Lithium is the gold standard treatment for bipolar disorder (BD). However, its mechanism of action is incompletely understood, and prediction of treatment outcomes is limited. In our previous multi-omics study of the Pharmacogenomics of Bipolar Disorder (PGBD) sample combining transcriptomic and genomic data, we found that focal adhesion, the extracellular matrix (ECM), and PI3K-Akt signaling networks were associated with response to lithium. In this study, we replicated the results of our previous study using network propagation methods in a genome-wide association study of an independent sample of 2,039 patients from the International Consortium on Lithium Genetics (ConLiGen) study. We identified functional enrichment in focal adhesion and PI3K-Akt pathways, but we did not find an association with the ECM pathway. Our results suggest that deficits in the neuronal growth cone and PI3K-Akt signaling, but not in ECM proteins, may influence response to lithium in BD. Up to 70% of patients are initially misdiagnosed, usually with major depressive disorder. On average, there is a delay of 8 years before the correct diagnosis of BD is made4. During this time, patients continue to suffer, may be treated with medications that make their illness course worse, and are at risk of suicide.Bipolar disorder (BD) is a chronic psychiatric illness that presents with episodes of mania, depression, and sometimes psychosis. Globally, it is the sixth leading cause of medical disability among people from 15 to 44 years old. Patients with BD are at a higher risk of suicide than those with any other psychiatric or medical illness. Some studies report that roughly 50% of patients will attempt suicide, and up to 20% of untreated patients will complete suicide5. Its mechanism of action is still not completely understood6. Many studies have investigated the neurotrophic effect of lithium. One theory posits that chronic administration of lithium inhibits glycogen synthase kinase 3 (GSK3β), a serine/threonine kinase.This leads to anti-apoptotic effects and improved cell structural stability10. GSK3β has also been shown to exhibit interactions with many pathways, including phosphorylation of several components of the PI3K/AKT/mTOR signaling network, as well as regulation of transcription for proteins bound to microtubules11. Another theory involves the phosphoinositol (PI) cycle. In the PI cycle, lithium inhibits inositol monophosphatase, which ultimately downregulates protein kinase C isozymes such as myristoylated alanine-rich C-kinase substrate (MARCKS). MARCKS is an actin-binding protein found in neuronal processes that is implicated in cytoskeletal restructuring. Its downregulation stabilizes the neuronal membrane and results in neurotrophic effects12. A more recent theory proposes that lithium alters the phosphorylation state of collapsin response mediator protein-2 (CRMP2). CRMP2 regulates cytoskeletal organization, particularly in dendritic spines14. Finally, a study using polygenic score modeling has indicated that the cholinergic and glutamatergic pathways may potentially serve as targets for lithium15. It is possible that lithium exerts its effects through multiple or all of these pathways. A single definitive model remains elusive, but interactions with neuronal cytoskeleton are possibly involved.Lithium is the gold standard treatment for BD16. A differential response to lithium has been previously demonstrated between induced pluripotent stem cell (iPSC) neurons derived from lithium responders and non-responders. The hyperexcitability of in vitro neurons derived from BD patients was reversed by lithium treatment, but only in those from patients who were lithium responders17. This finding is also supported by family studies, which found that the relatives of lithium responders were significantly more likely to be lithium responders as well19. These studies imply that patients with BD could be subcategorized based on biological differences which induce a divergent lithium response. There is a great need to better understand these differences in order to identify possible predictors of treatment response. However, dozens of previous candidate-gene association studies, genome-wide association studies (GWAS), and polygenic risk score analyses of lithium response in BD have failed to identify genetic variants with major effects. Given this pressing need to find pharmacogenetic predictors of response, more advanced methods in integrative genomic analysis are necessary16.Interestingly, there is a range of responses to treatment with lithium. Previous studies have reported that 20–30% of patients with BD are excellent responders, whereas over 40% fail to demonstrate any significant clinical improvement. These patient populations have been shown to differ from each other both phenotypically and genetically21.GWAS inherently face several limitations when used in isolation, including the challenge of genetic heterogeneity. In many disease processes with genetic associations, patients may carry diverse combinations of causal variants that impact multiple genes, creating a net effect across a particular pathway. GWAS of BD primarily detect variants of very small effect size consistent with a polygenic mode of transmission. Since each single nucleotide polymorphism (SNP) contributes only a tiny amount to the overall predisposition to BD, enormous sample sizes are required, and it can be difficult to surmise mechanisms of disease. Network approaches seek to address this biological reality by integrating GWAS results with known protein-protein interactions and other molecular networks. New causal genes may be identi ed by boosting their interactions with products of known causal genes22 of treatment response to lithium. After using network propagation to reprioritize candidate genes from GWAS data, we found significant overlap between both transcriptomic and GWAS results. The joint analysis yielded a 500 gene network significantly enriched in the following Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways: focal adhesion, ECM-receptor interaction, and PI3K-Akt signaling23. All three pathways play a role in axon growth and neuronal development24. Consistent with these results, post-mortem studies have found that in BD, neuronal populations may exhibit a decrease in number, size, and/or amount of dendritic spines25. Given that lithium may have downstream effects on these pathways, it is possible that genetic defects in focal adhesion pathways may provide both a mechanism for susceptibility to BD as well as a target for lithium treatment.We have recently reported a combined analysis of transcriptomic and GWAS data from the Pharmacogenomics of Bipolar Disorder (PGBD) study26. We reprioritized GWAS results using network methods to determine overlap with focal adhesion, ECM-receptor interaction, and PI3K-Akt signaling pathways.In this study, we aimed to replicate the results of our previous multi-omics study on a larger dataset of over 2,000 patients from the International Consortium on Lithium Genetics (ConLiGen)27 on 12/12/2022 for study GCST01248726. The data resulted from a GWAS of lithium response in 2,563 patients at 22 sites participating in the International Consortium on Lithium Genetics (ConLiGen). We utilized the summary statistics from a combined sample of 2,039 European ancestry individuals. In the ConLiGen study, data from over 6 million single nucleotide polymorphisms (SNPs) were tested for association with categorical and continuous retrospective ratings of lithium response using the Alda scale29. The Alda scale includes two scores: score A is a 0–10 retrospective rating of lifetime response, while score B captures factors reducing the confidence in score A such as lack of a documented lithium level, etc. In the ConLiGen study, under the continuous phenotype, participants were rated with the Alda A score, and individuals with a B score greater than 4 were excluded. We used the continuous rather than the dichotomous phenotype as a measure of treatment response because genome-wide significant association was detected with the continuous phenotype in the original GWAS. Quality control and statistical analysis methods are described in the original paper.Summary statistics were downloaded from the NHGRI-EBI GWAS Catalog30, a web-based platform for annotating, prioritizing, visualizing and interpreting GWAS results. We utilized the SNP2GENE function to map SNPs to genes and conduct SNP, gene-based, and gene-set analysis. We used all default settings, except for setting the maximum lead SNP p-value to 1×10e-5.We imported the ConLiGen summary statistics into FUMA 21.NAGA provided a reprioritized ranked list of 19,781 genes as output. We then entered the top 500 genes with the highest final heat scores into STRING, an online database that generates mapped networks based on protein-protein interactions31. STRING additionally analyzes for overrepresentation of user-inputted gene lists in established pathways, using the hypergeometric test32. Using this function, we tested our a priori hypotheses to identify functional enrichment of the NAGA-generated top 500 gene list in the KEGG hsa04510 focal adhesion pathway, KEGG hsa04512 ECM-receptor interaction, and KEGG hsa04151 PI3K-Akt signaling pathway33. P-values were corrected for multiple testing by STRING using the Benjamini–Hochberg procedure34.We input the ConLiGen summary statistics into NAGA , an online network propagation tool for pathway boosting and interpretation of genome-wide association studies35. A hypergeometric test was conducted to test for overrepresentation of the NAGA-generated 500 gene network in the 500 gene network generated in our previous study23.Overlap between the NAGA-generated top 500 gene list and the KEGG pathways was visualized using Cytoscape26. The study was conducted in two phases: GWAS 1 (n = 1065) and GWAS 2 (n = 1168). Sex and age were similar across both cohorts. Mean Alda scale A scores were 6.13 (SD = 3.13) and 6.52 (SD = 2.87), respectively. Mean Alda scale B scores were 1.78 (SD = 1.26) and 2.35 (SD = 1.16), respectively.The demographics of the sample can be found in the original ConLiGen studyAs reported in the original ConLiGen study, the only SNPs that were significant at a genome-wide significance level of 5e-08 were in linkage disequilibrium with the SNP rs74795342 on chromosome 21 . Using FUMA in our gene-wise analysis, no significant genes were found at a significance level of p < 0.05/18314 = 2.730e-6 . No gene-sets were found to be significant either, using p < 0.05 after Bonferonni correction. The most highly associated genes and gene-sets are listed in Supplemental Table 1 and Supplemental Table 2.23. Using the STRING analysis function, the top 500 reprioritized gene list generated by NAGA was found to be significantly enriched in both the KEGG hsa04510 focal adhesion pathway (p = 1.74e-06) and KEGG hsa04151 PI3K-Akt signaling pathway (p = 1.90e-07). Given the goal of replication and the small number of statistical tests, this was considered as a significant replication of our previous results in an independent sample for the focal adhesion and PI3K-Akt pathways. However, the KEGG hsa04512 ECM-receptor interaction pathway was not found to be significantly enriched.We first tested the three a priori pathways that were significant in our previous study, which had examined an independent sample23.There were 33 genes that were common to both networks.A hypergeometric test found significant overlap (p = 5.699e-07) between the 500 gene network generated by NAGA and the 500 gene network generated by network propagation analysis in our previous studyThe top 25 reprioritized genes produced by NAGA are listed in After testing the three a priori hypotheses based on previous results, we tested the top 500 NAGA gene list for enrichment in all pathways in STRING. The top 10 KEGG pathways found to be most strongly enriched are found in Supplemental Table 3. These include cancer and growth pathways  as well as the dopaminergic synapse pathway.p = .0050) inconsistent with a robust result. .We additionally used the STRING analysis function to test for functional enrichment of the top 100, 200, 300, and 400 reprioritized gene lists generated by NAGA in all three a priori KEGG pathways. The results agreed with the primary analysis, since all gene lists were significantly enriched in the KEGG hsa04510 focal adhesion pathway and KEGG hsa04151 PI3K-Akt signaling pathways at a level of p < .05. Only the top 100 reprioritized gene list was found to be significantly enriched in the KEGG hsa04512 ECM-receptor interaction pathway , and the17. Elevated neuroactivity in BD may induce vulnerability in neurons through impairment of focal adhesion pathways. Chronic elevation of neuroactivity has been shown to dramatically reduce surface expression of integrin β1 in animal models, leading to axonal and dendritic degeneration and eventually cell death41.Previously, neurons derived from induced pluripotent stem cells of patients with BD have been shown to exhibit hyperexcitability in vitro. This hyperexcitable phenotype was rescued by lithium only in neurons derived from lithium good responders44. Our results in this study are consistent with this hypothesis.Unsurprisingly, neurons in patients with BD have been shown to be present with smaller size, fewer numbers, and more limited branching. We had previously proposed that in lithium responders, this deficit is caused by deficits in focal adhesion and is rescued by lithium treatment. Furthermore, we proposed that in patients who are not lithium responders, focal adhesion is not dysregulated, and lithium is unable to address the relevant impairments45.Genes in associated cancer pathways show some overlap with focal adhesion as well, which suggests the possibility of shared mechanisms.  and the generalizability of the data-set, given that all participants were of European descent. Additionally, data was collected retrospectively. As a result, outcomes may be less accurate in determining response phenotypes21.This study also demonstrates the utility of network propagation methods, which can add power to GWAS with limited sample sizes. These methods are beneficial in identifying which genes and gene-sets are of interest to a disease process, but future research is still indicated for con rmationIn summary, we replicated our previous results reinforcing that genetic deficits in focal adhesion and PI3K-Akt signaling are associated with lithium response in BD patients. We hypothesize, as before, that malformed axonal growth cones result in shorter and less branched axons and susceptibility to BD in a subpopulation of patients who are lithium responders. This is also consistent with the idea that response to lithium results from a disease mechanism distinct from that of lithium non-responders. Furthermore, we propose that lithium rescues disrupted neuronal growth and axon extension processes by addressing deficits in focal adhesion. A better understanding of the pathophysiology of BD and lithium treatment may lead to the future development of drugs similar to lithium, as well as possible clinical predictors for treatment response.


Verbena officinalis (VO), a Chinese herbal medicine, exhibits anti-inflammatory, analgesic, immunomodulatory, and neuroprotective effects. Evidence suggests that VO regulates lipid metabolism; however, its role in AS remains unclear. In the present study, an integrated network pharmacology approach, molecular docking, and molecular dynamics simulation (MDS) were applied to examine the mechanism of VO against AS. Analysis revealed 209 potential targets for the 11 main ingredients in VO. Further, 2698 mechanistic targets for AS were identified, including 147 intersection targets between VO and AS. Quercetin, luteolin, and kaempferol were considered key ingredients for the treatment of AS based on a potential ingredient target–AS target network. GO analysis revealed that biological processes were primarily associated with responses to xenobiotic stimuli, cellular responses to lipids, and responses to hormones. Cell components were predominantly focused on the membrane microdomain, membrane raft, and caveola nucleus. Molecular functions were mainly focused on DNA-binding transcription factor binding, RNA polymerase II-specific DNA-binding transcription factor binding, and transcription factor binding. KEGG pathway enrichment analysis identified pathways in cancer, fluid shear stress, and atherosclerosis, with lipid and atherosclerosis being the most significantly enriched pathways. Molecular docking revealed that three key ingredients in VO  strongly interacted with three potential targets . Further, MDS revealed that quercetin had a stronger binding affinity for AKT1. These findings suggest that VO has beneficial effects on AS via these potential targets that are closely related to the lipid and atherosclerosis pathways. Our study utilized a new computer-aided drug design to identify key ingredients, potential targets, various biological processes, and multiple pathways associated with the clinical roles of VO in AS, which provides a comprehensive and systemic pharmacological explanation for the anti-atherosclerotic activity of VO.Lipid metabolism disorders may considerably contribute to the formation and development of atherosclerosis (AS). Traditional Chinese medicine has received considerable attention in recent years owing to its ability to treat lipid metabolism disorders using multiple components and targets.  SeveralVerbena officinalis (VO) is a commonly used herb with abundant sources, a low price, and low toxicity. It was originally published in Mingyi Bielu (Records of Famous Physicians). VO is a perennial herb that grows worldwide in Europe, America, North and Central Africa, Asia, and Australia , to develop an updated drug discovery method. In the present study, network pharmacology was used to examine the key ingredients in VO and its possible targets against AS. Further, molecular docking and MDS were used to explore the interaction between targets and active ingredients in VO. In addition to these techniques, we used a computerized target prediction system based on interactions among components in medicinal materials and targets and among genes and biological pathways to determine the relationship comprehensively and systematically between active ingredients in VO and their anti-atherosclerotic effects for the first time Figure 122.1http://tcmspw.com/tcmsp.php), ClassyFire (https://cfb.fiehnlab.ucdavis.edu/), GeneCards (http://www.genecards.org), DisGeNET (https://www.disgenet.org/), OMIM (https://www.omim.org/), PANTHER Classification System (http://pantherdb.org/), UniProt (https://www.uniprot.org), Venny2.1.0 (http://bioinfogp.cnb.csic.es/tools/venny), STRING (http://string-db.org/cgi/input.pl), DAVID (http://David.ncifcrf.gov), Bioinformatics (https://www.bioinformatics.com.cn/), Metascape (https://metascape.org/gp/index.html#/main/step1), PubChem (https://pubchem.ncbi.nlm.nih.gov/), Cytoscape 3.9.0 software, Smina software, Gromacs 2019.6, and Gmx_MMPBSA software.The databases and software used were as follows: Traditional Chinese Medicine Systems Pharmacology , followed by the construction and visualization of an interconnected network graph of the effective ingredients and targets using Cytoscape 3.9.0 software .The PPI network of the target proteins was constructed using the STRING11.5 database  and visu2.2.5H. sapiens using the Metascape database  developed by The formula above shows that a component known as inner energy contains E33.13.1.1Using TCMSP, all the active ingredients in VO were identified. By referring to the pharmacokinetic parameters, 12 components, including quercetin, luteolin, kaempferol, and beta-sitosterol, were screened Table 1.Apart from confirming the targets of the VO ingredients, we identified 209 potential targets using the UniProt database. To identify the targets of AS, keywords were entered into the GeneCards, DisGeNET, and OMIM databases; subsequently, 4,848, 2,044, and three targets, respectively, were screened. After combining the data from the three databases and removing duplicates, 2,698 AS targets were identified. To further determine the effectiveness of both VO and AS, a Venn diagram was constructed to summarize 147 VO-related anti-atherosclerotic targets , protein-modifying enzyme (PC00260), gene-specific transcriptional regulator (PC00264), transmembrane signal receptor (PC00197), intercellular signal molecule (PC00207), transporter (PC00227), and others; of these, metabolite interconversion enzymes (18.37%) were the most abundant class  was used as a measure of the system’s stability. During the six dynamic simulation sets, it fluctuated slightly but remained stable at 0.20–0.25 nm/0.5–0.6 nm after simulation for 25 ns Figure 84Traditional Chinese medicines can be used to treat diseases involving multiple targets and components. They exhibit the ability to inhibit endothelial dysfunction, platelet activation, lipid peroxidation, ROS production, and macrophage-induced AS . Howevervia the lipid and atherosclerotic pathways, inhibiting downstream NF-KB and reducing inflammatory factors  in susceptible individuals, leading to the formation of foam cells . AS depetability . Lipid mtability . Li explts on AS . These fvia multiple targets.We found that three key ingredients in VO  were linked to three AS target genes . We used molecular docking to dock the three active ingredients with the three related potential target proteins . The negvia a direct synergistic effect on multiple targets and pathways. Further experiments are warranted to verify the biological mechanism by which VO regulates AS signaling pathways because there is still no evidence that VO antagonizes AS in animals or clinical experiments.In the present study, the main VO ingredients, potential pharmacological targets, and signal pathways were identified for treating AS using a computer-aided drug design approach. Our study results indicate that VO ameliorates multiple pathological features of AS 5Using network pharmacology, molecular docking, and MDS, this study comprehensively and systematically revealed that the characteristics of the key ingredients in VO , potential targets , various biological processes , and multiple pathways  prevent the occurrence and development of AS, providing an important theoretical basis for clinical treatment of AS and related research.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Data were processed by TYC, YYG, XJY, and XY. The manuscript was drafted by TYC and YYG. Data acquisition, analysis, and interpretation were conducted by WY. All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.


Objective: To investigate the effects and mechanisms of Paeoniae radix rubra–Angelicae sinensis radix (P-A) drug pair in the treatment of rheumatoid arthritis (RA).Methods: Mass spectrometry was employed to accurately characterize the main components of the P-A drug pair. Network pharmacology was used to analyze the main components and pathways of the P-A drug pair in the treatment of RA, and Discovery Studio software was used to molecularly dock the key proteins on the pathway with their corresponding compounds. The levels of serum TNF-a, IL-1β, and IL-6 were measured by enzyme linked immunosorbent assay (ELISA). The histopathology of the ankle joint was observed by hematoxylin-eosin (HE) staining, and the positive expression of p-PI3K, p-IKK, p-NF-κB, and p-AKT in the synovial tissue of the ankle joint was detected by immunohistochemical analysis. Finally, the expression of PI3K, IKK, and AKT and their phosphorylation levels were determined by western blot in each group of rats.Results: Network pharmacology combined with molecular docking analysis revealed that the pharmacodynamic mechanism of the P-A drug pair for the treatment of RA may be related to the contents of caffeic acid, quercetin, paeoniflorin, and baicalein in the regulation of the expression of the PI3K/AKT/NF-κB signaling pathway and the targets of PIK3CA, PIK3R1, AKT1, HSP90AA1 and IKBKB in the pathway. Compared with the model group, the P-A drug pair significantly improved the pathological changes of the synovial tissue and reduced feet swelling in RA model rats. Moreover, it regulated the levels of TNF-α, IL-1β, and IL-6 in serum (p < 0.05). The results of the immunohistochemical analysis and western blot showed that the expression of PI3K, IKK, NF-κB, and AKT decreased after phosphorylation in the synovial tissue (p < 0.05).Conclusion: The P-A drug pair exhibited an inhibitory effect on the hyperactivation of the PI3K/AKT/NF-κB signaling pathway in the synovial membrane of RA rats. The mechanism may be related to the downregulation of the phosphorylation levels PI3K, IKK, NF-κB, and AKT, which in turn decreased inflammatory cell infiltration and synovial membrane proliferation. This technique can rapidly characterize the complex components of Chinese medicines . NetworkPaeoniae radix rubra and Angelicae sinensis radix were purchased from Shaanxi Sciendan Pharmaceutical Co., Ltd. and identified by Professor Cheng Huyin of Shaanxi University of Chinese Medicine. The rat TNF-α ELISA kit (MM-0180R1), rat IL-1β ELISA kit (MM-0047R1), and rat IL-6 ELISA kit (MM-0190R1) purchased from Jiangsu Enzyme Immunoassay Industrial Co.,Ltd. Bovine Type II collagen (L22S11C125306) was purchased from Shanghai Yuanye Bio-Technology Co.,Ltd., and full Frances adjuvant (J04GS149798) was purchased from Shaanxi Shuoyan Chemical Technology Co., Ltd. Antibodies against PI3K (ET1608-70), AKT (ET1609-47), p-AKT (ET1612-73), IKK (ET1611-15) and NF-κB (ET1604-27) were obtained from Hangzhou HuaAn Biotechnology Co.,Ltd., antibodies against p-PI3K (AF3241) was obtained from Affinity Biosciences Group Ltd., antibodies against p-IKK (bs-3232R) was obtained from Beijing Biosynthesis Biotechnology Co.,Ltd.In the treatment of RA, the maximum dose of Paeoniae radix rubra was 60 g, and the maximum dose of Angelicae sinensis radix was 50 g. Combined with the total prescription dosage for the treatment of the dampness heat obstruction type RA, and the common ratio of Angelicae sinensis radix and Paeoniae radix rubra is 1:1, the dosage is 40 g of Angelicae sinensis radix and 40 g of Paeoniae radix rubra . AccordiThe reference standards of chlorogenic acid, caffeic acid, ethyl gallate, 3-butyl-phthalide, benzoyloxypaeoniflorin, senkyunolide I, gallic acid, oxypaeoniflorin, vanillic acid, albiflorin, paeoniflorin, ferulic acid, 1,2,3,4,6-pentagalloylglucose, benzoylpaeoniflorin, paeonol, and ligustilide  were supplied by Shanghai Yuanye Bio-Technology Co., Ltd. Appropriate amounts of the reference standards were mixed and dissolved in methanol in a 10-mL measuring flask, then filtered and set aside.The determination was performed on Acquity UPLC®BEH C18 column  with mobile phases consisting of 0.1% formic acid acetonitrile (A) −0.1% formic acid aqueous solution (B) in gradient elution . The column temperature was 30°C; the flow rate was 0.2 mL/min; and the injection volume was 2 μL.A triple TOFTM 5600+ time-of-flight liquid mass spectrometer was used for quantitation. Electrospray ion source, positive and negative ion temperature were 600°C and 550°C, spray voltage was 5.5 kV and −4.5 kV respectively. Atomization gas was N2, scanning range was m/z100∼2000, cracking voltage was ±80V, collision energy was ±10 eV, and the total data collection time was 42 min.http://swisstargetprediction.ch/) (https://www.genecards.org/) (https://www.disgenet.org/) (https://www.ncbi.nlm.nih.gov/geo/) (p ≤ 0.05 and |logFC| ≥ 1) to determine differentially expressed genes and to generate volcano plots.The targets of the drug were predicted by Swiss TargetPrediction database(ion.ch/) . With “rds.org/)  and DisGet.org/) , the relov/geo/) , In addihttps://bioinfogp.cnb.csic.es/tools/venny/index.html), and the intersection was obtained. The final target was the target of action of the P-A drug pair in RA. Using the STRING platform (https://string-db.org/) (The screened active compound targets and RA targets were mapped by Venny 2.1.0(db.org/)  with thep < 0.05, and the key signaling pathways for the treatment of RA with the P-A drug pair were explored.Based on the core targets screened in the above process, GO and KEGG signaling pathway analysis were performed on the intersecting targets using the cluster Profiler package  in R 4.0https://pubchem.ncbi.nlm.nih.gov), and the PDB structure files of core targets were downloaded from PDB database (https://www.rcsb.org/)  . The LibSPF male SD rats, body weight 180–220 g, were purchased from Chengdu Dasuo Laboratory Animal Co., LTD., license number SCXK 2020–030. They were fed at humidity (50 ± 10) % and temperature (25 ± 2)°C and the animal experiment was approved by the Animal Experiment Ethics Committee of Shaanxi University of Chinese Medicine .n = 10): the normal control group, the model group, the Tripterygium glycoside tablet group (20 mg/kg)  , and theThe changes in the color of their body hair, mental and activity state, dietary habit, and joint swelling were observed daily.One hour after the last administration, 5 mL of blood was taken from the abdominal aorta of rats. The blood sample stood for 30 min and then was centrifuged at 3,000 r/min for 15 min to separate the serum. The levels of TNF-α, IL-1β, and IL-6 in the serum were determined by using an ELISA kit, according to the manufacturer’s instructions.The rats were executed by decervicalization, and their left hind feet were taken, fixed with 4% paraformaldehyde, decalcified with 10% EDTA solution, dehydrated by a series of ethanol gradient solutions, treated with xylene, embedded in paraffin, and sectioned. HE staining was used for routine staining. After dehydration, the sections were cleared with xylene and then sealed with neutral gum. The histopathological changes in the ankle joints of the rats were observed under a microscope.The left ankle joint tissue sections were dewaxed with xylene, and repaired antigen with sodium citrate buffer. After treating with 3% hydrogen peroxide, the sections were rinsed, sealed with 10% goat serum, and p-PI3K, p-IKK, p-NF-κB and p-AKT antibodies were added dropwise. The tissue sections were incubated overnight at 4°C. Biotin-labeled secondary antibodies were added for incubation, and the color developed by dropwise addition of diaminobenzidine chromogenic solution was observed. Hematoxylin was re-stained, dehydrated, cleared, sealed, and photographed. Quantitative analysis of the positive proteins was performed by using Image-Pro plus software.Protein was extracted from the synovial tissue of the right ankle of the rats, and the bicinchoninic acid method was used for protein quantification. Briefly, 9 µL of the protein sample was separated by 10% SDS-PAGE and closed with 5% skim milk for 1 h. The primary antibody was added and incubated overnight, then the secondary antibody was added for incubation. An enhanced chemiluminescence kit was used for color development, and protein bands were photographed in a gel imager system. The relative protein expression  was analyzed.p < 0.05 indicated that the difference was statistically significant.All data are expressed as the mean ± standard deviation, and Graph Pad Prism 9.0.0 software was used to process the data. One-way ANOVA and Tukey’s multiple comparisons tests were used to compare the data. The detected data were imported into Peak View 2.2 software, and NIST 2017, NIST E&L_HR-MS/MS_1.0, TCM Library 1.0, TCM MS/MS Library were used as the matching library of P-A drug pair. Combined with the mass spectrum information of each compound, comparison with the reference standards, reference to the relevant literature data, and compounds in the P-A drug pair were identified and classified. A total of 41 compounds were identified, among which 19 components were found in Paeoniae radix rubra, 19 components identified in Angelicae sinensis radix, and there were three compounds common to both herbs. Among them, compounds 9, 12, 14, 15, 19, 20, 21, 22, 23, 26, 28, 29, 32, 33, 39, 40 were identified as gallic acid, chlorogenic acid, caffeic acid, vanillic acid, ethyl gallate, oxypaeoniflorin, paeoniflorin, albiflorin, ferulic Acid, 1,2,3,4, 6-pentagalloylglucose, paeonol, benzoyloxypaeoniflorin, senkyunolide I, benzoylpaeoniflorin, ligustilide, 3-butyl-phthalide, respectively, by comparison with reference standards. The identification results are summarized in A total of 1,926 component targets were predicted by using Swiss Target Prediction database, and a total of 565 active component targets of the P-A drug pair were obtained after de-weighting. After combining the results of the GeneCards and the DisGeNET databases, a total of 5,792 RA-related targets were obtained after de-duplication. GSE206848 was filtered and normalized by using R software and related software packages to screen for differential genes, and a total of 2,805 differential genes were obtained for upregulated  and downregulated (830) RA. The volcano map of the differential genes is shown in The 57 RA targets of the P-A drug pair were imported into STRING database for analysis, and the confidence level was set to >0.9 to eliminate the isolated target proteins and obtain the protein interaction information. The network was imported into Cytoscape 3.7.2 software for visualization, and network topology analysis was conducted to obtain the PPI of the targets (57 nodes and 56 edges). As shown in p < 0.05, 680 related biological processes (BPs), 44 related cell compositions (CCs), and 56 related molecular functions (MFs) were screened . Compared with the model group, the levels of TNF-α, IL-1β, and IL-6 in the serum of rats in the high-dose P-A drug pair group decreased (p < 0.01), as shown in Compared with the normal control group, the levels of TNF-α, IL-1β, and IL-6 in the serum and ankle synovial tissue of rats in the model group increased (As shown in p < 0.0001), indicating that there was inflammatory injury in the ankle synovial tissue of rats in the model group. The P-A drug pair in the high and medium dose groups, p-PI3K, p-IKK, p-NF-κB, and p-AKT protein expression in ankle tissue decreased (p < 0.01), indicating that the expression of inflammatory factors in RA rats could be inhibited by the P-A drug pair.As shown in p < 0.001). The expression of p-PI3K/PI3K, p-IKK/IKK, and p-AKT/AKT was inhibited to varying degrees by the P-A drug pair in the high and medium dose groups, with significant differences compared with the model group (p < 0.01).As shown in The core targets PIK3R1, PIK3CA, AKT1, HSP90AA1 and IKBKB and their corresponding components in the PI3K/AKT/NF-κB pathway were docked by molecular docking technology. PIK3R1 and quercetin, PIK3CA and caffeic acid, AKT1 and quercetin, HSP90AA1 and paeoniflorin, and IKBKB and baicalin showed the best intermolecular affinity. The rat model of CIA was established by injecting bovine type II collagen emulsion subcutaneously, at the root of the tail and in the paw of the two hind feet. After 38 days of continuous gavage with the P-A drug pair, serum TNF-a, IL-1β, IL-6 levels were measured in each group by ELISA. Ankle joint histopathology was observed by HE staining, and the p-PI3K, p-IKK, p-NF-κB, and p-AKT protein positive expression in the ankle synovial tissues of each group was detected by immunohistochemical analysis. PI3K, IKK, and AKT protein expression and their phosphorylation levels in the ankle synovial tissue of each group were determined by western blot. Combining the results of each experiment, we believe that the mechanism of action of the P-A drug pair in the treatment of RA may be related to the action of some key compounds, such as caffeic acid, quercetin, paeoniflorin, and baicalein, on key targets, including PIK3R1, PIK3CA, AKT1, HSP90AA1, and IKBKB, to downregulate the phosphorylation levels of PI3K, IKK, and AKT. This, in turn, inhibits the release of inflammatory mediators TNF-α, IL-1β, and IL-6 and suppresses the overactivation of the PI3K/AKT/NF-κB signaling pathway in the synovial membrane of RA rats .RA is a chronic, progressive, and aggressive autoimmune disease with synovitis and extra-articular lesions as the main clinical manifestations . CurrentExcluding signaling pathways unrelated to the disease, the results of KEGG enrichment analysis showed that the drug treated RA by acting on PI3K-AKT, JAK-STAT, HIF-1, and other signaling pathways. The PI3K-Akt signaling pathway can regulate the release of inflammatory factors and the formation of enzymes related to proliferation, apoptosis, and inflammation to participate in the pathological process of RA. The PI3K-AKT signaling pathway was found to be widely present and aberrantly activated in RA synoviocytes . InhibitAccording to the above analysis results, in this study, the key proteins PIK3R1, PIK3CA, AKT1, HSP90AA1, and IKBKB and their corresponding components in the PI3K/AKT/NF-κB pathway were interlinked by molecular docking technology, and the results indicated that the binding stability between all the active compounds and the key targets was strong. In particular, quercetin, caffeic acid, paeoniflorin, and baicalin scored higher than the other compounds with the key targets. The results suggest that these four compounds are the active ingredients of the P-A drug pair for the treatment of RA. A previous study showed that quercetin reduced joint swelling and inflammation in mice with arthritis and alleviated joint damage . By inhiTNF-α, IL-1β, and IL-6 play an important role in the development and progression of RA. TNF-α induces endothelial cells to express adhesion molecules, promoting leukocyte adhesion and infiltration of the vascular endothelium. This leads to local inflammation, and it also directly stimulates collagenase synthesis in articular chondrocytes, promoting synovial inflammation . An incrIn this study, UPLC-Q-TOF-MS/MS technology was used to comprehensively and accurately identify the active components in the P-A drug pair. Moreover, its mechanism of action in the treatment of RA was further explored by using network pharmacology and animal experiments. The results provide an important theoretical basis for the further development of P-A drug pair for the treatment of RA.The P-A drug pair exhibited good therapeutic effects on RA rats. In this study, UPLC-Q-TOF-MS/MS was used to analyze and identify the key chemical components of the P-A drug pair, and network pharmacology to investigate the mechanism of action. The levels of serum TNF-a, IL-1β, and IL-6 were measured by enzyme linked immunosorbent assay (ELISA). The positive expression of p-PI3K, p-IKK, p-NF-κB, and p-AKT in the synovial tissue of the ankle joint was detected by immunohistochemical analysis. The expression of PI3K, IKK, and AKT and their phosphorylation levels were determined by western blot in each group of rats. It was speculated that components, such as caffeic acid, quercetin, paeoniflorin, and baicalein, might act on key targets, such as PIK3R1, PIK3CA, AKT1, HSP90AA1, and IKBKB, to downregulate the phosphorylation levels of PI3K, IKK, and AKT. Furthermore, the PI3K/AKT/NF-κB signaling pathway was regulated by inhibiting the release of inflammatory mediators TNF-α, IL-1β, and IL-6.in vitro or in vivo). In the following, we will carry out these studies, and we will also adopt cutting-edge methods and technologies based on experimental pharmacology, network pharmacology and multi-omics and their comprehensive analysis to investigate the specific effects and mechanisms of P-A drug pair on RA.Through the comprehensive analysis of network pharmacology and pharmacodynamics, this study preliminarily interpreted the effect and mechanism of P-A drug pair in the treatment of RA, and provided reference for the study of the effect and mechanism of Chinese herbal medicine in the treatment of RA. However, this study has some limitations. For example, the docking results should be verified by experiments such as Isothermal Titration Calorimetry and Surface Plasmon Resonance, and the P-A drug pair and its potential active components, targets and signaling pathways for the treatment of RA have not been fully studied and verified by experimental pharmacology (


Huntington disease (HD) is a neurodegenerative disease associated with polyglutamine expansion in the protein huntingtin (HTT). Although the length of the polyglutamine repeat correlates with age at disease onset and severity, psychological, cognitive and behavioral complications point to the existence of disease modifiers. Mitochondrial dysfunction and metabolic deregulation are both associated with the HD but, despite multi-omics characterization of patients and model systems, their mechanisms have remained elusive. Systems analysis of multi-omics data and its validation by using a yeast model could help to elucidate pathways that modulate protein aggregation. Metabolomics analysis of HD patients and of a yeast model of HD was, therefore, carried out. Our analysis showed a considerable overlap of deregulated metabolic pathways. Further, the multi-omics analysis showed deregulated pathways common in human, mice and yeast model systems, and those that are unique to them. The deregulated pathways include metabolic pathways of various amino acids, glutathione metabolism, longevity, autophagy and mitophagy. The addition of certain metabolites as well as gene knockouts targeting the deregulated metabolic and autophagy pathways in the yeast model system showed that these pathways do modulate protein aggregation. Taken together, our results showed that the modulation of deregulated pathways influences protein aggregation in HD, and has implications for progression and prognosis.This article has an associated First Person interview with the first author of the paper. Summary: Metabolomic analysis of Huntington disease (HD) patients and of a HD yeast model identified deregulated metabolic pathways. We used the yeast model to investigate their role in the aggregation of huntingtin protein and their implications in HD. HTT), resulting in an expanded polyQ stretch within the HTT protein, leading to neurodegeneration , Alzheimer disease (AD), Parkinson disease (PD) and amyotrophic lateral sclerosis (ALS) are incurable. These diseases are associated with the formation of protein aggregates that eventually leads to neuronal cell death . Despiteneration . Althougneration  and the neration . HD patineration .Genetic modifiers of HD with variable penetrance have been reported , 2019. PIn HD patients, pyruvate dehydrogenase (PDH) activity is impaired, which suggests it has a role in the initiation or propagation of disease . MRI andThe metabolic status of the cells has been shown to modulate the autophagy pathway . AggregaTranscriptomics analyses of the affected region in brains obtained from HD patients or model systems showed deregulation of signaling and metabolic pathways . MoreoveYeast has been used as a model system to study the role of pathways in many diseases . It has In this study, we metabolomically analyzed pre-symptomatic and symptomatic HD patients, individuals as familial controls as well as age- and sex-matched controls, and categorized the significantly different levels of metabolites into pathways. Furthermore, metabolomics analysis of the HD yeast model was carried out and the significantly different levels of metabolites were categorized into pathways. Comparative analysis of the metabolic pathways – obtained by analysis of GEO-based transcriptomics datasets as well as of proteomics and metabolomics datasets obtained from literature of patients and model systems  – have bn=5) and symptomatic (n=11) HD patients, as well as that of familial controls (n=11) and age- and sex-matched controls (n=4) were carried out. The patient cohort had been previously characterized considering the length of CAG repeats; outcome of multimodal magnetic resonance imaging (MRI), i.e. MRI, MRS, DTI, ADC scans; positron emission tomography (PET) scans and visual evoked potential (VEP) measurements  was carried out by comparing HD patient and control samples and using the significantly different metabolites. Our analysis categorized these significantly different metabolites into six pathways – three metabolic pathways of amino acids, i.e. (i) glycine, serine and threonine metabolism; (ii) valine, leucine and isoleucine biosynthesis; (iii) D-glutamine and D-glutamate metabolism, as well of (iv) purine metabolism, (v) aminoacyl-tRNA biosynthesis and (vi) nitrogen metabolism. Results are provided as a chord diagram, which provides details of pathways and associated metabolites E.600=0.5 in all culture samples). Importantly, these observations are consistent with previous studies , human (13 pathways) and mouse (12 pathways). Our analysis shows that seven metabolomics pathways were shared between all different datasets, whereas yeast and human datasets had three overlapping pathways between them. The common pathways that emerged between all three pooled datasets include aminoacyl-tRNA biosynthesis, metabolism of amino acids  and nitrogen metabolism G.Furthermore, we performed a commonality analysis for the shared pathways together with the associated metabolites between pooled datasets from human, mouse and yeast G. IntereTranscriptomics datasets were obtained from the GEO databases for yeast , brain hAnalysis of yeast transcriptomics data showed a total of seven pathways A. The trThe pathways obtained by analysis of individual yeast, mouse and human transcriptomics datasets were combined and commonality pathway analysis between the different species was carried out. Yeast and human datasets showed two shared pathways, which include the tricarboxylic acid  cycle and protein processing in the endoplasmic reticulum D. The moWe also separately performed pathway analysis of genes up- or downregulated in the transcriptomics datasets of yeast and mouse HD models, and HD patients . For HD Pathway analysis of genes upregulated in human NPCs datasets showed enrichment of the AGE-RAGE signaling pathway and regulation of inflammatory mediators of TRP channels, whereas that of downregulated genes showed enrichment of pathways involved in Hippo, Wnt, TGF-beta, MAPK and Rap1 signaling, axon guidance, cancer and glutamatergic synapse . PathwayProteomics HD mouse datasets analyzed include those of cortex, striatum, hippocampus  and synaCommonality analysis of human datasets showed two shared pathways for all the three omics datasets, i.e. the alanine, aspartate and glutamate metabolism as well as the purine metabolism pathway A. ProteoOur analysis of the mouse datasets showed 19 shared pathways between transcriptomics and proteomics datasets, eight shared pathways between proteomics and metabolics datasets, but only one shared pathway between transcriptomics and metabolomics datasets B. CommonThe HD yeast model 72Q was used for all further experiments. We identified shared metabolic pathways that are deregulated in HD patients as well as mouse and yeast models of HD, but also for those deregulated and shared between at least two of the systems. We found some shared pathways between HD patients and the mouse HD model, which include the metabolism of glutathione, tryptophan, glyoxylate and dicarboxylate, TCA cycle and longevity-regulating pathway D. To evaKGD2) substantially increased HTT protein aggregation (PYC1) also significantly increased HTT protein aggregation  has on HTT protein aggregation in the yeast model system. Addition of glutamine led to a significant increase in protein aggregates, whereas addition of alanine reduced protein aggregation . Therefore, our results show the importance of these pathways in modulating HTT protein aggregation.We found that human, yeast and mice metabolomics datasets show deregulation of alanine, aspartate and glutamate, as well as glutamate–glutamine pathways G. The alregation D. Furtheregation D. AdditiAAD6) and glutathione-dependent oxidoreductase and glutathione S-transferase (GRX4). Whereas knockout of AAD6 reduced protein aggregates, that of GRX4 did not cause any significant change in protein aggregation  significantly reduced HTT aggregation, whereas knockout of N-formyltyrosine oxidase (DIT2) led to significant increase in HTT aggregation  led to significantly increased HTT protein aggregation , which is involved in mitochondrial fission, led to significantly decreased HTT protein aggregation (CKA1) also led to increased HTT protein aggregation  gene lead to a significant increase in HTT protein aggregation. This might be caused by reduced flux through the TCA cycle, as the concentration of oxaloacetate might be limiting under these conditions. Further, the addition of malate significantly increased under this condition, whereas knockout of the cytosolic malate dehydrogenase gene MDH2 significantly reduced aggregation of HTT protein  – the conversion of glucose to ribose-5-phosphate – in the human proteomics dataset. Our results show that HTT protein aggregation in yeast was significantly increased following addition of Hed in HD , and HD ed in HD . Reduceded in HD . An incred in HD . Anothered in HD . A genomeroxides . Furthereroxides . Taken teroxides .BNA7 significantly reduced HTT protein aggregation, whereas knockout of DTI2 significantly increased it. Previous studies have also shown a role of tryptophan metabolism in HTT protein aggregation , Bangalore, India. The cohort included eleven symptomatic HD patients, five pre-symptomatic HD patients, 11 individuals as familial controls and four random individuals as age- and sex-matched controls. The study was approved  by the Joint Institutional Ethics committee (IEC) of Sri Sathya Sai Institute of Higher Learning (SSSIHL) and SSSIHMS, according to the ethical standards set out in the 1964 Declaration of Helsinki and its later amendments.Serum samples from HD patients, familial controls, and age- and sex-matched controls were collected for metabolomics analysis; 50 μl was used for extraction. The procedure for metabolite extraction and analysis was adapted from our previous studies  was used for normalization; Student's 600=0.8. Cells were then harvested and washed twice with double-distilled water (ddH2O) to remove traces of medium. Collected cell pellets were resuspended in 25 ml of electroporation buffer  and unlabeled (+/−)-jasmonic acid  were added to the extraction buffer (100 µl for 10 ml of extraction buffer). Each sample was then sonicated on ice for 30 s at a 5-10 pulse rate, following addition of 450 μl of cold chloroform and vortexed for 10 min. Thereafter, cold Millipore type1 water (150 μl per sample) was added and samples were vortexed for 10 min. Samples were then incubated at −20°C for 20 min, followed by centrifugation (1500 g) for 10 min at 4°C, upon which a two-layer separation was seen. Both the layers were then transferred together into a new tube and left to dry in a speed vac (miVac) for ∼40 min. After drying the pellets were resuspended in methanol:water (50:50), vortexed and sonicated on ice for 5 min. Samples were then transferred to a conditioned [methanol:water (50:50)] Amicon filter column  and centrifuged for 6 h at 16,200 g at 4°C. Filtrates were collected into collection tubes, and each entire volume was used for quantitative metabolomics.Samples of 8×10Of extracted metabolite samples, 2 μl were taken as the injection volume for quantitative metabolomics. Analyses were carried out using the Agilent 6490 iFunnel triple quadrupole LC–MS system. Samples analyses were carried out under positive and negative electron spray ionization, using an XBridge BEH Amide Column . Details of detected metabolites are shown in Agilent Masshunter Quantitative analysis software (Version No. B.07.00) was used to obtain the abundance of each metabolite and to set the peak integration time with respect to the corresponding retention time for each metabolite. Acquired metabolites were normalized, using internal standard (+/−)-jasmonic acid for those acquired in negative mode and L-tryptophan for those acquired in positive mode.t-test analysis was carried out using MetaboAnalyst 5.0 . Heatmaps for the different metabolites are shown in To identify different levels of metabolite in control and sample, two-way Student's https://www.metaboanalyst.ca), an online tool for MSEA. A hypergeometric test was employed as an enrichment method for the study. We used the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database as reference for our enrichment analysis. Significant metabolic pathways were selected when FDR values were ≤0.25.Previously published metabolome datasets were mined (sample details shown in https://molbiotools.com/listcompare.php) (as shown in https://cytoscape.org/), together with their respective levels of significantly different metabolites.Pathways identified in yeast and human metabolomics datasets were pooled with pathways found to be enriched in this study, i.e. 25Q vs 46Q, 25Q vs 72Q and 25Q vs 103Q for yeast and control vs disease, control vs pre-symptomatic and control vs symptomatic for HD patients C,F. Poolhttps://www.networkanalyst.ca/), the online tool for comprehensive gene expression profiling and network visual analytics, which uses the limma package for analysis of gene expression datasets. Expression levels of genes were considered significantly different at a log2-fold change of 1 and P≤0.05. The implications significantly different levels of gene expression have on the disease were explored using the enrichment analysis tool Enrichr (https://maayanlab.cloud/Enrichr/), and the KEGG pathway database as a reference. Overlapping pathways, resulting from pathway enrichment were then selected based on their significance (P≤0.05). Significant pathways from the datasets were taken for comparative analysis using the online tool Molbiotools multiple list comparator (https://molbiotools.com/listcompare.php)  analysis was done by using microarray datasets deposited at the National Centre for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. We retrieved the normalized series matrix file for each of the GSE IDs selected (sample details are shown in php) see A-D.P≤0.05. Significant proteins were tested on having any implications on biological pathways by using the enrichment analysis tool Enrichr. Results of the enrichment analysis were then screened for significant pathways (adjusted P≤0.05). Significant pathways enriched in these datasets were taken for comparative pathway analysis of datasets, visualized in Venn diagrams (https://molbiotools.com/listcompare.php) as shown in The proteomics profile datasets for HD in human and mouse were mined from the literature (for sample details see Significantly deregulated pathways identified after multi-omics analysis of human, mouse and yeast datasets were analyzed each for shared pathways A-C, and 10.1242/dmm.049492_sup1Supplementary informationClick here for additional data file.


Pathway-level survival analysis offers the opportunity to examine molecular pathways and immune signatures that influence patient outcomes. However, available survival analysis algorithms are limited in pathway-level function and lack a streamlined analytical process. Here we present a comprehensive pathway-level survival analysis suite, PATH-SURVEYOR, which includes a Shiny user interface with extensive features for systematic exploration of pathways and covariates in a Cox proportional-hazard model. Moreover, our framework offers an integrative strategy for performing Hazard Ratio ranked Gene Set Enrichment Analysis and pathway clustering. As an example, we applied our tool in a combined cohort of melanoma patients treated with checkpoint inhibition (ICI) and identified several immune populations and biomarkers predictive of ICI efficacy. We also analyzed gene expression data of pediatric acute myeloid leukemia (AML) and performed an inverse association of drug targets with the patient’s clinical endpoint. Our analysis derived several drug targets in high-risk KMT2A-fusion-positive patients, which were then validated in AML cell lines in the Genomics of Drug Sensitivity database. Altogether, the tool offers a comprehensive suite for pathway-level survival analysis and a user interface for exploring drug targets, molecular features, and immune populations at different resolutions.The online version contains supplementary material available at 10.1186/s12859-023-05393-y. Organizing biological knowledge into pathways facilitates the integrative analysis of gene expression data derived from RNA sequencing and proteomics profiling. Common pathway-level analysis tools, such as ENRICHR  and GSEAA one-stop tool for expression-based survival analyses.The ability to include multiple covariates inside the Cox-proportion hazard pathway model.The ability to summarize prioritized gene signatures into relevant clusters and pathway modules.The ability to perform hazard ratio ranked gene set enrichment analysis.With several drug screening databases now available with perturbed gene sets after drug treatment in cancer cell lines, survival analysis can also be utilized to perform drug screening by identifying drugs that can reverse expression associated with highly refractory diseases . For exaAltogether, survival analysis is a critical branch of statistics for analyzing the time-to-event, and our tool facilitates a comprehensive survival analysis of pathway-level scores.. Additional installation and usage instructions is available in the Additional file PATH-SURVEYOR is implemented in the R environment, and packages can be automatically installed during runtime. There are four major components of the PATH-SURVEYOR Fig. , which iPATH-SURVEYOR interactive mode provides the ability to analyze and visualize “on-the-fly” associations of immune signatures and pathways scores with a clinical endpoint Fig. A. The apP value is calculated on the likelihood ratio, wald test. An adjusted P value can be calculated based on Benjamini–Hochberg correction method. In the output table, genes and pathways are ranked by the likelihood ratio P value.To facilitate the identification of top high-risk pathways and genes, we have developed a pipeline to systematically assess pathways associated with hazard by a Cox proportional hazard function Fig. C. The usThe Connectivity Mode offers the user the ability to analyze the similarity between pathways associated with survival Fig. D. The haThe Jaccard score function J for two pathways A and B is defined asThe Jaccard matrix can be visualized as a heatmap.Next, the pathways can be clustered using the hclust function from R (v4.1.0) into k-groups (user-specified). Clusters can be visualized as a dendrogram. To overlap survival-associated gene expression, genes within the pathway can be displayed as a table with a flexible sorting feature and added annotation information.From the pipeline mode, we can derive a hazard ratio ranked gene list, which can then be applied as input to the Pre-Ranked-Hazard-Ratio GSEA R Shiny app Fig. E. This aTo demonstrate the functionalities of the PATH-SURVEYOR framework, we have included use-case examples of biomarker discovery in a cohort of immunotherapy-treated melanoma patients. We have also provided an example use-case strategy for drug repurposing in pediatric acute myeloid leukemia patients.To identify predictive biomarkers that facilitate patient selection of patients suitable for immune checkpoint inhibitor (ICI) treatment, we integrated 313 melanoma patients treated with ICI from Riaz et al. n = 51) , Hugo et1 , Hugo To leverage our framework for therapeutic discovery, we obtained the gene expression data and clinical annotation of 220 patients with the KMT2A fusion event from the National Cancer Institute TARGET pediatric acute myeloid leukemia (AML) 1031 cohort (0–22 years of age). The translocation event of the gene KMT2A, also known as mixed lineage leukemia (MLL), is frequently identified in pediatric AML. Through its multiple fusion partners arises a diverse patient population with a need for advanced risk stratification . ThroughPATH-SURVEYOR is designed to visualize and perform systematic survival analysis based on gene and pathway information. The application is designed for users with limited experience in programming as well as for advanced users to perform systematic high-throughput pathway screening. In the interactive mode, the Shiny application will ensure reproducibility and can be easily set up and applied in any cohort. In the pipeline mode, the user can apply univariate and multivariate analysis of pathway and patient covariates associated with patient survival outcomes. Our current application can also perform GSEA based on hazard ratio ranking as well as pathway clustering to examine shared gene and pathway features associated with survival, which is unique to PATH-SURVEYOR when compared with other tools, including TRGAted , UALCAN As more RNA sequencing and proteomics data are being captured in large clinical trials, generating user- interfaces will facilitate access to these data sets. Thus, we present PATH-SURVEYOR, a program for inferring survival through pathways, immune components, and drug-induced targets. We anticipate PATH-SURVEYOR will enable a collaborative environment for exploring pathway-level, drug targets, and immune features that are predictive of treatment efficacy, especially for high-risk malignancies.https://github.com/shawlab-moffitt/PATH-SURVEYOR-Suite. URL Links to the Input File Prep App: https://shawlab-moffitt.shinyapps.io/path_surveyor_fileprep/. URL Links to the PATH-SURVEYOR App: https://shawlab-moffitt.shinyapps.io/path_surveyor/. Preloaded Examples: https://shawlab-moffitt.shinyapps.io/path_surveyor_preloaded_example_aml/. https://shawlab-moffitt.shinyapps.io/path_surveyor_preloaded_example_melanomaici/. URL Links to the Connectivity Analysis App: https://shawlab-moffitt.shinyapps.io/pathway_connectivity/. Preloaded Example: https://shawlab-moffitt.shinyapps.io/pathway_connectivity_preloaded_example_melanomaici/. URL Links to the Hazard Ratio GSEA App: https://shawlab-moffitt.shinyapps.io/preranked_hazardratio_gsea/. Preloaded examples: https://shawlab-moffitt.shinyapps.io/preranked_hazardratio_gsea_preloaded_example_melanomaici/. Data and Code to the Example Use Cases: http://shawlab.science/shiny/PATH_SURVEYOR_ExampleUseCases/. Github Repository of the Supplementary Examples: https://github.com/shawlab-moffitt/PATH-SURVEYOR_Manuscript_Supplementary. Downloadable Instructions for setting up the Docker Images are available here: https://github.com/shawlab-moffitt/PATH-SURVEYOR-Suite/tree/main/7-PATH-SURVEYOR-Docker. Operating system: Platform independent. Programming language: R version 4.1 or higher. License: BSD License.Project name: PATH-SURVEYOR. Project home page: Additional file 1. Supplementary Figure S1.Additional file 2. Supplementary Figure S2.Additional file 3. Supplementary Figure S3.Additional file 4. Supplementary Figure S4.Additional file 5. Supplementary Figure S5.Additional file 6. Supplementary Figure S6.Additional file 7. Supplementary Figure S7.Additional file 8. Supplementary Table S1–S4.Additional file 9. Supplementary Method.Additional file 10. PATH-SURVEYOR-Suite-main.zip.


Massive cerebral infarction (MCI) causes severe neurological deficits, coma and can even result in death. Here, we identified hub genes and pathways after MCI by analyzing microarray data from a murine model of ischemic stroke and identified potential therapeutic agents for the treatment of MCI.n = 6 mice) and a middle cerebral artery occlusion (MCAO) group (n = 7 mice) were extracted to identify common differentially expressed genes (DEGs). After identifying gene interactions, we generated a protein-protein interaction (PPI) network with Cytoscape software. Then, the MCODE plug-in in Cytoscape was used to determine key sub-modules according to MCODE scores. Enrichment analyses were then conducted on DEGs in the key sub-modules to evaluate their biological functions. Furthermore, hub genes were identified by generating the intersections of several algorithms in the cytohubba plug-in; these genes were then verified in other datasets. Finally, we used Connectivity MAP (CMap) to identify potential agents for MCI therapy.Microarray expression profiling was performed using the GSE28731 and GSE32529 datasets from the Gene Expression Omnibus (GEO) database. Data from a sham group  analysis showed that the DEGs in this sub-module showed enrichment in inflammatory response, extracellular space and cytokine activity in terms of biological process, cellular component and molecular function, respectively. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that TNF signaling was the most enriched pathway. Myd88 and Ccl3) for ischemic injury. Further analysis identified TWS-119 as the best potential candidate for MCI therapy and that this target may be associated with TLR/MyD88 signaling.Bioinformatic analysis identified two hub genes ( Massive cerebral infarction (MCI) is a life-threatening subtype of stroke that can result in fatal intracranial hypertension caused by cerebral edema. Most patients die from cerebral herniation within 1 week and the one-year mortality rate associated with this condition can reach 70–80%  and functional pathways, thus enhancing our understanding of the mechanisms underlying disease occurrence and development at the molecular level  model is a classic mouse model of ischemia that mimics human MCI and develops a serious edema at 24 h after reperfusion . The RNA expression profiles of a MCAO model group without therapy along with a sham group were then obtained from the GSE28731 dataset (three MCAO mice and two sham mice) and GSE32529 (four pairs of MCAO and sham mice)  were downloaded from the GEO database (http://www.ncbi.nlm.nih.gov/geo/geo2r). The limma package was used to apply multiple-testing corrections on P-values to help correct for the occurrence of false positives. The Benjamini and Hochberg  method was then used to adjust P values to limit false positive errors. Volcano plots were then generated using the ggpubr and ggthemes packages in R. An online visualization tool  was then used to identify common DEGs and generate a Venn diagram.Differentially expressed RNAs between the MCAO and sham groups were identified as DEGs by the limma  package in R  with a minimum interaction score (median confidence) of 0.4; this network was then visualized by Cytoscape software (version 3.7.2)  database (http://david.ncifcrf.gov) to perform Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the DEGs in the most significant sub-module with a threshold of P < 0.05. We then identified the top 10 GO biological processes (BP), the top 10 GO molecular functions (MF), the top10 GO cellular compartments (CC) and the top15 signaling pathways. Bubble maps were then plotted using an online tool (http://www.bioinformatics.com.cn/).In order to clarify potential molecular mechanisms underlying MCI, we used the Database for annotation, visualization, and integration discovery platform  were regarded as hub genes . Connectivity scores ranging from −1 to 1 were then calculated by a query signature. A higher negative connectivity score indicated the enhanced ability of a drug to reverse the uploaded expression profiles  in the GEO database. Further details of the two GSE datasets are given in ano plot . The intano plot .After classifying nodes by degree, we generated a PPI network of DEGs that was composed of 154 nodes and 947 edges . FurtherP < 0.05). The top 10 GO terms for biological process (BP), cellular component (CC) and molecular function (MF) are depicted in To further study the biological functions of the 24 DEGs in the most significant sub-module, GO term and KEGG pathway analysis was performed. In total, 149 GO items and 46 KEGG pathways were identified (Myd88) and chemokine ligand 3 (Ccl3) were identified as hub genes by taking the intersection of 30 genes arising from 10 algorithms in Cytohubba. The ten algorithms were as follows: Betweenness, BottleNeck, Closeness, Degree, Density of Maximum Neighborhood Component (DMNC), EcCentricity, Edge Percolated Component (EPC), Maximal Clique Centrality (MCC), Maximum Neighborhood Component (MNC) and Radiality  had the highest negative enrichment scores and were considered as potential targets for the treatment of MCI. TWS-119 is a form of glycogen synthase kinase (GSK) inhibitor and was the most promising candidate for MCI with a score of −98.77. Lestaurtinib is a janus kinase inhibitor and had a score of −97.92 while tivozanib is a vascular endothelial growth factor receptor inhibitor and had a score of −97.85 . These pMyd88 and Ccl3 were hub genes and that TWS-119 was the most potential candidate for the therapeutic treatment of MCI.Massive cerebral infarction (MCI) is a severe cerebrovascular disease associated with high rates of disability and mortality. In the present study, we obtained microarray data relating to MCAO and identified 215 common DEGs. Then, we built a PPI network and determined key sub-modules. Furthermore, GO terms and KEGG pathway enrichment analysis were performed based on the DEGs in most key sub-module and inflammation was identified as an important process occurring after MCAO. Finally, we identified that Inflammatory response was identified as the most significantly enriched BP term along with cytokine activity as the most significant MF term, thus supporting the fact that local inflammatory response after acute stroke arises from the release of proinflammatory cytokines, especially IL-6, IL-1β, TNF and IL-17  and interleukin-1 receptor families. This protein plays a crucial role in the innate immune system, leukocyte activation in inflammatory response, and host defense against Gram-positive bacteria , is considered to be a cardinal element in the regulation of immune cell trafficking and the immune microenvironment in inflammation  inhibitor, was shown to attenuate acute lung injury by reducing neutrophil infiltration, which may also attenuate brain edema by inhibiting the JAK2/STAT3 pathway  is a specific GSK-3β inhibitor and has been reported to play a role in reducing brain edema and disruption of the blood-brain-barrier (BBB) in a MCAO model increasing the expression of ZO-1 and claudin-3 (Wang et al., Myd88 and Ccl3 expression in MCI is required.The present study has some limitations that need to be considered. For example, we did not verify the therapeutic efficiency of TWS-119 in MCAO mice. Moreover, further investigation of the precise effects of Myd88 and Ccl3 as hub genes and TWS119 as the most potential drug for MCI. Following MCAO, we identified enrichment of the TNF signaling pathway and intense inflammatory responses. Our findings provide new concepts for future research on the precise molecular mechanisms underlying MCI.In the present study, we identified The original contributions presented in the study are included in the article/AG analyzed data and was responsible for the first draft of this manuscript. BG and MZ created the figures. XS collected the data. WJ and DT designed the research and revised the draft manuscript. All authors approved the final manuscript.


Neovascular age-related macular degeneration (nAMD) is a leading cause of irreversible visual impairment in the elderly. The current management of nAMD is limited and involves regular intravitreal administration of anti-vascular endothelial growth factor (anti-VEGF). However, the effectiveness of these treatments is limited by overlapping and compensatory pathways leading to unresponsiveness to anti-VEGF treatments in a significant portion of nAMD patients. Therefore, a system view of pathways involved in pathophysiology of nAMD will have significant clinical value. The aim of this study was to identify proteins, miRNAs, long non-coding RNAs (lncRNAs), various metabolites, and single-nucleotide polymorphisms (SNPs) with a significant role in the pathogenesis of nAMD. To accomplish this goal, we conducted a multi-layer network analysis, which identified 30 key genes, six miRNAs, and four lncRNAs. We also found three key metabolites that are common with AMD, Alzheimer’s disease (AD) and schizophrenia. Moreover, we identified nine key SNPs and their related genes that are common among AMD, AD, schizophrenia, multiple sclerosis (MS), and Parkinson’s disease (PD). Thus, our findings suggest that there exists a connection between nAMD and the aforementioned neurodegenerative disorders. In addition, our study also demonstrates the effectiveness of using artificial intelligence, specifically the LSTM network, a fuzzy logic model, and genetic algorithms, to identify important metabolites in complex metabolic pathways to open new avenues for the design and/or repurposing of drugs for nAMD treatment. Formation of new blood vessels from pre-existing capillaries is essential for the pathogenesis of many diseases with a neovascular component, such as cancer, proliferative diabetic retinopathy (PDR), and exudative or neovascular age-related macular degeneration (nAMD) ,2,3. nAMSeveral mechanisms are under investigation for the development of new anti-angiogenic drugs to overcome the lack of response to anti-VEGF. These include the compensatory angiogenic pathways, vessel co-option, intussusceptive microvascular growth, and vascular mimicry ,11,12,13Network-based approaches can be used to study complex biological diseases and integrate multiple types of data. Disease-related genes tend to be arranged in clusters as disease modules. A disease module represents an interconnected set of mechanisms that are linked to a phenotype. Protein–protein interaction (PPI) network analysis is used to comprehensively investigate complex intracellular signaling pathways and identify disease-related genes. Indeed, topological analysis of the networks for evaluating different kinds of centralities and identification of hubs as highly connected nodes play fundamental roles in distinguishing novel targets and avoiding inefficient responses ,21. A geHere, we integrated key proteins that are involved in 12 related angiogenic signaling pathways. These included VEGF-VEGFR2, FGF-FGFR, EGF-EGFR, Dll4-Notch, TGFβ-ALK1, HGF/c-Met, angiopoietins/Tie receptors, Wnt/β-catenin, PDGF/PDGFR, Ephs/Ephrins, IGF-IGFR, factors related to vessel co-option, intussusceptive microvascular growth-related proteins, vascular mimicry-related factors, text mining data , angiogeThe nAMD-related modules and their components were identified using the NeDRex plugin through the utilization of two algorithms (MuST and DIAMOnD) . The SQSSix miRNAs  and fourteen essential genes were found when the miRNA-gene regulatory network was constructed using the aforesaid thirty-one genes as input data. Indeed, the 14 essential genes at this stage were the same genes that were related to miRNAs in the presented gene regulatory network A. In the−12); glyoxylate and dicarboxylate metabolism (FDR = 0.000176); arginine biosynthesis (FDR = 0.00562); alanine, aspartate, and glutamate metabolism (FDR = 0.0204); sphingolipid metabolism (FDR = 0.027); glycine, serine, and threonine metabolism (FDR = 0.0288); cysteine and methionine metabolism (FDR = 0.0288); valine, leucine, and isoleucine biosynthesis (0.0354); and taurine and hypotaurine metabolism (FDR = 0.0354). Furthermore, the SMPDB–metabolite enrichment analysis revealed two distinct pathways involving the urea cycle (FDR = 0.0308) and glycine and serine metabolism (FDR = 0.0458). Schematic figures of the metabolite enrichment results are presented in Three different layers of nAMD-related data  were subjected to the enrichment process. The results of enrichment on 31 identified genes showed that seven pathways involving Staphylococcus aureus infection  = 0.000152), protein digestion and absorption (FDR = 0.00031), ECM–receptor interaction (FDR = 0.00427), amoebiasis (FDR = 0.00492), the AGE-RAGE signaling pathway in diabetes complications (FDR = 0.00492), focal adhesion (FDR = 0.00492), and complement and coagulation cascades (FDR = 0.0401) represent significant influences. According to the miRNA enrichment findings, all six discovered miRNAs play a role in the first five pathways, which are significant in terms of FDR. These pathways included terpenoid backbone biosynthesis (FDR = 0.0022855), arachidonic acid metabolism (FDR = 0.0280283), the hippo signaling pathway—multiple species (FDR = 0.0280283), base excision repair (FDR = 0.0311627), and complement and coagulation cascades (FDR = 0.0311627). Applying two databases (KEGG and SMPDB) and 115 extracted metabolites as input data, metabolite enrichment analysis was carried out. Nine significant pathways were identified using the KEGG–metabolite enrichment analysis in terms of FDR. These included aminoacyl–tRNA biosynthesis (FDR = 7.02 × 10−12); glyoxylate and dicarboxylate metabolism (FDR = 0.000176); arginine biosynthesis (FDR = 0.00562); alanine, aspartate, and glutamate metabolism (FDR = 0.0204); sphingolipid metabolism (FDR = 0.027); glycine, serine, and threonine Metabolism (FDR = 0.0288); cysteine and methionine metabolism (FDR = 0.0288); valine, leucine, and isoleucine biosynthesis (FDR = 0.0354); and taurine and hypotaurine Metabolism (FDR = 0.0354).The false discovery rate (FDR) index, along with the KEGG and SMPDB databases, was utilized to identify significant pathways. The SMPDB database revealed two important pathways: the urea cycle (FDR = 0.0308) and glycine and serine metabolism (FDR = 0.0458). However, nine significant pathways were identified based on the KEGG database, including aminoacyl–tRNA biosynthesis (FDR = 7.02 × 10−12).The integration of the metabolic pathway topology analysis with the enrichment analysis of metabolic pathways is also crucial. Two different sorts of results were produced, depending on whether relative betweenness centrality (R-b C) or out-degree centrality (O-d C) was applied in the topological analysis. We conducted a joint pathway analysis to examine the connection between 115 distinct AMD-related metabolites obtained from published articles along with 31 crucial genes involved in these metabolic pathways. Three different sorts of findings were obtained depending on the chosen topology measure -3-hydroxybutyric acid, glycine, L-lysine, L-arginine, and pyruvic acid. Additionally, a connection was found between Alzheimer’s disease (AD) and four metabolites: glycine, L-lysine, L-arginine, and calcium A.The two-centrality metrics (degree and betweenness) in each of the two drawn networks (the SNP–Gene–Disease network and the SNP–Gene–Metabolite–Disease network) were used as input data in the mGWAS-Explorer database to identify 30 significant SNPs associated with nAMD disease. The genes to which these SNPs connect were first identified and included in the third list of crucial genes in nAMD. The second phase involved specifying shared SNPs between nAMD, Alzheimer’s disease (AD), multiple sclerosis (MS), Parkinson’s disease (PD), and schizophrenia. In a comparison between the 30 critical SNPs in nAMD with the total number of shared SNPs in nAMD and the four other neurodegenerative disorders (15 SNPs), 9 SNPs were found to be essential for the centrality parameters in nAMD disease and to be associated with a range of aforementioned neurodegenerative disorders. The main results were categorized into three axes: (i) the rs1061170 (corresponding to the complement factor H (CFH) gene) was common in nAMD, AD, MS, and schizophrenia; (ii) the rs699947 (corresponding to the VEGFA gene) was common in nAMD, AD, MS, PD, and schizophrenia; and (iii) the rs429358 (corresponding to the APOE gene) was common in nAMD, AD, MS, and PD B.The maximization progress using 300 iterations of the binary-GA is shown in To acquire a clear understanding of the nAMD condition, all the results were pooled in four tables. + metabolism (NMNAT1). Recent studies have also shown that the complement system plays a crucial role in AMD pathogenesis through regulation of ECM stability, inflammation, energy metabolism, lipid accumulation, and oxidative stress (OxS). Understanding the ECM’s structural components is important before considering how the complement system affects their function.The diagnostic and therapeutic molecular networks constructed for the nAMD pathogenesis consisted of 30 proteins. These were created by merging 18 genes from the disease modules, 14 genes from the gene regulatory network, and 6 genes from the AMD-SNP data, which were validated. The 30 identified genes were in three general families, including angiogenesis, inflammation, and metabolism. A detailed examination of the angiogenesis processes revealed six different subfamilies. These included the angiogenesis itself , ECM proteins , cytoskeleton remodeling , protein-mediated transport (ABCA4), proteasome degradation (UBC), cell viability (TNFRSF10A), and DNA repair (ERCC6). The subfamilies related to inflammation included the complement system , proteins involved in toll-like receptors (TLR4), the autophagy process (SQSTM1), and protein involved in PINK1-PRKN mediated mitophagy (TOM40). Finally, the metabolism subfamily was classified into three subfamilies, which included glucose metabolism (SLC16A8), lipoprotein metabolism (APOE), and NADECM molecules play a crucial role in modulating cellular functions and various processes, including angiogenesis. Collagen, laminin, and fibronectin are examples of ECM molecules that exhibit angioregulatory characteristics . Our finA 150 bp DNA element in the promoter region of the COL1A2 gene is a TGFβ-responsive element. TGFβ promotes the expression of PDGFA, fibronectin (FN1), and collagens (COL1A1 and COL4A1), with a prominent role in angiogenesis ,30,31. AThe Collagen type XIV α 1 chain (COL14A1) reveals its key roles in various processes such as ECM and collagen fibril organization. Moreover, collagen binding results showed that high expression of COL14A1 is associated with poor overall survival rates for breast cancer patients . CollageEarly stages of nAMD are associated with changes in Bruch’s membrane (BrM) and choriocapillaris ECM composition, which can be followed by BlamD, BlinD, and drusen development. These changes most likely produce a situation in which the underlying genetic risk is manifested. ECM protection against C3b deposition and inflammation is an inevitable process . To bettInfectious endophthalmitis or sterile uveitis/endophthalmitis are two types of problems that are related to intravitreal anti-VEGF injections. These are caused by pathogens or patient/delivery/medication-specific factors, respectively ,43. The ECM proteins like collagen can be assembled using fibronectin, a key component of the newly deposited ECM, as a template. The hallmark of late-stage DR and nAMD is neovascularization, which uses ECM production as a scaffold for the abnormal new vessel architecture. The fibronectin matrix and collagen turnover are impacted by changes in MMP activity that are crucial in the etiology of nAMD. PDGF and TGFβ contribute to proliferative vitreoretinopathy (PVR), nAMD, and PDR and are known to promote the formation of the fibronectin matrix . The ECMMechanical stress in RPE cells also increases gene expression in the axis of angiogenesis  and ECM  and has a prominent role in promoting aberrant angiogenesis in nAMD . The proTnfrsf10 null mutation in mice upregulate TNFRSF10A transcription and create age-dependent RPE abnormalities . Th. Thhttpsy 2020)) , STRING r 2020)) , Disnor y 2018)) , the Ther 2021)) , KEGG dir 2021)) ,267, andr 2020))  (gene lihttps://www.genome.jp/kegg/ (accessed on 1 January 2021)) and WikiPathways (https://www.wikipathways.org/index.php/WikiPathways (accessed on 19 November 2020)) databases [In the third step, various studies were considered, such as those on signaling pathways, protein–protein interaction networks, text-mining-related data, human gene–disease association networks, and microarray data. Several keywords consisting of 14 general headings were used in the process, such as “angiogenesis signaling pathways”, “vessel cooption-related factors”, “vascular mimicry-related factors”, “angiogenesis-related protein-protein interaction networks”, “angiogenesis-related inflammatory factors”, “endothelial cell metabolism”, “endoplasmic reticulum stress”, “angiogenesis-related immune checkpoints”, “autophagy signaling pathways”, “cytoskeleton remodeling factors”, “wound response”, “neurogenesis”, “aging-related factors”, “vision-related genes”, “vitamin D-related signaling pathways”, “G protein coupled receptor signaling pathways”, and “prostaglandin signaling pathways”. We then analyzed the Kyoto Encyclopedia of Genes and Genomes (KEGG)  implemented in Cytoscape software (version 3.7.2). In the second step, disease modules were identified using two different algorithms: Multi-Steiner Trees (MuST) and Disease Module Detection (DIAMOnD). Reconstruction of comprehensive nAMD and anti-VEGF-resistance-related networks for the Homo sapiens organism was conducted with the GeneMANIA plugin (version 3.5.1)  implemenThe CentiScaPe plugin (version 2.2)  was usedhttps://www.networkanalyst.ca/ (accessed on 15 November 2016)) [https://www.mirnet.ca (accessed on 18 December 2019)) and six miRNAs as the input data [Considering 31 genes as the input data, NetworkAnalyst (r 2016))  and miRTr 2016)) . Fourteeput data . At thishttps://www.metaboanalyst.ca/ (accessed on 1 December 2018)) was used for all analyses related to metabolites [At this point, the procedure of reviewing recent studies, data extraction, and classifications were performed in order to identify metabolites ,278,279 abolites .Two different types of networks—the SNP–Gene–Disease network and the SNP–Gene–Metabolite–Disease network—were reconstructed to address important SNPs related to nAMD disease via centrality parameters (degree and betweenness) and potential association of the nAMD-related SNP profile with other neurodegenerative disorders. The first 10 cases from each network were chosen based on their degree and betweenness centralities. A list of 30 critical SNPs was created by merging the results . A detaihttps://www.expressanalyst.ca/) (accessed on 18 February 2021) [https://www.ccb.uni-saarland.de/mieaa tool/ (accessed on 1 December 2022)) [https://www.metaboanalyst.ca/ (accessed on 9 September 2022)) [Functional enrichment analysis was performed at three distinct levels, including genes, miRNAs, and metabolites, using ExpressAnalyst (ry 2021) , microRNr 2022)) , and Metr 2022)) , respectMMIN, betweenness MMIN, degree MGDIN, and betweenness MGDIN) were normalized within the range of 0 to 1  and deep learning techniques. To achieve this goal, specific metabolite indicators and factors related to metabolic pathways underwent two stages of data pre-processing. Initially after the joint pathway analysis, a total of 33 crucial pathways were chosen based on FDR < 0.05 . Subsequf 0 to 1 . Each off 0 to 1 . After ef 0 to 1 . These rf 0 to 1 . Figure The first step in our methodology involved calculating a numerical merit for each metabolite. This merit was derived from four independent parameters: two degree values and two betweenness values. The degree values quantified the number of interactions a metabolite had with other metabolites and genes within the network. The betweenness values captured the centrality and influence of a metabolite in mediating interactions between other metabolites and genes. By considering these four parameters, we could comprehensively evaluate the importance of metabolites within the metabolic network. To integrate the four parameters into a single merit, a fuzzy logic model was developed. Fuzzy logic is a potent computational framework that simulates human thinking under ambiguity. It has emerged as a valuable tool in various domains, including decision making, control systems, and pattern recognition. In the context of metabolomics research, fuzzy logic presents a viable method for combining many factors and capturing the intricate interactions seen in metabolite networks. Fuzzy logic makes it possible to represent metabolite properties in a more understandable way and makes it easier to calculate a single merit by using linguistic variables and fuzzy membership functions ,297,298.In this research, the fuzzy logic model took the four parameters as inputs, and each input was represented by four triangular membership functions: Weak, Moderate, Good, and Excellent. These membership functions captured the linguistic values associated with inputs, allowing for a more intuitive representation of the metabolite’s characteristics. The fuzzy logic model processed these inputs and produced an output, termed Merit, which was also defined by four triangular membership functions representing Weak, Moderate, Good, and Excellent values within the range of 0–100. After calculating the numerical merit for all metabolites, the metabolic route database was updated. The calculated merit values were assigned to each metabolite within their respective routes. This process ensured that the importance of metabolites was reflected accurately within the database, providing a reliable foundation for subsequent analyses and evaluations. This process facilitated the development of a standardized and extensible algorithm for evaluating the importance of metabolites within metabolic routes and could be utilized to prioritize metabolites based on their merit values. It facilitated targeted investigations and interventions within metabolic pathways. The general flowchart of the process is shown in The input data for the second fuzzy logic model were derived from the table listing 33 significant pathways from which the outcomes of the first phase of the fuzzy logic model were implemented. During this stage, the impact and FDR of the pathway-related indicators (degree/betweenness/closeness) were analyzed, and their data were sorted into quartiles independently. The quartile results for the three indicators—degree, betweenness, and closeness impact—were established by defining their range as “Minimum to Q1 = D, Q1 to Q2 = C, Q2 to Q3 = B, and Q3 to Maximum = A”. However, the defined range for FDR was determined differently and is presented as “Minimum to Q1 = A, Q1 to Q2 = B, Q2 to Q3 = C, and Q3 to Maximum = D” . After eThe second step involved the calculation of the output merit for the metabolic route using fuzzy logic. This step considered four independent parameters that collectively reflected the performance of the metabolic route, namely degree impact, betweenness impact, closeness impact, and FDR. The degree impact, betweenness impact, and closeness impact parameters exhibited a better performance with higher values, while the FDR parameter demonstrated a better performance with a lower value. To integrate these four parameters and calculate a single output merit, a second fuzzy logic model was developed. This model consisted of four inputs and one output. Three inputs, namely degree impact, closeness impact, and FDR, were represented by combined triangular–trapezoidal membership functions, including Weak, Moderate, Good, and Excellent. The second input, betweenness impact, was represented by three combined triangular–trapezoidal membership functions, including Weak, Good, and Excellent, based on the network analysis values. The fuzzy logic model processed these inputs and generated an output termed the Output Merit of Metabolic Route, which was defined by four trapezoidal membership functions representing Weak, Moderate, Good, and Excellent values within the range of 0–100. The overall process flowchart is illustrated in The Long Short-Term Memory (LSTM) network, a type of recurrent neural network (RNN) capable of analyzing sequence and time-series data, is a network architecture that is ideally suited for the goal of creating an intelligent metabolic pathway classifier. By using a training set of sequences and goal values, an LSTM neural network can be used to predict a numerical answer within a sequence. An LSTM network processes input data as a recurrent neural network by looping over time steps and updating the network state. The network state includes data that have been stored for earlier time steps. A common LSTM network for regression starts with a sequence input layer and then moves on to an LSTM layer. A fully connected layer and a regression output layer make up the network’s final layers. The general architecture of an LSTM network is shown in To construct an LSTM network for sequence-to-one regression, a layer array was created containing a sequence input layer, an LSTM layer, a fully connected layer, and a regression output layer ,300. A s-The training was performed using the Adam optimizer.-The network was trained for 100,000 epochs. For larger data sets, a lower number of epochs may suffice for achieving a good fit.-The sequences and responses used for validation were specified.-The learning rate was set at 0.0005.-The network that gave the best validation loss, i.e., the lowest validation loss, was outputted.To specify the number of values to predict, a fully connected layer with a size matching the number of responses was included, followed by a regression layer. Here, there was a single response. The training options were specified as follows:To evaluate the performance of the developed model in predicting the outputs of unknown inputs, five metabolic pathways were used as test data. After training the model, these test data were used for evaluation of the model output. The mentioned process was repeated 10 times; two of these are shown in The integration of artificial intelligence (AI) in studying metabolic pathways has emerged as a valuable tool for identifying key metabolites that significantly influence diverse biological processes. The potential to discern these crucial metabolites can provide invaluable insights into various disease mechanisms, drug development, and therapeutic interventions. In the previous section of this research, a Long Short-Term Memory (LSTM) network-based model capable of processing 55 input metabolite sequences representing the metabolic routes was developed. The output merit of these metabolic routes was determined through a fuzzy logic model, which provided valuable context to our analysis. By searching through a 56-dimensional space, our objective was to optimize the model and pinpoint the most effective metabolites within the metabolic route. To achieve this, we leveraged the powerful genetic algorithm (GA) search and optimization technique, enabling us to efficiently explore the vast solution space. A comprehensive depiction of the entire process is presented in The strong optimization method known as the genetic algorithm (GA) is based on the concepts of natural selection and genetics. GAs have emerged as a popular and efficient technique for resolving challenging optimization issues in a variety of disciplines, including engineering, computer science, finance, and biology. By using a population of potential solutions and iteratively evolving them through selection, crossover, and mutation procedures, GAs simulate the process of evolution. GAs may effectively search huge solution spaces using this genetically inspired strategy, finding optimal or nearly optimal solutions to complex problems that would otherwise be difficult to solve using conventional optimization techniques. This section will delve into the fundamentals and uses of genetic algorithms, emphasizing their importance as a flexible and reliable approach for optimization ,302. To In summary, we identified 30 genes, six miRNAs, and four lncRNAs that may play significant roles in nAMD pathogenesis. We also found three key metabolites that drive AMD development that are also common with AD and schizophrenia. Moreover, we identified nine key SNPs and their related genes with a critical role in nAMD pathogenesis and that are common with AD, schizophrenia, MS, and PD. These results will contribute to the development of diagnostic and therapeutic biomarkers for nAMD in the near future and could open new avenues to the design and/or repurposing of drugs related to novel targets. In addition, in a family with an AMD patient, screening other family members for SNPs may be useful in predicting their susceptibility to other neurodegenerative diseases. Obviously, if SNPs shared between AMD and other neurodegenerative diseases are identified in these family members, the necessity of controlling metabolic, transcriptomic, and proteomic profiles may also be beneficial.This study also demonstrates the effectiveness of using artificial intelligence , a fuzzy logic model, and a genetic algorithm to identify important metabolites in complex metabolic pathways. The 25 chosen metabolites had a significant impact on the output quality of the metabolic pathways. Our findings shed light on the underlying mechanisms governing diverse biological processes. This study contributed to the broader comprehension of metabolic regulation, which has significant implications for drug development and precision medicine, by focusing on the most effective metabolites. We outline a promising method for exploring and unraveling complex biological networks, establishing the groundwork for future advancements in the field of metabolic and disease pathway analysis.