Actinidia chinensis Planch  in the treatment of colon cancer are still unclear. In the present study, the integration of network pharmacology, molecular docking, and cell experiments was employed to study the effective mechanism of ACP against colon cancer.As an anticancer Chinese herbal medicine, the effective components and mechanism of The Venn diagram and STRING database were used to construct the protein–protein interaction network (PPI) of ACP-colon cancer, and further topological analysis was used to obtain the key target genes of ACP in colon cancer. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to visualize the related functions and pathways. Molecular docking between key targets and compounds was determined using software such as AutoDockTools. Finally, the effect of ACP on CT26 cells was observed in vitro.The study identified 40 ACP-colon key targets, including CASP3, CDK2, GSK3B, and PIK3R1. GO and KEGG enrichment analyses found that these genes were involved in 211 biological processes and 92 pathways, among which pathways in cancer, PI3K-Akt, p53, and cell cycle might be the main pathways of ACP against colon cancer. Molecular docking verified that the key components of ACP could stably bind to the corresponding targets. The experimental results showed that ACP could inhibit proliferation, induce apoptosis, and downregulate the phosphorylation of PIK3R1, Akt, and GSK3B in CT26 cells.ACP is an anti-colon cancer herb with multiple components, and involvement of multiple target genes and signaling pathways. ACP can significantly inhibit proliferation and induce apoptosis of colon cancer cells, which may be closely related to the regulation of PI3K/AKT/GSK3B signal transduction. Colorectal cancer (CRC) is a common malignancy with the third and second highest incidence and mortality rates of 10.0% and 9.4% worldwide, respectively . The comActinidia chinensis Planch  is one of the representative anticancer herbal medicines that is able to play a therapeutic role in a variety of cancers. In both in vitro and in vivo experiments, Gao et al. [In recent years, TCM-based herbal medicines have gained more and more recognition worldwide and have been widely used in the field of anticancer, among which o et al.  discovero et al. . Furthero et al.  showed tThe majority of recent research has focused on examining TCM's mechanism of action via a single target and pathway; its multi-target and multi-pathway functions have not been taken into account. In line with the holistic idea of TCM, network pharmacology is a novel research approach developed by Andrew L. Hopkins that has the potential to transform the single-target, single-method research paradigm into a systematic, comprehensive investigation of the relationships between drugs, targets, and diseases , 8. Netwhttp://tcmspw.com/tcmsp.php) [http://www.tcmip.cn/ETCM/) [http://bidd.group/NPASS/index.php) [http://herb.ac.cn/) [The active compounds of ACP were collected from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform  , the Encn/ETCM/) , the Natdex.php) , and the.ac.cn/)  databasehttps://pubchem.ncbi.nlm.nih.gov/), and then submitted to the Similarity Ensemble Approach , STITCH (http://stitch.embl.de/), and SwissTargetPrediction (http://swisstargetprediction.ch/) databases to predict targets based on structure similarity [https://www.uniprot.org/). Max Tc ≥ 0.4, confidence score ≥ 0.4, and probability value ≥ 0.5 were used as screening criteria to predict the targets of ACP active ingredients.The SMILE numbers of the compounds were retrieved from the PubChem database (milarity –16. Convhttp://gepia2.cancer-pku.cn/#index) with the criteria of q value < 0.01 and fold change (FC) ≥ 2 [Colon cancer-related genes in the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases were identified using the Gene Expression Profiling Interactive Analysis (GEPIA2) online tool ((FC) ≥ 2 , 18.https://www.omicshare.com/tools/Home/Soft/venn). The above target genes were then uploaded to the STRING database (https://string-db.org/) [The target genes of ACP against colon cancer were analyzed using the Venn online tool (db.org/)  to constdb.org/)  was usedhttps://david.ncifcrf.gov/) [http://www.bioinformatics.com.cn/, http://vip.sangerbox.com/home.html) [The ACP target genes were imported into the DAVID 6.8 database (rf.gov/)  for the rf.gov/) –24. GO ame.html) .https://www.rcsb.org/) [https://projects.biotec.tu-dresden.de/plip-web/plip/) [The PDB format of protein structures was from the Protein Data Bank (PDB) (sb.org/) , and watsb.org/) . The prosb.org/)  and saveb/plip/)  and PyMOACP granules  were commercially produced by Sichuan Neo-green Pharmaceutical Technology Development Co., Ltd. . The product has a brownish-yellow granular texture and is soluble in water, so the aqueous extract of ACP granules was used for all subsequent experiments. RPMI-1640 medium, fetal bovine serum (FBS), penicillin–streptomycin, and trypsin were obtained from Gibco . The Cell Counting Kit-8 (CCK-8) assay, QuickBlock™ Blocking Buffer, and BCA Protein Assay Kit were purchased from Beyotime Biotechnology . FITC Annexin V apoptosis detection kit I was produced by BD Pharmingen™ . Antibodies against GAPDH, PIK3R1, p-PIK3R1, Akt, p-Akt, GSK3B, and p-GSK3B were purchased from Bioworld .Mouse colon cancer CT26 cells were obtained from The Cell Bank of Type Culture Collection of Chinese Academy of Sciences . CT26 cells were cultured in RPMI-1640 medium containing 10% FBS and 1% penicillin–streptomycin and placed in a 37 °C incubator containing 5% carbon dioxide, and logarithmic growth phase cells in good condition were taken for subsequent experiments.3/well in 96-well plates for 24 h, and then the cells were treated with different doses of ACP extracts . The CCK-8 kit was added after 48 h of drug action to make it 10% in each well and incubated at 37 °C, protected from light, for 2 h. The absorbance of each well at 450 nm was detected using a microplate reader. Cell survival (%) =  × 100%.CT26 cells were inoculated at 2 × 105/well. 24 h later, different concentrations of ACP extracts  were added and treated. After 48 h of drug action, the cells were collected, washed twice with cold PBS, and resuspended with binding buffer. Then, 5 μl each of Annexin V-FITC and PI were added sequentially and stained for 15 min at room temperature, and apoptosis was analyzed by flow cytometry.Logarithmic growth phase CT26 cells were taken and inoculated in 6-well plates at 1 × 10Protein expression and phosphorylation were evaluated by western blot . CT26 ceP < 0.05 was considered statistically significant.The experimental results were statistically analyzed by SPSS 24.0. The results were expressed as the mean ± standard deviation (SD). Two independent sample t test was applied when the data satisfied a normal distribution. A non-parametric rank-sum test was used for non-normally distributed data. Eleven ACP components were acquired from the TCMSP database, 31 components were obtained from the HERB database, and 12 components were obtained from NPASS. After deletion of the repeated components, 30 effective compounds were obtained , and the top 8 pathways (gene numbers ≥ 50) included metabolic pathways, pathways in cancer, lipid and atherosclerosis, PI3K-Akt signaling pathway, MAPK signaling pathway, chemical carcinogenesis-receptor activation, pathways of neurodegeneration-multiple diseases, and chemical carcinogenesis-reactive oxygen species , mainly including pathways in cancer, hepatitis B, lipid and atherosclerosis, cell cycle, cellular senescence, human cytomegalovirus infection, PI3K-Akt signaling pathway, p53 signaling pathway, measles, kaposi sarcoma-associated herpesvirus infection, Epstein-Barr virus infection, and human T-cell leukemia virus 1 infection  in a dose-dependent manner , as shown in Fig. In order to clarify the effect of ACP on colon cancer CT26 cells, we chose the CCK-8 assay to detect the cell viability of CT26 cells after treatment with different concentrations of ACP. The results showed that 1600–12800 μg/ml ACP significantly inhibited the proliferation of CT26 cells , Solanum nigrum L. (Longkui), Duchesnea indica (Andr.) Focke (Shemei), Scutellaria barbata D. Don (Banzhilian), and Viscum coloratum (Kom.) Nakai (Hujisheng). TBD can inhibit the growth of CRC, reduce the adverse effects of chemotherapy, relieve symptoms, and improve the immune function and quality of life in patients with CRC [Recent years have seen a dramatic shift in people's eating patterns and lifestyles due to the ongoing development of material living conditions. High-fat and low-dietary fiber diets, a lack of physical exercise, and other factors have contributed to the increasing incidence of CRC year by year, creating a greater health and economic burden. Existing treatments are effective in prolonging the survival time of patients with advanced CRC, but the 5-year survival rate of metastatic patients is still low, and all of these treatments are accompanied by varying degrees of side effects and complications . TCM haswith CRC –35. DespIn the present study, the key components and potential targets of ACP for the treatment of colon cancer were studied via network pharmacology and molecular docking to provide a feasible basis for the study and application of ACP. The results show that quercetin, emodin, kaempferol, and aloeemodin are effective, active components in ACP that exerts anti-colon cancer effect. Quercetin is a natural flavonoid that has powerful anti-tumor and antioxidant effects . NumerouIn addition to the above four key ingredients, other ingredients also affect the treatment of colon cancer. Sitosterol can effectively block the Wnt/β-catenin pathway by down-regulating the expression of the lymphoid enhancer-binding factor 1 (LEF-1) gene and its downstream targets, c-myc, survivin, and CCND1, and exerting anti-HCT116 cell proliferation and invasion effects . OleanolIn the present study, 40 key target genes of ACP for colon cancer treatment were identified. Caspase-3 (CASP3) is an apoptosis-related protein; Cyclin-dependent kinase 2 (CDK2) and cell cycle-related proteins cyclin B1 (CCNB1) are cell cycle regulatory proteins. Phosphoinositide-3-kinase regulatory subunit 1 (PIK3R1) is an important protein in the PI3K/Akt signaling pathway, and it is related to cell proliferation and apoptosis. Matrix metallopeptidase 9 (MMP9) is a matrix metalloproteinase that is closely related to metastasis. Glycogen synthase kinase 3 beta (GSK3B) is a multifunctional serine/threonine kinase that is involved in regulating cellular activities such as cell proliferation, differentiation, apoptosis, metabolism, and stem cell renewal, and its polymorphisms are associated with the development of CRC . TopoisoThe key targets of ACP, according to GO and pathway enrichment studies, may bind to proteins and double-stranded DNA, control the actions of protein kinases like serine, threonine, and histone, and participate in protein dimerization and other activities, thus influencing the pathways in cancer, hepatitis B, lipid, and atherosclerosis, cell cycle, cellular senescence, human cytomegalovirus infection, PI3K-Akt signaling pathway, p53 signaling pathway, measles, kaposi sarcoma-associated herpesvirus infection, Epstein-Barr virus infection, human T-cell leukemia virus 1 infection, IL-17 signaling pathway, prostate cancer, and progesterone-mediated oocyte maturation. They also participate in bioprocesses such as drug response, signal transduction, angiogenesis, cell migration, apoptosis, and cell cycle. Pathways in cancer are a collection of multiple pathways involving Wnt, PI3K-Akt, MAPK, and other tumor-related signaling pathways. The Wnt/β-catenin signaling pathway is a recognized driver of CRC. As a functional effector molecule of signaling, the modification and degradation of β-catenin is a key factor in the development of CRC and can participate in proliferation, stemness, apoptosis, EMT, invasion, and metastasis of CRC cells . The PI3Finally, based on the combination of KEGG pathway enrichment analysis and molecular docking results, we selected the PI3K-Akt signaling pathway as the target and observed the anticancer effects of ACP in vitro. The results showed that ACP inhibited the proliferation of CT26 cells in a dose-dependent manner, induced apoptosis, and inhibited the phosphorylation of PIK3R1, AKT, and GSK3B. PIK3R1 is the regulatory subunit of PI3K that could activate AKT through phosphorylation and then inhibit GSK3B through phosphorylation. GSK3B inhibits the expression of its downstream β-catenin and its target genes survivin, c-Myc, and cyclin D1 . SurviviHowever, our study has the following limitations: The relevant information included in the existing drug and target gene databases may be incomplete, which reduces the credibility of the prediction results; the role of ACP in the regulation of GSK3B downstream signaling molecules needs to be further investigated; and the present study only addressed the apoptosis-inducing effect of ACP in colon cancer cells and did not delve deeper into other roles of ACP on CRC such as invasion and metastasis. Nevertheless, our findings provide important preliminary information on the role of ACP in anti-CRC therapy, suggesting that ACP may be a potentially effective drug for the treatment of CRC.In summary, the active components of ACP for the treatment of colon cancer include quercetin, emodin, kaempferol, and aloeemodin, and the effective targets include CASP3, CDK2, GSK3B, PIK3R1, HSP90AA1, MMP9, TOP2A, CCNB1, and MYC, with pathways in cancer, cell cycle, cellular senescence, the PI3K-Akt signaling pathway and the p53 signaling pathway, which contribute to multiple bioprocesses, such as drug response, signal transduction, angiogenesis, cell migration, apoptosis, and cell cycle. ACP can also inhibit cell proliferation and induce apoptosis through a mechanism related to the regulation of PI3K/AKT/GSK3B signaling. This study systematically analyzed the active components, target genes, and signaling pathways of ACP in the treatment of colon cancer, thus providing a basis for the application and study of ACP.


Introduction: Previous studies indicated that Wuda Granule (WDG) has been applied in the treatment of gastrointestinal motility disorder (GMD), but the effect and underlying mechanisms is yet to be elucidated. This study aimed to explore the mechanism and pharmacological effect of WDG for GMD via network analysis, verification of animal experiments and clinical experiments.Methods: The chemical components of WDG were identified from the Traditional Chinese Medicine Systems Pharmacology Database , and the Encyclopedia of Traditional Chinese Medicine  according to oral bioavailability (OB) ≥ 20% and drug-likeness (DL) ≥ 0.10. The targets of WDG compounds were retrieved from the Swiss Target Prediction database (http://www.swisstargetprediction.ch/) and targets related to GMD were retrieved from GeneCards database (https://www.genecards.org/). Network analysis were performed to screen the key active compounds of WDG and its hub targets. Then the pharmacological effect of WDG were verified via vivo experiments in rats and clinical experiments.Results: The results showed that 117 effective active compounds of WDG were screened and 494 targets of WDG compounds targeting GMD were selected. These targets were involved in the biological process of inflammatory regulation and the regulation of gastrointestinal motility. The mechanism was mainly involved in the regulation of PI3K-Akt signaling pathway and Rap1 signaling pathway. In addition, molecular docking analysis suggested that eight key active compounds of WDG may be mainly responsible for the effect of WDG on GMD by targeting HARS, AKT, and PIK3CA, respectively. Animal experiments and clinical trials both suggested that WDG could exert therapeutical effect on GMD via inhibiting inflammation and promoting gastrointestinal motility, it could also improve digestive function of patients with laparoscopic colorectal cancer after surgery.Conclusion: This study was the first to demonstrate that WDG improved GMD mainly via inhibiting inflammatory level and promoting gastrointestinal motility, providing new insights for the understanding of WDG for GMD, inspiration for future research and reference for clinical strategy in terms of the treatment of GMD. Gastrointestinal motility disorder (GMD), affecting millions of patients worldwide , is an iArecae Semen , Panax ginseng , Lindera aggregata (Sims) Kosterm , Persicae Semen  and Fructus amomi  , with thousands of years of practice and experience, is a major source of new drugs. It is mainly comprised of natural products, and has been used in the prevention and treatment of human diseases . Wuda grm Lour.] . The majne, etc. . Additione, etc. . It has ne, etc. , but thein vivo experiments and clinical trials.Network analysis is an effective method for comprehensively analyzing complex networks using high-throughput analyses and computerized calculations . The holhttp://lsp.nwu.edu.cn/index.php), and the Encyclopedia of Traditional Chinese Medicine . Candidate compounds with oral bioavailability (OB) ≥ 20% and drug-likeness (DL) ≥ 0.10 of WDG were screened. The chemical structure formulae of the screened compounds was obtained from the PubChem database, and the targets of WDG compounds were retrieved from the Swiss Target Prediction database (http://www.swisstargetprediction.ch/).The chemical components of WDG were identified from the Traditional Chinese Medicine Systems Pharmacology Database , which is a comprehensive multifunctional database containing the therapeutic targets of all known human diseases. The query keywords used were “gastrointestinal mobility disorder”, “gastrointestinal motility, or “gastrointestinal peristalsis”. To exclude the interference of targets with low correlation with GMD, only targets with a relevance score greater than or equal to the median value of all the targets were included. Intersection targets between GMD proteins and WDG targets were screened by Venn analysis, and were considered as targets of WDG compounds with potential pharmacological therapeutic effects on GMD.The therapeutic targets of GMD were retrieved from GeneCards database (https://string-db.org/), which was used to search and predict protein-protein interactions, and identify core regulatory genes in the PPI network. Core targets with functional interactions were screened according to the degree value in the network after network topology analysis.The protein-protein interaction (PPI) of intersection targets was retrieved from the STRING database  and PPI networks were constructed, visualized, and analyzed using the open-source Cytoscape software (Version 3.7.1). Network topology analysis was conducted to determine important compounds and core targets of WDG in the HCT and PPI networks, respectively, which calculated the value of the degree between the nodes, thereby suggesting the interaction strength between the compounds, targets, or herbs.https://david.ncifcrf.gov/). An adjusted p value less than 0.05 indicated that the GO terms and the pathway were significantly associated with WDG. Finally, the results of GO and KEGG enrichment were visualized via the bubble plot using the “ggplot2” package of R software 4.2.0.To elucidate the potential mechanisms of action of WDG in the treatment of GMD, targets with twice more than the median value of degree in the PPI network were selected for gene ontology (GO) enrichment and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment analyses using the DAVID database  and * SDF format files of the screened active WDG compounds were downloaded from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/). The bioactive compounds of WDG (ligands) were examined for their putative antibacterial activities against key targets to determine efflux protein-ligand interactions through molecular docking analysis using the Schrodinger software suite.In order to determine whether the screened active compounds of WDG could bind to the core target, thus providing evidence for our predicted results on the active compounds of WDG and their potential therapeutical targets, we conducted molecular docking analysis to verify the results. The structures of the core targets were obtained from the PDB database  were purchased from the Guangdong Medical Laboratory Animal Center . Ethics approval for the study protocol was obtained from the Institutional Animal Care and Ethics Committee of Guangdong Provincial Hospital of Chinese Medicine (certificate number: 2017041).Forty rats were randomly divided into four groups: control group , Postoperative ileus (POI) group (POI rat + 0.9% NaCl), WDG group (POI rat + Wuda granule 500 mg/kg), and prucalopride group . Rats in the control and POI groups were administered 100 Before POI induction, the rats were denied food for 12 h and kept water-free for 6 h. After anesthesia, the rats were placed in the supine position and immobilized using tape following which their hair was shaved, the abdomen was sterilized with iodine solution, and the abdominal cavity was opened with surgical scissors. Thereafter, the small intestine to the cecum region was placed on sterilized gauze, and the entire small intestine was massaged three times using a sterile cotton swab . FinallyBlood collected from each group was placed in a centrifuge tube which was incubated at 4°C for 3 h and then centrifuged at 3000 rpm for 15 min. The upper serum was collected and used for Enzyme Linked Immunosorbent Assay (ELISA) of VIP and IL-10 according to the manufacturer’s instructions. Optical density was measured at 450 nm using a SpectraMax M5 autoreader.2 and 5% CO2 was continuously injected into the bath via the gas supply device. After 4 h of reperfusion, the rats were anesthetized by an intraperitoneal injection of 7% chloral hydrate. The abdomen was opened and the gastric antrum, duodenum, and jejunum were rinsed with Krebs solution at room temperature. Thereafter, the tissues were fixed to the bottom of a glass dish using a silicone plate, and the chassis was filled with Krebs solution. Finally, the mucosal layer was removed using tweezers. Based on the direction of the longitudinal muscle fiber, 8 mm × 2 mm longitudinal smooth muscle strips were cut using a blade following which each longitudinal muscle strip was suspended in the tissue chamber. One end of the strip was fixed to a hook at the bottom of the chamber and the other was connected to an external isometric force transducer. The strips were subjected to an initial tension of 1 g and were balanced for 60 min. The Krebs solution was replaced every 15 min during the balancing process, and the spontaneous contraction frequency and area under the curve were calculated. After the muscle strips contracted smoothly, 100 μL of acetylcholine (5–10 mol/L) was added to the muscle groove. The amplitude index, contraction tension, and contraction frequency of the muscle strips before and after the administration were recorded by Protowin.Half an hour before the preparation of the rat gastrointestinal smooth muscle specimen, cold water was added to the external circulation bath of the HV-4 isothermal tissue-organ perfusion system to maintain the temperature at 37°C. Thereafter, 10 mL of Krebs solution was injected into the bath, and a mixture of 95% OGastrointestinal motility disorder (GMD) is a common gastrointestinal disease that severely affects the patient’s life and physical wellbeing . It is tInformed consent was obtained from all the participants, and the study protocol was approved by the Ethics Committee of Guangdong Provincial Hospital of Chinese Medicine . This study was registered with the Chinese Clinical Trial Registry (registration number: ChiCTR-IPR-16008599). All participants were informed about the trial content and signed an informed consent form before the trial.Patients were recruited according to the following criteria:1) diagnosed with distinct histopathological evidence of colon or rectal cancer; 2) underwent colorectal cancer surgery at the Guangdong Provincial Hospital of TCM ; 3) the duration of the surgical procedure was within 2–4  h; 4) the duration under anesthesia was within 2.5–4.5  h; 5) did not experience serious complications, infections, adverse events, or secondary surgery during treatment; and 6) levels of serum albumin >27 g/L and prealbumin > 0.14 g/L.Forty patients with who had undergone laparoscopic colorectal cancer surgery were included in this study. Of these, 26 patients (14 of the WDG and 12 of the placebo group) completed the entire experiment, while 14 dropped out. Of the 14 who dropped out, eight patients were of the placebo group and six were of the WDG group. Of the eight patients of the placebo group who dropped out, three patients withdrew because the duration of surgery exceeded the time stipulated in the inclusion criteria, three patients withdrew because they could not tolerate the piezometric tube after the surgery, and two patients accidentally pulled out the piezometric tube due to pain and irritation at the port of surgery. Of the six patients of the WDG group who dropped out, three patients withdrew because the duration of surgery for these exceeded the time stipulated in the inclusion criteria, and three patients were unwilling to continue participating in the trial after surgery and voluntarily withdrew. All 26 patients who completed the entire experiment received the same fundamental treatment. The WDG treatment group received fundamental treatment plus WDG, whereas the placebo group received fundamental treatment only.Gastrointestinal pressure was measured using a gastrointestinal motility instrument  comprising a 24-channel high-resolution pressure measurement system. A gastrointestinal piezometer tube was sterilized and placed in the abdomen of the patient a day before surgery. Briefly, after topical anesthesia was administered to the nasal cavity, the tube was slowly inserted from one side of the nasal cavity. After successful placement, the tube was fixed. Monitoring of the gastrointestinal motility of the patient was initiated 2–3 h before the surgery. After surgery, gastrointestinal motility was monitored for 2–3 h on an empty stomach for 1–3 days. To measure gastrointestinal pressure, the patient was intubated, and the tube was located at the proximal end of the jejunum using radiography. According to the characteristics of the contraction wave, the pressure wave of the gastric antrum was wide with a high amplitude and that of the duodenum was narrow and sharp. The frequency of sinus contractions was approximately 3 contractions/min, and that of the duodenum and jejunum was 8–12 times/min.Migrating motor complex (MMC) parameters of the gastric antrum, duodenum, and jejunum, including the duration of phases I and II, the contraction amplitude and frequency of phase III, were mainly determined using the measurements of high-resolution gastrointestinal pressure. The MMC parameters were also related to the motor index (MI) of the jejunum, duration, MMCII phase amplitude, and MI. Clinical evaluation indicators of postoperative gastrointestinal motility recovery included the first postoperative gas level, duration of defecation, duration of postoperative liquid and semi-liquid diet recovery, duration of hospitalization, and hospitalization expenses.Blood samples were collected from the patients at 8:00 a.m. on the first, second, third, and seventh postoperative days. Each sample was centrifuged at 3000 rpm for 10 min at 4°C, following which 1.5 mL of the supernatant was transferred to a fresh centrifuge tube and stored at −80°C. Levels of IL-4, IL-6, TNF-α and endotoxin in serum and colon tissue were measured by ELISA kits according to the manufacturer’s instructions.p < 0.05 was considered statistically significant.All experimental data was presented as the mean ± standard deviation and was analyzed by one-way ANOVA test followed by Bonferroni test for multiple comparisons using GraphPad Prism software. Student’s t-tests and Mann–Whitney U tests was used to assess statistical significance between two groups . Two-factor ANOVA was used to compare different time points. A value of After removing duplication, 117 compounds of WDG were obtained according to OB ≥ 20% and DL ≥ 0.10. A total of 1053 predicted targets of the 117 WDG compounds were then identified. As shown in A total of 3073 GMD targets were screened in the GeneCards database after removing duplication. Subsequently, 494 intersection targets were selected via Venn analysis between the targets of the WDG compounds and GMD targets . The intThe results of KEGG pathway enrichment showed that WDG was mainly involved in the regulation of the PI3K-Akt and Rap1 signaling pathways , after ein vivo. The results showed that compared with the control group, the levels of TNF To verify the results of the network analysis, the regulation of inflammation and gastrointestinal motility by WDG was verified  TNF α , IL-1β , which indicated that WDG exerted a pharmacological effect on GMD by improving gastrointestinal motility.The duration of the MMC phase I was significantly increased; further, 1 day after surgery, the duration of MMC phase II, the number of MMC, and the contractive amplitude and motility of the gastric antrum, duodenum, and jejunum, were decreased compared to the levels before surgery  is a common gastrointestinal disease that severely affects the patient’s life and physical wellbeing . It can Gastrointestinal symptoms, are significant non-motor symptoms of GMD, and are represented by inflammation , which iGastrointestinal motility disorder is considered an idiopathic symptom of other diseases and is characterized by abnormal gastrointestinal contractions, dysphagia, gastroesophageal reflux disease, flatulence, severe constipation, vomiting, and abdominal distension . In GMD,Chinese herbal medicines, which consist of natural medicinal herbs, possess various complex chemical compositions owing to the herbal combinations, making it difficult to elucidate the mechanism of their therapeutic effects. A single herb or Chinese medicinal formula contains many phytochemical compounds, including alkaloids, terpenoids, and flavonoids. These medicines achieve the desired pharmacological effects mainly through a combination of various compounds. Many effective drugs in the clinic have been derived from Chinese herbal medicines, such as artemisinin , which not only increase the pharmacological effect, but also reduce the cost for patients and make the pharmacokinetics clearer. Thus, identifying the main chemical constituents responsible for the therapeutic function of Chinese herbal medicine is vital. In this study, we predicted eight key active compounds in WDG that may be responsible for its therapeutic effects. Previous studies have reported that beta-sitosterol , stearicThis study aimed to explore the potential pharmacological effects of WDG on GMD, and their underlying mechanisms, providing insight for the research on the mechanism of WDG on GMD. It firstly explored the mechanism of action of WDG in treating postoperative GMD through network analysis, animal experiments, and small sample clinical controlled studies. The results showed that WDG could promote the recovery of postoperative gastrointestinal motility via inhibiting the inflammatory process and promoting postoperative gastrointestinal motility recovery, providing the biochemical basis of WDG’s clinical efficacy. Although the results were encouraging, there still exist some limitations in this study. As a preliminary exploration, the relationship and crosstalk between the inflammatory mechanism of Wuda granules and the promoting gastrointestinal motility effect still lack further verification, which should be verified in the future research. Network analysis was mainly depended on compute calculation, it may lead to a very speculative hypothesis, which may result in false positives, further verification on the compounds of herbs should be conducted to confirm the results. Thus, in future, the research should be designed according to the following directions: 1) the pathway involving in inhibiting inflammation and promoting gastrointestinal motility by WDG should be designed to further verify the potential pathway of WDG based on the results of network analysis. 2) In clinic, high-quality RCT with large sample and multiple center should be designed to assess the anti-inflammation effect and effect of promoting gastrointestinal motility based on the small sample results of this study. 3) with multitopic analysis and sequencing techniques, the mechanism involving in the therapeutical effect of WDG should be confirmed with human sample. 4) as various effective compounds has been derived from Chinese herds medicine in recent years, the eight key compounds screened in this study should be further explored for their role in the alleviation of GMD with the purpose of developing plant-derived compounds that may have better therapeutic effects.This study firstly demonstrated that treatment with WDG alleviates GMD mainly by reducing inflammation and promoting gastrointestinal motility, multi-compounds and multi-targets are involved in this process, providing new insights to understand the effect of WDG treatment on GMD, which can inspire future research, and serve as a reference to devise clinical strategies in the treatment of GMD.


C. sativa is a plant suggested as a potential treatment for T2D, but how it works needs to be clarified. This study explored the pharmacological mechanism of C. sativa in treating T2D. We identified the active compounds in C. sativa and their targets. From there, we examined the genes associated with T2D and found overlapping genes. We conducted an enrichment analysis and created a protein–protein and target–compound interactions network. We confirmed the binding activities of the hub proteins and compounds with molecular docking. We identified thirteen active compounds from C. sativa, which have 150 therapeutic targets in T2D. The enrichment analysis showed that these proteins are involved in the hormone, lipid, and stress responses. They bind transcription factors and metals and participate in the insulin, PI3K/Akt, HIF-1, and FoxO signaling pathways. We found four hub proteins  that bind to the thirteen bioactive compounds. This was verified using molecular docking. Our findings suggest that C. sativa’s antidiabetic action is carried out through the insulin signaling pathway, with the participation of HIF-1 and FoxO.The incidence of type 2 diabetes (T2D) is rising, and finding new treatments is important.  Type 2 diabetes (T2D) is a complex, multifactorial disease that affects various body organs and causes changes in carbohydrate, lipid, and protein metabolism . It is cThe etiology of T2D includes insulin resistance, alterations in the secretion of this hormone, and alterations in the immune, gastrointestinal, and nervous systems . The treC. sativa is a plant native to central Asia, used for recreational, religious, and medicinal purposes. This plant contains a large number of compounds, such as cannabinoid phenols, non-cannabinoid phenols, flavonoids, terpenoids, alcohols, aldehydes, n-alkanes, wax esters, steroids, and alkaloids [lkaloids ,10. Tradlkaloids .C. Sativa has also been used to treat pain, nausea, and vomiting in patients with various diseases, such as rheumatism, epilepsy, and asthma [C. Sativa has also been used as a treatment for T2D, but the results have been controversial. One study reported that the recreational use of C. sativa may increase the risk of peripheral arterial occlusion, myocardial infarction, and renal disease in patients with T2D [C. sativa acts, considering the compounds it contains, has not been fully elucidated.Nevertheless, d asthma ,11. C. Swith T2D . On the with T2D , yet anowith T2D . HoweverC. sativa in T2D using network pharmacology and molecular docking to provide a reference for future drug development for treating T2D.Therefore, this study aimed to explore the components, targets, and pathways of https://tcmsp-e.com/tcmsp.php, accessed on 5 March 2023) [C. sativa to obtain the corresponding compounds and their related information. According to the absorption, distribution, metabolism, and excretion (ADME) protocols, the active compounds were screened and the criteria were an oral bioavailability (OB) of ≥20 and drug-likeness (DL) of ≥0.10, according to the criteria suggested by the same database and supported by two previous studies [We used the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform   and ente studies ,17.C. sativa were predicted using SwissTarget Prediction  [http://www.lilab-ecust.cn/pharmmapper/, accessed on 15 March 2023) [https://pubchem.ncbi.nlm.nih.gov/, accessed on 15 March 2023) [https://www.uniprot.org/, accessed on 15 March 2023) [Putative targets of the selected compounds of ch 2023)  and Pharch 2023) . The SDFch 2023)  was usedhttps://www.malacards.org/, accessed on 20 March 2023) [https://www.disgenet.org/, accessed on 20 March 2023) [http://ctdbase.org/, accessed on 20 March 2023) [The T2D-related genes were obtained by retrieving MalaCards ch 2023) , the Dis , the Dich 2023) . The MalC. sativa. To construct and visualize the Venn diagram, we employed the Venny 2.1 platform .Finally, we created a Venn diagram to show the intersection between the T2D-related genes and the predicted targets of the bioactive compounds from https://string-db.org/, accessed on 25 March 2023) [We constructed the protein–protein interaction network using the STRING database (ch 2023) , a freelch 2023) , which phttp://bioinformatics.sdstate.edu/go/, accessed on 27 March 2023) [p of <0.05 as cut-off values, displaying the top ten results.The intersecting genes were evaluated via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses using the Shiny GO 0.77 tool (ch 2023) , with anTo explain the relationship between the compounds and targets, a herb–compound–target network was built and visualized using Cytoscape.http://www.swissdock.ch/) [https://www.rcsb.org/) [https://alphafold.ebi.ac.uk/) [https://discover.3ds.com/discovery-studio-visualizer-download, accessed on 25 April 2023) [Finally, we made an intersection between the hub proteins of the protein–protein interaction network and the proteins of the target–bioactive-compound network; the selected proteins were docked with their respective bioactive compounds. Molecular docking was performed with the Swiss dock database (ock.ch/)  using thsb.org/)  or Alpha.ac.uk/) . For the.ac.uk/)  was usedil 2023) . Lower sC. sativa plant. However, after filtering the results with the parameters of an OB of ≥20 and DL of ≥0.10, there were 13 remaining compounds, including Apigenin, Arachidonic acid, Cannabinol, Caryophyllene oxide, Gamma-Linolenic acid, Gondoic acid, Linoleic Acid, Linolenic Acid, Luteolin, n-cis-Feruloyltyramine, Oleic Acid, Sitosterol, and Stigmasterol. Interestingly, both cannabidiol and tetrahydrocannabinol showed a low OB , for which reason they were not considered in this investigation; however, it is to be expected that, through other routes of administration, these molecules may have pharmacological effects. Through the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), we found 47 compounds from the C. sativa. In general, we found more targets on the PharmMapper platform, but the vast majority of targets matched on both platforms used. We used PharmMapper and SwissTarget Prediction to identify the targets corresponding to the bioactive compounds of We obtained 1300 genes associated with T2D from the MalaCards, DisGeNet, and Comparative Toxicogenomics Databases once the duplicates had been removed. Specifically, we found 590 genes in the MalaCards database, 958 genes in the DisGeNet database, and 234 genes in the Comparative Toxicogenomics Database (CTD).C. sativa were selected and considered as key targets. This diagram is shown in Subsequently, common T2D genes and plant-related targets were obtained using a Venn diagram. A total of 150 potential anti-T2D genes from C. sativa was mainly involved in cancer-related pathways, lipids, and insulin signaling (In the Gene Ontology (GO) enrichment, we showed the top 10 of the 150 overlapping targets of each enrichment. According to the results of our Biological Process (BP) A, the fuignaling D.The Protein–Protein interaction (PPI) network was constructed with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and imported into Cytoscape . In thissativa targets. This network can be seen in C. sativa. These targets were Aldo-keto reductase family 1 member B1 (AKR1B1), Albumin (ALB), Androgen receptor (AR), Cholinesterase (BCHE), Dipeptidyl peptidase 4 (DPP4), EGFR, ESR1, Estrogen receptor beta (ESR2), Prothrombin (F2), Vitamin D-binding protein (GC), Glycogen synthase kinase-3 beta (GSK3B), 11-beta-hydroxysteroid dehydrogenase 1 (HSD11B1), HSP90AA1, Insulin-like growth factor 1 receptor (IGF1R), Kinesin-like protein (KIF11), Mitogen-activated protein kinase 10 (MAPK10), MAPK14, MAPK8, Nitric oxide synthase, endothelial (NOS3), Mineralocorticoid receptor (NR3C2), 3-phosphoinositide-dependent protein kinase 1 (PDPK1), Sex hormone-binding globulin (SHBG), SRC, and Transthyretin (TTR).We constructed a compound–target network from the 150 common genes between the T2D and C. We then analyzed the intersection between the hub proteins of the protein–protein interaction network and the 24 targets that bound the 13 bioactive compounds. We found four common targets, EGFR, ESR1, HSP90AA1, and SRC; therefore, these targets were considered to be the most important. These four targets were then verified using a molecular docking analysis.−1  protocols (setting OB ≥ 20 and DL ≥ 0.10) and the number of targets, we found that Linoleic Acid, Linolenic Acid, and Gamma-Linolenic acid were the compounds that had the highest number of targets within the ADME parameters. Therefore, these three compounds could be thought to have the highest pharmacological activity in T2D. However, it should be considered that these compounds are not exclusive to Both Linoleic Acid and Linolenic Acid have been widely and inversely associated with T2D. Generally, it has been reported that a high intake of these fatty acids improves insulin resistance, reduces the risk of T2D, and always takes care of an adequate ratio between these two types of fatty acids. Specifically, omega-3 fatty acids, such as Linolenic acid, can suppress the expression of genes related to inflammation interacting with peroxisome proliferator-activated receptors, hepatocyte nuclear factor- 4a, and liver X receptor, or through mitigating the activation of the NF-kB transcription factor ,35,36,37C. sativa found in this study have been associated with T2D [Moreover, Gamma-Linolenic acid also seems to act on glucose metabolism . Specifiwith T2D ,45,46,47C. sativa in T2D corresponded to the response to hormones, stress, and lipids, among others. Therefore, these results may indicate that C. sativa can regulate the hormones involved in the pathophysiology of T2D, such as insulin [C. sativa are found in this organelle. Among the multiple alterations of T2D is mitochondrial dysfunction, leading to high levels of reactive oxygen species and low levels of ATP [C. sativa affect the mitochondria in patients with T2D. The main Molecular Functions enriched were those related to lipid binding and protein kinases. Protein kinases are a group of molecules that regulate a wide variety of cellular processes through protein phosphorylation and have been implicated in the pathophysiology of T2D. They have even been proposed as therapeutic targets [C. sativa also affects the activity of these proteins.Regarding the results of our gene ontology enrichment, the Biological Processes enriched by  insulin . We alsos of ATP . In othes of ATP . Therefo targets . HoweverC. sativa in the treatment of T2D. Interestingly, the insulin signaling pathway is regulated by C. sativa in T2D, as this would largely explain the protective effect of this plant for this disease, as previously reported in epidemiological studies [C. sativa compound, delta-nine tetrahydrocannabinol, can regulate this metabolic pathway, which supports our results [C. sativa may also be inhibiting HIF-1 [C. sativa also regulates glucose through this metabolic pathway.We found that the insulin signaling pathway, PI3K/Akt signaling pathway, Hypoxia-inducible factor 1 (HIF-1) signaling pathway, FoxO signaling pathway, and cancer-related pathways such as prostate cancer were the potential fundamental mechanisms of  studies ,14. The  studies . Other r results . The PI3ng HIF-1 . AnotherC. sativa, we found four key therapeutic targets  for the thirteen active compounds of C. sativa. These results were subsequently verified through molecular docking. As expected, according to the structure of cannabinol, most of the interactions were alkyl, pi-alkyl, and Van der Waals forces.From the analysis of the hub proteins of the protein–protein interaction network and the target–compound network of C. sativa bind to EGFR and ESR1, activate the PI3K/Akt pathway, and induce glycolysis, thus decreasing serum glucose levels. Similarly, HSP90AA1 and SRC are related to the PI3K/Akt pathway [EGFR and ESR1 are molecules mainly involved in cancer. However, it has also been shown that the PI3K/Akt pathway can be activated through these receptors. ESR1 induces Solute carrier family 2, facilitated by glucose transporter member 4 (GLUT4) translocation, and thus potentiates glycolysis ,56. Ther pathway ,58. In a pathway ,60,61.C. sativa on T2D. However, it also has some limitations, mainly because these results need to be experimentally verified. Another point to consider is that the TCMSP database only contains 47 compounds of C. sativa, when more than 500 plant compounds have been described. Another fact to think about regarding molecular docking is that this type of analysis only considers the interaction between the two molecules to be studied, when, in reality, many other molecules can compete for the same binding site; in addition, they are continuously in motion. The dosage of these compounds must also be considered to observe a pharmacological action. Finally, it is also possible that pharmacological interactions occur with drugs are approved to treat T2D, since it must be regarded that the administration of herbs does not replace the treatments accepted by medical associations, but only complements them.Therefore, this research provides valuable information on the mechanisms of action of thirteen different bioactive compounds from C. sativa, a plant with 509 human therapeutic targets. In our search for genes associated with T2D, we came across 1300 genes linked to this disease. By comparing these two sets of results, we found 150 genes that overlapped. We inferred from our analysis of these genes that their products involve cell membrane and mitochondria functions and hormone, lipid, and stress responses. Furthermore, we discovered that C. sativa’s antidiabetic properties are carried out through the insulin signaling pathway, specifically the PI3K/Akt pathway, in which HIF-1 and FoxO also play a role. Additionally, we identified four essential proteins  as therapeutic targets of C. sativa, all of which are linked to the PI3K/Akt pathway.After conducting our research, molecular docking in combination with network pharmacology contributed to a better understanding of the therapeutic mechanism of C. sativa in T2D. We pinpointed thirteen compounds with pharmacological activity in


Morinda officinalis How (MO) is a Rubiaceae plant, and its medicinal part is dried root, which is one of the “Four Southern Medicines” in China. At present, the plant MO breed seedlings mainly by cutting methods. Long-term asexual propagation makes pathogenic fungi accumulate in MO, leading to stem-base rot, which is caused by Fusarium oxysporum (Fon).Trichoderma harzianum and Pestalotiopsis sp. as biocontrol fungi to investigate their antagonistic ability to Fon through in vitro antagonism and pot experiments, and combined with transcriptome sequencing to explore the mechanism of biocontrol.In this study, we used Trichoderma harzianum and Pestalotiopsis sp. could inhibit the growth of Fon. In addition, Trichoderma harzianum and Pestalotiopsis sp. could also enhance the basic immunity to Fon by increasing the activities of defensive enzymes such as POD and SOD, chlorophyll content, soluble sugar content, and oligosaccharide content of MO. The mechanism of biological control of stem-base rot of MO was discussed by transcriptome technology. MO was treated with two treatments, root irrigation with biocontrol fungi or inoculation with Fon after root irrigation with biocontrol fungi. Transcriptome sequencing revealed that nearly 11,188 differentially expressed genes (DEGs) were involved in the process of inducing MO systemic resistance to Fon by biocontrol fungi. Meanwhile, Gene Ontology (GO) classification and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, as well as transcription factor (TFs) prediction showed that there were significant differences in the expression levels of MO roots under different treatments. Also, the genes of the “MAPK signaling pathway” and “plant hormone signaling pathway” were analyzed, in which the ERFs gene of the ethylene signal transduction pathway participated in the metabolism of glycosyl compounds. It is speculated that the ethylene signal may participate in the immune response of the sugar signal to the infection of Fon. After qRT-PCR verification of 10 DEGs related to the ethylene signal transduction pathway, the expression trend is consistent with the results of transcriptome sequencing, which proves the reliability of transcriptome sequencing.The results showed that both In conclusion, this study preliminarily identified the molecular mechanism of the biological control of MO stem-base rot and provided a scientific basis for further research on the prevention and control mechanism of MO stem-base rot. It is odampness . Accordidampness . From thum (Fon) . The disum (Fon) . The symum (Fon) . The disStreptomyces, Bacillus, and Trichoderma harzianum  and induced systemic resistance (ISR)  . Many ef pathway . Recent pathways . TherefoTrichoderma harzianum and Pestalotiopsis sp. which can be used for Fon control in MO. The results showed that biocontrol fungi could inhibit the growth of Fon in vitro. In addition, biocontrol fungi can enhance the immunity of plants to Fon by raising the activity of defensive enzymes, the content of chlorophyll, and the content of soluble sugar, and also affect the content of MO oligosaccharide. To explore the control mechanism of biocontrol fungi, transcriptome sequencing was used for analysis. A total of 921,532 unigenes were identified in MO, and transcriptome analysis showed that there were more than 10,000 DEGs in biocontrol fungi against Fon. These genes were annotated and enriched for GO functions and KEGG pathways, as well as predicted TFs, and validated using real-time PCR (qRT-PCR). The results showed that the expression levels of MO roots were significantly different after different biocontrol fungi treatments. We also analyzed the MAPK signaling pathway and plant hormone signaling pathway and related genes involved in the control process. In conclusion, this study aims to understand the mechanism of systemic resistance of MO to Fon induced by biocontrol fungi from the molecular level, providing a scientific basis for further analysis of the biocontrol mechanism.At present, chemical control is mainly used to control stem-base rot in MO, which can easily cause environmental pollution. In this study, we identified two efficient biocontrol fungi 22.1Morinda officinalis used in this experiment came from Deqing County, Guangdong, China, and was detoxified by tissue culture. When the tissue culture seedlings were rooted and transplanted into sterile nutrient soil for 6 months.Fusarium oxysporum (ON454550) and Trichoderma harzianum (ON454551) were isolated from the root of Morinda officinalis and identified and preserved by our research group. And a strain of Pestalotiopsis sp. (OR608750)with antibacterial activity proposed by the College of Forestry and Landscape Architecture, South China Agricultural University. The pathogens and biocontrol fungi were cultured on potato dextrose agar (PDA) medium at 25°C under dark conditions for 5 days. The microconidia suspension of the pathogens and biocontrol fungi used in greenhouse experiments was prepared as described by 2.2Trichoderma harzianum and Pestalotiopsis sp., we evaluated the effect of fungal antagonism and hyphal growth. For the fungal antagonism, we used the flat antagonism experiment on the PDA medium in vitro. The six-millimeter plugs from 5-day-old cultures of the pathogens and biocontrol fungi were inoculated on both ends of the PDA culture medium plate, and the distance between biocontrol fungi and pathogens was 5 cm. After incubation at 28°C for 7 days, the colonies’ diameters of pathogens were determined. The experiment was repeated three times with only pathogenic bacteria inoculated as control. The inhibition rate was calculated according to the following formula: inhibition rate (%) = [(colonies’ diameter of the control group–colonies’ diameter of treatment group)/colonies’ diameter of control group] × 100% , and sterile water was used as a mock control (NF). After 24 h, each treated plant was inoculated with 100 mL Fon spore suspension , and Fon alone was used as a control (F). Disease index (DI) was recorded and photographed 30 days after inoculation, and the roots of MO were harvested for RNA sequencing and determination of oligosaccharide content. In addition, the leaves of MO at different time points were harvested for the determination of physiological and biochemical indexes.To evaluate the biocontrol effect of biocontrol fungi on MO stem-base rot, a greenhouse experiment was carried out in this study. Six months after transplanting tissue culture seedlings of MO into sterile nutrient tanks, plants were treated with 100 mL single biocontrol fungus or combined biocontrol fungi , chlorophyll content, and soluble sugars during the process of biocontrol fungi triggering ISR to stem-base rot in MO. The above physiological and biochemical indicators were determined by using the corresponding detection kit, namely “The SOD detection kit” and “The POD detection kit” , “The Chlorophyll content test kit”  and “The Soluble sugar content detection kit”  according to the manufacturer’s instructions. The oligosaccharide content was determined according to the method of 2.5Total RNA was extracted by Total RNA Extractor (Trizol)  according to the manufacturer’s instructions. The concentration of RNA was measured by Qubit2.0 , and the integrity of RNA and genomic contamination were detected by agarose gel.Trichoderma harzianum or Pestalotiopsis sp. alone , Trichoderma harzianum mixed with Pestalotiopsis sp. (TP), and sterile water treatment (NF). The other 12 samples were pretreated with the above biocontrol fungi for 24 h and then challenged with Fon . All samples were taken from at least 5 MO roots and stored in a −80°C refrigerator. The total RNA of each sample was isolated by the above method.A total of 24 samples were used for library construction in this study. Of these, 12 samples were obtained from MO treated with The library was constructed using the qualified total RNA above. The first-strand cDNA was synthesized by the 1st Strand Synthesis Reaction Buffer , and the second cDNA strand was synthesized immediately after the reaction. After the terminal was repaired and the sequencing joint was connected, the junction product was purified and fragment size was sorted by magnetic beads. Finally, PCR amplification was performed with 2 × Super Canace™ High-Fidelity Mix and Primer mix reagent . Library amplification products were purified using Hieff NGS™ DNA Selection Beads  . The library was detected by agarose gel electrophoresis, and the recovered DNA was accurately quantified by a Qubit DNA detection kit . Illumina sequencing was carried out in the Shanghai Sangon Biotech, China, and performed using the Illumina MiSeq™ platform according to the manufacturer’s instructions .2.6de novo was assembled into transcripts using Trinity, and sequencing data were assembled into transcripts through three steps: Inchworm, Chrysalis, and Butterfly.The raw image data files obtained from Illumina Miseq™ were converted into raw sequenced reads by CASAVA Base Calling analysis, which contained the sequence information of the reads and the corresponding sequencing quality information. The raw data quality values and other information were counted, and the sequencing data quality of the samples was evaluated visually by using FastQC. Raw data from sequencing, which contains low-quality sequences with junctions. To ensure the quality of information analysis, the raw data must be filtered to obtain clean data. In this study, clean data The transcript obtained by Trinity assembly was deredundant, and the longest transcript in each transcript cluster was taken as Unigene, which was used as the reference sequence for subsequent analysis. The Unigene was compared with CDD, KOG, COG, NR, NT, PFAM, and GO databases using Blast alignment software, and the KEGG database was compared using KAAS alignment software to obtain the annotation information of Unigene.p-value  < 0.05, the function was considered to be significantly enriched. In addition, to identify all transcription factors (TFs) in the root of MO, all Unigene was compared to the plant transcription factor database for TFs prediction.All Unigene was compared with the reference sequence, and the comparison results were calculated by RSeQC. Then, according to the comparison results and the annotation file of the reference genome, the read count data was standardized by TMM, and then the difference analysis was conducted by DEGseq. In order to get a significant difference of gene will filter is set to: qValue <0.05, and the difference between multiples | FoldChange | > 2. Then all DEGs were subjected to GO and KEGG functional enrichment analysis, and when the corrected 2.7To confirm the transcriptome data, 10 DEGs related to the ethylene signaling pathway were randomly selected and verified . The tot2.8P≧ 0.05. p-value < 0.05 was considered statistically significant. All data were analyzed by analysis of variance using SPSS 20.0 .All bioassays and experiments were performed three times with 5 seedlings treated each time. Significant differences between means were compared using the LSD test (Fisher’s protected most significant difference test) at 33.1Trichoderma harzianum and Pestalotiopsis sp. have antagonistic activity against Fon in vitro (Trichoderma harzianum against Fon was 58.30% (p = 0.000), and the inhibition rate of Pestalotiopsis sp. against Fon was 33.81% (p = 0.000)  . Through= 0.000) .3.2Trichoderma harzianum and Pestalotiopsis sp. have the potential to control MO stem base rot. To test this hypothesis, we conducted a greenhouse experiment. After 30 days of inoculation with Fon, the disease symptoms of the control group were obvious, while the stem-base rot symptoms of MO treated with biocontrol fungi were significantly reduced. Among them, the combined treatment of Trichoderma harzianum and Pestalotiopsis sp. had the most obvious effect, with a control effect of 46.67% (p = 0.000), while the control effect of biocontrol fungus alone was 36.36% (p = 0.010)  . In addi= 0.010) .3.3the Trichoderma harzianum treatment group, the total chlorophyll content of other biocontrol fungi treatments had little change, while the total chlorophyll content of the Trichoderma harzianum treatment group would increase as time went by. The soluble sugar content of the biocontrol treatment was higher than that of the control group and showed a trend of increasing first and then decreasing as time went by The SOD activity and POD activity after each biocontrol treatment were significantly higher than that of the control group and showed a trend of first increasing and then decreasing as time went by ,B. These3.4The quality and concentration of all RNA samples were tested by Qubit2.0. The RNA quality of all samples was high, with an OD value above 2.0. The samples with unqualified concentration and quality were re-extracted to ensure that all samples met the requirements of the transcriptome sequencing library.A total of 583,671 million raw reads were obtained by high-throughput sequencing, and filtering of the raw data resulted in 522,375 million clean reads, representing 90% of the raw reads. The Q20 bases ranged from 97.79 to 98.46%, Q30 ranged from 92.82 to 94.68, and the GC amount ranged from 46.36 to 57.52%, indicating that the sequencing accuracy is high and can meet the requirements of subsequent analysis . The 1,3−5). The unigenes sequences were aligned to the KEGG database using the KAAS software. A total of 921,532 Unigenes were obtained annotated, of these, 176,943 (19.2%) were annotated to the CDD database, 204,656 (22.21%) were annotated to the PFAM database, 191,958 (20.83%) were annotated to the KEGG database, 228,126 (24.76%) were annotated to the KOG database, 268,497 (29.14%) were annotated to the GO database, 417,346 (45.29%) were annotated to the NR database, 47,654 (5.17%) were annotated in the NT database  using blast  were detected in the Pestalotiopsis sp. treatment group, and 105 DEGs  were detected in the co-treatment group with Trichoderma harzianum and Pestalotiopsis sp. In the samples pretreated with biocontrol bacteria, 2,332 DEGs  were detected in the Trichoderma harziamum group, and 4,745 DEGs  were detected in the Pestalotiopsis sp. group. A total of 2,672 DEGs  were detected in the co-treatment group of Trichoderma harzianum and Pestalotiopsis sp.Based on the above sequencing data, two control groups were established to study the control mechanism of different biocontrol bacteria on stem-base rot of MO. In one group, only treated with biocontrol fungi and compared with the sample treated with sterile water. The other group was inoculated with the pathogen Fon after being pretreated with biocontrol fungi for 24 h and compared with the sample inoculated with Fon after sterile water treatment. It was observed that the expression levels of a large number of genes in the root of MO were changed whether the biocontrol fungi were treated alone or pretreated before inoculation with pathogenic bacteria ,B. AccorThe selected DEGs were compared with the GO database for functional annotation. The GO database was divided into 3 categories, including “biological process,” “cellular component” and “molecular function,” which were further divided into 68 sub-categories. The results showed that compared with mock treatment or Fon inoculation alone, the most DEGs were annotated to the “cellular process,” “metabolic process” and “cell component organization or biogenesis” in the category of “biological process” in the libraries constructed from MO treated with biocontrol fungi alone or pretreated with biocontrol fungi and then inoculated with Fon. In the category of “cell component,” the number of DEGs annotated as “cell components,” “cells” and “organelles” was the most, while the number of DEGs annotated as “binding” and “catalytic activity” in the category of “molecular functions” was the most . These rThe selected DEGs were compared with the KEGG database to analyze the metabolic pathways in MO. According to the results , KEGG pa3.6Trichoderma harzianum alone, DEGs were obviously enriched in the “oxidation reduction process,” “antibiotic metabolism process” and “cell response to oxidative stress.” When treated with Pestalotiopsis sp. alone, DEGs were significantly enriched in the “endoplasmic reticulum subcompartment,” “epigenuclear membrane-endoplasmic reticulum network,” and “cytoplasmic stress granules.” After Trichoderma harzianum and Pestalotiopsis sp. were treated together, only the “mitochondrial respiratory chain supercomplex” function of cell group classification was significantly enriched in DEGs, which may be related to the low amount of DEGs in this treatment.GO functional enrichment analysis of DEGs in the group treated with biocontrol fungi alone showed that the functions of DEGs enriched by different biocontrol treatments were also different . When trTrichoderma harzianum, DEGs were significantly enriched in the “multicellular biological process,” “multicellular biological development,” “multicellular biological reproduction process,” “cytoplasmic ribosome,” “ribosome” and “molecular structural activity.” After pretreatment by Pestalotiopsis sp., DEGs were significantly enriched in “polysaccharide catabolic process,” “reaction to acidic chemicals,” “reaction to inorganic substances,” “extracellular region,” “cytoplasmic desmata,” “hydrolase activity, hydrolyzed O-linked glycosyl compounds”, “Hydrolase activity, acting on glycosyl bonds” function. After co-treatment with Trichoderma harzianum and Pestalotiopsis sp., DEGs significantly enriched “cytoplasmic translation,” “antibiotic catabolic process,” “organic nitrogen compound biosynthesis process,” “fungus-type cell wall,” “ribosome,” “structural components of ribosomes,” “formate dehydrogenase (NAD+) activity” and “structural molecular activity.”GO function enrichment analysis of DEGs in the biocontrol pretreatment group showed that DEGs after biocontrol pretreatment was significantly enriched in different functions . After pTrichoderma harzianum alone could be enriched into the “MAPK signal pathway,” “P53 signal pathway,” “cGMP-PKG signal pathway,” “carbon metabolism,” etc. The DEGs of the group treated with Pestalotiopsis sp. alone could be enriched into “MAPK signal pathway,” “starch and sucrose metabolism,” “fatty acid metabolism,” etc. DEGs in the co-treatment group of Trichoderma harzianum and Pestalotiopsis sp. could be enriched to “MAPK signaling pathway,” “phenylpropane synthesis pathway,” “carbon metabolism” and “tyrosine metabolism.” All the above three treatments can enrich MAPK signaling pathways related to plant immunity and carbohydrate metabolism, indicating that the basic immunity of plants can be improved after biocontrol bacteria alone treatment.By analyzing the enrichment of DEGs in the KEGG pathway of the group treated with biocontrol fungi alone, the results showed that the top 30 KEGG pathways of DEGs under different biocontrol bacteria treatments were different . The DEGthe Trichoderma harzianum pretreatment group, DEGs were significantly enriched in the “phenylpropane synthesis pathway” and “mutual conversion of pentose and glucuronic acid,” while in the Pestalotiopsis sp. pretreatment group, DEGs were significantly enriched in “MAPK signal pathway,” “phenylpropane synthesis pathway,” “plant hormone signal transduction” and “plant-pathogen interaction.” In the pretreatment group of Trichoderma harzianum and Pestalotiopsis sp., DEGs were significantly enriched in “MAPK signaling pathway,” “pentose and glucuronic acid interconversion” and “carbon metabolism.” In general, although the signal pathways of DEGs enrichment after pretreatment of different biocontrol bacteria are not consistent, all the above pathways can play an important role in the process of biological control.Enrichment in the KEGG pathway of DEGs pretreated with biocontrol fungi showed that the KEGG pathway related to plant immunity was significantly enriched in each group . In the 3.7Transcription factors play an important role in plant growth and development, hormone signal transduction, leaf senescence, and plant disease resistance. In order to explore which transcription factors are related to the mechanism of biological control of stem-based rot, the transcriptome sequence of the root of MO was compared and analyzed with the plant transcription factor database (PlnTFDB2) . The res3.8Trichoderma harzianum alone treatment, but it was down-regulated significantly after the other treatments. The gene of TRINITY_DN519710_c1_g1 was down-regulated after co-treatment with Trichoderma harzianum and Pestalotiopsis sp., and all other treatments were significantly up-regulated, while the gene of TRINITY_DN517401_c2_g1 was up-regulated after treated with Pestalotiopsis sp. alone, and all the other treatments were significantly down-regulated (Trichoderma harzianum pretreatment, and down-regulated after Pestalotiopsis sp. pretreatment, but all the DEGs were up-regulated after the co-pretreatment with two biocontrol fungi (To confirm the reliability of the RNA-seq data, 10 DEGs related to the ethylene signaling pathway were randomly selected for the qRT-PCR validation analysis. The results showed that after the biocontrol fungi were treated separately, the five DEGs of TRINITY_DN488910_c0_g1, TRINITY_DN529234_c1_g1, TRINITY_DN520737_c0_g2, TRINITY_DN520737_c0_g1, and TRINITY_DN505197_c1_g1 were all significantly down-regulated. TRINITY_DN537446_c0_g4 and TRINITY_DN533749_c2_g3 were up-regulated significantly, but the expression trend of some genes was inconsistent after different treatments. The TRINITY_DN532960_c2_g1 gene was up-regulated after egulated . After bol fungi . Althoug4streptomyces sclerous SM3-7 from the rhizosphere soil of corn, which showed good potted control against stem-base rot. Trichoderma verticillium HB20111 and Trichoderma harzianum TW21990 had a synergistic effect, and the control effect on stem rot at the wheat seedling stage was significantly higher than that of a single strain. However, biocontrol agents have seldom been used to control the stem-base rot of MO. In this study, we report for the first time that Trichoderma harzianum and Pestalotiopsis sp. can be used as biocontrol fungi against the stem-base rot of MO, and the control effect is better when they are used together (MO stem-base rot first appeared in 1979, researchers found MO stem-base, roots, and seeds can be infected with stem-base rot . MO stemtogether .Penicillium oxalicum QZ8 and Trichoderma asperellum QZ2 inhibit the growth of Fon through spatial competition. In addition to the competitive effect, biocontrol bacteria also identify plant pathogens, carry out entanglement and adsorption on pathogenic bacteria, and dissolve the mycelia of pathogenic bacteria, so as to parasitize in pathogenic bacteria and obtain the nutrients of pathogenic bacteria for growth and reproduction. For example, Trichoderma produces a series of enzymes to degrade the cell wall of Fusarium, and completes the process of hyperparasitism to Fusarium through recognition, contact, entrench, penetration, and parasitism. In our study, both Trichoderma harzianum and Pestalotiopsis sp. showed different degrees of resistance to the stem-base rot pathogen Fon, and parasitic on pathogenic bacteria mycelia through identification, entanglement or attachment, and finally penetrating and dissolving pathogenic bacteria mycelia (Fusarium. S. enissocaesilis strains IC10 and S. rochei strains Y28, or after the application of plant, hormones can induce ISR to Fon. Besides, some non-pathogenic fungi like Fusarium moniliforme can also induce ISR against Fon (In a common microecological environment, biocontrol bacteria compete with pathogenic bacteria for limited space and nutritional resources through rapid propagation and growth, thus limiting the growth of pathogenic bacteria, which is called competition. For example,  mycelia . In receinst Fon . In our inst Fon .At present, high-throughput transcriptome sequencing technology has been widely used to study the interaction mechanism between plants and pathogens, and a large number of functional genes related to plant disease resistance have been mined, revealing the pathogenesis of pathogenic bacteria and the molecular mechanism of plant disease resistance. However, there have been no reports on the mechanism of preventing and controlling MO stem-base rot from the transcriptome level . In thisArabidopsis thaliana, MPK3 and MPK6 can phosphorylate WRKY33 and increase its affinity for W-box elements, thereby promoting the expression of their target genes, such as genes involved in plant secondary metabolic pathways, such as PAD3 and CYP71A13, as well as genes involved in plant antimicrobial pathways, such as PDF1.2 and PR4. The expression of these genes enhances plant resistance to stem basal rot pathogens (Studies have shown that the MAPK signaling pathway plays a crucial role in promoting and protecting plant growth when plants are subjected to environmental stress . The resathogens . In summHormone-regulated genes play a crucial role in plant growth and development as well as in responding to biotic stresses . Among tin vitro antagonistic results showed that Trichoderma harzianum and Pestalotiopsis sp. Inhibited Fon. Combined with the results of the greenhouse experiments, it was verified that both fungi could be used as biocontrol fungi to control MO stem-base rot, and the detection of physiological and biochemical indexes reflected that a variety of metabolic processes and functions in the plant changed after the use of the two biocontrol fungi, such as increasing the activity of antioxidant enzymes, chlorophyll content, and soluble sugars, which reflected that biocontrol fungi could promote the response of MO to Fon stress from the side. Continuing to explore the biocontrol mechanism by transcriptome sequencing, the results showed that Trichoderma harzianum and Pestalotiopsis sp. could affect the gene expression of MO and regulate the signal transduction and phytohormone pathways related to Fon resistance, such as the MAPK signaling pathway and the ethylene signaling pathway, which could improve the disease resistance and growth of MO.Overall, the The data presented in the study are deposited in the NCBI repository, accession number PRJNA1043455.ZiC: Writing – original draft, Writing – review & editing. PH: Writing – review & editing. ZL: Writing – review & editing. ZeC: Writing – review & editing. JC: Writing – review & editing. XC: Writing – review & editing. TS: Writing – review & editing. PD: Writing – review & editing.


Many characteristics of the disorder are known, such as the phenotypic variability of the disease and the biological processes associated with it; however, the exact and systemic molecular mechanisms between the deleted area and its resulting clinical phenotypic expression, for example that of neuropsychiatric diseases, are not yet fully understood.Using previously published transcriptomics data (GEO:GSE59216), we constructed two datasets: one set compares 22q11DS patients experiencing neuropsychiatric diseases versus healthy controls, and the other set 22q11DS patients without neuropsychiatric diseases versus healthy controls. We modified and applied the pathway interaction method, originally proposed by Kelder et al. (2011), on a network created using the WikiPathways pathway repository and the STRING protein-protein interaction database. We identified genes and biological processes that were exclusively associated with the development of neuropsychiatric diseases among the 22q11DS patients. Compared with the 22q11DS patients without neuropsychiatric diseases, patients experiencing neuropsychiatric diseases showed significant overrepresentation of regulated genes involving the natural killer cell function and the PI3K/Akt signalling pathway, with affected genes being closely associated with downregulation of CRK like proto-oncogene adaptor protein. Both the pathway interaction and the pathway overrepresentation analysis observed the disruption of the same biological processes, even though the exact lists of genes collected by the two methods were different.Using the pathway interaction method, we were able to detect a molecular network that could possibly explain the development of neuropsychiatric diseases among the 22q11DS patients. This way, our method was able to complement the pathway overrepresentation analysis, by filling the knowledge gaps on how the affected pathways are linked to the original deletion on chromosome 22. We expect our pathway interaction method could be used for problems with similar contexts, where complex genetic mechanisms need to be identified to explain the resulting phenotypic plasticity.The online version contains supplementary material available at 10.1186/s13023-023-02953-6. In about 1 in 4000 live births an incident of 22q11DS 0, is a g. The twoOne major challenge of studying 22q11DS is to understand the molecular mechanisms between the deleted genes and the resulting disease phenotypes; previous research showed that the phenotypes vary widely among individuals, even though most patients share a common 3 Mb deletion , 10. In The complex genotype-phenotype variability of 22q11DS and the polygenic characteristics would benefit from novel bioinformatical approaches that utilize our current knowledge of complex interactions between biological materials. Previous efforts to organize this current knowledge by the bioinformatics community resulted in several large and publicly available databases, which can be easily exploited. In particular, pathway databases such as WikiPathways, Reactome, and KEGG, created from the literature and manually curated, delineate numerous biological interactions at the gene and molecular level –17. An iIn addition, networks can help us integrate omics data and biological interactome data for further analysis . PreviouThe complexity of molecular features involving 22q11DS makes it an attractive subject for network biology methods discussed above. In addition, a deeper understanding of the genotype-phenotype relationships of 22q11DS may help us gain richer insights into several other diseases that display polygenic traits, such as neuropsychiatric diseases, by identifying the collective characteristics of interacting genetic variants –34. In oIn this paper, we reimplemented Kelder et al.’s pathway interaction method to a transcriptomics 22q11DS dataset, originally published by Jalbrzikowski et al. , and demIn this study, we re-analysed a transcriptomics dataset originally created by Jalbrzikowski et al.  using a 22q11DS and colored yellow.After analysis of the transcriptomics dataset derived from peripheral blood, among a total 1,135 candidate pathways, the Psychiatric Group identified 167 pathways that included at least one significant path to the source , and 154 pathways with p-values less than 0.05. The Nonpsychiatric Group identified 246 significant pathways out of 255 pathways that have at least one significant path. The Psychiatric Group detected 116 genes contributing to the significant path network, and the Nonpsychiatric Group identified 185 such genes. The genes collected from the Psychiatric and Nonpsychiatric groups are presented in Fig. Our primary goal was to identify genes that were exclusively expressed among the autism spectrum disorder/psychosis patients. Figure Using the differential expression analysis for autism spectrum disorder and/or psychosis patients (Psychiatric Group), we found 217 differentially expressed genes, consisting of 98 downregulated and 119 upregulated genes. The same analysis involving patients without neuropsychiatric diseases (Nonpsychiatric Group) found 145 differentially expressed genes, among which the number of downregulated genes was 76 and that of upregulated genes was 69.22q11.2 copy number variation syndrome) to be the most significantly overexpressed, which is rather obvious considering this pathway included the highest number of genes that had been directly affected by the deletion. Besides, only one pathway - Transcriptional regulation of granulopoiesis (WP5001) - was included in both lists.Table Allograft Rejection-WP2328 and Neutrophil Degranulation-WP4049), general metabolism , and cancer-related pathways , overall consistent with the findings by the authors of the dataset [Amyloid fiber formation- WP3547 pathway was found in both groups. The complete results and associated genes were provided as Supplementary Material Both data groups observed overrepresentation of biological pathways associated with immune system . We then refined the interaction networks and identified a gene subnetwork that was exclusively associated with 22q11DS patients experiencing autism spectrum disorder/psychosis. The results were compared with that of pathway ORA.The pathway interaction method, originally proposed by Kelder et al. , analyseIn the subnetwork shown in Fig. Besides the association with NK cell functions via PDGFRB, a number of studies reported that the downregulation of the PI3K/Akt pathway is associated with autism spectrum disorder and schizophrenia –53. PIK3We can summarize our findings as follows. The differentiating feature of the Psychiatric Group over the Nonpsychiatric Group, i.e. the molecular signature of the 22q11DS patients who have experienced autism spectrum disorder and/or psychosis, compared to those who have not experienced such diseases, implies the involvement of NK cell functions and PI3K/Akt signalling pathway, as well as several genes such as CRKL, PDGFRB, and AKT1.Compared to the pathway interaction method demonstrated here, pathway ORA detects individual genes that are significantly differentially expressed and are contained in previously defined pathways. Among the identified significantly expressed neuropsychiatric genes, current literature suggests that PRF1, GZMB, ADORA2A, ADRB2, and IL1B play important roles regarding the NK cell function –63. On tIt would be beneficial to briefly compare the two methods we used. Considering our initial goal was to identify molecular interactions associated with the expression of autism spectrum disorder/psychosis among the 22q11DS patients, the pathway method seemed to offer a convincing way to identify a molecular subnetwork of the disease. One significant advantage of our method over the conventional pathway ORA is that the pathway interaction method can collectively detect genes of interest, where the connections between such gene sets indicate plausible biological relationships powered by prior knowledge instead of “guilt by association”. For example, GRB2, IL2, PIK3CA, and PIK3CB were not differentially expressed enough under the individual gene significance criteria, thus would not have been identified had we applied only pathway ORA, even though they have direct protein-protein interactions with other significant genes. On the other hand, pathway ORA seems to be better at identifying individual genes that are strongly expressed, regardless of the expression level of nearby genes. Another advantage of ORA is that gene significance criteria can be easily adjusted and modified, and can easily include any combination of variables, while the pathway interaction method depends only on gene weights, which first needs appropriate transformations. Our understanding is that the two methods complement each other and together reveal a more complete picture of the molecular interaction of 22q11DS, as shown in Fig. For our study, we have applied several adjustments to the original methods by Kelder et al. . First, The value of pathways in this context is that pathways offer relatively safe start and end points. Pathways (at least those in the WikiPathways repository) are created and supported by previous publications, so that analysis revolves around plausibly grouped genes, making the results easy to interpret. Computationally speaking, pathway nodes are excellent source and target nodes, since it is much more efficient to find significant paths between pathways than to examine paths between all candidate gene pairs. In our study, we were able to use an already existing pathway (22q11DS pathway) as the source node, as it incorporated the deleted genes and their close interaction partners that were previously known. However, we would like to emphasize the flexibility of our method as well, since even if no published pathway existed, one would be able to directly create an appropriate gene-pathway relationship and use it as a starting point. The pathway interaction method can also include transcription-factor targets or other types of interaction databases, in addition to or instead of protein-protein interactions to further extend the base network.As a final note, we would like to briefly discuss the data used. The dataset was obtained from whole blood rather than from brain cells, implying the tissue-specific gene expressions might have affected the key genes and pathways identified; for example, we found pathways involving innate immune systems and cell metabolism to be significantly over-expressed in both data groups. Considering our goal was to study the phenotypic expression of autism spectrum disorder and psychosis, actual brain cell expression data may provide clearer pictures on the dynamics between the deleted genes and related biological processes, for example, whether the perturbations in the cellular processes we observed was due to peripheral pathology or a secondary effect, as result of brain dysfunction. However, our dataset was still useful to examine the neuropsychiatric genes that might reveal crucial interactions via the immune-related responses. The research community also has discovered that neuropsychiatric functions and immune-related pathways tend to be closely associated , 68. We Challenges to understanding exact mechanisms of 22q11.2 Deletion Syndrome (22q11DS) originate from the high level of genotype-phenotype variability and the polygenic inheritance of the disease. In this paper, we explored the pathway interaction method to identify molecular signature of 22q11DS, especially that of patients experiencing autism spectrum disorder and/or psychosis. Using the method, we were able to identify and explain possible involvement of pathways including the NK cell functions pathway and genes such as CRKL for the development of autism and/or psychosis in 22q11DS patients. Our approach is flexible and can incorporate various knowledge of molecular dynamics, such as protein-protein interactions, and can be used to study other rare diseases with a similar context, where complex genetic mechanisms between mutated genes and the resulting phenotypes are in question.with autism spectrum disorder/psychosis (N = 66 + 19 = 85), and the second set contained that of healthy controls vs. patients without reported psychiatric symptoms (N = 27 + 66 = 93). We analysed the two datasets separately, using the approaches described below, and compared the results.For this study we used a publicly available transcriptomics dataset of whole-genome Illumina Human HT-12 microarray assays, obtained from whole blood RNA of a total 112 subjects , from a study originally published by Jalbrzikowski et al. , with GEwith vs. without neuropsychiatric phenotypes. First, we noted that the number of patients with neuropsychiatric phenotypes [We decided to conduct two separate analyses instead of directly comparing the patients enotypes  and the enotypes  were ratweight, was introduced from T statistics using the formula below, as suggested by Kelder et al. [We used R version 4.0.3 for our data analysis. As an initial step, we conducted a differential expression analysis at probe-level using the limma R package, version 3.46.0 . Gene-ler et al. . The weiThe weight variable inversely maps T statistics, which can take any real number , into a value within . A smaller value of weight indicates a large T-value, which can be interpreted as a possibly significant level of gene expression, and vice versa. According to Kelder et al., genes with an absolute T statistic > = 3 correspond to unadjusted p-value < = 0.004 .The pathway interaction analysis mostly followed the proposed method by Kelder et al., but we have made several adjustments in later steps to improve biological interpretability and contextual applicability. The original method by Kelder et al. was described visually as Supplementary Material source, and identified ‘significant paths’ that connected the source with another pathway node. A significant path was defined as a directed path comprising three or more edges, starting at WP4657 and ending at another candidate pathway node, with the edge weight sum below a certain threshold (lmax); the abbreviation ‘l’ originated from weighted ‘length’, which indicates the sum of edge weights). The minimum number of edges was introduced in order for each connected pair of pathways to include at least one protein-protein interaction. We applied the following algorithm to each candidate pathway :In the network constructed above, we called the 22q11DS pathway node (labeled WP4657) the Find the shortest path between the 22q11DS WP4657 pathway and the candidate pathway.max, stop the process; pick the next candidate pathway, and return to [Calculate the sum of edge weights of the shortest path, identified in step 1 above; if the sum is greater than lmax, check the length (number of edges) of the path, i.e. the number of edges.If the sum is less than lIf length is 2, remove the last edge.If length is greater than 2, remove all edges except the first and the last edge.Store the path information, pick the next candidate pathway, and return to .max; we first used 0.9 which was suggested by Kelder et al., i.e., a pathway would be selected if it contained at least one path with the sum of edge weights less than 0.9. Next, each candidate pathway was assigned an interaction score, using the formula below, where n was the total number of significant paths and li the weight sum of each significant path.Using the algorithm above, we first collected all pathways that had at least one significant path connected to the central 22q11DS pathway (the “source”). The list of pathways depends on the path threshold lFor pathways with at least one significant path, we also calculated empirical p-values to check the statistical significance, since pathways with large numbers of genes were likely to have many significant paths, resulting in high crude interaction scores. For each selected pathway, we created 1,000 modified networks, where the corresponding gene-pathway portion was replaced by the same number of randomly chosen genes. In each sample, the proportion of differentially expressed genes with weight below 0.8 was fixed, so that they are likely to contribute to significant paths if there exist appropriate protein-protein interactions. After calculating 1,000 random interaction scores from the networks, we computed the p-value of the pathways using the number of incidents that yielded the interaction score greater than or equal to the original score . Those hmax=0.9 for three-edge paths, and lmax=1.4 for longer ones. The reason was that, when the threshold lmax increases, we want to remove shorter paths with large weights. The detailed reasoning of the relaxed threshold was based on our sensitivity analysis, which is included as the Supplementary Material Once the list of pathways was finalized, we collected significant paths, this time with a relaxed threshold. The reasoning behind this was that we wanted to identify paths containing a relatively non-significant gene that still interacts with other genes that were significantly expressed. Expression of a gene is not constant over time and can be affected by activities of various other genes that interact with it, therefore, we wanted to find such genes (hence a meaningful collection of genes that interact with each other) whose partners had been significantly activated or repressed. Finally, all collected paths were filtered using the final threshold: lIn the final result, each selected pathway contained a pathway score and a list of genes that were connected via its significant paths. The resulting subnetwork was visualized using Cytoscape version 3.8.1 . Neuropswith autism spectrum disorder/psychosis and healthy individuals ; the second group contained 22q11DS patients without autism spectrum disorder/psychosis and healthy individuals .A major goal of our analysis was to identify meaningful biological interactions and key genes associated with autism spectrum disorder and psychosis of 22q11DS patients. To achieve this, we created two datasets from the GSE59216 dataset and ran two separate analyses using the approach described above. The first dataset included 22q11DS patients We identified the characteristics of genes and pathways derived from the Psychiatric Group, which were not detected by the Nonpsychiatric Group. To ensure certain genes were indeed associated with neuropsychiatric diseases, we downloaded the ‘Curated gene-disease associations’ dataset from the webpage of DisGeNet Version 7.0, published in January 2020 , and ext0.15 would indicate about 10% up-regulation compared to healthy controls). Next, with those differentially expressed genes, we used PathVisio to conduct pathway ORA [In the final step of our study, we compared the results of the pathway interaction method with that from the pathway overrepresentation analysis (ORA). Pathway ORA is an enrichment method which identifies pathways containing significant numbers (i.e. higher numbers than expected) of genes of interest, assuming such genes follow hypergeometric distribution . First, hway ORA , 74. In We note in passing that when conducting the overrepresentation analysis and selecting pathways from the interaction network, we used p-values instead of adjusted p-values. First, the reason was to make the analysis overall consistent with the original method proposed by Kelder et al., where the weights were computed using T-statistics, a direct basis of calculating p-values without further scaling. Second, in our overrepresentation analysis, p-value and log2FC score thresholds were selected so that the number of detected genes roughly match that from pathway interaction analysis, rather than to determine the statistical significance of the genes. In our future studies, we are interested in extending our analysis to give the full scope of weighting schemes as well as gene- or pathway-selecting criteria.https://github.com/woosubs/PathwayInteraction.An overview of our approach is summarized in Fig. Below is the link to the electronic supplementary material.Supplementary Material 1Supplementary Material 2Supplementary Material 3Supplementary Material 4Supplementary Material 5