To analyse the mechanism of Nardostachyos Radix et Rhizoma–Salidroside in the treatment of Premature Ventricular Brats by using network pharmacology and molecular docking and to provide the basis for developing the use of experimental and clinical traditional Chinese medicine. The chemical compositions of Nardostachyos Radix et Rhizoma and Salidroside were determined, and their related targets were predicted. The disease-related targets were obtained by searching the common disease databases Genecards, OMIM, Drugbank and DisGeNET, and the intersection between the predicted targets and the disease targets was determined. Then using the STRING database to set up the protein‒protein interactions (PPIs) network between Nardostachyos Radix et Rhizoma–Salidroside and the common targets of PVB. An “herb-ingredient-target” network was constructed and analyzed by Cytoscape3.7.2 software. Using the metascape database to analysis the predicted therapeutic targets based on the GO and KEGG. Finally, molecular docking technology was used toconfirm the capacity of the primary active ingredients of the 2 herbs to bind to central targets using the online CB-Dock2 database. 41 active components of Nardostachyos Radix et Rhizoma–Salidroside were detected, with 420 potential targets of action, with a total of 1688 PVB targets, and the top 10 core targets of herb-disease degree values were AKT1, TNF, GAPDH, SRC, PPARG, EGFR, PTGS2, ESR1, MMP9, and STAT3. KEGG analysis indicated that its mechanism may be related to the calcium signalling pathway, cancer signalling pathway, AGE-RAGE signalling pathway and other pathways. Molecular docking suggested that main of the active ingredients of the Nardostachyos Radix et Rhizoma–Salidroside pairs were well bound to the core targets. Based on novel network pharmacology and molecular docking validation research methods, we revealed for the first time the potential mechanism of Nardostachyos Radix et Rhizoma–Salidroside in PVB therapy. Traditional Chinese medicine (TCM) puts PVB into the scope of "palpitations", which started from "Yellow Emperor's Classic of Internal Medicine", and its disease is located in the heart. According to Chinese traditional medicine, the main pathological mechanisms are exogenous disease, injury due to diet, internal injury due to emotional disorder, physical deficiency and prolonged illness. The disease mechanism is mostly based on the deficiency of qi blood yin and yang of the original organism, followed by blood stasis and phlegm. At present, modern medical treatment mainly includes anti-arrhythmic drugs and surgery, which are effective but easily cause liver and kidney function damage in the long term, require higher costs and patient compliance is not high. In recent years, Chinese medicine has gradually embodied unique advantages in the treatment of PVB2.Premature ventricular beats (PVB), also known as ventricular presystole, is a common clinical arrhythmia that refers to spontaneous excitation of the ventricle that is not receiving signals from the sinus node, resulting in premature ventricular depolarisation. Clinically, some patients may have no obvious symptoms, while others may experience palpitations, dizziness, fatigue, chest tightness, etc.3, the main compounds in Nardostachyos Radix et Rhizoma (NRER) are terpenoids, volatile oils, flavonoids, coumarins, lignans, sugars, etc., and NRER neophytum can affect the cellular cAMP-PKA conduction pathway, inhibit calcium overload, and promote the restoration of cardiac function4; it can also inhibit cardiac hypertrophy by inhibiting the expansion of the cell surface area induced by angiotensin II (AngII)5. Salidroside(SAL) mainly contains ketones, phenylalkyl glycosides, coumarins, organic acids, polysaccharides, amino acids, vitamins, etc.6, which can activate voltage-gated potassium channel proteins and calcium channel proteins to stabilise ion channels on the cell membrane, alleviate myocardial fibrosis and eliminate ectopic rhythms7. Other study have shown that SAL has inhibitory effects on tumor cells8. The treatment of diseases by Chinese medicine compounds involves multitarget and multicomponent integrated regulation, and the construction of a complex network of hurbs and disease genes and proteins through network pharmacology is conducive to the development of new drugs and provides new ideas for the subsequent treatment of diseases9. At present, the mechanism of NRER-SAL action is still unknown, and there have not been studied in the treatment of PVB.According to modern pharmacological findings10. Discovering TCM from the system point of view and molecular level, the research range from "one target, one drug" to the new "network target, multi-component" model has established a new approach to TCM network pharmacology11. We all know that although traditional medicines provide the basis for the development of modern medicines, they still need to undergo discovery, isolation, and mechanistic studies12. Therefore, the early work is also very necessary.Network pharmacology is a new discipline based on systems biology theory, biological system network analysis and the selection of specific signal nodes for multi-target drug molecule design. TCM compounds have the characteristics of multiple targets and levels. This mechanism is similar to the completeness, systematization and comprehensiveness of network pharmacology, so network pharmacology is suitable for studying the pharmacological mechanism of Chinese medicine compound compoundsIn this study, network pharmacology and molecular docking methods were used to investigate the key targets and mechanisms of action of NRER-SAL for the treatment of PVB, the first time to explore the possible pharmacodynamic substances and potential targets of NRER-SAL in the treatment of PVB, which will provide guidance for further experiment research, clinical research and new medicinal development in the future Fig. .Figure 1http://lsp.nwu.edu.cn/tcmsp.php), a systematic pharmacological database and analytical platform database for traditional Chinese medicines and part of the literature collection at5, with two ADME attribute values of oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18, the active ingredient was entered into the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) to record the SMILES number.NRER: Based on the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, TCMSP  (http://www.swissadme.ch/). If the active ingredient was not found in PubChem, the chemical structure of the ingredient was searched through the literature and plotted in the SwissADME database, and high gastrointestinal absorption and drug-like properties (DL) ≥ 2 were taken as the screening conditions in the results and recorded.SAL: The active ingredients and chemical structures of SAL were collected through the literaturehttp://www.swisstargetprediction.ch/)15, and the study species was selected as "Homo sapiens". The study species was selected as "Homo sapiens", “probility > 0.1”, no target compounds were excluded, the predicted target information of the active ingredient was exported, and the gene name was recorded for follow-up work.The screened active ingredients of the above two herbal medicines were subjected to target prediction in the Swiss Target Prediction database (https://www.genecards.org), Online Mendelian Inheritance in Man database , Drugbank database (https://go.Drugbank.com/) and Disease Gene Network Database  for targets of PVB and summarised and deduplicated them.Using "premature ventricular beats" as the keyword, we screened the GeneCards database (http://www.bioinformatics.com.cn) to obtain the intersection target of the NRER-SAL pair for the treatment of PVB, which is the potential target of action of the two herbs for the treatment of the disease.The obtained predicted targets of drug composition and the multidatabase summary targets of the disease were used to construct a Venn diagram using the Microbiotics online data analysis visualisation platform , the species was selected as "Homo sapiens", and the interaction threshold was "highest confidence ≥ 0.4" to obtain the protein‒protein interaction network (PPI). Then the image and tsv file were saved. The results were then imported into CytoScape 3.7.2, and the software plug-in Centiscape2.2 was used to calculate the Closeness, Betweenness and Degree, where the Closeness reflects the tightness of the node to other nodes and the Betweenness indicates the degree of the node's pivotal importance in the network topology. The higher the Degree is, the higher the influence of the node in the network18, filtered and rearranged according to the degree value in ascending order.The intersecting targets were imported into the STRING11.0 database (19 (https://Metascape.org/), the species was limited to "Homo sapiens", and the screening condition was P < 0.01 by default. Gene Ontology (GO) functional enrichment analysis and Kyoto Gene and Genome (KEGG) pathway enrichment analysis22 were performed. GO functions included biological process (BP), cellular component (CC) and molecular function (MF), which were used to describe the target function. KEGG pathway enrichment analysis obtained the signaling pathways enriched in the core targets of PVB. The results of GO function were taken as the top 10, and the KEGG pathways were taken as the top 20 in terms of -log10  value from high to low. The results were visualised with the help of the microbiology platform and outputted in the form of bar charts and bubble charts, respectively.The intersecting targets were imported into the Metascape platformhttps://www.rcsb.org/) as pdb format files using the online CB-Dock2 database (http://clab.labshare.co.uk/cb-dock/php/index.php)24 for validation and visualisation analysis. In this paper, we apply a new blind docking tool called CB-Dock2, which focuses on improving docking accuracy. CB-Dock2 predicts the binding region of a given protein, calculates the center and size using a curvature based cavity detection method, and interconnects with the state-of-the-art docking software Autodock Vina. CB-Dock2 also ranks binding patterns based on Vina scores and provides an interactive 3D visualization of binding patterns. And the absolute values of the binding energies were ranked from high to low.The core target proteins with the top 10 degree values in the PPI network were selected for molecular docking validation with the 5 core components with the top degree values. The small molecule structures were obtained from the PubChem database, and the 3D structures of the obtained small molecules were saved in mol2 format, imported into openbabel 2.4.1 software for file conversion, and exported in pdb format. Protein structures were obtained from the PDB database  mainly involves oxidoreductase activity, heme binding, tetrapyrrole binding, etc.; the biological process (BP) mainly includes response to hormone, regulation of system process and positive regulation of phosphorylation, etc.; the cellular component (CC) includes cell membrane rafts, regulation of system process and positive regulation of phosphorylation, etc. As shown in the Fig. KEGG-enriched pathways analysed included pathways in cancer, Calcium signaling pathway, Neuroactive ligand‒receptor interaction, Proteoglycans in cancer, Ras Proteoglycans in cancer, Ras signaling pathway, Prolactin signaling pathway, MAPK signaling pathway, Phosphatidylinositol 3-kinase/Protein kinase B signaling pathway, PI3K-Akt signaling pathway, Epidermal growth factor receptor signaling pathway (EGFR tyrosine kinase inhibitor resistance), Estrogen signaling pathway , etc., as shown below Figs. https://www.bioinformatics.com.cn, an online platform for data analysis and visualization. It is generally believed that a binding energy < − 5 kcal/mol indicates that the protein has a better binding activity with the small molecule. The docking results of the top 5 binding energies are shown, as shown in the Fig. As shown in the Fig. Accompanying the accelerated pace of life and aging, unhealthy lifestyles have led to a rising trend in the incidence of PVB, which seriously affects the quality of life and the physiological and psychological health of patients. Premature ventricular contractions.In this study, we systematically analysed the mechanism of action of Ganoderma-Rhodiola medicinal is for the treatment of PVB by constructing a herb-PVB-related active ingredient-predictive target network and PPI and molecular docking.27. The current clinically used anti-arrhythmic proprietary Chinese medicines, such as ginseng pine nutritious heart capsules and stable heart particles, all contain NRER. SAL is a genus of Rhodiola in the family Rhodiolaceae, and there are many different species, such as Rhodiola grandiflora, Rhodiola alpina, and Rhodiola sacra. Modern research has found that it can be used in the cardiovascular system. Modern research has found that it has significant therapeutic effects in cardiovascular diseases such as coronary heart disease, heart failure, myocardial infarction and arrhythmia, and it can also reduce hypoxia-induced necrosis and apoptosis of cardiomyocytes28. Salidroside injection, cardioplegia capsules and oral solutions of salidroside have been widely used to treat cardiovascular diseases with marked effects29.NRER, contained in the "Herbal Gleanings", is mostly distributed in China's Sichuan, Gansu, Qinghai and other places. Chinese medicine believes that it has the effect of regulating qi to stop pain, open the depression to wake up the spleen, and reassure the effect of palpitations. Modern research has found that NRER has anticonvulsant epilepsy, antidepressant, anti-inflammatory, antibacterial, anti-arrhythmic and antioxidant effects and is widely used in the cardiovascular system, digestive system, endocrine system and nervous system30 SAL present in salidroside rhizome has anti-inflammatory, antioxidant and anti-apoptotic activities, which can reduce the occurrence of ventricular arrhythmia by improving myocardial ischemia and reperfusion. It was also hypothesised that Sal can affect the protein expression and phosphorylation level of myocardial Connexin43 (Cx43), correct the distribution disorder of Cx43, restore the continuity and uniformity of myocardial conduction under physiological conditions, and thus play a role in anti-ventricular premature expiration. Restoring the continuity and uniformity of myocardial conduction in the physiological state, thus exerting anti-ventricular arrhythmia effects33. Tamarixetin not only reduces the secretion of the proinflammatory cytokines interleukin 6 (IL-6), tumor necrosis factor alpha  and interleukin 12 (IL-12p70) and thus exerts anti-inflammatory effects34 but also inhibits apoptosis and the expression of fibrosis-related genes, reverses myocardial remodelling in the stress-overloaded heart35, and has a higher concentration in cardiac tissues after oral intake, which is considered to be a good cardiac target. cardiac targeting properties36. Tricin has been shown to increase erythrocyte count and haemoglobin concentration, with good protection against hypoxia-induced damage to cells; Tricin also has anti-inflammatory, anti-allergic and antitumour effects40. (R)-Naringenin is thought to modulate the voltage sensitivity of BKCa channels, accelerate the activation rate of the channel, change the channel gating characteristics and is not affected by intracellular Ca2+ concentration41. Herbacetin have potent anti-inflammatory properties by inhibiting the release of pro-inflammatory cytokines such as TNF-α and IL-1β, reducing cellular nitric oxide (NO) production, and reducing lipid droplets in the liver, altering the amount of fatty acid synthase (FAS), fatty acid beta-oxidation (β-oxidation), malic enzyme, glucose 6-phosphate dehydrogenase (G6PD) and carnitine palmitoyltransferase (CPT) in plasma and liver total cholesterol, triglycerides and free fatty acids42. Quercetin has the ability to affect ion channels and calcium homeostasis, inhibit cardiac fibrosis and inflammation, and modulate autophagy and apoptosis, thereby ameliorating ischaemia and reperfusion injury and producing benefits for arrhythmias43.Through the use of web-based pharmacological mining and organisation, it was found that the presence of a variety of components in NRER and SAL are associated with the treatment of ventricular premature expiration. It was found that44, and cardiac hypertrophy can lead to cardiac arrhythmias and, in serious cases, malignant arrhythmias. Cardiac hypertrophy can lead to cardiac arrhythmia and, in severe cases, malignant arrhythmia46. Some studies have shown that47 TNF-α can affect calcium homeostasis to induce cardiac hypertrophy, and TNF and IL-1β can increase Ca2+ leakage in the sarcoplasmic reticulum, leading to delayed cellular depolarisation and arrhythmias48. Some scholars have suggested that TNF may be related to the induction of electrical remodelling through sympathetic overinternalisation49. GAPDH is known as glyceraldehyde-3-phosphate dehydrogenase (GAPDH), which is the most important enzyme in cardiovascular hypertrophy. Phosphate dehydrogenase (GAPDH), of which there are four types according to their cellular localisation, GAPC, NP-GAPDH, GAPA/B and GAPCp, has been associated with damaged mitochondria in response to oxidative stress induced by cardiac ischaemia‒reperfusion injury50, and damage to the mitochondria can lead to fatal ventricular arrhythmias51. It was found that SRC-mediated tyrosine kinase phosphorylates HCN4 channel proteins, and the long-term effect can induce changes in current density that can rescue the surface expression of D553N for normal channel function52. According to the data, EGFR and its ligand EGF protect the mouse heart against perfusion-induced ischemic injury53, and ischaemia‒reperfusion injury in rat myocardium can be attenuated by selective EGFR kinase inhibitors54.The core targets obtained through PPI network analysis mainly include AKT1, TNF, GAPDH, SRC, EGFR, etc. AKT1 is one of the AKT genes, and when AKT1 is overexpressed in the heart after short-term activation, it induces reversible cardiac hypertrophy, and when activated persistently, it not only causes generalised cardiac hypertrophy but also exacerbates cardiac dysfunction2+ inwards flow during cardiomyocyte excitation and are also involved in the ionic basis of the phase 2 plateau phase in ventricular myocytes. It has been shown that activation of the PI3K/Akt pathway can lead to Cx43 phosphorylation55, and activated Akt exerts antiarrhythmic effects and reduces arrhythmias by affecting Cx43 and thereby reducing the functional impairment of gap junctions or increasing the area of gap junctions56. This pathway also affects the occurrence of cardiac action potentials by influencing ion channels (Ikr and ICAa)57. It has been shown58 that complex inflammatory processes contribute to myocardial conduction inhomogeneity by affecting inhomogeneous changes in Na+ channels and cellular coupling.KEGG enrichment showed that the main pathways of NRER-SAL for the treatment of PVB are the calcium signalling pathway, AGE/RAGE signalling pathway and PI3K-Akt signalling pathway. Normal cardiac physiological activity is generated by the sinus node to generate excitation and then downwards conduction. When the electrical activity is abnormal, the rhythm or frequency of the heart is disturbed. Calcium ions play a crucial role in myocardial contraction and diastole, and voltage-gated calcium channels (VGCCs), which exist on cardiomyocytes, are the key channels for Ca59. The minimum binding energy is the minimum energy required for the binding of molecules and proteins, which can evaluate the quality of the binding ability between molecules. The negative binding energy indicates that the two do not need to consume energy when combined, but also release energy, the structure is more stable, and the more likely to combine when they meet. The molecular docking results showed that the active ingredients of NRER-SAL have good affinity with the core targets MMP9, AKT1, TNF, GAPDH, SRC, ESR1, and EGFR, etc. which further validated the therapeutic effects of the two herbs on PVB.Most small molecule binding occurs in protein pockets or cavities, so discovering protein–ligand binding sites and conformations is particularly important in drug discovery. Blind docking is a powerful way to obtain this information, and the results can be used for future novel drug discovery and researchIn this study, we investigated the mechanism of action of NRER-SAL herbs pairing for the treatment of PVB through network pharmacological methods and concluded that it can be treated through multiple targets and pathways. The current results can provide a reference direction and guidance basis for subsequent experiments and clinics.Supplementary Information.


Lactococcus lactis W136, a probiotic bacterium, improves sinus-specific symptoms, SNOT-22, and mucosal aspect on endoscopy, accompanied by a reduction in sinus pathogens and an increase in protective bacteria. The present work explores the molecular mechanisms underpinning these observations using transcriptomics of the sinus mucosa.We have previously documented that in individuals with chronic rhinosinusitis (CRS) refractory to surgery, intranasal application of live L. lactis W136 clinical trial were used to probe epithelial responses to microbiome supplementation using a hypothesis-free bioinformatic analysis of gene expression analysis. Samples from twenty-four patients with CRS refractory to medical and surgical management were prospectively collected during a clinical trial assessing the effect of 14 days of BID nasal irrigation with 1.2 billion CFU of live L. lactis W136 probiotic bacteria . Endoscopically guided sinus brushings were collected as part of the initial study, with brushings performed immediately before and after treatment. Following RNA extraction, samples were assessed using the Illumina HumanHT-12 V4 BeadChip. Differential gene expression was calculated, and pathway enrichment analysis was performed to identify potentially implicated processes.Epithelial brushings collected prospectively as a sub-study of the Differentially identified transcripts and pathways were assessed for the overall population and the clinical phenotypes of CRSwNP and CRSsNP. Patterns of response to treatment were similar across all groups, implicating pathways for the regulation of immunity and epithelial cell regulation. These resemble the patterns of improvement observed following successful treatment with endoscopic sinus surgery or azithromycin.Gene expression profiling following the application of live bacteria to the diseased sinus epithelium highlights the implication of multiple components of the inflammation-microbiome-epithelial barrier axis implicated in CRS. These effects appear to involve both epithelial restoration and modulation of innate and adaptive immunity, supporting the potential interest of targeting the sinus epithelium and the microbiome as potential CRS therapies. Staphylococcus Aureus represents a frequent and problematic pathogen, particularly in CRSwNP and in the elderly population  is an inflammatory condition of the upper airways of uncertain origin which is characterised by disruption of normal homeostasis with ensuing tissue inflammation, microbiome dysbiosis, and epithelial dysfunction  Charactepulation . While ivia downstream effects, suggests that all three components represent targets for CRS treatment. While immune dysfunction has long been the principal target of therapy  and the CHUM Institutional Review Board and Ethics committee (Registration No. 12.288) prior to the trial performance . Briefly, twenty-four patients with CRS refractory to previous medical and surgical therapy received a 14-day course of BID sinus irrigations containing 1.2 × 109 CFU of live L. lactis W136. No patients had received oral corticosteroids or topical or systemic antibiotic therapy in the preceding 30 days. Only saline irrigation was allowed for symptom relief. Excluded were patients <18 years, CF, with technical reasons for endoscopic sinus surgery (ESS) failure, active sinus infection with purulence, pain and/or hyperthermia, or with immune suppression from disease or medication.Epithelial brushings collected prospectively as a sub-study of the lsewhere . ApprovaEpithelial brushing was collected under endoscopic control using a gastric cytology brush at the level of the frontal recess prior to the first probiotic application at D0, and the day following the last treatment at D14. Total RNA was extracted using RNeasy Mini Kit . Gene expression was performed at Genome Quebec Innovation Center  using the Illumina HumanHT-12 V4 BeadChip .Raw Illumina probe data were exported from BeadStudio and screened for quality. Pre-processing and statistical analysis were conducted using the R statistical language and software packages from Bioconductor as described by Huber et al. . Quantilt-test used to compare gene expression before and after intranasal administration of L. lactis at D0 and D14 respectively. P-values from the resulting comparison were adjusted for multiple testing according to the method of Benjamini and Hochberg : Hallmark   was performed using the Bioconductor's package FGSEA using gene sets from the Molecular Signature Database  .P-values were adjusted for multiple test corrections with FDR cut-offs of 0.05. GSEA was performed to assess whether a known biological pathway or sets of individual genes were significantly enriched among the genes ranked by the moderated t-test following the differential gene expression analysis.GSEA-associated pathways L. lactis W136 completed the study  All CRS (N = 20), (ii) CRSwNP subgroup (N = 16) (iii) CRSsNP subgroup (N = 4) and (iv) Responders (N = 10) (as defined by a reduction in SNOT-22 score of 8.9 or greater). The responders included both CRSwNP (N = 8) and CRSsNP (N = 2) patients. The transcriptomic assessment demonstrated differences associated with treatment. Individual gene transcripts with FDR  ≤ 5% and ≥1.2-fold-change in expression were noted only in the “All CRS” groups and in the CRSwNP subgroup . We . We L. lsubgroup . Comparasubgroup  reveal sLactococcus Lactis W136 induced upregulation of cellular cycle, growth, and proliferation pathways. While enriched hallmark pathways were similar between groups, the differences were more significant in ALL CRS and CRSwNP groups. Notably, a common mechanism appears to involve the upregulation of cell cycle and transcription , reactive oxygen species (ROS) regulation . The modulation of immunity was also indicated by the upregulation of signalling pathways  accompanied by downregulation of adaptive  and Type 1-associated immune responses . Differential expression in selected significant pathways demonstrates a consistent pattern of enhanced epithelial renewal in response to probiotic treatment. Reinforcement of this transcriptional signature in the population restricted to SNOT-22 responders indicated that these were associated with observed clinical improvements. Pathways were similar between phenotypes of CRSsNP and CRSwNP, suggesting a common mechanism of effect in both populations (A pathway enrichment analysis was performed to identify potential underlying mechanisms of response to treatment . Signifiulations . IdentifL. lactis W136 in patients with refractory CRS using transcriptomics. This complements our previously reported material on the microbiome changes observed with BID administration of 1.2 billion CFU of Lactococcus Lactis W136 directly to the nasal and sinus cavities (We explore mechanisms underpinning the response to intranasal administration of live cavities . A priorcavities  reportedcavities .L. lactis administration is associated with differential gene expression involving biological pathways not only implicated in the regulation of immunity, but also epithelial regulation, as suggested by the identification of pathways that contribute to cell proliferation, survival signalling, and DNA damage repair. Transcriptomic changes associated with L. lactis W136 probiotic therapy thus suggest a beneficial effect on cell cycle progression and restoration of epithelial function.The present work uses transcriptomic methods to understand the mechanistic underpinnings of the effects of topical probiotics on the nasal and sinus surface. We describe that Cellular and structural integrity is essential for the appropriate function of the epithelial barrier. We have previously documented that epithelial response to wounding is impaired in CRS . ImproveWe report that immune function was consistently downregulated following treatment, with decreases observed in interferon response, T cell infiltration and activation, and allograft reaction. Immune modulation is a feature of probiotics , thus itL. lactis administration was also associated with differential gene expression involving biological pathways implicated in epithelial regulation, as suggested by the identification of pathways that contribute to cell proliferation, survival signalling, and DNA damage repair. Transcriptomic changes associated with L. lactis W136 probiotic therapy thus suggest a beneficial effect on cell cycle progression and restoration of epithelial function. Cellular and structural integrity is essential for the appropriate function of the epithelial barrier (This is consistent with our results.  barrier .We have previously documented that epithelial response to wounding is impaired in CRS . ImproveL. lactis W136 probiotic supplementation on cellular and epithelial function were not expected, however, these are consistent with previous reports showing improved re-epithelialisation of keratinocytes following wounding with Lactobacillus rhamnosus GG lysate in a scratch assay model (L. lactis supplementation on the restoration of epithelial cell function and cell cycle progression. These changes were observed after only 14 days of treatment compared to the treatment length used in other studies, which is four months. Thus, the beneficial effect is expected to be greater with sustained L. lactis supplementation.The depth and breadth of the effects of ay model  and our L. lactis W136 influences multiple processes extending beyond immune regulation, suggesting effects on the epithelial barrier which can potentially modulate composition of the microbiome.Taken together, these findings demonstrate that the administration of This study is not a parallel group-controlled trial, and there is no direct comparison possible with saline-only irrigations. Nevertheless, in the clinical trial, saline irrigation-only use during the run-in period while was associated with aggravation of symptoms of CRS and deterioration of mucosal aspect following the withdrawal of medications. Also, the sample size is relatively small, particularly in the “CRSsNP” and “Responders” group which are smaller than the “overall population”. The pattern of response is nevertheless similar across groups and results thus should not be discounted.In addition, there is no record of whether saline alone influences gene expression. While saline irrigations have been shown not to influence microbiome composition in CRS patients  this hasL. lactis W136 probiotic therapy influences both epithelial restoration and modulation of innate and adaptive immunity, supporting the potential interest of targeting the sinus epithelium and the microbiome as potential CRS therapies.The transcriptomic assessment suggests improvements following the application of live bacteria to the diseased sinus epithelium in refractory CRS appear to involve multiple components of the inflammation-microbiome-epithelial barrier axis implicated in CRS.


Mouse models have been used extensively to study human coronary artery disease (CAD) or atherosclerosis and to test therapeutic targets. However, whether mouse and human share similar genetic factors and pathogenic mechanisms of atherosclerosis has not been thoroughly investigated in a data-driven manner. We conducted a cross-species comparison study to better understand atherosclerosis pathogenesis between species by leveraging multiomics data. Specifically, we compared genetically driven and thus CAD-causal gene networks and pathways, by using human GWAS of CAD from the CARDIoGRAMplusC4D consortium and mouse GWAS of atherosclerosis from the Hybrid Mouse Diversity Panel (HMDP) followed by integration with functional multiomics human (STARNET and GTEx) and mouse (HMDP) databases. We found that mouse and human shared >75% of CAD causal pathways. Based on network topology, we then predicted key regulatory genes for both the shared pathways and species-specific pathways, which were further validated through the use of single cell data and the latest CAD GWAS. In sum, our results should serve as a much-needed guidance for which human CAD-causal pathways can or cannot be further evaluated for novel CAD therapies using mouse models. Coronary artery disease (CAD) represents one of the leading causes of mortality worldwide  and is tMouse models have been extensively used to study atherosclerosis mechanisms and to test therapeutic drugs . There aPreviously, literature-based analyses revealed limited gene-level sharing between species but 70% sharing at the level of molecular pathways without considering the tissue context or gene regulatory networks . As liteTo understand the similarities and differences in CAD/atherosclerosis pathways and candidate disease regulatory genes in human and mouse, we used Mergeomics to integrate multiomics data including GWAS signals, tissue-specific eQTLs, and gene networks to predict causal CAD/atherosclerosis subnetworks and their potential regulator genes in each species . From thWe constructed co-expression networks from transcriptome data of 299 aortic artery, 173 coronary artery, and 175 liver tissue samples from the GTEx study  for humaFrom the human datasets, we identified 36 co-expression modules in the aortic artery, 21 modules in the coronary artery, and 33 liver modules using WGCNA, whereas with MEGENA we obtained 159, 168, and 168 modules, respectively. For mouse, we identified 52 aorta co-expression modules and 55 liver modules using WGCNA, and 207 aorta and 190 liver modules using MEGENA . We poolCanonical biological pathways and co-expression modules from both species were pooled together, where each gene set (a pathway or module) contains functionally associated genes. We then mapped the GWAS data, which contains the potential causal signals, with the gene sets through species- and tissue-specific eQTLs to identify potential causal gene sets for CAD/atherosclerosis in each species and tissue. Instead of using only the top individual genome-wide significant signals in CAD GWAS, we used the whole spectrum of GWAS single nucleotide polymorphisms (SNPs) and their associated p-values, which allowed us to consider moderate and subtle signals in addition to strong ones. GWAS SNPs were then mapped to genes through species- and tissue-specific eQTLs which represent the functional association between genes and expression single-nucleotide polymorphisms (eSNPs) examined in GWAS. For each gene set among the pooled pathways and modules, we mapped the member genes to eSNPs through vascular  or non-vascular tissue (liver in both species) eQTLs by matching the tissue type between eQTLs and co-expression modules. Then, we used the Marker Set Enrichment Analysis (MSEA) procedure from our Mergeomics tool  to test Among the 1823 canonical pathways and 1089 coexpression modules . Since a superset that was found as CAD-associated in one of the species may cover >90% of the genes in more than one superset found in the other species, the number of the shared supersets from each of the species can differ.Between vascular and liver tissues, we observed shared CAD pathways between tissues and species . These iIn the vascular tissue-driven analysis, 53 of the 73 human supersets (73%) and 60 of the 80 mouse supersets (75%) were found to be matched or preserved between species . The shaIn the liver-based analysis, 56 of the 74 human supersets (76%) and 51 of the 60 mouse supersets (85%) were preserved between species . The shaWe also identified species-specific processes in individual tissues . Human-sMouse-specific atherosclerosis mechanisms identified in the aorta include signaling by insulin receptor, EGFR, ERBB2, NGF, and TGF-beta signaling, axon guidance, VEGF and VEGFR pathways, and Tap63 and DeltaNp63 pathways . Some ofWe identified KDs, which are potential key regulatory genes, within the CAD/atherosclerosis-associated gene sets using the Key Driver Analysis (KDA) in the Mergeomics pipeline . The KDAZHX2, whose subnetwork neighbors are highly enriched for genes in a co-expression module annotated with RXR/VDR pathway, post translational protein modification, and endocytosis terms  and cardiac EGF pathways  or negatively correlated (e.g. Utrn) with aortic lesion area in the atherosclerosis HMDP aorta tissue, or positively  or negatively (e.g. Acadm and Ets2) correlated with aortic lesion area in the atherosclerosis HMDP liver  treatment. We found that the subnetworks driven by the KDs Acta2, Flna Mylk, and Myl9 exhibited significant changes in expression between diet conditions in smooth muscle cells  and FLNa, an actin-binding protein encoded by Flna, promotes SMC spreading, a hallmark characteristic in atherosclerosis development , we compared ls SMCs; , thus hiferation . Our finelopment . Lastly,function , which mOit3 and its predicted surrounding network genes showed significant changes in expression between healthy and NASH states across multiple cell types such as hepatic stellate cells, hepatocytes, mononuclear phagocytes, and T cells , which is an advanced stage of non-alcoholic fatty liver disease involving liver inflammation and damage and linked to obesity, insulin resistance, and CAD. We found that the KD  T cells .To ensure the observed gene expression changes in the KDs and their subnetwork genes validated above were not random in single cell data, we chose five random genes and their subnetwork genes as negative controls to test their expression changes between control and disease states. Indeed, few of these randomly selected genes showed statistically significant changes.–8) onto the shared CAD/atherosclerosis subnetworks between species. We found that KDs FOXC1 in the shared vascular network and ARNTL in the shared liver CAD network were among GWAS hits. Additional GWAS hits in the shared cross-species CAD/atherosclerosis subnetworks were peripheral nodes , which include established CAD processes , as well as more novel processes (e.g. asparagine N-linked glycosylation). Given the high ratio overlap between species for genetic pathways, we can highlight the importance of the in vivo mouse model for understanding human CAD. More importantly, our results shed light on when to utilize mouse models for translational benefit to humans by focusing on the shared pathways and networks, and when we should look to alternative animal models for processes which are not shared between mouse and human .Our data-driven multiomics analysis confirmed a number of causal pathways that were previously reported to be shared between species in a previous study based on literature review: platelet activation and signaling, metabolism of lipid and lipoproteins, cholesterol biosynthesis, cell cycle pathways, focal adhesion, and Jak-STAT signaling . The preOur tissue-specific network analysis also revealed interesting tissue-specificity in the shared cross-species pathways. For instance, unique vascular tissue pathways include apoptosis, vascular smooth muscle contractions, and RXR/VDR pathways, all of which have been previously linked to CAD. We observed that some of the causal CAD/atherosclerosis pathways shared between species in a single tissue were identified as species-specific in the other tissue. For instance, BCAA catabolism pathway was found to be shared between mouse and human in vascular tissue, whereas it was also identified as human-specific in liver. Similarly, the leukocyte transendothelial migration pathway was shared between species in liver, whereas it was human-specific in vascular tissues. We also identified species-specific pathways in each tissue individually. For instance, signaling by insulin receptor was found to be mouse-specific in aorta, but the diabetes pathway was impaired in both mouse and human liver. These results point to the importance of taking the tissue context into consideration when translating genes and pathways between mouse and human.ZHX2 was found to be one of the top KDs in an aorta coexpression module shared between mouse and human   —4. Findi and C5) .ARNTL as a key driver gene connected to two more recent CAD GWAS hits ASAP2 and ST5  study design for atherosclerosis was previously described . Aortic Proximal aortic lesion area had been carried out as previously described . The aorTissue collection process for the expression data from aorta and liver tissues that were used in our analysis was previously described . Whole aGenotypes for 625 mice from 95 inbred and recombinant inbred mouse strains were obtained from Jackson Laboratories using the Mouse Diversity Array . SNPs wiThe coronary artery disease genome-wide replication and meta-analysis (CARDIoGRAM) and the coronary artery disease genetics (C4D) consortium (CARDIoGRAMplusC4D) collaborated and combined genetic data from diverse large-scale studies to determine risk loci for CAD and myocardial infarction (MI) in the human genome. In our analysis, we used the 1000 genomes-based GWAS from the CARDIoGRAMplusC4D, which is a meta-analysis of GWASs mainly based on European, South Asian, and East Asian descent, and imputed them through the 1000 Genomes phase 1 v3 training set with 38 million variants . In totaWe used two different gene expression data resources: one from Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) , and theGTEx consortium provides a publicly available human tissue bank with an enormous amount of data encompassing the associations between genotype and tissue-specific gene expression patterns in 54 tissues . In our Tissue-specific coexpression networks were constructed from aorta and liver gene expression data of the HMDP mice  and fromAfter identifying the WGCNA and MEGENA modules, we annotated each module with its functions by using the previously curated biological pathways taken from MSigDB database  that incBayesian networks (BNs) represent a class of gene regulatory networks, which can demonstrate the directed causal relationships between genes by using genetic and gene expression information, as well as previously known regulatory relationships between genes. In the BNs, each edge is directed to a child node from a parent node, where the BN represents a multivariate probability distribution, and the state of each node is estimated by the states of its parent nodes. We leveraged the Reconstructing Integrative Molecular Bayesian Networks (RIMBANet) tool  to constP-values of corresponding SNPs from the disease GWAS of corresponding species. Then, we used a modified chi-square statistic for the enrichment assessment, which is summarized across a range of quantile-based cutoffs for the GWAS, instead of depending on a single GWAS p-value cutoff. This test analyzes the significance of enrichment for stronger disease-GWAS p-values, by comparing the GWAS p-values of a given eSNP set against the eSNP sets that were mapped from randomly generated gene sets. Our MSEA approach is based on a set of quantile-based cutoffs and is not based on a single GWAS p-value cutoff; hence it avoids artefacts while producing more stable enrichment scores. The definition of the modified chi-square statistics used in the MSEA is: O and E are the numbers of the observed and estimated positive findings, respectively (i.e. findings above the i-th quantile point); n is the number of quantile points , and κ=1 is a stability parameter diminishing the artefacts for small eSNP sets with low expected counts. For the MSEA procedure, an FDR <5% cutoff was used, which is estimated by the Benjamini-Hochberg method  and functional genomics data  were integrated using our Mergeomics pipeline . Canonicg method  to identAfter identifying the significant disease-associated gene sets (pathways or modules) at FDR <5%, to reduce the redundancy in our findings, we merged the overlapping gene sets into non-overlapping supersets if they significantly shared their member genes, which is defined by a gene-overlapping ratio of >33% and Bonferroni corrected one-tailed Fisher’s exact test (FET) p<5%. In some cases, a coexpression module and a canonical pathway, or two different coexpression modules, which were annotated with the same biological term, were not merged since they did not significantly share their member genes; hence we kept them as independent gene sets though they were annotated with similar or the same terms.The second step of the Mergeomics pipeline, Weighted Key Driver Analysis (wKDA), was used to predict key regulator, or key driver (KD), genes within the CAD-associated supersets. The wKDA maps the member genes in each superset onto a tissue-specific gene regulatory network, which is a Bayesian network (BN) in our study. wKDA has been demonstrated to predict biologically meaningful KDs in our previous studies . In the After identifying the putatively causal and genetic atherosclerosis and CAD-associated biological mechanisms using the MSEA, we compared our findings from mouse and human in a tissue-specific manner. To identify shared gene sets between species, we matched pathways and coexpression modules by name and annotation terms, as well as applied an overlap analysis to identify the common mechanisms. We calculated 2-sided  overlapping ratios between the CAD-associated mechanisms identified for mouse and human using the Jaccard index, A two-sided overlap that is >50% and a hypergeometric test adjusted p<5% orA one-sided overlap that is >90% and a hypergeometric test adjusted p<5%.Occasionally, multiple pathways in mouse were found to be a subset (one-sided overlap >90%) of a single pathway in human (or vice versa), or the two-sided overlapping analysis identified a pathway in one species to be matched to multiple pathways in the other species. Hence, the number of common pathways differs in each of the species as shown in FASTQ files from each study  for aort important study, the authors integrated genetic and genomic datasets from humans and mice to unveil shared networks and pathways associated with coronary artery disease. Their compelling analysis led to the identification of new regulatory genes and pathways in vascular tissues and in the liver, allowing for a more in-depth understanding of the pathogenesis of coronary artery disease.In this  This manuscript represents an elegant bioinformatics approach to addressing causal pathways in vascular and liver tissue related to atherosclerosis/coronary artery disease, including those shared by humans and mice and those that are specific to only one of these species. The authors constructed co-expression networks using bulk transcriptome data from human  and mouse (aorta) vascular and liver tissue. They mapped human CAD GWAS data onto these modules, mapped GWAS SNPs to putatively causal genes, identified pathways and modules enriched in CAD GWAS hits, assessed those shared between vascular and liver tissues and between humans and mice, determined key driver genes in CAD-associated supersets, and used mouse single-cell transcriptome data to infer the roles of specific vascular and liver cell types. The overall approach used by the authors is rigorous and provides new insights into potentially causal pathways in vascular tissue and liver involved in atherosclerosis/CAD that are shared between humans and mice as well as those that are species-specific. This approach could be applied to a variety of other common complex conditions. Summary:Mouse models are widely used to determine key molecular mechanisms of atherosclerosis, the underlying pathology that leads to coronary artery disease. The authors use various systems biology approaches, namely co-expression and Bayesian Network analysis, as well as key driver analysis, to identify co-regulated genes and pathways involved in human and mouse atherosclerosis in artery and liver tissues. They identify species-specific and tissue-specific pathways enriched for the genetic association signals obtained in genome-wide association studies of human and mouse cohorts.Strengths:The manuscript is well executed with appropriate analysis methods. It also provides a compelling series of results regarding mouse and human atherosclerosis. The following is the authors’ response to the original reviews.Reviewer #1 (Public Review):This manuscript represents an elegant bioinformatics approach to addressing causal pathways in vascular and liver tissue related to atherosclerosis/coronary artery disease, including those shared by humans and mice and those that are specific to only one of these species. The authors constructed co-expression networks using bulk transcriptome data from human  and mouse (aorta) vascular and liver tissue. They mapped human CAD GWAS data onto these modules, mapped GWAS SNPs to putatively causal genes, identified pathways and modules enriched in CAD GWAS hits, assessed those shared between vascular and liver tissues and between humans and mice, determined key driver genes in CAD-associated supersets, and used mouse single-cell transcriptome data to infer the roles of specific vascular and liver cell types. The overall approach used by the authors is rigorous and provides new insights into potentially causal pathways in vascular tissue and liver involved in atherosclerosis/CAD that are shared between humans and mice as well as those that are species-specific. This approach could be applied to a variety of other common complex conditions.The conclusions are largely supported by the analyses. Some specific comments:1. It appears that GWAS SNPs were mapped to genes solely through the use of eQTLs.Current methods involve a number of other complementary approaches to map GWAS SNPs to effector genes/transcripts and there is the thought that eQTLs may not necessarily be the best way to map causal genes.We agree with the reviewer that multiple approaches can be used to map GWAS SNPs to genes, and eQTLs is only one way to do so. We focused on eQTLs mainly because we aim to address tissue-specificity of eQTLs and the relative higher abundance of eQTLs compared to other tissue-specific functional genomics data, such as pQTLs and epiQTLs. We now acknowledge this limitation in the discussion section in our revised manuscript and point to future studies utilizing other approaches to map GWAS signals to downstream effectors.1. Given the critical causal role of circulating apoB lipoproteins in atherosclerosis in both mice and humans and the central role of the liver in regulating their levels, perhaps the authors could use the 'metabolism of lipids and lipoproteins' network in Fig 3B as a kind of 'positive control' to illustrate the overlap between mice and humans and the driver genes for this network.We appreciate the reviewer’s excellent suggestion and now elaborate the findings in Fig 3B as a positive control in the results section.1. Is it possible to infer the directionality of effect of key driver genes and pathways from these analyses? For example, ACADM was found to be a KD gene for a human-specific liver CAD superset pathway involving BCAA degradation. Are the authors able to determine or predict the effect of genetically increased expression of ACADM on BCAA metabolism and on CAD? Or the directionality of the effect of the hepatic KD gene OIT3 on hepatic and plasma lipids and atherosclerosis.The Bayesian networks only have information on which genes likely regulate the others but not the up or down-regulation direction, and the network key driver analysis only considers the enrichment of GWAS candidate genes in the neighborhood of each key driver. Therefore, it is not possible to directly infer whether increasing or decreasing a key driver will lead to up or downregulation of the downstream pathways based on our current analysis. We could, however, examine correlations of key driver genes with downstream genes, or disease traits in relevant datasets. For instance, we checked the mouse atherosclerosis HMDP datasets for the correlations between select key drivers and clinical traits and found various key drivers shared and species-specific in aorta and liver significantly correlate with aortic lesion area and other traits of interest such as LDL levels, and inflammatory cytokines. We have added these new findings into the results section and supplemental tables.1. While likely beyond the scope of this manuscript, the substantial amount of publicly available plasma proteomic and metabolomic data could be incorporated into this multiomic bioinformatic analysis. Many of the pathways involve secreted proteins or metabolites that would further inform the biology and the understanding of how these pathways relate to atherosclerosis.We appreciate the reviewer’s valuable suggestion. Here we focused on liver and aorta gene regulatory networks to understand the tissue-specific mechanisms at the gene level. Indeed, plasma proteomic and metabolomic data could be further incorporated in future studies to understand the pathways captured in the circulation that can capture cross-tissue interactions mediated by secreted proteins and metabolites from different tissues. We have addressed this as a future direction in the discussion section.The findings here will motivate the community of atherosclerosis investigators to pursue additional potential causal genes and pathways through computational and experimental approaches. It will also influence the approach around the use of the mouse model to test specific pathways and therapeutic approaches in atherosclerosis. In addition, the computational approach is robust and could (and likely will) be applied to a variety of other common complex conditions.Reviewer #2 (Public Review):Summary:Mouse models are widely used to determine key molecular mechanisms of atherosclerosis, the underlying pathology that leads to coronary artery disease. The authors use various systems biology approaches, namely co-expression and Bayesian Network analysis, as well as key driver analysis, to identify co-regulated genes and pathways involved in human and mouse atherosclerosis in artery and liver tissues. They identify species-specific and tissue-specific pathways enriched for the genetic association signals obtained in genome-wide association studies of human and mouse cohorts.Strengths:The manuscript is well executed with appropriate analysis methods. It also provides a compelling series of results regarding mouse and human atherosclerosis.Weaknesses:The manuscript has several weaknesses that should be acknowledged in the discussion. First, there are numerous models of mouse atherosclerosis; however, the HMDP atherosclerosis study uses only one model of mouse atherosclerosis, namely hyperlipidemic mice, due to the transgenic expression of human apolipoprotein ELeiden (APOE-Leiden) and human cholesteryl ester transfer protein (CETP). Therefore, when drawing general conclusions about mouse pathways not being identified in humans, caution is warranted. Other models of mouse atherosclerosis may be able to capture different aspects of human atherosclerosis.We appreciate the reviewer’s valuable insight! Indeed, the specific HMDP atherosclerosis model may miss important mouse pathways that could have overlapped with the human pathways. We have added this important point to the limitations section under the discussion to caution the interpretation of the human-specific pathways, as they could be present in mice but are missed by the specific HMDP atherosclerosis dataset used.Second, the mouse aorta tissue is atherosclerotic, whereas the atherosclerosis status of the GTEX aorta tissues is not known. Therefore, it is possible that some of the human or mouse-specific gene modules/pathways may be due to the difference in the disease status of the tissues from which the gene expression is obtained.We agree with the reviewer that GTEx vascular tissues have unclear atherosclerosis status. However, in addition to GTEx, we also included the human STARNET dataset which contains vascular tissues from human patients with CAD. Therefore, we believe the comparability of the human and mouse vascular tissue datasets is reasonable.Third, it is unclear how the sex of the mice  and the sex of the human donors affected the results. Did the authors regress out the influence of sex on gene expression in the human data before performing the co-expression and preservation studies? If not, this should be acknowledged.We acknowledge that the effect of sex in the mouse and human datasets were not regressed out in our analysis. We have added this under the limitations section.Fourth, some of the results are unexpected, and these should be discussed. For example, the authors identify that the leukocyte transendothelial migration pathway and PDGF signaling pathway are human-specific in their vascular tissue analysis. These pathways have been extensively described in mouse studies. Why do the authors think they identified these pathways as human-specific? Similarly, the authors identified gluconeogenesis and branched-chain amino acid catabolism as human and mouseshared modules in the vascular tissue. Is there evidence of the involvement of these pathways in atherosclerosis in vascular cells?We agree with the reviewer that these unexpected findings warrant further discussion. As pointed out by this reviewer, it is possible that the mouse HMDP atherosclerosis dataset cannot fully represent all mouse atherosclerosis biology and therefore missed the leukocyte migration and PDGF pathways that were identified in the human datasets. Regarding the surprising findings of pathways such as BCAA catabolism in vascular tissues, we acknowledge that future studies will need to further investigate such pathway predictions but also highlight that these pathway terms have many shared genes with more commonly known pathways such as the TCA cycle, which may indicate the involvement of energy metabolism in vascular tissues in CAD development. We have added these points to the discussion section under limitations and concluding remarks.Overall, acknowledging these drawbacks and adding points to the discussion will strengthen the manuscript.Reviewer #1 (Recommendations For The Authors):1. Could the authors comment on why MEGENA produces so many more co-expression modules per tissue than WCGNA?As described in the methods section, MEGENA uses a multi-scale clustering structure to generate network modules at different scales, with each scale representing a different compactness level of the modules. At lower compactness scales larger modules are generated; at higher compactness scales, smaller modules are generated. By using all modules obtained from different scales, the total number of modules is much larger than WGCNA which only generates a network at one scale.1. Much of the results section involves repeating in the text lists of pathways, modules, and genes that are also listed in Figures 2 and 3. The text in this part of the results could be used more productively to focus on illustrative examples or potential new biology.We have revised the results section to reduce repeating long lists of pathways, modules, and genes as suggested.Reviewer #2 (Recommendations For The Authors):In addition to the weaknesses I mentioned in the public review comments, there are a few minor issues that I outline below:1. The authors should introduce atherosclerosis as the underlying cause of CAD in theIntroduction. In fact, I believe there are many places in the manuscript where theauthors mean atherosclerosis instead of coronary artery disease, especially when presenting and discussing mouse results since the HMDP study did not examine the coronary arteries of mice. I believe the authors should make the appropriate changes throughout the manuscript.We have made the changes as suggested.1. The authors state in the introduction, "For example, mice tend to develop atherosclerotic lesions in the aorta and carotids, while humans often develop lesions in coronary arteries ." This is not entirely correct, so this sentence should be revised. Several models of mice show coronary artery atherosclerosis development, but most researchers study lesions in larger aortas. Further, humans develop lesions throughout the arterial tree, but perhaps what the authors meant was the most consequential plaque development is in the coronary arteries. Please rephrase.We have rephrased the statement as suggested.1. Last line of page 5 should read "...which will drive modules and pathways that are more likely..." not "derive"Typo corrected.


Network pharmacology analysis suggested that MAPK14, MAPK8, and NOS3 were the top three relevant gene targets for the three phytochemicals, and molecular docking demonstrated strong binding affinities of the phytochemicals to their coded proteins. Immunoblotting suggested that the AN-SG and BA-SG both showed prominent effects in inhibiting inducible nitric oxide synthase (iNOS)  and MAPKp-p38 (both p < 0.05) compared with those induced by the LPS stimulation only. The AN-SG combination exhibited greater inhibitions of the protein expressions of iNOS , which may partly explain the mechanisms of the synergy observed. This study established a practical approach to developing novel herbal-compound formulations using integrated network pharmacology analysis, molecular docking, and experimental bioassays. The study provides a scientific basis and new insight into the two synergistic combinations against neuroinflammation.Neuroinflammation plays an important role in the onset and progression of neurodegenerative diseases. The multicomponent and multitarget approach may provide a practical strategy to address the complex pathological mechanisms of neuroinflammation. This study aimed to develop synergistic herbal compound formulas to attenuate neuroinflammation using integrated network pharmacology, molecular docking, and experimental bioassays. Eight phytochemicals with anti-neuroinflammatory potential were selected in the present study. A compound-gene target-signaling pathway network was constructed to illustrate the mechanisms of action of each phytochemical and the interactions among them at the molecular level. Molecular docking was performed to verify the binding affinity of each phytochemical and its key gene targets. An experimental study was conducted to identify synergistic interactions among the eight phytochemicals, and the associated molecular mechanisms were examined by immunoblotting based on the findings from the network pharmacology analysis. Two paired combinations, andrographolide and 6-shogaol (AN-SG) (IC Neurodegenerative diseases are characterised by the progressive loss of neurons and the deposition of proteins manifested as altered physicochemical properties in the brain and peripheral organs . It is aα] that lead to the high permeability of the blood-brain barrier (BBB) and impaired neuronal survival  for the detection of nitrite production as an indicator of nitric oxide (NO)  and BA-SG  or media with vehicle (0.1% DMSO) for 1 h before the activation of LPS (1 μg/mL). After the incubation for 40 min or 24 h, cell pellets were harvested by centrifugation at 500 g for 5 min at 4°C. The cell pellets were mixed with radioimmunoprecipitation assay buffer  with 1% proteinase inhibitors , and their concentrations were elucidated using Pierce™ BCA Protein Assay Kit . The total proteins from each sample at 10 mg/mL were separated by the SDS-PAGE electrophoresis , and then the proteins were transferred to the PVDF membrane by the iBlot 2 gel transfer device . The membranes were incubated with 3% bovine serum albumin  dissolved in PBST [PBS buffer plus 1% tween 20 , p38 MAPK , and iNOS  overnight at 4°C. GAPDH  was used as a loading control. The primary antibodies were probed with antirabbit HRP conjugated secondary antibodies  at room temperature for 2 h. All these antibodies were purchased from Cell Signaling Technology (United States). The immunoreactive bands on the membranes were incubated by the SuperSignal West Pico Plus ECL kit  and visualised by the iBright CL750 . Specific bands were analysed, and the intensity was quantified using ImageJ software.N11 cells were grown in T75 cell flasks  until confluence. Cells were then treated with individual AN, 6-SG, and BA at 25 The CI model was applied to determine the interaction of phytochemicals in the NO assay. The dose-response curves of single and combined phytochemicals on the NO assay were generated from the laboratory experiments. The data was then input to the CompuSyn software 2.0  to generate the CI-fraction affected (Fa) curve, isobologram figure, and the CI values at all Fa values , 53. In 50 values were determined from the constructed dose-response curves. The statistical comparison between groups was conducted by one-way ANOVA with the Tukey test, and p < 0.05 was considered statistically significant.Statistical analysis was conducted using GraphPad Prism 9.0 software . The data were shown as mean ± standard error of the mean (SEM) from at least three individual experiments. The relative ICThe compound-gene target-signaling pathway network was constructed to reveal the pharmacological action of each phytochemical in relation to neuroinflammation . Our resThe phytochemicals-disease PPI interaction network for each of the eight phytochemicals was created using the STRING database  for MAPK14 and NOS3 are shown in Tables μM. HES, LU, and CU showed moderate cytotoxicity from 50 to 100 μM. In contrast, AN, BA, 6-SG, and GLY did not induce any cytotoxicity from 0 to 100 μM.The Alamar blue assay was conducted to examine the cytotoxicity of the eight phytochemicals on N11 cells. TE exhibited dose-dependent cytotoxicity from 10 to 100 p < 0.0001vs. blank control: 0.41 ± 0.12 ng/mL). BA, AN, LU, and 6-SG lowered LPS-induced NO expression levels in a dose-dependent manner , followed by LU , AN , and BA . CU and TE exhibited moderate NO inhibitory effects with IC50 values of 20.54 μg/mL (34.59 μM) and 17.25 μg/mL (27.70 μM), respectively. HES and GLY did not show any obvious inhibitory effects.The cellular neuroinflammation model was established by activating the microglial N11 cells with LPS, which led to an excessive amount of NO to 24.48 ± 0.21 ng/mL (t manner . Based oμg/mL) appeared to be more potent than their individuals, and the enhanced NO inhibitory activities were not associated with cytotoxicity. The rest of the pair-wised combinations did not show higher potencies than their individual components in general  or 6-SG alone . The CI model was then performed to evaluate the drug interaction responsible for the enhanced activity in the combination. The CI-Fa curve  exhibited a more prominent effect than that of BA (IC50 = 5.48 ± 0.83 μg/mL) or 6-SG (IC50 = 4.16 ± 0.46 μg/mL) alone (both p < 0.0001). The CI model revealed a synergistic interaction of BA and 6-SG when used together to suppress NO . When Fa values were at 0.5 (representing 50% of the NO inhibition), the isobologram in Both BA and 6-SG showed a dose-dependent inhibition of NO, as shown in press NO . At the The compound-gene targets-signaling pathway networks of the AN-SG and BA-SG combinations were built to understand the associated mechanisms of their synergistic interaction. As shown in To verify the network pharmacology analysis and investigate the synergistic mechanism of AN-SG and BA-SG combinations, the protein levels of phosphor-MAPKp38/MAPKp38 (p-p38/p38) and iNOS were investigated.μg/mL) led to significantly upregulated expressions of p-p38/p38 (p < 0.0001) and iNOS (p < 0.001) with fold increases of 2.06 ± 0.22 and 4.16 ± 0.08 in comparison to that of the untreated cells (Blank), respectively. AN, 6-SG, and AN-SG all significantly inhibited the increased fold change of p-p38/p38 (p < 0.05vs. LPS) and iNOS (p < 0.05vs. LPS). In addition, the inhibitory effect of the AN-SG combination was significantly greater than that of AN or 6-SG alone on iNOS (p < 0.05vs. AN or 6-SG).As shown in Figures μg/ml) caused elevated expressions of p-p38/p38 (p < 0.0001vs. Blank) and iNOS (p < 0.0001 vs. Blank) with fold increases of 2.01 ± 0.21 and 7.07 ± 0.49, respectively, in contrast to that of the untreated cells  and iNOS (p < 0.05 vs. LPS). It was noticed that the combined p-p38/p38 inhibitory effect was significantly higher than that of 6-SG alone.The LPS stimulation  are intracellular signaling regulators [The highly relevant gene targets for the antineuroinflammatory effects of the eight phytochemicals included MAPK14 and MAPK8. The top KEGG pathway involved in the actions of the eight phytochemicals was the MAPK signaling pathway. MAPKs are a family of serine/threonine protein kinases that regulate key biological processes as well as cellular responses to external stress signals . MAPKs agulators , 62, whigulators , 64. Incgulators . MAPK14 gulators . Anti-negulators . Our netκB) signaling and reduced expression of iNOS, resulting in decreased LPS-induced NO generation and ultimately suppressed neuroinflammatory response [Nitric oxide synthases (NOS) are the enzymes responsible for NO generation. To date, three distinct NOS isoforms have been identified, including neuronal NOS (nNOS/NOS1), inducible NOS (iNOS/NOS2), and endothelial NOS (eNOS/NOS3) . The excresponse . Thus, tresponse . Studiesresponse . In addiresponse . Apart fMolecular docking is commonly used to model the interaction between small molecules and target proteins  and to eIn vitro bioassays were carried out to validate the results of the network pharmacology analysis and to identify possible synergistic interactions among the eight phytochemicals. LPS-induced microglia N11 cells were used to test the individual and combined activities of the eight phytochemicals in inhibiting NO production. Our results suggested that LU, AN, BA, and 6-SG lowered LPS-induced NO expression levels in a dose-dependent manner. Interestingly, although HES and GLY showed high binding affinities to both MAPK14 and NOS3 proteins, they did not show any obvious inhibitory effect in the NO assay, which may be attributed to their broad and unspecific effects. Furthermore, our study demonstrated that the AN-SG and BA-SG combinations exhibited synergistic interaction in reducing NO production. To the best of our knowledge, it is the first study demonstrating synergistic interactions between AN and 6-SG and BA and 6-SG targeting neuroinflammation. Based on the constructed network, the MAPK14 and NOS3 gene targets were highly relevant for the anti-neuroinflammatory actions of the AN-SG and BA-SG combinations, both linking to the downstream production of iNOS protein expression [pression , 78. Ourvia the downregulation of NF-κB and MAPK signalling pathways. AN downregulated the protein levels of iNOS and cyclooxygenase-2 (COX-2) and mRNA expression in LPS-induced human keratinocyte HaCaT cells [κB [κB subunit and the dependent transcriptional activity of NF-κB by blocking the phosphorylation of NF-kappa-B inhibitor alpha(IκBα) and subsequent degradation of IκBα [κB kinases (IKK) and MAPK [κB and MAPK may be the key to the synergistic mechanism of the AN-SG and BA-SG combinations.Previous studies suggested that AN, BA, and 6-SG exhibited anti-neuroinflammatory activity aT cells . BA signells [κB . Similar of IκBα . 6-SG aland MAPK . Thus, tκB) and their target proteins by the two combinations will be conducted in the future. Moreover, it is evident that AN, 6-SG, and BA were associated with many other key gene targets and coded proteins, as displayed by the compound-gene target network. These gene targets and signalling pathways may also contribute to the synergistic effects of AN-SG and BA-SG against neuroinflammation.The current study only focused on analysing the MAPK pathway based on the network pharmacology results. More in-depth studies of other hub KEGG pathways (i.e. NF-The network pharmacology replies primarily to the currently available data from online databases, which may lead to a biased result . TherefoThe present study developed two novel herbal compound combinations, AN-SG and BA-SG, that showed synergistic anti-neuroinflammatory activities. The associated mechanisms underlying the observed synergy were explored through network pharmacology analysis and molecular docking. Network pharmacology demonstrated that MAPK14, MAPK8, and NOS3 were the main gene targets of AN, 6-SG, and BA, and the top KEGG pathway was the MAPK signaling pathway. Western blot analysis demonstrated that AN-SG and BA-SG showed prominent effects in inhibiting p-p38 MAPK and iNOS, which has partly validated the illustration from network pharmacology. The present study provides insight into the development of synergistic combinations targeting neuroinflammation with integrated network pharmacology, molecular docking, and experimental bioassays.


Objective: This is the first study to explore the mechanism of colchicine in treating coronary artery disease using network pharmacology and molecular docking technology, aiming to predict the key targets and main approaches of colchicine in treating coronary artery disease. It is expected to provide new ideas for research on disease mechanism and drug development.Methods: Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), Swiss Target Prediction and PharmMapper databases were used to obtain drug targets. GeneCards, Online Mendelian Inheritance in Man (OMIM), Therapeutic Target Database (TTD), DrugBank and DisGeNET databases were utilized to gain disease targets. The intersection of the two was taken to access the intersection targets of colchicine for the treatment of coronary artery disease. The Sting database was employed to analyze the protein-protein interaction network. Gene Ontology (GO) functional enrichment analysis was performed using Webgestalt database. Reactom database was applied for Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Molecular docking was simulated using AutoDock 4.2.6 and PyMOL2.4 software.Results: A total of 70 intersecting targets of colchicine for the treatment of coronary artery disease were obtained, and there were interactions among 50 targets. GO functional enrichment analysis yielded 13 biological processes, 18 cellular components and 16 molecular functions. 549 signaling pathways were obtained by KEGG enrichment analysis. The molecular docking results of key targets were generally good.Conclusion: Colchicine may treat coronary artery disease through targets such as Cytochrome c (CYCS), Myeloperoxidase (MPO) and Histone deacetylase 1 (HDAC1). The mechanism of action may be related to the cellular response to chemical stimulus and p75NTR-mediated negative regulation of cell cycle by SC1, which is valuable for further research exploration. However, this research still needs to be verified by experiments. Future research will explore new drugs for treating coronary artery disease from these targets. Coronary artery disease (CAD) is a common heart disease characterized by insufficient blood and oxygen supply to the myocardium . It is cColchicine, one of the oldest drugs still in use today, is derived from the dried bulb and seeds of the colchicum . It is aNetwork pharmacology is to establish a network model of effective drug targets, involving emerging interdisciplinary subjects such as artificial intelligence and big data . In the https://tcmsp-e.com/, accessed on 10 February 2022) was used to identify the targets of “Colchicine”. The Swiss Target Prediction database  was also used to predict the target of colchicine, and we use probability >0 as the screening condition to screen target. PharmMapper database  was utilized to predict the targets of colchicine, where we set up Generate Conformers as “yes”, Maximum Generated Conformations as “300” and Select Targets Set as “Druggable Pharmacophore Models ”. The Uniprot database  was used for matching the gene names of each target protein, and then the target genes from the three databases were pooled to obtain the drug targets of colchicine.The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform  , OMIM , DrugBank , DisGeNET , and TTD  databases were searched for “Coronary Artery Disease” to obtain the relevant disease targets. We used the default search function of the database, and included all CAD related targets provided by the database. The disease targets from the five databases were combined to obtain the disease targets for CAD. The drug targets were then intersected with the disease targets to obtain the intersecting targets, and the Venn diagram of the drug-disease intersecting targets was drawn using R-4.0.2-win software.The GeneCards  (https:/2), OMIM  . The protein type was set to “Homo sapiens”. The confidence level was set to >0.9. The free targets were deleted, and the protein-protein interaction network (PPI) was constructed. After importing the nodal relationship information into Cytoscape 3.8.0 software to calculate the degree, the key targets for colchicine in the treatment of CAD were predicted based on the degree . The restricted species was “H. sapiens”. The method was over characterization analysis. Using the genomic protein code as the reference set, GO functional enrichment analysis of biological process (BP), molecular function (MF) and cellular component (CC) was performed on the intersection targets.The drug-disease intersection targets were imported into the WebGestalt database  . The species was qualified as “H. sapiens”. p < 0.05 and FDR < 0.05 were set. KEGG pathway enrichment analysis was performed on the intersecting targets. GO functional enrichment analysis and KEGG enrichment analysis were mapped using R-4.0.2-win software, and drug-target-pathway mesh maps were mapped using Cytoscape 3.8.0 software.The drug-disease intersection targets were uploaded into the Reactome database   based on the PDB IDs. If multiple PDB IDs exist for the same target, the best protein crystal structure was selected based on the root mean square deviation of the resolution. The proteins were processed using AutoDock4.2.6 (https://autodock.scripps.edu/) and PyMOL2.4 (https://pymol.org/2/)  was used to predict the position of the docking box. All parameters were default standard parameters of the software or platform. Molecular docking was carried out after hydrogenation and electron addition to the processed proteins, and the binding activity was judged by the docking score. The docked protein-ligand complexes were imported into the PLIP platform  for analysis, and molecular docking patterns were drawn in combination with PyMOL2.4 software.The protein-data-bank (PDB) IDs of the intersecting targets were searched through the String network platform, and the corresponding protein structures were downloaded from the RCSB PDB database (ock4.2.6  (https:/.org/2/)  to removThe workflow is demonstrated in The TCMSP database indicated that colchicine has a Mol ID of MOL013158, a bioavailability of 3.85% and a drug-like property of 0.78. A total of 143 potential targets of colchicine were obtained through TCMSP, Swiss Target Prediction and PharmMapper databases.A total of 8520 CAD-related targets were obtained through GeneCards, OMIM, DrugBank, DisGeNET and TTD databases. After intersecting them with drug targets, 70 targets of colchicine for the treatment of CAD were obtained .Seventy intersecting targets were imported into the String database, and the PPI network revealed that there were free targets for ACADS, ACKR1, ACR, BLVRA, CCL24, DAB1, EDC3, FERD3L, FLOT2, FUT8, KLK1, MYNN, PMP2, PRPS1, REPS1, RPA1, SNF8, TCEA3, TOX and TUBB1. And interactions were present for the remaining 50 targets, as shown in The 70 intersecting targets were loaded into the Webgestalt database for GO functional enrichment analysis, and the results shown were as follows . 1) Ther70 intersecting targets were imported into the Reactom database for KEGG enrichment analysis. It revealed a total of 549 pathways for colchicine in the treatment of CAD, which involved cellular response to chemical stress, p75NTR-mediated SC1 negative regulation of cell cycle, transcriptional activation of mitochondrial biogenesis, Notch-HLH transcriptional pathway and FOXO-mediated transcription. The top 20 pathways are shown in Based on the results of the PPI, key targets with degree values > 5 were molecularly docked to the active ingredient colchicine. The stability between the two can be judged by the binding energy, which can be used to determine how well a protein binds to a ligand compound. When the binding energy of the ligand to the receptor is lower, the more stable the binding conformation of the two and the higher the possibility of interaction, which is generally evaluated at −5 kcal/mol . The resTarget prediction yielded a total of 70 targets for colchicine in the treatment of CAD. PPI network further screened 4 key targets with degree values > 10 and 12 key targets with degree values > 5, which may be potential mechanism of colchicine in the treatment of CAD. Both MPO and CYCS are key targets with the highest degree values, and there are numerous studies demonstrating the involvement of MPO in the development and progression of atherosclerosis . The lacCYCS is the gene encoding cytochrome C, which is a water-soluble protein located in the inner mitochondrial membrane and is the only peripheral protein in the electron transport chain that plays an important role in mitochondrial energy metabolism . ContinuANXA5, also known as membrane-linked protein A5, is a member of the Annexin family . It inhiHDAC1, histone deacetylase 1, is an enzyme that removes acetyl groups from lysine residues of histones and non-histones . HDAC1 hIn molecular docking, colchicine has a high probability of binding to three key targets with degree >10, such as MPO, CYCS and HDAC1, and other 11 key targets with degree >5. MPO is the key target with the highest degree value and lowest binding energy, and the binding energy for docking with colchicine is −8.7 kcal/mol, which is a good fit. Studies have revealed that colchicine can significantly reduce MPO, and this effect has been demonstrated in models of respiratory syncytial virus infection , ischemiIn the KEGG pathway enrichment analysis, cellular response to chemical stress and p75NTR-mediated SC1 negative regulation of cell cycle may be the main pathways for colchicine to treat CAD. The cellular response to chemical stress is the top-ranked signaling pathway, and CYCS, HDAC3, GSR, SOD2, SIN3B, NCOA2, PPARA, and BLVRA are all intersectional targets enriched in this pathway. Among them, CYCS and HDAC3 are key targets with degree >10, and GSR, SOD2 and SIN3B are the key targets with degree >5. The p75NTR-mediated SC1 negative regulation of cell cycle is the second-ranked signaling pathway, and three key targets, HDAC1, HDAC2, and HDAC3, are enriched in this pathway. They cover the entire range of histone deacetylases in this process and play an integral and important role in mediating the deacetylation of PRDM4 in this pathway . In GO fAlthough this study predicted potential targets and mechanisms for colchicine in the treatment of CAD, it still has some limitations (shown below). 1) Due to the limitations of the databases, some targets and mechanisms may not have been included in the analysis. Deftereos et al. showed that colchicine can reduce the incidence of in-stent restenosis by inhibiting the in-stent neointima , and SajThis study predicts the potential targets and mechanisms of action of colchicine in the treatment of CAD. Colchicine may treat CAD through targets such as CYCS, MPO and HDAC1. The mechanism of action may be related to the cellular response to chemical stimulus and p75NTR-mediated negative regulation of cell cycle by SC1, which is valuable for further research exploration. However, our research lacks experimental verification, and we look forward to future experimental studies to confirm our results and continue to explore new drugs for treating CAD on this basis.


The concept of “one-airway-one-disease”, coined over 20 years ago, may be an over-simplification of the links between allergic diseases. Genomic studies suggest that rhinitis alone and rhinitis with asthma are operated by distinct pathways. In this MeDALL (Mechanisms of the Development of Allergy) study, we leveraged the information of the human interactome to distinguish the molecular mechanisms associated with two phenotypes of allergic rhinitis: rhinitis alone and rhinitis in multimorbidity with asthma. We observed significant differences in the topology of the interactomes and in the pathways associated to each phenotype. In rhinitis alone, identified pathways included cell cycle, cytokine signalling, developmental biology, immune system, metabolism of proteins and signal transduction. In rhinitis and asthma multimorbidity, most pathways were related to signal transduction. The remaining few were related to cytokine signalling, immune system or developmental biology. Toll-like receptors and IL-17-mediated signalling were identified in rhinitis alone, while IL-33 was identified in rhinitis in multimorbidity. On the other hand, few pathways were associated with both phenotypes, most being associated with signal transduction pathways including estrogen-stimulated signalling. The only immune system pathway was FceRI-mediated MAPK activation. In conclusion, our findings suggest that rhinitis alone and rhinitis and asthma multimorbidity should be considered as two distinct diseases. However, clinically, two rhinitis (R) phenotypes can be identified: (i) R alone (affecting around 70–80% of patients with R) and (ii) R in multimorbidity with A (R + A), affecting 20–30%. On the other hand, the majority of patients with A have or have had rhinitis (R)1. Furthermore, airway remodelling, a constant feature of A, does simply not exist in R. It is also important to consider that the clinical, immunological and genetic differences between monosensitisation  and polysensitisation  and the link between polysensitisation and multimorbidity increase the heterogeneity of R. This suggests the existence of distinct molecular pathways in R + A and R alone2. In consequence, the concept of “one-airway-one-disease” (coined over 20 years ago) may be an oversimplification of the disease3.Allergic rhinitis tends to cluster with asthma (A) in multimorbidity4. An alternative approach is the characterisation of the molecular mechanisms of these diseases and their interactions. A complete characterisation of cellular function can only emerge from studying how gene products interact with one another, forming a dense molecular network known as the interactome, which can be defined as the representation of all interactions  among the gene products present at a given time within a cell. This is where the branch of systems biology, known as interactomics, comes into play, applying data mining and biostatistical methodologies (i) to identify molecular pathways and (ii) in general, to provide a molecular context that will facilitate the understanding of the complexity of many phenotypes. During the last decade, its analysis has provided important insights into the inner operations of the cell under different conditions including pathological ones6.Previous efforts to understand the links between R and R + A have focused on the atopic march sequence7. A MeDALL in silico study suggested the existence of a multimorbidity cluster between A, eczema and R, and that type 2 signalling pathways represent a relevant multimorbidity mechanism of allergic diseases8. The in silico analysis of the interactome at the cellular level implied the existence of differentiated multimorbidity mechanisms between A, eczema and R at cell type level, as well as mechanisms common to distinct cell types9. A transcriptomics study of samples from MeDALL birth cohorts identified a signature of eight genes associated to multimorbidity for A, R and eczema7. In this study, genes of R alone differed from those of R + A multimorbidity without any overlap.The MeDALL study, which was aimed at unravelling the complexity of allergic diseases, did show that the coexistence of eczema, R and A in the same child is more common than expected by chance alone, both in the presence and absence of IgE sensitisation. This suggests that these diseases share causal mechanismsIn this study, we used transcriptomic information obtained in MeDALL cohorts to compare the molecular mechanisms of R and R + A, assessing how the relationship between these diseases should be understood in a multimorbidity framework using an interactomics approach.10. The analysis comprised a cross-sectional study carried out in participants from three MeDALL cohorts using whole blood. It compared participants with single allergic disease  or with multimorbidity  to those without asthma, dermatitis or rhinitis and non-allergic participants. We characterised the molecular pathways associated to R alone and R + A using an interactomics approach.We used the transcriptomics data from Lemonnier et al. obtained in MeDALL (Mechanisms of the Development of Allergy)10.Three birth cohorts were used: BAMSE , INMA  and GINIplus 13. The full dataset is supplied in Supplementary Table Differentially expressed genes (DEGs) for R alone and for R + A were obtained from the MeDALL gene expression study6 experimentally determined protein–protein interactions in human cells14. Data was downloaded via the IntAct web-based tool at the European Bioinformatics Institute (https://www.ebi.ac.uk/intact/). Ensembl Gene IDs were used instead of HGNC names to avoid ambiguities. Self-interactions and expanded interactions were discarded. Interactomes are supplied in Supplementary Table The first-degree interactomes of the DEGs for R alone and for R + A were independently generated using the IntAct database, which contains a curated collection of > 1015, with the Reactome database as the source of functional information16 and the default gene background. Functional pathways were considered significant with FDR < 0.05. In order to simplify the functional annotation and remove redundancy, pathways associated to diseased or defective cellular processes were removed and we only considered pathways in the intermediate levels (levels 3 and 4) of the Reactome hierarchy. Furthermore, we assigned each pathway to a generic functional family  in order to help interpreting the results. We did this by (1) clustering all the pathways according to their Szymkiewicz-Simpson overlap17; (2) identifying the best partition of using the Pearson’s Gamma method18 implemented in the fpc R package; and (3) asigning each cluster in the best partition to a generic pathway (i.e. a Reactome pathway with > 800 genes) by means of a Fisher’s Exact Test19. Full functional annotation is available in Supplementary Table The interactomes were functionally annotated using the DAVID web-based tool20.All data mining and statistical analysis were carried out using the R programming languageAmong the 786 participants included in the analysis, 54.8% had no allergic disease. Among those with an allergic disease, 45% had asthma (61% with multimorbidity), 42% dermatitis (48%) and 51% rhinitis (49%). Asthma was more common in BAMSE (63%), dermatitis in INMA (74%) and rhinitis in GINIplus (79%). Fifty-five percent of the participants had no allergic disease  . Similarly, the interactome of R + A is 6.22 times denser than random expectation, which is also statistically significant . There were no DEGs common to R alone and R + A in the MeDALL study, but we identified 25 genes common to both interactomes, which implied a degree of interconnectedness significantly larger than random expectation .We generated interactomes for R alone and R + A, which can be seen as snapshots of the cellular mechanisms behind these conditions Fig. . The intFunctional annotation revealed marked differences in the molecular pathways of both phenotypes. Pathways specific to R alone Fig.  involvedThe pathways common to both R phenotypes are shown in Fig. 21. The high level of connectivity that we observed within the interactome of each phenotype, together with the lack of common DEGs, suggests that R alone and R + A are largely mechanistically different diseases, affecting different molecular pathways.Using topological and functional analysis, we identified a core of common mechanisms between the two phenotypes of R, but also found significant differences between both phenotypes. Densely interconnected groups of genes within the interactome are known to be contributors to the same pathological phenotypes22. Variation in the TLR genes has been associated with R in several candidate gene studies. A significant excess of rare variants in R patients was found in TLR1, TLR5, TLR7, TLR9 and TLR10 but not in TLR823. Children carrying a minor rs1927911 (TLR4) allele may be at a higher R risk24. In turn, TLR is strongly associated to MyD88 pathways, mediating in innate lymphoid cells type 2 (ILC2) activation and eosinophilic airway inflammation26. IL-17 was also identified, as were SUMO pathways, known to regulate many cellular processes including signal transduction and immune responses28. Furthermore, it is known that SUMOylation plays a critical role in the expression of TSLP in airway epithelial cells. Inhibition of SUMOylation attenuates house dust mite-induced epithelial barrier dysfunction29.TLRs stand out as strong drivers of R alone. TLRs are type I transmembrane receptors employed by the innate immune systemIL-1 and IL-33 activity—that seem to be driving R + A multimorbidity. IL-33 and IL1RL1 are among the most highly replicated susceptibility loci for A30, and IL-33 has a known role in infection-mediated A susceptibility31. There is an increase in FGFR (fibroblast growth factor receptor) signalling. The FGF/FGFR signalling system regulates a variety of biological processes, including embryogenesis, angiogenesis, wound repair and lung development32. It may be relevant in A remodelling. Interleukin-33 (IL-33) which belongs to the interleukin-1 (IL-1) family is an alarmin cytokine with critical roles in tissue homeostasis, pathogenic infection, inflammation, allergy and type 2 immunity. IL-33 transmits signals through its receptor IL-33R  which is expressed on the surface of T helper 2 (Th2) cells and group 2 innate lymphoid cells (ILC2s), thus inducing transcription of Th2-associated cytokine genes and host defense against pathogens33. IL33 and IL1RL1/ST2 are among the most highly replicated susceptibility loci for asthma35. However, IL-33 is not associated with rhinitis alone36.On the other hand, there are a number of mechanisms—such as Nf-kB-mediated signalling and 37. Future analysis could exploit tissue-specific transcriptomic data to highlight the differences between the epithelium of upper and lower airways39.The exposure of the airway epithelium to external stimuli such as allergens, microbes, and air pollution triggers the release of the alarmin cytokines IL-25, IL-33 and thymic stromal lymphopoietin (TSLP). IL-25, IL-33 and TSLP interact with their ligands, IL-17RA, IL1RL1 and TSLPR, expressed by cells including dendritic cells, ILC2 cells, endothelial cells, and fibroblasts. Alarmins play key roles in driving type 2-high, and to a lesser extent type 2-low responses, in asthmaRAS on B cells, CD209 signalling40, MAPK Kinase41, FceRI MAPK kinase activation42, ERK activation43, Raf kinases44 or VEGFA45.Finally, some signal transduction pathways common to R alone and R + A have an impact on the IgE-mediated immune response. They include activation of 10. This is the case for the present study and we were unable to include the A alone group. It is likely that A is almost always associated with R in children.One major problem is that there are no data on A without rhinitis. However, this is not the first study in which either the A population is too low or there is no signal for A alone47. Also, the human interactome is known to be biased towards certain genes of interest (a category that includes many disease-associated genes)48. However, non-biased interactomes have a much lower coverage, which makes them unsuitable for some topology-based studies49. Lastly, time-dependent and location-dependent interaction patterns are not captured in our study, which only considers an interactome static in time.Incompleteness and spurious interactions have for a long time been limitations in studies that make use of data from the human interactome. However, authors have argued that data noise does not limit a successful application of the interactome to the investigation of disease mechanisms50, mHealth-based studies51 and genomic approaches7 all support the existence of two distinct diseases: R alone and R with A multimorbidity. This study helps to better understand the differences between R and R + A and to refine the ARIA-MeDALL hypothesis on allergic diseases52. It also highlights the importance of IL-1753, IL-3354 and their interactions to understand the allergic multimorbidity.Clinical data, epidemiologic studiesThe interactomes of R alone and R + A showed topological characteristics that suggest that the cellular mechanisms involved are different for each phenotype. We identified mechanisms specific to R alone  and mechanisms specific to R + A .Supplementary Information.