Purpose: This study was designed to evaluate the pharmacological mechanisms of puerarin against oliguria in acute alcoholism via network pharmacology analysis combined with experimental verification.Methods: First, this study established an acute alcoholism rat model, compared the changes in urine volume in each group, and observed the therapeutic effect of puerarin by H&E staining, biochemical, RT-qPCR, and immunohistochemical analyses. Second, puerarin-related targets were searched in TCMS, PubChem, CNKI, Wanfang, PubMed, and GeenMedical Academic databases. Also, potential disease targets were obtained from the GeneCards, MalaCards, and NCBI-gene databases and genes with puerarin target gene intersections were screened out. The interaction network for co-predicted targets was obtained using the STRING database, and the core targets were imported into Cytoscape for visualization using DAVID Bioinformatics Resources 6.8. The essential genes were subjected to the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) pathway enrichment analyses to predict related biological processes and significant signaling pathways. Finally, molecular docking was used to examine the interaction of puerarin with key targets, and the core targets were validated further by RT-qPCR and Western blotting.Results: Compared to the model group, the urine volume of the rats was significantly increased after puerarin treatment, and the levels of anti-diuretic hormone (ADH) and aquaporin 2 (AQP2) expression were decreased. Searching the intersection of puerarin and acute alcoholism targets yielded 214 potential targets, 837 biological processes, and 185 signaling pathways involved. The molecular docking results indicated a good affinity between puerarin and key targets (cyclic adenosine monophosphate (cAMP), protein kinase A (PKA), cAMP-response element-binding protein (CREB), and c-Fos). RT-qPCR and Western blotting further verified that puerarin could down-regulate the expression of cAMP/PKA/CREB/c-Fos.Conclusion: This study identified the potential targets of puerarin against oliguria in rats with acute alcoholism using network pharmacology and animal experiments. The mechanism may be closely related to the cAMP signaling pathway. Alcoholism has become a severe public health problem in recent years, with epidemiology indicating that approximately 2.5 million people die yearly from excessive drinking . Acute aPueraria lobata (Willd.) Ohwi, according to the “Chinese Pharmacopoeia,” is cool, sweet, and pungent in taste and has the effect of quenching thirst and relieving alcoholism , that was left for at least 1 week after use for experiments. All animal experiments followed the Guidelines for the Care and Use of Laboratory Animals (license key: SYXK: K2018-0002).n = 12/group): control, ethanol (model), ethanol + puerarin (treatment), and puerarin. Before the experiment began, all rats fasted for 18 h and drank freely. The model and treatment groups were given 14 ml kg−1 , and blood was drawn from the inferior vena cava (3% heparin sodium anti-coagulation) before the kidneys and hypothalamus were removed.The SD rats were randomly divided into four groups , rabbit anti-cAMP , rabbit anti-PKA , rabbit anti-CREB , rabbit anti-c-Fos , and rabbit anti-CREB3L1 .Puerarin  was provided by Shanghai Xian Ding Biotechnology Co., Ltd. . Detection kits for CRE (Lot: C013-2-1) and BUN (Lot: C011-2-1) were provided by Nanjing Jiancheng Bioengineering Institute . Copeptin ELISA Kit (Lot: B165001) was provided by American BIM. 56° liquor (Lot: 20110923) was purchased from Beijing Hongxing Co., Ltd. . Endogenous peroxidase (HRP) and enzyme-labelled goat antibody mouse IgG polymer (Lot: PV-6002) were provided by Beijing Zhongshan Jinqiao Biotechnology Co., Ltd. . Antibodies used in the experiment are as follows: mouse anti-AQPThe steps involved in histopathological examination were as follows: 1) kidney and hypothalamus tissues were fixed with 4% paraformaldehyde for 48 h and then xylene twice, 5 min each time. 2) Rehydrated in an alcohol gradient of 100%, 95%, and 80%, 5 min each time, and washed in PBS three times for 5 min each. 3) The slices were immersed in hematoxylin for 5 min, followed by washing with PBS three times. 4) Added with 1% hydrochloric acid alcohol differentiation solution for 3  s, washed with PBS for three times, stained with eosin for 3 min, and washed again. 4) Dehydrated, transparent slices were sealed with a neutral gum. 5) Observed the pathological changes of kidney and hypothalamus tissues under a light microscope.via ELISA. Rat plasma samples and standards were added to a 96-well plate, horseradish peroxidase was added to all but the blank wells, and the plate was incubated in an incubator for 60 min. The samples were washed five times after removal, and then substrate solution was added and incubated at 37°C for 15 min in the dark. Finally, the stop solution was added to terminate the enzyme–substrate reaction. Within 15 min, the optical density (A) value of each well was measured at 450 nm, and the sample concentration was calculated using the standard curve.All operations were carried out precisely according to the instructions. For example, plasma copeptin levels were measured The steps involved in immunohistochemistry analysis were as follows: 1) fixed kidney slices were dehydrated and embedded in paraffin. 2) The slices were rehydrated in an alcohol gradient of 100%, 95%, and 80%, 5 min each time and washed in PBS three times for 5 min each. 3) After 3 min of repair at high temperature and high pressure, the slices were allowed to cool at room temperature before being blocked for 60 min with 5% goat serum. 4) In a humidified box, the slides were incubated overnight at 4°C with primary antibodies and then for 1 h with homologous secondary antibodies the next day at room temperature. 5) The slices were stained with DAB and observed at the best time for color development under a microscope. Fractionation of 1% hydrochloric acid–ethanol was carried out, hot water changed the nuclues to blue at 50°C for 3 min, and the dehydrated, transparent slices were sealed with a neutral gum. 6) Semi-quantitative analysis was conducted using Image-Pro Plus 6.0.http://www.tcmsp-e.com/) and PubChem (https://pubchem.ncbi.nlm.nih.gov/) databases, and supplement-related targets were obtained in combination with literature databases such as CNKI, Wanfang, GeenMedical Academic, and PubMed. Acute alcoholism targets were found using MalaCards , GeneCards (https://www.genecards.org/), and NCBI-gene (https://www.ncbi.nlm.nih.gov/gene) databases. After removing duplicate target genes, a Venn diagram was created to analyze the overlapping genes in order to identify the potential puerarin targets against acute alcoholism  is a versatile platform for assessing and integrating the PPI network. We imported overlapping potential genes into the STRING database to build the PPI network with the following parameters: the species as “Homo sapiens” and the minimum value of the combined score at 0.900. We obtained correlation data between the targets by removing the free protein. The data were then imported into Cytoscape 3.7.2 for the PPI network (STRING ( network .https://david.ncifcrf.gov/) was utilized for GO and KEGG pathway enrichment analyses. We identified significantly enriched pathways using p-values above 0.05 as thresholds for cellular component (CC), biological process (BP), and molecular function (MF), and the screened data were visualized using online software of Weishengxin (http://www.bioinformatics.com.cn/) (The DAVID database (com.cn/) .https://www.rcsb.org/). The active ingredient SDF file was obtained from PubChem. The Mol2 file was converted using Open Babel. Proteins were processed using AutoDock Tools 1.5.6 software as follows: separating proteins, adding non-polar hydrogen, balancing charge, and saving them as pdbqt format files. AutoDock Vina was used to perform docking and energy calculation between the processed active compound and the target protein, and then PyMOL was used for drawing  synthesized and provided the primers, and the primer sequences are shown in 2, 1:300; cAMP, 1:10,000; PKA, 1:1000; CREB, 1:1000; and c-Fos, 1:10,000) was added and incubated overnight at 4°C. The aforementioned washing steps were repeated three times the following day, and then a secondary antibody  was added and incubated for 60 min. After that, the membrane was washed with TBST thrice again for 10 min each time. The film was washed with the enhanced chemiluminescence reagent (ECL), and the film was exposed to the dark room. Finally, ImageJ was used to calculate the band reaction’s gray value.About 20 mg of tissue was placed in a homogenizer, and then 500 ul lysate was added. The homogenized tissue lysate was placed on an ice bath for 30 min before being centrifuged at 15,000 rpm (r = 4.0 cm) for 15 min to obtain the supernatant. The loading buffer was added in a 1:4 ratio, and the mixture was boiled at 100°C for 10 min to denature the protein. Then, the BCA kit was used to measure the protein concentration, and the loading amount was calculated. After that, SDS-PAGE gel vertical electrophoresis was performed at 60 V for 30 min and 90 V for 90 min. The membrane was tranferred at 300 V for 90 min, blocked with 5% non-fat milk powder for 90 min, washed three times with TBST for 10 min each time, and finally the primary antibodies , indicating that the acute alcoholism rat model was successfully established. However, the urine volume of rats in the treatment group was increased (p < 0.01), showing that puerarin could improve oliguria in rats with acute alcoholism.As shown in The glomeruli, proximal tubules, and distal tubules of SD rats were normal in structure under light microscopy, and no differences were observed in the capillary lumen and glomerular capsule lumen .p > 0.05), whereas the urea levels in the rats in the model group were relatively high (p < 0.001), and puerarin treatment had no effect (p > 0.05).As seen in + between the rats in each group (p > 0.05), while urine Na+ was lower in the model group, and puerarin treatment was ineffective (p > 0.05).The biochemical results indicated that there was no difference in plasma Nach group  (p > 0.0ffective  (p > 0.0p < 0.05), and both copeptin and ADH mRNA levels were reduced after puerarin administration (p < 0.05).This study examined copeptin and ADH mRNA levels to determine the effect of acute alcoholism on anti-diuretic hormone (ADH) in rats and whether puerarin could modulate it. Compared with the control group, plasma copeptin levels  and hypo2) is closely related to urine reabsorption. Immunohistochemistry , and puerarin treatment reversed these effects (p < 0.05).Water channel aquaporin-2  were found in MalaCards, GeneCards, and NCBI-gene databases. After removing duplicate genes, 6,959 targets in total were chosen. We combined the 6,959 potential target genes for acute alcoholism with the 310 puerarin target genes. A total of 214 intersections were thought to be possible puerarin targets for treating acute alcoholism .Following the submission of drug–disease common targets to the STRING database, medium confidence >0.9 was selected for the PPI analysis . The PPIAccording to the GO results, the set of crossed genes was enriched in 837 biological processes, most of which were related to positive gene expression regulation, apoptosis, inflammation response, signal transduction, and other biofeedback regulatory processes. The crossed genes were enriched in 107 cellular component expression processes, primarily related to cytoplasmic lysis, cytoplasm, nucleus, mitochondria, and so on. The crossed genes were enriched in 165 biological processes related to protein binding, enzyme binding, protein kinase binding, and so on. The KEGG results revealed the enrichment of 185 signaling pathways. The results of the top 10 GO biological processes and the four major signaling pathways were presented as bar and bubble plots in conjunction with expertise and a review of the literature .By analyzing the aforementioned 19 core targets and 4 major signaling pathways, we predicted that puerarin against oliguria in acute alcoholism might be closely related to the cAMP signaling pathway . The actcAMP, PKA, CREB, and c-Fos were the primary target genes for puerarin therapy of oliguria in rats with acute alcoholism. As a result, we examined the levels of cAMP, PKA, CREB, and c-Fos using RT-qPCR and Western blotting. As seen in p < 0.05). Puerarin treatment significantly reversed these effects (p < 0.05).Our network pharmacology analysis revealed that p < 0.05). They were considerably inhibited by puerarin treatment (p < 0.05).To better understand the relationship between the cAMP signaling pathway and oliguria in acute alcoholism, we examined an important transcription factor, cAMP-responsive element-binding protein-3 like-1 (CREB3L1), associated with ADH. The results of RT-qPCR  and WestPueraria lobata has a long history of alcoholism. Puerarin, one of the main active components of Pueraria lobata, accelerates alcohol metabolism, exhibits anti-inflammatory, antioxidant, neuroprotective properties, and regulates metabolism and diuresis  receptors on the renal collecting duct cells, AQP2-containing vesicles, to move from the cytoplasm to the apical membrane, resulting in the increase of water permeability of the apical membrane (2 was increased in the model group rats, indicating that oliguria in acute alcoholism was caused by excessive secretion of ADH from hypothalamic tissues to promote water reabsorption by the kidney.The kidneys are a vital excretory organ. The current study, however, discovered there were no significant morphological differences between the glomeruli and tubules, and no changes in blood creatinine levels in the control and model rats, implying that oliguria in acute alcoholism is not directly related to renal parenchymal injury. ADH, an important hormone for maintaining water–sodium homeostasis , is mainan bulge . ADH themembrane . ADH hasmembrane . The res+ concentrations can influence ADH secretion (+ were found to be lower in acute alcoholism rats, possibly because the renal tubules reabsorbed water and Na+, but plasma Na+ did not change significantly in a short time, maybe ADH was elevated, increasing water reabsorption and diluting blood, which is consistent with the findings of + concentration was not the main factor causing the increase of ADH in rats with acute alcoholism. To further clarify the reasons for the increased secretion of ADH through network pharmacology analysis, a total of 214 common targets of puerarin and acute alcoholism were collected, and 19 core targets for the treatment of acute alcoholism through the PPI core target interaction network, namely, c-Fos, MAPK1, STAT3, TNF, IL6, JUN, MAPK3, ESR1, EGFR, CAV1, ATK1, HIF1A, EP300, NFKB1, MAPK14, CREB1, CDKN1A, TP53, and RELA. This revealed that puerarin is linked to a variety of targets, including key targets for the treatment of acute alcoholism. Further target function enrichment analysis was performed in this study to elucidate how puerarin exerts its effects via these core targets. Puerarin was associated with cancer pathways, lipid and atherosclerosis, hepatitis B, cGMP signaling pathway, HIF-1 signaling pathway, TNF signaling pathway, cAMP signaling pathway, MAPK signaling pathway, and others according to KEGG enrichment analysis. Among the aforementioned targets and pathways, the cAMP signaling pathway is crucial for neuronal survival, growth and development, and differentiation (2 (Previous research has shown that increased plasma Naecretion . Howeverntiation  and partation (2 . One of ation (2 .As a second messenger, cAMP participates in many physiological processes and pathophysiological changes . Severalvia direct binding (CREB3L1 was discovered for the first time in long-term cultured mouse astrocytes . CREB3L1 binding , implyinIn this study, we used an experimental–network pharmacology–experimental validation method to show that puerarin was related to the cAMP pathway and could alleviate oliguria in rats with acute alcoholism. The possible mechanism of puerarin for treating oliguria in acute alcoholism is shown in


Background: Pregnancy-induced Hypertension (PIH) is a disease that causes serious maternal and fetal morbidity and mortality. Alisma Orientale (AO) has a long history of use as traditional Chinese medicine therapy for PIH. This study explores its potential mechanism and biosafety based on network pharmacology, network toxicology, molecular docking and molecular dynamics simulation.Methods: Compounds of AO were screened in TCMSP, TCM-ID, TCM@Taiwan, BATMAN, TOXNET and CTD database; PharmMapper and SwissTargetPrediction, GeneCards, DisGeNET and OMIM databases were used to predict the targets of AO anti-PIH. The protein-protein interaction analysis and the KEGG/GO enrichment analysis were applied by STRING and Metascape databases, respectively. Then, we constructed the “herb-compound-target-pathway-disease” map in Cytoscape software to show the core regulatory network. Finally, molecular docking and molecular dynamics simulation were applied to analyze binding affinity and reliability. The same procedure was conducted for network toxicology to illustrate the mechanisms of AO hepatotoxicity and nephrotoxicity.Results: 29 compounds with 78 potential targets associated with the therapeutic effect of AO on PIH, 10 compounds with 117 and 111 targets associated with AO induced hepatotoxicity and nephrotoxicity were obtained, respectively. The PPI network analysis showed that core therapeutic targets were IGF, MAPK1, AKT1 and EGFR, while PPARG and TNF were toxicity-related targets. Besides, GO/KEGG enrichment analysis showed that AO might modulate the PI3K-AKT and MAPK pathways in treating PIH and mainly interfere with the lipid and atherosclerosis pathways to induce liver and kidney injury. The “herb-compound-target-pathway-disease” network showed that triterpenoids were the main therapeutic compounds, such as Alisol B 23-Acetate and Alisol C, while emodin was the main toxic compounds. The results of molecular docking and molecular dynamics simulation also showed good binding affinity between core compounds and targets.Conclusion: This research illustrated the mechanism underlying the therapeutic effects of AO against PIH and AO induced hepato-nephrotoxicity. However, further experimental verification is warranted for optimal use of AO during clinical practice. Pregnancy-induced Hypertension (PIH) encompasses a series of serious pregnancy complications in the second trimester (after 20 weeks of gestation), including pre-eclampsia, eclampsia, chronic hypertension with pre-eclampsia and chronic hypertension with pregnancy . Little The complexity of the interactions also limits the efficacy of treatment. Currently available management consists of symptomatic treatment, including spasmolytics (magnesium sulfate), anticoagulants (aspirin), antihypertensive drugs, and termination of pregnancy as a last resort . NeverthTraditional Chinese medicine (TCM) therapy is an ancient practice used for more than 2000 years in China . The appAlisma Orientale (AO), also called Ze Xie in Chinese, is a high-efficacy and low-toxicity herb medicine primarily used in Southeast Asian countries. Its efficacy is mainly mediated by removing dampness and promoting water metabolism , accountThe concept of network pharmacology was first officially reported by the British Pharmacologist Hopkins in 2007 , while nIn this study, we applied network pharmacology to screen the main therapeutic compounds of AO and predicted the potential mechanism. In addition, we analyzed the potential hepato-nephrotoxicity mechanism of AO compounds by network toxicology during the treatment of PIH. Finally, molecular docking and molecular dynamics simulation analyses were used to estimate the binding stability. Overall, this study improves our current understanding of the potential pharmacodynamic and toxicity mechanism of AO in PIH patients and explores how to enhance efficacy and reduce toxicity. The detailed research content of this study is presented in the flow chart .http://lsp.nwu.edu.cn/tcmsp.php) database (http://bidd.group), BATMAN-TCM (http://bionet.ncpsb.org.cn/) and TCM@Taiwa (http://tcm.cmu.edu.tw/zh-tw/) database were used to acquire detailed information on identified AO compounds. Oral bioavailability (OB) refers to the dosage of drugs that enter the blood circulation through oral absorption and act on local tissues and organs to produce corresponding pharmacological effects. Drug-likeness (DL) is defined as the similarity of the chemical structure of a drug compared with known drugs. Both characteristics are important to assess the potential medicinal value of compounds (http://www.swissadme.ch) database according to their pharmacokinetic parameters. The toxicity compound of AO was further searched in Toxicology data network  database (https://toxnet.nlm.nih.gov/newtoxnet/ctd.htm) to analyze the biosafety profile of AO in the treatment of PIH  and SwissTargetPrediction database (http://www.swisstargetprediction.ch/) to predict the possible targets based on the spatial conformation. The uniport ID of the target was converted to the standardized gene name by Uniport (https://www.uniprot.org/) database. MeSH database (https://www.ncbi.nlm.nih.gov/mesh) was utilized to verify the standard name of the disease as “Pregnancy-induced Hypertension” “drug-induced liver injury” (DILI) and “drug-induced kidney injury” (DIKI) (https://www.genecards.org/), DisGeNET database (https://www.disgenet.org) and Online Mendelian Inheritance in Man (https://omim.org) database were retrieved to obtain potential targets using the keywords “Pregnancy-induced Hypertension” “drug-induced liver injury” and “drug-induced kidney injury”. After merging and removing duplicate targets, the intersected targets in the Venn plot were defined as potential targets of OA compounds during PIH treatment and toxic compounds associated with hepato-nephrotoxicity.The structural formulas of therapeutic and toxic compounds obtained in the previous step were input into the PharmMapper  (http://database  (http:// Uniport  (https:/” (DIKI) . Then, G” (DIKI)   database was used to construct a PPI network of the anti-PIH effect of AO, with species limited to “Homo sapiens” and interaction score >0.4. The Cytoscape plug-ins Cytohubba and MCODE (molecular complex detection) were used to analyze the topological parameters and select the core targets of AO anti-HIP with high accuracy and exhibit the complex relationship between disease and herb (A protein-protein interaction (PPI) network was generated to identify interacting proteins. STRING  (http://and herb . The samhttp://www.metascape.org/), and the cut-off p-value, min overlap and enrichment value were set to 0.01, 3 and 1.5, respectively. And q-value < 0.05 (Benjamini–Hochberg method) was used to remove the false positive enrichment results (http://www.bioinformatics.com.cn/) was used to visualize the enriched results as bar and bubble plots. Then, the KEGG (http://www.kegg.jp.org/) database was used to map and color the detailed target messages in the most significantly enriched pathway. Finally, the complex regulatory network of AO in the treatment of PIH was shown by the “herb-compound-target-pathway-disease” network, incorporating the elements of AO, therapeutic compound, potential target, and top 10 enriched pathway, and visualized by Cytoscape software . The same steps were used to analyze the core pathways of toxic AO compounds associated with hepato-nephrotoxicity.Functional enrichment analysis was conducted to better understand the functions of the screened genes. Gene Ontology (GO) enables the analysis of gene function based on the cellular compound (CC), molecular function (MF) and biological process (BP). Kyoto Encyclopedia of Genes and Genomes (KEGG) enables an understanding of the biological pathways associated with genes. Both were used to illustrate the core pathway and mechanism of AO anti-HIP. Targets were inputted in the Metascape database  (http:// results . The R pthe KEGG  (http://http://www.swissdock.ch/) is an online molecular docking (MD) tool that enables the calculation of the binding affinity of each binding site between small molecule ligand and receptor protein. The X-ray diffraction of the protein crystal structure of core targets was downloaded from the Protein Data Bank (PDB) database (www.rcsb.org) (https://www.3ds.com/) (The Swiss dock platform  (http://csb.org) . The resds.com/) .Molecular Dynamics simulations (MDS) were further conducted to investigate the stability of small molecule ligands in proteins by the “Standard Dynamics Cascade” unit of Discovery Studio2019 software for ligand-protein complexes with the lowest binding affinity after molecular docking . The ligThere were 77 compounds of AO obtained from TCMSP, TCM-ID, TCM@Taiwan and BATMAN database  Details. Based oAfter screening and removing duplicates, we identified 138 potential targets associated with 29 therapeutic compounds and 160 targets associated with 10 AO toxic compounds. Besides, 1136 putative targets of PIH, 2188 targets of drug-induced liver injury and 2094 targets of drug-induced kidney injury were retrieved from the GeneCards, DisGeNET and OMIM databases. 78 targets overlapped between therapeutic compounds of AO and PIH, while 117 and 111 targets overlapped between AO toxic compounds and liver and kidney injury were obtained by Venn  and combined score, and the maximal clique centrality (MCC) algorithm was further used to screen the core targets. The top 10 targets were AKT1, BCL2L1, CASP3, EGFR, ESR1, HSP90AA1, IGF1, MAPK1, MAPK8 and SRC . Besidesp < 0.01, q < 0.05), demonstrating the characteristic of multi-pathway of AO in the treatment of PIH. The top 10 biological processes included the response to hormone, regulation of kinase activity, enzyme-linked receptor protein signaling pathway, cellular response to hormone stimulus, regulation of MAPK cascade cellular response to lipid, cellular response to nitrogen compound, positive regulation of protein phosphorylation, transmembrane receptor protein tyrosine kinase and positive regulation of cell migration , and the lipid and atherosclerosis pathway was significantly associated with AO induced liver injury , the lipid and atherosclerosis pathway was also significantly associated with AO induced kidney injury  network was constructed by Cytoscape 3.9.1 to reveal the complex molecular mechanism and multi-compound, multi-target and multi-pathway characteristic of AO in the treatment of PIH . The majMolecular dynamics simulation represents an important technology for analyzing the ligand-protein complex’s conformational change and stability after docking. To explore the binding stability of Alisol B monoacetate with IGF1, the RMSD, RMSF curves, energies alternate tendency and hydrogen bonding heat map were calculated. Pregnancy-induced hypertension is a common disease during pregnancy, with a 10–12% incidence rate . ElevateAs mentioned above, oxidative stress, immune dysregulation, endothelial cell malfunction and other perplex factors have been associated with the pathogenesis of PIH. Unlike Western medicine which adopts a targeted approach, TCM fosters a more holistic approach based on the multi-compound, multi-target and multi-pathway of TCM drugs and has huge prospects for use as complementary and alternative medicine.Alisma Orientale, also known as “Ze Xie” in Chinese, is the most important herbal compound in Ze Xie decoction indicated for treating hypertension, according to ancient records. Modern studies have isolated more than 100 active compounds, mainly terpenoids and small amounts of flavonoids, alkaloids, phytosterols and fatty acids. It is worth mentioning that protostane triterpenoids are the characteristic compounds, including alisol A-I and their derivatives . It has Previous studies have demonstrated that AO plants, especially triterpene compounds, have diuretic activity. Zhang et al. reported that Alisol B, alisol B 23-acetate and other derivatives could interfere with the Na+, K+, and Cl− co-transport carrier on the luminal membrane of the Henle and sodium–chloride co-transporter in the renal distal convoluting tubule to exert diuretic action . It coulvia network toxicology.Few studies have hitherto assessed the toxicity and side effects of AO and its compounds to determine its biosafety profile . AccordiPPI network analysis showed that IGF, AKT1, EGFR and MAPK1 were the main targets of AO in the treatment of PIH; the molecular docking and molecular dynamics simulation results also exhibited good binding affinity and stability. Besides, KEGG pathway enrichment analysis showed that MAPK and PI3K-AKT signal pathways were significantly enriched. Insulin-like growth factor I (IGF1) can bind to the alpha subunit of IGF1 receptor and modulate the tyrosine kinase activity to initiate the down-stream signaling events activation of PI3K-AKT and Ras-MAPK pathways . It has in vivo and in vitro basic research is needed to confirm and extend our conclusions. And this was exactly what we need to do next, to verify the mechanisms of AO core compounds with therapeutic efficacy in PIH and hepato-nephrotoxicity in future research.In conclusion, we found that the main compounds of AO in treating PIH are triterpenoids, especially Alisol B, Alisol C and their derivatives, which target IGF, AKT1, EGFR and MAPK1 and mediate the MAPK and PI3K-AKT signal pathways to produce diuretic and immunomodulatory activities and regulate the function of endothelial cells, anti-vascular inflammation and oxidative stress. In addition, our study preliminarily indicated that the toxic compounds in AO were mainly emodin and small amounts of fatty acids, which may produce hepato-nephrotoxicity by interfering with lipid metabolism. Furthermore, our study suggests that an optimal therapeutic effect against PIH may be achieved by increasing the triterpenoid content and reducing toxic compounds through appropriate methods. However, some shortcomings in the present study affect the robustness of our findings to some extent. First, our study was based on the identified and main bioactive compounds of AO, which may have some selection bias. Second, and crucially, further This study was based on the bioinformatics analysis of network pharmacology, network toxicology, molecular docking and molecular dynamics simulation. The finding shows that the triterpenoids were the main therapeutic compounds and emodin was the main toxic compound of Alisma Orientale. And also illustrated the potential mechanism underlying the therapeutic effects of AO against PIH and AO induced hepato-nephrotoxicity. In short, our research could guide directions for subsequent basic experimental verification and provide points of view for enhance efficacy and reduce toxicity in clinical.


Obese type 2 diabetes mellitus (obese T2DM) is one of the prime diseases that endangers human health. Clinical studies have confirmed the ability of the Huanglian Huazhuo capsule to treat obese T2DM; however, its mechanism of action is still unclear. In this study, effects and mechanisms of the Huanglian Huazhuo capsule in obese T2DM were systematically investigated using network pharmacology and molecular docking techniques.  The active ingredients and targets of the Huanglian Huazhuo capsule were extracted from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Obese T2DM diabetes-related targets were retrieved from a geographic dataset combined with a gene card database. A protein-protein interaction (PPI) network was constructed to screen core targets. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using Database for Annotation Visualization and Integrated Discovery (DAVID). Interactions between potential targets and active compounds were assessed using molecular docking. Molecular docking was performed on the best core protein complexes obtained using molecular docking.  A total of 89 and 108 active ingredients and targets, respectively, were identified. Seven core targets were obtained using a topological analysis of the PPI network. The GO and KEGG pathway enrichment analyses showed that the effects of the Huanglian Huazhuo capsules were mediated by inflammation, lipid response, oxidative stress-related genes, and HIF-1 and IL-17 signaling pathways. Good binding ability was observed between the active compounds and screened targets using molecular docking.  The active ingredients, potential targets, and pathways of the Huanglian Huazhuo capsule for the treatment of obese T2DM were successfully predicted, providing a new strategy for further investigation of its molecular mechanisms. In addition, the potential active ingredients provide a reliable source for drug screening in obese T2DM. Diabetes mellitus (DM) is a metabolic endocrine disease characterized by glucose and fat metabolism disorders and increased plasma glucose levels, and in the symptomatic stage, it is characterized by excessive drinking, polyphagia, polyuria, weakness, wasting, or sweet-tasting urine . RelevanIn Chinese medicine, DM is not named but classifies as a “thirst disorder.” According to Traditional Chinese Medicine (TCM), the etiology of obese T2DM is complex, with congenital deficiency of endowment, emotional disorders, poor diet, and lack of exercise contributing to the disease. However, the congenital deficiency of endowment shows the most impact than others . In the In Chinese medicine, traditional pharmacological approaches are limited to mechanistic studies . In 2013https://old.tcmsp-e.com/tcmsp.php). “Huanglian, Huangbai, Danshen, Shanzha, and Zhiqiao” were the keywords of the compound. Furthermore, the full name of the target gene was converted into an abbreviation. TCMSP is a systematic pharmacology platform for Chinese herbal medicine, providing interactive data associated with the relationship among drugs, targets, and diseases [http://www.megabionet.org/tcmid/) provides data for TCM [The active components in the Huanglian Huazhuo capsule were retrieved using Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform  [https://www.rstudio.com/products/rstudio/) was |log FC| > 1, P < 0.05 [http://www.genecards.org/) [https://bioinfogp.cnb.csic.es/tools/venny/index.html), and the intersecting target was the action target of the Huanglian Huazhuo capsule in intervening obesity and diabetes.The mRNA expression profile of an obese T2DM sample (GSE166467) was searched in the gene expression synthesis dataset (GEO: gov/GEO) . The thrP < 0.05 . The DEGds.org/)  using thhttps://cytoscape.org/). The network diagram of “drugs-component-targets-diseases” was constructed, which was analyzed using the cytoNCA plug-in.The drugs, active ingredients, and intersection targets of drugs and diseases were input in the Cytoscape 3.8.0 software (https://cn.string-db.org/). The species was restricted to “Homo sapiens,” and data with confidence levels higher than 0.90 were selected. The obtained data were imported into the Cytoscape 3.8.0 software for analysis, and graphs were generated by identifying the core genes of the network.The data of drug-disease intersection genes were imported to the STRING website (https://mirrors.tuna.tsinghua.edu.cn/bioconductor), respectively. Enrichment analysis statistical filter values were set , and the screening results were visualized.Drug-disease intersection targets were subjected to GO and KEGG analyses using the RStudio 4.2.1 package and bioconductor package , and the water and protein impurities were removed using PyMOL (https://pymol.org/) software. The 2D structure of the active ingredient was downloaded from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) and imported into Chem3D software (https://www.chemdraw.com.cn/) to convert it into a 3D structure. The target proteins and small molecules were hydrogenated using AutoDockTools 1.5.6 software (https://autodock.scripps.edu/). The Grid Box was set up with the original ligand as the center, and the data were derived, followed by AutoDock Vina application for molecular docking. The target proteins and small molecules were visualized using the PyMoL software.The 3D structure of the target protein was downloaded from the Research Collaboratory for Structural Bioinformatics, Protein Data Bank (RSCB PDB) database . Of these, 19 were upregulated and 9 were downregulated in the obese T2DM . The heaA total of 222 active ingredient targets of the Huanglian Huazhuo capsule were identified using the TCMSP database, and 1056 genes associated with obese T2DM and 108 targets of the drug-disease intersection were obtained using the GeneCards database .The “Drug-Component-Target-Disease” network of the Huanglian Huazhuo capsule was mapped using the Cytoscape 3.8.0 software for obesity and DM treatment . The diaBased on the topological analysis, the target data generated from the STRING website was input into the Cytoscape 3.8.0 software. Greater than or equal to the median value was used as a filtering criterion, and the final topological parameter analysis yielded 18 nodes and 192 edges on the way to the PPI network, and STAT3, MAPK1, RELA, IL6, TNF, ESR1, and IL10 were identified as seven core targets .R language bioconductor package, and the GO and KEGG analyses were performed. The GO enrichment analysis included molecular function, biological pathway function, and cellular component. A bar chart was created using the top 10 entries (The common drug-disease targets were input into the  entries , with thP-values from the KEGG enrichment analysis, and the associated targets and compounds were selected to develop a compound-target-pathway network using the Cytoscape 3.8.0 software (Based on the gene ratios, the top 20 highly enriched pathways were screened . These 2software , contain−1, the molecules bind by themselves (Molecular docking is a theoretical simulation method to examine the interaction between molecules and predict their binding mode and affinity. The top three target proteins and small molecules were screened for docking. Generally, the lower the energy required for ligand-molecule binding, the easier the docking success. If the binding energy is < 0 kcal/molemselves . The molDM is a common chronic disease with increasing annual incidence. The number of patients with T2DM primarily characterized by insulin resistance is increasing . TherefoThe Huanglian Huazhuo capsule removes phlegm, accumulation, dampness, and blood stasis and invigorates spleen. Among them, Huanglian and Huangbai removes heat and dampness and releases fire and detoxify; Shanzha digests and removes accumulation, strengthens stomach, and removes stasis by eliminating Qi; Zhiqiao detoxifies and removes turbidity; and Danshen promotes blood circulation and removes blood stasis. However, the bioactive compounds of the Huanglian Huazhuo capsules and their mechanisms of action against obese T2DM remain unclear. Therefore, the potential targets and mechanisms of action of the Huanglian Huazhuo capsules were identified in obese T2DM cases using a network pharmacology strategy and molecular docking.β/NF-κB and c-Jun-N-terminal kinase signaling pathways in the adipose tissue by downregulating inflammatory events, thereby inhibiting obesity-induced insulin resistance [γ and SREBP-1c, thereby reducing adipose tissue accumulation and improving hyperlipidemia in mice with obesity and diabetes [A total of 88 active compounds from the TCMSP database were screened for the Huanglian Huazhuo capsules. Furthermore, 1056 obese T2DM disease targets were obtained from the geographic database. Hence, 108 putative Huanglian Huazhuo capsule-obese T2DM targets were identified. Based on the degree in the drug-component-target-disease network, the top seven active compounds of the Huanglian Huazhuo capsule were quercetin, beta-sitosterol, stigmasterol, kaempferol, luteolin, tanshinone IIA, and naringenin. Their effectiveness is supported by previous studies. Reportedly, quercetin attenuates lipid peroxidation, platelet aggregation, and capillary permeability and contains anti-inflammatory effects with therapeutic efficacy in obesity and T2DM . Beta-sisistance . Stigmassistance . Kaempfediabetes . Luteolidiabetes . Tanshindiabetes . Naringediabetes . The strdiabetes . STAT3, The KEGG and GO functional analyses showed that the effect of the Huanglian Huazhuo capsule in obese T2DM is associated with many biological processes, including inflammatory, lipopolysaccharide, and oxidative stress responses. In this study, the targets were enriched for lipid and inflammatory response pathways, such as HIF-1, IL-17, and TNF signaling pathways. HIF-1 reportedly promotes changes in the adipose tissues of patients with obesity, leading to the inhibition of adipocyte differentiation, adipocyte dysfunction, inflammation, insulin resistance, and T2D . An adipβ, and Wnt signaling pathways, and they closely associate with the inflammatory signaling pathway [Subsequently, seven key target proteins, such as STAT3, MAPK1, RELA, IL6, TNF, ESR1, IL10, and active compounds, including quercetin, beta-sitosterol, stigmasterol, kaempferol, luteolin tanshinone IIA, naringenin, were evaluated using molecular docking techniques. The binding affinities ranged from −5.1 to −9.0 kcal/mol, indicating that all targets possibly had good docking ability with the active compounds. The result suggests that these compounds may contribute to the effectiveness of the Huanglian Huazhuo capsules in obese T2DM treatment. Chronic low-grade inflammation with elevated levels of nonspecific inflammatory factors, T2DM is an important factor in T2DM development and its complications . The maj pathway –39. ThesHowever, the present study has some limitations. First, bioactive compounds and target data were retrieved from the literature and databases; hence, the reliability and accuracy of the predictions depend on the data quality. The active compounds in the Huanglian Huazhuo capsules can be analyzed using the lC/MS technique. In addition, metabolomics and pharmacokinetic studies may be advantageous. Second, data mining methods that require clinical trials and animal studies to confirm these findings were used.It is the first time that the pharmacological and molecular mechanisms of action of the Huanglian Huazhuo capsule have been systematically explored to treat obese T2DM using network pharmacology and molecular docking techniques. These bioinformatics and computational analyses suggest that quercetin, beta-sitosterol, stigmasterol, and kaempferol are possibly the main active compounds of the Huanglian Huazhuo capsule in obese T2DM treatment. In addition, the Huanglian Huazhuo capsule could treat obese T2DM by reducing pathological damage, inflammatory response, and oxidative stress via various pathways, such as HIF1, IL-17, and TNF. Overall, the present study focused on the multicomponent and multipathway nature and mechanism of action of the Huanglian Huazhuo capsule. These findings can guide the application and further develop the Huanglian Huazhuo capsules in obese T2DM treatment.


Pathway enrichment analysis has become a widely used knowledge-based approach for the interpretation of biomedical data. Its popularity has led to an explosion of both enrichment methods and pathway databases. While the elegance of pathway enrichment lies in its simplicity, multiple factors can impact the results of such an analysis, which may not be accounted for. Researchers may fail to give influential aspects their due, resorting instead to popular methods and gene set collections, or default settings. Despite ongoing efforts to establish set guidelines, meaningful results are still hampered by a lack of consensus or gold standards around how enrichment analysis should be conducted. Nonetheless, such concerns have prompted a series of benchmark studies specifically focused on evaluating the influence of various factors on pathway enrichment results. In this review, we organize and summarize the findings of these benchmarks to provide a comprehensive overview on the influence of these factors. Our work covers a broad spectrum of factors, spanning from methodological assumptions to those related to prior biological knowledge, such as pathway definitions and database choice. In doing so, we aim to shed light on how these aspects can lead to insignificant, uninteresting or even contradictory results. Finally, we conclude the review by proposing future benchmarks as well as solutions to overcome some of the challenges, which originate from the outlined factors. Pathway enrichment analysis has become one of the foremost methods for the interpretation of biological data as it facilitates the reduction of high-dimensional information to just a handful of biological processes underlying specific phenotypes. Over the last decade, the popularity of pathway enrichment analysis has led to the development of numerous different methods that can be categorized into three generations: (i) over-representation analysis (ORA), (ii) functional class scoring (FCS) and (ii) pathway topology (PT)-based, each of which adds an increasing layer of complexity to the analysis . ORA, thAn analogous but more philosophical debate in the community pertains to the choice of pathway or gene set database. Its selection is arguably one of the most decisive factors influencing the results of enrichment analyses as it determines the possible gene sets that can be enriched (i.e. genes within a gene set are enriched in an examined list of genes). The number of public databases has continued to grow in the past years in parallel with novel enrichment methods. However, the list of the most widely used databases has not changed in the last decade as enrichment analyses are predominantly conducted exclusively on one of the following three databases: KEGG , ReactomAt present, users are offered a wide spectrum of enrichment methods and databases when performing enrichment analyses. This poses a challenge when considering the numerous factors that play a role in results of enrichment analysis, which can lead to insignificant, irrelevant or even contradictory results. Thus, in recent years, several benchmark studies have been conducted to evaluate the effects of various aspects of pathway analysis for practical guidelines.In this work, we review the findings of major benchmarks conducted on different factors that influence the results of pathway enrichment analysis . The goaet al. . As a proxy to study the latter parameter , the authors evaluated the influence of single genes on the fraction of pathways that were considered enriched, assuming that a setup that fails to take into account PT is one in which individual genes have an equal impact on results. To that end, they found that TopologyGSA [P-values of target pathways change when topological information is incorporated, the authors found that both the ranks and P-values of target pathways decreased for PRS [P-values of target pathways (at times caused by pathway-specific effects).Ihnatova et al.  conducteologyGSA  and ClipologyGSA  yielded  for PRS  and CePahttps://academic.oup.com/bib), type I error rate, power, runtime and assessments of reproducibility across datasets, among others in https://academic.oup.com/bib). Furthermore, we outline additional methodological considerations, including the steps used in data preprocessing and biases, which arise from experiments (https://academic.oup.com/bib), the gene- and gene set–level statistics selected (https://academic.oup.com/bib), the applicability of enrichment analysis to various omics dataset types (https://academic.oup.com/bib) and the choice of background (https://academic.oup.com/bib).Besides the above-mentioned common measures and classifications, several comparative studies have used to draw distinctions between enrichment methods, the performance of methods on a number of additional aspects has also been benchmarked. We refer to the studies that evaluate other aspects, including accuracy (https://academic.oup.com/bib).Given the vast variety of enrichment methods, often with tunable settings, hundreds of methods and variants are at the disposal of life science researchers. As results can acutely vary according to the method selected, such a broad variability has prompted the development of tools to conduct enrichment analysis in concert. While the techniques to do so can differ, generally a consensus is taken across several methods to determine the final set of pathways that are interesting in some statistically significant way. Examples to do so include the R packages EGSEA , EnrichmWhile variations of enrichment methods have been among the most studied factors that influence the results of an enrichment analysis, there are several other considerations to be made in the design of an experiment to ensure biologically meaningful results. In this section, we introduce studies, including notable benchmarks, that have investigated the impact of additional factors on the results of enrichment analysis, such as database choice and pathway size.One of the most critical factors the results of an enrichment analysis can hinge upon is the choice of a reference pathway database(s). It is common practice for researchers to solely rely upon a single database for an enrichment analysis, which can be due, in part, to a researcher’s preferences, the popularity of a particular database or its ease of usage, among other factors. Indeed, we observed that the majority of studies that benchmarked the performance of enrichment methods  were almet al. [A first investigation on the importance of selecting a collection of gene sets was performed by Bateman et al. . In thiset al. , the autet al. ).Similar conclusions were drawn in our previous work  in whichet al. [P-value as their EcoCyc counterparts.An additional factor that is related to database choice is gene set (pathway) size, corresponding to the number of genes within a gene set for enrichment methods that do not consider PT, or the number of nodes (genes) and edges for those that do consider it. The effect of pathway size has recently been studied in Karp et al.  by compaet al. . Given tNotably, size differences between equivalent pathways have not only been examined for these two databases but also across other major resources, such as Reactome, and WikiPathways. In this work, the authors argue that using pathway definitions that span across several biological processes  can lead to misinterpretations as when these pathways are enriched, it is difficult to construe whether this implicates all or only a subset of the pathway. These broadly defined pathways can also be less informative, contributing little in terms of novelty to the overall understanding of the distinctions between the phenotypes under study. Nonetheless, smaller pathways can lead to exceedingly long results and overly strict multiple testing corrections .Possible solutions for mitigating the impact of gene set size on results are defining the minimum and maximum number of genes within a gene set (e.g. between 10 and 500), careful consideration of the enrichment analysis method selected (see ‘Hypothesis testing and significance assessment’ section) as well as addressing redundancies within gene sets, as proposed in . In theiet al. [While database choice and pathway size are two critical factors to consider, we foresee several approaches to offset the challenges they create. In the case of database choice, a study by Maleki et al.  proposedAnother solution that we propose is that the enrichment results generated from a reference database could be validated against an additional database using equivalent pathway mappings across them. By leveraging pathway mappings, one can assess the similarity between the results obtained from different databases  to confirm whether they are in consensus, or re-evaluate them if they are not. In earlier work, we leveraged this technique by generating equivalent pathway mappings across four pathway databases . A web tSimilarly, pathway mappings can also be employed to systematically study the impact of pathway size on results. Here, one could leverage hierarchical mappings (i.e. pathway A is part of pathway B) from pathway ontologies to evaluate whether related pathways are similarly enriched. Although a pathway ontology was earlier proposed by , it has et al. [The growth of biomedical literature is reflected in pathway databases as their pathway definitions change over time. A study by Wadi et al.  demonstret al. [While the factors mentioned thus far have each been benchmarked with regard to their impact on pathway enrichment results, there exist other factors that have not yet been explored in detail. First, at a more granular level, individual genes can also have an impact on results. A study by Ballouz et al.  raised thttps://academic.oup.com/bib). As a consequence, depending on the size of a database, the same pathway in one database may or may not be enriched in another after applying multiple testing correction. This is often the case when comparing popular databases, such as KEGG and Reactome, whose number of pathways can differ by an order of magnitude.A second factor that has not yet been investigated, which is related both to database updates and choice, is the size of a database measured by the number of pathways. This factor is not only important due to its correlation with the coverage of biological processes but also because the size of the database can influence the significance of the results when correcting for multiple testing , keeping in mind that without gold standards to conclude whether the results from any given method are biologically sound, objective evaluations can be difficult to make. Overall, we have found many inconsistencies in the performance of methods across metrics as well as across studies. While there is no consensus across studies on whether a specific method outperforms others, we have reported trends we have observed regarding the top-performing methods for each metric.Though we note that the performance of the majority of methods on these and other metrics is inconclusive, whether a particular method is a reasonable choice for a certain use case can depend on a number of factors, such as the goal of the experiment, the dataset in question or particulars of the gene set collection. Nevertheless, trade-offs between performances on certain metrics can be important considerations in the selection of a method. For example, given a dataset where changes in differential gene expression between experimental groups are subtle, a highly sensitive method can increase the likelihood of detecting a signal. Thus, a large number of gene sets that are truly significant can be identified, essentially ruling out nearly all gene sets that are not detected, albeit at the expense of producing a greater number of false positives. If, however, changes in differential gene expression between experimental groups are generally more pronounced, a method ranked high in specificity may be preferable to preclude the detection of too many gene sets, which can complicate interpretation.We have also examined comparative studies that have evaluated the differences between distinct categories of enrichment methods, such as how the null hypothesis is formulated and the sampling unit is defined, noting that the selection of one category of methods over another can have serious repercussions on the fraction of gene sets that are significant and their ranks. In addition, a major categorical distinction is drawn between topology- and non-topology-based methods, which have been reviewed in several benchmarks. We have found that, though topology-based approaches are more advanced, for some methods, the removal of topological information yields no differences in results, for other methods, it can improve results, and several are constrained in that they only cater to KEGG pathways . Finally, we reviewed studies that have assessed the influence of particular, modular aspects of a typical enrichment analysis as well as outlined additional aspects one must be cognizant of that can affect the behavior of a given method, which ultimately reflects in the overall results of an analysis.We have reviewed several other factors apart from enrichment methods, such as pathway size and database choice. Notably, the latter can be subjective, with both researcher preferences and distinct research goals taking precedence over set guidelines. However, we have outlined approaches that leverage pathway mappings to mitigate the effect of these factors. An additional aspect discussed in this review is the lack of regular updates to enrichment tools, which reflect updates made to pathway databases. Fortunately, this issue has, at least, partially been addressed by the adoption of API services by major pathway resources. Nevertheless, the amount of literature published on a daily basis continues to grow, making the task of maintaining up-to-date pathway definitions difficult, particularly for public and academic resources. Thus, we envisage that the path forward to address this shortfall is to improve interoperability across databases via mappings  or throuhttps://academic.oup.com/bib). Last, we foresee that the insights gained from multi-omics experiments will also be reflected in pathway definitions in two ways: (i) the appearance of ‘dynamic pathways’  and (ii) a shift from traditional gene sets to sets of multimodal biological entities.Finally, we would like to mention possible future benchmarks beyond the ones we have previously proposed. First, future benchmarks can benefit from the existence of a gold standard prioritization approach, for instance, one that leverages well-established pathway-disease associations from genetic disorders, similar to the assessment proposed in , which eIn conclusion, the effect of various factors on pathway enrichment analysis is apparent. Numerous studies have demonstrated how variations in the design of an enrichment analysis can lead to altogether different findings. At the extremes, comparative studies have shown how certain experimental setups can detect either all or no gene sets as interesting in some statistically significant way. We summarize the key findings of studies reviewed herein as follows:P-value for each gene set, one must bear in mind that disparate approaches can impact the results of an enrichment analysis, and depending on the approach taken, introduce false positives.One must be cognizant of how the null hypothesis is formulated (i.e. competitive or self-contained) as methods categorized into one or another approach behave differently in terms of the fraction of gene sets reported as significant, as well as their sensitivity to gene set size, sample size and sample heterogeneity. Self-contained methods also tend to have greater power than competitive methods and careful consideration should be made taking into account the proportion of genes that are differentially expressed in the dataset. Similarly, in order to calculate a Certain enrichment methods have been observed to be more or less robust to pathway and sample size than certain others. Sensitive methods may detect larger gene sets as significantly enriched and their sensitivity can be tied with whether they are competitive or self-contained methods. Not surprisingly, a method’s performance tends to deteriorate with decreasing sample size, although some methods are more robust on this factor than others.P-values of relevant pathways for some topology-based methods, yet this may not be the case for all. Nonetheless, some topology-based methods are limited or at least partial to specific pathway databases.Topology-based methods are intuitively more advanced than non-topology-based ones. Incorporation of topological information tends to improve the ranks and The selection of one gene set collection over another can lead to different results. Some collections or databases may be more suitable than others for a given dataset. The selection of a database is complicated by variable definitions of pathway boundaries as well as by redundancies and outdated pathway definitions.The errors from these steps that propagate through an enrichment analysis may be inconsequential at best and misleading at worst. Although there is no singular method or gene set collection/pathway database, which is advisable for enrichment analysis over all others, well-informed choices can be made and solutions to mitigate the impact of various factors are available. Furthermore, recently, many ensemble approaches have been developed so that users can benefit from multiple databases and/or methods.Pathway enrichment analysis is a widely used technique for the interpretation of biological dataIn recent years, the advent of a multitude of enrichment methods and pathway databases has led to several benchmarks to study the impact of various factors on the results of enrichment analysisThis review outlines key aspects of enrichment analysis and summarizes results of studies, which have evaluated their influenceWe propose solutions to mitigate the effect of these factors and identify possible future benchmarksS.M. and D.D.-F. wrote the manuscript. A.T.K. and M.H.-A. reviewed the manuscript. All authors have read and approved the final manuscript.Review_Supplement_bbac143Click here for additional data file.Methods_Benchmark_Supplement_bbac143Click here for additional data file.


Glioblastoma (GBM) is the most common primary intracranial tumor in the central nervous system, and resistance to temozolomide is an important reason for the failure of GBM treatment. We screened out that Solute Carrier Family 2 Member 10 (SLC2A10) is significantly highly expressed in GBM with a poor prognosis, which is also enriched in the NF-E2 p45-related factor 2 (NRF2) signalling pathway. The NRF2 signalling pathway is an important defence mechanism against ferroptosis. SLC2A10 related LINC02381 is highly expressed in GBM, which is localized in the cytoplasm/exosomes, and LINC02381 encoded micropeptides are localized in the exosomes. The micropeptide encoded by LINC02381 may be a potential treatment strategy for GBM, but the underlying mechanism of its function is not precise yet. We put forward the hypothesis: “The micropeptide encoded by LINC02381 regulates ferroptosis through the glucose transporter SLC2A10 in GBM.” This study innovatively used machine learning for micropeptide to provide personalized diagnosis and treatment plans for precise treatment of GBM, thereby promoting the development of translational medicine. The study aimed to help find new disease diagnoses and prognostic biomarkers and provide a new strategy for experimental scientists to design the downstream validation experiments.The online version contains supplementary material available at 10.1186/s12885-022-09972-9. We screened out that SLC2A10 was significantly highly expressed in GBM with a poor prognosis, also enriched in the NRF2 signalling pathway.SLC2A10 related LINC02381 is highly expressed in GBM, which is localized in the cytoplasm/exosomes, and LINC02381 encoded micropeptides are localized in the exosomes.We put forward the hypothesis: “The micropeptide encoded by LINC02381 regulates ferroptosis through the glucose transporter SLC2A10 in GBM.”The study aimed to find new disease diagnoses and prognostic biomarkers and provide a new strategy for experimental scientists to design the downstream validation experiments.Cancer research is increasingly developing into a serious public health problem worldwide. The pan-cancer analysis of the whole genome (PCAWG) project conducted the most comprehensive tumor genome analysis of the entire genome of 2658 tumors in 38 tumor types , compariMultidrug resistance, especially to Temozolomide (TMZ), causing tumors to recur frequently, is a challenge in the treatment of glioblastoma, and its underlying molecular mechanisms are unclear. Since the protection of the blood–brain barrier (BBB) prevents charged or macromolecules from accumulating at physically relevant concentrations in the tumor microenvironment, thus playing the role of a hemolytic tumor, the level of TMZ in the brain is only 40% of the blood content, so it is necessary to develop new treatments that enable the drug to pass through the BBB more effectively to prolong the patient’s survival time beyond the median survival of 15 months ; a machiWe have been working on basic research on gliomas and anti-tumor medicinal plants, including gene clusters, tumor markers , and nonMicropeptides are small open reading box-coded small peptides , less thFerroptosis is a regulated cell death caused by excessive lipid peroxidation, catalyzed by iron ions , which ihttps://www.gsea-msigdb.org/gsea/msigdb/search.jsp) and WikiGene [The human-based SWEETs are filtered with the Molecular Signatures Database (MSigDB) (WikiGene  databaseWikiGene  databasehttps://string-db.org) online platform to build the SWEETs Protein–protein interaction (PPI) network, we also obtained the protein information and combined functional enrichment analysis (Kyoto Encyclopedia of Genes and Genomes (KEGG) / Reactome pathway and Gene Ontology (G.O.) analysis).Using the STRING v11.5 , GEO (GSE51024) and Genotype-Tissue Expression (GTEx) database, and extracted data from tumor and normal tissue. After removing the batch effects, we calculated the differentially expressed genes (DEGs)  by limma package [We downloaded the expressional and clinical data from TCGA pan-cancer   and the http://www.snapgene.com/) was used to present G.C. content and location information and sequence information for LINC02381 and its encoded micropeptides, and iLoc-LncRNA [SnapGene Viewer 5.2.3 , the progress of glucose transporters in tumors and predict its potential function in tumors. The differential expression and prognosis relationship between pan-cancers are then clarified. In the case of glioblastoma, SWEETs expressed with significant differences were combined with c2.cp.kegg.v7.2.entrez.gmt and c5.go.v7.entrez.gmt, and found that the significantly highly expression of SWEETs (SLC2A10/SLC2A5/SLC2A9) in the WikiPathway database is significantly concentrated in the NRF2 signalling pathway. The NRF2 signalling pathway regulates glutathione stability, mitochondrial function, and lipid metabolism and is associated with ferroptosis.We screened out the key target SLC2A10 in GBM, further obtained the verified ceRNA mechanism through multiMiR, and found that LINC02381 was significantly highly expressed in GBM, and confirmed the expression of SLC2A10 and LINC02381 in GBM, LINC02381/hsa-miR-27a-3p/SLC2A10 has a potential ceRNA mechanism in GBM. At this stage, there is no study of the glucose transporter SLC2A10 and its associated LINC02381 in GBM. LncRNA and pri-miRNA were not involved in the noncoding RNA encoded micropeptide in GBM. Previous studies have revealed that circRNA-coded micropeptide AKT3-174aa, FBXW7-185aa, SHPRH-146aa, and PINT-87aa might be independent prognostic indicators and therapeutic targets in GBM [Noncoding RNA is abundant and stable in the bioactive ingredients of exosomes, which could regulate cell proliferation, invasion, angiogenesis, immune escape, and therapeutic resistance in GBM. The excellent biocompatibility of exosomes could also be transformed into therapeutic factor-targeted delivery systems to improve the accuracy and efficacy of drug treatment. Exosomes are expected to become new biomarkers and therapeutic agents for GBM, and explicitly targeting exosome noncoding RNA is a potential GBM treatment strategy with potential . HoweverNRF2 is a transcription factor that regulates cell defence and resists oxidative stress and plays a dual role in tumor progression . Many coWe screened 62 SWEETs in pan-cancer and found that they highly expressed with worse survival SLC2A10 as a critical biomarker in GBM and related to the NRF2 signalling pathway. We further obtained the verified relevant ceRNA mechanism, LINC02381/hsa-miR-27a-3p/SLC2A10. SLC2A10-related LINC02381 was also significantly highly expressed in GBM, which is located in cytoplasm/exosomes, and its encoded micropeptides were located in exosomes. Therefore, we propose the following hypothesis: LINC02381 encoded micropeptides regulate ferroptosis through SLC2A10 in GBM. The study aimed to find new disease diagnoses and prognostic biomarkers and provide a new strategy for experimental scientists to design the downstream validation experiments.Additional file 1: Table S1. The list of pan-cancer in this study.


Long noncoding RNAs (lncRNAs) constitute an emerging group of ncRNAs that modulate gene expression at the transcriptional or translational level. Koi herpesvirus (KHV), also known as Cyprinus herpesvirus type 3 (CyHV-3) and characterized by high pathogenicity and high mortality, has caused substantial economic losses in the common carp and koi carp fisheries industry. In this work, we sequenced the lncRNA and mRNA of host koi carp infected with KHV. A total of 20,178 DEmRNAs were obtained, of which 5021 mRNAs were upregulated and 15,157 mRNAs were downregulated. Both KEGG pathways and GO terms were enriched in many important immune pathways. The KEGG analysis showed that DEGs were significantly enriched in many important immune pathways, such as apoptosis, NOD-like receptor signaling pathway, Jak-STAT signaling pathway, TNF signaling pathway, IL-17 signaling pathway, NF-kappa B signaling pathway, and so on. Furthermore, a total of 32,697 novel lncRNA transcripts were obtained from koi carp immune tissues; 9459 of these genes were differentially expressed. Through antisense, cis-acting, and trans-acting analyses, the target genes of differentially expressed lncRNAs (DElncRNAs) were predicted. Gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses showed that the DElncRNA expression pattern was consistent with the differential mRNA expression pattern. The lncRNA–mRNA network analysis, which included many immune pathways, showed that after KHV infection, the expression of most lncRNAs and their target mRNAs were downregulated, suggesting that these lncRNAs engage in a positive regulatory relationship with their target mRNAs. Considering that many studies have shown that herpesviruses can escape the immune system by negatively regulating these immune pathways, we speculated that these lncRNAs play a significant role in KHV’s escape from host immunity. Furthermore, 10 immune-related genes and 20 lncRNAs were subsequently verified through RT–qPCR, to confirm the accuracy of the high-throughput sequencing results. In this study, we aimed to explore lncRNA functions in the immune response of lower vertebrates and provide a theoretical basis for the study of noncoding RNAs in teleosts. Therefore, exploring lncRNA expression in KHV-infected koi carp helped us better understand the biological role played by lncRNA-dependent pathways in aquaculture animal viral infection. Long noncoding RNAs (lncRNAs) constitute an emerging group of ncRNAs that can modulate gene expression at the transcriptional or translational level. LncRNAs, comprising a heterogeneous class of noncoding transcripts, are endogenous cellular RNAs that are longer than 200 nucleotides . Their lWith the complex paradigm of RNA-based gene regulation slowly being elucidated, an increasing body of evidence has indicated that lncRNAs play significant roles in regulating host–virus interactions during viral infection . VirusesIn addition, mounting evidence has revealed that many lncRNAs are differentially expressed during infection with severe acute respiratory syndrome coronavirus , enterovirus 71, vesicular stomatitis virus, influenza A virus in mice, Zika virus, rabies virus, and hepatitis C virus. Viral lncRNAs control the host immune response, by regulating the expression of network genes and viral proliferation ,10,11,12The total number of lncRNAs has been estimated to be encoded by ~20,000 transcripts; however, to date, only ~200 lncRNAs have been characterized. Although lncRNAs have been demonstrated to be involved in immune system regulation in mammals , the resKoi herpesvirus (KHV), also known as Cyprinus herpesvirus type 3 (CyHV-3), was first reported in Israel and the United States in 1998. To date, KHV has caused large economic losses in the common carp and koi carp fisheries industry because, at water temperatures ranging from 18 °C to 28 °C, this viral infection is characterized by high pathogenicity and high mortality. However, no effective treatment for the disease is currently available. KHV is a double-stranded DNA virus with a 295-kbp genome encoding 156 predicted genes ,15. The In this work, we sequenced lncRNAs and mRNAs in the host (koi carp) after infection with KHV. We aimed to explore lncRNA functions and the lncRNA-mRNA co-expression network in the immune response of lower vertebrates and provide a theoretical basis for the study of noncoding RNAs in teleosts. Specifically, exploring the expression of lncRNAs and the targeting of lncRNA to mRNA in KHV-infected koi carp may help us better understand the biological role played by lncRNA-dependent pathways in aquaculture animal virus infection.Koi fish weighing 158.13 ± 2.5 g were purchased from Huadiwan Market in Guangzhou City, Guangdong Province, China. After adapting to a circulating water system and being fed commercial feed every day for two weeks, the fish were randomly allocated to separate groups: an infected and uninfected group. The infected group was administered an intraperitoneal injection of 100 μL of CCB cell culture supernatant containing 100 PFU of KHV virus. The other group was the mock control and received an intraperitoneal injection of 100 μL of uninfected CCB cell culture supernatant, to eliminate the influence of the cell culture medium on the immune response. Three days after injection, we randomly selected uninfected and infected carp, and collected brain, gill, liver, spleen, and kidney tissue samples. Then, the tissues were mixed together and considered one fish sample. A total of three infected samples and three mock samples were ground with liquid nitrogen and incubated at −80 °C for the next step.A TRIzol reagent kit  was used for total RNA extraction, according to the manufacturer’s protocol. RNA quality was assessed with an Agilent 2100 Bioanalyzer  and verified using RNase free agarose gel electrophoresis. After total RNA was extracted, ribosomal RNAs (rRNAs) were removed to obtain a sample of mRNAs and ncRNAs. The enriched mRNAs and ncRNAs were fragmented into short sequences in fragmentation buffer and reverse transcribed into cDNA with random primers. Second-strand cDNA was synthesized using DNA polymerase I, RNase H, d NTP , and buffer. Next, the cDNA fragments were purified with a QiaQuick PCR extraction kit  and end repaired. Poly (A) was added to the sequence, which was then ligated to Illumina sequencing adapters. Then, uracil-N-glycosylase (UNG) was used to digest second-strand cDNA. The digested products were size-selected using agarose gel electrophoresis, amplified by PCR, and sequenced with an Illumina Nova Seq6000 from Gene Denovo Biotechnology Co., Ltd. .http://cpc.cbi.pku.edu.cn/, accessed on 1 March 2022), were used to assess the protein-coding potential of novel transcripts, on the basis of default parameters. Reads at the intersection of nonprotein-coding potential results were chosen as long noncoding RNAs. Based on this analysis, lncRNAs were categorized into five classes, according to their location relative to protein-coding genes: intergenic lncRNAs, bidirectional lncRNAs, intronic lncRNAs, antisense lncRNAs, and sense overlapping lncRNAs.Fastp (version 0.18.0) was used to filter the samples, to ensure high quality and clean reads. Parameters were selected to remove those reads that included adapter sequences or more than 10% unknown nucleotides, consisted of more than 50% low quality  bases, or for which short read alignment was required. Bowtie2 (version 2.2.8) was used for mapping reads to the ribosome RNA database. The rRNA mapped reads were then removed. The remaining reads were used to assemble and analyze the transcriptome. Two software programs, CNCI   and KEGG pathway analysis [The differentially expressed transcripts of coding RNAs and lncRNAs were analyzed between two different groups using DESeq2  softwarer > 0.95 and P < 0.001.Pearson correlation coefficients of lncRNAs and mRNAs were calculated using the corresponding matrix data, and then their correlation was tested. The competing endogenous RNA (ceRNA) mechanism was found to be significantly and positively correlated with the co-expression of lncRNA–mRNA, with the correlation coefficient The lncRNA–mRNA targeted pairs were constructed based on the targeting relationship between differentially expressed mRNAs (DEmRNAs) and DElncRNAs. First, six pathways involved in KHV infection, including apoptosis, TNFα, NOD-like, IFN, NF-κB, and Toll-like pathways, were selected as analysis targets. A total of 50 lncRNAs and mRNAs with the most significant expression were screened, and an interaction network diagram was generated.https://cytoscape.org/, accessed on 28 April 2022).A ceRNA regulatory network (lncRNA–mRNA) was constructed based on the co-targeted lncRNA–mRNA pairs and co-expressed lncRNA–mRNA pairs. Then, the ceRNA regulatory network was visualized with Cytoscape software  were used to perform qRT–PCR with Quant Studio 5 . Real-time PCR was performed in accordance with the ABI PRISM protocol, with a 50 µL reaction volume, consisting of 5 µL of template, 25 µL of SYBR® Green Realtime PCR Master Mix, 2 µL of each primer (10 µM), and 16 µL of ddH2O. The cycling conditions were as follows: 1 min at 95 °C, followed by 40 cycles at 95 °C for 15 s, 60 °C for 15 s, and 72 °C for 45 s.To validate the accuracy of the RNA-sequencing results, a total of ten DEmRNAs and 20 DElncRNAs were selected, and their expression was quantified by reverse transcription-quantitative PCR. The gene-specific primers used for these selected lncRNAs and mRNAs are shown in p < 0.05.The comparative CT method for relative quantification was used to calculate the relative gene expression level . StatistTranscriptomic data sequencing was performed with an Illumina NovaSeq 6500 platform. The sequencing of each sample generated more than 12 billion reads. After filtering adapters, low-quality reads and poly (A) reads, high-quality clean reads, accounting for more than 99% of the raw reads, were obtained . Then, tBased on the location of the transcripts in the reference genome, the transcripts were screened with CPC2 and CNCI software. Transcripts with a length ≥200 bp and exon number ≥2 were retained. In total, 32,697 novel lncRNA transcripts  were obtThe number of mRNAs and lncRNAs shared between the infection group and the control group is shown in P <0.05, GO enrichment analysis was performed, to further investigate the potential functions of DEmRNAs in common crap tissues in response to KHV infection. The results of the GO enrichment analysis of differentially expressed mRNAs indicated 5359 functional groups P <0.05, , includihttp://www.genome.jp/kegg/pathway.html (accessed on 20 May 2022)”. A total of 341 KEGG pathways were found to be enriched with DEGs  was performed using GSEA v4.0.2 software and the Molecular Signatures Database (Broad Institute). This analysis revealed several significantly enriched gene sets that regulated the antiviral immune response, including the MAPK signaling pathway, NF-kappa B signaling pathway, p53 signaling pathway, apoptosis, NOD-like receptor signaling pathway, and RIG-I-like receptor signaling pathway B. MoreovTo investigate the potential regulatory role played by lncRNAs in koi carp tissues, we used GO and KEGG analyses to predict lncRNA target gene functions. We predicted the target genes of lncRNAs via antisense analysis. We also performed colocalization (cis-acting) and expression correlation (trans-acting) analysis of lncRNAs and protein-coding genes. The results of the GO term-enriched target gene predictions obtained via these three methods were almost identical; the BP category genes were mainly enriched in cellular processes and single-organism processes, while the CC category genes were mainly enriched in cells and cell parts. The most significant MF category-enriched genes involved binding and catalytic activity A–C. The p < 0.004). The results showed that only a few lncRNAs were associated with single-targeted mRNAs; for example, MSTRG.9580.5 targeted caspase 3(CAPS3)  ; these p3(CAPS3) A, MSTRG.3(CAPS3) D, and MS3(CAPS3) E; most D3(CAPS3) A; MSTRG.3(CAPS3) E. In addA total of 20,178 DEmRNAs were obtained from the transcriptome database of common carp tissues. Of the mRNAs in the treatment group, 5021 mRNAs were upregulated, and 15,157 mRNAs were downregulated. Downregulated genes accounted for more than 75% of all DEGs. Both KEGG pathways and GO terms were enriched in some important immune pathways. The KEGG analysis showed that DEGs were significantly enriched in many important immune pathways, such as apoptosis, NOD-like receptor signaling pathway, Jak-STAT signaling pathway, TNF signaling pathway, IL-17 signaling pathway, NF-kappa B signaling pathway, C-type lectin receptor signaling pathway, Toll-like receptor signaling pathway, MAPK signaling pathway, Kaposi sarcoma-associated herpesvirus infection pathway, Epstein–Barr virus infection, T-cell receptor signaling pathway, Cytokine–cytokine receptor interaction pathway, and lysosome pathway. Many critical genes involved in these immune signaling pathways were downregulated, including p53, NOD1, NOD2, IL-1β, IL-6, IL-8, Jak, STAT1, TNF and TNFR, TRADD, IL-17RA, IL-17R, p65, p100, TRAF6, MyD88, TLR7, TLR9, CD80, CD38, CD45 aCD48, CCL3, CCL8, CCR3, CCR4, and CCR8. The GSEA results were compared with KEGG analysis results, which showed that most of the DEGs enriched in the MAPK signaling pathway, NF-Kappa B signaling pathway, p53 signaling pathway, apoptosis, NOD-like receptor signaling pathway, and Rig-I-like receptor signaling pathway were downregulated.These pathways play important roles in the innate immune response of fish host cells, to defend against viral infection. When viruses invade host cells, viral pathogen-associated molecular patterns (PAMPs) are recognized by host cell pattern recognition receptors (PRRs), primarily NOD-like receptors (NLRs), Toll-like receptors (TLRs), and RIG-I-like receptors (RLRs). Subsequently, PRRs stimulate signaling that triggers an innate immune response, inducing the NF-kappa B signaling pathway, Jak-STAT signaling pathway, cytokine–cytokine receptor interaction pathway, and TNF signaling pathway. Activation of these signaling pathways leads to the production of cytokines, such as interferon, interferon-related genes, inflammatory factors, and chemokines, which either inhibit viral replication directly, promote immune cell migration, or induce apoptosis, thereby inhibiting viral replication ,25,26.In the studied herpesviruses, especially Kaposi’s sarcoma-associated herpesvirus (KSHV), these signaling pathways play crucial roles. Proteins in Toll-like pathways and NOD-like pathways play significant roles during the life cycle of KSHV. KSHV can infect human plasmacytoid dendritic cells (pDCs), activate the expression of CD83 and CD86, and induced IFN-α secretion from pDCs. Further analysis showed that the induction of IFN-α is mediated through TLR9 signaling activation . MoreoveIn addition, the RIG-I-like receptor signaling pathway is critical during KSHV reactivation. RIG-I and its adaptor protein MAVS, two important mitochondrial antiviral proteins in the RIG-I-like receptor signaling pathway, can sense KSHV infection and suppress its replication following primary infection and/or viral reactivation. In fact, depletion of MAVS or RIG-I before KSHV reactivation leads to an increased viral load and infectious virus production, and MAVS depletion in latent KSHV-infected B cells leads to increased viral gene transcription . SignaliLncRNAs are produced from the genome and defined as transcripts >200 nucleotides in length that lack protein-coding potential . LncRNAsIn an integrative analysis of transcriptomic data, target genes of lncRNAs were predicted, mainly based on their functions: cis, antisense, or trans. Playing a cis role, the function of lncRNAs is related to adjacent protein-coding genes. LncRNAs located upstream and downstream of a protein-coding gene may regulate the expression of that gene. Playing an antisense role, similar to that of a microRNA (miRNA), the regulatory effect of a lncRNA is often related to complementary base pairing. Some antisense lncRNAs may regulate gene silencing, transcription, and mRNA stability, by binding to the mRNA of the justice chain. Playing a trans role, a lncRNA is related to co-expressed protein-coding genes. When lncRNAs are positively or negatively correlated with the expression levels of a certain gene, the target gene can be predicted via correlation analysis or co-expression analysis, using lncRNAs and protein-coding genes in two or more samples ,38.LncRNAs play critical roles in various biological processes and diseases, from cell differentiation and tissue organ development, to cancer metastasis. Many studies in mammals have shown that lncRNAs play important roles in the regulation of the immune response; for example, the TLR-stimulated lncRNA lincRNA-Cox2 can positively and negatively regulate the expression of distinct types of innate immune genes. Knocking down the long intergenic noncoding RNA (lincRNA) Cox2 disrupted the production of proinflammatory genes , while IFN-related genes were hyperactivated in the absence of the lincRNA Cox2 . In addiFurthermore, the lncRNA–mRNA network analysis of these immune pathways also showed that after KHV infection, the expression of most lncRNAs and their target mRNAs was downregulated, suggesting that these lncRNAs engage in a positive regulatory relationship with their target mRNAs. This network analysis will provide a reference for our future studies. Next, we will focus on verifying the targeted relationship of these lncRNA–mRNA pairs and discuss how these lncRNAs affect important immune pathways, to enable the immune escape of KHV. Moreover, as we previously inferred, KHV escapes from the host immune system by negatively regulating the lncRNA-associated immune pathways. Therefore, we believe that lncRNAs play a significant role in KHV escape from host immunity. KHV may not only regulate mRNA expression but also regulate lncRNA expression and even regulate important target mRNAs, by regulating lncRNA expression. In other words, KHV may directly regulate mRNA expression, on the one hand, and indirectly regulate mRNA through the regulation of lncRNA, on the other hand.In this study, 32,697 novel lncRNA transcripts  were obtained from koi carp tissues, and 9459 of these lncRNAs were differentially expressed: 1336 DElncRNAs were upregulated, and 8123 DElncRNAs were upregulated. The target genes of DElncRNAs were predicted through antisense, cis-acting, and trans-acting analyses. For target genes predicted using the trans-acting method, the KEGG enrichment result was consistent with the KEGG enrichment result of differentially expressed mRNAs. The main enriched pathways were apoptosis, the NOD-like receptor signaling pathway, Jak-STAT signaling pathway, TNF signaling pathway, IL-17 signaling pathway, NF-Kappa B signaling pathway, C-type lectin receptor signaling pathway, Toll-like receptor signaling pathway, the MAPK signaling pathway, Kaposi sarcoma-associated herpesvirus infection, Epstein–Barr virus infection, T-cell receptor signaling pathway, cytokine–cytokine receptor interactions, and lysosomes. The lncRNA–mRNA network analysis, which included many immune pathways, showed that the expression of most lncRNAs and their target mRNAs was downregulated. Considering that many studies have shown that herpesviruses can escape the immune system by negatively regulating these immune pathways, we speculate that these lncRNAs play a significant role in KHV’s escape from host immunity. The regulatory mechanism of these lncRNAs will be the focus of our future research.


Mammalian target of rapamycin (mTOR) is a serine/threonine kinase involved in a variety of cellular functions, such as cell proliferation, metabolism, autophagy, survival and cytoskeletal organization. Furthermore, mTOR is made up of three multisubunit complexes, mTOR complex 1, mTOR complex 2, and putative mTOR complex 3. In recent years, increasing evidence has suggested that mTOR plays important roles in the differentiation and immune responses of mesenchymal stem cells (MSCs). In addition, mTOR is a vital regulator of pivotal cellular and physiological functions, such as cell metabolism, survival and ageing, where it has emerged as a novel therapeutic target for ageing-related diseases. Therefore, the mTOR signaling may develop a large impact on the treatment of ageing-related diseases with MSCs. In this review, we discuss prospects for future research in this field. The mTOR protein plays an essential role in cell metabolism, promoting cell proliferation and survival through changes in energy and substance metabolism . The mTOThe term “mesenchymal stem cell” was coined in the end of the 20th century, and the criteria for defining MSCs were issued by the International Society for Cellular Therapy in 2006 . This deAt present, human MSCs have been used in the clinical treatment of various diseases . In earlThe mTOR protein belongs to the PI3K-related kinase family and is encoded by the mTOR gene, which is an evolutionarily conserved serine/threonine kinase . The mTOLipid synthesis is a key nutrient metabolic pathway that allows organisms to remain active even when energy is limited . Adipogede novo adipogenesis in liver and adipose tissue. mTORC1 has a positively regulation of sterol regulatory element binding protein (SREBP). mTORC1 promotes SREBP expression, maturation, and nuclear localization through an S6K1-dependent pathway or phosphorylates the phospholipid acid phosphatase lipin-1 and controls its nuclear translocation  and two regulatory subunits (β and γ), is an upstream kinase of mTOR, and the tumor-suppressor protein liver kinase B1 (LKB1) can inhibit the mTORC1 signaling pathway by activating AMPK and TSC2  Figure . AMPK ac pathway .MSCs differentiate into osteoblasts, which are involved in bone formation through the synthesis and sedimentation of mineralized extracellular matrix . Mature in vitro and in vivo  that are postnatal MSCs with self-renewing abilities and differentiate into osteoblasts/odontoblasts, adipocytes and nerve cells. According to a recent study, inhibition of the PI3K-Akt-mTOR pathway promotes osteogenic/dentine differentiation in SCAPs  in vivo . Recent  in vivo . With th in vivo . MiR-100 in vivo . Moreove in vivo .However, there is mounting evidence that the effect of mTOR-mediated autophagy is destructive in bone formation. Rapamycin can block osteoblast proliferation and differentiation in mic and rats . In contLimb skeletal elements develop from cartilage templates in a process called chondrogenesis; during chondrogenesis, the aggregated mesenchymal cells undergo a highly ordered process of proliferation and maturation . Chondro2S) can promote osteoclast formation by activating the PI3K/AKT/mTOR pathway to down-regulate autophagy (Pasteurella multocida toxin (PMT)  and are responsible for bone absorption . Enhanceutophagy . Similarin (PMT) . The actin (PMT) . In contin (PMT) .Myogenesis is a highly regulated multi-step process that refers to the transformation of progenitor cells into multi-nucleated and functional myofibers . Myogenein vitro and in vivo [ through the Akt/mTOR signaling pathway (AD is a complex, heterogeneous and severe neurodegenerative disease that represents a major form of dementia and is characterized by cognitive behavioral impairment, psychiatric symptoms, progressive cognitive decline, disorientation, behavioral changes and death . AD is t pathway .PD is an ageing-related neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra midbrain region and the presence of intracytoplasmic inclusions called Lewy bodies . When neOsteoporosis is a major risk cause of fracture or broken bones in later life . In oldein vivo and in vitro through mTOR and autophagy, and the regulation of amyloid β on BMSCs is dependent on mTOR, thus providing a possible mechanism for osteoporotic remodeling in AD patients (BMSCs isolated from elderly individuals or aged animals exhibit decreased bone marrow frequencies and proliferation rates and higher levels of ageing and ageing-related changes than young cells . It has patients .Atherosclerosis is a common age-related disease, and increasing age is one of the major risk factors for developing atherosclerosis . One of The mTOR signaling pathway is involved in the development of many human diseases, and its dysregulation has been reported in several pathological processes, especially in age-related human diseases and in mouse models of accelerated aging . In addiMoreover, the Notch pathway is one of several pro-proliferative pathways activated by mTOR . Notch sWe summarized relevant studies on the involvement of mTOR in the differentiation of adipocytes, bone and muscle. We found that the mTOR signaling pathway in MSCs is crucial. Inhibition or activation of this pathway can affect cell differentiation of MSCs. In addition, there are many mTOR related signaling pathways that have a certain impact on the immune response, and regulate angiogenesis, such as the Notch pathway. Therefore, in the future, the influences caused by mTOR may be regulated by other pathways. What are the difficulties in identifying these new approaches? What are the disadvantages of these new pathways compared to the mTOR pathway? Is the activation time of other regulatory pathways different from the time or stage of activation of the mTOR signaling pathway? Is it cost-effective to regulate MSC differentiation through these new pathways? These questions are all worth considering.In recent years, there has been a sharp increase in the number of articles on the activation and inhibition of the mTOR signaling pathway, and many researchers have focused on the development of innovative inhibitors. Most of the reported inhibitors target mTORC1 or PI3K and mTOR dual inhibitors, including competitive inhibitors and allosteric inhibitors. The discovery of rapamycin-insensitive mTORC2 stimulated the development of mTOR inhibitors that target the kinase domain . However