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Objective:To analyze the improvement effect of monk fruit on diabetic nephropathy(DN)by network pharmacology,and to elucidate its possible related mechanism.Methods:The Traditional Chinese Medicine Systems Pharmacology(TCMSP)Database was used to detect the active ingredients and their targets of monk fruit;the DN target genes were screened out by DisGeNET Database and Genecards Database;the key targets of monk fruit against DN were obtained by comparing the monk fruit with DN targets;protein-protein interaction(PPI)network diagram was constructed by STRING Database and Cytoscape software;Gene Ontology(GO)functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)signaling pathway enrichment analysis were performed by Cytoscape software.Molecular docking technology was used to predict the binding abilities of the core targets and the main active ingredients of monk fruit.Results:The TCMSP Database combined with the selection criteria was used to screen out a total of five active ingredients of monk fruit(ZINC03860434,Perlolyrine,beta-sitosterol,Kaempferol,and Flazin)as well as 85 targets represented by serine/threonine protein kinase 1(AKT1),transcription factor RELA,c-Jun N-terminal kinase(JUN),and tumor necrosis factor(TNF).Among them,Kaempferol contained the most targets.Among the 85 targets,34 were associated with DN.The GO functional enrichment analysis mainly included biological process(BP)such as oxidative stress,regulation of inflammation and apoptosis,and cell signaling transduction.The KEGG enrichment analysis included advanced glycosylation end product(AGE)-receptor of AGE(AGE-RAGE)signaling pathway,TNF signaling pathway,and C-type lectin receptor signaling pathway.The results molecular docking technology of the main active ingredients of monk fruit and DN target proteins showed that 5 kinds of molecular docking engergy were-8.00--5.00 kJ·mol-1.Conclusion:Kaempferol is the most effective active ingredient in the monk fruit for the treatment of DN,and its mechanism is mainly related to anti-inflammatory.
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BACKGROUND:The mechanisms and targets of alendronate in the treatment of osteoporosis still need to be investigated in depth. OBJECTIVE:To investigate the mechanism by which alendronate regulates bone metabolism in rats with osteoporosis and to perform a bioinformatics analysis of differentially expressed proteins. METHODS:Female Sprague-Dawley rats were randomly divided into three groups(n=12 per group):model group,alendronate group and sham-operated group.Animal models of osteoporosis were prepared using ovariectomy in the model and alendronate groups.At 4 weeks after modeling,rats in the alendronate group were gavaged with alendronate;the other two groups were given the equal volume of normal saline.After 12 weeks of continuous gavage,the bone mineral density of the tibia was measured and the lumbar spine of the rats was taken for proteomic analysis using Tandem mass tag-liquid chromatography-tandem mass spectrometry technique to identify differentially expressed proteins for gene ontology,Kyoto Encyclopedia of Genes and Genomes pathway and protein-protein interaction analysis. RESULTS AND CONCLUSION:There were 32 up-regulated proteins and 51 down-regulated proteins identified between the alendronate group and model group.Gene ontology enrichment analysis showed that the differentially expressed proteins were mainly involved in molecular functions,such as binding and catalytic activity,and in biological processes,such as cellular process and metabolic process.Kyoto Encylopedia of Genes and Genomes enrichment analysis showed that the differentially expressed proteins in the alendronate group and model group were mainly involved in the biosynthesis of pantothenate and coenzyme A.Protein-protein interaction analysis indicated that among the differentially expressed proteins in the alendronate group and model group,Hspa1l,Enpp3,Unc45a,Myh9 and Cant1 were located at the nodes of the protein-protein interaction network and were closely related to bone metabolism.Overall,these findings indicate that alendronate may regulate bone metabolism in the rat model of osteoporosis by regulating the expression of differentially expressed proteins and biosynthesis of pantothenate and coenzyme A.
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AIM: To identify immune-related key genes and the extent of immune cell infiltration in a oxygen-induced retinopathy(OIR)model by bioinformatics method.METHODS: Microarray data were obtained from the GEO database, differentially expressed genes(DEGs)were identified using the “limma” R package, GO function enrichment and KEGG pathway analysis were conducted, and immune cell infiltration based on the CIBERSORT algorithm was analyzed. Weighted gene co-expression network analysis(WGCNA)was used to screen DEGs in the immune-related gene module, constructing a protein-protein interaction(PPI)network using STRING online database and Cytoscape software, and further screening final target genes using the cytoHubba plug-in.RESULTS: A total of 467 DEGs were screened, including 270 up-regulated and 197 down-regulated genes. Helper T cell 2(Th2 cells), an immune cell type, exhibited significantly high expression levels(P<0.05). WGCNA analysis identified 66 differential genes in modules most closely related to immunity. After constructing the PPI network, 5 key genes were screened through plug-ins: von Willebrand factor(vWF), vascular endothelial growth factor A(VEGF-A), Serping1, apoptosis inducing factor 1(AIF1)and signal transducers and activators of transcription 3(STAT3).CONCLUSION: The findings of this study provide valuable insights into immune cell infiltration in OIR as well as key genes related to immunity through bioinformatics analysis, which can serve as a reference for further research and treatment of retinal neovascular diseases.
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ObjectiveTo identify the pharmacodynamic substances for the anti-inflammatory and analgesic effects of Bupleuri Radix by structure-activity omics. MethodA mouse model of pain was established with formaldehyde to examine the anti-inflammatory and analgesic effects of saikosaponins in vivo. The core targets of the active components in Bupleurum Radix for the anti-inflammatory and analgesic effects were screened from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform （TCMSP）， Online Mendelian Inheritance in Man （OMIM）， and Search Tool for Recurring Instances of Neighbouring Genes （STRING）. The key core targets with high binding affinity were screened based on the comprehensive score in the molecular docking between different types of saikosaponins and core targets. The structure-activity relationship was discussed and analyzed based on the binding of compounds to pharmacodynamic targets. ResultSaikosaponins alleviated the foot swelling induced by formaldehyde and reduced the content of prostaglandin E2 （PGE2） in the mouse model， showcasing a significant inhibitory effect on the inflammatory pain caused by PGE2. Nine components and 39 targets of saikosaponins， as well as 3 074 targets of anti-inflammatory and analgesic effects were screened out， and 22 common targets shared by saikosaponins and the effects were obtained as the direct targets. The protein-protein interaction （PPI） analysis showed that the main active components of Bupleurum Radix were saikosaponins a， b1， b2， b3， c， d， e， f， and v， and the key targets were fms-related receptor tyrosine kinase 1 （FLT1）， kinase insert domain receptor （KDR）， fibroblast growth factor 2 （FGF2）， vascular endothelial growth factor A （VEGFA）， and signal transducer and activator of transcription 3 （STAT3）. Molecular docking between saikosaponins and the top 5 targets with high degrees in PPI network analysis revealed 25 highly active docks， including 6 docks with scores of 5-6 and 18 docks with scores above 6. ConclusionThis study adopted structural-activity omics to analyze the material basis for the anti-inflammatory and analgesic effects of Bupleuri Radix in vivo， providing new ideas and methods for identifying the pharmacodynamic substances in traditional Chinese medicine.
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Objective To screen key autophagy-related genes in alcoholic hepatitis (AH) and investigate potential biomarkers and therapeutic targets for AH. Methods Two AH gene chips in Gene Expression Omnibus (GEO) and autophagy-related data sets obtained from MSigDB and GeneCards databases were used, and the key genes were verified and obtained by weighted gene co-expression network analysis (WGCNA). The screened key genes were subject to gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein-protein interaction (PPI) and immune infiltration analyses. Messenger RNA (mRNA)- microRNA (miRNA) network was constructed to analyze the expression differences of key autophagy-related genes during different stages of AH, which were further validated by real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) in the liver tissues of AH patients and mice. Results Eleven autophagy-related genes were screened in AH (EEF1A2, CFTR, SOX4, TREM2, CTHRC1, HSPB8, TUBB3, PRKAA2, RNASE1, MTCL1 and HGF), all of which were up-regulated. In the liver tissues of AH patients and mice, the relative expression levels of SOX4, TREM2, HSPB8 and PRKAA2 in the AH group were higher than those in the control group. Conclusions SOX4, TREM2, HSPB8 and PRKAA2 may be potential biomarkers and therapeutic targets for AH.
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Glutamate-ammonia ligase (GLUL, also known as glutamine synthetase) is a crucial enzyme that catalyzes ammonium and glutamate into glutamine in the ATP-dependent condensation. Although GLUL plays a critical role in multiple cancers, the expression and function of GLUL in gastric cancer remain unclear. In the present study, we have found that the expression level of GLUL was significantly lower in gastric cancer tissues compared with adjacent normal tissues, and correlated with N stage and TNM stage, and low GLUL expression predicted poor survival for gastric cancer patients. Knockdown of GLUL promoted the growth, migration, invasion and metastasis of gastric cancer cells in vitro and in vivo, and vice versa, which was independent of its enzyme activity. Mechanistically, GLUL competed with β-Catenin to bind to N-Cadherin, increased the stability of N-Cadherin and decreased the stability of β-Catenin by alerting their ubiquitination. Furthermore, there were lower N-Cadherin and higher β-Catenin expression levels in gastric cancer tissues compared with adjacent normal tissues. GLUL protein expression was correlated with that of N-Cadherin, and could be the independent prognostic factor in gastric cancer. Our findings reveal that GLUL stabilizes N-Cadherin by antagonizing β-Catenin to inhibit the progress of gastric cancer.
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AIM: To explore the key genes related to immunity and immune cell infiltration levels in diabetes retinopathy(DR)using bioinformatics.METHODS: Differential expression genes(DEGs)were obtained by “limma” R from Gene Expression Omnibus(GEO)data from September to October 2022, Gene ontology(GO)and Kyoto encyclopedia of genes and genomes(KEGG)were analyzed, and the infiltration of immune cell types in each sample was calculated based on CIBERSORT algorithm. Weighted gene co-expression network analysis(WGCNA)was used to screen for DEGs in immune-related gene modules. The protein-protein interaction(PPI)network was established by STRING online database and Cytoscape, and the hub genes were screened by MCODE and cytoHubba plug-ins.RESULTS: The results showed that 1 426 up-regulated and 206 down-regulated differential genes were screened, where 7 immune cell types, including B cell naive, Plasma cells, CD4+T cells, T cells regulatory(Tregs), Macrophages M0, Macrophages M1 and Neutrophils were significantly overexpressed(P<0.05), while others were low expressed(P<0.05). After WGCNA, a total of 820 DEGs were found in the modules most related to immunity. After constructing the PPI network, 10 key genes were screened using plug-ins, and two key genes were further screened using the expression amount of each differential gene in PPI: DLGAP5 and AURKB.CONCLUSION: This study used bioinformatics to screen the infiltration of immune cells and key genes related to immunity in patients with DR. These findings may provide evidences for future research, diagnosis, and treatment of DR.
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Objective:To investigate the complex Calculus Bovis-target-keratitis network and to explore the molecular mechanism of Calculus Bovis treating keratitis through network pharmacology. Methods:Genes related to keratitis were searched in the online DisGeNET database and the protein-protein interaction (PPI) network of keratitis-associated proteins was constructed.The components isolated and identified in Calculus Bovis were collected through the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, https: //tcmsp-e.com/tcmsp.php), Chemistry Database by Shanghai Institute of Organic Chemistry of CAS (http: //www.organchem.csdb.cn), and published literature.The canonical SMILES information of the collected components was exported, which were submitted to the SwissTargetPrediction platform to predict potential targets of the components.The active component-predicted target network of Calculus Bovis was constructed and merged with the PPI network of keratitis-associated proteins to build the active component-potential target network of Calculus Bovis and systemically investigate the potential targets and signal pathways of Calculus Bovis in treatment of keratitis.The component-target-pathway network was established to analyze the mechanism of Calculus Bovis treating keratitis. Results:Thirty-nine components isolated and identified in Calculus Bovis were searched and 65 target genes related to keratitis were screened.Of the 28 potential targets involved in Calculus Bovis treating keratitis, there were 7 direct targets, including tumor necrosis factor, caspase 1, Toll-like receptor 9, C-X-C motif chemokine ligand 8, interleukin-6, mitogen-activated protein kinase 8, neurotrophic receptor tyrosine kinase 1.The 28 potential targets were annotated to 12 entries for biological process, 18 for cellular components and 13 for molecular function.In the Kyoto encyclopedia of genes and genomes pathway enrichment analysis, 10 signal pathways were identified as enriched categories, which were mainly related to human cytomegalovirus infection, amoebiasis, antifolate resistance, PI3K-Akt signaling pathway, rheumatoid arthritis, apoptosis, cytokine-cytokine receptor interaction, malaria, non-alcoholic fatty liver disease, interleukin-17 signaling pathway. Conclusions:Calculus Bovis may play an adjuvant therapeutic effect on keratitis through anti-inflammatory, antibacterial, antiviral, immune regulation, inflammatory regulation and other functions.
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Pulmonary fibrosis (PF) is the pathological structure of incurable fibroproliferative lung diseases that are attributed to the repeated lung injury-caused failure of lung alveolar regeneration (LAR). Here, we report that repetitive lung damage results in a progressive accumulation of the transcriptional repressor SLUG in alveolar epithelial type II cells (AEC2s). The abnormal increased SLUG inhibits AEC2s from self-renewal and differentiation into alveolar epithelial type I cells (AEC1s). We found that the elevated SLUG represses the expression of the phosphate transporter SLC34A2 in AEC2s, which reduces intracellular phosphate and represses the phosphorylation of JNK and P38 MAPK, two critical kinases supporting LAR, leading to LAR failure. TRIB3, a stress sensor, interacts with the E3 ligase MDM2 to suppress SLUG degradation in AEC2s by impeding MDM2-catalyzed SLUG ubiquitination. Targeting SLUG degradation by disturbing the TRIB3/MDM2 interaction using a new synthetic staple peptide restores LAR capacity and exhibits potent therapeutic efficacy against experimental PF. Our study reveals a mechanism of the TRIB3-MDM2-SLUG-SLC34A2 axis causing the LAR failure in PF, which confers a potential strategy for treating patients with fibroproliferative lung diseases.
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Tuberculosis (TB) is one of the deadly diseases caused by Mycobacterium tuberculosis (Mtb), which presents a significant public health challenge. Treatment of TB relies on the combination of several anti-TB drugs to create shorter and safer regimens. Therefore, new anti-TB agents working by different mechanisms are urgently needed. FtsZ, a tubulin-like protein with GTPase activity, forms a dynamic Z-ring in cell division. Most of FtsZ inhibitors are designed to inhibit GTPase activity. In Mtb, the function of Z-ring is modulated by SepF, a FtsZ binding protein. The FtsZ/SepF interaction is essential for FtsZ bundling and localization at the site of division. Here, we established a yeast two-hybrid based screening system to identify inhibitors of FtsZ/SepF interaction in M. tuberculosis. Using this system, we found compound T0349 showing strong anti-Mtb activity but with low toxicity to other bacteria strains and mice. Moreover, we have demonstrated that T0349 binds specifically to SepF to block FtsZ/SepF interaction by GST pull-down, fluorescence polarization (FP), surface plasmon resonance (SPR) and CRISPRi knockdown assays. Furthermore, T0349 can inhibit bacterial cell division by inducing filamentation and abnormal septum. Our data demonstrated that FtsZ/SepF interaction is a promising anti-TB drug target for identifying agents with novel mechanisms.
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Objective To identify and verify the interacting protein of α-11 giardin, so as provide the experimental evidence for studies on the α-11 giardin function. Methods The yeast two-hybrid cDNA library of the Giardia lambia C2 strain and the bait plasmid of α-11 giardin were constructed. All proteins interacting with α-11 giardin were screened using the yeast two-hybrid system. α-11 giardin and all screened potential interacting protein genes were constructed into pBiFc-Vc-155 and pBiFc-Vn-173 plasmids, and co-transfected into the breast cancer cell line MDA-MB-231. The interactions between α-11 giardin and interacting proteins were verified using bimolecular fluorescence complementation (BiFC). Results The yeast two-hybrid G. lambia cDNA library which was quantified at 2.715 × 107 colony-forming units (CFU) and the bait plasmid containing α-11 giardin gene without an autoactivation activity were constructed. Following two-round positive screening with the yeast two-hybrid system, two potential proteins interacting with α-11 giardin were screened, including eukaryotic translation initiation factor 5A (EIF5A), calmodulin-dependent protein kinase (CAMKL) and nicotinamide adenine dinucleotide phosphate-specific glutamate dehydrogenase (NADP-GDH), hypothetical protein 1 (GL50803_95880), hypothetical protein 2 (GL50803_87261) and a protein from Giardia canis virus. The α-11 giardin and EIF5A genes were transfected into the pBiFc-Vc-155 and pBiFc-Vn-173 plasmids using BiFC, and the recombinant plasmids pBiFc-Vc-155-α-11 and pBiFc-Vn-173-EIF5A were co-tranfected into MDA-MB-231 cells, which displayed green fluorescence under a microscope, indicating the interaction between α-11 giardin and EIF5A protein in cells. Conclusion The yeast two-hybrid cDNA library of the G. lambia C2 strain has been successfully constructed, and six potential protein interacting with α-11 giardin have been identified, including EIF5A that interacts with α-11 giardin in cells.
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Purpose: Age?related macular degeneration (AMD) is the leading cause of irreversible blindness in older individuals. More studies focused on screening the genes, which may be correlated with the development of AMD. With advances in various technologies like multiple microarray datasets, researchers could identify differentially expressed genes (DEGs) more accurately. Exploring abnormal gene expression in disease status can help to understand pathophysiological changes in complex diseases. This study aims to identify the key genes and upstream regulators in AMD and reveal factors, especially genetic association, and the prognosis of the development of this disease. Methods: Data from expression profile GSE125564 and profile GSE29801 were obtained from the Gene Expression Omnibus (GEO) database. We analyzed DEGs using R software (version 3.6.3). Functional enrichment and PPI network analysis were performed using the R package and online database STRING (version 11.0). Results: We compared AMD with normal and found 68 up?regulated genes (URGs) and 25 down?regulated genes (DRGs). We also compared wet AMD with dry AMD and found 41 DRGs in dry AMD. Further work including PPI network analysis, GO classification, and KEGG analysis was done to find connections with AMD. The URGs were mainly enriched in the biological process such as DNA replication, nucleoplasm, extracellular exosome, and cadherin binding. Besides, DRGs were mainly enriched in these functions such as an integral component of membrane and formation of the blood?aqueous barrier (BAB). Conclusion: This study implied that core genes might involve in the process of AMD. Our findings may contribute to revealing the pathogenesis, developing new biomarkers, and raising strategies of treatment for AMD
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Identifying the key proteins among different types of human disease-causing coronaviruses is essential for the molecular mechanism and thereby designing potential drug molecules. Eight selected proteins of seven types of disease-causing coronaviruses, viz.SARS-CoV-2 (severe acute respiratory syndrome coronavirus2), SARS-CoV (severe acute respiratory syndrome coronavirus), MERS-CoV (middle east respiratory syndrome coronavirus), Human coronavirus OC43, Human coronavirus HKU1, Human coronavirus 229E and Human coronavirus NL63, were chosen for the comparison. Further, an attempt has been made to explore the most important host-pathogen interactions with a special focus on spike (RBD) protein region as this region deemed to be functionally most important. Epitope region was also identified which helps in the design of epitope-based vaccines. The structural comparison carried out among the seven types of human coronaviruses has revealed the molecular level details on the similarity among this series. This study has facilitated the identification of the important residues in the studied proteins which control the key functions such as viral replication and transmission. Thus, exploring the protein space in the family of coronaviruses, provide valuable insights into the molecular basis associated with the role of proteins and viral infections, which is expected to trigger the identification of the drug targets for coronaviruses infections, in a rational way.
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Identifying the key proteins among different types of human disease-causing coronaviruses is essential for the molecular mechanism and thereby designing potential drug molecules. Eight selected proteins of seven types of disease-causing coronaviruses, viz.SARS-CoV-2 (severe acute respiratory syndrome coronavirus2), SARS-CoV (severe acute respiratory syndrome coronavirus), MERS-CoV (middle east respiratory syndrome coronavirus), Human coronavirus OC43, Human coronavirus HKU1, Human coronavirus 229E and Human coronavirus NL63, were chosen for the comparison. Further, an attempt has been made to explore the most important host-pathogen interactions with a special focus on spike (RBD) protein region as this region deemed to be functionally most important. Epitope region was also identified which helps in the design of epitope-based vaccines. The structural comparison carried out among the seven types of human coronaviruses has revealed the molecular level details on the similarity among this series. This study has facilitated the identification of the important residues in the studied proteins which control the key functions such as viral replication and transmission. Thus, exploring the protein space in the family of coronaviruses, provide valuable insights into the molecular basis associated with the role of proteins and viral infections, which is expected to trigger the identification of the drug targets for coronaviruses infections, in a rational way.
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Identifying the key proteins among different types of human disease-causing coronaviruses is essential for the molecular mechanism and thereby designing potential drug molecules. Eight selected proteins of seven types of disease-causing coronaviruses, viz.SARS-CoV-2 (severe acute respiratory syndrome coronavirus2), SARS-CoV (severe acute respiratory syndrome coronavirus), MERS-CoV (middle east respiratory syndrome coronavirus), Human coronavirus OC43, Human coronavirus HKU1, Human coronavirus 229E and Human coronavirus NL63, were chosen for the comparison. Further, an attempt has been made to explore the most important host-pathogen interactions with a special focus on spike (RBD) protein region as this region deemed to be functionally most important. Epitope region was also identified which helps in the design of epitope-based vaccines. The structural comparison carried out among the seven types of human coronaviruses has revealed the molecular level details on the similarity among this series. This study has facilitated the identification of the important residues in the studied proteins which control the key functions such as viral replication and transmission. Thus, exploring the protein space in the family of coronaviruses, provide valuable insights into the molecular basis associated with the role of proteins and viral infections, which is expected to trigger the identification of the drug targets for coronaviruses infections, in a rational way.
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Bronchial asthma is a common chronic disease of airway inflammation, high mucus secretion and airway hyper responsiveness. The pathogenetic mechanisms of asthma remain unclear. In this study, we aimed at identifying genes playing an import role in disease-related pathways in airway epithelial cells of asthma patients. Microarray data GSE41861 of asthma airway epithelial cells was used to screen differentially expressed genes (DEGs) through GEO2R analysis. The weighted gene co-expression network analysis (WGCNA) was performed to identify gene co-expression network modules in bronchial asthma. The DAVID database was then used to perform functional and pathway enrichment analysis of these DEGs. In addition, we have conducted protein-protein interaction (PPI) network of DEGs by STRING, and eventually found key genes and significant modules. A total of 315 DEGs (111 up-regulated and 204 down-regulated) were identified between severe asthma and healthy individual, which were mainly involved in pathways of cilium assembly, cilium morphogenesis, axon guidance, positive regulation of fat cell differentiation, and positive regulation of cell substrate adhesion. A total of 60 genes in the black module and green module were considered to be correlated with the severity of asthma. Combining PPI network, several key genes were identified, such as BP2RY14, PTGS1, SLC18A2, SIGLEC6, RGS13, CPA3, and HPGDS. Our findings revealed several genes that may be involved in the process of development of bronchial asthma and potentially be candidate targets for diagnosis or therapy of bronchial asthma.
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@#Proximity-dependent biotinylation (PDB) uses biotin ligase fused to the protein of interest to biotinylate adjacent proteins, purify them with streptavidin beads, and then identify the biotinylated protein by mass spectrometry.This technology can be used to detect transient and/or low affinity interactions, provide a chance to learn more about membrane-less organelles and other subcellular structures that cannot be easily isolated or purified, and fill the gap in traditional methods.This article summarizes the technological development and application of PDB in recent years.
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Cyclin-dependent kinase 9 (CDK9) activity is correlated with worse outcomes of triple-negative breast cancer (TNBC) patients. The heterodimer between CDK9 with cyclin T1 is essential for maintaining the active state of the kinase and targeting this protein-protein interaction (PPI) may offer promising avenues for selective CDK9 inhibition. Herein, we designed and generated a library of metal complexes bearing the 7-chloro-2-phenylquinoline CˆN ligand and tested their activity against the CDK9-cyclin T1 PPI. Complex 1 bound to CDK9 via an enthalpically-driven binding mode, leading to disruption of the CDK9-cyclin T1 interaction in vitro and in cellulo. Importantly, complex 1 showed promising anti-metastatic activity against TNBC allografts in mice and was comparably active compared to cisplatin. To our knowledge, 1 is the first CDK9-cyclin T1 PPI inhibitor with anti-metastatic activity against TNBC. Complex 1 could serve as a new platform for the future design of more efficacious kinase inhibitors against cancer, including TNBC.
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Molecular glues are a class of small molecules that induce the formation of protein-protein interactions to confer new biological function or therapeutic effects. As a unique pharmacological modality, molecular glues could target proteins without druggable binding pockets. It exhibits a variety of functions, including regulating signal transduction, stabilization or degradation of targeted proteins, through sticking different proteins together. This review will summarize the development and current status of molecular glues derived from natural products and analogs by illustrating the discovery and interaction mechanism. We hope to present a systematic view, provide valuable clues for researchers and encourage them to explore more efficient and rational molecular glue discovery strategies.
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Background The rise of single cell RNA sequencing (scRNA-seq) and spatial transcriptome sequencing technologies has allowed for intensive study of lung diseases, but both have been poorly studied in silicosis. Objective To explore differentially expressed genes DEGs in silicosis macrophages by scRNA-seq combined with spatial transcriptome sequencing and analyze the potential diagnostic genes. Methods Male C57BL/6 mice (5-6 weeks old, 22-30 g) were randomly divided into 4 groups: normal saline (NS) group for 7 d, NS group for 56 d, SiO2 group for 7 d, and SiO2 group for 56 d, with 1 mouse in each group. A silicosis model was constructed by tracheal drip injection of SiO2 suspension (0.2 g·kg−1, 50 g·cm−2), and the control mice were given the same volume of NS. The right lung was removed for scRNA-seq and the left lung for spatial transcriptome sequencing on day 7 and day 56, respectively. Cell populations were captured using principal component analysis techniques and dimensionality reduction of uniform manifold approximation and projection. The Find Markers function in R language was applied to analyze the DEGs changes of macrophages in two groups of lung tissues, and the corresponding DEGs were subjected to Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes signaling pathway analysis, while STRING and CytoHubba plug-ins of Cytoscape software were applied to protein-protein interaction network analysis to screen out key (Hub) genes. Spatial transcriptome sequencing was used to explore the original location of Hub genes on lung tissue sections and their mapping in lung macrophages. Finally, the correlation of Hub gene expression levels in lung tissues of silicosis patients and mouse silicosis models was verified, the diagnostic efficacy of Hub gene using subject operating characteristic curves (ROC). In vitro experiments by applying cell viability assay were conducted to verify the changes in viability of mouse macrophages (RAW264.7) under SiO2 stimulation. Results The scRNA-seq revealed a total of 20 clusters captured and defined. The results of scRNA-seq and spatial transcriptome sequencing showed an increased number of macrophages in the lung tissue of the SiO2 group compared to the NS group and clustered in the focal areas. Among the 97 macrophage DEGs screened out, 75 were up-regulated genes, and mainly enriched in chemotaxis and migration of neutrophils, chemokine receptor binding, tumor necrosis factor signaling pathway, cytokine-cytokine receptor interaction pathway, and interleukin-17 signaling pathway; and 22 were down-regulated genes, and mainly enriched in late endosomes, peroxisome proliferator-activated receptors signaling pathway, and alcoholic liver disease signaling pathway. A total of 2 core modules and 3 Hub genes were screened out, including Ccl2, Ccl7, and Ptgs2. The scRNA-seq showed that they were expressed at elevated levels in the SiO2 group compared to the NS group and clustered in additional macrophages, and the spatial transcriptome sequencing showed that they clustered in inflammatory areas with nodular lesions. The CCL7 and PTGS2 expressions were increased in the lung tissue of SiO2 patients compared with the healthy subjects, and the areas under the working curve of the subjects were 0.850 and 0.786, respectively. The viability of RAW264.7 cells was enhanced under SiO2 stimulation at 3 h, 6 h, and 12 h compared to those without the stimulation (P<0.05). Conclusion Bioinformatics screening have identified 3 Hub genes (Ccl2, Ccl7, and Ptgs2)and 2 potential diagnostic genes (CCL7 and PTGS2) in the lung tissue of silicosis mice, which may be potential molecular markers of early-stage silicosis with implications for the development and prognosis of silicosis.