Astrocytes play a central role in the neuroimmune response by responding to CNS pathologies with diverse molecular and morphological changes during the process of reactive astrogliosis. Here, we used a computational biological network model and mathematical algorithms that allow the interpretation of high-throughput transcriptomic datasets in the context of known biology to study reactive astrogliosis. We gathered available mechanistic information from the literature into a comprehensive causal biological network (CBN) model of astrocyte reactivity. The CBN model was built in the Biological Expression Language, which is both human-readable and computable. We characterized the CBN with a network analysis of highly connected nodes and demonstrated that the CBN captures relevant astrocyte biology. Subsequently, we used the CBN and transcriptomic data to identify key molecular pathways driving the astrocyte phenotype in four CNS pathologies: samples from mouse models of lipopolysaccharide-induced endotoxemia, Alzheimer’s disease, and amyotrophic lateral sclerosis; and samples from multiple sclerosis patients. The astrocyte CBN provides a new tool to identify causal mechanisms and quantify astrogliosis based on transcriptomic data. Astrocytes also perform innate immune functions to protect the brain against pathogens. Astrocytes respond to most CNS pathologies, including traumatic brain injury, ischemic stroke, autoimmune neurological diseases, neurodegenerative diseases, microbial infections, and environmental toxin exposure. Reactive astrogliosis is characterized as a profound change in astrocyte phenotype after a CNS pathology. The pathology of reactive astrogliosis in most of these diseases has been linked to the innate immune functions of astrocytes. Astrocytes express receptors to recognize pathogen-associated molecular patterns (PAMP) and damage- or danger-associated molecular patterns (DAMP) that trigger an innate immune response. The diverse responses that can trigger these reactive astrocytes release a wide variety of effector molecules in an pathology-specific manner4.Astrocytes and microglia are the main neuroimmune cells of the central nervous system (CNS). In the healthy brain, astrocytes maintain ion and neurotransmitter homeostasis, provide metabolic support to neurons, and support synaptic activity5. However, recent studies have shown that the molecular activity of astrocytes is far more heterogeneous than the A1/A2 dichotomy and even A1-like pathologies can further be subdivided into at least 9 distinct gene sets that change drastically over time6. Furthermore, the categories of non-immune genes that contribute to the neurotoxicity/neuroprotective effects are vast, including epigenetic modifiers7, genes which encode toxic saturated lipids8, and genes which influence astrocyte-microglia communications9. Given this broad heterogeneity, there is a need for a tool to distinguish the molecular mechanisms driving the astrocyte phenotype in different CNS pathologies.Early transcriptome-profiling studies identified an “A1” molecular signature of 12 genes that was associated with a neurotoxic astrocyte subtype that emerged after exposure to specific cytokines secreted by microglia exposed to lipopolysaccharide (LPS), an “A2” molecular signature of 12 genes that was associated with a neurotrophic subtype after ischemic stroke, and a core set of pan-reactive genes that were not pathology-specific14. CBNs integrate a collection of causal statements curated from the scientific literature into a coherent computable network, which can aid in the interpretation of large-scale transcriptomic changes. The nodes within the network represent biological entities, such as mRNA, proteins, protein activities, and biological processes. The nodes are connected by edges indicating directional relationships , which gives the network a cause-and-effect backbone topology. A second scorable layer is generated by using known gene expression signatures associated with a subset of nodes in the network and comparing the signatures of these nodes to pathology-induced changes in gene expression values to infer the activity of the node in a dataset. This approach considers changes in the abundance of transcripts as a downstream response to node activity without assuming these transcripts produce active proteins. In this way, a single transcriptomic profile  can be used to determine the activity of the whole network14.Our framework utilizes an established causal biological network (CBN) model methodologyhttp://www.causalbionet.com/) and accessible to the public.In this study, we present a CBN model that describes important molecular events involved in astrocyte activity. We also show how transcriptomic data from models of amyotrophic lateral sclerosis (ALS), neuroinflammation caused by systemic LPS-induced endotoxemia, Alzheimer’s disease (AD), and multiple sclerosis (MS) can be assessed with this network model to get a mechanistic understanding of gene expression changes. This model is stored in the CBN database  is a human-readable, computable language that captures causal and correlative biological observations in statements consisting of two biological entities (nodes) connected by a relationship (edge). The gene signatures of the A1 vs. A2 astrocyte reactivity paradigm served as a starting point for identifying relevant pathways involved in astrogliosis15. We searched the literature for mechanistic papers published between 1994 and 2021 that described causal relationships between proteins encoded by the genes listed in the molecular signature of the A1 neuroinflammatory response induced by the bacterial toxin LPS, the A2 neurotrophic response induced by ischemic stroke, and the pan-reactive astrogliosis response15. These articles were supplemented with additional publications on astrogliosis in other CNS and retinal diseases also published up to 2021. The criteria to select publications were as follows: (1) The article was an original research article with a PubMed identifier, and (2) it studied the manipulation of astrocytes in a healthy or a non-cancerous CNS disease-related context. This led to the inclusion of studies using human biopsies, in vivo studies with mice and rats, and in vitro studies from primary astrocytes, organotypic slice cultures, and induced pluripotent stem cell-derived astrocytes. Mechanistic information originating from glioblastoma studies, other cancers, or immortalized cell lines were not included in the CBN. The journal impact factor or any other means of ranking publications was not considered. If the statements in the original research article were sufficiently supported by the results presented in the figures, the information was considered reliable and captured16. To retrieve causal relationships, the result sections from these articles were extracted for curation. Contradicting statements were captured without preferential treatment and with proper annotations . BEL statements curated from the literature were compiled into a CBN backbone by using the OpenBEL framework 3.0.0, an open-source compilation framework16. The CBN consists of biological entities (nodes) and the relationships between them. The network was visualized in Cytoscape ; the node and edge counts and most connected nodes in the network were obtained using the Network Analyzer tool in Cytoscape17.Biological Expression Language  to induce ischemic stroke. After scoring of the iNode database with transcriptomic data from treatment versus control samples, we selected iNodes with an adjusted p-value < 0.01 for data-driven network enhancement. Additional literature was identified to find evidence to support the addition of new nodes into the networks using the same criteria as those for publications selected for construction of the CBN backbone.The literature-derived CBN model backbone was expanded using a data-driven approach to identify additional molecular drives in astrogliosis that might have been missed during literature curation. The approach utilized a database of nearly 800 nodes with inferable activities (iNodes). The activity of these nodes in an experimental dataset can be inferred by comparing the differential mRNA abundances in the dataset with the known mRNAs regulated by the iNodes. The regulated mRNAs by the iNodes were derived from public datasets of mRNA changes in response to manipulation of the nodes . iNodes were inferred from transcriptomic data (GSE35338) published by Zamanian et al.18; (ii) GSE75431: astrocytes from the PS2APP mouse model of Alzheimer’s disease (AD) vs. non-trangenic controls18; (iii) GSE83670: astrocytes isolated from the normal-appearing white matter of human multiple sclerosis (MS) patients vs. astrocytes from controls19; and (iv) GSE69166: spinal cord astrocytes from the SOD1 G93A transgenic mouse model of ALS vs. nontransgenic controls20. The transcriptomic data were scored against the iNode database, and the iNodes with an adjusted inferred p-value < 0.01 were selected. The scoring results were uploaded to Cytoscape17 to visualize the iNode enrichment results in the context of the astrocyte CBN. The network model was orthologized to the equivalent mouse or human gene symbols to allow the mapping of the inferred nodes onto the network. Composite images for publication were generated with Inkscape . An overview of our approach is shown in Fig. We used four datasets that were not used for the data-driven network expansion to gain a mechanistic understanding of astrogliosis following different pathologies. The public datasets were: (i) GSE75246: astrocytes, neurons, and microglia from a mouse model of systemic LPS-induced endotoxemia vs. vehicle treated control cells9, we merged all three networks into one comprehensive network of astrocyte reactivity and updated the merged network with additional literature references.We initially constructed three CBNs to capture A1/A2 and pan-reactive astrocyte activity. The literature backbone of the A1 astrocyte activity CBN consisted of 401 nodes connected by 732 edges, supported by data from 62 publications. After data-driven expansion, the A1 CBN was supported by data from 76 relevant peer-reviewed articles. The expanded A1 network contained 502 nodes and 893 edges. The literature backbone of the A2 astrocyte activity CBN consisted of 334 nodes connected by 504 edges, supported by data from 43 publications. After data-driven expansion, the A2 astrocyte activity network model was computed from 81 publications and consisted of 535 nodes and 842 edges. The literature backbone of the pan-reactive astrocyte CBN consisted of 458 nodes connected by 816 edges, supported by data from 70 publications. After data-driven expansion, the pan-reactive astrocyte activity network model was computed from 123 publications and contained 879 nodes and 1587 edges. However, several mechanisms and publications overlapped between the networks, and given the heterogeneity of reactive astrocyte activitiesThe final merged astrocyte activity network model was computed from 235 publications ; their downstream effector MYD88 ; NF-κB complex; and the IκKB (Inhibitor of NF-κB kinase) complex were inferred to be upregulated in astrocyte and microglia samples. Only TLR2 and IκKB were inferred to be upregulated in neurons.The type I and II interferons and their receptors ; Interferon regulatory factors ; TNF-α (Tumor Necrosis Factor Alpha); the interleukin (IL)-6 family members IL-6, OSM (Oncostatin M), and LIF (Leukemia Inhibitory Factor); IL-19; IL-27; IL-1B and the IL-1 receptor complex were also inferred to be upregulated in astrocyte and microglia samples. Only LIF, IL-27, and IL-19 were inferred to be upregulated in neurons.N-methyl-4-phenylpyridium, and Apolipoprotein E (APOE)) were inferred to be upregulated in astrocytes and microglia. Other inferred upregulated signaling pathways in astrocytes and microglia included the p38 MAPK (Mitogen-Activated Protein Kinase) family; CSF2 (Colony Stimulating Factor 2); and FFAR3 (Free Fatty Acid Receptor 3).In addition to the inflammatory signaling pathways, proteins/chemicals/toxins associated with neurodegenerative diseases  was inferred to be downregulated in astrocyte and microglia samples. The JAK1 (Janus Kinase 1)—STAT1  pathway and the SOCS1 , which inhibits STAT1 signaling cascade; caspase 1 (CASP1), which formed part of the NLRP3  inflammasome together with IL-1B; the connexin-30 hemichannel (GJB6); the IFNGR1 (Interferon Gamma Receptor 1); and the RelA p65 subunit of the NF-κB complex were inferred to be upregulated in astrocytes, but not in microglia. The FOXO3 (Forkhead Box O3) cellular survival pathway was inferred to be upregulated in astrocyte samples but downregulated in microglia. PPARA (Peroxisome Proliferator-Activated Receptor (PPAR) Alpha) and the connexin-43 hemichannel (GJA1) were inferred to be downregulated in astrocytes and upregulated in microglia. Lactate and TGFB1 (Transforming Growth Factor Beta 1) were inferred to be downregulated in astrocytes but unchanged in microglia samples. An overview is given in Fig. 18. The PS2APP transgenic mouse is a model of AD characterized by the overexpression of the mutant alleles of amyloid precursor protein (APP) and presenilin (PSEN2), which induce an age-dependent beta-amyloid plaque pathology associated with astrogliosis in the hippocampus and frontolateral cortex21.The GSE75431 RNA-sequencing dataset contained the transcriptome of astrocytes isolated from the whole cortex of 7- and 13-month-old PS2APP transgenic mice and their non-transgenic littermatesThe analysis of the significantly scored iNodes in the context of the CBN model resulted in a smaller set of connected nodes (9 nodes) in the AD dataset than in the LPS dataset. After the inclusion of the NF-κB node , which was the most connected node in the CBN, 21 nodes were connected in the samples collected from 13-month-old animals. Most of these nodes were not significant in samples from the 7-month-old mice. The connected subgraph highlighted inferred decreases in dexamethasone and the NR3C1 glucocorticoid receptor; IL-6; the metabolic regulators PPARG and PPARGC1A (PPARG Coactivator 1 Alpha/PCG-1α); the mitochondrial protein kinase PINK1 (PTEN Induced Kinase 1); and the antioxidant NFE2L2 (Nuclear factor erythroid 2/NRF2).NRF1 (Nuclear Respiratory Factor 1); CHRNA7 (Cholinergic Receptor Nicotinic Alpha 7 Subunit); TNF-α and STAT1 were inferred to be upregulated. CHD2 (Chromodomain Helicase DNA Binding Protein 2) was inferred to be upregulated, but its downstream signaling through the FGFR (Fibroblast growth factor receptor) family, as well as the structural protein VIM (Vimentin), downstream of the FGFR family was inferred to be downregulated. Other structural markers, GFAP and OLIG2 (Oligodendrocyte Transcription Factor 2), as well as the cell-shape regulator RHOA (Ras homolog family member A) were inferred to be upregulated. An overview of these signaling cascades is shown in Fig. 19. The GSE83670 dataset compared Affymetrix microarray gene expression data from human astrocytes isolated by postmortem laser-capture microdissection from the normal-appearing white matter of MS patients and neurologically normal age- and sex-matched controls. The authors of this dataset studied astrocytes isolated from the normal-appearing white matter, rather than those from the lesion area, to identify factors that could initiate or prevent MS progression19.MS is an autoimmune disease with a strong inflammatory component characterized by demyelinated white-matter lesions in the brain and spinal cord. The white matter lesions are surrounded by areas of non-demyelinated normal-appearing white matterThe iNode enrichment analysis highlighted a set of 20 connected nodes in the astrocyte CBN. The anti-inflammatory glucocorticoid pathway (dexamethasone and the glucocorticoid receptor NR3C1) was inferred to be downregulated. TNF-α and the TNF receptor superfamily; downstream MAPK JNK  family; MAPK9; and MAPK14 were inferred to be upregulated; along with connexin-30 (GJB6). The ITPR (Inositol Triphosphate Receptor)—calcium—PRKC (Protein Kinase C) signaling pathway was also inferred to be upregulated. S100B  and its receptor AGER (Advanced Glycosylation End-Product Specific Receptor) were inferred to be downregulated, but the nodes up- and downstream from S100B, TP53 (Tumor Protein P53), and PTGS2 (Prostaglandin-endoperoxide Synthase 2/Cyclooxygenase-2), were inferred to be upregulated. Other inferred upregulated nodes included the ICAM1 (Intercellular Adhesion Molecule 1), and HBEGF . The EGFR , downstream of HBEGF, was inferred to be downregulated along with THBS1 (Thrombospondin 1) and ELK1 (ETS Transcription Factor ELK1). An overview of these changes is given in Fig. 22. We analyzed the GSE69166 dataset, which used an Affymetrix microarray to compare gene expression in astrocytes isolated by laser-capture microdissection from the spinal cord of 3- or 4-month-old SOD1 G93A ALS mice with those isolated from their non-transgenic littermates. In this mouse model, pre-symptomatic ALS occurred at the age of 2 months, symptomatic ALS at 3 months, and late-stage ALS at 4 months after birth. The authors showed that the astrocytes isolated from the SOD1 transgenic mice had an altered immune response, with increased expression of lysosomal, phagocytic, and chemokine genes starting at the symptomatic ALS stage and continuing through late-stage ALS. The SOD1 transgenic astrocytes also showed a reduction in homeostatic transcripts involved in potassium transport. With functional assays for the activity of the lysosomal gene β-hexosaminidase and the uptake of neuronal cellular debris, the authors confirmed that lysosomal enzyme activity and phagocytosis were increased in the SOD1 transgenic astrocytes20.ALS is the most common motor neuron disease in adults. It leads to the death of motor neurons, which causes progressive paralysis. Approximately 10% of ALS cases are familial, and mutations in the superoxide dismutase 1 (SOD1) gene account for approximately 15% of familial ALS casesIn the late stage of ALS, we identified a set of 51 significant nodes connected by 64 edges. The first mechanism highlighted was a general increase in immune and inflammatory signaling. The toll-like receptors  and their downstream effector MYD88 were inferred to be upregulated. Other immune and inflammatory factors, including a set of type I and II interferons and their receptors ; IRF3; PTGS2; TNF-α and the TNF receptor superfamily; and LIF were also inferred to be upregulated, as well as chemicals/toxins associated with neurodegenerative diseases (beta-amyloid and N-methyl-4-phenylpyridium), whilst the anti-inflammatory glucocorticoid receptor (NR3C1) and its ligand (dexamethasone) were inferred to be downregulated.The network also highlighted increased oxidative stress, with an inferred upregulation of the pro-oxidant hydrogen peroxide and downregulation of the antioxidant molecule ascorbate. The metabolic regulators PPAR family members PPARD and PPARG were inferred to be downregulated. Other inferred upregulated signaling pathways included the p38 MAPK family; ITPR and the purinergic receptors (P2RY2 and P2RX7) which converge downstream on calcium and PRKC signaling. The ILK1 (Integrin-Linked Kinase)—AKT1 (AKT serine/threonine-protein kinase)—FOXO1 (Forkhead Box O1) cellular survival pathway was inferred to be downregulated. In general, the directionality of the regulation was similar in the earlier stage of ALS, but their inferred fold-changes were smaller. An overview of the dysregulated signaling pathways is shown in Fig. 4. However, recent studies have shown that astrogliosis is a much more complex and highly heterogeneous process23, and the molecular signature of reactive astrogliosis is strongly dependent on the type of injury4. Here, we introduced a comprehensive CBN model to aid in the identification of relevant molecular processes related to astrogliosis in high-throughput datasets. Scientific statements derived from original research articles and scripted into BEL were assembled into a network model that captured the molecular processes involved in diverse types of astrocyte activation15. Although the network was constructed to capture both the neurotoxic and neurotrophic aspects of astrocyte activation, the most connected nodes in the network were related to pro-inflammatory processes and the astrocytic marker GFAP.Reactive astrogliosis plays a central role in CNS diseases and injuries. Traditionally, reactive astrogliosis has been identified by astrocyte hypertrophy and increased expression of GFAP. Later, a framework of neurotoxic A1 astrocytes, neurotrophic A2 astrocytes, and general pan-reactive astrocytes emerged24, was upregulated in ALS and LPS-treated mice. STAT1 was perturbed in LPS-treated and AD mice. The JAK/STAT signaling pathway is downstream of several cytokines and interferons, where it plays a role in immune functions and contributes to neuroinflammation25. Chemicals/proteins associated with neurodegenerative diseases , were inferred to be upregulated in the LPS-treated and ALS, but not in AD or MS astrocytes.We used the iNode enrichment analyses to infer the molecular drivers of reactive astrogliosis in four datasets: an LPS-induced mouse model of endotoxemia-associated neuroinflammation, the PS2APP mouse model of AD, the SOD1 G93A mouse model of ALS, and astrocytes isolated from the normal-appearing white matter of human patients with MS. The iNode analysis results mapped to the astrocyte CBN indicated that astrocytes isolated from both the ALS and LPS models had a highly inflammatory phenotype. Downstream, there was a divergence of the master-inflammatory regulator NF-κB. The NF-κB node reached significance in astrocytes from the LPS-induced inflammation dataset, but not in the ALS, AD, or MS datasets. The analysis also implicated downstream inflammatory mediators that overlapped between LPS and the neurodegenerative disease datasets. The p38 MAPK family, which is associated with several stages of the inflammatory process in neurodegenerative diseases26. OLIG2 is a transcription factor that plays an essential role in the differentiation of oligodendrocytes and neurons in the developing CNS. However, OLIG2 also has a role in astrocyte differentiation. While most astrocytes express GFAP, there are GFAP-negative astrocytes in the CNS. A proportion of these GFAP-negative astrocytes are positive for OLIG2. These astrocytes play a role in inhibitory neuronal transmission27. These structural proteins are often used in immunohistochemistry studies to characterize astrocytes because they label a large part of the injury and are expressed at higher levels after CNS injury28. The inferred changes in these marker proteins could reflect changes in either astrocyte morphology, or astrocyte numbers, as the formation of the glial scar involves both. The publication associated with GSE75246 did not show the morphology of the astrocytes18, however, the inferred increase of GFAP and OLIG2 in our analysis suggests changes in astrocyte morphology.The structural astrocyte marker GFAP and the transcription factor OLIG2 were inferred to be upregulated in late-stage AD, and vimentin was inferred to be downregulated in the AD mouse astrocytes. GFAP and vimentin are the two major intermediate filament proteins in astrocytes, and together, are key components for glial scar formation after injury28. S100B is a calcium-binding protein that can be released in the extracellular space from injured cells and its high serum level is usually a sign of CNS injury. AGER  is a pattern recognition receptor. S100B mediated activation of AGER activates an inflammatory phenotype that induces NF-κB signaling29. There is a feedback loop between S100B, and TP53, where S100B reduces the activity of TP53 in a calcium-dependent manner, and the S100B/TP53 interaction mediates the effects of S100B31. TP53 promotes astrocyte maturation, reduces proliferation, and enhances the expression of synapse-associated genes, whilst HBEGF-EGFR signaling forms a positive feedback loop which inhibits astrocyte maturation, promotes proliferation, and reduces the expression of synapse forming genes. The inferred reduced expression of EGFR and increased expression of TP53 could indicate a reduction in proliferation and increased maturation of astrocytes in MS. The predicted directionality of HBEGF was inconsistent with the directionality of EGFR, however, both HBEGF and EGFR are also involved in other signaling pathways apart from the pathways captured in the CBN32, possibly explaining the causal inconsistency. The MS astrocytes appeared to be less reactive in certain reactive astrocyte pathways, with decreases in the S100B-AGER inflammatory axis. The anti-inflammatory signaling through the glucocorticoid receptor was inferred to be downregulated, whilst inflammatory signaling through TNF-α and its receptor was inferred to be upregulated. The authors of the publication associated with the GSE83670 dataset proposed that these astrocytes played a neuroprotective role in MS19.The nodes inferred to be regulated in the astrocytes isolated from normal-appearing white matter in MS patients partially overlapped with the inflammatory and anti-inflammatory nodes highlighted in the other datasets. In addition, EGFR, S100B, and AGER were inferred to be decreased in MS. S100B is a cytoplasmic protein primarily expressed by astrocytesIn summary, we demonstrated how the reactive astrocyte CBN can be used in combination with the iNode enrichment analysis to identify drivers of astrocyte activation resulting from CNS pathologies. The current approach has some limitations. In the above analyses, we have limited the discussion to the connected nodes, however, several other network nodes were inferred to be regulated based on the transcriptomic datasets we used (shown in the Supplementary Information). It is likely that by focusing on sets of connected nodes, we missed some interesting candidate factors which were not connected to other perturbed nodes. Secondly, the mechanistic information extracted from transcriptomic data is limited to the extent of the iNode database. The inclusion of nodes in our scoring database is limited by the availability of high-quality public datasets in which these nodes were manipulated. Moreover, the biological context in public datasets varies and is often not astrocyte-specific. Hence, the mRNAs regulated by these nodes might be different in an astrocyte-specific context than in a general biological context. During the construction and data-driven expansion of the CBN, we were limited to pathways described in an astrocyte-specific context in the literature. As a result, not all the nodes suggested during data-driven expansion could be substantiated and there are likely parts of the signaling pathways missing. These nodes can be added to the model after experimental verification as the strength of the astrocyte-specific literature evidence grows. Lastly, while the use of the CBN tool helps to identify drives of astrocyte phenotypes, it will be worth further validating them with experimental data to support conclusions about the underlying disease biology.33. In this manuscript, we demonstrated that the astrocyte activation network can be used as a substrate for scoring high-throughput data to gain a disease-specific mechanistic understanding of the differences between diseased and healthy astrocytes and different stages of the disease. Once more transcriptomic data become available, the model can be used to assess gene expression profiles in individuals with CNS diseases for classifying subjects, generating hypotheses about the mode of action of novel drug candidates, and predicting the drivers of treatment outcomes. Ultimately, the astrocyte network model could provide a supporting tool for mechanistic disease investigation, tailored drug therapy, and precision medicine.In conclusion, because reactive astrogliosis is a highly heterogeneous process, representation of the diverse mechanisms in astrocyte activity processes through CBN models can be a powerful tool for systems medicineSupplementary Information.


Houttuynia cordata Thunb. (family: Saururaceae) (H. cordata), as has been reported, is a Chinese herbal medicine commonly used to treat upper respiratory tract infection and bronchitis. Our previous study has proven that sodium houttuyfonate  had beneficial effects in the prevention of pulmonary fibrosis (PF) induced by bleomycin (BLM) in mice. In the present study, network pharmacology was used to investigate the efficiency and potential mechanisms of H. cordata in PF treatment. Upon manual collection from the literature and databases such as TCMSP and TCM-ID, 10 known representative ingredients of H. cordata species were screened. Then, the prediction of the potential active ingredients, action targets, and signaling pathways were conducted through the Gene Ontology (GO), protein–protein interaction (PPI),and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. The results of network pharmacology prediction suggested that H. cordata may act through multiple signaling pathways to alleviate PF, including the phosphatidylinositol 3-kinase-protein kinase B (PI3K/AKT) pathways, mitogen-activated protein kinase (MAPK) pathways, the tumor necrosis factor (TNF) pathways, and interleukin-17 (IL-17) signaling pathways. Molecular docking experiments showed that the chemical constituents of H. cordata had good affinity with TNF, MAPK1, and AKT1, and using lipopolysaccharide (LPS)-induced A549 cells, a model was established to verify the anti-pulmonary fibrosis effects and related mechanisms of H. cordata–relevant constituents. Finally, these evidences collectively suggest H. cordata may alleviate PF progression via PI3K/Akt, MAPK, and TNF signaling pathways and provide novel insights to verify the mechanism of H. cordata in the treatment of PF.Pulmonary fibrosis, a common outcome of pulmonary interstitial disease of various different etiologies, is one of the most important causes of respiratory failure.  Pulmonary fibrosis (PF) is a chronic, progressive, and devastating interstitial lung disease mainly resulting from toxic insults, autoimmune injuries, drug-induced injuries, infectious injuries, or traumatic injuries. As can be affected by age, genetic predisposing factors, and environmental exposures, PF produces many different pathological types, which are mainly classified as idiopathic pulmonary fibrosis (IPF) and fibrosing alveolitis. PF is mainly characterized by diffuse pneumonia in the early period, secreting pro-inflammatory cytokines such as the tumor necrosis factor α (TNF-α), interleukin-1β (IL-1β), and IL-6. Alveolar epithelial cell (AEC) injury and excessive deposition of collagen in extracellular matrix (ECM) ultimately result in progressive scarring and loss of lung function . With apCentella asiatica (L.) Urban, Ophiopogon japonicus (L.f.) Ker-Gawl., and Houttuynia cordata Thunb. (family: Saururaceae) (H. cordata). The traditional Chinese medicine (TCM) mainly produces synergistic effects via the synergistic mechanism of actions with the active ingredients, enhancing functions, and producing less toxicity, which makes enormous contributions to modern drug development and disease treatment. Some active ingredients in the traditional Chinese medicine play a good role in the progression of PF, just like paclitaxel (PTX) , dual deficiency of the lung and spleen (Pi fei liang xu), Qi deficiency with blood stasis (Qi xu xue yu), and phlegm obstructing the vessels and collaterals (Tan zu mai luo). They were mainly treated by methods of invigorating the lungs and kidneys (Bu fei yi shen), fortifying the spleen and boosting the lung (Jian pi yi fei), promoting blood circulation and dredging collaterals (Huo xue tong luo), transforming dampness and dispelling phlegm (Hua shi qu tan), and harmonizing and percolating dampness (He jie shen shi). The treatment process used decoctions including Schisandra decoction, Bufei decoction, and Buyang Huanwu decoction , and heat-clearing and toxin-resolving Chinese medicinal materials such as el (PTX) , tanshinel (PTX) , and andel (PTX) .H. cordata, famous in China, Japan, Korea, and Northeast India for its medicinal properties, is used in the treatment of a number of diseases such as pneumonia, diabetes, and even SARS , and TNF-α  and TCM-ID . Active ingredients were retrieved from them. The chemical information of main active ingredients was collected from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) and the Canonical SMILES of all ingredients were traced back to PubChem (http://pubchem.ncbi.nih.gov). The major components in H. cordata were obtained by a literature review and screening of public databases. The potential therapeutic targets of the main active components were predicted by matching them in the TCMSP, SEA (https://sea.bkslab.org/), HitPick (http://mips.helmholtz-muenchen.de/hitpick/), SwissTargetPrediction (http://www.swisstargetprediction.ch/), and STITCH (http://stitch.embl.de/) tools. Among them, according to TC > 0.4 in SEA, according to precision >50% SwissTargetPrediction in HitPick, the top 15 with the highest scores were selected, and according to score >0.4 in stitch.The keyword “Houttuyniae Herba” was searched in TCMSP , GeneCards (https://www.genecards.org/), and OMIM (https://www.omim.org/) potential disease target analysis platforms were utilized to search for human genes related to PF. After data deduplication/integration, intersecting genes were obtained and considered as therapeutic targets relevant to PF.With “pulmonary fibrosis” as the keyword, DisGeNET (H. cordata–related targets and PF-related targets were imported into the Venn online tool (http://www.bioinformatics.com.cn/) to obtain the intersection and a Venn diagram, in which potential targets of H. cordata for the treatment of PF were revealed. The above potential target gene ID was identified and standardized through the UniProt database (https://www.uniprot.org). The target network of H. cordata major ingredients acting on PF was analyzed by Cytoscape 3.7.1 built-in tools.Active ingredients of H. cordata targets, common targets were used to build a PPI network using the STRING database (https://string-db.org/) and Cytoscape software. And the core target proteins were identified in the PPI network.To determine the key p < 0.05 and q < 0.05.The overlapped targets were further analyzed by the GO function analysis and KEGG enrichment analysis using the clusterProfiler package based on R language. The filtering thresholds for the retrieved results were http://www.rcsb.org/) and the optimal models visualized using PyMOL (2.0) software (http://www.pymol.org/2/) and AutoDockVina software (http://vina.scripps.edu/) were used to perform the molecular docking of active components and core targets.The binding ability of key components and key targets was verified to explore the accurate binding modes through molecular docking simulation. The PDB database , quercetin , and 2-undecanone  were purchased from Shanghai Yuanye Biotechnology Co., Ltd. .2 at 37°C. The EMT model of lipopolysaccharide (LPS)-induced A549 was reproduced in our study  and SNH, quercetin, and 2-undecanone alone or in combination for up to 24 h, respectively.A549 cells, obtained from the Cell Bank of the Chinese Academy of Sciences, were cultured in DMEM-H with 10% fetal bovine serum , 100 U/mL penicillin, and 100 ng/ml streptomycin with 5% COur study . For theThe cells were lysed in RIPA lysis buffer  with 1% phenylmethylsulfonylfluoride , 2% phosphorylated proteinase inhibitor , and 2% protease inhibitor . And 30 μg of the protein was separated using sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) (10%), and then was transferred onto a PVDF membrane. After blocking, the membranes were incubated overnight at 4°C with the primary antibody against β-actin , Phospho-AKT , AKT , p44/42 MAPK (ERK1/2) , Phospho-p44/42 MAPK (ERK1/2) , Phospho-p38 MAPK , and TNF-α . Next, membranes were washed in TBS-T several times, and then were incubated for 2 h in horseradish peroxidase (HRP)-linked anti-rabbit  or anti-mouse  at room temperature. After washing with TBS-T again, the protein bands were analyzed using ChemiScope 3500mini exposure instrument  with a chemiluminescence substrate.p < 0.05 were considered statistically significant.All values were expressed as mean ± standard deviation (SD), and data and analyses for graphing were processed with SPSS 25.0 statistical software and GraphPad Prism 8.0.2 software. Comparisons between groups were performed using one-way ANOVA. Values of H. cordata by a TCM network pharmacology–based strategy and provides ideas for H. cordata for further drug development. This workflow is shown in This study reveals the mechanism of the anti-pulmonary fibrosis pharmacological action of H. cordata’s chemical constituents were collected from TCMSP. A total of 10 main components were candidated after removing the redundant ones from the literature and public databases. The 10 components were as follows: hyperoside, quercetin, rutin, heriguard, houttuynin, 2-undecanone, isoquercitrin, quercitrin, β-stigmasterol, and myrcene , TNF, AKT1, VEGFA, TP53, IL-1β, and IL-6 were advised to be the core (hub) node owing to a high network degree value, betweenness centrality, and closeness centrality in the PPI regulatory network .p < 0.05 and q < 0.05, GO and KEGG pathway enrichment analyses of the 101 intersection targets were used to determine the biological functions of PF that were affected by H. cordata. GO enrichment items were composed of biological process (BP) terms, cell composition (CC) terms, and molecular function (MF) terms  terms . The BP lization . The actH. cordata were used for subsequent experimental studies.Three core target proteins  with high degrees were identified with nine active compounds  by AutoDock Vina. According to the results of the molecular docking activity, the compounds selected generally had a moderate binding potential with a good medicinal reference value . The docp < 0.05 or p < 0.01) , p-p44/42 MAPK (ERK1/2), p-p38 MAPK, and TNF-α protein in the drug group was significantly decreased compared with that in the model group ( < 0.01) . Our resPF is a progressive and severe condition characterized by mesenchymal cell proliferation and differentiation, ECM deposition, and remodeling of the lung parenchyma and airways. Although progress has been made in the research on PF, our understanding of its pathological mechanism remains rudimentary. There is still no complete cure for PF, and current therapies merely act to delay but not completely stop the progression of the disease.H. cordata, a Chinese herb with special odor, is mainly used to treat bronchitis and upper respiratory tract infection. And studies have reported that it can inhibit rapid PF by upregulating IFN-γ and inhibiting the TGF-β1/Smad pathway , an adduct compound of houttuynin and sodium bisulfite, was developed and put in use. Sodium new houttuyfonate has better water solubility and stability and can be transformed into houttuynin under physiological conditions. Previous studies have shown that the lung is the target organ of houttuynin, and it has good therapeutic effects on pneumonia  family, TNF, AKT, or the cytokine VEGF. The results of the GO functional enrichment analysis revealed that H. cordata is mainly involved in signal transduction, inflammatory immunity, regulation of chemokine production, etc. The KEGG enrichment analysis showed that the possible pathways involved in the anti-pulmonary fibrosis of H. cordata mainly included the phosphatidylinositol-3-kinase (PI3K)/Akt signaling pathway, MAPK signaling pathway, TNF signaling pathway, IL-17 signaling pathway, HIF-1 signaling pathway, and NOD-like receptor signaling pathway. All the above signaling pathways are related to inflammation and immune responses, indicating that H. cordata exerts immunomodulatory and anti-inflammatory effects through multiple pathways, exactly in accordance with our previous study which shows that sodium houttuyfonate reduces BLM-induced elevation of inflammatory cytokines such as IL-1, IL-6, and TNF-α in mice (308 and Ser473 of Akt will be phosphorylated with the help of 3-phosphoinositide–dependent protein kinase 1 (PDK1) and PDK2, Akt-activated , p-p44/42 MAPK (ERK1/2), p-p38 MAPK, and TNF-α via the PI3K/AKT, MAPK, and TNF signaling pathways. However, the mechanism needs more investigation in vitro and in vivo models.LPS, an endotoxin component of Gram-negative bacilli as an inflammatory inducer, is a vital promoter of acute lung injury (ALI) as well as promoting EMT formation in various cell types including alveolar epithelial cells (AECs) . A549 ceH. cordata in anti-pulmonary fibrosis, which involves multicomponents, multi-targets, and multi-pathways. An in-depth study should be considered by taking H. cordata as a novel anti-pulmonary fibrotic drug.Our present study has important implications for the current understanding of the molecular mechanism of


Guanxin-Shutong capsule (GXSTC), a combination of Mongolian medicines and traditional herbs, has been clinically proven to be effective in treating cerebrovascular diseases (CBVDs). However, the underlying pharmacological mechanisms of GXSTC in CBVDs remain largely unknown. In this study, a combination of systems pharmacology and experimental assessment approach was used to investigate the bioactive components, core targets, and possible mechanisms of GXSTC in the treatment of CBVDs. A total of 15 main components within GXSTC were identified using high-performance liquid chromatography coupled with diode array detector (HPLC-DAD) and a literature research. Fifty-five common genes were obtained by matching 252 potential genes of GXSTC with 462 CBVD-related genes. Seven core components in GXSTC and 12 core genes of GXSTC on CBVDs were further determined using the protein-protein interaction (PPI) and component-target-pathway (C-T-P) network analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis results predicted that the molecular mechanisms of GXSTC on CBVDs were mainly associated with the regulation of the vascular endothelial function, inflammatory response, and neuronal apoptosis. Molecular docking results suggested that almost all of core component-targets have an excellent binding activity . More importantly, in middle cerebral artery occlusion (MCAO) -injured rats, GXSTC significantly improved the neurological function, reduced the infarct volume, and decreased the percentage of impaired neurons in a dose-dependent manner. Western blotting results indicated that GXSTC markedly upregulated the expression of vascular endothelial growth factor A (VEGFA) and endothelial nitric oxide synthase (eNOS), while downregulating the expression of cyclooxygenase-2 (COX-2) and transcription factor AP-1 (c-Jun) in MCAO-injured rats. These findings confirmed our prediction that GXSTC exerts a multi-target synergetic mechanism in CBVDs by maintaining vascular endothelial function, inhibiting neuronal apoptosis and inflammatory processes. The results of this study may provide a theoretical basis for GXSTC research and the clinical application of GXSTC in CBVDs. Cerebrovascular diseases, characterized by stroke and other forms of neurological dysfunction and degeneration, are among the most common causes of morbidity and mortality worldwide, seriously affecting human health and life . Until nChoerospondias axillaris (Roxb.) B.L.Burtt and A.W.Hill , Salvia miltiorrhiza Bunge , Syzigium aromaticum (L.) Merr. and L.M.Perry , Dryobalanops aromatica C.F.Gaertn , and Bambusa textilis McClure   has been used for thousands of years to prevent and treat CBVDs. Guanxin-Shutong capsule, a combination of Mongolian medicines and traditional herbs, has been commonly used to treat angina pectoris and coronary heart diseases in China ; it consambusae]  at a weiambusae] . In receambusae] . HoweverSystems pharmacology is an effective tool for drug-target analysis with the advantages of a low cost, short cycle, and providing more comprehensive information. Moreover, it has gradually promoted the paradigm shift from the “one-target, one-drug” mode to a “network-targets, multiple-components” approach in drug discovery . CoincidIn this study, the mechanisms of action of GXSTC on CBVDs were investigated by combining systems pharmacology and experimental assessment. The detailed workflow is shown in GXSTC was supplied by Buchang Pharmaceuticals . The reference standards of gallic acid, ellagic acid, tanshinone IIA, cryptotanshinone, protocatechuic acid, and eugenol were obtained from the National Institute for the Control of Pharmaceutical and Biological Products . The reference standards of danshensu, 5-hydroxymethylfurfural, protocatechualdehyde, rosmarinic acid, salvianolic acid A, salvianolic acid B, tanshinone I, and dihydrotanshinone I were purchased from Baoji Chenguang Biotechnology Co. Ltd. Deionized water was purified by a Milli-Q water purification system . Acetonitrile and methanol (HPLC-grade) were purchased from Fisher Scientific . 2,3,5-triphenyltetrazolium chloride  was obtained from Sigma-Aldrich . A bicinchoninic acid (BCA) protein assay kit and a sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) kit were bought from Dingguo Changsheng Biotechnology . The running buffer (pH: 8.4–8.6), transfer buffer (pH: 8.4), and Tris-buffered saline (TBS) were purchased from Servicebio . Anti-VEGFA (ab46154) were bought from Abcam . Anti-β-actin (13E5) and anti-c-Jun (60A8) rabbit mAbs were bought from Cell Signaling Technology . Anti-eNOS (GB11086) and anti-COX2 (GB11077-2) rabbit polyclonal antibodies were obtained from Servicebio. HRP-conjugated AffiniPure goat anti-rabbit IgG  was purchased from ProteinTech Group . Anti-β-tubulin and other reagents were supplied by other Chinese suppliers.18 column . The mobile phase was composed of solvent A (acetonitrile) and solvent B (water containing 0.05% phosphoric acid) and the gradient procedure was set as follows: 0–15 min, 5–20% A; 15–18 min, 20–34% A; 18–30 min, 34–40% A; 30–35 min, 40–50% A; 35–40 min, 50–65% A; 40–60 min, 65–75% A; the UV detection wavelength was set at 274 nm.The GXSTC powder (0.5 g), and a mixture of 14 reference standards were dissolved in 50 ml methanol, respectively. After sonication for 30 min, the samples were filtered through 0.22 μm syringe filter, and 20 μL filtrate was injected into the HPLC-DAD system for analysis. HPLC analysis of GXSTC was performed by LC-20AT HPLC system  with an Agilent TC-Chttps://pubchem.ncbi.nlm.nih.gov/)      . Next, ter. 2.3) , BATMAN-r. 2016) , STITCH r. 5.18) , and Swir. 2019)  databasehttp://omim.org/) (https://www.genecards.org/) (https://www.ncbi.nlm.nih.gov/gene/) (http://bidd.nus.edu.sg/group/cjttd/) (https://www.drugbank.ca) (http://ctdbase.org/) (https://www.uniprot.org/)  , GeneCards.org/) , NCBI Gev/gene/) , TTD (ht/cjttd/) , and Drubank.ca)  databasese.org/) . To ensuot.org/)  databasehttps://cytoscape.org/, ver. 3.7.2)   network was established by using Cytoscape (. 3.7.2) . In addir. 11.0)  to obtaihttps://www.r-project.org/, ver. 3.6.2)  network was constructed to investigate the reciprocity of the components, targets and pathways by using Cytoscape.To investigate the potential molecular mechanisms of GXSTC on CBVDs, candidate targets were analyzed for GO functional and KEGG pathway enrichment analysis based on the “cluster profiler” and “org.Hs.eg.db packages” of the R project (. 3.6.2) . The enrhttps://www.rcsb.org/)  . Then, tn = 8 in each group): sham, middle cerebral artery occlusion, and MCAO + GXSTC pretreatment . These doses were chosen based on the dosage of GXSTC in clinical practice    and prevs a day) . Rats ins a day) . After 2After reperfusion, the neurological deficits were scored using a 5-point scale scoring system, as described previously . The detAfter reperfusion, the rats were deeply anesthetized and decapitated. The brains were rapidly removed and stored on ice for 15 min. The brains were cut into 2 mm coronal slices, stained with 2% TTC solution for 30 min, and then fixed with 4% paraformaldehyde solution at 4°C overnight. After staining, the slices were arranged in groups and photographed, and the infarct volume of each slice was measured by using Image Pro Plus 6.0. To exclude the confounding effect of brain edema, the corrected infarct volumes were calculated through the following formula:Finally, the infarct volume (%) in each group was expressed as the percentage of contralateral hemispheric volume.To evaluate the histological damage, rats were deeply euthanized and sacrificed at 24 h after reperfusion. Subsequently, the brains were quickly removed and immediately immersed in 4% paraformaldehyde. After fixation for 24 h, the brain tissues were dehydrated, embedded in paraffin and cut into 5 μm coronal slides. Finally, the brain slides were stained with H&E, and the histopathological changes were observed by using light microscopy at a ×400 magnification.β-actin , and β-tubulin  at 4°C overnight. Next, the membranes were rinsed in TBST for 10 min three times, and then incubated with HRP-labeled secondary antibody  for 1 h at room temperature. Finally, the blots were visualized by using an enhanced chemiluminescence method  and were analyzed using the ImageJ software .After 24 h of reperfusion, the right brain tissues were pooled and homogenized via sonication in RIPA lysis buffer  containing protease and phosphatase inhibitors . The total protein concentration in each sample was measured by a BCA protein assay kit according to the manufacturer’s instructions. Equal amounts of protein samples (30–60 μg per sample) were separated by using 8–10% SDS-PAGE gels and transferred to PVDF membranes. The membranes were blocked with 5% nonfat milk in TBS containing 0.1% TWEEN (TBST) at room temperature for 1 h, and then incubated with the following primary antibodies: anti-VEGFA , e-NOS (1:500), c-Jun , COX-2 , p < 0.05, and a significant statistical significance was set at p < 0.01.Statistical analysis was performed using GraphPad Prism 8.0.1 for Windows. All data, except for the neurologic score values, were expressed as mean ± standard deviation. Statistical differences between the various groups were analyzed using one-way analysis of variance (ANOVA). Statistical significance was set at Choerospondiatis fructus, eight components were primarily produced by Radix Salviae miltiorrhizae, and two components were derived from Salviae miltiorrhizae. However, only tanshinone IIA, salvianolic acid B, and borneol were used as marker components for the quality control of GXSTC in the Chinese Pharmacopeia. Among them, borneol was the most important constituent in Bomeolum and had a neuroprotective effect on cerebral ischemia, but it can hardly be detected by using HPLC. Therefore, borneol was also included as a candidate component for further analyses in this study. Detailed information and structures of these components are provided in A total of 14 main components in GXSTC were unambiguously identified by comparing the retention time with those of standard samples . Based oAccording to the predefined screening conditions, 252 targets related to the GXSTC components were collected . A visuaA total of 462 CBVD-related targets were collected from six public databases . After mp < 0.05, a total of 1,515 BPs, 47 CCs, 64 MFs, and 112 pathways were obtained , molecular function (MF) and cellular component (CC). According to the screening criteria of obtained , and theBased on the KEGG enrichment analysis results, we established a C-T-P network to systematically explain the mechanism of GXSTC on CBVDs. The network includes 48 nodes and 147 edges. The blue octagon represent 14 bioactive components of GXSTC, the orange ellipses represent 26 corresponding targets of components, and the rose-red V shapes represent the pathways related to CBVDs in the top 20 pathways . Seven cThe docking analysis between the seven core components and their corresponding core targets was carried out by using AutoDock Vina. Among them, EDN1 could not be docked because there is currently no suitable protein crystal structure available. Therefore, a total of 28 pairs of docking results were obtained, with an affinity ranging from −4.8 to −10.3 kcal/mol . Affinitp < 0.01, vs. sham group). The pretreatment with GXSTC (2.0 g/kg) significantly reduced the neurological deficits score , which suggested that the MCAO model was successfully established. Furthermore, the pretreatment with GXSTC (2.0 g/kg) significantly reduced the cerebral infarct volume from 38.19 to 18.56% . After pretreatment with GXSTC at doses of 1.0 and 2.0 g/kg, the rates of necrotic neurons were markedly decreased .The neuroprotective effect of GXSTC on brain injury was further confirmed by using H and E staining. As shown in p < 0.05, vs. MCAO group). With the increase in the GXSTC concentration, the expression of VEGFA and e-NOS gradually increased. Moreover, compared with the sham group, the expression of COX-2 and c-Jun was markedly increased in the MCAO group. However, the pretreatment with 1.0 and 2.0 g/kg GXSTC could significantly decreased the expression of COX-2 and c-Jun .Because the core targets were mainly associated with inflammatory responses, regulation of vascular endothelial function, and neuronal apoptosis, we chose VEGFA, e-NOS (NOS3), COX-2 (PTGS2), and c-Jun for further investigation. As shown in GXSTC, which is derived from a Mongolian medicine formulation, has been clinically proven to be an effective and safe TCM for the treatment of CBVDs. However, the bioactive components and multi-target mechanisms of GXSTC have not yet been clearly elucidated. In this study, 15 active components in GXSTC were identified by using HPLC-DAD and a literature research. Fifty-five common targets were screened by matching the potential targets of GXSTC with the CBVD-associated targets. Twelve core targets and seven core components were identified by using PPI and C-T-P network analysis. Enrichment analysis results predicted that the therapeutic effects of GXSTC against CBVDs may be involved in the regulation of vascular endothelial function, inflammatory response and neuronal apoptosis. Finally, molecular docking and various experiments  were carried out to assess the systems pharmacology prediction.in vitro and in vivo , neuronal apoptosis , and vascular endothelial function . Inflammation is a hallmark of stroke pathology and contributes to the aggravation of brain injury . PTGS2, GO functional and KEGG pathway enrichment analyses were performed to better understand the reciprocity of these common targets. The enriched results suggested that the molecular mechanisms of GXSTC on CBVDs were predominately involved in AGE-RAGE, HIF-1, TNF, IL-17, and the apoptosis signaling pathway. These results are consistent with those of previous studies showing that the above pathways play important roles in the progression and development of CBVDs . Among tTo further investigate the interactions between the core components and their corresponding core targets, molecular docking studies were performed to elucidate the binding modes. The results indicated that almost all of core components-targets had a good binding activity , and more than half of them had a stronger binding activity , which further confirmed the reliability of the systemic pharmacology prediction. More importantly, in MCAO-injured rats, GXSTC significantly improved the neurological function, reduced the infarct volume, and decreased the rate of impaired neurons in a dose-dependent manner. In addition, we selected vascular function-related proteins (VEGFA and eNOS), inflammation-related proteins , neuronal apoptosis-related protein (c-Jun) from 12 core targets to further assess the molecular mechanisms. These proteins were also enriched in the HIF-1, TNF, and IL-17 signaling pathways, and may be the center of the regulatory network of GXSTC against CBVDs. Western blot analyses indicated that GXSTC markedly upregulated the expression of VEGFA and e-NOS, and downregulated the expression of COX-2 and c-Jun in MCAO-induced rats. These findings provide the evidence that GXSTC exerts a multi-target synergetic effect on CBVDs by maintaining vascular endothelial function, inhibiting neuronal apoptosis and inflammatory processes. However, identifying the core components by using network pharmacological analysis alone has some limitations, such as that it neither reflect the effect intensity against CBVDs of these core components, nor illuminate the correlation between their concentration and efficacy. Therefore, these core components are worthy of further exploration. In addition, the three doses of GXSTC  used in this study are comparatively high and it remains unclear whether these doses will result in artefacts in our model. They are equivalent to 80, 160, and 320 mg/kg per day in humans, respectively (converted by using the body surface area) , so a loIn this study, an integrated systems pharmacology approach was used to investigate the bioactive components, therapeutic targets and explore the pharmacological mechanisms of GXSTC in treating CBVDs. The results suggested that the multi-target synergetic mechanism of GXSTC in CBVDs mainly involved three therapeutic modules, including maintaining vascular endothelial function, inhibiting neuronal apoptosis and inflammatory processes. Then, we selected the rat MCAO/reperfusion model to evaluate the protective effects of GXSTC on CBVDs and chosen several core proteins to assess the predicted molecular mechanisms, confirming the reliability of our method. In conclusion, our study gives an insight into the multi-target mechanisms by systems pharmacology approach, providing a scientific foundation for GXSTC research and its clinical application in treating CBVDs.


PAK1:SYT1, PAK1:RFC5, DCTN1:STX1A, and GRIA1:MAP2K4) were normally correlated in universal contexts. In summary, we systematically identified rewired pathways, disrupted pathway crosstalk circuits, and critical genes and gene links in schizophrenia transcriptomes.Transcriptomic studies of mental disorders using the human brain tissues have been limited, and gene expression signatures in schizophrenia (SCZ) remain elusive. In this study, we applied three differential co-expression methods to analyze five transcriptomic datasets (three RNA-Seq and two microarray datasets) derived from SCZ and matched normal postmortem brain samples. We aimed to uncover biological pathways where internal correlation structure was rewired or inter-coordination was disrupted in SCZ. In total, we identified 60 rewired pathways, many of which were related to neurotransmitter, synapse, immune, and cell adhesion. We found the hub genes, which were on the center of rewired pathways, were highly mutually consistent among the five datasets. The combinatory list of 92 hub genes was generally multi-functional, suggesting their complex and dynamic roles in SCZ pathophysiology. In our constructed pathway crosstalk network, we found “Clostridium neurotoxicity” and “signaling events mediated by focal adhesion kinase” had the highest interactions. We further identified disconnected gene links underlying the disrupted pathway crosstalk. Among them, four gene pairs ( Schizophrenia (SCZ) is one of the main mental disorders that disrupt both the physical and the social welfare of the affected subjects and their families. While genome-wide association studies have identified a good number of risk variants to SCZ, functional characterization and neurobiological mechanisms remain to be elucidated ,2. For oRegarding the bioinformatics analysis of such data, we noted that the majority of these original transcriptome studies have benefited from the gene co-expression approach, especially through the application of a popular tool, Weighted correlation network analysis (WGCNA) . By desiUnder a specific experimental condition, cellular processes or pathways are coordinated in a particular way to fulfill their programmed functions. Biological pathways are never static and have no definite boundaries in between. The interrogation of context-specific pathway interactions gives rise to pathway crosstalk networks ,31, whicThe successful application of differential co-expression approaches in other human disease studies, as well as the intriguing idea of combining differential co-expression networks and pathway crosstalk, has motivated us to carry out the present work of pathway-centric analysis of multiple SCZ transcriptome datasets. Specifically, we investigated disruption of expression correlation within cellular pathways in three RNA-Seq and two microarray datasets, all of which were based on SCZ-vs-control comparison. We performed pathway crosstalk analysis in the scaffold of the gene correlation-loss network, revealing a network of disrupted pathway connections in SCZ. With regard to the SCZ phenotype, our analysis results shed light on internally rewired pathways, important genes as pathway hubs, and disrupted coordination between key signaling pathways.The workflow started from integrating an SCZ transcriptome dataset with pathway curation knowledge, entailed both intra-pathway and between-pathway differential co-expression analyses, and finally culminated in the identification of schizophrenia-specific pathway crosstalk disruptions and accountable gene correlation losses .Five RNA-Seq/microarray datasets assaying various human brain regions were included in our study . RNAseq1® Human Genome U133 Plus 2.0 array. Functions in R packages “hgu133plus2.db” and “rma” were used for data pre-processing. Expression values for probe sets were averaged to the gene level. From the whole set of genes represented on the microarray platform, we retained 11,724 genes that overlapped with the working gene set of the three RNA-Seq datasets.GSE1 refers to a microarray dataset GSE21138 downloaded from Gene Expression Omnibus (GEO) for prefrontal cortex brain tissues (Brodmann region 46) . GSE2 rep-value for a coefficient was found less than 0.05, the corresponding covariate was deemed significantly influential on gene expression and the estimated coefficient was subtracted from the gene expression value. The numbers of genes significantly influenced by nuisance covariates were shown in As illustrated in previous studies ,39,40, cWe downloaded pathways curated in Pathway Commons , which ap-values. An analogous method, GSCA [p-value for each pathway out of each dataset, we performed the Fisher’s meta-test to summarize multiple dataset-specific p-values, thus making an overall conclusion on each pathway’s differential co-expression significance from all five datasets.The gene sets net correlations analysis (GSNCA)  implemenod, GSCA , was alsWithin one pathway, GSNCA summarizes the expression correlation profile for a gene with respect to all other peer genes, deriving a “weight” index for each gene. Briefly, GSNCA solves the weight vector for all pathway genes under each experimental condition separately and regards the pathways with the most remarkable weight vector changes as the most phenotype-relevant pathways. In one experimental condition, the gene with the highest weight value is designated as the hub gene, around which an intra-pathway gene co-expression network is constructed. The intra-pathway co-expression network is depicted as a union of the first and the second minimum spanning trees, which is identified by minimizing the total path length (sum of correlation distances). By definition, the hub gene of a pathway may not necessarily have the highest degree  because it is identified by virtue of the quantitative regulatory importance  rather than the degree .We used our R package DCGL   to identp < 0.001, binomial probability model). Microarray datasets presented a higher link overlapping fraction than the RNA-Seq datasets, possibly because of the closer histological relationship of microarray samples compared to the more distantly separated brain regions of the RNA-Seq datasets  and that gene pairs with positive correlation furthermore dominated the same-signed links (average fraction is 77.6%). Because it was recently reported  that a up < 0.05). The two modes of CSPN considered cross-pathway links in relation to a concerned gene set differently: in the “both” mode, links connecting genes of interest on both ends were considered, while in the “or” mode, links incident to genes of interest were considered.Following our previous practices ,34, herehttp://bioinformatics.psb.ugent.be/webtools/Venn/ (accessed on 11/22/2019)). Web service ToppFun [https://toppgene.cchmc.org/enrichment.jsp (accessed on 04/12/2021)) was invoked for examining enriched cellular components of pivotal genes.Venn diagrams overarching five object lists were rendered with an online tool from Dr. Prof Van de Peer’s Bioinformatics and Evolutionary Genomics group , each participating in two or more SCZ-disrupted pathways. We further identified the respective pathways that each multi-functioning gene showed up as a hub in the control or SCZ samples . The res pathway B. Other s in SCZ , becominp-value < 0.001, binomial probability model), though the actual number of joint links was technically too small to allow for sizable network inference , CSPN repeatedly revealed 12 disrupted pathway connections revolving around “Clostridium neurotoxicity” and “signaling events mediated by focal adhesion kinase” A. For veThe pathway crosstalk disruptions implicated many neuropsychiatric pathways, including neurotransmitter release cycle, clostridium neurotoxicity, L1CAM interactions, among others. Additionally, the network involved focal adhesion and immune pathways, themes of high relevance with SCZ per multiple lines of evidence ,51,56,57PAK1:SYT1, PAK1:RFC5, DCTN1:STX1A, and GRIA1:MAP2K4 are universally highly correlated (correlation coefficients close to or above 0.30). However, these pivotal inter-connections broke apart in SCZ transcriptomes, accounting for the observed pathway crosstalk disruptions to a large extent.In companion with the disrupted crosstalk of pathway entities A, we prePAK1:SYT1, PAK1:RFC5, DCTN1:STX1A, and GRIA1:MAP2K4). Put together, we reported SCZ-specific, internally rewired pathways, dynamic pathway hub genes, and disrupted pathway crosstalk connections. These results provide insights into a deep understanding of SCZ pathophysiological mechanisms.Due to the ethical standard for the collection of human brain tissue in research, transcriptome data have been very limited for schizophrenia (SCZ), and gene expression dysregulation mechanisms underlying SCZ remain elusive. Here we applied three established differential co-expression methods to analyze three RNA-Seq and two microarray datasets derived from schizophrenia and matched normal samples, representing one of the major efforts to integrate multiple transcriptome data using different computational approaches. In total, we sorted out 60 pathways of internal correlation rewiring  by summaCompared to the conventional differential expression approach, differential co-expression analysis represents a different yet complementary perspective on diseased transcriptomes. Methods purported to identify differentially co-expressed genes, gene connections, and gene sets have been released and improved during the past 15 years ,59,60. NCompared with the pathway rewiring results, pathway hub genes manifested a much higher level of consistency across datasets, where every pair of datasets shared a significantly high portion of pathway hubs . This coAggregating numerous gene expression signals to biological processes or functional modules is a common and powerful approach to disease transcriptomes. Analyzing co-expression or differential co-expression of functional gene modules has been a popular approach to dissecting psychiatric disorder’s transcriptomes, which features frequent applications of the well-known software WGCNA or rWGCNA . However


Network pharmacology was used to illuminate the targets and pathways of polybrominated diphenyl ethers (PBDEs) causing thyroid dysfunction. A protein-protein interaction (PPI) network was constructed. Molecular docking was applied to analyze PBDEs and key targets according to the network pharmacology results. A total of 247 targets were found to be related to 16 PBDEs. Ten key targets with direct action were identified, including the top five PIK3R1, MAPK1, SRC, RXRA, and TP53. Gene Ontology (GO) functional enrichment analysis identified 75 biological items. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis identified 62 pathways mainly related to the regulation of the thyroid hormone signaling pathway, MAPK signaling pathway, PI3K-Akt signaling, pathways in cancer, proteoglycans in cancer, progesterone-mediated oocyte maturation, and others. The molecular docking results showed that BDE-99, BDE-153, 5-OH-BDE47, 5′-OH-BDE99, 5-BDE47 sulfate, and 5′-BDE99 sulfate have a good binding effect with the kernel targets. PBDEs could interfere with the thyroid hormone endocrine through multiple targets and biological pathways, and metabolites demonstrated stronger effects than the prototypes. This research provides a basis for further research on the toxicological effects and molecular mechanisms of PBDEs and their metabolites. Furthermore, the application of network pharmacology to the study of the toxicity mechanisms of environmental pollutants provides a new methodology for environmental toxicology. Polybrominated diphenyl ethers (PBDEs) are persistent organic pollutants that exist widely in the environment. They are widely used in daily life and have been added as fire extinguishing materials to various polymer resins and plastics, furniture, television sets, interior decoration, and others . PBDEs a12HnBrn) and interference with the thyroid endocrine [The toxicological mechanism of PBDEs is the focus of research. The chemical general formula is Cndocrine . Meanwhindocrine . Similarndocrine . Most toThe pathogenesis of thyroid dysfunction is complicated, and influencing factors include sex, smoking, lifestyles, and variation in physiological status, such as that of the immune system, as well as other pathological indicators, such as glucose tolerance and cholesterol levels . Single Network pharmacology is a strong tool for studying the synergistic effect of multiple components and targets , 9. In 2Molecular docking is a major computing technology in the field of computer-aided drug research. In the early 1980s, Kuntz et al. proposed the application of molecular docking in drug discovery and biology . As one This study used network pharmacology in drug research and development for reference and applied it to the study of environmental toxicology to explore the mechanism of 16 PBDEs on thyroid endocrine disruption. Furthermore, molecular docking was used to verify the binding of active homologues and key targets. The methodology is aimed at studying the toxicological mechanism of PBDEs on thyroid dysfunction at the target and pathway levels from the integral action of multiple homologues.In our study, the potential effects and mechanisms of PBDE prototypes influencing thyroid dysfunction were analyzed by network pharmacology. Molecular interactions between PBDE prototypes, metabolites, and target receptors related to thyroid dysfunction were studied by molecular docking, which is shown in http://stitch.embl.de/), Swiss Target Prediction database (http://www.swisstargetprediction.ch/), SEA database (http://sea.edbc.org/), and Pharmmapper database (http://www.lilab-ecust.cn/pharmmapper). Then, the GeneCards database (http://www.genecards.org/) was used to search thyroid dysfunction-related genes. The Venny 2.1.0 online website (http://bioinfogp.cnb.csic.es/tools/venny/index.html) was used to obtain a Venn diagram. The STRING database (http://string-db.org/) was used to predict potential PPI networks. Cytoscape software (version 3.6.1) was used to draw the PBDE-thyroid dysfunction interaction network and screen key targets. Finally, the DAVID 6.8 database (http://david.ncifcrf.gov/) was used to perform Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis.Four databases were used to search target genes related to PBDEs, including the STITCH database (http://www.rcsb.org/) was used to download the crystal structures of protein targets. Chem3D 19.0 software was used to create PBDE chemical structures and minimize their energy. AutoDock Tools 1.5.6 [The RCSB Protein Data Bank (ls 1.5.6  and Autols 1.5.6  were usels 1.5.6  softwarels 1.5.6  softwarehttps://pubchem.ncbi.nlm.nih.gov/) and ChemSpider database (http://www.chemspider.com/) were used to acquire the 3D structures and the corresponding SMILES structural formulas of the compounds.Search PBDEs ubiquitous in the environment and human body and related to thyroid disorders in the literature through Web of Science, and the retrieval time remains the default –21. BDE-http://stitch.embl.de/), Swiss Target Prediction (http://www.swisstargetprediction.ch/), and SEA (http://sea.edbc.org/) databases. “Homo sapiens” was selected as the species to search for the desired targets. Similarly, the 3D structures of PBDEs were uploaded to the Pharmmapper database (http://www.lilab-ecust.cn/pharmmapper/), and “Homo sapiens” was selected as the Select Targets Set option.The SMILES structural formulas of the compounds in 3.1.1 were input into the STITCH (https://www.genecards.org/) was used to screen targets related to thyroid dysfunction with “Thyroid dysfunction” as the keyword.The GeneCards database (The gene names corresponding to PBDE- and thyroid dysfunction-related targets were intersected using the Venny 2.1.0 online website to obtain the common targets.https://string-db.org/), and the species was selected as “Homo sapiens.” The protein interaction network was constructed based on the nodes with a score greater than 0.9, while the discrete nodes were hidden. Then, the network was imported into Cytoscape 3.6.1 to construct a PPI network.The common targets of PBDE-thyroid dysfunction were imported into the STRING database , and GO enrichment and KEGG pathway analyses were carried out with P < 0.05 as the screening condition.The key targets obtained in http://www.rcsb.org/), and waters and ligands were removed by PyMOL software. The protein crystal structure was imported into AutoDock Tools 1.5.6 to hydrogenate, calculate the total charge, and add atomic types. Finally, the processed protein and PBDEs were docked with AutoDock Vina to verify the molecular binding. The visualization of docking results was realized by using PyMOL and LigPlot software.Based on the PBDE detected in the environment, BDE-47, BDE-99, BDE-100, BDE-153, and BDE-154 were selected, which are found at relatively high levels in the human body, as reported in the literature , 23. Thehttp://www.chemspider.com/) and PubChem database (http://pubchem.ncbi.nlm.nih.gov/) were used to determine the SMILES structural formulas of the compounds . Among them, 53 items were associated with the biological process (BP), 8 items were associated with the cellular component (CC), and 13 items were associated with the molecular function (MF), accounting for 71.6%, 10.8%, and 17.6% of the total GO enrichment, respectively. The top 10 items in BP, CC, and MF are shown in The 10 key target proteins were introduced into the DAVID 6.8 database. Seventy-four GO items were obtained by GO enrichment analysis  and the sulfate metabolites  were also selected. The top 5 kernel targets screened by network pharmacology, including PIK3R1  PDB cod. The doce target , 33. TheThe 2D visual analysis results of the molecular docking of PBDE prototypes, hydroxylated metabolites, and sulfate metabolites with key targets are shown in −1. Among them, the docking binding energy of BDE-47 with PIK3R1 was lower than that of ligand PTR. The docking interactions between PBDE prototypes and key targets mainly include hydrogen bond interactions, hydrophobic interactions, and π-π interactions. Specific descriptions were shown . The docking interactions between the natural ligand and the key targets mainly include hydrogen bonding and hydrophobic interactions. Specific descriptions are available  level is lower in the body of salmon exposed to the mixture of BDE-47 and BDE-99 than in those exposed to any of the homologues alone at the same concentration of PBDEs .The targets obtained by network pharmacology are applicable for PBDE prototypes and structural analogues such as metabolites. This structural extrapolation is also a characteristic of network pharmacology. Therefore, based on obtaining key targets from PBDE prototypes, the thyroid hormone endocrine interference mechanism of PBDE metabolites was studied further. Compared with the precursor, PBDE metabolites exhibit stronger thyroid hormone interference effects. As the phase I metabolite of PBDEs, OH-PBDEs are more similar in structure to thyroid hormones than the prototype, resulting in a stronger binding ability to TTR. The mechanism by which OH-PBDEs interfere with thyroid hormones can act as an effective competitor for binding to thyroid transporter TTR or directly to TRs, thus interfering with the balance of thyroid hormones in vivo , 35. StuThe number (monomer or mixture) and the metabolic form (hydroxylated or sulfate metabolites) will affect the thyroid hormone interference at different levels, resulting in varying degrees of damage to the body. Therefore, based on monomer research, more attention can be paid to mixtures and hydroxylated and sulfate metabolites to further clarify the toxicity mechanism of the mixture and various metabolites.Multiple key target proteins of PBDEs affecting thyroid function were screened by network pharmacology, indicating that PBDEs affect thyroid endocrine function through multiple targets acting on multiple pathways throughout the whole biological network.α) coding gene related to thyroid function. PIK3R1 RNA could be moderately expressed in the thyroid by regulating the PI3K-Akt signal pathway, which plays an important role in the development of thyroid cancer [Ten key target proteins were screened, and the top five were PIK3R1, MAPK1, SRC, RXRA, and TP53. PIK3R1 is a phosphatidylinositol-3-kinase regulatory subunit (P85d cancer , 37. MAPd cancer . SRC is d cancer . RXRA isd cancer . TP53 isd cancer . The topThe KEGG pathway analysis shows that pathways affected by PBDEs mainly involve the pathways in cancer, proteoglycans in cancer, progesterone-mediated oocyte maturation, PI3K-Akt signaling pathway, hepatitis C, thyroid hormone signaling pathway, prolactin signaling pathway, thyroid cancer, MAPK signaling pathway, and others. Genes PIK3R1, MAPK1, SRC, RXRA, and TP53 are involved in the thyroid hormone signaling pathway and are the top targets affected by PBDEs based on the analysis of the key target in The PI3K-Akt signaling pathway, thyroid hormone signaling pathway, and MAPK signaling pathway have been reported in previous studies. PBDEs inhibited the protein expression of estrogen receptor ER and GPR30 in hypothyroidism rats and further affected the expression of proteins in the PI3K/Akt and ERK1/2 signaling pathways . BDE-100The docking results show that the hydroxylated metabolites of PBDEs could produce more hydrogen bonding interactions and hydrophobic interactions than those of PBDE prototypes, and the binding activity was stronger. In addition, PBDE sulfate metabolites generally have more hydrogen bonds and lower binding energy than hydroxylated metabolites. This indicates that PBDE sulfate metabolites have a stronger binding activity to the five thyroid function-related targets than PBDE hydroxylated metabolites, which might be an important component causing thyroid hormone endocrine disruption.−1 indicates a certain binding activity, less than -5.0 kcal·mol−1 indicates good binding activity, and less than -7.0 kcal·mol−1 indicates strong binding activity [Systematic molecular docking generally believes that the binding energy between the molecule and the target has three levels: less than -4.25 kcal·molactivity . The molactivity . The expactivity . Studiesactivity , 48. ThiThe toxicity of the PBDE mixture is different from that of its monomer, and thus, a holistic study was carried out by network pharmacology. The results show that the top five targets PIK3R1, MAPK1, SRC, RXRA, and TP53 are concentrated in the thyroid hormone signaling pathway, which preliminarily indicates that PBDEs have a strong endocrine disrupting effect on thyroid hormones. Meanwhile, pathway analysis identified 62 pathways mainly related to the regulation of the thyroid hormone signaling pathway, MAPK signaling pathway, PI3K-Akt signaling, pathways in cancer, proteoglycans in cancer, and progesterone-mediated oocyte maturation, the last three of which are newly discovered pathways affected by PBDEs. The molecular docking results show that the selected PBDE prototypes and hydroxylated and sulfate metabolites could bind to the targets, and the binding effect was enhanced in turn. The research suggests that the study of pathways affected by PBDEs can be further explored in follow-up research, and more attention should be given to PBDE metabolites.