Matricaria aurea  is a widely used herb in Middle Eastern communities for many ailments, including diabetes mellitus, without any scientific basis to support this tradition. For the first time, this study aimed to investigate the possible antidiabetic activity of MA in a type 2 diabetic rat model, identify chemical constituents by LC-MS/MS, and then elucidate the molecular mechanism(s) using enzyme activity assays, q-RTPCR gene expression analysis, network pharmacology analysis, and molecular docking simulation. Our results demonstrated that only the polar hydroethanolic extract of MA had remarkable antidiabetic activity. Furthermore, it improved dyslipidemia, insulin resistance status, ALT, and AST levels. LC-MS/MS analysis of MA hydroethanolic extract identified 62 compounds, including the popular chamomile flavonoids apigenin and luteolin, other flavonoids and their glycosides, coumarin derivatives, and phenolic acids. Based on pharmacokinetic screening and literature, 46 compounds were chosen for subsequent network analysis, which linked to 364 candidate T2DM targets from various databases and literature. The network analysis identified 123 hub proteins, including insulin signaling and metabolic proteins: IRS1, IRS2, PIK3R1, AKT1, AKT2, MAPK1, MAPK3, and PCK1, inflammatory proteins: TNF and IL1B, antioxidant enzymes: CAT and SOD, and others. Subsequent filtering identified 40 crucial core targets (major hubs) of MA in T2DM treatment. Functional enrichment analyses of the candidate targets revealed that MA targets were mainly involved in the inflammatory module, energy-sensing/endocrine/metabolic module, and oxidative stress module. q-RTPCR gene expression analysis showed that MA hydroethanolic extract was able to significantly upregulate PIK3R1 and downregulate IL1B, PCK1, and MIR29A. Moreover, the activity of the antioxidant hub enzymes was substantially increased. Molecular docking scores were also consistent with the networks’ predictions. Based on experimental and computational analysis, this study revealed for the first time that MA exerted antidiabetic action via simultaneous modulation of multiple targets and pathways, including inflammatory pathways, energy-sensing/endocrine/metabolic pathways, and oxidative stress pathways.Type 2 diabetes mellitus (T2DM) is a heterogeneous disease with numerous abnormal targets and pathways involved in insulin resistance, low-grade inflammation, oxidative stress, beta cell dysfunction, and epigenetic factors. Botanical drugs provide a large chemical space that can modify various targets simultaneously.  Type 2 Diabetes Mellitus T2DM is a multifarious metabolic syndrome that has a strong association with hyperglycemia, hyperlipidemia, and obesity. It encompasses a lot of disturbances, including defective metabolism of carbohydrates, lipids, and proteins; oxidative stress; chronic low-grade inflammation; and epigenetic factors, such as regulatory microRNAs. T2DM has two major pathophysiological arms: resistance to the action of insulin, primarily in the liver and other insulin-sensitive tissues; and pancreatic beta cell secretory dysfunction . AbnormaOxidative stress linked to obesity and overnutrition has been proposed as a single unifying mechanism underlying insulin resistance, beta cell dysfunction, glucose intolerance, development of T2DM, and progression of the disease leading to macro and micro complications . OxidatiAnother player in the arena of T2DM biology is microRNAs, which have emerged as etiological agents, diagnostic and prognostic biomarkers in T2DM, and promising therapeutic targets. MicroRNA 29a has been reported to be one of the strongest candidate Mirna regulatory hubs in the T2DM gene network, as well as one of the most upregulated miRNAs in various insulin-sensitive tissues in T2DM patients and rodent models of diabetes. Several studies have found that abnormally high expression of hepatic miRNA 29a in high-fat diet and genetic animal models is causally linked to insulin resistance, hyperlipidemia, and prevents insulin from downregulating PEPCK, resulting in unrestrained gluconeogenesis. Molecularly, Mirna 29a targets the regulatory unit p85 of PI3-K, a key signaling molecule in the insulin transduction pathway, causing insulin signal truncation, and contributing to insulin resistance .Matricaria aurea, also known as golden chamomile  of action. Network pharmacology is a new theoretical framework and methodology grounded on the advancement of omics technologies, bioinformatics, systems biology, mathematics, and pharmacology to examine the impact of drugs on both the interactome and diseasome levels. Instead of the old paradigm of “one drug-one target-one disease,” network pharmacology adopts a new paradigm of “one or multi drugs-multi targets or multiple-component-therapeutics/network-targets.” This approach makes it extremely useful for analyzing drug combinations, especially botanical formulas .Matricaria aurea. Furthermore, it improved dyslipidemia, oxidative stress status, and gene expression of important genes related to T2DM in an experimental rat model, and the active constituents were identified using the UPLC-Q-TOF MS/MS system. Moreover, we used network pharmacology principles and methods to decipher the potential mechanism of action of MA in T2DM treatment. Finally, we checked the reliability of network predictions against molecular docking simulation, q-RTPCR data, and enzyme activity assay data, as shown in In this article, we report for the first time the antidiabetic activity of the polar hydroethanolic extract of Matricaria aurea (Loefl.) Sch. Bip. (Family: Asteraceae) were collected from its wild habitat in Wadi Habis (Latitude 31° 22′ 81″N and Longitude 27° 3′ 54″E), Mersa Matruh governorate, Northwestern Coast, Egypt. The plant identity was identified and authenticated by Dr. Omran Ghaly, Head of the Plant Taxonomy Unit, Desert Research Center, Matria, Cairo, Egypt. A voucher specimen was deposited at the herbarium of the Desert Research Center (CAIH) with code number: CAIH-970-R.The aerial parts of MA aerial parts were air-dried in the shade then crushed into a powder (3.45 kg). The MA powder was successively extracted as described by . In a Soad libitum basis throughout the experimental period, except when otherwise stated. Animal handling and experimental procedures were approved by the Research Ethics Committee of the Faculty of Pharmacy, Suez Canal University, Ismailia, Egypt (Code: 201803RA1), which are in accordance with the Guide for the Care and Use of Laboratory Animals .Healthy male Wistar rats, 5 weeks old and weighing 90–110 g were obtained from the national research center . Rats were left for 1 week for acclimatization before beginning the experiment. Throughout the experiment, rats were maintained on 12 h light/dark cycle and temperature control (20–24°C). The animals were fed a standard chow diet and given water on an Rats were divided into two dietary regimens at the beginning of the experiment: normal pellet diet  and high fat diet   at a dose of 20 mg/kg  or one oRats were fasted overnight, anesthetized with diethyl ether, and euthanized at the end of the experiment. Retroorbital blood samples were taken, and liver samples were collected and kept at −80°C for RNA extraction and enzyme activity assays. To obtain serum samples, blood samples were subjected to centrifugation at 3000 rpm for 15 min, and then the serum was stored at −80°C until the biochemical assay.Fasting blood glucose and serum insulin were estimated using the glucometer method  and rat insulin ELISA kit , respectively. Systemic insulin resistance was estimated using the homeostasis model assessment index for insulin resistance (HOMA-IR) using the following equation: dl)−63] .Serum total cholesterol (TC), triglycerides (TGs), high density lipoprotein-cholesterol (HDL-C), low density lipoprotein-cholesterol (LDL-C), alanine aminotransferase (ALT), and aspartate aminotransferase (AST) were calorimetrically quantified using commercial kits from  according to the spectrophotometric methods described in the manufacturer’s protocol.To determine oxidative stress and antioxidant markers in the liver tissue, malondialdehyde (MDA) , reduced™ Reverse Transcriptase . RT was carried out in a Mastercycler Gradient Thermocycler  using the following parameter values: 25°C for 10 min, followed by 37°C for 120 min, and finally, 85°C for 5 min, then held at 4°C. While mirna-29a was reverse transcribed using the TaqMan™ MicroRNA Reverse Transcription kit and a miRNA-29a specific stem–loop primer . The Thermocycler was programmed according to the following conditions: 16°C for 30 min, 42°C for 30 min, and 85°C for 5 min, then held at 4°C to perform miRNA RT. The relative expression of PCK1, p85α PIK3R1, IL-1β and miRNA-29a was quantified using the TaqMan primers and probes with the following assay IDs . 18s rRNA was used as an endogenous control . The PCR was performed on the StepOne™ Real-Time PCR System (Applied Biosystems) with the following conditions: 50°C for 2 min, then 95°C for 10 min, followed by 40 cycles of 95°C for 15 s and 60°C for 1 min. Relative gene expression was calculated by the 2−ΔΔCt method after normalization to 18s rRNA  according to the supplied manufacturer’s protocol. RNA concentration and purity were measured spectrophotometrically using the Nano Drop ND-1000 spectrophotometer . RNA was reverse transcribed to cDNA by the High-Capacity cDNA Reverse Transcription Kit, which uses the MultiScribe18s rRNA .2O: MeOH: ACN). To obtain complete solubility of the stock solution, the sample was vortexed and ultra-sonicated at 30 kHz for 10 min. 20 µl aliquot of the stock solution was again diluted with 1000 µl of the (2:1:1) H2O: MeOH: ACN, centrifuged at 10,000 rpm for 5 min before injection, and 10 µl (1 μg/ml) was used. For experiment confidence, the LC-MS analysis was also performed on blank and quality control samples/internal standard (IS). In both positive and negative modes, the sample was injected.A stock solution of the extract was made by dissolving 50 mg of aq.-ethanolic extract (5:95) in 1 ml of a (2:1:1) solvent mixture of water, methanol, and acetonitrile  from , which was preceded by an in-line filter disks pre-column . For each mode, the mobile phase contained two solvents: solvent (A) 5 mM ammonium formate in 1% methanol, pH adjusted to 3.0 by using formic acid, solvent (B) 5 mM ammonium formate in 1% methanol, adjusted to pH 8 using sodium hydroxide, and solvent (C) 100% acetonitrile. For the positive ion mode, solvents (A) and (C) were used, while for the negative ion mode, solvents (B) and (C) were used. The column temperature was maintained at 40°C, and the flow rate was set to 300 μl/min. The gradient elution was performed with the following parameters: 0–20 min, (90% (A) or (B), 10% (C); 21–25 min, (10% (A) or (B), 90% (C); 25.01–28 min, (90% (A) or (B), 10% (C)). (A) was used for the positive ion mode only, while solvent (B) was used for the negative ion mode only.The mass spectrometry (MS) was carried out on a Triple TOF 5600+ system with a Duo-Spray source in the ESI mode . In the positive mode, the sprayer capillary and declustering potential voltages were 4500 and 80 eV, respectively, and in the negative mode, -4500 and −80 V. The collision energies of 35 V (positive mode) and −35 V (negative mode) were used, together with CE spreading of 20 V and ion tolerance of 10 ppm. The TripleTOF5600+ was operated using an information-dependent acquisition (IDA) protocol. Analyst-TF 1.7.1 was used to collect batches of MS and MS/MS data. The IDA method was utilized to simultaneously collect full-scan MS and MS/MS information. The method used high-resolution survey spectra ranging from 50 to 1100 m/z, and the mass spectrometer was programmed to detect a 50-ms survey scan. Following that, the top 15 most intense ions were chosen for MS/MS fragmentation spectra acquisition after each scan .For non-targeting, small molecule comprehensive analysis of the sample, MS-DIAL 3.70 open-source software was utilized. ReSpect positive (2737 records) or ReSpect negative (1573 records) databases were used as reference databases, depending on the acquisition mode. The search parameters were set as MS1 and MS2 mass tolerance: 0.01 and 0.05 Da for data collection, for peak detection; minimum peak height: 100 amplitude, mass slice width: 0.05 Da, smoothing level: two scans, minimum peak width: six scans, for identification MS1 and MS2 tolerance: 0.2Da/each, for alignment; retention time tolerance: 0.05 min and MS1 tolerance: 0.25 Da .http://www.swissadme.ch/), including molecular weight, Log Po/w, hydrogen bond acceptor count, hydrogen bond donor count, and number of rotatable bonds. Based on the rule of five, these properties were applied to the drug likeness evaluation. The candidate bioactive chemicals were selected if they complied with two or more of the five drug-likeness filters  and had a bioavailability score of 0.55 or more, as proposed by the Swissadme tool, which allows for the computation of physicochemical properties and prediction of druglike nature, pharmacokinetic properties, and ADME parameters  retrieving targets matched only to “exact” compound and affinity filter of no more than 10,000 nM, Swiss Target Prediction (http://www.swisstargetprediction.ch/) with a score cutoff = 0.5, STITCH database (http://stitch.embl.de/) with a score cutoff 0.4, Therapeutic Target database , Comparative Toxicogenomics database , and the first bioinformatics analysis tool for molecular mechanism of traditional Chinese medicine BATMAN-TCM (http://bionet.ncpsb.org.cn/batman-tcm/) with a target prediction score cutoff 20 , DisGeNET (version 7.0) with a score cutoff of 0.2 (https://www.disgenet.org/), Therapeutic Target Database , and DRUGBANK (only targets for FDA approved drugs) (http://www.drugbank.ca). Targets were converted into HUGO Gene Nomenclature Committee (HGNC) approved gene symbols and then merged  to create a Venn diagram and analyze the intersection between the two sets of targets. The intersection between MA targets and T2DM targets was subsequently utilized to build our compound-target network and protein-protein interaction network and was considered as MA candidate targets against T2DM. To get protein-protein interaction data, overlapping targets were further used in the STRING database , and the screening parameters used were “Homo sapiens,” with a high interaction score of 0.7 as the minimum interaction score.To get overlapping targets between T2DM disease targets and MA chemical constituents’ targets, we used the online tool “calculate and draw custom Venn diagrams” (1) The Compounds-targets (C-T) network, which links MA compounds and their overlapping targets with T2DM. 2) The Target-Target or Protein-Protein interaction (T-T) network of common targets for both MA and T2DM common targets. 3) Compound-Core key target-Pathway (C-T-P) network.To decode the mechanism of action of MA against T2DM, we constructed the following three main networks: All the networks were constructed and visualized using Cytoscape (version 3.9.1), which is an open-source software for visualizing interactions between molecules, biomolecules, and biological pathways. In addition, many apps can work efficiently with cystoscope to analyze the network based on the equations of graph theory. Topological analysis was done using the network analyzer plug-in in Cytoscape. The fundamental units of a network are nodes and edges. Nodes represent drugs, proteins, or pathways, and edges represent interactions between them. Three topological properties, including degree centrality, closeness centrality, and betweenness centrality, were used to judge the importance of targets and compounds in the networks. The standard definition of degree is the number of direct neighbors of a node. The greater the number of a node’s direct links or edges, the greater its degree and influence in the network. A node’s betweenness centrality is defined as the number of shortest paths between all pairs of nodes that pass through the node. It represents the extent to which nodes stand between each other and is often used to find nodes that serve as a bridge from one part of a graph to another. Closeness centrality is considered a measure of how long it will take to spread information sequentially from one node to all other nodes. The higher the degree/closeness/betweenness centrality of a protein, the greater its importance in the protein-protein interaction network .https://maayanlab.cloud/Enrichr). To understand the molecular mechanisms and pathways of our targets, Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analysis was conducted on all 364 candidate targets by the David tool (https://david.ncifcrf.gov/home.jsp).Enrichment of Gene ontology (GO) terms; biological process (BP), molecular function (MF), and cellular component (CC) to understand genes’ functions was performed using Enricher (https://www.rcsb.org) was utilized to retrieve 11 PDB file formats of the core target proteins predicted by network analysis. The protein names and PDB IDs are as follows: AKT1 (1unq), AKT2 (1o6l), HIF1A (1h2m), IL6 , MAPK1 (6rq4), MAPK3 (4qtb), PPARA (2p54), PPARG (5y2o), SIRT1 (4zzi), TNF (2az5), and RELA (2ram). Ligands and proteins were further prepared for docking and converted to PDPQT file format using Biovia Discovery Studio and Autodock Tools. The Autodock Vina software was used to perform the docking simulation process, and the ligands with the lowest binding energy scores were chosen to visualize their interactions with proteins in the Biovia Discovery Studio software .Experimental data were presented as Mean ± SEM after analysis by the Statistical Package of Social Sciences (SPSS) program version 22, . One-way analysis of variance (ANOVA) followed by a Tukey’s multiple comparison test was performed for comparisons among groups. For a specific value to be regarded a statistically significant, the As shown in The cellular antioxidant defense mechanism was significantly diminished in diabetic control animals. In liver homogenate, the lipid peroxidation and oxidative stress parameter MDA was dramatically enhanced, while antioxidant indicators such as GSH, SOD, and CAT activity were significantly reduced. Administration of either Pioglitazone or MA extract significantly increased the activity of antioxidant enzymes CAT and SOD, along with GSH levels, while causing a significant decrement in MDA levels, as shown in To explore the molecular mechanism of MA in the treatment of T2DM, we selected PIK3R1, PCK1, IL1B, and MIR 29A to investigate their expression level changes by qRT-PCR. As exhibited in SeeA total of 62 compounds were identified by utilizing UPLC/MS-MS analysis. The total ion chromatogram (TIC) is depicted in Data mining of six databases led to the collection of 6931 targets in , which cetc. For a detailed look at compounds and target degrees, seeThe overlapped 364 targets were considered the potential candidate targets of MA against type 2 diabetes mellitus. 46 compounds of MA were connected to their corresponding 364 targets in a large compound-target network using cystoscope software, as shown in seeTo gain more insight about the importance of each target and its contribution to the pharmacological mechanism of action, a protein-protein interaction network was created by importing the 364 common targets in STRING db. As shown in Enrichment analysis based on GO and KEGG databases was performed to investigate the biological functions and pathways of MA putative target for T2DM treatment. GO analysis results suggested that MA targets are enriched in multiple biological processes linked directly to metabolism and T2DM, including glucose homeostasis, carbohydrate homeostasis, regulation of metabolic processes, cellular response to insulin stimulus, cytokine stimulus, reactive oxygen species, and oxidative stress. seeTo further uncover the potential pathways involved in the antidiabetic effects of MA, KEGG pathway enrichment analysis was carried out. A total of 25 top enriched pathways are identified and presented in To get a deeper look at the enrichment analysis of all MA targets, we managed to see the relationship between the 40 core targets (major hubs) extracted from the PPI network and the key pathways related to T2DM from the enrichment analysis of all MA targets and we asked how many core targets are found in each pathway. To answer this question, we constructed a compound-core target-pathway (C-T-P) network linking MA active compounds, core targets, and pathways, as depicted in Checking the validity of network predictions against other channels of evidence, such as experimental data and computational chemistry simulation is a critical step in elucidating the mechanism of action. Molecular docking simulation is one of the most important and commonly used tools for exploring the biological activity of drugs on proteins, with affinity or binding energy being the most important parameter. The molecule with the highest biological activity has the lowest value of this parameter. The docking score values determine the binding potential or affinity of drugs to their targets, as well as the stability of conformations. A docking score value of less than −7 indicates strong binding activity, values between −5 and −7 demonstrate good binding activity, and values in the range of −4.25 to −5 signify a certain binding activity . Out of In humans, the progression of type 2 diabetes could be divided into two distinct stages. The first stage is the transition from a healthy metabolism to a state of declining insulin action on peripheral tissues (insulin resistance) and prediabetes. Nutritional overload and obesity are thought to be associated with the development of this state by increasing the levels of circulating triglycerides and free fatty acids that deposit in the liver, muscle, and adipose tissue, resulting in insulin resistance. Despite all the abnormalities that occur in stage 1, the pancreatic beta cells are still able to compensate for insulin resistance by producing more insulin (hyperinsulinemia). To develop frank hyperglycemia and type 2 diabetes, there must be a second stage of transition from hyperfunctional beta cells that compensate for insulin resistance to beta cell failure/death that is characterized by normal or hypoinsulinemia and the inability to compensate for insulin resistance. In animal models, a high-fat diet is typically used to cause the first stage of insulin resistance and prediabetes, while chemical inducers such as STZ are usually used to cause the second stage of pancreatic B-cell death and hypoinsulinemia. Several studies have shown that a combination of HFD and STZ for induction of T2DM in rats is a better model for mimicking the natural history of the disease . T2DM isMatricaria aurea  is a widely used herb in Middle Eastern communities for a variety of ailments, including diabetes mellitus, despite a lack of scientific evidence to support such use   , analgesin vivo) , antibacin vivo) , 2021 bisolvents , and it solvents . In addisolvents . While Wsolvents . Zemestasolvents . Therefosolvents . Previousolvents . Furthersolvents . It has solvents .etc. Further filtering to obtain the network’s crucial key targets revealed a total of 40 crucial core targets through which MA may exert its effect, including AKT1, AKT2, IRS1, IRS2, PIK3R1, TNF, ILB, IL6, MAPK1, MAPK3, HIF-1, and FOXO. From the rigorous interpretation of these results, some important themes started to emerge. 1) Firstly, a single compound can regulate multiple targets on its own, whereas a single target can be regulated by many compounds; for instance, the inflammatory cytokine IL1B was modulated by more than half of all identified compounds. This observation highlights the essence of network polypharmacology and shows that simultaneously targeting numerous proteins associated with the disease may be more beneficial than targeting a single protein selectively. Moreover, the targeting of a single protein by numerous bioactive compounds suggested that MA bioactive compounds may have a synergistic effect in treating T2DM. 2) The major constituents of MA, including apigenin, luteolin, quercetin, chlorogenic acid, p-coumaric acid, and ferulic acid, were found to target about 80% of the total targets in the network model. We think that MA’s antidiabetic activity is very likely to be mediated by the major constituents’ actions on these targets. The minor constituents might contribute to the biological activity by synergistic or additive interactions with the same targets. Experimental studies of synergy are needed to validate or rule out this assumption. 3) Analysis of the relationships between the crucial core targets predicted by network analysis and the enriched pathways revealed that each of the top enriched pathways contains a large number of the core targets; for example, more than one-third of the core targets are found in the insulin signaling pathway and approximately half of the core targets are found in the Foxo signaling pathway. To emphasize this point, we think that the aggregation of a large proportion of the core targets predicted by PPI network analysis in the top pathways reflects the powerful prediction capability and reliability of such networks for inferring action mechanisms. 4) The fourth pattern revealed by network analysis was the ability of one target to be involved in more than one pathway, implying crosstalk and a close relationship between these pathways. For example, PIK3R1 is involved in nine of the most important pathways related to T2DM, such as the insulin signaling pathway, the FoxO signaling pathway, and so on. This observation captured network biology’s realization of the modularity nature of complex diseases such as diabetes. The importance of targets in this model stems from their importance in the disease module, whereas the importance of drugs stems from their ability to modulate essential hubs in the disease module. Moreover, GO and KEGG pathway enrichment analyses of the putative targets identified multiple pathways, mainly inflammatory pathways, oxidative stress pathways, and endocrine/energy-sensing/metabolic pathways.Establishing the antidiabetic activity of MA ethanolic extract prompted us to search for the molecular players that might contribute to the mechanism of MA’s antidiabetic effect. Firstly, we hypothesized that the chemical components of MA ethanolic extract could influence critical T2DM targets. Using UPLC QTOF MS/MS, we identified a total of 62 chemical compounds in MA ethanolic extract. Many of the identified compounds have previously been reported to be the major flavonoids and phenolics in chamomile, including Apigenin and its glycosides, Luteolin and its glycosides, Quercetin and its glycosides, p-coumaric acid, chlorogenic acid, and ferulic acid . Then, win vitro study that showed the anti-inflammatory activity of MA hydroethanolic extract via inhibiting MMPs proteins . Bulgari and others showed that the anti-inflammatory effect of chamomile at the gastric level was mediated through inhibition of metalloproteinase 9 (MMP9) and the NFKB pathway  in DNA, and hence plays a central role in the defense system of the cell in combating oxidative stress. Nrf2 regulates the transcription of antioxidative stress enzymes such as CAT, SOD, HMOX1, and glutathione peroxidase , HMG CoA reductase (HMGCR), and Acetyl-coA carboxylase (ACACA). In agreement with our proposal, previous studies have shown that MA ingredients, including Apigenin, luteolin, quercetin, ferulic acid, chlorogenic acid, isoorientin, naringenin, and resveratrol, ameliorate hyperglycemia and hyperlipidemia through activation of the AMPK signaling pathway and SIRT1 . In perfect agreement with our model’s predictions, a mechanistic study showed that chamomile ameliorates insulin resistance through transcriptional stimulation of PPARG and PPARA in the adipose tissue and liver, respectively . In addi studies . In perf studies .It is very interesting to find that natural compounds can modulate the targets of FDA-approved drugs, although they are chemically different from each other. According to Liu and others, the calculated chemical similarity scores between FDA-drugs and popular flavonoids were less than 0.6 for the majority of FDA-approved antidiabetic drugs, except some SGLT2 inhibitors . We condHypoxia and impaired adaptive responses to hypoxia in diabetic tissues as a result of inadequate HIF-1 stimulation seem to be pathogenic agents in the development of diabetes and its afflictions. However, the detailed mechanism has not yet been discovered . The outThe limitation of our study lies in its inability to dictate the extent to which every single mechanism contributes to the final observed phenotype. It is possible that the proposed different mechanisms collectively contribute and cooperate to improve the disease state. However, it is very likely that some regulated mechanisms contribute more to treatment than their rivals. These issues can be resolved in the future by further detailed mechanistic studies. Another limitation is the small number of animals used in the experiment. However, if we consider the large effect size needed to be detected (in blood glucose and other parameters), we will find that the small sample size is sufficient. Before considering this plant for advanced clinical trials in humans, larger studies using a higher number of animals are warranted in future studies. In addition, the network model in our study was based on the chemical profile of MA ethanolic extract, taking into account the minor constituents’ interactions. The role of minor constituents needs to be validated or refuted in further synergy studies.Matricaria aurea. Integrating different channels of evidence: experimental, computational biology, and computational chemistry, helped us to elucidate the general action mechanisms that mediate MA antidiabetic activity at the molecular level from a systems biology perspective. This mechanism can be summarized in modulation of inflammatory pathways, energy sensing/endocrine/metabolic pathways, as well as oxidative stress pathways. MA can serve as a source of new antidiabetics and nutraceutical formulations to improve diabetic state.In this study, we established for the first time the antidiabetic potential of


The Chinese herbal formula Shenyi (SY) is a prescription that was developed by the Department of Nephrology, Chinese People's Liberation Army General Hospital. This preparation is mainly used to treat chronic kidney disease (CKD) caused by Diabetic nephropathy (DN) and is effective. However, the active ingredients of SY, DN treatment-related molecular targets and the effector mechanisms are still unclear.The Traditional Chinese Medicine Systems Pharmacology (TCMSP) database and the Traditional Chinese Medicine and Chemical Component Database of Shanghai Institute of Organic Chemistry were used to screen the active ingredients in SY, the TCMSP database and Swiss Target Prediction database were used to collect the targets of the active ingredients of SY, and the Gene Cards and Online Mendelian Inheritance in Man (OMIM) databases were used to screen for DN pathogenesis targets. The intersections of the component targets and disease targets were mapped to obtain the therapeutic targets. The METASCAPE database was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the therapeutic targets. Cytoscape 3.7.2 was used to analyze topological parameters and construct a network of SY for the treatment of DN.Sixty-two active ingredients and 497 active ingredient effector targets in SY, 3260 DN-related targets, and 271 SY treatments for DN targets were identified. Among these targets, 17 were core targets, including AKT1, tumor necrosis factor (TNF), interleukin-6 (IL6), and TP53. The GO and KEGG enrichment analyses show that SY's therapeutic effects for DN occur mainly through pathways such as advanced glycation end product (AGE)-RAGE, PI3K-Akt, and IL-17.Multiple active ingredients in SY exhibit treatment effects on DN by affecting metabolism, inhibiting inflammation, and affecting cell structure growth. Diabetic nephropathy (DN) is the main cause of chronic kidney disease (CKD) and end-stage renal disease (ESRD) worldwide . DN is aAtractylodes macrocephala Koidz, Herba patriniae, Lobelia chinensis, Psoralea corylifolia, Coptis chinensis, Fructus Ligustri Lucidi, common burreed rhizome, and Vaccaria segetalis. This preparation is mainly used to treat CKD caused by DN and the effect is good. Related studies have shown that SY can inhibit glomerular sclerosis by inhibiting the expression of glomerular TGF-β  is a prescription that was developed by the Department of Nephrology, Chinese People's Liberation Army General Hospital. It consists of ar TGF-β . The spear TGF-β . In thisAtractylodes macrocephala Koidz,” “Herba patriniae,” “Lobelia chinensis,” “Coptis chinensis,” “Fructus Ligustri Lucidi,” “common Burreed rhizome,” and “Vaccaria segetalis” were used as keywords to search in the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database  (The PPI results of the 270 therapeutic targetminated) . Node siminated)  and 17 (minated)  targets P < 0.01), of which 2,712, 128, 248, and 361 pathways were identified in GOBP, GOCC, GOMF, and KEGG, respectively . KEGG enrichment analysis was performed on the 17 core targets to obtain a total of 161 pathways (P < 0.01). A lower P value corresponded to a greater degree of enrichment. P-values were used for ranking to select the 20 pathways with the highest degree of enrichment for analysis  to induce oxidative stress and promote inflammation and thrombosis, thereby resulting in diabetes-related vascular complications. Furthermore, the formation and accumulation of AGEs can promote mitochondrial peroxide synthesis, resulting in further cytotoxicity and causing further kidney injury. In addition, this pathway and the renin–angiotensin system (RAS) can result in progression of diabetic vasculopathy . Furthertabolism . The thetabolism , 32. TheKEGG enrichment analysis was carried out for the 17 core targets, and a high degree of overlap was noted between the results and the KEGG enrichment analysis of the initially selected 271 therapeutic targets, such as the PI3K-Akt signaling pathway, the AGE-RAGE signaling pathway in diabetic complications, and the IL-17 signaling pathway, showing the feasibility of employing the network pharmacology procedure used in this study to determine core therapeutic targets and pathways. The core target, AKT1, participates in metabolism, cell proliferation, cell survival, cell growth, and angiogenesis –36. TNF In this paper, network pharmacology was used to screen for targets related to SY treatment for DN, and gene enrichment analysis was used to predict the pathways involved in SY's treatment effects on DN. A literature search and analysis were performed on the active ingredients, therapeutic targets, and pathways of the Chinese herbal medicines contained in the formula, and the results indicated that SY has therapeutic effects on DN, which consistent with the results when SY is used in clinical practice. In summary, the treatment effect of SY on DN is the result of synergism among multiple components, multiple targets, and multiple pathways. Our study provides mechanistic evidence to supplemort SY as a treatment for DN in clinical practice and provides guidelines for subsequent SY and DN studies. The Chinese herbal formula Shenyi has been clinically used in the treatment of DN and has good curative effects. It can be developed as a drug for treating DN and preventing its progression to end-stage renal disease. The main purpose of this study is to predict the target pathway of compound Shenyi in the treatment of DN, and to provide a theoretical basis for better clinical development of drugs for the treatment of diabetic nephropathy. Since this research is based on the database mining research, the mechanism of drug action is only predicted, and no relevant experimental verification. Moreover, this paper uses multiple databases, and the algorithms of each database are different, which undoubtedly raises the question of the accuracy of network pharmacology analysis. In a follow-up study, we will carry out animal and cell-related experiments to verify the target pathway predicted in this study.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/KC carried out research design and implementation, research data collection and sorting, article conception and writing. YD carried out research planning and design and paper revision. SS and PL carried out some data sorting. XC and LL conducted research propose concepts and oversee the planning and execution of research activities. All authors contributed to the article and approved the submitted version.This work was supported by the National Natural Science Foundation of China (Grant No. 81830019) and the Beijing Natural Science Foundation (Grant No. 7202188).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Studies have found that apple pollen can restrain the activity of amylase. Therefore, we speculate that it may be prescribed to treat patients with type 2 diabetes mellitus (T2DM), while its chemical and pharmacologic profiles remain to be further explained. In this study, the potential bioactive compounds of apple pollen and the underlying mechanism of action were investigated by performing chemical and network pharmacology analysis. Therefore, HPLC-QTOF-MS/MS analysis based on chemical compound libraries was applied in identifying the chemical profiles of apple pollen and network pharmacology was adopted for predicting the potential targets of the active components of apple pollen. Initially, the chemical map of apple pollen was identified and characterized. Secondly, the potential targets of active compounds of apple pollen were predicted with the Swiss Target Prediction and PharmMapper databases, whereas targets of T2DM were collected from the GeneCards and OMIM database. Thereafter, the target of active compounds and T2DM targets established common targets using Venn. Afterwards, the common targets were imported into the STRING database in order to construct the protein-protein interaction (PPI) network and select the core targets of apple pollen treatment of T2DM. In addition, GO and KEGG signaling pathway enrichment analyses were conducted on the selected core targets using the DAVID database. As a result, totally 28 compounds were identified. Meanwhile, network pharmacological analysis showed that 3-hydroxy-3-methyl glutaric acid, 5-hydroxyindoleacetic acid, DL-3-phenyllactic acid, isorhamnetin-3-glucoside-4′-glucoside, isorhamnetin-3-O-glucoside, syringetin-3-O-galactoside, rhamnetin, m-coumaric acid, quercitrin, isorhamnetin-3-galactoside-6″-rhamnoside, and kaempferol-3-O-alpha-L-arabinoside might be the active compounds of apple pollen. Moreover, AKT1, PPARG, SRC, EGFR, CASP3, ESR1, and the other potential core targets might be involved in the treatment of T2DM by modulating the following pathways, containing insulin resistance, hepatitis C, pancreatic cancer, insulin signaling pathway, TNF signaling pathway, and PI3K-AKT signaling pathway. Quercitrin, kaempferol, and isorhamnetin-3-O-glucoside bound most stably to AKT1. Isorhamnetin-3-O-glucoside and quercitrin bound most stably to SRC. In addition, arachidonic acid bound most stably to PPARG. Type 2 diabetes mellitus (T2DM) is the main subtype of diabetes mellitus (DM), with insulin resistance (IR) being the main pathological feature. T2DM is characterized by the disorders of glucose metabolism, glucose metabolism-related enzymes, and lipid metabolism. The main complications include cardiovascular disease (CVD), renal failure (RF), peripheral neuropathy, and obesity, causing serious health damage and economic burdens on the patients , 2. At pMalus pumila Mill.) is a kind of apple pollen powder, which is extensively distributed throughout China and is usually collected from April to May in spring. Apple pollen has been reported to possess extensive pharmacological activities [Pollen is a kind of natural advanced nutrition worthy of development and research, not only rich in amino acids, vitamins, nucleic acids, flavonoids, unsaturated fatty acids, protein active enzymes , etc., btivities –15, suchtivities . Some sctivities  performetivities . Additiotivities . The maitivities , 18 inclHigh performance liquid chromatography coupled with electrospray ionization and quadrupole time-of-flight-mass spectrometry (HPLC-Q-TOF-MS/MS) is a powerful and reliable analytical technique developed to identify natural products , which iThe Agilent 1200 HPLC system interfaced with Agilent 6520 hybrid quadrupole time of flight mass spectrometer  was utilized in this study. The system was operated under the control of MassHunter Acquisition software (version B.04.00).Both methanol and formic acid (LC-MS grade) were purchased from Fisher Scientific , purified water was provided by Hangzhou Wahaha Group , and ethanol (AR grade) was obtained from Tianjin Tianli Chemical Reagents Ltd. .Qinguan pollen (batch number: 20140410) and Gala pollen (batch number: 20140401) were obtained from apple demonstration areas in the Northwest Agriculture and Forestry University .μm). The mobile phase consisted of A (0.1% formic acid in water) and B (0.1% formic acid in methanol). The following gradient program was adopted for separation: 0∼15 min, 10%∼20%; 15∼45 min, 20%∼40% B; 45∼75 min, 40%∼80% B; 75∼80 min, 80%∼90% B; and 80∼85 min, 90%∼100% B. The injection volume was 10 μL, the column temperature was set at 25°C, and the solvent flow rate was set at 0.8 mL·min−1.Chromatographic separation was performed using a Boston Green ODS-C18 column  was set at 8 L/min. Meanwhile, the heated capillary temperature was set at 350°C and the nebulizer pressure was set at 35 psi. Besides, the source parameter capillary voltage was set at 3500 V, the fragmenting voltage was 135 V, and the split ratio was 1 : 3. The collision energy for MS/MS analysis was tested within the range of 20–40 eV .μm membrane filter before the injection into the HPLC system. The sample injection volume was 10 µL.Approximately 1.0 g of each sample  was accurately weighed and put into a capped 100 mL conical flask. Thereafter, each respective sample was extracted with 50 mL ethanol by reflux extraction for 1 h. Then, the extract was filtered and dried, while the residue was reconstituted with 50 mL methanol and filtered through a 0.25 The accurate mass data of molecular ions were processed by adopting the Mass Hunter Acquisition software (version B.04.00). The obtained parent ion information and a large number of daughter ion fragments were analyzed by MassHunter software. The molecular formula and molecular weight were deduced by the “molecular search function” and matched with the MassBank standard spectrum to deduce the chemical structure and determine the final structure of the compound. The total ion chromatograms of Qinguan and Gala apple pollen obtained are illustrated in https://www.ncbi.nlm.nih.gov/pubmed/) and then uploaded into the SwissTargetPrediction database (https://www.swisstargetprediction.ch/) [https://www.lilab-ecust.cn/pharmmapper/). Obviously, the species was limited to “Homo sapiens” for target prediction and the minimum interaction threshold was set to “highest possible” >0.6 by comparing with the known active compounds. Secondly, T2DM-related targets were obtained from the DrugBank database (https://go.drugbank.com) and GeneCards database (https://www.genecards.org/) based on the key word search of “Type 2 Diabetes Mellitus.” Besides, the duplicate targets were eliminated.Initially, the canonical SMILES information of each identified compounds was obtained by the NCBI PubChem database (ion.ch/)  and Pharhttps://string-db.org/) by incorporating the overlapping targets between the candidate compound targets and the T2DM-related targets.The PPI network was constructed based on the STRING database (https://david.ncifcrf.gov) [GO functional annotation and KEGG pathway enrichment analyses of the common target genes were conducted using the DAVID database platform (crf.gov) , 23. TheThe core component target and ligand files were retrieved from the chemical database (Pub Chem) and the protein database (RCSB-Pdb). The Autodock Vina software was adopted for molecular docking of the core component target.The pharmacological network of “chemicals-targets-pathways” was constructed to screen the potential chemical components related to the therapeutic effects on T2DM . The actThe monitoring ion for detection and analysis is shown in β-D-glucoside, quercitrin, rhamnetin, m-coumaric acid, DL-3-phenyllacticacid, isorhamnetin-3-glucoside-4′-glucoside, isorhamnetin-3-O-glucoside, syringetin-3-O-galactoside, isorhamnetin-3-galactoside-6″-rhamnoside, and kaempferol-3-O-alpha-L-arabinoside [A total of 28 compounds were identified, which mainly included flavonoids and organic acids Tables  and 2. Abinoside , 24, 25.Based on a computer prediction method to calculate the identified compounds of apple pollen, this study obtained absorption parameters that could determine whether the chemical compositions could be absorbed. By employing Swiss Target Prediction and PharmMapper, a total of 196 targets were predicted. Additionally, totally 961 T2DM target genes were uncovered in the GeneCards database and OMIM database. Later, the above genes were intersected in the Venny, and 196 target genes for Apple pollen for T2DM were obtained . At the After comparatively analyzing 28 component targets and T2DM disease targets, 37 common potential targets were identified for apple pollen. The STRING database is a commonly used tool to predict PPI and produce the integrated and objective association networks. With the common protein targets as the input for network visualization, a diversified PPI network was created, which systematically summarized the interactions of apple pollen targets associated with T2DM treatment . The hubHomo sapiens,” the target gene name was modified to official gene symbol, and the threshold was set at P < 0.01. Thereafter, GO functional annotation and KEGG pathway enrichment analyses were performed.Thereafter, the drug-disease intersection genes were introduced into the DAVID database. Then, the target gene name list was input, the species was limited to “GO analysis of the common targets revealed that the biological process (BP) was mainly related to negative regulation of apoptotic process, signal transduction, steroid hormone mediated signaling pathway, peptidyl-serine phosphorylation, transcription initiation from RNA polymerase II promoter, positive regulation of transcription from RNA polymerase II promoter, and cellular response to insulin stimulus .KEGG pathway enrichment analysis of the aforementioned common target genes is presented in The network diagram of “Active Component Target-Gene-Pathway” was constructed in Cytoscape software . AccordiThe core components arachidonic acid, quercitrin, isorhamnetin-3-O-glucoside, and kaempferol were molecularly docked with the core targets AKT1, PPARG, and SRC and when the binding bond energy was <−5 kcal/mol, indicating good docking results for both. The molecular docking model was plotted using PyMOL  Figure .α-amylase. Therefore, we predict that apple pollen generates a therapeutic effect on T2DM.Over the past 30 years, the number of DM cases in China has increased significantly. Among them, T2DM accounts for 97.3%, whereas type 1 diabetes mellitus (T1DM) takes up nearly 5%, and other types account for only 0.7% . T2MD isThe HPLC-Q-TOF-MS/MS technique  helped tIn addition to active ingredients, we also successfully predicted drug targets of apple pollen. The major targets included AKT1, PPARC, EGFR, and SRC. AKT1 is one of 3 closely related serine/threonine-protein kinases  called the AKT kinase, regulating numerous processes including metabolism, proliferation, cell survival, growth, and angiogenesis. AKT is responsible for the regulation of glucose uptake by mediating insulin-induced translocation of the SLC2A4/GLUT4 glucose transporter to the cell surface (by similarity) . PPARG bα is closely related to the development of diabetes and its complications [α will accumulate in cells under high glucose hypoxia, aggravating the ischemic-hypoxic state of diabetic patients and promoting the development of diabetes. TNF can induce apoptosis by promoting the release of inflammatory factors [α is closely related to insulin resistance [α inhibiting insulin signaling by activating the NF-κB signaling pathway [α levels are positively correlated with the course of diabetic nephropathy, and the possible mechanism is that TNF-α damages diabetic glomerular tissue by producing inflammatory response cytokines and increasing microvascular permeability [α and monocyte chemoattractant protein-1 (MCP-1).Analysis of KEGG enrichment pathways identified totally 17 pathways that are closely related to the development of T2DM. Meanwhile, the relationship between several of these pathways and T2DM and its complications has been verified by experimental or clinical studies. For example, patients with hepatitis B or C are more likely to develop diabetes, and the possible mechanism lies in the fact that hepatitis B virus infection can lead to abnormal liver metabolism. Consequently, insulin resistance and hepatitis B virus can lead to hepatic glucose metabolism by impairing liver function and hepatitis B virus can cause abnormal liver glucose metabolism by impairing liver function . Besidesications . A large factors , and TNFsistance . The mec pathway . In addieability . The PI3eability  signalineability . Apple pAltogether 28 compounds were successfully identified in Qinguan pollen and Gala apple pollen, including flavonoids and organic acids. Among them, 17 compounds were identified for the first time in apple pollen, while the other 9 were previously reported .From the obtained total ion chromatograms of Qinguan pollen and Gala apple pollen, it was found that a few of chromatogram peaks did not match with the corresponding compounds, indicating the presence of some unknown chemical compositions in apple pollen, which should be further investigated.To conclude, different from chemical drugs, TCM is a multi-component and complex drug. Thus, it is challenging to pinpoint the mech-anisms of action of TCM. Each herb may contain many active ingredients that have single or multiple targets. As a result, it is difficult to pinpoint the mechanisms of action of TCM. However, in the traditional research of network pharmacology, the compounds are mostly collected from databases. Besides, some compounds cannot be detected in some TCM, which may yield false positive results.In this study, we use HPLC-Q-TOF-MS/MS based on multiple in-house chemical libraries coupled with network pharmacology, which was utilized to characterize the chemical compounds of apple pollen and investigate its underlying mechanism in treating T2DM.A total of 2 compounds were identified in apple pollen, among which, 20 were considered as the potential compounds. Apart from that, a total of 510 targets were predicted, and AKT1, PPARG, EGFR, SRC, ESR1 and MAPK1 were recognized as the main potential targets involved in T2DM , 41. MorThe above results reduce the prediction range and increase the accuracy of the prediction results, also providing important information for performing further pharmacological investigations on apple pollen.


A systems biological understanding of the multi-target effects of a drug requires prior knowledge of proteome-wide binding profiles. In order to achieve the above, we have utilized PharmMapper, a web-based tool that uses a reverse pharmacophore mapping approach , along with KEGG for exploring the pathway relationship. In the validation part of the study, predicted protein targets and signalling pathways were strengthened with existing datasets of DrugBank and antibody arrays specific to vascular endothelial growth factor (VEGF) signalling in the case of 5-fluorouracil as direct experimental evidence. The current systems toxicological method illustrates the potential of the above big-data in supporting the knowledge of onco-cardiological indications which may lead to the generation of a decision making catalogue in future therapeutic prescription.Onco-cardiology is critical for the management of cancer therapeutics since many of the anti-cancer agents are associated with cardiotoxicity. Therefore, the major aim of the current study is to employ a novel  Onco-cardiology is critical for the management of cancer therapeutics since many of the anti-cancer agents are associated with cardiotoxicity. In order to understand the basis of side-effects of the drugs, we need to explore and predict the so-called off-target or side effects. The need for prediction and management of cardiovascular effects of drugs is compelling. As individual target screening of anti-cancer drugs through biological experiments would be a haunting task, Human umbilical vein endothelial cells (HUVEC) were obtained from Lonza Group . VEGF Pathway Phospho Antibody Array was purchased from Full Moon BioSystems, Sunnyvale, USA. 5-Fluorouracil was commercially obtained from Sigma-Aldrich. All other reagents were commercially acquired in analytical reagent grade.Chemotherapeutic drugs (including its major metabolites) that are believed to be associated with cardio toxicity were identified and their relevant information were obtained through searching previous literature found in PubMed literature. The list of drugs and its metabolites used in the current study are illustrated in http://59.78.96.61/pharmmapper),17 which uses reverse pharmacophore mapping approach for the prediction of potential target for a given small molecule. PharmMapper server functions based on a large database named pharmacophore database (PharmTargetDB) which contains over 7000 receptor-based pharmacophore models. For each drug molecule, SDF file was obtained from PubChem and submitted to PharmMapper server and we obtained top 300 candidates.In order to predict the targets of our drugs and their metabolites, we used PharmMapper Server  (https://david.ncifcrf.gov) as described previously.20 In DAVID web server, the Kyoto Encyclopedia of Genes and Genomes (KEGG) database module was used to predict the cellular pathway association in which pathway terms enriched significantly (p < 0.05) were considered for further discussion.Using the drug-target protein list obtained from PharmMapper, a drug–protein matrix was prepared for all individual metabolites of each drug . The primary endothelial cell culture was administrated with 100 μM concentration of 5-FU for 8 hours. After the treatment schedule, cell lysate was collected and processed for the quantification of phosphorylation level in terms of relative abundance at specific phosphorylation sites of enzymes/substrates involved in vascular endothelial growth factor (VEGF) signalling pathway. For this purpose we used VEGF Pathway Phospho Antibody Array containing 185 antibodies  based on the supplier described protocol. Briefly, cells were scraped with lysis buffer and crushed with lysis beads to prepare cell lysate. Proteins were purified by using column provided in the array kit. Protein quantification was done by NanoDrop 2000 (ThermoFisher Scientific). Desirable amount of protein was biotinylated and incubated with antibody coated slides pre-blocked with milk protein. Finally, Cy3-streptavidin was added for the detection of biotin-conjugated antibody. Both control and treatment (5-FU) array slide were processed simultaneously. Fluorescent scanned images were converted in to list of fluorescence intensity signals of each spots in the array. Normalization of the level of phosphorylation was done with its corresponding non-phosphorylated form of target. Fold difference in phosphorylation level between conditions was calculated and 2-fold cut off criteria was applied to categorize up-regulated and down regulated phosphorylation targets between treatment conditions.http://www.maayanlab.net/KEA2/).21 After analysis, only the significantly enriched kinases were listed that are plausibly considered as targets of 5FU. The list of possible 5FU targets were intersected with PharmMapper predicted list for the evaluation of consistency between the prediction method and experimental validation.In order to explore novel 5-FU targeted kinases, we intended to infer the list of kinases associated with the list of differentially phosphorylated sites by 5-FU treatments. For this purpose we employed kinase enrichment analysis (KEA) – a web-based tool with an underlying database version 2 (www.drugbank.ca).22 To achieve this, the list of PharmMapper based predicted targets were intersected with target list provided in DrugBank  for the evaluation of consistency between the current prediction method and previous validations.In this approach, we compared the predicted targets list with the existing knowledge of drug target relationship available in DrugBank , reliable in silico screening of drug candidates and prediction of the functional effects of system perturbations using large-scale network is believed to be advantageous.24,25 The above fact prompted us to explore the novel adverse mechanisms (cardiotoxic side effect) of few anti-cancer drugs including its major metabolites based on the proteome scale multiple interaction profile on human targets through reverse pharmacophore and pathway enrichment strategies. After extensive literature survey and manual curation, we prioritized thirty four chemotherapeutic drugs of different groups with cardio-vascular side effects that were reported in different case studies and in original research articles. Further, the available pharmacokinetic information of the 34 cardiotoxic chemotherapeutic drugs was obtained for creating the list of drug metabolites. The extensive list of all the study related drug and their metabolites along with their PubChem ID are illustrated in Post-market identification of the adverse drug reactions (ADR) is an important public health issue. Since cytotoxic agents and targeted therapies that are used to treat cancer result in collateral damage to other tissues including the cardiovascular system, a growing number of clinical trials are now studying the long-term side effects of anticancer therapy, specifically cardiovascular events.P < 0.05) and targeted by all the 34 drugs was prepared  signalling, insulin signalling and focal adhesion which were enriched by all the drugs except cisplatin. VEGF receptors are major players in maintaining the cardiovascular homeostasis27 and also endothelial cell functions are highly dependent on VEGF signalling pathway.28 Next to VEGF, insulin pathway was also affected by all the drugs except cisplatin in our study. Altered insulin signalling is believed to cause endothelial dysfunction, atherosclerosis29 and other cardiovascular pathogenesis including coronary artery disease.30 Focal adhesion signalling involving focal adhesion components including β1 integrin, vinculin, focal adhesion kinase (FAK) is indispensible to various cardiac events ranging from embryonic heart development to mechanotransduction.31 Further, in cardiac remodeling point of view, FAK knockout mice showed increased cardiac hypertrophy upon Ang II stimulation.32 ErbB signalling was also affected by most of the drugs. Previous studies on ErbB signalling components ErbB1, ErbB2, ErbB3 and ErbB4 are differentially expressed and with their primary ligand, neuregulin-1 play substantial roles in embryonic heart development33 as well as in adult endothelial and cardiac functions.34,35 It is noteworthy that ErbB1/ErbB2 mutant mice suffer from cardiac dysfunction.36 The cardio-toxic effects of the well-known breast cancer drug, Trastuzumab was shown to affect cardiac function by interacting with ErbB2.37 PPARs signalling play a vital role in cardiovascular physiology and dysfunction including inflammation and circadian rhythm.38 PPAR protects cardiac system from oxidative stress, further inhibition of PPAR-α signalling results in cardiac damage.39 PPAR signalling was the 5th most common pathway affected by the chemotherapeutic drugs. In the renin–angiotensin (RAS) axis, angiotensin (Ang) II, the main effector of the RAS, is one of the major mediators of vascular remodeling in hypertension and plays critical role in stability of the cardiovascular microenvironment.40,41l-Arginine is the main source of nitric oxide (NO) generation via NO synthase (NOS) which play vital regulatory role in cardiovascular and renal physiology even under hormonal disorders.42 Overall, our results indicate that chemotherapeutic drugs commonly affect specific pathways required for the normal functioning of the cardiovascular system.The most recent ‘European Society of Cardiology’ guidelines indicate that the cardiovascular complications of cancer therapy can be sub classified as (1) cardiac complications including myocardial dysfunction and CHF, coronary artery disease, valvular heart disease, arrhythmias, and pericardial diseases and 2) vascular complications including arterial hypertension, thromboembolic event, peripheral vascular disease and stroke, and pulmonary hypertension.pathways  and 2 re vascularp-value less than 0.05) were interrogated with the top 100 PharmMapper protein target list of 5-FU which indicated that SRC, PDPK1, AKT1, PTK2/FAK1, RAF1 were present in both the 5-FU target list and PharmMapper predicted list  kinase enrichment analysis (KEA) and (2) DrugBank interrogation. In addition, we employed the phosphorylation antibody array to understand the effects of 5-FU on VEGF signalling pathway in terms of modulation of phosphorylation status of specific proteins using HUVEC cells. In order to explore the possible targets of 5-FU (down regulated phosphorylation of substrate due to the inhibition of upstream kinase by 5-FU), we predicted the corresponding upstream kinase through kinase enrichment analysis (KEA). KEA results revealed that upstream kinases such as SRC, AKT1, PDPK1, RAF1, BRAF, MAP3K8, PTK2, MET were significantly associated with a set of under-represented profile of downstream phosphoproteins (which in turn indicate the possibly inhibited upstream kinase targets) due to 5-FU treatment. The enriched kinase nodes are illustrated in ted list . Moreoveted list .17 Additionally, in the current study, we have support for the PharmMapper target prediction by (1) comparing DrugBank curated protein target list with PharmMapper-top 300 targets (2) comparing 5FU modulated VEGF signalling kinase list obtained from experimentally assisted kinase enrichment analysis with top 100 targets of 5FU from PharmMapper. DrugBank which combines detailed drug data with comprehensive drug target and drug action information drives us to indicate the positive overlapping nature of PharmMapper predicted targets with DrugBank primary/secondary targets for the following drugs including Idarubicin, 5-fluorouracil, Methotrexate, Paclitaxel, Docetaxel, Lapatinib, Sorafenib, Imatinib, Vemurafenib, Nilotinib, Pazopanib, Dasatinib and Tamoxifen. Further, our second method, prediction of targets was through Kinase Enrichment Analysis (KEA), which utilizes the prior knowledge of kinase–substrate interactions and links the lists of mammalian proteins/genes with the kinases that phosphorylate them.21 Similarly, a previous study utilized the kinase–substrate enrichment analysis (KSEA) to systematically infer the activation of kinase pathways from mass spectrometry-based phosphoproteomic analysis of Lapatinib resistance cells and acute myeloid leukemia (AML) cells.43,44 In this study, we utilized similar template in order to explore the possible kinase targets of 5-FU using VEGF array. We explored the upstream kinases that are believed to phosphorylate the VEGF signalling components that were down phosphorylated after 5-FU treatments in HUVEC. Among the 85 VEGF array phosphorylation substrates, 20 of them were found to be down regulated which in turn drove us to explore the upstream kinase through KEA as RAF1, PTK2/FAK1, SRC, PDPK1, and AKT1. This study was also successful in finding the predicted down regulated kinases as potential targets among the top 100 targets hits from PharmMapper database. The above evidence enrich support to the proof-of-concept of reverse pharmacophore mapping based prediction of targets which in turn strongly supports the proteome-wide prediction of cardio toxic mechanism of anti-cancer drugs before entering into the wet lab experiments. As pointed out in45 and demonstrated in a series of recent publications,46–67 user-friendly and publicly accessible web-servers represent the future direction for developing practically more useful prediction methods and computational tools. Actually, many practically useful web-servers have increasing impacts on medical science,16 driving medicinal chemistry into an unprecedented revolution,68 we shall make efforts in our future work to provide a web-server for the prediction approaches presented in this paper.In the validation aspect, we intend to point out that the PharmMapper protocol have been successfully utilized in more than two hundred articles after the first benchmarking test of Tamoxifen on finding the proper targets (proof of concept) among the top 300 pharmacophore candidates.In conclusion, the current study provides a detailed analysis on off-target-pathway-cardiovascular homeostasis relationship of anti-cancer drugs related to pathophysiology. Limitations of the current study are (1) the proteome target/background list is not the whole human proteome list which is restricted to the targets available in PharmMapper target list. (2) The study did not explore the relationship of drug-target in terms of stimulatory or inhibitory interaction and only predicted the interaction potential. Because of limited prediction potential we cannot absolutely prioritize the cardiotoxic/non cardiotoxic nature of drugs, whereas this data can be a reference model to partially understand the cardiotoxic mechanism of anti-cancer drugs.All authors reviewed critically and refined the manuscript. All authors declare that there is no conflict of interest.SG acknowledges financial support of INSPIRE programme sponsored and managed by the Department of Science and Technology. JM is thankful to Science and Engineering Research Board for young scientist award. LS convey her thanks to Department of Biotechnology for the fellowship. PG acknowledges financial support from UGC. SC is grateful to University Grants Commission (UGC) – Faculty Recharge Programme. All funding agencies are the different wings of Government of India.RA-008-C8RA02877J-s001


Drosophila fruit fly is an ideal model system to explore mechanisms of longevity and aging due to low cost, easy handling and maintenance, large number of progeny per adult, short life cycle and lifespan, relatively low number of paralogous genes, high evolutionary conservation of epigenetic mechanisms and signalling pathways, and availability of a wide range of tools to modulate gene expression in vivo. Here, we focus on the organization of the evolutionarily conserved signaling pathways whose components significantly influence the aging process and on the interconnections of these pathways with gene expression regulation.The spatio-temporal regulation of gene expression determines the fate and function of various cells and tissues and, as a consequence, the correct development and functioning of complex organisms. Certain mechanisms of gene activity regulation provide adequate cell responses to changes in environmental factors. Aside from gene expression disorders that lead to various pathologies, alterations of expression of particular genes were shown to significantly decrease or increase the lifespan in a wide range of organisms from yeast to human.  Drosophila, is that gradual deregulation and a violation in the functioning of signal transduction pathways can lead to diseases and death of organisms. Changes in the functioning of signaling pathways are known to result in reduction or lengthening of lifespan. For example, genetic manipulations with the insulin/insulin-like growth factor 1 (IGF-1) signaling (IIS) pathway and target of rapamycin (TOR) signaling pathway that receive signals from growth factors and nutrients (nutrient sensing), with the c-Jun NH2-terminal kinase (JNK) pathway (nutrient sensing and stress resistance), as well as with nuclear factor kappa B (NF-κB) pathway (inflammation and immune response) are associated with an increase in lifespan both in Drosophila and mammals [Aging is a fundamental process that is characteristic to some extent for all multicellular organisms and is most studied and understood in the evolutionary line of animals. In mammals, it is characterized by the functional loss of cells, tissues, and organs and is accompanied by the increasing susceptibility to many diseases, including cancer, neurodegeneration, cardiovascular diseases, and metabolic disorders of an organism . One of  mammals ,4. Thus,Drosophila is one of the most studied model organisms, and in this review we describe in detail the organization of IIS, TOR, NF-κB, and JNK signaling pathways and lifespan control in Drosophila. We also draw parallels between Drosophila and humans. The analysis carried out in the review reveals general patterns in the functioning of four signaling pathways and allows us to draw conclusions about the conservation of lifespan control mechanisms between Drosophila and mammals. We also provide an overview of the mechanisms of epigenetic control of gene expression associated with the activity of these signaling pathways, where known.Drosophila, fruit fly has been used as a model organism to identify conservative basic biological processes [Drosophila were carried out in the second decade of the last century, and since then this model animal has been extensively used for aging research [Drosophila and humans made Drosophila an indispensable model organism for identifying the pathways responsible for lifespan extension, as well as for understanding the complexity of the aging processes in eukaryotic organisms. Lastly, Drosophila and humans have similar classes of transcription factors [Investigations using mammalian models  require a large number of animals to be housed, maintained and subjected to invasive testing and therefore raise both economic and ethical concerns. Furthermore, studies on the aging of mammals are limited by their long lifespan. That is why non-vertebrate model organisms  are popular among researchers in the aging field. Since the beginning of the last century, when the first gene in the history of genetics was discovered in rocesses . The firresearch ,7. High  factors  and, thuDrosophila has homologs to ~75% of human disease genes [Drosophila practically possesses one significant advantage over mammalian models in the aging field, that is short life cycle and lifespan of ~60–70 days for laboratory isogenic wild-type strains and ~120–130 days for non-isogenic flies from natural populations [Drosophila overwinters in nature in the adult stage in vegetable warehouses, etc., wherever the temperature reaches 16 °C. At this temperature, the duration of lifespan of adult flies is significantly lengthened [se genes  and ~60%se genes ,11. UnliWolbachia, since this infection can influence lifespan [Health and longevity of fruit flies are affected by various factors such as diet, temperature, genetic and environmental interventions. Sexual differentiation has also significant effects on fly longevity. For instance, it was shown that male and female flies respond differently to dietary restriction (DR) . Greaterlifespan .Drosophila population. This is problematic, since variations in metrics between laboratories are quite common, leading to difficulties in comparison of lifespan effects. That is why comparison between survival curves of experimental and control flies and calculation of the significance level of differences between them (P) are performed using Kaplan–Meier method, log-rank test or Kolmogorov–Smirnov test [Despite the fact that lifespan assays involve the simple counting of dead flies over time, it can be complicated by inconsistencies in approaches to estimating survival curves and other statistics. For instance, standard metrics in lifespan assay are the assessment of mean, median, or maximum lifespans within a nov test .Drosophila may be also a useful for studying the epigenetic mechanisms of gene expression control involved in development of aging and longevity . Epigenetic alterations in Drosophila are known to be controlled by the diet [Drosophila can be used as a model to study how exactly environmental stimuli affect epigenetic alterations. Understanding of how gradual deregulation of regulatory systems and imbalance of epigenetic mechanisms of gene regulation result in aging progression will contribute to a faster concretization of approaches aimed at combating aging. Thus, Drosophila turned out to be a convenient object for studying epigenetic mechanisms of longevity and aging.Since the molecular mechanisms of gene regulation are rather evolutionarily conserved, the diet , temperaDrosophila, of cause, is one of the best analyzed/understood multicellular organism, more complex in comparison with C. elegans and yeast and is an ideal model system due to ease of genetic and environmental manipulations, completely sequenced genome, relatively low number of paralogous genes, the opportunity to investigate influence of the parameters such as gender ratio, resistance to pathogens, chemicals and environmental stress factors on longevity. In addition, Drosophila does not have programmed aging, as, for example, Arabidopsis, in which the death of an adult organism is associated with the maturation of seeds [Drosophila, aging apparently occurs as in humans: survival curves and mortality curves in these species look the same; moreover, the peak of reproduction in both species is shifted to early life periods [Drosophila exist as well. First, Drosophila goes through different developmental stages  which mammals do not have [Drosophila differs substantially from mammals at the organismal level, brain anatomy, cardiovascular system, and respiration systems. Third, flies have a less complex and adaptive immune system than mammals and do not maintain their body temperature. This can lead to the fact that the effects of drugs/inhibitors/geroprotectors can vary greatly between Drosophila and mammals .of seeds . In Dros periods . Howevernot have . Second,Drosophila, low redundancy/reduced number of paralogous genes in Drosophila in comparison to mammals does not allow reproducing on this organism the regulatory complexity that is inherent in mammals, in which paralogization of regulatory genes is one of the main mechanisms for acquiring complexity [From a molecular point of view, despite the general good conservation of basic signaling pathways and cellular processes between mammals and mplexity ,27,28. IDrosophila as a model organism, as well as the rapid rate of generation turnover in this species, coupled with the conservation of the main regulatory and signaling pathways, makes Drosophila indispensable in aging studies. Below, we look in detail at the main signaling pathways in Drosophila known to be important for controlling aging.Nevertheless, the comprehensibility (recognizability) of Drosophila point of view and concentrating on the extracellular signals–gene expression regulation axis, evolutionary conserved signaling pathways, which dysfunctions result in altered fly lifespan. Thereby, direct effects on cellular protein synthesis, e.g., through modulation of activity of translation initiation factors that are not mediated by changes in expression level of genes encoding them, are not covered. More in-depth information on the signaling pathways, including their peculiarities in different species from yeast to humans as well as descriptions of their branches not directly related to gene expression regulation, is available in other reviews (see references below).Below we briefly describe, from the Drosophila insulin network [Drosophila insulin-like peptides  of the fly proteins is provided in The insulin/insulin-like growth factor 1 (IGF-1) signaling (IIS) pathway is involved in regulation of various physiological processes, including growth, development, metabolism, stress responses and reproduction  into phosphatidylinositol -trisphosphate (PIP3) [3 serves as second messenger, activating protein kinase Pdk1, which in its turn phosphorylates protein kinase Akt  at Thr342 and thus activates it [3 and Akt, respectively [This interaction stimulates tyrosine-protein kinase activity of InR ,37,39 ane (PIP3) . PIP3 sevates it ,46,47,48vates it ,50 , results in a median adult lifespan extension of 9% and 13% for males and females, respectively [InR, chico and 14-3-3ε mutations also extend lifespan [05545InR/E19InR females demonstrated adult lifespan extension of 85%, while other homo- and transheterozygous InR mutants were found to be strongly or moderately short-lived [1chico males and females showed increase of median adult lifespan of 13% and 36%, respectively. At the same time, the effect of homozygous 1chico mutation on the adult lifespan is completely different in males and females; the former are slightly short-lived, whereas the lifespan of the latter is extended by 48% [Downregulation of several pathway components that negatively regulate Foxo activity was shown to extend lifespan . First, ectively . Similarectively . Second,lifespan ,52,67,68rt-lived . HeterozDrosophila lifespan can be extended by upregulation of antagonistic IIS pathway regulators. Thus, Pten or Foxo overexpression in adult head fat body or adult male muscles increased the median lifespan of flies [Similarly, the of flies ,70. In aof flies . Interesof flies ,72. Thusof flies ,74.05545InR/E19InR, 1chico/1chico and 1Akt/1Akt, result in delayed development and a dwarf phenotype due to decreased cell number and cell size [chico gene that means “small boy” in Spanish. In addition, the extension of female lifespan by modulating the expression level of the IIS pathway genes is often associated with reduced or delayed fecundity [Several homozygous loss-of-function mutations or transheterozygous combinations of loss-of-function and hypomorphic mutations of the IIS pathway genes, e.g., ell size ,76,77,78ecundity ,66,68,71ecundity ,79,80. MThus, it can be concluded that the IIS pathway performs a “pro-nutritional role”. It activates/suppresses protein synthesis and cell proliferation through the TOR signaling pathway (see below) in response to the presence of food/fasting conditions, respectively, and, at the same time, it suppresses cell proliferation through Foxo, mobilizing fat reserve in adipose tissues via Foxo activity upon fasting. The effects of lengthening lifespan upon suppression of the IIS signaling pathway may be associated with the redistribution of nutrients under fasting conditions, as well as slowing down cell divisions with a decrease in protein synthesis.Streptomyces hygroscopicus bacteria [Drosophila [Being interconnected with the IIS pathway, the target of rapamycin (TOR) signaling pathway coordinates cell growth responses to the availability of nutrients and energy , signalosophila ,99 and sosophila . Much leosophila ,102 -ATPase (V-ATPase) holoenzyme that constitutes a part of the TOR signaling pathway at the lysosome membrane  tubules, ovaries and testes, while the Secretory 13 (Sec13) gene encoding a component of GATOR2 is predominantly active in salivary glands, fat body, accessory glands and spermathecae. The catalytic and scaffold subunits of the PP2A phosphatase complex, Mts and PP2A-29B, are both expressed at high levels during Drosophila development, probably due to the involvement of this complex in many different cellular processes. Lastly, transcription factors regulated by the TOR pathway demonstrate high variability in their tissue-specific and developmental expression profiles  as well as by DR [Drosophila longevity [Inactivation of TORC1 caused by rapamycin treatment substantially increases the lifespan of of males . Interesas by DR . Also, ias by DR  and reduas by DR . Noticeaas by DR . Furtheras by DR . In thisongevity .2L7Tor/k17004Tor transheterozygous mutant flies with reduced activity of both TORC complexes are characterized by 20% longer median lifespan as well as a smaller body size than their counterparts carrying +Tor rescue transgenic construct [AMPKα gene and null mutations of the Sestrin and Nprl2 genes (the latter one encodes a subunit of the GATOR1 complex) were each shown to shorten the lifespan of Drosophila [onstruct . This sionstruct ,165. Accosophila ,166,167.osophila ,142,152.REPTOR and REPTOR-BP knockout animals of both sexes have drastically reduced lifespan [Mitf overexpression in the nervous system leads to ~10% decrease in the lifespan of females and flies were not viable when Mitf was overexpressed in all tissues [Myc overexpression and haploinsufficiency substantially shortens and extends the lifespan, respectively [Tif-IA and Polr1A genes encoding the essential activator of RNA Pol I and the largest specific subunit of this polymerase, respectively, were shown to extend the median lifespan by 8%, at least in females [Besides Maf1 described above, other transcription factors regulated by TORC1 also contribute to the fly lifespan. First, lifespan . Second, tissues . Third, ectively . Finally females .Mitf overexpression -ATPase genes, essentially autophagy genes) are more difficult to explain: on the one hand, autophagy activation in neurons can be harmful to flies, and on the other hand, additional expression of lysosomal vacuolar (H+)-ATPase proteins may increase the pool of Rag GTPases associated with them on the lysosomal membrane, and these could activate TORC1 more readily leading to a subsequent drop in lifespan.Thus, inactivation of transcription factors that promote animal survival upon nutrient restriction (REPTOR and REPTOR-BP), as well as an increase in the synthesis of ribosomal RNA genes due to Myc, reduces lifespan. On the contrary, moderate lengthening of lifespan results from a decrease in the expression of transcription factors that promote the synthesis of ribosomal RNA (Tif-IA or Polr1A). The negative effects of Drosophila and mammals, aging is associated with the nuclear factor kappa B (NF-κB) signaling pathway [Drosophila [Drosophila NF-κB factors—Dorsal (Dl), Dorsal-related immunity factor (Dif) and Relish (Rel)—regulate the insect humoral immunity that gets activated during infection [In different animal species, including  pathway . It has osophila . Fruit fosophila ,173,174 nfection ,176,177.Drosophila Imd pathway being presented to PGRP-LE in the cytosol [Drosophila inhibitor of apoptosis-2 (DIAP-2)-dependent polyubiquitylation [Activation of the Imd pathway occurs through direct recognition of Gram-negative bacterial diaminopimelic acid (DAP)-type peptidoglycan (PGN) by the PGN recognition protein LC (PGRP-LC) at the cell surface or by PGRP-LE receptor intracellularly ,179. PGR cytosol . The PGR cytosol ,185 and  cytosol . Ligand tylation . Activattylation . The E3 tylation ,189,190.tylation . Activattylation ,193,194.tylation ,196,197 Phosphorylated Rel-68 forms a complex with PCAF (Gcn5) (histone acetyltransferase (HAT) p300/CBP-associated factor) at target genes and activates transcription  Figure . Other rAkirin is an NF-κB co-factor required for Imd target gene activation by the Rel transcription factor  Figure . H3K4ac,Drosophila [Charon  is a member of the IκB superfamily and inhibits the activity of Rel. Charon fulfills the functional and structural criteria for an IκB protein. It was shown to predominantly reside in the nuclear fraction and regulate the Imd pathway at the level of Rel68:Rel68 homodimer suppressing the Rel68-driven induction of genes encoding AMPs  Figure . Caspar osophila . Transglosophila ,208. DowDrosophila has eight other Toll receptors (Toll-2 to Toll-9) and five other Spz proteins (Spz-2 to Spz-6) [The Toll signaling is activated in response to both Gram-positive cocci and fungi . The pato Spz-6) . The dimo Spz-6) ,217,218.o Spz-6) ,219,220 o Spz-6) ,222. Caco Spz-6) ,224. It o Spz-6) .imd, Dredd, Fadd and IKKβ , and the Transglutaminase (Tg) gene is highly expressed in crop of adult flies. As for the IIS and TOR pathways, only a few of the NF-κB pathway genes are active in all tissues and at all stages of development: effete (eff), bendless (ben) and Ubiquitin-conjugating enzyme variant 1A (Uev1A). Ben and Uev1A form a heterodimer that is required for Imd K63-linked polyubiquitinylation and its subsequent proteasome degradation [Drosophila, the Gram-positive bacterial response system that is triggered via the Imd pathway is constitutively ready to activate antimicrobial response genes. This may also have an adaptive value, and a decrease in the readiness of this system to fight infections due to mutations that disrupt the expression of its components, leads to a corresponding decrease in lifespan.Tissue-specific and developmental expression profiles of the NF-κB signaling pathway genes are not uniform . The majand IKKβ . Some otradation ,227,228.radation . Thus, iDrosophila lifespan (Toll (Tl) gene extends lifespan for about 12% and 2% in females and males, respectively [imd leads to about 15% and 12% increase of mean lifespan in heterozygous male and female flies [cact, pirk, ben, trbd, casp, Tg, Charon and nubbin (nub) genes were demonstrated to cause moderate decrease of Drosophila lifespan  in the lifespan compared with control, but RNAi of Rel in glia, neurons and intestine resulted in about 60%, 38% and 35% increase of the lifespan, respectively [Downregulation of some components of the NF-κB signaling pathway was shown to slightly extend lifespan . For insectively . Downregle flies . Downreglifespan  [173,190 control . At the ectively . Rel-nulectively .cact gene in neuroblasts increases the median lifespan of male and female flies for about 16% and 4%, respectively [Dif, PGRP-LC, PGRP-LE, Rel) negatively regulates fly lifespan. Finally, shortened lifespan was observed in Dif, PGRP-LC, PGRP-LE and Rel mutants [Upregulation of the ectively . Overexp mutants ,235,236 2-terminal kinase (JNK) is a conserved branch of the mitogen-activated protein kinase (MAPK) pathway [Drosophila, the JNK signaling can be loosely classified into “canonical” and “non-canonical” pathways. The term ‘‘canonical’’ is used to describe pathway components activated during development, especially during embryonic dorsal closure, which are also required for normal wound closure [The c-Jun NH pathway . JNK sig pathway  and regu pathway . In Dros closure ,241. “No closure ,241.Drosophila has only two TRAF proteins, Traf4 and Traf6 [Drosophila. Overexpression of Traf4 causes JNK pathway activation, while overexpression of Traf6 leads to activation of Drosophila NF-κB proteins Dif and Rel [“Non-canonical” signaling is activated by binding of the single tumor necrosis factor (TNF) ligand Eiger (Egr) to the TNF receptors Wengen (Wgn) and Grindelwald (Grnd) ,245,246 nd Traf6 ,249. Orind Traf6 ,251, sinnd Traf6 ,253,254. and Rel . In addi and Rel .Ligand-receptor binding initiates a conserved signaling cascade of MAPK superfamily kinases, which includes at least five JNKK kinases (JNKKKs)—Slipper (Slpr), Wallenda (Wnd), Tak1, Mekk1 and Apoptotic signal-regulating kinase 1 (Ask1) as well as at least two JNK kinases (JNKKs)—Hemipterous (Hep) and MAP kinase kinase 4 (Mkk4) ,260,261.puckered (puc) [unpaired (upd) gene encoding the Drosophila interleukin-6 (IL-6) homolog [Matrix metalloproteinase 1 (Mmp1) gene encoding a protease that degrades the extracellular matrix [Activated (phosphorylated) Bsk translocates into the nucleus and, in its turn, activates its downstream transcription factor Jun-related antigen (Jra), which heterodimerizes with the transcription factor Kayak (Kay) forming an active AP-1 transcriptional complex in the nucleus ,263 Fig. AP-1 tred (puc)  encoding homolog  and the r matrix . AP-1 alr matrix .Activation of the “canonical” and “non-canonical” JNK signaling pathways differs only upstream, but not downstream of the Hep component. The most upstream acting molecules identified for the “canonical” signaling during dorsal closure are nonreceptor tyrosine kinases of the Src family: Src42A, Src64B and Btk  Figure . Src42A Peroxiredoxin (Prx2), lethal (2) essential for life (l(2)efl) and Thor , which activity advances stress defense [Prx2 is able to suppress Bsk activity in neurons, indicating that Prx2 may also act as a negative feedback regulator of JNK in specific tissues [It is also known that JNK antagonizes IIS, since JNK signaling promotes Foxo nuclear localization  Figure . Foxo al defense ,271. OvePuc and Prx2 inhibition, JNK signaling is downregulated by the STRIPAK complex components, Striatin interacting protein (Strip) and Connector of kinase to AP-1 (Cka), which bind to and inactivate Hep/JNKK acting as negative regulators of JNK signaling [Along with ignaling ,276,277.The JNK pathway is activated not only through Wgn and Grnd receptors but also from the Imd pathway through Tak1  206,278278. JNK-grnd, Traf6, wnd, Ask1, hep and Mkk4 genes are expressed at very low to moderate levels with minimal variability across developmental stages and different tissues  are highly upregulated in virtually all tissues, and the corresponding products of these genes were found almost at all developmental stages  slightly extend Drosophila lifespan , JNK, AMPK, PKC and others and most of these kinases are able to phosphorylate several key regulators of glucose homeostasis as well as the insulin receptor, resulting in decreased activity of the PI3K/Akt . BesidesDrosophila, the interactions between the pathways are rather poorly understood. The IIS pathway, as the central pathway associated with nutrient intake, interacts with all other pathways and plays an important role not only in the regulation of longevity, but also in the development of cancer in Drosophila [In osophila . As for osophila .Drosophila and are interconnected through the Tak1 kinase [Drosophila [With regard to the the NF-κB and the JNK pathways, both of them appear to be involved in the immune response in 1 kinase ,227,278.1 kinase . In turn1 kinase . Interesosophila ,191. Theosophila ,282.Drosophila, each pathway consists of the numerous components, which are characterized by a larger number of paralogous subunits in human (Evolutionary conserved signaling pathways IIS, TOR, NF-κB and JNK play critical roles under stress conditions and aging. In in human . This inDrosophila, activation of the IIS and the TOR pathways leads to a shortening of lifespan, whereas activation of the JNK pathway suppresses the activity of TOR and causes an increase in lifespan. Violation of the NF-κB pathway results in a decrease in lifespan. The Imd sub-branch of the NF-κB pathway is constitutively ready for activation and both activating and repressing components of the IIS and the TOR pathways are constitutively expressed.In In closing, many questions regarding the links between the components of these signaling pathways and aging still remain. For instance, influence of downregulation or overexpression of genes on fly lifespan was studied only for 40–53% of the pathways’ components . Moreove


More and more evidence indicates sodium-glucose co-transporter 2 inhibitors (SGLT2is) may display clinical benefits for heart failure with preserved ejection fraction (HFpEF). However, the mechanisms of the action remain unclear.A systematic pharmacology-based strategy was applied for predicting the potential molecular mechanisms of SGLT2is in HFpEF. The potential targets of SGLT2is and HFpEF were contained from diverse databases. After networks were constructed, Metascape was applied to functional enrichment. Moreover, the key findings were validated through molecular docking.We obtained 487 SGLT2is related targets and 1505 HFpEF related targets. The networks showed the complex relationship of HFpEF-target-HFpEF. The results of functional enrichment analysis suggested that several biological processes, including muscle system process, inflammatory response, vasculature development, heart development, regulation of MAPK cascade, positive regulation of ion transport, negative regulation of cell population proliferation, cellular response to nitrogen compound, apoptotic signaling pathway, multicellular organismal homeostasis, response to oxidative stress, regulation of cell adhesion, positive regulation of cell death, response to growth factor, and cellular response to lipid, and signaling pathways, such as cardiomyopathy, cAMP signaling pathway, cytokine-cytokine receptor interaction, apoptosis, MAPK signaling pathway, HIF-1 signaling pathway, calcium signaling pathway, and NF-kappa B signaling pathway. Finally, we validated the interactions and combinations of SGLT2is and core targets.SGLT2is play the potential role of anti-HFpEF through the direct or indirect synergy of multiple targets and pathways. Our study promotes the explanation of the molecular mechanisms of SGLT2is in HFpEF.The online version contains supplementary material available at 10.1186/s12872-022-02693-8. Heart failure (HF) is a complex clinical syndrome that results from any structural or functional impairment of ventricular filling or ejection of blood . EjectioIn recent years, more and more evidence shows that hypoglycemic drugs also have cardiovascular benefits , 11. SodNetwork pharmacology is an interdisciplinary subject, its formation and development mainly benefit from artificial intelligence and big data analysis , 18. TheThe chemical structures of SGLT2is, namely empagliflozin, dapagliflozin, canagliflozin, and ertugliflozin, were obtained from PubChem  and impoWe used Metascape  for geneWe conducted the receptor-ligand molecular docking to assess these interactions . We chosP-value was set to 0.01, and the minimum enrichment was set to 1.5. For PPI enrichment, physical core database was chosen and the minimum and the maximum network sizes were set to 3 and 500, respectively.For pathway and process enrichment, the minimum overlap was set to 3, We firstly obtained the chemical structures of empagliflozin, dapagliflozin, canagliflozin, and ertugliflozin from PubChem  and 479 edges Fig. A. Then tThe overlaps between these targets associated with SGLT2is and HFpEF are shown in a Circos plot Fig. , we reveAccording to the GO analysis, the top 20 results were all attributed to biological processes Table A&B. DetaThrough the PPI network, we demonstrated that targets in HFpEF were enriched to blood circulation, muscle system process, and circulatory system process (Table S1), whereas targets in SGLT2is were enriched to phosphotransferase activity, kinase activity, and protein kinase activity . A total of 11 MCODE components were enriched . Through all targets, PPI network, response to reactive oxygen species, cellular response to oxidative stress, and cellular response to chemical stress were top 3 pathway and process enrichment results .The enrichment analysis in PaGenBase demonstrated that these targets were specific in smooth muscle, heart, and blood tissue and adipocyte, human umbilical vein endothelial cells, cardiac myocytes, THYIn the present study, the binding energies of four SGLT2is and 21 targets were all below -5.0 kcal/mol Fig. A, indicaWith the release of PRESERVED-HF , CANVAS In the present study, we included four SGLT2is, namely empagliflozin, dapagliflozin, canagliflozin, and ertugliflozin. We predicted the targets of SGLT2is in SwissTargetPrediction  and supp+/H+ exchanger to possibly improve cardiac function [+/H+ exchanger activity and reduced cytosolic Na+ [2+/calmodulin-dependent kinase II (CaMKII) activity and CaMKII-dependent sarcoplasmic reticulum Ca2+ leak [Different multidirectional mechanisms of SGLT2is could improve HF status . Howeverfunction , 72. Latolic Na+ . Additioa2+ leak , 75.A large number of studies have shown that HFpEF is a syndrome of over-activation of inflammatory, oxidative stress, and autophagy , which iThrough KEGG pathways enrichment analysis, we emphasized the cAMP signaling pathway, MAPK signaling pathway, HIF-1 signaling pathway, calcium signaling pathway, and apoptosis during the occurrence and development of HFpEF. These findings are in line with previous studies. MiR-665 inhibition can stabilize the cardiac function of HF rats via the cAMP signaling pathway via upregulation of the GLP1R . By switHowever, there were some limitations that we should pay attention to. Firstly, when we fished the targets from HFpEF, we found that some databases are not updated to HFpEF, only HF or chronic HF, which makes us inevitably lose some important targets. Moreover, we only generally analyzed the mechanisms of SGLT2is for HFpEF, but we still need to further study the single SGT2i, because the action mechanisms of different SGLT2is may not be completely consistent. In addition, the pathophysiology of HFpEF manifestations is highly heterogeneous , 85, morIn conclusion, we identified the synergistic pharmacological mechanisms of SGLT2is in HFpEF by the integrative virtual screening and network pharmacology method. Moreover, our main findings were validated with molecular docking. Our findings of the potential mechanisms of the direct or indirect synergy of multiple targets and pathways provide an optional therapy for HFpEF. However, more experimental and clinical validation is essential to reveal the effect of SGLT2is against HFpEF.Additional file 1. Tables S1, S2 and Figure S1–S7.