TP53, AKT1, CSF1R, and TGFBR1 were the core target genes. Enrichment analyses showed that the key signaling pathways were the MAPK and IL-17 signaling pathways. In vivo experiments confirmed that Chuanxiong and Dahuang pretreatments significantly inhibited the levels of SCr, BUN, UNAG, and UGGT in contrast media-induced acute kidney injury (CIAKI) rats (p < 0.001). The results of Western blotting showed that compared with the control group, the protein levels of p-p38/p38 MAPK, p53, and Bax in the contrast media-induced acute kidney injury group were significantly increased, and the levels of Bcl-2 were significantly reduced (p < 0.001). Chuanxiong and Dahuang interventions significantly reversed the expression levels of these proteins (p < 0.01). The localization and quantification of p-p53 expression in immunohistochemistry technology also support the aforementioned results. In conclusion, our data also suggest that Chuanxiong and Dahuang may inhibit tubular epithelial cell apoptosis and improve acute kidney injury and renal fibrosis by inhibiting p38 MAPK/p53 signaling.The molecular mechanisms of Rhizoma Chuanxiong  and Rhei Radix et Rhizoma  in treating acute kidney injury (AKI) and subsequent renal fibrosis (RF) were investigated in this study by applying network pharmacology and experimental validation. The results showed that aloe-emodin, (−)-catechin, beta-sitosterol, and folic acid were the core active ingredients, and  Conioselinum anthriscoides “Chuanxiong” (Apiaceae). Rhei Radix et Rhizoma  is mainly derived from the dried roots and rhizomes of Rheum officinale Baill. (Polygonaceae) and Rheum palmatum L. (Polygonaceae). Studies have shown that the chemical components in CX mainly include volatile oils, phenolic acids, alkaloids, and polysaccharides, which have good pharmacological activities on cardiovascular and cerebrovascular diseases and the nervous system, liver, and kidneys (C. anthriscoides “Chuanxiong” (Apiaceae)  and R. officinale Baill. (Polygonaceae)  are frequently used to dissipate stasis, activate blood, and remove toxicity (in vivo model.Acute kidney injury (AKI) is characterized by an abrupt loss of renal function, which mainly manifests as increased serum creatinine levels and decreased urine output . A meta- kidneys . The mai kidneys . In termtoxicity . A retrotoxicity . CX and toxicity . An herbtoxicity . In additoxicity . In multThe relevant chemical components of CX and DH were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and screened according to their pharmacokinetic characteristics . Target Target genes of AKI and RF were obtained from the GeneCards database, DisGeNET database , Online The protein–protein interaction (PPI) network comprises individual proteins that interact to participate in all aspects of life processes, including biological signal transmission, gene expression regulation, energy and material metabolism, and cell cycle regulation . After iCytoscape version 3.7.1 was used to construct an “herb–component–disease–target” network to reflect the complex relationship between CX and DH, and the potential target genes of AKI and RF . In the p-value <0.05 was used to screen the top biological processes or signaling pathways and visualize the results in the R language.The potential targets were imported into the Database for Annotation, Visualization, and Integrated Discovery (DAVID) for GO biological process and KEGG pathway enrichment analyses . A thresBased on the “herb–component–target” network, PPI network, and KEGG analysis, key active ingredients and core target genes were selected. To further confirm the more specific core genes, we verified the expression of the genes in the Gene Expression Omnibus (GEO) database. First, we downloaded the gene set associated with AKI from the GEO database. After sample quality assessment, data standardization, batch effect removal, probe annotation, etc., differentially expressed gene analysis was performed using the limma package in R software . FinallyBased on the “herb–component–target” network, PPI network, and KEGG analysis, key active ingredients and core target proteins were selected. The structural files of the active ingredients and target proteins were downloaded from the PubChem and Protein Data Bank (PDB) databases, respectively . Discovead libitum, except for the day of dehydration. The animal study was reviewed and approved by the Medicine Animal Ethics Committee of Shanghai University of Traditional Chinese Medicine .All chemicals were purchased from Sigma-Aldrich  unless otherwise stated. The contrast media iohexol (Omnipaque) was purchased from Amersham Health . A total of 32 adult 8–0-week-old male Sprague–Dawley rats weighing 200–250 g were purchased from the Shanghai Laboratory Animal Research Center. Rats were housed in an air-conditioned room at 23°C with a 12 h/12 h light/dark cycle. CX and DH were purchased from Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine. The CX and DH herbal pair (CX 9 g and DH 18 g) was placed in a round-bottomed flask, and eight times the volume of pure water was added and steeped at room temperature for 60 min and boiled for 30 min. Then, the filtrate was filtered through gauze, and six times the volume of pure water was added to the residue and boiled for 30 min and filtered. The two aforementioned filtrates were then combined and placed in a rotary evaporator. After they were rotated, the filtrate was concentrated at 60°C until it contained 2.25 g of raw drug per mL and then set aside. Food and water were provided A well-established rat model of CIAKI was used . A totalWestern blotting was performed as previously described . The pripost hoc multiple-comparison test was used to determine the significance of differences in multiple comparisons. Differences were considered significant if p < 0 05, highly significant if p < 0 01, and very highly significant if p < 0.001.Results are expressed as mean ± SD. One-way analysis of variance (ANOVA) with Tukey’s Among 281 chemical components of CX and DH retrieved from the TCMSP database and screened based on pharmacokinetic characteristics (OB ≥ 30% and DL ≥ 0.18), 23 chemical components were included in the analyses. After including five characteristic compounds , we ended up with 28 active ingredients. In addition to OB and DL, the molecular name, molecular ID, molecular weight (MW), and half-life (HL) are shown in A total of 7,453 AKI-related target genes and 6,581 RF-related target genes were retrieved in GeneCards, of which 1,258 and 668 target genes, respectively, met the screening criteria (relevance score ≥10). A total of 185 AKI-related target genes and 570 RF-related target genes were retrieved in DisGeNET, while nine AKI-related target genes and 14 RF-related target genes were retrieved in OMIM, and three AKI-related target genes and three RF-related target genes were retrieved in TTD. The target genes retrieved from the four databases were intersected and de-duplicated to obtain a total of 514 target genes. Specific target gene information for AKI and RF retrieved from the database is available in the TNF), vascular endothelial growth factor a (VEGFA), cellular tumor antigen p53 (TP53), transcription factor AP-1 (JUN), caspase-3 (CASP3), prostaglandin G/H synthase 2 (PTGS2), hypoxia-inducible factor 1 subunit alpha (HIF1A), epidermal growth factor (EGF), interleukin 1 beta (IL1B), and matrix metallopeptidase 9 (MMP9).A total of 44 potential targets were obtained after the intersection of disease and drug target genes . The 44 PTGS2, MAP2K1, HSP90AA1, RBP4, PPARG, MME, CASP3, NR3C2, etc.To deeply explore the relationship between the active ingredients, core targets, and disease targets of CX and DH, we used Cytoscape to build an “herb–component–target” network . The netp-value and the number of enriched genes, the MAPK signaling pathway is undoubtedly the most important of these pathways.To further explore the mechanism of CX and DH in the treatment of AKI and RF, we performed GO biological function annotation and KEGG signaling pathway analyses of the 44 target genes . GO enriTP53, AKT1, CSF1R, and TGFBR1 genes in the AKI group was significantly different from that in the control group (p < 0.05). Since TP53 is one of the most connected genes in the top 10 hub genes, we selected p53 encoded by TP53 as the target protein for subsequent molecular docking. The PDB ID of p53 is 6SL6 . Both CXDH and NAC pretreatments significantly inhibited SCr, BUN, UNAG, and UGGT in CIAKI rats, and the effects are similar (p < 0.001). Histological analysis . After CXDH intervention, the expression levels of all these proteins were reversed. The results of immunohistochemistry (IHC) techniques in p < 0.001). Taken together, all these findings suggest that CXDH inhibits the expression of p38 MAPK/p53-mediated apoptosis cascade in CIAKI rats and attenuates acute injury to renal tissue.To further verify the effect of CXDH on p38 MAPK/p53 signaling, the expression levels of p38 MAPK, phospho-p38 MAPK (p-p38), p53, Bax, and Bcl-2 were determined by enzyme-linked immunosorbent assay. As shown in AKT1, BCL2, CASP3, IL1B, JUN, MAP2K1, MDM2, MMP3, MYC, PPARG, PTGS2, TGFBR1, TNF, and TP53. Enrichment analysis showed that the regulation of apoptotic signaling pathway, epithelial cell migration, response to oxidative stress, and MAPK and IL-17 signaling pathways are key parts of the mechanism.AKI is an acute kidney disease characterized by acute changes in renal function . So far,The progression of AKI to CKD is a complex process involving the regulation of multiple cellular and signaling pathways, including inflammatory injury, cell cycle arrest, and cell death regulation, which can ultimately lead to or aggravate RF  He et a. During According to the aforementioned results, CX and DH may improve AKI and subsequent RF by regulating downstream apoptosis through the p38 MAPK/p53 pathway. The MAPK signaling pathway can transduce related extracellular stimulus signals into the cell and nucleus through the three-level kinase cascade pathway and participate in cell proliferation, growth, apoptosis, and other processes . This paThe MAPK signaling pathway plays an important role in acute kidney injury and subsequent RF. The present study emphasizes that inhibition of renal tubular epithelial cell apoptosis via p38 MAPK/p53 signaling may be an important renoprotective mechanism of CX and DH. Nevertheless, the present study is still incomplete, and the mechanisms of intervention for renal fibrosis require further evaluation in future studies.


Background: Yuquan Pill (YQW) is a modern concentrated pill preparation of six herbs, namely, Ge Gen (Pueraria lobata Ohwi), Di huang (Rehmannia glutinosa Libosch.), Tian Huafen (Trichosanthes kirilowii Maxim.), Mai Dong (Ophiopogon japonicus (L. f.) Ker Gawl.), Wu Weizi (Schisandra chinensis (Turcz.) Baill.) and Gan Cao . It is extensively used to treat type 2 diabetes-related glucose and lipid metabolism disorders. But what’s the pharmacodynamic substance and how it works in the treatment of Type 2 diabetes mellitus (T2DM) are still unclear.Purpose: The purpose of this study is to determine the likely pharmacological components and molecular mechanism of YQW’s intervention on T2DM by combining serum pharmacochemistry, network analysis and transcriptomics.Methods: The efficacy and prototypical components of blood entry were determined after oral administration of YQW aqueous solution to T2DM rats induced by high-fat feed and low-dose streptozotocin (STZ), and the key targets and pathways for these compounds to intervene in T2DM rats were predicted and integrated using network analysis and transcriptomics techniques.Results: In diabetic rats, YQW can lower TG, CHO, NO, and MDA levels (p < 0.05) while increasing HDL-C levels (p < 0.01), and protecting the liver and kidney. 22 prototype components  were found in the serum of rats after oral administration of YQW for 90 min, which might be used as a possible important ingredient for YQW to intervene in T2DM rats. 538 YQW pharmacodynamic components-related targets and 1,667 disease-related targets were projected through the PharmMapper database, with 217 common targets between the two, all of which were engaged in regulating PI3K-Akt, MAPK, Ras and FoxO signal pathway. Finally, the mRNA expression profiles of liver tissues from rats in the control, model, and YQW groups were investigated using high-throughput mRNA sequencing technology. YQW can regulate the abnormal expression of 89 differential genes in a disease state, including 28 genes with abnormally high expression and 61 genes with abnormally low expression. Five common genes  and two extensively used regulatory pathways  were revealed by the integrated transcriptomics and network analysis study.Conclusion: The mechanism of YQW’s intervention in T2DM rats could be linked to 22 important components like puerarin, daidzein, and glycyrrhetinic acid further activating PI3K-Akt and MAPK signaling pathways by regulating key targets Kit, Ppard, Ppara, Fabp4, and Tymp, and thus improving lipid metabolism disorder, oxidative stress, and inflammation levels in T2DM rats. On the topic, more research into the pharmacological ingredient foundation and mechanism of YQW intervention in T2DM rats can be done. About 425 million people in the world suffer from diabetes, of which T2DM and its complications affect the great majority of them . AccordiPueraria lobata (Willd.) Ohwi (gegen), Rehmannia glutinosa (Gaertn.) DC. (dihuang), Ophiopogon japonicus (Thunb.) Ker Gawl. (maidong), Trichosanthes kirilowii Maxim./Trichosanthes rosthornii Harms (tian huafen), Schisandra chinensis (Turcz.) Baill. (wu weizi) and Glycyrrhiza uralensis Fisch./Glycyrrhiza inflata Batalin/Glycyrrhiza glabra L. (gancao). Gegen is a monarch drug that enhances fluid production, quenches thirst, and reduces fever. It belongs to the lung and stomach meridians  of the ancient book Zhongfutang Public Selection Recipe, which made up of six medications: eridians . Ministeeridians . Gancao eridians . YQW haseridians ; dihuangeridians ; maidongeridians , but alsTherefore, this subject systematically analyzed the potential pharmacodynamic substances and mechanism of YQW intervention in T2DM rats using the serum pharmacochemistry research method, combining with network analysis and transcriptomics technology.Yuquan Pill ; Metformin Hydrochloride Tablets ; Streptozotocin ; Uralose ; Citric Acid-Sodium Citrate Buffer ; 45% High-fat feed (batch No. D12451. SPF (Beijing) Biotechnology Co., Ltd., China); NO assay kit ; MDA test kit ; mass spectrometry grade methanol, acetonitrile and formic acid ; ultrapure water (A.S.WATSON TM LIMITED); ME155DU electronic balance ; JA 2003B electronic balance ; KQ-300DB ultrasonic cleaning machine ; high-speed desktop refrigeration centrifuge ; Pipette ; Fully automated biochemistry analyser (BECKMAN COULTER); EPOCH enzyme labeller (BioTek); Eclipse Ti-E laser confocal microscope ; Panoramic section scanner, 3DHISTECH (HUNGARY); ACQUITY I-CLASS UPLC , SYNAPT G2-Si Q-TOF MS .2.0 g of YQW (Concentrated Pill) powder were weighed and ultrasonic extracted for 20 min with 20 mL of 75% methanol solution . The mixture was centrifuged twice at 13,000 r/min for 15 min each time after cooling to room temperature and the supernatant was removed.SPF grade SD rats  were acquired from SPF (Beijing) Biotechnology Co., Ltd. (Production license number: scxk (Beijing) 2019-0010, Quality certificate of experimental animals: No. 1103242011017624). These rats were cared and treated according to the regulations and general recommendations of China Laboratory Animal Management Regulations, and approved by the Animal Ethics Committee of Beijing University of Traditional Chinese Medicine .After 1 week of adaptive feeding in a standard environment, 10 rats were randomly selected as the control group and the remaining rats were fed 45% high-fat feed as the modeling group using the random number approach. The modeling rats received a single intraperitoneal injection of STZ 35 mg/kg  after 5 Finally, all rats were sacrificed using anesthetics following a 12-h fast, in accordance with laboratory animal ethics. To get the serum, a blood sample was taken from the abdominal aorta and centrifuged at 3,000 r/min for 15 min at 4°C. The supernatant was taken out of the mixture and split. A completely automated biochemical analyzer was used to assess blood glucose, TG, CHO, HDL-C, LDL-C, and SOD. NO and MDA were also measured according to the kit’s specifications, and the remaining serum was kept at −80°C until needed. The splenic glands were removed and washed with saline after the rats were executed, and the blood was removed from the surface with filter paper before being weighed on an electronic balance and the splenic index calculated.The liver and kidney were embedded in paraffin, dehydrated, and stained with H&E, and examined under a light microscope at 20 and 200 magnification after being fixed in 4% paraformaldehyde for 48 h.p < 0.05.The data is presented as continuous variables or qualitative descriptions depending on the type of data. GraphPad Prism 5 software was used to process the continuous variables, and the results were reported as mean ± SD. A statistically significant difference was defined as m/V) by intraperitoneal injection (dose administered was 0.4 mL/100 g) and blood was taken from the abdominal aorta.Blood was taken from the inner orbital canthus of the rats in the dosing group at 45 min after oral administration in the morning; at 90 min, the rats in each group were anesthetized with 25% urethane  after standing for 30 min. The sera of rats in each group were pooled at 45 and 90 min, then 1,000 μL of the combined serum were added 3 times of acetonitrile, vortex for 2 min, centrifuged at 3,000 r/min , and then blew dry with nitrogen at 40°C and redissolved with 100 μL methanol (enrichment 10 times). Finally, the redissolution fluid was centrifuged at 12,000 r/min , and the supernatant was separated for sample injection analysis using UPLC-Q-TOF/MS.in vivo blank and drug-contained serum).Chromatographic column: Waters ACQUITY BEH C18 column ; flow rate: 0.3 mL/min; column temperature: 35°C; injection volume: 5.0 μL.mobile phase: 0.1% formic acid in water (A) - acetonitrile (B); gradient elution procedure: (1) condition 1: 0–2 min, 5% B ∼ 5% B; 2–17 min, 5% B ∼ 98% B; 17–20 min, 98% B ∼ 98% B; 20–23 min, 98% B ∼ 5% B; 23–25 min, 5% B ∼ 5% B; (2) condition 2: 0–8 min, 2% B ∼ 9% B; 8–18 min, 9% B ∼ 32% B; 18–20 min, 32% B ∼ 89% B; 20–35 min, 89% B ∼ 100% B; 35–37 min, 100% B ∼ 2%B; 37–40 min, 2% B ∼ 2% B  was used for mass spectrometry analysis. Leucine-enkephalin was employed as an external reference for real-time calibration to guarantee that correct mass numbers were collected for each component of the molecule during data capture, yielding fragment ions http://www.megabionet.org/tcmid), TCMSP (http://tcmspw.com/tcmsp.php), ETCM (http://www.ehbio.com/ETCM), BATMAN-TCM (http://bionet.ncpsb.org/batman-tcm/). By means of TCM database, the exclusive database of YQW was created, and information on the chemical composition of the six herbs was collected, including chemical names, molecular formulas, structural formulae, and molecular weights.To collect information on the chemical composition of six herbs in YQW, the following online databases were used: TCMID  and UNIFI were utilized to process data and the unique database of YQW were used to identify chemical compositions. The major chemical composition of YQW was then identified and confirmed by extracting fragment ions and comparing it to relevant literature data based on the retention time (RT) and mass spectrum information of each chemical component under positive and negative ion modes.http://www.lilab-ecust.cn/pharmmapper/) was employed to anticipate the appropriate chemical component-targets in the prescription given above. Then, the GeneMANIA (http://genemania.org) protein interaction analysis tool was used to predicte the indirect targets connected to the direct targets. Finally, the Uniport (http://www.uniprot.org/) database was used to convert all targets into conventional gene names.The PharmMapper database (http://ctdbase.org/), Therapeutic Target Database (http://db.idrblab.net/ttd/), and GeneCards (http://www.genecards.org/) illness databases. The CTD database was filtered by looking for reported targets associated with T2DM that were tagged with “marker/mechanism”; the TTD database was filtered by looking for databases that clearly contained the disease “type 2 diabetes”; and the GeneCards database was filtered by having a relevance score of 26 or higher. Then, in Uniport, T2DM-targets were changed into the standard gene name. The targets of YQW in the treatment of T2DM were determined by intersecting the T2DM and composition targets.The term “type 2 diabetes” was used to found the targets associated to T2DM by searching the Comparative Toxicogenomics Database (https://cn.string-db.org) analysis platform. The network topology of the PPI network was then analyzed using Cytoscape 4.6.1 . According to the Degree value, the top five targets were chosen as critical objectives.To obtain the PPI network, the YQW targets in the therapy of T2DM were submitted to the String (Rattus norvegicus” was chosen for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) studies. The top 10 pathways in KEGG Pathways with different classification functions were chosen based on their p values and imported into the Omicshare (https://www.omicshare.com/) platform for pathway enrichment analysis to create a bubble map of signaling pathways; at the same time, the top 10 pathways in BP, CC, and MF were chosen based on their p values and imported into the microbiology online database to investigate the possible mechanism of YQW.All of the above-mentioned targets were uploaded to the String analysis platform’s “Multiple Proteins” section, and “Using the merge integration function, the “TCM-Component-Target” network was built in Cytoscape 4.6.1.http://www.personalbio.cn/) extracted and analysed nine samples from the three groups of rats. Electrophoresis, gel imaging system, and Agilent 2100 biological analyzer were used to ensure the quality, quantity, purity and integrity of the RNA samples. Finally, the Illumina HiSeq 2500 was used to sequence the samples.Shanghai Personal Biotechnology Co., Ltd.  for cluster analysis. In addition, GO enrichment analysis was performed using top GO and KEGG enrichment analysis was performed using the cluster profiler in this experiment.In this section, the back-regulated differential genes obtained from transcriptomics sequencing were integrated and analyzed with the major targets of YQW for the treatment of T2DM, taking the shared targets or shared pathways to understand the mechanism of action of YQW intervention in T2DM rats.###p < 0.001), indicating that the modelling was successful. The GLU of rats in the Met group decreased (**p < 0.01), indicating a better hypoglycaemic effect, while the GLU in the YQW-L and YQW-H groups showed a trend of decrease compared with the model group, but there was no significant difference compared with the model group. In addition, the YQW-L group reduced TG and CHO levels in T2DM rats (*p < 0.05) and was more effective than the Met group. Besides, compared with the model group, NO level was reduced in the Met and YQW-L groups (*p < 0.05), but there was no statistical significance in the YQW-H group. Compared with the model group, SOD level was increased in the Met group (**p < 0.01). Compared with the model group, the YQW-L group (*p < 0.05) showed a significant decrease in MDA level, while the Met group and YQW-H groups showed no statistically significant decrease. These results showed that the YQW-L group was able to regulate the increase in serum NO and MDA levels and the decrease in SOD levels in rats, and enhance the antioxidant capacity of rats. Finally, compared with the model group, the Met groups showed an increase in spleen index (*p < 0.05), and the YQW-L and YQW-H groups showed a tendency to increase, although there was no statistical difference, the original data could be found in Rats in the control group had normal drinking, diet and excretion, clean fur, good mental state and active behavior. On the contrary, the rats in the model group had obvious excessive drinking, excessive urination, thin body, rough and wet fur, poor mental state and wet bedding, which needed to be changed every day. As shown in 75% methanolic and aqueous extracts of YQW were analysed respectively in positive and negative ion mode and the basal peak ion flow diagrams are shown in A preliminary characterization of the 116 components in YQW was carried out by combining database comparison and literature reports. Among them of 20 components were derived from Gegen, 4 components from Dihuang, 3 components from Tianhuafen, 10 components from Maidong, 21 components from Wuweizi, and 58 components from Gancao. The main components include 59 flavonoids, 10 lignans, 9 triterpenes, 6 phenylpropanoids, 5 terpenoid, 5 steroids and 4 fatty acid, etc. The results are shown in Blank serum and YQW-containing serum were analysed in positive and negative ion mode according to the analytical conditions established in the UPLC-Q-TOF/MS technique, and the base-peak ion flow diagrams and the table of incoming blood prototype components are shown in in vitro chemical composition characterization, a preliminary characterization of the 22 prototypical components of YQW into blood after high and low administration to T2DM rats was carried out. They were listed in The retention time, precise molecular weight, error and high-energy fragmentation ion peak information of each peak were obtained by comparing the ion flow maps of blank serum and YQW-H and YQW-L containing serum. UNIFI data processing system was used to automatically match and identify them. Combined with the results of the p-values was carried out on the Omicshare platform to obtain bubble maps. The KEGG pathway enrichment analysis showed that of the 203 individual signalling pathways obtained, the top 20 signal transduction process were shown respectively in as shown in The 197 direct common targets were obtained by integrating 22 chemical composition targets and T2DM targets. The Venn diagram results are shown in To further understand the multifaceted mechanism of action of YQW-L inT2DM rats, RNA sequencing analysis were performed to obtain the mRNA expression of each sample in the control, model and YQW-L groups. By comparing the differential gene expression in each group, the screening revealed that the YQW-L group could reverse the abnormally high expression of 28 genes and the abnormally low expression of 61 genes the modle group compared with the normal group. The differential gene regressions are shown in Additional differential gene Venn diagrams and cluster analysis heat maps were used to represent the number of differential genes between the comparison groups, the overlap between the comparison groups and the correlation of expression between 9 individual samples in the three groups. The differential gene expression histogram and the cluster analysis heat map for each group were shown in In this section, the expression of differential genes in the model group compared to the normal group  and the differential genes back-regulated in the YQW-L group compared to the normal group were analysed respectively for GO Term enrichment. In addition, KEGG processes are mainly divided into metabolic processes, environmental information processing processes, disease processes, tissue system processes and cellular processes. The differential genes and the KEGG enrichment analysis of the retraced genes in the model group compared to the normal group  were also shown in Five shared targets of Kit, Ppard, Ppara, Fabp4 and Tymp were obtained by crossing the targets of pharmacodynamic components and disease predicted by network analysis with the differential targets obtained by transcriptome sequencing. And the shared passway were PI3K-Akt and MAPK signal pathway. As shown in Diabetes is a chronic disease with various consequences, and its prevalence is quickly increasing over the world, putting people’s health at risk . The T2Din vitro chemical properties of YQW. The precise mass number, retention period, and lysis rules were used to identify 22 chemical compounds in the blood prototype components. Flavonoids, lignans, and triterpenoids may be viable therapeutic chemicals for regulating lipid metabolism and antioxidation, such as puerarin . This result suggests that the active ingredient in YQW may improve the lipid metabolism disorder, oxidative stress and inflammation by regulateing these 5 key genes in T2DM rats. This study looks into the potential pharmacodynamic substances and mechanism of YQW in the treatment of T2DM, but how to explain the pharmacodynamic substance basis and mechanism of YQW in the treatment of T2DM in a systematic and comprehensive manner still requires a lot of detailed research.In summary, YQW groups can reverse the abnormally low or abnormally high expression of some genes. The active ingredients in YQW, such as puerarin, daidzein and glycyrrhetinic acid, may further activate the signalling pathways of PI3K/Akt, FoxO and AMPK by regulating the important proteins FoxO3, IL10, Ppargc1a and FoxO1, thereby reducing the level of inflammation, regulating lipid metabolism and protecting liver and kidney tissues. Five common targets, namely, Kit, Ppard, Ppara, Fabp4 and Tymp, were obtained by analyzing the targets screened with network pharmacology and the differential genes sequenced with transcriptomics, all of which were reversed after YQW administration (


Diabetic cardiomyopathy (DCM) is one of the most prevalent complications of diabetes with complex pathogenesis. YuNü-Jian (YNJ) is a traditional Chinese medicinal formula widely used for diabetes with hypoglycemic and cardioprotective effects. This study aims to investigate the actions and mechanisms of YNJ against DCM which has never been reported.Network pharmacology approach was used to predict the potential pathways and targets of YNJ on DCM. Molecular docking between hub targets and active components of YNJ was performed and visualized by AutoDock Vina and PyMOL. Then type 2 diabetic model was employed and intervened with YNJ for 10 weeks to further validate these critical targets.First, a total of 32 main ingredients of YNJ were identified and 700 potential targets were screened to construct herb-compound-target network. Then 94 differentially expressed genes of DCM were identified from GEO database. After that, PPI network of DCM and YNJ were generated from which hub genes  were assessed by topology analysis. Next, functional and pathway analysis indicated that the candidate targets were enriched in response to oxidative stress and Nrf2 signaling pathway. Furthermore, molecular docking revealed strong affinity between core targets and active components of YNJ. Finally, in rats with type 2 diabetes, YNJ obviously attenuated cardiac collagen accumulation and degree of fibrosis. Meanwhile, YNJ significantly upregulated protein expression of SIRT1, Nrf2 and NQO1 in diabetic myocardium.Collectively, our findings suggested that YNJ could effectively ameliorate cardiomyopathy induced by diabetes possibly through SIRT1/Nrf2/NQO1 signaling. The progressive pathophysiological changes of DCM consist of myocardial remodeling, diastolic/systolic dysfunction, heart failure, and even sudden death . AccordiAt present, management of DCM mainly focuses on lowering blood glucose and lipid levels with no reliable specific drugs. However, whether the diabetic patients developed diastolic dysfunction and progressed to heart failure was independent of HbA1c levels, blood pressure, and lipid control . Newer gIn traditional Chinese medicine, symptoms associated with DCM have been depicted as early as 1,400 years ago. According to the theory of Chinese medicine, the basic pathogenesis of DCM is dry heat and yin deficiency, which runs through the whole process of DCM . Thus, hin vivo and in vitro experiments. This makes it possible to investigate the interaction between herbal formulations and disease with multifactorial pathogenesis, which cannot be readily verified by conventional experimental modes based on the “one gene, one drug, one disease” paradigm  in the GEO database, drug target prediction, PPI network construction and topology analysis, KEGG pathway analysis, and GO biological analysis. Then, the molecular docking between the active ingredients and the predicted targets was carried out. Finally, a diabetic animal model was established to verify the improvement of cardiomyopathy by YNJ and the potential mechanism through the putative targets. The detailed strategy of this study is summarized in 22.1https://tcmsp-e.com/) platform. The oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18 of the absorption, distribution, metabolism, and excretion (ADME) parameters were used as the criteria for screening. In order to ensure the integrity and reliability of the study, active ingredients were supplemented through literature review. Those with high contents and explicit pharmacological effects but not in accord with the above criteria were also considered as active candidate ingredients  database, and the SMILES number corresponding to each compound was obtained after eliminating compounds that did not match those in PubChem. Then, the Swiss Target Prediction database (http://www.swisstargetprediction.ch/) was used to predict the targets of active compounds (https://www.uniprot.org/) for batch standardization, with species set to Homo sapiens.The screened Pubchem CID was input into the PubChem compound database  was applied to construct a network of active ingredients of YNJ and their targets. In the network diagram, a node represents an herb, an active ingredient, or a target, and the edge represents the interaction between them. The degree value of a node indicates the number of edges between the nodes in the network; the larger the value, the more likely the target is to be the key target of compounds.2.3https://www.ncbi.nlm.nih.gov/geo/), from which the GSE4745 dataset  was rigorously selected for further study. Samples from the GSE4745 dataset were grouped by R language (Version 4.1.0) to obtain significantly DEGs with adjusted p < 0.05 and |logFC| ≥ 2.0 as screening conditions. The DEGs were imported into the HCNC-Tools-HCOP (https://www.genenames.org/tools/hcop/) online tool for species conversion, preserving the homologous genes of rat and human, which are the DCM-related genes of human to be studied.The gene expression datasets analyzed in this study were obtained from the GEO database (ols-HCOP  (https:/2.4Protein–protein interaction (PPI) networks of YNJ targets and DCM-related DEGs were constructed and visually analyzed with the plug-in BisoGenet in Cytoscape software (Version 3.8.2). Core proteins were screened from the central network, which came from the intersection of the above two networks. The CytoNCA plug-in was adopted for multi-center topological parameter analysis and screening according to the degree sorting procedure. The core targets were evaluated according to three parameters: degree centrality (DC), closeness centrality (CC), and betweenness centrality (BC). The screening criteria were set as DC > 2 times of the median to obtain the network of core target from which the key genes for the treatment of DCM by YNJ were further identified with DC, CC, and BC > 1 time of the median.2.5https://metascape.org) was employed to perform GO and KEGG signal pathway analysis with the aforementioned predicted targets. GO biological function analysis mainly describe the function of target genes, including molecular function (MF), cellular components (CC), and biological processes (BP). KEGG enrichment analysis interprets the signaling pathway enriched by the common genes of YNJ active components and DCM. Terms with p < 0.01, minimum gene overlap (Min Overlap) of 3, and minimum enrichment factor (Min Enrichment) > 1.5 were collected and grouped according to the similarity of their members. The top 20 crucially significant pathways were selected for further study and are displayed in the advanced bubble chart colored by − log10 .Metascape (2.6http://www.pdb.org/). After adding hydrogen and calculating charges, the crystal structure of core components was downloaded from PubChem. AutoDock Vina software was used to dehydrate and hydrotreat the receptor protein and conduct molecular docking. Afterwards, free energy of binding  was obtained as the indicator of the binding likelihood. The more the negative scoring , the higher the binding force is between the compound and the protein. Generally, the binding energy ≤−5.0  kcal/mol can be considered as an excellent binding effect. Finally, PyMOL software was used for visualization.The 3D protein structure of key targets was downloaded from the RCSB Protein Data Bank . All rats were housed in standard conditions with a 12-h light/dark cycle at 22 ± 2°C and a humidity of 55 ± 5% with free access to food and water. After adaptation for 1 week, animals were divided randomly into a negative control (NC) and a model group. Rats in the model group were fed with a high-fat diet (HFD) for 4 weeks and then intraperitoneally injected with 25 mg/kg freshly prepared streptozotocin  dissolved in citrate buffer  three times every other week (2.7.2The successfully induced rats were randomly divided into diabetic mellitus (DM) and YNJ-treated group (YNJ). The preparation of YNJ involved mixing of decocting-free granules of Gypsum Fibrosum, Rehmanniae Radix Praeparata, Anemarrhenae Rhizoma, Ophiopogonis Radix, and Achyranthis Bidentatae Radix  with distilled water to a final drug concentration of 0.9 g/ml. YNJ was administrated intragastrically at a dose of 4.52 g/kg every day according to the clinical effective dose for diabetic patients. Rats in the NC and DM groups received equal amount of 0.9% saline with the same administration route and duration. After treatment with YNJ or saline for 10 consecutive weeks, all rats were anesthetized and euthanized. Ventricular tissues were rapidly excised and fixed in 4% paraformaldehyde or frozen at −80°C for later assays.2.7.3After fixation for 24 h, the cardiac tissues were dehydrated in ascending grades of alcohol and embedded in paraffin. Picro-Sirius red (PSR) staining was used to demonstrate cardiac fibrosis. Briefly, sections of 5 μm thickness were conventionally deparaffined with xylene and alcohol. Then, slides were incubated with PSR  for 10 min and viewed using a Nikon Eclipse Ci-L microscope . The collagen volume fraction  within the tissue was identified and assessed with Image Pro Plus 6.0 software as the ratio of stained fibrillar collagen area to the view area. The CVF from six random fields (×200) were averaged and used to show the collagen deposition of this sample.2.7.42O2 for 10 min. After blocking of nonspecific binding with normal serum for 20 min, the sections were incubated with primary antibody against SIRT1  at 4°C overnight. The slides were then incubated with secondary antibodies at 37°C for 30 min, stained with DAB, counterstained with Mayer’s hematoxylin, and visualized by a highly sensitive inverted microscope . Image analysis was performed by Image Pro Plus 6.0 software.Paraffin-embedded sections of 5 μm thickness were kept at 60°C for 3 h in the oven and dewaxed with xylene and hydrated in gradient ethyl alcohol. The slides were subjected to microwave antigen retrieval treatment followed by endogenous peroxidase deactivation using 3% H2.7.5Ventricular tissues were homogenized in lysis buffer and quantified for protein concentration with a commercial assay kit . Equal quantities of proteins were separated on 10%–12% SDS-PAGE under denaturing conditions and transferred onto a PVDF membrane. The membrane was blocked using 5% nonfat milk in TBS containing 0.05% Tween 20 (TBST) for 2 h, and then immunoblotted overnight at 4°C with primary antibody, followed by incubation with HRP-conjugated secondary antibody for 1 h at room temperature. The immunoreactive proteins were visualized with an ECL-detection kit  using a Tanon 6600  luminescent imaging workstation. Levels of protein were quantified using Image Pro Plus 6.0 software  for optical density values. All the target proteins were normalized by GAPDH and depicted as percentage of the NC. Rabbit anti-Nrf2 antibody (ab92946), rabbit anti-NQO1 antibody (ab34173), rabbit anti-GAPDH antibody (ab8245), and goat anti-rabbit IgG H&L (HRP) (ab6721) were all purchased from Abcam .2.7.6p-values less than 0.05 were considered to indicate statistically significant differences.All data were expressed as mean ± SD and analyzed and plotted using GraphPad Prism 5 (Version 5.01). One-way analysis of variance (ANOVA) and Tukey’s test were used for statistical analyses. 33.1The candidate bioactive components of YNJ were screened out from the TCMSP database with OB ≥ 30% and DL ≥ 0.18. Together with literature retrieval, we obtained 2, 13, 4, and 17 bioactive ingredients from Rehmannia Radix Praeparata, Anemarrhenae Rhizoma, Ophiopogonis Radix, and Achyranthis Bidentatae Radix, respectively. After removing the duplicate values and those without targets, 32 bioactive ingredients were screened out and eventually included in the follow-up study Table 1.3.2According to the filters described above, 3,579 candidate genes that were targeted by YNJ were harvested from the Swiss Target Prediction database. Then 700 common targets were obtained by overlap analysis, showing synergistical effects among these bioactive components.By using Cytoscape, we constructed a compound–target network to identify the relationship between YNJ bioactive components and their candidate targets. The network was composed of 736 nodes and 2,504 edges Figure 23.3p < 0.05, we obtained 113 DEGs, of which 26 were upregulated and 87 were downregulated. The related volcano map was created to show the distribution of these genes  is less than −7 in AutoDockTools. As shown in 3.7To further verify the cardioprotective effects of YNJ, diabetic rats were treated with YNJ for 10 weeks. Cardiac collagen accumulation and degree of fibrosis were evaluated by PSR staining. 3.8To clarify the effects of YNJ on the screened key targets against DCM, the expression of SIRT1, Nrf2, and NQO1 was detected by immunochemistry and Western blotting analysis. As shown in 4in vivo experiments to illustrate the potential mechanisms of YNJ for DCM.Although the treatment of DCM has been developed recently, practically effective interventions are scarce due to the complex etiology and pathogenic mechanisms of DCM. Based on the proprietary characteristics of multicomponents, multitargets, and multipathways, Chinese medicine has shown reliable therapeutic effects for DCM . In the Rehmanniae glutinosa, the minister medicine in YNJ, attenuated adriamycin-induced cytotoxicity and apoptosis in H9C2 cardiac muscle cells (Anemarrhena asphodeloides, attenuated isoproterenol-induced myocardial injury by inhibiting ER stress-mediated apoptosis pathways (Cardiac remodeling is the major structural abnormalities of DCM. Here, in the myocardium of diabetic rats, we found obvious collagen deposition and fibrosis, which could be markedly reversed by YNJ administration. Similar protective function of YNJ on pathological remodeling has also been found in coronary artery ligation-induced myocardium infarction rats . Meanwhile cells . Timosappathways . In combA. asphodeloides exhibited strong antioxidant activities (R. glutinosa is associated with the increase of Mn-SOD expression and GSH level as well as the resulting scavenging effect on free radicals in cardiac muscle cells (Achyranthes bidentata polypeptides showed similar anti-oxidant and protective capacity against myocardial ischemic/reperfusion injury in rats (It is well-documented that oxidative stress characterized by excessive ROS production is one of the leading factors in the development of DCM. The key pathological processes underlying cardiac remodeling in diabetes are highly redox sensitive, including cardiomyocyte hypertrophy, apoptosis, fibrosis, and diastolic/systolic dysfunction . Oxidatitivities . Moreovele cells . Achyran in rats . All theThe cellular antioxidant defense system plays vital roles in the protection against oxidative damage. Nrf2, a basic leucine zipper stress-responsive transcription factor, has been recognized as a crucial mediator in ameliorating oxidative stress and enhancing cell survival by inducing the expression of multiple antioxidants and cytoprotective proteins, particularly NQO1 and HO-1. Both expression level and transcriptional activation of Nrf2, together with its target gene NQO1 level, were decreased in diabetic animals and HG-treated primary neonatal rat cardiomyocytes . Moreove+-dependent histone deacetylase, regulates multiple biological processes, including redox hemostasis, inflammation, apoptosis, and cell metabolism. Decreased SIRT1 expression was detected in myocardial tissue of diabetic rats, along with declined antioxidant defenses and impaired cardiac morphology and function (+/NADH ratio, which was responsible for the antioxidant and antiapoptotic effects of NQO1 (SIRT1, a NADfunction . Conversfunction , 31, or function , 33. Morfunction . Moreovefunction . Simulta of NQO1 . Also, t of NQO1 . Thus, t of NQO1 . In this5In summary, activation of SIRT1 and Nrf2 signaling markedly enhances expression of antioxidant enzymes, attenuates oxidative damage, and is, thus, beneficial by retarding cardiac fibrosis induced by diabetes . The chrPublicly available datasets were analyzed in this study. This data can be found here: NCBI: GSE4745.FW conceived and designed the study and provided funding support. WW performed network pharmacology research, molecular docking, and animal experiments and wrote the first draft of manuscript. RL helped analyze the data and prepare the manuscript. YZ revised the manuscript and provided funding support. LW, YT, BD, and ST helped conduct the experiment. All authors contributed to the article and approved the submitted version.


Wumei San (WMS) is a traditional Chinese medicine that has been widely applied in the treatment of piglet diarrhea (PD). However, the mechanism of WMS in PD has not been investigated. In this study, the main active compounds of WMS and the target proteins were obtained from the Traditional Chinese Medicine Systematic Pharmacology, PubChem, and SwissTargetPrediction databases. The molecular targets of PD were identified using GeneCards, OMIM, and NCBI databases. The common targets of WMS and PD were screened out and converted into UniProt gene symbols. PD-related target genes were constructed into a protein-protein interaction network, which was further analyzed by the STRING online database. Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed to construct the component-target gene-disease network. Molecular docking was then used to examine the relationship between the core compounds and proteins. As a result, a total of 32 active compounds and 638 target genes of WMS were identified, and a WMS-compound-target network was successfully constructed. Through network pharmacology analysis, 14 core compounds in WMS that showed an effect on PD were identified. The targets revealed by GO and KEGG enrichment analysis were associated with the AGE-RAGE signaling pathway, PI3K-Akt signaling pathway, TNF signaling pathway, NOD-like receptor signaling pathway, IL-17 signaling pathway, and other pathways and physiological processes. Molecular docking analysis revealed that the active compounds in WMS spontaneously bind to their targets. The results indicated that WMS may regulate the local immune response and inflammatory factors mainly through the TNF signaling pathway, IL-17 signaling pathway, and other pathways. WMS is a promising treatment strategy for PD. This study provides new insights into the potential mechanism of WMS in PD. Piglet diarrhea (PD) is a common disease, especially in 1- to 3-month-old piglets, with a high incidence in farming production , 3. PD iAnji Prescription in Tang-Song period. WMS including Mume Fructus (MF), Coptis chinensis Franch. (CC), Curcuma longa L. (CL), Terminalia chebula Retz. (TC) and Diospyros kaki L.f. (DK) is now the latest recorded formula in Chinese Veterinary Pharmacopeia (2020 edition) II. Over thousands of years of evolution, only one or two herbs have changed in the prescription. MF, composed of organic acids, flavonoids and fatty acids, used to treat chronic cough, prolonged diarrhea, and other inflammation-related diseases  II. The Traditional Chinese Medicine Systematic Pharmacology (TCMSP) (https://www.tcmspw.com/tcmsp.php) database, which is a systems pharmacology resource for traditional Chinese medicines or related compounds and offers details on the absorption, distribution, metabolism, and excretion properties of a drug with potential biological effects at a systematic level, was used to identify the compounds in the herbs of MF, CC, CL, TC, and DK. Oral bioavailability (OB) and drug likeness (DL) are two essential characteristics of medications taken orally. OB and DL can assess the effectiveness of a drug's systemic circulation, and the resemblance between a molecule and a known medication, respectively. The putative active components were screened using OB and DL in this research. Active substances were identified from prior investigations and data from key databases of Chinese herbal medicine. Active compounds were then screened and considered as putative main constituents and retained using OB ≥30 % and DL ≥0.18 based on data from prior studies and pertinent Chinese herbal medicine databases (https://pubchem.ncbi.nlm.nih.gov/) and imported into the SwissTargetPrediction database with relevant parameters for prediction of target genes (probability > 0). The potential targets were then collected. The target genes corresponding to the compounds were uniformly standardized in UniProt (http://www.uniprot.org/).The formula of WMS containing MF, CC, CL, TC, and DK was obtained from atabases , 17. Foratabases . Active https://www.genecards.org/), OMIM (https://www.omim.org/), and NCBI (https://www.ncbi.nlm.nih.gov/gene/). All potentially relevant genes were obtained. All the disease gene targets were then normalized and converted into gene symbols through UniProt database after removing the redundancy.“Piglet diarrhea” was used as the keyword to explore disease-related genes at GeneCards (https://www.bioinformatics.com.cn/?keywords=%E6%96%87%E6%81%A9%E5%9B%BE).The predicted target genes of WMS and the projected target genes of PD were analyzed by Venn Diagram analysis , and the relationships among them were analyzed. Herbs, compounds, and targets were represented by nodes in the network, and their interactions were represented by edges connecting nodes.https://string-db.org/) (v 11.5) to draw a protein—Protein interaction (PPI) network. The organism was limited to “Sus scrofa.” The results showed the confidence of the interaction between the proteins by scores. The minimum required interaction score was selected at medium confidence data >0.4 to ensure the reliability of the analysis. The data from STRING database were then analyzed by the “Analysis Network” tool in Cytoscape 3.9.1 software to obtain a PPI network. The relevant parameters of degree (DC), betweenness centrality (BC), closeness centrality (CC), and stress were calculated for topology analysis on the PPI network to obtain the key targets.The potential target genes of WMS in PD were uploaded to STRING database (https://david.ncifcrf.gov/tools.jsp) for Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis with “Sus scrofa” as the species. P ≤ 0.01 was considered to be significantly enriched. The top 10 most significantly enriched GO biological processes (BP), cell component (CC), and molecular function (MF) and top 20 items of KEGG pathway results were then mapped (https://www.bioinformatics.com.cn/?keywords=pathway) to draw the enrichment bubble diagram.The potential targets from the intersection were imported into DAVID database (2021)  and saved in pdb format after removing solvent and organic through the PyMol software. The 2D chemical structure of the compounds was obtained from PubChem and converted to mol2 format after minimizing energy through Chem3D 18.0. The ligand and receptor were then converted to pdbqt file format through AutoDockTools-1.5.7. Molecular docking was performed by AutoDock Vina 1.1.2. A binding energy of <-4.25 kcal/mol indicates that the ligand and receptor molecules bind spontaneously. The binding energy of <-5.0 kcal/mol indicates that ligand and receptor molecules are stably bound. The binding energy of <-7.0 kcal/mol indicates that the two have strong binding activity. Results with strong binding force were selected and visualized by Pymol software.The 10 important compounds in accordance with the degree values in WMS were selected to dock to the top 10 core targets from the PPI analysis. The three-dimensional crystal structure of the target protein was downloaded from the RCSB database  described above, the compounds of WMS were screened out. After the removal of non-target compounds, 8, 14, 3, 8, and 4 natural compounds of TC, CC, CL, MF, and DK were obtained, respectively. After removing any duplicate compounds, a total of 34 active compounds in the WMS formula were obtained .TCMSP, PubChem, and SwissTargetPrediction databases were used to predict the target genes of compounds in WMS. The results identified 638 targets, including 325, 447, 58, 75 and 271 targets for TC, CC, CL, DK and MF, respectively.From the GeneCards website and OMIM, 352 genes were determined as highly likely to be associated with PD. The 352 candidate PD-associated genes were compared with the 638 target genes from WMS using Venn diagram analysis . A totalFrom the above data, a network of the complex interactions among components of WMS, effective compounds, and target genes was visualized using Cytoscape; the network comprised 133 nodes and 393 edges . The topin vivo. The network comprised 90 nodes and 607 edges. The results were then imported into Cytoscape to construct a network diagram.The 94 intersection genes were uploaded to the STRING database, and a PPI network was obtained depicting the BP of WMS treatment of PD The PPI network and relevant parameters were obtained with the Analysis Network tool in Cytoscape software. Using the four parameters DC, BC, CC and Stress, the indicators above the median value were selected as the key indicators, and two screenings were performed. The critical values of the first screening were DC >11, CC >0.4623, BC >0.0032, and Stress >255. Through topological analysis, 30 key targets were obtained and screened again. The screening criteria were DC >15, CC >0.6744, BC >0.0093, and Stress >60. Finally, a total of 14 key targets of WMS acting on PD were obtained. The specific screening strategy is shown in P < 0.01 were identified.After inputting common targets into the DAVID 2021 database, 145 KEGG pathways, 184 GO BP, 31 GO cell components and 57 GO molecular functions that met the enrichment criteria of The top 10 most significantly enriched GO BP were selected for analysis . The majThe top 20 most significantly enriched KEGG pathways were selected for analysis . The potUsing the herb, compound, target, and pathway analyses, an entire herb, chemical, target and pathway network was then constructed by Cytoscape using the top 20 signaling pathways. As shown in From the degree analysis in the network, the 10 main active compounds (2R)-5,7-dihydroxy-2-(4-hydroxyphenyl)chroman-4-one (MF3), epiberberine (CC8), moupinamide (CC11), berlambine (CC10), sennoside E_qt (TC2), quercetin (A1), obacunone (CC12), kaempferol (MF2), cheilanthifoline (TC7), and 7-dehydrosigmasterol (TC3) were chosen for molecular docking verification with the 10 core targets MAPK14, CASP3, ESR1, MAPK3, VEGFA, TNF, CCND1, HSP90AA, CAV1, and NOS3. The statistical results of binding energy are shown in Among the 100 receptor-ligand docking combinations, the binding energy of all molecules to proteins was <−5.0 kcal/mol, and 62 groups  had binding energy <−7.0 kcal/mol, which indicated that the main active ingredients in WMS have a strong binding activity with the core targets. The affinity of the compound obacunone to target NOS3 had the lowest binding energy of −11.1 kcal/mol. The binding modes of some key targets to the core active compounds are shown in PD is a common disease in pig farms with a high morbidity and mortality that leads to productivity loss. Antibiotics and medical zinc oxide are the main treatment strategies for PD in pig production . HoweverIn this study, network pharmacology was applied to analyze the effective components and mechanisms of WMS in the treatment of PD. A total of 32 active compounds in the WMS formula were obtained and 10 core compounds were identified. The compounds included terpenoids, flavonoids, alkaloids, and tannins. Flavonoids have the effects of reducing ulcerative colitis and anti-gastric ulcers, improving functional dyspepsia, inhibiting ileal motility, protecting gastric mucosa, and exerting antibacterial and antiviral effects . Quercet+ inward transport and the main transporter protein regulating Cl−/HCOThe mapped PPI network was analyzed and 14 core targets were identified: MAPK14, HSP90AA1, TNF, NOS3, RELA, CCND1, TLR4, VEGFA, MAPK3, CAV1, MAPK1, ESR1, CASP3, and ALB. Intestinal inflammation is one of the main internal causes of piglet diarrhea, and the expression of the intestinal cytokines is one of the main characteristics of intestinal inflammatory response. Previous studies showed that, the gene expression of IL-6, TNF-α and IL-β in jejunum of piglets were increased , 34. The colitis , 40. REL colitis . MAPK14, colitis . TLR4 ma colitis , 44. CAV colitis . VEGFR i colitis . Epiderm colitis . We specGO enrichment analysis showed that WMS may play a therapeutic role by regulating the immune response of piglets, modulating cell growth, and regulating apoptosis. PD is caused by impaired intestinal barrier function, disruption of intestinal flora homeostasis, and disturbances in intestinal chemical, mechanical, and immune barriers , 49. Thevia the PI3K-AKT pathway (PD progresses through a variety of pathophysiological processes, including stimulation of intestinal structural damage, digestive dysfunction, oxidative stress, and inflammation response. KEGG pathway enrichment analysis showed that the pharmacological effects of WMS in treating PD included effects on the AGE-RAGE signaling pathway, PI3K-Akt signaling pathway, TNF signaling pathway, NOD-like receptor signaling pathway, and IL-17 signaling pathway. As can be seen from the  pathway . IL17 is pathway . NOD-lik pathway . TogetheWMS is a traditional Chinese medicine formula that is effective in treating PD. In this study, network pharmacology combined with molecular docking was applied to explore the mechanism underlying the effect of WMS on PD. The results indicated that WMS may exert its anti-PD activities through the TNF signaling pathway and IL-17 signaling pathway. The AGE-RAGE signaling pathway, PI3K-Akt signaling pathway, and NOD-like receptor signaling pathway may also be involved. Further experiments are required to elucidate the precise mechanism by which WMS functions in PD.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.HHY wrote the manuscript. HHY, WL, XYJ, and GQY designed the figures and edited the manuscript. HHY supervised data analysis and manuscript editing, in cooperation with XYZ, WZ, and YHW. All authors contributed to the article and approved the submitted version.


Az1-mediated degradation of other substrates such as cyclin D1 (CCND1), DNp73 (TP73) or Mps1 regulates cell growth and centrosome amplification, and the currently known six Az1 substrates are all linked with tumorigenesis. To understand whether Az1-mediated protein degradation might play a role in regulating other cellular processes associated with tumorigenesis, we employed quantitative proteomics to identify novel Az1 substrates. Here, we describe the identification of LIM domain and actin-binding protein 1 (LIMA1), also known as epithelial protein lost in neoplasm (EPLIN), as a new Az1 target. Interestingly, between the two EPLIN isoforms (α and β), only EPLIN-β is a substrate of Az1. The interaction between EPLIN-β and Az1 appears to be indirect, and EPLIN-β is degraded by Az1 in a ubiquitination-independent manner. Az1 absence leads to elevated EPLIN-β levels, causing enhanced cellular migration. Consistently, higher LIMA1 levels correlate with poorer overall survival of colorectal cancer patients. Overall, this study identifies EPLIN-β as a novel Az1 substrate regulating cellular migration.Polyamines promote cellular proliferation. Their levels are controlled by ornithine decarboxylase antizyme 1 (Az Summary: EPLIN-β is degraded by ornithine decarboxylase antizyme 1 (Az1) in a proteasome-mediated, ubiquitination-independent manner. Az1 absence leads to elevated EPLIN-β levels, causing enhanced cellular migration. Polyamines, which include putrescine, spermidine and spermine, are bound to and modulate the functions of negatively charged molecules, such as DNA and RNA, as well as negatively charged proteins . They arODC1) is the rate-limiting enzyme regulating polyamine synthesis (OAZ1 (Az1), OAZ2 (Az2) and OAZ3 (Az3) in humans : ORF1 contains the translational start codon and also has a stop codon at a frameshift site, and ORF2 encodes most of the protein and lacks an initiation codon . A +1 ri1 induces ODC degradation , which is highly similar to ODC , Aurora A (AURKA), cyclin D1 (CCND1), Mps1 and Smad1 (1 is a negative regulator of DNp73 (TP53) tumor-suppressor family, that is overexpressed in a variety of cancers and the expression of which leads to resistance to a variety of chemotherapeutic drugs and metastasis  cells. These data therefore demonstrate that EPLIN-β is a novel Az1 substrate mediating cellular migration.Loss of EPLIN-α expression has been implicated in the progression of various cancers, such as oral, breast and prostate cancers, in which its expression was either downregulated or completely abolished compared to normal tissues, indicating a tumor-suppressor role for EPLIN-α , 2016. O1 substrates, we generated HCT116 human colorectal cells lacking Az1 using CRISPR-Cas9 genome editing. The aim was to identify proteins with elevated expression levels in the absence of Az1. Colorectal cells were chosen for the screen as polyamines are associated with the physiology of the human gut, and their levels are elevated in gastrointestinal tumors  in one clone  proteomics methodology (1−/− knockout (KO) cells and their wild-type (WT) controls. Changes in expression of greater than twofold between heavy and light proteins (H/L ratios) were considered significant  was second on the list. Hence, we focused our efforts in characterizing the role of Az1 in the regulation of EPLIN. As aforementioned, EPLIN is expressed as two isoforms generated from an alternative pre-mRNA splicing event  to block protein synthesis and subsequent chase indicated that the decay of EPLIN-β and ODC proteins was significantly delayed in the absence of Az1  led to a decrease in EPLIN-β levels, this was not the case in cells lacking Aznchanged E, furthee of Az1 F, sugges1 in H1299 cells to evaluate the direct impact of Az1 overexpression on EPLIN levels. Co-expression with Az1 led to a decrease in EPLIN-β but not EPLIN-α levels . Thus, we believe that the interaction between Az1-mediated EPLIN-β degradation occurs independent of ubiquitination, similar to other Az1 substrates. To that end, we co-expressed Az1 and EPLIN-β along with either WT ubiquitin, or one in which the seven lysine residues required for ubiquitin chain formation were substituted to arginine residues (i.e. Ubi-KO)  . EPLIN-βn status F. As a pn status G, as repn status . These d1–EPLIN-β interaction has a functional role in cellular growth regulation, we first evaluated the effects of Az1 deficiency on cellular growth and migration. Compared to control cell numbers, Az1−/− cell numbers increased significantly, leading to increased cellular colony formation  (1 on cellular growth and motility.To determine whether the Azormation A. Moreov0.39 mm) B, togeth1−/− clones   E. These LIMA1 expression was higher in tumor tissues compared to that in normal tissues in colorectal cancers, based on the University of Alabama at Birmingham Cancer data analysis Portal (UALCAN) website  (LIMA1 (EPLIN-β only) are correlated with a significantly lower survival probability  A. Furtheex.html)  indicate1 substrate. This work expands the repertoire of Az1 substrates to seven and also suggests the existence of many more substrates that are yet to be identified and characterized. Our proteomics analysis identified around ten substrates in HCT116 colorectal cells that were upregulated significantly (H/L>2) in the absence of Az1, of which we characterized EPLIN-β here, and the others are being currently investigated. We envisage that there might be other substrates being regulated in a cell-type-specific and context-dependent manner, and these are being explored in our laboratory. The growing list of Az1 substrates prompts us to speculate that Az1-mediated protein degradation might be a yet underexplored regulatory mechanism that could be critical in the regulation of cellular growth, and thus in cancer development, as well as in other physiological and pathological processes.This report describes EPLIN-β as a novel AzPolyamines are ubiquitously produced, and their levels are relatively high in rapidly growing cells. In particular, increased polyamines levels have been shown to directly correlate with disease activity and tumor burden . Polyami1, has been used to study the mechanistic basis of Az1-mediated degradation. Binding of Az1 to ODC leads to disruption of ODC homodimers, leading to the exposure of the C-terminal tail, which targets ODC to the proteasome in a ubiquitin-independent manner  compared to those with lower LIMA1 levels. This further indicates that EPLIN-α expression is either downregulated or undetectable in colorectal cancers, whereas EPLIN-β expression is upregulated, consistent with previous observations , leading to enhanced migration or invasion capabilities . Overexprvations .1 alone is insufficient, although it is necessary for EPLIN-β degradation. As such, the other EPLIN-α isoform, which is also able to bind to Az1, is not degraded by Az1. We speculate that although the binding region is common to both isoforms, the additional 160 amino acids of EPLIN-β are critical for degradation. Given the lack of these 160 amino acids in EPLIN-α, it is not degraded, albeit being able to bind. Therefore, structural predictions of the N-terminus of EPLIN-β were performed using the ‘AlphaFold Protein Structure Database’ , suggesting that the mechanism of degradation of various Az1 substrates might be different.This observation is similar to the p73 proteins, of which DNp73, but not the TAp73 isoform, is degraded by Azbind Az1 . This inoteasome . Interes1, but not Az2, is the key mediator of EPLIN-β degradation. Both silencing and overexpression of Az2 did not alter EPLIN-β levels. Thus, although polyamines can induce the frameshifting of both Az1 and Az2, only the former appears to be the major regulator of protein abundance, at least in the cellular systems used in our study. Thirdly, it is to be noted that many of the newly identified substrates have a much lower H/L ratio compared to that of ODC. This probably provides an explanation as to why other Az1 substrates have not been identified thus far. Moreover, we detected the interaction between Az1 and EPLIN-β using overexpression systems, as it is technically difficult to detect interaction of endogenous proteins, as antibodies against processed full-length Az1 are currently not available commercially.Secondly, Az1, we were not able to notice a binding using the respective purified proteins, as well as the LIM domain alone (data not shown). This suggests that although they do bind to each other, the interaction does not occur under isolated situations, indicating that either (1) the purified proteins are not correctly folded or in the correct conformation to bind each other, as we needed to use modified methods for purification of Az1/EPLIN-β, or (2) that some other unknown factors, particularly a protein co-factor, might be necessary for this interaction. Further biochemical studies are required to confirm whether this interaction is indeed direct or indirect.Finally, although EPLIN-β co-immunoprecipitated with Az1, mediating Az1-dependent cellular growth and migration  . These t1, ODC or AZIN have been described previously (The relevant plasmids were transfected using Lipofectamine 2000 (Invitrogen) in accordance with the manufacturer's instructions. Expression vectors  expressing full length EPLIN-α, EPLIN-β, Azeviously .13C615N2 L-lysine-2HCl (143 mg/ml) (0.1%) , 13C615N4 L-arginine-HCl (83 mg/ml) (0.1%)  and 1% penicillin-streptomycin solution ] or ‘light’  medium and the corresponding Az1-KO cells were cultured with ‘light’ or ‘heavy’ medium for ∼4–5 doublings, at a cell density of around 20–30% confluence. The cells were harvested, and normalized protein extracts were used for SILAC analysis. LDS buffer (Invitrogen) and reducing agent (Invitrogen) were added to the lysates and boiled for 5 min. The proteins were then separated on 4–12% NuPage Novex Bis–Tris Gels (Invitrogen); stained with a colloidal blue staining kit (Invitrogen) and digested with trypsin using in-gel digestion procedures , 10% dialyzed fetal bovine serum , ocedures .6 and a maximum injection time of 10 ms. The top twenty most intense peptide ions in each survey scan were sequentially isolated to an AGC target value of 5×104 with a resolution of 17,500 and fragmented using a normalized collision energy of 25. A dynamic exclusion of 10 s and isolation width of 2 m/z were applied. SILAC peptide and protein quantification was performed with MaxQuant version 1.5.0.30 (https://maxquant.org/) using default settings. Database searches of MS data were performed using UniProt human fasta (2017) (https://www.uniprot.org/proteomes/UP000005640) with tryptic specificity allowing a maximum of two missed cleavages, two labeled amino acids and an initial mass tolerance of 4.5 ppm for precursor ions and 0.5 Da for fragment ions. Cysteine carbamidomethylation was searched as a fixed modification, and N-acetylation and oxidized methionine were searched as variable modifications. Labeled arginine and lysine were specified as fixed modifications. Maximum false discovery rates were set to 0.01 for both proteins and peptides. Proteins were considered identified when supported by at least one unique peptide with a minimum length of seven amino acids. All mass spectrometry data have been deposited in the PRIDE repository with the accession number PXD039865.Tryptic peptides were analyzed using an EASY-nLC 1000 coupled to a Q Exactive Hybrid Quadrupole-Orbitrap (Thermo Fisher Scientific). The peptides were resolved and separated on a 50 cm analytical EASY-Spray column  equipped with a pre-column over a 120 min gradient ranging from 8 to 38% of 0.1% formic acid in 95% acetonitrile/water at a flow rate of 200 nl/min. Survey full-scan mass spectrometry (MS) spectra  were acquired with a resolution of 70,000, an automatic gain control (AGC) target of 3×10Cell lysates were prepared in lysis buffer containing 0.5% Nonidet P-40 (NP-40) with protease inhibitor cocktail and dithiothreitol as described previously . The totCell lysates, prepared as described above, were used for co-immunoprecipitation (IP) assays as described previously . Briefly1 or Az2 in HCT116 cells, CRISPR-Cas9 system was used as previously published  or Az2  were designed using the online tool CRISPOR (http://crispor.tefor.net/) and cloned into the vectors lentiCRISPRv2 (Addgene plasmid #52961) using Bbs1 enzyme. After validation by DNA sequencing, the sgRNA vectors were co-transfected with the corresponding packaging plasmids into 293T cells, and the virus supernatant was prepared. HCT116 cells were then transduced with polybrene  and viral supernatant, and selected on 0.5 μg/ml puromycin-containing medium for 10–12 days. Single colonies were randomly picked up into 96-well plates for genotyping by PCR and the knockouts were further validated by PCR using the Az1- or Az2-specific genomic primers.To knockout Azublished . Briefly1 KO cells were seeded into six-well plates at ∼60% confluence. After 24 h, the cells were treated with the protein synthesis inhibitor cycloheximide (Sigma-Aldrich) for the indicated durations before harvesting and analysis by immunoblotting.To determine EPLIN-β half-life, HCT parental and Az1 (as above) or FLAG-EPLIN-β (as above). After 24 h of transfection, cells were lysed in NP-40 lysis buffer and then incubated with anti-FLAG antibody  for 3 h and protein G-agarose beads for 2 h at 4°C. After washing three times, the ubiquitinated EPLIN-β was detected by immunoblotting using anti-His monoclonal antibody.For EPLIN-β ubiquitination analysis, H1299 cells were transfected with His-ubiquitin , Myc-Az1-KO cells were seeded into six-well plates with glass coverslips. The next day, the permeabilized cells were blocked for 1 h with PBS supplemented with 2% bovine serum albumin and 5% FBS, and incubated with primary anti-ODC and anti-EPLIN antibodies (as above) antibodies at 4°C overnight, at 1:200 dilution .HCT116 parental and Azdilution . After wTotal RNA was prepared from cells using TRIzol reagent (Invitrogen) according to the manufacturer's instructions. Then, 1.5–2 µg of total RNA was reverse transcribed into cDNA using SuperscriptII (Invitrogen). The sequences of real-time PCR primers and semi-quantitative reverse transcription-PCR analysis are the same as described .8453). Briefly, two restriction enzymes were used to linearize the vector followed by gel extraction. The insert shRNAs consist of two oligonucleotides that are complementary  – were plated on six-well plates and grown for 2 weeks. After rinsing three times with PBS, cells were fixed with 4% paraformaldehyde in PBS, and stained with 1% Crystal Violet.HCT116 cells – WT, Az4 cells/well in complete DMEM and cultured to confluence. The cells were then serum-starved overnight in DMEM, and the confluent cell monolayers were scraped with a yellow pipette tip to generate scratch wounds and were washed twice with Opti-MEM (Invitrogen) to remove cell debris  and <0.05 (*). The log-rank test was applied in the Kaplan–Meier survival examination. Statistical comparisons between two groups were carried out by two-tailed unpaired Student's t-test. A two-tailed P-value <0.05 was considered significant.The differences in mean values were considered significant at 10.1242/joces.260427_sup1Supplementary informationClick here for additional data file.


Recent efforts to repurpose existing drugs to different indications have been accompanied by a number of computational methods, which incorporate protein-protein interaction networks and signaling pathways, to aid with prioritizing existing targets and/or drugs. However, many of these existing methods are focused on integrating additional data that are only available for a small subset of diseases or conditions.OpenTargets.org. The Pathway2Targets algorithm takes a list of significant pathways as input, then retrieves and integrates public data for all targets within those pathways for a given condition. It also incorporates a weighting scheme that is customizable by the user to support a variety of use cases including target prioritization, drug repurposing, and identifying novel targets that are biologically relevant for a different indication.We have designed and implemented a new R-based open-source target prioritization and repurposing method that integrates both canonical intracellular signaling information from five public pathway databases and target information from public sources including p-value < 0.025) that included EGFR, VEGFA, and PTGS2. Interestingly, there was no statistically significant enrichment of targets for other cancers in this same list suggesting high specificity of the results. We also adjusted the weighting scheme to prioritize more novel targets for CRC. This second analysis revealed epidermal growth factor receptor (EGFR), phosphoinositide-3-kinase (PI3K), and two mitogen-activated protein kinases (MAPK14 and MAPK3). These observations suggest that our open-source method with a customizable weighting scheme can accurately prioritize targets that are specific and relevant to the disease or condition of interest, as well as targets that are at earlier stages of development. We anticipate that this method will complement other approaches to repurpose drugs for a variety of indications, which can contribute to the improvement of the quality of life and overall health of such patients.As a proof of concept, we applied this algorithm to a public colorectal cancer RNA-sequencing dataset with 144 case and control samples. Our analysis identified 430 targets and ~700 unique drugs based on differential gene expression and signaling pathway enrichment. We found that our highest-ranked predicted targets were significantly enriched in targets with FDA-approved therapeutics for colorectal cancer ( Substantial effort and resources have been devoted to identifying therapeutic treatments for many human diseases and conditions. The maladies could be caused by autoimmunity, uncontrolled cell growth, genetics, infection, and other acute or chronic ailments. Since moving a candidate treatment through the process of approval by the US Food and Drug Administration (FDA) is risky , often ti.e., indication)  . The benication) . Subsequication) . This isication) . The majication) , gene seication) , and/or ication)  for repuication) .Even with such recent advances, many prioritization algorithms rely on public or proprietary protein network data , with soGiven the specialization that is prevalent among many repurposing tools, the aim of the current study was to incorporate a novel, flexible, and customizable open-source target prioritization method into the Pathway2Targets algorithm, which would increase the number of supported use cases. This updated algorithm retrieves additional target information, clinical trial data, automatically fetches the reactome pathway diagrams for the signaling pathways with the highest number of targets, and accepts reactome pathway enrichments generated by the enrichr algorithm . This adGSE156451) to enable confident downstream repurposing analysis , determine quality control metrics with FastQC (www.bioinformatics.babraham.ac.uk/projects/fastqc/), map and quantify reads to the human GRCh38 transcriptome with Salmon  were then subjected to signaling pathway analysis using the Signal Pathway Impact Analysis (SPIA) algorithm with 3,000 bootstrap replicates to generate a null distribution for each of over 2,000 public signaling pathways  software . Brieflyh Salmon , and calh Salmon  by compah Salmon . Similarpathways , as repopathways . The lispathways , Reactompathways , Pathwaypathways , BioCartpathways .p-value < 0.05) generated by SPIA, although an output file from the enrichr algorithm would have also been compatible , together with the associated source databases, and retrieved all gene products that are members of each pathway. The diverse public identifiers used by each pathway database were then automatically converted and submitted to the Open Targets database to retrieve a smaller subset of the available target information. The data for ~10 fields associated with each target were then programmatically retrieved and stored for processing. These fields included target name, target symbol, target Ensembl identifier, therapeutic name, therapeutic type, modulation of the target, total tractability, maximum clinical phase, and approval status. The single output file combined these fields with the relevant pathway information for each target   . Examplend INSL5 .p-value correction to reduce the number of false-positive results. This pathway enrichment analysis identified 63 statistically significant  and biologically relevant intracellular signaling pathways including the inhibition of HIF-1 , activation of cell cycle , activation of DNA replication , activation of PLK1 , and inhibition of T-cell receptor signaling pathways   algorithm to the list of DEGs in CRC. The SPIA algorithm uses permutation and bootstrapping to generate a null distribution for each pathway, and then applies a Bonferroni e 0.001) .After identifying significantly affected signaling pathways, we then wanted to determine whether our updated Pathway2Targets software could be used for two purposes: (1) to predict existing late-stage therapeutics from the enriched signaling pathway data, as well as those that could potentially be repurposed for treating colorectal cancer and (2) to generate a ranked list of prioritized targets that were specific to colorectal cancer. To evaluate these outcomes, we used the new Pathway2Targets software to analyze our significant pathway results from the CRC dataset.Our Pathway2Targets analysis generated a list of 430 targets  and apprWe next wanted to quantify how many of the predicted targets were approved for either colorectal cancer and/or any cancer. To do so, we manually reviewed the top 50 targets, together with the attributes and default weighted scores. This analysis showed that the top 50 prioritized targets, using the default weighting parameters, included three approved CRC targets and 11 targets for other cancers. Given that many labs prefer to validate a much smaller number of results in the wet-lab, we reduced the top 50 to a list of 15 potential candidates for drug repurposing. After applying this filtering process, we observed three CRC targets  and five targets that had undergone one or more phase IV trials in any indication  were all found in the top 15 results from this list .p-value < 0.025), while there was no significant enrichment for all cancers . This result suggests that our approach is capable of predicting biologically relevant targets based on the specific signaling pathway profile of the disease/system being evaluated.To determine whether our selected parameters were able to accurately predict therapeutic candidates that would be specific for colorectal cancer or all cancers, we performed a hypergeometric statistical analysis of the top 15 targets. This analysis showed a significant enrichment of CRC targets in the top 15 predicted results  demonstrates that our flexible weighting scheme can accurately focus subsequent wet-lab validation on “hits” that are more likely to be effective, as has been done previously . It has Our second target prioritization analysis simulated a scenario that involved prioritizing targets when no/minimal approved drugs exist. Our customizable weighting scheme made this possible by quickly changing the weight parameters in the software. Interestingly, the results from this analysis identified many targets that are relevant to oncology-related indications including MTOR , MAPK14 The novelty of our Pathway2Targets target prediction algorithm consists of multiple factors such as open-source code, a single input file of significant pathways, a flexible weighting scheme, incorporation of signaling pathways, output of prioritized targets and therapeutics for repurposing open combines significant signaling pathway enrichment results with publicly available data and our custom weighting scheme can be integrated with safety and efficacy data to further complement ongoing prioritization and repurposing efforts. While the default weighting scheme appears to be capable of identifying relevant targets for CRC in our first analysis, the default parameters may not be universally applicable to all possible use cases. We envision that modifying the weighting scheme should adequately support a wider set of use cases.etc. It is imperative that the user provides high-quality data with minimal numbers of confounding variables, such as timepoints, as input. Although our predictions for CRC are enriched in approved targets, it is important to note that any target predictions made by this software should be confirmed in well-controlled registered clinical trials prior to use in the clinic. Users of the software should also keep in mind that the algorithm only identifies existing therapeutics that are present in the Open Targets platform. As such, it is unrealistic to expect the software to predict drugs that have not been publicly disclosed or that it performs docking predictions to identify novel molecules that bind to novel targets. Similarly, those applying the results to early-stage research should account for toxicity or off-target effects that are associated with a given therapeutic. Even with these caveats, we believe our algorithm can be applied across a broad variety of human diseases and conditions. To do so, users would only need to generate a list of DEGs from their disease of interest, run a pathway enrichment analysis using either SPIA or enrichr, and then use the pathway results as input to the Pathway2Targets software. Future collaborative work will be required to determine how Pathway2Targets can be integrated with precision medicine, artificial intelligence, or other approaches.We believe that these predictions of approved and novel targets for CRC show promise and that the software warrants additional investigation in other indications. Additional work will be required to determine the extent to which this software can be applied to other -omics technologies such as whole genome sequencing, chromatin immunoprecipitation sequencing (ChIP-seq), shotgun proteomics, We expect that researchers will be interested in applying this open-source target prioritization algorithm to predict the most relevant targets for a disease space regardless of competition in the field. However, some users may need to modify the default weights for one or more parameters in order to yield improved results for any given use case. Although additional future analyses are needed to confirm that this approach remains relevant in other human diseases and conditions, we expect that our flexible and customizable weighting approach will facilitate such efforts from gene expression and pathway data. In conclusion, we expect that this improved algorithm will facilitate future predictions of therapeutic targets that could be repurposed for other indications.This software is platform-independent and has been successfully tested on 64-bit RedHat Linux and on Mac OS 12.0 and 13.0.This software is written in R (version 3.6.1 or later).10.7717/peerj.16088/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.16088/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.16088/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.16088/supp-4Supplemental Information 4Click here for additional data file.10.7717/peerj.16088/supp-5Supplemental Information 5Click here for additional data file.10.7717/peerj.16088/supp-6Supplemental Information 6Click here for additional data file.10.7717/peerj.16088/supp-7Supplemental Information 7Click here for additional data file.