Identification of EML4 as a key hub gene for endometriosis and its molecular mechanism and potential drug prediction based on the GEO database

Objective:Key genes were screened to analyze molecular mechanisms and their drug targets of endometriosis by applying a bioinformatics approach.Methods:Gene expression profiles of endometriosis and healthy controls were obtained from the Gene Expression Omnibus database.Significant differentially expressed genes were screened using the limma package.Correlation pathways were screened by Spearman correlation analysis on the echinoderm microtubule-associated protein-like 4(EML4)and enrichment in endometriosis pathways and estimated by the GSVA package.Immune characteristics were assessed by the“ESTIMATE”R package.Potential regulatory pathways were determined by enrichment analysis.The SWISS-MODE website was used in homology modeling with EML4 and EML4 protein activity was predicted.VarElect was employed in molecular docking for screening potential compound inhibitors targeting endometriosis.Results:Ten endometriosis and 10 normal samples were included.EML4 was significantly upregulated in endometriosis(p<0.05).Thirty significantly correlated pathways involving 18 positive and 12 negative correlations,including GLYCOSAMINOGLYCAN_BIOSYNTHESIS_HEPARAN_SULFATE and GLYCOSPHINGOLIPID_BIOSYNTHESIS_GANGLIO_SERIES were screened between EML4 and endometriosis.Immunocorrelation analysis showed a significant difference in immune-related pathways in endometriosis and normal samples(p<0.05).In endometriosis,EML4 was associated with T-cell CD4 resting memory,activated mast cells,plasma cells,activated NK cells,M2 macrophages,and follicular helper T cells(p<0.05).Molecular docking identified five potential inhibitors of EML4,and compound DB05104(asimadoline)bound well to EML4 protein to exert its physiological effects.Conclusion:Differential gene expression and immune correlation analyses revealed that EML4 may affect endometriosis through multiple targets and pathways,the mechanism of which involved immune cell activation and infiltration.Molecular docking and dynamics simulation verified DB05104 as a potential inhibitor of EML4 and a powerful target for endometriosis treatment.

Preliminary delivery efficiency prediction of nanotherapeutics into crucial cell populations in bone marrow niche

Several crucial stromal cell populations regulate hematopoiesis and malignant diseases in bone marrow niches.Precise regulation of these cell types can remodel niches and develop new therapeutics.Multiple nanocarriers have been developed to transport drugs into the bone marrow selectively.However,the delivery efficiency of these nanotherapeutics into crucial niche cells is still unknown,and there is no method available for predicting delivery efficiency in these cell types.Here,we constructed a three-dimensional bone marrow niche composed of three crucial cell populations:endothelial cells(ECs),mesenchymal stromal cells(MSCs),and osteoblasts(OBs).Mimetic niches were used to detect the cellular uptake of three typical drug nanocarriers into ECs/MSCs/OBs in vitro.Less than 5%of nanocarriers were taken up by three stromal cell types,and most of themwere located in the extracellular matrix.Delivery efficiency in sinusoidal ECs,arteriole ECs,MSCs,and OBs in vivo was analyzed.The correlation analysis showed that the cellular uptake of three nanocarriers in crucial cell types in vitro is positively linear correlated with its delivery efficiency in vivo.The delivery efficiency into MSCs was remarkably higher than that into ECs and OBs,no matterwhat kind of nanocarrier.The overall efficiency into sinusoidal ECswas greatly lower than that into arteriole ECs.All nanocarriers were hard to be delivered into OBs(<1%).Our findings revealed that cell tropisms of nanocarriers with different compositions and ligand attachments in vivo could be predicted via detecting their cellular uptake in bone marrow niches in vitro.This study provided the methodology for niche-directed nanotherapeutics development.

Ligand Based Virtual Screening of Molecular Compounds in Drug Discovery Using GCAN Fingerprint and Ensemble Machine Learning Algorithm

The drug development process takes a long time since it requires sorting through a large number of inactive compounds from a large collection of compounds chosen for study and choosing just the most pertinent compounds that can bind to a disease protein.The use of virtual screening in pharmaceutical research is growing in popularity.During the early phases of medication research and development,it is crucial.Chemical compound searches are nowmore narrowly targeted.Because the databases containmore andmore ligands,thismethod needs to be quick and exact.Neural network fingerprints were created more effectively than the well-known Extended Connectivity Fingerprint(ECFP).Only the largest sub-graph is taken into consideration to learn the representation,despite the fact that the conventional graph network generates a better-encoded fingerprint.When using the average or maximum pooling layer,it also contains unrelated data.This article suggested the Graph Convolutional Attention Network(GCAN),a graph neural network with an attention mechanism,to address these problems.Additionally,it makes the nodes or sub-graphs that are used to create the molecular fingerprint more significant.The generated fingerprint is used to classify drugs using ensemble learning.As base classifiers,ensemble stacking is applied to Support Vector Machines(SVM),Random Forest,Nave Bayes,Decision Trees,AdaBoost,and Gradient Boosting.When compared to existing models,the proposed GCAN fingerprint with an ensemble model achieves relatively high accuracy,sensitivity,specificity,and area under the curve.Additionally,it is revealed that our ensemble learning with generated molecular fingerprint yields 91%accuracy,outperforming earlier approaches.

PREDICTION OF THE THERAPEUTIC EFFECTIVENESS OF NEW DRUGS FROM CLINICAL PHARMACOLOGY STUDIES

The development of new drugs for therapeutic purposes has become very expensive and time-consuming in American and European countries.It is estimated that on the average 50 to 100 million dollars and 10 or more years from the time of patenting are required to make a new drug available for general prescription. Every new drug needs to be charac-

Link Prediction based on Tensor Decomposition for the Knowledge Graph of COVID-19 Antiviral Drug

Due to the large-scale spread of COVID-19,which has a significant impact on human health and social economy,developing effective antiviral drugs for COVID-19 is vital to saving human lives.Various biomedical associations,e.g.,drug-virus and viral protein-host protein interactions,can be used for building biomedical knowledge graphs.Based on these sources,large-scale knowledge reasoning algorithms can be used to predict new links between antiviral drugs and viruses.To utilize the various heterogeneous biomedical associations,we proposed a fusion strategy to integrate the results of two tensor decomposition-based models(i.e.,CP-N3 and Compl Ex-N3).Sufficient experiments indicated that our method obtained high performance(MRR=0.2328).Compared with CP-N3,the mean reciprocal rank(MRR)is increased by 3.3%and compared with Compl Ex-N3,the MRR is increased by 3.5%.Meanwhile,we explored the relationship between the performance and relationship types,which indicated that there is a negative correlation(PCC=0.446,P-value=2.26 e-194)between the performance of triples predicted by our method and edge betweenness.

Screening of Biomarkers for Hypertension Susceptibility in Pregnancy

Objective:To study the differential lncRNA/mRNA expression profiles of placental tissues in patients with gestational hypertension,analyze their possible mechanisms of action,and explore their target genes and small molecule drug-related lncRNAs.Methods:Three patients with gestational hypertension who were treated in our hospital from May 2018 to May 2019 were selected as the research subjects and three healthy pregnant women who underwent a prenatal examination in the same hospital were selected as the control group.The placental tissues were taken from the patients.RNA-sequencing was performed to construct lncRNA/mRNA differential expression profiles;screening differentially expressed lncRNAs were used to predict target genes,and GO and KEGG enrichment analysis predicted the biological functions of target genes and the enriched signal pathways,respectively.Protein-protein interaction network,lncRNA-miRNAmRNA network,and differentially expressed genesmall molecule drug association networks were constructed.Results:RNA-seq analysis revealed 19 differentially expressed lncRNA(4 up-regulated;15 down-regulated)(P<0.05).Moreover,423 differentially expressed genes(DEGs)(84 up-regulated;339 downregulated)(P<0.05).GO and KEGG enrichment analysis found that gestational hypertension is mainly related to endothelial cell damage,inflammatory response,abnormal immune regulation,and abnormal trophoblast invasion.The PPI network and lncRNA-miRNA-mRNA network were constructed.Differentially expressed gene-drug small molecule prediction results found 19 pairs of differentially gene-small drug relationship pairs,mainly including antibody,inhibitor et al.Conclusion:Differently expressed lncRNAs in the placenta of patients with gestational hypertension can participate in the regulation of multiple biological functional levelrelated signal pathways through targeted regulation of their target genes,and play an important role in the occurrence and development of gestational hypertension.The predicted small molecule drug can be used as a reference for clinical treatment.

Bioinformatics analysis of SARS-CoV-2 infectionassociated immune injury and therapeutic prediction for COVID-19

Background:Severe acute respiratory syndrome coronavirus 2 is a highly contagious viral infection,without any available targeted therapies.The high mortality rate of COVID-19 is speculated to be related to immune damage.Methods:In this study,clinical bioinformatics analysis was conducted on transcriptome data of coronavirus infection.Results:Bioinformatics analysis revealed that the complex immune injury induced by coronavirus infection provoked dysfunction of numerous immune-related molecules and signaling pathways,including immune cells and toll-like receptor cascades.Production of numerous cytokines through the Th17 signaling pathway led to elevation in plasma levels of cytokines(including IL6,NF-kB,and TNF-a)followed by concurrent inflammatory storm,which mediates the autoimmune response.Several novel medications seemed to display therapeutic effects on immune damage associated with coronavirus infection.Conclusions:This study provided insights for further large-scale studies on the target therapy on reconciliation of immunological damage associated with COVID-19.

Liver tissue microbiota in nonalcoholic liver disease:a change in the paradigm of host-bacterial interactions

Nonalcoholic fatty liver disease(NAFLD)pathogenesis is explained by the complex relationship among diet and lifestyle-predisposing factors,the genetic variance of the nuclear and mitochondrial genome,associated phenotypic traits,and the yet not fully explored interactions with epigenetic and other environmental factors,including the microbiome.Despite the wealth of knowledge gained from molecular and genome-wide investigations in patients with NAFLD,the precise mechanisms that explain the variability of the histological phenotypes are not fully understood.Earlier studies of the gut microbiota in patients with NAFLD and nonalcoholic steatohepatitis(NASH)provided clues on the role of the fecal microbiome in the disease pathogenesis.Nevertheless,the composition of the gut microbiota does not fully explain tissue-specific mechanisms associated with the degree of disease severity,including liver inflammation,ballooning of hepatocytes,and fibrosis.The liver acts as a key filtration system of the whole body by receiving blood from the hepatic artery and the portal vein.Therefore,not only microbes would become entrapped in the complex liver anatomy but,more importantly,bacterial derived products that are likely to be potentially powerful stimuli for initiating the inflammatory response.Hence,the study of liver tissue microbiota offers the opportunity of changing the paradigm of host-NAFLD-microbial interactions from a“gut-centric”to a“liver-centric”approach.Here,we highlight the evidence on the role of liver tissue bacterial DNA in the biology of NAFLD and NASH.Besides,we provide evidence of metagenomic findings that can serve as the seed of further hypothesis-raising studies as well as can be leveraged to discover novel therapeutic targets.