Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli). While S. aureus generally induces a chronic and subclinical mastitis, E. coli is an important etiological pathogen resulting in an acute and clinical mastitis. The liver plays a central role in both, the metabolic and inflammatory physiology of the dairy cow, which is particularly challenged in the early lactation due to high metabolic and immunological demands. In the current study, we challenged the mammary glands of Holstein cows with S. aureus or E. coli, respectively, mimicking an early lactation infection. We compared the animals' liver transcriptomes with those of untreated controls to investigate the hepatic response of the individuals. Both, S. aureus and E. coli elicited systemic effects on the host after intramammary challenge and seemed to use pathogen-specific targeting strategies to bypass the innate immune system. The most striking result of our study is that we demonstrate for the first time that S. aureus intramammary challenge causes an immune response beyond the original local site of the mastitis. We found that in the peripheral liver tissue defined biological pathways are switched on in a coordinated manner to balance the immune response in the entire organism. TGFB1 signaling plays a crucial role in this context. Important pathways involving actin and integrin, key components of the cytoskeleton, were downregulated in the liver of S. aureus infected cows. In the hepatic transcriptome of E. coli infected cows, important components of the complement system were significantly lower expressed compared to the control cows. Notably, while S. aureus inhibits the cell signaling by Rho GTPases in the liver, E. coli switches the complement system off. Also, metabolic hepatic pathways  are affected after mammary gland challenge, demonstrating that the liver restricts metabolic tasks in favor of the predominant immune response after infection. Our results provide new insights for the infection-induced modifications of the dairy cow's hepatic transcriptome following mastitis.Mastitis is one of the major risks for public health and animal welfare in the dairy industry. Two of the most important pathogens to cause mastitis in dairy cattle are  In times of growing consumer awareness, the delivery of high-quality products from healthy animals is a high priority for animal farming and the food industry. One major potential risk for public health and animal welfare in the dairy industry is mastitis, the infection and inflammation of the mammary gland. It is one of the most frequent and costly single-animal diseases in dairy cows , raises Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli). While S. aureus generally induces a chronic and subclinical mastitis  rely on completely different virulence factors, e.g., exotoxins  in an infection model at the Clinic for Cattle at the University of Veterinary Medicine Hanover, in the following referred to as TiHo. The remaining five animals were brought to the Leibniz Institute for Farm Animal Biology in Dummerstorf, in the following referred to as FBN, to serve as a non-challenged control and to participate in a long-term model. The husbandry and sample collection of both experimental groups were previously described  and BHB (beta-hydroxybutyric acid) were determined using the ABX Pentra 400 . Differences between cohorts (TiHo vs. FBN) were evaluated in a Mixed linear model with fixed effects of day of sampling, cohort, and day of sampling × cohort interaction and a random animal effect with the lme4 package in R . These time points were related to the different peaks of inflammation in the mammary gland after S. aureus or E. coli challenge, according to previous studies  were grinded using the Precellys 24 tissue homogenizer . Total RNA was extracted using the NucleoSpin RNA II kit  via an on-column-purification including a modified DNase I digestion step as described by Weikard et al. . Subsequhttps://www.ebi.ac.uk/ena) and are publicly accessible under project number PRJEB33849, accession numbers ERR3466640 - ERR3466680 at EMBL-EBI.All RNAseq datasets are submitted to the ENA repository  assuming no, mild or severe clinical response to pathogen. In our analyses, we furthermore calculated the module membership for each gene and module, which is the correlation between the normalized gene expressions and eigengene value of a sample. We plotted the module membership against the correlation between normalized gene expression and infection identifier (representing a measure of gene trait significance) within modules. For pathway analyses, it is assumed that genes, which are highly interconnected within a network eigengene module, should be involved in closely linked or identical biological pathways. Thus, for those modules showing a high correlation between the module eigengene value for each sample and its infection group score, differentially expressed genes (DEGs) by pathogen comparison were modulewise submitted to Ingenuity Pathway Analysis  with a threshold for significance of padj < 0.05. In addition, we also tested for enriched KEGG pathways via DAVID . For data processing, we used SAMtools  as well ia DAVID . In the E. coli or S. aureus challenged animals, respectively.Finally, IPA pathway analyses were also performed for all DEGs in the comparison between samples from controls and RNAseq generated a total of 4.5 billion reads corresponding to an average of 110 million reads per liver sample. Ninety-eight percent of reads mapped at least once to the bovine reference genome UMD3.1 .S. aureus and E. coli and non-challenged control samples along PC  1 and PC2  there was no significant difference between individuals in the TiHo and FBN cohort .A multidimensional scaling (MDS) plot represents the distribution of hepatic transcriptome data from cows having received an intramammary challenge with  and PC2 . The resonducted . The cluS. aureus mammary gland infection is usually considered to be a subclinical inflammation with local pathological manifestation. However, the body temperature course data after the pathogen challenge monitored by the intravaginal logger indicated a clinical response to the challenge as demonstrated by a maximum mean body temperature exceeding 40.0°C in 83% of the S. aureus challenged animals during the course of challenge. This clinical pathogen response is supported by the hepatic transcriptome analysis, which revealed a total of 3,672 genes differently expressed between liver samples of S. aureus challenged cows compared to the control group  in the time period 24 h after challenge. The average maximum temperature of the animals was 41.8°C . There was a tendency for more DEGs with higher expression in E. coli challenged animals than with lower expression compared to untreated controls  against the correlation between normalized gene expression and infection category (representing a measure of gene-trait relationship significance). While modules with a significant correlation to the infection category showed a very clear linear relationship of those two parameters see , we did For subsequent pathway analyses, we focused on those modules within the upper quartile across all modules regarding the proportion of DEGs and also on those with a significant correlation to the infection identifier. There were a number of modules with a significant correlation to the infection identifier, which demonstrated pathogen-challenge specific differences in the proportion of DEGs . These mE. coli challenge, pathway enrichment analysis highlighted the modules turquoise, darkred, pink, green and blue  lower expressed in E. coli challenged animals compared to controls. However, they themselves were not assigned to this module, but to the turquoise module.The darkred module contained only 71 genes  but alsod status . They arE. coli challenge in the mammary gland is consistent with the prediction that upstream regulators with anti-inflammatory and attenuating cytokine signaling role like IL10 or SOCS1 are activated in the liver, and immune response stimulating factors like IL2 and IFNG are inactivated .Further modules, which display a clustering of samples by infection status are royalblue and grey60 . Yet, pain vitro challenges, where only those signaling pathways that are active within the respective cell are monitored by expression analysis. Furthermore, we decided to analyze the DEGs across all modules within a pathogen challenge, because the analysis of genes by cluster has the limitation that a gene can only be member of a single cluster, but is involved in multiple pathways. This is demonstrated e.g., by SREB1 and SREB2, which were predicted as upstream regulators of DEGs after E. coli challenge in the module darkred. However, they themselves were assigned to another module (turquoise). Thus, we complemented the cluster-based data analysis with pathway analyses across all DEGs with pathogen challenge, E. coli or S. aureus.When interpreting the results, it has to be considered that we have monitored the response of the animals in one tissue while the direct pathogen contact took place in another tissue. Thus, regulatory signals might not be directly measured at gene expression level in the liver, e.g., if the regulatory signal is a cytokine produced by blood monocytes. This is different to S. aureus challenge  Epithelial Adherens Junctions and Actin Cytoskeleton Signaling, (ii) ILK Signaling and Integrin Signaling, and (iii) RhoGDI Signaling  revealed a substantial number of 124 significantly enriched canonical pathways in the liver transcriptome in response to intramammary enge see . Analogoignaling .It was striking that numerous significantly enriched pathways were involved in the organization and maintenance of the cytoskeleton and/or intracellular or cell to cell signaling . All pathways with an indicated activation status in the Top 25 list of significantly enriched Ingenuity canonical pathways were inactivated except for RhoGDI Signaling and PTEN Signaling .S. aureus associated module brown, Epithelial Adherens Junction Signaling was the most significantly affected pathway  in our S. aureus challenge study, which reflects its substantial inactivation in infected animals , which we had also observed for the S. aureus associated module yellow.Analogous to the hway see  in the lskeleton , 47, whimals see . This paz-scores (−3.33) for the ILK (integrin-linked kinase) Signaling pathway and for the Integrin Signaling pathway (−4.23) show that both pathways were clearly inactivated in the liver of cows intramammary challenged with S. aureus  but also with the upregulation of the RhoGDI Signaling pathway, which was assigned a positive z-score of 3.02. The RhoGDI (Rho GDP-dissociation inhibitor) is described as an inhibitor of Rho family GTPases activity by hampering the dissociation of GDP, preventing the subsequent binding of GTP . To obtain information on the further transmission of the initial signals, the potential upstream regulators were monitored, which had been predicted for the DEGs by IPA. In response to S. aureus challenge, miRNAs were the most prominent upstream regulators in the hepatic transcriptome with the highest positive activation z-scores  of significantly enriched canonical pathways in the liver transcriptome . However, the main pathways identified as enriched after E. coli intramammary challenge differed from those obtained from the analysis of S. aureus challenged animals. This is in line with the differences detected in the modulewise data analysis. Key hepatic processes affected by E. coli challenge and attributed the highest activation scores, are (i) RXR Activation (and associated pathways) together with lipid metabolism , and (ii) Acute Phase Response and the Complement System , indicating a substantial downregulation of this pathway in E. coli challenged vs. control animals. This is in agreement with the positive activation score for the pathway LPS/IL-1 mediated Inhibition of RXR Function (z-score = 2.65). As presented in beta Oxidation in response to E. coli intramammary challenge also detected for the E. coli challenge associated module turquoise. Furthermore, due to the effects on the pathway LPS/IL-1 mediated Inhibition of RXR Function, a link is also provided for an immune response to the intramammary pathogen challenge also seen in the turquoise module.Modulewise analysis had indicated a major effect of padj < 0.40E-16) and Glycolysis/Gluconeogenesis (p < 0.5E-19). All genes encoding key enzymes in glycolysis/gluconeogenesis showed a lower transcription in the cows with intramammary E. coli challenge compared to controls  and particularly in the citrate cycle . E. coli challenge with an increased expression of key cytokine receptor genes .Acute Phase Response Signaling, an essential component within the innate immune responses, was within the top significantly enriched Ingenuity canonical pathways and also had one of the highest activation scores in the hepatic transcriptome comparing E. coli challenge. However, surprisingly and in contrast to the acute phase response pathway it displayed a low negative z-score indicating its strong and coordinated inactivation in E. coli challenged animals (z-score = −2.14). As shown in E. coli challenged cows compared to the control, while in contrast some inhibitors of the complement system  were correspondingly upregulated in the challenged animals.The Complement System pathway, which is as well an integral part of the innate immune system acting as bridge between innate and acquired immunity, was also highly significantly enriched with significantly DEGs after E. coli challenge (see p < 8.06E-83) and a high negative activation score. Indication on inactivation was obtained also for other known regulators of liver (lipid) metabolism . This has already been demonstrated by the results from the module turquoise. This module comprised the largest number of genes and showed a strong correlation of module membership of genes and gene-infection association  immune responses. Mammary gland challenge and infection by response  and no dresponse  demonstrS. aureus challenge negatively affected hepatic pathways directly involved in combating the invading pathogen. This is already visible from the volcano plot of DEGs  and classical extracellular activated kinase ERK1/2 MAP kinases have been identified as key integrators triggering cytoskeleton rearrangements. Signals from chemokine receptors and integrin molecules that coordinate these processes are transmitted to Rho family GTPases and passed on to cytoskeletal target proteins, which leads to the initiation of numerous processes including cell migration, morphogenesis, cytokinesis, and endo/exocytosis  are class II nuclear receptors; this subgroup includes inter alia liver X receptors (LXRs)  . While Ls (LXRs) . LXRs ars (LXRs)  and are s (LXRs) , 76. TheE. coli challenge is in accordance with data from the literature. Moreover, FGG, FGB, and A2M genes involved in APR signaling, were significantly upregulated in our analysis of DEGs.In recent years, several reports –76 shed E. coli LPS as well as proinflammatory cytokines TNFalpha and IL1beta decreased the abundance of RXR proteins in the liver of hamsters  infection and increased metabolic demands for milk production. The aspect of an increased metabolic challenge of the animal due to pathogen-specific adaptions of the hepatic transcriptome might be a new approach for the treatment and prevention of the disease via direct interventions not only directly at the mammary gland, but also at systemic level.These studies in the literature and our results from the hepatic transcriptome analysis consistently show that particularly E. coli challenge on the hepatic expression of the complement system genes. This comprised a lower expression of direct components of the complement system and a higher expression of genes encoding inhibitors of the complement system  in the challenged animals. Similar to the APR, the complement system is a powerful weapon of the innate immune response to fight against invading pathogens  governed branch] is incapable to react accordingly to the pathogen challenge while the APR seems to be activated, although with some balancing processes.There was a remarkable effect of athogens . HoweverE. coli 1303 (the same strain as used in our experiment) and analyzed mRNA from udder tissue on a microarray platform. They also found a significant transcriptional downregulation of some components of the complement system, especially the first component of the classical pathway (C1 complex) and components in the membrane attack complex (MAC). Interestingly, Günther et al. also found complement inhibitors, such as DAF (decay-accelerating factor 1) and C1QBP (complement C1q-binding protein), to be increased in infected mammary gland tissue. Our data indicate that this effect is also reflected in the liver, the main tissue synthesizing complement system components. Based on these data, we suggest that while intramammary E. coli challenge triggers transcriptional processes of the APR in the liver, it also induces in parallel an active shut-down of the complement system already at transcript level.Günther et al.  challengE. coli 1303 strain used in our study was studied by Leimbach et al.  displayed an inverse ratio of expression levels. In addition, while obviously major acute phase protein genes  were upregulated via STAT3/STAT3A in the course of challenge, TCF1/3/4 was significantly lower expressed with correspondingly lower expression of genes encoding albumin (ALB), transferrin (TF), or histidine rich protein (HRG), a peptide with antimicrobial activity against E. coli , indicating a considerable activation of this pathway in the livers of E. coli infected animals. Thus, the lower hepatic immunoinhibitor gene expression in response to S. aureus or E. coli intramammary challenge has initiated an activation of the immune response via upregulation of expression levels of target genes acting downstream in this pathway . This supports a well and tightly orchestrated regulation of the immune response beyond the local site of bacterial infection.In the hepatic transcriptome of intramammary S. aureus and E. coli, respectively. We found that both pathogens can attack the immune system in an important metabolic organ that is located at a considerable distance from the site of the animal's direct pathogen contact. Thus, only antibiotic therapy of cows suffering from mastitis might not be sufficient. Immunostimulant drugs could favor the course of the disease but would have to be used in a pathogen-specific manner. Especially for E. coli mastitis, the influences of the infection on the liver metabolism should not be neglected. Particularly in the early lactation, the cow's energy demand for milk production can hardly be covered by the energy consumed with the feed  are affected after mammary gland challenge, demonstrating that the liver reduces metabolic tasks in favor of the predominant immune response after infection. Therefore, a revised, pathogen-specific treatment regime for mastitis, which goes beyond mere antibiotic therapy, might be beneficial for the course of the infection.Both, S. aureus udder challenge causes an immune response beyond the original local site of the mastitis. We found that in the peripheral liver tissue defined biological pathways are switched on/off in a coordinated manner to balance the immune response in the entire organism. TGFB1 signaling plays a crucial role in this context.The most striking result of our study is that we demonstrate for the first time that https://www.ebi.ac.uk/ena, Project number PRJEB33849, accession numbers ERR3466640 - ERR3466680) at EMBL-EBI.RNA-Seq datasets are submitted to the ENA repository . For the TiHo cohort, the experiment was performed under the reference number 33.12-42502-04-15/2024 by the Lower Saxony Federal State Office for Consumer Protection and Food Safety. This study was submitted to and approved by the ethics committees of the Leibniz Institute for Farm Animal Biology and the University of Veterinary Medicine Hanover, respectively. All ethical evaluations were performed as required by the German Animal Care law and associated legislative regulations .RW, H-MS, WP, HZ, MH, H-JS, MS, SE, and CK designed research. AH, JB, MM, and LR performed research. AH, RW, DB, and CK analyzed data and wrote the paper. 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.


Astragalus Membranaceus (AM) is a commonly used medicine in clinical therapy of eye disorders in traditional Chinese medicine (TCM). In recent years, it is also used for treating DR, but the specific mechanism is unclear. Therefore, this study explores the potential mechanism of AM in DR treatment by using network pharmacology.  Diabetic retinopathy (DR) includes a series of typical lesions affected by retinal microvascular damage caused by diabetes mellitus (DM), which not only seriously damages the vision, affecting the life's quality of patients, but also brings a considerable burden to the family and society.  Based on the oral bioavailability (OB) and drug likeness (DL) of two ADME  parameters, Traditional Chinese Medicine Systems Pharmacology Database (TCMSP), Swiss Target Prediction platform, GeneCards, and OMIM database were used to predict and screen the active compounds of AM, the core targets of AM in DR treatment. The Metascape data platform was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on the core targets.  24 active compounds were obtained, such as quercetin, kaempferol, and astragaloside IV. There were 169 effective targets of AM in DR treatment, and the targets were further screened and finally, 38 core targets were obtained, such as VEGFA, AKT1, and IL-6. EGFR tyrosine kinase inhibitor resistance, AGE-RAGE signaling pathway in diabetic complications, PI3K-Akt signaling pathway, and other metabolic pathways participated in oxidative stress, cell apoptosis, angiogenesis signal transduction, inflammation, and other biological processes.  AM treats DR through multiple compounds, multiple targets, and multiple pathways. AM may play a role in the treatment of DR by targeting VEGFA, AKT1, and IL-6 and participating in oxidative stress, angiogenesis, and inflammation. Diabetes mellitus (DM) is a metabolic disorder caused by genetic and environment factors, and it has become a critical health problem worldwide due to its high prevalence and related disability and mortality . In a reRecently, traditional Chinese medicine (TCM) is gradually attracting public's attention owing to its clinical efficacy. Compared with western medicine, TCM is an important alternative strategy because of syndrome differentiation and holistic concept for treating DM . Also, tAstragalus Membranaceus (AM) is named “Huangqi” of TCM. AM was originally described in the Sheng Nong's Herbal Classic which is the Classic of Herbal Medicine in ancient TCM practice, which is the dry root of astragalus mongolicus or membranous astragalus [κB, and reduce the production of proinflammatory cytokines, such as IL-1β and TNF-α, which were induced by AGE. Moreover, it can inhibit the formation of retinal angiogenesis by downregulating the level of VEGF in Muller cells induced by hyperglycemia [tragalus . It is otragalus . Owing ttragalus . There wglycemia .However, the specific mechanism of action is not clear. In recent years, a new trend emerged by using network pharmacology to understand TCM. From the perspective of systems biology, network pharmacology explores the relationship between drugs-components-targets-diseases, predicts the potential mechanism of drugs to treat diseases, and provides a theoretical basis for effectiveness of TCM to treat the disease . Therefohttps://pubchem.ncbi.nlm.nih.gov/, 2020.6.04) platform was used, and the InCHIKey number or CAS number of the effective components was entered in order to obtain the 2D structural formula of the components. And then, the 2D structural formula was imported into the Swiss Target Prediction platform (http://www.swisstargetprediction.ch/), 2020.6.04 [Homo sapiens,” and the potential targets of the compounds with the condition of “probability ≥  0” were predicted. Finally, targets obtained from both databases were combined and deduplicated, and the targets of AM were obtained.“Huangqi” (AM) was retrieved in TCMSP and screened with OB ≥ 30% and DL ≥ 0.18. The chemical compounds of AM were obtained, and the compounds of AM from the literature reports were added. All the components of AM were combined. The PubChem platform (020.6.04 . The spehttps://tcmspw.com/tcmsp.php) is a database that is based on the framework of systems pharmacology for herbal medicine, which can predict the compound and target of herbal medicines by setting ADME  parameters [TCMSP: The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP)  in the human body, including OB and DL.Oral bioavailability (OB): OB refers to the speed and degree of absorption of the active compounds or active groups of the drug in the systemic circulation, and is a key parameter for evaluating whether the drug can be developed. Generally, molecules with OB ≥ 30% have good oral availability .Drug likeness (DL): the DL of all herbal medicines is acquired by calculation of Tanimoto coefficient. The Tanimoto coefficient is defined as follows :(1)fA,B=A is the molecular descriptor index of the compounds of herbal medicines to be tested in the TCMSP database and B is the average drug likeness index described by all 6511 small drug molecules in the DrugBank database. The descriptors of all molecules are calculated by the Dragon software . Generally, molecules with DL ≥ 0.18 have good drug likeness.Here, PubChem: PubChem is the world's largest collection platform of chemical information and search information of compounds, such as chemical and physical properties, biological activities, safety, and toxicity information and more and information can be obtained by importing name, molecular formula, structure, and other identifiers of compounds .Swiss Target Prediction: Swiss Target Prediction is a type of online analysis software for predicting targets of small molecules, which can import SMILES and structure of the molecule to predict the targets of small molecule compounds .https://www.genecards.org/, 2020.6.05) [https://omim.org/,2020.6.05) [The keyword “diabetic retinopathy” was searched in the GeneCards Database (20.6.05) , and the20.6.05) , Gene MaOnline Mendelian Inheritance in Man (OMIM) and GeneCards are two different databases that provide comprehensive information of all annotated and predicted human genes.http://jvenn.toulouse.inra.fr/app/example.html) [https://string-db.org/) was used and protein-protein interaction (PPI) was built. In the platform, “Multiple proteins” was selected and the organism was selected as “Homo sapiens.” The intersection targets of AM and DR were then imported, the medium confidence of minimum required interaction score was set as 0.4, and the remaining parameters were maintained at the default settings [The jvenn online platform (le.html)  was usedsettings . The filSTRING: it is a search tool for the retrieval of interacting genes/proteins, which is applied for predicting the PPI network and detecting the possible relationships.Cytoscape: it is a type of open-source software used to analyze and visualize biological networks .P value < 0.01, a minimum count of 3, and an enrichment factor >1.5 (the enrichment factor is the ratio between the observed counts and the counts expected by chance) were set. GO and KEGG pathway enrichment analyses on core targets were performed.The core targets of AM in DR treatment were imported into the Metascape platform, and in this, species was selected as “H sapiens,” “Custom analysis” was selected, and parameters of Metascape: it is a gene function annotation analysis tool, to realize the recognition of gene or protein function through the bioinformatics analysis of genes and proteins .Gene Ontology (GO): it is a bioinformatics tool used for annotating genes, gene products, and sequences to analyze their biological processes. GO includes 3 aspects: molecular function (MF); biological process (BP); cellular component (CC).KEGG: it is a database resource comprising information on the biological systems of genome sequences and other high-throughput data .Cytoscape 3.7.2 was used to construct the component-target network (C-T network).String online platform was used to build PPI of core targets, and then, PPI was visualized by Cytoscape 3.7.2.The top 20 KEGG pathways were screened, and Cytoscape 3.7.2 was used to construct the target-pathway network (T-P network).On the TCMSP platform, the ADME parameters of OB ≥ 30% and DL ≥ 0.18 were selected 20 active compounds were obtained, and then, according to the relevant literature , additio2D structural formulas of 24 compounds were obtained from PubChem, and then, compounds were imported into the Swiss Target Prediction platform to obtain the target information of 24 compounds. Targets were merged and deduplicated, and a total of 482 targets of AM were obtained. The component-target network (C-T network) was constructed  by usingThere were 217 targets of DR in the OMIM database, while there were 1753 targets of DR in the GeneCards database. The targets from the two databases were merged, the duplicates were removed, and finally, 1901 targets of DR were obtained. 482 targets of AM and 1901 targets of DR were imported into Venny2.1.0 online platform, and there were 169 common targets . 169 tar38 core targets were imported into the STRING platform, the PPI of the core targets were obtained , and finThere were 38 targets of AM on DR; the biological function of the 38 targets was elucidated. GO analysis and KEGG pathway analysis on 38 targets were performed. Based on GO results, there were 79 processes of molecular functions, such as protein kinase activity, protein serine/threonine kinase activity, and kinase binding; there were 418 biological processes, such as transmembrane receptor protein tyrosine kinase signaling pathway, positive regulation of cell migration, and positive regulation of cell motility; there were 42 cellular components, such as membrane raft, membrane microdomain, and membrane region. In addition, there were 123 pathways for results of KEGG.P value .Subsequently, we compared the results of GO and KEGG enrichment analyses for 38 key targets, and the results indicated that regulation of various kinase activities, response to insulin, and regulation of vascular endothelial cells played a main role in the process of AM in DR treatment. Furthermore, in delaying the progression of diabetic retinopathy, oxidative stress and inflammation could also play an important role . The actIn addition, there were several pathways, such as pathway in cancer, proteoglycans in cancer, and hepatitis bare, indicating AM might exhibit potential therapeutic effects on these diseases, while demonstrating that the progression of DR is influenced by other diseases. There were multiple targets, multiple pathways, and multiple processes involved in treatment of DR through AM. In addition, there were still some deficiencies in this study. The network pharmacology method was used to predict the targets and pathways of AM in DR treatment, which still need to be verified by future pharmacological studies.In summary, we found that AM in DR treatment has the characteristics of multiple components, multiple targets, and multiple pathways through network pharmacology. AGE-RAGE signaling pathway in diabetic complications and PI3K-Akt signaling pathway might play an important role in the treatment process, providing a theoretical basis for AM in DR treatment and a new direction for the development of drugs for DR.


Background: Ge-Gen-Qin-Lian Decoction (GGQLD), a traditional Chinese medicine (TCM) formula, has been widely used for ulcerative colitis (UC) in China, but the pharmacological mechanisms remain unclear. This research was designed to clarify the underlying pharmacological mechanism of GGQLD against UC.Method: In this research, a GGQLD-compound-target-UC network was constructed based on public databases to clarify the relationship between active compounds in GGQLD and potential targets. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses were performed to investigate biological functions associated with potential targets. A protein–protein interaction network was constructed to screen and evaluate hub genes and key active ingredients. Molecular docking was used to verify the activities of binding between hub targets and ingredients.Results: Finally, 83 potential therapeutic targets and 118 corresponding active ingredients were obtained by network pharmacology. Quercetin, kaempferol, wogonin, baicalein, and naringenin were identified as potential candidate ingredients. GO and KEGG enrichment analyses revealed that GGQLD had anti-inflammatory, antioxidative, and immunomodulatory effects. The effect of GGQLD on UC might be achieved by regulating the balance of cytokines  in the immune system and inflammation-related pathways, such as the IL-17 pathway and the Th17 cell differentiation pathway. In addition, molecular docking results demonstrated that the main active ingredient, quercetin, exhibited good affinity to hub targets.Conclusion: This research fully reflects the multicomponent and multitarget characteristics of GGQLD in the treatment of UC. Furthermore, the present study provided new insight into the mechanisms of GGQLD against UC. Ulcerative colitis (UC) is an idiopathic chronic inflammatory bowel disease (IBD) characterized by persistent inflammation of the entire large intestine that causes abdominal pain, bloody diarrhea, and fecal urgency . The incGe-Gen-Qin-Lian decoction (GGQLD), a well-known TCM formula, was first described in Shang Han Lun written by Zhongjing Zhang  and has Network pharmacology is a part of bioinformatics, integrating bioinformatics, and system medicine. Since network pharmacology can reflect the characteristics of multiple components and targets of TCM, it has been widely used in TCM research and advanced drug discovery in recent years . This prhttps://tcmspw.com/tcmsp.php) [All chemical ingredients of each herb in GGQLD were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) (msp.php) . TCMSP imsp.php) . It can msp.php) .Drug screening and evaluation in GGQLD were mainly identified by OB and DL administered by TCMSP. OB and DL are two important indicators in ADME. DL is a qualitative concept used in drug design for how druglikeness a substance is concerning factors such as bioavailability and helps optimize pharmacokinetic and pharmaceutical properties, such as solubility and chemical stability . OB is thttp://www.uniprot.org). Target information was set to Homo sapiens and aggregated for further analysis.The corresponding protein targets of each active ingredient in GGQLD were predicted using TCMSP. Predicted targets from TCMSP were extracted and converted to gene names using UniProtKB (https://www.disgenet.org/), GeneCards database (https://www.genecards.org/), and DrugBank database (https://www.drugbank.ca/). DisGenNET is a knowledge management platform integrating and standardizing data about disease-associated genes and variants from multiple sources. It also covers the full spectrum of human diseases as well as normal and abnormal traits [UC-related genes were retrieved from the DisGeNET database  network was constructed to clarify the relationship between active compounds in GGQLD and potential targets. This network was constructed and visualized using Cytoscape 3.7.2 software.P-values below 0.05 were considered statistically significant.To investigate biological functions associated with potential targets, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses via the ‘clusterProfiler’ R package. The ‘clusterProfiler’ R package serves as a user-friendly enrichment tool with integrated gene cluster analysis based on multiple resources . GO enrihttps://string-db.org/) to perform PPI analysis. STRING, an online PPI analysis database, currently features the largest number of organisms (5090) and proteins (24.6 million) and has very broad and diverse, benchmarked data sources [Homo sapiens’, and the minimum required interaction score was set to be high confidence (0.700). The PPI network was visualized with Cytoscape 3.7.2 software.Protein–protein interaction (PPI) is of pivotal importance in the regulation of biological systems and is consequently implicated in the development of disease states . The pot sources . The spehttps://david.ncifcrf.gov/), a web-based analysis tool. Gene counts >3 and a P-value below 0.05 were considered as the cutoff criteria. To reveal the relationship between hub genes and possible therapeutic components, the corresponding chemical compounds of hub genes were incorporated into a Sankey diagram.To further identify the hub genes in the potential targets, the top 10 hub genes were identified using MCC methods in ‘cytoHubba’ of Cytoscape 3.7.2 software . Furtherhttp://cao.labshare.cn/cb-dock/). CB-Dock is a protein-ligand docking method that automatically identifies the binding sites, calculates the center and size, customizes the docking box size according to the query ligands, and then performs molecular docking with AutoDock Vina [https://www.rcsb.org/), and SDF formats of ligands were obtained from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/), respectively. The styles of the ligand and receptor were set as ‘ball and stick’ and ‘cartoon’, respectively. The colors of the ligand and receptor were set as ‘By Element’ and ‘By Chain’, respectively. The vina sore represents a certain binding activity between a ligand and a protein. The lower the vina score is, the more stable the ligand binds to the protein.Molecular docking was used to predict the activities of binding of proteins to compounds via CB-Dock , a total of 139 ingredients were identified, including 14 components of Huanglian, 35 components of Huang Qin, 4 components of Gegen, and 92 components of Gancao. Some compounds are shared by two or more herbs, such as quercetin (MOL000098), which is shared by Huanglian and Gancao; beta-sitosterol (MOL000358), which is shared by Gegen and Huang Qin; coptisine (MOL001458) and epiberberine (MOL002897), which are shared by Huanglian and Huang Qin; and formononetin (MOL000392), which is shared by Gegen and Gancao. Detailed information regarding these ingredients is shown in Supplementary Table S1.Potential targets in GGQLD were predicted in TCMSP. In total, 259 targets were identified for 139 bioactive compounds (4 compounds did not receive predictions). Afterward, predicted targets from the TCMSP were extracted and converted to gene names using UniProtKB. From the results, we can see that many compounds were predicted to target the same proteins, such as 62 ingredients targeted to PPARG.The identification of UC-related targets was searched using the keyword ‘ulcerative colitis’ from the following resources: DisGeNET database, GeneCards database, and DrugBank database. A total of 5273 genes were searched, including 914 genes from the DisGeNET database, 4356 genes from the GeneCards database, and 64 genes from the DrugBank database, and 17 FDA-approved compounds were identified for the treatment of UC. As a result, there are 776 common targets shared by at least two databases, which were retained as UC-related targets.Based on the GGQLD target database and UC-related target database, 83 overlapping genes were identified as potential targets for GGQLD against UC, as shown in the Venn diagram . The resThere were 118 active ingredients corresponding to the 83 potential targets. To better understand the complex relationships between all bioactive compounds in GGQLD and their UC-related targets, a G-U network was constructed . There wTo further validate whether biological functions enriched by candidate targets as mentioned above were correlated with UC, GO and KEGG enrichment analyses were performed via R. Three GO terms were analyzed, which included CC, MF, and BP. The top 10 GO enrichment results for each term are shown in Additionally, 123 KEGG pathways were found. KEGG enrichment analysis showed that the 83 potential targets were closely related to several critical pathways associated with UC, such as ‘IL-17 signaling pathway’, ‘tumor necrosis factor (TNF) signaling pathway’, ‘Th17 cell differentiation’, ‘inflammatory bowel disease’, ‘Hypoxia-inducible factor (HIF)-1 signaling pathway’, and ‘inflammatory bowel disease’. The top 20 enriched KEGG signaling pathways are shown in To further investigate the relationship between the 83 overlapping genes, the PPIs were analyzed using the STRING online database, and high confidence target protein interaction data with a score >0.7 were selected. The obtained PPI network file was imported into Cytoscape 3.7.2 software for visualization. We found 77 nodes and 609 interactions in the PPI network, and the results are shown in According to the PPI network, the top 10 core nodes were identified, ranked by MCC in the ‘cytoHubba’ plugin. Thus, 10 genes were identified as hub genes: IL-6, TNF, STAT3, IL-1β, CXCL8, CCL2, intracellular adhesion molecule-1 (ICAM1), IL-10, IL-4, and IL-2, as shown in To further validate potential targets in UC, we performed molecular docking on quercetin with the 10 hub genes . Docking analysis successfully predicted Vina scores, which were all negative and less than −6, between quercetin and the 10 hub genes; the results are listed in In vivo and in vitro studies have provided multiple lines of evidence indicating that some ingredients of GGQLD, such as berberine and glabridin, have definite beneficial effects against UC [Currently, TCM is being more frequently chosen by patients to treat UC, and many TCM formulas have been successfully utilized for the treatment of UC ,37, inclainst UC . Given tainst UC . Here, wConsistent with previous studies, BP enrichment results indicated that a large number of genes were associated with oxidative stress, a key etiological factor of UC, including ‘reactive oxygen species metabolic process’, ‘response to reactive oxygen species’, ‘response to oxidative stress’, ‘regulation of reactive oxygen species metabolic process’, and ‘cellular response to oxidative stress’. The gastrointestinal tract is prone to reactive oxygen species (ROS) attack. Alterations in the balance between ROS production and the capacity to rapidly detoxify reactive intermediates lead to oxidative stress, which is an essential factor in the pathogenesis of gastrointestinal mucosal disease . During Additionally, the KEGG results indicate that most of the disease-related pathways are immune and inflammatory, tumor-related signaling pathways, and virus infection-related signaling pathways, most of which have been reported to be closely related to UC. For example, IL-17, a key mediator in the pathogenesis of intestinal inflammation, is upregulated in inflamed mucosa from UC patients. The disease severity in UC patients is also correlated with the IL-17 level in peripheral blood mononuclear cells . In addiTherefore, we further constructed a PPI network to unravel the complex molecular relationships underlying the potential targets, and 10 hub genes, namely, IL-6, TNF, STAT3, IL-1β, CXCL8, CCL2, ICAM1, IL-10, IL-4, and IL-2, were identified by ‘cytoHubba’. Most of them are cytokines, such as proinflammatory cytokines , and anti-inflammatory cytokines . Cytokines are structurally diverse proteins with profound functional relevance to the maintenance of physiological homeostasis, including chemokines, interferons, interleukins, lymphokines, and tumor necrosis factors . CytokinCytokines are crucial for the maintenance of the immune system. For example, IL-10 is a key immunosuppressive cytokine expressed by many cell types and is particularly important in maintaining intestinal microbe-immune homeostasis. Polymorphisms in the IL-10 locus confer risk for UC, deficient in either IL-10 or IL-10 receptor exhibit severe intestinal inflammation and marked pro-inflammatory cytokine secretion in mice and humans . IL-10 pCytokine signaling pathways involving transcription factors of the signal transducers and activators of transcription (STAT) family play a key role in the pathogenesis of IBD . STAT3 aThe Sankey diagram indicated that quercetin corresponds to most of the hub targets. Quercetin is a plant-derived polyphenolic compound belonging to the flavonols (a subclass of flavonoids) and has shown beneficial effects in the prevention and/or treatment of several pathological conditions due to its antioxidant, anti-inflammatory, antifibrotic, antimicrobial, and antitumoral activities . In the Cytokines that either promote or suppress intestinal inflammation have led to some efficacious therapeutics for IBD . In geneCurrent research based on network pharmacology could provide a novel and systematic analysis method for the research of Chinese herbal formulas. However, it should be noted that there were some limitations in our article. On the one hand, focusing on validated target genes may exclude potential targets that have not been experimentally validated. On the other hand, there is a lack of verification analyses in our research. Thus, further clinical and basic research is needed to validate our results and to elucidate the molecular mechanism in GGQLD.Overall, the present study suggests that quercetin, kaempferol, wogonin, baicalein, and naringenin may be the main active ingredients in GGQLD. Cytokines  might be potential therapeutic targets of GGQLD in UC. Furthermore, the effect of GGQLD on UC might be achieved by regulating the balance of cytokines in the immune system and inflammation-related pathways, such as the IL-17 pathway and Th17 cell differentiation pathway. Last but not least, this research showed that network pharmacology is a powerful tool for identifying active compounds and potential targets derived from TCM.Click here for additional data file.


Fucosterol, a sterol isolated from brown algae, has been demonstrated to have anti-cancer properties. However, the effects and underlying molecular mechanism of fucosterol on non-small cell lung cancer remain to be elucidated. In this study, the corresponding targets of fucosterol were obtained from PharmMapper, and NSCLC related targets were gathered from the GeneCards database, and the candidate targets of fucosterol-treated NSCLC were predicted. The mechanism of fucosterol against NSCLC was identified in DAVID6.8 by enrichment analysis of GO and KEGG, and protein–protein interaction data were collected from STRING database. The hub gene GRB2 was further screened out and verified by molecular docking. Moreover, the relationship of GRB2 expression and immune infiltrates were analyzed by the TIMER database. The results of network pharmacology suggest that fucosterol acts against candidate targets, such as MAPK1, EGFR, GRB2, IGF2, MAPK8, and SRC, which regulate biological processes including negative regulation of the apoptotic process, peptidyl-tyrosine phosphorylation, positive regulation of cell proliferation. The Raf/MEK/ERK signaling pathway initiated by GRB2 showed to be significant in treating NSCLC. In conclusion, our study indicates that fucosterol may suppress NSCLC progression by targeting GRB2 activated the Raf/MEK/ERK signaling pathway, which laying a theoretical foundation for further research and providing scientific support for the development of new drugs. Non-small cell lung cancer (NSCLC) accounts for about 85% of lung cancer2, and mainly includes lung squamous cell carcinoma(LUSC), lung adenocarcinoma(LUAD)3. The principal treatment methods of NSCLC are chemotherapy, surgery, radiotherapy and targeted therapy4, but the five-year survival rate as low as 18%, and may lead to serious side effects and drug resistance6. Therefore, there is an urgent need to develop effective drugs for the treatment of NSCLC.Lung cancer is the leading cause of cancer death worldwide8. Most of marine plants extracts have been confirmed to have a variety of bioactivities, including anti-cancer10, anti-inflammatory12, anti-viral14, etc. Marine medicines derived from marine plant extracts have received more and more attention. Fucosterol belongs to algal phytosterol in ethanol extract of brown algae , which has been proved to have multiple biological activities, including antioxidant17, anti-inflammatory20, anticancer21, antimicrobial22, anti-depression23, etc. Previous researches have reported fucosterol on anti-cervical cancer21, anti-leukemia24, anti-colorectal cancer25, etc., but there are few studies on the mechanism of fucosterol in treating NSCLC about its potential therapeutic targets and related pathways in detail.An estimated 25% of Earth's total species lies in marine species. Many compounds in the ocean have special biological activities and chemical structures recognized as potential medicines for many diseases26, identify new drug targets27, and better explain the mechanism of interactions between bioactive molecules and cellular pathways. Molecular docking technology is an advantageous tool for modernization research, which can achieve virtual screening of drugs28. Reverse molecular docking refers to the docking of a small molecule drug in a group of potential binding cavities of clinically relevant large molecule targets. Through detailed analysis of its binding characteristics, the potential small molecule targets are identified and ranked according to the binding tightness30.In the past few years, network pharmacology, as a new discipline based on system biology and multi-direction pharmacology, can effectively implement the predictive analysis of drug action mechanismsIn this study, we employed network pharmacology and molecular docking to explore the effects and mechanisms of fucosterol on NSCLC. A detailed description of the procedure is shown in Fig. https://pubchem.ncbi.nlm.nih.gov/) was used to retrieve the 3D chemical structure of fucosterol, saved it as “sdf” format, and converted to the mol2 format by Pymol2.4 for minimizing the MM2 energy.The PubChem database (http://www.lilab-ecust.cn/pharmmapper/) to determine the fucosterol-related predictive targets33. Set parameters: Generate Conformers: Yes; Maximum Generated Conformations: 300; Select Target Set: Human Protein Target Only; Number of Reserved Matched Targets: 300. The results of predictive targets with z-score > 0 as screening criteria were retained for further analysis.Fucosterol's mol2 format was uploaded to the PharmMappper database (http://www.uniprot.org/) to collect the official symbols with the species limited to “Homo sapiens” by eliminating duplicate and nonstandard targets.Fucosterol-related predictive targets were input into UniProtKB database (https://www.genecards.org/) using the phrase “non-small cell lung cancer” as a keyword, and the top 500 targets were retained according to the relevance score. The predictive targets of fucosterol were compared with the NSCLC-related targets in BioVenn (http://www.biovenn.nl/index.php) to identify the candidate targets of fucosterol for NSCLC treatment.NSCLC-related targets were gathered from GeneCards database (https://string-db.org/) with uploading the candidate targets while the species limited to “Homo sapiens” and the confidence score > 0.700 .The data on protein–protein interaction (PPI) were obtained from STRING database 11.0 (https://david.ncifcrf.gov/home.jsp) was applied to carry out GO enrichment analysis and KEGG pathway enrichment analysis of the candidate targets. First, the species option was selected as “Homo sapiens”. Second, four analysis items were selected: KEGG pathway, cellular components, molecular functions, and biological processes. Third, enriched GO terms and KEGG pathways were screened for P-value ≤ 0.05 using the Bonferroni correction. Finally, based on the third step, KEGG pathway and the top 20 items of GO with ranking by their P-value were chosen for further analysis.DAVID 6.8 (https://cytoscape.org/) was utilized for network topology analysis and network visualization. Construct the following networks: (1) Cluster 1 for NSCLC network; (2) Fucosterol-NSCLC PPI network; (3) Important biological processes and the candidate targets crosstalk network; (4) Target-pathway interaction network.The Cytoscape3.7.2 software , was obtained from X-ray diffraction in Protein Data Bank, and the 3D structure of fucosterol (compound CID: 5281328) and Lymecycline (compound CID: 54707177) were obtained from Pubchem. Lymecycline was used as a positive control. Then, we used AutoDock 4 which based on the standard docking procedure for a rigid protein and a flexible ligand. At first, we used pymol2.4 to remove the water molecules and the original ligands from the GRB2, and then used AutoDock4 to add hydrogen and used the vina to dock the GRB2 with fucosterol and Lymecycline, and then selected the compound with the best binding effect. Finally, we used pymol2.4 and ligplot2.3 to observe and analyze the interaction and binding mode between ligand and receptor.https://atb.uq.edu.au/register.py). To ensure the total charge neutrality of the simulated system, the corresponding amount of sodium ions were added to the system to replace water molecules to produce solvent boxes of the appropriate size. Initial the energy of 50,000 steps of the whole system was minimized by (EM) at 300 K. Subsequently, the systems were equilibrated by position restraint simulations of NVT and NPT ensembles. Equilibrated systems were used to simulate a 50 ns no restraint production run. In the end MD analyses were performed, this includes root mean square deviation (RMSD), root mean square fluctuations (RMSF) and Hydrogen bond analysis. The data of RMSD, RMSF and Hydrogen bond analysis is generated by Origin2019b (https://www.originlab.com/).Lymecycline and fucosterol with GRB2 were rationally docked in our previous work. This complex was analyzed by all atom molecular dynamics (MD) simulations for 50 ns. All the simulations were carried out with Gromacs 2020.2 software package and gromos54a7_atb.ff force field. The force field parameters of fucosterol and Lymecycline was generated by ATB (http://timer.cistrome.org), a friendly comprehensive tool set for systematical analysis of immune infiltrates across diverse cancer types38, was carried out. There were many immune cells in the TIMER database. We chose CD4 + T cells, CD8 + T cells, B cells, neutrophils, macrophages, myeloid dendritic cells to conduct a new analysis. Besides, we performed the association between gene GRB2 and EGFR after adjusting tumor purity in the correction section in TIMER2.0 database.In order to explore the correlation between GRB2 expression and immune infiltrates in LUAD, the TIMER2.0 database , a tool for assessing the functions of 54,675 genes and 10,188 tumor tissue samples, including breast cancer, ovarian cancer, lung cancer, and gastric cancer.The correlation between GRB2 expression and survival in lung adenocarcinoma was analyzed by Kaplan–Meier plotter  data for NSCLC, and clustered to find out the topology module of NSCLC’s PPI network. The cluster 1 was retained since it is the most significant for PPI network of NSCLC Fig. , and theIn order to construct the interaction network between proteins and dig out the core regulatory genes, the fucosterol-NSCLC PPI network of the candidate targets was performed Fig. . Fucostep-value ≤ 0.05) of each module in Fig. GO enrichment yields a deeper understanding on the gene function and biological significance of the candidate targets of fucosterol treated NSCLC on a systematic level. To obtain the biological processes, molecular functions, and cellular components of the candidate targets, we performed a GO enrichment analysis, and displayed the top 20 significantly terms , all of them can activate the downstream pathway–Raf/MEK/ERK signaling pathway. Therefore, the three signal transduction pathways were selected as key signaling pathways for fucosterol in the treatment of NSCLC for integration and further analysis.To explain better the mechanisms of fucosterol treatment of NSCLC at the pathway level, 73 pathways were obtained by mapping 37 candidate targets , CD8 + T cells, CD4 + T cells , macrophages, neutrophils and myeloid dendritic cells, suggesting that the GRB2 expression was mainly related to the immune infiltration of CD8 + T cells, CD4 + T cells, macrophages, neutrophils and myeloid dendritic cells. Additionally, Fig. To further explore the direct relationship between GRB2 and NSCLC, we conducted the analysis of the relationship between GRB2 expression and immune infiltrates on TIMER(2.0) database. As shown in Fig. 58. Hence, finding effective targeted drugs to anti-lung cancer has become an urgent problem to be solved. Network pharmacology can analyze and explain the complexity between biological systems, diseases, and drugs from a network perspective, becoming a frontier method of drug discovery60. Molecular docking is a structure-based drug design method, which can predict the affinity and binding pattern through the interaction between ligands and receptors, accelerate the design and screening of drugs, and provide a basis for future experimental detection62. Molecular dynamics simulate ligand target complexes in a given system to explore their stability and flexibility64. Therefore, based on network pharmacology and molecular docking simulation ligand target binding, the mechanism research of fucosterol therapy for NSCLC may serve as the foundation for the development of NSCLC targeted drugs in the future.NSCLC is characterized by high malignancy, low 5-year survival rate and poor prognosis. However, for patients with advanced lung cancer, having specific predictive biomarkers and receiving targeted therapy or immunotherapy significantly improve quality of life and progression-free survival (PFS) compared to chemotherapy44. Through the NSCLC-related pathway map, we discover that GRB2 can be acted as the trigger or initiation signal of the Raf/MEK/ERK pathway by PI3K-Akt signaling pathway and ErbB signaling pathway, which may be crucial for fucosterol in the treatment of NSCLC.Based on the reverse of molecular docking technology for predicting fucosterol targets, and compare with NSCLC related targets, we obtained 37 candidate targets. Fucosterol-NSCLC PPI network analysis uncovered that fucosterol probably exerted pharmacological effects on NSCLC via 37 candidate targets, including 6 hub genes: GRB2, EGFR, MAPK1, SRC, IGF2, MAPK8. According to GO biological process analysis, 37 candidate targets were found to mainly responsible for cell proliferation, apoptosis, migration, signal transduction and angiogenesis. In addition, KEGG analysis of candidate targets disclosed that fucosterol probably has therapeutic effect on NSCLC through multiple pathways like PI3K-Akt signaling pathway, VEGF signaling pathway, ErbB signaling pathway. Furthermore, it is reported that fucosterol has antiproliferative effects on human lung cancer cells by inducing apoptosis, cell cycle arrest and targeting of Raf/MEK/ERK signaling pathway, such as A549 and SK-LU-1 cancer cells, in addition, fucosterol could also inhibit the growth of xenografted tumours in miceBased on molecular docking, the combination of fucosterol and GRB2 can be discussed. At the same time, Lymecycline was selected as the positive control to provide the basis for future experiments. We chose to complete many key structures with higher resolution crystal structures, and then the docking site was selected in the SH3 region of GRB2, so as to influence the cell signal transduction events and achieve the therapeutic effect of cancer. RMSD, RMSF and hydrogen bond analysis were calculated using molecular dynamics simulations to infer the basic properties of ligand-target complexes-stability and flexibility. The stability of RMSD and RMSF was essential to infer good binding affinity, while hydrogen bond analysis was to compare the binding of phytosterol and Lymecycline to GRB2. The results show that the combination of GRB2 and fucosterol is stable.66. Grb2 is a downstream protein of epidermal growth factor receptor (EGFR) that is known to be closely related to NSCLC, and may serve as an adaptor protein to bind to phosphorylated tyrosine in the EGFR, thereby linking receptor activation to intracellular signaling cascade69. Simultaneously, recent research has demonstrated that the balance of Grb2 monomer–dimer is a determinant of its normal and carcinogenic functions70. Grb2 regulating angiogenesis and cell movement is highly overexpressed in tumors and may be useful as a target for anticancer agents. In the present study, we found Grb2 can be targeted by fucosterol and activate the Raf/MEK/ERK pathway to achieve the purpose for treating NSCLC73.Growth factor receptor-bound protein 2 (Grb2), a universally expressed adaptor protein, which plays a pivotal downstream mediator role in a variety of oncogenes signaling pathways and has a significant effect on signal transduction in normal and cancer cellsIn summary, our study indicated the molecular and pharmacological mechanism of fucosterol against NSCLC from a systematic perspective. We unveil that GRB2 can serve as an anticancer target in fucosterol to initiate the Raf/MEK/ERK pathway for treating NSCLC. This strategy provides a new idea of anti-NSCLC and lays a foundation for the development of new medicines. Nonetheless, network pharmacology has certain limitations, and more experiments are needed to verify the validity of our findings. Moreover, we hope that our study will be useful for fostering innovative research of marine drugs against cancers.


Increasing demands for the supply of biopharmaceuticals have propelled the advancement of metabolic engineering and synthetic biology strategies for biomanufacturing of bioactive natural products. Using metabolically engineered microbes as the bioproduction hosts, a variety of natural products including terpenes, flavonoids, alkaloids, and cannabinoids have been synthesized through the construction and expression of known and newly found biosynthetic genes primarily from model and non-model plants. The employment of omics technology and machine learning (ML) platforms as high throughput analytical tools has been increasingly leveraged in promoting data-guided optimization of targeted biosynthetic pathways and enhancement of the microbial production capacity, thereby representing a critical debottlenecking approach in improving and streamlining natural products biomanufacturing. To this end, this mini review summarizes recent efforts that utilize omics platforms and ML tools in strain optimization and prototyping and discusses the beneficial uses of omics-enabled discovery of plant biosynthetic genes in the production of complex plant-based natural products by bioengineered microbes. Plant natural products represent an enormous resource for chemical and biotechnological production of biopharmaceuticals and natural products-based drugs where about 50–70% of all anti-infective agents in clinical use are being provided and inspired by natural products . As of 2Driven by the increased availability of bioinformatics tools and high throughput instruments, including next generation sequencing (NGS) and mass spectrometry (MS), omics technologies have been prominently used as principal tools in systems biology research aimed at elucidating the underlying molecular mechanisms behind cellular functions and interplays among biomolecules in biological systems . Omics tImportantly, the adoption of single or multi-omics in natural products studies has seen the increment of omics-guided discovery of known and novel metabolites, biosynthetic genes, and regulatory elements from model and non-model plants. By employing transcriptome-guided gene mining and microbial engineering strategies, a number of natural products from previously incomplete and gapped pathways, such as opiate alkaloid noscapine and cannabinoids, have since been produced in microbial hosts, thereby opening up new and exciting opportunities in natural products biomanufacturing using bioengineered microbes as the preferred bioproduction platform . BiomanuEscherichia coli and Saccharomyces cerevisiae where the computational sets of stoichiometric and mass-balanced metabolic reactions in the microbes were derived from genomics-guided experimental analysis including flux balance analysis (FBA) and elementary node analysis  pathway  cycle, and acetyl-CoA biosynthesis were found to be important in particular by downregulating pyruvate synthase (YDBK) gene that culminated in higher specific production of limonene. In genome engineered S. cerevisiae, the use of flux and metabolomic analysis has aided the functional expression of a heterologous 1-deoxy-D-xylulose 5-phosphate (DXP) pathway, the alternative IPP-producing pathway by which combinatorial expression of IspG  and IspH (4-hydroxyl-3-methylbut-2-enyl diphosphate reductase) enzymes was tested to overcome the poor conversion of 2-C-methyl-D-erythritol-2,4-cyclodiphosphate (MEcPP) and the limited NADPH coenzyme availability -overproducing S. cerevisiae revealed distinct transcriptional changes of genes related to sugars and amino acids transport in S288c and CEN.PK background strains that aided in the efforts of systematically modulating the final production of p-CA with up to 20–50% improvement  and Catharanthus roseus (Madagascar periwinkle) have emerged as the model medicinal plants with regard to the employment of multi-omics approaches in the comprehensive analysis of the benzylisoquinoline alkaloids (BIAs) and monoterpenoid indole alkaloids (MIAs) biosynthetic pathways, respectively  where the key redox and hydrolase enzymes for the conversion of stemmadenine to tabersonine or catharanthine were successfully identified via proteome analysis and transcriptome gene mining (Polygonum minus (Persicaria minor) essential for pathway reconstruction and natural product biosynthesis in engineered microbes (The employment of single- and multi-omic tools has brought about a systematic biology-informed pipeline for discovering and biomanufacturing of new-to-nature plant-derived compounds using systems and synthetic biology platforms . In the ectively . Using me mining . These omicrobes .S)- and (R)-reticuline was first demonstrated in engineered S. cerevisiae through BIA pathway reconstitution that includes the expression of the enzymes norcoclaurine synthase (NCS) and reticuline epimerase (CYP82Y2) from opium poppy P. somniferum  from Nicotiana tabacum and tropane alkaloid biosynthesis genes from Anisodus acutangulus , enabled pathway reconstruction and directed biosynthesis of the desired phenylpropanoid compounds in engineered S. cerevisiae using glucose as carbon source , and ribosomal binding site (RBS) in addition to the use of ML-assisted directed evolution and semi-rational protein engineering strategies (S. cerevisiae (in vivo p-CA production in tyrosine ammonia lyase (TAL1)-expressing S. cerevisiae in accordance to the strengths of de novo and native 5′UTR with weak and high predicted protein abundance  gene under the control low-strength synthetic s14 promoter enhanced the production of amorphadiene in engineered E. coli (de novo design and screening of synthetic RBS for MVA pathway engineering and bioproduction of limonene where model training was conducted on expression levels of HMG-CoA synthase (HMGS), HMG-CoA reductase (HMGR), MVA kinase (MK), and IPP delta-isomerase (IDI) using multiple combinations of RBS sequences. The constructed library of 32 RBS combinations was then built and tested in combination with terpene-synthesizing pGL403 plasmid construct that resulted in the identification and selection of high-performing E. coli strains with improved limonene titer over 1.5–3-fold  and vinorine hydroxylase (VH) essential in sarpagan and ajmalan alkaloid biosynthesis that could be useful in the Build and Test of these high-value bioproducts in engineered microbial chassis , the applications of omics and ML tools in strain and bioproduct development are set to be the cornerstone in industrial biomanufacturing of biobased chemicals and pharmaceuticals.The combination of omics datasets and ML strategies is expected to drive the production of natural products and other biobased chemicals especially in terms of biosynthetic pathway inference, refactoring, and optimization. The self-organizing map (SOM) approach represents an unsupervised NN method useful in the identification of new enzymes using plant transcriptome datasets to complement common gene co-expression analysis, such as differentially expressed genes (DEG) method . The SOM chassis , 2018. Amodeling . In theitruction . Interespipeline . Thanks hemicals . In lineOverall, it is envisioned that the employment of data-centered omics and ML platforms will lead to more streamlined and less resource-intensive biomanufacturing strategies and accelerate strain prototyping pipelines that have been a major stumbling block in the translation of bioproduct development from laboratory to market. Omics-guided microbial engineering and ML-assisted biomanufacturing will therefore bring about data-driven biomanufacturing pipelines that can be expanded to include metagenome datasets and accelerate the bioproduction of industrially relevant biomolecules and drugs tailored to the pressing needs of medical, agricultural, environmental, and industrial sectors.ABR, SNB, HB, and NSS all contributed toward the writing and editing of this manuscript. All authors contributed to the article and approved the submitted version.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.


Compared to the conventional differential expression approach, differential coexpression analysis represents a different yet complementary perspective into diseased transcriptomes. In particular, global loss of transcriptome correlation was previously observed in aging mice, and a most recent study found genetic and environmental perturbations on human subjects tended to cause universal attenuation of transcriptome coherence. While methodological progresses surrounding differential coexpression have helped with research on several human diseases, there has not been an investigation of coexpression disruptions in chronic kidney disease (CKD) yet.RNA-seq was performed on total RNAs of kidney tissue samples from 140 CKD patients. A combination of differential coexpression methods were employed to analyze the transcriptome transition in CKD from the early, mild phase to the late, severe kidney damage phase.Regulation of nuclear SMAD2/3 signaling demonstrating the most remarkable intra-pathway correlation rewiring. Moreover, the pathway Signaling events mediated by focal adhesion kinase displayed significantly weakened crosstalk with seven pathways, including Regulation of nuclear SMAD2/3 signaling. Well-known relevant genes, such as ACTN4, were characterized with widespread correlation disassociation with partners from a wide array of signaling pathways.We discovered a global expression correlation attenuation in CKD progression, with pathway Altogether, our analysis reported a global expression correlation attenuation within and between key signaling pathways in chronic kidney disease, and presented a list of vanishing hub genes and disrupted correlations within and between key signaling pathways, illuminating on the pathophysiological mechanisms of CKD progression. Chronic kidney disease (CKD) entails gradual loss of kidney function leading to end-stage renal disease, precipitating the need for renal replacement therapies. The early stages of CKD, stages 1–2, have little signs or symptoms and the disease is often not detected until the later stages [Whereas transcriptome data are most typically analyzed to find differentially expressed genes, an alternative analysis strategy –3 is gaiThe interactions among separate cellular pathways are referred to as pathway crosstalk –13, whicRegulation of nuclear SMAD2/3 signaling demonstrating the most remarkable intra-pathway correlation rewiring. In concordance with this global trend of correlation attenuation, 43 genes lost their hub statuses established in early CKD transcriptomes, including ACTN4, ARF6, MAP2K7, and SRCAP. Moreover, the pathway Signaling events mediated by focal adhesion kinase displayed significantly weakened crosstalk with seven pathways, including Regulation of nuclear SMAD2/3 signaling. Our analysis results proposed that vanishing hub genes and attenuated correlation within and between pathways may underpin the pathophysiological mechanisms of CKD advancement.While methodological progresses surrounding differential coexpression have helped with research on several human diseases –20, therA total of 140 patients with different stages of CKD and a total of 25 control donors from Shaanxi Traditional Chinese Medicine Hospital, Xi’an No. 4 Hospital, and Baoji Central Hospital were included in our study. Patients with acute kidney injury, liver disease, active vasculitis, gastrointestinal pathology or cancer were excluded from the study. All study participants were ethnically Han Chinese. Patients . Total RNA extraction with ribosomal RNA removal (Ribo-Zero), RNA quality control, strand-specific library construction (Illumina), and 150 bp pair end RNA sequencing (Illumina) were conducted by Novogene. RNA-Seq data were quality controlled according to multi-perspective guideline  using QCn = 16) and an eGFR < 60 group (n = 17), assigning normal, CKD1, and CKD2 to the former and CKD3–5 categories to the latter. Hence, by comparing between early and late CKD, we sought to elucidate cellular network rewiring mechanisms concurrent with the development of CKD from a mild, medically amenable stage to the severe end stage of kidney failure. This primary RNA-Seq dataset was denoted as CKD.According to the National Kidney Foundation, eGFR is the best measurement for kidney function and is used to stage kidney disease. Patients with eGFR < 60 (CKD stage 3 to 5) are considered sufferring serious chronic kidney disease clinically. Also due to that the current differential coexpression approaches are generally incapable of handling minute sample size per category, we dichotomized all samples to an eGFR> 60 group  as the threshold. The three datasets contributed 28,574, 15,022, and 17,533 genes at this step, and an intersection among the three sources resulted in 11,400 shared genes. The number of considered genes eventually shrank to 2766 after constraining to well-defined pathways (see below). All correlation values were calculated using the Pearson method.The method DCe from R package DCGL (v2.0)  was emplFrom Pathway Commons , we obtap-value for all 369 pathways in each of the three transcriptome datasets. We also let GSNCA output the hub gene and the schematic gene wiring network for each pathway. In GSNCA’s terminology, a hub is a gene that has the maximum weight, and the gene wiring network is the union of the first and second minimum spanning trees, which were identified through minimizing the total path length (sum of correlation distances). A hub gene and a pathway intra-wiring network are associated with one pathway under one experimental condition.Gene Sets Net Correlations Analysis (GSNCA)  was exerp < 0.05 were kept in the pathway crosstalk map.Huang et al. devised an algorithm  to identp values out of the hypergeometric tests was visualized in barplots , we identified differentially coexpressed links, differentially coexpressed genes, significantly rewired pathways, and pathway hub genes. We assessed the overlapping significance among five data sources using the hypergeometric test, where the total number of candidate entities were 369 for pathways, 2766 for genes, and 3,823,995 Given these reproducibility results from repetitive sub-datasets, we believed those differential coexpression methods were capable of yielding statistically stable results when different yet same-natured samples were recruited to represent a phenotype. The much lower (and mostly insignificant) result consistency among three distinct data sources prompted us to speculate that our three datasets bore considerable disparity in their molecular mechanisms, despite all being related to kidney diseases. In our formal workflow, we primarily focused on the RNA-seq dataset (denoted “CKD”), integrating GSE62792 and GSE37171 as auxiliary data only in partial analyses. In particular, with respect to the less mutually consistent pathway-level results, we resorted to all three datasets to compile a list of focused pathways that were significantly rewired per two data sources or more.DCGL categorized its identified differentially coexpressed links according to the signs of correlation values in the two compared conditions. From dataset CKD, we noted an overwhelming dominance (85.4%) of decreased-positive links Fig. a, thoughp values from individual datasets, and compiled 27 focused pathways (Table p < 0.01) and had an aggregate p below 0.01. In decreasing aggregate p, Regulation of nuclear SMAD2/3 signaling (originating from PID) emerges as the most noteworthy pathway, showing p values 0.012, 0.0010, and 0.0020 in CKD, GSE62792, and GSE37171, respectively. In concordance with the pervasive disassociation trend between genes, most of these focused pathways displayed far-flung correlation loss among their member genes  maintained its hub status in CKD advancement; all other 43 genes . The significantly rewired pathways featured 40 and 40 hub genes in early CKD and late CKD, respectively, which shared only six genes. Of the 27 focused pathways out of combined evidence from three datasets, only three had a constant hub in early CKD and late CKD Table . Since ap ≤ 0.01).We evaluated the statistical significance of the observed hub constancy rate 3/27) and hub retention rate (1/44) by comparing them against two empirical distributions, which resulted from random permutation of patients’ class labels  is a critical regulator of cell movement and is implicated in TGF-beta signal transduction in CKD [Signaling events mediated by focal adhesion kinase consists of 53 genes derived from PID, sharing only two members with the 71 genes of Regulation of nuclear SMAD2/3 signaling. A total of 73 cross-pathway correlation links accounted for the disrupted crosstalk between Signaling events mediated by focal adhesion kinase and Regulation of nuclear SMAD2/3 signaling  [MYLK) [PLCG1) [The significant disruption of the individual pathway-pathway crosstalks was attributed to correlation-attenuated gene pairs traversing pathway boundaries  , diabeti) [MYLK) , and par [PLCG1) .Compared to the conventional differential expression approach, differential coexpression analysis represents a different yet complementary perspective into diseased transcriptomes. Methods purposed for identification of differentially coexpressed genes, gene connections, and gene sets have been invented and improved in nearly two decades. While a negative voice questioned the potential confounding between differential expression and differential coexpression , more stRegulation of nuclear SMAD2/3 signaling and Signaling events mediated by focal adhesion kinase and highlighted critical genes such as ACTN4 in the context of transcriptome correlation network. Plenty of researches have proved the role of Smad2/3 in kidney disease. Smad2 and Smad3, two major downstream mediators of transforming growth factor-β1 (TGF-β1), play a dominant role in kidney dysfunction and renal fibrosis [In this work, we applied a variety of differential coexpression oriented methods to analyze the transcriptome transition from early stage CKD patients to late stage CKD patients, making innovative discoveries at multiple levels covering vanishing hub genes, disassociated gene links, and disrupted pathway crosstalks. Our results recapitulated well-known CKD pathways such as fibrosis . Smad2 afibrosis –54. Smadfibrosis , 56. Thefibrosis . Knock-dfibrosis  and arisfibrosis .Signaling events mediated by focal adhesion kinase as the pathway centered on the disassembled pathway crosstalk network during CKD progression. PTK2, a non-receptor tyrosine kinase, is one of the first molecules recruited to focal adhesions in response to external mechanical stimuli that controls cell migration, cell proliferation and cell survival in kidney [PTK2 has the ability to regulate AKT, PI3K, MAPK, Yes-associated protein, integrin α, TGF-β1 and α-smooth muscle actin (α-SMA) [PTK2 to focal adhesions [PTK2 promoted the expression of monocyte chemoattractant protein-1 and cell migration to accelerate the progression of various glomerular diseases [PTK2 was required for phosphorylation of ACTN4, a vanishing hub gene with substantial contribution to pathway crosstalk disruption in CKD progression. ACTN4 is closely associated with CKD, especially focal segmental glomerulosclerosis (FSGS) [ACTN4 has been identified as a cause of familial focal segmental glomerulosclerosis [ACTN4 mutations can be distinguished from other podocyte diseases [ACTN4 knock-in mice developed extensive and irrecoverable reductions compared with those isolated from wild type mice, and mutant cells were more likely to detach upon stretch [ACTN4 can enhance NF-κB activity in podocytes which aggravates podocyte injury [In the present study, we identified n kidney , 61. PTK (α-SMA) –63. ECM dhesions . In addidiseases . Interess (FSGS) , 67. Mutclerosis , and thediseases . Podocyt stretch . In addie injury .ACTN4 and those pathway connections incident to Signaling events mediated by focal adhesion kinase, may propel specific biological hypotheses on CKD molecular mechanisms.Additional pathways and genes in our results may provide worthwhile research targets in future CKD studies. The disassociated gene links and disrupted pathway crosstalks identified by analyses, such as those gene links revolving around Notably, we discovered a global expression correlation attenuation within and between key signaling pathways in CKD progression. A similar trend of global loss of transcriptome correlation was previously observed in aging mice . MoreoveRegulation of nuclear SMAD2/3 signaling demonstrating the most remarkable intra-pathway correlation rewiring. We identified 27 focused pathways that significantly changed the internal gene-gene expression correlation in CKD advancement, and enumerated 44 presumably CKD-relevant genes on account of their vanishing hub roles in the collapsed pathways. Moreover, we went further to delineate a disrupted pathway crosstalk map centered upon Signaling events mediated by focal adhesion kinase; well-known relevant genes (such as ACTN4) and relevant pathways  were found involved in these inter-pathway disassociations.In this study, we performed RNA-Seq transcriptome profiling of five stages of chronic kidney disease patients and analyzed the transcriptome correlation disruptions accompanying CKD progression in the context of signaling pathways using a combination of differential coexpression methods. Overall, a global expression correlation attenuation was observed in CKD progression, with pathway Additional file 1: Figure S1. Universal correlation attenuation within focused pathways. All focused pathways listed in Table Figure S2. Fourteen vanishing hub genes had statistically significant differential expression between CKD stages (FDR< 0.3). Differential expression analysis was performed via Comulative Link Models for ordinal regression. Figure S3. Disrupted pathway crosstalk map inferred from the union network of decreased gene links from all three datasets. The background gene-gene network comprised 47,218 correlation-loss edges. Node size and edge width are proportional to the statistical significance of correlation loss . Each edge was labelled with the p value out of CSPN analysis. Figure S4. Correlation-attenuated gene pairs traverse pathway boundaries shedding light on disrupted pathway crosstalks. Figure Table S1. Clinical and demographic baseline characteristics of controls and patients with CKD. Results are expressed as the means ± SD, *P< 0.05, **P< 0.01. Table S2. Except GCLM, all 44 hub genes in early CKD pathway-wise co-expression networks lost their hub status in late CKD. These genes were identified as differentially coexpressed genes by DCGL as well. Table S3. Correlation-attenuated gene pairs that traverse pathway boundaries.