The four major pathways in gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) including chromatin remodeling, DNA damage repair, activation of mTOR signaling, and telomere maintenance were mediated by some critical molecules and constituted critical processes of regulation in cancer-causing processes. However, the interplay and potential role of these pathway-related molecules in the tumor microenvironment of the primary and metastatic site remained unknown.We systematically evaluated the mRNA expression of 34 molecules associated with the four pathways in 227 GEP−NEN samples from 5 datasets. We assigned the samples into two expression patterns of pathway-related molecules by an unsupervised clustering method. Subsequently, we explored the specific cell-related molecules, especially immune and stromal cells using the WGCNA method, based on differentially expressed genes (DEGs) responsible for the different patterns of pathway-related molecules, which provided a new method to qualify the pathway-related subtypes of individual tumors, then the PC_Score and PI_Score scoring systems were also constructed using obtained specific cell-related molecules. Furthermore, we performed the association of pathway-related subtypes with characteristics of immune landscape in primary and metastatic GEP-NENs.We demonstrated that the specific pathway-related molecules  were associated with cytolytic activity. Then we identified the two distinct patterns of pathway-related molecules, which were characteristic with a significantly distinct immune landscape. Using WGCNA, we also identified the fibroblast-related molecules, including ASPN, COL10A1, COL3A1, EDNRA, MYL9, PRELP, RAB31, SPARC, and THBS2, and immune-related molecules including CASP1, CCL5, CTSS, CYBRD1, PMP22, and TFEC. Based on these specific markers, we identified four distinct pathway-related subtypes, characterized by immune and fibrotic enriched (I/FE), immune enriched (IE), fibrotic enriched (FE), and immune and fibrotic desert (I/FD), of which I/FE was characteristic with the highest PC_Score and PI_Score whereas I/FD presents the opposite trend. I/FE was positively correlated with immune landscape of T-cell activation and immunosuppression. Furthermore, the I/FE marked GEP-NENs with increased immune activation scores . Importantly, the four distinct pathway-related subtypes were not conserved in different tumor sites, because I/FE was lacking in the liver metastatic site even though IE, FE, and I/FD also could be observed in the metastatic site.This study was the first to perform a comprehensive analysis of the four major pathways in GEP-NENs. We demonstrated the potential function of these pathway-related molecules in immune landscapes. Our findings indicated that the primary and metastatic GEP-NENs had distinct antitumor phenotypes. This work highlighted the interplay and potential clinical utility of these pathway-related molecules in GEP-NENs. Gastroenteropancreatic neuroendocrine neoplasms (GEP−NENs), a rare tumor, accounted for about 1% of all malignancies and were the second most common form of gastrointestinal malignancy with a mortality rate of 60% . One of Currently, the prerequisites for good immunotherapy were the preexistence of immune checkpoints and the higher infiltration of effector CD8 T cells . The prePlenty of studies had pointed that the mutations of GEP-NENs, including point mutations and gene fusions, had been found in molecules associated with four main pathways: chromatin remodeling, DNA damage repair, activation of mTOR signaling, and telomere maintenance , 8, 9. HPublic gene expression data were retrieved in Gene Expression Omnibus (GEO). Five GEO cohorts  were used as discovery groups which include 204 GEP-NENs and 23 normal samples while another cohort (GSE98894) was set as the validated group including 130 primary samples and 69 liver metastases. The batch effects from non-biotechnological bias was corrected by the “sva” package , an R BiThe specific pathway-related molecules associated with GEP-NENs include 11 telomere maintenance  , 9 mTOR We classified all GEP-NNE samples into two distinct subtypes based on the expression of pathway-related molecules. The empirical Bayesian approach of the “limma” R package was used to identify DEGs between different pathway-related patterns , with thWe conducted gene set variation analysis (GSVA) enrichment analysis using “GSVA” R packages to investigate biological processes among different pathway-associated patterns . GSVA enUsing WGCNA , we coulWe quantify the infiltration level of 28 immune cell types using the single−sample gene−set enrichment analysis (ssGSEA) method, which provided the enrichment score to measure the abundance of immune cells. Gene set signatures of these immune cell types were obtained from a previous study . The ssGi × Expi),where i means the specific phenotype-related genes.1) The specific phenotype-related genes were identified. We used the specific pathway-related genes which had a strong correlation with cytolytic activity to conduct unsupervised hierarchical clustering. The DEGs between the subgroups were obtained using the limma R package. Only these DEGs which had a correlation to cell fractions with a threshold of MM >0.7 and GS >0.6 using WGCNA were used to conduct further exploration. We performed a univariate logistic regression model to calculate the odds radios of GEP-NEN occurrence. Then DEGs related to tumor occurrence were screened to construct a scoring system. 2) A scoring system was constructed. After obtaining the odds radios of each specific gene score, we then define the PC_Score and PI_Score of each patient: PC_Score/PI_Score = , MLH1 , TSC1 , ATRX , and ATR . The clinical tissue samples used in this study were histopathologically and clinically diagnosed at Fudan University Shanghai Cancer Center from 2014 to 2019. Two characteristics were used for scoring the expression of markers in slices: overall stain intensity  and a score representing the percentage of tumor cells that were stained . An immunohistochemistry (IHC) score was then calculated by multiplying the values of the two characteristics. Based on the IHC score, patients were divided into two groups: high expression (IHC score ≥median) and low expression (IHC score < median).Spearman and distance correlation were used to calculate the correlation coefficient of these pathway-related molecules. The Wilcoxon test was used to compare the differences. To assess the specific gene associated with tumor occurrence, a univariate logistic regression model was used to calculate the odds ratio (OR). Because of the correlation between the four specific pathways and tumor occurrence, the “simDesign” package of R was used to identify the best cutoff point of each gene. All statistical analyses considered P < 0.05 as statistical significance.2 >0.2, P < 0.001), named PTEN, CHEK2, SMARCA4, MLH1, TSC1, TSC2, ATRX, and ATR  with clinical information, we collected microarrays of pancreatic neuroendocrine tumors from 132 tissues, a total of 34 patients, including 30 adjacent tissues and 102 tumor tissues. We performed immune scores on these 132 tissues by IHC . The results indicated that PC_Score of cluster 1 was significantly higher than that of cluster 2, and similar results were also found in PI_Score. Interestingly, while we used the above 620 different genes between cluster 1 and cluster 2 to perform consensus clustering, the same two subgroups were also obtained, named gene_cluster_A and gene_cluster_B. gene_cluster_A had a significantly lower PC_Score and PI_Score than gene_cluster_B . Consistent with the observations from the discovered cohort, the five-pathway-related molecules  were correlated in the replication cohorts  and established a new PI_Score and PC_Score scoring model. In the subgroups, we assessed the immune status of GEP-NENs by exploring three essential processes in the immunity process : first, Our findings supported recent evidence that pathway-related molecules could drive T-cell infiltration and antitumor response, but the relationship of these genes with immunogenicity was still difficultly explored because of the lack of public data. Upregulation of oncogenic pathways playing critical role in immunosuppression had been suggested . For exaTogether, this study showed that specific pathway-related molecules were associated with transcriptional and cellular metrics of immune landscapes including effective T-cell response and APC response processes in GEP-NENs, both of which were required for antitumor immunity and could potentially serve as predictors of response to immunotherapy. We hoped that this correlation in GEP-NENs could find out the potential role of these pathway-related molecules in recomposing antitumor immunity in GEP-NENs. Indeed, the five-pathway-related molecules in our panel had been confirmed to be associated with immune cell infiltration and response to immune-checkpoint blockade, which provided an indication for a therapeutic strategy. Our findings might also be applicable for advanced GEP-NENs including liver metastasis. Understanding the underlying immunobiology in GEP-NENs might establish a new method to drive effector immune cell infiltration into the periphery of tumor cells and help more patients apply immunotherapeutic strategies.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/SJ, XY and YQ designed the study. XL, HG, and XX developed the methodology. XL, HG, XX, ZY, WZ, FW, JC, YZ, and XC performed the data analysis. XL, HG, and XX drafted the manuscript. All authors contributed to the article and approved the submitted version.This study was funded by the National Natural Science Foundation of China (U21A20374), Shanghai Municipal Science and Technology Major Project (21JC1401500), Scientific Innovation Project of Shanghai Education Committee (2019-01-07-00-07-E00057), Clinical Research Plan of Shanghai Hospital Development Center (SHDC2020CR1006A), Xuhui District Artificial Intelligence Medical Hospital Cooperation Project (2021-011), Shanghai Municipal Science and Technology Commission (20ZR1471100), National Natural Science Foundation of China , Commission of Health and Family Planning (2018YQ06), and Shanghai Municipal Science and Technology Commission (19QA1402100).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Applying Petri net analysis techniques, we found 279 pathways, which describe signal flows from receptor activation to cellular response, representing the combinatorial diversity of functional pathways.120 pathways steered the cell to survival, whereas 58 and 35 pathways led to apoptosis and necroptosis, respectively. For 65 pathways, the triggered response was not deterministic and led to multiple possible outcomes. We investigated the in silico knockout behavior and identified important checkpoints of the TNFR1 signaling pathway in terms of ubiquitination within complex I and the gene expression dependent on NF-κB, which controls the caspase activity in complex II and apoptosis induction. Despite not knowing enough kinetic data of sufficient quality, we estimated system’s dynamics using a discrete, semi-quantitative Petri net model.The paper describes a mathematical model of the molecular switches of cell survival, apoptosis, and necroptosis in cellular signaling pathways initiated by tumor necrosis factor 1. Based on experimental findings in the literature, we constructed a Petri net model based on detailed molecular reactions of the molecular players, protein complexes, post-translational modifications, and cross talk. The model comprises 118 biochemical entities, 130 reactions, and 299 edges. We verified the model by evaluating invariant properties of the system at steady state and by  in silico knockout analyses to the Petri net model and could identify important checkpoints of the tumor necrosis factor 1 signaling pathway.It is still a challenge to develop mechanistic models for big molecular systems without the knowledge of enough kinetic parameters of sufficient quality. At the same time, more qualitative and semi-quantitative data have been produced in increasing numbers, e.g., by new high-throughput technologies. This has generated demands for new concepts at appropriate abstraction levels. The Petri net formalism enables the integration of qualitative as well as quantitative data and provides algorithms and methods for model verification and model simulation. Moreover, Petri nets exhibit a clear and coherent visualization. Here, we modeled the molecular switches between cell survival, apoptosis, and necroptosis induced by tumor necrosis factor 1. We were interested not only in an exhaustive exploration of all possible signaling pathways, but also in finding the system’s checkpoints. Our Petri net model comprises 118 biochemical entities, 130 reactions, and 299 edges. We found 279 pathways that describe signal flows from receptor activation to cellular response.120 pathways steered the cell to survival, whereas 58 and 35 pathways led to apoptosis and necroptosis, respectively. For 65 pathways, the triggered response was not deterministic, leading to multiple possible outcomes. We applied  S1 Text.The tumor necrosis factor receptor 1 (TNFR1) controls pivotal cellular processes involved in immunity and developmental processes ,3. ApoptWhereas apoptosis is a well-known and well-studied pathway, the regulation and function of the necroptosis pathway has just recently been discovered and is still under study ,8. NecroAlternatively to cell death, the activation of nuclear factor κ-light-chain-enhancer of activated B cells (NF-κB) initiates the gene expression of mainly pro-inflammatory and anti-apoptotic operating genes . TherefoFig 1. The activity and function of RIP1 is sensitively controlled [The regulation of the opposing signaling cascades has been often considered as a molecular switch. Receptor-interacting protein 1 (RIP1) seems to have a pivotal function in modulating the controversial outcomes since it is an essential signaling node in all pathways, see ntrolled , for exantrolled  and assintrolled . Linear ntrolled –23. The ntrolled ,24.Although high-throughput technologies have provided many experimental data, there is a lack regarding the quality, quantity, and completeness of the data. Computational models are powerful approaches to represent and understand the complexity of biological systems. Computational systems biology can provide information on the system-wide behavior without knowing kinetic parameters. Systematic analyses can gain new insights of regulation, reveal correlations in diseases and pathologies, and can suggest potential targets for therapeutic treatment ,26. Newlet al. have proposed a Boolean model of the processes of apoptosis, which considers several stimuli [et al. have merged two existing models to an ODE model with pro- and anti-apoptotic responses of TNFR1 signaling [et al. have introduced a hybrid model, covering the stimulation of seven receptors and 22 cytokine stimuli in immunological pathways [et al. have investigated effects of different A20 feedback implementations for the NF-κB signaling dynamics, applying ODE modeling techniques [The TNFR1 signaling pathway has often been a subject of mathematical modeling . The mod stimuli . Schliemignaling . Melas epathways . Very rechniques . All theThe optimal modeling approach should be guided by the current knowledge and the amount and quality of the available data of the biological system under study and by the questions that should be addressed. For signaling pathways, many qualitative information is available, although exact parameters and kinetic data are scarce. The reason is that signaling systems exhibit kinetics, which is hard to elucidate in experimental investigations. Therefore, biological systems often lack of sufficient data. Only small-sized systems have been modeled and analyzed using equation-based approaches, for example, the IκB-NF-κB signaling module . Qualitatransitions visualized as squares or rectangles. The chemical or biological entities, like proteins, DNA, RNA, or metabolites, are modeled as places drawn as circles. Places and transitions are connected via directed edges. To model the dynamics of a system, we used movable objects—the tokens. Places can carry tokens, which represent a number of an entity, e.g., one mole of a compound. Tokens can move through the system according to specific firing rules. Starting with an initial distribution of the tokens on the places that could correspond to a physiological state, we interpret a flow of tokens as a flow of substances or a flow of signals. An important feature of PNs is the availability of mathematically proven techniques for verification of consistency and of correctness of a model [place invariants and transition invariants. Place invariants are sets of places (entities) that describe the conservation of substances. A transition invariant is a set of transitions that represents a specific fundamental pathway. Transition invariants decompose the PN model into modules. The module of a transition invariant is the subnetwork defined by its transitions (reactions), the places connected to the transitions, and edges between the transitions and places. For biological models, transition invariants represent functional modules. Each reaction should be part of a transition invariant, otherwise the reaction could be removed from the model or the model contains an error. Thus, transition invariants can be applied to check the model for consistency and partly also for its correctness. For more detailed information, we refer to section “Materials and Methods”.We apply a mathematical formalism called Petri nets (PNs) that enables to explore the dynamics in a non-deterministic way. For model construction, we need the knowledge on the chemical reactions, such as complex formation, phosphorylation, ubiquitination, or metabolic reactions. This active part of the system is modeled as  a model ,61. In pin silico knockout analysis to investigate and discuss the system’s behavior of the PN. This includes a detailed discussion of the molecular switch behavior in the TNFR1-induced signaling pathway.In this paper, we were interested in an exhaustive modeling of the molecular switch behavior of the TFNR1-induced signaling pathway, covering the NF-κB pathway, apoptosis, and necroptotic processes. Our model primarily focuses on the TNFR1-mediated pathways because TNFR1 is a ubiquitous membrane receptor, which has been studied intensively by numerous experimental groups. We abstained from incorporating further receptors. An extension of the model to include other relevant cell death-inducing receptors, as, e.g., the FAS receptor, would significantly increase the complexity of the model but is a worthwhile task for future work. Here, we developed a semi-quantitative PN model and applied invariant-based methods and in vivo conditions is difficult. The lack of kinetic parameters motivated us to work at a higher level of abstraction. We chose the Petri net formalism [It is crucial to find the suitable level of abstraction and scope of model with regard to the available knowledge of the signaling pathway and the questions to be addressed. Significant aspects are the availability, amount, quality, and reliability of experimental findings. For the TNFR1 signaling system, kinetic data are scarce and do not cover the entire scope of processes. We cannot build a fully quantitative model because the required knowledge of stoichiometry, concentration, and other kinetic parameters is lacking for many processes. Note that, experimental investigation of detailed kinetics under relevant ormalism ,32 as thormalism , see FigInformation on the stoichiometry was lacking for the majority of reactions. We abstained to speculate about reaction kinetics, as, e.g., the Michalis-Menten kinetics, that might be reasonable for some of the reactions. Instead, we abstracted processes by a single transition if not more detailed information was available. The PN model might ignore the detailed kinetics of many reactions. Note that, a detailed kinetic model of the entire TFNR1 pathway is currently out of the scope of any theoretical approach. Moreover, detailed kinetics is mostly available for specific cell lines and specific organisms. The specificity for cell line and organism reduces the relevant experimental data that can be integrated into a kinetic model. The PN approach enables the further development of a model of high abstraction level to a detailed kinetic model if the relevant information becomes available.Fig 1 schematically illustrates the molecular processes of TNFR1 signal transduction. This pathway is initiated by the stimulation of the TNF receptor followed by the formation of complex I and a diversity of consecutive and concurrent molecular processes. An example is the translocation of NF-κB into the nucleus, which facilitates gene expression activity and transcription of proteins like IκB, A20, cellular FLICE-inhibitory protein (cFLIPL), B-cell lymphoma 2 (BCL-2), and X-linked inhibitor of apoptosis protein (XIAP). The transcription of these proteins affects, e.g., the regulation of the TNFR1 signaling pathway. The formation of complex IIa, complex IIb, and the necrosome may induce either apoptosis or necroptosis.Materials and Methods”. Based on the processes illustrated in Fig 1, we constructed a PN model to analyze the broad combinatorial spectra of signaling pathways. The model comprises stoichiometry relations for well-studied processes in combination with the abstraction of a simple transition for processes with unknown stoichiometry or controversial experimental findings. Fig 2 represents the PN model with 118 places, 130 transitions, and 299 edges. For a SBML version of the Petri net, we refer to , respectively. In Fig 2, signal cascades towards NF-κB activation, apoptosis, and necroptosis are highlighted blue, green, and red, respectively. A dot in a circle indicates a place with one token in the initial marking. Gray circles represent logical places, which appear at several locations of the network layout and represent one unique place of the same name in the PN. On the left side, the layout separately shows the synthesis of 26 housekeeping proteins that are required for maintaining the basic cellular function. The input and output transitions were labeled according to their biological meaning. All other transitions were consecutively numbered. Input transitions (squares without outgoing edges) represent syntheses of proteins. Output transitions (squares without incoming edges) model the diverse cellular outcomes, like apoptosis, necroptosis, or survival as well as degradation and dissociation processes for proteins and protein complexes, respectively. The places were labeled according to the biological meaning, e.g., by the names of a protein, a modified protein, or a protein complex. To ensure correctness and completeness of the model to the greatest possible extent, we applied the invariant analysis.In the following, we refer to the PN terminology, which we explain in detail in section “Table 1.The five place invariants (PIs) of the PN, all containing two places, represented the conservation of the proteins IκB, A20, XIAP, cFLIPL, and BCL-2, see Fig 3 depicts the regulation of NF-B activity highlighting the place invariant PI1. PI1 describes the conservation of the gene of IκB, which was neither produced nor degraded. The total token load never changes for the two places and with regard to the initial marking, the token is either allocated to place IkB_g or place NF-kB:IkB_g. In the initial marking, a token was assigned to place IkB_g, which represents the gene of IκB. The activated transcription factor can bind to the gene (transition T26) and induces the transcription of the mRNA. Following the transcription, the gene as well as the transcription factor dissociated, and both were regained (transition T27). The other PIs of the PN, PI2, PI3, PI4, PI5, featured a similar regulatory motif.S5 Table. All TIs contained input and output transitions. Only TI18 was a trivial TI, which described the synthesis and degradation of NF-κB. 33 of the 48 TIs represented incomplete signaling pathways, so-called dissected pathways. Dissected pathways do not cover a pathway from receptor activation to cell response. S5 Table indicated dissected pathways by TI numbers in bold face. S1 (TI15) and S2 (TI9) Figs show examples of dissected pathways. A dissected pathway ignores interrelation with other pathways, see, e.g., the missing activation of NF-κB that is necessary to induce the A20 feedback regulation of TI9. For the list of TIs and their biological interpretations, we refer to S5 Table. The remaining 15 TIs were also Manatee invariants (MIs) that describe complete signaling pathways, i.e., from the receptor activation to the cell response [Materials and Methods”. For the verification of a biological PN, we postulated as important criterion that every TI should be biologically meaningful.The examination of transition invariants (TIs) is an important analysis for the verification of the PN since they reflect basic dynamic patterns. The TNFR1 PN was covered by 48 TIs, wherein each transition was part of a TI. All 48 TIs represented reasonable signal flows, see response , see sec2 colored green in Fig 4 comprised the formation of complex I, along with its ubiquitination with K63 and M1 Ub chains, the dissociation of complex I by ubiquitination via CYLD recruited to LUBAC, the formation of complex IIb, and extrinsic activation of caspase 3, which induces apoptosis. TI2 described a reasonable signal flow in the PN and hence corresponded to a possible pathway mediated by TNFR1 signal transduction.Exemplarily, TIS6 Table. Each of the 279 MIs represented a unique pathway of the molecular switch between cell survival, apoptosis, and necroptosis. Exemplarily, Fig 5 highlights MI7 that combined three TIs, TI9 colored blue, TI15 colored red, and TI18 colored green. MI7 exemplified typical mutual dependencies of TIs that make isolated TIs nonfunctional. The red signal flow described by TI15 required NF-κB, i.e., a token on place NF-κB, as well as a token on place CI (complex I). NF-κB was provided by transition Syn_NF-κB of the green TI18. Complex I was provided by transition T13 of the blue TI9. Vice versa, the signal flow described by the blue TI9 cannot work without NF-κB in the nucleus, i.e., a token on place NF-κB_n. Translocation of NF-κB into the nucleus required an active transition T25 of the red invariant TI15.Overall, we found 279 MIs, see 9S2 Fig describes the dissected pathway of the A20 feedback regulation in complex I. MI7, see Fig 5, which includes TI9 colored blue, represents a complete signal flow. MI7 includes the A20 feedback loop and covers the signal flow of complex I formation and activation of NF-κB with its translocation into the nucleus and gene expression of IκB and A20. The inhibitor, IκB, terminates gene expression and restores the inhibitory complex of NF-κB and IκB in the cytosol. A20 binds to complex I, leads to the dissociation of the complex, and prevents the formation of complex II. For other MIs that contain TI9, see Figs.TIS6 Table. For space reasons, we abstained from a discussion of each individual pathway. We classified the MIs according to their biological outcome. We assigned 166 MI-induced subnetworks to unique cell response, either survival, apoptosis, or necroptosis. We assigned 65 MIs to multiple cell responses and called them ambiguous pathways. An ambiguous pathway covers, e.g., the inhibition of MOMP induction, which would result in cell survival and apoptosis induction via the extrinsic pathway. In this special case, MOMP induction is part of the intrinsic pathway, but extrinsic apoptosis induction is still possible. Thus, the MOMP induction would be classified as an apoptosis pathway. This was true for 48 MIs of the 113 MIs, so they were all considered for the classification, overall 214 (48 + 166) MIs.Each of the 279 MIs denoted a complete and unique signaling pathway, see Fig 6. We neglected the 65 ambiguous pathways because they either could trigger both types of cell death, apoptosis and necroptosis, or represent housekeeping pathways without induction of a specific cellular response. A simple example of a housekeeping pathway is the synthesis and degradation of NF-κB described by TI18 colored green in Fig 5. Note that, TI18 also corresponds to MI18. For pathways that can trigger both types of cell death, accurate quantitative simulations would be required to determine the stochastic chance of the cell to end up either in apoptosis or necroptosis.The largest fraction of 56% of the pathways steered the cell to survival, whereas 27% and 17% of the pathways led to apoptosis and necroptosis, respectively, see in silico knockout analysis we wanted to get the number of blocked molecular species downstream of the pathways [in silico knockout analysis for all proteins and the complete set of proteins and protein complexes. We selected all transitions, which represent protein syntheses, and all places of the PN model except for the places of a PI. In total, we considered 31 transitions and 108 places in the knockout matrix illustrated in Fig 7. The knockout was performed applying the software tool isiKnock [The knowledge of the combinatorial diversity of pathways enabled us to estimate the vulnerability of the system to perturbations, caused, for example, by knockouts of proteins. We examined the PN for its robustness properties and vulnerability, applying knockout studies on MIs. In the pathways . We perfisiKnock  based onFig 8 shows a bar plot of the percentage of the network that becomes inoperable, if we would knockout the synthesis of a specific protein. We ranked the proteins according to the percentage of affected pathway entities. The dissection of the hierarchy of a pathway is important for potential application in therapeutic interventions. A protein that is a player more upstream in the pathway may have also an impact on other downstream branches in an undesired form of crosstalk. Therefore, a later intervention of the pathway is often more favorable because it acts more specifically [Fig 8) and TNFR were top-ranked as they are essential upstream in each pathway. RIP1 was an important node, as it plays key roles in NF-κB activation, apoptosis, and necroptosis. However, not all branches of the network were RIP1-dependent, like apoptosis mediated via complex IIa. Only housekeeping pathways remained unaffected. Proteins of the intrinsic apoptotic branch, necroptosis, and proteins upregulated by NF-κB had more specific functions in the molecular switch and got a lower ranking.All protein knockouts affected at least one pathway component, which emphasized that all proteins have a specific function in the network. ifically . Some prWe performed a clustering of the knockout data of the complete in silico knockout matrix in The three major branches of the TNFR1 signaling pathway are illustrated in in silico knockout for an additional verification of the PN model and for a discussion of the molecular switch behavior. Whereas some knockout effects were obvious, others can only be derived from network analysis. In the following, we discuss exemplary knockouts in detail. Fig 10 shows a subsection of the in silico knockout matrix in Fig 7. We selected 20 proteins for single knockout and determined the effects for 21 pathway entities, see S7 Table. The additional two rows in Fig 10 represent the effect of the multiple knockouts for SMAC mimetic and the impairment of translation by cycloheximide. The selection should allow to deduce the impact of the proteins on the molecular switching behavior. Therefore, we examined the effect of the in silico knockouts on selected pathway entities, which represent important signaling nodes and cover all pathways, including complex I (CI), complex IIa (CIIa), complex IIb (CIIb), apoptosome, and necrosome (RIP1:RIP3). To perform a detailed analysis for the knockout of all proteins in all network components is out of the scope of the paper.We employed the in silico single and multiple knockouts of the submatrix in Fig 10 summarized in S7 Table.In the following, we explicitly describe the results of Knockout of BAX (row 1): There were six red entries. Obviously, the knockout of BAX had a negative effect on the complex formation of BCL-2 and BAX (BCL-2:BAX), but an impact on the activation of CASP9 in the apoptosome . The activation of CASP3 and CASP8 were affected .Knockout of cFLIPS (row 2): We observed only one red entry for the complex of cFLIPS bound to complex IIb (CIIb:cFLIPs). cFLIPS can promote necroptosis induction in complex IIb. Since other pathways exist that can induce necroptosis, the knockout of cFLIPS had no direct effect on necroptosis induction.Knockout of cIAP1/2, TRAF2 : Both rows had the same ten red entries affecting the formation of complex I and the NF-κB-dependent gene expression as well as the feedback and crosstalk regulation of the target genes. This emphasized the direct regulation of both proteins since cIAP1/2 requires TRAF2 for recruitment.Knockout of CYLD (row 4): There was one red entry for the complex of CYLD bound to complex I (CI:CYLD). CYLD promotes the dissociation of complex I and the formation of complex II. Since several pathways also cover downstream processes, no additional effects were observed.Knockout of FADD, procaspase 8 : Both rows exhibited the same 14 red entries all associated to apoptosis processes. Only the survival pathways and necroptosis induction were still functional. This emphasized the direct regulation of both proteins since procaspase 8 requires FADD for recruitment.Knockout of IKK, LUBAC, NEMO, TAK1 : All four rows had the same nine red entries, which indicated the strong relation of the proteins in the Ub-dependent regulation in complex I. All red entries affected the downstream activation of NF-κB and the regulation of the target genes, while complex II formation and cell death induction remained functional.Knockout of MLKL (row 8): We got only one red entry for activated MLKL located at the plasma membrane prior necroptosis induction (MLKL_PM). Since this refers to the last step in the necroptosis pathway, necroptosis induction was hampered.Knockout of NF-κB (row 10): We had eight red entries, all referring to NF-κB regulation via IκB and the regulation of NF-κB-dependent genes.Knockout of procaspase 3 (row 11): We observed two red entries. The knockout affected CASP3 activation and CASP3 inhibition by XIAP .Knockout of procaspase 9 (row 13): We had four red entries, all referring to the processes of the regulation of procaspase 9 in the apoptosome via XIAP and SMAC.Knockout of RIP1 (row 14): We got 14 red entries, affecting the formation of complex I and the induction of necroptosis. Only apoptosis processes were still functioning since RIP1 is a major player in the TNFR1 signal transduction pathway.Knockout of RIP3 (row 15): We had two red entries. The formation of the necrosome and the activation of MLKL were affected by the knockout of RIP3 .Knockout of TNF, TNFR1, TRADD : All three rows showed the same 20 red entries, affecting all places except the nuclear NF-κB (NF-kB_n), due to the turnover of NF-κB, which remains unaffected by the knockout. Since the three proteins initialized the pathway, all downstream pathway components were affected by the knockouts.Effect of SMAC mimetic by multiple knockout of cIAP1/2 and XIAP (row 21): We had ten red entries, all referring to the formation of complex I, NF-κB-dependent gene expression and XIAP regulation. Only apoptosis and necroptosis induction remained functional.Effect of cycloheximide by multiple knockout of IκB, A20, XIAP, cFLIPL, and BCL-2 (row 22): We got seven red entries, mimicking the effect of cycloheximide, which impaired the translation of upregulated genes. This predicted that only the cell death pathways remain unaffected. Upon TNF stimulation, most cells did not exert cell death because of rapid gene expression of cFLIPL, cIAP2, XIAP, and BCL-2, which may inhibit cell death signaling [ignaling . The treignaling .The study of TNFR1 signal transduction has a long history and revealed many theories. Each theory has its own the individual focus and may reflect alternative viewpoints ,70. ContIncompleteness and diversity of the data, limited knowledge, and uncertainties in the literature are general challenges for all modeling techniques. Therefore, we used experimentally determined data published in peer-reviewed and high-ranking journals for model creation. We started with review papers. Because of the expertise of physicians in the group, we were mainly interested in mammalian processes. We did not perform any wet-lab experiments, but discussed contradicting findings in the literature with experimentalists, exhibiting expertise in the relevant topic. The application of techniques to verify theoretical consistency of the model and to check the biological interpretation for correctness was mandatory to get confidence into the model.The PN covers signaling processes of cell survival, apoptosis, and necroptosis. The PN model compiled the current view of the TNFR1 signaling pathway. During the development of the model, well-established views of molecular regulations had been superseded by other proposed regulatory mechanisms. An example is the regulation of A20, which operates as a deubiquitinating enzyme in the feedback regulation of NF-κB signaling. Originally, its suppressive role in NF-κB signaling has been assigned to the proteasomal degradation of RIP1 by a K48-linked Ub tag . RecentlOn the contrary, less-understood processes could not be integrated in the PN model since the exact mechanism of regulation was not entirely characterized. Important aspects that need further investigation are, for example, the effect of RIP1 phosphorylation and the regulation by ubiquitination within complex II. Further, the exact mechanism of necroptosis execution and the mode of action of MLKL remains to be identified. Further experimental efforts are necessary to get a clearer picture of the TNFR1-mediated signaling pathways and to provide information to support a sound theoretical investigation.To investigate networks of pathways in systems biology profoundly, the determination of all possible signal flows was obligatory. The mathematical approach of place invariants (PIs) and transition invariants (TIs) explained substance conservation and the basic system’s behavior, respectively. TI-induced subnetworks represented functional modules. Manatee invariants (MIs) constructed by linear combination of TIs described complete functional signal flows in a network that operates at steady state. The complexity of the computation of MIs was related to the number of TIs and the possible linear combinations. For the PN of TNFR1 signal transduction, the number of MIs highly increased with regard to the number of TIs, from 48 TIs to 279 MIs. The analysis revealed substance conservation, basic dynamics of the system, and all complete signaling pathways.Knockout analysis: Knockout analysis was applied for classification of pathways, ranking of pathway’s entities, and clustering of processes. The deduction of the regulation of signal transduction via knockout experiments was not an easy task since the pathway components were involved in several processes. Further, the variation of results between cell types, type of experiment, and working group, had an essential influence on the diversity of the data. The in silico knockout analysis could reveal obvious relations, expected dependencies, and predictions of effects that were not yet experimentally proven. Not in every case, the results of the knockout prediction did match the experimental knockouts. On the one hand, the TI or MI analysis may not capture all relevant pathway dependencies due to an insufficiently detailed modeling of the processes, or the knockout behavior is dependent on other signal flows, too, which were not explicitly included in the PN model. On the other hand, the experiments may be obtained for a specific cell type and may not be applicable to all cells. For such cases, we suggest to adapt the PN model of TNFR1 signal transduction to a specific cell type.Pathway classification: The result was in accordance with the expected biological behavior because most cells exhibited a robust survival response and suppressed the cell death induction [nduction . The disnduction . Some prRobustness describes an inherent quality of systems and aims to maintain and ensure the correct function of a system [a system . AlternaPharmaceutical therapies: The TNFR1 signaling pathway will always be a target of cytoprotective or cytotoxic therapies since it controls opposing responses and has a major function in immunity and development [in silico knockout was in accordance with the experimental findings of SMAC-mimetic treatment. Upon TNF-α stimulation, most cells do not exert cell death because of rapid gene expression of cFLIPL, cIAP2, XIAP, and BCL-2, which inhibit cell death signaling [elopment ,17. The elopment . For canelopment . SMAC mielopment . It inteelopment ,75. The ignaling . The treignaling .The molecular switch: The determination of specific checkpoints of the system was important to intervene the signaling cascade in a desired manner. The survival response was very robust to perturbations. Therefore, we needed to determine the factors that overcome this robust response and promote cell death pathways. We determined the important checkpoints in complex I in terms of the ubiquitination within complex I and the activation of NF-κB-dependent gene expression. The impairment of ubiquitination, e.g., by SMAC mimetic or the in silico knockout of TRAF2 and cIAP, favored the induction of apoptosis and necroptosis. The upregulated genes by NF-κB negatively controlled cell death signaling. We showed that the impairment of NF-κB activation, e.g., by knockout of proteins of complex I like LUBAC, and the translation of upregulated genes, e.g., by simulating a cycloheximide treatment, promoted cell death induction.Ubiquitinated RIP1 has been reported to have a scaffold function for the required kinases, TAK1 and IKK, in complex I and to promote cell survival . DeubiquPhosphorylated RIP1 has been reported to inhibit kinase-dependent induction of cell death, following TNFR1 ligation . Severalin silico knockout predictions for IKK, NEMO, TAK1, and LUBAC because only apoptosis induction and necroptosis induction remained functional after a single knockout of either IKK, NEMO, TAK1, or LUBAC.Another checkpoint to enhance the resistance to cell death induction is the NF-κB-dependent gene expression. Only full activation of IKK leads to NF-κB activation ,71. It hL is regulated by NF-κB activation. cFLIPL is a homolog of CASP8 and competes with CASP8 to form a heterodimer and prevents full activation of CASP8. If NF-κB activation is blocked, the level of cFLIPL decreases, leading to the induction of apoptosis [in silico knockout of cFLIPL, BCL-2, and XIAP was in accordance with the expected effects of the drug cycloheximide.The level of cFLIPpoptosis . Other tpoptosis . CycloheL is usually upregulated at the time point at which complex II can has been assembled in the cytosol and inhibits caspase activation. The two isoforms of cFLIP differentially regulate the activity of complex II [L, binding to CASP8 and FADD, has a survival function, blocking apoptosis and necroptosis, cFLIPS, binding to CASP8, inhibits full activation of caspase activity [The checkpoints that mediate signal transduction in complex I and from complex I to complex II are well-characterized. But the exact regulation within complex II has not been entirely clarified. In complex II, the checkpoints mainly control caspase activity. TRADD needs to dissociate from complex I and binds to FADD to provide a platform for CASP8 recruitment and apoptosis induction . cFLIPL mplex II . While cactivity ,90. Theractivity . CYLD isactivity . Also A2activity .places and one for the active elements called transitions. For biochemical systems, the places model biological entities, for example, proteins, ligands, protein complexes, genes, transcripts, metabolites, and other chemical compounds. Transitions stand for the reactions transforming one place into another, for example, chemical reactions, phosphorylation, ubiquitination, complex formation, and other. The directed edges connect only vertices of different type. Places with outgoing edges are called pre-places and places with ingoing edges post-places, with respect to the transition the edges connect with the considered place. Edges are labeled by integers.Petri nets (PNs) represent a graph theory-based mathematical formalism to model systems of concurrent processes ,32. PNs P, T, F, W, m0) with:Formally, we define a PN as a quintuple N =  ∪ (T × P) is the set of flow relations or edges, resp.W: F → N defines the edge weights.m0: P → N0 is the initial marking.tokens that are located on the places. A token represents a discrete unit of an entity, for example, one mole of a chemical compound or one molecule. A token distribution defines a system state and is given by the marking, m, which is a vector of the size of the set of places, |P|. The initial marking, m0, describes the initial state of the system before starting a simulation. The marking is illustrated by points on the corresponding places or by integer numbers.For classical PNs called P/T nets (Place/Transition nets), the dynamics is performed by movable objects named firing rules. In P/T nets, firing rules are timeless, meaning that the tokens on the pre-places are removed at the same time as the tokens are produced on the post-places, see Fig 11.Tokens move through a PN following specific x and a scalar a:In the following, we adopt the notations for a vector x = a means that all components of x are identical to a,x≥a means that no component of x is less than a but x = a is excluded,x>a means that all components of x are larger than a.and C and a vector x, a product C x implies that the number of columns of the matrix C is the same as the number of components of the vector, x.Even if not explicitly specified, all implicit relations are assumed to be satisfied. For example, for a matrix We modeled the PN as an open system, meaning that all proteins of the pathway are synthesized and degraded. There are transitions without pre-places for synthesis and transitions without post-places for degradation. The only exceptions were genes that induce the synthesis of proteins in a controlled manner and, therefore, formed specific patterns in the PN model.Among other properties, invariants characterize a PN. This property always holds at steady state independent of the system state and of the initial marking . Invariatransition invariants (TIs) and place invariants (PIs). Both are based on the incidence matrix of the PN, C: P×T, with P as the set of places and T as the set of transitions, see also the definition of PNs in the previous subsection “Petri nets”. An entry in C indicates the change in the number of tokens on the considered place (row), when the considered transition (column) fires. The incidence matrix describes the topology of a pure PN in a unique way. A pure PN does not contain loops, meaning bidirectional edges, between a transition and a place.We define m0 = 0, i.e., keeps the system state invariant. A TI is defined as a Parikh vector, x: T→ℕ, of a firing sequence that fulfills the equation Δm = C x = 0. The number of firings per transition is given by the elements of x. An integer solution x is a true invariant if it has no negative components, i.e., x≥0. The set of transitions, whose components in x are positive, i.e., x > 0, defines the support of the TI, supp(x). A TI, x, is minimal if no other solution, x‘, exists with supp(x) ⊇ supp(x’), and the largest common divisor of all elements of x equals one. We consider minimal, non-negative, integer solutions, x, as a TI. A PN is covered by TIs (CTI) if every transition is a member of at least one TI. For metabolic systems, a TI, then called an elementary mode, describes a biological pathway at steady state [A TI is a superset of transitions, whose firing sequence reestablishes an arbitrary initial marking, Δdy state . The condy state . The anady state . The CTIdy state .x: T→ℕ, of the equation y CT = 0, where CT denotes the transposed incidence matrix. The definition of a minimal and true PI is analogous to the definition of a minimal and true TI. A PN is covered by PIs (CPI), if every place is member of at least one PI.Analogously to TIs, we define a PI as an integer solution, et al, 2017.The criterion for network verification demanded for a plausible biological interpretation for every TI. For signal transduction networks, the determination of all possible signal flows from the signal reception to cellular response is elementary to derive insights of the behavior of the biological system. This is a necessary prerequisite for the biological verification of the PN model as well as follow-up studies. The PN model described the molecular processes of TNFR1 signal transduction on a high level of detail. To allow for network analysis, we could have modeled the processes in a more abstract way but this would have simplified the processes, and the model might have lost important regulations. Therefore, we decided to adapt the TI analysis to obtain biologically meaningful results in terms of complete biological pathways, while keeping the high level of detail of the PN model. We introduced so-called Manatee invariants as linear combinations of TIs to ensure that the TI covers a signaling pathway from receptor activation to cell response. For the detailed definition and derivation of MIs, we refer to  Amstein Each invariant induces a subnetwork. A TI-induced or MI-induced subnetwork is formed by the transitions of the TI or MI, respectively, and the places and edges in between. Analogously, a PI-induced subnetwork is defined by the places of the PI and transitions and edges in between. Invariants decompose the PN into subnetworks that can overlap. These subnetworks should be biologically relevant and should represent biologically functional modules.in silico knockout analysis supports a profound investigation of a comprehensive PN model of a signaling pathway [Knockout studies or perturbation studies are suitable methods to reveal the vulnerable parts of a system. The  pathway . We defihttps://github.com/MolBIFFM/MonaLisa/tree/master/store. The knockout was performed applying the open-source software isiKnock [For model reconstruction and analysis, we used the open-source software MonaLisa ,41, avaiisiKnock  based onisiKnock  with theisiKnock  and the isiKnock  as distaS1 Text(DOCX)Click here for additional data file.S1 TableFor each transition, we list its name, describe the biological meaning, and give references to relevant literature. For degradation and synthesis of proteins, we give no reference to literature.(DOCX)Click here for additional data file.S2 Tablein vivo experiments are given.For transitions, references and specifications of cell lines that have been used in relevant (DOCX)Click here for additional data file.S3 TableFor each place, its name and biological meaning are given. For abbreviations applied to name places, we refer to (DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table9. TI9 induces A20 feedback regulation but ignores the interrelation of this process with a necessary activation of NF-κB, see text.For each TI, the number, the names of the transitions, and the biological meaning are given. The number is highlighted by bold face if the TI covers a dissected pathway. 33 TIs represent dissected pathways. An example of a dissected pathway is TI(DOCX)Click here for additional data file.S6 TableFor each MI, its number, the number(s) of the transition invariants (TIs), and the names of transitions are given. For descriptions of transitions, we refer to (DOCX)Click here for additional data file.S7 Tablein silico knockout matrix of The list interprets the (DOCX)Click here for additional data file.S1 Fig15 highlighted in red describes the activation of NF-κB, the degradation of IκB, the gene expression of IκB, and the interaction of complex I with the inhibitory complex of NF-κB and I κB. The pathway relies on a former production of complex I, place CI. The assembly process of complex I is not part of TI15 and hence, TI15 represents an incomplete pathway.TI(DOCX)Click here for additional data file.S2 Fig9 highlighted in blue describes the assembly of complex I and the dissociation of complex I via A20. The transcription of A20 relies on a former translocation of NF-κB into the nucleus. The translocation of NF-κB into the nucleus is not part of TI9 and hence, TI9 represents an incomplete pathway.TI(DOCX)Click here for additional data file.S3 Fig131 is the linear combination of four transitions invariants (TIs), TI4 highlighted in dark green, TI9 highlighted in blue, TI15 highlighted in red, and TI18 highlighted in green. Note that, the TIs, i.e., their color code in the figure, may overlap. For detailed information on the TIs, we refer to S5 Table. MI131 represents a possible signal flow that is induced by the binding of TNF to the receptor TNFR, see TI9 and TI4, highlighted in blue and in dark green, respectively. The assembly of complex I, place CI, is part of TI9. MI131 combines the assembly of complex I with the degradation of complex I, see TI15, highlighted in red, and the synthesis of NF-κB, see TI18, highlighted in green. MI131 includes the inhibition of apoptosis by cFLIP, see TI4, highlighted in dark green. MI131 represents a complete pathway. MI131 resolves all relevant preconditions and interrelation of processes, as, e.g., the A20 feedback loop is accompanied by a preceding activation of NF-κB.MI(DOCX)Click here for additional data file.S4 Fig209 is the linear combination of four transition invariants (TI), TI9, highlighted in blue, TI15, highlighted in red, TI16, highlighted in purple, and TI18, highlighted in green. Note that, the transition invariants, i.e., their color code in the figure, may overlap. For detailed information on the transition invariants, we refer to 209 represents a possible signal flow that is induced by the binding of TNF to the receptor TNFR, see TI9, highlighted in blue. The assembly of complex I, place CI, is part of TI9. MI209 combines the assembly of complex I with the degradation of complex I, see TI16, highlighted in purple, the synthesis of NF-κB, see TI18, highlighted in green, and the translocation of NF-κB into the nucleus followed by the induction of transcription of IκB, see TI15, highlighted in red. MI209 represents a complete pathway. MI209 resolves all relevant preconditions and interrelation of processes, as, e.g., the A20 feedback loop is accompanied by a preceding activation of NF-κB.MI(DOCX)Click here for additional data file.S1 Data(XML)Click here for additional data file.


Zearalenone (ZEA) widely exists in moldy grains, which seriously destroys the fertility of females. Isorhamnetin, a natural flavonoid, has extensive of pharmacological activities. However, the beneficial effect and the underlying molecular mechanism of isorhamnetin involvement in ZEA-induced porcine oocyte damage have not been investigated./L) for 44 h at 39 ℃. ZEA (5 μmol/L) and isorhamnetin (10 μmol/L) were selected for subsequent studies. Polar body exclusion rate, apoptosis rate and apoptosis related proteins, ROS levels and SOD2 protein, mitochondrial membrane potential and distribution, endoplasmic reticulum distribution and proteins expression, and PI3K, Akt and p-Akt proteins expression of oocytes were detected. In addition, the effect of PI3K antagonist (LY294002) on oocyte nuclear maturation and apoptosis were used to determine the involvement of PI3K/Akt signaling pathway.Oocytes were treated with different concentrations of ZEA  and isorhamnetin  and the distribution rate of normal endoplasmic reticulum showed that isorhamnetin relieved ZEA-caused endoplasmic reticulum stress. Mechanistically, isorhamnetin decreased Bax/Bcl-2 protein expression and inhibited ZEA-induced apoptosis through PI3K/Akt signaling pathway.Collectively, these results suggest that isorhamnetin protects oocytes from ZEA-caused damage through PI3K/Akt signaling pathway, which enhances meiotic maturation and mitochondrial function, and inhibits early apoptosis, oxidative stress and endoplasmic reticulum stress in porcine oocytes. Our study provides a new strategy for solving the reproductive toxicity induced by ZEA and treating woman infertility.A possible mechanism by which isorhamnetin protected porcine oocytes from ZEA-induced damage. Isorhamnetin inhibited meiosis arrest and apoptosis of porcine oocytes induced by ZEA through the PI3K/Akt signaling pathway. Moreover, isorhamnetin repaired ZEA-induced oocyte damage by alleviating oxidative stress, mitochondrial dysfunction and ER stress.The online version contains supplementary material available at 10.1186/s40104-022-00809-w. Mammalian oocytes provide most of the maternal genetic materials, cytoplasmic components, organelles and membranes for embryonic development . OocytesZearalenone (ZEA), a mycotoxin like estrogen, is widely distributed in contaminated foods such as corn, oats and millet , 8. ZEA Isorhamnetin, a flavonoid compound, is present in man’s daily diet, such as pears , onion  and peanPI3K/Akt signaling pathway has the functions of regulating oocyte maturation . StudiesIn this study, we explored whether isorhamnetin could exert a protective effect on ZEA-induced damage in porcine oocytes. Moreover, the signaling pathway of isorhamnetin involved in this cytoprotective effect was studied. We found that isorhamnetin therapy suppressed ZEA-induced damage by promoting meiotic maturation and mitochondrial function, and inhibiting early apoptosis, oxidative stress and ER stress in porcine oocytes. Our results show that isorhamnetin acts as a natural and non-toxic additive to protect ZEA-induced damage in porcine oocytes.Isorhamnetin  was primely dissolved in dimethyl sulfoxide (DMSO) and stored at − 20 °C. The concentration of isorhamnetin stock solution was 100 mmol/L. Before use, the final concentration of isorhamnetin was diluted with culture medium. PI3K antagonist  was obtained from Beyotime . Antibodies information: GAPDH (Cat#:60004–1-1g) and goat anti-rabbit (Cat#:31460) /mouse (Cat#:31430) IgG (H + L)-HRP were purchased from Proteintech ; Bcl-2 (Cat#:01556), Cleaved caspase-3 (Cat#:02117), Bax (Cat#:01637), SOD2 (Cat#:02506), p-Akt (Cat#:P001a), Akt (Cat#:0003b), PI3K (Cat#:02894), GRP78 (Cat#:03157) and CHOP (Cat#:00880) were purchased from WanleiBio .L-cysteine, 10 IU/mL pregnant mare serumgona dotropin (Ningbo Second Hormone), 0.91 mmol/L sodium pyruvate, 10% porcine follicle fluid, 10 IU/mL follicle stimulating hormone (Ningbo Second Hormone), 1 mg/mL poly, 3.05 mmol/L glucose and 10 ng/mL epidermal growth factor for 44 h at 39 ℃ in 5% CO2. Other chemicals and reagents not mentioned were purchased from sigma Aldrich . According to the nuclear maturation rate of oocytes and previous reports [Ovaries were obtained from a local abattoir  and stored in 37 °C phosphate-buffered saline (PBS) containing 1% antibiotic. Follicular fluid was harvested from healthy antral follicles using a 10-mL disposable syringe (26-gauge needle). More than three layers of COCs were collected by stereomicroscopy. COCs were washed two or three times in TCM199  containing 10 mmol/L 4-(2-hydroxy-ethyl)-1-piperazineethanesulfonic acid and 5% fetal bovine serum . Every 50–60 COCs was transferred into 500 μL TCM199 medium with 10% FBS, 10 IU/mL human chorionic gonadotropin , 1% antibiotics (penicillin−streptomycin solution), 0.57 mmol/L  reports , 29, ZEAAfter maturation (44 h), cumulus cells around oocytes were removed with 1 mg/mL hyaluronidase. Nextly, oocytes were cultured with Hoechst 33342 (Beyotime) for 25 min. Stained oocytes were washed three times (PBS) and examined with inverted fluorescence microscope , oocytes with two chromatin bright spots were identified as mature oocytes. Each treatment group had 20–30 oocytes for maturation analysis. Data were obtained from at least three independent replicate experiments.The denuded oocytes were incubated with 60 μL binding buffer containing 2.5 μL of Annexin-V-FITC (Beyotime) for 25 min. After washing three times (PBS), the apoptosis rate of positive oocytes was detected by inverted fluorescence microscope (Olympus). There were 50–60 oocytes for apoptosis detection in each treatment group. Data were obtained from at least three independent replicate experiments.Oocytes were loaded with 2 μmol/L  probe for 30 min. After washing, the fluorescence signal of oocytes was collected by fluorescence microscope (Olympus). The fluorescence intensity of the ROS signals was calculated with the ImageJ. Each treatment group had 50–60 oocytes for ROS fluorescence detection. Data were obtained from at least three independent replicate experiments.Denuded oocytes were stained with 10 µmol/L JC-1 solution  for 35 min. Then, the oocytes were washed and examined immediately under inverted fluorescence microscopy (Olympus). Microphotographs were calculated by ImageJ Software. Mitochondrial membrane potential was the ratio of red to green fluorescence intensity. 20–30 oocytes were tested for mitochondrial membrane potential. Data were obtained from at least three independent replicate experiments.Oocytes were loaded with Mito-Tracker Red probe  for 45 min. After washing, oocytes were mounted on glass slides and observed by fluorescence microscopy (Olympus). Normal mitochondria were evenly distributed, while accumulative distribution, peripheral distribution and semi-peripheral distribution were considered to be abnormal mitochondrial distribution. 20–30 oocytes were tested for mitochondrial distribution. Data were obtained from at least four independent replicate experiments.Oocytes were loaded with ER-Tracker Blue probe  for 35 min. Oocytes were captured under fluorescence microscope (Olympus) after washing three times. Normal/abnormal endoplasmic reticulum was determined by the same method as mitochondria (2.8). 20–30 oocytes were tested for ER distribution. Data were obtained from at least four independent replicate experiments.100–150 oocytes were lysed in 16 μL lysis buffer (Beyotime) containing protease inhibitor. 5 × loading buffer (Beyotime) was mixed with each lysed sample and subsequently boiled at 100 ℃ for 8 min. Total lysate was size fractionated with sodium dodecyl sulfate–polyacrylamide gels and transferred to nitrocellulose membrane. Membrane was blocked in 5% skimmed milk powder for 1.5 h and incubated at 4 °C overnight with primary antibodies. Washing thrice (TBST), membrane was incubated with secondary antibody for 2 h. Membrane was washed and covered with 1:1 mix of chemiluminescence . The band of protein was calculated by ImageJ software. Data were obtained from at least three independent replicate experiments.P-value less than 0.05 was considered to be significant. At least three biological replicates were used for each treatment.Statistical analyses was achieved with the Graphpad Prism (6.0) and SPSS (18.0) software. Data were showed as mean ± SEM and analyzed by one-way ANOVA followed by the Duncan’s test. The P < 0.05). Combined with previous studies [To investigate whether ZEA exposure affects oocyte maturation, porcine oocytes were treated with ZEA  for 44 h. Following ZEA treatment, oocyte maturation rate was decreased in a dose-dependent manner. As shown in Fig.  studies  and the P < 0.05). Particularly, the 10 μmol/L isorhamnetin was the most effective in repairing ZEA inhibition of oocyte maturation. However, there was no significant change in oocyte maturation rate between the ZEA group, ZEA + 5 μmol/L isorhamnetin group and ZEA + 30 μmol/L isorhamnetin group.To explore the beneficial effect of isorhamnetin on ZEA-caused damage, oocytes were co-cultured with ZEA (5 μmol/L) and isorhamnetin  during IVM. As indicated in Fig. P < 0.01; Fig. P < 0.05; Fig. 10 μmol/L and 20 μmol/L isorhamnetin also inhibited the apoptosis of oocytes exposed to 5 μmol/L ZEA . These results indicated the beneficial role of isorhamnetin in ZEA-exposed oocytes damage was positively correlated with the inhibition of mitochondrial dysfunction.Mitochondria are important subcellular targets of ROS-induced cell damage. Nextly, the effect of isorhamnetin on mitochondrial dysfunction  in ZEA-exposed oocytes was analyzed by JC-1 and Mitotracker red staining. As shown in Figs. P < 0.01; Fig. P < 0.05; Fig. P = 0.16; Fig. Porcine oocytes were treated with ER-Tracker Blue to observe the effect of isorhamnetin on ER stress induced by ZEA. As shown in Fig. P < 0.05; Fig. P < 0.05; Fig. To clarify the protective mechanism of isorhamnetin in alleviating ZEA-caused oocyte damage. The activated forms of PI3K, Akt and p-Akt were detected in porcine oocytes. As shown in Fig. P < 0.05). In addition, isorhamnetin inhibited ZEA-induced oocyte apoptosis  was cultured with ZEA and ISO in COCs for 44 h, and then the nuclear maturation and apoptosis of oocyte were evaluated. As shown in Fig. 4002 was ZEA widely exists in moldy grains and causes irreversible damage to the reproductive system of animals and humans . Pigs arZEA interferes with oocyte development, which is manifested as reduced oocyte maturation rate and abnormal oocyte morphology . ZEA indZEA induces oxidative stress and stimulates ROS generation, and the excessive ROS production promotes ZEA-induced cell death . Xu et aPTGS2, TNFAIP6, PTX3 and HAS2) [2) significantly reduced the expansion degree of COCs compared with the control group (0.117 ± 0.036 mm2) [Cumulus cells control meiotic resumption of oocytes by regulating follicular microenvironment . In addind HAS2) . In vitr036 mm2) . ConsistPGC1a, ATP5B, pdss2 and pdss1) to inhibit meiosis of porcine oocytes [PGC-1 gene and prevented obesity-related mitochondrial dysfunction in 3T3-L1 cells [It has been reported that the expansion of cumulus cells was closely related to organelles rearrangement of porcine oocytes . First o oocytes . Consist oocytes . MeanwhiL1 cells . These fATF4 and CHOP gene expression to induce ER stress in porcine oocytes [The correct folding of functional proteins in ER is a prerequisite for maintaining oocyte maturation . GRP78 i oocytes . In addi oocytes , 54. In  oocytes . Isorham oocytes . These rPI3K/Akt plays an important role in oocyte maturation, granulosa proliferation and primordial follicle recruitment . GrowingIn conclusion, our study demonstrated that ZEA exposure induced reproductive toxicity during porcine oocyte maturation. Isorhamnetin prevented the ZEA-induced meiotic arrest, apoptosis, oxidative stress, mitochondrial dysfunction, ER stress through the PI3K/Akt signaling pathways, thereby alleviating oocytes damage caused by ZEA. These findings suggest that isorhamnetin has a great application prospect in alleviating ZEA-induced oocyte damage. Thus, animals and humans will be healthier, and feed products and foods will be safer.Additional file 1. Explanation for the low maturation rate of porcine oocytes.Additional file 2. The raw western blot images.


Acacia Nilotica (AN) has long been used as a folk cure for asthma, but little is known about how AN could possibly modulate this disease. Thus, an in-silico molecular mechanism for AN’s anti-asthmatic action was elucidated utilizing network pharmacology and molecular docking techniques. DPED, PubChem, Binding DB, DisGeNET, DAVID, and STRING were a few databases used to collect network data. MOE 2015.10 software was used for molecular docking. Out of 51 searched compounds of AN, eighteen compounds interacted with human target genes, a total of 189 compounds-related genes, and 2096 asthma-related genes were found in public databases, with 80 overlapping genes between them. AKT1, EGFR, VEGFA, and HSP90AB were the hub genes, whereas quercetin and apigenin were the most active components. p13AKT and MAPK signaling pathways were found to be the primary target of AN. Outcomes of network pharmacology and molecular docking predicted that AN might exert its anti-asthmatic effect probably by altering the p13AKT and MAPK signaling pathway. Asthma has been described as a common long-term inflammatory condition of the respiratory airways. Airway inflammation, hyperresponsiveness, and remodeling are the prime manifestations of asthma. The recurrence of the disease, along with the high expense of available treatment, make it life-threatening sometimes . Many trAcacia Nilotica (AN) possesses a wide range of biological attributes. Acacia trees are abundant in Saudi Arabia, India, Sudan, Pakistan, Egypt, and Sri Lanka and are used by ordinary people as a nutritional supplement and medicinal agent [Acacia Nilotica possesses anticancer, antispasmodic, antipyretic, anti-diabetic, antifungal, antiviral, antibacterial, and antihypertensive properties [al agent . Adding operties . In Pakioperties . The lacThe network pharmacology approach allows for studying the interaction among compounds, genes, and diseases. This provides a better understanding of the contribution of any particular test compound in the treatment or prevention of any specific disease by targeting the genes involved in disease pathogenesis. Network pharmacology employs a multifunctional approach to elucidate the mechanism of action of multiple compounds rather than concentrating on interactions between a single molecule and a single target. This makes it possible to discover novel, natural pharmacological treatments and preventative measures with novel mechanisms of action . TherebyThe literature retrieval and DPED database found 51 compounds in AN, the names and molecular formulae reported in Acacia Nilotica asthma targets.Through the STITCH and BindingDB databases, 189 genes were targeted by 18 phytoconstituents of AN . 2096 AsTo investigate the relationship between active compounds and prospective targets, Cytoscape was used to build a network between potential targets and active compounds. A compound-target network was constructed with 80 possible target genes and 18 active compounds in AN. Using a “network analyzer”, we discovered that the compound-target network has 206 edges and 99 nodes .In a compound-target network, the degree of 18 active compounds was examined . FlavoneThe association between the overlapping genes are revealed through protein-protein interaction studies. Interrelationship between genes involved in a particular disease pathophysiology can be established by PPI network. These overlapping genes were submitted to STRING version 11.5 and high confidence protein interaction data with a score >0.7 was chosen for PPI network building . The higCytoscape was used to visualize the PPI network, and 76 nodes and 227 edges were found . The HubGO annotations and KEGG pathway analysis were performed on 80 anti-asthmatic targets to demonstrate the molecular mechanism of AN in the treatment of asthma. Go analysis of target genes of AN’s targets was related to the regulation of inflammation, response to oxygen levels, response to hypoxia, protein kinase B signaling, and many others. The cellular compartment (CC) includes the apical part of the cell, basolateral plasma membrane, and mast cell granules. At the same time, molecular function (MF) provides transcription co-activity binding, nuclear receptor activity, ligand-activated transcription factor activity, and many others, as shown in p ˂ 0.05. According to KEGG analysis, as shown in The top 10 GO annotations   and ten Network analysis was used to investigate the mechanism of AN in asthma. DAVID analysis was used to determine the top 10 enriched pathways, and Cytoscape was used to generate the target-pathway-compound network. The network contained 110 nodes and 335 edges, with 18 active components, 80 potential targets, and ten critical pathways  the best-docked pose binding energy prediction using MOE (Molecular operating environment) scoring system and (ii) Hydrogen bond information of the top-ranked pose. Each compound was docked in 10 distinct positions throughout the docking run. The recovered molecules were first sorted using the pre-validated methodology, and then the visualization approach was used to determine the inhibitor binding mode that is best based on the inhibitor’s critical interactions with the active site residues. Acacia Nilotica’s mechanism in treating asthma.Chronic inflammation is a hallmark of asthma, and airway immune inflammation contributes significantly to asthmatic symptoms such as inflammation, hyperresponsiveness, reversible airway restriction, and remodeling . TraditiEighteen compounds were associated with AN’s ability to alleviate asthma symptoms, including four flavonoids, three terpenoids, three phenolic acids, two phenolics, one diterpene, one sterol, and carotenoid. AN’s therapeutic impact on asthma appears primarily due to terpenoids and flavonoids. Compounds-genes network analysis indicated that quercetin and apigenin were the most potent active components against asthma. A literature review of the anti-asthmatic activity of quercetin and apigenin further confirmed this finding. Both compounds displayed significant anti-asthmatic activity in animal models of asthma ,11,12,13Acacia Nilotica’s anti-asthmatic potential is probably due to its immunomodulatory and anti-inflammatory potential. In this study, the molecular mechanism of Acacia Nilotica was clarified using network pharmacology and molecular docking techniques. The present study had some limitations. The current work is based on an in silico approach; detailed in vivo and in vitro studies are required to validate these findings and point out the precise mechanism of action.Seventy-eight signaling pathways, targeted by 18 compounds of AN and 80 target genes, including the PI3K-AKT pathway, MAPK signaling pathway, and Ras signaling pathway, were revealed by KEGG analysis. PI3K-AKT signaling pathway was found to be the highly enriched pathway targeting 16 overlapping genes compared to the other screened pathways. AKT pathway has been shown to reduce cell proliferation in airway smooth muscle cells in asthmatic conditions . Thus, bhttps://pubchem.ncbi.nlm.nih.gov/) accessed on 23 March 2022 [https://biosig.lab.uq.edu.au/pkcsm/prediction) was used to conduct ADMET analysis on selected constituents, accessed on 27 March 2022. Active components were selected when the ADMET evaluation results were deemed acceptable. Compounds which have appropriate intestinal absorption, excretion and no toxicity were selected for further evaluation.The bioactive ingredients of AN were searched using Dr. Duke’s Phytochemical and Ethno botanical Database (DPED), Web of Science, and Google Scholar, while the SMILES and molecular formulas of constituents were discovered using PubChem (https://www.bindingdb.org/bind/index.jsp), accessed on 28 March 2022, was used using the “homo sapiens” setting to predict target genes for selected compounds based on SMILES. The “minimum needed interaction score” was set to “high confidence (0.700)” during Binding DB prediction. The public database DisGeNET  was used to identify asthma-related target genes. A Venn diagram was used to identify and illustrate the overlapping genes between the compounds and the asthma target genes [Binding DB  was used to construct, display, and analyze the network of interactions based on the Binding DB prediction results for constituents and overlapping genes [Cytoscape ver. 3.9.1  was used to gather information on protein-protein interactions between the target proteins of selected AN components (PPI). The website calculated a score for each protein’s mutual information. The stronger the contact between the two target proteins, the higher the score. Since high confidence data >0.7 were used to ensure accuracy and reliability, the study was considered reliable. The obtained protein interaction data were imported into the Cytoscape 3.9.1 application to generate a PPI protein interaction network. The CytoHubba plug-in was employed for the identification of Hub genes [An online database called STRING, version 11.5,  [The selected target genes were analyzed for GO and KEGG analysis by using (David) v 6.8 software. Cellular components (CC), molecular functions, biological processes were analyzed for GO analysis. KEGG pathway enrichment analysis was done to predict a possible molecular mechanisms of AN against asthma. GO KEGG pathway bar charts were made with SRPLOT (er 2022) .It is possible to perform a pathway enrichment analysis using KEGG as well as pathway functional annotations for a given gene set. The Cytoscape 3.9.1 software was used to create the compound–target–pathway network seen in MOE 2015 (Molecular operating environment) was employed as the molecular docking tool. Crystal structure of AKT1, EGFR, VEGFA and Hsp90AB1 were found in the protein data bank . The builder tool of MOE was used to prepare the structures of apigenin and quercetin. The MOE tool’s Energy minimization algorithm was used to reduce the energy of the protein molecule. The following variables were used to minimize energy; 0.05 Gradient, MMFF94X + Solvation Force Field, and Current Geometry Chiral Constraint. When the root mean square gradient dropped below 0.05, energy minimization was stopped. Ten distinct docked conformations for each compound were produced once the active site was chosen. For binding pattern analysis, the compound’s lowest energy conformation was chosen. The minimized protein structure was used as the docking template .For the first time, the active components and mechanisms of AN’s anti-asthma properties were studied using network pharmacology. According to network pharmacology results, quercetin and apigenin were found to be the most enriched constituents of AN, and the AKT1, EGFR, VEGFA, and HSP90AB were enriched hub genes, respectively. The fundamental mechanism of AN against asthma was the deactivation of the p13AKT signaling pathway, which may aid in the prevention and progression of asthma and other inflammatory diseases. Based on network pharmacology and molecular docking-based prediction, it can be concluded that AN might be considered a natural source of asthma management; however, detailed mechanistic studies are required to validate this hypothesis.


Danhong injection (DHI) is widely used in the treatment of cardiovascular and cerebrovascular diseases, and its safety and effectiveness have been widely recognized and applied in China. However, the potential molecular mechanism of action for the treatment of arrhythmia is not fully understood. in vitro cell experiments, we explored the active compounds of DHI for the treatment of arrhythmia and predicted the potential targets of the drug to investigate its mechanism of action.  In this study, through network pharmacology and in vitro arrhythmia model using human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), in which calcium transients were recorded to evaluate the status of arrhythmia. Next, the active compounds and key targets in the treatment of arrhythmia were identified through network pharmacology and molecular docking, and the key signaling pathways related to the treatment of arrhythmia were analyzed. Furthermore, we used real-time quantitative reverse transcription PCR (qRT–PCR) to verify the expression levels of key genes.  First, the potential therapeutic effect of DHI on arrhythmia was investigated in an in vitro human cardiomyocyte model. To further dissect the underlying molecular basis of this observation, network pharmacology analysis was performed, and the results showed that there were 108 crosstargets between DHI and arrhythmia. Moreover, 30 of these targets, such as AKT1 and HMOX1, were key genes. In addition, the mRNA expression of AKT1 and HMOX1 could be regulated by DHI.  Early afterdepolarizations (EADs) were observed during aconitine treatment in hiPSC-CMs, and the proarrhythmic effect of aconitine was partially rescued by DHI, indicating that the antiarrhythmic role of DHI was verified in an in vitro model, presumably because of its multitarget regulatory mechanism. Key genes, such as AKT1 and HMOX1, may contribute to the antiarrhythmic role of DHI in the heart. DHI can alleviate aconitine-induced arrhythmia in an  Salvia miltiorrhiza and safflower have the effects of promoting blood circulation, being anti-inflammatory and anticoagulant, and dredging collaterals  × 100% [Cell viability was detected by a CCK-8 kit assay . hiPSC-CMs were cultured in a 5% CO2 incubator for another 4 h. We next recorded the Ca2+ signal of hiPSC-CMs to evaluate the status of arrhythmia modeling.hiPSC-CMs were plated in 6-well plates, and the cells were divided into 3 groups: control group (Group A), aconitine group (Group B), and aconitine+DHI group (Group C). We added aconitine for 4 h to model arrhythmia . Then, wμM Fluo-4-AM  at 37°C for 30 min and washed with Tyrode. Then, the samples were transferred to an inverted microscope to record calcium signals. A confocal microscope  was employed for imaging. ImageJ and PeakCaller were used to analyze calcium signals.The calcium signals of hiPSC-CMs were observed and recorded by fluorescence imaging. hiPSC-CMs were incubated in Tyrode's solution containing 5 Salvia miltiorrhiza and safflower were retrieved from TCMSP (http://lsp.nwu.edu.) [The chemical constituents of wu.edu.) . The phawu.edu.) .The active ingredients of DHI were cross-referenced with arrhythmia targets, primarily by cross-referencing five datasets, including Online Mendelian Inheritance in Man (OMIM) , Therapehttps://string-db.org/) [https://david. ncifcrf.GOv/) [P < 0.05 was set as the screening condition for which the difference was statistically significant.The targets of DHI and arrhythmia diseases were substituted into the STRING database (db.org/) , and mulrf.GOv/)  and R lahttps://www.rcsb.org/) [Cytoscape  softwaresb.org/) . We downTotal RNA was extracted from hiPSC-CMs according to the instruction manual of the Evo M-MLV RT kit . The first-strand template cDNA was synthesized by reverse transcription. The expression levels of Akt1 and HMOX1 were detected by qRT–PCR using SYBR Green qPCR Mix  with appropriate dilutions of cDNA solution and corresponding primers. GAPDH was used as an internal control gene. All reactions were 3 replicates. The primers used in this study are shown in Supplementary Table P < 0.05 was considered significantly different.All statistical analyses were performed using SPSS 20.0 software, and the results are expressed as the mean ± standard error (SE). One-way analysis of variance (ANOVA) was applied to analyze the statistical significance of the differences between the different groups, and n = 18 − 24, P < 0.01), which was rescued by DHI  (Early afterdepolarizations (EADs) were observed during aconitine treatment in hiPSC-CMs, and the proarrhythmic effect of aconitine was partially rescued by DHI . Becaused by DHI . Moreove < 0.01) . Notably < 0.01) .in vitro, we next investigated the active compounds and key targets of DHI in the treatment of arrhythmia. The chemical constituents of DHI were searched in the TCMSP database, and 136 were found in Salvia miltiorrhiza and 98 in safflower (Supplementary Table https://www.UniProt.org/) database was used to correct and retrieve the corresponding target protein names. As a result, 59 predicted targets of Salvia miltiorrhiza and 259 predicted targets of safflower were obtained. Using “arrhythmia” as the keyword, we searched 5 databases, including OMIM, TTD, PharmGKB, DrugBank, and GeneCards, and found 1337 arrhythmia-related target genes, which were selected as candidate targets  and 1066 edges. Degree and betweenness are important parameters in the network analysis, because the degree of a node refers to the number of connections between the node and other nodes, and the greater the degree of a node, the more nodes connected to it. The betweenness refers to the proportion of the shortest path through the node in the total path, and the greater the degree and the betweenness, the more important the node was. As shown in Supplementary Table Salvia miltiorrhiza and safflower, which indicates that DHI plays a role in the treatment of arrhythmia through multiple components, targets, and signaling pathways.GO function analysis is mainly used to describe the function of genes and proteins, and three categories of cellular function, molecular function, and biological function are included. The signaling pathways enriched by the common targets of DHI and arrhythmia were obtained by KEGG enrichment analysis. GO analysis enriched 30 items , includiThe initial PPI network string analysis consisted of 108 nodes, as shown in The expression of AKT1 increased in the aconitine arrhythmia group, while the expression of AKT1 was decreased by treatment with DHI and sotalol, a known antiarrhythmia drug . Similar2+ plays an irreplaceable role in myocardial excitation-contraction coupling (ECC) [2+ regulation in cardiomyocytes may lead to arrhythmias [2+ signaling [Arrhythmia is an important type of cardiovascular disease that can be caused alone or accompanied by other cardiovascular diseases. DHI has the effects of antiatherosclerosis, inhibition of oxidative stress, antithrombosis, and reversal of ventricular remodeling , 40. At ng (ECC) , and thehythmias . At the ignaling . We founα/Akt/GSK3β and nuclear factor kappa-B (NF-κB) signaling pathways may reverse left ventricular hypertrophy and improve myocardial status [The regulation of the PI3K-Akt signaling pathway includes regulating downstream signaling pathways to influence apoptosis and promote proliferation to play a protective role in cardiomyocytes. The PI3K-Akt/eNOS signaling pathway plays an important role in mediating apoptosis . In addil status . Thus, tIk and ICa) and change the susceptibility to arrhythmias such as atrial fibrillation [2+, biliverdin, and carbon monoxide. As an antioxidant system, induced HOMX1 has a certain protective effect on various oxidative stress-related diseases, such as atrial fibrillation (AF) [in vitro [In the drug-ingredient-target network of DHI, Akt1 and HMOX1 are the two key gene targets of DHI against arrhythmia. Both are important cardioprotective genes. Akt is a protooncogene belonging to the serine/threonine family of protein kinases, including the three isoforms Akt1, Akt2, and Akt3 , 51. Theillation . Moreoveillation . Cardiacillation , 56. As ion (AF) , 58. Proion (AF) . Moreovein vitro , and thein vitro .Molecular docking predicts the interaction between receptor and drug molecules according to the characteristics of receptor and drug molecules. Molecular docking of network pharmacology will help researchers better understand the mechanism of TCM in treating diseases . Severalin vitro experiments are based on hiPSC-CMs, which can more accurately reflect the human response to drugs. Second, we verified the effect of DHI on potential targets more accurately by combining hiPSC-CM experiments and network pharmacology. There are some limitations. First, these experimental results are based on cell experiments rather than clinical trials and animal experiments. In addition, the composition of TCM is complex, so the specific effect of compounds in DHI remains to be further verified. Last but not least, there are various types of arrhythmias, and the pathogenesis of different types also differs. Therefore, the therapeutic effect of DHI on specific type of arrhythmias deserves further exploration.There are some advantages in our study. First, the In this study, network pharmacology and cell experiments were used to identify the main biologically active components of DHI and to study the potential mechanism of DHI in the treatment of arrhythmia. DHI may play a myocardial protective role against arrhythmia by regulating the PI3K-Akt signaling pathway and key genes, including Akt1 and HMOX1. This study provides a theoretical basis for the further study of DHI in arrhythmia treatment.