Therapeutic target database update 2022: facilitating drug discovery with enriched comparative data of targeted agents

Comprehensive applications of the artificial intelligence technology in new drug research and development

Purpose

Target-based strategy is a prevalent means of drug research and development (R&D), since targets provide effector molecules of drug action and offer the foundation of pharmacological investigation. Recently, the artificial intelligence (AI) technology has been utilized in various stages of drug R&D, where AI-assisted experimental methods show higher efficiency than sole experimental ones. It is a critical need to give a comprehensive review of AI applications in drug R &D for biopharmaceutical field.

Methods

Relevant literatures about AI-assisted drug R&D were collected from the public databases (Including Google Scholar, Web of Science, PubMed, IEEE Xplore Digital Library, Springer, and ScienceDirect) through a keyword searching strategy with the following terms [("Artificial Intelligence" OR "Knowledge Graph" OR "Machine Learning") AND ("Drug Target Identification" OR "New Drug Development")].

Results

In this review, we first introduced common strategies and novel trends of drug R&D, followed by characteristic description of AI algorithms widely used in drug R&D. Subsequently, we depicted detailed applications of AI algorithms in target identification, lead compound identification and optimization, drug repurposing, and drug analytical platform construction. Finally, we discussed the challenges and prospects of AI-assisted methods for drug discovery.

Conclusion

Collectively, this review provides comprehensive overview of AI applications in drug R&D and presents future perspectives for biopharmaceutical field, which may promote the development of drug industry.

Connecting the dots: Computational network analysis for disease insight and drug repurposing

Network biology is a powerful framework for studying the structure, function, and dynamics of biological systems, offering insights into the balance between health and disease states. The field is seeing rapid progress in all of its aspects: data availability, network synthesis, network analytics, and impactful applications in medicine and drug development. We review the most recent and significant results in network biomedicine, with a focus on the latest data, analytics, software resources, and applications in medicine. We also discuss what in our view are the likely directions of impactful development over the next few years.

Screening of promising molecules against potential drug targets in Yersinia pestis by integrative pan and subtractive genomics, docking and simulation approach

This study focuses on Yersinia pestis, the bacterium responsible for plague, which posed a severe threat to public health in history. Despite the availability of antibiotics treatment, the emergence of antibiotic resistance in this pathogen has increased challenges of controlling the infections and plague outbreaks. The development of new drug targets and therapies is urgently needed. This research aims to identify novel protein targets from 28 Y. pestis strains by the integrative pan-genomic and subtractive genomics approach. Additionally, it seeks to screen out potential safe and effective alternative therapies against these targets via high-throughput virtual screening. Targets should lack homology to human, gut microbiota, and known human 'anti-targets', while should exhibit essentiality for pathogen's survival and virulence, druggability, antibiotic resistance, and broad spectrum across multiple pathogenic bacteria. We identified two promising targets: the aminotransferase class I/class II domain-containing protein and 3-oxoacyl-[acyl-carrier-protein] synthase 2. These proteins were modeled using AlphaFold2, validated through several structural analyses, and were subjected to molecular docking and ADMET analysis. Molecular dynamics simulations determined the stability of the ligand-target complexes, providing potential therapeutic options against Y. pestis.

DrugMAP 2.0: molecular atlas and pharma-information of all drugs

The escalating costs and high failure rates have decelerated the pace of drug development, which amplifies the research interests in developing combinatorial/repurposed drugs and understanding off-target adverse drug reaction (ADR). In other words, it is demanded to delineate the molecular atlas and pharma-information for the combinatorial/repurposed drugs and off-target interactions. However, such invaluable data were inadequately covered by existing databases. In this study, a major update was thus conducted to the DrugMAP, which accumulated (a) 20831 combinatorial drugs and their interacting atlas involving 1583 pharmacologically important molecules; (b) 842 repurposed drugs and their interacting atlas with 795 molecules; (c) 3260 off-targets relevant to the ADRs of 2731 drugs and (d) various types of pharmaceutical information, including diverse ADMET properties, versatile diseases, and various ADRs/off-targets. With the growing demands for discovering combinatorial/repurposed therapies and the rapidly emerging interest in AI-based drug discovery, DrugMAP was highly expected to act as an indispensable supplement to existing databases facilitating drug discovery, which was accessible at: https://idrblab.org/drugmap/.

Predicting prospective therapeutic targets of Bombyx batryticatus for managing diabetic kidney disease through network pharmacology analysis

We conducted network pharmacology and molecular docking analyses, and executed in vitro experiments to assess the mechanisms and prospective targets associated with the bioactive components of Bombyx batryticatus in the treatment of diabetic kidney disease (DKD). The bioactive components and potential targets of B batryticatus were sourced from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. Using 5 disease databases, we conducted a comprehensive screening of potential disease targets specifically associated with DKD. Common targets shared between the bioactive components and disease targets were identified through the use of the R package, and subsequently, a protein-protein interaction network was established using data from the STRING database. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses pertaining to the identified common targets were conducted using the Database for Annotation, Visualization, and Integrated Discovery. Molecular docking simulations involving the bioactive components and their corresponding targets were modeled through AutoDock Vina and Pymol. Finally, to corroborate and validate these findings, experimental assays at the cellular level were conducted. Six bioactive compounds and 142 associated targets were identified for B batryticatus. Among the 796 disease targets associated with DKD, 56 targets were identified. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed the involvement of these shared targets in diverse biological processes and signaling pathways, notably the PI3K-Akt signaling pathway. Molecular docking analyses indicated a favorable binding interaction between quercetin, the principal bioactive compound in B batryticatus, and RAC-alpha serine/threonine-protein kinase. Subsequently, in vitro experiments substantiated the inhibitory effect of quercetin on the phosphorylation level of PI3K and Akt. The present study provides theoretical evidence for a comprehensive exploration of the mechanisms and molecular targets by which B batryticatus imparts protective effects against DKD.

Neuromolecular and behavioral effects of cannabidiol on depressive-associated behaviors and neuropathic pain conditions in mice

BACKGROUND AND AIMS

Neuropathic pain (NP) has a high incidence in the general population, is closely related to anxiety disorders, and has a negative impact on the quality of life. Cannabidiol (CBD), as a natural product, has been extensively studied for its potential therapeutic effects on symptoms such as pain and depression (DP). However, the mechanism of CBD in improving NP with depression is not fully understood.

METHODS

First, we used bioinformatics tools to deeply mine the intersection genes associated with NP, DP, and CBD. Secondly, the core targets were screened by Protein-protein interaction network, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analysis, molecular docking and molecular dynamics simulation. Next, the effects of CBD intervention on pain and depressive behaviors in the spinal nerve ligation (SNL) mouse model were evaluated using behavioral tests, and dose-response curves were plotted. After the optimal intervention dose was determined, the core targets were verified by Western blot (WB) and Quantitative Polymerase Chain Reaction (qPCR). Finally, we investigated the potential mechanism of CBD by Nissl staining, Immunofluorescence (IF) and Transmission Electron Microscopy (TEM).

RESULTS

A total of five core genes of CBD most associated with NP and DP were screened by bioinformatics analysis, including PTGS2, GPR55, SOD1, CYP1A2 and NQO1. Behavioral test results showed that CBD by intraperitoneal administration 5 mg/kg can significantly improve the pain behavior and depressive state of SNL mice. WB, qPCR, IF, and TEM experiments further confirmed the regulatory effects of CBD on key molecules.

CONCLUSION

In this study, we found five targets of CBD in the treatment of NP with DP. These findings provide further theoretical and experimental basis for CBD as a potential therapeutic agent.

Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery

INTRODUCTION

Small molecules often bind to multiple targets, a behavior termed polypharmacology. Anticipating polypharmacology is essential for drug discovery since unknown off-targets can modulate safety and efficacy - profoundly affecting drug discovery success. Unfortunately, experimental methods to assess selectivity present significant limitations and drugs still fail in the clinic due to unanticipated off-targets. Computational methods are a cost-effective, complementary approach to predict polypharmacology.

AREAS COVERED

This review aims to provide a comprehensive overview of the state of polypharmacology prediction and discuss its strengths and limitations, covering both classical cheminformatics methods and bioinformatic approaches. The authors review available data sources, paying close attention to their different coverage. The authors then discuss major algorithms grouped by the types of data that they exploit using selected examples.

EXPERT OPINION

Polypharmacology prediction has made impressive progress over the last decades and contributed to identify many off-targets. However, data incompleteness currently limits most approaches to comprehensively predict selectivity. Moreover, our limited agreement on model assessment challenges the identification of the best algorithms - which at present show modest performance in prospective real-world applications. Despite these limitations, the exponential increase of multidisciplinary Big Data and AI hold much potential to better polypharmacology prediction and de-risk drug discovery.

Application of artificial intelligence in drug design: A review

Artificial intelligence (AI) is a field of computer science that involves acquiring information, developing rule bases, and mimicking human behaviour. The fundamental concept behind AI is to create intelligent computer systems that can operate with minimal human intervention or without any intervention at all. These rule-based systems are developed using various machine learning and deep learning models, enabling them to solve complex problems. AI is integrated with these models to learn, understand, and analyse provided data. The rapid advancement of Artificial Intelligence (AI) is reshaping numerous industries, with the pharmaceutical sector experiencing a notable transformation. AI is increasingly being employed to automate, optimize, and personalize various facets of the pharmaceutical industry, particularly in pharmacological research. Traditional drug development methods areknown for being time-consuming, expensive, and less efficient, often taking around a decade and costing billions of dollars. The integration of artificial intelligence (AI) techniques addresses these challenges by enabling the examination of compounds with desired properties from a vast pool of input drugs. Furthermore, it plays a crucial role in drug screening by predicting toxicity, bioactivity, ADME properties (absorption, distribution, metabolism, and excretion), physicochemical properties, and more. AI enhances the drug design process by improving the efficiency and accuracy of predicting drug behaviour, interactions, and properties. These approaches further significantly improve the precision of drug discovery processes and decrease clinical trial costs leading to the development of more effective drugs.

CytoCellDB: a comprehensive resource for exploring extrachromosomal DNA in cancer cell lines

Recently, the cancer community has gained a heightened awareness of the roles of extrachromosomal DNA (ecDNA) in cancer proliferation, drug resistance and epigenetic remodeling. However, a hindrance to studying ecDNA is the lack of available cancer model systems that express ecDNA. Increasing our awareness of which model systems express ecDNA will advance our understanding of fundamental ecDNA biology and unlock a wealth of potential targeting strategies for ecDNA-driven cancers. To bridge this gap, we created CytoCellDB, a resource that provides karyotype annotations for cell lines within the Cancer Dependency Map (DepMap) and the Cancer Cell Line Encyclopedia (CCLE). We identify 139 cell lines that express ecDNA, a 200% increase from what is currently known. We expanded the total number of cancer cell lines with ecDNA annotations to 577, which is a 400% increase, covering 31% of cell lines in CCLE/DepMap. We experimentally validate several cell lines that we predict express ecDNA or homogeneous staining regions (HSRs). We demonstrate that CytoCellDB can be used to characterize aneuploidy alongside other molecular phenotypes, (gene essentialities, drug sensitivities, gene expression). We anticipate that CytoCellDB will advance cytogenomics research as well as provide insights into strategies for developing therapeutics that overcome ecDNA-driven drug resistance.

Therapeutic effect and mechanisms of traditional Chinese medicine compound (Qilong capsule) in the treatment of ischemic stroke

Background Qilong capsule (QLC) is a well-known traditional Chinese medicine compound extensively used in clinical practice. It has been approved by the China's FDA for the treatment of ischemic stroke (IS). In our clinical trial involving QLC (ClinicalTrials.gov identifier: NCT03174535), we observed the potential of QLC to improve neurological function in IS patients at the 24th week, while ensuring their safety. However, the effectiveness of QLC beyond the initial 12-week period remains uncertain, and the precise mechanisms underlying its action in IS have not been fully elucidated. Purpose In order to further explore the clinical efficacy of QLC in treating IS beyond the initial 12-week period and systematically elucidate its underlying mechanisms. Study Design This study employed an interdisciplinary integration strategy that combines post hoc analysis of clinical trials, transcriptome sequencing, integrated bioinformatics analysis, and animal experiments. Methods In this study, we conducted a post-hoc analysis with 2302 participants to evaluate the effectiveness of QLC at the 12th week. The primary outcome was the proportion of patients achieving functional independence at the 12th week, defined as a score of 0-2 on the modified Rankin Scale (mRS), which ranges from 0 (no symptoms) to 6 (death). Subsequently, we employed RNA sequencing (RNA-Seq) and quantitative reverse transcription polymerase chain reaction (RT-qPCR) techniques in the QLC trial to investigate the potential molecular mechanisms underlying the therapeutic effect of QLC in IS. Simultaneously, we utilized integrated bioinformatics analyses driven by external multi-source data and algorithms to further supplement the exploration and validation of QLC's therapeutic mechanism in treating IS. This encompassed network pharmacology analysis and analyses at the mRNA, cellular, and pathway levels focusing on core targets. Additionally, we developed a disease risk prediction model using machine learning. By identifying differentially expressed core genes (DECGs) between the normal and IS groups, we quantitatively predicted IS occurrence. Furthermore, to assess its protective effects and determine the key regulated pathway, we conducted experiments using a middle cerebral artery occlusion and reperfusion (MACO/R) rat model. Results Our findings demonstrated that the combination of QLC and conventional treatment (CT) significantly improved the proportion of patients achieving functional independence (mRS score 0-2) at the 12th week compared to CT alone (n = 2,302, 88.65 % vs 87.33 %, p = 0.3337; n = 600, 91.33 % vs 84.67 %, p = 0.0165). Transcriptome data revealed that the potential underlying mechanism of QLC for IS is related to the regulation of the NF-κB inflammatory pathway. The RT-qPCR results demonstrated that the regulatory trends of key genes, such as MD-2, were consistent with those observed in the RNA-Seq analysis. Integrated bioinformatics analysis elucidated that QLC regulates the NF-κB signaling pathway by identifying core targets, and machine learning was utilized to forecast the risk of IS onset. The MACO/R rat model experiment confirmed that QLC exerts its anti-CIRI effects by inhibiting the MD-2/TLR-4/NF-κB signaling axis. Conclusion: Our interdisciplinary integration study has demonstrated that the combination of QLC with CT exhibits significant superiority over CT alone in improving functional independence in patients at the 12th week. The potential mechanism underlying QLC's therapeutic effect in IS involves the inhibition of the MD-2/TLR4/NF-κB inflammatory signaling pathway, thereby attenuating cerebral ischemia/reperfusion inflammatory injury and facilitating neurofunctional recovery. The novelty and innovative potential of this study primarily lie in the novel finding that QLC significantly enhances the proportion of patients achieving functional independence (mRS score 0-2) at the 12th week. Furthermore, employing a "multilevel-multimethod" integrated research approach, we elucidated the potential mechanism underlying QLC's therapeutic effect in IS.

B. glomerulata promotes neuroprotection against ischemic stroke by inhibiting apoptosis through the activation of PI3K/AKT/mTOR pathway

BACKGROUND

Brassaiopsis glomerulata (Blum) Regel (B.glomerulata) is recognized as a traditional Chinese medicine (TCM) primarily used for promoting blood circulation and removing stasis. It is frequently utilized in the treatment of injuries resulting from falls and bumps.

PURPOSE

Despite its effective use in clinical treatment for ischemic stroke (IS), there are currently no reports on its composition and mechanism of action, which affects its promotion. The study investigated the chemical components and molecular mechanisms of B.glomerulata, with the following components: UPLC-Q-TOF-MS, network pharmacology Analysis and experimental verification in vivo and vitro.

METHODS

The effect of B.glomerulata on interfering with ischemic stroke was assessed on MCAO/R rats and ORD cell model. Then the compositional analysis was conducted using UPLC-Q-TOF-MS. Furthermore, network pharmacology and molecular docking techniques were explored to identify potential targets and pathways. The predicted mechanisms of action were ultimately confirmed by immunohistochemistry and protein blotting.

RESULTS

B. glomerulata exhibited neuroprotective effects in MCAO/R rats by reductions in hippocampal and cortical neuronal damage, brain infarction, and cerebral edema. Both in vivo and in vitro experiments demonstrated that it decreased ROS and MDA levels, increased SOD and GSH levels, thereby inhibiting oxidative stress. Moreover, the improvements in neuronal morphology and the modulation of Nissl bodies suggested a potential mechanism underlying its neuroprotective action. Additionally, B.glomerulata exhibited concentration-dependent reductions in Bax and Caspase-3 expressions, along with increases in GFAP, Bcl2/Bax ratio, p-PI3K, p-AKT, and p-mTOR levels.

CONCLUSION

B.glomerulata exhibited neuroprotective effects against cerebral ischemia-reperfusion injury both in vivo and in vitro. It prevented oxidative stress damage and inhibited apoptosis of ischemic stroke through the PI3K/AKT/mTOR pathway.

Preliminary investigation on the mechanism of anti-periodontitis effect of Scutellariae Radix based on bioinformatics analysis and in vitro verification

Objective

To investigate the material basis, targets and molecular mechanism of Scutellariae Radix against periodontitis to provide theoretical basis for clinical applications.

Materials and methods

The active compounds and targets of Scutellariae Radix were obtained from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database, and the periodontitis-related targets were collected by integrating Online Mendelian Inheritance in Man (OMIM), Therapeutic Target Database (TTD), Genecards and Gene Expression Omnibus (GEO) database together. The potential targets of Scutellariae Radix against periodontitis were obtained from the intersection of two target sets. Metascape database was used for Gene Ontology (GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Discovery Studio software was used for molecular docking between key targets and compounds to evaluate their binding affinity. Western blot was used to check the expression of PTGS2 and MMP9 to verify the regulatory effects of baicalein, the main active compound of Scutellariae Radix, on human periodontal ligament stem cells (hPDLSCs) cultured under inflammatory environment which induced by lipopolysaccharide (LPS).

Results

15 active compounds of Scutellariae Radix and 53 common targets for periodontitis treatment were identified. Among these targets, the 10 core targets were AKT1, IL-6, TNF, VEGFA, TP53, PTGS2, CASP3, JUN, MMP9 and HIF1A. GO and KEGG analysis mainly focused on response to LPS and pathways in cancer. Molecular docking showed that the main active compounds had good binding affinity with key targets. Cell experiments confirmed that baicalein can interfere the expression of pro-inflammatory factors PTGS2 and MMP9 proteins and exert anti-inflammatory effects.

Conclusion

Current study preliminarily analyzed the mechanism of Scutellariae Radix against periodontitis, which provide a new idea for the utilization of Scutellariae Radix and the development of novel medicine for the clinical treatment of periodontitis.

Exhaustive local chemical space exploration using a transformer model

How many near-neighbors does a molecule have? This fundamental question in chemistry is crucial for molecular optimization problems under the similarity principle assumption. Generative models can sample molecules from a vast chemical space but lack explicit knowledge about molecular similarity. Therefore, these models need guidance from reinforcement learning to sample a relevant similar chemical space. However, they still miss a mechanism to measure the coverage of a specific region of the chemical space. To overcome these limitations, a source-target molecular transformer model, regularized via a similarity kernel function, is proposed. Trained on a largest dataset of ≥200 billion molecular pairs, the model enforces a direct relationship between generating a target molecule and its similarity to a source molecule. Results indicate that the regularization term significantly improves the correlation between generation probability and molecular similarity, enabling exhaustive exploration of molecule near-neighborhoods.

Revolutionizing adjuvant development: harnessing AI for next-generation cancer vaccines

With the COVID-19 pandemic, the importance of vaccines has been widely recognized and has led to increased research and development efforts. Vaccines also play a crucial role in cancer treatment by activating the immune system to target and destroy cancer cells. However, enhancing the efficacy of cancer vaccines remains a challenge. Adjuvants, which enhance the immune response to antigens and improve vaccine effectiveness, have faced limitations in recent years, resulting in few novel adjuvants being identified. The advancement of artificial intelligence (AI) technology in drug development has provided a foundation for adjuvant screening and application, leading to a diversification of adjuvants. This article reviews the significant role of tumor vaccines in basic research and clinical treatment and explores the use of AI technology to screen novel adjuvants from databases. The findings of this review offer valuable insights for the development of new adjuvants for next-generation vaccines.

Orostachys malacophylla (pall.) fisch extracts alleviate intestinal inflammation in Drosophila

ETHNOPHARMACOLOGICAL RELEVANCE

Orostachys malacophylla (Pall.) Fisch (O. malacophylla) is a succulent herbaceous plant that is the Orostachys genus of Crassulaceae family. O. malacophylla has been widely used as a traditional Chinese medicine with antioxidant, anti-inflammatory, anti-febrile, antidote, anti-Toxoplasma gondii properties. However, the biological function of alleviating intestinal inflammation and key bioactive compounds were still unknown.

AIM OF THE STUDY

We used a Drosophila model to study the protective effects and bioactive compounds of O. malacophylla water extract (OMWE) and butanol extract (OMBE) on intestinal inflammation.

MATERIALS AND METHODS

Drosophila intestinal inflammation was induced by oral invasion of dextran sodium sulfate (DSS) or Erwinia carotovora carotovora 15 (Ecc15). We revealed the protective effects of two extracts by determining intestinal reactive oxygen species (ROS) and antimicrobial peptide (AMP) levels and intestinal integrity, and using network pharmacology analysis to identify bioactive compounds.

RESULTS

We demonstrated that both OMWE and OMBE could ameliorate the detrimental effects of DSS, including a decreased survival rate, elevated ROS levels, increased cell death, excessive proliferation of ISCs, acid-base imbalance, and disruption of intestinal integrity. Moreover, the overabundance of lipid droplets (LDs) and AMPs by Ecc15 infection is mitigated by these extracts, thereby enhancing the flies' resistance to adverse stimuli. In addition, we used widely targeted metabolomics and network pharmacology analysis to identify bioactive compounds associated with IBD healing that are present in OMWE and OMBE.

CONCLUSIONS

In summary, our research indicates that OMWE and OMBE significantly mitigate intestinal inflammation and have the potential to be effective therapeutic agents for IBD in humans.

Active Targets and Potential Mechanisms of Erhuang Quzhi Formula in Treating NAFLD: Network Analysis and Experimental Assessment

The purpose of this research was to investigate the main active components, potential targets of action, and pharmacological mechanisms of Erhuang Quzhi Formula (EHQZF) against NAFLD using network pharmacology, molecular docking, and experimental validation. The main active chemical components of EHQZF and the potential targets for treating NAFLD were extracted and analyzed. The PPI network diagram of "Traditional Chinese Medicine-Active Ingredients-Core Targets" was constructed and the GO, KEGG, and molecular docking analysis were carried out. Identification of components in traditional Chinese medicine compounds was conducted by LC-MS. NAFLD models were established and relevant pathologic indicators and Western blot were analyzed in vivo and ex vivo. Totally 8 herbs attributed to the liver meridian and 20 corresponding targets of NAFLD were obtained from EHQZF. Flavonoids and phenolic acids as the main components of EHQZF treated NAFLD through the MAPK/AKT signaling pathway. Pathway enrichment analysis focused on the MAPK/AKT signaling pathway and apoptosis signaling pathway. Molecular docking showed that Quercetin and Luteolin had stable binding structures with AKT1, STAT3, and other targets. Experiments showed that EHQZF reduced lipid accumulation, regulated changes in adipose tissue, inhibited the MAPK/AKT signaling pathway and exert multiple components, several targets, and multiple pathway interactions to treat NAFLD.

Exploring therapeutic targets and molecular mechanisms for treating diabetes mellitus-associated heart failure with Qishen Yiqi dropping pills: A network pharmacology and bioinformatics approach

Diabetes mellitus (DM) and heart failure frequently coexist, presenting significant public health challenges. QiShenYiQi Dropping Pills (QSDP) are widely employed in the treatment of diabetes mellitus concomitant with heart failure (DM-HF). Nevertheless, the precise mechanisms underlying their efficacy have yet to be elucidated. Active ingredients and likely targets of QSDP were retrieved from the TCMSP and UniProt databases. Genes associated with DM-HF were pinpointed through searches in the GeneCards, OMIM, DisGeNET, and TTD databases. Differential genes connected to DM-HF were sourced from the GEO database. Enrichment analyses via gene ontology and Kyoto Encyclopedia of Genes and Genomes pathways, as well as immune infiltration assessments, were conducted using R software. Further analysis involved employing molecular docking strategies to explore the interactions between the identified targets and active substances in QSDP that are pertinent to DM-HF treatment. This investigation effectively discerned 108 active compounds and 257 targets relevant to QSDP. A protein-protein interaction network was constructed, highlighting 6 central targets for DM-HF treatment via QSDP. Gene ontology enrichment analysis predominantly linked these targets with responses to hypoxia, metabolism of reactive oxygen species, and cytokine receptor interactions. Analysis of Kyoto Encyclopedia of Genes and Genomes pathways demonstrated that these targets mainly participate in pathways linked to diabetic complications, such as AGE-RAGE signaling, dyslipidemia, arteriosclerosis, the HIF-1 signaling pathway, and the tumor necrosis factor signaling pathway. Further, immune infiltration analysis implied that QSDP's mechanism in treating DM-HF might involve immune-mediated inflammation and crucial signaling pathways. Additionally, molecular docking studies showed that the active substances in QSDP have strong binding affinities with these identified targets. This research presents a new model for addressing DM-HF through the use of QSDP, providing novel insights into incorporating traditional Chinese medicine (TCM) principles in the clinical treatment of DM-HF. The implications of these findings are substantial for both clinical application and further scientific inquiry.

DL-3-n-Butylphthalide Ameliorates Post-stroke Emotional Disorders by Suppressing Neuroinflammation and PANoptosis

Post-stroke emotional disorders such as post-stroke anxiety and post-stroke depression are typical symptoms in patients with stroke. They are closely associated with poor prognosis and low quality of life. The State Food and Drug Administration of China has approved DL-3-n-butylphthalide (NBP) as a treatment for ischemic stroke (IS). Clinical research has shown that NBP alleviates anxiety and depressive symptoms in patients with IS. Therefore, this study explored the role and molecular mechanisms of NBP in cases of post-stroke emotional disorders using network pharmacology and experimental validation. The results showed that NBP treatment significantly increased the percentage of time spent in the center of the middle cerebral artery occlusion (MCAO) rats in the open field test and the percentage of sucrose consumption in the sucrose preference test. Network pharmacology results suggest that NBP may regulate neuroinflammation and cell death. Further experiments revealed that NBP inhibited the toll-like receptor 4/nuclear factor kappa B signaling pathway, decreased the level of pro-inflammatory cytokines, including tumor necrosis factor-α, interleukin-1β, and interleukin-6, and M1-type microglia markers (CD68, inducible nitric oxide synthase), and reduced the expression of PANoptosis-related molecules including caspase-1, caspase-3, caspase-8, gasdermin D, and mixed lineage kinase domain-like protein in the hippocampus of the MACO rats. These findings demonstrate that the mechanisms through which NBP ameliorates post-stroke emotional disorders in rats are associated with inhibiting neuroinflammation and PANoptosis, providing a new strategy and experimental basis for treating post-stroke emotional disorders.

Changing Landscape of Antimicrobial Resistance in Neonatal Sepsis: An in silico Analyses of Multidrug Resistance in Klebsiella pneumoniae

BACKGROUND

Neonatal sepsis poses a critical healthcare concern, as multidrug-resistant Klebsiella pneumoniae ( K. pneumoniae ) infections are on the rise. Understanding the antimicrobial susceptibility patterns and underlying resistance mechanism is crucial for effective treatment.

OBJECTIVES

This study aimed to comprehensively investigate the antimicrobial susceptibility patterns of K. pneumoniae strains responsible for neonatal sepsis using in silico tools. We sought to identify trends and explore reasons for varying resistance levels, particularly for β-lactams and fluoroquinolone.

METHODS

K. pneumoniae isolated from neonates at Kanchi Kamakoti CHILDS Trust Hospital (2017-2020) were analyzed for antimicrobial resistance. Elevated resistance to β-lactam and fluoroquinolone antibiotics was further investigated through molecular docking and interaction analysis. β-lactam affinity with penicillin-binding proteins and β-lactamases was examined. Mutations in ParC and GyrA responsible for quinolone resistance were introduced to investigate ciprofloxacin interactions.

RESULTS

Of 111 K. pneumoniae blood sepsis isolates in neonates, high resistance was detected to β-lactams such as cefixime (85.91%, n = 71), ceftriaxone (84.9%, n = 106), cefotaxime (84.9%, n = 82) and fluoroquinolone (ciprofloxacin- 79.44%, n = 107). Molecular docking revealed low β-lactam binding toward penicillin-binding proteins and higher affinities for β-lactamases, attributing to the reduced β-lactam efficiency. Additionally, ciprofloxacin showed decreased affinity toward mutant ParC and GyrA in comparison to their corresponding wild-type proteins.

CONCLUSION

Our study elucidates altered resistance profiles in neonatal sepsis caused by K. pneumoniae , highlighting mechanisms of β-lactam and fluoroquinolone resistance. It underscores the urgent need for the development of sustainable therapeutic alternatives to address the rising antimicrobial resistance in neonatal sepsis.

From genetic associations to genes: methods, applications, and challenges

Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.

Network pharmacology and biological verification of morusin's therapeutic mechanisms in inhibiting nasopharyngeal carcinoma growth

Nasopharyngeal carcinoma (NPC) presents a significant therapeutic challenge due to its aggressive nature and limited treatment options. Although morusin, a compound found in traditional Chinese medicines, exhibits significant tumor-inhibiting properties, its specific effects on NPC proliferation remain unclear. This study aims to elucidate the inhibitory effects of morusin on NPC survival and proliferation while exploring the underlying mechanisms through the utilization of network pharmacology, molecular docking, and experimental validation in vitro and in vivo. Network pharmacology analysis identified 117 potential targets of morusin against NPC, with 8 hub targets including AKT1, BCL2, CASP3, CTNNB1, ESR1, HSP90AA1, MMP9, STAT3, and the IL-17 signaling pathway. Further investigation of public data indicated that the expression levels of BLC2, CASP3, CTNNB1, HSP90AA1, and STAT3 in NPC tissue were significantly elevated compared to normal nasopharyngeal tissue. Docking studies exposed robust binding activity between morusin and key gene molecules. Additionally, biological assays demonstrated that morusin effectively inhibits NPC growth both in vivo and in vitro. Through a comprehensive investigation, this study identified the pharmacological mechanisms essential for morusin-induced inhibition of NPC growth by targeting multiple molecular targets and signaling pathways. These findings show the potential to contribute to the development of novel clinical agents for treating NPC.

The Role of AI in Drug Discovery

The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This review paper elucidates the multifaceted applications of AI across various stages of drug development, highlighting significant advancements and methodologies. It delves into AI's instrumental role in drug design, polypharmacology, chemical synthesis, drug repurposing, and the prediction of drug properties such as toxicity, bioactivity, and physicochemical characteristics. Despite AI's promising advancements, the paper also addresses the challenges and limitations encountered in the field, including data quality, generalizability, computational demands, and ethical considerations. By offering a comprehensive overview of AI's role in drug discovery, this paper underscores the technology's potential to significantly enhance drug development, while also acknowledging the hurdles that must be overcome to fully realize its benefits.

DrugRepoBank: a comprehensive database and discovery platform for accelerating drug repositioning

In recent years, drug repositioning has emerged as a promising alternative to the time-consuming, expensive and risky process of developing new drugs for diseases. However, the current database for drug repositioning faces several issues, including insufficient data volume, restricted data types, algorithm inaccuracies resulting from the neglect of multidimensional or heterogeneous data, a lack of systematic organization of literature data associated with drug repositioning, limited analytical capabilities and user-unfriendly webpage interfaces. Hence, we have established the first all-encompassing database called DrugRepoBank, consisting of two main modules: the 'Literature' module and the 'Prediction' module. The 'Literature' module serves as the largest repository of literature-supported drug repositioning data with experimental evidence, encompassing 169 repositioned drugs from 134 articles from 1 January 2000 to 1 July 2023. The 'Prediction' module employs 18 efficient algorithms, including similarity-based, artificial-intelligence-based, signature-based and network-based methods to predict repositioned drug candidates. The DrugRepoBank features an interactive and user-friendly web interface and offers comprehensive functionalities such as bioinformatics analysis of disease signatures. When users provide information about a drug, target or disease of interest, DrugRepoBank offers new indications and targets for the drug, proposes new drugs that bind to the target or suggests potential drugs for the queried disease. Additionally, it provides basic information about drugs, targets or diseases, along with supporting literature. We utilize three case studies to demonstrate the feasibility and effectiveness of predictively repositioned drugs within DrugRepoBank. The establishment of the DrugRepoBank database will significantly accelerate the pace of drug repositioning. Database URL:  https://awi.cuhk.edu.cn/DrugRepoBank.

Identifying proteomic risk factors for overall, aggressive, and early onset prostate cancer using Mendelian Randomisation and tumour spatial transcriptomics

BACKGROUND

Understanding the role of circulating proteins in prostate cancer risk can reveal key biological pathways and identify novel targets for cancer prevention.

METHODS

We investigated the association of 2002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis-pQTL Mendelian randomisation (MR) and colocalisation. Findings for proteins with support from both MR, after correction for multiple-testing, and colocalisation were replicated using two independent cancer GWAS, one of European and one of African ancestry. Proteins with evidence of prostate-specific tissue expression were additionally investigated using spatial transcriptomic data in prostate tumour tissue to assess their role in tumour aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets.

FINDINGS

We identified 20 proteins genetically linked to prostate cancer risk (14 for overall [8 specific], 7 for aggressive [3 specific], and 8 for early onset disease [2 specific]), of which the majority replicated where data were available. Among these were proteins associated with aggressive disease, such as PPA2 [Odds Ratio (OR) per 1 SD increment = 2.13, 95% CI: 1.54-2.93], PYY [OR = 1.87, 95% CI: 1.43-2.44] and PRSS3 [OR = 0.80, 95% CI: 0.73-0.89], and those associated with early onset disease, including EHPB1 [OR = 2.89, 95% CI: 1.99-4.21], POGLUT3 [OR = 0.76, 95% CI: 0.67-0.86] and TPM3 [OR = 0.47, 95% CI: 0.34-0.64]. We confirmed an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80-0.82], and also found an inverse association with both aggressive [OR = 0.84, 95% CI: 0.82-0.86] and early onset disease [OR = 0.71, 95% CI: 0.68-0.74]. Using spatial transcriptomics data, we identified MSMB as the genome-wide top-most predictive gene to distinguish benign regions from high grade cancer regions that comparatively had five-fold lower MSMB expression. Additionally, ten proteins that were associated with prostate cancer risk also mapped to existing therapeutic interventions.

INTERPRETATION

Our findings emphasise the importance of proteomics for improving our understanding of prostate cancer aetiology and of opportunities for novel therapeutic interventions. Additionally, we demonstrate the added benefit of in-depth functional analyses to triangulate the role of risk proteins in the clinical aggressiveness of prostate tumours. Using these integrated methods, we identify a subset of risk proteins associated with aggressive and early onset disease as priorities for investigation for the future prevention and treatment of prostate cancer.

FUNDING

This work was supported by Cancer Research UK (grant no. C8221/A29017).

Network Pharmacology Based Elucidation of Molecular Mechanisms of Laoke Formula for Treatment of Advanced Non-Small Cell Lung Cancer

OBJECTIVE

To explore the specific pharmacological molecular mechanisms of Laoke Formula (LK) on treating advanced non-small cell lung cancer (NSCLC) based on clinical application, network pharmacology and experimental validation.

METHODS

Kaplan-Meier method and Cox regression analysis were used to evaluate the survival benefit of Chinese medicine (CM) treatment in 296 patients with NSCLC in Tianjin Medical University Cancer Institute and Hospital from January 2011 to December 2015. The compounds of LK were screened using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, and the corresponding targets were performed from Swiss Target Prediction. NSCLC-related targets were obtained from Therapeutic Target Database and Comparative Toxicogenomics Database. Key compounds and targets were identified from the compound-target-disease network and protein-protein interaction (PPI) network analysis, respectively. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis were used to predict the potential signaling pathways involved in the treatment of advanced NSCLC with LK. The binding affinities between key ingredients and targets were further verified using molecular docking. Finally, A549 cell proliferation and migration assay were used to evaluate the antitumor activity of LK. Western blot was used to further verify the expression of key target proteins related to the predicted pathways.

RESULTS

Kaplan-Meier survival analysis showed that the overall survival of the CM group was longer than that of the non-CM group (36 months vs. 26 months), and COX regression analysis showed that LK treatment was an independent favorable prognostic factor (P=0.027). Next, 97 components and 86 potential targets were included in the network pharmacology, KEGG and GO analyses, and the results indicated that LK was associated with proliferation and apoptosis. Moreover, molecular docking revealed a good binding affinity between the key ingredients and targets. In vitro, A549 cell proliferation and migration assay showed that the biological inhibition effect was more obvious with the increase of LK concentration (P<0.05). And decreased expressions of nuclear factor κB1 (NF-κB1), epidermal growth factor receptor (EGFR) and AKT serine/threonine kinase 1 (AKT1) and increased expression of p53 (P<0.05) indicated the inhibitory effect of LK on NSCLC by Western blot.

CONCLUSION

LK inhibits NSCLC by inhibiting EGFR/phosphoinositide 3-kinase (PI3K)/AKT signaling pathway, NFκB signaling pathway and inducing apoptosis, which provides evidence for the therapeutic mechanism of LK to increase overall survival in NSCLC patients.

Evolutionary screening of precision oncology biomarkers and its applications in prognostic model construction

Biomarker screening is critical for precision oncology. However, one of the main challenges in precision oncology is that the screened biomarkers often fail to achieve the expected clinical effects and are rarely approved by regulatory authorities. Considering the close association between cancer pathogenesis and the evolutionary events of organisms, we first explored the evolutionary feature underlying clinically approved biomarkers, and two evolutionary features of approved biomarkers (Ohnologs and specific evolutionary stages of genes) were identified. Subsequently, we utilized evolutionary features for screening potential prognostic biomarkers in four common cancers: head and neck squamous cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, and lung squamous cell carcinoma. Finally, we constructed an evolution-strengthened prognostic model (ESPM) for cancers. These models can predict cancer patients' survival time across different cancer cohorts effectively and perform better than conventional models. In summary, our study highlights the application potentials of evolutionary information in precision oncology biomarker screening.

Xanthoceras sorbifolium leaves alleviate hyperuricemic nephropathy by inhibiting the PI3K/AKT signaling pathway to regulate uric acid transport

ETHNOPHARMACOLOGICAL RELEVANCE

In China, Xanthoceras sorbifolium Bunge was first documented as "Wen Guan Hua" in the "Jiu Huang Ben Cao" in 1406 A.D. According to the "National Compilation of Chinese Herbal Medicine," X. sorbifolium leaves are sweet and flat in nature and can dispel wind and dampness, suggesting that their extract can be used to treat rheumatoid arthritis. X. sorbifolium Bunge has also been used to treat arteriosclerosis, hyperlipidemia, hypertension, chronic hepatitis, and rheumatism, complications associated with hyperuricemic nephropathy (HN), a condition characterized by kidney damage resulting from high levels of uric acid (UA) in the blood.

AIM OF THE STUDY

The purpose of this study was to investigate the effects and underlying mechanisms of a 70% ethanol extract from X. sorbifolium leaves (EX) in alleviating HN.

MATERIALS AND METHODS

A mouse model of hyperuricemia was established to initially evaluate the hypouricemic effects and determine the effective dose of EX. Phytochemical analyses were conducted using ultra high-performance liquid chromatography and liquid chromatography-mass spectroscopy. The potential key pathways of EX in the alleviation of HN were inferred using network pharmacology and bioinformatics. An HN rat model was then established, and experiments including biomarker detection, western blotting, reverse transcription quantitative polymerase chain reaction, immunohistochemical and Masson's trichrome staining, and transmission electron microscopy were conducted to evaluate the effect of EX on UA transporter expression in vitro.

RESULTS

Network pharmacology and bioinformatics analyses revealed that the phosphoinositide 3-kinase (PI3K)/AKT signaling pathway was the key pathway for the alleviation of HN progression by EX. EX treatment reduced serum biomarkers in HN rats, downregulated the expression of p-PI3K, p-AKT, glucose transporter 9 (GLUT9), urate transporter 1 (URAT1), Collagen I, matrix metalloproteinase (MMP)-2, and MMP-9, and upregulated the expression of ATP binding cassette subfamily G member 2 (ABCG2) to improve renal interstitial fibrosis in HN rats. A high content of both quercitrin and cynaroside were identified in EX; their administration inhibited the increased expression of GLUT9 and URAT1 in damaged HK-2 cells.

CONCLUSION

Our study provides evidence that EX alleviates HN. The potential mechanism underlying this effect may be the regulation of UA transporters, such as GLUT9 and URAT1, by limiting the activation of the PI3K/AKT signaling pathway to improve renal injury.

IMMUNOTAR - Integrative prioritization of cell surface targets for cancer immunotherapy

Cancer remains a leading cause of mortality globally. Recent improvements in survival have been facilitated by the development of less toxic immunotherapies; however, identifying targets for immunotherapies remains a challenge in the field. To address this challenge, we developed IMMUNOTAR, a computational tool that systematically prioritizes and identifies candidate immunotherapeutic targets. IMMUNOTAR integrates user-provided RNA-sequencing or proteomics data with quantitative features extracted from publicly available databases based on predefined optimal immunotherapeutic target criteria and quantitatively prioritizes potential surface protein targets. We demonstrate the utility and flexibility of IMMUNOTAR using three distinct datasets, validating its effectiveness in identifying both known and new potential immunotherapeutic targets within the analyzed cancer phenotypes. Overall, IMMUNOTAR enables the compilation of data from multiple sources into a unified platform, allowing users to simultaneously evaluate surface proteins across diverse criteria. By streamlining target identification, IMMUNOTAR empowers researchers to efficiently allocate resources and accelerate immunotherapy development.

Evaluating the Causal Effects of Circulating Proteome on the Risk of Sepsis and Related Outcomes

The current investigation deployed Mendelian randomization (MR) to elucidate the causal relationship between circulating proteins and sepsis. A rigorous two-sample MR analysis evaluated the effect of plasma proteins on the sepsis susceptibility. To affirm the integrity of MR findings, a suite of supplementary analyses, including Bayesian colocalization, Steiger filtering, the assessment of protein-altering polymorphisms, and the correlation between expression quantitative trait loci and protein quantitative trait loci (pQTLs), was employed. The study further integrated the examination of protein-protein interactions and pathway enrichment, along with the identification of pharmacologically actionable targets, to advance our comprehension and outline potential sepsis therapies. Subsequent analyses leveraging cis-pQTLs within MR studies unveiled noteworthy relationships: 94 specific proteins exhibited significant links with sepsis-related 28 day mortality, while 96 distinct proteins correlated with survival outcomes in sepsis. Furthermore, incorporating both cis- and trans-pQTLs in MR investigations revealed more comprehensive findings, associating 201 unique proteins with sepsis-related 28 day mortality and 199 distinct proteins with survival outcomes in sepsis. Markedly, colocalization analyses confirmed that eight of these proteins exhibited prominent evidence for colocalization, emphasizing their potential criticality in sepsis pathophysiology. Further in silico analyses were conducted to delineate putative regulatory networks and to highlight prospective drug targets among these proteins. Employing the MR methodology has shed light on plasma proteins implicated in the etiopathogenesis of sepsis. This novel approach unveiled numerous biomarkers and targets, providing a scientific rationale for the development of new therapeutic strategies and prophylactic measures against sepsis.

Enhanced optical imaging and fluorescent labeling for visualizing drug molecules within living organisms

The visualization of drugs in living systems has become key techniques in modern therapeutics. Recent advancements in optical imaging technologies and molecular design strategies have revolutionized drug visualization. At the subcellular level, super-resolution microscopy has allowed exploration of the molecular landscape within individual cells and the cellular response to drugs. Moving beyond subcellular imaging, researchers have integrated multiple modes, like optical near-infrared II imaging, to study the complex spatiotemporal interactions between drugs and their surroundings. By combining these visualization approaches, researchers gain supplementary information on physiological parameters, metabolic activity, and tissue composition, leading to a comprehensive understanding of drug behavior. This review focuses on cutting-edge technologies in drug visualization, particularly fluorescence imaging, and the main types of fluorescent molecules used. Additionally, we discuss current challenges and prospects in targeted drug research, emphasizing the importance of multidisciplinary cooperation in advancing drug visualization. With the integration of advanced imaging technology and molecular design, drug visualization has the potential to redefine our understanding of pharmacology, enabling the analysis of drug micro-dynamics in subcellular environments from new perspectives and deepening pharmacological research to the levels of the cell and organelles.

Potential drug targets for gastroesophageal reflux disease and Barrett's esophagus identified through Mendelian randomization analysis

Gastroesophageal reflux disease (GERD) is a prevalent chronic ailment, and present therapeutic approaches are not always effective. This study aimed to find new drug targets for GERD and Barrett's esophagus (BE). We obtained genetic instruments for GERD, BE, and 2004 plasma proteins from recently published genome-wide association studies (GWAS), and Mendelian randomization (MR) was employed to explore potential drug targets. We further winnowed down MR-prioritized proteins through replication, reverse causality testing, colocalization analysis, phenotype scanning, and Phenome-wide MR. Furthermore, we constructed a protein-protein interaction network, unveiling potential associations among candidate proteins. Simultaneously, we acquired mRNA expression quantitative trait loci (eQTL) data from another GWAS encompassing four different tissues to identify additional drug targets. Meanwhile, we searched drug databases to evaluate these targets. Under Bonferroni correction (P < 4.8 × 10-5), we identified 11 plasma proteins significantly associated with GERD. Among these, 7 are protective proteins (MSP, GPX1, ERBB3, BT3A3, ANTR2, CCM2, and DECR2), while 4 are detrimental proteins (TMEM106B, DUSP13, C1-INH, and LINGO1). Ultimately, C1-INH and DECR2 successfully passed the screening process and exhibited similar directional causal effects on BE. Further analysis of eQTLs highlighted 4 potential drug targets, including EDEM3, PBX3, MEIS1-AS3, and NME7. The search of drug databases further supported our conclusions. Our study indicated that the plasma proteins C1-INH and DECR2, along with 4 genes (EDEM3, PBX3, MEIS1-AS3, and NME7), may represent potential drug targets for GERD and BE, warranting further investigation.

Mechanism of Cnidii Fructus in the Treatment of Infertility Based on Network Pharmacology and Molecular Docking Analysis Technology

Infertility is a condition characterized by a low fertility rate, which significantly affects the physical and mental health of women of reproductive age. Typically, the treatment duration is prolonged, and the therapeutic outcomes are often unsatisfactory. Professor Cheng-yao He, a renowned expert in traditional Chinese medicine, commonly uses the herb Cnidii Fructus (SCZ) for the treatment of infertility. However, the exact mechanism remains unclear, and there is limited research available on this topic. The active ingredients of SCZ were obtained from the traditional chinese medicine system pharmacology (TCMSP) database and screened for pharmacokinetics (PK), involving absorption, distribution, metabolism, and excretion (ADME). Target prediction was performed by SwissTargetPrediction database, and infertility-related disease targets were searched in GeneCards, TTD, DrugBank, and OMIM database. The protein-protein interaction (PPI) network was constructed using the STRING database (Version 11.5) and analyzed by Cytoscape software (Version 3.9.1). Additionally, the target genes were subjected to biological enrichment analysis in the Metascape database, including gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis, and the "Disease-Ingredient-pathway-target" network was constructed using Cytoscape software. With the assistance of AutoDockVina, Ligplot, and PyMOL software, a validation of Molecular docking results and a visualization of the results were performed. This study identified 11 retained active ingredients of SCZ, 447 drug targets, 233 of which were related to infertility, and 5393 disease targets. GO enrichment analysis mainly involved 221 biological processes such as cellular response to chemical stress and gland development. KEGG enrichment analysis mainly involved 68 pathways such as thyroid hormone signaling pathway, estrogen signaling pathway, FOXO signaling pathway, and PI3K/Akt signaling pathway. Molecular docking showed that the core active ingredients of SCZ, including Ammidin, Diosmetin, Xanthoxylin N, and Prangenidin, had strong binding abilities with core targets such as MDM2, MTOR, CCND1, EGFR, and AKT1. This study preliminarily demonstrated that SCZ may act on the PI3K/Akt signaling pathway, exerting its therapeutic effects on infertility by improving energy metabolism disorders and endometrial receptivity, inducing primordial follicle activation, regulating oocyte proliferation, differentiation, and apoptosis, and promoting the release of dominant follicles.

The Glabridin from Huangqin Decoction Prevents the Development of Ulcerative Colitis into Colitis-Associated Colorectal Cancer by Modulating MMP1/MMP3 Activity

BACKGROUND AND AIM

Huangqin decoction (HQD) is a Chinese medicine used to treat colitis and colorectal cancer (CRC). However, the specific compounds and mechanisms of HQD remain unclear despite its good curative clinical results. Through bioinformatics, network pharmacology, and experiments, this study aims to explore the progressive mechanisms of colitis-associated colorectal cancer (CAC) from ulcerative colitis (UC) while examining the protective effects of HQD and its compounds against this.

METHODS

Bioinformatics was utilized to identify the hub genes between UC and CRC, and their clinical predictive significance, function, and expression were validated. Employing network pharmacology in combination with hub genes, key targets of HQD for preventing the development of UC into CAC were identified. Molecular docking and molecular dynamics (MD) were utilized to procure compounds that effectively bind to these targets and their transcription factors (TFs). Finally, the expression and mechanism of key targets were demonstrated in mice with UC or CAC.

RESULTS

(1) Joint analysis of UC and CRC gene sets resulted in 14 hub genes, mainly related to extracellular matrix receptor binding, biological processes in the extracellular matrix, focal adhesion and neutrophil migration; (2) Network pharmacology results show HQD has 133 core targets for treating UC and CRC, acting on extracellular matrix, inflammatory bowel disease, chemical carcinogen receptor activation and other pathways; (3) The intersection of hub genes and core targets yielded two key targets, MMP1 and MMP3; (4) STAT3 is a shared TF of MMP1 and MMP3. (5) Molecular docking and MD verified that the dockings between Glabridin and STAT3/MMP1/MMP3 are stable and reliable; (6) In murine vivo experiments verified that Glabridin reduces inflammation, extracellular matrix degradation, and the occurrence of epithelial-mesenchymal transition to prevent UC transforming into CAC by inhibiting the phosphorylation of STAT3 and regulating the activity of MMP1/3.

Study on therapeutic mechanism of total salvianolic acids against myocardial ischemia-reperfusion injury based on network pharmacology, molecular docking, and experimental study

ETHNOPHARMACOLOGICAL RELEVANCE

Radix Salviae miltiorrhizae, also known as Danshen in Chinese, effectively activates the blood and resolves stasis. Total salvianolic acids (SA) is the main active ingredient of Danshen, and related preparations, such as salvianolate injection are commonly used clinically to treat myocardial ischemia-reperfusion injury (MIRI). However, the potential targets and key active ingredients of SA have not been sufficiently investigated.

AIM OF THE STUDY

This study aimed to investigate the mechanism of action of SA in treating MIRI.

MATERIALS AND METHODS

Network pharmacology and molecular docking techniques were used to predict SA targets against MIRI. The key acting pathway of SA were validated by performing experiments in a rat MIRI model.

RESULTS

Twenty potential ingredients and 54 targets of SA in treating MIRI were identified. Ingredient-target-pathway network analysis revealed that salvianolic acid B and rosmarinic acid had the highest degree value. Pathway enrichment analysis showed that SA may regulate MIRI through the IL-17 signaling pathway, and this result was confirmed in the rat MIRI experiment.

CONCLUSION

The results of this study indicate that SA may protect MIRI by regulating the IL-17 pathway.

The role of interleukin-20 in liver disease: Functions, mechanisms and clinical applications

Liver disease is a severe public health concern worldwide. There is a close relationship between the liver and cytokines, and liver inflammation from a variety of causes leads to the release and activation of cytokines. The functions of cytokines are complex and variable, and are closely related to their cellular origin, target molecules and mode of action. Interleukin (IL)-20 has been studied as a pro-inflammatory cytokine that is expressed and regulated in some diseases. Furthermore, accumulating evidences has shown that IL-20 is highly expressed in clinical samples from patients with liver disease, promoting the production of pro-inflammatory molecules involved in liver disease progression, and antagonists of IL-20 can effectively inhibit liver injury and produce protective effects. This review highlights the potential of targeting IL-20 in liver diseases, elucidates the potential mechanisms of IL-20 inducing liver injury, and suggests multiple viable strategies to mitigate the pro-inflammatory response to IL-20. Genomic CRISPR/Cas9-based screens may be a feasible way to further explore the signaling pathways and regulation of IL-20 in liver diseases. Nanovector systems targeting IL-20 offer new possibilities for the treatment and prevention of liver diseases.

Potential mechanisms of traditional Chinese medicine in treating insomnia: A network pharmacology, GEO validation, and molecular-docking study

The purpose of this study is to investigate the potential mechanisms of Chinese herbs for the treatment of insomnia using a combination of data mining, network pharmacology, and molecular-docking validation. All the prescriptions for insomnia treated by the academician Qi Wang from 2020 to 2022 were collected. The Ancient and Modern Medical Case Cloud Platform v2.3 was used to identify high-frequency Chinese medicinal herbs and the core prescription. The Traditional Chinese Medicine Systems Pharmacology and UniProt databases were utilized to predict the effective active components and targets of the core herbs. Insomnia-related targets were collected from 4 databases. The intersecting targets were utilized to build a protein-protein interaction network and conduct gene ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis using the STRING database, Cytoscape software, and clusterProfiler package. Gene chip data (GSE208668) were obtained from the Gene Expression Omnibus database. The limma package was applied to identify differentially expressed genes (DEGs) between insomnia patients and healthy controls. To create a "transcription factor (TF)-miRNA-mRNA" network, the differentially expressed miRNAs were entered into the TransmiR, FunRich, Targetscan, and miRDB databases. Subsequently, the overlapping targets were validated using the DEGs, and further validations were conducted through molecular docking and molecular dynamics simulations. Among the 117 prescriptions, 65 herbs and a core prescription were identified. Network pharmacology and bioinformatics analysis revealed that active components such as β-sitosterol, stigmasterol, and canadine acted on hub targets, including interleukin-6, caspase-3, and hypoxia-inducible factor-1α. In GSE208668, 6417 DEGs and 7 differentially expressed miRNAs were identified. A "TF-miRNA-mRNA" network was constructed by 4 "TF-miRNA" interaction pairs and 66 "miRNA-mRNA" interaction pairs. Downstream mRNAs exert therapeutic effects on insomnia by regulating circadian rhythm. Molecular-docking analyses demonstrated good docking between core components and hub targets. Molecular dynamics simulation displayed the strong stability of the complex formed by small molecule and target. The core prescription by the academician Qi Wang for treating insomnia, which involves multiple components, targets, and pathways, showed the potential to improve sleep, providing a basis for clinical treatment of insomnia.

Unraveling the Global Proteome and Phosphoproteome of Prostate Cancer Patient-Derived Xenografts

Resistance to androgen-deprivation therapies leads to metastatic castration-resistant prostate cancer (mCRPC) of adenocarcinoma (AdCa) origin that can transform into emergent aggressive variant prostate cancer (AVPC), which has neuroendocrine (NE)-like features. In this work, we used LuCaP patient-derived xenograft (PDX) tumors, clinically relevant models that reflect and retain key features of the tumor from advanced prostate cancer patients. Here we performed proteome and phosphoproteome characterization of 48 LuCaP PDX tumors and identified over 94,000 peptides and 9,700 phosphopeptides corresponding to 7,738 proteins. We compared 15 NE versus 33 AdCa samples, which included six different PDX tumors for each group in biological replicates, and identified 309 unique proteins and 476 unique phosphopeptides that were significantly altered and corresponded to proteins that are known to distinguish these two phenotypes. Assessment of concordance from PDX tumor-matched protein and mRNA revealed increased dissonance in transcriptionally regulated proteins in NE and metabolite interconversion enzymes in AdCa.

IMPLICATIONS

Overall, our study highlights the importance of protein-based identification when compared with RNA and provides a rich resource of new and feasible targets for clinical assay development and in understanding the underlying biology of these tumors.

G-K BertDTA: A graph representation learning and semantic embedding-based framework for drug-target affinity prediction

Developing new drugs is costly, time-consuming, and risky. Drug-target affinity (DTA), indicating the binding capability between drugs and target proteins, is a crucial indicator for drug development. Accurately predicting interaction strength between new drug-target pairs by analyzing previous experiments aids in screening potential drug molecules, repurposing them, and developing safe and effective medicines. Existing computational models for DTA prediction rely on strings or single-graph neural networks, lacking consideration of protein structure and molecular semantic information, leading to limited accuracy. Our experiments demonstrate that string-based methods may overlook protein conformations, causing a high root mean square error (RMSE) of 3.584 in affinity due to a lack of spatial context. Single graph networks also underperform on topology features, with a 6% lower confidence interval (CI) for activity classification. Absent semantic information also limits generalization across diverse compounds, resulting in 18% increment in RMSE and 5% in misclassifications within quantifications study, restricting potential drug discovery. To address these limitations, we propose G-K BertDTA, a novel framework for accurate DTA prediction incorporating protein features, molecular semantic features, and molecular structural information. In this proposed model, we represent drugs as graphs, with a GIN employed to learn the molecular topological information. For the extraction of protein structural features, we utilize a DenseNet architecture. A knowledge-based BERT semantic model is incorporated to obtain rich pre-trained semantic embeddings, thereby enhancing the feature information. We extensively evaluated our proposed approach on the publicly available benchmark datasets (i.e., KIBA and Davis), and experimental results demonstrate the promising performance of our method, which consistently outperforms previous state-of-the-art approaches. Code is available at https://github.com/AmbitYuki/G-K-BertDTA.

Discovery of the potential biomarkers for early diagnosis of endometrial cancer via integrating metabolomics and transcriptomics

Endometrial cancer (EC) is one of the most common malignant tumors in women, and the increasing incidence and mortality pose a serious threat to the public health. Early diagnosis of EC could prolong the survival period and optimize the survivorship, greatly alleviating patients' suffering and social medical pressure. In this study, we collected urine and serum samples from the recruited patients, analyzed the samples using LC-MS approach, and identified the differential metabolites through metabolomic analysis. Then, the differentially expressed genes were identified through the systematic transcriptomic analysis of EC-related dataset from Gene Expression Omnibus (GEO), followed by network profiling of metabolic-reaction-enzyme-gene. In this experiment, a total of 83 differential metabolites and 19 hub genes were discovered, of which 10 different metabolites and 3 hub genes were further evaluated as more potential biomarkers based on network analysis. According to the KEGG enrichment analysis, the potential biomarkers and gene-encoded proteins were found to be involved in the arginine and proline metabolism, histidine metabolism, and pyrimidine metabolism, which was of significance for the early diagnosis of EC. In particular, the combination of metabolites (histamine, 1-methylhistamine, and methylimidazole acetaldehyde) as well as the combination of RRM2, TYMS and TK1 exerted more accurate discrimination abilities between EC and healthy groups, providing more criteria for the early diagnosis of EC.

Wogonin inhibits the migration and invasion of fibroblast-like synoviocytes by targeting PI3K/AKT/NF-κB pathway in rheumatoid arthritis

BACKGROUND

Rheumatoid arthritis (RA) is currently an autoimmune inflammatory disease with an unclear pathogenesis. Fibroblast-like synoviocytes (FLSs) have tumor-like properties, and their activation and secretion of pro-inflammatory factors are important factors in joint destruction. Wogonin (5,7-dihydroxy-8-methoxyflavone), a natural flavonoid isolated from Scutellaria baicalensis root, has been shown to have significant anti-inflammatory, anti-oxidative stress, and anti-tumor effects in a variety of diseases. However, the role of wogonin in RA has not yet been demonstrated.

PURPOSE

To investigate the inhibitory effect of wogonin on the invasive behavior of fibroblast-like synoviocytes and to explore the mechanism of action of wogonin in RA.

METHODS

CCK-8, EdU, cell migration and invasion, immunofluorescence staining, RT-qPCR, and protein blot analysis were used to study the inhibitory effects of wogonin on migration, invasion, and pro-inflammatory cytokine overexpression in the immortalized rheumatoid synovial cell line MH7A. The therapeutic effects of wogonin were validated in vivo using arthritis scores and histopathological evaluation of collagen-induced arthritis mice.

RESULTS

Wogonin inhibited the migration and invasion of MH7A cells, reduced the production of TNF-α, IL-1β, IL-6, MMP-3 and MMP-9, and increased the expression of IL-10. Moreover, wogonin also inhibited the myofibrillar differentiation of MH7A cells, increased the expression of E-cadherin (E-Cad) and decreased the expression of α-smooth muscle actin (α-SMA). In addition, wogonin treatment effectively ameliorated joint destruction in CIA mice. Further molecular mechanism studies showed that wogonin treatment significantly inhibited the activation of PI3K/AKT/NF-κB signaling pathway in TNF-α-induced arthritic FLSs.

CONCLUSION

Wogonin effectively inhibits migration, invasion and pro-inflammatory cytokine production of RA fibroblast-like synoviocytes through the PI3K/AKT/NF-κB pathway, and thus wogonin, as a natural flavonoid, has great potential for treating RA.

The landscape of small-molecule prodrugs

Prodrugs are derivatives with superior properties compared with the parent active pharmaceutical ingredient (API), which undergo biotransformation after administration to generate the API in situ. Although sharing this general characteristic, prodrugs encompass a wide range of different chemical structures, therapeutic indications and properties. Here we provide the first holistic analysis of the current landscape of approved prodrugs using cheminformatics and data science approaches to reveal trends in prodrug development. We highlight rationales that underlie prodrug design, their indications, mechanisms of API release, the chemistry of promoieties added to APIs to form prodrugs and the market impact of prodrugs. On the basis of this analysis, we discuss strengths and limitations of current prodrug approaches and suggest areas for future development.

Advances in machine intelligence-driven virtual screening approaches for big-data

Virtual screening (VS) is an integral and ever-evolving domain of drug discovery framework. The VS is traditionally classified into ligand-based (LB) and structure-based (SB) approaches. Machine intelligence or artificial intelligence has wide applications in the drug discovery domain to reduce time and resource consumption. In combination with machine intelligence algorithms, VS has emerged into revolutionarily progressive technology that learns within robust decision orders for data curation and hit molecule screening from large VS libraries in minutes or hours. The exponential growth of chemical and biological data has evolved as "big-data" in the public domain demands modern and advanced machine intelligence-driven VS approaches to screen hit molecules from ultra-large VS libraries. VS has evolved from an individual approach (LB and SB) to integrated LB and SB techniques to explore various ligand and target protein aspects for the enhanced rate of appropriate hit molecule prediction. Current trends demand advanced and intelligent solutions to handle enormous data in drug discovery domain for screening and optimizing hits or lead with fewer or no false positive hits. Following the big-data drift and tremendous growth in computational architecture, we presented this review. Here, the article categorized and emphasized individual VS techniques, detailed literature presented for machine learning implementation, modern machine intelligence approaches, and limitations and deliberated the future prospects.

DRSPRING: Graph convolutional network (GCN)-Based drug synergy prediction utilizing drug-induced gene expression profile

Great efforts have been made over the years to identify novel drug pairs with synergistic effects. Although numerous computational approaches have been proposed to analyze diverse types of biological big data, the pharmacogenomic profiles, presumably the most direct proxy of drug effects, have been rarely used due to the data sparsity problem. In this study, we developed a composite deep-learning-based model that predicts the drug synergy effect utilizing pharmacogenomic profiles as well as molecular properties. Graph convolutional network (GCN) was used to represent and integrate the chemical structure, genetic interactions, drug-target information, and gene expression profiles of cell lines. Insufficient amount of pharmacogenomic data, i.e., drug-induced expression profiles from the LINCS project, was resolved by augmenting the data with the predicted profiles. Our method learned and predicted the Loewe synergy score in the DrugComb database and achieved a better or comparable performance compared to other published methods in a benchmark test. We also investigated contribution of various input features, which highlighted the value of basal gene expression and pharmacogenomic profiles of each cell line. Importantly, DRSPRING (DRug Synergy PRediction by INtegrated GCN) can be applied to any drug pairs and any cell lines, greatly expanding its applicability compared to previous methods.

Symptom-based drug prediction of lifestyle-related chronic diseases using unsupervised machine learning techniques

BACKGROUND AND OBJECTIVES

Lifestyle-related diseases (LSDs) impose a substantial economic burden on patients and health care services. LSDs are chronic in nature and can directly affect the heart and lungs. Therapeutic interventions only based on symptoms can be crucial for prompt treatment initiation in LSDs, as symptoms are the first information available to clinicians. So, this work aims to apply unsupervised machine learning (ML) techniques for developing models to predict drugs from symptoms for LSDs, with a specific focus on pulmonary and heart diseases.

METHODS

The drug-disease and disease-symptom associations of 143 LSDs, 1271 drugs, and 305 symptoms were used to compute direct associations between drugs and symptoms. ML models with four different algorithms - K-Means, Bisecting K-Means, Mean Shift, and Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) - were developed to cluster the drugs using symptoms as features. The optimal model was saved in a server for the development of a web application. A web application was developed to perform the prediction based on the optimal model.

RESULTS

The Bisecting K-means model showed the best performance with a silhouette coefficient of 0.647 and generated 138 drug clusters. The drugs within the optimal clusters showed good similarity based on i) gene ontology annotations of the gene targets, ii) chemical ontology annotations, and iii) maximum common substructure of the drugs. In the web application, the model also provides a confidence score for each predicted drug while predicting from a new set of input symptoms.

CONCLUSION

In summary, direct associations between drugs and symptoms were computed, and those were used to develop a symptom-based drug prediction tool for LSDs with unsupervised ML models. The ML-based prediction can provide a second opinion to clinicians to aid their decision-making for early treatment of LSD patients. The web application (URL - http://bicresources.jcbose.ac.in/ssaha4/sdldpred) can provide a simple interface for all end-users to perform the ML-based prediction.

Intranasal administration of the essential oil from Perillae Folium ameliorates social defeat stress-induced behavioral impairments in mice

ETHNOPHARMACOLOGICAL RELEVANCE

Perillae Folium, the leaves and twigs of Perilla frutescens (L.) Britton, has been included in many traditional Chinese medicine herbal formulas to treat depression. However, the precise antidepressant mechanism of the essential oil from Perillae Folium (PFEO) has not been fully investigated.

AIM OF THE STUDY

To assess the effects and potential mechanisms of PFEO on depression using animal models and network pharmacology analysis.

MATERIALS AND METHODS

PFEO was intranasally administered to a mouse model of social defeat stress (SDS). The antidepressant effects of PFEO on SDS-induced mice were evaluated using behavioral tests. Enzyme-linked immunosorbent assay (ELISA) and western blot were performed to measure the levels of depression-related biomarkers in the hippocampus and serum of the mice. The chemical compounds of PFEO were determined using gas chromatography-mass spectrometry (GC-MS). Network pharmacology and molecular docking analyses were conducted to investigate the potential bioactive components of PFEO and the mechanisms underlying the antidepressant effects. To validate the mechanisms of the bioactive compounds, in vitro models using PC12 and BV2 cells were established and the blood-brain barrier (BBB) permeability was evaluated.

RESULTS

The intranasal administration of PFEO suppressed SDS-induced depression in mice by increasing the time spent in the social zone and the social interactions in the social interaction test and by decreasing the immobility time in the tail suspension and forced swimming tests. Moreover, the PFEO treatment reduced the SDS-induced anxiety-like behavior, as inferred from the increased activity in the central zone observed in the open field test and in the open arms observed in the elevated plus maze test. PFEO administration recovered the SDS-induced decrease in the levels of 5-HT, NE, gamma-aminobutyric acid (GABA), and p-ERK in the hippocampus of mice. Furthermore, the increased serum corticosterone level was also attenuated by the PFEO treatment. A total of 21 volatile compounds were detected in PFEO using GC-MS, among which elemicin (15.52%), apiol (15.16%), and perillaldehyde (12.79%) were the most abundant ones. The PFEO compounds targeted 32 depression-associated genes, which were mainly related to neural cells and neurotransmission pathways. Molecular docking indicated good binding affinities between the bioactive components of PFEO (apiol, β-caryophyllene, elemicin, and myristicin) and the key targets, including ACHE, IL1B, IL6, MAOB, SLC6A2, SLC6A3, SLC6A4, and tumor necrosis factor. Among the four compounds, β-caryophyllene, elemicin, and myristicin were more effective in reducing neurotoxicity and neuroinflammation. Elemicin showed the highest BBB permeability rate.

CONCLUSIONS

This study shows the antidepressant activities of PFEO in an SDS-induced mouse model and suggests its potential mechanisms of action: regulation of the corticosterone levels, hippocampal neurotransmitters, and ERK signaling. Apiol, β-caryophyllene, elemicin, and myristicin may be the main contributors to the observed effects induced by PFEO. Further studies are needed to fully elucidate the underlying mechanisms and the main PFEO bioactive components.

Screening of anti-melanoma compounds from Morus alba L.: Sanggenon C promotes melanoma cell apoptosis by disrupting intracellular Ca2+ homeostasis

ETHNOPHARMACOLOGICAL RELEVANCE

Morus alba L. is a widespread plant that has long been considered to have remarkable medical values, including anti-inflammation in Traditional Chinese Medicine (TCM). The components of Morus Alba L. constituents have been extensively studied and have been shown to have high prospects for cancer therapy. However, limited investigations have been done on the bioactive compounds in Morus alba L.

AIM OF THE STUDY

This study aimed to systematically examine the anticancer properties of 28 commercially available compounds from Morus alba L. against melanoma cells in vitro. Additionally, the anticancer mechanisms of the bioactive compound exhibiting the most significant potential were further studied.

MATERIALS AND METHODS

The anti-proliferative effects of Morus alba L.-derived compounds on melanoma cells were determined by colony formation assays. Their effects on cell viability and apoptosis were determined using the CCK8 assay and flow cytometry, respectively. The binding affinity of identified Morus alba L. compounds with anticancer activities towards melanoma targets was analyzed via molecular docking. The molecular mechanism of Sanggenon C was explored using soft agar assays, EdU incorporation assays, flow cytometry, western blotting, transcriptome analysis, and xenograft assays.

RESULTS

Based on colony formation assays, 11 compounds at 20 μM significantly inhibited colony growth on a panel of melanoma cells. These compounds displayed IC50 values (half maximal inhibitory concentrations) ranging from 5 μM to 30 μM. Importantly, six compounds were identified as novel anti-melanoma agents, including Sanggenon C, 3'-Geranyl-3-prenyl-2',4',5,7-tetrahydroxyflavone, Moracin P, Moracin O, Kuwanon A, and Kuwanon E. Among them, Sanggenon C showed the most potent effects, with an IC50 of about 5 μM, significantly reducing proliferation and inducing apoptosis in melanoma cells. Based on the xenograft model assay, Sanggenon C significantly inhibited melanoma cell proliferation in vivo. Sanggenon C triggered ER stress in a dose-dependent manner, which further disrupted cellular calcium ion (Ca2+) homeostasis. The Ca2+ chelator BAPTA partially restored cell apoptosis induced by Sanggenon C, confirming that Ca2+ signaling contributed to the anticancer activity of Sanggenon C against melanoma.

CONCLUSIONS

In our study, 11 compounds demonstrated anti-melanoma properties. Notably, Sanggenon C was found to promote apoptosis by disrupting the intracellular calcium homeostasis in melanoma cells. This study provides valuable information for the future development of novel cancer therapeutic agents from Morus alba L.

Differential gene expression and miRNA regulatory network in coronary slow flow

Coronary slow flow (CSF) is characterized by slow progression of coronary angiography without epicardial stenosis. The aim of this study was to explore the potential biomarkers and regulatory mechanism for CSF. Peripheral blood mononuclear cells from 3 cases of CSF and 3 healthy controls were collected for high-throughput sequencing of mRNA and miRNA, respectively. The differentially expressed mRNAs (DE-mRNAs) and miRNAs (DE-miRNAs) was identified. A total of 117 DE-mRNAs and 32 DE-miRNAs were obtained and they were mainly enriched in immune and inflammatory responses. Twenty-six DE-mRNAs were the predicted target genes for miRNAs by RAID, and then the regulatory network of 15 miRNAs were constructed. In addition, through the PPI network, we identified the three genes (FPR1, FPR2 and CXCR4) with larger degrees as hub genes. Among them, FPR1 was regulated by hsa-miR-342-3p, hsa-let-7c-5p and hsa-miR-197-3p and participated in the immune response. Finally, we validated the differential expression of hub genes and key miRNAs between 20 CSF and 20 control. Moreover, we found that miR-342-3p has a targeted regulatory relationship with FPR1, and their expression is negatively correlated. Then we established a hypoxia/reoxygenation (H/R) HUVEC model and detected FPR1, cell proliferation and apoptosis. Transfection with miR-342-3p mimics can significantly promote the proliferation of HUVEC under H/R conditions. FPR1 were associated with CSF as a biomarker and may be regulated by miR-342-3p potential biomarkers.

Network Pharmacology Analysis of Liquid-Cultured Armillaria ostoyae Mycelial Metabolites and Their Molecular Mechanism of Action against Gastric Cancer

Armillaria sp. are traditional edible medicinal mushrooms with various health functions; however, the relationship between their composition and efficacy has not yet been determined. Here, the ethanol extract of liquid-cultured Armillaria ostoyae mycelia (AOME), a pure wild Armillaria sp. strain, was analyzed using UHPLC-QTOF/MS, network pharmacology, and molecular docking techniques. The obtained extract affects various metabolic pathways, such as JAK/STAT and PI3K/AKT. The extract also contains important compounds such as 4-(dimethylamino)-N-[7-(hydroxyamino)-7-oxoheptyl] benzamide, isoliquiritigenin, and 7-hydroxycoumarin. Moreover, the extract targets key proteins, including EGFR, SCR, and IL6, to suppress the progression of gastric cancer, thereby synergistically inhibiting cancer development. The molecular docking analyses indicated that the main compounds stably bind to the target proteins. The final cell culture experimental data showed that the ethanol extract inhibited MGC-803 gastric cancer cells. In summary, our research revealed the beneficial components of AOME for treating gastric cancer and its associated molecular pathways. However, further research is needed to confirm its effectiveness and safety in gastric cancer patients.

Network pharmacology, molecular docking and experimental study of CEP in nasopharyngeal carcinoma

ETHNOPHARMACOLOGICAL RELEVANCE

The Stephania cephalantha Hayata is an important traditional medicinal plant widely used in traditional medicine to treat cancer. Cepharanthine (CEP) was extracted from the roots of Stephania cephalantha Hayata. It has been found to exhibit anticancer activity in different types of cancer cells. Nevertheless, the activity of CEP against nasopharyngeal carcinoma (NPC) and its underlying mechanism warrant further investigation.

AIMS OF THE STUDY

NPC is an invasive and highly metastatic malignancy that affects the head and neck region. This research aimed to investigate the pharmacological properties and underlying mechanism of CEP against NPC, aiming to offer novel perspectives on treating NPC using CEP.

MATERIALS AND METHODS

In vitro, the pharmacological activity of CEP against NPC was evaluated using the CCK-8 assay. To predict and elucidate the anticancer mechanism of CEP against NPC, we employed network pharmacology, conducted molecular docking analysis, and performed Western blot experiments. In vivo validation was performed through a nude mice xenograft model of human NPC, Western blot and immunohistochemical (IHC) assays to confirm pharmacological activity and the mechanism.

RESULTS

In a dose-dependent manner, the proliferation and clonogenic capacity of NPC cells were significantly inhibited by CEP. Additionally, NPC cell migration was suppressed by CEP. The results obtained from network pharmacology experiments revealed that anti-NPC effect of CEP was associated with 8 core targets, including EGFR, AKT1, PIK3CA, and mTOR. By performing molecular docking, the binding capacity of CEP to the candidate core proteins (EGFR, AKT1, PIK3CA, and mTOR) was predicted, resulting in docking energies of -10.0 kcal/mol for EGFR, -12.4 kcal/mol for PIK3CA, -10.8 kcal/mol for AKT1, and -8.6 kcal/mol for mTOR. The Western blot analysis showed that CEP effectively suppressed the expression of EGFR and the phosphorylation levels of downstream signaling proteins, including PI3K, AKT, mTOR, and ERK. After CEP intervention, a noteworthy decrease in tumor size, without inducing any toxicity, was observed in NPC xenograft nude mice undergoing in vivo treatment. Additionally, IHC analysis demonstrated a significant reduction in the expression levels of EGFR and Ki-67 following CEP treatment.

CONCLUSION

CEP exhibits significant pharmacological effects on NPC, and its mechanistic action involves restraining the activation of the EGFR/PI3K/AKT pathway. CEP represents a promising pharmaceutical agent for addressing and mitigating NPC.

Unveiling Gene Interactions in Alzheimer's Disease by Integrating Genetic and Epigenetic Data with a Network-Based Approach

Alzheimer's Disease (AD) is a complex disease and the leading cause of dementia in older people. We aimed to uncover aspects of AD's pathogenesis that may contribute to drug repurposing efforts by integrating DNA methylation and genetic data. Implementing the network-based tool, a dense module search of genome-wide association studies (dmGWAS), we integrated a large-scale GWAS dataset with DNA methylation data to identify gene network modules associated with AD. Our analysis yielded 286 significant gene network modules. Notably, the foremost module included the BIN1 gene, showing the largest GWAS signal, and the GNAS gene, the most significantly hypermethylated. We conducted Web-based Cell-type-Specific Enrichment Analysis (WebCSEA) on genes within the top 10% of dmGWAS modules, highlighting monocyte as the most significant cell type (p < 5 × 10-12). Functional enrichment analysis revealed Gene Ontology Biological Process terms relevant to AD pathology (adjusted p < 0.05). Additionally, drug target enrichment identified five FDA-approved targets (p-value = 0.03) for further research. In summary, dmGWAS integration of genetic and epigenetic signals unveiled new gene interactions related to AD, offering promising avenues for future studies.