanoma, measured from the granular layer up to the deepest melanoma cell in the dermis, is by far the most significant prognostic variable and is correlated with outcome . HoweverThe analysis of clinicopathological parameters of thick CM are depicted in n = 140), showed no significant association between the development of metastasis within five years and the classical prognostic variables  plus the additional investigated histopathological variables . After adjustment for the classical prognostic variables, no significant associations were found between outcome and all investigated parameters in the multivariate analysis (n = 141), where we obtained the same results in the multivariate analysis  from the UZleuven database We included 24 patients with thick primary melanoma (≥2 mm) who consented to this study, of whom 6 (25%) were women and 18 (75%) men. We grouped 24 cases into 12 pairs of melanomas that were matched for all clinicopathological prognostic markers but that differed in outcome  . BreslowOf the 24 samples, 23 samples passed the quality control and underwent RNA-sequencing and bioinformatics analysis.From the 29,457 genes included in the Leuven dataset, we identified 830 genes differentially expressed between M− and M+ (398 overexpressed in M− and 432 overexpressed in M+) However, after correction for multiple testing with the Benjamini–Hochberg method, no significantly differentially expressed genes (DEGs) were found.Since complex and heterogeneous phenotypes are best explained by small but coordinated differences in functionally correlated genes, that may therefore not be statistically significant if analyzed separately, we performed pathway analysis. The top ten upregulated pathway in thick M− are depicted in p-value of 9.99 × 10−5 . From the 20,807 genes, we found 1864 differentially expressed (1016 upregulated in M− and 848 upregulated in M+). No genes remained significant after multiple testing adjustment. The TWEAK pathway in this dataset reported a Despite recent advances in understanding its pathogenesis, melanoma is still the deadliest form of skin cancer. Understanding the molecular mechanisms involved in the development of thick CM, as well as the knowledge of their interplay, has great potential to assist in the clinical management of patients with thick CM. The metastatic process itself in CM is not yet fully understood and gene expression profiling studies for thick primary cutaneous melanomas that did not metastasize are scarce and understudied in the literature. The biology of thick CMs is still largely unexplored and, so far, the underlying mechanisms of metastasis in thick CM have not been investigated with a “multi-omics” approach. In this study we explored the clinical, pathological and molecular differences between non-metastasizing (M−) and metastasizing (M+) thick CMs in order to find elements that could help to explain their different clinical behavior and gain insight into mechanisms of metastasis in CM. Moreover, we tried to find metastatization hallmarks for thick melanomas in order to better stratify patients for prognosis and treatment.A retrospective analysis of the clinicopathological data of 140 patients with thick CM (≥2.0 mm) from a UZ Leuven cohort with a follow up of at least 5 years revealed that the outcome of these patients was not correlated with any of the known clinical  or histological  parameters. We also found these observations in the Leeds database. This confirmed that other parameters need to be found to better stratify these selected group of patients (Breslow ≥ 2 mm) than the ones already used in the clinic  [These preliminary data encouraged us to enroll patients with thick CM for advanced molecular analysis. In the UZ database and Leeds database, no significantly differentially expressed genes were found, which might be indicative for multifactorial drivers of metastasis, since complex and heterogeneous phenotypes are best explained by small differences in functionally correlated genes. To gain further insight into the molecular changes that occur in thick CM, we also carried out genes set analysis. Pathway enrichment analysis revealed the TWEAK pathway as a potential driving mechanism of metastasis, particularly the submodule that regulates cell death. Hence, our study confirmed for the first time that the apoptosis module of the TWEAK pathway is upregulated in thick M− and identified a gene signature associated with non-metastasizing melanomas. In addition to the TWEAK pathway, the oestrogen receptor pathway was also identified in our gene-set analysis, but we excluded this pathway because hormone-related studies are often multifactorial, and from the literature we could not draw a coherent conclusion on genes that were involved and upregulated in the estrogen receptor pathway in thick CM ,17.According to the literature, the TWEAK/Fn14 signaling pathway plays a key role in cancer. While TWEAK and Fn14 gene expression is low in normal healthy tissues, increased expression of these genes has been observed in many solid primary tumor types  ,22,23,24Our results provide evidence regarding the differential biological behavior of thick melanoma; however, whether our data are also confirmed at the protein level awaits further studies using immunohistochemical techniques on larger numbers of thick melanomas. Our findings highlight the molecular biomarkers that can be used for prognostic purposes and provide information on the underlying mechanisms of outcome in thick CM. Detailed molecular knowledge of thick CM biology will further be instrumental in the specification of new biomarkers for risk stratification, which will improve the clinical management of this disease. In this regard, knowing the apoptotic biomarkers among thick CM patients may be helpful to find better treatment options.n = 4) and at the Melanoma Institute Australia in Sydney, Australia (n = 14) and Maria Sklodowska-Curie National Research Institute of Oncology in Warsaw, Poland (n = 6). These 24 cases were grouped into 12 pairs of melanomas that were matched for all clinic pathological prognostic markers but that differed in outcome . Hence, the study cohort consisted of 12 melanomas that did metastasize and 12 that did not. Each of these 12 pairs were matched for all other prognostic factors as depicted in Patients with high Breslow thickness melanomas with available remaining FFPE tissue were identified from the files of the pathology departments at University Hospitals in Leuven, Belgium , Melanoma Institute Australia and Maria Sklodowska-Curie National Research Institute of Oncology.The pathology of each melanoma of our patient cohort was reviewed by four expert dermatopathologists . Clinical data available included age , sex, site of involvement , histological subtype, mitotic index, ulceration, Breslow thickness and AJCC stage at diagnosis . The cliSeven consecutive 5 µm sections were prepared from FFPE tissue samples. The first and last sections were stained with hematoxylin & eosin  and used as a control for the presence of a vertical growth phase and a required tumor cell percentage of at least 10%. After deparaffinization with Histo-Clear and ethanol, tumor foci marked by a pathologist on the H & E section were manually macrodissected. Tissue was digested with proteinase K; RNA extraction was performed using RNeasy FFPE Kit  according to the manufacturer’s instructions, and RNA was quantified using the Qubit 2.0 fluorometer . This study is based on analysis of bulk-RNA, derived from the vertical growth phase of thick CM, and therefore does not take heterogeneity in RNA expression into consideration.Sequence libraries were prepared with the Lexogen QuantSeq 3′ mRNA-Seq Library prep kit according to the manufacturer’s protocol. Samples were indexed to allow for multiplexing. Library quality and size range was assessed using a Bioanalyzer (Agilent Technologies) with the DNA 1000 kit  according to the manufacturer’s recommendations. Libraries were diluted to a final concentration of 2 nM and subsequently sequenced on an Illumina HiSeq4000 platform according to the manufacturer’s recommendations. Single-end reads of 50 bp length were produced with a minimum of 1 M reads per sample.http://www.bioinformatics.babraham.ac.uk/projects/fastqc (accessed on 10 September 2021). Adapters were filtered with ea-utils fastq-mcf v1.05. (Erik Aronesty (2011). ea-utils: “Command-line tools for processing biological sequencing data”; Ea-utils: Fastq processing utilities. https://github.com/ExpressionAnalysis/ea-utils (accessed on 10 September 2021)) Splice-aware alignment was performed with STAR v2.6.1b against the human reference genome hg38 using the default parameters. Reads mapping to multiple loci in the reference genome were discarded [Quality control of raw reads was performed with FastQC v0.11.7 (Andrews S. (2010)). FastQC: a quality control tool for high throughput sequence data. Available online at: iscarded . Resultiiscarded .p-values were adjusted for multiple comparisons with the Benjamini–Hochberg procedure, which controls false discovery rate (FDR). Pathway analysis was performed using Piano R package for enriched gene set analysis [p-value), Stouffer , Reporter , PAGE , Tail Strength , GSEA , Mean (logFC), Median (logFC), Sum (logFC), MaxMean . For the gene-sets we used the Molecular Signatures Database, curated pathways (c2), canonical pathways (cp), version 7.2. Gene-sets with more than 100 genes capturing general biology were removed from downstream analysis (Raw mRNA expression data for UZ Leuven database: primary accession –PRJEB47586 and secondary accession-ERP131867).Quantification of reads per gene was performed with HT-seq Count v2.7.14. Differential gene expression analysis was done comparing thick M- melanomas versus thick M+ melanomas with the DESeq2  R-packaganalysis . Within The results of our analysis were validated using the Leeds dataset . The norIn the present study, we characterized the transcriptome of thick melanoma M− versus M+ in a precisely matched patient data set. We identified a gene signature associated with non-metastasizing melanomas. In particular, the TWEAK-induced activation of the non-canonical NF-κB signaling pathway, which induces production of TNF-alpha and sensitizes tumor cells to death, may play a protective role in thick non-metastasizing melanomas. The exploitation of these genes and pathway may open future therapeutic avenues.


Narrow spectrum of action through limited molecular targets and unforeseen drug-related toxicities have been the main reasons for drug failures at the phase I clinical trials in complex diseases. Most plant-derived compounds with medicinal values possess poly-pharmacologic properties with overall good tolerability, and, thus, are appropriate in the management of complex diseases, especially cancers. However, methodological limitations impede attempts to catalogue targeted processes and infer systemic mechanisms of action. While most of the current understanding of these compounds is based on reductive methods, it is increasingly becoming clear that holistic techniques, leveraging current improvements in omic data collection and bioinformatics methods, are better suited for elucidating their systemic effects. Thus, we developed and implemented an integrative systems biology pipeline to study these compounds and reveal their mechanism of actions on breast cancer cell lines.Transcriptome data from compound-treated breast cancer cell lines, representing triple negative (TN), luminal A (ER+) and HER2+ tumour types, were mapped on human protein interactome to construct targeted subnetworks. The subnetworks were analysed for enriched oncogenic signalling pathways. Pathway redundancy was reduced by constructing pathway-pathway interaction networks, and the sets of overlapping genes were subsequently used to infer pathway crosstalk. The resulting filtered pathways were mapped on oncogenesis processes to evaluate their anti-carcinogenic effectiveness, and thus putative mechanisms of action.The signalling pathways regulated by Actein, Withaferin A, Indole-3-Carbinol and Compound Kushen, which are extensively researched compounds, were shown to be projected on a set of oncogenesis processes at the transcriptomic level in different breast cancer subtypes. The enrichment of well-known tumour driving genes indicate that these compounds indirectly dysregulate cancer driving pathways in the subnetworks.The proposed framework infers the mechanisms of action of potential drug candidates from their enriched protein interaction subnetworks and oncogenic signalling pathways. It also provides a systematic approach for evaluating such compounds in polygenic complex diseases. In addition, the plant-based compounds used here show poly-pharmacologic mechanism of action by targeting subnetworks enriched with cancer driving genes. This network perspective supports the need for a systemic drug-target evaluation for lead compounds prior to efficacy experiments.The online version contains supplementary material available at 10.1186/s12906-021-03340-z. While reductionism-based approaches generated most of the drugs and drug targets known today, drug-human interactions are rather complex and the mechanism of action of most pharmacologically effective drugs results from the perturbation of cellular networks . Thus, aMost cancers are driven by multiple genetic mutations and epigenetic dysregulations , 4 interExisting methods for drug discovery and drug target identification are highly efficient in elucidating the mechanism of action of anti-microbial drugs. However, studies have consistently demonstrated that this simple framework is inefficient in addressing drug action in complex and multi-factorial disease systems. In such systems, limiting drug research to targeting single disease biomarkers is one of the main causes of drug failures in clinical trials , 2, 8. DCimicifuga foetida medicinal plant [Sophora flavescens and Heterosimilax chinensis medicinal plants [Experimental evidences from separate studies on the use of plant-based drugs in cancer cells have strongly suggested a multi-targeting therapeutic strategy. In fact, ancient civilizations relied on plant-based compounds due to their relatively low systemic toxicities and ability to simultaneously treat multiple closely-related disease conditions . Actein,al plant , Withafeal plant  plant, Cl plants , and indl plants , are amol plants –17. Yet,Network biology is a holistic approach in systems biology to understand biological systems, where biomolecules and their binary interactions are projected onto a graph to depict molecular relationships . NowadayIn this study, we developed a data-centric computational framework to determine the mechanism of action of plant derived natural products on breast cancer cell lines. In this approach, we mapped compound-treated breast cancer transcriptome data  , actein p-value correction to control for false discovery rates (FDR). Fold change and FDR cut-offs were simultaneously used to select differentially expressed genes.The expression datasets included microarray expression profiles and RNA-seq counts and, therefore, platform specific protocols were followed. For the microarray derived datasets , probeset mapping was performed by choosing the probe with the maximum average expression value among multiple probesets of a gene. For RNA-seq data (CKI), we selected only those genes with above zero counts in at least two samples in either control or treatment group. Overall, we log2 normalized all the pre-processed datasets. Subsequently, we used LIMMA  package The challenge of discovering most-connected drug specific subnetworks in the human protein-protein interaction network was solved using KeyPathwayMiner (KPM) , one of −3 (for indole-3-carbinol and withaferin A) or 1 × 10−2 (for actein and CKI) to identify differentially expressed genes. Thus, our approach is strict; with the intention of reducing the rate of false positives and retaining only important features. These two cut-offs were used to assign binary values to all the genes in a dataset. Specifically, we used ‘1’ to denote differentially expressed genes based on our criteria, and ‘0’ for other genes. In the subnetwork construction, significantly changed and physically interacting proteins are used. These interconnected proteins essentially denote drug-targeted cellular pathways. We allowed a maximum of 5 non-differentially expressed genes in each subnetwork solution, a parameter available in KPM. For the priori human PPIN, we used BioGRID [In our analysis, we made a few modifications to the input data and constraints as we describe next. We applied a uniform fold-change cut-off of 2 and a varied FDR cut-off of 5 × 10 BioGRID    Cytoscapthe TCGA  clinicalWe used enrichR  package Pi and Pj, with enriched gene sets, Gi and Gj, we computed the Jaccard index (J) using the formula below:Oncogenic signalling pathways do not function in isolation but are known to crosstalk with each other while redirecting cellular processes. Construction of pathway interaction networks has been previously applied to visually elaborate the pathway-pathway interrelationships and infer associated biological phenomenon , 40. On This evaluates to the number of genes common in the two pathways divided by the total number of genes in both pathways without repeats. Hence, Jaccard index takes values between 0 and 1, and, using this metric, the proportional similarity between two pathways can be deduced. Here, we defined two pathways to be either in crosstalk or similar based on their Jaccard scores. We relied on a cut-off of 0.60 and 0.25 to infer pathway redundancy and pathway crosstalk respectively. Since we used multiple pathway databases , triple negative and human epidermal receptor 2 positive (HER2+); based on hormone receptor (oestrogen receptor (ER) and progesterone receptor (PR) hormones) and HER2 protein expression . While tNetwork mapping and subnetwork scoring approaches have been extensively used in integrative biology field to discover active disease- and drug-specific modules in various experiments , 44, 45.Overall, we observed a compound- and breast cancer subtype-specific pattern based on the number of proteins and their interactions. Thus, it is deducible from these results that the different compounds studied had substantial differential and specific effects on the activity of the underlying protein interaction networks in the disease subtypes. With the differences in the number of targeted proteins, this deduction reinforces the dominant idea that no two drugs have a similar mechanism of action in complex diseases , 46. As An overarching question is whether the genes enriched in the subnetwork solutions have any significance in breast cancer prognosis. In therapeutic terms, effective anti-carcinogenic drug candidates are known to regulate a niche of known proto-oncogenes in a disease network. To address this, network centrality measures can be used to identify topologically important target nodes (genes) in the subnetwork solutions . In diseBetweenness and degree centrality scores for all genes in the subnetworks are given in Supplementary Table K = 5 in KPM-based subnetwork construction step, the top five genes mainly consisted of genes with non-significant differential expression but with the highest degree and betweenness centrality scores. Survival analysis found APP, TRIM25 and ELAVL1 to have significant associations with overall survival  in breast cancer. Overexpression of APP and TRIM25 in cancer patients was associated with low overall survival and the reverse was true for ELAVL1  were found to be the most frequent across all subnetworks  pathway enrichment was applied to all the genes in a subnetwork, (ii) only oncogenic signalling pathways were retained, (iii) to identify and filter out redundant pathways coming from different databases, pathway-pathway correlation networks were constructed (iv) the final list of pathways was mapped on three major oncology related processes based on their semantic similarity to the 10 canonical oncogenic signalling pathways  see Met section.As described in the methods section, we performed pathway enrichment analysis using the genes in each identified subnetwork. An important factor in this systemic approach is the interconnectivity of the proteins used in pathway enrichment analysis. Thus, it is obvious that the enriched pathways are connected due to the shared targeted-network proteins. To illustrate this, first we eliminated all those pathways which were unrelated to cancer. Supplementary Table , and (ii) the interacting pathways are well-known to interact in literature acting as sub-pathways through the activation of the main pathway, as in the case of ‘apoptosis’, ‘TNF’ and ‘IL17’ in Fig. We observed a characteristic clustering of related pathway terms across the various enrichment results. For instance, in the actein treated MDA-MB-453 dataset, we identified 10 pathway clusters out of 21 enriched pathways; only 5 of these  were identified as members of the defined oncogenic signalling pathways (see Methods). An examination of the various pathway clusters from all the datasets revealed two important features: (i) the clustered pathways were either semantically related or from the same database with similar functions, as in the case of ‘NRF2’ and ‘Nuclear receptor meta-pathway’ pathways in Fig. Next, to infer biological significance, we applied a two-tier approach. First, we relied on the predefined canonical oncogenic signalling pathways see  section Systems pharmacology has evolved as a data-driven approach to bridge the gap between the increasing amounts of compound/drug perturbation data and drug discovery through systematic evaluations , 55. It Actein has been widely studied in breast cancer due to its effects on various biological processes in various cancers , 57–59. CKI is an ancient formulation in the Chinese pharmacopoeia, and mixed results have been reported in breast cancer . The groThe therapeutic effectiveness of indole-3-carbinol is well defined in oestrogen receptor driven cancers , 65. CarThe characteristic anti-cancer effects of Withaferin A is well anchored in scientific reports –70 and sWhereas this work attempts to associate the various targeted networks with carcinogenesis processes to explain the mechanism of action of poly-pharmacologic compounds, a major limitation arises on enumerating their therapeutic values. For instance, enrichment of a pathway in either up- or down-regulated subnetworks may not necessarily be directly translated as activation or inactivation of the related pathway-defined cellular process, as the same process may be targets of other co−/dys-regulated pathways by the same drug. In vitro reports on the activity of the different compounds on cell lines –14 showeAnother challenge experienced in this approach is the un-directionality of protein interactomes. Thus, given the inherent directionality in signalling pathways, our future studies will incorporate directed networks from an ensemble of databases, by drawing on their comprehensiveness to construct all-inclusive interaction networks.Additionally, given the poly-pharmacologic properties found here, simulations on the effect of different combinations to determine synergistic and antagonistic combinations and side-effects would provide more information. Regan-Fendt et al.  recentlyLiterature evidence from other in vitro studies on both breast and other cancers were shown to support some of our predictions on the systemic effects of the studied compounds. This suggests the method may be valuable in identifying the systemic molecular effects of pleiotropic compounds during drug screening. Additionally, it may be used to select the appropriate compound based on targeted pathways or biological processes associated with disease. However, more in vitro studies are needed to validate the predictions in the respective cell types before wider adoption of this methodology.Overall, this study generated two main outputs: (i) proposed a data-driven framework for elucidating the mechanism of action of pleiotropic natural products using transcriptome data and protein interactome and (ii) demonstrated that plant-derived drugs  are capable of simultaneously regulating multiple carcinogenesis processes in breast cancer. Thus, this network-centric method can extract subtle systemic drug effects on cellular pathways and provides a better approach to the abortive exquisite ‘target’ approach in studying poly-pharmacologic compounds. Although breast cancer datasets were used to prove the concept, the approach can also be easily applied to other cancers. We anticipate that the proposed framework will be instrumental in accelerating evaluation of poly-pharmacologic compounds for applications in pre-clinical drug efficacy research for oncology precision medicine and other complex diseases.Additional file 1: Supplementary Fig. 1. Principal component analysis (PCA) results of transcriptome samples for each dataset illustrating the distribution of variance in the first two components considered for sample separation. PC1: principal component 1, PC2: principal component 2. (a) actein on MDA-MB-453, (b) CKI on MCF-7, (c) Indole-3-Carbinol on MCF-7, (d) Indole-3-Carbinol on MDA-MB-231, (e) Indole-3-Carbinol on MDA-MB-436, (f) Indole-3-Carbinol on T47D, (g) Indole-3-Carbinol on ZR751, (h) Withaferin A on MCF-7 and (i) Withaferin A on MDA-MB-231.Additional file 2: Supplementary Fig. 2. Average gene expresion profiles of most frequent central genes in the compound-targeted subnetworks based on TCGA datasets. a-e) Box-plots showing gene-phenotype  association.Additional file 3: Supplementary Fig. 3. Pathway-pathway interaction networks based on shared enriched genes illustrating functional pathway cross-talk. a-f: represents networks of pathways targeted by CKI on MCF-7, I3C on MCF-7, I3C on MDA-MB-436, I3C on T47D, I3C on ZR751 and WA on MCF-7. CKI: Compound Kushen Injection, I3C: Indole 3-Carbinol, WA: Withaferin A.Additional file 4: Supplementary Table 1. Summary of the transcriptome datasets used and the molecular profiles of the cell lines. The columns Controls and Treatments list the number of samples in each case. .Additional file 5: Supplementary Table 2. Summary of the differential expression analysis results. The number of differentially expressed genes under the respective plant-derived drugs/compounds are given in the table. DEG: Differentially expressed genes, FDR: False discovery rate, FC: Fold change.Additional file 6: Supplementary Table 3. Results of subnetwork betweenness- and degree centrality analysis.Additional file 7: Supplementary Table 4. Pathways enriched in whole subnetworks. FDR < 0.05.Additional file 8: Supplementary Table 5. Enriched pathways in up- and down-regulated subnetworks. FDR < 0.05.Additional file 9: Supplementary Table 6. An example of Actein targeted oncogenesis processes illustrating the approach used in grouping the oncogenic signaling pathways into different cancer pathophysiological processes based on the pathways’ enriched genes.