The identification and understanding of metabolic pathways is a key aspect in crop improvement and drug design. The common approach for their detection is based on gene annotation and ontology. Correlation-based network analysis, where metabolites are arranged into network formation, is used as a complentary tool. Here, we demonstrate the detection of metabolic pathways based on correlation-based network analysis combined with machine-learning techniques. Metabolites of known tomato pathways, non-tomato pathways, and random sets of metabolites were mapped as subgraphs onto metabolite correlation networks of the tomato pericarp. Network features were computed for each subgraph, generating a machine-learning model. The model predicted the presence of the β-alanine-degradation-I, tryptophan-degradation-VII-via-indole-3-pyruvate (yet unknown to plants), the β-alanine-biosynthesis-III, and the melibiose-degradation pathway, although melibiose was not part of the networks. In vivo assays validated the presence of the melibiose-degradation pathway. For the remaining pathways only some of the genes encoding regulatory enzymes were detected. David Toubiana et al. report the combination of correlation-based network analysis and machine-learning techniques to detect metabolic pathways from quantitative metabolic data. They demonstrate the utility of their method by studying metabolic pathways in tomato. The reconstruction of metabolic networks follows a defined set of steps; initiated at the known biochemistry, genomics, and physiology, followed by the governing of the physico-chemical constraints, proceeded by flux distribution predictions, and finalized by the determination of which of the offered solutions translate into meaningful physiological states3. Regardless of whether or not they are fully validated, metabolic pathways are collected in databases of genome-scale hypernetworks5, e.g., PlantCyc (http://www.plantcyc.org/)6, BioCyc (http://biocyc.org/)7, and KEGG (http://www.genome.jp/kegg/)8.The reconstruction of metabolic pathways is a complex process based on a constraint-based bottom-up approach; such reconstruction typically uses gene annotation and ontology, computational derivation, and discrete manual curation, requiring a priori knowledge of the stoichiometry between compounds, thermodynamic information of the pathway’s reactome, as well as its cellular compartmentalization, and other factors. Due to the complexity of reconstruction, metabolic pathways are more often predicted computationally rather than on substantial experimental evidence9, which are subsequently transformed into network form, where nodes represent the metabolites and the links between them the correlation coefficients.Complementary to the constraint-based approach, metabolite networks—constituted on high-throughput data metabolite profiles—provide an attractive method for studying the coordinated behavior of metabolites without the need for a priori knowledge. Metabolite profiles are correlated based on mathematically defined (dis)similarity measures14 or collections of different varieties or cultivars17 as they provide a large sample size, which stabilizes the correlation and reduces the error rate. Correlation-based network analysis (CNA) explores the structural properties of graphs that can be used to interpret metabolite networks and even postulate hypotheses18. Nonetheless, although CNA and graph theory are equipped with a myriad of tools21, many studies limit themselves to employing CNA for the study of the global structure and relationships of metabolite data. For the current study, we exploit the tools from graph theory.Metabolite correlation-based networks are often reconstructed based on the exploitation of the natural variability of mapping populations22. An ML approach to predict metabolic pathways in bacteria has been proposed based on properties of metabolic pathways as defined in genome-scale networks23. Although various ML algorithms exist to tackle problems for studying metabolic profiles, the power of ML algorithms has been underutilized in the analysis of metabolic correlation networks.Machine learning (ML) employs a collection of techniques that allow computers to learn from existing data without being explicitly programmedIn this study, we delved deep into the possibilities of CNA and ML by combining them to predict metabolic pathways in correlation networks in the pericarp of a tomato introgression line population. We demonstrate that this method can be essentially used for functional metabolomics. We do so by mapping existing metabolic pathways onto the metabolite correlation networks followed by the computation of a set of network properties for each pathway to derive an ML model. The resulting ML model was then used to predict the existence of yet unidentified pathways based on the mapping of pathways onto the correlation networks and computation of the same set of network properties. To validate the model, we applied several in vivo experiments on the positively predicted, yet unidentified pathways.To the best of our knowledge this is the first study that employs structural analysis of metabolite correlation networks in order to identify metabolic pathways.http://www.plantcyc.org/) is a collection of metabolic pathways found in plants. TomatoCyc is the subset of PlantCyc containing metabolic pathways found in tomato—notwithstanding the possibility that some of the remaining PlantCyc pathways may also be found in tomato. The methods introduced in this paper facilitated the identification of previously unknown metabolic pathways within the tomato pericarp using supervised ML techniques combined with metabolite CNA. It does so based solely on reactions and may not be used to predict differences in catalytic activity.The identification of metabolic pathways is a key aspect in understanding the metabolism of an organism of interest. PlantCyc (Given a set of tomato pathways (positive instances) and a set of pathways that do not exist in tomato (negative instances), a supervised ML model was induced in order to classify any given pathway (test instance) as either tomato (positive) or non-tomato (negative). A set of numeric profiles (feature vectors) of positive and negative instances (the training set) was utilized by ML algorithms during the training phase in order to induce such a model.24 as presented in Toubiana et al.11. The original dataset10 contained metabolic profiles of the central metabolism of the tomato fruit for three different harvesting seasons, hereinafter referred to as seasons I, II, and III. For each season, a weighted, undirected CN was constructed. Network links were weighted according to their correlation coefficient, allowing negative values. The CN for season I included 75 nodes, corresponding to the 75 metabolites, and 473 links; the CN for the season II was composed of 75 nodes and 869 links, while the CN for season III had 78 nodes and 338 links. Each pathway analyzed (train or test) was represented as a group of nodes in each one of the three CNs. A numeric profile was computed for each group of nodes in each CN (for details see the Methods section). Pathways that were part of the PlantCyc and MetaCyc (https://metacyc.org/) collections but not found in TomatoCyc were used to train and induce ML models. A workflow of the current study is presented in Fig. The numeric profiles of metabolic pathways were computed from tomato CNs based on the tomato introgression line mapping populationIn total, the three seasons and the corresponding CNs contained 109 different metabolites, i.e., nodes, while 52 common metabolites were contained in all three CNs. Out of the 589 metabolic pathways listed in TomatoCyc, 169 pathways were identified to be mapped as a subgraph onto the three CNs. The mapping was partial in a sense that it allowed omitting compounds from the pathways that were not found in the 52 common metabolites. In other words, at least two compounds of a given pathway needed to intersect with the common set of 52 metabolites in order to be considered for pathway mapping. Consequently, only the pathway’s corresponding compounds were mapped followed by feature computation.p-value 2.631e-06).The superpathway of lysine, threonine, and methionine biosynthesis II, had 36% of its compounds within the networks, which resulted in the largest of all subgraphs. In total, 67 pathways were represented by exactly two compounds, while for three pathways all of their compounds were found in the CNs. The same analysis was repeated for the remaining 625 non-tomato plant pathways, identifying 33 pathways that shared at least two compounds with the tomato metabolite CNs. For the non-plant MetaCyc pathways, 151 pathways were identified that shared at least two or more compounds with the CNs. In both cases (tomato and non-tomato MPs), the largest number of compounds shared with the CNs was 18. Supplementary Fig. The aforementioned 169 tomato pathways were used as the positive instances in the training set , which is often used as the pivotal measure.25.We applied various classifier algorithms Fig.  and also26. Figure In order to identify the most contributing features and reduce potential overfitting, the features were evaluated using InfoGainWe observed that the 12 highest ranked features (out of the 20-top seeded features) Fig.  correspoNetwork centrality properties measure the importance of a node or link for maintaining the cohesiveness of a network. Here, the distribution of the centrality-related features ; and two pathways corresponding to PlantCyc: the melibiose degradation and the β-alanine biosynthesis III pathways.2. The first conversion also produces 2-oxoglutarate and L-glutamate, the second conversion produces an acetyl CoA NADH. In tomato Solyc12g006450 codes for β-alanine aminotransferase and Solyc01g106080 for malonate semialdehyde dehydrogenase. The presence of both genes was validated by performing PCR on DNA extracted from M82 tomatoes. A single amplicon was detected for both genes  to 3-oxoproponate and then via malonate semialdehyde dehydrogenase  to CO27. Four loci associated with genes coding for α-galactosidase were detected within the tomato genome on chromosomes 3-6  for Solyc03g019790 on chromosome 3 between the parental line M82 and IL 3-1 . To test for the presence of α-galactosidase, immunological analysis was performed against corresponding antibodies raised against α-galactosidase from barley29, revealing different amounts of the enzyme in M82 and the tested introgression lines  to the sugars galactose and glucose.2.1.22 tnes Fig. . In ordenes Fig. . Quantitnes Fig.  vs. putanes Fig.  is presenes Fig. .Fig. 6InSolyc09g064430). Also here we performed PCR on DNA extracted from M82 tomatoes to validat its presence Fig. The β-alanine biosynthesis III is one-step pathway, where L-aspartate is converted to β-alanine via aspartate 1-decarboxylase / L-tyrosine decarboxylase  and health care (drug design). The constraint-based approach is the common (non-curated) method for proposing the existence of metabolic pathways in an organism; in this approach, genes regulating metabolic pathways are organized into genome-scale networks8, complemented by enrichment analysis based on expression data (gene ontology) for the identification of key metabolic processes. However, this approach ignores the post-regulatory mechanisms taking place between the genetic, enzymatic, and metabolic levels of the cell.Understanding of the activity of metabolic pathways in the context of complex metabolic coordinationThe approach demonstrated here is based on quantitative measurements of metabolites, and by that effectively accounts for post-transcriptional and post-translational events, circumventing the need for gene data integration. We showed that metabolic correlation-based networks incorporate more information about the cellular activity than has been attributed to them so far. In fact, our study shows that metabolic pathways are deeply embedded into metabolic CNs and shape their topological structure.10. Additionally, 85 metabolic pathways unassociated with tomato from the MetaCyc collection as well as 85 random subsets of metabolites were added to the training set as negative instances. During the first trials of the study, initial ML models classified some tomato-unassociated pathways as tomato metabolic pathways, although their corresponding nodes in the CN did not correlate. We attribute this behavior to the fact that the initial ML models were trained using only tomato-associated metabolic pathways (positive instances from TomatoCyc) and tomato-unassociated metabolic pathways (negative instances from MetaCyc); thus, only a few negative instances contained uncorrelated metabolites within the studied CNs, not providing sufficient negative examples for the ML classifier to learn from. This observation led to the necessity to include also random subsets of metabolites as negative instances.To detect metabolic pathways within metabolic CNs, network analysis was combined with ML techniques. In this analysis, 169 known metabolic pathways in tomato (positive instances) were mapped as subgraphs onto the three metabolite CNs of the pericarp of a tomato introgression line mapping population26 was applied, reducing the complete feature set to the 20 most relevant features  a set of 148  and 66 non-plant species metabolic pathways (MetaCyc), was generated. Similar to the pathways in the training set, each pathway in the test-set was mapped onto the three different CNs, followed by feature computation Fig. . Based o33. The named metabolic pathway is attributed with energy conservation in form of a CoA ester during the oxidation of an aldehyde32. The L-tryptophan VII pathway has been documented in non-pathogenic bacteria associated with plants34. Given that the set of metabolites and genes (and likely enzymes) is also present in tomato, it is probable to assume both metabolic pathways fulfill a similar purpose here.Limited in vivo analyses were performed on metabolic pathways with high prediction scores. Representative for MetaCyc, the β-alanine degradation I pathway and the L-tryptophan degradation VII (via indole-3-pyrtuvate) were chosen. The presence of all genes regulating both metabolic pathways were validated by performing PCR on DNA Fig. . The setSolyc09g064430. In plants, only the aspartate 1-decarboxylation is associated with aspartate 1-decarboxylase. In Archaea also L-tyrosine decarboxylase has been shown to mediate the decarboxylation of aspartate35. Here, the presence of the gene encoding an L-tyrosine decarboxylase was validated via PCR performed on DNA extracted from M82 tomatoes  dataset. However, it is important to highlight that the approach presented here may be used to predict metabolic pathways but it cannot be used to predict differences in catalytic activity.The actual power of the novel method for metabolic pathway detection presented here is revealed when placing the results in context of the initial datasets. Twenty years into metabolomics and tools for the definition of metabolites on an individual level are still laggingThe usage of CNA combined with ML techniques will greatly contribute to metabolite pathway prediction and identification in incomplete datasets. In addition, the identification of metabolic pathways may be more accurate, as CNs are based on quantitative metabolic data taking into account all post-regulatory mechanisms occurring along the cellular machinery – a capability that is absent in the constraint-based pathway identification. Finally, as metabolic profiling can be performed independent of an annotated genome, the identification and prediction of metabolic pathways can be applied to virtually all organisms.24 as generated for Schauer et al.10 were used. The dataset was composed of metabolite profiles of the central metabolism from three different harvesting seasons (field experiments) in three different years, hereinafter referred to as seasons I, II, and III. Each metabolite profile was based on 4 to 6 biological replicates. For each season a weighted, undirected metabolite CN was constructed as described by Toubiana et al.11. Network nodes represented metabolites and network links were weighted according to their Pearson correlation coefficient, allowing negative values. Spurious correlations, where |r| ≤ 0.3 and p ≥ 0.01, were removed . For more details of network construction we refer the reader to the subsequent sections.To construct metabolite CNs the metabolic profiles of the tomato pericarp of an introgression line mapping populationhttps://github.com/toubiana/CNA_combined_with_ML. The resulting feature-value datasets for positive and negative instances are supplied as Supplementary Data Metabolic pathway mapping and onto CNs was achieved with pathways from the PlantCyc and MetaCyc databases (see below for details). Feature computation was achieved with R code as provided in n ≥ 3 biological replicates. A priori statistical tests were performed followed by adequate data transformation where necessary. Gene information for PCR performed on selected genes is provided in Supplementary Data Statistical tests for validation of metabolic pathways was performed with Gi = , where Vi is the set of nodes corresponding to metabolites found in the dataset of season i, E is the set of links between them, and link weights (w:E→R) correspond to the Pearson correlation coefficient. In the rest of this paper we will use the terms nodes and metabolites interchangeably. The constructed CN for season I was composed of Metabolite CNs are represented as weighted networks http://www.plantcyc.org/) version PMN 10.0 was used, listing 1214 pathways, composed of 6200 reactions, involving 152,416 enzymes and 5138 compounds. For tomato pathways, the TomatoCyc database version 1.0 within PlantCyc was used, listing 589 pathways, composed of 3379 reactions, involving 7106 enzymes and 2557 compounds. Finally, the MetaCyc pathway database (http://metacyc.org/) version 20.0 was used, listing 2454 pathways from 2788 different organisms, composed of 13,533 reactions, involving 11,041 enzymes and 13,191 compounds. MetaCyc pathways that were also found in PlantCyc were regarded as a part of the PlantCyc pathway collection. In addition, random sets of two to 18 metabolites were generated, corresponding to the minimum and maximum length of coherent pathways identified in all three networks. Only metabolic pathways that that shared at least two compounds with all three CNs were relevant for subsequent analysis. Of the 3043 metabolic pathways, only 320 such pathways were identified.As a plant pathway reference, the PlantCyc database . The set of communities is denoted as k is the index of a community in CN of season i. A pathway j can be represented as a subset of metabolites in the CN of season i, denoted as Second, we applied various community detection algorithmsj. We denoted the neighborhood of the node v in the CN for season i as the following: v. It is possible to compute various features from the neighborhoods of nodes in each pathway.Next, we computed structural features from the neighborhoods of each pathway j. The Mixed neighborhoods feature accounts for all nodes that are significantly correlated to more than one metabolite within the pathway j, but not all of them. We note that these two features are reminiscent of the symmetric difference as defined in set theory. In fact, for two nodes, the Distinct neighborhoods feature is equal to the size of the symmetric difference of their neighborhoods. However, for a larger number of nodes both features are different from the symmetric difference.The Distinct neighborhoods feature accounts for all nodes that are significantly correlated to exactly one metabolite within the pathway Finally, metabolic pathways were mapped as subgraphs onto the different CNs. Two types of subgraphs were considered: conjunctive subgraphs and extended subgraphs. Conjunctive subgraphs included all nodes in 38 and standard libraries in R39.The complete list of 148 features and their verbal definitions can be found in Supplementary Data 40 version 3.6.11.The dataset we analyzed included 339 pathways for which 444 features were computed. A large number of features may impair the ability of an ML model to generalize beyond the data points used to produce it - a phenomenon known as overfitting. In an effort to avoid overfitting and identify the most contributing features, we selected the features with the highest information gain. This procedure reduces the entropy of the class variable after analyzing the value for the feature. For the current study, the top-20 ranked features . ML algorithm tuning (a.k.a hyperparameter optimization) was performed applying a trial and error approach.Given an instance whose class is unknown, a trained ML model assigns a probability of that instance being positive (a tomato pathway) or negative (a non-tomato pathway). If the probability of an instance having a positive class is above some predefined threshold, then the predicted class of that instance is positive. Standard performance metrics can be used to compare the predicted classes assigned to the pathways vs. their true classes, i.e., the true positive rate , false positive rate (FPR), precision, and F-measure. In addition, the performance of ML models can be described by the receiver operating characteristic (ROC) curve, which is created by plotting the TPR as a function of the FPR at different threshold levels. The AUC under the ROC curve of ‘1’ indicates a perfect classifier. The AUC is often used as the pivotal measure, because it does not require specifying the threshold and it is independent of the proportion of positive and negative instances in the dataset.There are several procedures that can be used to evaluate the ability of an ML model to predict the class of previously unseen instances. The most popular method is k-fold cross-validation, where the dataset is divided into k equal  subsets. Each subset is then removed from the dataset in its turn. An ML model is trained based on the remaining subsets. The trained model is applied on every instance in the removed subset, and the predicted class is recorded. Eventually after k iterations all instances in the dataset will be assigned a predicted class vs. their original true class. Cross-validation is typically used to prove the stability of a given ML algorithm and assess whether or not the trained model is prone to overfitting. On one hand, a larger number of folds results in a larger number of instances in the training set during each iteration and consequently renders more accurate models. On the other hand, a larger k requires training more ML models during the evaluation, which increases the computational resources required.41.Due to the large number of ML algorithms evaluated, 10-fold cross-validation was used to select the best ML algorithm for the current study. Once the best ML algorithm was chosen, we increased the number of folds to the maximal possible value (339 pathways in our case) in order to obtain the most accurate in silico evaluation results. This special case of k-fold cross validation is known as leave-one-out cross-validation (LOOCV)40 software, version 3.6.11. For the current study, the best model was achieved using the random forest algorithm and an equal distribution between MetaCyc and randomly engineered pathways  as negative instances to the dataset43. Therefore, all of the positive instances were used for training, along with 85 randomly selected MetaCyc pathways and the same number of randomly selected non-pathways. In total, 170 negative instances were produced  with the primers shown in supplementary Table M82 tomato fruits were freeze lyophilized and grounded to a fine powder prior to extraction. Total genomic DNA was extracted with Hexadecyl trimethyl-ammonium bromide (CTAB)http://www.bio-rad.com/). First-strand cDNA was synthesized in a 10 μL volume, containing 350 ng of plant total RNA by using an iScript cDNA Synthesis Kit (Bio-Rad). The reaction was carried out using 1:10 dilutions of cDNA. PCR was hot started at 95 °C and carried out for 40 cycles composed of 95 °C for 20 s, 65 °C for 20 s, and 72 °C for 30 s. Amplification was monitored in real-time using an iCycler IQ multicolor real time PCR Detection System (Bio-Rad). The list of primers (Supplementary Table http://bioinfo.ut.ee/primer3-0.4.0/primer3). The relative contents of transcripts was determined by the 2–ΔΔCt method46 based on the normalization of expression data with regard to the expression of one reference gene. The reference genes were SGN-U314153 and SGN-U316474, characterized by constitutive expression47. The differences of Ct (ΔCt) between the control and target were compared.For relative quantitative RT-PCR, total RNA was extracted from the mesocarp tissue (excluding the seed) from the ILs of interest and M82 using an Aurum Total RNA Kit according to the manufacturer’s instructions ]. The homogenate was centrifuged 20,000 × g for 10 minutes at 4 °C, and the protein concentration was determined in the supernatant using a Bradford assay48. Proteins (20–30 µg) were separated by SDS-PAGE, and transferred to a polyvinylidene difluoride membrane . Blotting and incubation with a primary antibody raised against alpha-galactosidase from barley were performed as described by Chrost and Krupinska29. As a secondary antibody, a peroxidase-coupled anti-rabbit serum was used for visualization of immunoreactive protein bands.Plant tissues were frozen in liquid N49 with certain modifications. Mesocarp tissue (excluding the seed) from 5-10 fully mature tomato fruits weighing 50 g was cut into small pieces and suspended with 50 mL 1 M NaCl (pH was adjusted to six). The suspension was homogenized with glass beads at 4 °C for a period of 30 s. The resultant suspension was stirred for one hour at 4 °C, filtered through cheesecloth, and centrifuged at 12,000 × g for 20 min. The supernatant fraction was loaded onto PD SpinTrap G-25 columns , pre-equilibrated with 20 mM sodium acetate/acetic acid buffer (pH 4.75). The desalted protein was then eluted with the same buffer (20 mM sodium acetate/acetic acid) and used immediately for enzyme assaying.Enzyme extraction and assay were performed as previously described in Sozzi et al.50 using p-nitrophenyl-α-D-galactopyranoside  as substrate. The assay mixture, composed of—40 μL of 26 mM pNPGal, 50 µL of 100 mM acetate buffer (pH 4.5), and 40 µL of 0.2% BSA, was pre-incubated at 37 °C for two minutes, and the enzymatic reaction was initiated by the addition of 20 µL of the crude extract. Following 15min of incubation at 37 °C, the enzymatic reaction was terminated by adding 100 µL of 0.4 M Na2CO3, and the released yellow colored p-nitrophenol was determined spectrophotometrically at 410 nm. A blank solution absent the protein was run concurrently, and the appropriate correction was made.In order to test for α-galactosidase activity, aliquots of crude protein extract were assayed as previously described51. Pure standard of melibiose (purchased from Sigma) was diluted in methanol and run in different quantities to build calibration curves. In the standard, two peaks were identified (1MEOX) (8TMS) main-product and by-product (C37H89NO11Si8) MW 948 RI 2837 and 2868 by library RT 41.8 and 42.1 min. Extract sample (300 µL) was injected (1 µL) with and without spiked-in standard. Identification and annotation of melibiose was achieved based on comparison to an authentic standard. In addition, control samples with spiked-in non-labeled standards were also used to confirm coelution. Metabolite identity was further matched against publically available databases  following a procedure optimized for tomato tissueFurther information on research design is available in the Supplementary InformationSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Description of Supplementary DataReporting Summary


Persistent and intractable hiccups bring serious inconvenience to patients’ work and daily life, and impair their quality of life. Relevant studies showed that acupuncture therapy might be effective in treating persistent and intractable hiccups. However, there is no consistent conclusion so far. The aim of our research is to investigate the safeties and effectiveness of acupuncture in treating patients with persistent and intractable hiccups.We will search randomized controlled trials (RCTs) using acupuncture therapy to treat persistent and intractable hiccups in the following 6 English electronic databases and 3 Chinese electronic databases: MEDLINE, EMBASE, the Cochrane Library, Web of Science, PsycINFO, Allied and Alternative Medicine (AMED), Chinese National Knowledge Infrastructure (CNKI), Chinese Scientific Journals Database (VIP) and Wanfang data. The cure rate and the total effective rate will be considered as the primary outcomes. Complete cessation within a given period post-treatment of hiccups, changes in frequency or intensity of hiccups, concomitant symptom score, and adverse events will be considered as secondary outcomes. We will use Endnote software 9.1 for studies selection, Review Manager software 5.3, and STATA 13.0 software for analysis and synthesis.we will synthesize current studies to evaluate the the safeties and effectiveness of acupuncture for persistent and intractable hiccups.Our study will provide evidence of acupuncture therapy for persistent and intractable hiccups. It was reported that in the US, roughly 4000 patients require hospitalization for managing hiccups per year. Sometimes hiccups are considered trivial because they are often self-limiting, while they can be persistent and intractable occasionally, which are torturous to some patients. Persistent and intractable hiccups can be a manifestation of deep-seated pathology, correlating with gastrointestinal diseases, central nervous system, primary pulmonary and cardiovascular problems, psychogenic problems, infection, and etc.–7 It has an impact on the daily life of patients, such as insomnia, anxiety, exacerbation of malnutrition, aspiration, pneumonia, wound dehiscence , and even death in the extreme condition.–3Hiccups (singultus), are known as an involuntary, spasmodic contraction of the diaphragm and intercostal muscles somewhile which causing air disturbance, followed by the abrupt closure of the glottis.–11 In 2016, a systematic review and meta-analysis concluded that acupuncture was effective on intractable hiccup. However, due to methodological defects, the conclusions are not reliable. At present, there is no specific evidence that ensured the effectiveness and safeties of acupuncture on this topic. The aim of our research is to evaluate the effectiveness and safeties of acupuncture therapy for persistent and intractable hiccups.Drugs, such as chlorpromazine, metoclopramide, baclofen, haloperidol, gabapentin, and benzodiazepines have been used in persistent and intractable hiccups. Owing to undesirable adverse reactions, doctors and patients are seeking for non-pharmacological therapy. Acupuncture is one of the non-drug related treatments for persistent and intractable hiccups, which is widely adopted in China. It has been reported that acupuncture therapy may work in persistent and intractable hiccups.22.1The protocol of our study has been registered on the International Prospective Register of Systematic Reviews (PROSPERO) . The protocol is conducted strictly based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) guidelines.2.22.2.1The randomized controlled trials (RCTs) of acupuncture therapy for persistent and intractable hiccups will be included.2.2.2 with or without organic lesions. There is no restriction on age, gender, or race.We will include the study involving participants diagnosed with persistent or intractable hiccups (duration lasting > 48 hours)2.2.3We will include studies in which intervention group using acupuncture therapy  as the main intervention, and the control group using non-acupuncture therapy  or sham acupuncture.2.2.4Primary outcome(1)The cure rate ;(2)The total effective rate . as following: curative: after treatment, hiccups cease, and other accompanying symptoms disappears; effective: shorter duration of hiccups or decreased frequency of hiccups or it just recurred occasionally after treatment; invalid: after treatment, there are no observable changes for duration and frequency of hiccups.We define curative effectSecondary outcomes(1)Complete cessation within a certain time frame post-treatment of hiccups;(2)Change in frequency or intensity of hiccups;(3)Concomitant symptom score .(4)Adverse events related to interventions.2.2.5The diagnosis of the subjects not clear will be excluded; studies with acupuncture therapy not selected as the main treatment in the intervention group will be excluded; the unavailable full text will be excluded; duplicated data will be excluded; data cannot be extracted will be excluded.2.32.3.1The following 6 English electronic databases and 3 Chinese electronic databases from inception to October 2019 will be searched: MEDLINE, EMBASE, the Cochrane Library, Web of Science, PsycINFO, Allied and Alternative Medicine (AMED), Chinese National Knowledge Infrastructure (CNKI), Chinese Scientific Journals Database (VIP) and Wanfang data.2.3.2http://www.chictr.org.cn, http://www.ClinicalTrial.gov, and ISRCTN Register. In addition, the relevant grey literature will be searched from the Health Management Information Database (HMIC), OpenSIGLE Database, and the National Technical Information Service (NTIS). Experts in the field will be consulted for relevant researches.Relevant references will be reviewed and screened. Moreover, the following registration website of the clinical trial will be searched: WHO ICTRP, 2.4Following the principle of combining subject words with text words, we will search the electronic databases with search terms for acupuncture therapy, persistent and intractable hiccups, and randomized controlled trial. There will be no restriction on languages and publication dates. Taking MEDLINE as an example, the specific search strategy is as Table 2.52.5.1The retrieved researches will be imported in Endnote software 9.1 to remove duplicates. The abstracts and titles of 2 independent researchers (JZ and QWX) will be screened according to the established inclusion and exclusion criteria, after that, we will screen the full text for the second selection. Two researchers (JZ and QWX) will cross-check the included studies. In case of disagreements, the third researcher (FRL) will be involved. The detailed screening process will be shown in the following PRISMA-P flow diagram  and the extracted information includes: author, publication year, country, characteristics of participants, hiccups type, concomitant diseases, the details of intervention and comparisons, outcomes and the specific data, adverse events, follow-up, results, conclusions, sources of funds, conflicts of interest, ethical approval and methodological quality. Two researchers (GXX and ZHY) will cross-check the data extraction. In case of disagreements, the third researcher (FRL) will be involved. We will contact the author for more information if necessary.2.5.3 We will evaluate the risk of bias from the following 6 domains: selection, performance, attrition, detection, reporting, and other sources of bias. The risks of bias will be marked as three levels: low , high (meet none of the criteria), and unclear . Two researchers (HY and JL) will cross-check after assessment. In case of disagreements, the third researcher (FRL) will be involved.The risk of bias of each included RCT will be evaluated by 2 independent researchers (HY and JL) according to the guidance from the Cochrane Handbook of Systematic Reviews of Interventions.2.6If there is a possibility to perform a meta-analysis, we will use Review Manager software (RevMan5.3) and STATA 13.0 software to conduct all data analyses. The random-effect model will be selected to analyze all the data. Descriptive analysis will be conducted if there is significant statistical heterogeneity.2.6.1Mean difference (MD) will be used to evaluate if the extracted date for continuous outcomes  have the same unit. Otherwise, standardized mean difference (SMD) will be used. For dichotomous outcomes , the number of participants, rate ratio (RR) will be used to analyze. For both continuous outcomes and dichotomous outcomes, we will set the confidence intervals (CIs) to 95%.2.6.2We will contact the corresponding author for detailed information for any missing or insufficient data. We will exclude these researches if we still cannot get accurate data after contacting the corresponding author.2.6.3I2 test and the Chi2 test). When I2 < 50%, small heterogeneity will be prompted. When I2 ≥ 50%, significant heterogeneity will be prompted.Heterogeneity will be assessed by qualitative analysis (comparing the included studies’ characteristics) and quantitative analysis  through assessing the quality by the Grading of Recommendations Assessment, Development, and Evaluation (GRADE)2.8Since our study have no connection with individual patient data, thus, it is not necessary for ethical approval. The results of our systematic review will be reported on a peer-reviewed journal or relevant conferences to provide the implication of acupuncture therapy for patients with persistent and intractable hiccups.3 concluded that acupuncture was effective on intractable hiccup through systematic review and meta-analysis, but its quality of the methodology was extremely low according to AMSTAR 2 scale: there was no explicit statement on the review methods established prior, nor comprehensive literature search strategy and so on, affecting the credibility of the conclusion. So, the validity is still unclear. We will conduct this systematic review to evaluate the effectiveness and safeties of acupuncture therapy for persistent and intractable hiccups strictly following the Cochrane Handbook for Systematic Reviews of Interventions, to provide different perspectives and guidance for doctors.Persistent and intractable hiccups bring serious inconvenience to patients’ work and daily life, damaging their quality of life. So far, there is no satisfactory treatment for persistent and intractable hiccups. Acupuncture treatment might be effective. Although 1 studyConceptualization: Han Yang, Jun Zhou.Methodology: Han Yang, Rufei Zhang, Juan Li, Jun Zhou, Guixing Xu, Zihan Yin.Supervision: Fanrong Liang.Writing – original draft: Han Yang, Ying Cheng, Qiwei Xiao.Writing – review & editing: Han Yang, Rufei Zhang, Juan Li, Ling Zhao.


Serratia marcescens outer membrane phospholipase A gene (OM-PLA1) and expressed its 33 KDa protein in engineered Escherichia coli BL21(DE3). OM-PLA1 activity reached 18.9 U mL−1 with the induction of 0.6 mM isopropyl β-D-1-thiogalactopyranoside for 4 h. The optimum temperature and pH were 50°C and 7.5, respectively. Mg2+, Ca2+, Co2+, and Mn2+ at 0.1 mM L−1 significantly increased OM-PLA1 activity. The kinetic equations of OM-PLA1 and Lecitase Ultra were y = 13.7x+0.74  and y = 24.42x+0.58 , respectively. The phosphorus content decreased from 22.6 to 9.3 mg kg−1 with the addition of 15 units of free recombinant OM-PLA1 into 150 g of crude rapeseed oil. OM-PLA1 has the close degumming efficiency with Lecitase Ultra. The S. marcescens outer membrane phospholipase gene (OM-PLA1) possessed higher substrate affinity and catalytic efficiency than Lecitase Ultra. This study provides an alternative approach to achieve crude vegetable oil degumming with enzymatic technology.Crude oil degumming by phospholipid removal is crucial to guarantee oil quality. Phospholipase degumming could produce green vegetable oil by reducing energy consumption and protecting the environment. To develop a novel phospholipase for oil degumming, we cloned the  Crude vegetable oil mainly comprises trace elements, free fatty acids, triglycerides, sterols, phospholipids, heavy metals, and other minor impurities  for heterologous expression. The enzymatic characteristics of OM-PLA1 and free enzymatic degumming of rapeseed oil were also investigated  was cloned and preserved in the College of Food and Biological Engineering, Hefei University of Technology. The biological and chemical reagents, pEASY-T1 cloning kit, pEASY-Blunt E1 expression kit, Escherichia coli DH 5α, and E. coli BL21(DE3) were purchased from Transgen Biotech. The gel imaging system and the PCR amplification, SDS-PAGE, and electrophoresis devices were obtained from Bio-RAD. Primer synthesis and gene sequencing were performed by Sangon Corporation.The HG326223.1 were used to amplify the S. marcescens OM-PLA1 gene. A pEASY-T1 cloning vector with the OM-PLA1 gene was transformed into E. coli DH5α for sequencing identification. The confirmed S. marcescens OM-PLA1 gene was inserted into a pEASY-Blunt E1 expression vector. T7 promoter primer and T7 terminator primer were used to amplify the insertion sequence for sequencing. The vector embracing OM-PLA1 with correct insertion location was identified. The upstream sequence of OM-PLA1 is close to the T7 promoter. The identified recombinant expression vector was transformed into E. coli BL21(DE3) for heterologous expression.The upstream primer 5′-TATGCGCATTTTGTCAGGGA-3′ and downstream primer 5′-GATTACATAATATCGTTCAGC-3′ based on the Genbank sequence E. coli BL21(DE3) was induced by adding IPTG in an expression broth. The OM-PLA1 engineered E. coli BL21(DE3) was inoculated into a 50 mL Erlenmeyer flask loaded with 5 mL of LB liquid medium containing 100 μg of mL ampicillin at 37°C and 250 rpm shaking speed. When the cell concentration was 0.5 OD600, 100 mM IPTG was sucked out and injected into the Erlenmeyer flask. The condensed IPTG was diluted to 0–1.2 mM for the induction of recombinant OM-PLA1 at 37°C and 250 rpm shaking speed. The broth was centrifuged at 10,000 rpm for 5 min, and then the upper broth was taken for the activity detection of OM-PLA1. The effects of 0–8 h of induction time on OM-PLA1 expression were also investigated.The expression of OM-PLA1 in engineered 2+ firmly through three sites and reduce the leakage of Ni2+ to protein samples during purification. ProteinIso Ni-IDA Resin has a specific adsorption capacity for the labeled proteins. The labeled proteins were bound to NI-IDA purification medium, whereas the unbounded proteins were washed down. The detailed processing steps are as follows: (1) ProteinIso Ni-IDA Resin was re-suspended with an equilibrium liquid containing 50 mM NaH2PO4, 300 mM NaCl, 10 mM imidazole, and 10 mM Tris base adjusted to pH 8; (2) the supernatant of the fermentation broth was obtained by centrifugation at 10,000 rpm for 10 min. The supernatant was further filtered through a 0.45 μm aperture. The filtered sample was prepared by diluting the supernatant 10 times; (3) after sampling, the column was washed with 10-fold equilibrium liquid; (4) the target protein was eluted by adding equilibrium liquid adjusted to pH 5. The protein profile of S. marcescens OM-PLA1 eluted from the resin was analyzed via SDS-PAGE  in accordance with the product protocol. IDA can chelate Ni0 as abscissa and ordinate, respectively. Km (mM) and Vmax (mM min−1) were calculated using Lineweaver–Burk method  spectroscopy . The protein profiles determined via SDS-PAGE showed that the size of OM-PLA1 expressed by engineered E. coli BL21(DE3) was appropriately 33 KDa. With the addition of 0.6 mM IPTG, SDS-PAGE indicated that the expression level of the recombinant OMP-LA1 positively increased when the treatment time was 0.5–4.5 h (The .5–4.5 h .E. coli BL21(DE3) reached 18.9 U mL−1, which was the highest among the set concentrations of IPTG. Almost no OM-PLA1 activity in the broth from wild-type E. coli BL21(DE3) was observed in the control (E. coli BL21(DE3) reached 18.7 U mL−1, which was the highest concentration achieved among the set treatment times . The kinetic equation of Lecitase Ultra was y = 24.42x+0.58 . By contrast, OM-PLA1 possessed lower enzyme activity and higher substrate affinity than Lecitase Ultra.The kinetic equations of OM-PLA1 and Lecitase Ultra were drawn based on Lineweaver–Burk equation under the same conditions . The kin−1  and ln v = 15.68–4691.69/T (R2 = 0.991), respectively. The activation energy of Lecitase Ultra was higher than that of OM-PLA1 according to the absolute value of the equation slope. OM-PLA1 possessed higher catalysis efficiency than Lecitase Ultra. When the same temperature was increased, OM-PLA1 activity had lower increase rate than Lecitase Ultra.The Arrhenius plot method was used to investigate the relationship between initial hydrolysis speed and reaction temperature . The Arr2+, Mg2+, Co2+, and Mn2+ at 0.1 mM L−1 remarkably increased OM-PLA1 activity. Cu2+, Zn2+, SDS, EDTA, and EGTA at 1 mM L−1 considerably decreased OM-PLA1 activity. By contrast, 1 mM L−1 K+, Al3+, and 1 mM L−1 Na+ did not remarkably affect OM-PLA1 activity. The kinetic parameters of OM-PLA1 were determined by calculating the consumption amount by using the gradient concentrations of soybean lecithin as the catalytic substrate.The effect of factors on OM-PLA1 activity was investigated under the conditions of 50°C and pH 7.5 . Ca2+, M−1 Mg2+ . The lowest valley value decreased to −24.82 deg·cm2·dmol−1·10−3 from −13.35 deg·cm2·dmol−1·10−3 at the wavelength of 220 nm. The structure types of OM-PLA1 indicated that 0.1 mM L−1 Mg2+ significantly increased helix proportion . The results indicated that the secondary structure change caused by metal ion Mg2+ increased the enzyme activity.CD spectrum was used to determine the secondary structure of OM-PLA1 under the condition of 0.1 mM L−1 Mg2+ . The higoportion . The add−1 generally satisfied the requirement for edible oil. OM-PLA1 and Lecitase Ultra were used to degum crude rapeseed oil under the same conditions. The effect of treatment time on oil degumming was investigated by determining the phosphorus content . At the initial catalytic stage, OM-PLA1 possessed almost the same degumming efficiency with Lecitase Ultra (<1.5 h). After treatment for 2 h, OM-PLA1 had lower residual activity than Lecitase Ultra. Therefore, the enzyme stability of OM-PLA1 was slightly lower than that of commercialized Lecitase Ultra during oil degumming.Phosphorus contents below 10 mg kg content . Catalys−1 phosphorus under the same conditions. Degumming was performed at 50°C for 2 h at a shaking speed of 150 rpm  could meet the need for the enzymatic degumming of crude rapeseed oil. OM-PLA1 possessed lower catalysis efficiency than Lecitase Ultra when the amount of enzyme was >15 units. OM-PLA1 possessed almost the same catalysis efficiency compared with Lecitase Ultra. However, OM-PLA1 still had a large space to be modified to improve its catalytic performance.Different amounts of OM-PLA1 and Lecitase Ultra were added into 150 g of crude rapeseed oil adjusted to pH 7.5 containing 22.6 mg kg 150 rpm . The phoThermomyces lanuginosus lipase and Fusarium oxysporum phospholipase A1. The stability of T. lanuginosus lipase is combined to the A1 phospholipase activity  to form metal active sites  and exogenous ligands  degumming effect of OM-PLA1 on other crude oil vegetable oils; (2) further exploring large-scale industrial applications to reduce the loss of enzyme activity; (3) combination use of OM-PLA1 and PLC for crude oil degumming; and (4) enzyme immobilization for recycling and reuse.In the present study, the expression of the S. marcescens OM-PLA1 gene was expressed in engineered E. coli BL21(DE3). The highest activity of the recombinant OM-PLA1 with a size of appropriately 33 KDa reached 18.9 U mL−1. The optimum temperature and pH were 50°C and 7.5, respectively. The Km values of OM-PLA1 and Lecitase Ultra were 18.53 and 42.1 mM, respectively. The Vmax values of OM-PLA1 and Lecitase Ultra were 1.35 and 1.72 mM min−1, respectively. The P content in 150 g of crude rapeseed oil decreased from 22.6 mg kg−1 to 9.3 mg kg−1 with the addition of 15 units of free OM-PLA1. OM-PLA1 possessed almost the same catalysis efficiency and lower enzymatic stability in comparison with Lecitase Ultra. The results provide an alternative phospholipase material for oil degumming.The HG326223.1.The datasets generated for this study can be found in the Genbank sequence PY designed the experiment. YW performed experiment. SuJ performed characteristic determination. WX wrote the manuscript. ZZ analyzed the conclusions. ZH analyzed the data. DM measured the enzymatic activity. ShJ provided the research ideas. Y-HY searched the literature. All authors read and approved the final manuscript.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.


Mahai capsules (MHC) have been deemed to be an effective herb combination for treatment of cardiovascular diseases (CVD) development and improvement of the life quality of CVD patients. To systematically explore the mechanisms of MHC in CVD, a network pharmacology approach mainly comprising target prediction, network construction, biological process and pathway analysis, and related diseases was adopted in this study.  We collected the bioactive compounds and potential targets of MHC through the TCMSP servers. Candidate targets related to CVD were collected from Therapeutic Targets Database and PharmGkb database and analyzed using ClueGO plugin in Cytoscape. KEGG pathway was enriched and analyzed through the EnrichR platform, and protein-protein interaction networks were calculated by STRING platform. The compound-target, target-disease, and compound-target-disease networks were constructed using Cytoscape. B signaling pathway.  A total of 303 targets of the 57 active ingredients in MHC were obtained. The network analysis showed that PTGS2, PTGS1, HSP90, Scn1a, estrogen receptor, calmodulin, and thrombin were identified as key targets of MHC in the treatment of CVD. The functional enrichment analysis indicated that MHC probably produced the therapeutic effects against CVD by synergistically regulating many biological pathways, such as PI3K-Akt, TNF, HIF-1, FoxO, apoptosis, calcium, T-cell receptor, VEGF, and NF-kappa  In summary, the analysis of the complete profile of the pharmacological properties, as well as the elucidation of targets, networks, and pathways, can further illuminate that the underlying mechanisms of MHC in CVD might be strongly associated with its synergic regulation of inflammation, apoptosis, and immune function, and provide new clues for its future development of therapeutic strategies and basic research. Cardiovascular diseases (CVD) are a class of the degenerative chronic diseases such as atherosclerosis, heart failure, hypertensive, aneurysms, and thromboembolism . CVD marIn China, various TCM have achieved great success in the prevention and treatment of CVD. Danshen dripping pills and Danhong injection are notable examples that were confirmed by clinical trials in their protective effects against CVD –9. SimilEven though there was considerable benefit with such multicomponent drugs, understanding the scientific material basis and underlying mechanism of TCM herbal formulas are still required in the treatment of CVD at the molecular level and from a systematic perspective. Most herbal medicines containing enormous bioactive compounds and lots of related multiple targets also complicate the pharmacological research, making it difficult to clarify their pharmacological mechanism only by traditional experimental approaches. With the rapid development of life science and computer science, various virtual screening tools and bioinformatic database have been developed to explore the interactions between the complex ingredients and the huge amount of target genes in TCM , 18. TheIn this paper, we aim to employ virtual screening databases and integrate bioinformatics analyses to explore the relationships between MHC and targets, which show the network of drug and related targets on whole level. This might enable us to investigate the pharmacological mechanism of how MHC exerts the protective effects on CVD.Candidate compounds for Hedysarum Multijugum Maxim , Strychni Semen , Angelicae Sinensis Radix , Caulis Piperis Kadsurae , Homalomena Occulta (Lour.) Schott , and Radix Rhei Et Rhizome  were retrieved from the TCMSP database (ref.). Identification of ADME  properties by the TCMSP database was employed to screen the composite compounds. In the current study, OB  and DL (prediction of drug-likeness) identify the potential bioactive compounds of MHC. The ingredients satisfying the criteria of OB ≥30% and DL ≥0.18 are retained and treated as candidate molecules of MHC for subsequent analysis.To gather information on interactions between active functional compounds in MHC and associated genes, the bioactive compounds and potential targets were collected through the TCMSP servers. Known CVD-related targets were collected from existing resources, including Therapeutic Targets Database and PharmGkb database, which provide comprehensive information on bioactive compounds and targets interactions, and relationships among compounds, targets, and diseases. All obtained proteins were subjected to PharmGkb or TTD to detect the relationships between candidate molecules, targets, and CVD.To investigate relationship between the compounds in MHC and their targets in CVD, we construct the network through network visualization software in Cytoscape 3.6.1. This software was used to integrate data and analysis and visualize complex interaction networks. In networks, nodes represent compounds or proteins and edges indicate compound-target gene interactions. Three types of networks, for example, compounds-targets (C-T), targets-diseases (T-D), and compounds-targets-diseases (C-T-D) networks, were constructed and visualized using Cytoscape. For nodes in the complex network, three indicators were calculated to reveal its features.p value was used to examine the significance of the GO terms enrichment. The GO terms that have a p value of ≤0.05 were regarded as significant and interesting.ClueGO, which is a plugin integrated in Cytoscape, can be employed to conduct GO analysis to create functionally organized term networks and comprehensively visualize functionally grouped terms for understanding the biological significances . The p vp value threshold of 0.05 was used for pathway discovery. In this study, we chose the top ten KEGG terms to explore the related pathways.Enrichr is an integrative web-based platform that includes new gene-set libraries, an alternative approach to rank enriched terms, and various interactive visualization approaches to display enrichment results . The KEGhttps://string-db.org/) was employed to construct PPI networks with the species limited to “homo sapiens” [String , followed by PTGS1 (degree = 31), HSP90 (degree = 31), Scn1a (degree = 28), estrogen receptor (degree = 16), calmodulin (degree = 16), and thrombin (degree = 15), demonstrating their potential therapeutic effects for treating CVD. Among them, the target prostaglandin G/H synthase 2 (PTGS2) with the highest degree (40) and prostaglandin G/H synthase 1 (PTGS1) with a degree of 31 can be modulated by the compounds in MHC, indicating their vital role in helping to treat CVDs. Similarly, heat shock protein 90 (HSP90), a key enzyme in the blood coagulation system, was predicted to be regulated by 31 chemicals. The details of the relationship between activate compounds and targets are described in β-adrenergic receptor; the top diseases that were most relevant to MHC were as follows: atherosclerosis, cardiovascular disease, hypertension, thrombosis, and neurodegenerative diseases, implying that MHC may be also effective in the treatment of these diseases. Thrombin, a main target of MHC, was associated with coronary atherosclerosis, thrombosis, and thrombotic disease in the T-D network. Based on these findings, MHC contained numerous effective substances with different pharmacologic properties that may act on multiple targets with potential synergistic effects.To gain better insight into the diseases that could be modified by MHC, a T-D network was constructed on the strength of predicted targets and the corresponding diseases . The genThen, we mapped all candidate compounds with their corresponding targets onto these diseases. After discarding the targets without participating in any related CVDS and the corresponding compounds, a compounds-targets-diseases network was constructed with 87 nodes  and 127 edges . For insTo clarify the multiple mechanisms of MHC on CVD from a systematic level, we performed an enrichment analysis for the biological process (BP), molecular function (MF), and cellular component (CC) of the retrieved protein targets of MHC. As shown in B signaling pathway (hsa04064). Among them, the PI3K-Akt and TNF signaling pathways have the highest combined scores, which imply the vital roles in the treatment and prevention of CVDs. In addition, 6 signaling pathways including HIF-1, FoxO, VEGF, TLR, MAPK, and NF-κB signaling pathways are also important pathways capable of regulating anti-inflammatory, neuroprotective, and antioxidative effects. This suggests that the potential targets of MHC may be involved in various pathways, showing their specific mechanism of action to modulate CVDs.To investigate the underlying mechanism of MHC, the targets were further mapped to pathways, and the top 10 pathways are listed in The protein-protein network was constructed via mapping the putative targets into the String platform. After excluding isolated nodes, the protein interaction network induced by MHC was composed of 221 nodes (proteins) and 3865 edges . The topThe efficacy of MHC has been verified through accumulated considerable clinical experiences. However, it is difficult to illuminate the mechanism of MHC from the perspective of modern medicine because of the complex composition. The network pharmacological analysis provides new approaches and perspectives for the study of complicated Chinese medicine formula. In the present study, we used the network pharmacology approach to illuminate the scientific material basis and multiple underlying mechanisms of MHC in CVDs treatment from a systematic perspective. The pharmacodynamic compounds and potential targets, network analysis of elements such as active ingredients and potential targets, GO and KEGG pathway enrichment analysis, and protein-protein interaction were used to investigate the relationships between active molecules and related proteins of CVD.HQ, MQ, DG, HFT, QN, and DH are the most commonly used herbal medicines to treat CVDs. In our work, with the help of the ADME evaluation system, 444 active ingredients were identified, 75 of which could interact with 303 direct targets by drug targeting. Recent researches have shown that some active ingredients in MHC have biological activity against CVDs, which confirm the bioinformatics data analyzed in our study and highlights the credibility of the network pharmacology system. For instance, isorhamnetin has been proven to exert cardiovascular protective effects through multiple mechanisms, including antioxidative, anti-inflammatory, and antiproliferative effects. Various pharmacological studies showed that isorhamnetin protects against cardiac hypertrophy and can exhibit positive effect on hypoxia/reoxygenation-induced injury by attenuating apoptosis and oxidative stress , 26. Kae2, which is responsible for production of inflammatory prostaglandins. Previous relevant studies have defined that the increased expression of COX-2 and COX-1 may contribute to the development of inflammatory diseases due to the fact that they can facilitate the transcription of several cytokines associated with disease progression. COX-2 and COX-1 have been acknowledged as classic therapeutic targets of nonsteroidal anti-inflammatory drugs (NSAIDs), which bind the site corresponding to the COX active site. Inhibition of the COX with NSAIDs acutely reduces inflammation, pain, and fever, and long-term use of these drugs reduces fatal thrombotic events, as well as the development of colon cancer and Alzheimer's disease. A recent research suggested that the inhibition of COX-2 may activate the MAPK pathway and reduce inflammatory response and improve myocardial remodeling in mice with myocardial infarction. Various activity compounds in TCM exert protective effects in the vascular or cardiac injury via the signaling or downstream processing of COX-2 [β signaling pathway, the downregulation of HSP 90 can inhibit the activation of myocardial fibroblast, which is a pathological signature of myocardial fibrosis [As we know, MHC probably exerts its therapeutic effect on CVD by binding and regulating particular protein targets. The analytical result of the C-T network displayed an average degree of 13 per compounds and 5.3 per target proteins, respectively. Among the 57 compounds with corresponding targets, 99 were capable of acting on more than 2 targets and 44 linked with more than 13 target proteins. The key node proteins were suggested to be important targets in the treatment of CVD. Among them, the targets prostaglandin G/H synthase 2  with the highest degree 40) and prostaglandin G/H synthase 1  with a degree of 31 can be modulated by the compounds in MHC, indicating their vital role in helping to treat CVDs. COX-2 and COX-1 are constitutively expressed in the endothelium, brain and various tissues in physiological conditions, and convert arachidonate to prostaglandin Hof COX-2 –39. In a and prosfibrosis . In lighβ, IL-6, and TNF) derived from the vessel wall or blood have been proven to be associated with vascular risk and predictive of future cardiovascular events. Inflammatory processes and their failure to resolve is firmly established as central to the progress of cardiovascular diseases. Several emerging lines of evidence support the hypothesis that inhibition of the inflammatory processes by targeting of the cytokines might serve as an efficient system to attenuate myocardial and arterial injury, reduce disease progression, and promote healing. Our results have shown that the protective effects of MHC may be related to the inhibition of cytokines thereby suppressing chronic inflammatory process. Besides, one of the key reasons for the occurrence and development of CVD is the apoptotic process. In the present study the “regulation of apoptotic process (GO:0042981)” and “apoptotic process (GO:0006915)” are both enriched in the top ranked GO BP terms. Apoptosis is defined as a highly regulated form of cell death and can be regulated by genetic or pharmacologic interventions. Regulation of apoptosis is a hopeful choice of treatment for CVDs and disorders such as myocardial infarction, ischemia/reperfusion injury, chemotherapy cardiotoxicity, and heart failure. In the process of apoptotic, TP53, IL-6, TGF-β, TNF-α, IKBKB, and EGFR are considered to be important related proteins. The “cellular response to oxidative stress (GO:0034599)” and “cellular response to reactive oxygen species (GO:0034614)” were also enriched in our results. It is established that elevated oxidative stress could be a key factor in the development of complex biochemical, structural, and functional changes associated with CVD. Recently, different research groups have provided evidences that pharmacological approaches to counteract excessive accumulation of ROS are sufficient in the prevention or treatment of heart failure. Besides, endothelial dysfunction and vascular remodeling caused by IL-6, TGF-β, TNF-α, EGFR, and VEGFA are also some of the core features of CVD.By analyzing the GO-enriched results, we found that MHC may have certain effects in regulating cytokine-related biological processes, such as “cellular response to cytokine stimulus (GO:0071345)” and “cytokine-mediated signaling pathway (GO:0019221)”. Cytokines (such as IL-1κB pathways. Most signaling pathways significantly enriched by targets were associated with multiple chronic inflammatory diseases, not merely CVD, which is consistent with the enriched GO terms in our study. Among 177 pathways, PI3K-Akt and TNF signaling as critical pathways regulate the process of apoptosis, inflammation, and oxidative stress in the development and severity of CVD. For instance, the master factors contributing to the initiation and evolution of inflammatory responses in cardiovascular tissues include the elevated level of TNF and the activation of NF-κB in the TNF signaling pathway. Notably, our research indicated that the main biological function of MHC in CVD is negatively regulating the TNF signaling pathway by the interaction between bioactivity compounds and the potential targets including TNF, VCAM1, COX-2, MMP3, MMP9, IKBKB, IL-1β, IL-6, CCL2, CXCL2, CXCL10, caspase-3, and caspase-8. Moreover, our results also show that MHC can exert the protective effect by modulating the PI3K-Akt signaling pathway. In the cardiovascular system, a great number of studies targeting PI3K/Akt have elucidated the contribution of this pathway to cardiac and vascular function regulation both in the normal and diseased states. GSK-3β, AKT1, eNOS, and VEGFA, which have been screened as the targets of MHC, are considered to modulate important cellular physiological processes in the vessels, heart, and brain. The eNOS protein shows pharmacological properties by producing NO, which can rapidly diffuse across cell membranes to act as a potent paracrine mediator. The PI3K/Akt signaling pathway has been proven to be involved in the resistance response to hypoxia ischemia. It can regulate the expression of HIF-1α, which further modulates the expression of downstream targets that related to glucose metabolism and angiogenesis to facilitate ischemic adaptation [Our results showed that MHC integrated various signaling pathways which have been testified as accurate target pathways to modulate CVD, such as TNF, PI3K-Akt, HIF-1, FoxO, apoptosis, calcium, MAPK, Toll-like receptor, VEGF, and NF-aptation . In cardaptation . Based oThe current study uses a network pharmacology approach that combines active compounds, potential targets, GO, and KEGG enrichment analysis to investigate the molecular mechanism of MHC against CVDs from a systematic perspective. Among these crucial biological functions, 303 targets were identified as key active factors involved in the 177 related pathways. We found that our prediction-based results were generally consistent with previous research on pathways and diseases treated with MHC extracts. Furthermore, we can suggest more comprehensive mechanisms of therapeutic effects of MHC in terms of target proteins, pathways, and diseases than manual reviews of the literature. Nonetheless, more experimental researches were warranted to validate these hypotheses, and experimental verification of the potential effective compounds after candidate screening is needed, which will lay a foundation for further experimental research and clinical rational application of MHC.


Muscle weakness is a common consequence of both aging (sarcopenia) and neuromuscular disorders (NMD). Whilst genome-wide association (GWA) studies have identified genetic variants associated with grip strength  and NMDs, including multiple sclerosis (MS), myasthenia gravis (MG) and amyotrophic lateral sclerosis (ALS), it is not known whether there are common mechanisms between these phenotypes. To examine this, we have integrated GS and NMD associated genetic variants  in a multimorbid analysis that leverages high-throughput chromatin interaction (Hi-C) data and expression quantitative trait loci data to identify target genes . Biological pathways enriched by these genes were then identified using next-generation pathway enrichment analysis. Lastly, druggable genes were identified using drug gene interaction (DGI) database. We identified gene regulatory mechanisms associated with GS, MG, MS, and ALS. The SNPs associated with GS regulate a subset of genes that are also regulated by the SNPs of MS, MG, and ALS. Yet, we did not find any genes commonly regulated by all four phenotype associated SNPs. By contrast, we identified significant enrichment in three pathways  that are commonly affected by the gene regulatory mechanisms associated with all four phenotypes. 13% of the genes we identified were known drug targets, and GS shares at least one druggable gene and pathway with each of the NMD phenotypes. We have identified significant biological overlaps between GS and NMD, demonstrating the potential for spatial genetic analysis to identify common mechanisms between potential multimorbid phenotypes. Collectively, our results form the foundation for a shift from a gene to a pathway-based approach to the rationale design of therapeutic interventions and treatments for NMD. Age-associated changes in skeletal muscle structure and composition are influenced by various factors including genetics, physical inactivity, malnutrition, hormonal and environmental changes . UnderstMG is an autoimmune disease caused by autoantibodies (Abs) binding to postsynaptic region and impairing the function of acetylcholine receptors (AChRs). Three main pathogenic mechanisms mediated by these Abs underlie muscle weakness in MG: (1) IgG Abs bind to complement factors leading to the formation of a membrane attack complex; (2) specific IgG cross-linking of AChRs increase endocytosis and degradation of AChRs; or (3) blockage of AChR ion channels by Abs prevents the binding of acetylcholine to AChR . These rALS is a fatal motor neuron disease that causes progressive degeneration of upper and lower motor neurons in the brain and spinal cord leading to muscle weakness, paralysis and death. In ALS, skeletal muscle harbors the characteristic protein aggregates   whose buMS is an autoimmune-mediated demyelinating disease of CNS. A cascade of events involving autoreactive T cells, B cells, microglia, astrocytes induce demyelination, inflammation and neurodegeneration in MS . ChangesTo date, there has been only one genome-wide association (GWA) study on the genetic features of cachexia, and only for body mass loss in COPD and cancer . HoweverCo-occurrences of MG:MS , MG:ALS 1  Consortium web-based knowledgebase 2 identif3) to identify pathways enriched for eGenes from GS, MG. MS and ALS. Analyses were performed using default parameters as described by Ahsan and Draghici  was used to compare eGene lists for each phenotype to identify intersections between all possible phenotype combinations. To test if gene overlaps were non-random, we performed bootstrap analysis using 10000 randomly chosen sets of genes [from a background set of all significant eGenes  from GTEx (Version 8)] of the same size as the number of eGenes for each phenotype.4) which integrates information on gene druggability and drug-gene interactions from 30 different sources , MG (n = 18), MS (n = 285), and ALS (n = 135) SNPs  SNPs . Approxi35) SNPs . We then35) SNPs .We hypothesized that the emergent genome organization in muscle would be more relevant to gene regulation in understanding the biological underpinnings of sarcopenia and NMD. Therefore, we repeated the CoDeS3D analysis using genomic organization data captured in psoas muscle tissue  Figure . We idenn = 18452) that were captured having spatial contacts with any of the four phenotype associated SNP sets. The results were consistent with what we saw using the all eGenes background set. However, the overlap between GS:MS:ALS was no longer significant for the immortalized cell line dataset using only those genes with a spatial connection  between MG and MS eQTLs; LS eQTLs . The 25 LS eQTLs . SimilarLS eQTLs .5. We identified one eQTL (rs9270986) associated with MG in LD (R2 = 0.96) with a single eQTL (rs3129889) associated with MS. Consistent with our hypothesis, we did not find any other eQTLs in strong LD (R2 > 0.8) (Genes in the HLA-region were overrepresented in the overlapping eGene sets. We hypothesized that the observed overlaps in the HLA region were not due to the effect of Linkage disequilibrium (LD) between eQTLs in that region. We derived the LDmatrix between all four phenotype associated SNPs located across chromosome 6 using LDlink2 > 0.8) . CollectGradual muscle wasting and weakness is a comorbidity that we hypothesized would be reflected in shared biological pathways between MS, MG, ALS, and GS. Using iPathwayGuide, we identified considerable number of pathways  and overShared eGenes explained some of the observed co-occurrence in pathways; >50% of the shared pathways observed for GS:MG and GS:MS  contained both shared and condition-specific eGenes. By contrast, 99% of shared pathways identified for GS and ALS were due to co-occurrence of condition-specific eGenes. Similarly, all of the overlapping pathways (GS:MS and GS:ALS) identified using eGenes that were derived from the psoas muscle dataset were due to the co-occurrence of condition-specific eGenes . Thus, peGenes associated with NMD also co-occur in shared pathways, consistent with the existence of multimorbidity between these conditions . NotablyThe eGenes associated with GS, MG, MS, and ALS that were identified using the psoas muscle dataset also co-occurred in biological pathways . The shan = 536) as a background set. Ten thousand random samples were generated of the same size as the number of phenotype associated pathways. Bootstrap analysis reinforced that all pathway overlaps identified  between phenotypes were statistically more significant than expected by chance . Again, similar results were observed from both analyses , we determined that 13% of the eGenes we identified are druggable , 10. NotWe identified entirely unique sets of druggable eGenes for GS (4 eGenes), MS (18 eGenes) and ALS (1 eGene) from the psoas muscle that were not found in immortalized cell lines . Three oThis is the first report of shared biological mechanisms between genetic variants associated with the generalized age-associated decline of muscle strength (as measured by hand GS) and muscle weakness/wasting caused by neuromuscular disorders . The existence of shared eGenes, whose expression correlates with condition-associated SNPs, is consistent with subtle changes within combinatorial gene regulatory mechanisms contributing to the risk of generalized aspects of muscle weakness. Moreover, the observation of the co-occurrence of condition-specific eGenes within shared biological pathways provides insights into the mechanisms associated with the risk of development of generalized age- and disease-associated muscle weakness.C2, TAP2, MICB) specific to each of the phenotypes within immune system-related pathways is consistent with these pathways having a significant role in the development of age and disease-related muscle weakness. Similarly, we determined that the expression level of the SLC25A12 gene is associated with rs2044469 (GS), rs4953911, and rs882300 (MS) and, rs1400816 (ALS). Notably, alterations to SLC25A12 expression affect mitochondrial structure, function and biogenesis and are associated with age-related muscle dysfunction , axon guidance (hsa04360) and alcoholism (hsa05034) pathways contain eGenes whose regulation is associated with genetic variants from each of the four phenotypes. The axon guidance pathway guides axon outgrowth and plays a pivotal role in forming a properly functional nervous system . Axon guLTA; and ALS: CX3CR1) within the cytokine-cytokine receptor interaction pathway. The co-occurrence of shared and phenotype-specific eGenes within these pathway overlaps informs on potential pharmacological side-effects associated with therapeutic treatment  across cell types and lineages . HoweverThere are alternative explanations for the identification of more eGenes using Hi-C data from the immortalized cell lines  than froIn conclusion, we have identified the existence of common genetic mechanisms by which neuromuscular diseases and aging cause muscle weakness and wasting. The shared gene regulatory mechanisms and the shared pathways identified between the phenotypes represent novel therapeutic targets and highlight possible mechanisms for drug side-effects and other complications.https://github.com/Genome3d/codes3d-v1. R and python scripts used for data cleaning and generating figures are available in figshare with the identifier 10.17608/k6.auckland.9795350. R Studio version 1.2.1335 was used for all R scripts. Python version 2.7.15 was used for all the python scripts.CoDeS3D pipeline is available at All datasets generated for this study are included in the article /Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this manuscript.SG performed analysis, interpreted the data and wrote the manuscript. WS contributed to the data interpretation and commented on the manuscript. MW commented on the manuscript. DC-S commented on the manuscript. ES contributed to the data interpretation and commented on the manuscript. JO’S directed the study, co-wrote the manuscript, contributed to data interpretation and commented on the manuscript. JO’S is a guarantor for this manuscript.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.


The present study aimed to uncover the pharmacological function and underlying mechanism of puerarin as a potential treatment for COVID‐19, using an in silico methodology, including network pharmacology and molecular docking. The pivotal targets of puerarin to treat COVID‐19 were identified and included the epidermal growth factor receptor (EGFR), tumour necrosis factor (TNF), tumour protein p53 (TP53), caspase 3 (CASP3), RELA proto‐oncogene (RELA), Fos proto‐oncogene (FOS), caspase 8 (CASP8), prostaglandin‐endoperoxide synthase 2 (PTGS2), interleukin 2 (IL2), protein kinase CB (PRKCB), B cell lymphoma/leukaemia gene‐2 (BCL2), protein kinase CA (PRKCA), nitric oxide synthase 3 (NOS3) and peroxisome proliferator–activated receptor gamma (PPARG). Functionally, the anti–COVID‐19 action of puerarin was associated with the suppression of oxidative stress and inflammatory cascades, and cell apoptosis. The signalling pathways of puerarin to treat COVID‐19 included modulation of the pathways of apoptosis, IL‐17 signalling, mitogen‐activated protein kinase (MAPK) signalling and TNF signalling. Molecular docking data illustrated the binding capacity of puerarin with COVID‐19 and the effective anti–COVID‐19 activity of puerarin. Taken together, our current network pharmacology–based findings revealed the pharmacological role of puerarin in the treatment of COVID‐19. Furthermore, the bioinformatic findings elucidated that some of these pivotal targets might serve as potential molecular markers for detecting COVID‐19. Meanwhile, other COVID‐19–nosopoietic genes were acquired using the gene‐disease associations (DisGeNET) and GeneCards databases. Additionally, these acknowledged genes of puerarin and COVID‐19 were mapped prior to emendation using the UniProt tool. After functional enrichment analysis using FunRich software, all anti‐COVID‐19 biotargets of puerarin were screened and identified, as reported elsewhere.2.2The total acknowledged targets of puerarin and COVID‐19 were used to further determine and construct a functional protein association network via the STRING database following a specific algorithm. In addition, these merged targets of puerarin and COVID‐19 were used to construct a network of protein‐protein interactions (PPI) using the Cytoscape software. As revealed by topological parameters using the Network Analyzer tool, the pivotal targets of puerarin to treat COVID‐19 were identified and visualized.2.3q‐value cut‐off = 0.05 for enrichment prior to plotting the corresponding bubble chart, histogram and Circos circle chart.Using the R language packages, such as ‘ClusterProfiler’, ‘ReactomePA’, ‘org.Hs.eg.Db’ and ‘GOplot’ in the R language (3.6.1), the enrichment analysis and visualization of the biological process and KEGG pathways of the intersection targets were performed and obtained accordingly. Moreover, the information on gene annotation resulted from ‘org.Hs.eg.Db’, p‐value cut‐off = 0.05, 2.4Using the chemical‐protein binding method, the pivotal targets were screened out and identified for puerarin‐based molecular docking analysis. After searching for specific proteins through the PDB database, 5R84 protein was selected to dock with the puerarin compound. The ChemBio3D Draw in Chem Bio Office 2010 software was used to conduct the three‐dimensional structure of puerarin before docking the molecular structures using AutoDock Vina software. The rationality of the docking parameter setting was assessed according to the root‐mean‐square deviation (RMSD) of the ligand molecule. The RMSD ≤ 4 Å was the threshold for the conformation of the ligand molecule.33.1As revealed in Figure 3.2EGFR, TNF, TP53, CASP3, RELA, FOS, CASP8, PTGS2, IL2, PRKCB, BCL2, PRKCA, NOS3 and PPARG, as shown in Figure All the mapped intersection proteins were input into Cytoscape 3.7.1 software to calculate the topological parameters of the interaction network of targets and functionally related proteins of puerarin in the treatment of COVID‐19. The algorithm from the network freedom median degree of 6.129 and freedom maximum degree of 15 was used to identify the pivotal targets of puerarin to treat COVID‐19. Therefore, the pivotal target screening condition range was set to 7‐15, and finally, 14 pivotal targets were obtained. These genes included 3.3P‐adjust < .05) of pivotal targets were identified and were mainly involved in hepatitis B, human cytomegalovirus infection, AGE‐RAGE signalling pathway in diabetic complications, sphingolipid signalling pathway, human immunodeficiency virus 1 infection, apoptosis, measles, IL‐17 signalling pathway, MAPK signalling pathway, HIF‐1 signalling pathway, TNF signalling pathway, fluid shear stress and atherosclerosis, oxytocin signalling pathway, hepatitis C, leishmaniasis, influenza A, colorectal cancer, microRNAs in cancer, small‐cell lung cancer and Kaposi sarcoma–associated herpesvirus infection ‐KEGG‐COVID‐19 was created using Cytoscape 3.7.1 software, as shown in Figure 3.5From the interaction diagram of the original ligand and the protein‐ligand complex, the data showed the amino acid residues HIS‐163 and GLU‐166, where the original ligand GWS binds to the 5R84 protein. In the three‐dimensional picture of puerarin, it is observed that the binding residues of hydrogen bonded with TYR‐54, ASN‐142, GLU‐166 and GLN‐189. In addition, puerarin formed hydrogen bonds with the amino acid residues TYR‐54, ASN‐142, GLU‐166 and GLN‐189 in the 5R84 protein ; Formal analysis ; Investigation ; Validation . Chen Huang: Data curation ; Formal analysis ; Methodology ; Writing‐review & editing (lead). Ka Wu: Resources ; Software ; Visualization . Yu Li: Data curation ; Formal analysis ; Investigation ; Software . Xiao Liang: Methodology ; Resources ; Software . Min Su: Funding acquisition ; Supervision ; Writing‐original draft . Rong Li: Conceptualization ; Funding acquisition ; Project administration .Table S1Click here for additional data file.Table S2Click here for additional data file.Table S3Click here for additional data file.


Fuxin mixture (FXHJ) is a prescription for the treatment of heart failure. It has been shown to be effective in clinical trials, but its active ingredients and mechanism of action are not completely clear, which limits its clinical application and international promotion. In this study, we used network pharmacology to find, conclude, and summarize the mechanism of FXHJ in the treatment of heart failure. From FXHJ, we found 39 active ingredients and 47 action targets. Next, we constructed the action network and was conducted enrichment analysis. The results showed that FXHJ mainly treated heart failure by regulating the MAPK signaling pathway, PI3KAkt signaling pathway, cAMP signaling pathway, TNF signaling pathway, toll-like receptor signaling pathway, VEGF signaling pathway, NF-kappa B signaling pathway, and the apoptotic signaling molecule BCL2. Through the research method of network pharmacology, this study summarized the preliminary experiments of the research group and revealed the probable mechanism of FXHJ in the treatment of heart failure to a certain extent, which provided some ideas for the development of new drugs. Heart failure (HF) is a closed result of most cardiovascular diseases, which may be caused by damage to ventricular filling from various structural or functional disorders of the heart . In obseFXHJ is a prescription for the treatment of heart failure obtained by Dr. Xue through the combination of clinical experience and literature data. It is composed of Aconiti Lateralis Radix Praeparata (FZ), Angelicae Sinensis Radix (DG), Phellodendri Chinrnsis Cortex (HB), Lepidii Semen Descurainiae Semen (TLZ), Epimrdii Herba (YYH), and Alisma Orientale (ZX). Preliminary clinical trials of the research group have shown that FXHJ can significantly improve cardiac function, delay left ventricular remodeling, extend the 6WMT distance, improve exercise tolerance, reduce plasma BNP level, and improve patients' quality of life .Due to the multicomponent and multitarget characteristics of Traditional Chinese medicine, it is difficult for us to clearly understand its mechanism of action. Therefore, we turn our attention to network pharmacology. Network pharmacology is an emerging discipline based on the integration of systems biology, molecular biology, pharmacology, and a variety of network computing platforms in the context of the era of big data . BecauseThe mechanism of multicomponent and multitarget action of TCM has hindered the establishment of an evidence-based model of TCM and the study of toxic and side effects. Therefore, the concept of “equivalent molecular group” is suggested in this study. The various components in FXHJ can be roughly classified into three categories in the treatment of heart failure. The first had no association with the treatment of heart failure. The second had a positive effect on the treatment of heart failure, which we called active compounds. The third had a negative effect on the treatment of heart failure. Although the three components acted differently, on the whole, their final results were affirmative. After eliminating the inactive components, we take the sum of the components of positive and negative effects and turn them into the equivalent molecular group. In short, an equivalent molecular group is a collection of disease-related components, whether they play a positive or negative role in a disease.In this study, we used the method of network pharmacology to explore the equivalent molecular group of FXHJ through the previous experimental results and elucidated the material basis and potential mechanism of action of FXHJ in the treatment of heart failure so as to provide some ideas for the development of new drugs.http://tcmspw.com/tcmsp.php) is an open and comprehensive database of Chinese medicine ingredients and action targets. In clinical treatment, TCM is often used by oral administration. Oral bioavailability (OB) and drug-likeness (DL), two ADME-related models, are the main variables affecting the absorption of drugs from the gastrointestinal tract. Therefore, we screened bioactive components under the conditions of OB ≥ 30% and DL ≥ 0.18. Next, we find the target from the list of compounds. At the same time, we used the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) to standardize the names of active ingredients. In order to make the results concise and convenient, we utilized the Uniprot database (https://www.uniprot.org/) to to convert the protein name of the target into the gene name. Then, we take the union of all the consequences and delete the duplicates. In order to get the disease target of heart failure, we used “heart failure” as the keyword to retrieve the target of heart failure in the Genecards database (https://www.genecards.org/). After the intersection of the target of FXHJ and the target of heart failure, we obtained the exact target of FXHJ in the treatment of heart failure.Chinese medicine systematic pharmacology database (TCMSP)  to obtain KEGG signaling pathway data. We analyzed the results with P value less than 0.05 to obtain the results. According to the enrichment results and the preceding experimental results, we obtained the functional pathway of FXHJ in the treatment of heart failure. After that, we screened the effective targets and components in reverse according to the action pathway and obtained the equivalent molecular group.Direct targets were recorded into David 6.8 database , download the 3d structure of the active ingredient file (“SDF” format). Open Babel is used to hydrogenate atoms in molecular structure. Select the MMFF94 field to add the charge and minimize energy. Finally, AutoDcok Tools are utilized to convert the compound into a PDBQT format file.In the PubChem website (http://www.rcsb.org/).Download the crystal structure of the key targets molecule from the PDB website , TNF (2AZ5), PIK3CG (2CHZ), PTGS2 (5KIR), and MAPK1 (5NHV). The docking results are shown in We visualized the combination of ligand and receptor with the highest Affinity, as shown in In the treatment of heart failure, how to slow or even reverse ventricular remodeling has grown up to be a hot topic in recent years. Transforming growth factor beta, angiotensin II, aldosterone and endothelin, and myocardial damage or stress produce cytokines and chemokines can induce the formation of muscle fibroblasts , 11. In P < 0.01) [There are three subclasses in the MAPK signaling pathway, namely ERK, JNK, and p38MAPK. Cipolletta et al. demonstrated that the inhibition of p-ERK volume could delay cardiac hypertrophy and improve cardiac volume . Our pre < 0.01) .β cytokine downstream of PI3KAkt, which can be inactivated by phosphorylation, is a key negative regulator of cardiac hypertrophy [The active form of PI3K can activate AKT, thereby promoting cell proliferation and inhibiting the apoptotic pathway. Overexpression of PI3K can cause cardiac hypertrophy. The GSK3ertrophy . Our preertrophy .β1-AR-cAMP-PKA pathway in previous experiments and found that compared with the model group, both the FXHJ group and the captopril group could reduce the plasma cAMP level, increase the cAMP reserve of cardiomyocytes, improve cardiac function, and delay ventricular remodeling [cAMP ACTS as excitation-coupled phosphorylation of cardiomyocyte proteins, including l-type calcium channels, sarcoplasmic reticulum ATPase 2 regulatory protein phosphor, clonidine receptor 2, phosphatase 1 inhibitors, and various contractile proteins . Lin B emodeling .α (TNF-α) and the apoptotic signaling molecule BCL2 play important roles in the progression of heart failure. Liu W et al.'s serum examination of patients with chronic heart failure found that, compared with the healthy control group, the serum TNF in patients with heart failure was significantly increased, while bcl-2 was significantly decreased [α levels exacerbate heart failure. The optimal use of diuretics, ACE inhibitors, beta-blockers, and standard treatment with digoxin for heart failure can significantly reduce circulating TNF levels [α in rats with heart failure, improve the expression of the BCL2 gene, and thus inhibit a variety of myocardial cell apoptosis pathways [Tumor necrosis factor ecreased . StudiesF levels . SiltaneF levels . Our prepathways , 23.In addition, through the research method of network pharmacology, we analyzed and found that the mechanism of action of FXHJ in the treatment of heart failure may also be related to the toll-like receptor signaling pathway, VEGF signaling pathway, and NF-kappa B signaling pathway. Studies have shown that 8 weeks after knocking out NF-kappa B subunit P50/NF–B1 in mice with myocardial infarction, the degree of ventricular expansion is mild, and ventricular systolic function is preserved. Therefore, the absence of the NF-kappa B subunit P50 can improve the prognosis of heart failure caused by myocardial infarction. At the same time, inhibition of the NF-kappa B signaling pathway can inhibit ventricular remodeling to a certain extent, which may be related to the reduction of preinflammatory response and the regulation of extracellular matrix . InhibitThe analysis of the equivalent molecular group shows that there is no ZX in it. This is because the active components of ZX are mainly Alisol A 24-acetate and Alisol B, which have similar structures to aldosterone and its antagonists. They compete for aldosterone receptors to inhibit reabsorption in different parts of the tubules, inhibiting water reabsorption and increasing urine output . This beβ-sitosterol promotes apoptosis by regulating the ERK and PI3KAkt signaling pathways [β [β factor and cAMP and inhibiting the expression of the NF-kappa B signaling pathway [Some of the components in the equivalent molecular group have been studied experimentally, which can provide some reference for the further selection of the components. Moon DO et al. demonstrated that pathways , which ihways [β –37. Min hways [β . Meanwhi pathway –41. Alth pathway , 43, oth pathway , 45. Thi pathway , a mecha pathway , 48. Gao pathway .Results of molecular docking showed that the Affinity of ligand and receptor was higher. This indicated that the ligand could well bind to the receptor, thus inhibiting the expression of this pathway. As mentioned above, inhibiting these three pathways can delay the progression of heart failure.Interestingly, it can be evident from However, there were a few shortcomings in this study. Due to limited funds, this study did not obtain the blood composition of FXHJ by mass spectrometry but selected database screening, which may cause certain errors. Secondly, although animal experiments and clinical trials have shown that FXHJ has a beneficial therapeutic effect, there is still a lack of experimental research on some components. Further research is needed to determine whether these components will cause side effects.In summary, this study revealed the mechanism of FXHJ in the treatment of heart failure through the research method of network pharmacology and summarized the previous experimental results of the research group. It wishes to point out the direction for new drug development and further research. At the same time, the concept of the equivalent molecular group provides innovative ideas for the transformation and research of the modernization of TCM.