Meta-QTL analysis for mining of candidate genes and constitutive gene network development for fungal disease resistance in maize(Zea mays L.)

The development of resistant maize cultivars is the most effective and sustainable approach to combat fungal diseases.Over the last three decades,many quantitative trait loci(QTL)mapping studies reported numerous QTL for fungal disease resistance(FDR)in maize.However,different genetic backgrounds of germplasm and differing QTL analysis algorithms limit the use of identified QTL for comparative studies.The meta-QTL(MQTL)analysis is the meta-analysis of multiple QTL experiments,which entails broader allelic coverage and helps in the combined analysis of diverse QTL mapping studies revealing common genomic regions for target traits.In the present study,128(33.59%)out of 381 reported QTL(from 82 studies)for FDR could be projected on the maize genome through MQTL analysis.It revealed 38 MQTL for FDR(12 diseases)on all chromosomes except chromosome 10.Five MQTL namely 1_4,2_4,3_2,3_4,and 5_4 were linked with multiple FDR.Total of 1910 candidate genes were identified for all the MQTL regions,with protein kinase gene families,TFs,pathogenesis-related,and disease-responsive proteins directly or indirectly associated with FDR.The comparison of physical positions of marker-traits association(MTAs)from genome-wide association studies with genes underlying MQTL interval verified the presence of QTL/candidate genes for particular diseases.The linked markers to MQTL and putative candidate genes underlying identified MQTL can be further validated in the germplasm through marker screening and expression studies.The study also attempted to unravel the underlying mechanism for FDR resistance by analyzing the constitutive gene network,which will be a useful resource to understand the molecular mechanism of defense-response of a particular disease and multiple FDR in maize.

Creutzfeldt-Jakob disease presenting as Korsakoff syndrome caused by E196A mutation in PRNP gene:A case report

BACKGROUND Prion diseases are a group of degenerative nerve diseases that are caused by infectious prion proteins or gene mutations.In humans,prion diseases result from mutations in the prion protein gene(PRNP).Only a limited number of cases involving a specific PRNP mutation at codon 196(E196A)have been reported.The coexistence of Korsakoff syndrome in patients with Creutzfeldt-Jakob disease(CJD)caused by E196A mutation has not been documented in the existing literature.CASE SUMMARY A 61-year-old Chinese man initially presented with Korsakoff syndrome,followed by rapid-onset dementia,visual hallucinations,akinetic mutism,myoclonus,and hyperthermia.The patient had no significant personal or familial medical history.Magnetic resonance imaging of the brain revealed extensive hyperintense signals in the cortex,while positron emission tomography/computed tomography showed a diffuse reduction in cerebral cortex metabolism.Routine biochemical and microorganism testing of the cerebrospinal fluid(CSF)yielded normal results.Tests for thyroid function,human immunodeficiency virus,syphilis,vitamin B1 and B12 levels,and autoimmune rheumatic disorders were normal.Blood and CSF tests for autoimmune encephalitis and autoantibody-associated paraneoplastic syndrome yielded negative results.A test for 14-3-3 protein in the CSF yielded negative results.Whole-genome sequencing revealed a diseasecausing mutation in PRNP.The patient succumbed to the illness 11 months after the initial symptom onset.CONCLUSION Korsakoff syndrome,typically associated with alcohol intoxication,also manifests in CJD patients.Individuals with CJD along with PRNP E196A mutation may present with Korsakoff syndrome.

Identification of Potential Therapeutic Targets of Alzheimer's Disease By Weighted Gene Co-Expression Network Analysis

Objective Alzheimer's disease(AD)is the most common cause of dementia.The pathophysiology of the disease mostly remains unearthed,thereby challenging drug development for AD.This study aims to screen high throughput gene expression data using weighted co-expression network analysis(WGCNA)to explore the potential therapeutic targets.Methods The dataset of GSE36980 was obtained from the Gene Expression Omnibus(GEO)database.Normalization,quality control,filtration,and soft-threshold calculation were carried out before clustering the co-expressed genes into different modules.Furthermore,the correlation coefiidents between the modules and clinical traits were computed to identify the key modules.Gene ontology and pathway enrichment analyses were performed on the key module genes.The STRING database was used to construct the protein-protein interaction(PPI)networks,which were further analyzed by Cytoscape app(MCODE).Finally,validation of hub genes was conducted by external GEO datasets of GSE 1297 and GSE 28146.Results Co-expressed genes were clustered into 27 modules,among which 6 modules were identified as the key module relating to AD occurrence.These key modules are primarily involved in chemical synaptic transmission(G0:0007268),the tricarboxylic acid(TCA)cycle and respiratory electron transport(R-HSA-1428517).WDR47,OXCT1,C3orfl4,ATP6V1A,SLC25A14,NAPB were found as the hub genes and their expression were validated by external datasets.Conclusions Through modules co-expression network analyses and PPI network analyses,we identified the hub genes of AD,including WDR47,0XCT1,C3orfl4i ATP6V1A,SLC25A14 and NAPB.Among them,three hub genes(ATP6V1A,SLC25A14,OXCT1)might contribute to AD pathogenesis through pathway of TCA cycle.

Identification and Evaluation of Insect and Disease Resistance in Transgenic Cry1Ab13-1 and NPR1 Maize

PCR detection,quantitative real-time PCR(q-RTPCR),outdoor insect resistance,and disease resistance identification were carried out for the detection of genetic stability and disease resistance through generations(T2,T3,and T4)in transgenic maize germplasms(S3002 and 349)containing the bivalent genes(insect resistance gene Cry1Ab13-1 and disease resistance gene NPR1)and their corresponding wild type.Results indicated that the target genes Cry1Ab13-1 and NPR1 were successfully transferred into both germplasms through tested generations;q-PCR confirmed the expression of Cry1Ab13-1 and NPR1 genes in roots,stems,and leaves of tested maize plants.In addition,S3002 and 349 bivalent gene-transformed lines exhibited resistance to large leaf spots and corn borer in the field evaluation compared to the wild type.Our study confirmed that Cry1Ab13-1 and NPR1 bivalent genes enhanced the resistance against maize borer and large leaf spot disease and can stably inherit.These findings could be exploited for improving other cultivated maize varieties.

Identifying Biomarkers for Diabetic Kidney Disease Using GraphSAGE Neural Network

Diabetic Kidney Disease (DKD) is a common chronic complication of diabetes. Despite advancements in accurately identifying biomarkers for detecting and diagnosing this harmful disease, there remains an urgent need for new biomarkers to enable early detection of DKD. In this study, we modeled publicly available transcriptome datasets as a graph problem and used GraphSAGE Neural Networks (GNNs) to identify potential biomarkers. The GraphSAGE model effectively learned representations that captured the intricate interactions, dependencies among genes, and disease-specific gene expression patterns necessary to classify samples as DKD and Control. We finally extracted the features of importance;the identified set of genes exhibited an impressive ability to distinguish between healthy and unhealthy samples, even though these genes differ from previous research findings. The unexpected biomarker variations in this study suggest more exploration and validation studies for discovering biomarkers in DKD. In conclusion, our study showcases the effectiveness of modeling transcriptome data as a graph problem, demonstrates the use of GraphSAGE models for biomarker discovery in DKD, and advocates for integrating advanced machine-learning techniques in DKD biomarker research, emphasizing the need for a holistic approach to unravel the intricacies of biological systems.

Multi-target mechanism of triphala in cardio-cerebral vascular diseases based on network pharmacology

OBJECTIVE Numerous references made clear that triphala is revered as a multiuse therapeutic and perhaps even panacea historically.Nevertheless,the protective mechanism of triphala on cardio-cerebral vascular diseases(CCVDs)remains not comprehensive understanding.Hence,a network pharmacology-based method was suggested in this study to address this problem.METHODS This study was based on network pharmacology and bioinformatics analysis.Information on compounds in herbal medicines of triphala formula was acquired from public databases.Oral bioavailability as well as drug-likeness were screened by using absorption,distribution,metabolism,and excretion(ADME)criteria.Then,components of triphala,candidate targets of each component and known therapeutic targets of CCVDs were collected.Compound-target gene and compounds-CCVDs target networks were created through network pharmacology data sources.In addition,key targets and pathway enrichment were analyzed by STRING database and DAVID database.Moreover,we verified three of the key targets(PTGS2,MMP9 and IL-6)predicted by using Western blotting analysis.RESULTS Network analysis determined 132 compounds in three herbal medicines that were subjected to ADME screening,and 23 compounds as well as 65 genes formed the principal pathways linked to CCVDs.And 10 compounds,which actually linked to more than three genes,are determined as crucial chemicals.Core genes in this network were IL-6,TNF,VEGFA,PTGS2,CXCL8,TP53,CCL2,IL-10,MMP9 and SERPINE1.And pathways in cancer,TNF signaling path⁃way,neuroactive ligand-receptor interaction,etc.related to CCVDs were identified.In vitro experiments,the results indi⁃cated that compared with the control group(no treatment),PTGS2,MMP9 and IL-6 were up-regulated by treatment of 10μg·L^-1 TNF-α,while pretreatment with 20-80 mg·L^-1 triphala could significantly inhibit the expression of PTGS2,MMP9 and IL-6.With increasing Triphala concentration,the expression of PTGS2,MMP9 and IL-6 decreased.CON⁃CLUSION Complex components and pharmacological mechanism of triphala,and obtained some potential therapeutic targets of CCVDs,which could provide theoretical basis for the research and development of new drugs for treating CCVDs.

Gene therapy in Parkinson's disease: targeting the endplasmic reticulum proteostasis network

Parkinson’s disease（PD）is the second most common neurodegenerative disease affecting 1%of the population over 60 years of age.The progressive degeneration of dopaminergic neurons at the substantia nigra pars compacta（SNpc）results in a severe and gradual depletion of dopamine content in the striatum,a phenomena that is responsible for the characteristic motor symptoms of this disease.

Screening of NKX2-5,GATA4,ZIC3 gene mutations in sporadic congenital simple heart disease in Hainan Province

Objective:Congenital heart disease(CHD)is caused by abnormal cardiac development,which is the most common congenital malformation at home and abroad.NKX2-5,GATA4 and ZIC3 have been shown to be associated with CHD.This experiment explored the relationship between NKX2-5,GATA4 and ZIC3 gene mutations and sporadic CHD in Hainan Province.Methods:To collect 210 sporadic CHD patients in Hainan,the DNA of patients was extracted from blood,and the target gene fragments were amplified.Using high-resolution melting(HRM)and DNA sequencing technology,and we analyzed the sequences of NKX2-5,GATA4 and ZIC3 genes.Results:NKX2-5,GATA4 and ZIC3 genes were sequenced in 210 CHD patients,and seven gene mutations were found,including NKX2-5 heterozygous missense mutation(c.178G>T)and three heterozygous mutations in GATA4(c.677C>T,c.928A>G,c.1123G>A),three heterozygous mutations in ZIC3(c.19G>C,c.1255C>G,c.1348C>T),in which NKX2-5(c.178G>T),GATA4(c.1123G>A),and ZIC3(c.1255C>G,c.1348C>T)are new mutation sites.These gene mutations were predicted to be pathogenic mutations by bioinformatics software.Conclusion:Conclusion:Seven gene mutations were found in 210 patients,and it was the first report that the gene mutations of NKX2-5,GATA4 and ZIC3 in Hainan Province associated with the pathogenesis of CHD.

Screening key target genes for coronavirus disease 2019 based on bioinformatics and gene network

Objective:To use bioinformatics and gene networks to screen key target genes of coronavirus disease 2019,which provides references for clinical research and development of drugs for coronavirus disease 2019.Methods:Target genes related to coronavirus disease 2019 were screened in the GeneCards and National Center for Biotechnology Information databases,and the obtained gene data were imported into the Database for Annotation,Visualization and Integrated Discovery(Version 6.8)database to collect the related information about pathways and genes.The genes enriched in the first 20 pathways and the genes whose occurrence frequency≥5 were imported into the String database respectively to construct protein-protein interaction network diagram and compare the two network diagrams.Results:TNF,IL-6,IL-2,IL-8,CXCL8,IL1B,CCL2,IFNG,STAT1,MAPK1,MAPK3,MAPK8,TP53 and RELA are ranked top in the two network diagrams,and the frequency of occurrence in the first 20 pathways was≥5.Conclusion:The incidence of coronavirus disease 2019 is associated with multiple signaling pathways,including influenza A,pathways in cancer,toll-like receptor signaling pathway,hypoxia-inducible factor-1 signaling pathway,et al.TNF,IL-6,IL-2,IL-8,CXCL8,IL1B,CCL2,IFNG,STAT1,MAPK1,MAPK3,MAPK8,TP53 and RELA are closely related to coronavirus disease 2019,which needs to be further studied.By analyzing the pathways of the genes related to coronavirus disease 2019 and the interactive network diagrams between the genes,it is helpful to understand the pathogenesis of the disease and provide a reference for clinical research and development of effective drugs for coronavirus disease 2019.

lncRNACNN3-206 activates intestinal epithelial cell apoptosis and invasion by sponging miR-212, an implication for Crohn’s disease

BACKGROUND Statistics indicate that the incidence of Crohn’s disease(CD)is rising in many countries.The poor understanding on the pathological mechanism has limited the development of effective therapy against this disease.Previous studies showed that long noncoding RNAs(lncRNAs)could be involved in autoimmune diseases including CD,but the detailed molecular mechanisms remain unclear.AIM To identify the differentially expressed lncRNAs in the intestinal mucosa associated with CD,and to characterize their pathogenic role(s)and related mechanisms.METHODS The differential expression of lncRNAs was screened by high-throughput RNA sequencing,and the top candidate genes were validated in an expanded cohort by real-time PCR.The regulatory network was predicted by bioinformatic software and competitive endogenous RNA analysis,and was characterized in Caco-2 and HT-29 cell culture using methods of cell transfection,real-time PCR,Western blotting analysis,flow cytometry,and cell migration and invasion assays.Finally,these findings were confirmed in vivo using a CD animal model.RESULTS The 3'end of lncRNACNN3-206 and the 3’UTR of Caspase10 contain highaffinity miR212 binding sites.lncRNACNN3-206 expression was found to be significantly increased in intestinal lesions of CD patients.Activation of the lncRNACNN3-206-miR-212-Caspase10 regulatory network led to increased apoptosis,migration and invasion in intestinal epithelial cells.Knockdown of lncRNACNN3-206 expression alleviated intestinal mucosal inflammation and tissue damage in the CD mouse model.CONCLUSION lncRNACNN3-206 may play a key role in CD pathogenesis.lncRNACNN3-206 could be a therapeutic target for CD treatment.

Analysis of hippocampal gene expression profile of Alzheimer's disease model rats using genome chip bioinformatics

In this study, an Alzheimer＇s disease model was established in rats through stereotactic injection of condensed amyloid beta 1-40 into the bilateral hippocampus, and the changes of gene expression profile in the hippocampus of rat models and sham-operated rats were compared by genome expression profiling analysis. Results showed that the expression of 50 genes was significantly up-regulated （fold change 〉 2）, while 21 genes were significantly down-regulated in the hippocampus of Alzheimer＇s disease model rats （fold change 〈 0.5） compared with the sham-operation group. The differentially expressed genes are involved in many functions, such as brain nerve system development, neuronal differentiation and functional regulation, cellular growth, differentiation and apoptosis, synaptogenesis and plasticity, inflammatory and immune responses, ion channels/transporters, signal transduction, cell material/energy metabolism. Our findings indicate that several genes were abnormally expressed in the metabolic and signal transduction pathways in the hippocampus of amyloid beta 1 40-induced rat model of Alzheimer＇s disease, thereby affecting the hippocampal and brain functions.

Computational and theoretical modeling of intermediate filament networks:Structure,mechanics and disease

Intermediate filaments, in addition to microtubules and actin microfilaments, are one of the three major components of the cytoskeleton in eukaryotic cells. It was discovered during the recent decades that in most cells, intermediate filament proteins play key roles to reinforce cells subjected to large-deformation, and that they participate in signal transduction, and it was proposed that their nanome- chanical properties are critical to perform those functions. However, it is still poorly understood how the nanoscopic structure, as well as the combination of chemical composition, molecular structure and interfacial properties of these protein molecules contribute to the biomechanical properties of filaments and filament networks. Here we review recent progress in computational and theoretical studies of the intermediate filaments network at various levels in the protein＇s structure. A multiple scale method is discussed, used to couple molecular modeling with atomistic detail to larger-scale material properties of the networked material. It is shown that a finer-trains-coarser method- ology as discussed here provides a useful tool in understanding the biomechanical property and disease mechanism of intermediate filaments, coupling experiment and simulation. It further allows us to improve the understanding of associated disease mechanisms and lays the foundation for engineering the mechanical properties of biomaterials.

MicroRNA and mRNA profiling of cerebral cortex in a transgenic mouse model of Alzheimer’s disease by RNA sequencing

In a previous study,we found that long non-coding genes in Alzheimer’s disease(AD)are a result of endogenous gene disorders caused by the recruitment of microRNA(miRNA)and mRNA,and that miR-200a-3p and other representative miRNAs can mediate cognitive impairment and thus serve as new biomarkers for AD.In this study,we investigated the abnormal expression of miRNA and mRNA and the pathogenesis of AD at the epigenetic level.To this aim,we performed RNA sequencing and an integrative analysis of the cerebral cortex of the widely used amyloid precursor protein and presenilin-1 double transgenic mouse model of AD.Overall,129 mRNAs and 68 miRNAs were aberrantly expressed.Among these,eight down-regulated miRNAs and seven up-regulated miRNAs appeared as promising noninvasive biomarkers and therapeutic targets.The main enriched signaling pathways involved mitogen-activated kinase protein,phosphatidylinositol 3-kinase-protein kinase B,mechanistic target of rapamycin kinase,forkhead box O,and autophagy.An miRNA-mRNA network between dysregulated miRNAs and corresponding target genes connected with AD progression was also constructed.These miRNAs and mRNAs are potential biomarkers and therapeutic targets for new treatment strategies,early diagnosis,and prevention of AD.The present results provide a novel perspective on the role of miRNAs and mRNAs in AD.This study was approved by the Experimental Animal Care and Use Committee of Institute of Medicinal Biotechnology of Beijing,China(approval No.IMB-201909-D6)on September 6,2019.

Protective effects of proanthocyanidins on beta-amyloid peptide (25-35)-induced PC12 cell apoptosis by blocking S-phase and increasing p53 gene expression

BACKGROUND： Current studies related to the effects of proanthocyanidins on Alzheimer＇s disease have focused primarily on the signal transduction pathway of cellular apoptosis. However, the influence of p53 gene expression on cell cycle regulation, with regard to the protective mechanisms of proanthocyanidins, has not been reported. OBJECTIVE： To observe the effect of proanthocyanidins on cell cycle distribution, cellular apoptosis and p53 gene expression in β-amyloid peptide （25-35） （Aβ25-35）-induced PC12 cells cultured in serum-free media, and to investigate the molecular neuroprotective mechanisms of proanthocyanidins with regard to cell cycle regulation. DESIGN, TIME AND SETTING： A parallel, controlled, at the Institute of Biochemistry and Molecular Biology cellular, and molecular study was performed Guangdong Medical College from July 2006 to July 2008. MATERIALS： Proanthocyanidins were provided by Nanjing Xuezi Medical and Chemical Research Center, China; Aβ25-35 was provided by Sigma, USA; PC12 cells were provided by the Institute of Basic Medical Science, Academy of Military Medical Sciences; and rabbit anti-p53 polyclonal antibody was provided by Santa Cruz Biotechnology, USA. METHODS： PC12 cells were cultured in serum-free media for 24 hours. Cells from the model group were treated with 25 μmol/L Aβ25-35 for 24 hours. Cells in the drug protection group were pre-treated with 30 mg/L proanthocyanidins for 1 hour and then treated with 25 μmol/LAβ2^-35 for 24 hours. The control group was not treated. MAIN OUTCOME MEASURES： Flow cytometry was used to detect cell cycle distribution and rate of apoptosis; reverse-transcriptase polymerase chain reaction was used to detect p53 mRNA expression; and Western blot was used to detect p53 protein expression. RESULTS： After treating with 25 μmol/LAβ25-35 for 24 hours, the rate of apoptosis and the percentage of cells in S phase were significantly increased （P 〈 0.01 ）, and p53 mRNA and protein expressions were decreased. Pretreatment with proanthocyanidins for 1 hour blocked the increase in apoptosis and the percentage of cells in S phase in Aβ25-35-induced PC12 cells （P 〈 0.01 ） and increased p53 mRNA and protein expressions. CONCLUSION： Proanthocyanidins blocked apoptosis and S-phase arrest in Aβ25-35-induced PC12 cells cultured in serum-free media. The protective mechanism could be related to increased p53 mRNA and protein expressions.

基于融合特征与GA-SVM算法的脑疾病基因预测

单一生物数据网络提供的特征信息是十分受限的,针对这一问题,提出了一种基于半监督自编码器的多网络特征融合方法,丰富特征信息。此外,为解决在人为设置模型的超参数时,易出现模型性能较低、陷入局部最优等问题,进一步提出了利用遗传算法优化支持向量机(GA-SVM算法)模型的方法,提高脑部疾病基因的预测性能。构建来自不同数据源的相似性数据网络,利用重启随机游走算法从四个数据网络中提取特征,通过半监督自编码器进行处理及融合,在十折交叉验证的策略下使用GA-SVM算法模型预测脑部疾病基因,并与其他算法进行比较。实验结果表明,在PD数据集上的AUC和AUPR值分别为0.805、0.792,而在MDD数据集上的AUC和AUPR值分别为0.825、0.823,均优于已有的预测模型,有效证明了该方法能够提高脑部疾病基因的预测效果。

Semi-Global Inference in Phenotype-Protein Network

Discovering genetic basis of diseases is an important goal and a challenging problem in bioinformatics research. Inspired by network-based global inference approach, Semi-global inference method is proposed to capture the complex associations between phenotypes and genes. The proposed method integrates phenotype similarities and protein-protein interactions, and it establishes the profile vectors of phenotypes and proteins. Then the relevance between each candidate gene and the target phenotype is evaluated. Candidate genes are then ranked according to relevance mark and genes that are potentially associated with target disease are identified based on this ranking. The model selects nodes in integrated phenotype-protein network for inference, by exploiting Phenotype Similarity Threshold (PST), which throws lights on selection of similar phenotypes for gene prediction problem. Different vector relevance metrics for computing the relevance marks of candidate genes are discussed. The performance of the model is evaluated on Online Mendelian Inheritance in Man (OMIM) data sets and experimental evaluation shows high performance of proposed Semi-global method outperforms existing global inference methods.

Prioritization of orphan disease-causing genes using topological feature and GO similarity between proteins in interaction networks

Identification of disease-causing genes among a large number of candidates is a fundamental challenge in human disease studies.However,it is still time-consuming and laborious to determine the real disease-causing genes by biological experiments.With the advances of the high-throughput techniques,a large number of protein-protein interactions have been produced.Therefore,to address this issue,several methods based on protein interaction network have been proposed.In this paper,we propose a shortest path-based algorithm,named SPranker,to prioritize disease-causing genes in protein interaction networks.Considering the fact that diseases with similar phenotypes are generally caused by functionally related genes,we further propose an improved algorithm SPGOranker by integrating the semantic similarity of gene ontology(GO)annotations.SPGOranker not only considers the topological similarity between protein pairs in a protein interaction network but also takes their functional similarity into account.The proposed algorithms SPranker and SPGOranker were applied to 1598 known orphan disease-causing genes from 172 orphan diseases and compared with three state-of-the-art approaches,ICN,VS and RWR.The experimental results show that SPranker and SPGOranker outperform ICN,VS,and RWR for the prioritization of orphan disease-causing genes.Importantly,for the case study of severe combined immunodeficiency,SPranker and SPGOranker predict several novel causal genes.

Community Detection in Disease-Gene Network Based on Principal Component Analysis

The identification of communities is imperative in the understanding of network structures and functions.Using community detection algorithms in biological networks, the community structure of biological networks can be determined, which is helpful in analyzing the topological structures and predicting the behaviors of biological networks. In this paper, we analyze the diseasome network using a new method called disease-gene network detecting algorithm based on principal component analysis, which can be used to investigate the connection between nodes within the same group. Experimental results on real-world networks have demonstrated that our algorithm is more efficient in detecting community structures when compared with other well-known results.

NOGEA: A Network-oriented Gene Entropy Approach for Dissecting Disease Comorbidity and Drug Repositioning

Rapid development of high-throughput technologies has permitted the identification of an increasing number of disease-associated genes(DAGs),which are important for understanding disease initiation and developing precision therapeutics.However,DAGs often contain large amounts of redundant or false positive information,leading to difficulties in quantifying and prioritizing potential relationships between these DAGs and human diseases.In this study,a networkoriented gene entropy approach(NOGEA)is proposed for accurately inferring master genes that contribute to specific diseases by quantitatively calculating their perturbation abilities on directed disease-specific gene networks.In addition,we confirmed that the master genes identified by NOGEA have a high reliability for predicting disease-specific initiation events and progression risk.Master genes may also be used to extract the underlying information of different diseases,thus revealing mechanisms of disease comorbidity.More importantly,approved therapeutic targets are topologically localized in a small neighborhood of master genes in the interactome network,which provides a new way for predicting drug-disease associations.Through this method,11 old drugs were newly identified and predicted to be effective for treating pancreatic cancer and then validated by in vitro experiments.Collectively,the NOGEA was useful for identifying master genes that control disease initiation and co-occurrence,thus providing a valuable strategy for drug efficacy screening and repositioning.NOGEA codes are publicly available at https://github.com/guozihuaa/NOGEA.

基于“肾虚络瘀”理论探究川芎治疗糖尿病肾病的有效成分及作用机制

目的:揭示川芎治疗糖尿病肾病(Diabetic Kidney Disease,DKD)的药理机制,探索治疗DKD的新策略。方法:采用中药系统药理学数据库与分析平台(TCMSP)和Swiss Target Prediction数据库检索川芎的成分和药物靶点。用GSE30528数据集鉴定DKD肾小球相关的差异表达基因。将药物靶点和疾病靶点的交集作为潜在治疗靶点。利用Cytoscape软件构建蛋白质-蛋白质相互作用(Protein-protein Interaction,PPI)网络和药物-化合物-靶点网络。基于Bio GPS数据库对核心靶标的组织和器官分布进行评估。采用R软件进行基因本体论(GO)富集分析及京都基因与基因组百科全书(KEGG)通路分析,采用R软件进行功能注释。通过微生信平台进行基因集富集分析。采用Schrodinger和Lig Plus软件进行分子对接。对分子对接得到的最佳核心复合物进行分子动力学模拟。结果:经过筛选分析,获得6种药物活性成分和285个治疗DKD预测靶点。杨梅酮、川芎萘呋内酯和川芎醌被推测为主要活性成分。非受体酪氨酸激酶(Non-receptor tyrosine Kinase,SRC)、信号转导与转录激活因子3(Signal Transducer and Activator of Transcription 3,STAT3)、表皮生长因子受体(Epidermal Growth Factor Receptor,EGFR)和雌激素受体1(Estrogen Receptor 1,ESR1)等靶标可能发挥关键作用。富集分析结果表明,川芎可能通过调节鞘脂、钙、胰岛素抵抗、血管内皮生长因子(Vascular Endothelial Growth Factor,VEGF)、低氧诱导因子-1(Hypoxia-inducible Factor-1,HIF-1)、晚期糖基化终末产物-晚期糖基化终末产物受体(Advanced Glycation End Product-Advanced Glycosylation End Product Specific Receptor,AGE-RAGE)、局灶粘连等信号通路发挥治疗DKD的作用。在分子对接中,川芎醌与SRC表现出最佳的结合能,分子动力学亦证明了两者结合的稳定性。结论:基于生物信息学,预测了川芎治疗DKD的有效成分、潜在靶点和通路,为今后临床应用川芎治疗DKD提供了实验基础。此外,SRC可能是DKD的潜在治疗靶点,川芎醌可能是治疗DKD的潜在化合物,为DKD的药物筛选提供了可能性。