To predict the target of the active ingredient of lotus leaf for lowering fat and losing weight. Explore its multicomponent, multitarget, multipath mechanism.  Screen the main active ingredients of lotus leaves through the TCMSP database, and use the TCMSP database to predict the potential targets of the active ingredients. Obtain obesity-related targets from the human genome annotation (GeneCards) database. Use Venn software to take the intersection of the two to obtain the effect target of the lotus leaf lipid-lowering and weight-reducing effects. Use Cytoscape 3.6.0 software to construct an effective ingredient-target network. Use the STRING database to construct an intersection target protein interaction (PPI) network, visualize it with Cytoscape 3.6.0 software, and perform network topology analysis to obtain the core target. Use the DAVID database to perform gene ontology (GO) and metabolic pathway (KEGG) enrichment analysis for the above targets. Use AutoDockTools software for molecular docking to verify the binding strength.  A total of 15 main active ingredients such as quercetin, isorhamnetin, sitosterol, and kaempferol were obtained, which can act on 135 targets related to obesity. These targets are significantly enriched in multiple GO and KEGG entries such as hypoxia response, positive regulation of gene expression, response to toxic substances, aging, and positive regulation of RNA polymerase II promoter transcription. Molecular docking shows that flavonoids such as quercetin have better binding to the target protein Akt1.  The lipid-lowering and weight-reducing effects of lotus leaf embody the characteristics of multicomponent, multitarget, and multipathway of traditional Chinese medicine, which provides a certain scientific basis for the screening and in-depth study of the effective ingredients of lotus leaf. The lotus leaf belongs to the leaf of the Nymphaeaceae plant lotus, which is a medicinal and food homologous plant published by the Ministry of Health . The lotTraditional Chinese medicine has the characteristics of multicomponent, multitarget, and synergistic effects between each component. Network pharmacology is based on the theory of systems biology, and it is a new technology to explain the drug's action and its mechanism –10. InteUse the Computational Systems Biology Laboratory (TCMSP) to retrieve all the active ingredient data of lotus leaves. It is an important evaluation index for drugs to participate in the process of absorption, distribution, metabolism, and excretion. Taking oral bioavailability (OB) and drug-like properties (DL) as screening conditions, set oral bioavailability (OB) ≥30%, and drug-like properties (DL) ≥0.18 to obtain active compounds that meet the conditions. Obtain the corresponding target points of each active ingredient in TCMSP , 13.https://www.genecards.org). Deduplicate, set the correlation score to be greater than 1, and screen out disease targets [Search for obesity-related disease targets through the GeneCards database . The minimum interaction threshold is set to “highest confidence” (>0.4) to obtain the PPI network and obtain protein interaction information. Use Cytoscape 3.6.0 software to draw the PPI network diagram, and screen out the core targets according to the node degree value [Enter the lotus leaf lipid-lowering and weight-loss targets in the STRING database  database to download the structure of small molecule ligands. Enter the core target protein screened under item 1.2 into the RCSB PDB (http://www.rcsb.org/) database, and download the 3D structure of the target protein. Prepare ligand files and receptor files, and use AutoDockTools software for molecular docking [Enter the main active ingredients selected under 1.1 into the PubChem  ≥30% and drug-like activity (DL) ≥0.18. The results are given in https://www.genecards.org) database was searched with “Obesity” as the keyword, and 9,510 obesity-related target genes were obtained. Take 2569 genes with a correlation score greater than 1 and draw a Venn diagram of the targets of the active ingredients in the lotus leaf and the targets of obesity-related diseases. The results are shown in Through the TSMSP database, 189 target proteins of 15 active ingredients in the lotus leaf were retrieved, and the target proteins and gene information were corrected through the STRING database . The GenThe lotus leaf-components-target-obesity network was constructed by Cytoscape 3.6.0 software. The results are shown in It can be seen from Import the obtained 135 intersection targets into the STRING database to obtain the PPI relationship. The results are shown in The top 10 targets in terms of the degree value are protein kinase B1 (Akt1), interleukin 6 (IL6), tumor necrosis protein p53 (TP53), caspase 3 (CASP3), JUN protein (JUN), myeloma virus oncogene homolog (MYC), epidermal growth factor receptor (EGFR), epidermal growth factor (EGF), and mitogen activated protein kinase 8 (MAPK8). These targets may be the key targets for the lotus leaf to lower fat and lose weight.P < 0.05). GO analysis consists of three parts: biological process (BP), cellular component (CC), and molecular function (MF). Among them, there are 479 BP entries, 44 CC entries, and 99 MF entries [P value, the top 10 GO items are listed, respectively, as shown in A total of 622 GO entries were screened in the DAVID database (It can be seen from P < 0.05) [112 KEGG-enriched signal pathways were screened by the DAVID database ( < 0.05) . Select Analysis shows that the targets are significantly enriched in the hepatitis B signaling pathway, hypoxia-inducible factor-1 (HIF-1) signaling pathway, tumor necrosis factor (TNF) signaling pathway, Chagas disease signaling pathway, and multiple signaling pathways related to cancer. The HIF-1 signaling pathway is shown in Search the three-dimensional structure of the interaction between quercetin (MOL000098), the active ingredient of lotus leaf, and the target Akt1 through the PDB database, download the MOL structural formula of the compound, and import them into the AutoDockTools software, respectively . The binThis study uses the method of network pharmacology to explore the mechanism of the effect of lotus leaf in reducing fat and reducing weight. From the “lotus leaf-components-targets-obesity” network diagram, it can be seen that compounds such as quercetin, kaempferol, isorhamnetin, O-nornopine, and papaverine are important nodes in the network. It is speculated that these compounds may be the material basis of lotus leaf's lipid-lowering and weight-reducing effects. The PPI network of the intersection target of lotus leaf and obesity shows that protein kinase B1 (Akt1), interleukin 6 (IL6), tumor necrosis protein p53 (TP 53), caspase 3 (CASP3), JUN protein (JUN), myeloma virus oncogene homolog (MYC), epidermal growth factor receptor (EGFR), epidermal growth factor (EGF), and mitogen-activated protein kinase 8 (MAPK8) interact with multiple compounds and have high degrees and play a key role in the network diagram. It is suggested that it may be the key target of lotus leaf's lipid-lowering and weight-reducing effects. The GO function is significantly enriched in biological processes such as the positive regulation of gene expression and the positive regulation of RNA polymerase II promoter transcription. KEGG pathway enrichment analysis results show that lotus leaf can play a weight loss effect through the hypoxia-inducible factor-1 (HIF-1) signaling pathway, tumor necrosis factor (TNF) signaling pathway, and multiple signaling pathways related to cancer. The results of molecular docking show that flavonoids such as quercetin have good binding to the target protein Akt1, which provides a strong basis for network pharmacology to predict the reliability of the target. On the basis of network pharmacology, this study discussed the active ingredients of lotus leaf and its multitarget and multipathway characteristics in the process of reducing fat and losing weight. Preliminary explanation of the mechanism of the lotus leaf weight loss effect is provided for the further in vivo and in vitro experimental verification of the lotus leaf weight loss activity and the screening and evaluation of traditional Chinese medicine for weight loss.


If somatic stem cells would be able to maintain their regenerative capacity over time, this might, to a great extent, resolve rejuvenation issues. Unfortunately, the pool of somatic stem cells is limited, and they undergo cell aging with a consequent loss of functionality. During the last decade, low molecular weight compounds that are able to induce or enhance cell reprogramming have been reported. They were named “Small Molecules” (SMs) and might present definite advantages compared to the exogenous introduction of stemness-related transcription factors (e.g. Yamanaka’s factors). Here, we undertook a systemic analysis of SMs and their potential gene targets. Data mining and curation lead to the identification of 92 SMs. The SM targets fall into three major functional categories: epigenetics, cell signaling, and metabolic “switchers”. All these categories appear to be required in each SM cocktail to induce cell reprogramming. Remarkably, many enriched pathways of SM targets are related to aging, longevity, and age-related diseases, thus connecting them with cell reprogramming. The network analysis indicates that SM targets are highly interconnected and form protein-protein networks of a scale-free topology. The extremely high contribution of hubs to network connectivity suggests that (i) cell reprogramming may require SM targets to act cooperatively, and (ii) their network organization might ensure robustness by resistance to random failures. All in all, further investigation of SMs and their relationship with longevity regulators will be helpful for developing optimal SM cocktails for cell reprogramming with a perspective for rejuvenation and life span extension. Theylication , 14. Conin silico study we performed: (i) a comprehensive data mining of SMs; (ii) the characterization of SMs and SM cocktails, including assessing their protein targets and possible interactions between them; (iii) the analysis of pathways targeted by SMs, (iv) the comparison of targets and pathways of SM cocktails with those of the OSKM TFs, and (v) screening for SMs as human metabolites.In this We first compiled a full list of SMs established thus far, based on a keyword meta-analysis of the literature. Comprehensive data mining with subsequent curation (see Methods) resulted in a total of 92 chemical compounds  that canThe analysis of the basic biological activities of the collected SMs revealed that they fall into three major categories –5: (i) sThe SMs with signaling activity represent the largest group . The mentioned compounds can replace Oct4 [2CO3) which promote glycolysis are mandatory components of each reprogramming cocktail. Finally, all the cocktails include one or more epigenetic modifiers: HDAC inhibitors , DNMT inhibitors (5-aza-dC), the inhibitor of LSD1 acting on histone H3 (Parnate), and the inhibitors of histone methyltransferases . The common SMs are presented in the reprogramming cocktails in descending order: CHIR99021 = RepSox (n = 7), VPA = Forskolin (n = 6), Parnate (n = 5), DZNep (n = 4), AM 580 (n = 3), EPZ004777 (n = 2); other SMs are found only in one cocktail  for extracting the chemical-protein interactions. Then, using the DAVID bioinformatics tools [To get further insight into the mechanisms of chemically-induced reprogramming, we carried out an enrichment analysis for SM protein targets. For that purpose, we first used the STITCH database , AMPK signaling (p = 5.7E-17), Insulin signaling (p = 2.3E-13), FoxO signaling (p = 4.2E-23), and pathways involved in cell-cell and cell-extracellular matrix interactions . Also, SM targets are over-presented in the signaling pathways associated with age-related diseases, including different types of cancer, type II diabetes mellitus (p = 7.7E-08), amyotrophic lateral sclerosis (p = 2.3E-04), and Alzheimer's disease (p = 2.5E-03). Among the enriched pathways are numerous growth-promoting pathways, cell survival  or cell death  signaling. Many enriched pathways are related to immune and inflammatory responses. Among them are the pathways related to innate immunity , specific immune responses , and inflammatory signaling , etc. Not surprisingly, the enriched pathways include regulation of cell cycle (p = 3.5E-09), cell differentiation , and Signaling pathways regulating pluripotency of stem cells (p = 2.8E-06).To further evaluate to what extent the SM targets interact between themselves, we determined their protein-protein interactions (PPIs), annotated in the BioGRID database . These dP(k) = 221 x k-1.16) follows a power-law distribution of connectivity and indicates that the PPI network of SM targets has a scale-free topology, with an extremely high contribution of hubs to the average network connectivity.Next, we aimed to understand the topology of the constructed network. To address this point, we calculated the distribution of node connectivity. The regression equation in Using the same approach, we built the chemical-protein interaction and PPI networks for the ten SM cocktails used thus far for chemical reprogramming see . As seenIt seems plausible that the cocktails for chemical cell reprogramming and TFs for iP, specifically Yamanaka’s factors (OSKM), have common targets . However2), organooxygen  and organonitrogen (Spermidine) compounds, and prenol lipids (Retinoic acid). Other “natural” SMs represent nutrients that integrate into the human body when consuming products of plant metabolism (n = 11). Interestingly, several of these compounds  are components of plant extracts that have been already shown to improve healthspan, in particular stress resistance and cognitive abilities [Most SMs are artificially synthesized chemicals. Of special interest is whether among the SMs are compounds that are natural (human) metabolites or their analogs. Overlapping the 92 SMs with the molecules found in the Human Metabolome Database - HMDB  gives a bilities . Severalbilities .Furthermore, using STITCH tools , we founUntil now, the selection of SMs for chemically-induced pluripotency or cell reprogramming was done mainly on an empirical basis, and no analysis of SMs and their targets has been undertaken. Several reviews published in the past –32 focusin vitro [in vivo on i4F reprogrammable mice [Functional analysis of SMs and their targets revealed that they are distributed between three major categories: epigenetics, intra- and inter-cellular signaling, and metabolic “switchers”. All these categories appear to be mandatorily presented in each SM cocktail to induce cell reprogramming. Specifically, it seems that sufficient components for a “minimal reprogramming” cocktail have to include an inhibitor of HDAC (e.g. VPA or NaB), an inhibitor of TGFβ signaling (e.g. RepSox), and GSK3-inhibiting SMs (e.g. CHIR99021 or LiCl). This assumption was further confirmed by the KEGG pathways enrichment analysis. The unusually significant enrichment of epigenetic and signaling pathways highlights their importance in chemical iP. Remarkably, many enriched pathways were related to aging, longevity and age-related diseases, thus presumably connecting them with the processes of cell reprogramming. This notion has recently been supported experimentally by demonstrating induction of cellular senescence by activation of OSKM, in vitro  and alsoble mice –40. Yet,ble mice . Anotherble mice . In turnble mice . For uncble mice –46, howeIt is still a matter of debate whether SMs act independently of each other in triggering cell reprogramming, or if they act in a cooperative, epistatic manner. The latter suggests the interactions between their targets, including direct  interactions. With this in mind, we analyzed the connectivity and interconnectivity of targets of SMs and SM cocktails. The network analysis indicates that their targets are highly interconnected and form PPI networks with a scale-free topology that confers robustness and persistent connectivity. This means that: (i) the SM targets probably act in a cooperative manner to induce cell reprogramming; (ii) a scale-free topology of SM targets ensures higher integrity of the network and its resistance to random attacks , 48, thuRecently, we hypothesized that cell reprogramming is a natural process that is triggered and regulated via two major networks – a genetic one  and a chemical one  , 49. In Further supporting our hypothesis are the data on the common targets of SM cocktails and Yamanaka’s factors. This comparison revealed an insignificant overlap between the SM cocktails’ targets and OSKM, except for Cocktail #7. The lack of common targets between the cocktails and Yamanaka factors was quite a surprising observation. More prominent overlap was however observed between pathways, meaning that despite different targets, both SM cocktails and Yamanaka’s factors “use” more or less the same pathways.Altogether, this suggests that the two systems, chemical (SMs) and genetic (TFs), might cooperate to increase the efficiency of cell reprogramming. Interestingly, the overlapping pathways for SM cocktails and OSKM targets are mainly cancer- or virus-related but not related to key reprogramming processes, such as demethylation and chromatin decondensation or pluripotency pathways, as it might be expected. One of the reasons could be rooted in statistical issues. In in vivo. In this regard, testing SM cocktails in the naked mole-rat model could be of particular interest as induction of pluripotency in the cells of this animal requires special conditions and is not always achievable [Although beyond the scope of the present study, it is worth mentioning that there is a significant overlap between the collected 92 SMs and the compounds found in the DrugAge database   and Google Scholar (https://scholar.google.com/). Additional data about the chemical and biological properties of SMs were obtained from PubChem [https://pubchem.ncbi.nlm.nih.gov/ and from the Human Metabolome Database (HMDB) [http://www.hmdb.ca/. Briefly, HMDB contains the collection of small molecules found in the human body, including nucleic acids, carbohydrates, lipids, peptides, amino acids, organic acids, biogenic amines, vitamins, minerals, food additives, drugs, cosmetics, contaminants, pollutants, and other chemicals that enter the human body [Data on SMs for cell reprogramming were gathered from publicly available literature, using PubMed NCBI  , http://man body .The papers were searched using the following keywords: “induced pluripotency”, “chemically induced pluripotency”, “chemical reprogramming”, “chemically induced dedifferentiation”, “induction of pluripotency by small molecules”. In order to be included in the analysis, each article had to contain data: (i) on SM(s) or their cocktail(s) that either induced or enhanced cellular reprogramming; (ii) on the bioactivity of the SMs; and (iii) on the SM dosage and cell type. According to their role in cell reprogramming, the compounds found were divided into two major groups of molecules: iP inducers and iP enhancers. Since it was not always possible to definitely link the compounds to one of the groups, as in some cases a given compound was considered an inducer and in other cases an enhancer, these entities were marked as “inducer and/or enhancer”. From each paper the following data were collected and manually curated: (i) the name(s) of SM(s) that either induce or enhance pluripotency, with or without TFs; (ii) the effect of SM(s) on the iP efficiency; and (iii) whether a given SM can substitute the pluripotency-associated TFs. The collected SMs were organized in a table as shown in http://stitch.embl.de/, one of the largest repositories of chemical-protein interactions [To determine the protein targets of the collected SMs, we used the STITCH database (version 5.0), ractions , which iractions .https://www.grnpedia.org/trrust/, was used. The overlaps between gene targets of drug cocktails and OSKM transcription factors were calculated using only the genes that are present in both STITCH and TRRUST databases. In order to compute the overlap between gene targets of SMs and GenAge [https://genomics.senescence.info/genes/index.html, two lists of longevity-associated genes (LAGs) were used: i) the manually curated list of human LAGs from GenAge, build 20 and ii) the human orthologs of model organisms LAGs from GenAge, build 20. Orthologs of genes were computed using a script developed in our lab, that queries the database InParanoid 8 [https://inparanoid.sbc.su.se/cgi-bin/index.cgi. For stringency, we selected for each gene only inparalogs with scores of 1.0. The significance of the overlaps with GenAge [https://genomics.senescence.info/cells/, was computed using Fisher’s exact test.To obtain the list of OSKM transcription factors the TRRUST database , https:/d GenAge , https:/ranoid 8 , https:/h GenAge  and CellThe overlaps between: i) the list of SMs and HMDB, and ii) the list of SMs and DrugAge  were calhttps://david.ncifcrf.gov. Statistical significance of enrichment was evaluated using default parameters set in DAVID. A threshold of 0.001 was used for the adjusted P-value.Functional and pathway enrichment analyses were performed with the DAVID Bioinformatics Resources tool, version 6.8 , https:/http://thebiogrid.org, human interactome, Build 3.5.177. The PPI network construction and analyses were performed using Cytoscape [http://www.cytoscape.org, version 3.7.1. Prior to any network analyses, genetic interactions, self-loops, duplicate edges and interactions with proteins from other species were removed from the interactome, and the remaining network was used as a control. The interconnectivity was computed as the fraction of nodes in the largest connected component out of the input gene set, by using the breadth-first search algorithm. Modeling the relationship between node subset size and interconnectivity in the human interactome was carried out by randomly sampling subsets of nodes in the interactome, with a sample size varying from 50 to 17,600 nodes (step of 50). In this case, sampling was performed 100 times for each subset size. In order to evaluate the statistical significance of the observed network interconnectivity for cocktails and SMs gene targets, random sampling from the BioGRID network was performed 1000 times, for a subset of nodes of equal size to each evaluated network. For each set of random samplings, average interconnectivity, standard deviation and z-score of the observed interconnectivity were computed.Protein-protein interaction (PPI) data were taken from the BioGRID database , http://ytoscape , http://For a joint protein-drug network, the protein targets of the collected SMs, determined from the STITCH database, were used together with PPIs from BioGRID.Valproic acid CHIR99021 (C)RepSox (6)Parnate (T)Forskolin (F)DZNepTTNPBNaBLiClSB431542Valproic acidCHIR99021RepSoxTrichostatin A (TSA)Li2CO3TranilastBrdUCHIR99021RepSoxForskolinVPACHIR99021RepSoxParnateForskolinAM 580EPZ004777VPACHIR99021RepSoxParnateForskolinAM 580DZNep5-aza-dCSGC0946EPZ004777VPACHIR99021RepSOXParnateForskolinDZNepVPACHIR99021RepSoxParnateForskolinAM 580DZNepCHIR99021NaBcyclic pifithrin-a (ɑ)A-83-01ThiazovivinPD0325901Supplementary FiguresSupplementary Table 1Supplementary Table 2Supplementary Table 3Supplementary Table 4Supplementary Table 5Supplementary Table 6Supplementary Table 7Supplementary Table 8Supplementary Table 9Supplementary Table 10


To investigate the potential mechanism of action of Yi-Qi-Huo-Xue-Tong-Luo formula (YQHXTLF) in the treatment of diabetic peripheral neuropathy (DPN).  Network pharmacology and molecular docking techniques were used in this study. Firstly, the active ingredients and the corresponding targets of YQHXTLF were retrieved using the Traditional Chinese Medicine Systems Pharmacology (TCMSP) platform; subsequently, the targets related to DPN were retrieved using GeneCards, Online Mendelian Inheritance in Man (OMIM), Pharmgkb, Therapeutic Target Database (TTD) and Drugbank databases; the common targets of YQHXTLF and DPN were obtained by Venn diagram; afterwards, the “YQHXTLF Pharmacodynamic Component-DPN Target” regulatory network was visualized using Cytoscape 3.6.1 software, and Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on the potential targets using R 3.6.3 software. Finally, molecular docking of the main chemical components in the PPI network with the core targets was verified by Autodock Vina software.  A total of 86 active ingredients and 229 targets in YQHXTLF were screened, and 81 active ingredients and 110 targets were identified to be closely related to diabetic peripheral neuropathy disease. PPI network mapping identified TP53, MAPK1, JUN, and STAT3 as possible core targets. KEGG pathway analysis showed that these targets are mostly involved in AGE-RAGE signaling pathway in diabetic complications, TNF signaling pathway, and MAPK signaling pathway. The molecular docking results showed that the main chemical components of YQHXTLF have a stable binding activity to the core pivotal targets.  YQHXTLF may act on TP53, MAPK1, JUN, and STAT3 to regulate inflammatory response, apoptosis, or proliferation as a molecular mechanism for the treatment of diabetic peripheral neuropathy, reflecting its multitarget and multipathway action, and providing new ideas to further uncover its pharmacological basis and mechanism of action. Diabetic peripheral neuropathy (DPN) is one of the most common and serious microvascular complications of diabetes which is characterized by pain, sensory abnormalities, and loss of sensation , 2. It hYi-Qi-Huo-Xue-Tong-Luo formula (YQHXTLF) is an in-hospital preparation for the prevention and control of diabetic peripheral neuropathy at Anhui Provincial Hospital of Traditional Chinese Medicine. It consists of 7 Chinese herbal medicines: Astragalus (Huangqi), Radix Angelicae Sinensis (Danggui), Radix et Rhizoma Dioscoreae (Dihuang), Radix et Rhizoma Yanhusuo (Yanhusuo), Radix et Rhizoma Puerariae (Gegen), Radix et Rhizoma Chrysanthemum (Jixueteng), and Radix et Rhizoma Weilingensis (Weilingxian). The combination of all the herbs in this formula can treat both the symptoms and the root cause of pain by treating Qi and blood together, which can benefit Qi and invigorate blood, as well as promote circulation and relieve pain. Preliminary clinical studies have shown that this formula can alleviate abnormal skin sensation, numbness, and tingling in the limbs and improve motor nerve conduction speed and sensory nerve conduction speed in DPN patients . Basic rNetwork pharmacology is a new discipline involving the analysis of drug-target-disease network associations . It provIn this study, we use network pharmacology as a tool to further analyze the possible targets, molecular mechanisms, biological processes, and pathways of YQHXTLF for the treatment of DPN. We modeled the interrelationship between the targets of DPN and elucidated the synergistic mechanism between the active components of Chinese medicine, providing insights into the interrelationship and changes between Chinese medicine and diseases from the perspective of biological networks, which provided new possibilities and directions for the treatment of DPN. The flow chart of this study is shown in https://www.tcmspw.com) analysis platform [https://www.uniprot.org/) with the species “homo sapiens.”The Traditional Chinese Medicine Systems Pharmacology , the OMIM database (https://www.omim), the Pharmgkb database (https://www.pharmgkb.org/), the TTD database (http://bidd.nus.edu.sg/group/cjttd/), and the Drugbank data (https://www.drugbank.ca/). For the targets in GeneCards, only those with a score ≥ 10 were chosen. The targets obtained from the above four databases were then integrated to construct a DPN-related target set.We searched several important databases with the keyword "diabetic peripheral neuropathy", including the GeneCards database (https://www.cytoscape.org/) [The overlapping targets were considered to be the common targets of YQHXTLF and DPN. Cytoscape   was thenThe intersecting gene targets of YQHXTLF and DPN were imported into the STRING protein interaction analysis platform, and the protein classification was set to “homo sapiens” with a maximum confidence level of ≥0.7, hiding the unlinked nodes in the network. Protein interaction network analysis was performed, and the TSV file was downloaded. Cytoscape 3.6.1 software was then imported to construct protein-protein interaction (PPI) network maps. The hub targets in the PPI network were screened using the cytoHubba plugin, with darker node colors representing higher scores . The Molp value < 0.05 and q value < 0.05 were considered statistically significant.Gene Ontology (GO) functional analysis is mainly used to describe the function of gene targets, including biological process, cellular component, and molecular function. KEGG enrichment analysis can obtain the signal pathways enriched by the common targets of YQHXTLF and DPN. By using R 3.6.3 software-related R packages , GO enrichment analysis and KEGG pathway enrichment analysis were conducted for the intersection target genes of YQHXTLF-DPN. According to Fisher's test, a https://pubchem.ncbi.nlm.nih.gov/) as the small-molecule ligand file for molecular docking. The core target protein receptor structure file was obtained from the PDB online database (https://www.rcsb.org). The CB-Dock software was used for molecular docking, and the score of the molecular docking complexes was calculated using the Vina program to evaluate their binding activity.Molecular docking validation is achieved through the CB-Dock server , a dockiThe active ingredients were screened by searching the TCMSP database with a threshold value of OB ≥ 30% and DL ≥ 0 : 18. A total of 86 active ingredients derived from YQHXTLF were screened. Among them, 20 ingredients were from Astragalus, 2 from Radix Angelicae Sinensis, 4 from Radix Puerariae, 24 from Spatholobi Caulis, 2 from Rehmanniae Radix, 7 from Radix Clematidis, and 49 from Corydalis Rhizoma. The search for their corresponding targets showed that 86 compounds acted on 229 targets. The basic information of the 86 active compounds is shown in Using the keyword “Diabetic peripheral neuropathy,” the GeneCards website screened 1521 genes related to diabetic peripheral neuropathy, the OMIM database screened 118, the Pharmgkb database retrieved 198, the TTD online website retrieved 32, and the Drugbank database retrieved 20. After removing duplicate targets and standardized gene names, a total of 1736 DPN potential action targets were screened.https://hiplot.com.cn) was used to show the intersection of TQHXTLF and DPN targets, and Venn diagrams were drawn to obtain a total of 110 candidate targets for TQHXTLF and DPN, as shown in The Hiplot research data visualization platform  was used to evaluate the bonding strength between the docked molecules, and the value of the Vina score indicated some binding activity between the proteins and the compounds. The smaller the binding energy (Vina score), the higher the affinity of the receptor and ligand and the more stable the binding of the compound to the target site. The binding energies of the four active ingredients and the corresponding target proteins were all less than -5.0, indicating good docking and high binding activity . MoleculκB to reduce apoptosis [β and TNF-α, thereby inhibiting the neuroimmune activation of microglia and alleviating the progression of diabetic neuropathy [In this study, we used network pharmacology and molecular docking techniques to analyze the potential molecular biological mechanisms of YQHXTLF for the treatment of DPN. We firstly screened the active ingredients of YQHXTLF using the criteria of OB ≥ 30% and DL ≥ 0.18 and obtained 94 compounds, 229 potential targets, and 110 targets overlapping with DPN to construct a compound-disease target regulatory network. Through the network analysis, 81 components in this prescription may act on 110 targets to exert the therapeutic effect. These identified compounds, particularly the four compounds including quercetin, luteolin, kaempferol, and formononetin, were linked to more than 10 targets, indicating that these compounds might play a vital role in the process of DPN treatment. Quercetin is a flavonoid which is widely found in nature and has been proved to have a great effect on antibacterial, anti-inflammatory, and antiallergic therapy . Quercetpoptosis . Quercetpoptosis , 37. Lutpoptosis . Luteolipoptosis . Kaempfepoptosis , 41. It uropathy . Formonouropathy . Formonouropathy , 45. TheκB and tissue-type fibrinogen activator inhibitor-1 (PAI-1) expression, further activating inflammatory factors, transforming growth factor-β (TGF-β), and vascular endothelial growth factor (VEGF), leading to altered vascular function and peripheral neurological microangiopathy [α (TNF-α) is a proinflammatory factor involved in peripheral nerve injury. It stimulates monocytes and endothelial cells to secrete IL-1β and IL-6 and other inflammatory factors, which have toxic effects on neurons and glial cells and lead to demyelination [α inhibits nitric oxide synthase (NOS) activity in vascular endothelial cells, resulting in reduced NO-induced vasodilation, which leads to endothelial dysfunction and neurotrophic vascular damage and induces neuropathy [α can activate the c-jun amino-terminal kinase JNK signaling pathway, leading to apoptosis [The exact etiology of DPN is still unclear, and its pathogenesis may be related to mitochondrial dysfunction and oxidative stress, polyol pathway activation, advanced glycosylation end products, and endoplasmic reticulum stress due to prolonged and severe hyperglycaemia , 47. In giopathy , 53. Numgiopathy , 55. STAgiopathy . Activatgiopathy . MAPK isgiopathy . MAPKs cgiopathy . Tumor nlination . TNF-α iuropathy . In addipoptosis . TP53, cpoptosis . TP53 hapoptosis , and TP5poptosis , so it iIn summary, this study analyzed the potential molecular biological mechanisms of YQHXTLF in the treatment of DPN throughnetwork pharmacology and molecular docking approach. The results showed that the active components of YQHXTLF in DPN treatment were composed of 81 compounds, among which quercetin, luteolin, kaempferol, and formononetin were the important active components. Moreover, a total of 110 target genes were screened, among which TP53, MAPK1, JUN, and STAT3 are possible core targets. The pathways involved in the treatment of DPN by YQHXTLF may be related to the AGE-RAGE signaling pathway, TNF signaling pathway, and MAPK signaling pathway, reflecting the multicomponent, multitarget, and multipathway biological properties of YQHXTLF. However, the network pharmacology is only a reasonable prediction of the mechanism of action of the herbal compound for the treatment of DPN based on the data mining perspective, which can provide a reference for the study of its therapeutic mechanism. We will be followed by animal and clinical experiments to validate the screened targets based on this prediction analysis, to provide more scientific evidence for its clinical application.


Pathway enrichment analysis (PEA) is a well-established methodology for interpreting a list of genes and proteins of interest related to a condition under investigation. This paper aims to extend our previous work in which we introduced a preliminary comparative analysis of pathway enrichment analysis tools. We extended the earlier work by providing more case studies, comparing BiP enrichment performance with other well-known PEA software tools.PEA uses pathway information to discover connections between a list of genes and proteins as well as biological mechanisms, helping researchers to overcome the problem of explaining biological entity lists of interest disconnected from the biological context.We compared the results of BiP with some existing pathway enrichment analysis tools comprising Centrality-based Pathway Enrichment, pathDIP, and Signaling Pathway Impact Analysis, considering three cancer types , for a total of six datasets  obtained from the The Cancer Genome Atlas and Gene Expression Omnibus databases. We measured the similarities between the overlap of the enrichment results obtained using each couple of cancer datasets related to the same cancer.As a result, BiP identified some well-known pathways related to the investigated cancer type, validated by the available literature. We also used the Jaccard and meet-min indices to evaluate the stability and the similarity between the enrichment results obtained from each couple of cancer datasets. The obtained results show that BiP provides more stable enrichment results than other tools.The online version contains supplementary material available at 10.1186/s12859-021-04297-z. Over the past decade, we have witnessed an increase in the production and availability of omics data aided by the continuous development and refinement of Next Generation Sequencing (NGS), Genome-Wide Association Studies (GWAS), gene expressions, and SNP microarrays platforms, also known as High-Throughput (HT) methodologies. Their ability to produce a massive amount of data has spurred the development of several pipelines for data analysis –4. SeverBiological pathways are human representations of the existent biomolecules’ interactions regulating cellular functions in healthy and diseased conditions and how cells can interact with the external environment. Biological pathways are networks where nodes represent biomolecules, and edges represent the interaction among them. The pathway representation as a network has contributed to the development of several network alignment , and deeBiological pathways are fundamental in analyzing, contextualizing and interpreting omics data. The main pathway categories are three: Signalling Pathways, Metabolic Pathways and Regulatory Pathways. Several pathway databases including Kyoto Encyclopedia of Gene and Genome (KEGG) , MetabolMany molecular and cell biologists face a common question in their research: how to link pathways to a specific genes or proteins list? The availability of biological information in a digital format enables the automatic elaboration of these pathway databases for different knowledge discovery tasks.In particular, Pathway Enrichment Analysis (PEA) is a well-established approach to gain insight into the underlying biological mechanism of a differentially expressed list of genes and proteins of interest. PEA can use the information in pathway databases to elucidate the link between the genes and proteins of interest and the biological mechanisms affected in the biological pathways. The three principal categories of PEA methods are: (1) Over Represented Analysis (ORA); (2) Gene Set Enrichment Analysis (GSEA); and (3) Topological Enrichment Analysis (TEA).Self-contained null hypothesis methods assume that no genes in the gene list are associated with the phenotype. Competitive null hypothesis methods assume that genes in the gene list have a higher probability of being associated with the phenotype than genes outside the gene list. In contrast, the TEA methods need a genes and proteins list along with the network topology information.The first two categories of methods perform enrichment analysis using a list of genes, proteins, SNPs or mRNA as input. Moreover, GSEA methods include self-contained or competitive null-hypothesis approaches. PEA methods endeavor to help researchers decipher biological entities of interest disconnected from the biological context, expediting their findings’ validation. Analyzing lists of biological entities at the functional pathway-level can provide more explanatory power than analyzing a list of independent entities.Hypergeometric function [Fisher’s Test [Several PEA software tools are available, among those: BioPAX-Parser (BiP) , Centralfunction  to assesr’s Test . CePa anr’s Test . In contr’s Test  evaluatiBiP and pathDIP can perform pathways enrichment analysis using a list of genes and proteins of interest without providing any additional information or data manipulation in the phenomena under investigation. In contrast, CePa and SPIA require users to provide additional network information, such as the interactions among the genes and proteins of interest and other network topology information, to perform TEA.CePa, pathDIP, and SPIA allow performing PEA only using the previously collected and integrated pathway information. Conversely, BiP can perform PEA employing users’ downloaded pathways information from KEGG, Reactome or any other pathway database compliant with the Biological Pathway Exchange (BioPAX) format . In thisWe evaluate the BiP’s pathway enrichment effectiveness using three TCGA cancer data sets and three GEO gene expression data sets, related respectively with colorectal (CC), endometrial (EC), and thyroid (TC) cancer. We used the six gene lists along with KEGG and Reactome pathway databases to evaluate the enrichment results. This way, it is possible to assess if the enrichment results obtained from each tool are similar, when varying the input gene list and keeping the database unchanged.Although some works, such as , 29, usep value p valueJaccard and meet-min similarities indices to evaluate the tools’ stability . The obtained results proved that BiP is more solid in terms of number and similarity of enriched pathways than other existing tools. Also, BiP can identify some well-known cancer pathways omitted by other compared software frameworks.For each couple of cancer enrichment results we computed the pathway overlap (intersection) and the union between the two enrichments, selecting the first top 10 pathways  and Bonferroni. BiP can enrich lists of genes and proteins using KEGG, Reactome and other available pathway databases compliant with the BioPAX format. BiP can be freely downloaded as a stand-alone application at [BiP  can perfation at .t-test. The background gene list is the list of genes of a specific microarray platform. The differential gene list and the background gene list must use the same identifiers to represent the gene symbol or protein identifier. CePa is an R application freely available at [CePa  performslable at .Fisher’s Exact Test, providing correction for multiple hypothesis testing through two different methods: Bonferroni and FDR. pathDIP integrates pathway data from 22 databases, including KEGG and Reactome. pathDIP is publicly available at [pathDIP  is an inlable at .p value, combining the enrichment and perturbation p values. SPIA needs a set of differentially expressed genes and their fold changes and pathways topology to compute PEA from the condition under investigation. Differentially expressed genes need to be in Entrez gene IDs format. SPIA performs PEA by using pathway information from the KEGG database. SPIA is an R program freely available at [Signaling Pathway Impact Analysis (SPIA)  combineslable at .The main differences among the surveyed PEA frameworks are summarised as follows. CePa and SPIA are all available as R packages, meaning the user needs some basic programming knowledge. Conversely, BiP efficiently performs PEA through a simple graphical user interface (GUI), that allows loading only a list of genes or proteins, selecting which pathway database use, setting the p value significance threshold and choosing the destination folder where to store the results. The only requirement to use BiP is to have previously installed Java, no additional libraries are necessary. pathDIP is a web application that allows performing enrichment analysis through a graphical interface just by loading a list of protein or gene identifiers and returning the pathways in which those identifiers are involved.PEA software frameworks are available as stand-alone software, web-based applications or program libraries. The first two categories are usually more convenient to use, as they do not require analytical skills or programming abilities. Program libraries are coded in C, Java, R and Python languages, allowing them to automate the process through scripting analysis pipelines. User skills and the cost-benefit ratio of time invested in orchestrating everything necessary to run the analysis may influence selecting software platforms and program libraries. In the following we present a description of some well-known PEA software frameworks.Fisher’s Exact Test, along with Bonferroni and FDR correctors. Finally, SPIA calculates pathway enrichment combining the classical enrichment analysis with the measure of the perturbation on a given pathway under a given condition, evaluating a node’s neighbourhood.All the PEA software tools are different in terms of pathway enrichment calculation. BiP employs the Hypergeometric Test, along with FDR and Bonferroni correctors to calculate pathway enrichment. CePa computes pathway enrichment using topological information  and gene-set information. pathDip computes pathway enrichment using the BiP and pathDIP can perform pathways enrichment analysis using a list of genes and proteins of interest without providing any additional information or data manipulation in the phenomena under investigation. In contrast, CePa and SPIA require users to provide additional network information, such as the interactions among the genes and proteins of interest and other network topology information, to perform TEA.Finally, BiP is the only enrichment analysis tool that allows users to select the pathway database that has to be used to enrich the condition under investigation.All the PEA software tools and the related algorithms are well established in providing biological context in -omics studies, particularly to analyze the biological molecules, where summarizing the overall biology of a particular disease by pathways enhances interpretability. BiP, CePa, pathDIP and SPIA are some of the available gold standards tools in pathway enrichment analysis.Most PEA tools evaluate enrichments in a very similar manner; we chose CePa, pathDip and SPIA as representative PEA tools due to their popularity and ability to test a breadth of data sources similar to that of BiP.The Genomic Regions Enrichment of Annotations Tool (GREAT)  is a webg:Profiler  primary COAD [GSE41011 [THCA [GSE65144 [UCEC [GSE63678 [TCGA and GEO databases respectively, to test BiP.We downloaded three couples of cancer datasets: colorectal cancer (COAD  and GSE4GSE41011 ), thyroiGSE65144 ), and en44 [UCEC , and GSEThe Gene Expression Omnibus (GEO) database is a public functional genomics database including high-throughput gene expression, chips, and microarrays data.The Cancer Genome Atlas (TCGA) makes publicly available molecular and clinical information for more than 33 different types of human cancers, including exome , single nucleotide polymorphism (SNP), DNA methylation, transcriptome (mRNA), microRNA (miRNA) and proteome. TCGA data are accessible through the NCI Genomic Data Commons (GDC) data portal, GDC Legacy Archive, and the Broad Institute’s GDAC Firehose.UCEC dataset comprises 560 cases and 22, 162 probes. The GSE41011 dataset contains 30, 968 probes and 19 cases, GSE65144 includes 12 cases and 54, 675 probes and, finally, GSE63678 comprises 7 cases and 22, 277 probes. Here cases indicate the positive samples, while probes indicate the dimension of the microarray .The COAD dataset contains 750 cases and 21, 224 probes, THCA includes 681 cases and 13, 564 probes, Table  We downloaded biological pathways database in BioPAX format for each available organism from Reactome , but we Reactome is an open-source freely available curated relational database of signaling and metabolic molecules and their relations organized into biological pathways and processes. Reactome uses pathway steps that indicate any event in biology that changes the state of a biological molecule. Molecules such as nucleic acids, proteins, complexes, and small molecules participating in reactions form a network of biological interactions called pathways.KEGG is a database that integrates genomic, chemical and systemic functional information to analyze gene functions. KEGG contains various types of data classified as systems information, genomic information, chemical information and health information. The central KEGG element is the molecular network, representing systemic functions of the cell and the organism.Mutated genes (MGenes) were downloaded from cBioPortal , 48 for The cBioPortal for Cancer Genomics provides a Web resource for graphically analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, gene expression, etc data. The cBioPortal allows to integrate multiple data types at the gene level and then query for the presence of specific biological events in each sample . Data integration includes CNAs, mRNA and microRNA (miRNA) expression, and so on.p value by reducing the type I error in the null hypothesis due to multiple comparisons.GEO2R is an online framework with which users can separate the samples into groups and select the differential expressed genes (DEG). We used the False Discovery Rate (FDR) corrector to adjust the Both downloaded genes and pathways data sets have been used as input for the four PEA frameworks to get new biological insights.We evaluated each tool’s capability to enrich the same relevant pathways using different data sets related to the same condition. Thus, it was not needed to perform any batch effect removal.Additional file Hypergeometric function Equation mentclass2pt{minimi-th smallest p value out the total number of the p value for the performed experiment, N represents the number of performed tests, and i is the number of accepted p value using the i-th p value threshold.k is the number of performed individual tests.To balance the errors due to multiple tests, we implemented the False Discovery Rate (FDR) corrector along with the Bonferroni’s corrector. Equations  To assess the BiP abilities to detect essential pathways influenced by the genes under investigation, we compared the pathway enrichment results by using the two gene lists from the same disease obtained by BiP with respect to those provided by CePa, pathDIP, and SPIA, using the following similarity indices:Jaccard similarity index (JI) measures the percentage of similarity between the two enrichment sets. Equation  (1) meet-min index (mi) [meet-min index is reported in Eq. mi index ranges between 0, that indicates no containment between the two sets, and 1 that indicates a perfect containment of a set into another.(2) dex (mi)  evaluateWe chose to use Jaccard and meet-min indices as similarity measures for the following reasons. The Jaccard index is a common trend to compare populations by determining what percent of objects identified were present in both populations, which means that the Jaccard index evaluates the similarity of two sets. Meet-min is a measure that can assess the similarity (the common objects into the two sets). The containment between two sets or one set contains the other, which means that the meet-min can evaluate both similarity and containment between two sets.This section compares the BiP, CePa, pathDIP and SPIA capability to perform pathway enrichment analysis using the six gene lists obtained from the cancer datasets in Table  We compared BiP with pathDIP , CePa 1 and SPIACOAD and GSE41011- and using the KEGG database.In this subsection, we present the pathway enrichment results obtained by all the software tools by analyzing the two gene lists related to Colorectal Cancer (CC) -p value COAD and GSE41011 genes lists  pathways . Bernal et al. [“telomeres” in contributing to genomic instability in cancer. Tong et al. [“ChREBP” plays a critical role in redirecting glucose metabolism to anabolic pathways as well as suppressing p53 activity. Esteban-Jurado et al. [“Fanconi anemia DNA damage repair pathway” performs an important role in germline predisposition to colorectal cancer. Fernández-Briera et al. [“NCAM” (but not the neurite outgrowth) is responsible of worse prognosis and lymph nodes metastasis in colorectal cancer. To the best of our knowledge, we could not find any evidence for the “Switching of origins to a post-replicative state” pathway.CePa enrichment results include the following KEGG pathways. Authors show in  that Kloes in CC , 60. Sates in CC  describer, while  highlighs et al.  reviews l et al.  evaluateg et al.  shows tho et al.  evaluatea et al.  describep value COAD and GSE41011 genes lists ”, “Human T-cell leukemia virus 1 infection”, “Cardiac muscle contraction” pathways and the endometrial cancer. Konno et al. [“Cellular senescence” pathway in suppressing proliferating, and stem cell-like phenotype of aggressive endometrial cancer cells. Nevadunsky et al. [“oxidative phosphorylation” in both endometrial and non-endometrial cancer types. Wong et al. [“apoptosis” in endometrial cancer. Huo et al. [“Mineral absorption” pathway in the underlying biological mechanisms driving the tumorgenesis of endometrial cancer.Among the pathways enriched by pathDIP we could not find any link for the o et al.  delineaty et al.  assessesg et al.  demonstro et al.  describeSPIA did not enrich any pathway from both EC genes lists , the extracellular-signal-regulated kinases in colorectal cancer. Mita et al. [“Cell Cycle, Mitotic”, “Cell Cycle” and “Cell Cycle Checkpoints” pathways, are related to colorectal cancer. Furthermore, survivin-transcription is controlled by specific sequences in the promoter region, and it increases during “Mitotic G1-G1/S phases” [“Mitotic G2-G2/M phases” [p value relevance. A recent manuscript published by Reilly et al. [“DNA repair” pathway in colorectal cancer enriched by BiP. Finally, [“Cellular responses to stress” pathway in colorectal cancer.Hughes et al.  describen et al. , Tomonagn et al.  describeg et al.  describea et al. , Kuerbita et al.  desribe  phases” , 105, an phases” , 106, oty et al.  describeFinally,  describeCOAD and GSE65144, we identified 59 and 704 significant pathways ”, “RNA Polymerase II Transcription” and “Generic Transcription Pathway”) with endometrial cancer. Takai et al. [“Signaling by Receptor Tyrosine Kinases” and endometrial cancer. Yu et al. [“ERBB2 and ERBB4 signaling pathways” and the endometrial cancer, while [“Rho GTPases signaling” in cancer. Deregulation of the cell cycle is famously linked to cancer development [Mehlen et al.  describen et al. , Mäkinenn et al.  report ti et al.  shows thu et al. , Change u et al. , 119 pror, while  reviews elopment , while telopment .UCEC and GSE63678, we identified 22 and 215 significant pathways  with endometrial cancer. At the best of our knowledge, it was not possible to identify any link between “Metabolism of proteins”, “Innate Immune System”, and “Translation” pathways with endometrial cancer. [“immune system” in endometrial cancer development.Baylin et al. , and Mäk cancer.  delineatTHCA and GSE65144, we identified 374 and 921 relevant pathways . Ząbczyńska et al. [Nucera et al.  reports g et al.  describer, while  describea et al.  describeTHCA and GSE65144, we identified 28 and 405 relevant pathways . We couldn’t find a link between the “Metabolism”, “metabolism of proteins”, “Immune System”, “Post-translational protein modification”, “Neutrophil degranulation”, “Disease” pathways and thyroid cancer. describeTo compare the pathway enrichment results obtained from BiP with CePa, pathDIP, and SPIA, we used the same six gene lists obtained from the cancer datasets listed in Table p value For each couple of enrichments, we measured the total number of the enriched pathway with Jaccard index able to measure the similarity between two sets and the meet-min index that can assess the containment property between two groups. High Jaccard index values indicate that the two enrichments are similar, while high values of meet-min index suggest that the smallest set is contained in the biggest. Figures  Jaccard and meet-min indices values respectively for each software tool applied using KEGG or Reactome, to each cancer data set.To measure the similarity between the pairs of pathway enrichments obtained from the investigated cancer genes lists, we adopted the Jaccard and meet-min indexes values in almost all comparisons, revealing that BiP is more stable than the other methods. Only in two cases, CePa obtains better Jaccard values when enriching CC and TC gene lists. Result due to the relatively similar number of enriched pathways in the two enrichment results , endometrial (EC), and thyroid (TC) cancers. The goal of PEA was to evaluate the capability of BiP to identify relevant pathways for the three types of cancer under investigation with respect to the other tools.p value insulin signaling pathway , all of which are well recognized pathways whose dysregulation can contribute in accelerate the risk of CC development and progression. It is worth to note that, in the intersection between the top 10 enriched pathway from both COAD and GSE41011 data sets, only BiP identified as novel candidate risk pathways the Metabolic pathway , which p value Thyroid hormone pathway  or an integrative pathway approach (pathDIP) led to more biologically consistent results.The results obtained by BiP show good performance in terms of the number of relevant enriched pathways, especially compared to SPIA. BiP shows higher Jaccard and meet-min indexes values in almost all similarity comparisons results obtained from different gene lists of the same cancer types than CePa, pathDIP, and SPIA, suggesting BiP’s efficacy to achieve more reliable results. Of course, part of the boost in performance may be due to using more recently update versions of Reactome and KEGG databases, considering the difference in the size of the two databases.As future works, we are improving the enrichment approach to limit the number of enriched pathways, ensuring at the same time the best possible result accuracy and improving their effectiveness. Finally, we will extend the BiP parsing capability to make it compatible with as many pathway representation formats as possible, such as SBML, GMTL, and XML-like.Additional file 1. Pathway Enrichment Analysisuser guide illustrating how to download genes or proteins data setsfrom GEO and TCGA databases and highlighting how to use thedownloaded data sets with the surveyed PEA software tools.Additional file 2. The list of relevant enriched pathways(p value Additional file 3. The list of relevant enriched pathways(p value Additional file 4. The list of relevant enriched pathways(p value Additional file 5. The list of relevant enriched pathways(p value


In the present study, we aimed to explore whether common genetic targets or signaling pathways existed in chemical cystitis.Gene Expression Omnibus (GEO) database was used to search the related gene expression profiles. The differentially expressed genes (DEGs) were identified by using GEO2R. The DAVID 6.8 Beta and R software were used to perform Kyoto Encyclopedia of Genes and Genomes pathway analysis and Gene Ontology function analysis of DEGs. The protein‐protein interaction network was constructed by STRING 11.0 to reveal the potential gene interactions. The expression of cyclin‐dependent kinase 1 (Cdk1) at the messnger RNA (mRNA) and protein levels was examined by real‐time polymerase chain reaction (PCR) and Western blot analysis analysis, respectively.The GEO database was searched, and the gene expression profiles of GSE55986 and GSE68539 were downloaded. A total of 262 DEGs and 356 DEGs were identified from GSE55986 and GSE68539, respectively. We found that the p53 signaling pathway might play a key role in the development of chemical cystitis, and Cdk1 acted as a crucial gene in the p53 signaling pathway. Moreover, the experimental results of real‐time PCR and Western blot analysis analysis demonstrated that the expression of Cdk1 at the mRNA and protein levels in cystitis tissues was significantly increased in different animal models of chemical cystitis compared with the control group.Cdk1 might be a potential pathogenic genetic target for chemical cystitis. Cdk1 shows to be a potential pathogenic genetic target for chemical cystitis Cdk1cyclin‐dependent kinase 1CYPcyclophosphamideDEGsdifferential expression genesGEOGene Expression OmnibusICinterstitial cystitisLPSlipopolysaccharide1Chemical cystitis becomes increasingly common with economic and social development. It is an important type of nonbacterial bladder inflammation. The most familiar clinical symptoms of chemical cystitis are unexplained urinary frequency and pelvic pain.As classic animal models of chemical‐induced cystitis, ketamine‐induced model, and cyclophosphamide (CYP)‐induced model can reflect the pathological progression, which are widely applied in the related basic research of nonbacterial bladder inflammation.Some trials have shown that the mouse model induced by lipopolysaccharide (LPS) is reliable as it can also reflect the pathology of chemical‐induced cystitis.22.1The series matrix files of GSE55986 and GSE68539 were downloaded from the GEO database. The platform of GSE55986 was GPL6246 (MoGene‐1_0‐st) Affymetrix Mouse Gene 1.0 ST Array (transcript [gene] version), while the platform of GSE68539 was GPL15523 Phalanx Mouse OneArray Ver 2. Three normal samples  and three CYP‐induced samples  were extracted from the GSE55986 profile. Eight normal samples  and eight ketamine‐induced samples  were extracted from the GSE68539 profile. R software was adopted to evaluate the data quality.2.2p values  and Log fold change (|logFC|) were important statistical indexes in the GEO2R results. If the adj. p value was less than .01 and |logFC| was greater than 1, the genes were identified as DEGs. Moreover, R ggplot2 and pheatmap were used to present these DEGs in the volcano plot and heatmap.DEGs were screened by GEO2R, which allowed users to compare two or more samples of the GEO series to identify genes expressed under different experimental conditions. The adjusted 2.3p < .01 was considered statistically significant.The DAVID 6.8 Beta was used for functional enrichment analyses. First, the DEGs extracted from two GEO series were respectively up‐loaded to DAVID 6.8 Beta. Then the KEGG pathway analysis and GO enrichment analysis of DEGs were performed by the DAVID functional annotation tool. The results were expressed in bubble plots by using R software. 2.4Search Tool for the Retrieval of Interacting Genes (version 11.0) is a database of known and predicted protein–protein interactions (PPIs), including direct  and indirect  associations. In our present study, the PPI network of seven co‐DEGs was constructed by STRING 11.0 to reveal the potential gene interactions. To visualize the interactions between the shared gene and other genes involved in the p53 signaling pathway of two animal models, the related genes in the PPI network were presented respectively.2.5Female wild‐type C57BL/6 mice (20–25 g) were obtained from HFK Bio‐Technology Company  and housed in the SPF‐grade animal facility under the controlled conditions as follows: 70% ± 4% relative humidity, 24 ± 1°C and a 12‐h light/dark cycle. The mice were given free access to tap water and food. All the animal‐related procedures were conducted following the guidelines established by the Institutional Animal Care and Use Committee of Suzhou Ninth Hospital Affiliated to Soochow University (wj2016065), and all animal‐related experiments were performed following the ARRIVE guidelines. In the ketamine‐induced model, the mice were administered with 30 mg/kg/day ketamine (Sigma‐Aldrich) via intraperitoneal injection for 12 weeks as previously described.2.6n = 3–5 in each group). Quantitative PCR (qPCR) was performed according to the instructions. Trizol Reagent was obtained from Invitrogen and used to isolate total RNA. Subsequently, 1 µg purified RNA was reversely transcribed into complementary DNA using Revert Aid First Strand cDNA Synthesis Kit (Fermentas). qPCR was carried out on an ABI 7500 system (Applied Biosystems) by using SYBR green PCR Master Mix (Invitrogen). The relative expression of Cdk1 was calculated using the 2‐∆∆Ct method, and glyceraldehyde 3‐phosphate dehydrogenase was selected as a housekeeping gene.Cystitis tissues were harvested after animal experiments, immediately placed on dry ice, and stored at −80°C (2.7Mouse cystitis tissue was homogenized in a lysis buffer supplemented with 1% protease inhibitor cocktail. The protein concentration was determined by a BCA protein assay kit (Pierce). Equal amounts of proteins were subjected to sodium dodecyl sulfate‐polyacrylamide gels using 10% gels and electrotransferred onto nitrocellulose membranes. The blots were blocked in 5% (wt/vol) nonfat dry milk in 0.1% Tris‐buffered saline/Tween 20 (TBST) for 1 h, followed by incubation with primary antibodies against CDK1 (Invitrogen) at a dilution 0.5 µg/ml and β‐actin  at a dilution of 1:1000 overnight at 4°C. The membranes were washed with TBST and incubated with secondary antibodies (TIANGEN) for 1 h. β‐actin was used as a loading control. Immunoreactive bands were visualized using ECL chemiluminescence (Thermo Company), and the band densities were analyzed using ImageJ software .2.8SEM. Statistical analysis was performed by SPSS 23.0 Statistics software using Student's t test. p < .05 was considered statistically significant.All data were presented by mean ± 33.1A total of 262 DEGs and 356 DEGs were identified from GSE55986 and GSE68539 using the GEO2R online tool, respectively. CYP‐induced DEGs and ketamine‐induced DEGs were respectively visualized in the volcano plot Figure . Venn an3.2p value was ≤.01. In our present study, 262 DEGs extracted from GSE55986 were significantly enriched in 64 BPs, while 356 DEGs extracted from GSE68539 were significantly enriched in two BPs. The most enriched GO terms of 262 DEGs were respectively related to the positive regulation of cyclin‐dependent protein serine/threonine kinase activity and microtubule bundle formation in BP, protein binding and kinase activity in MF, and replication fork and condensed nuclear chromosome in CC , molecular functions (MFs), and cellular components (CCs). According to the results of functional enrichment analyses, the GO terms were significantly representative if the C Figure . The mosC Figure . Besidess Figure , while 3s Figure .3.3KEGG pathway analysis was performed in DAVID using the bioinformatic approach. A total of 18 KEGG pathways were detected in both GEO series. There was only one shared KEGG pathway between the two series, namely the p53 signaling pathway. Venn diagram showed the only KEGG pathway by Venn analysis.3.4Venn analysis was performed to analyze the related genes in the p53 signaling pathway of the two models Figure . Subsequ3.5We studied the effects of Cdk1 expression in different animal models. Gene expression of Cdk1 was validated by qPCR and Western blot analysis analysis. Figure 4The unknown pathogenesis of chemical cystitis remains a challenge for clinicians, and a better understanding of the molecular mechanism of this disease is essential for developing more effective treatments.Our bioinformatic study showed that the p53 signaling pathway might be the key molecular pathological mechanism no matter in the ketamine‐induced model or CYC‐induced model. Our data indicated that the p53 signaling pathway played a key role in the disease progression of chemical‐induced cystitis. It is well known that p53 is a type of tumor suppressor protein.According to our study, Cdk1 was the core gene in the p53 signaling pathway, and some previous studies have revealed that Cdk1 can phosphorylate p53.5Collectively, our bioinformatic analysis of two animal models revealed that the p53 signaling pathway was a key biological pathway in the disease progression of chemical‐induced cystitis. Moreover, Cdk1 was identified as a potential pathogenic genetic target for chemical cystitis according to bioinformatic analysis and experimental study. Taken together, our findings provided valuable insights into the molecular mechanisms of chemical cystitis.The authors declare that there are no conflict of interests.Kun Wang and Huaping Du conceived and designed the study. Dong Xue and Kun Wang performed the experiment and prepared Figures All the procedures of animal experiments were in accordance with the guidelines established by the Institutional Animal Care and Use Committee of Soochow University.


Phellodendron amurense Rupr. (PAR) and Atractylodes lancea (Thunb.) DC. (ALD), can be used in the treatment of RA. Currently, most studies focus on the anti-inflammatory mechanism of PAR and ALD and are less focused on their coordinated molecular mechanism. In this research, we established an integrative pharmacological strategy to explore the coordinated molecular mechanism of the two herbs of Ermiao Powder in treating RA. To explore the potential coordinated mechanism of PAR and ALD, we firstly developed a novel mathematical model to calculate the contribution score of 126 active components and 85 active components, which contributed 90% of the total contribution scores that were retained to construct the coordinated functional space. Then, the knapsack algorithm was applied to identify the core coordinated functional components from the 85 active components. Finally, we obtained the potential coordinated functional components group (CFCG) with 37 components, including wogonin, paeonol, ethyl caffeate, and magnoflorine. Also, functional enrichment analysis was performed on the targets of CFCG to explore the potential coordinated molecular mechanisms of PAR and ALD. The results indicated that the CFCG could treat RA by coordinated targeting to the genes involved in immunity and inflammation-related signal pathways, such as phosphatidylinositol 3‑kinase/protein kinase B signaling pathway, mitogen-activated protein kinase signaling pathway, tumor necrosis factor signaling pathway, and nuclear factor-kappa B signaling pathway. The docking and in vitro experiments were used to predict the affinity and validate the effect of CFCG and further confirm the reliability of our method. Our integrative pharmacological strategy, including CFCG identification and verification, can provide the methodological references for exploring the coordinated mechanism of TCM in treating complex diseases and contribute to improving our understanding of the coordinated mechanism.As a systemic inflammatory arthritis disease, rheumatoid arthritis (RA) is complex and hereditary. Traditional Chinese medicine (TCM) has evident advantages in treating complex diseases, and a variety of TCM formulas have been reported that have effective treatment on RA. Clinical and pharmacological studies showed that Ermiao Powder, which consists of  Rheumatoid arthritis (RA) is autoimmune arthritis with the characteristics of systemic inflammation, persistent synovitis, and autoantibodies . It is rTraditional Chinese medicine (TCM) has a long history of development, and it has accumulated a large number of theories and rich clinical experience. TCM can effectively treat RA with high safety and few adverse effects . IncreasPhellodendron amurense Rupr. (PAR) (15 g) and Atractylodes lancea (Thunb.) DC. (ALD) (15 g). Due to the function of anti-inflammatory, EMP has been widely used in treating RA. More studies focused on analyzing the anti-inflammatory mechanism of PAR and ALD separately or the anti-inflammatory effects of EMP. EMP is comprised of two herbs: in vitro experiments. The results demonstrated the coordinated mechanism of CFCG from PAR and ALD in treating RA. Our study’s principal novelty is the design of a reliable optimization model and reverse-engineering strategy, which can obtain the core components group with the smallest number of components and the most extensive coverage of function from EMP. It could provide a methodologic reference for exploring the coordinated mechanism of TCM in treating complex diseases and prescriptions optimization.Toward this end, we explored the coordinated molecular mechanisms of EMP in treating RA through a network pharmacology method. We screened active components from the components of PAR and ALD obtained from the Traditional Chinese Medicine Database and Analysis Platform (TCMSP), and 126 components that meet Lipinski's rules were retained. Then, the coordinated functional space prediction model was applied to quantify the effect of 126 active components on RA, and we identified the coordinated functional space composed of 85 components. Through the knapsack algorithm, we identified A coordinated functional components group (CFCG) from the 85 active components. We also explored the potential coordinated mechanism of CFCG by the functional enrichment analysis and molecular docking. The results indicated the potential co-functional effect of CFCG, and they can target genes related to inflammation and immunity. Finally, we validated the effect of CFCG through https://tcmsp-e.com/)  . Open Ba-e.com/) .http://www.swissadme.ch/index.php) (From SwissADME (dex.php) , we obtadex.php) .http://sea.bkslab.org/) (http://mips.helmholtz-muenchen.de/hitpick/cgi-bin/index.cgi?content=targetPrediction.html) (SwissTargetPrediction (http://www.swisstargetprediction.ch/) (The target genes of active components were predicted through Similarity Ensemble Approach (SEA) (ab.org/) , HitPickon.html) , and Swiion.ch/) . Then, tThe component-target (C-T) network was constructed based on active components and their target genes by Cytoscape (version 3.6.0) . The CytTo analyze the effect of each active component of PAR and ALD on RA, a coordinated functional space prediction model was constructed to calculate the contribution score of each component to the coordinated functional space as follows:i represents the eccentricity. CompHi represents the eccentricity of each component only in PAR, and CompCi represents components that only consist in ALD. Cedge is the edge count of each component, Tedge is the edge count of all the component’s targets, and Tarj represents the sum eccentricity value of all the component’s targets. NL (Cij) represents the value of min–max normalization to Cij, and NL (dosei) represents the value of min–max normalization to each component’s dose in EMP.In the equation discussed earlier, i represents the component, and j is one of its targets. For each component, CSC represents the contribution score, and CompAccording to the coordinated functional space prediction models, we obtained each component's contribution score. The top 85 components were selected; the sum of their contribution score accounts for 90% of the total contribution score of all components.Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis for targets of components in this study and RA-related genes was performed by R package “clusterProfiler,” respectively .To identify the core coordinated functional components of the top 85 active components that have the greatest effects in EMP, we applied the knapsack algorithm. For the 900 targets of the 85 active components, the target–target interaction was identified according to the protein–protein interaction (PPI) network. Then, the degrees of the 85 active components and the 900 targets were obtained by the Cytoscape plugin NetworkAnalyzer. The 473 targets whose degrees were over the median degree of 900 targets and 85 components were retained.Next, the knapsack algorithm was performed for the 85 components and their 473 targets.i represents the component i that was selected or not, and Di is the sum degree of its targets. N is set to 426, which is the largest number of targets in the knapsack. ni represents the count of targets of component i. fiN represents the largest sum degrees of the targets of components in the knapsack, whereas the number of targets is N. In short, when the number of targets in the knapsack accounts for 90% (426) of the 473 targets, the number of components put in the knapsack is the least, and the sum of the degrees of these 426 targets is the largest. CFCG, which consists of 37 components, was identified.In the formula, khttps://zinc.docking.org/) (https://pubchem.ncbi.nlm.nih.gov) databases (http://www.rcsb.org)   and PubCatabases . The procsb.org) . Auto Docsb.org)  and Autocsb.org)  were usecsb.org)  were useWogonin (≥98% purity by high-performance liquid chromatography) was purchased from Jingzhu Biotechnology Co., Ltd. . Paeonol, magnoflorine (≥98% purity by high-performance liquid chromatography), and ethyl caffeate were purchased from Jiangsu Yongjian Pharmaceutical Co., Ltd. . Moreover, fetal bovine serum and Dulbecco’s modified Eagle’s medium, which were required for the experiments, were obtained from Gibco . Also, LPS was acquired from Sigma-Aldrich Co., Ltd. .2. The culture medium needed to change every 1–2 days. RAW264.7 cells were blown down and passaged at a ratio of 1:2 or 1:3 when they had reached approximately 80% confluence. The cells (2 × 104 per/well) were then seeded in 96-well plates until they reached 80% confluence. Next, the cells were treated with wogonin, paeonol, ethyl caffeate, and magnoflorine for 2 h and treated with LPS (1 μg/ml) for 24 h subsequently.Mouse macrophage RAW264.7 cells were acquired from the cell bank of the Chinese Academy of Sciences . The RAW264.7 cells were cultured in complete Dulbecco’s modified Eagle’s medium with 8% fetal bovine serum, then incubated in a constant temperature incubator at 37°C with an atmosphere of 5% CO5 cells/ml. The cells reached approximately 80% confluence after 24 h of incubation. Then, the cells were treated with various concentrations of wogonin , paeonol , ethyl caffeate , and magnoflorine . Subsequently, we added 10-μl CCK8 (Dojindo) in each well. After incubation for 3 h, cell viability (CCK8 activity) was quantified through the absorbance at 450 nm measured by a microplate reader (Tecan infinite M200).The CCK-8 was used to assay the cell viability. To test the cytotoxicity of four components in our experiment, RAW264.7 cells were placed in 96-well plates with a density of 1 × 10After 2 h of incubation of the RAW264.7 cells with caffeic acid, wogonin, paeonol, ethyl caffeate, and magnoflorine and 24 h of incubation with LPS (1 μg/ml), we collected the culture supernatant and mixed it with total nitric oxide assay kit (Beyotime) for NO assay. Then, the microplate reader (Tecan infinite M200) was used to assay the absorbance at 540 nm.RAW264.7cells were lysed in radioimmunoprecipitation assay lysis buffer  containing protease suppressor. The protein concentration was quantified by the bicinchoninic acid protein assay kit . An equal amount of protein was separated by sodium dodecyl sulfate–polyacrylamide gel electrophoresis and then transferred onto polyvinylidene fluoride membranes . After blocking in 5% blocking in QuickBlock Western  for 10 min, the proteins on the membrane were incubated with following primary antibodies at 4°C overnight: Akt , p-Akt , p38 MAPK , p-p38 MAPK , p44/42 MAPK (Erk1/2) , p-p44/42 MAPK (Erk1/2) , NF-κB p65 , p-NF-κB p65 , and TNF-α . Following four washes in Tris-buffered saline with Tween, proteins were incubated with secondary antibodies for 1 h at room temperature. Antibody signal was detected using Clarity Western enhanced chemiluminescence substrate . The β-actin served as an endogenous reference.post-hoc test was used to compare more than two groups. Results were considered statistically significant if the p-value was <0.05.To compare the anti-inflammatory effects of four components, GraphPad Prism 5 was used for statistical analysis. Student's t-test for the comparison of two groups was utilized to analyze the significance of differences, whereas a one-way analysis of variance followed by a Dunnett SwissTargetPrediction. The C-T networks were constructed by these components and their targets. Then, a coordinated functional space prediction model and knapsack algorithm were applied to demonstrate the co-functional mechanism of components from EMP in treating RA   . We then7.8e−47) . In the vs. PAR own targets, p < 0.000021; 341 targets vs. ALD own targets, p < 0.0036)  . It alsoTo explore the potential effect of the 85 active components, we performed GO and KEGG pathway analysis for RA pathogenic genes, 924 targets of the 126 active components and 900 targets of the 85 active components, respectively. The RA pathogenic genes set contains 281 known pathogenic genes which have been confirmed by at least five previous studies. We found that the targets of 85 components contain 61 of the 281 RA pathogenic genes . The resIt is known that the characteristics of the TCM mechanism are multicomponent and multi-target; the results of our analysis for the co-functional effect of PAR and ALD in EMP demonstrated the phenomenon. We found that 305 of the 85 actives components' targets were shared by components in PAR and others in ALD . The resBased on the knapsack algorithm performed for the 85 components and their 473 key targets, we identified 37 active components as the key coordinated functional components . GO and To further explore the co-functional effects of CFCG, we dissect the results of GO and KEGG pathway enrichment analysis performed on the 426 targets .The results of the GO analysis showed that the targets were enriched in biological processes which play an important role in RA, such as response to oxygen levels (GO:0070482), response to hypoxia (GO:0001666), regulation of innate immune response (GO:0045088), immune response-activating cell surface receptor signaling pathway (GO:0002429), and T cell activation (GO:0042110)  Figure . RA is aThe result of KEGG pathway enrichment analysis also indicated that the targets were involved in pathways related to inflammation and immunity, such as the PI3K-Akt signaling pathway (hsa04151), MAPK signaling pathway (hsa04010), TNF signaling pathway (hsa04668), NF-κB signaling pathway (hsa04064), T cell receptor (TCR) signaling pathway (hsa04660), and cyclic adenosine 3',5'-monophosphate signaling pathway (hsa04024) .To further explore the coordinated mechanism of the CFCG in treating RA, we constructed a comprehensive pathway with four pathways, including hsa04151, hsa04010, hsa04668, and hsa04064 . We defiTo explore the co-functional effect of the CFCG, we also conducted molecular docking for 37 components with a three-dimensional conformer and 131 proteins coded by the 32 target genes . A totalin vitro experiments. We detected the four components’ anti-inflammatory effects through RAW264.7 cells induced by LPS.To further validate the results of the identification of CFCG, four components  from CFCG were selected for NO assay was applied to detect the effects of wogonin, paeonol, ethyl caffeate, and magnoflorine with different concentrations on RAW264.7 cells' viability. As compared with the control group, the four concentrations of wogonin , paeonol , ethyl caffeate , and magnoflorine  had no influence on cell viability; the four concentrations of these components were used for following experiments .p < 0.0001 in a concentration-dependent manner , paeonol , ethyl caffeate , and magnoflorine  significantly decreased the extracellular NO levels with t manner . The resvia reducing the activities of the PI3K-Akt signaling pathway, MAPK signaling pathway, TNF signaling pathway, and NF-κB signaling pathway. Also, it also validated the reliability and accuracy of the identification of CFCG.Furthermore, to validate the results of the comprehensive pathway, Western blotting was applied to detect the activities of the PI3K-Akt signaling pathway, MAPK signaling pathway, NF-κB signaling pathway, and TNF signaling pathway in RAW264.7 cells . CompareAs a chronic and systemic autoimmune inflammatory disease , the mulRecently, the flourishing development of network pharmacology has contributed to an emerging trend of TCM network pharmacology . TCM forIn this study, we explored the potential coordinated functional space of PAR and ALD based on the C-T networks. We found that 341 targets were shared by the two herbs. It indicated the potential co-functional therapeutic effect of PAR and ALD in EMP. Then, we constructed the coordinated functional space prediction models and performed the knapsack algorithm to identify the key coordinated functional components of the potential coordinated functional space. Thirty-seven components were defined as the key coordinated functional components. The results of GO and KEGG pathway enrichment analysis on the targets of CFCG showed that the 426 targets were enriched in 86.96 and 97.92% of GO-BP terms and KEGG pathway of the overlap of RA-related genes and 924 targets of 126 active components. The CFCG identified by our method can cover the most function information of the original active components from EMP. The functional enrichment analysis results also indicated that the CFCG could coordinate the treatment of RA by regulating the genes that involved in biological processes and signaling pathways related to inflammation and immunity, such as oxygen levels (GO:0070482), response to hypoxia (GO:0001666), regulation of innate immune response (GO:0045088), immune response-activating cell surface receptor signaling pathway (GO:0002429), T cell activation (GO:0042110), PI3K-Akt signaling pathway (hsa04151), MAPK signaling pathway (hsa04010), TNF signaling pathway (hsa04668), and NF-κB signaling pathway (hsa04064). It has been reported that the PI3K-Akt signaling pathway is involved in regulating the expression of inflammatory cytokines and plays an important role in the development and progression of various types of inflammation . The devin vitro experiments. PAR and ALD play a coordinating role by regulating genes related to oxygen levels, immune response, synthesis of inflammatory mediators, and so on. Our strategy has the function of compound optimization and can further explore the coordinated mechanism of the CFCG, which was identified in the optimization process. Specifically, the components screened by the coordinated functional space prediction model can cover the most information of the original active components from EMP, and the reverse optimization method based on the targets of CFCG can also retain most targets of the screened components from our model. The identification of CFCG may contribute to improving our understanding of the coordinated mechanism of TCM in treating complex diseases. Moreover, the strategy can reduce the experiment time and cost of the studies on the mechanisms of TCM formulas. It can provide a reference for TCM mechanism research and ideas for the development of new drugs. However, our research lacks sufficient experimental verification. More components from CFCG and their targets should be selected to verify the reliability of our strategy. The determination of the content of herbs' components is also a difficult problem, and the effect of the components is related to the content. It requires further experimental verification. Our research mainly focuses on exploring the coordinated mechanism of herbs and the identification of CFCG. Although the CFCG plays a major therapeutic role, the effect of other components cannot be ignored. Further research is needed to analyze the effect of non-CFCG.In summary, we decoded and validated the molecular mechanisms of EMP in treating RA based on system pharmacological model, knapsack algorithm, molecular docking, and


The study aimed to identify yet unknown and uncharacterized long non-coding RNAs (lncRNAs) in treatment-naïve ulcerative colitis (UC), and to define their possible roles in UC pathogenesis. For that purpose, accurate quantification methods for lncRNA transcript detection, multiple and “stringent” strategies were applied. New insights in the regulation of functional genes and pathways of relevance for UC through expression of lncRNAs are expected.The study was based on sequencing data derived from a data set consisting of treatment-naïve UC patients (n = 14) and control subjects (n = 16). Two complementary aligners were used to identify lncRNAs. Several different steps were used to validate differential expression including plotting the reads over the annotation for manual inspection. To help determine potential lncRNA involvement in biological processes, KEGG pathway enrichment was done on protein-coding genes which co-expressed with the lncRNAs.A total of 99 lncRNAs were identified in UC. The lncRNAs which were not previously characterized (n = 15) in UC or other autoimmune diseases were selected for down-stream analysis. In total, 602 protein-coding genes correlated with the uncharacterized lncRNAs. KEGG pathway enrichment analysis revealed involvement of lncRNAs in two significantly enriched pathways, lipid and atherosclerosis, and T-cell receptor signaling.This study identified a set of 15 yet uncharacterized lncRNAs which may be of importance for UC pathogenesis. These lncRNAs may serve as potential diagnostic biomarkers and might be of use for the development of UC treatment strategies in the future. DespiteLncRNAs play an important role in tumor development and carcinogenesis and have been suggested to be biomarkers for diagnosis and prognosis . A growiLncRNAs, which are RNAs with a length greater than 200 nucleotides, are poorly conserved . Their r22.1Gene expression data of mucosal gene expression were obtained from the Gene expression Omnibus (GEO) GSE128682 and represent sequencing data obtained from mucosal biopsies of treatment-naïve UC patients (n = 14) and normal control subjects (n = 16) .2.2https://www.gencodegenes.org/) [https://github.com/gpertea/stringtie/releases) [https://www.bioconductor.org/packages/release/bioc/html/ballgown.html) [https://bioconductor.org/packages/release/bioc/html/GenomicRanges.html) [A schematic overview of the data analysis methodological approach is shown in es.org/)  was usedeleases)  was usedwn.html)  stattestes.html) . Grangeshttps://bioconductor.org/packages/release/bioc/html/biomaRt.html). in latest Ensembl annotation. Finally, each significantly lncRNA was inspected visually  [Co-expression analysis was used to select potential protein coding target transcripts for the fifteen uncharacterized lncRNAs depicted in PA.html)  was usedPA.html)  using ge33.1DESeq2 on the STAR generated gene count matrix gave a total of 8615 differentially expressed (DE) lncRNA genes (padj <0.05) coding for a total of 28182 lncRNA transcripts . BallgowVisual inspection of lncRNAs candidates was done by plotting read coverage of lncRNAs over the exon structure defined in the genome reference annotation . A total3.2vice versa  . In addice versa . Among t3.3Genes corresponding to the correlated protein-coding transcripts were used for KEGG pathway enrichment. Two significantly enriched pathways with padj and qvalue <0.05 could be identified, the T cell receptor pathway and the lipid and atherosclerosis pathway. Seventeen and twelve genes, which co-expressed with the uncharacterized lncRNAs were found to be enriched in both pathways. Among them VAV3, lymphocyte cytosolic protein 2 (LCP2), and inducible T cell co-stimulator (ICOS), both of which play a role in vascular endothelial cell integrity , NK-cell4In this study, differentially expressed lncRNAs in treatment-naïve UC were explored by applying accurate quantification methods for lncRNA transcript detection. This study provides new knowledge of 15 previously uncharacterized lncRNAs which may be involvedin the regulation of the lipid and atherosclerosis and T cell receptor signaling pathways.Accurate quantification of lncRNA transcripts is challenging. Therefore, several complementary methods along with visual inspection were applied to generate a set of lncRNAs that distinguish between UC samples and controls . The majInitial PCAs of the uncharacterized lncRNAs A and theIn the lipid and atherosclerosis pathway, lncRNA AC001611.2 expression correlated positively with four genes  whereas six other lncRNAs correlated negatively with the same genes A. PerhapLncRNAs may also play a role in T cell apoptosis during UC. LncRNAs AL354743.2 and LINC0113 correlated positively with the STAT3 transcription factor which induces the transcription of BCL2 and BCL-XL in T cells. The expression of these anti-apoptotic genes can increase the resistance of pathogenic T cells of lamina propria to apoptosis, leading to prolonged inflammation .The T cell receptor-signaling pathway was the second significantly enriched pathway. Several genes involved in this pathway such as PTPRC (CD45), NFATc1, and RASGRP1 were differentially expressed in UC B. The exThe expression of lncRNA AC110611.2 correlated with numerous protein-coding transcripts . Apart fin vitro and in vivo experiments. To what extent the uncharacterized lncRNAs contribute to the regulation of the T-cell receptor signaling pathway during UC progression has to be explored in more depth in the future.Many of the up-regulated lncRNAs found by this methodological approach have been already identified and shown to be associated with IBD pathogenesis , such as5This study revealed 15 lncRNAs, which have not been functionally annotated previously and which may be involved in the pathogenesis of UC. The applied methodological approaches together with a visual inspection of read counts over the annotation was key to identifying lncRNA's that were differentially regulated. The results may provide new potential diagnostic biomarkers and therapeutic targets for ulcerative colitis which may improve the understanding of the molecular pathogenesis of UC. However, if lncRNAs are going to be of use as future biomarkers for UC, more reliable approaches for lncRNAs profiling and reliable lncRNA quantification methods are required.Not applicableNot applicable.Not applicable.All data are within the manuscript and its Supporting Information files.Mithlesh Kumar Ray: Formal analysis, Validation, writing, Software, reviewing the final draft. Christopher G. Fenton: Data curation, Methodology, Investigation, Visualization, Validation, Software, Writing – review & editing. Ruth H. Paulssen: Conceptualization, Investigation, Project administration, Resources, Methodology, Supervision, writing, Writing – original draft.The authors declare no conflict of interests regarding the publication of this manuscript.