Therapeutic target database 2020: enriched resource for facilitating research and early development of targeted therapeutics

Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives

Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a quarter of them are emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, and diabetes. However, only 44 unique transporters (∼9.8% of the SLC superfamily) with 3D structures and specific binding sites have been reported. To design innovative and effective drugs targeting diverse SLCs, there are a number of obstacles that need to be overcome. However, computational chemistry, including physics-based molecular modeling and machine learning- and deep learning-based artificial intelligence (AI), provides an alternative and complementary way to the classical drug discovery approach. Here, we present a comprehensive overview on recent advances and existing challenges of the computational techniques in structure-based drug design of SLCs from three main aspects: (i) characterizing multiple conformations of the proteins during the functional process of transportation, (ii) identifying druggability sites especially the cryptic allosteric ones on the transporters for substrates and drugs binding, and (iii) discovering diverse small molecules or synthetic protein binders targeting the binding sites. This work is expected to provide guidelines for a deep understanding of the structure and function of the SLC superfamily to facilitate rational design of novel modulators of the transporters with the aid of state-of-the-art computational chemistry technologies including artificial intelligence.

Network pharmacology and transcriptomic profiling elucidate the therapeutic effects of Ranunculus ternatus Thunb on liver fibrosis via MK3-NF-κB inhibition

Activation of hepatic stellate cells (HSCs) is critical in the progression of liver fibrosis and is a promising target for anti-hepatic fibrosis drug development. Moreover, effective pharmacological interventions targeting this pathomechanism are scarce. Our study confirms the therapeutic value of β-sitosterol, a major constituent of Ranunculus ternatus Thunb, in hepatic fibrosis and identifies its underlying mechanisms. After treatment with β-sitosterol, CCL4-induced hepatic fibrosis was reversed in mice, while inflammatory and hepatic fibrosis indices were improved. Meanwhile, we explored the molecular mechanism of β-sitosterol treatment for hepatic fibrosis and, based on RNA-seq results, found that the ameliorative effect of β-sitosterol on hepatic fibrosis was associated with the MK3 and NF-κB signalling pathways. MK3, an important kinase in the MAPK pathway, plays a role in transmitting upstream and downstream signals, whereas the NF-κB signalling pathway has been shown to be associated with HSC activation. We verified the interaction between MK3 and IκB in HSC cells using endogenous Co-IP, whereas β-sitosterol reduced the binding of MK3 to IκB and the activation of the NF-κB signalling pathway. Our findings reveal the mechanism of β-sitosterol in the treatment of liver fibrosis, suggesting that β-sitosterol may be a promising drug for the treatment of liver fibrosis and deserves further investigation.

Inhibition of IL-17 signaling in macrophages underlies the anti-arthritic effects of halofuginone hydrobromide: Network pharmacology, molecular docking, and experimental validation

BACKGROUND

Rheumatoid arthritis (RA) is a prevalent autoimmune disease marked by chronic synovitis as well as cartilage and bone destruction. Halofuginone hydrobromide (HF), a bioactive compound derived from the Chinese herbal plant Dichroa febrifuga Lour., has demonstrated substantial anti-arthritic effects in RA. Nevertheless, the molecular mechanisms responsible for the anti-RA effects of HF remain unclear.

METHODS

This study employed a combination of network pharmacology, molecular docking, and experimental validation to investigate potential targets of HF in RA.

RESULTS

Network pharmacology analyses identified 109 differentially expressed genes (DEGs) resulting from HF treatment in RA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses unveiled a robust association between these DEGs and the IL-17 signaling pathway. Subsequently, a protein-protein interaction (PPI) network analysis revealed 10 core DEGs, that is, EGFR, MMP9, TLR4, ESR1, MMP2, PPARG, MAPK1, JAK2, STAT1, and MAPK8. Among them, MMP9 displayed the greatest binding energy for HF. In an in vitro assay, HF significantly inhibited the activity of inflammatory macrophages, and regulated the IL-17 signaling pathway by decreasing the levels of IL-17 C, p-NF-κB, and MMP9.

CONCLUSION

In summary, these findings suggest that HF has the potential to inhibit the activation of inflammatory macrophages through its regulation of the IL-17 signaling pathway, underscoring its potential in the suppression of immune-mediated inflammation in RA.

Protective Effects of Red Ginseng Against Tacrine-Induced Hepatotoxicity: An Integrated Approach with Network Pharmacology and Experimental Validation

Introduction

Tacrine, an FDA-approved acetylcholinesterase inhibitor, has shown efficacy in treating Alzheimer's disease, but its clinical use is limited by hepatotoxicity. This study investigates the protective effects of red ginseng against tacrine-induced hepatotoxicity, focusing on oxidative stress.

Methods

A network depicting the interaction between compounds and targets was constructed for RG. Effect of RG was determined by MTT and FACS analysis with cells stained by rhodamine 123. Proteins were extracted and subjected to immunoblotting for apoptosis-related proteins.

Results

The outcomes of the network analysis revealed a significant association, with 20 out of 82 identified primary RG targets aligning with those involved in oxidative liver damage including notable interactions within the AMPK pathway. in vitro experiments showed that RG, particularly at 1000μg/mL, mitigated tacrine-induced apoptosis and mitochondrial damage, while activating the LKB1-mediated AMPK pathway and Hippo-Yap signaling. In mice, RG also protected the liver injury induced by tacrine, as similar protective effects to silymarin, a well-known drug for liver toxicity protection.

Discussion

Our study reveals the potential of RG in mitigating tacrine-induced hepatotoxicity, suggesting the administration of natural products like RG to reduce toxicity in Alzheimer's disease treatment.

Danggui Jixueteng decoction for the treatment of myelosuppression after chemotherapy: A combined metabolomics and network pharmacology analysis

Objective

This study aimed to explore the mechanism of the Danggui Jixueteng decoction (DJD) in treating Myelosuppression after chemotherapy (MAC) through network pharmacology and metabolomics.

Methods

We obtained the chemical structures of DJD compounds from TCMSP and PubMed. SwissTargetPrediction, STITCH, CTD, GeneCards, and OMIM were utilized to acquire component targets and MAC-related targets. We identified the key compounds, core targets, main biological processes, and signaling pathways related to DJD by constructing and analyzing related networks. The main active compounds and key proteins of DJD in treating AA were confirmed by molecular docking. A MAC rat model was established through intraperitoneal injection of cyclophosphamide to confirm DJD's effect on the bone marrow hematopoietic system. Untargeted metabolomics analyzed serum metabolite differences between MAC rats and the control group, and before and after DJD treatment, to explore DJD's mechanism in treating MAC.

Results

Of the 93 active compounds identified under screening conditions, 275 compound targets and 3113 MAC-related targets were obtained, including 95 intersecting targets; AKT1, STAT3, CASP3, and JUN were key proteins in MAC treatment. The phosphatidylinositol-3-kinase/RAC-alpha serine/threonine-protein kinase (PI3K/AKT) signaling pathway may play a crucial role in MAC treatment with DJD. Molecular docking results showed good docking effects of key protein AKT1 with luteolin, β-sitosterol, kaempferol, and glycyrrhizal chalcone A. In vivo experiments indicated that, compared to the model group, in the DJD group, levels of WBCs, RBCs, HGB, and PLTs in peripheral blood cells, thymus index increased, spleen index decreased, serum IL-3, GM-CSF levels increased, and IL-6, TNF-α, and VEGF levels decreased (p < 0.01); the pathological morphology of femoral bone marrow improved. Eleven differential metabolites were identified as differential serum metabolites, mainly concentrated in phenylalanine, tyrosine, and tryptophan biosynthesis pathways, phenylalanine metabolism, and arachidonic acid metabolism.

Conclusion

This study revealed that DJD's therapeutic effects are due to multiple ingredients, targets, and pathways. DJD may activate the PI3K/AKT signaling pathway, promote hematopoietic-related cytokine production, regulate related metabolic pathways, and effectively alleviate cyclophosphamide-induced myelosuppression after chemotherapy in rats.

Longitudinal plasma proteomic analysis identifies biomarkers and combinational targets for anti-PD1-resistant cancer patients

The response rate of anti-PD1 therapy is limited, and the influence of anti-PD1 therapy on cancer patients is unclear. To address these challenges, we conducted a longitudinal analysis of plasma proteomic changes with anti-PD1 therapy in non-small cell lung cancer (NSCLC), alveolar soft part sarcoma (ASPS), and lymphoma patients. We included 339 plasma samples before and after anti-PD1 therapy from 193 patients with NSCLC, ASPS, or lymphoma. The plasma proteins were detected using data-independent acquisition-mass spectrometry and customable antibody microarrays. Differential proteomic characteristics in responders (R) and non-responders (NR) before and after anti-PD1 therapy were elucidated. A total of 1019 proteins were detected using our in-depth proteomics platform and distributed across 10-12 orders of abundance. By comparing the differential plasma proteome expression between R and NR groups, 50, 206, and 268 proteins were identified in NSCLC, ASPS, and lymphoma patients, respectively. Th17, IL-17, and JAK-STAT signal pathways were identified upregulated in NR group, while cellular senescence and transcriptional misregulation pathways were activated in R group. Longitudinal proteomics analysis revealed the IL-17 signaling pathway was downregulated after treatment. Consistently, many proteins were identified as potential combinatorial therapeutic targets (e.g., IL-17A and CD22). Five noninvasive biomarkers (FLT4, SFTPB, GNPTG, F5, and IL-17A) were further validated in an independent lymphoma cohort (n = 39), and another three noninvasive biomarkers (KIT, CCL3, and TNFSF1) were validated in NSCLC cohort (n = 76). Our results provide molecular insights into the anti-PD1 therapy in cancer patients and identify new therapeutic strategies for anti-PD1-resistant patients.

Profiling Kinase Activities for Precision Oncology in Diffuse Gastric Cancer

Gastric cancer (GC) remains a leading cause of cancer-related mortality globally. This is due to the fact that majority of the cases of GC are diagnosed at an advanced stage when the treatment options are limited and prognosis is poor. The diffuse subtype of gastric cancer (DGC) under Lauren's classification is more aggressive and usually occurs in younger patients than the intestinal subtype. The concept of personalized medicine is leading to the identification of multiple biomarkers in a large variety of cancers using different combinations of omics technologies. Proteomic changes including post-translational modifications are crucial in oncogenesis. We analyzed the phosphoproteome of DGC by using paired fresh frozen tumor and adjacent normal tissue from five patients diagnosed with DGC. We found proteins involved in the epithelial-to-mesenchymal transition (EMT), c-MYC pathway, and semaphorin pathways to be differentially phosphorylated in DGC tissues. We identified three kinases, namely, bromodomain adjacent to the zinc finger domain 1B (BAZ1B), WNK lysine-deficient protein kinase 1 (WNK1), and myosin light-chain kinase (MLCK) to be hyperphosphorylated, and one kinase, AP2-associated protein kinase 1 (AAK1), to be hypophosphorylated. LMNA hyperphosphorylation at serine 392 (S392) was demonstrated in DGC using immunohistochemistry. Importantly, we have detected heparin-binding growth factor (HDGF), heat shock protein 90 (HSP90), and FTH1 as potential therapeutic targets in DGC, as drugs targeting these proteins are currently under investigation in clinical trials. Although these new findings need to be replicated in larger study samples, they advance our understanding of signaling alterations in DGC, which could lead to potentially novel actionable targets in GC.

The effect of Astragali Radix-Radix Angelica Sinensis on acute kidney injury: a network pharmacology and molecular docking study

Background

Acute kidney injury (AKI) is a devastating clinical syndrome with high mortality rate attributed to lack of effective treatment. The herbal pair of Astragali Radix (AR) and Radix Angelica Sinensis (RAS) is a commonly prescribed herbal formula or is added to other traditional Chinese medicine (TCM) prescriptions for the treatment of kidney diseases. AR-RAS has certain protective effects on AKI in experiments, but the relevant mechanisms have yet to be clear. So this study aims to explore the mechanism of action of AR-RAS in AKI by combining network pharmacology and molecular docking methods.

Methods

In Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), the major AR-RAS chemical components and associated action targets were found and screened. The DrugBank and GeneCards databases were used to find AKI-related targets. The targets that are in close relationship with AKI were obtained from Therapeutic Target database (TTD), Online Mendelian Inheritance in Man (OMIM), and PharmGKB databases. The "herb-active ingredient-target" network was drawn by Cytoscape 3.8.0 software. The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database was used to build the protein-protein interaction network. Bioconductor/R was used to examine Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. AR-RAS components and critical targets were docked using the AutoDock Vina program.

Results

A compound-target network, built by screening and analyzing the results, allowed to identify 19 active components and 101 possible therapeutic targets for AKI. The main ingredients were quercetin, kaempferol, 7-o-methylisocronulatol, formononetin and isorhamnetin. The key targets included AKT serine/threonine kinase 1 (AKT1), nuclear receptor coactivator 1 (NCOA1), JUN, estrogen receptor alpha (ESR1) and mitogen-activated protein kinase 8 (MAPK8). These molecules are targeted by pathways such as the calcium signaling route, the tumor necrosis factor (TNF) signaling pathway and the interleukin-17 (IL-17) signaling pathway, as well as the development of T helper 17 cells. Molecular docking demonstrated that AR-active RAS components exhibited strong binding activities to probable targets of AKI.

Conclusions

We described here the potential active ingredients, possible targets responsible for the efficacy of AR-RAS in AKI treatment, providing a theoretical basis for further research.

Identification of potential crucial genes and therapeutic targets for epilepsy

BACKGROUND

Epilepsy, a central neurological disorder, has a complex genetic architecture. There is some evidence suggesting that genetic factors play a role in both the occurrence of epilepsy and its treatment. However, the genetic determinants of epilepsy are largely unknown. This study aimed to identify potential therapeutic targets for epilepsy.

METHODS

Differentially expressed genes (DEGs) were extracted from the expression profiles of GSE44031 and GSE1834. Gene co-expression analysis was used to confirm the regulatory relationship between newly discovered epilepsy candidate genes and known epilepsy genes. Expression quantitative trait loci analysis was conducted to determine if epilepsy risk single-nucleotide polymorphisms regulate DEGs' expression in human brain tissue. Finally, protein-protein interaction analysis and drug-gene interaction analysis were performed to assess the role of DEGs in epilepsy treatment.

RESULTS

The study found that the protein tyrosine phosphatase receptor-type O gene (PTPRO) and the growth arrest and DNA damage inducible alpha gene (GADD45A) were significantly upregulated in epileptic rats compared to controls in both datasets. Gene co-expression analysis revealed that PTPRO was co-expressed with RBP4, NDN, PAK3, FOXG1, IDS, and IDS, and GADD45A was co-expressed with LRRK2 in human brain tissue. Expression quantitative trait loci analysis suggested that epilepsy risk single-nucleotide polymorphisms could be responsible for the altered PTPRO and GADD45A expression in human brain tissue. Moreover, the protein encoded by GADD45A had a direct interaction with approved antiepileptic drug targets, and GADD45A interacts with genistein and cisplatin.

CONCLUSIONS

The results of this study highlight PTPRO and GADD45A as potential genes for the diagnosis and treatment of epilepsy.

An integrated RNA-Seq and network pharmacology approach for exploring the preventive effect of Corydalis bungeana Turcz. Extract and Acetylcorynoline on LPS-induced acute lung injury

ETHNOPHARMACOLOGICAL RELEVANCE

Corydalis bungeana Turcz. (KDD) is a Chinese herbal medicine with anti-inflammatory, lung cleansing, detoxification and other functions. Clinically, it is commonly used to treat respiratory infections. This study uses ALI as the research model, which is consistent with the clinical use of KDD. Acetylcorynoline (AC) is the main alkaloid component of the KDD extracts, and network pharmacology studies suggest that it may be the main active ingredient in the prevention of ALI.

AIM OF THE STUDY

The aim of this study is to explore the underlying mechanisms and to study the efficacy material basis of KDD in anti-ALI effect by LPS-induced mice and using a combination of RNA sequencing (RNA-Seq) technology and network pharmacology.

MATERIALS AND METHODS

Establish a mouse model of ALI by intraperitoneal injection of LPS (5 mg/kg). The main active ingredients of KDD were identified and analyzed by high performance liquid chromatography with quadrupole time-of-flight mass spectrometry (HPLC-QTOF-MS) and network pharmacology. IL-18, IL-1β, and IL-6 levels in serum and bronchoalveolar lavage fluid (BALF), lung histopathological changes, and lung myeloperoxidase (MPO) activity were assessed. We investigated the possible molecular mechanisms of KDD and AC in an LPS-induced mouse ALI models with RNA-Seq technology. In addition, the anti-inflammatory effect of AC was verified in vitro by establishing an LPS-stimulated RAW264.7 inflammation model. Molecular docking further validated AC as the efficacy material basis of KDD in anti-ALI.

RESULTS

Based on HPLC-QTOF-MS technology and network pharmacology, KDD is more strongly associated with lung tissue, and that AC may be the main active ingredient of KDD. Subsequently, in vivo experiments results showed that KDD and AC reduced the levels of pro-inflammatory cytokines in serum and BALF, reduced MPO levels and reduced inflammatory damage in the lungs. To elucidate its underlying mechanism, based on RNA-Seq analysis techniques performed in lung tissue, enrichment analysis showed that KDD and AC intervened through the NLR signaling pathway, thereby mitigating LPS-induced ALI. Then, RT-qPCR, IF, WB and other technologies were used to verify the anti-ALI core difference genes of KDD and AC from the gene transcription and protein expression levels of the NLR signaling pathway, and confirmed the anti-ALI. In vitro experimental results also showed that AC has anti-inflammatory effects in RAW264.7. Finally, the biotransformation and molecular docking results also further indicated that AC is the active ingredient of KDD in anti-ALI.

CONCLUSIONS

Studies have shown that KDD has a good therapeutic effect on ALI, and AC is the main pharmacodynamic material basis for its therapeutic effect in ALI.

Protective effect of Tibetan medicine Qiwei Tiexie pills on liver injury induced by acetaminophen overdose: An integrated strategy of network pharmacology, metabolomics and transcriptomics

BACKGROUND

Drug-induced liver injury, particularly from acetaminophen (APAP), has emerged as a significant public health concern. Unfortunately, there is currently no effective treatment strategy available. Qiwei Tiexie pills (QWTX), a traditional Tibetan medicine, have demonstrated considerable clinical efficacy in treating various liver diseases. Nevertheless, the protective effect of QWTX against drug-induced liver injury and its underlying mechanism remains poorly understood.

PURPOSE

This study aimed to assess the therapeutic potential of QWTX, a Tibetan medicine, in an animal model of APAP-induced liver injury. Additionally, we sought to investigate the molecular mechanism through which QWTX exerts its effects.

METHODS

We employed LC-MS and network pharmacology to predict the potential targets of QWTX in drug-induced liver injury. Subsequently, we employed HE staining, transcriptomics, metabolomics, and qRT-PCR to analyze the mechanism underlying QWTX treatment in drug-induced liver injury.

RESULTS

Network pharmacology analysis revealed that the active components of QWTX are involved in inflammatory and drug metabolism-related pathways. In mouse models, pretreatment with QWTX effectively mitigated the elevated levels of serum aspartate aminotransferase (AST), alanine aminotransferase (ALT), and inflammatory factors (IL-1β, IL-6, and TNF-α) induced by APAP overdose. Moreover, APAP inhibited 1459 differentially expressed genes (DEGs) and 874 differential accumulation metabolites (DAMs), while QWTX promoted their expression. Conversely, APAP promoted 874 genes and 119 metabolites, which were inhibited by QWTX. Further analysis demonstrated that QWTX ameliorated the metabolic disorders induced by APAP overdose and potentially exerted a protective effect by inhibiting the expression of critical genes in crucial inflammatory pathways. QWTX also up-regulated antioxidant enzymes, thereby mitigating the oxidative stress resulting from APAP overdose.

CONCLUSION

QWTX treatment effectively protects against APAP-induced liver damage in mice. Transcriptomic and metabolomic analyses further revealed that QWTX ameliorated hepatic metabolic disorders induced by APAP overdose while significantly suppressing the inflammatory response and oxidative stress associated with drug-induced liver injury. This study provides a new insight into the treatment of drug-induced liver injury by the TCM system and provides a basis for the development of new therapies for drug-induced liver injury by QWTX and its active ingredients.

Evaluation of the Mechanism of Yishan Formula in Treating Breast Cancer Based on Network Pharmacology and Experimental Verification

BACKGROUND

Yishan formula (YSF) has a significant effect on the treatment of breast cancer, which can improve the quality of life and prolong the survival of patients with breast cancer; however, its mechanism of action is unknown.

OBJECTIVE

In this study, network pharmacology and molecular docking methods have been used to explore the potential pharmacological effects of the YSF, and the predicted targets have been validated by in vitro experiments.

METHODS

Active components and targets of the YSF were obtained from the TCMSP and Swiss target prediction website. Four databases, namely GeneCards, OMIM, TTD, and DisGeNET, were used to search for disease targets. The Cytoscape v3.9.0 software was utilized to draw the network of drug-component-target and selected core targets. DAVID database was used to analyze the biological functions and pathways of key targets. Finally, molecular docking and in vitro experiments have been used to verify the hub genes.

RESULTS

Through data collection from the database, 157 active components and 618 genes implicated in breast cancer were obtained and treated using the YSF. After screening, the main active components (kaempferol, quercetin, isorhamnetin, dinatin, luteolin, and tamarixetin) and key genes (AKT1, TP53, TNF, IL6, EGFR, SRC, VEGFA, STAT3, MAPK3, and JUN) were obtained. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that the YSF could affect the progression of breast cancer by regulating biological processes, such as signal transduction, cell proliferation and apoptosis, protein phosphorylation, as well as PI3K-Akt, Rap1, MAPK, FOXO, HIF-1, and other related signaling pathways. Molecular docking suggested that IL6 with isorhamnetin, MAPK3 with kaempferol, and EGFR with luteolin have strong binding energy. The experiment further verified that YSF can control the development of breast cancer by inhibiting the expression of the hub genes.

CONCLUSION

This study showed that resistance to breast cancer may be achieved by the synergy of multiple active components, target genes, and signal pathways, which can provide new avenues for breast cancer-targeted therapy.

The Potential Mechanism of Liujunzi Decoction in the Treatment of Breast Cancer based on Network Pharmacology and Molecular Docking Technology

BACKGROUND

Liujunzi Decoction (LJZD) is a potential clinical treatment for Breast Cancer (BC), but the active ingredients and mechanisms underlying its effectiveness remain unclear.

OBJECTIVE

The study aimed to investigate the target gene of LJZD compatibility and the possible mechanism of action in the treatment of breast cancer by using network pharmacology and molecular docking.

METHODS

Based on TCMSP, ETCM, and BATMAN database searching and screening to obtain the ingredients of LJZD, the related targets were obtained. Breast cancer-related targets were collected through GEO, Geencards, OMIM, and other databases, and drug-disease Venn diagrams were drawn by R. The PPI network map was constructed by using Cytoscape. The intersecting targets were imported into the STRING database, and the core targets were analyzed and screened. The intersected targets were analyzed by the DAVID database for GO and KEGG enrichment. AutoDock Vina and Gromacs were used for molecular docking and simulation of the core targets and active ingredients.

RESULTS

126 active ingredients of LJZD were obtained; 241 targets related to breast cancer were sought after screening, and 180 intersection targets were identified through Venn diagram analysis. The core targets were FOS and ESR1. KEGG enrichment analysis mainly involved PI3K/Akt, MAPK, and other signaling pathways.

CONCLUSION

This study has explored the possible targets and signaling pathways of LJZD in treating breast cancer through network pharmacology and bioinformatics analysis. Molecular docking and simulation have further validated the potential mechanism of action of LJZD in breast cancer treatment, providing essential experimental data for future studies.

Strychni Semen Combined with Atractylodes Macrocephala Koidz Attenuates Rheumatoid Arthritis by Regulating Apoptosis

BACKGROUND

Rheumatoid Arthritis (RA) is a chronic autoimmune disease that can lead to joint pain and disability, and seriously impact patients' quality of life. Strychni Semen combined with Atractylodes Macrocephala koidz (SA) have pronounced curative effect on RA, and there is no poisoning of Strychni Semen (SS). However, its pharmacological mechanisms are still unclear.

OBJECTIVE

In this study, we aimed to investigate the pharmacological mechanisms of Strychni Semen combined with Atractylodes Macrocephala Koidz (SA) for the treatment of RA.

METHODS

We used network pharmacology to screen the active components of SA and predict the targets and pathways involved. Results originating from network pharmacology were then verified by animal experiments.

RESULTS

Network pharmacology identified 81 active ingredients and 141 targets of SA; 2640 disease- related genes were also identified. The core targets of SA for the treatment of RA included ALB, IL-6, TNF and IL-1β. A total of 354 gene ontology terms were identified by Gene ontology (GO) enrichment analysis. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis results showed that SA was closely associated with TNF signaling pathways in the treatment of RA. Furthermore, according to the predicted results of network pharmacology, we established a rat model of Adjuvant Arthritis (AA) for in vivo experiments. Analysis showed that each treatment group led to an improvement in paw swelling, immune organ coefficient and synovial tissue morphology in AA rats to different degrees, inhibit the expression levels of IL-1β, TNF-α and IL-6, upregulated the levels of Fas, Bax and Caspase 3, down-regulated the expression levels of Fas-L, Bcl-2 and p53.

CONCLUSION

SA has an anti-RA effect, the mechanism underlying the therapeutic action of SA in AA rats was related to the regulation of apoptosis signaling pathways.

Drug repurposing based on differentially expressed genes suggests drug combinations with possible synergistic effects in treatment of lung adenocarcinoma

Lung adenocarcinoma is one of the leading causes of cancer-related mortality globally. Various treatment approaches and drugs had little influence on overall survival; thus, new drugs and treatment strategies are needed. Drug repositioning (repurposing) seems a favorable approach considering that developing new drugs needs much more time and costs. We performed a meta-analysis on 6 microarray datasets to obtain the main genes with significantly altered expression in lung adenocarcinoma. Following that, we found major gene clusters and hub genes. We assessed their enrichment in biological pathways to get insight into the underlying biological process involved in lung adenocarcinoma pathogenesis. The L1000 database was explored for drug perturbations that might reverse the expression of differentially expressed genes in lung adenocarcinoma. We evaluated the potential drug combinations that interact the most with hub genes and hence have the most potential to reverse the disease process. A total of 2148 differentially expressed genes were identified. Six main gene clusters and 27 significant hub genes mainly involved in cell cycle regulation have been identified. By assessing the interaction between 3 drugs and hub genes and information gained from previous clinical investigations, we suggested the three possible repurposed drug combinations, Vorinostat - Dorsomorphin, PP-110 - Dorsomorphin, and Puromycin - Vorinostat with a high chance of success in clinical trials.

Integrated bioinformatics and network pharmacology to explore the therapeutic target and molecular mechanisms of Bailing capsule on polycystic ovary syndrome

BACKGROUND

Polycystic ovary syndrome (PCOS) is a complex endocrine and metabolic disorder that is common in women of reproductive age. The clinical features of PCOS include hyperandrogenemia and polycystic ovarian changes. Bailing capsule (BL), a proprietary Chinese medicine that contains fermented Cordyceps sinensis powder, has been applied to treat PCOS. However, the specific active ingredients of BL and its mechanisms of action are yet to be elucidated.

METHODS

Initially, the effectiveness of BL on PCOS model mice was evaluated. Subsequently, the active ingredients of BL were searched in the TCMSP and TCM Systems Pharmacology databases, and their targets were predicted using Swiss Target Prediction and SEA databases. Furthermore, the GEO gene database was used to screen for differentially expressed genes (DEGs) related to PCOS. Data from Gene Card, OMIM, DDT, and Drugbank databases were then combined to establish a PCOS disease gene library. Cross targets were imported into the STRING database to construct a protein-protein interaction network. In addition, GO and KEGG pathway enrichment analyses were performed using Metascape and DAVID databases and visualized using Cytoscape software and R 4.2.3. The core targets were docked with SYBYL-X software, and their expressions in PCOS mice were further verified using qPCR.

RESULTS

The core active ingredients of BL were identified to be linoleyl acetate, cholesteryl palmitate, arachidonic acid, among others. Microarray data sets from four groups containing disease and normal samples were obtained from the GEO database. A total of 491 DEGs and 106 drug-disease cross genes were selected. Estrous cycle and ovarian lesions were found to be improved in PCOS model mice following BL treatment. While the levels of testosterone, progesterone, and prolactin decreased, that of estradiol increased. qPCR findings indicated that the expressions of JAK2, PPARG, PI3K, and AKT1 were upregulated, whereas those of ESR1 and IRS1 were downregulated in PCOS model mice. After the administration of BL, the expressions of associated genes were regulated. This study demonstrated that BL exerted anti-PCOS effects via PIK3CA, ESR1, AKT, PPARG, and IRS1 targets affecting PI3K-Akt signaling pathways.

DISCUSSION

This research clarified the multicomponent, multitarget, and multichannel action of BL and provided a theoretical reference for further investigations on its pharmacological basis and molecular mechanisms against PCOS.

Recent advances in anti-inflammatory active components and action mechanisms of natural medicines

Inflammation is a series of reactions caused by the body's resistance to external biological stimuli. Inflammation affects the occurrence and development of many diseases. Anti-inflammatory drugs have been used widely to treat inflammatory diseases, but long-term use can cause toxic side-effects and affect human functions. As immunomodulators with long-term conditioning effects and no drug residues, natural products are being investigated increasingly for the treatment of inflammatory diseases. In this review, we focus on the inflammatory process and cellular mechanisms in the development of diseases such as inflammatory bowel disease, atherosclerosis, and coronavirus disease-2019. Also, we focus on three signaling pathways (Nuclear factor-kappa B, p38 mitogen-activated protein kinase, Janus kinase/signal transducer and activator of transcription-3) to explain the anti-inflammatory effect of natural products. In addition, we also classified common natural products based on secondary metabolites and explained the association between current bidirectional prediction progress of natural product targets and inflammatory diseases.

Mechanisms of action underlying Shentong Zhuyu decoction based treatment of rheumatoid arthritis using systems biology and computer-aided drug design

Rheumatoid arthritis is an autoimmune disease characterized by chronic polyarticular pain, for which no cure currently exists. In Chinese medicine, rheumatoid arthritis (RA) is believed to be caused by phlegm and blood stagnation. Shentong Zhuyu decoction can be used to treat RA, as it promotes blood circulation, resolves blood stasis, and relieves pain. In our study, we used network pharmacology and computer-aided drug design to evaluate the components, active compounds, and targets of Shentong Zhuyu decoction (STZY). Our results suggest that STZY contains active compounds such as quercetin, luteolin, and formononetin that regulate immune network targets. RA associated genes are enriched in pathways including those associated with nuclear factor kappa B, phosphatidylinositol-3-kinase/AKT, and hypoxia inducible factor 1 signaling. The main active compounds in STZY (quercetin and luteolin) were derived from Achyranthis Bidentatae Radix, Carthami Flos, licorice, Cyperi Rhizoma, and Myrrha and targeted the pro-inflammatory cytokines interleukin 2, interleukin 1 alpha, interleukin 1 beta, and interleukin 6. In addition, the compounds quercetin, luteolin, and formononetin in these herbs can target the anti-inflammatory cytokines interleukin 4 and interleukin 10. Our results suggest that STZY can balance the immune network, promote an anti-inflammatory environment, and reduce the clinical symptoms of RA. Based on the close relationship between inflammatory response and osteoclast formation, we hypothesized that STZY may inhibit inflammation and alleviate bone destruction in RA. Our findings indicate that STZY can treat RA through multiple components, targets, and pathways. This study may provide a reference for the clinical application of STZY in RA treatment.

Network pharmacology identifies the inhibitory effect of Yiqiyangyinquyu prescription on salivary gland inflammation in Sjögren's syndrome

This study aimed to explore the mode of action of Yiqiyangyinquyu prescription (YP) against Sjögren's syndrome (SS) by combining network pharmacology with molecular docking techniques. YP's active components and target proteins were identified using the BATMAN-traditional Chinese medicine database. Concurrently, targets associated with SS were extracted from databases, including Genecards, Online Mendelian Inheritance in Man, and Therapeutic Target Database. The standard targets were then imported into the STRING database to construct a protein-protein interaction network. We then conducted gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses, which were succeeded by molecular docking studies to validate core active components and key targets. Finally, in vitro experiments and molecular dynamics simulation were conducted to substantiate the therapeutic efficacy of YP in treating SS. A total of 206 intersection targets and 46 active compounds were identified. Gene ontology analysis unveiled that YP targets were primarily enriched in cellular responses to chemical stress, inflammation, and cell proliferation. Key enriched signaling pathways encompassed the interleukin 17, hypoxia-inducible factor-1, tumor necrosis factor (TNF-α), and advanced glycation end products-receptor for AGEs (AGE-RAGE) signaling pathways. Molecular docking results demonstrated high-affinity between neotanshinone C, tanshiquinone B, miltionone I, TNF-α, interleukin 1 beta (IL-1β), and interleukin 6 (IL-6). Noteworthy, TNF-α, considered the most important gene in YP against SS, binds to YP most stably, which was further validated by molecular dynamics simulation. In vitro experiments confirmed YP's capacity to reduce TNF-α, IL-1β, and IL-6 expression, effectively alleviating SS-related inflammation. YP demonstrated a significant anti-inflammatory effect by suppressing inflammatory cytokines (TNF-α, IL-6, and IL-1β), providing experimental evidence for its clinical application in treating SS.

Compound musk injection in the treatment of ischemic stroke: A network analysis of the mechanism of action

BACKGROUND

Ischemic stroke (IS) is affected by a wide range of factors and has certain treatment limitations. Studies have reported that compound musk injection (CMI) is effective in the treatment of IS, however, its mechanism of action is still unclear.

METHODS

The main active ingredients in CMI were retrieved from HERB, TCMSP and BATMAN databases, and the relevant targets were predicted by Swiss Target Prediction platform. MalaCards, OMIM, DrugBank, DisGeNET, Genecards and TTD databases were used to obtain the genes related to IS. The intersection of drugs and disease targets was used to construct protein-protein interaction networks, and gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed. AutoDock Vina software was used for molecular docking, and cell experiments were conducted to verify the results. Reverse transcription-polymerase chain reaction (RT-PCR) was used to detect the expression level of relative mRNA in cells.

RESULTS

Network analysis and molecular docking results showed that the key targets of CMI in the treatment of IS were SRC, TP53, PIK3R1, MAPK3, PIK3CA, MAPK1, etc. KEGG pathway enrichment analysis mainly involved PI3K/Akt signaling pathway, Rap1 signaling pathway and MAPK signaling pathway. The molecular docking results all showed that the key ingredients were strong binding activity with the key targets. The quantitative RT-PCR results indicated that CMI may increase the expression of PIK3CA, MAPK3 mRNA and decrease the expression of SRC mRNA.

CONCLUSIONS

CMI can treat IS by regulating pathways and targets related to inflammatory response and apoptosis in a multi-component manner.

New cytotoxic dammarane type saponins from Ziziphus spina-christi

Cancer is the world's second-leading cause of death. Drug development efforts frequently focus on medicinal plants since they are a valuable source of anticancer medications. A phytochemical investigation of the edible Ziziphus spina-christi (F. Rhamnaceae) leaf extract afforded two new dammarane type saponins identified as christinin E and F (1, 2), along with the known compound christinin A (3). Different cancer cell lines, such as lung cancer (A549), glioblastoma (U87), breast cancer (MDA-MB-231), and colorectal carcinoma (CT-26) cell lines, were used to investigate the extracted compounds' cytotoxic properties. Our findings showed significant effects on all the tested cell lines at varying concentrations (1, 5, 10, and 20 µg/mL). The three compounds exhibited potent activity at low concentrations (< 10 μg/mL), as evidenced by their low IC50 values. To further investigate the complex relationships between these identified cancer-relevant biological targets and to identify critical targets in the pathogenesis of the disease, we turned to network pharmacology and in silico-based investigations. Following this, in silico-based analysis (e.g., inverse docking, ΔG calculation, and molecular dynamics simulation) was performed on the structures of the isolated compounds to identify additional potential targets for these compounds and their likely interactions with various signalling pathways relevant to this disease. Based on our findings, Z. spina-christi's compounds showed promise as potential anti-cancer therapeutic leads in the future.

Therapeutic potential of Lianhua Qingke in airway mucus hypersecretion of acute exacerbation of chronic obstructive pulmonary disease

BACKGROUND

Lianhua Qingke (LHQK) is an effective traditional Chinese medicine used for treating acute tracheobronchitis. In this study, we evaluated the effectiveness of LHQK in managing airway mucus hypersecretion in the acute exacerbation of chronic obstructive pulmonary disease (AECOPD).

METHODS

The AECOPD model was established by subjecting male Wistar rats to 12 weeks of cigarette smoke (CS) exposure (80 cigarettes/day, 5 days/week for 12 weeks) and intratracheal lipopolysaccharide (LPS) exposure (200 μg, on days 1, 14, and 84). The rats were divided into six groups: control (room air exposure), model (CS + LPS exposure), LHQK (LHQK-L, LHQK-M, and LHQK-H), and a positive control group (Ambroxol). H&E staining, and AB-PAS staining were used to evaluate lung tissue pathology, inflammatory responses, and goblet cell hyperplasia. RT-qPCR, immunohistochemistry, immunofluorescence and ELISA were utilized to analyze the transcription, expression and secretion of proteins related to mucus production in vivo and in the human airway epithelial cell line NCI-H292 in vitro. To predict and screen the active ingredients of LHQK, network pharmacology analysis and NF-κB reporter system analysis were employed.

RESULTS

LHQK treatment could ameliorate AECOPD-triggered pulmonary structure damage, inflammatory cell infiltration, and pro-inflammatory cytokine production. AB-PAS and immunofluorescence staining with CCSP and Muc5ac antibodies showed that LHQK reduced goblet cell hyperplasia, probably by inhibiting the transdifferentiation of Club cells into goblet cells. RT-qPCR and immunohistochemistry of Muc5ac and APQ5 showed that LHQK modulated mucus homeostasis by suppressing Muc5ac transcription and hypersecretion in vivo and in vitro, and maintaining the balance between Muc5ac and AQP5 expression. Network pharmacology analysis and NF-κB luciferase reporter system analysis provided insights into the active ingredients of LHQK that may help control airway mucus hypersecretion and regulate inflammation.

CONCLUSION

LHQK demonstrated therapeutic effects in AECOPD by reducing inflammation, suppressing goblet cell hyperplasia, preventing Club cell transdifferentiation, reducing Muc5ac hypersecretion, and modulating airway mucus homeostasis. These findings support the clinical use of LHQK as a potential treatment for AECOPD.

Qili Qiangxin (QLQX) capsule as a multi-functional traditional Chinese medicine in treating chronic heart failure (CHF): A review of ingredients, molecular, cellular, and pharmacological mechanisms

Chronic heart failure (CHF) is a key part of cardiovascular continuum. Under the guidance of the theory of vessel-collateral doctrine, the present study proposes therapeutic benefits of Qili Qiangxin (QLQX) capsules, an innovative Chinese medicine, on chronic heart failure. The studies show that multiple targets of the drug on CHF, including enhancing myocardial systole, promoting urine excretion, inhibiting excessive activation of the neuroendocrine system, preventing ventricular remodeling by inhibiting inflammatory response, myocardial fibrosis, apoptosis and autophagy, enhancing myocardial energy metabolism, promoting angiogenesis, and improving endothelial function. Investigation on the effects and mechanism of the drug is beneficial to the treatment of chronic heart failure (CHF) through multiple targets and/or signaling pathways. Meanwhile, it provides new insights to further understand other refractory diseases in the cardiovascular continuum, and it also has an important theoretical and practical significance in enhancing prevention and therapeutic effect of traditional Chinese medicine for these diseases.

Screening Plasma Proteins for the Putative Drug Targets for Carpal Tunnel Syndrome

Carpal tunnel syndrome (CTS) is one of the most common work-related musculoskeletal disorders. The present study sought to identify putative causal proteins for CTS. We conducted a two-sample Mendelian randomization (MR) analysis to evaluate the causal association between 2859 plasma proteins (N = 35,559) and CTS (N = 1,239,680) based on the published GWAS summary statistics. Then we replicated the significant associations using an independent plasma proteome GWAS (N = 10,708). Sensitivity analyses were conducted to validate the robustness of MR results. Multivariate MR and mediation analyses were conducted to evaluate the mediation effects of body mass index (BMI), type 2 diabetes (T2D), and arm tissue composition on the association between putative causal proteins and CTS. Colocalization analysis was used to examine whether the identified proteins and CTS shared causal variant(s). Finally, we evaluated druggability of the identified proteins. Ten plasma proteins were identified as putative causal markers for CTS, including sCD14, PVR, LTOR3, CTSS, SIGIRR, IFNL3, ASPN, TM11D, ASIP, and ITIH1. Sensitivity analyses and reverse MR analysis validated the robustness of their causal effects. Arm tissue composition, BMI, and T2D may play a fully/partial mediating role in the causal relationships of ASIP, TM11D, IFNL3, PVR, and LTOR3 with CTS. The association of ASPN and sCD14 with CTS were supported by colocalization analysis. Druggability assessment demonstrated that sCD14, CTSS, TM11D, and IFNL3 were potential drug therapeutic targets. The present study identified several potential plasma proteins that were causally associated with CTS risk, providing new insights into the pathogenesis of protein-mediated CTS and offering potential targets for new therapies.

The Important Role of Transporter Structures in Drug Disposition, Efficacy, and Toxicity

The ATP-binding cassette (ABC) and solute carrier (SLC) transporters are critical determinants of drug disposition, clinical efficacy, and toxicity as they specifically mediate the influx and efflux of various substrates and drugs. ABC transporters can modulate the pharmacokinetics of many drugs via mediating the translocation of drugs across biologic membranes. SLC transporters are important drug targets involved in the uptake of a broad range of compounds across the membrane. However, high-resolution experimental structures have been reported for a very limited number of transporters, which limits the study of their physiologic functions. In this review, we collected structural information on ABC and SLC transporters and described the application of computational methods in structure prediction. Taking P-glycoprotein (ABCB1) and serotonin transporter (SLC6A4) as examples, we assessed the pivotal role of structure in transport mechanisms, details of ligand-receptor interactions, drug selectivity, the molecular mechanisms of drug-drug interactions, and differences caused by genetic polymorphisms. The data collected contributes toward safer and more effective pharmacological treatments. SIGNIFICANCE STATEMENT: The experimental structure of ATP-binding cassette and solute carrier transporters was collected, and the application of computational methods in structure prediction was described. P-glycoprotein and serotonin transporter were used as examples to reveal the pivotal role of structure in transport mechanisms, drug selectivity, the molecular mechanisms of drug-drug interactions, and differences caused by genetic polymorphisms.

Exploring the pharmacological mechanism of Wuzhuyu decoction on hepatocellular carcinoma using network pharmacology

BACKGROUND

Wuzhuyu decoction, a traditional Chinese medicinal formula, is effective in treating hepatocellular carcinoma (HCC).

AIM

To explore the potential mechanism of action of Wuzhuyu decoction against HCC.

METHODS

The active components of each Chinese herbal medicinal ingredient in Wuzhuyu decoction and their targets were obtained from the Traditional Chinese Medicine Database and Analysis Platform. HCC was used as a search query in GeneCards, Online Mendelian Inheritance in Man, Malacards, DisGeNET, Therapeutic Target Database, and Comparative Toxicogenomics Database. The overlapping targets of the Wuzhuyu decoction and HCC were defined, and then protein-protein interaction, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed. CytoHubba was used to select hub genes, and their binding activities and key active components were verified using molecular docking.

RESULTS

A total of 764 compounds, 77 active compounds, and 204 potential target genes were identified in Wuzhuyu decoction. For HCC, 9468 potential therapeutic target genes were identified by combining the results from the six databases and removing duplicates. A total of 179 overlapping targets of Wuzhuyu decoction and HCC were defined, including 10 hub genes (tumor necrosis factor, interleukin-6, AKT1, TP53, caspase-3, mitogen-activated protein kinase 1, epidermal growth factor receptor, MYC, mitogen-activated protein kinase 8, and JUN). There were six main active components (quercetin, kaempferol, ginsenoside Rh2, rutaecarpine, β-carotene, and β-sitosterol) that may act on hub genes to treat HCC in Wuzhuyu decoction. Kyoto Encyclopedia of Genes and Genomes enrichment analysis mainly involved the mitogen-activated protein kinase, p53, phosphatidylinositol-4,5-bisphosphate 3-kinase-Akt, Janus kinase-signal transducer of activators of transcription, and Hippo signaling pathways. Further verification based on molecular docking results showed that the small molecule compounds (quercetin, kaempferol, ginsenoside Rh2, rutaecarpine, β-carotene, and β-sitosterol) contained in Wuzhuyu decoction generally have excellent binding affinity to the macromolecular target proteins encoded by the top 10 genes.

CONCLUSION

This study revealed that Wuzhuyu decoction may be a latent multicomponent, multitarget, and multipathway treatment for HCC. It provided novel insights for verifying the mechanism of Wuzhuyu decoction in the treatment of HCC.

MSFC: a new feature construction method for accurate diagnosis of mass spectrometry data

Mass spectrometry technology can realize dynamic detection of many complex matrix samples in a simple, rapid, compassionate, precise, and high-throughput manner and has become an indispensable tool in accurate diagnosis. The mass spectrometry data analysis is mainly to analyze all metabolites in the organism quantitatively and to find the relative relationship between metabolites and physiological and pathological changes. A feature construction of mass spectrometry data (MSFS) method is proposed to construct the features of the original mass spectrometry data, so as to reduce the noise in the mass spectrometry data, reduce the redundancy of the original data and improve the information content of the data. Chi-square test is used to select the optimal non-redundant feature subset from high-dimensional features. And the optimal feature subset is visually analyzed and corresponds to the original mass spectrum interval. Training in 10 kinds of supervised learning models, and evaluating the classification effect of the models through various evaluation indexes. Taking two public mass spectrometry datasets as examples, the feasibility of the method proposed in this paper is verified. In the coronary heart disease dataset, during the identification process of mixed batch samples, the classification accuracy on the test set reached 1.000; During the recognition process, the classification accuracy on the test set advanced to 0.979. On the colorectal liver metastases data set, the classification accuracy on the test set reached 1.000. This paper attempts to use a new raw mass spectrometry data preprocessing method to realize the alignment operation of the raw mass spectrometry data, which significantly improves the classification accuracy and provides another new idea for mass spectrometry data analysis. Compared with MetaboAnalyst software and existing experimental results, the method proposed in this paper has obtained better classification results.

Analyzing the research landscape: Mapping frontiers and hot spots in anti-cancer research using bibliometric analysis and research network pharmacology

Introduction: Network pharmacology has emerged as a forefront and hotspot in anti-cancer. Traditional anti-cancer drugs are limited by the paradigm of "one cancer, one target, one drug," making it difficult to address the challenges of recurrence and drug resistance. However, the main advantage of network pharmacology lies in its approach from the perspective of molecular network relationships, employing a "one arrow, multiple targets" strategy, which provides a novel pathway for developing anti-cancer drugs. This study employed a bibliometric analysis method to examine network pharmacology's application and research progress in cancer treatment from January 2008 to May 2023. This research will contribute to revealing its forefront and hotspots, offering new insights and methodologies for future investigations. Methods: We conducted a literature search on network pharmacology research in anti-cancer (NPART) from January 2008 to May 2023, utilizing scientific databases such as Web of Science Core Collection (WoSCC) and PubMed to retrieve relevant research articles and reviews. Additionally, we employed visualization tools such as Citespace, SCImago Graphica, and VOSviewer to perform bibliometric analysis. Results: This study encompassed 3,018 articles, with 2,210 articles from WoSCC and 808 from PubMed. Firstly, an analysis of the annual national publication trends and citation counts indicated that China and the United States are the primary contributing countries in this field. Secondly, the recent keyword analysis revealed emerging research hotspots in "tumor microenvironment," "anti-cancer drugs," and "traditional Chinese medicine (TCM). " Furthermore, the literature clustering analysis demonstrated that "calycosin," "molecular mechanism," "molecular docking," and "anti-cancer agents" were widely recognized research hotspots and forefront areas in 2023, garnering significant attention and citations in this field. Ultimately, we analyzed the application of NPART and the challenges. Conclusion: This study represents the first comprehensive analysis paper based on bibliometric methods, aiming to investigate the forefront hotspots of network pharmacology in anti-cancer research. The findings of this study will facilitate researchers in swiftly comprehending the current research trends and forefront hotspots in the domain of network pharmacology in cancer research.

Exploring the Mechanisms of Modified Bu-Shen-Yi-Qi Decoction for COPD-Related Osteoporosis Therapy via Transcriptomics and Network Pharmacology Approach

Purpose

To investigate the effectiveness of modified Bu-Shen-Yi-Qi decoction (MBSYQ) in the treatment of osteoporosis associated with chronic obstructive pulmonary disease (COPD) and its underlying mechanisms of action.

Methods

Disease targets, active ingredients and targets were predicted by TTD, CTD, DisGeNET, HERB (BenCaoZuJian as its Chinese name), and multiple-TCM databases; In addition, the screened targets were performed via the online platforms DAVID 6.8 and Metascape for GO and KEGG pathway enrichment analysis; The relationship between the MBSYQ and core targets were verified by molecular docking technique. Then we established a COPD-associated osteoporosis rat model by passive 24-week cigarette exposure. We assessed the efficacy of MBSYQ by lung histopathology assessment and distal femur/the first lumbar vertebra (L1) microstructural assay. In addition, we performed tibial RNA sequencing, which was validated by RT-PCR and Western blot.

Results

Screening revealed that the 350 active compounds of MBSYQ anchored 228 therapeutic targets for COPD-related osteoporosis; KEGG pathway enrichment analysis showed that the key targets mainly regulated MAPK and PI3K/AKT signaling pathways. In vivo studies showed that MBSYQ treatment alleviated pathological alterations in lung tissue, and reversed the bone loss and microstructure damage in the femur/L1 of model rats. The RNA seq indicated that MBSYQ could upregulate genes associated with anti-oxidative stress and aerobic respiration. The GSEA analysis displayed that MAPK and PI3K/AKT pathways were inhibited by CS exposure and activated by MBSYQ.

Conclusion

MBSYQ is effective in the prevention and treatment of COPD-related osteoporosis, partially achieved by improving oxygen metabolism and activating MAPK and PI3K/AKT pathways.

Network pharmacology and molecular docking to explore the potential mechanism of urolithin A in combined allergic rhinitis and asthma syndrome

This research used network pharmacology, molecular docking, in vivo studies, and other techniques to investigate the biological activity and mechanism of action of urolithin A (UA) in treating combined allergic rhinitis and asthma syndrome (CARAS). Urolithin A and potential related targets of allergic rhinitis and asthma were searched from the public databases. Then, bioinformatics analyses were given to protein-protein interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG). Subsequently, molecular docking and molecular dynamic simulation were performed, aiming at predicting the binding of the active compound to the core target. Finally, in vivo experiment was conducted for further validation. A total of 45 common targets of allergic rhinitis and urolithin A and 62 common targets of asthma and urolithin A were identified, among which six common core targets were screened with Cytoscape. Molecular docking indicated that these core targets had good binding activity to urolithin A, which was further confirmed by molecular dynamics simulation. In the CARAS mouse model, urolithin A showed anti-inflammatory properties. The biological activity and regulatory network of UA on CARAS were revealed, and the anti-inflammatory effect of UA was clarified, which could be associated with the equilibrium of the immune system's Th1/Th2 cells.

Network Pharmacology-Based Approach to Investigate the Active Ingredients and Therapeutic Mechanisms of Jingu Tongxiao Pill against Osteoarthritis

This study aimed to investigate the active ingredients and therapeutic mechanisms of Jingu Tongxiao Pill (JGTXP), a commonly used Chinese patent medicine, in treating osteoarthritis (OA) via network pharmacology analysis combined with experimental validation. First, we administered JGTXP to rat plasma and identified the candidate active compounds. Next, target prediction, protein-protein interaction, compound-target network construction, gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted for JGTXP. Lastly, the network-derived key targets and pathways were validated in vitro and in vivo. Finally, we identified 106 compounds in JGTXP and 24 absorbed compounds in the rat plasma. Network analysis revealed that JGTXP interferes with OA mainly via regulating the inflammatory response, collagen catabolic process, and osteoclast differentiation, and the nuclear factor kappa B (NF-κB) signaling pathway plays a pivotal role in these processes. Experimentally, JGTXP exerted potential protective effects on articular cartilage and inhibited expression of inflammatory mediators and collagen catabolism-related proteins, including interleukin 1 beta (IL-1β), interleukin 6, tumor necrosis factor alpha (TNF-α), and matrix metalloproteinase (MMP) 3 and MMP13, in a papain-induced OA rat model. Consistently, mRNA expression levels of these factors and nitric oxide release were suppressed by JGTXP in an LPS-induced RAW 264.7 inflammation model. The reporter gene assay showed that JGTXP could reduce the transcriptional activity of NF-κB. Consecutive western blot analysis demonstrated that nuclear NF-κB p65, inducible nitric oxide synthase (iNOS), and cyclooxygenase 2 (COX-2) expression were inhibited while cytoplasmic NF-κB p65 was upregulated by JGTXP. Using a combination of chemical profiling, network pharmacology analysis, and experimental validation, we preliminarily clarified the active ingredients of JGTXP intervention for OA and demonstrated that JGTXP ameliorates OA, at least partially, by regulating the NF-κB signaling pathway.

Empirical comparison and analysis of machine learning-based approaches for druggable protein identification

Efficiently and precisely identifying drug targets is crucial for developing and discovering potential medications. While conventional experimental approaches can accurately pinpoint these targets, they suffer from time constraints and are not easily adaptable to high-throughput processes. On the other hand, computational approaches, particularly those utilizing machine learning (ML), offer an efficient means to accelerate the prediction of druggable proteins based solely on their primary sequences. Recently, several state-of-the-art computational methods have been developed for predicting and analyzing druggable proteins. These computational methods showed high diversity in terms of benchmark datasets, feature extraction schemes, ML algorithms, evaluation strategies and webserver/software usability. Thus, our objective is to reexamine these computational approaches and conduct a comprehensive assessment of their strengths and weaknesses across multiple aspects. In this study, we deliver the first comprehensive survey regarding the state-of-the-art computational approaches for in silico prediction of druggable proteins. First, we provided information regarding the existing benchmark datasets and the types of ML methods employed. Second, we investigated the effectiveness of these computational methods in druggable protein identification for each benchmark dataset. Third, we summarized the important features used in this field and the existing webserver/software. Finally, we addressed the present constraints of the existing methods and offer valuable guidance to the scientific community in designing and developing novel prediction models. We anticipate that this comprehensive review will provide crucial information for the development of more accurate and efficient druggable protein predictors.

A novel FCTF evaluation and prediction model for food efficacy based on association rule mining

Introduction

Food-components-target-function (FCTF) is an evaluation and prediction model based on association rule mining (ARM) and network interaction analysis, which is an innovative exploration of interdisciplinary integration in the food field.

Methods

Using the components as the basis, the targets and functions are comprehensively explored in various databases and platforms under the guidance of the ARM concept. The focused active components, key targets and preferred efficacy are then analyzed by different interaction calculations. The FCTF model is particularly suitable for preliminary studies of medicinal plants in remote and poor areas.

Results

The FCTF model of the local medicinal food Laoxianghuang focuses on the efficacy of digestive system cancers and neurological diseases, with key targets ACE, PTGS2, CYP2C19 and corresponding active components citronellal, trans-nerolidol, linalool, geraniol, α-terpineol, cadinene and α-pinene.

Discussion

Centuries of traditional experience point to the efficacy of Laoxianghuang in alleviating digestive disorders, and our established FCTF model of Laoxianghuang not only demonstrates this but also extends to its possible adjunctive efficacy in neurological diseases, which deserves later exploration. The FCTF model is based on the main line of components to target and efficacy and optimizes the research level from different dimensions and aspects of interaction analysis, hoping to make some contribution to the future development of the food discipline.

Micro-simulation insights into the functional and mechanistic understanding of glycyrrhizin against asthma

Asthma is a common chronic respiratory disease, which causes inflammation and airway stenosis, leading to dyspnea, wheezing and chest tightness. Using transgelin-2 as a target, we virtually screened the lead compound glycyrrhizin from the self-built database of anti-asthma compounds by molecular docking technology, and found that it had anti-inflammatory, anti-oxidative and anti-asthma pharmacological effects. Then, molecular dynamics simulations were used to confirm the stability of the glycyrrhizin-transgelin-2 complex from a dynamic perspective, and the hydrophilic domains of glycyrrhizin was found to have the effect of targeting transgelin-2. Due to the self-assembly properties of glycyrrhizin, we explored the formation process and mechanism of the self-assembly system using self-assembly simulations, and found that hydrogen bonding and hydrophobic interactions were the main driving forces. Because of the synergistic effect of glycyrrhizin and salbutamol in improving asthma, we revealed the mechanism through simulation, and believed that salbutamol adhered to the surface of the glycyrrhizin nano-drug delivery system through hydrogen bonding and hydrophobic interactions, using the targeting effect of the hydrophilic domains of glycyrrhizin to reach the pathological parts and play a synergistic anti-asthmatic role. Finally, we used network pharmacology to predict the molecular mechanisms of glycyrrhizin against asthma, which indicated the direction for its clinical transformation.

DCABM-TCM: A Database of Constituents Absorbed into the Blood and Metabolites of Traditional Chinese Medicine

Traditional Chinese medicine (TCM) not only maintains the health of Asian people but also provides a great resource of active natural products for modern drug development. Herein, we developed a Database of Constituents Absorbed into the Blood and Metabolites of TCM (DCABM-TCM), the first database systematically collecting blood constituents of TCM prescriptions and herbs, including prototypes and metabolites experimentally detected in the blood, together with the corresponding detailed detection conditions through manual literature mining. The DCABM-TCM has collected 1816 blood constituents with chemical structures of 192 prescriptions and 194 herbs and integrated their related annotations, including physicochemical, absorption, distribution, metabolism, excretion, and toxicity properties, and associated targets, pathways, and diseases. Furthermore, the DCABM-TCM supported two blood constituent-based analysis functions, the network pharmacology analysis for TCM molecular mechanism elucidation, and the target/pathway/disease-based screening of candidate blood constituents, herbs, or prescriptions for TCM-based drug discovery. The DCABM-TCM is freely accessible at http://bionet.ncpsb.org.cn/dcabm-tcm/. The DCABM-TCM will contribute to the elucidation of effective constituents and molecular mechanism of TCMs and the discovery of TCM-derived drug-like compounds that are both bioactive and bioavailable.

Comprehensive survey of target prediction web servers for Traditional Chinese Medicine

Traditional Chinese medicine (TCM) is characterized by multi-components, multiple targets, and complex mechanisms of action and therefore has significant advantages in treating diseases. However, the clinical application of TCM prescriptions is limited due to the difficulty in elucidating the effective substances and the lack of current scientific evidence on the mechanisms of action. In recent years, the development of network pharmacology based on drug systems research has provided a new approach for understanding the complex systems represented by TCM. The determination of drug targets is the core of TCM network pharmacology research. Over the past years, many web tools for drug targets with various features have been developed to facilitate target prediction, significantly promoting drug discovery. Therefore, this review introduces the widely used web tools for compound-target interaction prediction databases and web resources in TCM pharmacology research, and it compares and analyzes each web tool based on their basic properties, including the underlying theory, algorithms, datasets, and search results. Finally, we present the remaining challenges for the promising future of compound-target interaction prediction in TCM pharmacology research. This work may guide researchers in choosing web tools for target prediction and may also help develop more TCM tools based on these existing resources.

Active components and molecular mechanisms of Sagacious Confucius' Pillow Elixir to treat cognitive impairment based on systems pharmacology

BACKGROUND

Sagacious Confucius' Pillow Elixir (SCPE) is a common clinical prescription to treat cognitive impairment (CI) in East Asia.

OBJECTIVE

To predict the active components of SCPE, identify the associated signaling pathway, and explore the molecular mechanism using systems pharmacology and an animal study.

METHODS

Systems pharmacology and Python programming language-based molecular docking were used to select and analyze the active components and targets. Senescence-accelerated prone 8 mice were used as a CI model. The molecular mechanism was evaluated using the water maze test, neuropathological observation, cerebrospinal fluid microdialysis, and Western blotting.

RESULTS

Thirty active components were revealed by screening relevant databases and performing topological analysis. Additionally, 376 differentially expressed genes for CI were identified. Pathway enrichment analysis, protein-protein interaction (PPI) network analysis and molecular docking indicated that SCPE played a crucial role in modulating the PI3K/Akt/mTOR signaling pathway, and 23 SCPE components interacted with it. In the CI model, SCPE improved cognitive function, increased the levels of the neurotransmitter 5-hydroxytryptamine (5-HT) and metabolite 5-hydroxyindole acetic acid (5-HIAA), ameliorated pathological damage and regulated the PI3K/AKT/mTOR signaling pathway. SCPE increased the LC3-II/LC3-I, p-PI3K p85/PI3K p85, p-AKT/AKT, and p-mTOR/mTOR protein expression ratios and inhibited P62 expression in the hippocampal tissue of the CI model.

CONCLUSION

Our study revealed that 23 active SCPE components improve CI by increasing the levels of the neurotransmitter 5-HT and metabolite 5-HIAA, suppressing pathological injury and regulating the PI3K/Akt/mTOR signaling pathway to improve cognitive function.

Repositioning of clinically approved drug Bazi Bushen capsule for treatment of Aizheimer's disease using network pharmacology approach and in vitro experimental validation

Aims

To explore the new indications and key mechanism of Bazi Bushen capsule (BZBS) by network pharmacology and in vitro experiment.

Methods

The ingredients library of BZBS was constructed by retrieving multiple TCM databases. The potential target profiles of the components were predicted by target prediction algorithms based on different principles, and validated by using known activity data. The target spectrum of BZBS with high reliability was screened by considering the source of the targets and the node degree in compound-target (C-T) network. Subsequently, new indications for BZBS were predicted by disease ontology (DO) enrichment analysis and initially validated by GO and KEGG pathway enrichment analysis. Furthermore, the target sets of BZBS acting on AD signaling pathway were identified by intersection analysis. Based on STRING database, the PPI network of target was constructed and their node degree was calculated. Two Alzheimer's disease (AD) cell models, BV-2 and SH-SY5Y, were used to preliminarily verify the anti-AD efficacy and mechanism of BZBS in vitro.

Results

In total, 1499 non-repeated ingredients were obtained from 16 herbs in BZBS formula, and 1320 BZBS targets with high confidence were predicted. Disease enrichment results strongly suggested that BZBS formula has the potential to be used in the treatment of AD. GO and KEGG enrichment results provide a preliminary verification of this point. Among them, 113 functional targets of BZBS belong to AD pathway. A PPI network containing 113 functional targets and 1051 edges for the treatment of AD was constructed. In vitro experiments showed that BZBS could significantly reduce the release of TNF-α and IL-6 and the expression of COX-2 and PSEN1 in Aβ_25-35-induced BV-2 cells, which may be related to the regulation of ERK_1/2/NF-κB signaling pathway. BZBS reduced the apoptosis rate of Aβ_25-35 induced SH-SY5Y cells, significantly increased mitochondrial membrane potential, reduced the expression of Caspase3 active fragment and PSEN1, and increased the expression of IDE. This may be related to the regulation of GSK-3β/β-catenin signaling pathway.

Conclusions

BZBS formula has a potential use in the treatment of AD, which is achieved through regulation of ERK_1/2, NF-κB signaling pathways, and GSK-3β/β-catenin signaling pathway. Furthermore, the network pharmacology technology is a feasible drug repurposing strategy to reposition new clinical use of approved TCM and explore the mechanism of action. The study lays a foundation for the subsequent in-depth study of BZBS in the treatment of AD and provides a basis for its application in the clinical treatment of AD.

Pharmacological effects and mechanisms of YiYiFuZi powder in chronic heart disease revealed by metabolomics and network pharmacology

Introduction: YiYiFuZi powder (YYFZ) is a classical formula in Chinese medicine, which is commonly used clinically for the treatment of Chronic Heart Disease (CHD), but it's pharmacological effects and mechanism of action are currently unclear. Methods: An adriamycin-induced CHD model rat was established to evaluate the pharmacological effects of YYFZ on CHD by the results of inflammatory factor level, histopathology and echocardiography. Metabolomic studies were performed on rat plasma using UPLC-Q-TOF/MS to screen biomarkers and enrich metabolic pathways; network pharmacology analysis was also performed to obtain the potential targets and pathways of YYFZ for the treatment of CHD. Results: The results showed that YYFZ significantly reduced the levels of TNF-α and BNP in the serum of rats, alleviated the disorder of cardiomyocyte arrangement and inflammatory cell infiltration, and improved the cardiac function of rats with CHD. The metabolomic analysis identified a total of 19 metabolites, related to amino acid metabolism, fatty acid metabolism, and other metabolic pathways. Network pharmacology showed that YYFZ acts through PI3K/Akt signaling pathway, MAPK signaling pathway and Ras signaling pathway. Discussion: YYFZ treatment of CHD modulates blood metabolic pattern and several protein phosphorylation cascades but importance specific changes for therapeutic effect require further studies.

GraphCPIs: A novel graph-based computational model for potential compound-protein interactions

Identifying proteins that interact with drug compounds has been recognized as an important part in the process of drug discovery. Despite extensive efforts that have been invested in predicting compound-protein interactions (CPIs), existing traditional methods still face several challenges. The computer-aided methods can identify high-quality CPI candidates instantaneously. In this research, a novel model is named GraphCPIs, proposed to improve the CPI prediction accuracy. First, we establish the adjacent matrix of entities connected to both drugs and proteins from the collected dataset. Then, the feature representation of nodes could be obtained by using the graph convolutional network and Grarep embedding model. Finally, an extreme gradient boosting (XGBoost) classifier is exploited to identify potential CPIs based on the stacked two kinds of features. The results demonstrate that GraphCPIs achieves the best performance, whose average predictive accuracy rate reaches 90.09%, average area under the receiver operating characteristic curve is 0.9572, and the average area under the precision and recall curve is 0.9621. Moreover, comparative experiments reveal that our method surpasses the state-of-the-art approaches in the field of accuracy and other indicators with the same experimental environment. We believe that the GraphCPIs model will provide valuable insight to discover novel candidate drug-related proteins.

Machine learning on large scale perturbation screens for SARS-CoV-2 host factors identifies β-catenin/CBP inhibitor PRI-724 as a potent antiviral

Expanding antiviral treatment options against SARS-CoV-2 remains crucial as the virus evolves under selection pressure which already led to the emergence of several drug resistant strains. Broad spectrum host-directed antivirals (HDA) are promising therapeutic options, however the robust identification of relevant host factors by CRISPR/Cas9 or RNA interference screens remains challenging due to low consistency in the resulting hits. To address this issue, we employed machine learning, based on experimental data from several knockout screens and a drug screen. We trained classifiers using genes essential for virus life cycle obtained from the knockout screens. The machines based their predictions on features describing cellular localization, protein domains, annotated gene sets from Gene Ontology, gene and protein sequences, and experimental data from proteomics, phospho-proteomics, protein interaction and transcriptomic profiles of SARS-CoV-2 infected cells. The models reached a remarkable performance suggesting patterns of intrinsic data consistency. The predicted HDF were enriched in sets of genes particularly encoding development, morphogenesis, and neural processes. Focusing on development and morphogenesis-associated gene sets, we found β-catenin to be central and selected PRI-724, a canonical β-catenin/CBP disruptor, as a potential HDA. PRI-724 limited infection with SARS-CoV-2 variants, SARS-CoV-1, MERS-CoV and IAV in different cell line models. We detected a concentration-dependent reduction in cytopathic effects, viral RNA replication, and infectious virus production in SARS-CoV-2 and SARS-CoV-1-infected cells. Independent of virus infection, PRI-724 treatment caused cell cycle deregulation which substantiates its potential as a broad spectrum antiviral. Our proposed machine learning concept supports focusing and accelerating the discovery of host dependency factors and identification of potential host-directed antivirals.

Tuo-Min-Ding-Chuan Decoction Alleviates Airway Inflammations in the Allergic Asthmatic Mice Model by Regulating TLR4-NLRP3 Pathway-Mediated Pyroptosis: A Network Pharmacology and Experimental Verification Study

Background

Tuo-Min-Ding-Chuan Decoction (TMDCD) is an effective traditional Chinese medicine (TCM) formula granule for allergic asthma (AA). Previous studies proved its effects on controlling airway inflammations, while the specific mechanism was not clear.

Methods

We conducted a network pharmacology study to explore the molecular mechanism of TMDCD against AA with the public databases of TCMSP. Then, HUB genes were screened with the STRING database. DAVID database performed GO annotation and KEGG functional enrichment analysis of HUB genes, and it was verified with molecular docking by Autodock. Then, we built a classic ovalbumin-induced allergic asthma mice model to explore the mechanism of anti-inflammation effects of TMDCD.

Results

In the network pharmacology study, we found out that the potential mechanism of TMDCD against AA might be related to NOD-like receptor (NLR) signaling pathway and Toll-like receptor (TLR) signaling pathway. In the experiment, TMDCD showed remarkable effects on alleviating airway inflammations, airway hyperresponsiveness (AHR), and airway remodeling in the asthmatic mice model. Further molecular biology and immunohistochemistry experiments suggested TMDCD could repress TLR4-NLRP3 pathway-mediated pyroptosis-related gene transcriptions to inhibit expressions of target proteins.

Conclusion

TMDCD could alleviate asthmatic mice model airway inflammations by regulating TLR4-NLRP3 pathway-mediated pyroptosis.

The elucidation of the anti-inflammatory mechanism of EMO in rheumatoid arthritis through an integrative approach combining bioinformatics and experimental verification

Introduction: Emodin (EMO), a natural derivative of the anthraquinone family mainly extracted from rhubarb (Rheum palmatum), has previously been demonstrated to possess superior anti-inflammatory properties from a single target or pathway. In order to explore the underlying mechanism of action of EMO against rheumatoid arthritis (RA), a network pharmacology approach was employed. Methods: A gene expression profile from GSE55457 available from the Gene Expression Omnibus (GEO) database was used to identify the targets of EMO action. Further, single cell RNA sequencing data from GEO database of RA patients (GSE159117) were downloaded and analysed. To further investigate the anti-RA effect of EMO on MH7A cells, the expression of IL-6 and IL-1β were monitored. Finally, RNA-seq analyses were conducted on synovial fibroblasts from EMO-treated. Result: We screened the key targets of EMO against RA using network pharmacology methods, including HMGB1, STAT1, EGR1, NR3C1, EGFR, MAPK14, CASP3, CASP1, IL4, IL13, IKBKB and FN1, and their reliability was verified using ROC curve. Single-cell RNA sequencing data analysis showed that these core target proteins mainly played a role by modulating monocytes. The anti-RA effect of EMO was further verified with MH7A cells, which showed that EMO could block cell differentiation and reduce the expression of IL-6 and IL-1β. WB experiments confirmed that EMO could affect the expression of COX2, HMBG1 and the phosphorylation of p38. Finally, sequencing of synovial fibroblasts from rats treated with EMO showed consistent results with those predicted and verified, further proving the anti-inflammatory effect of EMO. Conclusion: Our research shows that EMO inhibits inflammatory response of rheumatoid arthritis (RA) by targeting HMGB1, STAT1, EGR1, NR3C1, EGFR, MAPK14, CASP3, CASP1, IL4, IL13, IKBKB, FN1 and Monocytes/macrophages.

Association between the SLC6A11 rs2304725 and GABRG2 rs211037 polymorphisms and drug-resistant epilepsy: a meta-analysis

Background: Previous studies have shown that SLC6A11 and GABRG2 are linked to drug-resistant epilepsy (DRE), although there have been conflicting results in the literature. In this study, we systematically assessed the relationship between DRE and these two genes. Methods: We systematically searched the PubMed, Embase, Cochrane Library, Web of Science, Google Scholar, Wanfang Data, CNKI, and VIP databases. To clarify whether heterogeneity existed between studies, tools such as the Q-test and I ² statistic were selected. According to study heterogeneity, we chose fixed- or random-effects models for analysis. We then used the chi-squared ratio to evaluate any bias of the experimental data. Results: In total, 11 trials and 3,813 patients were selected. To investigate the relationship with DRE, we performed model tests on the two genes separately. The results showed that SLC6A11 rs2304725 had no significant correlation with DRE risk in the allele, dominant, recessive, and additive models in a pooled population. However, for the over-dominant model, DRE was correlated with rs2304725 (OR = 1.08, 95% CI: 0.92-1.27, p = 0.33) in a pooled population. Similarly, rs211037 was weakly significantly correlated with DRE for the dominant, recessive, over-dominant, and additive models in a pooled population. The subgroup analysis results showed that rs211037 expressed a genetic risk of DRE in allele (OR = 1.01, 95% CI: 0.76-1.35, p = 0.94), dominant (OR = 1.08, 95% CI: 0.77-1.50, p = 0.65), and additive models (OR = 1.14, 95% CI: 0.62-2.09, p = 0.67) in an Asian population. Conclusion: In this meta-analysis, our results showed that SLC6A11 rs2304725 and GABRG2 rs211037 are not significantly correlated with DRE. However, in the over-dominant model, rs2304725 was significantly correlated with DRE. Likewise, rs211037 conveyed a genetic risk for DRE in an Asian population in the allele, dominant, and additive models.

BERT2OME: Prediction of 2'-O-Methylation Modifications From RNA Sequence by Transformer Architecture Based on BERT

Recent work on language models has resulted in state-of-the-art performance on various language tasks. Among these, Bidirectional Encoder Representations from Transformers (BERT) has focused on contextualizing word embeddings to extract context and semantics of the words. On the other hand, post-transcriptional 2'-O-methylation (Nm) RNA modification is important in various cellular tasks and related to a number of diseases. The existing high-throughput experimental techniques take longer time to detect these modifications, and costly in exploring these functional processes. Here, to deeply understand the associated biological processes faster, we come up with an efficient method Bert2Ome to infer 2'-O-methylation RNA modification sites from RNA sequences. Bert2Ome combines BERT-based model with convolutional neural networks (CNN) to infer the relationship between the modification sites and RNA sequence content. Unlike the methods proposed so far, Bert2Ome assumes each given RNA sequence as a text and focuses on improving the modification prediction performance by integrating the pretrained deep learning-based language model BERT. Additionally, our transformer-based approach could infer modification sites across multiple species. According to 5-fold cross-validation, human and mouse accuracies were 99.15% and 94.35% respectively. Similarly, ROC AUC scores were 0.99, 0.94 for the same species. Detailed results show that Bert2Ome reduces the time consumed in biological experiments and outperforms the existing approaches across different datasets and species over multiple metrics. Additionally, deep learning approaches such as 2D CNNs are more promising in learning BERT attributes than more conventional machine learning methods.

Integrated LC-MS/MS and network pharmacology approach for predictingactive ingredients and pharmacological mechanisms of Tribulus terrestris L. against cardiac diseases

Tribulus terrestris L. (Gokshura) is a medicinal herb used for treating cardiac diseases and several other diseases. However, the active ingredients and the possible mechanism of action for treating cardiac diseases remain unclear. Hence, the study was designed to identify the active ingredients and to explore the potential mechanism of action of Tribulus terrestris L. for treating cardiac diseases by an integrated approach of metabolomics and network pharmacology. We performed HPLC-QTOF-MS/MS analysis to identify putative compounds and network pharmacology approach for predictive key targets and pathways. Using molecular docking and molecular dynamics simulation, we identified the active ingredients in Tribulus terrestris L. that can act as putative lead compounds to treat cardiac diseases. A total of 55 putative compounds were identified using methanolic extract of Tribulus terrestris L. using HPLC-QTOF-MS/MS analysis. Network pharmacology analysis predicted 32 human protein targets from 25 secondary metabolites, which have shown direct interaction with cardiac diseases. Based on the degrees of interaction, the hub targets such as TACR1, F2, F2R, ADRA1B, CHRM5, ADRA1A, ADRA1D, HTR2B, and AVPR1A were identified. In silico molecular docking and simulation resulted in the identification of active ingredients such as Kaempferol 3-rutinoside 7-glucuronide, Keioside, rutin, moupinamide, aurantiamide, quercetin-3-o-α-rhamnoside, tribuloside, and 3'',6''- Di-O-p-coumaroyltrifolin against hub protein targets. Hence, these compounds could be potential lead compounds for treating cardiac diseases. A further assessment of its efficacy can be made based on in vivo and in vitro studies for better understanding and strong assertion.Communicated by Ramaswamy H. Sarma.

OncoRTT: Predicting novel oncology-related therapeutic targets using BERT embeddings and omics features

Late-stage drug development failures are usually a consequence of ineffective targets. Thus, proper target identification is needed, which may be possible using computational approaches. The reason being, effective targets have disease-relevant biological functions, and omics data unveil the proteins involved in these functions. Also, properties that favor the existence of binding between drug and target are deducible from the protein’s amino acid sequence. In this work, we developed OncoRTT, a deep learning (DL)-based method for predicting novel therapeutic targets. OncoRTT is designed to reduce suboptimal target selection by identifying novel targets based on features of known effective targets using DL approaches. First, we created the “OncologyTT” datasets, which include genes/proteins associated with ten prevalent cancer types. Then, we generated three sets of features for all genes: omics features, the proteins’ amino-acid sequence BERT embeddings, and the integrated features to train and test the DL classifiers separately. The models achieved high prediction performances in terms of area under the curve (AUC), i.e., AUC greater than 0.88 for all cancer types, with a maximum of 0.95 for leukemia. Also, OncoRTT outperformed the state-of-the-art method using their data in five out of seven cancer types commonly assessed by both methods. Furthermore, OncoRTT predicts novel therapeutic targets using new test data related to the seven cancer types. We further corroborated these results with other validation evidence using the Open Targets Platform and a case study focused on the top-10 predicted therapeutic targets for lung cancer.

Systematic analysis on the mechanism of Zhizi-Bopi decoction against hepatitis B via network pharmacology and molecular docking

PURPOSE

Zhizi-Bopi decoction (ZZBPD) is a classic herbal formula with wide clinical applications in treating liver diseases including hepatitis B. However, the mechanism needs to be elucidated.

METHODS

Chemical components of ZZBPD were identified by ultra-high-performance liquid chromatography coupled with time-of-flight mass spectrometry (UHPLC-TOF-MS). Then we used network pharmacology to identify their potential targets. Network construction, coupled with protein-protein interaction and enrichment analysis was used to identify representative components and core targets. Finally, molecular docking simulation was conducted to further refine the drug-target interaction.

RESULTS

One hundred and forty-eight active compounds were identified in ZZBPD, targeting 779 genes/proteins, among which 174 were related to hepatitis B. ZZBPD mainly influences the progression of hepatitis B through the hepatitis B pathway (hsa05161) via core anti-HBV targets (AKT1, PIK3CA, PIK3R1, SRC, TNF, MAPK1, and MAPK3). Enrichment analysis indicated that ZZBPD can also potentially regulate lipid metabolism and enhance cell survival. Molecular docking suggested that the representative active compounds can bind to the core anti-HBV targets with high affinity.

CONCLUSION

The potential molecular mechanisms of ZZBPD in hepatitis B treatment were identified using network pharmacology and molecular docking approaches. The results serve as an important basis for the modernization of ZZBPD.

Integrated Multi-Omics Techniques and Network Pharmacology Analysis to Explore the Material Basis and Mechanism of Simiao Pill in the Treatment of Rheumatoid Arthritis

The Simiao pill (SMP) is a classic prescription that has shown anti-inflammatory, analgesic, and immunomodulatory effects and is clinically used to treat inflammatory diseases, such as rheumatoid arthritis (RA) and gouty arthritis, for which the effects and mechanism of action remain largely unknown. In this study, serum samples from RA rats were analyzed using ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry based metabolomics technology and liquid chromatography with tandem mass spectrometry proteomics technology together with network pharmacology to explore the pharmacodynamic substances of SMP. To further verify the above results, we constructed a fibroblast-like synoviocyte (FLS) cell model and administered phellodendrine for the test. All these clues suggested that SMP can significantly reduce the level of interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α) in complete Freund's adjuvant rat serum and improve the degree of foot swelling; combined with metabolomics, proteomics, and network pharmacological technology, it is determined that SMP plays a therapeutic role through the inflammatory pathway, and phellodendrine is found to be one of the pharmacodynamic substances. By constructing an FLS model, it is further determined that phellodendrine could effectively inhibit the activity of synovial cells and reduce the expression level of inflammatory factors by downregulating the expression level of related proteins in the TLR4-MyD88-IRAK4-MAPK signal pathway to alleviate joint inflammation and cartilage injury. Overall, these findings suggested that phellodendrine is an effective component of SMP in the treatment of RA.

Network pharmacology, molecular docking, and experimental validation to explore the potential mechanism of Long Mu Qing Xin mixture for the treatment of attention deficit hyperactivity disorder

Background: Long Mu Qing Xin Mixture (LMQXM) has shown potentially positive effects in alleviating attention deficit hyperactivity disorder (ADHD); however, the action mechanism is still not fully understood. This study aimed to predict the potential mechanism of LMQXM for ADHD using network pharmacology and molecular docking, which were then validated using animal experiments.

Methods: Network pharmacology and molecular docking techniques were used to predict the core targets and potential pathways of LMQXMQ for ADHD, and KEGG pathway enrichment analysis revealed the potential significance of dopamine (DA) and cyclic adenosine monophosphate (cAMP) signaling pathways. To verify the hypothesis, we conducted an animal experiment. In the animal experiment, the young spontaneously hypertensive rats (SHRs) were randomly divided into the model group (SHR), the methylphenidate hydrochloride group (MPH, 4.22 mg/kg), and 3 LMQXM groups (low-dose (LD) group, 5.28 ml/kg; medium-dose (MD) group, 10.56 ml/kg; and high-dose (HD) group, 21.12 ml/kg), and administered by gavage for 4 weeks; the WKY rats were set as the control group. The open field test and Morris water maze test were used to evaluate the behavioral performance of rats, high performance liquid chromatography mass spectrometry (LC-MS) was used to analyze DA levels in the prefrontal cortex (PFC) and striatum of rats, ELISA was used to detect cAMP concentrations in the PFC and striatum, and immunohistochemistry and qPCR were used to analyze positive cell expression and mRNA expression for indicators related to DA and cAMP pathways.

Results: The results showed that beta-sitosterol, stigmasterol, rhynchophylline, baicalein, and formononetin might be key components of LMQXM for ADHD and that these components bind well to the core targets, DA receptors (DRD1 and DRD2). Furthermore, LMQXM might act through the DA and cAMP signaling pathways. In the animal experiment, we found that MPH and LMQXM-MD controlled hyperactivity and improved learning and memory in SHRs, while LMQXM-HD only controlled hyperactivity in SHRs; meanwhile, MPH and LMQXM-MD upregulated DA and cAMP levels, mean optical density (MOD) of cAMP, and MOD and mRNA expression of DRD1 and PKA in the prefrontal cortex (PFC) and striatum of SHRs, while LMQXM-LD and LMQXM-HD upregulated DA and cAMP levels in the striatum, MOD of cAMP in the PFC, and mRNA expression of PKA in the PFC. However, we did not find a significant regulatory effect of LMQXM on DRD2.

Conclusion: To sum up, this study demonstrated that LMQXM may increase DA levels mainly by activating the cAMP/PKA signaling pathway through DRD1, thereby controlling the behavioral disorders of SHRs, which is most effective at moderate doses, and this may be a key mechanism for LMQXM in the treatment of ADHD.